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--- title: Left ventricular hypertrophy, carotid atherosclerosis, and cognitive impairment in peritoneal dialysis patients authors: - Xuejing Zhu - Ran Jing - XiaoPing Li - Wanfen Zhang - Yushang Tang - Tongqiang Liu journal: BMC Cardiovascular Disorders year: 2023 pmcid: PMC9996916 doi: 10.1186/s12872-023-03130-0 license: CC BY 4.0 --- # Left ventricular hypertrophy, carotid atherosclerosis, and cognitive impairment in peritoneal dialysis patients ## Abstract ### Background Left ventricular hypertrophy (LVH) and carotid atherosclerosis (CAS) have been identified as factors associated with cognitive impairment (CI) but have not been studied in patients undergoing peritoneal dialysis (PD). This study investigated the relationship between LVH and CAS and cognitive function in patients undergoing PD. ### Methods In this single-center cross-sectional study, the clinically stable patients who were over 18 years of age and had undergone PD for at least 3 months were enrolled. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), which included seven areas: visuospatial/executive function, naming, attention, language, abstraction, delayed recall, and orientation. LVH was defined as LVMI > 46.7 g/m2.7 in women and LVMI > 49.2 g/m2.7 in men. CAS was defined as carotid intima-media thickness ≥ 1.0 mm and/or the presence of plaque. ### Results A total of 207 patients undergoing PD were recruited, with an average age of 52.14 ± 14.93 years and a median PD duration of 8 months (5–19 months). The CI rate was $56\%$, and the prevalence of CAS was $53.6\%$. LVH occurred in 110 patients ($53.1\%$). Patients in the LVH group tended to be older, and had a higher body mass index, a higher pulse pressure, a higher male proportion, a lower ejection fraction, a higher prevalence of cardiovascular disease and CI, and a lower MoCA scores. Multivariate logistic regression analysis was conducted to analyze the association between LVH and CI (OR, 10.087; $95\%$ confidence interval, 2.966–34.307). And the association between LVH and CI was still supported after propensity matching scores. CAS was not significantly associated with CI. ### Conclusion LVH is independently associated with CI in patients undergoing PD, while CAS is not significantly associated with CI. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12872-023-03130-0. ## Introduction Peritoneal dialysis (PD), also known as home PD, requires patients to be able to self-supervise and self-manage [1], which to a certain extent depends on normal cognitive function [2]. Unfortunately, according to relevant studies, the incidence of cognitive impairment (CI) in this population is 28–$66\%$ [3]. CI increases the incidence of complications such as peritonitis, cardiovascular and cerebrovascular disorders, and calcium and phosphorus metabolism disorders in dialysis patients and increases the hospitalization rate and mortality of patients [4–6]. CI is also recognized as an independent predictor of survival in dialysis patients. Therefore, there is an urgent need to explore and identify the risk factors leading to CI in dialysis patients and perform active intervention. There are many risk factors for CI in patients undergoing PD, including general demographic characteristics (age, gender, socioeconomic status, and education level), traditional factors (such as hypertension, diabetes [7, 8], and cardiovascular risk factors [9–12]), non-traditional factors (such as hyperparathyroidism, anemia, inflammation [13], and malnutrition [14]), and uremia- or dialysis-related factors [15, 16]. Among them, cardiovascular risk factors are also the main cause of death in patients undergoing PD [17–19]. Left ventricular hypertrophy (LVH) and carotid atherosclerosis (CAS) are major predictors of cardiovascular disease (CVD) [20]. In recent years, Elias and Kaffashian et al. showed that LVH was negatively correlated with cognitive function [21–25], and the association might not only be indirect. LVH independently leads to cognitive decline and CI [26–32], and hemodialysis patients with CVDs have worse processing speed and executive function [9]. One possible mechanism underlying the relationship between LVH and CI is the presence of white matter damage [33]. However, there is still a lack of research on the correlation between LVH and cognitive function in patients undergoing PD. In addition, CAS includes carotid stiffness, increased carotid intima-media thickness, carotid stenosis, and carotid plaque characteristics [34, 35]. Although traditionally thought to primarily cause ischemic stroke, there is an increasing body of evidence showing that carotid atherosclerosis contributes to the development of CI and dementia. It has been shown that atherosclerosis is an independent risk factor for CI [35–45]. However, there is still a lack of relevant studies on patients undergoing PD. Therefore, this cross-sectional study aimed to investigate whether LVH and CAS were independently associated with CI during PD. ## Study design and participants This study was a cross-sectional observational survey of patients undergoing PD. The study has been approved by the Ethics Committee of Changzhou Second People's Hospital. From January 2017 to July 2022, patients undergoing continuous ambulatory peritoneal dialysis (CAPD) hospitalized in the Department of Nephrology, Changzhou Second Hospital Affiliated to Nanjing Medical University were screened. Inclusion criteria were as follows: age > 18 years; undergoing CAPD for ≥ 3 months and clinically stable; and able to undergo all measurements and fill in all questionnaires. Exclusion criteria were as follows: kidney transplantation, systemic infections, acute cardiovascular events, active hepatitis, or cancer; surgery or trauma in the month before the study; and any neurological conditions such as dementia or Parkinson's disease, a history of mental illness such as depression or schizophrenia, and any other serious health conditions such as malignancy, severe infections, or impairments in vision, hearing, speech, or comprehension skills. All participants received a conventional glucose-based lactate-buffered PD solution. ## Clinical characteristics Demographic characteristics and complications were recorded, including age, sex, body mass index (BMI), education level, dialysis duration, systolic blood pressure, diastolic blood pressure, pulse pressure, smoking, diabetes mellitus, hypertension, primary kidney disease, and history of CVD. Education level was recorded as the highest school level for which a diploma was obtained: primary school or below, middle school, high school and beyond. Primary kidney disease includes chronic glomerulonephritis, diabetic nephropathy, and others (such as IgA nephropathy, nephrotic syndrome, lupus nephritis, Sjogren's syndrome, and ANCA-associated vasculitis). CVD information was obtained from medical history reviews, and CVD was recorded if any of the following conditions were present: angina pectoris, grade III or IV congestive heart failure (as classified based on the guidelines of the New York Heart Association), transient ischemic attack, myocardial infarction, and cerebrovascular accident. ## Laboratory methods Blood samples were collected after overnight fasting while continuing PD treatment, and relevant indicators were determined using standardized equipment and procedures, including serum albumin, AST, ALT, triglyceride, total cholesterol, low-density lipoprotein, high-density lipoprotein, phosphorus, calcium, sodium, potassium, parathyroid hormone, urea nitrogen, creatinine, uric acid, glycosylated hemoglobin, fasting blood glucose, high-sensitivity C-reactive protein (hsCRP), and hemoglobin. To rule out cognitive decline secondary to nutritional or endocrine changes, thyroid hormone levels in the blood were measured by standard laboratory methods. Dialysis adequacy was defined based on total Kt/V and creatinine clearance (Ccr). ## Echocardiographic measurements Echocardiography was performed by trained sonographers at our hospital using a Philips iE33 Doppler echocardiography system with a real-time 3D probe X3-1 (frequency 1–3 MHz) (Philips, Best, The Netherlands). Left ventricular end-diastolic diameter (LVDd), septal thickness (IVST), left ventricular posterior wall thickness (LVPWT), and ejection fraction (EF) were measured. All the above data were averaged over three cardiac cycles. Left ventricular mass (LVM) was estimated by the formula of Devereux et al.: LVM (g) = 0.8 × 1.04 [(LVID + IVS + PWT)3 − (LVIDd)3] + 0.6.LVM was normalized for body height to the 2.7 (LVMI). LVH was defined as LVMI > 46.7 g/m2.7 in women and LVMI > 49.2 g/m2.7 in men [46]. Cardiac parameter, Relative wall thickness (RWT), was calculated as the ratio of twice the posterior wall thickness divided by the left ventricular internal diameter in diastole, and a value over 0.42 cm was defined as an elevated RWT [47].Four categories of left ventricular geometry were defined: [1] normal (normal RWT and LVMI); [2] eccentric hypertrophy (normal RWT and high LVMI); [3] concentric hypertrophy (high RWT and high LVMI); and [4] concentric remodeling (high RWT and normal LVMI). ## Carotid ultrasound measurement Echocardiography was performed by a well-trained sonographer in our hospital using a Sonos5500 color Doppler ultrasound diagnostic instrument with a probe frequency of 3–11 MHz. The patient was placed in the supine position, and transverse 2D images of the bilateral common carotid artery, the extracranial segment of the internal carotid artery, the external carotid artery, and the carotid bifurcation were acquired segment by segment, and the intima of the wall and the presence of plaque were observed. According to guidelines of the American Heart Association, CAS is defined as carotid intima-media thickness ≥ 1.0 mm and/or the presence of plaque [48–50]. ## Cognitive function measurement All participants were tested for cognitive function using the Chinese version of the Montreal Cognitive Assessment (MoCA), which assesses seven cognitive domains, namely visuospatial/executive functioning, naming, attention, language, abstraction, delayed recall, and orientation. For patients with less than 12 years of education, MoCA scores were increased by 1 point to correct for the bias caused by educational level. Possible scores range from 0 to 30, with higher scores indicating better cognitive status. CI was defined as a total MoCA score of < 26, and a score of ≥ 26 was considered to indicate normal cognition. Assessments of cognitive function were performed in a separate room with 1 medical staff to 1 patient. In total, 4 medical staff members participated in this study as observers and all completed a training program that taught them the methods and processes to ensure the integrity and accuracy of the assessment. ## Statistical analyses SPSS version 25.0 and GraphPad Prism 9.4.1 [681] were used for statistical analysis and plotting. Normally distributed continuous variables are presented as mean and standard deviation. Non-normally distributed continuous variables are presented as medians and interquartile ranges. Categorical variables are expressed as frequencies and percentages. The independent sample t-test, the Mann–Whitney U test, and the chi-square test were used to compare the differences of continuous variables and categorical variables between the two groups. All variables with a significance level of $P \leq 0.10$ on the univariate test were included in further multivariate analyses. Three linear regression models were then developed. Model 1 was the basic model, including LVH and accepted demographic data (age, sex, BMI, and education level). Model 2 was adjusted for cardiovascular risk factors (diabetes mellitus, CVD, common CAS, smoking history, beta-blockers, pulse pressure, and EF) based on Model 1. Model 3 was adjusted for laboratory parameters (hemoglobin, albumin, hsCRP, and Ccr) based on Model 2. Next, the risk factors of CI were analyzed by multivariate logistic regression analysis. To reduce the effect of possible selection bias, the effect of the small number of patients with LVH, and the effect of the relatively large number of associations on the reliability of the multivariable model and to adjust for the effects of other potential confounders without reducing the ratio of events per variable, we further performed sensitivity analyses for LVH by propensity score matching. Propensity score matching was performed using the following variables: age, sex, BMI, RRF, CVD, pulse pressure, and EF. The maximum difference in propensity scores that allowed matching was 0.02, and all test levels were two-sided, with $P \leq 0.05$ indicating statistical significance. ## Basic characteristics Of 268 patients undergoing PD screened for inclusion in this study, 207 ($77.2\%$) agreed to participate and completed the MoCA questionnaire. The general characteristics of our participants were as follows: age, 52.14 ± 14.93 years; BMI, 22.99 ± 3.66; and dialysis duration, 8 months (5–19 months). Males accounted for $51.2\%$ of the study population. Of the study population, $96.1\%$ had hypertension and $29\%$ had diabetes. The prevalence of LV hypertrophy as assessed by echocardiography was $53.1\%$. The prevalence of CAS was $53.6\%$. ## Clinical features of left ventricular hypertrophy and carotid atherosclerosis Subjects were grouped according to LVH. As shown in Table 1, the comparison of demographic and biochemical data revealed significant differences in age, sex, BMI, CVDs, pulse pressure, and EF between the two groups. Age, pulse pressure, and CVD were higher in the LVH group than in the non-LVH (NLVH) group. The EF was lower in the LVH group than in the NLVH group. Other variables including antihypertensive drugs, hypertension, diabetes, triglycerides, total cholesterol, and CAS did not differ significantly (Table 1).Table 1Demographic and cardiovascular data were compared between LVH and NLVH groupsCharacteristicsNLVH ($$n = 97$$)LVH ($$n = 110$$)T/Z/X2PAge (years)48.36 ± 14.8255.46 ± 14.27− 3.5090.001BMI (kg/m2)22.41 ± 4.0723.55 ± 3.14− 2.2610.025Males n (%)41 (42.3)65 (31.4)5.8390.016Diuretics n (%)13 (13.4)11 [10]0.5820.446Beta-blockers n (%)48 (49.5)50 (45.5)0.3360.562Calcium channel blockers n (%)80 (82.5)99 [90]2.4960.114ACE/ARB inhibitors n (%)18 (18.6)16 (14.5)0.6040.437Cardiovascular disease n (%)6 (6.2)19 (17.3)5.9670.015Hypertension n (%)92 (94.8)107 (97.3)0.8170.366Diabetes mellitus n (%)23 (23.7)36 (32.7)2.0560.152Smoking History n (%)38 (39.2)55 [50]2.4410.118Systolic BP (mmHg)141.05 ± 23.07146.9 ± 19.49− 1.9770.050Diastolic BP (mmHg)87.12 ± 15.0586.49 ± 12.060.3310.741Pulse pressure (mmHg)53.93 ± 15.660.41 ± 15.1− 3.0340.003Total cholesterol, (mmol/L)4.37 ± 1.014.38 ± 0.99− 0.1010.92triglyceride, (mmol/L)1.3 (0.93–1.88)1.35 (0.87–1.83)− 0.1160.907HDL cholesterol (mmol/L)1 (0.88–1.26)0.98 (0.80–1.2)− 1.4730.141LDL cholesterol (mmol/L)2.08 ± 0.681.99 ± 0.650.9540.341LV ejection fraction (%)60 (56.5–63)57 (53–61)− 3.782 < 0.001CAS n (%)47 (48.5)64 (58.2)1.9620.161Values for categorical variables are given as number (percentage); values for continuous variables, as mean ± standard deviation or median [interquartile range]ACE Angiotensin Converting Enzyme; ARB Angiotensin Receptor Blocker; BMI Body mass index; BP, Blood pressure; CAS Carotid atherosclerosis; HDL High-density lipoprotein; LDL Low-density lipoprotein; LVMI Left ventricular Mass Index; LVH Left ventricular hypertrophy; NLVH Non Left ventricular hypertrophy; LV Left ventricle Participants were divided into two groups according to the occurrence of CAS. As shown in Additional file 1: Table S1, there were significant differences in the use of beta-blockers and the proportion of people smoking between the two groups, while there were no significant differences in other variables including age, BMI, gender, hypertension, diabetes, and antihypertensive drugs (Additional file 1: Table S1). ## Left ventricular hypertrophy, carotid atherosclerosis, and cognitive function In the present study, the prevalence of CI was $56\%$. The incidence of CI was significantly higher in the LVH group ($74.5\%$) than in the NLVH group ($35.1\%$) ($P \leq 0.001$). In addition, the total MoCA score in the LVH group was significantly lower than that in the NLVH group (Fig. 1H). In addition, there were significant differences in visuospatial/executive function ($P \leq 0.001$) (Fig. 1A), language ($P \leq 0.001$) (Fig. 1D), delayed recall ($P \leq 0.001$) (Fig. 1F), attention ($$P \leq 0.007$$) (Fig. 1C), abstract function ($$P \leq 0.028$$) (Fig. 1E), and orientation ($$P \leq 0.026$$) (Fig. 1G). There was no significant difference in naming ($$P \leq 0.119$$) (Fig. 1B).Fig. 1Comparison of cognitive domains between LVH and NLVH groups. Note: ** = $P \leq 0.001.$ = $P \leq 0.01.$* = $P \leq 0.05.$ ns = no significance. Abbreviations: NLVH Non-left ventricular hypertrophy, LVH Left ventricular hypertrophy In addition, in the left ventricular geometry, there were 79 cases ($38.2\%$) in the normal group, 52 ($25.1\%$) in eccentric hypertrophy, 58 ($28.0\%$) in centripetal hypertrophy, and 19 ($9.2\%$) in centripetal remodeling. The incidence of cognitive impairment in centripetal hypertrophy ($79.3\%$) was slightly higher than that in centripetal hypertrophy (69.2), and the difference was not statistically significant ($P \leq 0.05$) (Table 2).Table 2Differences in left ventricular geometry and cognitive impairment in peritoneal dialysis patientsNormal ($$n = 78$$)Eccentric hypertrophy ($$n = 52$$)Concentric hypertrophy ($$n = 58$$)Concentric remodeling ($$n = 19$$)PCI,n (%)24 (30.8)36 (69.2)ad46 (79.3)be10 (52.6)cf < 0.001Values for categorical variables are given as number (percentage)CI Cognition ImpairmentaP < 0.001, Normal group versus Eccentric hypertrophy groupbP < 0.001, Normal group versus Concentric hypertrophy groupcP < 0.05, Normal group versus Concentric remodeling groupdP > 0.05, Eccentric hypertrophy group versus Concentric hypertrophy groupeP > 0.05,Eccentric hypertrophy group versus Concentric remodeling groupfP < 0.01, Concentric hypertrophy group versus Concentric remodeling group In the CAS group, there was no significant difference in the total MoCA score between the two groups ($$P \leq 0.152$$), but there was a significant difference in the proportion of CI between the two groups ($$P \leq 0.014$$). In the subgroup with normal cognitive function, patient demographics, laboratory data, and comorbidities (age, education, diabetes, hemoglobin, albumin, CRP, CAS percentage, Ccr, LVMI, and LVH percentage) were significantly different compared with the CI group (Additional file 2: Table S2). All these variables and the influencing factors of LVH and CAS were included in the subsequent analysis as recognized confounding factors. Three models were established by multivariate logistic regression analysis. Model 1 included LVH and demographic data (age, BMI, sex, and education); Model 2 was adjusted for cardiovascular risk factors (diabetes mellitus, CVD, smoking history, beta-blockers, pulse pressure, EF, and CAS) based on Model 1. Model 3 was adjusted for laboratory indicators (hemoglobin, albumin, and CRP) based on Model 2. It was found that the association between LVH and CI was not weakened (Table 3). Model 4 was established after propensity score matching (Table 4). After adjusting for age, education level, diabetes mellitus, hemoglobin, albumin, hsCRP, and Ccr, LVH was still independently associated with CI in Model 4.Table 3Association of left ventricular hypertrophy with cognitive impairment by univariable and multivariable logistic regression analysisVariableLVHOR ($95\%$ CI)P ValueUnivariable5.426 (2.983–9.871) < 0.001Multivariable model 16.148 (2.785–13.569) < 0.001Multivariable model 26.291 (2.700–14.657) < 0.001Multivariable model 310.087 (2.966–34.307) < 0.001Multivariable model 1: adjusted for demographic and clinical measures (including age, sex, body mass index, level of education). Model 2: model 1plus cardiovascular risk factors (including diabetes mellitus, cardiovascular disease, smoking history, beta-blockers, pulse pressure, CAS and LV ejection fraction) Model 3: model 2plus laboratory measures (including hemoglobin, albumin, Ccr and high-sensitivity C-reactive protein). OR Odds ratio; $95\%$ CI $95\%$ Confidence intervalTable 4Univariate and multivariate logistic regression analyses after propensity score matching showed an association between left ventricular hypertrophy and cognitive impairmentVariableLVHOR ($95\%$ CI)P ValueUnivariable4.312 (2.109–8.816) < 0.001Multivariable model 49.214 (2.039–36.766)0.002Model 4: adjusted for age, level of education, diabetes mellitus, hemoglobin, albumin and high-sensitivity C-reactive protein after a propensity score match. The following variables were used for propensity score matching: age, sex, body mass index, cardiovascular disease, pulse pressure, LV ejection fraction. OR Odds ratio; $95\%$ CI $95\%$ confidence interval In addition, after adjusting for LVH and all confounding factors, a regression model was constructed (Additional file 3: Table S3), and LVH was independently associated with CI in patients undergoing PD ($P \leq 0.001$). CAS was not an independent risk factor for CI. ## Discussion In the present study, in addition to established risk factors such as age and education level, LVH was found to be a new potential risk factor for CI in patients undergoing PD, especially with significant differences in visuospatial/executive function, delayed memory, language, and attention. CAS is not an independent risk factor for CI in patients undergoing PD. The prevalence of CI based on MoCA scores in this PD population was $56\%$, which is similar to the percentages reported by Salazar-Felix [51] and Yi et al. [ 52] ($65\%$ and $49.9\%$, respectively). In addition, a systematic review and meta-analysis showed that the prevalence of CI in patients undergoing PD ranged from $3.3\%$ to $74.5\%$ [53], which could be attributed to the different neuropsychological tests used. Nasreddine et al. [ 54] found that compared with MMSE, MoCA had a higher sensitivity in detecting early CI ($78\%$ and $18\%$) and had good specificity ($87\%$) and positive and negative predictive values ($89\%$ and $91\%$, respectively). Moreover, it has been reported that the MoCA test is more sensitive in assessing the cognitive ability of patients undergoing PD [54], especially in visuospatial/executive function and language. In epidemiological studies, cardiovascular risk factors are the leading cause of death in patients undergoing PD, but an accurate prediction based on comprehensive risk factors remains challenging. LVH is a typical sign of cardiac end-organ damage, and LVM is the comprehensive temporal correlation between high blood pressure and other cardiovascular risk factors, which can be used as a marker of the chronicity and degree of elevated blood pressure, as well as an indicator of the long-term burden of vascular risk factors [21]. In addition, multiple meta-analyses [29, 30, 32, 55] have shown a positive correlation between LVH and CI. Therefore, we hypothesized that there would be such an association in patients undergoing PD. In the present study, the proportion of LVH in patients undergoing PD with CI was significantly higher than that in the non-CI (NCI) group, and LVH remained an independent predictor of CI after adjusting for demographic data, cardiovascular risk factors, laboratory parameters, and propensity score matching. In end-stage renal disease (ESRD) treated by dialysis, fluid overload and arterial hypertension often contribute to a combination of eccentric and concentric hypertrophy, which may be influenced both by inadequate volume and blood pressure control [56]. In a study [57], $86.4\%$ of the patients overall had LVH; among these, $56.3\%$ had concentric LVH, $30.1\%$ had eccentric LVH, $6.8\%$ had concentric remodeling and only $6.8\%$ of the patients had normal LV geometry. However, in our study, the proportion of centripetal hypertrophy ($28.0\%$) was slightly higher than that of eccentric hypertrophy ($25.1\%$). The cause of this outcome is obscure, but most probably satisfactory volume and BP control played a central role. In addition, the incidence of cognitive impairment in centripetal hypertrophy was higher than that in eccentric hypertrophy, but the incidence was not statistically significant. One possible mechanism for the relationship between LVH and cognitive dysfunction is the presence of white matter damage [33]. In individuals with cardiovascular risk factors, white matter damage has been found in regions of the dorsolateral prefrontal and anterolateral orbital circuits [58]. Dysfunction of these frontal subcortical circuits is characterized by impaired executive function and memory [59]. These results are in agreement with our study. The LVH group of patients undergoing PD had significant differences in visuospatial/executive function, language, and delayed recall (all $P \leq 0.001$). Zhong et al.[43]demonstrated a significant association between middle-aged CAS and cognitive function. Unfortunately, we found that the incidence of atherosclerosis in patients undergoing PD was significantly different between the CI and NCI groups, but there was no significant association between atherosclerosis and CI after adjusting for confounding factors. This consideration may be related to the insufficient sample size and cognitive scoring tests, and further large-scale, multicenter studies are needed to draw definite conclusions. Similar to the general population, age, education level, and diabetes mellitus are related to the cognitive degree of patients undergoing PD. However, we also found that hsCRP, albumin, and hemoglobin were independently associated with CI in patients undergoing PD. This is similar to the study of Stivelman et al. [ 60]. Higher albumin and hemoglobin levels are protective factors for cognitive function [61, 62]. The underlying mechanism of albumin may lie in its role as an antioxidant during inflammation, reducing oxidative damage in the pathogenesis of CI.In addition, the hemoglobin of patients with peritoneal dialysis in this study (86.00 ± 13.62 g/L) was slightly lower than that in Yin [63] et al. ’s study (93.0 ± 19.6), which was considered to be related to the poor compliance of patients with peritoneal dialysis in this study, low return rate, and insufficient dosage of erythropitin. The strength of this study was that it was the first to reveal an association between LVH and CAS and CI in patients undergoing PD. There are some limitations to the study, however. First, as a single-center study, the small sample size may limit its generality. Second, this study has a cross-sectional design, so causal inferences cannot be made. In addition, we only applied MoCA to assess cognition. MoCA is a short screening tool that does not fully assess cognitive function the way neuropsychological tests do. The use of magnetic resonance imaging is recommended to identify asymptomatic cerebral infarction, microbleeds, and white matter disease. ## Conclusions We showed that LVH is independently associated with CI in patients undergoing PD, especially in visuospatial/executive functions, delayed memory, language, and attention. Although the mechanism underlying the association between LVH and CI in patients undergoing PD has not been clarified, new directions for further research are proposed. Early identification of LVH has significant benefits in reducing the prevalence of CI. In addition, CAS was not significantly associated with CI after adjusting for confounding factors. ## Supplementary Information Additional file 1: Table S1. 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--- title: Effect of environmental variance-based resilience selection on the gut metabolome of rabbits authors: - Cristina Casto-Rebollo - María José Argente - María Luz García - Agustín Blasco - Noelia Ibáñez-Escriche journal: 'Genetics, Selection, Evolution : GSE' year: 2023 pmcid: PMC9996918 doi: 10.1186/s12711-023-00791-5 license: CC BY 4.0 --- # Effect of environmental variance-based resilience selection on the gut metabolome of rabbits ## Abstract ### Background Gut metabolites are key actors in host-microbiota crosstalk with effect on health. The study of the gut metabolome is an emerging topic in livestock, which can help understand its effect on key traits such as animal resilience and welfare. Animal resilience has now become a major trait of interest because of the high demand for more sustainable production. Composition of the gut microbiome can reveal mechanisms that underlie animal resilience because of its influence on host immunity. Environmental variance (VE), specifically the residual variance, is one measure of resilience. The aim of this study was to identify gut metabolites that underlie differences in the resilience potential of animals originating from a divergent selection for VE of litter size (LS). We performed an untargeted gut metabolome analysis in two divergent rabbit populations for low ($$n = 13$$) and high ($$n = 13$$) VE of LS. Partial least square-discriminant analysis was undertaken, and Bayesian statistics were computed to determine dissimilarities in the gut metabolites between these two rabbit populations. ### Results We identified 15 metabolites that discriminate rabbits from the divergent populations with a prediction performance of $99.2\%$ and $90.4\%$ for the resilient and non-resilient populations, respectively. These metabolites were suggested to be biomarkers of animal resilience as they were the most reliable. Among these, five that derived from the microbiota metabolism (3-(4-hydroxyphenyl)lactate, 5-aminovalerate, and equol, N6-acetyllysine, and serine), were suggested to be indicators of dissimilarities in the microbiome composition between the rabbit populations. The abundances of acylcarnitines and metabolites derived from the phenylalanine, tyrosine, and tryptophan metabolism were low in the resilient population and these pathways can, therefore impact the inflammatory response and health status of animals. ### Conclusions This is the first study to identify gut metabolites that could act as potential resilience biomarkers. The results support differences in resilience between the two studied rabbit populations that were generated by selection for VE of LS. Furthermore, selection for VE of LS modified the gut metabolome, which could be another factor that modulates animal resilience. Further studies are needed to determine the causal role of these metabolites in health and disease. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12711-023-00791-5. ## Background Gut metabolites are key actors in host-microbiota crosstalk and can have either beneficial or detrimental effects on the host [1–3]. They can act in the gut or travel through the plasma to reach the host’s tissues, i.e. they can influence the functions of the liver, brain, and immune system [4]. Gut metabolites can be derived from (i) the microbiota, due to the conversion of non-digestible components from the diet or to de novo synthesis, (ii) the host, and (iii) the diet. Metabolites from the host can also be modified by the gut microbiota [5, 6]. In livestock, the study of the gut metabolome is an emerging topic. Recently, differences in the gut metabolome have been found to be associated with traits such as feed efficiency [7, 8] and milk yield protein [9]. Thus, the study of the gut metabolome can help to expand the knowledge on the interactions between the gut and peripheral tissues, and also to understand its effect on key traits such as methane emission and animal resilience. Animal resilience has become a major trait of interest in livestock because of the high demand for more sustainable livestock systems. Resilience is the ability of animals to maintain or quickly recover their production performance when an environmental perturbation occurs [10]. Since environmental variance (VE) is highly correlated with animal resilience, it is an interesting trait for measuring resilience [10–12]. Indeed, animals with a low VE for a given trait seem to cope better with environmental disturbances [11, 12]. Quantitative genetics and genomic studies in different species underline the importance of the immune system in modulating animal resilience [13–16]. The gut metabolome is closely related to the modulation of the immune system [3, 17], thus its study could provide insight into the mechanisms that underlie animal resilience. A previous metagenomic study found that microbiome composition (genes and species) differs between two rabbit populations that had been selected for high and low VE of litter size (LS) [18]. These two lines showed a notable genetic response to this selection, and also correlated responses in resilience indicators that were measured after a vaccination challenge [12] or immediately following first parity [14]. The present study is an extension of a previous metagenome study [18] and its aim was to identify gut metabolites that are related to the resilience potential of these rabbit populations. The gut microbiome is a complex ecosystem that is strongly influenced by environmental factors [19, 20] and the origin of metabolites [6]. Reducing the impact of confounding factors is necessary to correctly decipher variability in the gut metabolome that underlies complex traits. The rabbit populations used in this study are coetaneous and were selected under the same environmental conditions and diet for 13 generations, and also showed differences in resilience potential [12, 14]. Thus, they constitute an exceptional resource to identify gut metabolites that can act as biomarkers of animal resilience. ## Methods The rabbits used in this study belonged to the 13th generation of a divergent selection experiment for high and low VE of LS that was carried out at the University Miguel Hernández of Elche (Spain) [21]. Cecum samples were collected from 28 does (14 from each population) that were sacrificed after their first parity by intravenous injection of sodium thiopental at a dose of 50 mg/kg of body weight (Thiobarbital, B. Braun Medical S. A., Barcelona, Spain). The samples were homogenized in 50-mL Falcon tubes and aliquoted in 2-mL cryotubes for immediate snap-freezing in liquid nitrogen and storage at − 80 °C until they were processed. Untargeted metabolite analysis of the gut content was conducted on the Metabolon Discovery HD4 platform. The samples were prepared by the Hamilton Company's automated MicroLab STAR® system. Prior to extraction, several recovery standards were added for quality control purposes. Proteins in the samples were precipitated with methanol under vigorous shaking for two min, followed by centrifugation to recover chemically diverse metabolites. The resulting extract was divided into five aliquots and the TurboVap® (Zymark) evaporator was used to remove organic solvents. The metabolites in the gut were profiled by Ultrahigh Performance Liquid Chromatography (UPLC) and Tandem Mass Spectrometry (UPLC-MS/MS) with negative and positive ion mode electrospray ionization (ESI). All methods used a Waters ACQUITY UPLC system (Waters, Milford, MA, USA) and a Q-Exactive high resolution/accurate mass spectrometer (Thermo Fisher Scientific) interfaced with a heated electrospray ionization (HESI-II) source and an Orbitrap mass analyser operated at 35,000 mass resolution. The aliquots were dried and resuspended in solvents that are compatible with the method used and that contain standards at fixed concentrations to ensure injection and chromatographic consistency. Quality control samples were injected throughout the platform run to remove artifacts and background noise and to distinguish biological variability from process/instrument variability. Among the five aliquots, two were analysed by two separate reverse phase (RP)/UPLC-MS/MS methods with the acidic positive ion mode ESI. One was chromatographically-optimized for more hydrophilic compounds and the other for more hydrophobic compounds. To detect the former, the aliquot was gradient-eluted from a C18 column (Waters UPLC BEH C18-2.1 × 100 mm, 1.7 µm) using a water and methanol solution that contained $0.05\%$ perfluoropentanoic acid (PFPA) and $0.1\%$ formic acid (FA). To detect the latter, the aliquot was gradient-eluted from the same C18 column but using an overall higher organic solution composed of methanol, acetonitrile, water, $0.05\%$ PFPA, and $0.01\%$ FA. The third aliquot was analysed by RP/UPLC-MS/MS with the basic negative ion mode ESI using a separate dedicated C18 column and eluted with methanol, water, and 6.5 mM of ammonium bicarbonate at pH 8. The fourth aliquot was analysed via a negative ion mode ESI with a HILIC column (Waters UPLC BEH Amide 2.1 × 150 mm, 1.7 µm), using a gradient of water and acetonitrile with 10 mM ammonium formate at pH 10.8. The last aliquot was reserved for backup. Raw data files were obtained after tandem mass spectrometry analysis by alternating between MS and data-dependent MSn scans, using dynamic exclusion. The scan range varied slightly between chromatography methods but covered a 70 to 1000 mass to charge ratio (m/z). Raw data were extracted, peaks were identified, and quality control was processed on the Metabolon hardware and software. In total, 765 metabolites were identified by a library that included three criteria of more than 3300 authenticated standard components: retention time/index (RI), mass to charge ratio (m/z), and chromatographic data, including MS/MS spectral data. All three criteria can be used to distinguish and differentiate metabolites. Metabolite quantification was based on the area-under-the-curve of the detected peaks. All statistical analyses were done in R [22]. A principal component analysis was computed using 480 of the 765 metabolites that had no missing values. Of the 28 animals, 13 animals remained in the datasets from both the low (resilient) and the high (non-resilient) VE of LS populations. Metabolites with more than $20\%$ missing values [23] within each population were considered uninformative and were removed from the dataset. The remaining missing values were replaced by half of the minimum peak intensity identified by the UPLC-MS/MS method to which each metabolite belonged. Due to the compositional nature of metabolomic data [24], the data on 654 metabolites from the 26 samples were transformed using the additive log-ratio (ALR) transformation, as follows:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ALR\left(\mathrm{j}\|ref\right)=\mathrm{log}\left(\frac{{x}_{\mathrm{j}}}{{x}_{ref}}\right)=\mathrm{log}\left({x}_{\mathrm{j}}\right)-\mathrm{log}\left({x}_{ref}\right),$$\end{document}ALRj\|ref=logxjxref=logxj-logxref,where the number of total ALR is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{j}$$\end{document}j-1, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{j}$$\end{document}j being the total number of variables in the dataset and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{ref}$$\end{document}xref is the reference variable (uracil nucleotide) with the lowest coefficient of variation. Procrustes correlation was performed to check for lack of isometry in the transformed dataset [25]. ALR-transformed data were auto-scaled to a mean of 0 and a standard deviation (SD) of 1. A partial least square-discriminant analysis (PLS-DA) was performed to identify the most relevant metabolites for discriminating rabbits from the resilient and non-resilient populations. The PLS-DA model was computed using the mixOmics package in R [26], using a categorical vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf{y}$$\end{document}y that indicates the rabbit population for each sample (high or low VE), and a matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf{X}$$\end{document}X with the ALR of each metabolite for each sample. The optimal number of components was that with the lowest balance error rate (BER) for Mahalanobis distance, computed by fourfold cross-validation repeated 100 times. Metabolites that had a contribution to model prediction or variable important prediction (VIP) less than 1 were removed from the dataset, and a new PLS-DA model was computed [27]. PLS-DA model computing and variable selection were performed until the lowest BER was achieved. The prediction performance of the final model was validated using a confusion matrix and a permuted-confusion matrix. The former was constructed by fourfold cross-validation repeated 10,000 times using the Mahalanobis distance to predict the rabbit populations. The accuracy and precision of the model were calculated considering that the resilient population was the true positive value. We also computed a permuted-confusion matrix by randomizing the categorical \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf{y}$$\end{document}y vector to check whether the prediction performance of the final models was spurious, i.e. whether the percentage of true positives in the permuted-confusion matrix was far from the $50\%$ expected under random assignment of two categories. Bayesian statistics were used to determine the relevance of the difference in the metabolite abundance between the two rabbit populations using a model that included a single effect for line and flat priors for all unknowns. Marginal posterior distributions of the unknowns were estimated by Monte Carlo Markov chains (Gibbs sampling) using four chains with a length of 50,000 iterations, a lag of 10, and a burn-in of 1000 iterations. The posterior mean of the difference in metabolite abundance was estimated as the mean of the marginal posterior distribution of the difference between the resilient and the non-resilient populations. These differences were quantified in units of SD of each metabolite. The probability of the difference [28] being greater (if the difference is positive) or less (if negative) than 0 (P0) was also calculated. To establish a threshold for the identification of relevant metabolites an approximation of the false discover rate (FDR) of Storey [29] was calculated based on the cumulative posterior error probability (PEP), similar to the q-value. The PEP was calculated as (1 − 0)/0.5. We assumed a cumulative PEP of 0.05, meaning that approximately $5\%$ of the relevant metabolites identified were allowed to be false positives. Then, we performed an analysis for assigning the biological origin of each relevant metabolite using the metOrigin tool [30]. A full record of the method used is in Additional file 1. ## Results This study included 13 rabbits from a resilient and 13 rabbits from a non-resilient population, and for which 765 untargeted metabolites were identified from their guts. The Bayesian statistical analysis identified 66 metabolites with relevant differences (> 0.67 SD units) in abundance between the two populations (see Additional file 2: Table S1). The PLS-DA model for these 66 metabolites showed a prediction performance of 71 and $90\%$ for the non-resilient and resilient animals, respectively. The most representative pathways were the long-chain fatty acylcarnitines (LCFA) metabolism, histidine metabolism, endocannabinoid metabolites, glycine, serine, and threonine metabolism, and tryptophan metabolism (Fig. 1a) and (see Additional file 2: Table S1). Of these 66 relevant metabolites, $29\%$ were associated with a co-metabolism because they can be produced by both the host and the microbiota, $12\%$ were associated with the microbiota (de novo synthesis), $27\%$ were associated with other sources ($24\%$ food related and $3\%$ drug related), and $32\%$ could not be traced back to their origin (Fig. 1b).Fig. 1Pathway and biological origin of metabolites with a relevant difference in abundance between the divergent rabbit populations. a Pathways of the 66 metabolites identified with a relevant difference in abundance between the resilient and non-resilient rabbit populations. b Biological origin of the 66 metabolites with a relevant difference in abundance between the two rabbit populations. “ Co-metabolism” refers to metabolites that are shared between the host and the microbiota. “ Microbiota” are microbiota-derived metabolites. “ Food related” are metabolites obtained from the diet. “ Drug” refers to drug-related metabolites. “ Unknown” refers to metabolites with unknown biological origin We also performed an optimized PLS-DA to identify the most relevant metabolites, i.e. those that reached the highest prediction performance, and found 15 metabolites with a prediction performance of 99.2 and $90.4\%$ for, respectively, animals from the resilient and the non-resilient populations (Fig. 2a), i.e. behenoylcarnitine (C22), arachidoylcarnitine, ethyl beta-glucopyranoside, 3-(4-hydroxyphenyl)lactate, 5-aminovalerate, glycerophosphoglycerol, succinylcarnitine, equol, cysteine s-sulfate, betaine, serine, palmitoyl dihydrosphingomyelin, thiamine, and aconitate. These 15 metabolites are proposed as potential resilience biomarkers due to the low error achieved by the model to predict the divergent population that they belonged to. Thirteen of these 15 metabolites matched with those that were identified in the Bayesian analysis as differing in abundance between the rabbits from the divergent populations (see Additional file 2: Table S1). The two non-overlapping metabolites, aconitate and thiamine, showed the lowest contribution to the optimized PLS-DA model (Fig. 2b) and a difference in abundance of 0.5 units of SD. Of the 13 most reliable metabolites, five appeared to be derived from the diet (behenoylcarnitine, arachidoylcarnitine, succinylcarnitine, betaine, and palmitoyl dihihydrosphingomyelin), three from the microbiota (3-(4-hydroxyphenyl)lactate, 5-aminovalerate, and equol), and two from the co-metabolism between the host and the microbiota (N6-acetyllysine, and serine). For the remaining three metabolites, ethyl beta-glucopyranoside, glycerophosphoglycerol, and cysteine s-sulfate, no origin was determined. Fig. 2Final PLS-DA model. a Representation of the first (Comp 1) and second (Comp 2) components of the PLS-DA used to discriminate rabbits from the resilient (red) and non-resilient (blue) populations. b Representation of posterior mean differences in units of standard deviation (SD) and variable importance on prediction (VIP) of metabolites included in the final PLS-DA model. Blue dots are relevant metabolites identified as more abundant in the non-resilient population. Red dots are the relevant metabolites identified with greater abundance in the resilient population. Black dots are metabolites included in the final PLS-DA model but that did not exceed both VIP > 1 and a posterior mean of the differences > 0.5 SD ## Discussion The study of the gut metabolome can help unravel its effects on key traits in livestock. In this study, we found differences in the metabolite profile (see Additional file 2: Table S1) between rabbits from divergent populations for VE of LS with differences in resilience potential [12]. The Bayesian analysis identified 66 metabolites with differences in abundance between rabbits from the divergent populations and good PLS-DA prediction performance to classify population origin. However, 13 of these 66 metabolites achieved the highest prediction performance to classify the resilient from the non-resilient animals. Hence, these metabolites were proposed as potential biomarkers for resilience. Our study also showed that 27 of the 66 metabolites and five of the 13 candidate resilience biomarkers (see Additional file 2: Table S1) originated from the microbiota (Fig. 1b). These results suggest that the microbiome composition differs between the two rabbit populations, in agreement with a previous metagenomic study for genes and taxa using the same populations [18]. In addition, we found that 16 of the 66 metabolites and (5 of the 13 candidate resilience biomarkers (see Additional file 2: Table S1) showed a biological origin related to the diet (Fig. 1b). These results suggest that the rabbits from the resilient and non-resilient populations differ in their feeding behaviour and/or the use of dietary compounds, either because of the host itself or their microbiota. Relevant functions were identified for four of the five resilience biomarkers that were related to microbiota-derived metabolites (equol, 3-(4-hydroxyphenyl)lactate, 5-aminovalerate, N6-acetyl lysine, and serine). Equol (0.93 SD unit difference between the two populations), which derives from the daidzein metabolism, can develop neuroprotective effects [31] and trigger an immune response since it enhances macrophages and protects from oxidative stress [32]. As the rabbits were sacrificed after their first parity, the high levels of equol in the resilient animals may have helped to reduce the inflammatory response triggered by farrowing, which is a highly stressful event for the dam. Farrowing may also have increased the levels of the 3-(4-hydroxyphenyl)lactate biomarker (− 1.04 SD units difference) in the rabbits from the non-resilient population, which is a metabolite that derives from the degradation of tyrosine and has been associated with non-alcoholic hepatic liver diseases [33]. This metabolite could be involved in a gut-liver crosstalk based on differences found in the plasma levels of cholesterol and triglycerides between the animals of these two populations (after a challenge) [12, 14]. The 5-aminovalerate and N6-acetyl lysine metabolites are products of the degradation of lysine (KEGG ID: C00431); 5-aminovalerate in the presence of betaine, which is another resilience biomarker identified in our study (Fig. 2b) acts as a methyl substrate donor to form 5-aminovaleric acid betaine [34]. 5-aminovaleric acid betaine may not be identified correctly, thus its role in health and disease is still unclear (see Haikonen et al. [ 34] for more information). We did not find any evidence for the implication of N6-acetyl lysine in pathways related to animal resilience. However, catabolism of the identified resilience biomarker serine (-0.69 unit of SD) was suggested to be related to adaptation of pathogens during the inflammation process [35]. To support the relevance of this pathway, glycine, an interconverted molecule to serine (KEGG ID: C00037), was identified with a difference of − 0.68 SD unit between the two populations. Although it is not known how the serine levels act in the non-resilient population, it would be relevant to study its implication in animal resilience given its role in modulating bacterial pathogenesis. We also identified other metabolites derived from the aromatic amino acids (AAA) metabolism (such as the abovementioned 3-(4-hydroxyphenyl) lactate) (Table 1). This is consistent with the differences in AAA biosynthesis genes (chorismite mutase and lyase) found in a previous metagenomic study using the same rabbit populations [18]. AAA metabolisms can control health and disease [36], by acting directly on the gut or on distal organs (i.e. liver, kidney or brain) [37], as well as modulate inflammatory bowel [37–39] and liver diseases such as hepatic inflammation and steatosis [37, 40]. Our results showed high levels of kynurenine and anthranilate (Table 1) in the rabbits from the non-resilient population, which showed higher levels of CRP (an inflammation biomarker) [12]. High levels of these two metabolites were also found in individuals that were under stress with inflammation [41]. As the animals were sacrificed after their first parity, the higher levels of kynurenine and anthranilate in the animals from the non-resilient population could pinpoint higher susceptibility to stress and inflammation in this population. Unexpectedly, the level of indole was found to be lower in the resilient rabbits (Table 1). This metabolite has protective functions in the gut by maintaining the intestinal barrier integrity and immune homeostasis, thus limiting dysbiosis during an inflammation response [42, 43]. An in-depth study would be needed to understand the role of the metabolites derived from the tryptophan metabolism on animal resilience. Table 1Metabolites from the aromatic amino acids (AAA) metabolism that had relevant differences between the non-resilient and resilient populationsPathwayMetaboliteµH-LP0HPD95Tyrosine metabolism3-(4-hydroxyphenyl)lactate− 1.0499.67[− 1.55, − 0.22]Phenylalanine metabolismN-acetylphenylalanine− 0.8298.17[− 1.57, − 0.04]Phenyllactate0.7196.38[− 0.04, 1.52]Tryptophan metabolismKynurenine− 0.7997.80[− 1.55, − 0.22]Anthranilate− 0.7597.27[− 1.51, 0.04]Oxindolylalanine− 0.7496.91[− 1.51, 0.03]Indole− 0.6995.77[− 1.49, 0.10]µH-L: posterior mean of the differences between the non-resilient and resilient populationsP0: probability of the difference being greater (if the difference is positive) or less (if negative) than 0HPD95: high posterior density interval of $95\%$ The metabolites behenoylcarnitine, arachidoylcarnitine, steroylcarnitine, palmitoylcarnitine, and formiminoglutamate, were also highlighted. Behenoylcarnitine and arachidoylcarnitine were identified as potential resilience biomarkers by PLS-DA, while the other three only showed relevant differences in their abundance between the divergent populations (see Additional file 2: Table S1). The first four metabolites are long-chain fatty acylcarnitines (LCFA), which are biomarkers of gut dysbiosis [44] and it has been shown that high levels of LCFA in the gut are a biomarker for inflammatory bowel diseases due to mitochondrial dysfunction in the colonocytes [45]. Correct integrity and functionality of the intestinal epithelial barrier and colonocytes are essential to gut immunity homeostasis and pathogenesis [46–48]. These results suggest that differences in the assimilation of long-chain fatty acids by the gut for energy purposes could influence gut integrity and immunity. The fifth metabolite, formiminoglutamate, belongs to the histidine catabolism to l-glutamate pathway. Lower abundance of this metabolite was found in the resilient animals (see Additional file 2: Table S1), which is in line with a previous metagenomic study that reported higher levels of glutamate formiminotransferase in animals from the resilient population [18]. Our findings suggest that there are differences in l-glutamate metabolism between the two rabbit populations. Glutamate is an important neurotransmitter that can act in the gut, spinal cord, and brain, participates in the gut-brain axis, and influences inflammatory response [49]. For the potential resilience biomarker metabolites palmitoyl dihydrosphingomyelin, ethyl beta glucopyranoside, glycerophosphoglycerol, and cysteine-s-sulfate (Fig. 2b), no hypotheses about biological mechanisms affecting animal resilience can be made because their effects on the host are still unclear. However, we suggest that these metabolites are important for predicting and classifying the rabbits into the two rabbit divergent populations. ## Conclusions This is the first study to identify gut metabolites that could act as potential biomarkers for resilience. Our results agree with the differences in resilience potential of these two rabbit populations, which were generated by divergent selection for VE of LS. These differences could be due to the levels of acylcarnitines and of metabolites derived from amino acid metabolism, such as aromatic amino acids (tryptophan, phenylalanine and tyrosine), glycine, serine, and glutamate metabolism. Moreover, relevant metabolites, such as 3-(4-hydroxyphenyl)lactate could be involved in host-gut microbiota crosstalk. Selection for environmental variance has modified the gut metabolome, which could thus be another contributor to the differences in resilience between the rabbits from these divergent populations. However, further studies are needed to properly determine the origin and mode of action of each metabolite to unravel their causal role in health and disease, as well as in host-gut microbiota crosstalk. ## Supplementary Information Additional file 1. Full pipeline with all the metabolomic analyses. Additional file 2: Table S1. Results of the Bayesian statistical analysis. The file includes (from left to right) the metabolite ID, posterior mean of the differences among the resilient and non-resilient rabbit populations (meanDiff), the probability of the difference being higher (if the difference is positive) or lower (if negative) than 0 (P0), the highest posterior interval density of $95\%$ (HPD95), the chemical name of the metabolites and the general and specific pathways in which they are involved, the posterior error probability (PEP), the cumulative PEP, the metabolites identified by the Bayesian analysis (Bayes), the metabolites identified by the PLS-DA (PLS), and the biological origin determined for each metabolite. ## References 1. de Vos WM, Tilg H, Van Hul M, Cani PD. Gut microbiome and health: mechanistic insights. Gut (2022.0) 71 1020-1032. DOI: 10.1136/gutjnl-2021-326789 2. Manor O, Dai CL, Kornilov SA, Smith B, Price ND, Lovejoy JC. Health and disease markers correlate with gut microbiome composition across thousands of people. 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--- title: 'Device-measured movement behaviors and cardiac biomarkers in older adults without major cardiovascular disease: the Seniors-ENRICA-2 study' authors: - Blanca Fabre-Estremera - Antonio Buño-Soto - Esther García-Esquinas - Verónica Cabanas-Sánchez - David Martínez-Gómez - Fernando Rodríguez-Artalejo - Rosario Ortolá journal: European Review of Aging and Physical Activity year: 2023 pmcid: PMC9996928 doi: 10.1186/s11556-023-00313-8 license: CC BY 4.0 --- # Device-measured movement behaviors and cardiac biomarkers in older adults without major cardiovascular disease: the Seniors-ENRICA-2 study ## Abstract ### Background High-sensitivity cardiac troponin T (hs-cTnT) and N-terminal pro-brain natriuretic peptide (NT-proBNP) are biomarkers of myocardial infarction and heart failure, respectively, and indicate cardiovascular risk. Since low physical activity (PA) and sedentary behavior (SB) are also associated with higher cardiovascular risk, and this association could be a consequence of higher levels of cardiac biomarkers, we examined the association of device-measured movement behaviors with hs-cTnT and NT-proBNP in older men and women without major cardiovascular disease (CVD). ### Methods We used data from 1939 older adults from the Seniors-ENRICA-2 study. Accelerometers were used to assess time spent in sleep, SB, light PA (LPA), and moderate-to-vigorous PA (MVPA). Linear regression models were fitted separately in eight strata defined by sex, by median total PA time, and by the presence of subclinical cardiac damage according to cardiac biomarkers levels. ### Results In the less active men with subclinical cardiac damage, spending 30 min/day more of MVPA was associated with a mean percentage difference (MPD) ($95\%$ confidence interval) in hs-cTnT of − 13.1 (− 18.3, − 7.5); MPDs in NT-proBNP per 30 min/day increment were 5.8 (2.7, 8.9) for SB, − 19.3 (− 25.4, − 12.7) for LPA and − 23.1 (− 30.7, − 14.6) for MVPA. In women with subclinical cardiac damage who were less physically active, 30 min/day more of SB, LPA and MVPA were associated with MPDs in hs-cTnT of 2.1 (0.7, 3.6), − 5.1 (− 8.3, − 1.7) and − 17.5 (− 22.9, − 11.7), respectively, whereas in those more active, LPA and MVPA were associated with MPDs of 4.1 (1.2, 7.2) and − 5.4 (− 8.7, − 2.0), respectively. No associations were found with NT-proBNP in women. ### Conclusions The relationship between movement behaviors and cardiac biomarkers in older adults without major CVD depends on sex, subclinical cardiac damage and PA level. More PA and less SB were generally related to lower cardiac biomarkers levels among less active individuals with subclinical cardiac damage, with greater benefits for hs-cTnT in women than men and no benefits for NT-proBNP in women. ### Supplementary Information The online version contains supplementary material available at 10.1186/s11556-023-00313-8. ## Introduction Cardiovascular diseases (CVD) are the leading cause of death worldwide, especially among individuals > 65 years, where they accounted for $42\%$ of all deaths in 2019 [1]. While serum cardiac troponins and natriuretic peptides are useful for the clinical management of acute myocardial infarction and heart failure, respectively, in the general population, these two biomarkers are related to subclinical cardiac damage and indicate CVD risk [2–4]. Cardiac troponin (cTn) is a heterotrimeric complex (troponin T, troponin C and troponin I) that regulates the interaction between actin and myosin filaments in cardiac muscle. As cTnT and cTnI are highly specific for cardiac myocytes, both have become the standard biomarkers for risk stratification in patients with suspected acute coronary syndrome and for the diagnosis of myocardial infarction [5, 6]. An important step forward has been the development of high-sensitivity assays, which detect concentrations 10–100 times lower than those of conventional assays. The clinical decision value for acute myocardial infarction is the 99th percentile (p99) of the reference population, preferably stratified by sex, which facilitates earlier treatment or exclusion resulting in better outcomes [5–7]. The most commonly used biomarkers for the diagnosis of heart failure and cardiac dysfunction are the B-type natriuretic peptides, mostly synthesized and secreted by left ventricle myocytes: N-terminal pro-B-type natriuretic peptide (NT-proBNP) and biologically active B-type natriuretic peptide. The recommended cut-off values to exclude heart failure in a non-acute setting are 35 and 125 pg/mL for BNP and NT-proBNP, respectively. In addition, an age-dependent cutoff value of NT-proBNP may be more useful in this setting [8, 9]. Physical inactivity has been consistently associated with a significant increase in CVD risk and a decrease in life expectancy [10–12]. In older adults, total physical activity (PA) has been shown to have a favorable impact on metabolic disease [13], hypertension [14], premature mortality [15] and leukocyte telomere length [16], a hallmark of aging. Moderate-to-vigorous PA (MVPA) has also been associated with significant protection from coronary heart disease (CHD) [17, 18]. Conversely, sedentary behavior (SB) has been inversely associated with all-cause mortality, CVD mortality and cancer mortality, and also with a lower incidence of CVD, cancer and type-2 diabetes in older adults, independently of PA [19]. Additionally, sleep duration has been related to CVD risk [20, 21]. Therefore, a balance between different movement behaviors (sleep, SB and different intensities of PA) is strongly encouraged [22]. Most previous research on the relationship between movement behaviors and cardiac biomarkers has only focused on PA in younger active individuals, reporting exercise-induced troponin elevations above the p99 [23, 24]. However, few studies have investigated the association between movement behaviors and cardiac biomarkers in older adults [25–27]. This is important because such association could explain the effect of movement behaviors on CVD risk in old age. To our knowledge, only one study has analyzed cardiac biomarkers concentrations in older adults with different levels of movement behaviors, finding that MVPA may be more important in protecting against cardiac health deterioration in less active individuals [27]. However, the mentioned study only included men and did not consider 24-hour movement behaviors, including sleep, or other accelerometry variables of interest, such as bouted time or mean movement intensity. Also, as with many preventive interventions, the effect of PA may depend on the level of cardiac damage or CVD risk [28]. Therefore, we aimed to investigate the association between device-measured movement behaviors and serum high-sensitivity cardiac troponin T (hs-cTnT) and NT-proBNP in older men and women without major CVD. We hypothesized that this association depends on baseline levels of cardiac biomarkers as well as the level of PA. ## Study design and participants Data came from the Seniors-ENRICA-2 cohort [29]. Participants were recruited between 2015 and 2017 by stratified random sampling of all community-dwelling individuals aged 65 years and older holding a national healthcare card and living in two districts of the city of Madrid (Spain) and four large surrounding towns. Initially, a computer-assisted telephone interview was conducted to collect socio-demographic, lifestyle and morbidity data. Next, two home visits by study staff were done to perform a physical examination, obtain a diet history, place a wrist accelerometer, and obtain serum samples. ## Device-measured movement behaviors Each participant in the study received an ActiGraph GT9X accelerometer (ActiGraph Inc., Pensacola, FL, USA), and was asked to wear it on their non-dominant wrist (to minimize misclassification of arm movements during sedentary activities as physical activity) for seven consecutive days without removing it unless it was for bathing or swimming. Details on the processing of the accelerometer data have been published elsewhere [30]. The raw accelerometer data were processed using the GGIR package (v.1.7–0, https://cran.r-project.orgweb/packages/GGIR/) in R [31]. SB and PA intensities were identified using previously proposed thresholds for the Euclidean Norm of the raw accelerations Minus One (ENMO), in milligravitational units (mg): < 45 mg for SB, 45–99 mg for light PA (LPA), and ≥ 100 mg for MVPA [32]. Sleep periods were detected with an automatized algorithm [33]. Total PA time was the result of the sum of time in LPA and MVPA. Time in sedentary bouts ≥30 min, and in MVPA bouts ≥10 min were also registered, considering bouts in each behavior when $80\%$ of the minimum required time met the threshold criteria. The number of sedentary breaks was estimated by subtracting 1 to the number of sedentary blocks, regardless of duration. Mean movement intensity was estimated with the daily mean of acceleration in mg. To avoid SB and PA underestimation [34], participants were included if they had at least 4 valid days (≥3 weekdays and ≥ 1 weekend-day), in which they wore the accelerometer ≥16 h/day. Non-wear time and time with abnormally high accelerations (i.e., ≥5.5 g) were imputed using the mean of the acceleration recorded for each participant during the corresponding time intervals. ## Cardiac biomarkers Fasting venous blood samples were collected from the arm of each participant in RST tubes with thrombin-based clot activator and polymer gel (Becton Dickinson). The tubes were centrifuged at 3.000 rpm for 10 minutes within 3 h of collection and serum was aliquoted, frozen at − 80 °C and stored up to 3.6 years at the Department of Preventive Medicine and Public Health, Universidad Autónoma de Madrid. Serum hs-cTnT and NT-proBNP were measured between July 2019 and June 2020 on a cobas®6000 analyzer (Roche Diagnostics) using an electrochemiluminescence Elecsys® immunoassay, at the Department of Laboratory Medicine, ‘La Paz’ University Hospital (Madrid). The hs-cTnT and the NT-proBNP assays have a limit of detection of 3 pg/mL and 10 pg/mL, respectively. The assays were performed using the manufacturer’s calibrators and quality controls. For hs-cTnT, the inter-assay coefficient of variation was $5.10\%$ for a mean concentration of 26.86 pg/mL and $3.60\%$ for a mean concentration of 2000.96 pg/mL. For NT-proBNP, the inter-assay coefficient of variation was $7.28\%$ for a mean concentration of 134.92 pg/mL and $8.33\%$ for a mean concentration of 4609.34 pg/mL. The Roche hs-cTnT assay has a sex-specific 99th percentile upper reference limit (URL) of 9.0 ng/L for females and 16.8 ng/L for males. ## Potential confounders We also collected information on sociodemographic and lifestyle characteristics, including sex, age, educational level, tobacco smoking and alcohol consumption. Food consumption and energy intake (kcal/day) were obtained from a validated diet history [35]; the diet quality was estimated with the Mediterranean Diet Adherence Screener (MEDAS), which ranges from 0 to 14, with higher scores indicating higher quality [36]. Height and weight were measured by trained staff under standardized conditions using electronic scales (model Seca 841, precision to 0.1 kg) and portable extendable stadiometers (model Ka We 44444Seca), with the participants barefoot and lightly clothed, and body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Systolic blood pressure (SBP) in mmHg was measured three times separated by 1–2-minute intervals, by trained study staff under standardized conditions with validated automatic devices (Omron model M6), using the mean of the second and third measurements for analyses. Fasting serum glucose, total cholesterol, triglycerides and creatinine were measured on Atellica® Solution-CH chemistry analyzer (Siemens Healthineers) using colorimetric enzymatic methods. LDL-cholesterol measurement depended on triglycerides levels: if triglycerides < 250 mg/dL, LDL-cholesterol was calculated with the Friedewald formula (LDL = total cholesterol - triglycerides/5 - HDL), and if triglycerides ≥250 mg/dL, LDL-cholesterol was determined on Atellica® Solution-CH chemistry analyzer (Siemens Healthineers) by a colorimetric enzymatic method [37]. The estimated glomerular filtration rate (eGFR) was calculated with the Chronic Kidney Disease - Epidemiology Collaboration (CKD-EPI) eq. [ 38], and CKD defined as an eGFR < 60 mL/min/1.73m2. Lastly, the presence of major CVD was determined by medical diagnosis of acute myocardial infarction, stroke, chronic heart failure or atrial fibrillation recorded in the Primary Care database from the Community of Madrid (Spain). ## Statistical analysis Analyses were performed separately in men and women, and by PA and subclinical cardiac damage, as these two variables also modified the study associations. Participants were classified as less or more active according to median total PA time (3.53 h/day) and the presence or absence of subclinical cardiac damage, determined by high baseline levels of hs-cTnT and/or NT-proBNP. The cutoff values used for hs-cTnT were based on the Fourth Universal Definition of Myocardial Infarction, which considers that the term myocardial injury should be used when there is evidence of elevated cTn values with at least one value above the sex-specific p99 URL (for the Roche hs-cTnT assay, in men: 16.8 pg/dL, in women: 9.0 pg/mL) [7]; those for NT-proBNP were based on the European Society of Cardiology guidelines, which consider levels ≤75 pg/mL if aged 65–75 years, and ≤ 250 pg/mL if > 75 years, to exclude heart failure in non-acute settings [8, 9]. Cardiac biomarker levels according to study participant characteristics were summarized with geometric means and geometric standard deviation factors, as their distributions were positively skewed. Associations between time in each movement behavior and hs-cTnT or NT-proBNP were analyzed by linear regression with log-transformed cardiac biomarker levels to achieve parametric distributions. Results were summarized with mean percentage differences (MPD), and their $95\%$ confidence interval (CI), in cardiac biomarkers per 30 min/day increments in sleep, SB, LPA or MVPA, which were obtained by subtracting 1 from the exponentiated β coefficients in the regression models, and multiplying the result by 100. Three models were built with incremental adjustment for potential confounders: Model 1 adjusted for sex, age, and educational level; Model 2 further adjusted for tobacco smoking, alcohol consumption, MEDAS score, and energy intake; and Model 3 further adjusted for BMI, SBP, serum glucose and LDL-cholesterol levels, and eGFR. Since the results from the three models were very similar, only the fully adjusted ones are presented. Also, dose-response associations were evaluated by modeling time in each movement behavior as restricted cubic splines, with models for sleep and SB adjusted for MVPA time, and models for LPA and MVPA adjusted for SB time. P values for non-linearity were calculated by testing the null hypothesis that the coefficient of the second spline equals 0 using Wald tests. Associations of the other accelerometry variables with hs-cTnT or NT-proBNP levels were examined by modeling 30 min/day increments (for time in bouts) or 1-SD (standard deviation) increments (for number of sedentary breaks and mean movement intensity), using the same statistical procedures. To account for potential false-positive results due to multiple testing, we calculated the $5\%$ false discovery rate for all the comparisons using the Benjamini-Hochberg procedure [39], and adjusted the statistical significance accordingly. Analyses were performed with Stata®, version 16 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX:StataCorp LLC). ## Results From the 3.273 study participants in the Seniors-ENRICA-2 study, we excluded 643 with a previous diagnosis of major CVD and 8 without information about it, 586 without valid accelerometry records (478 without accelerometry measurements and 108 not meeting the wearing time requirements), 46 without hs-cTnT and/or NT-proBNP determinations, and 51 who lacked data on potential confounders. Thus, the analytical sample included 1.939 individuals. Study participants had a mean age of 71.5 years and $55.65\%$ were women. Supplemental Table 1 and Table 1 show the characteristics of study participants and cardiac biomarker concentrations in each stratum, respectively. Cardiac marker levels were higher in the less active participants than in the more active ones (Table 1). hs-cTnT levels were higher in men, older participants, and those with higher energy intake, higher SBP, higher glycaemia, lower LDL-cholesterol, and CKD, whereas NT-proBNP levels were higher in older participants and those with normal weight, higher glycaemia and CKD.Table 1Cardiac biomarkers by characteristics of study participants, stratified by subclinical cardiac damage and PA timehs-cTnT (pg/mL)NT-proBNP (pg/mL)No subclinical cardiac damageaSubclinical cardiac damageaNo subclinical cardiac damageaSubclinical cardiac damageaLow PA timebHigh PA timebLow PA timebHigh PA timebLow PA timebHigh PA timebLow PA timebHigh PA timebnGM (GSD)nGM (GSD)nGM (GSD)nGM (GSD)nGM (GSD)nGM (GSD)nGM (GSD)nGM (GSD)Total4388.19 (1.36)4527.76 (1.36)53212.22 (1.63)51711.23 (1.55)43850.43 (1.70)45245.96 (1.64)532127.57 (1.99)517120.90 (1.78)Sex Men2679.33 (1.31)2349.21 (1.32)19013.61 (1.66)16912.33 (1.54)26747.55 (1.68)23442.85 (1.65)190132.19 (2.17)169121.52 (1.88) Women1716.69 (1.23)2186.54 (1.25)34210.09 (1.56)3488.83 (1.50)17155.29 (1.72)21849.56 (1.62)342125.07 (1.88)348120.61 (1.73)Age (years) 65–701897.89 (1.33)2847.44 (1.36)1749.74 (1.54)2338.57 (1.49)18941.94 (1.46)28442.01 (1.57)174111.51 (1.85)233115.32 (1.66) ≥702498.43 (1.34)1688.46 (1.33)35812.03 (1.65)28411.02 (1.56)24958.01 (1.80)16853.51 (1.70)358136.19 (2.03)284125.69 (1.87)Educational level ≤ Primary2488.18 (1.34)2897.83 (1.35)34311.53 (1.69)3529.89 (1.55)24851.69 (1.74)28947.12 (1.62)343126.21 (2.01)352122.04 (1.82) Secondary978.30 (1.35)837.69 (1.40)9011.10 (1.53)899.82 (1.57)9748.62 (1.60)8342.13 (1.71)90127.95 (2.15)89116.88 (1.69) University938.12 (1.32)807.84 (1.36)9910.34 (1.52)769.66 (1.58)9349.06 (1.71)8045.99 (1.65)99132.06 (1.74)76120.47 (1.74)Tobacco smoking Non-smoker2027.80 (1.35)2157.44 (1.33)31610.93 (1.65)3109.68 (1.55)20254.79 (1.73)21545.59 (1.65)316126.11 (1.98)310119.85 (1.84) Former smoker1808.61 (1.32)2008.11 (1.36)16511.79 (1.59)1689.95 (1.60)18046.33 (1.66)20045.75 (1.62)165122.20 (1.94)168115.74 (1.65) Current smoker568.35 (1.35)378.35 (1.41)5111.29 (1.62)3910.75 (1.44)5649.14 (1.70)3749.45 (1.74)51157.46 (2.12)39156.44 (1.78)Alcohol consumption Non- drinker767.39 (1.29)667.14 (1.28)13211.60 (1.75)1039.22 (1.53)7657.55 (1.75)6650.31 (1.53)132136.81 (2.03)103117.04 (1.66) Former drinker218.17 (1.36)227.68 (1.25)3913.16 (1.99)3310.70 (1.52)2149.54 (1.74)2247.08 (1.59)39140.49 (2.10)33117.79 (1.66) Moderate drinkerc2388.29 (1.35)2407.78 (1.38)26810.76 (1.54)2649.94 (1.54)23849.71 (1.68)24046.70 (1.63)268123.14 (1.96)264117.10 (1.77) Heavy drinker1038.60 (1.34)1248.26 (1.37)9311.33 (1.56)1179.96 (1.62)10347.46 (1.68)12442.31 (1.73)93122.83 (1.94)117134.69 (1.94)MEDAS score (0–14) ≤61648.15 (1.35)1377.70 (1.36)18411.64 (1.75)1739.68 (1.57)16448.79 (1.75)13745.71 (1.63)184125.48 (2.00)173118.97 (1.76) 7–81868.16 (1.33)1897.74 (1.35)25611.01 (1.57)2329.88 (1.56)18651.30 (1.71)18947.20 (1.57)256128.38 (1.97)232118.42 (1.75) ≥9888.35 (1.34)1268.02 (1.37)9211.04 (1.56)11210.02 (1.54)8851.75 (1.59)12644.44 (1.77)92129.54 (2.02)112129.40 (1.87)Energy intake (kcal/day) < 18001207.29 (1.32)1306.92 (1.30)22710.77 (1.76)1808.86 (1.48)12056.93 (1.81)13045.75 (1.58)227129.81 (2.04)180121.83 (1.72) 180–20501618.34 (1.34)1377.62 (1.36)16211.54 (1.56)19810.05 (1.55)16148.77 (1.69)13747.06 (1.65)162131.95 (1.97)198123.43 (1.85) > 20501578.79 (1.33)1858.64 (1.35)14311.62 (1.50)13910.94 (1.63)15747.58 (1.61)18545.32 (1.69)143119.44 (1.91)139116.24 (1.76)BMI (kg/m2) < 251038.07 (1.35)1387.58 (1.35)12710.92 (1.65)1729.40 (1.51)10356.11 (1.65)13848.47 (1.68)127133.58 (1.92)172127.01 (1.70) 25–302278.28 (1.34)2377.85 (1.36)23210.99 (1.65)23210.30 (1.59)22750.39 (1.68)23745.80 (1.62)232121.96 (1.95)232115.65 (1.86) ≥301088.14 (1.34)778.08 (1.37)17311.78 (1.59)1739.59 (1.56)10845.63 (1.78)7742.25 (1.66)173131.00 (2.08)173123.31 (1.73)SBP (mmHg) < 1301788.13 (1.37)2017.50 (1.37)20110.76 (1.63)2269.57 (1.52)17852.74 (1.73)20144.37 (1.66)201120.68 (1.88)226114.91 (1.74) ≥1302608.23 (1.32)2518.06 (1.34)33111.52 (1.63)29110.06 (1.59)26048.91 (1.68)25147.29 (1.63)331131.94 (2.04)291125.77 (1.81)Serum glucose (mg/dL) < 1002677.96 (1.35)3027.64 (1.37)33910.71 (1.59)3809.53 (1.53)26752.76 (1.72)30247.04 (1.62)339132.41 (1.93)380122.67 (1.74) ≥1001718.58 (1.33)1508.15 (1.33)19312.20 (1.69)13710.76 (1.63)17147.00 (1.67)15043.87 (1.69)193119.49 (2.07)137116.14 (1.89)Serum LDL-cholesterol (mmol/L) < 1303198.39 (1.35)2867.91 (1.37)38111.75 (1.63)33910.31 (1.58)31951.69 (1.72)28645.81 (1.69)381132.71 (2.00)339119.61 (1.79) ≥1301197.69 (1.32)1667.63 (1.33)15110.00 (1.62)1789.01 (1.50)11947.20 (1.66)16646.24 (1.57)151115.47 (1.93)178123.41 (1.77)eGFRd (mL/min/1.73m2) ≥604278.18 (1.34)4447.77 (1.36)48110.72 (1.58)4899.75 (1.56)42750.30 (1.70)44445.80 (1.64)481123.22 (1.94)489120.04 (1.77) < 60118.81 (1.37)810.11 (1.18)5117.29 (1.81)2811.70 (1.49)1155.66 (1.63)856.07 (1.75)51176.98 (2.21)28137.13 (1.90)BMI body mass index, eGFR estimated Glomerular Filtration Rate, GM geometric mean, GSD geometric standard deviation factor, hs-cTnT high-sensitivity cardiac troponin T, MEDAS Mediterranean Diet Adherence Screener, NT-proBNP N-terminal pro-B-type natriuretic peptide, PA physical activity, SBP systolic blood pressureaSubclinical cardiac damage: hs-cTnT >p99 (16.8 pg/mL in men and 9.0 pg/mL in women) and/or NT-proBNP >cutoff (75 pg/mL if age ≤ 75 years, 250 pg/mL if age > 75 years)bLow PA time: total PA time ≤ 3.53 h/day; high PA time: total PA time > 3.53 h/daycModerate drinker: < 10 g/day in women and < 20 g/day in mendEstimated Glomerular Filtration Rate by the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation Participants wore the accelerometer for a mean (SD) time of 22.83 (2.17) hours per day during 6.67 (0.64) valid days. Supplemental Table 2 shows time spent in each movement behavior among men and women in each stratum. Men spent more time in SB and MVPA and less time sleeping and in LPA than women. Less active participants (those with a total PA time ≤ 3.53 h/day) spent more time sleeping (men, $33\%$; women, $34\%$) and in SB (men, $57\%$; women, $56\%$), and less time in LPA (men, $7\%$; women, $9\%$) and MVPA (men, $3\%$; women, $2\%$) than the more active participants (31, 32, 50, 49, 12, 14, 7 and $6\%$, respectively). Compared with participants without subclinical cardiac damage, those with it spent more time in LPA (men only) and less time in MVPA (women only). Associations of time in each movement behavior with cardiac biomarkers in men and women with and without subclinical cardiac damage are shown with restricted cubic splines in Figs. 1 and 2 and Supplemental Figs. 1–2. Among participants with subclinical cardiac damage, there was evidence of departure from linearity in the associations with hs-cTnT of MVPA in men ($p \leq 0.001$) and of LPA in women ($p \leq 0.001$) (Fig. 1), and in the associations with NT-proBNP, of SB, LPA and MVPA in men ($$p \leq 0.010$$, $$p \leq 0.005$$ and $p \leq 0.001$, respectively) (Fig. 2). Such evidence was not found in participants without subclinical cardiac damage, except between LPA and NT-proBNP in men ($$p \leq 0.031$$) (Supplemental Figs. 1–2). The fact that study associations varied between males and females, between those with and without subclinical cardiac damage, and that it was not linear across PA level, further supported our decision to stratify the main analyses by sex, subclinical cardiac damage and PA level. Fig. 1Association of each movement behavior with hs-cTnT in men and women with subclinical cardiac damage. Restricted cubic splines whose values are geometric means ($95\%$ confidence interval) of hs-cTnThs-cTnT: high-sensitivity cardiac troponin T; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity; PA: physical activity; SB: sedentary behaviorSubclinical cardiac damage: hs-cTnT >p99 (16.8 pg/mL in men and 9.0 pg/mL in women) and/or NT-proBNP >cutoff (75 pg/mL if age ≤ 75 years, 250 pg/mL if age > 75 years). Linear regression models adjusted for sex, age, educational level (primary or less, secondary, or university), smoking status (never, former, or current), alcohol consumption (never, moderate, heavy, or former), energy intake (kcal/day), Mediterranean Diet Adherence Screener (MEDAS) score, body mass index (kg/m2), serum glucose (mg/dL), serum LDL-cholesterol (mg/dL), systolic blood pressure (mmHg) and glomerular filtration rate. Models for sleep and SB further adjusted for MVPA time, and models for LPA and MVPA further adjusted for SB timeFig. 2Association of each movement behavior with NT-proBNP in men and women with subclinical cardiac damage. Restricted cubic splines whose values are geometric means ($95\%$ confidence interval) of NT-proBNPLPA: light physical activity; MVPA: moderate-to-vigorous physical activity; NT-proBNP: N-terminal pro-B-type natriuretic peptide; PA: physical activity; SB: sedentary behaviorSubclinical cardiac damage: hs-cTnT >p99 (16.8 pg/mL in men and 9.0 pg/mL in women) and/or NT-proBNP >cutoff (75 pg/mL if age ≤ 75 years, 250 pg/mL if age > 75 years). Linear regression models adjusted for sex, age, educational level (primary or less, secondary, or university), smoking status (never, former, or current), alcohol consumption (never, moderate, heavy, or former), energy intake (kcal/day), Mediterranean Diet Adherence Screener (MEDAS) score, body mass index (kg/m2), serum glucose (mg/dL), serum LDL-cholesterol (mg/dL), systolic blood pressure (mmHg) and glomerular filtration rate. Models for sleep and SB further adjusted for MVPA time, and models for LPA and MVPA further adjusted for SB time Table 2 and Supplemental Table 3 summarize the association of increments in each movement behavior with cardiac biomarkers in men and women, stratified by subclinical cardiac damage and PA time. Among men, no associations were found between movement behaviors and cardiac biomarker levels, except in the group of less active men with subclinical cardiac damage, where all movement behaviors except sleep were related with cardiac biomarkers levels. Thus, in this group, spending 30 min/day more in SB was related to higher NT-proBNP levels, with a MPD ($95\%$ CI) of 5.8 (2.7, 8.9) (Table 2), and accumulating SB time in bouts ≥30 min did not modify much the association (Supplemental Table 3). There was also an inverse association of the number of sedentary breaks with NT-proBNP (MPD per 1-SD increment of − 25.4 [− 33.1, − 16.8]), but not with hs-cTnT (Supplemental Table 3). Also in this group of less active men with subclinical cardiac damage, more LPA was linked to lower NT-proBNP levels (− 19.3 [− 25.4, − 12.7] per 30 min/day increment in LPA) and more time in MVPA was associated with lower levels of both cardiac biomarkers (− 13.1 [− 18.3, − 7.5] per 30 min/day increment for hs-cTnT and − 23.1 [− 30.7, − 14.6] for NT-proBNP) (Table 2). Accumulating MVPA time in bouts ≥10 min did not modify much the association with NT-proBNP levels (− 23.6 [− 37.2. -7.0]), but the association with hs-cTnT was greatly reduced and became non-significant (Supplemental Table 3). Lastly, a strong inverse association was found between mean movement intensity and NT-proBNP levels (− 27.8 [− 35.9, − 18.6] per 1-SD increment), but not with hs-cTnT (Supplemental Table 3).Table 2Association of movement behaviors with cardiac biomarkers in men and women, stratified by subclinical cardiac damage and PA timeMenWomenNo subclinical cardiac damagebSubclinical cardiac damagebNo subclinical cardiac damagebSubclinical cardiac damagebLow PA timecn = 267High PA timecn = 234Low PA timecn = 190High PA timecn = 169Low PA timecn = 171High PA timecn = 218Low PA timebn = 342High PA timecn = 348hs-cTnT Sleep− 0.6 (− 2.4, 1.3)− 0.4 (− 2.7, 1.9)− 1.7 (− 3.6, 0.2)− 0.9 (− 3.6, 1.9) 0.1 (− 2.2, 2.4)− 0.4 (− 3.0, 2.2)− 0.4 (− 2.0, 1.2)− 2.3 (− 4.1, − 0.4) SB 0.2 (− 1.4, 1.9) 0.1 (− 1.8, 2.0) 3.3 (1.4, 5.1)− 0.7 (− 2.9, 1.4)− 1.1 (− 3.4, 1.2)− 1.0 (− 3.0, 0.9) 2.1 (0.7, 3.6)d− 1.5 (− 0.1, 3.1) LPA 1.9 (− 2.4, 6.5) 1.0 (− 3.1, 5.2)− 3.9 (− 8.4, 0.8) 2.3 (− 1.9, 6.7) 2.2 (− 2.7, 7.3) 2.8 (− 0.8, 6.6)− 5.1 (− 8.3, − 1.7)d 4.1 (1.2, 7.2)d MVPA− 1.1 (− 7.0, 5.3)− 0.2 (− 4.0, 3.8) − 13.1 (− 18.3, − 7.5)d 3.4 (− 1.3, 8.2) 6.4 (− 2.3, 16.0) 1.8 (− 2.0, 5.7)− 17.5 (− 22.9, − 11.7)d− 5.4 (− 8.7, − 2.0)dNT-proBNP Sleep 1.5 (− 1.6, 4.7) 2.2 (− 1.7, 6.3)− 0.8 (− 3.9, 2.5)− 2.0 (− 6.5, 2.6) 0.2 (− 3.5, 4.1) 0.6 (− 3.7, 5.0) 2.3 (− 0.3, 5.1) 1.8 (− 1.3, 5.0) SB− 1.8 (− 4.5, 1.0) 0.2 (− 2.9, 3.5) 5.8 (2.7, 8.9)d− 0.6 (− 4.1, 3.0) 0.6 (− 3.2, 4.6)− 0.7 (− 3.9, 2.6)− 0.6 (− 3.0, 1.8)− 1.7 (− 4.2, 0.8) LPA 8.1 (0.5, 16.2)− 1.2 (− 7.7, 5.7)−19.3 (− 25.4, − 12.7)d 1.0 (− 5.8, 8.3)− 1.6 (− 9.3, 6.7) 3.4 (− 2.6, 9.8)− 4.9 (− 10.1, 0.7) 0.4 (− 4.3, 5.3) MVPA−6.7 (− 15.9, 3.4)−4.8 (− 10.8, 1.6)−23.1 (− 30.7, − 14.6)d 7.0 (− 0.9, 15.5)−7.4 (− 19.8, 6.8)− 2.1 (− 8.1, 4.3)− 11.4 (− 20.9, − 0.8)− 0.0 (− 5.8, 6.1)Values are mean percentage differencesa ($95\%$ confidence interval) in each cardiac biomarker per 30 min/day increment in each movement behavior in each stratumLinear regression models adjusted for sex, age, educational level (primary or less, secondary, or university), smoking status (never, former, or current), alcohol consumption (never, moderate, heavy, or former), energy intake (kcal/day), Mediterranean Diet Adherence Screener (MEDAS) score, body mass index (kg/m2), serum glucose (mg/dL), serum LDL-cholesterol (mg/dL), systolic blood pressure (mmHg) and glomerular filtration rate (mL/min)aMean percentage differences were calculated by subtracting 1 from the exponentiated β-coefficients in the regression models with log-transformed values of cardiac biomarkers and multiplying the result by 100bSubclinical cardiac damage: hs-cTnT >p99 (16.8 pg/mL in men and 9.0 pg/mL in women) and/or NT-proBNP >cutoff (75 pg/mL if age ≤ 75 years, 250 pg/mL if age > 75 years)cLow PA time: total PA time ≤ 3.53 h/day; high PA time: total PA time > 3.53 h/daydStatistically significant association when using a false discovery rate of $5\%$. hs-cTnT: high-sensitivity cardiac troponin T; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity; NT-proBNP: N-terminal pro-B-type natriuretic peptide; PA: physical activity; SB: sedentary behavior Among women, no associations were found between movement behaviors and NT-proBNP levels, or between movement behaviors and hs-cTnT levels in those without subclinical cardiac damage. However, in women with subclinical cardiac damage, all movement behaviors except sleep were related with hs-cTnT levels, with differences according to the level of PA. Thus, spending more time in SB was linked to higher hs-cTnT levels only among the less active women with subclinical cardiac damage, with a MPD of 2.1 (0.7, 3.6) per 30 min/day increment (Table 2), and accumulating SB time in bouts ≥30 min did not modify much the association (Supplemental Table 3). Spending more time in LPA was related to lower hs-cTnT levels in the less active women with subclinical cardiac damage (− 5.1 [− 8.3, − 1.7] per 30 min/day increment in LPA), whereas in the more active ones, hs-cTnT levels were higher for more time in LPA (4.1 [1.2, 7.2] per 30 min/day increment) and for more sedentary breaks (9.4 [4.5, 14.6] per 1-SD increment) (Table 2, Supplemental Table 3). Spending more time in MVPA was associated with lower hs-cTnT levels among less active women with subclinical cardiac damage (MDP of − 17.5 [− 22.9, − 11.7] per 30 min/day), and also, although to a lesser extent, in the more active ones (− 5.4 [− 8.7, − 2.0]) (Table 2). When considering accumulated time in MVPA bouts ≥10 min, the association strengthened in the less active group (− 28.4 [− 41.1, − 13.0] per 30 min/day increment), but weakened in the more active one, becoming non-significant (Supplemental Table 3). Lastly, an inverse association was found between mean movement intensity and hs-cTnT levels only in less active women with subclinical cardiac damage (MDP per 1-SD increment of − 16.0 [− 22.2, − 9.4]) (Supplemental Table 3). ## Discussion In our study of Spanish older adults without major CVD, the relationship between movement behaviors and cardiac biomarkers levels depends on sex, subclinical cardiac damage and PA level. The strongest associations were observed in less active individuals with subclinical cardiac damage, in whom more PA and less SB were generally related to lower levels of hs-cTnT and NT-proBNP. Regular PA is one of the cornerstones of prevention and treatment of many chronic diseases, such as CHD [17], diabetes mellitus [40], hypertension [41] or obesity. However, in previous investigations, the dose-response relationship of more PA and less sedentariness with lower incidence of CVD [42] and lower all-cause mortality [15] in older adults was not linear. A meta-analysis on the effect of adherence to moderate-intensity PA (MPA) recommendations on the risk of CHD [18] showed that, compared with inactive participants, those performing the minimum recommended amount (150 min/week MPA) or the amount recommended for additional benefits (300 min/week MPA) had a $14\%$ and a $20\%$ lower risk of CHD, respectively. However, in those with higher PA, the risk reduction was only slightly higher than in those with 300 min/week MPA. Lastly, less active participants also had a significantly lower risk of CHD than the inactive ones, suggesting that doing some PA is better than doing nothing [43]. The non-linear associations of PA with cardiac biomarkers observed in our study in participants with subclinical cardiac damage are in line with this meta-analysis, as well as the lower levels of hs-cTnT found for more PA in less active women with subclinical cardiac damage and of both biomarkers in men. However, in contrast to previous studies that also reported a non-linear relationship between sleep and all-cause mortality and CVD, with the lowest risk for 6–8 h/day compared to short (< 6 h/day) [20] or long sleep duration (> 8–9 h/day) [20, 21], no evidence of a relationship with cardiac biomarker levels was found in our study. Our results in men are also in line with those reported by Parsons et al. [ 27]. Like us, they observed a non-linear relationship of PA with cardiac biomarker levels in older men, and suggested that MVPA may be more important in protecting against cardiac health deterioration in less active men, consistent with the widely known health benefits of MVPA [17, 18]. However, while their results suggested that LPA could also play a role for hs-cTnT in less active men, ours support a more important role for NT-proBNP levels. SB also seemed to be more important in our study than in theirs for NT-proBNP levels at low levels of PA. Interestingly, in a post hoc analysis of the aforementioned study, movement behaviors were not associated with NT-proBNP among less active men with normal blood pressure, but only in those with hypertension, a group with higher NT-proBNP levels possibly consisting of individuals with subclinical cardiac damage, similar to our finding associations only among less active men with subclinical cardiac damage. The stronger associations of movement behaviors with NT-proBNP levels than with hs-cTnT levels in men with subclinical cardiac damage found in our study, and also reported by Parsons et al. [ 27], may be due to the different pathophysiological mechanisms involved in the production of each biomarker: cardiomyocyte injury for hs-cTnT and myocardial stretch for NT-proBNP [44]. The main difference between men and women in our study was the absence of associations for NT-proBNP observed in women. This may be explained by the stronger reported association of NT-proBNP with incident heart failure in men, and the stronger and earlier activation of the natriuretic peptide system in men [45, 46]. However, the associations with hs-cTnT observed in women and men in our study were consistent, although somewhat stronger in women, possibly due to the sex differences in PA intensity. Thus, although our findings among older women with subclinical cardiac damage who were more physically active, in whom more LPA and more sedentary breaks were linked to higher hs-cTnT, were unexpected, it is possible that when PA is already high, doing more LPA, which can mean also doing more sedentary breaks, does not add any benefit, possibly because it could even replace MVPA. In fact, using isotemporal substitution models, 30 min/day more of MVPA at the expense of LPA was associated with an $11.7\%$ lower hs-cTnT level, suggesting that to obtain more benefits within the same PA time, the intensity of PA should be increased. However, given that men spend more time in MVPA and less in LPA than women (Supplementary Table 2), more active men with subclinical cardiac damage would not obtain any benefit from doing more PA, and less active men would obtain fewer benefits than less active women. Regarding bouts, the most recent WHO PA guidelines [43] do not require PA to be performed in bouts of sufficient duration because new evidence shows that PA of any duration is associated with better health outcomes, including all-cause mortality [15] and multimorbidity [47]. In fact, we found that, among less active men with subclinical cardiac damage, the association with NT-proBNP did not vary much when MVPA time was accumulated in bouts ≥10 min, and the association with hs-cTnT was even lost. However, the fact that among less active women with subclinical cardiac damage (who perform very little MVPA, as shown in Supplemental Table 2) the association with hs-cTnT strengthened when MVPA time was accumulated in bouts ≥10 min suggests that the less active an individual is, the more important it is to increase the intensity of PA. Cardiac biomarkers are also good indicators of CVD risk. A study in older men without CVD followed for 9 years has shown that a higher NT-proBNP was associated with an increased CVD risk [48]. Another investigation in a middle-aged European population over a 20-year follow-up reported that hs-cTnI is an independent predictor of CVD events, so those participants with hs-cTnI levels ≥12.7 pg/mL had 2.5 times the risk than those with non-detectable hs-cTnI levels. Interestingly, when cTnI was also measured by a high-sensitivity assay, the association remained significant even for those individuals with undetectable levels in the conventional assay [49]. Our results support that the association between movement behaviors and cardiac biomarkers depends on their baseline levels as well as the PA level and suggest that less active individuals with subclinical cardiac damage would obtain more benefits from moving more and sitting less. However, identifying the mechanisms involved in the benefits of movement behaviors on CVD through the improvement of cardiac biomarkers requires further research. Our study has several strengths. In addition to the large sample size, the main strength is the use of accelerometry, which allowed objective assessment of different movement behaviors, including bouts, number of sedentary breaks or mean movement intensity. Another strength is that cTnT was determined by hs assays. Furthermore, although the analysis plan was not pre-registered, standard statistical procedures were used, specifically adjusting for potential confounders and stratifying by modifiers of the study associations, such as sex, level of PA and subclinical cardiac damage, to reduce the risk of bias. However, some limitations should be acknowledged. The main weakness is its cross-sectional design, which precludes making causal inferences. Wrist accelerometers have good wear-time compliance, but lower accuracy than those placed on the hip or the thigh, as they may not be able to distinguish between sitting and standing and may misclassify arm movements during sedentary activities as PA (particularly when used on the dominant wrist). Moreover, we did not check whether study participants wore the device on the non-dominant wrist all the time. Additionally, as the cardiac biomarkers were measured in frozen stored samples, the possibility of a variable decrease in concentration during long-term storage cannot be excluded, so some measurement error may have occurred. Also, as in any observational study, residual confounding may persist despite models were adjusted for many potential confounders. Furthermore, the large number of stratified analyses performed precluded additional stratification by age (e.g., below and above 75 years), because the resulting analyses would have insufficient statistical power to assess if age influenced the study results. Lastly, our results may not be generalizable to younger age groups, non-European populations, or even non-Mediterranean populations. ## Conclusions In older men and women without major CVD, the link between movement behaviors and cardiac biomarkers depends on sex, subclinical cardiac damage and level of PA. Engaging in more activity and reducing sedentariness is generally more beneficial for participants who are initially less active and suffer subclinical cardiac damage, with greater benefits for hs-cTnT in women than in men and no benefit for NT-proBNP in women. 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--- title: Duhuo Jisheng Decoction suppresses apoptosis and mitochondrial dysfunction in human nucleus pulposus cells by miR-494/SIRT3/mitophagy signal axis authors: - Wei Liu - Xiaolong Zhao - Xuejian Wu journal: Journal of Orthopaedic Surgery and Research year: 2023 pmcid: PMC9996943 doi: 10.1186/s13018-023-03669-w license: CC BY 4.0 --- # Duhuo Jisheng Decoction suppresses apoptosis and mitochondrial dysfunction in human nucleus pulposus cells by miR-494/SIRT3/mitophagy signal axis ## Abstract ### Background Increasing evidence suggests that mitophagy is responsible for the pathogenesis of intervertebral disk (IVD) degeneration. Previous studies have shown that Duhuo Jisheng Decoction (DHJSD), a classic Fangji of traditional Chinese medicine, can delay IVD degeneration; however, its specific mechanism of action is unknown. In this study, we investigated the mechanism by which DHJSD treatment prevented IVD degeneration in IL-1β-treated human nucleus pulposus (NP) cells in vitro. ### Methods Cell Counting Kit-8 was performed to explore the effects of DHJSD on the viability of NP cells exposed to IL-1β. The mechanism by which DHJSD delays IVD degeneration was explored using luciferase reporter assay, RT-qPCR, western blotting, TUNEL assay, mitophagy detection assay, Mito-SOX, Mitotracker and in situ hybridization. ### Results We observed that DHJSD enhanced the viability of NP cells treated with IL-1β in a concentration-time dependent approach. Moreover, DHJSD lessened IL-1β-induced NP apoptosis and mitochondrial dysfunction and activated mitophagy in NP cells treated with IL-1β. Mitophagy suppressor cyclosporin A reversed the beneficial impacts of DHJSD in NP cells. In addition, the differential expression of miR-494 regulated IL-1β-induced NP apoptosis and mitochondrial dysfunction, and the protective impact of miR-494 on NP cells treated with IL-1β was achieved by mitophagy activation, which was regulated by its target gene, sirtuin 3 (SIRT3). Finally, we observed that DHJSD treatment could effectively delay IL-1β-induced NP apoptosis by affecting the miR-494/SIRT3/mitophagy signal axis. ### Conclusions These results show that the miR-494/SIRT3/mitophagy signaling pathway is responsible for the apoptosis and mitochondrial dysfunction of NP cells and that DHJSD may exert protective effects against IVD degeneration by regulating the miR-494/SIRT3/mitophagy signal axis. ## Introduction Low back pain (LBP) due to intervertebral disk (IVD) degeneration is very common in modern society, causing a heavy medical and socioeconomic burden [1, 2]. Studies have shown that the nucleus pulposus (NP) is the center of IVD function and the earliest site of degeneration during IVD degeneration and that excessive apoptosis of NP cells (NPCs) leads to a decreased number of cells, which is an important pathological basis of IVD degeneration [3, 4]. However, the specific mechanism leading to the apoptosis of NPCs remains unclear. In mammals, the main function of the mitochondria is energy synthesis and coordination of biological activities that regulate active oxygen production, cell metabolism and apoptosis [5–7]. Studies have shown that the mitochondrial apoptosis pathway is involved in the apoptosis of NPCs [8, 9]. Mitochondrial apoptosis pathways are activated by various factors, resulting in increased permeability of the external membrane of the mitochondria and the release of cytochrome C (Cyt-c) into the cytoplasm, which binds to apoptosis activators and activates caspase-3, leading to a cascade of reactions that result in apoptosis [10]. Reportedly, oxidative stress (OS) caused by IL-1β and other factors may increase the mitochondrial apoptosis pathway of NPCs resulting in IVD degeneration [11–13]. Therefore, inhibition of NPC apoptosis induced by IL-1β can be an important therapeutic target for treating IVD degeneration. With the intracellular degradation of cytoplasmic macromolecules and dysfunctional organelles, autophagy is crucial for cell homeostasis and survival under stressful conditions. This homeostasis course involves isolation of the cytoplasmic components of the biomembrane autophagosome, fusion of the autophagosome with the lysosome and digestion of the cargo in the lysosome referred to as autophagy flux [14]. A special type of autophagy, mitophagy, selectively degrades damaged mitochondria through the autophagy pathway to maintain the homomorphism of mitochondrial dynamics and reduce mitochondrial dysfunction during OS [15]. Mitophagy dysfunction has been implicated in many degenerative disorders such as IVD degeneration, neurodegenerative disorders and osteoarthritis [16, 17]. The basal level of mitophagy maintains the stability of NPCs. During OS, mitophagy is activated to clear damaged mitochondria and reduce the apoptosis of the mitochondrial pathway of NPCs [18]. Therefore, mitophagy activation can effectively delay the progression of IVD degeneration. Sirtuin 3 (SIRT3) is located in mitochondria, which is a kind of protein deacetylase. Its activity depends on the auxiliary group nicotinamide adenine dinucleotide, and it is involved in regulating senescence, apoptosis, autophagy and mitophagy. A study by Wang et al. showed that SIRT3 knockdown aggravated apoptosis, senescence and mitochondrial dysfunction, whereas SIRT3 overexpression exerted opposing effects in the NPCs treated with tert-butyl hydroperoxide [19]. A recent study confirmed that SIRT3 is an important protective agent against osteoarthritis by increasing the autophagic flux [20]. Mitophagy mediated by SIRT3 plays an important role in the pathophysiology of IVD degeneration as an important link in clearing mitochondrial reactive oxygen species (ROS) and maintaining mitochondrial stability. The regulation of SIRT3 expression and its mediated mitophagy can play a significant role in delaying IVD degeneration [21]. More and more evidence showed that microRNAs regulate the biological process of NP cells and play an important role in IVD degeneration. We previously confirmed that miR-494 was significantly overexpressed during the progression of IVD degeneration. Downregulating miR-494 can delay the apoptosis of NPCs and the degeneration of the extracellular matrix and promote the repair of intervertebral disks [22]. However, the functional activity of miR-494 in NPCs still needs further investigation. Several studies have shown that traditional Chinese medicines (TCM) are useful in treating LBP alongside other significant clinical effects [23, 24]. Duhuo Jisheng Decoction (DHJSD), derived from “Bei Ji Qian Jin Yao Fang” of the Tang Dynasty, is composed of 15 Chinese herbs including Radix glycyrrhizae, Panax ginseng, Radix achyranthis bidentatae, Eucommiae ulmoidis cortex, Poria cocos, Cortex cinnamomi, Radix paeoniae alba, Radix rehmanniae, Radix angelicae sinensis, Rhizoma chuanxiong, Herba Asari, Radix saposhnikoviae, Radix gentianae macrophyllae, Ramulus loranthi and Radix angelicae pubescentis, and the main active ingredients are osthol, gentiopicroside, loganic acid and paeoniflorin. The combination of these herbs can eliminate pathogenic factors and play the role of removing rheumatism, relieving arthralgia, tonifying the liver and kidneys and supplementing qi and blood. At present, DHJSD is broadly employed in the clinical treatment of lumbar disk herniation because of its anti-inflammatory and analgesic effects [25, 26]. A meta-analysis showed that modified DHJSD had a more favorable effect on the treatment of lumbar disk herniation than Western medicine, and there were no obvious adverse events [25]. Studies have shown that DHJSD delays the SDF-1-induced inflammatory response by regulating the CXCR4/NF-B signaling axis, thereby delaying IVD degeneration [27]. Our previous study demonstrated that DHJSD delayed the compression-induced degeneration of NP extracellular matrix and NPCs apoptosis by activating autophagy [28]. However, the effect of DHJSD on mitophagy and mitochondrial apoptosis of NPCs requires further investigation. In the pathological process of IVD degeneration, the NPC produces excessive inflammatory factors, which triggers the subsequent degeneration process, among which IL-1β and TNF-α are the most widely reported inflammatory factors. Our previous research had successfully established the degeneration model of NPCs by using IL-1β [22, 29]. In accordance with the relevant literature, we used IL-1β to make the degeneration model of NPCs in this study. Then, DHJSD was used to intervene in degenerated NPCs, and the effect of DHJSD on IL-1β treated NPCs was detected. We observed that DHJSD could reduce mitochondrial dysfunction and apoptosis of NPCs caused by IL-1β through activating mitophagy. Our study has revealed that SIRT3 constitutes the target gene of miR-494, which affects IL-1β-induced NPC apoptosis through SIRT3-regulated mitophagy. Finally, we observed that DHJSD suppressed the IL-1β-induced NPC mitochondrial apoptosis by regulating the miR-494/SIRT3/mitophagy signaling pathway. Our study highlights the important role of the miR-494/SIRT3/mitophagy signal axis in IVD degeneration and explores the anti-apoptotic mechanism of DHJSD on NPCs. ## Materials and reagents DHJSD comprised 6 g each of Radix glycyrrhizae, Panax ginseng, Radix achyranthis bidentatae, Eucommiae ulmoidis cortex, Poria cocos, Cortex cinnamomi, Radix paeoniae alba, Radix rehmanniae, Radix angelicae sinensis, Rhizoma chuanxiong, Herba Asari, Radix saposhnikoviae, Radix gentianae macrophyllae, Ramulus loranthi and 9 g of Radix angelicae pubescentis. The above-mentioned herbs were provided by the First Hospital of Wuhan. The specific preparation method of DHJSD has been previously reported [28]. It was formulated by the Pharmacy Department of the Wuhan First Hospital to contain 1 g/mL of crude drug. The stock solution was cooled at room temperature and stored at 4 °C prior to usage. In subsequent experiments, we diluted the DHJSD stock solution to ultimate contents of 500, 400, 300, 200 and 100 μg/mL, adopting DMEM/F12 medium with $15\%$ fetal bovine serum (FBS). ## Sample acquisition We collected NP tissue of 10 patients (5 females and 5 males, aged 11–18 years) undergoing surgery for idiopathic scoliosis in the Wuhan First Hospital from August 2021 to January 2022. All patients were examined by MRI before surgery and graded based on the Pfirrmann grading standard. Of the 10 samples acquired, five were grades I and II, respectively; they were all normal NP samples. ## Isolation and incubation of NPCs The collected NP tissues were cut into 0.5 × 0.5 × 0.5 mm3 tissue pieces and treated with $0.2\%$ type II collagenase for 4 h at 37 °C. The cell suspension was placed in a low-speed centrifuge and centrifuged for 3 min at 155 × g, after which the cells were collected. Then, the NPCs were cultured in a culture flask with DMEM/F12 medium containing $15\%$ FBS and placed in a $5\%$ CO2 and 37 °C incubator. The medium was altered every 2 ~ 3 days, and when it reached 80 ~ $90\%$ confluence, we used $0.25\%$ trypsin–EDTA solution for passage. The first generation of NPCs was used for subsequent cell viability testing and intervention experiments. ## NPCs viability assay The Cell Count Kit-8 method (Dojindo, Japan) was used to determine how DHJSD affects NPC viability. We trypsinized the good growth state of NPCs, inoculated them in a 96-well plate (5 × 103 cells per well) and then incubated them at 37 °C for 24 h. After the completion of the adsorption course, we treat the NPCs with or without IL-1β (10 ng/mL) (Sino Biological Inc., North Wales, PA, USA) and different DHJSD concentrations (500, 400, 300, 200 and 100 μg /mL) for 24 h or various treatment periods (72, 48, 36, 24 and 12 h) at the same concentration (300 μg/mL). The cells were washed at a certain time with PBS, and 100 μL of DMEM containing 10 μL CCK-8 solution was added to all wells and incubated at 37 °C for 2 h. After that, we measured OD at a wavelength of 450 nm using the plate spectrophotometer (Thermo Scientific, USA) to calculate the vitality of NPCs. ## Experimental design and cell treatments First, we treated the NPCs with DHJSD (300 μg/mL) for 24 h before administrating IL-1β to investigate the effect of DHJSD on NPCs. After that, we pretreated NPCs with DHJSD (300 μg/mL) alone or combined with cyclosporin A (1 μM) for 24 h before IL-1β administration. To explore how miR-494 affected NPCs, we designed a miR-494 inhibitor and mimic and their negative control and synthesized them through GenePharma (Shanghai, China). They were then transfected into NPCs using lipofectamine 2000 before IL-1β administration. To knock down SIRT3 expression, scrambled siRNA (siScr) and short interfering (si) RNA targeting SIRT3 (si-sirt3) were designed and bought from GenePharma (Shanghai, China). The NPCs were co-transfected by adopting miR-494 suppressor (150 nM) and si-sirt3 (100 nM) for 48 h using lipofectamine 2000. Finally, NPCs were either treated with DHJSD alone or pretransfected with miR-494 mimic (50 nM) or si-sirt3 (100 nM) for 24 h to explore the role of mir-494/SIRT3/mitophagy signal axis on DHJSD activity. ## EdU (5-Ethynyl-2′-deoxyuridine) assay Here, 3104 cells of NPCs were seeded in 24-well plates. Cells were treated with IL-1β (10 ng/mL) with or without DHJSD (300 μg/mL) for 24 h and then tested following the manufacturer’s instructions (BeyoClick™ EdU Cell Proliferation Kit with Alexa Fluor 594). DAPI was used to stain the cell nucleus before observation. Images were captured under a microscope (Olympus). ## Luciferase reporter assay The bioinformatics algorithms and related databases were used to search for the potential targets for miR-494. SIRT3 was confirmed to have an assumed binding site on miR-494. Wild-type (WT) and mutant (MUT) 3′-UTR fragments containing the assumed miR-494 binding site were amplified and inserted into the pGL3 vector (RiboBio). HEK 293 cells were seeded in a 6-well plate, grown in an incubator at 37 °C for 24 h with $5\%$ CO2 and then co-transfected with 100 ng of pGL3 vector harboring MUT 3′-UTR or WT and 40 nM of miR-Scr or miR-494 mimic employing transfection reagent, lipofectamine 2000. The cells were harvested in 48 h to detect luciferase activity through a dual luciferase reporter assay kit (Promega, Madison, Wisconsin, USA). ## Real-time quantitative PCR (RT-qPCR) The RNA of NPCs was extracted by applying the TRIzol reagent (Invitrogen) based on the manufacturer’s instructions. The RNA content was determined at a wavelength of 260 nm using a spectrometer. Reverse transcription of 1 μg total RNA was used for synthesizing cDNA, and a reaction volume of 10 μL (4.5 μL diluted cDNA, 0.25 μL primers and 5 μL 2 × SYBR Master Mix) was used for PCR amplification. The cycle threshold was recorded. The target gene expression level was normalized to the GAPDH level, and the miR-494 level was normalized to that of U6. The expression of SIRT3 and miR-494 was calculated using the 2−ΔΔCt approach. The primers employed are provided in Table 1.Table 1List of primers employed in RT-PCRNamePrimerSequenceSizeHomo GAPDHForward5′- TCAAGAAGGTGGTGAAGCAGG -3′115 bpReverse5′- TCAAAGGTGGAGGAGTGGGT -3′Homo SIRT3Forward5’- CTTACTAGAGTGCGGCGGT-3’220 bpReverse5’- ACAGGTCCACTCATCTTCGT-3’U6Forward5 ‘- CGCTTCGGCAGCACATATAC -3’Reverse5 ‘- AAATATGGAACGCTTCACGA -3’hsa-miR-494Forward5 ‘-TGCGCAGGTTGTCCGTGTTGTCT-3 ‘Reverse5′- CCAGTGCAGGGTCCGAGGTATT-3′ ## Western blotting (WB) Mitochondrial, cytoplasmic and total proteins were extracted, and the corresponding kit (Beyotime) was used to detect the content. Thereafter, 25 µg of protein was subjected to sodium dodecylsulfate-polyacrylamide gel electrophoresis. The protein was transferred to a polyvinylidene fluoride film (Millipore, Billerica, Massachusetts, USA) using a semidry method. The polyvinylidene fluoride film was soaked in TBST containing $5\%$ skimmed milk powder and sealed with a shaker for 2 h at room temperature. The blots were incubated overnight, with the primary antibodies diluted from 1:500 to 1:1000. The antibodies were used against the proteins listed below: Parkin (ab77924), PINK1 (ab23707), Bax (ab32503), Bcl-2 (ab32124), Cyt-c (ab110325), Collagen II (ab34712), Adamts5(ab41037) (Abcam, Cambridge, UK); P62 (#5114), LC3 (#2775), SIRT3 (#2627S), GAPDH (#5174), Caspase-3 (#9662) and Cleaved-caspase-3 (#9664) (Cell Signaling Technology; Danvers, Massachusetts, USA); VDAC1 (sc-32063) (Santa Cruz Biotechnology; Dallas, Texas, USA); Aggrecan (13880-1-AP) and MMP3 (17873-1-AP) (Wuhan Sanying, Wuhan, China). After rinsing the film, the proper secondary antibody was incubated through the blot for 1 h at 25 °C. The film’s gray values were analyzed after darkroom exposure using Image J software v1.46 (NIH, Bethesda, MD, USA). ## TUNEL assay NPCs in all groups were collected and fixed for 1 h with $4\%$ paraformaldehyde. They were cultured with $0.1\%$ Triton X-100 (TX) for 10 min and rinsed with PBS three times. According to the manufacturer’s instructions, the cells were stained utilizing the In Situ Cell Death Detection Kit (12156792910; Roche Applied Science, Indianapolis, IN, USA) and 40, 6-diamino-2-phenylindole (DAPI). The liquid on the slide was wiped dry using absorbent paper, and the slide was mounted with a mounting solution containing an anti-fluorescence quencher. The image was collected by observation under a fluorescence microscope (Olympus). ## Mitophagy detection assay The mitophagy detection kit was used to detect mitophagy in NPCs. The NPCs were seeded in the logarithmic growth period on a 6-well plate covered with cell slides at 2 × 105 per well. The NPCs were cultured at 37 °C overnight under $5\%$ CO2 saturated humidity and treated as depicted above. Next, the cells were incubated with 100 nM Mtphagy Dye working solution for 30 min at 37 °C for mitochondrial probe staining. After that, the cells were washed twice with PBS and subjected to IL-1β for 24 h. After culturing for 24 h, 1 μmol/L LYSO DYE working solution was added to the cells and then incubated for 30 min at 37 °C. Finally, the NPCs were observed under a Laser Scanning Microscope (LSM) (ZEISSLSM780, Germany). ## Measurement of mitochondrial ROS and mitochondrial membrane potential (MMP) Mitochondrial ROS and MMP were measured by Mito-SOX (40778ES50; Yeasen biotech, Shanghai, China) and Mitotracker (C1048, Beyotime). The NPCs were seeded in the logarithmic growth period on a 6-well plate, cultured at 37 °C overnight under $5\%$ CO2 saturated humidity and treated as the experimental design. The NPCs were stained and fixed according to the manufacturer’s instructions. Finally, the Mito-Sox and the Mitotracker intensity were observed using the LSM. ## In situ hybridization We used the high hybridization efficiency and specificity of oligonucleotide probes for examining miR-494 expression in NPCs. Cy3 labeled oligonucleotide probes and complementary to mature miR-494 and disruption probes were obtained from Sangon Biotech Co., Shanghai, China. ISH reactions were performed as previously described [30]. ## Statistical analysis IBM SPSS v25.0 (IBM, Armonk, New York) was used for all data analyses. Outcomes were exhibited as the mean ± SD. Student’s t test and ANOVA were for statistical analyses. Tukey’s test was used to test between group variations. Statistical significance was considered at $p \leq 0.05.$ ## Effects of DHJSD on vitality, proliferation and extracellular matrix metabolism of NPCs exposed to IL-1β We examined how DHJSD affected the viability of NPCs exposed to IL-1β at various concentrations of DHJSD pretreated NPCs before incubating them for 24 h with IL-1β. DHJSD significantly enhanced the activity of NPCs treated with IL-1β in a concentration-dependent manner, and the strongest impact was observed at 300 μg/mL (Fig. 1a). Subsequently, DHJSD (300 μg/mL) was added to the NPCs pretreated with IL-1β, and the viability of NPCs was tested at various time points. The results showed that NPC viability gradually increased as the treatment time extended. After 24 h of treatment, the vitality of NPCs reached the highest point, and then, the effect of DHJSD gradually decreased (Fig. 1b). Therefore, DHJSD enhanced the viability of NPCs exposed to IL-1β in a time-concentration-dependent method. Next, NPCs were treated with 300 μg/mL DHJSD for 24 h in the follow-up experiment. IDD is characterized by a decrease in the number of NP cells and their extracellular matrix products. EDU results showed that the proliferation of the NPCs decreased after IL-1β treatment, however, the intervention of DHJSD could promote the proliferation of NPCs (Fig. 1c). Through WB, we observed that DHJSD increased the expression of collagen II and aggrecan and decreased the expression of MMP3 and adamts5, which indicates that DHJSD can reverse the decrease of extracellular matrix of NP caused by IL-1β (Fig. 1d). The above results show that DHJSD has a potentially important role in the process of IVD degeneration. Fig. 1DHJSD enhances the viability of IL-1β-exposed NP cells (NPCs). a NPCs treated with or without IL-1β and various DHJSD concentrations for 24 h. b NPCs treated with or without IL-1β and 300 µg/mL DHJSD for different phases. NPCs were untreated, treated with IL-1β (10 ng/mL) alone, or with IL-1β (10 ng/mL) and DHJSD (300 µg/mL). c Representative fluorescence images with EDU staining and quantitative analyses of EDU-positive cells (scale bar: 50 μm, original magnification ×200) d The protein content of collagen II, aggrecan, MMP3 and adamts5 of NPCs treated above. All experiments were performed three times in duplicate, and data are shown as average ± SD ($$n = 3$$). $P \leq 0.05$ compared with the control, #$P \leq 0.05$ in comparison to the IL-1β group ## Effects of DHJSD on IL-1β-induced apoptosis and mitochondrial dysfunction in NPCs The mitochondrial path modulates the cell apoptotic events through mitochondrial membrane permeabilization and subsequent proapoptotic protein release. First, the expression of apoptosis-associated proteins was detected with WB. In comparison to the control, IL-1β treatment upregulated Bax expression and the ratio of cleaved caspase-3/caspase-3 but downregulated those of Bcl-2 in human NPCs. Nonetheless, DHJSD treatment delayed the protein expression alterations in apoptotic marker genes in human NPCs induced by IL-1β exposure (Fig. 2a). We examined the cellular localization of the proapoptotic protein (Cyt-c). Their release from the mitochondria to the cytoplasm is crucial to the caspase activation apoptotic event. WB analyses proved that the ratio of mitochondrial to cytoplasmic Cyt-c was reduced by IL-1β treatment. However, DHJSD treatment delayed the trend of decreasing ratio (Fig. 2b). Second, TUNEL staining results also showed that the increased rate of apoptosis in human NPCs subjected to IL-1β alone was noticeably lessened in the DHJSD-treated group (Fig. 2c). Moreover, Mito-SOX fluorescence intensity was higher in the IL-1β-treated group than in the control group. After pretreatment with DHJSD, NPC fluorescence intensity decreased remarkably compared to that of the IL-1β-treated group (Fig. 2d). Finally, Mitotracker assay outcomes suggested that DHJSD inhibited IL-1β-induced mitochondrial membrane potential loss as well (Fig. 2e). These results demonstrated that DHJSD attenuates IL-1β-induced apoptosis and mitochondrial dysfunction in NPCs. Fig. 2Effects of DHJSD on IL-1β-induced apoptosis and mitochondrial dysfunction in NPCs. NPCs were untreated, treated with IL-1β (10 ng/mL) alone, or with IL-1β (10 ng/mL) and DHJSD (300 µg/mL). a The protein content of Bcl-2 and Bax and the ratio of cleaved caspase-3/caspase-3 of NPCs treated above. b Representative western blotting (WB) assay and quantitation of the cytochrome c (Cyt-c) content in cytoplasmic and mitochondrial extracts. c Representative fluorescence images with TUNEL staining and quantitative analyses of TUNEL-positive cells (scale bar: 50 μm, original magnification ×200). d Representative fluorescence images with Mito-Sox and quantitative analyses of the fluorescence intensity (original magnification ×1000, scale bar: 10 μm). e Representative fluorescence images with Mitotracker Red and quantitative analyses of the fluorescence intensity (original magnification ×1000, scale bar: 10 μm). All experiments were performed three times in duplicate, and data are shown as average ± SD ($$n = 3$$). * $P \leq 0.05$ vs. the control. # $P \leq 0.05$ vs. the IL-1β group ## DHJSD lessened IL-1β-induced NPC apoptosis by promoting mitophagy To evaluate how DHJSD affected mitophagy in human NPCs exposed to IL-1β, the expression of mitophagy-associated factors were identified through WB assay. IL-1β lessened the expression of Parkin, PINK1 and LC3-II but increased that of p62 (Fig. 3a). In contrast, DHJSD promoted the expression of Parkin, PINK1 and LC3-II and decreased that of p62, while those reversed distinctly when co-treated with a mitochondrial autophagy inhibitor (cyclosporin A) [18]. Next, mitochondria-dependent apoptosis was explored through WB and TUNEL assay. Cyclosporin A increased the translocation of Cyt-c limited by DHJSD treatment. Similarly, DHJSD reduced Bax expression and the ratio of cleaved caspase-3/caspase-3 but raised Bcl-2 expression; however, the impact was reversed by cyclosporin A (Fig. 3b and c). WB apoptosis results were further confirmed by TUNEL assay (Fig. 3d). These results suggest that DHJSD prevents NPCs from mitochondria-dependent apoptosis by promoting mitophagy. Fig. 3DHJSD attenuates the IL-1β-induced apoptosis of NPCs by promoting mitophagy. NP cells were untreated, treated with IL-1β (10 ng/mL) alone, with IL-1β (10 ng/mL) and DHJSD (300 µg/mL), or with cyclosporin A (1 μM), IL-1β (10 ng/mL) and DHJSD (300 µg/mL). a The protein content of Parkin, PINK1, LC3-II and P62 of NPCs treated above. b The protein content of Bcl-2 and Bax and the ratio of cleaved caspase-3/caspase-3 of NPCs treated above. c Representative WB assay and quantitation of the Cyt-c content in cytoplasmic and mitochondrial extracts. d Representative fluorescence images with TUNEL staining and quantitative analyses of TUNEL-positive cells (scale bar: 50 μm, original magnification ×200). All experiments were performed three times in duplicate, and data are shown as average ± SD ($$n = 3$$). * $P \leq 0.05$ vs. the control. # $P \leq 0.05$ vs. the IL-1β group. & < 0.05 vs. the DHJSD + IL-1β group ## Effects of miR-494 on IL-1β-induced apoptosis and mitochondrial dysfunction in NPCs Although we have previously proved that mir-494 plays an important role in IVD degeneration, the role of mir-494 in IL-1β-induced degeneration of NPCs still needs further study. MiR-494 expression in NPCs was measured using qRT-PCR. The miR-494 expression was higher in NPCs exposed to IL-1β than in the controls (Fig. 4a). The results were identified through in situ hybridization (Fig. 4b). To evaluate how miR-494 affected IL-1β-induced apoptosis and mitochondrial dysfunction in human NPCs, transfected human NPCs were exposed to IL-1β using miR-494 suppressor, suppressor control, miR-494 mimic or mimic control. Transfection efficiency of miR-494 mimic and miR-494 inhibitor was verified through qRT-PCR (Fig. 4c). First, we detected the expression of apoptosis-associated proteins using WB. Compared to the control, miR-494 overexpression upregulated Bax expression and the ratio of cleaved caspase-3/caspase-3 but downregulated Bcl-2 expression in human NPCs. However, the miR-494 inhibitor caused an inhibition of the protein expression alterations in apoptotic marker genes in human NPCs induced by IL-1β exposure (Fig. 4d). Cyt-c cellular localization was examined by WB. The results proved that the ratio of mitochondrial to cytoplasmic Cyt-c was reduced by miR-494 overexpression. However, the miR-494 inhibitor delayed the trend of decreasing ratio (Fig. 4e). Second, TUNEL staining results also showed that miR-494 overexpression raised NPC apoptosis and that low miR-494 expression delayed the IL-1β-induced apoptosis of NPCs (Fig. 4f). Additionally, Mito-SOX fluorescence intensity was higher in the group transfected with miR-494 mimic than in the control group. After pretreatment with miR-494 inhibitor, the fluorescence intensity of NPCs decreased noticeably in comparison to that of the control group (Fig. 4g). Finally, the Mitotracker assay results indicated that miR-494 regulated IL-1β-induced mitochondrial membrane potential loss (Fig. 4h).Fig. 4Effects of miR-494 on IL-1β-induced apoptosis and mitochondrial dysfunction in NPCs. NP cells were untreated or treated with IL-1β (10 ng/mL). a mRNA expression of miR-494 of NPCs treated above. b Representative fluorescence images with ISH and quantitative analysis (original magnification ×400, scale bar: 25 μm). c Transfection efficiency of miR-494 mimic and miR-494 inhibitor were measured using qRT-PCR. Data are shown as average ± SD. * $P \leq 0.05$ vs. the control. NPCs were untreated, treated with IL-1β (10 ng/mL) alone or with IL-1β (10 ng/mL) and mimic control or miR-494 suppressor, inhibitor control or miR-494 mimic. d The protein content of Bcl-2 and Bax and the ratio of cleaved caspase-3/caspase-3 of NPCs treated above. e Representative WB assay and quantitation of the Cyt-c content in cytoplasmic and mitochondrial extracts. f Representative fluorescence images with TUNEL staining and quantitative analyses of TUNEL-positive cells (scale bar: 50 μm, original magnification ×200). g Representative fluorescence images with Mito-Sox and quantitative analyses of the fluorescence intensity (original magnification × 1000, scale bar: 10 μm). h Representative fluorescence images with Mitotracker Red and quantitative analyses of the fluorescence intensity (original magnification ×1000, scale bar: 10 μm). All experiments were performed three times in duplicate, and data are shown as average ± SD ($$n = 3$$). * $P \leq 0.05$ vs. the IL-1β + mimic control. # $P \leq 0.05$ vs. the IL-1β + inhibitor control ## SIRT3 is a direct target of miR-494 The 3'-UTR of SIRT3 had a complementary sequence to the miR-494 seed sequence (Fig. 5a). Subsequently, we examined if miR-494 bound to the projected site directly in the 3'-UTR of SIRT3. The LR vector containing WT or mutated (MUT) SIRT3 3'-UTR sequence was transfected in HEK 293 cells. miR-494 overexpression effectively lessened WT luciferase activity but did not reduce the activity of the mutant reporter gene, implying that miR-494 targets SIRT3 3'-UTR directly (Fig. 5b). This inhibitory impact was identified through SIRT3 expression analyses. The results showed that miR-494 overexpression suppressed the expression of sirt3 mRNA and protein in NPCs, and miR-494 inhibition raised the SIRT3 mRNA and protein levels (Fig. 5c and d).Fig. 5SIRT3 is a direct target of miR-494. a Assumed miR-494 target site in the 3′-UTR of the human SIRT3 transcript projected through bioinformatics analyses. b Luciferase activity in HEK 293 cells co-transfected with miR-494 mimic or mimic control and WT or MUT SIRT3 3′-UTR constructs. c mRNA expression of SIRT3 of NPCs treated above. d The SIRT3 protein content of NPCs treated above. All experiments were performed three times in duplicate, and data are shown as average ± SD ($$n = 3$$). * $P \leq 0.05$ vs. mimic control. # $P \leq 0.05$ vs. the suppressor control ## Downregulation of miR-494 expression delayed IL-1β-induced apoptosis of NPCs by promoting SIRT3-regulated mitophagy To verify whether the downregulation of miR-494 expression delayed IL-1β-induced apoptosis of NPCs by promoting SIRT3-regulated mitophagy, we knocked down SIRT3 in human NPCs by transfecting short interfering RNA against SIRT3 or negative control. Si-sirt3 knockout efficiency was verified through qRT-PCR and WB (Fig. 6a and b). Si-sirt3 increased the translocation of Cyt-c limited by miR-494 inhibitor. Similarly, miR-494 inhibition decreased Bax levels, the ratio of cleaved caspase-3/caspase-3 and raised Bcl-2 expression. The impacts were attenuated by the addition of si-sirt3 (Fig. 6c and d). WB results of apoptosis were further confirmed by the TUNEL assay (Fig. 6e). WB was conducted for the detection of the expression of mitophagy-related factors. As shown in Fig. 6f, we observed that the downregulation of miR-494 expression might raise the expression of Parkin, PINK1, LC3-II and decrease p62 expression, while those reversed distinctly when transfected with si-sirt3. These results suggest that SIRT3-regulated mitophagy is crucial to miR-494 delayed IL-1β-induced apoptosis of NPCs. Fig. 6Downregulation of miR-494 expression delay IL-1β-induced apoptosis of NPCs by promoting SIRT3-regulated mitophagy. a, b Transfection efficiency of si-sirt3 was measured using qRT-PCR and WB separately. NPCs were untreated, treated with IL-1β (10 ng/mL) alone, with IL-1β (10 ng/mL) and suppressor control or miR-494 suppressor, or with IL-1β (10 ng/mL) and miR-494 inhibitor and siScr or si-sirt3. c The protein content of Bcl-2 and Bax and the ratio of cleaved caspase-3/caspase-3 of NPCs treated above. d Representative WB assay and quantitation of Cyt-c content in cytoplasmic and mitochondrial extracts. e Representative fluorescence images with TUNEL staining and quantitative analyses of TUNEL-positive cells (scale bar: 50 μm, original magnification ×200). f The protein content of Parkin, PINK1, LC3-II and p62 of NPCs treated above. All experiments were performed three times in duplicate, and data are shown as average ± SD ($$n = 3$$). * $P \leq 0.05$ vs. the IL-1β + suppressor control. # $P \leq 0.05$ vs. the IL-1β + miR-494 suppressor + siScr group ## DHJSD attenuated the IL-1β-induced apoptosis of NPCs by miR-494/SIRT3/ mitophagy signal axis Our results show that DHJSD downregulated miR-494 expression and upregulated SIRT3 expression than the control (Fig. 7a and b). To investigate whether DHJSD lessens IL-1β-induced apoptosis of NPCs through the miR-494/SIRT3/mitophagy signal axis, miR-494 mimic or si-sirt3 was transfected in NPCs. First, mitochondria-dependent apoptosis was explored through WB and TUNEL assay. DHJSD lessened Bax expression, the ratio of cleaved caspase-3/caspase-3 and raised Bcl-2 expression; however, this effect was reversed by miR-494 mimic and si-sirt3 (Fig. 7c and 7d). Likewise, we observed that miR-494 mimic and si-sirt3 increased the translocation of Cyt-c, which was limited by DHJSD treatment. WB results of apoptosis were further confirmed by the TUNEL assay (Fig. 7e). Next, we examined mitophagy in NPCs through WB assay, and the results showed that DHJSD might raise the expression of LC3-II, PINK1 and Parkin and decrease p62 expression, while those reversed distinctly when transfected with miR-494 mimic or si-sirt3 (Fig. 7f). Finally, mitophagy detection experiments showed that miR-494 mimic or si-sirt3 transfection significantly reduced mitophagy and lysosome co-localization signals compared with the DHJSD treatment group alone (Fig. 7g). These results suggest that DHJSD lessens the IL-1β-induced apoptosis of NPCs by miR-494/SIRT3-regulated mitophagy. Fig. 7DHJSD attenuates the IL-1β-induced apoptosis of NPCs by miR-494/SIRT3/mitophagy signal axis. NP cells were untreated, treated with IL-1β (10 ng/mL) alone, or with IL-1β (10 ng/mL) and DHJSD (300 µg/mL). a MiR-494 expression of NP cells treated above was measured using qRT-PCR and ISH separately (scale bar: 25 μm, original magnification ×400). b SIRT3 expression of NPCs treated above was measured using qRT-PCR and WB separately. NPCs were untreated, treated with IL-1β (10 ng/mL) alone, with IL-1β (10 ng/mL) and DHJSD (300 µg/mL), or with IL-1β (10 ng/mL) and DHJSD (300 µg/mL) and mimic control, miR-494 mimic, siScr or si-sirt3. c The protein content of Bcl-2 and Bax and the ratio of cleaved caspase-3/caspase-3 of NPCs treated above. d Representative WB assay and quantitation of Cyt-c content in cytoplasmic and mitochondrial extracts. e Representative fluorescence images with TUNEL staining and quantitative analyses of TUNEL-positive cells (scale bar: 50 μm, original magnification ×200). f The protein content of Parkin, PINK1, LC3-II and p62 of NPCs treated above. g The representative images of mitophagy in NPCs treated above were detected through the mitophagy detection kit, in which red staining denotes mitophagy, green denotes lysosomes, and yellow denotes co-localization of lysosomes and mitophagy (original magnification ×1000, scale bar: 10 μm). All experiments were performed three times in duplicate, and data are shown as average ± SD ($$n = 3$$). * $P \leq 0.05$ vs. the IL-1β + DHJSD + mimic control. # $P \leq 0.05$ vs. the IL-1β + DHJSD + siScr group ## Discussion LBP constitutes a severe public health problem and the second most typical clinical symptom after respiratory disease [31]. The most typical reason for LBP is degenerative changes in the disk and its secondary pathological processes [32]. Nonsteroidal anti-inflammatory medicines are broadly employed in the treatment of disk degeneration. These drugs regulate effective temporary relief and inflammation of LBP; however, they cannot reverse the disk degeneration process and cause more adverse reactions [33]. Recently, TCM was observed to be highly efficacious in disk degeneration treatment and had almost no adverse reactions, sparking the interest of many researchers. In this study, we observed that DHJSD delayed the IL-1β-induced apoptosis of NPCs and mitochondrial function damage by activating mitophagy. Studies have shown that mitochondrial apoptosis pathways are responsible for NPC apoptosis. Intracellular damage or OS conditions and excessive ROS can cause lipid peroxidation of mitochondrial membranes, Bcl-2 protein family activation to form protein holes, mitochondrial PT holes to open, apoptosis active substances released from the mitochondria to the cytoplasm, causing a downstream caspase-9 activation, and further caspase-3 activation, triggering a caspase-cascade reaction, and ultimately lead to apoptosis [10]. Ding et al. conducted a study on stressed NPC and observed that stress stimulation could damage cell mitochondria and induce apoptosis [34]. Shen studies have shown that IL-1β intervention can reduce mitochondrial membrane potential levels, reduce the Bcl-2/Bax ratio and increase Cyt-c release into the cytoplasm, which in turn causes NPCs apoptosis [35]. In this study, DHJSD reduced ROS production, stabilized the mitochondrial membrane, improved the Bcl-2/Bax ratio, reduced Cyt-c release to the cytoplasm from the mitochondria and reduced Cleaved-caspase-3 expression and thus delayed NPC apoptosis compared with the IL-1β group. Therefore, we speculate that IL-1β-induced NPC mitochondrial apoptosis is involved in the IVD degeneration course, and that DHJSD can reduce NPC apoptosis and delay IVD degeneration. Previous studies have demonstrated a basic level of autophagy in normal NPCs. This mainly removes misfolded proteins by autophagy lysosomes to maintain cell homeostasis. After treatment with inflammatory factors such as IL-1β, the autophagy level in NPCs decreased and the apoptosis of NPCs increased. Further research shows that activating autophagy can protect NP cells from excessive apoptosis [36, 37]. Reportedly, mitophagy can degrade damaged mitochondria to maintain NP intracellular homeostasis and is responsible for the effects of OS and apoptosis. A study by Hu et al. showed that promoting Nrf2/SIRT3-dependent mitochondrial autophagy can suppress NPC apoptosis and improve the IVD degeneration course [21]. Lin observed that Urolithin A, a compound from natural herbs, inhibits TBHP-induced mitochondrial apoptosis in NPCs via mitophagy regulated by the AMPK signal axis [38]. Recent studies have confirmed that various TCMs exert pharmacological effects through mitophagy regulation [39, 40]. Therefore, we speculate that the anti-apoptotic impact of DHJSD is related to mitophagy. PINK1, Parkin and LC3-II are the key proteins for initiating mitochondrial autophagy, and p62 is an essential protein for autophagy degradation. We used these proteins as markers to evaluate mitophagy. We observed that DHJSD raised the expression of LC3-II, Parkin and PINK1; however, p62 expression was lessened after treatment with DHJSD. Combined with immunofluorescence results, this shows that DHJSD promotes mitophagy. Additionally, the pretreatment of mitochondrial autophagy inhibition reversed the anti-apoptotic effect of DHJSD. Based on the above results, we conclude that the anti-apoptotic effect of DHJSD is achieved through the mitochondrial pathway to promote autophagy in NPCs. Our previous studies have shown that miR-494 is responsible for NPC apoptosis and IVD degeneration [22]. The miR-494 expression was raised in clinical samples of IVD degeneration. Decreasing miR-494 expression can reduce NPC apoptosis resulting from TNF-a. Previous in vivo experiments have shown that lowering miR-494 expression can delay the IVD degeneration process [41, 42]. Bioinformatics target projection has confirmed SIRT3 as an assumed target for miR-494 [43]. SIRT3 modulates numerous cellular courses and is a known cell-protective gene. Specifically, SIRT3 regulates mitophagy to prevent NPC apoptosis [19, 44]. Given the role of miR-494, SIRT3 and mitophagy in NPC apoptosis, we studied the specific mechanism of the miR-494/SIRT3/mitophagy signal axis in NPC apoptosis. We modulated miR-494 expression in NPCs and then applied the IL-1β intervention. The results showed that the differential expression of miR-494 can regulate IL-1β-induced apoptosis and mitochondrial dysfunction in NPCs. Further, we verified that SIRT3 was the target gene of miR-494, and the anti-apoptosis and mitochondrial autophagy effects of miR-494 were significantly weakened upon blocking SIRT3 expression. These results suggest that miR-494/SIRT3/mitophagy is responsible for the apoptosis of NP induced by IL-1β. To further explore the pharmacological effects of DHJSD, we transfected miR-494 mimic or si-sirt3 in NPCs in advance to increase miR-494 expression or reduce SIRT3 expression and then treated with DHJSD and IL-1β. The results showed that pretreatment of miR-494 mimic or si-sirt3 reversed the anti-apoptotic and pro-mitophagy effects of DHJSD. Based on these results, we speculate that DHJSD can delay the IL-1β-induced apoptosis of NPCs by promoting miR-494/SIRT3-regulated mitophagy. This experiment had some limitations. First, although this study shows that the DHJSD plays an anti-apoptosis effect by regulating the miR-494/SIRT3/mitophagy signal axis, the specific mechanisms of miR-494 expression modulation by DHJSD are unknown. Our previous research showed that DHJSD could delay IVD degeneration by blocking the activity of p38MAPK [28]. Another study showed that vx745, by selectively inhibiting the activation of p38MAPK, can reduce the expression of miR-494 [43]. Therefore, we suspected that DHJSD may reduce the expression of miR-494 by inhibiting the activation of p38MAPK. However, DHJSD is a multi-herb formula containing plentiful bioactive ingredients, and its pharmacological efficacy could be derived from the synergistic actions of the multi-ingredients modulating multi-pathways in a whole system level. As for the detailed mechanism of miR-494 expression modulation by DHJSD, we need further exploration in the follow-up experiment. Second, this study was limited to the cell level, and no in vivo studies were conducted. For follow-up experiments, the rat IVD degeneration model can be used to study how DHJSD delays IVD degeneration and its mechanism of action. ## Conclusions This study has confirmed the therapeutic impact of DHJSD on IVD degeneration. 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--- title: 'Potential biomarker proteins for aspiration pneumonia detected by shotgun proteomics using buccal mucosa samples: a cross-sectional case–control study' authors: - Kohei Ogura - Maho Endo - Takashi Hase - Hitomi Negami - Kohsuke Tsuchiya - Takumi Nishiuchi - Takeshi Suzuki - Kazuhiro Ogai - Hiromi Sanada - Shigefumi Okamoto - Junko Sugama journal: Clinical Proteomics year: 2023 pmcid: PMC9996945 doi: 10.1186/s12014-023-09398-w license: CC BY 4.0 --- # Potential biomarker proteins for aspiration pneumonia detected by shotgun proteomics using buccal mucosa samples: a cross-sectional case–control study ## Abstract ### Background Aspiration pneumonia (AP), which is a major cause of death in the elderly, does present with typical symptoms in the early stages of onset, thus it is difficult to detect and treat at an early stage. In this study, we identified biomarkers that are useful for the detection of AP and focused on salivary proteins, which may be collected non-invasively. Because expectorating saliva is often difficult for elderly people, we collected salivary proteins from the buccal mucosa. ### Methods We collected samples from the buccal mucosa of six patients with AP and six control patients (no AP) in an acute-care hospital. Following protein precipitation using trichloroacetic acid and washing with acetone, the samples were analyzed by liquid chromatography and tandem mass spectrometry (LC–MS/MS). We also determined the levels of cytokines and chemokines in non-precipitated samples from buccal mucosa. ### Results Comparative quantitative analysis of LC–MS/MS spectra revealed 55 highly (P values < 0.10) abundant proteins with high FDR confidence (q values < 0.01) and high coverage (> $50\%$) in the AP group compared with the control group. Among the 55 proteins, the protein abundances of four proteins (protein S100-A7A, eukaryotic translation initiation factor 1, Serpin B4, and peptidoglycan recognition protein 1) in the AP group showed a negative correlation with the time post-onset; these proteins are promising AP biomarker candidates. In addition, the abundance of C-reactive protein (CRP) in oral samples was highly correlated with serum CRP levels, suggesting that oral CRP levels may be used as a surrogate to predict serum CRP in AP patients. A multiplex cytokine/chemokine assay revealed that MCP-1 tended to be low, indicating unresponsiveness of MCP-1 and its downstream immune pathways in AP. ### Conclusion Our findings suggest that oral salivary proteins, which are obtained non-invasively, can be utilized for the detection of AP. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12014-023-09398-w. ## Background Aspiration pneumonia (AP) is caused by inhaling saliva, food, or vomit, which results in bacterial infection [1–3]. Aspiration, defined as the inhalation of oropharyngeal or gastric contents into the larynx and lower respiratory tract, is often the result of impaired swallowing resulting from dysphagia, head/neck/esophageal cancers, esophageal stricture, chronic obstructive pulmonary disease, or seizures. This allows oral and/or gastric contents to enter the lung, especially in patients with an inefficient cough reflex [1]. In addition to swallowing, impaired consciousness, because of degenerative neurologic disease or cardiac arrest, is also a risk factor for AP [2, 4] Patients with bacterial AP need to be treated promptly with antibiotics. Delay in diagnosis and treatment can result in prolonged hospital stay, additional complications, and eventually death [5]. However, pneumonia symptoms such as cough and fever often do not appear in the early stages. This absence of symptoms restricts the early detection and treatment of AP. Although the detection of causative bacteria results in prompt treatment with antibiotics, it is often difficult to distinguish infectious and noninfectious oral bacteria. Oral bacteria are present at various sites within the human oral cavity [6]. Recent reports have indicated that lung microbiota is involved in pneumonia in addition to the oral microbiota [7, 8]. Boaden et al. identified 103 different bacterial phylotypes from the oral microbiota of patients with acute stroke [9]. One study identified 67 pathogens in 95 institutionalized elderly patients with severe AP [4]. These reports indicate that host immune system-derived biomarkers, but not causative bacteria, are useful for detecting AP in the early stage. It is unclear whether AP represents a distinct entity from typical pneumonia [1, 10, 11]. Based on a previous report which estimated that AP accounts $5\%$–$15\%$ of the cases of community-acquired pneumonia, Mandell et al. proposed that AP should not be considered a distinct entity, but rather part of a continuum that also includes community- and hospital-acquired cases of pneumonia [1]. Recent studies have indicated that the composition of salivary proteins reflects oral and systemic conditions [12]. For example, salivary proteins may apply to the detection of localized oral diseases, such as head and neck cancer [13] and Sjogren's syndrome [14, 15], as well as systemic diseases, such as diabetes mellitus [16–18], and viral infections [19]. Based on reports regarding a relationship between oral proteomics and disease, we suspect that some salivary proteins may be used as biomarkers for the detection of AP at the early stage of onset. It is not easy for elderly people, particularly bedridden patients with neurologic or cerebrovascular disease, to eject a sufficient volume of saliva. In addition, saliva production is likely decreased because of decreased chewing frequency or drug treatment. In the present study, we collected saliva proteins from the buccal mucosa, where the ostia of Stensen’s ducts are located. By cleaning the buccal mucosa before sample collection, contaminates are readily removed. Using shotgun proteomics for a comparative quantitative analysis between AP and control patients, we identified eight candidate AP biomarkers. We also found a significant correlation between serum and oral C-reactive protein (CRP) levels. In addition, we evaluated a panel of cytokines and chemokines to determine the responsiveness of immune-related proteins. ## Subjects We collected samples from the buccal mucosa of six AP and six control patients in an acute-care hospital in Ishikawa Prefecture (Japan) from September 2021 to December 2021. The characteristics of the 12 patients are listed in Tables 1 and Additional file 1: Table S1. Five of the six AP patients and one of the six controls had been treated with antimicrobial agents at the time of collection. Five of the AP patients had a history of AP. Because we selected the six patients who had never been previously diagnosed with AP as controls, this study is a cross-sectional case–control study. Medical information (age, gender, body mass index, underlying diseases, blood test data, and dietary intake method) was obtained from the electronic medical records. Oral conditions were assessed by the Oral Health Assessment Tool (OHAT) [20]. The number of remaining teeth and the presence of intra-oral bleeding were determined and the buccal mucosa was assessed for dryness. Table 1Characteristics of the subjectsAP ($$n = 6$$)Ctrl ($$n = 6$$)P-value†Age (years)87.2 ± 5.578.3 ± 10.30.132Body Mass Index15.3 ± 0.221.6 ± 3.60.002Blood data CRP (mg/dl)4.99 ± 4.271.80 ± 2.860.132 WBC (103/µl)8.07 ± 3.588.57 ± 4.301.000 Alb (g/dl)2.78 ± 0.592.80 ± 1.621.000Oral states Residual teeth6.8 ± 8.58.2 ± 12.80.818 OHAT3.2 ± 2.21.2 ± 1.20.093†P values were calculated by Mann–Whitney U testAlbumin concentrations of the AP ($$n = 4$$) and control ($$n = 4$$) patients. The other data were obtained from the AP ($$n = 6$$) and control ($$n = 6$$) patients ## Sample collection from the buccal mucosa Before sample collection, a dentist confirmed that at least 2 h had passed since the previous meal. After removing visible food residue, samples were collected from the buccal mucosa using a Hummingood sponge brush (Molten Corporation, Hiroshima, Japan), which had been dipped into 5 mL saline in a 50 mL tube and squeezed briefly onto the side of the tube. The samples were collected by placing the brush on the buccal mucosa, rubbing “back and forth” 10 times at a rate of 1 rub/second. The sponge was returned to the saline-containing tube, pressed, and squeezed tightly. After collection, the samples were stored at − 20 °C, thawed, and centrifuged at 3,000 × g for 5 min at 4 °C. The supernatants were used for further analysis by LC–MS/MS and multiplex assays. ## LC–MS/MS The supernatant was precipitated using trichloroacetic acid (TCA) and washed with acetone. The precipitate was air-dried at room temperature and dissolved in 40 μL of 6 M urea containing 50 mM triethylammonium bicarbonate. After measuring the protein concentration using Pierce BCA Protein Assay Kit (ThermoFisher Scientific), 1 μg of protein was incubated with 5 mM tris(2-carboxyethyl)phosphine) for 30 min at 37 °C under dark conditions, alkylated with 24 mM iodoacetamide for 30 min at room temperature, and digested with trypsin (Promega) at a trypsin: protein ratio of 1:10. After desalination using Pierce C18 Spin Tips & Columns (ThermoFisher Scientific) and acidification with $1\%$ trifluoroacetic acid, the digested peptides were loaded onto the nanoliquid chromatography EASY-nLC 1200 system (ThermoFisher Scientific). This system is equipped with a precolumn (Acclaim PepMap100 C18 column: inner diameter, 75 μm, length, 20 mm, particle size, 3.0 µm; ThermoFisher Scientific) and analytical column (Acclaim PepMap100 C18 column: inner diameter, 75 μm; length, 150 mm, particle size, 3.0 µm; ThermoFisher Scientific) equilibrated with $0.1\%$ formic acid. Next, peptide elution is performed using a linear gradient ($0\%$–$35\%$) of acetonitrile at a flow rate of 300 mL/min. The eluted peptides were ionized with a spray voltage of 2 kV (ion transfer tube temperature, 275 °C) and detected using tandem mass spectrometry (LC–MS/MS; Thermo Orbitrap QE plus, ThermoFisher Scientific) in the data-dependent acquisition mode using Xcalibur (version 4.0; Thermo Fisher Scientific). Mass spectra with 375–1,500 m/z were obtained with a resolution of 70,000 full width at half maximum. ## Quantification of LC–MS/MS data Comparative analysis of the detected protein and label-free quantitation was performed using Proteome Discoverer software version 2.2.0.388 (Thermo Fisher Scientific). The proteins were searched against UniProtKB/Swiss-Prot human database (taxonomy_id:9606). Oxidation of methionines and carbamidomethylation of cysteines were set as variable and fixed modification, respectively. The mass tolerance was set to 10 ppm. Two missed cleavages by trypsin were permitted. Further, target-decoy approach was used to determine the false discovery rate (FDR). Peptide-to-spectrum match data were obtained at an FDR of $1\%$, and the abundances were normalized by total peptide amounts. ## Multiplex cytokine assay After measuring protein concentration using the BCA Protein Assay Kit, the supernatants (not precipitated by TCA) were applied to a LEGENDplex Human Inflammation Panel 1 (13-plex: IL-1β, IFN-α2, IFN-γ, TNF-α, MCP-1, IL-6, IL-8, IL-10, IL-12p70, IL-17A, IL-18, IL-23, IL-33) in a V-bottom Plate (BioLegend, San Diego, USA). The concentrations of the target proteins were standardized to total protein concentration. In our shotgun proteomics analysis, we did not detect peaks for IL-1β, IL-6, TNF-α, or MCP-1 (Additional file 1: Table S2). In the multiplex cytokine/chemokine assay, the values for IL-6 and TNF-α were under the limit of detection (IL-6, < 6.80 pg/mL; TNF-α, < 0.73 pg/mL) in most of the oral samples. Using the supernatant without TCA precipitation, we also conducted a multiplex cytokine/chemokine assay. The protein concentrations of the supernatant ranged from 0.012 to 0.27 mg/mL, which were likely dependent on the strength of rubbing. Among the 13 cytokine and chemokine proteins, IL-1β, MCP-1, IL-8, and IL-18 were detected in all 12 samples. The concentrations were normalized to the total protein concentration. There were no significant differences between the AP and control groups, although MCP-1 levels tended to be lower ($$P \leq 0.065$$ by Mann–Whitney U test; Fig. 3).Fig. 3Results of the multiplex cytokines/chemokines assay. P values were calculated by the Mann–Whitney U test ## Sample collection and patient information We collected buccal mucosa samples from AP ($$n = 6$$, age 79–93 years old) and non-AP (Control; $$n = 6$$, age 66–93 years old) patients. Three samples (AP#3, AP#5, and AP#6) were collected from the patients with Parkinson’s disease, three (AP#2, AP#4, and Control#1) from those with Alzheimer's disease, and two (Control#2 and Control#6) with Lacunar infarction. The duration from the onset to the collection time varied from 1 to 18 days (Additional file 1: Table S1). Body mass index was low in the AP group (Table 1). No significant differences were observed in the blood for CRP concentration, several white blood cells, or serum albumin concentration. While there was no difference in the number of residual teeth, the OHAT score tended to be higher in the AP group, indicating an unhealthy oral state of the AP patients. ## Comparative quantitative analysis of oral proteins After TCA precipitation and acetone wash, protein solutions were obtained with concentrations ranging from 0.65 to 2.49 mg/mL. LC–MS/MS analysis detected 3,528 proteins including 3,253, 157, and 118 proteins at high- (q value < 0.01), middle- (0.01 < q value < 0.05), and low-confidence levels, respectively, based on their FDR (Additional file 2: Figure S1 and Additional file 1: Table S2). No significant difference was observed in the abundance distribution (Additional file 2: Figure S2). Principal component analysis (PCA) revealed that AP #4 was distinct from the other 11 samples; there was no significant difference in PCA profiles of the AP and control groups (Additional file 2: Figure S3). Although Proteome Discoverer software version 2.2, which applies the Minora nodes, is a powerful tool for label-free quantification [21], ANOVA adjusted using the Benjamini–Hochberg method, and not using nonparametric analysis such as Mann–Whitney U test, is available to calculate P values (Additional file 1: Table S2). In the present study, we compared the protein abundance obtained using the software. Among the 3253 proteins with high confidence, 638 had high coverage (> $50\%$); of those, 601 proteins were detected in all 12 samples. Then, we compared protein abundances of the 601 proteins between the AP and control groups. Abundance of 18 proteins, including aldo–keto reductase family 1 member B10, interleukin-36 receptor antagonist protein, and caspase-14, were significantly high in the AP group ($P \leq 0.05$ by Mann–Whitney U test) (Additional file 1: Table S3). Further, 37 proteins, including chloride intracellular channel protein 3, protein S100-A7A, and serpin B4, had high abundance in the AP group (0.1 > P ≥ 0.05 by Mann–Whitney U test; Additional file 1: Table S3). Next, we performed the gene ontology enrichment analysis on the proteins with significantly high abundance in the AP group [22, 23]. Significant results ($P \leq 0.05$ and FDR < 0.05) were observed for three processes: nitrobenzene metabolic process, peptide antigen assembly with MHC class I protein complex, and cellular detoxification of nitrogen compound. ## Serum and oral C-reactive protein Ouellet-Morin et al. reported a moderate-to-strong association between CRP measured in saliva and serum ($r = 0.72$) [24]. In the present study, there was no significant difference in CRP concentration either in the blood ($$P \leq 0.16$$) or oral cavity ($$P \leq 0.71$$) between the AP and control groups. Nonetheless, Spearman's correlation coefficient was 0.86 ($$P \leq 0.001$$) between blood and oral CRP levels, indicating that serum CRP may be predicted non-invasively using oral CRP values, which is consistent with the previous report [24] (Fig. 1). It is unclear why the abundance of oral CRP was relatively high in Control #3.Fig. 1Correlation between serum and oral CRP levels (Spearman's rank correlation coefficient) ## Detected proteins As shown in Additional file 1: Table S1, the time between the day of onset and collection ranged from 1 to 18 days. Next, we examined the correlation between the time from onset (Additional file 1: Table S1) and abundance of the proteins in the AP group (Additional file 1: Table S2). Among the 55 highly expressed proteins ($P \leq 0.10$ by Mann–Whitney U test) in the AP group, negative correlation was shown by 4 proteins, viz., protein S100-A7A (Uniprot ID, Q86SG5), eukaryotic translation initiation factor 1 (P41567), serpin B4 (P48594), and peptidoglycan recognition protein 1 (O75594) ($P \leq 0.10$ by Spearman’s test; Table 2 and Fig. 2).Table 2Proteins with higher abundance between the AP and control groupsAccessionDescriptionExp. q-value: CombinedCoverage [%]Abundance Ratio: (AP)/(Control)aP valuebCorrelation coefficientcP valuedQ86SG5Protein S100-A7A0696.510.065− 0.7710.072P41567eukaryotic translation initiation factor 10623.030.093− 0.8290.042P48594Serpin B40672.60.065− 0.7710.072O75594peptidoglycan recognition protein 10531.230.093− 0.8290.042The proteins were detected in all 12 samples with a coverage of > $50\%$aAbundance Ratio was calculated using Proteome Discoverer software version 2.2bP values were calculated by Mann–Whitney U test using the protein abundance (AP vs. Control)cCorrelation coefficients between days post-onset and protein abundance in the AP groupdP values were calculated by Spearman’s testFig. 2Four proteins expressed at high levels in the AP samples. The proteins detected in all 12 samples are listed in Table 2. The protein abundance between the six AP and six control samples (A–D) and the correlation between time post-onset and protein abundance in the six AP samples (E–H) are shown ## S100 protein family The S100 proteins, a family of calcium-binding cytosolic proteins, are known as damage-associated molecular pattern molecules and they exhibit a variety of intracellular and extracellular functions [25]. Protein S100A-7A (Ratio (AP/Control) = 6.51 by Proteins Discoverer software version 2.2) was higher ($$P \leq 0.065$$ by Mann–Whitney U test) in the AP group; however, there was no difference in the levels of other S100 protein family members. ## Cytokines and chemokines The LC–MS/MS detected some interleukins (ILs), however, there was no difference in IL-1α, IL-8, or IL-18 levels between the groups. The IL-36 cytokines, which include IL-36α, IL-36β, IL-36γ and IL-36Ra, belong to the IL-1 family and exert pro-inflammatory effects on various target cells, such as keratinocytes, synoviocytes, dendritic cells, and T cells [26]. Ramadas et al. showed that IL-36γ is upregulated in airway epithelial cells and involved in chemokine (neutrophil chemoattractants CXCL1 and CXCL2) production and neutrophil influx in mice challenged with a house dust mite extract [27]. In contrast, the abundance of the IL-36 receptor antagonist protein was significantly higher in AP samples compared with the control samples ($$P \leq 0.041$$). Our data suggest that various IL-36-related signaling pathways are involved in the onset of AP. ## Non-salivary proteins In the Human Body Fluid Proteome database, 2,871 proteins have been registered as saliva proteins as of May 2022 [28], and 1,973 of the 3,528 proteins detected in the present study were registered as salivary proteins in the database. Of the 1,555 non-salivary proteins, 130 were in high abundance in the AP group, whereas only six were detected with > $20\%$ coverage. Mago Nashi Homolog 2 (Magoh2) was detected in five of the six AP samples (coverage = $39\%$), but not in any of the Control samples. Although Magoh proteins contribute to exon junction complexes [29], it is unclear whether the Magoh2 protein is involved in the onset of AP. ## Discussion Underlying diseases can affect the composition of oral proteins. Figura et al. reported lower concentrations of S100-A16, ARP$\frac{2}{3}$, and VPS4B in the saliva of the Parkinson’s disease group compared with the healthy control group [30]. Although the three proteins were detected with high confidence in the present study, there was no significant difference between the three Parkinson’s disease samples (AP#3, AP#5, and AP#6) and the other nine samples. Concerning Alzheimer's disease, Contini et al. reported higher levels of S100A8, S100A9, α-defensins, and cystatins A and B in patients with Alzheimer's disease compared with healthy volunteers [31]. Although our analysis detected α-defensin, there was no significant difference between the three samples from Alzheimer's disease patients (AP#2, AP#4, and Control#1) and the other nine samples. These data suggest that the changes in the protein concentration disappear in patients suffering from AP. Absence of significant difference between subsets may be attributed to the limited size of our cohort study. The S100 proteins, a family of calcium-binding cytosolic proteins, are known as damage-associated molecular pattern molecules and exhibit a variety of intracellular and extracellular functions [25]. The S100 protein family consists of approximately 20 members, which are not only involved in cell proliferation, differentiation, migration, and apoptosis but are also thought to be closely related to cancer and neurodegenerative diseases [32]. Of these, S100-A7 is abundant in the saliva of patients with systemic sclerosis [33] and has recently been reported to act as an antimicrobial peptide [34, 35]. The S100-A7 is produced in [36] epithelial cells on the tongue and has been shown to exhibit antimicrobial activity against Escherichia coli (E. coli) [37]. The S100-A7A may be a useful biomarker for AP. Human serpins are a family of endogenous protease inhibitors with several biological functions [38]. As Bao et al. reviewed, serpin family proteins are involved in host–pathogen interactions [39]. Jiang et al. reported that α-antitrypsin, a serpin superfamily member, promotes lung defense against Pseudomonas aeruginosa by inhibiting neutrophil elastase-mediated host defense protein degradation in mice [40]. Moreover, serpin A1 suppresses the mediators of lipopolysaccharide-mediated proinflammation [41, 42]. Association of serpin 4B to immunity and/or infection remains unclear. Peptidoglycan is an essential component of the bacterial cell envelope [43]. Peptidoglycan recognition proteins recognize bacterial peptidoglycans and are involved in promoting antibacterial immunity and inflammation [44]. For example, human peptidoglycan recognition protein 1 exhibits bactericidal activity and is found in body fluids such as serum, sweat, and saliva [45]. In this study, we observed a high abundance of peptidoglycan recognition protein 1 ($$P \leq 0.093$$ by Mann–Whitney U test) (AP/Control ratio = 2.6, Additional file 1: Table S2). In addition, its abundance showed a significantly negative correlation with the time post-onset ($$P \leq 0.042$$ by Spearman’s test, Additional file 1: Table S4). Based on our data and previous reports, peptidoglycan recognition protein 1 and protein S100A-7A may be useful biomarkers of AP. Cytokines and chemokines are known markers of inflammation in response to bacterial infection. Although MCP-1 is a chemokine that recruits monocytes to the foci of active inflammation [46, 47], it was detected in all samples by the multiplex assay and the values tended to be lower in the AP group compared with the control group (Fig. 3). McGrath-Morrow et al. showed that in a lower respiratory tract model of E. coli infection, the host defense against the bacterium was mediated by MCP-1 and its receptor, CCR2 [47]. Low MCP-1 levels in AP patients may explain the reduced resistance to infection. ## Limitations As mentioned in Introduction, it remains unclear whether AP is distinct from typical pneumonia [1, 10, 11]. To clarify this, patients with typical pneumonia should be recruited as controls and compared with those having AP. However, in the current study, we could not recruit such patients as controls, suggesting that the potential biomarkers discovered in this study are not specific to AP in cases where there is any difference between AP and typical pneumonia. ## Conclusion In this study, we identified putative biomarkers applicable to the detection of AP at an early stage. We found four candidate proteins that may be considered biomarkers of AP. This study had several limitations, which included the varied duration from onset to collection (1–18 days post-onset). It remains unclear whether the candidate proteins identified in this study increase or decrease in the early stages of the disease. To address this issue, long-term prospective studies need to be conducted that evaluate samples from pre-onset to the onset of AP. ## Supplementary Information Additional file 1: Table S1. Characteristics of the 12 patients. Table S2. Detected Proteins. ( P values were calculated by ANOVA and adjusted by the Benjamini–Hochberg method in Proteome Discoverer software version 2.2.0.388 (Thermo Fisher Scientific).). Table S3. 602 proteins with high confidence (FDR) and high coverages (>$50\%$). ( P values were calculated using protein abundance (columns F-Q) by Mann–Whitney U test.). Table S4. Correlation between days post-onset and the proteins. ( P values were calculated by Spearman's test).Additional file 2: Figure S1. Distribution of abundance. This figure was prepared by Proteome Discoverer software version 2.2.0.388 (Thermo Fisher Scientific). Figure S2. Volcano plots of the proteomic data. The plots were generated using the abundance ratio = Log2(AP/Control). P values were calculated by ANOVA and adjusted by the Benjamini–Hochberg method in Proteome Discoverer software version 2.2.0.388 (Thermo Fisher Scientific). Figure S3. Principal component analysis of the 12 samples. This figure was prepared by Proteome Discoverer software version 2.2.0.388 (Thermo Fisher Scientific). ## References 1. Longo DL, Mandell LA, Niederman MS. Aspiration pneumonia. N Engl J Med (2019.0) 380 651-663. DOI: 10.1056/NEJMRA1714562 2. DiBardino DM, Wunderink RG. Aspiration pneumonia: a review of modern trends. J Crit Care (2015.0) 30 40-48. DOI: 10.1016/J.JCRC.2014.07.011 3. 3.Care PY, Aul P, Arik EM. Primary Care A SPIRATION P NEUMONITIS AND A SPIRATION P NEUMONIA. N Engl J Med. www.nejm.org. 2001. 4. Marik PE. Aspiration pneumonitis and aspiration pneumonia. N Engl J Med (2001.0) 344 665-671. 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--- title: 'Free sugar intake from snacks and beverages in Canadian preschool- and toddler-aged children: a cross-sectional study' authors: - Jessica Yu - Anisha Mahajan - Gerarda Darlington - Andrea C. Buchholz - Alison M. Duncan - Jess Haines - David W. L. Ma journal: BMC Nutrition year: 2023 pmcid: PMC9996946 doi: 10.1186/s40795-023-00702-3 license: CC BY 4.0 --- # Free sugar intake from snacks and beverages in Canadian preschool- and toddler-aged children: a cross-sectional study ## Abstract ### Background Excess consumption of free sugar (FS) increases the risk of dental caries and unhealthy weight gain. However, the contribution of snacks and beverages to young children’s FS intake is not well understood. The purpose of this study was to determine FS intake from snacks and beverages among preschool-aged Canadian children. ### Methods This cross-sectional study examined baseline data from 267 children 1.5 to 5 y enrolled in the Guelph Family Health Study. Dietary assessment was completed over a 24-h period using ASA24-Canada-2016 to, 1) estimate the proportion of children whose FS intake from snacks and beverages consumed exceeded $5\%$ total energy intake (TE) and $10\%$ TE, and 2) identify the top snack and beverage sources of FS. ### Results FS contributed 10.6 ± $6.9\%$ TE (mean ± SD). 30 and $8\%$ of children consumed ≥ $5\%$ TE and ≥ $10\%$ TE from snack FS, respectively. Furthermore, 17 and $7\%$ of children consumed ≥ $5\%$ TE and ≥ $10\%$ TE from beverages FS, respectively. Snacks and beverages accounted for 49 ± $30.9\%$ of FS energy. Top snack sources of FS (% children, children’s %TE from FS) were bakery products ($55\%$, $2.4\%$), candy and sweet condiments ($21\%$, $3.0\%$), and sugar-containing beverages ($20\%$, $4.1\%$). Top sugar-containing beverage sources of FS ($48\%$, $5.3\%$) were $100\%$ fruit juice ($22\%$, $4.6\%$) and flavored milk ($11\%$, $3.1\%$). ### Conclusions Snacks and beverages contributed nearly half of FS intake among a sample of young children in Canada. Thus, long-term monitoring of snacking behavior and consumption of FS is warranted. These findings may help inform nutritional strategies and public policies to improve diet quality and FS intake in preschool-aged children. ### Trial registration The Clinical Trial Registry number is NCT02939261 from clinicaltrials.gov. Date of Registration: October 20, 2016. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40795-023-00702-3. ## Background The World Health Organization (WHO) recommends that free sugar (FS) intake be limited to < $10\%$ of total energy (TE) to reduce the risk of unhealthy weight gain and < $5\%$ of TE to reduce the risk of dental caries [1]. According to the WHO, FS is defined as monosaccharides and disaccharides added to foods and beverages by the manufacturer, cook or consumer, and sugars naturally present in honey, syrups, fruit juices and fruit juice concentrates [1]. Studies investigating preschool- and toddler-aged children have found that 70–$96\%$ of children exceed the WHO free sugar intake recommendation of < $5\%$ TE [2–6], which is of concern because of short term consequences such as dental caries and since dietary habits formed in young children have been found to track into adulthood [1, 7]. Canada’s new dietary guidelines [8] have adopted the WHO recommendation that FS intake be limited to < $10\%$ TE. Overall reduction of FS through several strategies including the preparation of nutritious snacks with little to no added sugars and the selection of water as the beverage of choice is encouraged [8]. However, the contribution of snacks and beverages to FS intake remains poorly understood, which is concerning because snacking is becoming more prevalent and contributes > $25\%$ TE in children living in Canada and the United States (US) [9–13]. Studies in these countries reveal promising data that sweet beverage consumption is decreasing in children; however, intake still remains high [14, 15]. Continuous monitoring is necessary given evidence that FS intakes from beverages have particularly adverse impacts on cardiovascular disease risk [1, 16]. While it is known that many young children exceed recommendations for FS, few studies have explored specific food or beverage intake and sources of FS [2, 4, 5]. Understanding the top sources of FS can help inform approaches to reduce sugar intake among young children. This study investigated the contribution of snacks, beverages, and their categories (e.g., bakery products, frozen desserts, candy and sweet condiments, flavored milk, $100\%$ fruit juice) to FS intake among preschool-aged Canadian children. The objectives of the present study were to investigate the proportion of children whose FS intakes exceed < $5\%$ TE and < $10\%$TE through snacks and beverages; and to identify leading snack and beverage sources of FS. ## Ethics approval and consent to participate The present study adhered to the guidelines of the Declaration of Helsinki and was approved by the University of Guelph Research Ethics Board (REB#17-07-003). Parents provided written informed consent. ## Study design This is a cross-sectional secondary analysis of baseline data obtained from children participating in the Guelph Family Health Study-Full Study cohort. Details describing the study design and recruitment have been published elsewhere [17]. The aim of this study was to investigate FS intake from snack and beverage categories among preschool-aged children. ## Setting and participants The Guelph Family Health Study is an ongoing family-based behavior change intervention study (NCT02939261). Between 2017–2020, families with at least one child between the ages of 1.5 and 5 y and who were not planning to move in the next year were recruited from Guelph-Wellington areas via the Guelph Family Health Team, Guelph Community Health Centre, community events, and social media platforms. ## Dietary assessment A single 24-h dietary assessment was completed at baseline for each child by one parent using the online Automated Self-Administered 24-h (ASA24) Dietary Assessment Tool 2016-Canadian version (National Cancer Institute, Bethesda, MD). The Canadian version of ASA24 calculates the nutrient profile of reported dietary intake using the Food Patterns Equivalents Database (FPED), Food and Nutrients Database for Dietary Studies (FNDDS), and Canadian Nutrient File (CNF). Energy and nutrient intakes were screened for outliers using the adjusted box plot method [18] and implausible dietary intakes. Total sugar and added sugar intakes were calculated by ASA24. FS intake was defined as added sugar plus sugar from $100\%$ fruit juice (includes fruit juice concentrate diluted to single strength and fruit juice that is not from concentrate) [1, 19]; and determined using a standardized and semi-automated stepwise approach as described in Additional File 1. ## Snacks, beverages, and their categories Snacks were defined as foods (i.e., food snacks) and beverages excluding water (i.e., beverage snacks) consumed between parent-identified meals as per ASA24 meal occasion coding. This is consistent with previous research investigating snacking in preschool-aged children using 24-h recall [13, 20, 21]. Beverages were defined as all beverages (including water) consumed at any meal or snack during the 24-h recall. Items were classified into snack and beverage categories by two data analysts to ensure quality of data entry. Snack and beverage items were classified into 15 categories and several subcategories using a classification system adapted from Bernstein et al. [ 22] (See Additional file 2). These categories included sugar-focused major food groups created based on the Canadian Food and Drug Regulation’s Schedule M food categories [22]. FS intakes were determined from these sources. ## Statistical analyses Statistical analyses were completed using SAS® University Edition version 9.4 (SAS Institute, Inc., Cary, NC). The percent contributions (mean ± SD) of snacks, beverages, and their FS to TE and total FS energy were estimated. These data were used to determine the proportion of children whose FS intakes exceeded < $5\%$ TE and < $10\%$ TE from snacks or beverage alone. To identify snack and beverage sources of FS, the proportion of children consuming each snack and beverage category (See Additional file 2) and each category’s FS contribution to TE were determined. The frequency and percent of children consuming each snack and beverage category (regardless of serving size) over 24-h were reported. For each participant, the energy consumed from FS per snack and beverage category relative to TE (%TE from FS) was determined by summing FS energy for the snack or beverage category, dividing by the participant’s TE and then multiplying by 100. Next, participants’ %TE from FS (mean, $95\%$ CI) was calculated for snack and beverage categories consumed by at least 30 participants (to ensure adequate data for a meaningful summary). Generalized estimating equations were used to account for any dependence between sibling participants [23]. ## Sample characteristics A total of 322 children were enrolled in the Guelph Family Health Study full study cohort. Data were excluded from ASA24 records that were incomplete ($$n = 28$$), included breastmilk ($$n = 15$$) or had sugar intakes that were considered both an outlier (less than the 25th percentile minus 1.5 times the IQR or greater than the 75th percentile plus 1.5 times the IQR) and implausible ($$n = 12$$). Thus, the final cross-sectional analysis included baseline data from 267 ($$n = 129$$ boys, $$n = 138$$ girls) children from 210 families. Additional file 3 presents a participant flow chart. In the final analytical sample, over half ($52\%$) of families had a household annual income of ≥ $100,000 Canadian and the majority ($79\%$) had at least one parent with a university degree or higher (Table 1). Most families ($73\%$) had one child included in the analysis. The majority of children ($77\%$) were White with an average age of 3.6 ± 1.2 y (mean ± SD). TE in the sample of children was summarized as 1411 ± 385 kcal/d (mean ± SD).Table 1Demographic characteristics of families and participantsFamily Characteristics ($$n = 210$$)n (%)Household Income (Canadian Dollars) < $60,00033 [16] $60,000 – $99,99956 [27] ≥ $100,000110 [52] Did not answer11 [5]Highest level of education obtained by at least one parent Some university, some college or technical school, high school graduate13 [6] College graduate31 [15] University graduate63 [30] Postgraduate training or degree103 [49]Child(ren) included in this analysis 1 child154 [73] 2 or 3 children56 [27]Participant Characteristics ($$n = 267$$)n (%)Child Ethnicity White205 [77] Other55 [21] Did not answer7 [3]Child Age in years, Mean ± SD3.6 ± 1.2Child SexN (%), Mean age (years) ± SD Male129 [48], 3.6 ± 1.3 Female138 [52], 3.5 ± 1.2Child Total Energy Intake in kcal, Mean ± SD1411 ± 385 ## Snacks and beverages: free sugar energy Among all children, FS contributed an average of $10.6\%$ of TE (Table 2). Furthermore, FS from snacks and beverages contributed an average of $5.7\%$ of TE, which is half of all energy contributed by different sources of FS. Individually, snacks contributed approximately one-third and beverages amounted to one-fifth of FS energy. Table 2Percent of children’s total energy contributed by free sugar, snacks, and beveragesa% TE(mean ± SD)% total FS energy(mean ± SD)Total FS10.6 ± 6.9-FS from snacks3.9 ± 4.036.5 ± 28.1FS from food snacks3.1 ± 3.429.9 ± 26.7FS from beverage snacks0.8 ± 2.26.6 ± 16.4FS from beverages2.6 ± 4.219.3 ± 26.0FS Free sugar, TE total energyaSample included ($$n = 267$$) non-breastfed Canadian children 1.5–5 years from the Guelph Family Healthy Study. Total FS is of all meals, snacks, and beverages consumed over 24-h. Beverage snacks are beverages (excluding water) consumed during snacking occasions (i.e., between meals), whereas beverage items, including water and FS from beverage snacks, were consumed any time within the 24-h recall ## Food snacks, beverage snacks, and free sugar energy Almost all ($97\%$) children consumed snacks and most ($91\%$) consumed ≥ 2 snack items (Table 3). An additional table shows all snack categories assessed (See Additional File 4). Thirty percent and $8\%$ of children consumed ≥ $5\%$ TE and ≥ $10\%$ TE from snack FS, respectively (Fig. 1).Table 3Snack intake by snack categories and their contributions to free sugar energy intake among childrenaSnack CategoryNumber of Children (Percent)Children’s %TE from FSMean ($95\%$ CI)≥ 11≥ 2ALL SNACKS260 [97]17 [6]243 [91]4.0 (3.5 – 4.5)BEVERAGE SNACKS121 [45]81 [30]40 [15]1.8 (1.3 – 2.3) Sugar-Containing Beverages53 [20]41 [15]12 [4]4.1 (3.2 – 4.9) Plain Milk73 [27]49 [18]24 [9]0FOOD SNACKS257 [96]24 [9]233 [87]3.2 (2.8 – 3.6) Candy and Sweet Condiments56 [21]50 [19]6 [2]3.0 (2.3 – 3.8) Bakery Products148 [55]108 [40]40 [15]2.4 (2.1 – 2.7) Dairy Products and Alternates94 [35]69 [26]25 [9]1.1 (0.7 – 1.6) Savory Snacks111 [42]93 [35]18 [7]0.3 (0.2 – 0.5) Fruits191 [72]86 [32]105 [39]0.5 (0.3 – 0.8) Vegetables and Legumes (except fried potatoes)44 [16]30 [11]14 [5]0.01 (0.0 – 0.03) Cereals and Grain Products29 [11]24 [9]5 [2]- Nuts and Seeds42 [16]35 [13]7 [3]0.1 (0.1 – 0.2) Mixed Dishes, Sides and Entrees16 [6] ~ ~ - Frozen Desserts12 [4] ~ ~ - Meats, Eggs and Substitutes11 [4] ~ ~ -FS Free Sugar, TE Total EnergyaThis table summarizes the number and percent of children 1.5–5 years who consumed different snack categories, relative to the total number of children ($$n = 267$$). Percent was calculated to identify the proportion of children who consumed snack categories at least once (≥ 1) over 24-h, then calculated for the more discrete categories of one time [1] or two or more times (≥ 2). Not all children were reported to consume a snack ($$n = 7$$). Mean children’s %TE from FS was calculated for categories that were consumed by ≥ 30 children. For these calculations, only data from children who consumed each respective category were used (i.e., data from children who did not consume the snack category over 24-h were not included in the mean). Generalized estimating equations were used to account for any dependence between sibling participants [23]. For categories consumed by < 30 children, mean %TE from FS was not calculated and was instead denoted with a dash. Categories consumed and/or breakdowns were not reported and were denoted as ~ whenever < 5 children were identified. Sugar-containing beverages included beverages with sugars added during processing + $100\%$ fruit juice. An expanded version of this table including food and beverage items included in each snack category can be found in Additional File 4Fig. 1Percent of children consuming 0 to < 5, 5 to < 10 or ≥ $10\%$ of TE from snack free sugar. Sample included ($$n = 267$$) Canadian children 1.5-5y. The ≥ $10\%$ group ranges from 10 to $23\%$ of TE. FS, Free Sugar; TE, Total Energy Among children who consumed snacks, snack FS contributed $4.0\%$ TE. Although food snacks ($96\%$) were consumed by more children than beverage snacks ($45\%$) and food snacks contributed more FS than beverage snacks, the single snack category that contributed the most %TE from FS was sugar-containing beverages (SCBs) followed by candy and sweet condiments, and bakery products. These categories were also consumed by large proportions of children: $20\%$ for SCBs, $21\%$ for candy and sweet condiments, and $55\%$ for bakery products. Health Canada recommends that most sugars come from fruit, vegetables, and unsweetened dairy products [8, 24]. The proportions of children consuming these snacks were $72\%$ for fruits, $27\%$ for plain milk, $17\%$ for cheese, $16\%$ for vegetables and legumes, and $4\%$ for plain yogurt. ## Beverages and free sugar energy In this sample, 17 and $7\%$ of children consumed ≥ $5\%$ TE and ≥ $10\%$ TE from beverage FS, respectively (Fig. 2). Almost all ($99\%$) children consumed beverages ≥ 2 times over 24-h (Table 4).Fig. 2Percent of children consuming 0 to < 5, 5 to < 10 or ≥ $10\%$ of TE from beverage free sugar. Sample included ($$n = 267$$) Canadian children 1.5-5y. The ≥ $10\%$ group ranges from 10 to $26\%$ of TE. FS, Free Sugar; TE, Total EnergyTable 4Beverage intake by beverage categories and their contribution to free sugar energy intake among childrenaBeverage CategoryNumber of Children (Percent)Children’s %TE from FSMean ($95\%$ CI)≥ 11≥ 2All Beverages265 [99]2 (0.7)263 (98.5)2.6 (2.1 – 3.1)Sugar-Containing Beverages129 [48]79 [30]50 [19]5.3 (4.5 – 6.1)$100\%$ Fruit Juice58 [22]48 [18]10 [4]4.6 (3.7 – 5.6)Flavored Milk29 [11] ~ ~ 3.1 (2.3 – 4.0)Water245 [92]31 [12]214 [80]0Plain Milk180 [67]66 [25]114 [43]0Smoothies23 [9] ~ ~ -Plant-Based Beverages21 [8]15 [6]6 [2]-Fruit Drinks16 [6] ~ ~ -Yogurt Beverages15 [6] ~ ~ -Hot Beverages7 [3] ~ ~ -Regular Soft Drink ~ ~ ~ -Sports Drinks ~ ~ ~ -Diet Soft Drink000-Energy Drinks000-Vegetable Drinks000-aThis table summarizes the number and percent of children 1.5–5 years who consumed different beverage categories, relative to the total number of children ($$n = 267$$). Percent was calculated to identify the proportion of children who consumed beverage categories at least once (≥ 1) over 24-h, then calculated for the more discrete categories of one time [1] or two or more times (≥ 2). Not all children were reported to consume a snack ($$n = 7$$). Not all children were reported to consume a beverage ($$n = 2$$). Mean children’s %TE from FS was calculated for categories that were consumed by ≥ 30 children. For these calculations, only data from children who consumed each respective category were used (i.e., data from children who did not consume the beverage category over 24-h were not included in the mean). Generalized estimating equations were used to account for any dependence between sibling participants [23]. For categories consumed by < 30 children, mean %TE from FS was not calculated and was instead denoted with a dash. Categories consumed and/or breakdowns were not reported and were denoted as ~ whenever < 5 children were identified. Sugar-containing beverages include beverages with sugars added during processing + $100\%$ fruit juice. In this sample, this includes flavored milk, smoothies, sweetened plant-based beverages, fruit drinks, yogurt beverages, sweetened hot beverages, and $100\%$ fruit juiceFS Free Sugar, TE Total Energy SCBs, consumed by nearly half ($49\%$) of children, included $100\%$ fruit juice, flavored milk, smoothies, sweetened plant-based beverages, fruit drinks, flavored yogurt beverages, sweetened hot beverages, regular soft drinks, and sports drinks. More children consumed SCBs once ($30\%$) compared to multiple times ($19\%$) over 24-h. FS from SCBs contributed a mean of $5.3\%$ TE. The SCB that was consumed by the highest proportion of children and contributed the greatest %TE from FS among children was $100\%$ fruit juice followed by flavored milk. Smoothies, plant-based beverages, fruit drinks, yogurt beverages, and hot beverages were each consumed by fewer than $8\%$ of children. Regular soft drinks and sports drinks were rarely consumed. Diet soft drinks, energy drinks, and vegetable drinks were not reported in any ASA24-records. Water and plain milk were the beverage categories consumed by the largest proportions of children (92 and $68\%$, respectively) and were more commonly consumed multiple times than once over 24-h. ## Discussion In this study, we determined the proportion of children whose FS intake from snacks and beverages exceeds $5\%$ TE and $10\%$ TE, respectively; and identified leading snack and beverage sources of FS. Overall, we found that FS contributed $10.6\%$ TE and that snacks and beverages combined contributed nearly half of all FS energy. To understand the breakdown of FS sources, examination revealed that 30 and $8\%$ of children exceeded $5\%$ TE and $10\%$ TE through snack FS, respectively. We acknowledge that beverages are not only consumed during snacking occasions, but also throughout the day. Therefore, we completed further examination of FS from beverages, which showed that 17 and $6\%$ of children exceeded $5\%$ TE and $10\%$ TE through beverages, respectively. Both snack and beverage analyses reveal excess contribution to FS intake. Finally, SCBs, bakery products, and candy and sweet condiments were the top snack and beverage sources of FS energy. The WHO and Health Canada recognize snacks and beverages as potential areas to reduce FS intake and improve diet quality [1, 8]. Snacking is increasing in young children and the present study found that snacks contributed over one-quarter ($26.6\%$) of TE, similar to preschoolers from Canadian ($27\%$) and US ($28\%$) national datasets [9, 12, 13]. FS energy as a proportion of TE was lower in our study ($10.6\%$) than a recent analysis of children 1–8 y from Canadian Community Health Survey (CCHS)-2015 ($13.8\%$) [4] but still exceeded $10\%$ TE [1]. The findings that SCBs, bakery products, and candy and sweet condiments were the top snack sources of FS are consistent with previous studies [4, 5] and provide new information that these sources are commonly consumed during snacking occasions. SCBs are of particular concern due to their association with cardiometabolic risk [1, 16]. Despite reductions in SCB intake among young Canadian children [15], our findings that SCBs (particularly $100\%$ fruit juice and flavored milk) were consumed by nearly half of the children and contributed FS greater than $5\%$ TE reinforces that SCBs are still a concern among preschoolers and toddlers. Possible strategies to reduce FS from snacks and beverages include replacing with healthy alternatives, reducing frequency of consumption, and reducing portion size. Replacing top snack and beverage sources of FS with fruits and vegetables during snacking occasions can limit FS intake and improve diet quality in young children, as demonstrated by Reale et al. [ 25]. Health Canada recommends a healthy eating pattern where most sugars come from fruit, vegetables, and unsweetened dairy products, items, which contain no FS [8, 24]. In our study, fruits and plain milk are snack and beverage categories that were consumed by a large proportion of children. Substituting sweetened items such as flavored yogurt with plain yogurt plus unsweetened fruits, and flavored milk with plain milk, can reduce FS intake while retaining the nutrients these items offer. Reducing portion size and frequency of consumption of sweetened snacks and beverages may also reduce FS intake. A previous study of a sample of 26 US preschoolers found that providing children with a larger size of beverage as a snack increased beverage and/or food intake and serving $100\%$ fruit juice led to greater overall snack energy intake [26]. Given that SCBs were mostly consumed once a day among children in our study, portion size of beverages may be a larger contributor of excess FS intake than frequency of consumption. Furthermore, our data suggest that children who consume SCBs during snack time, consume more FS from that snack category than from any other snack category (Table 3). Some limitations should be considered when interpreting our study results. Most participants came from middle-income households, had educated parents, and were White. Given that household income, parental education and ethnicity have been associated with diet quality and sweet snack and beverage intake, our findings may not be generalizable to children of diverse backgrounds [9, 27, 28]. To reduce participant burden, FS was calculated from a single day of dietary recall, which may vary from usual intake, and thus cannot be directly compared to WHO FS recommendations. Finally, using ASA24-Canada-2016 to calculate FS resulted in the use of data from multiple databases (i.e., FPED, CNF-2015, FNDDS) with sometimes inconsistent added sugar and total sugar values. In particular, “muffins with fruits/and or nuts” and “lemonade” are items where FS sometimes exceeded total sugar, suggesting an overestimation of FS in these items although, of note, these items were consumed by 11 and 5 children, respectively. Despite these potential overestimations, the contribution of FS to TE was lower in our study compared to children from the CCHS-2015 ($10.6\%$ TE in 1.5 – 5 y versus $13.8\%$ TE in 1–8 y children), which analyzed FS using a different methodology [4]. Strengths of this study include the focus on how snack and beverage categories contribute to FS intake among young Canadian children. Furthermore, this study assessed FS as a percentage of TE, a measure used by various health agencies [1, 8]. ## Conclusions In this sample of young Canadian preschool-aged children, nearly half of FS energy intake came from snacks and beverages. Our study provides evidence that even at a young age, children consume snacks and beverages containing FS throughout the day at levels higher than currently recommended. We also identified the major sources of FS including bakery products, candy and sweet condiments, $100\%$ fruit juice, and flavored milk. These findings help to inform nutrition recommendations and potential policy to address the contribution of sugar in the diet quality of preschool-aged children. ## Supplementary Information Additional file 1. Stepwise determination of free sugars from ASA24-Canada-2016 data. Additional file 2. Categories and subcategories of snacks and beverages (Adapted from Bernstein et al., 2016).Additional file 3. Participant flow chart. Additional file 4. Snack intake by major and minor snack categories and their contribution to free sugar energy intake among children. Additional file 5. Guelph Family Health Study consortium members. ## References 1. 1.WHOGuideline: sugars intake for adults and children2015Geneva, SwitzerlandWorld Health Organization. Guideline: sugars intake for adults and children (2015.0) 2. Devenish G, Golley R, Mukhtar A, Begley A, Ha D, Do L, Scott JA. Free sugars intake, sources and determinants of high consumption among Australian 2-year-olds in the SMILE cohort. Nutrients (2019.0) 11 161. DOI: 10.3390/nu11010161 3. Crowe M, O’Sullivan M, Cassetti O, O’Sullivan A. Estimation and consumption pattern of free sugar intake in 3-year-old Irish preschool children. Eur J Nutr (2020.0) 59 2065-2074. DOI: 10.1007/s00394-019-02056-8 4. 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--- title: 'Measurement properties of the EQ-5D-5L in sub-health: evidence based on primary health care workers in China' authors: - Yueyue Liu - Chuchuan Wan - Xiaoyu Xi journal: Health and Quality of Life Outcomes year: 2023 pmcid: PMC9996950 doi: 10.1186/s12955-023-02105-1 license: CC BY 4.0 --- # Measurement properties of the EQ-5D-5L in sub-health: evidence based on primary health care workers in China ## Abstract ### Background Sub-health which is the state between health and disease is a major global public health challenge. As a reversible stage, sub-health can work as a effective tool for the early detection or prevention of chronic disease. The EQ-5D-5L (5L) is a widely used, generic preference-based instrument while its validity in measuring sub-health is not clear. The aim of the study was thus to assess its measurement properties in individuals with sub-health in China. ### Methods The data used were from a nationwide cross-sectional survey conducted among primary health care workers who were selected on the basis of convenience and voluntariness. The questionnaire was composited of 5L, Sub-Health Measurement Scale V1.0 (SHMS V1.0), social-demographic characteristics and a question assessing the presence of disease. Missing values and ceiling effects of 5L were calculated. The convergent validity of 5L utility and VAS scores was tested by assessing their correlations with SHMS V1.0 using Spearman’s correlation coefficient. The known-groups validity of 5L utility and VAS scores was assessed by comparing their values between subgroups defined by SHMS V1.0 scores using the Kruskal–Wallis test. We also did an analysis in subgroups according to different regions of China. ### Results A total of 2063 respondents were included in the analysis. No missing data were observed for the 5L dimensions and only one missing value was for the VAS score. 5L showed strong overall ceiling effects ($71.1\%$). The ceiling effects were slightly weaker on the “pain/discomfort” ($82.3\%$) and “anxiety/depression” ($79.5\%$) dimensions compared with the other three dimensions (nearly $100\%$). The 5L weakly correlated with SHMS V1.0: the correlation coefficients were mainly between 0.2 and 0.3 for the two scores. 5L was yet not sensitive in distinguishing subgroups of respondents with different levels of sub-health, especially the subgroups with adjacent health status ($p \leq 0.05$). The results of subgroup analysis were generally consistent with those of the full sample. ### Conclusions It appears that the measurement properties of EQ-5D-5L in individuals with sub-health are not satisfactory in China. We thus should be cautious to use it in the population. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12955-023-02105-1. ## Background In parallel with the change of living environment and the increasing pace of life, more and more people are in the state of sub-health [1], which refers to the state between health and disease that does not meet the criterion for health nor the clinical diagnostic criteria for diseases according to modern medicine [2]. Chinese scholar Wang Yuxue first formally introduced its concept in the 1980s [3]. It is derived from traditional Chinese medicine (TCM) characterized by a decline in vitality, physiological function and the capacity for adaptation over a certain period of time [2, 4]. In many countries, much attention has been paid on perceived poor health “medically unexplained symptoms (MUS)” [5]. They are two concepts that have some similarities but are not exactly identical. MUS contains a series of clinical defined conditions commonly with diagnostic criteria mainly focusing on physiological symptomatic outcomes like pain and fatigue [6, 7]. However, sub-health is not a strict clinical concept and should be considered as a dynamic process. It includes physical, mental and social adaptation performance of the subject, which highlights both psychological and social factors, so it carries a wider connotation than MUS [3, 8, 9]. Sub-health now is a major global public health challenge [6, 7, 10, 11]. Previous investigations conducted in China within different groups of people have shown that $60\%$-$70\%$ of surveyed individuals are sub-healthy [12, 13]. Sub-health is a low-quality health state and people in this state are typified by impaired health related quality of life (HRQoL) [6, 7, 10]. They may frequently suffer from physical, mental and social interactional problems, like fatigue, pain, sleep disorder, depression, agitation, fear, inability to assume appropriate social roles and so on. As a reversible stage, sub-health has a bidirectional transformation to health or disease so it can work as a effective tool for the early detection or prevention of chronic disease [4, 14–17]. Hence, it is necessary to accurately measure sub-health status, which can help to promote early interventions in the population and thus to avoid the generation of disease and further to reduce the burden of disease and healthcare expenditure. Sub-health is rich in connotation and should be comprehensively assessed from various aspects. Currently the assessment and measurement of sub-health is based on individual symptoms and social background, physiological and biochemical test results, relevant TCM theories, specific questionnaires and scales, or a combination of those methods [18]. However, there is no standardized criterion. Instrument-based measurement can reflect the subjective and multidimensional manifestations of sub-health in a relatively objective manner, which is a widely used sub-health measurement method. Its advantages include being quantitative and easy-to-use. The instruments used in current sub-health researches include Sub-Health Measurement Scale V1.0 (SHMS V1.0) [19], Suboptimal Health Status Questionnaire-25 (SHSQ-25) [20, 21], Sub-Health Self-Rating Scale (SHSRS) [22], Self-Rated Health Measurement Scale Version1.0 (SRHMS V1.0) [23, 24], etc. Among them, the SHMS V1.0 developed by Xu et al. is a reliable and valid tool widely used for measuring sub-health (Cronbach's alpha coefficient of 0.917 and KMO statistic of 0.927) [1, 12, 13, 19, 25–29]. Although those scales can reflect the health status of individuals, the variety of them hinders the result comparisons between studies using different scales due to not exactly consistent development ideas behind those scales. Moreover, the instruments cannot provide health utilities, thus cannot be used in economic evaluations. The EQ-5D-5L (5L) is a widely used, generic preference-based instrument with good reliability and validity in many specific groups of people [30–32]. If it is a valid sub-health measurement instrument, health utilities in sub-health and result comparisons among different studies could be available, which lay a foundation for further researches. However, to the best of our knowledge, there is a lack of study on the performance of 5L in sub-healthy population at present. Hence, the study aimed to assess whether the 5L can effectively reflect the impact of sub-health on HRQoL using the SHMS V1.0 as an external standard. Given the fact that primary health care workers in China are at high risk of being sub-health due to their long-term heavy workloads [33], the study was conducted based on data from primary health care workers in China. ## Sampling and data collection The study used data from a cross-sectional survey conducted in primary health care institutions across multiple cities in mainland China from July to August 2021. The definition of primary health care institutions in this survey refers to the relevant provisions of Law of the people’s Republic of China on the Promotion of Basic Medical and Health Care. They mainly include township (street) health centers, community health service centers (stations), village health offices, infirmaries and clinics. Respondents meeting the following criteria were included in the analysis: [1] Full-time health care workers of the institutions; [2] ≧1 year of working experience; [3] Not clinically diagnosed with any disease. The study sample was selected on the basis of convenience and voluntariness. First, thirty one provinces, autonomous regions and municipalities in mainland China were divided into four region groups (ten in the Eastern Region, six in the Central Region, twelve in the Western Region, and three in the Northeast Region) according to their economic levels and future development strategies. Then investigators from all provinces, autonomous regions and municipalities included in each region were recruited respectively. After receiving uniform training, investigators visited at least two local primary care institutions by convenience to conduct face-to-face questionnaire survey. At the study site, according to the principle of voluntariness, investigators invited health care workers to participate in the survey, providing them with the purposes, contents, and requirements of the survey. Then, investigators confirmed with consenting individuals the time and undisturbed places for the survey. After being reminded of the bullet points and prompts in questions, the respondents answered questionnaires independently on removable electronic devices (mobile phone, tablet, etc.) provided by the investigators, and the data were uploaded to the electronic database system in real time. We assigned data auditors to examine the collected data in time. If many missing items or logical errors were found, the questionnaire will be discarded and fed back to the corresponding investigator so as to timely conduct the investigation again. The questionnaire relevant to this study was composited of social-demographic characteristics (including age, gender, height, weight, education level, years of experience, professional title, annual income, type of household, marital status, region and location of healthcare institution), 5L, SHMS V1.0 and a question assessing the presence of disease. The order of the SHMS V1.0 and 5Lwas randomly assigned to avoid order effect. The two scales’ items were respectively set as 5L and SHMS V1.0 question groups. Then we set up two “element packages” containing the 5L and SHMS V1.0 question sets in reverse order. In each questionnaire, only one “element package” was randomly displayed to realize the randomization of the response order of the two scales. The protocol of this study was approved by the Ethics Committee of China Pharmaceutical University (No. CPU2019015). Informed consent was obtained from all individual participants included in the study. ## SHMS V1.0 SHMS V1.0 has been widely used in college students, residents in different regions, and has showed good reliability and validity in measuring sub-health [25–27]. Its Cronbach's alpha coefficient was 0.939 for our sample. SHMS V1.0 is composited of three subscales, ten dimensions and 39 items in total, each in a Likert 5-point format, with four general items of sub-health (GS). The three subscales are physical sub-health scale (PS), mental sub-health scale (MS) and social sub-health scale (SS). PS contains four dimensions of physical symptom, organic function, physical mobility function, and vitality. MS includes three dimensions of positive emotion, psychosocial symptom and cognitive function. SS contains three dimensions of social adaptability, social resource and social support. For SHMS V1.0, the conversion score (sc) is adopted to do analysis. It is defined as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{s}}_{{\text{c}}} = \frac{{s_{r} - s_{{r\left({min} \right)}} }}{{s_{{r\left({max} \right)}} - s_{{r\left({min} \right)}} }} \times 100,$$\end{document}sc=sr-srminsrmax-srmin×100,where sr is the raw score calculated by summing the corresponding item scores except ones of four overall assessment items. sr(min) is the theoretical minimum score of sr and sr(max) is the theoretical maximum score of sr. Higher conversion scores mean better health status. ## EQ-5D-5L 5L is a generic preference-based instrument for measuring health and consists of two parts: a descriptive system and a visual analog scale (VAS). The system comprises five dimensions of mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, with each consisting of only one item. Each item has five levels of response describing no, slight, moderate, severe, and extreme problems (“1” being the no problems and “5” being the extreme problems). The 5L can describe a total of 3125 potential health states of the respondent on the day of survey, with “11,111” being the full health and “55,555” the worst health state. EQ-VAS is a vertical line with a scale ranging from 0 to 100, with 100 on the top representing the “best imaginable health state” and 0 at the bottom representing the “worst imaginable health state”. In the study, the responses to the five dimensions of 5L were converted into utility scores using the Chinese 5L value set [34]. ## Data analysis Descriptive statistics including mean, standard deviation (SD) and percentage were used to present characteristics of the study sample. The mean values (SD) of 5L utility and EQ-VAS scores were respectively calculated and the missing data of 5L were examined. The distributions of responses to different dimensions and the proportion of respondents reporting the full health of 5L were also reported. We assessed the convergent validity of 5L by analyzing its relationship with SHMS V1.0, using Spearman’s correlation coefficient. We hypothesized that the respective correlation of 5L utility and EQ-VAS scores with SHMS V1.0 total and subscale scores would be moderate or strong, as well as the similar dimensions from the two instruments including Pain/discomfort and Organic function; Mobility and Physical mobility function; Pain/discomfort and Physical mobility function; Usual activities and Vitality; Anxiety/depression and Psychosocial symptom. The values of correlation coefficients less than 0.3 were considered to be weak, values between 0.3 and 0.49 were regarded as moderate, and values of 0.5 and above as strong [35]. The known-groups validity of 5L was assessed by identifying whether subgroups known to be different in health status could be distinguished by 5L’s utility and VAS scores [36]. Five known-groups (i.e., health, mild sub-health, moderate sub-health, severe sub-health and illness) were defined according to SHMS V1.0 total, PS, MS or SS score respectively. The demarcation score boundaries were determined according to the demarcation norms of the SHMS V1.0 in Chinese civil servants (Additional file 1) [37]. Since both the distributions of utility and VAS scores were skewed, the non-parametric Kruskal–Wallis H rank test with pairwise comparisons was performed to assess their known-groups validity. We hypothesized that the higher the SHMS V1.0 total, PS, MS or SS scores, the higher the 5L utility and EQ-VAS scores. Since the responses to preference-based HRQoL scales may vary among different regions [38], we also analyzed the convergent validity and known-groups validity in subgroups determined by different regions of China (i.e., the Eastern, Central, Western and Northeast Regions). Microsoft® Excel 2016 and IBM SPSS 26 were used for data analysis. All effects were considered statistically significant at $p \leq 0.05.$ ## Descriptive statistics A total of 2167 questionnaires from 913 primary health care institutions nationwide were returned and 2063 respondents ($34.7\%$ in the Eastern Region, $25.8\%$ in the Central Region, $31.8\%$ in the Western Region, and $7.7\%$ in the Northeast Region) were included in the final analysis, with the response rate being $95.2\%$. Table 1 displays characteristics of the study respondents. Six hundred and thirty-four ($30.7\%$) were male and 1429 ($69.3\%$) were female. Their mean age (SD) was 37.6 (9.6) years. Table 1Characteristics of the study respondentsn (%)GenderMale634 (30.7)Female1429 (69.3)Age (years)Mean ± SDa37.6 ± 9.6 < 35833 (40.4)[35, 60)1195 (57.9) ≥ 6035 (1.7)Body mass index (kg/m2) < 18.5193 (9.4)[18.5, 24)1403 (68.0)[24, 28)408 (19.8)[28, 30)19 (0.9) ≥ 3040 (1.9)EducationSecondary school or lower185 (9.0)College degree837 (40.6)Bachelor’s degree896 (43.4)Master's degree136 (6.6)PhD degree9 (0.4)Years of experience (years) < 5416 (20.2)[5, 10)477 (23.1)[10, 20)628 (30.4) ≥ 20542 (26.3)Professional titleJunior title1149 (55.7)Intermediate title673 (32.6)Associate senior title138 (6.7)Senior title46 (2.2)No title57 (2.8)Annual income level (RMB) < 50,000651 (31.6)[50,000, 100,000)928 (45.0)[100,000, 200,000)417 (20.2) ≥ 200,00067 (3.2)Household typeAgricultural764 (37.0)Non-agricultural1299 (63.0)Marital statusSingle368 (17.8)Married1676 (81.2)Others19 (0.9)RegionEastern Region716 (34.7)Central Region532 (25.8)Western Region657 (31.8)Northeast Region158 (7.7)Location of healthcare instituteCity1114 (54.0)Rural area949 (46.0)aStandard deviation There were no missing responses to the 5L dimensions and only one missing value for the VAS score. The mean values (SD) of 5L utility and VAS scores were 0.974 (0.057) and 86.4 (14.9), respectively. A total of 1466 ($71.1\%$) respondents reported full health (“11,111”). The distributions of the responses to 5L are shown in Table 2. Nearly all respondents reported no problems on the mobility, self-care and usual activities dimensions. Around $80\%$ reported no problems on the dimensions "pain/discomfort" and "anxiety/depression", and nearly $20\%$ reported slight problems (Level 2). Almost no respondents reported serious or extreme problems on all of the dimensions (Level 4 and Level 5).Table 2Distributions of the responses to the EQ-5D-5L dimensionsEQ-5D-5L dimensionLevel1 (%)Level2 (%)Level3 (%)Level4 (%)Level5 (%)Mobility2020 (97.9)38 (1.8)3 (0.1)0 (0.0)2 (0.1)Self-care2056 (99.7)3 (0.1)1 (0.0)0 (0.0)3 (0.1)Usual activities2029 (98.4)32 (1.6)1 (0.0)0 (0.0)1 (0.0)Pain/discomfort1698 (82.3)344 (16.7)19 (0.9)1 (0.0)1 (0.0)Anxiety/depression1641 (79.5)386 (18.7)33 (1.6)2 (0.1)1 (0.0) ## Convergent validity The 5L utility score weakly correlated with SHMS V1.0 scores. Comparing the correlations of 5L utility score with SHMS V1.0 total and the three subscales scores, the 5L utility score most strongly correlated with the SHMS V1.0 total score and weakliest related to the SS score. The correlations between the EQ-VAS and SHMS V1.0 scores were similar but they were generally better than those between the 5L utility score and SHMS V1.0 scores (Table 3). The correlation coefficients for the five pairs of potentially relevant dimensions between SHMS V1.0 and 5L were all lower than 0.3 (weak correlation). Correlation coefficient of the SHMS V1.0 vitality dimension and the 5L usual activities dimension was only -0.056 (Table 4).Table 3Convergent validity of the EQ-5D-5L utility score and EQ-VAS scoreEQ-5D-5L utility scoreEQ-VAS scoreSHMS V1.0 total score0.2300.307SHMS V1.0 PS score0.2270.267SHMS V1.0 MS score0.2020.282SHMS V1.0 SS score0.1380.219EQ-VAS0.3671.000$p \leq 0.01$Table 4The correlations between similar dimensions from SHMS V1.0 and EQ-5D-5LSHMS V1.0EQ-5D-5LSpearman’s Correlation coefficientOrganic function (P2)Pain/discomfort− 0.185Physical mobility function (P3)Mobility− 0.099Physical mobility function (P3)Pain/discomfort− 0.185*Vitality (P4)Usual activities− 0.056Psychosocial symptom (M2)Anxiety/depression− 0.208***$p \leq 0.01$, $p \leq 0.05$ The results of the subgroup analysis were generally consistent: correlation coefficients were generally lower than 0.3 (Additional file 2). The correlations of 5L utility score and EQ-VAS with SHMS V1.0 were best respectively in the Central Region and Northeast Region. The 5L weakliest correlated with SHMS V1.0 SS score for all subgroups. The correlations between EQ-VAS and SHMS V1.0 in all regions were slightly better compared with those between the 5L utility score and SHMS V1.0. ## Known-groups validity The mean values (SD) of 5L utility score and EQ-VAS score for each group are shown in Table 5. The utility and VAS scores of 5L were both significantly different in at least two of the five groups divided by overall, physical, mental or social health status ($p \leq 0.001$, Table 5). The Kruskal–Wallis H value of the 5L utility score in the physical health was 113.1 which was higher than those in the mental health (87.5) and social health (43.0). The EQ-VAS also had the lowest Kruskal–Wallis H value in social health (101.2) compared with those in physical and mental health (130.2 and 155.9). The 5L utility score did not significantly distinguish the “illness” and “severe sub-health” groups nor the “health” and “mild sub-health” groups in overall, physical, mental and social health ($p \leq 0.05$, Additional file 3). EQ-VAS was also insensitive in distinguishing between adjacent health status groups. Subgroup analysis had generally consistent results (Additional file 2).Table 5Known-groups validity of EQ-5D-5L utility score and EQ-VAS scoren(%)EQ-5D-5L utility score Mean (SDa)/Mean rankEQ-VAS score Mean (SDa)/Mean rankOverall health statusHealth584 (28.3)0.986 (0.036)/1158.4389.9 (13.3)/1235.16Mild sub-health514 (24.9)0.977 (0.075)/1077.7288.4 (12.0)/1110.39Moderate sub-health755 (36.6)0.969 (0.050)/957.8984.2 (16.2)/904.39Severe sub-health123 (6.0)0.964 (0.051)/885.4980.7 (17.3)/787.83Illness87 (4.2)0.937 (0.088)/763.4477.7 (16.7)/657.77Kruskal–Wallis H103.432170.634p value0.0000.000Physical health statusHealth726 (35.2)0.981 (0.065)/1122.8889.2 (12.8)/1180.01Mild sub-health528 (25.6)0.981 (0.044)/1097.7187.3 (15.1)/1081.64Moderate sub-health603 (29.2)0.969 (0.049)/952.8185.1 (13.8)/915.57Severe sub-health127 (6.2)0.952 (0.058)/780.9178.5 (18.2)/703.97Illness79 (3.8)0.938 (0.087)/765.7677.0 (22.8)/756.00Kruskal–Wallis H113.093130.167p value0.0000.000Mental health statusHealth488 (23.7)0.986 (0.034)/1154.3289.8 (13.9)/1241.48Mild sub-health438 (21.2)0.977 (0.077)/1078.2388.4 (14.0)/1136.45Moderate sub-health83 (140.3)0.972 (0.050)/1001.3585.3 (14.8)/952.33Severe sub-health208 (10.1)0.963 (0.057)/896.0881.9 (16.0)/796.18Illness98 (4.8)0.943 (0.078)/764.0378.9 (16.5)/698.16Kruskal–Wallis H87.543155.853p value0.0000.000Social health statusHealth367 (17.8)0.982 (0.042)/1123.4790.1 (12.9)/1237.30Mild sub-health510 (24.7)0.977 (0.048)/1072.7686.8 (15.2)/1061.11Moderate sub-health848 (41.1)0.974 (0.066)/1019.7186.6 (14.2)/1020.05Severe sub-health211 (10.2)0.962 (0.062)/901.4082.0 (15.9)/805.94Illness127 (6.2)0.964 (0.052)/903.0280.2 (18.1)/777.21Kruskal–Wallis H43.000101.237p value0.0000.000aStandard deviation ## Discussion This is the first study to assess the measurement performance of 5L in measuring the effect of sub-health on HRQoL, filling a gap in the field of the applicability of 5L in sub-healthy population and laying a foundation for further research in sub-health measurement. Based on the results of a representative sample of primary health care workers in China, the measurement properties of 5L in sub-health may not be satisfactory. Many studies have reported that the 5L shows good measurement properties in many types of cancer [30], hemophilia [31], osteoarthritis [32], etc. The health status of people with clinically diagnosed disease usually declines significantly, while the majority ($86\%$) of the respondents in sub-health of this study were mild to moderate, the difference between whose health status and full health was not obvious. The possible reason could be that the 5L, although a new version of EQ-5D, is still insensitive to the health difference between slightly declined health status and full health. There were no missing responses to the all 5L dimensions and the instrument had good completion rate, which was consistent with the results of several studies before [32, 39, 40], suggesting its good feasibility in the population. On the other hand, around $70\%$ of respondents reported “no problems” on all the 5L dimensions. This result is higher than that of the general population ($50.8\%$) [41]. The possible reason is that the general population includes people with disease, while the primary health care workers in the study are engaged in their daily work without disease. The proportions of subjects reporting “no problems” on the “pain/discomfort” and “anxiety/depression” dimensions were $82.3\%$ and $79.5\%$, respectively. Compared with the other three dimensions accounting for nearly $100\%$, the ceiling effects were slightly weaker on those two specific dimensions. Previous studies in specific patient and general populations have also reported that “anxiety/depression” and “pain/discomfort” were two dimensions presenting relatively more frequent problems [42–44]. It also reflected that the mental problems were important factors affecting the health of primary health care workers in China, which was similar to the existing report [33]. The convergent validity of 5L utility score in measuring sub-health was poor. Its correlations with the SHMS V1.0 overall and three subscales scores were low, especially with the SHMS V1.0 SS score. As a sub-health specific scale, SHMS V1.0 covers a wide range of dimensions in the physical, mental and social health; whereas the 5L contains only five dimensions and lacks dimension related to social health. In terms of similar dimensions between the two instruments, the SHMS V1.0 items are profounder and richer, and the measured content is more comprehensive and detailed. Thus it can detect different health status of the population effectively. For example, the SHMS V1.0 “organic function” dimension includes measurements of vision and hearing in addition to measurements of gastrointestinal and head pain or discomfort. In contrast to the 5L “anxiety/depression” dimension consisting of a single item, the SHMS V1.0 “psychosocial symptom” dimension contains 5 items specifically measuring feelings like nervousness, fear, loneliness and so on. Those feelings often accompany or easily exacerbate to anxiety or depression, so the SHMS V1.0 can capture less severe changes in psychological symptoms. We were surprised that a very low correlation between the "mobility" dimension of the 5L and a similar dimension of the SHMS V1.0, "physical mobility function" (r = − 0.099) was identified. The same was true for 5L "usual activity" dimension and SHMS V1.0 "vitality" dimension (r = − 0.056). In addition, the multiple items within a certain dimension facilitate multiple reflections around that dimension by subjects, and thus better reflect the true situation. The correlations between the EQ-VAS and the SHMS V1.0 overall and three subscale scores were not strong but better compared with those between 5L utility score and SHMS V1.0 scores. It indicates to some extent that the EQ-VAS is able to reflect health dimensions that the 5L descriptive system do not include, which corresponds to the prior finding [45]. Based on the Kruskal–Wallis H values and the results of pairwise comparisons of 5L across the overall, physical, mental and social health, the 5L had the poorest known-groups validity in social health and was not sufficiently effective in distinguishing groups of respondents with different levels of sub-health, especially for the respondents with adjacent health status. The known-groups validity of EQ-VAS score was slightly better compared with the 5L utility score. The possible explanation is that the respondents may provide a global assessment of their health on the EQ-VAS, which is not limited to the five dimensions of 5L. Previous studies have shown that the 5L could hardly reflect the effects of fatigue, interpersonal relationships, sleep, vision, hearing and drug-induced adverse reactions (such as loss of libido and hair loss) on HRQoL [46–50]. Those factors are important for determining whether an individual is in full health or with varying degrees of sub-health. In addition, 5L takes the same day as the recall period which may not be conducive to the measurement of sub-health characterizing as a long-term chronic state. There are some limitations to the study. Although SHMS V1.0 is a widely used sub-health measurement instrument, choosing it as an external criterion may still be not enough thus influencing the accuracy of sub-health measurement and the assessment of measurement properties of 5L. The value set used to calculate 5L utility score was derived from a previous study based on a sample of urban residents from five cities in China [35], but $37\%$ of the sample in this study was agricultural households, which might have caused potential bias. In addition, because the study was based on a cross-sectional survey, we cannot evaluate the responsiveness of 5L in the sub-healthy population. ## Conclusions To conclude, it appears that the EQ-5D-5L lacks measurement properties in measuring HRQoL in sub-healthy population in China. Hence, we should be cautious to use it in some sub-healthy groups whose health is close to full health. ## Supplementary Information Additional file 1. Demarcation norms of the SHMS V1.0 total and subscales scores in Chinese civil servants. Additional file 2. Convergent and known-groups validity of 5L utility score and EQ-VAS score in subgroups divided by different regions of China. Additional file 3. 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--- title: DNA methylation analysis identifies key transcription factors involved in mesenchymal stem cell osteogenic differentiation authors: - Rodolfo Gómez - Matt J. Barter - Ana Alonso-Pérez - Andrew J. Skelton - Carole Proctor - Gabriel Herrero-Beaumont - David A. Young journal: Biological Research year: 2023 pmcid: PMC9996951 doi: 10.1186/s40659-023-00417-6 license: CC BY 4.0 --- # DNA methylation analysis identifies key transcription factors involved in mesenchymal stem cell osteogenic differentiation ## Abstract ### Background Knowledge about regulating transcription factors (TFs) for osteoblastogenesis from mesenchymal stem cells (MSCs) is limited. Therefore, we investigated the relationship between genomic regions subject to DNA-methylation changes during osteoblastogenesis and the TFs known to directly interact with these regulatory regions. ### Results The genome-wide DNA-methylation signature of MSCs differentiated to osteoblasts and adipocytes was determined using the Illumina HumanMethylation450 BeadChip array. During adipogenesis no CpGs passed our test for significant methylation changes. Oppositely, during osteoblastogenesis we identified 2462 differently significantly methylated CpGs (adj. $p \leq 0.05$). These resided outside of CpGs islands and were significantly enriched in enhancer regions. We confirmed the correlation between DNA-methylation and gene expression. Accordingly, we developed a bioinformatic tool to analyse differentially methylated regions and the TFs interacting with them. By overlaying our osteoblastogenesis differentially methylated regions with ENCODE TF ChIP-seq data we obtained a set of candidate TFs associated to DNA-methylation changes. Among them, ZEB1 TF was highly related with DNA-methylation. Using RNA interference, we confirmed that ZEB1, and ZEB2, played a key role in adipogenesis and osteoblastogenesis processes. For clinical relevance, ZEB1 mRNA expression in human bone samples was evaluated. This expression positively correlated with weight, body mass index, and PPARγ expression. ### Conclusions In this work we describe an osteoblastogenesis-associated DNA-methylation profile and, using these data, validate a novel computational tool to identify key TFs associated to age-related disease processes. By means of this tool we identified and confirmed ZEB TFs as mediators involved in the MSCs differentiation to osteoblasts and adipocytes, and obesity-related bone adiposity. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40659-023-00417-6. ## Introduction Musculoskeletal disorders (MSD) are a set of pathologies that affect the locomotor system (bones, joints, peri-articular structures, and muscles). Research interest in these diseases has increased due to their elevated prevalence because of an ageing demographic resulting in a burgeoning economic cost to national health systems [1, 2]. In fact, $20\%$ of Europeans undergo treatment or take supplements to help them cope with their MSD [3]. Besides ageing, the increase in the prevalence of major MSD conditions is also associated with an increase in obesity [4, 5] and a sedentary lifestyle [6, 7]. A common link amongst major MSDs are alterations to bone, such as osteoporosis and osteopenia, which have been associated with an increase in patient fragility [8] and in turn to a reduced life expectancy [9]. Bone alterations are also present in other MSDs, such as osteoarthritis where osteoporotic and sclerotic bone regions co-exist and both are linked to disease progression [10]. Likewise, in rheumatoid arthritis, as well as in other inflammatory arthropathies, there are local and systemic bone alterations [11]. Approximately $15\%$ of the human skeleton is renewed every year [12]. This means that maintenance of an optimal bone mass depends on the precise balance between bone resorption and bone formation. When this balance is altered pathological conditions such as osteoporosis or osteopetrosis occur. Bone formation is carried out by osteoblasts, which share with adipocytes, their mesenchymal origin [12]. Therefore, the process of bone formation in turn depends on the balance between the differentiation of mesenchymal stem cells (MSCs) towards osteoblast or adipocyte cell fates [12]. It is considered that both processes, osteoblastogenesis and adipogenesis, are competing and reciprocal [12, 13]. In fact, bone marrow adiposity has been associated with bone loss in several MSDs as well as in other associated pathologies or conditions such as obesity and ageing [12, 14]. The commitment of the MSC towards osteoblast or adipocyte cell fate is controlled by certain transcription factors (TFs) such as runt related TF 2 (Runx2) and SP7 for osteoblastogenesis, and peroxisome proliferator activated receptor gamma (PPARγ) and C/EBPs [13] for adipogenesis. These TFs integrate the cell environment and the signalling of diverse pathways helping to initiate or block the differentiation process [13]. This can potentially have clinical implications for example, long-term pharmacological activation of PPARγ, which promotes adipogenesis and inhibits osteoblastogenesis, increases fracture rates among patients with diabetes [15]. Advances have been made investigating the role of TFs and their associated signalling networks in osteoblastogenesis and adipogenesis, however, despite the well-established transcriptional cascade associated with adipocyte differentiation [16] a better understanding of the TF network involved in osteoblast differentiation is required. TF activity can be inhibited by DNA methylation through the blockade of their interaction with the DNA [17]. This inhibition has been associated with repression of gene expression, which supports the key role of DNA methylation in multiple processes, including development and tumorigenesis [17, 18]. DNA methylation is considered a heritable repressive epigenetic mark that consists of the covalent addition of a methyl group in the 5′ position of a cytosine in a CpG dinucleotide [18]. The abundance of these CpGs in the genome is not homogeneous, with more CpG-rich regions being present at TF-binding sites [17]. As a result DNA methylation has been proposed as a major regulator of TF activity [17]. There is limited information about the TFs that regulate the osteoblastogenesis process. Therefore, by means of a combined genome-wide methylation analysis and bioinformatic approach and using the link between DNA methylation and TF activity, we identified the TFs potentially affected by the changes in the DNA methylation during osteoblastogenesis. Our analysis suggested the DNA binding sites for the TF ZEB1/ZEB2 (Zinc Finger E-Box Binding Homeobox $\frac{1}{2}$) were hyper-methylated due to the process of osteoblast differentiation. Functional data confirmed the role of these TFs on both osteoblastogenesis and adipogenesis processes, emphasising the relevance of DNA methylation on the activity of key TFs during cellular differentiation. Consistent with this, ZEB1 expression in human pathological bone samples revealed a potential link between this TF and metabolic-mediated bone alterations. ## Reagents IGF-1 was purchased from Peprotech (Rocky Hill, NJ, USA). DMEM, Foetal bovine serum, β-Glycerol Phosphate, dexamethasone, ascorbic acid 2-phosphate, insulin, 3-Isobutyl-1-methylxanthine, indomethacin, rosiglitazone, Cetylpyridinium, Oil Red, Alizarin Red were purchased from Sigma-Aldrich (St. Louise, MO, USA). Other products were also purchased from Sigma-Aldrich unless otherwise indicated. ## Cell culture MSCs were purchased from Lonza (BAS, Switzerland). All the cells came from women donors, MSCs were cultured, characterized, and their trilineage potential was determined as previously described [19, 20]. The procedure for their culture and differentiation to osteoblast or adipocytes was performed as we previously described [21]. ## Cytological staining Cells undergoing osteoblast differentiation were fixed in $70\%$ cold ethanol (5 min, − 20 ºC). After drying the wells, to reveal calcium-rich mineralisation deposits the cells were incubated at room temperature with a solution of Alizarin Red (40 mM, pH 4.2) for 20–30 min. Prior to acquiring the images, the cells were gently washed with distilled water to avoid unspecific staining. For quantitation, the staining was eluted with $10\%$ (w/v) Cetylpyridinium solubilized in 10 mM sodium phosphate buffer (pH 7.0), and the absorbance measured at 570 nm. Cells undergoing adipogenesis were fixed with formalin for 1 h. After washing the wells with distilled water and $60\%$ isopropanol the wells were dried. To reveal the presence of lipid droplets the cells were stained with a $21\%$ (w/v) solution of Oil Red O for 10 min. Prior to acquiring the images, the wells were gently washed with distilled water to avoid unspecific staining. Staining images were quantified using the image analysis software ImageJ. ## Bone samples Bone samples from patients were obtained after total knee/hip replacement surgery for osteoarthritic and osteoporotic conditions. Healthy bone was from cadavers. Both healthy and pathological bone samples were comparable in terms of age and sex. The Ethics Committee for Research at Santiago-Lugo Area approved the protocol. Informed consent was obtained from all patients or patients’ families. Clinical data regarding weight and height was obtained from the clinical records, where available. BMI was calculated as weight (kg)/height (m)2. To isolate bone RNA, bone explants were obtained assuring only trabecular bone was processed, thus without fat, cartilage, nor other fibroblastic or stromal tissues. Bone was repeatedly washed with Phosphate Buffered Saline until clean, frozen to − 80 ºC, and pulverised using a CellCrusher (Cellcrusher, Co. Cork, Ireland) following the manufacturer instructions. Per each 500 µl of bone powder, 1 ml of TriReagent was added to perform RNA extraction. ## RNA extraction and real-time reverse transcription PCR Cell cultures were disrupted, and RNA extraction was performed using Qiagen RNeasy mini kit (Qiagen, Crawley, UK) following manufacturer instructions. Alternatively, when the experiments were performed in 96-well plates cell cultures were disrupted in Ambion Cells-to-cDNA II Cell Lysis buffer (Life Technologies, Carlsbad, CA, USA). Total RNA was then extracted and converted to cDNA using M-MLV reverse transcriptase (Invitrogen, Waltham, MA, USA) and RT-PCR was performed using TaqMan® probes. Changes in gene expression levels were calculated as described previously [21]. ## DNA methylation and RNA expression arrays Global DNA methylation analysis was performed using the Illumina Infinium HumanMethylation450K BeadChip array using DNA from three donor samples for each condition analysed (undifferentiated MSCs, MSCs differentiated to adipocytes, and MSCs differentiated to osteoblasts). All the samples were derived from the same donor to reduce variability. DNA, isolated using DNeasy Blood & Tissue Kit (Qiagen GmbH, Hilden, Germany), was bisulphite converted using the EpiTect® 96 Bisulphite Kit (Qiagen GmbH, Hilden, Germany), and 200 ng of bisulfite converted DNA was analysed using the array by the service provider Edinburgh Clinical Research Facility. The raw data were extracted using GenomeStudio (Illumina, San Diego, CA, USA) which provides the methylation data as β values: β = M/(M + U), where M and U represent the fluorescent signal of the methylation and unmethylated probes respectively. β values range from 0 (no methylation) to 1 ($100\%$ methylation). For gene expression, an Illumina HumanHT-12 v4 Expression BeadChip array was performed using 200 ng of RNA [with an RNA integrity score > 7 (Agilent bioanalyzer 2100)] for each sample. Three samples from different donors and for each condition (undifferentiated MSCs, and MSCs differentiated to osteoblasts) were analysed. The RNA samples were processed according to the manufacturer’s protocol and the array was performed by Central Biotechnology Services, Cardiff University. Gene expression data were analysed essentially as previously described [19]. All data are available on request. ## Data analysis HumanMethylation450K BeadChip data were normalised and analysed using the R language and the Tost analysis pipeline [22]. Further analyses to identify differentially methylated CpGs during differentiation were performed essentially using our own scripts developed from the limma package [23]. Data were transformed from β values to the more statistically valid M-values [24]. To explore the relationship between the distribution of differentially methylated CpGs and TF-binding and thus activity during the differentiation process we created an R-script, Regulatory Element Interrogation Script (REINS), available as an R Markdown document (Additional file 1). This script allows the download, management and overlap of the information provided by the ENCODE database [25], essentially ChIP-seq information about the genome-wide binding sites for 161 TFs in 91 different cell types, and differently methylated CpGs from any source including HumanMethylation450K BeadChip arrays. The script normalizes as a percentage the number of CpGs associated to a TF with the total number of CpGs. This allows the comparison of the overlap among different TFs with both hypo- or hyper-methylated CpGs. This normalization, “TF Relevance” (TFR), was performed using the following equations:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$TFR\,\,hypomethylation = \frac{{no \,of\,hypomethylated\,CpGs\,overlapping\,the\,TF}}{{Total\,no \,of\,hypomethylated\,CpGs}}\times100$$\end{document}TFRhypomethylation=noofhypomethylatedCpGsoverlappingtheTFTotalnoofhypomethylatedCpGs×100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$TFR\,\,hypermethylation = \frac{{no \,of\,hypermethylated\,CpGs\,overlapping\,the\,TF}}{{Total\,no \,of\,hypermethylated\,CpGs}}\times{100}$$\end{document}TFRhypermethylation=noofhypermethylatedCpGsoverlappingtheTFTotalnoofhypermethylatedCpGs×100 Finally, for the same TF, an unbalance in its ratio between TFRs for the hypo- and hyper-methylation (RRT, Relative Relevance of a TF) was calculated.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RRT\left(Relative Relevance of a Transcription Factor\right)=\frac{TFR\, hypomethylation}{TFR \,hypermethylation}$$\end{document}RRTRelativeRelevanceofaTranscriptionFactor=TFRhypomethylationTFRhypermethylation ## Computational modelling A computational model of BMP2 signalling was constructed to evaluate ALPL induction in the context of ZEB TFs activity. The design and construction of this model is described in Additional file 2. ## RNA-mediated interference (ZEBs expression inhibition) For siRNA transfection 50 nM siRNA was transfected into $50\%$ confluent MSCs using Dharmafect™ 1 lipid reagent (Thermo Fisher, Waltham, MA, USA). MSC were plated at a density of 5000 cells/well in 96-well plates for 24 h then media were replaced with osteogenic or adipogenic medium. After day 7 of differentiation the expression of key marker genes of differentiation was evaluated by RT-PCR. Cell cultures were cultured for 14 or 21 days to evaluate lipid accumulation or mineral deposition, respectively. Dharmacon siRNA SMARTpools® (Thermo Fisher, Waltham, MA, USA) of 4 specific siRNA duplexes (total of 50 nM siRNA) were used to target ZEB1 and ZEB2 TFs. Depletion of gene-specific mRNA levels was calculated by comparison of expression levels with cells transfected with 50 nM siCONTROL (non-targeting siRNA 2, cat. 001210–02; Dharmacon, Lafayette, CO, USA). ## Statistical analysis Data are expressed as mean ± standard error of the mean (SEM) for at least 3 independent experiments. Statistical differences were determined using a one-way analysis of variance or Kruskal–Wallis test, followed by a Bonferoni or Dunn’s post-hoc test, respectively, or Student’s t test or Mann–Whitney when appropriate. Contingency table statistical analysis was performed using Fisher exact tests. Correlations were assessed with Spearman tests. Statistical analysis was performed using Prism software (GraphPad Software Inc), $p \leq 0.05$ was considered significant. The statistical analysis of the array was performed using R software and Bioconductor R-packages [23]. ## Phenotypical characterization of MSC differentiation to osteoblasts and adipocytes We examined the DNA methylation profile associated with the differentiation of human MSC to osteoblasts and adipocytes. MSCs were differentiated in adipogenic or osteoblastogenic media for 14 or 21 days, respectively. Differentiation was confirmed by cytological staining with alizarin red for osteoblast and oil red for adipocytes (Fig. 1A). To further confirm the differentiation well-characterised osteoblast and adipocyte differentiation marker genes were measured by RT-PCR with expression consistent with the expected differentiation status and staining (Fig. 1B and C).Fig. 1MSCs differentiation to osteoblasts and adipocytes. A Three donors MSCs differentiated to osteoblasts (21 days) and adipocytes (14 days) were stained with alizarin red and oil red, respectively. B Expression of osteoblast marker genes in MSCs differentiated to osteoblasts. C Expression of adipocyte marker genes in MSCs differentiated to osteoblasts. All the experiments were performed at least in 3 different donors. Data is expressed as mean ± SEM. * $p \leq 0.05$, *$p \leq 0.01.$ All* gene expression data were normalised to the reference gene, GAPDH, and expressed as fold relative to the control samples ## Global DNA methylation of MSC differentiation DNA was extracted from MSC and differentiated cells and their global CpG methylation profile was assayed. For osteoblastogenesis, 2462 CpGs (1984 hypo-methylated and 478 hyper-methylated) showed a significant (adjusted p ≤ 0.05) change in their methylation (Fig. 2A) (Additional file 3). The overlapping genes of these CpGs (Additional file 3) included many in osteoblast metabolism such as RUNX2, GPNMB, CTSK, WNT5A, COL1A1 and PTH1R. However, surprisingly, only significant changes in CpG methylation were observed for osteoblastogenic differentiation (Fig. 2B), which involves a limited role for DNA methylation in establishing and maintaining the adipogenic phenotype (Additional file 4) (Fig. 2B).Fig. 2Analysis of the differently methylated CpGs. A Volcano plot representing the differentially methylated CpGs (M-values) associated with the differentiation of MSCs to osteoblasts. Significant (adjusted p ≤ 0.05) changes in CpG methylation (red). B Heatmap and clustering of the methylation values (M-values) of 2 462 CpGs differently methylated for MSCs differentiated to osteoblast. yellow: hypomethylated; red: hypermethylated. C Distribution of CpGs according to their location in relation to CpG islands. The number of CpGs were normalised according to the total CpGs in the array and the total amount of significant CpGs in each group (hypo- and hyper-methylated). Green line represents the percentage of hypo-methylated CpGs. Red line represents the percentage of hyper-methylated CpGs. Boxes identify a significant relationship between methylation and location. Broken grey line represents the expected distribution of the CpGs. $p \leq 0.01$, $p \leq 0.0001.$ D Distribution of the differentially methylated CpGs according to the different chromatin states. The bars represent the percentage of significantly hypo- (green) and hyper- (red) differentially methylated CpGs normalised according to the number of CpGs present in each state (on the array). E Analysis of the methylation profile of all the CpGs studied in the array that overlap different chromatin states. Violin plots representing the 15 different chromatin states with methylation level represented from 0 to 1. For the statistical analysis a cut off of $0.1\%$ in the difference of methylation was applied. The insulator state ($$p \leq 2.78$$E−2) and the heterochromatin state ($$p \leq 6.33$$E−37) were more methylated in OB than in MSC. The TXF elongation ($$p \leq 9.82$$E−30), TXF transition ($$p \leq 1.31$$E−09), and weak enhancer ($$p \leq 2.45$$E−06) states were less methylated in OB than in MSC. * $p \leq 0.001$ ## Genomic topology of the differentially methylated CpGs during osteoblastogenesis We examined the distribution of the 2462 differentially methylated CpGs within the genome and in relation to CpG island features. We found that osteoblastogenesis-dependent methylation remodelling was significantly associated with “open sea regions”. In contrast, methylation remodelling was less frequent than expected at CpG islands and their shores (Fig. 2C). Interestingly, only at “open sea regions” were the proportion of hypo-methylated CpGs higher than those hyper-methylated (Fig. 2C). Chromatin profiling is a powerful tool to detect regulatory features within the underlying DNA [26]. Therefore, we overlapped the location of the osteoblastogenesis differentially hypo- or hyper-methylated CpGs with the location of the fifteen chromatin states used to segment the genome by ENCODE for the mesoderm cell-line GM12878 [26], normalising for CpG location frequency bias from the array. A significantly higher frequency of differentially methylated CpGs were located at enhancer, active transcription, and heterochromatin chromatin states (Additional file 6). CpGs defined to be located within insulator, poised promoter or polycomb-repressed states were more frequently hyper-methylated, while the reciprocal was evident at transcription elongation state where CpGs were more frequently hypo-methylated (Fig. 2D). Next, we determined the methylation levels of all CpGs for each chromatin state in either the initial MSCs or following differentiation into osteoblasts. As predicted, promoter regions were very hypo-methylated while regions associated with transcription elongation or heterochromatin were hyper-methylated. When comparing CpG methylation levels for the two cell states, MSC and osteoblasts, data revealed that the methylation of the CpGs that overlapped with the insulator state and with the heterochromatin state were significantly higher in the osteoblasts than MSCs whilst those associated with chromatin states for transcription transition and elongation, and weak enhancer activity were lower (cut off $0.1\%$ value; $p \leq 0.001$) (Fig. 2E). ## Correlation of DNA methylation and gene expression To correlate DNA methylation with gene expression, we performed whole genome transcriptome analysis of RNA from the donor MSCs and differentiated osteoblasts. This analysis identified 2315 genes/transcripts as significantly differentially expressed (adjusted $p \leq 0.05$) during the differentiation process (Fig. 3A) (Additional file 5). The analysis of this gene list using Ingenuity Pathway Analysis (IPA) identified that the major bone related pathways were significantly enriched (osteoblast differentiation, WNT pathway activation) (Table 1), (Additional file 7). Interestingly, there was also a significant enrichment in genes associated with adipogenesis inhibition (Table 1), supporting the fact that osteoblastogenesis and adipogenesis are opposing cell fates. Fig. 3Relationship among methylation, gene expression and TFs. A Volcano plot representing the differentially expressed genes (adj. p ≤ 0.05, red) associated with the differentiation of MSCs to osteoblasts. B Lowess curve representing the correlation between the expression of genes in arbitrary units that had CpGs differently methylated in their 5’UTR and the methylation of these CpGs (M values). C Association of Hypo- and Hyper methylated CpGs to the chromatin interactions mediated by the RNA polymerase II (POL II). Data from chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) was obtained from ENCODE repository. Bars represent the number of CpGs that overlap (or not) to the POL II-mediated interactions. The methylation of CpGs overlapping or not POL II regions is significantly different $$p \leq 0.0436$$ (Fisher’s exact test). D Output of the REINS algorithm that overlaps hypo- of hypermethylated CpGs and TFs to suggest potential activity. Bars represent the metric RRT (Relative Relevance of a TF). RRT is the ratio of the percentage enrichment in hypomethylated CpGs vs the percentage enrichment in hypermethylated CpGs for a given TF. Bars represent the log2 of the RRT for all the TF studiedTable 1.Enrichment of the major bone related pathwaysPathwayp valueCell differentiation1.11E−26Increased osteoblasts differentiation9.80E−11Increase differentiation of bone3.94E−13Increased alkaline phosphatase2.17E−05WNT pathway activation6.46E−04Inhibition of MMPs1.41E−03Inhibition of accumulation of lipids8.56E−12Inhibition of synthesis of lipid1.07E−09Upstream regulatorp valueTGFB1 inhibited3.73E−26ERBB2 inhibited3.15E−23 Next, we explored whether gene expression and DNA methylation levels correlated in both MSCs and differentiated osteoblast. Using the methylation level of the significantly differentially methylated CpGs we observed a significant negative correlation only in osteoblasts (MSCs: r = − 0.0242, $$p \leq 0.6055$$ vs Osteoblasts: r = -− 0.377, $$p \leq 0.0031$$). Furthermore, when focussing on the methylation level of significantly differentially methylated CpGs that occur in 5’UTR regions, the negative correlation with gene expression improved for osteoblasts (r = − 0.2500; $$p \leq 0.0499$$), in line with data for other cell-types [27]. This correlation further improved when analysing genes above a robust expression level threshold (> $\frac{1}{3}$ of the maximum expression) (r = − 0.6289; $$p \leq 0.0013$$) (Fig. 3B). ## Osteoblastogenesis DNA methylation changes and POL II-chromatin interaction loci DNA methylation changes do not correlate with the gene expression and biochemical changes that occur during adipogenesis. Similarly, though DNA methylation did associate with osteoblastogenesis the correlation with gene expression was low, except for the described 5’UTR gene region CpGs and genes with a robust expression level. Using Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET), Li et al. [ 28] defined regions of the genome, essentially enhancers, topologically-associated with RNA polymerase II (RNAPII). Following the same approach, we determined, using ENCODE data for the cell-line MCF7, if the genomic loci of our significantly differentially methylated CpGs during osteoblastogenesis were RNAPII bound regions -indicative of active transcription. We observed that there was a significantly different ($$p \leq 0.0436$$) association of hypo- versus hyper-methylated CpGs with RNAPII regions, with a greater overlap for hypo-methylated CpGs. ( Fig. 3C)—implying that hypo-methylation of CpGs during osteoblastogenesis correlates with active gene expression. ## Identifying a link between DNA methylation and TF-binding sites We investigated whether our DNA methylation signature could identify key TFs that drive or elicit MSC differentiation towards osteoblasts. To do this we designed an R script named REINS that overlaps and normalizes changes in DNA methylation with the TF-binding sites determined by ChIP-seq. For our analysis we used ChIP-seq data derived from the ENCODE project (161 TFs studied across 91 different cell-types). To validate the script we used publicly available data of DNA methylation from different tissues and cells, namely; adipose tissue, muscle, pancreas, thymus, and human pluripotent stem cells (hPSCs) [29]. The polycomb repressive complex 2 (PRC2) related TFs (EZH2, SUZ12, CTBP2) have been tightly associated with the epigenetic repression of stem cell genes during cell differentiation [30]. Thus, the identification of the differential activation state of these TFs [30, 31] on hPSCs (hypo-methylated > hyper-methylated) in comparison with other tissues (hyper-methylated > hypo-methylated) was considered a positive validation (Additional file 8A–C). Likewise, the prediction of the potential critical role of the transcription factor ZEB1 on the thymus (hypo-methylated > hyper-methylated) was also considered a positive validation (Additional file 8D). After the validation, we used REINS with the DNA methylation data of MSCs differentiated to osteoblasts. TFs were sorted according to their normalized ratio of enrichment for binding sites for hypo-/hyper-differentially methylated CpGs, with values > 0 more enriched for de-methylation and < 0 more enriched for methylation (Fig. 3D). The top significant ($p \leq 0.001$) TFs enriched, whose binding sites were relatively more hypo-methylated, included the bone anabolism related TFs SMARCC1, MAFK, JUNB, FOS, JUN and STAT3 [32]. Factors that promote osteoblast differentiation are frequently inhibitors of adipocyte differentiation, and vice versa [13]. Accordingly, we noted that a significant proportion of the top significant ($p \leq 0.001$) TFs whose binding sites were hyper-methylated were associated with the promotion of adipogenesis (EZH2, ZEB1, NFY, ZNF143, SIN3A, SREBP1, SUZ12, SAP30 and CTBP2) [33–39]. ## Expression of ZEB TFs during MSC differentiation to osteoblasts To validate the data obtained with REINS, we picked the TF most associated to DNA hyper-methylation during osteoblastogenesis, ZEB1 (log2RRT = -− 3.053). Thus, we evaluated the expression of ZEB1 during our in vitro differentiation model. Counterintuitively, based on the methylation data, ZEB1 mRNA expression increased during osteoblastogenesis (Fig. 4A), which was also positively correlated with the induction of ALPL expression, a differentiation marker (Fig. 4B). Interestingly, ZEB1 consensus binding sites can also be occupied by a related TF with repressor activity, ZEB2. ZEB2 mRNA (ΔCt value) expression (data not shown) was relatively higher than ZEB1 in MSC but showed a similar increase during osteoblastogenesis (Fig. 4C). However, early in the osteoblastogenesis the kinetics of ZEB1 and ZEB2 induction differed, as evidenced by the significantly different induction ratio ZEB1/ZEB2 at day 3 (Fig. 4D). To examine how the induction of both ZEB factors, and their different induction-kinetics, could be involved in osteoblastogenesis we constructed a computational model, which included known functions of ZEB1/ZEB2 in bone morphogenic protein (BMP) signalling [40] (Additional file 9). Since we only wanted to investigate the coexistence and interaction of both ZEB TFs in this model, we did not include other signalling pathways relevant for differentiation. Confirming the experimental data, in the model ALPL expression was induced during differentiation despite the presence of the repressor activity of ZEB2 (Fig. 4E, F), mainly due to the change in the induction kinetics of ZEB1 and ZEB2.Fig. 4ZEB1 and ZEB2 expression during MSC differentiation to osteoblasts. A ZEB1 gene expression represented in arbitrary units along the differentiation procedure. B Correlation of the gene expression of ALPL and ZEB1 along the differentiation. Spearman $r = 0.6977.$ $$p \leq 0.0006.$$ C ZEB2 gene expression represented in arbitrary units along the differentiation. D Ratio of induction of ZEB1 and ZEB2. All the experiments were performed in at least 5 different donors. Data are expressed as means ± SEM. * $p \leq 0.05$, ** $p \leq 0.01.$ E Output from one simulation of the computational modelling. The panel represents the ratio of the mRNA expression of ZEB1/ZEB2 along 21 (virtual) days of differentiation. F Output from one simulation of the computational modelling. The panel shows the increased ALPL mRNA along 21 (virtual) days of differentiation ## Role of ZEBs TFs on MSC differentiation Since ZEB1 is involved in both adipose and bone metabolism and the information on the function of ZEB2 is limited we studied the contribution of both TFs to the differentiation of MSC to adipocytes and osteoblasts. We depleted MSC of either ZEB1 or ZEB2 by siRNA prior to their differentiation to osteoblasts or adipocytes (Fig. 5A, B). After 7 days of differentiation the depletion of ZEB1 expression did not alter the expression of osteoblast differentiation markers genes but did significantly reduce the expression of adipogenic markers (Fig. 5C–F). ZEB2 depletion enhanced ALPL expression (Fig. 5C) but not RUNX2 (Fig. 5D) and though adipogenic marker genes were increased this was not significant (Fig. 5E, F) unless compared to their expression level following ZEB1 loss. Fig. 5Expression of osteoblastogenesis and adipogenesis differentiation marker genes. Cells were treated with siRNA to deplete ZEB1 and ZEB2 mRNA expression, or Control non-targeting siRNA. A ZEB1 and B ZEB2 gene expression in control MSCs and MSCs treated with siRNA to knock-down ZEB1 or ZEB2 mRNA expression. All experiments were performed in at least in 4 donors. Data are expressed as mean ± SEM in arbitrary units. C RUNX2 and D ALPL gene expression in 7 day differentiated MSCs to osteoblast. E FABP4 and F Adiponectin gene expression in 7 day differentiated MSCs to adipocytes. All the experiments were performed with at least 4 donors. G and H Staining of calcium and lipid deposits in MSCs differentiated to osteoblasts and adipocytes. G Alizarin red staining of calcium deposits in MSCs differentiated to osteoblasts for 21 days. Cells were treated or not with siRNA to deplete ZEB1 and ZEB2 gene expression. Lower panel: alizarin red staining quantification expressed in arbitrary units. H Oil red staining of lipid deposits in MSCs differentiated to adipocytes for 14 days. Cells were treated or not with siRNA to deplete ZEB1 and ZEB2 mRNA expression Lower panel: oil red staining quantification expressed in arbitrary units. All the experiments were performed in at least 4 donors. Data are expressed as mean ± SEM in arbitrary units. $p \leq 0.01$, * $p \leq 0.001$ Although the transient depletion of either ZEB1 or ZEB2 did not significantly alter the mineralization in fully differentiated (day 21) osteoblasts (Fig. 5G), significant differences were observed in the lipid accumulation between fully differentiated adipocytes with an inhibited expression of ZEB2 compared with ZEB1 (Fig. 5H). These data again pointed that the alteration of ZEB1/ZEB2 relative levels at the start of differentiation affected the outcome of the adipogenic process. ## Bone ZEB1 expression positively correlates with body weight and BMI Regarding the observed implication of ZEB1 in adipogenic metabolism, we aimed to determine whether the expression of this TF was modulated in certain pathologies or physiological situations where bone integrity is affected. Neither osteoarthritic nor osteoporotic bone exhibited variations of ZEB1 expression in comparison to healthy bone (Fig. 6A, B).Fig. 6ZEB1 expression in bone correlates with donor weight and BMI, but not disease status. A ZEB1 expression in bone from osteoarthritic patients and patients without bone-related pathologies Healthy bone $$n = 6$$, Osteoarthritic bone $$n = 7$$, Osteoporotic bone $$n = 6$.$ Data are expressed as mean ± SEM in arbitrary units. B ZEB1 expression in bone from osteoporotic patients and patients without bone-related pathologies. Data are expressed as mean ± SEM in arbitrary units. C Correlation of ZEB1 expression in bone vs donor weight (kg). D Correlation of ZEB1 expression in bone vs BMI (weight (kg)/height (m)2). E Correlation of ZEB1 expression in bone vs expression of PPARG, a major adipogenic inducer. F Correlation of PPARG expression in bone vs weight (kg). G Correlation of PPARG expression in bone vs BMI. * $p \leq 0.05$, $p \leq 0.01$ As previously described, ZEB1 is involved in obesity development. Considering that obesity is defined as increased fat accumulation, we used body weight to investigate whether bone ZEB1 expression was linked to this pathology, identifying a significant correlation between the expression of this TF and body weight (Fig. 6C). Moreover, to discriminate among healthy, overweight, and obese patients we explored the same correlation using BMI instead of weight, which further improved the correlation coefficient (Fig. 6D). Obesity has been associated with bone adiposity [12]. Likewise, bone adiposity has been linked to the activation of PPARγ [41]. Thus, we aimed to determine whether PPARG expression correlated with ZEB1 expression in bone samples. Data obtained showed a positive correlation between both genes (Fig. 6E), however PPARG expression did not correlate with weight nor BMI (Fig. 6F, G), indicating a relevant role for ZEB1 in obesity-associated bone alterations. ## Discussion Here we have described the methylation profile of MSCs undergoing osteoblastogenesis and shown that changes in DNA-methylation occur outside of CpG islands. Likewise, we have associated these methylation changes to established chromatin states and linked the changes in DNA-methylation and gene expression. We have also developed a software tool to investigate the relationship between TFs and DNA-methylation profiles, which we used to define a set of candidate TFs potentially linked to the activation or repression of osteoblastogenesis. Accordingly, we validated the role of one of these inactivated TFs, ZEB1, and its opposing counterpart, ZEB2, on the differentiation of MSCs. We observed that modulating the ratio of these opposing factors affected both osteo- and adipogenesis, experimentally validating our bioinformatic tool. Bone alterations are a common link amongst many musculoskeletal pathologies and have been associated with increased patient fragility [8]. Further, bone-marrow adiposity increases with age in part because of an increase in MSC differentiation to adipocytes rather than osteoblasts [4, 42, 43]. Epigenomic state has been studied and no significant methylation changes were found in adipogenesis [44, 45], however, similar osteoblastogenic information is limited [12]. As a result, we determined DNA-methylation signature of both processes with an aim of identifying regulatory elements and TFs involved. In this work we were able to determine the changes in the methylation profile associated with MSC osteoblastogenesis, which revealed that the methylation remodelling occurred outside of the CpG islands, consistent with previous reports describing methylation differences between tissues [46], and suggested that DNA methylation could contribute to, or reinforce, the differentiation process. Additionally, by overlaying chromatin profiling data [26], we observed that osteoblastogenic DNA methylation remodelling (both hypo- and hyper-methylation) occurred at enhancer chromatin states. Interestingly, an increase in methylation was evident in the CpGs of insulator, poised promoter, and polycomb-repressed states, with the opposite true for transcription elongation state. This not only added a new layer of evidence to the involvement of DNA methylation on the regulation of osteoblastogenesis but also described the insulators as important points of regulation. DNA-binding of the well-characterized insulator CCCTC-binding factor is highly sensitive to DNA methylation [47], which suggests that osteoblastogenesis-associated methylation changes could alter CCCTC-binding factor binding, and therefore alter 3D genome architecture and thus influence function [47–49]. Unexpectedly, we did not identify any significant changes in the DNA methylation profile during adipogenesis. Supporting this observation, Noer et al. [ 50] described that DNA methylation of adipogenic promoters did not reflect transcriptional status, nor potential for gene expression, in adipocytes differentiated from MSCs. Likewise, it was determined that DNA methylation remained stable during adipocyte differentiation, implying that DNA methylation may not be a determinant of the adipogenic differentiation process [45]. Conversely, crude pharmacological inhibition of DNA methylation (with 5-aza-2′-deoxycytidine) in 3T3-L1 preadipocytes inhibited adipogenesis but promoted osteoblastogenesis [51]. MSCs are clearly delicately balanced for their differentiation commitment with regards adipo-osteogenic differentiation of MSCs [12, 13]. Our data suggests that adipogenesis can be achieved without a substantial modification of the default methylation profile of MSCs and that this phenotype is somewhat plastic. When MSC undergo osteogenic differentiation, the phenotype may be more stable due to its maintenance through a defined DNA methylation programme. Accordingly, during osteoblastogenesis we observed a negative correlation between DNA methylation and medium to highly expressed genes, and a significant link between DNA hypo-methylation and DNA regions related to active gene expression (RNAPOLII regions). Supporting this hypothesis we observed through our novel bioinformatic tool “REINS” that DNA hyper-methylation during osteoblastogenesis was enriched at binding sites of several pro-adipogenic transcriptional regulators [33–39, 52–54]—thus having the potential to inhibit their DNA-binding and therefore activity. Also enriched in osteoblastogenic hyper-methylated regions were binding sites for the transcriptional repressor SETDB1, a H3–K9 histone methyltransferase. SETDB1 binds to methylated DNA via a methyl-CpG-binding domain [55], thus enrichment of this factor is consistent with its described role as an inhibitor of adipogenesis [56]. Although REINS was validated because it identified the inactive epigenetic repression state of PRC2 TFs on hPSCs, the script is currently limited to the data of the 161 TFs present in the ChIP-seq data from the ENCODE project. As a result, the analysis is biased against TFs that regulate osteoblast metabolism and differentiation for which ChIP-seq data is lacking. Among the TF potentially inactive or repressed during the osteoblastogenesis, we studied ZEB1, which had the largest enrichment in the ratio DNA hyper-methylated/ hypo-methylated predicted by REINS. ZEB1 is a zinc-finger protein involved in adipocyte differentiation in mouse, as well as in obesity development in humans [33, 57]. It is also involved in thymus and skeletal development [58, 59] with its deletion in mouse being related to craniofacial abnormalities (shortened jaw), limb defects, and fusion of ribs [59]. Consistent with these data, REINS also predicted the potential activation state of this TF on thymus and adipose tissues (Additional file 8D). However, the potential inactive state predicted for ZEB1 in osteoblastogenesis by the tool was contrary to its reported activities promoting BMP2-mediated ALPL expression [40], skeletal abnormalities presented in ZEB1-null mice [59], and our observed increase in ZEB1 mRNA expression during osteoblastogenesis, which correlated with ALPL mRNA expression. In fact, ZEB1, along with orthologous ZEB2, are members of the ZEB family of transcription factors, and both can bind to E-box sequences CACCT(G). ZEB2 deficiency in humans has also been associated with craniofacial abnormalities, such as the excessive growth of the jaw (Mowat-Wilson syndrome) [60]. Interestingly, the functionality of ZEB proteins appear cellular-context dependent and, of relevance here, ZEB1 and ZEB2 have reported opposing activities (enhancer and repressor, respectively) upon activation of osteoblastic TGFβ or BMP signalling pathways [40, 61, 62]. Bone marrow has a higher level of ZEB2 expression compared to ZEB1 expression [63], which is consistent with their expression levels in our MSCs. Although both factors increased in mRNA expression during osteoblastogenesis, the ratio of these inductions (ZEB1 vs ZEB2) significantly increased at day 3 of differentiation. These data may suggest ZEB1 could be more active in early differentiation. At the late stages of differentiation, however, when ZEB2 would be predicted to dominate, we found a potential link of ZEB binding sites with hyper-methylation, which we speculate is to reduce the impact of ZEB2 acting repressively on osteoblastogenesis or to regulate the potential ZEB1-mediated adipogenic behaviour described previously [33, 57]. In line with this, our computational model revealed that the presence of the repressive ZEB2 to be compatible with the induction of ALPL along the differentiation process. To examine this hypothesis, we depleted ZEB1 and ZEB2 in MSCs prior to inducing their differentiation to osteoblasts and adipocytes. Removal of ZEB2 enhanced expression of the osteoblast differentiation marker ALPL, again supporting its role as an osteoblastogenic repressor. Interestingly, ZEB1 depletion did not affect osteoblast differentiation or mineralization suggesting that the role of ZEB1 during the process could be limited or only evident upon removal of the repressive ZEB2. To the contrary, ZEB1 depletion in MSCs significantly reduced the expression of the adipogenic differentiation markers, consistent with its role as an adipogenic promoter [33]. ZEB1 depletion partially reduced lipid accumulation, though this did not reach significance possibly due to the transient nature of siRNA-mediated depletion after the 14 days of differentiation required for lipid generation. Meanwhile, ZEB2 depletion increased adipogenic gene expression and oil staining when compared with the levels in ZEB1 depleted cells, again suggesting that the modulation of the ratio of the expression of these TFs in the MSCs regulates the adipogenic process. Regarding the essential role of ZEB1 in adipogenesis, we observed a positive correlation between bone ZEB1 expression and body weight or BMI. These data are consistent with the described relation between ZEB1 and obesity [57] and evidence how this metabolic pathology not only affects fat tissue, but also bone. Underpinning these correlations, we have found that ZEB1 also correlated with PPARG in bone samples. In agreement with this, it has been reported that ZEB1 knockdown diminished PPARG expression in 3T3-L1 cells [33]. Since PPARG is a major adipocytic inducer, and its activation has been associated with bone adiposity and fragility [15], the positive correlation of ZEB1 with this TF could support the role for ZEB1 in bone adiposity. Interestingly, PPARG expression did not correlate with weight or BMI, which suggests that the obesity effects on bone adiposity might imply ZEB1 modulation rather than major adipogenesis inducer PPARG. In conclusion, in this work we have characterised the DNA-methylation changes that occur during MSC-osteoblastogenesis. We propose that DNA methylation may be important for imprinting and maintaining the osteoblastic phenotype but not that of adipocytes. We present a script that could help to identify key TFs-associated with a given process through the analysis of DNA-methylation. Moreover, we have validated the algorithm on tissue-specific and osteoblastogenic DNA-methylation signatures, identifying ZEB TF ratios as modulators of differentiation of MSCs to osteoblasts and adipocytes, and ZEB1 as a critical TF in obesity-related bone adiposity. ## Supplementary Information Additional file 1: REINS NCL 2020. Script, Regulatory Element Interrogation Script (REINS), available as an R Markdown document, to explore the relationship between the distribution of differentially methylated CpGs and TF-binding and thus activity during the differentiation process. Additional file 2: Model design. A computational model of Bmp2 signalling was constructed to evaluate ALP induction in the context of ZEB TFs activity. Additional file 3: CPG list and overlapping genes. List of differently methylated CPGs associated to osteoblast differentiation and their overlapping genes. Additional file 4: CpG_OB_AP_MSC. Methylation status (M-values) of CPGs differently methylated across the MSC differentiation to osteoblast and adipocyte. Additional file 5: RNA array. Differently expressed genes between stem cells differentiated (21 days) or not to osteoblast. Additional file 6: Figure S1. Observed vs expected ratio of CpGs enrichment among the different chromatin states and their cumulative methylation profile in MSCs. A) The bars represent the ratio of the percentage of differently methylated CpGs vs the percentage of the total CpGs studied for each chromatin state. B) Cumulative plot of the methylation of the CpGs overlapping top enriched (Strong_ Enhancer, Weak_ Enhancer, and Txn_Transition) and depleted chromatin states (Active_promoter and Poised_ Promoter). Y axis: cumulative percentage of CpGs overlapping to each chromatin state. X axis: fold change methylation MSCs vs Osteoblasts. Additional file 7: Figure S2. Output of ingenuity pathway analysis. Representation of the significant enrichment on ‘inhibition of MMPs’ as well as ‘WNT pathway activation’. Additional file 8: Figure S3. Validation of REINS algorithm. Transcription factor relevance (TFR) metric for hypo (Ho)- and hypermethylated (Hr) CpGs associated to MSCs differentiation to osteoblasts (OB), to different tissues (muscle, adipose tissue, pancreas, thymus) and to human pluripotent stem cells (hPSC). A) TFR for the repressor TF CTBP2. B) TFR for the TF EZH2 a member of the polycomb repressive complex 2 (PRC2). 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--- title: Caffeine ameliorates the metabolic syndrome in diet-induced obese mice through regulating the gut microbiota and serum metabolism authors: - Li Chen - Xian-jun Wang - Jie-xin Chen - Jing-cheng Yang - Xian-Bin Cai - Yong-song Chen journal: Diabetology & Metabolic Syndrome year: 2023 pmcid: PMC9996965 doi: 10.1186/s13098-023-00993-3 license: CC BY 4.0 --- # Caffeine ameliorates the metabolic syndrome in diet-induced obese mice through regulating the gut microbiota and serum metabolism ## Abstract ### Objective Obesity is associated with gut microbiota disorders, which has been related to developing metabolic syndromes. The research aims to investigate the effects of caffeine treatment on insulin resistance, intestinal microbiota composition and serum metabolomic changes in high-fat diet (HFD)-induced obesity mice. ### Methods Eight-week-old male C57BL/6 J mice were fed a normal chow diet (NCD) or HFD with or without different concentrations of caffeine. After 12 weeks of treatment, body weight, insulin resistance, serum lipid profiles, gut microbiota and serum metabolomic profiles were assessed. ### Results Caffeine intervention improved the metabolic syndrome in HFD-fed mice, such as serum lipid disorders and insulin resistance. 16S rRNA Sequencing analysis revealed that caffeine increased the relative abundance of Dubosiella, Bifidobacterium and Desulfovibrio and decreased that of Bacteroides, Lactobacillus and Lactococcus to reverse HFD-fed obesity in mice. Additionally, Caffeine Supplementation also altered serum metabolomics, mainly focusing on lipid metabolism, bile acid metabolism and energy metabolism. Caffeine increased its metabolite 1,7-Dimethylxanthine, which was positively correlated with Dubosiella. ### Conclusions Caffeine exerts a beneficial effect on insulin resistance in HFD-mice, and the underlying mechanism may be partly related to altered gut microbiota and bile acid metabolism. ## Introduction Obesity is a serious issue worldwide, and its prevalence continues to rise in most countries [1]. Obesity can be simply summarized as the imbalance between energy intake and expenditure resulting in the accumulation of excess energy in the body, leading to metabolic disorders in the organism [2]. Many kinds of research show it is closely related to type 2 diabetes, non-alcoholic fatty liver disease, and heart diseases [3, 4]. Moreover, Insulin resistance (IR) has long been a problem carried by obesity itself. Although taking diet pills is currently the most popular way to lose weight, they still have some side effects and may improve insulin resistance to a limited extent. For example, Metformin can cause adverse gastrointestinal reactions [5]. This is why an urgent search for a functional food that fights obesity while improving insulin resistance is critical. Coffee and tea are the food products most consumed worldwide [6, 7]. Long-term consumption of tea or coffee has a wide range of health-promoting effects, including anti-obesity [8], prevention of Type II Diabetes [9], as well as inhibition of the development of diabetic complications [10]. Caffeine is the main active ingredient of tea and coffee. A study of green tea containing caffeine suggests that caffeine may be its main anti-obesity ingredient [11]. The beneficial metabolic effects of caffeine on obesity include the downregulation of inflammatory factors expression in the circulation and various tissues, the upregulation of adipocyte lipolysis, the inhibition of lipid synthesis and hepatic steatosis, a reduction in fat mass and the improvement of fatty liver, hepatic/systemic insulin resistance, which have been reported in both humans and animals [12–15]. In addition, in nonobese research models, caffeine was also found to inhibit neonatal cord blood mononuclear cells from releasing TNF-α, improving fructose-induced insulin resistance in mice by enhancing central insulin signaling and Glut4 expression in skeletal muscle to reverse rat ageing-induced insulin resistance [16–18]. Many reports show the gut microbiota has been linked to inflammatory bowel disease [19], obesity [20], autism spectrum disorders [21] and immune system disorders [22] among others. Some components of coffee might have affected the gut microbiota [23–25]. A recent study has shown that EGCG and caffeine, the main component of green tea, can modulate the gut microbiome to fight obesity [26]. Fubrick tea has been confirmed to improve metabolic disorders by regulating intestinal microbiome and caffeine metabolism [27], and the close relationship between gut microbiome and metabolomics suggests that caffeine may affect gut microbiome. What's more, it has been shown that caffeine-induced sleep restriction alters the gut microbiome in mice [28]. However, the relationship between how caffeine affects the gut microbes and its improvement in metabolic syndrome has not been reported. Metabolomics has been widely used because of its potential to help researchers identify the involvement of different biomarkers. Therefore, we focus on investigating whether caffeine, a main component of coffee, ameliorates insulin resistance in diet-induced obesity mice in association with gut microbiota alterations and explores its underlying mechanism through plasma metabolites. ## Preparation of caffeine solution Caffeine (Sigma-Aldrich, C0750) was respectively dissolved in sterile water to get a concentration of low-dose caffeine solution (0.5 g/L) and high-dose caffeine solution (1 g/L) for feeding to high-fat mice until mice were sacrificed. ## Animal experiments Forty male C57BL/6 J mice (Charles River) weighing 20–25 g were bred at Shantou University Medical College (Shantou, China). All animals lived in a specific pathogen-free environment with free access to food and water. After all, mice were acclimatized to the research environment for one week; ten mice were randomly fed with a normal chow diet (NCD group). In contrast, the remaining 30 mice were fed with a $60\%$ high-fat diet (ResearchDiet, D12492) and randomly divided into three groups to fed with the sterile water (HFD group), low-dose caffeine solution (caffeine low-dose group) and high-dose caffeine solution (caffeine high-dose group) at the same time. The ten mice in the same group were randomly labelled and housed in two standard cages (five mice per cage). The body weight of mice and the amount of food intake were measured weekly. ## Biochemical analysis of serum samples At the end of the experiment, all mice were anaesthetized with sodium pentobarbital (50 mg/kg) intraperitoneally. The eyes were removed to collect the blood samples in 5 mL Vacutainer tubes containing the chelating agent ethylene diamine tetraacetic acid (EDTA). The samples were centrifuged at 4 ℃ for 15 min, and plasma samples were collected and stored at − 80 °C. Serum random blood glucose and lipid profiles such as Total cholesterol (TC), total triglycerides (TG), High-density lipoproteins (HDL) and Low-density lipoproteins (LDL) were measured with an Abbott Architect c16000 instrument (The First Affiliated Hospital of Shantou University Medical College). ## Intraperitoneal glucose tolerance test (IPGTT) After 12 weeks of caffeine intervention, all mice fasted for 8 h. Using blood glucose test strips, measured the blood glucose level at 0 min. Immediately after that, all animals were intraperitoneally injected with $20\%$ glucose solution(1 g/kg). Subsequently, blood glucose values were respectively measured at 30, 60, 90, and 120 min. Plot the blood glucose-time curve and computer the area under the IPGTT curve (AUC). ## Intraperitoneal insulin tolerance test (IPITT) The blood glucose values of each animal given adequate diet and water were measured as the blood glucose values at 0 min. Afterwards, blood glucose levels were measured at 30,60,90 and 120 min after giving each mouse an intraperitoneal injection of 0.75U/kg of insulin with fasting without water. The blood glucose-time curve and the AUC of IPITT are similar to that described above. ## Sequencing Total genomic DNA from 39 samples was obtained by hexadecyl trimethyl ammonium bromide (CTAB)/sodium dodecyl sulfate (SDS) method, and the concentration and purity of DNA were detected by $1\%$ agarosegels. The DNA was diluted to 1 ng/μl with sterile water. 16S rRNA was amplified using barcode-containing specific primers 341F-806R (V3-V4). After quantifying and identifying PCR products using 1X loading buffer (containing SYBR green) and $2\%$ agarose gel, the mixed PCR products were purified using the AxyPrepDNA Gel Extraction Kit (AXYGEN). Sequencing libraries were generated using the NEB Next® Ultra™ DNA Library Prep Kit for Illumina (NEB, USA) according to the manufacturer's recommendations with index codes added. Library quality was evaluated on a Qubit@ 2.0 Fluorometer (Thermo Scientific) and an Agilent Bioanalyzer 2100 system. Finally, the library was sequenced on the Illumina Miseq/HiSeq2500 platform to obtain 250 bp/300 bp paired-end reads. ## Data analysis Paired-end reads from raw DNA fragments were merged using FLASH and assigned to each sample based on a unique barcode. Sequence analysis was performed by the UPARSE software package using the UPARSE-OTU and UPARSE-OTUref algorithms. Sequences with ≥ $97\%$ similarity were assigned to the same OTU. We select a representative sequence for each OTU and use the RDP classifier to annotate the taxonomic information of each representative sequence. Histograms of the relative abundance of species at the phylum and genus levels show the community structure of each grouping at different taxonomic levels. Alpha Diversity metrics such as ace and chao1 were used to Analyze Microbiota evenness and diversity. To confirm differences in the taxonomic abundance of individuals between the two groups, STAMP software was used. LEfSe was used to quantify biomarkers within different groups. The raw data was converted to. mzXML format by ProteoWizard, and imported into the XCMS software for peak alignment, retention time correction and peak area extraction. Subsequently, the data extracted by XCMS were subjected to metabolite structure identification, data pre-processing, data quality evaluation and finally, data analysis. The data acquired were sample normalized, log-transformed (base 10) and auto-scaled on MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/MetaboA-alyst/ModuleView.xhtml). Partial Least Squares—Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) are used to perform multivariate statistical analyses. The variable importance in projection (VIP) values showing the contribution of each variable to the classification were measured and counted in the OPLS-DA model. The student's t-test P values lower than 0.05 indicated statistically significant differences. Volcano plots further revealed changes in metabolites, with FC > 1.2 being upregulation and FC < 0.8 being downregulation. Lastly, we selected important differential metabolites for metabolite pathway studies in the KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.kegg.jp/) database. ## Sample extraction Plasma samples collected as above were thawed at 4 °C. An appropriate amount of sample and pre-cooled methanol/acetonitrile(1:1,v/v) were mixed and centrifuged for 20 min(14000 g, 4 ℃). The supernatant was used for LC-MS/MS Analysis. ## LC–MS/MS analysis The serum samples were detected by a UHPLC (1290 Infinity LC, Agilent Technologies). The column temperature, flow rate and injection volume were 25 °C, 0.5 mL/min and 2 μL. The mobile phase consisted of A (water + 25 mM ammonium acetate + 25 mM ammonia) and B (acetonitrile). The gradient elution program was as follows: 0–0.5 min $95\%$B; 0.5–7 min, B linearly change from 95 to $65\%$; 7–8 min, B from $65\%$ linear change to $40\%$; 8–9 min, B maintained at $40\%$; 9–9.1 min, B linear change from 40 to $95\%$; 9.1–12 min, B maintained at $95\%$. Samples are analyzed randomly and continuously to reduce errors caused by fluctuations in the instrument detection signals. The separated samples were analyzed using a quadrupole time-of-flight (AB Sciex TripleTOF 6600) with electrospray ionization in positive and negative ion modes, as described in a previous study [29]. ## Omics association analysis The relationships between different bacterial lineages and metabolites were obtained by correlation analysis using Spearman’s correlation method for the critical differential metabolites screened by metabolomics and Intestinal microbes that differed significantly at the genus level by 16S sequencing. Correlation heatmap and hierarchical clustering analysis were performed on R language (4.1.2) software. ## Statistical Analysis All data are expressed as mean ± SEM. Statistical analysis between the control and experiment groups was performed using One-way ANOVA on SPSS 25 software. The Bonferroni post hoc test and the Dunnett T3 post hoc were respectively used for conformity to chi-square and non-conformity chi-square. P-value < 0.05 was considered statistically significant. Graphs were made using GraphPad Prism 8 software. ## Caffeine mitigated metabolic disorders in mice fed with a high-fat diet Metabolic syndrome is characterized by obesity, diabetes, impaired glucose regulation, and dyslipidemia. As shown in Fig. 1A, B, body weight gain and liver weight were increased in HFD mice in comparison with NCD mice($p \leq 0.001$), while caffeine high-dose treatment significantly reduced these indices in comparison with HFD mice($p \leq 0.01$). Compared with the NCD mice, serum triglyceride (TG), total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels were elevated. However, the high-density lipoprotein cholesterol (HDL-C) level was also increased($p \leq 0.001$). Consequently, caffeine high-dose significantly downregulated TC and LDL-C levels and upregulated HDL-C levels (Fig. 1C, $p \leq 0.01$; $p \leq 0.05$). Similarly, caffeine high-dose reduced plasma random blood glucose in HFD mice (Fig. 1B, $p \leq 0.05$). IPGTT and IPITT tested insulin resistance. Our results indicated that supplementation with high caffeine dose rather than a low dose decreased the AUC of GTT and ITT curve, increasing HFD in mice (Fig. 2; $p \leq 0.001$; $p \leq 0.05$). Overall, caffeine high-dose improved the metabolic disorders associated with high-fat diet-induced obesity. Fig. 1Caffeine alleviated the Hyperlipidemia in the HFD-mice. ( A) Caffeine high-dose treatment decreased the body weight and liver weight in obese mice. ( B) Caffeine high-dose significantly improved Hyperlipidemia compared with the HFD group. c Caffeine high-dose intervention decreased random blood sugar in obese mice. NCD, HFD, caffeine low-dose and caffeine high-dose (n == $\frac{9}{10}$ per group) groups. Data are presented as the mean± SEM. * $p \leq 0.05$; $p \leq 0.0$l;$p \leq 0.001$ (HFD group versus NCD group, caffeine low-dose group, caffeine high-dose group)Fig. 2The AUC of IPGTT and IPITT were respectively decreased in insulin resistance mice after caffeine treatment. NCD, HFD, caffeine low-dose and caffeine high-dose (n == 5 per group) groups ## Caffeine remodeled the gut microbiota in HFD mice Growing evidence indicates Gut microbiota* dysbiosis has been repeatedly seen in insulin resistance. To investigate whether caffeine affects the gut microbiota, we collected faeces at 12 weeks of NCD, HFD and caffeine high-dose + HFD mice and then profiled the microbiota composition by 16S rRNA gene sequencing. We performed Splicing, filtering, and chimaera removal on 100897 raw tags from 29 faecal samples to get 83789 clean sequences clustered into 1821 operational taxonomic units (OTUs) with consistently higher than $97\%$ for further analysis. As observed on a Venn diagram (Fig. 3A), 767 OUT were shared among the three groups, and each group had unique OTUs. Ace and chao1, representative Alpha analyses indices (Fig. 3B), showed HFD decreased microbiota species richness and diversity, while caffeine seemed to reverse this change (with no statistical significance). As expected, Firmicutes was the most abundant phylum of all samples; however, caffeine seems not to affect Firmicutes. We observed caffeine decreased further Bacteroidetes, which may be associated with a reduction in Bacteroides at the genus level (Fig. 4). Among the significant microbial communities at the genus level, the relative abundance of Dubosiella and Desulfovibrio was decreased by HFD mice compared with the NCD mice, while caffeine increased these two microbiotas. Moreover, it also raised Faecalibaculum and Blautia. At the same time, caffeine decreased the relative abundance of Lactobacillus, Romboutsia, Lactococcus and Erysipelatoclostridium increased by HFD mice (Fig. 4). We did a Welch t-test with an abundance index for the HFD and caffeine high-dose groups to further explore whether caffeine affected the faecal microbiota. We found 19 OTUs significantly different ($p \leq 0.05$). As shown in Fig. 5, we found the differences between Dubosiella, Desulfovibrio and Lactobacillus were statistically significant. Furthermore, caffeine elevated Ruminococcaceae UCG-004, Bifidobacterium, [Eubacterium] brachy group, Ruminococcaceae UGC-014, Enterococcus, Alkanindiges, Haliangium, Holdemania and Ruminococcaceae UCG-009 and decreased Gemella, Allobaculum, Helicobacter, Geothermobacter, Tyzzerella, [Eubacterium] no datum group and Aerococcus. Similarly, we used LDA Effect Size (LEfSe) analysis to identify communities or species that significantly impacted the sample delineation shown in Fig. 6. Species with LDA values greater than 4 were set as statistically different markers between groups (Fig. 7).Fig. 3Caffeine treatment altered the number and Alpha diversity of gut microbiota in HFD-mice. ( A) A Venn diagram in the three groups. ( B) Ace and chaol indexes were higher in the caffeine high-dose group than in the HFD groupFig. 4Caffeine high-dose treatment altered the relative abundances of some gut microbiota at the phylum and genus levelFig. 5The welch t-test of gut microbiota between HFD and caffeine high-does groupFig. 6LEfSe analysis identified the most differentially abundant taxa of gut microbiota between the HFD group and caffeine high-dose ($$n = 9$$/10 per group) groupsFig. 7LDA scores ofLEfSe analysis> 4 are shown ## Caffeine changes plasma metabolites in high-fat diet mice PCA is a statistical method that can predict the species with the most enormous difference between groups, which is an unsupervised model; while PLS-DA is a supervised model, which can maximize the difference between groups according to a predefined classification, which has better separation than PCA. The PLS-DA plot showed that there was a clear separation between NCD and HFD and caffeine high-dose groups (Fig. 8). To identify differential metabolites, the OPLS-DA and its corresponding variable importance of projection (VIP) were applied. Consistent with the PLS-DA results, the OPLS-DA model also showed NCD group could be well distinguished from the HFD group, and significant distinctions were observed between HFD and caffeine high-dose group (Figs. 9, 10). In addition, compared to the NCD group, the volcano plot showed that 168 metabolites were downregulated and 383 metabolites were upregulated under high-fat diet induction; caffeine high-dose in obese mice resulted in 24 metabolites downregulated and 53 metabolites upregulated in serum (Figs. 11, 12).Fig. 8PLS-DA scores of serum metabolites in high-dose groups of NCD, HFD and caffeine high-dose groupFig. 9Scores plots of OPLS-DA of serum metabolites between NCD and HFD groupsFig. 10Scores plots of OPLS-DA of serum metabolites between Caffiene and HFD groupsFig. 11The volcano plot of serum metabolites between NCD and HFD groupsFig. 12The volcano plot of serum metabolites between Caffiene and HFD groups Next, we used differential metabolites (FC > 1.2 or FC < 0.8, $p \leq 0.05$, VIP > 1.0) for metabolic pathway screening. Compared with the NCD group, the top 8 KEGG pathways that changed in the HFD group mainly included Starch and sucrose metabolism, Fructose and mannose metabolism, Pentose phosphate pathway, Glycerophospholipid metabolism and caffeine metabolism, which focuses on energy metabolism(Fig. 13); while the pathways changed by caffeine high-dose mainly included Linoleic acid metabolism, Sulfur metabolism, Primary bile acid biosynthesis, Caffeine metabolism, Histidine metabolism, Purine metabolism, Pyruvate metabolism, Glycine, serine and threonine metabolism, Biosynthesis of unsaturated fatty acids and Steroid hormone biosynthesis could be seen(Fig. 14). The Specific trends of variation of differential metabolites contained in metabolic pathways are shown in Table 1.Fig. 13Summary of pathway analysis of serum samples between NCD and HFD groupFig. 14Summary of pathway analysis of serum samples between between HFD and caffeine high-dose groupTable 1The differential metabolites in the pathway after caffeine treatmentMetabolitesVIPFCTrendPathwayN v HH v CN v HH v CN v HH v CLinoleic acid–1.56–1.34–↑a, iAdenosine 5′- phosphosulfate1.331.940.51.89↑##↑b, fAdenosine 5′- monophosphate1.261.940.521.79↓##↑fPyruvaldehyde1.091.552.410.49↑#↓gCholic acid–1.99–0.45–↓*cGlycocholic acid1.041.990.531.55↓#↑c1,7-Dimethylxanthine-3.34-116.04-↑**dN-methylhistamine1.071.731.370.77↑#↓h22R-Hydroxycholesterol1.241.660.391.9↓##↑jNCD, HFD and caffeine high-dose ($$n = 9$$/10 per group) groups. # $p \leq 0.05$ as compared to NCD group; ##$p \leq 0.01$ as compared to NCD group; ##$p \leq 0.001$ as compared to NCD group; $p \leq 0.05$ as compared to HFD group; $p \leq 0.01$ as compared to HFD group; $p \leq 0.001$ as compared to HFD group; ↑, content increased; ↓, content decreased; vs, versus; N, NCD group; H, HFD group; C, caffeine high-dose; VIP, variable importance of projection; FC, fold change. a: Linoleic acid metabolism; b: Sulfur metabolism; c: Primary bile acid biosynthesis; d: Caffeine metabolism; e: Histidine metabolism; f: Purine metabolism; g: Pyruvate metabolism; h: Glycine, serine and threonine metabolism; i: Biosynthesis of unsaturated fatty acids; j: Steroid hormone biosynthesis ## Correlation analysis of gut microbiota, serum untargeted metabolites, physiological data and insulin resistance indicators Correlation analysis of serum untargeted metabolites and gut microbiota at genus level in the NCD, HFD and caffeine high-dose groups was presented by correlation heatmap. As shown in Fig. 15, caffeine metabolomics such as 1,7-Dimethylxanthine showed positive correlations with Dubosiella, Alkaninndiges and Faecalibaculum. Pyruvaldehype was positive correlations with Allobaculum. In addition, Desulfovibrio, Dubosiella, and Alkaninndiges correlated positively with most of the metabolites. N-methylhistamine and Cholic acid showed negative correlations with some gut microbiota. In addition, Bifidobacterium, Desulfovibrio and Ruminococcaceae showed negative correlations with and Lactobacillus, Lactococcus, Faecalibaculum, and Allobaculum showed positive correlations with physiological data and insulin resistance indicators (Figs. 16, 17).Fig. 15Correlation Analysis of Gut Microbiota and Serum Metabolomics. The colors of grids indicate the correlation analysis value of spearman's correlation analysis. Grids in red represent positive correlations (correlation analysis value more than 0.1), while grids in blue represent negative correlations (correlation analysis value less than -0.1). Color coding scale represents the correlation analysis value from heatmap, the deeper red or blue indicates higher correlation values. $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$Fig. 16Correlation Analysis of Gut Microbiota and Physiological data. The colors of grids indicate the correlation analysis value of spearman's correlation analysis. Grids in red represent positive correlations (correlation analysis value more than 0.1), while grids in blue represent negative correlations (correlation analysis value less than -0.1). Color coding scale represents the correlation analysis value from heatmap, the deeper red or blue indicates higher correlation values. $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$Fig. 17Correlation Analysis of Gut Microbiota and Insulin resistance indicator. The Y-axis indicates the different abundance of the gut microbiota. The colors of grids indicate the correlation analysis value of spearman's correlation analysis. Grids in red represent positive correlations (correlation analysis value more than 0.1), while grids in blue represent negative correlations (correlation analysis value less than -0.1). Color coding scale represents the correlation analysis value from heatmap, the deeper red or blue indicates higher correlation values. $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$ ## Discussion In our study, we found that caffeine high-dose could reduce body weight and liver weight, and improve hyperlipidemia and insulin resistance in the HFD mice, demonstrating caffeine has a therapeutic effect on metabolic syndromes, which is consistent with previous studies [15–17]. Moreover, we elucidate the mechanism of this anti- metabolic syndrome, which may involve gut microbiota, bile acid metabolism, lipid metabolism, and energy metabolism. The nature of the intestinal microbiome has been shown to be closely related to IR in several human and animal model studies [30, 31]. 16S rRNA sequencing was used to investigate how caffeine affects the composition of the gut microbiome. Compared to the NCD group, HFD mice have a substantial reduction in the abundance of Bacteroidetes and a proportional increase in Firmicutes, similar to previous studies [20]. However, caffeine further reduced Bacteroidetes, which seems likely that caffeine has its unique effect on the intestinal microbiome. Bacteroidetes is a Gram-negative bacterium containing LPS, a potent activator of toll-like receptor 4(TLR4), which causes inflammatory responses and cytokine expression and secretion. In addition, Lower Bacteroidetes may be associated with lower Bacteroides at the genus level. Bacteroides, Lactobacillus and Lactococcus containing bile salt hydrolase (BSH) can hydrolyze conjugated bile acids into unconjugated bile acids [37]. It has been reported that conjugated bile acid is a nuclear farnesoid X receptor (FXR) antagonist [33]. Decreased BSH leads to increased conjugated bile acids, which increases the conversion of cholesterol to bile acids and reduces lipid accumulation. Likewise, in our plasma data, cholic acid, which was elevated in insulin-resistant mice and humans [9, 34], was decreased by caffeine, while glycocholic acid was increased. This may suggest that the improvement of dyslipidemia by caffeine may be related to bile acid metabolism. Bifidobacterium is a probiotic. In a human report, 16 healthy subjects drank a daily dose of 3 cups of coffee for three weeks, which raised the number of Bifidobacterium compared with human faeces before coffee consumption [35]. This bifidogenic effect of coffee was also discovered in animal models [32]. Meanwhile, caffeine also increased this probiotic in the NAFLD patients [36]. In accordance with our results, which hint the bifidogenic effect of coffee may be related to caffeine. Moreover, Bifidobacterium and Ruminococcaceae are short-chain fatty acid (SCFAs)-producing bacteria; SCFAs have been shown to increase gut barrier function [37] and stimulate pancreatic islet beta-cell growth and proliferation, regulate the body's insulin sensitivity [38]. Desulfovibrio, significantly elevated by caffeine, was shown to produce acetic acid and regulate liver lipid metabolism in mice to alleviate non-alcoholic fatty liver disease [39]; to produce H2S, which maintains lipid metabolism balance [40] and reduces systemic inflammation [41]. In addition, the present study also shown Bifidobacterium, Ruminococcaceae and Desulfovibrio were negatively associated with obesity indicators. Serum metabolomics showed that the serum metabolites of the three groups were separated from each other. However, caffeine did not move the plasma metabolites of HFD mice closer to that of NCD mice, which may be the effect of caffeine as a foreign substance in the metabolism. Elevations of caffeine metabolites such as 1,7-Dimethylxanthine in plasma may illustrate this point. In our data, 1,7-Dimethylxanthine was positively correlated with Dubosiella, Alkaninndiges and Faecalibaculum, possibly indicating that changes in the intestinal microbiome are associated with caffeine metabolism. Dubosiella significantly elevated in response to caffeine, is not yet well studied. Still, this bacterium was negatively correlated with most inflammatory factors and indicators of obesity and positively associated with butyric acid [42, 43]. Moreover, Dubosiella is* positively correlated with Adenosine 5'-monophosphate (AMP) and Linoleic acid, which Implies the relationship between Dubosiella and energy metabolism. Metabolic pathway enrichment analysis showed that caffeine could regulate Steroid hormone biosynthesis, Primary bile acid biosynthesis, Biosynthesis of unsaturated fatty acids, Glycine, serine and threonine metabolism, Linoleic acid metabolism, Pyruvate metabolism and so on. The Biosynthesis of steroid hormones, the Biosynthesis of primary bile acids and the Biosynthesis of unsaturated fatty acids are closely related to lipid metabolism. Disturbances in lipid metabolism often accompany insulin resistance. In a study of the activity and function of the cytochrome P450 side-chain cleavage enzyme (CYP11A1), the rate-limiting enzyme for the conversion of cholesterol to pregnenolone, it was found that incubation of isolated mitochondria with the cholesterol analogue 22R-hydroxycholesterol resulted in the efficient formation of pregnenolone, the direct precursor for the synthesis of all steroid hormones [44]. Estrogen has been reported to increase insulin sensitivity [45]. Similarly, 22R-hydroxycholesterol is a natural ligand for the liver X receptor [LXR], and activation of LXR improves TNFα-induced insulin resistance in brown adipocytes [46]. All of the above suggests that the improvement of insulin resistance by caffeine may be related to steroid hormone synthesis. Glycine, serine, threonine, linoleic acid and pyruvate acid ultimately produce energy metabolism through the tricarboxylic acid cycle. Caffeine is known to activate beta receptors, increase plasma cAMP concentrations and promote lipolysis and beta-oxidation [47]. ## Conclusion This study illustrates the ameliorative effect of caffeine on diet-induced obesity mice by combining 16S rRNA Sequencing and plasma metabolomics. Caffeine modulates plasma lipid metabolism disorders, ameliorates insulin resistance, and may change the gut microbial composition by promoting beneficial bacteria and reducing harmful bacteria. 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--- title: 'Unhealthy lifestyle associated with increased risk of macro- and micro-vascular comorbidities in patients with long-duration type 2 diabetes: results from the Taiwan Diabetes Registry' authors: - Li-Ju Ho - Wayne Huey-Herng Sheu - Su-Huey Lo - Yen-Po Yeh - Chii-Min Hwu - Chien-Ning Huang - Chang-Hsun Hsieh - Feng-Chih Kuo journal: Diabetology & Metabolic Syndrome year: 2023 pmcid: PMC9996995 doi: 10.1186/s13098-023-01018-9 license: CC BY 4.0 --- # Unhealthy lifestyle associated with increased risk of macro- and micro-vascular comorbidities in patients with long-duration type 2 diabetes: results from the Taiwan Diabetes Registry ## Abstract ### Background Unhealthy lifestyle has been associated with obesity and type 2 diabetes. Whereas its association with vascular complications in patients with long-duration of type 2 diabetes is still uncertain. ### Methods A total of 1188 patients with long-duration of type 2 diabetes from the Taiwan Diabetes Registry (TDR) data were analyzed. We stratified the severity of unhealthy lifestyle via scoring three factors (sleep duration <7 or >9 h, sit duration ≥ 8h, and meal numbers ≥ with night snack) and analyzed their associations with the development of vascular complications using logistic regression analysis. Besides, we also included 3285 patients with newly diagnosed type 2 diabetes as the comparison. ### Results Increased numbers of factors that stand for unhealthy lifestyle were significantly associated with the development of cardiovascular disease, peripheral arterial occlusion disease (PAOD) and nephropathy in patients with long-duration of type 2 diabetes. After adjusting multiple covariables, having ≥ 2 factors of unhealthy lifestyle remained significant associations with cardiovascular disease and PAOD, with an odds ratio (OR) of 2.09 ($95\%$ confidence interval [CI] 1.18–3.69) and 2.68 ($95\%$ CI 1.21–5.90), respectively. Among individual factor for unhealthy lifestyle behaviors, we revealed that eating ≥ 4 meals per day with night snack increased the risk of cardiovascular disease and nephropathy after multivariable adjustment (OR of 2.60, $95\%$ CI 1.28–5.30; OR of 2.54, $95\%$ CI 1.52–4.26, respectively). Whereas sit duration for ≥ 8 h per day increased the risk of PAOD (OR of 4.32, $95\%$ CI 2.38–7.84). ### Conclusion Unhealthy lifestyle is associated with increased prevalence of macro- and micro-vascular comorbidities in Taiwanese patients with long-duration type 2 diabetes. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13098-023-01018-9. ## Introduction Type 2 diabetes mellitus (T2DM) has been well recognized as an important cardiovascular risk highly associated with several vascular comorbidities. Atherosclerotic macrovascular complications affect arteries that supply the heart, brain, and lower extremities resulting in cardiovascular disease, cerebrovascular disease, and peripheral arterial occlusive disease (PAOD), respectively [1, 2]. Diabetes also frequently accompanied by several microvascular complications, namely neuropathy, nephropathy, and retinopathy [2]. In fact, vascular diseases are the leading causes of morbidity and mortality in patients with diabetes [3]. Overall, their life expectancy is about 7–10 years shorter than people without diabetes [4]. Besides, diabetic nephropathy and retinopathy are the main contributors to end-stage renal disease and blindness which further impair their quality of life and self-care ability. Despite significant progression in the newly developed anti-hyperglycemic agents, there are still considerable T2DM patients could not achieve adequately glycemic control [5]. One important and usually ignored issue is the dysregulated lifestyle behaviors that disturbed normal physical activities are commonly presented in patients with diabetes. Indeed, strategies to facilitate behavior change have been recommended in the standards of diabetes care [6] and recent American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) consensus conducted in June 2022 also emphasized the importance of 24-h physical behaviors for management of hyperglycemia in T2DM [7]. Considering the dysregulated life behaviors might be adjustable at the early stage of diabetes, we aim to investigate their long-lasting impacts on the diabetes associated macro- and micro-vascular comorbidities, which are currently still lack of evidence. Unhealthy lifestyle mediated disruption of circadian rhythm is known to have lots of deleterious impacts on health [8]. For example, using the recommended 7–9 h sleep duration in adults [9] as the reference group, sleep deprivation was associated with increased risk for general obesity and abdominal obesity [10]. Likewise, ≥ 8 h of sitting time per day has been shown positively associated with incident diabetes in obese women [11]. Regarding to the eating pattern, a delayed eating schedule [12] or nighttime snacking [13] can trigger detrimental influence on the body metabolism. Disturbed circadian rhythm might influence the vascular system as well. Using cardiac magnetic resonance to define the post-reperfusion infarct size in patients with ST-segment elevation myocardial infarction, Zhao et al. [ 14] recently disclosed individuals with shift work had augmented reperfusion injury. Similar finding was observed in the ischemic stroke animal model, female rats underwent early exposure to shifted light–dark cycles presented elevated circulating cytokines with greater post-stroke mortality [15]. Based on these reports, unhealthy lifestyle that disturb day-night cycles very likely impedes the normal vascular regulation as encountering atherosclerotic vascular disease [16]. However, direct evidence linking unhealthy lifestyle behaviors to the diabetes associated vascular diseases is still very limited. Therefore, in this project, we utilized the data obtained from the Taiwan Diabetes Registry containing detailed assessment of the medical documentation to explore this issue. The Taiwan Diabetes Registry (TDR) is a nationwide, multicenter study to assess real-world clinical practices and outcomes for patients with diabetes in Taiwan. The data obtained from the TDR include collection of anthropometric parameters, laboratory examinations, foot assessment and medical records allowing detailed evaluation of diabetes-associated macrovascular and microvascular complications. Participants also completed some questionnaires containing information of sleep duration, sitting time per day and dietary habit with meal frequency. Then, we defined the unhealthy lifestyle behaviors based on previous reports related to three factors: sleep [10], sitting [11] and diet [12, 13]. The severity of unhealthy lifestyle was further stratified by the numbers of disturbed factors and associated with diabetes related vascular comorbidities. Our main purpose is to characterize the risky lifestyle behaviors which potentially pose a threat of vascular complications to subjects with diabetes. Ultimately, we hope that these insights will emphasize the importance of behavioral metabolic control that can be utilized as therapeutic targets to minimize the risks of vascular complications in populations with diabetes. ## Study design and ethics statement The Taiwan Diabetes Registry (TDR) is a national, observational study launched by the Diabetes Association of Republic of China (R.O.C., Taiwan) in October 2015 and designed for prospective follow-up with a total of 14 medical centers, 44 regional and local hospitals and 37 general practice clinics participated in the program. Three groups of patients were recruited in the TDR including those with type 1 diabetes mellitus, patients with newly diagnosed (within 6 months) T2DM and patients with long-duration of T2DM who had previously jointed the surveys conducted by the Taiwan Association diabetes educators [17, 18]. Therefore, the patients with long-duration of T2DM have detailed records of previous medical history with regular follow-up (more than 2 outpatient visits per year). In the registration of database, informed consent was obtained, some questionnaires were completed, clinical information and various diabetes-related medical records were collected using an electronic portal (e-portal) web-based platform. This study was approved by the Joint Institute Review Board in Taiwan (protocol number: 14-S-012), and the Institutional Review Board of the Tri-Service General Hospital (TSGHIRB No. 1-104-05-157). Currently, only cross-sectional data of first registration were released and were utilized in this study to evaluate the association between unhealthy lifestyle behaviors and long-term diabetic vascular complications. To do this, we mainly analyzed dataset from 1188 patients with long-duration of T2DM (13 individuals were excluded due to incomplete or incorrect information). Besides, data of 3285 individuals with newly diagnosed T2DM were also analyzed as the comparison to support the potential long-lasing impacts of dysregulated lifestyle on the diabetes associated vascular comorbidities. ## Data collection Information included general characteristics of diabetes, personal and family history, past disease history, lifestyle patterns (smoking, drinking, betel nut chewing, diet habit, hours of sleep and daily activity), physical examination (height, weight, blood pressure, waist and hip circumference), laboratory data (blood glucose, HbA1c, lipids, renal and liver function, urine analysis), foot assessment, cardiovascular and microvascular complications of diabetes, diabetes education and self-management status, hospitalization history, and medications used were all documented in the web-based platform of TDR by the diabetes educators and health care providers. Additionally, all the participants completed some questionnaires such as the EuroQol 5-dimension (EQ-5D), the Patient Health Questionnaire-9 (PHQ-9), and the International Physical Activity Questionnaire (IPAQ). The EQ-5D is a 5-dimension questionnaire (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) developed by the EuroQol group in the 1980s to assess health-related quality of life (QOL) [19]. The other questionnaire, PHQ-9, contains 9 items and is one of the easy tools used to screen for the presence and severity of depression and to monitor response to treatment [20]. Then, the IPAQ is a comprehensive instrument that can be used internationally to measure health-related physical activity (PA) in populations [21]. The IPAQ can provide practitioners with an estimate of physical activity and sedentary behavior. ## Three factors for stratifying unhealthy lifestyle behaviors According to the clinical information obtained from the web-based platform and various self-administered questionnaires, three factors including sleep duration, sit duration, and meals numbers with night snack ingestion were utilized to assess lifestyle behaviors of study subjects. Based on previous reports [10–13], sleep duration for less than 7 h or more than 9 h per day, sit duration for ≥ 8 h a day, and ingestion for ≥ 4 meals with night snack were defined as dysregulated lifestyle in this study. Patients with 0 factor, 1 factor and ≥ 2 factors of unhealthy lifestyle behaviors were stratified to assess their prevalence and associations with diabetes related macro- and micro-vascular diseases. The role of each factor on different vascular comorbidity was further assessed as well. ## Definition of vascular complications of diabetes Macrovascular complications are composed of cardiovascular disease, cerebrovascular disease, and peripheral arterial occlusive disease (PAOD); whereas microvascular complications comprise neuropathy, nephropathy, and retinopathy [1, 2]. In this study, patients with any one of the listed conditions including coronary heart disease, myocardial ischemia and/or infarction, angina, congestive heart failure, arrhythmia, and a history of percutaneous transluminal coronary angioplasty (PTCA) or coronary artery bypass graft surgery (CABG) were referred to have cardiovascular disease. The cerebrovascular disease is defined as a group of diseases including transient ischemic attack (TIA), ischemic stroke, and hemorrhagic stroke. PAOD is defined as a composite of following status, such as having symptom of intermittent claudication, abnormal foot assessment with reduced or absent pulse over dorsalis pedis artery and/or posterior tibial artery, and a history of percutaneous transluminal angioplasty (PTA), peripheral artery bypass surgery, or amputation. Moreover, diabetic polyneuropathy comprises patients who had neurologic symptoms or aberrant neurologic physical examinations such as decrease/loss of vibratory or pinprick sensation tested by hemi-quantified tuning fork and single-stranded nylon, respectively, on either foot. Patients with diabetic retinopathy are defined as those who had one of the following conditions including macular degeneration, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), blindness, or receiving laser therapy of retina in the past. Estimated glomerular filtration rate (eGFR), expressed in ml/min/1.73 m2, was calculated using the equation from Modification of Diet in Renal Disease (MDRD) [22]. Finally, diabetic kidney disease (DKD) in this study was defined as eGFR < 60 ml/min/1.73 m2 or albuminuria defined as a spot urine albumin to creatinine ratio (UACR) ≥ 30 mg/g. ## Statistical analyses Statistical analysis was carried out using SPSS Statistics for Windows version 22.0 (IBM SPSS Inc., Chicago, IL, USA). For descriptive analysis, continuous variables were shown as mean and standard deviation (S.D.), and categorical variables were expressed as numbers (n) and percentages (%). The differences of continuous variables among three groups were analyzed using the Kruskal–Wallis test due to non-normal distribution (checked by one-sample Kolmogorov–Smirnov test), and the chi-square test was used to compare the categorical variables. The univariate and multivariate logistic regression models were further applied to analyze the impact of dysregulated lifestyle behaviors to specific vascular complications of T2DM. In the multivariate logistic regression, covariables related to demographic characteristics and socioeconomic status were chosen, particularly those that reach statistical significance in univariate analysis. The crude OR was shown in the univariate logistic regression test, whereas the adjusted OR was referred to the results in the multivariate logistic regression model. Statistical significance was defined as a p-value of <0.05. ## Results A total of 1188 T2DM patients (625 males and 563 females) with mean age of 65.9 years and average DM duration of 14.0 years were enrolled. Table 1 presents the demographic characteristics of the study populations stratified via scoring the number of factors (0 factor, 1 factor, and ≥ 2 factors) that stand for unhealthy lifestyle behaviors. Factors indicating dysregulated lifestyle were referred to sleep duration (sleep < 7 h or > 9 h a day), sit duration (sit for ≥ 8 h per day), and frequency of meals (≥ 4 meals per day with night snack ingestion). Overall, there were $41.1\%$ ($$n = 488$$) patients with 0 factor, $45.1\%$ ($$n = 536$$) patients with 1 factor, and $13.8\%$ ($$n = 164$$) patients with ≥ 2 factors analyzed in this study. Generally, females had more unfavorable dysregulated lifestyle than males ($$p \leq 0.013$$). The levels of waist circumference, fasting glucose, glycated hemoglobin, triglyceride and creatinine were significantly increased as having increased number of dysregulated lifestyle behaviors (p-value of 0.024, 0.045, 0.019, 0.011, and 0.002, respectively). Additionally, highly educated persons with junior college degree or above were less prone to lifestyle dysregulation ($$p \leq 0.023$$). In contrast to the married subjects, patients who were single, divorced or widowed tended to behave in unhealthy life pattern ($$p \leq 0.021$$). Notably, it also revealed that patients who took metformin or sulfonylurea (SU) were less likely to have lifestyle dysregulation (p-value of 0.016, and 0.006, respectively), while patients who used glucagon-like peptide 1 receptor agonist (GLP1RA) and insulin injection as therapeutic agents of T2DM had more dysregulated lifestyle behaviors (p-value of 0.011 and 0.001, respectively). However, there was no difference between the three subgroups in receiving other oral hypoglycemic agents, including alpha-glucosidase inhibitor (AGI), thiazolidinedione (TZD), sodium-glucose co-transporter 2 inhibitor (SGLT2i), and dipeptidyl peptidase 4 inhibitor (DPP4i). Regarding residence in urban (capital city) or rural areas, the three subgroups also did not reach statistical differences. Table 1Basic characteristics of long-duration T2DM patients divided by number of factors that stand for unhealthy lifestyleAll0 factor1 factor≥ 2 factorsp-value($$n = 1188$$)($$n = 488$$)($$n = 536$$)($$n = 164$$)Age (years)65.9 (11.4)65.8 (10.8)65.5 (11.5)67.5 (12.9)0.191Male % (n)$52.6\%$ [625]$57.4\%$ [280]$50.4\%$ [270]$45.7\%$ [75]0.013BMI (kg/m2)26.2 (4.2)26.0 (4.0)26.3 (4.1)26.8 (4.7)0.103Waist (cm)90.8 (10.6)90.1 (9.9)90.7 (10.8)93.0 (12.0)0.024Waist-to-hip ratio0.93 (0.07)0.93 (0.07)0.93 (0.07)0.94 (0.08)0.182Systolic BP (mmHg)132.8 (16.3)132 [16]133 [16]136 [20]0.105Diastolic BP (mmHg)74.4 (10.6)74 [10]74 [10]76 [13]0.058Fasting glucose (mg/dL)141.7 (46.2)140 [46]141 [44]151 [55]0.045HbA1c (%)7.6 (1.3)7.5 (1.3)7.6 (1.2)7.8 (1.5)0.019LDL cholesterol (mg/dL)89.9 (26.4)89 [25]90 [27]92 [29]0.704Triglyceride (mg/dL)133.2 (104.2)127 [89]134 [115]149 [109]0.011Creatinine (mg/dL)1.1 (0.9)1.1 (0.9)1.1 (0.9)1.3 (1.1)0.002ALT (U/L)28.7 (21.1)28 [21]29 [20]29 [24]0.718Years of diabetes14.0 (8.5)13.9 (8.4)13.7 (8.3)15.3 (9.3)0.252Smoking % (n)$28.0\%$ [333]$29.1\%$ [142]$26.5\%$ [142]$29.9\%$ [49]0.554Alcohol drinking % (n)$19.2\%$ [228]$22.1\%$ [108]$17.2\%$ [92]$17.1\%$ [28]0.100Low education % (n)$5.2\%$ [62]$4.7\%$ [23]$5.2\%$ [28]$6.7\%$ [11]0.611High education % (n)$16.9\%$ [201]$20.3\%$ [99]$15.3\%$ [82]$12.2\%$ [20]0.023Capital residence % (n)$8.8\%$ [104]$8.8\%$ [43]$8.6\%$ [46]$9.1\%$ [15]0.974Married % (n)$83.7\%$ [994]$84.6\%$ [413]$85.1\%$ [456]$76.2\%$ [125]0.021Statin % (n)$48.8\%$ [551]$45.1\%$ [220]$47.8\%$ [256]$45.7\%$ [75]0.696Metformin % (n)$74.9\%$ [846]$71.7\%$ [350]$73.1\%$ [392]$63.4\%$ [104]0.016AGI % (n)$12.3\%$ [139]$11.3\%$ [55]$12.5\%$ [67]$10.4\%$ [17]0.692SU % (n)$51.9\%$ [586]$48\%$ [234]$53.4\%$ [286]$40.2\%$ [66]0.006*TZD % (n)$7.7\%$ [87]$7\%$ [34]$8.2\%$ [44]$5.5\%$ [9]0.455SGLT2i % (n)$3.4\%$ [38]$2\%$ [10]$3.7\%$ [20]$4.9\%$ [8]0.141DPP4i % (n)$38.4\%$ [434]$38.1\%$ [186]$35.8\%$ [192]$34.1\%$ [56]0.486GLP1RA % (n)$1.3\%$ [15]$0.6\%$ [3]$1.1\%$ [6]$3.7\%$ [6]0.011Insulin % (n)$28.5\%$ [338]$26.8\%$ [131]$26.3\%$ [141]$40.2\%$ [66]0.001*Number of factors standing for unhealthy lifestyle: scoring by sleep duration, sit duration and frequency of meals and night snack. Continuous variables were analyzed using the Kruskal–Wallis test and are presented as mean values and (standard deviation); Categorical variables were analyzed using the Chi-square test and are presented as percentages (number)BMI, body mass index; BP, blood pressure; HbA1c, glycated hemoglobin; LDL, low density lipoprotein; ALT, alanine aminotransferase; AGI, alpha glucosidase inhibitor; SU, sulfonylurea; TZD, thiazolidinedione; SGLT2i, sodium-glucose co-transporter 2 inhibitor; DPP4i, dipeptidyl peptidase 4 inhibitor; GLP1RA, glucagon-like peptide 1 receptor agonist$p \leq 0.05$; $p \leq 0.01$; the bold values refer to variables with $p \leq 0.05$ Since patients with more dysregulated lifestyle behaviors presented central fat accumulation, increased levels of triglyceride, creatinine and less adequate glycemic control as shown in Table 1, we further assessed whether increased number of factors that disturbed normal lifestyle linked to elevated prevalence of macro- and micro-vascular comorbidities in patients with long-duration of T2DM as illustrated in Table 2. Notably, the prevalence of cardiovascular disease are doubling in patients with ≥ 2 factors as comparing to the prevalence rate in those with 0 factor or 1 factor (p-value of 0.001). Similarly, the risk of peripheral arterial occlusion disease (PAOD) and nephropathy were significantly elevated among individuals with higher number of factors (p-valuate of 0.014 and 0.037, respectively). While there is no statistically significant difference on the prevalence of cerebrovascular disease, polyneuropathy, and retinopathy among these three subgroups. Then, we further performed univariate and multivariate logistic regression to evaluate the associations between the number of dysregulated lifestyle factors and cardiovascular disease, PAOD, and nephropathy. Univariable logistic regression analysis disclosed that patients with ≥ 2 factors of lifestyle dysregulation presented significantly increased associations with the development of cardiovascular disease, PAOD, and nephropathy with a crude odds ratio (OR) of 2.51 ($95\%$ confidence interval (CI) 1.47–4.28, $$p \leq 0.001$$) for cardiovascular disease, crude OR of 2.94 ($95\%$ CI 1.39–6.24, $$p \leq 0.005$$) for PAOD, and crude OR of 1.46 ($95\%$ CI 1.02–2.09, $$p \leq 0.040$$) for nephropathy (Table 3). Particularly, after adjustment of multiple variables including age, gender, BMI, DM duration, married or not, history of smoking, history of alcohol drinking, education status, income status and living in capital city or not, patients with more than 2 unfavorable lifestyle behaviors still maintained significantly higher probability of cardiovascular disease and PAOD, with an adjusted OR of 2.09 ($95\%$ CI 1.18–3.69, $$p \leq 0.012$$) and 2.68 ($95\%$ CI 1.21–5.90, $$p \leq 0.015$$), respectively (Table 3). For further demonstrating the existence of unhealthy lifestyle at the early stage of T2DM and its potential long-lasting impacts, the same analysis was conducted in 3285 individuals with newly diagnosed T2DM from the TDR data as shown in Additional file 1: Tables S1 and S2. Intriguingly, we found the prevalence rates of 0 factor, 1 factor, and ≥ 2 factors of unhealthy lifestyle are very similar among patients with long-duration and newly diagnosed of T2DM with respective $41.1\%$ and $39.6\%$ for 0 factor, $45.1\%$ and $46.1\%$ for 1 factor, and $13.8\%$ and $14.3\%$ for ≥ 2 factors (Additional file 1: Table S1). Whereas the associations between the number of dysregulated lifestyle factors and cardiovascular disease, PAOD, and nephropathy could not be observed in these patients with newly diagnosed T2DM (Additional file 1: Table S2). These results indicated the unfavorable 24-h physical behaviors already existed at the initial diagnosis of T2DM and likely have important long-lasting influence on the subsequent development of diabetes associated vascular complications. Table 2Prevalence of diabetes associated comorbidities in long-duration T2DM patients divided by number of factors that stand for unhealthy lifestyle0 factor1 factor≥ 2 factorsp-value($$n = 488$$)($$n = 536$$)($$n = 164$$)DM associated comorbiditiesCardiovascular disease % (n)$7.6\%$ [37]$7.8\%$ [42]$16.5\%$ [27]0.001Cerebrovascular disease % (n)$2.0\%$ [10]$3.0\%$ [16]$4.3\%$ [7]0.307PAOD % (n)$3.1\%$ [15]$4.7\%$ [25]$8.5\%$ [14]0.014Polyneuropathy % (n)$12.3\%$ [60]$15.1\%$ [81]$15.2\%$ [25]0.379Retinopathy % (n)$19.7\%$ [96]$24.6\%$ [132]$19.5\%$ [32]0.117Nephropathy % (n)$36.1\%$ [176]$34.1\%$ [183]$45.1\%$ [74]0.037Number of factors standing for unhealthy lifestyle: scoring by sleep duration, sit duration and frequency of meals and night snack. Categorical variables were analyzed using the Chi-square test and are presented as percentages (number)PAOD, Peripheral arterial occlusion disease$p \leq 0.05$; $p \leq 0.01$; the bold values refer to variables with $p \leq 0.05$Table 3Odds ratios for cardiovascular disease, PAOD and nephropathy divided by number of factors in patients with long-duration T2DM1 factor≥ 2 factorsOR ($95\%$ CI), p-valueOR ($95\%$ CI), p-valueCardiovascular diseaseCrude OR1.03 (0.65–1.63), 0.9032.51 (1.47–4.28), 0.001§Adjusted OR1.05 (0.65–1.70), 0.8312.09 (1.18–3.69), 0.012PAODCrude OR1.54 (0.80–2.96), 0.1932.94 (1.39–6.24), 0.005*§Adjusted OR1.49 (0.76–2.91), 0.2482.68 (1.21–5.90), 0.015NephropathyCrude OR0.92 (0.71–1.19), 0.5191.46 (1.02–2.09), 0.040§Adjusted OR0.89 (0.69–1.16), 0.4041.25 (0.86–1.81), 0.251Number of factors standing for unhealthy lifestyle: scoring by sleep duration, sit duration and frequency of meals and night snack. The group of 0 factor was used as the reference. Univariable (crude OR) and multivariable (adjusted OR) logistic regression were performedOR, odds ratio; CI, confidence intervals. § Adjusted for age, sex, BMI, DM duration, marital status, education, income, capital residence, smoking status, drinking status$p \leq 0.05$; $p \leq 0.01$; the bold values refer to variables with $p \leq 0.05$ In order to understand the potential roles of specific lifestyle dysregulation on associated vascular complications (cardiovascular disease, PAOD, and nephropathy), we further performed univariate and multivariate logistic regression analysis. The favorable lifestyle behaviors matching to sleep duration, sitting time and frequency of meals were respectively used as the reference (Table 4). Remarkably, the disturbed eating behavior with ≥ 4 meals per day and night snack ingestion were highlighted to have strong and specific associations with the occurrence of cardiovascular disease and nephropathy, which were remained significantly even after adjusting several variables using multivariate logistic regression analysis, with an adjusted OR of 2.60 ($95\%$ CI 1.28–5.30, $$p \leq 0.009$$) for cardiovascular disease and 2.54 ($95\%$ CI 1.52–4.26, $p \leq 0.001$) for nephropathy. Besides, the presence of sedentary lifestyle with sit duration ≥ 8 h a day was solely and significantly associated with PAOD even after multivariable adjustment, with an adjusted OR of 4.32 ($95\%$ CI 2.38–7.84, $p \leq 0.001$). However, T2DM patients with reduced or prolonged sleep duration have no significant associations with the development of cardiovascular disease, PAOD or nephropathy as comparing to those with normal 7 to 9 h sleep duration (Table 4).Table 4Odds ratios for cardiovascular disease, PAOD and nephropathy divided by individual factor in patients with long-duration T2DMSleep durationSit durationMeal numbers and nigh snack(< 7 or > 9 h, $$n = 557$$)(≥ 8 h, $$n = 245$$)(≥ 4 with night snack, $$n = 68$$)OR ($95\%$ CI), p-valueOR ($95\%$ CI), p-valueOR ($95\%$ CI), p-valueCardiovascular diseaseCrude OR1.31 (0.88–1.96), 0.1851.53 (0.97–2.40), 0.0672.49 (1.28–4.84), 0.007§Adjusted OR1.31 (0.86–1.99), 0.2071.23 (0.76–2.01), 0.4032.60 (1.28–5.30), 0.009PAODCrude OR0.77 (0.44–1.34), 0.3564.20 (2.42–7.31), < 0.0011.74 (0.67–4.51), 0.258§Adjusted OR0.71 (0.40–1.26), 0.2424.32 (2.38–7.84), < 0.0011.69 (0.63–4.53), 0.296NephropathyCrude OR0.86 (0.68–1.10), 0.2271.29 (0.97–1.72), 0.0822.33 (1.42–3.81), 0.001§Adjusted OR0.81 (0.63–1.04), 0.0911.13 (0.84–1.53), 0.4142.54 (1.52–4.26), < 0.001*Sleep duration between 7 and 9 h, sit duration < 8 h, and other frequency of meals (≦3 or ≥ 4 without nigh snack) were used as the reference, respectively. Univariable (crude OR) and multivariable (adjusted OR) logistic regression were performedOR, odds ratio; CI, confidence intervals. § Adjusted for age, sex, BMI, DM duration, marital status, education, income, capital residence, smoking status, drinking status$p \leq 0.05$; *$p \leq 0.01$; the bold values refer to variables with $p \leq 0.05$ ## Discussion In this cross-sectional analysis of a nationwide, multicenter diabetes registry cohort in Taiwan, we revealed both long-duration ($$n = 1188$$) and newly diagnosed ($$n = 3285$$) T2DM patients presented high prevalence of unhealthy lifestyle with around $60\%$ of those possessed at least one unfavorable 24-h lifestyle behavior as counting the daily sleep duration, sitting time and meal frequency. Therefore, the status of dysregulated lifestyle should be evaluated and corrected as soon as T2DM was diagnosed. We also found some subgroups of long-duration T2DM patients are less prone for severe lifestyle dysregulation including males, highly educated persons and those who are married, suggesting these unfavorable lifestyle behaviors are related to the socioeconomic status and might be adjustable through education. We also found having more factors disturbing normal lifestyle behaviors was associated with several detrimental cardiometabolic parameters, such as increased waist circumference, higher levels of triglyceride, creatinine and poor glycemic control. More importantly, the associations between unhealthy lifestyle and diabetes related vascular comorbidities could only be observed in patients with long-duration of T2DM, indicating these unfavorable lifestyle behaviors are likely to have long-lasting impacts for the development of vascular complications in T2DM patients. To the best of our knowledge, this was the first study trying to investigate the associations between lifestyle behaviors and vascular complications of diabetes. Our data showed there is significantly increased prevalence and associations of cardiovascular disease, PAOD and nephropathy in long-duration T2DM patients having ≥ 2 risk factors of lifestyle dysregulation. Whereas only cardiovascular disease and PAOD remained statistically significant associations after adjusting covariables. Overall, our results highlighted that unhealthy lifestyle could not only deteriorate the cardiometabolic parameters, but also contribute to the incidences of cardiovascular illnesses in T2DM. Theoretically, it seems to be very reasonable because diabetes and metabolic syndrome are well-established risks for cardiovascular disease. Notably, several types of adverse cardiovascular events including unstable angina [23], acute myocardial infarction [23, 24], sudden cardiac death [24, 25] were shown to present circadian variation with the highest incidence occurs in the awaking daytime. The morning peak in adverse cardiovascular events may relate to the beginning of awaking activity [23], increased sympathetic tone, more aggregable platelets with increased coagulation, more vulnerable atherosclerotic plaque, and higher plasma cortisol and epinephrine levels [23, 26]. Additionally, dysregulated lifestyle behaviors associated circadian disruption also increases inflammatory markers including CRP [27, 28], TNF-alpha [27, 28], IL-6 [27], IL-10 [28], and resistin [27]. Moreover, a recent prospective investigation in patients with ST-segment elevation myocardial infarction further demonstrated that dysregulated lifestyle such as shift work was associated with augmented reperfusion injury and increased risks of major adverse cardiac events during a median follow-up of 5.0 years [14]. Collectively, the presence of lifestyle dysregulation conveys pleiotropic effects on triggering cardiovascular diseases, thereby providing a pathophysiologic link between unhealthy lifestyle and vascular complications in patients with long-term T2DM as observed in this study. Moreover, our study also found that specific lifestyle dysregulation was associated with specific vascular complication in patients with T2DM. Those with unfavorable eating habits had higher probabilities for developing cardiovascular disease and nephropathy, whereas the sedentary lifestyle was significantly associated with PAOD. In this study, we defined the misaligned eating behavior as meal frequency with ≥ 4 meals per day along with night snack intake, which likely combines the mixed effect of increased eating occasions, delayed eating time with night supper ingestion, and possible irregular eating schedule. Although light is the primary zeitgeber of the central clock [29], peripheral clocks derived from various peripheral organs are influenced by food supply [30]. A recent study disclosed the timing of nutrient delivery could influence the cardiometabolic health via alterations in peripheral clocks, most notably in the liver [31]. In other words, food is one of the external synchronizers of peripheral clocks. For example, changes in the timing and frequency of food intake may lead to an uncoupling of peripheral oscillators from the central pacemaker. As a result, the feeding/fasting behavior entrains clock genes and regulates various aspects of metabolism. Indeed, many studies have proved that disturbed eating time and frequency can trigger the development of obesity [30, 32], type 2 diabetes [32], and cardiovascular disease [33]. Although how unhealthy eating habit steers the increased cardiometabolic diseases remains obscure, various hormones and enzymes are likely engaged. Unusual feeding pattern may modify the circadian rhythm via hormones involved in metabolism, including insulin, glucagon, cortisol, adiponectin, and leptin [30]. Liver* genes also encode enzymes involved in food processing and expressed in a rhythmic pattern [34]. Adipose tissues also participated in regulating circadian clock network. Some glucocorticoid metabolism-related genes and the transcription factor peroxisome proliferator activated receptor γ (PPARγ), are part of the intrinsic clock controlled genes [35]. Besides, a large number of intestinal enzymes and hormones are also exhibited in a circadian manner and are synchronized by food [30]. Among these, incretins as diet induced gut peptides must be mentioned. The incretin hormone glucagon-like peptide-1 (GLP-1) was physiologically secreted from intestine upon eating and functions to augment insulin secretion, prolong gastric emptying and inhibit appetite [36]. A recent animal study further disclosed the circadian release of GLP-1 was regulated by the core clock gene, Bmal1 [37]. Importantly, GLP-1 receptor agonist as an injectable anti-diabetic medication has been demonstrated to conduct effective body weight loss along with protective effects for major adverse cardiovascular events and diabetic nephropathy [38]. Therefore, our findings further emphasize the importance of incretin-based therapy in T2DM. Taken together, changes of feeding time/frequency or ingestion of high-energy meals may alter the rhythmic clocks on multiple organs and have decisive influences on cardiometabolic consequences in T2DM. Last but not least, our data also showed that sedentary lifestyle with sitting duration for more than 8 h a day was significantly associated with PAOD. The causal relationship between long sitting time and PAOD is still uncertain. Currently, only few studies investigated the status of sedentary lifestyle and its impact on people with PAOD. Based on a 5-year follow-up study, the incidence of mobility loss and unable to continuously walk for 6 min was significantly increased in people with PAOD compared to persons who have a normal value of ankle-brachial index [39]. Similarly, greater sitting hours per day and slower outdoor walking speed were found to be associated with faster annual decline in distance of 6-min walk and calf muscle, respectively, in participants with PAOD [40]. Therefore, it is likely that the long sitting behavior might be a consequence of muscular functional impairment happened in patients with PAOD. Moreover, early identification and modification of the sedentary behavior might be important for ameliorating the functional decline in patients with PAOD. In this study, there are still several limitations. First, this is a cross-sectional analysis, and the causal relationship cannot be ascertained. Second, other behaviors (e.g., exercise strength/frequency/duration and the work pattern with different light/dark cycle) can also interact with the intrinsic clock network and contribute to the cardiovascular diseases but are not included in our investigation. Third, in evaluating the sleep status, we merely counted the average sleep duration without assessing the sleep quality or social jet lag. Finally, we did not obtain the information about food contents from participants during our analysis. We only focused on the meal frequency with or without night snack consumption rather than dietary profiles such as total calories and percentage of macronutrients intake. As a result, some attentions should be given while interpreting our results. The major strength of current study is using a nationwide, multicenter diabetes registry cohort under a web-based platform. Second, we concurrently assess the status of lifestyle dysregulation and its associations with macro- and micro-vascular complications in two groups of patients (long-duration and newly diagnosed T2DM). This approach emphasized the potential long-term impact of unhealthy lifestyle behaviors on vascular comorbidities in T2DM. In conclusion, the present study showed that unhealthy lifestyle is associated with increased prevalence of diabetic macrovascular and microvascular complications in Taiwanese population. Specifically, patients with disturbed eating habits had higher probabilities of developing cardiovascular disease and nephropathy, whereas the sedentary lifestyle was significantly associated with PAOD. 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--- title: Cardiovascular and psychosocial risks among patients below age 50 with acute myocardial infarction authors: - Åshild Faresjö - Jan-Erik Karlsson - Henrik Segerberg - Andrea Lebena - Tomas Faresjö journal: BMC Cardiovascular Disorders year: 2023 pmcid: PMC9996997 doi: 10.1186/s12872-023-03134-w license: CC BY 4.0 --- # Cardiovascular and psychosocial risks among patients below age 50 with acute myocardial infarction ## Abstract ### Background Despite improvements in the treatment and prevention of cardiovascular disease since the 1960s, the incidence of cardiovascular diseases among young people has remained the same for many years. This study aimed to compare the clinical and psychosocial attributes of young persons affected by myocardial infarction under the age of 50 years compared to middle-aged myocardial infarction patients 51–65 years old. ### Methods Data from patients with a documented STEMI or NSTEMI elevated acute myocardial infarction in the age groups up to 65 years, were collected from cardiology clinics at three hospitals in southeast Sweden. The Stressheart study comprised a total of 213 acute myocardial infarction patients, of which $$n = 33$$ ($15.5\%$) were under 50 years of age and $$n = 180$$ ($84.5\%$) were middle-aged, (51–65 years). These acute myocardial infarction patients filled in a questionnaire at discharge from the hospital and further information through documentation of data in their medical records. ### Results Blood pressure was significantly higher in young compared to middle-aged patients. For diastolic blood pressure ($$p \leq 0.003$$), systolic blood pressure ($$p \leq 0.028$$), and mean arterial pressure ($$p \leq 0.005$$). Young AMI patients had a higher ($$p \leq 0.030$$) body mass index (BMI) than the middle-aged. Young AMI patients were reported to be more stressed ($$p \leq 0.042$$), had more frequently experienced a serious life event the previous year ($$p \leq 0.029$$), and felt less energetic ($$p \leq 0.044$$) than middle-aged AMI patients. ### Conclusions This study revealed that persons under the age of 50 affected by acute myocardial infarction exhibit traditional cardiovascular risk factors like high blood pressure, and higher BMI, and were more exposed to some psychosocial risk factors. The risk profile of young persons under age 50 affected by AMI was in these respects more exaugurated than for middle-aged persons with AMI. This study underlines the importance of the early discovery of those at increased risk and encourages preventative actions to focus on both clinical and psychosocial risk factors. ## Introduction Cardiovascular disease (CVD) has long been a steadily growing public health problem globally, with heart disease being the most common cause of death in the US in 2020 [1]. Death from CVD seemed to hit its peak in the US in the 1960s, and since there has been a decline in this number [1]. Furthermore, between 1990 and 2016 the disability-adjusted life years (DALY) adjusted by age decreased by $28.7\%$. Globally this indicates that the preventive work against CVD has been quite successful [2]. Although this has mainly been directed to the older population, there is still a lack of preventive measures for young myocardial infarction patients under the age of 55 [3]. The decline in mortality from CVD has largely been accomplished by a better understanding of the disease and its risk factors, and the development of more effective treatment and preventative actions. The approximately $47\%$ decrease in mortality in the US between 1980 and 2000, was due to improved treatment and around $44\%$ due to changes in risk factors [4]. In recent decades there has been an increased incidence of obesity and diabetes mellitus type 2 [4]. Recent research revealed that long-term stress with elevated cortisol concentrations before the onset of acute myocardial infarction (AMI) could be of importance, and the AMI could not fully be explained by only classical risk factors [5, 6]. Psychosocial and socioeconomic factors like socioeconomic status, occupation, education, and social support have not by themselves been shown to have a direct impact on mortality, but rather play a part in the context of developing depression and other risk factors. However, the feeling of low control has also by itself been associated with CVD [7–9]. Myocardial infarction before 46 years of age is quite rare and accounts for only approximately $10\%$ of all male AMI cases, [10]. It is nonetheless a public health problem and because of the long remaining life expectancy of these patients, it should not be disregarded by preventative strategies. Despite this, a lack of awareness and a poorer understanding of risk factors has been suggested to be the reason for the standstill in incidence rates among young myocardial infarction (MI) patients [3]. The risk profile is yet to be fully understood, but according to previous studies, it mirrors that of middle-aged and older MI patients, with some exceptions. Classical risk factors seem to have an even greater impact on the risk of CVD for younger AMI patients [11]. This yields especially smoking, abnormal lipids, hypertension, and diabetes which have been shown to occur more frequently in young MI patients. Also they tend to be more exposed to some adverse psychosocial factors such as stress [12–16]. For younger persons in working life, anxiety disorders with symptoms like chest pain and breathlessness that resembles acute coronary syndrome could emerge [17]. Panic disorder has been suggested as a risk factor for cardiovascular disease and even as a trigger for acute coronary syndrome [18]. Myocardial infarction with non-obstructive coronary arteries (MINOCA) has been shown to occur more frequently in young MI patients as compared to older, with coronary spasm, structural dysfunction, and thrombotic disorders being some of the potential causes for MINOCA [19, 20]. Plaque rupture is the most common cause of MI in all ages [21]. An atherosclerotic plaque develops from a fatty streak, containing a deposition of low-dense lipoproteins (LDL)-particles and inflammatory cells, into an unstable plaque with an expanding necrotic core. When the plaque eventually ruptures it exposes the blood to the pro-coagulatory matrix, which causes the formation of a thrombus and might occlude the vessel [22]. However, the pathophysiological differences between younger and older MI patients are still largely unexplored. The overall aim of this study was to explore the potential differences in cardiovascular risk profiles and psychosocial factors between younger and middle-aged patients affected by an acute myocardial infarction. ## Subjects The study population was collected from three hospitals in southeast Sweden, one university hospital and two regional hospitals (but all three integrated into the university organization). Inclusion criteria were ST-segment elevation acute myocardial infarction (STEMI) or non-ST segment elevation acute myocardial infarction (NSTEMI), and an age of 65 years or younger. Exclusion criteria were not long enough hair (< 1 cm) on the vertex area, not speaking Swedish, or diagnosis of Addison’s disease or Cushing’s syndrome. The participants in the study were included when discharged from the hospital, in general, 2–3 days after the AMI. The participants were then asked to fill in a questionnaire and cut a piece of hair for cortisol measurement. After the patient´s written consent, some data from the medical records were also collected. ## Data collection The questionnaire used was that of the STRESSHEART study which was composed of validated questions from the SCAPIS study [5, 23]. The participants answered questions about previous diseases and medication, as well as the heredity of MI and stroke. Perceived stress was measured on a Likert scale from 1 to 10, and participants got to answer questions about whether they had experienced a serious life event such as divorce, disease, or death in the family in the last year. There were also general questions about the patient’s psychosocial health, such as how they perceived their general health, and how often they felt calm, energetic, or sad. Some demographic and lifestyle factors were also collected, including smoking history, alcohol consumption, sleep habits, and physical exercise. Questions about socioeconomic like education, occupation, civil status, and ethnicity were also included. From the medical records data was collected about the patient’s systolic and diastolic blood pressure, heart rate, weight, length, body mass index (BMI), and hip and waist circumference. The measurements SBP/DBP/ and heart rate were measured for all the AMI cases when discharged from the hospital clinic. A calculation of the mean arterial pressure (MAP) and the waist-hip ratio was also done. Hypertension was defined as previously diagnosed hypertension by a physician. Obesity was defined as a BMI ≥ 30 (kg/m2). The total number of classical risk factors was calculated for each patient; the risk factors were hypertension, obesity, hyperlipidemia, diabetes mellitus, current smoking, and heredity for MI or stroke. Heredity was defined as the incidence of AMI or stroke in first-degree relatives at any age. The inclusion day was used for the analysis of the seasonal onset of AMI. The cortisol concentration in hair was analyzed with an in-house radioimmune assay [24]. ## Statistical analysis All analyses were performed using SPSS version 28. Descriptive statistics were presented using frequencies and proportions for categorical variables and means or medians for continuous variables. To compare frequencies for categorical variables chi-squared or Fisher’s exact test was used. To calculate the risk ratio, OR and $95\%$ CI were applied. Cortisol concentration medians and IQR were compared using ANOVA. For some variables in Tables 2–4, there were some internal dropouts, but the calculation of the percentage was based on the actual number that answered the specific question. The population was analyzed based on their age at the onset of AMI. Young MI cases were defined as ≤ 50 years and middle age was defined as 51–65 years. Figure 1 was drafted using Excel. The significance level was set by a p-value < 0.05.Fig. 1Comparisons of the onset in different seasons of myocardial infarction among AMI patients below age 50 ($$n = 33$$) and middle-aged (51–65 years) AMI patients ($$n = 180$$) The study was approved by the Ethical Review Board at Linköping University (Dnr 2016-79-31, Dnr 2016-453-32, Dnr 2017-106-32). All participants in the study gave their written consent for participation. ## Results A total of $$n = 213$$ AMI patients up to the age of 65, were enrolled in the study. Of these $$n = 33$$ was defined as younger (under the age of 50) and $$n = 180$$ as middle-aged (between 51 and 65 years) with a median age of 47 for the young group and 61 years for the middle-aged group. Females constitute $27.2\%$ of the total AMI cases. Among the young AMI group, $30.3\%$ were women, and $26.6\%$ were females in the middle-aged group, see Table 1.Table 1The characteristics of the study population of younger AMI patients (below 50 years) and middle-aged AMI patients (51–65 years)VariablesAMI patients below age 50Middle-aged AMI patients 51–65 yearsFemale ($$n = 10$$)Male ($$n = 23$$)Female ($$n = 48$$)Male ($$n = 132$$)Age (median)45.5476161Waist circumference median (IQR)96 [24]101 [16]93 [24]102 [14]Waist-hip ratio median (IQR)0.89 (0.07)1.01 (0.06)0.91 (0.08)1.01 (0.06) Higher mean blood pressure could be seen in the young MI group when compared to the middle-aged, with systolic blood pressure 131 mmHg versus 124 mmHg; ($$p \leq 0.028$$), diastolic blood pressure 84 mmHg versus 78 mmHg; ($$p \leq 0.003$$) and mean arterial blood pressure 100 mmHg versus 93 mmHg; ($$p \leq 0.005$$). There was only a small difference seen in waist circumference and the waist-hip ratio between younger and older female MI cases, as shown in Table 1. Younger patients had a higher median BMI when compared to the middle-aged 29 versus 27; ($$p \leq 0.031$$). Obesity tends to be more common among the younger MI cases compared with older MI cases $39.4\%$ versus $21.9\%$; ($$p \leq 0.055$$). The median cortisol concentration tends to be higher among the young AMI cases compared to older MI, however not significant (75.7 pg/mg vs. 70.2 pg/mg), see Table 2.Table 2Clinical measurements, and medical history for younger AMI patients below age 50 ($$n = 33$$) and middle-aged (51–65 years) AMI cases ($$n = 180$$)VariablesAMI patients below age 50Middle-aged AMI patients (51–65 years)p-valueClinical valuesSystolic blood pressure, mean (SD)131 [19]124 [17]0.03Diastolic blood pressure, mean (SD)84 [12]78 [11]0.003Mean arterial pressure, mean (SD)100 [14]93 [12]0.005Resting heart rate, mean (SD)74 [12]72 [12]0.20Length, mean (SD)176 [10]175 [9]0.66BMI, median (IQR)29 [6]27 [5]0.03Obesity, % (n)39.4 [13]21.9 [39]0.05Cortisol level, median (IQR)75.7 (202.1)70.2 (151.6)0.7VariablesAMI patients below age 50Middle-aged AMI patients (51–65 years)p-valueMedical cardiovascular History% (n)% (n)Earlier MI18.8 [6]27.5 [39]0.27Angina pectoris6.3 [2]11.9 [17]0.36Atrial fibrillation0 [0]2.1 [3]0.40Heart failure0 [0]3.5 [5]0.27Stroke3.0 [1]6.3 [9]0.46Hypertension27.3 [9]37.3 [53]0.28Hyperlipidemia18.8 [6]23.4 [34]0.47Percutaneous coronary intervention9.4 [3]18.9 [27]0.17Known hereditary MI42.4 [14]41.7 [75]0.35Known hereditary Stroke24.2 [8]35.1 [47]0.24Diabetes type 10 [0]2.8 [4]0.33Diabetes type 215.2 [5]12.0 [17]0.62 There were not any significant differences in the patient’s medical history. However, heart failure, atrial fibrillation, and diabetes type 1 tended to be more common in the middle-aged group compared to the younger as well as tendencies of increased risk among the middle-aged cases concerning previous angina pectoris, stroke, and percutaneous coronary intervention, see Table 2. Cardiovascular medication in general like anticoagulants, anti-hypertensive, and anti-hyperlipidemia were more frequently reported among middle-aged MI patients, see Table 4. For risk factors like smoking, no differences were seen, however, alcohol consumption was more frequent among the younger MI cases ($35.3\%$ vs. $20.9\%$). Physical activity was equal frequent between the two groups. Fewer sleep hours during the night as well as poor sleep quality were more common among the younger MI cases. A high level of perceived stress was also more pronounced among the younger MI group, see Table 3. Young patients were more likely to have experienced a serious life event in the last year ($61.8\%$ vs. $45.5\%$; $$p \leq 0.03$$). The young patients were also more prone to answer never or seldom when asked how often they felt energetic compared to the older MI cases ($48.5\%$ vs. $26.8\%$; $$p \leq 0.03$$). Low level of education was to some extent higher among the older as well as the number born outside Sweden. The estimated economic situation and occupation were quite equally distributed, except for the higher frequency of academics and chief positions among the middle-aged group, see Table 4.Table 3Comparisons of lifestyle risk factors between AMI patients below age 50 ($$n = 33$$) and middle-aged (51–65 years) AMI patients ($$n = 180$$)VariablesAMI patientsbelow age 50Middle-aged AMI patients (51–65 years)p-value%n%nSmoker yes24.2828.3510.67Drink alcohol0.14 Rarely39.41342.475 Sometimes24.2836.765 Quite often36.41220.937Physical activity0.96 Non/sporadic57.61960.8107 Regular30.31029.051 Intensive regular12.1410.218Sleep habits0.36 Poor/very poor33.31123.433 Quite good45.51558.983 Very good21.2717.725 Sleep less than 7 h. night54.51838.3540.08 Sleep more than 7 h. night45.51561.787Perceived everyday stress0.22 Never0.004.58 Sometimes60.62068.5122 Always39.41327.048Perceived stress (median, IQR) rating (1–10) on a VAS scale7 [3]6 [5]0.03Table 4Medications and psychosocial factors among AMI patients below age 50 ($$n = 33$$) and middle-aged (51–65 years) AMI patients ($$n = 180$$)VariablesAMI patients below age 50Middle-aged AMI patients (51–65 years)p-value%n%nMedications Antihypertensive21.2732.7590.07 Anti-hyperlipidemia9.1318.3330.13 Anticoagulants0.0011.1200.03 Cardiovascular medications3.0118.3330.02 Diabetes medications12.1411.7210.70 Steroid based medications15.2515.6280.79Psychosocial factorsEducation0.19 Low12.1425.746 Medium66.72253.696 High21.2720.737Occupations0.88 Workers54.51853.592 Civil servants30.31027.347 Academic /boss15.2518.031 Retired0.001.22Ethnicity0.05Swedish97.03284.4151 Not Swedish3.0115.628Economy0.90 Bad18.2618.533 Good81.82781.5145Civil status0.50 Single18.2622.941 Married/cohabited81.22777.1138Encountered serious life events last year0.03 Yes60.62041.573 No39.41358.5103How often have you felt calm?0.78 Never/seldom30.31030.254 Sometimes36.41229.653 Most of the time33.31140.272How often have you felt energetic?0.03 Never48.51626.838 Sometimes15.2531.745 Most of the time36.41241.559How often have you felt sad?0.89 Never66.72265.593 Sometimes24.2822.532 Most of the time9.1312.017 Some slight differences in the seasonal distribution on the onset of AMI could also be seen, both groups had a downward slope during the summertime. The younger had two peaks, one during spring and the second in autumn, and the middle-aged had their peak during the winter months, see Fig. 1. ## The main findings The risks for cardiovascular disorders are generally increased with age. If younger people suffer a serious cardiac event there must likely be some explanations for their cardiac risk profiles. In this study, we found that younger person who suffers an acute myocardial infarction are more likely to have higher blood pressure, both systolic and diastolic, and they are also more likely to be obese than middle-aged MI patients. On the other hand, middle-aged were more likely to have heart failure and to use anticoagulants and cardiovascular medications. There were also some differences in psychosocial health, where the younger MI persons reported higher levels of stress, had more often experienced a serious life event last year, and reported themselves to be less energetic. Younger MI persons having higher diastolic blood pressure are in accordance with previous findings in some other studies [13, 14]. According to a sub-study within the Framingham heart study, isolated diastolic hypertension is a better model for understanding cardiovascular risk than isolated systolic hypertension for young people under 50 years of age [13]. The present study found higher systolic hypertension, diastolic hypertension, and mean arterial pressure, for young MI patients. The higher mean hypertension and systolic hypertension in young patients might indicate that young MI patients are generally more burdened by high blood pressure than older MI patients, which may be because they are less medicated for hypertension [25]. The importance of obesity as a risk factor for cardiovascular diseases in a young population and how it differs from the general MI patient have been described earlier. Obesity has been shown to increase the risk of CVD in young populations [15]. In this study, BMI was higher in young MI patients compared to middle-aged MI patients, but no difference was evident in waist circumference or waist-hip ratio. This could indicate that BMI might be a better measurement for determining the cardiovascular risks in young people compared to middle-aged. The risk of having previous cardiovascular diagnoses like angina pectoris, heart failure, percutan coronary intervention as well as a stroke, was two times higher among older MI patients as expected. No difference in the prevalence of diagnosed hypertension, hyperlipidemia, or diabetes mellitus was seen, but a slightly increased risk among the older group appeared. Most other studies defined heredity as the incidence of MI in a family member at an early age, below 55 years for male first-degree relatives or below 65 years for females [16, 25]. The classic type A behavior with high demands on themselves, impatient, and generally hostile and very competitive, tend to have a higher risk of coronary heart disease, which could not be explained by individual factors such as hypertension, diabetes, serum levels of cholesterol, or smoking [26, 27]. An increased cortisol concentration one month before MI, a measure of long-term stress, was seen among the younger study population, however not significant. The recovery prognosis after an acute myocardial infarction among younger patients has been shown to be related to financial barriers to health care [28]. Socioeconomic factors might play a role in functional recovery after myocardial infarction [29]). Chronic stress related to either job or marital strain was found to be associated with long-term adverse outcomes after acute myocardial infarction [30]. The presence of diabetes mellitus could increase the general risk of mortality after myocardial infarction, but young adults with diabetes mellitus experienced significant improvements [31]. Our findings also reveal that young MI patients are more likely to have experienced serious life events the year before the infarction and feel less energetic compared to middle-aged MI patients. There was no difference between age groups regarding experienced life events in previous years, indicating that a temporal aspect might be of importance. Since atherosclerotic lesions take many years to develop the idea that young MI patients have less coronary stenosis is not so farfetched, which has also been shown in previous studies [17, 18]. It can therefore be assumed that coronary occlusion is not the only cause of MI in these patients. This could indicate myocardial infarction with non-obstructive coronary arteries that there might be alternative causes for MI in young patients related to such as coronary spasms, structural dysfunction, and thrombotic disorders, but further studies are needed to understand the linkage to age [17, 18]. A seasonal difference in the onset of MI could also be seen in this study, where both groups had a decrease in the summer, while the older group had their peak during winter and the younger during autumn and even a small peak appeared during spring in this group. An earlier study of MI cases found a similar seasonal pattern, decreased occurrence from winter to autumn and from spring to summer. This was seen in both men and women, in different age groups, and in most geographic areas. In-hospital case fatality rates for AMI also followed a seasonal pattern, with a peak of $9\%$ in winter [32]. This recent study almost followed the same pattern, but the younger ones had a more distinct peak in the autumn. Another recent study observed seasonal variation of incidence and in-hospital mortality and sex-specific differences regarding the seasonal variation of in-hospital mortality [33]. Seasonal variation in the onset of AMI could be affected by independent environmental and biological variables. ## Strengths and limitations A major strength of this study is the number of variables included, which provides quite an extensive description of both the clinical and the psychosocial profile. The number of included AMI cases is however a limitation. The cross-sectional design of the study also limits the potential for truly understanding the association between cardiovascular risk factors and how these affect the risk for AMI in the younger. Many of the analyzed variables are based on self-estimations made by the participants, such as perceived stress, alcohol consumption, smoking habits, sleep, and exercise habits. All these retrospective questions based on individual perceptions could lead to some risks for recall bias. Especially questions about stress and self-estimated well-being can also have been affected by the myocardial infarction closely before their participation, mainly by making them remember the past as worse than it was, but this recall bias was minimized, since all participants filled in the questionnaire directly when they were discharged from the hospital, in general, only 2–3 days after the cardiac event. ## Conclusions This study highlights the cardiovascular and psychosocial risk factors for young persons affected by myocardial infarction. Young MI patients are a group where little has improved over the years. The young patients protruded regarding higher blood pressure, higher BMI, increased self-reported stress, and even higher cortisol concentrations, they had more often experienced a serious life event and felt less energetic the year before the MI. These younger persons thereby follow a pattern where the traditional cardiovascular risk factors are evident before serious cardiac events. All these factors are preventable. This study underlines the importance of the early discovery of those at increased risk and encourages preventative actions to focus on both clinical and psychosocial risk factors. ## References 1. Shields M, Haque A, de Ganzalez Barrington A. Leading vauses of death in the US during the COID-19 pandemic, March 2020 to October 2021. JAMA Intern Med (2022) 182 883-886. DOI: 10.1001/jamainternmed.2022.2476 2. Hay SI, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F. 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--- title: 'Leisure-time physical activity trajectories from adolescence to adulthood in relation to several activity domains: a 27-year longitudinal study' authors: - Frida Kathrine Sofie Mathisen - Torbjørn Torsheim - Coral Falco - Bente Wold journal: The International Journal of Behavioral Nutrition and Physical Activity year: 2023 pmcid: PMC9996998 doi: 10.1186/s12966-023-01430-4 license: CC BY 4.0 --- # Leisure-time physical activity trajectories from adolescence to adulthood in relation to several activity domains: a 27-year longitudinal study ## Abstract ### Background Insufficient physical activity (PA) levels among adolescents and adults make promoting PA a public health priority. Although most people exhibit low or decreasing levels of PA, other groups increase or maintain high levels of activity. These different groups may engage differently in activity domains during their leisure time. This study aimed to identify distinct trajectories of leisure-time vigorous physical activity (LVPA) and to explore whether these trajectories are characterised by differences in four activity domains (participation in organised sports clubs, diversity in leisure-time activities, outdoor recreation, and peer PA) over the life course. ### Methods Data were drawn from the Norwegian Longitudinal Health Behaviour Study. The sample of participants ($$n = 1103$$, $45.5\%$ female) was surveyed 10 times from age 13 years in 1990 to age 40 years in 2017. LVPA trajectories were identified using latent class growth analysis, and mean differences in activity domains were studied using the one-step BCH approach. ### Results Four trajectories were identified: active ($9\%$), increasingly active ($12\%$), decreasingly active ($25\%$), and low active ($54\%$). Overall, this analysis showed a declining tendency in LVPA from age 13 to 40 years except for the increasingly active trajectory. Belonging to a trajectory with a higher LVPA level was related to higher mean levels of the included activity domains. Compared with those in the increasing trajectory, people belonging to the decreasing trajectory reported higher mean participation levels in and age at becoming a member of sports clubs, diversity in leisure-time activities, and best friend’s activity level during adolescence. However, in young adulthood, people in the increasingly active trajectory reported significantly higher mean levels for the same variables. ### Conclusions The development of LVPA from adolescence to adulthood is heterogeneous, suggesting the need for targeted health promotion initiatives. The largest trajectory group included more than 50 percent and was characterized by low levels of LVPA, less engagement in PA domains and fewer active friends. There seems to be little carry-over effect of engagement in organised sports in adolescence regarding level of LVPA later in life. Changes in social surroundings throughout the life span, such as having friends who are more or less engaged in PA, may assist or hinder health enhancing engagement in LVPA. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12966-023-01430-4. ## Background Physical activity (PA) is well established as a predictor of lifetime health and is essential to the inclusion of health promotion in global health policies and local interventions. Nevertheless, global estimates show that $27.5\%$ of adults [1] and $81\%$ of adolescents [2] do not meet the global recommendations for aerobic exercise, which is at least 150–300 min of moderate-to-vigorous intensity, or at least 75–150 min of vigorous intensity, or a combination of those throughout the week [3]. As pointed out by the recommendations, there seems to be an additional health benefit related to vigorous physical activity, and it has been shown that for the same amount of total PA, higher proportions of PA with vigorous intensity are related to lower mortality [4]. Research related to the mechanisms that promote lifelong PA and decrease inactivity is needed, especially research based on longitudinal data. Longitudinal data allow researchers to examine the life-course patterns of PA over time and across various life events and transitions [5]. To understand further these changes over time and differences between individuals, four approaches have been suggested by Telama [6]: the carry-over value hypothesis, ability and readiness hypothesis, habit-formation hypothesis, and self-selection hypothesis. The carry-over value hypothesis suggests that adults continue to engage in activities they participated in at a young age. The ability and readiness hypothesis suggests that earlier experience and the basic skills connected to this experience contribute to the maintenance of or re-engagement in PA despite participation in different types of activity. The habit-formation hypothesis suggests that behaviour is repeated because it is a habit and that this behaviour is based not only on planned behaviour but is automatic and performed with less awareness. The self-selection hypothesis acknowledges a hereditary disposition to fitness and motor performance in some people, which makes them engage in PA more often in adolescence and adulthood. These hypotheses can be used individually to explain the possible paths of lifelong patterns of PA or they can be applied in a cumulative way through the combination of more than one hypothesis to explain the establishment of an active lifestyle. A growing body of research has used finite mixture modelling to investigate the development of PA from a life-course perspective. The literature was recently summarised in a systematic review [7]. The number of previous studies looking at the development of PA from childhood or adolescence to adulthood is limited, and only four studies, based on only two different data materials, both population-based studies from Finland, were identified in the systematic review. These studies identified three or five latent leisure-time PA trajectories and that the most significant proportion of people follow a stable moderate or persistently low level of PA. One study identified two additional trajectories in addition to the three prevalent trajectories (steady high, moderate, or low level of leisure-time PA) that comprised an increasingly active or a decreasingly active trajectory [8]. Emerging research interest in the domains related to PA (i.e., the context in which PA occurs) has also contributed to the knowledge about lifelong engagement in leisure-time PA [9]. Diverse patterns of engagement in PA, both organised and non-organised, in team sports or individual sports, and in a wide variety of activities during adolescence are related to the activity level in adulthood (e.g., [10–15]). Organised sport provides structures for social interaction and skill development, which are thought to contribute to the development of lifelong PA by establishing habits, abilities, and continued participation. A previous study [16] indicated that participation in sports clubs is associated with a sustained or increased PA pattern and, correspondingly, drop out from organised sport with a decrease in PA from a high level. Kjønniksen and colleagues [12] found that diversity in leisure-time activities (the number of activities participated in) at age 15 years was more strongly related to later activity level at age 23 years than was engagement in specific activities. Earlier experiences with PA and sports make it easy to maintain or re-engage in PA, especially if the newer form of PA differs from the earlier activity [6]. Previous research also indicates that broad and varied experiences during adolescence affect PA habits later in life [11], a finding that is consistent with the ability and readiness hypothesis [6]. Another domain related to PA, and possibly relevant to the Nordic context, is the concept of nature-related outdoor recreation, friluftsliv, which is considered a core social and cultural value in Norway [17]. Different forms of outdoor activities may support PA throughout the entire life course. Participation in outdoor PA influences positive attitudes towards PA and contributes to positive activity habits [18]. PA during childhood and adolescence is usually performed with peers, and peers appear to influence the individual’s PA level significantly through behavioural modelling, peer pressure, group norms, and co-participation [19]. When entering adulthood, friends and partners may act as critical agents for activity and inactivity. There is an evidence gap in the research literature needed to develop a more nuanced understanding of PA development from adolescence into adulthood [7, 9]. This study aimed to identify distinct trajectories of leisure-time vigorous physical activity (LVPA) and to explore whether these trajectories are characterised by differences related to activity domains over the life course. The activity domains included were participation in organised sports, diversity in leisure-time activities, outdoor recreation, and active peers. ## Study sample Data were drawn from the Norwegian Longitudinal Health Behaviour Study. The study involved participants from 22 randomly selected schools in Hordaland in Western Norway. The sample was geographically limited to this region, which allowed the researchers to maintain close contact with the participants at the beginning of the project to establish a good foundation for this cohort study. A total of 924 students (414 girls, $44.8\%$) participated in the first survey in 1990. This was $78\%$ of the initial sample of 1195 students, and the average age was 13.3 years. During the two subsequent data collections in school, any new student in any randomly selected school was invited to participate. This meant that a total of 1105 people participated in the survey at least once over the 27 years ($89\%$ of the total invited sample of 1242). Written consent was given by parents before participation in the survey. Participants were surveyed 10 times (1990, 1991, 1992, 1993, 1995, 1996, 1998, 2000, 2007, and 2017). For the first three times, the survey was conducted during school hours and the students completed the self-completed questionnaires in class. After that, the questionnaire was distributed by post. Participants were also given an option to respond online for the last two surveys. The questionnaire was distributed during October, with greater variation in time of completion when the survey was sent by post. More information about the sample is found in Additional file 1. Of the total sample of 1105 participants, only participants having at least one measure of LVPA over the 10 measurement points were included in the analyses, which gave a sample of 1103 participants ($45.5\%$ female). ## Outcome measure The outcome measure was LVPA. To assess the participants’ level of LVPA, a previously used item from The Health Behaviour in School-aged Children (HBSC) Study: WHO Collaborative Cross-national Study was included in the questionnaire [20]. The question reads, “Outside school hours, how often do you do sports or exercise to the extent that you become out of breath or sweat?” The following response categories are offered (coding in parenthesis): Every day [7], 4–6 times a week [5], 2–3 times a week (2.5), Once a week [1], Once a month (0.25), Less than once a month [0], and Never [0]. In 1993, the first part of the question was changed to “Outside school hours/work”. This question was included at all 10 measurement points and has previously been identified as having acceptable to good reliability in an Australian sample [21] and overall good reliability in a Norwegian sample aged 13–18 years [22]. Validity has been found to be fair when correlated with maximal oxygen uptake, especially among girls [22]. ## Membership in sports clubs The participants’ membership status in organised sports was assessed using the question, “Are you a member of a sports club or sports association?” The following response categories were offered: Yes [1], No, but I have been a member before [0], and No, I have never been a member of a sports club [0]. This item was included eight times and was excluded at ages 19 and 21 years. ## Age at becoming a member of a sports club The participants were asked retrospectively four times from 1990–1993 the question, “When did you become a member of a sports club?”. The response categories were I have never been a member of a sports club or sports association [0] and I became a member when I was about …. years old, with response options of 1–13, 14, 15 or 16 years. Data from these four measurements were recoded into one where the most initial response was preferred. ## Diversity in leisure-time activities A list of alternative types of sport or exercise was provided to respondents at the ages of 15 years (33 alternatives), 23 years (33 alternatives, exercise in a fitness centre was added and orienteering was removed), and 40 years (20 alternatives). See complete list of all activities in Additional file 2. Participants recorded the frequency level for all activities using four response categories: Several times a week, Once a week, Less than once a week, and Never. A count variable was computed to measure diversity in leisure-time activities. Performance of a sport or exercise at any frequency was counted in. At age 15 years, the mean level of self-reported diversity in leisure-time activities was significantly higher in the active and decreasingly active trajectories than in the low active and increasingly active trajectories ($p \leq 0.05$). At age 30 years, the active and increasingly active trajectories exhibited greater diversity than the low active and decreasingly active trajectories. From the age of 30 to 40 years, diversity decreased in all trajectories, and the mean diversity in leisure-time activities did not differ significantly between the active, decreasingly active, and increasingly active trajectories. However, all three differed significantly from the low active trajectory. Consistent with the ability and readiness hypothesis [6], we found that respondents in the trajectories exhibiting a stable engagement in a diversity of leisure-time activities either increased or maintained their relatively high LVPA level. By contrast, respondents in the decreasingly active trajectory showed a greater decrease in diversity in leisure-time activities from adolescence to young adulthood. Engagement in multiple sports and PAs during adolescence may provide an important base for developing motor skills and promoting long-lasting engagement in higher LVPA level later in life. This association has been shown in longitudinal studies [11, 12, 36]. Continuing to participate in several different sports or activities throughout adolescence and into adulthood may have contributed to the maintenance of a higher LVPA level, even though the number of activities the respondents participated in at age 40 years did not differ between the active, increasingly active, and decreasingly active trajectories. ## Outdoor recreation The questions about outdoor recreational activity were, “How often do you usually do outdoor activity in summer? Outdoor recreation in summer can include hiking, swimming, cycling, or fishing” and “How often do you usually do outdoor activity in winter? Outdoor recreation in winter can include hiking, fishing or cross-country skiing”. The following response categories were offered: Four times a week or more often [4], 2–3 times a week [3], Once a week [2], Less than once a week [1], and Never [0]. These items were included at ages 13, 14, 15, 16, 23, 30, and 40 years. People in the low active trajectory reported significantly lower ($p \leq 0.05$) mean levels of outdoor recreation than those following the active trajectory at all measurement points, except during summer at age 13 years. The mean level also differed significantly between the low active and decreasingly active trajectory at most of the measurement points during adolescence. However, from age 23 years, the mean levels did not differ between these two trajectories except for outdoor recreation during the winter at age 40 years, when the low active class reported significantly lower mean levels. The low active trajectory class reported significantly lower outdoor recreation levels than those in the increasingly active trajectory class except at age 13 and in winter at age 15 years. At age 23, we also found significantly lower ($p \leq 0.05$) mean levels of outdoor recreation during both summer and winter for the decreasingly active trajectory than for the active trajectory, and during winter compared to the increasingly active trajectory. At age 30, both the active and the increasingly active trajectories showed significantly higher ($p \leq 0.05$) mean levels of outdoor recreation than the decreasingly active trajectory. At age 40 years, there was a significant ($p \leq 0.05$) lower mean level of outdoor recreation during winter in the decreasingly active trajectory compared with the active trajectory. All trajectories showed relatively high levels of outdoor recreation during both summer and winter during adolescence. Engagement in outdoor recreation is closely linked to cultural characteristics in Norway, and it is common to spend leisure time outdoors, especially during weekends and holidays [12]. We found significant differences indicating that respondents in the low active trajectory engaged less in these activities than the other trajectories. However, our findings do not allow us to determine how outdoor recreation contributed to the LVPA level. By being accessible to all and not requiring membership or special equipment, outdoor recreation may contribute to LVPA throughout the life course more broadly than organised sports or PA at fitness centres. In our study, respondents following the low active trajectory seemed to be less active in outdoor recreation during winter compared with the other trajectories. Outdoor recreation in winter can be challenging in a Nordic country like Norway. Such seasonal changes may be important to consider when planning initiatives to promote activity, at least for outdoor recreation during winter among those in the low active trajectory. ## Active friends Peer PA was assessed using two questionnaire items. The first related to the number of friends participating in sports and included the question, “How many of your friends do sports or exercise?”. The response categories were Almost all [4], More than half [3], About half [2], Less than half [1], and None [0]. This item was measured at ages 13, 15, and 18 years. The second peer item related to the level of sports and exercise performed by the participant’s best friend and read, “Does your best friend do sports and exercise?” The responses options were Four times a week or more [4], 2–3 times a week [3], Once a week [2], Less than once a week [1], Never [0], and I do not have a best friend (missing). This item was measured at ages 13, 15, and 23 years. Gender (binary, measured at baseline), having children (yes/no, measured at ages 21, 23, 30 and 40 years), income (gross income in intervals of 100 000 NOK, measured at ages 23, 30 and 40 years), and body mass index (BMI, measured at ages 15, 23 and 40 years), which was calculated based on self-reported height and weight, were included to describe the characteristics of the different trajectory classes because previous research has found them to be related to the development of lifetime PA [7, 8, 16, 23–28]. ## Statistical analysis The data were managed, and descriptive statistics were calculated using IBM SPSS (version 27.0). The data were converted to Mplus (version 8.7 [29]) for the latent class growth analysis (LCGA). The level of statistical significance was 0.05. Prior to the LCGA, the data on LVPA from all 10 measurement points were modelled in a latent growth model to explore what number of growth parameters suited the data best. Three models were tested, with two (intercept and slope factor), three (intercept, slope, and quadratic slope factor) and four (intercept, slope, quadratic, and cubic slope factor) growth parameters. The best fit, determined by the highest comparative fit index (CFI = 0.911) and the lowest root mean square error of approximation (RMSEA = 0.05) was obtained when the model included four growth parameters. The LCGA was then fitted using the same growth parameters. ## Latent class growth analysis Trajectories for LVPA were identified using LCGA, a type of group-based trajectory model, which makes it possible to identify latent classes of individuals based on their joint growth trajectories over time [30]. The LVPA variable was treated as continuous in the analysis. Missing data were assumed to be missing at random (MAR) and addressed using full information maximum likelihood estimation (FIML). The model parameters were estimated using the maximum likelihood estimator with robust standard errors (MLR). The number of classes was determined by testing the model fit for two, three, four, five, six, and seven latent trajectory classes. Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, average posterior probability > 0.70 for within-group membership, the Vuong–Lo–Mendell–Rubin test (VLMR), and the bootstrap likelihood ratio test (BLRT) were used to assess the fit and interpretability of the model and number of latent trajectory classes [31]. When assessing the class enumeration, we did not find that the AIC and BIC values reached a low point and started to increase. The VLMR test was significant only for the two-class and four-class models ($p \leq 0.05$). However, the proposed more reliable BLRT was significant for all models. The enumeration was therefore based on entropy and the posterior probability of class membership. Theoretical and empiric support for the four-class solution was also used [31]. The qualitative assessment of the three-class model and five-class model vs the four-class model supported the decision because the three-class solution provided fewer nuances and the five-class solution had two similar and (almost) parallel classes. Figures showing plots of the two- to seven-class models are presented in Additional file 3. ## Distal outcome model using the Block–Croon–Hagenaars (BCH) approach The mean differences in the above-mentioned variables related to activity domains (e.g., membership in sports clubs, diversity in leisure-time activity, peer PA) measured in adolescence, young adulthood, and adulthood were studied across the trajectory classes. To do this, we used the one-step automatic BCH approach. Using this approach to estimate a distal outcome model allowed us to avoid shift of the latent class trajectories so that they were no longer measured only by the LVPA indicator. Instead, the BCH method avoids a shift in the latent classes (i.e., LVPA trajectories) by using a weighted multiple group analysis in the final stage [32]. In the BCH approach, the auxiliary variables are treated as continuous. ## Quality assessment The Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) [31] checklist was used to ensure the quality of the analysis (see Additional file 4). ## Drop out analysis Drop out analysis were undertaken by comparing baseline values of LVPA, membership in sports club, age at becoming a member of a sport club, outdoor recreation, number of active friends and best friend’s activity level to examine whether there was a difference between those who dropped out of the study before age 40 and the 455 respondents who did not. Independent sample t-tests showed no statistically significant differences between these two groups ($p \leq 0.05$). ## Participants and descriptive statistics Among the total sample, $17\%$ of the participants completed all 10 repeated measurements of LVPA; $15\%$ completed nine measurements, $11\%$ eight, $9\%$ seven, $10\%$ six, $9\%$ five, $11\%$ four, $10\%$ three, $5\%$ two, and $4\%$ one. Table 1 shows the descriptive statistics for all included variables at all 10 measurement points from 1990 to 2017.Table 1Descriptive statistics of included variables by measurement pointYear1990199119921993199519961998200020072017Respondent age13141516181921233040N924958936789779643634630536455Coding of time in LCGA-10-9-8-7-5-4-20717LVPA (0–7) n912952945708777639583628533447 Mean3.183.113.002.552.242.112.021.881.701.99 S.E0.070.070.070.080.080.080.080.080.070.08Membership in sports clubs (No [0], Yes [1]) n913953940708776628533447 Percentage member65.462.458.552.238.826.823.130.5Diversity in leisure-time activities n927627452 Mean13.579.027.57 S.E0.210.180.16Outdoor recreation, summer [0-4] n917949946703628533446 mean3.303.123.302.992.842.923.01 S.E0.030.030.030.040.040.040.04Outdoor recreation, winter [0-4] n915949945704628534443 mean2.942.552.552.232.032.232.32 S.E.0.030.030.040.040.040.040.05Number of active friends (0–4) n904934772 Mean3.162.752.15 S.E0.040.040.05Best friend’s activity level (0–4) n878934626 Mean2.652.581.93 S.E0.040.040.05Gender (Boy [0], Girl [1]) na1103 Percentage of girls45.6BMI n886606443 Mean20.1823.2825.52 S.E0.080.150.18Having children (No [0], Yes [1]) n585628536449 Percentage having children7.016.455.085.3Income (2000: 1–6; 2007: 1–8; 2017: 1–10) n626534446 Mean1.663.786.45 S.E0.040.070.10The range for the measure of diversity in leisure-time activity was in 1992 0 to 33; in 2000 0–27; and in 2017 0–20. The range for the measure of BMI was in 1992 13.72–33.91; in 2000 14.69–53.98; in 2017 18.36–41.40LCGA Latent class growth analysis, LVPA Leisure-time vigorous physical activity, BMI Body mass indexaThe n is based on data from all measurement points ## LVPA trajectories Four LVPA trajectories were identified from adolescence to adulthood (Fig. 1). Based on their development, the trajectories were named (ranked from smallest to largest and with the percentage given in parentheses): active ($9\%$), increasingly active ($12\%$), decreasingly active ($25\%$), and low active ($54\%$). The final number of trajectories was selected based on the described fit statistics (Table 2).Fig. 1Leisure-time vigorous physical activity trajectories ($$n = 1103$$)Table 2Latent Class Growth Analysis (LCGA) based on the total sampleNo. of classesAICBICBLRTVLMREntropyAverage Latent Class Probabilities for Most Likely Latent Class Membership (%)Sample Size Per Class Based on Most Likely Class MembershipThe Number of Random Start Values and Final Iterations129,901.4629,971.54––––––1001103200, 20228,520.1728,615.28p <.05p <$\frac{.050.77392}{94324}$/779200, 20328,248.5528,368.69p <.05p =$\frac{.070.69089}{77}$/$\frac{85612}{342}$/149200, 20428,090.9728,236.14p <.05p <$\frac{.050.70384}{78}$/$\frac{87}{75102}$/$\frac{276}{597}$/128200, 20527,997.8828,168.08p <.05p =$\frac{.200.66477}{72}$/$\frac{71}{82}$/$\frac{87122}{292}$/$\frac{169}{471}$/49200, 20627,928.5128,123.74p <.05p =$\frac{.610.66481}{85}$/$\frac{70}{71}$/$\frac{72}{86483}$/$\frac{46}{268}$/$\frac{121}{141}$/44200, 20727,871.3828,091.64p <.05p =$\frac{.110.65387}{84}$/$\frac{78}{74}$/$\frac{72}{71}$/$\frac{6241}{34}$/$\frac{432}{166}$/$\frac{62}{190}$/178200, 20AIC Akaike information criterion, BIC Bayesian information criterion, BLRT Bootstrap likelihood ratio test, VLMR Vuong-Lo-Mendell-Rubin testThe class solution considered optimal is presented in bold The active and decreasingly active trajectories started with the highest LVPA level at the baseline. From here, the active trajectory continued to exhibit higher levels of LVPA than the three other trajectories. The decreasingly active trajectory showed a continuous decrease in activity level from adolescence to young adulthood but exhibited a more stable pattern after age 23 years. The low active trajectory had the lowest mean LVPA at age 13 years and showed a decreasing level of LVPA until the age of 18 years. From here, this trajectory continued at about the same low level until the age of 40 years. The increasing trajectory started with about the same estimated mean LVPA level as the low active trajectory. However, it ended at about the same LVPA level as the active trajectory at age 40 years and was the only trajectory in which the mean LVPA level increased from age 13 to age 40 years. ## Characteristics related to activity domains across the LVPA trajectories The mean and standard error of all included auxiliary variables across all four trajectories and indications of significant differences between each trajectory are shown in Table 3. The development within the four trajectories is illustrated in Fig. 2.Table 3Distal outcome model using the Block-Croon-Hagenaars (BCH) approach for included activity domains, demographic and socioeconomic variablesActiveaDecreasingly activebLow activecIncreasingly activedpMeanS.EMeanS.EMeanS.EMeanS.Ea vs. ba vs. ca vs. db vs. cb vs. dc vs. dMembership in sports clubs Age 130.900.040.910.030.460.030.660.060.773 < 0.0010.003 < 0.0010.0010.004 Age 140.870.050.920.030.370.030.760.060.362 < 0.0010.158 < 0.0010.018 < 0.001 Age 150.880.050.840.040.350.030.700.060.540 < 0.0010.022 < 0.0010.057 < 0.001 Age 160.880.050.780.040.250.030.650.070.190 < 0.0010.012 < 0.0010.137 < 0.001 Age 180.840.060.620.050.120.020.580.070.005 < 0.0010.003 < 0.0010.649 < 0.001 Age 230.670.080.250.050.110.020.570.07 < 0.001 < 0.0010.3860.0260.002 < 0.001 Age 300.450.100.230.050.140.030.390.070.0610.0020.6100.1300.1170.004 Age 400.580.100.310.060.210.030.440.090.030 < 0.0010.3230.1770.2320.016 Age when first member7.200.287.620.198.440.148.540.340.253 < 0.0010.0040.0010.0290.798 Number of times reported member4.180.253.660.151.410.093.660.250.098 < 0.0010.153 < 0.0011.000 < 0.001Diversity in physical activities Age 1515.610.8016.160.5311.770.3613.410.710.592 < 0.0010.049 < 0.0010.0040.052 Age 2312.520.659.260.497.420.2811.920.56 < 0.001 < 0.0010.5080.0020.001 < 0.001 Age 409.620.648.350.386.500.268.710.570.109 < 0.0010.306 < 0.0010.6230.001Outdoor recreation Age 13, summer3.400.123.390.073.200.053.400.110.9220.1100.9810.0400.9390.110 Age 13, winter3.070.123.160.082.80.052.870.120.5850.0370.243 < 0.0010.0570.640 Age 14, summer3.280.123.160.082.980.063.390.100.4500.0200.4730.0840.0990.001 Age 14, winter2.890.132.750.082.330.062.700.120.384 < 0.0010.306 < 0.0010.7850.006 Age 15, summer3.500.93.420.073.160.053.380.110.5220.0010.4270.0080.8010.079 Age 15, winter2.990.132.820.092.290.062.650.130.301 < 0.0010.065 < 0.0010.3040.014 Age 16, summer3.340.133.090.092.800.073.180.120.127 < 0.0010.3580.0160.5820.009 Age 16, winter2.760.162.510.091.890.072.420.130.202 < 0.0010.105 < 0.0010.5780.001 Age 23, summer3.430.142.920.102.580.073.230.120.007 < 0.0010.3170.0080.073 < 0.001 Age 23, winter2.950.171.980.121.710.062.610.14 < 0.001 < 0.0010.1450.0540.002 < 0.001 Age 30, summer3.270.162.810.122.760.073.440.100.0270.0020.3940.689 < 0.001 < 0.001 Age 30, winter2.810.202.120.121.940.072.980.130.006 < 0.0010.5060.216 < 0.001 < 0.001 Age 40, summer3.350.143.000.102.750.083.630.110.056 < 0.0010.1270.069 < 0.001 < 0.001 Age 40, winter2.920.152.350.122.050.082.750.160.006 < 0.0010.4470.0470.060 < 0.001Number of active friends Age 133.600.103.570.072.790.073.310.140.796 < 0.0010.099 < 0.0010.1170.002 Age 153.430.123.270.102.260.072.950.150.350 < 0.0010.015 < 0.0010.090 < 0.001 Age 183.060.152.400.131.680.072.600.170.002 < 0.0010.053 < 0.0010.382 < 0.001Best friend’s activity level Age 133.160.133.120.092.390.062.260.140.848 < 0.001 < 0.001 < 0.001 < 0.0010.434 Age 153.490.112.950.102.200.072.530.14 < 0.001 < 0.001 < 0.001 < 0.0010.0210.048 Age 232.780.231.860.151.540.082.780.180.002 < 0.0010.9900.071 < 0.001 < 0.001 Gender0.220.050.300.040.560.020.550.060.206 < 0.001 < 0.001 < 0.0010.0020.939Body mass index Age 1520.110.3120.160.1820.180.1420.290.270.9030.8490.6860.9520.7290.733 Age 2323.790.4523.290.3723.130.2523.460.500.4220.1930.6400.7520.7950.581 Age 4024.390.6225.880.4025.620.3125.300.750.0590.0690.3710.6380.5320.711Having children Age 210.020.030.050.030.110.020.010.020.3800.0030.9470.0980.3300.003 Age 230.100.050.190.050.190.020.080.040.2650.1130.7400.9050.1230.037 Age 300.430.100.530.060.610.040.470.080.3910.0690.7280.2720.5740.122 Age 400.870.070.960.030.810.030.790.070.2460.4570.4450.0020.0360.776Income Age 231.640.151.750.111.680.051.480.110.5940.7840.4020.6260.1180.115 Age 305.000.353.860.183.620.103.530.220.008 < 0.0010.0010.2760.2830.734 Age 407.780.436.900.255.820.166.860.410.101 < 0.0010.1390.0010.9300.027Statistically significant ($p \leq 0.05$) results are marked with bold fontFig. 2Mean values for activity domains measured multiple times (A = membership in organised sports clubs; B = number of active friends; C = best friend’s activity level; D = Outdoor recreation in summer; E = Outdoor recreation in winter; F = diversity in leisure-time activities) across the leisure-time vigorous physical activity trajectories ## Organised sports At the baseline, the active and decreasingly active trajectories showed significantly higher membership levels in organised sports clubs compared with the other two trajectories ($p \leq 0.01$). From age 13 to 30 years, all trajectories showed a decrease in membership. However, the level of membership in sports clubs increased in the increasingly active trajectory from 0.67 at age 13 years to 0.76 at age 14 years and then decreased as for the other trajectory classes. From age 30 to 40 years, the mean level of membership in sports clubs increased among all four trajectories. The active and low active trajectories differed significantly at all measurement points ($p \leq 0.05$). The decreasingly and increasingly active trajectories differed significantly ($p \leq 0.05$) at ages 13 and 14 years, when the decreasingly active trajectory showed higher participation levels. These trajectories did not differ at ages 15 to 18 years, but at age 23 years, the level of membership in sports clubs decreased more in the decreasingly active trajectory, which made the level in the increasingly active trajectory significantly higher ($p \leq 0.01$). At age 40 years, the membership level in organised sports was significantly higher in the active and increasingly active trajectories compared with the low active trajectory ($p \leq 0.05$). Those inthe increasingly active trajectory reported the highest mean age at first participation in organised sports. However, the number of times reported being a member was not significantly lower than the active and the decreasingly active trajectories, whose mean age when becoming a member was younger. ## Active peers The number of active friends decreased in all classes from age 13 to 23 years. The number of active friends was significantly higher ($p \leq 0.01$) at all three measurement points in the active, decreasingly active, and increasingly active trajectories than in the low active trajectory. At age 15 years, the difference was also significant ($p \leq 0.05$) between the active and increasingly active trajectories. At age 18 years, the active and the decreasingly active trajectories differed significantly ($p \leq 0.01$). At ages 13 and 15 years, those in the active trajectory reported significantly higher ($p \leq 0.001$) mean level of best friend’s PA compared with the low active and increasingly active trajectories. At age 13 years, the reported level of best friend’s PA differed significantly ($p \leq 0.05$) between the low active and increasingly active trajectories compared with the decreasingly active trajectory. At age 15 years, best friend’s PA differed significantly ($p \leq 0.05$) between all trajectories. However, at age 23 years, the active and increasingly active trajectories reported the same mean best friend’s PA, which was significantly higher ($p \leq 0.01$) than that reported for the other two trajectories. As supported by a previous study [19], peers can affect PA behaviour across, and in addition to, other domains. The peers’ activity levels may contribute to an increase or decrease in a person’s LVPA level. For example, the periods of adolescence and transition into young adulthood carry multiple opportunities to establish new relationships. We found an increase in the best friend’s activity level during the transition from adolescence to young adulthood among those in the increasingly active trajectory but a decrease among those in the decreasingly active trajectory. These findings emphasise the importance of peers to the development of LVPA and highlight how health promotion interventions should consider including peer relationships in initiatives to promote PA [19]. ## Related demographic variables Mean BMI did not differ significantly between the four trajectories. Having children was more frequently reported in the low active trajectory at age 21 years, when this frequency differed significantly ($p \leq 0.01$) from those of the active and increasingly active trajectories. At age 23 years, the frequency of having children differed ($p \leq 0.05$) only between the low active and increasingly active trajectories. At age 40 years, the frequency of having children differed significantly ($p \leq 0.05$) between the decreasingly active and the low active and increasingly active trajectories. Income at age 23 years did not differ significantly between the four trajectories, although the mean income at age 30 years was significantly higher ($p \leq 0.01$) in the active trajectory than in the other trajectories. At age 40 years, the low active trajectory had a significantly lower ($p \leq 0.05$) mean income than the other trajectories. ## Discussion In this study, we aimed to identify developmental patterns of LVPA in a Norwegian sample with a follow-up of 27 years. We identified four LVPA trajectory classes from early adolescence to middle adulthood; these results partly support the findings of two cohort studies from Finland [7, 33]. The largest trajectory identified was the low active trajectory, which included slightly more than half of the sample ($54\%$). Previous studies have also found a proportionally larger trajectory class with a persistently low PA level [8, 33–35]. Those following the low active trajectory reported the lowest mean LVPA level at all measurement points and showed considerable stability in their development. These results support the findings of a systematic review that showed that the low active trajectories appear to be more stable than more active trajectories [7] and a previous study showing that inactivity tracks better than activity [6]. The active trajectory appears at the other end of the spectrum and had the highest average level of LVPA at all measurement points, although it represented only $9\%$ of the sample. A larger decreasingly active trajectory ($25\%$ of the sample) was identified at about the same high level of weekly LVPA as the active trajectory at age 13 years. From the age of 13 to 23 years, the weekly LVPA level decreased markedly in this trajectory. However, the LVPA level did not decline to the level of the low active trajectory, a finding that is consistent with previous research [7]. It is possible that high LVPA levels during adolescence may have contributed to a later decline in PA and prevented an earlier onset of lower activity. An increasingly active trajectory, representing $12\%$ of the sample, was identified that exhibited a slightly different development from the other trajectory classes because the weekly LVPA increased from age 13 to 40 years. Attention has focused on establishing habits when promoting lifelong engagement in PA. As indicated in the habit-formation hypothesis [6], repetition and routine are needed to form lasting habits. People in the active trajectory class are highly engaged in all activity domains from adolescence to adulthood. This consistency in engagement probably entails repeated PA behaviour over time, which increases the potential for establishing long-lasting PA habits. The respondents following the decreasing trajectory may be labelled “early bloomers”. They had a high engagement in various PA domains in early adolescence, much like those in the active trajectory. However, their engagement and LVPA level declined from adolescence to young adulthood. The transition from adolescence to early adulthood reflects changes in the types of activities that are available and other critical life transitions such as moving away from home or starting higher education. As indicated in the carry-over value hypothesis, such changes can make continuing PA difficult if the possibility of engaging in the same type of activity as before is reduced [6]. It seems that people in the decreasingly active trajectory continue to engage in various activities during adolescence, which suggests some habitual behaviour but of lower intensity. The findings of the current study also suggest that the people in the increasing trajectory were “late bloomers”, with a higher LVPA level during young adulthood and adulthood than in adolescence. However, the late onset does not seem to have hindered the positive development of a physically active lifestyle. The increasingly active trajectory was the only trajectory whose activity level continued to increase in young adulthood. The ability and readiness hypothesis suggests that experiences in PA and sports, such as organized sports, and the related basic skills, make it easier to continue with PA or to re-engage after a break. Thus, previous experience is valuable even though the type of activities and domains may differ at a later stage [6], for instance, PA at fitness centres or organised sports clubs for students. ## Membership in organised sports clubs The active trajectory was characterised by higher participation in organised sports. However, at age 13 years, almost half the respondents in the low active trajectory also reported membership in organised sports, which suggests that membership alone does not necessarily guarantee higher levels of LVPA among adolescents. Similar results based on objectively measured PA have been reported in a youth sample from Finland [16]. There are several possible reasons why membership in organised sports did not ensure higher levels of LVPA for those in the low active trajectory in our study. One reason is the nature of the measurement used in this study, which did not include the frequency, duration, or intensity of PA related to the respondents’ engagement in sports clubs. The respondents may be members of an organised sports club but may participate seldom or have low engagement. Negative experiences (e.g., low motivation or mastery, conflict with coaches or peers) may also explain why those in the low active trajectory did not continue to participate in organised sports for extended periods, as indicated by the low average frequency of reported club membership. Therefore, their experiences with organised sports may have been too brief to make a lasting impact on their PA during the life course. Respondents following the decreasingly active trajectory also reported high levels of membership in sports clubs, becoming a member of a sports club at an early age, and being a member at multiple measurement points. However, there did not seem to be a carry-over effect of these prior experiences, as shown by their decreasing activity level from age 13 to 23 years. This might be related to factors outside of sports clubs, such as injury, new interests and priorities, or different experiences with sports clubs or activities. Therefore, it is important to acknowledge that the same activity domain can contribute to both positive and negative experiences, which may influence the development of LVPA over time in different directions. ## Demographic and socio-economic status across the LVPA trajectories The active and decreasingly active trajectories were more prevalent among males than females, which is consistent with earlier reports [7, 16, 25]. In contrast to previous research on the relationship between BMI and PA development [23, 26, 27], our study did not find differences in these parameters between the four trajectories. At age 21 years, the low active trajectory was characterised by more respondents having children compared with the two trajectories with the highest LVPA level at that time. Previous research has found that having a child is negatively associated with overall PA [24], that having children increases the odds of belonging to the decreasingly active trajectory [8], and that, among women, having children has a strong negative effect on the number of sports practised [28]. At age 40 years, a large proportion of the respondents reported having children. In Norway, most organised sports clubs rely on parental engagement and voluntary work, which may explain the increase in sports club membership at age 40 years among the respondents in our study. Our findings of significant differences in income between the active and low active trajectories are consistent with those of studies analysed in a recent systematic review [7] and suggest that mean income is higher in the active trajectory. ## Limitations This study has some limitations. Self-reporting might lead to over- or under-reporting [37]. We used a single item regarding frequency to measure LVPA, which may have oversimplified this phenomenon with many different dimensions, such as type of activity or duration. However, use of this single-item question has been shown to have acceptable reliability and validity [21, 38]. Further validation studies are needed [39], including validation across the life course. Adding a measure of moderate physical activity or sedentary behaviour could have enriched our analysis, however these types of measurements were not available in the data material. We assessed participation in organised sports using a single item related to sport club membership. Membership does not necessarily indicate active participation, and having more items related to sports participation, such as frequency and level of activity, may have strengthened our analysis. This is also relevant to many of the other items related to activity domains. For example, to assess the diversity in leisure-time activities, we created a sum score based on more detailed information related to the types of activities and frequencies. Excluding this sum score and analysing specific details may have added valuable information about the degree of active participation in different activities across the four trajectories. However, for some activities, the number of respondents was too low, and we would not have been able to include the whole range of different activities in the analyses. The analyses did not control for any variables, and the differences between the classes on the auxiliary variables may reflect an effect of confounders. We did not assess the strength of the associations or compare them with each other but examined only the different characteristics of the four latent trajectory classes grouped according to LVPA. Lastly, to summarise data, a reduction is sometimes needed. In group-based models, one reduces by approximation and by grouping, and comparing individuals who are not entirely homogenous [30]. Therefore, it is essential to recognise that trajectory group membership is not definite because the LCGA gives only the probability of following a trajectory. Further, our results are not necessarily generalisable to other populations as the studied sample represents a population from western Norway. However, the baseline mean of LVPA at age 13 in this sample (collected in October/November 1990) was almost identical to that of the nationally representative HBSC study sample of 13-year-olds ($$n = 1616$$) collected in November 1989, suggesting that the study sample from western Norway at baseline was representative of Norwegian youth in the relevant age group [12]. The main strength of the present study is its longitudinal design and long-time follow-up with many measurement points, relatively large sample size, and comprehensive use of self-reported measures of LVPA and engagement in different activity domains. In addition, using finite mixture modelling of longitudinal data provides new information about the complexity of PA behaviour over that provided by population-based mean levels. ## Conclusions Four life-course trajectories of LVPA were identified in this 27-year longitudinal study in Norway. Primarily, the findings suggest heterogeneity in the development of LVPA across different life periods. Secondly, the largest trajectory group included more than half of the respondents and was characterised by a low LVPA level and low engagement in the four activity domains, calling for increased recruitment into different activity domains for children and adolescents at high risk of falling into this trajectory. Thirdly, engagement in organised sports in adolescence does not seem to have a sustainable carry-over effect on the level of LVPA later in life. For organised sports clubs to contribute to life-long PA, there needs to be more awareness on drop-out and retention. Lastly, those belonging to the more active trajectories reported having more active peers, indicating that the social impact of friends influences the activity level. In the development of PA, changing social surroundings during life may assist or hinder health enhancing engagement in LVPA. This and the heterogeneity in LVPA development from adolescence to adulthood highlights the need for targeted and age group specific health promotion initiatives. ## Supplementary Information Additional file 1. Information about the recruitment and representativeness of the sample, consent, data collection and how missing data was handled. Additional file 2. Table showing the different types of leisure-time activities that were included in the questionnaire in 1992, 2000 and 2017. These data were used to make the measure of diversity in leisure-time activities. Additional file 3. The plots for the one- to seven-class solutions for the LCGA are shown. The plots show the sample mean of leisure-time vigorous physical activity (times per week) from age 13 to age 40 ($$n = 1103$$).Additional file 4. Completed checklist for guidelines for reporting on latent trajectory studies (GRoLTS).Additional file 5. Completed STROBE checklist. ## References 1. Guthold R, Stevens GA, Riley LM, Bull FC. 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--- title: Prevalence of anemia and its associated factors among patients with type 2 diabetes mellitus in a referral diabetic clinic in the north of Iran authors: - Reyhane Hizomi Arani - Farima Fakhri - Mohammad Naeimi Tabiee - Fatemeh Talebi - Zahra Talebi - Negin Rashidi - Maryam Zahedi journal: BMC Endocrine Disorders year: 2023 pmcid: PMC9997001 doi: 10.1186/s12902-023-01306-5 license: CC BY 4.0 --- # Prevalence of anemia and its associated factors among patients with type 2 diabetes mellitus in a referral diabetic clinic in the north of Iran ## Abstract ### Purpose This study intended to investigate the prevalence of anemia and its associated factors among patients with type 2 diabetes mellitus (T2DM) in Gorgan, Iran. ### Methods This cross-sectional study was conducted on 415 (109 men) patients with T2DM referred to the referral diabetes clinic of Sayad Shirazi Hospital in Gorgan in 2021. Demographic information, anthropometric indices, past medical history, and some laboratory data on cell counts, serum blood glucose, HbA1c, creatinine, lipid/iron profiles, and urinary albumin were collected. The univariable and multivariable logistic regression analysis was applied to compute odds ratios (ORs) and $95\%$ confidence intervals (CI) for potential associated factors, using SPSS version 21. The multivariable Model was adjusted for obesity, Hb A1c, T2DM duration, using glucose-lowering drugs (GLDs), chronic kidney disease (CKD), albuminuria, hypertriglyceridemia, and hypercholesterolemia. ### Results The prevalence of anemia was $21.5\%$ [$95\%$CI: 17.6-25.7] among our total participants. The corresponding values for men and women were 20.2 (13.1-29.0) and 21.9 (17.4-27.0), respectively. The adjusted model revealed that obesity (OR, 1.94 [$95\%$ CI, 1.17–3.23]), T2DM duration for more than five years (OR, 3.12 [1.78–5.47]), albuminuria (OR, 6.37 [3.13–10.91]), chronic kidney disease (OR, 4.30 [2.83–7.29]) and hypertriglyceridemia (OR, 1.72 [1.21–2.77]) were significantly associated with prevalent anemia among patients with T2DM. Moreover, using insulin separately or in combination with oral GLDs associated positively with the prevalence of anemia with ORs of 2.60 [1.42-6.42] and 1.87 [1.30-4.37], respectively. ### Conclusion Anemia had a high prevalence among patients with T2DM in the north of Iran (about $22\%$), which is associated with obesity, hypertriglyceridemia, duration of T2DM, and diabetic kidney disease. ## Introduction Anemia is a condition in which the oxygen-carrying capacity of blood can not meet the physiological needs of the body, due to the decreased erythrocyte mass or hemoglobin concentration [1]. The prevalence of anemia is about $27\%$ worldwide, which makes it a public health problem with the greatest impact on developing countries (they account for more than $89\%$ of the anemia burden) [2]. Based on global burden disease (GBD) reports, Iran, as a developing country, anemia has the age-standardized prevalence and years lived with disability (YLDs) of $23.0\%$ and 677 (per 100,000), respectively [2], which was more prominent among adult patients with diabetes mellitus (DM) ($30.4\%$) [3]. Nearly 4.5 million cases of DM were detected among Iranian adults in 2011. It is estimated that about 9.2 million of Iranian individuals will be affected by DM by the year 2030. This significant growth in the disease incidence reveals the high burden of DM in Iran, especially when taking into account the impact of its complications [4, 5, 46]. It has been reported that the patients with DM are twice more prone to anemia compared to the individuals without DM [6–8]. Anemia could also increase the risk of end-stage renal and cardiovascular diseases, hospitalization, and premature death in patients with DM [9]. Moreover, it plays a role in the progression and development of macrovascular and microvascular complications of DM [10]. Hence, it could affect the patients’ quality of life and their healthcare costs [11, 46]. Many researchers have studied the potential associated factors with anemia such as age, gender, diabetic nephropathy, glycemic control, antihypertensive medications, and glucose-lowering drugs (GLDs) [12, 13]. Both DM and anemia have some similar symptoms like numbness or coldness in the extremities, pale skin, and shortness of breath. This might result in remaining anemia unrecognized in a considerable number of patients with DM [6]. Therefore, early diagnosis and management of anemia can be recommended as an essential strategy to reduce its adverse effects. Despite the high prevalence of anemia and its effect on DM complications, in Iran, there are limited studies examining its associated factors. Hence, this study aimed to estimate the prevalence of anemia and its association factors among patients with type 2 diabetes mellitus (T2DM) in a referral diabetes clinic in Gorgan, located in the north of Iran. ## Study population This clinical-based cross-sectional study included outpatients with T2DM being under treatment at the referral diabetes clinic of Sayad Shirazi hospital in Gorgan in 2021. The sample size was calculated based on this formula: N = (pq/e2) * z1−/α2. Here, p was the anticipation of anemia prevalence of $19.6\%$ in the diabetic population, [14] $q = 1$ - p; e was an allowable error ($5\%$); and Z1−α/2= 1.96. So, 242 participants were an acceptable sample size for this study. To mitigate selection bias, patients were selected by a systematic random sampling technique among volunteer patients with T2DM. In this method, the selection of the first subject is done randomly and then the subsequent subjects are selected by a periodic process. The exclusion criteria were: [1] age ≤ 18 or ≥ 75 years, [2] T2DM duration less than 1 year [3] known hematologic diseases (thalassemia, lymphoma, and leukemia) or other systemic disorders (such as infectious diseases) that could result in anemia, [4] presence of an acute condition (such as acute bleeding) or hospitalization within the last two weeks before sampling, [5] blood transfusions in the three months before sampling, [6] pregnancy, [7] type 1 DM, [8] smoking, [9] missing clinical and demographic data. Finally, 415 (109 men) eligible participants remained for our analysis. The Ethics Committee of the Golestan University of Medical Sciences approved this study (ethics Code: IR.GOUMS.REC.1398.170). Verbal informed consent was obtained from all subjects, and they were also assured that their personal information would remain confidential. All methods of this study were performed in accordance with the relevant guidelines and regulations. ## Measurements A trained interviewer recorded patients’ information, including demographic characteristics, duration of T2DM, and medical history. The height and weight of patients without shoes and with light clothes to the nearest 100 g were measured using the same device (Seca weighing scale, made in Germany), and body mass index (BMI) was calculated by dividing weight (kg) into height squared (meters). After 8 to 12 h of overnight fasting, a blood sample was taken from all participants to measure cell counts, iron profiles (total iron-binding capacity (TIBC), hemoglobin concentration, serum ferritin, and iron level), triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), fasting plasma glucose (FPG), and glycated hemoglobin (Hb A1c). Also, urine sample was taken from the participants. All of them were measured in a same lab with same kits, devices, and methods. FPG was measured by the colorimetric glucose oxidase method (Human, Heidelberg Germany). Furthermore, TC, HDL-C, and triglyceride were measured by enzymatic method using a proper kit (Lipid, Pars Azmoon Co., Karaj, Iran). Also, if triglyceride levels were < 400 mg/dL, the Friedewald formula (LDL = total cholesterol - (HDL + TG/5)) was applied for calculating LDL-C, and if triglycerides were ≥ 400 mg/dL, LDL-C was measured by direct assay. Hb A1C was assessed by column chromatography. TIBC was measured by the chemical precipitation method. Also, ferritin was measured by the immunoassay method using a gamma counter. Urinary albumin excretion was assessed by the immunoturbidometry method of a 24 hour’s urine collection. Serum creatinine levels were measured using kinetic colorimetric Jaffe with a sensitivity of 0.2 mg/dL (range, 0.2–15 mg/dL). The Modification of Diet in Renal Disease (MDRD) equation was employed for calculating the estimated glomerular filtration rate (e-GFR) of the participants [15, 16]. ## Definition of outcomes and variables Anemia was considered as hemoglobin less than 12 g/dL in women and less than 13 g/dL in men according to the World Health Organization (WHO) criteria [12]. DM was defined as FPG ≥ 126 mg/dL and/or Hb A1c ≥ 6.5 or taking any GLDs. The participants were categorized into two groups: BMI < 30 kg/m2 (normal/overweight) and ≥ 30 kg/m2 (obese). Chronic kidney disease (CKD) was considered as e-GFR lower than 60 mL/ min/1.73m2 according to the kidney disease outcomes quality initiative (KDQOI) guidelines [17]. Albuminuria was considered as urinary albumin creatinine ratio (ACR) of 30 mg or more in 24 hours’ urine collection. Hypertriglyceridemia was defined as TG more than 150 mg/dL and hypercholesterolemia was considered TC more than 200 mg/dL. ## Data analysis Statistical analyses were performed using SPSS version 21 for Windows (Chicago, Illinois, USA). To report the quantitative variables, mean (standard deviation: SD) and median (inter-quartile range) were used for variables with normal and highly skewed distribution, respectively. To compare baseline characteristics of quantitative variables between anemic and non-anemic groups, the t-test and the Mann-Whitney test were used for variables with normal and highly skewed distribution, respectively. The prevalence [$95\%$ confidence interval (CI)] of each group of categorical variables were estimated and compared by Chi-square test. The univariable and multivariable logistic regression analyses were performed for categorical variables to evaluate thet association between variables and anemia by reporting odds ratios (ORs) with $95\%$ CI. Only covariates with a p-value <0.20 in the univariable analysis were then selected to enter the multivariable analysis. The multivariable model is adjusted for obesity status, Hb A1c (Hb A1c ≤ $7\%$ as reference), T2DM duration (less than five years as reference), GLDs usage (oral as reference), prevalent CKD, prevalent albuminuria, hypertriglyceridemia, and hypercholesterolemia. ## Results The study population consisted of 415 participants with a mean (SD) age of 57.5 (8.6). The prevalence of anemia was $21.5\%$ ($95\%$ CI: 17.6-25.7) among our total participants. The corresponding values for our male and female participants were 20.2 (13.1-29.0) and 21.9 (17.4-27.0), respectively. The baseline characteristics of participants are shown in Table 1. Generally, compared to T2DM patients without anemia, T2DM patients with anemia had a longer T2DM duration and higher levels of FPG, HDL-C, and triglycerides. Iron indices (iron, ferritin, and TIBC) were similar among patients with and without anemia, considering this point that we defined anemia based on hemoglobin, and both groups may have negative iron balances. Table 2 shows the prevalence of anemia in different subgroups. Anemia was more prevalent among obese individuals compared to non-obese ones. In addition, the prevalence of anemia was higher in participants with more than five years of T2DM duration than in ones with less than five years. Moreover, participants with Hb A1C > $7\%$ had a higher prevalence of anemia than ones with Hb A1C ≤ $7\%$. Also, patients with albuminuria and CKD were more likely to have anemia. Furthermore, participants who used a combination of insulin and oral GLDs or used insulin separately were more likely to have anemia compared to those who were treated with oral GLDs only. Finally, participants with hypertriglyceridemia had a higher prevalence of anemia than those with low triglyceride levels. Multivariable logistic regression analysis (Table 3) showed that the presence of obesity (OR, 1.94 [$95\%$CI, 1.17–3.23]), T2DM duration > 5 years (OR, 3.12 [CI, 1.78–5.47]), albuminuria (OR, 6.37 [CI, 3.13–10.91]), CKD (OR, 4.30 [CI, 2.83-7.29]), as well as hypertriglyceridemia (OR, 1.72 [CI, 1.21–2.77]),had the independent positive associations with anemia. Furthermore, using insulin separately or in combination with oral GLDs associated positively with the prevalence of anemia with ORs of 2.60 [CI, 1.42–5.42] and (OR, 1.87 [CI, 1.30–4.37])), respectively. ## Discussion In this clinic-based study conducted in 2021, about one-fifth of the Gorgan diabetic women and men were found to have anemia. Obesity, T2DM duration > 5 years, albuminuria, CKD, hypertriglyceridemia, as well as using insulin both with and without oral GLDs were independently associated with the prevalence of anemia among our participants. Several studies have reported the prevalence of anemia among patients with T2DM to be high, especially among developing countries. Our results were similar to the previous study from Iran (among residents of Tehran city) in 2014 ($30.4\%$) [3]. A recent meta-analysis estimated a prevalence of $35\%$ for anemia among patients with T2DM in Africa [18]. Compared to ours, other studies conducted in Kuwait [6], Malaysia [19], Brazil [20], Greece (among patients with DKD stages 2–4) [21, 22], Saudi [23], England [24], and Pakistan [25], revealed a higher prevalence of anemia among patients with T2DM, which were $29.7\%$, $31.7\%$, $34.2\%$, $34.7\%$, $47.8\%$, $55.5\%$, $59\%$, and $63\%$, respectively; however, a study from India [10] and a community-based cohort from Australia [8] showed a lower prevalence of $12.13\%$ and $11.5\%$, respectively. These differences may be due to the quality of healthcare services, such as accessibility of patients to visit a specialist and laboratory testing besides. For instance, in developed countries, the participants are in close follow-up in specialized centers, therefore were not representative of the whole diabetic population. Selection bias could also be the underlying cause of the difference;furthermore, the differences could be the result of variations in studies’ methodology and participant characteristics such as lifestyle, feeding habits, type of GLD usage, duration of T2DM, ethnicity, and mean age [21]. The current study showed that T2DM duration > 5 years (regardless of glycemic indices and nephropathy) had a strong independent association with anemia, in line with some other studies [8, 26]. It seems that chronic hyperglycemia could decrease erythropoiesis and increase red blood cells (RBCs) destruction due to more exposure to inflammation and oxidative stress, and bone marrow impairment [27, 28]. In this study, although the levels of FPG and HbA1c were higher among diabetics with anemia, statistically they were not associated with anemia. However, some studies showed lower HbA1c among patients with anemia [29, 30]. They explained that decreased hemoglobin concentration and the enhanced RBC turnover in the anemia of chronic diseases could reduce the glycation process, and consequently lead to falsely reporting lower HbA1c levels [31]. Despite the lack of significant association between glucose indices and the prevalence of anemia, using insulin had a significant association in the present study. Insulin users had potentially worse baseline characteristics rather than non-insulin users; also, using insulin could be a representative of poor control DM or prolonged diabetes or those with complications (allocation bias) [32]. So this result is in agreement with some other studies reporting that poor control DM has an association with anemia [12, 33]. Considering that neuropathy is common in patients with poor glycemic control, one of the reasons for the increased risk of anemia is impairment of production and release of the erythropoietin stimulated by the autonomic nervous system [34]. Furthermore, DM could negatively affect the interstitial and peritubular structures of kidney (where erythropoietin is produced), and anemia could be the result of decreased erythropoietin production by kidney failure. Moreover, the exposure of erythroblasts or mature erythrocytes to oxidative stress (due to glucose toxicity) could cause erythrocyte dysfunction. Besides, metformin is the first-line choice for T2DM management unless in patients with e-GFR < 45 ml/min/1.73 m2, according to ADA 2022 guidelines. It has been reported that metformin interferes with Cyanocobalamin absorption and is associated with vitamin B12 deficiency, resulting in an increased risk of anemia among patients with T2DM [35]. We found that obesity (BMI ≥ 30 kg/m2) and hypertriglyceridemia(triglycerides> 150 mg/dL) were independently associated with the prevalence of anemia after adjustment with other confounders. This could be explained by the hypothesis that the adipose tissue is the source of different cytokines. The increase in inflammatory activity of adipose tissue in obese patients could cause an increase in hepcidin levels, leading to a reduction in serum iron [36]. However, some studies reported an “obesity paradox” in anemia; they observed that normal/overweight T2DM patients were more anemic than obese patients [29].They also claimed that overnutrition may be associated with increased consumption of protein, iron, and other micronutrients, which have a protective effect against iron and/ or B12 deficiency in the diabetic population [37]. We found that albuminuria > 30 mg/24hr and e-GFR≤60 ml/min/1.73 m2, as two important parameters of diabetic kidney disease (DKD), had a significant independent association with anemia among patients with T2DM. Our findings were in accordance with other studies showing a higher frequency of anemia in patients with T2DM and nephropathy [38, 39]. A large multicenter US study of the Kidney Early Evaluation Program (KEEP) demonstrated that the development of anemia in patients with DKD had a statistical relationship with the severity of albuminuria and e-GFR decline [40]. DM could also damage tubulointerstitial tissues (associated with the degree of albuminuria) in the early stage, even before any reduction in e-GFR. It could cause decreased erythropoietin production and iron metabolism impairment leading to reduced production of RBCs [41]. Blood urea may be increased due to renal dysfunction, and it could negatively affect RBC’s lifespan [22]. We found no significant sex-difference in the prevalence of anemia, in contrast to other studies [12, 42]. As the mean age of our participants was 57.8 years, men and women were similar due to decreased occurrence of anemia in women due to menopause. Moreover, in the Iranian diabetic society, men and women have identical diets, education, and health awareness [43]. The present study has several strengths. First, we assessed various baseline characteristics, such as different laboratory and clinical data. We considered different potential associated factors in our model in a sufficient sample size. Thus, we could discuss some important independent associated factors in detail. Second, we randomly selected our participants out of a referral center in Gorgan that could enhance the ability to generalize our results to other Iranian populations (due to different ethnic groups living in Gorgan, including indigenous inhabitants, Turkman, Sistani, and Baloch). Finally, to the best of our knowledge, it is the first report on the prevalence of anemia among a diabetic population in the north of Iran. This research had several limitations. First, as it was a cross-sectional study, we could not show the casualty, so a longitudinal study is needed to assess the relationship over time. Second, we did not consider some drug usage including iron supplements and erythropoietin in the case of CKD. Third, we did not record dietary patterns, particularly iron intake was not considered in our study, although all patients were routinely educated about diet and health care in our referral diabetes clinic. Fourth, we did not measure erythropoietin, B12, and folate levels in our participants, so the lack of definition of anemia etiology was another limitation. Fifth, we did not exclude patients with high blood pressure [39] and patients who have occupations leading to anemia (for example occupations contacting with blood toxins, such as lead, benzol, amino- nitro compounds of benzol, arsenic hydrogen, etc.) [ 44, 45]. Finally, considering only one center for this study was the other part of our limitations. In conclusion, we demonstrated a high prevalence of anemia among patients with T2DM in one of the referral diabetic clinic in the north of Iran, which was associated with obesity, hypertriglyceridemia, duration of T2DM, and renal dysfunction. It is necessary to screen and evaluate anemic conditions in diabetic populations. They must be warned against the potential risk and complications of anemia and the importance of regular screening, especially among those with stated associated factors. Table 1Baseline characteristics of 415 participants with T2DMTotal($$n = 415$$)Without Anemia ($$n = 89$$)With Anemia ($$n = 326$$)P-valueAge (year)57.8 (9.0)57.4(8.5)57.9(9.1)0.742BMI (kg/m2)28.4 (4.0)28.2(4.2)28.3(4.0)0.939Diabetes duration (month)10.7 (6.2)7.2(4.8)11.1(6.6) < 0.001 FPG (mg/dL)180.1 (141–244)162.2[129-198]185.0[145-257] < 0.001 Hb A1c (%)8.1 (8.9)8.5(1.8)8.9(1.7)0.176TC (mg/dL)175.7 (3.2)165.4 (0.7)198.5 (3.9)0.073LDL-C(mg/dL)76.8 (59–103)83.5[67-103]75.0[58-103]0.108HDL-C(mg/dL)48.4 (43–64)46.0[38-55]49.0[45-67] 0.009 Triglyceride(mg/dL)158.9 (106–225)140 (101–182)164.0[108-237] 0.014 e-GFR (ml/min/1.73 m2)82.1 (3.5)86.3(2.5)80.9(3.8)0.202Hemoglobin(g/dL)12.0 (1.4)13.2(1.6)11.7(1.4) < 0.001 Ferritin (µg/L)37.1 (21–72)34.0[27-65]37.9[20-74]0.740Iron (µg/dL)82.1 (59–98)79.0[65-98]83.0[57-98]0.254TIBC (µg/dL)351.7 (36.2)347.1(33.0)352.9(37.1)0.292Data were presented as mean (SD); FPG, LDL-C, HDL-C, Triglyceride, Ferritin and Iron were presented as median (interquartile range [IQR]); For comparison between the two groups, t-test for data with normal distribution and Mann-Whitney test ones with abnormal distribution; BMI: body mass index; FPG: fasting plasma glucose; Hb A1c: Hemoglobin A1c; TC: total cholesterol; LDL-C: low density lipoprotein cholesterol; HDL-C: high density lipoprotein cholesterol; e-GFR: estimated glomerular filtration rate e-GFR: estimated glomerular filtration rate; TIBC: total iron binding capacity; T2DM: Type 2 diabetes mellitus Table 2Prevalence of anemia among patients with T2DM in different subgroupCase/TotalCrude prevalence % ($95\%$ CI)P-value Obesity 0.032 Non-obese$\frac{53}{29218.2}$ (13.9–23.1)Obese$\frac{36}{13227.3}$(19.9–35.7) Gender 0.708Women$\frac{67}{30621.9}$(17.4–27.0)Men$\frac{22}{10920.2}$(13.1–29.0) Diabetes Duration <0.001 < 5 years$\frac{18}{16211.1}$(6.7–17.0)> 5 years$\frac{71}{25328.1}$(22.6–34.0) Hb A1c 0.001 ≥ $\frac{711}{10610.4}$(5.3–17.8)> $\frac{778}{30925.2}$(20.5–30.5) Albuminuria <0.001 No$\frac{64}{37517.1}$(13.4–21.3)Yes$\frac{25}{4062.5}$(45.8-77.3) GLD <0.001 Oral$\frac{52}{30017.3}$(13.2–22.1)Insulin$\frac{24}{7731.2}$(21.1–42.7)Oral + Insulin$\frac{13}{3834.2}$(19.6–51.4) CKD <0.001 No$\frac{62}{36217.1}$ (13.4–21.4)Yes$\frac{27}{5350.9}$(36.8-64.9) Triglyceride 0.023 ≤ $\frac{15039}{22617.3}$ (12.6–22.8)> $\frac{15050}{18926.5}$ (20.3–33.4) TC 0.066≤ $\frac{20067}{34019.7}$(15.6–24.3)> $\frac{20022}{7529.3}$ (19.4–41.0) LDL-C 0.688≤ $\frac{10065}{29622.0}$(17.4–27.1)> $\frac{10024}{11920.2}$ (13.4–28.5) HDL-C 0.240≤ $\frac{4063}{27223.2}$ (18.3–28.6)> $\frac{4026}{14318.2}$(12.2-25.5)Each group of categorical variables compared by Chi-square test; GLD: glucose lowering drug; CKD: chronic kidney disease; TC: total cholesterol; LDL-C: low density lipoprotein cholesterol; HDL-C: high density lipoprotein cholesterol; T2DM: Type 2 diabetes mellitus Table 3Crude and adjusted odds ratios of associated factors with prevalent anemia among patients with T2DMCrude OR ($95\%$ CI)Adjusted OR ($95\%$ CI)P-valueObesityNon-obese11obese2.28 (1.72–3.19)1.94 (1.17–3.23) 0.010 Hb A1c >711≤71.61 (1.31–2.12)1.15 (0.60–3.10)0.610 Diabetes Duration <5 years11≥ 5 years4.23 (2.16–6.01)3.12 (1.78–5.47) <0.001 Albuminuria No11Yes7.31 (4.61–12.54)6.37 (3.13–10.91) <0.001 GLD Oral11Insulin2.42 (1.89–5.17)1.87 (1.30–4.37) 0.039 Oral + Insulin3.19 (2.03–6.73)2.60 (1.42–5.42) 0.046 CKD No11Yes5.87 (3.51–8.15)4.30 (2.83–7.29) <0.001 Triglyceride ≤ 15011> 1502.31 (2.79–3.98)1.72 (1.21–2.77) 0.024 TC ≤20011> 2002.09 (0.67–5.45)1.58 (0.84–4.88)0.124Multivariable logistic regression analysis was performed in 2 levels: [1] without adjustment (crude odds ratios (ORs) and $95\%$ CI); [2] full adjustment, which is adjusted for obesity, Hb A1c, diabetes duration, GLDs usage, CKD, albuminuria, triglyceride and TC. 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--- title: 'Pediatric sleep-disordered breathing in Shanghai: characteristics, independent risk factors and its association with malocclusion' authors: - Yuanyuan Li - Xianqin Tong - Shuai Wang - Liming Yu - Gang Yang - Jinqiu Feng - Yuehua Liu journal: BMC Oral Health year: 2023 pmcid: PMC9997003 doi: 10.1186/s12903-023-02810-9 license: CC BY 4.0 --- # Pediatric sleep-disordered breathing in Shanghai: characteristics, independent risk factors and its association with malocclusion ## Abstract ### Objectives This study aimed to determine the prevalence and independent risk factors of SDB, and explore its association with malocclusion among 6–11-year-old children in Shanghai, China. ### Methods A cluster sampling procedure was adopted in this cross-sectional study. Pediatric Sleep Questionnaire (PSQ) was applied to evaluate the presence of SDB. Questionnaires including PSQ, medical history, family history, and daily habits/environment were completed by parents under instruction, and oral examinations were implemented by well-trained orthodontists. Multivariable logistic regression was applied to identify independent risk factors for SDB. Chi-square tests and Spearman's Rank Correlation were used to estimate the relationship between SDB and malocclusion. ### Results A total of 3433 subjects (1788 males and 1645 females) were included in the study. The SDB prevalence was about $17.7\%$. Allergic rhinitis (OR 1.39, $95\%$ CI 1.09–1.79), adenotonsillar hypertrophy (OR 2.39, $95\%$ CI 1.82–3.19), paternal snoring (OR 1.97, $95\%$ CI 1.53–2.53), and maternal snoring (OR 1.35, $95\%$ CI 1.05–1.73) were independent risk factors for SDB. The SDB prevalence was higher in children with retrusive mandibles than in proper or excessive ones. No significant difference was observed in the correlation between SDB and lateral facial profile, mandible plane angle, constricted dental arch form, the severity of anterior overjet and overbite, degree of crowding and spacing, and the presence of crossbite and open bite. ### Conclusions The prevalence of SDB in primary students in the Chinese urban population was high and highly associated with mandible retrusion. The independent risk factors included Allergic rhinitis, adenotonsillar hypertrophy, paternal snoring, and maternal snoring. More efforts should be made to enhance public education about SDB and related dental-maxillofacial abnormalities. ## Introduction Sleep-disordered breathing (SDB) is a common syndrome characterized by upper airway (UA) dysfunction during sleep, and subsequent sleep disruption and ventilation abnormalities. It includes a range of clinical entities of varying severity from primary snoring to obstructive sleep apnea/hypopnea syndrome (OSAHS) in children of all ages [1]. Epidemiological studies in different age groups and regions showed that the prevalence of SDB varied greatly, ranging from 7 to $27.6\%$ [2–8]. Adenoid vegetations and/or adenotonsillar hypertrophy, and craniofacial abnormalities associated with decreased UA volume are considered the main predisposing factors for SDB [1, 9–12]. Other risk factors such as obesity and neuromuscular disorders have been identified [11, 13]. There are also studies indicating that SDB has been associated with several controversial risk factors, including a history of prematurity, allergic diseases, asthma, tobacco exposure, and parental history of adenotonsillectomy [1, 3, 7, 14–16]. Several studies suggested that breastfeeding may be a protective factor against SDB in childhood [12, 17, 18]. while other studies did not support these results. Such discrepancies in potential risk/protective factors may be due to ethnicity and age differences among study populations and how SDB is defined. SDB in children is commonly correlated with craniofacial dentofacial features and may be related to dental-maxillofacial abnormalities [9, 19–21]. It was reported that patients with SDB often experience alterations in the tongue’s position, and posture of the head and neck, which may break the balance of oral and peri-oral muscles, consequently posing a negative impact on the development of the craniofacial skeleton and dental occlusion according to Moss’s theory [22]. Lyra et al. [ 23] conducted a cross-sectional work of 390 children and found that overjet, anterior open bite, and posterior crossbite were significantly associated with SDB. Two earlier cross-sectional studies reported a correlation between SDB and open bite or convex facial profile respectively [24, 25]. Although children with SDB and malocclusion may share some anatomical features, the relationship between SDB and malocclusion in children is still controversial. The incidence of atopic diseases and adenotonsillar hypertrophy, and the living conditions of children in China are changing rapidly. Meanwhile, the prevalence of SDB may be changing as well. The prevalence of dentofacial deformity among the Chinese population of children and adolescents, as reported [26, 27], has increased by $27\%$ since the 1960s. With people’s emerging attention to mouth breathing, the orthodontic consultation rate caused by mouth breathing has hugely increased in China. Quite a few clinicians suggested that children with SDB should be routinely examined by orthodontists. However, SDB and malocclusion are still issues rarely mentioned by parents in well-child visits and by primary health care providers. This study aimed to assess the prevalence and risk factors of SDB and its relation to malocclusion in primary school children in Shanghai, China. This study enrolled 3433 children of 6–11 years and included an assessment of SDB risk factors and potentially related malocclusion derived from previous reports and our clinic experience. ## Study design and participants Shanghai Stomatological Hospital conducted an epidemiological survey of the oral health status of primary school students in Shanghai. A cluster sampling procedure was adopted in this cross-sectional study. Four primary schools were randomly selected in the suburban district (Minhang district) and two in the urban district (Jingan district). The students in grades one to five were identified as potential subjects for the survey. The exclusive criteria were as follows: tooth agenesis, orofacial clefts, and other congenital malformation; children unable to cooperate; whose guardians do not agree to join the study. This study was approved by the Ethical Committee of the Shanghai Stomatological Hospital on 24 December 2021 (Certificate Number 2021–028) and started after that day. Informed consent was obtained from the guardians of subjects before the survey. ## Procedure The research was mainly comprised of questionnaires completed by parents under the instruction and examinations implemented by orthodontists. ## Questionnaire The first part of the questionnaire was about general information on subjects such as sex, birthday, height, and weight. The second part was Pediatric Sleep Questionnaires (PSQ), which were applied to evaluate the possible presence of SDB. The PSQ includes questions about the frequency and severity of snoring and apnea, mouth breathing, daytime sleepiness, the problem of attention or behaviour, and other pediatric symptoms of SDB. The average score of all items greater than 0.33 was defined as the presence of SDB [28]. The third part of the questionnaire contained 8 questions about medical history and family history. The Last part was about daily habits/environment for SDB, such as tobacco smoke exposure (more than 20 min a day recorded as yes), sleeping position (supine, lateral, or prone), frequency of using air conditioner, air humidifier, and air cleaner (more than 30 days/year recorded as commonly used) in the house. ## Examination The examinations were implemented by five orthodontists with more than 3 years of clinical experience. Before the examination, the orthodontists had accomplished standardized training. The inter-examiner reliability was insured by Cohen’s kappa coefficient (value > 0.8). The examinations were performed in schools’ infirmaries using portable lighting and disposable mouth mirrors. Each examiner was assigned a recorder to fill in the paper version of the health check form simultaneously. 13 items were included in the form. Neck circumference: measured in the middle of the neck using tape. Lateral facial profile: concave, upright, or protruding. It was assessed by the aesthetic plane (from nasal tip to pogonion of soft tissue) while the subject was sitting in a comfortable upright position. Mandible development: excessive, proper, or retrusive. The position of the upper lip was used as a reference. Sagittal relationship of molar occlusion: Angle class I, II or III. The classic Angle classification was applied. Mandible plane angle: low, average, or high. It was assessed by the Frankfort plane. Upper dental arch: normal or constricted maxillary dental arch. Lower dental arch: normal or constricted mandible dental arch. Overjet of anterior teeth: 0–3 mm: normal; 3–5 mm: mild; 5–8 mm: moderate; > 8 mm: severe. The distance from the palatal surface of the most protruded maxillary incisor to the labial surface of the corresponding mandibular incisor. Crossbite or edge-to-edge: present or absent. It was considered present if the maxillary incisor occluded lingually to or onto the corresponding mandibular incisor. Overbite of anterior teeth: ≤ $\frac{1}{3}$, normal; $\frac{1}{3}$–$\frac{1}{2}$, mild; $\frac{1}{2}$–$\frac{2}{3}$, moderate; > $\frac{2}{3}$, severe. It was assessed by the coverage of the mandibular incisors by most of the maxillary incisors. Open bite: present or absent. It was considered present if the overlap of the lower incisors by upper incisors in the vertical plane was less than 0 mm. Teeth crowding: 0 mm: normal; 0–4 mm: mild; 4–8 mm: moderate; > 8 mm: severe. Teeth spacing: 0 mm: normal; 0–2 mm: mild; 2–4 mm moderate; > 4 mm: severe. ## Statistical analysis The paper version of the health check form of subjects was input to Epidata software by two data processors from Shanghai KNOWLANDS MedPharm Consulting Co. Ltd. One inspector was responsible for quality control. Data were analyzed using SPSS Statistics 23 (SPSS Inc., Chicago, IL, USA) software package, adopting the both-sided significance level of $5\%$. Continuous variables were expressed as mean ± standard deviation. The Student’s t-test was used for continuous variables including height, weight, BMI, and neck circumference. We assessed the significance of differences by the Chi-square test for categorical variables. Based on Chi-square tests, variables with a p value ≤ 0.05 were selected for the pool of potential risk factors in building multivariate models. Multivariable logistic regression was applied to identify risk factors for SDB. The relationship between the prevalence of SDB and dental-maxillofacial development was evaluated using the Chi-square tests and Spearman correlation analyses. ## Results Among 4230 students from the six schools, 3853 families agreed to participate in the survey, 3499 returned the questionnaire, and 3457 children completed the oral examination. After excluding data with obvious errors, a total of 3433 subjects (1788 males and 1645 females, mean age of 8.53 ± 1.47 years) were contained in the study. The characteristics of subjects and distribution of children with SDB were summarized in Tables 1 and 2. The prevalence of SDB among primary school children defined by PSQ was about $17.7\%$. Moreover, it was much higher in boys ($21.0\%$) than in girls ($14.0\%$) ($P \leq 0.001$). No difference was observed between age groups ($p \leq 0.05$) (Table 2). Greater body weight ($P \leq 0.05$) and BMI ($P \leq 0.001$) can be observed in children with SDB. Neck circumference was longer in SDB children, but no significant difference was observed ($p \leq 0.05$) (Table 1).Table 1Basic information of non-SDB and SDB childrenTotal ($$n = 3433$$)Male ($$n = 1788$$)Female ($$n = 1645$$)P (male vs. female)Non-SDB ($$n = 2827$$)SDB ($$n = 606$$)P (N-SDB vs. SDB)Height (cm)134.7 ± 10.3135.0 ± 9.8134.3 ± 10.8< 0.05134.8 ± 10.3134.3 ± 9.90.37Weight (kg)32.0 ± 9.032.8 ± 8.931.2 ± 8.9< 0.00131.9 ± 8.932.9 ± 9.5< 0.05BMI17.5 ± 3.917.8 ± 3.817.1 ± 4.0< 0.00117.3 ± 3.818.0 ± 4.5< 0.001NC (cm)28.1 ± 2.628.6 ± 2.728.1 ± 2.6< 0.00128.1 ± 2.628.3 ± 2.70.06BMI body mass index, NC neck circumferenceThe Student’s t-tests were usedTable 2The prevalence of SDB in different age groups and sex groupsTotal (n)Non-SDBSDBPrevalence of SDB (%)χ2PAge (years)6–768557610915.99.360.0537–877565512015.58–982266216019.59–1054643311320.710–1160550110417.2Sexmale1788141337521.028.31< 0.001female1645141423114.0Total3433282760617.7––Chi-squared tests were used The potential risk factors for SDB were described in Table 3. Allergic rhinitis and adenotonsillar hypertrophy are well-known risk factors for SDB in children. In this study, the prevalence of SDB in children with medical histories of allergic rhinitis, adenotonsillar hypertrophy, or asthma or tympanitis was much higher than that in children without these medical histories ($P \leq 0.001$). As shown in Table 3, parents’ rhinitis history or parents’ snoring were associated with SDB ($P \leq 0.001$). As for daily habits, tobacco exposure and sleeping position were associated with the prevalence of SDB. Children exposed to tobacco for more than 20 min a day had a higher prevalence of SDB ($P \leq 0.01$). The prevalence of SDB was significantly higher in children who usually slept in prone positions than in supine or lateral positions ($P \leq 0.001$). The frequency of using an air conditioner, air humidifier, and air cleaner in the house did not correlate with SDB ($p \leq 0.05$).Table 3Univariate analysis of potential risk factors for SDB in childrenTotalNon-SDBSDBPrevalence (%)χ2P valueMedical historyAllergic rhinitisYes130999331624.168.9< 0.001No1999174025913.0Adenotonsillar hypertrophyYes39126013133.599.3< 0.001No2748237237613.7AsthmaYes1641194527.411.17< 0.001No3231268354817.0TympanitisYes3062287825.514.2< 0.001No3127259952816.9Family historyPaternal rhinitis historyYes105583721820.710.81< 0.001No2315194437116.0Maternal rhinitis historyYes78962116821.310.43< 0.001No2600217642416.3Paternal snoringYes2127167245521.458.79< 0.001No1157103312410.7Maternal snoringYes63447815624.627.61< 0.001No2613220141215.8Daily habits/environmentsTobacco exposureYes83666317320.77.31< 0.01No2579215142816.6Sleeping positionSupine115695620017.314.16< 0.001Lateral1970164232816.7Prone3072297825.4Air conditionerCommonly4013326917.20.0620.8Rarely3032249553717.7Air humidifierCommonly5844859917.00.2370.63Rarely2849234250717.8Air cleanerCommonly90675814816.31.470.23Rarely2527206945818.1Chi-square tests were used In Fig. 1, multivariate logistic regression showed that allergic rhinitis (OR 1.39, $95\%$ CI 1.09–1.79), adenotonsillar hypertrophy (OR 2.39, $95\%$ CI 1.82–3.19), paternal snoring (OR 1.97, $95\%$ CI 1.53–2.53), and maternal snoring (OR 1.35, $95\%$ CI 1.05–1.73) were independent risk factors of SDB. However, the medical histories of asthma or tympanitis, parents’ rhinitis history or tobacco exposure or sleeping position, and SDB did not demonstrate significant association by multivariate regression analysis. Fig. 1Multivariate regression analysis of risk factors for SDB Among the dental-maxillofacial variables, only mandible development was significantly associated with SDB (Table 4). Children with SDB were more susceptible to retrusion of the mandible (χ2 = 6.82, $P \leq 0.05$). Children with protruding profiles intended to have a higher frequency of SDB than those in upright/concave profiles, although it did not show a statistical difference ($p \leq 0.05$). Children with molar occlusion of Angle class I showed a non-significantly lower SDB prevalence than Angle class II and III occlusion. Spearman analyses of the correlation between SDB and mandible plane angle, constricted dental arch form, the severity of anterior overjet and overbite, degree of crowding and spacing, and the presence of crossbite and open bite were conducted, but no significant correlation was observed in this study (Table 5).Table 4The association between SDB and dental-maxillofacial growthTotalNon-SDBSDBPrevalence of SDB (%)χ2P valueLateral facial profileConcave128 ($3.8\%$)1072116.41.430.49Upright2103 ($61.8\%$)174435917.1Protruding1174 ($34.4\%$)95521918.7Mandible developmentExcessive52 ($1.6\%$)44815.46.820.03Proper2409 ($73.8\%$)200140816.9Retrusive802 ($24.6\%$)63416820.9Molar occlusionClass I2064 ($63.4\%$)171834616.84.440.11Class II996 ($30.6\%$)79919719.8Class III198 ($6.0\%$)1603819.2Mandible plane angleLow203 ($6.3\%$)1634019.70.710.70Average2462 ($76.3\%$)203143117.5High560 ($17.4\%$)45810218.2Chi-square tests were used Statistically significant at $P \leq 0.05$Table 5Spearman correlation analysis of SDB and malocclusionsRhoP valueUpper dental arch− 0.010.56Lower dental arch0.000.86Anterior overjet0.020.31Anterior crossbite0.480.49Anterior overbite− 0.010.60Anterior open bite0.410.52Maxillary crowding0.020.33Mandibular crowding− 0.010.79Maxillary spacing0.010.49Mandibular spacing0.000.84Rho Spearman's Rank Correlation Coefficient ## Discussion SDB can cause various complications, such as abnormal growth and cardiovascular, immunological, and metabolic disorders, affecting a child’s health and quality of life for many years [29]. Currently, SDB-related dentofacial abnormalities draw more attention from parents and doctors in communities than that decades ago. We call for more efforts from local health authorities to enhance public propaganda and education on prevention measurements for SDB and related diseases. Meanwhile, more attention should be paid to further studies on dental-maxillofacial growth. The golden standard method for SDB diagnosis is polysomnography (PSG). However, there are restrictions on applying PSG in large-scale population-based epidemiological surveys and health screenings [30]. Therefore, the PSQ, a tool validated to have a sensitivity of $85\%$ and a specificity of $87\%$ for identifying children with SDB, was used in this study [28]. Li et al. [ 31] confirmed the applicability and generalizability of the Chinese version of PSQ in an extensive epidemiological survey of pediatric SDB in a cross-sectional study in China. A total of 3433 subjects (1788 males and 1645 females) aged 6–11 years from primary school in Shanghai were included in this study. The overall prevalence of SDB was $17.7\%$. It was reported that the prevalence of pediatric SDB ranged from 8.5 to $11.3\%$ in a large cohort of primary schools in Japan [32]. In another study of a random sample of 3–11-year-old children in Wuxi, China, SDB prevalence was about $13.4\%$ [2]. The prevalence of SDB in this survey was higher than previously reported, which may be attributed to the discrepancy in population subgroups, age-range, and environments. The study showed that SDB was much more common in boys ($21.0\%$) than in girls ($14.0\%$). This was consistent with many previous study reports [3, 33, 34]. Children with SDB weighed about 1 kg more than children without SDB (non-SDB) and averaged about 0.7 points higher in BMI. Although some studies did not observe the association between SDB and weight [23], it is almost accepted that obesity plays an important role in the pathophysiology of pediatric SDB [11, 35, 36]. Different from adults, the major risk factor of pediatric OSAHS is currently reported as adeno-tonsillar hypertrophy [37, 38]. Adenotonsillar hypertrophy directly leads to the narrowing of the retropalatal part of the upper airway, which is usually the most common site of obstruction due to the smallest cross-sectional. The most frequent cases of a high incidence of SDB caused by adenotonsillar hypertrophy were found in 3–5 years old children [39]. It is generally accepted that the number of respiratory infections decreased in 6–11-year-old children, and their SDB is usually associated more closely with allergic disease [4, 40, 41]. Allergic rhinitis may influence sleeping by several mechanisms, and the main point is that the increased airway resistance is caused by nasal congestion due to the nasal mucosa allergic inflammatory. Using PSG and a questionnaire, Liu et al. studied allergic rhinitis’s influence on SDB. They found that despite the high prevalence of allergic rhinitis in children with SDB, allergic rhinitis was only linked to behavioral problems rather than the aggravating factor for SDB when assessed by PSG [42]. In this study, we found a high frequency of SDB with rhinitis ($24.1\%$ vs. $13.0\%$), adenotonsillar hypertrophy ($33.5\%$ vs. $13.7\%$), asthma ($27.4\%$ vs. $17.0\%$) and tympanitis ($25.5\%$ vs. $16.9\%$). The multivariate logistic regression showed that allergic rhinitis (OR 1.39) and adenotonsillar hypertrophy (OR 2.39) were risk factors for SDB, while asthma and tympanitis were not. Some studies identified asthma as a vital factor for SDB [2, 43]. However, we did not obtain a positive result, possibly due to the low proportion of children with asthma and the inability to distinguish between rhinitis and asthma. Parental snoring has long been identified as a significant risk factor for pediatric SDB in different ethnic groups [44, 45]. Kannan et al. [ 46] explored the predictors of childhood habitual snoring in a birth cohort and found that parental habitual snoring was consistently associated with childhood habitual snoring from ages 1 to 7. Our study supported these observations and proposed that paternal snoring (OR 1.97) and maternal snoring (OR 1.35) were risk factors for SDB. We also evaluated the relationship between daily habits/environment and SDB prevalence. We did not observe an association between the frequency of using air conditioners, air humidifiers, and air cleaners and the SDB prevalence. It was speculated that tobacco exposure and air pollution might be linked to an increased risk of respiratory diseases such as allergies, airway inflammation, and oedema, resulting in an increased prevalence of SDB. In our study, children exposed to tobacco for more than 20 min a day had a higher prevalence of SDB ($20.7\%$ vs. $16.6\%$). However, the differences were not significant in multivariate logistic regression analysis. As for sleep position, the prevalence of SDB was much higher in children who usually slept in the prone position ($25.4\%$) than in the supine ($17.3\%$) or lateral position ($16.7\%$). Sleep in the prone position is more commonly seen in children than adults, and it differs from the supine position incontrolling the cardiovascular, respiratory, and thermoregulatory systems. Numerous clinicians believe that sleeping in a supine position is more likely to induce the collapse of the upper airway and lead to SDB. However, this conjecture was proved to be a fault in this study and previous research which was also conducted in China [2], although the differences were insignificantin multivariate logistic regression analysis. Dentofacial development is a complex biological process that could be affected and remodeled by various risk factors breaking the balance. The prevalence of dentofacial deformity among the Chinese population of children and adolescents, as reported [26, 27], has increased by $27\%$ since the 1960s. SDB harms the healthy development of children but may also be associated with malocclusion due to some craniofacial anatomical characteristics. Our cross-sectional research explored if SDB is associated with malocclusion in 6–11-year-old children in China. We discovered that children with protruding profiles intended to have a higher frequency of SDB than upright/concave profiles, although it did not show a statistical difference. Children with molar occlusion of Angle class I showed a non-significantly lower SDB prevalence than Angle class II and III occlusion. Furthermore, the incidence of SDB in children with mandible retraction was significantly higher. A follow-up study examined 6–8-year-old children from Finland at baseline and 2.2 years later and reported that children with SDB were more likelyhave the convex facial profile and mandibular retrusion than children without SDB [47], which was in agreement with previous works [25, 48]. However, the association between SDB and pediatric dentofacial deformity remained controversial. Lyra et al. [ 23] suggested that the prevalence of SDB was highly correlated with posterior crossbite and anterior open bite, but not with molar relationship or anterior overjet/overbite. It was reported that the dimensions of UA are smaller in children with large overjet and constricted dental arch, which might predispose them to SDB [49, 50], However,our study did not detect an association between overjet and dental arch. In a systematic review, Hansen et al. 2022 checked out the 1996 literature, and four papers were included last. They hold that no exact association can be ascertained between specific malocclusion and SDB in children [51]. We speculate that craniofacial morphology and dental occlusion seem to have complex, multifactor pathogenesis in children. Further research is needed to ascertain if SDB is associated with some specific malocclusion traits. Although the results of our study cannot confirm a definite risk of SDB to malocclusion but found a high prevalence of SDB in children with retrusive mandible, we suggested regular comprehensive epidemiological research of malocclusion and dental-maxillofacial healthy development guidance was needful for primary students. The main limitation of this report was the definition of SDB. Due to the epidemiological nature of the study, the sample size was too large to obtain PSG data. A questionnaire completed by the guardian was used to evaluate the presence of SDB. Although PSQ is a widely used methods to assess SDB, some guardians may have needed to be more explicit about their children’s sleeping patterns and filled out PSQ inaccurately. Secondly, information regarding medical history reported by parents were not verified via thorough medical evaluation and documentation in medical records. Furthermore, some variables selected as a qualitative representative of malocclusion, the lateral profile, for example, might not satisfy a more detailed quantified analysis in the present study. We encourage that complementary examinations such as radiography and cephalometry be used to determine the malocclusion traits in further studies. In conclusion, allergic rhinitis, adenotonsillar hypertrophy, paternal snoring, and maternal snoring were independent risk factors for pediatric SDB. In the study population, the prevalence of SDB was associated with retrusive mandible. We encourage patients with a history of allergic rhinitis, adenotonsillar hypertrophy, and paternal/maternal snoring, in addition to having mandibular retrusion should be referred for medical diagnosis of SDB, such as PSG monitoring, in order to prevent and minimize adverse effects of SDB on individuals lives. 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--- title: IL-22 alters gut microbiota composition and function to increase aryl hydrocarbon receptor activity in mice and humans authors: - Jordan S. Mar - Naruhisa Ota - Nick D. Pokorzynski - Yutian Peng - Allan Jaochico - Dewakar Sangaraju - Elizabeth Skippington - Annemarie N. Lekkerkerker - Michael E. Rothenberg - Man-Wah Tan - Tangsheng Yi - Mary E. Keir journal: Microbiome year: 2023 pmcid: PMC9997005 doi: 10.1186/s40168-023-01486-1 license: CC BY 4.0 --- # IL-22 alters gut microbiota composition and function to increase aryl hydrocarbon receptor activity in mice and humans ## Abstract ### Background IL-22 is induced by aryl hydrocarbon receptor (AhR) signaling and plays a critical role in gastrointestinal barrier function through effects on antimicrobial protein production, mucus secretion, and epithelial cell differentiation and proliferation, giving it the potential to modulate the microbiome through these direct and indirect effects. Furthermore, the microbiome can in turn influence IL-22 production through the synthesis of L-tryptophan (L-Trp)-derived AhR ligands, creating the prospect of a host-microbiome feedback loop. We evaluated the impact IL-22 may have on the gut microbiome and its ability to activate host AhR signaling by observing changes in gut microbiome composition, function, and AhR ligand production following exogenous IL-22 treatment in both mice and humans. ### Results Microbiome alterations were observed across the gastrointestinal tract of IL-22-treated mice, accompanied by an increased microbial functional capacity for L-Trp metabolism. Bacterially derived indole derivatives were increased in stool from IL-22-treated mice and correlated with increased fecal AhR activity. In humans, reduced fecal concentrations of indole derivatives in ulcerative colitis (UC) patients compared to healthy volunteers were accompanied by a trend towards reduced fecal AhR activity. Following exogenous IL-22 treatment in UC patients, both fecal AhR activity and concentrations of indole derivatives increased over time compared to placebo-treated UC patients. ### Conclusions Overall, our findings indicate IL-22 shapes gut microbiome composition and function, which leads to increased AhR signaling and suggests exogenous IL-22 modulation of the microbiome may have functional significance in a disease setting. Video Abstract ### Supplementary Information The online version contains supplementary material available at 10.1186/s40168-023-01486-1. ## Background Cytokines are secreted proteins that mediate cell–cell communication between immune cells and play key roles in activating pathways involved in immunity and host response to microbes. IL-22, a cytokine secreted by T cells and type 3 innate lymphoid cells (ILC3s) [1, 2], has shown a unique capacity among cytokines to signal to and regulate epithelial cells, inducing a broad range of responses. IL-22 is produced by T cells and ILC3s in response to IL-23 secreted by activated dendritic cells and macrophages [3]. IL-22 signals through its cognate receptor, a heterodimer of IL-10R2 and IL-22RA1, which is predominantly expressed on epithelial cells lining mucosal barriers [4, 5]. Restricted expression of IL-22RA1 effectively targets IL-22 activity to mucosal barriers comprising the primary sites of host-microbe interactions in the skin, airway epithelium, and gastrointestinal (GI) tract. Through its effects on epithelial cells, IL-22 may regulate microbiome composition and function via both direct and indirect mechanisms. Upon IL-22 binding, intestinal epithelial cells (IECs) upregulate proliferative and anti-apoptotic pathways, antimicrobial protein (AMP) secretion, and mucin production and fucosylation [6]. IL-22-induced AMP secretion by IECs has the potential to cull select bacterial clades, acting as a negative selective pressure on the microbiota. In a mouse model of GI infection, robust IL-22-induced AMP activity provided protection against Citrobacter rodentium infection [7]. Conversely, IL-22-regulated mucosal glycosylation selects for commensal organisms that drive colonization resistance to Clostridioides difficile [8]. This suggests increases in mucin availability and altered fucosylation downstream of IL-22 signaling may select for microbes capable of metabolizing these host-derived sugars, acting as a positive selective pressure. Even in the absence of pathogens, homeostatic IL-22 activity may contribute to microbiome composition. Deletion of IL-23, the IL-23 receptor, or IL-22 itself all result in altered gut microbiome composition and increased sensitivity to chemically induced colitis in mice [9–12]. While exogenous administration of IL-22 ameliorates this phenotype, transferring the microbiome of IL-22 or IL-23 pathway-deficient animals to wild-type mice leads to reduced AMP and IL-22 expression in the gut and, again, increased sensitivity to chemically induced colitis [9, 12, 13]. In sum, IL-22 may play a role in shaping the gut microbiome to exclude potentially pathogenic or pro-inflammatory organisms that may contribute to GI inflammatory diseases such as ulcerative colitis (UC), a type of inflammatory bowel disease (IBD) that is characterized by inflammation of the intestinal mucosa. Patients with UC have been shown to have reduced microbial diversity broadly characterized by reduced microbial richness and uneven abundance distribution in comparison to healthy subjects [14]. Microbes can influence the host immune system through the effects of secreted metabolites or modification of host-derived factors [15, 16]. A classic example of this are short-chain fatty acids (SCFA), which are derived exclusively from microbial fermentation and can regulate colonic regulatory T cell (Treg) numbers; inhibit NF-κB activation in lamina propria cells and peripheral blood mononuclear cells; and enhance antimicrobial activity of intestinal macrophages [17–19]. More recently, microbial metabolism of L-tryptophan (L-Trp), specifically the production of indole derivatives, has been identified for its role in generating ligands for the aryl hydrocarbon receptor (AhR). AhR is a ligand-activated transcription factor that regulates a number of immune processes, including the development, maintenance, and function of ILC3s, γδ T cells, Th22 cells, and Th17 cells [20]. Mice with reduced AhR activity display increased sensitivity to colitis, intestinal candidiasis, and alcoholic liver disease [13, 21–23]. Supplementation with L-Trp-metabolizing microbes or microbially derived L-Trp metabolites, such as indole derivatives, rescues this phenotype [13, 21–23]. Reductions in both SCFA and tryptophan metabolites have been described in IBD patients [13, 24]. Given its activity at the mucosal barrier, IL-22 may act as a critical node for host-microbe communication through its role as a downstream target of ligand-bound AhR. Multiple AhR transcription factor binding sites are found in the IL-22 locus, allowing AhR to regulate the development of ILC3s as well as IL-22 gene expression [6, 20, 25]. Additionally, AhR-deficient animals produce significantly less IL-22 [25]. This suggests AhR can act as a microbial sensor of sorts; detecting products of microbial metabolism and directing the host immune system to respond in kind [20]. Loss of IL-22 is associated with altered microbiome composition and function as well as reduced AhR activation, suggesting potential involvement in a host-microbe feedback loop in the gut that, combined, may increase susceptibility to colitis [12, 13]. Products of microbial metabolism can activate AhR and upregulate IL-22 production, leading to increased AMP and mucin production in IECs which in turn contribute to reduced colitis susceptibility and may shape microbiome composition and function [6]. To address the potential role of IL-22 in this putative feedback loop and in UC, we evaluated the effect of exogenous IL-22, in the form of a fusion protein of IL-22 and the Fc portion of IgG4 (IL-22Fc), on the GI microbiome, microbial L-Trp metabolism, and AhR activity in mice and UC patients, a disease associated with dysbiosis, altered microbial L-Trp metabolism, and reduced AhR activity in the GI tract [13, 21, 26–28]. In doing so, we observed an IL-22Fc-induced shift in microbiota composition and microbial L-Trp metabolism that led to increased fecal AhR activity in both mice and UC patients treated with IL-22Fc. ## IL-22Fc treatment alters microbiota composition along the GI tract in mice To assess the impact of IL-22Fc treatment on the GI microbiome, previously co-housed wild-type adult mice were randomly re-caged into two, separately housed treatment groups, and injected intraperitoneally with either IL-22Fc or anti-Ragweed (aRW, isotype control antibody) (Fig S1A, see the “Methods” section). After 2 weeks of treatment, animals were euthanized and ileal, colonic, and fecal samples were collected for DNA isolation and subsequent 16S-V4 rRNA gene sequencing to characterize the bacterial microbiota. Shannon’s diversity was unaltered following IL-22Fc treatment (Fig. 1A), though a trend towards reduced richness was observed (Fig. S1B). Analysis of Bray-Curtis distances revealed a significant compositional shift in the bacterial microbiota of IL-22Fc-treated mice compared to isotype-treated animals across all three GI regions while maintaining the relative biogeographical structure of these tissue-specific communities (Fig. 1B).Fig. 1IL-22Fc consistently alters microbiota composition along the GI tract in mice. A Shannon’s diversity following IL-22Fc or aRW treatment. B NMDS ordination plot of Bray-Curtis distances. Dashed ellipses represent the $95\%$ CI for the centroid of each stratification group. r2 calculated by PERMANOVA. For A and B, triangle, square, and circle markers indicate ileal, colonic, and fecal samples, respectively. Teal and red markers indicate animals treated with IL-22Fc ($$n = 8$$) or isotype control ($$n = 8$$), respectively. C Taxonomic tree depicting all bacterial genera detected within the ileum, colon, or stool of IL-22Fc or aRW-treated mice. The outer heatmap displays the difference in mean, normalized abundance of a given bacterial genera between IL-22Fc and aRW-treated mice (Log2(∆)) in the ileum, colon, or stool. All non-zero Log2(∆) values have a BH-adjusted p value <0.05. Highly IL-22Fc responsive genera, defined as the top ten genera based on average Log2(∆) and significant enrichment or depletion in IL-22Fc-treated animals across multiple tissues, are labeled. Note that only seven IL-22Fc-enriched genera met these criteria To identify highly IL-22Fc responsive genera, we first identified genera that were significantly enriched (or depleted) in multiple tissues of IL-22Fc treated mice (i.e., ileum, colon, and/or stool) based on differential abundance (DA) testing utilizing DESeq2 [29] (Fig. 1C, Table S1-3). We then ranked these multi-tissue enriched (or depleted) genera by their average difference in normalized abundance between IL-22Fc and aRW-treated mice (Log2(∆)) across these tissues to identify those with the greatest effect size, designating the top ten genera based on this ranking as highly IL-22Fc enriched (or depleted). Based on this criteria, seven genera were identified as highly IL-22Fc enriched (only seven genera were significantly enriched in multiple tissues): Bacteroides, 28-4 (a member of the Lachnospiraceae family), Clostridium-AJ, Altered Schaedler Flora 356 (ASF-356), Erysipelatoclostridium, CAG-41 (a poorly characterized member of the Clostridia class), and Eubacterium-D, suggesting IL-22Fc treatment creates a more permissive environment that allows these organisms to thrive (Fig. 1C and S1C). Conversely, based on the above criteria, Turicibacter, CAG-873 (a member of the Muribaculaceae family), Dubosiella, Bifidobacterium, CAG-95 (a member of the Lachnospiraceae family), Clostridium, Ruminococcus-B, an unclassified genus within the Ruminococcaceae family (Ruminococcaceae unclassified), Romboutsia, and Eubacterium-I represented the top ten genera that were highly depleted following IL-22Fc treatment (Figs. 1C and S1D). To ensure these observations were robust to the statistical method implemented for the identification of DA genera and not an artifact of the DESeq2 approach, we performed two additional, conceptually different statistical approaches for DA testing: nonparametric Wilcoxon rank-sum testing as well as compositional data analysis utilizing ALDEx2 [30] as recommended by Nearing et al. ( see the “Methods” section) [31]. When comparing the statistical results of DESeq2, Wilcoxon rank-sum, and ALDEx2, significant DA genera (BH-corrected p value <0.05) were consistently identified across the three approaches (Table S1-3). In a minority of genera that were only identified as significant by DESeq2, they frequently were nominally significant based on Wilcoxon rank-sum and/or ALDEx2 analysis (uncorrected p value <0.05) and routinely demonstrated small effect sizes compared to multi-significant genera. Combined, these findings demonstrate the robustness of our observations and confirm IL-22 as a robust microbiome modulator capable of altering the steady-state microbial community along the GI tract of healthy mice. ## IL-22Fc treatment alters microbial tryptophan metabolism L-Trp and indole derivatives of L-Trp are capable of shaping the intestinal environment via activation of AhR [20]. Given the reciprocal regulation of L-Trp metabolism and IL-22 production, we initially utilized additional stool samples collected at the same time from the same mouse experiment as stool samples used for microbiota profiling (i.e., after 2 weeks of either aRW or IL-22Fc treatment, Fig. S1A) to assess changes in fecal concentrations of microbially derived L-Trp metabolites following IL-22Fc treatment (see the “Methods” section). Fecal pellets from IL-22Fc-treated animals had increased concentrations of L-Trp, indole-3-carboxaldehyde (I3C), an established AhR ligand [20], and 5-hydroxyindoleacetate (5-HIAA) compared to controls (Fig. 2A). The remaining microbially derived L-Trp metabolites in our panel were unchanged (Fig. S3A). Of note, a previous study describing the role of microbially derived L-Trp derivatives in the context of Card9 deficiency did not observe an enrichment of tryptamine (a potential precursor to indole derivative synthesis) in animals expressing higher levels of IL-22 nor were fecal tryptamine concentrations associated with IBD status in humans [13]. This suggests that increased synthesis of indole derivatives proceeds through an alternative precursor, such as indole pyruvate, or changes in fecal tryptamine concentrations following increased IL-22 levels are undetectable due to rapid metabolic pathway kinetics quickly converting excess tryptamine to downstream products. In a separate replicate murine experiment, stool samples were collected at days 0, 8, and 14 to longitudinally assess changes in fecal concentrations of microbially derived L-Trp metabolites after IL-22Fc exposure compared to baseline pre-exposure levels. L-Trp, I3C, and two additional microbially derived AhR ligands, indole-3-propionic acid (IPA) and indole-3-acetic acid (IAA) [20], showed a greater increase from the baseline in IL-22Fc-treated animals compared to control-treated animals (Figs. 2B and S3B). The remaining microbially derived L-Trp metabolites (including 5-HIAA, which was not detected in this experiment) showed no difference between IL-22Fc and control treated animals. L-Trp is an essential amino acid obtained via diet or microbial synthesis while I3C, IPA, and IAA are predominantly synthesized by the microbiome via the metabolism of L-Trp [20, 32, 33] (Fig. S2A). Functional analysis of the microbiome utilizing PICRUSt2, a software tool developed for predicting functional abundances based on 16S rRNA gene sequencing [34], showed predicted metagenomic counts mapping to the L-Trp biosynthesis pathway were significantly increased following IL-22Fc treatment (Fig. 2C). Consistent with this observation, the predicted metagenomic counts of both tryptophan synthase and tryptophanase, enzymes responsible for the synthesis of L-Trp and indole derivatives such as I3C [33], were also increased throughout the GI tract following IL-22Fc treatment (Fig. 2D) though tryptophan decarboxylase, indole pyruvate decarboxylase, and monoamine oxidase (additional enzymes involved in the synthesis of indole derivatives) were not called by PICRUSt2 prediction. Furthermore, a greater proportion of the Amplicon Sequence Variants (ASVs) representing the highly IL-22Fc enriched genera from our 16S-V4 rRNA gene sequencing data were predicted to encode tryptophan synthase compared to ASVs representing the highly IL-22Fc depleted genera while tryptophanase prevalence did not significantly differ (Fig. S3C). Specifically, the predicted prevalence of tryptophan synthase was observed in all ASVs representing the highly IL-22Fc-enriched genera except for those of Eubacterium-D (Fig. S3D), the genus with the lowest overall abundance and effect size among the highly IL-22Fc enriched genera (Fig. S1C). While only ASVs representing Romboutsia and Bacteroides were predicted to encode tryptophanase (Fig. S3D), the high overall abundance of Bacteroides compared to Romboutsia may indicate an amplified ability of Bacteroides to influence concentrations of indole derivatives such as I3C in vivo, leading to the observed increase in fecal concentrations of I3C (Fig. 2A, B). 5-HIAA, while predominately a product of host serotonin metabolism, can also be synthesized by select microbiota members [35, 36] (Fig. S2A). Predicted metagenomic counts of aldehyde dehydrogenase, responsible for 5-HIAA synthesis, decreased with IL-22Fc treatment and the prevalence of this enzyme did not differ significantly between ASVs representing highly IL-22Fc-enriched and depleted genera (Figs. 2D and S2C-D), indicating IL-22Fc driven shifts in the microbiota were not directly responsible for the observed increase in fecal 5-HIAA concentration. Combined, these observations suggest IL-22Fc exposure alters microbial L-Trp metabolism in the gut, resulting in increased concentrations of L-Trp and indole derivatives. Fig. 2IL-22Fc alters microbial tryptophan metabolism in mice. A Fecal concentrations of L-Trp, I3C, and 5-HIAA following IL-22Fc or aRW treatment. B Log2 fold change from baseline in fecal concentrations of L-Trp, I3C, and 5-HIAA. C Predicted metagenomic counts, as calculated by PICRUSt2, mapping to the L-tryptophan biosynthesis pathway in IL-22Fc or aRW-treated animals. D Predicted metagenomic counts, as calculated by PICRUSt2, of microbial enzymes capable of synthesizing tryptophan (tryptophan synthase), indole (tryptophanase), and 5-HIAA (aldehyde dehydrogenase). Statistical significance determined by rank-sum test ($p \leq 0.05$, $p \leq 0.01$). Triangle, square, and circle markers indicate ileal, colonic, and fecal samples, respectively. Teal and red markers indicate animals treated with IL-22Fc ($$n = 8$$) or isotype control ($$n = 8$$), respectively ## Fecal AhR activity increases following IL-22Fc treatment IL-22Fc treatment modulates the microbiome in mice, which may indirectly influence host activity through elevated AhR ligand production, including L-Trp metabolites. We next evaluated the broader effects of IL-22Fc and associated microbiome changes on AhR activity using a well-characterized in vitro AhR reporter cell line [37]. AhR activity was subsequently compared with metabolomic and predicted metagenomic results. Fecal pellets from IL-22Fc-treated animals displayed increased AhR activation compared to the control group (Fig. 3A), indicating an IL-22Fc-associated increase in AhR ligands consistent with our metabolomic findings. Despite fecal AhR activity strongly correlating with fecal concentrations of L-Trp, I3C, and 5-HIAA (Fig. S4A), only predicted metagenomic counts of tryptophan synthase and tryptophanase positively correlated with AhR activity, regardless of GI location (Figs. 3B and S4B-C), suggesting microbially derived L-Trp and I3C (rather than 5-HIAA) is driving increased AhR activity associated with IL-22Fc treatment. In support of this, exposing the AhR reporter cell line to 100uM of L-Trp or I3C led to significantly higher AhR activation compared to 5-HIAA, with L-Trp producing the greatest effect (Fig. 3C).Fig. 3Microbially derived AhR ligand availability in the intestine increases following IL-22Fc treatment in mice. A AhR activity following treatment with prepared fecal samples obtained from IL-22Fc or aRW-treated animals, reported as Log2(fold change over unstimulated wells). B Scatterplot of predicted fecal metagenomic counts of tryptophan synthase, tryptophanase, and aldehyde dehydrogenase vs. fecal AhR activity. Spearman’s rank correlation coefficient (rho) was calculated to determine correlation strength. C AhR activity, reported as Log2(fold change over unstimulated wells), following treatment with 100uM of either L-Trp, I3C, or 5-HIAA (see the “Methods” section). D AhR activity following treatment with sterile-filtered bacterial culture supernatant from bacterial isolates representing either highly IL-22Fc depleted ($$n = 14$$) or enriched ($$n = 8$$) genera, reported as Log2(fold change over matched sterile culture media). E Proportion of bacterial isolates representing highly IL-22Fc-depleted or -enriched genera capable of activating AhR in vitro, as defined by an in vitro AhR activity statistically greater than that of matched sterile culture media (see Fig. S5). For A and B, teal and red markers indicate animals treated with IL-22Fc ($$n = 8$$) or isotype control ($$n = 8$$), respectively. For panels A, C, and D, all samples were measured in triplicate and statistical significance was determined by T test. For panel E, the statistical significance was determined by Barnard’s test. For all panels, $p \leq 0.05$ and *$p \leq 0.01$ To further evaluate the potential production of AhR ligands by highly IL-22Fc responsive bacterial genera, we used isolates with ≥$97\%$ 16S rRNA gene sequence similarity to that of ASVs representing the highly IL-22Fc responsive genera identified earlier and assessed their capacity for AhR ligand production in vitro using the same reporter cell line as above. Strains representing the Bifidobacterium ($$n = 3$$), CAG-873 ($$n = 1$$), Clostridium ($$n = 2$$), Romboutsia ($$n = 1$$), Dubosiella ($$n = 1$$), Turicibacter ($$n = 1$$), Bacteroides ($$n = 3$$), Clostridium-AJ ($$n = 3$$), and Erysipelatoclostridium ($$n = 1$$) genera were obtained from public bacterial strain collections (Table S4). Additionally, utilizing stool samples collected from the previously described mouse experiments, we isolated additional strains representing Bifidobacterium ($$n = 1$$), CAG-873 ($$n = 4$$), and Bacteroides ($$n = 1$$) (Table S4). All told, we obtained fourteen and eight strains representing highly IL-22Fc enriched and depleted genera, respectively. To assess their ability to activate AhR in vitro, all strains were grown in liquid growth media for 48 h after which culture supernatant was collected, sterile filtered, and assessed for AhR activation relative to matched sterile growth media (see the “Methods” sections). Overall, culture supernatant from highly IL-22Fc-enriched strains induced numerically greater AhR activity compared to that of highly IL-22Fc-depleted strains (Fig. 3D). Furthermore, the proportion of highly IL-22Fc-enriched strains with a relative AhR activity significantly greater than that of matched sterile culture media (indicating their ability to activate AhR) was significantly higher than that of highly IL-22Fc-depleted strains, with culture supernatant from one Bacteroides strain (GNE6609) and two Clostridium-AJ strains (GNE6686, GNE6624) activating AhR above background while no strains representing highly IL-22Fc depleted genera did so (Figs. 3E and S5). Of note, culture supernatant from an additional Bacteroides strain (GNE6603) also induced AhR activity compared to matched sterile growth media but this signal was not significant due to an outlier replicate (Fig. S5B). Publicly available whole-genome sequence data for these four AhR activating strains (GNE6609 [Bacteroides thetaiotaomicron VPI-5482; GCF_000011065.1], GNE6603 [Bacteroides faecis* MAJ27; GCF_900106755.1], GNE6686 [Ruminococcaceae bacterium D16; GCA_000177015.3], and GNE6624 [Clostridiales bacterium Choco116; GCA_003202955.1]) was analyzed to look for putative presence of tryptophan synthase, tryptophanase, indole pyruvate decarboxylase, tryptophan decarboxylase, monoamine oxidase, and aldehyde dehydrogenase homologs, key bacterial enzymes involved in L-Trp synthesis and subsequent conversion to AhR activating indole derivatives (Fig. S2). Consistent with our predictions based on PICRUSt2 analysis, putative tryptophan synthesis homologs were detected in the genome of all four AhR activating strains and tryptophanase homologs were present in the two AhR activating Bacteroides strains (GNE6609 and GNE6603) while tryptophan decarboxylase and monoamine oxidase homologs were not detected in any AhR activating strain (Table S5). Interestingly, putative indole pyruvate decarboxylase homologs were also present in all four AhR activating strains, despite PICRUSt2 predictions not indicating such, and aldehyde dehydrogenase homologs were detected in three of these strains (GNE6609, GNE6686, and GNE6624) (Table S5). Combined, these observations suggest all four AhR activating strains are capable of synthesizing L-Trp and L-Trp-derived AhR ligands. As such, targeted metabolomics was performed on culture supernatants from all four AhR activating strains to assess their ability to synthesize L-Trp-derived AhR ligands in vitro. Culture supernatant collected from the Clostridium-AJ strains (GNE6686 and GNE6624) had increased concentrations of IAA, culture supernatant from Bacteroides strain GNE6609 had increased levels of L-Trp, and culture supernatant from Bacteroides strain GNE6603 had increased levels of both IAA and L-Trp relative to matched sterile growth media (Fig. S5C), indicating these strains can synthesize IAA and/or L-Trp in vitro (consistent with our PICRUSt2 and WGS analysis). All told, these results further indicate that altered L-Trp metabolism associated with IL-22Fc-induced changes in GI microbiome composition leads to increased AhR activity in the gut. ## IL-22Fc administration increases fecal AhR activity in humans Experiments in mouse models suggested IL-22Fc treatment alters microbiome composition and subsequent AhR ligand production. UC patients have been previously shown to have microbial dysbiosis, altered microbial L-Trp metabolism, and reduced AhR activity and treatment with an exogenous IL-22 pathway is being evaluated as a therapy for UC [13, 21, 26, 28, 38–40]. We next evaluated the effects of IL-22Fc on fecal L-Trp metabolite concentrations, AhR activity, and microbiota composition in UC patients participating in a phase 1b randomized, blinded, placebo-controlled multiple ascending-dose study evaluating the safety, tolerability, pharmacokinetics, and pharmacodynamics of repeat intravenous dosing of efmarodocokin alfa, a fusion protein that links IL-22 with human immunoglobulin G4 (NCT02749630) [41]. At screening, prior to drug administration, fecal levels of the microbially synthesized indole derivatives I3C and IPA were reduced in UC patients compared to healthy volunteers (HV) while fecal L-Trp levels were unchanged (Fig. 4A). This was accompanied by increased fecal concentrations of L-Kynurenine (L-Kyn, Fig. S6A) in UC, suggesting a shunting of L-Trp metabolism towards host pathways. Consistent with these findings, UC stool also elicited reduced AhR activity in our in vitro AhR reporter assay, though this did not meet statistical significance (Fig. S6B). Baseline modified Mayo Clinic Scores (mMCS) were available for the subset of screened UC patients that met the study enrollment criteria (including diagnosis of UC with a Mayo Endoscopic Subscore of ≥2 points by central reading at screening) and were enrolled in the clinical trial [41]. When stratified by mMCS, we observed reduced IPA coupled with significantly increased L-Kyn in severe UC (mMCS >5) compared to moderate UC (mMCS ≤5), with levels of L-Trp and I3C being similar amongst moderate and severe UC (Fig. S6C). Severe UC was also accompanied by reduced fecal AhR activity compared to moderate UC, though this again did not meet statistical significance (Fig. S6D). Additionally, we previously described microbial dysbiosis in the fecal microbiota of enrolled UC participants compared to HV, which was broadly characterized by reduced diversity [41]. Human and mouse microbiota do not completely overlap, and we observed five of the highly IL-22Fc enriched and eight of the highly IL-22Fc depleted genera in the fecal microbiota of enrolled subjects out of the 17 highly IL-22Fc responsive bacterial genera identified in our mouse experiment (Figs. 1C and S1C-D). Of these, the mean Log2(fold change in abundance at screening between UC and HV) of the IL-22Fc depleted genera was greater than that of the IL-22Fc enriched genera, indicating IL-22Fc depleted genera tended to be enriched in UC, though this trend was not significant (Fig. S7A). Looking at the individual genera, only Clostridium, Ruminococcaceae unclassified, and Eubacterium-D were significantly enriched in UC participants compared to HV at screening while no genera were significantly depleted (Fig. S7B-C). These observations are in line with prior studies describing depleted indole concentration, increased L-Kyn levels, and reduced AhR activity accompanying microbial dysbiosis in the stool of IBD patients [13, 23, 28].Fig. 4Efmarodocokin alfa administration increases fecal AhR activity in humans. A Fecal concentrations of L-Trp, I3C, and IPA at screening in HV ($$n = 69$$) compared to UC ($$n = 46$$). P value determined by rank-sum test. B AUC of Log2(fold change from screening) for fecal AhR activity in enrolled UC patients following administration of efmarodocokin alfa ($$n = 8$$) or placebo ($$n = 5$$). C Scatterplot of fecal AhR activity vs. fecal concentrations of I3C and IPA at screening only ($$n = 23$$) and at all visits ($$n = 104$$) in enrolled UC patients. Spearman’s rank correlation coefficient (rho) was calculated to determine correlation strength. D Mean AUC of Log2(fold change from screening) for normalized abundance of IL-22Fc-depleted ($$n = 7$$) or -enriched ($$n = 5$$) genera detected in enrolled UC patients receiving efmarodocokin alfa treatment. E Mean ΔAUC (AUC in efmarodocokin alfa-treated UC patients—AUC in placebo-treated UC patients) of IL-22Fc-depleted ($$n = 7$$) or -enriched ($$n = 5$$) genera detected in enrolled UC patients. For panels B, D, and E, only enrolled UC patients with a complete sample collection history (screening, day 29, day 43, day 64, and day 85) were considered for AUC calculations. P value determined by Student’s T test Following the screening, enrolled subjects were administered either placebo or efmarodocokin alfa according to one of three dosing regimens (see the “Methods” section), and fecal L-Trp metabolite concentrations, AhR activity, and microbiota composition were further evaluated post-treatment. In addition to screening samples, stool samples were collected from enrolled participants on days 29, 43, 64, and 85. Fecal L-Trp metabolite concentrations, AhR activity, and IL-22Fc responsive bacterial genera abundances were assessed in these samples, normalized to screening levels, and the area under the Log2(fold change from screening) curve (AUC) was calculated for each participant to determine the impact of efmarodocokin alfa compared to placebo over the duration of treatment. As determined by AUC comparisons, UC participants receiving efmarodocokin alfa showed a statistically significant increase in fecal AhR activity compared to placebo-treated participants that was accompanied by an increase in fecal L-Trp, ICA, and IPA, and reduction in fecal L-Kyn, (Figs. 4B, S6E), suggesting efmarodocokin alfa alters AhR activity and L-Trp metabolism in the GI tract of humans. Furthermore, fecal AhR activation strongly correlated with fecal ICA and, to a lesser extent, fecal IPA concentrations in enrolled UC participants both at screening and when considering all visits (Fig. 4C), consistent with our observations in mice. Clinical remission (defined as attaining a mMCS ≤2, Mayo rectal bleeding (RB) subscore of 0, and other Mayo subscores of ≤1) were observed in 5 out of 18 UC participants treated with efmarodocokin alfa compared to 0 of 6 placebo-treated participants, making comparisons between efmarodocokin alfa remitters and non-remitters difficult due the small group sizes [41]. With respect to the fecal microbiota, we previously described a general correction in UC-dysbiosis following efmarodocokin alfa treatment whereby bacterial genera enriched in UC participants compared to HV at screening tended to be depleted following efmarodocokin alfa treatment and vice versa [41]. We next focused on the subset of IL-22Fc-responsive bacterial genera identified in mice that were also present in UC participants. All of the highly IL-22Fc-depleted genera identified in mice and detected in enrolled UC patients tended to be depleted in efmarodocokin alfa-treated UC participants compared to placebo, based on AUC analysis, while the opposite was true of the IL-22Fc-enriched genera, though to a lesser extent (Fig. S8). While these trends were not statistical significant on an individual genus level, grouping these genera together based on their response in mice (i.e., IL-22Fc depleted vs. enriched) indicated the average AUC of IL-22Fc-depleted genera in efmarodocokin alfa-treated UC patients was significantly less than that of the IL-22Fc-enriched genera (Fig. 4D). Additionally, when taking into account the average AUC of these genera in placebo-treated UC participants to calculate ΔAUC (AUC in efmarodocokin alfa-treated UC patients—AUC in placebo-treated UC patients), the average ΔAUC of IL-22Fc-depleted genera was less than that of IL-22F-enriched genera (Fig. 4E). These observations suggest the IL-22Fc responsiveness of key bacterial genera is conserved across mice and humans and, combined with our fecal metabolite and AhR activity findings, provide evidence that IL-22Fc exposure consistently impacts the microbiome/L-Trp/AhR axis in the gut of both mice and humans, warranting future validation in a large cohort. ## Discussion The microbiome is a key regulator of IL-22 activity via the synthesis of L-Trp-derived AhR ligands [13, 21, 22, 42, 43]. Subsequent IL-22-driven upregulation of AMP secretion, mucin production, and fucosylation by IECs suggests IL-22 in turn shapes the microbiome. We found that IL-22Fc administration resulted in a significant shift in microbiome composition throughout the GI tract of healthy mice, suggesting exogenous IL-22 treatment is a robust microbiome regulator. IL-22Fc also increased L-Trp and its associated metabolites I3C and 5-HIAA, all of which activated AhR in vitro, as well as overall AhR activity in stool samples. Functional analysis of the microbiome showed an increase in microbial L-Trp metabolism genes following IL-22Fc treatment that correlated with AhR activity. Finally, UC patients treated with efmarodocokin alfa, an IL-22Fc fusion protein under evaluation in the clinic, showed increased AhR activity that correlated with fecal ICA and IPA levels and accompanied changes in fecal microbiome composition consistent with that of IL-22F-treated mice, demonstrating consistent effects of IL-22Fc between mice and humans. Exogenous IL-22 treatment is currently being investigated in multiple disease settings [44] (NCT02749630, NCT04539470, NCT02833389). Characterizing the impact increased IL-22 activity has on the microbiome is essential to understanding the therapeutic value of exogenous IL-22 treatment, especially in a disease setting such as IBD where microbial dysbiosis is a defining feature [26, 27]. Microbiome modification through specific enrichment and depletion of bacterial taxa could be a key mechanism of efficacy following treatment with exogenous IL-22. A number of the IL-22Fc-enriched taxa identified here were previously described to play beneficial roles in host immune regulation. ASF356, Bacteroides, Lachnospiraceae 28-4, and Clostridium-AJ members are robust producers of SCFAs [17, 45–47], microbially derived fermentation products that are associated with increased colonic Treg numbers, inhibition of NF-κB activation, and enhanced antimicrobial activity of intestinal macrophages [17, 45–47]. Furthermore, direct supplementation with certain Bacteroides members protects against murine models of colitis, graft-versus-host disease (GVHD), and metabolic syndrome [45, 48, 49]. IL-22Fc-depleted microbes, on the other hand, tend to be associated with pro-inflammatory responses. Turicibacter, Dubosiella, Ruminococcus, and Muribaculaceae members are reported to be enriched in both murine and human colitis, metabolic syndrome, and GVHD [43, 50–59], with both Ruminococcus and Muribaculaceae members directly demonstrating pro-inflammatory effects in vitro and in DSS-induced colitis, respectively [60, 61]. These effects are consistent with observations in UC patients undergoing IL-22Fc treatment as microbes that were differentially abundant in UC patients in comparison to healthy subjects at baseline were preferentially altered following IL-22Fc treatment with microbes enriched in UC compared to healthy subjects tending to be depleted following IL-22Fc treatment (and vice versa) [41]. Together, these results suggest treatment with IL-22 may correct disease-associated dysbiosis via selection for beneficial microbes and against disease associated taxa. Altering microbial L-Trp metabolism is a mechanism by which IL-22Fc-driven microbiome modulation may be beneficial in a disease setting. Reduced concentrations of microbially synthesized L-Trp metabolites (specifically indole derivatives such as ICA and IPA) were found in UC stool compared to healthy volunteers, consistent with previous studies [13, 21, 28]. Indole derivatives confer protection in murine models of colitis, largely via AhR activation, leading to increased IL-22 production by ILC3s and T cells that drives improved intestinal barrier integrity, reduced inflammation, and, ultimately, disease amelioration [20, 22, 28, 62–64]. In our studies, IL-22Fc treatment increased fecal concentrations of microbially derived ICA and IPA, which correlated with increased fecal AhR activity in both mice and humans. Furthermore, predicted metagenomics, WGS analysis, and in vitro culture of IL-22Fc-enriched microbes indicated these organisms have increased capacity for AhR ligand production. This suggests that, in addition to established direct effects of IL-22Fc on the host, IL-22Fc treatment may also confer additional benefits via a feedback loop along the IL-22/microbiome/AhR axis to induce a robust, sustained response. Although our data support IL-22 as a key regulator of gut microbiome composition and function resulting in increased AhR activity, more work will need to be done, particularly in human subjects, to confirm these findings. Though we provide evidence of IL-22Fc-induced shifts in microbiome composition, we can only hypothesize this is via increased AMP and mucin secretion and future studies are necessary to determine the specific mechanisms involved. Utilization of co-culture systems and further functional analysis of bacterial clades identified here to be enriched or depleted by IL-22Fc treatment may also provide additional insight. The IL-22Fc responsive bacterial strains examined in this study were grown using the preferred in vitro growth conditions for each organism, which limited full evaluation of the functional capacity of bacteria using different media or substrates. As L-Trp and L-Trp precursor availability may vary depending on growth media, it is possible the ability of each strain to activate AhR may vary with growth conditions as well. It is also worth noting that the in vitro behavior of these select microbes may differ from their behavior within a complex gut ecosystem with respect to L-Trp metabolism and AhR ligand synthesis. Additionally, while the consistency among mouse and human observations presented here strengthens the translatability of our findings, the small study size in humans necessitates further validation in a larger cohort, ideally including a greater longitudinal aspect to better characterize the durability of IL-22Fc-associated changes in the GI microbiome and metabolome. To that end, a larger ongoing phase 2 clinical trial of efmarodocokin alfa may yield samples capable of addressing this question (NCT03558152, NCT03650413). ## Conclusions Our current work expands on the relationship between the GI microbiome and IL-22 by establishing IL-22 as a clear microbiome modulator in both mice and humans. We observed microbiome modulation, increased fecal concentrations of key indole derivatives, and amplified AhR activity in healthy mice following IL-22Fc treatment. Bacterial genera modulated by IL-22Fc treatment were associated with L-Trp metabolism through functional analysis and generated differential AhR ligand production in vitro. Finally, UC patients were observed to have depleted concentrations of AhR ligands and reduced AhR activity in stool samples in comparison to healthy subjects, both of which increased following efmarodocokin alpha treatment. Our findings support a model in which microbial dysbiosis leading to altered L-Trp metabolism, reduced AhR activation, and insufficient IL-22 levels can promote a vicious cycle fostering a loss of GI homeostasis and suggest that supplemental IL-22 may reverse this process, reshaping the microbiome to increase AhR activity and enhance a virtuous cycle to help regain a homeostatic state. ## Murine IL-22Fc treatment experimental design Six- to eight-week-old, female C57BL/6J mice ($$n = 16$$) were purchased from The Jackson Laboratory and allowed to acclimatize in our facilities for 2 weeks prior to the start of the study. After the acclimatization period during which animals were co-housed, mice were randomly re-caged on study day 0 into two treatment groups and separately housed for the remainder of the experiment: IL-22Fc treated ($$n = 8$$, four mice per cage) and control ($$n = 8$$, four mice per cage). Animals were treated with 50 ug/mouse of either IL-22Fc or anti-ragweed isotype control via intraperitoneal injection three times a week and euthanized on day 14. Immediately prior to euthanization, mice were allowed to defecate directly into sterile microcentrifuge tubes. Collected fecal pellets were snap-frozen on dry ice until able to be stored at –80°C. Following euthanization, the terminal ileum (about 3 cm) and the terminal third of the colon (about 3-cm tissue, starting from the rectal end, minus the anus) were collected from each animal. The luminal contents of ileal and colonic samples were removed by gentle squeezing with the forceps, and the tissue was preserved in RNAlater at 4°C overnight and then stored at –80°C. All animal experiments were approved by the Genentech Institutional Animal Care and Use Committee. ## DNA extraction DNA was extracted from ileal, colonic, and fecal samples using the QIAGEN Allprep DNA/RNA 96 Kit (Cat. No. # 80311) according to a previously described protocol [41]. Briefly, samples were transferred to a QIAGEN PowerBead Plate containing 650 μL of lysis buffer RLT + β-mercaptoethanol and mechanically lysed using a QIAGEN TissueLyzer II (Cat. No. # 85300). The lysate was then transferred to the AllPrep 96 DNA Plate and the QIAGEN protocol for “Simultaneous Purification of DNA and RNA from Tissues Using Spin Technology” was followed beginning at step 5. For positive and negative DNA extraction controls, mock microbial communities and molecular-grade water were carried through the entire protocol, respectively. ## Bacterial 16S-V4 rRNA gene sequencing Bacterial 16S-V4 rRNA gene sequencing libraries were created as previously described [41, 65]. Briefly, PCR amplification of the 16S rRNA gene was conducted in triplicate for each sample using barcoded primers targeting the V4 region [65]. Blank extractions were used as a template for negative controls to monitor for 16S rRNA gene contamination. Following PCR, triplicates were pooled, purified, and normalized. All microbiome sequence data obtained from murine experiments is available from the Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra, BioProject: PRJNA782012). All microbiome sequence data obtained from clinical trial NCT02749630 is available from the European Genome-Phenome Archive (https://ega-archive.org/, EGAS00001006172). ## 16S-V4 rRNA gene sequence data processing QIIMEv2019.7 was used to process the 16S-V4 rRNA gene sequence data as previously described [41, 66]. Briefly, raw sequence data was demultiplexed, read trimming was performed to remove regions of low sequence quality, and paired-end reads were denoised, dereplicated, and chimera filtered with DADA2 [67]. Taxonomy was assigned based on the V4 region of the Genome Taxonomy Database 16S rRNA gene sequence database (r86.1) [68, 69]. Resulting in abundance tables were rarefied to 49,000 reads per sample. ## PICRUSt2 Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) was used to predict microbiota functional gene abundances from 16S-V4 rRNA gene sequence data [34]. Counts of Enzyme Commission Numbers (EC) were predicted from rarefied abundance tables and representative sequences with the picrust2_pipeline.py function, accounting for the predicted 16S rRNA gene copy number. ## Microbiome analysis ⍺- and β-diversity measures were calculated in QIIMEv2019.7. All statistical analyses were conducted in the R statistical environment [70]. A Wilcoxon rank-sum test was performed to compare ⍺-diversity between experimental groups differences (e.g., aRW vs. IL-22Fc). Bray-Curtis distance matrices were visualized via NMDS and permutational multivariate analysis of variance (PERMANOVA) was used to determine relationships between metadata (i.e., experimental group) and bacterial microbiota composition in the R statistical environment using the vegan package [71]. To identify significantly enriched or depleted bacterial genera, the DESeq2 R-package was used as described by McMurdie et al. and implemented by Nearing et al. [ 29, 31, 72]. Significant genera were defined as those having a Benjamini-Hochberg adjusted p value <0.05 [73]. Only genera with at least 10 reads detected in at least $20\%$ of samples were considered for differential abundance testing. Nonparametric differential abundance testing was performed by subjecting rarefied count data to a Wilcoxon rank-sum test followed by Benjamini-Hochberg p value correction, as implemented by Nearing et al. [ 31]. Compositional data analysis for differential abundance testing was conducted using ALDEx2 as implemented by Nearing et al. [ 30, 31]. A Wilcoxon rank-sum test was performed to identify differentially enriched predicted metagenomic functions. ## Bacterial strains and growth condition Bacterial strains were obtained from Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ), Japan Collection of Microorganisms (JCM), Biodefense and Emerging Infections Research Resources Repository (BEI Resources), or isolated in the house (Table S4). A starter culture for each strain was grown in the indicated growth media in a Coy anaerobic chamber (Coy Laboratory) at 37°C for 72 h. Aliquots of the starter culture were then normalized to 0.05 OD600 in 5 mL of fresh growth media. Cultures grown in Chopped Meat Medium with Carbohydrates (CMC, Anaerobe Systems Catalog #AS-823), as well as sterile, negative control CMC, were filtered through a 100-μm cell strainer prior to OD600 measurement. Following normalization, cultures were incubated at 37°C for 48 h, after which cultures were centrifuged at 14,000 rpm for 10 min, filtered using a 0.2-μm syringe filter, and the resulting filtered supernatants were stored at −80°C until use for assessing in vitro AhR activation potential and metabolomics. ## Bacterial genome analysis TBLASTN 2.11.0+ [74] was used to compare TnaA [E. coli], TrpA [E. coli], TrpB [E. coli],TDC [Staphylococcus epidermidis], DDC [Bacillus licheniformis], IpdC [Azospirillum], AofH [Bacillus subtilis], and AldA [E. coli] protein sequences to publicly available sequenced bacterial genomes for isolates GNE6609, GNE6603, GNE6686, and GNE6624 (Genbank: GCF_000011065.1, GCF_900106755.1, GCA_000177015.3, and GCA_003202955.1). Searches yielding high-scoring segment pairs (HSPs) satisfying e value ≤ 0.1 and query coverage ≥ $60\%$ were considered putative evidence of the presence of the query protein in the target genome. ## Metabolomics A quantitative targeted metabolomics panel-based LC-MS/MS method, which included analysis of Tryptophan metabolism pathway metabolites, was performed. Analysis was also performed by a pre-qualified liquid chromatography-tandem mass spectrometry (LC-MS/MS) quantitation method which includes 20 tryptophan metabolism pathway metabolites (see the list of analytes in the supplementary table S6) and 17 stable isotope-labeled internal standards for quantitation by surrogate matrix (methanol) approach as described previously for other panels [75, 76]. L-Trp metabolism panel sample preparation includes protein precipitation using organic solvent methanol containing stable isotope-labeled internal standards for optimal recovery. Isomeric or closest eluting stable isotope-labeled internal standards were used for analytes with no corresponding stable isotope-labeled internal standards. Sample batch analysis included surrogate matrix calibration curves for each analyte, and batch performance was evaluated based on the accuracy and precision of quality control samples such as sample pool quality control samples, surrogate matrix quality control samples, and calibration curve samples. After satisfactory sample batch analysis, concentration data for each metabolite was reported in ng/g or μg/g (nM or μM) for fecal samples in a predetermined standardized data format. See Supplementary Table S6 for list of panel analytes, stable isotope-labeled internal standards, and additional protocol details. ## AhR activity assay AhR activity reporter cell line H1L6.1c3 was obtained from the Denison lab at UC Davis and utilized to assess the AhR activation potential of fecal samples, metabolites, and bacterial culture supernatants according to previously published protocols [37]. In brief, fecal samples were prepared by resuspending fecal pellets in sterile PBS to a final concentration of 100 mg/mL, centrifuging at 4°C to pellet particulate matter, and filtering the supernatant through a sterile 0.22-uM cellulose filter. Prepared fecal samples were stored at −80°C until needed. H1L6.1c3 cells were treated with 100uL of prepared fecal samples diluted 1:10 in growth media [Alpha-Minimal Essential Media (MEM; Invitrogen, #12000-063) containing $10\%$ fetal bovine serum] and incubated at 33°C in the presence of $5\%$ CO2 for 24 h, afterwhich the luciferase activity was measured. For bacterial culture supernatants, H1L6.1c3 cells were treated with 100uL of sterile-filtered bacterial culture supernatant diluted 1:10 in growth media [Alpha-Minimal Essential Media (MEM; Invitrogen, #12000-063) containing $10\%$ fetal bovine serum] and incubated at 33°C in the presence of $5\%$ CO2 for 24 h, after which luciferase activity was measured. For IL-22Fc responsive fecal metabolites (L-Trp, I3C, and 5-HIAA), H1L6.1c3 cells were treated with 100uL of growth media [Alpha-Minimal Essential Media (MEM; Invitrogen, #12000-063) containing $10\%$ fetal bovine serum] containing 100uM of either L-Trp, I3C, or 5-HIAA and incubated at 33°C in the presence of $5\%$ CO2 for 4 h, afterwhich the luciferase activity was measured metabolites. All samples were measured in triplicate. Statistically significant differences in AhR activation potential were determined by Wilcoxon rank-sum test. ## IL-22Fc Ph1b study protocol Healthy volunteers and UC patients with moderate to severe ulcerative colitis were enrolled in a phase 1b multicenter, randomized, observer-blinded, placebo-controlled study to evaluate the safety, tolerability, pharmacokinetics, and pharmacodynamics of efmarodocokin alfa [41]. Briefly, 69 HV and 46 UC patients screened for this study provided baseline stool samples. Twenty-four UC patients met the study enrollment criteria (including diagnosis of UC with a Mayo Endoscopic Subscore of ≥2 points by central reading at screening) and were administered placebo ($$n = 6$$) or efmarodocokin alfa ($$n = 18$$) intravenously at doses ranging from 30 to 90 μg/kg either biweekly or monthly. Stool samples were obtained predose and at defined post-dose time points, with missing timepoints treated as absent. A total of five placebo-treated UC patients and nine efmarodocokin alfa-treated UC patients had complete stool sample collection history for the entire study (screening, day 29, day 43, day 64, and day 85). Note that fecal samples for one efmarodocokin alfa-treated UC patient were exhausted prior to performing the in vitro AhR activity assay, leading to $$n = 5$$ placebo-treated UC patients and $$n = 8$$ efmarodocokin alfa-treated UC patients for this analysis. ## Supplementary Information Additional file 1: Figure S1. Supplement to IL-22Fc microbiota response in mice. ( A) After a two week acclimatization period during which animals were co-housed, six- to eight-week-old, female C57BL/6J mice ($$n = 16$$) were randomly re-caged on study day 0 into two treatment groups and separately housed for the remainder of the experiment: IL-22Fc treated ($$n = 8$$, four mice per cage) and control ($$n = 8$$, four mice per cage). Animals were treated with 50 ug/mouse of either IL-22Fc or anti-ragweed isotype control via intraperitoneal injection three times a week for two weeks (as indicated by the red arrow), and euthanized on day 14. See methods for additional details. ( B) Richness following IL-22Fc or aRW treatment. P-values determined by T-test. ( C-D) Normalized abundance in control and IL-22Fc treated animals of the top highly IL-22Fc responsive bacterial genera consistently enriched (C) or depleted (D). Statistical significance determined by DESeq2 and corrected for false discovery. * BH-corrected p-value <0.05. For all plots, triangle, square, and circle markers indicate ileal, colonic, and fecal samples respectively. Teal and red markers indicate animals treated with (IL-22Fc ($$n = 8$$) or isotype control ($$n = 8$$), respectively, respectively. Figure S2. Tryptophan metabolism pathways. ( A) Depiction of well established tryptophan metabolism pathways relevant to the GI microbiome. As the functional capacity of the gut microbiome is highly complex and yet to be fully characterized, additional, as yet unidentified microbial metabolic pathways involving L-Trp metabolism may exist. Predominantly microbiome derived enzymes and metabolites are highlighted in green [13, 20]. L-Trp: L-Tryptophan, 5-HT: Serotonin, 5-HIAAld: 5-Hydroxyindoleacetaldehyde, 5-HIAA: 5-Hydroxyindoleacetic acid, L-Kyn: L-Kynurenine, ILA: Indole Lactic Acid, IPA: Indole Propionic Acid, IAAld: Indole Acetaldehyde, IAA: Indole Acetic Acid, I3C: Indole-3-Carboxaldehyde. Figure S3. Supplement to IL-22Fc tryptophan metabolism response in mice. ( A) Fecal concentrations of additional microbially derived L-Trp metabolites included in our panel that were not significantly altered by IL-22Fc treatment. Teal and red markers indicate animals treated with IL-22Fc ($$n = 8$$) or isotype control ($$n = 8$$), respectively. ( B) Log2(Fold change from baseline) in fecal concentrations of additional microbially derived L-Trp metabolites included in our panel. Statistical significance determined by Rank-Sum Test (. $p \leq 0.1$, * $p \leq 0.05$, ** $p \leq 0.01$). ( C) Predicted prevalence of tryptophan synthase, tryptophanase, and aldehyde dehydrogenase among ASVs representing the highly IL-22Fc enriched or depleted bacterial genera. Statistical significance determined by Barnard’s Test (* $p \leq 0.05$). ( D) Predicted prevalence of tryptophan synthase, tryptophanase, and aldehyde dehydrogenase among ASVs representing the highly IL-22Fc enriched or depleted bacterial genera, stratified by genus. Teal and Red bars indicate bacterial genera enriched or depleted following IL-22Fc treatment, respectively. Figure S4. Supplement to Intestinal AhR Signaling following IL-22Fc treatment in mice. ( A) Scatterplot of L-Trp, I3C, and 5-HIAA fecal concentration vs. fecal AhR activity. ( B-C) Scatterplot of predicted ileal (B) or colonic (C) metagenomic counts of tryptophan synthase, tryptophanase, and aldehyde dehydrogenase vs. fecal AhR activity. For all panels, Spearman's rank correlation coefficient (rho) was calculated to determine correlation strength. Figure S5. Supplement to Proxy Strain AhR Activity and Metabolite Production. ( A-B) AhR activity, reported as Log2(fold change over matched sterile culture media) of sterile filtered culture supernatant from bacterial isolates representing the highly IL-22Fc depleted (A) or enriched (B) genera, stratified by bacterial isolate. ( C) Metabolite production, reported as Log2(fold change over matched sterile culture media) of sterile filtered culture supernatant from bacterial isolates that induced AhR activity, stratified by bacterial isolate. Culture supernatants were collected and measured in triplicate for all panels. Statistical significance indicates relative AhR activity or metabolite concentration is statistically greater than zero as tested by one sample T-test (* $p \leq 0.05$, ** $p \leq 0.01$), indicating an in vitro AhR activity or metabolite concentration is statistically greater than that of matched sterile culture media. Figure S6. Supplement to efmarodocokin alfa effect on fecal metabolite concentrations in humans. ( A) Fecal concentrations of L-Kyn at screening in HV ($$n = 69$$) compared to UC ($$n = 46$$). P-value determined by Rank-Sum test. ( B) Fecal AhR Activity (reported as fold change over unstimulated wells) at screening in HV ($$n = 69$$) and UC ($$n = 46$$). P-value determined by T-test. ( C) Fecal concentrations of L-Trp, I3C, IPA, and L-Kyn at screening in HV ($$n = 69$$), moderate UC (mMCS ≤ 5, $$n = 10$$), and severe UC (mMCS > 5, $$n = 13$$). P-value determined by Rank-Sum test. ( D) Fecal AhR Activity (reported as fold change over unstimulated wells) at screening in HV ($$n = 69$$) moderate UC (mMCS ≤ 5, $$n = 10$$), and severe UC (mMCS > 5, $$n = 13$$). P-value determined by T-test. ( E) AUC of Log2(fold change from screening) for fecal concentrations of L-Trp, I3C, IPA, and L-Kyn in enrolled UC participants following administration of efmarodocokin alfa ($$n = 9$$) or placebo ($$n = 5$$). P-value determined by T-Test. For panel E, only enrolled UC participants with a complete sample collection history (screening, day 29, day 43, day 64, and day 85) were considered for AUC calculations. Figure S7. Supplement to efmarodocokin alfa effect on fecal microbiota in enrolled trial participants. ( A) Mean Log2(fold change in abundance at screening between enrolled UC and HV) of the IL-22Fc depleted ($$n = 8$$) and IL-22Fc enriched ($$n = 5$$) genera present in the fecal microbiome of enrolled trial participants at screening. P-value determined by T-test. ( B) Normalized abundance of highly IL-22Fc depleted (B) or enriched (C) genera in the fecal microbiome of enrolled HV ($$n = 39$$) and UC ($$n = 24$$) participants at screening. P-value determined by DESeq2 analysis. Figure S8. Supplement to efmarodocokin alfa effect on fecal microbiota AUC in humans. AUC of Log2(fold change from screening) for normalized abundance of IL-22Fc depleted (A) or enriched (B) genera detected in enrolled UC patients receiving either placebo ($$n = 5$$) or efmarodocokin alfa ($$n = 9$$) treatment. Only enrolled UC patients with a complete sample collection history (screening, day 29, day 43, day 64, and day 85) were considered for AUC calculations. Note that CAG-873 was not detected in any enrolled UC patients with a complete sampling history. P-value determined by T-Test. Additional file 2: Table S1-3. Differential Abundance Testing Results. To identify significantly enriched or depleted bacterial genera in the (S1) ileum, (S2) colon, and (S3) stool of mice treated with IL-22Fc compared to placebo, the DESeq2 R-package was used as described by McMurdie et al. [ 72]. We performed two additional, conceptually different statistical approaches for DA testing, nonparametric Wilcoxon rank-sum testing as well as compositional data analysis utilizing ALDEx2 to ensure our observations were robust to the statistical method implemented. Effect sizes, nominal p-values, and BH-adjusted p-values for all statistical approaches are displayed. Only genera with at least 10 reads detected in at least $20\%$ of samples were considered for differential abundance testing. Table S4. Bacterial Strain Information. Strain ID, source, strain name, whole genome and 16S rRNA gene sequence accession number, preferred growth medium, and proxy taxonomy (i.e. experimentally identified bacterial taxa this strain is representing based on ≥$97\%$ 16S rRNA gene sequence similarity) for all bacterial strains used in this study. All strains sourced from Genentech (i.e.: Source = ‘Genentech’) were isolated directly from mice utilized in the described experiments while all other strains were acquired from the indicated culture collection. Table S5. Evidence for the capacity to produce tryptophan derived AhR ligands in sequenced genomes of putative AhR activating bacterial strains. Shown are the results of genome-wide BLAST comparisons of TnaA [E. coli], TrpA [E. coli], TrpB [E. coli], TDC [Staphylococcus epidermidis], DDC [Bacillus licheniformis], IpdC [Azospirillum], AofH [Bacillus subtilis] and AldA [E. coli] protein query sequences against sequenced genomes of bacterial strains GNE6609, GNE6603, GNE6686, and GNE6624. Each line within a field represents a high-scoring segment pair (HSP) yielded by tBLASTn searches of query protein sequences against the corresponding translated bacterial isolate genome with evalue ≤ 0.1. Genes are considered putatively present if BLAST searches yield at least one HSP satisfying query coverage ≥ $60\%$. Table S6. List of metabolomics panel analytes, stable isotope labeled internal standards, and additional protocol details. ## References 1. Takatori H, Kanno Y, Watford WT, Tato CM, Weiss G, Ivanov II. Lymphoid tissue inducer–like cells are an innate source of IL-17 and IL-22. J Exp Medicine. (2009.0) 206 35-41. 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--- title: Association of triglyceride-glucose index with atherosclerotic cardiovascular disease and mortality among familial hypercholesterolemia patients authors: - Jun Wen - Qi Pan - Lei-Lei Du - Jing-Jing Song - Yu-Peng Liu - Xiang-Bin Meng - Kuo Zhang - Jun Gao - Chun-Li Shao - Wen-Yao Wang - Hao Zhou - Yi-Da Tang journal: Diabetology & Metabolic Syndrome year: 2023 pmcid: PMC9997009 doi: 10.1186/s13098-023-01009-w license: CC BY 4.0 --- # Association of triglyceride-glucose index with atherosclerotic cardiovascular disease and mortality among familial hypercholesterolemia patients ## Abstract ### Background Familial hypercholesterolemia (FH) is an inherited metabolic disorder with a high level of low-density lipoprotein cholesterol and the worse prognosis. The triglyceride-glucose (TyG) index, an emerging tool to reflect insulin resistance (IR), is positively associated with a higher risk of atherosclerotic cardiovascular disease (ASCVD) in healthy individuals, but the value of TyG index has never been evaluated in FH patients. This study aimed to determine the association between the TyG index and glucose metabolic indicators, insulin resistance (IR) status, the risk of ASCVD and mortality among FH patients. ### Methods Data from National Health and Nutrition Examination Survey (NHANES) 1999–2018 were utilized. 941 FH individuals with TyG index information were included and categorized into three groups: < 8.5, 8.5–9.0, and > 9.0. Spearman correlation analysis was used to test the association of TyG index and various established glucose metabolism-related indicators. Logistic and Cox regression analysis were used to assess the association of TyG index with ASCVD and mortality. The possible nonlinear relationships between TyG index and the all-cause or cardiovascular death were further evaluated on a continuous scale with restricted cubic spline (RCS) curves. ### Results TyG index was positively associated with fasting glucose, HbA1c, fasting insulin and the homeostatic model assessment of insulin resistance (HOMA-IR) index (all $p \leq 0.001$). The risk of ASCVD increased by $74\%$ with every 1 unit increase of TyG index ($95\%$CI: 1.15–2.63, $$p \leq 0.01$$). During the median 114-month follow-up, 151 all-cause death and 57 cardiovascular death were recorded. Strong U/J-shaped relations were observed according to the RCS results ($$p \leq 0.0083$$ and 0.0046 for all-cause and cardiovascular death). A higher TyG index was independently associated with both all-cause death and cardiovascular death. Results remained similar among FH patients with IR (HOMA-IR ≥ 2.69). Moreover, addition of TyG index showed helpful discrimination of both survival from all-cause death and cardiovascular death ($p \leq 0.05$). ### Conclusion TyG index was applicable to reflect glucose metabolism status in FH adults, and a high TyG index was an independent risk factor of both ASCVD and mortality. ## Introduction Atherosclerosis cardiovascular disease (ASCVD) is the leading cause of death in both developed and developing countries [1]. Atherosclerotic plaque formation and development is the most important pathophysiological process in ASCVD, which is associated with endothelial cell injury, inflammation, oxidative stress, lipid and other metabolic alterations, and thrombosis [2, 3]. Familial hypercholesterolemia (FH) is an inherited metabolic disorder resulting in lifetime exposure to high levels of low-density lipoprotein cholesterol (LDL-C) and consequently, an elevated risk of ASCVD [4, 5]. However, despite the heavy cardiovascular metabolic burden, the prevalence of type 2 diabetes (T2D), another common risk factor of ASCVD, is lower in FH patients compared with unaffected relatives [6] or the normal population [7, 8]. Given the idea, the glucolipid metabolism of FH patients seems to be different from normal persons, although there has not been convincing explanation. Besides, the combination of FH and T2D doubles the risk of cardiovascular disease in persons with FH [7, 8]. Given the glucolipid discord and the detrimental synergistic effects, it’s essential to verify the efficacy of established glucolipid metabolism-related biomarkers and develop novel biomarkers among FH patients [9]. The triglyceride-glucose (TyG) index, calculated as Ln (fasting triglycerides [TG, mg/dl] × fasting blood glucose [mg/dl]/2), is an emerging tool to reflect insulin resistance (IR) [10]. IR is the earlier stage and principal characteristic of T2D and also leads to a cluster of abnormalities including accelerated atherosclerosis, hypertension or polycystic ovarian syndrome [11, 12]. Recent studies revealed the relationship between TyG index and pro-atherosclerotic factors such as inflammation, endothelial dysfunction, glucolipid metabolism disorders and thrombosis [13–15]. Therefore, it’s not surprising that the TyG index was positively associated with a higher prevalence of a series of diseases such as symptomatic coronary artery disease and all-cause mortality [10, 16]. However, the effects of IR (refer to higher TyG index) on cardiovascular health, and the value of TyG index to reflect IR and glucose metabolic status as well as predict ASCVD and mortality risks in FH patients has not been evaluated. In view of the above, data from a nationally representative sample of FH individuals from National Health and Nutrition Examination Surveys (NHANES) were utilized to determine the association between the TyG index and glucose metabolic indicators, IR status, the risk of ASCVD and mortality among FH adults. ## Study population NHANES is a two-year-cycle cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) of America, involving a home interview and a medical examination, offering demographics, socioeconomic status, dietary and health information as well as physical and physiological measurements of U.S. population [17]. NHANES study protocol was approved by The National Center for Health Statistics (NCHS) ethics committee (Protocol #2011–17, https://www.cdc.gov/nchs/nhanes/irba98.htm). Informed consent from all the participants was obtained before participating. There were 1456 out of 116876 participants in the survey diagnosed as FH in accordance with a Dutch Lipid Clinical Network (DLCN) index that was higher than or equal to 3 points, as described previously [18]. Among them, TyG index was available in 1120 individuals ($$n = 336$$ excluded). Participants ($$n = 179$$) were excluded due to loss of follow-up. Therefore, a total of 941 individuals (530 females and 411 males), is the final study population (Fig. 1). The study population was divided into 3 groups: < 8.5, 8.5–9.0, > 9.0 to make the participants number in the groups comparable, as reported previously [19].Fig. 1Flowchart of the study population ## Data collection Information on socioeconomic conditions, behavior and history of diseases was obtained through questionnaires by experienced interviewers. Drinking was defined as having at least 12 alcohol drinks per year; smoking was defined as having smoked ≥ 100 cigarettes in life [20]. History of ASCVD, hypertension or diabetes was defined as self-reported physician diagnosis [21]. History of ASCVD was defined as self-reported physician diagnosis. “ Has a doctor or other health professional ever told {you/SP} that {you/s/he} had a coronary heart disease/angina, also called angina pectoris/heart attack (also called myocardial infarction)/stroke?” was a question on the medical conditions section of the household questionnaires via home interview, and those who answered “yes” were deemed to have a history of ASCVD. Body mass index (BMI) was calculated using weight (kg)/height (m2). Income status of the family was described with poverty-income ratio, which was the ratio of family income to the poverty threshold. TyG index was calculated as Ln (fasting triglycerides [TG, mg/dl] × fasting blood glucose [mg/dl]/2) [10]. Laboratory results were obtained from serum specimens when they visited the mobile examination center and vials were stored under appropriate frozen (− 30 °C) conditions until they were shipped to National Center for Environmental Health for testing [22]. The original homeostatic model assessment insulin resistance index (HOMA-IR) was calculated as fasting insulin × fasting blood glucose/22.5 [23]. Homeostatic model assessment insulin sensitivity index (HOMA-IS) was calculated as 1/HOMA-IR. Details for each variable measurement is published on the NHANES website [24]. ## Follow-up and endpoints The period of follow-up lasted from the date of the interview through the last follow-up time, Dec 31 2019, or the date of death, whichever came first. Records from the NDI provided information on these including participants' causes of death. The endpoints for this study included all-cause mortality and cardiovascular death, which encompassed cardiac death (e.g., sudden cardiac death and myocardial infarction) and vascular death (e.g., stroke) [1]. The median follow-up duration is 114 months (interquartile range, 57–159 months). The maximum follow-up duration is 248 months. ## Statistical analysis Data were analyzed using SPSS complex sample module version 22.0 (IBM Corp, Armonk, NY) and R (version 4.2.2). The Kolmogorov–Smirnov normality test was adopted to test the normality of continuous variables. Normally distributed variables were described with mean ± standard deviation (SD). Variance analysis was adopted to compare the mean levels while Chi-square tests were chosen to compare the percentages of categorical variables across the different groups. Spearman correlation analysis was used to test the association of TyG index and various established glucose metabolism-related indicators (fasting blood glucose, fasting insulin, HbA1c, HOMA-IR and HOMA-IS index. Both univariable and multivariable-adjusted logistic regression were used to calculate the odds ratio (OR) with $95\%$ confidence interval (CI) for the relationship between TyG index and ASCVD. The possible nonlinear relationships between TyG index and the all-cause or cardiovascular death were further evaluated on a continuous scale with restricted cubic spline (RCS) curves based on the multivariable Cox proportional hazards models, with four nodes at the fixed percentiles of $5\%$, $35\%$, $65\%$ and $95\%$ of the distribution of TyG index. The event-free survival rates among the groups were estimated by the Kaplan–Meier method and compared by the log-rank test. Cox regression analysis was used to assess the association between TyG index and mortality. The following variables were utilized as covariates in the study population: age, gender, BMI, smoke, drink, HOMA-IR, low-density lipoprotein cholesterol (LDL-C), creatinine and hypertension. To assess the added prognostic value of TyG index beyond the original model, C-index was calculated, using predict.coxph function to predict Cox model with predict value type = "survival". Furthermore, a sensitivity analysis was applied to further investigate the association of TyG index with ASCVD and mortality in FH patients with IR (HOMA-IR ≥ 2.69) [25]. A two-sided $p \leq 0.05$ was considered statistically significant. ## Demographic characteristics of the study population Among the 941 FH participants in this study, 204 ($21.68\%$) had TyG index < 8.5 (low TyG index), 311 ($33.05\%$) had TyG index ≥ 8.5 and ≤ 9.0 (moderate TyG index), and 426 ($45.27\%$) had TyG index > 9.0 (high TyG index; Table 1). As expected, those with higher TyG index had higher levels of fasting blood glucose, fasting total glyceride (TG), fasting insulin, HbA1c and HOMA-IR index (all $p \leq 0.001$), in addition to higher occurrence rate of diabetes and impaired fasting blood glucose (IFG; $p \leq 0.001$) than those with lower TyG index. Besides, individuals with higher TyG index were also more likely to be older, white and had higher BMI, systolic blood pressure (SBP), fasting total cholesterol (TC) and LDL-C as well as lower high-density lipoprotein cholesterol (HDL-C; all $p \leq 0.05$) compared with those with relatively lower TyG index. There was no significant difference among the four groups in DLCN score, poverty-income ratio, drink, smoke, hypertension history, diastolic blood pressure (DBP), alanine aminotransferase (ALT), aspartate aminotransferase (AST) as well as serum creatinine ($p \leq 0.05$).Table 1Demographic characteristics of the study populationTyG < 8.5 ($$n = 204$$)TyG 8.5–9 ($$n = 311$$)TyG > 9 ($$n = 426$$)p valueAge (SD) (y.o.)50.0 (15.5)50.6 (16.0)53.6 (14.4)0.004Female (%)125 ($61.3\%$)164 ($52.7\%$)241 ($56.6\%$)0.159Race (%) < 0.001 White89 ($43.6\%$)163 ($52.4\%$)228 ($53.5\%$) Black64 ($31.4\%$)62 ($19.9\%$)57 ($13.4\%$) Mexican American13 ($6.37\%$)40 ($12.9\%$)70 ($16.4\%$) Other38 ($18.6\%$)46 ($14.8\%$)71 ($16.7\%$) BMI (SD) (kg/m2)28.6 (7.37)29.3 (6.31)30.4 (6.60)0.004 Poverty ratio (SD)2.46 (1.65)2.29 (1.56)2.34 (1.55)0.504 DLCN score (SD)3.51 (0.79)3.57 (0.84)3.61 (0.99)0.397DM status (%) < 0.001 DM16 ($7.92\%$)38 ($12.8\%$)159 ($39.2\%$) IFG12 ($5.94\%$)32 ($10.7\%$)55 ($13.5\%$) IGT11 ($5.45\%$)26 ($8.72\%$)27 ($6.65\%$) No163 ($80.7\%$)202 ($67.8\%$)165 ($40.6\%$)Education status (%)0.005 College or above108 ($53.2\%$)147 ($47.4\%$)165 ($38.9\%$) High school or equivalent50 ($24.6\%$)72 ($23.2\%$)111 ($26.2\%$) Less than high school45 ($22.2\%$)91 ($29.4\%$)148 ($34.9\%$) Drink (%)132 ($64.7\%$)187 ($60.1\%$)235 ($55.2\%$)0.064 Smoke (%)107 ($52.7\%$)176 ($57.5\%$)246 ($57.7\%$)0.748 ASCVD (%)59 ($29.1\%$)89 ($29.1\%$)146 ($34.4\%$)0.126 Hypertension (%)98 ($48.0\%$)145 ($46.6\%$)223 ($52.3\%$)0.275 SBP (SD) (mmHg)126 (23.0)125 (19.3)129 (20.7)0.025 DBP (SD) (mmHg)71.8 (13.0)70.7 (12.6)72.1 (13.6)0.388 HbA1c (SD) (%)5.5 (0.5)5.7 (0.7)6.5 (2.0) < 0.001 Fasting insulin (μU/mL)9.15 (6.45)13.9 (28.7)16.6 (17.3) < 0.001Fasting glucose (SD) (mmol/L)5.35 (0.66)5.59 (0.93)7.32 (3.56) < 0.001 HOMA-IR index (SD)2.22 (1.70)3.52 (7.29)5.61 (8.81) < 0.001 ALT (SD) (U/L)23.9 (15.7)26.8 (27.0)34.2 (96.5)0.139 AST (SD) (U/L)26.4 (21.6)26.8 (31.2)27.0 (18.7)0.952 Creatinine (SD) (μmol/L)76.1 (22.3)75.8 (26.3)77.8 (32.4)0.593 Fasting TG (SD) (mg/dL)79.3 (18.6)128 (24.9)216 (72.1) < 0.001 Fasting TC (SD) (mg/dL)255 (56.5)262 (53.0)280 (60.6) < 0.001 HDL-C (SD) (mg/dL)61.2 (17.9)52.3 (13.6)47.9 (13.7) < 0.001 LDL-C (SD) (mg/dL)178 (53.3)184 (49.0)190 (55.7)0.038TyG, triglyceride-glucose index; SD, standard deviation; BMI, body mass index; DLCN, Dutch Lipid Clinical Network; DM, diabetes mellitus; IFG, impaired fasting blood glucose; IGT, impaired glucose tolerance; ASCVD, atherosclerotic cardiovascular diseases; DBP, diastolic blood pressure; SBP, systolic blood pressure; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TG, total glyceride; TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol ## Correlation between TyG index and established glucose metabolism indicators among FH patients As a novel indicator of IR, the diagnostic value of TyG index in FH population was examined by assessing the association with those well-recognized indicators reflecting glucose metabolism status including fasting blood glucose, HbA1c, fasting insulin, HOMA-IR and HOMA-IS. As shown in Table 2, TyG index was positively associated with fasting glucose, HbA1c, fasting insulin and HOMA-IR index, and negatively associated with HOMA-IS (all $p \leq 0.001$) among FH patients, indicating that TyG index was applicable to FH individuals to reflect the glucose metabolism status. Table 2Correlation between TyG index and established glucose metabolism-related indicatorsVariablesCorrelation coefficient (r)p valueFasting glucose (mmol/L)0.462 < 0.001HbA1c (%)0.333 < 0.001Fasting insulin (μU/mL)0.272 < 0.001HOMA-IR0.407 < 0.001HOMA-IS-0.407 < 0.001HOMA-IR, homeostatic model assessment insulin resistance index; HOMA-IS, homeostatic model assessment insulin sensitivity index ## Association of TyG index with ASCVD among FH patients Given the idea that TyG index is associated with the development and prognosis of cardio-cerebrovascular diseases [26, 27], we evaluated the relationship in FH population by conducting a multivariable-adjusted logistic analysis (Table 3). After adjustment with age, gender, BMI, smoke, drink, HOMA-IR, LDL-C, creatinine and hypertension, the OR was 2.38 ($95\%$ CI 1.25–4.53, $$p \leq 0.01$$) in the group with high TyG index compared with the group with low TyG index; while no significant difference was observed between the group with moderate and low TyG index (OR, 1.45; $95\%$CI 0.73–2.89, $$p \leq 0.29$$). For every 1 unit increase of TyG index, the risk of ASCVD increased by $74\%$ after adjustment ($95\%$CI 1.15–2.63, $$p \leq 0.01$$). In the sensitivity analysis, TyG index remained significantly associated with ASCVD in FH patients with IR (Crude OR, 1.52, $95\%$CI 1.09–2.13, $$p \leq 0.02$$; Adjusted OR, 2.14; $95\%$CI 1.19–3.86, $$p \leq 0.01$$). These results indicated that increase of TyG index was independently associated with the elevation of ASCVD risk among FH adults. Table 3Odd ratios of TyG index with ASCVD among FH participantsNO. Events/subjectsCrude ORp valueAdjusted ORp valueLow TyG (< 8.5)$\frac{59}{204}$RefRefModerate TyG (8.5–9.0)$\frac{89}{3110.94}$ (0.67–1.46)0.941.45 (0.73–2.89)0.29High TyG (> 9.0)$\frac{146}{4261.28}$ (0.89–1.84)0.182.38 (1.25–4.53)0.01All FH participants Every 1 unit increase of TyG$\frac{294}{9411.26}$ (1.02–1.57)0.031.74 (1.15–2.63)0.01FH participants with IR Every 1 unit increase of TyG$\frac{166}{4581.52}$ (1.09–2.13)0.022.14 (1.19–3.86)0.01OR, odd ratios; Ref. reference, IR, insulin resistanceAdjusted model: age, gender, BMI, smoke, drink, HOMA-IR, LDL-C, creatinine and hypertension ## Association of TyG index with mortality among FH patients In this study, the median follow-up duration is 114 months (interquartile range, 57–159 months). In Fig. 2, we used RCS to flexibly model and visualize the relationship with all-cause and cardiovascular mortality in FH adults. The risk of both all-cause and cardiovascular mortality was relatively flat until around 8.6 of TyG index, and then started to increase rapidly afterwards (p for non-linearity = 0.0083 and 0.0046, respectively). The study population was divided into three groups (< 8.5, 8.5–9.0 and > 9.0) due to the strong U/J-shaped relation between TyG index and mortality. Figure 3 depicts the cumulative hazard of all-cause death and cardiovascular death in the groups with different TyG index. FH patients reported > 9.0 had lower survival probability compared with those reported low or moderate TyG index (log-rank $$p \leq 0.035$$). No significant difference was observed in the incidence of cardiovascular mortality (log-rank $$p \leq 0.093$$). Cox analysis was utilized to further assess the association of TyG index with all-cause death and cardiovascular death (Table 4). After multivariable adjustment, TyG index was independently associated with both all-cause death (HR, 1.55; $95\%$CI 1.10–2.18, $$p \leq 0.01$$ for every 1 unit increase of TyG index) and cardiovascular death (HR, 1.79; $95\%$CI 1.04–3.09, $$p \leq 0.04$$ for every 1 unit increase of TyG index). In sensitivity analysis (Table 5), TyG index remained significantly associated with all-cause and cardiovascular death in FH patients with IR (HR, 1.91; $95\%$CI 1.12–3.24, $$p \leq 0.02$$ and HR, 2.73; $95\%$CI 1.11–6.74, $$p \leq 0.03$$, respectively). The adjusted HR was 1.61 ($95\%$CI 1.07–2.42, $$p \leq 0.02$$) for all-cause death and 2.09 ($95\%$CI 1.02–4.30, $$p \leq 0.045$$) for cardiovascular death in the group with high TyG index comparing with the group with moderate TyG index. However, no significant difference was observed when comparing the two groups reported low and moderate TyG index (HR, 1.19; $95\%$CI 0.71–2.00, $$p \leq 0.51$$ for all-cause death; HR, 2.19; $95\%$CI 0.96–4.97, $$p \leq 0.06$$ for cardiovascular death). In Cox prediction models, C-statistic values were 0.489 ($95\%$CI 0.437–0.542) and 0.408 ($95\%$CI 0.324–0.491) for survival from all-cause death and cardiovascular death with traditional risk factors, respectively (Table 6). Addition of TyG index to the original model resulted in significant improvements in discrimination of both survival from all-cause death (C-statistic 0.483; $95\%$CI 0.432–0.535, $$p \leq 0.042$$) and cardiovascular death (C-statistic 0.397; $95\%$CI 0.315–0.479, $$p \leq 0.042$$). These results demonstrated that TyG index was an independent risk factor for all-cause death and cardiovascular death among FH adults. Fig. 2Restricted cubic spline curves (RCS) for the relationship between TyG index and the all-cause death A and cardiovascular death BFig. 3Cumulative hazard of all-cause death A and cardiovascular death B across the groups with different TyG indexTable 4Hazard ratios of TyG index with all-cause and cardiovascular mortality among FH participantsNO. Events/subjectsCrude HRp valueAdjusted HRp valueAll-cause death$\frac{151}{941}$Low TyG (< 8.5)$\frac{26}{2041.05}$ (0.64–1.71)0.851.19 (0.71–2.00)0.51Moderate TyG (8.5–9.0)$\frac{42}{311}$RefRefHigh TyG (> 9.0)$\frac{83}{4261.55}$ (1.07–2.24)0.021.61 (1.07–2.42)0.02Every 1 unit increase of TyG1.54 (1.16–2.06)0.0031.55 (1.10–2.18)0.01Cardiovascular death$\frac{57}{941}$Low TyG (< 8.5)$\frac{14}{2041.96}$ (0.91–4.23)0.092.19 (0.96–4.97)0.06Moderate TyG (8.5–9.0)$\frac{12}{311}$RefRefHigh TyG (> 9.0)$\frac{31}{4262.02}$ (1.04–3.94)0.042.09 (1.02–4.30)0.045Every 1 unit increase of TyG1.68 (1.06–2.67)0.031.79 (1.04–3.09)0.04HR, hazard ratiosAdjusted model: age, gender, BMI, smoke, drink, HOMA-IR, LDL-C, creatinine and hypertensionTable 5Hazard ratios of TyG index with all-cause and cardiovascular mortality among FH participants with IRNO. Events/subjectsCrude HRp valueAdjusted HRp valueAll-cause death$\frac{66}{458}$Every 1 unit increase of TyG2.10 (1.37–3.23)0.0011.91 (1.12–3.24)0.02Cardiovascular death$\frac{28}{458}$Every 1 unit increase of TyG2.54 (1.32–4.88)0.012.73 (1.11–6.74)0.03Adjusted model: age, gender, BMI, smoke, drink, HOMA-IR, LDL-C, creatinine and hypertensionTable 6C-index of TyG for predicting all-cause and cardiovascular death in FH patientsModelsC-statistic ($95\%$ CI) p valueAll-cause death Original model 0.489 (0.437–0.542)Ref Original model + TyG0.483 (0.432–0.535)0.042Cardiovascular death Original model0.408 (0.324–0.491)Ref Original model + TyG0.397 (0.315–0.479)0.042The C-index was calculated by using the predict.coxph function to predict the Cox model with predicted value type = "survival"Original model included age, gender, BMI, smoke, drink, HOMA-IR, LDL-C, creatinine and hypertension ## Discussion Herein, we combined NHANES data from 1999 to 2018, and a total of 941 FH participants with TyG index and follow-up data accessible were finally included. The capabilities of TyG index to reflect glucose metabolism status (hyperglycemia and IR) and predict the risks of ASCVD and mortality were preliminarily verified. As a cost-effective tool, TyG index integrates fasting glucose and triglycerides levels and could provide an early relevant clinical evaluation of glucolipid metabolic disorder such as IR, and potential prediction value of ASCVD and mortality risks. To the best of our knowledge, this is the first study to examine the value of TyG index among adults with FH. As a hallmark of T2D, IR is a state of decreased sensitivity and responsiveness to the action of insulin [28]. Arguably, the gold standards of IR diagnosis are euglycemic insulin clamp and intravenous glucose tolerance testing; however, they have not been applied in clinical practice due to invasiveness and high cost [29]. TyG index is used as a novel marker of IR in healthy individuals, according to a considerable number of studies since 2014. In a Korean study with 5354 middle-aged nondiabetic individuals enrolled, the risk of diabetes onset was fourfold higher in the highest quartile compared with the lowest quartile (relative risk, 4.10; $95\%$CI 2.70–6.21) [30]. In a White European cohort with 4820 participants, the HR was 5.59 ($95\%$ CI 3.51–8.91) in the fourth quartile vs. the bottom quartile [31]. However, it’s important to note that the applicability of TyG index to detect IR among specific populations with metabolism characteristics should be further evaluated in theory because TyG index largely depends on the glucolipid metabolic status. For instance, the risk of T2D in lean Koreans increased along with the increase of TyG index with HRs of in each quartile were 1.00, 1.63 ($95\%$CI 1.18–2.24), 2.30 ($95\%$CI 1.68–3.14) and 3.67 ($95\%$CI 2.71–4.98), respectively [32]. Whereas, TyG index had lower sensitivity and specificity compared with HOMA-IR, as reported in a study based on healthy Argentinean children aged 9.3 ± 2.2 years old [33]. FH is a common inherited condition leading to significant metabolism disorders, characterized by high LDL-C level. Interestingly, in spite of the heavy cardiovascular metabolic burden, the prevalence of T2D is lower in FH patients [6–8]. Furthermore, Mendelian randomization analysis suggested a significant association between gene variants determining higher LDL-C levels and a lower risk of T2D [34]. Given the glucolipid metabolism features and the high prevalence of FH (estimated at 1 in 200), efforts to investigate the capability of TyG index to evaluate glucose metabolism disorders among FH patients are required [9]. Results in this pilot study demonstrated that TyG index was positively associated with well-recognized indicators such as fasting blood glucose (r, 0.462; $p \leq 0.001$), HbA1c (r, 0.333; $p \leq 0.001$), fasting insulin (r, 0.272; $p \leq 0.001$) and HOMA-IR (r, 0.407; $p \leq 0.001$) in FH population. Therefore, TyG index seems to be applicable to FH patients. Despite major advances in understanding of the disease and effective therapies such as lipid-lowering drugs and dietary interventions, FH is still underdiagnosed and undertreated [35]. As a result, FH is an important risk factor of ASCVD and premature deaths [36]. IR and T2D have also been reported to increase the risk of ASCVD by exerting harmful effects on the vascular smooth muscle cells, macrophages and endothelium [37]. The effects of T2D on the risk of cardiovascular disease in FH patients were evaluated by Climent et al. where the OR was 2.01 ($95\%$CI 1.18–3.43, $$p \leq 0.01$$), suggesting T2D and IR led to additional ASCVD risk [7]. As a result, it’s essential to develop reliable and convenient tools to detect IR and predict ASCVD and mortality risks in FH population. As demonstrated in several large clinical studies, TyG index is associated with the development and prognosis of cardiovascular diseases [26, 27]. In a prospective study including a total of 1655 nondiabetic patients with acute coronary syndrome with LDL-C below 1.8 mmol/L, a high TyG index level (≥ 8.33) was associated with a higher incidence of acute myocardial infarction ($21.2\%$ vs. $15.2\%$, $$p \leq 0.014$$), larger infarct size (described by cardiac injury biomarkers), and higher incidence of revascularization ($8.9\%$ vs. $5.0\%$, $$p \leq 0.035$$) [38]. In another study focused on elderly acute coronary syndrome patients, TyG index increased by $28\%$ ($95\%$CI 1.06–1.56, p for trend = 0.02) for each SD increase in the TyG index. Herein, we found that high TyG index acted as an independent risk factor of ASCVD and mortality in FH adults. To be specific, the risk of ASCVD, all-cause death and cardiovascular death increased by $74\%$ after adjustment ($95\%$CI 1.15–2.63, $$p \leq 0.01$$), $55\%$ ($95\%$CI 1.10–2.18, $$p \leq 0.01$$) and $79\%$ ($95\%$CI 1.04–3.09, $$p \leq 0.04$$) for every 1 unit increase of TyG index after multivariable adjustment. These results indicated that IR plays an essential, detrimental role in FH patients, which could be another explanation for the residual risks of FH. Evaluation and treatment of IR should also be emphasized since most of current therapies focus on the management of LDL-C [35]. Besides, we also noticed that the combination of TyG index and the traditional model led to significant improvements in Cox prediction models of both survival from all-cause mortality (0.483 vs. 0.489, $$p \leq 0.042$$) and cardiovascular mortality (0.397 vs. 0.408, $$p \leq 0.042$$). Surprisingly, a strong U/J-shaped relation was observed according to the RCS results ($$p \leq 0.0083$$ for all-cause death and 0.0046 for cardiovascular death, respectively) and the moderate TyG index group had the lowest risk of mortality. When compared with the moderate TyG index group, the low TyG index group had a trend toward an increased risk of all-cause and cardiovascular mortality (HR, 1.19; $95\%$CI 0.71–2.00 and HR, 2.19; $95\%$CI 0.96–4.97, respectively), despite no significant differences. A potential cause of the phenomenon is the effect of certain parameters which could not be adjusted, such as hypoglycemia. TyG index was significantly correlated with blood glucose (r, 0.462; $p \leq 0.001$). The low TyG index group had a trend of worse prognosis, which may be caused by lower blood glucose. Nevertheless, we failed to provide robust statistical evidence on the elevated risk of mortality in the low TyG index group compared with the moderate TyG index group mainly due to the limited sample size. The current study has several limitations to be noted. Firstly, the small sample size may have limited the statistical power to detect some associations as significant when comparing different groups, as mentioned above. However, up to 114 months of median follow-up duration helps to improve statistical efficiency. Secondly, the data of TyG index was obtained only at baseline and it’s hard to control for possible changes in blood glucose and TG during the follow-up in theory. However, it’s still considered a valid method to evaluate the long-term effects of TyG index according to a large number of reports [32, 39, 40]. Thirdly, the cut-off of the TyG index in this report was based on the RCS results. Therefore, more investigations based on other populations are required to explore whether the $\frac{8.5}{9.0}$ cut-off is universal. Lastly, although the adjustment model incorporated the most available demographic and clinical parameters, some residual or unmeasured confounding variables such as laboratory results related with thrombogenesis and coagulation could have affected the results. Conclusively, results in this pilot study suggested that TyG index was applicable to reflect glucose metabolism status in FH adults, and a high TyG index was an independent risk factor of ASCVD and all-cause mortality in the same population. ## References 1. Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W. 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--- title: 'Dose response relationship between food insecurity and quality of life in United States adults: 2016–2017' authors: - Sanjay Bhandari - Jennifer A. Campbell - Rebekah J. Walker - Abigail Thorgerson - Aprill Z. Dawson - Leonard E. Egede journal: Health and Quality of Life Outcomes year: 2023 pmcid: PMC9997014 doi: 10.1186/s12955-023-02103-3 license: CC BY 4.0 --- # Dose response relationship between food insecurity and quality of life in United States adults: 2016–2017 ## Abstract ### Background Food insecurity is associated with worse general health rating, but little research exists investigating whether there is a dose response relationship across levels of food security and mental and physical health domains at the population level. ### Methods Data from the Medical Expenditure Panel Survey (2016–2017) with US adults aged 18 years and older was used. The physical component score (PCS) and mental component score (MCS) of Quality of Life, served as the outcome measures. Four categories of food insecurity (high, marginal, low, very low food security) served as the primary independent variable. Linear regression was used to run unadjusted followed by adjusted models. Separate models were run for PCS and MCS. ### Results In a sample of US adults, $16.1\%$ reported some degree of food insecurity. For PCS, marginal (β = − 2.54 ($p \leq 0.001$), low (β = − 3.41, ($p \leq 0.001$), and very low (β = − 5.62, ($p \leq 0.001$) food security was associated with worse PCS scores, compared to adults with high food security. For MCS, marginal (β = − 3.90 ($p \leq 0.001$), low (β = − 4.79, ($p \leq 0.001$), and very low (β = − 9.72, ($p \leq 0.001$) food security was associated with worse MCS scores, compared to adults with high food security. ### Conclusion Increasing levels of food insecurity were associated with decreased physical and mental health quality of life scores. This relationship was not explained by demographic factors, socioeconomic factors, insurance, or comorbidity burden. This study suggests work is needed to mitigate the impact of social risk, such as food insecurity, on quality of life in adults, and understand pathways and mechanisms for this relationship. ## Introduction Food insecurity is defined as the limited or uncertain access to, or ability to acquire, nutritionally adequate food (USDA). In 2020, over 38 million or $11.8\%$ of US adults lived in food insecure households, with $8\%$ reporting low food security and $3.8\%$ reporting very low food security [1]. Evidence shows food insecurity is strongly associated with a range of health conditions, such as hypertension, coronary artery disease, diabetes, hepatitis, stroke, cancer, asthma, arthritis, chronic obstructive pulmonary disease, kidney disease, depression, and limitations in activities [2–5]. Food insecure individuals have higher rates of emergency department visits and hospitalizations even after accounting for other socioeconomic factors [6]. Further, food insecurity poses a substantial financial burden on society accounting for an annual $77.5 billion in additional health care expenditure [6, 7]. Given its prevalence, associated adverse health outcomes, and increased healthcare costs, growing attention towards addressing food insecurity as a social need is taking place across several professional medical societies, health insurance payers, and healthcare organizations who have invested in and recommend screening for food insecurity with referral to food resources [8–12]. While such attention is serving to grow the evidence base for how to address food insecurity at a population level, there remain gaps in understanding the impact that food insecurity has on health status at the population level. Specifically, there is need to further understand the relationship between food insecurity and patient reported outcomes, namely health related quality of life (HRQoL), individual perception of one’s physical and mental health status as well the factors that influence their functioning [13], to further inform food insecurity interventions from a holistic standpoint. Although various studies have highlighted the inverse relationship between food insecurity and HRQoL in general [14–21], there is limited knowledge on whether a dose–response relationship exists between food insecurity and HRQoL and whether the relationship is consistent across both physical and mental health domains of HRQoL at the population level. Therefore, this paper aims to address this gap by examining the dose–response relationship between food insecurity and HRQoL using a nationally representative sample of US adults. We hypothesize that worsening levels of food insecurity will be associated with worsening HRQoL and this dose–response relationship will be consistent across both physical and mental domains of HRQoL. ## Data source The 2016 and 2017 Medical Expenditure Panel Survey (MEPS) was analyzed as a cross-sectional analysis. These two years included detailed questions on food insecurity and included measures of HRQOL. The full year consolidated files and food security files from the household component files were used. MEPS is a national survey that collects data from U.S. citizens and their families on items such as health services, employment, and insurance. This survey began recording in 1996 and is overseen by the Agency for Healthcare Research and Quality (AHRQ) [22]. The sample included adults 18 and older who serve as the reference person in the food insecurity file. ## Quality of life The primary outcomes are physical component summary (PCS) and mental component summary (MCS), both of which are continuous measures. The MEPS Self-Administered Questionnaire uses the Short Form-12 Health Survey Version 2 forming two summary scores based on responses to these questions for PCS and MCS. Both MCS and PCS assess four key domains, respectively. For MCS these include: [1] vitality; [2] social functioning; [3] role emotional; and [4] mental health. For PCS these include: [1] functioning; [2] role physical; [3] bodily pain; and [4] general self-rated health. Example questions include: “How much of the time during the past 4 weeks have you felt calm and peaceful?”, and “During the past 4 weeks, how much did pain interfere with your normal work?”. Information from all 12 questions were used as part of the scoring algorithms for both the PCS and the MCS, which was completed by AHRQ [22]. Both scales are scored based on an adult population mean of 50, with a standard deviation of 10. In both scales higher scores represents higher functioning [22]. ## Food insecurity The primary independent variable is household food security. This variable is based on the USDA 10-item Adult Food Security Scale and previous literature [23, 24]. The raw score was created by adding the affirmative answers to the following prompts:“How often in the last 30 days has anyone in the household worried whether food would run out before getting money to buy more?” Score of 1 if responded ‘often’ or ‘sometimes’. “How often in the last 30 days did the food purchased not last and the person/household didn't have money to get more?” Score of 1 if responded ‘often’ or ‘sometimes’. “How often in the last 30 days could the person/household not afford to eat balanced meals?” Score of 1 if responded ‘often’ or ‘sometimes’. “In the last 30 days, did the person/household reduce or skip meals because there wasn't enough money for food?” Score of 1 if responded ‘yes’. “How many meals were skipped in the last 30 days?” Score of 1 if responded with 3 + days. “In the last 30 days, did the person/household ever eat less because there wasn't enough money for food?” Score of 1 if responded ‘yes’. “In the last 30 days, was the person/household ever hungry but didn't eat because there wasn't enough money for food?” Score of 1 if responded ‘yes’. “In the last 30 days, did anyone in the household lose weight because there wasn't enough money for food?” Score of 1 if responded ‘yes’. “In the last 30 days, did anyone in the household not eat for a whole day because there wasn't enough money for food?” Score of 1 if responded ‘yes’. “How many days in the last 30 days did anyone in the household not eat for a whole day because there wasn't enough money for food?” Score of 1 if responded with 3 + days. The score was adjusted so that those who answered 3 + days to, “how many meals were skipped in the last 30 days?” as well as to, “how many days in the last 30 days did anyone in the household not eat for a whole day because there wasn't enough money for food?” had an additional 1 added to their overall score. This addition is based on Dean et al. [ 23] to provide comparability with the USDA Economic Research Service guidelines by aligning the 30-day window in MEPS questions to the 12-month window in USDA questions [23]. The intent of this scoring is to appropriately capture the severity of food insecurity when skipping meals or not eating for an entire day occurs multiple times. Missing was defined as refusing to answer, responding with “I don’t know” or “Not ascertained” to all food insecurity questions. If an individual answered at least one of the questions, they had a score calculated using the items with a response in the dataset. The raw score was grouped into 4 categories: high food security (score of 0), marginal food security (score of 1–2), low food security (score of 3–5), and very low food security (score of 6–11). ## Covariates The covariates included age (18–44, 45–64, 65 +), sex (male, female),race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Hispanic, other), education (less than high school, high school graduate, college or more), employment (employed, not employed), region (Midwest, Northwest, South, West), poverty (poor/near poor: ≤ $124\%$ poverty line, low income: 125–$199\%$ poverty line, middle income: 200–$399\%$ poverty line, high income: ≥ $400\%$ poverty line), and insurance (private, public, uninsured). ## Statistical analyses Population estimates were created using sampling weights as recommended by MEPS. Sample demographics were reported as percentages and/or mean with standard deviation. Primary outcomes were PCS and MCS scores treated as continuous variables, while the primary independent variable for each model was the 4 category food security variable with full food security as the reference group. Covariates included age, sex, race/ethnicity, education, region, poverty level, insurance status, and employment status. Initial models included unadjusted linear regression for the independent relationship between food security and HRQOL (PCS and MCS respectively). Then, fully adjusted linear regression models were run with PCS and MCS as separate outcome variables, the 4-category food security variable as the primary independent variable, and age, sex, race/ethnicity, education, region, poverty level, insurance status, and employment status as covariates. Covariates were included based on conceptual relevance and/or bivariate p-values < 0.25. All analyses were done in R package (Version 4.0.3). A p-value less than 0.05 was used to determine statistical significance. ## Results The total sample included 26,196 adult participants with food security data, representing 275,829,365 non-institutionalized United States adults aged 18 years and older. The sample characteristics are given in Table 1 with $83.9\%$ of participants indicating full food security, $6.6\%$ having marginal food security, $4.9\%$ having low food security, and $4.6\%$ having very low food security. Table 1Sample characteristics of United States adults: 2016–2017CharacteristicsTotal sample $$n = 26$$,196 (weighted %)Age18–4440.345–6435.865 + 23.9SexMale47.5Female52.5Race/ethnicityNon-hispanic white65.4Non-hispanic black12.5Hispanic13.8Other8.3EducationLess than high school10.8High school grad27.1College or more61.7EmploymentEmployed64.0Not employed35.9RegionMidwest21.4Northeast17.5South37.8West23.2PovertyPoor/near poor17.8Low income13.1Middle income28.6High income40.5InsurancePrivate69.4Public23.1Uninsured7.5Food insecurityHigh food security83.9Marginal food security6.6Low food security4.9Very low food security4.6Quality of lifePhysical component49.22 (range: 4.41–74.07)Mental component51.79 (range 0.04–75.64) The unadjusted and adjusted linear regression models for PCS are shown in Table 2. In the unadjusted model, those with marginal food security had a 2.91 decrease in PCS, on average, compared to those with full food security (β = − 2.91, $95\%$ CI [− 3.65, − 2.16], p-value < 0.001). Those with low food security had a 4.45 decrease (β = − 4.45, $95\%$ CI [− 5.20, − 3.69], p-value < 0.001), on average, and those with very low food security had a 7.29 decrease, on average (β = − 7.29, $95\%$ CI [− 8.24, − 6.34], p-values < 0.001). When adjusted by age, sex, race/ethnicity, education, region, poverty level, insurance status, and employment status, those with marginal food security had a 2.54 decrease in PCS, on average, compared to those with full food security (β = − 2.54, $95\%$ CI [− 3.14, − 1.94], p-value < 0.001). Those with low food security had a 3.41 decrease, on average, compared to those with full food security (β = − 3.41, $95\%$ CI [− 4.03, − 2.80], p-value < 0.001) and those with very low food security had a 5.62 decrease, on average, compared to those with full food security (β = − 5.62, $95\%$ CI [− 6.36, − 4.85], p-values < 0.001).Table 2Unadjusted and Adjusted Linear Regression Model Examining the Association between Food Insecurity and PCS, United States Adults: 2016–2017Food security categoryPCS quality of LifeUnadjustedAdjustedβ (CI)β (CI)Marginal food security− 2.91* (− 3.65; − 2.16)− 2.54* (− 3.14; − 1.94)Low food security− 4.45* (− 5.20; − 3.69)− 3.41* (− 4.03; − 2.80)Very low food security− 7.29* (− 8.24; − 6.34)− 5.62* (− 6.39; − 4.85)Ref: High Food Security. * $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001.$ Adjusted for age, sex, race/ethnicity, education, employment, region, poverty level, insurance status The unadjusted and adjusted linear regression models for MCS are shown in Table 3. In the unadjusted model, those with marginal food security had a 4.70 decrease in MCS, on average, compared to those with full food security (β = − 4.70, $95\%$ CI [− 5.33, − 4.07], p-value < 0.001). Those with low food security had a 5.81 decrease, on average, compared to those with full food security (β = − 5.81, $95\%$ CI [− 6.43, − 5.18], p-value < 0.001); and those with very low food security had a 11.24 decrease, on average, compared to those with full food security (β = − 11.24, $95\%$ CI [− 12.12, − 10.37], (p-values < 0.001). When adjusted by age, sex, race/ethnicity, education, region, poverty level, insurance status, and employment status, those with marginal food security had a 3.90 decrease in MCS, on average, compared to those with full food security (β = − 3.90, $95\%$ CI [− 4.53, − 3.28], p-value < 0.001). Those with low food security had a 4.79 decrease, on average, compared to those with full food security, (β = − 4.79, $95\%$ [− 5.45, − 4.12], p-value < 0.001); and those with very low food security had a 9.72 decrease, on average, compared to those with full food security (β = − 9.72, $95\%$ CI [− 10.62, − 8.82], (p-values < 0.001).Table 3Unadjusted and adjusted linear regression model examining the association between food insecurity and MCS, United States adults: 2016–2017Food security categoryMCS quality of LifeUnadjustedAdjustedβ (CI)β (CI)Marginal food security− 4.70 (− 5.33; − 4.07)− 3.90* (− 4.53; − 3.28)Low food security− 5.81* (− 6.43; − 5.18)− 4.79* (− 5.45; − 4.12)Very low food security− 11.24* (− 12.12; − 10.37)− 9.72* (− 10.62; − 8.82)Ref: High Food Security. * $p \leq 0.05$; $p \leq 0.01$; $p \leq 0.001.$ Adjusted for age, sex, race/ethnicity, education, employment, region, poverty level, insurance status ## Discussion Overall, in a nationally representative sample of approximately 2.8 million US adults, this study shows that $16.1\%$ of the US population reported experiencing marginal, low, or very low food security. Increasing levels of food insecurity were found to be associated with worsening physical and mental HRQoL scores, consistent with our hypotheses. Specifically, for physical health related quality of life, after adjusting for demographic factors, marginal food security was significantly associated with 2.54 lower points, low food security with 3.41 lower points, and very low food security with 5.62 lower points, compared to adults with full food security. For mental health related quality of life, marginal food security was significantly associated with 3.90 lower points, low food security was associated with 4.79 lower points, and very low food security was associated with 9.72 lower points compared to adults with full food security. These findings add to our current understanding by demonstrating at a population level that a dose–response relationship exists between increasing levels of food insecurity and both physical and mental health related quality of life. While few studies have examined the dose–response relationship between food insecurity and HRQoL at a population level, these findings are consistent with the current literature. For example, existing evidence shows the relationship between food insecurity and mental health outcomes, underscoring the presence of worse mental health outcomes, including depression, in food insecure individuals [17, 19, 21]. However, many of these studies have been limited in generalizability by focusing on specific sub-populations with chronic conditions and lacking national representation. Further, many studies have used inconsistent measures of quality of life. For example, two recent studies used nationally representative samples of the US population and evaluated the association between food insecurity and quality of life using various validated patient-reported outcome scales [25, 26]. While both studies highlight the negative association between food insecurity and HRQoL, the study by Hammer et al. specifically showed variability of the association based on the scales used, with the recommended measure being the abbreviated derivative of the present study’s measure of HRQoL. In addition, the current findings show that marginal food security is significantly associated with lower HRQoL compared to those with full food security. This is a notable finding as marginal food security and full food security are often collapsed into one food secure category [3]. Consistent with existing evidence showing that marginal food security is related to worse health outcomes across the life span for health and well-being [27–29], these findings add to the current body of literature highlighting the need to examine food insecurity across categories, rather than collapsing into a binary indicator. The implications of our study findings are multifold. Food insecurity is included as one of the five core domains of the Centers for Medicare & Medicaid Services (CMS)’s Accountable Health Communities Health-Related Social Needs Screening Tool.(The Centers for Medicare & Medicaid Services) The Supplemental Nutrition Assistance Program (SNAP), the nation’s largest federally funded food program to counteract food insecurity, serves approximately 1 in 7 Americans; enrollment in SNAP has been shown to be linked with improved outcomes across various dimensions, including improvement in food insecurity measures, reduction in mortality and poverty, lower healthcare costs, and improved medication adherence [30–34] Given the strong inverse relationship of food insecurity and HRQoL, it is critical to incorporate HRQoL in the current food interventions and public health initiatives to effectively address food insecurity from a holistic standpoint. Owing to HRQoL’s role as a predictor for treatment success, and as an important patient reported outcome in medical decision-making and evaluation of practices and policies, the number of research studies reporting on HRQoL has steadily increased in the past decades [35–37]. Evidence shows worse HRQoL is associated with increased mortality and healthcare utilization across various groups of patient populations [38–40]. Therefore, it is critical to inform interventions that will reduce rates of food insecurity as a way to improve HRQoL. More specifically, given the current study’s findings demonstrating the dose–response relationship between food insecurity with HRQoL, future research should focus on reversing the major decline in HRQoL across all gradients and identify whether the effect will differ by the duration or modalities of intervention (directly providing food, food vouchers, monetary assistance, etc.). Additionally, as availability of data on HRQoL will help researchers to further evaluate the relationship between food insecurity and HRQoL, the current findings can inform efforts to better evaluate HRQoL at the population level by health insurance payers encouraging collection of data through value-based payment models in addition to addressing food insecurity. Existing food programs such as SNAP can also consider routinely collecting HRQoL data from its participants to substantiate robust effectiveness of the program, which can also serve as a valuable information for federal government/Congress regarding SNAP program. Finally, given the findings that the dose response relationship between food security categories and HRQOL are independent of sociodemographic factors and comorbidity, it will be important to understand the potential pathways and mechanisms of these effects. Longitudinal studies and studies that use path analysis or structural equation modeling may be warranted to better understand these relationships and identify potential targets for intervention. Although there are several strengths of our study including a nationally representative sample and incorporation of both physical and mental health components of HRQoL measure, a few limitations exist. First, given the cross-sectional nature of the study, causality between food insecurity (exposure variable) and HRQoL (outcome) cannot be inferred. Second, recall biases cannot be ruled out as the data are based on self-report. Finally, there might be some residual confounders that were not accounted for. ## Conclusion In conclusion, our study showed that increasing levels of food insecurity were associated with worsening physical and mental health related quality of life scores in a dose–response relationship. Specifically, this study found that marginal food security was significantly associated with worse HRQoL compared to full food security. This relationship across categories of food security was not explained by demographic factors, socioeconomic factors, or insurance status. Since HRQoL is an important patient reported outcome and is associated with various clinical outcomes, further research is needed to mitigate the impact of food insecurity on HRQoL in adults. Specifically, next steps in the field should emphasize identification of potential pathways and mechanisms of the relationship as well as development of interventions to reduce rates of food insecurity as a way to improve HRQoL across both physical and mental health domains. ## References 1. 1.Coleman-Jensen A, Rabbitt MP, Gregory CA, Singh A. US Department of Agriculture Economic Research Service. Economic Research Report ERR-298: Household Food Security in the United States in 2020. 2021. 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--- title: Predictive value of estimated pulse wave velocity for cardiovascular and all-cause mortality in individuals with obesity authors: - Daidi Li - Feng Cao - Wenke Cheng - Yanyan Xu - Chuang Yang journal: Diabetology & Metabolic Syndrome year: 2023 pmcid: PMC9997019 doi: 10.1186/s13098-023-01011-2 license: CC BY 4.0 --- # Predictive value of estimated pulse wave velocity for cardiovascular and all-cause mortality in individuals with obesity ## Abstract ### Background Estimated pulse wave velocity (ePWV) has revealed excellent performance in predicting cardiovascular disease (CVD) risk. However, whether ePWV predicts all-cause mortality and CVD mortality in populations with obesity remains elusive. ### Methods We performed a prospective cohort including 49,116 participants from the National Health and Nutrition Examination Survey from 2005 to 2014. Arterial stiffness was evaluated by ePWV. Weighted univariate, multivariate Cox regression and receiver operating characteristic curve (ROC) analysis was used to assess the effects of ePWV on the risk of all-cause and CVD mortality. In addition, the two-piecewise linear regression analysis was used to describe the trend of ePWV affecting mortality and identify the thresholds that significantly affect mortality. ### Results A total of 9929 participants with obesity with ePWV data and 833 deaths were enrolled. Based on the multivariate Cox regression results, the high ePWV group had a 1.25-fold higher risk of all-cause mortality and a 5.76-fold higher risk of CVD mortality than the low-ePWV group. All-cause and CVD mortality risk increased by $123\%$ and $44\%$, respectively, for every 1 m/s increase in ePWV. ROC results showed that ePWV had an excellent accuracy in predicting all-cause mortality (AUC = 0.801) and CVD mortality (AUC = 0.806). Furthermore, the two-piecewise linear regression analysis exhibited that the minimal threshold at which ePWV affected participant mortality was 6.7 m/s for all-cause mortality and 7.2 m/s for CVD mortality. ### Conclusions ePWV was an independent risk factor for mortality in populations with obesity. High ePWV levels were associated with an increased all-cause and CVD mortality. Thus, ePWV can be considered a novel biomarker to assess mortality risk in patients with obesity. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13098-023-01011-2. ## Introduction Obesity (BMI ≥ 30 kg/m2) is the excessive accumulation or abnormal distribution of body fat and is closely related to metabolic disorders which can be divided into class I obesity (BMI, 30.0–34.9 kg/m2), class II obesity (BMI, 35.0–39.9 kg/m2) and class III obesity(BMI ≥ 40 kg/m2) based on BMI [1]. At present, obesity is a growing global public health issue that affects both adults and children [2, 3]. Since nutrient metabolism is essential for the survival, growth and development of all organisms, obesity is associated with a wide range of diseases including dyslipidemia, cardiovascular diseases (CVD), type 2 diabetes mellitus, hypertension, hepatic steatosis, stroke, gallbladder diseases, osteoarthritis and sleep apnea, increasing the risk of mortality for patients with obesity [4]. Hence, it has crucial to identify clinical indicators linked to mortality caused by obesity or obesity-related complications. Arterial stiffness plays a crucial role in the development of CVD and is strongly associated with mortality in populations with obesity [5]. Pulse wave velocity (PWV) is reported to be a novel non-invasive measurement of artery stiffness [6]. Several methods of measuring PWV have emerged over the past several years, such as carotid-femoral PWV (cfPWV) and brachial-ankle PWV (baPWV), of which cfPWV has been considered the standard method used to measure artery stiffness [7]. Although both cfPWV and baPWV have standardized measurement procedures [8], they require specialized and expensive devices that are rarely available in a primary hospital, thus, limiting their application in population screening [9]. To address this issue, researchers used estimated pulse wave velocity (ePWV) to assess the degree of aortic stiffness using an algorithm including age and mean blood pressure (MBP) [10]. At present, ePWV is considered an alternative to cfPWV [11], which may have critical importance in forecasting morbidity and mortality of people with obesity with different complications [12]. In this study, we analyzed the correlation between e-PWV and mortality in patients with obesity. ## Study design and population This was a prospective cohort with data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2014, with follow-up till December 31, 2019. The data comprised interviews, home or mobile physical examinations and laboratory tests. It followed a complex, stratified and multi-stage probability design concept and was audited and managed by the National Centre for Health Statistics. The survey was performed every 2 years; all participants signed informed consent; and further specific information, sampling methods and data collection procedures can be obtained here [13]. Data from a total of 49,116 participants were collected in five cycles from NHANES between 2005 and 2014 (2 years/cycle), and 9929 participants with obesity (BMI ≥ 30 kg/m2)[1] were identified. We excluded 859 participants who were diagnosed with cancer, 186 participants who were pregnant, and 456 without ePWV data. Further, we excluded 113 participants who died within 2 years of follow-up for reducing the potential reverse causation bias; thus, 8315 participants were eventually included in this study. Elaborated information is available at https://wwwn.cdc.gov/Nchs/Nhanes/. ## Measurement of ePWV ePWV was calculated using the following algorithm [14]: ePWV = 9.587–0.402 * age + 4.560 * 10–3 * age2 -2.621 * 10–5 * age2 * MBP + 3.176 * 10–3 * MBP * age—1.832 * 10–2 * MBP. In this algorithm, age was measured in years, and MBP was calculated as diastolic BP (DBP) + 0.4 * [systolic BP -DBP]. Participants’ BP was measured after 5 min of quiet rest. These operations were conducted by NHANES technicians. The average of at least three measurements was used as the BP value. ## Study endpoints The main outcomes in this study included CVD and all-cause mortality. All-cause mortality was the sum of all deaths whereas CVD mortality was diagnosed per the International Classification of Diseases version 10 codes (ICD-10 I00-I09, I11, I13 or I20-I51). ## Covariates We collected and categorized covariates such as age (≤ 60 years and > 60 years), sex (male/female), race (non-Hispanic white people, non-Hispanic Black people, Mexican Americans, etc.), educational level (less than grade 9, 9 − 11 grade/graduated from high school or equivalent and college graduated or above), marriage (unmarried, married, separated, divorced, widowed and those living with partner/others), family income, smoking and drinking status. The smoking status was classified into the following: Never smoked (< 100 cigarettes/session), previously smoked (> 100 cigarettes/session, currently not smoking) and current smoker (> 100 cigarettes/session, either on some days or every day) [15]. Drinking status was categorized as non-drinkers (< 12 drinks in life), ever drinking in the last year (alcohol or 12 drinks in life, currently not drinking), mild/moderate drinkers (over the past year: females, once/day or less; males, twice/day or less), heavy drinkers (over the past year: females, more than once/day; males, > twice/day) [16]. Medical history and medication use were collected via family interviews and mobile examination centers using standardized questionnaires. The specific details for collecting these covariates can be obtained from the NHANES Laboratory/Medical Technician Procedure Manual [13]. ## Statistical analysis Appropriate weighting (MEC2yr) was conducted in the statistical analysis. In population baseline characteristics, continuous variables were expressed as weighted means (standard errors) and categorical variables as unweighted counts (weighted %). Hazard ratios (HRs) and $95\%$ confidence intervals (CIs) of ePWV with all-cause and CVD mortality were assessed using survey-weighted cox regression models. As most of the data were skewed, the Mann-Whitney test was used to compare the two groups for continuous variables and the chi-squared test was used to compare the categorical variables. From baseline characteristics, confounders were selected according to their association with the outcome of interest or a change in the effect estimate of > $10\%$ [17]. Additional file 1: Table S1 depicts the variables with a contribution of > $10\%$ to each result. Meanwhile, for the missing data, we obtained five data sets by multiple imputations, and the pooled multivariate cox regression results were regarded as sensitivity analysis and the results were shown in Additional file 1: Table S2. Regarding the models in this study, we eventually made the following adjustments. For model 1 in all-cause and CVD mortality, we adjusted age, race and gender. For model 2 in all-cause mortality, we adjusted age, gender, race, educational level, marital status, poverty income ratio (PIR), waist, hemoglobin (Hb), HbA1c, fasting plasma glucose (FPG), alanine aminotransferase (ALT), tuberculosis (TB), creatinine, low-density lipoproteins (LDL), C-reactive protein (CRP), osteoporosis, chronic kidney disease (CKD), arthritis, CVD, diabetes mellitus (DM), hyperlipidemia, hypertension, antihypertensive medication, diabetes medications, alcohol use and smoke. For model 2 in CVD mortality, we adjusted age, race, educational level, marital status, BMI, PIR, waist, Hb, HbA1c, FPG, ALT, aspartate aminotransferase (AST), TB, creatinine, low-density lipoproteins (LDL), osteoporosis, CKD, arthritis, CVD, DM, hyperlipidemia, hypertension, antihypertensive medication, diabetes medications, alcohol use and smoke. Subgroup analysis was conducted by following demographic covariates and CVD-risk factors including sex (male, female), age (< 60 years and ≥ 60 years), BMI (30–34.9,35–39.9, ≥ 40 kg/m2), race (non-Hispanic white people, non-Hispanic Black people, Mexican Americans, etc.), history of CVD (no/yes), and history of hypertension (no/yes), history of DM (no/yes), history of Asthma (no/yes), history of hyperlipidemia (no/yes), history of Arthritis (no/yes), history of Osteoporosis (no/yes), history of CKD (no/yes), and P-values for interaction were obtained. In addition, receiver operating characteristic (ROC) curve was used to assess the predictive value of ePWV for all-cause and CVD mortality. Furthermore, a generalized additive model was used to evaluate the association between ePWV and the risk of mortality [18], and P-values for non-linear regression were obtained using log-likelihood ratio tests. If a non-linear association was observed, a two-piecewise linear regression model was used to calculate the inflection point where the ratio of ePWV to mortality significantly changes in the smooth curve [19]. All statistical analyses were performed by R software (Version 4.2.1, http://www.R-project.org, The R Foundation) and EmpowerStats (Version 4.2.0, www.R-project.org, X&Y Solutions, Inc., Boston, MA). $P \leq 0.05$ was considered statistically significant. ## Baseline characteristics in the study Data were collected from a total of 8315 participants with obesity, and those data represented 64,916,705 individuals. After a median follow-up of 101 months (interquartile range 75 − 134 months), 833 all-cause mortality deaths and 240 CVD deaths occurred. The median level of ePWV was 7.87 m/s, and the low-ePWV group and the high-ePWV group included 4175 and 4158 participants, respectively. The specific results of survey-weighted baseline characteristics with high ePWV and low ePWV levels in participants with obesity are indicated in Table 1.Table 1Survey-weighted baseline characteristics of obese participants (representing 64,916,705 individuals) stratified by ePWV median levelsVariablesLow ePWV(4.78–7.87 m/s)$$n = 4157$$High ePWV(7.87–16.26 m/s)$$n = 4158$$P-valueVariablesLow ePWV(4.78–7.87 m/s)$$n = 4157$$High ePWV(7.87–16.26 m/s)$$n = 4158$$P-valueRepresented size35,722,92629,193,779No2748 (98.61)3045 (92.58)BMI (kg/m2)35.72 (0.12)35.88 (0.13)0.162Yes42 (1.39)265 (7.42)PIR2.70 (0.05)3.05 (0.05) < 0.001Arthritis < 0.001Waist (cm)112.71 (0.29)115.64 (0.30) < 0.001No3476 (83.15)2151 (52.65)HB (g/dL)14.32 (0.04)14.28 (0.04) < 0.001Yes677 (16.85)1996 (47.35)HBA1C (%)5.61 (0.02)6.09 (0.03) < 0.001CKD < 0.001FPG (mg/dL)106.67 (0.74)120.46 (1.62) < 0.001No3547 (91.52)2745 (74.66)AST (U/L)26.02 (0.23)26.60 (0.34)0.017Yes380 (8.48)1224 (26.34)ALT (U/L)30.53 (0.34)27.29 (0.40) < 0.001DM < 0.001TB (μmol/L)11.50 (0.12)12.06 (0.13) < 0.001No3396 (88.34)2584 (68.05)Creatinine(μmol/L)75.57 (0.47)82.69 (0.56) < 0.001Yes561 (11.66)1574 (31.95)HDL (mmol/L)1.18 (0.01)1.27 (0.01) < 0.001CVD < 0.001TC (mmol/L)5.05 (0.02)5.14 (0.03)0.011No3397 (96.41)2584 (82.89)LDL (mmol/L)3.06 (0.03)2.98 (0.03) < 0.001Yes159 (3.59)789 (17.11)TG (mmol/L)1.66 (0.04)1.86 (0.05) < 0.001Hypertension < 0.001CRP (mg/dL)0.60 (0.02)0.57 (0.02)0.316No2977 (71.63)998 (27.32)Age < 0.001Yes1180 (28.37)3160 (72.68) < 60 years4133 (99.45)1745 (51.61)Hyperlipidemia < 0.001 ≥ 60 years24 (0.55)2413 (48.39)No1107 (25.06)688 (15.24)Gender0.529Yes3050 (74.94)3470 (84.76) Women2293 (51.90)2265 (52.89)Antihypertensive medication < 0.001 Men1864 (48.10)1893 (47.11)No4000 (96.10)3499 (85.31)Race < 0.001Yes157 (3.90)656 (14.69) Non-Hispanic white1591 (59.61)1736 (70.27)Diabetes medications < 0.001 Non-Hispanic black1079 (15.76)1223 (15.29)No3846 (93.21)3113 (79.13) Mexican American842 (13.04)681(6.94)Yes311 (6.79)1042 (20.87) Other races645 (11.59)518 (7.50)Alcohol user < 0.001Education Levels < 0.001Never501 (9.82)636 (12.46) Less than 9th grade315 (4.79)604 (7.42)Former552 (13.02)1081(22.74) 9-11th grade/high school grade or equivalent1691 (37.50)1725 (39.59)Mild/moderate1034 (27.26)1229 (34.12) College graduate or above2146 (57.71)1827 (52.99)Heavy1714 (42.76)958 (25.26)Marital Status < 0.001Refused/Don't know356 (7.14)254 (5.41) Never married1128 (24.23)363 (8.28)Smoke status < 0.001 Married2011 (53.09)2286 (60.93)Never2546 (59.81)2176 (51.76) Separated163 (2.76)156 (2.41)Former642 (17.63)1376 (34.24) Divorced362 (9.14)640 (14.92)Current967 (22.56)603 (14.0) Widowed36 (0.63)572 (10.28) Living with partner/others457 (10.13)141 (3.19)Asthma0.003 No3404 (81.86)3510 (84.63) Yes747 (18.14)646 (15.37)Osteoporosis < 0.001Continuous variables are expressed as weighted mean (Standard error, SE). Categorical variables are expressed as counts (weighted %)ePWV estimated pulse wave velocity, CVD cardiovascular diseases, PIR Poverty income ratio, CKD Chronic kidney disease, BMI body mass index, HB hemoglobin, FPG fasting plasma glucose, TB Total bilirubin, TC Total cholesterol, LDL low-density lipoprotein cholesterol, TG triglycerides, HDL high-density lipoprotein cholesterol, ALT alanine aminotransferase, AST aspartate aminotransferase, HbA1C glycated hemoglobinA1c ## Univariate and multivariate cox regression analyses of the association between ePWV and all-cause and CVD mortality Table 2 depicts the association between ePWV levels and the risk of all-cause and CVD mortality using weighted univariable and multivariable cox regression analyses in participants with obesity. During the follow-up of this investigation, there were 722 deaths in the high-ePWV group and 111 in the low-ePWV group, respectively. We further analyzed the effect of ePWV levels on all-cause and CVD mortality using univariate cox regression, and the crude model demonstrated that high ePWV level (7.87 − 16.26 m/s) was an independent risk factor in both all-cause mortality (HR = 6.36, $95\%$ CI 4.92 − 8.22, $P \leq 0.001$) and CVD mortality (HR = 8.7, $95\%$ CI 4.57 − 16.55, $P \leq 0.001$). In addition, every 1 m/s-increase in ePWV increased the risk of both all-cause mortality (HR = 1.65, $95\%$ CI 1.59 − 1.72, $P \leq 0.001$) and CVD mortality (HR = 1.77, $95\%$ CI 1.61 − 1.85, $P \leq 0.001$).Table 2Weighted univariate and multivariate Cox regression to assess the association between ePWV levels and the risk of all-cause and cardiovascular disease mortality in obese participantsLow ePWV(4.78–7.87 m/s)High ePWV(7.87–16.26 m/s)p-valueEvery 1 m/s increase in ePWVp-valueAll-cause mortality Number of deaths111722833 Crude modelRef6.36 (4.92–8.22) < 0.0011.65 (1.59–1.72) < 0.001 Model 1Ref2.64 (1.83–3.80) < 0.0011.47 (1.38–1.57) < 0.001 Model 2Ref2.25 (1.12–4.90)0.0242.23 (1.26–3.93)0.006CVD Mortality Number of deaths24216240 Crude modelRef8.70 (4.57–16.55) < 0.0011.77 (1.61–1.85) < 0.001 Model 1Ref2.60 (1.12–6.02)0.0261.43 (1.29–1.61) < 0.001 Model 2 †Ref6.76 (1.09–41.92)0.0401.44 (1.14–1.82)0.003Model 1 adjusted age, race, genderModel 2Age, Gender, Race, Education levels, Marital Status, PIR, Waist, HB, HBA1c, FPG, ALT, TB, Creatinine, LDL, CRP, Osteoporosis, CKD, Arthritis, CVD, DM, Hyperlipidemia, Hypertension, Antihypertensive medication, Diabetes medications, Alcohol use, SmokeModel 2 † Age, Race, Education levels, Marital Status, BMI, PIR, Waist, HB, HBA1c, FPG, ALT, AST, TB, Creatinine, LDL, Osteoporosis, CKD, Arthritis, CVD, DM, Hyperlipidemia, Hypertension, Antihypertensive medication, Diabetes medications, Alcohol use, Smoke Next, multivariate cox regression analysis was conducted to evaluate ePWV levels and the risk of all-cause and CVD mortality in participants with obesity (Table 2). Three models were constructed after adjusting for different covariates. In all these models, high ePWV level was an independent risk factor for all-cause and CVD mortality. Besides, every 1 m/s-increase in ePWV was associated with a $123\%$ and $44\%$ increase in the risk of all-cause mortality (HR = 2.23, $95\%$ CI 1.26 − 3.93, $$P \leq 0.006$$) and CVD mortality (HR = 1.44, $95\%$ CI 1.14 − 1.82, $$P \leq 0.003$$) in the corresponding model 2 with maximum adjusted covariates, respectively. ## Subgroup analysis As indicated in Fig. 1 and 2, subgroup analyses were performed to assess the association of ePWV with all-cause and CVD mortality based on the different clinical characteristics of the participants. In most subgroups, the results for ePWV and all-cause mortality were consistent ($P \leq 0.05$, Fig. 1). However, the relationship between ePWV and all-cause mortality was slightly different in the subgroups with histories of CVD ($$P \leq 0.018$$ for interaction), DM ($$P \leq 0.002$$ for interaction), or CKD ($$P \leq 0.031$$ for interaction). In addition, the results for ePWV and CVD mortality were consistent in all CVD mortality subgroups (all P for interaction were > 0.05, Fig. 2).Fig. 1Subgroups analyses of the correlation between clinical characteristics and all-cause mortality. The model was adjusted for age, gender, body mass index (BMI), race, education levels, marital status, poverty income ratio (PIR), waist, hemoglobin (Hb), HbA1c, fasting plasma glucose (FPG), alanine aminotransferase (ALT), tuberculosis (TB), creatinine, low-density lipoproteins (LDL), C-reactive protein (CRP), osteoporosis, chronic kidney disease (CKD), arthritis, cardiovascular disease (CVD), diabetes mellitus (DM), hyperlipidemia, hypertension, antihypertensive medication, diabetes medications, alcohol use and smokeFig. 2Subgroups analyses of the correlation between clinical characteristics and cardiovascular disease (CVD) mortality. The model was adjusted for age, body mass index (BMI), race, education levels, marital status, poverty income ratio (PIR), waist, hemoglobin (Hb), HbA1c, fasting plasma glucose (FPG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), tuberculosis (TB), creatinine, low-density lipoproteins (LDL), osteoporosis, chronic kidney disease (CKD), arthritis, cardiovascular disease (CVD), diabetes mellitus (DM), hyperlipidemia, hypertension, antihypertensive medication, diabetes medications, alcohol use and smoke ## Diagnostic value of ePWV ROC analysis was constructed to evaluated the diagnostic value of ePWV in all-cause mortality and CVD mortality. The results demonstrated that ePWV had a certain accuracy in predicting all-cause mortality (AUC = 0.801, $95\%$ CI 0.79–0.82, $P \leq 0.001$) and CVD mortality (AUC = 0.806, $95\%$ CI 78–0.83, $P \leq 0.001$). The ePWV cutoff point value for the group with all-cause mortality, where the sensitivity and specificity were highest (0.79 and 0.68, respectively) was 8.7 m/s. And for the group with CVD mortality, where the sensitivity and specificity were highest (0.75 and 0.73, respectively) was 9.3 m/s (Fig. 3A and B).Fig. 3Receiver operating characteristics (ROC) curves for ePWV as a predictor of all-cause mortality A and CVD mortality B ## Two-piecewise linear regression model to assess the association between ePWV and the risk of all-cause and CVD mortality As indicated in Table 3 and Additional file 1: Table S3, a two-piecewise linear regression model was used to assess the association between the risk of ePWV and all-cause and CVD mortality based on respective model 2. ePWV had a non-linear association with the risk of both all-cause mortality and CVD mortality (non-linear $P \leq 0.001$) with two inflection points for each group (6.7 m/s and 8.7 m/s for all-cause mortality; 7.2 m/s and 8.5 m/s for CVD mortality). A positive correlation was observed between ePWV and all-cause mortality when ePWV > 6.7 m/s ($P \leq 0.001$), and the ePWV was positively correlated with CVD mortality when ePWV > 7.2 m/s ($P \leq 0.001$, Table 3).Table 3The results of two-piecewise linear regression model for ePWV and the risk of all-cause and CVD mortality in obese participantsOutcomeInflection-point of ePWV (m/s)HR$95\%$ CIP-valueAll-causeMortality < 6.70.50.1–2.00.3146.7–8.73.21.9–5.7 < 0.001 ≥ 8.71.31.2–1.6 < 0.001CVD Mortality < 7.20.50.1–2.00.2457.2–8.516.12.0–126.80.008 ≥ 8.51.31.1–1.6 < 0.001HRs have been fully adjusted for confounders, which are the same as the variables adjusted for Model 2 in Table2 (Model 2 for all-cause mortality, Model 2 † for CVD mortality, respectively) Meanwhile, a steeper growth curve was observed in all-cause mortality and CVD mortality when ePWV values were 6.7 − 8.7 m/s and 7.2 − 8.5 m/s, respectively. In this interval, every 1 m/s-increase in ePWV was associated with a 3.2-fold ($95\%$ CI 1.9 − 5.7, $P \leq 0.001$) and 16.1-fold ($95\%$ CI 2.0 − 126.8, $$P \leq 0.008$$) increase in the risk of all-cause mortality and CVD mortality. This demonstrated that the all-cause mortality increased more readily when ePWV values fell within this interval. Afterwards, smooth growth curves were observed when ePWV > 8.7 m/s (HR = 1.3, $95\%$ CI 1.2 − 1.6, $P \leq 0.001$) and 8.5 m/s (HR = 1.3, $95\%$ CI 1.1 − 1.6, $P \leq 0.001$) for all-cause mortality and CVD mortality, respectively (Table 3). The results for all-cause mortality and CVD mortality are demonstrated in Figs. 4A and B.Fig. 4Visualization curves of two-piecewise linear regression model for ePWV and the risk for all-cause mortality (A) and CVD mortality (B). The solid line and long dashed line represent the estimated odds ratio and its $95\%$ confidence interval. ePWV: estimated pulse wave velocity Finally, we explored the effect of ePWV on all-cause mortality and CVD mortality in different BMI, and the results were shown in Additional file 1: Table S3. For the participants with a BMI in 30–40 kg/m2, a steeper growth curve was observed in all-cause mortality and CVD mortality when ePWV values were below 8.45 m/s (HR = 2.77, $95\%$ CI 1.7 − 4.5, $P \leq 0.001$) and 8.73 m/s (HR = 5.05, $95\%$ CI 1.49 − 17.18, $P \leq 0.001$), respectively. And for the participants with a BMI ≥ 40 kg/m2, a similar steeper curve was found when ePWV < 11.34 m/s for CVD mortality. However, there was no significant correlation between ePWV and all-cause mortality and CVD mortality when ePWV > 8.45 m/s (HR = 1.41, $95\%$ CI 1.00 − 1.99, $$P \leq 0.05$$) and 11.34 m/s (HR = 0.88, $95\%$ CI 0.17 − 4.57, $$P \leq 0.883$$). ## Discussion This study was the first large retrospective observational study based on individuals with obesity to examine the relationship between ePWV levels and all-cause and CVD mortality. Our findings can be summarized as follows, [1] ePWV was an independent risk factor for mortality in populations with obesity; [2] higher ePWV was significantly associated with an increased risk of both all-cause and CVD mortality; [3] ePWV is non-linearly related to the risk of all-cause and CVD mortality. Each 1 m/s-increase in ePWV was associated with a $123\%$ and $44\%$ increase in the risk of all-cause mortality and CVD mortality, respectively. Subgroup analysis indicated that the association between ePWV and all-cause mortality was differed by the histories of CVD, DM and CKD, the increase of ePWV may pose an increased risk of all-cause mortality in the participants without a history of CVD, DM or CKD.The underlying mechanism is still unclear. ePWV is an indicator to assess the degree of arterial stiffness. Increased arterial stiffness is an important factor in the development and progression of chronic diseases including CVD, DM and CKD, hypertension, asthma, hyperlipidemia, arthritis, osteoporosis, etc. [ 20–22]. The caused may include the following aspects. Firstly, arterial stiffness generates a dramatic increase in blood pressure, leading to cerebrovascular accidents, aneurysms and other accidents [23]. Secondly, arterial stiffness affects blood circulation, reducing the delivery of oxygen and nutrients and leading to impaired organ function, thereby increasing the risk of all-cause mortality [24]. However, the interweaving of multiple factors may weaken the relationship between ePWV and all-cause mortality. Besides, patients with a history of CVD, DM and CKD, early intervention and treatment are often effective in reducing the risk of mortality [25, 26] which further weakens the association between ePWV and all-cause mortality. Many studies have considered ePWV > 10 m/s an important risk factor for the development of related diseases [27–29]. However, Further analysis is required because this division based solely on median value cannot capture the overall trend in predicting disease risk. In this study, based on the median ePWV value, we demonstrated that ePWV > 7.87 m/s is a significant risk factor for all-cause and CVD mortality. However, on further analysis using the two-piecewise linear regression model for ePWV and the risk of all-cause and CVD mortality, we demonstrated that the threshold value for distinguishing the ePWV hazard effect was 6.7 m/s for all-cause mortality and 7.2 m/s for CVD mortality, respectively. Since they are all below the median value, some participants with ePWV values between the median (7.87 m/s) and threshold values (6.7 m/s or 7.2 m/s) may be categorised as belonging to the low-risk group. Obesity is correlated with increased arterial stiffness [30, 31]. The structural and functional changes of the intima, middle layer and outer layer of the vasculature are the main causes of arterial stiffness in patients with obesity, which can be regulated by plasma factors such as aldosterone and insulin [32]. Besides, abnormal distribution, immune cell dysfunction and extracellular matrix remodeling are all important factors of arterial stiffness in patients with obesity [33–35]. Meanwhile, metabolic dysfunction increases insulin and aldosterone levels, activating the endothelial Na+ channel [32]. This subsequently causes an impaired release of nitric oxide (NO) produced by vascular endothelial cells, restricted diastole of vascular smooth muscle cells, stimulation of tissue remodeling and the development of fibrosis and thus vascular stiffness [36]. In addition, adipokines such as adiponectin and leptin, secreted by adipocytes, are also closely associated with arterial stiffness [37]. Plasma circulating adiponectin levels are inversely correlated with total body fat mass [38]. Adiponectin exerts anti-inflammatory and anti-fibrotic effects and enhances insulin sensitivity [39]. In addition, adiponectin reduces endothelial cell apoptosis [40], increases NO utilisation and improves vascular endothelial dysfunction [41]. Previous studies have revealed that low adiponectin levels are a risk factor for arterial stiffness [42, 43]. Plasma leptin levels increase linearly with increasing body weight, and leptin induces hypertension and endothelial dysfunction using aldosterone-dependent methods [44]. A large cross-sectional study demonstrated a synergistic relationship between high plasma leptin and low adiponectin levels and the progression of arterial stiffness [45]. In addition, the results of a meta-analysis revealed that leptin was positively correlated with arterial stiffness [46]. Arterial stiffness has been reported to exhibit a correlation with the morbidity and mortality of hypertension and CVD [47, 48]. Although cf-PWV and baPWV are widely regarded as the gold standard for assessing arterial stiffness in a non-invasive manner to predict the risk of CVD [8, 9], they necessitate specialized knowledge and expensive instrumentation. Compared with cf-PWV, ePWV is a convenient and affordable indicator that combines age and BP to evaluate arterial stiffness. In recent years, ePWV has widely been used as an indicator to predict disease risk in various populations, including the general population, patients with stroke, and those with CVD [27, 28, 49]. Moreover, ePWV revealed a better performance than the Framingham risk score (FRS) in predicting CVD ($$P \leq 0.044$$) and overall mortality ($P \leq 0.001$); however, no significant difference was observed between baPWV and FRS ($P \leq 0.05$ for both) [29]. Therefore, ePWV is suitable for widespread screening and self-monitoring usage in the population. In this study, we discovered the steeper growth curve for participants with ePWVs of 6.7 − 8.7 m/s for all-cause mortality and 6.2 − 8.5 m/s for CVD mortality, respectively. This indicates that until ePWV reaches a high threshold, the risk of all-cause mortality increases more quickly, and high BP is a distinctive feature of arterial stiffness [50], which is associated with reduced arterial compliance and causes increased myocardial fiber contractility and left ventricular load and ventricular diastolic dysfunction [51, 52]. Heart failure is caused by sudden, severe BP swings, which increase the risk of patient death [53]. This may explain the sharp rise in patient mortality before the specific threshold at which ePWV increased. The ventricles become tolerant owing to the gradual rise in BP, which delays the rate of mortality. Another possible reason could be that participants with higher ePWV were more conscious of their health status and thus sought medical assistance and made lifestyle improvements. This caused a slightly downward trend in all-cause mortality and CVD mortality. ## Strengths and limitations This study was a pioneering assessment of the predictive value of ePWV on all-cause and CVD mortality in a population with obesity. We demonstrated the dynamic model of ePWV affecting all-cause mortality and CVD mortality, which can be used to assess the prognosis of populations with obesity. However, our study had some limitations. First, our results are only partially representative of this region as the population used in this survey is primarily from parts of the United States and cannot be representative of all regions of the world. Second, Asian-Americans and native Americans have different diagnostic criteria for obesity, which may cause some bias into the results. Last, this study did not collect comprehensive information on participants’ drugs or other chronic diseases, which might have partially affected the findings. ## Conclusion ePWV was an independent risk factor for mortality in the populations with obesity. High ePWV levels were associated with increased all-cause mortality and cardiovascular mortality. Therefore, ePWV can be considered a novel biomarker to assess the risk of mortality in patients with obesity. ## Supplementary Information Additional file 1: Table S1. Selected covariates. Table S2. Survey-weighted multivariate Cox regression performed to assess the ePWV levels and the risk of all-cause and CVD mortality after multiple imputation of 5 data sets. Table S3. The results of two-piecewise linear regression model for ePWV and the risk of all-cause and CVD mortality in obese participants with different BMI. ## References 1. Aronne LJ. Classification of obesity and assessment of obesity-related health risks. 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--- title: Fibroblast growth factor 21 is expressed and secreted from skeletal muscle following electrical stimulation via extracellular ATP activation of the PI3K/Akt/mTOR signaling pathway authors: - Manuel Arias-Calderón - Mariana Casas - Julián Balanta-Melo - Camilo Morales-Jiménez - Nadia Hernández - Paola Llanos - Enrique Jaimovich - Sonja Buvinic journal: Frontiers in Endocrinology year: 2023 pmcid: PMC9997036 doi: 10.3389/fendo.2023.1059020 license: CC BY 4.0 --- # Fibroblast growth factor 21 is expressed and secreted from skeletal muscle following electrical stimulation via extracellular ATP activation of the PI3K/Akt/mTOR signaling pathway ## Abstract Fibroblast growth factor 21 (FGF21) is a hormone involved in the regulation of lipid, glucose, and energy metabolism. Although it is released mainly from the liver, in recent years it has been shown that it is a “myokine”, synthesized in skeletal muscles after exercise and stress conditions through an Akt-dependent pathway and secreted for mediating autocrine and endocrine roles. To date, the molecular mechanism for the pathophysiological regulation of FGF21 production in skeletal muscle is not totally understood. We have previously demonstrated that muscle membrane depolarization controls gene expression through extracellular ATP (eATP) signaling, by a mechanism defined as “Excitation-Transcription coupling”. eATP signaling regulates the expression and secretion of interleukin 6, a well-defined myokine, and activates the Akt/mTOR signaling pathway. This work aimed to study the effect of electrical stimulation in the regulation of both production and secretion of skeletal muscle FGF21, through eATP signaling and PI3K/Akt pathway. Our results show that electrical stimulation increases both mRNA and protein (intracellular and secreted) levels of FGF21, dependent on an extracellular ATP signaling mechanism in skeletal muscle. Using pharmacological inhibitors, we demonstrated that FGF21 production and secretion from muscle requires the activation of the P2YR/PI3K/Akt/mTOR signaling pathway. These results confirm skeletal muscle as a source of FGF21 in physiological conditions and unveil a new molecular mechanism for regulating FGF21 production in this tissue. Our results will allow to identify new molecular targets to understand the regulation of FGF21 both in physiological and pathological conditions, such as exercise, aging, insulin resistance, and Duchenne muscular dystrophy, all characterized by an alteration in both FGF21 levels and ATP signaling components. These data reinforce that eATP signaling is a relevant mechanism for myokine expression in skeletal muscle. ## Introduction Fibroblast growth factor 21 (FGF21) is a pleiotropic peptide hormone. The physiology of FGF21 is largely complex because it is synthesized and secreted by several organs and can act on multiple target tissues in either a paracrine or an endocrine fashion [1, 2]. First described expressed in liver cells [2], it is now well considered an adipokine [3], myokine [4], and cardiomyokine [5]. Furthermore, the molecular mechanism of FGF21 signaling is complex and involves several FGF receptors (FGFRs) as well as an obligate coreceptor, β-klotho (KLB). Tissue specificity of FGF21 signaling is conferred by the co-expression of a given FGF receptor and KLB [6]. Some of the main physiological effects of FGF21 described to date are to increase the incorporation of glucose into cells, increase sensitivity to insulin, promote the use of fats in metabolism, decrease body mass index and glycemia and decrease insulin levels. It is a starvation-like hormone with metabolic functions that lead to maintaining fuel support to tissues [1, 7, 8]. In skeletal muscle, FGF21 is poorly expressed at rest [9, 10]. However, different physiological and pathological conditions promote the expression and secretion of FGF21 in muscle. Muscle-derived FGF21 increases with starvation, endoplasmic reticulum stress, and mitochondrial dysfunctions (4, 11–14), as a response and adaptation factor to cellular stress mechanisms [15, 16]. It has been proposed as a biomarker of muscle-specific mitochondrial disorders [17, 18]. In particular, it has been described that events that alter mitochondrial function and increase oxidative stress levels could be the stimuli that induce FGF21 expression in skeletal muscle [11, 19, 20]. Insulin has also been described as a strong stimulus for FGF21 expression in skeletal muscle. Both insulin infusion in healthy young men and pathological hyperinsulinemic condition renders to an elevated FGF21 expression in skeletal muscle [9]. It has been described that exercise is also an important stimulus for the regulation of FGF21 expression in skeletal muscle. Aerobic exercise has been strongly associated with the increase in FGF21 plasma levels in humans (as reviewed in [21, 22]); it has been suggested derived from the liver, but tissue source of plasmatic FGF21 has not been addressed in those reports (23–28). Emerging evidence suggests that FGF21 from skeletal muscle could also contribute to increased plasma levels after exercise [27]. The fact that FGF21 levels also increase in response to exercise fits to myokines definition: they are produced and secreted in response to muscle contractile activity [29], dependent on the depolarization of the sarcolemma. The expression of FGF21 in muscle in response to stress signals and during exercise makes sense. It has been described that cellular stress responses are associated with the molecular mechanisms of exercise and that exercise could induce its beneficial effects and mediate adaptation mechanisms in skeletal muscle through controlled stress signals, also regulating the expression of myokines (15, 16, 30–33). A paradoxical situation, that illustrates the complexity of the FGF21 pathways, is that while its plasma levels increase during exercise, they also do so in processes of metabolic deregulation, such as obesity or liver diseases [22]. In these latter cases, exercise even reduces plasma and liver levels of FGF21, and overexpresses its signaling pathway in the liver [34]. In this way, it is now suggested that both the stimuli and the local/systemic responses depend on the source organ that produces FGF21. In this way, an increase in “metabolic” plasma FGF21 (released from the liver or adipocytes) would not be equivalent to “exercise” FGF21 (released by skeletal muscles) [22]. Exercise increases FGF21 levels associated with protein kinase B/Akt1 protein activation [15, 24, 35]. FGF21 expression and secretion is improved in C2C12 muscle cells transduced with a constitutively active form of Akt1 [4]. In addition, muscle-specific Akt1 transgenic mice increase both mRNA and protein level of FGF21 in muscles, as well as FGF21 serum levels [4]. An increase in muscle FGF21 has been also described in a transgenic animal model with constitutively-activated mammalian target protein of Rapamycin (mTOR) [36]. These data suggest that FGF21 is under the control of the PI3K-Akt-mTORC1 signaling pathway in skeletal muscle. Direct effects of FGF21 on muscle cells have been also described, suggesting a putative autocrine/paracrine role of this myokine. It has been described that FGF21 induces glucose uptake in skeletal muscle, through a mechanism that does not involve the canonic Akt-dependent glucose uptake pathway [37]. We recently published that FGF21 regulates glucose uptake in adult skeletal muscle fibers through a mechanism dependent on both GLUT4 translocation to cell surface and atypical PKC-ζ activation [38]. On the other hand, Zhou et al. have described that the expression of FGF21 is increased in muscles of mdx mice, a model of Duchenne muscular dystrophy [39, 40]. Also, it has been described that FGF21 could mediate muscle plasticity processes by inducing an increase in aerobic fibers, both in vitro and in vivo [41]. Moreover, FGF21 is responsible for muscle atrophy associated with metabolic alterations [12, 42]. To date, the exercise-cellular stress relationship appears to be the main axis for the induction of FGF21 in skeletal muscle, and that the production of this myokine would be associated with the activation of the Akt pathway. However, the molecular mechanism that regulates the expression and secretion of this factor from normal skeletal muscle is still unknown, as is the initial stimulus that would allow the activation of the pathway associated with the Akt protein for the regulation of muscle FGF21. Classically, the activation of Akt is associated with the activation of the PI3K protein through stimulation of receptor tyrosine kinases or G protein-coupled receptors [43]. Downstream of Akt is the protein mTORC1 which has also been associated with controlling FGF21 in skeletal muscle [36]. This signaling pathway is activated by exercise, an important muscle FGF21-inducing stimulus [24]. Therefore, it is interesting to study a mechanism that relates the activation of this pathway in normal muscle conditions, unlike most of the published data that use an altered expression of FGF21 by genetic tools or in pathological conditions. We have previously described that exercise adaptation mechanisms in skeletal muscle are regulated by extracellular ATP (eATP)-mediated signaling, which activates the Excitation-Transcription (ET) coupling (44–47). eATP is released from muscle cells after membrane depolarization and activates their P2X/P2Y purinergic receptors to evoke cytosolic Ca2+ transients related to gene expression [45, 47]. This mechanism is also related to the synthesis and release of interleukin 6 (IL6), a well-known myokine, in response to electrical stimulation [48, 49]. Consequently, the ET-coupling, through signaling mediated by eATP, is a possible pathway to study as a molecular mechanism that associates exercise with the production of myokines in skeletal muscle. It has been described that ET-coupling activates Akt in muscle fibers in response to electrical stimulation, through the PI3K/Akt pathway [50]. Moreover, we have recently demonstrated that eATP induces protein synthesis in whole flexor digitorum brevis (FDB) muscle in vitro through the P2Y/PI3K/Akt/mTOR signaling pathway [51]. In addition, eATP signaling has been shown to play a role in cellular stress-dependent adaptation, via reactive oxygen species [52]. Recent evidence from our laboratory indicates that ET-coupling is related to mitochondrial stress events in response to electrical stimulation, via IP3-dependent Ca+2 signals [53]. Both Akt activation and cellular stress signals are related to FGF21 production in skeletal muscle, so it is interesting to study the role of eATP in the control of FGF21 expression and secretion in skeletal muscle. The relationship of the signaling via eATP with the regulation of FGF21 is also supported by reports of direct effects of FGF21 on skeletal muscle (glucose uptake [37], formation of aerobic fibers [41], control of muscle mass [12] and its alterations in pathological conditions such as Duchenne muscular dystrophy [40]), situations in which our laboratory has reported that signaling mediated by eATP participates directly, or is altered [47, 50, 54]. Considering this background, it is necessary to elucidate the molecular mechanism that controls the expression and secretion of FGF21 in skeletal muscle. We here show that electrical stimulation elicits FGF21 synthesis and secretion in skeletal muscle, by eATP activation of a P2YR/PI3K/Akt/mTORC1 pathway. These results allow us to identify the extracellular ATP-dependent signaling pathway as a new target to modulate the production of FGF21 in skeletal muscle, as well as to incorporate FGF21 as one of the genes regulated by ET coupling. ## Materials and methods All procedures involving animals were approved by the Institutional Animal Care and Use Committee of the Faculty of Dentistry of Universidad de Chile (Certificate N° 061501). The results are reported following the ARRIVE guidelines. ## Muscle dissection and stimulation Male BALB/c mice (8 weeks old, 18-25 g) were obtained from the Experimental Platform of the Faculty of Dentistry (Universidad de Chile). Standard animal room conditions (48–$50\%$ humidity; 20 ± 2°C; 12 h light/dark cycle), and ad libitum water and food (LabDiet® JL Rat and Mouse/Auto 6F 5K67) were maintained. FDB muscles were isolated from BALB/c mice as previously described [46], and stabilized for 2 h in DMEM (Thermo Fisher Scientific, MA, USA) supplemented with 1 mM sodium pyruvate (Sigma-Aldrich Corp, St. Louis, MO, USA), 100 U/mL penicillin (Thermo Fisher Scientific, MA, USA), 100 µg/mL streptomycin (Thermo Fisher Scientific, MA, USA) and $1\%$ horse serum (Thermo Fisher Scientific, MA, USA), at 37° C. ## Isolation of adult skeletal fibers Isolated fibers from the FDB muscle were obtained by enzymatic digestion with collagenase type II (90 min with 400 U ml−1) and mechanic dissociation with fire-polished Pasteur pipettes, as previously described [46]. The isolated fibers were seeded in ECM-coated dishes and used 20 h after seeding. ## Electrostimulation in vitro or in situ Isolated FDB muscle fibers were electrically stimulated in vitro following the protocol previously established in our laboratory [47]. A field electrode, covering the entire surface of the plate on which the isolated fibers are cultured, connected to a Grass S48 pulse generator was used. The stimulation was performed at 20 Hz (270 pulses, 0.3 ms each; 2 mV), a frequency that induces the maximum release of ATP from the muscle fibers [47]. For in-situ stimulation, the same equipment and stimulation pattern described were used. In this experimental condition, male BALB/c mice (6-8 weeks) were anesthetized by intraperitoneal injection of 80 mg/kg ketamine and 8 mg/kg xylazine. Subsequently, an incision was made at the level of the lower extremities, in the upper lateral part of the gastrocnemius muscle, to directly stimulate the sciatic nerve (20 Hz, 270 pulses, 0.3 ms each; 0.3 mV). After stimulation, the animal was euthanized by cervical dislocation, the FDB muscles were dissected and kept in DMEM medium (Thermo Fisher Scientific, MA, USA) supplemented with $10\%$ fetal bovine serum (Biological Industries; CT, USA), at 37°C for 2 h before processing for protein analysis. In this model, an FDB muscle was considered as the experimental condition, and the contralateral muscle was used as intra-animal control, a muscle that underwent the same surgical procedure, but without the application of electrical stimulation. ## ATP concentration- and time-response curves. Effect of antagonists and blockers To determine the effect of ATP (Adenosine 5′-triphosphate, Sigma-Aldrich Corp, St. Louis, MO, USA) on either FGF21 protein or mRNA levels, isolated fiber cultures or FDB muscles were previously serum-starved for 2 h (DMEM culture medium without horse serum). Subsequently, muscles were stimulated with exogenous ATP at selected concentrations (0.1-100 µM), for different times (30-360 min). When blockers or inhibitors were used, they were incubated 30 min before and during the stimulation with ATP. Evaluation of changes in mRNA and protein levels in response to ATP was performed in the presence of 100 μM Suramin (Sigma-Aldrich Corp, St. Louis, MO, USA), 25 μM Nifedipine (Sigma-Aldrich Corp, St. Louis, MO, USA), 100 nM Rapamycin (Sigma-Aldrich Corp, St. Louis, MO, USA), 50 μM LY294002 (Cell Signaling Technology, Danvers, MA, EEUU), 10 μM Akt VIII (Sigma-Aldrich Corp, St. Louis, MO, USA) 30 μM Cycloheximide (Sigma-Aldrich Corp, St. Louis, MO, USA) or 0.5 μM actinomycin-D (Sigma-Aldrich Corp, St. Louis, MO, USA). ## Total RNA extraction, reverse transcription and quantitative real-time PCR Total mRNA was obtained from cell cultures using Trizol™ reagent (Life Technologies, CA, USA), according to the manufacturer’s instructions. cDNA was obtained from 2 µg of total RNA by using the High-Capacity cDNA Reverse Transcription Kit (#4368814, Applied Biosystems, CA, USA), as indicated by the manufacturer´s protocol. The qRT-PCR was carried out in the StepOne™ Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) using the Brilliant III Ultra-Fast SYBR® Green QPCR Master Mix (#600882, Agilent Technologies, CA, USA). The sequences of the primers used to amplify the cDNA were: FGF21 (600 nM) sense: TACACAGATGACGACCAAGA; antisense: GGCTTCAGACTGGTACACAT; and Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (400 nM) sense: CAACTTTGGCATTGTGGAAG, antisense: CTGCTTCACCACCTTCTTG. All primers were standardized to render an efficiency between $95\%$ and $105\%$. The thermocycling protocol included 95 °C for 3 min followed by 40 cycles of 95 °C for 20 s and 60 °C for 20 s. The amplification procedure was verified by melting curve analysis. The results were normalized to GAPDH expression (housekeeping) and reported according to the 2-ΔΔCT method [55]. ## Quantitative measurement of secreted FGF21 To determine the concentration of FGF21 secreted into the culture medium from FDB muscle stimulated with ATP, the commercial Mouse FGF21 ELISA kit ab212160 (Abcam, Cambridge, U.K) was used following the instructions provided by the manufacturer. ## Immunoblot FDB muscles were processed with a rotor/stator tissue homogenizer (Biospec, OK, USA) in 150 µl of ice-cold lysis buffer (20 mM Tris-HCl, $1\%$ Triton X-100, 2 mM EDTA, 10 mM Na3VO4, 20 mM NaF, 10 mM sodium pyrophosphate, 150 mM NaCl, 1 mM PMSF, 1:200 protease inhibitor cocktail Calbiochem Set III, pH 7.4). The cell lysates were sonicated for 3 min, incubated on ice for 30 min, and centrifuged to remove debris. The protein concentration was determined by the turbidimetric assay with sulfosalicylic acid. Proteins resolution by $10\%$ SDS-PAGE and immunoblot were performed as previously detailed [48]. Protein staining was performed with the RapidStepTM enhanced chemiluminescence (ECL) reagent (EDM Millipore, MA, USA). Images were acquired in an Amersham Imager 600 (GE Healthcare Life Sciences, PA, USA) and densitometry was analyzed with the ImageJ Software (NIH, MA, USA). Monoclonal antibodies were used for detection of FGF21 (0.4 μg/ml, # ab171941, Abcam, Cambridge, UK) or the loading control GAPDH (1 μg/ml, #G9545, Sigma-Aldrich Corp, St. Louis, MO, USA). ## Statistical analysis Data of n experiments were expressed as mean ± standard error of the mean (SEM). Non-parametric tests were used to evaluate significance. Mann-Whitney test was used for comparing a single condition with a control. For multiple comparisons, the Kruskal Wallis test followed by the Dunn post hoc test was used. A p value < 0.05 was considered statistically significant. Statistical analyzes were performed using the Graph Pad Prism 6 software (CA, USA). ## Electrical stimulation increases FGF21 mRNA and protein levels, through eATP signaling Electrical stimulation (20 Hz, 270 pulses, 0.3 ms each) of FDB isolated muscle fibers evoked a significant increase in FGF21 mRNA levels measured at different times after stimulation; the peak was reached at 30 min with more than 25-fold increase (Figure 1A). Figure 1: Electrical stimulation increases FGF21 expression dependent on the eATP signaling pathway in skeletal muscle. (A) Electrical stimulation (ES, 20 Hz, 270 pulses, 0.3 ms each) evokes a transient increase in FGF21 mRNA levels in FDB isolated muscle fibers. n=6; p<0.05; **p<0.001 vs Control; Kruskal-Wallis with Dunn’s post-hoc test. (B) In situ electrical stimulation (20 Hz, 270 pulses, 0.3 ms each) of sciatic nerve evokes an increase in FGF21 protein levels in whole-FDB muscle, 120 min after stimulation. n=4; p<0.05 vs Control; Mann-Whitney test. (C) Nifedipine (25 μM), a blocker of Cav1.1-pannexin-1 communication, and suramin (100 μM), a non-selective P2Y/P2X receptors antagonist, both reduce the increase in FGF21 mRNA levels evoked by electrical stimulation (20 Hz, 270 pulses, 0.3 ms each), 30 min after stimulation, in FDB isolated muscle fibers. n=4; n.s., not significant; **p<0.001 vs Basal; Kruskal-Wallis with Dunn’s post-hoc test. To analyze changes in protein expression of FGF21 after membrane depolarization, an in situ electrical stimulation of the sciatic nerve was performed in mice, which corresponds to the neural branch that innervates the entire hindlimb, including the FDB. A two-fold increase in FGF21 protein level was observed in FDB whole muscle 120 min after electrical stimulation (Figure 1B). We tested the hypothesis that FGF21 expression could be mediated by the pathway that links electrical stimulation to ATP release through pannexin-1 channels activated by the voltage sensor Cav1.1 to stimulate P2Y purinergic receptors. To that aim, we studied the increase in FGF21 mRNA levels after electrical stimulation in the presence of drugs that block either the Cav1.1-pannexin-1 communication (nifedipine) or the P2Y purinergic receptors (Suramin). Both drugs reduced FGF21 mRNA expression to levels not significantly different from basal (Figure 1C), suggesting that indeed the effect of electrical stimulation is mediated by the ATP release signaling process. To further explore this mechanism, we incubated muscle fibers at different times in the presence of 100 µM eATP. The increase in FGF21 mRNA peaked at 30 min with a 10-fold increase (Figure 2A). Confirming the role of the purinergic signaling, in fibers incubated with 100 μM suramin this increase was abolished (Figure 2B). Accordingly, FGF21 protein expression increased in FDB muscles after 120-min incubation with 100 µM ATP (Figure 2C). A dose-response curve with different ATP concentrations showed that maximal protein expression occurs at 3 µM extracellular ATP with little or no reduction at higher concentrations (Figure 2D). Of note, the increase in protein expression was also inhibited by incubation with 100 µM suramin (Figure 2E). Figure 2: Exogenous ATP promotes FGF21 expression and secretion in skeletal muscle. (A) Exogenous ATP (100 μM) evokes a transient increase in FGF21 mRNA levels in FDB isolated muscle fibers. n=6; p<0.001 vs Control; Kruskal-Wallis with Dunn’s post-hoc test. (B) Suramin (100 μM), a non-selective P2Y/P2X receptors antagonist, decreases the effect of exogenous ATP (100 μM) on FGF21 mRNA levels, 30 min after stimulation, in FDB isolated muscle fibers. n=4; n.s., not significant; p<0.001; Kruskal-Wallis with Dunn’s post-hoc test. (C) Exogenous 100 μM ATP increases FGF-21 protein level in whole-FDB muscle extracts, at 120 min. n=3; p<0.05 vs 0; Kruskal-Wallis with Dunn’s post-hoc test. (D) Exogenous ATP stimulation increases FGF-21 protein levels from 3 μM in whole-FDB muscle. n=4; n.s., not significant; p<0.05, p<0.01 vs 0; Kruskal-Wallis with Dunn’s post-hoc test. (E) Suramin (100 μM), a non-selective P2Y/P2X receptors antagonist, decreases the effect of exogenous 100 μM ATP on FGF21 protein levels, at 120 min of stimulation, in whole-FDB muscle extracts. n=4; n.s., not significant; p<0.05; Kruskal-Wallis with Dunnett’s post-hoc test. (F) Exogenous 3 μM ATP stimulation increases FGF21 secretion to extracellular medium from whole-FDB muscle, at 240 min. n=4; p<0.05, p<0.01 vs Control; Kruskal-Wallis with Dunn’s post-hoc test. The inset shows that 3 μM ATP concentration also increases mRNA levels of FGF21 in skeletal muscle fibers, as previously demonstrated with 100 μM eATP (C). An important question about newly produced FGF21 is whether it is stored/degraded or secreted from muscle fibers. The secretion of FGF21 was addressed by ELISA assays in the extracellular media of muscle fibers incubated with 3 µM extracellular ATP. This reduced ATP concentration was used considering that was the smallest concentration that evoked significant increases in FGF21 protein expression (Figure 2D), and that this concentration has been reported to selectively activates P2Y but not P2X purinergic receptors [56, 57]. A significant 30-fold increase in secreted FGF21 was observed at 240 min incubation with exogenous ATP (Figure 2F). As a validation of previous results with 100 µM ATP, it was demonstrated that the 3 µM concentration also increases FGF21 mRNA expression after 30-min incubation (Figure 2F, inset). ## FGF21 expression and secretion is regulated by transcriptional activation via the PI3K-Akt-mTOR pathway To make sure that mRNA expression, protein synthesis and secretion of FGF21 are indeed mediated by the transcriptional machinery of the skeletal muscle fiber, we studied the effect of 3 µM exogenous ATP in the whole FDB muscle in the presence of either 30 µM cycloheximide (a general inhibitor of translation) or 0.5 µM actinomycin-D (a general inhibitor of transcription). As shown in Figure 3, both inhibitors completely abolished the effect of ATP on FGF21 mRNA levels (Figure 3A), FGF21 protein levels (Figure 3B) and FGF21 secreted levels (Figure 3C), indicating that this process is regulated by transcription. Figure 3: ATP stimulation increases FGF21 secretion, protein content, and mRNA levels in whole-FDB muscle, through a transcription-mediated mechanism. Cycloheximide (30 μM; Ciclohex), a general translation inhibitor, and Actinomycin-D (0.5 μM; Act-D), a general transcription inhibitor, both abolished the effect of 3 μM ATP stimulation over mRNA (A), protein (B), and secreted (C) FGF21 levels. n=4; n.s., not significant; p<0.05, *p<0.01 vs non-ATP Control; Mann-Whitney test. Considering than Akt has been described as a classical regulator of FGF21 [36, 43], and our previous reports showing that the eATP pathway in skeletal muscle activates the Akt signaling pathway [50, 51], we studied the PI3K-Akt-mTOR signaling pathway as a putative target downstream the P2Y receptors for regulation of FGF21 expression in skeletal muscle. LY294002 (50 μM), a general PI3K inhibitor, Akt VIII (10 μM), an Akt inhibitor, and Rapamycin (100 nM), a mTORC1 inhibitor, all blocked the stimulation effect of 3 μM ATP over mRNA, protein, and secreted FGF21 levels, in whole-FDB muscle (Figures 4A–C). This is strong evidence in favor of the involvement of PI3K-Akt-mTOR pathway downstream of purinergic stimuli in skeletal muscle fibers. Figure 4: Pharmacological inhibition of PI3K-Akt-mTOR pathway abolished the increase in mRNA, protein and secreted FGF21 levels evoked by ATP stimulation, in whole-FDB muscle. LY294002 (50 μM), a general PI3K inhibitor, Akt VIII (10 μM), an Akt inhibitor, and Rapamycin (100 nM), a mTORC1 inhibitor, all blocked the 3 μM ATP stimulation effect on mRNA (A), protein (B), and secreted (C) FGF21 levels, in whole-FDB muscle. n=4; n.s., not significant; p<0.05, *p<0.01, vs non-ATP Control; Mann-Whitney test. ## Discussion In recent years, FGF21 has gained attention as an important regulator of metabolic processes at the systemic level, with multiple beneficial effects in different pathologies such as diabetes, insulin resistance, and obesity, among others [42, 58, 59]. FGF21 is expressed in different tissues in humans and murine research models, one of them being skeletal muscle, in which it has been categorized as a myokine [29, 32, 42]. However, the muscle regulation mechanisms of this factor have been mainly associated with pathologies or physiological alterations that induce the expression of FGF21 [42], so its production in muscle under physiological conditions is still controversial. One of the main physiological stimuli reported to regulate FGF21 in skeletal muscle is exercise (35, 58–60). However, the molecular mechanism by which exercise could induce the expression and secretion of FGF21 is unknown. We here demonstrate for the first time the role of electrical stimulation through extracellular ATP in the regulation of the expression, synthesis, and secretion of FGF21 by skeletal muscle through the activation of the P2YR/PI3K/Akt/mTORC1 pathway, as summarized in the graphic model of Figure 5. These results allow us to identify the extracellular ATP-dependent signaling pathway as a new study target to modulate the production of FGF21 in skeletal muscle and incorporate FGF21 as one of the genes associated with the control of ET coupling, acting as a paracrine regulator of muscle plasticity. Figure 5: Proposed model for the regulation of FGF21 expression and secretion by electrical stimulation, dependent on extracellular ATP signaling and activation of the P2YR/PI3K/Akt/mTORC1 pathway in mouse skeletal muscle. Electrical stimulation is sensed by the dihydropyridine receptor (CaV1.1) that, as we previously reported, evokes ATP release from muscle cells through Pannexin-1 hemichannels (PnX1). eATP stimulates P2Y receptors (P2YR) that activate the PI3K/Akt/mTORC1 signaling pathway to promote FGF21 expression and secretion. Created with BioRender.com. At the molecular level, exercise can be studied in vitro in muscle cell cultures through the application of electrical stimulation [61], thus allowing the study of molecular events related to exercise and its responses, such as the control of the production of myokines. In this context, it is interesting to study whether the electrical stimulus regulates the expression, synthesis, and secretion of FGF21 in skeletal muscle. Our laboratory has reported that the electrical stimulation induces the release of ATP into the extracellular space, acting as a relevant mediator of ET coupling [44, 45]. This pathway controls the expression of different genes associated with muscle plasticity, including IL-6 as a myokine [48, 49]. However, until now, a relationship between this pathway and the expression of FGF21 has not been studied. The electrical stimulation parameters that promote the highest ATP release through Pannexin1 from skeletal fibers in previous studies (20 Hz, 270 pulses, 0.3 ms each) [47] increased levels of FGF21 mRNA, intracellular protein, and secreted protein. The depolarization-evoked increase in FGF21 mRNA was abolished when P2Y/P2X receptors were antagonized by Suramin, reinforcing the role of eATP in this process. The effect of as low as 3 µM of exogenous ATP to promote FGF21 protein expression suggests that P2Y receptors are involved because they respond to nM-low µM ATP concentrations. In contrast, P2X requires high µM to mM ATP for activating [56, 57]. Within the subtypes of P2Y receptors identified to date, it has been described that the P2Y2 receptor is strongly expressed in isolated FDB muscle fibers [49] and that it is also part of the protein complex involved in the ET coupling [44], which suggests that this receptor could be involved in the regulation of FGF21 dependent on extracellular ATP. However, experiments are required to demonstrate the participation of P2Y2R in this mechanism specifically. In addition, the depolarization-evoked increase in FGF21 mRNA was abolished after Nifedipine incubation, which we have reported disturbs the relation between the voltage sensor Cav1.1 and the ATP releaser conduit Pannexin 1 [62]. Interestingly, we have demonstrated that Cav1.1, Pannexin 1, P2Y receptors, and signaling molecules such as PI3K are joined as a multiprotein complex in the T-tubule of the skeletal muscle [44]. The increased FGF21 mRNA in skeletal isolated fibers electrically stimulated was reinforced with a more physiological approach using in situ sciatic nerve stimulation. When electrical stimulation was applied to the sciatic nerve in anesthetized mice, there was an increase in protein levels of FGF21 in the FDB muscle. Therefore, in a model where the neuromuscular junction is physiologically working, the result is similar than observed in isolated skeletal muscle fibers directly stimulated. The direct incubation of whole FDB muscles with 100 μM exogenous ATP in vitro, showed a time-dependent increase in FGF21 mRNA and intracellular protein, which was abolished by preincubation with Suramin. Hence, direct activation of P2Y/P2X receptors promotes FGF21 expression. Interestingly, the maximal value in mRNA level of FGF21 evoked by eATP was observed at 30 min, while intracellular protein level was at 120 min and secreted FGF21 level was at 240 min. The latter suggests that secretion is linked to the expression and synthesis of FGF21; therefore, secretion does not operate as an independent mechanism. The differential time course for the increase in mRNA-protein-secretion levels suggests that the secretion of FGF21 requires the de novo synthesis of this factor. The same has been described for the family of endocrine growth factors, a classification in which FGF21 is found, in which its synthesis leads to its rapid secretion, without maintaining intracellular storage [19, 63]. That hypothesis was confirmed in the current work when muscle stimulation with exogenous ATP was addressed after incubation with Actinomycin D (transcription blocker) or Cycloheximide (translation blocker). Both treatments prevented the ATP-evoked increase in FGF21 mRNA, intracellular protein, and secretion. It has been described that the regulation of FGF21expression is associated with different signaling proteins. Izumiya et al. observed increased levels of FGF21 (mRNA and protein) in a transgenic model for constitutively active Akt, muscle-specific, suggesting that FGF21 expression depends on Akt activity in skeletal muscle [4]. The same authors describe the participation of PI3K in the expression of this myokine [4]. Furthermore, Guridi and colleagues have suggested that mTORC1 is also an important regulator of muscle production of FGF21 [36]. In the current work, we tested the hypothesis that extracellular ATP activates the Akt-dependent signaling pathway to induce the expression and secretion of FGF21. Osorio-Fuentealba et al. observed an increase in PI3K-dependent Akt phosphorylation (Thr308 and Ser473) in response to stimulation with extracellular ATP in a myotube model, demonstrating an Akt activation mediated by purinergic receptor signaling [50]. In agreement, we have recently published that 3 μM ATP is sufficient to activate the PI3K/Akt/mTORC1 signaling pathway within 20 min of stimulation in mouse FDB muscle [51]. These data support our hypothesis that the regulation of FGF21 expression by extracellular ATP would be related to the activation of the PI3K/Akt/mTORC1/ATF4 signaling pathway in muscle. In the current work, exogenous ATP showed a concentration-dependent effect for increasing the FGF21 protein expression in the whole FDB muscle. Interestingly, the maximal mRNA expression was observed with 3 μM ATP, the same concentration that evokes the largest Akt activation [51]. The time with exogenous ATP required for Akt activation is 5-7 min, and for mTOR activation 20 min [51], faster than the 30 min required for FGF21 mRNA expression observed in this work. That reinforces the idea of a timeline of molecular events. The increased levels of mRNA, intracellular protein, and secretion of FGF21 evoked by exogenous ATP were abolished after preincubation with pharmacological blockers of PI3K (LY294002), Akt (Akt VIII) and mTORC1 (Rapamycin). The latter confirms that the PI3K/Akt/mTORC1 pathway is required for the eATP-evoked control of FGF21 expression. That agrees with previously published data that involved these proteins in the regulation of FGF21 expression [4, 36, 64]. Previously published data showed the involvement of components of the Akt/mTORC1 pathway in contexts in which the proteins were constitutively activated or blocked with molecular tools and/or transgenic animals. These systems force metabolic pathways and activate cellular stress pathways, which influenced the classification of FGF21 as a myokine only expressed under these stress conditions in skeletal muscle [15, 16, 65]. However, our results show the participation of this pathway in a physiological context for skeletal muscle, given that no process has been genetically manipulated to induce a model of metabolic alteration. In the same way, the stimulation carried out with eATP was on muscle cultured in a medium with glucose and aminoacids, so a metabolic stress condition is not generated. Therefore, these results allow us to demonstrate the expression of FGF21 in skeletal muscle under normal exercise conditions, a situation that has remained controversial in the literature. Circulating levels of FGF21 have been reported to increase in response to acute training in mice and healthy humans (23–28). One study shows that acute exercise also increases the FGF21 expression in liver and skeletal muscle in mice and humans [27]. However, another study mentions that there are no changes in the expression of muscular FGF21 and that the increase in plasmatic levels after exercise responds only to increased expression of hepatic FGF21 [24]. A possible explanation for the latter observation could be that the study of Kim et al. only assessed FGF21 mRNA in skeletal muscle, which may not directly correlate with protein levels. Or on the other hand, they could be evaluating time points in which the kinetics of mRNA increase is not detected. Our approaches on isolated muscle fibers showed that an electrical stimulus that promotes ET-coupling and favors the oxidative fiber phenotype [24] induces both the expression and secretion of FGF21 from skeletal muscle cells. It has been described that extracellular ATP-mediated signaling induces IP3-dependent increases in intracellular Ca2+ in skeletal muscle cells [44, 45, 47]. In the current work, the participation of Ca2+ signals in the regulation of FGF21 was not directly evaluated, which prevents us from suggesting or ruling out the involvement of this second messenger in the pathway proposed for eATP-evoked FGF21 expression. However, it has recently been reported that eATP induces transient intracellular Ca2+ signals that induce mTOR activation and protein synthesis, through the modulation of a specific calcium dependent PI3K isoform [66]. Accordingly, it is likely that this second messenger could regulate FGF21 expression in a way complementary to the pathway proposed from the current work. Additional studies are required to determine the participation of IP3-dependent Ca2+ signals in regulating the expression of FGF21 or other myokines in skeletal muscle. The beneficial effects of FGF21 have been mostly related to its action over adipose tissue and liver [1]. Rosales et al. recently demonstrated that FGF21 promotes glucose uptake in skeletal muscle fibers, independent of Akt but dependent on PKC-ζ downstream of PI3K and GLUT4 translocation [38]. Those findings, combined with our data that demonstrate FGF21 expression and secretion after electrical stimulation, strongly suggest that FGF21 could be an autocrine/paracrine signaling molecule, secreted during muscle activity to improve glucose uptake for muscle metabolic demands. Although plasma FGF21 levels have been described as biomarkers of metabolic disorders or mitochondrial myopathies (67–69), its release from muscle has been associated with improvements in metabolic function [42, 70, 71]. Consequently, it is complex to assign a favorable or harmful role to the circulating levels of FGF21 “per se” for metabolic health. Apparently, it would depend on the tissue source, type of stimulus, and interaction with other secreted molecules [42]. Recombinant FGF21 is unsuitable for clinical use owing to poor pharmacokinetic profiles, short half-life, inactivation in plasma, and instability in solution (reviewed in [70]. Therefore, the prescription of specific exercise protocols could be addressed to allow an endogenous increase in plasma FGF21. In conclusion, the results presented in the current work show for the first time the role of electrical stimulation through extracellular ATP in regulating the expression, synthesis, and secretion of FGF21 through activating the P2YR/PI3K/Akt/mTORC1 pathway in skeletal muscle. These results allow us to identify the extracellular ATP-dependent signaling pathway as a new study target to modulate the production of FGF21 in skeletal muscle and incorporate FGF21 as one of the genes associated with the control of Excitation-Transcription Coupling, acting as a paracrine regulator of muscle plasticity. Determining this ATP-dependent molecular mechanism for regulating FGF21 allows the reopening of the debate on its expression in basal conditions and regulated by physiological stimuli. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The animal study was reviewed and approved by Institutional Animal Care and Use Committee of the Faculty of Dentistry of Universidad de Chile (Certificate N° 061501). ## Author contributions Conceptualization, MA-C, EJ and SB. Methodology, MA-C, MC, JB-M, CM-J, NH, PL and SB. Formal analysis, MA-C and SB. Funding acquisition, EJ and SB. Data interpretation and discussion, MA-C, MC, PL, EJ and SB. Project administration, NH and SB. Supervision, SB. Writing—original draft preparation, EJ and SB. Writing—review and editing, MA-C, PL, EJ and SB. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer ACF declared a shared affiliation with several of the authors MA-C, MC, JB-M, PL, NH, EJ and SB to the handling editor at time of review. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Kaur N, Gare SR, Shen J, Raja R, Fonseka O, Liu W. Multi-organ FGF21-FGFR1 signaling in metabolic health and disease. Front Cardiovasc Med (2022) 9. DOI: 10.3389/fcvm.2022.962561 2. 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--- title: United Network for Organ Sharing (UNOS) Database Analysis of Factors Associated With Kidney Transplant Time on Waiting List journal: Cureus year: 2023 pmcid: PMC9997046 doi: 10.7759/cureus.34679 license: CC BY 3.0 --- # United Network for Organ Sharing (UNOS) Database Analysis of Factors Associated With Kidney Transplant Time on Waiting List ## Abstract Introduction: In the United States (U.S.), African Americans and other minority groups have longer wait times for kidney transplantation than Caucasians. To date, many studies analyzing time spent on the waitlist for each race/ethnicity have been done. However, there are few to no studies examining waitlist time after the 2019 policy changes to the geographic distribution of donated kidneys. Methods: Data collected from the National Organ Procurement and Transplantation Network database were used to analyze associations between race and time spent on the waitlist for a kidney transplant in the U.S. Additional sub-categorical data were analyzed to determine further associations and potential covariates, such as gender, age, citizenship, primary source of payment, region of transplant center, BMI, Kidney Donor Profile Index (KDPI), renal diagnosis, and presence/type of diabetes. Data were analyzed using odds ratios and validated by Bonferroni-Holm’s corrected chi-square tests at confidence intervals of $95\%$ to determine if there are statistically significant differences between transplant time spent on the waitlist and ethnicity, as well as age, diagnosis category, region of transplant center, and KDPI. Results: Statistically significant increased odds of remaining on the transplant list at two years existed for all non-white races/ethnicities, except those identifying as multiracial. Asian American candidates had the greatest odds of remaining on the waitlist greater than two years in comparison to white candidates: 1.51 times that of a patient categorized as white (odds ratio [OR] 1.51, confidence interval [CI] 1.44-1.57). African American/Black, (OR 1.38, CI 1.34-1.43) Pacific Islander (OR 1.38, CI 1.17-1.63), Hispanic candidates (OR 1.37, CI 1.32-1.41), and American Indian or Native Alaskan candidates (OR 1.23, CI 1.12-1.46) also had increased odds of remaining on the transplant waitlist greater than two years compared to white candidates. Discussion: In this study, ethnic disparities persisted as a barrier for non-white individuals receiving treatment for end-stage kidney disease, specifically in the context of time spent on the waitlist for a kidney transplant. Further research is needed regarding the causes of these disparities in time spent on the waitlist, such as cultural restrictions in organ donation, racial differences in parameters for organ match, and institutionalized racism in health care practitioners. ## Introduction In 2022, the United States (U.S.) organ transplant system achieved its one-millionth transplant, a milestone in organ transplant history [1]. The United Network for Organ Sharing (UNOS) serves as the nation’s transplant system under contract with the federal government. While organ procurement and transplants have become more streamlined, there are specific populations that have a more difficult time securing a transplantable organ, such as a kidney. Kidney transplant patients are the largest population of organ transplant recipients and the largest population of those remaining on the organ transplant waitlist. In 2021, more than 24,000 people received a kidney transplant out of the 41,354 organ transplants. However, nearly 66,000 eligible kidney transplant patients remained on the greater than 106,000 patient waitlist, and 6,427 ($24.5\%$) donated kidneys went un-transplanted [1]. While the number of transplants continues to increase, the National Academies of Sciences, Engineering, and Medicine (NASEM) submitted a committee report to improve equity and accountability of the U.S. organ transplant system following their study on deceased donor organ procurement, allocation, and distribution [1]. The committee recommended four action areas to the current US organ transplant system to achieve equity in five years, with specific changes to kidney organ transplants [1]. As the NASEM committee report emphasizes, equity is still not achieved within the organ transplant system, and the importance of understanding that inequity regarding kidney transplantation cannot be underestimated as kidney transplants make up the majority of transplant and waitlisted patients. Indications for kidney transplant In the U. S., nearly 786,000 patients live with end-stage renal disease (ESRD) [2]. ESRD is the terminal stage of chronic kidney disease (CKD), defined as a glomerular filtration rate of less than 15 mL/min [3]. Patients with CKD have a reduced quality of life and premature mortality and are characterized by decreasing kidney function that can manifest as metabolic and electrolyte abnormality, metabolic acidosis, anemia, and symptoms, such as pericarditis, pleuritis, confusion, myoclonus, seizures, fluid overload, hypertension, peripheral neuropathy, restless leg, malnutrition, nausea, vomiting, etc. [ 3,4]. The cause of CKD and ESRD is kidney injury. Kidney injury can have multiple etiologies, with the most common being diabetes and hypertension [3]. Patients with ESRD require intervention, including recurrent dialysis and/or kidney transplant [3]. Patients are referred to a nephrologist to discuss the potential of kidney transplant when the estimated glomerular filtration rate (eGFR) is reduced to 30 or below, although patients who are asymptomatic may not initiate dialysis until eGFR is much lower [3]. While most patients with ESRD require dialysis, few may be eligible and undergo renal transplants. Kidney transplantation, while not a cure for renal disease, can increase a patient's quality of life and increase life expectancy [3]. To be eligible for a renal transplant, patients must join the waitlist maintained by United Network for Organ Sharing (UNOS). There are multiple factors that determine whether a patient receives a kidney transplant, including blood type, location, size of the patient compared to the donor, and in some cases, age and severity of the disease [5]. Recent changes have been made to the algorithm to determine organ allocation, including age matching of the donor and recipient (also named longevity matching) in 2014, the distance between donor and recipient instead of using region boundaries in 2019, and assigning priority for hard-to-match candidates who are highly sensitized like patients with higher antibody level due to previous transplants, blood transfusions or pregnancies in 2019 [4]. Not all patients who are placed on the transplant list can receive an organ depending on current health status, comorbidities, etc. However additional systemic factors also play a role in the access and ability of a patient to receive a renal transplant. Kidney transplantation disparity While the prevalence of ESRD has been stably increasing within the U.S. particular populations have an increased prevalence [3]. Minority populations, particularly Black and Hispanic populations, are more likely to have ESRD compared to the non-Hispanic white population (3.4 and 1.5 times higher, respectively) [2,3,6,7]. Additionally, ESRD is more prevalent in men and people over the age of 65 years old [3]. There is also a difference in prevalence regarding geographic location within the U.S., with those who live in the southern states, such as Arkansas, Louisiana, Texas, and Oklahoma, more likely to have ESRD compared to those living in the northeast [6]. Although minority populations have an increased prevalence of ESRD, their time spent on the waiting list continues to remain unequal. Published reports show that Black patients are less likely to be referred for transplant evaluation, are diagnosed with ESRD at lower eGFR, are delayed in transplantation registration, progress slower through the waiting list, and are ultimately less likely to receive a transplant compared to the non-Hispanic white population [6-10]. Continued criticism exists over the use of race-based kidney filtration equations that may overestimate filtration rate and misrepresent the severity of minority (specifically Blacks) kidney function, including what can be done to change it (e.g., creating new algorithms) [11]. In response to the racial and ethnic disparity in kidney transplantation protocol, changes have been made in three installments: 1) in 2003, the UNOS allocation policy was changed to eliminate priority based on HLA-B matching; 2) in 2014, UNOS changed the kidney allocation system to include first dialysis date as well as time entered onto the transplant list as the wait-time is one of the largest variables to determine kidney allocation; and 3) in December 2019, UNOS changed the region criteria to prioritize patients based on a 250-mile radius of procurement instead of using the fixed donation service areas created in the 1980s [12-14]. Studies conducted after the policy changes to determine the effectiveness in reducing racial/ethnic disparities showed that while there was a decrease in disparity, parity was not achieved regarding the transplant rate and time spent on the waitlist [13,15-17]. Few studies after 2014 and even fewer after 2019 have been conducted to determine if disparity has decreased in other areas after the new policy was installed. Within this context, we sought to examine the role of ethnicity/race along with geographic location, socioeconomic status, age, comorbidities, Kidney Donor Profile Index (KDPI), BMI, and gender in renal transplantation wait time disparities. Although a recent analysis shows that the incidence of kidney transplantation for Black and white candidates is equivalent, it was hypothesized that ethnic and racial disparity still exists regarding the time spent on the Kidney Transplant Wait-List, which translates to a longer time spent receiving dialysis, associated with increased mortality, morbidity, and acute rejection of transplants, and decreased quality of life, for minority patients [9,18,19]. ## Materials and methods Data on all waitlisted candidates for donor kidney transplantation in the National Organ Procurement and Transplantation Network (OPTN) database listed on or before November 2020 were included in the analysis. Selection of censoring of data included using the OPTN advanced data report tool. Candidates listed for simultaneous kidney-pancreas transplants or other multi-organ transplants were excluded from the analysis. The data encompassed all adults, ages 18 years and older; pediatric candidates were excluded. Candidate ethnicity was determined from the candidate race variable in the OPTN database and was grouped into seven subcategories: White, Black, Hispanic, Asian, American Indian/Alaskan Native, Pacific Islander, and Multiracial. Any candidate with a race/ethnicity variable listed as unknown was excluded from the OPTN advanced search. The waiting list population data included the time spent on the waiting list for each ethnicity/race in the following categories: <30 days; 30 to <90 days; 90 days to <6 months; six months to <1 year; one year to <2 years; two years to <3 years; three years to <5 years; and five or more years. Additional sub-categorical data were collected regarding the wait time of each ethnicity/race for potential covariates, such as gender, age, citizenship, the primary source of payment, region of transplant center, BMI, Kidney Donor Profile Index (KDPI), renal diagnosis, and presence/type of diabetes. Time on the waiting list was calculated from the date of the listing, regardless of status, either active or inactive, to the time of the data collection. The waitlist candidates were then subcategorized by those who had been on the list for less than two years, two years and beyond, three years and beyond, and five years and beyond. Data were analyzed using odds ratios and validated by Bonferroni-Holm’s corrected chi-square tests at confidence intervals of $95\%$ to determine if there are statistically significant differences between transplant time spent on the waitlist and ethnicity, as well as age, diagnosis category, region of transplant center, and KDPI. All statistical analysis was performed using Microsoft Excel. ## Results Characteristics of the sample There were 91,830 patients included in the study; $35.3\%$ ($$n = 35$$,407) were listed as white, $31.6\%$ ($$n = 29$.058$) were listed as Black, $21.7\%$ ($$n = 19$$,883) were listed as Hispanic, $9.2\%$ ($$n = 8$$,419) were listed as Asian, $0.9\%$ ($$n = 851$$) were listed as American Indian/Alaskan Native, $0.6\%$ ($$n = 568$$) were listed as Pacific Islander, and $1.2\%$ ($$n = 1$$,063) were listed as multiracial. It is important to note that some patients may have been counted in multiple race and ethnic (i.e., Hispanic) categories. The median wait-list time was in the “1 year to <2 years” category; however, that time was not indicative of all patients' time on the waitlist, especially regarding race/ethnicity. Associations between race/ethnicity and waitlist wait times A statistically significant association was found between race and waitlist waiting times. All non-white races examined, other than those identifying as multiracial, had statistically significant increased odds of remaining on the kidney transplant list greater than two years when compared to persons who identified as white. As can be seen in Table 1, the odds ratio and $95\%$ confidence interval of each ethnicity/race, with the exception of multiracial, who remain on the waitlist at two, three, and five years are greater than one, which represents the odds of a white patient remaining on the waitlist at those same time intervals. Table 1 \| Unnamed: 0 \| Unnamed: 1 \| All Wait Times \| Less than 2 Years versus 2+ Years Wait Times \| Less than 2 Years versus 2+ Years Wait Times.1 \| Less than 2 Years versus 2+ Years Wait Times.2 \| Less than 2 Years versus 2+ Years Wait Times.3 \| Less than 2 Years versus 2+ Years Wait Times.4 \| Less than 2 Years versus 2+ Years Wait Times.5 \| Less than 2 Years versus 2+ Years Wait Times.6 \| Less than 2 Years versus 2+ Years Wait Times.7 \| Less than 2 Years versus 2+ Years Wait Times.8 \| Less than 2 Years versus 2+ Years Wait Times.9 \| Less than 2 Years versus 2+ Years Wait Times.10 \| Less than 2 Years versus 2+ Years Wait Times.11 \| Less than 2 Years versus 2+ Years Wait Times.12 \| Less than 2 Years versus 2+ Years Wait Times.13 \| Less than 2 Years versus 2+ Years Wait Times.14 \| Less than 2 Years versus 2+ Years Wait Times.15 \| Less than 2 Years versus 2+ Years Wait Times.16 \| Less than 2 Years versus 2+ Years Wait Times.17 \| Less than 2 Years versus 2+ Years Wait Times.18 \| Less than 2 Years versus 2+ Years Wait Times.19 \| Less than 2 Years versus 2+ Years Wait Times.20 \| Less than 2 Years versus 2+ Years Wait Times.21 \| Less than 2 Years versus 2+ Years Wait Times.22 \| Less than 2 Years versus 2+ Years Wait Times.23 \| Less than 2 Years versus 2+ Years Wait Times.24 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| Black \| Black \| Black \| Black \| Hispanic \| Hispanic \| Hispanic \| Hispanic \| Asian \| Asian \| Asian \| Asian \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Pacific Islander \| Pacific Islander \| Pacific Islander \| Pacific Islander \| Multiracial \| Multiracial \| Multiracial \| Multiracial \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| \| All Patients \| All Patients \| 0.00000 \| 0.00000 \| 1.384 \| 1.342 \| 1.428 \| 1 \| 1.365 \| 1.319 \| 1.414 \| 1 \| 1.513 \| 1.444 \| 1.586 \| 1 \| 1.278 \| 1.116 \| 1.463 \| 1 \| 1.38 \| 1.17 \| 1.627 \| 1 \| 1.115 \| 0.987 \| 1.26 \| 0 \| \| \| \| All Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| Less than 3 Years versus 3+ Years Wait Times \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 3+ Years with 95% Confidence Intervals \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| Black \| Black \| Black \| Black \| Hispanic \| Hispanic \| Hispanic \| Hispanic \| Asian \| Asian \| Asian \| Asian \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Pacific Islander \| Pacific Islander \| Pacific Islander \| Pacific Islander \| Multiracial \| Multiracial \| Multiracial \| Multiracial \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| \| All Patients \| All Patients \| 0.000000 \| 0.00000 \| 1.447 \| 1.399 \| 1.496 \| 1 \| 1.403 \| 1.351 \| 1.457 \| 1 \| 1.551 \| 1.476 \| 1.63 \| 1 \| 1.312 \| 1.135 \| 1.516 \| 1 \| 1.388 \| 1.166 \| 1.652 \| 1 \| 1.057 \| 0.923 \| 1.21 \| 0 \| \| \| \| All Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| Less than 5 Years versus 5+ Years Wait Times \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 5+ Years with 95% Confidence Intervals \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| Black \| Black \| Black \| Black \| Hispanic \| Hispanic \| Hispanic \| Hispanic \| Asian \| Asian \| Asian \| Asian \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Pacific Islander \| Pacific Islander \| Pacific Islander \| Pacific Islander \| Multiracial \| Multiracial \| Multiracial \| Multiracial \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| \| All Patients \| All Patients \| 0.00000 \| 0.00000 \| 1.528 \| 1.461 \| 1.599 \| 1 \| 1.448 \| 1.376 \| 1.523 \| 1 \| 1.638 \| 1.536 \| 1.747 \| 1 \| 1.367 \| 1.129 \| 1.656 \| 1 \| 1.78 \| 1.438 \| 2.203 \| 1 \| 0.939 \| 0.771 \| 1.144 \| 0 \| Figure 1 also demonstrates visually the odds ratio and $95\%$ confidence interval of each ethnicity/race versus the odds of a white patient remaining on the waitlist at those same time intervals. Figure 1: Odds ratio comparisons of whites to other racial/ethnic groups for kidney transplants in years at 95% confidence intervals2+ refers to two or more years; 3+ refers to three or more years; 5+ refers to five or more years. Table 2 reports the odds ratio and $95\%$ confidence interval of each race/ethnicity who remain on the waitlist for two or more years remains statistically significant for all subcategories that were included in the study, indicating a general effect not being driven by any one factor. The size of this effect, however, may differ when analyzing the subcategories of data and it should be noted that the effect size was insufficiently large for the relatively small count of candidates who identified as American Indian/Alaskan Native, Pacific Islander, and multiracial to remain statistically significant. Additionally, the sub-categorical data was not analyzed for wait-list times greater than three years. Table 2 \| Unnamed: 0 \| Unnamed: 1 \| All Wait Times \| Less than 2 Years versus 2+ Years Wait Times \| Less than 2 Years versus 2+ Years Wait Times.1 \| Less than 2 Years versus 2+ Years Wait Times.2 \| Less than 2 Years versus 2+ Years Wait Times.3 \| Less than 2 Years versus 2+ Years Wait Times.4 \| Less than 2 Years versus 2+ Years Wait Times.5 \| Less than 2 Years versus 2+ Years Wait Times.6 \| Less than 2 Years versus 2+ Years Wait Times.7 \| Less than 2 Years versus 2+ Years Wait Times.8 \| Less than 2 Years versus 2+ Years Wait Times.9 \| Less than 2 Years versus 2+ Years Wait Times.10 \| Less than 2 Years versus 2+ Years Wait Times.11 \| Less than 2 Years versus 2+ Years Wait Times.12 \| Less than 2 Years versus 2+ Years Wait Times.13 \| Less than 2 Years versus 2+ Years Wait Times.14 \| Less than 2 Years versus 2+ Years Wait Times.15 \| Less than 2 Years versus 2+ Years Wait Times.16 \| Less than 2 Years versus 2+ Years Wait Times.17 \| Less than 2 Years versus 2+ Years Wait Times.18 \| Less than 2 Years versus 2+ Years Wait Times.19 \| Less than 2 Years versus 2+ Years Wait Times.20 \| Less than 2 Years versus 2+ Years Wait Times.21 \| Less than 2 Years versus 2+ Years Wait Times.22 \| Less than 2 Years versus 2+ Years Wait Times.23 \| Less than 2 Years versus 2+ Years Wait Times.24 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| Odds Ratios vs. Whites for Waiting 2+ Years with 95% Confidence Intervals \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| Black \| Black \| Black \| Black \| Hispanic \| Hispanic \| Hispanic \| Hispanic \| Asian \| Asian \| Asian \| Asian \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Amer. Indian/Alaska Native \| Pacific Islander \| Pacific Islander \| Pacific Islander \| Pacific Islander \| Multiracial \| Multiracial \| Multiracial \| Multiracial \| \| Category \| Subcategory \| Bonferroni-Holms Corrected P-value \| Bonferroni-Holms Corrected P-value \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| OR \| OR_LB \| OR_UB \| Sig \| \| All Patients \| All Patients \| 0 \| 0 \| 1.384 \| 1.342 \| 1.428 \| 1 \| 1.365 \| 1.319 \| 1.414 \| 1 \| 1.513 \| 1.444 \| 1.586 \| 1 \| 1.278 \| 1.116 \| 1.463 \| 1 \| 1.38 \| 1.17 \| 1.627 \| 1 \| 1.115 \| 0.987 \| 1.26 \| 0 \| \| Gender \| Male \| 0 \| 0 \| 1.407 \| 1.352 \| 1.463 \| 1 \| 1.393 \| 1.333 \| 1.454 \| 1 \| 1.484 \| 1.397 \| 1.576 \| 1 \| 1.344 \| 1.121 \| 1.611 \| 1 \| 1.34 \| 1.071 \| 1.677 \| 1 \| 1.215 \| 1.035 \| 1.427 \| 1 \| \| Gender \| Female \| 0 \| 0 \| 1.348 \| 1.283 \| 1.418 \| 1 \| 1.319 \| 1.244 \| 1.397 \| 1 \| 1.554 \| 1.442 \| 1.674 \| 1 \| 1.194 \| 0.973 \| 1.465 \| 0 \| 1.421 \| 1.114 \| 1.812 \| 1 \| 0.989 \| 0.818 \| 1.195 \| 0 \| \| Age \| 18-34 Years \| 0.00021 \| 0.00019 \| 1.357 \| 1.207 \| 1.526 \| 1 \| 1.296 \| 1.154 \| 1.455 \| 1 \| 1.319 \| 1.097 \| 1.586 \| 1 \| 1.179 \| 0.725 \| 1.918 \| 0 \| 1.126 \| 0.615 \| 2.062 \| 0 \| 0.921 \| 0.641 \| 1.321 \| 0 \| \| Age \| 35-49 Years \| 0 \| 0 \| 1.481 \| 1.383 \| 1.585 \| 1 \| 1.465 \| 1.359 \| 1.58 \| 1 \| 1.458 \| 1.316 \| 1.616 \| 1 \| 1.188 \| 0.904 \| 1.562 \| 0 \| 1.518 \| 1.106 \| 2.083 \| 1 \| 1.14 \| 0.901 \| 1.443 \| 0 \| \| Age \| 50-64 Years \| 0 \| 0 \| 1.418 \| 1.352 \| 1.486 \| 1 \| 1.438 \| 1.363 \| 1.517 \| 1 \| 1.526 \| 1.42 \| 1.64 \| 1 \| 1.391 \| 1.133 \| 1.709 \| 1 \| 1.401 \| 1.101 \| 1.783 \| 1 \| 1.373 \| 1.129 \| 1.671 \| 1 \| \| Age \| 65 + \| 0 \| 0 \| 1.406 \| 1.322 \| 1.496 \| 1 \| 1.587 \| 1.47 \| 1.714 \| 1 \| 1.661 \| 1.52 \| 1.814 \| 1 \| 1.569 \| 1.169 \| 2.106 \| 1 \| 1.455 \| 0.971 \| 2.179 \| 0 \| 1.219 \| 0.912 \| 1.63 \| 0 \| \| Citizenship \| U.S. Citizen \| 0 \| 0 \| 1.376 \| 1.334 \| 1.42 \| 1 \| 1.283 \| 1.233 \| 1.335 \| 1 \| 1.455 \| 1.381 \| 1.532 \| 1 \| 1.285 \| 1.12 \| 1.474 \| 1 \| 1.344 \| 1.122 \| 1.61 \| 1 \| 1.116 \| 0.985 \| 1.263 \| 0 \| \| Citizenship \| Non-U.S. Citizen \| na \| 0.2049 \| 1.086 \| 0.881 \| 1.339 \| 0 \| 1.178 \| 0.995 \| 1.395 \| 0 \| 1.33 \| 1.104 \| 1.603 \| 1 \| 1.041 \| 0.298 \| 3.635 \| 0 \| 1.276 \| 0.815 \| 1.996 \| 0 \| 0.603 \| 0.282 \| 1.289 \| 0 \| \| Primary Source of Payment \| Private insurance \| 0 \| 0 \| 1.38 \| 1.316 \| 1.446 \| 1 \| 1.222 \| 1.157 \| 1.291 \| 1 \| 1.403 \| 1.314 \| 1.498 \| 1 \| 1.039 \| 0.824 \| 1.309 \| 0 \| 1.335 \| 1.04 \| 1.714 \| 1 \| 0.998 \| 0.836 \| 1.191 \| 0 \| \| Primary Source of Payment \| All Public insurance \| 0 \| 0 \| 1.41 \| 1.352 \| 1.47 \| 1 \| 1.475 \| 1.409 \| 1.545 \| 1 \| 1.597 \| 1.492 \| 1.709 \| 1 \| 1.462 \| 1.233 \| 1.732 \| 1 \| 1.435 \| 1.15 \| 1.792 \| 1 \| 1.192 \| 1.005 \| 1.414 \| 1 \| \| Region of Center \| Region 1 \| na \| 0.01834 \| 1.319 \| 1.139 \| 1.528 \| 1 \| 1.078 \| 0.911 \| 1.276 \| 0 \| 1.248 \| 0.991 \| 1.572 \| 0 \| 0.778 \| 0.276 \| 2.191 \| 0 \| na \| na \| na \| na \| 0.718 \| 0.383 \| 1.344 \| 0 \| \| Region of Center \| Region 2 \| na \| 0 \| 1.456 \| 1.348 \| 1.573 \| 1 \| 1.061 \| 0.929 \| 1.213 \| 0 \| 1.089 \| 0.942 \| 1.258 \| 0 \| 1.336 \| 0.555 \| 3.215 \| 0 \| 0.89 \| 0.316 \| 2.506 \| 0 \| 0.89 \| 0.55 \| 1.441 \| 0 \| \| Region of Center \| Region 3 \| na \| 0 \| 1.507 \| 1.391 \| 1.632 \| 1 \| 0.995 \| 0.878 \| 1.128 \| 0 \| 1.292 \| 1.044 \| 1.598 \| 1 \| 1.177 \| 0.668 \| 2.074 \| 0 \| 0.963 \| 0.342 \| 2.711 \| 0 \| 0.391 \| 0.211 \| 0.724 \| 1 \| \| Region of Center \| Region 4 \| na \| 0 \| 1.496 \| 1.333 \| 1.678 \| 1 \| 1.234 \| 1.109 \| 1.372 \| 1 \| 1.192 \| 0.967 \| 1.47 \| 0 \| 0.789 \| 0.473 \| 1.317 \| 0 \| 0.689 \| 0.302 \| 1.571 \| 0 \| 0.63 \| 0.346 \| 1.145 \| 0 \| \| Region of Center \| Region 5 \| 0 \| 0 \| 1.493 \| 1.349 \| 1.651 \| 1 \| 1.247 \| 1.163 \| 1.336 \| 1 \| 1.371 \| 1.262 \| 1.489 \| 1 \| 1.226 \| 0.99 \| 1.517 \| 0 \| 1.355 \| 1.088 \| 1.689 \| 1 \| 0.97 \| 0.783 \| 1.202 \| 0 \| \| Region of Center \| Region 6 \| 0.04944 \| 0.10018 \| 1.14 \| 0.857 \| 1.517 \| 0 \| 0.782 \| 0.596 \| 1.024 \| 0 \| 1.2 \| 0.972 \| 1.483 \| 0 \| 0.908 \| 0.502 \| 1.641 \| 0 \| 0.843 \| 0.567 \| 1.254 \| 0 \| 1.921 \| 1.212 \| 3.045 \| 1 \| \| Region of Center \| Region 7 \| na \| 0.00077 \| 1.332 \| 1.191 \| 1.489 \| 1 \| 1.047 \| 0.907 \| 1.209 \| 0 \| 1.223 \| 1.022 \| 1.463 \| 1 \| 1.096 \| 0.797 \| 1.508 \| 0 \| 1.291 \| 0.322 \| 5.171 \| 0 \| 1.537 \| 1.014 \| 2.329 \| 1 \| \| Region of Center \| Region 8 \| na \| 0.00018 \| 1.552 \| 1.313 \| 1.836 \| 1 \| 1.468 \| 1.197 \| 1.8 \| 1 \| 1.07 \| 0.776 \| 1.478 \| 0 \| 1.209 \| 0.624 \| 2.344 \| 0 \| 1.182 \| 0.333 \| 4.201 \| 0 \| 1.273 \| 0.777 \| 2.085 \| 0 \| \| Region of Center \| Region 9 \| na \| 0 \| 1.55 \| 1.388 \| 1.731 \| 1 \| 1.343 \| 1.183 \| 1.526 \| 1 \| 1.395 \| 1.198 \| 1.625 \| 1 \| 1.341 \| 0.628 \| 2.865 \| 0 \| 2.49 \| 0.455 \| 13.624 \| 0 \| 0.639 \| 0.366 \| 1.118 \| 0 \| \| Region of Center \| Region 10 \| na \| 0 \| 1.554 \| 1.382 \| 1.747 \| 1 \| 0.916 \| 0.686 \| 1.223 \| 0 \| 0.816 \| 0.589 \| 1.131 \| 0 \| 1.387 \| 0.546 \| 3.525 \| 0 \| 1.156 \| 0.326 \| 4.106 \| 0 \| 1.885 \| 1.065 \| 3.337 \| 1 \| \| Region of Center \| Region 11 \| na \| 0.01812 \| 1.213 \| 1.112 \| 1.325 \| 1 \| 1.02 \| 0.803 \| 1.295 \| 0 \| 0.915 \| 0.698 \| 1.199 \| 0 \| 1.015 \| 0.563 \| 1.827 \| 0 \| 0.966 \| 0.282 \| 3.307 \| 0 \| 1.294 \| 0.924 \| 1.813 \| 0 \| \| Region of Center \| All Regions Excluding 5 & 6 \| 0 \| 0 \| 1.422 \| 1.376 \| 1.47 \| 1 \| 1.142 \| 1.092 \| 1.195 \| 1 \| 1.246 \| 1.167 \| 1.331 \| 1 \| 1.074 \| 0.888 \| 1.298 \| 0 \| 0.857 \| 0.579 \| 1.267 \| 0 \| 0.984 \| 0.838 \| 1.155 \| 0 \| \| BMI \| 18.5-<25 \| 0 \| 0 \| 1.357 \| 1.264 \| 1.457 \| 1 \| 1.271 \| 1.178 \| 1.37 \| 1 \| 1.483 \| 1.363 \| 1.614 \| 1 \| 1.713 \| 1.205 \| 2.435 \| 1 \| 1.052 \| 0.63 \| 1.756 \| 0 \| 0.967 \| 0.728 \| 1.286 \| 0 \| \| BMI \| 25-<30 \| 0 \| 0 \| 1.404 \| 1.325 \| 1.488 \| 1 \| 1.445 \| 1.359 \| 1.537 \| 1 \| 1.501 \| 1.381 \| 1.632 \| 1 \| 1.062 \| 0.833 \| 1.355 \| 0 \| 1.337 \| 0.969 \| 1.844 \| 0 \| 1.015 \| 0.795 \| 1.296 \| 0 \| \| BMI \| 30-<35 \| 0 \| 0 \| 1.388 \| 1.306 \| 1.475 \| 1 \| 1.34 \| 1.251 \| 1.436 \| 1 \| 1.525 \| 1.363 \| 1.707 \| 1 \| 1.269 \| 0.994 \| 1.62 \| 0 \| 1.548 \| 1.144 \| 2.095 \| 1 \| 1.143 \| 0.903 \| 1.446 \| 0 \| \| BMI \| 35-<50 \| 0 \| 0 \| 1.325 \| 1.229 \| 1.428 \| 1 \| 1.313 \| 1.191 \| 1.447 \| 1 \| 1.407 \| 1.151 \| 1.719 \| 1 \| 1.523 \| 1.105 \| 2.1 \| 1 \| 1.254 \| 0.861 \| 1.828 \| 0 \| 1.266 \| 0.935 \| 1.714 \| 0 \| \| KDPI \| <86 \| 0 \| 0 \| 1.384 \| 1.342 \| 1.428 \| 1 \| 1.365 \| 1.319 \| 1.414 \| 1 \| 1.513 \| 1.444 \| 1.586 \| 1 \| 1.278 \| 1.116 \| 1.463 \| 1 \| 1.38 \| 1.17 \| 1.627 \| 1 \| 1.115 \| 0.987 \| 1.26 \| 0 \| \| KDPI \| 86+ \| 0 \| 0 \| 1.373 \| 1.318 \| 1.431 \| 1 \| 1.379 \| 1.318 \| 1.443 \| 1 \| 1.614 \| 1.519 \| 1.714 \| 1 \| 1.307 \| 1.114 \| 1.535 \| 1 \| 1.642 \| 1.344 \| 2.005 \| 1 \| 1.099 \| 0.948 \| 1.276 \| 0 \| \| Diagnosis \| Diabetes \| 0 \| 0 \| 1.36 \| 1.287 \| 1.436 \| 1 \| 1.549 \| 1.464 \| 1.639 \| 1 \| 1.693 \| 1.567 \| 1.828 \| 1 \| 1.535 \| 1.274 \| 1.849 \| 1 \| 1.492 \| 1.189 \| 1.873 \| 1 \| 1.261 \| 1.031 \| 1.543 \| 1 \| \| Diagnosis \| Glomerular Disease \| 0 \| 0 \| 1.426 \| 1.317 \| 1.544 \| 1 \| 1.267 \| 1.16 \| 1.383 \| 1 \| 1.44 \| 1.299 \| 1.597 \| 1 \| 1.122 \| 0.784 \| 1.606 \| 0 \| 1.152 \| 0.746 \| 1.778 \| 0 \| 0.928 \| 0.693 \| 1.243 \| 0 \| \| Diagnosis \| Hypertensive Nephrosclerosis \| 0 \| 0 \| 1.402 \| 1.305 \| 1.506 \| 1 \| 1.304 \| 1.187 \| 1.431 \| 1 \| 1.5 \| 1.331 \| 1.691 \| 1 \| 1.259 \| 0.807 \| 1.965 \| 0 \| 1.497 \| 0.934 \| 2.397 \| 0 \| 1.281 \| 0.918 \| 1.787 \| 0 \| \| Diagnosis \| Polycystic Kidneys \| na \| 0 \| 1.492 \| 1.302 \| 1.709 \| 1 \| 1.336 \| 1.154 \| 1.547 \| 1 \| 1.203 \| 0.961 \| 1.506 \| 0 \| 1.125 \| 0.558 \| 2.269 \| 0 \| 2.17 \| 0.771 \| 6.109 \| 0 \| 0.89 \| 0.533 \| 1.487 \| 0 \| \| Diagnosis \| Retransplant/Graft Failure \| na \| 0 \| 1.417 \| 1.263 \| 1.59 \| 1 \| 1.34 \| 1.154 \| 1.555 \| 1 \| 1.511 \| 1.252 \| 1.824 \| 1 \| 1.587 \| 0.874 \| 2.882 \| 0 \| 1.276 \| 0.54 \| 3.014 \| 0 \| 1.206 \| 0.812 \| 1.792 \| 0 \| \| Diagnosis \| Tubular and Interstitial Diseases \| na \| 0.23662 \| 1.043 \| 0.855 \| 1.272 \| 0 \| 0.963 \| 0.778 \| 1.192 \| 0 \| 1.123 \| 0.828 \| 1.522 \| 0 \| 0.332 \| 0.132 \| 0.835 \| 1 \| 0.601 \| 0.175 \| 2.059 \| 0 \| 1.191 \| 0.592 \| 2.399 \| 0 \| \| Diabetes \| No \| 0 \| 0 \| 1.392 \| 1.336 \| 1.451 \| 1 \| 1.257 \| 1.197 \| 1.32 \| 1 \| 1.42 \| 1.331 \| 1.514 \| 1 \| 1.122 \| 0.898 \| 1.401 \| 0 \| 1.495 \| 1.146 \| 1.95 \| 1 \| 1.122 \| 0.95 \| 1.326 \| 0 \| \| Diabetes \| Type I \| na \| 0.269 \| 1.211 \| 1.033 \| 1.419 \| 1 \| 1.242 \| 1.032 \| 1.495 \| 1 \| 1.484 \| 0.987 \| 2.232 \| 0 \| 0.606 \| 0.189 \| 1.939 \| 0 \| 0.909 \| 0.217 \| 3.814 \| 0 \| 0.679 \| 0.351 \| 1.314 \| 0 \| \| Diabetes \| Type II \| 0 \| 0 \| 1.399 \| 1.33 \| 1.473 \| 1 \| 1.508 \| 1.429 \| 1.592 \| 1 \| 1.653 \| 1.538 \| 1.776 \| 1 \| 1.471 \| 1.232 \| 1.755 \| 1 \| 1.358 \| 1.095 \| 1.684 \| 1 \| 1.157 \| 0.955 \| 1.401 \| 0 \| Although candidates who identified as Black, Hispanic, Asian, American Indian/Alaskan Native, or Pacific Islander maintained higher odds of remaining on the waitlist than candidates identifying as white, each race/ethnicity had varying odds. The odds of a candidate categorized as Black waiting two or more years for a transplant is 1.38 times that of a candidate categorized as White, with a $95\%$ confidence interval of (1.34, 1.43). The odds of a candidate categorized as Hispanic waiting two or more years for a transplant is 1.37 times that of a patient categorized as white, with a $95\%$ confidence interval of (1.32, 1.41). The odds of a candidate categorized as Asian waiting two or more years for a transplant is 1.51 times that of a candidate categorized as white, with a $95\%$ confidence interval of (1.44, 1.59). The odds of a candidate categorized as American Indian or Alaska Native waiting two or more years for a transplant is 1.28 times that of a candidate categorized as white, with a $95\%$ confidence interval of (1.12, 1.46). The odds of a candidate categorized as a Pacific Islander waiting two or more years for a transplant is 1.38 times that of a candidate categorized as white, with a $95\%$ confidence interval of (1.17, 1.63). The odds of a candidate categorized as multiracial waiting two or more years for a transplant is 1.12 times that of a candidate categorized as white, with a $95\%$ confidence interval of (0.99, 1.26). Notably, this is not statistically significant, indicating that this is not contributing to the overall statistically significant association between race and wait time. ## Discussion As the prevalence of ESRD increases and the number of kidneys required for kidney transplantation increases, the current organ distribution system will need to continually undergo revision to provide patients with the best quality of life and prognosis. While the number of successful transplants continues to increase, thousands of people remain on the transplant list, with kidney transplant patients making up the majority. Although it has been widely documented that ESRD disproportionately affects minority, elderly, and southern patients, the current organ distribution system has historically been unable to adequately provide for those populations. Policy changes over the past two decades have been directed at improving the areas that have created disparity, such as HLA matching, incorporating first-time requiring dialysis as a parameter to determine kidney allocation, and updating kidney distribution based on distance from the geographic location of organ procurement [12-14]. Reassessment of patients’ time spent on the waitlist after these changes have been made is a good indicator of the success of these policies, as increasing time on the waitlist and a lengthened time requiring dialysis increases morbidity, mortality, and acute organ rejection and decreases patients’ quality of life [18]. Although prevention of progression to ESRD is the ideal goal, for a patient who requires a placement on the kidney transplant list the outcome should not be affected by their immutable characteristics. Ethnicity as a major factor in time spent on the kidney transplant waitlist In this study, ethnic disparities persisted for non-white individuals receiving treatment for end-stage kidney disease, specifically in the context of time spent on the waitlist for a kidney transplant. These differences persisted even up to a five-year wait time. Despite changes in organ allocation policy, minorities waitlisted for renal transplantation remain disadvantaged compared with patients who identify as white. Although Black and Hispanic/Latinx populations remain on the wait time longer than white populations, Asian American patients were found to be most disadvantaged. Asian American patients had the greatest odds of remaining on the waitlist greater than two years and greater than three years in comparison to white patients: 1.51 times more likely to remain on the list after two years and 1.55 times more likely to remain on the list after three years than that of a patient who identified as white. Additionally, patients that identified as Pacific Islanders were 1.78 times more likely to remain on the list after five years than that of a patient who identified as white, the highest odds among all ethnic/racial groups on the waitlist for 5+ years. Previous studies assessing the 2014 policy changes on the kidney allocation system (KAS) found that the number of Asian patients on the waitlist decreased but continued to have the highest wait-listing rate, the rate at which added to the list [17]. In 2019, Asians made up $8.5\%$ of the organ transplant waitlist, but only $24.7\%$ of those patients received transplants, whereas $48.8\%$ of white patients on the transplant list received transplants [20]. Few studies exist to determine the reason for this disparity. A qualitative study of organ donation-related attitudes and beliefs of three Asian ethnic groups located in the greater Philadelphia metropolitan area: Chinese, Filipino, and Vietnamese Americans found that Asian communities had beliefs that may limit the relatability of U.S. organ donation and transplant campaigns, especially among older populations [21]. It has been postulated that Asian Americans would benefit more from a micro-targeted culturally competent family approach, as Asian Americans were mainly supportive of organ donation and transplants if kept in the family [21]. Additionally, nearly one-third of the Asian participants interviewed during that study cited religious beliefs and the idea that they needed to remain whole with all of their organs as a barrier to organ donation [21]. While this research helped elucidate opinions on donation, it does not provide insight into barriers of Asian patients who remain on the kidney transplant list. More research needs to be conducted to better understand why Asian patients have higher odds of remaining on the transplant list longer than white Americans. Pacific Islanders make up $0.6\%$ of US Transplant waitlist candidates but only $0.2\%$ of the US population [22]. While the research regarding Asian American transplant patients is limited, even fewer studies exist that analyze the views and opinions of Pacific Islanders. Additionally, only $25\%$ of Pacific Islanders on the waitlist received a transplant versus $48.8\%$ of the white population [22]. Native Hawaiians, a segment of the Pacific Islander population, are more likely to be diagnosed with diabetes, cardiovascular disease, higher obesity rates, and more likely to be diagnosed with chronic liver disease when compared to white Hawaiians [22]. Limited research on this population leaves many questions as to why at five years, they are 1.78 times more likely to remain on the kidney transplant list compared to patients that identify as White. More research is required to explain this finding. Recent articles researching changes in waitlist times after the kidney allocation system policies were updated were unable to determine the effects on the Native Hawaiian Pacific Islander population [17]. Study limitations While this study included a large sample and an up-to-date database (including data collected one year after major policy changes made in 2019 by UNOS) and findings may be considered generalizable to the population's studies, it has its limitations. The results cannot define a change to the waitlist overtime; inactive patients may have more heavily impacted smaller minority populations, inequality between the geographic location of candidates could not be assessed, socioeconomic status was not easily quantified, and the population is only representative of the adult patient population with ESRD. Associations between race/ethnicity and odds of remaining on the waitlist as compared to candidates that identify as white cannot define changes to the waitlist and time spent on the waitlist post versus pre-policy change completed in 2019, as there are no previous odds ratio studies completed before the policy change. These findings can only describe an existing inequality/disparity that exists at the time of data gathering. Additionally, as status seven candidates were not specifically excluded from the data, some of the candidates may remain on the waitlist even if an organ was offered. A status seven candidate is a candidate that is temporarily unsuitable for transplant due to reasons such as the candidate being too sick for transplant, lacking insurance, having medical, surgical, or psychosocial contraindications, or the patient chooses not to receive the organ [23]. Zhang et al. concluded that while racial disparities continue to exist on the waitlist, the racial disparity reduction seen after the kidney allocation system was updated in 2014 was due to a “steeper decline in inactive waitlisting among minorities and a greater proportion of actively waitlisted minority patient” [17]. Although the data were collected after a large amount of inactive or status 7 candidates were removed from the list due to changes in the kidney allocation system, the odds ratio may still be slightly affected by the limited numbers of candidates from some ethnic/racial groups [17]. Furthermore, it should be noted that while people were removed from the list due to inactive status, the literature shows that candidates who identify as white are more likely to move from inactive to active status, allowing them to receive a transplant than patients who identify as an ethnic/racial minority group [16]. Another limitation is the inability to further evaluate and analyze the geographic location of the patient. Candidate data from all 11 regions within the U.S. were collected and analyzed as a covariate, and while the odds ratio for some ethnic/racial minorities remained statistically significant throughout most regions, such as candidates that identified as Black who had increased odds regardless of location, with the exception of region 6, which includes Alaska, Hawaii, Idaho, Montana, Oregon, and Washington, smaller groups such as Asian American, Native Hawaiian and Pacific Islander, and American Indian Alaskan native did not remain statistically significant [24]. This may be due to the limited number of candidates in the specific regions or may indicate that there is a regional disparity that deserves greater evaluation. Previous data that analyzed the location and geographic disparity caused by the organ allocation system also utilized Designated Service Areas (DSA), which may not be able to be directly compared with regions of OPTN [25]. This study also neglected to account for data that can identify the socioeconomic status of the candidates on the waitlist. Patzer and McClellan found that low socioeconomic status is correlated with kidney disease progression and access to kidney transplantation [19]. In addition, low SES is further associated with an increased incidence of CKD, progression to end-stage renal disease, inadequate dialysis treatment, reduced access to kidney transplantation, and poor health outcomes [19]. While this study evaluates insurance type, it is not the best indicator of socioeconomic status. Previous studies have shown that the insurance type (e.g., private, Medicaid, or no insurance) of the candidates on the waitlist has been a barrier to transplant, can exacerbate disparity, or lead to worse survival outcomes [26,27]. In this study, the type of insurance did not seem to impact the odds, as the increased odds remained statistically significant regardless of private or public insurance. Due to limitations in data sets, further subcategorization and patients with no insurance were not specified. Although previous studies have shown similar racial/ethnic disparities in pediatric patients with ESRD, this population was not considered in this study [28]. This study was limited to adult candidates and did not analyze pediatric patients under age 18, and further evaluation is warranted. Implications for future research After the data in this study were analyzed, OPTN made updates to race and ethnicity reporting in January 2022, allowing race and ethnicity to be reported instead of categorizing individuals based on ethnicity. The change was made because some individuals identify as two or more ethnicities based on the previous categorization, such as Hispanics that identify as white. Ethnicity misclassification that was inherent in the data collected by OPTN may have influenced the outcome of this study, and further research should be done to determine if changes to data reporting have impacted the wait time. ## Conclusions Misinformation and lack of emphasis on change of what was previously considered race-based medicine may still impact individuals who identify as a minority or a non-white American. As racial and ethnic disparities have been seen in waitlist time for organ transplantation, including kidney transplantation, this research highlights health disparities in nephrology, or more specifically for individuals who are receiving treatment for end-stage kidney disease. Overall, a statistically significant association was found between race and waitlist waiting times. All non-white candidates examined, other than those identifying as multiracial, had statistically significant increased odds of remaining on the kidney transplant list greater than two years when compared to persons who identified as white. Asian American candidates had the greatest odds of remaining on the waitlist greater than two years in comparison to white candidates: 1.51 times more likely to remain on the list after two years than a candidate who identified as white. African American and Pacific Islander candidates had odds of remaining on the transplant waitlist greater than two years which was slightly lower than that of Asian Americans in comparison to white patients: 1.38 times that of candidates who identified as white. Among odds ratios that are statistically different from white candidates, Hispanic candidates, followed by Native American and Alaskan candidates, had the smallest odds of remaining on the waitlist greater than two years in comparison to white candidates: 1.37 times and 1.28 times that of candidates that identified as white, respectively. Recognizing these disparities may prompt researchers to further investigate the causes of this disparity and assist in changing policy to close the gap in future transplantation practice. Further research should be conducted on the causes of these disparities in time spent on the waitlist, cultural restrictions in organ donation, racial differences in parameters for organ match, as well as the possible role of institutionalized racism in health care practices and practitioners. ## References 1. Dageforde LA, English RA, Kizer KW. Achieving equity in organ transplantation: recommendations for action based on the National Academies of Sciences, Engineering, and Medicine Report. Transplantation (2023) 107 291-296. PMID: 36584378 2. Kidney Disease Statistics for the United States. (2022) 3. Hashmi MF, Benjamin O, Lappin SL. End-stage renal disease. (2022) 4. Curham GC, Tonelli M. Overview of the management of chronic kidney disease in adults. UpToDate (2022) 5. The Kidney Transplant Waitlist-What You Need to Know. (2022) 6. Gupta R, Woo K, Yi JA. Epidemiology of end-stage kidney disease. Semin Vasc Surg (2021) 34 71-78. PMID: 33757639 7. Johansen KL, Chertow GM, Foley RN. 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--- title: Is obesity related to enhanced neural reactivity to visual food cues? A review and meta-analysis authors: - Filip Morys - Isabel García-García - Alain Dagher journal: Social Cognitive and Affective Neuroscience year: 2020 pmcid: PMC9997070 doi: 10.1093/scan/nsaa113 license: CC BY 4.0 --- # Is obesity related to enhanced neural reactivity to visual food cues? A review and meta-analysis ## Abstract Theoretical work suggests that obesity is related to enhanced incentive salience of food cues. However, evidence from both behavioral and neuroimaging studies on the topic is mixed. In this work, we review the literature on cue reactivity in obesity and perform a preregistered meta-analysis of studies investigating effects of obesity on brain responses to passive food pictures viewing. Further, we examine whether age influences brain responses to food cues in obesity. In the meta-analysis, we included 13 studies of children and adults that investigated group differences (obese vs lean) in responses to food vs non-food pictures viewing. While we found no significant differences in the overall meta-analysis, we show that age significantly influences brain response differences to food cues in the left insula and the left fusiform gyrus. In the left insula, obese vs lean brain differences in response to food cues decreased with age, while in the left fusiform gyrus the pattern was opposite. Our results suggest that there is little evidence for obesity-related differences in responses to food cues and that such differences might be mediated by additional factors that are often not considered. ## Introduction Individuals vary in their susceptibility to obesity. An oft-proposed explanation is that an environment abundant in appetizing food cues triggers different levels of hunger and eating in different people. According to this explanation, individuals more susceptible to omnipresent food cues might overconsume palatable foods and have difficulty to restrict their caloric intake, which may lead to obesity (Polivy et al., 2008). However, there are few systematic reviews of cue-reactivity in obesity that unequivocally support the model. The underlying theory is that foods and food-related cues can come to act as conditioned stimuli that predict the rewarding effects of ingestion. Food constituents, such as calories (Sclafani et al., 2011), are unconditioned stimuli that elicit unconditioned metabolic responses (Jansen, 1998; Hill, 2007). When reliably paired with caloric intake, food cues such as the sight or smell of food might become conditioned cues that ultimately invoke conditioned responses such as hunger, food craving, salivation or brain activity (Jansen, 1998; Hill, 2007; van den Akker et al., 2014). Several researchers have proposed that reactivity to food cues forms part of a trait that combines enhanced appetitive drive and reduced inhibitory control, which renders some individuals vulnerable to uncontrolled eating in an obesogenic environment (Vainik et al., 2019). Some studies have related reactivity to food cues to high body mass index (BMI) and obesity (Stice et al., 2009 2010; Demos et al., 2012; van den Akker et al., 2014; Pursey et al., 2014). However, the literature contains a number of conflicting findings on the topic (see Boswell and Kober, 2016). This is largely due to the diversity of paradigms and outcome measures used. Classically, one of the main outcome measures in food cue reactivity paradigms is self-reported cue-induced food craving, which is a strong and conscious desire to eat (Jansen, 1998; Jansen et al., 2011; Boswell and Kober, 2016). It is different from trait craving, which is seen as a desire to eat arising independently of any external cues (Cepeda-Benito et al., 2000; Boswell and Kober, 2016). Both types of cravings can be measured with standardized questionnaires, such as the State and Trait Food-Cravings Questionnaire (Cepeda-Benito et al., 2000), or using visual analog scales (Nederkoorn et al., 2000). The second category of outcome measures in studies on food cue reactivity entails peripheral physiological measures, such as insulin levels changes (Jastreboff et al., 2013; Kroemer et al., 2013), vagal responses (Udo et al., 2014), heart-rate and heart-rate variability changes (Nederkoorn et al., 2000), or salivation (Boyland et al., 2017), among others. Finally, cue reactivity can be assessed with neurocognitive measures such as functional magnetic resonance imaging (fMRI) or electroencephalography (EEG) (van der Laan et al., 2011), eye-tracking (Doolan et al., 2014; Mehl et al., 2017), or cognitive paradigms (e.g. Morys et al., 2018; Oliva et al., 2019), as reviewed below. A number of fMRI studies have been designed around the hypothesis that individuals with obesity show neurobehavioral alterations in response to food. Perhaps the simplest way of testing this hypothesis is via the presentation of passively viewed food images. These designs allow the comparison between fMRI activity in response to visual food stimuli vs non-food items (usually objects). Such paradigms have been used to investigate potential behavioral causes of unhealthy weight gain, such as making sub-optimal food choices, low cognitive control in response to food stimuli or dysregulation in emotional control. Investigations of cue reactivity leave a large freedom to the researchers in terms of designing experiments and analyzing resulting datasets (Smeets et al., 2019). For example, while designing passive food picture viewing paradigms, one might consider contrasting reactivity to food pictures with non-food pictures (e.g. Davids et al., 2010), or high-calorie with low-calorie food pictures (e.g. Frank et al., 2014). Similarly, while investigating obesity-related changes in cue reactivity, experimenters tend to contrast obese groups with lean groups (e.g. Mehl et al., 2018), correlate cue reactivity with obesity measures (e.g. BMI; Boswell and Kober, 2016) or only investigate cue reactivity in obese individuals without a control group (e.g. Luo et al., 2013). In the following sections of this article, we will focus on neurocognitive measures of cue reactivity in obesity, especially on neural correlates of passive food picture viewing. Specifically, we wish to test the hypothesis that there is either greater appetitive or reduced self-regulation response to food cues in obesity and that this is reflected in fMRI experiments. All the studies reviewed below meet a priori defined quality criteria. The most relevant criteria here are that all the studies examine fMRI differences between a condition of interest and a control condition and include obese and lean individuals. ## Passive image viewing in obesity Studies investigating differences between obese and lean individuals in brain responses to food picture viewing have produced mixed findings. Bruce and colleagues showed that in obese children ($$n = 10$$), as opposed to healthy weight children ($$n = 10$$), food vs non-food pictures viewing elicits higher brain activations in the prefrontal cortex (PFC), both pre- and post-meal (Bruce et al., 2010). Lower post-meal reductions in brain activity in obese vs lean children were also observed in the limbic and reward processing regions, e.g. the nucleus accumbens. The authors concluded that this shows hyperreactivity of obese children to food cues and reduced satiety effects. Such interpretation is in line with a study by Rapuano and colleagues ($$n = 78$$) showing that children with a higher genetic risk for obesity have increased activation of the nucleus accumbens to food advertisement (Rapuano et al., 2017). Similarly, Davids and colleagues ($$n = 44$$) showed an increased activation in the dorsolateral prefrontal cortex (dlPFC) in response to food cues in obese vs lean children, but also increased caudate and hippocampal activations in lean vs obese children (Davids et al., 2010). Studies in adults showed increased brain response to food pictures in obese vs lean individuals in the insula (Stoeckel et al., 2008; Martin et al., 2010; Oltmanns et al., 2012; Scharmüller et al., 2012), caudate (Rothemund et al., 2007; Stoeckel et al., 2008; Nummenmaa et al., 2012), orbitofrontal cortex, amygdala, nucleus accumbens, anterior cingulate cortex, pallidum, putamen and hippocampus (Rothemund et al., 2007; Stoeckel et al., 2008; Martin et al., 2010; Oltmanns et al., 2012), or the PFC (Martin et al., 2010; Dimitropoulos et al., 2012). Generally, those regions are involved in processing of food cues (van der Laan et al., 2011), but also play a role in dietary self-control (Han et al., 2018; Neseliler et al., 2019), reward and emotional processing, working memory (Pursey et al., 2014), and interoception (Rahmani and Rahmani, 2019). Meta-analytical studies have shown that some of these brain regions, such as the nucleus accumbens, caudate, putamen or amygdala, are also activated in substance dependent individuals in response to drug cues (Tang et al., 2012; García-García et al., 2014), making a conceptual link between cue reactivity in addiction and obesity. In contrast to these findings, decreased brain activation in obese vs lean individuals in response to food picture viewing has been shown in the anterior cingulate, lingual and superior occipital gyri (Heni et al., 2014), superior frontal gyrus (Nummenmaa et al., 2012), precentral gyrus, cingulate gyrus, dlPFC (Dimitropoulos et al., 2012) and the temporal lobe (Martin et al., 2010). Some of those regions overlap with those mentioned above and are also engaged in processing of food cues and dietary self-control (van der Laan et al., 2011; Han et al., 2018). Although reduced brain activation to food-cues may reflect impaired self-regulation, especially when it involves prefrontal areas, there remain inconsistencies in the neuroimaging findings. For instance, a number of studies did not show significant differences between lean and obese individuals for passive food picture viewing (Murdaugh et al., 2012; García-García et al., 2013b; Frank et al., 2014; Doornweerd et al., 2018; Morys et al., 2018). In their systematic review, Pursey and colleagues outline a large number of brain structures that show higher brain activity in obese vs lean participants in response to visual food cues (Pursey et al., 2014). This review, however, fails to differentiate between contrasts used in various studies (e.g. food > non-food cues, or high-calorie food > low-calorie food) and does not mention studies with no significant findings or findings where lean individuals had higher brain activity than obese individuals. A review by van der Akker and colleagues seems to further support the notion that obese individuals show higher responses to food cues than lean individuals (van den Akker et al., 2014). A contrasting view is presented in a behavioral meta-analysis by Boswell and Kober, who found no effect of BMI group on cue reactivity measures (Boswell and Kober, 2016). We believe that such large inconsistencies in the literature warrant a well-controlled meta-analysis on cue responsivity differences in obese and lean individuals. ## Reward sensitivity and decision-making. Eating entails making food choices concerning what, how much, when or with whom to eat. These and other questions have been examined in the context of obesity. Specifically, studies have investigated whether obesity is associated with functional brain differences during food-related decision-making. A study on children ($$n = 141$$), for instance, asked participants to perform food choices (either select the food displayed or reject it) under three conditions: the consideration of how healthy the food was, the consideration of tastiness, and a free (‘natural’) choice condition. Across all conditions, a higher BMI was associated with lower activity in the left dlPFC while selecting the food item displayed. This correlation was also found during the consideration of tastiness, which probably influenced the results most. The authors suggested that lower activity in the dlPFC might reflect lower cognitive control, which might jeopardize weight-loss interventions (van Meer et al., 2019). These findings, however, might be difficult to integrate with findings from another study on portion size choices. In this study, participants ($$n = 36$$) were asked to select how much food they wanted to eat for lunch that day from a series of food items displayed in the fMRI. The right inferior frontal operculum showed higher fMRI activity in overweight participants relative to lean individuals (Veit et al., 2019). From a neuroeconomic perspective, food-related decision-making requires the integration of costs and benefits associated with each available choice (Essex and Zald, 2010). In this vein, auction tasks measure the willingness to pay for food and non-food items displayed during fMRI (Plassmann et al., 2007). These tasks provide a direct measurement of current subjective value. An fMRI study using an auction task ($$n = 81$$) showed that obese and overweight individuals paid more money for highly palatable foods than lean participants. Moreover, participants with obesity showed higher fMRI activity in the caudate, accumbens and anterior cingulate cortex relative to lean participants (Verdejo-Román et al., 2017). A similar auction task was used to show that these brain regions track the caloric density of food (Tang et al., 2014). Together, these studies raise the possibility that individuals with obesity might attribute a greater subjective value to palatable food. Finally, food cues can affect decision-making not only in food domains, but, among others, in monetary paradigms. In the field of obesity, one fMRI study has examined monetary delay-discounting in obese and lean individuals (Morys et al., 2018, $$n = 36$$). Here, obese individuals showed less delay discounting (decreased impulsivity) when exposed to negative gustatory cues, which was related to decreased activity of the left dlPFC. This was, in turn, related to altered brain connectivity between the dlPFC and the ventromedial PFC, posterior cingulate gyrus and parietal cortex. Importantly, however, visual priming with food cues did not alter decision-making processes in obese or lean individuals. This suggests a lack of differences in visual food cue reactivity between obese and lean individuals, while pointing to the fact that more proximal cues (e.g. taste) might affect decision-making processes differently depending on weight status. ## Inhibitory control paradigms. In general, obese and lean individuals seem to differ in terms of their inhibitory control (Vainik et al., 2013). A question remains, however, whether such differences are specific to food cues or can be generalized to other domains. Two of the tasks measuring inhibitory control are the Stop-Signal Reaction Task and Go/No-Go task. These tasks have been adapted from their original designs to incorporate food stimuli (Loeber et al., 2012; Mühlberg et al., 2016). In general, behavioral studies using the food versions of these tasks have reported similar performance in obese and lean participants (Loeber et al., 2012; Mühlberg et al., 2016). In a similar vein, a neuroimaging study from Carbine et al. compared fMRI responses to high vs low calorie foods using a Go/No-Go task ($$n = 54$$). The authors did not find an effect of obesity on this type of inhibitory control (Carbine et al., 2018). Together, these findings suggest that obese and lean individuals have comparable inhibitory control responses towards food stimuli, both behaviorally and in terms of brain activity. Another task, the approach/avoidance paradigm, allows testing food cue reactivity in the context of behavioral conflict (Mehl et al., 2018, 2019). A behavioral study by Mehl and colleagues ($$n = 60$$) showed increased approach for food cues in obese compared to lean individuals (Mehl et al., 2018). A follow-up fMRI study by the same group ($$n = 33$$) showed that approach bias for unhealthy food cues is related to increased activity in the right angular gyrus, while decreasing this bias by means of cognitive bias modification decreases the activity in the right angular gyrus and its connectivity to the right dorsal striatum (Mehl et al., 2019). Those findings, however, were identified in a group of obese individuals only and not contrasted with a group of lean individuals. ## Influences on food cue reactivity independent of weight status Factors beyond weight status might also influence food cue reactivity. Since the main focus of this paper is to investigate how weight status influences food cue reactivity, we will only briefly introduce other factors thought to influence neural responses to food cues. Lawrence and colleagues ($$n = 25$$) found that activity of the ventromedial PFC to food stimuli was positively related to self-reported hunger (Lawrence et al., 2012). In the same study, activity of the nucleus accumbens predicted BMI in individuals with low self-control. Cosme and colleagues corroborated these results and found evidence that neural food cue reactivity was related to self-control ($$n = 94$$, Cosme et al., 2019). Craving was also shown to be associated with neural food cue reactivity in the ventral striatum, anterior cingulate and orbitofrontal cortex ($$n = 60$$, Giuliani and Pfeifer, 2015). Interestingly, dietary restraint was associated with neural response to milkshake receipt in the orbitofrontal and dlPFC, but not to anticipated milkshake receipt or food pictures presentation ($$n = 39$$, Burger and Stice, 2011). Finally, and provided that emotions can affect eating behavior (Macht, 2008), the emotional context might also influence the neurobehavioral processing of food. In this vein, a study ($$n = 58$$) designed an emotional priming task to test whether the processing of food vs non-food pictures differed depending on the emotional context (i.e. negative, neutral or positive). The authors found that liking rates and amygdala activity differed according to emotional priming. Adiposity, however, measured using waist circumference, did not have an effect on the results (García-García et al., 2019). In line with this, a study by Lopez and colleagues found that the activity of the inferior frontal gyrus in response to passive food viewing was lower in individuals with low desire to eat and high positive mood ($$n = 75$$, Lopez et al., 2016). ## Meta-analysis aims Conflicting findings in available literature regarding neural correlates of cue reactivity in obesity might arise from a number of factors: small sample sizes, control conditions used (e.g. low-calorie foods or non-food objects), lack of control conditions, lack of a control group, region-of-interest (ROI) vs whole-brain fMRI analysis and, others. To overcome these limitations and provide an objective assessment of cue reactivity in obesity, we perform a meta-analysis with a focus on studies investigating obese vs lean group differences in passive food picture viewing paradigms. Such analysis enables us to provide evidence for and elucidate mechanisms of food cue reactivity differences between obese and lean individuals. Based on previous reviews on the topic of obesity and reward processing (Stice et al., 2009; García-García et al., 2013a; van den Akker et al., 2014; Pursey et al., 2014), we hypothesized that obese individuals would show higher fMRI activity in response to visual food stimuli than lean individuals in predominantly reward-related brain regions, such as the nucleus accumbens, caudate, pallidum, putamen, ventromedial and orbitofrontal cortex. In contrast, we also hypothesized that lower brain activity in obese vs lean individuals will be observed in the dlPFC and the temporal cortex, perhaps reflecting reduced inhibitory control. These hypotheses are an extended version of our preregistered hypotheses. ## Materials and methods Methods and analysis strategies used for the meta-analysis were preregistered prior to data collection. Protocols along with files used for the meta-analysis are available at https://osf.io/d53e$\frac{6}{.}$ ## Study selection Morys and García-García independently performed a literature search in the following scientific databases: PubMed, Scopus and Google Scholar. Keywords included 1) obesity-related terms, such as ‘obesity’, or ‘obese’ or ‘overweight’, 2) ‘food’ and 3) ‘fMRI’, or ‘MRI’, or ‘brain’. The results were then cross-validated between the authors and fitting articles were selected for further analysis. Each included study had to meet all of the following criteria: 1) studies using fMRI measured whole-brain activity as outcome measures 2) studies investigating group differences in cue reactivity between obese and lean individuals, 3) studies using food vs non-food pictures contrast, 4) studies reporting cluster peak coordinates and t-statistics or z-statistics for each cluster, if significant results were found. The meta-analysis also included articles reporting non-significant findings. We included articles that investigated cue reactivity in children and in adult samples. Main exclusion criteria were: 1) studies on clinical populations (e.g. individuals with depression or type II diabetes), 2) lack of a (control) group of lean individuals, 3) studies using fMRI paradigms other than passive viewing (these studies, however, have been reviewed in the section ‘Influence of food pictures on maladaptive behaviors in obesity’). To the best of our knowledge, all the studies included were performed on independent (i.e. non-overlapping) samples of participants. ## Seed-based d mapping meta-analysis We conducted a meta-analysis using seed-based d-mapping (SDM, https://www.sdmproject.com/). This meta-analytic method allows the combination of fMRI studies using their cluster peak coordinates and effect sizes to find reliable, common patterns of activations in the brain for a specific effect of interest. It includes positive features from other meta-analytical methods, such as activation likelihood estimation or multi-kernel density analysis (e.g. weighting meta-analytic values by sample size of studies and using random-effects models), and extends those methods by adding certain improvements (Wager et al., 2007; Radua and Mataix-Cols, 2009; Radua et al., 2012). One of the improvements is the possibility of including both positive and negative effects in one analysis. Another one is including effect sizes from single studies to derive meta-analytic results. Here, we investigated whether food cue reactivity differences between lean and obese individuals have consistent neural correlates. Additionally, because we recently found that some brain volume correlates of obesity might be age-dependent (García-García et al., 2019), we investigated whether age influences brain mechanisms of cue reactivity in obesity. To this end, we performed meta-regression analysis with SDM software using age as a covariate of interest. For this analysis, a study by Martin and colleagues (Berridge et al., 2010) was excluded because the authors did not report age of participants. We also investigated whether gender influenced weight group differences in neural responses to food vs non-food cues. Lastly, we performed a ROI analysis to investigate weight group differences in food cue reactivity in pre-defined ROIs derived from a previous meta-analysis investigating main effects of viewing food vs non-food stimuli (van der Laan et al., 2011). This analysis deviated from our preregistration protocol and is reported as an exploratory analysis. The ROIs from this study include the orbitofrontal cortex, inferior frontal gyrus, insula, amygdala and several visual areas. In assessment of the results, we used threshold-free cluster enhancement (TFCE) multiple comparison correction with a 0.05 threshold (‘threshold-free’ in TFCE refers to the cluster-forming threshold; Smith and Nichols, 2009) after 1000 permutations with a cluster extent > 100 voxels. For significant clusters, we performed Egger test for asymmetry of the funnel plot to investigate potential publication bias. ## Articles included in the meta-analysis In our database search, we identified 13 studies that fulfilled our criteria. Two of the studies investigated cue reactivity in children, while 11 studies investigated adults aged 18 to 75 years. The meta-analysis included 407 individuals. Details can be found in Table 1. Table 1. \| Study \| Year published \| Sample size obese \| Sample size lean \| Mean age \| BMI obese \| BMI lean \| Visual food stimuli \| Control stimuli \| Found significant clusters \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Bruce et al. \| 2010 \| 10 \| 10 \| 13 \| 31.3 \| 18.8 \| Appetizing low- and high-calorie food pictures \| Pictures of animals \| Yes \| \| Davids et al. \| 2010 \| 22 \| 22 \| 14 \| 29.4 \| 19.7 \| High-calorie food pictures \| Neutral pictures of landscapes, buildings and work-related situations, pleasant pictures of babies, young animals, children playing. \| Yes \| \| Dimitropoulos et al. \| 2012 \| 22 \| 16 \| 25 \| 31.6 \| 22.7 \| Low- and high-calorie food pictures \| Furniture pictures \| Yes \| \| Doornweerd et al. \| 2018 \| 16 \| 16 \| 50 \| 28.4 \| 24.4 \| Low- and high-calorie food pictures \| Non-food items, such as trees, flowers, rocks, and bricks \| No \| \| Frank et al. \| 2014 \| 11 \| 11 \| 40 \| 40.2 \| 21.4 \| Low- and high-calorie food pictures \| Non-food pictures \| No \| \| Garcia-Garcia et al. \| 2013 \| 18 \| 19 \| 33 \| 34.9 \| 22.4 \| Low- and high-calorie salty and sweet food pictures \| Rewarding non-food stimuli \| No \| \| Heni et al. \| 2013 \| 12 \| 12 \| 24 \| 30.5 \| 21.2 \| Low- and high-calorie food pictures \| Pictures of objects with no association with eating \| Yes \| \| Martin et al. \| 2010 \| 10 \| 10 \| - \| 34.0 \| 22.1 \| General food pictures \| Gaussian blurred unrecognizable images of food and tools \| Yes \| \| Morys et al. \| 2018 \| 24 \| 27 \| 27 \| 34.3 \| 22.1 \| Positive food pictures \| Scrambled, unrecognizable versions of positive food pictures \| No \| \| Murdaugh et al. \| 2012 \| 25 \| 13 \| 47 \| 32.9 \| 22.6 \| Low- and high-calorie food pictures \| Pictures of cars \| No \| \| Nummenmaa et al. \| 2012 \| 19 \| 16 \| 47 \| 43.9 \| 24.1 \| Appetizing and bland food pictures \| Pictures of cars \| Yes \| \| Oltmanns et al. \| 2012 \| 10 \| 10 \| Age range: 20–45 \| 35.1 \| - \| High-calorie sweet and savory food pictures \| Non-food objects of use \| Yes \| \| Rothemund et al. \| 2007 \| 13 \| 13 \| 30 \| 36.3 \| 20.9 \| Low- and high-calorie food pictures \| Neutral non-food related stimuli \| Yes \| ## Meta-analysis results Contrary to our hypothesis, there were no significant differences in brain activity in obese vs lean participants for the contrasts of food > non-food pictures. However, meta-regression with age as a covariate revealed a significant cluster within the left posterior insula/Rolandic operculum, which was negatively related to age (peak MNI coordinates: −48, −8, 6; z-value: −1.416, size: 321 voxels; Figure 1, Table 2), and a cluster in the left fusiform gyrus which was positively related to age (peak MNI coordinates: −34, −54, −12; z-value: 3.681, size: 400 voxels; Figure 1, Table 2). Egger tests for funnel plot asymmetry performed for the peak voxel in those clusters did not show a significant publication bias (insula: t[10] = 0.02, $$P \leq 0.984$$; fusiform gyrus: t[10] = −0.02 $$P \leq 0.984$$). Upon further investigation, correlation analysis revealed that BMI in the lean groups was significantly related to mean age of samples included in the meta-analysis ($r = 0.845$, $$P \leq 0.001$$). This might mean that the clusters significantly related to age might in fact be related to the BMI of lean groups. We tested this in an additional meta-regression analysis where we included age as a predictor and regressed out effects of BMI of the lean group. In this analysis, we found that only the cluster in the left insula remained significantly related to age. This indicates that the cluster in the left fusiform gyrus was likely related to BMI of the lean group and not to the age of participants. We did not find an effect of gender on weight group food cue reactivity. In order to further evaluate the possibility of weight group effects on cue reactivity, we performed an exploratory analysis using ROIs derived from a previous food cue meta-analysis (van der Laan et al., 2011). There were no significant differences in any ROIs, even with small-volume correction. ## Discussion In this article, we first reviewed the existing literature and then performed a pre-registered meta-analysis on food cue reactivity in obese individuals. Previous theoretical work suggests that obesity is related to an enhanced salience response towards visual food stimuli (Berridge et al., 2010). Moreover, greater brain responses to the sight of food have been proposed as a neural vulnerability factor for the development of obesity (Stice and Burger, 2019). However, although some exceptions exist (van Meer et al., 2019), most studies examining fMRI reactivity to food cues during decision-making, reward sensitivity or cognitive control tasks found no effects of BMI (Carbine et al., 2018; Morys et al., 2018; Adise et al., 2019). At the same time, results of neuroimaging studies investigating the influence of obesity on passive viewing of food cues are mixed. To investigate this issue further, we performed a meta-analysis of 13 studies with 407 participants investigating the influence of obesity on brain responses to food cues. We did not find evidence for overall altered cue processing in obese individuals. This is in line with a previous meta-analysis showing no influence of BMI on behavioral cue reactivity and craving (Boswell and Kober, 2016). However, we found two clusters in the brain that displayed an age-dependent relationship in this context: one in the left insula, which was negatively related to age, and one in the left fusiform gyrus, which was positively related to age. We did not find evidence for publication bias for either region. In our analyses, we only included studies that used appropriate control conditions (i.e. non-food cues) and control groups (lean individuals). The first cluster was negatively related to age, which means that in children and young adults there was a weight group difference in left insula response to viewing food vs non-food pictures (Figure 1). This difference, however, decreased in older adults. Interestingly, a similar cluster in the left insula was previously observed to be activated for viewing food vs non-food pictures, independent of BMI (van der Laan et al., 2011; Huerta et al., 2014). Activation of the anterior and middle insula was also reported in studies investigating taste processing (Small, 2006), cephalic phase response (Tomasi et al., 2009) or food craving (Pelchat et al., 2004). Finally, insula was implicated in a meta-analysis of studies requiring subjects to intentionally regulate their level of craving in response to food (Han et al., 2018). The effect was BMI dependent. The second cluster in which we found age-dependent effects of cue reactivity in obese vs lean individuals was located in the left fusiform gyrus, a structure also related to food picture viewing (van der Laan et al., 2011). The fusiform gyrus plays an important role in object recognition (Grill-Spector et al., 2001) and in the case of food vs neutral stimuli viewing is possibly related to increased attention for and visual processing of food images (Killgore and Yurgelun-Todd, 2007; van der Laan et al., 2011). Our age-dependent results are in line with a meta-analysis by van Meer and colleagues who showed that a similar brain region was activated to a higher degree in adults than in children/adolescents to food picture viewing (Van Meer et al., 2014). In our study, weight group difference in activation of this brain region as a response to food cues increased with age (Figure 1). Van Meer explains this finding as meaning that food cues gain salience in adults as compared to children, which is reflected in higher activity in the left fusiform gyrus. In the context of our study, however, such an interpretation should be considered carefully, since a follow-up analysis revealed that the cluster in the left fusiform gyrus is likely related to BMI in the lean group and not to age per se. Our findings stand in contrast with the conclusions from previous reviews on the topic of cue reactivity in obesity (Stice et al., 2009; van den Akker et al., 2014; Pursey et al., 2014), which posit that there are indeed group differences in neural responses to food cues and that these differences present similarities with those found in substance addictions (García-García et al., 2014). Interestingly, these reviews did not consider age effects and concluded that obesity is related to altered neural processing of food cues independent of age. This is contrary to our meta-analytic findings. These reviews, however, included studies using other stimuli than food pictures (e.g. gustatory stimuli, such as milkshakes), neglected findings from studies reporting no group differences and might have overinterpreted findings that are not methodologically robust. We believe that failing to include studies with null findings, coupled with reviewing studies that used inadequate statistical thresholding and underpowered sample sizes (Eklund et al., 2016), might have led to incorrect conclusions. Such issues become evident when the neuroimaging literature is contrasted with the behavioral literature, as a meta-analysis performed by Boswell and Kober did not find evidence for the influence of BMI on food cue reactivity (Boswell and Kober, 2016). Here, the authors also showed that food cue reactivity predicts weight-gain. This is in line with other longitudinal findings in the neuroimaging domain showing that neural responses to food cues predict weight gain. For example, higher activity in the nucleus accumbens in response to food pictures tends to predict weight over a 6 months of follow-up (Demos et al., 2012). In their study in 2018, Stice and Yokum showed that activity in the motor processing areas, but not in the striatum, predicts BMI gain over 3 years (Stice and Yokum, 2018). Conversely, fMRI has also been used in individuals undergoing weight-loss programs: food cue reactivity in brain areas related to reward (Murdaugh et al., 2012), and self-regulation (Neseliler et al., 2019) both predicted successful outcomes. Therefore, a question remains how cue reactivity can be independent of BMI and obesity status but also predict future weight fluctuations (Boswell and Kober, 2016). Van der Akker and colleagues claim that cue reactivity might lead to increased food intake and weight gain only in more impulsive individuals (van den Akker et al., 2014). This suggests that there are multiple factors beyond BMI that need to be taken into account when investigating food cue reactivity. Some of those factors were reviewed in the Introduction to this article and include dietary restraint, self-control, hunger or food craving. Our meta-regression analysis shows that age might be another such factor. In addition, the nature of food cues (e.g. visual, olfactory or gustatory) might also differentially affect cue reactivity in obese individuals (Morys et al., 2018). Another possibility is that pathological eating (such as binge eating or food addiction patterns) might mediate the effects of obesity on fMRI responses to the sight of food. Unfortunately, findings from fMRI studies on compulsive overeating are notably inconsistent (García-García et al., 2020), and future research should consider both obesity and eating behavior to further test this possibility. One limitation of the current meta-analysis is the use of BMI as a measurement of obesity. In some cases, individuals with high muscle mass who are not obese might be placed in the obese group based solely on BMI values. Further, only 3 studies included in the analysis reported the methods in which BMI was measured (self-reported vs measured), which might constitute an additional confound in the analysis as people tend to underreport their weight and overreport their height (Sherry et al., 2007; Merrill and Richardson, 2009). However, without the explicit knowledge of the methods used to measure BMI and body composition of individuals included in the studies we were not able to correct for this in our analyses. Overall, our review and meta-analysis show that there is scant evidence for food cue reactivity differences between lean and obese individuals. Our findings show that only two brain areas were related to weight group differences in visual processing of food cues and that these effects were age-related. Hence, additional factors contributing to neural correlates of food picture viewing in lean and obese individuals, such as age, self-control, food craving, impulsivity, hunger or dietary restraint need to be investigated in future studies. We propose that studies of better quality—using large sample sizes, appropriate statistical thresholding and ideally preregistered designs and analyses plans—should be performed to investigate in depth how those additional factors influence cue reactivity in obesity. In addition, a meta-analysis of behavioral cue-reactivity studies investigating effects of age might shed more light on and replicate the current neuroimaging-based findings. ## Funding This work was supported by a Foundation Scheme award to A.D. from the Canadian Institutes of Health Research. 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--- title: 'Morphometry of the lateral orbitofrontal cortex is associated with eating dispositions in early adolescence: findings from a large population-based study' authors: - Peter A Hall - John R Best - James Danckert - Elliott A Beaton - Jessica A Lee journal: Social Cognitive and Affective Neuroscience year: 2021 pmcid: PMC9997071 doi: 10.1093/scan/nsab084 license: CC BY 4.0 --- # Morphometry of the lateral orbitofrontal cortex is associated with eating dispositions in early adolescence: findings from a large population-based study ## Abstract Early adolescence is a critical period for eating behaviors as children gain autonomy around food choice and peer influences increase in potency. From a neurodevelopmental perspective, significant structural changes take place in the prefrontal cortex during this time, including the orbitofrontal cortex (OFC), which is involved in socially contextualized decision-making. We examined the morphological features of the OFC in relation to food choice in a sample of 10 309 early adolescent children from the Adolescent Brain and Cognitive Development Study. Structural parameters of the OFC and insula were examined for relationships with two important aspects of food choice: limiting the consumption of fast/fried food and maximizing the consumption of nutritious foods. Raw, partially adjusted and fully adjusted models were evaluated. Findings revealed that a larger surface area of the lateral OFC was associated with higher odds of limiting fast/fried food consumption in raw [odds ratio (OR) = 1.07, confidence interval (CI): 1.02, 1.12, $$P \leq 0.002$$, PFDR = 0.012], partially adjusted (OR = 1.11, CI: 1.03, 1.19, $$P \leq 0.004$$, PFDR = 0.024) and fully adjusted models (OR = 1.11, CI: 1.03, 1.19, $$P \leq 0.006$$, PFDR = 0.036). In contrast, a larger insula volume was associated with lower odds of maximizing healthy foods in raw (OR = 0.94, CI: 0.91, 0.97, $P \leq 0.001$, PFDR = 0.003) and partially adjusted (OR = 0.93, CI: 0.88, 0.98, $$P \leq 0.008$$, PFDR = 0.048) models. These findings refine our understanding of the OFC as a network node implicated in socially mediated eating behaviors. ## INTRODUCTION The orbitofrontal cortex (OFC) receives information about the taste, smell, sight and texture of food and represents this in terms of its reward value in the medial OFC, areas 13 and 11 (Grabenhorst and Rolls, 2011; Levy and Glimcher, 2012; Rolls, 2016a, 2016b, 2021; Padoa-Schioppa and Conen, 2017; Hirokawa et al., 2019). The OFC represents many other types of reward-related information as well, including that of a social nature (Rolls et al., 1994, 2006; Hornak et al., 1996, 2003; Rolls, 2019). In contrast, the lateral OFC responds to many aversive, subjectively unpleasant or unexpected events (O’Doherty et al., 2001; Kringelbach and Rolls, 2003; Rolls et al., 2003, 2020; Deng et al., 2017; Xie et al., 2021). Importantly, the lateral OFC is directly involved in reversal, in that damage to this region impairs the reversal or stopping of a behavior and increases impulsivity (Rolls et al., 1994, 2020b; Berlin et al., 2004; Hornak et al., 2004; Aron et al., 2014). Research on adolescent brain development (see Dahl, 2004 for a review) suggests that emotional and reward-related reactivity develops earlier than stopping abilities in the adolescent brain, meaning that adolescence is a time where subtle variations in the medial or lateral OFC, respectively, may have substantial implications for eating of foods that require self-control to avoid (e.g. calorie-dense fast or fried food items) and, to a lesser extent, the selection of nutritious foods that may be less hedonically appealing. ## Early adolescence, eating and the OFC In adolescence, the prefrontal cortex (PFC) undergoes significant morphological changes and coincident with these are equally noteworthy changes in the social environment (Luna et al., 2001; Larsen and Luna, 2018). From late childhood to early adolescence, the social network becomes broader and more impactful in many areas of function (Christie and Viner, 2005; Curtis, 2015). Eating behaviors and the social environment of eating both change considerably as youth gain autonomy around food choices, particularly with respect to accessing calorie-dense food items, which might be available both within and outside of the household. Amplifying such dynamics further is the newfound emotional significance of food choice, which can generate strong feelings of not only affiliation, acceptance and pleasure but also shame, disgust and guilt. As such, brain structures that connect socioemotional decision-making with homeostatic needs may be quite consequential for developing eating habits. Prior studies examining the structural aspects of the human brain and eating outcomes have shown that body mass index (BMI) is negatively associated with gray matter volume (GMV) in the PFC, insular cortex and other medial structures, including the amygdala (Bobb et al., 2014; Debette et al., 2014). Other studies have revealed significant associations between volumetric parameters and obesogenic behaviors, such as inactivity and eating practices (Erickson et al., 2010; Gu et al., 2015; Papenberg et al., 2016). Finally, a recent meta-analysis of 21 studies (with a cumulative total of 5885 participants) found that a lower GMV in the medial PFC (including the OFC) is one of the most reliable correlates of obesity (García-García et al., 2019; Chen et al., 2020). A recent analysis of the Adolescent Brain and Cognitive Development (ABCD) Study examined BMI (but not eating behaviors) in relation to functional and morphological aspects of the brain, using whole brain analyses (Adise et al., 2021). Findings suggested widespread structural brain features associated with weight gain and baseline BMI, such as cortical surface area, thickness and subcortical volume. Two earlier studies using a smaller subset of the findings found similar morphological associations with BMI, most centrally, reduced cortical thickness in the PFC (Ronan et al., 2020; Laurent et al., 2020). ## Prior studies on OFC structure and eating patterns Only a handful of prior studies have examined associations between brain structural parameters and eating patterns. In one of these (Song et al., 2019), it was found that unrestrained eating was negatively correlated with GMV of the cingulate cortex, in a small sample ($$n = 159$$) of Chinese women. In a larger sample ($$n = 629$$) of young and middle-aged adults, self-reported hedonic eating symptoms were associated with larger right lateral OFC thickness over and above BMI; no other neuroanatomical correlates of food addiction were found (Beyer et al., 2019). Finally, comparing overweight and obese to normal-weight participants in a case–control study ($$n = 139$$), Cohen and colleagues found that a greater OFC volume predicted more high-quality food choice tendencies recorded in a 3-day food diary among overweight and obese individuals, although medial and lateral subregions of the OFC were combined in this case (Cohen et al., 2011). Functional imaging studies have similarly implicated the OFC in food cue reactivity and subjective hunger states (van der Laan et al., 2011; Tang et al., 2012; Chen and Zeffiro, 2020). ## The current investigation Although several empirical studies and one meta-analysis have confirmed reliable associations between OFC structural parameters and BMI, relatively few studies have focussed on eating patterns that might give rise to BMI. Likewise, most prior studies of this nature have included large proportions of middle-aged and older adults, wherein brain structural parameters might represent the consequence of decades of adiposity and associated physiological states (e.g. hypertension and impaired glycemic control). For this reason, studies of OFC structural parameters in earlier life might be particularly informative. The ABCD is a large-scale, multi-center study of adolescent brain development and cognition (Jernigan et al., 2018) and provides a unique opportunity to examine such effects. This study has recruited more than 11 000 9- and 10-year-olds to date, all of whom have undergone assessment of lifestyle behaviors (including eating habits) and brain imaging, the latter including a structural magnetic resonance imaging (MRI) scan. The purpose of the current investigation is to examine the association between structural aspects of the OFC, its subregions and eating tendencies in a large sample of young adolescents. It is hypothesized, based on prior studies, that a larger OFC volume or surface area will predict more adaptive eating patterns across demographic features of the sample, because of the higher capacity to perform complex value computation from multiple value sources (including relative health value, hedonic appeal and approval of others). Second, it is anticipated that such effects will be mediated by measures of value computation (e.g. delay discounting) and emotional regulation capacity. Finally, it is hypothesized that morphological aspects of the insula will be associated with eating tendencies, but with less consistency, due to lower loading of function on value computations that may influence choice behavior itself, and more encoding of salience. Such effects will be particularly evident with respect to calorie-dense food items and will remain after controlling for demographic characteristics, methodological variables and BMI. ## Participants The ABCD Study is a large-scale prospective study following cohorts of 9- and 10-year-olds forward in time over the adolescent years into adulthood, with a projected 10-year time horizon (Jernigan et al., 2018; https://abcdstudy.org/). The current investigation involves the first two waves of data collection, which includes a baseline MRI scan (Wave 1), and assessments of eating habits (Wave 2; corresponding to ages 11 and 12 years). Participant demographic characteristics are presented in Table 1. The sample consists of all individuals with baseline MRI scan and Wave 2 parent-reported eating habits ($$n = 10$$ 309). The ABCD Study involved a consortium of 21 data collection sites in major metropolitan areas across the continental United States. The original recruited sample comprises 11 880 participants from Data Release 2.0.1, aged between 9 and 10 years of age at baseline (Wave 1). Baseline visits occurred between September 2016 and October 2018. Table 1. \| Characteristic \| Characteristic.1 \| \| --- \| --- \| \| Age in months, baseline [mean (s.d.)] \| 119.0 (7.5) \| \| Age in months, follow-up [mean (s.d.)] \| 131.1 (7.7) \| \| Sex \| Sex \| \| Female \| 4931 (48) \| \| Male \| 5378 (52) \| \| Child race \| Child race \| \| American Indian/Native American \| 57 (0.6) \| \| Asian Indian \| 47 (0.5) \| \| Black/African American \| 1576 (15) \| \| Chinese \| 80 (0.8) \| \| Don’t know \| 73 (0.7) \| \| Filipino \| 47 (0.5) \| \| Guamanian \| 1 (<0.1) \| \| Japanese \| 11 (0.1) \| \| Korean \| 21 (0.2) \| \| Native Hawaiian \| 3 (<0.1) \| \| Other Asian \| 26 (0.3) \| \| Other Pacific Islander \| 13 (0.1) \| \| Other race \| 431 (4.2) \| \| Refused \| 44 (0.4) \| \| Samoan \| 3 (<0.1) \| \| Vietnamese \| 20 (0.2) \| \| White \| 7834 (76) \| \| Missing \| 22 \| \| Child Hispanic ethnicity \| Child Hispanic ethnicity \| \| Yes \| 2038 (20) \| \| No \| 8148 (80) \| \| Missing \| 123 \| \| Family income level [mean (s.d.)] \| 7.3 (2.4) \| \| Missing \| 806 \| \| Primary parent education, years [mean (s.d.)] \| 16.7 (2.7) \| \| Missing \| 11 \| \| Second parent/partner education, years [mean (s.d.)] \| 16.5 (3.0) \| \| Missing \| 2006 \| \| Healthy food consumption (whole grains, green leafy veggies, other veggies and berries) \| Healthy food consumption (whole grains, green leafy veggies, other veggies and berries) \| \| 0 (none of these categories) \| 490 (4.8) \| \| 1 (1 of these categories) \| 1432 (14) \| \| 2 (2 of these categories) \| 2760 (27) \| \| 3 (3 of these categories) \| 3224 (31) \| \| 4 (all of these categories) \| 2403 (23) \| \| Fast/fried food consumption less than once per week \| 6820 (66) \| \| Body mass index z-score, baseline [mean (s.d.)] \| 0.5 (1.8) \| \| Missing \| 761 \| \| Body mass index z-score, follow-up [mean (s.d.)] \| 0.9 (1.8) \| \| Missing \| 1152 \| \| Emotional Stroop congruent mean reaction time (ms) [mean (s.d.)] \| 1087.9 (125.0) \| \| Missing \| 146 \| \| Emotional Stroop incongruent mean reaction time (ms) [mean (s.d.)] \| 1164.1 (136.0) \| \| Missing \| 150 \| \| Delay discounting (area under the hyperbolic discounting curve) [mean (s.d.)] \| 0.5 (0.3) \| \| Missing \| 149 \| \| Lateral orbitofrontal cortex, thickness (mm) [mean (s.d.)] \| 3.0 (0.1) \| \| Lateral orbitofrontal cortex, area (mm2) [mean (s.d.)] \| 5572.3 (598.1) \| \| Lateral orbitofrontal cortex, volume (mm3) [mean (s.d.)] \| 18 669.2 (2032.8) \| \| Medial orbitofrontal cortex, thickness (mm) [mean (s.d.)] \| 2.7 (0.2) \| \| Medial orbitofrontal cortex, area (mm2) [mean (s.d.)] \| 3710.6 (427.2) \| \| Medial orbitofrontal cortex, volume (mm3) [mean (s.d.)] \| 11 974.8 (1441.9) \| \| Insula, thickness (mm) [mean (s.d.)] \| 3.3 (0.1) \| \| Insula, area (mm2) [mean (s.d.)] \| 4545.4 (490.8) \| \| Insula, volume (mm3) [mean (s.d.)] \| 15 200.6 (1692.0) \| \| Cerebral cortex, mean thickness (mm) [mean (s.d.)] \| 2.8 (0.1) \| \| Cerebral cortex, total area (mm2) [mean (s.d.)] \| 1 86 269.7 (18 098.0) \| \| Cerebral cortex, total volume (mm3) [mean (s.d.)] \| 5 96 059.6 (56 700.3) \| Each individual ABCD Study site obtained local institutional ethical review board approval, and centralized institutional approval for the ABCD Study was obtained by the University of California, San Diego. For each participating child, written and informed consent was provided by each caregiver; each child provided written assent. The current analysis was approved by the ethical review board of the institution of the lead author (the University of Waterloo, Waterloo, Canada). ## MRI and preprocessing. The ABCD consortium performed the neuroimaging and preprocessing. Images of brain structure were acquired using MRI. Cortical volume, area and thickness were estimated using Freesurfer v5.3.0 (Dale et al., 1999). Images were subjected to manual checking by technicians to evaluate the image quality (intensity inhomogeneity, underestimation of white matter, pial overestimation and magnetic susceptibility artifact). Those assigned a score of 1 for passable image quality were used for the current investigation. Further details regarding quality control procedures are described in Hagler et al. [ 2019]. Brain morphological features (area, volume and thickness) for the OFC and insula were obtained from automated anatomical parcellation using the Desikan atlas–based classification (Desikan et al., 2006) as part of the standard FreeSurfer pipeline [region of interest (ROI) names in the atlas: lh-insula, rh-insula, lh-medialorbitofrontal, rh-medialorbitofrontal, lh-lateralorbitofrontal, rh-lateralorbitofrontal]. For each structure, left and right hemispheres were combined, and in the case of the OFC, medial and lateral subregions were examined separately. ## Anthropometrics. To assess BMI, separate weight and height measurements were taken and averaged together; from this average score, BMI (zBMI) was calculated using corresponding z-scores according to the World Health Organization Child Growth Standards (World Health Organization, 2006). This was done using the R package ‘zscorer’ (Myatt and Guevarra, 2019). BMI z-scores <5 were assumed to be invalid and recoded as missing. ## Demographics. Household income was collected via caregiver report using the following categories: $5000, $5000–$11 999, $12 000–$15 999, $16 000–$24 999, $25 000–$34 999, $35 000–$49 999, $50 000–$74 999, $75 000–$99 999, $100 000–$199 999 and ≥$200 000. Prior to being included in the statistical models, income was categorized into tertiles (<$50 000 vs $50 000–$99 999 vs $100,000 and above). Sex-at-birth was reported at baseline by parents in accordance with sex listed on the child’s birth certificate; all subsequent analyses used this as the indicator of participant sex. Child race/ethnicity was reported by parents at this time as well. ## Delay discounting and emotional regulation. The negative and positive urgency sub-scales from the Urgency Perseverance Premeditation Sensation seeking UPPS-P for Children Short Form (ABCD-version) were collected at baseline and were included as measures of emotion regulation (Barch et al., 2018). Delay discounting is a behavioral measure of impulsive decision-making (Johnson et al., 2008) and was collected at Wave 2. The participant makes several choices between a small-immediate hypothetical reward right now and a constant hypothetical $100 reward at different future time points (6 h, 1 day, 1 week, 1 month, 3 months, 1 year and 5 years). Each block of choices features the same delay to the larger reward, and the immediate reward is titrated after each choice until both the smaller-sooner reward and the delayed-$100 reward have equal subjective value to the participant. An ‘indifference point’ is calculated that represents the small-immediate amount deemed to have the same subjective value as the $100 delayed reward at each of the seven delay intervals. These values were then converted to an area under the hyperbolic curve, which quantifies the degree of discounting of delayed rewards (Myerson et al., 2001). ## Emotional Stroop task. This task is an adaptation of the classic Stroop paradigm (Stroop, 1935), which is intended to measure cognitive control with emotionally salient stimuli (Başgöze et al., 2015; Banich et al., 2019). Stimuli consist of pairs of face images and emotion words denoting positive and negative emotional states, which can be either congruent with each other or incongruent with each other, in terms of valence (the word ‘happy’ paired with a happy face image; the word ‘sad’ paired with a happy face image). The location of the word varied from trial to trial. Two test blocks were completed: the first included $75\%$ congruent and $25\%$ incongruent trials; the second block included $50\%$ congruent and $50\%$ incongruent trials. Accuracy and reaction time are recorded, and longer reaction times on incongruent relative to congruent trials were the metric of interference (i.e. low cognitive control). For the current study, we used the mean response time on incongruent trials across block and emotion subtype, adjusted for congruent response time, as the outcome measure. ## Eating behaviors. At 1-year follow-up, the primary parent completed a nutrition questionnaire, which included 13 questions on food categories eaten framed as follows: ‘In a typical week, does your child eat…’ (Morris et al., 2015). For the current study, we constructed two outcome measures from responses to this questionnaire. First, we used the response to the item regarding consumption of fried or fast food less than once per week as a binary outcome. Second, we summed the responses across four items (greater than three servings of whole grains per day, greater than six servings of green leafy vegetables per week, at least one serving of other vegetables per day and at least two servings of berries per week) to create a healthy food variable that ranged from 0 (none of the healthy item servings met) to 4 (all of the healthy item servings met). As such, both eating behavior metrics were scored such that responding ‘yes’ indicates a healthier food choice in relation to the target food type(s) (i.e. ‘yes’ = limiting fast and fried food; ‘yes’= maximizing servings of vegetables, berries and whole grains). Several items from this complete ABCD parent-report measure involving protein sources were omitted because of their less definitive roles in relation to healthy eating outcomes and/or confounding with vegetarian status (e.g. processed meat products). ## Statistical analyses All statistical analyses were conducted using R version 4.0.3 (r-project.org). To evaluate the association between baseline structural MRI markers and 1-year nutritional outcomes, two sets of regression models were constructed. Consumption of fast and fried foods less than once a week (yes vs no) was modeled using logistic regression, whereas consumption of healthy foods (including whole grains, green leafy vegetables, other vegetables and berries) was modeled using ordinal regression. For both sets of regression models, each of the a priori structural brain measures were evaluated without covariates and then with covariates. In the covariate-adjusted models, the primary covariates were baseline child age, sex, race and ethnicity, family income and total cortical volume, total cortical area or mean cortical thickness, depending on whether the primary brain structure predictor represented volume, area or thickness; zBMI was examined as an additional covariate in a third set of models. As such, each outcome was examined in raw (Model 1), covariate-adjusted (Model 2) and covariate + zBMI adjusted (Model 3) predictive models. Given the substantial sex differences often observed in research involving eating and disproportionate social pressure around eating for adolescent girls, we also examined sex as a moderator. Brain variables were standardized (mean = 0, s.d. = 1) to facilitate interpretation and comparison across brain variables. Estimates from these models were converted to odds ratios (necessitated by the binomial eating predictor variables, as collected by the ABCD Study), which are presented along with the $95\%$ confidence intervals (CIs) and P values. Corrections for false discovery rate (FDR) were applied across three brain regions (i.e. lateral OFC, medial OFC and insula) and both morphological outcomes (i.e. surface area and volume), as per Benjamini and Hochberg [2000]. Statistical significance was set at PFDR < 0.05 for each estimate of interest. Secondary analyses employed linear regression to determine whether structural MRI parameters were associated with performance and measures of delay discounting and emotional regulation. These included the negative and positive urgency scales from the UPPS, the delay discounting area under the curve and the emotional Stroop incongruent response time adjusted for congruent response time. Models were constructed without covariates and then with the same set of covariates included in the primary models. ## Primary analyses Descriptive statistics for the sample ($$n = 10$$ 309) can be found in Table 1. Analysis of structural parameters and eating habits is presented in Table 2, in raw and adjusted forms. Findings from raw and partially adjusted models revealed that larger lateral OFC surface area was associated with higher odds of limited fast and fried food consumption [adjusted odds ratio (OR) = 1.11, $95\%$ CI: 1.03, 1.19, PFDR = 0.024]; this effect remained strong following further adjustment for BMI. Medial OFC was not observed to be associated with eating habits, with all FDR-corrected P values >0.20. Larger insula volume was associated with a lower odds of maximizing nutritious foods (adjusted OR = 0.93, $95\%$ CI: 0.88, 0.98, PFDR = 0.048), although this did not survive further adjustment for BMI. Figures 1 and 2 depict the morphological substrates for each ROI and the primary findings regarding OFC area, respectively. Effect sizes (Hedge’s g) predicting odds of limiting fast/fried food consumption and maximizing nutritious foods are depicted in Figure 3, for the lateral and medial OFC subregions and for the insula. Analysis of the left and right hemispheres separately revealed that both left and right lateral OFC areas were associated with greater odds of limiting fast/fried food consumption (adjusted OR = 1.09, $95\%$ CI: 1.03, 1.17 and adjusted OR = 1.07, $95\%$ CI: 1.01, 1.15, respectively). Similarly, the negative association between insula volume and healthy food consumption was observed in the left hemisphere (adjusted OR = 0.94, $95\%$ CI: 0.89, 1.00) and right hemisphere (adjusted OR = 0.94, $95\%$ CI: 0.89, 0.99). See Supplemental Table 1 for details. ## Secondary analyses The association between lateral OFC area with delay discounting and emotional Stroop performance is shown in Table 3 and with negative and positive urgency in Table 4. After accounting for the covariates, the lateral OFC area was not significantly associated with any of these measures. We further considered whether income tertile, sex or age might moderate the association between brain predictor and nutritional outcome; however, we failed to detect any significant interactions despite substantial statistical power (all Ps > 0.05), suggesting that the associations between these brain regions and nutrition outcomes are similar for males and females and across family income. Sex differences were examined as well, given the large difference in eating expectations in the American context for girls vs boys. None of the tests of sex-based moderation of OFC effects were significant, suggesting that OFC effects on eating were relatively uniform for girls and boys in the sample. However, insula surface area was significantly more impactful on fast/fried food consumption for girls (OR = 1.14, CI: 1.05, 1.25) than for boys (OR = 1.03, CI: 0.95, 1.12; χ2 [1] = 4.08, $$P \leq 0.043$$); ORs for each sex category and outcome variable are presented in Figure 3. No additional sex differences emerged with respect to insula volume. ## Discussion In this investigation, it was found that a larger surface area of the lateral OFC was associated with stronger tendencies to limit consumption of appetitive but unhealthy calorie-dense foods (i.e. fast/fried foods) among early adolescent boys and girls. This effect remained robust after accounting for demographics, methodological variables and BMI. The absolute magnitude of the effect was modest but reliable after stringent correction for FDR. As such, there was reasonable support for our primary hypothesis. Inconsistent with our primary hypotheses, there was no evidence of a mediation of the abovementioned effects by delay discounting task performance or a self-reported measure of emotional control. With respect to the insula, larger surface area and volume predicted a stronger tendency to limit fast/fried foods, and this association was significantly greater in magnitude for girls than for boys. On the other hand, a larger insular surface area was associated with weaker tendencies to maximize nutritious foods. Our findings are consistent with other animal and human research implicating the OFC in eating and food processing more generally (Simmons et al., 2005; Spierling et al., 2020; Rolls, 2021), as well as body mass (Chen et al., 2020), although the medial OFC is sometimes implicated more so than lateral (Shott et al., 2015). Given that the lateral OFC is involved in changing or stopping behavior when punishment or non-reward stimuli are received (Rolls, 2019; Rolls et al., 2020a), it is hypothesized that the larger surface of the lateral OFC may encode a greater sensitivity to the non-rewarding or punishing attributes of high-calorie food indulgence, such as social sanctions and norm violations. Such contingencies may be furnished by peer groups—which are disproportionately influential at this time—or even in the home environment, by siblings, parents or others in the home. The effects of the insula are also notable, given the involvement of the insula in salience detection (anterior insula; Craig, 2009) and mobilization of organismic responses to physiological states of homeostatic significance (posterior insula; Menon and Uddin, 2010). In general, the foods included in the vegetables/berries/grains item were nutritious but of low caloric density, meaning that a physiological drive toward calorie maximization would tend to result in lower odds of endorsing this item; indeed, that is what was found. The parcellation employed did not allow for separate consideration of anterior and posterior aspects of the insula, unfortunately; future studies may further disentangle the relative role of each. Nonetheless, it is noteworthy that pathologies involving overactive insular function may involve ascription of undue emotional valence to mundane or unimportant stimuli, as we might see in anxiety disorders and the personality disposition of neuroticism; there is some potential that insular involvement in eating may work through a similar mechanism (i.e. hyper-emotional significance of food objects, rendered salient in the everyday environment). The home environment and parental eating behaviors may be implicated in this respect as well, given that much of the eating activity in early adolescence is still in the family home. The parents also provide access to different food types, and so the home food cue array is partially modulated by parents. Some of the strengths of this investigation included a large and broadly representative sample, which allowed for the possibility of subregion analysis within the OFC and highly precise estimates of most estimates of association. Although other studies have identified the OFC as an important structure in the prediction of BMI, to our knowledge none have previously examined medial and lateral subregions separately and with eating behaviors as the target. The consideration of these separately appears to be somewhat consequential, given that lateral subregions seemed to predict eating tendencies more reliably than medial OFC subregions. Additionally, the precise parameter estimates allow us to confidently declare null findings to be definitively null. Finally, the population-representative nature of the sample allows for generalizability to the US adolescent population. The selection of participants on the cusp of adolescence (9–10 years) is a strength in that it reduces the impact of lifelong adiposity on the brain, as an alternative explanation for why associations are observed. Most existing studies documenting associations between brain structural parameters and BMI, for instance, have employed samples wherein the majority of participants are in middle age to late life, meaning that obesity status is maintained for decades prior to measurement of brain structure or function. Any effects of obesity on the brain may influence correlations between structural parameters and predictors of interest, including eating behaviors. In this case, the majority of the sample is not yet obese, and those who have significant adiposity have likely had it only for months to years, rather than years to decades. This makes us more confident that brain structural parameters represent premorbid facets of the brain that may predict future adiposity, via eating behaviors. Some limitations exist. First, longitudinal (i.e. multi-year) follow-ups might be more informative in terms of confirming temporal associations between OFC characteristic emergence and later eating trajectories; although the MRI data were collected at baseline and eating behaviors at follow-up, directionality cannot be conclusively disentangled without further follow-up measurements. Second, the effects observed, strictly speaking, are not large in magnitude. However, given that the OFC is thought to only participate in value computation and comparison, it stands to reason that feed forward to lateral PFC subregions would be required for motor implementation (i.e. actual implementation of a value preference in manifest eating behaviors; Padoa-Schioppa and Conen, 2017). From this perspective, the magnitude of the OFC effects observed here should be as expected. With respect to measurement, the eating behavior measures were parental reports, which may have eliminated self-presentational biases, which can be substantial among adolescents. However, parental reports may have also introduced inaccuracy in estimation, given that parents do not have full access to the dietary history of their children, even over the course of a given time interval. Ultimately, these influences would tend to attenuate the strength of correlations between eating measures and structural parameters, however. With respect to null findings, some null effects may be produced by a lack of sensitivity of the binomial eating outcome, which may have also attenuated any potential mediating effects involving behavioral measures tested in this investigation. Further, although our focus in this review was on brain systems that model some facets of social context in eating decisions (and therefore a focus on the OFC), we did not include in our ROI analyses subcortical structures that might mediate indulgent eating behaviors, such as the nucleus accumbens and ventral tegmental area (Volkow et al., 2011). Investigating these areas may represent a useful future direction. Likewise, patterns of interconnectivity of these and the current ROIs may be useful in this respect, and the insula in particular, as it has many theoretically meaningful connections of significance to eating-related pathologies (Frank et al., 2013). Finally, the morphological features of the insula extracted for the current investigation are not isomorphic with the primary taste cortex, given that the taste cortex is only a small part of the anterior insula. Unfortunately, the atlas used for parcellation in ABCD data does not include the subregions of the insula. As such, it is the case that the relationship between the insula and any eating habits is as likely to reflect somatic awareness and any other insular functions over and above taste per se. Broadly speaking, our findings fit well with an interpretation of insular function as involving salience detection in any case. ## Conclusion In conclusion, we examined the morphological aspects of the OFC and insula and their associations with several aspects of eating behaviors among early adolescents in a large, representative population dataset. We found that a larger lateral OFC surface area reliably predicted less frequent indulgence in calorie-dense food items (fried or fast foods). In contrast, a larger insula volume was associated with less consumption of foods that are of high nutritive value but of relatively low-to-moderate calorie density. Our findings are broadly consistent with the idea that the lateral OFC regulates some aspects of indulgence, possibly in concert with increasing social pressures to regulate during the early adolescent years. A highly sensitive insular processing in contrast may render adolescents relatively more sensitive to highly salient foods rather than more nutritive options available in their eating environment. ## Funding The data used in for the analyses presented in this paper are from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org; National Institutes of Mental Health (NIMH) National Data Archive (NDA)). The ABCD Study is a large multi-site longitudinal study of adolescent development involving 21 sites and more than 11,000 children (aged 9 to 10 years of age at baseline), who will be followed forward for 10 years. The ABCD Study is supported by the National Institutes of Health (NIH) and federal partners under the following awards: U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A complete list of funding support and partners can be found at the following link: https://abcdstudy.org/federal-partners.html. A complete list of participating sites and investigators can be found here: https://abcdstudy.org/consortium_members/. 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--- title: The orbitofrontal cortex, food reward, body weight and obesity authors: - Edmund T Rolls journal: Social Cognitive and Affective Neuroscience year: 2021 pmcid: PMC9997078 doi: 10.1093/scan/nsab044 license: CC BY 4.0 --- # The orbitofrontal cortex, food reward, body weight and obesity ## Abstract In primates including humans, the orbitofrontal cortex is the key brain region representing the reward value and subjective pleasantness of the sight, smell, taste and texture of food. At stages of processing before this, in the insular taste cortex and inferior temporal visual cortex, the identity of the food is represented, but not its affective value. In rodents, the whole organisation of reward systems appears to be different, with reward value reflected earlier in processing systems. In primates and humans, the amygdala is overshadowed by the great development of the orbitofrontal cortex. Social and cognitive factors exert a top-down influence on the orbitofrontal cortex, to modulate the reward value of food that is represented in the orbitofrontal cortex. Recent evidence shows that even in the resting state, with no food present as a stimulus, the liking for food, and probably as a consequence of that body mass index, is correlated with the functional connectivity of the orbitofrontal cortex and ventromedial prefrontal cortex. This suggests that individual differences in these orbitofrontal cortex reward systems contribute to individual differences in food pleasantness and obesity. Implications of how these reward systems in the brain operate for understanding, preventing and treating obesity are described. ## Introduction Research is described at the neuronal level that shows that in primates, the reward value of the sight, smell, taste and oral texture of food is represented in the orbitofrontal cortex, but not at earlier stages of processing. It is shown that this is a different type of organisation from what appears to be present in rodents. Research is then described, which shows in human (fMRI) Functional Magnetic Resonance imaging investigations that the organisation is similar to that in other primates, in that the pleasantness of the sight, smell, taste and oral texture of food is represented in the orbitofrontal cortex, but not at earlier stages such as in the taste insula. This is extended, by showing that in humans, social and cognitive factors such as word-level information that the food is rich and delicious modulates the activations produced by the smell and taste of food in the orbitofrontal cortex. Moreover, paying attention to the pleasantness of the food rather than its physical properties increases activations produced by food reward in the orbitofrontal cortex. Then, it is shown that even in the resting state, when no food is present, the liking of the individual for sweet foods, and as a probable consequence, the body mass index (BMI), is related to the functional connectivity of the reward-related orbitofrontal cortex with action-related systems such as the anterior cingulate cortex. This is related to individual differences in food reward systems that arise, it is proposed, by variation useful in evolutionary processes. This provides a foundation for understanding food reward systems in the brain, and their relation to appetite control and body weight, in humans. The organisation of the pathways for food reward in primates including humans shown in Figures 1 and 2 is based on the evidence described next. What is described here refers to primates including humans unless otherwise stated. Largely unimodal taste, olfactory, oral texture and visual sensory inputs that represent what object is represented but not its reward value converge in the orbitofrontal cortex to form multimodal representations that encode food reward. The neuron-level evidence comes from macaques, as this is the best neuron-level evidence that is related to the processing in humans. A unique feature of the approach here is that it combines extensive complementary evidence from the most relevant neuron-level studies with fMRI investigations in humans about food reward systems in the orbitofrontal cortex. Fig. 1.: Schematic diagram showing some of the gustatory, olfactory, visual and somatosensory pathways to the orbitofrontal cortex, and some of the outputs of the orbitofrontal cortex, in primates. The secondary taste cortex and the secondary olfactory cortex are within the orbitofrontal cortex. V1—primary visual cortex. V4—visual cortical area V4. Tier 1: the column of brain regions including and below the inferior temporal visual cortex represents brain regions in which ‘what’ stimulus is present is made explicit in the neuronal representation, but not its reward or affective value, which are represented in the next tier of brain regions (Tier 2), the orbitofrontal cortex and amygdala, and in the anterior cingulate cortex. In Tier 3 areas beyond these such as medial prefrontal cortex area 10, choices or decisions about reward value are taken (Rolls, 2008b, 2014; Rolls and Deco, 2010). Top-down control of affective reward systems by cognition and by selective attention from the dorsolateral prefrontal cortex is also indicated. Medial PFC 10/VMPFC—ventromedial prefrontal cortex area 10; VPMpc—ventralposteromedial thalamic nucleus, the thalamic nucleus for taste. Fig. 2.: Some of the pathways involved in processing food-related stimuli are shown on this lateral view of the primate brain (macaque). Connections from the primary taste and olfactory cortices to the orbitofrontal cortex and amygdala are shown. Connections are also shown in the ‘ventral visual system’ from V1 to V2, V4, the inferior temporal visual cortex, etc., with some connections reaching the amygdala and orbitofrontal cortex. In addition, connections from the somatosensory cortical areas 1, 2 and 3 that reach the orbitofrontal cortex directly and via the insular cortex and that reach the amygdala via the insular cortex are shown. as, arcuate sulcus; cal, calcarine sulcus; cs, central sulcus; lf, lateral (or Sylvian) fissure; lun, lunate sulcus; ps, principal sulcus; io, inferior occipital sulcus; ip, intraparietal sulcus (which has been opened to reveal some of the areas it contains); sts, superior temporal sulcus (which has been opened to reveal some of the areas it contains). AIT, anterior inferior temporal cortex; FST, visual motion processing area; LIP, lateral intraparietal area; MST, visual motion processing area; MT, visual motion processing area (also called V5); PIT, posterior inferior temporal cortex; STP, superior temporal plane; TA, architectonic area including auditory association cortex; TE, architectonic area including high-order visual association cortex and some of its subareas TEa and TEm; TG, architectonic area in the temporal pole; V1-V4, visual areas V1–V4; VIP, ventral intraparietal area; TEO, architectonic area including posterior visual association cortex. The numerals refer to architectonic areas and have the following approximate functional equivalence: 1–3, somatosensory cortex (posterior to the central sulcus); 4, motor cortex; 5, superior parietal lobule; 7a, inferior parietal lobule, visual part; 7b, inferior parietal lobule, somatosensory part; 6, lateral premotor cortex; 8, frontal eye field; 12, part of orbitofrontal cortex; 46, dorsolateral prefrontal cortex. The orbitofrontal cortex in humans and macaques largely corresponds, as shown in Figure 3. Evidence is described here that the medial orbitofrontal cortex areas 13 and 11 represent food reward value, with convergence of taste, olfactory, visual and somatosensory inputs onto neurons that represent reward value. The medial orbitofrontal cortex represents many other types of reward value (Rolls, 2019a,b; Rolls et al., 2020a; Xie et al., 2021a). The lateral orbitofrontal cortex (red in Figure 3) represents unpleasant stimuli, for example unpleasant odours (Rolls et al., 2003a; Rolls, 2019b), and not obtaining an expected reward such as a food reward (Thorpe et al., 1983) or emotional reward (Kringelbach and Rolls, 2003) or monetary reward (Rolls et al., 2020b; Xie et al., 2021a). The taste, olfactory, visual, somatosensory and auditory anatomical pathways in macaques by which the inputs reach the orbitofrontal cortex are described elsewhere (Ongür and Price, 2000; Rolls, 2015, 2019b, 2021). Tractography (Hsu et al., 2020) and functional connectivity (Du et al., 2020) of the human orbitofrontal cortex show similar connectivity to the macaque. The ventromedial prefrontal cortex (VMPFC) on the medial wall of the frontal lobes (see Figure 3) has connections from the orbitofrontal cortex (Carmichael and Price, 1996; Ongür and Price, 2000; Du et al., 2020; Hsu et al., 2020) and is implicated in decision-making about reward value, rather than representing reward value on a continuous scale as in the orbitofrontal cortex (Rolls and Grabenhorst, 2008; Grabenhorst et al., 2008b; Rolls et al., 2010b,c; Grabenhorst and Rolls, 2011; Glascher et al., 2012; Rolls, 2019b). Rodents may have no granular orbitofrontal cortex areas 13, 11 and 12 corresponding to these areas in primates including humans (see Figure 3), and the whole organisation of the rodent brain systems for taste and related processing is very different to that of macaques, as shown below and elsewhere (Rolls, 2016a,c, 2019b, 2021). Hence, focus on these systems in primates including humans is important for understanding food reward systems in humans, and that is the approach taken here. Fig. 3.: The orbitofrontal (below) and medial prefrontal including anterior cingulate (above) cortical areas in humans, macaque monkeys and rats. (A) Medial (top) and orbital (bottom) areas of the human frontal cortex (Öngür et al., 2003). The medial orbitofrontal cortex is shown in green (areas 13 and 11) and the lateral orbitofrontal cortex in red (area 12). Almost all of the human orbitofrontal cortex except area 13a is granular. Agranular cortex is shown in dark grey. Black shows olfactory regions posterior to the orbitofrontal cortex. The ventromedial prefrontal cortex is the area shown as 10 m and below that towards 11 m. The anterior cingulate cortex comprises areas 32 and 24, with the subgenual area 25. The part of area 45 shown is the orbital part of the inferior frontal gyrus pars triangularis. (B) Medial (top) and orbital (bottom) areas of the macaque frontal cortex. Conventions as in (B). (C) Medial (top) and lateral (bottom) areas of rat frontal cortex [which is thought to have no granular orbitofrontal cortex equivalent to the primate including human granular orbitofrontal cortex areas 11, 13 and 12 (Passingham and Wise, 2012)]. Rostral is to the left in all drawings. Top row: dorsal is up in all drawings. Bottom row: in (A) and (B), lateral is up; in (C), dorsal is up. Not to scale. Abbreviations: AC, anterior cingulate cortex; AON, anterior olfactory nucleus; cc, corpus callosum; Fr2 second frontal area; Ia, agranular insular cortex; ig, induseum griseum; IL, infralimbic cortex; LO, lateral orbital cortex; MO, medial orbital cortex: OB, olfactory bulb; Pr, piriform (olfactory) cortex; PL, prelimbic cortex; tt, tenia tecta; VO, ventral orbital cortex; Subdivisions of areas are labelled caudal (c); inferior (i), lateral (l), medial (m); orbital (o), posterior or polar (p), rostral(r), or by arbitrary designation (a, b). [Adapted from Passingham and Wise (2012)]. (a) Adapted from Ongur, Ferry, and Price (2003) Architectonic subdivision of the human orbital and medial prefrontal cortex, Journal of Comparative Neurology 460: 425–449 (Öngür et al., 2003). (b) Adapted from Carmichael and Price (1994) Architectonic subdivision of the orbital and medial prefrontal cortex in the macaque monkey, Journal of Comparative Neurology 346: 366–402 (Carmichael and Price, 1994). (c) Adapted from Palomero–Gallagher and Zilles (2004) Isocortex, in Paxinos, George ed., The Rat Nervous System, 3e, pp. 729–757 (Palomero-Gallagher and Zilles, 2004). ## Taste reward neurons in the orbitofrontal cortex A secondary cortical taste area in primates was discovered by Rolls and colleagues (Thorpe et al., 1983; Rolls et al., 1989, 1990) in the orbitofrontal cortex (Rolls, 2019b), extending several millimetres in front of the insular primary taste cortex. This is defined as a secondary cortical taste area, for it receives direct inputs from the primary taste cortex, as shown by a combined neurophysiological and anatomical pathway tracing investigation (Baylis et al., 1995). Different neurons in this region respond not only to each of the four classical prototypical tastes sweet, salt, bitter and sour (Rolls et al., 1990, 2003b; Verhagen et al., 2003; Kadohisa et al., 2005b), but also to umami tastants such as glutamate (which is present in many natural foods such as tomatoes, mushrooms and human milk) (Baylis and Rolls, 1991) and inosine monophosphate (which is present in meat and some fish such as tuna) (Rolls et al., 1996a). In addition, other orbitofrontal cortex neurons respond to water (Rolls et al., 1990), and others to somatosensory stimuli including viscosity, grittiness (Rolls et al., 2003b), astringency as exemplified by tannic acid (Critchley and Rolls, 1996a) and capsaicin (Rolls et al., 2003b; Kadohisa et al., 2004). Fat in food in the mouth is also represented by some neurons in the orbitofrontal cortex (Rolls et al., 1999; Verhagen et al., 2003), and texture is important, for such neurons typically respond not only to foods such as cream and milk containing fat, but also to paraffin oil (which is a pure hydrocarbon) and to silicone oil ((Si(CH3)2O)n). The responses of these oral fat-encoding neurons are not related to free fatty acids such as linoleic or lauric acid (Verhagen et al., 2003; Kadohisa et al., 2005b; Rolls, 2011), and the fat responsiveness of these primate orbitofrontal cortex neurons is therefore not related to fatty acid sensing (Gilbertson et al., 1997; Gilbertson, 1998), but instead to oral texture sensing (Rolls, 2020). The transduction mechanism reflects the coefficient of sliding friction (Rolls et al., 2018), paving the way for the development of new foods with the pleasant mouthfeel of fat but designed nutritional content (Rolls, 2020). In addition, we have shown that some neurons in the orbitofrontal cortex (and also insular taste cortex and amygdala) reflect the temperature of substances in the mouth (Kadohisa et al., 2004, 2005a,b; Verhagen et al., 2004). Some of the coding principles are illustrated by the two neurons shown in Figure 4. The two neurons each have their independent tuning to the set of stimuli. It is this independent tuning or coding with sparse distributed representations that underlies the ability of the brain to represent the exact nature of a stimulus or event, and this applies to taste in addition to other sensory modalities including smell (Rolls et al., 1996c, 2010a; Rolls and Treves, 2011; Rolls, 2015, 2016b, 2021). This tuning also provides a foundation for the implementation of sensory-specific satiety (Rolls, 2014, 2015), as described below. Taste responses are found in a large mediolateral extent of the orbitofrontal cortex (Critchley and Rolls, 1996a; Pritchard et al., 2005; Rolls and Grabenhorst, 2008; Rolls, 2008a, 2015). Fig. 4.: Independent coding of food-related stimuli shown by the responses of two orbitofrontal cortex neurons to taste and oral somatosensory inputs. a. Firing rates (mean ± SEM) of viscosity-sensitive neuron bk244 that did not have taste responses, in that it did not respond differentially to the different taste stimuli. The firing rates are shown for the viscosity series (carboxymethylcellulose 1–10 000 centiPoise), for the gritty stimulus (1000 cP carboxymethylcellulose with Fillite microspheres), for the taste stimuli 1 M glucose (Gluc), 0.1 M NaCl, 0.1 M MSG, 0.01 M HCl and 0.001 M QuinineHCl, and for fruit juice (BJ). Spont = spontaneous firing rate. b. Firing rates (mean ± SEM) of viscosity-sensitive neuron bo34, which had responses to some taste stimuli and had no response to the oils (mineral oil, vegetable oil, safflower oil and coconut oil, which have viscosities that are all close to 50 cP). The neuron did not respond to the gritty stimulus in a way that was unexpected given the viscosity of the stimulus, was taste tuned and did respond to capsaicin. (After Rolls, Verhagen and Kadohisa 2003). The majority of these orbitofrontal cortex neurons with food-related taste or oral texture responses represent food reward value, in that their responses decrease to zero during feeding to satiety (Critchley and Rolls, 1996c), as illustrated in Figure 5 for the sweet taste of glucose. This procedure is sometimes called reward devaluation and shows that the neurons only respond to food when it is rewarding. Further, feeding to satiety with fat (e.g. cream) decreases the responses of the fat-responsive neurons to zero on the food eaten to satiety, providing evidence that they encode the reward value of fat in the mouth (Rolls et al., 1999). Fig. 5.: The effect of feeding to satiety with glucose solution on the responses (firing rate ± SEM) of a neuron in the orbitofrontal (secondary taste) cortex to the taste of glucose (open circles) and of blackcurrant juice (BJ). The spontaneous firing rate is also indicated (SA). Below the neuronal response data, the behavioural measure of the acceptance or rejection of the solution on a scale from +2 (strong acceptance) to −2 (strong rejection) is shown. The solution used to feed to satiety was 20% glucose. The monkey was fed 50 ml of the solution at each stage of the experiment as indicated along the abscissa, until he was satiated as shown by whether he accepted or rejected the solution. Pre is the firing rate of the neuron before the satiety experiment started. (After Rolls et al., 1989). These taste and oral texture neurons show that sensory-specific satiety is implemented in the orbitofrontal cortex, in that as illustrated in Figure 5, orbitofrontal cortex neurons decrease their responses to the food eaten to satiety, but not to other foods, and this applies to taste neurons and to fat texture neurons (Rolls et al., 1999) (and to neurons that respond to the sight and smell of food, as shown below). In fact this is how Edmund Rolls discovered sensory-specific satiety, one of the most important single factors that influence the amount of food eaten in a meal. The subjective correlate of this modulation is that food tastes pleasant when hungry and tastes hedonically neutral when it has been eaten to satiety. The discovery of sensory-specific satiety was made by recording from neurons in the lateral hypothalamus that receive inputs from the orbitofrontal cortex (Rolls, 1981; Rolls et al., 1986). The hypothalamic neuron being recorded from was responding to the sight of food, a sweet taste, and stopped responding after feeding to satiety with that taste. Rolls at that stage pulled a peanut out of his pocket and offered it to the monkey, and the lateral hypothalamic neuron gave a massive response to the sight of the peanut. It was clear within 3 or 4 presentations that something important was happening here, for the expectation was that after feeding to satiety, hypothalamic reward neurons would no longer respond to food. However, Rolls offered the peanut, and then banana, to the macaque, which avidly ate it. He went on to satiate the monkey with banana and the neuron stopped responding to banana, but still responded to peanuts. And he found that what the hypothalamic neuron still responded to, the monkey would find rewarding and would eat it (Rolls et al., 1986). Edmund Rolls quickly went on to show with colleagues that sensory-specific satiety was present in humans, and ran generations of Oxford undergraduates on sensory-specific satiety paradigms, showing that they showed sensory-specific satiety for food, and that variety of taste and flavour in a meal was a major factor in influencing how much food is eaten in a meal (Rolls and Rolls, 1977, 1997; Rolls et al., 1981a,b, 1982, 1983a,b, 1984; Hetherington, 2007). Further, it was shown in an Ethiopian refugee camp that there is a long-term form of sensory-specific satiety, which needs to be allowed for when designing foods to be offered on a long time scale (Rolls and De Waal, 1985). Sensory-specific satiety is present in the primate orbito-frontal cortex, but not at earlier stages of processing including the insular–opercular primary taste cortex (Rolls et al., 1988; Yaxley et al., 1988) and the nucleus of the solitary tract (Yaxley et al., 1985), where the responses reflect factors such as the intensity of the taste, which is little affected by satiety (Rolls et al., 1983c; Rolls and Grabenhorst, 2008). Sensory-specific satiety is probably implemented at least in part by adaptation of the synaptic afferents to orbitofrontal cortex neurons with a time course of the order of the length of a course of a meal (Rolls and Rolls, 1997; Rolls, 2019b). It is complemented by visceral and other satiety-related signals that reach the orbitofrontal cortex (from the nucleus of the solitary tract, via thalamic, insular visceral cortex, and possibly hypothalamic nuclei) and there modulate the representation of food, resulting in an output that reflects the reward (or appetitive) value of each food (Rolls, 2014, 2015, 2016c, 2019b). Sensory-specific satiety for reward value implemented in the orbitofrontal cortex is found not only for food, but also probably for every other type of reward, and for no punishing stimuli, and is probably a major evolutionary adaptation to help animals to obtain not only a wide range of nutrients, but also the wide range of rewards that are essential for reproductive success (Rolls, 2014, 2019b). Sensory-specific satiety, that is, sensory-specific reward devaluation, is thus a major principle of operation implemented in the orbitofrontal cortex but not at earlier stages of processing in primates (Figure 1). ## Taste neurons before the orbitofrontal cortex Taste information reaches the orbitofrontal cortex from the insular taste cortex (Baylis et al., 1995). The primary taste cortex is in the anterior (granular) insula and adjoining frontal operculum in macaques (and humans) and receives taste inputs via the nucleus of the solitary tract and the thalamus (VPMpc, ventralposteromedial thalamic nucleus, and pars parvocellularis) (Rolls, 2015). The taste insula contains taste neurons tuned to sweet, salt, bitter, sour (Scott et al., 1986b; Yaxley et al., 1990; Scott and Plata-Salaman, 1999; Rolls and Scott, 2003) and umami as exemplified by monosodium glutamate (MSG; Baylis and Rolls, 1991; Rolls et al., 1996a). It also contains neurons that encode oral somatosensory stimuli including viscosity, fat texture, temperature and capsaicin (Verhagen et al., 2004). Some neurons in the primary taste cortex respond to particular combinations of taste and oral texture stimuli, but macaque insular taste cortex neurons do not respond to olfactory stimuli or visual stimuli such as the sight of food (Verhagen et al., 2004). Neurons in the primate insular and frontal opercular primary taste cortex do not represent the reward value of taste, that is the appetite for a food, in that their firing is not decreased to zero by feeding the taste to satiety (Rolls et al., 1988; Yaxley et al., 1988). Neural processing peripheral to the primary taste cortex is consistent with this, with taste responses found in the rostral part of the nucleus of the solitary tract (Scott et al., 1986a) that are not influenced by feeding to satiety (Yaxley et al., 1985). This is an important principle of operation of reward systems in primates including humans: sensory processing and perceptual representations take place in cortical areas before the orbitofrontal cortex; and reward processing is implemented in the orbitofrontal cortex (Figure 1). Part of the evolutionary adaptive value of this is that objects can be recognised and their locations, etc. can be remembered even when they are not rewarding, because sensory processing and perception is kept separate from reward value and hedonics in primates including humans, as shown in Figure 1 (Rolls, 2014, 2019b). ## Taste reward activations in humans fMRI studies in humans are important in that they provide evidence that the same rules of operation of food reward brain systems apply in humans, although they cannot provide anything like the precision of the evidence available from single neuron studies about exactly what is represented in terms of separate stimuli, because tens of thousands of neurons are being averaged across at a time. Human studies are valuable in another way too, for they allow effects of word level cognitive modulations of reward systems to be investigated. Different regions of the human orbitofrontal cortex can be activated by pleasant (sucrose or glucose) or by aversive (e.g. quinine or sodium chloride) taste stimuli (Zald et al., 1998, 2002; O’Doherty et al., 2001). Umami taste stimuli, of which an exemplar is MSG and which captures what is described as the taste of protein, activate the insular (primary), orbitofrontal (secondary) and anterior cingulate [tertiary (Rolls, 2008a)] taste cortical areas (de Araujo et al., 2003a; Rolls, 2009). Sensory-specific satiety (and thus reward value) is also reflected in the activations in the human orbitofrontal cortex, in that in a study with real foods with taste, texture and olfactory components, it was found that after feeding to satiety with tomato juice, the activations of the orbitofrontal cortex to tomato juice decreased to zero but not of chocolate milk; whereas after feeding to satiety with chocolate milk, the opposite occurred (Kringelbach et al., 2003). This study thus provided evidence that the subjective pleasantness of the flavour of food and sensory-specific satiety are represented in the human orbitofrontal cortex. Another type of evidence about reward value in the human orbitofrontal cortex comes from the discovery that the subjective pleasantness of taste stimuli as reported consciously by humans is linearly related to activations in the medial orbitofrontal cortex/ventromedial prefrontal cortex, as shown in Figure 6 (Grabenhorst and Rolls, 2008). The same was found in a region to which the medial orbitofrontal cortex projects (Du et al., 2020; Hsu et al., 2020), the pregenual anterior cingulate cortex (Figure 6) (Grabenhorst and Rolls, 2008), which is involved in actions made to obtain rewarding stimuli (Rolls, 2019c). Consistent with what is found at the neuronal level in primates, activations in the human taste insula were linearly related to the subjective intensity but not pleasantness of the stimulus (Figure 6) (Grabenhorst and Rolls, 2008). Fig. 6.: Effect of paying attention to the pleasantness vs the intensity of a taste stimulus, MSG. a. Top: A significant difference related to the taste period was found in the taste insula at [42 18–14] z = 2.42 P < 0.05 (indicated by the cursor) and in the mid insula at [40 −2 4] z = 3.03 P < 0.025. Middle: Taste Insula. Right: The parameter estimates (mean ± SEM across subjects) for the activation at the specified coordinate for the conditions of paying attention to pleasantness or to intensity. The parameter estimates were significantly different for the taste insula t = 4.5, df = 10, P = 0.001. Left: The correlation between the intensity ratings and the activation (% BOLD change) at the specified coordinate (r = 0.91, df = 14, P ≪ 0.001). Bottom: Mid Insula. Right: The parameter estimates (mean ± SEM across subjects) for the activation at the specified coordinate for the conditions of paying attention to pleasantness or to intensity. The parameter estimates were significantly different for the mid insula t = 5.02, df = 10, P = 0.001. Left: The correlation between the intensity ratings and the activation (% BOLD change) at the specified coordinate (r = 0.89, df = 15, P ≪ 0.001). The taste stimulus, MSG, was identical on all trials. b. Top: A significant difference related to the taste period was found in the medial orbitofrontal cortex at [–6 14 −20] z = 3.81 P < 0.003 (towards the back of the area of activation shown) and in the pregenual cingulate cortex at [–4 46–8] z = 2.90 P < 0.04 (at the cursor). Middle: Medial orbitofrontal cortex. Right: The parameter estimates (mean ± SEM across subjects) for the activation at the specified coordinate for the conditions of paying attention to pleasantness or to intensity. The parameter estimates were significantly different for the orbitofrontal cortex t = 7.27, df = 11, P < 10–4. Left: The correlation between the pleasantness ratings and the activation (% BOLD change) at the specified coordinate (r = 0.94, df = 8, P ≪ 0.001). Bottom: Pregenual cingulate cortex. Conventions as above. Right: The parameter estimates were significantly different for the pregenual cingulate cortex t = 8.70, df = 11, P < 10−5. Left: The correlation between the pleasantness ratings and the activation (% BOLD change) at the specified coordinate (r = 0.89, df = 8, P = 0.001). The taste stimulus, 0.1 M MSG, was identical on all trials. [After (Grabenhorst and Rolls, 2008)]. Further evidence about processing in the insular taste cortex is described elsewhere (Small et al., 1999; O’Doherty et al., 2001; de Araujo et al., 2003a, 2012; Grabenhorst and Rolls, 2008; Small, 2010; Rolls, 2015, 2016a,c). In the mid-insular cortex, there is a somatosensory representation of oral texture (de Araujo and Rolls, 2004), which might be unpleasant, and this region can sometimes be activated by taste stimuli as illustrated in Figure 6. If the insular taste cortex in humans is activated by odours, this may be because of taste recalled through backprojection pathways (Rolls, 2016b) from the more anterior agranular insular cortex, which is multimodal (de Araujo et al., 2003b), or from the orbitofrontal cortex. What is encoded in the human insula is the identity/intensity of the taste, not its hedonic/reward value, in that activations in the insula correlate with the intensity ratings but not the pleasantness ratings of the taste (Figure 6) and in that activations in the human insula are modulated by selective attention to the intensity of the taste, as opposed to its pleasantness (Figure 6) (Grabenhorst and Rolls, 2008, 2010; Rolls et al., 2008; Ge et al., 2012; Luo et al., 2013; Rolls, 2013). The texture-related unpleasantness of some oral stimuli is represented in frontal opercular areas that are close to the insular taste cortex (Rolls et al., 2015). This region [and for that matter the taste insula (Verhagen et al., 2004; Kadohisa et al., 2005b)] includes oral somatosensory inputs, and care must be taken to ensure that mouth grimaces, etc. do not occur differentially to the stimuli being used. For example, a small reduction in the activation produced to an aversive taste in this insular/opercular region occurred when it was accompanied by a visual stimulus that led to an expectancy that the taste would not be aversive (Nitschke et al., 2006), but it would be important to show that mouth movements were not the cause of this small effect. ## Olfactory food reward neurons in the orbitofrontal cortex Some primate orbitofrontal cortex neurons respond well to olfactory stimuli (Critchley and Rolls, 1996b; Rolls et al., 1996b, 2010a). For many of these olfactory neurons, the response is also related to tastes (Critchley and Rolls, 1996b), and the olfactory representations can be learned by olfactory to taste association learning (Rolls et al., 1996b), providing evidence that the orbitofrontal cortex can remap odours from the olfactory gene–specified representation (Buck and Axel, 1991; Mombaerts, 2006) into a representation where the ‘meaning’ in terms of the association of the odour with other stimuli is paramount. Flavours are built by learning in the orbitofrontal cortex as combinations of taste and olfactory inputs, with oral texture also often being a component (Rolls et al., 1996b). The olfactory to taste association learning is slow, taking 30–60 trials to reverse, so that flavour representations are somewhat stable (Rolls et al., 1996b). The representation of information about odour and taste by primate orbitofrontal cortex neurons (Rolls et al., 1996c, 2010a) is approximately independent by different neurons, in that the information increases approximately linearly with the number of neurons (Rolls et al., 2010a). The Shannon mutual information between the taste and odour stimuli and the neuronal firing is measured in bits (with two bits needed for example to perfectly discriminate four stimuli), and the linear increase in information with the number of neurons (for tens of neurons) provides evidence that the coding by different neurons is independent, enabling the total number of stimuli that can be discriminated to rise exponentially with the number of neurons (because information is a log measure) (Rolls et al., 2010a; Rolls and Treves, 2011; Rolls, 2021). This is a fundamental aspect of brain computation that applies also in the orbitofrontal cortex (Rolls, 2021). Many primate olfactory orbitofrontal neurons encode the reward value of odour, not only in that their responses often reflect the taste primary reinforcer with which an odour is associated (Critchley and Rolls, 1996b; Rolls et al., 1996b), but also in that their activity is decreased in a sensory-specific satiety way by feeding a particular food to satiety (Critchley and Rolls, 1996c). ## Olfactory food reward activations in the human orbitofrontal cortex In humans, there is strong and consistent activation of the orbitofrontal cortex by olfactory stimuli (Zatorre et al., 1992; Francis et al., 1999; Rolls et al., 2003a). This region represents the reward value and pleasantness of odour, as shown by a sensory-specific satiety experiment with banana vs vanilla odour (O’Doherty et al., 2000), and these reward-specific activations have been confirmed, with evidence too that activations in the pyriform (primary olfactory) cortex were not decreased by odour devaluation by satiety (Gottfried, 2015; Howard et al., 2015). Further, pleasant odours tend to activate the medial, and unpleasant odours the more lateral, orbitofrontal cortex (Rolls et al., 2003a), adding to the evidence that it is a principle that there is a hedonic map in the orbitofrontal cortex, and also in the anterior cingulate cortex, which receives inputs from the orbitofrontal cortex (Rolls and Grabenhorst, 2008; Grabenhorst and Rolls, 2011; Rolls, 2014; Du et al., 2020; Hsu et al., 2020). The primary olfactory (pyriform) cortex represents the identity and intensity of odour in that activations there correlate with the subjective intensity of the odour, and the orbitofrontal and anterior cingulate cortices represent the reward value of odour, in that activations there correlate with the subjective pleasantness (medially) or unpleasantness (laterally) of odour (Rolls et al., 2003a, 2008, 2009; Grabenhorst et al., 2007; Rolls and Grabenhorst, 2008; Grabenhorst and Rolls, 2011; Rolls, 2014). ## Neuronal activity Taste and olfactory pathways are brought together in the orbitofrontal cortex where flavour is formed by learned associations at the neuronal level between these inputs (see Figure 1) (Rolls and Baylis, 1994; Critchley and Rolls, 1996b; Rolls et al., 1996c). Visual inputs also become associated by learning in the orbitofrontal cortex with the taste of food to represent the sight of food and contribute to flavour (Thorpe et al., 1983; Rolls et al., 1996b). Olfactory-to-taste associative learning by these orbitofrontal cortex neurons may take 30–40 trials to reverse an olfactory-to-taste discrimination task, and this slow learning may help to make a flavour stable (Rolls et al., 1996b). Olfactory neurons are found in a considerable anterior–posterior extent of the primate orbitofrontal cortex, extending far into areas 11 and 14 (Rolls and Baylis, 1994; Critchley and Rolls, 1996b,c; Rolls et al., 1996b,c), and are not restricted to a posterior region as some have thought (Gottfried and Zald, 2005). Visual-to-taste association learning and its reversal by neurons in the orbitofrontal cortex can take place in as little as one trial (Thorpe et al., 1983; Rolls et al., 1996b; Deco and Rolls, 2005a). This has clear adaptive value in enabling particular foods with a good or bad taste to be learned and recognized quickly, important in foraging and in food selection for ingestion. The visual inputs reach the orbitofrontal cortex from the inferior temporal visual cortex, where neurons respond to visual objects independently of their reward value (e.g. taste) as shown by satiety and reversal learning tests (Rolls et al., 1977; Rolls, 2008b, 2012b). The visual-to-taste associations are thus learned in the orbitofrontal cortex (Rolls, 2014, 2019b, 2021). These orbitofrontal cortex visual–taste neurons thus respond to expected value (Rolls, 2014). ## Taste–olfactory convergence shown by activations in humans Taste and olfactory conjunction analyses, and the measurement of supradditive effects that provide evidence for convergence and interactions in fMRI investigations, showed convergence for taste (sucrose) and odour (strawberry) in the orbitofrontal and anterior cingulate cortex, and activations in these regions were correlated with the pleasantness ratings given by the participants (de Araujo et al., 2003b; Small et al., 2004; Small and Prescott, 2005). These results provide evidence on the neural substrate for the convergence of taste and olfactory stimuli to produce flavour in humans, and where the pleasantness of flavour is represented in the human brain (Rolls, 2014, 2015). The first region where the effects of this olfactory–taste convergence are found is in an agranular part of what cytoarchitecturally is the insula (Ia) that is topologically found in the posterior orbitofrontal cortex, although it is anterior to the insular taste cortex and posterior to the granular orbitofrontal cortex (de Araujo et al., 2003b; Rolls, 2015, 2016a). McCabe and Rolls [2007] have shown that the convergence of taste and olfactory information in the orbitofrontal cortex appears to be important for the delicious flavour of umami. They showed that when glutamate is given in combination with a consonant, savoury, odour (vegetable), the resulting flavour can be much more pleasant than the glutamate taste or vegetable odour alone, and that this reflected activations in the pregenual cingulate cortex and medial orbitofrontal cortex. The principle is that certain sensory combinations can produce very pleasant food stimuli, which may of course be important in driving food intake, and that these combinations are formed in the brain far beyond the taste or olfactory receptors (Rolls, 2009). O’Doherty et al. [ 2002] showed that visual stimuli associated with the taste of glucose activate the orbitofrontal cortex and some connected areas, consistent with the primate neurophysiology. Simmons et al. [ 2005] found that showing pictures of foods, compared to pictures of places, can also activate the orbitofrontal cortex. Similarly, the orbitofrontal cortex and connected areas were also found to be activated after presentation of food stimuli to food-deprived subjects (Wang et al., 2004). ## The neuroeconomics of food reward value in the orbitofrontal cortex The reward value representations in the primate orbitofrontal cortex of taste, olfactory and flavour stimuli are appropriate for economic decision-making in a number of ways (Rolls, 2014, 2015). First, the responses of orbitofrontal cortex neurons reflect the quality of the commodity or ‘good’ (e.g. the sight or taste of food) multiplied by the amount available (Padoa-Schioppa and Assad, 2006; Padoa-Schioppa, 2011; Padoa-Schioppa and Conen, 2017). Moreover, these neurons reflect the value of reward stimuli and not actions made to obtain the rewards (Thorpe et al., 1983; Rolls et al., 1990; Verhagen et al., 2003; Padoa-Schioppa and Assad, 2006; Rolls, 2014, 2019b). In humans, activations in the ventromedial prefrontal cortex reflect the ‘subjective value’ of foods (where ‘subjective value’ in economics refers to what is chosen by an individual rather than to conscious subjective pleasantness (Rolls, 2014, 2015), measured by the willingness to pay for foods in an auction task (Plassmann et al., 2007)). More generally, there is evidence that the orbitofrontal cortex represents value on a continuous scale, whereas the ventromedial prefrontal cortex is implicated in choices, i.e. decision-making, between stimuli with different values (Rolls and Grabenhorst, 2008; Grabenhorst et al., 2008b, 2010; Grabenhorst and Rolls, 2009, 2011; Rolls et al., 2009, 2010b,c; Rolls et al., 2010d; Glascher et al., 2012; Rolls, 2014, 2019b). ## Representations in the orbitofrontal cortex of reward value on a common scale but not in a common currency For decision-making, it is important that representations of reward value are on a common scale (so that they can be compared), but are not in a common currency of general reward value, for the specific reward must be represented to guide actions appropriate for obtaining that particular reward (Rolls, 2014, 2015, 2019b, 2021). To investigate whether specific reward representations are on a common scale of reward value, we performed an fMRI study in which we were able to show that even fundamentally different primary rewards, taste in the mouth and warmth on the hand, produced activations in the human orbitofrontal cortex that were scaled to the same range (Grabenhorst et al., 2010). Further fMRI studies are consistent with this (Levy and Glimcher, 2012). These reward value representations in the orbitofrontal cortex are thus in a form suitable for making decisions about whether to for example choose and eat a particular food, with the attractor network decision-making mechanisms now starting to be understood (Wang, 2002; Rolls and Deco, 2010; Rolls et al., 2010b,c,d; Grabenhorst and Rolls, 2011; Rolls, 2014, 2015, 2016b, 2021). ## Top-down cognitive effects on taste, olfactory and flavour food reward processing in the orbitofrontal cortex: a route for social influences on eating Social factors, for example if a person is informed by another individual or by advertising that a food is in some way good or delicious, can influence eating behaviour. One route by which this can happen is by top-down, cognitive and social, influences on the orbitofrontal cortex food reward system (see Figure 1, ‘Cognitive and attentional top-down bias’). To what extent does cognition influence the hedonics of food-related stimuli, and how far down into the sensory system does the cognitive influence reach? We measured the activation to a standard test odour (isovaleric acid combined with cheddar cheese odour, presented orthonasally using an olfactometer) that was paired with a descriptor word on a screen, which on different trials was ‘Cheddar cheese’ or ‘Body odor’. Participants rated the affective value of the standard test odour, isovaleric acid, as significantly more pleasant when labelled ‘Cheddar Cheese’ than when labelled ‘Body odor’, and these effects reflected activations in the medial orbitofrontal cortex and pregenual cingulate cortex (de Araujo et al., 2005). The implication is that cognitive factors can have profound effects on our responses to the hedonic and sensory properties of food, in that these effects are manifest quite far down into sensory and hedonic processing (in the orbitofrontal cortex, see Figure 1), so that hedonic representations of odours are affected (de Araujo et al., 2005). Similar cognitive effects and mechanisms have now been found for the taste and flavour of food, where the cognitive word level descriptor was for example ‘rich delicious flavor’ and activations to flavour were increased in the orbitofrontal cortex and regions to which it projects including the pregenual cingulate cortex and ventral striatum, but were not influenced in the insular primary taste cortex where activations reflected the intensity (concentration) of the stimuli (Grabenhorst et al., 2008a) (see Figure 7). Cognitive factors can also influence the release of the hunger-related hormone ghrelin (Crum et al., 2011). If self-control of reward-related processing is required, the dorsolateral prefrontal cortex may be involved in the attentional and related aspects of the processing (Hare et al., 2009; Rolls, 2014; Lowe et al., 2019). Fig. 7.: Cognitive modulation of flavour reward processing in the brain. a. The medial orbitofrontal cortex was more strongly activated when a flavour stimulus was labelled ‘rich and delicious flavor’ (MSGVrich) than when it was labelled ‘boiled vegetable water’ (MSGVbasic) [–8 28 −20]. (The flavour stimulus, MSGV, was the taste 0.1 M MSG + 0.005 M inosine 5ʹmonophosphate combined with a consonant 0.4% vegetable odour.) b. The timecourse of the BOLD signals for the two conditions. c. The peak values of the BOLD signal (mean across subjects ± Statistical Parametric Mapping (SEM)) were significantly different (t = 3.06, df = 11, P = 0.01). d. The BOLD signal in the medial orbitofrontal cortex was correlated with the subjective pleasantness ratings of taste and flavour, as shown by the SPM analysis, and as illustrated (mean across subjects ± SEM, r = 0.86, P < 0.001). [After (Grabenhorst et al., 2008a)]. These top-down cognitive word-level effects on food reward systems in the orbitofrontal cortex are likely to be an important route by which social influences, and advertising, can influence food reward value, food choice and the amount of food eaten. Other social influences may well by similar top-down biased competition (Deco and Rolls, 2005b; Rolls, 2013, 2021) modulate the orbitofrontal cortex food reward system in a similar way. ## Top-down selective attention to affective value vs intensity biases reward representations in the orbitofrontal cortex: another route for social influences on eating Selective attention is another way in which social factors may bias the ways in which humans respond to food. When humans are asked to pay selective attention to the pleasantness of a food, there is a top-down modulation of food reward representations in the orbitofrontal cortex to taste, flavour and olfactory food-related stimuli. On the other hand, selective attention to the intensity of the taste, flavour, etc. modulates activations in areas such as the insular primary taste cortex (see Figure 5) (Grabenhorst and Rolls, 2008, 2010; Rolls et al., 2008; Ge et al., 2012; Luo et al., 2013; Rolls, 2013). A source of this top-down modulation by attention of reward processing in the orbitofrontal cortex is the executive system in the dorsolateral prefrontal cortex (Luo et al., 2013), and this is of interest in relation to how the executive system controls behaviour towards rewards (cf. Lowe et al., 2019). This differential biasing of brain regions engaged in processing a sensory stimulus depending on whether the cognitive demand is for affect-related vs more sensory-related processing may be an important aspect of cognition and attention, which have implications for how strongly the reward system is driven by food, and thus for eating and the control of appetite (Grabenhorst and Rolls, 2008, 2011; Rolls et al., 2008; Rolls, 2012a, 2013, 2014). The top-down modulations of processing by cognitive, social and executive function factors have many implications for investigations of taste, olfactory and other sensory processing, for the development of new food products, and for understanding obesity. ## Individual differences in the orbitofrontal cortex food reward system, and their association with obesity and BMI An important hypothesis is that different humans may have reward systems that differ in how strongly their reward systems are activated, driven by the sensory and cognitive factors that make taste, olfactory and flavour stimuli attractive. In a test of this, we showed that activations to the sight and flavour of chocolate in the orbitofrontal and pregenual cingulate cortex were much higher in chocolate cravers than non-cravers (Rolls and McCabe, 2007), although there were no differences at the level of the insular taste cortex. This provides evidence that differences in specific reward systems, and not necessarily in earlier sensory processing, can lead to individual differences in behaviour to taste, olfactory and flavour stimuli. This is consistent with the hypothesis that part of the way in which evolution results in effective specific reward systems is by utilizing natural variation in these reward systems, and selecting for reward systems that lead to reproductive success (Rolls, 2014, 2018). This concept that individual differences in responsiveness to food reward are reflected in brain activations in regions related to the control food intake (Beaver et al., 2006; Rolls and McCabe, 2007) may provide a way for understanding and helping to control food intake and obesity (Rolls, 2012a, 2014, 2016c). There is evidence from a number of studies (many relatively small scale with typically fewer than 200 participants) that the structure and function of the orbitofrontal cortex and related regions are related to obesity (Lowe et al., 2019). The following studies are provided as examples. Fibre density measured with tractography was reported to be higher between regions such as the putamen, pallidum and midbrain and the posterior parietal cortex (Gupta et al., 2015). On the other hand, lower grey matter volume of the orbitofrontal cortex, VMPFC, anterior cingulate, striatum and insula is associated with obesity (Shott et al., 2015; Lowe et al., 2019). Higher metabolism of the orbitofrontal cortex (measured with positron emission tomography) was associated with a high BMI in elderly females (Sala et al., 2019). Food addiction scores ($$n = 39$$) were correlated with greater activation to the anticipation of food of the orbitofrontal cortex, anterior cingulate cortex and amygdala (Gearhardt et al., 2011). Higher responses of the orbitofrontal cortex to visual food cues have been found in obese people (Pursey et al., 2014). Inhibitory control of behaviour by an executive function system in the dorsolateral prefrontal cortex may be one way in which food intake control is maintained (Lowe et al., 2019), and this might include top-down cognitive and executive control of the orbitofrontal cortex. To investigate whether there are inherent differences between individuals in terms of their orbitofrontal cortex reward systems, we analysed in a very large scale study with 31 536 participants whole-brain functional connectivity in the resting state when no food was available to investigate whether the functional connectivity of parts of the brain is associated with individuals’ liking for sweet foods, and a possible consequence of this, their BMI (Rolls et al., 2021). ( Functional connectivity is measured by the correlation between the Blood Oxygenation-Level Dependent (BOLD) signals between each pair of brain areas, with a higher functional connectivity implying that the systems are influencing each other more.) In 31 536 humans from the UK Biobank it was found that increased resting state connectivities of the orbitofrontal cortex/VMPFC especially with the anterior cingulate cortex, were correlated with the liking for sweet foods (False Discovery Rate (FDR) $P \leq 0.05$). In the same data set, it was found that the functional connectivities of the orbitofrontal cortex were positively correlated with the BMI (FDR $P \leq 0.001$). Moreover, in a sample of 494 534 people, the ‘liking for sweet foods’ was correlated with their BMI ($r = 0.06$, $P \leq 10$−124) (Rolls et al., 2021). The correlation between the functional connectivity of the orbitofrontal cortex (relative to that of other brain areas) and the BMI was cross-validated in 569 participants from the Human Connectome Project (Rolls et al., 2021). Further, higher functional connectivity involving the orbitofrontal cortex was associated with high BMI (≥30) compared to a mid-BMI group (22–25). Moreover, relative to other brain areas, low orbitofrontal cortex functional connectivity was associated with low BMI (≤20.8) compared to the mid-BMI group. The latter is interesting, because it is consistent with the hypothesis that lower functional connectivity of the orbitofrontal cortex reward system is associated with low BMI. It was proposed that high BMI relates to increased efficacy of orbitofrontal cortex food reward systems relative to other brain areas, and low BMI to decreased efficacy. It is of interest that this was found in the resting state, when the participants were not being stimulated by the sight or taste of food, so may be an underlying individual difference in brain connectivity (Rolls et al., 2021). The hypothesis thus is that the increased functional connectivity of the orbitofrontal cortex even when no food is present may be an individual difference that does influence how rewarding food is for an individual, and the increased body weight that may be related to higher eating of such foods. This hypothesis relates to the much broader hypothesis that a driving factor in evolution may be variation in the reward value of different specific types of reward in different individuals, which provides a fundamental basis of personality, that is, individual differences (Rolls, 2014, 2018). In the present case, the implication is that the variation in the connectivity of food reward systems in the brain may lead some individuals to like food more, which of course can be adaptive in some environments, and that this can in some environments, especially when food is highly palatable and readily available, be associated with a high body weight/BMI (Rolls, 2014, 2016c). Further light is cast on the underlying mechanisms by the finding that it is possible to predict sensation-seeking from the functional connectivity between the medial orbitofrontal cortex and anterior cingulate cortex (Wan et al., 2020). The implication is that the reward-related medial orbitofrontal cortex system by its connections to the action-related cingulate cortex (Rolls, 2019c) can strongly drive reward-related seeking behaviour. ## The orbitofrontal cortex is a food reward system, and not a habit or response or action system In the primate orbitofrontal cortex, neurons respond to the reward value of sensory stimuli, and do not respond to motor responses (Thorpe et al., 1983; Rolls and Baylis, 1994; Critchley and Rolls, 1996b; Rolls et al., 1996b; Wallis and Miller, 2003; Padoa-Schioppa and Assad, 2006; Grattan and Glimcher, 2014). Reward value is a property of stimuli, and this is what is represented in the primate including human orbitofrontal cortex (Rolls, 2019b, 2021). One way in which reward systems influence behaviour is via the cingulate cortex, which implements goal-related learning of actions that is under the control of the reward value of the goal, for example obtaining food (Rolls, 2021) (see Figure 1). The concept is that the posterior cingulate cortex receives information about actions being performed from the parietal cortex; receives information about whether the action was rewarded from the orbitofrontal cortex; learns the appropriate actions to obtain the rewards and avoid the punishers; and sends the output from the midcingulate cortex to premotor cortical areas (Rolls, 2019c, 2021). A second way in which the orbitofrontal cortex influences behaviour is via its projections to the striatum, to reinforce stimulus–response habits, which once stamped in, result in the responses being performed when the stimulus is received even if the stimulus is no longer rewarding (Rolls, 2014, 2021). The reinforcement signal from the orbitofrontal cortex may act directly in the striatum, but also via its influence on dopamine neurons via the ventral striatum and habenula (Rolls, 2017, 2021). It is normally the case that motivated behaviour is performed for the reward or goal, and it is only when a habit or stimulus–response behaviour becomes established that eating is no longer under the control of the reward (Berridge et al., 2009); so normally goal-directed ‘liking’ predicts motivation or ‘wanting’, but when the habit system is involved, the behaviour can become unlinked from liking (Rolls, 2014, 2015). As described below, the rodent orbitofrontal cortex is not functionally homologous to the primate orbitofrontal cortex, because the rodent orbitofrontal cortex has representations of behavioural responses (Wilson et al., 2014; Sharpe et al., 2015; Rolls, 2019b, 2021). ## Orbitofrontal cortex food reward systems and their relation to conditioned appetite and conditioned satiety Gut and other post-ingestive consequences on a longer time scale can influence food reward mechanisms. For example, if the food has a high energy value, then gradually humans learn to eat less of that flavour of food, in what is termed conditioned satiety (Booth, 1985). If the food has a low energy value, then more of it is consumed by learning over a few meals, and this is termed conditioned appetite (or ‘appetition’) (Booth, 1985; Sclafani, 2013). Thus, post-ingestive consequences of eating can by learning influence the sensory (taste, olfactory, etc.) reward value of food, and the same type of associative learning between the flavour of a food and its post-ingestive consequences can account for the findings (de Araujo et al., 2020) that hungry animals learn from gut signals to choose a food with significant energy content (they ‘like and want it’). Thus, associative learning between the flavour of a food and its post-ingestive consequences appears to be the mechanism (Sclafani, 2013), rather than gut signals being what is primarily rewarding (de Araujo et al., 2020). Consistent with my view, food reward that can reinforce actions is not found when the food directly enters the stomach unless large volumes are delivered (Nicolaidis and Rowland, 1976, 1977; Rolls, 2014), partly because the time course is too slow for each aliquot that enters the stomach to act as a discrete reward for an action. So even if vagal afferent stimulation can induce reward (Han et al., 2018), the time course of this route and the fact that food accumulates in the stomach and drains steadily into the duodenum makes this a poor system for reinforcing individual actions, but instead a suitable slow signal for slow associative learning of associations between flavour reward in the mouth and food in the gut. Consistent with this evidence, humans report that intragastric feeding is neither pleasant nor rewarding, as is well known in clinical medicine (Rolls, 2014). In more detail, during sham feeding when food drains from the stomach, whether the individual eats is under the control of the sight, smell and taste of the food, which acts as the reward for eating. A tiny drop of food is sufficient to reward and maintain the behaviour. If the food is no longer delivered to be tasted and swallowed, then the sham feeding soon stops. That is the evidence that it is the sight, smell and taste of food that provide food reward (Rolls, 2014). Moreover, the subjective pleasantness of the food is related to its flavour as signalled by taste, oral texture and odour. By contrast, when food is delivered directly into the stomach, it is not very rewarding, in that enormous quantities, for example one-quarter of the capacity of the stomach, have to be delivered in order for the animal to slowly learn to deliver food to the stomach (Nicolaidis and Rowland, 1976, 1977; Rolls, 2014). By contrast, food in the gut acts as a satiety signal, to switch off reward. A very telling observation is that if after eating to satiety the stomach is drained of food, feeding resumes immediately (Gibbs et al., 1981). This proves that a gut signal acts by producing satiety and by influencing the operationally defined reward value of food, which is whether an individual works for the taste, smell and sight of the food, i.e. for the sensory properties of the food. There is much evidence that modulation of the sensory reward or appetitive value of a food by gut signals is also relevant to clinical conditions, including obesity (Monteiro and Batterham, 2017; Makaronidis and Batterham, 2018). Strong further evidence for the importance of taste, olfactory, visual and oral texture cues in producing food reward value comes from studies of sensory-specific satiety and the effects of variety on food intake (Rolls et al., 1981a,b, 1983c, 1989; Critchley and Rolls, 1996c; Rolls and Rolls, 1997; Kringelbach et al., 2003), which cannot be accounted for by the gut reward signals that have been discussed (De Araujo et al., 2020). Rolls’ theory, therefore, is that taste, olfactory, oral texture and visual food reward systems determine whether food is eaten, and that gut signals modulate these sensory food reward systems, both by short-term satiety signals and by longer-term conditioning of the reward value of the sensory properties (taste, texture, smell and sight) of particular foods (Rolls, 2014, 2016c). That is, while humans are eating in a meal, the reward value and pleasure of food are produced by its sensory properties including its taste, texture, smell and sight. This reward value is reduced by sensory-specific satiety [implemented it is suggested by the adaptation of synapses bringing these sensory inputs onto neurons in the primate orbitofrontal cortex (Rolls, 2014)], by gut signals including gastric distension which rely on food entering the duodenum (Gibbs et al., 1981) and by post-absorptive effects that accumulate during a meal (Rolls, 2014). Over the longer term, the reward value of the sensory properties of a food can be conditioned by its nutritional consequences (Booth, 1985; Sclafani, 2013; Rolls, 2014, 2016c), and that is a relatively slow conditioning effect on the reward value produced by the sight, taste, texture and smell of food. The evidence thus is that the taste and flavour (including its oral texture) of a food is a primary, unlearned reward, and that the reward value can be modulated later in life by associative learning between the taste and flavour of food and its post-ingestive consequences. Further evidence for an innate liking for different tastes, which shows that taste is a primary, unlearned, reinforcer, is that very young rat pups display different reactivities to different tastes for at least some of which there has been no opportunity for conditioning (Kehoe and Blass, 1985). Further consistent evidence from humans is that we found greater reactivity of the agranular insular taste area, and the supracallosal cingulate cortex where aversive stimuli are represented (Rolls, 2019c), to the taste and texture of vegetable juice in young adults of student age than in older age groups (Rolls et al., 2015). This probably relates to the well-known dislike in young individuals of vegetables such as Brussels sprouts that are somewhat bitter (Rolls et al., 2015). [ The agranular insular taste area is just anterior to the primary insular taste cortex in which the unpleasantness of these stimuli was not represented (Rolls et al., 2015), consistent with the evidence about the insular taste cortex representing taste identity and intensity but not hedonics described above.] The implications are that stimuli such as taste and oral texture are primary reinforcers and that later in life post-ingestive gut-related consequences of the food eaten can be associated by learning with the taste of food that has been recently eaten in the processes known as conditioned appetite and conditioned satiety (Sclafani, 2013; Rolls, 2014). ## Unpleasant stimuli and non-reward in the lateral orbitofrontal cortex Many unpleasant stimuli, including unpleasant odours, are represented in the lateral orbitofrontal cortex area 12 (Rolls et al., 2003a, 2020a; Grabenhorst and Rolls, 2011; Rolls, 2019b), which then connects with the supracallosal anterior cingulate cortex (Rolls, 2019c; Du et al., 2020; Hsu et al., 2020). This part of the orbitofrontal cortex via its influence of the supracallosal anterior cingulate cortex may contribute to food choice by representing unpleasant aspects of food stimuli, such as in young adults the bitterness present in vegetable juice, to which older participants are much less sensitive (Rolls et al., 2015). Different neurons in the orbitofrontal cortex respond when a visually signalled expected taste reward is not obtained, that is, to negative reward prediction error (Thorpe et al., 1983; Rolls and Grabenhorst, 2008; Rolls, 2014, 2019b). Activations in the lateral orbitofrontal cortex occur when an expected reward is not obtained, and reversal of choice should occur (Kringelbach and Rolls, 2003; Rolls et al., 2020b). Moreover, damage to the human orbitofrontal cortex impairs this reward reversal behaviour and also is associated with impulsiveness, which may reflect insensitivity to non-reward (Rolls et al., 1994; Berlin et al., 2004, 2005; Hornak et al., 2004). This system may be involved in controlling food choice behaviour, by stopping behaviour when eating may be appropriate. Indeed, frontotemporal dementia is associated with disorders of eating of this type (Ahmed et al., 2019) that may be accounted for in the way just described. Similarly, undersensitivity or poor top-down control of this lateral orbitofrontal cortex system may contribute to disinhibited over-eating and obesity. Over-sensitivity and over-connectivity of this lateral orbitofrontal cortex system non-reward system are associated with depression (Rolls, 2018; Rolls et al., 2020a; Xie et al., 2021b). ## Food reward systems in humans and other primates compared to those in rodents Emphasis is placed here on research in primates and humans, because there is evidence that the rodent taste and food reward systems operate somewhat differently (Rolls, 2014; Rolls, 2015, 2016a, 2021). In brief, the taste system is different in rodents in that there is a pontine taste area, which then projects subcortically, but in primates there is no pontine taste area and cortical processing is performed first (Scott and Small, 2009; Small and Scott, 2009; Rolls, 2016a). Second, in rodents, the taste and olfactory systems are modulated peripherally [in the nucleus of the solitary tract and the olfactory bulb, respectively (Pager et al., 1972; Palouzier-Paulignan et al., 2012)] by hunger so that reward is represented peripherally and is entangled with sensory processing, whereas in primates and humans food perception is separated from its reward value (Figure 1) (Rolls, 2014). A perceptual correlate of this is that when humans feed to satiety, the intensity of the flavour changes very little, whereas the pleasantness of the flavour decreases to zero (Rolls et al., 1983c; Rolls and Rolls, 1997), showing that in humans’ perceptual representations of taste and olfaction are kept separate from hedonic representations. This is adaptive, in that we do not go blind to the sight, taste and smell of food after eating it to satiety and can, therefore, still learn about where food is located in the environment even when we are not hungry (Rolls, 2014). Third, the orbitofrontal cortex is very little developed in rodents (with only an agranular part) (Wise, 2008; Passingham and Wise, 2012) (Figure 3), yet is one of the major brain areas involved in taste and olfactory processing, and emotion and motivation, in primates including humans (Rolls, 2014, 2019b, 2021). Fourth, the rodent visual system is far less developed than the primate visual system (Rolls, 2021), and the reward value of the sight of food is very important in finding and selecting food in humans and other primates and is a major influence on the primate orbitofrontal cortex reward system, as described above. These findings make the rodent taste, olfactory and visual systems a poor model of neural food reward processing in humans, and for that reason emphasis is placed here on discoveries in primates and humans (Rolls, 2014; Rolls, 2015, 2016a, 2019b, 2021). ## The amygdala The amygdala is a structure in the temporal lobe with somewhat similar connections to the orbitofrontal cortex (see Figure 1). The amygdala has been present in evolution for much longer than the primate orbitofrontal cortex and appears to differ from the orbitofrontal cortex in that it cannot implement one-trial, rule-based, visual discrimination reversal when the taste or flavour associated with the visual stimulus is reversed (Rolls, 2014, 2021). The primate amygdala contains neurons that respond to taste and oral texture (Sanghera et al., 1979; Scott et al., 1993; Kadohisa et al., 2005a,b). Some neurons respond to visual stimuli associated with reinforcers such as taste, but do not reflect the reinforcing properties very specifically, do not rapidly learn and reverse visual-to-taste associations, and are much less affected by reward devaluation by feeding to satiety than are orbitofrontal cortex neurons (Sanghera et al., 1979; Yan and Scott, 1996; Wilson and Rolls, 2005; Kadohisa et al., 2005a, 2005b; Rolls, 2014). The primate orbitofrontal cortex appears to be much more closely involved in flexible (rapidly learned, and affected by reward devaluation) reward representations than is the primate amygdala (Rolls, 2014, 2019b, 2021). Fat texture, oral viscosity and temperature, for some neurons in combination with taste, and also the sight and smell of food, are represented in the macaque amygdala (Rolls and Scott, 2003; Kadohisa et al., 2005a,b). Interestingly, the responses of these amygdala neurons do not correlate well with the preferences of the macaques for the oral stimuli (Kadohisa et al., 2005b), and feeding to satiety does not produce the large reduction in the responses of amygdala neurons to food (Yan and Scott, 1996; Rolls and Scott, 2003) that is typical of orbitofrontal cortex neurons. We found activation of the human amygdala by the taste of glucose (Francis et al., 1999). Extending this study, O’Doherty et al. [ 2001] showed that the human amygdala was as much activated by the affectively pleasant taste of glucose as by the affectively negative taste of NaCl, and thus provided evidence that the human amygdala is not especially involved in processing aversive as compared to rewarding stimuli. Zald et al. ( 1998; 2002) also showed that the human amygdala responds to aversive (e.g. quinine) and to sucrose taste stimuli. Rolls has compared and contrasted the roles of the orbitofrontal cortex vs the amygdala in behaviour (Rolls, 2014, 2019b, 2021). ## Beyond reward value to decision-making in the ventromedial prefrontal cortex Representations of the reward value of food, and their subjective correlate the pleasantness of food, are fundamental in determining appetite and processes such as food-related economic decision-making (Padoa-Schioppa, 2011; Padoa-Schioppa and Cai, 2011; Rolls, 2014). But after the reward evaluation, a decision has to be made about whether to seek for and consume the reward. We are now starting to understand how the brain takes decisions (Wang, 2002; Rolls and Deco, 2010; Deco et al., 2013; Rolls, 2014, 2021), and this has implications for whether a reward of a particular value will be selected (Rolls and Grabenhorst, 2008; Rolls, 2008b, 2014, 2021; Rolls and Deco, 2010; Grabenhorst and Rolls, 2011; Deco et al., 2013). A tier of processing beyond the orbitofrontal cortex, in the ventromedial prefrontal cortex area 10 (see Figure 3), becomes engaged when choices are made between odour stimuli based on their pleasantness (Grabenhorst et al., 2008b; Rolls et al., 2010b,c,d) (Tier 3 in Figure 1). For example, activations in this area are larger when humans make a decision about which of two odours they prefer, compared to only rating the odours on a continuous scale of reward value (Grabenhorst et al., 2008b). The activations found during this decision-making are similar to those predicted from the attractor network model of decision-making (Rolls et al., 2010b,c; Rolls, 2021). ## Conclusions Analysis of the orbitofrontal cortex shows how it represents the reward value of the taste, texture, smell and sight of food. This is a key system involved in the control of food intake. Moreover, individual differences in the orbitofrontal cortex reward system are correlated with the liking for sweet foods and BMI (Rolls et al., 2021), indicating that this food reward system plays a role in the control of body weight. Although the correlation was not high in this investigation, it was highly significant, and was based on the reported liking for sweet foods, which is only one simple and limited measure of food reward, and higher correlations might be expected with fuller measures of food reward. The analysis of food reward systems in the orbitofrontal cortex leads to better understanding of the factors that are likely to influence eating behaviour, including sensory-specific satiety and variety in what is readily available; the high palatability of many modern foods in relation to satiety signals that evolved before these highly palatable foods became available; and social, cognitive and executive control influences on orbitofrontal cortex food reward systems. In fact, it has been suggested that in order to control obesity, it may be important to understand all the factors that may contribute to high food intake, because unless all are controlled, overeating may occur (Rolls, 2016c). The factors are described in more detail elsewhere (Rolls, 2016c), but include genetic factors; endocrine factors and how they affect brain reward systems as well as metabolism; the delicate balance between orbitofrontal cortex food reward systems that may be overdriven in the modern environment, and satiety signals; the high palatability of modern foods; sensory-specific satiety and the effect of variety on food intake; food saliency and portion size, effects that relate to the importance of the sight of food in humans and that relate to advertising; the fact that food is readily available at many times of the day, which may disturb the normal timing between meals; the high energy density of foods that make it difficult for satiety signals to operate before energy intake is high; a high eating rate, which can have similar effects; and stress (Rolls, 2016c). ## Funding This research was supported by the Medical Research Council. ## Conflict of interest The author is not aware of any affiliations, memberships, funding or financial holdings that might be perceived as affecting the objectivity of this paper. ## References 1. 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--- title: Oral health related quality of life and the prevalence of ageusia and xerostomia in active and recovered COVID-19 Patients authors: - Mahnoor K.M. Saleem - Abhishek Lal - Naseer Ahmed - Maria S. Abbasi - Fahim Vohra - Tariq Abduljabbar journal: PeerJ year: 2023 pmcid: PMC9997189 doi: 10.7717/peerj.14860 license: CC BY 4.0 --- # Oral health related quality of life and the prevalence of ageusia and xerostomia in active and recovered COVID-19 Patients ## Abstract ### Background Salivary disturbance is associated with patients who either have an active coronavirus disease 2019 (COVID-19) or have recovered from coronavirus infection along with loss of taste sensation. In addition, COVID-19 infection can drastically compromise quality of life of individuals. ### Objective This study aimed to analyze xerostomia, ageusia and the oral health impact in coronavirus disease-19 patients utilizing the Xerostomia Inventory scale-(XI) and the Oral Health Impact Profile-14. ### Methods In this cross-sectional survey-based study, data was collected from 301 patients who suffered and recovered from COVID-19. Using Google Forms, a questionnaire was developed and circulated amongst those who were infected and recovered from coronavirus infection. The Xerostomia Inventory (XI) and Oral Health Impact Profile-14 were used to assess the degree and quality of life. A paired T-test and Chi-square test were used to analyze the effect on xerostomia inventory scale-(XI) and OHIP-14 scale scores. A p-value of 0.05 was considered as statistically significant. ### Results Among 301 participants, $54.8\%$ were females. The prevalence of xerostomia in participants with active COVID-19 disease was $39.53\%$ and after recovery $34.88\%$. The total OHIP-14 scores for patients in the active phase of infection was 12.09, while 12.68 in recovered patients. A significant difference was found between the mean scores of the xerostomia inventory scale-11 and OHIP-14 in active and recovered COVID patients. ### Conclusion A higher prevalence of xerostomia was found in COVID-19 infected patients ($39.53\%$) compared to recovered patients ($34.88\%$). In addition, more than $70\%$ reported aguesia. COVID-19 had a significantly higher compromising impact on oral function of active infected patients compared to recovered patients. ## Introduction The recent coronavirus disease-19 (COVID-19) pandemic is the great challenge to the health care system and the general population. Along with the global health crisis, it has also brought an unprecedented socio-economic crisis, which continues to have a devastating effect (Xie et al., 2020; Ali et al., 2020). Patients with COVID-19 present with mild flu-like condition leading to life-threatening multi-organ failure, lasting for up to two weeks, with high mortality among patients with co-morbidities (Ahmed et al., 2020c; Ahmed et al., 2020a; Neto et al., 2020; Abbasi et al., 2020; Zhou et al., 2020). The infected individuals primarily contract the virus from other infected humans by respiratory droplets produced by sneezing and coughing. Primary symptoms that are displayed by the infected patient include fever, dry cough, sore throat, shortness of breath, and myalgia (Wang et al., 2020b). Furthermore, as the disease progresses, other symptoms which may develop include disturbed taste and smell sensations, along with gastric upset such as diarrhea and constipation (Ali et al., 2020; Neto et al., 2020; Abduljabbar et al., 2020). Literature suggests that the viral infection of the salivary gland cells may alter the salivary constituents and composition. Critical salivary compounds like proteins, enzymes and hormones play an important part in perception of taste. Therefore, it is likely that the ageusia associated in COVID-19 patients is the result of changes in salivary composition associated with the salivary gland infection (Abduljabbar et al., 2020). The mechanism of COVID-19 disease as presented in previous studies, includes an interaction with angiotensin-converting enzyme inhibitor 2 (ACE-2) receptors, which are present throughout the body including lungs, blood vessels, brain, and salivary glands (Sarfaraz et al., 2020). Additionally, tissues present in the body that express ACE-2 receptors are prone to invasion of this virus and may display various symptoms according to the system being affected. Since ACE-2 receptors are present in the salivary glands, there is a possibility that the coronavirus can invade the salivary glands and cause inflammatory reactions leading to acute and chronic sialadenitis and dry mouth (Wang et al., 2020a; Fantozzi et al., 2020; Freni et al., 2020). Xerostomia is a condition whereby secretion or production of saliva is reduced causing compromised oral functions (Villa, Connell & Abati, 2014; Mulyani et al., 2019). It is associated with burning sensation, abnormal taste, dysarthria, dysphagia, dysgeusia, and halitosis, having a negative impact on the quality of life (Abduljabbar et al., 2020; Millsop, Wang & Fazel, 2017; Niklander et al., 2017). It occurs in $5.5\%$ to $46\%$ of the general world population with females being most affected (Anwar et al., 2022). It is also common in healthy individuals, although most commonly associated with many underlying systemic conditions. Increasing age along with polypharmacy are reported as risk markers for xerostomia especially inhaled anti-asthmatic drugs (Kishore et al., 2022). Other agents that contribute to xerostomia are caffeine, alcohol, tobacco, and carbonated beverages. COVID-19 infection along with xerostomia will compromise the oral health-related quality of life of patients and an improvement in quality of life for recovered individuals is expected. However, to our knowledge from indexed literature, there limited evidence related to oral health and quality of life of active and recovered COVID-19 patients. Timely identification and management of COVID-19 induced dry mouth could prove to be beneficial for the already suffering patient to maintain their diet and health with improved quality of life. Therefore, this study aimed to investigate the possible correlation between coronavirus and Xerostomia and to determine the prevalence of ageusia and xerostomia in active and recovered COVID-19 patients. In addition, the impact of coronavirus on quality of life regarding oral health will also be assessed. ## Study sample and ethical statement In this cross sectional analytical study, using the convenience sampling method, adults who had an active COVID-19 and later on recovered from it were included. Children and adolescents along with those who were not infected with coronavirus were excluded. The sample size was calculated through Open-Epi software. Considering the minimum frequency of xerostomia to be $45.9\%$. The power of the test was 80. A confidence interval of $95\%$. The margin of error is $5\%$. The estimated sample size for this study was 350 participants. The information of the participants collected through the survey was kept anonymous and confidential. Participation was voluntary and all participants completed informed consent. This study was conducted under the approval of the Ethical and Review Committee of Altamash Institute of Dental Medicine. ( AIDM/EC/$\frac{08}{2020}$/05). Initially, a short message about whether to be tested positive for coronavirus or not was sent to 750 individuals. Out of 750 individuals, 400 reported to be tested positive for SARS-CoV-2, and 350 agreed to participate in this study. ## Questionnaire and data collection A questionnaire was constructed and validated through face and content validation process, furthermore, the reliability of the questionnaire was assessed through intra-class correlation and the internal consistency of items assessed was 0.92. The questionnaire was sent through email, and their responses were recorded. Forty-nine incomplete forms were excluded. A total of 301 completed questionnaires were received and included in the study. The questionnaire consisted of two sections. The first included the demography of the patients including age, gender, occupation, level of education and smoking habits, systemic diseases, and finally if any medicines were being taken. The second section was focused on whether the patients experienced xerostomia symptoms or not, and how their quality of life regarding oral health was affected during active infection and after they recovered from it, using validated measurement tools. Xerostomia and the oral health impact of the coronavirus was measured using the Xerostomia Inventory Scale (XI) (Thomson et al., 1999) and Oral Health Impact Profile-14 (OHIP-14) scale (Slade, 1997), both being self-administered. Both the Xerostomia Inventory Scale (XI) and Oral Health Impact Profile-14 (OHIP-14) scale are adapted in the present study in accordance with there published license. The Xerostomia Inventory scale (XI) comprised of a total of 11 questions, with each question having five options as follows: 1 = Never, 2 = Hardly ever, 3 = Occasionally, 4 = Fairly often, and 5 = Very often. The Oral Health Impact Profile-14 scale comprised of a total of 14 questions related to seven dimensions: Functional limitation, physical pain, psychological discomfort, physical disability, psychological disability, social disability, and handicap. Each question in the OHIP-14 scale has five options as follows: 0 = Never, 1 = Rarely, 2 = Sometimes, 3 = Repeatedly, and 4 = Always. In both of the scales used, options in each question have its own numerical value which was added at the end to obtain a total score for each participant. For the Xerostomia Inventory scale (XI), the minimum score was 11 and the maximum was 55, with higher scores depicting greater severity in oral dryness. In the OHIP-14 scale, the minimum score was 0 with 56 being the maximum, and a score above 10 indicated poor Quality of Life (QoL). The questionnaire was composed of two parts, the first part consisted of Xerostomia Inventory and Oral Health Impact Profile scales questions which the patients were asked to fill when they had active coronavirus infection. The second part of the questionnaire also had similar questions of the Xerostomia Inventory and Oral Health Impact Profile scales but these were addressing symptoms after recovery from coronavirus infection. The total score for each of the two scales in both parts of the questionnaire was added up. The total scores of OHIP and XI scales during active coronavirus infection were compared with scores of OHIP and XI scales when participants had recovered from it. ## Statistical analysis Descriptive statistics were performed to obtain, the frequency, percentages, mean scores, and standard deviations of the quantitative and qualitative variables in the study i.e., age, gender, education status, drugs, smoking and systematic illness history, total score, questionnaire items, and domains. Paired T-test and Chi-square test were used to analyze the effect of mean xerostomia inventory (XI) and OHIP-14 scale scores on age, gender, and COVID-19 patients. A p-value of ≤ 0.05 was considered statistically significant. ## Results A total of 301 COVID-19 patients participated in the study in two phases (infection and recovery). The response rate from participants was $86\%$. More than one-half ($54.8\%$) of the participants were females. The majority of participants (126 ($41.9\%$)) were undergraduates by education status and mostly students (130 ($43.2\%$)) from occupation. More than two-thirds of participants were nonsmokers as far as habits are concerned. While 87 ($28.9\%$) participants were taking some medicines, bronchodilators ($6.89\%$) were the most common medicine used by the participants. Similarly, 81 ($26.9\%$) participants were suffering from a systematic illness, and hypertension was the most common illness 52 ($17.3\%$). Furthermore, 167 ($55.48\%$) participants were younger than 30 years of age, 121 ($40.19\%$) were from the age group of 31 to 60 years and 13 patients were older than 60 years of age as presented in Table 1. Table 1 \| Variables \| Factors \| n \| (%) \| \| --- \| --- \| --- \| --- \| \| Gender \| Male \| 136 \| 45.2 \| \| Gender \| Female \| 165 \| 54.8 \| \| Age \| 18–30 years \| 167 \| 55.5 \| \| Age \| 31–40 years \| 50 \| 16.6 \| \| Age \| 41–50 years \| 42 \| 14.0 \| \| Age \| 51–60 years \| 29 \| 9.6 \| \| Age \| Above 60 years \| 13 \| 4.3 \| \| Education Status \| Undergraduate \| 126 \| 41.9 \| \| Education Status \| Graduate \| 93 \| 30.9 \| \| Education Status \| Post-graduate \| 48 \| 15.9 \| \| Education Status \| Below Undergraduate \| 34 \| 11.3 \| \| Occupation \| Student \| 130 \| 43.2 \| \| Occupation \| Business \| 56 \| 18.6 \| \| Occupation \| Healthcare Professional \| 35 \| 11.6 \| \| Occupation \| Engineer \| 13 \| 4.3 \| \| Occupation \| Teacher \| 17 \| 5.6 \| \| Occupation \| Labor work \| 1 \| 0.3 \| \| Occupation \| Others \| 49 \| 16.3 \| \| Do you smoke? \| Yes \| 59 \| 19.6 \| \| Do you smoke? \| No \| 242 \| 80.4 \| \| How many cigarettes per day? \| 1 \| 216 \| 71.8 \| \| How many cigarettes per day? \| 2 \| 37 \| 12.3 \| \| How many cigarettes per day? \| 4 \| 12 \| 4.0 \| \| How many cigarettes per day? \| 6 \| 15 \| 5.0 \| \| How many cigarettes per day? \| More than 6 \| 21 \| 8.6 \| \| Are you taking any medicines currently? \| Yes \| 87 \| 28.9 \| \| Are you taking any medicines currently? \| No \| 214 \| 71.1 \| \| Are you suffering from any other disease currently? \| Yes \| 81 \| 26.9 \| \| Are you suffering from any other disease currently? \| No \| 220 \| 73.1 \| The distribution of xerostomia inventory scale XI score for active and recovered COVID-19 patients is presented in Table 2. On the basis of scores 3, 4 and 5 from item 4 of the xerostomia inventory scale, the prevalence of xerostomia in participants with active COVID-19 disease was $39.53\%$ and after recovery was $34.88\%$. The mean summative xerostomia inventory scale score was 22.36 ± 12.96 in active COVID-19 patients while in recovered patients 20.85 ± 12.59 as shown in Fig. 1. A significant difference was found between the mean scores of the xerostomia inventory scale among infected and recovered patients (paired t-test: $$p \leq 0.011$$). Additionally, a significant difference was also found between gender and xerostomia inventory scale scores in active ($$p \leq 0.002$$) and recovered COVID-19 patients ($$p \leq 0.001$$) (Chi-square test). Xerostomia was prevalent in young individuals between 18 to 30 years of age, with $17.60\%$ in active while $15.61\%$ in recovered cases. For older individuals of 60 years and above, xerostomia was in $4.98\%$ and $4.31\%$ active and recovered cases respectively. A significant difference was seen between the age of participants and xerostomia inventory scale scores in the active phase of COVID-19 infection (Chi-square test: $$p \leq 0.016$$). However, no significant difference ($$p \leq 0.473$$) was observed between age and recovered patient scores. The distribution of oral health impact profile scale 14 scores for active and recovered COVID-19 patients is described in Table 3. The total OHIP-14 score for patients in the active phase of infection was 12.09 ± 14.49, while 12.68 ± 12.43 in recovered patients as presented in Fig. 1. The prevalence of different domains in the OHIP-14 scale was as follows: Functional limitation was seen in 224 ($74.41\%$) participants when assessed by scores 3 and 4 (Q1 and Q2). While physical pain was observed in 40 ($13.28\%$) after summing up score 3 and 4 (Q3 and Q4). Psychological discomfort was found in 74 (24.58) (Q5 and Q6). Physical disability was seen in 81($26.91\%$) (Q7 and Q8). Psychological disability was found in 89 ($29.56\%$) participants (Q9 and 10), whereas social disability (Q11, Q12) was evident in 86 ($28.57\%$) participants. Being handicap was (Q13 and Q14) found in 77 ($25.58\%$) subjects respectively (patients with active COVID-19 infection). Additionally, the highest individual OHIP-14 scale score was recorded for taste loss ($70.09\%$) amongst the participants as described in Table 3. Table 3 \| Q \| OHIP scale \| COVID-19 Patients \| COVID-19 Patients.1 \| COVID-19 Patients.2 \| COVID-19 Patients.3 \| COVID-19 Patients.4 \| COVID-19 Patients.5 \| COVID-19 Patients.6 \| COVID-19 Patients.7 \| COVID-19 Patients.8 \| COVID-19 Patients.9 \| COVID-19 Patients.10 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| S \| Never \| Never \| Rarely \| Rarely \| Sometimes \| Sometimes \| Repeatedly \| Repeatedly \| Always \| Always \| \| \| \| \| n \| % \| n \| % \| n \| % \| n \| % \| n \| % \| \| 1.0 \| Have you had trouble pronouncing any word? \| A \| 174 \| 57.8 \| 47 \| 15.6 \| 67 \| 22.3 \| 11 \| 3.7 \| 2 \| 0.7 \| \| 1.0 \| Have you had trouble pronouncing any word? \| R \| 189 \| 62.8 \| 41 \| 13.6 \| 48 \| 15.9 \| 20 \| 6.6 \| 3 \| 1.0 \| \| 2.0 \| Have you felt that your sense of taste has worsened? \| A \| 24 \| 8.0 \| 15 \| 5.0 \| 51 \| 16.9 \| 163 \| 54.2 \| 48 \| 15.9 \| \| 2.0 \| Have you felt that your sense of taste has worsened? \| R \| 22 \| 7.3 \| 9 \| 3.0 \| 49 \| 16.3 \| 176 \| 58.5 \| 45 \| 15.0 \| \| 3.0 \| Have you had painful aching in your mouth? \| A \| 186 \| 61.8 \| 54 \| 17.9 \| 37 \| 12.3 \| 14 \| 4.7 \| 10 \| 3.3 \| \| 3.0 \| Have you had painful aching in your mouth? \| R \| 182 \| 60.5 \| 61 \| 20.3 \| 35 \| 11.6 \| 16 \| 5.3 \| 7 \| 2.3 \| \| 4.0 \| Have you found it uncomfortable to eat any foods? \| A \| 184 \| 61.1 \| 44 \| 14.6 \| 57 \| 18.9 \| 9 \| 3.0 \| 7 \| 2.3 \| \| 4.0 \| Have you found it uncomfortable to eat any foods? \| R \| 183 \| 60.8 \| 49 \| 16.3 \| 50 \| 16.6 \| 17 \| 5.6 \| 2 \| 0.7 \| \| 5.0 \| Have you been self-conscious? \| A \| 140 \| 46.5 \| 42 \| 14.0 \| 74 \| 24.6 \| 21 \| 7.0 \| 24 \| 8.0 \| \| 5.0 \| Have you been self-conscious? \| R \| 154 \| 51.2 \| 37 \| 12.3 \| 75 \| 24.9 \| 15 \| 5.0 \| 20 \| 6.6 \| \| 6.0 \| Have you felt tense? \| A \| 103 \| 34.2 \| 51 \| 16.9 \| 73 \| 24.3 \| 50 \| 16.6 \| 24 \| 8.0 \| \| 6.0 \| Have you felt tense? \| R \| 110 \| 36.5 \| 65 \| 21.6 \| 89 \| 29.6 \| 26 \| 8.6 \| 11 \| 3.7 \| \| 7.0 \| Have your diet been unsatisfactory? \| A \| 135 \| 44.9 \| 47 \| 15.6 \| 73 \| 24.3 \| 27 \| 9.0 \| 19 \| 6.3 \| \| 7.0 \| Have your diet been unsatisfactory? \| R \| 152 \| 50.5 \| 42 \| 14.0 \| 75 \| 24.9 \| 18 \| 6.0 \| 14 \| 4.7 \| \| 8.0 \| Have you had to interrupt meals? \| A \| 144 \| 47.8 \| 58 \| 19.3 \| 64 \| 21.3 \| 26 \| 8.6 \| 9 \| 3.0 \| \| 8.0 \| Have you had to interrupt meals? \| R \| 165 \| 54.8 \| 52 \| 17.3 \| 65 \| 21.6 \| 13 \| 4.3 \| 6 \| 2.0 \| \| 9.0 \| Have you found it difficult to relax? \| A \| 120 \| 39.9 \| 47 \| 15.6 \| 70 \| 23.3 \| 39 \| 13.0 \| 25 \| 8.3 \| \| 9.0 \| Have you found it difficult to relax? \| R \| 121 \| 40.2 \| 72 \| 23.9 \| 69 \| 22.9 \| 23 \| 7.6 \| 16 \| 5.3 \| \| 10.0 \| Have you been a bit embarrassed? \| A \| 148 \| 49.2 \| 43 \| 14.3 \| 85 \| 28.2 \| 16 \| 5.3 \| 9 \| 3.0 \| \| 10.0 \| Have you been a bit embarrassed? \| R \| 156 \| 51.8 \| 50 \| 16.6 \| 70 \| 23.3 \| 13 \| 4.3 \| 12 \| 4.0 \| \| 11.0 \| Have you been a bit irritable with other \| A \| 127 \| 42.2 \| 46 \| 15.3 \| 79 \| 26.2 \| 32 \| 10.6 \| 17 \| 5.6 \| \| 11.0 \| Have you been a bit irritable with other \| R \| 142 \| 47.2 \| 53 \| 17.6 \| 76 \| 25.2 \| 21 \| 7.0 \| 9 \| 3.0 \| \| 12.0 \| Have you had difficulty doing your usual job? \| A \| 146 \| 48.5 \| 42 \| 14.0 \| 76 \| 15.2 \| 27 \| 9.0 \| 10 \| 3.3 \| \| 12.0 \| Have you had difficulty doing your usual job? \| R \| 166 \| 55.1 \| 53 \| 17.6 \| 52 \| 17.3 \| 17 \| 5.6 \| 13 \| 4.3 \| \| 13.0 \| Have you felt that life, in general, was less satisfying? \| A \| 144 \| 47.8 \| 35 \| 11.6 \| 81 \| 26.9 \| 26 \| 8.6 \| 15 \| 5.0 \| \| 13.0 \| Have you felt that life, in general, was less satisfying? \| R \| 159 \| 52.8 \| 39 \| 13.0 \| 58 \| 19.3 \| 30 \| 10.0 \| 15 \| 5.0 \| \| 14.0 \| Have been totally unable to function? \| A \| 166 \| 55.1 \| 40 \| 13.3 \| 59 \| 19.6 \| 26 \| 8.6 \| 10 \| 3.3 \| \| 14.0 \| Have been totally unable to function? \| R \| 174 \| 57.8 \| 51 \| 16.9 \| 50 \| 16.6 \| 20 \| 6.6 \| 6 \| 2.0 \| The domains of OHIP-14 scale in COVID-19 recovered patients were observed in the study. The functional limitation was compromised in 54 ($17.94\%$) participants when assessed by scores 3 and 4 (Q1 and Q2), while physical pain was felt by 42 ($13.95\%$) individuals, when summed up by score 3 and 4 (Q3 and Q4). Psychological discomfort was evident in 72 ($23.92\%$) participants (item Q5 and Q6). Physical disability was seen in 51 ($16.94\%$) subjects (Q7 and Q8) and psychological disability was found in 64 ($21.26\%$) (Q9 and Q10). Social disability was apparent in 60 ($19.93.\%$) participants (Q11 and Q12) and handicap was found in 71 ($23.58\%$) patients (Q13 and Q14) respectively. Similarly, the highest individual OHIP-14 scale score was recorded for taste loss ($73.42\%$) amongst the participants (sum of negative answers 3 and 4 in item number 2) as presented in Table 3. There was a significant difference (paired t-test: $$p \leq 0.001$$) between the mean scores of the OHIP-14 scale in active and recovered COVID-19 patients. Furthermore, a significant difference was also found between gender and OHIP-14 scale score in active COVID-19 patients (Chi-square tests: $$p \leq 0.002$$) and recovered patients ($$p \leq 0.003$$). The loss of taste was prevalent in young individuals 18 to 30 years of age ($42.19\%$) while ($10.29\%$). in older individuals 60 years and above. Moreover, there was no significant difference between the age of participants and OHIP-14 scores in active and recovered patients (Chi-square test: $$p \leq 0.076$$, $$p \leq 0.069$$) as shown in Table 4. Table 4 \| Variable \| Xerostomia inventory scale \| Xerostomia inventory scale.1 \| \| --- \| --- \| --- \| \| \| Infected \| Recovered \| \| Gender \| 0.002* \| 0.001* \| \| Age \| 0.016* \| 0.473 \| \| OHIP-14 scale \| OHIP-14 scale \| OHIP-14 scale \| \| Gender \| 0.002* \| 0.003* \| \| Age \| 0.076 \| 0.069 \| ## Discussion Currently, COVID-19 is one of the major threats to the health of human life. Its early detection is critical in prevention and management of the disease. In the present study, it was observed COVID-19 was more prevalent in females then and majority of the participants were younger than 30 years of age which is in contrast to a study by Ali et al. [ 2020] which reported that adult male patients with a median age between 34–59 years have a greater incidence of developing SARS-CoV-2 infection. Our findings are supported by the fact that $63\%$ of population of the country where the data was collected (Pakistan) is comprised of youth aged individuals (Hafeez & Fasih, 2018). The present study also supports the findings of Berlin et al. [ 2020] who suggested that the published data on the association of COVID-19 with smoking status are only descriptive and conclusions from it cannot be drawn (Ahmed et al., 2020b). Furthermore, evidence suggests that underlying health conditions such as hypertension, diabetes, and coronary heart disease increase the risk of morbidity in COVID-19 patients (Abbasi et al., 2020; Wang et al., 2020a; Berlin et al., 2020). Nearly $27\%$ of the participants in the present study suffered with systematic illness with hypertension being the most common. Studies by Zhou et al. [ 2020] and Wang et al. ( 2020a) are also in accordance to the findings by the current study as majority ($48\%$) of patients had comorbidity, with hypertension being the most common followed by diabetes (Abbasi et al., 2020). Furthermore, this study reported on the prevalence of xerostomia in patients with active COVID-19 disease, which was $39.53\%$ and after recovery $34.88\%$. Earlier studies have also reported that COVID-19 patients experiences xerostomia though the temporal sequence of xerostomia and COVID-19 diagnosis is not clear and warrants further exploration (Abrar et al., 2021; Anzar et al., 2021; Rodríguez, Romera & Villarroel, 2020). In addition, xerostomia is known to be related to a wide range of viral infections (Nasiri, 2020) and SARS-CoV-1 has been shown to infect epithelial cells in salivary gland ducts in rhesus macaques (Nigar et al., 2022). Moreover, Freni et al. [ 2020] reported xerostomia in COVID-19 patients with a statistically significant difference between active and recovered patients (Fantozzi et al., 2020). This is similar to the observations in the present study, as a significant difference was found between the mean scores of xerostomia inventory scale between active and recovered patients. Additionally, a significant difference was also found between gender and xerostomia inventory scale scores in active and recovered COVID-19 patients with the female having more prevalence. This corresponds to the study by Biadsee et al. [ 2020], where $56.2\%$ experienced xerostomia with females having more prevalence ($61\%$). Xerostomia is multifactorial and is commonly associated with old age and polypharmacy (Mulyani et al., 2019; Nigar et al., 2022). Other factors include emotional stress and anxiety disorders, along with agents like caffeine, alcohol, tobacco, and carbonated beverages (Ahmed et al., 2020c). Many of these contributing factors are also associated with COVID-19 patients, so the exact cause of xerostomia in COVID-19 is yet to be discovered, either it is due to the mentioned conditions or due to viral infection of salivary glands impeding the salivary flow (Mulyani et al., 2019; Nasiri, 2020; Ahmed et al., 2020c; Fathi et al., 2021). As all the above-mentioned studies are observational and the literature lacks objective evidence, therefore, it is recommended that objective evaluation with validated, repeatable and standardized tests to establish the frequency, extent, and cause of xerostomia in COVID-19 patients are performed. COVID-19, affects the oral health-related quality of life of patients and OHIP-14 score among patients in the present study with active infection and recovery were 12.09 ± 14.49 and 12.68 ± 12.43 respectively. This is in line with the study by Niklander et al. [ 2017] with OHIP-14 scores among COVID-19 patients to be 20.1 ± 14.32 (Millsop, Wang & Fazel, 2017). A high prevalence of functional limitation was seen in participants of present study, which includes difficulty in speech and loss/altered taste. These findings are in accordance with a study by Vaira et al. [ 2020] in which majority of the patients reported gustatory disorders during SARS-CoV-2 infection (Chen et al., 2020). Similar findings are also observed in other studies (Mohamud et al., 2020; Giacomelli et al., 2020). Moreover, a smaller number of patients also complained of a painful mouth and discomfort in eating food which is also supported by Lechien et al. [ 2020] who reported loss of appetite and dysphagia amongst COVID-19 patients (Wee et al., 2020). It is also established that COVID-19 is associated with psychiatric implications including delirium, depression, anxiety, and insomnia (Holmes et al., 2020). Other coronaviruses also induce psychopathological sequelae through direct viral infection of the central nervous system (CNS) or indirectly via an immune response (Holmes et al., 2020; Wu et al., 2020). Moreover, psychological stressors such as social isolation, the psychological impact of a novel severe and potentially fatal illness, concerns about infecting others, and stigma are implicated in COVID-19 related psychiatric manifestation (Mazza et al., 2020). In the present study as well, psychological and social disability along with handicap symptoms were observed in more than a quarter of subjects. The study showed that xerostomia and ageusia are associated with COVID-19 infections and quality of life including difficulty in eating, speech difficulty, social and psychological disorders are augmented in infection. Therefore, it is important that a detailed and comprehensive intraoral assessment should be performed in patients that were diagnosed with COVID-19 in order to establish any oral manifestation that might be related. Moreover, the dentist should improve the examination of salivary glands and saliva flow in order to perform early diagnoses related to changes in the glandular parenchyma that might be affected by the virus. The study has some limitations. First, the sample size is small and includes mild-moderate patients, thus cannot represent general COVID-19 patients. Some of the COVID-19 survivors that we were able to reach did not want to participate in the study. In addition, the salivary flow before the infection and other factors like stress, that might have affected the oral health could not have been evaluated. The occurrence of xerostomia is a possible outcome of the viral infection in the patients observed, however it should also be associated with treatment of the infection. In addition, it may be essential to carry out the measurement of the salivary flow before and after the COVID-19 diagnosis to demonstrate a close correlation of it with the virus. The study targeted only COVID-19 patients. There may be a bias because people who have xerostomia or any oral problem would intend to reply to the questionnaire. 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--- title: METTL3-mediated m6A modification of has_circ_0007905 promotes age-related cataract progression through miR-6749-3p/EIF4EBP1 authors: - Rui Li - Haohao Zhu - Qian Li - Jiancen Tang - Yiping Jin - Hongping Cui journal: PeerJ year: 2023 pmcid: PMC9997201 doi: 10.7717/peerj.14863 license: CC BY 4.0 --- # METTL3-mediated m6A modification of has_circ_0007905 promotes age-related cataract progression through miR-6749-3p/EIF4EBP1 ## Abstract Many cases of blindness are caused by age-related cataracts (ARCs). N6-methyladenosine (m6A)-modified circRNA widely participates in disease progression. However, the role of m6A modification of circRNA in ARC is unclear. We mined and elucidated the functions and mechanisms of key circRNAs with m6A modification involved in ARC progression. The GSE153722 dataset was used to mine m6A-mediated key circRNA. Loss-of-function assays and rescue assays were used to explore the effect and mechanism of circRNA on ARC cell proliferation and apoptosis. Has_circ_0007905 was a hypermethylated and upregulated expression in the ARC group relative to the control group both in vivo and in vitro. Silencing of has_circ_0007905 promoted proliferation and inhibited the apoptosis of HLE-B3 cells. METTL3 was upregulated in HLE-B3 cells after ARC modeling and had four binding sites with has_circ_0007905 and a mediated m6A modification of has_circ_0007905. Proliferation was significantly inhibited and apoptosis of HLE-B3 cells was facilitated by METTL3 overexpression, whereas these effects were prevented by has_circ_0007905 silencing. Silencing of has_circ_0007905 led to an alteration in the transcriptome landscape. Differentially expressed genes were mainly involved in immune-related processes and pathways. EIF4EBP1 overexpression promoted apoptosis and suppressed proliferation, and also significantly reversed effects of has_circ_0007905 silencing. Moreover, miR-6749-3p significantly decreased the luciferase activities of wild type plasmids with both of has_circ_0007905 and EIF4EBP1. MiR-6749-3p inhibitor blocked elevation in proliferation and reduced EIF4EBP1 expression and apoptosis conferred by has_circ_0007905 silencing. We reveal for the first time that the commitment of ARC progression is guided by METTL3/has_circ_0007905/miR-6749-3p/EIF4EBP1 axis, and the results provide new insights into ARC pathology. ## Introduction Age-related cataract (ARC) is classified into three subtypes based on the different regions of opacity within the lens: posterior subcapsular, cortical, and nuclear (Shiels & Hejtmancik, 2017). Currently, ARC contributes to the most significant number of blindness cases caused by cataracts worldwide and is strongly associated with age (Lawrence, Fedorowicz & Van Zuuren, 2015). However, the dual challenges of an increasingly aging population and treatment reliance on cataract surgery have called for the development of new and effective therapeutic strategies for ARC. N6-methyladenosine (m6A) refers to the insertion of a methyl substituent on adenosine at the N6 position. It most prevalently occurs in the stop codons and 3′ untranslated region with a conserved motif of RRACH (R = G or A; and H = A, C, or U) (Luo et al., 2022). The m6A modification process includes m6A installation by “writers” (m6A methyltransferases), m6A removal by “erasers” (m6A demethylases), and m6A recognition by “readers” (recognition proteins) (Chen & Wong, 2020). Benefiting from m6A involvement in regulating RNA translation, splicing, and stability, m6A plays an essential biological role in a variety of diseases and cellular functions (Chen et al., 2022), including cancer (Jing et al., 2021) and neurologic disorders (Yen & Chen, 2021). It has been shown in a recent review that m6A modification also plays a vital role in the pathogenesis of diabetic retinopathy (Kumari et al., 2021). It was suggested in a report on transcriptome-wide m6A methylome sequencing of the anterior lens capsule of high myopia patients that m6A may modulate the composition of the extracellular matrix by proteins that alter fundus anatomy in high myopia (Wen et al., 2021). Overall, m6A plays a critical role in ocular diseases. Furthermore, methylase METTL3 repressed proliferation and promoted apoptosis of human lens epithelial cells in diabetic cataracts (Yang et al., 2020), indicating that cataract pathological progression is related to m6A modification. However, the mechanism by which m6A is involved in ARC progression is still largely unknown. Intriguingly, abundant m6A modifications in circRNAs similar to mRNA have been proven in emerging studies (Liu et al., 2022a). CircRNAs are a class of stable and ubiquitous noncoding RNAs highly conserved in mammals. The latest study reported that the m6A abundance of total circRNAs was reduced in the lens epithelium cells of ARC patients. m6A regulators, such as METTL14, WTAP, and ALKBH5, were significantly upregulated in ARC tissue compared to the normal lens (Li et al., 2020). It is suggested by these results that m6A may participate in ARC by regulating circRNAs. However, the study of circRNA RNA methylation is still at an early stage, and the pathogenesis of the m6A modification of circRNA in ARC has not yet been reported. In this study, we intended to screen the critical circRNAs in ARC through MeRIP-seq and sequencing data of circRNAs and clarify the function of candidate circRNAs in lens epithelial cells through cellular experiments. Finally, transcriptome sequencing was used to explore the downstream molecular mechanisms of the circRNAs. For the first time, we reveal the role and molecular mechanism of m6A modification of circRNAs in ARC progression. ## Data downloaded from the Gene Expression Omnibus (GEO) database The circRNA-seq data and MeRIP-seq data were downloaded from the GEO database under accession number GSE153722 (six ARC vs. six normal). The differential expression analysis of circRNA-seq and MeRIP-seq was performed using the R package DESeq under the cut-off criteria: —log2FC—≥ 1. The m6A-enriched circRNA in the normal control and ARC samples were analyzed. The intersection of differentially expressed circRNAs and differentially methylated m6A-enriched circRNAs served as candidate circRNAs. Additionally, the RNA-protein binding site between has_circ_0007905 and METTL3 was predicted using the RBPsuite software (http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/). The potential m6A modification sites of has_circ_0007905 were predicted using SRAMP software (http://www.cuilab.cn/sramp). ## Sample collection of crystalline lens from ARC patients Crystalline lens samples were obtained from conventional continuous curvilinear capsulorhexis in ARC patients ($$n = 10$$, each sample with three replicates), with no vascular contact or damage to the iris or other intraocular tissues. Patients with complex cataracts due to high myopia, trauma, uveitis, glaucoma, or other systemic diseases, such as hypertension and diabetes, were excluded from the study. Transparent lenses removed from normal subjects with shallow anterior chambers served as controls ($$n = 2$$, each sample with three replicates). The “shallow anterior chamber” is only a potential risk factor for glaucoma, but there is currently no related disease, and it also belongs to normal people. In this group, the clinical option was to deepen the anterior chamber through preventive lens surgery to remove the potential glaucoma risk. Signed informed consent was obtained from all patients. Detailed clinical data for each individual human subject are shown in Table S1. This study was approved by the Ethics Committee of Shanghai East Hospital, School of Medicine, Tongji University ([2021] Audit Research No. 79). ## RT-qPCR analysis Total RNA was extracted from crystalline lens samples and HLE-B3 cells using TRIzol (Invitrogen). The concentration and purity of total RNA were measured using a microspectrophotometer (Tiangen, Beijing, China). High-quality RNA was stored at −80 °C for subsequent RT-qPCR and RNA sequencing. Next, reverse transcription was conducted using a RevertAid™ First Strand cDNA Synthesis Kit (K16225; Thermo Fisher, Waltham, MA, USA). cDNA amplification was performed by 2 × PCR Master Mix (Roche, Basel, Switzerland) on the ABI Q6 Flex Real-time PCR system (Applied Biosystems, Foster City, CA, USA). Gene expression was calculated using the 2−ΔΔCT method. All primers used in this study are shown in Table S2. ## Cell culture and ultraviolet B (UVB) irradiation and transfection The human lens epithelial cell line of HLE-B3 cells was purchased from BFB BLUEFBIO (BFN60805970; Bluebio (Yantai) Bio-Pharmaceutical Co., Ltd., Yantai, China) and cultured in high glucose DMEM (10-013-CVRC; Corning, Shanghai, China) supplemented with $10\%$ FBS (10099, GIBCO) and $1\%$ PS (E607011, Sangon). HLE-B3 cells were maintained in an incubator with a $5\%$ CO2 atmosphere at 37 °C. To construct ARC cell model, HLE-B3 cells were exposed to UVB light (from top to bottom) for 10 min, according to a previous study, with slight modifications in exposure time (Xiang et al., 2019). For the knockdown of has_circ_0007905 and METLL3, siRNAs were designed and synthesized by GenePharma. The has-miR-6749-3p mimic and has-miR-6749-3p inhibitor were also synthesized by GenePharma to overexpress and knockdown the has-miR-6749-3p, respectively. For overexpression of EIF4EBP1, the full length of EIF4EBP1 was cloned into pCDNA3.1 vectors (OE-EIF4EBP1), and the blank vector served as NC. Transfection was conducted using Lipofectamine 2000 reagent (Invitrogen) according to the manufacturer’s instructions. Briefly, when the fusion rate of the cells reached 80–$90\%$, the fresh medium was replaced. All siRNA, vectors, and Lipofectamine 2000 reagents were diluted with OPTI-MEM (5 µL:45 µL). Next, diluted Lipofectamine and RNA sequences were mixed for 20 min and added to the cell sample. ## MeRIP-qPCR analysis Isolated RNA was purified and fragmented into fragments with ∼200 bp using RNA Fragmentation Reagent (Invitrogen, Waltham, MA, USA). Around $10\%$ of the volume of fragmented RNA was set aside as an input group. Magnetic beads A/G were mixed with anti-m6A antibodies and set aside. Next, fragmented RNA was incubated with the prepared anti-m6A antibody in an immunoprecipitation buffer, and anti-immunoglobulin G (IgG) served as a negative control. After incubation, RNA was eluted from the beads and subjected to RT-qPCR analysis. ## CCK8 assay The proliferation of HLE-B3 cells was assessed using a CCK-8 kit (C0037; Beyotime Biotechnology, Shanghai, China). Briefly, HLE-B3 cells were processed into single-cell suspensions with a density of 1 × 104 cells/mL and were seeded into a 6-well plate with 1000 cells/well. Each sample set had six replicates. At the indicated times of 0, 24, 48, 72, and 96 h, 10 µL CCK-8 solution was added to each well. The absorbance value at 450 nm was detected after 1 h. ## Flow cytometry At 6 h post-transfection, the medium was replaced with fresh medium. The cells were cultured for 48 h for flow cytometry. The cells were harvested and gently washed with PBS. Cells were resuspended with 195 µL of 1 × Binding Buffer to a cell density of 2–5 × 105 cells/mL. A total of 5 µL of Annexin V-FITC (C1062; Beyotime, Shanghai, China) was added to the cell resuspension and incubated for 15 min at room temperature in the dark. Next, the cells were washed with 200 µL 1 × Binding Buffer and centrifuged at 1000 rpm for 5 min to remove the supernatant. Finally, the cells were resuspended in 190 µL of 1 × Binding Buffer, and 10 µL of propidium iodide was added. A flow cytometry assay was performed within 4 h. ## TUNEL analysis The apoptosis of HLE-B3 cells was assessed with the One Step TUNEL Apoptosis Assay Kit (green fluorescence) (C1086; Beyotime Biotechnology, Shanghai, China). Briefly, HLE-B3 cells were fixed with $4\%$ paraformaldehyde for 30 min and then washed with PBS, followed by permeabilization in PBS containing $0.3\%$ Triton X-100 for 5 min. Afterward, 50 µL TUNEL solution was added to the HLE-B3 cells and incubated for 60 min in the dark. Lastly, HLE-B3 cells were sealed with antifluorescence quenching sealing tablets and photographed on a fluorescence microscope. ## Transcriptome sequencing RNA extracted from HLE-B3 cells after has_circ_0007905 knockdown ($$n = 3$$) or NC ($$n = 3$$) was used for transcriptome sequencing. RNA sequencing was performed by Yingbio Technology (Shanghai, China) on an Illumina HiSeq 2500 platform. The statistical power of this experimental design was calculated in RNASeqPower and was 0.896. The raw data were filtered using the FastQC method. Clean reads were subjected to screening of differentially expressed genes (DEGs) using the DEGSeq algorithm upon thresholds of Log2FC >1 or <-1 and FDR <0.05. GO and KEGG enrichment were analyzed on DEGs. The RNA-seq data generated in this study are available in the Sequence Read Archive under accession number PRJNA868911. ## Western blot HLE-B3 cells were harvested and lysed using a lysis buffer, and the protein concentration of cell extracts was quantified with a BSA kit. Next, appropriately 20 µg protein was loaded on 10 SDS-PAGE gels and transferred into PVDF membranes, followed by blocking with TBST solution containing $5\%$ skim milk at 4 °C for 3 h. Membranes were incubated with primary antibodies at 4 °C overnight and then incubated with secondary antibodies of Goat Anti-Mouse IgG H&L (HRP) (1:1000, ab205719; Abcam, Cambridge, UK) and Goat Anti-Rabbit IgG H&L (HRP) (1:20000, ab6721; Abcam, Cambridge, UK). Finally, the protein bands were imaged using the Bio-Rad ChemiDoc XRS system. Primary antibodies, including GAPDH (1:2000, 60004-1-Lg; Proteintech, Rosemont, IL, USA), METTL3 (1:1000, 15073-I-AP; Proteintech), and EIF4EBP1 (1:2000, ab32024; Abcam, Cambridge, UK), were used. ## RNA immunoprecipitation (RIP) The RIP assay was performed in this study using the Magna RIP RNA-Binding Protein IP Kit (Millipore, Burlington, MA, USA) according to the product instruction. Cells were collected and washed twice with pre-cooled PBS and centrifuged at 1500 rpm for 5 min at 4 °C to discard supernatant, followed by adding RIP Lysis Buffer to lyse the cells on ice for 5 min. Diluted 50 µL of magnetic beads in 100 µL of RIP Wash Buffer was used. The sample was incubated with 5 µg of Argonaute-2 antibody or 1 µg IgG antibody for 30 min at room temperature with rotation. After light centrifugation, the supernatant was removed, and the precipitate was resuspended in 0.5 mL RIP Wash Buffer. After that, 900 µL of RIP Immunoprecipitation Buffer was added to the bead-antibody mixture and then incubated with 100 cell lysis products overnight at 4 °C with rotation. Finally, 150 µL proteinase K buffer was added to the complex product and incubated at 55 °C for 30 min, followed by RNA extraction for RT-qPCR. ## Dual-luciferase activity assay HLE-B3 cells were seeded into 96-well plates and co-transfected with has-miR-6749-3p mimic/NC and psiCHECK™-2 vectors containing has_circ_0007905 wild type (WT) or mutant (Mut) 3′ UTR sequence, and psiCHECK™-2 vectors containing EIF4EBP1 WT or Mut 3′ UTR sequence. At 48 h after transfection, the cells were lysed and incubated with firefly luciferase buffer to detect firefly luciferase activity, followed by incubation with Renilla luciferase to detect firefly luciferase activity (Promega, Madison, WI, USA). ## Statistical analysis Data analysis was executed using GraphPad Prism 9.0. The t-test was used to assess significant differences between two groups, and one-way analysis of variance (ANOVA) following Turkey’s test to assess significant differences among three groups. All data are displayed as mean ± standard deviation (mean ± SD), and a P-value lower than 0.05 was considered significant. ## Identification of hypermethylated-upregulated expression of circRNAs Data from GSE153722 were analyzed to screen differentially expressed circRNAs and differentially methylated circRNAs. According to GSE153722 circRNA-seq data, a total of 220 differentially expressed circRNAs were identified, including 117 upregulated and 103 downregulated in the ARC group compared to the NC group (Fig. 1A). According to GSE153722 MeRIP-seq data, a total of 220 differentially methylated circRNAs were identified, including 102 hypermethylation circRNAs and 118 hypomethylation circRNAs in the ARC group compared to the NC group (Fig. 1A). Subsequently, as shown in Fig. 1A, there were 50 overlapping circRNAs that satisfied the hypermethylation upregulated in the ARC group. These overlapping circRNAs have attracted our attention. To further narrow the scope of candidate hypermethylated-upregulated expression circRNAs, five circRNAs (has_circ_0007905, has_circ_0065244, has_circ_0003949, has_circ_0022997, and has_circ_0035228) with the most significantly changed were selected for validation. The distribution of m6A peaks of the five circRNAs is shown in Fig. 1B, and they all harbored more than three m6A peaks. We also examined the expression of five candidate circRNAs in ARC tissue using RT-qPCR. Compared with the NC group, only the expression of has_circ_0007905 ($$P \leq 0.00114$$) was upregulated in the ARC group, whereas the remaining four circRNA expressions were opposite (Fig. 1C). Next, we determined the expression pattern and m6A level of has_circ_0007905 in ARC cell model induced by UVB exposure. As expected, a significant increase of has_circ_0007905 expression in HLE-B3 cells after UVB exposure was observed (Fig. 1D; $$P \leq 0.00292$$). The total m6A level of has_circ_0007905 was also significantly elevated in the UVB treatment group compared with the control group (Fig. 1E; $$P \leq 0.00543$$). According to the annotation in UCSC Genome Browser on Human (GRCh37/hg19) (https://genome.mdc-berlin.de/index.html), has_circ_0007905 was formed by the back-splicing of exons 1 and 4 (Fig. 1F). The back-splicing site of has_circ_0007905 was confirmed with Sanger sequencing (Fig. 1G). Collectively, we obtained the dataset of hypermethylated-upregulated expression circRNAs in ARC tissue and focused on has_circ_0007905. Figure 1: Has_circ_0007905 promotes apoptosis and inhibits the proliferation of lens epithelial cells.(A) Four-quadrant volcano plot. The abscissa represents the differentially expressed circRNAs, and the ordinate represents the differentially methylated circRNAs. (B) The distribution of m6A peaks of five circRNAs. m6A enrichment was shown by immunoprecipitation (IP) in red, and the input control was in blue. (C) The expression of five candidate circRNAs in clinical ARC tissue (n = 10) and transparent lenses (n = 3) was measured using RT-qPCR. (D) The expression of has_circ_0007905 in HLE-B3 cells after UVB exposure was measured using RT-qPCR. (E) The m6A level of has_circ_0007905 in HLE-B3 cells after UVB exposure was measured using MeRIP-qPCR. (G) Sanger sequencing was performed to verify the back-splicing site of has_circ_0007905. (F) Diagram illustrating that has_circ_0007905 is generated from back-splicing between exons 1 and 4 of chromosome 1. ns indicates P > 0.05, an asterisk () indicates P < 0.05, two asterisks () indicate P < 0.01, three asterisks () indicate P < 0.001. ## Silencing of has_circ_0007905 promotes proliferation and inhibits apoptosis of lens epithelial cells To further investigate the function of has_circ_0007905 in ARC progression in vitro, we interfered with has_circ_0007905 expression in the HLE-B3 cell line using two siRNA sequences. We confirmed that has_circ_0007905 expression was significantly reduced upon the presence of siRNA, especially siRNA-1 (si-has_circ_0007905-1; $P \leq 0.0001$) (Fig. 2A). Meanwhile, CCK-8 assays indicated that silencing of has_circ_0007905 significantly boosted the proliferation of HLE-B3 cells (Fig. 2B; $P \leq 0.0001$). TUNEL staining demonstrated that apoptosis of HLE-B3 cells was significantly decreased after silencing of has_circ_0007905 compared with the NC group (Fig. 2C). These results highlight that has_circ_0007905 promotes the progression of ARC in vitro. Figure 2: METTL3 mediates the m6A modification of has_circ_0007905.(A) The silencing efficacy of has_circ_0007905 was confirmed by RT-qPCR. (B) The proliferation of HLE-B3 cells after silencing of has_circ_0007905 was measured by CCK-8 assays. (C) The apoptosis of HLE-B3 cells after silencing of has_circ_0007905 was measured by TUNEL staining. (D) The expression of five candidate m6A modification enzymes in clinical ARC tissue (n = 10) and transparent lenses (n = 3) were measured using RT-qPCR. (E) Four RNA-protein binding sites between has_circ_0007905 and METTL3 were predicted using the RBPsuite database. (F) Seven potential m6A modification sites of has_circ_0007905 were predicted by SRAMP. The mRNA (G) and protein (H) expression of METTL3 in HLE-B3 cells after UVB exposure was measured using RT-qPCR and western blot, respectively. ns indicates P > 0.05, an asterisk () indicates P < 0.05, two asterisks () indicate P < 0.01, three asterisks () indicates P < 0.001. ## METTL3 mediates m6A modification of has_circ_0007905 As we all know, m6A modification is driven by the m6A writer composed of METTL3, METTL14, and WTAP, which are removed by m6A erasers, such as FTO and ALKBH5 (Deng et al., 2018). To further examine which enzyme is engaged in m6A modification of has_circ_0007905, we detected the expression of METTL3, METTL14, WTAP, FTO, and ALKBH5 in ARC tissues. Intriguingly, only METTL3 expression was significantly upregulated in ARC tissues compared to the NC group (Fig. 2D; $P \leq 0.0001$). Four RNA-protein binding sites between has_circ_0007905 and METTL3 (Fig. 2E) were predicted by the RBPsuite database (http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/). Seven potential m6A modification sites in has_circ_0007905 were also expected using SRAMP (http://www.cuilab.cn/sramp) (Fig. 2F). Moreover, the mRNA ($$P \leq 0.00882$$) and protein ($$P \leq 0.00162$$) expressions of METTL3 were significantly elevated in HLE-B3 cells upon UVB exposure (Figs. 2G and 2H). Thus, we initially guessed that the m6A modification of has_circ_0007905 was mediated by METTL3. ## METTL3 inhibits proliferation and promotes apoptosis of lens epithelial cells via has_circ_0007905 Four siRNAs against METTL3 were synthesized to explore the functional role of METT L3 in ARC progression in vitro. It was shown in the results of RT-qPCR and western blot that the mRNA and protein expression of METTL3 was significantly decreased after transfection with siRNA-968. Thus, siRNA-968 was used for METTL3 knockdown experiments (Figs. 3A and 3B). Expectedly, METTL3 knockdown significantly diminished the expression (Fig. 3C; $$P \leq 0.012$$) and m6A modification level (Fig. 3D; $P \leq 0.0001$) of has_circ_0007905. In addition, METTL3 knockdown significantly facilitated proliferation (Fig. 3E; $$P \leq 0.0202$$) and inhibited apoptosis detected by flow cytometric assay (Fig. 3F; $$P \leq 0.010$$) (Fig. S1) and TUNEL (Fig. 3G) of HLE-B3 cells. These results suggested that knockdown of METTL3 attenuated ARC progression in vitro. Figure 3: METTL3 inhibits proliferation and promotes apoptosis via has_circ_0007905 in lens epithelial cells.(A–B) Knockdown efficacy of METTL3 was confirmed by RT-qPCR and western blot. (C) The expression of has_circ_0007905 in HLE-B3 cells after the knockdown of METTL3 was measured using RT-qPCR. (D) The m6A level of has_circ_0007905 in HLE-B3 cells after the knockdown of METTL3 was measured using MeRIP-qPCR. (E) The proliferation of HLE-B3 cells after silencing METTL3 was measured by CCK-8 assays. The apoptosis of HLE-B3 cells after silencing of METTL3 was measured by flow cytometry (F) and TUNEL staining (G). The effect of simultaneous METTL3 overexpression and silencing of has_circ_0007905 on the proliferation (H) and apoptosis (I–J) of HLE-B3 cells was measured using CCK-8 assays, flow cytometry, and TUNEL staining, respectively. An asterisk () indicates P < 0.05, three asterisks (**) indicate P < 0.001. We conducted rescue experiments to further clarify whether the function of METTL3 in ARC progression is has_circ_0007905-dependent. CCK-8 assay demonstrated that the proliferation of HLE-B3 cells was significantly impaired ($P \leq 0.0001$) by the overexpression of METTL3. However, the proliferation was significantly restored by the simultaneous METTL3 overexpression and silencing of has_circ_0007905 ($P \leq 0.0001$) (Fig. 3H). In contrast, overexpression of METTL3 significantly accelerated apoptosis of HLE-B3 cells. Still, this phenotypic effect was prevented by has_circ_0007905 silencing, which was detected using flow cytometric assay (Fig. 3I; NC vs. OE-METTL3, $P \leq 0.0001$, OE-METTL3 vs. OE-METTL3+ si-circ_0007905, $$P \leq 0.0071$$) (Fig. S2) and TUNEL (Fig. 3J). Therefore, we concluded that METTL3 promotes ARC progression in vitro via has_circ_0007905 in an m6A-dependent manner. ## Silencing of has_circ_0007905 leads to an alteration in transcriptome landscape in lens epithelial cells One of the mechanisms of circRNA is to regulate the expression of target genes through the ceRNA mechanism. Thus, we mined the downstream target genes of has_circ_0007905 using transcriptome sequencing. We found that has_circ_0007905 interference resulted in 707 mRNAs downregulation and 2184 mRNAs upregulation compared with the NC group (Fig. 4A). These DEGs were clearly clustered into two branches (Fig. 4B). To investigate the physiological significance of DEGs in ARC progression, GO and KEGG pathway enrichment were performed for DEGs. GO enrichment revealed that these DEGs were mainly associated with the immune-related process, interestingly, 13 of the 20 high-ranking significantly enriched GO entries were related to immune processes, such as the immune system process, inflammatory response, immune response, and innate immune response (Fig. 4C). KEGG pathway enrichment showed that these DEGs were mainly involved in immune-related pathways, including cytokine-cytokine receptor interaction, complement and coagulation cascades, and proliferation-related pathways, such as the PI3K-Akt and NF-kappa B signaling pathways (Fig. 4D). These results implicated that has_circ_0007905 interference may be involved in the progression of ARC by affecting the immune response processes and proliferation-related pathways of HLE-B3 cells through DEGs. Figure 4: Silencing of has_circ_0007905 leads to an altered transcriptome landscape in lens epithelial cells.Volcano plot (A) and Heat map (B) of the differentially expressed genes (DEGs) between the NC group and the has_circ_0007905 silencing group. (C) GO enrichment analysis based on DEGs. (D) KEGG pathway enrichment analysis based on DEGs. (E) The expression of six DEGs in RNA-Seq was verified by RT-qPCR. To verify the reliability of the sequencing results, five down-regulated DEGs in the has_circ_0007905 interference group with high abundance and significant P-values were selected for RT-qPCR. Compared with the NC group, the expression of these five DEGs was significantly reduced in the has_circ_0007905 interference group (Fig. 4E). Given that ARC manifests as cellular senescence and loss and diminished proliferation, we focused on a target gene associated with proliferation and apoptosis (Yang et al., 2020). EIF4EBP1 has been reported to be associated with proliferation and apoptosis (Zhang & Su, 2022), so we focused on EIF4EBP1. ## Has_circ_0007905 inhibits proliferation and promotes apoptosis via EIF4EBP1 in lens epithelial cells To explore the function of EIF4EBP1 in ARC progression in vitro, we constructed plasmids that overexpressed EIF4EBP1 and transfected them into HLE-B3 cells. A significant increase in EIF4EBP1 expression at mRNA and protein levels was confirmed by RT-qPCR (Fig. 5A; $P \leq 0.0001$) and western blot (Fig. 5B; $$P \leq 0.00330$$) after overexpression of EIF4EBP1. Moreover, we observed that proliferation was significantly prevented (Fig. 5C; $P \leq 0.0001$) and that apoptosis was enhanced (Fig. 5D) in HLE-B3 cells after EIF4EBP1 overexpression, indicating that EIF4EBP1 contributes to ARC progression in vitro. EIF4EBP1 expression was significantly reduced by has_circ_0007905 interference ($$P \leq 0.0007$$) and this effect could be reversed with EIF4EBP1 overexpression ($$P \leq 0.0344$$) (Fig. 5E). The CCK8 results showed that the excessive proliferation caused by has_circ_0007905 interference was significantly attenuated by EIF4EBP1 overexpression (Fig. 5F; $P \leq 0.0001$). TUNEL staining proved that has_circ_0007905 interference resulted in the attenuation of apoptosis, which was significantly rescued by EIF4EBP1 overexpression (Fig. 5G). Taken together, these results supported that has_circ_0007905 inhibits proliferation and promotes apoptosis via EIF4EBP1 in HLE-B3 cells. Figure 5: Has_circ_0007905 inhibits proliferation and promotes apoptosis via EIF4EBP1 in lens epithelial cells.The mRNA (A) and protein (B) expression of EIF4EBP1 in HLE-B3 cells after overexpression of EIF4EBP1. The proliferation (C) and apoptosis (D) of HLE-B3 cells after overexpression of EIF4EBP1 were measured using CCK-8 assays and TUNEL staining, respectively. The effects of simultaneous METTL3 overexpression and silencing of has_circ_0007905 on EIF4EBP1 protein expression (E), proliferation (F), and apoptosis (G) of HLE-B3 cells were measured using western blot, CCK-8 assays, and TUNEL staining, respectively. An asterisk () indicates P < 0.05, three asterisks (**) indicate P < 0.001. ## Has_circ_0007905 inhibits proliferation and promotes apoptosis via miR-6749-3 p/EIF4EBP1 in lens epithelial cells To confirm the mode of interaction between has_circ_0007905 and EIF4EBP1, RIP-PCR and AGO2-dependent AGO2-RIP were performed, respectively. The results showed that the enrichment of EIF4EBP1 in the absence of an AGO2 antibody was not affected by the knockdown of has_circ_0007905 (Fig. 6A; $$P \leq 0.942$$). However, the enrichment of EIF4EBP1 was significantly reduced by has_circ_0007905 interference in the presence of an AGO2 antibody (Fig. 6A; $$P \leq 0.027$$). These results suggested that EIF4EBP1 is regulated by has_circ_0007905 through an AGO2-dependent ceRNA mechanism. Figure 6: Has_circ_0007905 inhibits proliferation and promotes apoptosis via miR-6749-3p/EIF4EBP1 in lens epithelial cells.(A) RIP-qPCR to detect the binding between has_circ_0007905 and EIF4EBP1. (B) AGO2-RIP-qCPR detects whether the binding of has_circ_0007905 and EIF4EBP1 is AGO protein dependent. (C) Venn diagram of the predicted miRNA of has_circ_0007905 and EIF4EBP1. (D) Five miRNAs were selected for RT-qPCR validation. (E) WT binding sites and MUT binding sites of miR-6749-3p and has_circ_0007905/EIF4EBP1. The red font indicates complementary paired bases, and the blue font indicates mutation sites. (F) The binding relationship between has_circ_0007905 and miR-6749-3p was verified using a dual-luciferase reporter assay. (G) The binding relationship between EIF4EBP1 and miR-6749-3p was verified using dual-luciferase reporter assay. The effects of simultaneous has_circ_0007905 silencing and miR-6749-3p inhibitor on EIF4EBP1 protein expression (H), proliferation (I), and apoptosis (J) of HLE-B3 cells were measured using western blot, CCK-8 assays, and TUNEL staining, respectively. ns indicates P > 0.05, an asterisk () indicates P < 0.05, three asterisks (**) P < 0.001. Given that has_circ_0007905 regulates the expression of EIF4EBP1 through the ceRNA mechanism, we next explored the miRNA that acts as a communication bridge between has_circ_0007905 and EIF4EBP1. According to the RNAhybrid and Miranda databases, has_circ_0007905 and EIF4EBP1 potentially bind 259 and 513 miRNAs, respectively. Among these, 103 miRNAs were shared by has_circ_0007905 and EIF4EBP1 (Fig. 6A). Five miRNAs with the highest binding energy were selected for RT-qPCR validation. It was found that only the expression of miR-6749-3p was significantly upregulated after the knockdown of has_circ_0007905 (Fig. 6B; $$P \leq 0.0272$$). Furthermore, the WT binding sites and MUT binding sites of miR-6749-3p and has_circ_0007905/EIF4EBP1 displayed in Fig. 6C. As shown in Fig. 6D, miR-6749-3p mimic significantly decreased the luciferase activities of has_circ_0007905 in the cells carrying the WT plasmid rather than the MUT plasmid ($P \leq 0.0001$), also reduced the luciferase activities of EIF4EBP1-WT ($$P \leq 0.000561$$) (Fig. 6E). The decrease in EIF4EBP1 expression induced by has_circ_0007905 silencing ($$P \leq 0.0007$$) was significantly reversed by the miR-6749-3p inhibitor ($$P \leq 0.0103$$) (Fig. 6F). Importantly, has_circ_0007905 silencing conferred that the elevation in the proliferation of HLE-B3 cells was significantly mitigated by the miR-6749-3p inhibitor (Fig. 6G). The effects of has_circ_0007905 silencing on apoptosis were also significantly blocked by the miR-6749-3p inhibitor (Fig. 6H). In summary, these results demonstrated that has_circ_0007905 functions as a sponge for miR-6749-3p to regulate EIF4EBP1 expression to involve the proliferation and apoptosis of HLE-B3 cells. ## Discussion Emerging studies have proved that the vital role of epigenetic modifications (Li et al., 2020; Wang et al., 2015; Wang et al., 2017) and circRNA (Liang et al., 2020; Liu et al., 2021; Liu et al., 2022b) in the pathogenesis of ARC. However, the role of m6A modification on circRNA during ARC has not been revealed yet. To the best of our knowledge, our study is the first to investigate the function and mechanism of m6A modification in circRNA in ARC. We uncovered that METTL3-mediated m6A modification of has_circ_0007905 promotes apoptosis and inhibits the proliferation of HLE-B3 cells through miR-6749-3p/EIF4EBP1, leading to ARC progression. METTL3 is an m6A methylase catalytic center (Wang, Doxtader & Nam, 2016). METTL3-mediated m6A modification of RNAs has a non-negligible role in eye-related diseases, such as proliferative vitreoretinopathy (Wang, Doxtader & Nam, 2016) and diabetes-induced pericyte dysfunction (Suo et al., 2022). Currently, limited by the short study period in which circRNA catalyzed by METTL3 for m6A modification can mediate ARC progression has not yet been reported. In 2020, Yang et al. revealed that METTL3 contributes to diabetic cataract progression by stabilizing ICAM-1 mRNA stability to promote the proliferation and apoptosis of HLE cells (Yang et al., 2020). This study affirms the important role of METTL3 in diabetic cataract but not in those of circRNA or ARC. The report by Li et al. was similar to our results. In that study, m6A-tagged circRNAs in ARC were characterized using MeRIP-seq, regrettably, they did not reveal any specific functions or mechanisms of m6A modification on circRNAs (Li et al., 2020). Our findings addressed deficiencies in those two studies. We demonstrated that m6A modified has_circ_0007905 is mediated by METTL3, which promoting apoptosis and inhibiting proliferation in ARC. In the present study, has_circ_0007905 with hypermethylation and upregulation in ARC tissues relative to control tissue was identified. Has_circ_0007905 was first reported by Huang et al., compared with paired para-cancerous cervical tissues, has_circ_0007905 was upregulated in cervical cancer, suggesting a cancer-promoting potential (Huang, Chen & Huang, 2021). Has_circ_0007905 was produced from the STX6 mRNA located on chr1 (q25, 3) thereby it was termed as circSTX6 by Meng et al. Pancreatic ductal adenocarcinoma progression in vitro and in vivo in a ceRNA manner through sponging miR-449b-5p was also provoked by has_circ_0007905 (Meng et al., 2022). There is no more research on has_circ_0007905. Still, our conclusion is consistent with those two studies, in which it was shown that has_circ_0007905 has pathogenic potentials. Conclusively, these data indicated that has_circ_0007905 was involved in ARC by promoting apoptosis and inhibiting proliferation of HLE-B3 cells. Among the mechanisms of circRNA function, ceRNA theory is commonly considered (Qi et al., 2015), this was also the case for has_circ_0007905 in this study. We found that has_circ_0007905 could directly bind miR-6749-3p to release EIF4EBP1 mRNA, and either overexpression of EIF4EBP1 or inhibition of miR-6749-3p could alleviate the effects of has_circ_0007905 silencing on the proliferation and apoptosis of HLE-B3 cells. Except for the fact that miR-6749-3p from serum can be used as a diagnostic biomarker of intervertebral disc degeneration together with miR-766-3p and miR-4632-5p (Cui et al., 2020b), there is no other study on miR-6749-3p. This unique report implicates the engagement of miR-6749-3p in disease progression, and our study enriches our knowledge related to miR-6749-3p. However, more research is needed to support our conclusions. Moreover, EIF4EBP1, the target gene of has_circ_0007905/miR-6749-3p, has been reported to be extensively involved in cell proliferation and apoptosis, such as in acute myeloid leukemia cells (Jiang et al., 2021), ovarian cancer cells (Lee, Kim & Jeon, 2016), and extravillous trophoblast cells (Dong et al., 2022). Nonetheless, the role of EIF4EBP1 in the proliferation and apoptosis of ARC cells has not been reported. Thus, this is the first report on the role of EIF4EBP1 in ARC. This is the first report indicating that the has_circ_0007905/miR-6749-3p/EIF4EBP1 axis is involved in ARC cell proliferation and apoptosis, which will benefit our understanding of ARC pathogenesis. Some interesting “contradictory results” should be highlighted. Generally, the up-regulated gene is a promoting factor of disease, which should promote cell proliferation. However, in our study, silencing of up-regulated has_circ_0007905 promotes proliferation and inhibits apoptosis of HLE-B3 cells. In fact, this result is not contradictory, because the pathological logic of “Generally” is usually applied to tumors, but does not apply in many diseases, especially in cataracts. In ARC, human lens epithelial cells apoptosis and proliferation inhibition is an initiating element in cataract development. Thus, in many cataract studies, knockdown of upregulated pathogenic genes promotes proliferative and inhibits apoptosis, and these studies support our conclusion. For example, lncRNA TUG1 expression was significantly higher in the anterior lens capsules of ARC than that in the normal anterior lens capsules, and si-TUG1 significantly inhibited apoptosis of human lens epithelial cell line SRA$\frac{01}{04}$ (Li et al., 2017); similarly, lncRNA TUG1 expression was significantly upregulated in SRA$\frac{01}{04}$ cells following oxidative stress, and si-TUG1 significantly increased cell viability and reduced levels of apoptosis (Shen & Zhou, 2021). Moreover, circHIPK3 was down-regulated in human lens epithelium samples of ARC patients, overexpression of circHIPK3 mediated the promotion of proliferation and inhibition of apoptosis (Cui, Wang & Huang, 2020a), while silencing of circHIPK3 significantly decreased cell viability and proliferation, and accelerated apoptosis upon oxidative stress in primary cultured human lens epithelial cells (Liu et al., 2018). Similar results were observed in circZNF292 (Xu et al., 2021), circ_0060,144 (Liu et al., 2022b), and circMRE11A_013 (Liu et al., 2021). In short, the result of up-regulated has_circ_0007905 promotes proliferation and inhibits apoptosis of HLE-B3 cells is not a contradictory result. ## Conclusion In summary, the expression of has_circ_0007905 in HLE-B3 cells was promoted by METTL3-mediated m6A modification of has_circ_0007905. The upregulation of has_circ_0007905 promotes the expression of EIF4EBP1 by sponging miR-6749-3p, ultimately promoting apoptosis and inhibiting the proliferation of HLE-B3 cells and leading to ARC progression. The present study breaks down the existing knowledge boundaries and contributes to the development of circRNA-based ARC-targeting drugs. ## References 1. Chen D, Cheung H, Lau HC, Yu J, Wong CC. N(6)-Methyladenosine RNA-Binding Protein YTHDF1 in gastrointestinal cancers: function, molecular mechanism and clinical implication. Cancers (2022) 14 3489. 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--- title: TLR4 signaling modulates extracellular matrix production in the lamina cribrosa authors: - Emma K. Geiduschek - Paige D. Milne - Philip Mzyk - Timur A. Mavlyutov - Colleen M. McDowell journal: Frontiers in Ophthalmology year: 2022 pmcid: PMC9997209 doi: 10.3389/fopht.2022.968381 license: CC BY 4.0 --- # TLR4 signaling modulates extracellular matrix production in the lamina cribrosa ## Abstract The optic nerve head (ONH) is a place of vulnerability during glaucoma progression due to increased intraocular pressure damaging the retinal ganglion cell axons. The molecular signaling pathways involved in generating glaucomatous ONH damage has not been fully elucidated. There is a great deal of evidence that pro-fibrotic TGFβ2 signaling is involved in modulating the ECM environment within the lamina cribrosa (LC) region of the ONH. Here we investigated the role of signaling crosstalk between the TGFβ2 pathway and the toll-like receptor 4 (TLR4) pathway within the LC. ECM deposition was examined between healthy and glaucomatous human ONH sections, finding increases in fibronectin and fibronectin extra domain A (FN-EDA) an isoform of fibronectin known to be a damage associated molecular pattern (DAMP) that can activate TLR4 signaling. In human LC cell cultures derived from healthy donor eyes, inhibition of TLR4 signaling blocked TGFβ2 induced FN and FN-EDA expression. Activation of TLR4 by cellular FN (cFN) containing the EDA isoform increased both total FN production and Collagen-1 production and this effect was dependent on TLR4 signaling. These studies identify TGFβ2-TLR4 signaling crosstalk in LC cells of the ONH as a novel pathway regulating ECM and DAMP production. ## Introduction Cupping of the optic nerve head (ONH), thinning and loss of the retinal nerve fiber layer, and characteristic visual field defects are all clinical features of glaucoma [1]. Elevated intraocular pressure (IOP) is the most important risk factor for both the development and progression of glaucoma [2]. Current glaucoma therapy involves decreasing IOP by suppression of aqueous humor formation, enhancing uveoscleral outflow or, most recently, directly targeting the trabecular meshwork. However, these therapies are not uniformly effective, and this therapy generally only slows the progression of vision loss over time (3–5). This highlights the crucial need for more effecting glaucoma treatments and an increased understanding of the molecular and cellular mechanisms of disease progression. The ONH contains retinal ganglion cell (RGC) axon bundles and support tissues and cells. The lamina cribrosa (LC) is the main structural component of the ONH. The LC is a mesh-like connective tissue structure through which the RGC axons pass as they exit the eye to form the myelinated extraocular optic nerve. The LC is composed of a series of interconnected lamellar beams made up of elastin, collagens, laminin, and heparin sulfate proteoglycan. The LC provides support for the RGC axons and resident cells. The LC region is populated by three major cell types, glial fibrillary acidic protein (GFAP)-positive ONH astrocytes, microglia and α-smooth muscle actin (α-SMA) positive LC cells. The resident LC cells are located within the LC beams, and the ONH astrocytes are located both in longitudinal columns along RGC axon bundles and in transverse orientation investing multiple LC beams [6]. Resident microglia are regularly spaced throughout the normal ONH in the walls of blood vessels, within the glial columns, and in the LC [7]. The ONH region progressively remodels during glaucoma, leading to ONH cupping as well as mechanical failure and fibrosis of the LC; however, the cellular and molecular mechanisms responsible for this remodeling are not fully understood. The ONH remains the most vulnerable point for the RGC axons, where they are most susceptible to elevated IOP, as the RGC axons have to turn 90° to enter the ONH and traverse the LC [8]. This region is particularly susceptible to pressure because at the ONH, the sclera thins to form the ECM structure that allows RGC axons to exit the eye. Dysregulation of ECM in the LC causes increased fibrosis, elastosis, thickening of the connective tissue septae surrounding the ON fibers, and a thickening of the basement membranes involving altered collagen fibers and disorganized distribution and deposition of elastin, causing mechanical failure which in turn exacerbates ONH and RGC axon damage [6, 9, 10]. Deposition of ECM causes the LC to initially undergo thickening and posterior migration. Eventual shearing and collapse of the LC plates leads to a thin fibrotic connective tissue structure/scar. ECM remodeling adversely affects the capacity of the LC to support RGC axons and predisposes RGCs to axonal compression, disruption of axoplasmic flow, and apoptosis. Both the LC cells and ONH astrocytes are responsible for supporting the RGC axons by synthesizing growth factors and ECM (6, 11–13). Several cytokines are known to regulate the production and modulation of ECM, including Toll like receptor 4 (TLR4) signaling as previously described in other fibrotic diseases (14–16). TLR4 was first discovered as the receptor for lipopolysaccharide (LPS) [17], but can also be activated by endogenous ligands, known as DAMPs, damage associated molecular patterns. DAMPs are generated in situ as a result of injury, cell damage, ECM remodeling, and oxidative stress [18, 19]. TLR4 is known to be expressed in astrocytes, microglia, and LC cells in the human ONH (20–22). TLR4 pathway related genes, downstream ECM genes, and DAMPs such as tenascin-C and heat shock proteins have been identified as differentially expressed in the human ONH and retina in glaucoma (23–25). Importantly, TLR4 gene polymorphisms have been associated with enhanced glaucoma risk in some populations (26–28). The role of TLR4 signaling in modifying the ECM and fibrotic environment has been studied in hepatic and renal fibrosis, scleroderma, as well as in Tlr4 mutant mice (14–16). Recently, we identified TLR4 signaling as an important regulator of the ECM in the TM and ocular hypertension [29]. In addition, DAMPs (including tenascin C, FN-EDA, heat shock proteins, and hyaluronan) have been shown to activate TLR4 and augment TGFβ signaling and downstream fibrotic responses [14, 30, 31], and DAMPs have been identified in the glaucomatous ONH of both mice and humans [32]. Importantly, we previously identified the DAMP FN-EDA increases IOP in mice through the TLR4 signaling pathway [33]. In addition, numerous studies have identified elevated aqueous humor levels of TGFβ2 in glaucoma patients (34–37). We and others have shown that TGFβ2 treatment of trabecular meshwork (TM) cells alters the ECM composition (29, 38–40) and induces ECM cross-linking (41–43). In the posterior segment, TGFβ2 is also the predominant TGFβ isoform in the ONH and astrocytes, LC cells, and activated microglia are known to express and secrete TGFβ2, with increased expression of TGFβ2 documented in the glaucomatous ONH (25, 44–47). TGFβ2 treatment of ONH astrocytes and LC cells in vitro increases ECM protein synthesis and secretion via canonical Smad signaling [44, 45]. This dysregulation of ECM components could contribute to the ONH fibrotic environment in the LC in glaucoma. Here we demonstrate crosstalk between the TGFβ2 and TLR4 signaling pathways in primary ONH LC cells and show the DAMP, FN-EDA, is increased in the human glaucomatous LC suggesting this DAMP may have important implications in TLR4 activation and signaling in the glaucomatous ONH. ## Human donor eyes Human donor eyes were obtained from the Lions Eye Bank of Wisconsin (Madison, WI) within 24 h of death. The eyes were obtained and managed in compliance with the Declaration of Helsinki. The human eyes used for IHC experiments ranged from 59 to 80 years old. Within 24 hours post time of death, human eyes are fixed in $4\%$ paraformaldehyde for 24 hours at 4°C, rinsed with 1X PBS, then cryoprotected in $30\%$ sucrose in PBS for another 48 hours at 4°C. The eye was embedded into optimum cutting temperature embedding medium (Sakura Finetek 4583, Sakura Finetek USA, Inc., Torrance, CA) in 25mm x 20mm x 5mm Tissue-Tek cryomolds (Sakura Finetek 4557) and frozen on a prechilled metal block. After which, cryosections at 10μm intervals were cut from the frozen eyecups before being stored at -80°C until immunostaining. Glaucoma diagnosis is based off patient medial history report and visual field defects. Primary human ONH LC cell strains were isolated from normal (nonglaucomatous) donor eyes (ages 59-74) from the Lions Eye Bank of Wisconsin or received as a kind gift from Dr. Abe Clark at the University of North Texas Health Science Center and characterized as previously described [11, 12, 48]. All donor tissues were obtained and managed according to the guidelines in the Declaration of Helsinki for research involving human tissue. Cells were cultured and maintained in Ham’s F-10 growth media containing $10\%$ FBS L-glutamine (0.292 mg/mL), and penicillin (100units/mL)/streptomycin (0.1mg/mL) in a humid chamber at 37°C in $5\%$ CO2. The medium what replaced every 2-3 days. ## Immunohistochemistry Standard procedures for IHC were utilized as previously described [33]. The OCT was removed via two washes in ddH2O for 2 minutes each before dried by subsequent washing in $70\%$ ethanol for 2 minutes, $100\%$ ethanol for 2 minutes, and then left to dry at RT for 10 minutes. Sections were then rinsed with 1X PBS for 5 minutes before incubated in $0.1\%$ Triton X (Sigma-Aldrich REFX100) at RT for 15 minutes to permeabilize cell membranes. Slides were then blocked in Superblock Blocking Buffer in PBS (REF37580, Thermo Fisher Scientific) for 60 minutes at RT before incubated at 4°C overnight with FN (Sigma-Aldrich Corp., F3648) at 1:250, and FN-EDA (Abcam, ab6328) at 1:100 dilution. Primary antibody was washed off with four rinses in 1X PBS for 5 minutes each before slides were incubated in the appropriate secondary antibody for 2 hours at RT; Alexa Fluor 488 donkey anti-rabbit IgG (A21206, Invitrogen – Thermo Fisher Scientific) at 1:200 dilution and Alexa Fluor 594 donkey anti-mouse IgG (A21203, Invitrogen – Thermo Fisher Scientific) at 1:200 dilution. Slides were washed 5 times in 1X PBS for 5 minutes each and mounted with Prolong Gold mounting medium containing DAPI (Invitrogen-Molecular Probes). Image acquisition was performed using Zeiss Axio Imager Z2 microscope. All images were taken at 40X magnification, scale bar represents 20μm. ## TLR4 inhibition and activation Primary human ONH LC cells were grown to confluency and pretreated with the selective TLR4 inhibitor, TAK-242 (In vivoGen, San Diego, CA, USA) at 15μM for 2 hours. TAK-242 is a cyclohexene derivative that specifically inhibits TLR4 signaling by binding to the intracellular domain of TLR4 and blocking downstream signaling. We previously performed a dose response curve using TAK-242 in primary human trabecular meshwork cells in culture and determined 15μM had the greatest inhibitory effect without affecting cell viability [29]. Cells were then treated with TGFβ2 (5ng/mL) and/or TAK-242 (15μM) for 72 hours in serum-free medium. For TLR4 activation studies, hONH LC cultures were grown to confluency and treated with cellular fibronectin (cFN) (10μg/mL) containing the FN-EDA isoform (F2518; Sigma-Aldrich Corp., St. Louis, MO, USA) and/or TGFβ2 (5ng/mL), and/or TAK-242 (15μM) for 72 hours in serum-free medium. Western blot and immunocytochemistry experiments were performed as described below. ## Immunocytochemistry Primary human ONH LC cells were seeded on 12-well plates on coverslips and grown to confluency. After undergoing TLR4 inhibition and activation treatments as previously described for 72 hours, cells were washed with 1X PBS, fixed with $4\%$ paraformaldehyde (PFA), permeabilized with $0.95\%$ Triton X-100 in PBS, and blocked using Superblock Blocking Buffer in PBS (REF37580, Thermo Fisher Scientific) for 60 minutes at room temperature. Cells were labeled overnight at 4°C with rabbit anti-Fibronectin (F3648, Sigma-Aldrich) at a 1:100 dilution, or rabbit anti-collagen-1 (NB600-408, Novus Biologicals) at a 1:100 dilution in Superblock Blocking Buffer in PBS. Treatment without the primary antibody was used as a negative control. Coverslips were then incubated for at room temperature 2 hours using Alexa Fluor 488 donkey anti-rabbit IgG (REFA21206, Invitrogen – Thermo Fisher Scientific) at a 1:200 dilution. Coverslips were mounted to slides with Prolong Gold mounting medium containing DAPI (Invitrogen-Molecular Probes). Image acquisition was performed using Zeiss Axio Imager Z2 microscope. Images were taken at X20 or X40 magnification; scale bar represents 20μm. ## Western blot analysis Primary human ONH LC cells were treated as previously described above for 48 or 72 hours. Cell lysates were extracted using lysis buffer (Pierce RIPA Buffer, REF89901, Thermo Fisher Scientific; cOmplete Mini, EDTA-Free protease inhibitor cocktail, REF11836170001, Sigma-Aldrich; PhosSTOP REF04906837001 Sigma-Aldrich), and the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific REF23225) was used to estimate protein concentrations of each sample. Each loading sample contained 10μg of protein and the appropriate amount of 4X Protein Loading Buffer (Li-Core REF928-40004). Samples were boiled for 10 minutes, then separated using a 4-$12\%$ Bolt™ Bis-Tris Plus mini gel (Invitrogen, NW04120BOX). Proteins from electrophoresed gels were transferred to polyvinylidene (PVDF) membranes (Bio-Rad Immun-Blot, REF1620177) for an hour at a constant 20V using the Mini Gel Tank wet transfer (Invitrogen, REFA25977). Membranes were left to dry for 45 minutes before using Revert™ 700 Total Protein Stain (Li-Core REF926-11021) to confirm equal loading for samples. After destaining of total protein stain, membranes were blocked for one hour at room temperature with Intercept Blocking Buffer (Li-Cor, REF927-60001). Membranes were immunolabeled overnight at 4°C with primary antibodies: GAPDH (1:5000, Cell Signal REF97166S), Fibronectin (1:1000, Sigma -Aldrich F3648), and/or FN-EDA (1:500, Abcam, ab6328), diluted in Intercept Blocking Buffer. Blots were washed three times for 5 minutes each with 1X TBS-T and then incubated for 1 hour with the appropriate secondary antibodies at a 1:20,000 dilution in Intercept Blocking Buffer (Li-Core Goat anti-rabbit IRDye 800CW; Li-Core Goat anti-rabbit IRDye 680RD; Li-Core Goat anti-mouse IRDye 800CW). Membranes were then imaged on a Licor OdysseyCLx system. Each experiment was repeated 2-3 times in each individual hONH LC strain, and a total of 2-3 independent hONH strains were tested. Band intensity for proteins of interest and total protein were measured using Image Studio Lite (LI-COR Biosciences, Lincoln, NE, USA). Each target protein densitometry value was normalized against either its corresponding GAPDH or total protein value as indicated, and the fold change was calculated to control. Fold changes are represented as the mean +/- SEM. Statistical significance determined by a 1-way ANOVA and subsequent Tukey’s post hoc analysis comparing all treatments. ## FN and the DAMP FN-EDA are increased in the LC region of the glaucomatous human ONH Increased fibrosis of the human ONH during glaucoma disease progression has been well established. The lamina cribrosa forms sieve-like layers of ECM which allow RGC axons to exit. The ECM is primarily made of collagens, elastin, and laminin [49], all of which have been shown to be increased in the glaucomatous ONH [50, 51]. In addition, it is known that fibronectin is present in the LC region [52]. In the present study, the human ONH (Figure 1A) including the prelaminar (Figure 1B), lamina cribrosa (Figure 1C), and retrolaminar (Figure 1D) regions were analyzed for changes in FN and FN-EDA expression. Composite images (Figures 1E, H, K, N, Q, T) show the overlay of FN and FN-EDA in the LC region. Here, we show an increase of fibronectin protein expression in the LC region of human glaucomatous donor eyes (Figures 1O, R, U) compared to normal non-glaucoma control donor eyes (Figures 1F, I, L). We also demonstrate an increase in the FN-EDA isoform, a known DAMP and activator of TLR4, in the LC region of human glaucomatous donor eyes (Figures 1P, S, V) compared to normal non-glaucoma control donor eyes (Figures 1G, J, M). These data suggest that FN and the FN-EDA isoform may have important implications in the development of glaucomatous ONH damage. Figure 1: FN and FN-EDA expression in the LC region of normal and glaucomatous human ONH. (A) A cross-section of a hONH with (B–D) inserts of representative imaging locations of the (B) prelaminar, (C) LC, and (D) retrolaminar sections of the hONH. Immunohistochemistry images of the LC region of (E–M) healthy or (N–V) glaucomatous hONHs from donor eyes. Images show an increase in FN (O, R, U) and FN-EDA (P, S, V) expression in the LC region of glaucomatous eyes compared to healthy individuals (F, I, L, G, J, M respectively). Scale bar represents 20μm unless otherwise stated. [(A) 5X magnification, (B–U) 40X magnification. FN=green; FN-EDA=red; DAPI=blue.]. ## Dissection and isolation of the human ONH generates monocultures of LC cells In order to test the function and role of DAMPs such as FN-EDA in LC cells, primary LC cells were isolated and cultured from human donor eyes. Following previously established protocols [48], the ONH was dissected and the ONH explant placed into culture to propagate LC cells (Figures 2A–C). The isolation and characterization of the monocultures of LC cells was performed as previously described by Lopez et al. [ 48]. Here representative images (Figures 2E–G) and western blots (Figure 2D) show isolated LC cells are negative for GFAP and positive for αSMA, previously determined indicators of LC cells [48]. In total we characterized 4 independent LC cell strains from different donors with no history of ocular disease. Figure 2: Isolation and characterization of LC cells from hONH explants. (A–C) Progressive removal of the RPE, peripapillary sclera, and ON from the ONH in initial isolation. The ONH explant was then cultured to isolate the LC section as previously described (48). (D) Representative western immunoblot from two different cell strains for GFAP and αSMA. ONH LC cells were positive for αSMA and negative for GFAP. (E–G) Immunocytochemistry staining of hONH LC cells were positive for αSMA (F) and negative for GFAP (G). 40X magnification, Scale bar represents 100μm. ## Inhibition of TLR4 signaling blocks TGFβ2-induced increases of ECM production in primary human ONH LC cells It is well established that TGFβ2 signaling increases in glaucoma and is known to affect the ONH during disease progression. Here, we show that inhibition of TLR4 by the selective inhibitor, TAK-242, blocks TGFβ2 dependent increases of total FN and the DAMP FN-EDA protein expression. Primary hONH LC cells were treated with TGFβ2 (5ng/mL) and/or TAK-242 (15μM) for 72 hours. As previously reported, TGFβ2 induces FN expression in LC cells [44, 45]. However, TLR4 signaling inhibition significantly decreases FN (Figure 3A) and FN-EDA (Figure 3B) protein expression compared to GAPDH control (Figures 3C, D). Both FN and FN-EDA protein levels returned to control levels, with no significant differences between control and TAK-242 + TGFβ2 treated cells ($$n = 3$$ primary hONH LC cell strains, each repeated in 2-3 independent experiments). As expected, there was little FN-EDA in the control treated cells as the presence of FN-EDA is typically minimal in normal healthy cells [53]. This data suggests that TLR4 signaling is necessary for TGFβ2 induced fibrosis in LC cells. Figure 3: Inhibition of TLR4 blocks TGFβ2-induced ECM protein expression. (A, B) Primary hONH LC cells (n = 3 strains, each repeated in 2-3 independent experiments) were pretreated with TAK-242 for 2 hours, and subsequently treated with TGFβ2 (5 ng/mL) and/or TAK-242 (15μM) for 72 hours. Western immunoblot for (A) FN and (B) FN-EDA show that inhibition of TLR4 signaling via TAK-242 blocks the fibrotic effects of TGFβ2. Protein expression is normalized to GAPDH signal. (C) Representative immunoblots of FN and respective GAPDH protein expression, and (D) FN-EDA and respective GAPDH protein expression. Statistical significance was determined by 1-way ANOVA and Tukey’s post hoc analysis. P < 0.05, *P < 0.01. Cellular FN-EDA is an isoform of FN and has previously been shown to be a ligand for TLR4 [19, 54]. Here we show that TLR4 inhibition prevents cFN induced ECM protein expression in primary ONH LC cells (Figure 4). Human ONH LC cells were grown to confluency on coverslips and treated with TGFβ2 (5ng/mL), TAK-242 (15μM), and/or cFN (10μg/mL). As expected, TGFβ2 increased FN (Figure 4B) and COL1 (Figure 4L) protein expression compared to control. This increase was dependent on TLR4 signaling, as the addition of the selective TLR4 inhibitor TAK-242 prevented TGFβ2-induced increases of both FN (Figure 4D) and COL1 (Figure 4N). The selective TLR4 inhibitor TAK-242 also prevented the cFN-induced increases of both FN and COL1 (Figures 4H, I, Q, R) compared to cFN treatment alone (Figures 4F, O). These expression changes were statistically significant (Figures 4E, J, S, T). Each experiment was repeated in 4 independent hONH LC cell strains. This data suggests a TGFβ2 – TLR4 signaling crosstalk in the ONH. Figure 4: TLR4 signaling is necessary and sufficient for ECM production in hONH LC cells. Primary hONH LC cells (n = 4 cell strains) were grown to confluency on coverslips and left untreated for control (A, K) or treated with TGFβ2 (B, D, G, I, L, N, P, R) and/or cFN (F–I, O–R). Additionally, cells were pretreated with the selective TLR4 inhibitor TAK-242 (C, D, H, I, M, N, Q, R) for 2 hours, followed by treatment with TGFβ2 and/or cFN for 72 hours. Quantification of the immunocytochemistry normalized to untreated control (E, S) and cFN treatment (J, T). Scale bar represents 50μm, P < 0.05, P < 0.01, P < 0.001, **P < 0.0001). ## Discussion In primary open angle glaucoma (POAG) the extracellular matrix (ECM) of the LC is disturbed and remodeled resulting in mechanical failure and fibrosis. Cupping of the ONH and changes to the ECM of the LC are associated with disorganized and increased deposition of collagen and elastin fibers [4, 9, 10]. Early histological analysis of the glaucomatous ONH in humans and animal models demonstrated increases in collagen IV, elastin and tenascin [9, 25, 55, 56]. In advanced glaucoma, histological analysis revealed a collapse of the LC plates and the formation of a fibrotic network of connective tissue. Both the LC cells and ONH astrocytes are responsible for supporting the RGC axons by synthesizing growth factors and ECM. Dysregulation of the ECM production and remodeling leads to glaucomatous changes in the ONH and RGC axon damage. The pathogenic and molecular pathways responsible for the structural changes of the LC in POAG are not completely understood. However, it is well established that aqueous humor levels of TGFβ2 are elevated in POAG patients (34–37) and there is an increase in the levels of TGFβ2 in the ONH [25, 47]. ONH astrocytes, LC cells, and activated microglia express and secrete TGFβ2 (44–46). Treatment of ONH astrocytes and LC cells with exogenous TGFβ2 increases ECM protein synthesis and secretion as well as phosphorylation of canonical Smad$\frac{2}{3}$ signaling proteins in both cell types [44, 45]. Exogenous TGFβ2 also increases co-localization of pSmad$\frac{2}{3}$ with Co-Smad4 in the nucleus of ONH astrocytes and LC cells [44, 45]. Knockdown of connective tissue growth factor (CTGF), a downstream signaling factor of TGFβ2, blocks the induction of ECM proteins by TGFβ2 in ONH astrocytes [57]. The dysregulation of these ECM components could contribute to the fibrotic environment and basement membrane thickening in the LC of the ONH in glaucoma. In summary, these data suggest that TGFβ2 regulates the expression of ECM proteins in the ONH and the effects of TGFβ2 signaling are a major component in the development of glaucomatous ONH damage. Here we show that there is crosstalk between the TGFβ2 and TLR4 signaling pathways in ONH LC cells, and this signaling crosstalk may also extend to ONH astrocytes and microglia cells contributing to glaucomatous ONH damage. TLR4 signaling is known to affect not only immune responses, but also initiate fibrotic responses in several disease states. In addition, certain alleles of the TLR4 gene are associated with an increased risk of glaucoma in some populations (26–28). Interestingly, an increase in tenascin C, a large ECM glycoprotein and DAMP, has previously been reported to be increased in the glaucomatous ONH [25, 32]. Here, we show an additional DAMP, FN-EDA, to be elevated in the glaucomatous ONH and modulate TLR4-TGFβ2 signaling crosstalk in LC cells. TLR4-TGFβ2 signaling crosstalk is likely regulated by the TGFβ pseudoreceptor BMP and activin membrane-bound inhibitor (BAMBI). Bambi is known to be expressed in both human ONH astrocytes and LC cells [58], and TLR4 activation downregulates Bambi expression, which enhances TGFβ signaling leading to increased ECM production [14, 15]. BAMBI downregulation by TLR4 is regulated by a MyD88-NFκB-dependent pathway [15, 30, 31]. BAMBI functions to inhibit TGFβ signaling by cooperating with SMAD7 and impairing SMAD3 activation, while knockdown of Bambi expression enhances TGFβ signaling [59]. These data suggest a crosstalk between TLR4 and TGFβ signaling pathways in LC cells (Figure 5). Activation of TLR4 downregulates BAMBI leading to unopposed TGFβ signaling and fibrogenesis. Since the fibrotic response leads to the accumulation of endogenous TLR4 ligands such as FN-EDA and tenascin C, a feed-forward loop could develop leading to a further progression of the fibrotic response. Future studies will elucidate the exact molecular mechanism of TLR4 and TGFβ signaling crosstalk in the ONH. Figure 5: Crosstalk of TGFβ2 – TLR4 signaling in LC Cells. TGFβ2 activates the TGFβ receptor complex, phosphorylating Smad2/3. pSmad2/3 forms a complex with Smad 4, which translocates to the nucleus to act as a transcription factor increasing pro-fibrotic and pro-inflammatory gene transcription, including the production of DAMPs. These DAMPs are then able to activate TLR4 signaling, increasing NFκB through the MyD88 dependent pathway. NFκB translocates to the nucleus acting as a transcription factor, inhibiting the transcription of Bambi, which acts as a negative regulator of the TGFβ2 signaling cascade. Thus, these two pathways act in a feedforward loop. The structure of the collagenous lamina cribrosa beams are disrupted in glaucoma. Increases of COL4 are seen in the LC region of ONH [56], and LC primary cell lines isolated from human glaucomatous optic nerve heads have significantly higher COL1 and COL5 mRNA expression than healthy control lines [60]. COL1 protein expression also increases in other fibrotic diseases [61], and collagen VI staining also increases in other tissue types during stress [62]. Concurrently, there is a marked loosening of the collagen matrix and significant loss of collagen fibers in the LC region of the ONH in POAG human tissue [55]. Both COLVIII and COLXIII mRNA are downregulated when comparing LC cell mRNA synthesis from healthy versus glaucomatous derived primary cell cultures [60]. This all suggests a disruption of collagen fibrils and density leading towards disease progression and pathogenic ECM modifications. Our results recapitulate previous literature in that TGFβ2 treatment increases COL1 deposition [22], and we further show that this increase is TLR4 dependent. In addition, we demonstrate that FN-EDA treatment is also sufficient in increasing COL1 expression in a TLR4-dependent manner. Future studies will look at other individual collagen subtype changes in glaucoma progression in the LC. In conclusion, we show novel findings highlighting increased total FN and increased FN-EDA expression in the LC region of the glaucomatous human ONH. TGFβ2-TLR4 crosstalk in hONH LC cells is involved in the production and regulation of the ECM. Both TGFβ2 and cFN containing the EDA isoform can increase ECM protein expression in LC cells, as well as increase the production of the DAMP FN-EDA, and inhibition of TLR4 blocks these effects. These results provide insights into a novel pathway in the ONH region that could be driving glaucoma disease progression and eventual loss of vision. Understanding these cellular signaling mechanisms behind ONH damage offer new targets for developing further treatment therapies. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. ## Author contributions CM developed all ideas and experimental designs, analyzed data, and wrote the manuscript. EKG performed all histological, cellular, and molecular experiments, analyzed data, isolated and characterized ONH cells for culture, and wrote the manuscript. PDM assisted with cell culture maintenance and molecular experiments. PM isolated ONH tissue for culture. TM dissected and embedded human donor eyes for histology. All authors contributed to the article and approved the submitted version. ## Funding This work was supported in part by National Institute of Health R01EY02652 (CM), William and Phyllis Huffman Research Professorship (CM), and the McPherson Eye Research Institute Grant Summit Program (CM). This work was also supported in part by an Unrestricted Grant from Research to Prevent Blindness to the UW-Madison Department of Ophthalmology and Visual Sciences, the Core Grant for Vision Research from the NIH to the University of Wisconsin-Madison (P30 EY016665), and a T32 EY027721 to the Department of Ophthalmology and Visual Sciences University of Wisconsin-Madison (PM). ## Acknowledgments The authors would like to thank Dr. Abe Clark and Dr. Tara Tovar-Vidales for providing several LC cells strains for our studies. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: A review on hypo-cholesterolemic activity of Nigella sativa seeds and its extracts authors: - Karmegam Uma Maheswari - K Dilara - S Vadivel - Priscilla Johnson - Selvaraj Jayaraman journal: Bioinformation year: 2022 pmcid: PMC9997490 doi: 10.6026/97320630018343 license: CC BY 3.0 --- # A review on hypo-cholesterolemic activity of Nigella sativa seeds and its extracts ## Abstract Nigella sativa (N. sativa) (Family Ranunculaceae) is a popular therapeutic herb in many parts of the world. It is widely used in traditional medical systems such as Unani, Ayurveda and Siddha. Seeds and oil have a long history of folkloric use in many medicinal and culinary systems. The seeds of N. sativa have long been used to treat a variety of illnesses and disorders. Studies on N. sativa and its therapeutic potential have been investigated. This includes anti-diabetic, anticancer, immune-modulatory, analgesic, antimicrobial, anti-inflammatory, spasmolytic, bronchodilator, hepato-protective, renal protective, gastro-protective, antioxidant properties, and several others. Nigella sativa contains thymoquinone. This is a bioactive component of the essential oil with medicinal benefits. Therefore, it is of interest to report a comprehensive data on the therapeutic usefulness of N. sativa in hypo-cholesterolemic activity. ## Background: Cholesterol is a necessary component of all living organisms; without it, the organism couldn't work efficiently and would die [1]. One of the most important cardiac risk factors has been identified as elevated plasma cholesterol (hypercholesterolemia). Serum cholesterol levels are directly associated to cardiac morbidity and mortality. Many studies have found that high serum cholesterol levels alter the biochemical properties of blood components and artery intima, facilitating the progression of atherosclerosis. [ 2]. Hypercholesterolemia has been shown to produce oxidative stress by inducing free radical-mediated lipoprotein peroxidation. This stress is caused by a mismatch between the production of free radicals and the antioxidant defense system's efficacy. The activity of antioxidant enzymes have been found to be abnormal in those who have cardiovascular disease [3]. Lowering cholesterol levels was the prime goal in preventing the occurrence of coronary heart disease (CHD). Cholesterol lowering and dietary modifications have also been included in health care strategies to guard against CHD [4]. The optimal intake to keep preferable blood lipids levels, which could protect against heart disease, has attracted people's curiosity. Elevated levels of serum total cholesterol, low density lipoprotein cholesterol (LDL), and triglyceride have indeed been associated with a greater risk of heart disease, whereas elevated levels of high-density lipoprotein cholesterol (HDL) have indeed been related to a lower risk of heart disease [5]. As a result, a dietary pattern that successfully lowers TC, LDL, and triglyceride levels while preserving or increasing HDL is preferred. Aside from medicine, diet composition plays an essential role in blood lipid and lipoprotein concentration regulation. Plant-based medicines are well-known for their therapeutic benefits because they have little or no adverse effects [6]. Traditional remedies have gained a lot of popularity in various parts of the world during the last 20 years or so. Extensive study on different plant species' medicinal principles and potential is causing traditional medicines all around the world to be reevaluated. Despite advances in conventional chemistry and pharmacology in the development of successful pharmaceuticals, the plant kingdom may still be a useful source of new medicines. Isoflavones, phytosterols, saponins, fibers, polyphenols, flavonoids, and ascorbic acid are only a few of the metabolites produced by plants, and their role in lipid and antioxidant metabolism has sparked attention [7]. Since several studies demonstrated its wide spectrum of pharmacological potential, Nigella sativa (N. sativa) (Family Ranunculaceae) is emerging as a wonder herb with a rich historical and religious heritage. Black seed is the popular name for N. sativa. Nigella sativa is a plant native to Southern Europe, North Africa, and Southwest Asia that is grown in a variety of nations across the world, including the Middle East Mediterranean region, South Europe, India, Pakistan, Syria, Turkey, and Saudi Arabia [8]. Nigella sativa seeds and oil have been utilized for millennia in the treatment of a variety of diseases all throughout the world. It is also a significant medicine in Indian traditional medical systems such as Unani and Ayurveda [9]. Crude oil extracted from the seeds of N. sativa demonstrated a wide range of therapeutic actions. Nigella sativa seed oil can also help with headaches, flatulence, blood homeostasis issues, rheumatism, and other inflammatory disorders [10]. Therefore, it is of interest to report a comprehensive data on the therapeutic usefulness of N. sativa in hypo-cholesterolemic activity. ## Chemical composition of black seeds: Since then, the several active chemicals have been extracted, recognized, and published in various black seed types. Thymoquinone ($30\%$ -$48\%$), thymohydroquinone, dithymoquinone, p-cymene ($7\%$ -$15\%$), carvacrol ($6\%$ -$12\%$), 4-terpineol ($2\%$ -$7\%$), t-anethol ($1\%$ -$4\%$), sesquiterpene longifolene ($1\%$ -$8\%$), -pinene, and thymol are the most important active chemicals. In trace proportions, black seeds also contain additional chemicals. Isoquinoline alkaloids, such as nigellicimine and nigellicimine-N-oxide, and pyrazol alkaloids, or indazole ring containing alkaloids, such as nigellidine and nigellicine, are found in seeds. Furthermore, the seeds of N. sativa contain alpha-hederin, a water-soluble pentacyclic triterpene, as well as saponin, a possible anticancer agent [11]. ## Atherosclerosis activity: The most common cause of sickness and mortality in the world is atherosclerosis. [ 12 - Check with author] It is a complex disease with numerous risk factors. Atherosclerosis is caused by a combination of causes, one of which is a high level of cholesterol. Lowering cholesterol levels with medication or dietary changes may lower the risk of coronary heart disease (CHD) [13]. Several plants have been identified in traditional medicine, with some of them delivering excellent relief to those suffering from cardiovascular illnesses. Botanical dietary supplements can help your heart health and prevent atherosclerosis in several ways [13]. Many herbal substances have been found in recent research to lower plasma triacylglycerol (TG) and total cholesterol (TC) levels while increasing high-density lipoproteins (HDL) levels, lowering the risk of coronary heart disease (CHD)[12 - check with author]. Nigella sativa (NS) has been found to provide a variety of health benefits, including hypoglycemic, hypocholesterolemic, and antioxidant properties [14]. The choleretic impact of NS is a key mechanism that may explain the plant's lipid-lowering and atheroprotective benefits. Treatment with NS seed powder and seed oil was found to lower blood total and LDL cholesterol levels while increasing HDL cholesterol levels in hypercholesterolemic rabbits in one research. NS's lipid-modulating effects were accompanied by smaller atherosclerotic plaques and a lower intima/media ratio [15]. The impact of N. sativa seeds powder (1000 mg/kg) and oil (500 mg/kg) on atherosclerosis in diet-induced hypercholesterolemia rabbits was studied for eight weeks in comparison to simvastatin (10 mg/kg). The results group treated with N. sativa seeds powder or oil considerably decreased arterial wall lipid deposition, TC and LDL, and increased HDL. Furthermore, plaque development halted and lowered the intima/media ratio significantly [16 - check with author]. In diet-induced hypercholesterolemia rabbits, N. sativa seeds powder (100 mg/kg/day) dramatically decreased serum levels of TC, TG, and LDL-C and elevated HDL-C during a four-week period [17]. Arachidonic acid (AA)-triggered platelet accumulation and blood coagulation were inhibited by the methanol soluble part of the N. sativa seed oil. 2-(2-methoxypropyl)-5-methyl-1,4-benzenediol, thymol, and carvacrol, among other separated oil constituents, had a substantially higher effect on AA-triggered platelet accumulation and blood coagulation than aspirin [18]. Furthermore, fatty streak production in the left and right coronary arteries, as well as the aorta, was dramatically reduced. According to the findings, N. sativa's anti-inflammatory and antioxidant capabilities may be responsible for this impact [19]. In hypercholesterolemia rabbits, therapies with honeys (a resinous hive product obtained by honeybees from diverse plant origin) and TQ resulted in significant reductions in serum TC, LDL-C, triglycerides, and thiobarbituric acid-reactive concentrations, as well as increased HDL-C and glutathione content. Propolis and TQ had a preventive role against hypercholesterolemia-induced aortic tissue damage, according to histopathological analysis. The findings also revealed that antioxidant mechanisms may be involved in the protective effects of propolis and TQ [20]. Table 1(see PDF) illustrates the antiatherogenic and anti-platelet actions of N. sativa and its components. ## Effect Nigella sativa seed extracts in animal studies: The effects of methanol extracts (810 mg/kg) and volatile oil (410 mg/kg) of N. sativa seed on hyperlipidaemia rats revealed that therapies with plant considerably decreased plasma triglycerides (TG), total cholesterol (TC), extremely low density lipoproteins (VLDL-C), low density lipoproteins (LDL-C), -hydroxy-methylglutaryl-CoA reductase activity, and improved higher densities lipoproteins (HDL-C) concentration [21]. Intra-gastric gavage of N. sativa seed petroleum ether extract decreased fasting plasma insulin and TG levels while increasing HDL-C [22]. Besides that, oral feeding of N. sativa seed fixed oil (1 ml/kg) to rats for 12 weeks decreased TC, TG, hyperglycemia, leukocytes, and platelet counts while increasing hematocrit and haemoglobin levels [23]. In rats, therapies with N. sativa seed oil (800 mg/ kg, p.o. for 4 weeks) reduced blood TC, LDL, and TG while increasing serum HDL. Medication with N. sativa (50, 100, 200, 300, 400, 500 mg/day) reduced blood TC, LDL, and TG while enhancing the HDL/LDL ratio in normal rats. [ 24]. An ethanolic extract of N. sativa seed (0.5, 1 and 1.5 mg/kg, ip) was shown to prevent adrenaline-induced dyslipidemia and left ventricular hypertrophy in rats. In adrenaline-induced dyslipidaemia mice, injection of the plant for two weeks significantly reduced TC, TG, LDL-C, and elevated HDL-C. Furthermore, eight weeks of treatment with N. sativa boosted antioxidative activity and reduced left ventricular hypertrophy and cardiomyocyte size. [ 25]. N. sativa In Sprague Dawley rats, (1000 mg/kg/day) exhibited a significant reduction in TC, TG, LDL-C, and an elevation in HDL-C when compared to simvastatin, a synthetic antihyperlipidemic medication. The findings indicated that N. sativa has the potential to be employed as an antihyperlipidemic medication with no negative side effects. [ 26]. The impact of N. sativa seed squashed therapy (7.5 g/kg/day) on TC, TG, LDL-C, and MDA was been observed in a rabbit model of hyperlipidemi. Furthermore, treatment with N. sativa ($5\%$) significantly reduced TC and LDL-C in hypercholesterolemia rabbits. [ 27]. The impact of several N. sativa extracts on lipid levels in ovariectomized as an experimental animals of menopause has been examined, and the findings show that distinct N. sativa extracts considerably lowered sugar levels and LDL-C, however variations in TC, TG, and HDL-C were not significant [28]. Data shows that treatment with either N. sativa (0.4 mg/kg) or olive oil (0.4 mg/kg) drastically diminished TC, TG, LDL-C, and VLDL-C in a mouse model of hyperlipidemia. The value of HDL-C in the olive oil group, on the other hand, was considerably higher than in the N. sativa group. [ 29]. The effects of N. sativa acetone extraction (0, 0.2, 0.4 percent) and N. sativa seed powder (0, 1.5, 2.5, 3.0 percent) on hyperlipidaemia laying hens over 4 weeks demonstrated that feeding either with the seed powder or acetone extracts of the plant seeds significantly lowered TC and TG. [ 30]. In Pekin ducklings, supplementing with N. sativa seed ($2\%$) decreased HDL-C while increasing TC, TG, LDL-C, and VLDL-C. [31]. Canola oil and N. sativa seed powder considerably lowered TC and LDL-C while increasing HDL-C in a non-significant way [32]. In addition, therapy with N. sativa (30 mg/kg, po) enhanced HDL-C while decreasing LDL-C [33]. In albino rats, palm oil enhanced TC and LDL-C levels and reduced HDL-C levels at 24 weeks, and though therapies with N. sativa dramatically decreased TC and LDL-C levels and increased HDL-C levels [34]. In hyper cholesterolemia rabbits, feed with $5\%$ N. sativa dramatically reduced arterial wall lipid accumulation, TC, and LDL. TQ (10 mg/kg) lowered TC, TG, and LDL-C while increasing HDL-C in a rabbit model of atherosclerosis for eight weeks. The alterations in lipid profile were not significant. The impacts of N. sativa and also its constituents on lipid profile were seen in Table 2(see PDF). ## Role of N. sativa in clinical studies: The impacts of N. sativa seed powder on blood cholesterol, HDL, LDL-c, and TG in menopausal women of 2 categories were explored in a clinical research. The treatment group got N. sativa powder (500 mg) capsules, while the placebo group got placebo capsules (wheat germ, 100 mg). For a period of two months, capsules of N. sativa powder have been given orally at a quantity of 1 g after breakfast daily. According to the data, N. sativa significantly increased serum HDL-C while significantly lowering LDL-C, TC, TG, and FBG [35]. In people with metabolic syndrome, treatment with N. sativa seed oil considerably elevated blood HDL-C and lowered LDL-C [36]. In hypercholesterolemia patients, oral dose with N. sativa powder at a dose of 1 g everyday prior to actually breakfast for 2 months lowered blood levels of TC, LDL, and TG while increasing HDL [37]. In addition, after months of therapy with N. sativa powder (500 mg) and statin (10-20 mg) in people with acute coronary syndrome in Multan, Pakistan, serum TC, LDL, and TG levels were considerably lower than in the statin (10-20 mg) alone group [38]. The effects of an eight-week oral administration of N. sativa seed in male people with moderate dyslipidemia and high blood pressure were also studied. Patients were randomly categorized into three groups: a placebo, 100 mg of N. sativa extract two times a day, or 200 mg of N. sativa extract two times a day. The serum levels of TC, TG, LDL, SBP, and DBP in N. sativa extract groups decreased significantly in a dose-dependent manner. [ 39]. The impacts of N. sativa on glucose, uric acid, TG, cholesterol, blood urea nitrogen (BUN), and creatinine in ordinary healthy individuals were studied in two groups: I) the test group received N. sativa powder (500 mg) capsules twice daily, and II) the placebo group got brown sugar (500 mg) capsules twice daily. The findings showed that N. sativa considerably lowered sugar levels and cholesterol levels [40]. Another human investigation found favorable impacts of the capsulated N. sativa powder (500 mg) on blood pressure, serum total cholesterol, LDL cholesterol, triglycerides, and fasting blood sugar, but the results were not statistically significant due to the small sample size. [ 41]. The impact of N. sativa intake (2 g/day) and aerobic exercises on lipid profile in inactive obese females over an eight-week period also revealed that the plant and aerobics together resulted in huge upgrades in blood LDL-C and HDL-C [42]. The impact of 2.5 ml N. sativa seed oil orally twice in patients with metabolic syndrome was compared to atorvastatin 10 mg once per day, metformin 500 mg twice a day, atenolol 50 mg once per day, and amlodipine 5 mg daily for at least a period of six weeks. Treatment with N. sativa greatly reduced blood levels of LDL-C and improved HDL-C [43]. In healthy patients, taking 2.5 ml N. sativa seed oil two times a day for 8 weeks lowered fasting blood cholesterol, LDL, TG, glucose, and HbA1C levels considerably [44]. Furthermore, obese women who took 3 g of N. sativa seed oil every day for two months saw a substantial reduction in TG, VLDL, weight, and waist circumference [45]. In hyperlipidemia patients, diet with N. sativa 2 g/day for 4 weeks resulted in favorable reductions in blood levels of TC, TG, and LDL [46]. In individuals with hypertriglyceridemia, saboose-asapghol (Plantago ovata) 4 g and N. sativa 2 g two times a day for 90 days dramatically lowered serum TG levels [47]. In hyperlipidemia patients, two tea spoons (about 9.0 g) of N. sativa seed per day compared to gemfibrozil 600 mg twice daily for eight weeks dramatically lowered blood levels of TC, TG, and LDL-C and elevated HDL-C [48]. In hyperlipidemia patients, the benefits of N. sativa were compared to nicotinic acid together with a low-fat diet and physical activity in three groups: For two months, the effects of I) a placebo, II) 2 tea spoons N. sativa after breakfast, and III) niacin 2 g in divided dosages after breakfast, lunch, and dinner were studied. In hyperlipidemia patients, N. sativa and niacin dramatically lowered serum LDL-C levels while increasing HDL-C levels [49]. In hypercholesterolemia, a combination of N. sativa seeds (50 mg/kg) and honey reduced serum TC, TG, TC: HDL-C, as well as SBP, DBP, and elevated HDL-C. In dyslipidemia patients, a combination of N. sativa seeds (500 mg), garlic oil (250 mg), and simvastatin (10 mg) capsules given once daily after dinner lowered serum TC, TG, LDL-C, and Non-HDL while increasing HDL-C [50]. ## Conclusion: According to several findings, N. sativa and its constituents have hypocholesterolemic properties. However, further research is needed to determine the specific molecular and cellular basis of N. sativa's hypo-cholesterolemic properties and the effects of its constituents. Furthermore, more clinical research into the effects of the plant and its ingredients on hypo-cholesterolemic effects was required. ## References 1. Rahmati-Ahmadabad S. Life Sci. (2019) 224 139. PMID: 30922848 2. Porter FD. J Lipid Res. (2011) 52 6. PMID: 20929975 3. Pizzino G. Oxid Med Cell Longev. (2017) 2017 8416763. 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--- title: Immediate and antecedent causes of mortality in hospitalised Indian patients with COVID-19 authors: - BY Keerthi - K Saritha - Chirali Shah - Vimala Thomas - Vikram Cheryala journal: Bioinformation year: 2022 pmcid: PMC9997491 doi: 10.6026/97320630018402 license: CC BY 3.0 --- # Immediate and antecedent causes of mortality in hospitalised Indian patients with COVID-19 ## Abstract It is of interest to assess the immediate and antecedent causes of mortality amongst adult COVID-19 infected patients with or without comorbidities admitted in an exclusive COVID-19 hospital was conducted the between August 2020 to May 2021. The immediate and antecedent causes were collected from the medical certificate of cause of death (MCCD). Remaining data was extracted from the hospital’s record. ICMR protocol was used to grade severity of illness at admission into mild, moderate and severe categories. Clinical status during hospitalisation and most recent radiographic and laboratory data were used to assess disease progression and outcome. This study includes data from 571 people, who died at our centre between August 2020 and May 2021. Patients registered without any co-morbidity were 146 with mean age of 57.53 years; ($\frac{33}{146}$) were females and ($\frac{110}{46}$) males. Hypertension (274, $47.99\%$) was found in a moderately large number of patients followed by diabetes (225, $39.4\%$) and anaemia (199, $34.6\%$). Increase in risk of mortality of COVID-19 was found maximum in patients with acute respiratory distress syndrome ($72.33\%$), followed by secondary infections ($6.83\%$). Mortality recorded in this study was mainly in males of older age (50 years and above) with at least one co-morbidity. Anaemia was also prevalent amongst these patients and considered as an independent factor for mortality. Hence, recording of comorbidities and haemoglobin levels may help as a guideline to develop risk stratification and management of patients with COVID-19 to reduce overall mortality. ## Background: The novel corona virus (SARS-CoV-2) is an emerging disease that was first diagnosed in China and has been declared as a pandemic by the World Health Organisation on 11th March 2020. This virus belongs to the family of beta corona virus that causes severe acute respiratory syndrome [1]. SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) has infected tens of millions of individuals around the world, leading to substantial mortality [2] A number of factors have been linked to an increased risk of COVID-19-related catastrophic consequences and mortality [3] With increasing age, mortality appears to soar exponentially. Higher risk is also linked to male gender, obesity, socioeconomic deprivation, and a variety of pathologies [4,5]. COVID-19 is primarily a respiratory illness. It can also cause symptoms that are not related to the lungs. Thrombotic complications, myocardial dysfunction and arrhythmia, acute coronary syndromes, acute kidney injury, gastrointestinal symptoms, hepato-cellular injury, hyperglycaemia and ketosis, neurologic illnesses, ocular symptoms, and dermatologic complications are some of the conditions that can occur [6]. However, there is scarcity of data on how the factors linked to COVID-19 mortality are associated with different comorbidities. Advanced age is also considered as a substantial risk factor of mortality from any cause. It is possible that COVID-19 infection merely increases everyone's chance of death by a constant factor, or that some things have a different impact on COVID-19 mortality. COVID-19-relatedmortality cannot be attributable to another disease (e.g., cancer) and should be counted separately from pre-existing conditions suspected of precipitating a severe course of COVID-19 [7, 8]. Day of death was identified as the time between the inception of symptoms and signs and the moment of death [9]. The vital causes of death recorded due to COVID-19 disease are acute respiratory distress syndrome, pneumonia and comorbidities. [ 8] In most of the cases it is likely that COVID -19 is the underlying cause of death (UCOD) which leads to ARDS resulting in mortality amongst positive patients. While recording form 4 medical certification of cause of death (MCCD), it is necessary to underline the pre -existing comorbidities that might have contributed to severity and mortality of patients [7]. Studies done on mortality with this infection suggested that; patients who died due to COVID-19 infection suffered from at least one of the comorbidities such as hypertension, diabetes, heart disease and cancer [10,11]. There is a lack of literature on studies assessing immediate and antecedent causes of mortality amongCOVID-19 infected patients, along with association of comorbidities. Therefore, it s of interest to assess immediate and antecedent cause of mortality amongst patients with COVID-19 infection admitted in an exclusive COVID-19 hospital. ## Study setting and design: A retrospective, observational single-centre study was designed to assess major causes of mortality associated with COVID-19 infection. Inclusion criteria were marked as death due to COVID-19 amongst the patients with and without comorbidities. Case sheets of all the deceased COVID-19 infected patients aged 18 or more who died during hospitalisation between August 2020 till May 2021, with SARS CoV-2 confirmed by RT-PCR or rapid antigen test (RAT) were taken on record. Demographic and clinical characteristics such as age, gender, day of illness at admission, symptoms at admission, duration of hospital stay, severity at admission, underlying chronic disease histories (diabetes, hypertension, cardiovascular disease, respiratory disease and cancer) and day of death from symptom onset were extracted from the hospital's record. Since the data were collected during the peak of the pandemic, information regarding onset and duration of symptoms at time of admission was missing from a few patients (15 cases). ICMR protocol was used to grade severity of illness at admission into mild, moderate and severe categories. The days of illness was divided into early presentation which represents patients suffering from disease > 7 days, and late presentation in which onset of illness was <7 days. Clinical status during hospitalisation and most recent radiographic and laboratory data were used to assess disease progression and outcome. Study outcome was mortality, defined as the proportion of patients who died from COVID- 19 in the hospital. The immediate and antecedent causes of mortality were collected from the MCCD and confirmed after a comprehensive review of the case data. The cause of mortality was categorised as immediate, antecedent and due to pre-existing comorbidities. The ICD 10 code was used to record the various causes of death. ## Ethical approval: The study was conducted after seeking approval from institutional scientific and research committee. Informed consent was taken at time of admission and permission was sought to access the deceased person's medical records. ## Statistical analysis: The R software version 4.1.1 and Microsoft Excel were used to analyse the data. A frequency table was used to show categorical variables. Continuous variables were shown in Mean SD/ Median (Min, Max) format. The Chi-square test was used to determine if categorical variables are interdependent. The effect of several variables on the likelihood of developing severe COVID-19 infection is estimated using uni-variate and multivariate logistic regressions. Statistical significance is indicated by a P-value of less than or equal to 0.05. ## Characteristics of the subjects: The ages of patients ranged from 23 to 93, with a mean age of 57.67 ± 13.08 years. The majority of the participants were over the age of 50-59 ($27.5\%$) followed by 60-69 ($25.92\%$) years. The gender ratio was 1.52:1, with 60.25 % of males and 39.75 % of females. Severity of illness at the time of admission was notable, with 505 ($88.44\%$) patients in severe condition. ICU admission was required for 443 patients ($77.58\%$), whereas ward admission was required for 128 (22.42 %). In $94.57\%$ of the subjects, the day of death was within 7 days. Admission was delayed (> 7 days) in $48.69\%$ of the cases. In $54.82\%$ of cases, the length of hospital stay was more than seven days. Information about day of death was available for only 566 patients as compared to total 571 since patients admitted in severe stages could give data on when the infection started. Only 564 subjects had detailed information regarding their symptoms on their day of admission (day of illness from the onset of symptoms) resulting in a smaller number of patients (Table 1 - see PDF). ## Immediate and antecedent causes of mortality: Among all SARS-CoV-2 deaths, the most common immediate cause of mortality was identified as acute respiratory failure ($71.8\%$) as shown in Figure 1a. Pathologies associated with mortality (Table 2 - see PDF) were sepsis ($8.06\%$), cardiac failure ($3.8\%$) as shown in Figure 1b and cardiogenic shock ($3.68\%$) diabetic ketoacidosis ($3.5\%$) as shown in Figure 1c(see PDF). Although a clear pattern is not observed in pathologies responsible for mortality due to COVID-19, most of the cases had at least a single comorbidity (Figure 1 - see PDF). Patients registered without any comorbidity were 146 with mean age of 57.53 years; ($\frac{33}{146}$) were females and ($\frac{110}{46}$) males. Increase in risk of mortality of COVID-19 was found maximum in patients with acute respiratory distress ($72.33\%$), followed by secondary infections ($6.83\%$), COVID pneumonia ($6.13\%$) and infection ($5.25\%$). Myocardial infarction and cardiac dysrhythmia were also found prominent in patients with COVID infection ($4.55\%$ and $2.28\%$ respectively). ## Risk factors associated with severe infection: Age, duration of hospital stays, symptoms at admission such as sore throat, dyspnea, myalgia, diarrhoea, headache, vomiting, and cardiovascular diseases are observed to have significant association with severe infection, according to univariate logistic regression. The risk of severe COVID-19 infection rose by 0.97 % per unit increase in age. When comparing patients with a hospital stay of ≥ 7 to those with a hospital stay of < 7, the odds of developing severe COVID increased by 0.42 %. The odds of having severe COVID rise by 0.44, 2.51, 0.53, 0.34, 0.18, 0.22, and 0.37 % for those who have a sore throat, dyspnea, myalgia, diarrhoea, headache, vomiting, and cardiovascular illness, respectively (Table 3 - see PDF). In multivariate logistic regression (Table 3 - see PDF) also similar association was observed with, age ($$P \leq 0.0102$$), hospital stay ($$P \leq 0.0010$$), headache ($$P \leq 0.0151$$), vomiting ($$P \leq 0.0085$$), total leucocyte count ($$P \leq 0.0446$$), and neutrophil count ($$P \leq 0.0305$$); all had a significant effect on severity. The probabilities of severe COVID-19 infection rise by 0.97, 1.22, and 0.8 % with each unit increase in age, total leucocyte count, and neutrophil count, respectively. ## Trend of age and gender of COVID-19 related mortality in first and second wave: The first wave peaked at the end of August 2020 and was followed by a progressive decrease with very few cases admitted in hospital by mid of November and December. The number of patients who died in first wave in the hospital were 49; out of which 21 ($42.86\%$) were ≥70 years of age (Figure 2a - see PDF), 13 ($26.53\%$) were in between 60-69 years, 10 ($20.21\%$) belonged to age group of 50-59 and remaining $10\%$ aged below 50. The second wave peaked around mid March of 2021 and declined around May 2021. The number of patients deceased in the centre were around 522. Maximum i.e 143 ($25.04\%$) deaths were observed in the age group of 50-59 years followed by 142 ($24.87\%$)between 60-69years, 116 deaths ($20.23\%$)above ≥ 70 years and remaining $10.2\%$ of deceased were below 50 years. The patients who died were significantly older (70 years and above) in the first wave with a mortality rate of $42.86\%$ and in the younger age group (18 to 50 years) mortality was around $4.08\%$. However, in the second wave younger patient's mortality was higher ($25.04\%$) than the older patients. However, in both waves, the male (Figure 2b - see PDF) mortality rate was higher ($63.23\%$ and $62.87\%$) than females ($36.73\%$ and $37.13\%$). According to the data obtained, the second wave of COVID-19 affected younger patients with at least one of comorbidities than the first. However, more studies should be conducted to compare mortality related to COVID'S first and second waves. Figure 2(see PDF) below visualises the findings trend. ## Discussion: The objective of this study was to observe the immediate and antecedent cause of mortality in patients suffering from COVID-19. In our study, an increase in mortality was reported in older age groups (59 years to 70 years); similar patterns were observed in other countries affected by COVID-19. In a hospital-based study of India on 425 patients, reported old age (65 years and above) as a confounding factor to increasing risk of death in COVID-19 patients.[4] In research from Asia, Europe, and North America, age-specific mortality rates were relatively similar. [ 12] A plausible reason for this age-related mortality could be chronic medical conditions and lower-level of immunity. We found an overall mortality higher in wave 2 ($37.33\%$ females and $62.87\%$ male) than in the first wave ($36.7\%$ females and $63\%$ males). Patients of age 50 to 70 years recorded an increase in mortality in the 2nd wave ($25.04\%$) compared to the 1st wave ($20.4\%$), with a higher number of mortalities in male patients than females in both the waves. In other age groups also, mortality was observed higher in the second wave. Similarly, in a retrospective study from North India, that included 10 tertiary care units, concluded an increase in mortality in the patients in wave 2 as compared to wave.[13] However, a study done by Jain et al.., showed no significant increase in mortality rate amongst both the waves. [ 14] Interestingly the result of study in Spain showed a decrease in mortality rates by $13.2\%$ in the second wave as compared to ($24\%$) in the first wave. [ 15] The increase in mortality rate during the second wave was reported by many countries, despite improved treatment protocol. This could be attributed to the lack of COVID- 19 appropriate behaviour (mask use and maintaining social distancing) amongst the people after vanishing of 1st wave and due to the urge of returning to normalcy. However, there is a dearth of articles comparing mortality rate amongst first and second waves. Hence, more studies should be done in order to provide evidence-based results. ## Symptoms at time of admission: COVID-19 is difficult to identify since its symptoms are often mistaken with those of other chronic disorders. As a result, analysing and mining relationships between symptoms could aid in diagnosis. SARSCoV-2 can produce severe respiratory symptoms, such as fever and dry cough, similar to middle-east respiratory syndrome coronavirus (MERSCoV), which also causes similar respiratory infections. Some people experience digestive symptoms first, rather than respiratory symptoms, such as decreased appetite, lethargy, nausea, vomiting, and diarrhoea. Diarrhoea is a zoonotic symptom that $20\%$ to $25\%$ of MERSCoV or SARS-CoV affected people [16]. Symptoms of the neurological system, such as headaches, and cardiovascular symptoms, such as palpitation and chest discomfort, were also common in many patients. All these symptoms were experienced by the subjects in this study. It was discovered that gender differences in symptoms were statistically significant, albeit of a smaller extent. In our study, fever, cough, and shortness of breath were more common in men than in women; however, all other symptoms were equally or more common in female patients. In a group of non-hospitalized COVID-19 patients in Poland, there were more variations in symptoms with lack of appetite ($55\%$ of women, 36 % of males) and loss of taste ($53\%$ women, 40 % men) [17]. ## Comorbidities: After adjusting for confounders and comparing hypertensive patients to non-hypertensive patients, Gao and colleagues showed that hypertensive patients had a significantly higher risk of mortality from COVID-19. [ 18] However, they did not emphasise diabetes as a risk factor. Reviews on hypertension mentioned it as a major factor for clinical outcomes in patients with COVID-19. [ 19] In our study, hypertension and diabetes were marked as a major independent and combined risk factor for worst outcomes associated with COVID-19. Findings of our study also demonstrate that hypertension alone contributes to a small degree of risk for developing severe infections; but not leading to death nor development of acute respiratory failure. These findings were strong enough to withstand various sensitivity analyses and adjustments for other concomitant conditions. It's worth noting that hypertension's risk is amplified by its confounding impact on Diabetes Mellitus-Type 2. Furthermore, neither hypertension nor T2DM enhanced the risk of mortality once ARDS/respiratory failure was established. Significantly, hyperglycaemia during hospitalisation was the key driver of increased risk of all outcomes, more so than a history of T2DM, but elevated blood pressure was less contributory. Other comorbidities that were identified as risk factors for negative outcomes included advanced age, male sex, and history of other cardiovascular diseases, chronic kidney disease, and malignancies. Chronic lung illness was found to be a risk factor for severe infection development but not for mortality, ARDS, or respiratory failure. Meta-analysis that included outcomes from 16 publications, identified hypertension, chronic kidney failure, type-2 diabetes, cardiovascular disease and obstructive pulmonary disease as significant factors associated with the development of serious illness, where patient might require ICU admission and mechanical ventilators. [ 20] However T2DM has a significant importance for the worst outcome like death. Since this was not a patient based meta-analysis, the study lacks to prove role of independent and dependent contribution of hypertension and T2DM. [ 20] Based on univariate analysis; a meta-analysis conducted by Tain et al.. reported that T2DM alone as a risk factor of mortality due to COVID-19. However, this study did not mention the contribution of hypertension alone in severity of disease. [ 11] According to the present study, it was analysed that; type 2 diabetes mellitus alone is responsible for more serious illness than hypertension and/ both the comorbidities together and any other conditions. More specific studies are required to analyse the severe outcome of disease associated with comorbidities. ## Haematological findings: The outcome of the current study suggests haematological abnormalities especially anaemia having a significant association with COVID-19 and mortality. It has been documented those respiratory diseases combined with anaemia have significant effect on outcomes and increases mortality [21]. In community acquired pneumonia, anaemia is predominant amongst the patients with nearly 7 to $12\%$ [22]. In the study by Zhou et al. [ 3] frequency of anaemia was recorded around $15\%$ of 199 patients. Similarly, in cohort study of 267 subjects with COVID-19, $16\%$ of patients had anaemia at the time of admission, whereas increase in incidence to $53\%$ was seen during hospitalization [22]. In our study, the prevalence of anaemia was reported $34.6\%$, which is much higher than reported in the study by Zhou et al.. [3]. Due to a paucity of research on anaemia in COVID-19 patients; the true prevalence of anaemia in COVID-19 patients is unknown. According to results of multivariate analysis, there is significant increase in total leucocyte counts (10.64 ± 6.18; 8.87) in patients with severe infection versus non-severe (10.54 ± 5.57) leading to death. A multi centre retrospective study in China found that leukocytosis on admission of COVID-19 patients was linked to a higher probability of mortality in the hospital [23]. Non-survivors were also substantially more likely than survivors (P0.001) to have leukocytosis, according to Zhou et al. [ 24]. Findings of current study indicate no significant difference in lymphocyte and platelet counts amongst the severe and non-severe cases. Yang et al. [ 25] showed that there is an insignificant difference in lymphocytes between severe and non-severe patients and it is said that lymphopenia is not specific for COVID patients, but also prominent in elderly. Several studies suggest lymphopenia as a major contributor amongst the fatality related to COVID-19 [26-29]. Lymphocyte count is indicated as a major prognostic tool to measure severity of infection in patients with COVID-19 [30]. Data shows that the deceased were mainly of age group (50-59 years) hence, age could not be the contributor in severity of disease. However, the extent of lymphopenia and its progression among COVID-19 patients may be determined by the patients' age and clinical condition [29]. In the majority of studies, increased neutrophil count was a prominent finding [26,28,29]. In the study conducted in Singapore on 138 hospitalised patients resulted in an increase of neutrophil count amongst the patients admitted to ICU (11.6x109/L vs 3.5x109/L) [28]. Similarly, in this study neutrophil count was marked higher in severe infection patients (7.78) than non- severe patients (7.65) respectively. Qin et al. [ 31] and Gong et al. [ 32] observed significantly higher neutrophil counts in severe than non-severe patients ($P \leq 0.001$), and it was also shown in a study by Li et al. among non-survivors compared to survivors. [ 33] Another research on 82 dead COVID-19 patients by Zhang et al. found that 74.3 percent of them had neutrophilia on admission, which rose to $100\%$ in the 24 hours before death. [ 10] The cytokine storm that characterises COVID-19 sickness could be linked to the occurrence of neutrophilia. However, because neutrophilia can be caused by bacterial co-infections and the medication used to treat the condition like corticosteroids, cautious interpretation is essential. Despite the fact that the predictive capacities of haematological parameters for COVID-19 patients vary between studies, the need to include these data for early identification of high-risk patients requiring intensive care should be mandatory. Strength and limitations: This is amongst a very few studies done on causes of mortality amongst the patients who died due to COVID-19. Hence, this helps to provide an insight of comorbidities and clinical outcomes responsible for mortality; which indeed is the strength of this study. However, this study has limitations where in; this is a single centre study hence, generalising the findings can be challenging. Also, we have measured comorbidities associated with death due to COVID-19 and have not emphasised much on pathological findings. ## Conclusion: In this observational study, most cases of COVID-19 deaths were males over 50 years of age with different comorbidities such as, diabetes, hypertension, cardiovascular and respiratory diseases. Comorbidities are indeed risk factors that increase chances of developing ARDS leading to worst outcomes. Haematological abnormality such as anaemia was also prevalent and was independently associated with mortality amongst the patients. Moreover, the patients with comorbidities and haematological abnormalities are at a higher risk of developing severe infection such as, ARDS and death due to COVID-19. Hence, recording of comorbidities and changing haemoglobin levels throughout hospitalisation may help as a guideline to develop risk stratification and management of patients with COVID-19 to reduce overall mortality. ## References 1. Zhu N. 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--- title: Linking co-expression modules with phenotypes authors: - Rakesh Kumar - Krishna Kumar Ojha - Harlokesh Narayan Yadav - Vijay Kumar Singh journal: Bioinformation year: 2022 pmcid: PMC9997497 doi: 10.6026/97320630018438 license: CC BY 3.0 --- # Linking co-expression modules with phenotypes ## Abstract The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost depends upon the percentage of variance captured by the reduced 1D vector. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. This method works with clinical traits represented as binary class labels and can be adopted for clinical traits measured in continuous scale by dividing samples in two groups around median value. Application of the AOR method on test data for muscle gene expression profiles identifies modules significantly associated with diabetes status. ## Background: In recent years the transcriptomic data analysis has witness a shift from gene level analysis to gene modules level analysis [1,2]. Module level analysis aims to identify set of co-expressed/co-regulated genes from transcriptomic data [3,4]. Further downstream analysis includes identification of intra-modular hubs, investigation of relationship between co-expression modules and the comparison of network topology of different networks [5, 6]. Generally module as whole is summarized with either meta-gene representing average expression of genes for each sample or with module eigen genes which can be considered as the best summary of standardized module expression data [7]. The module eigen gene of a given module is defined as the first principal component of the standardized expression profiles [8]. To find modules that relate to a clinical trait of interest, the module eigen genes are correlated with the clinical trait of interest [9,10]. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. ## The AOR algorithm to assess module-trait association: The method works such that where clinical traits are distinct binary class (positive or negative). Given the total number of samples is m and out of which k belongs to negative class, the method examines the expression pattern of modules across the samples and identify modules which tend to have significant association with the clinical trait of interest. The method is based on the observation that the samples of the two class will show clear distinction in distribution of ranks if they are arranged according to expression values of a gene having true differential expression. However, in module level analysis instead of a single gene we deal with set of genes assigned to the module. Therefore, in order to find sample ranking based on expression of modules as whole, a matrix with n rows, equal to the number of genes in the module and m columns, equal to the number of total samples was created. Each row of the matrix contains samples arranged in ascending order as per the expression values of genes. A position vector was created by calculating the negative class sample frequency for each column of the matrix. For a module which is not related to clinical trait, the first k largest frequencies will be uniformly distributed across the position vector. However, a module having significant relation with clinical trait will cause larger frequencies to concentrate towards one end of the position vector. The index of first k largest frequencies were summed to get a score Gs. A significantly lower Gs score represents lower expression of module in negative class sample and a significantly higher score represents higher expression of module in negative class sample. ## Calculation of score Gs A module Μ; was defined as collection of genes gi($i = 1...$n) having similar expression pattern across the samples sj($j = 1...$m). The expression information of giε Μ across samples can be stored in a nxm matrix E where value Eij represents the expression information of ith gene in jth sample. A sample sj was assigned to set L0 if it belongs to negative class and to set L1 if belongs to positive class. The expression matrix E was converted to nxm index matrix where value Iij was set as per Eq. [ 1]. ( see PDF) ## Assessing the significance of score Gs A null distribution of score Gs was calculated by assuming that the module is not associated with the clinical trait. In case of no association, the position vector ν will have uniform distribution of first k largest values. Therefore, in order to assess the significance of Gs score of a module having n number of genes following steps were performed: [1] False modules were created by randomly selecting n genes from the total number of genes for which expression data is available. [2] The score Gs for False module was calculated. [3] Step 1 and 2 were repeated 1000 time to generate a null distribution of score Gs. A module with Gs score greater than \|Μ;±(1.96xS)\| was considered significantly associated with negative class samples. Where Μ; and S are mean and standard deviation of the null distribution respectively. The score Gs was further converted to scaled Gsscore (Gsscore) and he standardised Gsscore (Gsstandardised). The score Gswas scaled to have value between -1 and +1 using Eq. [ 2]. ( see PDF) ## Results: The R-package WGCNA [11] was used to identify set of co-expressed genes from muscle transcriptome of healthy (NGT) and diabetes (T2D) subjects [12]. In total WGCNA has identified 30 modules having tightly co-expressed genes grouped together. In order to assess the association between modules and subject diabetes status we applied the AOR and module Eigen gene (as implemented in WGCNA package) method to expression data of each of the module. Using the cutoff of p value < 0.001 thirteen modules were found significantly associated with subject diabetes status using AOR method whereas module Eigen gene method was not able to produce statistical significant result for any of the module (Figure 1 - see PDF). This showed that the subject diabetes status have significant affect on muscle gene expression. ## Discussion: In this study, analysis of rank (AOR) method has been used to assess the association between clinical traits and co-expression modules. Application of the method on muscle gene expression profile identified significant association between module and subject diabetes status highlighting importance of AOR method in identifying hidden patterns across gene expression profiles. While interpreting the results of the AOR method both type of score Gsscaledand Gsstandardised should be taken into consideration along with the obtained p-value. The significant p-value obtained using AOR method for module-trait association just indicates that, more number of negative class samples, than expected by chance, are concentrated at the beginning/end of the position vector. The similar sign for Gsstandardised and Gsscaledscore for a module supports deregulation of genes belonging to concerned module between negative and positive class samples. However, opposite signs for Gsstandardisedand Gsscaledsuggest the existence of expression heterogeneity within negative class samples with respect to expression of genes belonging to concern module. ## Conclusion: The AOR method helps in identifying hidden patterns from gene expression data and can provide deeper insights into disease biology by discovering co-expression modules linked to clinical-traits. ## References 1. Choobdar S. Nature Methods (2019) 16 843. PMID: 31471613 2. Saelens W. Nature Communications (2018) 9 1. PMID: 29545622 3. Mao Y. Molecular Medicine Reports (2020) 22 1155. 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--- title: In vitro antioxidant and anti-diabetic analysis of Andrographis echioides and Andrographis paniculata ethanol extract authors: - Chevuru Sai Shreya Reddy - Gayatri Devi Ramalingam - J Selvaraj - A Jothi Priya journal: Bioinformation year: 2022 pmcid: PMC9997502 doi: 10.6026/97320630018337 license: CC BY 3.0 --- # In vitro antioxidant and anti-diabetic analysis of Andrographis echioides and Andrographis paniculata ethanol extract ## Abstract It is of interest to analyse and compare the antioxidant and anti-diabetic activity of ethanolic extracts of Andrographis echioides and Andrographis paniculata. Andrographis echioides and Andrographis paniculata were collected from a local farm. In vitro antioxidant activity was assessed by the potential of Piperine, Lupeol, beta sitosterol; DPPH free radical scavenging assay was performed by Liyana Pathirana and Shahidi method. In vitro anti-diabetic activity was assessed by alpha amylase inhibitory activity and alpha glucosidase inhibitory activity. The data were analysed by one-way-ANOVA to check the statistical significance among the groups and considered at the levels of $p \leq 0.05.$ Both the ethanolic extracts of Andrographis echioides and Andrographis paniculata showed significant antioxidant and anti-diabetic potential in a dose-dependent manner (100-500µg) and can be used as potential antidiabetic agents. Similar to antioxidant potential, Andrographis paniculata exhibited an increased anti-diabetic potential compared to Andrographis echioides. Data shows that the ethanolic extracts of Andrographis echioides and Andrographis paniculata possessed antioxidant and anti-diabetic activity and hence our present findings conclude that both plants can be considered for the development of natural drugs for the management of diabetes. ## Background: The number of people suffering with diabetes in India has increased from 26 million in 1990 to 65 million in 2016 [1]. The species of these plants may represent a source of new hypoglycemic compounds for developing better remedies to treat diabetic patients without serious side effects [2]. The oxygen which is indispensable for life, under uncertain conditions has harmful effects on the human body [3]. Most of the harmful effects of oxygen are due to formation and activation of certain chemical compounds, known as ROS, which have a tendency to donate oxygen to other compounds [4]. Free radicals and antioxidants have become commonly used terms in modern discussion and relatability of disease mechanisms [5]. Andrographis echioides and Andrographis paniculata were given much attention in recent times, because of the therapeutics, pharmaceutical and health protective value as potential plants for treating the Dengue fever [6]. Hence, it has a very high demand and usage in international markets. Andrographolide, Dehydroandrographolide, Neoandrographolide and deoxyandrographolide are the main bioactive compounds present in these plants [7]. Andrographis echioides is located in the dry lands of South Asian countries. Andrographis paniculata is used as a herbal medicine in India, Bangladesh, China and Hong Kong [8,9]. Andrographis paniculata has a broad range of pharmacological effects including anticancer, antidiarrheal, anti hepatitis, anti microbial, anti malarial, and antioxidant activities [10]. Our team has extensive knowledge and research experience that has translated into high quality publications [11- 13, 14-19, 20-30]. Recent demand has increased for Andrographis echioides and Andrographis paniculata which led to the research of these plants in treating various diseases. Therefore, it is of interest to document the antioxidant and antidiabetic activity of Andrographis echioides and Andrographis paniculata ethanol extracts were compared in vitro. ## Chemicals: All chemicals and reagents used for this research work were purchased from Sigma Chemical Company St. Louis, MO, USA; Invitrogen, USA; Eurofins Genomics India Pvt Ltd, Bangalore, India; New England Biolabs (NEB), USA. ## Collection of plant material: Andrographis echioides and Andrographis paniculata were collected from Chennai District, Tamil Nadu, India. The species were identified and authenticated at the Department of Centre for Advanced Study in Botany, University of Madras, and Chennai, India. The bark leaves and flower parts of the plant were shade-dried, cut into small pieces and coarsely powdered. The coarse powder was used for extraction with ethanol. ## Preparation of plant extracts: 1kg of dry powders from leaves from both plants was taken in individual aspirator bottles; 3 liters of ethanol was used and the mixture was shaken occasionally for 72 hours. Then the extract was filtered. This procedure was repeated three times and all extracts were decanted and pooled. The extracts were filtered before drying using whatman filter paper no 2 on a Buchner funnel and the solvent was removed by vacuum distillation in a rotary evaporator at 40°C; the extracts were placed in pre-weighed flasks before drying. ## Assessment of in vitro anti-diabetic activity by plant extract Inhibition of albumin denaturation: The anti-diabetic activity of the plant extract was studied by the inhibition of albumin denaturation technique which was studied according to the methods of Mizushima and Kobayash, 1968 and Sakat et al. [ 2010] followed with minor modifications. The reaction mixture consisted of test extracts and $1\%$ aqueous solution of bovine albumin fraction, pH of the reaction mixture was adjusted using a small amount of 1N HCl. The plant extract with increase in concentration (100 to 500 µg/ml) were incubated at 37°C for 20 min and then heated to 51°C for 20 min, after cooling the samples the turbidity was measured at 660nm.(UVVisible Spectrophotometer Model 371, Elico India Ltd) The experiment was performed in triplicate. In this study, Aspirin was used as a standard anti-diabetic drug. Calculation: % Inhibition=100-((A1 -A2)/A0)100). ## Statistical analysis: The data were analysed statistically using one way analysis of variance (ONE-WAY ANOVA). Duncan Multiple range test was used to analyze the statistical significance between groups. The levels of significance were considered at the levels of $p \leq 0.05.$ ## Alpha amylase inhibitory activity of Andrographis echioides In the present study, Andrographis echioides significantly ($p \leq 0.05$) increased the Alpha amylase inhibitory activity in a dose-dependent manner (100-500µg/ml). However, 400 and 500µg concentrations exhibited the maximum activity in inhibiting the alpha amylase (Figure 1 - see PDF), suggesting that the plant has potential antidiabetic activity. ## Alpha amylase inhibitory activity of Andrographis paniculata: In the present study, Andrographis paniculata* significantly ($p \leq 0.05$) increased the Alpha amylase inhibitory activity in a dose-dependent manner (100-500µg/ml). However, 400 and 500µg concentrations exhibited the maximum activity in inhibiting the alpha amylase (Figure 2 - see PDF), suggesting that the plant has potential antidiabetic activity. ## Alpha glucosidase inhibitory activity Alpha glucosidase inhibitory activity of Andrographis echioides In the present study, Andrographis echioides significantly ($p \leq 0.05$) increased the Alpha glucosidase inhibitory activity in a dose-dependent manner (100-500µg/ml). However, 400 and 500µg concentrations exhibited the maximum activity in inhibiting the alpha amylase (Figure 3 - see PDF), suggesting that the plant has potential antidiabetic activity. ## Alpha glucosidase inhibitory activity of Andrographis paniculata: In the present study, Andrographis paniculata significantly ($p \leq 0.05$) increased the Alpha glucosidase inhibitory activity in a dose-dependent manner (100-500µg/ml). However, 400 and 500µg concentrations exhibited the maximum activity in inhibiting the alpha amylase (Figure 3 - see PDF), suggesting that the plant has potential anti-diabetic activity. ## In vitro antioxidant activity DPPH radical scavenging activity of Andrographis echioides In the present study, Andrographis Andrographis echioides significantly ($p \leq 0.05$) increased the DPPH radical scavenging activity in a dose-dependent manner (100-500µg/ml). However, 400 and 500µg concentrations exhibited the maximum activity in inhibiting the alpha amylase (Figure 5 - see PDF), suggesting that the plant has potential antidiabetic activity. ## DPPH radical scavenging activity of Andrographis paniculata Data shows that Andrographis paniculata significantly ($p \leq 0.05$) increased the DPPH radical scavenging activity in a dose-dependent manner (100-500µg/ml). However, 400 and 500µg concentrations exhibited the maximum activity in inhibiting the alpha amylase (Figure 6 - see PDF), suggesting that the plant has potential antidiabetic activity. ## Discussion: Antioxidant activity of aqueous seed extract of Andrographis echioides and Andrographis paniculata was determined by DPPH free radical scavenging assay. Free radicals / molecules possessing an unpaired electron lead to oxidative stress. The effects of the antioxidants on DPPH free radical scavenging was considered to be due to their hydrogen donating ability. The results obtained in the study show that both the species exhibit significant antioxidant activity as compared with the standard Vitamin C. Andrographis paniculata significantly exhibited a higher antioxidant activity as compared to the Andrographis echioides [31 - check with author]. Further studies may be needed to find out the potential health benefits of the extracts in prevention and scavenging of free radicals. In the study, in vitro α-amylase inhibitory activity and α glucosidase inhibitory activity of extracts of Andrographis echioides and Andrographis paniculata were studied. Results revealed a dose dependent increase in % of inhibitory activity. The extracts showed potent anti diabetic activity in a dose dependent manner, with comparison and increased in a dose dependent manner as compared to the standard. There is a positive relationship between the extracts and ability to inhibit α-glucosidase and α-amylase [32]. Outcome of this study indicates that extracts of Andrographis echioides and Andrographis paniculata could be used as potential antidiabetic agents [33 - check with author]. Similar to antioxidant potential, Andrographis paniculata exhibited an increased anti-diabetic potential compared to Andrographis echioides. In Andrographis echioides, the whole plant is beneficial in controlling the blood glucose level and reducing or preventing diabetes. Andrographis echioides improves the lipid metabolism and prevents diabetic complications from lipid peroxidation and antioxidant systems. This could be useful for prevention or early treatment of diabetic disorders [34]. Ethanol extract of this plant possesses significant antihyperglycemic, antihyperlipidemic, and antioxidant effects in alloxan induced diabetic rats. Andrographis echioides, the whole plant parts - leaf, stem, roots, all are beneficial in controlling blood sugar and can be used in treatment of diabetes. The Andrographis paniculata can be used in treatment in reducing sugar levels, controlling diabetes [35]. Andrographis paniculata is potentially developed as an alternative anti-diabetic agent [36]. Andrographis paniculata give the best anti-diabetic activity to treat obese diabetic conditions [37]. The effects of combination of extracts from Andrographis paniculata [Burm.f.] Nees and Azadirachta indica A is known [38]. Just blood glucose levels were lowered more efficiently than compared to single extract Combination has potential to develop as an anti-diabetic agent. The aqueous and ethanolic extracts of Andrographis paniculata are capable of exhibiting significant blood sugar lowering effects [2]. Andrographis echioides, the whole plant exhibits antioxidant properties. Different extracts of Andrographis echioides can be very effective antioxidants and it could protect biological systems against the oxidative stress including ageing, cancer, diabetes and cardiovascular disorder. Andrographis echioides is a good natural antioxidant source and has higher antioxidant potential [39]. Andrographis paniculata possess antioxidant properties and can be used in treating various disorders. Andrographis paniculata leaves possess anti-inflammatory, antioxidant and analgesic properties [40]. The components get involved in the antioxidant activity of Andrographis paniculata [41]. The antioxidant property can be delayed and prevent oxidation process and forms readily oxidizable substrates and it reduces to low concentration. It is used in lung infection treatments. A limitation of the study is that the sample size cannot be generalised over a population of a particular area. Sample size is very less as we are confined to few samples of the plant. ## Conclusion: Data shows that Andrographis echioides and Andrographis paniculata possess both antioxidant and anti-diabetic properties in ethanol extracts. 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--- title: Associations between Accelerometer-Measured Physical Activity and Fecal Microbiota in Adults with Overweight and Obesity authors: - RILEY L. HUGHES - DOMINIKA M. PINDUS - NAIMAN A. KHAN - NICHOLAS A. BURD - HANNAH D. HOLSCHER journal: Medicine and Science in Sports and Exercise year: 2022 pmcid: PMC9997628 doi: 10.1249/MSS.0000000000003096 license: CC BY 4.0 --- # Associations between Accelerometer-Measured Physical Activity and Fecal Microbiota in Adults with Overweight and Obesity ## Body Physical activity (PA) is defined as any body movement produced by skeletal muscles that requires energy expenditure [1,2]. Engagement in any PA intensity reduces the risk of premature mortality and a myriad of chronic diseases [1,3,4] and promotes physical and mental health [3]. When the intensity of PA is considered, higher moderate-to-vigorous PA (MVPA) of any duration can benefit cardiometabolic and cardiovascular health and decrease the risk of premature mortality [5]. However, growing evidence suggests that light-intensity PA is also associated with a reduced risk of premature mortality [4] and could help improve cardiometabolic health [6]. Conversely, physical inactivity (i.e., noncompliance with PA guidelines of ≥150 min of moderate, >75 min of vigorous, or any equivalent combination of the two intensities per week) [7] was identified as the fourth leading risk factor for global mortality by the World Health Organization [8] and has a higher prevalence than all other risk factors [9]. Sedentary time (ST) is associated with greater mortality as well as gastrointestinal inflammation (i.e., increased circulating lipopolysaccharide) and noncommunicable diseases including colorectal cancer, cardiovascular disease, and diabetes, independent of recreational PA [10,11]. ST is not the same as physical inactivity or lack of exercise, as individuals who engage in regular PA and exercise may also have high levels of ST [12]. Less than $10\%$ of Americans meet the recommended amount of weekly MVPA based on accelerometry [13]. Importantly, PA is a modifiable lifestyle factor, and evidence suggests that even small increases in daily PA can provide health benefits [3,4,9]. However, the mechanisms by which PA prevents disease and improves health are not fully understood [1]. In addition, there remains a large degree of interindividual variability in response to regular PA and exercise (i.e., planned, structured, and repetitive PA with the goal to maintain or improve fitness) [2,14]. Much of this variability remains unexplained, although genetic factors are thought to contribute as well as compensatory metabolic and behavioral changes in response to increases in daily PA [14,15]. Recent discussions have concluded that “-omics” data, including the gut microbiota, may help elucidate mechanisms underlying connections between daily PA and health and variability in the health effects of daily PA [1]. The effects of the gut microbiota extend far beyond the gastrointestinal system, influencing systemic functions including metabolism and immunity [16]. These effects are mediated in part by the production of metabolites [17] (e.g., short-chain fatty acids (SCFA) and branched-chain fatty acids (BCFA)) that influence host systems and metabolic pathways [16]. Variability in the composition of the gut microbiome [18] has fueled research on the relationship between features of the gut microbiota, such as diversity or the presence, absence, or the amount of certain taxa, and host health. In addition, the gut microbiota is not a fixed trait but instead responds to environmental stimuli and is a malleable part of the human supraorganism [19,20]. Diet is a lifestyle factor that directly influences the gut microbiota by providing substrates for microbial metabolism [21]. In contrast, PA may influence the gut microbiota via more indirect mechanisms such as alterations in substrate utilization, gut transit time, bile acid profile, and signaling pathways in muscle and immune cells [19]. Most of the research on PA and the gut microbiota has focused on aerobic-based exercise [19], a subcategory of PA, and has often focused on athletic populations. These studies have suggested that exercise influences the gut microbiota composition, often increasing the abundance of health-associated bacteria such as Lactobacillus, Bifidobacterium, and Akkermansia as well as increasing SCFA production and the abundance of butyrate-producing taxa [19]. Large, cohort studies that have collected gut microbiota samples and metadata, including self-reported PA or exercise data, have not yet reported associations between the gut microbiota and PA [22,23]. Few studies have investigated the relationship between objectively measured, total daily PA and PA intensity and the gut microbiota, particularly in young to middle-age adults [24]. The objective of this cross-sectional analysis is to assess whether total daily PA and PA intensities or ST and prolonged ST are associated with differences in the gut microbiota composition or SCFA profile of adults with overweight or obesity. We hypothesized that greater total daily PA and less ST would be associated with greater relative abundance of health-associated bacteria such as Bifidobacterium, Lactobacillus, and Akkermansia and SCFA- and butyrate-producing microbes such as Faecalibacterium as well as correspondingly greater fecal SCFA concentrations. Characterizing relations between total daily PA and PA intensities, ST, and prolonged ST and the fecal microbiota will help narrow the gap in understanding how daily PA contributes to human health. ## ABSTRACT ### Purpose We aimed to assess whether total daily physical activity (PA), PA intensities, sedentary time (ST), and prolonged ST are associated with differences in the gut microbiota composition or short-chain fatty acid (SCFA) profile of adults with overweight or obesity. ### Methods Cross-sectional associations between total daily PA (counts per minute), PA intensities (light and moderate-to-vigorous (MVPA)), ST, prolonged ST, and fecal microbiota composition were assessed in adults ($$n = 124$$) between 25 and 45 yr of age with body mass index ≥25 kg·m−2. Fecal microbiota composition was assessed with 16S rRNA gene sequencing. Daily PA and ST were measured with a hip-worn ActiGraph wGT3X-BT accelerometer. ### Results Daily PA volume and intensity were positively associated with relative abundance of Faecalibacterium ($$P \leq 0.04$$) and negatively associated with the abundances of Alistipes, Parabacteroides, and Gemmiger ($$P \leq 0.003$$–0.04) as well as the concentrations of acetate, butyrate, and total SCFA (all $$P \leq 0.04$$). Conversely, ST was negatively associated with abundance of Faecalibacterium but positively associated with the abundances of taxa, including Ruminococcaceae, Parabacteroides, Alistipes, and Gemmiger. Clustering of participants based on whether they met PA recommendations suggested that SCFA profiles differed between individuals who did and did not meet PA recommendations. K-means clustering based on percent of time spent in MVPA and ST also identified differences in fecal microbiota composition between cluster 1 (lower MVPA, higher ST) and cluster 2 (higher MVPA, lower ST), including a higher abundance of Alistipes in cluster 1. ### Conclusions The current analysis suggests a beneficial association of daily PA on the fecal microbiota and a negative association of ST, particularly with respect to the associations of these variables with the genera Faecalibacterium, a butyrate-producing taxon. ## Study design This cross-sectional analysis was performed on previously collected baseline data (before randomization/intervention) from the Persea americana for Total Health study [25]. Adults between 25 and 45 yr of age with a body mass index (BMI; kg·m−2) ≥25.0 were enrolled in this study. Study exclusion criteria included the following: 1) BMI <25.0 kg·m−2, 2) pregnancy or lactating, 3) current tobacco use, 4) previous diagnosis of metabolic or gastrointestinal disease, 5) food allergies or intolerances, 6) use of medications that impact normal bowel function, or 7) malabsorptive or restrictive bariatric surgery within the previous 2 yr [25]. Study procedures were administered in accordance with the Declaration of Helsinki and were approved by the University of Illinois Institutional Review Board. This trial is registered at www.ClinicalTrials.gov as NCT02740439. Baseline dietary intake was assessed using the Dietary History Questionnaire II, a standardized and validated tool for nutritional assessment developed by the National Cancer Institute [26,27]. Baseline dietary fiber intake (in grams per 1000 kcal) using the US Department of Agriculture values from the Dietary History Questionnaire was used in the linear analyses, as described hereinafter. Healthy Eating Index (HEI) total score was also compared between clusters, as described hereinafter. ## Fecal microbiota and metabolites Participants collected fecal samples on their own and delivered the samples within 15 min of defecation as previously described [25]. Briefly, upon arrival to the laboratory, samples were homogenized, placed in aliquots, flash frozen, and stored at −80°C for microbiota analysis. Fecal DNA was isolated, and the V4 region of the 16S ribosomal RNA gene was amplified then sequenced at the WM Keck Biotechnology Center as previously described [25]. Sequences were demultiplexed in Quantitative Insights Into Microbial Ecology version 2 (QIIME2) version 2019.4, and amplicon sequence variants were generated using the DADA2 version 1.10.1 denoise-single plugin using default settings after dereplication and standard quality-filtering procedures (e.g., the removal of sequencing-related barcodes, sequences with quality scores <20, and chimeric sequences) [25]. Taxonomy was assigned using the q2-feature-classifier command with default parameters in QIIME2, and sequences were matched against the Greengenes 13_8 database [25]. Fecal aliquots for volatile fatty acid analysis were weighed, acidified with 2 N HCl, and stored at −20°C until analysis. SCFA (butyrate, propionate, and acetate) and BCFA (isobutyrate, valerate, and isovalerate) concentrations were quantified using GC-LC (180 cm × 4 mm i.d. glass column with $10\%$ SP-1200/$1\%$ HVFA H3PO4 on $\frac{80}{100}$ mesh Chromosorb WAW; Hewlett-Packard 5890A Series II gas chromatograph; Supelco, Inc., Bellefonte, PA) and normalized on a dry matter basis (in micromoles per gram) [25]. ## Accelerometry A triaxial wGT3X-BT accelerometer (ActiGraph LLC., Pensacola, FL; 3.3 × 4.6 × 1.5 cm; 19 g; dynamic range, ±8g) was used to measure total daily PA, time spent in PA intensities, ST, and prolonged ST. The accelerometer was worn for 7 consecutive days on the right axillary line during waking hours, except for water-based activities. The raw accelerationsignal was sampled continuously at 100 Hz. Acceleration data were converted to vertical axis counts over 60-s epochs using ActiLife software (version 6.13.3; ActiGraph LLC., Pensacola, FL). Non–wear time was defined as 60 consecutive minutes of 0 counts [28] and excluded from the analyses. Only participants with at least 4 d with at least 10 h·d−1 of wear time were included in the analyses [29]. Counts per minute was used as a measure of total daily PA [4]. PA intensities were defined as follows: ≥100 to 2019 (light) and MVPA ≥2020 counts per minute, and expressed in minutes per day [29]. ST was defined as <100 accelerometer counts per minute; prolonged ST was defined as time spent in sedentary bouts lasting ≥30 consecutive minutes [30] and not allowing for tolerance time ≥100 counts per minute [31]. PA and ST variables are defined and described in Supplemental Table 1 (Supplemental Digital Content 1, http://links.lww.com/MSS/C763). Participants were classified according to adherence to the aerobic portion of PA recommendations [7]. Specifically, those engaging in 150 min·wk−1 of MVPA-equivalent PA were classified as meeting PA recommendations. MVPA-equivalent PA was expressed as minutes of moderate PA plus twice the minutes spent in vigorous PA. ## Statistical analyses Before analyses, all PA variables except for counts per minute (light PA, MVPA minutes per day), ST, and prolonged ST were adjusted for accelerometer wear time using the residuals method [32]. Each variable was regressed on wear time, and unstandardized residuals were saved. The predicted value of the PA or ST variable was then computed using the mean wear time of the sample as a constant and added to the unstandardized residuals saved from the respective simple regression models for each PA and ST variable. Associations between wear-time adjusted PA variables, ST, prolonged ST and fecal microbiota composition, and SCFA concentrations were first assessed using multivariate linear regression models. In R (version 4.0.4), phyloseq (version 1.34.0) was used to glom taxa at the phylum and genus levels. The top 5 phyla and top 20 genera were selected for analysis in the linear models. The Firmicutes-to-Bacteroidetes ratio and α-diversity (Faith’s PD and Shannon diversity) were also computed and used in the linear model analysis. Age, sex, BMI, and baseline dietary fiber intake were used as covariates in the model. Prolonged ST was further adjusted for total ST. Linearity assumptions were verified visually and sensitivity analysis for statistically significant outcomes with apparent outliers was performed to verify results. Two strategies were used to group participants. First, participants were categorized on the basis of whether or not they met the recommendations for PA [7]. K-means clustering was also used to categorize participants based on percent of time spent in MVPA and ST. Differences between clusters in age, sex, BMI, HEI total score, Firmicutes/Bacteroidetes (F/B) ratio, diversity indices, and SCFA were defined using Student’s t-tests or χ2 tests (for sex). Clusters were then analyzed to determine whether gut microbiota composition was significantly different between clusters using Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) [33] and DESeq2 [34]. The formula used for the ANCOM-BC function included the same covariates as the linear models (i.e., age, sex, BMI, and baseline dietary fiber intake). Results were considered significant if the P value was <0.05. Because of the exploratory nature of these analyses, results were not corrected for multiple hypothesis testing [35]. ## Participant characteristics Of the 163 participants who underwent baseline testing, 124 participants had both microbiota and accelerometer data. The characteristics of these participants are shown in Table 1. TABLE 1 \| Unnamed: 0 \| Mean \| Range \| \| --- \| --- \| --- \| \| Age (yr), mean ± SD \| 34.9 ± 5.9 \| 25–46 \| \| Sex \| \| \| \| Men (n) \| 44 \| \| \| Women (n) \| 80 \| \| \| Weight (kg), mean ± SD \| 94.5 ± 17.4 \| 63.3–134.5 \| \| BMI (kg·m−2), mean ± SD \| 32.5 ± 5.4 \| 25.0–48.0 \| \| PA and ST \| \| \| \| CPM \| 307.4 ± 106.5 \| 106.5–617 \| \| Light (min·d−1) \| 259.2 ± 59.9 \| 115.7–427.4 \| \| MVPA (min·d−1) \| 34.0 ± 19.1 \| 1.2–95.8 \| \| ST (min·d−1) \| 615.3 ± 66.9 \| 411.6–775.8 \| \| Prolonged ST (min·d−1) \| 196.0 ± 77.5 \| 45.0–449.0 \| ## Linear model associations The PA variables can be grouped into three categories: total daily PA (counts per minute), PA intensity (light, MVPA), and ST (ST and prolonged ST). Total daily PA and time spent in PA intensities were positively associated with relative abundance of Faecalibacterium (counts per minute, light PA) but negatively associated with relative abundance of Alistipes (counts per minute, light, MVPA), Parabacteroides (light), and Gemmiger (counts per minute) as well as concentrations of acetate (counts per minute, MVPA), butyrate (counts per minute), and total SCFA (counts per minute, MVPA; Fig. 1). However, after removal of gut microbiota and SCFA outliers via sensitivity analysis, there was only a trending relationship between Alistipes and counts per minute ($$P \leq 0.08$$), whereas the associations between Alistipes and MVPA ($$P \leq 0.17$$), and the association between butyrate and counts per minute ($$P \leq 0.33$$) were no longer statistically significant. Adjusting MVPA for ST revealed a negative association between MVPA and the SCFA/BCFA ratio ($$P \leq 0.03$$; Supplemental Table 2, Supplemental Digital Content 2, Linear model results, http://links.lww.com/MSS/C764). Conversely, ST was negatively associated with relative abundance of Faecalibacterium but positively associated with relative abundances of several taxa (Ruminococcaceae, Parabacteroides, Alistipes, and Gemmiger; Fig. 2). After adjusting ST for MVPA, only the association with Alistipes remained statistically significant ($$P \leq 0.01$$), although there was only a trending association after sensitivity analysis ($$P \leq 0.08$$; Supplemental Table 2, Supplemental Digital Content 2, Linear model results, http://links.lww.com/MSS/C764). Prolonged ST, adjusted for ST, was positively associated with Actinobacteria and Blautia (Fig. 2). These associations remained significant after also adjusting for MVPA ($$P \leq 0.03$$ and $$P \leq 0.03$$, respectively; Supplemental Table 2, Supplemental Digital Content 2, Linear model results, http://links.lww.com/MSS/C764). FIGURE 1: PA association with gut microbiota. Linear models controlled for age, sex, BMI, and baseline dietary fiber intake indicated significant associations between total PA and gut microbiota composition (proportions). CPM, counts per minute; DM, dry matter. FIGURE 2: ST association with gut microbiota. Linear models controlled for age, sex, BMI, and baseline dietary fiber intake indicated significant associations between PA volume and gut microbiota composition (proportions). ## Clustering based on PA recommendations and K-means clustering Clustering participants based on whether they complied with the aerobic portion of PA recommendations resulted in two clusters: compliers ($$n = 88$$) and noncompliers ($$n = 36$$). These two clusters did not differ in age, BMI, baseline dietary fiber intake, or HEI total score but were different in their sex distribution ($$P \leq 0.05$$; Supplemental Table 3, Supplemental Digital Content 3, Meet PA recommendations sex demographics, http://links.lww.com/MSS/C765). The microbiota composition of these two clusters was also compared. F/B ratio and α-diversity were not different between the two groups, but there were statistically significant differences in SCFA profiles (Supplemental Fig. 1, Supplemental Digital Content 4, Differences in SCFA profiles based on meeting PA recommendations, http://links.lww.com/MSS/C766). Participants who did not meet weekly PA recommendations had lower concentrations of isovalerate and a higher SCFA/BCFA ratio compared with those who did meet recommendations. Comparison of the two groups using DESeq2 (Supplemental Fig. 2, Supplemental Digital Content 4, Meet PA cluster DESeq2 comparison, http://links.lww.com/MSS/C766, and Supplemental Table 4, Supplemental Digital Content 5, Meet PA recommendations DESeq2 results, http://links.lww.com/MSS/C767) also revealed differences in the relative abundances of several taxa, although analyses using ANCOM-BC did not reveal differences between groups (Supplemental Table 5, Supplemental Digital Content 6, Meet PA recommendations ANCOM-BC results, http://links.lww.com/MSS/C768). K-means clustering was used to group participants based on the percent of time spent in MVPA and ST, in accordance with previous research [30]. Both elbow and silhouette methods were used to determine optimal number of clusters [36]. A k-value of two was chosen, which resulted in cluster 1 ($$n = 71$$) and cluster 2 ($$n = 53$$) (Fig. 3). FIGURE 3: K-means clustering. K-means clustering based on percent of time spent in MVPA and ST with a k-value of two. Percent of time spent in MVPA and ST, age, sex, BMI, baseline fiber intake, HEI total score, SCFAs, F/B ratio, and α-diversity (Faith’s PD and Shannon diversity) were compared between the two clusters using t-tests or χ2 test for sex. There were no statistically significant differences in age, sex, BMI, HEI total score, SCFAs, F/B ratio, or α-diversity between the two clusters. Clusters differed in percent of time spent in MVPA and ST, with cluster 1 showing lower percent of time spent in MVPA (mean ± SD, $3.2\%$ ± $1.9\%$ vs $4.4\%$ ± $2.3\%$) and higher percent of ST (mean ± SD, $72.8\%$ ± $4.4\%$ vs $60.9\%$ ± $4.8\%$; Supplemental Fig. 3, Supplemental Digital Content 4, K-means cluster PA comparison, http://links.lww.com/MSS/C766). ANCOM-BC and DESeq2 were used to compare the fecal microbial composition of the two clusters. DESeq2 identified two taxa, Alistipes and Clostridiales, that were significantly different between the two clusters, both showing higher relative abundance in cluster 1 (Fig. 4 and Supplemental Table 6, Supplemental Digital Content 7, K-means clustering DESeq2 results, http://links.lww.com/MSS/C769). Analysis with ANCOM-BC also revealed Alistipes as significantly different between clusters as well as Ruminococcus, Coprobacillus, and Lachnobacterium (Fig. 5 and Supplemental Table 7, Supplemental Digital Content 8, K-means clustering ANCOM-BC results, http://links.lww.com/MSS/C770). FIGURE 4: K-means cluster DESeq2 comparison. DESeq2 identified two taxa, Alistipes and Clostridiales, that were significantly different between clusters. Relative abundance of taxa displayed as proportions. FIGURE 5: K-means cluster ANCOM-BC comparison. ANCOM-BC identified five taxa, Alistipes, Ruminococcus, rc4–4, Coprobacillus, and Lachnobacterium, that were significantly different between clusters. Relative abundance of taxa displayed as proportions. ## DISCUSSION The current analysis is novel in that it is the only study, to our knowledge, that investigates the associations between gut microbiota composition and objectively measured PA in a population of adults with overweight and obesity. The results of our analyses indicate that there are associations between total daily PA, time spent in specific PA intensities (light PA and MVPA), and the fecal microbiota in this cohort of younger and middle-age adults with overweight and obesity. The current analysis found associations between PA and ST and gut microbiota taxa that have been previously reported in associations between exercise or PA and the gut microbiota, although the directions of the associations in the current findings do not always match previous reports. For instance, ST showed positive associations with Parabacteroides and Gemmiger. These findings are in contrast with previous reports that these taxa were positively associated with onset of exercise in inactive older women [35] and juvenile rats [36], respectively. Comparison of individuals who did and did not meet weekly PA recommendations using DESeq2 also revealed differences in abundances of individual taxa such as Ruminococcus (family Lachnospiraceae), YS2, and Haemophilus, although ANCOM-BC revealed no significant results. Bressa et al. [ 24] reported Haemophilus to be increased in active women, which aligns with the results reported herein. According to the analyses using ANCOM-BC, Lachnobacterium and Coprobacillus relative abundances were higher in k-means cluster 2 (higher MVPA, lower ST). Lachnobacterium has previously been reported to be more abundant in individuals with low levels of PA [37], which is in contrast with the current analysis. Coprobacillus was decreased in athletes supplemented with Lactobacillus plantarum PS128 compared with a placebo group [38]. This genus has also been associated with long-term intake of a Western-style diet [39]. The current analysis revealed associations between PA and SCFA-producing taxa as well as SCFA profiles. Linear models revealed that light PA was positively associated with relative abundance of Faecalibacterium. In contrast, ST showed a negative association with Faecalibacterium. The positive associations between counts per minute and light PA and Faecalibacterium are in agreement with previous studies investigating both total daily PA and exercise [24,40,41]. Faecalibacterium is one of the most abundant butyrate-producing bacteria in the gastrointestinal tract and is proposed to have additional anti-inflammatory properties [42]. Butyrate is an SCFA with beneficial health effects, including anti-inflammatory and immunomodulatory effects [42]. Thus, the positive association between PA and this taxon has beneficial implications for human health, whereas the negative association between ST and this taxon could negatively impact gastrointestinal and metabolic health. Cluster analysis based on k-means clustering also revealed differences in SCFA-producing taxa. Ruminococcus (family Ruminococcaceae) was higher in cluster 1 (lower MVPA, higher ST) according to the analyses utilizing ANCOM-BC, reflecting the positive association between ST and Ruminococcaceae herein. This finding is also in agreement with results from Bressa et al. [ 24] that Ruminococcus was higher in sedentary versus active women. Ruminococci have been deemed key symbionts of the gut ecosystem because of their ability to metabolize complex polysaccharides and generate SCFA [43]. However, SCFA has been reported to be elevated in adults with obesity in some studies [44], suggesting that SCFA-producing bacteria may also play a role in weight gain under certain circumstances by increasing energy harvest. The positive association between ST and Ruminococcaceae may reflect this association in the current cohort of adults with overweight and obesity. Previous research has shown that aerobic-based exercise has different effects on fecal SCFA concentrations in lean and obese individuals, increasing concentrations in lean individuals but not in individuals with obesity [45,46]. This could explain the lack of positive association in the current cohort between total daily PA and fecal SCFA concentrations (acetate, butyrate, and total SCFA). Cluster analysis based on whether participants met weekly PA recommendations also revealed differences in SCFA profiles, including lower isovalerate concentrations and higher SCFA/BCFA ratio in those who did not meet the PA recommendations. Lastly, the current analysis revealed associations between PA and taxa that have been implicated in interindividual variability in response to exercise. Our results of both the DESeq2 and ANCOM-BC analyses revealed that there was a higher abundance of Alistipes in k-means cluster 1 (lower MVPA, higher ST). This supports the finding from the linear models in the current analysis that total daily PA and light PA were negatively associated with relative abundance of Alistipes, and ST was positively associated with relative abundance of this taxon. This finding is also in agreement with previous studies that have reported a negative association between PA and Alistipes [47] or that a higher abundance of Alistipes was associated with greater ST or a nonathletic lifestyle [47,48]. The role of the Alistipes genus in the gut ecosystem is not well understood [49]. Some evidence suggests that Alistipes is associated with pathogenicity in colorectal cancer and depression, but other evidence suggests that it is protective against diseases such as liver fibrosis, colitis, cancer immunotherapy, and cardiovascular disease [49]. Intriguingly, Alistipes has been found to decrease in responders and increase in nonresponders after an exercise intervention aimed to improve glucose homeostasis and insulin sensitivity in men with prediabetes [46]. Further research on this genus in the context of PA is needed to understand its health effects, its ability to detect nonresponders to exercise for different health outcomes, and its potential to improve response when decreased using strategies such as dietary modification. A limitation of the current report includes the cross-sectional nature of the analyses, which precludes the ability to determine causality. In addition, potentially informative variables such as transit time were not measured. There was also a lack of diversity in the current cohort with respect to BMI. Only individuals with BMI ≥25 kg·m−2 were enrolled in the study, and the average BMI of the participants used for the current analysis was >30 kg·m−2. This may have selected for a population with a smaller range of daily PA in terms of higher PA intensity. The limited range of daily PA intensity may have impeded our ability to detect statistically significant associations with or differences in fecal microbiota composition based on PA intensity. This may also have contributed to differences in the current findings relative to those reported in the exercise literature, which often involve athletes or structured exercise interventions. Common findings of these studies include increases in Bifidobacterium, Lactobacillus, and Akkermansia as well as butyrate and butyrate-producing taxa in response to exercise [19]. More intense or vigorous exercise may be needed to induce these specific changes in the gut microbiota of sedentary individuals, as well as those with overweight or obesity [50]. As mentioned previously, exercise is a subcategory of PA but is distinct in that it only includes planned, structured PA and is done with the objective of improving or maintaining physical fitness [2]. Although this type of PA is beneficial for human health and physical fitness, evidence also suggests that small amounts of exercise may not be able to overcome the detrimental effects of a sedentary lifestyle and that increased daily PA (steps, standing, etc.) may be just as important for metabolic health, cancer, and mortality risk [51,52]. Thus, it is important to distinguish between these two aspects of PA as they may have distinct effects on the gut microbiota and health in different contexts. Because of these limitations, the current analysis was considered exploratory, and therefore, P values were not adjusted. Thus, there is a higher likelihood of false discoveries, and results should be considered preliminary. Intervention studies are needed to assess connections between the fecal microbiota and PA in a broader range of individuals, as well as with a broader range of potential modifying factors, such as medication use and transit time [53], to better elucidate the effects of PA on the gut microbiota as well as determine whether aspects of PA such as type, intensity, frequency, or duration influence the effect on the gut microbiota. This research may elucidate some of the mechanisms by which the gut microbiota mediates the health benefits of PA. Current research suggests that these mechanisms may include improved gut barrier function, insulin sensitivity, and mental health as well as reduced inflammation [20]. Furthermore, emerging research on the gut–muscle and gut–bone axes indicates that the gut microbiota may contribute to the effects of exercise on muscle hypertrophy and bone health [20]. In addition, future research on the topic of interindividual variability in response to daily PA should investigate the potential effect of differences in the gut microbiota. This could lead to gut-targeted dietary recommendations or probiotic supplements that could complement daily PA to enhance or enable beneficial metabolic responses in previously nonresponsive or less responsive individuals. ## CONCLUSIONS In summary, the current analysis provides novel insights into the relationship between objectively measured PA and the gut microbiota composition of individuals with overweight and obesity that suggest potential beneficial or protective effects of PA on the gut microbiota, such as higher abundance of Faecalibacterium and lower abundance of Alistipes. This work provides a reference and foundation for future research on this topic. ## References 1. Neufer PD, Bamman MM, Muoio DM. Understanding the cellular and molecular mechanisms of physical activity-induced health benefits. 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--- title: Extensive set of African ancestry-informative markers (AIMs) to study ancestry and population health authors: - Samantha Boudeau - Meganathan P. Ramakodi - Yan Zhou - Jeffrey C. Liu - Camille Ragin - Rob J. Kulathinal journal: Frontiers in Genetics year: 2023 pmcid: PMC9997643 doi: 10.3389/fgene.2023.1061781 license: CC BY 4.0 --- # Extensive set of African ancestry-informative markers (AIMs) to study ancestry and population health ## Abstract Introduction: Human populations are often highly structured due to differences in genetic ancestry among groups, posing difficulties in associating genes with diseases. Ancestry-informative markers (AIMs) aid in the detection of population stratification and provide an alternative approach to map population-specific alleles to disease. Here, we identify and characterize a novel set of African AIMs that separate populations of African ancestry from other global populations including those of European ancestry. Methods: Using data from the 1000 Genomes Project, highly informative SNP markers from five African subpopulations were selected based on estimates of informativeness (In) and compared against the European population to generate a final set of 46,737 African ancestry-informative markers (AIMs). The AIMs identified were validated using an independent set and functionally annotated using tools like SIFT, PolyPhen. They were also investigated for representation of commonly used SNP arrays. Results: This set of African AIMs effectively separates populations of African ancestry from other global populations and further identifies substructure between populations of African ancestry. When a subset of these AIMs was studied in an independent dataset, they differentiated people who self-identify as African American or Black from those who identify their ancestry as primarily European. Most of the AIMs were found to be in their intergenic and intronic regions with only $0.6\%$ in the coding regions of the genome. Most of the commonly used SNP array investigated contained less than $10\%$ of the AIMs. Discussion: While several functional annotations of both coding and non-coding African AIMs are supported by the literature and linked these high-frequency African alleles to diseases in African populations, more effort is needed to map genes to diseases in these genetically diverse subpopulations. The relative dearth of these African AIMs on current genotyping platforms (the array with the highest fraction, llumina’s Omni 5, harbors less than a quarter of AIMs), further demonstrates a greater need to better represent historically understudied populations. ## Introduction Racial health disparities in populations of African descent have been extensively documented and in the United States these disparities have been observed in many diseases (Tsai et al., 2011). For example, African Americans have significantly greater mortality and morbidity for asthma and are nearly five times more likely to be diagnosed with primary open-angle glaucoma compared to Americans with European ancestry (Barnes, 2010; Cole et al., 2021). Based on data from US cancer registries for all malignancies combined, African Americans have worse cancer incidence and survival rates compared with European Americans (Özdemir & Dotto, 2017). Not surprisingly, there are certain cancers for which the racial health disparity is more pronounced. In head and neck cancer, African Americans possess poorer survival rates than their European American counterparts even though they have a similar incidence rate (Daraei & Moore, 2015; Zavala et al., 2021). Breast cancer also has pronounced racial disparities with African American women having a $40\%$ higher mortality rate, younger age at diagnosis, and higher incidence of aggressive forms of the disease. Interestingly, African American males also have a higher incidence of breast cancer compared to European American males (Stringer-Reasor et al., 2021). Compared to men of European ancestry, men of African or Afro-Caribbean ancestry have been found to have a higher risk of developing more aggressive forms of prostate cancer at a younger age (McHugh et al., 2021). Socioeconomic factors including healthcare access, geographical factors, lifestyle, and other behavioral factors are routinely used to explain racial health disparities in cancer (Khan et al., 2019). Yet, in head and neck cancer studies, much of this disparity remains after accounting for socioeconomic factors (Molina et al., 2008) and access to healthcare (Ragin et al., 2011), suggesting a genetic basis to these differences. Much of the literature investigating racial health disparities has relied on self-identified race as a proxy for genetic ancestry. However, current characteristics for determining race, including skin color, geography, and language, are often too vague to capture true genetic ancestry in disease studies (Hunt and Megyesi, 2008). Ancestry-informative markers (AIMs) are SNPs with highly differing allele frequencies between different populations, and the differences in frequency tends to be an order of magnitude greater than the difference among continental subpopulations (Tian et al., 2006). More recently, AIMs are being integrated in biomedical studies to study associations between genetic ancestry and health as a more accurate measure of genetic ancestry. AIMs panels are heavily used in admixture mapping in studies seeking to identify disease-associated loci in admixed populations such as African Americans (Chen et al., 2010). AIMs allow for the study of both global and local ancestry association with disease which can lead to the identification of population-specific disease loci. Loci that are associated with increased disease risk in a population are likely to be found in regions of the genome with a high percentage of ancestry for that population (Zhang et al., 2014). While studies have shown that only 1,500–2,500 SNPs are necessary to detect ancestral chromosomal regions in admixed populations (Winkler et al., 2010), a comprehensive AIMs set is required for a finer mapping of disease loci. In this work, we generate a novel panel of 46,437 African ancestry-informative markers that were identified using European and African subpopulations genotype data from Phase 3 of the 1000 Genomes Project (1KGP) (Sudmant et al., 2015). This work compares populations of African and European ancestries to identify SNPs that will be highly informative for African ancestry as well as differentiating Africans from other continental groups. Additionally, this AIM set will provide an important reference panel to investigate genetic ancestry in understudied admixed populations such as African Americans and Afro-Caribbeans. While most African AIMs found in the literature are comprised of SNPs from just two subpopulations, namely CEU (US-European) and YRI (Yoruba) (Zhang et al., 2014), this panel provides a more extensive accounting of each of the 1KGP subpopulations in Europe (EUR) and Africa (AFR). The most highly differentiated SNPs from each AFR subpopulation relative to the EUR population based on informativeness estimates common to all AFR subpopulations were pooled to generate this panel. The African AIMs were validated using an independent dataset of 1,448 individuals where approximately one half is of European ancestry and the other half is of varying degree of African ancestry. They were characterized for function and disease association with several gene-trait associations specific to African populations corroborated in the literature. We also find that these SNPs are under-represented in major genotyping platforms that are in current use. This work identifies a robust new panel of SNPs found in high frequency across continental African populations that have the potential to link population-specific mutations and disease, thus, providing much needed foundational data to examine understudied African populations. ## Data source and AIMs identification Ancestry-informative markers (AIMs) were identified using SNP data from the 1000 Genomes Project (1KGP) (1000 Genomes Project 2012; 1000 20151000 Genomes Project Consortium et al., 2015). The genotype data of African (AFR) populations (Gambian, Gambia [GWD]; Esan, Nigeria [ESN]; Luhya, Kenya [LWK]; Mende, Sierra Leone [MSL]; Yoruba, Nigeria [YRI]), and European (EUR) populations (Utah residents with European ancestry [CEU]; Finnish, Finland [FIN], British, England & Scotland [GBR]; Iberian, Spain [IBS]; Tuscany, Italy [TSI]) were downloaded from the 1KGP database (2013 release). The ancestry informativeness (In) of each genetic variant was estimated separately for six different combinations of AFR datasets (AFR-S1 to AFR-S6) and the combined EUR dataset (Figure 1) using the tool, infocalc (Rosenberg et al., 2003). Genetic variants with In≥ 0.25 were considered as AIMs. The analyses resulted in six separate AIMs subpanels which were subsequently intersected to identify 46,737 common African AIMs. FIGURE 1: Development of the African AIMs panel for this study. Genotype data from African (AFR) populations (Gambian, Gambia [GWD]; Esan, Nigeria [ESN]; Luhya, Kenya [LWK]; Mende, Sierra Leone [MSL]; Yoruba, Nigeria [YRI]) and European (EUR) populations (Utah residents with European ancestry [CEU]; Finnish, Finland [FIN], British; England & Scotland [GBR]; Iberian, Spain [IBS]; Tuscany, Italy [TSI]) from the 1000 Genomes Project database. AFR dataset combinations (AFR-S1 to AFR-S6) and the combined EUR dataset were generated based on the ancestry informativeness (ln) of each variant. Genetic variants with In≥ 0.25 were considered as AIMs. Each AFR subset was compared against the combined EUR set resulting in six different AIMs subsets. The SNPs common to all six AIMs subset were extracted to generate the set of 46,737 African AIMs used in this study. ## Population genomic differentiation analysis To validate population-specific properties of the identified AIMs, we estimated the pairwise fixation index (FST) for the seven 1KGP subpopulations with significant African ancestry (YRI, ESN, MSL, GWD, LWK, ACB, ASW) against the European population. Using the 1KGP data for these populations, we extracted SNPs identified as AIMs on the 22 autosomes using BCFTools (Danecek et al., 2021). Weir-FST estimates were calculated using VCFTools (Danecek et al., 2011) and then visualized via violin plots. We performed a principal component analysis on the AIMs set to visualize their ability to differentiate between the subpopulations of African ancestry and the other subpopulations from the 1KGP. Using BCFTools, the 1KGP VCF files were converted to their binary version (.bcf). PLINK was used to prune and merge the chromosome files into one. Eigenvectors were generated and used for PCA analysis comparing the different populations. ## AIMs validation To test the ability of the identified AIMs to separate populations of African ancestry from those of European ancestry, we evaluated them on an independent dataset of 1,472 individuals. These individuals were recruited as part of a cohort of head and neck cancer study as cases and controls to be genotyped for an IRB-approved related study. A custom Illumina sequencing array was designed by adding a subset of the AIMs from this panel to the Illumina GSA backbone (Infinium Global Screening Array-24 Kit). DNA was extracted from the biospecimen collected from study participants as described in Blackman et al. [ 2018] and genotyped using the custom array. The AIMs included in the array are a subset of the 47 K that are not in linkage disequilibrium. After excluding retired SNPs, updated and combined SNPs, as well as SNPs whose probes were not able to be made, 11,377 probes were added to the GSA array. The number of AIMs successfully genotyped by all samples across two batches totaled 9,566 and the genotype data for 9,385 SNPs were used as the validation SNP subset via principal component analysis. ## Functional characterization We annotated and evaluated the functional role of these genetic variants by running our list of AIMs through ANNOVAR (Wang et al., 2010) using the hg19 human reference genome. The pathogenicity of the AIMs was evaluated using multiple functional effect predictors: polymorphism phenotyping: PolyPhen2 (Adzhubei et al., 2013); sorting intolerant from tolerant: SIFT (Kumar et al., 2009); and two machine learning methods: metaSVM (support vector machine) and metaLR (logistic regression) (Dong et al., 2015). ## Overlap with current platforms We also surveyed the representation of our AIMs set among common genotyping array platforms which may indicate how likely they are to be found in the literature as associated with known disease mutations. We downloaded the manifests for 25 commonly used genotyping arrays from Illumina and Affymetrix (ThermoFisher Scientific) and ascertained the number of SNPs from the AIMs set that appear on the sequencing arrays. Lastly, we estimated the fraction of our African AIMs represented on each array platform. These AIMs were then compared to a large list of imputed SNPs to estimate which of the AIMs could be imputed. ## Genomic characterization of African AIMs In this study, genotype data derived from five continental African and five European subpopulations were analyzed to obtain a generalized AIMs panel for the African populations represented in the 1KGP data (Figure 1). This African-European continental comparison initially generated six AIMs subpanels ranging from 51,907 to 59,422 SNPs with each subpanel including all African populations except for one. The number of AIMs obtained from each dataset are shown in Figure 2A. All six AIMs subpanels were intersected and a total of 46,737 AIMs commonly found in each of the six subpanels were retrieved for further analyses. The usefulness of these AIMs for assessment of population structure was analyzed using data from a separate and ongoing project pointing towards the utility of this AIMs panel to study population admixture in African populations (Ramakodi et al., 2017). FIGURE 2: (Continued) To characterize the distribution of these African AIMs in the genome and to evaluate their functional roles, we partitioned 1 Mbp windows across each chromosome. Figure 2B shows a genome-wide distribution of African AIMs with a significantly high fraction found on the X-chromosome. Most autosomal 1 Mbp windows have fewer than 300 SNPs, although there are several windows with a much higher SNP density. Chromosome 17 and the X-chromosome possess regions with a much higher density of SNPs, with some chromosomal regions having up to 970 SNPs per 1 MB window. Windows that are AIMs-rich are generally distributed evenly across each chromosome (e.g., chromosomes 1 and 12) with no obvious clusters. However, there appears to be some clusters of AIMs-rich windows, as is the case of the X-chromosome harboring a large cluster near its centromere. Additionally, some of the shorter chromosomes (e.g., chr8, 12, 14, 15, 17, and 20) appear to have a higher fraction of African AIMs relative to the longer chromosomes (e.g., chr2, 3, 5, 6, and 7). To investigate whether the regions enriched for AIMs correlate with diseases in the literature, we extracted studies from the GWAS catalog that identified AIMs from this set as associated with cancer and plotted the loci (colored dots) onto a karyotype plot (Figure 2B). AIMs, rs12916300 and rs12913832, mapped to HERC2 (chr15) and rs694339 in the CBLN2 (chr18) genes were associated with colorectal cancer risk in a European sample (Hofer et al., 2017) (Figure 2B). In an African American sample, AIM rs7252505 found in the GPATCH1 gene (chr19) was associated with colorectal cancer risk (H. Wang et al., 2017) (Figure 2B). Two intronic AIMs (rs13267382: chr8, LINC00536; rs9952980: chr18, SLC14A2) and two intergenic AIMs (rs10832963: chr11, SPTY2D1 - SRSF3P1; rs11814448: chr10) were also found to be associated with breast cancer in samples of European and Asian ancestry (Michailidou et al., 2017). Figure 2C shows the distribution of SNPs across different genomic regions and the potentially consequential roles they play in genome function. Most of the AIMs are found in two genomic regions, with just over half, $51.4\%$, found in the intergenic regions of the genome and $39.1\%$ in intronic regions. The remaining SNPs are distributed in smaller fractions in intronic non-coding RNAs ($5.8\%$), 3-prime UTRs ($1.1\%$), with $0.7\%$ found within 1 Mb downstream of genes and $0.6\%$ found upstream of genes. Only 338 SNPs, $0.6\%$ of the entire AIMs set, were found to be in coding regions of the genome. Interestingly, when compared to all autosomal SNPs in the 1000 Genomes Project dataset, this AIMs set has significantly fewer exonic (p-value < 2.2e-16, Fisher’s Exact test) and 5-prime UTR (p-value = 0.04685, Fisher’s Exact test) SNPs than expected. ## Substructure in populations of African ancestry The importance of population substructure in association studies is becoming more apparent as they can confound observed genetic associations when ignored and hinder us from elucidating the genetic bases of disease. Consequently, understanding whether this set of African AIMs adequately identifies population substructure within and between populations with shared African ancestry is an important goal. Figure 3 shows the distribution of the fixation index of these AIMs in the different sub-populations of African ancestry as estimated against the EUR super-population. Based on Weir-FST statistics, there is a uniform distribution of FST estimates for the continental AFR subpopulations with the four West African subpopulations (ESN, YRI, GWD, MSL). The violin plots in Figure 3 show the most similarity. The ACB subpopulation shows a slightly different distribution compared to the AFR subpopulations though its plot still reflects a high degree of dissimilarity from the EUR population and close relationship to the AFR subpopulation. The ASW violin plot reveals the most divergent distribution and a much lower FST minimum relative to the other subpopulations. FIGURE 3: Distribution of FST among autosomal AIMs. FST was estimated for each African subpopulation (ESN, YRI, GWD, MSL, and LWK) against the European population using data from the 1000 Genomes Project. Additionally, estimates of FST were also calculated for the two other subpopulations in the dataset with high African ancestry, the Afro-Caribbean population from Barbados (ACB), and African Americans from the United States (ASW). FST was estimated for biallelic autosomal AIMs only and shows differences in the distribution in the FST estimates between the populations. The generated African AIMs were evaluated for their ability to properly differentiate populations of African ancestry from populations of European ancestry and the other continental ancestral groups such East Asia, South Asia, and the Americas. Using SNPs from the 1000 Genomes Project, principal component analysis reveals that this AIMs set separates African subpopulations from all other populations (Figure 4A). Based on these AIMs, the AFR subpopulations are effectively segregated from the EUR subpopulations in addition to other global ancestry groups that were not included in the generation of the set (Figure 4A). The PCA plots also show that the AIMs were able to detect population substructure within populations admixed with African ancestries such as the admixed American populations, ASW and ACB. Figure 4B shows that PC1 separates the AFR subpopulations from the EUR subpopulations and to a lesser extent it shows some degree of separation between the AFR subpopulations, explaining $16.94\%$ of the total variance of the sampled genetic variation. PC2 and PC3 explained a combined additional $15.24\%$ of the total genetic variance. The five continental African (AFR) subpopulations appear to cluster more tightly together while the populations of African ancestry in Southwest United States (ASW) and the African Caribbean in Barbados (ACB) subpopulations show some spread toward the European (EUR) population cluster. FIGURE 4: (Continued). Using an independent dataset of racially self-identified individuals from the United States, we were able to show that our validation subset of 9,385 markers effectively separated individuals of European ancestry from those of African ancestry (Figure 4C). Additionally, these AIMs detected population differences within the group of individuals with African ancestry without being able to fully differentiate the sub-populations of African ancestry (continental African, African American, Caribbean of African ancestry). ## AIM allele frequencies and representation in common genotyping arrays Minor allele frequency (MAF) reflects how common an allele is in a population with low frequency alleles often associated with disease phenotypes making them markers of high interest. In Figure 5, columns 1–5 display the MAF for the five continental AFR populations (ESN, GWD, LWK, MSL, YRI), column 6: the Afro-Caribbean population, column 7: African Americans in the Southwest, and column 8: the European population. The five AFR subpopulations appear to have similar MAFs for the different SNPs while the frequencies are noticeably different in the ACB and ASW populations. The EUR populations appear to have much lower MAFs for these SNPs. The seven columns (1–7) with populations of African ancestry show further evidence of population substructure. We also tried to predict pathogenicity using prediction tools such as SIFT (sorting intolerant from tolerant) and PolyPhen2 (polymorphism phenotyping). Column 9 shows the SIFT results and column 10 shows the results from PolyPhen2 with damaging mutations labeled in purple (Figure 5). There are a few instances where some SNPs are identified as damaging by both predictors and these SNPs appear to be more concentrated in SNPs with higher MAFs in the AFR populations. Interestingly, many of the SNPs predicted to be damaging by SIFT have very low MAF in the EUR population. We also compared other pathogenicity tools, such as metaSVM and metalR (Dong et al., 2015), that provide an aggregate prediction score from multiple individual tools and these tools only identified two AIMs as damaging: rs12186491 maps to SPINK6, a serine protease inhibitor kazal-type 6 gene which has been found to regulate nasopharyngeal carcinoma metastasis through EGFR signaling, and rs6601495 encodes for Retinitis Pigmentosa 1-Like 1 Protein which is associated with diseases like occult macular dystrophy and retinitis pigmentosa (Zheng et al., 2017; Noel and MacDonald, 2020). FIGURE 5: Distribution of minor allele frequencies in African AIMs. AIMs found in the coding regions of the genome and carrying non-synonymous mutations are juxtaposed next to the SIFT and Polyphen2 scores for each SNPs. Of the 338 exonic SNPs, 164 are non-synonymous mutations, 149 of which were scored by the tools. Excluding the X-linked SNPs and multiallelic sites, only 112 SNPs were non-synonymous and are plotted here. The color gradient for the MAF ranges from white (0) to red (0.6); the gradient for SIFT range from purple (0, damaging) to pink (1, benign), while the gradient for Polyphen2 ranges from pink (0, benign) to purple (1, damaging). Generally, functional or pathogenicity information on the SNPs contained in this set of AIMs is scarce which may be a byproduct of being underrepresented in currently available genotyping platforms. An analysis of 25 popular genotyping array platforms from Illumina and Affymetrix reveals that most of the commercial arrays surveyed included less than $10\%$ of the African AIMs (Figure 6). Combined, only 19,239 of the 46,737 AIMs in this set were found among the 8,708,293 unique SNPs included in these 25 commercial arrays. When compared to a current list of over 13 million variants (imputed SNPs from Neale lab, https://github.com/Nealelab/UK_Biobank_GWAS) that were imputed against a diverse panel, 43,647 of the 46,737 AIMs were found, indicating that most of the SNPs could be imputed. FIGURE 6: Percentage of African AIMs from this study that are represented in commercial genotyping arrays. Twenty-five commonly used genotyping platforms were chosen from Illumina and Affymetrix (y-Axis) and the fraction of SNPs differentiating the African population from others (i.e., AIMs from this study) is listed for each array. Platforms are ranked from most representative of the AIMs panel from this study to the least. ## Discussion Undetected population structure can lead to spurious findings in genetic association studies. With the increased reliance on these studies to identify genetic markers associated with disease, identification and correction for population stratification are critical as both environmental and genetic factors can affect disease risk between populations or subpopulations (Enoch et al., 2006). In this work, we developed a set of African ancestry-informative SNPs that differentiates populations of African ancestry from others and identifies substructure within populations of African ancestry based on estimates of informativeness. We identified 46,737 African ancestry-informative markers from five African subpopulations using Phase 31,000 Genomes Project data and our results suggest they convincingly aggregate populations based on their genetic ancestry and effectively separate populations of African ancestry from other major ancestral populations (Figure 4). Although the AIMs were identified from a comparison between AFR subpopulations and a combined EUR reference, Figure 4 shows that they suitably isolate populations of African ancestry from those of other continental groups including East Asian, South Asian, and groups with American ancestry. While there are several existing sets of ancestry-informative SNPs claiming to differentiate African populations from others, they often come with limitations including that they are estimated from one subpopulation of African ancestry, usually YRI, and one of European ancestry, usually CEU (Keene et al., 2008; Cheng et al., 2009; Tandon et al., 2011; Zeng et al., 2016), though sometimes they include ASW (Chen et al., 2010). This AIM panel provides a more extensive and comprehensive set of pan-African SNPs that can help improve the accuracy of African ancestry estimates since it was established from multiple subpopulations from both the European and African populations. We developed our AIMs set to exclude SNPs that are specific to just one African subpopulation and are unlikely to be informative outside the context of that subpopulation. We also detected substructure between subpopulations of African ancestry when admixed populations with significant African ancestry, namely ASW and ACB, were introduced (Figure 2). However, this AIMs panel of markers does not fully differentiate between subpopulations of African ancestry. This distinction highlights the consideration that should be given to both between and within population variation to effectively control for population stratification in genetic studies. As advanced in Enoch et al. [ 2006] and originally by Lewontin [1972], while there is significant diversity between populations, the bulk of human diversity is found within populations. We evaluated the subset of AIMs that are in protein-coding regions for pathogenicity and association with disease as certain disease-causing variants have been found at highly differing frequencies across populations (Patterson et al., 2004). Using tools such as SIFT, Polyphen, metaSVM, and metaLR via ANNOVAR, we investigated which changes may lead to a loss of function in their associated proteins. These tools predict how non-synonymous mutations affect protein function (Flanagan et al., 2010). $0.64\%$ of the AIMs are located in exonic regions of the genome and of that SNP pool, only the $48.52\%$ fraction that are non-synonymous mutations were used for pathogenicity prediction. There is also a lack of agreement between each of the prediction tools which makes it challenging to interpret the predictions made for the SNPs identified as damaging. Further investigation is needed to contextualize these AIMs and elucidate their potential implications in disease. Although most of our identified AIMs are in non-coding regions, they can still play a role in the genetic basis of health disparities and how genetic ancestry can influence disease risk, although these association data are sparser. One such case of that demonstrates the functional impact of non-coding AIMs from association literature is the African ancestry variant, rs72725854, a rare variant found in an enhancer region at 8q24 which has been shown to regulate multiple lnRNA genes and the MYC oncogene to influence prostate cancer risk in men of African ancestry (Darst et al., 2020; Walavalkar et al., 2020). Additionally, the GWAS literature is ripe with SNPs that have been found to be associated with disease phenotypes such as differential survival in head and neck cancer, differences in prostate cancer risk, and differences in diagnosis stage in breast cancer (Al-Alem et al., 2014; Irizarry-Ramírez et al., 2017; Ramakodi et al., 2017). Certain chromosomes stood out as having a higher proportion of AIMs relative to their size such as the X-chromosome and chromosomes 4, 8, 12, 14, 15, and 17, all of which have a higher proportion of AIMs than the much larger chromosome 1. Interestingly, loci on some of these chromosomes have been identified in the literature as associated with diseases in populations of African ancestry including loci 8p23 and 8q24 and asthma risk and the association of rs75853687 on chromosome 5 with alloimmunization in sickle cells patients (Williams et al., 2018; Daya et al., 2019). Over eight variants associated with prostate cancer risk have been identified on loci 8q24 (Han et al., 2016). This AIMs panel will significantly contribute to the ease with which the field integrates African genetic ancestry in population genetics studies but there remain some limitations. The 1000 Genomes Project was used to identify the AIMs for this set although it only includes six African sub-populations and does not completely encapsulate the rich genetic history of the African continent. Moreover, the included populations have small sample sizes which might not be fully representative of the diversity in those populations. Lastly, Phase 3 data from the 1KGP was sampled at relatively low depth (7.4x) which can make it challenging to identify less common, rarer variants in the population (Byrska-Bishop et al., 2022). The underrepresentation of these African AIMs on commonly used commercial genotyping arrays also contributes to the scarcity of information about their involvement of disease. However, many of these SNPs may be imputable using currently available African-inclusive panels. We searched a list of over 13 million imputable variants generated from UK BioBank data imputed using a panel made up of data from the Haplotype reference consortium, UK10K, as well as the 1000 Genomes Project reference panels, for the presence of our identified African AIMs. Out of these 46,737 AIMs, 43,647 were found on the list of imputable variants, which is expected considering that 1KPG data was included in the panel used to impute the SNPs. However, many populations in Africa remain poorly studied and imputing variants for those could be challenging. As discussed by Martin et al. [ 2018], imputation panels and resources have a European bias, and sequencing initiatives are biased for West African populations, thus, ignoring much of the total genetic diversity on the continent. This is a particular concern for the imputation of African genomes as the literature suggests a higher rate of genetic diversity and a lower rate of linkage disequilibrium in African populations (Bentley et al., 2020). Furthermore, most reference panels are currently not publicly available or use too small of a sample size to impute effectively (Gurdasani et al., 2015; Mathias et al., 2016; Taliun et al., 2021). The development of ancestry-informative markers within the framework of population disease risk estimation presents an exciting opportunity to investigate the genetic bases of health disparities across heterogeneous populations with people from different genetic histories. Population structure can have significant implications for genomics studies and simply controlling for them is not always enough to successfully account for population stratification and to avoid such pitfalls as spurious associations (Enoch et al., 2006). As demonstrated here, having a highly specific African AIMs set can help detect ancestry differences between populations of African ancestry and others but it can also identify substructure within populations of African ancestry that should be further surveyed in association studies. This approach increases statistical power and can lead to the identification of true associations between the SNP markers of interest and the disease/trait in association studies. Many studies have identified associations between genetic ancestry and health and, more recently, there is a trend toward the identification of AIMs associated with disease though the studies of African ancestry are still few. Studies like these allow for increased granularity in the analysis of ancestry and health. Considering that self-reported race is an inconsistent and unreliable substitute for genetic ancestry, the AIMs set presented here provides a means for researchers to uncover the impact of ancestry on disease and phenotype. These African AIMs, allowing researchers to apply a set of markers spanning the whole genome, will hopefully provide new avenues to study disease genetics in a large, diverse, and understudied population, and helps elucidate the contribution of local ancestry to disease risk and health. ## Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: 1000 Genomes project. The list of ancestry-informative markers developed in this study is available in Supplementary Table S1. ## Author contributions SB performed the analyses, generated the tables and figures, wrote the first draft of the manuscript, and managed all subsequent drafts. RK and CR contributed to the conception and design of the study. MR developed the AIMs pipeline, identified the AIMs set. YZ designed the custom array and processed the genotype data. RK, CR, SB, and MR wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1061781/full#supplementary-material ## References 1. An integrated map of genetic variation from 1,092 human genomes. Nature (2012) 491 56-65. DOI: 10.1038/nature11632 2. Adzhubei I., Jordan D. 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--- title: 'Medication work among nonagenarians: a qualitative study of the Newcastle 85+ cohort participants at 97 years old' authors: - Joy Adamson journal: The British Journal of General Practice year: 2023 pmcid: PMC9997653 doi: 10.3399/BJGP.2022.0188 license: CC BY 4.0 --- # Medication work among nonagenarians: a qualitative study of the Newcastle 85+ cohort participants at 97 years old ## Abstract ### Background People aged ≥85 years are the fastest growing section of our population across most high-income countries. A majority live with multiple long-term conditions and frailty, but there is limited understanding of how the associated polypharmacy is experienced by this group. ### Aim To explore the experiences of medication management among nonagenarians and the implications for primary care practice. ### Design and setting Qualitative analysis of medication work in nonagenarians from a purposive sample of survivors of the Newcastle 85+ study (a longitudinal cohort study). ### Method Semi-structured interviews ($$n = 20$$) were conducted, transcribed verbatim, and analysed using a thematic approach. ### Results In most cases, although considerable work is associated with self-management of medication use, it is generally not experienced as problematic by the older people themselves. Taking medications is habitualised into everyday routines and practices, and is experienced in much the same way as other activities of daily living. For some, the work associated with medications has been relinquished (either partially or wholly) to others, minimising the burden experienced by the individual. Exceptions to this were found when disruptions to these steady states occurred, for example, following a new medical diagnosis with associated medication changes or a major life event. ### Conclusion This study has shown a high level of acceptance of the work associated with medications among this group and trust in the prescribers to provide the most appropriate care. Medicines optimisation should build on this trust and be presented as personalised, evidence-based care. ## INTRODUCTION People aged ≥85 years are the fastest growing sector of the population in the UK and other high-income countries; the UK population aged ≥85 years is predicted to more than double over the next 20 years, reaching 3.6 million by 2039.1 This has important implications for health and social care services, since the majority of those aged ≥95 years live with multiple long-term conditions and the associated polypharmacy.2 Taking multiple medications has been found to be associated with a range of negative health outcomes including adverse drug events, drug-interactions, functional decline, cognitive impairment, falls, and urinary incontinence.3–5 Medicines optimisation is a key feature of health policy, advocating a person-centred approach to safe and effective medicines use.6 Central to a medicines optimisation approach is an understanding of how individuals experience and respond to their medications alongside a sense of their desired level of involvement in decision making about their medications.7,8 Several reviews have highlighted important aspects of the patient perspective on medication optimisation including the significance of identity and personal values; individual understanding of medications and morbidity; the physical characteristics of the medications; perceived effects (and adverse effects); the professional/patient relationship; as well as practical aspects such as cost and access to health care. Friends and family have been found to support adherence to medication in practical ways, but their role can be underestimated by health professionals.9–13 Despite the disproportionate burden of multiple long-term conditions and multiple-medication use faced by older people, the lived experiences of how they incorporate their medications into everyday life are underrepresented in the existing literature. The aim of this study is, therefore, to explore and understand the experiences of medication use among the oldest old (people aged 97 years) to inform medication optimisation practices within primary care service delivery. ## METHOD An exploratory in-depth qualitative interview study was conducted with a sample of surviving participants from the Newcastle 85+ study. A member of the main study’s public involvement group was involved in the study design, co-developed participant information leaflets, and contributed to the interview topic guide. ## Population and recruitment The Newcastle 85+ study is an observational cohort study of people born in 1921, who reached the age of 85 years during the year of 2006 when recruitment commenced and were registered with a participating general practice in Newcastle upon Tyne or North Tyneside Primary Care Trusts in the UK.14 Data were gathered by two methods, a general practice record review and a multidimensional health assessment conducted in the participants’ usual residence by a trained research nurse. A wide variety of information on their health, family and social circumstances, and use of health and care services was collected.14 Following baseline assessment, participants were re-assessed at 18, 36, and 60 months, and 10-year follow up at aged 95 years.15 This qualitative study purposively sampled from the 80 surviving participants in the 10-year follow up. To reflect the diversity within the cohort and to ensure maximum variation, a sampling frame that included sex (gender), place of residence (living independently, with family members, or residential care), morbidity, and frailty (as captured by a disability measure based on no difficulty with 17 activities of daily living) was used.14 Potential participants were contacted via mail and a telephone call to request participation in the study. ## Data collection and analysis Interviews were conducted between August 2018 and February 2019. Using a semi-structured topic guide (see Supplementary Appendix S1) they explored several aspects of the participants’ day-to-day experiences including their health and health care, social participation, and thoughts about the future. Interviews were conducted by one of the authors, a research nurse with experience of qualitative methods, who had no previous relationship with the participants. All interviews took place in the participants’ own dwelling and lasted approximately 1 hour (ranging from 36 minutes to 2 hours); some participants opted to have a family member present to support them during the interview. Family members contributed to the interviews in practical ways, such as helping with hearing difficulties, filling in details that participants had difficulty remembering, and sometimes adding their own view of the participants’ stories. Informed consent was obtained from all participants following the establishment of capacity. Interviews were audiorecorded and fully transcribed verbatim. Data were analysed thematically.16,17 A systematic approach was taken, which included: detailed familiarisation; identification and indexing of key themes; and contextualising and interpreting these themes in relation to the broader dataset. Initial coding (carried out by the same author taking the interview) was inductive, whereby coding labels were placed on sections of text. During this process regular meetings were held with another author, a senior qualitative researcher, to discuss codes and themes. Initial findings were presented to a patient representative for comment. This article is based on further development of the inductively identified theme ‘coping with medication’. Further concept development was finalised by the senior qualitative researcher and discussed with the wider research team, drawing on the concept of ‘work’, which has been used within medical sociology to facilitate the understanding of how individuals manage chronic illness.18 Constant comparison was conducted, in order to compare data across codes/themes and cases.19 In particular ‘negative cases’ were explored; that is, the data from those participants who described an experience of medication management that was different from the majority, enhancing the rigour of the analysis.19 Pseudonyms have been given to all participants. ## RESULTS Interviews were conducted with 20 participants ($$n = 13$$ female, $$n = 7$$ male), the majority ($$n = 14$$) still lived in their own homes and encompassed a range of older people with a range of severity of disability (see Table 1). Eighteen out of 20 participants were taking ≥4 regular medications (ranging from none to ten); one had decided against taking preventative/disease modifying treatment; and the other was using analgesics on an ‘as required’ basis. Five participants were supported during the interview by a family member/carer. Table 1. \| Participant \| Sex \| Disability \| Housing \| \| --- \| --- \| --- \| --- \| \| Russell \| Male \| Moderate \| Care home \| \| Pauline \| Female \| Mild \| Owner occupier \| \| Jack \| Male \| Mild \| Owner occupier \| \| Joe \| Male \| Mild \| Owner occupier \| \| Eileen \| Female \| Mild \| Owner occupier \| \| Anne \| Female \| Severe \| Rented home (LA) \| \| Maureen \| Female \| Moderate \| Rented home (private) \| \| Margaret \| Female \| Severe \| Rented home (LA) \| \| Pamela \| Female \| Mild \| Rented home (private) \| \| Malcolm \| Male \| \| Owner occupier \| \| Bob \| Male \| Moderate \| Care home (LA) \| \| Jean \| Female \| Moderate \| Owner occupier \| \| Mary \| Female \| Severe \| Sheltered housing \| \| Cath \| Female \| Mild \| Owner occupier \| \| Carol \| Female \| Moderate \| Care home \| \| Adele \| Female \| Mild \| Sheltered housing \| \| Penny \| Female \| Mild \| Owner occupier \| \| Tony \| Male \| Mild \| Owner occupier \| \| Alan \| Male \| Mild \| Owner occupier \| \| Angela \| Female \| Mild \| Owner occupier \| ## Everyday work involved in medication use Notable in the participants’ descriptions of their medication use is the degree of work involved in taking multiple medications: from organising the prescription to collecting and storing the medication, knowing the correct dosage times, physically taking the medication, and monitoring for adverse effects. Analysis uncovered three overlapping subthemes relating to different types of work involved including the ‘emotional work’ individuals undertake, relating to how they feel about their medication use; and the ‘cognitive work’ required to understand the reasons for, and effects of, the prescribed medications in the context of their health literacy. Both cognitive and emotional work underpinned the decisions made on accepting or rejecting a medication regimen. This, in turn, governed the ‘instrumental work’ associated with having the right medication available to take at the right time — including ordering and collecting prescriptions, storing, and then physically taking the medications. Yet perhaps surprisingly, for most, this work was not considered a burden: Participant (P):‘I’ve got six on the prescriptions, regular prescriptions. Apart from those, which are prescribed by the medical group, I take little … What do you call them? … Extra tablets that are my own choice. So there would be about 10 probably. ’Interviewer (I):‘How do you feel about taking that number of tablets?’P:‘Of the six on the prescription, I think there are about four — at least — that I’ve had to take all my life for various … I’ve got anaemia, pernicious anaemia. Yes, yes. So I’ve got to take little things like that. It doesn’t bother me … No trouble at all, no.’ ( Joe, Male [M]) It would appear there is an apparent paradox between the work involved in medications management and the associated burden, however, this can be explained in terms of the degree to which, over time, medication use has become accepted, adhered to, and habitualised. ## Habitualised management The majority of participants had long since accepted the need for prescribed medications in a matter-of-fact way, without evidence of actively engaging with any significant decision making around this on a day-to-day basis. To a general question about their day so far, one participant responded: ‘Well I got up this morning, had a shower, got dressed and then I came through and had some toast and marmalade, I took my tablets, washed my breakfast things, put my little bit of washing out, run the sweeper over the floor and that was it. ’(Maureen, Female [F]) This was a typical answer, suggesting that for many participants that were interviewed, medication use is an accepted and firmly embedded part of their autonomous daily routine, associated with minimal burden. One ubiquitous element in the descriptions of instrumental work was the presence of routines, whether these are daily, weekly, or monthly. The weekly visit to the pharmacy on a Friday was a fixed point in this participant’s routine: ‘Hair tomorrow, I go and get my hair done tomorrow, 9.30 am, then tablets on Friday. I go up for them to the chemist. They could deliver it, but I said, “Well, I never know when I’m gonna be in.” So, I’d rather go up for them myself. So, I get them on a Friday, when I get some ham or something and some rolls for when [daughter’s name] comes Saturday and we have our lunch. ’(Mary, F) Various strategies to optimise adherence were described, and demonstrated medication use was completely entrenched into daily routine practices reflecting that, for many participants, their lives were more generally characterised by domestic routines. For example, some placed medication in different locations around the house to be at hand when needed: ‘I’m usually setting my breakfast up about 8.00 am, and the washing up and putting the next day’s medication in the two little containers, one I take up to bed for the morning’s medication, and the night-time one I keep in there. ’(Tony, M) The importance of routine was reaffirmed when participants described lapses in adherence were most often associated with changes in their normal pattern of activity: ‘Say for instance on Sunday night there. I forgot to take them. No, Saturday night it must have been. With my son. We had been out all day and came in late at night. About 10.00 pm it was when we came in. I was so eager getting undressed and getting into bed after having a wash that I forgot to take my tablets. It’s just occasions like that when I might I forget you see. ’(Jack, M) For most participants much of the cognitive and emotional work involved in decision making relating to medication adherence had taken place in the past and generally no longer required further processing on a day-to-day basis. This is not to imply that participants were necessarily passive in their interest or understanding of medications. While this varied, some indicated a good medication literacy: ‘There are not so many now but the important, the immune system one, MMF [mycophenolate mofetil], I used to be on 250 mcg capsules morning and evening, and then [name of doctor] after tests, the antibodies that had been causing the encephalitis, I was having these encephalitis fits … Then they found 10 times the normal amount of this particular antibody so they put is [me] on that MMF.’(Tony, M) Many participants had taken medications (sometimes the same medications) for years and even in the absence of direct evidence of effectiveness, many of those interviewed were still strongly committed to taking their prescribed medications regularly. The motivation for ongoing medication adherence most commonly articulated was a trust in the medical professional prescribing the medication: I:‘You feel as though they’re helping you, the medications?’P:‘Well, they’re bound to be. They’re bound to be, [interviewer’s name], or I wouldn’t be getting them.’ ( Jean, F) This trust appeared to be based on a combination of the stated accepted expertise of the professionals (based on the symbolic capital of the prescribing clinicians), but also indicated implicitly by the lack of any adverse events or side effects mentioned expressly in the accounts: ‘It doesn’t worry me, because it’s only three things and I know it’s not doing me any harm. It’s for my own good. I just swallow them, full stop. I don’t even think. I just take them and get on with it. ’(Jean, F) Only a few participants described occasional incidents of swallowing problems and adverse effects. While the majority of the responders had reached a steady state in their medication management, this was not to suggest that no cognitive or emotional work was being undertaken. For most, there was still an implicit monitoring of effectiveness or side effects from medications by the participants. Some were content that their medications were beneficial, often citing their own longevity as proof, however, this seemed to vary by medication class and the availability of evidence on which to base these perceptions. For example, medication for analgesia was given as an example, where effectiveness could be easily monitored: ‘My taking the codeine and paracetamol, it doesn’t make things completely back to normal, but I think it helps. The rest of them, I probably don’t notice the difference. I know my blood pressure is normal, so I know the tablets are controlling that. ’(Cath, F) I:‘Is there anything that you are able to do to ease the pain or to make your knees better?’P:‘I have ointments, but nothing seems to do any good. And I take ibuprofen tablets, but they don’t seem to help much there.’ ( Margaret, F) For longstanding difficulties associated with particular medication use, participants articulated the autonomous solutions they had derived in order to achieve an acceptable balance between the potential benefits of the medication and the negative impacts of adherence: ‘Occasionally I drop them off, but then I’m probably advised to restart them. I mean, the one for osteoporosis, the alendronic acid one, anybody who takes it will tell you, it’s a real misery. You aren’t supposed to lie down, or have anything to eat for half an hour before. The nurse might say, take it in the night, but you don’t want to be sitting up in the early morning, or the middle of the night. It really is a nuisance to take. The rest of the tablets you just take them and that’s that. ’(Cath, F) Participants appeared to derive comfort from the familiar routines and satisfaction from performing these routines competently, this role fulfilment may help to foster a positive sense of identity as well as demonstrating (and protecting) their autonomy, especially for participants who were growing more dependent in other areas of their lives. For this group of the oldest old, as would be expected, previous practices relating to medication management had been challenging for some due to physical and/or cognitive decline. This meant relinquishing some of their autonomy over the maintenance of their health and had to call on their social capital to fulfil the task of medication management: I:‘Are there things that are difficult for you nowadays to go on holiday?’P:‘Oh, yes. It’s awkward for medication. [ Daughter’s name]’s used to it but I get quite mixed up with that, checking it before and keeping it in order while we’re away.’ ( Tony, M) ## Diminishing autonomy The process of relinquishing autonomy is slow, adjusting to physical and cognitive change occurs over time and new routine practices are gradually formed seeking assistance across a range of activities of daily living, in a stepwise fashion. In terms of medications, in some cases elements of the instrumental work associated with medicines was shared or relinquished to members of the participants social network, diminishing the potential burden experienced: P:‘I sit at the table, and [daughter’s name] brings them. She helps me, now, to get the blooming things out. I sit and do 4 weeks at a time. ’Daughter:‘They’re all written down. She follows the names. I bring all the boxes, the Tupperware boxes out, and then she puts them all in and does them for … She sits and does them.’ ( Eileen, F) Doctors’ surgeries and pharmacies typically offer multiple means of communication to order repeat prescriptions, including in person, telephone, and internet. Nevertheless, a combination of sight, hearing, and mobility impairments made accessing these services difficult for some, and family, friends, or paid carers were supporting this work. In the most extreme cases, participants did not describe any burden associated with medication management as they had relinquished responsibility for the mainstay of the associated work into either informal or formal support networks. For example, one participant no longer knew the names of any of her medications or the reasons for why she took them. She was living in her own home with carers attending four times a day to assist her with personal care, meals, and medications: P:‘Well, to tell you the truth, I don’t know what I take.’ [ laughter]I:‘Who looks after your medicines, then?’P:‘Oh, the girls.’ ( Pauline, F) This participant was still committed to taking her medications and did so regularly, with the assistance of her care workers. Doing less of the instrumental work associated with medications appeared to be closely associated with a lack of cognitive or emotional work including knowing which drug they were taking and why, or feeling anxious about their medication use. This also tended to happen in a context where participants had given up or were giving up responsibility for other areas of their life into the trusted domain of professionals and/or family members. ## Disrupted management There were two clear exceptions within the sample, who fell outside of ‘habitual management’ having suffered a disruption that had led to a change in their medication management practices. The first example was a woman with a new health problem struggling to adjust. She described her medications as: ‘Bane of me life. ’(Pamela, F) She was still taking the medications, but she had experienced some severe adverse effects and was anxious that the doses were still not correct. Being outside of the ‘steady state’ where greater cognitive and emotional work were implied in order to understand the change in health status and the consequences of this in terms of medicines management, were associated with much greater feelings of burden. The second exception was a man who had stopped all medications, no longer visited his GP, and had refused investigations of a skin lesion. He described his choice, saying: ‘I decided I would live with it, until my time on this planet was up. ’(Bob, M) This rejection of medication and other forms of medical intervention have been a recent change in his outlook, following a major disruptive life event — the death of his wife — and he feels he no longer wants to prolong his own life, taking the autonomous decision to cease his medication use alongside other forms of health care. ## Summary This study, which has explored medication work among nonagenarians, found that in most cases, although medication use requires emotional, cognitive, and instrumental work it is generally not experienced as problematic. Medication use is habitualised into everyday routines and practices, like ‘having toast and marmalade’, and is regarded in much the same way as other activities of daily living. Following (what can be) years of using medication for a specific indication, most issues associated with perceived effectiveness or adverse effects have been addressed and routines have been established. These older people take an autonomous role in choosing to adhere to medication regimens and practical ways of realising this aim. For some, the work associated with medications has been relinquished (either partially or wholly) alongside their diminishing autonomy, minimising the burden experienced by the individual. Exceptions to this were found when disruptions to these steady states occurred, for example, following a new medical diagnosis with associated medication changes or a major life event. ## Strengths and limitations The study presents a rare opportunity to understand the experiences of nonagenarians. Narratives relating to the everyday lives of participants were generated through the interview process; therefore, medications were talked about as part of these accounts, rather than in answer to specific questions relating to particular medications. As such, the data were able to show the significance of medication work as part of their lives as a whole and capture their implicit views on medications and how they coped with these. However, a lack of direct questioning may have limited findings with regards to medicines efficacy and adverse effects in some cases. All of those interviewed were longstanding participants in the Newcastle 85+ cohort study and, as such, were all from a small area of the UK, White British, and had shown a willing commitment to participation in research. This may have limited the range of accounts that were obtained and hence transferability to other populations. It is also important to note that findings may be subject to a cohort effect and may not have transferability to future generations when they reach this age range. Participants were offered the opportunity to have someone present at the interviews — five interviews were conducted with the participant and a carer. It is possible that the participant provided a different account than they would have unaccompanied. However, this is unlikely to have changed the main messages of the study. ## Comparison with existing literature Several of the points raised in this study echo findings from previous research, for example, daily routines being used as memory aids and that forgetting medication was worsened when routines were disturbed; being diagnosed with a life-limiting illness impacts on perceived value of certain medications; the use of physical medication organisation systems (for example, dosette boxes) to assist with the management of complex medication use; and the importance of social support.20–27 However, there are also features from the previous literature that were largely absent from the accounts generated in this study. For example, studies have indicated a range of instrumental difficulties experienced by individuals taking multiple medications, including: the collection of medications, obtaining repeat prescriptions, and the cost of medications. These issues did not feature in this current research, possibly explained by the older age of the sample and the policy context within which the data were collected. The research took place in the UK, therefore all prescriptions for those aged >60 years were free of charge. Most pharmacies have services in place to assist with repeat prescriptions and have home delivery options on request. The participants in this study had established routines of performing this work themselves, or with the help of formal or informal support. Likewise, there was little mention of adverse effects, swallowing difficulties, and medication volume, which have been highlighted in previous work.20–24 Again, it is possible that such issues may have been addressed earlier in the individual’s ‘medications career’ and were, therefore, no longer a significant issue for participants who were in a steady state of medication management. Studies including younger populations often highlight the burden felt by participants when medication use interferes with social interactions and other daily activities;20,21,25–27 however, this also did not feature heavily in the accounts of the current study’s participants. This may, in part, be due to fewer competing priorities within this population sample of the nonagenarians, and that medication work was seen as integral to everyday activities, rather than in conflict with it. It is likely that identity work (that is, any modifications to an individual’s identity) resulting from the onset of chronic illness and associated medication use has been completed by this group, having an established acceptance of any physical and mental decline experienced by the age of 97 years. Being able to cope with or adjust to a complex medication regimen signified a personal success and can produce feelings of self-worth and, by extension, senses of health and wellbeing.22 ## Importance for research and practice As stated above, the data presented here was generated through qualitative interviews intended to explore participants’ day-to-day experiences. Medication optimisation was not the sole focus of the interviews. Further research would benefit from considering more specifically, and in more detail, the interaction between older people, their specific (multiple) medications, and their primary care practitioners. This could address important issues relating to medication reviews, the appropriateness of medications, and safe autonomy relating to medication practices at home. Findings from this study provide insights that may be of help to clinicians in approaching patient-centred discussions with those from the oldest old cohort, through a greater understanding of the range of experiences they are likely to encounter among this population group. The study found that, for many participants in this cohort, there was an unquestioning acceptance of medications and a great deal of trust in prescribing clinicians. Policy documentation has been lacking in guidance for clinicians around helping these supporting parties manage regimens on behalf of older people. It is important to note that for this age group the greatest burden from medication use happens when a disruption occurs; support in terms of information provision, regular review, and follow-up should be provided to minimise the impact of disruptions. Given the international focus of deprescribing and reducing inappropriate polypharmacy28 this study has several important policy implications. Structured medication reviews are incorporated into routine clinical practice, with many pharmacists now specifically employed to optimise medications in older people. This study’s findings suggest that medication reviews should be tailored to the needs of the individual, and a standardised approach for older people may not be appropriate, especially among nonagenarians. The desired level of patient involvement in decision making about medications should also be acknowledged. Indeed, any approach to reduce inappropriate medication in older people should focus on their clinical and physical, as well as their social and psychological, needs.29 Tools such as STOPP/START,30 which are used to identify potential inappropriate medication in older people, are helpful in this regard, but should not be solely used as a way to reduce or stop medication. This study has shown that participants continued to take their medication because they thought they were ‘bound to be’ helping. The timing of medication review for this cohort may also be important: in addition to having a medication review over a defined time period, a medication review could also be triggered by any significant change in prescribing (for example, initiating a new medication) or life event for the patient. Central to the success of a shared decision-making approach is an understanding of how individuals experience and respond to their medications alongside a sense of their desired level of involvement in decision making about their medications. This study has provided insights into how nonagenarians interact with their medications. It has shown a high level of acceptance of the work of medication management among this group and trust in the prescribers to provide the most appropriate care. This suggests that any rationalisation of medications must build on this trust and be presented as personalised, evidence-based care. ## Funding This project was funded by the National Institute for Health and Care Research (NIHR) Research for Patient Benefit Programme (grant reference: PB-PG-1217-20025). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. ## Ethical approval Ethical approval for the original Newcastle 85+ study was obtained from the Newcastle and North Tyneside 1 Research Ethics Committee (reference: 06/Q$\frac{0905}{2}$) in 2006, and a substantial amendment to carry out the work described in this article was approved by the North East — Newcastle and North Tyneside 1 Research Ethics Committee (Substantial Amendment no.23, 16 July 2019). ## Provenance Freely submitted; externally peer reviewed. ## Competing interests The authors have declared no competing interests. ## Discuss this article Contribute and read comments about this article: bjgp.org/letters ## References 1. 1. Office for National Statistics (ONS) National population projections: 2014- based statistical bulletin2015LondonONS. National population projections: 2014- based statistical bulletin (2015) 2. 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--- title: 'Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records' authors: - Cathy Qi journal: The British Journal of General Practice year: 2023 pmcid: PMC9997656 doi: 10.3399/BJGP.2022.0353 license: CC BY 4.0 --- # Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records ## Abstract ### Background The COVID-19 pandemic has directly and indirectly had an impact on health service provision owing to surges and sustained pressures on the system. The effects of these pressures on the management of long-term or chronic conditions are not fully understood. ### Aim To explore the effects of COVID-19 on the recorded incidence of 17 long-term conditions. ### Design and setting This was an observational retrospective population data linkage study on the population of Wales using primary and secondary care data within the Secure Anonymised Information Linkage (SAIL) Databank. ### Method Monthly rates of new diagnosis between 2000 and 2021 are presented for each long-term condition. Incidence rates post-2020 were compared with expected rates predicted using time series modelling of pre-2020 trends. The proportion of annual incidence is presented by sociodemographic factors: age, sex, social deprivation, ethnicity, frailty, and learning disability. ### Results A total of 5 476 012 diagnoses from 2 257 992 individuals are included. Incidence rates from 2020 to 2021 were lower than mean expected rates across all conditions. The largest relative deficit in incidence was in chronic obstructive pulmonary disease corresponding to 343 ($95\%$ confidence interval = 230 to 456) undiagnosed patients per 100 000 population, followed by depression, type 2 diabetes, hypertension, anxiety disorders, and asthma. A GP practice of 10 000 patients might have over 400 undiagnosed long-term conditions. No notable differences between sociodemographic profiles of post- and pre-2020 incidences were observed. ### Conclusion There is a potential backlog of undiagnosed patients with multiple long-term conditions. Resources are required to tackle anticipated workload as part of COVID-19 recovery, particularly in primary care. ## INTRODUCTION The COVID-19 pandemic has had both a direct and indirect impact on the health and care system.1 Direct effects are those of COVID-19-related illnesses.2 Indirect effects are highly heterogeneous and include delays in cancer services and postponement of elective surgery and other non-urgent treatments owing to surge pressures on the system. 1 For example, it has been estimated that around 28 million operations were cancelled or postponed globally during the peak 12 weeks of the pandemic’s first wave.3 The impact on non-urgent treatments include harm from cessation or delay of screening services and the management of long-term conditions.1 A ‘long-term’ or chronic condition is a condition that cannot presently be cured but is controlled by medication and/or other treatment/therapies, for example, diabetes and asthma.4 Long-term conditions are associated with increasing age and deprivation, and the number of people with multiple long-term conditions (multimorbidity) is increasing.4 Patients with long-term conditions are more intensive users of health and social care services, and before the pandemic accounted for $50\%$ of GP appointments, $64\%$ of outpatient appointments, and $70\%$ of all inpatient bed days.4 In primary care, a call and recall system is used to manage long-term conditions, which is offered to patients after a specific diagnosis is made and recorded in condition registries. Primary care activity was substantially reduced in the early months of the pandemic and, when activity returned to more usual levels in late 2020, acute care displaced much planned care such as long-term condition monitoring and review.5 It is unknown whether this has resulted in ongoing delays in diagnosis and management for long-term conditions. Routinely collected data provide an opportunity to examine changes in recorded diagnoses. The Secure Anonymised Information Linkage (SAIL) Databank (www.saildatabank.com) contains data from $84\%$ of the GPs and all hospital inpatient and day case activity in Wales.6–8 In the current study, historic trends in the incidence rates of 17 long-term conditions were examined, and rates in 2020 and 2021 compared with expected rates over these 2 years had the previous trends continued without interruption. In addition, changes in the characteristics of patients with recorded diagnoses were examined to inform resource allocation. ## METHOD This was an observational retrospective study reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. ## Data sources Anonymised individual-level, population-scale data sources were accessed within the SAIL Databank.6–11 Conditions treated in hospital are recorded using International Classification of Diseases version 10 (ICD-10) codes in the Patient Episode Dataset for Wales (PEDW) dataset. Diagnoses from GP records are coded using Read v2 codes in the Welsh Longitudinal General Practice (WLGP) dataset. The Welsh Demographic Service Dataset was used to link birth, death, sex, and lower layer super output area (LSOA). LSOAs are an output geography created for the 2011 Census and, on average, an LSOA contains the homes of 1500 residents.12 Ethnicity categories were identified from 26 linked data sources (Supplementary Table S1). ## Study cohort Residents of Wales diagnosed for the first time with at least one of 17 long-term conditions between January 2000 and December 2021 were identified using ICD-10 or Read v2 codes (Supplementary Tables S2 and S3). The conditions included were anxiety disorders, asthma, atrial fibrillation, coronary heart disease (CHD), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes mellitus, epilepsy, heart failure, hypertension, inflammatory bowel disease (IBD), osteoporosis, peripheral vascular disease (PVD), rheumatoid arthritis, and stroke and transient ischaemic attack (TIA). These conditions comprise most of the general practice ‘Quality and Outcomes (QOF) Framework’.13 In addition, individuals diagnosed with three diabetes subtypes (type 1, type 2, undetermined) were identified using an algorithm.14 ‘Undetermined type diabetes’ was assigned when criteria for type 1 or type 2 were not met. The final study dataset excluded records missing week of birth or sex, or where the diagnosis date was before birth or after death dates. ## Variables Monthly incidence was derived from the number of individuals diagnosed with a long-term condition for the first time, each month. Age at the earliest found diagnosis date was categorised (<20, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, ≥90 years). Sex was male/female. Ethnic groups were analysed using harmonised Office for National Statistics (ONS) categories (White/Black/Asian/Mixed/other/unknown). Deprivation was derived from the LSOA code at the time of diagnosis mapped to the 2019 Welsh Index of Multiple Deprivation15 and categorised in quintiles (1, most deprived, to 5, least deprived). Frailty was based on an internationally established cumulative deficit model that utilises an electronic Frailty Index (eFI).16–18 eFI scores were used to categorise individuals as: fit, mild, moderate, or severely frail using 10 years of previous WLGP data from date of diagnosis. Individuals without sufficient coverage of GP data were assigned to a missing category. Learning disability status (yes/no) was identified for the study cohort using Read v2 codes (Supplementary Table S4). Socioeconomic categories with one to four counts were rounded to five to prevent accidental disclosure and the excess counts deducted from an unknown/missing/adjacent category. ## Outcomes The primary outcome measure was the monthly incidence rates for each long-term condition. This was derived for the full study period from January 2000 to December 2021. The primary analysis used data from January 2015 to December 2021; the primary outcome was the relative difference between observed and expected incidence rates from 2020 to 2021. The secondary outcome was the annual number and proportion of incident cases by each sociodemographic and clinical subgroup. ## Statistical analysis Monthly incidence rates were derived from the number of new diagnoses occurring each month × 100 000/population size and presented descriptively for the full study period. Population size was estimated from individuals registered to GPs in Wales on 1 July of each year; a breakdown by age group, sex, and social deprivation was presented to check population stability over time. The population size of Wales published by the ONS19 was extracted to estimate coverage achieved by the GP-registered population size. Three-month rolling averages were derived from the mean rate of the month in question, the previous, and the following month. A seasonal autoregressive integrated moving average (SARIMA) model on monthly incidence data from January 2015 to December 2019 was fitted to predict the expected incidence rate (and $95\%$ confidence intervals [CIs]) for each month in 2020 and 2021. Model selection is described in Supplementary Box S1. The difference between the total observed and predicted (lower and upper $95\%$ CI bound) rates was calculated over the 2-year period, and for 2020 and 2021 separately. Percentage differences were (observed – expected) × 100/expected rates. Counts and percentages of individuals by demographic groups were presented for each year from 2000 to 2021, and for 2015–2019 and 2020–2021. Each of the 17 long-term conditions and three diabetes subgroups was examined and analysed separately. As sensitivity analyses, the primary analysis was repeated on the number of cases, unadjusted for population. Statistical analyses were performed using R V4.1.2. ## Public involvement A public partner contributed public or patient perspective to stakeholder discussions at each stage of the study, including interpretation of the significance and potential impact of the results. ## RESULTS There were 5 476 012 diagnoses of long-term conditions identified between January 2000 and December 2021 belonging to 2 257 992 individuals after minor exclusions (Figure 1). Coverage of the population of Wales using GP data in SAIL (Supplementary Table S5) was high (>$80\%$ from 2003, and >$85\%$ from 2015). Supplementary Table S6 shows that population demographics in the GP population were generally stable from 2000 to 2021. Figure 1.: Study flowchart: numbers presented are number of diagnoses (number of individuals). Data were extracted in two ways: (a) via using a ‘diabetes algorithm’ to identify individuals diagnosed with type 1, type 2, or undetermined type diabetes, (b) via using International Classification of Diseases (version 10) and Read codes to identify individuals diagnosed with one or more of 17 conditions (including diabetes mellitus). For (a), the identification algorithm selected the earliest diagnosis date per individual. For (b), the number of diagnoses refers to the number of unique diagnosis dates available, where a diagnosis date is defined as having one or more diagnosis codes recorded on that day. The final dataset included the earliest recorded diagnosis date for each individual per condition. CHD = coronary heart disease. CKD = chronic kidney disease. COPD = chronic obstructive pulmonary disease. IBD = inflammatory bowel disease. PEDW = Patient Episode Database for Wales. PVD = peripheral vascular disease. TIA = transient ischaemic attack. WLGP = Welsh Longitudinal General Practice. A fully interactive dashboard showing incidence counts and rates from 2000 to 2021 for all 17 long-term conditions and diabetes subtypes is available here: https://envhe.shinyapps.io/wales-cec-ltc-incidence/ (source code: https://gitlab.com/envhe/wales-cec-ltc-incidence-shiny-dashboard). Figure 2 shows monthly incidence rates from 2015 to 2021, and predicted rates from 2020 by condition. There was an abrupt reduction around March to April 2020 across all conditions, followed by a general upward trend in subsequent months. Figure 2.: Monthly observed number of diagnoses per 100 000 population from 2015 to 2021 for 17 long-term conditions and three diabetes subtypes (type 1/type 2/undetermined). For 2020 and 2021, monthly predicted number of diagnoses per 100 000 are also shown with 95% confidence intervals indicated by the shaded region. Monthly observed data are overlaid with 3-month rolling averages (solid line). CKD = chronic kidney disease. COPD = chronic obstructive pulmonary disease. PVD = peripheral vascular disease. TIA = transient ischaemic attack. Table 1 shows the difference in the total observed and expected incidence rates over 2020–2021 by condition. Observed incidence was lower than mean expected incidence for all conditions, except type 1 diabetes. Predicted rates are not available for osteoporosis as a SARIMA model was not fitted because of inconsistent trends in 2015–2019 data. Table 1. \| Condition \| 2020 and 2021 \| 2020 and 2021.1 \| 2020 and 2021.2 \| 2020 and 2021.3 \| \| --- \| --- \| --- \| --- \| --- \| \| Condition \| Observed \| Predicted (95% CI) \| Change (95% CI) \| Percentage change (95% CI) \| \| COPD \| 549 \| 892 (779 to 1005) \| −343 (−456 to −230) \| −38.4 (−45.4 to −29.5) \| \| Depression \| 1800 \| 2512 (2194 to 2830) \| −712 (−1031 to −394) \| −28.3 (−36.4 to −17.9) \| \| Type 2 diabetes \| 837 \| 1136 (871 to 1401) \| −300 (−565 to −34) \| −26.4 (−40.3 to −3.9) \| \| Hypertension \| 1663 \| 2231 (1979 to 2483) \| −568 (−820 to −316) \| −25.5 (−33 to −16.0) \| \| Anxiety disorders \| 2503 \| 3333 (2784 to 3882) \| −830 (−1379 to −281) \| −24.9 (−35.5 to −10.1) \| \| Asthma \| 756 \| 1006 (898 to 1114) \| −250 (−358 to −142) \| −24.9 (−32.2 to −15.9) \| \| Diabetes mellitus \| 999 \| 1314 (952 to 1676) \| −315 (−677 to 47) \| −24 (−40.4 to 4.9) \| \| Rheumatoid arthritis \| 148 \| 192 (142 to 243) \| −45 (−95 to 6) \| −23.1 (−39.0 to 4.0) \| \| PVD \| 341 \| 430 (375 to 485) \| −90 (−145 to −35) \| −20.8 (−29.8 to −9.2) \| \| Inflammatory bowel disease \| 147 \| 183 (152 to 214) \| −36 (−67 to −5) \| −19.8 (−31.4 to −3.4) \| \| Undetermined type diabetes \| 123 \| 147 (116 to 178) \| −24 (−55 to 7) \| −16.3 (−31.0 to 6.1) \| \| CHD \| 671 \| 774 (680 to 869) \| −103 (−198 to −9) \| −13.3 (−22.8 to −1.3) \| \| Heart failure \| 756 \| 871 (753 to 990) \| −116 (−234 to 3) \| −13.3 (−23.6 to 0.4) \| \| CKD \| 1462 \| 1678 (1496 to 1861) \| −217 (−399 to −34) \| −12.9 (−21.5 to −2.3) \| \| Epilepsy \| 159 \| 182 (143 to 220) \| −23 (−61 to 16) \| −12.4 (−27.9 to 11.4) \| \| Atrial fibrillation \| 1158 \| 1304 (1145 to 1463) \| −146 (−305 to 13) \| −11.2 (−20.8 to 1.1) \| \| Stroke and TIA \| 592 \| 647 (554 to 740) \| −55 (−148 to 38) \| −8.5 (−20.0 to 6.9) \| \| Dementia \| 1050 \| 1135 (991 to 1279) \| −85 (−229 to 59) \| −7.5 (−17.9 to 6.0) \| \| Type 1 diabetes \| 41 \| 38 (22 to 53) \| 3 (−12 to 19) \| 8.6 (−22.8 to 83.3) \| Conditions with the largest relative deficit in diagnoses were COPD, depression, type 2 diabetes, hypertension, anxiety disorders, and asthma. Observed rates for COPD were $38.4\%$ ($95\%$ CI = 29.5 to 45.4) lower than expected, corresponding to an undiagnosed population of 343 ($95\%$ CI = 230 to 456) per 100 000 individuals. Anxiety disorders had the largest absolute undiagnosed population of 830 ($95\%$ CI = 281 to 1379) per 100 000. Compared with 2020, estimated differences for 2021 were similar for COPD and anxiety disorders, and smaller, but with larger $95\%$ CIs, among most other conditions (Supplementary Table S7). Figure 2 suggests that there may still be an overall lag in diagnoses in 2021 for most conditions. Incidence rates for some conditions were close to pre-pandemic levels by the end of 2021; others (for example, PVD and stroke and TIA) were approaching predicted rates near the start of 2021 but dropped again towards the end of the year. The estimated rate of underdiagnosis for diabetes mellitus was 178 ($95\%$ CI = 57 to 299) in 2020 and 137 ($95\%$ CI = −104 to 378) in 2021, similar to corresponding estimates for type 2 diabetes of 168 ($95\%$ CI = 72 to 263) in 2020 and 132 ($95\%$ CI = −38 to 302) in 2021, whereas the estimated underdiagnosis for type 1 diabetes was 0 ($95\%$ CI = −8 to 7) in 2020 and −3 ($95\%$ CI = −11 to 5) in 2021. Results from analysis of incidence counts unadjusted for population size (Supplementary Tables S8 and S9) were consistent with primary findings. SARIMA model specification and estimated parameters for analysis of incidence rates and counts are shown in Supplementary Tables S10 and S11, respectively. Supplementary Tables S12 to S31 show annual incidence by sociodemographic factors from 2015 to 2021. The study dashboard (link before) includes data from 2000. There was no notable difference between the distribution of cases among categories in 2020 and/or 2021 compared with preceding years for any of the sociodemographic factors, indicating that, although overall rates of diagnosis decreased, influences of sociodemographic characteristics on being diagnosed did not drastically differ pre- and post-2020. Type 1 diabetes was the only condition with an estimated mean net gain in incidence of $8.6\%$ ($95\%$ CI = −22.8 to 83.3) (Table 1). Given that type 1 diabetes is diagnosed in younger patients (around $75\%$ <50 years old), whether diagnosis trends differed between younger (<50 years) and older (>50 years) populations was investigated (Supplementary Figure S1). Most conditions were rare in those aged <50 years (monthly rate <10 per 100 000), but among the remaining conditions, trends within age groups were similar to aggregate trends, including for depression, anxiety, and asthma. As further post hoc exploration, Supplementary Figures S2 and S3 show that incidence trends by sex and social deprivation groups were also similar. ## Summary From 2020 to 2021, there were deficits in recorded incidences across multiple long-term conditions, likely an indirect effect of the COVID-19 pandemic. Increasing demand and workforce vacancies could have affected availability of appointments and postponed diagnostic tests. A typical general practice of 10 000 patients might have over 400 undiagnosed long-term conditions (some potentially occurring in the same individuals). Observed incidence for some conditions (for example, heart failure and stroke and TIA) increased and declined again during 2021; this could reflect changes in healthcare pressures between the alpha wave (September 2020 to March 2021) and the delta wave (June 2021 to December 2021) in Wales. Other conditions were approaching pre-pandemic levels towards the end of 2021 (for example, asthma), which could reflect condition-specific ‘catch-up’ activity but an excess would be needed to reach net expected numbers. ## Strengths and limitations This study included multiple conditions, mostly selected from the QOF framework, previously used to monitor and reward performance in primary care, thus electronic coding quality is generally good, although this can vary between individual clinicians and practices. Overall data coverage was close to the full population of Wales. The assumption that trends in 2015–2019 would persist if COVID-19 had not occurred could not be tested. Possible interactions between COVID-19 and prognosis were not accounted for, for example, excess mortality could partially explain the persistent reduction in incidence and could have led to an overestimation of expected rates. However, given that underdiagnosis is evident in a wide range of conditions and in those aged <50 years, non-presentation and recording may be the biggest issue. ## Comparison with existing literature Observational studies conducted in Spain have reported reduced incidence of multiple chronic diseases in 2020,20 and substantial reductions in clinical indicators for control and treatment of chronic disease in March and April 2020.21 A UK-based study using primary care data reported reduced incidences of depression ($47.1\%$) and anxiety ($40.8\%$) in Wales, Scotland, and Northern Ireland, especially among working-age adults registered at practices in more deprived areas.22 The current study included longer-term data showing there is likely still a lag for most conditions as services have resumed pre-pandemic activity. Further, the pandemic has exacerbated an already high prevalence of undiagnosed COPD.23,24 UK pandemic guidance to postpone tests that may increase the respiratory transmission of viral infections, including spirometry, likely contributed.25 This might also explain the difference in lag towards the end of 2021 between asthma and COPD, as spirometry is needed to diagnose COPD whereas a diagnosis of asthma is based more on the clinical history. Reductions in hospital admissions for infectious exacerbation of COPD following the national lockdown in Wales26 could also in part explain the reduction in incidence rates. The absence of deficits in recorded incidence for type 1 diabetes is likely condition-specific, rather than owing to a younger patient population, as type 1 diabetes inevitably presents soon after symptom onset and there were no indications that overall trends were confounded by age. Other studies have reported increased incidence in 2020–2021, mostly in younger patients (<18 years)27–30 and increased risk following COVID-19 infections27,28 although it is unclear if the association is causative. ## Implications for research and practice Rectifying this backlog of case identification and consequent management deficits is likely to require specific strategic and operational planning at the level of primary care organisations. Targeted catch-up initiatives are unlikely to be feasible because of the lack of sociodemographic characterisation of the missing diagnoses. Consideration for specific resource allocation to enable healthcare staff time to be committed to searching records, testing, and screening risk groups (for example, across cardiovascular conditions) is needed. Governments and policymakers may need to identify such specific funding to tackle this workload as part of COVID-19 recovery, alongside other higher-profile patient needs such as cancer care and elective surgery. General or condition-specific patient advocacy organisations and charitable foundations may have a role in ‘championing’ for patients with potentially relevant symptoms to present to primary care (as advocated also, for example, with potential cancer symptoms),31 or to seek attendance and ‘health checks’ among infrequent attenders. Further research is ongoing to identify exactly what deficits in condition management, health outcomes, and impact on health services have occurred. ## Funding This work was funded by the Wales COVID-19 Evidence Centre, funded by Health and Care Research Wales. This work was supported by the Con-COV team funded by the Medical Research Council (grant number: MR/V$\frac{028367}{1}$). This work was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF), and the Wellcome Trust. This work was supported by the ADR Wales programme of work. The ADR Wales programme of work is aligned to the priority themes as identified in the Welsh Government’s national strategy: Prosperity for All. ADR Wales brings together data science experts at Swansea University Medical School, staff from the Wales Institute of Social and Economic Research, Data and Methods (WISERD) at Cardiff University, and specialist teams within the Welsh Government to develop new evidence that supports Prosperity for All by using the SAIL Databank at Swansea University, to link and analyse anonymised data. ADR Wales is part of the Economic and Social Research Council (part of UK Research and Innovation) funded ADR UK (grant ES/S$\frac{007393}{1}$). ## Ethical approval All research conducted has been completed under the permission and approval of the SAIL independent Information Governance Review Panel (IGRP) project number 0911. ## Data The raw data sources are described in detail in the methods, which were accessed and analysed within a Trusted Research Environment (TRE). Extracting the data from the TRE is prohibited as a condition of use. Accredited researchers can apply to access the SAIL Databank via a governed approval process and is independent of the study authors (https://saildatabank.com/). The main datasets used are: Patient Episode Dataset for Wales (PEDW) dataset, Welsh Longitudinal General Practice (WLGP) dataset, and the Welsh Demographic Service Dataset. Tabulated data and results are available for readers to access within the dashboard (link above). ## Provenance Freely submitted; externally peer reviewed. ## Competing interests All authors completed the ICMJE uniform disclosure form at http://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; Adrian Edwards declares a role as Director of the Wales Covid-19 Evidence Centre as part of university employment, receiving no further payments; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work. All other authors have declared no competing interests. ## Discuss this article Contribute and read comments about this article: bjgp.org/letters ## References 1. 1. 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--- title: Diversity of arterial cell and phenotypic heterogeneity induced by high-fat and high-cholesterol diet authors: - Jieqi Wen - Rongsong Ling - Ruiyue Chen - Siyan Zhang - Yarong Dai - Tingtao Zhang - Fanyu Guo - Qingxin Wang - Guixin Wang - Yizhou Jiang journal: Frontiers in Cell and Developmental Biology year: 2023 pmcid: PMC9997679 doi: 10.3389/fcell.2023.971091 license: CC BY 4.0 --- # Diversity of arterial cell and phenotypic heterogeneity induced by high-fat and high-cholesterol diet ## Abstract Lipid metabolism disorder is the basis of atherosclerotic lesions, in which cholesterol and low-density lipoprotein (LDL) is the main factor involved with the atherosclerotic development. A high-fat and high-cholesterol diet can lead to this disorder in the human body, thus accelerating the process of disease. The development of single-cell RNA sequencing in recent years has opened the possibility to unbiasedly map cellular heterogeneity with high throughput and high resolution; alterations mediated by a high-fat and high-cholesterol diet at the single-cell transcriptomic level can be explored with this mean afterward. We assessed the aortic arch of 16-week old Apoe−/− mice of two control groups (12 weeks of chow diet) and two HFD groups (12 weeks of high fat, high cholesterol diet) to process single-cell suspension and use single-cell RNA sequencing to anatomize the transcripts of 5,416 cells from the control group and 2,739 from the HFD group. Through unsupervised clustering, 14 cell types were divided and defined. Among these cells, the cellular heterogeneity exhibited in endothelial cells and immune cells is the most prominent. Subsequent screening delineated ten endothelial cell subsets with various function based on gene expression profiling. The distribution of endothelial cells and immune cells differs significantly between the control group versus the HFD one. The existence of pathways that inhibit atherosclerosis was found in both dysfunctional endothelial cells and foam cells. Our data provide a comprehensive transcriptional landscape of aortic arch cells and unravel the cellular heterogeneity brought by a high-fat and high-cholesterol diet. All these findings open new perspectives at the transcriptomic level to studying the pathology of atherosclerosis. ## 1 Introduction As a chronic inflammatory disease, atherosclerosis is the major cause of myocardial and cerebral infarction and ischemia of the extremities, the underlying cause of about $50\%$ of all deaths (Lusis, 2000). A disorder of lipid metabolism is the basis of atherosclerotic lesions, with cholesterol and low-density lipoprotein (LDL) acting as chief instigators (Lu and Daugherty, 2015). Several kinds of cells and cytokines are involved in the atherosclerotic progression. Lesion of the diseased aorta originated from its intima. Minimally oxidized LDL diminishes nitric oxide to increase the permeability of endothelial layer and triggers endothelial cells to produce cytokines and chemokines, which helps monocytes pass through endothelial monolayer and convert into macrophages. Lipid accumulates here topically, and oxidation and modification of LDL happen under the presence of different enzymes and ROS. Macrophages then differentiate into foam cells with the help of highly oxidized LDL and gather. Under the regulation of cytokines, smooth muscle cells (SMCs) migrate into the inner lumen, and proliferate and secrete extracellular matrix (ECM) to promote the hyperplasia of fibrous cap and formation of plaque (Libby, 2000). Then macrophage and inflammatory T cells secrete various cytokines and enzymes to degrade matrix (Schönbeck et al., 2000), make the lesional plaque fragile, and induce calcification and precipitation. Finally the rupture of the plaque and the exposure of lipid core into blood recruit platelet initiate thrombosis. This blocks artery lumen and contributes to the ischemia or necrosis of organs or tissues supplied by the artery (Lusis, 2000). It is clear that endothelial cells and macrophages play important roles in the development of atherosclerosis. Endothelial injury and repair are novel theories explaining pathogenesis of atherosclerosis (Mannarino and Pirro, 2008). Endothelial cells (ECs) are involved in a great range of homeostatic functions (Durand and Gutterman, 2013) through anti-coagulant, antithrombotic, and anti-inflammatory activity. Normally, endothelial cells regulate vascular tone, cell adhesion, and SMC proliferation (Paone et al., 2019). However, these functions lapse when pathological conditions appear and cause endothelial dysfunction (Werner et al., 2006). Most atherosclerotic risk factors can activate endothelial cells to secretes chemokines, cytokines, adhesion molecules, and intracellular adhesion molecules, hence aggregate immune cells (Davignon and Ganz, 2004). Macrophages play a vital role in the initiation of atherosclerosis and growth of plaque, while continuous inflammation leads to its apoptosis. In the absence of effective exocytosis, the accumulation of cell debris and apoptosis promotes the formation of a necrotic core in atherosclerotic plaques. By thoroughly assessing the transcriptional landscape of aortic cells from mice administered with different diets, a panorama of the differences between normal and diseased endothelial cells can be looked at with the help of single-cell RNA sequencing. Western diet-mediated changes in immune cells, especially macrophages in the aortic arch, were also a focus in this study. ## 2.1 Mice Our mice strain came from the Nanjing Model Animal Resource Information Platform. Apoe−/− male C57BL/6 mice (16 weeks old) were used to establish control and experimental group. APOE is often produced in monocytes and macrophages (Curtiss et al., 2000) and plays a critical role in blood lipid metabolism (Chen et al., 2017) as ligands for receptors that clear chylomicron and VLDL residue (Meir and Leitersdorf, 2004). So when APOE is knocked out, total cholesterol in plasma increases (Maganto-Garcia, Tarrio, and Lichtman, 2012), and the effect is multiplied especially under a high-fat and high-cholesterol diet. Female mice secrete estrogen, which lowers the content of LDL in plasma and enhances endovascular blood coagulation (Aryan et al., 2020). For the experimental group, to accelerate the progression of atherosclerosis, the mice were fed with high-fat and high-cholesterol food for about 12 weeks after they had been weaned (4 weeks old); this group is referred to as the Western diet (HFD) group for short. ( Formula of high fat, high cholesterol diet: $20\%$ sucrose, $15\%$ lard, $1.2\%$ cholesterol, $0.2\%$ sodium cholate, $10\%$ casein, $0.6\%$ calcium hydrogen phosphate, $0.4\%$ stone powder, $0.4\%$ premix and $52.2\%$ basal feed.) Meanwhile, another group of mice, the control group, was administered with a chow diet. Mice were euthanized after 12 weeks of being administered different diets. Animal studies were performed in compliance with ethical guidelines and use of animals, and the experimental protocol was approved by the Shenzhen University Animal Care and Use Committee. ## 2.2 Aorta dissection and single-cell suspension After being euthanized, mice were locally perfused with cold PBS to remove the peripheral blood remaining in the aorta. The aorta were then harvested and incubated using the enzyme mix in the Lung Dissociated Kit (Cat# 130-095-927, Miltenyi Biotec). The aortic arch was cut into pieces before being immersed with the enzyme mix, and then the reaction was performed at 37°C for 40 min on a rotator. The product was then filtered through a 70 um strainer to remove the extra tissue, and the strainer was washed three times with Dulbecco’s modified Eagle’s medium (DMEM). The cell suspension was centrifuged for 5 min at 500×g, at 25°C. Pour off the supernatants and resuspend cells with DMEM again to obtain the final suspension. ## 2.3 scRNAseq Single-cell suspension of the two aortas from each group was pooled together as one sample. Single-cell RNA sequencing, library construction, and quality control were executed using the illumina-HiSeq3000 platform by Genergy Bio-technology (Shanghai) Co., Ltd. Dying cells with mitochondrial RNA above $30\%$ and cells lacking information with UMI<200 were eliminated during the quality control, and 8,155 dissociated cells, with 5,416 cells from the control group and 2,739 from the HFD one, were filtered out thereafter. The median reads per cell were 3,955 for the control group and 3,421 for the HFD group, and transcripts detected per cell in the two groups were 1,364 and 1,069 respectively. ( Figure 1A). FIGURE 1: Transcriptome map of aortic cells extracted from both non-diseased and atherosclerotic Apoe−/− mice. (A) Schematic diagram of the experimental flow. (B) t-Stochastic neighbor embedding (t-SNE) representation of gene expression information in single cells from control (ND) group and Western diet (HFD) group. Colors denote different clusters (left), cell types (middle), and groups (right). (C) Heatmap of five representative genes for each aortic cell type. (D) Dot plot demonstrates distinct marker of each cluster. Size of dot corresponds to proportion of cells expressing each transcript, and dot color corresponds to expression level of transcript. (E) Heatmap showing the top 30 upregulated genes (ordered by decreasing P value) in each cluster, with representative genes selected for biological identification of each cluster marked on the left and biological identification on the right. The color from yellow to purple symbolizes the expression levels from high to low. (Scale: log2 fold change) (F) Gene expression patterns projected onto t-SNE plot profile expression of six significant cell markers. Endothelial cell: Pecam1 and Cdh5; Fibroblast: Col3a1; Immune cell: Ptprc; Smooth muscle cell: Acta2; Adipocyte: Cfd. ## 2.4 Data processing and visualization The Cell Ranger Single-cell Software Suite was used to demultiplex the experimental data; Illumina’s bcl2fastq was wrapped around by using the mkfastq command. The calculations based on UMI-tools were used to control quality of RNA sequencing. Sample libraries balancing was carried out for the number of estimated reads per cell and then ran on the illumine-HiSeq300. Based on the 10x Genomics documentation (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cellranger), demultiplexing, alignment filtering, barcode counting, UMI counting, and gene expression estimation were performed by Cell Ranger software on each sample. The LogNormalize algorithm worked in normalization. To compare experimental groups with normalized sequencing-depth and expression data, the IntegrateData (Seurat) was used to aggregate the gene expression estimates from each sample. Seurat (version 3.2.0) and R (version 4.0.0) package were used in downstream analysis. Cells with less than 200 genes were detected, and more than $30\%$ of the mitochondrial gene count were filtered out as low quality or dying cells. Dimension reduction was then performed on normalized and logarithmized data by three stages of analysis, including the selection of variable genes, principal component analysis (nPCs = 50), and t-Distributed Stochastic Neighbor Embedding (t-SNE) with RunTSNE:dims = 1–30. Then the cell clustering was performed using the original Louvain algorithm (resolution = 0.9). We used the FindAllMarkers function to perform Wilcoxon rank-sum test based on the normalized data to identify gene markers in each cluster. The Seurat package FindAllMarkers was used to analyze the differentially expressed genes (DEGs) between the two groups. The cowplot (version 1.1.1) and ggplot2 (version 3.3.5) were used for graphing. Sorting of endothelial cells were then performed with canonical markers Pecam1 and Cdh5 based on the estimated amount of EC in the total sample with resolution 0.4. ## 2.5 Serum collection The mice to be sampled were individually isolated and kept in fasting conditions for 6 h, blood was collected below the jaw and stored in tubes without endotoxin. The samples were kept at 37°C for 1, 2 h to solidify the blood; blood was then left to clot overnight at 4°C. Afterwards, the serum was naturally precipitated, and centrifugation was performed for 10 min at 3,000 r/min at 4°C. Samples should be stored at −80°C if not used immediately. ## 2.6 Detection of CHO and TG in serum The concentration of cholesterol and triglyceride were tested in serum with the Cholesterol Kit (CHOD-PAP Method) (Cat# 020080, Biosino) and Triglyceride Kit (GPO-PAP Method) (Cat# F001-2, Biosino). Prepare work solution. Mix work solution and cholesterol/triglyceride calibrator in different ratio to prepare cholesterol/triglyceride with different step concentrations. Mix work solution and sample in 100:1. The measured the concentration of cholesterol/triglyceride in serum using spectrophotometric method. ## 2.7 Statistical analysis The statistical data of differentially expressed genes (DEGs) were calculated by Wilcoxon rank-sum test algorithm, and the threshold value of p-value was 0.05. The error bars in the bar graph represent standard error of mean (SEM). Two-tailed unpaired t-test and two-tailed Mann-Whitney test were used for statistical analysis unless stated otherwise. Statistical analysis was performed using GraphPad Prism version 8.0.2 or R version 3.2.0. ## 2.8 Single-cell suspension and flow cytometry To prepare aortic cell suspension, fresh descending aorta and aortic root fragments, harvested from Apoe−/− male C57BL/6 mice (18 weeks old) fed with a Western diet and normal diet, three in each group, were incubated by an enzyme mix with 0.2 mg/mL Liberase (Roche, 5,401,054,001) and 2 U/mL Elastase (Sigma-Aldrich, E1250), with HBSS as the solvent. Digestion was done by rotating at 37°C in an oven for an hour. The product was filtered through the 35 um strainer and washed with HBSS. Cells were collected by centrifugation at room temperature, 500 xg for 5 min. The supernatant was discarded and the cells resuspended with staining buffer ($3\%$ BSA and $1\%$NaN in PBS). Each group of cell suspension mentioned above was divided into four parts for incubation with the following four antibodies. Incubation was performed for 1 h at 4°C in darkness. Separate isotype controls for each antibody were also prepared. Secondary antibodies labeled with fluorescent dye were diluted with $3\%$ BSA and used to resuspend cells at room temperature for 30 min in darkness. Cells were washed with PBS by centrifugation at 400 g for 5 min twice. Finally, cells were resuspended with cold staining buffer ($3\%$ BSA and $1\%$NaN in PBS), the cell number was counted, and flow cytometry was performed. Cells were sorted by flow cytometry (CytoFLEX, Beckman Coulter) and analyzed with flow cytometer (CytoFLEX, Beckman Coulter) (version 2.0). ## 3.1 Cell map of whole aortic arch Whole-cell map of the aorta arch mapped by single-cell sequencing techniques has often been mentioned in the previous studies (Kalluri et al., 2019; Zhao et al., 2021). This study provides a more comprehensive map of the transcriptional information of all aortic cells. Cells from control and HFD group were pooled together and distinguished into 27 cell clusters corresponding to 10 different cell types through unsupervised clustering done using Seurat and then were visualized by the dimension reduction via t-stochastic neighbor embedding (Figure 1B). To define the identity of each cell cluster, we performed differential expression analysis between each cluster and assigned a specific identity to each cluster based on the established lineage-specific marker genes (Figure 1C). Some of these marker genes may were only evenly and slightly upregulated or just topically upregulated in certain clusters (Figure 1D), but this could not exclude the role of these marker genes in identifying cell clusters. ( Kalluri et al., 2019). The largest population of cells in this study is endothelial cells, accounting for $43.2\%$ (Supplementary Figure 1A). Under such high-resolution sequencing, seven endothelial cell clusters were distinguished despite the similarity of partial expression profiles among the clusters. Different types of immune cells were distributed in nine clusters unevenly and account for $28.8\%$, the second largest population. Occupying a proportion of $18.7\%$, fibroblasts identified by particularly positive expression of genes encoding fibronectin (like Col3a1, Fbn1, Mfap5, Gsn, and Pdgfra) were divided into four clusters. Traditional matrix fibroblasts in cluster three and eight were characterized by notable expression of Fn1 and Mfap4 respectively. Enhanced expression of Mgp and Dcn indicates that fibroblasts in cluster 12 were involved in composition of ECM and collagen, while differentiating matrix fibroblasts in cluster 13 were featured with high expression of Gsn and Col6a2, two genes involved in ECM processing. Except fibroblasts in cluster 12, which exist exclusively in the control group, all other fibroblast subsets exist in both groups. Smooth muscle cells are always defined by their usual marker Acta2. This population also showed a rise of Tagln, Rgs5, Myh9, and Myl11. Other cell types include erythroid cell (Hbb-bt, Hbb-bs, Hba-a1, Bpgm, and Alas2), cycling basal cells (Stmn1, H2afz, Hmgb2, Top2a, and Tubb5), clara cells (Scgb1a1, Bpifa1, Scgb3a2, Cyp2f2, and Bpifb1), mesothelial cells (Upk3b, Gpm6a, Msln, Nkain4, and Lrrn4), adipocytes (Cfd, Scd1, Adipoq, Ucp1, and Slc36a2), and neurons (Prnp, Kcna1, Kcna2, Vwa1, and Mbp). Among these, cycling basal cells, clara cells, mesothelial cells, and adipocytes have not been identified in similar studies (Figures 1C–F; Supplementary Figures 1A–C). ## 3.2 Single-cell profile helps determine functionally distinct endothelial cell populations Sorting of specific types of cells was performed based on single-cell gene profile and dependent on identity genes or specific conditions. 2,785 cells with positive expression of endothelial canonical markers Pecam1 and Cdh5 were selected as endothelial cell lines from the total cell repository, with 1786 from the control group and 999 from the HFD group. These cells were divided into ten subpopulations with specific identification (Figures 2A, B; Supplementary Figure 2A). FIGURE 2: Differentiation of ten distinct vascular endothelial subpopulations. (A) t-Stochastic neighbor embedding representation of potential aortic endothelial cells extracted from total cell repertoire and separate them into 10 clusters. (B) Heatmap identifying top 10 upregulated genes of each endothelial subpopulation. (C) Violin plots of log-transformed gene expression of canonical endothelial markers distinguish these ten endothelial subpopulations from all other aortic cells. (D) Dot plot of functional markers showing the characteristics of each subpopulation. (Lipid handling: Lpl, Gpihbp1, Mgll, Cd36; Angiogenesis: Pgf, Nrp2, Tmsb10; Adhesion factor: Vwf, Vcam1, Cd9; Inflammation: Cxcl2, Tyrobp; Collagen forming: Col3a1, Col1a1) (E) Violin plots of log-transformed gene expression of selected markers demonstrating heterogeneity in each cluster. Violin plot y-axis demonstrates normalized transcript expression values. (F) Expression pattern of selected genes in endothelial subpopulation 1–3 showed heterogeneity and homogeneity between the three clusters. (Common: Gpihbp1, Cdh5; EC1 higher: Mgll, Slc6a6; EC2 higher: Aqp1; EC3 higher: Tgfbr3, Tmsb4x, Tmsb10, Tubb5). Positive expression of EC canonical markers Nos3 (Knowles et al., 2000), Ptprb, and Notch1 in these selected cells distinguish them from other vascular cells and support the lineage assignment as ECs. Genes encoding proteins worked in endothelial adhesion and angiogenesis, such as Pecam1 (Sauteur et al., 2014; Lim et al., 2019), Cdh5, and Egfl7 (Charpentier et al., 2013; Usuba et al., 2019), which also showed enrichment in these cells (Figure 2C; Supplementary Figure 2B). Genes with average log-fold enrichment >2 and p-value <0.01 are the first choice to be used in distinguishing between individual endothelial cell subpopulations. Expression of different functional genes demonstrates the functional multiplicity of these endothelial cells. Markers that have a role in the transporting and metabolism of lipids Lpl (Lutz et al., 2001), Gpihbp1 (Allan et al., 2017), Mgll (Nomura et al., 2010), and Cd36 (Jabs et al., 2018; Gerbod-Giannone et al., 2019) were upregulated in almost all clusters except EC 4, 6, and 10. Cells in EC 4 and 10 worked in cell adhesion (Vwf and Vcam1), while cells in EC six were inflammatory (Cxcl2 and Tyrobp). Of note, the gene uniquely expressed in endothelial colony-forming cells, Cytl1, showed a particular rise in EC 4, suggesting it is a genuine endothelial precursor cell; because endothelial colony-forming cells was thought to be a late endothelial precursor cell with a strong angiogenic function distinct from classical endothelial cells (d’Audigier et al., 2018). This type of endothelial cell has not been mentioned in similar studies. It uniquely performed an undetectable expression of Lpl and presented a higher expression of Nos3. Lyvel, a validated lymphatic endothelial cell marker that plays a role in lymphatic reactions such as leukocyte trafficking and helping to clear acute inflammatory response after myocardial infarction (Okuda et al., 2012; Vieira et al., 2018; Jackson, 2019), showed an exclusive positive expression in EC 10. In addition to the transportation or metabolism of lipids, other endothelial subpopulations also present with different characteristics. Genes related to angiogenesis and cell proliferation, such as Pgf, Nrp2, and Tmsb10, have higher expression levels in EC 3. In VECs, PPARγ plays a protective role by increasing nitric oxide bioavailability and preventing oxidative stress. As a PPARγ target gene, RBP7 (Hu et al., 2017; Fang and Sigmund, 2020) is enriched in VECs and upregulated in EC5 in this study, which suggests it as EC against AS. Smooth muscle cell marker Myl9 exhibited a high expression in SMC-like EC in EC 7. Rgs5, which is involved in the induction of endothelial apoptosis (Lovschall et al., 2007), also performed significant upregulation in EC 7. Encoding proteins involved in B cell proliferation, Igkc, Ighm, and Cd79a (Kedmi et al., 2011), present a specific rise in EC eight and are identified as B cell-like EC. Fibrosing EC was identified with greatly increased Gsn, Dcn, and genes that play a role in collagen forming (Col1a2, Col3a1, Col1a1) (Figures 2B, D, E; Supplementary Figure 2C). Among different gene markers, there exists a notable negative correlation between Cd36 and Vcam1. All endothelial subpopulations, except EC 4, 6, and 10, expressed higher Cd36 and have a reduced expression of Vcam1, while the three excluded subpopulations showed inverse expression. Interestingly, Pecam1 and Cdh5 also performed similar negative correlation in these endothelial cells. From previous analysis, EC 1–3 were found to lack genes with log-fold enrichment >1, indicating that these clusters lack significant features. However, by further analysis, we observed that the genes involved in cell proliferation and angiogenesis presented progressively elevated expression levels from EC1 to EC3, suggesting that they may be continuous phenotypic gradients rather than conventionally different subpopulations (Figure 2F). Analysis focused on the top 30 upregulated genes of endothelial subpopulations 1–3 further revealed that the expression profiles of some cells in cluster EC1 overlap with those of EC2, while almost all of the cells in cluster EC3 have the expression features of EC2 (Supplementary Figure 2D). ## 3.3 Comparison of transcriptional map between endothelial cells from two groups reveals diet-dependent genetic variance Various endothelial subpopulations were distributed differently in the control group versus the HFD group. In total, the cells that exist preferentially in the HFD group indicated biological processes related with the development of mainly atherosclerosis. These subpopulations role in lipid clearance, utilization and storage, and exhibit property to against atherosclerosis but they also maintain role in inflammation and endothelial cell apoptosis. In contrast, those endothelial cells distributed mainly in the control group were involved in cell proliferating. Subpopulations EC1-3, which are assumed to have a developmental relationship, showed a distinct distribution between the two groups. EC1 has a biased distribution in the HFD group, while the other two were dominated by cells from the control group (Figures 3A, B). FIGURE 3: Homogeneity and heterogeneity between vascular endothelial cell of two origins uncover cellular variation respond to diet alteration. (A) t-Stochastic neighbor embedding representation of potential aortic endothelial cells extracted from total cell repertoire showing cellular origin in different colors. (B) Percentage of each endothelial population within total endothelial cells. Left: control (ND) group; Right: Western diet (HFD) group. (C–E) Transcriptional profiling of endothelial cells in ND versus HFD group notes diet-dependent genes with transcriptional upregulation in all HFD clusters. EC indicates endothelial cells (C) or diet dependent genes that are cluster specific (D) and conserved genes under regulation of diet. (E). The transcriptional profiles of endothelial cells in the two experimental groups can help to reveal which biological processes are regulated by diet. In addition, the biological processes not influenced by diet were listed. We have summarized all endothelial cell markers that were used in recent single-cell studies of atherosclerosis (Table 1) as a reference. TABLE 1 \| Gene name \| Significance \| \| --- \| --- \| \| HHcy \| Involved in potentiates atherosclerosis mainly through endothelial injury and inflammatory activation Ma et al. (2022) \| \| Mgp \| Promoted ECs proliferation, migration, and tube formation Ni et al. (2022); ECM gene upregulated in HFD group, EndMT + EC and pro-inflammatory EC Zhao et al. (2021) \| \| Nectin \| Biasedly expressed in early stage of carotid atherosclerosis S. Li et al. (2021) \| \| Egr1 \| Most enriched in regulatory regions in human vein and artery endothelial cells and has been predicted to act as a significant regulator of ECs under oscillatory shear stress S. Li et al. (2021), involved in cellular growth and development Huang et al. (2021) \| \| Klf2 \| Significant regulator of anti-inflammatory response and maintenance of vascular integrity S. Li et al. (2021); Shear-sensitive gene F. Li et al. (2021); biasedly expressed in EndMT− ECs Zhao et al. (2021) \| \| Klf4 \| Significant regulator of anti-inflammatory response and maintenance of vascular integrity S. Li et al. (2021); biasedly expressed in EndMT− ECs Zhao et al. (2021) \| \| Gja4, Gja5, Arli5, Cd58 \| Expressed in EC related to coagulation cascade, viral myocarditis, and type I diabetes mellitus S. Li et al. (2021) \| \| Sphk1 \| Involved in endothelial permeability S. Li et al. (2021) \| \| Igfbp4, Plvap, Aqp1, Myc \| Expressed in EC, mainly involved in ribosome-associated pathways, fluid shear stress in atherosclerosis, cancer proteoglycan, and leukocyte trans-endothelial migration S. Li et al. (2021) \| \| Klk10 \| Unique EC marker (Williams et al., 2022) inhibits endothelial inflammation, endothelial barrier dysfunction, and reduces endothelial migration and tube formation (F. Li et al., 2021) \| \| Vcam1 \| Inflammatory related marker Quiles-Jiménez et al. (2021) expressed in EC located in the lesser curvature of the aorta F. Li et al. (2021) and EndMT + cells Zhao et al. (2021). Expressed by activated endothelium, facilitates adhesion and transmigration of leukocytes, such as monocytes and T cells Depuydt et al. (2020) \| \| VLDLR \| Involved in uptake of lipoproteins, promoting foam cell formation under conditions of increased native or oxidized lipoproteins Liu et al. (2021) \| \| Cd36 \| Receptor for oxidized low-density lipoprotein gene for lipid metabolism, biasedly expressed in lipid-handling EC F. Li et al. (2021); Zhao et al. (2021) (ref2) \| \| ICAM-1 \| Shear-sensitive gene (Li et al., 2021), pro-inflammatory gene Sorokin et al. (2020); Zhao et al. (2021) (ref2,3) expressed in EndMT + ECs Zhao et al. (2021) (ref2) \| \| BMP4 \| Hear-sensitive gene F. Li et al. (2021), involved in ECM organization Zhao et al. (2021) \| \| Ang2, EZF/GKLF \| Hear-sensitive genes F. Li et al. (2021) \| \| Cavin2 \| EC marker, involved in maintenance and function of endothelial cells F Li et al. (2021) \| \| Nos3 \| Antiatherosclerosis gene F. Li et al. (2021) \| \| Clec3b, S100a4, Fmo2 \| Higher in early stage F. Li et al. (2021) \| \| Cxcl2, Cxcl12, Jun, Tcf4 \| Higher in late stage F. Li et al. (2021) \| \| Il6 \| Inflammatory gene Sorokin et al. (2020) higher in late stage F. Li et al. (2021) \| \| Icam2 \| EC marker F. Li et al. (2021) involved in immunity and inflammation Huang et al. (2021) \| \| Egfl7 Gu et al. (2019); F. Li et al. (2021), Vwf (F. Li et al. (2021); Zhao et al. (2021), Cytl1 F. Li et al. (2021), Cdh5 Garrido et al. (2021); Huang et al. (2021); Zhao et al. (2021), Cd34 Van Kuijk et al. (2019); Depuydt et al. (2020); Slenders et al. (2021), Pecam1 Gu et al. (2019); Van Kuijk et al. (2019); Depuydt et al. (2020); Zhao et al. (2021); F. Li et al. (2021); Huang et al. (2021); S. Li et al. (2021); Brandt et al. (2022); Burger et al. (2022); Ni et al. (2022); Pinheiro-de-Sousa et al. (2022) \| Classical EC markers \| \| Fn1 \| ECM gene biasedly expressed in HFD group, EndMT + EC and pro-inflammatory EC Zhao et al. (2021) \| \| Tgfbr2, Bgn \| ECM genes biasedly expressed in EndMT + EC Zhao et al. (2021) \| \| Fgl2, Il7, Abca1, Eln \| Biasedly expressed in EndMT + EC Zhao et al. (2021) \| \| Ccl21a \| Biasedly expressed in EndMT + EC Zhao et al. (2021) and lymphatic endothelial cell Cai et al. (2020) \| \| Lpl, Gpihbp1 \| Biasedly expressed in lipid-handling EC Zhao et al. (2021) \| \| Vim \| Biasedly expressed in HFD group and pro-inflammatory EC Zhao et al. (2021) \| \| Dcn \| ECM gene (Zhao et al., 2021), involved in immunity and inflammation Huang et al. (2021), biasedly expressed in HFD group, EndMT + EC, and pro-inflammatory EC \| \| Gapdh, Fabp4, Mgll \| Involved in fatty acid metabolism, biasedly expressed in EndMT− EC Zhao et al. (2021) \| \| Ctsb, Ctsz \| Involved in ECM degradation Zhao et al. (2021) \| \| Fabp5 \| Biasedly expressed in HFD group Zhao et al. (2021) \| \| Cxcl6, Nfkbiz Zhao et al. (2021) \| Pro-inflammatory gene biasedly expressed in EndMT + ECs Huang et al. (2021) \| \| Lrg1, Ptprb, Acvrl1, Tmem100 \| Angiogenesis-related gene Huang et al. (2021) \| \| Adamts1, Cd74, Cebpb, Ctla2a, Fcgrt, Kdm6b, Lcn2, Nfkbia, Sgk1 \| Involved in immunity and inflammation Huang et al. (2021) \| \| Ecscr, Gpr56, Pcdh1, Tmsb10 \| Involved in cellular chemotaxis Huang et al. (2021) \| \| Bmpr2, Ccdc85b, Fosb, Id3, Oaz1, Pfkbfb3, Tspan8 \| Involved in cellular growth and development Huang et al. (2021) \| \| Cxcr3, Lyve1 \| Biasedly expressed in lymphatic endothelial cell Cai et al. (2020) \| All genes enhanced in the HFD group are related to atherosclerosis. Some indicate the progression of disease, such as Adam15 (Langer et al., 2005; Oksala et al., 2009), Lpl (Dugi et al., 1997), and Gadph (Perrotta et al., 2014), which play a role in pathological neovascularization, lipid utilization and storage, and innate immunity, respectively. Some play roles in the dysfunction of endothelial cells, such as Podxl which functions as an anti-adhesive molecule (Shoji et al., 2018) and Eng which regulates endothelial cell shape changes in response to blood flow and is required for normal structure (McAllister et al., 1994; Vicen et al., 2019). Some are anti-atherosclerotic, such as Cxcl12, which plays a protective role after myocardial infarction (Hartmann et al., 2015), and Rbp7, which helps to increase nitric oxide bioavailability (Figure 3C). Cluster-specific markers also performed variously in the two groups and further confirmed the effect of diet. Cytl1 and Vcam1, markers of genuine endothelial precursor cells, showed unchanged expression under different types of diet, as does the marker of fibrosing EC, Col1a1. Fbln5, which is reinduced in atherosclerotic lesions, showed a rise in the HFD group. Similarly, canonical B cell marker Igkc and Ighm showed a reduced expression under the regulation of the Western diet (Figure 3D). The cells in the experimental group lost a lot of primary endothelial cell functions, such as cell formation, adhesion, and contraction, but inflammatory responses and some lipid-related functions (markers including Gpihbp1, Cd36, and Fabp4) were still retained (Figure 3E). ## 3.4 Identification and diet-dependent variation of aortic immune cells under single-cell RNA sequencing Immune cells are another emphasis in this study, and we define Ptprc + cells as immune cells. 1,615 cells were filtered out from the total cell population, with 1,083 from the control group and 532 from the HFD group. These cells were reclustered into 14 subpopulations. Identification of these cells was done based on the transcriptional profiles with the application of CIBERSORT (Figures 4A, B; Supplementary Figure 3A). FIGURE 4: Identification of each immune cell cluster and their distribution reveal constitution of aortic immune cells and effect of diet. (A) T-Stochastic neighbor embedding (t-SNE) representation of gene expression information in aortic Ptptrc + immune cells. Cells from both control and Western diet group (right) are distinct into 14 clusters (left); CIBERSORT helps with their identification (middle). (B) Heatmap showing the top 10 upregulated genes in each cluster of immune cells, with biological identification of each cluster marked on the right. (C) Percentage of each immune cell type within total immune cells. Left: control (ND) group; Right: Western diet (HFD) group. (D) Dot plot showing the expression levels of representative marker genes for each immune cell type. (E) Violin plots of log-transformed gene expression of Ptprc distinguish these immune subpopulations from all other aortic cells. Constitution of immune cells in the control group versus the HFD group reveals the great effect created by the Western diet. The most obvious heterogeneity brought by the Western diet was that B cells and T cells dominated the control group, accounting for $27.98\%$ and $23.18\%$ respectively, while macrophages had the largest proportion in HFD group, up to $45.49\%$. These 3 cell types dominate the immune cell. Another variance was the increase of dendritic cells and decrease of neutrophil (Figures 4A, C; Supplementary Figure 3A). Macrophage had the largest proportion in all immune cells are were divided into four clusters, all showing a common expression of C1qa (De Couto et al., 2019). Pf4, an M2 macrophage marker, was expressed the highest in cluster4 and cluster6, while foam cell marker Cd68 and Abcg1 was enriched in cluster7. The proportion of B cell and T cell were similar. It should be noted that B cells (Cd79a, Cd79b, and Ly6d) existed exclusively in the control group. T cells (Trbc1, Trbc2 (Lefranc, 2014), Cd3d, and Cd3g) consisted of natural killer CD4+ T cells, Rora (Kästle et al., 2017), Il7r (Al-Mossawi et al., 2019), Icos (Niu et al., 2018) and Cd4, and cytotoxic CD8+ T cells, Cd8a and Cd8b1. The remainder of immune cell populations included two clusters of dendritic cells (DCs) with gene signatures Cd209a (cluster 3), Cst4 and Clec9a (cluster 14), two clusters of neutrophils (cluster 10: S100a8 and S100a9; cluster 10 and 12: Gsr (Yan et al., 2013); cluster 12: highly expressed Ly6c2, a marker of monocyte), and three other clusters of Ptprc + cells (Figures 4B, D; Supplementary Figure 3B). Regardless of type, all showed a high expression of Ptprc (Figure 4E). ## 3.5 Differentiation of macrophage and heterogeneity brought by wester diet As previously seen, four clusters were defined as macrophage. Common enrichment of C1qa (De Couto et al., 2019), C1qb (Giladi et al., 2018), C1qc (Zhao et al., 2020), and Mafb (Goudot et al., 2017) further confirmed the identity of macrophage (Figure 5A). The proportion of macrophage across all immune cells increased due to the appearance of foam cells according to the constitution of macrophage in the control group versus the HFD group (Supplementary Figure 4A). In the meantime, the proportion of M2 macrophage decreased. The remainder are M2-like macrophages and inflammatory macrophages (Figure 5B). FIGURE 5: Gene expression signature of macrophage and its variation under western diet. (A) Violin plots of log-transformed gene expression of canonical macrophage markers in macrophages and all other aortic immune cells. (B) Percentage of each macrophage type. (C) Violin plots profile gene expression of markers for the five classes of macrophages in these selected macrophages. (D) Heterogeneity within macrophage of two origins demonstrated by dotplot. Single-cell differential expression pattern helps differentiate these four macrophage subsets from one another, and specific enrichment of these genes in each subset helps further determine their identification. Previous studies established a gene pool for various macrophages. Despite greater expression of M2 macrophage markers Folr2, Mrc1, and Cbr2, M2 macrophage also had characteristics of resident macrophage (F13a1 (Beckers et al., 2017) and Lyve1 (Ensan et al., 2016)). The expression profile of M2-like macrophages is similar to M2 macrophages, inferred by a transitional relationship between them. Meanwhile, due to the high expression of the monocyte marker Ccr2, we speculate that it is the progenitor of M2 macrophages. Foam cells were identified because the relative upregulation of Cd68 and Abcg1 can promotes cholesterol accumulation, which matters in the formation of foam cells (Cheng et al., 2016). These cells also have an exclusively positive expression of Spp1. Spp1 encodes osteopontin, which is a factor related to the severity of lesions. Another related factor is cathepsin, encoded by Ctsb, Ctsd, and Ctsz (Cochain et al., 2018), which was expressed higher in the foam cell. This was also the case with Trem2, a putative factor in Trem2-high macrophages except osteopontin and cathepsin. With significant upregulation of several chemokines (Ccl3, Ccl4, Cxcl1, Cxcl2, and Cxcl16) that play a role in inflammation, inflammatory macrophages were identified (Figure 5C; Supplementary Figure 4B). The effect of diet on macrophages can also be seen from genetic regulation under the Western diet. These genes were involved in biological pathways such as cholesterol transporting (Abca1, Abcg1, and Pltp), development of adipose tissue (Plin2), inflammation (Lgals3 and Ccl8), and cell apoptosis (Ifitm2 and Ctsd) (Figure 5D). ## 3.6 Differential expression of Pecam1 and Cdh5 between different regions of the aorta Pecam1 and Cdh5 are a pair of genes used to identify endothelial cells in this study. According to the scRNA sequencing data, an endothelial subpopulation, identified as genuine endothelial precursor cells specifically, was higher in Pecam1 and lower in Cdh5 compared to other endothelial subpopulations (Figure 1F). This subpopulation expressed Cytl1 exclusively (Figure 1D). The Cytl1+ cells were found in three clusters in further analysis of sorted Pecam1+ Cdh5+ endothelial cells (Figure 2E), and retained the characteristics of high Pecam1 expression and low Cdh5 expression (Figure 2C). Upregulated Vcam1 expression level is another characteristic of these cells. In the comparison of gene expression levels for endothelial cells isolated from a normal diet versus Western diet, Pecam1 and Cdh5 were listed in top 100 differentially expressed genes (DEGs), with Pecam1 lowered and Cdh5 enhanced in the Western diet group (Figure 3E). Flow cytometry helped identify the distinct spatial location of high Pecam1 (Figure 6A) and low Cdh5 (Figure 6B) expression. Greater curvature of the aorta demonstrates less presence of high Pecam1-expressing cells and more presence of low Cdh5-expressing cells. The preference in location was found in both diet groups. Otherwise, the Western diet presented mild inhibition of the increase of low Cdh5-expressing cells. FIGURE 6: Identification of genetic heterogeneity in situ within endothelial cells. Flow cytometry of mouse aortic endothelial cells demonstrating heterogeneity of (A) Pecam1, (B) Cdh5, and (C) Vcam1 between greater (arcus aortae) and lesser (descending aorta) curvature of aorta, showing the influence of a Western diet. Flow cytometry also helped identify the distinct spatial location of high Vcam1 expression (Figure 6C). High Vcam1-expressing cells showed a preference in descending aorta compared to the greater curvature of the aortic root under a high fat diet. A contrasting result was found under normal conditions. ## 4 Discussion Known as the region with disturbed blood flow, the aortic arch is composed of different cell types and is relevant in the progression of cardiovascular disease and is the research object in this study. With well-established single-cell RNA sequencing, we characterized different types of cells in the aortic arch and mining gene expression changes that occur within these cells under the influence of diet. We differentiated 14 classes of cells from the total sample, including endothelial cells, fibroblasts, smooth muscle cells, mesothelial cells, adipocytes, neurons, and immune cell populations, which were distributed in 27 clusters. Notably, the identification of cycling basal cell, clara cell, mesothelial cell, and adipocyte was new. Heterogeneous gene expression pattern distinguishes 10 subpopulations of endothelial cells with distinct functions and reveals which biological processes may be affected or remain unchanged under a Western diet. This study identifies genuine endothelial precursor cells for the first time in such studies, complementing the transcriptional information blank of this cell. Another class of cells focused on in this study was immune cells, especially macrophages. Seven immune cell types were distributed in 14 immune cell clusters, and four clusters of macrophages expressed different characteristics. A genes expression map uncovered the multiplicity of aortic cells and demonstrated that the Western diet accelerates atherosclerotic development by regulating the role of the genes in various biological processes. In the total aortic cell pool, the seven endothelial subpopulations were similar in the expression levels of Pecam1 and Cdh5. The cell adhesion molecules encoded by Pecam1 and Cdh5 are essential for leukocyte trans-endothelial migration (Dasgupta et al., 2009) and the maintenance of vascular lumen homeostasis (Lampugnani et al., 2010). Negative expression correlation between these two genes was observed in each endothelial subpopulation. A genuine endothelial precursor cell exhibited higher Pecam1 and lower Cdh5 gene expression levels compared to the other endothelial cell clusters. Another gene pair showing similar negative correlation between endothelial precursor cells and other endothelial cells is Vcam1 and Cd36. Based on the conclusions of previous single-cell studies, Vcam1 and Cd36 were relatively biased to be lower and higher expressed in classic endothelial cells and endothelial cell roles in lipid treating. A classic endothelial cell tends to be distributed in descending aorta with less curvature, while other endothelial cells tend to be distributed in the arterial root with greater curvature (Kalluri et al., 2019). Genuine endothelial precursor cells exhibited the same expression pattern as the classic endothelial cells mentioned previously. According to the relationship between the two gene pairs, the endothelial precursor cells involved in this study can be speculated to be like classic endothelial cells, possibly located at sites with smaller curvature. Sites with less curvature often suffer attenuated lipid accumulation and lower atherosclerotic risk rates. The result of flow cytometry supports the speculation on the location of genuine endothelial precursor cells and demonstrates the influence of the Western diet on this cell type. Nos3, known for its promotion effects in nitric oxide production and vasodilation, was significantly upregulated in these precursor cells, further indicating the lower atherosclerotic risk rate within it (Lusis, 2000). Data in the STRING database showed a co-expression relationship between Cdh5, Pecam1, and Vcam1, while Cd36 was not involved. However, whether the supposed relevance among these proteins are reliable and how to localize them remains to be addressed. Flow cytometry was not used in this study, instead only algorithm was used in the cell sorting. Endothelial cells sorted this way in the study showed a similar proportion of genuine endothelial precursor cells in both experimental groups. Except genuine endothelial precursor cells, inflammatory endothelial cells also had a higher expression of Pecam1 and Vcam1 as well as a lower expression of Cdh5 and Cd36. The two endothelial cell subpopulations existing in both the control and HFD group indicated that some precursor cells and inflammatory cells from advanced atherosclerosis were clustered together with cells in a normal condition. It can be speculated that the transcriptional profiles of these cells in the late disease and normal states are similar, but whether they locate in atherosclerotic plaque remains to be determined. Distribution of EC1-3 and expression profile of them indicates great probability that endothelial develops from the state of EC3 into EC1 during the disease progression. During the process, endothelial went through dysfunction and lost the function of angiogenesis and cell proliferation. The Western diet can accelerate the progression of atherosclerosis (Supplementary Figures 5A–D) as well as endothelial dysfunction. Genes that showed an increased expression indicated the character of dysfunctional endothelial cells. These dysfunctional endothelial cells lost cell adhesion function (Podxl), which is one of the key functions in normal endothelial cells. Meanwhile, based on certain responses, based on certain responses, they appeared to inhibit the further dysfunction of endothelial cells (Hu et al., 2017), for example, changing shape in response to blood flow in order to keep normal structure (Eng) or promoting the production of nitric oxide (Rbp7) (Fang and Sigmund, 2020). In accordance with previous findings, Cxcl2 is enriched in endothelial cells in advanced atherosclerosis and suppresses atherosclerosis after myocardial infarction (F. Li et al., 2021). As a key enzyme in triglyceride metabolism, Tubb5 plays an important role in removing lipids from the blood, as well as in lipid utilization and storage (Weinstock et al., 1995). The enrichment of these biological processes suggests that in most cases, feedback inhibitory pathways for atherosclerosis development are activated in dysfunctional endothelial cells, potentially limiting further plaque expansion by inhibiting lipid accumulation and regulating vasodilation in the lesion. Both Fbln5 and Vcam1 were upregulated in genuine endothelial precursor cells of mice with advanced atherosclerosis administered with a Western diet, implying that it induced more leukocytes that migrated to sites of inflammation. At the same time, lipid-related functions in these cells were not as active as in other endothelial cells, suggesting their absence in proatherogenic biological processes such as cholesterol transport, lipid utilization, or storage. The results showed that some immune cells were absent in the HFD group, such as B cells and CD4 + T cells, indicating that the Western diet may inhibit the differentiation of aortic immune cells. A recent study pointed out that B1a and B1b lymphocytes produce IgM to inactivate oxidation-specific epitopes on LDL and thereby protect against atherosclerosis (Pattarabanjird et al., 2022). Therefore, it can be speculated that B cells initiate protective mechanisms in atherosclerosis, while the Western diet reduces the number of B cells, thus aggravating atherosclerosis. In accordance with a recent study published in Frontiers in Immunology, which indicated that neutrophils and APC-like neutrophils were dominant in the blood of hyperlipidemic patients rather than healthy patients (Zhao et al., 2022), neutrophils and dedicated antigen-presenting dendritic cells (APC-DC) were found to be higher in the HFD group than in the control group. However, in this study, there was no more research into the role of APC-DC in atherosclerosis. This could be further explored in the future. The proportion of macrophages observed in Ptprc + cells increases with disease progression, similar to the findings of other researchers (Galkina et al., 2006; Cochain et al., 2018). In contrast, the proportional increase in macrophages observed in this study was caused by foam cells rather than resident macrophages or inflammatory macrophages. These foam cells were specifically enriched with the validated markers of Trem2 high macrophage. It can be inferred that the transition from macrophages to foam cells occurs in the late stages of disease and the cell of origin may be highly expressing Trem2. During the progression of atherosclerosis, the production of foam cells is inseparable from the high oxidation of LDL. High oxidization assists LDL recognition by the scavenger receptor Cd68 and thus uptake by macrophages occurs rapidly. Clearly, the significant upregulation of Cd68 with the above principle could explain the emergence of foam cells in this study. The role of Abca1, Abcg1, and Pltp in foam cells is particularly apparent in the late stage of atherosclerosis. They help with the efflux of cholesterol in macrophages and uptake into HDL, hence promote the conversion of LDL to HDL. This facilitation of HDL synthesis predicts an inhibited conversion of macrophages to foam cells and can be one of the manifestations of feedback inhibition in advanced atherosclerosis, which slows down the further development of disease. In conclusion, transcriptional information of aortic cells, especially VEC, was mapped using single-cell RNA sequencing. Significantly, we identified 10 endothelial subpopulations and established candidate marker genes of each subpopulation. In addition, all endothelial candidate markers identified in previous research of atherosclerosis with single-cell RNA sequencing were confirmed in this study. Notably, by observing its transcriptional signature, we found genuine endothelial precursor cells, which had not been mentioned in previous studies. Compared with previous studies, the results of this study broaden the understanding of endothelial cells and further enrich existing information about different endothelial cell markers. In addition, comparison of gene expression profiles and constitutions of cell between the health and atherosclerotic group can help in understanding the post-modification induced by a high-fat high-cholesterol diet in various cells, especially endothelial cells and immune cells. Understanding the pathways involved in these modifications may contribute to further exploration of pathogenesis of atherosclerotic lesions. There are some limitations to this study, which may comprise further areas of research. Validation of several newly demonstrated genes or pathways has not yet been carried out. Traditional methods such as immunohistochemistry and western blot could be used to validate the distribution of these genes and pathways more accurately. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE206239. ## Ethics statement The animal study was reviewed and approved by Shenzhen University Animal Care and Use Committee. ## Author contributions JW designed the research and performed the experiments and RL performed data analysis; both contributed equally to this manuscript. JW wrote the article. RC conducted critical editing. SZ provided data support. YD, TZ, FG, QW, and GW helped in reagent and material preparation. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2023.971091/full#supplementary-material ## Abbreviations DC, Dendritic cell; EC, Endothelial cell; ECM, Extracellular matrix; HDL, High-density lipoprotein; HFD, Western diet; LDL, Low-density lipoprotein; SMC, Smooth muscle cell; t-SNE, t-stochastic neighbor embedding; VEC, Vascular endothelial cell. ## References 1. Al-Mossawi H., Yager N., Taylor C. A., Lau E., Danielli S., de Wit J.. Context-specific regulation of surface and soluble IL7R expression by an autoimmune risk allele. Nat. Commun. (2019) 10 4575. DOI: 10.1038/s41467-019-12393-1 2. Allan C. M., Jung C. J., Larsson M., Heizer P. J., Tu Y., Sandoval N. P.. 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--- title: 'Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development' authors: - Carolyn Clausen - Bennett Leventhal - Øystein Nytrø - Roman Koposov - Thomas Brox Røst - Odd Sverre Westbye - Kaban Koochakpour - Thomas Frodl - Line Stien - Norbert Skokauskas journal: Frontiers in Psychiatry year: 2023 pmcid: PMC9997712 doi: 10.3389/fpsyt.2023.1033724 license: CC BY 4.0 --- # Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development ## Abstract ### Introduction Child and adolescent mental health services (CAMHS) clinical decision support system (CDSS) provides clinicians with real-time support as they assess and treat patients. CDSS can integrate diverse clinical data for identifying child and adolescent mental health needs earlier and more comprehensively. Individualized Digital Decision Assist System (IDDEAS) has the potential to improve quality of care with enhanced efficiency and effectiveness. ### Methods We examined IDDEAS usability and functionality in a prototype for attention deficit hyperactivity disorder (ADHD), using a user-centered design process and qualitative methods with child and adolescent psychiatrists and clinical psychologists. Participants were recruited from Norwegian CAMHS and were randomly assigned patient case vignettes for clinical evaluation, with and without IDDEAS. Semi-structured interviews were conducted as one part of testing the usability of the prototype following a five-question interview guide. All interviews were recorded, transcribed, and analyzed following qualitative content analysis. ### Results Participants were the first 20 individuals from the larger IDDEAS prototype usability study. Seven participants explicitly stated a need for integration with the patient electronic health record system. Three participants commended the step-by-step guidance as potentially helpful for novice clinicians. One participant did not like the aesthetics of the IDDEAS at this stage. All participants were pleased about the display of the patient information along with guidelines and suggested that wider guideline coverage will make IDDEAS much more useful. Overall, participants emphasized the importance of maintaining the clinician as the decision-maker in the clinical process, and the overall potential utility of IDDEAS within Norwegian CAMHS. ### Conclusion Child and adolescent mental health services psychiatrists and psychologists expressed strong support for the IDDEAS clinical decision support system if better integrated in daily workflow. Further usability assessments and identification of additional IDDEAS requirements are necessary. A fully functioning, integrated version of IDDEAS has the potential to be an important support for clinicians in the early identification of risks for youth mental disorders and contribute to improved assessment and treatment of children and adolescents. ## Introduction Mental health is a key component of overall health. Mental disorders are amongst the most common and debilitating clinical challenges. For example, depression is one of the leading causes of disability worldwide [1]. Furthermore, following the first year of the COVID-19 pandemic, the global prevalence of depression and anxiety increased by $25\%$ [1]. While all people are susceptible to developing mental health problems, children and teenagers are most vulnerable, with $75\%$ of all life-time mental disorders having their onset in childhood and adolescence [2, 3]. In addition, environmental factors are more likely to negatively impact the developing brain, increasing the risk for mental disorders in youth and children [1, 4]. Despite this, access to and availability of timely CAMHS is limited [4]. Without appropriate early interventions, children and adolescent mental health symptoms can evolve into potentially lifelong mental disorders, yet $70\%$ of those experiencing mental health problems go without receiving appropriate care (1, 4–6). As part of routine health care, children and adolescents should, but rarely do, receive early assessments for risks associated with mental disorders [7, 8]. Detecting and managing these risks as early as possible can help to reduce costs of services as well as societal costs, and ultimately, help alleviate the high demand for more complex treatment services [8, 9]. CAMHS expansion requires not only redistributed health budgets to allocate a greater share of funding toward mental health, but also investment in additional technological resources and mental health informatics [1, 4]. Telepsychiatry or virtual reality (VR) exposure therapy exercises, for example, have proven to be effective mental health care [10, 11]. Other health information technologies (HIT), such as clinical decision support systems (CDSSs), may have even more potential for service enhancement [12, 13]. A CDSS is a tool designed to improve healthcare delivery by enhancing precision and timeliness of medical decisions through provision of support based on targeted clinical knowledge and patient health information [14]. CDSSs are designed for various specific purposes, such as risk identification, diagnostics, and prescription management support [9, 12, 14]. They can be developed to provide support with the use of clinical practice guidelines, as well as employing artificial intelligence (AI) to map aggregated patient health record data, commonly referred to as “big data” [11, 15, 16]. Big data analytics and mental health informatics using AI can provide evidence from multiple sources to allow for an aggregation of knowledge, account for multifaceted patient situations, and gain important insights for future approaches to care [16, 17]. Because of the challenge in juxtaposing normative clinical guidelines, with empirical evidence in the form of care patterns, developing a CDSS requires collaborative, multi-disciplinary efforts to ensure a cohesive balance between the technological innovation and the clinical workflow [12, 18, 19]. Human computer interaction (HCI) and user-centered design (UCD) methods allow for simulated experimental and observational approaches that provide valuable insight into user workflow and clinician problem-solving needs. This process informs development, based on close collaborations with the end-users throughout innovation and research (13, 20–22). Clinical decision support systems have found some significant success in general medicine and adult mental health but have yet to be adequately developed and implemented to CAMHS [18, 23, 24]. The development of a CDSS for CAMHS faces systematic obstacles, including the lack of coordination amongst services and the limited accessibility of patient health data records used to develop a CDSS for CAMHS [25]. While standardized clinical practice guidelines can be easily modeled for inclusion in a CDSS as part of an electronic health record (EHR) platform, providing decision support based on local practice patterns embedded in aggregated patient data can be challenging, as it requires access to hybrid and multi-source clinical data with approval from ethical committees and adherence to data protection regulations (i.e., General Data Protection Regulations-GDPR) alike [11, 14, 16, 20]. Despite the challenges, the integration of health data has continued to exhibit potential for improving healthcare services [16]. Continued digital development, utilizing previously collected patient health data, has the potential to provide innovative solutions to acknowledge limitations within health services [4]. With the digitalization of health services across specializations, integration of additional information and data from other information systems, could provide clinicians with transparent and holistic insight into a patient’s current needs [14, 16]. Exploiting all possibilities of digital solutions within a CDSS, not only limited to patient health information from the EHR system but additionally encompassing digital case notes and hospital information systems, could provide a more efficient way to address the dynamics involved within CAMHS [11]. In Norway, the Individualized Digital Decision Assist System (IDDEAS) will be the first CDSS in CAMHS that uses both “big data” analytics and standardized clinical guidelines. Norwegian CAMHS are facing substantial increasing demand amidst the COVID-19 pandemic, like elsewhere in the world [26, 27]. In 2021, almost 65,000 Norwegian children and adolescents received mental health care – a $14\%$ increase from the previous year [26]. Furthermore, over the course of the year nearly 36,000 referrals for mental health care have been reported for children and young people [26]. The Norwegian National Association of Child and Adolescent Mental Health Services (N-BUP), established in 1958, has historically been responsible for providing a basis to connect all CAMHS in Norway and continuing to promote coordination and sharing knowledge amongst CAMHS [28]. While N-BUP actively helps to facilitate the dissemination and sharing of important CAMHS information through research and management conferences annually, there is still invaluable CAMHS knowledge that has yet to be utilized- previously collected CAMHS individual patient EHR data (i.e., BUP-data) [29]. The previously established EHR system of BUP-data was the first of its kind in Norway to be able to provide data comparisons on an individual patient basis [29]. While the EHR system has been replaced, utilizing the knowledge acquired within BUP-data, in combination with standardized clinical practice guidelines, has the potential to provide Norwegian CAMHS with additional support to meet the mental health needs of children and adolescents [30]. Upon receiving access to this invaluable resource, with support from N-BUP, and in close collaboration with its’ clinicians, the IDDEAS project is developing and researching a CDSS to provide clinicians in Norwegian CAMHS with real-time decision support, in part by BUP-data, but also with standardized clinical guidelines, including DSM-5 and ICD-10 [11]. The IDDEAS prototype is in the process of formative usability testing, including this qualitative study. This study aimed to understand CAMHS clinicians’ overall perceptions of IDDEAS prototype usability while also examining potential barriers to implementation and specific needs to be met in the development of the CDSS. The objectives of this study were to 1) explore clinicians decision-making processes; 2) investigate the perceived usability and functionality of the IDDEAS prototype; and 3) identify the user-perspectives on IDDEAS, to inform continued development and feasibility within Norwegian CAMHS. ## Study design This is a mixed-methods study to evaluate IDDEAS, a decision support system for diagnosis and treatment of children and adolescents in Norwegian CAMHS. The IDDEAS project is organized into the following stages: [1] The Assessment of Needs and Preparation of IDDEAS; [2] The Development of the IDDEAS CDSS model; [3] The Evaluation of the IDDEAS CDSS; and [4] Implementation and Dissemination (see Figure 1). This qualitative study reports on the interviews conducted as one component of the usability evaluation of the first IDDEAS prototype [11]. FIGURE 1: Individualized Digital Decision Assist System (IDDEAS) project study design overview. This evaluation process utilizes user-centered design (UCD) methods, with the testing of the CDSS conducted in phases of developmental iterations. The UCD methods include formative usability sessions [12, 31], cognitive walk-through/think-aloud procedures [5, 32], iterative development with end-users, and utilization of both qualitative and quantitative methods of inquiry [31, 33]. As part of UCD, the iterative development of the CDSS involves continuous collaboration with CAMHS clinicians. The specific methods and the development plan are detailed in the IDDEAS project protocol [11]. The present study serves as the first usability test, using UCD methods to investigate Norwegian CAMHS clinicians’ perceptions of the usability, utility, and overall functionality of the IDDEAS prototype. ## IDDEAS prototype The IDDEAS prototype allows for exploration of the ability of IDDEAS guidelines to provide decision support for diagnosis and treatment of attention deficit and hyperactivity disorder (ADHD) (see Figure 2). ADHD is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, ultimately causing impaired functioning for the individual [9]. The IDDEAS prototype at this stage uses ADHD as the first clinical model paradigm. Preparation of IDDEAS includes the validation of the clinical materials and the user-interface. The IDDEAS guidelines were previously validated by the IDDEAS clinical research team using the DSM-5 and ICD-10 criteria. Focus groups were used to pre-test content prior to the IDDEAS prototype evaluation. FIGURE 2: Individualized Digital Decision Assist System prototype software screenshot. Each IDDEAS prototype evaluation session included having a clinician participant complete a concurrent, cognitive walk through/think-aloud procedure, as they critically appraised hypothetical patient case scenarios developed from real cases within CAMHS. A total of 20 patient case scenarios were collaboratively designed and validated by the IDDEAS team (BL, NS, RK). Out of the 20 possible cases, each participant was randomly assigned four to assess, two of which were to be assessed while using the IDDEAS prototype (ADHD modeled guidelines) and two without. Use of IDDEAS was similarly randomly assigned. Throughout the assessment of the four cases, participants were asked to follow a think-aloud procedure and provide a concurrent walk through of the clinical procedure they would follow if the patients were real. They were also asked to provide additional patient information they perceived to be potentially necessary to complete their clinical assessment. Finally, participants were asked to provide their overall perceptions of the IDDEAS prototype and its usability, functionality, and potential utility. ## Setting and sampling The participants ($$n = 20$$) were those who first participated from the larger cohort evaluation of the IDDEAS prototype [11]. We (CC) directly contacted all potential participants who had been recommended by N-BUP board members and those from a random list of service providers. To promote privacy and confidentiality, an invitation email with background information about the IDDEAS team and consortium, as well as the project’s scope and aims, was sent to each potential participant. We (CC) met with each participant prior to the evaluation session in order to go through the proposed study procedure, as well as to provide participants with an opportunity to get acquainted and ask any potential remaining questions. Upon agreeing to participate in the study, each participant created their own profile on the IDDEAS portal and in accordance with the Norwegian Centre for Research Data (NSD) protocol, completed the informed consent process. Initial focus group discussions and pre-testing sessions were conducted beginning in March 2020, with the interviews taking place until Spring 2022. ## Research instrument A semi-structured interview guide with five questions was developed collaboratively by the IDDEAS team, based on the specific research question and the overall objectives of the IDDEAS project. The interview guide was created following the Mayring qualitative content analysis (QCA) approach [34] and is similar to those implemented by Schaaf et al. [ 12] and Baysari et al. [ 30]. The final interview guide was confirmed by the IDDEAS team and translated, making it available in both English and Norwegian (see Supplementary Appendix 1). We (CC) conducted preliminary internal testing with members of the IDDEAS team. After the internal testing, a small focus group interview was held with four Norwegian CAMHS psychologists and psychiatrists who all met the inclusion criteria for the qualitative study. Participants were deemed eligible for inclusion if they were either a child and adolescent psychiatrist or psychologist. All potential participants who did not meet the inclusion criteria were excluded. Study participants were given the option to choose to complete their interview in English or Norwegian. ## Data collection The interviews took place at the end of the IDDEAS prototype usability evaluation sessions. The study was conducted following UCD methodology and standardized criteria for qualitative research, including the consolidated criteria for reporting qualitative research (COREQ) and the Standards for Reporting Qualitative Research (SRQR) [35]. The first author (CC) was responsible for interviewing the participants. The research question, interview guide, and qualitative data categorization system were all developed by CC and verified by the IDDEAS team. After completion of informed consent and establishing a profile with the IDDEAS portal,1 participants were invited to meet with CC, either in person or online. Due to COVID-19 meeting regulations and safety requirements, all invitations sent out were via the Microsoft Teams online platform. All interviews were recorded and transcribed, word-for-word. All interviews were conducted directly following the completion of the IDDEAS prototype assessment’s case appraisal procedure. All interviews took place within one session and no interviews were repeated or redone. All transcripts were saved within a secure, password protected zip file and stored on the Norwegian University of Science and Technology (NTNU) secure server in preparation for data analysis. No personal or sensitive data was included in accordance with the Norwegian Centre for Research Data (NSD) protocol requirements and research data management permission granted (reference code: 100166). ## Data analysis In line with QCA methods, a category system and coding rules were developed for the qualitative data analysis. The system was based on the research question and the study’s objectives, with the specific categories developed to determine which textual passages to take into consideration. Following an inductive category development procedure, the categories are tentative and deduced step-by-step, as applicable. The proposed categories were presented to the IDDEAS team members for theoretical structure verification prior to application to data material and formative/summative checks of reliability. Theoretical based definitions, examples of applicable text passages, and coding rules for each category, were collated within a coding agenda [36]. As suggested by Mayring [34, 37], falling within the range of 10–$50\%$, $35\%$ of the transcribed material was checked with the preliminary categories and assessed for adequate representation of theoretical foundation and encompassing the text content. The proposed categories and the coding agenda were presented to the IDDEAS team and underwent revision before completing data analysis. The categories were revised from three main categories and 12 subcategories in Version 1 to a total of 11 subcategories in Version 2 (see Supplementary Appendix 2 for more details). The final category system consisted of three main categories and eleven subcategories (see Figure 3). FIGURE 3: Qualitative content analysis applied main categories and subcategories. All text passages from the interview transcripts were extracted and organized following the deductive category application model [34]. The content-analytical coding rules were followed, to keep the process of category application as controlled as possible and to determine the most appropriate category. If there was a text passage that could not be assigned to a category, this was discussed with the IDDEAS team. After assigning all text passages to categories, all included within each category were then summarized and an example quotation was extracted for representation of the content. The extracted quotations that best represented the content of the category were chosen to represent the main findings. Any quotations in Norwegian were translated to English. ## Participants The participants represent ten CAMHS clinics. Most participants identified as men ($$n = 11$$) with the rest identifying as women ($$n = 9$$). Fourteen were CAMHS psychologists, while 6 were CAMHS psychiatrists. The participants had varied experience working in CAMHS: no participants worked in CAMHS for less than 6 months, one participant had worked in CAMHS for 6–12 months; two participants worked in CAMHS for 1–4 years, and 17 reported to have worked in CAMHS for 4 years or more. The IDDEAS prototype evaluation session duration ranged from 30′20′′ to 93′51′′ with the 5-question interview mean duration of 6′45′′ ranging from 1′15′′ to 11′34′′. ## Main results by category The following sections present the results organized by the deductive QCA categories. We provide example comments from each category. Three main categories were extracted: [1] Patient information, [2] Software Functionality, [3] Usability and Overall Experience (see Figure 3). ## Patient information and referral information required Most of the participants reported concern about insufficient patient information available during the evaluation session. Participants who raised this issue acknowledged that they understood that the evaluation procedure was to intentionally include hypothetical patient case scenarios with limited clinical data, as well as the limited ability to engage with the IDDEAS prototype at this stage in its development. One participant noted: Participants shared that insufficient patient information made it difficult to arrive at one diagnosis and indicated a need for more information in order to adequately utilize suggestions. Participants acknowledged they were missing important information, such as what is currently established about the patient, and the ability for the information to be adjusted accordingly to keep up to date. One participant said: Finally, it was not easy for participants to speculate how it would be to use the system in the future to input their own patient information changes or adapt to changes in patient data between sessions. It was stated that decision support could be very useful when additional referral information is available and, for junior colleagues, guidance on how to find missing information could be beneficial. One participant offered the following suggestion: ## Symptom history and services received All participants found it important to have information about the patient’s previous symptoms, the diagnostic/treatment history, and any previous services. Participants commonly explained that there was often insufficient information available on the family context and the relationship with parents. For the clinicians to feel they can adequately assess the current patient information, there is a need for thorough presentation of patient history and any collaborative services accessed that inform the patient’s assessment. Multiple participants explained the importance of always having multiple hypotheses for patients, without knowing all services received already. More specifically, participants noted that while there may be patient symptom history available, information from additional services involved in the care was not adequately presented. For example: ## Electronic health record information presentation All participants reacted positively to the presentation of the patient information directly adjacent to the guideline support. Participants noted the importance of being able to navigate between the guidelines and the patient information seamlessly, and being able to track location in the guideline, while maintaining access to patient information visible on the other side of the screen. One participant explained: Participants noted that not only is displaying the patient information and criteria side-by-side advantageous, but it could be potentially important to have the patient EHR data integrated with the decision support in the future. For example: ## Validity of content for CAMHS Participants’ perceptions of the IDDEAS prototype functionality varied. Some participants had problems interpreting guideline content; they did not like the phrasing and found instructions difficult to understand. Some participants questioned future functionality of IDDEAS with the prototype guidelines requiring the participants to click through all guidelines support materials, regardless of whether they might need to review that specific information or not. For example, one participant stated: While this participant was one who seemed hesitant about the need for providing guideline support throughout the clinical process, others spoke highly of the fact that the IDDEAS ADHD guidelines were detailed and encompassed all components of standardized guidelines. Overall, most participants were pleased to be provided with guideline support that matched what they would instinctively do in their current practice and took comfort in knowing it would be available as novice clinicians might need explicit step-by-step guideline support through their assessment. As one clinician explained: With another participant explaining the potential benefits for novice clinicians by stating: ## Aesthetic and design of user interface In terms of the aesthetics and design of the IDDEAS prototype, all but one participant found the prototype to be adequate. These participants reported that the text was easy to read, and the guidelines were easy to use. The participant who did not find the aesthetics and design to be adequate noted that the IDDEAS prototype was not aesthetically pleasing due to being too much like a webpage. Most participants emphasized the simplicity as a positive design element. One participant stated: ## Approach to support layout Several clinicians reported trouble with the decision tree guideline format which requires “Yes” or “No” responses when criteria are met, or not. “ I do not know” option was also voiced, as illustrated by a statement bellow. Another participant explained further: However, most felt positive about the decision tree as it helped them structure their thought process and identify components of the guideline support that they liked and other aspects that could be changed for the next IDDEAS version. Participants found that the guideline decision support layout, in step-by-step, informative guideline criteria boxes, helped to structure the clinical assessment process: While this participant found the guideline-based support helped structure their efforts, they also noted that this layout could also negatively impact their work, if they were unable to track their progress in applying the guidelines; they feared losing track of progress if they closed one guideline box. Participants also mentioned a significant concern about their inability to move directly to guideline criteria to allow for investigating differential diagnoses (i.e., investigate inattention criteria met instead of assessing for hyperactivity) rather than going through the entire ADHD guideline following along with the predetermined sequence of the guideline decision support boxes provided, one-at-a-time. Participants were also clear about the need to expand clinical guidelines beyond ADHD in order to address comorbidities and appropriately address symptoms commonly displayed across multiple disorders. This issue also suggested the need to have multiple guidelines and criteria available to allow for navigation from general to specific components of diagnostic criteria. One clinician explained: ## Satisfaction Most participants indicated that they were not entirely clear about the potential usefulness and helpfulness of limited (to ADHD only) IDDEAS. However, most were hopeful and intrigued by IDDEAS and were interested in its potential even though the prototype had limited utility, as they could not use it in an interaction with “real patient” information. Despite limitations of the current prototype, it was judged to be easy to use and participants were interested in seeing the ongoing developments. It seemed clear to all that IDDEAS’ usefulness will increase with the expansion of the diagnostic decision tree and the ability to see whether patient symptoms lie along a threshold. A participant stated that they liked the tool because it helped them to structure their thoughts about the diagnosis. One participant reflected on the ease of use and user-friendliness specifically: ## Learnability Learnability in this context refers to the ability to learn how to use the IDDEAS prototype. There were mixed thoughts about the “learnability” of IDDEAS. Participants noted that IDDEAS is intuitive, and there is potential for improved ease of use and helpfulness based on the positive degree of learnability. While some reported initial challenges, it did not take too much time to understand how to go through the system. One clinician explained that they would enjoy learning how to interact with the system in the future, over the current approach to clinical care: There were some barriers to the learnability of IDDEAS due to the user interface. More specifically, some participants specified that they found it difficult to use and interpret the prompts. One participant explained: Another elaborated further, to explain: Some participants explained that when there was too much going on within the layout of the interface, it can be challenging. Participants suggested that adjusting the symbols indicating where to click and the wording used in the notifications could make it easier to learn. One stated: ## Efficiency The efficiency of using the IDDEAS prototype was discussed both in terms of the current approaches to guideline provision and the potential improvement with developments. Participants found it hard to assess the efficiency of IDDEAS at this time, largely due to the limited capacity of the prototype. Participants discussed that without seeing the entire program, it is difficult to fully appreciate the actual potential for IDDEAS and its contribution to practice efficiency and quality improvement. One participant noted that they found it difficult to determine IDDEAS to be usable and useful at this stage due to the phrasing of the alerts in guideline boxes causing some delay. For example, understanding the intention of the “decline/accept recommendation” support message provided within the guideline and becoming acquainted with what exactly this was prompting them to do throughout their patient assessment procedure. Additionally noted was the requirement to click through each guideline support box and all criteria included within the ADHD guideline, negatively impacting the efficiency of their clinical procedure. One participant explained: ## Memorability Memorability in this study refers to the ability of the user to remember the task at hand and the components involved in the procedure. Overall, participants spoke positively of the ability to follow the workflow to assess a patient while using the IDDEAS, even though this may be perceived differently from clinician to clinician, particularly based on their experience. One participant explained: ## Errors Participants reported at times having encountered errors with the guideline support (i.e., “Not Supported” message displayed upon acceptance of a recommendation) and the navigation buttons (i.e., inability to close one guideline box without exiting entirety). Additionally, participants specifically discussed encountering glitches with the system generating repeat guideline boxes, the inability to access specific criteria when clicking yes in response to prompt suggestion, or falsely notifying the participant that the guideline is over when they have selected to reject the recommendation and continue their assessment. Participants specified that the errors encountered with the prototype made them find it less usable and appropriate at this stage of development. One participant explained in reference to the inability to access more of the guideline upon declining the guideline recommendation: On the other hand, another clinician stated simply in reference to a glitch encountered: ## Discussion This study represents the first phase in the development of the IDDEAS CDSS. It is a qualitive study of how CAMHS clinicians perceived the usability of the IDDEAS prototype. As IDDEAS is developed iteratively and in collaboration with the end-users, revisions and adaptations are expected. This qualitative study provides valuable initial information about the usability of IDDEAS, while also identifying needs based on input from potential end-users, CAMHS clinicians. Our study suggests that the first IDDEAS CDSS prototype needs to be adapted and adjusted to be perceived as usable and helpful. However, more importantly, there is a consensus amongst stakeholders that there is great potential for its usefulness with further development, as well as an eagerness for engagement in helping to inform the future development of the IDDEAS CDSS. Clinicians were able to use the simulated explorative procedure to evaluate the usability of the IDDEAS prototype, and the potential for useful and helpful future versions of IDDEAS. Our experimental procedures allowed the clinicians to reflect on IDDEAS and suggest what could be better or different. While there was limited patient information and an inability to interact with a fully formed CDSS, the study allowed for us to learn about clinicians’ preferences with respect to what they need and do not need from the CDSS in CAMHS and EHR integration [38]. Similarly, other CDSS development studies that have used hypothetical case scenarios found similar limitations dependent upon the state of the CDSS prototype but still identified important takeaways for the systems further development, including close integration of the patient information from the EHR [31]. The main consensus elicited from our findings was the importance of quickly being able to identify patient information that is missing at the time of assessment. In this case, clinicians specified that with growing demand for services it is important to be able to efficiently determine whether a patient referral to CAMHS might be rejected or accepted. Furthermore, with global pandemics seemingly becoming a global societal norm, improved timeliness, and overall efficiency of the provision of care within CAMHS could potentially greatly benefit from further incorporation of well tested and validated HIT, such as a CDSS, as long as it is developed in accordance with end user needs. Based on our findings, a guideline-based decision support system was helpful, but it needs to be able to provide clinicians with customized suggestions as to which clinical guidelines to reference, based on changes in the patient’s health status as well as services previously accessed. Interacting with the guideline-based support provided clinicians an opportunity to reflect on what they feel is lacking in the platforms currently used in CAMHS and speculate how IDDEAS could help to meet these needs in the future. It is also important to acknowledge that the guideline functionality serves as only one component of the overall functionality of the CDSS. The “decision tree” formatting might not optimally serve all clinicians, despite the formatting of the IDDEAS user interface and overall functionality design of the platform that could provide non-linear-based support for dynamic clinical care. In accordance with the need for improved coordination among services involved in CAMHS [4, 25], the IDDEAS project follows the Local Early Appropriate and Precise (LEAP) model [39]. Our results reaffirm that as CAMHS in Norway depend on information coming from other services (i.e., educational and psychological counseling service, and the primary care provider), it is important to ensure the available patient information not only covers their current health status but also any previous care received from other social, school and health services [39, 40]. This close collaboration provides the opportunity for customized guideline suggestions and availability of information about involved services, while also allowing early identification of risks. Early risk identification is a critical component of CAMHS to prevent the onset of mental health disorders [9]. Individualized Digital Decision Assist System development will continue to work toward full EHR integration, to keep collaborative efforts in CAMHS coordinated and ultimately to help to provide clinicians with accurate, efficient, and early clinical decision support through efficient and early identification of risks and provision of early intervention. With direct integration with the EHR, it will be possible to examine the potential for identifying previously addressed symptoms, while also identifying potential comorbidities by flagging relevant overlaps across multiple guidelines [5]. An integrated CDSS potentially provides specific, adapted suggestions relevant to the care of an individual patient, thus allowing the clinician to determine the extent to which they need to review other materials. Maintaining this autonomy for the clinicians in CAMHS, allowing for them to be the decision-makers, is important for the acceptance and utility of future IDDEAS versions. As found by Kortteisto et al. [ 22] for the end-users to find a CDSS useful, they need to first trust it. As reported by Sutton et al. [ 13], diagnostic support based on patient data can be an advantage while also prove potentially harmful if users’ distrust what is provided by the CDSS. Graphical displays of statistics, access to scientific literature, and references to local EHR patient data patterns were all mentioned as examples of potential future design factors that help reassure clinicians of the CDSS trustworthiness while keeping the clinician as the main decision-maker [40]. The inclusion of support based on BUP-data is important to the clinicians as the end-users but is also important to service users [41]. Service users in Norway want to be more involved in their care, including understanding the components of services received and sharing their data for the improvement of overall services [41]. In general, clinicians want transparent presentations of EHR data that informs the decision support and recommendations provided for all stakeholders involved, making it more likely that stakeholders will trust and use the CDSS [21]. The results suggest that several attributes of the IDDEAS prototype should be addressed in the next version of IDDEAS (see Table 1). For the development of IDDEAS following UCD methods and an overall iterative approach [11, 30], we expect continued identification of usability barriers and limitations to the design of IDDEAS, as seen commonly in other CDSS development studies [25, 38, 42, 43]. For example, as similarly found by Baysari et al. [ 30] or Giordanengo et al. [ 43], it is important to design the system from the perspective of the end-user and finding overall improved perceptions of the system usability with the well-integrated EHR [44]. The formative usability iterations underlying the design of IDDEAS promotes the ability to adjust as needed to meet the local coordinated CAMHS requirements, such as the provision of data-based recommendations from close integration with the patient EHR in the future. While there were several propositions for how to overcome the currently limiting attributes of the IDDEAS prototype, it will be important to also assess the identified barriers of the next prototype and provide a comparison to be able to understand the usability and potential utility of IDDEAS. TABLE 1 \| Attribute \| Perceived strength \| Perceived limitation \| Proposed development \| \| --- \| --- \| --- \| --- \| \| The “accept recommendation” guideline support box \| Shows the clinician that they are still in charge \| Can be unclear for some clinicians how recommendation comes about \| Show summary confirmation of what the clinician selected and optional box for where recommendation came from \| \| “Criteria not fulfilled” notification guideline support box \| Allows for the clinician to see what they know and what they do not know \| Can be unclear based on phrasing of information in box \| Provide notification for recommendation with evidence optional to access (BUP-data statistics- regional/department examples); simplify wording and appearance to be clear \| \| Structured layout of guideline support boxes \| Provides clinician with reminders for the important information to acquire within the assessment steps and following the structured diagnostic process ensuring reliable and standardized diagnostic procedure \| Inability to navigate through the guideline outside of the step-by-step structured support boxes and change the order of assessment, when necessary, given the specific patient context \| Provide option to click through guideline boxes to see criteria without selecting- ability to access all guideline boxes regardless of recommendation; display score of fulfilled criteria to show current suggestion for diagnosis, maintains control for clinician yet clear support \| \| Visual display and aesthetics of user interface \| Simple and minimalistic is good, not distracting \| Webpage layout and not intuitive of how to navigate \| Keep simple and minimalistic but also modern to help with engagement; provide navigation labels to allow for clinicians to explore how to navigate interface/use support \| \| “Decision tree” style provision of decision support \| Helped to organize structure of thought process and to keep track of decision making in line with the diagnostic criteria \| Can feel like support is forcing a decision to be made with only yes or no option; does not fully reflect complexity of patient situations in CAMHS \| Including a “I do not know” option so marked items will accumulate into box with summary of missing information to be able to efficiently acknowledge what is not known; use the diagnostic tree where you could look at symptoms falling above or below threshold for assessing the diagnosis \| \| Accessibility of specific guideline criteria within guidelines \| Can scroll through previously accessed guideline components \| Limited guideline capacity to show all components until navigated through tree \| Should provide option for going back/forward into the guideline specifics; if missing information should be able to look up where guideline provides support for criteria \| \| Display of guideline support on user- interface next to patient information \| Providing guideline support side by side with patient information could improve efficiency of clinical care; more quickly acknowledged what was needed because of accessibility \| Decision support is available next to patient information but not connected so cannot interact with guideline and save any previously noted criteria met by patient \| Future design of system should be able to have guideline support integrated with the patient to provide alerts relative to patients’ health information; ability to update criteria in between sessions and adjusts decision support provided to improve efficiency and overall coordination of services \| ## Strengths and limitations There are multiple strengths and limitations of this study. First, there is a potential for bias. As participants were recruited by convenience sampling, this potentially attracted clinicians who may be already interested in innovation and potentially more comfortable interacting with technological solutions. While the focus of the study was to identify the CAMHS clinicians’ perceptions of the IDDEAS prototype, it was a limitation that patients and their families were not involved as well [9, 25]. Furthermore, as this is one component of a larger study within a multiple part project, the generalizability of our findings is limited. However, our sample size is in accordance with qualitative methodology standards and allows for un-saturated qualitative data and provides development information that will be used to design and execute larger scale IDDEAS usability studies. Additionally, the materials used in addition to the IDDEAS prototype (i.e., patient cases) also proved to have relative limitations, potentially deterring from the ability to assess the usability of the system attributes. However, as seen with other CDSS development studies following UCD designs, the patient cases provide a base for early on prototype testing and thus were intentionally designed to illicit important information for future research and help to identify current clinical needs. Despite these limitations, there were strengths to the study as well. It was a strength to have the opportunity to work closely with the child and adolescent psychiatrists and psychologists who will ultimately be IDDEAS end-users. With the help and support from N-BUP we were able to recruit participants from multiple regions of Norway to help inform our continued development of IDDEAS. Furthermore, as participants were not provided with access to the IDDEAS platform prior to the usability evaluation and interviews, the study provides an authentic overview of the overall perceived usability, and specifically the learnability of IDDEAS at this stage. Our future research efforts will include iterations designated for service users testing and assessment of needs for optimal IDDEAS development. Despite qualitative research being highly dependent upon subjective interpretations and the relative competence of both the researcher and interviewee [12, 13], the combination of inductive and deductive qualitative categories within the QCA was found to be in line with findings from other studies [13]. The use of COREQ and SRSS allowed for us to minimize possible bias and ensure adherence to previously validated qualitative standards. The IDDEAS project’s focus on formative usability assessments and prototype development allows for close collaboration with potential end-users in experimental settings, compensating for what could be deemed the limitation of qualitative interview inquiry. The mixed methods used in the usability evaluation will provide additional approaches to quantifying our findings, while also still allowing for efficiently identifying CAMHS clinicians’ current needs and how IDDEAS can meet those needs. ## Conclusion Child and adolescent mental health services psychiatrists and psychologists shared the need for a completed IDDEAS clinical decision support system, especially if it is integrated within their clinical care processes; specifically, the electronic health record. Participants are eager to engage with the next phase, the dynamic high-fidelity prototype. Further usability assessments and identification of additional requirements for IDDEAS is necessary before feasibility testing and implementation. The findings from this study can help inform future IDDEAS development. A fully functioning, integrated version of IDDEAS has the potential to be an important contribution to support clinicians in the early identification of risks for mental disorders as well as full assessment and treatment of children and adolescents. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Regional Committee for Medical and Health Research Ethics, Southeast (REK Sør-Øst). The patients/participants provided their written informed consent to participate in this study. ## Author contributions CC was responsible for conducting the data collection, data analysis, and formulation of the manuscript. NS and BL were responsible for providing the guidance and feedback throughout the development of the original manuscript and contributing with edits to the manuscript. All authors contributed with their suggestions throughout the development of the final manuscript and read and approved of the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer FS declared a past co-authorship with the author NS to the handling editor. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1033724/full#supplementary-material ## References 1. 1.World Health Organization [WHO],. Covid-19 pandemic triggers 25% increase in prevalence of anxiety and depression worldwide. (2022). Available online at: https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide (accessed March 5, 2022).. (2022) 2. Kessler R. 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--- title: 'Investigating the Association of Patient Body Mass Index With Posterior Subcutaneous Fat Thickness in the Cervical Spine: A Retrospective Radiographic Study' journal: Cureus year: 2023 pmcid: PMC9997731 doi: 10.7759/cureus.34739 license: CC BY 3.0 --- # Investigating the Association of Patient Body Mass Index With Posterior Subcutaneous Fat Thickness in the Cervical Spine: A Retrospective Radiographic Study ## Abstract Introduction: Although BMI is often used as a surrogate for posterior cervical subcutaneous fat thickness (SFT), the association of BMI with cervical SFT is unknown. We performed a retrospective radiographic study to analyze the relationship between BMI and cervical SFT. Methods: This was a retrospective cohort study of patients with cervical CT scans. SFT was assessed by measuring the distance (mm) from the spinous processes of C2-C7 to the skin edge. Pearson correlations and linear regression were used to analyze the relationship between BMI and SFT. One-way ANOVA was used to analyze differences in C2-C7 distances while stratifying by BMI. Results: A total of 96 patients were included. BMI had a moderate correlation with average C2-C7 ($r = 0.546$, $p \leq 0.05$) SFT, and a weak to moderate correlation with each individual C2-C7 distance. The strongest correlation was at the C7 level ($r = 0.583$, $p \leq 0.05$). These analyses remained significant controlling for potential confounders of patient age, sex, and diabetes. No difference was found in the average C2-C7 distance in patients with BMIs of 25-30 compared to those with BMIs of 30-40 ($$p \leq 0.996$$), whereas in patients with BMI <25 and BMI >40, differences were significant ($p \leq 0.05$). Conclusions: BMI is not strongly correlated with SFT in the cervical spine. Although BMI less than 25 or greater than 40 is correlated with respectively decreased or increased cervical SFT, BMI of 25-40 is not correlated with cervical SFT. This is clinically important information for surgeons counseling patients on perioperative risk before undergoing cervical spine procedures, namely infection. Further research delineating the relationship between posterior SFT and surgical site infection in the cervical spine is warranted. ## Introduction Surgical site infection (SSI) is a known complication following cervical spine surgery, with rates as high $0.07\%$ after anterior cervical surgery [1] and up to $18\%$ after posterior surgery [2,3]. Risk factors for SSI include increased BMI, truncal obesity, smoking, diabetes, longer operative times, and a history of prior SSI [4,5]. Obesity and BMI have gained much attention in regard to preoperative planning and counseling patients, especially as the rates of obesity continue to rise in the United States. The Centers for Disease Control (CDC) defines obesity as a BMI of 30 or higher, and severe obesity as a BMI of 40 or higher. The prevalence of obesity in 2017-2018 was $42.4\%$, as compared to $30.5\%$ in 1999-2000. Similarly, the rate of severe obesity increased from $4.7\%$ to $9.2\%$ in that same time period [6]. A BMI of 30 or above was shown to have an increased risk of postoperative infection in patients undergoing spine surgery, with an odds ratio of 2.13 [5]. The increasing prevalence of obesity and known complications intraoperatively and postoperatively have led to the advocacy for diligent preoperative counseling and optimization on the part of the spine surgeon [7,8]. Diet and exercise counseling, as well as referral to bariatric surgeons when appropriate, should all be considered to help optimize surgical outcomes in elective spine procedures for obese patients [7,8]. Although patient BMI is often used preoperatively to assess the risk for postoperative infection, it must be recognized that this has potential limitations in certain patients. BMI is simply a function of a patient’s height and weight, and because of this, it cannot reliably measure regional fat distribution in the operative area of interest [9,10]. This is important in spinal surgery because while a patient may have a high BMI, this could be due to truncal obesity, and may not necessarily reflect the amount of adipose tissue at their spinal surgical site. This realization has led to the postulation that local subcutaneous fat thickness (SFT) may be a better predictor of SSI than BMI. In the lumbar spine, BMI and obesity were not significantly related to SSI, but rather, SFT did show a significant relationship, with a $6\%$ increase in odds of infection for every 1-mm increase in local SFT [9]. BMI and fat thickness were found to be only weakly correlated (r2=0.44) in the lumbar spine [9]. In a previous study assessing patients who underwent posterior cervical fusion (PSF), it was shown that BMI and obesity were not significant risk factors for postoperative infection, but increased SFT at the C5 level was [11]. To our knowledge, the association between patient BMI and the thickness of the posterior cervical subcutaneous fat has not been previously reported. The limited literature on this topic leaves a void in knowledge on whether or not BMI can be used as a reliable surrogate marker for local subcutaneous fat when counseling patients preoperatively on the potential risks of undergoing a cervical spine procedure. We performed a retrospective cohort imaging review study to test the hypothesis that patient BMI is not strongly correlated with local SFT throughout the cervical spine. ## Materials and methods Study design and inclusion/exclusion criteria This retrospective cohort study was granted an exemption by the Institutional Review Board at the University of Michigan, Ann Arbor, Michigan, United States. A manual electronic chart review of the senior author's (IA) clinic patients from January 1, 2018, to December 31, 2019, was used to identify patients. Patients were eligible if they were at least 18 years old and presenting to the spine clinic with a non-contrast CT scan of the cervical spine. Additional inclusion criteria used were: CT scans had to be preoperative, and a BMI or height and weight within three months of the CT scan had to be recorded in the patient chart. Patients were excluded if they had previous cervical spine surgery, if there was significant coronal or sagittal cervical deformity present, or if the CT scan did not allow adequate visualization and measurement of all pertinent levels for this study. Outcomes and measurements Electronic medical charts were reviewed to collect demographic patient data including age, sex, BMI, and presence of diabetes. A single study member (JP) then performed all relevant measurements of the CT scans for consistency. The measurements obtained included the distance (mm) from the spinous processes of the C2-C7 levels to the skin edge, measured parallel to the disc space at the respective level on sagittal images (Figure 1). For patients with large posterior skin folds, the measurements at these levels were obtained by extending out to a line that connected the skin above and below the fold, in order to help eliminate any artificial inaccuracy due to patient anatomy or position during the CT (Figure 2). Each measurement was saved and reviewed by two additional study members (JP, IA) to ensure accuracy and consistency. Any discrepancy in measurement warranted a re-measurement of that specific patient. Figure 1: Representative sagittal cervical CT scan showing standard C2-C7 measurements Figure 2: Representative sagittal cervical CT scan showing measurements in a patient with a large posterior skin fold Statistical analyses Using an alpha of 0.05 and power of $80\%$, we calculated an estimated sample size of 50 patients. To improve the precision of the results and allow for subgroup analyses, we exceeded this sample size. Statistical analyses were performed using IBM SPSS Statistics for Macintosh, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States) and Stata Statistical Software: Release 14/MP (2015; StataCorp LP, College Station, Texas, United States). Bivariate Pearson’s correlation analyses were used to determine the correlation between BMI and posterior SFT at individual levels from C2-C7, as well as between BMI and the average total C2-C7 distances. Correlations were stratified with $r = 0.30$ to 0.50 indicating a weak relationship, $r = 0.50$ to 0.70 indicating a moderate relationship, and r > 0.70 indicating a strong relationship [12,13]. These same analyses were then put in a linear regression model controlling for patient age, gender, and the presence of diabetes. One-way ANOVAs were performed to assess for differences in average C2-C7 distances while stratifying by BMI groups of less than 25 (normal), 25 to less than 30 (overweight), 30 to less than 40 (obese), and greater than 40 (morbid obesity). A $p \leq 0.05$ was used to determine the statistical significance for all tests. ## Results A total number of 96 patients with a mean age of 62.0 years (± 17.7) were included in the final analysis, with $45.8\%$ ($$n = 44$$) being female. The average patient BMI was 28.2 (±6.3). Patient demographics are shown in Table 1. The average distances (mm) at each individual level from C2-C7 to the skin edge for the cohort were as follows: C2 44.6 (±12.8), C3 45.9 (±12.9), C4 44.4 (±13.7), C5 41.5 (±13.8), C6 35.7 (±12.3), and C7 28.7 (±10.9). Table 1 \| Age (years), mean (SD) \| Unnamed: 1 \| 62.0 (17.7) \| \| --- \| --- \| --- \| \| Gender, N (%) \| Male \| 52 (54.2%) \| \| \| Female \| 44 (45.8%) \| \| BMI, mean (SD) \| \| 28.2 (6.3) \| \| BMI Range \| \| 17.99-50.15 \| \| Diabetes, N (%) \| Yes \| 24 (25%) \| \| \| No \| 72 (75%) \| The total average distance (mm) from the C2-C7 spinous processes to the skin edge was found to be 40.1 (±11.6). When analyzing the relationship between the average total C2-C7 distance and patient BMI using bivariate Pearson correlations, a moderate relationship was found ($r = 0.546$, p = <0.05) (Figure 3). This relationship also remained significant using a linear regression model controlling for patient age, sex, and the presence of diabetes ($p \leq 0.05$), with an R2 of 0.421. Figure 3: Graphical representation of the association between patient BMI and the average C2-C7 subcutaneous fat thickness BMI had a weak to moderate correlation with SFT at each individual level of C2-C7. The strongest bivariate Pearson correlation was seen at the C7 level ($r = 0.583$). The remaining levels showed correlations of: C2 ($r = 0.511$), C3 ($r = 0.481$), C4 ($r = 0.473$), C5 ($r = 0.439$), and C6 ($r = 0.508$). All p-values were significant at <0.05. Linear regression models controlling for patient age, sex, and the presence of diabetes remained significant at all levels (p= <0.05). Patients were stratified into four groups based on BMI: Group 1= BMI <25, Group 2= BMI 25 to <30, Group 3= BMI 30 to <40, and Group 4=BMI>40. Group 1 ($$n = 34$$) had an average C2-C7 SFT of 31.9 mm (±7.7), while those in Group 2 ($$n = 32$$) had an average C2-C7 SFT of 43.3 mm (±9.9) ($p \leq 0.05$). Patients in Group 3 ($$n = 25$$) had an average C2-C7 SFT of 43.8 mm (±10.8). The average C2-C7 SFT of those in Group 4 ($$n = 5$$) was 57.1 mm (±11.5). One-way ANOVA analyses showed that the average total C2-C7 SFT was significantly lower in Group 1 compared to the other three BMI groups. Similarly, Group 4 had a significantly higher C2-C7 average SFT compared to the other three groups. However, those in groups 2 and 3 were not significantly different from each other ($$p \leq 0.996$$). See Table 2 and Figure 4 for further details. ## Discussion Our data indicate that BMI did not have a strong correlation with cervical SFT. When stratifying the cohort into BMI groups of <25, 25 to <30, 30 to <40, and >40, it was shown that the weak-moderate correlation that was found is likely driven by those at the extremes of BMI. This is demonstrated by the fact that those with BMI<25 or BMI>40 did have significantly different SFT compared to the other groups; however, those with a BMI of 25 to <30 and 30 to <40, were not significantly different from each other. Clinically, the results as a whole suggest that solely using patient BMI as a surrogate for local SFT and SSI risk is not reliable. This is especially true for those at the norm with BMIs of 25-40, who showed no significant differences in their SFT. Preoperative identification of risk factors that increase the potential for perioperative complications is an essential component of surgical decision-making. Understanding these risks and how to potentially minimize them are critical to shared decision-making with the patient. While the absolute percentages of SSIs after cervical spine surgery are relatively low [1,2,14,15], the effect on patient outcomes and the financial impact on the health system, are certainly not inconsequential. A retrospective study of patients with SSI after spine surgery showed an average secondary readmission stay of 9.6 days and an average cost of treatment of $19,642 USD [3]. A systematic review showed a mortality rate attributable to spine SSI to range from $1.1\%$ to $2.3\%$, and also showed that spine patients incur double the healthcare costs when they develop a postoperative infection [16]. Although BMI has previously been shown to be a risk factor for SSI, recent studies have challenged this and shown that local SFT in the lumbar and cervical spine, rather than BMI, is actually a better predictor of SSI [9,11]. Local SFT, however, is not a routinely obtained measurement. Instead, BMI is often used as a surrogate measure due to its perceived correlation with SFT and ease of use in registry-based studies. However, no prior study to our knowledge has directly investigated the relationship between BMI and local SFT in the cervical spine. In the lumbar spine, Lee et al. showed weak correlations ($r = 0.41$-0.49) between BMI and local SFT [9]. In the cervical spine, Mehta et al. reported a weak correlation ($r = 0.23$) between BMI and SFT at the C5 level, but no other cervical levels were analyzed, and this was not the primary outcome of interest in their study [11]. The results of the present study are in concordance with prior studies in the lumbar spine demonstrating a correlation that was not strong between BMI and local SFT. At best, the correlation was moderate, primarily at the C7 level. Strengths and limitations The strengths of this study include the relatively large cohort, consistent CT-based measurements reviewed by three authors, and the fact that this is the first study to directly investigate the relationship between BMI and local posterior cervical SFT. Limitations include the fact that this was a single-center and single-surgeon experience, as well as the retrospective nature of the study. Further, it should be clear that the present study does not investigate the relationship between BMI or SFT and SSI. It is possible that SFT may be more strongly correlated with postoperative SSI than BMI, but this hypothesis requires further investigation. In theory, in any future effect that is observed, increased posterior SFT should only have a direct impact on infection in posterior cervical cases. However, we also plan to include anterior cervical procedures in future studies. ## Conclusions Patient BMI is not strongly correlated with posterior SFT in the cervical spine. Although BMI less than 25 or greater than 40 is correlated with respectively decreased or increased cervical SFT, BMI of 25-40 did not show a significant correlation with cervical SFT. This is clinically important information for surgeons counseling patients on perioperative risk before undergoing cervical spine procedures, namely infection. In theory, increased posterior SFT can place patients at higher risk of infection after posterior cervical spine surgery, independent of patient BMI. This factor is not actively considered by most surgeons during the preoperative evaluation. Further research delineating the relationship between posterior SFT and SSI in the cervical spine is warranted. ## References 1. Ghobrial GM, Harrop JS, Sasso RC. 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--- title: 'How public health authorities can use pathogen genomics in health protection practice: a consensus-building Delphi study conducted in the United Kingdom' authors: - Nicholas Killough - Lynsey Patterson - Sharon J. Peacock - Declan T. Bradley journal: Microbial Genomics year: 2023 pmcid: PMC9997744 doi: 10.1099/mgen.0.000912 license: CC BY 4.0 --- # How public health authorities can use pathogen genomics in health protection practice: a consensus-building Delphi study conducted in the United Kingdom ## Abstract Pathogen sequencing guided understanding of SARS-CoV-2 evolution during the COVID-19 pandemic. Many health systems developed pathogen genomics services to monitor SARS-CoV-2. There are no agreed guidelines about how pathogen genomic information should be used in public health practice. We undertook a modified Delphi study in three rounds to develop expert consensus statements about how genomic information should be used. Our aim was to inform health protection policy, planning and practice. Participants were from organisations that produced or used pathogen genomics information in the United Kingdom. The first round posed questions derived from a rapid literature review. Responses informed statements for the subsequent rounds. Consensus was accepted when 70 % or more of the responses were strongly agree/agree, or 70 % were disagree/strongly disagree on the five-point Likert scale. Consensus was achieved in 26 (96 %) of 27 statements. We grouped the statements into six categories: monitoring the emergence of new variants; understanding the epidemiological context of genomic data; using genomic data in outbreak risk assessment and risk management; prioritising the use of limited sequencing capacity; sequencing service performance; and sequencing service capability. The expert consensus statements will help guide public health authorities and policymakers to integrate pathogen genomics in health protection practice. ## Data Summary The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. The survey responses are included in Supplementary Materials. ## Introduction The specialist area of pathogen genomics has assumed increasing prominence during the COVID-19 pandemic, and sequencing of SARS-CoV-2 has been performed on a much greater scale than for any previous pathogen [1]. The COVID-19 Genomics UK (COG-UK) Consortium was established rapidly in response to the COVID-19 pandemic. COG-UK leveraged expertise, people and equipment from diverse organisations as part of a single national sequencing programme. Its role in research, surveillance and policy-making has been central to the delivery of a pathogen genomics service and capability for the United Kingdom (UK). The rapid development of consortium tools allowed for harmonised analysis and reporting, and sharing of data and intelligence in the UK and internationally [2]. Sequence data and lineages were made publicly available through GISAID and the COG-UK website [3–5]. Sequencing has informed our understanding of the evolution of the SARS-CoV-2 virus. However, there is limited practical guidance for health protection teams in public health authorities about how pathogen genomics data should be used in practice; for example, in terms of risk assessment and risk management of cases, clusters and outbreaks. Policy-makers and service commissioners also need information to support decisions about how to fund pathogen genomics services. The Delphi consensus-building research method aims to develop expert consensus statements [6]. This method is appropriate where the goal is to allow a group of individuals to take part in ‘structured communication’ about a complex issue [7], such as developing clinical guidelines. For example, the Delphi method was used in the context of COVID-19 to create expert clinical practice statements on the management of respiratory failure [8]. As part of the Public Health Agency’s COG-UK-funded Public health Risk Assessment using Genomic Methods And Tools In Context (PRAGMATIC) Study, we planned and undertook a Delphi study. Our study aimed to develop a set of expert consensus statements that could guide public health authorities about how they should use pathogen sequencing to support health protection risk assessment and outbreak management. ## Study design To identify baseline information to inform the development of our study, in September 2021 we undertook a rapid review of published reports on the use of pathogen sequencing in public health practice. We searched PubMed, using the following keywords and phrases: ‘public health genomic sequencing’, ‘whole genome sequencing in public health’, and ‘sequencing and public health action’. We restricted the results to those published in English between January 2018 and August 2021. We chose not to undertake a formal systematic literature review due to our assessment that it would not be feasible to conduct a systematic review, Delphi study and to implement the findings in our practice during the 6 month funded timeline of our project. We therefore chose to use a modified Delphi method in which we asked the expert respondents to provide free-text responses in the first round of the study to inform the development of the second-round statements [9, 10]. The major themes identified in our rapid review highlighted the importance of specific contexts, such as the use of sequencing in identifying variants, the investigation of the effects of transmission, immune escape and severity of disease. It was reported that sequencing played a key role in outbreak investigation within healthcare and residential care settings. The literature described methods of interpretation and dissemination of sequencing results to key stakeholders. These themes formed the basis of our round one questions. ## Participant recruitment Forty-four people were invited by email to participate in the study. The invitees were from institutions associated with the production or use of pathogen genomics information in the UK, including COVID-19 Genomics UK Consortium (COG-UK) members, the four UK public health agencies, healthcare providers and academic researchers. Participants were initially identified through our professional networks, and onwards through a snowballing approach. The participants were from a range of professional backgrounds, including scientists, clinicians, policymakers, researchers, and public health practitioners. Thirty-three potential participants were identified through their roles in the UK’s four public health agencies (in health protection or pathogen genomics services), eight were primarily affiliated to academic organisations, and three were affiliated to National Health Service or Health and Social Care Trust laboratories. Nineteen of 33 invitees from public health agencies agreed to participate (58 %), five of eight primarily academic invitees agreed (63 %), and one of three Trust laboratory invitees agreed (33 %). The research team did not participate in the study as respondents. We chose not to collect demographic characteristics of respondents as due to the small number of participants we assessed that it would be possible to identify individuals by the unique combinations of characteristics, which would have undermined the anonymous nature of the study. We asked whether the participant was a producer or user (or both) of pathogen sequence data. If participants missed a round, they were eligible to take part in a later round, as this is thought to reduce the risk of false consensus and to better reflect the views of the invited participants [11]. ## Delphi process We invited the participants to take part in three rounds of an online survey, which took place between 5 and 12 January 2022, 1 and 10 March 2022, and 11 March and 22 March 2022. One reminder was sent to all participants during each round. The first round comprised twenty questions with free-text answer fields (Table S1, available in the online version of this article). From this, we developed the second-round statements, which required responses on the Likert Scale from 1 (strongly agree) to 5 (strongly disagree) (Supplementary Materials). Consensus was accepted when 70 % or more of the responses were either strongly agree/agree, or alternatively, 70 % or more of responses were disagree/strongly disagree, on the five-point Likert scale [12–15]. When consensus was not achieved, statements were revised to improve the clarity or specificity, and were carried through into a subsequent round. The Delphi exercise ended after round three if statements did not reach consensus. ## Data analysis and reporting The expert consensus statements were tabulated and presented alongside explanatory notes informed by the authors’ summary of the participants’ free-text responses. We followed the Conducting and Reporting of Delphi Studies (CREDES) guidance [6]. ## Study population Twenty-five participants (57 %) responded to the initial invite and agreed to participate in the study. Twelve to fourteen of those responded to each survey round. They were a mix of people who produced and used pathogen genomic data (Table 1; Fig. 1). ## Delphi round one completion Twenty questions with a free-text answer field were asked in round one. Fourteen of 25 participants responded. The questions were answered with a range of completeness. Thirteen out of the 20 (65 %) questions were answered by more than ten participants while seven of the 20 (35 %) questions were answered by fewer than eight participants (Table 2). The full responses are in Supplementary Materials. Table 2. \| Round \| Participants \| Questions \| Consensus \| Consensus not achieved \| \| --- \| --- \| --- \| --- \| --- \| \| 2 \| 12/25 (48 %) \| 26 \| 18 (69 %) \| 8 (31 %) \| \| 3 \| 14/25 (56 %) \| 9 \| 8 (89 %) \| 1 (11 %) \| ## Delphi rounds two and three completion In round two, a consensus was reached for 18 (69 %) of the 26 statements presented (Summarised in Table 3, full results in Supplementary Materials). In round three, one question did not reach consensus. We concluded that consensus would not be achieved following a further round of questioning for this statement (Table 3). The Delphi study was concluded after round three. Table 3. \| Question \| Strongly agree / Agree No. (%) \| Neither agree nor disagree No. (%) \| Strongly disagree / Disagree No. (%) \| \| --- \| --- \| --- \| --- \| \| Round 2 \| Round 2 \| Round 2 \| Round 2 \| \| Q2. Public health authorities should use pathogen genomic sequencing to detect the emergence of new variants. \| 12 (100.0) \| 0 (0) \| 0 (0) \| \| Q3. Public health authorities should use pathogen genomic sequencing as a surveillance tool to monitor geographical spread of variants over time. \| 12 (100.0) \| 0 (0) \| 0 (0) \| \| Q4. Public health authorities and healthcare providers should use pathogen genomic sequencing in outbreak investigations in health care settings to rule in or rule out transmission events. \| 10 (83.3) \| 2 (16.7) \| 0.0 \| \| Q5. Public health authorities should use pathogen genomic data to inform estimates of epidemic growth. \| 10 (83.3) \| 2 (16.7) \| 0.0 \| \| Q6. Public health authorities should use pathogen genomic data to inform estimates of clinical severity of the disease. \| 11 (91.7) \| 1 (8.3) \| 0.0 \| \| Q7. Public health authorities should use pathogen genomic data to detect changes in risk of infection in specific settings e.g., schools, care homes etc. \| 9 (75.0) \| 3 (25.0) \| 0.0 \| \| Q8a. Pathogen genomic information should inform evaluation of the effect of: non-pharmaceutical interventions. \| 6 (50.0) \| 4 (33.3) \| 2 (16.7) \| \| Q8b. Pathogen genomic information should inform evaluation of the effect of: vaccines. \| 11 (91.7) \| 1 (8.3) \| 0.0 \| \| Q8c. Pathogen genomic information should inform evaluation of the effect of: pharmaceutical therapeutic treatments. \| 10 (83.3) \| 2 (16.7) \| 0.0 \| \| Q9. Pathogen genomic information should be linked to contextual epidemiological information to facilitate risk assessment. \| 11 (91.7) \| 1 (8.3) \| 0.0 \| \| Q10. Public health authorities should prioritise sequencing from outbreaks where it appears there is greater than expected disease severity. \| 11 (91.7) \| 1 (8.33) \| 0.0 \| \| Q11. Public health authorities should sequence enough randomly selected samples to enable unbiased surveillance. \| 12 (100.0) \| 0.0 \| 0.0 \| \| Q12. Public health authorities should prioritise sequencing of samples from vaccinated people. \| 8 (66.7) \| 3 (25.0) \| 1 (8.3) \| \| Q13. Public health authorities should prioritise sequencing of samples from people who have had multiple episodes of infection. \| 10 (83.3) \| 1 (8.3) \| 1 (8.3) \| \| Q14. If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards vulnerable populations. \| 8 (66.7) \| 1 (8.3) \| 3 (25.0) \| \| Q15. If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards outbreak investigation. \| 7 (58.3) \| 3 (25.0) \| 2 (16.7) \| \| Q16. If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards populations in whom new variants might be present e.g., international travellers and immunocompromised people. \| 12 (100.0) \| 0.0 \| 0.0 \| \| Q17. Public health authorities should use pathogen genomic data to de-escalate potential outbreaks that were identified through epidemiological links. \| 7 (58.3) \| 4 (33.3) \| 1 (8.3) \| \| Q18. Timeliness of genomic sequencing results is important to allow results to be acted upon by public health authorities. \| 12 (100.0) \| 0.0 \| 0.0 \| \| Q19. Clinical teams need timely access to sequence results to inform treatment and infection control decisions. \| 11 (91.7) \| 0.0 \| 1 (8.3) \| \| Q20. A minimum of ten percent of all positive COVID-19 samples should be sequenced. \| 7 (58.3) \| 5 (41.7) \| 0.00 \| \| Q21. Public health authorities should use tools such as rapid genotyping for surveillance of lineages. \| 9 (75.0) \| 3 (25.0) \| 0.00 \| \| Q22. Public health authorities should analyse sequence and/or single nucleotide polymorphism data as part of investigation of outbreaks. \| 8 (66.7) \| 3 (25.0) \| 1 (8.3) \| \| Q23. Analysts should exclude sequences below a defined sequence coverage from analysis when investigating transmission events. \| 4 (33.3) \| 6 (50.0) \| 2 (16.7) \| \| Q24. Public health authorities should use bioinformatics tools to elicit unsuspected transmission events. \| 10 (83.3) \| 2 (16.7) \| 0.0 \| \| Q25. Public health authorities should ensure training is provided for health protection, infection control and clinical teams on the interpretation of sequencing results. \| 12 (100.0) \| 0.0 \| 0.0 \| \| Round 3 \| Round 3 \| Round 3 \| Round 3 \| \| Q2. If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards people who are at greater risk of adverse outcomes from infection. \| 10 (71.4) \| 0.00 \| 4 (28.6) \| \| Q3a. If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards outbreak investigations: In closed settings, such as care homes and hospitals. \| 13 (92.9) \| 1 (7.1) \| 0.0 \| \| Q3b. If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards outbreak investigations: In community outbreaks, such as at public events or functions. \| 10 (71.4) \| 4 (28.6) \| 0.0 \| \| Q4a. Public health authorities should use pathogen genomic data to de-escalate potential outbreaks that were identified through epidemiological links: In closed settings, such as care homes and hospitals. \| 13 (92.9) \| 1 (7.1) \| 0.0 \| \| Q4b. Public health authorities should use pathogen genomic data to de-escalate potential outbreaks that were identified through epidemiological links: in community outbreaks, such as at public events or functions. \| 10 (71.4) \| 3 (21.5) \| 1 (7.1) \| \| Q5. Pathogen genomic information should inform evaluation of the effect of non-pharmaceutical interventions. \| 10 (71.4) \| 4 (28.6) \| 0.0 \| \| Q6. The proportion of samples sequenced should reflect the epidemiological context. \| 12 (85.7) \| 2 (14.3) \| 0.0 \| \| Q7. Public health authorities should analyse sequencing data in more detail than lineage as part of the investigation of outbreaks. \| 13 (92.9) \| 0.0 \| 1 (7.1) \| \| Q8. Analysts should only include sequences above defined sequence coverage depth when investigating transmission events. (This is an overall quality control value provided for all sequencing results). \| 8 (57.1) \| 4 (28.6) \| 2 (14.3) \| \| Questions that failed to reach consensus \| Questions that failed to reach consensus \| Questions that failed to reach consensus \| Questions that failed to reach consensus \| \| Round 2: Analysts should exclude sequences below a defined sequence coverage from analysis when investigating transmission events. \| 4 (33.3) \| 6 (50.0) \| 2 (16.7) \| \| Round 3: Analysts should only include sequences above defined sequence coverage depth when investigating transmission events. (This is an overall quality control value provided for all sequencing results). \| 8 (57.1) \| 4 (28.6) \| 2 (14.3) \| ## Statements that achieved consensus The statements that achieved consensus, with accompanying explanatory notes, are presented in Table 4. Table 4. \| Statement no. \| Expert statement \| Explanation \| \| --- \| --- \| --- \| \| Monitoring the emergence of new variants \| Monitoring the emergence of new variants \| Monitoring the emergence of new variants \| \| 1. \| Public health authorities should use pathogen genomic sequencing to detect the emergence of new variants. \| Participants and published literature highlighted the importance of sequencing for detecting the evolution and emergence of new variants of pathogens [19, 20]. \| \| 2. \| Public health authorities should use pathogen genomic sequencing as a surveillance tool to monitor geographical spread of variants over time. \| Strong support was given to the use of sequencing as a tool to monitor the geographical spread of variants after their initial introduction to a population [21]. \| \| 3. \| Public health authorities should sequence enough randomly selected samples to enable unbiased surveillance. \| Participants highlighted the importance of unbiased surveillance to ensuring that variants are detected as part of routine surveillance. It was emphasised that this is essential in detecting changes in the prevalence of variants and ensuring the variants believed to be extinct are no longer detected [22]. \| \| Understanding the epidemiological context of genomic sequence data \| Understanding the epidemiological context of genomic sequence data \| Understanding the epidemiological context of genomic sequence data \| \| 4. \| The proportion of samples sequenced should reflect the epidemiological context. \| The absolute number and relative proportion of positive samples sequenced will affect the confidence that can be placed on the results for risk assessment and decision-making. ECDC has published guidelines that pathogen genomics surveillance systems should sequence a minimum of 10 % of positive samples and they discuss the importance of linking this information to local public health systems [23–25]. \| \| 5. \| Pathogen genomic information should be linked to contextual epidemiological information to facilitate risk assessment. \| Sequencing may be used as a tool to support or discount potential transmission events in those identified as having epidemiological links. Epidemiological, demographic and clinical data can supplement genomic data as part of analyses of severity and vaccine escape. When the aim is to interrupt community transmission, ongoing contact tracing of linked clusters may help prioritise the sequencing of community isolates [26]. \| \| Use of genomic data in outbreak risk assessment and risk management \| Use of genomic data in outbreak risk assessment and risk management \| Use of genomic data in outbreak risk assessment and risk management \| \| 6. \| Public health authorities and healthcare providers should use pathogen genomic sequencing in outbreak investigations in health care settings to rule in or rule out transmission events. \| Much published work has focused on the potential of genomics to identify unrecognised connections between cases, but less attention has been given to the importance of ruling out transmission events, which can effectively disprove transmission between individuals and thus de-escalate a situation. This has recently been addressed with the development of tools like the HOCI Sequence Reporting Tool (SRT) [27]. Tools should contextualise the probability of transmission. It was suggested by participants that maximum likelihood phylogenetic trees should be used, at least at a local level and that further detail should be provided with time-scaled trees, such as those that can be created with IQTREE-2 or BEAST 2 [28, 29]. \| \| 7. \| Public health authorities should use pathogen genomic data to de-escalate potential outbreaks that were identified through epidemiological links in closed settings, such as care homes and hospitals. \| Sequence results have demonstrated the ability to de-escalate outbreaks by excluding nosocomial transmission when multiple external infection introductions have been identified [27, 30]. This may facilitate the reallocation of resources away from potential incidents that were shown to be unrelated cases. \| \| 8. \| Public health authorities should use pathogen genomic data to de-escalate potential outbreaks that were identified through epidemiological links in community outbreaks, such as at public events or functions. \| When assessing outbreaks in a community setting identifying the source of infection can be very difficult. Pathogen sequencing has helped demonstrate transmission that can often occur in large community settings such as public events [31]. \| \| 9. \| Public health authorities should use pathogen genomic data to inform estimates of epidemic growth. \| Pathogen genomics is essential in assessing the spread of emerging variants. It has played a key role in the understanding of the effects of mutations on the growth advantage of new variants [32]. \| \| 10. \| Public health authorities should use pathogen genomic data to inform estimates of clinical severity of the disease. \| During the COVID-19 pandemic, the transition between variants has been accompanied by differences in clinical severity and vaccine effectiveness. Linked pathogen genomic data is important for measuring these factors [33]. \| \| 11. \| Public health authorities should use pathogen genomic data to detect changes in risk of infection in specific settings e.g., schools, care homes etc. \| Understanding the relative attack rates associated with different lineages or variants in specific setting may help public health authorities assess and manage risk in those settings [27]. \| \| 12. \| Pathogen genomic information should inform evaluation of the effect of vaccines and pharmaceutical therapeutic treatments. \| In the context of COVID-19, sequence data have supported detection of reductions in vaccine effectiveness associated with the emergence of new variants [34–36] and informed booster vaccine schedules [37]. The use of novel COVID-19 therapies has been guided by knowledge of the variant at an individual and population level [38]. \| \| 13. \| Pathogen genomic information should inform evaluation of the effect of non-pharmaceutical interventions. \| The effectiveness of individual actions (such as social distancing and personal protective equipment) and societal restrictions (such as the mandated closure of places of business and education) were affected by SARS-CoV-2 variants, with respect to both the transmissibility and severity. Timely genomic data was important for managing these situations [39–42]. \| \| Prioritising the use of limited sequencing capacity \| Prioritising the use of limited sequencing capacity \| Prioritising the use of limited sequencing capacity \| \| 14. \| Public health authorities should prioritise sequencing from outbreaks where it appears there is greater than expected disease severity. \| Outbreaks with a significantly greater than expected case hospitalisation ratio or case fatality ratio could be an early sign of a variant with greater inherent severity or immune escape, and should result in sequencing to assess this risk [43, 44]. \| \| 15. \| Public health authorities should prioritise sequencing of samples from vaccinated people. \| New variants of SARS-CoV-2 that became dominant after the introduction of the vaccination programme were associated with progressively reduced vaccine effectiveness, possibly due to the selection pressure in the context of high prevalence and high vaccination coverage. Identifying changes in vaccine effectiveness provides opportunities for changes to vaccines or to introduce additional doses to manage the impact of such antigenic changes [43]. \| \| 16. \| Public health authorities should prioritise sequencing of samples from people who have had multiple episodes of infection. \| Reinfection may indicate immune escape due to the emergence of new variants, and should be monitored using integrated genomic and epidemiological data [45, 46]. \| \| 17. \| If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards outbreak investigations in closed settings, such as care homes and hospitals. \| Participants emphasised the importance of using genomic information to determine the need for implementing additional protective measures in closed settings during outbreaks [47]. Application of stricter measures may be required in an outbreak of a high-risk variant in a closed setting such as in a care home whereas, an outbreak of the dominant lineage may not warrant the same additional measures. In a hospital however, we see a far more open setting with introductions possible from the community at a more regular interval. In this case we see a greater proportion of spread to wards from outpatient clinics, staff as well as between wards. In this scenario, a more consistent and higher coverage of sequencing may be of use to identify problematic areas and implement IPC measures earlier e.g., improved ventilation, reinforcing mask wearing rules, hand hygiene, etc [48]. \| \| 18. \| If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards populations in whom new variants might be present e.g., international travellers. \| When a new variant emerges in one place, there will be a lead time before it reaches other locations. International borders present an opportunity to detect new variants by effectively sampling people who have recent exposure in other regions. Early awareness of new variants can inform the response to their arrival in a timely way [49, 50]. \| \| 19. \| If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards populations in whom new variants might be present e.g., immunocompromised people. \| The risk of the evolution of new variants may be increased in individuals who are immunocompromised, due to the selection pressure arising from ineffective immune response and consequent longer infection duration. Participants believed that these individuals should be prioritised due to the opportunity to detect new variants in the first person in whom a variant arose and the opportunity to respond to this situation [51]. People who receive treatment with antiviral or antibody therapies, which may create a selection pressure towards resistance, may also be prioritised. \| \| 20. \| If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards people who are at greater risk of adverse outcomes from infection. \| When there is diversity of variants in circulation, information about which variants are responsible for infection in demographic groups that experience severe outcomes may help inform public health risk assessment and management, and wider policy choices aimed at reducing harm [52]. \| \| 21. \| If it is not possible to sequence all samples, public health authorities should direct sequencing capacity towards outbreak investigations in community outbreaks, such as at public events or functions. \| The ability to identify the transmission pathways may highlight opportunities for future prevention, or alternatively, sequencing may disprove transmission and change the risk assessment of behaviours or events [53]. \| \| Sequencing service performance \| Sequencing service performance \| Sequencing service performance \| \| 22. \| Timeliness of genomic sequencing results is important to allow results to be acted upon by public health authorities. \| Timely results enable the assessment of the growth rates of new lineages and can inform advice about mitigation. Rapid genotyping can be a useful adjunct to sequencing for the monitoring of new variants but requires a high level of specificity [54]. Timely genomic results can help inform public health risk management in outbreaks [44]. \| \| 23. \| Clinical teams need timely access to sequence results to inform treatment and infection control decisions. \| In the early stages of an outbreak of a new variant, sequencing results have been used to inform treatment decisions, which is therefore time-sensitive [55]. Identifying the introduction and transmission of a lineage within a hospital, for example, can reveal breaks in infection control and inform infection prevention and control measures [40, 47]. \| \| Sequencing service capability \| Sequencing service capability \| Sequencing service capability \| \| 24. \| Public health authorities should ensure training is provided for health protection, infection control and clinical teams on the interpretation of sequencing results. \| Interpretation and analysis of pathogen genomics results require a range of multidisciplinary skills. Participants highlighted: data processing and epidemiological analysis; outbreak investigation and management; infection prevention and control methods and factors associated with transmission; knowledge of pathogen biology and evolution; understanding of genomics terms, such as SNP-distance, time to most recent common ancestor (TMRCA), cluster and lineage; ability to interpret phylogenetic trees; understanding of the process and limitations of sequencing; understanding of what sequencing data represents and how it is generated; understanding of the process for selecting samples and the proportion of samples being sequenced at a given; understanding the limitations of SARS-CoV-2 sequencing; the ability to integrate genomic cluster data with epidemiological data at scale; knowledge of infection prevention and control (IPC) practice; understanding of the IPC implications for genomically-linked and genomically-refuted clusters; understanding the limitations of epidemiological information; and understanding the application of genomics in investigating vaccine effectiveness and immune escape. \| \| 25. \| Public health authorities should use tools such as rapid genotyping for surveillance of lineages. \| Genotyping methods have supported epidemiological surveillance and public health response, but preparation of relevant assays requires prior knowledge of the haplotypic relationship between specific SNPs and a lineage of interest. It is a compromise of increased speed (and reduced cost) compared to the more detailed genomic information provided by sequencing [53]. Genotyping provides less resolution and cannot be used to create transmission trees, for example. \| \| 26. \| Public health authorities should use bioinformatics tools to elicit unsuspected transmission events. \| With the increasing availability and the development of bioinformatics tools like CIVET and outbreaker2 the ability to investigate unsuspected transmission events has become more comprehensive. These tools allow investigators to look beyond the local level of outbreaks and build connections not previously identified [56, 57]. \| \| 27. \| Public health authorities should analyse sequencing data in more detail than lineage as part of the investigation of outbreaks. \| Genomic information at the level of genomic lineage can indicate where transmission has not occurred. It is common, however, when a lineage is dominant, for most infections to be of the same lineage. More detailed analysis can provide further information in outbreak scenarios of the same lineage [57]. Tools that can infer the probability of transmission, like those developed in the HOCI study, should be used to investigate outbreaks [27]. Where possible maximum likelihood phylogenetic trees should be used. \| ## Statements that did not reach consensus Statements that did not reach consensus after the third Delphi round are presented in Table 5. Table 5. \| Statement no. \| Expert statement \| Explanation \| \| --- \| --- \| --- \| \| Use of genomic data in transmission investigations \| Use of genomic data in transmission investigations \| Use of genomic data in transmission investigations \| \| 1. \| Analysts should only include sequences above defined sequence coverage depth when investigating transmission events. (This is an overall quality control value provided for all sequencing results). \| In the first iteration of this statement, responses demonstrated a need for greater clarity about the implications of sequencing coverage for interpretation of lineage calls. The statement was twice revised for precision and clarity, and did not reach consensus. \| ## Pathogen genomics bioinformatics tools described in expert responses During the completion of the first round of free-text answers, a series of bioinformatics tools were suggested for analysis of sequence data (Table 6). Table 6. \| Tool \| Synopsis \| \| --- \| --- \| \| A2B COVID \| ‘A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events’ [58]. \| \| BEAST 2 \| ‘BEAST two is a cross-platform programme for Bayesian phylogenetic analysis of molecular sequences. It estimates rooted, time-measured phylogenies using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology’ [29]. \| \| Civet \| ‘Cluster Investigation and Virus Epidemiology Tool civet is a tool developed with 'real-time' genomics in mind. Using a background phylogeny, such as the large phylogeny available through the COG-UK infrastructure on CLIMB, civet will generate a report for a set of sequences of interest i.e., an outbreak investigation’ [56]. \| \| HOCI Sequence Reporting Tool (SRT) \| ‘The COG-UK Consortium Hospital-Onset COVID-19 Infections (COG-UK HOCI) study aims to evaluate whether the use of rapid whole-genome sequencing of SARS-CoV-2, supported by a novel probabilistic reporting methodology, can inform infection prevention and control (IPC) practice within NHS hospital settings’ [27]. \| \| IQTREE-2 \| ‘IQTREE-2 is a fast and effective stochastic algorithm to infer phylogenetic trees by maximum likelihood. IQ-TREE compares favourably to RAxML and PhyML in terms of likelihoods with similar computing time’ [28]. \| \| Transcluster \| ‘Transcluster is an R package for inferring and viewing transmission clusters from sequence alignments and sample dates’ [59]. \| ## Discussion The COVID-19 pandemic highlighted the need for guidance on the use of pathogen sequencing for policymakers and practitioners. Our study provides expert consensus statements to support investigators and researchers to develop services. The statements should inform the development of systems and processes for delivering and monitoring pathogen genomics services, and inform practical operational use of pathogen genomic information by public health teams. The themes and statements may be used as a self-assessment tool by public health agencies in the development of their capabilities. Delphi studies can also reveal points of professional controversy or disagreement, i.e. dissensus. Our study only resulted in one statement about which there was disagreement, which was about the potential use of sequence data in outbreak investigation where the data did not pass (unspecified) thresholds of sequence depth or quality. This may reflect differing technical knowledge of participants, or true disagreement about the technical limits at which data can be interpreted with validity. The qualitative responses from participants in round one revealed information about bioinformatics tools for practical use of pathogen genomics data in public health practice, which may be useful for teams that are developing their capabilities. Pathogen genomic sequencing is now used as part of the public health management of infection with Mycobacterium tuberculosis, Campylobacter spp., Staphylococcus aureus, Escherichia coli, Shigella spp., Listeria spp. and Salmonella spp. [ 16, 17]. The use of pathogen genomics in public health practice may inform direct clinical care and the effectiveness of outbreak management [18]. The COVID-19 pandemic highlighted the need for public health agencies to have multidisciplinary pathogen genomics capability, supported by effective data infrastructure and processes. Timely genomic results integrated with epidemiological data can support public health teams to assess and manage the risk of outbreaks and wider epidemics in real time. HDR-UK and CLIMB-COVID have supported the integration of COG-UK genomic data with pseudonymised data for research purposes, and genomic data are available through the trusted research environments of the UK nations. The consensus statements reached in our study reflect a specific point in the management of the COVID-19 pandemic, and the capability, capacity and maturity of UK pathogen genomics services. Though there are generalisable lessons from this work beyond COVID-19 and into the future, as the policy, testing, practice, methods and funding situations evolve over time, the findings may become less relevant. The Delphi method is readily usable in a new situation or context, should a need to update recommendations develop. ## Strengths and limitations As the participants were individuals who created and used pathogen genomics data during the COVID-19 pandemic the findings highlight the real-world, practical issues that they faced in the implementation and application of a pathogen genomics service. The number of participants was relatively low, and we limited invitations to people who were working within the UK public health and genomics system. The timing of our study coincided with the emergence of the Omicron variant, which doubtless competed for the attention of our invited potential participants. The short windows for completion of the study rounds may have limited the response rate. The Delphi study method inherently incorporates some subjectivity from the participants and the researchers, which we have aimed to mitigate through our transparent adherence to a conduct and reporting standard, and open data sharing with our report [7]. There is no standard method for confirming consensus in Delphi studies [14]. The 70 % threshold for agreement that we used reflects a degree of consensus, about which there is no objective gold standard to measure against. Alternative rules or thresholds could have resulted in variation in our results. Our study focused on gaining expert consensus from individuals who work in pathogen services, or who directly use the information that those services produce. As such, the study did not have patient, public or service user participants. We did not have any patient or public members of our research team. 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--- title: Comprehensive analysis of m6A modification lncRNAs in high glucose and TNF-α induced human umbilical vein endothelial cells authors: - Li Shan - Mingfei Guo - Yaji Dai - Liangbing Wei - Wei Zhang - Jiarong Gao journal: Medicine year: 2023 pmcid: PMC9997758 doi: 10.1097/MD.0000000000033133 license: CC BY 4.0 --- # Comprehensive analysis of m6A modification lncRNAs in high glucose and TNF-α induced human umbilical vein endothelial cells ## Abstract N6-methyladenosine (m6A) RNA methylation, as a reversible epigenetic modification of mammalian mRNA, holds a critical role in multiple biological processes. m6A modification in Long non-coding RNAs (lncRNAs) has increasingly attracted more attention in recent years, especially in diabetics, with or without metabolic syndrome. We investigated via m6A-sequencing and RNA-sequencing the differentially expressed m6A modification lncRNAs by high glucose and TNF-α induced endothelial cell dysfunction in human umbilical vein endothelial cells. Additionally, gene ontology and kyoto encyclopedia of genes and genomes analyses were performed to analyze the biological functions and pathways for the target of mRNAs. Lastly, a competing endogenous RNA network was established to further reveal a regulatory relationship between lncRNAs, miRNAs and mRNAs. A total of 754 differentially m6A-methylated lncRNAs were identified, including 168 up-regulated lncRNAs and 266 down-regulated lncRNAs. Then, 119 significantly different lncRNAs were screened out, of which 60 hypermethylated lncRNAs and 59 hypomethylated lncRNAs. Moreover, 122 differentially expressed lncRNAs were filtered, containing 14 up-regulated mRNAs and 18 down-regulated lncRNAs. Gene ontology and kyoto encyclopedia of genes and genomes analyses analyses revealed these targets were mainly associated with metabolic process, HIF-1 signaling pathway, and other biological processes. The competing endogenous RNA network revealed the regulatory relationship between lncRNAs, miRNAs and mRNAs, providing potential targets for the treatment and prevention of diabetic endothelial cell dysfunction. This comprehensive analysis for lncRNAs m6A modification in high glucose and TNF-α-induced human umbilical vein endothelial cells not only demonstrated the understanding of characteristics of endothelial cell dysfunction, but also provided the new targets for the clinical treatment of diabetes. Private information from individuals will not be published. This systematic review also does not involve endangering participant rights. Ethical approval will not be required. The results may be published in a peer-reviewed journal or disseminated at relevant conferences. ## 1. Introduction Type 2 diabetes mellitus (T2DM), is a chronic endocrine and metabolic disorder syndrome, accounting for more than $90\%$ of diabetic patients.[1,2] It is characterized by hyperglycemia, hyperlipidemia, hypertension and insulin resistance. T2DM, which is common along with complications such as diabetic nephropathy, retinopathy, and vascular disease.[3,4] Accumulating evidence suggested that patients with T2DM have an increased risk of vascular complications. Meanwhile, the development of coronary heart disease, cardiomyopathy, arrhythmias and peripheral artery disease are the primary cause of mortality in T2DM.[5,6] Owing to its complex and diverse pathogenesis, the mechanism of the impairment of cardiovascular disease in diabetes has not been fully established. However, oxidative stress mediated by hyperglycemia and activation of inflammatory damage have been recognized as key underlying events. In addition, many clinical studies have demonstrated that vascular endothelial cell proliferation, basement membrane thickening, and deposition of hyaline-like substances may contribute to the mechanism of diabetic vascular disease.[7,8] Long non-coding RNAs (lncRNAs) are defined as RNA molecules with a transcript length of more than 200 nt. Several studies have shown that lncRNAs participate in many life activities, such as dosage compensation effect, epigenetic regulation, cell cycle and differentiation regulation.[9,10] Abnormal expression of lncRNAs has been found to play an important role in multiple diseases, especially in T2DM and its complications. Although it has gained increasingly deep research, the underlying molecular mechanism of lncRNAs was still mostly uncharacterized in diabetic angiopathies. N6-methyladenosine (m6A), the most prevalent and abundant internal modification of RNA in eukaryotic cells, which plays essential roles in mRNA metabolism and multiple biological processes.[11,12] The dysregulation of m6A modification is associated with almost every stage of RNA metabolism, ranging from RNA splicing, nuclear export and translation to stability.[13] Current studies have identified that aberrant m6A methylation in diabetic angiopathies, including heart failure, vascular calcification and pulmonary hypertension.[14–16] However, the molecular mechanism of m6A methylation in multifarious diseases is still not fully understood. Therefore, proclaiming the potential of m6A machinery as novel targets for prevention and treatment diabetic angiopathies will attract more attention. In addition, accumulating research has demonstrated that abundant lncRNAs are also highly modified with m6A to perform their functions. Whereas the pathogenic mechanism of m6A modification lncRNAs in diabetic atherosclerosis have not been comprehensively clarified. In addition, TNF-α as proinflammatory cytokines is an important trigger of endothelial cell dysfunction. Meanwhile, it has been demonstrated they play a crucial role in promoting endothelial cell dysfunction via diverse mechanisms, such as oxidative stress and inflammatory response. Accordingly, high glucose and TNF-α induced endothelial cell dysfunction model in human umbilical vein endothelial cells (HUVECs) was established to reveal the function of m6A modification lncRNAs in atherosclerosis. It also provides a reference for exploring new targets for the diagnosis and treatment of atherosclerosis. ## 2.1. Cell culture and pretreatment HUVECs were purchased from iCell Biological Technology (Shanghai, China). HUVECs cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with $10\%$ fetal bovine serum (FBS; BI), 100 U/mL penicillin, 100 mg/mL streptomycin at 37°C in $5\%$ CO2-humidified incubator. To estimate the effects of high glucose on HUVECs, the cells were pretreated with medium containing 5.5 mM glucose, and then subjected to high glucose and TNF-α treatment. Meanwhile, 25 mM mannitol was used as an osmolarity control condition. Lastly, culture medium concentration was generated by adding 25 mM glucose and 5 ng/mL TNF-α as the final qualification.[17] Afterwards, different treatment cells in two groups were cultured in an incubator for 48 hours for the further experiment. ## 2.2. MeRIP and RNA library preparation and sequencing Total RNA was extracted using TRIzol reagent (Invitrogen, CA), according to the manufacturer’s protocol. RNA integrity and quality were analyzed via nanodrop and gel electrophoresis. m6A RNA immunoprecipitate was performed by the GenSeqTM m6A-MeRIP Kit (GenSeq Inc., Malaysia), according to the manufacturer’s instructions. Both the input samples without immunoprecipitation and the m6A samples with immunoprecipitation were used for RNA-seq library generation. The library quality was estimated with Bioptic Qsep100 Analyzer (Bioptic lnc., Taiwan, China). MeRIP-Seq service was rendered by Shanghai Biotechnology Corporation (Shanghai, China). Library sequencing was transacted on an illumina NovaSeq 6000 with 150bp paired-end reads. ## 2.3. Data analysis of MeRIP and RNA sequencing The quality of the original sequencing data was evaluated through FastQC software (v0.11.7). The raw reads were trimmed using Cutadapt (v2.5) and HISAT2 software (v2.1.0) was aligned to the Ensembl database (GRCh38/hg38). Cutadapt (v2.5) was used to trim adapters and filter for sequences, remaining reads were aligned to the human Ensemble database (GRCh38/hg38). The MeRIP enriched peaks were identified using exomePeak (v2.13.2). Differentially m6A-methylated lncRNAs peaks between the control and model group were analyzed using exomepeak software by Poisson–Gamma performed. Identified m6A peaks that $P \leq .05$ were chosen for the denovo motif analysis using HOMER (v4.10.4). The gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed for the differentially methylated related genes. The competing endogenous RNA network was constructed through the Cytoscape software (v3.6.1). The targets of lncRNAs were predicted from the LnaACTdb and LncTarD tools. ## 3.1. Characteristics of m6A methylation of lncRNAs A total of 7581 m6A peaks were detected within 557 lncRNAs in control group, while 5983 m6A peaks were recognized within 459 lncRNAs in model group (Fig. 1A and B). In addition, 5461 m6A peaks and 65 lncRNAs were identified in both groups. Incidentally, 4937 m6A peaks were all shared in the two groups, including 2492 up-regulated peaks and 2445 down-regulated peaks (Fig. 1C). Meanwhile, 434 differently m6A methylated lncRNAs were screened, containing 168 up-regulated lncRNAs and 266 down-regulated lncRNAs (Fig. 1D). Overall, these results indicated that the degree of the m6A modification in lncRNAs was higher in the control group than in the model group. Figure 1.: Overview of m6A methylation within lncRNAs in HUVECs. (A) Venn diagram showing the numbers of m6A peaks in the two groups. (B) Venn diagram showing the numbers of lncRNAs in the two groups. (C) Volcano plots displaying the differentially expressed m6A peaks between the two groups. (D) Volcano plots displaying the differentially expressed m6A methylated lncRNAs between the two groups. HUVECs = human umbilical vein endothelial cells, lncRNAs = long non-coding RNAs, m6A = N6-methyladenosine. The differently m6A methylated lncRNAs were screened under the conditions of fold change > 2 and $P \leq .05.$ Eventually, 119 significant differentially m6A methylated lncRNAs were selected from 754 lncRNAs, including 60 hypermethylated lncRNAs and 59 hypomethylated lncRNAs. The top 10 hypermethylated or hypomethylated lncRNAs are presented in Table 1. Table 1 \| Gene name \| Chromosome \| P value \| log2FC \| Regulation \| \| --- \| --- \| --- \| --- \| --- \| \| AC004837.3 \| 7 \| 1.8197e-08 \| 5.11 \| Hypermethylated \| \| SDCBP2-AS1 \| 20 \| 2.95121e-05 \| 4.53 \| Hypermethylated \| \| XIST \| X \| 0.001548817 \| 4.44 \| Hypermethylated \| \| HHIP-AS1 \| 4 \| 2.18776e-06 \| 4.43 \| Hypermethylated \| \| NEAT1 \| 11 \| 0.00162181 \| 3.85 \| Hypermethylated \| \| KLF3-AS1 \| 4 \| 7.94328e-12 \| 3.53 \| Hypermethylated \| \| AC090772.3 \| 18 \| 0.002238721 \| 3.45 \| Hypermethylated \| \| AC145207.3 \| 17 \| 1.58489e-15 \| 3.38 \| Hypermethylated \| \| AC079921.1 \| 4 \| 1.86209e-09 \| 3.33 \| Hypermethylated \| \| NAV2-AS2 \| 11 \| 1.1749e-06 \| 2.81 \| Hypermethylated \| \| AC020978.6 \| 16 \| 0.001174898 \| −4.61 \| Hypomethylated \| \| ADAMTS9-AS1 \| 3 \| 1.25893e-11 \| −4.66 \| Hypomethylated \| \| LINC02407 \| 12 \| 9.12011e-09 \| −4.72 \| Hypomethylated \| \| AC021078.1 \| 5 \| 9.77237e-09 \| −4.86 \| Hypomethylated \| \| KF456478.1 \| 19 \| 9.12011e-06 \| −4.9 \| Hypomethylated \| \| HAGLR \| 2 \| 8.70964e-05 \| −5.11 \| Hypomethylated \| \| LINC02577 \| 7 \| 0.000158489 \| −5.38 \| Hypomethylated \| \| AP4B1-AS1 \| 1 \| 6.30957e-13 \| −5.52 \| Hypomethylated \| \| LINC00607 \| 2 \| 7.94328e-23 \| −5.62 \| Hypomethylated \| \| AC124283.1 \| 17 \| 3.1622800000000002e-18 \| −6.46 \| Hypomethylated \| ## 3.2. Distribution of differentially m6A methylated lncRNAs To illustrate the distribution of differentially m6A methylated peaks across chromosomes, we analyzed the enrichment level of m6A methylated peaks on each chromosome. The lncRNAs peaks were primarily located on chromosomes 11, chromosomes 12, chromosomes 17, and chromosomes X (Fig. 2A). Meanwhile, hypermethylated lncRNAs peaks were primarily located on chromosomes 11 ($15.02\%$), chromosomes 17 ($6.44\%$), and chromosomes X ($8.15\%$), hypomethylated lncRNAs peaks were principally concentrate on chromosomes 12 ($7.16\%$), chromosomes 16 ($9.24\%$), and chromosomes 17 ($8.78\%$) (Fig. 2B). Figure 2.: Distribution of differentially m6A methylated lncRNAs. (A) Distribution of m6A modification lncRNAs on chromosomes. (B) Distribution of differentially methylated lncRNAs on chromosomes. (C) and (D) Distribution sites of differentially methylated lncRNAs on chromosomes. CDS = coding sequences, lncRNAs = long non-coding RNAs, m6A = N6-methyladenosine. Inasmuch to further expose the positional relationship of m6A methylated lncRNAs, we divided into the following categories, containing coding sequences, 3′-untranslated regions (UTRs), 5′-UTRs, and exon. In control group, m6A methylated lncRNAs levels were increased in the exon ($93.350\%$ vs $92.25\%$) and 3′-UTRs ($3.80\%$ vs $3.21\%$), when compared to the model group (Fig. 2C). On the contrary, m6A methylated lncRNAs levels were decreased in coding sequences when compared to the model (2.85 % vs 4.28 %). Moreover, approximately $0.27\%$ of the m6A methylated lncRNAs were only appeared in the 5′-UTRs region of the model group (Fig. 2D). ## 3.3. Abundance of m6A peaks and conserved m6A motifs in lncRNAs Regarding the abundance of the m6A peaks in lncRNAs, we found that $77.13\%$ of the lncRNAs in the control group contained m6A peaks, which appeared marginally more than the unimodal value calculated at $75.86\%$ in the model group. The respective percentages comparing different numbers of peaks were also determined with two peaks, three peaks, and more than three peaks being 15.81 versus 16.66, $3.92\%$ versus 5.10 and $3.14\%$ versus $2.38\%$, respectively, for the control versus model group (Fig. 3A). Figure 3.: Abundance of m6A peaks and the conserved m6A modified motif in lncRNAs (A) Proportions of lncRNAs harboring different numbers of m6A peaks in the two groups. (B) The sequence motifs of the m6A-containing peak regions in the two groups. lncRNAs = long non-coding RNAs, m6A = N6-methyladenosine. To analyze the conserved motif of m6A methylated lncRNAs, we selected the sequences of the peaks with the highest enrichment factor in two groups. The motif sequence was compared with the peaks with the highest enrichment ratio of lncRNA. It was found that GGACCG sequence was one of the conserved motif sequences of lncRNA based on P value (Fig. 3B). ## 3.4. Synopsis of differentially expressed lncRNAs in HUVECs As shown in Figure 4A, the results indicated that these lncRNAs have different expression patterns in the two groups. There were 32 significant differentially expressed lncRNAs in HUVECs, which were 14 up-regulated and 266 down regulated (Fig. 4B). The top 10 up-regulated and down-regulated lncRNAs are listed in Table 2. ## 3.5. Association of m6A methylation and expression of lncRNAs Combining the results of methylation sequencing and RNA sequencing, we found that 185 lncRNAs were hypermethylated, of which was only 4 differentially expressed lncRNAs were up-expressed. A total of 292 lncRNAs were hypomethylated, among them, 13 lncRNAs were down-expressed (Fig. 5A). Figure 5.: The association between lncRNAs m6A methylation and expression. (A) Venn diagram showing the relationship between m6A modification and expression. (B) Four-quadrant diagram of the relationship between lncRNAs methylation and expression. lncRNAs = long non-coding RNAs, m6A = N6-methyladenosine. To analyze the correlation between lncRNAs methylation level and expression level, a correlation graph was constructed using the fold enrichment of lncRNA m6A methylation and expression value in terms of FPKM. The results demonstrated that there was a statistically significant positive correlation between methylation and expression levels of lncRNAs in the control and model groups (Fig. 5B). ## 3.6. Functional of differentially m6A methylation and expression of lncRNAs Owing to manifest the role of differentially methylated and expression of lncRNAs in the occurrence and development of endothelial cell dysfunction, GO and KEGG pathway analyses were performed on the genes located near differentially lncRNAs. GO results revealed that these genes primarily participate in the metabolic process, positive regulation of biological process, biological regulation, and cellular process in the biological process category. In terms of cellular component, genes are associated with cell part, organelle part, and so on. In terms of molecular function, genes are primarily involved in contains binding (Fig. 6A). Meanwhile, we have demonstrated that five lncRNAs MEG3, MALAT1, FTX, XIST, and NEAT1 were mainly concentrated on the GO analysis, which deserves more attention in future research. Figure 6.: Functional analysis of mRNAs located near differentially methylated lncRNAs. (A) GO enrichment analysis of genes near m6A methylated lncRNAs. (B) KEGG enrichment analysis of genes near m6A methylated lncRNAs. GO = gene ontology, KEGG = kyoto encyclopedia of genes and genomes, lncRNAs = long non-coding RNAs, m6A = N6-methyladenosine. KEGG analysis found that the most important signaling pathways associated with genes were significantly enriched in PI3K-Akt signaling pathway, FOXO signaling pathway, pancreatic cancer and HIF-1 signaling pathway (Fig. 6B). These genes, such as PI3K, FOXO, HIF-1α, TGF-1β, MAPK1, EGFR, and BCL2 participated in the function of these pathways and accelerated the degree of endothelial cell dysfunction. ## 3.7. Construction of lncRNA-miRNA-mRNA network Five significant lncRNAs were screened out from 51 differentially methylated and expression lncRNAs, of which were associated with endothelial cell dysfunction. The network of lncRNA-miRNA-mRNA was constructed by Cytoscape software. It consisted of the screened lncRNAs and combined with predicted corresponding miRNAs and mRNAs from the LnaACTdb and LncTarD software. The network contained 5 lncRNAs, 231 miRNAs, and 273 mRNAs (Fig. 7). It is evident from the competing endogenous RNA network that lncRNAs regulate the complex relationship between miRNAs and mRNAs. The network will contribute to understanding of the functions and mechanisms of lncRNAs in endothelial cell dysfunction. Figure 7.: The networks of lncRNA-miRNA-mRNA regulation. Green represent the lncRNAs, genes in red are coding miRNA and mRNAs. lncRNAs = long non-coding RNAs. ## 4. Discussion In this research, we have investigated the m6A modification lncRNAs by high glucose and TNF-α influenced in HUVECs. The results performed significantly different between control and model groups with the abundance and distribution of m6A modification. In addition, the ratio of m6A modified lncRNAs in control group was remarkably greater than the model group, which indicated m6A modification down regulation of lncRNA expression in the model group. Meanwhile, previous research demonstrated that METTL14-mediated m6A methylation modification suppressed pyroptosis and diabetic cardiomyopathy by down-regulating TINCR.[18] Interestingly, the m6A methylation of XIST was prominently reduced, but the level of XIST was obviously over expression.[19] Notwithstanding, no studies have authenticated the absolute regulatory relationship between m6A methylation levels and lncRNA expression, which requires further in-depth research. The targets of m6A methylation lncRNAs were analyzed through GO enrichment, including biological processes, molecular function and cellular components. Biological processes results were mainly involved in metabolic process, positive regulation of biological process, biological regulation, and cellular process. Molecular function enrichment analysis was mostly concentrated on binding. Meanwhile, cellular components consist of macromolecular complex, cell part, and organelle part. These biological processes were mostly related to glucose and lipid metabolism, indicating that m6A modified lncRNAs perhaps involved in regulating insulin secretion and reducing blood glucose. GO analysis results revealed that majority of the m6A modified lncRNAs were hypomethylated sites, of which specific mechanisms still need further investigation. KEGG pathways demonstrated that most of m6A modified lncRNAs were down-regulated in the model group. As an upstream pathway of FOXO, PI3K/Akt could reduce Akt phosphorylation and activate insulin secretion to reduce the level of blood glucose. Additionally, research suggests that regulating PI3K/Akt pathway alleviates oxidative stress and inhibits apoptosis in diabetic cardiomyopathy, together with attenuated diabetic vasculopathy.[20,21] FOXO signaling pathway could regulate insulin signaling, gluconeogenesis and immune cell migration in diabetes. Previous research has declared that metformin might alleviated HUVECs apoptosis and vascular endothelial injury by regulating FOXO protein.[22] Meanwhile, activated PI3K/Akt might regulate glycolysis through the HIF-1α pathway and reduce vascular damage under hypoxic conditions.[23] Altogether, these m6A modified lncRNAs mediated pathways could harmonize blood glucose, diminish the degree of atherosclerosis and alleviate endothelial cell dysfunction. In addition, our results manifested that lncRNAs NEAT1, XIST, MALAT1, FTX, and MEG3 were differentially expressed in two groups, which might play a crucial role in m6A modification and endothelial cell dysfunction of HUVECs. NEAT1 has been reported to participate in multiple diabetic metabolic syndrome, which accelerates the occurrence and development of diabetic nephropathy by sponging miR-23c.[24] Others have claimed that lncRNA NEAT1 regulated diabetic retinal epithelial-mesenchymal transition via regulating miR-204/SOX4 axis.[25] Our research demonstrated that the expression level of NEAT1 was significantly higher in the model group, which might be related to m6A modification. LncRNA XIST was downregulated in high glucose treated podocytes, accompanied with increased apoptosis of podocytes. Furthermore, lncRNA XIST protects podocyte from high glucose-induced cell injury by sponging miR-30 and regulating AVEN expression in diabetic nephropathy.[26] Additionally, m6A modification of lncRNAs levels has been observed in various prime aggravators of diabetic nephropathy pathogenesis.[27] The targets of XIST were involved in BCL2, STAT3, MAPK1, and VEGF, which mainly concentrate on the immune regulation, oxidative stress, inflammatory response. What’s more, current studies suggest that the lncRNAs NEAT1, XIST, and MALAT1 participate in the pathogenesis of T2DM and highlight their potential as diagnostic biomarkers, especially MALAT1, which is highly expressed in the serum of patients with coronary atherosclerosis heart disease, and it has high value in the diagnosis and prediction of in-stent restenosis.[28] Inhibition of MALAT1 has the potential to protect the retina from oxidative damage and to prevent diabetic retinopathy.[29] FTX has been reported to be associated with several tumor progressions, such as hepatocellular carcinoma,[30] renal cell carcinoma,[31] and colorectal cancer.[32] Nevertheless, we have revealed that it was associated with endothelial cell dysfunction, and further experiments are required and validate their effects and mechanisms. LncRNA MEG3 is related with multiple biological processes, containing proliferation, apoptosis and inflammation response. Previous research has illustrated MEG3 could alleviate high glucose inducing apoptosis and inflammation via inhibiting NF-κB pathway by targeting miR-34a/SIRT1 axis.[33] In addition, ROCK2, TNF, NOTCH1, and HIF-1α have also played crucial regulatory roles during atherogenesis. Accordingly, controlling blood glucose and reducing the stimulation of inflammatory factors have obvious promoting effects on reducing vascular damage and improving atherosclerosis. However, our study had a few limitations regarding the analysis of m6A-sequencing and RNA-sequencing. All the results were only based on association studies and bioinformatic analyses, which need further experiments to verify the results. In addition, the functions of m6A modification lncRNAs and their targets related to endothelial dysfunction require further investigation. ## 5. Conclusion In summary, we firstly established a comprehensive analysis to investigate the m6A modification of lncRNAs in HUVECs to predict the mechanism of high glucose and TNF-α induced endothelial cell dysfunction. Despite the above limitations, our research still provides a new theoretical basis for the study of diabetic endothelial cell dysfunction, as well as new targets and reference for clinical treatment of atherosclerosis. ## Author contributions Conceptualization: Jiarong Gao. Data curation: Wei Zhang. Funding acquisition: Jiarong Gao. Investigation: Liangbing Wei. Methodology: Liangbing Wei, Wei Zhang. Project administration: Jiarong Gao. Resources: Li Shan. Software: Mingfei Guo, Yaji Dai. Validation: Mingfei Guo, Yaji Dai. Writing – original draft: Li Shan, Mingfei Guo. Writing – review & editing: Yaji Dai. ## References 1. Ferguson D, Finck BN. Emerging therapeutic approaches for the treatment of NAFLD and type 2 diabetes mellitus.. Nat Rev Endocrinol (2021) 17 484-95. 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--- title: 'Hand-Schüller-Christian syndrome combined with empty sella syndrome: A case report and literature review' authors: - Wei Ji - Xiaoyang Chen journal: Medicine year: 2023 pmcid: PMC9997762 doi: 10.1097/MD.0000000000033216 license: CC BY 4.0 --- # Hand-Schüller-Christian syndrome combined with empty sella syndrome: A case report and literature review ## Rational: Hand-Schüller-Christian syndrome (HCS) is a rare disease with little clinical awareness, but the condition is more dangerous, and it combines with empty sella syndrome (ESS) which is extremely rare. ### Presentation: A 26-year-old male patient who had proptosis, headaches, and diabetes insipidus for more than 10 years, and chronic cough and wheeze for 8 years presented to our hospital due to an abrupt onset of chest pain for 2 days. ### Diagnosis: Hand-Schüller-Christian syndrome is diagnosed based on the typical clinical manifestations of diabetes insipidus and bilateral proptosis, magnetic resonance imaging (MRI) pituitary imaging and pathology. Empty sella syndrome is diagnosed based on hormonal indicators, clinical manifestations and MRI pituitary scan results. Type 1 respiratory failure and severe pneumonia can be diagnosed based on the results of clinical examination, chest imaging (including chest x-ray and computed tomography), pathology and blood gas analysis. Left pneumothorax can be diagnosed with chest imaging. ### Interventions: “Meropenem and Cefdinir” were given for antimicrobrial coverage, “Desmopressin acetate” for anti-diuretic treatment, “Forcodine” for cough relief, “Ambroxol and acetylcysteine” for phlegm reduction, and continuous closed chest drainage was performed. ### Outcomes: The patient discharged after cough, wheezing, headache and other symptoms improved, and vital signs were stable. The patient has been followed up once a month for 17 months ongoing after discharge. At present, symptoms such as cough, sputum, and wheezing have improved considerably, and the mMRC score of dyspnea is 2 points. The reexamination of the chest X-ray shows that the absorption of lung exudates is better than before, and there is no recurrence of pneumothorax. ### Lessons: Consider whether isolated diabetic insipidus is related to HSC, and if so, conduct an MRI, a biopsy, and other examinations as soon as possible. ## 1. Introduction Hand-Schüller-Christian syndrome (HSC) was first proposed by Lichtenstein in 1953, commonly known as Langerhans cell histiocytosis (LCH).[1] HSC is a group of rare disorders of uncertain etiology, with bone damage, diabetes insipidus, and proptosis as its hallmark symptoms.[2] In 1968, Kaufman made the first report about empty sella syndrome (ESS). The subarachnoid space protrudes into the sella under the pressure of the cerebrospinal fluid, causing the sella to enlarge and the pituitary gland to compress and deform as a result of coloboma or absence of the diaphragm sella turcica, or atrophy of the pituitary gland.[3,4] Symptoms of ESS include headaches, vision and visual field disorders, endocrine dysfunction, amongst others.[4] HSC and ESS are rarely reported in domestic and foreign literature, especially HSC. Therefore, clinical awareness is not high. We report a case of Hand-Schüller-Christian Syndrome combined with ESS in a patient with severe pneumonia and respiratory failure, aiming to improve the understanding of the combination of HSC and ESS to reduce the rate of misdiagnosis and missed diagnosis. ## 2. Case report A 26-year-old male patient who had been experiencing an abrupt onset of chest pain for 2 days went to the emergency department of our hospital was admitted to the respiratory medicine department for treatment after a computed tomography scan revealed a pneumothorax. During the course of treatment, it was discovered that the patient had developed a thirst for more than 10 years without any apparent explanation and that it persisted even after drinking more than 5 L of water every day. Over the next few years, the patient developed bilateral proptosis, headaches, a cough, and wheezing, and suffered 2 epileptic seizures. The patient was eventually identified with “histiocytosis” through a tissue biopsy after several outpatient medical appointments. On admission the patient’s blood pressure was $\frac{126}{77}$ mm Hg, temperature was 36.3°C, heart rate was 94 beats/min, and respiratory was 30 beats/min. He was a short and odd-looking patient who had poor nutrition and mental retardation. We did not palpate a pleural rub, nor abnormalities in percussion of the chest wall. There were sporadic moist rales audible in both lungs, with reduced breath sounds in the left lung. There were no audible signs of dry rales or pleural friction rub. No precordial prominence was present, and the apical beat was situated in the fifth intercostal space 0.5 cm medial to the left mid-clavicular line. There is no palpable tremor and pericardial friction. The patient has normal cardiac boundary, and has regular heart rhythm and normal heart sounds. Laboratory results showed carbon dioxide partial pressure was 46 mm Hg, oxygen partial pressure was 57 mm Hg, and oxygenation index was 196 mm Hg. Blood examination showed a white blood cell count of 21.18 × 109/L, neutrophils of 18.48 × 109/L, and a hemoglobin of 134 g/L. Fasting blood glucose was 3.45 mmol/L, and 2-hour postprandial blood glucose was 5.63 mmol/L. Fasting c-peptide and 2-hour postprandial c-peptide were normal. Cortisol was normal at different periods. Sputum analysis, bacterial sputum smear, fungal sputum smear, tuberculosis sputum smear, and vasculitis-related autoantibody detection were all unremarkable. Adrenocorticotropic hormone was 28.25 pg/mL, parathyroid hormone was 45.97 pg/mL, human growth hormone was 1 ng/mL, luteinizing hormone was 0.974 mIU/mL, follicle-stimulating hormone was 1.34 mIU/mL, progesterone was 0.103 ng/mL, prolactin was 6.22 ng/mL, testosterone was 0.396 ng/mL, estradiol was 7.42 pg/mL. On ultrasound (Fig. 1), the patient was found to have breast development, testicular atrophy, bladder wall thickening and irregular-jagged alterations. On computed tomography and X-ray (Fig. 1), the patient was found to have left pneumothorax and bilateral interstitial lung lesions. On magnetic resonance imaging (Fig. 1), the patient’s sella is replaced by a liquid signal foci, the anterior pituitary tissue is thin, the posterior pituitary is not shown, and the slope is thickened showing a ground glass shape. Figure 1.: Ultrasound scrotum, ultrasound scrotal, ultrasound urinary, chest X-ray breast, CT chest, and MRI brain. CT = computed tomography, MRI = magnetic resonance imaging. We managed the patient with meropenem and cefdinir, desmopressin acetate, continuous closed drainage of the thoracic cavity, fluid support, and other treatments. The patient was discharged after the discomfort of wheezing, coughing and sputum subsided. Following discharge, the patient underwent routine followed-up once a month for 17 months. At present, the symptoms of cough, expectoration, wheezing, and other symptoms have all improved significantly, and the mMRC score for dyspnea is 2 points. The review chest X-ray reveals that there has been no recurrence of pneumothorax, and improvement of the pleural effusion. ( The patient has provided informed consent for publication of the case.) ## 3. Discussion Hand-Schüller-Christian Syndrome is a very rare disease that can strike anyone at any age, with a peak incidence at 1 to 3 years of age. It affects more males than females, and has an incidence of 4 to 8 per million in children, but the incidence in adults is just 1 to 2 in a million.[5,6] Many patients are unable to receive the proper diagnosis and treatment because of the low incidence, limited clinical awareness, and high missed diagnostic rate of this condition.[6] HSC frequently involves a number of organs and systems, although it can also involve only one, and the most vulnerable tissues include lymph nodes, bone, skin, and the lung. The most common symptom of HSC is solitary pulmonary LCH.[1,6] The patient’s age and the level of lesion involvement affect the prognosis, tending to be worse in younger patients with a multiple sites of disease.[7] Histiocytosis that occurs in infancy often has an acute onset and rapid progression. There are various degrees of damage, and the chances of survival through adulthood are quite slim. Death usually occurs more than a few weeks to 2 years after the first sign of illness.[7,8] The etiology of HSC is currently unknown, though current literature suggests genetic mutation, tumor microenvironment, and viral infection may play roles.[9] There is debate regarding whether harm is caused by the dendritic cells’ malignant mutation or the inflammatory infiltration, but typical clinical manifestations and imaging findings aid diagnosis.[9,10] The gold-standard for diagnosis is a combination of clinical presentation and pathological demonstration of LCH, but clear imaging findings can also aid in diagnosis.[5,6,8,11] The patient has been treated with individual strategy and obtain successful outcomes.[12] ESS was once perceived by the general public as a rare disease,[13,14] but thanks to ongoing advances in medical technology, its incidence in population imaging censuses have increased from $5.5\%$ to $35\%$. ESS can be divided into primary and secondary forms.[12,13] The early stages of ESS are frequently asymptomatic, though a small percentage of individuals experience unusual symptoms such as headache, visual impairment, cerebrospinal fluid rhinorrhea, and endocrine disorders. Though pathogenesis of ESS is still unclear, prevailing theories[12,15] include abnormal sella development, continuous or intermittent increase in cerebrospinal fluid pressure, obesity, and genetic factors. The standard for ESS diagnosis is undoubtedly imaging.[13,15] Treatment is not necessary for asymptomatic ESS, but continuous follow-up and treatment is necessary to monitor for symptom development. In this case, we report a rare combination of HSC and ESS that has not previously been reported in domestic and foreign literature. The patient suffered from HCS at an early age. HCS lesions can affect the bones of the head, leading to the loss of the integrity of the diaphragm sella turcica. Subarachnoid herniation into the sella may cause ESS after the anatomical structure changes. Furthermore, the diabetes insipidus in this case has not been cured or considerably controlled during many years of treatment. The long-term presence of diabetes insipidus may be explained by the coexistence of HCS lesions involving the head and ESS, which cause endocrine disorders and aberrant hormone secretion. A definite association has been noted in the literature in recent years between ESS and the HSC that induced diabetes insipidus in patients as well as between interstitial lung disease and HSC.[16,17] However, because there are no other secondary causes, ESS may also be primary. Although the symptoms and signs of diabetes insipidus and exophthalmos may be caused by HSC and ESS, the coexistence of HCS and ESS may not be relevant. In general, HCS is considered a pediatric disorder, but it can also occur in adults, and it is often challenging to trace the cause in isolated diabetes insipidus.[2] HCS is a rare disease with little clinical awareness, but the condition is dangerous. Missed diagnosis or misdiagnosis of this disease will delay diagnosis and treatment and possibly even have an adverse effect on prognosis. Therefore, it is crucial for clinicians to determine whether diabetes insipidus is a part of HSC. Early diagnosis of the patient’s illness and knowledge of the relationships between diseases will make it possible to treat the patient more effectively and at an earlier stage. The prognosis is excellent if HCS just affects the skin, and in some patients the condition is self-limiting. However the condition becomes extremely hazardous if it affects other systems. Death may occur within a few days of illness onset, usually from secondary systemic infections or organ failure. 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--- title: 'Prognostic value of elevated plasma angiotensin-converting enzyme 2 in cardiometabolic diseases: A review' authors: - Gang Zhou - Jingchen Liu journal: Medicine year: 2023 pmcid: PMC9997766 doi: 10.1097/MD.0000000000033251 license: CC BY 4.0 --- # Prognostic value of elevated plasma angiotensin-converting enzyme 2 in cardiometabolic diseases: A review ## Abstract Angiotensin-converting enzyme 2, as an internal anti regulator of the renin-angiotensin hormone cascade reaction, plays a protective role in vasodilation, inhibition of fibrosis, and initiation of anti-inflammatory and antioxidative stress by degrading angiotensin II and generating angiotensin (1–7). Multiple studies have shown that plasma angiotensin-converting enzyme 2 activity is low in healthy populations without significant cardiometabolic disease, and elevated plasma angiotensin-converting enzyme 2 levels can be used as a novel biomarker of abnormal myocardial structure and/or adverse events in cardiometabolic diseases. This article aims to elaborate the determinants of plasma angiotensin-converting enzyme 2 concentration, the relevance between angiotensin-converting enzyme 2 and cardiometabolic disease risk markers, and its relative importance compared with known cardiovascular disease risk factors. Confronted with the known cardiovascular risk factors, plasma angiotensin-converting enzyme 2 (ACE2) concentration uniformly emerged as a firm predictor of abnormal myocardial structure and/or adverse events in cardiometabolic diseases and may improve the risk prediction of cardiometabolic diseases when combined with other conventional risk factors. Cardiovascular disease is the leading cause of death worldwide, while the renin-angiotensin system is the main hormone cascade system involved in the pathophysiology of cardiovascular disease. A multi-ancestry global cohort study from the general population by Narula et al revealed that plasma ACE2 concentration was strongly associated with cardiometabolic disease and might be an easily measurable indicator of renin-angiotensin system disorder. The association between this atypical hormone disorder marker and cardiometabolic disease is isolated from conventional cardiac risk factors and brain natriuretic peptide, suggesting that a clearer comprehending of the changes in plasma ACE2 concentration and activity may help us to improve the risk prediction of cardiometabolic disease, guide early diagnosis and feasible therapies, and develop and test new therapeutic targets. ## 1. Introduction Cardiovascular disease is the leading cause of death worldwide,[1] while the renin-angiotensin system is the main hormone cascade system involved in the pathophysiology of cardiovascular disease. A multi-ancestry global cohort study from the general population by Narula et al[2] revealed that plasma ACE2 concentration was strongly associated with cardiometabolic disease and might be an easily measurable indicator of renin-angiotensin system disorder. The association between this atypical hormone disorder marker and cardiometabolic disease is isolated from conventional cardiac risk factors and brain natriuretic peptide, suggesting that a clearer comprehending of the changes in plasma ACE2 concentration and activity may help us to improve the risk prediction of cardiometabolic disease, guide early diagnosis and feasible therapies, and develop and test new therapeutic targets. In the renin-angiotensin system (RAS), the angiotensin-converting enzyme (ACE) converts angiotensin (Ang) I into angiotensin II (Ang II) which mediates vasoconstriction, sodium and water retention, cardiac remodeling, fibrosis, inflammation, endothelial dysfunction, and oxidative stress through the angiotensin type 1 receptor.[3] As an endogenous anti-regulatory factor for the renin-angiotensin hormone cascade reaction, ACE2 converts Ang I and Ang II (the preferred substrate, the affinity of ACE2 to Ang II is about 400 times higher than that of Ang I.[4] Therefore, ACE2 mainly hydrolyzes Ang II in vivo) into Ang (1–9) and Ang (1–7).[5] Ang (1–9) has a biological effect on anti-myocardial fibrosis through the Ang II type 2 receptor.[6,7] Ang (1–7) can also be generated by ACE cleavage of Ang (1–9), which acts on the G protein-coupled receptor Mas pathway,[8,9] and plays a protective role in vasodilation, inhibition of fibrosis, and initiation of anti-inflammatory and antioxidant stress.[10] ACE2 counteracts the effect of Ang II by degrading Ang II and generating Ang (1–7). The ultimate effect of RAS activation depends on the balance between plasma Ang (1–7)/ Ang II levels, which determines the availability of different angiotensin peptides and thus determines the balance between pro-inflammatory and anti-inflammatory pathways as well as pro-fibrotic and antifibrotic pathways. Decreased Ang (1–7)/ Ang II ratio was related to worsening cardiovascular adverse events and longer hospitalization. ## 2.1. Biochemistry and regulation of ACE2 ACE2 is the first homologous gene of human ACE, cloned from the cDNA library of human lymphoma and heart failure patients.[11] ACE2 is an intact type 1 membrane protein that is highly expressed in the heart, blood vessels, lungs, and kidneys.[5,12,13] It exists not only in membrane-bound cells but also in free form in circulating plasma by the cleavage of the extracellular domain protein by (tumor necrosis factor-α converting enzyme also known as ADAM17, a resolvase, and metalloproteinase).[14] Studies have shown that there were 2 different soluble forms of ACE2 shedding in primary cultured human airway epithelial cells.[15] This indicates that the shedding process may involve 1 or more enzymes, and further research is required to determine the mechanism of ACE2 shedding as well as the involvement of other shedding enzymes and their real cleavage sites. The mechanism of elevated plasma ACE2 concentration is still an active research field, which may be a complex interaction involving cell expression, hydrolysis of shedding enzymes, and impaired plasma clearance rate which affects plasma concentration. Previous studies have revealed that ACE2 might be posttranscriptional regulated by miR-421 which can be used as a novel potential therapeutic target to modulate ACE2 expression in diseases.[16] ## 2.2. Genotyping and genetic analysis Narula et al[2] found that 2 gene loci associated with ACE2 concentration had genome-wide significance through genome-wide meta-analysis. One of the gene loci was the variant rs5936022 encoded by the X chromosome. The increased variant rs5936022 was linked with the elevated ACE2 expression in multiple brain regions.[17,18] Another rs2464190, located upstream of the HNF1A gene locus, might be the main regulator of ACE2 levels since its promoter region possesses 3 HNF1A combining sites. HNF1A could induce upper cellular ACE2 expression in islet cells,[19] this special variation was also associated with the susceptibility of coronary artery disease and type 2 diabetes.[20,21] ## 2.3. The relevance between ACE2 and cardiometabolic disease risk factors Multiple studies have revealed that plasma ACE2 activity was low in healthy population without significant cardiometabolic disease, and elevated plasma ACE2 levels could be used as a novel predictor of abnormal myocardial structure and/or adverse events in cardiometabolic diseases involving heart failure (HF), myocardial infarction, atrial fibrillation, coronary artery disease, stroke, and aortic stenosis.[22–28] Elevated plasma ACE2 activity also was an independent predictor of sensitivity and specificity for all-cause mortality.[23,28] Compared with the established clinical risk factors (smoking, diabetes, hypertension, hyperlipidemia, and high body mass index), plasma ACE2 concentration consistently emerged as a strong predictor of cardiovascular diseases or death.[2] ## 2.4. The activity of ACE2 Plasma ACE2 activity was inhibited by endogenous inhibitor in vivo, this inhibition was dose-dependent. the endogenous inhibitor could be removed by anion exchange chromatography,[29] and was not affected by decreased renal function,[30] but it is still unclear whether other cardiovascular risk factors or diseases influence internal inhibitor levels. The study has shown that plasma ACE2 levels might be parallel to the expression of tissue ACE2 and the constant shedding rate in normal physiology.[31] In the study by Narula et al[2], the gene variation rs5936022, associated with plasma ACE2 levels, was related to elevated ACE2 expression in the vascular system, brain as well as heart, indicating that elevated plasma ACE2 levels reflected elevated ACE2 synthesis in tissue. On the contrary, Ramchand et al[28] have shown that elevated plasma ACE2 concentration was associated with decreased myocardial ACE2 gene expression in patients with aortic stenosis. It remains to be a problem that how to simultaneously measure the ACE2 activity in human tissues and plasma to identify whether increased plasma ACE2 activity is related to elevated ACE2 mRNA synthesis and/or elevated ACE2 shedding in tissues. ## 2.5. Determinants of plasma ACE2 concentration Studies have shown that plasma ACE2 levels in males were significantly higher than in females.[23,25] The difference in ACE2 expression between genders is also recorded in diverse human tissues involving the renal cortex, heart, and adipose tissue.[18] Considering that the genes encoding ACE2 are located on the X chromosome, X chromosome deactivation and escapement may lead to phenotypic differences and tissue specificity differences between genders.[32] Although men are associated with a higher concentration of ACE2, There is no significant evidence to suggest that ACE2 has a heterogeneous effect on major adverse cardiovascular outcomes between men and women.[2] Poorly is known about the biological significance of elevated ACE2 concentration in men and its relationship with gender susceptibility to cardiovascular diseases. Narula et al[2] revealed that gender was the most important factor determining the plasma ACE2 concentration, followed by race, body mass index, diabetes, age, systolic blood pressure, smoking status, and low-density lipoprotein. To investigate whether there is a potential causal relationship between clinical risk factors and ACE2 levels, Narula et al[2] showed that genetically higher body mass index and higher risk of type 2 diabetes were associated with elevated plasma ACE2 levels through the Mendelian randomization analysis, However, there was no significant correlation between genetic susceptibility to smoking, elevated low-density lipoprotein and systolic blood pressure with plasma ACE2 levels. In regression analyses of patients with heart failure, ACE2 activity was not associated with glomerular filtration rate and C-reactive protein.[22] ## 2.6. ACE2 and antihypertensive therapies ACE inhibitors do not seem to affect ACE2 activity because they act on different sites.[33] Previous studies have also confirmed that there was no significant difference in plasma ACE2 levels between the use of angiotensin receptor blockers, ACE inhibitors, calcium channel blockers, β blockers, or diuretics.[2,28] ## 3. ACE2 and cardiovascular disease Previous studies have evaluated the prognosis value of plasma ACE2 levels in patients with confirmed cardiovascular disease. In patients with obstructive coronary artery disease, HF, aortic stenosis as well as atrial fibrillation, elevated ACE2 activity was related to the elevated risk of cardiac dysfunction and adverse outcomes.[23–25,28] This means that increased plasma ACE2 activity may act as a positively biological indicator of these diseases or their severity. Plasma ACE2 levels also reflect changes in cardiac structure. In patients with atrial fibrillation, elevated plasma ACE2 activity was related to left atrial structural remodeling.[24] In patients with HF, increased plasma ACE2 levels independently predicted cardiovascular outcomes (hospitalization of heart failure, heart transplantation, or death) and were positively related to imaging indicators of ventricular remodeling as well as function disorder after 34 months of follow up.[31] Similarly, plasma ACE2 activity was associated with infarct size and more severe left ventricular remodeling after myocardial infarction with ST-segment elevation.[26] ## 3.1. ACE2 and coronary artery disease Previous studies have shown a significant increase in plasma ACE2 activity after myocardial infarction.[26] However, Ramchand et al[23] showed that plasma ACE2 activity increased even without acute myocardial injury in patients with obstructive coronary disease, which might reflect the potential atherosclerosis of the coronary artery rather than myocardial infarction. This suggests that plasma ACE2 activity may be an indicator of atherosclerosis. In patients with non-dialysis chronic kidney disease, circulating ACE2 activity was associated with silent atherosclerosis in carotid and peripheral vessels.[34] Studies have shown that ACE2 was expressed in vascular endothelial cells, macrophages, and smooth muscle cells in aortic and coronary atherosclerotic lesions.[35,36] ACE2 was present in atherosclerotic blood vessels during coronary artery bypass surgery in patients with coronary heart disease.[10] Experimental studies have shown that ACE2 overexpression could promote the stability of atherosclerotic plaques and reduce atherosclerotic lesions.[30] ACE2 tissue activity was lower in stable advanced atherosclerotic lesions than in early and ruptured atherosclerotic lesions.[35] In the rabbit atherosclerosis model, silencing the tumor necrosis factor-α converting enzyme gene enhanced plaque stability and improved vascular remodeling.[37] These findings reinforce the important anti-regulation role of ACE2 in atherosclerosis and suggest that the regulation of ACE2 may provide an option for the treatment of patients with atherosclerosis in the future. ## 3.2. ACE2 and heart failure In patients with suspicious HF, serum ACE2 activity was firmly related to brain natriuretic peptide (BNP) levels, left ventricular ejection fraction, and clinical diagnosis of HF. Úri et al[22] demonstrated that serum ACE2 activity had a diagnostic value in distinguishing heart failure with preserved ejection fraction (HFpEF) from heart failure with reduced ejection fraction (HFrEF) and it was associated with cardiovascular disease progression, ACE2 activity was significantly increased in hypertensive state and further increased in HFrEF but not in HFpEF when hypertension develops into HF. This indicates that serum ACE2 activity may be a selective biomarker for systolic dysfunction and has no correlation with the severity of diastolic dysfunction. Compared with serum ACE2 activity, plasma BNP and amino-terminal pro-B-type natriuretic peptide levels are forceful independent predictors of heart failure incidence and mortality, but they have poor selectivity to distinguish HFpEF from HFrEF because they have barely statistical difference.[38] Current studies of heart failure showed a significant positive relevance between circulating ACE2 activity and BNP levels.[23,28] In Chagas disease, Plasma ACE2 activity was an independent prognostic biological marker and similarly valid to BNP, the combination of ACE2 activity and BNP levels enhanced the predictive value of adverse cardiac outcomes.[39] Compared with patients without ACE2 or BNP elevation or only 1 biomarker elevation, patients with elevated plasma ACE2 activity and elevated BNP were more likely to have higher mortality. Since the determination of plasma ACE2 has a prognostic value that cannot be detected by BNP alone, the combination of these 2 biological markers may improve the medical decision-making capacity of aortic stenosis. Importantly, more than $40\%$ of patients with elevated ACE2 activity had normal BNP levels.[28] Even if the extra adjustment for BNP, elevated ACE2 levels were still related to a higher risk of myocardial infarction, stroke, diabetes as well as death. The relationship between ACE2 and HF was corresponding but decreased after adjusting for BNP.[2] ## 3.3. ACE2 and aortic stenosis In patients with aortic stenosis, increased plasma ACE2 concentration was related to decreased myocardial ACE2 gene expression, abnormal myocardial structure as well as more severe myocardial fibrosis, and was an independent predictor of mortality. Plasma ACE2 levels were correlated with the degree of valvular calcification and left ventricular end-diastolic volume, but not with left ventricular ejection fraction and the severity of aortic stenosis.[28] ## 3.4. ACE2 and stroke The rat experiment showed that after 4 hours of focal ischemia caused by acute ischemic stroke, the serum ACE2 activity initially decreased, and then rebounded within 3 days.[40] Preclinical studies on acute ischemic stroke indicated that the activation of ACE2/Ang (1–7)/Mas axis through targeted intervention could induce neuroprotective effects.[41] Bennion et al[27] showed that serum ACE2 activity was significantly correlated with ischemic stroke, but did not correlate with the infarction area of stroke. This study left 2 problems: using enzyme activity determination to provide data on the levels of functional plasma ACE2, but currently, there is no way to determine the tissue sources of ACE2. We can only speculate that the circulating ACE2 concentration is related to the tissue ACE2 concentration. ACE2 works in both membrane binding and soluble states. Ischemic cell death may lead to increased release of these 2 types, so it is not clear which 1 may be the main source of serum ACE2 activity during the stroke. Future studies need to assess the role and effect of ACE2 shedding in different tissues in stroke. ## 4. ACE2 and Ang (1–7)/Ang II ratio The previous study has shown that the expression and shedding of ACE2 elevated under hypoxia.[42] After the supplementation of recombinant Human ACE2(rhACE2), Ang II was effectively converted into Ang (1–7), significantly increasing the Ang (1–7)/Ang II ratio.[43] Ang (1–7)/Ang II ratio undergoes dynamic changes in disease. In patients with chronic heart failure receiving angiotensin-converting enzyme inhibition, plasma Ang II was inhibited, while plasma Ang (1–7) was increased. On the contrary, plasma Ang II was increased and Ang (1–7) level was decreased in patients with acute heart failure receiving angiotensin receptor blockers.[43] In the stage of chronic heart failure, elevated Ang (1–7) levels enhanced heart function and reversed pathological remodeling. When it reached the end-stage heart failure, tissue RAS was activated, which could result in increased myocardial Ang II levels and had nothing to do with systemic RAS. The circulating Ang II levels increased in patients above III degrees by the functional classification of the New York Heart Association (NYHA), while the circulating Ang (1–7) levels were higher in patients with NYHA functional class I/II degree. Regardless of ejection fraction, Ang (1–7)/Ang II ratio was significantly higher in patients with NYHA functional class I/II than III/IV.[43] This suggested that ACE2 may act as a predictor for disease progression since it reflected the severity of HF in the NYHA functional classification. It also indicated that targeted therapies, gene therapy, for instance, aimed to improve Ang (1–7) levels, may be a feasible treatment strategy. ## 5. ACE2 and other diseases related to cardiovascular disorders Roberts et al showed that the plasma ACE2 activity seemed to have increased in all chronic kidney disease (CKD) groups including predialysis, transplant patients, and even dialysis patients when compared with the historical samples of healthy subjects. this study suggested that although plasma ACE2 activity tends to increase in CKD patients, it may be a compensatory mechanism to reduce Ang II overactivity, and plasma ACE2 activity was relatively deficient at end-stage renal disease and dialysis initiation. This demonstrated that ACE2 may be involved as a novel biomarker in the progression of CKD. Narula et al[2] showed that elevated plasma ACE2 levels were related to the genetically upper body mass index and a biggish risk of type 2 diabetes. This manifested that ACE2 has a significant metabolic meaning. ACE2 provided significant renal protection in patients with diabetes, while ACE2 deficiency aggravated diabetic kidney injury, rhACE2 has therapeutic effects in experimental Alport syndrome and diabetic nephropathy.[44,45] The results of Marfella et al[46] showed that high glucose environment was more conducive to the formation of glycosylated ACE2, and the expression of glycosylated ACE2 in cardiomyocytes was strongly correlated with blood glucose control. ACE2 and glycosylated ACE2 were lower in patients with good glycemic control (HbA1c < $7\%$) than in patients with poor glycemic control (HbA1c > $7\%$). In other words, poor blood glucose control leads to increased glycosylated ACE2 levels in cardiomyocytes. Therefore, the expression of glycosylated ACE2 in type 2 diabetes mellitus (T2DM) patients is higher than that in non-T2DM patients, and promotes myocardial fibrosis by reducing the expression of Ang 1 to 9, Ang 1 to 7, and MasR, resulting in impaired cardiac function. Thus, the degree of myocardial fibrosis in T2DM patients is higher than that in non-T2DM patients. Since the discovery of ACE2, more than 20 years of basic biological and physiological knowledge has been accumulated, many follow up studies have indicated that ACE2 may act as a potential target for the prevention and treatment of chronic inflammation and inflammatory diseases. Recently, ACE2 has received extensive attention as the cellular receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which resulted in coronavirus disease (COVID-19)-related pneumonia. COVID-19 patients developed pneumonia due to accelerated injury in patients with multiple organ failures partly due to inflammatory cytokine storm induced by type 1 and type 2 T helper cells dysfunction and immune cells overactivation.[47–51] Lung function and pathological damage were improved after injection of rhACE2, inflammation was also alleviated.[52] Respiratory dysfunction and hypoxemia induced by patients with COVID-19, resulting in acute myocardial injury and chronic damage to the cardiovascular system. Severe symptoms are highly possible to occur in patients with cardiovascular disease if infected with SARS-CoV-2 which was triggered by the binding of the spike protein of the virus to ACE2. The binding decreased the protective function of ACE2. Interestingly, the trimer Spike protein in SARS-CoV-2-infected cells can be cleaved into S1 and S2 subunits, the S1 subunit contains a complete receptor-binding domain that binds to ACE2, thereby reducing the virus’s infectivity to neighboring cells by inducing ACE2 downregulation and reducing the number of ACE2 receptor molecules on its surface.[53] D’Onofrio et al[54] have shown that the expression of total ACE2 and glycosylated ACE2 in cardiomyocytes of DM patients is higher than that of non-DM patients, which is conducive to the entry of SARS-CoV-2 into host cells to increase the susceptibility of diabetic patients to COVID-19 infection and leads to worse prognosis. Therefore, reducing the expression of total ACE2 and glycosylated ACE2 may be a therapeutic target for DM patients to reduce COVID-19 infection. ## 6. Discussions Numerous studies have shown that elevated plasma ACE2 levels could be used as a novel biological predictor of abnormal myocardial structure and/or adverse events in cardiometabolic diseases involving HF, myocardial infarction, aortic stenosis, atrial fibrillation, coronary artery disease, and stroke. The study of plasma ACE2 will help us to improve the risk prediction of cardiometabolic disease, guide clinically feasible therapies, and develop new therapeutic targets. However, future studies also need to solve the following problems: To determine the mechanism of ACE2 shedding, and the involvement of other shedding enzymes as well as the actual cleavage sites involved; How to simultaneously determine the ACE2 activity in tissues and circulation; The relationship between tissue ACE2 activity and circulating ACE2 activity; How to determine the critical value of plasma ACE2 to meet the optimal combination of sensitivity and specificity simultaneously. ## Author contributions Conceptualization: Gang Zhou. Formal analysis: Gang Zhou. Software: Gang Zhou. Supervision: Jingchen Liu. Writing – original draft: Gang Zhou. Writing – review & editing: Gang Zhou, Jingchen Liu. ## References 1. Benjamin EJ, Blaha MJ, Chiuve SE. Heart disease and stroke statistics-2017 update: a report from the American heart association.. Circulation (2017.0) 135 e146-603. PMID: 28122885 2. Narula S, Yusuf S, Chong M. 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--- title: 'Identification of molecular subtypes and prognostic signatures based on transient receptor potential channel-related genes to predict the prognostic risk of hepatocellular carcinoma: A review' authors: - Dongyang Wu - Qingshan Cai - Dong Liu - Ganggang Zuo - Shudong Li - Liyou Liu - Jianxing Zheng journal: Medicine year: 2023 pmcid: PMC9997768 doi: 10.1097/MD.0000000000033228 license: CC BY 4.0 --- # Identification of molecular subtypes and prognostic signatures based on transient receptor potential channel-related genes to predict the prognostic risk of hepatocellular carcinoma: A review ## Abstract Abnormal transient receptor potential (TRP) channel function interferes with intracellular calcium-based signaling and causes malignant phenotypes. However, the effects of TRP channel-related genes on hepatocellular carcinoma (HCC) remain unclear. This study aimed to identify HCC molecular subtypes and prognostic signatures based on TRP channel-related genes to predict prognostic risks. Unsupervised hierarchical clustering was applied to identify HCC molecular subtypes using the expression data of TRP channel-related genes. This was followed by a comparison of the clinical and immune microenvironment characteristics between the resulting subtypes. After screening for differentially expressed genes among subtypes, prognostic signatures were identified to construct risk score-based prognostic and nomogram models and predict HCC survival. Finally, tumor drug sensitivities were predicted and compared between the risk groups. Sixteen TRP channel-related genes that were differentially expressed between HCC and non-tumorous tissues were used to identify 2 subtypes. Cluster 1 had higher TRP scores, better survival status, and lower levels of clinical malignancy. Immune-related analyses also revealed higher infiltration of M1 macrophages and higher immune and stromal scores in Cluster 1 than in Cluster 2. After screening differentially expressed genes between subtypes, 6 prognostic signatures were identified to construct prognostic and nomogram models. The potential of these models to assess the prognostic risk of HCC was further validated. Furthermore, Cluster 1 was more distributed in the low-risk group, with higher drug sensitivities. Two HCC subtypes were identified, of which Cluster 1 was associated with a favorable prognosis. Prognostic signatures related to TRP channel genes and molecular subtypes can be used to predict HCC risk. ## 1. Introduction Hepatocellular carcinoma (HCC) is a primary malignant hepatocyte tumor, accounting for approximately $80\%$ of liver cancer cases worldwide.[1,2] According to annual projections by the World Health Organization, >1 million patients are expected to die of liver cancer by 2030.[3] The progression of cirrhosis is the biggest risk factor for HCC, and it is estimated that $90\%$ of patients with underlying cirrhosis will eventually develop HCC.[4] Previous studies have demonstrated the beneficial chemopreventive effect of insulin-sensitizing drugs or statins against HCC occurrence.[5,6] Surgical resection, radiofrequency ablation, and liver transplantation are the first therapy choices for early HCC, with a 5-year survival rate of $70\%$.[7,8] During the last 15 years several aspects of HCC scenario have changed, as well as its management.[9,10] However, these therapies are ineffective in advanced stages (such as in metastatic HCC) where tumor resection is impossible, resulting in a 5-year survival rate as low as $2.5\%$.[11] A series of genetic events contribute to the pathogenesis and progression of HCC, $25\%$ of which result from inducible mutations. These molecular alterations determine the abnormal proliferation of tumor cells and their subsequent invasion.[12] Therefore, biomarkers characterizing molecular genetic mechanisms are essential for assessing disease prognosis and predicting treatment responses. Transient receptor potential (TRP) channels are a class of cation channels that perform signal transduction upon activation by changing the membrane potential or intracellular calcium (Ca2+) concentration[13] Ca2+ signaling is crucial for regulating key cellular events, including gene transcription, movement and contraction, energy production, and channel control.[14] The mammalian TRP superfamily consists of 28 nonselective cation-permeable channels that can be divided into 6 subfamilies based on sequence homology.[15] In cancer, mutations in TRP channel-encoding genes generate TRP channels with abnormal functions that interfere with normal intracellular Ca2+ distribution patterns, resulting in the dysregulation of downstream effectors and enhanced cancer-specific pathological features.[16] For example, the expression of TRPV2 has been implicated in the drug-induced cytotoxicity and stemness of HCC.[17,18] In addition, the expression of TRPV2 increases as liver disease progresses from normal liver to chronic hepatitis and then to cirrhosis and may serve as a prognostic marker for patients with HCC.[19] Another TRP superfamily gene, TRPC7, is highly expressed in viral hepatitis B-associated HCC and may be a potential therapeutic target or diagnostic marker.[20] Therefore, genes encoding TRP channels may play crucial roles in the progression of HCC. However, the understanding of the role of all 28 channel-related genes is still incomplete, and systematic studies on their impact on the prognosis of HCC are lacking. To this end, we analyzed 28 TRP channel genes from public expression data and established HCC molecular subtypes and prognostic signatures to stratify and predict the prognostic risk of HCC. We further constructed and validated a risk prognosis model and analyzed the resulting prognostic values reported for the immune microenvironment characteristics and drug sensitivity of TRP channel genes to comprehensively elucidate the potential role of these genes in HCC. ## 2.1. Data sourcing and preprocessing Standardized gene expression profiles and clinical follow-up data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database. After removing samples with no overall survival data, 365 HCC samples with prognostic information were retained. The microarray dataset GSE14520[21] downloaded from the Gene Expression Omnibus database was defined as an independent external validation cohort. GSE14520 is present on the sequencing platform of the GPL3921 [HT_HG-U133A] Affymetrix HT Human Genome U133A Array and contains 221 HCC samples with valid prognostic data. ## 2.2. Subtype identification based on TRP channel genes Data on the 28 TRP channel genes used in this study are provided in a relevant published article.[22] We compared expression levels between HCC and non-tumorous samples and used Pearson correlation analysis to assess potential associations. We identified the corresponding HCC subtypes based on TRP channel-related genes with significant differences in expression, which were subjected to unsupervised hierarchical clustering using R 3.6.1 ConsensusClusterPlus Version 1.54.0 (http://www.bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.html). The optimal k value ranged from 2 to 6. The TRP score in each sample was determined by enrichment scores computed by the gene set variation analysis algorithm using the R gene set variation analysis package version 1.36.2 (http://bioconductor.org/packages/release/bioc/html/GSVA.html)[23] and subsequently compared among subtypes using the Wilcoxon test to verify the rationality of HCC subtypes. ## 2.3. Association analysis between HCC subtypes and clinical features To evaluate the correlation between prognostic survival and different HCC subtypes, Kaplan–Meier (KM) curves were generated using the R survival package Version 2.41-1 (http://bioconductor.org/packages/survivalr/).[24] The clinical characteristics of patients with HCC (including age, sex, tumor grade, pathological stage, pathological TNM classification, and exposure to radiation therapy) were analyzed. Furthermore, the relationships between subtypes and these clinical features were assessed using the chi-square test, and statistical significance was set at $P \leq .05.$ ## 2.4. Comparison of immune microenvironment among subtypes In this study, we applied 2 algorithms to evaluate the immune microenvironment status of HCC samples and compared immune infiltration among different HCC subtypes using the Wilcoxon signed-rank test. For immune infiltration analysis, CIBERSORT (httRiskscore://cibersort.stanford.edu/index.php)[25] was used to compute the proportions of 22 types of immune cells, whereas ESTIMATE[26] was used to estimate the stromal and immune scores of the tumor samples. ## 2.5. Screening of differentially expressed genes (DEGs) among HCC subtypes To observe the possible existence of different molecular mechanisms underlying HCC subtypes, DEGs between these subtypes were screened using linear regression and empirical Bayesian methods provided by the limma package, version 3.10.3 (http://www.bioconductor.org/packages/2.9/bioc/html/limma.html).[27] The obtained P values were adjusted for multiple tests using the Benjamini–Hochberg method, and genes characterized by an adjusted P value < 0.05 and \|log2fold change\| > 1 were considered to have significant differential expression. ## 2.6. Identification of prognostic signatures Based on the obtained DEGs, univariate Cox regression analysis was used to screen the expression levels of genes significantly associated with survival with a set threshold of $P \leq .01.$ The least absolute shrinkage and selection operator algorithm was then used to define an optimal lambda value, followed by subsequent assessment of prognostic signatures (10-fold cross-validation) using the R package for the LARS algorithm version 1.2 (https://cran.r-project.org/web/packages/lars/index.html). ## 2.7. Generation and validation of the risk score-based prognostic model Furthermore, the prognostic gene signatures were subjected to stepwise Cox regression analysis using the R survminer package version 0.4.9 (https://cran.rstudio.com/web/packages/survminer/index.html) to establish a risk score-based prognostic model. The risk score was calculated using the following formula: where β is the regression coefficient, h0(t) is the baseline risk function, and h(t, X) is the risk function associated with X (covariate) at time t. We further calculated the risk scores of each sample in TCGA and GSE14520 cohorts and grouped the samples according to their median values. The samples were divided into high-risk prognosis (HRP) and low-risk prognosis (LRP) groups, and a KM curve was created to estimate the difference in the actual prognosis. ## 2.8. Analysis of prognostic independence to construct a nomogram model We assessed clinical characteristics using univariate and multivariate Cox regression analyses to screen for independent prognostic factors at a threshold value of $P \leq .05.$ The R.rms package version 5.1-2 (https://cran.r-project.org/web/packages/rms/index.html)[28] was used to construct a nomogram model to predict the survival probability of patients with HCC. ## 2.9. Drug sensitivity analysis The Genomics of Drug Sensitivity in Cancer database was used to estimate the sensitivity of each sample to chemotherapy drugs, followed by quantification of $50\%$ inhibitory concentration (IC50) values using the R pRRophetic package (https://github.com/paulgeeleher/pRRophetic).[29] The IC50 values of 138 chemotherapy drugs were compared between the HRP and LRP groups using the Wilcoxon signed-rank test. ## 2.10. Association analysis of prognostic risk and HCC subtype To observe the relationship between HCC subtypes and risk groups, we compared the distribution differences of subtypes between the HRP and LRP groups. The results were visualized using a bar chart obtained with the R. galluvial package version 0.12.3 (https://CRAN.R-project.org/package=ggalluvial).[30] ## 3.1. Two HCC subtypes were identified based on the analysis of TRP channel-related genes To analyze the expression patterns of the 28 TRP channel-related genes, their expression profiles were compared between HCC and non-tumorous samples. Sixteen TRP channel-related genes showed significant differences in expression levels between the 2 groups (Fig. 1A). Correlations between the expression of these genes were also analyzed; the results suggested that TRPV4 and TRPV6 had the strongest positive correlation, whereas TRPM2 and TRPM7 had the strongest negative correlation (Fig. 1B). The 16 key TRP-related genes were then incorporated into the unsupervised cluster analysis, and the optimal subtype number was defined as $k = 2$ (Fig. 1C). The proportion of ambiguous clustering was then determined to verify the stability of the clustering results, and the optimal value of $k = 2$ was confirmed (Fig. 1D). The TRP score of each sample was calculated and compared between the 2 clusters. The TRP score of Cluster 1 was significantly higher than that of Cluster 2 (Fig. 1E). Survival analysis also indicated a poor prognosis in patients with a low TRP score (Fig. 1F). These results highlight that a higher TRP value, as exhibited by Cluster 1, may be a favorable prognostic factor. Figure 1.: Two HCC subtypes associated with prognosis were identified based on 16 key TRP channel-related genes. (A) Differences in the expression of 28 TRP channel-related genes between HCC and normal tissue samples. P < .05, P < .01, P < .001, **P < .0001. (B) Correlation between the expression of 16 key TRP channel-related genes. (C) Consensus clustering cumulative distribution function with k = 2–6. (D) Verification graph based on the proportion of ambiguous clusters. (E) Difference in TRP score between the 2 subtypes. (F) KM curve showing the difference in survival between patients with high and low TRP scores. HCC = hepatocellular carcinoma, KM = Kaplan–Meier, TRP = transient receptor potential. ## 3.2. Differences in clinical features and prognosis between subtypes To further explore the relationship between clinical characteristics and HCC subtypes, we conducted a survival analysis to compare the survival differences between Clusters 1 and 2 (Fig. 2A). The KM curve suggested a better prognosis for patients in Cluster 1, which is consistent with previous results. In addition, the distribution of expression of the 16 key TRP-related genes associated with different clinical features was visualized using a heatmap (Fig. 2B). Comparison among subtypes revealed that the distribution of Cluster 2 was significantly associated with the later period of pathologic T, pathologic N, and pathologic stage (Fig. 2C). This suggests that Cluster 2 might possess a prognostic risk value, indicating the deterioration of clinical manifestations. Figure 2.: Differences in survival and the distribution of clinical characteristics between cluster 1 and cluster 2. (A) The KM curve shows a significant difference in survival probability between the 2 clusters. (B) Heatmap reveals the expression patterns of 16 key TRP genes reported to different clinical features. (C) The distribution proportions of clusters 1 and 2 varied significantly reported to clinical characteristics. KM = Kaplan–Meier, TRP = transient receptor potential. ## 3.3. Analysis of immune microenvironment characteristics based on HCC subtype Based on the expression matrix of HCC samples, the CIBERSORT algorithm was employed to calculate the fractions of immune cells in each sample. Using a threshold value of $P \leq .05$, 10 types of immune cells were found to infiltrate significantly differently between Clusters 1 and 2 (Fig. 3A). Among these, M1 and M2 macrophages exhibited the most significant difference between the 2 groups; their infiltration levels were higher in Cluster 1 than in Cluster 2. The ESTIMATE algorithm was used to compute the stromal and immune scores for each sample. Compared to Cluster 2, Cluster 1 presented higher immune and stromal scores (Fig. 3B), which also indicated a higher level of non-tumor cell infiltration. Figure 3.: Comparison of immune cell infiltration between cluster 1 and cluster 2 using CIBERSORT and ESTIMATE algorithms. (A) Infiltration differences in 22 types of immune cells between the 2 clusters were evaluated using the CIBERSORT algorithm. P < .05, P < .01, P < .001. (B) The violin plot depicts the difference in stromal and immune scores between clusters 1 and 2, which were calculated using the ESTIMATE algorithm. ## 3.4. Optimal prognostic signatures were screened based on DEGs between HCC subtypes To further explore the differences in molecular regulatory mechanisms among the subtypes, we performed a differential analysis of gene expression levels between Clusters 1 and 2. The patterns of the DEGs between the 2 groups are shown in Figure 4A. A total of 148 upregulated and 530 downregulated DEGs were identified according to the set thresholds (Fig. 4B). Univariate Cox regression analysis was performed to select the 212 DEGs that were significantly associated with prognosis. The least absolute shrinkage and selection operator algorithm was used to screen for optimal candidates, and 16 key genes were selected at lambda = 16 (Fig. 4C). Finally, stepwise Cox regression was used to define the optimal gene set, which constituted the basis for constructing the prognostic model. This set contained 6 prognostic signatures (matrix metalloproteinase (MMP) 1, SPP1, BRSK2, OGN, PPARGC1A, and FTCD) (Fig. 4D). Figure 4.: Screening of prognostic signatures for model construction based on differentially expressed genes (DEGs) between cluster 1 and cluster 2. (A) Heatmap showing the expression patterns of 678 DEGs between clusters 1 and 2. (B) The volcano plot exhibits 148 upregulated and 530 downregulated DEGs, with screening standards of adjusted P value < 0.05, and \|log2FC\| > 1. (C) LASSO coefficient distribution and likelihood bias. (D) The optimal gene set which included 6 prognostic signatures was identified using univariate Cox regression analysis. P < .05, *P < .01. LASSO = least absolute shrinkage and selection operator. ## 3.5. Prognostic model construction and efficacy verification Based on the regression coefficients of the 6 prognostic signatures and their levels in the TCGA dataset, a risk score-based prognostic model was constructed. The distributions of the risk score, survival time, and 6 prognostic signatures for each sample are shown in Figure 5A. These results suggest that the risk of death increases with the risk score. Furthermore, the expression of MMP1, SPP1, and BRSK2 was higher in the HRP group than in the LRP group. The KM curve confirmed that the actual survival of patients in the LRP group was significantly higher than that of patients in the HRP group (Fig. 5B). A time-dependent receiver operating characteristic curve was created to assess the specificity and sensitivity of the model in predicting prognostic risks; the area under the curve (AUC) of the 1-, 3-, and 5-year prediction were 0.749, 0.742, and 0.712, respectively (Fig. 5C), which represented a high predictive performance of the model. The performance of the model based on the 6 prognostic signatures was analyzed using the GSE14520 validation dataset. The results suggested that with an increase in the risk score, patient survival time and gene expression levels tended to change (Fig. 5D). The patients in the HRP group also had a significantly lower likelihood of survival (Fig. 5E). In addition, all AUC values were >0.6, indicating that the prognostic model based on the validation cohort had the potential to predict prognostic risk (Fig. 5F). Figure 5.: Construction and verification of a risk score-based prognostic model in training and validation cohorts. (A and D) Distribution of risk score, survival time, and expression of 6 signatures in (A) TCGA and (D) GSE14520 datasets. (B and E) KM curves show survival differences between the HRP and LRP groups in both (B) TCGA and (E) GSE14520 datasets. (C and F) ROC curves depicting the sensitivity and specificity of the (C) TCGA and (F) GSE14520 datasets in predicting the prognostic risks of HCC. HCC = hepatocellular carcinoma, HRP = high-risk prognosis, KM = Kaplan–Meier, LRP = low-risk prognosis, ROC = receiver operating characteristic, TCGA = The Cancer Genome Atlas. ## 3.6. Screening of independent prognostic factors to construct a nomogram survival prediction model We applied univariate Cox regression analysis for the clinical characteristics of all HCC samples in the TCGA dataset and selected factors with $P \leq .05$ to further conduct the multivariate Cox regression analysis (Fig. 6A and B). Then, the factors “pathological T” and “risk groups” were identified with prognostic independence to establish a nomogram model (Fig. 6C). The calibration curves confirmed that the predicted overall survival at 1, 3, and 5 years using the nomogram model fit well with the actual values (Fig. 6D). In the KM curve, patients in the HRP group (as predicted by the nomogram model) also showed a significantly unfavorable prognosis compared to those in the LRP group (Fig. 6E). Additionally, the AUC of the 1-, 3-, and 5-year receiver operating characteristic curves were 0.761, 0.739, and 0.703, respectively, confirming the predictive reliability of the nomogram model (Fig. 6F). Figure 6.: Identification of independent prognostic factors to construct a prognostic predictive nomogram model. (A and B) (A) Univariate and (B) multivariate regression analyses were performed to identify the independent prognostic factors. (C) Factors of the pathological T and risk groups were incorporated to generate a nomogram model. (D) The calibration curve illustrates consistency between the actual and predicted values of the nomogram model. (E) The KM curve indicates a significant difference in survival between patients with high and low prognostic risk as predicted by the nomogram model. The 1-, 3-, and 5-year survival ROC curves confirmed the predictive capability of the nomogram model. KM = Kaplan–Meier, ROC = receiver operating characteristic. ## 3.7. Associations between prognostic risk with HCC subtypes and drug sensitivity To observe the relationship between the HCC subtypes and risk groups, we compared the distribution of the 2 clusters between the HRP and LRP groups. The IC50 values of all drugs were significantly higher in the LRP group than in the HRP group (Fig. 7A), indicating that the high-risk group has higher drug sensitivities. Finally, 64 of the 138 chemotherapeutic drugs showed significantly different IC50 values between the HRP and LRP groups. Using box plots (Fig. 7B), we listed the differences in IC50 values between the 2 groups for the 6 chemotherapy drugs frequently used for HCC. The results showed that the IC50 values of these drugs were lower in the high-risk group than in the lower-risk group, indicating that differences in drug sensitivity may be a source of prognostic risk for patients with HCC. Figure 7.: Difference of distribution of the 2 clusters and comparative drug sensitivity between HRP and LRP groups. (A) Comparison of the distributions of 2 clusters between the HRP and LRP groups. (B) Box plots showing differences between the 2 groups in IC50 values of 6 frequently used chemotherapy drugs for HCC. HCC = hepatocellular carcinoma, HRP = high-risk prognosis, IC50 = 50% inhibitory concentration, LRP = low-risk prognosis. ## 4. Discussion In this study, 16 of the 28 TRP channel-related genes were differentially expressed between HCC and non-tumorous sample tissues. Based on these key TRP channel-related genes, we identified 2 molecular HCC subtypes, of which Cluster 1 had a higher TRP score and possibly a more favorable prognosis. Analysis of prognostic factors and clinical features confirmed the prognostic characteristics of Cluster 1. Compared with patients in Cluster 2, more patients in Cluster 1 were distributed in the early stage of HCC and were characterized by a low malignant level of the disease. After screening the DEGs between Clusters 1 and 2 and constructing the prognostic model, we found that prognostic signatures based on the HCC subtype could predict prognostic risks effectively. Moreover, patients in Cluster 1 were more likely to be distributed in the low-risk group than those in Cluster 2. These results corroborate each other, demonstrating the reliability of subtype-based clustering and the successful construction of the prognostic model and confirming the important role of the 16 TRP channel-related genes in predicting the prognosis of HCC. Taken together, the prognostic signatures related to TRP channel genes and molecular subtypes obtained in this study can be used in clinical applications to predict HCC risk. Identification of the immune microenvironment characteristics of the 2 subtypes revealed that the most significant difference between Clusters 1 and 2 was in the infiltration levels of M1 macrophages. Macrophages play a vital role in many pathophysiological processes such as inflammation, tissue repair, and metabolism. The polarization of M1/M2 macrophages may affect immune escape, carcinogenesis, and metastasis in HCC[31,32] However, in our study, no difference between clusters was detected in terms of the infiltration level of M2 macrophages. However, a relevant study identified DEGs between high- and low-infiltrating M1 macrophages as predictors of HCC prognosis[33] The activation of Notch signaling in HCC can mediate the differentiation of macrophages into M1 macrophages, thereby promoting inflammation and anti-tumor activity.[34] In this study, a significantly increased infiltration of M1 macrophages was observed in Cluster 1, indicating the activation of the inflammatory response and anti-tumor activity against HCC. This is why patients in Cluster 1 showed better clinical outcomes. M1 macrophages play an anti-tumor role by inhibiting tumor cell proliferation and metastasis,[35] which is consistent with our findings. Another finding is that the activation of the TRP family-related gene, TRPC5 (which in this study was highly expressed in paracarcinoma tissues), may inhibit macrophage differentiation by regulating the Akt/IκB/NF-κB signaling pathway.[36] Therefore, we further speculated that TRPC5 could cause increased infiltration of M1 macrophages, thereby reducing the invasiveness of tumor cells, inhibiting their malignant phenotype, and promoting a favorable prognosis for patients with HCC. However, the regulatory mechanism of M1 macrophages and the role of M1/M2 polarization in HCC remain to be experimentally explored. Considering the differences in overall survival and clinical phenotypes between the 2 HCC subtypes, we further analyzed the DEGs to explore the underlying molecular regulatory differences between Clusters 1 and 2 and screened prognostic signatures for the construction of a prognostic model and prognostic risk prediction. Among these, FTCD, PPARGC1A, OGN, BRSK2, SPP1, and MMP1 have important prognostic values and can be used to define prognostic risks. Specifically, FTCD, PPARGC1A, and OGN are protective factors against HCC and are associated with a better prognosis. Chen et al confirmed our findings and proposed that low expression of FTCD is associated with poor prognosis and an aggressive tumor phenotype in HCC.[37] The sensitivity and specificity of FTCD expression levels in differentiating normal or liver cirrhosis from early well-differentiated HCCs are $90\%$ and $86.7\%$, respectively.[38] Conversely, BRSK2, SPP1, and MMP1 expression levels were associated with a higher risk of poor prognosis in this study. Li et al further demonstrated that BRSK2 is positively associated with cancer status, has a prognostic risk for HCC, and may be involved in the regulatory mechanism of m6A methylation.[39] SPP1 is also a signature gene in HCC tissues that enhances the proliferation of tumor cells and is closely related to tumor progression.[40] More importantly, SPP1 mediates the crosstalk between HCC cells and macrophages and triggers the polarization of macrophages towards the M2 phenotype.[41] However, the association between these genes and the TRP family has rarely been studied, except for MMP1. MMP1 is upregulated in patients with HCC and is associated with a poor prognosis.[42] Pharmacological inhibition and gene silencing of TRPA1 downregulates MMP1 production in osteoarthritis,[43] whereas activation of TRPV1 increases the expression of MMP1.[44] However, in the present study, specific levels of TRPA1 and TRPV1 were not detected in HCC tissues. Therefore, the relationship between the expression of these prognostic gene signatures and the TRP channel genes requires further confirmation. There were several limitations in this study. First, there was no analysis of the potential associations between key TRP genes, M1 macrophage infiltration, and prognostic signatures. Second, no experimental verification of the diagnostic and prognostic values of key genes was performed. Third, all analysis was based on public databases, and no functional assay was conducted. Hence, in subsequent studies, we will focus on the interaction between M1/M2 macrophage polarization and TRP channel signaling, as well as on their actual impact on the prognosis of HCC. ## 5. Conclusion In this study, 2 HCC subtypes were identified based on key TRP channel-related genes. Patients in Cluster 1 were prone to a favorable prognosis and a lower level of tumor malignancy. 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--- title: 'Effect of ertugliflozin on renal function and cardiovascular outcomes in patients with type 2 diabetes mellitus: A systematic review and meta-analysis' authors: - Qian Cheng - Shupeng Zou - Chengyang Feng - Chan Xu - Yazheng Zhao - Xuan Shi - Minghui Sun journal: Medicine year: 2023 pmcid: PMC9997778 doi: 10.1097/MD.0000000000033198 license: CC BY 4.0 --- # Effect of ertugliflozin on renal function and cardiovascular outcomes in patients with type 2 diabetes mellitus: A systematic review and meta-analysis ## Background: The global prevalence of type 2 diabetes mellitus (T2DM) is growing yearly. The efficacy of ertugliflozin (ERT), a recently licensed anti-diabetic drug, has been widely reported. However, additional evidence-based data is required to ensure its safety. In particular, convincing evidence on the effects of ERT on renal function and cardiovascular outcomes is needed. ### Methods: We searched PubMed, Cochrane Library, Embase, and Web of Science for randomized placebo-controlled trials of ERT for T2DM published up to August 11, 2022. Cardiovascular events here mainly refer to acute myocardial infarction and angina pectoris (AP) (including stable AP and unstable AP). The estimated glomerular filtration rate (eGFR) was used to measure renal function. The pooled results are risk ratios (RRs) and $95\%$ confidence intervals (CIs). Two participants worked independently to extract data. ### Results: We searched 1516 documents and filtered the titles, abstracts, and full text, 45 papers were left. Seven trials met the inclusion criteria and were ultimately included in the meta-analysis. The meta-analysis found that ERT reduced eGFR by 0.60 mL·min−1·1.733 m−2 ($95\%$ CI: −1.02–−0.17, $$P \leq .006$$) in patients with T2DM when used for no more than 52 weeks and these differences were statistically significant. Compared with placebo, ERT did not increase the risk of acute myocardial infarction (RR 1.00, $95\%$ CI: 0.83–1.20, $$P \leq .333$$) and AP (RR 0.85, $95\%$ CI: 0.69–1.05, $$P \leq .497$$). However, the fact that these differences were not statistically significant. ### Conclusion: This meta-analysis shows that ERT reduces eGFR over time in people with T2DM but is safe in the incidence of specific cardiovascular events. ## 1. Introduction Diabetes is becoming more common worldwide, and the International Diabetes Federation predicts that 537 million people will have diabetes by 2021 (both diagnosed and undiagnosed). This figure is expected to rise by $46\%$ to 783 million by 2045.[1] Type 2 diabetes is becoming increasingly common across the world. Type 2 diabetes mellitus (T2DM) accounts for about $90\%$ of the overall population.[2,3] There are several complications associated with type 2 diabetes, which may be classified into 2 groups: microvascular and macrovascular. Cardiovascular disease is a macrovascular problem, whereas nephropathy is a microvascular consequence.[4] According to current research, most T2DM patients die from cardiovascular disease, with coronary atherosclerotic heart disease and heart failure (HF) being the primary causes of mortality.[5–8] Furthermore, recent research has indicated that people with diabetes are more likely than non-diabetics to experience HF and lower ejection fraction.[9,10] Furthermore, a survey found that individuals with atherothrombosis and T2DM had a $30\%$ greater risk of HF hospitalization than those with atherothrombosis but no T2DM.[10] Also, diabetes is the primary cause of end-stage renal disease,[11,12] and diabetes can result in chronic kidney disease (diabetic nephropathy), which involves 20 to $40\%$ of diabetics.[13,14] Interestingly, chronic kidney disease usually appears approximately ten years after type 1 diabetes has been diagnosed, but at that time, type 2 diabetes has already been diagnosed.[5] Therefore, in the absence of overt symptoms, diabetic nephropathy has a decreased estimated glomerular filtration rate (eGFR) as one of its clinical diagnostic criteria. Similarly, when glycated hemoglobin (HbA1c) was >$7.0\%$, each $1\%$ rise in HbA1c was linked with a $38\%$ increase in mortality risk from macrovascular events. When HbA1c was >$6.5\%$, the risk of microvascular death increased by $40\%$ for every $1\%$ increase in HbA1c.[15] As a result, renal function and cardiovascular events significantly influence the survival of type 2 diabetes patients. Ertugliflozin (ERT) is a new generation of sodium-glucose cotransporter-2 inhibitors that can lower blood glucose by reducing glucose reabsorption in the kidney and increasing glucose excretion in the urine.[16,17] Although the hypoglycemic impact of ERT has been extensively reported in previous research, there is no consensus on its effect on eGFR in T2DM patients. Linong Ji[18] and Samuel Dagogo-Jack[19] discovered that ERT when compared to a placebo group, slowed the drop in eGFR. However, in a VERTIS CV sub-study,[20] eGFR fell first in the ERT group compared to the placebo group and remained steady above baseline levels throughout the therapy. In the most recent meta-analysis of ERT, safety and tolerability have been reported.[16] However, its safety studies did not include its effect on eGFR. So, what effect does ERT have on eGFR in T2DM patients? We conduct this meta-analysis to address that question. In addition, issues about the influence on cardiovascular events in T2DM patients must be investigated. According to the findings of the VERTIS CV Trial, ERT reduced the total occurrence rate of hospitalization for heart failure (HHF) (risk ratio [RR] 0.70, $95\%$ confidence interval [CI]: 0.56–0.87) and total HHF/CV mortality (RR 0.83, $95\%$ CI: 0.72–0.96).[6] However, the effect on other cardiovascular events, such as acute myocardial infarction (AMI) and angina pectoris (AP), has not been observed. This meta-analysis will also address these concerns. ## 2. Patients and methods We performed a systematic review and meta-analysis according to the preferred reporting items for systematic review and meta-analyses guidelines. The systematic review protocol has been registered in the PROSPERO database (International Prospective Register of Systematic Reviews, https://www.crd.york.ac.uk/prospero; registration number CRD42022332437). ## 3. Data sources and searches From conception through August 2022, we did a comprehensive search of the PubMed, Cochrane Library, Embase, and Web of Science databases using the terms “ertugliflozin,” “type 2 diabetes,” and associated phrases. We manually reviewed the references of chosen research, relevant meta-analyses, and review papers, to verify that no eligible trials were ignored. The search is restricted to English language articles only. EndNote X9 (Clarivate, Philadelphia, PA) is used to manage and edit pertinent documents, as well as to eliminate duplicate files. ## 4. Study inclusion and exclusion criteria The following were the criteria for inclusion in the literature. T2DM patients are the target recipient; the individuals were 18 years old and had HbA1c levels ranging from 8.0 to $8.21\%$; the papers included were randomized controlled trials that compared ERT to placebo; and renal markers of eGFR or cardiovascular events (AMI and AP) have been documented. The following were the exclusion criteria: all participants had type 1 diabetes mellitus; the research was not a randomized controlled experiment; and reusing raw data or doing secondary analysis. ## 5. Data extraction and quality assessment Two reviewers did the initial screening of the literature by reading the titles and abstracts of the literature back-to-back. Documents with incomplete information in the title and abstract, as well as those that met the initial screening criteria, were then read in detail. Finally, for literature that met the inclusion criteria, its authors, year of publication, clinical trial registration number, interventions, controls, mean age, percentage of men, mean glycated hemoglobin, mean body mass index (BMI), mean eGFR, and length of treatment were recorded. The final renal function index was eGFR; the eligible cardiovascular outcome events were AMI and AP (including stable AP and unstable AP). The Cochrane Collaboration’s risk of bias tool was used to evaluate the included literature regarding random sequence generation, allocation concealment, blinding quality (including for participants, investigators, and analysts), data integrity, selective reporting, and other biases. Each article scored high, low, or uncertain based on the related criteria. Two reviewers worked separately on literature screening and data extraction, and discrepancies were addressed through conversation. If the discussion failed to achieve a consensus, a third reviewer was consulted. R4.2.1 was used for the analysis (https://www.r-project.org/). ## 6. Data synthesis and analysis Continuous variables were expressed as mean ± standard deviation (SD). When only standard errors were reported, the following formula was used for conversion: SD = SE·√n. weighted mean differences (WMDs) and associated $95\%$ CI were selected as the pooled effect size for continuous variables. For dichotomous variables, RR and their $95\%$ CI were used to describe the risk of cardiovascular events. Stata SE17.0 (Stata Corporation, College Station, TX) was used for data analysis, and $P \leq .05$ was considered a statistically significant difference. Heterogeneity was calculated using the Cochrane Q statistic and expressed as I2. When I2 ≤ $40\%$, it was considered low heterogeneity; when $40\%$ < I2 ≤ $70\%$, it was regarded as moderate heterogeneity; when $70\%$ < I2 ≤ $100\%$, it was considered as high heterogeneity. Suppose the P value was ≥.05 and I2 ≤ $40\%$, it indicated less heterogeneity among the included trials, and a fixed-effects model was used; if not, we used a random-effects model. When there is heterogeneity, we deal with it through these 2 approaches. Subgroup analysis was done based on age (≥55 and <60 years, ≥60 years), duration of T2DM (≥7 years, <7 years), dose (ERT 5 mg, ERT 15 mg), BMI (≥31, <31), HbA1c (≥$8.1\%$ and < $8.5\%$, <$8.1\%$) and eGFR (≥30 and <60, ≥60) (see Figures S1–S7, Supplemental Digital Content, http://links.lww.com/MD/I627; http://links.lww.com/MD/I628; http://links.lww.com/MD/I629; http://links.lww.com/MD/I630; http://links.lww.com/MD/I631; http://links.lww.com/MD/I632; http://links.lww.com/MD/I633, which showed the results of subgroup analysis). Sensitivity analysis was conducted to find the source of heterogeneity by eliminating the included literature one by one (see Figure S8, Supplemental Digital Content, http://links.lww.com/MD/I634, which showed the sensitivity analysis result). However, all subgroup and interaction analyses (P for interaction) in this meta-analysis were done to obtain more credible results. The Egger test evaluated publication bias in Stata SE17.0. $P \leq .05$ indicates possible publication bias (see Figure S9, Supplemental Digital Content, http://links.lww.com/MD/I635, which showed the result of the Egger test). ## 7.1. Characteristics of the included literature We searched for a total of 1516 papers. After removing 474 duplicates, 1042 remained. The titles and abstracts of these 1042 papers were screened, and 45 papers were retained for further review. Finally, after a detailed reading of these 45 papers, 7 met the inclusion criteria (Fig. 1). Figure 1.: PRISMA flow diagram of eligible randomized controlled trials. PRISMA = Preferred Reporting Items for Systematic Review and Meta-Analyses. The characteristics of the included trials are presented in Table 1. Finally, 7 RCTs were included in this meta-analysis, with a total of 11,091 T2DM patients.[16,18,21–25] Eleven thousand ninety-one patients with T2DM were randomly assigned to receive 5 mg of ERT, 15 mg of ERT, or a placebo. The mean age of the participants ranged from 54 to 68. Four RCTs included participants mainly from North America, South America, Europe, and South Africa, 1 RCT had subjects mainly from Asia (including mainland China, Hong Kong, Taiwan, South Korea, and the Philippines), and the other 2 RCTs did not give relevant information. The 7 RCTs’ glycated hemoglobin baseline averages varied from 8.0 to $8.21\%$. Six RCTs had baseline mean eGFR values between 46.6 and 99.3 mL·min−1·1.73 m−2, and no information was reported for the other RCT. All literature was published between 2015 and 2021. Table 1 provides more details for the remainder. Table 1 \| Study \| Year \| NCT number \| N \| Intervention \| Control \| Age (mean ± SD) \| Men (n) \| HbA1C (mean ± SD) (%) \| BMI (mean ± SD) (kg·m−2) \| eGFR (mean ± SD) (mL·min−1·1.73 m−2) \| Treatment duration (wk) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Amin et al \| 2015 \| NCT01059825 \| 328 \| ERT – 5mg \| Placebo \| 54.44 ± 8.63 \| 213 (64.9%) \| 8.11 ± 1.14 \| 30.4 ± 5.41 \| / \| 12 \| \| Steven et al \| 2017 \| NCT01958671 \| 461 \| ERT – 5mgERT – 15mg \| Placebo \| 56.4 ± 11.0 \| 261 (56.6%) \| 8.21 ± 0.98 \| 33.0 ± 6.7 \| 87.7 ± 18.6 \| 26 \| \| Samuel et al \| 2017 \| NCT02036515 \| 462 \| ERT – 5mgERT – 15mg \| Placebo \| 59.1 ± 9.0 \| 263 (56.9%) \| 8.0 ± 0.9 \| 30.8 ± 6.0 \| 87.9 ± 16.9 \| 52 \| \| George et al \| 2018 \| NCT01986855 \| 467 \| ERT – 5mgERT – 15mg \| Placebo \| 67.3 ± 8.6 \| 231 (49.5%) \| 8.2 ± 0.9 \| 32.5 ± 6.1 \| 46.6 ± 8.8 \| 52 \| \| Silvina et al \| 2019 \| NCT02033889 \| 621 \| ERT – 5mgERT – 15mg \| Placebo \| 56.6 ± 8.8 \| 288 (46.4%) \| 8.12 ± 0.90 \| 31.1 ± 4.7 \| 90.5 ± 19.3 \| 26 \| \| Linong et al \| 2019 \| NCT02630706 \| 506 \| ERT – 5mgERT – 15mg \| Placebo \| 56.5 ± 9.1 \| 281 (55.5%) \| 8.1 ± 0.9 \| 26.0 ± 3.2 \| 99.3 ± 19.7 \| 26 \| \| David et al \| 2021 \| NCT01986881 \| 8246 \| ERT – 5mgERT – 15mg \| Placebo \| 64.4 ± 8.1 \| 5769 (69.7%) \| 8.2 ± 1.0 \| 32.0 ± 5.4 \| 76.0 ± 20.9 \| 52 \| ## 7.2. Quality assessment of individual trials The Cochrane Collaboration tool was used to evaluate the included studies, and Figure 2A and B summarize the risk of bias. All the examined literature had low chance of biases related to random sequence generation, allocation concealment, blinded implementation quality, and other biases. Regarding the completeness of the data, 3 RCTs were low-risk, and the remaining 4 did not provide sufficient information to be judged. For reporting bias, only 1 RCT was unclear, and the remaining 5 were low-risk. Figure 2.: (A) Risk of bias graph and (B) risk of bias summary for included RCTs. RCT = randomized controlled trial. ## 7.3. Effect of ERT on laboratory changes in renal function For treatment durations up to 52 weeks, the analysis revealed a significant reduction in eGFR in the ERT group compared to the placebo group (SMD −0.60, $95\%$ CI: −1.02–−0.17, $$P \leq .006$$) (Fig. 3). Subsequently, we performed subgroup analyses for age (see Figure S1, Supplemental Digital Content, http://links.lww.com/MD/I627), duration of T2DM (see Figure S2, Supplemental Digital Content, http://links.lww.com/MD/I628), dose (see Figure S3, Supplemental Digital Content, http://links.lww.com/MD/I629), BMI (see Figure S4, Supplemental Digital Content, http://links.lww.com/MD/I630), glycated hemoglobin (see Figure S5, Supplemental Digital Content, http://links.lww.com/MD/I631), eGFR (see Figure S6, Supplemental Digital Content, http://links.lww.com/MD/I632), and length of treatment (see Figure S7, Supplemental Digital Content, http://links.lww.com/MD/I633), respectively. Subgroup analysis showed that in terms of eGFR, there was a more significant decline in those ≥60 years old (WMD −0.71, $95\%$ CI: −1.18–−0.24, $$P \leq .003$$) than in those ≥55 and <60 years old (WMD −0.13, $95\%$ CI: −1.10–0.85, $$P \leq .798$$); those with ≥7 years of T2DM duration (WMD −0.53, $95\%$ CI: −0.97–−0.10, $$P \leq .016$$) than those with <7 years (WMD −1.86, $95\%$ CI: −3.80–0.08, $$P \leq .060$$); those on ERT 15 mg (WMD −0.89, $95\%$ CI: −1.49–−0.29, $$P \leq .004$$) than those on 5 mg (WMD −0.31, $95\%$ CI: −0.91–0.29, $$P \leq .307$$). We also found that eGFR decreased more in those with BMI ≥ 31 kg·m−2 (WMD −0.77, $95\%$ CI: −1.22–−0.33, $$P \leq .001$$) than in those <31 kg·m−2 (WMD 1.32, $95\%$ CI: −0.13–2.78, $$P \leq .075$$); glycated hemoglobin ≥ $8.1\%$ and <$8.5\%$ (WMD −0.62, $95\%$ CI: −1.05–−0.18, $$P \leq .006$$) than those <$8.1\%$ (WMD −0.29, $95\%$ CI: −2.07–1.49, $$P \leq .751$$); those with baseline eGFR ≥ 30 and <60 mL·min−1·1.73 m−2 (WMD −1.58, $95\%$ CI: −2.70–−0.46, $$P \leq .006$$) than those ≥60 mL·min−1·1.73 m−2 (WMD −0.43, $95\%$ CI: −0.89–0.02, $$P \leq .063$$); and more in those treated for 52 weeks (WMD −0.62, $95\%$ CI: −1.07–−0.16, $$P \leq .008$$) than in those treated for 26 weeks (WMD −0.49, $95\%$ CI: −1.60–−0.62, $$P \leq .384$$). ERT significantly reduced eGFR for BMI ≥ 31 kg·m−2 compared with BMI < 31 kg·m−2, with a P interaction of 0.007. Figure 3.: Forest plot of effects of ertugliflozin on eGFR in patients with T2DM. eGFR = estimated glomerular filtration rate, T2DM = type 2 diabetes mellitus. ## 7.4. Effect of ERT on cardiovascular outcomes A total of 9503 patients were included in the 4 RCTs which reported associated cardiovascular events. AMI and AP are the main cardiovascular events reported in the 4 RCTs. The pooled analysis did not show significant changes in the following cardiovascular events associated with ERT compared with placebo. However, the risk of AP (RR 0.85, $95\%$ CI: 0.69–1.05, $$P \leq .497$$) showed a downward trend. There was no significant difference between ERT and placebo in the risk of AMI (RR 1.00, $95\%$ CI: 0.83–1.20, $$P \leq .333$$) (Fig. 4). Figure 4.: Forest plot of effects of ertugliflozin on the composite of AMI and AP of the T2DM participant. AMI = acute myocardial infarction, AP = angina pectoris, T2DM = type 2 diabetes mellitus. ## 8. Discussion T2DM has produced a considerable disease burden by raising the risk of cardiovascular, renal, and other consequences; it has become the most severe public health concern in China and throughout the world, and it is one of the most severe chronic illnesses endangering human health today.[26] According to published research, sodium-glucose cotransporter type-2 inhibitor (SGLT2i) is cardioprotective, and this protection is not dependent on its glucose-lowering actions.[27] There is also some indication that SGLT2i can protect the kidneys of T2DM patients. A meta-analysis found that SGLT2is lower the risk of proteinuria, acute kidney damage, and renal transplantation in T2DM patients.[28] ERT, a new generation of SGLT2i, has been proven to lower the risk of cardiovascular disease, particularly HHF hospitalization.[29] However, the effect on AMI and AP (including stable AP and unstable AP) has not been documented. Furthermore, ERT has been shown to impact eGFR in T2DM patients. The findings of the VERTIS MONO Phase A study revealed that after 26 weeks of therapy, the mean (SD) eGFR change from baseline was 0.5 (11.8) and −1.3 (10.1) mL·min−1·1.73 m−2 for ERT 5 and 15 mg, respectively, compared to a change of 1.4 (11.2) mL·min−1·1.73 m−2 for placebo.[22] VERTIS SITA2 showed similar findings.[19] Another result from the VERTIS CV trial showed that after 52 weeks of treatment, the mean (SD) eGFR change from baseline was −0.5 (13.0) and −1.2 (13.0) mL·min−1·1.73 m−2 for ERT 5 and 15 mg, respectively compared with a change of −0.3 (13.2) mL·min−1·1.73 m−2 with placebo.[20] As a result, the findings of previous studies on how ERT affects eGFR in T2DM patients are not entirely consistent. Therefore, this meta-analysis examined the available data to determine the relationship between ERT and associated cardiovascular events. In light of this, this meta-analysis also assessed the changes in eGFR and the risk of cardiovascular events in T2DM patients receiving ERT. According to our findings, there was no apparent difference between the effects of ERT and placebo on AMI and AP (including stable AP and unstable AP) in people with type 2 diabetes. Especially in AP, ERT tends to reduce the risk of occurrence. The overall findings indicate that ERT decreases the risk of these 2 cardiovascular events (RR 0.93, $95\%$ CI: 0.81–1.07, $$P \leq .398$$); however, this reduction is not statistically significant. Another meta-analysis revealed similar findings. Li Liu’s study concluded that ERT reduced systolic blood pressure by 2.57 mm Hg and diastolic blood pressure by 1.15 mm Hg in the T2DM population compared to the control group.[30] According to the findings of the HONEST research, those with high blood pressure and diabetes are 2.8 times more likely to suffer cardiovascular disease than those who only have diabetes.[31] These findings suggest that lowering blood pressure may decrease the risk of cardiovascular events in T2DM patients.[32] Our results showed that ERT significantly reduced eGFR in patients with T2DM (SMD −0.60, $95\%$ CI: −1.02–0.17, $$P \leq .006$$). This result was more reliable in T2DM patients with a BMI over 31 (P interaction = 0.007). In addition, people with T2DM and one of the following conditions also showed a significant decrease in eGFR after treatment with ERT: ≥60 years old, those with ≥7 years of T2DM duration, those on ERT 15mg, glycated hemoglobin ≥ $8.1\%$ and <$8.5\%$, those with baseline eGFR ≥ 30 and <60 mL·min−1·1.73 m−2, those treated for 52 weeks (All P values < 0.05). The mechanism may be that ERT inhibits the reabsorption of glucose by the renal tubules, which in turn leads to a decrease in the eGFR. In the long term, these effects of ERT may help to protect the metabolic function of renal tubular cells and delay the decline of eGFR.[33] These results suggest that those patients with T2DM who are overly obese, elderly, have had T2DM for a long time, have high baseline glucose levels, or have poor baseline renal function are more likely to experience a decrease in eGFR during treatment with ERT. Our study had certain limitations, although being quite comprehensive, and the subgroup and sensitivity analysis results were consistent with the main pooled results. Because of a lack of data from the original trial, therapy beyond 52 weeks could not be examined. The results are somewhat restricted because there was no comparison of ERT to other SGLT2is. As a result, more convincing results require more extended treatment duration data and comparisons with other SGLT2is. ## 9. Conclusion Our meta-analysis demonstrated that ERT was safe for AMI and AP. However, in terms of renal function, ERT produces a decline in eGFR over time, particularly in patients who are obese, old, have high basal glucose, and have a low baseline eGFR. As a result, while using ERT in these patients, greater attention should be paid to monitoring their renal function indicators to avoid renal damage. ## Author contributions Data curation: Chengyang Feng. Investigation: Chan Xu, Xuan Shi. 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--- title: Postoperative Infection and Revision Surgery Rates in Foot and Ankle Surgery Without Routine Prescription of Prophylactic Antibiotics authors: - Neal Huang - Daniel T. Miles - Connor R. Read - Charles C. White - Richard D. Murray - Andrew W. Wilson - Jesse F. Doty journal: JAAOS Global Research & Reviews year: 2023 pmcid: PMC9997784 doi: 10.5435/JAAOSGlobal-D-23-00015 license: CC BY 4.0 --- # Postoperative Infection and Revision Surgery Rates in Foot and Ankle Surgery Without Routine Prescription of Prophylactic Antibiotics ## Body Surgical site infections (SSIs) are associated with increased patient morbidity and increased healthcare costs.1,2 The Centers for Disease Control and Prevention has estimated an annual cost of $3.3 billion and a mortality rate of $3\%$ related to SSI.3 Specifically, the incidence of foot and ankle SSI in a systematic review was $2\%$ to $4\%$ with rates reported as high as $9\%$.4 Antibiotics are routinely prescribed in the outpatient setting to manage or prevent SSI, although this may not be without some degree of risk. Antibiotic-resistant organisms are an increasing and evolving problem when trying to treat clinical infections. Recent Centers for Disease Control and Prevention estimates suggest that two million annual antibiotic-resistant infections lead up to 23,000 yearly deaths.5 Physicians and healthcare systems must be cognizant of their routine prescribing practices and the possible implications of such. Many healthcare institutions now have initiatives to decrease antimicrobial resistance by developing antibiotic stewardship programs. The development of structured guidelines and clinical pathways further promotes the safest prescribing practices. For decades, general surgery and trauma publications were referenced to suggest the routine efficacy of perioperative surgical antibiotic prophylaxis in preventing SSI in major operations.6-8 Ruta et al9 conducted a survey of foot and ankle surgeons regarding postoperative antibiotics for outpatient surgeries. Seventy-five percent of responding surgeons reported the use of oral postoperative antibiotic prophylaxis in some patients while $16\%$ of respondents prescribed an oral postoperative antibiotic to all patients. A paucity of literature regarding the most efficacious utility of antibiotic prophylaxis in foot and ankle surgery leaves surgeons without clear guidance on antibiotic administration. With the growing pressure of antimicrobial stewardship in medicine, it is necessary to establish whether current prescribing patterns are effective or necessary in limiting infections. The purpose of this study was to determine the incidence of SSI and subsequent effect on revision surgery rates in patients not receiving oral postoperative antibiotic prophylaxis. Secondary identification of high-risk comorbidities that predispose patients to getting an SSI may warrant a lower threshold for antibiotic prophylaxis. The authors have hypothesized that outpatient surgery without routine postoperative antibiotic prophylaxis leads to acceptable surgical outcomes. ## Introduction: Surgical site infections (SSIs) are associated with patient morbidity and increased healthcare costs. Limited literature in foot and ankle surgery provides guidance about routine administration of postoperative antibiotic prophylaxis. The purpose of this study was to examine the incidence and revision surgery rates of SSI in outpatient foot and ankle surgeries in patients not receiving oral postoperative antibiotic prophylaxis. ### Methods: A retrospective review of all outpatient surgeries ($$n = 1517$$) conducted by a single surgeon in a tertiary referral academic center was conducted through electronic medical records. Incidence of SSI, revision surgery rate, and associated risk factors were determined. The median follow-up was 6 months. ### Results: Postoperative infection occurred in $2.9\%$ ($$n = 44$$) of the surgeries conducted, with $0.9\%$ of patients ($$n = 14$$) requiring return to the operating room. Thirty patients ($2.0\%$) were diagnosed with simple superficial infections, which resolved with local wound care and oral antibiotics. Diabetes (adjusted odds ratio, 2.09; $95\%$ confidence interval, 1.00 to 4.38; $$P \leq 0.049$$) and increasing age (adjusted odds ratio, 1.02; $95\%$ confidence interval, 1.00 to 1.04; $$P \leq 0.016$$) were significantly associated with postoperative infection. ### Discussion: This study demonstrated low postoperative infection and revision surgery rates without the routine prescription of prophylactic postoperative antibiotics. Increasing age and diabetes are signficant risk factors for developing a postoperative infection. ## Study Population A retrospective cohort study was approved by the university scientific review committee and the institutional review board at the hospital system where all procedures were conducted. An electronic health record query identified 1685 patients who underwent outpatient elective surgery with a single fellowship-trained orthopaedic foot and ankle surgeon over a four-year period from January 1, 2017, to May 1, 2020. Inclusion criteria consisted of patients of all ages, and any Current Procedural Terminology code, undergoing same-day discharge surgical procedures. Patients who were admitted to the hospital postoperatively for 23-hour observation, inpatient stays, and those who underwent a surgical procedure for a preexisting infection were excluded. All patients received preoperative antibiotics of cefazolin, and if they were allergic to cephalosporins, vancomycin was to be given. Per surgeon preference, no patients received any immediate postoperative antibiotics. In total, 1517 patients met the inclusion criteria (Table 1). The median age was 47 years (interquartile range [IQR], 32 to 59 years). The median body mass index (BMI) was 29.6 kg/m2 (IQR, 25.2 to 34.8 kg/m2). Male patients represented $34.1\%$ of the cohort, and female patients represented $65.9\%$. The median follow-up was 6 months (IQR, 3 to 9 months). Table 1 \| Characteristic \| Total(n = 1517) \| Infection \| Infection.1 \| Infection.2 \| \| --- \| --- \| --- \| --- \| --- \| \| Characteristic \| Total(n = 1517) \| Overall (n = 44) \| Superficial(n = 30) \| Deep(n = 14) \| \| Age \| \| \| \| \| \| Median (IQR) \| 47 (32 to 59) \| 52 (45 to 61) \| 57 (46 to 61) \| 46 (41 to 61) \| \| <20 \| 155 (10.2%) \| 1 (2.3%) \| 1 (3.3%) \| 0 (0.0%) \| \| 20-39 \| 396 (26.1%) \| 5 (11.4%) \| 2 (6.7%) \| 3 (21.4%) \| \| 40-59 \| 615 (40.5%) \| 23 (52.3%) \| 16 (53.3%) \| 7 (50.0%) \| \| ≥60 \| 351 (23.1%) \| 15 (34.1%) \| 11 (36.7%) \| 4 (28.6%) \| \| Sex \| \| \| \| \| \| Male \| 518 (34.1%) \| 20 (45.5%) \| 14 (46.7%) \| 6 (42.6%) \| \| Female \| 999 (65.9%) \| 24 (54.5%) \| 16 (53.3%) \| 8 (57.1%) \| \| Follow-up (mo) \| 6 (3 to 9) \| 4.5 (3 to 10) \| 4 (3 to 7.2) \| 5.5 (2.7 to 12.2) \| \| BMI \| \| \| \| \| \| Median (IQR) \| 29.6 (25.2 to 34.8) \| 29.6 (25.2 to 34.8) \| 32.6 (26.5 to 40.4) \| 31.4 (25.3 to 35.7) \| \| <25 \| 347 (22.9%) \| 8 (18.2%) \| 5 (16.7%) \| 3 (21.4%) \| \| 25-29 \| 436 (28.7%) \| 11 (25.0%) \| 8 (26.7%) \| 3 (21.4%) \| \| 30-39 \| 566 (37.3%) \| 15 (34.1%) \| 9 (30.0%) \| 6 (42.9%) \| \| ≥40 \| 168 (11.1%) \| 10 (22.7%) \| 8 (26.7%) \| 2 (14.3%) \| \| Preoperative antibiotics \| \| \| \| \| \| Cefazolin \| 1428 (94.1%) \| 43 (97.7%) \| 29 (96.7%) \| 14 (100.0%) \| \| Clindamycin \| 2 (0.1%) \| 0 (0.0%) \| 0 (0.0%) \| 0 (0.0%) \| \| Vancomycin \| 87 (5.8%) \| 1 (2.3%) \| 1 (3.3%) \| 0 (0.0%) \| \| Diabetes \| 170 (11.2%) \| 11 (25.0%) \| 9 (30.0%) \| 2 (14.3%) \| \| Tobacco use \| 466 (30.7%) \| 18 (40.9%) \| 12 (40.0%) \| 6 (42.9%) \| \| Hypertension \| 468 (30.9%) \| 19 (43.2%) \| 12 (40.0%) \| 7 (50.0%) \| \| COPD \| 20 (1.3%) \| 1 (2.3%) \| 1 (3.3%) \| 0 (0.0%) \| \| Cardiovascular disease \| 47 (3.1%) \| 2 (4.5%) \| 2 (6.7%) \| 0 (0.0%) \| \| Stroke \| 16 (1.1%) \| 1 (2.3%) \| 0 (0.0%) \| 1 (7.1%) \| \| Cancer \| 59 (3.9%) \| 1 (2.3%) \| 0 (0.0%) \| 1 (7.1%) \| \| Hypothyroidism \| 92 (6.1%) \| 5 (11.4%) \| 4 (13.3%) \| 1 (7.1%) \| \| Thyroid disease \| 58 (3.8%) \| 0 (0.0%) \| 0 (0.0%) \| 0 (0.0%) \| \| Rheumatoid arthritis \| 41 (2.7%) \| 2 (4.5%) \| 1 (3.3%) \| 1 (7.1%) \| \| Gout \| 28 (1.8%) \| 3 (6.8%) \| 3 (10.0%) \| 0 (0.0%) \| \| Anemia \| 24 (1.6%) \| 2 (4.5%) \| 1 (3.3%) \| 1 (7.1%) \| \| Neuropathy \| 21 (1.4%) \| 2 (4.5%) \| 2 (6.7%) \| 0 (0.0%) \| \| DVT history \| 16 (1.1%) \| 0 (0.0%) \| 0 (0.0%) \| 0 (0.0%) \| \| MRSA history \| 11 (0.7%) \| 0 (0.0%) \| 0 (0.0%) \| 0 (0.0%) \| \| Hyperthyroidism \| 7 (0.5%) \| 0 (0.0%) \| 0 (0.0%) \| 0 (0.0%) \| ## Measured Outcomes The primary outcome measured was SSI rate and the subsequent need for revision surgery because of infection. Parameters reviewed included age, sex, BMI, tobacco use, medical comorbidities, procedure type, length of postoperative follow-up, need for wound care, and revision surgery rates. Patients who were subsequently prescribed antibiotics, because of wound healing concerns, at the time of outpatient follow-up, were further subcategorized as prophylactic antibiotics, superficial infections, and deep infections. ## Definition of Infection Superficial infections were defined by erythema, wound drainage, suture abscesses, and excessive warmth at the surgical site. Superficial wound dehiscence without any clinical signs of infection were not counted as infected, and if antibiotics were given, these patients were categorized as prophylactic antibiotics. Deep infections were delineated as those who required a return to the operating room for irrigation and débridement. ## Statistical Analysis Statistical analyses were conducted with IBM SPSS Statistics version 27.0 (IBM). Tests were conducted 2-tailed and a $P \leq 0.05$ defined statistical significance. Patient characteristics are expressed as frequencies (%) and median (IQR). Normality was assessed by using the Shapiro-Wilk test ($P \leq 0.05$) and Q-Q plot. Chi-square test, Fisher's exact test, and univariate logistic regression were used to determine signficant associations between infection and risk factors. To adjust for confounding, multivariate logistic regression was used with clinically and statistically significant variables. Linearity of continuous variables was assessed with the Box-Tidwell procedure. Model fit was assessed with the Hosmer-Lemeshow test ($P \leq 0.05$). Multicollinearity was absent, as assessed by tolerance values greater than 0.1. Fewer than five and one percent of the standardized residuals laid outside two and three standard deviations. Cook's distance values more than 1.0 were absent. ## Results Forty-four patients ($2.9\%$) experienced postoperative infection, and 14 patients ($0.9\%$) required returning to the operating room because of infection. Of these 14 cases, 10 index surgeries involved the ankle and hindfoot. These procedures consisted of two ankle fractures, one pilon fracture, one talus fracture, two Achilles repairs, two peroneal tendon repairs with lateral ligament repairs, one tarsal coalition excision, and one anterior tibialis tendon repair. The remaining four cases were forefoot deformity corrections. The median time between index surgery and revision surgery was 77 days (IQR, 39 to 169 days). Eleven of the 14 revision surgery patients ($78.6\%$) were older than 40 years. Eight patients ($57.2\%$) had a BMI greater than 30 kg/m2. Seven patients ($50.0\%$) had hypertension, and six ($42.9\%$) used tobacco. Thirty patients ($2.0\%$) were diagnosed as simple superficial infections that resolved with local wound care. Treatment also consisted of a prescription of Bactrim $\frac{800}{160}$ mg two times per day for 7 to 10 days (27 of 30) or clindamycin 300 mg three times per day for 7 days (2 of 30). One superficial infection resolved without antibiotics. Twenty-seven of the superficial patients ($90.0\%$) were older than 40 years. Seventeen patients ($56.7\%$) had a BMI greater than 30 kg/m2. Twelve patients ($40.0\%$) had hypertension and used tobacco. Diabetes was the most signficant risk factor and accounted for $30.0\%$ (9 of 30) of the superficial infections (adjusted odds ratio [OR], 2.53; $95\%$ confidence interval [CI], 1.09 to 5.87; $$P \leq 0.031$$). Overall, patients older than 40 years accounted for $86.4\%$ of the postoperative infections (38 of 44), highlighting increasing age as a significant predictor (adjusted OR, 1.02; $95\%$ CI, 1.00 to 1.04; $$P \leq 0.016$$). Eleven of the 44 patients ($25.0\%$) had diabetes, which was the most signficant risk factor in both univariate and multivariate analyses (adjusted OR, 2.09; $95\%$ CI, 1.00 to 4.38; $$P \leq 0.049$$) (Tables 2 and 3). Ten patients ($22.7\%$) had a BMI greater than 40 kg/m2. Cefazolin was the most frequently used preoperative antibiotic ($97.7\%$). Eighteen patients ($1.1\%$) developed superficial wound dehiscence without clinical signs of infection, of whom 10 patients received prophylactic antibiotics for treatment. ## Discussion Based on historical data, systemic perioperative antibiotic prophylaxis has been routinely prescribed for orthopaedic procedures for decades. This study demonstrated low infection and revision surgery rates after elective outpatient foot and ankle surgery without the routine use of postoperative prophylactic antibiotics. Increasing age and diabetes were signficant risk factors of postoperative infection. No other patient demographic or comorbidity showed an increased risk of superficial infection or deep infection requiring débridement in the operating room. We report an infection rate of $2.9\%$ and a revision surgery rate for infection of $0.9\%$, which are within the previously reported parameters (range, $3.0\%$ to $4.8\%$) using postoperative antibiotic prophylaxis.10-12 There are limited studies in foot and ankle research for the use of routine prophylactic postoperative antibiotics. Frederick et al13 reported a $2.3\%$ infection rate with $0.8\%$ deep infection rate in 1227 patients who underwent foot and ankle surgery and did not receive postoperative oral antibiotics. No significant difference was found when this group was compared with a postoperative antibiotic prescription group with a $3.4\%$ infection rate ($$P \leq 0.08$$). Our results support these findings. Multiple modifiable and nonmodifiable patient comorbidities have been suggested to increase the risk of postoperative infection.10-12 Diabetes with increased hemoglobin A1c preoperatively is a modifiable risk factor that has a direct correlation with postoperative infection.10,14-16 Patients with diabetes were at an increased risk of developing a postoperative infection (OR, 2.75, $$P \leq 0.007$$) in a univariate analysis and were at an increased risk of superficial infection but failed to be associated with return to the operating room. This lack of significance is likely due to the low number of cases requiring revision surgery. Multivariate regression analysis demonstrated increased risk (adjusted OR, 2.09; $$P \leq 0.049$$) in developing postoperative infections when adjusting for age, BMI, and sex. Obesity is another risk factor that has been implicated in postoperative infection and wound complications across orthopaedics.17 This study did find that $56.8\%$ of the infection patients had a BMI of >30 kg/m2 and $22.7\%$ had a BMI of >40 kg/m2. Olsen et al18 found having a BMI of >30 kg/m2 to be a signficant predictor of both superficial and deep infections (OR, 1.68) in the treatment of ankle fractures. To our knowledge, this is the first study to show the detrimental effect that obesity, independent of diabetes, can have on patients undergoing elective foot and ankle surgery. The implication of increasing age on postoperative infection has been heavily disputed. The effect of age on infection is important to examine because it is a nonmodifiable risk factor. Multiple studies have shown associations with increasing age and postoperative infection.19,20 This suggests a positive role for prophylactic antibiotic considerations in the elderly. However, this does not come without signficant risk as a population disproportionately affected by opportunistic infections from antibiotics.21 Our study demonstrated an age-related propensity for postoperative superficial infections that were treated with antibiotics and subsequently resolved without additional sequelae. Patients who were 40 years or older accounted for $86.4\%$ of the postoperative infections. Carl et al11 found a similar signficant association with increasing age when comparing the average age of the infection group (age 55 years) with that of the no-infection group (age 45 years). Wiewiorski et al,20 in a prospective study, demonstrated age 60 years and older to be an independent risk factor of wound complications in an elective foot and ankle population. Age as a causative factor of infection is difficult to determine because older individuals typically have more comorbidities. However, increasing age was significant in developing a superficial infection after correcting for confounding comorbidities (adjusted OR, 1.03 per year; $$P \leq 0.019$$). This suggests that it would be beneficial for prophylactic antibiotics to be prescribed in an older population because they are at higher risk of superficial infections. Our results add to the growing body of literature that age likely plays a role in developing infection postoperatively. This study is limited by its retrospective design and lack of a true control group. Historical infection rates were used as a control group, but population demographics may be different. This study also depended heavily on electronic medical records with the presumption of accurate patient data and follow-up infection documentation. Therefore, there was no standardization of antibiotic prescription practice that may leave room for subjective judgment. Multivariate logistic regression analysis is also susceptible to bias because it cannot adjust for all unidentified risk factors that could be clinically relevant. In addition, quantification of diabetes was not conducted. To the authors’ knowledge, this is the largest population on the topic to date and the study's main strength. An additional strength is the variety of procedures included in this study represent those typically experienced by foot and ankle surgeons. This study demonstrated low postoperative infection and revision surgery rates without prescribing prophylactic postoperative antibiotics in outpatient foot and ankle surgery patients. We identified increasing age and diabetes as signficant risk factors for developing a postoperative infection. No other patient demographic or comorbidity showed an increased risk of superficial infection or deep infection. These findings suggest that outpatient foot and ankle surgery without routine postoperative antibiotic prophylaxis leads to an acceptable postoperative SSI incidence. ## References 1. Astagneau P, Rioux C, Golliot F, Brücker G. Morbidity and mortality associated with surgical site infections: Results from the 1997-1999 INCISO surveillance. J Hosp Infect (2001.0) 48 267-274. PMID: 11461127 2. Ban KA, Minei JP, Laronga C. American college of surgeons and surgical infection society: Surgical site infection guidelines, 2016 update. J Am Coll Surg (2017.0) 224 59-74. PMID: 27915053 3. Surgical site infection event 4. Modha MRK, Morriss-Roberts C, Smither M, Larholt J, Reilly I. Antibiotic prophylaxis in foot and ankle surgery: A systematic review of the literature. J Foot Ankle Res (2018.0) 11 61. 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--- title: 'Clinical efficacy of supplementing qi dispelling wind and activating blood circulation method in the treatment of IgA nephropathy: A meta-analysis' authors: - Zhiyu Pan - Mingming Zhao - Meiying Chang - Xiujie Shi - Sijia Ma - Yu Zhang journal: Medicine year: 2023 pmcid: PMC9997787 doi: 10.1097/MD.0000000000033123 license: CC BY 4.0 --- # Clinical efficacy of supplementing qi dispelling wind and activating blood circulation method in the treatment of IgA nephropathy: A meta-analysis ## Background: IgA nephropathy (IgAN) is a common primary glomerular disease, and supplementing qi dispelling wind and activating blood is commonly used as a treatment method in Chinese medicine. However, the existing studies have small sample sizes. This study aimed to use a meta-analysis to explore the clinical efficacy of this method and to systematically introduce this effective treatment. ### Methods: We searched for randomized controlled trial studies on supplementing qi dispelling wind and activating blood circulation methods for IgAN indexed in the China National Knowledge Infrastructure, Wanfang Data, Chongqing VIP, SinoMed, PubMed, EMBASE, and Web of Science databases, which were interrogated from database inception to January 2022. Combining the inclusion and exclusion criteria to screen the literature, we included a total of 15 eligible studies; the quality of the included studies was evaluated using the risk of bias assessment tool of the Cochrane System Revies Manual 5.4. The outcome indexes were extracted, and a meta-analysis was performed using Review Manager 5.4 software. ### Results: Fifteen articles were included in this review. A meta-analysis of the results led to the conclusion that supplementing qi dispelling wind and activating blood circulation prescription has beneficial effects on the total effective rate [odds ratios = 3.95, $95\%$ confidence interval (CI) 2.76–5.67], and can reduce 24-hour urinary protein quantity (mean deviation = −0.35, $95\%$ CI −0.54 to −0.16) and serum creatinine (mean deviation = −15.41,$95\%$ CI −28.39 to −2.44) without impact normal level of alanine transaminase, hemoglobin, and serum albumin. ### Conclusions: Supplementing qi dispelling wind and activating blood can significantly improve renal function and reduce 24-hour urinary protein quantity levels in patients with IgAN compared to the use of non-Chinese medicine treatment. This finding provides a rationale for using this method in the clinical treatment of IgAN. ## 1. Introduction IgA nephropathy is the most common primary glomerular disease in the world, accounting for $47.5\%$ to $52.66\%$ of all glomerular diseases and has a significant increasing trend.[1]Most patients follow an asymptomatic to less symptomatic course and GFR loss.[2] Up to $40\%$ of these patients eventually develop end-stage renal failure within 10 to 20 years.[3] Studies have shown that the lower the level of proteinuria in patients with IgA nephropathy, the lower the risk of end-stage renal disease.[4–6] As mentioned in the 2021 kidney disease: improving global outcomes guidelines, patients with 0.75 to 1g/day have a higher risk of renal function decline, and the current treatment for IgA nephropathy is mainly renin-angiotensin-aldosterone system (RAAS) blockade.[7]Long-term clinical practice has shown that Chinese medicine has advantages in combination with conventional treatment for IgA nephropathy. It can not only reduce proteinuria and protect renal function but also reduce the occurrence of adverse events.[8–10] Supplementing qi dispelling wind and activating blood method (YQH method) is widely used in the Chinese treatment of IgA nephropathy; however, due to the small sample sizes and different research focuses,[11,12] it is difficult to make accurate judgments on the therapeutic effects of YQH prescription in the treatment of IgA nephropathy. Hence, to further identify the therapeutic effects of YQH prescription on IgA nephropathy, 15 randomized controlled trials (RCTs) were selected using this method, and a meta-analysis was conducted to provide a theoretical basis for the treatment of IgA nephropathy using YQH prescription. ## 2.1. Literature retrieval We performed a literature search of PubMed, EMBASE, Web of Sciences, China Biology Medicine disc (CBM), China National Knowledge Infrastructure, VIP, and Wanfang Data. Relevant articles were searched using the following search terms: “IgA Nephropathy” or “Berger’s Disease” and “Chinese Medicine” and “Clinical Trial.” ## 2.3. Literature screening Two researchers independently screened the studies based on the inclusion criteria. The titles and abstracts were first read to exclude studies that did not meet the inclusion criteria. If a disagreement was encountered, the decision was made at the discretion of a third expert. ## 2.4. Data extraction First author, year of publication, sample age, gender, treatment regimens for the treatment and control groups, treatment period, outcome measurements, composing of YQH prescription. All data were cross-checked and transferred to the RevMan software (5.4). ## 2.5. Quality assessment Two reviewers independently used the Cochrane Handbook for Systematic Reviews of Interventions to evaluate the risk of bias of the included studies. The criteria were as follows: Random sequence generation; Allocation concealment; Blinding of participants and personnel; Blinding of outcome assessment; Incomplete outcome data; Selective reporting, and; Other bias. Each criterion has 3 degrees: low, high, and unclear risk of bias. ## 2.6. Statistical analysis Outcome indicators were analyzed using Review Manager (5.4) provided by the Cochrane Collaboration Software, and heterogeneity was assessed using the Chi-square test. Heterogeneity analysis results of P ≤.05 or I²≥$50\%$ indicated the presence of heterogeneity in multiple independent studies, which were analyzed using a random effects model. Heterogeneity test results of $P \leq .05$ or I² < $50\%$ indicated that there was no heterogeneity in multiple independent trials, and a fixed effect model was adopted. Funnel plots were used to assess potential reporting bias when more than 10 eligible studies were included. Count data was expressed as $95\%$ confidence interval (CI) and odds ratios (OR). A sensitivity analysis was conducted to assess the robustness of the combined effects of the included studies. ## 3. Results Literature search: A total of 158 studies were identified from 7 English and Chinese databases (Fig. 1). After removing the duplicates, 108 articles were selected. After careful reading of the abstracts, 87 articles were kept based on the inclusion. We retrieved and reviewed the full-text articles. Sixty-three studies were excluded owing to factors such as duplicate publications and inability to obtain available data. Twenty-four RCTs of them were eligible. Nine articles were excluded because they did not provide a complete drug composition or had an inappropriate control group design. Fifteen eligible randomized controlled studies were included in this meta-analysis. The characteristics of the selected studies are presented in Table 1, and the prescriptions are presented in the Supplemental Digital Content, see table, Supplemental Digital Content http://links.lww.com/MD/I558, which demonstrates the medicine composition of the included prescriptions. ## 3.1. Quality evaluation of the included literature In terms of random assignment, 7 of the 15 studies were classified as unclear risk because they only mentioned “random. ,”[13–19] and 2 studies were considered high risk.[20,21] Only 4 studies were rated as low risk due to allocation concealment.[22–25] Three of the 15 studies reported a blinded process; they were rated as low risk.[22–24] All 15 studies were rated as low risk due to the availability of complete outcome data. Only 3 studies reported bias, they were rated as high risk.[14,18,24] The other bias was categorized as unclear because of insufficient information to assess the risk. The quality evaluation of the included articles was dominated by low risk scores. However, due to the proportion of unclear risk, and the presence of high risk items which are mainly found in allocation concealment and selective reporting. The overall quality of the studies included in this review were not high, respectively (Figs. 2 and 3). Figure 2.: Bar chart of the risk of bias assessment of the included literature. Figure 3.: Risk of bias assessment of the included literature. ## 3.2.1. Total effective rate. Thirteen studies reported the total effective rate of YQH methods in patients with IgA nephropathy.[13–19,21–23,25–27] Forest plots obtained from the meta-analysis showed an OR of 3.95, $95\%$ CI of 2.76 to 5.67, and I²=$0\%$, which indicates that the overall effectiveness of the experimental group was significantly higher than that of the control group. ( Fig. 4A) Figure 4.: Forest plot showing the effect of YQH method for IgA nephropathy. (A) Total effective rate. (B) 24hPRO. (C) RBC-M. (D) SCr. (E) BUN. (F) ALT. (G) ALB. (H) Hb. (I) CCr. 24hPRO = 24-hour urinary protein quantity, BUN = blood urea nitrogen, CCr = endogenous creatinine clearance, Hb = hemoglobin, RBC-M = urine sediment erythrocyte count, SCr = serum creatinine, YQH method = Supplementing qi dispelling wind and activating blood method. ## 3.2.2. 24-hour urinary protein quantity (24hPRO). Thirteen articles reported changes in 24hPRO[13–19,21–23,25–27] with an effect size of mean deviation (MD) = −0.35, $95\%$ CI −0.54 to −0.16 based on forest plots, and a test for effect size of $Z = 3.65$, $$P \leq .0003$$; heterogeneity analysis of ($P \leq .00001$), I²=$94\%$, which suggests heterogeneity in the 13 articles and the need for a random effect model. The aggregated results of 13 RCTs suggested that YQH prescription showed favorable effects for decreasing 24hPRO in IgA nephropathy. ( Fig. 4B) ## 3.2.3. Urine sediment erythrocyte count (RBC-M). According to the RBC-M changes in the forest plot, the effect size of the 5 selected trials[13,16,18,25,26] was MD = −0.87 and $95\%$ CI −2.29 to 0.54; the test of the effect size was $Z = 1.21$, $$P \leq .23$$; and the heterogeneity analysis ($P \leq .00001$), I²=$96\%$ suggests that there is heterogeneity in 5 articles; therefore, a random effect model was used. This illustrates that the RBC-M levels of the experimental group were not notably different from those of the control group ($P \leq .05$). ( Fig. 4C) ## 3.2.4. Serum creatinine (SCr). Eight articles reported changes in SCr[15–17,19,22,23,26,27] with an effect size of MD = −13.12, $95\%$ CI −23.83 to −2.40 based on forest plots, and a test for effect size of $Z = 2.4$ and $$P \leq .02$$; heterogeneity analysis of ($P \leq .00001$), I²=$92\%$, which suggests heterogeneity in the 8 articles and the need for a random effect model. The aggregated results of 8 RCTs suggested that YQH prescription showed favorable effects in decreasing serum creatinine of IgA nephropathy. ( Fig. 4D) ## 3.2.5. Blood urea nitrogen (BUN). According to the BUN changes in the forest plot, the effect size of the 4 selected trials[15,16,19,23] was MD = 0.10, $95\%$ CI −0.71 to 0.90, the test of the effect size was $Z = 0.23$ and $$P \leq .82$$, and heterogeneity analysis ($P \leq .00001$), I²=$89\%$ suggests that there is heterogeneity in 4 articles; therefore, a random effect model was used. This illustrates that the BUN levels of the experimental group were not notably different from those of the control group ($P \leq .05$). ( Fig. 4E) ## 3.2.6. Alanine transaminase (ALT). Three articles reported changes in ALT[15,20,22] with an effect size of MD = 0.77 and $95\%$ CI −4.61 to 6.15 based on forest plots, and a test for effect size of $Z = 0.28$ and $$P \leq .78$$; heterogeneity analysis of ($P \leq .03$), I²=$71\%$, which suggests heterogeneity in the 3 articles and the need for a random effect model. This illustrates that the ALT levels of the experimental group were not notably different from those of the control group. ( Fig. 4F) ## 3.2.7. ALB. According to the ALB changes in the forest plot, the effect size of the 4 selected trials[16,17,20,23] was MD = 2.26 and $95\%$ CI −1.97 to 6.49; the test of the effect size was $Z = 1.05$ and $$P \leq .30$$; and the heterogeneity analysis ($P \leq .00001$), I²=$90\%$, suggests that there is heterogeneity in 4 articles; therefore, a random effect model was used. This illustrates that the ALB levels in the experimental group were not notably different from those of the control group ($P \leq .05$). ( Fig. 4G) ## 3.2.8. Hemoglobin (Hb). Three articles reported changes in ALT[20,22,23] with an effect size of MD = 0.42 and $95\%$ CI −0.57 to 1.41 base on forest plots, and a test for effect size of $Z = 0.83$ and $$P \leq .41$$; heterogeneity analysis of ($$P \leq .76$$), I²=$0\%$, which suggests no heterogeneity in the 3 articles and the need for a fixed effect model. This illustrates that the Hb levels of the experimental group were not notably different from those of the control group (Fig. 4H). ## 3.2.9. Endogenous creatinine clearance (CCr). According to the CCr changes in the forest plot, the effect size of the 3 selected trials[13,19,26] was MD = −0.28 and $95\%$ CI −4.50 to 3.93; the test of the effect size was $Z = 0.13$, $$P \leq .90$$; and the heterogeneity analysis ($$P \leq .43$$), I²=$0\%$ suggests that there is no heterogeneity in 3 articles; therefore, a fixed effect model was used. This illustrates that the CCr levels of the experimental group were not notably different from those of the control group ($P \leq .05$). ( Fig. 4I) ## 3.2.10. Publication bias. Funnel plots were used to measure publication bias, and 24hPRO and total effective rate had > 10 articles with an asymmetric distribution of their funnel plots, indicating possible publication bias. ( Fig. 5) Figure 5.: Funnel plots for publication bias. (A) 24hPRO. (B) Total effective rate. 24hPRO = 24-hour urinary protein quantity, ALT = alanine transaminase. ## 4. Discussion IgA nephropathy is an immune complex-mediated primary glomerular disease characterized by IgA deposition in the mesangial region and proliferation of mesangial cells. It is the most common type of chronic nephritis, with clinical manifestations of mild to moderate proteinuria and microscopic hematuria.[28] Approximately $40\%$ of patients develop end stage renal disease after the diagnosis of IgA nephropathy,[3] and persistent unremitting proteinuria is a risk factor for the deterioration of renal function in patients with IgA nephropathy.[4–6] kidney disease: improving global outcomes recommends the use of basic therapies, such as RAAS blockers to control blood pressure, including angiotensin converting enzyme inhibitors or angiotensin receptor antagonists, for IgA nephropathy with 24 hours urine protein quantification > 1 g.[7] However, in clinical practice, angiotensin converting enzyme inhibitors and angiotensin receptor antagonists have limited efficacy in reducing proteinuria in IgA nephropathy,[7,29] the results are not as good as they should be. Many clinical patients with IgA nephropathy have difficulty achieving complete remission of proteinuria after treatment with RAAS blockers. Therefore, it is important to actively seek to reduce proteinuria in IgA nephropathy based on conventional treatment in combination with Chinese medicine to stop the process of pathological damage.[10] According to traditional Chinese medicine, Qi deficiency, blood stasis, and wind evil are present throughout the development of IgA nephropathy.[30–33] The main pathogenesis is the Qi deficiency of spleen and kidney that is the key internal factors,[34,35] and wind evil disturb the collaterals that is the initiating factor,[33] and blood stasis aggravate the progression of the disease in the later stages.[36] The functions of the spleen and kidney play a role in this disease, for the essence of the grain and water relies on the governing roles of them to travel through the veins and channels. If they have dysfunction, the essence of grain and water will leak out in the urine, which may manifest as proteinuria and hematuria.[37] As the Inner Canon of Huangdi said, wind evil is a guide for various diseases and is characterized by opening-dispersing, migrant, and variable.[38] Patients with IgA nephropathy often have cold, tonsillitis, or other illnesses at the beginning of the disease.[39,40] The kidney meridians follow the throat and tongue, so wind evil can enter the kidney through the meridians and disturb the function of the kidney, resulting in foamy urine and proteinuria.[41] Wind evil is often combined with other evils, such as heat and dampness. It can further damage the function of kidney, so the IgA nephropathy can be recurrent, delayed, and difficult to cure. The basic functions of the kidney are conducted by the glomerulus and tubules. The glomerulus is a mass of capillaries, and the tubules are surrounded by vessels. The collaterals of the kidney are similar to the glomerulus in structure and function.[42,43] Blood stasis in collaterals can cause proteinuria and renal hypofunction, which makes the disease prolonged and difficult to treat.[44] In our study, we hoped to find out the efficacy of the YQH method in the treatment of IgA nephropathy by meta-analysis. This was previously unexamined. We analyzed the results of 24hPRO, SCr, nitrogen, liver function, and albumin in the 15 included papers by RevMan, and found that the OR of total effective rate was 3.95 with $95\%$ CI of 2.76 to 5.67; The MD value of 24hPRO was −0.35, $95\%$ CI was −0.54 to −0.16, and the MD value of SCr was −13.12, $95\%$ CI was −23.83 to −2.4, which means, for IgA nephropathy, the YQH method combined with conventional treatment had more significant clinical efficacy than conventional treatment used alone. However, there was no significant improvement in serum levels of BUN and CCr. The results also showed that the application of YQH method did not increase the serum level of ALT, nor does it decrease the serum level of Hb and ALB, which means that the method did not have an adverse effect on patients with IgA nephropathy. There was heterogeneity in 24-hour urine protein and serum creatinine levels, and the sources of heterogeneity may come from; Different cycles of medication; The baseline levels of the included patients were inconsistent; The drugs used in the control group were different in each study; The choice of Chinese medicine when using YQH prescription was different, and; Inconsistencies in detection methods. This study found good clinical efficacy of the YQH method in patients with IgA nephropathy, it can reduce 24hPRO and SCr without impact the normal level of ALT Hb and serum albumin. There are also limitations. For example, the sample sizes of the included studies were small, and the methodological quality of the RCTs was not high, which may have had an impact on the treatment outcome. Although the language of the study literature was not restricted, all included literature was from China, which means that the majority of all included studies were in Asian populations and multicenter large sample RCTs in different regions should be performed to complement the results of this study in the future. And the experimental design of traditional Chinese medicine treatment needs to be improved. ## 5. Conclusion In summary, the present study showed that the combination of the YQH method was effective compared with conventional treatment used alone for IgA nephropathy. It can significantly reduce urinary protein levels, and protect kidney function without impact the serum levels of ALT Hb and serum albumin. Therefore, it can be considered as an alternative therapy to supplement traditional therapies. ## Author contributions Conceptualization: Zhiyu Pan, Sijia Ma. Data curation: Zhiyu Pan, Xiujie Shi. Formal analysis: Zhiyu Pan. Investigation: Zhiyu Pan, Mingming Zhao, Yu Zhang. Methodology: Zhiyu Pan, Mingming Zhao, Yu Zhang. Project administration: Meiying Chang, Yu Zhang. Resources: Xiujie Shi, Yu Zhang. Software: Meiying Chang. Supervision: Mingming Zhao, Yu Zhang. Validation: Xiujie Shi, Yu Zhang. Visualization: Yu Zhang. Writing – original draft: Zhiyu Pan, Meiying Chang, Sijia Ma. Writing – review & editing: Zhiyu Pan. ## References 1. Rajasekaran A, Julian BA, Rizk DV. IgA nephropathy: an interesting autoimmune kidney disease.. Am J Med Sci (2021) 361 176-94. PMID: 33309134 2. Floege J, Rauen T, Tang SCW. Current treatment of IgA nephropathy.. Semin Immunopathol (2021) 43 717-28. PMID: 34495361 3. Yeo SC, Cheung CK, Barratt J. 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--- title: 'Research progress of mechanisms of fat necrosis after autologous fat grafting: A review' authors: - Shenzhen Gao - Baixue Lu - Rong Zhou - Weicheng Gao journal: Medicine year: 2023 pmcid: PMC9997804 doi: 10.1097/MD.0000000000033220 license: CC BY 4.0 --- # Research progress of mechanisms of fat necrosis after autologous fat grafting: A review ## Abstract Currently, autologous fat grafting is the common surgery employed in the department of plastic and cosmetic surgery. Complications after fat grafting (such as fat necrosis, calcification, and fat embolism) are the difficulties and hotspots of the current research. Fat necrosis is one of the most common complications after fat grafting, which directly affects the survival rate and surgical effect. In recent years, researchers in various countries have achieved great results on the mechanism of fat necrosis through further clinical and basic studies. We summarize recent research progress on fat necrosis in order to provide theoretical basis for diminishing it. ## 1. Introduction Autologous adipose provides multiple benefits (rich sources, easy harvest, soft texture, flexibility, and low immunogenicity) making it an ideal substitute filler for reconstructive surgery. Although autologous fat grafting become increasingly popular, it creates adverse outcomes including fat necrosis, low survival rate, and fat embolism, which plague surgeons and patients. Fat necrosis is a sterile inflammatory process in the presence of damaging factors, with adipose tissue liquefaction, fibrosis, and calcification.[1,2] As the mechanism of fat necrosis is complex and not very clear, how to reduce it has become a hot and difficult point in the field of fat grafting in recent years. In this review, the science and theory behind fat necrosis are summarized. ## 2. History Fat necrosis was first reported in the traumatic breast by Lee and Adair,[3] which manifested as palpable hard nodules similar to breast tumors. In 1930, Cookson[4] observed that the nodules incorporated a body of giant cells filled with foam-like cytoplasm and foreign-body large cells, surrounded by fibroblasts. In 1947, Adair and Munzer[5] considered that fat necrosis was produced by a benign aseptic inflammation, acting as various forms of liquefied necrosis, cyst, and calcification by the analysis of the lump formed after breast reconstruction, containing substantial necrotic fat. There were lots of literature regarding fat necrosis after trauma, lumpectomy, implant removal, and so forth. However, fat necrosis associated with autologous was reported until Bircoll[6] got an excellent result over breast augmentation with autologous liposuction in 1987. In 1990, Vizcaino and Montilla[7] touched multiple smooth, firm, and round nodules, with a X-ray examination showing cystic masses by injection of autologous nonvascularized fat in the breast. The histological analysis of masses revealed cystic contents comprised of necrotic fat and fibrous tracts without calcification. Maillard[8] achieved similar outcomes with Vizcaino and the cystic wall with slight calcification. Har-Shai et al[9] detected the liponecrotic cyst by injecting autologous fat obtained by liposuction into the cheek and indicated that leaking oil from adipocyte modulates the inflammatory and granulomatous response to fat necrosis and predisposes to cystic formation. In 2009, the American Society of Plastic Surgeons revised the assessment concerning the current applications and safety of autologous fat grafts and mentioned that the main adverse event was related to fat necrosis, as was perceived to interfere with breast cancer screening and result in graft loss.[10] Since then, significant attention to fat necrosis related to autologous fat transfer has been well described. ## 3. Pathogenesis Clinically, it is hard to distinguish calcification between fat necrosis and breast cancer on imaging; thus, identification of distinct pathological features of fat necrosis helps clarify its diagnosis and assess the severity degree and stages. Fat necrosis is the product created by sterile inflammation, as the result of aseptic saponification of lipases in blood and tissues.[2,11,12] Histopathological examination is the gold standard for the diagnosis of fat necrosis. Histologically, numerous fibroblasts, coenocytic giant cells, and lipid-filled macrophages clustered around varying vesicles merged by ruptured fat cells in the grafting area.[13] Berg et al[14] subdivided fat necrosis into 3 stages according to pathological manifestations: early stage, where fragments of fat cells were visible; middle stage, where red blood cells and phagocytes infiltrated; late stage, where multinucleate giant cells, hemosiderin, and calcification were noted. It was noticed that aggregated phagocytes around the necrotic region were mostly M2 macrophages through autologous fat grafting in mice.[15] In a similar experiment, Kato et al[16] revealed that all of the adipocytes and adipose-derived stem cells were dead, with the occupation of extracellular matrix, oil cysts, as well as calcification. The cyst wall consisted of both inner and outer layers in the oil cyst with a long history of autologous fat injection into breast augmentation. From the comparison between short and long case history, calcification was limited to the innermost fibrous area in the former and was stronger on both internal and external layers in the latter.[17] Under the transmission electron microscope, grafts were characterized by cellular swelling, mitochondrial cristae loss, chromatin condensation, and broken cytomembrane.[18] The above indicates that fat necrosis may present diverse appearances in different phases. ## 4. Associated mechanisms Fat necrosis after autologous fat grafting is the consequence of multiple inflammatory factors and tissue cells in response to specific stimuli. Precious studies indicated that the pathogenesis of fat necrosis includes ischemia and hypoxia, white fat browning, and fibrosis. ## 4.1. Hypoxia The survival of autologous fat grafts depended on the reestablishment of blood circulation.[11,19] Following the implantation into the recipient area, in the early stage (generally within 4 weeks), tissue fluid provided nutrients, and neovascularization allowed for graft survival at a later stage. Without adequate nutrients meeting adipose tissue requirements, portions underwent liquefied death. Fat absorption following revascularization mediated apoptosis without the inflammation associated with membrane rupture or lipid droplet leakage.[20,21] Accordingly, in the context of hypoxia fat necrosis provoked graft volume depression rather than apoptosis. More oxygen was needed in adipose tissue than in others because of higher oxygen tension in subcutis. Within the same volume, the bloodstream was richer in adipose than skeletal muscle; hence, fatty tissue was susceptible to ischemic necrosis in the presence of dissection of vessels.[11,22] Inflammatory reflection induced release agents (tumor necrosis factor-α [TNF-α], interleukin [IL]-6, etc.) to defend against microenvironmental changes in the setting of hypoxia, but excess agents could destroy normal adipocytes. The less inflammatory response observed in fat flap with vascular anastomosis could reduce the fat necrosis morbidity for a comparison between the autologous free fat and fat flap grafts with vascular anastomosis in mice by Oashi et al[23] and Mashiko and Yoshimura[20] observed through autologous fat grafting experiments in mice that the majority of adipocytes started to die by the time the oxygen tension was below half initially; while oxygen continued to decrease, endothelial cells and hemogenic cells suffered an injury. In the condition of subcutaneous oxygen tension beneath the threshold of 30 to 35 mm Hg (normal is 50–60 mm Hg), there was irreversible damage to adipocytes; nevertheless, dying adipocytes could be reversed to be healthy in case of higher oxygen tension (about $60\%$ or more of normal pressure).[15] Hypoxia-inducible factor-1α and fibroblast growth factor-2 were up-regulated in severe hypoxia, involving the occurrence of fat necrosis. During the trial of the mice model lacking oxygen, Eto et al[24] demonstrated that adipose-derived stem cells, precursor cells, and stromal cells worked together to diminish the cyst structure. Consequently, to avoid negative outcomes after the autologous fat graft, restoration of blood supply for implantation is required. ## 4.2. Browning Aside from 2 classic types of fat tissue, white adipose tissue (WAT) for storing the energy and brown adipose tissue, there was a novel fat tissue known as “beige” or “brite” adipose tissue, which localized at the inguinal, axillary, and subcutaneous tissues as WAT did, with similar morbidity and function to brown adipose tissue. Beige adipocytes harbored multilocular lipid droplets and abundant mitochondria with high expression of uncoupling protein-1, prone to release high heat. Under the stimulation of cold or drugs, the quantity and activity of beige fat increased significantly in the subcutaneous white fat areas, which was known as the “ browning of WAT.”[25–27] The origin of beige fat during browning was still debated: part of scholars believed that it derived from adipose precursor cell differentiation, and others argued that it was converted by white adipocytes. Browning of WAT could be induced after the procedure of autologous fat transfer in mice by Hoppela et al[28] When female abdominal fat was implanted to the back of nude mice by fat aspiration, graft browning was noted mainly in the late stage, which was recognized as an adoptive and reversible process. After removal of stimuli and inducible agents, the beige fat could transform into white fat; whereas factors were not eliminated, beige fat could remain for a long time and gradually necrotize.[29] Browning fat could be found in fat necrosis, not in well-survived adipose tissue while human adipose tissue was transferred into the back of nude mice by Liu et al.[30] They discussed that the degree of browning is closely related to the illness of the local environment and fat necrosis, as was not a natural process after autologous fat grafting. The authors also found that M1 macrophages secrete IL-6 after fat grafting, promoting the polarization of M2 macrophages, which in turn induced the browning of white fat, but there was no evidence elucidating the exact pathway of it.[30] It has been shown that beige fat was characterized by strong metabolism and high mitochondrial protein; compared to white fat, beige fat was more vulnerable to topical pro-apoptotic factors, inflammatory response, and hypoxia, which made it more susceptible to necrosis.[26,27] Beige fat metabolizes vigorously and consumes more oxygen, further worsening the local hypoxia environment and leading to a vicious cycle. Browning of WAT could be back towards the initial state following the withdrawal of triggers; therefore, by deepening the basic science of browning, the burgeoning surgeon may diminish the occurrence of undesirable fat necrosis. ## 4.3. Fibrosis Fibrosis is a process of tissue repair engaged by fibrous connective tissue in response to the loss of cells and tissue, which is marked by excessive deposition of extracellular matrix (ECM).[31] In fat necrosis following fat grafting, necrotic fat was retained at the graft region for a long while, surrounded by a fibrous cyst wall; a persistent cyst wall would result in chronic inflammation, but its thickness would block the entrance of inflammatory cells and factors and absorption of oil.[32,33] As such, persistence of oil cysts would induce long-term chronic inflammation with progressive calcification and fibrosis. After autologous fat grafting in the mice model, first of all, neutrophils arrived at the grafts to swallow detrimental elements and secret inflammatory mediators within 24 hours, which was involved in the fibrous process by metalloproteinase-9 decomposing ECM.[17] Liu et al[31] found that long-term oil cysts had a larger size and stronger fibrosis with a lack of neutrophils; thus, the authors considered that upregulation of neutrophils could reduce the fibrotic effect, but the excess would damage other healthy tissues. Macrophages are the critical cells associated with postnecrotic fibrosis. Within the area of fat necrosis, ruptured adipocytes caused a local inflammatory response, with neutrophils acting as front-line inflammatory cells that gradually subsided after 3 days; at this time, large amounts of macrophages accumulated to engulf the necrotic cells and removed cellular debris and contents. Kato et al[16] studied autologous fat grafting in mice and observed that necrotic adipose tissue was initially infiltrated by M1 macrophages, which released TNF-α, IL-6, IL-2, and other inflammatory factors to eliminate necrotic stuff; when the necrotic substance was so much that M1 macrophages could not absorb all of it, M2 macrophages progressively substituted M1 macrophages as the dominant cells, partially forming fibrous capsules to encapsulate necrotic tissue and the others encapsulating M1 macrophages containing lipid droplets to a crown-like structure. Tanaka et al[34] suggested that M2 macrophages could express C-type agglutinin, which stimulated myofibroblasts to secrete collagen and promoted the production of coronal-like structures. By transplanting human abdominal adipose tissue to the back of nude mice, Cai et al[35] revealed that M2 macrophages in the fat necrosis area could highly express transforming growth factor-β, contributing to fibroblast proliferation and differentiation, and the synthesis of multiple ECM proteins by adipose precursor cells. In addition, M2 macrophages could also secrete collagen by themselves, and in the presence of a lack of M2 macrophages, a decrease in type I collagen, type VI collagen, capsule wall thickness and fibrosis was evident. Abnormality of accumulated lipid allowed for necrosis of macrophage foam cells across the RIPK3/MLKL signaling pathway, which was produced by the fact that macrophages phagocytosed broken adipocytes, and the release of inflammatory factors and chemokines (TNF-α, IL-1α, IL-6, and monocyte chemotactic protein-1), facilitating the secretion of enormous collagen by fibroblasts.[36] The size and quantity of fat determined the fibrous level of fat necrosis. Fat droplets less than 1mm in diameter can be completely absorbed within a few weeks, while those with a diameter of more than 3 mm are more potential to die. After transplanting fat aspirated from the human abdomen to the back of nude mice, where more oil was transplanted, the more obvious was the liquefied necrosis, macrophage inflammatory infiltration, and fibrosis of the grafts.[37] Fibrosis manifests histopathologically as oil cysts and calcification, and clinically as long-standing palpable hard nodules, local skin retraction or thickening as well as inflammatory reactions such as redness, swelling, and heat pain in patients, giving rise to repeated postoperative visits. Additional studies are necessary to substantiate these findings and the utility of each method, which helps to provide strategies for precaution and treatment of fat necrosis, and new levels of improved patient satisfaction with superb aesthetic results will follow. ## 5. Conclusion Autologous fat grafting has been practiced widely today, it is vital for plastic surgeons to be familiar with its negative outcomes, with special care for fat necrosis, which determined the survival rate of transplanted fat. Moreover, fat necrosis causes calcification, cysts, and inflammatory reactions that can interfere with disease diagnosis and treatment. Currently, studies on fat necrosis mainly focused on the histopathology of fat necrosis masses, fat grafting in mice, etc. The mechanisms include the establishment of blood transport, browning of white fat, fibrosis after fat necrosis, etc. There is little evidence focusing on the molecular mechanisms of fat necrosis. A comprehensive summary of the science and theory of fat necrosis behind autologous adipocyte grafting, as well as its rich history is described. 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--- title: 'Investigating the mechanism of Tongqiao Huoxue decotion in the treatment of allergic rhinitis based on network pharmacology and molecular docking: A review' authors: - Fang Zhang - Jiani Wu - Qu Shen - Zhiling Chen - Zukang Qiao journal: Medicine year: 2023 pmcid: PMC9997813 doi: 10.1097/MD.0000000000033190 license: CC BY 4.0 --- # Investigating the mechanism of Tongqiao Huoxue decotion in the treatment of allergic rhinitis based on network pharmacology and molecular docking: A review ## Abstract Allergic rhinitis is prone to recurrence, and clinical treatments focus on control symptoms; however there is no radical cure. Our aim was to use network pharmacology and molecular docking to reveal the hub genes, biological functions, and signaling pathways of Tongqiao Huoxue decoction against allergic rhinitis. First, the chemical components and target genes of Tongqiao Huoxue decoction were obtained from the Traditional Chinese Medicine Systems Pharmacology database. Similarly, allergic rhinitis targets were screened using online Mendelian Inheritance In Man and GeneCards database. Then, all potential targets of Tongqiao Huoxue decoction in the treatment of allergic rhinitis were identified, the Venn diagram was portrayed using R software, and protein-protein interaction network was built using String. The hub genes were analyzed using enrichment analyses. Finally, molecular docking was used to verify the reliability of the key gene prediction. The core targets for Tongqiao Huoxue decoction to improve allergic rhinitis were AKT1, TP53, IL6, and so on. The enrichment analysis results showed that Tongqiao Huoxue decoction treatment in allergic rhinitis might be involved in the AGE-RAGE signaling pathway and fluid shear stress and atherosclerosis pathway. The molecular docking verification indicated that its ingredients bound well to the core targets of allergic rhinitis, and stigmasterol’s docking ability with TNF (−12.73 kcal/mol) is particularly prominent. Based on these findings, it may be deduced that stigmasterol treated allergic rhinitis by acting on TNF targets. But, this conclusion needs to be confirmed by further in vitro and in vivo trials. ## 1. Introduction Allergic rhinitis (AR) is a nasal mucosa atopic disorder mediated by immunoglobulin E (IgE) and is characterized by nasal pruritus, clear rhinorrhea, sneezing, nasal congestion, post-nasal drip, and pale discoloration of the nasal mucosa.[1] The incidence of AR is $19\%$ among adults and $22\%$ among children in China, and boys showing a higher prevalence than girls.[2,3] The global prevalence of AR is between $10\%$ and $40\%$.[4] AR as a chronic disease what does not cause death, but it imposes a heavy socioeconomic burden on patients and seriously impairs their quality of life.[5,6] Although the etiology of AR is still unclear, it is environmental, genetic, and epigenetic that strongly associated with onset of AR according to some current reports.[7] AR can be triggered while exposed to allergens including pollen, mold, dust, egg, seafood, and soybean, et al.[8] Health education for patients, irritant and allergen avoidance measures, pharmacotherapy, allergen immunotherapy, biologics, nasal irrigation, acupuncture and surgery are present common therapeutic measures for patients of AR. But there are no radical cure for AR, and clinical treatments are mainly used to control symptoms of AR.[9] Moreover, the efficacious of traditional Chinese medicine (TCM) against AR have been proven and TCM treatment without obvious adverse effects.[10] Tongqiao Huoxue decoction (THD), originated from “Corrections on the errors of medical works” written by the Qing Dynasty physician Wang Qingren is a traditional Chinese prescription, and it’s ingredients include 6 grams of red red peony, 6 grams of Chuanxiong, 6 grams of safflower, 9 grams of peach kernels, 9 grams of ginger 0.15 grams of musk, and 7 red dates. The main therapeutic effect of THD is to improve local blood circulation by dispersing blood stasis and dredging collateral. TCM theory believes that nose is connected to the brain, and the level of interleukin-6 in the blood serum closely relating with the increased occurrence of AR[11] can significantly be reduced by THD. Therefore, THD can be used to treat nasal diseases and the addition and subtraction of Chinese medicine drugs was carried out on the basis of THD in this study.[12] Through the summary of clinical treatment experience, it is found that some Chinese medicines such as comfrey, madder and lnk lotus have excellent efficacy in the treatment of AR. Based on this discovery, we added and subtracted the composition of THD to formulate a new THD, which includes astragalus, codonopsis ginseng, poria, Baizhu, Chuanxiong, red peony, madder, comfrey, lnk lotus, licorice, and the effective of ingredients modified THD has been clinically proven. However the drug active mechanisms of ingredients modified THD in the treatment of AR are still unclear and need to be further explored. In this research, network pharmacology was firstly applied to identified the active ingredients and targets of THD in the treatment of AR. Next, the protein-protein interaction (PPI) network analysis and enrichment analysis were used to predict the critical molecules of THD for AR treatment. Lastly, molecular docking was performed to reveal molecular regulatory mechanisms in a high-throughput manner.[13] ## 2.1. Screening active components of THD The active ingredients of THD were collected through the Traditional Chinese Medicine Systems Pharmacology (TCMSP, http://tcsmpw.com/tcsmp.php) Database. Oral bioavailability (OB) ≥ $30\%$ and drug-likeness (DL) ≥ 0.18 were set as the thresholds for screening the active components in AR. Then, the targets of these active ingredients were obtained from this website. The aggregated targets were input into Uniprot (http://www.uniprot.org/) to obtain gene symbols and gene IDs. ## 2.2. Collecting the disease-related targets “Allergic Rhinitis” was used as the keyword to search all the possible targets against AR in the online Mendelian Inheritance in Man database (http://www.omin.org/), and GeneCards database (relevance score ≥ 2) (http://www.genecards.org/). The AR-related targets from different databases were merged and dereplicated. Finally, these targets were unified as gene names on UniProt. ## 2.3. Venn diagram of targets between drugs and disease The intersection target genes between both AR and THD were obtained through Venn 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/). The coincident target genes were displayed in the overlapping domain after amalgamation and striking out the duplicates. ## 2.4. Analysis of PPI network PPI network of AR and THD reclosing target genes was depicted using the STRING database (http://string-db.org/). According to the corresponding calculation method the species was set as “Homo sapiens” and the threshold was set as 0.9 to show the PPI network. PPI network was visualized in Cytoscape 3.9.1 software and algorithms of cytoHubba plug-in: the Degree Value were used to sift the top 10 hub targets form the network. ## 2.5. Construction of drug-compound-target genes network The screened ingredients and the overlapping target genes were utilized to build the active component-target-AR network using Cytoscape 3.7.3 software. In addition, the top six components in the Degree value of THD were selected to dock with key genes. ## 2.6. Functional enrichment analyses The Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed using Bioconductor package and clusterProfiler package in R 4.2.1 software under the condition of $P \leq .05$ and q < 0.05. Finally, the results were presented in bar and bubble graphs. ## 2.7. Molecular docking To explore the relationship and action mechanisms between candidate proteins and active ingredients, molecular docking simulations were conducted to evaluate the strength and mode of interactions between components and hub targets. Crystal structures of critical targets protein receptors were acquired from the Protein Data Bank database (http://www.rcsb.org/) in Protein Data Bank format. The active component structure as ligands was downloaded from the PubChem compound database (http://pubchem.ncbi.nlm.nih.gov/). After removing the water molecules and organic compounds from ligands and proteins and adding non-polar hydrogen bridge to them by PyMol 2.6.0 software, the format of the molecular ligands and proteins was transformed into pdbqt format. Subsequently, the docking of ligands and proteins was performed by AutoDockTools 1.5.7 software. Each group of molecular docking was run 50 times, and the ionization energy was calculated. The minimum energy value was selected as the docking affinity. Finally, the docking results were visualized using PyMol software. ## 3.1. Active compounds in THD and candidate targets 232 potential active ingredients of THD were retrieved form TCMSP database under the screening conditions of OB ≥ $30\%$ and DL ≥ 0.18. Since THD contains a total of 11 flavors of Chinese medicine, and there many active ingredients in each flavor of Chinese medicine, only the two active ingredients with the highest OB in each flavor of Chinese medicine are listed (Table 1). 153 corresponding underlying target genes with THD were collected form TCMSP and a sum of 814 for AR were identified from the GeneCards and OMIM. Table 1 \| Mol ID \| Herbs \| Active compounds \| OB \| DL \| \| --- \| --- \| --- \| --- \| --- \| \| MOL000442 \| astragalus \| 1,7-Dihydroxy-3,9-dimethoxypterocarpene \| 109.99 \| 0.48 \| \| MOL000439 \| astragalus \| Isomucronulatol-7,2’-di-O-glucosiole \| 74.69 \| 0.62 \| \| MOL008411 \| codonopsis ginseng \| 11-Hydroxyrankinidine \| 65.95 \| 0.66 \| \| MOL008407 \| codonopsis ginseng \| (8S,9S,10R,13R,14S,17R)-17-[(E,2R,5S)-5-ethyl-6-methylhept-3-en-2-yl]-10,13-dimethyl-1,2,4,7,8,9,11,12,14,15,16,17-dodecahydrocyclopenta[a]phenanthren-3-one \| 65.9 \| 0.76 \| \| MOL000273 \| poria \| (2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-6-methylhept-5-enoic acid \| 44.17 \| 0.81 \| \| MOL000275 \| poria \| trametenolic acid \| 43.51 \| 0.8 \| \| MOL000020 \| Baizhu \| 12-senecioyl-2E,8E,10E-atractylentriol \| 63.37 \| 0.22 \| \| MOL000021 \| Baizhu \| 14-acetyl-12-senecioyl-2E,8E,10E-atractylentriol \| 62.4 \| 0.31 \| \| MOL001494 \| Chuanxiong \| Mandenol \| 68.96 \| 0.19 \| \| MOL002135 \| Chuanxiong \| Myricanone \| 65.95 \| 0.51 \| \| MOL004355 \| Red peony \| Spinasterol \| 65.33 \| 0.76 \| \| MOL000449 \| Red peony \| Stigmasterol \| 65.08 \| 0.76 \| \| MOL007735 \| comfrey \| Des-O-methyllasiodiplodin \| 75.08 \| 0.2 \| \| MOL002883 \| comfrey \| Ethyl oleate (NF) \| 73.09 \| 0.19 \| \| MOL003283 \| madder \| (2R,3R,4S)-4-(4-hydroxy-3-methoxy-phenyl)-7-methoxy-2,3-dimethylol-tetralin-6-ol \| 102.89 \| 0.39 \| \| MOL000358 \| madder \| beta-sitosterol \| 7712.0 \| 0.75 \| \| MOL001790 \| lnk lotus \| Linarin \| 72.13 \| 0.71 \| \| MOL001689 \| lnk lotus \| acacetin \| 69.94 \| 0.24 \| \| MOL001484 \| licorice \| Inermine \| 90.78 \| 0.54 \| \| MOL001792 \| licorice \| DFV \| 83.71 \| 0.18 \| ## 3.2. Intersective targets between THD and AR The 153 ingredients-related target genes and 814 AR-associated targets were imported into the Venn 2.1 software to draw the Venn diagram (Fig. 1). Among them, 151 were common targets of THD and AR. Figure 1.: Venn diagram of the interactive targets of THD and AR. The cyan circle represents the targets genes of AR, the red section represents the target genes of THD, and the intersection of the two circles represents the target genes THD for AR. AR = allergic rhinitis, THD = Tongqiao Huoxue decoction. ## 3.3. Drug-compound-target network The candidate targets and corresponding compounds were analyzed by Cytoscape 3.7.3 software to construct a drug-compound-target network to show the relationship between them more intuitively (Fig. 2). The “network analyzer” function in the Cytoscape software was used to perform network topology analysis. according to the degree value, the top five key components of THD treatment AR are ethyl oleate (NF), mandenol, perlolyrine, spinasterol, and stigmasterol. Figure 2.: The drug-compound-target network of THD and AR, HQ: astragalus, DS: codonopsis ginseng, FL: poria, BZ: Baizhu, CX: Chuanxiong, CS: red peony, ZC: comfrey, QC: madder, MHL: lnk lotus, GC: licorice. AR = allergic rhinitis, THD = Tongqiao Huoxue decoction. ## 3.4. Analysis of protein-protein interaction network and screening hub targets The PPI network derived from STRING database was plotted to explore the complex interactions among these 151 intersectant target genes of THD and AR. Subsequently, the PPI network of interactive proteins was inputted into Cytosacpe software for visualization. The core target proteins of THD therapeutic AR were calculated by count screening, as shown in Figures 3 and 4. The interaction of target proteins and these proteins was presented by nodes and edges, respectively. Besides, the darker the color and the larger the size of the node, manifested the higher Degree value (the connectivity between nodes). The top 10 in terms of degree value are AKT1, TP53, IL6, TNF, CASP3, MAPK3, HIF1A, VEGFA, ESR1, MYC. It indicated that these targets mentioned above played an important role in THD in the treatment of AR. Figure 3.: The PPI network. PPI network explored the complex interactions among these 151 intersectant target genes of THD and AR. AR = allergic rhinitis, PPI = protein-protein interaction, THD = Tongqiao Huoxue decoction. Figure 4.: Network diagram of the hub proteins of THD against AR. The darker the color and the larger the size of the node, manifested the higher Degree value. The top 10 genes in terms of degree value are AKT1, TP53, IL6, TNF, CASP3, MAPK3, HIF1A, VEGFA, ESR1, MYC. AR = allergic rhinitis, THD = Tongqiao Huoxue decoction. ## 3.5. GO and KEGG enrichment analysis Figures 5 and 6 showed the most significant 10 results of GO functional enrichment analysis in terms of biological process (BP), cellular component (CC), and molecular function (MF), respectively. These 1514 relevant BPs primarily focused on cellular responses to various substances, including hormone, organic cyclic compound, inorganic substance, and lipid, etc. Consistently, the results of GO analysis suggested that transcription regulator complex (CC), membrane microdomain (CC), membrane raft (CC), and transcription factor binding (MF) of DNA − binding and RNA polymerase II − specific DNA − binding were important ways for THD to affect AR. Figure 5.: the babble chart of GO enrichment analysis. The most significant 10 results of GO functional enrichment analysis in terms of biological process, cellular component, and molecular function. GO = gene ontology. Figure 6.: The bar diagram of GO enrichment analysis. GO = gene ontology. The 151 proteins further resulted in 190 KEGG pathways, and the top 20 critical signaling pathways were shown in Figure 7. KEGG pathway enrichment analysis identified these mechanism-related pathways, such as lipid and atherosclerosis, chemical carcinogenesis, AGE-RAGE signaling pathway in diabetic complications, receptor activation, fluid shear stress and atherosclerosis, and human cytomegalovirus infection. Additionally, AGE-RAGE signaling pathway and fluid shear stress and atherosclerosis will be explored further as crucial pathways (Figures 8 and 9). Figure 7.: The bar plot of KEGG enrichment analysis. The top 20 items ranked by −log10(P) value, gene count, and rich factor. KEGG = kyoto encyclopedia of genes and genomes. Figure 8.: The AGE-RAGE signaling pathway of potential target genes of THD treatment in AR. AR = allergic rhinitis, KEGG = kyoto encyclopedia of genes and genomes, THD = Tongqiao Huoxue decoction. Figure 9.: The fluid shear stress and atherosclerosis pathway of potential target genes of THD treatment in AR. AR = allergic rhinitis, KEGG = kyoto encyclopedia of genes and genomes, THD = Tongqiao Huoxue decoction. ## 3.6. Validation of molecular docking The 10 core target gene-encoded proteins in PPI network, containing AKT1, TP53, IL6, TNF, CASP3, MAPK3, HIF1A, VEGFA, ESR1, and MYC, were docked with five active components respectively by AutoDockTool 1.5.7 software. The binding energy of each component and the protein was obtained, and the heat-map plot was drawn in Figure 10. The binding energies were lower than −5 kcal/mol, indicated solid binding effects. According to the predicted results from molecular docking, spinasterol has the strongest molecular docking ability, and its docking ability with TNF was particularly prominent. The complex produced of each compound with its least energy-efficient protein was visualized using PyMOL software as in Figure 11. Figure 10.: Heat map of binding capacity between key targets and the bioactive compounds. Figure 11.: Molecular models of the binding of (A) TP 53 with mandenol, (B) TP53 with ethyl oleate, (C) TNF with spinasterol, (D) TNF with stigmasterol, (E) TNF with perlolyrine. TNF = tumor necrosis factor. ## 4. Discussion AR is a very common disorder that occurs especially in adolescents. Although AR is not a serious illness, it is clinically relevant because it underlies many complications, is a major risk factor for poor asthma control, and not only is detrimental to health but also has societal costs without effective treatment.[14] When exposed to allergens, IgE acts on mast cells, which in turn release a series of cytokines such as histamine, tumor necrosis factor-α (TNF-α), and leukotriene, resulting in further aggravation of AR.[15] The THD mentioned in this study has been shown to be effective in controlling the symptoms of AR and reducing the number of AR attacks in patients. The theoretical concepts of network pharmacology are coincide with the multi-components and multi-targets of TCM, it can serve as a significant method to analyze drugs, components, and diseases targets. In this study, a total 232 compounds of THD were found in the TCSMP platform, ethyl oleate (NF), mandenol, perlolyrine, spinasterol, and stigmasterol were identified as core active compounds. Mandenol (ethyl linoleate) by down-regulating inducible nitric oxide syntheses and cyclocxygenase-2 expression and thereby reducing nitric oxide, TNF-α, interleukin-1β, interleukin-6, and prostaglandin E2 production induced by macrophages, inhibited inflammatory activity.[16] In addition to exerting anticoagulant effects by increasing the content of cAMP and cGMP in platelets and relieving platelet aggregation, perlolyrine could also combat the vasoconstriction effect of posterior pituitary hormones.[17] Spinasterol possessed several important pharmacological properties for example anti-inflammation, antiulcer, neuroprotection and anti-pain. And its ant-inflammatory mechanisms included inhibition of cyclooxygenases, antagonism of TRPV1 receptor and attenuation of proinflammatory cytokines and mediators.[18] There is research found that stigmasterol reduced the release of inflammatory factors and the level of oxidative stress induced by interleukin-1β through sterol regulatory element binding transcription factor 2, which indicated the protective effect of stigmasterol on cells.[19] Ethyl oleate could enhance drug OB by both elevating drug solubility and promoting lymphatic transport.[20] Subsequently, 151 intersective target genes of THD and AR were obtained. Base on the PPI network analysis and topology algorithms in Cytoscape software, AKT1, TP53, IL6, TNF, CASP3, MAPK3, HIF1A, VEGFA, ESR1, and MYC were considered the hub target proteins in the treatment of AR. The activation of PI3K/AKT/mTOR pathway promotes abduction autophagy of macrophages.[21] The study of Xiaohan et al[22] confirmed that AKT1 was closely related to cell proliferation and AR could be alleviated by activating AKT1. MYC as a cancer-causing gene expresses three proteins: MYC-1, MYC-2, and MYC-3. The MYC proteins were involved in the regulation of a variety of cellular processes including cell growth, cell cycle, differentiation, apoptosis, angiogenesis, and metabolism et al.[23] In the Bioinformatics analysis of nasal epithelial cell gene expression in seasonal and perennial allergic rhinitis founds that AR patients allergic to house dust mite had significant down-regulation of MYC.[24] The variation of TP53 in amino acid at codon 72 promotes mucous cell hyperplasia by reducing Bcl-2 mRNA half-life and stability.[25] TNF was an important cytokine in the pathology of AR that TNF-α disrupted tight junctions of human airway epithelium and promotes inflammatory cytokines release and TNF-α triggers airway constriction, hyperresponsiveness, and sputum neutrophilia.[26] ESR contains ERα, ERβ, and G protein-coupled estrogen receptor (GPER), of which GPER was significant in immune regulation.[27] It has been demonstrated that GPER-specific receptor agonist G-1 could attenuated the symptoms of AR mice and inhabited the TH2 cell associated inflammatory response by decreasing the level of IL-4, IL-5, and IL-13, and elevating Treg cells proportion and the specific cytokines IL-10 and Foxp3 transcription factor.[28] MAPK3 was one of the MAPK family effectors could regulate the expression of related genes, the transcription, and translation of inflammatory factors.[29] GO annotation and pathway enrichment analyses showed that the major biological processes contained response to hormone, cellular response to organic cyclic compound, cellular response to lipid, membrane raft, membrane microdomain, DNA-binding transcription factor binding, and nuclear receptor activity et al And indicated it may be possible to delay the development of AR by preventing these biological processes form taking place. In the KEGG enrichment analysis of hub genes, the effect of THD against AR may be related to lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and fluid shear stress and atherosclerosis. The activation of AGE-RAGE signaling pathway led to increased RAGE expression and oxidative stressors, and blockaded of RAGE signal transduction may be a key measure for the prevention of deleterious consequences of oxidative stress, particularly in chronic disease.[30] After RAGE activation, the elevate of reactive oxygen species (ROS) products and then the oxidative stress response was activated, which means that numerous intracellular structures, such as cellular membranes, proteins, lipids, and DNA were disrupted.[31] Endothelial response to fluid shear stress (FSS) is a critical element of vascular homeostasis.[32] Multi-directional and chaotic FSS caused curved and branched arteries more and this areas with unstable flow were more susceptible to infection and inflammation.[33] The binding mode and binding capacity of active components in THD to potential targets in AR were probed by molecular docking. Our results showed that the active compounds in THD had low binding energy and high binding capacity to TNF, TP53, ESR, and MYC, and the docking scores were mostly < -5kcal/mol which indicated that these protein may be potential binding targets of compounds. It is necessary to state the present several limitations:Firstly, our conclusion was based on these reviewed and predicted data from online databases, this may have led our results being incomplete without unproven and undocumented compounds or targets. Secondlym absorption pathways, metabolic forms of bioactives in THD should be studied. Lastly, our study could only initially explained the mechanism of THD to treat AR based on the theoretical results of network pharmacology and molecular docking. The experiments in vivo and vitro were needed to further verify the main regulatory targets and pathways of action about THD’s treatment in AR. ## 5. Conclusion In summary, our analysis identified TNF, TP53, and MYC as central genes associated with THD anti-AR and validated the reliability of predicted central genes using molecular docking techniques. These hub relevant targets may through its function of regulating cell growth, cycle, apoptosis, and angiogenesis, promoting mucous cell hyperplasia, and decreasing inflammatory cytokines release and through AGE-RAGE signaling pathway in diabetic complications, and fluid shear stress and atherosclerosis pathway to played its role in relieving inflammatory, anti-oxidative stress response, and promoting cell proliferation to improve AR. This conclusion proved that THD had the characteristics of combined action of multi-targets and multi-pathways, and provided new directions for exploring the potential mechanism of THD’s treatment in AR. 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--- title: The efficacy and safety of combined GLP-1RA and basal insulin therapy among inadequately controlled T2D with premixed insulin therapy authors: - Jhih-Syuan Liu - Sheng-Chiang Su - Feng-Chih Kuo - Peng-Fei Li - Chia-Luen Huang - Li-Ju Ho - Kuan-Chan Chen - Yi-Chen Liu - Chih-Ping Lin - An-Che Cheng - Chien-Hsing Lee - Fu-Huang Lin - Yi-Jen Hung - Hsin-Ya Liu - Chieh-Hua Lu - Chang-Hsun Hsieh journal: Medicine year: 2023 pmcid: PMC9997828 doi: 10.1097/MD.0000000000033167 license: CC BY 4.0 --- # The efficacy and safety of combined GLP-1RA and basal insulin therapy among inadequately controlled T2D with premixed insulin therapy ## Abstract This study investigated the effect of a combination of glucagon-like peptide-1 receptor agonist (GLP-1 RA) and basal insulin (BI) in poorly controlled type 2 diabetes mellitus previously treated with premixed insulin. The possible therapeutic benefit of the subject is mainly hoped to provide a direction for optimizing treatment options to reduce the risk of hypoglycemia and weight gain. A single-arm, open-label study was conducted. The antidiabetic regimen was switched to GLP-1 RA plus BI to replace previous treatment with premixed insulin in type 2 diabetes mellitus subjects. After 3 months of treatment modification, GLP-1 RA plus BI was compared for superior outcomes by continuous glucose monitoring system. There were 34 subjects at the beginning, 4 withdrew due to gastrointestinal discomfort, and finally 30 subjects completed the trial, of which $43\%$ were male; the average age was 58 ± 9 years old, and the average duration of diabetes was 12 ± 6 years, the baseline glycated hemoglobin level was 8.6 ± 0.9 %. The initial insulin dose of premixed insulin was 61 ± 18 units, and the final insulin dose of GLP-1 RA + BI was 32 ± 12 units ($P \leq .001$). Time out of range (from $59\%$–$42\%$), time-in-range (from $39\%$–$56\%$) as well as glucose variability index including standard deviation also improved, mean magnitude of glycemic excursions, mean daily difference and continuous population in continuous glucose monitoring system, continuous overall net glycemic action (CONGA). Also noted was a decrease in body weight (from 70.9 kg–68.6 kg) and body mass index (all P values <.05). It provided important information for physicians to decide to modify therapeutic strategy as individualized needs. ## 1. Introduction The incidence and prevalence of type 2 diabetes mellitus (T2D) has been growing worldwide recently.[1] Owing to progressive nature of T2D, especially decline of insulin secretion,[2] many patients eventually require insulin therapy usually initialized with a long-acting (basal) formulation daily according to current guideline.[3] There are 3 therapeutic strategies of intensified injectable therapy when failed in oral antidiabetic agents and basal insulin therapy, which including adding short-acting insulin analogue (basal plus or basal-bolus), or adding GLP 1RA, or shifting to premixed insulin with individualized advantages and shortages in current treatment guideline.[4] The choice of treatment regimen may base on efficacy, side effects of drugs (including hypoglycemia and weight gain), patient’s preference, and atherosclerotic cardiovascular risk benefits. Furthermore, glucose variability (GV) is a glucose index via continuous glucose monitoring system (CGMS) and closely correlated to diabetic complications according to the previous studies.[5,6] If basal insulin has been titrated to an acceptable fasting blood glucose without optimal glycated hemoglobin (HbA1C) level, treatment may be intensified by changing to premixed insulin formulations. Moreover, flexible premixed insulin therapy regimens were the better option for these patients to cover postprandial insulin need.[7] However, clinical inertia among clinical physicians and patients, would be difficult to control hyperglycemia with complex therapeutic formula and/or lack of patient compliance. Therefore, premixed insulins contain fixed percentages of intermediate and short acting, may cause hardly dosage adjustment. It has also led not only to more events of hyperglycemia and hypoglycemia rebound hyperglycemia (Somogyi effect),[8] but also higher GV.[9] Body weight gain is also another expected result for long-tern use of insulin.[10] Glucagon-like peptide-1 receptor agonists (GLP-1 RA), 1 hormone of incretin families, can enhance glucose-dependent insulin secretion and suppress glucagon production of pancreas.[11] GLP-1 RAs are also attractive options for the treatment of T2D because they effectively lower HbA1C fasting plasma glucose, postprandial glucose, and body weight.[12] Therefore, GLP-1 RAs have a low risk of hypoglycemia and stable GV.[13] Not like insulin, GLP-1 RAs have fixed dosage injection to control sugar and rarely monitor blood sugar.[14] The combination of GLP-1 RA plus basal insulin has been proved as a treatment option to intensify insulin therapy in T2D. The benefits of combined therapy include lowering serum glucose and body weight, and comfortable accessibility.[15,16] However, it is unclear whether T2D patients who have previously received premixed insulin therapy and poor blood sugar control can increase the compliance of injection therapy due to the enhanced control of blood sugar and body weight through the combined injection of GLP-1-RA and basal insulin. The purpose of this study was to evaluate the feasibility, efficacy, safety, and GV of using combined injectable GLP1-RA and basal insulin therapy instead of twice-daily premixed insulin therapy in poorly controlled T2D patients. ## 2.1. Subjects and study design The subjects were enrolled form the outpatient department of Tri-Service General Hospital between January 2018 and October 2019. All study subjects were anonymous, and informed consent was obtained prior to participation. The study proposal was reviewed and approved by the institutional review board of Tri-Service General Hospital It is a single-arm, open, observational study. The subjects with documented T2D diagnosis longer than 3 months with aged from 40 to 80 with HbA1C levels between $7.0\%$ to $11.0\%$ under treatment of premixed insulin, NovoMix® 30 ($30\%$ insulin aspart and $70\%$ insulin aspart protamine) with or without combination with metformin were enrolled into the study. The patients with Alanine transaminase (ALT) and Aspartate transaminase (AST) > 3 times normal, and estimated GFR < 30mL/minute/1.73m2, or major systemic disease were excluded from the study. After enrollment, patients were scheduled for laboratory tests and insertion of CGMS after an 8 to 10 hours NPO. Patients were kept treating with premixed insulin for another week during CGMS insertion by experienced staff. After 1 week, antidiabetic regimen was changed to insulin glargine with an initial dose $40\%$ to $50\%$ of the previous total daily dose of premixed insulin. At the same time liraglutide was also started with an initial dose of 0.6 mg/day with subsequent up-titration to 1.2 mg/day after 1 week, if well tolerated. Repaglinide 1 to 2 mg 3 times per day were prescribed to reach the goal of postprandial glucose level < 180 mg/dL. Insulin glargine dose was regularly up-titrated at weekly interval according to fasting plasma glucose to reach goal of 90 to 130 mg/dL or reaching insulin dose of $50\%$ of patient’s weight. After a total treatment duration of 12 weeks, another CGMS procedure were performed again The glycemic index, clinical cardiovascular risk profiles, safety issues (body weight and hypoglycemia), and GV indices from CGMS before and after 3 months treatment modification was evaluated. Body mass index (BMI) was calculated as body weight (kg)/height (m2). Systolic blood pressure and diastolic blood pressure were measured in the right arm of seated individuals by using a standard mercury sphygmomanometer. ## 2.2. Laboratory measurements After maintaining a fasting state for 12 hours, blood samples were obtained from each participant to measure plasma glucose, HbA1C, creatinine, and lipid profiles. Serum total cholesterol, triglyceride, and low-density lipoprotein cholesterol (LDL-C) were determined using the dry, multilayer analytical slide method in the Fuji Dri-Chem 3000 analyzer (Fuji Photo Film Corporation, Tokyo, Japan). The levels of HbA1C were evaluated by ion-exchange high-pressure liquid chromatography method (BIO-RAD VARIANT II, Los Angeles, CA). Plasma glucose concentrations were determined by the glucose oxidase method on a Beckman Glucose Analyzer II (Beckman Instruments, Fullerton, CA). ## 2.3. Details of CGM insertion (iPRO 2™ CGM system, MMT-7741, medtronic) and glucose variability indices CGM measures glucose in interstitial fluid through subcutaneous sensor and saves data in the recorder every 5 minutes. It has been validated by several studies and was also known to provide a very well correlation between blood and interstitial fluid glucose values.[17] A minimum of 3 self-monitoring of blood glucose values per day from glucometer are needed to calibrate the glucose sensor data. CGM measurements provided several important information of GV, including time per day within target glucose range (time-in-range, glucose levels between 70–180 mg/dL), time-above target glucose range (time-above-range, glucose levels > 180 mg/dL), and time-below target glucose range (Time-Below-Range, glucose levels < 70 mg/dL). GV indices, which included standard deviation (SD), (magnitude of glycemic excursions [MAGE], average of blood glucose excursions exceeding 1 SD of the mean blood glucose value), (mean of daily differences [MODD], the absolute difference between the paired CGMS values obtained during 2 successive days (minimum and maximum SD days), and (continuous overall net glycemic action [CONGA], SD of differences between observed blood glucose reading and an observed blood glucose level).[18,19] ## 2.4. Statistical analysis Arithmetic means and standard deviations (SD) were calculated for the variables measured at least on an interval scale. Categorical data were presented as numbers (n) and percentages (%). For changes in clinical characteristics, biochemistry profiles, and CGM data at 2 different study points, Paired t test or Wilcoxon-Signed rank test was performed as applicable. Spearman correlation coefficients, with the changes of CGM record as dependent variables, was used to study the association and independent determinants of covariates. A P value of < 0.05 was considered to be statistically significant. All statistical analyses were performed using SPSS Inc. 26.0 software (SPSS, Chicago, IL). The number of patients reporting adverse events during the different treatments was recorded. ## 3. Results As shown in Table 1, we enrolled a total of 34 patients with T2D who received premix insulin and changed it to GLP-1RA + basal insulin. Four of them quit the trial project because of gastrointestinal discomfort caused by GLP-1RA. Finally, a total of 30 subjects with $43\%$ males; mean age of 58 ± 9 years, mean diabetes duration of 12 ± 6 years and baseline HbA1C level of 8.6 ± 0.9 % were included. There were a total of 14 people with an average dose of 1321 mg daily, whether to change the needle or continue to use metformin during the treatment, and the dose of metformin remained unchanged. After modification of treatment strategy, there were statistical significances in reduction of body weight 2.0 kg ($P \leq .001$), HbA1C of $1.0\%$ ($P \leq .001$) (Fig. 1A and B), and BMI of 0.9 kg/m2 ($P \leq .001$), lowing fasting plasma glucose of 35 mg/dL ($$P \leq .004$$), and LDL-C of 11 mg/dL ($$P \leq .012$$) (Table 1). The initial dose of premixed insulin was 61 ± 18 units, and the final dose of GLP-1 RA + basal insulin was 32 ± 12 units. There was a statistically significant difference in the final dosage of insulin between the 2 groups, with a P value of <.001(Table 1). There were improved estimated HbA1c by $1\%$ ($$P \leq .018$$), time-above-range by $17\%$ ($$P \leq .002$$), time-in-range by $17\%$ ($$P \leq .001$$) (Table 1). In Figure 2A and B, the data showed the end results of the study that improved of standard deviation (SD) of 11 ($$P \leq .002$$), MODD by 7 mg/dL ($$P \leq .032$$), MAGE by 18 mg/dL ($$P \leq .016$$), and CONGA of 19.8 mg/dL ($$P \leq .006$$). However, time-below-range (TBR) was not different between these 2 treatment strategies. Moreover, the improvement of HbA1C level is significantly positive correlation to narrowing range of main index about GV (SD, MAGE, MODD, and CONGA) ($R = 0.481$, 0.495, 0.584, and 0.623) ($$P \leq .007$$, 0.005, 0.001, and < 0.001) (Table 2). ## 4. Discussion In this observational study, we found that combined basal insulin plus GLP1 RA therapy achieved HbA1C level improvement and weight reductions, diminished glucose fluctuation with safety profiles than premixed insulin regimen among patients of T2D who needed intensification to injectable therapy. It provided clinical judgement for physician when treating patients with T2D individually. The progressive nature course of T2D indicates that many individuals need multiple therapeutic strategies to maintain optimal glycemic targets.[20,21] Updated guidelines from the American Diabetes Association and American Association of Clinical Endocrinologists recommend to consider a combination of GLP-1 RA therapies in T2D with high risks or established atherosclerotic cardiovascular disease, independent on individualized HbA1C target.[4,22] Moreover, basal insulin is the most convenient initial insulin regimen and can be add-on to metformin and other antidiabetic agents.[23] The combination injectable therapy of GLP-1 RA and basal insulin gets powerful glucose-lowering function and minimize weight gain and hypoglycemic events, comparing with premixed insulin twice-daily regimen.[18,24] Our study concept is also similar to recently published studies, except that they used iGlaLixi fixed combination therapy.[25,26] Similar to our results, body weight, HbA1c, and therapeutic total insulin dose can be adjusted by changing the therapeutic strategy improved by GLP-1RA plus basal insulin; and also did not increase the risk of hypoglycemia. Our results combined GLP1RA and basal insulin showed beneficial lipid profiles than premixed insulin in T2D subjects. This benefit comes from therapy with GLP1RA. GLP-1 can also regulate cholesterol and triglycerides by several different pathways. GLP-1R signaling could decrease VLDL-TG production from liver, reduces TG content by modulating key hepatic enzymes of lipid metabolism, and interferes hepatocyte de novo lipogenesis and lipid β-oxidation, as well as, also could modify reverse cholesterol transport.[27] Another study revealed that the BMI was significantly reduced by the GLP-1 RA treatment, however, the degree of LDL-C reduction was not correlation with that of BMI in the GLP-1RA subjects regardless of statin use.[28] In our study, we reported significant reductions in total and LDL-C for previous premixed insulin treated T2D after shifting to a combination therapy of GLP1-RA and basal insulin. This mechanism for the lowering level of LDL-C is still exactly unclear, but might be partially related to weight loss and less oral-intake. Furthermore, some factors other than GLP-1 effect such as intensive lifestyle factors may influence the result. GV is an alternative marker for chronic diabetic complications for patients of T2D. In the VARIATION study, T2D patients under combination therapy of basal insulin with a GLP-1 RA reach well glycemic control and the lowest glucose variability, comparing with subjects with other common insulin therapy regimens including premixed contents. Furthermore, TBR of CGM among combined treatment group is significantly lower than a premixed insulin treated 1.[29] It similar in our research, where indicators (SD, MAGE, MODD, CONGA) of GV in CGM as well as biochemical and estimated HbA1C level were improved. The complementary effects of the combination of GLP-1 RA with basal insulin, lowering both postprandial and fasting hyperglycemia,[30] may lead to narrowing GV for this combination strategy. Nevertheless, in our research, TBR of CGM between premixed insulin and combined treatment groups did not significantly change (Table 1). We find that our study subjects had higher HbA1C level (HbA1C $8.6\%$ in our study vs $6.9\%$–$7.0\%$ in VARIATION trial) under premixed insulin control with insufficient dosage adjustment. It might be explained that they had a relatively higher sugar level all the time and then led to fewer hypoglycemic events. However, combination therapy of basal insulin and GLP-1 RA still had low episodes of hypoglycemia in both studies (1.9 %). High GV means wide fluctuation in glucose levels result in acute change among hyperglycemia and hypoglycemia, may cause significant clinical complications.[31,32] Several studies demonstrated that diet control, exercise, and antidiabetic medication could stabilize GV and minimize of hypoglycemia, resulting in improving a series of vascular injury responses.[33,34] The FLAT-SUGAR Trial also concluded that GLP-1 RA with basal insulin minimized GV, reduced body weight, and decreased several cardiometabolic risk markers, comparing with basal-bolus insulin regimen therapy, without correlation to HbA1C change.[15] However, there was still limited data to explore the correlation between improvement of HbA1C and GV previously. In clinical, they’re important markers for evaluation of glycemic control of DM, but independent means for each other. In our study, it an interesting finding, but difficult to be explained. We hypothesize that pleiotropic effect of GLP-1 could reach both target of optimal level of HbA1C and range of GV.[35,36] In addition, basal insulin component with Neutral Protamine Hagedorn (NPH) shifting to insulin glargine U300 could also share the same benefits.[37,38] However, further studies for the phenomenon should be surveyed in the future. Several limitations in our study were noted. The observational study may be considered first, we only record separately once HbA1C level and CGM data in groups under premixed insulin (before) and GLP-1 RA + basal insulin (after) treatment within 3 months, thus the results should be interpreted with caution. Second, enrolled patients had a different degree of diabetes education across the study. For example, few study subjects not always adjusted the basal and premixed insulin dose throughout the study to achieve optimal pre- or postprandial sugar levels. Next, we did not indicate unique 1 research staff, blinded to study assignment and projects, to download the CGM data, when a separate research associate was responsible for participants’ evaluation and data collection. And then, another limitation is that we could not assure the patients adherence to diet control, exercise, and medication although we prove that they did regular visit during the OPD follow-up. After that, the results of this study were obtained in an only-Chinese population in Taiwan. Thus, the generalization of our result to other populations is limited. Finally, a weak point of this study is the smaller number of subjects included, which makes generalized results not really confident. In the future, large study analyses are necessary to account for the combined GLP-1 RA and basal insulin therapy comparing with multiple dose injection of premixed insulin treatment. Nevertheless, our study was only designed to confirm the principle of safe and therapeutic potential of combined GLP1-RA and long-acting basal insulin, which was illustrated in our results. In summary, combined both GLP-1 RA and basal insulin therapy showed a significant improvement of glycemic indices, cardiovascular benefits, and GV among patients with uncontrolled T2D on previous therapy with premixed insulin. It provided important information for physicians to choose suitable therapeutic strategy as individualized needs. A larger scaled study is necessary to be conducted to validate these findings. ## Acknowledgments The authors thank all individuals who participated in the study. ## Author contributions Conceptualization: Sheng-Chiang Su, Feng-Chih Kuo, Peng-Fei Li, Chia-Luen Huang, Li-Ju Ho, Kuan-Chan Chen, Yi-Chen Liu, Chih-Ping Lin, An-Che Cheng, Chien-Hsing Lee, Yi-Jen Hung, Hsin-Ya Liu. Formal analysis: Fu-Huang Lin. Writing – original draft: Jhih-Syuan Liu. 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--- title: Depression and suicidal ideation among individuals with type-2 diabetes mellitus, a cross-sectional study from an urban slum area of Karachi, Pakistan authors: - Hina Sharif - Shah Sumaya Jan - Sana Sharif - Tooba Seemi - Hira Naeem - Zahida Jawed journal: Frontiers in Public Health year: 2023 pmcid: PMC9997841 doi: 10.3389/fpubh.2023.1135964 license: CC BY 4.0 --- # Depression and suicidal ideation among individuals with type-2 diabetes mellitus, a cross-sectional study from an urban slum area of Karachi, Pakistan ## Abstract ### Background Suicidal thoughts and depression are associated with patients with diabetes, especially patients with low socioeconomic backgrounds and prolonged illness. ### Objective We aimed to estimate suicidal thoughts and depression among patients with type 2 diabetes (T2D) in the slums of Karachi. ### Methods This cross-sectional study was conducted across 38 locations in the slums of Karachi to understand depression, suicidal thoughts, and other supporting factors of depression associated with T2D. The three-item Oslo Social Support Scale, the Patient Health Questionnaire-9 (PHQ-9) scale, and the Ask Suicide Screening Questions were used to screen the patients. ### Results A total of 504 study participants were interviewed, with a response rate of $98\%$. The prevalence of depression among patients with diabetes was $30.83\%$, and suicidal ideation was $20.39\%$. In the final multivariate analysis, being socioeconomically poor, physically disabled, and having poor social support were independent predictors of depression. ### Conclusion Diabetes, low socioeconomic level, a lack of social support, and physical disability were all linked to depression. Therefore, trained health providers should conduct an early depression-focused routine screening for patients with diabetes. ## Introduction Diabetes is considered one of the world's largest growing epidemics, and globally, many countries have declared it a public health emergency [1]. As per the latest reports from the Centers for Disease Control and Prevention (CDC), ~26 million ($8.3\%$) adults and children in the United States alone have type 1 diabetes (T1DM) or type 2 diabetes (T2DM). Undoubtedly, T2DM consists of most of the chunk of it. Around the globe, more than 347 million people have diabetes, with an estimated prevalence of $9.8\%$ in men and slightly lower than $9.3\%$ in women [2]. By 2030, 438,000,000 individuals are anticipated to develop diabetes, making the disease more prevalent globally. In low- and middle-income countries, diabetes accounts for more than $70\%$ of morbidity and $88\%$ of deaths [3, 4]. In 2002, the International Diabetes Federation estimated that 33,000,000 cases of diabetes existed in Pakistan alone, affecting $26.7\%$ of Pakistani adults [5]. This figure is exceptionally high and keeps adding year after year. There are also grounds to suspect that many people go undetected, which would significantly increase the prevalence and the likelihood of complications from untreated conditions. Diabetes is a lifelong chronic disease that is psychologically distressing for a patient. A new diagnosis of diabetes is a significant burden in a patient's life. Many go through classic stages of grief, denial, anger, depression, and acceptance [6]. Due to its persistent and significant burden placed on people with diabetes in terms of self-management of the disease, people with diabetes face a variety of consequences because of their long-term condition, including lifestyle changes and responses to long-term treatment, concerns about complications, need for continuous monitoring of glycemic control, associated disability, and symptoms that interfere with their daily life. Depressive disorder is one of the most common and overwhelming psychiatric disorders in people with diabetes. Studies have shown that depression is a common comorbidity in people with diabetes [7, 8]. The prevalence of depression in patients with DM ranges from 7 to $84\%$ compared to an estimated prevalence of only 3–$4\%$ in the general population [9, 10]. The presence of depression in patients with diabetes mellitus is associated with financial stress, poor general health due to associated comorbidities, and poor glycemic control. It also worsens diabetes prognosis, increases treatment non-compliance, reduces the quality of life, prolongs diabetes recovery, and increases mortality [11]. Depression is also a significant risk factor for hospitalization and diabetes-related complications. Furthermore, such patients may experience psychological and psychosocial problems at the individual and interpersonal levels, all of which contribute significantly to and are correlated with depression and can lead to suicide in some circumstances. Location-specific prevalence and related characteristics exist in Pakistan. In regular hospitals, psychiatric aspects of chronic illnesses like diabetes are rarely considered. Most earlier investigations on the prevalence of depression and associated variables among people with diabetes were carried out in designated specialized diabetes care centers. This study aimed to determine the prevalence of depression and suicidal ideation in individuals with type 2 diabetes from an urban slum in Karachi, Pakistan. The results of this study will help develop more effective programs to treat comorbid diabetes and depression. In addition, the study results will serve as a basis for other researchers aiming to conduct large-scale studies in slum areas of Pakistan. Those interested in exploring the relationship between depression, suicide, and type 2 diabetes will also benefit from this study. ## Study area A cross-sectional observational study of a disadvantaged community in Karachi, Pakistan, was conducted. From August 2022 through September 2022, we gathered and evaluated primary data from SINA Health, Education, and Welfare Trust (SINA) clinics. SINA is a non-profit organization assisting slum populations since 1998 through a network of 38 clinic facilities, including three mobile vans in slum regions. Each year, these clinics assist nearly one million individuals. They provide high-quality primary healthcare to the poor, particularly women, children, and adolescents. ## Study design and period This was an institution-based cross-sectional study conducted from August to September 2022. ## Source and study population The source population included all patients with diabetes being followed up in the outpatient department. The study population included all patients with diabetes seen for follow-up during the data-collecting period. ## Inclusion and exclusion criteria All patients with diabetes older than 18 years who could communicate freely were enrolled. Those using antidepressant medications for depressed symptoms were eliminated since antidepressant medications might disguise depression signs and symptoms. Patients with DM who were newly diagnosed at the time of data collection were excluded from the research because adjustment disorder is more frequent in newly diagnosed patients than a full-blown depressive symptom. Finally, this study excluded diabetic individuals who were extremely ill. ## Study variables Depression and suicidal ideation were the dependent variables. Independent variables included age, gender, marital status, ethnicity, religion, educational and occupational status, clinical factors (kind of diabetes, fasting blood sugar level, duration of diabetes, treatment type), and psychosocial factors (social support). ## Sample size determination Since depression prevalence was not known in the local population, especially in slums, the anticipated prevalence of $50\%$ was used to determine the sample size, with a $95\%$ confidence level with $5\%$ absolute precision and a $30\%$ non-response rate. The sample size with that came out to be around 499. ## Sampling technique and procedure At the outset of the study, all patients with diabetes who were being followed up in the outpatient department of SINA clinics were approached with the study's objectives. Initially, 504 patients expressed an interest in taking part in the study. However, four participants were initially eliminated because they needed to give written informed consent, and seven more were excluded because they needed to fulfill the inclusion criteria. The remaining 493, however, were included in the study. ## Method of data collection and tools Face-to-face interviews were used to gather data using a pretested semi-structured questionnaire that included socio-demographic parameters, clinical features, the three-item Oslo Social Support Scale, the Patient Health Questionnaire-9 (PHQ-9) scale, and the Ask Suicide Screening Questions to screen suicidal ideation among those found with depression. The socio-demographic and clinical information was evaluated using questionnaires modified by studying related literature and the patient's medical records. The initial stage was to collect demographic information and treatment regimen-specific questions from patients, such as age, gender, education, material status, income, residential location (urban or rural), ethnicity, diabetes history, length of disease, and smoking/tobacco usage. The outcome variable (depression) was assessed using the PHQ-9 [12]. It comprises nine items on a four-point Likert scale that assess each of the nine DSM-IV depression criteria. Patients were asked to recollect depressed symptoms within the previous 2 weeks, with answers ranging from 0 (not) to 3 (nearly every day). A PHQ-9 score of 5 indicated depression. PHQ-9 is the most extensively used depression screening instrument; the PHQ-9's final question tackles passive suicidal thoughts. The PHQ-9 is frequently utilized in primary care settings, where the clinic population has fewer patients who screen positive for suicide risk. If a patient affirms passive ideation on the PHQ-9, a more extensive suicide risk assessment should be administered. In our study, we administered the Ask Suicide Screening Questions (ASQ) Toolkit developed by the National Institute of Mental Health (NIMH), a standardized suicide risk screening tool certified for medical patients aged eight and older. It consists of four yes/no screening questions, takes 20 s to administer, and provides a toolbox with safety tips, worksheets, scripts, brochures, and paths. Moreover, the amount of social support was determined by asking patients to rate the level of support they got from family and friends using the Oslo 3 social support scale. The scale was numbered from 3 to 14. Participants with scores of 3–8, 9–11, and 12–14 out of 14 were assessed to have poor, moderate, or high social support. ## Data quality assurance To ensure uniformity, the questionnaire was translated from English to the local language (i.e., Urdu, Punjabi, Sindhi, Balochi, Pashto, and Kashmiri) and then back to English. Before data collection, the local questionnaire version was pretested on $5\%$ of the total research participants with obtained Cronbach's alpha of 0.839. Based on the pretest results, a slight change was made to the questionnaire's content. In addition, the questionnaire content and data-collecting techniques were taught to data collectors. ## Data processing and analysis All data obtained were reviewed for completeness and consistency before being input into Microsoft Excel 2007 and exported to SPSS version 23 for analysis. To describe the socio-demographic features, clinical variables, and depression, descriptive statistics (frequencies, tables, percentages, and averages) were generated. Bivariate and multivariate logistic regression analyses were performed. To avoid possible confounders, variables with p-values < 0.05 in the bivariate model were added to the multivariate analysis. Variables with p-values of < 0.05 were deemed statistical predictors of depression in the multivariate model. The strength of the link was measured using the odds ratio with a $95\%$ confidence interval. ## Results A total of 504 participants were approached for the study with a $2\%$ non-response rate, so 493 participants fulfilling the inclusion criteria were finally taken up for final analysis. Table 1 shows the sociodemographic analysis of the studied participants. The mean age of participants was 48.3 (SD± 12.8) years. Maximum participants were female ($78.9\%$), in the age group of 46–55 years ($34.5\%$), were illiterate ($70.0\%$), married ($96.0\%$), had no source of income ($81.5\%$), lived in urban setup ($94.8\%$), has no employment ($81.5\%$), were Urdu speaking ($20.4\%$), has a family history of diabetes mellitus ($70.4\%$), were non-regular to daily exercise regimen ($67.2\%$), no history of smoking ($81.1\%$). Table 1 \| Demographic characteristics \| Co-variables \| n (%) \| \| --- \| --- \| --- \| \| Sex \| Male \| 104 (21.1) \| \| \| Female \| 389 (78.9) \| \| Age groups \| 18–24 \| 3 (0.6) \| \| \| 25–35 \| 21 (4.2) \| \| \| 36–45 \| 106 (21.5) \| \| \| 46–55 \| 174 (34.5) \| \| \| 56–65 \| 123 (24.9) \| \| \| ≥66 \| 63 (13.2) \| \| Education \| Illiterate \| 344 (70.0) \| \| \| Primary \| 55 (11.4) \| \| \| Secondary \| 60 (12.2) \| \| \| Intermediate \| 18 (3.7) \| \| \| Graduate \| 7 (1.4) \| \| \| Postgraduate \| 6 (1.4) \| \| Marital status \| Married \| 484 (96.0) \| \| \| Unmarried \| 6 (1.2) \| \| Income \| No income \| 411 (81.5) \| \| \| < 13,000 \| 28 (5.5) \| \| \| 13,001–39,000 \| 41 (8.13) \| \| \| 39,001–64,000 \| 3 (0.6) \| \| \| 64,001 and above \| 7 (1.38) \| \| Residential location \| Urban \| 478 (94.8) \| \| \| Rural \| 12 (2.3) \| \| Employment \| Yes \| 77 (15.2) \| \| \| No \| 411 (81.5) \| \| Ethnicity \| Urdu speaking \| 103 (20.4) \| \| \| Punjabi \| 50 (10.1) \| \| \| Sindhi \| 33 (6.1) \| \| \| Baloch \| 12 (2.3) \| \| \| Abbottabad \| 9 (1.7) \| \| \| Bengali \| 16 (3.17) \| \| \| Hazara \| 1 (0.2) \| \| \| Pathan \| 208 (41) \| \| \| Saraiki \| 11 (2.1) \| \| \| Hindko \| 11 (2.2) \| \| \| Kashmiri \| 11 (2.2) \| \| Diabetes family history \| Yes \| 355 (70.4) \| \| \| No \| 151 (26.6) \| \| Exercise \| Yes \| 152 (30.8) \| \| \| No \| 339 (67.2) \| \| Smoking \| Yes \| 81 (16.0) \| \| \| No \| 409 (81.1) \| Of the surveyed participants, ($37.7\%$) were diagnosed when they were more than 55 years of age, while ($35.2\%$) of participants were diagnosed between ages 45–54. The majority ($35.9\%$) were suffering from the illness for 1–5 years duration, ($58.3\%$) were on oral hypoglycemics agents (OHA) while (and $37.5\%$) were using both OHA and insulin for their treatment. The majority (76.45) had < 3 drugs prescribed for their treatment each day, while $52.9\%$ showed non-compliance as they reported that they had no money to buy medicines for their disease. The majority ($60.4\%$) had fast blood glucose levels within 101–125 mg/dl, and $5.6\%$ of the patients with diabetes-reported disability arising from long-standing and poor control of diabetes mellitus. Furthermore, in our study, $34.0\%$ of the participants had poor social support during the disease, as shown in Table 2. Table 2 \| Clinical and psychosocial characteristics \| Co-variables \| n (%) \| \| --- \| --- \| --- \| \| Age at diagnosis \| 18–30 \| 21 (4.2) \| \| \| 31–45 \| 109 (22.1) \| \| \| 45–54 \| 174 (35.2) \| \| \| ≥55 \| 186 (37.7) \| \| Duration of DM (in year) \| 1–5 \| 181 (35.9) \| \| \| 6–10 \| 142 (28.1) \| \| \| ≥10 \| 166 (32.9) \| \| DM Rx regime \| Insulin \| 7 (1.4) \| \| \| Oral hypoglycemic \| 294 (58.3) \| \| \| Insulin plus oral \| 189 (37.5) \| \| Duration of DM Rx (in year) \| 1–5 \| 181 (35.9) \| \| \| 6–10 \| 142 (28.1) \| \| \| ≥10 \| 166 (32.9) \| \| No. of prescribed medication administered per day \| ≤ 3 \| 377 (76.4) \| \| \| ≥4 \| 116 (23.6) \| \| Compliant with medication' \| Yes \| 392 (79.5) \| \| \| No \| 101 (20.4) \| \| Reasons for non-compliance (n = 202) \| No money to buy it \| 107 (52.9) \| \| \| Drug side effect \| 53 (26.2) \| \| \| Others \| 42 (20.7) \| \| Ever measured FBG \| Yes \| 362 (73.4) \| \| \| No \| 131 (26.5) \| \| FBG level (in mg/dl) \| ≤ 100 \| 58 (11.7) \| \| \| 101–125 \| 298 (60.4) \| \| \| ≥126 \| 137 (27.7) \| \| DM complication \| Yes \| 127 (25.7) \| \| \| No \| 366 (74.2) \| \| Physical disability \| Yes \| 28 (5.6) \| \| \| No \| 465 (94.4) \| \| Social support \| Poor \| 168 (34.0) \| \| \| Intermediate \| 139 (28.1) \| \| \| Strong \| 186 (37.7) \| Depression was prevalent in 30.83 % of diabetes individuals ($95\%$ CI: 26.4, 35.2). Of the 493 individuals, 10.1, 7.5, 5.0, and $2.6\%$ met the criteria for mild, moderate, moderately severe, and severe depression, respectively. In this study, living in an urban area, having a fast blood glucose (FBG) level of 126 mg/dl, being physically disabled due to long-standing diabetes mellitus, and having little or poor social support during illness were all related to depression in the bivariate analysis (Table 3). However, being physically disabled (AOR: 4.70, $95\%$CI = 1.28, 6.17) and having low or poor social support (AOR: 3.41, $95\%$CI = 1.76, 6.36) were revealed to be independent predictors of depression among diabetic patients in the multivariate analysis (Table 4) In this study, $35.5\%$ of patients had wished to be dead in the past week, while $24.3\%$ f felt that their family would be better off if they were dead. Moreover, $20.3\%$ thought about killing themselves, while $11.8\%$ attempted to kill themselves. Furthermore, we found $15.1\%$ of participants with an acute positive screen (imminent risk identified) who require a STAT safety/full mental health evaluation as shown in Table 5. ## Discussion This study aimed to determine the prevalence and risk factors for depression in individuals with diabetes mellitus at the outpatient department of SINA clinics in an urban slum in Karachi, Pakistan. According to the current study, the prevalence of depression is $30.83\%$ of individuals suffering from diabetes mellitus. The current study's findings were consistent with a cross-sectional investigation conducted in Egypt ($33.3\%$) and Bahrain ($35\%$), respectively [13, 14]. The prevalence reported in this study was also higher than that reported by Engidaw et al. [ 11] and Waitzfelder et al. [ 7] in their study from Ethiopia and USA, respectively. However, much higher prevalence rates of depression among patients with diabetes were reported from studies undertaken in India ($43.4\%$) and the Islamic Republic of Iran ($73.4\%$), respectively [9, 15]. The disparity might be attributed to disparities in evaluation instruments, healthcare delivery systems, educational status, lifestyle, and social contact. In Egypt, for example, the MADRS screening test was used to assess depression. On the contrary, the Beck Depression Inventory (BDI) was used to measure depression in studies conducted in Iran and Ethiopia. Another cause for the disparity might be different study conditions and different ethnicity of the individuals taken up for the study. On the other hand, the findings of this research were more significant than those done in Malaysia, the University of Gondar diabetes clinic, and the Black Lion Specialized Hospital, with 12.3, 15.4, and $13\%$, respectively (16–18). One reason for the disparity might be the varied healthcare systems and the fact that the PHQ-9 cutoff score is 5. Furthermore, all the initial research was done in specialist hospitals. Those treated in a specialty hospital may receive more thorough care due to ample qualified human resources. In our setting, such high-quality health facilities and resources were not available. We also investigated the risk factors for depression in people with diabetes mellitus. As a result, diabetic patients with poor social support were 3.34 times more likely to develop depression than diabetic patients with good social support. This finding was consistent with research done in Ethiopia at the Felege-Hiwot referral and Black Lion Specialized Hospitals [19, 20]. This might be because social isolation decreases social support, which can have a negative impact on physical and mental health. Diabetes therapy may be delayed due to a lack of social support. If treatment is delayed, the patient will show signs of potential signs of diabetes complications, which predispose the patient to various psychological problems such as depression [21]. Patients with diabetes who were physically impaired were 4.7 times as likely than non-disabled individuals to be depressed. The cause might be that physical infirmity leads to unemployment, fewer educational possibilities, and social contacts, all of which may predispose the patient to depression. Another cause might be that persons with physical disabilities do not get enough physical activity [22]. Furthermore, it was found that those who were socioeconomically poor were 4.2 times more at risk of having depression than those who were socioeconomically stable. Earlier studies have also reported that low levels of household income are linked to a variety of lifelong mental illnesses and suicide attempts, and a decrease in household income is linked to an increased risk of incident mental disorders [23]. Furthermore, the suicidal ideation among depressed patients with diabetes was analyzed using the Ask Suicide Screening Questionnaire for suicidal ideation, in which it was found that $20.39\%$ had suicidal ideation thoughts. Suicidal ideation and attempt rates have been reported as high as 26.4 and $13.3\%$, respectively [24, 25]. Some investigations on suicidal ideation and attempts in patients with diabetes found that suicidal risk increases in diabetics [26, 27]. However, two investigations indicated that patients with diabetes had a lower risk of suicidal thoughts than healthy controls and patients with other medical disorders [28, 29]. Depression has been identified as the most frequent psychological illness among people with diabetes who attempted suicide [30]. ## Strengths and limitations of the study The study's strengths were the use of a relatively large sample size with a reasonable response rate and the use of validated techniques. The current study also has several significant limitations that should be considered when interpreting the findings. Because this study was done in health institutions, the findings may not accurately reflect the depression of all patients with diabetes. The study's cross-sectional design does not prove a definitive cause-and-effect relationship. ## Conclusion Depression was connected with having type II diabetes, inadequate family and community support, and being physically impaired. Clinicians must prioritize patients with diabetes who are physically disabled and have limited social support. Early diagnosis and treatment of depressive symptoms as a standard component of diabetes care is suggested for doctors who deal closely with patients with diabetes. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by SINA-ETHICAL REVIEW BOARD (SINA-ERB). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions HS proposed the study design and study concept and prepared the manuscript. SJ helped in the literature review and finalized the results. TS and HN gathered and sorted out the data. SS was involved in data analysis and the result interpretation. SJ and ZJ supervised the overall project and reviewed the final version of the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabetes Care. (2004) 27 1047-53. DOI: 10.2337/diacare.27.5.1047 2. 2.World Health Organization. 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--- title: Neurometabolite alterations in traumatic brain injury and associations with chronic pain authors: - Linda E. Robayo - Varan Govind - Teddy Salan - Nicholas P. Cherup - Sulaiman Sheriff - Andrew A. Maudsley - Eva Widerström-Noga journal: Frontiers in Neuroscience year: 2023 pmcid: PMC9997848 doi: 10.3389/fnins.2023.1125128 license: CC BY 4.0 --- # Neurometabolite alterations in traumatic brain injury and associations with chronic pain ## Abstract Traumatic brain injury (TBI) can lead to a variety of comorbidities, including chronic pain. Although brain tissue metabolite alterations have been extensively examined in several chronic pain populations, it has received less attention in people with TBI. Thus, the primary aim of this study was to compare brain tissue metabolite levels in people with TBI and chronic pain ($$n = 16$$), TBI without chronic pain ($$n = 17$$), and pain-free healthy controls ($$n = 31$$). The metabolite data were obtained from participants using whole-brain proton magnetic resonance spectroscopic imaging (1H-MRSI) at 3 Tesla. The metabolite data included N-acetylaspartate, myo-inositol, total choline, glutamate plus glutamine, and total creatine. Associations between N-acetylaspartate levels and pain severity, neuropathic pain symptom severity, and psychological variables, including anxiety, depression, post-traumatic stress disorder (PTSD), and post-concussive symptoms, were also explored. Our results demonstrate N-acetylaspartate, myo-inositol, total choline, and total creatine alterations in pain-related brain regions such as the frontal region, cingulum, postcentral gyrus, and thalamus in individuals with TBI with and without chronic pain. Additionally, NAA levels in the left and right frontal lobe regions were positively correlated with post-concussive symptoms; and NAA levels within the left frontal region were also positively correlated with neuropathic pain symptom severity, depression, and PTSD symptoms in the TBI with chronic pain group. These results suggest that neuronal integrity or density in the prefrontal cortex, a critical region for nociception and pain modulation, is associated with the severity of neuropathic pain symptoms and psychological comorbidities following TBI. Our data suggest that a combination of neuronal loss or dysfunction and maladaptive neuroplasticity may contribute to the development of persistent pain following TBI, although no causal relationship can be determined based on these data. ## 1. Introduction Survivors of traumatic brain injury (TBI) experience a wide range of physical and mental health problems, including sensorimotor, behavioral, and neuropsychological deficits (Ponsford et al., 2014; Bramlett and Dietrich, 2015; Gardner and Zafonte, 2016; Irvine and Clark, 2018; Ng and Lee, 2019). It is estimated that TBI contributed to approximately 224,000 annual hospitalizations and over 64,000 deaths in 2020 in the United States (Centers for Disease Control and Prevention [CDC], 2022). Domestic surveillance data reveal that between 3.2 and 5.3 million individuals are currently living with at least one TBI-related disability, which further emphasizes the burden of TBI in the US population (Centers for Disease Control and Prevention [CDC], 2015). Clinical studies suggest that many TBI-associated symptoms, including pain (Prins et al., 2013; Rabinowitz and Levin, 2014; Bramlett and Dietrich, 2015; Irvine and Clark, 2018; Ng and Lee, 2019; Pavlovic et al., 2019; Capizzi et al., 2020), persist over an extended period. Incidentally, more than $50\%$ of individuals with TBI report chronic pain (Lahz and Bryant, 1996; Nampiaparampil, 2008). While pathophysiological mechanisms underlying the development of chronic pain after TBI are not entirely understood, those individuals reporting chronic pain frequently present with a combination of characteristics that resemble those of neuropathic pain conditions, including sensory alterations and psychological comorbidities (Ofek and Defrin, 2007; Widerström-Noga et al., 2016; Irvine and Clark, 2018; Khoury and Benavides, 2018; Bouferguène et al., 2019; Leung, 2020; Robayo et al., 2022). Thus, chronic neuropathic pain can be a consequence of trauma or diseases involving the central or peripheral nervous system, such as TBI (Ofek and Defrin, 2007; Scholz et al., 2019; Robayo et al., 2022). Findings from preclinical TBI research can offer insights into the molecular and cellular basis for developing chronic neuropathic pain symptoms, including central and peripheral sensitization, prolonged disinhibition within thalamocortical circuits, and the derangement of glial cell functions (Ji et al., 2013; Groh et al., 2018; den Boer et al., 2019). In humans, significant mechanistic insights have been gained through pain phenotyping based on symptoms, signs, and biomarkers, including brain imaging. Indeed, neuroimaging studies have identified both functional and structural abnormalities within specific pain-related brain regions in individuals reporting neuropathic pain (Geha and Apkarian, 2005; Moisset and Bouhassira, 2007; Alshelh et al., 2016; Mills et al., 2018). Numerous brain regions, including the thalamus, anterior cingulate cortex (ACC), insula, prefrontal cortex (PFC), and somatosensory cortices (S1 and S2), are involved in processing sensory-discriminative (e.g., location and intensity), cognitive-evaluative, and affective-emotional aspects of pain (Bushnell et al., 2013; Kuner and Kuner, 2021; Mercer Lindsay et al., 2021). Each of these regions uniquely contributes to the overall modulation and resulting perceptual experience (Davis and Moayedi, 2013; Bastuji et al., 2016; Garcia-Larrea and Bastuji, 2018). The prefrontal cortex, has been implicated in attentional and evaluative processes of pain, including attention, anticipation, learning, and cognitive control (Bushnell et al., 2013; Tan and Kuner, 2021). Common findings reported in the literature include increased prefrontal cortex functional activation and decreased gray matter volume in individuals with chronic pain (Seminowicz and Moayedi, 2017). Interestingly, the activity of the prefrontal cortex can be modulated by practicing mindfulness and meditation (Allen et al., 2012). The insula has been implicated in coding pain intensity, whereby electrical stimulation of this structure appears to elicit pain, and lesions affecting the insula can lead to the development of neuropathic pain (Garcia-Larrea et al., 2010; Garcia-Larrea and Peyron, 2013). The insula receives input from the spinothalamic tract and has also been implicated in mediating complex cognitive processes, including emotional awareness (Craig, 2009; Starr et al., 2009). The anterior cingulate cortex (ACC) and cingulum are components of the limbic system, thought to be involved in the affective-emotional components of the pain experience (Bushnell et al., 2013; Kuner and Kuner, 2021). The ACC can modulate both sensory and affective aspects of pain via activation of various receptor systems, including μ-opioid and gamma-aminobutyric acid (GABA) receptors, and activation of the periaqueductal gray (PAG) (Fuchs et al., 2014; Benarroch, 2020). Preclinical studies show that the ACC can undergo profound functional and structural changes during acute and chronic pain (Bliss et al., 2016), and such changes have also been associated with depression in human patients (Holtzheimer et al., 2017). The postcentral gyrus or primary somatosensory cortex is commonly activated during induced pain and implicated in the sensory-discriminative (e.g., location and intensity) components of the pain experience (Apkarian et al., 2005; Kuner and Kuner, 2021; Tan and Kuner, 2021). Greater functional connectivity between the ACC and postcentral gyrus has been observed in adults with chronic pain (Youssef et al., 2019). Lastly, the thalamus is the primary relay station for sensory information, and it also communicates with the cortex (Apkarian et al., 2005; Kuner and Kuner, 2021). Furthermore, the thalamus is rich in opioid receptors and, therefore, may also be involved in pain modulation (Apkarian et al., 2005). Chronic neuropathic pain has been associated with increased activation of the ACC, insula, PFC, S1, and S2 and decreased thalamic activity (Moisset and Bouhassira, 2007; Friebel et al., 2011; Lin, 2014), accompanied by an altered thalamocortical rhythm (Walton and Llinás, 2010; Alshelh et al., 2016). Similarly, studies evaluating structural abnormalities found lower volume in the thalamus, ACC, PFC, and anterior insula, and greater volume in the periaqueductal gray (PAG) and posterior insula in individuals with neuropathic pain compared to healthy controls (Pan et al., 2015; Henssen et al., 2019). These findings suggest that abnormalities in cortical and subcortical brain regions may alter the perception and modulation of pain signals (i.e., maladaptive pain processing) (Kuner and Flor, 2017) and result in chronic neuropathic pain. Brain imaging modalities such as magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are often used to identify brain tissue and metabolic abnormalities associated with TBI (Brooks W. M. et al., 2001; Croall et al., 2015; Faghihi et al., 2017; Smith et al., 2019) and neuropathic pain conditions (Geha and Apkarian, 2005; Moisset and Bouhassira, 2007; Alshelh et al., 2016; Mills et al., 2018). Specifically, proton magnetic resonance spectroscopy imaging (1H-MRSI) is a non-invasive MRI technique that allows the acquisition of spectroscopic data for several brain metabolites, including N-acetyl aspartate (NAA- a proxy for neuronal density and viability), total choline (tCho – an indicator of cell membrane integrity), glutamate and glutamine (Glx – excitatory neurotransmitter and its precursor), total creatine (tCre – a biomarker of cellular energetics), and myo-inositol (m-Ins – an indicator for glial cell density and inflammation) (Miller et al., 1996; Govindaraju et al., 2000; Brooks W. M. et al., 2001; Bak et al., 2006; Moffett et al., 2007; Govind et al., 2010; Chang et al., 2013; Croall et al., 2015; Bartnik-Olson et al., 2019). While metabolite alterations across brain regions have been examined in multiple chronic pain conditions (Fukui et al., 2006; Chang et al., 2013; Widerström-Noga et al., 2013; Ito et al., 2017; Levins et al., 2019; Peek et al., 2020), including neuropathic pain (Chang et al., 2013; Widerström-Noga et al., 2015), only a few research studies have focused on those with TBI and comorbid pain (Widerström-Noga et al., 2016; Lin et al., 2022). Moreover, prior TBI studies have utilized single-voxel or multi-voxel-single slice or multi-slice MRS techniques (Babikian et al., 2010; Widerström-Noga et al., 2016) for the evaluation of brain metabolites within a region or multiple regions of interest (ROIs). However, these limited spatial coverage MRS techniques may not be sufficient to enclose injured brain regions spread across the brain; this might limit assessments of brain metabolites in cortical and subcortical brain regions responsible for the processing of pain (Lin et al., 2022). The use of a whole-brain 1H-MRSI technique in individuals with TBI (Govind et al., 2010; Maudsley et al., 2015; Lin et al., 2022) has largely overcome the spatial coverage limitation of the brain and permits the evaluation of metabolite alterations following TBI during acute and subacute phases (Govind et al., 2010; Harris et al., 2012; DeVience et al., 2017; Bartnik-Olson et al., 2021). Yet, there remains limited research investigating changes in NAA, tCho, Glx, tCre, and m-Ins among those reporting pain in the chronic disease stage. Our laboratory has previously assessed the concentration of metabolite levels and their respective ratios among those with TBI and subacute pain (Widerström-Noga et al., 2016). However, no studies have examined such alterations in those with chronic pain following TBI. Furthermore, there is limited prior research that evaluated brain metabolite differences among individuals after TBI with- and without pain and pain-free healthy controls. Such information may provide insight into potential mechanisms associated with the development of chronic pain in those with TBI. Therefore, the purpose of this study was to obtain whole-brain 1H-MRSI measures in participants with chronic TBI and pain, and compare their metabolite profiles to those without pain and healthy controls across several pain-relevant brain regions. It was hypothesized that participants with chronic TBI and comorbid pain would exhibit altered brain metabolism compared to those with TBI and no pain and healthy controls. We also examined associations between metabolite levels, chronic pain symptoms, and psychological measures among the TBI with pain group. ## 2.1. Study participants Sixty-eight adult participants were enrolled in this study. Of them, thirty-one were healthy controls (HC, $$n = 31$$), and thirty-seven had closed-head TBI (TBI, $$n = 37$$). Participants with TBI were categorized into those with chronic pain (TBI-P, $$n = 19$$) and those without pain (TBI-NP, $$n = 18$$). Demographic and injury characteristics for all participants are shown in Table 1. In addition, all participants were: [1] fluent in English, [2] free from significant cognitive impairment using the MMSE-2 (PAR, 2020), [3] with no recent history of alcohol or drug abuse, [4] with no severe major depression, and [5] free from other neurological diseases (e.g., multiple sclerosis) or trauma (e.g., spinal cord injury). Enrolled TBI participants had experienced their injury six to two hundred seventy-six months before the start of the study and were thus considered to be in the chronic time period following injury (Mayer et al., 2017). The severity of TBI in each participant was determined based on the Glasgow Coma Scale (GCS) score when available. Participants were recruited through advertisements posted at the University of Miami Medical Campus and the Health and Human Services/National Institute on Disability, Independent Living, and Rehabilitation Research (HHS/NIDILRR) organization, South Florida TBI Model System center, the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study center within the University of Miami (UM), and by word of mouth. TBI participants provided medical records with proof of head/brain injury obtained from their medical care provider or insurance company unless they were directly referred from the HHS/NIDILRR, South Florida TBI Model System center, or the TRACK-TBI study center. The institutional review board of the University of Miami approved the study protocol, and all participants signed an informed consent form before participation. The data presented in this article is a subset of a more extensive study involving pain, quantitative sensory and psychological/psychosocial evaluations, and brain imaging in individuals with TBI. Pain, quantitative sensory, and psychological/psychosocial data of this larger cohort have been published (Robayo et al., 2022). TABLE 1 \| Variable \| HC mean (SD) \| TBI-NP mean (SD) \| TBI-P mean (SD) \| p \| \| --- \| --- \| --- \| --- \| --- \| \| Gender (n) \| \| \| \| 0.870a \| \| Male \| 15 \| 9 \| 9 \| \| \| Female \| 16 \| 8 \| 7 \| \| \| Age (years) \| 27.71 (7.72) \| 28.53 (8.16) \| 35.19 (10.77) \| 0.040b \| \| MMSE-2 \| 28.65 (1.31) \| 28.47 (2.12) \| 27.81 (1.94) \| 0.330b \| \| Years of education \| 15.32 (3.11) \| 15.94 (2.73) \| 13.88 (1.78) \| 0.079c \| \| Age at TBI (years) \| - \| 21.88 (8.43) \| 31.50 (11.34) \| 0.006c \| \| Time after TBI (months) \| - \| 79.47 (74.17) \| 43.97 (26.88) \| 0.233c \| \| GCS* \| - \| 8.13 (4.94) \| 10.57 (4.69) \| 0.483c \| \| Cause of injury (n) MVA Other \| - - \| 12 5 \| 8 8 \| 0.279d \| ## 2.2.1. Data acquisition Magnetic resonance spectroscopic imaging data from all participants ($$n = 68$$) were acquired using a 3 Tesla MRI scanner (Siemens Skyra) with a 20-channel receive-only head coil. The protocol included T1-weighted MPRAGE (magnetization-prepared acquisition rapid gradient echo; 1-mm isotropic resolution), T2-weighted gradient echo, FLAIR (fluid-attenuated inversion recovery), and whole-brain MR spectroscopic imaging (axial orientation, TE = 17.6 ms, TR = 1551 ms, field of view: 280 × 280 × 180 mm3, slab thickness: 135 mm, matrix: 50 × 50 × 18, nominal voxel volume: 0.31 cm3, and ∼ 17 min acquisition time). ## 2.2.2. Spectral processing RS data were processed using the MIDAS package (Maudsley et al., 2006) to obtain signal-normalized (institutional units), and CSF partial volume corrected metabolite maps at an approximate spatial volume of 1 cm3. Metabolite concentrations were obtained for NAA, tCre, tCho, Glx, and m-Ins. Individual subject T1-weighted MRI data were spatially transformed into a T1-weighted MRI in the Montreal Neurological Institute (MNI) template space. The resulting spatial transformation was subsequently applied to the MRSI data. We used a modified version (47 regions only, see Supplementary Table 1) of the anatomic parcellation-based Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002) in the MNI template space for obtaining metabolite data for each respective brain region. The quality of MRS data used for analysis was controlled by applying criteria such as linewidth (≤ 12 Hz), tissue fraction (> $70\%$ gray matter plus white matter), and outlier removal (> 3 SD). To improve the quality of MRS data obtained for analysis, spectra from multiple voxels within a selected brain region were summed to create a spectrum (e.g., thalamus) using the Map INTegrated spectrum (MINT) tool in the MIDAS package (Maudsley et al., 2006). In addition, metabolite ratios (NAA/tCre, m-Ins/tCre, Cho/tCre, Glx/tCre, and Glx/m-Ins) were calculated by dividing corresponding metabolite values per area. Data from four TBI participants were excluded from the final analysis due to motion artifacts, insufficient water signal suppression, and significant signal distortions. ## 2.2.3. Regions of interest Data from ten pain-related brain regions of interest were selected for analysis. These included the left and right frontal, insula, cingulum anterior, postcentral, and thalamus (see Figure 1A), which are involved in the processing of sensory-discriminative (e.g., location and intensity), cognitive-evaluative, and affective-emotional aspects of nociception and pain (Bushnell et al., 2013; Kuner and Kuner, 2021; Mercer Lindsay et al., 2021). These ROIs were selected because metabolite alterations in these regions have been consistently observed in chronic pain conditions (Ito et al., 2017; Levins et al., 2019; Peek et al., 2020; Cruz-Almeida and Porges, 2021), including neuropathic pain (Chang et al., 2013; Widerström-Noga et al., 2015). The frontal region, which includes the prefrontal cortex, has been implicated in attentional and evaluative processes of pain (Bushnell et al., 2013; Kuner and Kuner, 2021; Tan and Kuner, 2021). The insula has been implicated in coding pain intensity (Garcia-Larrea et al., 2010; Garcia-Larrea and Peyron, 2013). The anterior cingulate cortex (ACC) and cingulum are components of the limbic system, thought to be involved in the affective-emotional components of the pain experience (Bushnell et al., 2013; Kuner and Kuner, 2021; Tan and Kuner, 2021). The postcentral gyrus or primary somatosensory cortex is commonly activated during induced pain and implicated in the sensory-discriminative (e.g., location and intensity) components of pain (Apkarian et al., 2005; Kuner and Kuner, 2021; Tan and Kuner, 2021). Lastly, the thalamus is the primary relay station for sensory information, communicates with the cortex and is also be involved in pain modulation (Apkarian et al., 2005). FIGURE 1: (A) Coronal, sagittal and axial slices showing the regions of interest (ROIs), including the frontal, cingulum anterior, insula, postcentral, and thalamus from left and right hemispheres, superimposed on template T1 MRI and obtained using the AAL atlas (63). (B) Representative spectrum from the thalamus. Abbreviations used for anterior (A) and right (R). ## 2.3. Clinical variables Pain and psychological function measures were obtained by interview using validated questionnaires. ## 2.3.1. Pain The pain severity subscale of the multidimensional pain inventory (MPI) (Kerns et al., 1985) was used to assess overall pain severity on a 0-6 scale. In addition, neuropathic pain symptoms were evaluated using the NPSI (Bouhassira et al., 2004). Total NPSI scores were obtained by summing all the individual scores for ten questions assessing the presence and severity of [1] spontaneous pain with burning, squeezing, or pressure characteristics; [2] painful attacks with electric shocks or stabbing characteristics; [3] pain provoked or increased by brushing, pressure, or contact with something cold on the painful area; and [4] abnormal sensations including pins and needles, and tingling. ## 2.3.2. Psychological function The Beck anxiety inventory (BAI) (Beck et al., 1988) and Beck depression inventory (BDI) (Steer et al., 2001) were used to evaluate the presence and severity of 21 anxiety and depression symptoms, respectively. Participants were asked to rate their symptoms over the past two weeks using a number ranging from 0 to 3, with increasing scores reflecting greater symptomatology. Similarly, the post-traumatic stress disorder (PTSD) checklist-civilian version, PCL-C (Weathers, 1993; Ruggiero et al., 2003; Haarbauer-Krupa et al., 2017), was used to evaluate the presence and severity of PTSD symptoms. The Rivermead Post-concussion Symptoms Questionnaire Rivermead was also used to assess awareness of cognitive, emotional, and physical symptoms following TBI (King et al., 1995). ## 2.4. Statistical analysis Independent variables were assessed for normality and homogeneity of variances (homoscedasticity) using Shapiro-Wilk and Levene’s tests, respectively. Mean values for each of the metabolites (NAA, m-Ins, tCho, Glx and tCre) were compared across the three groups (HC versus TBI-NP and TBI-P) using multiple non-parametric ANCOVAs (Quade, 1967) with age as a covariate. LSD pairwise comparisons were obtained. In addition, metabolite ratios (NAA/tCre, m-Ins/tCre, Cho/tCre, Glx/tCre, and Glx/m-Ins) were compared using Kruskal-Wallis Tests. Statistical analyses were conducted using SPSS. Results were considered significant if values were p ≤ 0.05 after being corrected for multiple comparisons using the Benjamini and Hochberg false discovery rate (FDR) method (Benjamini and Hochberg, 1995). Data from the frontal, anterior cingulum, insula, postcentral gyrus, and thalamic regions were analyzed separately for both the left and right hemispheres. To evaluate the associations between clinical variables (e.g., pain and psychological) and the metabolite measures in the TBI-P group, we conducted partial Pearson correlations with age as a covariate. ## 3.1. Pain and psychological characteristics of participants Pain and psychological characteristics are indicated in Table 2. TABLE 2 \| Variable \| HC mean (SD) \| TBI-NP mean (SD) \| TBI-P mean (SD) \| p \| Pairwise comparisons \| Pairwise comparisons.1 \| Pairwise comparisons.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| HC vs. TBI-P \| TBI-NP vs. TBI-P \| HC vs. TBI-NP \| \| Total NPSI \| – \| – \| 37.38 (29.29) \| – \| – \| – \| – \| \| Pain severity \| – \| – \| 3.98 (1.37) \| – \| – \| – \| – \| \| Total BAI \| 5.39 (7.92) \| 5.23 (6.06) \| 21.69 (11.86) \| <0.001a \| <0.001 \| 0.002 \| ns \| \| Total BDI \| 4.52 (5.45) \| 6.76 (7.19) \| 18.50 (10.24) \| <0.001a \| <0.001 \| 0.008 \| ns \| \| Total PCL-C \| 25.65 (10.20) \| 24.76 (10.34) \| 41.56 (16.58) \| 0.002a \| 0.004 \| 0.008 \| ns \| \| Total Rivermead \| – \| 14.06 (12.34) \| 36.69 (16.17) \| <0.001b \| – \| – \| – \| ## 3.2. Metabolite differences between the HC, TBI-NP and TBI-P groups Results from non-parametric ANCOVA are presented in Tables 3-7, with Figure 2 displaying age-corrected means and pairwise comparisons for each metabolite (i.e., NAA, tCho, m-Ins, Glx, and tCre) in the selected ROIs for each group. Relative to HC, those within the TBI-NP group showed significantly lower NAA in nearly all brain regions, excluding the left postcentral ROI. These participants also displayed significantly lower m-Ins within the right postcentral region and significantly lower Glx and tCre within the right and left thalamus. When those in the TBI-P group were compared to HC, significantly lower NAA was also observed in the left and right anterior cingulum, left postcentral region, and the right thalamus. Similarly, tCho was significantly lower in the left frontal, left postcentral, right cingulum, and left and right thalamus, with tCre showing significantly lower values in the left postcentral region. As indicated in Table 1, “time after TBI” is not significantly different between TBI groups (TBI-NP vs. TBI-P). However, we understand that this variable covers a large range. For that, we conducted a sub-analysis to test if “time after TBI” affect the between-group results. Independent-Samples Mann-Whitney U tests, between a short time after TBI ($$n = 17$$, mean = 24.15) vs. a long time after TBI ($$n = 16$$, mean = 102.75) groups, indicated that there was no effect of “time after TBI” on metabolite levels, except for m-Ins on the postcentral right region ($U = 71.00$, $$p \leq 0.02$$). Myo-Inositol levels were significantly higher in the group with a shorter time after TBI (mean rank = 20.82) vs. a longer time after TBI (mean rank = 12.94). We then conducted an additional non-parametric ANCOVA for myo-inositol in the postcentral right region, controlling for “age” and “time after TBI.” Similar to our previously reported results, there was no significant difference between TBI-P vs. TBI-NP groups, F [1, 31] = 0.278, $$p \leq 0.602$$, suggesting that even though “time after TBI” may affect myo-inositol levels, the effect was not strong. Comparisons among the groups of NAA/tCre, Cho/tCre, Glx/tCre, m-Ins/tCre, and Glx/m-Ins ratios did not indicate significant differences between TBI-NP and TBI-P relative to controls (data not shown), except for higher m-Ins/Cre and lower Glx/m-Ins ratios in the right insula. Although uncorrected p-values were significant ($$p \leq 0.034$$ and $$p \leq 0.018$$), they did not survive FDR correction for multiple tests. ## 3.3. Associations with pain, psychological and injury variables Correlation coefficients between metabolite levels and clinical variables for the TBI-P group are provided in Table 8. Age was used as a covariate in the analysis to control for any potential confounding effect that age may have on metabolite measures. Significant positive correlations were found between 1) left frontal NAA levels and total NPSI scores, BDI and PTSD symptoms, and Rivermead scores, and 2) right frontal NAA levels with pain severity and Rivermead scores. Additionally, left postcentral NAA values were significantly correlated to total NPSI scores, suggesting some relationship between the severity of neuropathic pain symptoms and altered NAA levels. TABLE 8 \| N-acetylaspartate (NAA) \| Pain variables \| Pain variables.1 \| Psychological variables \| Psychological variables.1 \| Psychological variables.2 \| Psychological variables.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| N-acetylaspartate (NAA) \| Total NPSI \| Pain Severity \| BDI \| BAI \| PCL-C \| Rivermead \| \| N-acetylaspartate (NAA) \| r values \| r values \| r values \| r values \| r values \| r values \| \| Cingulum Ant L \| −0.012 \| 0.177 \| −0.133 \| −0.246 \| −0.245 \| 0.026 \| \| Cingulum Ant R \| 0.323 \| 0.479 \| 0.162 \| 0.219 \| 0.096 \| 0.367 \| \| Frontal L \| 0.599* \| 0.511 \| 0.589* \| 0.485 \| 0.535* \| 0.556* \| \| Frontal R \| 0.519 \| 0.524* \| 0.414 \| 0.470 \| 0.325 \| 0.577* \| \| Insula L \| 0.444 \| 0.386 \| 0.192 \| 0.062 \| 0.011 \| 0.351 \| \| Insula R \| 0.326 \| 0.198 \| −0.172 \| −0.005 \| −0.289 \| 0.151 \| \| Postcentral L \| 0.555* \| 0.442 \| 0.303 \| 0.342 \| 0.419 \| 0.367 \| \| Postcentral R \| 0.299 \| 0.138 \| 0.037 \| 0.068 \| 0.058 \| 0.38 \| \| Thalamus L \| −0.005 \| −0.196 \| −0.076 \| −0.344 \| −0.165 \| 0.165 \| \| Thalamus R \| 0.243 \| 0.116 \| −0.086 \| −0.099 \| −0.177 \| 0.286 \| ## 4. Discussion Whole-brain 1H-MRSI can provide in vivo brain tissue metabolite levels following TBI (Govindaraju et al., 2000; Govind et al., 2010). This type of information can help elucidate the relationship between brain tissue metabolite alterations in people with chronic TBI and co-occurring chronic pain. In this study, we compared NAA, tCho, Glx, tCre, and m-Ins levels and their respective ratios across frontal, cingulate, insula, somatosensory, and thalamic regions in participants living with chronic TBI and healthy controls. We also examined the relationships between metabolite levels and pain and psychological measures in those with chronic pain following TBI. Relative to controls, those in the TBI and chronic pain group exhibited significantly lower NAA in the left and right anterior cingulum, left postcentral region, and the right thalamus. Similarly, tCho was significantly lower in the left frontal, left postcentral, right cingulum, and left and right thalamus, with tCre showing significantly lower values in the left postcentral region. Conversely, those with TBI without chronic pain also showed significantly lower NAA in nearly all selected ROIs, excluding the left postcentral. These participants also displayed significantly lower m-Ins within the right postcentral region and significantly less Glx and tCre within the right and left thalamus. In addition, we observed significant correlations between left frontal NAA levels and poorer BDI scores, worse PTSD symptoms, and higher post-concussive symptoms among those reporting chronic pain. Similarly, left frontal NAA levels were correlated to pain severity and post-concussive symptoms, while left postcentral NAA was correlated with total NPSI scores. These findings may indicate long-term alterations among specific metabolite levels, such as NAA, that could be used as biomarkers for psychological dysfunction and chronic pain, including neuropathic pain symptoms, following TBI. Although cross-sectional and longitudinal studies have identified acute and subacute reductions in NAA or NAA/Cr as clinical indicators of neurological dysfunction, some report recovery to values approximating healthy controls (Govind et al., 2010; Maudsley et al., 2015, 2017). In general, lower NAA levels or NAA/tCre ratios are associated with worse TBI outcomes (Govind et al., 2010; Croall et al., 2015; Holshouser et al., 2019) and with chronic neuropathic pain conditions (Pattany et al., 2002; Fukui et al., 2006; Sorensen et al., 2008). Our findings support these observations and reveal TBI-associated alterations in NAA levels across numerous ROIs relative to healthy controls. Lower NAA levels in participants with TBI during the chronic stage likely reflect a reduction in the metabolic integrity and overall viability of neurons housed across pain-related structures, as NAA is one of the most abundant and highly concentrated neuronal metabolites responsible for mitochondrial bioenergetics and the transfer of intermediary energy substrates to various glial cells, including oligodendrocytes (Govindaraju et al., 2000; Moffett et al., 2013). These findings align with those of Lin and colleagues [2022], who recently used whole-brain 1H-MRSI to examine metabolite differences in participants suffering from moderate to severe TBI at least 12 months after their initial injury. Although Lin and colleagues [2022] reported lower NAA and its ratios across several ROI, metabolite levels were not statistically different in the TBI group compared to healthy controls following FDR correction for multiple comparisons (Lin et al., 2022). Our study develops this line of inquiry further and confirms metabolite alterations in people with TBI during the chronic stage, with p values surviving multiple comparisons. Biochemical and structural abnormalities among key pain regions, including the frontal cortex (Glodzik-Sobanska et al., 2007), insula (Widerström-Noga et al., 2016), the primary somatosensory cortex (Ferris et al., 2021), and thalamus (Gustin et al., 2014), have been identified following neurotrauma, suggesting an emergent schematic of disrupted metabolic and structural integrity, and reorganization of existing neural tissue. To our knowledge, no studies have delineated metabolite alterations in those with chronic pain following TBI. However, our findings may also indicate that brain tissue metabolite alterations following TBI are necessary but not sufficient for developing chronic pain. Thus, a combination of cellular and molecular mechanisms, including altered neuronal and glial cell function or loss within the pain circuitry, may lead to maladaptive plasticity favoring persistent pain symptoms post-TBI. Indeed, maladaptive plasticity has been associated with neuropathic pain (Costigan et al., 2009). Although not significantly different from the non-pain TBI group, apparent higher metabolite levels in the bilateral frontal and insular regions, right postcentral, and right thalamus were observed among those with chronic pain. This observation may indicate a shift toward increased metabolic activity (hyperactivity) or loss of inhibitory control associated with a state of central sensitization within pain-related brain regions, also supported by positive correlations between NAA levels in the left and right frontal and left postcentral regions and pain measures. Similarly, NAA levels in the left and right frontal regions were also positively correlated with measures of depression, post-traumatic stress disorder, and post-concussion symptoms. Neuroimaging studies suggest that abnormal frontal executive connectivity largely contributes to developing chronic pain symptoms where the intensity and temporality are often comprised of affective and motivational elements (Davis and Moayedi, 2013). Circuitry housed within frontal brain regions is thought to provide top-down attentional control of salient sensory information while also representing the emotional content of pain within more midline regions (Kragel et al., 2018). The interpretation of tCho signal alterations within the selected ROIs is difficult to parse, mainly because the tCho signal includes contributions from free choline (Cho), glycerophosphorylcholine (GPC), and phosphorylcholine (PC) (Govindaraju et al., 2000). Cho is required for the synthesis of neuronal and glial membrane phospholipids (e.g., phosphatidylcholine) and the neurotransmitter acetylcholine (Ach), which is involved in sensory, motor, and cognitive function. GPC acts as a cerebral osmolyte, whereas PC is involved in the synthesis of myelin sheath-associated compounds (e.g., sphingomyelin), which is essential for axonal integrity (Govindaraju et al., 2000; Leipelt and Merrill, 2004; Javaid et al., 2021). Higher tCho levels have been consistently reported during the acute and subacute TBI stages, and this increase has been attributed to breakdown products of cell membranes and myelin and astrogliosis (Govind et al., 2010). Contrary to this general finding, both TBI groups showed lower tCho levels when compared to the healthy control group in the left thalamus. Moreover, those in the TBI and chronic pain group had lower tCho levels within the right cingulum, left frontal, left postcentral, and the right thalamic regions when compared to healthy controls. These reductions likely suggest permanent tissue damage and no further possibility of structural repairs, which manifest as a decreased tCho profile that differs from those in the acute and subacute injury stages. Future research with larger sample sizes is required to confirm this finding. Ultimately, our results showing lower tCho in the cingulum anterior, frontal, postcentral, and thalamic regions in the TBI and chronic pain group relative to the healthy control group may suggest either one or a combination of the following: [1] decreased membrane turnover or impaired cholinergic neurotransmission (i.e., Cho), [2] reduced cell volumes (i.e., GPC), or [3] decreased myelin synthesis (i.e., PC) in the chronic stage. As with tCho levels, the impact of lower m-Ins is difficult to delineate, given limited findings across previously published MRS studies (Kierans et al., 2014). Indeed, prior evidence suggests that m-Ins is a crucial marker for glial cell health and also serves as an osmolyte – with higher levels reflecting greater neuronal dysfunction through alterations in calcium signaling and other ion mediated transduction pathways (Fisher et al., 2002; Croall et al., 2015). Similar to tCho, studies in acute and subacute TBI generally report elevated m-Ins levels or its ratios (Garnett et al., 2000a; Kierans et al., 2014). Our results indicate that those with TBI and chronic pain presented with lower m-Ins levels across the left postcentral, and both the right and left thalamus regions when compared to healthy controls. This result differs from those reported by Yoon and colleagues (Yoon et al., 2005), who observed elevated m-Ins/Cre ratios among those with moderate to severe TBI five-months after their injury. Once again, reduced neurometabolite levels may suggest tissue damage and metabolic dysfunction within neural networks responsible for both the gating of sensory information (i.e., thalamus) and processing of such information within higher order brain centers (i.e., left postcentral region). Additional studies in long term TBI are required to replicate such findings. TBI-associated increases in tCre (creatine/phosphocreatine) have been documented with limited evidence suggesting that increased levels may reflect a combination of neuronal cell death paired with low grade gliosis (Garnett et al., 2001). While authors have noted altered tCre and its ratios in the acute (Gasparovic et al., 2009) and chronic TBI stages (Cadoux-Hudson et al., 1990), many assume constant levels of this metabolite when acquiring such measures, making observable variations difficult to contextualize across studies. Indeed, one study found no change in overall tCre levels in seven acute TBI cases (Garnett et al., 2001), while another observed increases in the neurometabolite that also correlated to more severe emotional distress and executive function performance in those with mild injuries (Gasparovic et al., 2009). That said, tCre represents an intracellular estimate of energy production, and when combined with the quantification of adenosine di/triphosphate (ADP and ATP), and inorganic phosphate (Pi), may provide a high-resolution marker for the metabolism of discrete brain regions associated with the processing of chronic pain symptoms. Based on the current findings and similar to those observed for m-Ins and lower tCre levels in the postcentral region of the TBI with chronic pain group relative to the healthy control group may suggest reduced glial cell density or volume (i.e., m-Ins) and reduced cellular energetics (i.e., tCre) (Govindaraju et al., 2000; Croall et al., 2015) within the left somatosensory cortex. Future studies are needed in larger cohorts to better examine variations in tCre years after TBI. Increases in Glx levels within gray and white matter have been associated with poor neurological outcomes in individuals with TBI (Ashwal et al., 2004; Shutter et al., 2004), possibly reflecting excitotoxicity. However, lower Glx levels have also been detected in individuals with TBI compared to controls (Kubas et al., 2010; Yeo et al., 2011). Such findings may indicate downregulation of glutamate or glutamine production as a neuroprotective adaptation to neuronal death (Ramonet et al., 2004) or an overall reduced neuronal activity (Yeo et al., 2011). Based on a recent meta-analysis (Joyce et al., 2022), differences in findings may be attributed to biologically relevant (e.g., brain region) and technical factors (e.g., data acquisition). Results from Joyce et al. also showed that Glx was commonly unaffected, similar to our results, where no significant difference was found among groups with respect to all regions of interest except for the thalamus. Ultimately, the global perception of pain is contingent on the proper integration of excitatory and inhibitory processes across brain networks, where functional and structural connectivity within these networks is essential for processing sensory-discriminative, affective-motivational, and cognitive-evaluative dimensions of pain (Treede et al., 2000; Apkarian et al., 2005; Kuner and Kuner, 2021; Mercer Lindsay et al., 2021; Tan and Kuner, 2021). Exposure of cortical and subcortical tissue to external forces of an appropriate magnitude may cause diffuse axonal injury and brain swelling (Werner and Engelhard, 2007). Coincidently, secondary injury pathologies such as neuroinflammation, abnormal monoaminergic signaling, and mitochondrial dysfunction may prompt a maladaptive reorganization of existing pain circuitry (Irvine and Clark, 2018) following TBI. This notion is further corroborated by parallel cell signaling pathologies that have been observed in those with spinal cord injury (SCI), painful diabetic neuropathy, trigeminal neuralgia, and complex regional pain syndrome (CRPS) (Chang et al., 2013). ## 4.1. Limitations The associations between metabolite levels and severity of initial TBI were not investigated in this study but have been reported previously by us and others in cross-sectional and longitudinal studies (Garnett et al., 2000a,b; Govind et al., 2010), showing conflicting results. Thus, associations between metabolites in the context of initial TBI severity remain to be fully elucidated, as well as any influence of the mechanism of injury (e.g., MVA vs. sports injury), pain medication, and damage to the brain tissue microstructure. In addition, the relatively small sample size of the present study may have affected the statistical power when comparing both TBI groups (with and without chronic pain), so future studies should be conducted with larger sample sizes to further expand and validate the presented results. Furthermore, age and age at TBI were significantly different among groups. Participants in the TBI and chronic pain group were significantly older than those in the TBI without chronic pain group. For that reason, we controlled for age in our between-group comparisons to account for possible confounding effects of age on metabolite levels (Brooks J. C. W. et al., 2001; Grachev and Apkarian, 2001; Reyngoudt et al., 2012). Interestingly, age at TBI were higher in the TBI and chronic pain group than in the TBI without chronic pain group, suggesting that an older age at TBI may predispose people to chronic pain. This observation is consistent with previous literature reporting that an older age at TBI was related to greater disability (Forslund et al., 2019; Rabinowitz et al., 2021). However, any association between age at TBI and chronic pain remains to be fully elucidated, as well as the effects of age at TBI on long-term neurometabolite alterations. ## 5. Conclusion The results of the present study suggest that those with chronic TBI present with significant neurometabolite alterations within pain-related brain regions. The extent of these alterations varied among the metabolite measured and the regions of interest. The presence of chronic pain was associated with significantly lower NAA, m-Ins, tCho and tCre levels across pain-related regions relative to controls, and with higher (although non-significant) NAA and tCre levels in the frontal, insula, right postcentral and thalamus regions compared to individuals without chronic pain. We also observed significant correlations between NAA levels with measures of pain and psychological factors, further emphasizing the relationship between the severity of pain and the extent of psychological distress among those with TBI. Altogether, these results may suggest that metabolite alterations are necessary but not sufficient for the maintenance of chronic pain post-injury and that additional underlying mechanisms, including maladaptive plasticity and functional reorganization in pain-related brain regions, may play a role in the development of chronic pain. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Home \| FITBIR (nih.gov). ## Ethics statement The studies involving human participants were reviewed and approved by University of Miami Institutional Review Board. The patients/participants provided their written informed consent to participate in this study. ## Author contributions EW-N, VG, and AM designed the study. LR, VG, TS, and SS analyzed the proton-magnetic resonance spectroscopic imaging data. LR, EW-N, and NC analyzed the pain and psychological measures. NC, LR, and TS prepared the manuscript draft. All authors substantially contributed to the interpretation of data and manuscript revision. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Clinical criteria to exclude acute vascular pathology on CT angiogram in patients with dizziness authors: - Long H. Tu - Ajay Malhotra - Arjun K. Venkatesh - Richard A. Taylor - Kevin N. Sheth - Reza Yaesoubi - Howard P. Forman - Soundari Sureshanand - Dhasakumar Navaratnam journal: PLOS ONE year: 2023 pmcid: PMC9997874 doi: 10.1371/journal.pone.0280752 license: CC BY 4.0 --- # Clinical criteria to exclude acute vascular pathology on CT angiogram in patients with dizziness ## Abstract ### Background Patients presenting to the emergency department (ED) with dizziness may be imaged via CTA head and neck to detect acute vascular pathology including large vessel occlusion. We identify commonly documented clinical variables which could delineate dizzy patients with near zero risk of acute vascular abnormality on CTA. ### Methods We performed a cross-sectional analysis of adult ED encounters with chief complaint of dizziness and CTA head and neck imaging at three EDs between $\frac{1}{1}$/2014-$\frac{12}{31}$/2017. A decision rule was derived to exclude acute vascular pathology tested on a separate validation cohort; sensitivity analysis was performed using dizzy “stroke code” presentations. ### Results Testing, validation, and sensitivity analysis cohorts were composed of 1072, 357, and 81 cases with 41, 6, and 12 instances of acute vascular pathology respectively. The decision rule had the following features: no past medical history of stroke, arterial dissection, or transient ischemic attack (including unexplained aphasia, incoordination, or ataxia); no history of coronary artery disease, diabetes, migraines, current/long-term smoker, and current/long-term anti-coagulation or anti-platelet medication use. In the derivation phase, the rule had a sensitivity of $100\%$ ($95\%$ CI: 0.91–1.00), specificity of $59\%$ ($95\%$ CI: 0.56–0.62), and negative predictive value of $100\%$ ($95\%$ CI: 0.99–1.00). In the validation phase, the rule had a sensitivity of $100\%$ ($95\%$ CI: 0.61–1.00), specificity of $53\%$ ($95\%$ CI: 0.48–0.58), and negative predictive value of $100\%$ ($95\%$ CI: 0.98–1.00). The rule performed similarly on dizzy stroke codes and was more sensitive/predictive than all NIHSS cut-offs. CTAs for dizziness might be avoidable in $52\%$ ($95\%$ CI: 0.47–0.57) of cases. ### Conclusions A collection of clinical factors may be able to “exclude” acute vascular pathology in up to half of patients imaged by CTA for dizziness. These findings require further development and prospective validation, though could improve the evaluation of dizzy patients in the ED. ## Introduction Posterior circulation stroke in patients presenting with dizziness is difficult to diagnose–delayed or missed diagnosis may occur in $37\%$ of such cases on first medical contact [1]. An underlying ischemic stroke is present in 3–$5\%$ of patients presenting dizziness to the emergency department (ED) [2]. Large vessel occlusion (LVO) is the underlying etiology in approximately a third of posterior circulation ischemic strokes [3, 4]. LVO is therefore expected in ~$1\%$ of dizzy patients presenting to the ED. The National Institutes of Health Stroke Scale (NIHSS) is the most commonly used predictor in pre-hospital/pre-CTA risk assessment for LVO, however even a NIHSS of zero does not guarantee absence of LVO [5, 6]. Stroke or vessel occlusion with low NIHSS is more common in the posterior circulation and with non-focal presentations such as dizziness [5, 7]. Other acute vascular pathologies detectable on CTA include dissection and medium/small vessel occlusion; rarely, even ruptured aneurysm may initially present as dizziness [2, 8–10]. The early detection of these abnormalities may also impact patient management [11]. Therefore, predictive tools used to reassure against the need for emergent CTA or pre-hospital diversion to centers with endovascular capability would ideally consider these entities as well. In the absence of vessel abnormalities on CTA, underlying stroke is better detected by MRI or specialized bedside maneuvers [2, 12, 13]. Excluding the need for CTA may improve diagnosis by facilitating triage to these more sensitive modalities. We hypothesize that there is a collection of clinical variables that can delineate a subpopulation of dizzy patients in whom there is a near zero probability of acute vascular pathology detectable on CTA. Such a collection of variables could form the basis of a decision rule guiding the selection of patients for CTA versus alternative testing. In this study, we perform a retrospective analysis to derive a decision rule excluding vascular pathology in dizzy ED patients. We then validate the rule on a temporally separate validation cohort and assess applicability to a similar group of “stroke code” presentations with dizziness. We compare performance to the NIHSS and estimate the proportion of CTA exams which are potentially avoidable. ## Setting and design We performed a cross-sectional analysis based on adult patient visits to one of three EDs in a healthcare system between $\frac{1}{1}$/2014-$\frac{12}{31}$/2017. The first ED is a comprehensive stroke center, the second is a primary stroke center, the last is a smaller community ED. Adult (age ≥18 years) patient encounters with a chief complaint of dizziness who received CTA head and neck imaging were included for the analysis. Adult “stroke code” encounters during the same time period, with NIHSS ≤ 7 and a positive review of systems for dizziness were also obtained for a sensitivity analysis in patients presenting within the treatment window with high suspicion of stroke. An NIHSS cut off 7 was chosen based on prior literature showing that patients with NIHSS > 7 have greater risk of LVO, independent of presenting with dizziness [6, 14]. Additional details of patient eligibility for CTA imaging are provided in Item 1 in S1 File. The study was approved by the Yale University IRB with consent waived. The decision rule was developed with reference to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines [15]. ## Data collection Established vascular risk factors for stroke/LVO were identified based on a review of major recent literature and used to inform the first phase of feature selection [16–20] [Item 2 in S1 File]. Clinical data expected to be available at the time of an imaging order were extracted from the electronic medical record database (EPIC)–including but not limited to recognized risk factors. Demographic information, past medical history (PMH), review of systems (ROS), and physical exam (PE) findings were obtained. Past medical history data also captured categories of medication use, smoking, and other substance use. Physical exam findings included systems-based exam findings as well as NIHSS and Glasgow Coma Scale data. All associated CTA head and neck exam reports were obtained. ## Case categorization CTA head and neck exam reports were categorized based on the presence of acute vascular abnormality. Acute vascular pathology was defined as large vessel occlusion, smaller arterial or venous occlusion, non-occlusive dissection, and aneurysm or other vascular lesion with hemorrhage. Operational definition of LVO is provided in Item 3 in S1 File. Various etiologies were included to account for the possibility that some patients with dizziness who do not have LVO may have other acute pathology whose acute management would be altered if detected on CTA. A decision rule excluding LVO but missing other entities requiring acute intervention would be less clinically useful. Age indeterminate though potentially acute findings were categorized as “positive” cases; this categorization was performed to maximize sensitivity even at the cost of potentially reduced specificity. ## Feature coding and missing data One-hot encoding was used to covert PMH data into categorical variables. Instead of grouping past medical history elements into categories (e.g., history of cancer or history of diabetes), specific diagnosis codes were included, to assess differential risk that may arise from different severity or manifestations of medical conditions. ROS and PE findings were represented by two binary variables, one representing occurrence of a “pertinent positive” (e.g., positive review of systems for headache) the other when occurring as a “pertinent negative” (e.g., negative review of systems for nausea). Absent or missing documentation was therefore represented as a null value for both pertinent positive and pertinent negative variables. Numerical values (e.g., age, pain rating) were coded as continuous variables. Additional details of feature encoding are given in Item 4 in S1 File. ## Feature selection The study population was split into training and validation cohorts consisting of the first $75\%$ and last $25\%$ of included patient encounters, respectively (corresponding to encounters before and after 14:27 on $\frac{5}{12}$/2017). Features that were rarely documented in the medical record and therefore impractical for use in a decision rule were excluded. We initially assessed the 200 most documented PMH features, 100 most commonly ROS features, all numerical vital signs, and the 100 most commonly PE features; these were drawn from an initial pool of 1500, 186, and 266 of PMH, ROS, and PE features and covered $74.5\%$, $96.9\%$, and $97.5\%$ of instances of documentation in these categories respectively. Features were then filtered to remove those with low relationship to the target variable based on Chi-Squared test, leaving the 100 highest ranked features. ## Decision rule derivation via sequential covering A decision tree was generated of sufficient depth to categorize all cases using the CART (Classification and Regression Trees) algorithm. Features were also ranked on random forest (Gini) importance to assess ability to separate positive and negative cases not just in the single tree, but across an ensemble of trees [21]; we used 100 trees, each with access to all top 100 Chi-Squared ranked features and allowed sufficient depth to categorize all cases. Features were then extracted to a decision rule in two phases. First, established vascular risk factors with high Gini importance (rank ≤ 5) were added to the list. Second, other (less-recognized) potential predictors were identified, based on highest Gini importance and location on the decision path predicting the largest leaf node without acute findings. Continuous variables were required to have a monotonic relationship with the target variable for inclusion, to account for the potential bias of Gini criteria toward high cardinality features [22]. Each time a feature was added to the list in either phase, the decision tree was re-created using cases not excluded by the evolving decision rule. Gini importance and decision tree structure were therefore used to obtain a list of features accounting for all positive instances of the target variable (i.e., via sequential covering [23]). Lastly, features on the list describing differing manifestations of a single condition were grouped into clinically coherent categories to arrive at the final decision rule. See Item 5 in S1 File for a schematic of this process. ## Decision rule validation and sensitivity analysis The decision rule was applied to the temporally separated testing cohort, with performance compared to the NIHSS. A sensitivity analysis for hyperacute presentations was also performed, by applying the rule to “stroke code” presentations with dizziness (within the review of systems) and a NIHSS ≤ 7. ## Performance measures and statistical analysis Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated, with $95\%$ confidence intervals (CI) using the Wilson score method [24]. The proportion of patients predicted to have a negative exam (potentially avoidable CTAs) was also reported with $95\%$ CI using Wilson score [24]. ## Software tools Data management, coding, and analysis were done with Python (Version 3.7; Python Software Foundation; Delaware, US) in the PyCharm Integrated Development Environment (Version 2020.2.2; JetBrains; Prague, Czech Republic). Statistical and data analytic packages included pandas, sklearn, dtreeviz, and tableone [25]. Manual review and categorization of CTA findings was performed in Microsoft Excel (Version 2203; Microsoft; Redmond, Washington, US). ## Case characteristics (training and testing cohort) During the study period, 15,483 adult patients presented to the ED with a chief complaint of dizziness. Of these, 1,429 ($9.2\%$), received a CTA head and neck exam, and were included for analysis. Review of CTA head and neck results revealed 47 ($3.3\%$) cases of acute vascular pathology ($$n = 31$$, $2.2\%$ for LVO only) [Fig 1]. This set of cases was used to produce the training and validation cohorts. Demographic and clinical characteristics of patients with and without acute vascular pathology are provided in Table 1. Details of the positive cases are given in Item 6 in S1 File. Fig 1: Flow of patients to categorization of acute vascular abnormality on CTA and subsequent temporal separation of cases into a training and validation cohort. TABLE_PLACEHOLDER:Table 1 The $\frac{75}{25}$ temporal split of the study population into training and validation cohorts generated with data sets with 1072 and 357 cases, respectively. There were 41 cases of acute vascular pathology in the training set and 6 in the validation set (28 and 3 cases of LVO respectively) [Fig 1]. ## Decision rule list Generation of a decision rule from the training cohort resulted in a collection of 23 features delineating a subset of encounters with zero cases of acute pathology. Summarizing these into clinical categories, we arrive at a decision rule predicting absence of acute vascular pathology in patients without a history of any of the following: stroke/transient ischemic attack (TIA), unexplained speech difficulty/ataxia or visual disturbances (i.e., possible prior stroke/TIA), dissection, coronary artery disease, diabetes, migraines, active/long-term smoking, and current long-term anti-platelet or anti-coagulant use [Table 2]. Table 2 \| Broad Clinical Category \| Features \| \| --- \| --- \| \| Adult ED patient presenting with a chief complaint of dizziness • Excluding those who may have an alternative indication for neurovascular imaging, aside from dizziness \| Inclusion Criteria: Adult ED patients with chief complaint of dizziness.Exclusion Criteria: Other chief complaints, where dizziness is not the predominant concern, though may be an associated symptom (e.g., focal neurologic deficit, trauma, headache) \| \| No PMH of cerebrovascular event, specifically: • Dissection, stroke, or TIA (unexplained dysarthria/aphasia, ataxia, incoordination, and visual disturbances) \| 1. PMH–Dissection of vertebral artery2. PMH–Transient ischemic attack (TIA), and cerebral infarction without residual deficits3. PMH–Other cerebral infarction4. PMH–Aphasia5. PMH–Dysarthria and anarthria6. PMH–Ataxic gait7. PMH–Ataxia, unspecified8. PMH–Lack of coordination9. PMH–Other lack of coordination10. PMH–Visual Disturbances \| \| No PMH of specific vascular risk factors: • Coronary artery disease, diabetes, and current/long-term smoking, and migraines \| 11. PMH—Coronary atherosclerosis of native coronary artery12. PMH—Atherosclerotic heart disease of native coronary artery without angina pectoris13. PMH—Presence of aortocoronary bypass graft14. PMH—Type II or unspecified type diabetes mellitus without mention of complication, not stated as uncontrolled15. PMH—Encounter for long-term (current) use of insulin16. PMH—Long term (current) use of insulin17. PMH—Nicotine dependence, cigarettes, uncomplicated18. Smoking–active19. PMH—Migraine, unspecified, without mention of intractable migraine without mention of status migrainosus20. PMH—Migraine, unspecified, not intractable, without status migrainosus \| \| No current specific long-term medication use: • Anticoagulation or anti-platelet agents \| 21. PMH—Encounter for long-term (current) use of aspirin22. PMH—Long term (current) use of antithrombotics/antiplatelets23. PMH—Long term (current) use of anticoagulants \| ## Decision rule performance In the derivation phase, the rule applied to 603 of 1072 ($56\%$) cases, excluding all 41 cases of pathology (28 cases of LVO). In the derivation set, the rule had a sensitivity of $100\%$ ($95\%$ CI: 0.91–1.00), specificity of $59\%$ ($95\%$ CI: 0.56–0.62), positive predictive value of $9\%$ ($95\%$ CI: 0.07–0.12), and negative predictive value of $100\%$ ($95\%$ CI: 0.99–1.00). A two-by-two table of patients excluded (vs. not excluded) by the decision rule and those with (vs. without) acute vascular pathology in the derivation phase is given in Item 8 in S1 File. Application of the decision rule to the validation cohort predicted absence of acute vascular pathology in 185 of 357 ($52\%$) cases, excluding all 6 cases of pathology (3 cases of LVO). This corresponds to a sensitivity of $100.0\%$ ($95\%$ CI: 0.61–1.00), specificity of $53\%$ ($95\%$ CI: 0.48–0.58), positive predictive value of $4\%$ ($95\%$ CI: 0.02–0.07), and negative predictive value of $100\%$ ($95\%$ CI: 0.98–1.00). The proportion of patients with near-zero risk and potentially avoidable CTAs was $52\%$ (CI: 0.47–0.57). The separate cohort used for sensitivity analysis (“stroke codes,” dizziness, NIHSS ≤ 7) consisted of 81 cases with 12 acute vascular findings (10 LVO). Application of the decision rule predicted absence of acute pathology in 35 patients ($43\%$), excluding all cases of acute pathology. Sensitivity was 1.00 ($95\%$ CI: 0.76–1.00), specificity 0.51 ($95\%$ CI: 0.39–0.62), positive predictive value $26\%$ ($95\%$ CI: 0.16–0.40), and negative predictive value was $100\%$ ($95\%$ CI: 0.90–1.00). The proportion of dizzy “stroke code” presents with near-zero risk and potentially avoidable CTAs was $43\%$ ($95\%$ CI: 0.33–0.54). Performance of the decision rule in the validation phase compared to NIHSS cut-offs is given in Table 3. Results of the derivation phase and sensitivity analysis are provided separately [Item 9 in S1 File]. Details of NIHSS documentation and performance are given in the Item 10 in S1 File. Table 3 \| Test \| Sensitivity \| Specificity \| Positive Predictive Value (PPV) \| Negative Predictive Value (NPV) \| Predicted Negative (PN) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| NIHSS ≤ 8 \| 0.00 (0.00–0.43) \| 0.03 (0.01–0.11) \| 0.00 (0.00–0.06) \| 0.29 (0.08–0.64) \| 0.10 (0.05–0.20) \| \| NIHSS ≤ 7 \| 0.00 (0.00–0.43) \| 0.03 (0.01–0.11) \| 0.00 (0.00–0.06) \| 0.29 (0.08–0.64) \| 0.10 (0.05–0.20) \| \| NIHSS ≤ 6 \| 0.00 (0.00–0.43) \| 0.05 (0.02–0.13) \| 0.00 (0.00–0.06) \| 0.38 (0.14–0.69) \| 0.12 (0.06–0.22) \| \| NIHSS ≤ 5 \| 0.20 (0.04–0.62) \| 0.08 (0.04–0.18) \| 0.02 (0.00–0.09) \| 0.56 (0.27–0.81) \| 0.13 (0.07–0.23) \| \| NIHSS ≤ 4 \| 0.20 (0.04–0.62) \| 0.10 (0.05–0.18) \| 0.02 (0.00–0.09) \| 0.60 (0.31–0.83) \| 0.15 (0.08–0.25) \| \| NIHSS ≤ 3 \| 0.20 (0.04–0.62) \| 0.13 (0.07–0.23) \| 0.02 (0.00–0.10) \| 0.67 (0.39–0.86) \| 0.18 (0.11–0.29) \| \| NIHSS ≤ 2 \| 0.40 (0.12–0.77) \| 0.18 (0.10–0.29) \| 0.04 (0.01–0.13) \| 0.79 (0.52–0.92) \| 0.21 (0.13–0.32) \| \| NIHSS ≤ 1 \| 0.40 (0.12–0.77) \| 0.29 (0.19–0.41) \| 0.04 (0.01–0.15) \| 0.86 (0.65–0.95) \| 0.31 (0.22–0.43) \| \| NIHSS = 0 \| 0.60 (0.23–0.88) \| 0.53 (0.41–0.65) \| 0.09 (0.03–0.24) \| 0.94 (0.81–0.98) \| 0.52 (0.40–0.64) \| \| Decision Rule (validation) \| 1.00 (0.61–1.00) \| 0.53 (0.48–0.58) \| 0.04 (0.02–0.07) \| 1.00 (0.98–1.00) \| 0.52 (0.47–0.57) \| ## Discussion In this study, we used retrospective data and a sequential covering approach to derive a decision rule excluding large vessel occlusion and other vascular pathology in patients presenting with dizziness. The resultant rule consists of well-recognized vascular risk factors and as well as other features identified based on random forest importance. The decision rule predicts absence of acute vascular pathology in patients who do not have any of the following past medical history features: Fig 2: Visualization of a potential decision rule to exclude acute vascular pathology in adult ED patients with chief complaint of dizziness.Clinical criteria are grouped into closely related categories. Further development and validation are needed prior to application in clinical contexts. In the testing cohort, the decision rule excluded acute vascular pathology with $100\%$ sensitivity and $53\%$ specificity, The rule was more sensitive than conventionally used NIHSS thresholds (NIHSS ≤ 7), as well as NIHSS = 0. Similar performance of the rule, $100\%$ sensitivity and $51\%$ specificity, was seen when applied to “stroke code” presentations with dizziness and with relatively lower risk of anterior circulation LVO (NIHSS ≤ 7). High sensitivity suggests utility as a “rule out” tool; non-applicability of the rule does necessarily indicate need for CTA or presence of underlying pathology. The decision rule applied to $52\%$ of cases in the validation cohort and $43\%$ of cases in the sensitivity analysis, suggesting that nearly half of CTA head and neck examinations might be avoidable, if such a tool were sufficiently reassuring to defer neurovascular imaging. Dizziness is a non-specific symptom that accompanies a wide range of neurologic, cardiovascular, psychiatric, and other disease processes [2]. In this study, we focused on those patients in whom dizziness is the presenting complaint, and in whom there may be no alternative presentations warranting neurovascular evaluation. For example, patients who are dizzy in the setting of trauma or thunderclap headache would receive neurovascular evaluation for other reasons. Our decision rule suggests that documented (otherwise unexplained) speech, coordination, and vision abnormalities be considered potential prior stroke or TIA for the purposes of risk stratification. This would be consistent with prior research showing that as many as $90\%$ of posterior circulation TIAs may be misdiagnosed on first medical contact [26]. Among established vascular risk factors, smoking status and diabetes were included in the decision rule; hypertension and hyperlipidemia were conspicuously absent. It is conceivable that these or other risk factors for stroke would provide predictive value if the rule were updated or validated with additional data sets. Medical history elements seemed to be much more strongly predictive than review of system or physical exam findings, which may reflect the pre-selection of ED visits with dizziness as a primary concern. A notable difference between the risk factors highlighted by our study and existing predictive tools is the inclusion of migraine history as a predictive factor. Migraine with aura is a well-described, though perhaps underappreciated, risk factor for both dissection and stroke [27–29]. Our study suggests potential value within prediction tools alongside more established vascular disease processes. Prior research suggests that vascular imaging to evaluate for large vessel occlusion may be cost-effective in acute minor stroke patients even when the risk of LVO is low ($0.2\%$) [30]. Predictive tools for vascular pathology in stroke need to have near-perfect sensitivity to appropriately defer neurovascular evaluation. The collection of risk factors highlighted by our study suggests that subpopulations of patients with dizziness may exist with near-zero risk for pathology detectable on CTA. None of the patients excluded by the decision rule (in the training, validation, or sensitivity analysis cohorts) had an acute abnormality on CTA. Future research could assess the potential role of such a decision rule in conjunction with existing bedside exams and risk stratification tools. Strokes in the posterior circulation are less often caused by LVO than by cardiac embolism or lacunar mechanism [3]. The decision rule developed in this work may assist in distinguishing underlying etiologies by excluding patients with stenotic atherosclerosis/vasculopathy who subsequently develop vessel occlusion. CTA is less sensitive for subtle ischemic events that arise from distal embolic and other etiologies [2]. Patients with minimal risk for LVO, but still suspected to have stroke might could be prioritized for more sensitive MRI, HINTS (head impulse, nystagmus, and test of skew), or subspecialist consultation. Vascular imaging could be deferred in this cohort and performed only if alternative indications arise. ## Limitations Our study has some limitations. The first of these is the retrospective nature of data collection. Documentation in ED medical records is known to be inconsistent [31], which limits incorporation of predictive factors not appearing in the medical chart. Decision rule performance would also be impacted by medical history elements which may be present but not documented. Prospective and external validation would help address these limitations. Our data precede the COVID-19 pandemic; therefore, any potential thromboembolic risk from COVID-19 infection or related to rare vaccination side effects [32] are not captured. This possibility could be incorporated in future studies. Considering age-indeterminate findings on CTA to be potentially acute allowed us to err on the side of maximizing sensitivity, though could have reduced specificity for detection of acute abnormality. The consideration of a large and varied set of features allowed us to identify risk factors which may have been underappreciated, though could also reduce generalizability. The derivation of predictive tools for dizzy patients is complicated by the low prevalence of acute abnormality on imaging and therefore fewer positive events. We expect the need to refine the decision rule and estimates of performance with subsequent work using additional datasets. Most of the study limitations would have reduced the ability to identify patients at low risk; yet, we still found that half of patients may not have required CTA. ## Conclusion Our study suggests that a collection of clinical variables may delineate a sizeable subpopulation of dizzy patients receiving CTA in whom there is a near zero risk of acute vascular pathology including LVO. A decision rule composed of these predominantly well-recognized risk factors could apply to as many as half of patients currently evaluated by head and neck CTA. Further development and validation of this predictive tool would help guide efficient diagnosis and management of dizzy patients in the emergency department. ## References 1. Arch A.E.. Missed Ischemic Stroke Diagnosis in the Emergency Department by Emergency Medicine and Neurology Services. Stroke (2016.0) 47 668-73. DOI: 10.1161/STROKEAHA.115.010613 2. Saber Tehrani A.S.. Diagnosing Stroke in Acute Dizziness and Vertigo: Pitfalls and Pearls. 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--- title: Modeling the progression of Type 2 diabetes with underlying obesity authors: - Boya Yang - Jiaxu Li - Michael J. Haller - Desmond A. Schatz - Libin Rong journal: PLOS Computational Biology year: 2023 pmcid: PMC9997875 doi: 10.1371/journal.pcbi.1010914 license: CC BY 4.0 --- # Modeling the progression of Type 2 diabetes with underlying obesity ## Abstract Environmentally induced or epigenetic-related beta-cell dysfunction and insulin resistance play a critical role in the progression to diabetes. We developed a mathematical modeling framework capable of studying the progression to diabetes incorporating various diabetogenic factors. Considering the heightened risk of beta-cell defects induced by obesity, we focused on the obesity-diabetes model to further investigate the influence of obesity on beta-cell function and glucose regulation. The model characterizes individualized glucose and insulin dynamics over the span of a lifetime. We then fit the model to the longitudinal data of the Pima Indian population, which captures both the fluctuations and long-term trends of glucose levels. As predicted, controlling or eradicating the obesity-related factor can alleviate, postpone, or even reverse diabetes. Furthermore, our results reveal that distinct abnormalities of beta-cell function and levels of insulin resistance among individuals contribute to different risks of diabetes. This study may encourage precise interventions to prevent diabetes and facilitate individualized patient treatment. ## Author summary Mathematical models depicting the progression of Type 2 diabetes can be used to study the durable effect of anti-diabetic agents at different disease stages. However, few existing models have been capable of depicting the long-term dynamics of diabetes progression. Mounting evidence has indicated that hyperinsulinemia aggravates insulin resistance no matter in which order they occur. We developed a generalized model which incorporates the effect of hyperinsulinemia on insulin resistance and enables the quantitative study of the impact of diverse diabetogenic factors on beta-cell dysfunction. Specifying the diabetogenic factor to be obesity-related in the modeling framework, we generated an obesity-related diabetes model to investigate the influence of obesity on the glucose regulatory system. The long-course glucose and insulin trajectories depicted by the obesity-diabetes model are consistent with clinical observations and capture the trend of the longitudinal glucose data of the Pima Indian tribe. This work may have the potential to help develop therapeutic strategies for diabetes. ## Introduction Mathematical modeling has been a valuable approach to delineating the glucose metabolic system. Various mathematical models have been proposed over the last decades to study type 2 diabetes mellitus (T2D) focusing on different aspects [1–7]. A well-accepted model depicting the beta-cell mass dynamics along with the progression of T2D is developed by Topp et al. [ 1]. Topp’s model became a foundation for other models to study diabetes progression. To describe the effects of treatment on the time-course of insulin sensitivity and progressively impaired beta-cell function, De Winter et al. [ 2] developed a population pharmacodynamic model that consists of the differential equations of glucose, insulin, and glycosylated hemoglobin A1c. In [3], Gaetano et al. formulated a model of pancreatic islet compensation to depict the concurrent evolution of beta-cell mass, pancreatic beta-cell replication reserve, glycemia, and insulinemia. Wang studied the hypothesis that intermittent insulin secretion allows beta-cells to rest and be re-sensitized [4]. To explain the observation that fasting hyperinsulinemia can precede hyperglycemia by up to decades, Ha, Sherman, and their colleagues extended Topp’s model by relating beta-cell proliferation to the secretory workload of beta-cells [5], which can amplify the impact of small changes of glucose. The model successfully demonstrated the effect of weight loss and bariatric surgery on the glucose regulatory system. Despite all these efforts, a model that describes a long-course evolution of diabetes integrating diabetogenic factors (e.g. obesogenic environment) remains lacking. The primary causal factor for the progression of T2D has been believed to be insulin resistance induced by the interaction of insulin resistance genes and obesity which drives the hypersecretion of beta cells for insulin compensation. Esser et al. [ 8] remarked that this prevailing belief has since been modified: beta cell dysfunction, manifested as impaired insulin secretion, is the primary abnormality, with insulin resistance driving the hypersecretion of beta cells to maintain normal glucose levels. However, in the presence of an early beta cell dysfunction triggered by environmental factors and genetics, the increased beta cell secretory demand cannot be met by inadequate insulin, and dysglycemia occurs subsequently [8]. A recent and new perspective on T2D progression (Fig 1) claims that beta-cell hyper-responsiveness, induced by epigenetic-related or environmental factors (e.g. high-fat diets), causes hyperinsulinemia, with this aftereffect being the driving source of insulin resistance for individuals at risk of T2D [9–17]. The rationale behind this view is such that insulin resistance is a defense mechanism against insulin-induced metabolic stress rather than being harmful [13], as indicated by recent clinical studies in humans [18]. Johnson [18] and Corkey et al. [ 19] proposed hyperinsulinemia per se to be a manifestation of beta cell dysfunction including both the excessive and deficient insulin secretion stimulated by glucose as well as other nutrients. The mechanisms of T2D progression, including the order of each pathological stage, may vary among populations with different genotypes [18, 20]. In vivo studies have shown that hyperinsulinemia can cause insulin resistance for at least the phenotype of obesity-related diabetes [21–24]. Overall, surges of evidence indicate that hyperinsulinemia aggravates insulin resistance although the order of their occurrence remains under discussion and study [15, 25–27]. The field exploring the pathogenesis of T2D has come to the agreement on the critical role of beta-cell dysfunction in the progression to diabetes. Further understanding of the mechanisms underlying beta-cell dysfunction would contribute to better preventative and therapeutic interventions for the disease. Fig 1: A conceptual framework that views insulin hypersecretion as the upstream of insulin resistance and the development of T2D.Different from the view that hyperinsulinemia is the downstream of insulin resistance, it proposes that hyperinsulinemia results from the hyperresponsive beta-cells to hostile environment, and insulin resistance is an adaptive mechanism protecting critical tissues from insulin-induced metabolic stress [13]. In this paper, we developed mathematical models to investigate the significance of beta-cell dysfunction on diabetes progression. We first considered which pathogenic factors lead to the dysfunction of beta-cells, as the glucose regulatory system of the human body can maintain homeostasis without external interference factors. The risks of beta-cell dysfunction and the development of T2D are related to sustained exposure to fuel excess, or the failure to store excess fuel properly [19], as well as the crosstalk interactions with other endocrine diseases [28]. Beta-cell function (total secretion of all beta-cells) and blood glucose levels are under the control of diverse hormones such as epinephrine, growth hormone, glucocorticoids, and thyroxine. Abnormal levels of these hormones can lead to an increased risk of hyperglycemia [29]. In consideration of these observations, we incorporated an interference factor into the glucose regulatory model, accounting for the progressive impact of the diabetogenic factor on the beta-cell function and the longitudinal regulation of glucose. We formulated a base model for the undisturbed glucose-insulin regulatory system, upon which, we built the diabetes progression model framework with a generalized diabetogenic factor. We then specified the pathogenic factor to be obesity-related and construct the corresponding obesity-diabetes model. The simulated dynamics of the obesity-diabetes model delineate the long-term impact of obesity on the trends of glucose and insulin levels. We validated the model on the longitudinal data of the Pima Indian population, which characterizes the individualized glucose variations and predicts the glucose trajectories over their lifespan. Our model is capable of revealing distinct stages of the progression of diabetes (i.e., the euglycemic with hyperinsulinemia stage, prediabetic stage, and overt diabetic stage) and may assist physicians in designing optimal therapy with the individualized prediction of the disease evolution. Additionally, sensitivity tests on some key parameters of the obesity-related diabetes model were conducted to demonstrate the robustness of the model, and the analysis of the biological significance for these parameters sheds light on efficient treatment strategies for diabetes. We provide a holistic analysis of diabetes progression, including the identification of individualized obese thresholds of diabetogenic risk, the role of ethnic disparities in varied tolerance to the obesogenic environment, and the effectiveness of Roux-en-Y gastric bypass (RYGB) surgery. Thus, our modeling framework may be impactful to study individualized diabetic intervention and the effects of new therapies. ## Patient data The patient data were obtained from the work by Mason et al. [ 30]. We studied 11 sets of data from Pima Indians of the Gila River Indian Community in Arizona, who took part in a longitudinal study of diabetes. The subjects over the age of 5 were invited every two years to examine their plasma glucose using a 75-g oral glucose tolerance test (OGTT). The selected subjects experienced no less than 10 non-diabetic biennial OGTT before developing hyperglycemia (2-h plasma glucose ≥ 200mg/dl). The OGTT measurements were taken before the initial diabetes diagnosis and the treatment. A majority of the time course glucose data are characterized by an initial linear trend accompanied with a sharp rise at the end (Figs 1, 3 in ref. [ 30]). ## A phenomenological diabetes progression model The human body relies upon a compact control of blood glucose levels (70–100 mg/dl) to maintain its normal function. Insulin, synthesized in pancreas within the beta-cells, boosts the glucose utilization by target cells and plays an essential role in blood glucose regulation. High blood glucose levels induce the release of insulin from beta-cell secretory granules, which helps reduce the glucose concentration to the normal level. When the glucose level descends, the secretion of insulin ceases gradually. For an individual free from diabetogenic events, glucose homeostasis can be maintained by the glucose regulatory system. We first formulate a glucose-insulin regulation model for the euglycemic state, which serves as a base model for building our diabetes progression models. Very few models have accommodated the impact of hyperinsulinemia on insulin sensitivity [6]. We build the basic model upon the model of Topp et al. [ 1], where depicting the glucose-insulin dynamics in a slow time-scale (from days to years) becomes achievable by incorporating the functional pancreatic beta-cell mass equation into the system. The Topp model is dGdt=Gin-g1G-CGI,dIdt=s1G2G2+s2β-kI,dβdt=(-d0+r1G-r2G2)β, where G (mg/dl), I (μU/ml), β (mg) stand for the plasma glucose concentration, insulin concentration and the mass of functional beta-cells (preserving appropriate insulin production and secretion) at time t (days), respectively. The parameter Gin stands for the average rate of glucose infusion per day (with meal ingestion as a main source), including the hepatic glucose production. The term g1G represents insulin-independent uptake of glucose, mainly by brain cells and nerve cells. In contrast, the term CGI depicts the insulin-dependent uptake of glucose, mostly by fat cells and muscle cells in the human body. In particular, the coefficient C (ml/μU/day) stands for insulin sensitivity. The insulin secretion from beta-cells is assumed to be triggered by increased glucose levels in the form of the Hill function with coefficient 2, and the parameter s1 represents the secretory capacity per beta-cell. The insulin clearance rate is denoted by k (/day). The functional beta-cell mass is hypothesized to respond to glucose with a pattern similar to a downward parabola: moderate amount of glucose promotes the growth of beta-cells, while a high glucose level exacerbates beta-cell apoptosis, resulting in the decrease of functional beta-cell mass. The Topp model is not sufficient to explain the occurrence of hyperinsulinemia before or during the prediabetic stage, since the unnoticeable change of glucose during these two stages cannot induce apparent increase of beta-cell mass [5]. Additionally, in Topp’s model [1], the insulin sensitivity C is assumed to be a constant for the entire life or an artificial time-dependent decreasing function ae−rt with a, r > 0. Changing the value of insulin sensitivity C and the maximal releasing rate s1 in the model is still incapable of affecting the glucose level corresponding to the steady state [6]. This indicates that the Topp model cannot present the connection between varying degrees of impaired insulin signalling and the severity of hyperglycemia. To characterize the hyperinsulinemia-induced insulin resistance, we propose the insulin sensitivity to be a decreasing function of the insulin level, and assume that the insulin sensitivity function C(I) strictly decreases from C[0] to a positive number r0 when I increases. This is consistent with the finding of some studies that insulin sensitivity can be enhanced with reduced circulating insulin [31, 32]. Furthermore, in the Topp model, the glucose level is assumed to be an explicit factor regulating beta-cell mass. In vivo experiments have indicated that the replication of primary beta-cells is stimulated directly by insulin, instead of glucose, and the effects of glucose on beta-cell replication can be disclosed by glucose-induced insulin release and signaling [33, 34]. In particular, Johnson et al. proposed the “Sweet Spot” hypothesis, which speculates that moderately increased local insulin prompts the compensatory beta-cell hyperplasia, but once the elevated local insulin exceeds a certain level, it would cease to protect beta-cells and the beta-cell mass is anticipated to decrease [34]. Inspired by the formulation of the beta-cell equation in work [4], we model the net growth rate of functional beta-cell mass with a nonlinear function f3(I) that depends on the insulin level. In view of the net effect of beta-cell proliferation, neogenesis, and apoptosis on the growth rate of the functional beta-cell mass, f3(I) is assumed to take the form f3(I)=m1I/(I2+m22)-m3, where m1, m2, m3 > 0. The function has two positive roots I1, I2 (I1 < I2) and f3(I) > 0 in the interval (I1, I2), while f3(I) < 0 when I > I2. With this function, when the insulin level exceeds the bound I2, the functional beta-cell mass will decrease to diminish the insulin secretion, dragging the insulin level down to I2 over time. That is, our base model is designed upon the assumption that the normal self-regulating function of beta-cells on insulin secretion maintains the homeostasis of insulin and glucose levels without interference factors from the hostile environment. ## Model for undisturbed glucose-insulin regulatory system The base model is given by dGdt=Gin-f2(G)-C(I)GI, [1] dIdt=f1(G)β-kI, [2] dβdt=f3(I)β, [3] where f2(G) = g1G, C(I)=r0+r1r2+er3I, f1(G)=s1G2G2+s2, f3(I)=m1II2+(m2)2-m3; all parameters in the model are positive. Our rationale of incorporating the functional beta-cell mass into this model is to exhibit the long-course variation of beta-cell function, as we consider the integration of beta-cell secretory function (the ability to produce, store and release insulin) per cell and the functional beta-cell mass as an indicator of beta-cell function. When the normal regulation of the glucose-insulin system is disturbed, dysglycemia may occur, the progression of which can be elucidated by the diabetes progression models below. ## A generalized diabetes progression model The culprits of diabetes may vary for different subgroups of diabetic patients, which implies the distinction of possible interference factors to the glucose regulation system. Nevertheless, the underlying mechanisms through which the factors lead to dysglycemia are common. Numerous studies indicate that glycemia is primarily attributed to excess hepatic glucose output and abnormal insulin secretion and utilization [35]. Of note, beta-cell function is regulated by various mechanisms, not limited to glucose utilization [12]. Thus, confining the model for beta-cell function only with the variables of glucose and insulin may impede the study of beta-cell dysfunction. We aim to test through an in-silico approach how the T2D progression is affected by certain pathological factors. Here we propose a general form of diabetes progression model with a pathological factor X that is to be specified: dGdt=Gin+p1(X)-f2(G)-C(I)GI, [4] dIdt=f1(G)p2(X)β-kI, [5] dβdt=(f3(I)+p3(X))β, [6] where X is a bounded variable with a real value; all the variables in the system are in the time scale of days: p1(X) is incorporated into Eq [1] to stand for the increased hepatic glucose production caused by the pathological factor; p2(X) integrated into Eq [2] symbolizes the impact of the factor on the insulin secretion rate; p3(X) is incorporated to Eq [3] to describe the abnormal response of beta-cells to a hostile environment that develops in a slow time scale. The exact forms of the influence functions pi(X) ($i = 1$, 2, 3) will be determined with X being an obesity-related factor in Section. We assume that p1(X) = 0, p2(X) = 1, and p3(X) = 0 when $X = 0$ so the model is in accordance with the undisturbed glucose-insulin regulatory model when no diabetogenic factors exist in normal subjects. ## Obesity-related diabetes model Environmental changes, including the changes of dietary habits and activity levels, are correlated to the epidemic incidence of diabetes [11, 36]. These changes interfere with the normal regulation of the glucose-insulin system [10, 12]. The upsurge in obesity has been closely linked to the increased prevalence of diabetes. Longitudinal studies have shown that the increase in the body mass index (BMI) over time is predominant among the risk factors for the raise in diabetes prevalence, while a mild decrease of BMI can induce a significant reduction in the risk of diabetes [37, 38]. Many mechanistic studies investigated the obesity-diabetes connection, but the view that obesity is the direct cause of diabetes is controversial [39]. Corkey et al. suggested that sustained exposure to excess fuel, or the failure of decreasing circulating fuel levels, stimulates and maintains basal hyperinsulinemia [19]. In addition, excessive calorigenic nutrients increase the production of reactive oxygen species (ROS) and the ectopic deposit of lipids in liver and pancreas, which may cause hepatic insulin resistance and beta-cell dysfunction [19, 40–43]. Although whether excess ROS and liver and pancreas fat are the primary causative factors for T2D has not been fully affirmed at the present stage, diabetes may share common pathogenic factors with obesity, at least for the subgroup of obesity-related diabetes [15]. We name the co-factor as the obesity-related factor hereinafter. To quantitatively study the impact of the obesity-related factor on diabetes, we introduce the factor into the GIβ model. As the progression of obesity leads to a worse impact on the GIβ regulatory system, we set X to be the severity of the obese-related factor and quantify it as a variable with an upper bound of 1. Considering obesity is attributable to the progression of hepatic insulin resistance and gluconeogenesis [44], we design the elevated hepatic glucose production p1(X) to be a power function of X, which can be determined by the extent to which the pathogenic factor impacts the glucose generation rate. Prior research has established the association between obesity and a modest expansion of beta-cell mass in non-diabetic subjects [36]. In contrast, there is also a growing body of evidence that the accumulation of toxic metabolites (including ROS) within beta-cells in obese patients accelerates beta-cell apoptosis, leading to the progression to overt diabetes [45]. Thus, we assume functional beta-cell mass undergoes the impact of X in a pattern of a downward parabola and construct the influence function p3(X) with the form in Eq [9]. Moreover, some experimental data demonstrate that insulin hypersecretion in obese subjects is attributed more to an increase in beta-cell secretory function than to an increase in beta-cell mass [36, 46]. For simplicity, we assume the beta-cell secretion function is linearly increasing with X, as shown in Eq [8]. Furthermore, considering that obesity would progress slowly for an individual to a maximum limit, we assume X follows a logistic growth, given in Eq [10]. p1(X)=h1Xα, [7] p2(X)=1+h2X, [8] p3(X)=q1X(q2-X), [9] dXdt=n1X(n2-X). [ 10] Here h1 represents the extent to which the gluconeogenesis rate is elevated by the factor; α determines how fast p1(X) changes with respect to X; h2X describes the increased insulin secretion rate induced by the factor; p3(X) stands for the influence of X on the net growth rate of functional beta-cell mass. The influence is positive when the values of X stay below q2, and the influence becomes negative when the level of X exceeds q2; that is, q2 describes the beta-cell tolerance for the pathogenic factor. The parameter n1 represents the growth rate of X in a specific environment; n2 stands for the maximal level of obesity that an individual would reach progressively (n2 ≤ 1). The GIβ dynamics may behave differently with varied parameter values. In particular, different parameter values in C(I) can reveal distinct insulin resistance profiles in the obesity-related diabetes group. ## Results According to the American Diabetes Association (ADA), the fasting glucose levels for euglycemia, prediabetes, and diabetes are less than 100 mg/dl, 100 mg/dl to 125 mg/dl, and higher than 125 mg/dl, respectively [47]. The normal range of fasting insulin differs slightly between labs. Here we considered 5—20 μU/mL as the reference range for normal fasting insulin and adopt I ≥ 25 μU/mL as the criterion of hyperinsulinemia [48–50]. Throughout the numerical studies, we interpreted the simulation results according to the above ADA’s definition. The numerical method that solves the differential equations is the Runge-Kutta 4th-order algorithm embedded in the commercial software, Berkeley Madonna. The step size was set to be 0.005. Using proper sets of parameter values for the undisturbed glucose-insulin regulatory model, as shown in Table 1, straightforward computation indicates the base model has a steady state (for fasting) at (G, I, β) = (100 mg/dl, 20 μU/ml, 300). The parameter values and steady state may vary among individuals. In this work, excluding the values of Gin, g1, s2, and k, we choose values of the other parameters to guarantee the consistency of the simulated glucose-insulin dynamics with clinical observations. Table 1 \| Parameters* \| Set A \| Set B \| Unit \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| r 0 \| 0.019 \| 0.036 \| ml ⋅ μU−1 ⋅ day−1 \| see text \| \| r 1 \| 1.98 \| 29.32 \| ml ⋅ μU−1 ⋅ day−1 \| see text \| \| r 2 \| 3.088 \| 81.45 \| — \| see text \| \| r 3 \| 0.05 \| 0.11 \| — \| see text \| \| G in \| 864.0 \| 864.0 \| mg ⋅ dl−1 ⋅ day−1 \| [1] \| \| g 1 \| 1.44 \| 1.44 \| day −1 \| [1] \| \| s 1 \| 86.4 \| 86.4 \| μU ⋅ ml−1 ⋅ day−1 \| see text \| \| s 2 \| 20000.0 \| 20000.0 \| mg2 ⋅ dl−2 \| [1] \| \| k \| 432.0 \| 432.0 \| day −1 \| [1] \| \| m 1 \| 0.1 \| 0.1 \| day −1 \| see text \| \| m 2 \| 100.0 \| 100.0 \| μU ⋅ ml−1 \| see text \| \| m 3 \| 0.004 \| 0.004 \| day −1 \| see text \| \| h 1 \| 300.0 \| 300.0 \| mg ⋅ dl−1 ⋅ day−1 \| see text \| \| α \| 13.0 \| 13.0 \| — \| see text \| \| h 2 \| 0.1 \| 0.1 \| — \| see text \| \| q 1 \| 0.04 \| 0.025 \| day −1 \| see text \| \| q 2 \| 0.5 \| 0.5 \| — \| see text \| \| n 1 \| 0.0005 \| 0.0005 \| — \| see text \| \| n 2 \| 1.0 \| 1.0 \| — \| see text \| ## Impact of the obesity-related factor on the progression of diabetes We created a virtual patient A with the parameter values in set A of Table 1 and the initial condition of (G, I, β, X) = (100 mg/dl, 20 μU/ml, 300, 0.01) at a fasting state. We further assume the obesity severity can gradually reach the upper bound of 1 over the lifespan of the virtual patient. The simulation results of the obesity-related diabetes model, shown in Fig 2, demonstrate that the obesity-related factor can lead to the elevation of the person’s insulin level and patient A would develop hyperinsulinemia in about 7 years (2631 days). During the first seven years, the glucose levels are below 102 mg/dl with slightly increased insulin levels. However, the insulin level rises higher and quicker in the first hyperinsulinemic stage, and the glucose level progressively increases to 125 mg/dl in 17 years (6063 days). The result that hyperinsulinemia precedes the onset of diabetes of patient A for 9.6 years, is consistent with the clinical phenomenon that fasting hyperinsulinemia, as a prediction of diabetes, may precede hyperglycemia by up to decades [51]. Fig 2: Dynamics of glucose, insulin and functional beta-cell mass level for patient A with severe obesity (X approaches 1 progressively).This figure depicts the impact of severe obesity on the glucose regulatory system. The pathogenic factor X initially overstimulates the beta-cell function of this patient. The insulin level exceeds 25 μU/ml after day 2631, which represents the onset of hyperinsulinemia. However, at this time, the glucose level is 102 mg/dl, which is close to normal. As X continuously increases, the functional beta-cell mass keeps rising before day 6089 and then decreases afterwards. Correspondingly, the insulin level retains increasing before day 7015 and then descends. The hyperinsulinemic stage ends after day 9373 and insulin deficiency gradually occurs afterwards. In the hyperinsulinemic stage, patient A experiences some fluctuations in the glucose levels. The glucose level exceeds the threshold of diabetic stage on day 6063, yet decreasing for a while after reaching the level 151 mg/dl on day 7134. Subsequent to the appearance of beta-cell failure and insulin deficiency, a sharp rise of the glucose level occurs, transitioning patient A to overt diabetes. As the beta-cell function starts to fail but has not completely failed, due to the mitigated hyperinsulinemia, the insulin sensitivity gradually increases, which pauses the progression of the ultimate hyperglycemia and improves the glucose level from 151 mg/dl to 117 mg/dl. The fluctuations of glucose levels have also been observed in some clinical data (such as the Pima Indian data that will be presented later for data fitting in this paper). Nevertheless, insulin deficiency occurs over time subsequent to the occurrence of beta-cell failure. The massive and fast decline of insulin levels leads to the upsurge of glucose and transitions the patient to overt diabetes in the end (Fig 2). In the simulation results of Fig 2, we assume that the insulin sensitivity function C(I) depends only on the insulin level. The predicted dynamics of the insulin sensitivity are shown in Fig 3A (dashed line). Although a decreasing function C(I) can describe an enhanced insulin sensitivity with reduced circulating insulin, there is a limitation of using this function. As insulin decreases to a value lower than the initial level, insulin sensitivity is predicted to become greater than its initial value (Fig 3A dashed line). This seems to contradict the known physiology that insulin sensitivity decreases progressively with advancing diabetes and with obesity. To overcome this limitation, we assume that the insulin sensitivity function also depends on the obesity-related factor X (i.e. assuming C(I, X) decreases as X increases), accounting for the elevated insulin resistance driven by increased adiposity and free fatty acids [15, 52]. Choosing a specific function of X-induced insulin resistance, we plot the time evolution of insulin sensitivity C(I, X) in Fig 3A (solid line). Model simulation with the new function C(I, X) shows that the insulin sensitivity decreases before the onset of diabetes, then increases slightly for a while due to diminished insulin-induced resistance, and then goes back to the decreasing trend with the progression of obesity. This is consistent with the long-term behavior of insulin sensitivity. We also compare the dynamics of glucose, insulin and β-cell mass using the two functions C(I) and C(I, X) and find no significant difference in the GIβ dynamics (Fig 3B–3D, dashed vs. solid line). This result is not surprising as reduced insulin sensitivity only plays a significant role in transiting subjects from euglycemia to the onset of diabetes. In the diabetic stage, the defect of beta-cell function and mass is the dominant factor driving the deterioration of the disease [8]. For these reasons, we use the original function C(I) that minimizes the number of model parameters in later simulations and data fitting to the Pima Indian patients. Fig 3: Comparison of the insulin sensitivity and GIβ dynamics using C(I) and C(I, X) in the model.The function C(I, X) is chosen to be C(I)·(1-0.8X4X4+0.54), where C(I) is given in model (1). The other parameters maintain the same as in Fig 2. Graph (a) shows the time evolution of the insulin sensitivity C(I) in the dashed curve and C(I, X) in the solid curve. Graphs (b), (c), and (d) exhibit the corresponding GIβ dynamics under the two insulin sensitivity functions. ## Controlling/eradicating the obesity-related factor alleviates/reverses diabetes Distinct risk levels under the obesogenic environment, quantified by the different upper bound values of X, have varied influences on the GIβ system. Suppose that patient A is exposed to a mild obesogenic environment, where the maximal value that the obesity-related factor X would increase to is 0.17. The initial condition of X is set to be close to zero. The other parameters remain the same as those in Fig 2. The simulation results, presented in Fig 4, show that hyperinsulinemia would occur in 42 years (15460 days) and stay for the remainder of the life of patient A. Additionally, the patient would not become diabetic until 55 years (20053 days) later, a delay of 38 years relative to the condition of the uncontrolled obese severity in Fig 2. In contrast with the advanced diabetes that patient A would develop under the severe obesogenic environment, a moderate glycemic level of less than 130 mg/dl can remain in the later life of patient A with the alleviated obesity-related factor. The apparent alleviation of the disease progression rate and severity with the reduced level of obesity supports the recommendation of physicians for maintaining a healthy lifestyle. Fig 4: Dynamics of glucose, insulin and functional beta-cell mass level for patient A with mild obesity (X approaches 0.17 progressively).This figure describes the impact of the controlled obesity-related factor on the alleviation of diabetes. In contrast to the occurrence of beta-cell failure and subsequent insulin deficiency in Fig 2, hyperinsulinemia would occur to patient A on day 15460 and stay in the remainder of the life of this patient with undamaged pancreatic function. The glucose levels can be controlled below 125 mg/dl before day 20053, indicating a deferral of 38 years to develop diabetes compared with the condition in Fig 2. In addition, a moderate glycemic level, less than 130 mg/dl, can be maintained in the later life of this patient. We further investigated the evolution of diabetes with altered upper bound values of X, representing the varied maximal severities that the obesity-related factor can develop to. The results reveal 0.11 as the threshold value of the upper bound, separating the euglycemic and diabetic states for patient A. Below this threshold value, the X-induced beta-cell dysfunction is mild and the influence of moderately elevated insulin can be counteracted by temperate insulin resistance or glucagon to avoid the occurrence of hypoglycemia. On the other hand, when X exceeds 0.11 gradually, the excessive beta-cell secretory response leads to considerably high insulin, followed by severe insulin resistance and extra hepatic glucose production. There are two cases subsequent to this scenario. If the elevation of the X value stops at a moderate level, the expansion of beta cell mass would halt and the glucose can stay at a medium-high level. In the case that the X value continues to increase, the beta-cells cannot endure the excessive adverse pressure from the hostile environment, and initiate self-destruction. Consequently, beta-cell failure and insulin deficiency occur. The glucose would rise to an extremely high level, which can cause coma in certain extreme occasion. Moreover, the obesity-related diabetes model can reveal the success of RYGB surgery in promptly reversing diabetes. Pories et al. proposed that the gastrointestinal diabetogenic signal of a patient disappears right after RYGB, followed by the correction of the fasting hyperinsulinemia [10]. We hypothesize that the obesity-related factor can be eradicated quickly from diabetic patients after the surgery. Suppose patient A takes the gastric bypass surgery on the 9300th day, that is, the intervention is performed before the patient would go through the severe diabetic stage. The factor is assumed to decrease approximately to zero in one week, as shown in Fig 5D. The simulation results illustrate that in contrast to the upsurge of glucose level after day 9300 without any intervention, the glucose levels are reduced to 100 mg/dl following surgery for one week. Meanwhile, the high insulin levels decline quickly and remain normal after the first week. These results are in agreement with the clinical data that show the correction of hyperinsulinemia and a significant reduction of fasting glucose even in the first week after surgery [53]. The study [53] suggests that the reduced glucose production, rather than the increased glucose disposal, contributes to the amelioration of diabetes after surgery. This effect is demonstrated in our model, as the extra hepatic glucose production p1(X) would be instantly removed along with the vanishment of the pathological factor. Moreover, the terminated decline of functional beta-cell mass after surgery, shown in Fig 5C, represents the improved beta-cell function. This underlies the improved postprandial insulin secretion for patients undergoing the surgery. Fig 5: Altered dynamics of glucose, insulin and functional beta-cell mass level for patient A after taking the Roux-en-Y gastric bypass surgery.The patient is assumed to take the surgery on day 9300 and the obesity-related factor X is postulated to decrease exponentially to zero in approximately a week. The GIβ dynamics with taking the Roux-en-Y surgery are plotted in solid curves and the dashed curves represent the expected GIβ trajectories without taking the intervention. The solid curves are zoomed in to show the variations of the GIβ levels during the week after surgery. All of the other parameters here remain the same as those in Fig 2, except for those in the equation of X. Graph (a) illustrates that in contrast to the upsurge of glucose level after day 9300 without any intervention, the glucose levels are reduced to 100 mg/dl following surgery for one week. Graph (b) and (c) demonstrate the bariatric surgery can promptly halt the occurrence of beta-cell failure and insulin deficiency. Normal levels of insulin can be maintained after the first week. ## Distinct levels of insulin resistance and beta-cell function contribute to different risks of diabetes Ethnic distinction in the incidence of T2D has been recorded in the clinical literature. In particular, higher insulin resistance and up-regulated beta-cell function in African Americans, compared with non-Hispanic whites, are documented in most available data and are suggested to elevate the risk of T2D [54]. The underlying mechanism causing this disparity in the risks of diabetes may be explained by the obesity-related diabetes model. We then selected a virtual patient B who has a lower insulin resistance level than patient A, and assume the obesity-related factor has a less up-regulating effect on the beta-cell function of patient B. The parameter setting for this patient is listed in the Set B of Table 1. We investigated the GIβ dynamics with an initial euglycemic state under the progressive severe obesogenic environment. Fig 6 shows that the hyperinsulinemic stage of patient B lasts 3.3 years (1212 days), shorter than patient A with the same obese severity, and the maximal insulin level he would reach is 27.6 μU/mL, lower than the highest level that patient A attains. During this hyperinsulinemic stage, the glucose levels of patient B are well controlled beneath 111.6 mg/dl. Additionally, he would not step into the diabetic stage until 26.5 years (9676 days) afterwards, which is 11.5 years later than patient A does. The comparisons of diabetic progression between patient A and patient B theoretically exhibit the clinical observation that the risk and progression rate of developing diabetes varies with ethnic disparities, even if the cohorts are exposed under the same obesogenic environment. Fig 6: Dynamics of glucose, insulin and functional beta-cell mass for patient B with severe obesity (X approaches 1 progressively).Patient B is assumed to have a lower insulin resistance level and less beta-cell hyperresponsiveness to the obesity-related factor than patient A does. Graph (b) shows the hyperinsulinemic stage of patient B lasts for 15.2 years, which is 3.3 years shorter than patient A does in Fig 2. The maximal insulin level he would develop is 39 μU/ml, which is 27.6 μU/ml lower than the highest level that patient A attains. During this hyperinsulinemic stage, the glucose levels can be well controlled beneath 111.6 mg/dl. Additionally, patient B would not step into the diabetic stage until day 9676, which is 11.5 years later than patient A does. We subsequently considered the GIβ dynamics of patient B in the mild obesogenic environment, where X gradually increases to the maximum value of 0.17 with an initial value close to zero. All the other parameter values remain the same as those in Fig 6. The simulation results, presented in Fig 7, illustrate that the mild obese-related factor would slightly increase the glucose level of patient B to 104.8 mg/dl but the elevation would be counteracted afterward by an upsurge of insulin. Although the glucose level would return to the normal reference range before the insulin level exceeds the hyperinsulinemic threshold 25 μU/ml, the insulin level of patient B undergoes another elevation of 10.6 unit driven by the continual hyperresponsiveness of beta-cells to the increasing pathogenic risk. In the end, the glucose concentration would stay stable at a euglycemic level of 96.8 mg/dl. The different glucose dynamics compared with those in Figs 2, 4 and 6 suggest that the effects of a lower insulin resistance level, reduced up-regulated beta-cell function and mitigated obesogenic environment together contribute to the successful control of blood glucose. Moreover, the comparison of the GIβ dynamics between Figs 4 and 7 reveals that the difference in insulin resistance levels and beta-cell function among individuals can provide an explanation of the phenomenon that some individuals with mild obesity are able to remain euglycemic while others may not. Further numerical investigation exhibits the threshold value of X for patient B to develop diabetes is 0.41. The increase of the threshold value compared with the value of patient A, indicates that the lower insulin resistance and less up-regulated beta-cell response can increase the tolerance of patient B to the obesogenic environment. Fig 7: Dynamics of glucose, insulin and functional beta-cell mass level for patient B with mild obesity (X approaches 0.17 progressively).In this case, the factor X can only increase the glucose level to 104.8 mg/dl and the elevation is counteracted afterward by an upsurge of insulin. The insulin level remains moderately high after day 11010, driven by the continual hyperresponsiveness of beta-cells to the pathogenic risk. At the end, the glucose concentration of patient B can stay stable at a euglycemic level with 96.8 mg/dl. ## Sensitivity tests on parameters We performed sensitivity tests on the key parameters h1, α, h2, q1, and q2 in the obesity-related diabetes model, and investigate the biological influence of these parameters on the disease progression. In the graphs (a)-(f) of Supplemental S1 Fig, we examined the effects of different parameter values in p1(X) on the GIβ dynamics. The results depict that the dynamics of insulin stay the same and the dynamics of glucose are slighted changed when the value of h1 is varied up to 10 percent. As the value of α changes up to 10 percent, the alterations of the GIβ dynamics are unnoticeable. Graphs (a), (b), and (c) in Supplemental S2 Fig demonstrate that the change of the GIβ dynamics is negligible when we vary h2, the X-induced insulin secretion rate, up to 10 percent. Graphs (a)-(f) in Supplemental S3 Fig depict the different GIβ dynamics where the parameter values in p3(X) are varied up to 10 percent. The shifts of the GIβ curves, corresponding to the different parameter values of q1 and q2 in graph (a)-(f), only represent the varied onset time and severity of diabetes. Overall, the variation of these parameter values has no impact on the analysis and general conclusions obtained from our modeling work. Moreover, the sensitivity results reveal the impact of these parameters on the GIβ dynamics. Graphs (b) and (e) in Supplemental S3 Fig illustrate that the increase of q1, representing higher beta-cell susceptibility to the hostile environment, can reinforce hyperinsulinemia. Compared with the elevation of q1, the increase of q2, which stands for the enlarged tolerance of beta-cells to the pathological factor, contributes more to the high insulin level. The graphs (c) and (f) indicate that the influence of the factor X on beta-cells is not the sole determinant of functional beta-cell mass. When the increment of q1 or q2 exceeds a certain level, the insulin concentration would be significantly excessive, which instead prevents the expansion of functional beta-cell mass to slow down the insulin secretion. This reveals the regulation of functional beta-cell mass is one of the defensive approaches to reducing insulin-induced metabolic stress. Additionally, the graphs (f) and (d) demonstrate the increased beta-cell tolerance to the pathogenic factor would defer the occurrence of beta-cell failure and delay the onset of diabetes. We further make significant changes of the value of q1 to highlight its effect on the GIβ dynamics. The graph (g) demonstrates the considerable decrease of q1 can alleviate or even prevent the progression of hyperglycemia, which is enabled by the avoidance of beta-cell failure and insulin deficiency, as shown in the graphs (h) and (i). Further investigations on the significance of h1 are shown in the graphs (g) and (i) of Supplemental S1 Fig. These graphs illustrate that a substantial increase of h1 can speed up diabetes progression and elevate the steady state of the glucose level. This suggests that approaches to reducing hepatic gluconeogenesis are important in the treatment of hyperglycemia. Nevertheless, decreasing the value of h1 alone cannot prevent the development of hyperglycemia. In addition, although the variations of h1 have insignificant effects on the insulin dynamics, as shown in the graph (h), the dynamics of functional beta-cell mass alter with different values of h1. This reveals the indirect influence of glucose level on the regulation of functional beta-cell mass. Graphs (d)-(f) in Supplemental S2 Fig exhibit the influence of significantly elevated h2 on the GIβ dynamics. Although the considerable increase of h2 contributes to an apparent up-shift of the insulin curve, the curve of the functional beta-cell mass shifts down in the meantime. That is, the factor-induced expansion of functional beta-cell mass would be diminished apparently when the factor-induced insulin secretion rate is elevated substantially in the same pathogenic environment. This suggests the mass and the insulin releasing function together represent the total function of beta-cells, and the beta-cells may reduce its reproductive effort when the insulin releasing function is enhanced. Lastly, the severity of the pathogenic factor has substantial impact on the diabetes progression. In the obesity-related diabetes model, the dynamics of factor X are determined by the parameters n1 and n2. The comparison of the GIβ dynamics between Figs 4 and 7 has demonstrated the influence of the diminished value of n2 on disease alleviation or prevention. We further investigated the effect of n1 on the progression of hyperglycemia, as shown in Supplemental S4 Fig. These graphs exhibit that the significantly decreased value of n1, leads to a remarkably lowered value of X at each time point, inducing considerable alleviation or delay of the disease. Thus, mitigating the progression rate of the diabetogenic factor contributes to the amelioration of the disease. ## Best fits of the obesity-related diabetes model to Pima Indian data and its implication We fitted G(t) in the obesity-related diabetes model to 11 sets of glucose data of the Pima Indians [30]. The data fitting was performed by the commercial software package Berkeley Madonna. An optimal set of parameters were determined from the best fitting, searching for the minimum root mean square (RMS) between the model prediction and the data with the following formula RMS=∑$i = 1$n[G(ti)-G^(ti)]2n In the formula, G^(ti) represents the glucose level at time ti predicted by the model, and G(ti) is the corresponding data at ti. To avoid over-fitting, we kept all the parameter values used for the simulation of the base model except those in the insulin sensitivity function C(I), as the change of insulin sensitivity level has major influence on the disease progression. In addition, we fixed n2 to be 1 and relied on the variation of n1 to present different dynamics of X that patients may develop. The best fits, shown in Figs 8, 9 and 10, characterize the trend of glucose variations presented in the patients. In particular, each outbreak period displayed in the data sets is captured by our fits. The corresponding parameter values to the best fits are listed in Table 2. The estimated parameter values exhibit significant differences in distinct characteristics of the longitudinal T2D data, which are consistent with the parameter analysis results. Detailed explanations and possible biological mechanisms for these parametric variations are discussed below. Fig 8: Obesity-related diabetes model fit to plasma glucose data for Pima Indian #1–#6.Overall, the glucose levels of these patients were steadily trending upwards. Fig 9: Obesity-related diabetes model fit to plasma glucose data for Pima Indian #7–#9.These data* exhibit waveringly trends before the outbreak.* Fig 10: Obesity-related diabetes model fit to plasma glucose data with large fluctuations for Pima Indian #10 and #11. TABLE_PLACEHOLDER:Table 2 Among the patient data sets, the data of Pima Indian #6 describe a euglycemic level, and the data of patient #10 and 11 depict relatively well-controlled glucose levels. Which parameters play a key role in the disease control and prevention? A thorough comparison of the parameter values among the Pima Indian patients reveals that the small value of parameter q1, representing reduced susceptibility of beta-cells to the hostile environment, is vital for the control of diabetes. We further compared the parametric differences within the Pima Indian #6, 10, and 11. The Pima Indian #6 had higher insulin sensitivity and lower q1 than Pima Indian #10 and 11. However, the estimated value of n1 for Pima Indian #6 is considerably larger than those for Pima Indian #10 and 11, which implies Pima Indian #6 was exposed under a worse diabetogenic environment. This indicates a strong defensive function of beta-cells can counteract the adverse impact of diabetogenic factors, contributing to the disease prevention. Thus, treatment approaches to suppress the detrimental effect of the diabetogenic factors on the beta-cells may be more efficient than the efforts to improve the hostile environment, such as exercise and diet control. In addition, compared with Pima Indian #10, Pima Indian #11 who had higher insulin sensitivity, exhibited higher rate of glucose elevation with larger values of q1, q2, h1. This suggests the monotherapy targeting insulin resistance may not be sufficient to cure diabetes. We further investigated the possible factors driving Pima Indian #8 and #9 to develop diabetes significantly earlier than other Pima Indian patients. The clear distinction between the parameter values of Pima Indian #8, #9 and other patients only lies in the parameter n1. That is, Pima Indian #8 and #9 had higher values of n1 than other patients, except for the value of Pima Indian #6. The revealed influence of n1 on diabetic progression is in line with the result of parameter analysis for n1 (Supplemental S4 Fig), which provides theoretical evidence for employing the approaches to ameliorate the hostile environment as a treatment strategy. In addition, the impact of the high n1 value on the glucose regulatory system of Pima Indian #6 was offset by the remarkably low q1 value. This observation is consistent with the clinical phenomenon that some individuals are able to maintain euglycemic although they are exposed to a severe diabetogenic environment. A close-up view of the data sets for Pima Indian #5 and #7 exhibits that they experienced shorter periods of the diabetic outbreak than other patients. The steep upsurge of the glucose curves is underlined by the well-controlled glucose level before the outbreak. In particular, Pima Indian #5 spent only two years in developing severe diabetes from euglycemia, and this patient had been able to maintain a euglycemic state for 50 years before the outbreak. A scrutiny of the parameter values for all the patients reveals that lower values of h1 and n1 for Pima Indian #5 and #7, which represents lessened X-induced hepatic gluconeogenesis, may account for the well-managed glucose level before the diabetic outbreak. This highlights the importance of reducing hepatic gluconeogenesis for the treatment of hyperglycemia. ## Discussion Mathematical models of diabetes progression play a crucial role in the study of the durable effect of anti-diabetic agents at different disease stages. However, few mathematical models have been proposed to investigate the long-course diabetes evolution owing to the inherent difficulty in capturing the complexity of diabetes. Even fewer models, if any, have been constructed to incorporate the impact of hostile environment (diabetogenic factors) on beta-cell dysfunction and the progression of diabetes, which hinders the quantitative study of the disease course with new treatment strategies. To alleviate this challenge, we established mathematical models studying beta-cell dysfunction and the disease evolution induced by potential pathogenic factors. Based on the beta-cell mass regulation model of Topp et al. [ 1], we constructed a base model for the undisturbed glucose-insulin regulatory system. We revised the insulin-dependent glucose uptake in the Topp model by setting the insulin sensitivity as a decreasing function of insulin level to represent hyperinsulinemia-induced insulin resistance. The effect of glucose on functional beta-cell mass in the model has been replaced by the signal transduced by insulin, in view of the observation that there is no apparent elevation of glucose level to trigger beta-cell compensation during the prediabetic stage. Building upon the base model, we incorporated the environmental-induced or epigenetic-related diabetogenic factor, an inducement of beta-cell dysfunction, into the glucose regulatory system, formulating a general phenomenological diabetes progression model. The impacts of the pathogenic factor X on the dynamics of glucose, insulin and functional beta-cell mass are symbolized by incorporating the influence functions p1(X), p2(X), and p3(X) to the corresponding equations, representing the X-induced hepatic glucose production rate, insulin releasing rate and functional beta-cell mass net growth rate, respectively. Numerical investigations of the obesity-related diabetes model illustrated the impact of the obesity-related factor on the progression of diabetes. For an individual exposed to a severe obesogenic environment, the result shows the obesity-related factor can induce the occurrence of hyperinsulinemia after about 7 years’ progression, the commencement of which precedes the outset of diabetes for almost 10 years. In addition, the insulin level of the patient starts to decrease in 19 years and insulin deficiency occurs over time subsequent to the appearance of beta-cell failure. The longtime glucose and insulin dynamics depicted by the model are in good agreement with the clinical observations of diabetes progression. We further studied the influence of controlled or eradicated obesity-related factor on the disease evolution. With the assumption of exposing the same individual to a mild obesogenic environment, the simulation result manifests apparent alleviation of the disease progression rate and severity. This supports the finding that weight loss of obese patients helps alleviate diabetes. Analysis of varied upper bound values of the factor reveals an individualized threshold of the diabetogenic risk, separating the euglycemic and diabetic states. When the value of the obesity-related pathogenic factor stays below the threshold, the individual can stay euglycemic with certain degree of obesity. In contrast, an increasing excess of the threshold leads to the deterioration of diabetes. Such individualized threshold values, which provide warning signs of obese levels for taking necessary interventions to prevent the commencement of diabetes, may be valuable for clinical decision making. Although it may seem obscure how to quantify the obesity-related factor clinically, a closely associated concept of personal fat threshold (PFT) has already been utilized to design diet plans for diabetic patients [7, 55]. The PFT is determined by the extent of intra-pancreatic and intra-hepatic fat accumulation, as well as the individual sensitivity to local biochemical effects of superfluous lipids [55]. This perspective is proposed upon one of the clinical observations that the reversal of recent onset T2D can be achieved with a reduction of the pancreas and liver fat, even for nonobese people with T2D, by following a weight loss dietary regimen [56]. Overall, the quantification of the obesity-related factor can be accomplished feasibly with personalized clinical indices. Bariatric surgery which corresponds to the case of an eradicated obesity-related factor in our model, has the remarkable and durable ability to reverse diabetes. Sherman et al. simulated the metabolic consequences of bariatric surgery, with certain assumptions on the parameters of hepatic glucose production, beta-cell function, and insulin sensitivity in the model [5]. Their model was constructed with the hypothesis that the beta-cell mass would increase initially to compensate for insulin resistance, which differs from our proposition that hyperinsulinemia is the upstream of insulin resistance for obesity-related diabetes. Our obesity-related diabetes model also successfully depicted the effects of RYGB surgery on the prompt reversal of diabetes, with an assumption that the diabetogenic factor would be eliminated quickly after the surgery. This highlights the potential of utilizing our model for clinical decision-making. The underlying mechanism causing the ethnic disparities in the risks of diabetes can also be demonstrated by our obesity-related diabetes model. A higher insulin resistance and up-regulated beta-cell function in certain racial groups are suggested to elevate the risk of T2D [54]. We investigate the altered GIβ dynamics for a patient when the insulin resistance level and the up-regulating impact of the obesity-related factor on beta-cell function are lowered. The simulation result demonstrated that under the same obesogenic environment, this patient experienced an ameliorated disease state, and the time for the patient to step into the diabetic stage was delayed for longer than 11 years. Further investigations exhibited that the threshold value of the factor for this patient developing diabetes increases by three folds, indicating that the lower insulin resistance and reduced up-regulated beta-cell response may increase the tolerance of an individual to the obesogenic environment. Moreover, when this patient is exposed to a mitigated obesogenic environment, euglycemia (Fig 7) can be achieved successfully with tolerable beta-cell up-regulation. This is in line with the clinical observation that not all individuals with basal hyperinsulinemia would develop diabetes. We performed sensitivity tests on the key parameters in our obesity-related diabetes model and the results demonstrated the robustness of the model. The analysis of the biological significance for these parameters also sheds light on the possible efficient treatment strategies. The parameters h1, q1, q2, n1 and n2 have been shown to carry primary influence on the disease progression. The decreased value of n1 and n2, corresponding to the alleviated severity of the obesity-related factor from the hostile environment, can delay or even prevent the diabetes progression. This demonstrates the benefit of controlling the diabetogenic factors to the treatment of diabetes, as presented in the clinical data [56]. Likewise, the lower value of q1, representing a lower beta-cell susceptibility towards the pathogenic factor, contributes to slowing down or preventing the diabetes progression. In contrast, the increase of q2, representing improved tolerance of beta-cells to the factor, can retard the disease evolution. Therefore, the approaches that desensitize the beta-cells to diabetogenic factors can be potent for diabetic therapy. Furthermore, the decline of h1, which stands for the reduction of the pathogenic factor-induced hepatic gluconeogenesis, can slow down the diabetes progression and abate the final steady state of glucose level. Nevertheless, decreasing the value of h1 alone cannot prevent the development of hyperglycemia, which implies the monotherapy of suppressing gluconeogenesis is not sufficient to cure diabetes. The effects of reduced values of h1, q1, n1 on the control and prevention of diabetes were exemplified by the model fitting to the Pima Indian data. The overall results from the simulation and data fitting indicate that the disease prevention can be achieved by ameliorating the diabetogenic environment or decreasing the susceptibility of beta-cells to the environment. The monotherapy of reducing hepatic gluconeogenesis or insulin resistance can retard the disease evolution, yet is inadequate to halt diabetes progression. Nevertheless, the Roux-en-Y surgery, which eradicates both the factor-induced hepatic gluconeogenesis and beta-cell dysfunction, has shown fast and lasting therapeutic effects on the reversal of diabetes. By fitting the obesity-related diabetes model to the longitudinal T2D data of the Pima Indian tribe [30], we obtained individualized parameter values for the patients, as well as the predicted glucose trajectories over their lifespan. The best fits well delineate the trend of glucose variations presented in the data. Each outbreak period displayed in the data sets was sufficiently captured by our fits. In addition, the distinct characteristics among the data sets were able to be apprehended by the differences of estimated parameter values, which confirms the biological significance of the parameters. All of these highlight the feasibility of applying our model to studying the diabetes progression clinically. The anticipated glucose trajectories may also assist clinicians in determining the optimal treatment strategies at different diabetic stages. In particular, intensive therapy, such as bariatric surgery, could be an option for patients in the severe diabetic stage when moderate interventions, like medication treatment, are often insufficient. With the predicted progression trajectories, our model may help physicians decide the optimal time to apply such surgery for an ideal prognosis. Although our model provides a good fit to the longitudinal data of Pima Indian patients, there remains unclarity whether hyperinsulinemia or insulin resistance comes first. Incorporating the impact of hyperinsulinemia on insulin resistance in the model is important to investigate the diabetes progression for patients who develop hyperinsulinemia prior to insulin resistance. In the first simulation (Fig 2), we assume the insulin sensitivity function C(I) depends only on the insulin level. The limitation of this assumption is that insulin sensitivity can become greater than its initial value when insulin decreases to below a certain level. Assuming the insulin sensitivity function also depends on the obesity-related factor X can overcome this limitation (Fig 3A). However, introducing a function of X to the insulin sensitivity function involves more parameters, bringing more challenges to data fitting. Of note, we did not observe a significant change in the glucose-insulin dynamics using the two functions C(I) and C(I, X) (Fig 3B–3D). Thus, we used the function C(I) with fewer parameters in model fitting to the Pima Indian patients. In view of the limited data, we also found it challenging to obtain precise estimates of all the parameters in our models. Some of the parameter values that guarantee the consistency of simulated glucose-insulin dynamics with clinical observations were adopted, as we aim to establish phenomenological models describing the diabetes progression, instead of providing precise estimates of all the parameters. The measurement of beta-cell mass itself is intractable, hampering optimal parameter calibrations for all established models in the literature incorporating the beta-cell mass. Hopefully, future development of measurement technology in this field can provide handy data for further model validation. In summary, we established a novel mathematical model that introduces a general pathogenic factor into the glucose regulatory system. 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--- title: Therapeutic potential of vitamin D against bisphenol A-induced spleen injury in Swiss albino mice authors: - Mohamed A. Al-Griw - Hanan N. Balog - Taher Shaibi - Mohamed Fouzi Elmoaket - Iman Said Ali AbuGamja - Ahlam Bashir AlBadawi - Ghalia Shamlan - Ammar Alfarga - Areej A. Eskandrani - Afnan M. Alnajeebi - Nouf A. Babteen - Wafa S. Alansari - Rabia Alghazeer journal: PLOS ONE year: 2023 pmcid: PMC9997876 doi: 10.1371/journal.pone.0280719 license: CC BY 4.0 --- # Therapeutic potential of vitamin D against bisphenol A-induced spleen injury in Swiss albino mice ## Abstract Bisphenol A (BPA), a ubiquitous plasticizer, is capable of producing oxidative splenic injury, and ultimately led to spleen pathology. Further, a link between VitD levels and oxidative stress was reported. Hence the role of VitD in BPA-induced oxidative splenic injury was investigated in this study. Sixty male and female Swiss albino mice (3.5 weeks old) were randomly divided into control and treated groups 12 mice in each (six males and six females). The control groups were further divided into sham (no treatment) and vehicle (sterile corn oil), whereas the treatment group was divided into VitD (2,195 IU/kg), BPA (50 μg/kg), and BPA+VitD (50 μg/kg + 2,195 IU/kg) groups. For six weeks, the animals were dosed intraperitoneally (i.p). One week later, at 10.5 weeks old, mice were sacrificed for biochemical and histological analyses. Findings showed BPA triggered neurobehavioral abnormalities and spleen injury with increased apoptotic indices (e.g. DNA fragmentation) in both sexes. A significant increase was found in lipid peroxidation marker, MDA in splenic tissue, and leukocytosis. Conversely, VitD treatment altered this scenario into motor performance preservation, reducing oxidative splenic injury with a decrease in the percent apoptotic index. This protection was significantly correlated with preserving leukocyte counts and reduced MDA levels in both genders. It can be concluded from the above findings that VitD treatment has an ameliorative effect on oxidative splenic injury induced by BPA, highlighting the continuous crosstalk between oxidative stress and the VitD signaling pathway. ## Introduction There are increasing evidence that environmental exposures during intrauterine and postnatal periods or early life determine the phenotypic outcome and vulnerability to diseases in later life [1–3]. An endocrine disruptor, bisphenol A (BPA) has been used to produce epoxy resins and polycarbonate plastics [4]. BPA is essentially used for coating food cans and plastic food or beverage containers, resulting in regular exposure to humans [5, 6]. Therefore, continuous BPA exposure [5] is a severe cause of concern due to its detrimental effects on different body organs [6]. BPA exposure interferes with human health, leading to immune perturbations [4–7]. Spleen is an essential component of the immune system and performs vital functions, such as removal of degenerated and aged RBCs, extramedullary hematopoiesis, and elimination of particulate matter and harmful bacteria from the circulation [8, 9]. Because of its crucial role in the immune system, the spleen serves as an ideal organ for evaluating the effect of environmental toxicant BPA that interfere with normal hormone regulation of the immune response [6, 8, 10]. Therefore, the etiologies that dysregulate normal functions of the spleen also impair immune performance [6, 7, 8, 11, 12]. A lack of the underlying molecular mechanism that explains the diverse and pleiotropic impact on human health following BPA exposure led to further studies. Evidence suggests that lifestyle factors and exposure to environmental toxicants during developmental stages contribute to the generation of reactive oxygen species (ROS) and cause oxidative injury [10]. ROS-induced protein modifications are the underlying cause of several diseases [13]. The ROS, including the superoxide anion and hydroxyl radicals, impair DNA repair, enzyme activities, and oxygen utilization and depletes glutathione levels. Protein modifying effects of ROS leads to the production of lipid peroxidation-derived aldehydes and carbonyls such as 4-hydroxynonenal (HNE)-protein adducts and malondialdehyde (MDA). ROS-modified proteins can elicit an autoimmune response, which may result in the development of autoimmune disorders [8]. Furthermore, elevated protein carbonyls and MDA levels were found in patients with autoimmune disease [10, 12, 14]. Overproduction of ROS due to xenobiotic exposure leads to oxidative stress and its harmful consequences [15]. Oxidative stress is an underlying mechanism that can explain the correlation between BPA exposure and its adverse health effects [2, 5]. Vitamin D (VitD) is an integral part of immune system regulation and electrolyte reabsorption [16]. VitD2 and D3 are the two primary variants of VitD [16]. The 1,2,5-dihydroxy VitD3, also known as cholecalciferol, is the hormonally active form of VitD, vital in bone metabolism and calcium homeostasis [16]. Recently, a negative correlation between VitD levels and BPA exposure was reported [17–19]. A link between VitD levels and biomarkers related to oxidative stress and inflammation was suggested [20–22]. Although it plays a beneficial role in many biological processes [22], the protective role of VitD against oxidative response mediated spleen injury remains to be elucidated. To the best of our knowledge, there is no/little data on the effects of VitD on BPA-induced oxidative splenic injury, therefore, therefore, we aim to investigate the possible protective role of VitD in BPA-induced oxidative splenic injury. ## Animals Sixty male and female Swiss albino mice (3.5 weeks old) weighing approximately 17.1±1.8g were obtained from the Faculty of Sciences, University of Tripoli, Tripoli, Libya. All ethical regulations set by the Research Ethics Committee at Biotechnology Research Center, University of Tripoli, Libya, were followed to conduct animal work. Next, ethical approval was obtained from the Bioethics Committee at the Biotechnology Research Center before the commencement of the study (BEC-BTRC 10–2020). Every possible effort and precaution were taken to minimize pain to the animals throughout the experimental procedures. Animals were maintained in a well-ventilated facility following a 12-hour light/dark cycle and ambient temperature (26 ± 2°C). All animals received a standard diet and drinking water ad libitum. The diet was purchased from Research Diet Inc. ## Experimental design The animals were randomly divided into control and treated groups 12 mice in each (six males and six females). The control groups were further divided into sham (no treatment) and vehicle (sterile corn oil), whereas the treatment group was divided into VitD (2,195 IU/kg), BPA (50 μg/kg), and BPA+VitD (50 μg/kg + 2,195 IU/kg) groups. The BPA and VitD doses were administered based on previously recommended doses [23–27]. Corn oil, VitD, and BPA were administrated daily, intraperitoneally (i.p), for six weeks. One week later, at 10.5 weeks old, animals were sacrificed, and blood and spleen samples were collected for biochemical and histological examinations (Fig 1). Fig 1: Schematic of the treatment procedure. The mice were administered intraperitoneally with 50 μg VitD, 2195 IU BPA/kg body weight, either alone or in combination, respectively, for six weeks. ## Clinical assessment Survival of animals was measured throughout the study, mid-morning and late afternoon, for behavioral and clinical changes. Additionally, the death of mice occurring overnight was documented the following day, and three independent observers assessed the cause of the death to eliminate treatment-related deaths. ## Body and spleen weight The body weight of mice at the beginning and end of the experiment was measured to determine changes in the body weight. The spleen was excised from the mice at the end of the treatment period, and to record any changes in spleen weight, their weights were compared to the spleen of control mice. ## Motor activity measurement Motor performance was evaluated throughout the study by following a previously reported method [28, 29]. Briefly, the mice were subjected to regular training sessions on a rotating apparatus from week 4 to week 10. Motor performance was documented twice a week for six weeks. Three trials, each lasting for a minute, were carried out per session, and average values were calculated as the mean rotation number during five counting periods per hour. ## Peripheral blood and tissue harvesting At 9.5 weeks old, the mice were anesthetized, and their blood samples were collected by cardiac puncture and placed in EDTA containing tubes. The animals were sacrificed under anesthesia with $1\%$ ketamine. and their spleens were excised and weighed. A portion of the spleen was washed with the washing buffer and homogenized, and the supernatant obtained was used for lipid peroxidation assay. A portion of the splenic tissue was fixed in $10\%$ formalin solution and stored for histological analysis. ## Leukocyte counting The leukocytes (white blood cells) present in blood were viewed and counted on a blood film [7]. The blood film was air-dried, stained with Giemsa stain for 15 minutes, washed several times under tap water, air-dried, and mounted with Canada balsam. Differential cell counts were determined by observing the slides under an oil immersion microscope (Leica, Germany) using 40× magnification. ## Histopathology processing and analysis The splenic tissue was fixed with $10\%$ formalin for an hour after removal. Next, the slides were prepared according to the previously mentioned reports [7, 30]. The tissues were dehydrated by immersing overnight in $80\%$ isopropyl alcohol for 60 minutes. The tissues were washed twice with xylene for an hour, and the wax-impregnated tissues were embedded in paraffin blocks, mounted, and 3-micron thick sections were cut using a microtome. The sections were floated in a tissue floatation bath at 40°C and placed on egg albumin and glycerol-smeared glass slides, which were melted at 60°C for 5 minutes and allowed to cool. Sections were deparaffinized in xylene for 10 minutes, washed in absolute isopropanol, and stained with Ehrlich’s hematoxylin for 8 minutes. The excess stain was removed by washing the slides under tap water and immersing them in acid alcohol ($8.3\%$ HCl in $70\%$ alcohol). The sections were washed for 10 minutes under running tap water to facilitate bluing (slow alkalization), were counter-stained for a minute in $1\%$ aqueous eosin, and washed in tap water to remove excess stain. Next, the stained slides were dehydrated at 60°C for 5 minutes, cooled, and mounted on a DPX mount. The sections were wetted in xylene and inverted onto a mount and placed on the coverslip for examination under a microscope. Tissue injury scores encompassing follicle degeneration, inflammation (macrophage), vascular congestion, and edema were established and compared among the study groups. On a scoring scale from 0 to 4, a score of 0 was assigned for no splenic tissue changes, 1 for little changes, 2 for moderate changes, 3 for marked changes, and 4 for very distinct changes. Above four, the scoring criteria were averaged for each section, and the average was considered a replicate. The tissue sections were examined under a light microscope (Leica, Germany) with lower and higher magnifications and imaged. A pathologist evaluated changes to tissue architecture. ## DNA integrity analysis DNA was extracted using a commercial kit (Qiagen, Germany). Briefly, the tissue samples (25 mg) were minced and homogenized in a DNA lysis buffer containing proteinase K, followed by overnight incubation at 56°C. The homogenate was treated with RNase A and loaded onto a spin column. The bound DNA was eluted using a Tris-EDTA (TE) buffer. The purity and integrity of the extracted DNA were determined by spectroscopy and agarose gel electrophoresis, respectively. ## Lipid peroxidation assay The thiobarbituric acid reactive substances were used for measuring the malondialdehyde (MDA) levels [30–32]. Briefly, the splenic tissue was homogenized in phosphate-buffered saline using a tissue homogenizer (IKA, RW 20.n, Germany), and the homogenate obtained was centrifuged. A volume of 0.5 mL supernatant of the homogenate was mixed with a 2 mL reagent ($0.37\%$ thiobarbituric acid, 0.24 N HCl, and $15\%$ TCA). The mixture was boiled at 100°C for 15 minutes, cooled, and centrifuged at 3,000 rpm for 10 minutes. The absorbance of the supernatant was measured at 532 nm, and the unknown concentration of the TBA-MDA adducts was calculated from the standard curve generated using different concentrations of 1, 1, 3, 3-tetra methoxy propane and expressed as nmol/mL. ## Statistical analysis Statistical analysis was carried out using the Graph Pad Prism software version 7.0. To determine statistically significant changes between male and female mice in all the groups, two-way ANOVA was followed by Dunnett’s post hoc test. The normal distribution of the data was verified by skewness and kurtosis detection and homogeneity of variances by Levene’s Test. Data are represented as mean ± SD ($$n = 12$$). Significance was set at $p \leq 0.05.$ ## Survivability of mice The clinical observations revealed no behavioral changes, mortality, or toxicity in all the groups in response to different treatments. ## Effect of VitD on motor activity The results showed no difference between the treatment groups and the sham and vehicle control group (Fig 2). Fig 2: Effect of BPA and VitD on the motor activity in male and female mice.Data are represented as mean ± SD (n = 12), and P < 0.05 is represented as and P < 0.01 is represented as *. However, an increase in motor activity was measured only in female mice treated with BPA compared to the females in the control group ($$P \leq 0.03$$; Fig 2). No such enhancement in motor activity was observed in the female mice treated with BPA+VitD. The activity was significantly decreased compared to the BPA-treated female mice group ($$P \leq 0.0081$$; Fig 2). No difference in motor activities was observed between other groups, and no sex-based difference in motor activities was noticed in the rest of the treatment groups. ## Effect of VitD and BPA on body and spleen weight The changes in the body and spleen weight are presented in Fig 3A and 3B, respectively. Fig 3: Effect of VitD and BPA on mice body and spleen weight.(A) Quantification of body weight, (B) Quantification of spleen weight. Data are represented as mean ± SD (n = 12), and P < 0.01 is represented as , while P < 0.001 is represented as *. Compared to male and female control mice, the body weight of male mice treated with BPA was significantly ($$P \leq 0.009$$), while reduced in BPA-treated female mice ($$P \leq 0.01$$). Next, the body weight of BPA-treated female mice was significantly lower than BPA-treated male mice ($$P \leq 0.0001$$). Similarly, the body weight of female mice treated with BPA+VitD was significantly lower compared to the BPA+VitD treated male mice ($$P \leq 0.0028$$). The weight of the spleen was significantly increased in both male and female mice compared to their control counterparts ($$P \leq 0.0001$$). However, the weight of the spleen was unchanged in male mice treated with BPA+VitD compared to the weight of the spleen in BPA only treated male mice, while it significantly decreased in female mice treated with BPA+VitD compared to the weight of the spleen in BPA only treated female mice ($$P \leq 0.0021$$). Next, no significant difference was found in spleen weight between BPA+VitD and BPA-treated male mice. ## VitD attenuates leukocyte count after BPA exposure The data showed that the BPA-treated group decreased leukocyte (WBCs) counts compared to the control group (Fig 4A–4F). Fig 4: VitD attenuates leukocyte counts following BPA exposure.The mice were divided into different groups: control, vehicle, VitD, BPA, or BPA+VitD. The quantification of (A) WBCs, (B) monocytes, (C) neutrophils, (D), basophils, (E) lymphocytes, and (F) eosinophils. Data are represented as mean ± SD (n = 12), P < 0.05 is represented as , and P < 0.01 is represented as *. In males, BPA increased the total WBCs ($$P \leq 0.008$$) and lymphocyte counts (Fig 4A and 4B) and reduced the neutrophil counts (Fig 4C). In females, BPA increased the total WBCs, monocytes, and neutrophils counts (Fig 4A–4C) and decreased the basophils and eosinophils counts (Fig 4D and 4F). Conversely, VitD treatment exerted favorable effects on the WBC counts (Fig 4). Specifically, VitD treatment enhanced the total WBC and monocyte counts in BPA male mice compared to the untreated BPA male mice (Fig 4A and 4B). While, VitD treatment modulated the total WBCs, monocytes, basophils, and eosinophils counts in BPA female mice compared to the untreated BPA-treated female mice (Fig 4A, 4B, 4D and 4F). ## VitD reduces spleen pathology induced by BPA exposure The effect of VitD and BPA on spleen histology is represented in Fig 5. Fig 5: Histopathological analysis in hematoxylin and eosin (H&E) stained spleen of control and vehicle-treated mice (40×).(A) The splenic tissues of male and female control mice at lower magnification (panels Ai and ii) showed a typical structure of the spleen. The lymphatic nodules showed a prominent germinal center composed of the white pulp (long arrow) surrounded by splenic cords and venous sinuses composed of the red pulp (short arrow). The splenic tissues of male and female control mice (panels Ai’ and ii’) showed splenic trabeculae (long arrow) and lymphocytic aggregation and part of white pulp in the lymphatic nodules (short arrow). (B) The splenic tissue of male and female vehicle-treated mice at lower magnification (panels Bi and ii) showed lymphatic nodules with a prominent germinal center composed of the white pulp (long arrow). Also, at higher magnification, the splenic tissues of male and female vehicle-treated mice (panels Bi’ and ii’) showed white pulp composed of lymphatic nodules and prominent germinal center (long arrow) and red pulp composed of splenic cords and venous sinuses (short arrow). Histological examination revealed a significant reversal of adverse effects of BPA in VitD-treated mice. VitD attenuated follicle degeneration, inflammation (macrophage), and vascular congestion (Fig 5). Splenic tissues of BPA+VitD-treated male and female mice demonstrated minimal degeneration and congestion to the splenic tissues of BPA-treated male and female mice. Similarly, a significant difference was found between male and female control groups (Figs 5–7). Fig 6: Histopathological analysis in hematoxylin and eosin (H&E) stained spleen of the VitD-treated mice (40 X).The splenic tissues of male and female VitD-treated mice at high (panels Ci and ii) and low (panels Ci’ and ii’) magnification exhibit a part of the reactive germinal center (long arrow) with congested sinusoids (long arrow) and a few scattered macrophages (short arrow). Fig 7: Histopathological analysis in hematoxylin and eosin (H&E) stained spleen of the BPA-treated mice and BPA+VitD-treated mice (40×).(D) The splenic tissues of male and female BPA-treated mice at lower magnification (panels Di and ii) showed hyperplasia of the splenic lymphoid follicles within the white pulp (long arrow), and the red pulp showed dilatation and congestion of the splenic blood vessels (short arrow). At lower magnification, the splenic tissues of male and female BPA-treated mice (panels Di’ and ii’) showed significant hyperplasia of the splenic lymphoid follicles (long arrow) surrounded by marked red pulp congestion (short arrow) and macrophage accumulation at the periphery of the tissues (long arrow) and massive hemosiderosis (short arrow). (E) The splenic tissues of male and female BPA+VitD-treated mice at lower magnification (panels Ei and ii) revealed hyperplasia of the splenic lymphoid follicles, surrounded by congested sinusoids with macrophage accumulation. At higher magnification, the splenic tissues of male and female BPA+VitD-treated mice (panels Ei’ and ii’) showed marked sinusoidal congestion with macrophage accumulation. Tissue injury scores are shown in Table 1. Follicle degeneration, inflammation (macrophage), and vascular congestion were compared among the different groups, and no significant difference was found in the mean tissue injury scores between the male and female control groups ($P \leq 0.05$; Table 1). The mean tissue injury scores were higher in the BPA-treated male and female mice compared to control ($P \leq 0.05$; Table 1). The BPA+VitD-treated male and female mice demonstrated significantly lower scores than BPA-treated mice. Based on these results, VitD significantly reduced tissue injury in the spleen of mice ($P \leq 0.05$; Table 1). Table 1 \| Criteria of scoring \| Control \| Control.1 \| Vehicle \| Vehicle.1 \| VitD \| VitD.1 \| BPA \| BPA.1 \| BPA+VitD \| BPA+VitD.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Criteria of scoring \| Male \| Female \| Male \| Female \| Male \| Female \| Male \| Female \| Male \| Female \| \| Follicle degeneration \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 3 \| 3 \| 2 \| 2 \| \| Inflammation (macrophage) \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 4 \| 4 \| 3 \| 3 \| \| Vascular congestion \| 0 \| 0 \| 0 \| 0 \| 1 \| 1 \| 4 \| 4 \| 3 \| 3 \| \| Averaged scores \| 0.6 \| 0.6 \| 0.6 \| 0.6 \| 1 \| 1 \| 3.7 \| 3.7 \| 2.7 \| 2.7 \| To understand the effect of treatment on cell necrosis in the splenocytes, we analyzed the total nuclear genomic DNA from the splenic tissues of mice by agarose gel electrophoresis (Fig 8A). Fig 8: VitD reduces the genomic DNA integrity in BPA-treated mice.(A) Agarose gel electrophoresis of DNA isolated from the splenic tissues of control (lane 1), BPA-treated (lane 2), and BPA+VitD-treated (lane 3) mice. Data are represented as mean ± SD (n = 12), and P < 0.01 is represented. The results showed that DNA from the control group was largely intact and exhibited no/little internucleosomal DNA fragmentation (Fig 8A, lane 1). However, the DNA from the male and female BPA-treated groups exhibited an apoptotic DNA ladder in gel electrophoresis (Fig 8A, lane 2). In contrast, the treatment with VitD enhanced the internucleosomal DNA integrity (Fig 8A, lane 3). Quantitative analysis showed that treatment with BPA resulted in a decrease in the DNA concentration compared to the respective DNA concentration of the control group (Fig 8). Conversely, VitD treatment preserved the DNA integrity compared to the BPA treatment group (Fig 8B). No significant difference in the DNA integrity and quantity was observed between the adult males and females in all experimental groups. ## VitD reduces lipid peroxidation in the spleen after BPA exposure Herein, we analyzed the effect of BPA on oxidative stress in the splenic tissues by assessing levels of MDA, a marker of lipid peroxidation. Also, we studied the possible potential role of VitD in protecting the splenic tissue architecture against oxidative lipid damage. Initially, we found that BPA augmented the splenic MDA levels in males and females compared to the MDA levels of the control group ($P \leq 0.0001$ and $P \leq 0.0001$, respectively; Fig 9). Fig 9: VitD reduces BPA-induced lipid peroxidation.Data are represented as mean ± SD (n = 12), and P < 0.05 is represented as , P < 0.01 is represented as , and P < 0.001 is represented as . Conversely, VitD significantly decreased the MDA levels in the splenic tissues of females but not males compared to the MDA levels in the untreated female BPA group ($$P \leq 0.0021$$; Fig 9). No significant difference was found in the oxidative stress biomarker, MDA, observed between males and females in all experimental groups. ## Discussion The current work demonstrated that treatment with BPA markedly altered the motor activity and body weight, decreased the spleen weight, increased splenic tissue injury scores, and ultimately led to spleen pathology. Also, a marked increase was found in lipid peroxidation. VitD treatment significantly reversed the pathological changes. To the best of our knowledge, this is the first study to demonstrate that VitD abrogates oxidative stress-induced splenic injury after treatment with BPA in mice. Our findings suggest that a VitD can act as a therapeutically potent agent in reversing BPA-induced pathophysiological changes. Environmental exposure to BPA is a significant threat to public health worldwide. Several factors like environmental toxicants, unhealthy diet, or stress can promote different pathologies [30, 33–36]. Studies indicated a strong link between exposure to environmental toxicants, immune perturbations, [4, 5, 37]. BPA is a potent environmental toxicant that can negatively affect the immune system even at lower doses [4, 38–41]. VitD plays a vital role in maintaining Ca2+ homeostasis and bone metabolism [16, 22]. The beneficial effects of VitD significantly augment the protective function of the innate immune system [23]. VitD and its receptor activator have recently been shown to exert a protective effect in ischemia and reperfusion injury [42]. Also, VitD3 reversed kidney damage caused by reperfusion injury [43]. Similarly, paricalcitol, the VitD receptor activator, restored renal ischemia-reperfusion-induced damages [44]. Given the importance of VitD in normal physiology, we sought to assess the possible protective efficacy of VitD against BPA-induced spleen pathology. In this study, BPA was found to trigger motor abnormalities in males and females, and that co-treatment of BPA and VitD led to a significant reduce in BPA-induced motor abnormalities [45–48]. These findings are consistent with several previous reports, wherein BPA was shown to cause developmental motor abnormalities, and VitD treatment negatively correlated with the risk of motor abnormalities [49]. Exposure to BPA during developmental stages, particularly in the pre or postnatal stages, was correlated with increased body weight [6, 8, 50–53]. Notably, the effect of BPA on body weight was reported to be gender-independent [6, 8]. In contrast, BPA at a dose concentration of 0.1 mg/kg and ≥466 mg/kg/day significantly decreased the body weight compared to the body weight of untreated control mice [54]. Similarly, an absolute (>$22\%$) and relative (>$10\%$) decrease in the weight of rat livers were reported after exposure to BPA [55, 56]. Our findings confirm that exposure to BPA changed the body and spleen weight in adult male and female mice. The role of VitD on body weight modulation has been controversial. BPA exposure for six weeks decreased the body and spleen weights but to non-significant levels, which supports a previous study, where no change in the body weight was noted in response to VitD [57]. Blood components control the immune response kinetics [58]. Leukocytes or WBCs are the body’s primary defense against pathogenic challenges, which lead to antibody production and leukocytosis. Polymorphonuclear leukocytes with granular cytoplasm impart protection by releasing histamines. They lead to phagocytosis against invading pathogens and antigens, leading to inflammation [58]. The immune response defense is curtailed by exposure to BPA, leading to the depletion of eosinophils, basophils, and neutrophils [59] and impairment of WBC production in the hematopoietic stem cells of the bone marrow [59]. The impairment of WBC production leads to BPA-induced oxidative stress [60]. In the present study, we found that BPA led to marked alterations in the WBC counts compared to the control. BPA induced a significant increase in the total WBC and lymphocyte counts in males, and reduced the monocyte, neutrophil, and basophil counts. In females, BPA induced a significant increase in the total WBC, monocyte, and lymphocyte counts and caused a reduction in the neutrophil, basophil, and eosinophil counts. VitD was found to improve the hematological parameters in mice [57]. Moreover, researchers reported that VitD enhances WBC counts [61]. Similarly, in this study, co-treatment with VitD reversed the effect of BPA on WBC counts. Specifically, VitD enhanced the counts of all WBC types in male mice compared to the counts in BPA-treated male mice. The co-treatment with VitD modulated the total WBC, neutrophil, and eosinophil counts but not monocyte and lymphocyte counts compared to the counts of BPA-treated female mice. Increasing evidence documented that the dose and route of BPA administration determine its toxic effects. For example, BPA administration at 125 mg/kg/day via oral route demonstrated an adverse effect on the liver and kidneys [25, 62, 63]. Cellular and microanatomical abnormalities of the spleen and hematopoietic and immunomodulatory functions are affected by the dose of BPA and the sex of the animal [6]. BPA at a dose of 5 mg/kg/day exerted severe toxicity in the liver and kidneys, while BPA at 50 or 600 mg/kg/day failed to affect these organs [25, 62]. In the present study, we found that the treatment with BPA augmented the spleen pathology in male and female mice, including the cellular and microstructural alterations and depletion of lymphocytes in the white pulp accompanied by pyknotic cell aggregation. Also, narrow lumen, ruptured wall of central arterioles, changes to red pulp such as increased neutrophil and macrophage counts, and nests of pyknotic cells were noted. Increased susceptibility of splenocytes to apoptosis may lead to spleen pyknosis, an underlying mechanism leading to immune senescence and autoimmune diseases [64]. Also, we found that DNA from the BPA-treated mice exhibited apoptotic DNA ladder in gel electrophoresis. The formation of the DNA ladder (180–200 bp fragments) can be analyzed by agarose gel electrophoresis [65]. Similar to another study [66], increased apoptotic splenocytes were found in the BPA-treated male and female mice compared to control mice in the present investigation. VitD has been shown to prevent adverse effects of environmental toxins on spleen morphology [67] and ischemia-reperfusion injury of the ovary in animals [42]. Interestingly, improved spleen morphology and tissue architecture in VitD administrated mice were observed. Several studies stressed the association between BPA exposure and the generation of oxidative stress and its role in the detrimental health effects [12]. Moreover, oxidative stress is the causal mechanism involved in the toxicity associated with BPA [5, 68–70]. Oxidative stress leads to several changes, including membrane injury, mitochondrial dysfunction, and DNA damage in splenocytes [66]. Thus, BPA-induced ROS generation and subsequent oxidative stress might be involved in toxicity [5]. In epidemiological studies, increased lipid peroxidation marker, MDA, was correlated with the BPA exposure, and in fact, its elevated levels were found in patients with autoimmune diseases [10]. To substantiate the effect of oxidative stress on BPA-induced spleen pathology, we measured the MDA levels in the splenic tissue homogenates in all experimental groups. Here, we found that BPA significantly increased the MDA levels in the splenic tissues of the adult male and female mice compared to their corresponding MDA levels in the control group. Vitamins play a vital role in abrogating oxidative stress due to their antioxidant and anti-apoptotic properties. For instance, vitamin E supplementation attenuated apoptotic changes in the ovaries and caused reperfusion and ischemia [71]. VitD is a protective agent in many disorders [72, 73]. Many studies showed a strong correlation between VitD and oxidative stress [20–22, 74]. Researchers showed that ischemia and reperfusion-induced injury to the liver can be prevented with VitD supplementation [75]. Augmented MDA levels resulting from the non-alcoholic fatty liver disease were ameliorated after VitD supplementation [76]. In contrast, researchers found that VitD supplementation for twelve weeks reduced glutathione (GSH) and MDA levels while keeping the nitric oxide (NO) levels constant [77]. In this study, VitD treatment led to a marked reduction in the BPA induced-oxidative stress marker, MDA, in the splenic tissues of mice, independent of their gender. ## Conclusion Our findings led us to the conclusion that exposure to BPA caused splenic oxidative damage by affecting the status of oxidants and ultimately led to spleen pathology. Notably, the concurrent co-treatment with VitD demonstrated protective effects on the spleen architecture and WBC counts and afforded protection against BPA toxicity. However, this study fails to pinpoint the precise mechanism(s) VitD exerts its action. Nevertheless, the abovementioned data indicates that the favorable effects of VitD are mediated by its ability to block oxidative stress. 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--- title: Impact of angiotensin-converting enzyme inhibitors versus angiotensin receptor blockers on clinical outcomes in hypertensive patients with acute myocardial infarction authors: - Jae-Geun Lee - Seung-Jae Joo - Song-Yi Kim - Joon-Hyouk Choi - Ki Yung Boo - Jin-Yong Hwang - Seung-Ho Hur - Myung Ho Jeong journal: PLOS ONE year: 2023 pmcid: PMC9997890 doi: 10.1371/journal.pone.0281460 license: CC BY 4.0 --- # Impact of angiotensin-converting enzyme inhibitors versus angiotensin receptor blockers on clinical outcomes in hypertensive patients with acute myocardial infarction ## Abstract There has been a concern that angiotensin receptor blockers (ARB) may increase myocardial infarction (MI) in hypertensive patients compared with other classes of anti-hypertensive drugs. Angiotensin-converting enzyme inhibitor (ACEI) is recommended as a first-line inhibitor of renin-angiotensin system (RASI) in patients with acute MI (AMI), but ARB is also frequently used to control blood pressure. This study investigated the association of ARB vs. ACEI with the long-term clinical outcomes in hypertensive patients with AMI. Among patients enrolled in the nationwide AMI database of South Korea, the KAMIR-NIH, 4,827 hypertensive patients, who survived the initial attack and were taking ARB or ACEI at discharge, were selected for this study. ARB therapy was associated with higher incidence of 2-year major adverse cardiac events, cardiac death, all-cause death, MI than ACEI therapy in entire cohort. After propensity score-matching, ARB therapy was still associated with higher incidence of 2-year cardiac death (hazard ratio [HR], 1.60; $95\%$ confidence interval [CI], 1.20–2.14; $$P \leq 0.001$$), all-cause death (HR, 1.81; $95\%$ CI, 1.44–2.28; $P \leq 0.001$), and MI (HR, 1.76; $95\%$ CI, 1.25–2.46; $$P \leq 0.001$$) than the ACEI therapy. It was concluded that ARB therapy at discharge in hypertensive patients with AMI was inferior to ACEI therapy with regard to the incidence of CD, all-cause death, and MI at 2-year. These data suggested that ACEI be a more appropriate RASI than ARB to control BP in hypertensive patients with AMI. ## Introduction The renin–angiotensin system (RAS) plays an important role in the development of hypertension and is also associated with the pathogenesis and progression of atherosclerosis, leading to cardiovascular (CV) disease such as myocardial infarction (MI) [1]. Angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARB) are recommended as important drugs for lowering blood pressure (BP) [2]. The use of ACEI is also recommended in patients with ST-segment elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI), when they have anterior infarction, heart failure (HF), left ventricular (LV) systolic dysfunction, or diabetes mellitus (DM), unless contraindicated. ARB therapy is an alternative to ACEI therapy for patients with acute MI (AMI) who are intolerant to ACEI [3, 4]. Numerous studies demonstrated the beneficial role of ACEI in patients with AMI, and ARB was non-inferior to ACEI [5–7]. Nowadays, ARB is increasingly used in patients with hypertension, HF, diabetic nephropathy, and other clinical conditions [8], and its use is not limited to subjects who complain of side-effects of ACEI such as cough or angioedema. Unfortunately, the “ARB-MI paradox” was suggested after the Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial [9], a study comparing the efficacy of valsartan with the calcium-channel blocker (CCB), amlodipine, in patients with hypertension. Despite the same degree of BP lowering, valsartan was associated with a significantly higher risk of fatal and nonfatal MI when compared with amlodipine. And given that ACEI had been shown to reduce CV events, including MI, it has been argued that ARB may increase the risk of MI. Nevertheless, it is unclear whether ARB increases the recurrence of MI compared with ACEI in hypertensive patients after AMI. Therefore, we conducted the study to compare the clinical outcomes between ARB and ACEI treatment in hypertensive patients with AMI. ## Study population and data collection The study population was selected from the Korean Acute Myocardial Infarction Registry-National Institutes of Health (KAMIR-NIH) [10]. KAMIR-NIH is a nation-wide, prospective, multicenter, web-based observational cohort study aiming to develop a prognostic and surveillance index for patients with AMI. Patients who were hospitalized primarily for AMI and signed informed consents were consecutively enrolled from November 2011 to October 2015. This study was conducted according to the ethical guidelines of the Declaration of Helsinki. The study protocol was approved by the ethics committee at Chonnam National University Hospital, Republic of Korea (IRB No. CNUH-2011-172) and the institutional review boards of all participating hospitals approved the study protocol. Written informed consents were obtained from participating patients or legal representative. Data were collected by the attending physician with the assistance of a trained clinical research coordinator, via a web-based case report form in the clinical data management system of the Korea NIH. Patients, who died during index hospitalization, did not have hypertension, were prescribed neither ACEI nor ARB, or both ACEI and ARB at discharge, did not undergo echocardiographic study, and had incomplete clinical data, were excluded. AMI was diagnosed when there was an evidence of myocardial necrosis (a rise and/or fall in cardiac biomarker, preferably cardiac troponin), and at least one of the following: [1] symptoms of ischemia, [2] new or presumed new significant ST-segment-T wave changes or a new left bundle branch block, [3] a development of pathologic Q waves in the electrocardiogram, [4] an imaging evidence of the new loss of viable myocardium or new regional wall motion abnormality, and [5] the identification of an intracoronary thrombus by angiography [11]. Hypertension was defined as values ≥140 mmHg of systolic BP (SBP) and/or ≥90 mmHg of diastolic BP (DBP) during the initial hospitalization [12, 13]. Patients with a history of hypertension or antihypertensive treatment on the interview were also considered to have hypertension. Coronary reperfusion included reperfusion by percutaneous coronary intervention (PCI), thrombolysis, or coronary artery bypass graft (CABG), MI with non-obstructed coronary arteries (MINOCA) [3], and myocardial bridge. LV systolic function was evaluated by the echocardiographic study during the initial hospitalization. ## Clinical endpoints and definition The primary clinical endpoint was the occurrence of major adverse cardiac events (MACE), which was a composite of cardiac death (CD), MI, revascularization, and re-admission due to HF during the 2-year follow-up period. Although the recurrence of MI was the main focus, it was a secondary endpoint in this study because the primary endpoint of the KAMIR-NIH study was defined as MACE [10]. Other secondary endpoints were CD, revascularization, re-admission due to HF, all-cause death, stroke, stent thrombosis, 2-year major adverse cardiac and cerebrovascular events (MACCE) which was a composite of the primary endpoint and stroke, and 2-year MACE with non-cardiac death (NCD). All deaths were considered to be associated with cardiac problems, unless a definite non-cardiac cause was established. Revascularization included repeated PCI or CABG on either target or non-target vessels. The staged PCI was excluded from revascularization. The clinical follow-ups were routinely performed by visiting the hospital at 6-, 12-, 24-, and 36-month and whenever any clinical events occurred. If patients did not visit the hospitals, the outcome data were assessed by telephone interview. Clinical events were not centrally adjudicated. The physician identified all events and the principal investigator of each hospital confirmed them. ## Statistical analysis For continuous variables, data were expressed as mean ± standard deviation or median (interquartile range) and differences between the two groups were evaluated using the unpaired t-test or Mann-Whitney U test. For discrete variables, differences were expressed as counts and percentages and were analyzed with the χ2 test between the two groups. To adjust for any potential confounders, propensity score-matching (PSM) analysis was performed using the logistic regression model with all available variables that could be of potential relevance: age, gender, body mass index (BMI), history of smoking, Killip class on admission, BP, heart rate, LV ejection fraction (LVEF), CV risk factors or co-morbidity (hypertension, diabetes mellitus, hyperlipidemia, prior HF, prior stroke, prior MI, and prior angina), initial estimated glomerular filtration rate (eGFR) by Modification of Diet in Renal Disease (MDRD) equation, co-medications (aspirin, P2Y12 inhibitors, CCB, beta-blockers and statins) at discharge and types of MI (STEMI or NSTEMI). Patients in the ARB group were 1:1 matched to those in the ACEI group according to propensity score with nearest neighbor matching algorithm. Subjects were matched with a caliper width equal to 0.1 of the standard deviation of the propensity score. The efficacy of the propensity score model was assessed by estimating standardized differences for each covariate between groups. Survival curves for clinical endpoints and cumulative event rates with incidence rates per 100 patient-years up to 2-year were generated using Kaplan–Meier estimates. Cox-proportional hazard models were used to assess the adjusted hazard ratio (HR) comparing the two groups and their $95\%$ confidence interval (CI) for each clinical endpoint. Subgroups that were defined post-hoc according to demographic and clinical characteristics included age (<75 & ≥75 years), gender, diabetes mellitus, Killip class, LVEF (<$50\%$ & ≥$50\%$), beta-blockers at discharge, type of MI, multi-vessel disease and infarct-related artery. All data were processed with SPSS version 23 (IBM Co, Armonk, NY, US) and R version 3.1.3 (R Foundation for Statistical Computing, Vienna, Austria). For all analyses, a two-sided $p \leq 0.05$ was considered to be statistically significant. ## Results Total 13,624 consecutive patients were enrolled in the KAMIR-NIH. After excluding 8,797 patients (252 patients who died during index hospitalization, 6,044 patients without hypertension, 1,284 patients with neither ACEI nor ARB at discharge, 45 patients with both ACEI and ARB at discharge, 1,153 patients without echocardiographic data, and 19 patients with incomplete data), 4,827 hypertensive patients with either ACEI or ARB at discharge were analyzed in this study (Fig 1). ACEI or ARB was prescribed at the discretion of attending physicians. More ACEI were used at discharge. After PSM, 1,967 patients in each group were selected. Fig 1: Selection of patients for analysis.ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; KAMIR-NIH, Korean Acute Myocardial Infarction Registry-National Institutes of Health; PSM, propensity score-matching. ## Baseline clinical characteristics In the entire cohort, patients with ARB at discharge were older, and had more diabetes mellitus, prior MI, prior angina, prior HF and eGFR <60 mL/min/1.73m2 compared to those with ACEI (Table 1). On the other hand, patients with ACEI at discharge were more male, more current smoker, and had more STEMI, more treated with P2Y12 inhibitors or beta-blockers at discharge compared to those with ARB. The baseline LVEF of ARB group was higher than that of ACEI group. After PSM, these baseline differences between two groups were well balanced (Table 1). Overall reperfusion rate was $95\%$, and PCI with drug-eluting stents was the main method of coronary reperfusion in the entire and PSM cohorts. Table 1 \| Unnamed: 0 \| Entire cohort \| Entire cohort.1 \| Entire cohort.2 \| Entire cohort.3 \| Propensity score-matched patients \| Propensity score-matched patients.1 \| Propensity score-matched patients.2 \| Propensity score-matched patients.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variables \| ACEI \| ARB \| P value \| SD \| ACEI \| ARB \| P value \| SD \| \| Variables \| (n = 2604) \| (n = 2223) \| P value \| SD \| (n = 1967) \| (n = 1967) \| P value \| SD \| \| Age, years \| 65.7 ± 12.0 \| 67.5 ± 11.4 \| <0.001 \| 0.16 \| 66.9 ± 11.5 \| 67.1 ± 11.5 \| 0.650 \| 0.02 \| \| Male \| 1817 (69.8) \| 1406 (63.2) \| <0.001 \| -0.14 \| 1305 (66.3) \| 1290 (65.6) \| 0.638 \| -0.02 \| \| SBP at admission \| 135.2 ± 28.2 \| 133.4 ± 27.5 \| 0.028 \| -0.14 \| 132.7 ± 29.4 \| 132.4 ± 28.5 \| 0.546 \| -0.02 \| \| DBP at admission \| 80.3 ± 16.6 \| 80.5 ± 16.3 \| 0.668 \| 0.05 \| 79.4 ± 17.1 \| 80.1 ± 17.3 \| 0.296 \| 0.03 \| \| Killip class ≥ II \| 532 (20.4) \| 493 (22.2) \| 0.148 \| 0.04 \| 414 (21.0) \| 429 (21.8) \| 0.586 \| 0.02 \| \| Body mass index, kg/m2 \| 24.3 ± 3.3 \| 24.3 ± 3.6 \| 0.580 \| 0.02 \| 24.3 ± 3.3 \| 24.3 ± 3.6 \| 0.798 \| 0.01 \| \| Current smoker \| 879 (33.8) \| 594 (26.7) \| <0.001 \| -0.16 \| 559 (28.4) \| 553 (28.1) \| 0.859 \| -0.03 \| \| Diabetes mellitus \| 847 (32.5) \| 932 (41.9) \| <0.001 \| 0.19 \| 747 (38.0) \| 768 (39.0) \| 0.512 \| 0.01 \| \| Dyslipidemia \| 368 (14.1) \| 305 (13.7) \| 0.708 \| -0.01 \| 276 (14.0) \| 275 (14.0) \| >0.999 \| -0.01 \| \| Prior MI \| 191 (7.3) \| 238 (10.7) \| <0.001 \| 0.11 \| 173 (8.8) \| 181 (9.2) \| 0.697 \| 0.01 \| \| Prior angina pectoris \| 249 (9.6) \| 327 (14.7) \| <0.001 \| 0.15 \| 231 (11.7) \| 256 (13.0) \| 0.245 \| 0.03 \| \| Prior heart failure \| 40 (1.5) \| 53 (2.4) \| 0.036 \| 0.06 \| 38 (1.9) \| 43 (2.2) \| 0.654 \| 0.02 \| \| Prior stroke \| 248 (9.5) \| 209 (9.4) \| 0.921 \| 0.00 \| 191 (9.7) \| 189 (9.6) \| 0.957 \| 0.02 \| \| eGFR<60 mL/min/1.73m2 \| 563 (21.6) \| 611 (27.5) \| <0.001 \| 0.13 \| 481 (24.5) \| 501 (25.5) \| 0.484 \| 0.02 \| \| LVEF, % \| 51.2 ± 10.9 \| 53.8 ± 11.3 \| <0.001 \| 0.22 \| 52.8 ± 10.6 \| 53.1 ± 11.2 \| 0.504 \| 0.02 \| \| STEMI \| 1316 (50.5) \| 810 (36.4) \| <0.001 \| -0.29 \| 814 (41.4) \| 793 (40.3) \| 0.517 \| -0.02 \| \| Coronary reperfusiona \| 2479 (95.2) \| 2115 (95.1) \| 0.946 \| 0.00 \| 1876 (95.4) \| 1873 (95.2) \| 0.880 \| -0.03 \| \| SBP at discharge \| 116.9 ± 15.1 \| 115.9 ± 16.0 \| 0.036 \| \| 117.3 ± 17.1 \| 115.5 ± 15.7 \| 0.001 \| \| \| DBP at discharge \| 69.9 ± 10.2 \| 69.0 ± 10.1 \| 0.002 \| \| 70.0 ± 10.3 \| 68.9 ± 10.1 \| 0.002 \| \| \| Medications at discharge \| Medications at discharge \| Medications at discharge \| Medications at discharge \| Medications at discharge \| Medications at discharge \| Medications at discharge \| Medications at discharge \| Medications at discharge \| \| Aspirin \| 2601 (99.9) \| 2219 (99.8) \| 0.710 \| -0.02 \| 1964 (99.8) \| 1963 (99.8) \| >0.999 \| -0.01 \| \| P2Y12 inhibitors \| 2544 (97.7) \| 2124 (95.5) \| <0.001 \| -0.10 \| 1911 (97.2) \| 1906 (96.9) \| 0.708 \| -0.01 \| \| Beta-blockers \| 2390 (91.8) \| 1914 (86.1) \| <0.001 \| -0.16 \| 1762 (89.6) \| 1737 (88.3) \| 0.222 \| -0.04 \| \| Statins \| 2476 (95.1) \| 2093 (94.2) \| 0.158 \| -0.04 \| 1967 (100.0) \| 1967 (100.0) \| >0.999 \| -0.01 \| ## Clinical outcomes Two-year follow-up rate was $94\%$ and $97\%$ in the entire and PSM cohorts, respectively. In the entire cohort, $43\%$ of patients with ACEI at discharge continued to take ACEI at 1-year, but $36\%$ had cross-over to ARB. On the other hand, $82\%$ of patients with ARB at discharge continued to take ARB, and only $1.4\%$ had cross-over to ACEI. Also, at 2-year, $34\%$ of patients with ACEI at discharge continued to take ACEI, and $38\%$ had cross-over to ARB. Among patients with ARB at discharge, $70\%$ of patients continued to take ARB, and only $1.3\%$ had cross-over to ACEI. Cross-over rates in PSM cohort showed a similar pattern. In entire cohort, the ARB therapy at discharge was associated with higher incidence of MACE, CD, all-cause death, MI, Stroke, MACCE and MACE with NCD at 2-year than the ACEI therapy at discharge (Table 2). However, there was no significant difference in the incidence of revascularization, re-hospitalization due to HF and stent thrombosis between two groups. After PSM, the ARB therapy at discharge was still associated with higher incidence of CD (HR, 1.60; $95\%$ CI, 1.20–2.14; $$P \leq 0.001$$), all-cause death (HR, 1.81; $95\%$ CI, 1.44–2.28; $P \leq 0.001$), MI (HR, 1.76; $95\%$ CI, 1.25–2.46; $$P \leq 0.001$$), Stroke (HR, 1.97; $95\%$ CI, 1.26–3.09; $$P \leq 0.003$$), MACCE (HR, 1.20; $95\%$ CI, 1.04–1.39; $$P \leq 0.015$$) and MACE with NCD (HR, 1.22; $95\%$ CI, 1.05–1.41; $$P \leq 0.008$$) than the ACEI therapy at discharge (Table 2, Fig 2). Likewise, 1-year CD, all-cause death, MI, Stroke, MACCE and MACE with NCD were significantly higher in patients with the ARB therapy at discharge in entire and PSM cohorts (S1 Table). Fig 2: Kaplan-Meier curves and adjusted hazard ratios for 2-year clinical events in propensity score-matched patients with ARB vs. ACEI.(A) All-cause death. (B) Cardiac death. (C) Myocardial infarction. ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; CI, confidence interval; HR, hazard ratio. TABLE_PLACEHOLDER:Table 2 Compared with ACEI therapy, the inferior association between the ARB therapy at discharge and 2-year MI appeared to be consistent across a series of subgroups, including age, gender, diabetes mellitus, Killip class, LVEF, beta-blockers at discharge, and type of MI (Fig 3). In PSM cohort with reduced LVEF (<$50\%$), ARB therapy at discharge was associated with a significantly higher incidence of CD, all-cause death, MI, and MACE with NCD at 2-year than the ACEI therapy at discharge. On the other hand, in PSM cohort with preserved LVEF (≥$50\%$), the incidence of MI was not different between the ACEI and ARB therapy, but the incidence of all-cause death, CD, stroke and MACCE was higher in ARB group (S2 Table and S1 Fig). Fig 3: Subgroup analysis for myocardial infarction in propensity score-matched patients with ARB vs. ACEI.ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; CI, confidence interval; HR, hazard ratio; MI; myocardial infarction; NSTEMI, non-ST elevation myocardial infarction; LAD, left anterior descending artery; LVEF, left ventricular ejection fraction; STEMI, ST-elevation myocardial infarction. In propensity score-matched cohort, BP at discharge in ARB group were lower than that in ACEI group (SBP; 115.5 ± 15.7mmHg, vs. 117.3±15.5mmHg; $$P \leq 0.001$$, DBP; 68.9±10.1mmHg vs. 70.0±10.3mmHg; $$P \leq 0.002$$), however, BP at the admission, 1- and 2-year were not different (S2 Fig). We performed 1-year landmark analysis for the incidence of clinical events from 1 to 2-year among patients who were event-free at 1-year. The number of stable patients who were event-free at 1-year was 4,174 out of total 4,827 patients. At 1-year follow-up, 3,547 patients were taking RAS inhibitors (RASI). After excluding 41 patients taking both ACEI and ARB, 2,444 patients were taking ARB and 1,062 patients were taking ACEI. After PSM, 680 patients in each group were selected. The incidence of MACE and recurrent MI at 2-year was not statistically different, indicating that there was no difference between ARB and ACEI in clinical events in patients who were relatively stable after AMI (Fig 4). Fig 4: Landmark analysis for MACE and recurrent MI among patients who were event-free at 1-year after propensity score matching.ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; CI, confidence interval; HR, hazard ratio, MACE, major adverse cardiac events; MI, myocardial infarction. Perindopril ($50\%$) and ramipril ($40\%$) were the major ACEI’s, and candesartan ($35\%$), losartan ($24\%$), telmisartan ($20\%$) and valsartan ($14\%$) were the major ARB’s that prescribed at discharge (Table 3). All RASI were used in lower doses than those recommended in the guidelines. ARB’s association with higher incidence of 2-year MI than ACEI was consistent across the generic names of ARB’s without a significant interaction (Fig 5). Fig 5: Adjusted hazard ratios of 2-year recurrent myocardial infarction in propensity score-matched cohort with ARB vs. ACEI according to generic names of ARB.ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; CI, confidence interval. TABLE_PLACEHOLDER:Table 3 ## Discussion The main findings of this study are that ARB therapy at discharge was inferior to ACEI therapy with regard to the incidence of CD, and all-cause death, and MI in patients with hypertension and AMI, up to 2-year of follow-up. In patients who had no clinical events until 1-year, there was no difference in the incidence of clinical events between ACEI and ARB therapy at 2-year follow-up. In other cohort studies [14, 15], approximately 46~$67\%$ of patients with AMI received RASI. In this study, $78\%$ of hypertensive subjects with AMI received either ACEI or ARB at discharge, and ARB was prescribed in $46\%$ of those patients; this result reflects the “real world” practice in hypertensive patients with AMI. In patients with AMI, optimal medical therapy plays an important role for secondary prevention, and RASI is one of the important drugs when they have hypertension. ACEI is recommend as a first-line RASI for hypertensive patients with AMI [16], and when they are intolerant to ACEI, ARB is an alternative RASI to be prescribed at discharge. However, despite this recommendation, ARB is occasionally used as the first line RASI because ARB has an advantage of better tolerability than ACEI. There has been a long-standing debate that ARB has less preventive effects on all-causes death, CV death, and CV events than ACEI [17, 18]. There were meta-analyses that focused on this issue [19, 20]. In patients with diabetes mellitus or without HF, ACEI reduced all-cause death, CV death, and MI when compared with either active drugs or placebo, but ARB showed no benefits for these outcomes. However, the control event rate, which affected the efficacy of RASI therapy, has been lowered since 2000 because of more wide use of statin therapy and strict BP control in hypertensive patients. The relatively lower control event rate of the major ARB trials which were performed after 2000 may explain the lack of clinical benefits of ARB. Indeed, the meta-analysis of head-to-head comparison trials showed similar clinical outcomes between ARB and ACEI [19]. In patients with AMI, the clinical trial and observational studies comparing relative efficacy of ACEIs and ARBs on long-term clinical outcomes showed inconsistent results. In the Valsartan in Acute Myocardial Infarction trial which compared valsartan and captopril in MI patients with HF or LV systolic dysfunction, valsartan was not inferior to captopril in reducing the incidence of all-cause death, cardiac death, and MI [6, 21] and one registry data of patients with AMI showed that ACEI and ARB had similar risks of cardiac death or MI up to 1-year follow-up [22]. However, other observational studies of patients with AMI showed that ARB was inferior to ACEI in reducing all-cause death, MACE or any repeat revascularization [23–25]. In a recent registry study of patients with AMI without a history of hypertension [26], ACEI therapy was associated with reduced incidences of MACE, any repeat revascularization, stroke, and re-hospitalization due to HF than ARB therapy, but MI was not significantly different between ACEI and ARB therapy. In our study of hypertensive patients with AMI, the cumulative incidences of cardiac death, all-cause death, and MI at 2-year was higher in ARB group than in the ACEI group, but in patients without events until 1-year, clinical outcomes were not different between the two groups. These findings suggest that ACEI in hypertensive patients with AMI is associated with better clinical outcomes in the initial period compared with ARB, but after stabilization from the acute attack at 1-year, clinical outcomes are similar regardless of which RASI is administered. There are plausible mechanisms about “ARB-MI paradox”. ACEI inhibits the formation angiotensin II (Ang II) to prevent its pathological effects on endothelial function, CV remodeling, and the progression of atherosclerosis. ACEI also prevents the breakdown of bradykinin, resulting in additional cardioprotective effects. However, ARB selectively blocks Ang II type 1 receptors, which leads to a marked counter up-regulation of Ang II. The augmented stimulation of Ang II type 2 receptor was shown to promote the release of leukocyte-dependent matrix metalloproteinase-1 and resultant atherosclerotic plaque rupture. It may also lead to apoptosis and inhibition of angiogenesis which have a potential to decrease collateral vessel growth even in ischemic conditions [27]. These mechanisms may explain the superiority of ACEI over ARB in reducing MACE, CV death, MI, revascularization, and re-hospitalization due to HF in patients with AMI. ## Limitations This study has several limitations. First, this study analyzed a non-randomized, observational registry data. The prescription and selection of RASI was at the discretion of an attending physician. The information why physicians prescribed ACEI or ARB at discharge was not available. Although we performed a PSM analysis to account for the potential confounding factors, other unmeasured, residual variables as well as selection bias could not be completely controlled. However, a randomized clinical trial of head-to-head comparison between ACEI and ARB in hypertensive patients with AMI is very difficult to be performed. In this respect, observational registry data may answer which RASI has better clinical outcomes despite the inherent limitations. Second, because patients’ medications were recorded only at discharge, 1-year and 2-year, we could not ascertain whether patients actually obtained them, took them as prescribed, and adhered for two years. In addition, a large cross-over was observed in patients with ACEI or ARB during 2 years. However, taking ACEI from the hospital discharge was associated with better clinical outcomes than ARB. Third, the clinical events were not centrally adjudicated, but instead, identified by an attending physician and confirmed by the principal investigator of each hospital. As a result, some clinical events may not have been captured in the database. 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--- title: 'Association between parental absence during childhood and metabolic syndrome during adulthood: A cross-sectional study in rural Khanh Hoa, Vietnam' authors: - Rachana Manandhar Shrestha - Tetsuya Mizoue - Thuy Thi Phuong Pham - Ami Fukunaga - Dong Van Hoang - Chau Que Nguyen - Danh Cong Phan - Masahiko Hachiya - Dong Van Huynh - Huy Xuan Le - Hung Thai Do - Yosuke Inoue journal: PLOS ONE year: 2023 pmcid: PMC9997891 doi: 10.1371/journal.pone.0282731 license: CC BY 4.0 --- # Association between parental absence during childhood and metabolic syndrome during adulthood: A cross-sectional study in rural Khanh Hoa, Vietnam ## Abstract ### Background This study aimed to determine the association between parental absence during childhood and metabolic syndrome (MetS) in adulthood among middle-aged adults in rural Khanh Hoa province, Vietnam. Given that broader literature on adverse childhood experiences (ACEs) suggests a strong positive association between ACEs and cardiometabolic risk or diseases, we hypothesized that parental absence during childhood, which is a major component of ACEs, is more likely to cause MetS in adulthood. ### Methods Data were obtained from the baseline survey of the Khanh Hoa Cardiovascular Study, in which 3000 residents aged between 40 to 60 years participated. MetS was assessed using the modified Adult Treatment Panel III (ATP III) criteria. It was considered parental absence if the participants had experienced parental absence due to death, divorce, or out-migration before three or between three to 15 years. We used multiple logistic regression analyses to examine the association between parental absence during childhood and metabolic syndrome during adulthood. ### Results There was no significant association between parental absence and MetS; adjusted odds ratio [AOR] was 0.97 ($95\%$ confidence interval [CI] = 0.76–1.22) for those who experienced parental absence between three to 15 years and the corresponding figure for those who experienced it before three years was 0.93 ($95\%$ CI = 0.72–1.20). No significant associations were observed when these were examined for the causes of parental absence. ### Conclusion This study did not support our hypothesis of an association between parental absence during childhood and metabolic syndrome during adulthood. Parental absence may not be a predictor of MetS among Vietnamese people in rural communities. ## Background Parental absence in early life is traumatic and has long-lasting health effects [1, 2]. For example, a meta-analysis of nine studies from seven countries by Simbi et al. [ 3] reported that people who had experienced parental loss before 18 years of age were twice more likely to develop depression during adulthood. Previous studies also have suggested that parental loss is associated with other mental health outcomes including anxiety and schizophrenia [4–6]. Compared to mental health studies that have been extensively focused on parental absence during childhood, there is little research regarding parental absence on physical health during adulthood. Given that broader literature on adverse childhood experiences (ACEs) suggests a strong positive association between ACEs and cardiometabolic risk or diseases [7, 8], parental absence during childhood, a major component of ACEs, could cause a higher cardiometabolic risk in adulthood. A prospective study by Chen et al. [ 9] reported that parental loss during childhood tended to be associated with increased hypertension risk during adulthood. Moreover, a cross-sectional study of 135 Italian adult participants (mean age: 43.8 years old) by Alciati et al. [ 10] reported a significant association between parental loss during childhood and metabolic syndrome (MetS) in adulthood, although it was restricted to those with severe obesity (body mass index [BMI] ≥40 kg/m2). This study was conducted to examine the association between parental absence during childhood and MetS in adulthood in Vietnam, where the prevalence of MetS was reported to range from $14.0\%$ to $21.6\%$ since 2014 to 2019 [11–13]. We believe Vietnam can be important research setting to examine the association for several reasons. First, many middle-aged adults might have lost or got separated from their parents during and after the Vietnam War (1955–1975). Previous studies have suggested that parental loss in childhood is associated with a worsening of cardiovascular health in adulthood [14–16]. Hence, those who experienced it during and after the war could be at a higher cardiometabolic risk. Second, the health impact of ACEs due to parental absence might have been exacerbated by lifestyle changes during the economic growth since the 1990s (e.g., westernization in diet). As suggested by the Developmental Origins of Health and Disease (DOHaD) hypothesis [17], the mismatch between the nutritional environment in childhood and that in adulthood might have contributed to the increase in the number of cardiovascular diseases (CVDs) [18, 19]. This study thus aimed to examine the association between parental absence during childhood and the prevalence of MetS in adulthood among the same study population. We hypothesized that those who experienced parental absence during childhood were more likely to have MetS in adulthood, and that this association would be stronger if they experienced it earlier in childhood (i.e., before the age of three) than later in childhood. ## Study design and participants Data for this study came from a baseline survey of the Khanh Hoa Cardiovascular Study (KHCS), which is an ongoing cohort study among 3000 rural residents which aimed at examining the determinants of CVD events, especially those specific to Vietnamese society. From a district in Khanh Hoa Province, eight communes, which were socio-economically average communities in rural Vietnam, were chosen as study sites. The eligible participants for KHCS were those who were living in the study communes for more than six months and aged 40 to 59 years at the time of recruitment. Commune health center staff members prepared the lists of all eligible participants in each commune and recruited participants until the target number (i.e., 3000 participants) was reached (consent rate: 75–$87\%$) (i.e., convenient sampling). Eligible participants were asked to participate in the survey in fasting state. Exclusion criteria for this study were: being unable to provide informed consent, who had a plan to move out of the community within one-year, pregnant women and those who gave birth within one year, those who had CVD events in the past, and those being institutionalized. The baseline survey of the KHCS was conducted between June 2019 and 2020. The detailed information of the survey is available in our previous studies by Inoue et al. [ 20] and Chau et al. [ 21]. The study procedure was approved by the Research Ethics Committee at NCGM (approval number: NCGM-G-003172-03), the Ethical Committee of the Pasteur Institute in Nha Trang, Vietnam (approval number: $\frac{02}{2019}$/HDDD-IPN), and the Ethical Committee of the University of Tokyo (approval number: 2021007NI). Written informed consent was obtained from all participants. ## Anthropometric and biochemical measurements Waist circumference was measured at the umbilical level in the standing position using a measuring tape. Systolic and diastolic blood pressures were measured twice with participants seated and their arms supported at the heart level using an electric sphygmomanometer (Omron, HEM1020, Tokyo, Japan). They were instructed to rest for at least five minutes before the first measurement. Two measurements were used to calculate the mean blood pressure. Plasma fasting glucose, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, and triglycerides were measured using a Cobas 8000 (Roche, Switzerland). ## Parental absence (exposure) We considered it as parental absence if participants reported experiencing it before the age of three or between three and 15 years and due to the following causes: death, divorce, or out-migration for more than one year. Participants responded to the following questions: “Before you turned 15 years, did your parents die, get divorced or leave you for migratory work for a continuous period of more than one year?” and “Before you turned three years, did your parents die, get divorced or leave you for migratory work for a continuous period of more than one year?”. Response options included three categories (yes, no, and cannot recall), which were further grouped into two categories (no/cannot recall = 0 and yes = 1). We then categorized the participants into three categories: those who experienced parental absence before three years of age, those who experienced parental absence between three to 15 years, and those who did not have such experiences. To determine whether different causes of parental absence resulted in different associations, we also categorized participants according to parental absence experience by three different causes (i.e., death, divorce, and separation due to out-migration). ## Metabolic syndrome (outcome) Metabolic syndrome was defined according to the modified National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria [22] and Asian-specific cut-offs were employed for waist circumference, which had been used in previous studies in Vietnam [11, 12]. Specifically, participants were classified as having MetS if they met three or more of the following five components: [1] fasting plasma glucose ≥ 5.6 mmol/L (100 mg/dL) or on anti-diabetic medication, [2] systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥85 mmHg or on antihypertensive medication, [3] HDL-C <1.04 mmol/L (40 mg/dL) for men and HDL-C < 1.29 mmol/L (50 mg/dL) for women, [4] triglycerides ≥ 1.7 mmol/L (150 mg/dL) or on lipid-lowering medication, [5] and waist circumference ≥ 90 cm for men and ≥ 80 cm for women. ## Covariates We collected information on the following socio-demographic information using a questionnaire: age (in years), sex (male or female), marital status (married or unmarried), educational attainment (less than primary school, primary school, middle school, or high school or higher), occupation (government employee, non-government employee, self-employed, farmer or fisherman, housewife, other, or not working), and household income (low, middle, or high). Information on monthly household income was obtained by asking the representatives to choose from the following categories in Vietnamese Dong (VND)(≤1,000,000; 1,000,001 to ≤ 2,000,000; 2,000,001 to ≤ 3,000,000; 3,000,001 to ≤ 4,000,000; 4,000,001 to ≤ 6,000,000; 6,000,001 to ≤ 8,000,000; 8,000,001 to ≤ 12,000,000; 12,000,001 to ≤ 16,000,000; ≤ 16,000,001 to ≤ 20,000,000; > 20,000,000; or do not know), which was divided by the square root of the number of household members (i.e., equivalized income) and then grouped into tertiles (low, middle, or high). Childhood socioeconomic status was defined using the response to “How would you rate your family’s socioeconomic status when you were 15 years according to standards of that time?”. Response options included five categories ranging from low to high, which we further categorized into (low, lower-middle, or middle/ middle-high/ high) due to the small sample size in the upper two categories. We also collected information on the following health-related behaviors: smoking status (never, former, or current), and alcohol drinking (do not drink, less than one standard drink, 1–1.9 standard drink, or ≥2 standard drinks per day), and physical activity (calculated by total metabolic equivalent task) categorized into three groups (<600 METs, 600–1200 METs, or >1200 METs) based on the Global Physical Activity Questionnaire [23]. ## Statistical analysis Multiple imputations were used to account for those with missing information regarding household income ($$n = 33$$). Specifically, a multinomial logistic model was used to generate 20 datasets (200 iterations). The estimates were combined using Rubin’s rule [24]. The baseline characteristics of the participants, stratified by parental absence status, were expressed as means and standard deviations (SD) for continuous variables and numbers and proportions for categorical variables. To examine the association between parental absence by timing and causes and MetS, we conducted multiple logistic regression analyses to estimate odds ratios (ORs) and $95\%$ confidence intervals (CIs) of MetS. In model 1, we adjusted for age and sex. We also adjusted for childhood socioeconomic status (i.e., a confounder in the association between parental absence and MetS) in model 2 (the main model). In model 3, we further adjusted for marital status, job category, education, household income, smoking, alcohol consumption, and physical activity as possible mediators linking parental absence and MetS. The statistical significance level was set at $p \leq 0.05.$ All analyses were conducted using Stata ver.15.0 (College Station, TX, USA). ## Results Table 1 shows the participants’ general characteristics according to parental absence status. Of the total participants, 636 ($21.2\%$) had experienced parental absence before 15 years of age. The mean age of study participants was 49.9 years (SD = 5.5), $61.3\%$ were female, and $89.3\%$ were married, and the proportions were similar among those who had and had not experienced parental absence during their childhood. Participants who had parental absence experience before 3 years and between 3–15 years were more likely to have education level below primary school ($15.6\%$ and $17.3\%$ vs. $10.5\%$), low socioeconomic status ($45.3\%$ and $51.0\%$ vs. $37.0\%$), low household income ($38.7\%$ and $38.9\%$ vs. $31.9\%$) and less likely to be a government employee ($6.9\%$ and $6.1\%$ vs. $10.7\%$) compared to those who did not have such experience. The mean waist circumference, systolic and diastolic blood pressure, fasting blood glucose, high-density lipoprotein and triglycerides were similar between those who had and did not have such experience during their childhood. Table 1 \| Unnamed: 0 \| Unnamed: 1 \| Parental absence \| Parental absence.1 \| Parental absence.2 \| \| --- \| --- \| --- \| --- \| --- \| \| Variables \| All participants \| No \| Yes (3 - <15 yo) \| Yes (< 3 yo) \| \| \| (N = 3000) \| (n = 2364) \| (n = 347) \| (n = 289) \| \| Age (years), mean [SD] \| 49.9 [5.5] \| 49.6 [5.5] \| 50.9 [5.5] \| 50.6 [5.2] \| \| Sex (female), n (%) \| 1,840 (61.3) \| 1,439 (60.9) \| 218 (62.8) \| 183 (63.3) \| \| Ethnicity (Kinh), n (%) \| 2,970 (99.0) \| 2,337 (98.69) \| 346 (99.7) \| 287 (99.3) \| \| Marital status (currently married), n (%) \| 2,680 (89.3) \| 2,125 (89.9) \| 304 (87.6) \| 251 (86.8) \| \| Education, n (%) \| \| \| \| \| \| Less than primary school \| 352 (11.7) \| 247 (10.5) \| 60 (17.3) \| 45 (15.6) \| \| Primary school \| 863 (28.8) \| 655 (27.7) \| 124 (35.7) \| 84 (29.1) \| \| Middle school \| 1,068 (35.6) \| 862 (36.5) \| 108 (31.1) \| 98 (33.9) \| \| High school or higher \| 717 (23.9) \| 600 (25.4) \| 55 (15.9) \| 62 (21.4) \| \| Occupation, n (%) \| \| \| \| \| \| Government employee \| 295 (9.8) \| 254 (10.7) \| 21 (6.1) \| 20 (6.9) \| \| Non-government employee \| 483 (16.1) \| 386 (16.3) \| 57 (16.4) \| 40 (13.8) \| \| Self-employed \| 595 (19.8) \| 469 (19.8) \| 79 (22.8) \| 47 (16.3) \| \| Farmer/fisherman \| 870 (29.0) \| 676 (28.6) \| 102 (29.4) \| 92 (31.8) \| \| Houseworker \| 527 (17.6) \| 412 (17.4) \| 52 (15.0) \| 63 (21.8) \| \| Other \| 111 (3.7) \| 85 (3.6) \| 15 (4.3) \| 11 (3.8) \| \| Not working \| 119 (4.0) \| 82 (3.5) \| 21 (6.1) \| 16 (5.5) \| \| Household income, n (%) \| \| \| \| \| \| Low \| 1002 (33.4) \| 755 (31.9) \| 135 (38.9) \| 112 (38.7) \| \| Middle \| 1045 (34.8) \| 843 (35.7) \| 114 (32.9) \| 88 (30.5) \| \| High \| 920 (30.7) \| 741 (31.3) \| 93 (26.8) \| 86 (29.8) \| \| Missing \| 33 (1.1) \| 25 (1.1) \| 5 (1.4) \| 3 (1.0) \| \| Childhood socioeconomic status, n (%) \| \| \| \| \| \| Low \| 1,183 (39.4) \| 875 (37.0) \| 177 (51.0) \| 131 (45.3) \| \| Middle-low \| 764 (25.5) \| 593 (25.1) \| 88 (25.4) \| 83 (28.7) \| \| Middle/ middle-high/ high \| 1,053 (35.1) \| 896 (37.9) \| 82 (23.6) \| 75 (26.0) \| \| Smoking, n (%) \| \| \| \| \| \| Never \| 2,037 (67.9) \| 1,605 (67.8) \| 237 (68.3) \| 195 (67.5) \| \| Former \| 349 (11.6) \| 273 (11.5) \| 43 (12.4) \| 33 (11.4) \| \| Current \| 614 (20.5) \| 486 (20.6) \| 67 (19.3) \| 61 (21.1) \| \| Alcohol consumption, n (%) \| \| \| \| \| \| Don’t drink \| 2,114 (70.5) \| 1,655 (70.0) \| 251 (72.3) \| 208 (72.0) \| \| <1 standard drink \| 416 (13.9) \| 326 (13.8) \| 46 (13.3) \| 44 (15.2) \| \| 1–1.9 standard drink \| 201 (6.7) \| 165 (7.0) \| 24 (6.9) \| 12 (4.1) \| \| ≥2 standard drink \| 269 (8.9) \| 218 (9.2) \| 26 (7.5) \| 25 (8.7) \| \| Physical activity (METs minutes/week), n (%) \| \| \| \| \| \| <600 \| 252 (8.4) \| 201 (8.5) \| 25 (7.2) \| 26 (9.0) \| \| 600–1200 \| 120 (4.0) \| 93 (3.9) \| 20 (5.8) \| 7 (2.4) \| \| >1200 \| 2628 (87.6) \| 2070 (87.6) \| 302 (87.0) \| 256 (88.6) \| \| Waist circumference (cm), mean [SD] \| 81.1 [8.5] \| 81.1 [8.5] \| 80.6 [8.3] \| 81.1 [8.6] \| \| Systolic blood pressure (mmHg), mean [SD] \| 130.9 [19.1] \| 130.8 [18.8] \| 131.4 [19.2] \| 131.1 [21.1] \| \| Diastolic blood pressure (mmHg), mean [SD] \| 83.9 [12.1] \| 83.9 [12.0] \| 84.1 [12.5] \| 83.9 [13.2] \| \| Fasting blood glucose (mmol/L), mean [SD] \| 5.5 [1.5] \| 5.5 [1.4] \| 5.6 [1.6] \| 5.7 [2.1] \| \| High-density lipoprotein cholesterol (mmol/L), mean [SD] \| 1.3 [0.3] \| 1.3 [0.3] \| 1.3 [0.3] \| 1.3 [0.3] \| \| Triglycerides (mmol/L), mean [SD] \| 2.0 [1.5] \| 2.0 [1.5] \| 1.9 [1.3] \| 1.9 [1.4] \| Table 2 depicts the results of multiple logistic regression analysis investigating the association between parental absence by the timing (before three years or between three and 15 years) and MetS. We did not find a significant association between parental absence and MetS among the study participants who had experienced parental absence between three and 15 years (e.g., model 2; AOR = 0.97, $95\%$ CI = 0.76–1.22) and those who experienced it before three years (Model 2; AOR = 0.93, $95\%$ CI = 0.72–1.20). The associations were statistically insignificant in model 3. Table 2 \| Parental absence \| Those with/without MetS \| AOR \| 95% CI \| \| --- \| --- \| --- \| --- \| \| Model 1 \| \| \| \| \| No \| 887 / 1477 \| 1.0 \| Ref. \| \| Yes (3 - <15 yo) \| 133 / 214 \| 0.96 \| 0.76–1.21 \| \| Yes (<3 yo) \| 107 / 182 \| 0.92 \| 0.71–1.19 \| \| Model 2 \| \| \| \| \| No \| 887 / 1477 \| 1.0 \| Ref. \| \| Yes (3 - <15 yo) \| 133 / 214 \| 0.97 \| 0.76–1.22 \| \| Yes (<3 yo) \| 107 / 182 \| 0.93 \| 0.72–1.20 \| \| Model 3 \| \| \| \| \| No \| 887 / 1477 \| 1.0 \| Ref. \| \| Yes (3 - <15 yo) \| 133 / 214 \| 0.93 \| 0.73–1.19 \| \| Yes (<3 yo) \| 107 / 182 \| 0.94 \| 0.73–1.23 \| Table 3 demonstrates the results of the association between parental absence by cause (death, divorce, or out-migration) and MetS. There was no significant association between parental absence by cause (i.e., death, divorce, and out-migration) during childhood and MetS in adulthood. Table 3 \| Parental absence \| Those with/without MetS \| AOR \| 95% CI \| \| --- \| --- \| --- \| --- \| \| Due to death(s) \| \| \| \| \| No \| 942 / 1579 \| 1.0 \| Ref. \| \| Yes (3 - <15 yo) \| 111 / 169 \| 1.01 \| 0.78–1.31 \| \| Yes (<3 yo) \| 74 / 125 \| 0.93 \| 0.69–1.26 \| \| Due to divorce \| \| \| \| \| No \| 1111 / 1841 \| 1.0 \| Ref. \| \| Yes (3 - <15 yo) \| 8 / 15 \| 0.95 \| 0.40–2.27 \| \| Yes (<3 yo) \| 8 / 17 \| 0.79 \| 0.33–1.85 \| \| Due to migratory work \| \| \| \| \| No \| 1070 / 1765 \| 1.0 \| Ref. \| \| Yes (3 - <15 yo) \| 22 / 49 \| 0.72 \| 0.42–1.20 \| \| Yes (<3 yo) \| 35 / 59 \| 0.94 \| 0.61–1.45 \| ## Discussion In this study, no significant associations were found between parental absence and MetS among 3,000 middle-aged community dwellers in rural Khanh Hoa Province, Vietnam. This non-significant association was also observed when specific associations related to different causes of parental absence (i.e., death, divorce, and out-migration) were examined. The non-significant associations between parental absence and MetS did not support our hypothesis, which was based on studies by Alciati et al. [ 10] that reported a significant association between parental loss and MetS among severely obese subjects, and Chen et al. [ 9] that tended to show a significant association between parental death during childhood and increased hypertension risk during adulthood, a component of MetS. However, our findings align with a previous study conducted in China by Schooling et al. [ 25], which reported null association between parental death during childhood and adult cardiovascular risks such as blood pressure, fasting glucose, LDL-cholesterol and HDL-cholesterol. In addition, a few studies conducted among Western populations also reported null findings when they examined the specific association between parental divorce and marital discord (types of parental absence) and CVDs [26, 27]. In contrast to the study participants in Alciati et al. ’s study [10] who were severely obese and whose parents might have been genetically susceptible to obesity or MetS, and earlier death, our study participants as well as most of the previous studies included normal population. This is one of the reasons for the discrepancy. There are several possible interpretations of the non-significant associations. First, the participants could have also experienced stressful life events during their childhood, due to domestic turmoil during and after the Vietnam War. Hence, parental absence may not be the only strong predictor of MetS later in life. Second, those who experienced parental absence might have received care and support from extended family members, which might have lessened the negative effect of parental absence. The Vietnam War was so long that by the time the study participants were born (around 1960–1979), the adults might have been already accustomed to the conditions of war and were more likely to support children who had been separated from their parents. Third, those who were more severely affected by parental absence might have died earlier, and thus, did not participate in our study (i.e., survival bias). This supposition was supported by Brown et al. [ 28], who showed a significant association between ACEs and an increased risk of premature mortality among 17,337 adults in the United States. However, it should be reminded that these interpretations are not in line with our previous finding that parental absence in childhood was significantly associated with adulthood depressive symptoms among the same participants as in this study [20]. More specifically, those who experienced parental absence at 3 - < 15 years old and before 3 years old were 1.21 times and 1.41 times more likely to have depressive symptoms, respectively. This discrepancy in the study results indicates that the impact of parental absence during childhood did have a long-lasting health effect that stretches across the life course, but it can vary depending on the health outcomes under study. Another possible interpretation is that compared to individuals in developed countries where unhealthy foods are readily available that can increase cardiometabolic risk, those in Vietnam had not lived in an environment where they could afford to excessively consume an unhealthy diet as a result of unhealthy stress coping behaviors; such a difference might have underlain the null finding observed in our study. This study has a few limitations that should be considered while interpreting the results. First, although parental absence is unlikely to be forgotten, details (timing and length of separation with parent/s) regarding the event that had occurred in the remote past might be subject to recall bias. Second, the participants might have been hesitant to report their parents’ marital conflict (i.e., social desirability bias). Third, several variables could explain the association between parental absence and MetS, such as stressful childhood life events and the availability of social or financial support after the experience. Moreover, information on whether the experience involved losing either the mother or father or both parents could have facilitated our understanding of the impact of parental loss. Fourth, as mentioned before, the study findings might be subject to survival bias. Fifth, the number of participants with MetS might not have been sufficient to determine the associations, particularly when we examined the association in relation to parental absence caused by specific reasons; it should be mentioned that the sample size was not calculated specifically for this study. Finally, the participants might not have fully represented rural populations in Vietnam, as we only chose one district in one province in Vietnam. ## Conclusion This study did not find any evidence of a significant association between parental absence in childhood and MetS in adulthood among middle-aged dwellers in rural Khanh Hoa Province, Vietnam. The association remained non-significant when we considered the causes and timing of parental absence. ## References 1. Phillips SP, Carver L. Early Parental Loss and Self-Rated Health of Older Women and Men: A Population-Based, Multi-Country Study. PLoS One (2015) 10 e0120762. DOI: 10.1371/journal.pone.0120762 2. Ellis J, Dowrick C, Lloyd-Williams M. The long-term impact of early parental death: lessons from a narrative study. J R Soc Med (2013) 106 57-67. DOI: 10.1177/0141076812472623 3. Simbi CMC, Zhang Y, Wang Z. Early parental loss in childhood and depression in adults: A systematic review and meta-analysis of case-controlled studies. 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--- title: Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea authors: - Fatemeh Amiri - Ali Moghadam - Ahmad Tahmasebi - Ali Niazi journal: PLOS ONE year: 2023 pmcid: PMC9997893 doi: 10.1371/journal.pone.0277293 license: CC BY 4.0 --- # Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea ## Abstract The medicinal plant Digitalis purpurea produces cardiac glycosides that are useful in the pharmaceutical industry. These bioactive compounds are in high demand due to ethnobotany’s application to therapeutic procedures. Recent studies have investigated the role of integrative analysis of multi-omics data in understanding cellular metabolic status through systems metabolic engineering approach, as well as its application to genetically engineering metabolic pathways. In spite of numerous omics experiments, most molecular mechanisms involved in metabolic pathways biosynthesis in D. purpurea remain unclear. Using R Package Weighted Gene Co-expression Network Analysis, co-expression analysis was performed on the transcriptome and metabolome data. As a result of our study, we identified transcription factors, transcriptional regulators, protein kinases, transporters, non-coding RNAs, and hub genes that are involved in the production of secondary metabolites. Since jasmonates are involved in the biosynthesis of cardiac glycosides, the candidate genes for Scarecrow-Like Protein 14 (SCL14), Delta24-sterol reductase (DWF1), HYDRA1 (HYD1), and Jasmonate-ZIM domain3 (JAZ3) were validated under methyl jasmonate treatment (MeJA, 100 μM). Despite early induction of JAZ3, which affected downstream genes, it was dramatically suppressed after 48 hours. SCL14, which targets DWF1, and HYD1, which induces cholesterol and cardiac glycoside biosynthesis, were both promoted. The correlation between key genes and main metabolites and validation of expression patterns provide a unique insight into the biosynthesis mechanisms of cardiac glycosides in D. purpurea. ## Introduction The human has implemented medical plants for therapeutic purposes or disease prevention with the purpose of surviving since ancient times [1–4]. Most of the industries, specifically the pharmaceutical industry, implement secondary metabolites [5]. According to the estimations, $25\%$ of prescribed drugs in industrialized nations contain natural plant products [6]. Inkwood research website reported that the plant-derivative drug turnover was $26,621 million in 2017; also, it is estimated to be $53,850 million by 2026 [7]. Due to the lack of accurate information regarding the molecular mechanisms and gene networks involved in the regulation of metabolic pathways in most of the medicinal plants, researchers discover the proposed biological pathways-involved target genes through integrative omics data such as comparative co-expression analysis [8, 9]. Despite the clinical and chemical significance of the above-mentioned plants, there is not a comprehensive study on metabolite biosynthesis; therefore, characterization of key genes involved in secondary metabolites synthesis could be informative and practical to improve the yield of valuable metabolites. Specifically, secondary metabolites are produced in certain plant species. The extraction and refining of these products within specialized cells is difficult at certain stages of the plant life cycle under specific conditions and in small amounts [10, 11]. Key genes that affect the whole network and increase the secondary metabolites amounts and production pathways [9] could be detected using systems biology approaches [12, 13]. Therefore, the integrative omics approach is an efficient biological analysis of systems [12, 13] that leads to a better understanding of transcriptome and metabolome interactions, as well as valuable secondary metabolites-related gene networks [9, 14, 15]. The gene networks key genes could be detected through various functional genomics studies, such as Quantitative Trait Locus (QTL), Genome-Wide Association Studies (GWAS), forward and reverse mutagenesis screens, targeted mutagenesis approaches, and omics techniques [11, 13]. Using the co-expression network analysis, Tahmasebi et al. [ 2018] identified important modules associated with the secondary metabolism in Echinacea purpurea [14]. The co-expression analysis is widely applied to the biosynthesis of secondary metabolites [16–19], stem lodging resistance regulation in Brassica napus [20], identification of critical modules and candidate genes of drought resistance in Triticum aestivum [21], regulation of flower and fruit development in Fragaria vesca [22], and identification of genes involved in resistance responses to powdery mildew in Hordeum vulgare L. var. nudum [23]. Biological networking is considered as an appropriate approach to detect the metabolic pathways and resources involved in the production of secondary metabolites [10, 24]. According to the investigations on various organisms, genes with similar functional roles tend to be co-expressed [24]. In most of the cases, including non-model organisms, the co-expression analysis is a straightforward approach to predict the gene functions [24, 25]. Weighted Gene Co-expression Network Analysis (WGCNA) is frequently applied to detect modules with similar expression profiles, which can be included in similar biological processes or pathways [24, 26, 27]. In other words, the functionally-related genes tend to be co-expressed [22, 24] and the mentioned method is a powerful tool to identify the correlation between gene expression profiles and phenotypes, as well as new metabolic pathways in plants [27]. Current study applied the co-expression analysis to identify hub genes with secondary metabolites biosynthesis, including the biosynthesis of dioscin in Dioscorea nipponica [28], monoterpenoids, fatty acid derivatives, isoflavonoids, and anthocyanins biosynthesis in Echinacea purpurea [14], tanshinone biosynthesis in Salvia miltiorrhiza [16], terpenoid biosynthesis pathways in Matricaria recutita and Chamaemelum nobile [17], as well as flavonoid metabolism regulation in Camellia sinensis [19]. Digitalis purpurea (L.) (Foxglove, Common Foxglove, and Purple Foxglove) is a medicinal and ornamental plant [6, 29] that produces valuable bioactive metabolites, such as cardiac glycosides (acetyldigitoxin, digitoxin, digoxin, gitaloxigenin, gitaloxin, gitoxin, purpurea glycoside A, purpurea glycoside B, and strospeside), flavonoids (cyaniding and digicitrin), anthraquinones, saponins (digitonin) and phenylethanoid glycosides (calceolarioside, cornoside, forsythiaside, plantainoside, plantamajoside, and sceptroside) [6, 29]. Purpurea glycoside A (glucodigitoxin) and purpurea glycoside B are the principal glycosides in the fresh leaves, which are converted into digitoxin and gitoxin respectively, which normally predominate in the dried leaf [30]. Gitoxin plays an important role in treating breast cancer [6]. Evatromonoside, is precursor of digitoxigenin bis-digitoxoside while digitoxigenin bis-digitoxoside is precursor of digitoxin which has been used as cardiac drug [31]. Digitoxin as an in-use medication has anti-HSV (herpes simplex virus type) activity which actively inhibits HSV-1 replication [6]. In addition, it suppresses hypersecretion of IL-8 from cultured CF (cystic fibrosis) lung epithelial cells [6]. It is worth noting, digitoxin could induce apoptosis in tumor cells [6, 29]. Therefore, digitoxin could be a novel drug class antiviral mechanism and a candidate drug for suppressing IL-8-dependent lung inflammation in CF and potential anticancer drugs [6, 29]. The pharmacological activity of the glycosidal extract of D. heywoodii is related to gitoxin derivatives (digitalinum verum and strospeside) [32]. Generally, cardiac glycosides are efficiently applied to treatments for heart diseases and cancers [6, 29]; moreover, most of these compounds biosynthesis-involved gene functions are unknown. To achieve a better understanding of specific molecular mechanisms of secondary metabolism-related genes of this medicinal plant, the correlation between transcriptome and metabolome was studied using WGCNA. In addition, the major modules and hub genes were identified. Eventually, other novel agents correlated with the biosynthesis of secondary metabolites, such as transcription factors (TFs), transcriptional regulators (TRs), protein kinases (PKs), transporters, and mRNA-like non-coding RNAs (mlncRNAs) were identified through the functional analysis. To confirm the integrative data analysis, the candidate genes were validated after methyl jasmonate (MeJA) treatment. ## Data collection and preprocessing Fig 1 represents the data collection and preprocessing steps (Fig 1). The normalized RNA-Seq data of eight different tissues of D. purpurea, including mature flower, immature flower, sepals mature flower, sepals immature flower, immature leaf, young leaf, mature leaf petiole, and young leaf petiole, were retrieved from the Medicinal Plant Genomics Resource database (http://medicinalplantgenomics.msu.edu/) [33]. In addition, metabolomic information of major metabolites, such as digitoxigenin bis-digitoxoside (C35H54O10), digitoxin (C41H64O13), gitoxin (C41H64O14), glucodigitoxin (C47H74O18), and strospeside (C30H46O9), within the tissues, which were in accordance with transcriptome, was retrieved from the Plant/Eukaryotic and Microbial Systems Resource [34] (S1 Table). According to this database, liquid chromatography/time-of-flight/mass spectrometry (LC/TOF/MS) method was applied to achieve the metabolome profiles. First, the transcript expression level and the quantity of secondary metabolites within eight plant tissues of transcriptomic and metabolomic data were classified correspondently according to a comparative analysis using R Package WGCNA. Transcripts with low expression values observed in the transcriptomic data were also filtered out using the genefilter package based on the variance filtering through varFilter function. Fig 1: Flowchart of gene co-expression network analysis in D. purpurea.Data collection and analysis towards downstream analyses are shown. ## Gene co-expression network construction Gene co-expression networks were achieved through the WGCNA package in R space [35]. After transcripts filtration with low expression; the expression filtered matrix was included in the WGCNA workflow. The scale-free topology criterion was implemented to determine the soft threshold power, which is defined as the similarity relationships between gene pairs by calculating the unsigned Pearson’s correlation matrix [36]. The network was constructed using a step-by-step network construction method, which was on the basis of adjacency matrix construction and its consequent turning into the topological overlap matrix (TOM) that it was applied with the purpose of describing the interconnection among genes and finally calling the hierarchical clustering function [36]. Next, modules with a minimum module size of 30 were identified, and then, those with similar expression profiles were merged through the Dynamic Tree Cut method with a CutHeight of 0.25 [35]. Finally, the eigengene values of the modules were calculated to evaluate the module-trait (secondary metabolites) relationships and detect the most significant association of the modules to the secondary metabolites [36]. To quantify the associations of individual genes with traits of interest (digitoxigenin bis-digitoxoside), the correlation between individual genes and the trait (digitoxigenin bis-digitoxoside) was defined as Gene Significance (GS) [37]. In addition, a quantitative measure of module membership (MM) and gene expression profile were defined with the purpose of quantifying how close a gene is to a given module [35]. Generally, if GS and MM were highly correlated, it would imply that genes were the highly important elements for the modules and were most significantly associated with the trait [37, 38]. Finally, genes with a high significance for digitoxigenin bis-digitoxoside and high MM in chocolate3 module were identified. ## Eigengene network visualization through WGCNA functions A plot summary of the eigengene network was generated according to the plotEigengeneNetworks convenient function [35]. The trait (digitoxigenin bis-digitoxoside) was added to eigengenes with the purpose of understanding how the trait fit into the eigengene network. In fact, a sample trait, such as digitoxigenin bis-digitoxoside, could be incorporated as an additional node of the eigengene network [35]. The adjacency between the sample trait and an eigengene sometimes could be considered as the eigengene significance [35]. Therefore, we evaluated the relationship between each module and digitoxigenin bis-digitoxoside by correlating the eigengenes for each module to digitoxigenin bis-digitoxoside and consequently, the eigengene dendrogram and heatmap identified groups of correlated eigengenes called meta-modules [35]. Module-module relationship, which is also called meta-module, is a group of correlated eigengenes with the correlation of eigengenes of at least 0.5 [35, 37]. Meta-modules are defined as tight clusters of modules [35, 37] and groups of highly correlated eigengenes [39]. ## Functional annotation and enrichment analysis To convert the transcript sequences to the orthologues, BLASTX with E-value ≤ 10−5 was applied against the Arabidopsis Information Resource (TAIR). The functional enrichment analysis of modules was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) [40] for categories of Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was also carried out in the web-based DAVID [40]. P-value < 0.01 was considered to be significant; moreover, the identification and classification of TFs, TRs, and PKs were carried out through applying the transcript sequences to BLASTX search against the iTAK database [41]. To identify transporters, BLASTX was carried out on transcript sequences against the transporter classification database (TCDB) with E-value ≤ 10−20 [42]. Wu et al. [ 2012] identified 2660 mlncRNAs candidates, which were considered as an emerging class of regulators, using a computational mlncRNA identification pipeline in D. purpurea [43]. After the creation of the mlncRNAs-derived local database through CLC Genomics Workbench 11.0, the searching procedure was carried out for all of the transcripts in the significant major modules using BLASTN with a cut-off E-value ≤ 10−5 to uncover important mlncRNAs. ## Identification of hub genes The most efficient genes of the selected modules were detected through the computational analysis of the connectivity between nodes. Using the CytoHubba plugin of Cytoscape software with Maximal Clique Centrality (MCC) function, the edge count-based identification of the top 20 hub genes was carried out in each selected module. Hub genes have regulatory roles and impact on the downstream pathways [12]. They are involved in basic processes, including the protein synthesis and secondary metabolite biosynthesis. The hub gene SCL14 was identified in the blue2 module, which was associated with the secondary metabolites of digitoxigenin bis-digitoxoside and gitoxin (S2 Table). This gene encodes a member of TFs GRAS family (Fig 11A and S9 Table). It is a TF of the scarecrow-like protein subfamily and interacts with TGA II TF that affects the transcription of stress-responsive genes. GRAS proteins are involved in gibberellic acid (GA) signaling, phytochrome A signal transduction detoxification, biotic, and abiotic stress-related response process, and development [90, 91]. This TF interacts with DWF1, which is involved in the biosynthesis of steroid (ath00100) and converts substances (S2 Fig). This enzyme was observed in coral3 module and showed a high positive correlation and significant P-value with glucodigitoxin and strospeside (S2 Table). Fig 11: A total number of 140 hub genes in seven modules.The top 20 hub genes in each selected module, including blue2 (A), chocolate3 (B), coral3 (C), coral4 (D), darkorange2 (E), lightpink4 (F), and lightsteelblue (G) modules. Nodes represent genes in the network and red nodes indicate the hub genes. The gray line connecting two nodes indicates their connection. The hub gene ATP-binding cassette G21 (AT3G25620, ABCG21) is related to the secondary metabolites of digitoxigenin bis-digitoxoside and gitoxin in the blue2 module (Fig 11A and S2 and S9 Tables). The hub gene ATP-binding cassette G22 (AT5G06530, ABCG22) is related to the secondary metabolites of digitoxigenin bis-digitoxoside in the lightsteelblue module (Fig 11G and S2 and S9 Tables). The G-type ABC transporters are involved in the terpenoids transportation [52]. Another hub gene is BIGPETAL TF (AT1G59640, BPE), which controls the petal size and was identified in the blue2 module related to the secondary metabolites of digitoxigenin bis-digitoxoside and gitoxin (Fig 11A and S2 and S9 Tables). Furthermore, TCP5 (AT5G60970) is a hub gene and encodes a TF within lightsteelblue module, which showed a correlation with digitoxigenin bis-digitoxoside (Fig 11G and S2 and S9 Tables). MYB81 (AT2G26960) is a hub gene and observed in chocolate3 module that was significantly associated with digitoxigenin bis-digitoxoside. The hub gene Trihelix transcription factor GT-2 (AT1G76890, GT-2), which encoded a TF, was identified in the blue2 module that was associated with digitoxigenin bis-digitoxoside and gitoxin (Fig 11A and S2 and S9 Tables). One of the key genes that produced the secondary metabolites of the above-mentioned medicinal plant within the lightsteelblue module was UDP-glycosyltransferase 85A7 (AT1G22340, UGT85A7). This hub gene was related to the secondary metabolite biosynthesis of digitoxigenin bis-digitoxoside and cardenolides [43] (Fig 11G and S2 and S9 Tables). Among the hub genes that carry sugar compounds, SWEET15 (AT5G13170) was identified in the lightsteelblue module, which was associated with digitoxigenin bis-digitoxoside (Fig 11G and S2 and S9 Tables). There is also another hub gene that acts as PKs, such as ATAXIA-Telangiectasia Mutated (AT3G48190, ATM). It was observed in the darkorange2 module significantly associated with digitoxigenin bis-digitoxoside (Fig 11E and S2 and S9 Tables). The hub gene Cysteine-rich receptor-like protein kinase 8 (AT4G23160, CRK8) belongs to RLK-Pelle family, which was identified in the blue2 module that was associated with the secondary metabolites of digitoxigenin bis-digitoxoside and gitoxin (Fig 11A and S2 and S9 Tables). The hub gene IPK plays a vital role in terpenoid backbone biosynthesis (ath00900). It was identified in chocolate3 module which was significantly related to digitoxigenin bis-digitoxoside (Orthology: K06981) (Fig 11B and S1 Fig and S2 and S9 Tables). These hub genes were identified as promising candidates that improved the production of secondary metabolites. As it could be observed in the networks, several hub genes had the highest degree of connectivity among all 140 identified hub genes (Fig 11A–11G and S9 Table). Fig 12 represents the interactions of hub proteins through STRING database and showed the minimum required interaction score on the highest confidence (0.900) (Fig 12 and S10 Table). According to the GO, their activities in response to stimulus (GO:0050896), cellular macromolecule metabolic process (GO:0044260), metabolic process (GO:0008152), and cellular process (GO:0009987) are respectively shown in red, blue, green, and yellow (Fig 12). The interaction between nodes represented in Fig12 and related scores are provided in S10 Table (Fig 12 and S10 Table). Annotations of each node are also illustrated in (S11 Table). Fig 12: The protein-protein interactions of hub proteins.Response to the stimulus (GO:0050896), cellular macromolecule metabolic process (GO:0044260), metabolic process (GO:0008152), and cellular process (GO:0009987) are respectively shown in red, blue, green, and yellow. ## Plant materials and growth conditions Two-year-old D. purpurea were purchased from the Sepahan flower & ornamental plants international market (Isfahan, Iran). The plants that were in the flowering phase were placed under a 16-h light: 8-h dark cycle at 25°C for two weeks to be compatible with the new conditions in the glasshouse (S1 Fig). ## Stress conditions The adopted plants were sprayed and watered with the solution of 100 μM MeJA (plus $0.1\%$ Tween-20) in $0.1\%$ ethanol. The control plants also were sprayed and watered with $0.1\%$ Tween-20 in $0.1\%$ ethanol. All the pots were covered with the plastics. The leaf samples were collected at 3, 6, 24, and 48 hours after treatment (S1 Fig). The collected leaves were immediately frozen in liquid nitrogen and stored at −80°C until used for RNA extraction. ## RNA extraction, DNase treatment, and cDNA synthesis Total RNA was extracted from leaf samples using the Column RNA Isolation Kit (DENAzist Asia, Mashhad Iran) according to the manufacturer’s instructions. The quantity and concentration of RNA was measured using a NanoDrop ND 1000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The integrity and quality of RNA was checked by visual observation of 28S and 18S rRNA bands on a $1\%$ agarose gel. Prior to use, RNA samples were stored at −80°C. DNase treatment of RNA was carried out using the RNase-free DNase kit (Thermo Fisher Scientific, Waltham, Massachusetts, USA) according to the manufacturer’s instructions. The quality and quantity of treated RNA were rechecked by NanoDrop and agarose gel respectively. Then, 1 μg of DNase-treated RNA was used for first-strand cDNA synthesis using SinaClon BioScience kit (Karaj, Iran) according to the manufacturer’s instructions. The cDNA samples were stored at −20°C prior to use. ## Candidate genes Based on integrative data analysis, four following genes were candidates for the validation. Jasmonate-ZIM domain3 (JAZ3) was identified in the coral3 module that showed a high positive correlation and significant P-value with glucodigitoxin and strospeside (S2 Table). JAZ3 was chosen to know whether it responds to MeJA induction or not. The JAZ family genes are key repressors in the JA signal transduction pathway. Scarecrow-Like Protein 14 (SCL14) is a hub gene in the blue2 module and interacts with TGA II TFs including TGACG sequence-specific binding protein 2 (TGA2), Ocs-element-binding factor 5 (TGA5), and TGACG motif-binding factor 6 (TGA6) that affects the transcription of stress-responsive genes. It was associated with the secondary metabolites of digitoxigenin bis-digitoxoside and gitoxin (S2 Table). Delta24-sterol reductase (DWF1) is SCL14 downstream gene that is involved in the steroid biosynthesis (S2 Fig). This enzyme was identified in the coral3 module that showed a high positive correlation and significant P-value with glucodigitoxin and strospeside (S2 Table). HYDRA1 (HYD1) was identified in the darkorange2 module, which significantly was associated with digitoxigenin bis-digitoxoside and was involved in the process of steroid biosynthesis (S2 Fig and S2 Table). In fact, HYD1 converts 4α-methylcholesta-8,24-dien-3β-ol to 4α-methylcholest-7,24-dien-3β-ol and DWF1 converts desmosterol to cholesterol (precursor of cardiac glycosides) in the steroid biosynthesis pathway. The steroid biosynthesis pathway is one of the putative biosynthetic pathways of plant cardiac glycosides. ## Primer design Primers were designed using Allele ID 7 and Vector NTI 11 software for the reference and candidate genes (Table 1). The primers were designed based on the aligned nucleotide file. In this project to be more precise, we quantified the final expressions based on the means of reference gene of actin and other four genes. Table 1 \| Gene \| Forward \| Reverse \| Accession number \| Ta (°C) \| PCR product (bp) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| DWF1 \| CTTCTCACTCTTGCGACCTT \| GGATTCCAGCCACACACT \| AT3G19820 \| 55 \| 156 \| \| SCL14 \| CGCTGTTCCACTTCTCCGCCAT \| CCTGCCACTGCTTGTATGTCTCT \| AT1G07530 \| 61 \| 172 \| \| HYD1 \| GAACCCTCATTTCCTTGCCGAAGT \| CCCAAAGACACGCCAAACTGAAGA \| AT1G20050 \| 55 \| 195 \| \| JAZ3 \| GTCGGTGTGCGTGTATGA \| ATGGATGCTGGAACTGGC \| AT4G34990 \| 62 \| 123 \| \| Actin \| GTCTCTCACAATTTCCTTCTCAG \| GCTCTCCCACACGCTATT \| AT2G37620 \| 55 \| 126 \| ## RT-qPCR to validation of candidate genes First, primer specificity was confirmed by PCR and sequence analysis. To minimize pipetting error, the cDNA samples were diluted 1:20 by using nuclease-free water, and 5 μL cDNA was used for RT-qPCR. Relative RT-qPCR was performed in a 20 μL volume containing 5 μL cDNA (diluted), 10 μL RealQ Plus 2x Master Mix Green (Sinuhebiotech, Shiraz, Iran), 0.7 μL of 10 μM primers (10 μM). The amplification reactions were carried out in a Line-gene K thermal cycler (Bioer, China) under the following conditions: 15 s at 95°C, 45 cycles of 94°C for 15 s, Ta temperature for 15 s, and 72°C for 20 s. After 45 cycles, the specificity of the amplifications was tested by melting curve analysis by heating from 50 to 95°C. All amplification reactions were repeated two times under identical conditions and included a negative control and 3 standard samples. ## RT-qPCR data analysis The relative expression of candidate genes was calculated based on the threshold cycle (CT) method. The CT for each sample was calculated using the Line-gene K software. When replicate PCRs are run on the same sample, it is more appropriate to average CT data before performing the 2─ΔΔCT calculation. The actin gene was used as the reference gene for data normalization. The determined mean CT values for the candidate and internal reference gene were used in equation 2─ΔΔCT = (CT, candidate genes ─ CT, housekeeping genes) Time x ─ (CT, candidate genes ─ CT, housekeeping genes) Time x [44]. Time x represents the expression of the candidate genes at any time point in control and treated plants. The fold change ratios of the genes were normalized to internal control genes and were calculated relative to the expression at any time in control plants. ## Statistical analysis Analysis of variance followed by Duncan’s multiple range test was performed using MINITAB (Minitab, Inc., Pennsylvania, USA). In all cases, differences were regarded to be statistically significant at P-value ≤ 0.05 level. All experiments were performed in triplicate, analyzed using the GraphPad Prism software (GraphPad Software, USA). ## Co-expression network analysis D. purpurea is a medicinal plant that produces various cardiac and steroidal glycosides [6, 29]. The secondary metabolite biosynthesis pathways have not been sufficiently investigated so far; therefore, it would be very useful to find the secondary metabolite synthesis-involved hub genes in the context of metabolic engineering. Among 32341 unigenes derived from eight mentioned samples, 16185 filtered unigenes were merged with five major metabolites (S1 Table). Systems biology and integrative multi-omics studies provided an opportunity to study the important aspects of metabolic processes and complexities of the transcriptome and metabolome in non-model plants [14]. Combining the transcriptome with metabolome data could lead to an accurate network control of the biosynthesis of secondary metabolites. The WGCNA package was applied to find different modules of co-expressed genes and the correlation of the secondary metabolites with hub genes in each module [14]. Using WGCNA algorithms, the adjacency matrix was substituted with the weighted adjacency matrix by raising the correlations to the power of 16, which was selected through the scale-free topology criterion [35, 36]. The scale-free topology model was not improved after the power increase. At this power, the high mean number of connections was maintained; moreover [36], the scale-free topology fit index reached to 0.9 at the mentioned power, which was selected to produce a hierarchical clustering tree (Fig 2). Fig 2: Selection of an appropriate soft threshold power of β.Left; the scale-free fit index of power (β) was estimated to be 16 based on the threshold limit of 0.9. Right; the mean connectivity versus soft-thresholding power. The highly interconnected genes with similar expression profiles were clustered to a module based on TOM-based dissimilarity matrix [35, 36]. Therefore, the module network dendrogram was constructed through clustering Module Eigengene (ME) distances (Fig 3). First, 180 modules were identified, and then, 34 distinct modules were generated in different colors through dynamic tree cut and merged dynamic (Figs 3 and 4). Fig 3: Clustering of 180 modules.The horizontal red line shows the height cut of 0.25, which corresponds to the correlation of 0.75, to merging the modules. Fig 4: Clustering of genes and modules.The cluster dendrogram at the top of the plot shows co-expressed genes. The branches and color bands at the bottom of the plot represent the assigned module. ## Correlated modules with the secondary metabolites Results showed that 34 modules were related to five major secondary metabolites based on the Pearson correlation coefficient and P-value. Higher correlation, significant P-value, with secondary metabolites profiles, were observed for seven modules including coral3, lightpink4, chocolate3, blue2, coral4, darkorange2, and lightsteelblue modules (Fig 5 and S2 Table). Fig 5: Correlation of modules and secondary metabolites.Module Eigengenes (MEs) and secondary metabolites are respectively represented by each row and column. Each cell contains the corresponding correlation at the top and P-value at the bottom. The positive correlation of the module with secondary metabolites and the negative correlation are respectively shown in red and green. The white spectrum indicates the inexistence of modules and secondary metabolites correlations. The coral3 module showed a high positive correlation with strospeside and glucodigitoxin (Fig 5 and S2 Table); also, it was involved in the processes of terpenoid backbone biosynthesis, steroid biosynthesis, carotenoid biosynthesis, diterpenoid biosynthesis, flavonoid biosynthesis, flavone and flavonol biosynthesis, sesquiterpenoid, and triterpenoid biosynthesis (S3 Table). The darkorange2 and lightsteelblue modules were significantly associated with digitoxigenin bis-digitoxoside (Fig 5 and S2 Table) and involved steroid biosynthesis-related genes (S3 Table). The lightpink4 module significantly associated with glucodigitoxin (Fig 5 and S2 Table) was involved in steroid biosynthesis (S3 Table), and the chocolate3 module with high positive correlation and high significant level with digitoxigenin bis-digitoxoside (Fig 5 and S2 Table), was involved in terpenoid backbone biosynthesis (S2 Fig and S3 Table). It was found that the blue2 and coral4 modules were correlated with digitoxigenin bis-digitoxoside and gitoxin (Fig 5 and S2 Table). S3 Table represents data of some genes in selected modules involved in the process of secondary metabolites biosynthesis (S3 Table). The MM and GS were calculated in order to investigate the correlations between individual genes and metabolites. As it could be observed in Fig 6, the strongest connectivity interaction is between chocolate3 module and digitoxigenin bis-digitoxoside (Fig 6). The chocolate3 module showed the most significant correlation between MM and GS ($r = 0.5$, $$p \leq 3.1$$e-06) (Fig 6). The high correlation between GS and MM indicated the considerable role of the modules key genes in the underlying biological functions of secondary metabolites synthesis. Functional annotation and enrichment analysis of chocolate3 indicated that three transcripts including Plastid Terminal Oxidase (AT4G22260, IM), Isopentenyl Phosphate Kinase (AT1G26640, IPK), and Polypeptide 2 (AT2G29090, ABAH2) were involved in terpenoid metabolic (GO:0006721) and lipid biosynthetic processes (GO:0008610) (Fig 6). Fig 6: The scatterplot of Gene Significance (GS) for digitoxigenin bis-digitoxoside vs. Module Membership (MM) in chocolate3.The high significant correlation between GS and MM for Digitoxigenin bis-digitoxoside in the chocolate3 module. The Module Eigengene adjacency was represented by hierarchical clustering and heatmap (Figs 7 and 8). A Module Eigengene summarizes the gene expression profile of each module. A dendrogram of the eigengenes and metabolites and a heatmap of their relationships were provided to evaluate the relationship between each module and digitoxigenin bis-digitoxoside (Figs 7 and 8). The dendrogram and heatmap indicated that chocolate3 module and digitoxigenin bis-digitoxoside are highly related (Figs 7 and 8). Conversely, the royalblue2 and tan2 modules are highly related, this meta-module is inversely correlated with digitoxigenin bis-digitoxoside (Figs 7 and 8). Squares of red color along the diagonal are the meta-modules. Eigengenes were represented by I, J and etc.; for example, EJ denotes the eigengene (module) of the Jth module [39] (Fig 8). Fig 7: Eigengene dendrogram of the modules and digitoxigenin bis-digitoxoside.A hierarchical clustering dendrogram of eigengenes. The dissimilarity of EI and EJ is shown by 1- cor (EI; EJ). Fig 8: The heatmap plot of adjacencies in the eigengene network, including digitoxigenin bis-digitoxoside.Each row and column in the heatmap corresponds to one Module Eigengene (labeled by color) and or Digitoxigenin bis-digitoxoside. Low and high adjacencies (negative and positive correlations) are shown in blue and red. The connection strength (adjacency) between eigengenes I and J are defined as A I J = (1 + cor (EI; EJ))/2. ## Gene ontology (GO) and KEGG pathway analysis To conduct the functional analysis of D. purpurea transcriptome, all unigenes of the candidate modules were selected to search against the TAIR database using BLASTX with E-value ≤ 10−5. Among the 1760 unigenes, only 1250 were protein-coding. Then, the functional annotation and enrichment analyses were carried out (Figs 9 and 10). Fig 9: The gene ontology analysis of candidate modules.Gene ontology analysis of candidate modules classified into three functional categories including Biological Process, Molecular Function, and, Cellular Component. Fig 10: The KEGG analysis of candidate modules.KEGG analysis of candidate modules is performed by the DAVID database. All of the above-mentioned modules’ annotated unigenes were searched against the DAVID database to predict their functions. Using GO analysis, unigenes were classified into three distinct categories of BP, MF, and CC. Considering the BP category, GO term with the most genes was oxidation-reduction process (GO:0055114) (Fig 9). Jasmonic acid (JA) biosynthetic process (GO:0009695) is a key GO term shown in the GO analysis (Fig 9). JAs are recognized as signals in the plant stress responses, development processes, biosynthesis, and proper accumulation of secondary metabolites [45, 46]. In fact, they lead to a variety of biological responses in plants, such as defense responses to attacks by herbivorous insects or necrotrophic pathogens, biological responses to the injuries, increased production of secondary metabolites, male sterility, sex-determination of plants, and growth inhibition [47]. Moreover, JAs are associated with oxylipin biosynthetic process (GO:0031408), which constitute a family of oxylipins with the capability of inducing the expression of genes that code for enzymes catalyzing the formation of various secondary metabolites [48] (Fig 9). In addition, secondary metabolites act as defense molecules [46, 48]; therefore, the biosynthesis and proper accumulation of secondary metabolites lead to the plants defensive responses such as response to wounding (GO:0009611) (Fig 9). Also, they form defense proteins, such as proteinase inhibitors (PINs), in wounded tomato leaves with JA signaling pathways [45]. A number of secondary metabolites including nicotine, anthocyanins, glucosinolates, and terpenoid indole alkaloids (TIAs) are synthesized from proteinogenic amino acids [45]. Therefore, GO terms, such as aromatic amino acid family biosynthetic process (GO:0009073) and cellular amino acid biosynthetic process (GO:0008652) are associated with the biosynthesis of secondary metabolites [45] (Fig 9). Aromatic amino acid (AAA) family biosynthetic process (GO:0009073) is formed as a result of specific chemical reactions and pathways (Fig 9). It is noteworthy that AAAs include L-phenylalanine, L-tyrosine, and L-tryptophan involved in the protein synthesis [49, 50]. In addition, they are precursors for a wide range of secondary metabolites, various pigment compounds, and plant hormones, such as auxin and salicylate [49, 50]. In fact, AAA biosynthesis and degradation are considered as starting points for a large variety of secondary metabolites [50]. For example, tryptophan is a precursor for the synthesis of auxins, phytoalexins, glucosinolates, and alkaloids [49, 50]. Tyrosine is a direct precursor of coumarate in the phenylpropanoid pathway, as well as the synthesis of tyramine [49, 50] and meta-tyrosine, which is a non-proteogenic amino acid [50]. Tyrosine catabolism also leads to the synthesis of isoquinoline alkaloids, while phenylalanine is a precursor for a class of sulfur-containing secondary metabolites, which is called phenylalanine glucosinolates, and volatile compounds including phenylpropanoids, benzenoids, phenylpropenes, and nitrogenous [49, 50]. The conversion of phenylalanine to cinnamate leads to its further metabolism to p-coumaroyl CoA [50]. It is involved in the stress-related mediating responses [50]. Auxin transport (GO:0060918), response to auxin (GO:0009733), and auxin-activated signaling pathway (GO:0009734) are considered as important GO terms (Fig 9). Tryptophan is a precursor to the family of auxin hormones [49, 50]. Indole-3-acetic acid (IAA) is the most abundant auxin required for almost all of the major developmental processes in plants including embryogenesis, seedling growth, root elongation, vascular patterning, gravitropism, and flower development [50]. Chorismate biosynthetic process (GO:0009423) is associated with AAA biosynthesis (Fig 9). In plants, chorismate is a precursor of AAA and a wide range of aromatic secondary metabolites [49, 50]. In addition, it is an initial compound for the biosynthesis of folates, such as tetrahydrofolate or vitamin B9, pigments, and isochorismate pathway to salicylate [49, 50]. Considering MF category, the most abundant of targets are enriched for ATPase activity to ATPase activity coupled to transmembrane movement of substances (GO:0042626) and transferase activity of transferring hexosyl groups (GO:0016758) (Fig 9). The former (GO:0042626) is associated with the ABC transporter mechanism; also, their mechanism is driven by ATP hydrolysis in order to act as the exporters and importers [51, 52] (Fig 9). It should be noted regarding the CC category that the most frequent targets are enriched for chloroplast (GO:0009507), plasma membrane (GO:0005886), and integral component of membrane (GO:0016021) (Fig 9). Most secondary metabolites stored in the vacuole are secreted to the apoplast [52]; moreover, secondary metabolites are produced in different subcellular compartments. Results showed that the biosynthetic pathway of secondary metabolites is consistent with the transport system; for example, terpenoids are transported by the G-type ABC transporter [52]. These transporters are localized in most plant cell membranes and classified as possibly vacuolar [51] (Fig 9). Therefore, the category of CC respectively includes 45, 48, 135, and 225 transcripts in vacuole (GO:0005773), vacuolar membrane (GO:0005774), membrane (GO:0016020), and plasma membrane (GO:0005886) (Fig 9). The KEGG pathway enrichment analysis of candidate modules were conducted with the purpose of detecting the significant pathways. Results showed that there were five molecular pathways with P-value ≤ 0.01 detected using DAVID database [40] (Fig 10). Among these molecular pathways, the number of genes involved in metabolic pathways (ath01100), secondary metabolites biosynthesis (ath01110), antibiotics biosynthesis (ath01130), phenylalanine, tyrosine and tryptophan biosynthesis (ath00400), and arginine and proline metabolism (ath00330) were respectively determined to be 132, 80, 40, 11, and nine (Fig 10). The most important pathway was the secondary metabolites biosynthesis (ath01110) (Fig 10) in which genes were involved in terpenoid backbone biosynthesis (ath00900), sesquiterpenoid and triterpenoid (ath00909), steroid biosynthesis (ath00100), carotenoid biosynthesis (ath00906), diterpenoid biosynthesis (ath00904), flavonoid biosynthesis (ath00941), as well as flavone, and flavonol biosynthesis (ath00944) (S3 Table). The putative biosynthetic pathway of plant cardiac glycosides roughly comprised the biosynthesis of terpenoid backbone, steroid, and cardenolide [43] (S2 Fig). As an example, Deoxy-D-xylulose-5-phosphate synthase (AT4G15560, DXS1), Geranyl diphosphate synthase 1 (AT2G34630, GPS1), Delta14—sterol reductase (AT3G52940, FK), and DWF1 genes were involved in terpenoid backbone and steroid biosynthesis, respectively and in pairs (S2 Fig and S3 Table). All of the above-mentioned genes were identified in coral3 module that was associated with strospeside and glucodigitoxin (S2 Table). HYD1 and Sugar-Dependent 1 (AT5G04040, SDP1) were identified in the darkorange2 and lightsteelblue modules, respectively, which significantly were associated with digitoxigenin bis-digitoxoside and were involved in the process of steroid biosynthesis (S2 Fig and S2 and S3 Tables). ## Identification of TFs TFs are key elements of plant metabolic engineering and regulatory proteins that improve the production of secondary metabolites [48, 53, 54]. Moreover, they regulate enzyme expression through integrating internal and external signals [53]. For example, a number of TF families including Apetal2/ethylene responsive factor (AP2/ERF), WRKY, basic helix-loop-helix (bHLH), basic leucine zipper (bZIP), MYB, and NAM, ATAF and CUC (NAC) are involved in biotic and abiotic stress responses through mediating biosynthesis and accumulation of secondary metabolites [55]. In the current study, TFs within candidate modules were identified and classified using iTAK database [41]. According to the observations, a total number of 89 TF-encoding genes belong to 30 families, including bHLH, MYB, bZIP, Cys2/His2-type (C2H2), AP2/ERF-ERF, C2C2-GATA, WRKY, GRAS, and others (S4 Table). In addition, C2H2 TF was identified in the six candidate modules (S4 Table). C2H2 TF (mtfA) regulates mycotoxin sterigmatocystin production and other secondary metabolism gene clusters, such as genes responsible for the synthesis of terrequinone and penicillin in Aspergillus nidulans [56, 57]. C2H2 Zinc-Finger family in drought, heat, and also salt responses in Populus trichocarpa [58]. Most of TF families, such as C2H2, AP2/ERF-ERF, and bHLH, contain gibberellic acid-mediated signaling pathway [14]; also, they are correlated with darkorange2, coral3, and lightpink4 modules (S4 Table). bHLH TFs can regulate the biosynthesis of the secondary metabolites including flavonoid, glucosinolates, isoquinoline alkaloid, nicotine alkaloid, diterpenoid phytoalexins, saponins, and anthocyanin [45, 48, 55]. The overexpression of bHLH TFs of Triterpene Saponin Biosynthesis Activating Regulator1 (TSAR1) or Triterpene Saponin Biosynthesis Activating Regulator2 (TSAR2) in Medicago truncatula increases the transcript levels of known triterpene saponin biosynthetic genes, as well as accumulation of triterpene saponins [55, 59]. It is noteworthy that bHLH and bZIP TF families regulate diterpenoid phytoalexins biosynthesis in Oryza sativa, which defend against the invasions of the blast pathogen [55]. The AP2/ERF-ERF and WRKY TF families act as regulatory proteins of Catharanthus terpenoid indole alkaloids and terpene biosynthesis [60]. In addition, JA-stimulated artemisinin biosynthesis within Artemisia annua is mediated by two AP2/ERF-type TFs, and AaERF1 and AaERF2, as the overexpression of mentioned factors increases artemisinin accumulation in transgenic A. annua [61]. AP2/ERF TF family acts as a key regulator in the plant developments and stress responses [61]. The NAC TF family is involved in the anthocyanin accumulation within Arabidopsis thaliana and fruit crops [62, 63]. There was only one NAC TF observed in the darkorange2 module (S4 Table). NAC and bZIP TFs play vital roles in response to drought stress in O. sativa [64]. PtrNAC72 in Poncirus trifoliata regulates putrescine biosynthesis [55], while ANACO32 acts as a negative regulator of anthocyanin biosynthesis in A. thaliana [55]. HbNAC1 is involved in latex biosynthesis and drought tolerance in Hevea brasiliensis [55]. Secondary metabolites, such as glucosinolates, flavonoids, Hydroxycinnamic acid amides (HCAAs), and proanthocynanins are also mediated by MYB proteins [55]. In the current study, GRAS, Teosinte branched 1, Cycloidea, Proliferating cell factors (TCP), and Trihelix families were identified as the hub genes. GRAS proteins play a key regulatory role in the plant development, abiotic stress, and phytochrome signaling [14] that are present in the blue2 and coral3 modules (S4 Table). GRAS TF family is the regulator of GA3 signaling and biosynthesis [14]; also, it interacts with DWF1 (1.3.1.72) that is involved in the biosynthesis of steroids (ath00100). TCPs belong to the plant-specific bHLH TF family and are considered as key regulators of diverse developmental processes [65]. In A. thaliana, mTCP3 expression induces the biosynthesis and accumulation of proanthocyanidins within endothelium and the outer seed coat layers; moreover, it activates many enzymatic and regulatory genes involved in the flavonoid biosynthesis [65]. TCPs are also involved in the biosynthesis of plant hormones, such as brassinosteroids and jasmonic acids [65]. Trihelix transcription factors (TTFs), known as GT factors, are photoresponsive proteins that regulate environment-responsive secondary metabolisms [66]. PatGT-1 acts as a negative regulator in the production of patchoulol through repressing genes in the pathway in Pogostemon cablin [66]. The coral3 module involves the maximum member of bZIP, GRAS, and MYB TF families (S4 Table). It is noteworthy that bZIP TF (AabZIP1) is involved in ABA signaling to regulate artemisinin biosynthesis in A. annua [67]. In addition, ELONGATED HYPOCOTYL (HY5), which is considered as a bZIP TF, plays a crucial role in the light-mediated transcriptional regulation of terpene synthase. For example, AtTPS03 is involved in terpenoid biosynthesis in A. thaliana [68]. MYB, MYB-related, and forkhead-associated (FHA) TF families were involved in terpenoids and polyketides metabolisms in Clinopodium chinense [69]. MYB TFs are crucial for the terpenoid backbone biosynthesis [69]. ## Identification of TRs As it was observed for TFs, the TRs can regulate the gene expression at the transcriptional level; moreover, their identification was carried out through iTAK database [41]. A total number of 26 TR-encoding genes from 11 families, such as mitochondrial transcription termination factors (mTERF), Auxin/indole-3-acetic acid (AUX/IAA), and GCN5-Related N-Acetyltransferases family (GNAT), was identified (S5 Table). Thirteen TRs were contained in coral3 module (S5 Table). In the current study, the most common TRs were AUX/IAA and mTERF (S5 Table). AUX/IAA family members were identified as short-lived nuclear proteins [70, 71], which could act as hub factors and regulate the gene expression in auxin signaling transduction [71]. According to an investigation carried out by Poutrain et al. [ 2011] on Catharanthus roseus, it was found that auxin negatively regulated the biosynthesis of monoterpenoid indole alkaloids (MIAs) and CrIAA1 availability through a feedback mechanism [70]. In Solanum lycopersicum cv MicroTom, the positive regulation of mycorrhization and strigolactone biosynthesis was carried out by Sl-IAA27 through regulating the expression level of NODULATION SIGNALING PATHWAY1 (NSP1) [72]. In fact, AUX/IAA proteins are known as transcriptional repressors that mediate diverse physiological and developmental processes in plants; moreover, they are involved in the process of responding to stress [71, 73]. The mTERFs are key regulators of organellar gene expression (OGE) in mitochondria and chloroplasts, which can be implicated in all organellar gene expression steps ranging from the regulation of transcription to tRNAs maturation and regulation of translation [74]. In addition, mTERF proteins play a vital role in abiotic stress acclimation including excess light, UV-B exposure, heat, or altered salinity [74]. ## Identification of PKs PKs add a phosphate group to certain amino acids of proteins. These phosphorylated proteins lead to the mechanisms of signal transduction that could improve the production of secondary metabolites [9]. In fact, PKs are identified as essential regulators of plant growth and development, including developmental patterning, hormone signaling, stress responses, and disease resistance [75–78]. In the seven modules, five families of PKs, including Receptor-like kinase-Pellel (RLK-Pelle), Cyclin-dependent kinases (CMGC), Calcium- and calmodulin-regulated kinase (CAMK), Sterility (STE), and Tyrosine kinase-like (TKL) PKs, were identified. It was also found that RLK-Pelle was involved in all selected modules (S6 Table). RLK-Pelle protein, which was considered as the largest family of PKs, played a key role in developmental processes of meristem proliferation regulation, organ specification, reproduction, and hormone signal transduction [79]. Also, it could act in the signaling networks that involved abiotic and biotic environmental stimuli [79]. Cowpea RLK-Pelle group allocated in the plasma membrane led us to achieve an understanding of the extracellular ligands and their role in activating the downstream pathways [80]. Moreover, it acted as a transmembrane protein with extracellular receptor domains and intracellular kinase domains [80]. C2H2 TFs were associated with the promoter of the RLK-Pelle group. A number of C2H2 members were involved in the process of pathogen defense [80]. RLK-Pelle was recognized as the largest kinase group in the grapevine [81]. More than half of RLK-Pelle members were down-regulated in most of the tissues during the development, which indicated that they may have negative regulatory functions [81]. Several grapevine RLK-Pelle families were highly co-expressed, which suggested their possible interactions in the process of plant stress response signaling [81]. CAMK PKs were identified in the coral3 and lightsteelblue modules with four encoding genes (S6 Table). The grapevine CAMK (CAMK_CAMKL-CHK1) and TKL (TKL-Pl-4) proteins were up-regulated in response to stresses that caused dehydration, such as salt, PEG, and drought [81]. TKL PK was observed in coral3 module (S6 Table). The TKL family was found to be involved in the processes of growth, development, and stress response [82]. CMGC was also identified in the coral3 and darkorange2 modules with four encoding genes (S6 Table). According to the estimations, CMGC would localize the nucleus and cytoplasm. Moreover, it was down-regulated in response to salt, PEG, and drought treatments, while up-regulated in response to heat stress in the grapevine and cowpea [80, 81]. Many non-RLK groups, including CAMK, CMGC, STE, and TKL could positively regulate the plant growth [81]. ## Identification of transporters Transporters are molecules essential for plant development that are involved in the plant transport system [83, 84]. In metabolic engineering, achieving a deep understanding of transport mechanisms and subcellular distribution of biosynthesized phytochemicals is crucial for the successful metabolic engineering of medicinal plants [85]. Using the TCDB database, 84 transporter families were identified (S7 Table). The ATP-binding cassette (ABC) transporter, Nitrate-peptide transporter (NRT), Multidrug and toxic compound extrusion (MATE), and Purine permease (PUP) are considered as transporters involved in the movement of secondary metabolites [52]. Secondary metabolites are transported in the intercellular, intracellular, and intratissue fashion [85]. An ABC family of C-type transporters is presumed in the vacuolar transport of anthocyanins [85]. NRTs transport nitrates and peptides, while MATE transporters act as proton antiporters [52]. In addition, a number of plant MATE transporters are involved in xenobiotic efflux, Fe translocation, Aluminium detoxification, and hormone signaling [52]. MATE transporters are involved in the vacuolar transporter for flavonoids and anthocyanin vacuolar transportation [85]. Most metabolites are transported through membranes, such as the carbohydrate transport in which the SWEET transporters lead to the nectar secretion, plant-microbe interaction, and embryo development [86]. Four transporters of the Sweet, PQ-loop, Saliva, and MtN3 family were identified in the candidate modules (S7 Table). In Petunia axillaris, PaSWEET1 would supply sugar as energy for flowering and volatile biosynthesis [86]. The ABC proteins act as transporters of a diverse set of substrates including plant hormones, secondary metabolites, and lipid monomers [86]. We identified six transporters of the ABC superfamily (S7 Table). The mentioned proteins were localized in most plant cell membranes [51] and played a vital role in the process of plant development and defense mechanisms [84, 86]. G-type transporters ABC family was involved in the volatile release and formation of cutin, wax, and suberin [52, 86]. Terpenoids were transported by the G-type ABC transporters [52]. According to the findings, Nicotiana plumbaginifolia pleiotropic drug resistance1 (NpPDR1) was a G-type ABC transporter involved in the resistance to fungal and oomycete pathogens through sclareol transmission to the plant surface [52]. N. tabacum pleiotropic drug resistance1 (NtPDR1), which is a diterpene transporter, was also another G-type ABC transporter involved in the plant defense procedure through transporting various anti-fungal diterpenes, such as sclareol, manool, cembrene, and eucalyptol [52]. In our analysis, 27 transporters from the Mechanosensitive Calcium Channel (MCA) family were found (S7 Table). MCA1 and MCA2, mechanosensitive calcium channels in Arabidopsis, were involved in a cold-induced increase in [Ca2+]cyt and regulation of cold tolerance through a pathway other than the C-repeat binding factor/dehydration-responsive element binding-dependent (CBF/DREB1-dependent) pathway [87]. The Drug/Metabolite Transporter (DMT) superfamily with nine transporters, The Eukaryotic Nuclear Pore Complex (E-NPC) family with eight transporters, The Multidrug/Oligosaccharide-lipid/Polysaccharide (MOP) Flippase superfamily with seven transporters, The Domain of Unknown Function 3339 (DUF3339) family, The Major Facilitator Superfamily (MFS), The Amino Acid/Auxin Permease (AAAP) superfamily, The ATP-binding Cassette (YOP1) family and The H+- or Na+-translocating F-type, as well as V-type and A-type ATPase (F-ATPase) superfamily with six transporters, were the most identified and investigated transporters (S7 Table). ## Identification of mlncRNAs The mlncRNAs are considered as a subset of long noncoding RNAs (lncRNAs) and a new group of regulatory elements [88], which can be spliced, capped and polyadenylated [43]. In plants, mlncRNAs have regulatory roles in phosphate-starvation response, gender specific expression, nodulation, development, and response to hormones; also, they can affect cellular activity through specific sequences and RNA-folding structures [89]. A number of mlncRNAs play crucial roles in the organ development and defense responses through producing microRNAs; moreover, they have regulatory roles in plants through generating siRNAs [43]. It was found that the majority of D. purpurea mlncRNAs were species-specific and a considerable number of them showed tissue specific expression and involvement in plant development, cold and dehydration stress responses, and secondary metabolism [43]. Wu et al. [ 2012] confirmed that a number of mlncRNAs showed sense or antisense homology with protein-coding genes involved in secondary metabolism in D. purpurea [43]. For example, the 3’ UTR of D. purpurea 4-Hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (HDS) contained a 90 bp region with $87\%$ identities to mlncR8. HDS was involved in the terpenoid backbone biosynthesis. In addition, the 5’ UTR of D. purpurea Solanesyl diphosphate synthase (SPS) contained a 102 bp region that was highly complementary to mlncR31. SPS was involved in the biosynthesis of ubiquinone and plastoquinone [43]. In Digitalis nervosa, Salimi et al. [ 2018] showed that the expression levels of Δ5–3β-hydroxysteroid dehydrogenase (a key gene in cardenolides biosynthesis, 3β-HSD), mlncRNA23, mlncRNA28, and mlncRNA30 across different tissues under normal conditions within leaves and roots were respectively high and low [88]. It indicated the adjacent relationship between 3β-HSD and three mlncRNAs through an unknown mechanism. Also, it revealed that the possible accumulation of mentioned transcripts in the aerial parts of plants was associated with secondary metabolites biosynthesis site [88]. Due to the fact that the expression levels of three mlncRNAs and 3β-HSD under stress conditions are similar and decreased, mlncRNAs are more possibly stress-responsive [88]. In the current study, four mlncRNAs were detected for coral3 module including JO461863 (mlncR13), JO460006 (mlncR6), JO466327, and JO462746 (S8 Table). Moreover, this module is associated with the secondary metabolites of glucodigitoxin and strospeside (S2 Table). The expression levels of mlncR6 were also higher in leaves and roots [43]. A region with significant similarity to the CDS of SNF1-related protein kinase (SnRK, JO461197) was included in mlncR6; also, expression of this gene was positively correlated with mlncR6 in cold and dehydration stresses [43]. ## Validation of candidate genes To investigate the induction effect of MeJA on JAZ3, SCL14, DWF1, and HYD1 expression patterns, biennial plants were treated with 100 μM MeJA at the mentioned time points (Fig 13). In this study, JAZ3 was chosen as a candidate gene to know whether it responds to MeJA induction or not. Jasmonates are the best recognized key signal transducers that stimulate the overproduction of secondary metabolites [92]. Pérez-Alonso et al. [ 2014] evaluated the elicitors including MeJA, induced production of cardenolide in D. purpurea L. [93]. They showed that digoxin and digitoxin contents were increased by 80 and 100 μM MeJA respectively [93]. Rad et al. [ 2022] evaluated the induction of secondary metabolites in D. purpurea by applying polyamines and MeJA in suspension cultures [94]. El-Sayed et al. [ 2019] confirmed that the highest Digoxin production was obtained by using malt extract autolysate medium supplemented by MeJA [95]. Fig 13: The relative expression of candidate genes.The expression pattern of JAZ3, SCL14, DWF1, and HYD1 was evaluated under 100 μM MeJA treatment in Digitalis purpurea L. X axis represents the fold change of the expression value. Y axis represents time points. Vertical bars indicate ± SE of the mean (n = 3). The different letters on columns represent the significant difference given by Duncan’s multiple range tests (P-value ≤ 0.05). There were no significant differences between equal letters (P-value ≤ 0.05). Although the expression of JAZ3 is early induced, a significant suppression is observed after 48 h. Other key genes particularly SCL14, targeting DWF1, and HYD1, inducing cholesterol biosynthesis and cardiac glycoside content, showed a significant increase in the expression. ## Response of JAZ3 to exogenous MeJA Although JAZ3 showed no the significant expression at 3 h after MeJA treatment, it was significantly induced after 6 h and reached to the highest expression after 24 h, and decreased after 48 h (Fig 13A). This indicated that JAZ3 was responsive to MeJA induction. The JAZ family are key transcriptional repressors in the JA signal transduction pathways [96–98]. Under normal conditions, JA content is maintained at a relatively low level, in which the JAZ repressor interacts with MYC2. This interaction inhibits downstream JA signaling responses including fertility process, root growth, senescence progress, secondary metabolite accumulation, and defense pathways. Large amounts of accumulated JA perceived by coronatine-insensitive 1(COI1) and JAZ proteins are degraded via the SCFCOI1 (Skp/Cullin/F-box) complex and leading to the activation of JA responsive genes and TFs (MYC2, MYC3, MYC4, MYB75, and others). In fact, COI1 binds the JA conjugate JA-isoleucine (JA-Ile) and this interaction enables the SCFCOI complex to recognize JAZ proteins [96–98]. It is worth noting that, accumulation of JAs decreased after stress, and the expression of JA biosynthesis genes increased [99]. In the JA biosynthesis pathway, linolenic acid as the substrate for the biosynthesis of JA, convert to 12-oxo-phytodienoic acid (OPDA) by lipoxygenase (LOX), allene oxide synthase (AOS) and allene oxide cyclase (AOC). Then, OPDA is reduced by 12-oxo-phytodienoic acid reductase (OPR), to generate JA [45]. It seems that the increased expression of JAZ3 at 6 h and 24 h after MeJA treatment is because of degradation of JAZ proteins and reduction of JA content (Fig 13A). Han and Luthe [2022] investigated the effect of response to Fall Armyworm (FAW) feeding on the accumulation of JA in caterpillar-resistant maize [99]. They showed the accumulation of JA continued until 1 h and then decreased at 6 h and 12 h. Moreover, JA biosynthesis genes in response to FAW feeding and exogenous application of MeJA were increased at 6 h in maize inbred lineTx601 and at 12 h in maize inbred line Mp708 [99]. Based on the following research, it is possible that the decrease in JAZ3 expression 48 h after MeJA treatment will increase the secondary metabolites. Shi et al. [ 2016] found that over-expression of SmJAZ3 in hairy roots produced lower levels of tanshinone whereas down-regulation of SmJAZs enhanced tanshione production [100]. Ju et al. [ 2019] discovered that the transcript abundance of JAZ3 was rapidly up-regulated by MeJA treatment at 3 h, 6 h, and 12 h and was slowly and mildly up-regulated by ABA treatment at 24 h and finally the JAZ3 was rapidly and completely degraded by MeJA treatment in bread wheat and Arabidopsis [101]. Shoji et al. [ 2008] showed that jasmonate-induced up-regulation of nicotine biosynthesis is mediated by tobacco COI1 and JAZ repressors [102]. Li et al. [ 2021] confirmed that expression level of SmJAZ3 was increased at 0.5 h and decreased at 1 h then increased from 2 h until reached to the highest level at 12 h after 100 μM MeJA elicitation in S. miltiorrhiza [103]. Han and Luthe [2022] showed the exogenous application of $0.01\%$ MeJA was affected on the expression of ZmJAZ3 as the highest level of expression was observed at 6 h after treatment in caterpillar-resistant maize [99]. In response to the exogenous application of MeJA, the highest expression of ZmLOX, ZmAOS, and ZmOPR2 as JA biosynthesis, genes were observed at 6 h in maize inbred line Tx601 and the highest expression of ZmLOX, and ZmOPR2 were detected at 12 h but the highest expression of ZmAOS was seen at 6 h in maize inbred line Mp708 [99]. According to GO analysis, JA biosynthetic (GO:0009695) and oxylipin biosynthetic (GO:0031408) processes are recognized as signals in response to the stresses and biosynthesis, and accumulation of secondary metabolites like TIAs, ginsenoside, taxol, MIAs, and artemisinin which are produce via biosynthesis of terpenoids and steroids pathway [45, 46, 48] that are according to our results as shown in Fig 9. In fact, it seems that the induced expression of JAZ3 from 6 h to 24 h after MeJA elicitation is the cause of decrease in the accumulation of JAs and degradation of JAZ proteins at these point times (Fig 13A). ## Response of SCL14 to exogenous MeJA Based on our results, the expression of SCL14 encoding a GRAS TF was induced at 3 h and reached to the highest level at 6 h after 100 μM MeJA treatment and then decreased (Fig 13B). This gene is critical for indirect defense [104]. This gene is an additional component in the regulation and activation of some detoxification genes that are putatively involved in the detoxification of xenobiotics [105]. In fact, SCL14, TGA2, TGA5, and TGA6 are involved in the detoxification by inducing the expression of a subset of the genes. For example, the scl14, tga2, tga5, and tga6 mutants in Arabidopsis were more susceptible to toxic doses of isonicotinic acid and 2,4,6-triiodobenzoic acid [105]. These genes control the majority of cis-jasmone-induced genes and cis-jasmone-induced defense [104]. In addition, MYC TFs as activators of the JA responsive signals, and TGA TFs as well-known players in the SA responsive signals and in detoxifying mechanisms but altogether, both of them mediate the responses to xenobiotics and their combined action is required for the complete activation of the responses [106]. Moreover, cis-acting element of activation sequence-1 (as-1) in the promoters of defense- and stress-related genes is mainly activated by the TGA II TF under auxin- and SA-mediated stimuli [105, 106]. SCL14 bind to the promoters containing (as-1)–like cis-elements too [106, 107]. Finally, under JA induction, MYC2 binds to the G-boxes and stringently requires the presence of TGA TFs at the as-1-like cis-acting element [108]. It seems that high expression of SCL14 under MeJA treatment (Fig 13B) is because of degradation of JAZ3, and activation of MYC TFs (including MYC2, MYC3, and MYC4) and JA responsive genes. In addition, high expression of SCL14 under MeJA treatment could be associated with the requirement for the presence of TGA TFs at the as-1-like element for MYC2 [108]. According to GO and KEGG pathway analysis, transport auxin (GO:0060918), auxin responsive genes (GO:0009733), and auxin-activated signaling pathways (GO:0009734) are considered as important GO terms (Fig 9) because of the activation of as-1 cis–acting element by the TGA II TFs responsive to auxin and SA mediated stimuli. Chorismate biosynthetic process (GO:0009423) (Fig 9) is associated with aromatic amino acid family biosynthetic process (GO:0009073) and phenylalanine, tyrosine, and tryptophan biosynthesis (ath00400) (Fig 10). In plants, chorismate is a precursor of AAA and an initial compound for isochorismate pathway to SA [49, 50]. In addition, AAAs are precursors for a wide range of secondary metabolites, various pigment compounds, and plant hormones, such as auxin and SA [49, 50]. Thus, high expression of SCL14 can be related to the interaction with TGA TFs that are well-known players in the SA responsive signals (Fig 13B). SCL14 interacts with DWF1 which is involved in the biosynthesis of steroids (ath00100) including cholesterol that is a precursor of cardiac glycosides. In fact, in this pathway cholesterol is converted into the C21 steroid and cardiac glycoside via a series of enzyme catalytic reactions [109]. SCL14 was associated with biosynthesis of the secondary metabolites of digitoxigenin bis-digitoxoside and gitoxin (S2 Table) and DWF1 showed a high positive and significant correlation with glucodigitoxin and strospeside biosynthesis (S2 Table). It seems that the induced expression of SCL14 under MeJA treatment (Fig 14) promotes the expression of DWF1 (Fig 14), therefore enhances cholesterol biosynthesis as precursor of cardiac glycosides. Fig 14: The comparative relative expression of the candidate genes under 100 μM MeJA treatment in Digitalis purpurea L.Here, the expression pattern of JAZ3, SCL14, DWF1, and HYD1 is compared. X axis represents the fold change of the expression of candidate genes. Y axis represents time points. Vertical bars indicate ± SE of the mean (n = 3). The different letters on columns represent the significant difference given by Duncan’s multiple range tests (P-value ≤ 0.05). There were no significant differences between equal letters (P-value ≤ 0.05). The interesting point is that the expression of HYD1, involved in the biosynthesis of cholesterol and subsequently the cardiac glycosides, shows a stable increase after 48 h. In contrast, other genes are suppressing and JAZ3, inducing the downstream genes, shows a significant suppression. ## Response of DWF1 to exogenous MeJA The expression of DWF1 increased after all time points as the highest expression was observed at 24 h (Fig 13C). This gene is downstream of SCL14 TF, therefore it targets DWF1 (Fig 13C). It seems that high expression of SCL14 and DWF1 are associated with each other under MeJA treatment (Fig 14) and enhance cholesterol biosynthesis. Razdan et al. [ 2017] showed that the expression of WsDWF1 increased at 6 h, 12 h, 24 h, 48 h, and 72 h after 0.1 mM MeJA treatment in Withania somnifera as the highest expression was observed at 48 h [110]. They confirmed that the highest withanolides accumulation was detected at 48 h [110]. Upadhyay et al. [ 2014] observed the DWF1 accumulation at 5 h and decreased at 12 h under MeJA treatment in Asparagus racemosus [111]. Ciura et al. [ 2017] discovered that 100 μM MeJA treatment enhances the production of diosgenin in fenugreek (Trigonella foenum-graecum) tissues [112]. Wang et al. [ 2012] presented that overexpression of wild-type and mutant BjHMGS1 (3-hydroxy-3-methylglutaryl-CoA synthase 1) in Arabidopsis, up-regulates the genes involved in sterol biosynthesis like DWF1 [113]. DWF1 is located downstream of HMGS1. HMGS1 enhanced seed germination and sterol content, stress tolerance, reduced hydrogen peroxide (H2O2)–induced cell death, and increased resistance to Botrytis cinerea [113]. This study indicated that overexpression of upstream genes induces expression of downstream genes and enhanced secondary metabolite content. Mehmandoust Rad et al. [ 2020] showed that the application of polyamines and MeJA in D. purpurea had significant effects on the expression of 1-Deoxy-d-Xylulose 5-phosphate Reductoisomerase (DXR) and contents of cardenolide and digitoxin and enhanced the expression of DXR and contents of cardenolide and digitoxin [114]. Thus, it is possible that by increasing the expression of DWF1 under MeJA treatment, it increases the expression of the downstream genes like 3β-HSD and increases the amount of cardiac glycosides. The expression of 3β-HSD was increased under MeJA treatment and pregnenolone was converted into progesterone [115]. In steroidal sapogenin biosynthesis pathway, HYD1 converts 4α-methylcholesta-8,24-dien-3β-ol to 4α-methylcholest-7,24-dien-3β-ol and DWF1 converts desmosterol to cholesterol [112]. ## Response of HYD1 to exogenous MeJA The expression of HYD1 increased and showed the highest expression at 48 h after treatment (Fig 13D). Two genes, DWF1 and HYD1 are involved in the steroid biosynthesis pathway. It seems that high expression of DWF1 and HYD1 are associated with each other under MeJA treatment (Fig 14) so that enhances cholesterol biosynthesis. HYD1 was associated with digitoxigenin bis-digitoxoside. Zhang et al. [ 2017] performed transcriptome analysis in leaves, roots, adventitious roots, and calli of Periploca sepium and identified higher expression of HYD1. Many genes like HYD1 were significantly up-regulated in adventitious roots and calli but DWF1 was significantly down-regulated in leaves [109]. Ciura et al. [ 2018] found that the highest diosgenin content was observed after treatment with 100 μM MeJA in T. foenum-graecum and the expression of the genes coding HYD1 and DWF1 was elevated [116]. Thus, it is possible that by increasing the expression of HYD1 under MeJA treatment, it increases the expression of the downstream genes and increases the amount of cardiac glycosides. According to GO and KEGG pathway analysis, endoplasmic reticulum membrane (GO:0005789) and integral component of membrane (GO:0016021) GO terms are associated with localization of DWF1 [117]. The secondary metabolite biosynthesis (ath01110) (Fig 10) is related to the genes of HYD1 and DWF1 that are involved in the steroid biosynthesis pathway resulting in the formation of precursors of cardiac glycosides [109]. ## Key genes and other genes effective on the cardiac glycosides production In this study, WGCNA analysis has been used to identify key genes and pathways associated with biosynthesis of secondary metabolites in D. purpurea based on a systemic view and integration of metabolomics and transcriptomics data. Based on a systemic view, upstream genes were chosen as candidate genes. DWF1 and HYD1 were involved in the process of steroid biosynthesis to produce precursors of cardiac glycosides (cholesterol). DWF1 is downstream of SCL14. In this study, the expression of SCL14, DWF1, and HYD1 increased at all-time points. It seems that increasing the expression of these genes increases cholesterol as a precursor and the amount of cardiac glycosides is likely to increase. Cholesterol is the starting point of cardenolide formation in Digitalis spp. [ 31]. The expression of 3β-HSD was increased at 1 h under 0.1 mM MeJA in D. nervosa [115]. P5βR and P5βR2 were increased at 4 h and 8 h and decreased at 24 h and 48 h under 100 μM MeJA treatment in D. purpurea [118]. In cardenolide biosynthesis first, sterols transform to pregnenolone then, this pregnene is converted by 3β-HSD into progesterone [118]. Progesterone converts to 5β-pregnan-3,20-dione by Progesterone 5β-reductase (P5βR) [118]. In this pathway, P5βR2 catalyzes the 5β-reduction of the Δ4 double bond of several steroids [118]. Ordinal hydroxylations at C14 and C21 and lactone ring formation at C17 lead to aglycone digitoxigenin [118]. Finally, digitoxigenin bis-digitoxoside converts to digitoxin, which is a lipid soluble cardiac glycoside and precursor of glucodigitoxin and gitoxin [31, 119, 120]. Munkert et al. [ 2014] showed that MeJA treatment enhanced the transcription of 3β-HSDs (EcHSD2 and EcHSD3) and the accumulation of erysimoside and helveticoside in Erysimum crepidifolium [121]. Finally, an increase in the expression of upstream genes has been observed under MeJA treatment, and the amount of cardiac glycosides also increases [114]. Increasing the expression of SCL14 as a hub gene, affects the whole network and induces the expression of DWF1 and HYD1. Thus an increase in the expression of candidate genes from WGCNA analysis under methyl jasmonate treatment was expected and confirmed bioinformatic results. ## Conclusions Cardiac glycosides are mainly generated through the members of Digitalis genus. In this study, for the first time, an attempt has been made to identify key genes and pathways associated with biosynthesis of secondary metabolites based on a systemic view and combination of transcriptome and metabolome data in D. purpurea. Based on the systems biology insight, the candidate genes could be effective in enhancing the production of secondary metabolites. MeJA treatment enhanced transcription of JAZ3, HYD1, SCL14, and DWF1. DWF1 showed a high positive correlation with glucodigitoxin and strospeside and HYD1 was associated with digitoxigenin bis-digitoxoside. Based on WGCNA, SCL14 was a hub gene affecting on whole metabolite network under MeJA treatment. The key genes of DWF1 and HYD1 induce amount of precursors of cardiac glycosides. It is recommended that future research be carried out on the manipulation of metabolic pathways and metabolic engineering of the introduced genes to increase the production of valuable metabolites in D. purpurea. ## References 1. 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--- title: Testicular histone hyperacetylation in mice by valproic acid administration affects the next generation by changes in sperm DNA methylation authors: - Kazuya Sakai - Kenshiro Hara - Kentaro Tanemura journal: PLOS ONE year: 2023 pmcid: PMC9997898 doi: 10.1371/journal.pone.0282898 license: CC BY 4.0 --- # Testicular histone hyperacetylation in mice by valproic acid administration affects the next generation by changes in sperm DNA methylation ## Abstract Various studies have described epigenetic inheritance through sperms. However, the detailed mechanisms remain unclear. In this study, we focused on DNA methylation in mice treated with valproic acid (VPA), an inducer of epigenomic changes, and analyzed the treatment effects on the sperm from the next generation of mice. The administration of 200 mg/kg/day VPA to mice for 4 weeks caused transient histone hyperacetylation in the testes and DNA methylation changes in the sperm, including promoter CpGs of genes related to brain function. Oocytes fertilized with VPA-treated mouse sperm showed methylation fluctuations at the morula stage. Pups that were fathered by these mice also showed behavioral changes in the light/dark transition test after maturation. Brain RNA-seq of these mice showed that the expression of genes related to neural functions were altered. Comparison of the sperm DNA methylation status of the next generation of mice with that of the parental generation revealed the disappearance of methylation changes observed in the sperm of the parental generation. These findings suggest that VPA-induced histone hyperacetylation may have brain function-related effects on the next generation through changes in sperm DNA methylation. ## Introduction With the development of medicine and industry, the types and amounts of chemical substances in animals, including humans, have dramatically increased. Pharmaceutical and cosmetic chemicals and pesticides have enriched our lives. However, exposure to these chemicals affects the nervous, immune, and reproductive systems of animals [1]. In particular, chemicals with the potential for reproductive toxicity need to be carefully evaluated as they can significantly impact the next-generation and ecosystems. This challenge is further complicated because intergenerational effects via epigenetic changes in the reproductive system cannot be excluded [2]. Various chemicals, such as nicotine [3], arsenic [4], and vinclozolin [5], reportedly display intergenerational or transgenerational effects in mammals. These toxic effects of germline epigenetics are difficult to assess because they may have adverse effects on the next generation, even if they do not cause toxic effects to the exposed generation. Therefore, understanding the mechanism of the intergenerational effects of epigenetics is necessary to properly assess toxicity. Valproic acid (VPA) is widely used in the treatment of epilepsy, bipolar disorder, and migraine headaches [6, 7]. The major pharmacological activity of VPA is the increased concentration of gamma-aminobutyric acid (GABA) in the brain due to the inhibition of GABA transaminase and succinic semialdehyde dehydrogenase activity. These inhibitions may suppress the seizures [8]. VPA also inhibits histone deacetylases (HDACs), which are key regulators of chromatin structure [9]. Although VPA is less toxic to the administered generation, its use in pregnant women is contraindicated because of its reported fetal teratogenicity and association with intellectual disability [10]. Based on these clinical findings, rodents exposed in utero to VPA have been used as animal models of autism spectrum disorder (ASD) [11–13]. Although a recent study reported that VPA triggers behavioral alterations in mice [14]. The effects of intrauterine exposure to VPA are thought to be mediated by various pharmacological effects of VPA that act directly on the fetus. However, the effects of paternal VPA ingestion are mediated by sperm and thus through germline epigenomic changes. Epigenetics is the study of mitotically or meiotically heritable changes in gene expression or cellular phenotypes that occur without altering DNA sequences [15]. During spermatogenesis, spermatids undergo various epigenetic modifications including DNA methylation, histone modifications, and small non-coding RNA loading [16]. Although most sperm histones (approximately $95\%$ in mice and $90\%$ in humans, depending on the species [17]) are replaced by protamine at the end of spermatogenesis, various histone modifications are established during spermatogenesis and may be involved in the regulation of gene expression and other epigenetic modifications [18, 19]. Histone acetylation primarily alters chromatin conformation by relaxing nucleosome interactions, thereby promoting transcription [20]. Since VPA inhibits HDAC, the administration of VPA is expected to enhance the acetylation of spermatid histones, which may induce other epigenomic changes, such as DNA methylation in sperm, via structural changes in chromatin. DNA methylation is the most well-studied epigenetic modification in mammals and primarily involves the addition of a methyl group to the C5 position of cytosine. DNA methylation plays a role in regulating gene expression by recruiting proteins involved in repressing gene expression and by inhibiting the binding of transcription factors to DNA [21]. Methylated sperm DNA is demethylated immediately after fertilization. Some loci may evade demethylation and be transmitted to the next generation [22]. However, no study has examined the effects of VPA-induced histone hyperacetylation during spermatogenesis on DNA methylation in mature spermatozoa. If intergenerational effects via sperm DNA methylation is observed in VPA-treated mice, this can be used as a simple model for assessing epigenetic intergenerational toxicity or examining the mechanisms of epigenetic inheritance. Accordingly, in this study, we investigated the effects of VPA-treated sperm on the next generation of mice, focusing on DNA methylation. ## Animals and chemicals Male C57Bl/6N mice purchased from SLC (Shizuoka, Japan) were maintained in a temperature- and humidity-controlled room with a 12-h light/dark cycle and free access to food and water. Animals associated with tissue or cell sampling were euthanized by cervical dislocation after intraperitoneal injection of an anesthetic mixture consisting of medetomidine (0.3 mg/kg, Meiji Seika Pharma, Tokyo, Japan), midazolam (4 mg/kg; Sandoz, Tokyo, Japan), and butorphanol (5 mg/kg, Meiji Seika Pharma); VPA was purchased from Sigma-Aldrich (St. Louis, MO, USA). All animal care and experimental procedures complied with the regulations for animal experiments and related activities of the Tohoku University. This study was approved by the Tohoku University Institutional Animal Care and Use Committee. This study was conducted in accordance with the ARRIVE guidelines. ## Treatment protocols A total of 68 animals were divided into two groups of 34 animals each, with 4–6 animals per cage. The control group was administered a drug vehicle (saline). The VPA group was administered 200 mg/kg VPA. VPA and saline were administered daily via intraperitoneal injection to mice for 4 weeks from 6 to 10 weeks of age. The timing of this administration was determined based on the chemical effects that occurred before sexual maturity and epigenomic effects after maturity [23, 24]. Two weeks after the final dose, 38 animals were sacrificed at 12 weeks of age and used for in vitro fertilization and sperm DNA methylation analysis. This 2-week rest period facilitates the drug-affected sperm to reach the cauda epididymis and eliminates the acute effects of drug administration, enabling chronic effects to be studied. For western blot analysis, 18 animals were sacrificed 0, 3, and 7 days after the final dose. To obtain F1 offspring, 12 mice at 12 weeks of age from each group were mated with untreated, virgin, 12-week-old C57BL/6N female mice. Mating was carried out by keeping two female mice for each male mouse in the same cage. Successful mating was confirmed by the presence of sperm plugs. The resulting pups were raised to 12 weeks of age and then subjected to behavioral tests and brain RNA-seq analysis. The F1 offspring used for the behavioral experiments were individually kept from 4 weeks of age to prevent hierarchy in the home cage from affecting behavior. ## Quantification of testicular histone acetylation using multiplex fluorescent western blotting Testes from each group were collected and homogenized in Tris-buffered saline containing a protease inhibitor cocktail (Nacalai Tesque, Kyoto, Japan). The lysates were centrifuged, and the supernatants were suspended in an equivalent volume of 2× sample buffer (Nacalai Tesque), sonicated, and boiled for 5 min. Testicular protein extracts were separated using $15\%$ sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride membranes. Membrane blocking was performed by incubating the samples in Blocking One (Nacalai Tesque) for 1 h at room temperature. After washing with phosphate-buffered saline, the membranes were incubated overnight at 4°C with a combination of the following antibodies: [1] rabbit polyclonal anti-histone H3 (acetyl Lys 9) antibody (diluted 1:1000, ab10812, Abcam, Cambridge, UK) and goat polyclonal anti-histone H3 antibody (diluted 1:1000, ab12079, Abcam); [2] rabbit polyclonal anti-histone H3 (acetyl Lys 27) antibody (diluted 1:1000, ab4729, Abcam) and anti-histone H3 antibody (diluted 1:1000, ab12079). The membranes were washed with phosphate-buffered saline containing $0.1\%$ Tween 20 and treated with Alexa Fluor 555-labeled anti-rabbit and Alexa Fluor 633-labeled anti-goat secondary antibodies (diluted 1:2000) for 2 h at room temperature. After washing, multiplex fluorescent blot images were obtained using the ChemiDoc MP imaging system (Bio-Rad, Hercules, CA, USA). The fluorescence intensity of the bands was quantified using the Image Lab version 4.1 software (Bio-Rad) and normalized relative to that of histone H3. ## Collection of mouse spermatozoa Cauda epididymides were collected from both groups, cut using micro-spring scissors, squeezed, and transferred to 1 mL of human tubal fluid (HTF) medium [25]. The medium consisted of 101.6 mM NaCl, 4.7 mM KCl, 0.37 mM K2PO4, 0.2 mM MgSO4∙7H2O, 2 mM CaCl2, 25 mM NaHCO3, 2.78 mM glucose, 0.33 mM sodium pyruvate, 21.4 mM, sodium lactate, 286 mg/L penicillin G, 228 mg/L streptomycin, and 5 mg/mL bovine serum albumin. After incubation for 60 min at 37.5°C in $5\%$ CO2 in humidified air, the upper layer of the medium containing motile and capacitated sperm was collected. Sperm from 10 animals in each group were used for subsequent IVF experiments, and sperm from 3 animals in each group were used for DNA extraction. ## In vitro fertilization For oocyte collection, 4-week-old naïve C57Bl/6N females were superovulated by intraperitoneal injection of 5 IU pregnant mare serum gonadotropin (Asuka Animal Health, Tokyo, Japan), followed by 5 IU human chorionic gonadotropin (hCG; Mochida Pharmaceutical, Tokyo, Japan) 48 h later. Fifteen hours after hCG injection, the animals were sacrificed and their oviducts removed. The cumulus-oocyte complexes were collected in drops of HTF medium containing approximately 7 × 105 cells/mL sperm from 12-week-old mice from control or VPA groups. In vitro fertilization was performed by co-incubating oocytes with sperm in HTF medium drops for 4 h at 37.5°C in an atmosphere of $5\%$ CO2 in humidified air. After incubation, the oocytes were washed by gentle pipetting in potassium simplex optimized medium [26] using a glass pipette. The oocytes were transferred to new potassium simplex optimized medium drops and incubated for 72 h at 37.5°C in $5\%$ CO2 in humidified air. After incubation, morphologically normal morular embryos were selected and collected. ## Extraction of sperm DNA Sperm DNA was extracted using a modified phenol/chloroform method [27]. Spermatozoa were suspended in lysis buffer (0.14 mM β-mercaptoethanol, 0.24 mg/ml proteinase K, 150 mM NaCl, 10 mM Tris-HCl [pH 8.0], 10 mM ethylene diamine tetraacetic acid [pH 8.0], and $0.1\%$ sodium dodecyl sulfate) and incubated at 55°C overnight. After incubation, 400 μL of a phenol/chloroform/isoamyl alcohol (25:24:1) mixture (Nacalai Tesque) was added, and the resulting mixture was vortexed. The mixture was centrifuged at room temperature for 10 min at 12000 × g, and the upper aqueous phase was transferred to a new tube. This step of adding phenol/chloroform/isoamyl alcohol and centrifuging was repeated three times. Next, 400 μL of chloroform was added to the aqueous solution, vortexed, and centrifuged. The upper aqueous phase was transferred to a new tube containing 2 μL of ethachinmate (Nippon Gene, Tokyo, Japan), 40 μL of 3 M sodium acetate, and 800 μL of $100\%$ ethanol and incubated for 30 min at −80°C. After incubation, the mixture was centrifuged at 4°C for 30 min at 12000 × g. The supernatant was removed, and 500 μL of $70\%$ ethanol was added, vortexed, and centrifuged for 10 min at 12000 × g. The supernatant was removed, and the DNA pellet remaining in the tube was dried at room temperature and dissolved in Tris-ethylene diamine tetraacetic acid buffer. ## Sperm DNA methylation analysis Sperm DNA was extracted as described above from VPA-treated mice, VPA_F1, and control mice ($$n = 3$$ each). Subsequently, DNA methylation analysis was performed using WGBS, and DNA bisulfite conversion was performed using an EZ DNA Methylation-Gold Kit (Zymo Research, Orange, CA, USA). Further, DNA libraries were constructed using the Accel-NGS Methyl-Seq DNA Library Kit (Integrated DNA Technologies, Coralville, IA, USA) or Abclonal Scale Methyl-DNA Lib Prep Kit for Illumina (Abclonal, Tokyo, Japan) according to the manufacturer’s instructions. The DNA libraries were used for 150 bp paired-end sequencing on a Novaseq 6000 platform (Illumina, San Diego, CA, USA). Adapter trimming was performed using the Trim Galore 0.5.0 program (RRID: SCR_011847) and Trimmomatic program [28]. The resulting reads were quality checked using FastQC version 0.11.7 [29]. The reads were aligned to the mouse genome mm10 using Bismark version 0.22.3 [30] with default parameters. Gene annotation and methylation level calculations were performed using the methylKit package [31] in R (https://www.r-project.org/). Promoters were defined as regions 2 kb upstream of the transcription start sites. The DMCs were identified as cytosines in the promoter regions whose methylation rate showed a P-value < 0.05, as determined by t-test, and the mean methylation rate difference was ≥ $20\%$. Similarly, regions that contained at least 10 CpGs within 300 bp and whose average difference in methylation rates was ≥ $20\%$ were extracted as DMRs. The genomic regions rich in acetylated histones K9 and K27 located in the vicinity of the DMRs (± 1 kb) were identified using the Peak Browser of ChIP-Atlas [32], where the threshold for significance was set to 50 (q-value < 1E-05) and cell type as mouse male germ line (testis, male germ cells, spermatogonia, spermatogenic cells, round spermatids, and spermatids). Pathway analysis using IPA software version 68752261 (QIAGEN, Valencia, CA, USA) was performed for genes containing DMC in the promoter region. ## Morula DNA methylation analysis DNA methylation analysis was performed by the WGBS method using morulas derived from VPA-treated and control mouse sperms. In this experiment, 250–300 morulas derived from three to four mouse sperms per sample were used. Morula DNA was extracted using NucleoSpin Tissue XS (TaKaRa Bio, Shiga, Japan). Next, DNA bisulfite conversion, library construction, and sequencing were performed using the same kits and methods as in the sperm DNA methylation analysis experiments described above. The resulting reads were adapter-trimmed, quality checked, and aligned with the mouse reference genome mm10. Methylation rates were then calculated for each CpG, for which data existed in all samples, using the methylKit package in R. We compared these CpG methylation rates and examined the similarities between samples using principal component analysis (PCA). ## Behavioral test Fifteen male F1 mice were selected from each group to have the smallest variance in body weight at 12 weeks of age and were subjected to behavioral tests. Behavioral tests included the open field, light/dark transition, and context/cued fear conditioning tests, as previously described [33, 34]. The procedures and equipment used in the behavioral experiments described below are minor modifications to those used in a previous report by Saito et al [33]. The measured values and images were analyzed using Image OF2, Image LD2, and Image FZ2 software (O’Hara & Co., Ltd.) developed using the public domain ImageJ program [35]. The experimental tests were conducted between 10:00 and 15:00. The experiments were performed in a soundproof box (78 × 63 × 65 [H] cm) made of white-colored wood, which was equipped with an audio speaker and a light source. Background noise of approximately 50 dB was applied during the experiments. After each trial, the apparatus was cleaned with water and wiped dry. ## Open field test Locomotor activity was measured for 10 min using an OF apparatus made of white plastic (50 × 50 × 30 [H] cm). The LED light system was positioned approximately 50 cm above the center of the field (25 lx at the center of the field). The behavior was measured using a charge-coupled device (CCD) camera positioned above the center of the apparatus. ## Light/Dark transition test The apparatus consisted of a cage (21 × 42 × 25 [H] cm) divided into two chambers by a partition with an opening. One chamber was made of white plastic and was brightly illuminated (250 lx, light box). The other chamber, made of black plastic, was dark (5 lx, dark box). Behavior was measured using a CCD camera positioned above each chamber. The mice were placed in a dark box and allowed to move freely between the two chambers through the opening for 5 min. ## Contextual/Cued fear conditioning test The apparatus consisted of a conditioning chamber (test chamber; 17 × 10 × 10 [H] cm) made of clear plastic with a ceiling. The chamber floor had stainless steel rods (2 mm diameter) spaced 5 mm apart that delivered an electric foot to the feet of the mice. The inner wall of the chamber was covered with black and white plastic strips. The LED light system was positioned approximately 50 cm above the chamber (50 lx at the center of the floor). Behavior was measured using a CCD camera positioned above the center of the chamber. During the conditioning trial, mice were individually placed in the conditioning chamber and, after 40 s, were given six tone-shock pairings (20 s of tone at 65 dB, directly followed by 2 s of 0.08 mA electric shock), each separated by 60 s. The mice were then returned to their home cage. The next day, as a contextual fear test, the participants were returned to the conditioning chamber for 6 min without tone or shock. The day after that, for the cued fear test, they were placed in a novel chamber (with a different design and lacking plastic black and white stripes and stainless steel rods). After 3 min, a conditioning tone (with no shock) was presented for 3 min. The freezing response of the mice was measured using Image FZ2 as a consecutive 2-s period of immobility. Freezing rate (%) was calculated as follows: (freezing/session time) × 100. ## Brain RNA-seq RNA-seq was performed to obtain the brain RNA expression profiles of the F1 mice subjected to behavioral experiments. Four mice in each group were sacrificed after the behavioral experiments. Total RNA was extracted from the brain tissue (whole brain, excluding the olfactory bulb and cerebellum) using the NucleoSpin RNA kit (TaKaRa Bio). The concentrations of the RNA samples were determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The quality was checked using the 5200 Fragment Analyzer System (Agilent, Santa Clara, CA, USA) and Agilent HS RNA kit (Agilent). Sequence libraries were then constructed using the MGIEasy RNA Directional Library Prep Set (MGI Tech; Shenzhen, China). The quality of the library was checked using the 5200 Fragment Analyzer System with dsDNA 915 Reagent Kit (Agilent). One hundred base-pair paired-end sequencing was performed on the DNBSEQ-G400 platform using the DNBSEQ-G400RS high-throughput sequencing kit (MGI Tech). The resulting raw reads were quality-checked using the Sickle program. Adapter trimming was performed using the Cutadapt 1.16 program. Trimmed reads were mapped against mm10 using the HISAT2 program, followed by annotation and transcript quantification using StringTie. Transcript expression levels were compared to detect differentially expressed genes (DEGs) using edgeR; DEGs were defined as genes with P-values < 0.05 and two-fold or greater expression variation by exactTest. These were used for pathway analysis using IPA software version 68752261 (QIAGEN). ## Statistical analyses Data from western blotting and behavioral analysis are presented as the mean ± S.E. or the mean + S.E., using Student’s t-test for comparisons. Some behavioral analysis data are depicted using box-and-whisker plots (consisting of the median, interquartile range, minimum and maximum values, and outliers). For sequencing data, we used a t-test as well as the exactTest in the edgeR package of R. Statistical significance was set at $P \leq 0.05.$ All statistical analyses, except pathway analysis and graphic drawing, were performed in R. P-values in the pathway analysis were calculated using the IPA software. ## Effect of VPA administration on testicular histone acetylation During the period of VPA or saline administration, all animals were healthy and had no clinical abnormalities, including testicular toxicity. To determine whether VPA administration to mice increases testicular histone acetylation, a comparative analysis of acetylated histones H3K9 and H3K27 was performed using multiplex fluorescent western blotting (Fig 1A), and the unprocessed blots are depicted in S1 and S2 Raw images. Acetylation of both H3K9 and H3K27 was significantly increased compared to that in the controls in the testes collected immediately after VPA administration. However, acetylation was reduced to the same level as that in the controls in testes collected 3 and 7 days after the end of treatment (Fig 1B). These findings indicate that VPA administration transiently increases histone acetylation in the mouse testes. Fig 1: Testicular histone acetylation levels determined using multiplex fluorescent western blotting.(A) All blotting images of the control and VPA-treated groups are depicted. On the left is histone H3K9 and on the right the bands of histone H3K27, and from the top row, immediately after treatment with VPA or saline, 3 days later, 7 days later, the histone acetylation bands, histone H3 bands, merged bands images. Histone acetylation and histone H3 bands images are shown in monochrome. (B) The bars show the fluorescent signals of histone acetylation at each sampling point. The signals are standardized to the histone H3 signal, with value relative to the control group as 1. Data are shown as mean ± S.E. (n = 3, P < 0.05).* ## Sperm DNA methylation analysis of VPA-treated mice To investigate the effects of VPA administration on DNA methylation in mouse spermatozoa, a genome-wide DNA methylation analysis was performed using whole-genome bisulfite sequencing (WGBS). Significant methylation changes were detected in the cytosines of 1863 CpG contexts in the promoter region. Of these, 846 were hypermethylated, and 1017 were hypomethylated in the VPA-treated group (Fig 2A). Pathway analysis of the downstream genes of these promoters was then performed. This analysis revealed that many genes were associated with brain function-related pathways that included “axonal guidance” and “amyloid processing” in the downstream genes of promoters, containing hypermethylated CpGs. Various pathways that included “sulfate activation for sulfonation” and “RAR activation” were detected in the downstream genes of promoters, including hypomethylated CpGs (Fig 2B). Similarly, we searched for upstream regulators of these genes using Ingenuity pathways analysis (IPA) and found that many genes with hypermethylated CpGs in their promoters were regulated by HDAC. Tumor protein P53 (TP53) showed the highest P-value as an upstream regulator in genes with promoters containing hypomethylated CpG sites (Fig 2C). We explored regional methylation changes and found 187 differentially methylated regions (DMRs) across all chromosomes. We then searched for acetylated histone H3(K9 and K27)-rich genomic regions in the vicinity of these DMRs (±1 kb) using public ChIP-seq data provided by ChIP-Atlas and found that many DMRs were in the vicinity of acetylated histone H3 (Fig 2D). Collectively, these results indicate that sperm DNA methylation is altered by VPA administration. In particular, the results suggest that hypermethylation in the promoter regions can be attributed to the inhibition of HDACs by VPA and that many methylation changes occur in genes related to brain function. Fig 2: Sperm DNA methylation analysis of VPA-treated mice using whole-genome bisulfite sequencing (WGBS).(A) Heat map showing cytosines in the CpG context that were significantly changed in promoter methylation. The number of significantly hypermethylated and hypomethylated cytosines in the VPA-treated group compared with the control group is shown in the pie chart. P-value < 0.05 by t-test and whose mean methylation rate difference was ≥ 20% were considered significant (n = 3). (B) Top canonical pathways generated by Ingenuity Pathway Analysis (IPA), using the downstream genes of promoters with altered methylation as input. The dashed line indicates −log10 (0.05). (C) Upstream regulators predicted by IPA, using downstream genes of the promoters with altered methylation as input. (D) Positions of differentially methylated regions (DMRs) found in the VPA-treated group compared with those of the control group are indicated by triangle markers on the ideogram. The positions of acetylated histone H3 (K9 and K27) which have been reported to be present in the vicinity of DMRs (±1 kb) in the male germline are also indicated by rectangular markers on the ideogram. The regions that contained at least 10 CpGs within 300 bp and whose average difference in methylation rate was ≥ 20% were extracted as DMRs. Genomic regions with a score of 50 or higher in the ChIP-Atlas Peak Browser were designated as acetylated histone regions. ## DNA methylation analysis of morula derived from VPA-treated mice sperm To determine whether sperm-derived DNA methylation changes were maintained in the embryo after fertilization, we performed methylation analysis of the morula produced by conventional in vitro fertilization (IVF). The degree of methylation of each CpG was similar between the groups, with low to moderate methylation rates (mean methylation in the control group: $19\%$, VPA-treated group: $20\%$). However, a relatively large number of hypermethylated CpGs was also found in embryos derived from VPA-treated mouse sperm (Fig 3A). Principal component analysis (PCA) was then performed on all the samples to examine the uniformity of the methylation data across samples. The VPA group data tended to be scattered, whereas the control group had similar data (Fig 3B). Fig 3: Morula DNA methylation analysis using whole-genome bisulfite sequencing.(A) Comparison of CpG methylation levels in morula derived from sperm of control and VPA-treated groups. Only CpGs with sequencing reads common to all samples are shown (1109 CpGs in total). The average methylation levels are indicated. CpGs with > 20% hypermethylation compared with that of the control group are shown in red, those with > 10% hypermethylation in orange, and those with > 10% hypomethylation in blue. (B) Principal component analysis (PCA) using methylation reads distribution for each sample. ## Behavioral analysis of F1 mice To examine the effects of VPA-treated mouse sperm on the next generation, behavioral tests were performed on each group of F1 male mice. Forty-five F1 male mice were obtained from the control group and 52 from the VPA-treated group, all of which were healthy. Fifteen animals from each group were selected for the behavioral experiment to minimize the variance in body weight. The next generation of control mice (Ctrl_F1) weighed an average of 24.7 g, and the next generation of VPA-treated mice (VPA_F1) weighed an average of 25.0 g. The behavioral tests included an open field, light/dark transition, and fear conditioning tests. In the open field test, no significant differences were observed between VPA_F1 and Ctrl_F1 mice (Fig 4A). In the light/dark transition test, the time spent in the light box was significantly increased in VPA_F1 compared with that in Ctrl_F1 (Fig 4B). In the fear conditioning test, no significant differences were observed between the groups in any of the experiments (conditioning, contextual, and cued tests) performed over a 3-day period (Fig 4C). Fig 4: Results of behavioral tests for VPA-treated F1 and control group F1 mice.(A) Results of the open field test are shown in the box-and-whisker plots with overlaid dot plots representing individual data (P < 0.05, Student’s t-test, n = 15 per group). The median is marked by a bold line inside the box. The boxes show the 25th–75th percentiles. The whiskers outside the box indicate the range from minimum to maximum values. Data outside the box + 1.5 × interquartile range (IQR) are shown as outliers. Distance traveled (cm), time spent in the center area (sec), average speed (cm/s), and total moving episode number are shown. (B) Results of the light/dark transition test. Distance traveled in the light-box (cm), time spent in the light box (sec), number of times the two boxes have been moved back and forth, time to first enter the light box after the start of the test (sec) are shown. (C) Results of the contextual/cued fear conditioning test expressed as time courses of the freezing scores (%, mean + S.E., n = 15 per group). For the conditioning trial and contextual fear test, the total freezing time was used for statistical analysis by Student’s t-test. For the cued fear test, the total freezing time in the second half (180–360 s) was used.* ## Brain gene expression analysis The slight changes in the behavioral tests prompted brain RNA-seq analysis in both F1 groups. A variety of gene expression changes were evident in these brains. In the VPA_F1 group, 20 genes were found to be significantly (FDR < 0.05) upregulated, and 4 genes were significantly downregulated (Fig 5A). Pathway analysis using these genes detected a majority of the top pathways related to neuronal function, including endocannabinoid neuronal synapse pathway, dopamine-DARPP32 feedback in cAMP signaling, and CREB signaling in neurons (Fig 5B). Fig 5: Brain RNA-seq for VPA-treated F1 (VPA_F1) and control group F1 (Ctrl_F1) mice.(A) Genes with P-value < 0.05 and two-fold change are shown in the heatmap (n = 4). q-value ≤ 0.05 is indicated by arrowheads, and the gene name is attached. (B) Top canonical pathways generated by Ingenuity Pathway Analysis (IPA), using the genes with P-values < 0.05 and two-fold or greater expression as input. The dashed line indicates −log10(0.05). ## Sperm DNA methylation analysis of F1 mice To investigate whether the changes observed in the F1 generation are transmitted to the F2 generation, we performed DNA methylation analysis of sperm obtained from the F1 mice and compared the results with those of the F0 generation. The CpGs that showed significant methylation changes in the promoter region of the F0 generation samples showed no significant changes in that of the F1 generation samples, as the differences were resolved in both Ctrl_F1 and VPA_F1 groups (Fig 6A). Similarly, only 13 DMR regions were detected in the F1 generation, compared with nearly 200 regions in the F0 generation (Fig 6B). Fig 6: Sperm DNA methylation analysis of VPA-treated F1 mice using whole-genome bisulfite sequencing.(A) Heat map comparison of all samples of sperm DNA methylation levels, including data from the next generation of VPA-treated mice (VPA_F1) and next generation of control group mice (Ctrl_F1) at promoter CpG loci where significant changes were observed in the VPA-treated generation (F0 generation, shown in Fig 2A). The left six columns indicate the F0 generation and right six columns the F1 generation. (B) Positions of differentially methylated regions found in the VPA_F1 group compared with those of the Ctrl_F1 group are indicated by markers on the ideogram. ## Discussion Some studies have suggested that nicotine [3], arsenic [4], and other substances affect the next generation through DNA methylation. However, the underlying mechanism remains unclear. We attempted to induce relaxation of chromatin structures through testicular histone hyperacetylation by administering VPA, an HDAC inhibitor. We hypothesized that the relaxation of chromatin structure during spermatogenesis would disturb DNA methylation changes in the sperm and that some of these changes would be inherited by the next generation. As hypothesized, VPA administration induced histone hyperacetylation in testes. This hyperacetylation was comparable with that of the control group at least 3 days after the end of treatment, indicating that the effect of VPA was transient. Since the treatment in this study was daily administration of VPA for 4 weeks, it is expected that spermatozoa formed during this period differentiated while maintaining histone hyperacetylation. Considering that the transit time of the epididymis in mice is approximately 10 days [36] and that the time required for spermatogenesis is 34.5 days [37], the spermatozoa used for DNA methylation analysis in this study are presumed to have included highly acetylated histones during most of the formation process. The DNA methylation analysis revealed transient histone hyperacetylation during spermatogenesis, and DNA methylation changes remained in the sperm. Many of the DMRs we found are located near acetylated histone regions, suggesting that HDAC inhibition by VPA is linked to DNA methylation changes. The number and distribution of promoter differentially methylated cytosines (DMCs) and genome-wide DMRs suggested that disturbed, rather than directional, methylation changes occurred in the VPA-treated sperm genome. Notably, our pathway analysis suggested that brain function-related genes were enriched downstream of the promoter where hypermethylated DMCs were located. Moreover, in the upstream regulator analysis, HDAC showed high P-values, suggesting that CpG hypermethylation may contain directional changes attributable to VPA administration. The sperm-derived genome undergoes active demethylation in the male pronucleus immediately after fertilization via Tet3-mediated DNA hydroxylation [38]. However, some regions may escape demethylation and be transmitted to the next generation [22]. We performed IVF using VPA-administered mouse sperm, cultured them to the morula stage, and analyzed DNA methylation. Since the maternally derived genome has diluted DNA methylation upon cell division due to the weak function of the maintenance DNA methyltransferase 1 (DNMT1), morula DNA is expected to exhibit poor methylation [39]. RNA-seq analysis did not provide a satisfactory amount of data owing to low inputs, and identification of statistically evident differentially methylated loci was not possible. However, as hypothesized, an overall low methylation rate was evident. In addition, some CpGs from the VPA-administered mouse-derived morula remained slightly (but still highly) methylated, and the methylation rates tended to vary among samples. Although these differences are not necessarily keys to epigenetic inheritance, VPA treatment likely causes qualitative differences in sperm. We observed behavioral changes in VPA_F1. The increase in light duration in the light/dark test observed in VPA_F1 mice suggests a decrease in anxiety-related behavior. Our RNA-seq analysis identified many differentially expressed genes (DEGs) in the brain of VPA_F1 mice, and our pathway analysis detected many neural function-related pathways. Genes involved in many of these pathways included Grin2a and Grin2b, which encode N-methyl-D-aspartate (NMDA) receptor subunits, both of which were significantly downregulated in the VPA_F1 group. Further, NMDA receptors play an important role in various neural activities such as synaptic transmission, synaptic plasticity, and neurodevelopment in the central nervous system [40]. Since the inhibition or mutation of NMDA receptors induces various behavioral abnormalities [41], we suggest that the behavioral changes observed in this study also involve a reduction in Grin2a and Grin2b expression. However, whether these changes affect protein expression or these gene expression changes are due to DNA methylation remains unresolved and should be investigated. Several studies have reported the epigenomic effects of environmental factors over multiple generations [42, 43]. However, in our study, methylation changes in the sperm DNA of VPA-treated mice mostly disappeared in the F1 generation. Thus, if the intergenerational effects observed in our study were due to DNA methylation, transmission across multiple generations would be unlikely. We hypothesized that VPA treatment relaxes chromatin and increases susceptibility to epigenomic changes, but does not cause directional changes in DNA methylation. Therefore, DNA methylation changes that would persist for multiple generations would not occur. In this study, we demonstrated that VPA administration to mice alters sperm DNA methylation through histone hyperacetylation during spermatogenesis, which affects the next generation. We believe that this phenomenon is due to the synergistic effects of chromatin relaxation, which promotes epigenetic changes, and the pharmacological effects of VPA itself, such as HDAC inhibition and involvement in GABA signaling. Since a recent study suggests that VPA affects DNA modification independently of histone acetylation, future studies should validate our results with HDAC inhibitors other than VPA, such as trichostatin A [44]. If the changes observed in this study are due to enhanced epigenomic susceptibility caused by chromatin relaxation, inducing stronger epigenomic inheritance is possible by combining the treatment with environmental stimuli, which are known to cause epigenomic inheritance. Future studies must focus on a more detailed analysis of DNA methylation and gene expression during embryonic and brain development and aim to confirm that the changes in brain gene expression observed in F1 individuals contribute to behavioral changes as effects on functional proteins. ## References 1. 1World Health, O., et al. State of the science of endocrine disrupting chemicals 2012: summary for decision-makers. Geneva: World Health Organization; 2013.. (2013) 2. Shaw JLA, Judy JD, Kumar A, Bertsch P, Wang MB, Kirby JK. Incorporating transgenerational epigenetic inheritance into ecological risk assessment frameworks. Environ Sci Technol (2017) 51 9433-9445. DOI: 10.1021/acs.est.7b01094 3. 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--- title: 'Effectiveness of nordic walking in patients with asthma: A study protocol of a randomized controlled trial' authors: - María Vilanova-Pereira - Cristina Jácome - Manuel Jorge Rial Prado - Margarita Barral-Fernández - Marina Blanco Aparicio - Lara Fontán García-Boente - Ana Lista-Paz journal: PLOS ONE year: 2023 pmcid: PMC9997906 doi: 10.1371/journal.pone.0281007 license: CC BY 4.0 --- # Effectiveness of nordic walking in patients with asthma: A study protocol of a randomized controlled trial ## Abstract ### Background Patients with asthma often consider their symptomatology a barrier to exercise, leading to a reduced physical activity level. This study aims to determine whether the effect of a Nordic walking (NW) training program plus education and usual care is superior to educational and usual care only, in terms of exercise tolerance and other health-related outcomes in patients with asthma. The second aim is to explore the patients’ experience with the NW program. ### Methods A randomized controlled trial will be conducted with 114 adults with asthma recruited in sanitary area of A Coruña, Spain. Participants will be randomized to NW or control groups in blocks of six and in the same proportion in each group. Participants in the NW group will enrol in supervised sessions during eight weeks, three times/week. All participants will receive three educational sessions on asthma self-management plus usual care (S1 Appendix). Outcomes such as exercise tolerance (primary outcome), physical activity level, asthma-related symptoms and asthma control, dyspnea, lung function, handgrip strength, health related quality of life, quality of sleep, treatment adherence and healthcare resources use will be measured pre and postintervention, and at three and six months of follow-up. Participants in the NW group will additionally participate in focus groups. ### Discussion This is the first study analysing the effect of NW in patients with asthma. NW combined with education and usual care is expected to improve exercise tolerance, but also asthma-related outcomes. If this hypothesis is confirmed, a new community-based therapeutic approach will be available for patients with asthma. ### Trial registration Study registered in ClinicalTrials.gov with number of register NCT05482620. ## Introduction Asthma is one of the world´s largest non-communicable diseases [1], affecting 339 million people [2]. It is considered a major social and health concern, being associated with a high economic burden, related to days out of work and school, emergency department visits, hospitalizations and mortality [3, 4]. Asthma is characterized by a reversible airway obstruction presented with any combination of the following manifestations and symptoms: wheezing, dyspnea, cough, chest tightness, mucus hypersecretion and airway hyperresponsiveness [5]. Due to these symptoms, patients with asthma, in comparison to their peers, are characterized by lower levels of physical activity [6–10]. They also commonly exercise at lower intensities, mostly because they believe that their disease is a barrier to exercise [9, 11, 12]. In its turn, inactivity can lead to worsening in asthma symptoms and control, health-related quality of life (HRQoL) [13], and a decline in lung function, particularly in forced expiratory volume in one second (FEV1), in FEV1/forced vital capacity (FVC) ratio, and peak expiratory flow (PEF) [11, 14, 15]. According to the Global Initiative for Asthma (GINA) [16], adults with asthma should engage in physical activity and exercise programs for their overall health benefit. Nevertheless, recommendations on how to perform physical activity or exercise as a part of their treatment is missing in this guideline. There is some evidence showing a positive effect of exercise programs, such as walking, jogging or cycling, reducing exacerbations and rescue medication use [17], and improving asthma-related symptoms, asthma control, lung function [18], exercise tolerance, and HRQoL [19]. Nordic walking (NW) is one of the exercise programs that could have potential benefits for people with asthma. The main difference between NW and conventional walking is the use of two poles to boost the walk, involving for instance the upper limb during the march. It was born in Finland during the 1930s as a cross-country skiers training, and it was been developed, transitioning from skiers to university and educative fields, and then to literature and scientific publications [20, 21]. The evidence is still scarce, but it has shown benefits in exercise tolerance and HRQoL in other chronic conditions, such as metabolic syndrome, musculoskeletal disorders, Parkinson disease [22–24], cardiovascular diseases [25] and chronic obstructive pulmonary disease (COPD) [26]. In COPD, NW improved exercise tolerance (measured as meters walked in six-minute walking test-6MWT); daily physical activity levels; dyspnea; anxiety and HRQoL. COPD and asthma are both respiratory diseases that share common symptoms and signs, such as cough, dyspnea and limited air flow, as well as limited exercise tolerance. Therefore, we hypothesize that similar benefits with NW can also be achieved in patients with asthma. Thus, the primary aim of this clinical trial is to determine whether the effect of a NW training program plus education and usual care is superior to education and usual care only, in terms of exercise tolerance and other health-related outcomes, in patients with asthma. Secondary, we aim to explore the patients’ experience with the NW program. ## Study design This is a two-arm, parallel, randomized controlled trial (RCT) to analyse the effects of an eight-week NW program plus education and usual care compared to education and usual care alone in patients with asthma (NCT05482620). Assessments will take place at baseline, after 8-weeks and then at three and six months of follow-up. This study will be conducted in A Coruña, Spain between June 2022 and June 2024. This protocol is reported according to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines (S1 Checklist) [27]. ## Eligibility criteria Subjects will be included if they have a clinical diagnosis of asthma, have at least 18 years old, have the desire of take part in the study, and the ability to understand and sign the informed consent. Exclusion criteria include: smokers, diagnosis of other respiratory disease; occurrence of an asthma exacerbation or respiratory tract infection in the last four weeks; acute myocardial infarction in the last six months; cardiac arrhythmia with a IIIb or superior grade in Lown scale; gait disorders due to neuro-muscle-skeletal system problems; comorbidities that implies reduced ability to exercise (e.g., severe anaemia, electrolyte imbalance, hyperthyroidism) [28, 29]. Subjects that are already engaging in exercise sessions of more than 30 minutes per day with a moderate or vigorous intensity; participated in a pulmonary rehabilitation program in the last three months [30], and are pregnant and lactating women will be excluded. In addition, subjects that present contraindications to perform cardiovascular exercise, following the American Heart Association [31] and to perform the 6MWT following the American Thoracic Society / European Respiratory Society (ATS/ERS) [32] criteria, will be excluded too. ## Sample size To estimate the sample size needed, we used the tool created by the Clinic Epidemiologic and Biostatistics service from A Coruña University Hospital Complex (CHUAC) (http://bitly.ws/sDIp). This calculation was based on the 6MWT minimum clinically important difference in patients with asthma, which is set at 26 meters [33]. Using a standard deviation (SD) of 45.49 meters, obtained from a previous pilot study [34], and considering a power of $80\%$, an alpha level of 0.05, and a dropout rate of $15\%$, a total sample size of 114 is estimated (57 in each group). ## Recruitment Subjects will be screened by physicians during consultations in the pulmonology and allergy services from the pulmonology and allergy services from A Coruña sanitary area and in primary care centres. Subjects eligible in the study will be listed, and one researcher will be responsible to randomise blocks of six to either the NW group or control group (CG) in the same proportion. Randomization will be performed via a computer program for randomization (http://randomization.com/). The allocation will be made attending to the identification (ID) number, which will be then linked to the participants’ name in the list. The order will be unchangeable, once assigned. ## Blinding (masking) The assignment to the groups will be made after the baseline evaluation. The physiotherapist in charge of the evaluation will be concealed from which group belongs each subject, and data obtained will be indexed in the database, using study ID numbers only to identify data. Due to the nature of the interventions, the physiotherapist in charge of conducting it, will not be blinded to the allocation. ## Intervention Participants in both groups will attend three educational sessions together after the baseline assessment (S1 Appendix) (before the NW program starts for the experimental group). Participants will only know the result of the randomization after the education. Educational sessions will be conducted with groups of approximately 6–10 patients. Sessions will be facilitated by a physiotherapist, and will take place in the Faculty of Physiotherapy of The University of A Coruña. Different concepts and guidance related to asthma self-management will be addressed, based on an informative brochure built by the research team. This brochure will be provided to the participants at the end of the education component. If patients miss one session, they will be phone called to wonder about reasons, to be encouraged to assist next sessions and an individual education session will be scheduled to address the missed topic. Besides, patients should keep their usual care, namely attend their regular medical appointments, take the medication under prescription and follow physician recommendations as usual. Participants in the NW group will additionally enrol in an eight-week NW program [35] of three sessions per week (total of 24 sessions). Feasibility of the NW program has previously been tested in four patients, who showed a good acceptance and satisfaction with intervention received [34]. Each session will last one hour and include: a warm-up period of 15 minutes with articular mobility, body-weight exercises with walking poles, and five minutes of walking without poles; 30 minutes of NW, with an intensity of 70–$85\%$ theoretical maximum heart rate (HRmax = 206.9 –(0.7 x age)) [36]; cold-down period of five minutes of relaxed walk without poles, stretching and breathing exercises. NW program will be provided by the same physiotherapist in charge of the educational component, who was trained in NW in Finland. When participants fail one exercise session, he/she will be called to wonder about reasons and to be encouraged to participate in the next session. After finishing the eight-week period, participants will be encouraged to continue by themselves the NW sessions and a pair of poles will be provided to achieve this objective. ## Primary outcome The primary outcome will be exercise tolerance, measured by the distance walked during the 6MWT, a reliable and validated test in patients with asthma [37], which is strongly related with important clinical outcomes as dyspnea and fatigue perceived [32]. ## Secondary outcomes Exercise tolerance will also be assessed through the number of repetitions during the one-minute sit-to-stand test (1MSTST). This test is focused in lower limb function, especially quadriceps force [38]. The secondary outcome measurements will include physical activity level (accelerometry, International Physical Activity Questionnaire -IPAQ, and patient’s diary), asthma-related symptoms and asthma control (Control of Allergic Rhinitis and Asthma Test -CARAT and patient’s diary), dyspnea (modified Medical Research Council -mMRC, and Borg scale), lung function (spirometry), handgrip strength (hand dynamometry), HRQoL (European Quality of Life Questionnaire– 5 dimensions– 5 levels -EQ-5D-5L, mini Asthma Quality of Life Questionnaire -miniAQLQ), quality of sleep (Pittsburgh Quality of Sleep Index -PQSI), treatment adherence (Test of Adhesion to Inhalers -TAI, and patient’s diary), and healthcare resources use (patient’s diary). ## Other measures Respiratory muscle strength (maximum inspiratory -MIP and expiratory pressure -MEP) will be assessed to characterize sample, since no change with NW program is expected. Correlation with other outcomes such as exercise tolerance and physical activity will be explored [39]. Adverse effects reported by the participants will be registered. Qualitative data will be collected through focus groups to a better understanding of participants’ experience with NW, namely: experience and satisfaction with the intervention received, facilitators and barriers to their participation perceived improvement in asthma management and the way of afront their disease. ## Data collection methods All the assessments will take place at the Faculty of Physiotherapy in The University of A Coruña. Assessments will be performed by the same trained physiotherapist (different from the one in charge of interventions), blinded to the group allocation. The evaluator has been trained to perform these assessments, in which she also has previous experience. To avoid the risk that participants reveal to which group they belong during the first evaluation, the allocation will be only made after all baseline measurements and the education component. The evaluator will be also blinded during the following assessment visits. In the case this blinding is compromised due to the possible comments of the participants, this will be reported. When a participant fails an evaluation appointment, the evaluator will contact him/her in order to propose a date to a new appointment. After reading the information sheet and signing the informed consent, an anamnesis will be performed in order to collect the sociodemographic variables (age, sex, occupation…), past history (e.g., surgical, comorbidities, etc.) and to confirm the fulfilment of the eligibility criteria. All questionnaires, excepting IPAQ, will be self-completed by the participants after a brief explanation of the evaluator. The evaluator will be available to clarify any doubts during the questionnaires filling and at the end will check if all questions were covered. All outcomes will be measured at baseline, postintervention, and at three and six months follow-up, except respiratory muscle strength (measured only at baseline) and treatment adherence and healthcare resources use (not analysed at baseline). Adverse effects related to following described outcomes, or during intervention, will be registered if pertinent. Exercise tolerance will be measured with 6MWT and 1MSTS. To perform the 6MWT, an indoor corridor 30 metres long will be used, following ATS/ERS guidelines [32]. Before and after the test, vital signs (heart rate, oxygen saturation and blood pressure), fatigue of the lower limbs and dyspnea (using the modified Borg scale 1–10) will be recorded [40]. Two tests will be performed, with a minimum rest of 30 minutes, and the longest distance of the two tests will be selected for the analysis. For the 1MSTST, an armless chair will be used to ensure the upper limbs are not taking part in action. Subjects will be instructed to perform as many sit-to-stand movements that they can do in one minute [41, 42]. Accelerometry and the IPAQ, short form will be used to assess physical activity. The accelerometer MoveMonitor DynaPort®MM+ (McRoberts, The Hague, the Netherlands) will be used to register physical activity during seven days. Time lying down, sitting, standing up and walking, number of steps per day, intensity of movement, kilocalories consumed and metabolic equivalent of task (MET), will be analysed. Validity of accelerometer has been documented in COPD [43] and this tool has been used in previous studies in patients with asthma [44]. Besides, we will use IPAQ-short form, administrated through personal interview. The questionnaire has been validated in the Spanish population [45] and results will be analysed following the indications of the IPAQ group [46]. Also, number of steps per day should be recorded by patients in their patient’s diary, registered by a Google Fit ® Smartphone App (Google LLC, Mountain View, CA, USA); and register of their physical activity or exercise performed on the non-session’s day and once the sessions period has finished. Asthma-related symptoms and asthma control will be measured using the CARAT, validated in patients with asthma [47], which has been used in Spanish population [48]. Additional analysis will be made from data extracted from the patient´s diary, in terms of dyspnea measured with modified Borg scale, expectoration, cough, perception of wheezing, work absence due to asthma symptoms or avoidance of doing some activity due to same reason. Related to dyspnea, it will be assessed also during personal interview using the mMRC scale [49]. In addition, modified Borg scale will be used before and after each NW session [40]. Lung function will be assessed with a spirometry following international recommendations of ATS/ERS [50]. FEV1, FVC, FEV1/FVC, forced expiratory flow at 25 and $75\%$ of the pulmonary volume (FEF25-$75\%$) and PEF will be recorded, obtained with a spirometer (Datospir® 120C, Sibelmed, Barcelona, Spain). Moreover, PEF will be assessed every day by patients in their diary. A peak-flow meter will be provided to each patient by the study. Handgrip strength will be measured in both hands, registering data in kilograms, and using Jamar® hand-dynamometer (Performance Health, Warrenville, IL, USA) and following Mathiowetz et al. protocol [51]. HRQoL will be assessed using a generic and asthma-specific questionnaires. EQ-5D-5L has been validated in patients with asthma [52] and in Spanish population [53]. MiniAQLQ has been validated to use in this population [54], and has already been used in Spanish context [55, 56]. Quality of sleep will be assessed using PQSI, validated in Spanish [57]. Related to treatment adherence, inhaler adherence will be evaluated using the TAI, validated in patients with asthma and in the Spanish population [58]. Also, participants should note in their diary when, what and in which dosage need take medicine in order to relieve symptoms. Healthcare resources use will be analysed as well taking in count data from patient’s diary (emergency service or unscheduled medical visits due to asthmatic symptoms). Respiratory muscle strength will be measured by MIP and MEP, following Spanish Society of Pneumology and Thoracic Surgery (SEPAR) recommendations [59]. A digital manometer MicroRPM® (Vyaire Medical GmbH, Hoechberg, Germany), a flanged mouthpiece and the PUMA® software (Vyaire Medical GmbH, Hoechberg, Germany) will be used. At least six acceptable measurements are required and three of them should be reproductible, with less than $5\%$ of difference between them. Qualitative data will be obtained through focus group meetings, moderated by an experienced researcher. Another researcher will take part in the meeting to observe and take notes. These two researchers, will start introducing themselves and then moderator will give a clear explanation of the aim of the meeting before starting. Then, following a semi-structured guide with open-ended questions (S2 Appendix), focus groups will be conducted in order to gather a better understanding of participants experience. Around 15 participants will participate in focus groups [60], after receiving the NW program. Every NW group will constitute a focus group. Focus groups will be conducted in a room of the Faculty of Physiotherapy, in the University of A Coruña, and are expected to last about 90 minutes. Focus groups will be recorded (video and audio) and transcribed under consent of participants. Participants dropping out of the study, if any, will be contacted to record possible reasons, and in case they are from the NW group they will also asked regarding the possibility to participate in the focus group and to provide data registered in their diary. ## Participant timeline Participants will be assessed at baseline (T0), post-intervention (T1), and then at three (T2) and six months (T3) follow-up, as shown in Fig 1. After intervention, only NW group will be interviewed through focus groups. Fig 1: Timeline of recruitment, allocation and assessments.Measured before allocation.* ## Data management and dissemination Following what is established in Regulation (EU) $\frac{2016}{679}$, it will be strictly respected the confidentiality of personal and health data of participants. Any data relative to participants will be stored coded with an alphanumeric ID. All paper information will be stored in locked filing cabinet in the Faculty of Physiotherapy, The University of A Coruña. The principal researcher (ALP) and the physiotherapist in charge of intervention (MVP) of this study will be the only ones with access to this data. Results of this study will be disseminated through scientific publications (original articles, abstracts) and communications in national and international congresses. Nonetheless, in none of the dissemination strategies, the identification of participants will be revealed. Three of the authors (ALP, MVP and CJ) will compose the data monitoring committee. ## Ethics This study has been approved by the Clinical Research Ethics Committee (CEIC) of Galicia (Spain) in July 20th, 2020 (code number $\frac{2019}{574}$) (S3–S6 Appendices). During the study, truthful and understandable information will be provided to the participants, referred to the objectives of this trial, to the procedures (during assessments and intervention), contraindications of them, and possible complications that can arise during it development (any inconvenient is expected, despite those referred from physical activity and regular healthcare practice). Both oral and written information will be provided, and informed consent will be obtained written. Any important protocol modification will be communicated to CEIC. All changes will be registered as modifications in the clinical trial register database (www.clinicaltrials.gov) with register number NCT05482620. ## Statistical analysis Quantitative data will be expressed as mean and SD when normally distributed and as median and quartiles (Q1-Q3) when not normally distributed. Normality will be explored with Kolmogorov-Smirnov tests. To compare outcomes between NW group and CG, ANOVA tests for repeated measures or Kruskal-Wallis tests will be used, as appropriate in function of normality of data distribution. All this data will be analysed using SPSS (IBM Corp., Armonk, NY, USA) program, version 26.00. To analyse qualitative data, two of the researchers, following Braun et al. [ 61] recommendations, will deep read all transcripts and will make notes through text, to generate initial codes independently from each other. Then, the text will be re-read, searching and reviewing themes in relation to these codes, to be able to define and name the themes. After that, report can be produced. Another member of the team will check data to ensure credibility and trustworthy of findings, and through peer debriefing technique [62], researchers can be aware of their interpretation with the possibility to clean them of preconceptions or wrong assumptions. Agreement will be measured through agreement percentages, calculated as the number of thematic units in which evaluators agree divided by total number of units. Also kappa of Cohen will be used, considering >0.81 almost a perfect agreement [63]. ## Results and discussion This RCT aims to assess effectiveness of NW in patients with asthma. The study has been prospectively registered, and to our knowledge, it is the first study exploring this topic. Nordic walking is still a recent and novel exercise training modality, with few studies published in the health field. Based on previous studies about exercise programs [17–19], we hypothesise that the NW program in patients with asthma will increase exercise tolerance, physical activity level, asthma-related symptoms, asthma control, and HRQoL. Due to the characteristics of the intervention (involvement of the upper limbs), handgrip strength could also get improved [64]. We are expecting that lung function remains unchanged [65, 66], but as there is some controversy in current literature, we have decided to collect this data at all time-points [18, 19]. If the beneficial effects of NW are demonstrated, a new community-based therapeutic approach can be added to the current recommended and implemented in clinical practice. Nordic walking is affordable and feasible in the community, taking advantage of blue and green spaces in every patient´s environment–following Urban Training® concept–that have already shown benefits in terms of physical activity in patients with COPD [67]. Indirectly, the demonstration of our hypothesis could lead to added benefits, if the use of rescue medication, or medical visits (emergency department or unscheduled visits) are reduced: deriving from that reduction in healthcare resources use, a mitigation in socio-sanitary costs could be achieved. ## Strengthens and weaknesses The NW program proposed in this RCT has been previously tested in a feasibility pilot study in patients with asthma. We found that NW is a well-tolerated and satisfactory activity, and people specially enjoy the possibility of make an activity group. Also, this pilot study allowed us to estimate the sample size needed [34]. Participants will be recruited during two years, and in consequence, NW program will be conducted across the seasons, bringing different challenges, such as exposure to environment allergens for patients with allergic asthma. This augmented exposure happens specially from January to June, in which pollen concentration are higher, and maybe during this period an attempt to schedule sessions in hours with lower pollen concentrations (hours with warm temperatures [68]) could be sought [69]. Due to the nature of the intervention, neither participants nor physiotherapist in charge are blinded to allocation. The possible bias can be minimized by the presence of a blinded evaluator. Nevertheless, after the intervention the evaluator´s blinding could be compromised due to possible comments of participants. If this happens, this will be reported. Finally, some of the outcomes included in this study have already been analysed in patients with asthma after an exercise training programme and were shown to be sensitive to change [17–19, 26]. In addition, we will use both quantitative and qualitative methods, which can give a wider understanding of the feasibility of NW for patients, permitting analysing also the experience of participants with the exercise program. To our knowledge, this is the second study planning a focus group evaluation of NW training [70], and the third study approaching any qualitative study method, taking in account focus groups [71]. ## Conclusion Nordic Walking could be a new-community based therapeutic approach in patients with asthma, being affordable and feasible. Combined with educational and usual care, NW could improve exercises tolerance and other asthma related outcomes. ## References 1. 1World Health Organization. Chronic respiratory diseases: asthma. 2020 [cited 2021 Oct 11]. In: World Health Organization [Internet] Geneva: World Health Organization. Available from: https://www.who.int/news-room/q-a-detail/chronic-respiratory-diseases-asthma. 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--- title: Correlations between parameters of glycaemic variability and foetal growth, neonatal hypoglycaemia and hyperbilirubinemia in women with gestational diabetes authors: - Immacolata Blasi - Jessica Daolio - Valeria Pugni - Giuseppina Comitini - Marcello Morciano - Giorgio Grassi - Tullia Todros - Giancarlo Gargano - Lorenzo Aguzzoli journal: PLOS ONE year: 2023 pmcid: PMC9997917 doi: 10.1371/journal.pone.0282895 license: CC BY 4.0 --- # Correlations between parameters of glycaemic variability and foetal growth, neonatal hypoglycaemia and hyperbilirubinemia in women with gestational diabetes ## Abstract The diagnosis of gestational diabetes mellitus (GDM) is important to prevent maternal and neonatal complications. This study aimed to investigate the feasibility of parameters of glycaemic variability to predict neonatal complications in women with GDM. A retrospective study was conducted on pregnant women tested positive at the oral glucose tolerance test (OGTT) during 16–18 or 24–28 weeks of gestation. Glycaemic measures were extracted from patients’ glucometers and expanded to obtain parameters of glycaemic variability. Data on pregnancy outcomes were obtained from clinical folders. Descriptive group-level analysis was used to assess trends in glycaemic measures and foetal outcomes. Twelve patients were included and analysed, accounting for 111 weeks of observations. The analysis of trends in parameters of glycaemic variability showed spikes of glycaemic mean, high blood glucose index and J-index at 30–31 weeks of gestation for cases with foetal macrosomia, defined as foetal growth >90° percentile, neonatal hypoglycaemia and hyperbilirubinemia. Specific trends in parameters of glycaemic variability observed at third trimester correlate with foetal outcomes. Further research is awaited to provide evidence that monitoring of glycaemic variability trends could be more clinically informative and useful than standard glycaemic checks to manage women with GDM at delivery. ## Introduction Gestational diabetes mellitus (GDM) affects 3–$7\%$ of pregnancies [1]. The joint presence of insulin resistance and the subsequent increase in levels of postprandial glucose during pregnancy allow us to characterize the pregnancy as "diabetogenic". In pregnancies affected by with GDM, the inability of pancreatic β-cells to compensate for insulin resistance leads to inadequate insulin activity and reduced insulin sensitivity, both at central and peripheral levels, which alter the physiological glycaemic balance. GDM exposes pregnant women to higher risks of preeclampsia (PE), caesarean delivery, macrosomia, neonatal hypoglycaemia and hyperbilirubinemia, respiratory distress syndrome, and gestational type 2 diabetes mellitus in the following years [2]. In 2008, the Hyperglycaemia Adverse Pregnancy Outcome study (HAPO study) showed that increased levels of maternal glycaemia are associated with adverse neonatal outcomes [3]. Careful monitoring of glycaemic levels is therefore crucial to maintain glycaemia within the physiological range. As such, the glycaemic variability resulting from glycaemic fluctuations are considered a risk factor for the development of diabetes-related complications [4]. In women affected by GDM, foetal macrosomia is a recurrent complication associated with hyperglycaemia and high levels of glycaemic variability. Foetal macrosomia in women with GDM is associated with an increased risk of the following: (i) maternal complications, such as the increased possibility of caesarean delivery, operative vaginal delivery and perineal lacerations; (ii) short-term neonatal complications, such as still birth, shoulder dystocia, Erb’s palsy, hypoxia and acidosis; and (iii) long-term neonatal complications, such as metabolic syndrome, type 2 diabetes mellitus, obesity and insulin resistance [5,6]. In 2007, Herranz et al. [ 7] compared the mean overall, pre- and postprandial glucose levels and the percentage of glucose readings above and below target and glycated haemoglobin (HbA1c) of 37 Large for Gestational Age (LGA) infants and 36 Appropriate for Gestational Age (AGA) infants from mothers with type 1 diabetes mellitus. The study found no statistically significant difference in the preconception glycaemic parameters between the two groups but showed a significant association between the parameters and LGA infants during the third trimester of pregnancy. As such, the authors concluded that glycaemic fluctuations are the best predictors of macrosomia [7]. On the one hand, high levels of basal glycaemia explain only a small percentage ($12\%$) of glycaemic fluctuations and foetal macrosomia; on the other hand, high levels of postprandial glycaemia account for $40\%$ of cases with elevated foetal weights [8]. Therefore, HbA1c levels used to assess the mean levels of glucose 4–6 weeks before delivery is not associated with foetal weight at birth [9]. In 2012, Mazze et al. [ 10] compared the glycaemic patterns from four groups of women experiencing physiological changes in pregnancy, pregnancy complicated by GDM, diagnosed with type 1 diabetes mellitus and nonpregnant women. The results showed that glycaemic patterns differed by $20\%$ when pregnant and nonpregnant women were compared. As such, the authors concluded that further research is required to better define the role of glycaemic fluctuations, to improve the therapeutic approach and to reduce the incidence of maternal and foetal complications related to GDM. The aim of this study is twofold. First, to establish the association, if any, between different measures of glycaemic variability in pregnancies affected by GDM. Second, it investigates the presence of differentiated trends in these measures for those which experienced birth weights above the 90th percentile, neonatal hyperglycaemia and/or hyperbilirubinemia, and those who did not. ## Materials and methods This was a retrospective observational study collecting data on pregnant women treated at the Department of Obstetrics and Gynaecology in collaboration with the Endocrinology and Metabolism Unit at the “Santa Maria Nuova” hospital, AUSL–IRCCS in Reggio Emilia, Italy. The Ethics Committee AREA VASTA NORD of Azienda USL-IRCCS di Reggio Emilia, Italy, approved the study with approval n$\frac{.2021}{0003450.}$ The methodology is in accord with the Declaration of Helsinki. In accordance with study protocol approved, data were treated anonymously after collection. ## Participants In the present study, we included all pregnant women carrying a singleton pregnancy and receiving the diagnosis of GDM in the period between December 2015 and March 2016 at our hospital. The diagnosis of GDM was based on a 75-gram oral glucose tolerance test (OGTT) performed at 16–18 or 24–28 weeks of gestation [11]. We excluded twin pregnancies, women affected by pre-pregnancy type 1 or type 2 diabetes mellitus, and those with chronic hypertension or other pre-pregnancy diseases. According to clinical practice, all the participants received a specific diet to follow and instructions on how to measure and report glycaemia data at home until delivery. Each patient was asked (i) to collect the glycaemic profile measuring glucose levels by fingertip blood tests at baseline, and one hour after each meal once a week, and at different times in the remaining days of the week, and (ii) to attend the Endocrinology and Metabolism Unit every two weeks for insulin therapy or four weeks for diet therapy. Insulin therapy was given because of a trend in glucose levels ˃95 mg/dL at baseline or ˃140 mg/dL one hour after meals, after at least a 2-week period of diet therapy. Glycaemic data were collected in our database to calculate parameters of glycaemic variability as detailed below. Maternal characteristics and data on pregnancy outcome were obtained from clinical folders and were recorded in our database as well. ## Obstetric and foetal outcomes collected The following obstetric and foetal outcomes were recorded. With regard to obstetric outcomes, complications, such as PE and cholestasis, type of delivery, labor induction and delivery blood loss were considered. With regard to foetal outcomes, we recorded data on foetal weight and length, Apgar score and gestational age at birth. The eventual presence of neonatal hypoglycaemia and hyperbilirubinemia requiring phototherapy, distress, congenital malformations, shoulder dystocia, trauma during birth, stillbirth, and perinatal death were recorded as well. Of note, we used the most widely definition of neonatal hypoglycaemia to diagnose it, namely a glucose concentration of <40 mg/dl (2.6 mmol/l) in late preterm and term babies more than a few hours old [12–14], and the European Standards of Care for Newborn Health (EFCNI) to diagnose the condition of neonatal hyperbilirubinemia, namely it appears within 24 hours of birth following the detection of a bilirubin level >15 mg/dl (259 μmol/L), with an increase of >5 mg/die [15]. Given the absence of general agreement about the definition of macrosomia [6], for the purpose of this study, newborns weighted ˃4 kg were considered macrosomic. To identify LGA and SGA infants defined by the birth weight above the 90th percentile and below the 10th percentile, respectively, we referred to the Italian neonatal anthropometric values of reference [16]. ## Parameters of glycaemic variability collected The parameters of glycaemic variability were calculated by EasyGV software [17]. The parameters considered were: the glycaemic mean (GM), defined as the arithmetic mean of all blood glucose values measured by women; the glycaemic mean value (GMV), defined as weighted average of the glycaemic means divided by the number of measurements and compared to an ideal average blood glucose value [18]; the mean amplitude of glucose excursions (MAGE), which quantifies the main glycaemic variations [19]; the classical standard deviation (SD); the high blood glucose index (HBGI) and the low blood glucose index (LBGI), which indicate the frequency and the amplitudes of hyperglycaemic and hypoglycaemic events, respectively, and define the risks for patients to experience adverse glycaemic events [20]; the J-index, resulting from the means of glycaemic measures combined with the SDs of glycaemic values [21]; and the mean absolute glucose (MAG), as the sum of differences of consecutive glycaemic values divided by the total number of hours of observation [22]. All these parameters indicate data fluctuations, with higher values indicating higher variability. ## Statistical analysis First, Kendall’s correlation analyses were used to examine the presence of correlations among the parameters of glycaemic variability. Second, we plotted parameter values over time with a linear interpolation function to graphically verify the presence of temporal trends. Finally, we graphically assessed the presence of differentiated trends for the parameters of glycaemic variability with foetal study outcome. For this, we defined two groups according to whether the foetal weight was above or below the 90th percentile, the presence and absence of neonatal hypoglycaemia and hyperbilirubinemia, as well. In Figs 1, 2 and 3, we displayed adjusted means for pre-pregnancy BMI and the administration or not of insulin therapy obtained after Anova regressions, with covariates set to the group-specific mean values. We also computed un-weighted group average (and $95\%$ confidence intervals, CI) of GM and then performed a two-sample T-test on the equality of means (allowing for unequal variances) between those of women with adverse neonatal outcomes (LGA or Macrosomia) affected by obesity and those with physiological neonatal outcomes. All analyses were performed with the STATA software, version 17. Fig 1: Trends in parameters of glycaemic variability by foetal growth.GM = Glycaemic Mean; GMV = Glycaemic Mean Value; HBGI = High Blood Glucose Index; LBGI = Low Blood Glucose Index; MAG = Mean Absolute Glucose; MAGE = Mean Amplitude of Glucose Excursions; SD = Standard Deviation. Adjusted values reported (see the Statistical analysis sub-section for details). Fig 2: Trends in parameters of glycaemic variability by neonatal hypoglycaemia.GM = Glycaemic Mean; GMV = Glycaemic Mean Value; HBGI = High Blood Glucose Index; LBGI = Low Blood Glucose Index; MAG = Mean Absolute Glucose; MAGE = Mean Amplitude of Glucose Excursions; SD = Standard Deviation. Adjusted values reported (see the Statistical analysis sub-section for details). Fig 3: Trends in parameters of glycaemic variability by neonatal hyperbilirubinemia.GM = Glycaemic Mean; GMV = Glycaemic Mean Value; HBGI = High Blood Glucose Index; LBGI = Low Blood Glucose Index; MAG = Mean Absolute Glucose; MAGE = Mean Amplitude of Glucose Excursions; SD = Standard Deviation. Adjusted values reported (see the Statistical analysis sub-section for details). ## Results and discussion In the study period, we recruited 12 pregnant women affected by GDM. The mean age of participants was 36 years old, and the mean BMI indicated overweight. Participants gained a physiological weight, in average. All women received the diagnosis of GDM at 24–28 weeks, except for one woman who received it at 16–18 weeks. All women underwent diet therapy, with three women requiring also insulin therapy. According to standard clinical practice, all women treated with diet- and/or insulin- therapy achieved acceptable blood glucose levels. All new-borns had Apgar scores >7 at 5’. Macrosomia and LGA condition were identified in three babies, whose mothers presented a gestational weight gain greater than (case 2 and case 5) and less than (case 11) the Institute of Medicine (IOM) recommendations [23]. Neonatal hypoglycaemia was found in three out of twelve babies. Neonatal hyperbilirubinemia was found in four babies. Two out of three pre-term babies were admitted to neonatal intensive care unit (NICU). Specifically, one baby had respiratory distress, hypocalcaemia and sepsis. Detailed characteristics and pregnancy outcomes were reported in Table 1 for each participant. Table 1 \| Unnamed: 0 \| Gestational characteristics \| Gestational characteristics.1 \| Gestational characteristics.2 \| Gestational characteristics.3 \| Gestational characteristics.4 \| Gestational characteristics.5 \| Maternal characteristics at delivery \| Maternal characteristics at delivery.1 \| Maternal characteristics at delivery.2 \| Maternal characteristics at delivery.3 \| Maternal characteristics at delivery.4 \| Maternal characteristics at delivery.5 \| Maternal characteristics at delivery.6 \| Foetal characteristics and outcomes \| Foetal characteristics and outcomes.1 \| Foetal characteristics and outcomes.2 \| Foetal characteristics and outcomes.3 \| Foetal characteristics and outcomes.4 \| Foetal characteristics and outcomes.5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Age (years) \| Parity \| BMI (kg/m 2 ) \| GDM diagnosis (gestational week) \| Diet therapy \| Insulin therapy \| BMI (kg/m 2 ) \| Weight gain (kg) \| Gestational week \| Labor induction \| Type of delivery \| Blood loss (ml) \| Complications \| Gender \| Weight (kg)—Percentile \| Length (cm)—Percentile \| Apgar 1 minute \| Apgar 5 minutes \| Outcomes \| \| Case 1 \| 39 \| 0 \| 27.0 \| 26 \| Yes \| No \| 33.0 \| 14 \| 40 \| Yes \| Vaginal \| 300 \| \| Male \| 3.715–75 \| 52–80 \| 9 \| 10 \| Hyperbilirubinemia \| \| Case 2 \| 38 \| 2002 \| 37.9 \| 26 \| Yes \| No \| 42.4 \| 14 \| 39 \| No \| Vaginal \| 250 \| \| Male \| 4.245–98 \| 52–80 \| 9 \| 10 \| Macrosomia \| \| Case 3 \| 32 \| 2101 \| 28.8 \| 26 \| Yes \| No \| 33.3 \| 10 \| 35 \| Yes \| Iterative C-section \| 300 \| Cholestasis; PE \| Male \| 2.495–42 \| 47–51 \| 9 \| 10 \| Pre-term birth, Hypoglycaemia, Hyperbilirubinemia, NICU admission \| \| Case 4 \| 40 \| 2101 \| 26.9 \| 17 \| Yes \| Yes, initiate at 25 weeks \| 26.2 \| 6 \| 38 \| No \| C-section due to breech presentation \| 300 \| \| Female \| 2.940–26 \| 48–25 \| 9 \| 10 \| / \| \| Case 5 \| 34 \| 1001 \| 29.0 \| 26 \| Yes \| No \| 33.1 \| 12 \| 39 \| No \| Iterative C-section \| 400 \| \| Male \| 3.980–92 \| 52–80 \| 10 \| 10 \| LGA \| \| Case 6 \| 32 \| 3201 \| 23.8 \| 27 \| Yes \| No \| 27.5 \| 10 \| 39 \| Yes \| Vaginal \| 50 \| \| Female \| 3.370–53 \| 51–75 \| 10 \| 10 \| / \| \| Case 7 \| 42 \| 2101 \| 20.1 \| 27 \| Yes \| No \| 24.9 \| 14 \| 34 \| No \| Iterative C-section and breech presentation \| 800 \| PE; Abdominal bleeding after delivery treated requiring operative laparoscopy \| Male \| 2.134–40 \| 46–60 \| 8 \| 9 \| Pre-term birth, Respiratory distress, Hypocalcaemia,Sepsis,NICU admission \| \| Case 8 \| 34 \| 0 \| 31.0 \| 27 \| Yes \| No \| 33.0 \| 6 \| 40 \| Yes \| C-section due to CTG abnormalities \| 200 \| \| Male \| 3.155–21 \| 50–34 \| 9 \| 9 \| / \| \| Case 9 * \| na \| 0 \| na \| 27 \| Yes \| Yes, initiated at 35 weeks \| na \| na \| 36 \| No \| Vaginal \| 50 \| \| Male \| 3.064–81 \| 50–89 \| 10 \| 10 \| Pre-term birth \| \| Case 10 \| 41 \| 1001 \| 33.9 \| 26 \| Yes \| No \| 36.6 \| 6 \| 38 \| No \| Vaginal \| 300 \| \| Male \| 3.350–55 \| 51–73 \| 9 \| 10 \| Hypoglycaemia \| \| Case 11 \| 26 \| 0 \| 38.5 \| 26 \| Yes \| Yes, initiated at 26 weeks \| 40.0 \| 4 \| 37 \| Yes \| Vaginal \| 450 \| Cholestasis \| Male \| 3.715–96 \| 50–71 \| 9 \| 10 \| LGAHypoglycaemia, Hyperbilirubinemia, \| \| Case 12 \| 38 \| 1001 \| na \| 26 \| Yes \| No \| na \| 14 \| 37 \| No \| Vaginal (PROM) \| 100 \| \| Female \| 2.695–21 \| 49–59 \| 9 \| 10 \| Hyperbilirubinemia \| ## Parameters of glycaemic variability The statistical analysis was conducted on a sample consisting of 111 weeks of observations, accounting for a collection of glycaemic measures over a period of 9.25 weeks in average per woman. The mean values of glycaemic variability parameters are reported in S1 Table. As shown in Table 2, the analysis of glycaemic variability revealed positive and statistically significant (p-value <0.01) correlations of GM with all other measures but not with GMV and LBGI (negative correlation, p-value <0.01). On the other hand, no significant correlations were found for GMV and HBGI, HBGI and LBGI, LBGI and J-index. Table 2 \| Unnamed: 0 \| GM \| GMV \| MAGE \| SD \| HBGI \| LBGI \| J-index \| MAG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| GM \| 1 \| \| \| \| \| \| \| \| \| GMV \| -0.4236* \| 1 \| \| \| \| \| \| \| \| MAGE \| 0.2775* \| 0.2380* \| 1 \| \| \| \| \| \| \| SD \| 0.2621* \| 0.3012* \| 0.7140* \| 1 \| \| \| \| \| \| HBGI \| 0.5260* \| -0.077 \| 0.5571* \| 0.5520* \| 1 \| \| \| \| \| LBGI \| -0.2822* \| 0.7079* \| 0.2948* \| 0.3833* \| -0.0362 \| 1 \| \| \| \| J-index \| 0.6896* \| -0.1197 \| 0.5443* \| 0.5725* \| 0.7106* \| -0.0108 \| 1 \| \| \| MAG \| 0.1867* \| 0.3140* \| 0.5813* \| 0.7202* \| 0.3957* \| 0.4265* \| 0.4421* \| 1.0 \| A significant decrease over time occurred mainly for the HBGI measure, in particular at the beginning of the observational window [S1 Fig]. The adjusted analysis in Figs 1, 2 and 3 revealed differentiated trends of glycaemic measures by whether the birth weight was above the 90th percentile, and with cases of neonatal hypoglycaemia and of hyperbilirubinemia. Specifically, for the three observed macrosomic cases (Fig 1), we found spikes in the GM, GMV, MAGE, SD, HBGI and J-index values around weeks 30–31. A no clear pattern was found for LBGI whereas the MAG values associated to macrosomic cases were generally higher than what observed for non-macrosomic cases. For the three observed cases with neonatal hypoglycaemia (Fig 2), we found spikes in the GM, HBGI and J-index values also around weeks 30–31 of gestation. A no clear pattern was found for GMV whereas MAGE, SD and MAG values associated to hypoglycaemic cases were generally lower than what observed for euglycaemic cases. The LBGI values associated to hypoglycaemic cases showed an opposite trend, revealing a drop around weeks 30–31 of gestation. For the four observed cases with neonatal hyperbilirubinemia (Fig 3), we also found spikes in the GM, HBGI and J-index values around weeks 30–31 of gestation. A no clear pattern was found for GMV whereas MAGE, SD, MAG and LBGI values associated to cases with neonatal hyperbilirubinemia were generally lower than what observed for other cases. We also performed a test to assess whether the mean of GM differs significantly between the group of women with LGA/macrosomia fetuses (cases 2, 5 and 11) and those without (S1 Table). The corresponding two-tailed p-value was 0.2372, which is greater than 0.05. We concluded that the difference of means in GM between the group of women with macrosomia (mean 6.06, $95\%$ CI: 5.50–6.63) and the group without (mean 5.84, $95\%$ CI: 5.64; 6.05) was not statistically different from 0. ## Discussion In this study, we analysed parameters of glycaemic variability from a small cohort of patients adopting a pilot study-like approach. Our findings show that, in women with GDM, parameters of glycaemic variability reveal different trends at 30–31 weeks of gestation, according to the occurrence of foetal macrosomia, neonatal hypoglycaemia and hyperbilirubinemia. Specifically, we found that spikes of GM, HBGI and J-index values are similar among all the three conditions, and did not vary after adjustment for pre-pregnancy BMI and the administration or not of insulin therapy as known confounding factors for adverse neonatal outcomes. From a different perspective, we firstly provided a footprint of three foetal outcomes related to GDM at the beginning of third trimester, clinically challenging to manage women with GDM until delivery and newborns immediately after birth. One could hypothesize that the observed spikes could be related to low glycaemic control requiring insulin therapy rather than to a single parameter of estimated glycaemic variability. Due to the small sample size, we are not able to neither accept nor refuse such hypothesis, but future research is required to clarify this issue. The risk of diabetes-related complications is illustrated as the diagonal arrow of a geometric cube whose three-dimensional coordinates on the three axes are basal, postprandial glycaemia, and glycaemic fluctuations. The therapeutic approach for diabetes should aim to reduce the values of such coordinates to reduce the diagonal arrow measure of the cube represented by diabetes-related complications [4]. With regard to GDM, it is associated with adverse pregnancy outcomes due to which proper glycaemic monitoring and treatments are widely acknowledged. In fact, the suboptimal glycaemic control could lead to a higher incidence of foetal macrosomia and other composite outcomes [24] in the same way by which GDM treatments limited to standard diet therapy [25] and routine care [26] determine when compared to GDM insulin treatment. In this context, the use of parameters of glycaemic variability to monitor GDM are still experimental and supported by limited literature. The relationship between glycaemic variability and GDM has been firstly reviewed in 2020 by Yu W. et al [2020] [27]. Authors found that glycaemic variability is significantly higher in women with GDM compared to pregnant women without GDM, but failed to find consistent conclusions with regard to the relationship between glycaemic fluctuations in women with GDM and the occurrence of adverse neonatal events [27]. In light of this, results from this study are consistent with those from studies reporting that greater glycaemic fluctuations are more likely to cause adverse neonatal outcomes [28–30]. Of note, unlike these studies, we obtained our results via self-monitoring of blood glucose (SMBG) rather than CGM strategy. The risk of foetal macrosomia in pregnant women with type 1 and type 2 diabetes mellitus ranges between $48\%$ and $62\%$, and it is 2–3 times higher in women with GDM than in normal pregnant women [31]. Hence, it is important for women with GDM to maintain glycaemic levels within the normal range. Our body devotes much effort to maintaining glycaemia within the normal range, and vice versa, blood glucose could damage our body. Pregnancy in women with normal glucose metabolism is characterized by fasting levels of blood glucose that are lower compared to the non-pregnant status, due to insulin-independent glucose uptake by the fetus and placenta, and by mild postprandial hyperglycemia and carbohydrate intolerance as a result of diabetogenic placental hormones. “ Flat blood glucose profiles” is however a condition characterized by glycemic excursions on average between 60 and 140mg /dl. “ Flat blood glucose profiles” even in women with normal glucose tolerance it is characterized by a certain degree of glycemic variability favored by the excursion of postprandial glycaemia [32]. With this in mind, GDM monitoring has been ameliorated, but there is insufficient evidence to discriminate which parameters are able to predict foetal growth, as they are not properly informative about continuous glycaemic levels and variability. In addition, the relationship between glycaemic variability and foetal complications is poorly understood and investigated in women with GDM. In these women, small periods of transient hyperglycaemia seem enough to induce foetal growth acceleration responsible for macrosomia at birth. It is therefore of huge importance to monitor glycaemic variability in pregnant women affected by GDM. In 2011, Dalfrà et al. compared novel parameters of glycaemic variability, such as MAGE, glycaemic mean, SD, interquartile range (IQR), continuous overlapping net glycaemic action (CONGA), LGBI and HBGI, in two groups of pregnant women, the first affected by type 1 diabetes mellitus and the second with GDM, to healthy pregnant controls [33]. The study demonstrated that parameters of glycaemic variability reach higher levels in pregnant women with type 1 diabetes mellitus than in those with GDM and healthy controls. During the third trimester, parameters of glycaemic variability tend to decrease in women with type 1 diabetes mellitus and, on the contrary, increase in women with GDM, especially when insulin therapy is needed. Continuous glucose monitoring revealed alterations of the glycaemic profile in $61.3\%$ of women with GDM for which insulin administration was required. The findings of the aforementioned study highlight that MAGE, GMV and SD reach higher levels in women with insulin-treated GDM than in women with diet therapy-treated GDM during the second trimester, and it is in this period that GMV and HBGI values predict and are correlated with asymmetric macrosomia. This measure was defined by the ponderal index value (PI). In women with type 1 diabetes mellitus, PI correlates with HBGI in the first trimester, CONGA and IQR in the second trimester, and GMV and SD in the third trimester. The CONGA parameter has been recently introduced to express glycaemic variability in a defined period, such as every day, throughout a continuous glucose analysis [34]. In none of the three groups or trimesters did glycated haemoglobin correlate with parameters of foetal growth. In accordance with the literature, we found that glycaemic variability shows clinically-relevant trends with foetal outcomes regarding foetal weight above >90° percentile, neonatal hypoglycaemia and hyperbilirubinemia. The development of GDM during pregnancy may constitutes a transient condition for which, in our reality, CGM together with the application of subcutaneous abdominal probes are not applied conversely to what occurs in case of type 1 or type 2 diabetes. Our study investigated glycaemic variability by SMBG rather than CGM strategies for 48–72 hours as reported in previous studies. Although this approach could be inaccurate due to the lack of CGM data, on the other side, it allows to speculate that, in case of GDM, parameters of glycaemic variability could switch from being experimental to clinically informative, as we have firstly provided initial evidence to suggest their ability to early detect, and therefore prevent, potential adverse foetal outcomes. Considering that parameters were obtained from women’s glycaemic glucometers without the application of subcutaneous probes (of note, this approach is carried out in experimental conditions), it could be also speculated that some glycaemic variability parameters are better suited to a survey consisting of fewer observations during the day and allow the identification of pregnant women with GDM that are at higher risk of developing complications than others. Interestingly, a semiparametric statistical approach was proposed by Gupta R et al. [ 35] to identify the rate of progression in maternal glucose concentrations in specific gestational periods of LGA and AGA babies of mothers with type 1 diabetes mellitus. The study showed that time-specific fluctuations in glucose level velocity and changes in glucose velocity differ across gestational age in the same woman and between women delivering LGA and AGA infants. As such, in the first trimester, mothers delivering LGA infants show higher accelerations of glucose levels than those delivering AGA infants, suggesting the risk of neonatal hypoglycaemia. In the third trimester, after a steady state of glucose concentrations for both groups, there is a sharp decline for LGA foetuses, which indicates a higher risk of neonatal hypoglycaemia compared to AGA foetuses [35]. In our opinion, the strength of our study involves the following issue. It provides the basis for the clinical contextualisation of experimental parameters of glycaemic variability, in terms of sensibility and specificity for women with GDM, and potentially for women affected by other types of diabetes. This is important in case of transient forms of diabetes, such as GDM. To this regard, the study by Bapajeva G et al., [ 2022], showed that pre-existing insulin-dependent GDM increased the risk for pregnancy complications compared to other GDM types, such as the insulin independent form [36]. Of note, we found of interest the role of myo-inositol and D-chiro-inositol for the prevention and treatment of metabolic disorders, such as GDM [37,38], but we await future studies to fully examine the beneficial effects. Furthermore, the prevention of adverse neonatal events is fundamental to avoid short- and long-term complications on newborns’ development, and requires many clinical efforts, ranging from early detection of trigger factors, such as GDM, to administration of care factors, such as diet and/or insulin therapy. Insights from this study show that the strategy based on parameters of glycaemic variability may be useful to tailor glycaemic control and care in pregnant women until delivery and, therefore, prevent adverse neonatal outcomes associated with GDM. Moreover, glycaemic variability is considered a new concept of glycaemic control, and it is higher in women with GDM compared to women without GDM [27]. As the concept of hyperglycaemia is profoundly changed so far [39], this study helps to broaden the landscape of glycaemic management, placing a special emphasis on women with GDM, for which there are limited data. The limits of the study involve the small, but well-characterized sample size and the presence of pre-gestational overweight/obesity among women included in the study. Although obtained with a small simple size, our results should be considered preliminary and interpreted through the lens of a pilot study conducted with an exploratory intend, that aimed at contributing to the limited scientific knowledge on glycaemic variability in pregnant women. Increased BMI, and advanced age as well, prior to pregnancy are risk factors for pregnancy complications, with maternal obesity being the most important predictor of pregnancy complications in women with GDM [40]. Obesity constitutes an independent factor associated with foetal macrosomia, in light of which we believe it could make sense to further investigate our findings in different subjects. Pregnancy outcome can be good for both mothers with GDM and children with a timely and adequate approach [41]. With regard to the relationship between macrosomia or LGA, and maternal obesity, we examined maternal birth weight in the three cases of LGA or Macrosomia in the light of IOM recommendations. Case 2 (obese patient before pregnancy) and case 5 (overweight patient before pregnancy) presented a gestational weight gain greater than the IOM recommendations, whereas case 11 (obese patient before pregnancy) manifested a gestational weight gain less than the IOM recommendations. Gestational weight gains greater than or less than guideline recommendations, compared with weight gains within recommended levels, is associated with higher risk of adverse maternal and infant outcomes [41]. Based on limited sample size, further conclusions on the relationship between macrosomia or LGA, and maternal obesity cannot be drawn. However, we carefully speculate on how GDM contributes to predict macrosomia or LGA through a specific trend of glycaemic variability. Another limitation of this study involves the lack of data from CGM. In fact, the EasyGV software used in this study typically calculates parameters of glycaemic variability from CGM data, rather than glucometers. Unfortunately, CGM data are not available for patients included in this study for reason independent of authors’ intention. In fact, in our clinical practice the development of GDM does not constitute an indication per se requiring CGM. As consequence, validation of results with different methods are warranted in order to expand the role of glycaemic variability as a tool in patients with transitional diabetic conditions, such as pregnant women, and to explore the most optimal method to measure glycaemic variability. Although found of interest in other related studies (see e.g. [42–47]), we also did not measure HOMA-IR, insulin levels and “time in range” in our cohorts of pregnant women with GDM. ## Conclusions Our study shows the presence of differentiated trends of glycaemic measures when the birth weight was above the 90th percentile, and with cases of hypoglycaemia and of hyperbilirubinemia. We found spikes in the values of some parameters (particularly GM, HBGI and J-index) with adverse foetal outcomes particularly around weeks 30–31 of gestation. We are confident that any future insight will contribute to improve GDM management and treatment, as the relationship between glycaemic variability and gestational complications constitutes a novel and intriguing field of research. ## References 1. Engelgau MM, Herman WH, Smith PJ, German RR, Aubert RE. The epidemiology of diabetes and pregnancy in the U.S., 1988. Diabetes Care (1995) 18 1029-33. DOI: 10.2337/diacare.18.7.1029 2. 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--- title: Serum biomarkers and anti-flavivirus antibodies at presentation as indicators of severe dengue authors: - Cynthia Bernal - Sara Ping - Alejandra Rojas - Oliver Caballero - Victoria Stittleburg - Yvalena de Guillén - Patricia Langjahr - Benjamin A. Pinsky - Marta Von-Horoch - Patricia Luraschi - Sandra Cabral - María Cecilia Sánchez - Aurelia Torres - Fátima Cardozo - Jesse J. Waggoner journal: PLOS Neglected Tropical Diseases year: 2023 pmcid: PMC9997924 doi: 10.1371/journal.pntd.0010750 license: CC BY 4.0 --- # Serum biomarkers and anti-flavivirus antibodies at presentation as indicators of severe dengue ## Abstract ### Background Dengue is the most common vector-borne viral disease worldwide. Most cases are mild, but some evolve into severe dengue (SD), with high lethality. Therefore, it is important to identify biomarkers of severe disease to improve outcomes and judiciously utilize resources. ### Methods/Principal findings One hundred forty-five confirmed dengue cases (median age, 42; range <1–91 years), enrolled from February 2018 to March 2020, were selected from an ongoing study of suspected arboviral infections in metropolitan Asunción, Paraguay. Cases included dengue virus types 1, 2, and 4, and severity was categorized according to the 2009 World Health Organization guidelines. Testing for anti-dengue virus IgM and IgG and serum biomarkers (lipopolysaccharide binding protein and chymase) was performed on acute-phase sera in plate-based ELISAs; in addition, a multiplex ELISA platform was used to measure anti-dengue virus and anti-Zika virus IgM and IgG. Complete blood counts and chemistries were performed at the discretion of the care team. Age, gender, and pre-existing comorbidities were associated with SD vs. dengue with/without warning signs in logistic regression with odds ratios (ORs) of 1.07 (per year; $95\%$ confidence interval, 1.03, 1.11), 0.20 (female; 0.05,0.77), and 2.09 (presence; 1.26, 3.48) respectively. In binary logistic regression, for every unit increase in anti-DENV IgG in the multiplex platform, odds of SD increased by 2.54 (1.19–5.42). Platelet count, lymphocyte percent, and elevated chymase were associated with SD in a combined logistic regression model with ORs of 0.99 (1,000/μL; 0.98,0.999), 0.92 (%; 0.86,0.98), and 1.17 (mg/mL; 1.03,1.33) respectively. ### Conclusions Multiple, readily available factors were associated with SD in this population. These findings will aid in the early detection of potentially severe dengue cases and inform the development of new prognostics for use in acute-phase and serial samples from dengue cases. ## Author summary Dengue fever is an acute disease caused by dengue virus and transmitted to humans through the bite of infected Aedes mosquitoes. Dengue is the most common vector-borne viral disease worldwide affecting an estimated 50–100 million people and with 10,000 dengue-related deaths each year. Currently, there is no specific treatment, and safe and effective vaccines have not been fully implemented. Most dengue cases present with nonspecific mild symptoms, but some will evolve into severe dengue, which can be fatal. Early detection and subsequent timely treatment have been shown to decrease mortality among severe cases. Therefore, it is very important to identify biomarkers for the early identification of cases at risk for progression to severe disease. In this study we analyze demographic factors, clinical laboratory data, lipopolysaccharide binding protein and chymase to evaluate associations with disease severity. This study was carried out in Paraguay, which is a hyperendemic country for dengue where the disease has been understudied. A number of factors were found to be associated with severe disease in this population, including patient age, male gender, presence of comorbid illnesses, low platelet count, low lymphocyte percentage, and elevated chymase level. ## Introduction Dengue is a common acute febrile illness in tropical and subtropical regions of the world and accounts for upwards of $10\%$ of such illnesses in areas of endemicity [1–4]. Over the past 30 years, dengue incidence and associated deaths have increased both in those residing in and travelers returning from areas of endemicity [5, 6]. An estimated 50–100 million dengue cases and 10,000 dengue-related deaths annually occur worldwide from infection with one of the four types of dengue virus (DENV, genus Flaviviridae) [1, 3–5, 7]. Dengue severity ranges dramatically from a mild subclinical illness to dengue fever and clinically severe dengue with plasma leakage, hemorrhage, and/or end-organ dysfunction [1, 3, 4, 8, 9]. Timely diagnosis and the initiation of appropriate supportive care improves clinical outcomes and can lower mortality in clinically severe dengue from $20\%$ to <$1\%$ [1, 3, 4, 10, 11]. Although clinically severe cases represent a minority of dengue cases overall, fatal and hospitalized non-fatal cases account for over half of the $8.9 billion USD annual economic burden of dengue [9, 12]. Therefore, early identification of cases at increased risk for developing clinically severe dengue could both improve clinical outcomes and alleviate the economic burden caused by dengue on resource constrained medical systems [13]. Clinically severe dengue results from a complex interplay of virus [14–17], host [18–25], and epidemiologic factors [1, 9, 18]. The manifestations of severe dengue also differ based on patient age, with children more commonly developing plasma leakage compared to hemorrhage in adults [9, 26, 27]. Studies have identified associations between the detection and/or concentration of various molecules or gene transcripts and severe dengue [28–33]. One group of biomarkers that has been studied are proteins released during mast cell degranulation: vascular endothelial growth factor (VEGF) and the proteases tryptase and chymase [34–44]. In studies of patients from South and Southeast Asia, chymase was associated with and predictive of the development of clinically severe dengue [34, 35, 37, 38, 41]. Chymase release from mast cells occurs in the presence of DENV and may be increased by pre-existing anti-DENV IgG antibodies [36, 37]. According to a single study in mice, chymase release may be blocked by antibodies against viral non-structural protein 1 (NS1) [45]. Lipopolysaccharide (LPS) and lipopolysaccharide binding protein (LBP) are another set of molecules that have higher levels in dengue cases compared to healthy controls and in clinically severe cases compared to dengue fever, which could indicate their usefulness as a predictor of severity [46–48]. Elevated levels of circulating LPS and LBP result from derangements in gut permeability, potentially leading to bacterial translocation, bacteremia, and worsened outcomes. Finally, numerous clinical laboratory findings have been associated with clinically severe dengue, such as thrombocytopenia, lymphopenia, and evidence of liver or kidney injury [16, 20, 27, 49–51]. These may either define cases as clinically severe with end-organ dysfunction or predict the development of severe dengue through detection of changes over the course of illness [3]. The objective of the current study was to evaluate biomarkers of dengue severity among participants enrolled in an ongoing study of acute arboviral illness in the metropolitan area of Asunción, Paraguay. Paraguay is hyperendemic for dengue, with sustained viral circulation since 1999 and large disease outbreaks occurring every 2–5 years. In 2018, predominant circulation of DENV-1 was recorded [52], and in 2019–2020, this shifted to DENV-4, resulting in the largest outbreak in the country’s history [53]. Previous studies from Paraguay have found an increased risk of clinically severe dengue with DENV-2 and secondary infections [54–56]. However, dengue, and in particular biomarkers of severe disease, remains understudied in the country [57, 58]. Previously, our group evaluated anti-DENV and anti-ZIKV NS1 IgG levels among dengue cases in 2018 using a multiplex serological assay, the pGOLD assay [59]. Anti-DENV IgG levels in the pGOLD assay correlated with focus reduction neutralization test (FRNT50) titers, and an association was observed between hospitalization and detection of both anti-DENV and anti-ZIKV IgG. However, hospitalization is an inexact measure of clinical dengue severity. Therefore, in the current study, we sought to evaluate this earlier finding and levels of chymase and LBP as indicators of dengue severity among participants categorized according to the 2009 World Health Organization guidelines [3]. ## Ethics statement The study protocol was reviewed and approved by the Scientific and Ethics Committee of the Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Asunción (IICS-UNA, IRB00011984), and the Emory University Institutional Review Board (IRB00000569). Written informed consent was obtained from all the participants or their health care decision maker. ## Clinical samples Individuals included in the current study were enrolled in an ongoing parent study of suspected arboviral infections in the Asunción metropolitan area between February 2018 and March 2020. Participants of both genders and all ages were enrolled as outpatients at IICS-UNA in all study years and in the emergency care/inpatient facilities of Hospital Villa Elisa, 2018, and Hospital Central of the Instituto de Previsión Social, 2019–2020. Inclusion criteria for the parent study were an acute illness including two or more of the following symptoms: fever (measured or subjective), red eyes, rash, joint pain involving more than one joint, and/or diffuse muscle pain. Patients with fever and no other localizing signs or symptoms were also included. Day 1 was defined as the first day of symptoms. One hundred forty-five participants with acute dengue and up to 7 days of symptoms were selected for the current cross-sectional analysis from a total study population of 1566 cases of suspected arboviral illness. Cases were classified according to the 2009 WHO criteria as dengue without warning signs (DWS-), dengue with warning signs (DWS+) and severe dengue (SD) [3]. Cases were classified during the initial visit, and the final classification used for this study was upgraded if the case evolved over time to a more severe category following presentation. For categorization as DWS+, it was necessary to have at least one warning sign. For categorization as SD, an individual had to develop at least one criterion for SD during the clinical course. To maximize study power, all SD cases in the parent study were included in this analysis. A mixture of DWS- and DWS+ cases was then selected to achieve a representative distribution of participants based on age, days of symptoms, comorbidities, and gender from across the study period and to maintain an even distribution of these two categories. The number of included cases was limited by sample volume and availability of demographic and clinical data. ## Laboratory testing Acute-phase serum or plasma samples were collected during the initial visit for study enrollment and transported to the IICS-UNA laboratory. Samples were tested for DENV NS1 antigen using the Standard Q Dengue Duo rapid immunochromatographic test (SD Biosensor, Suwon, South Korea) according to manufacturer recommendations. Qualitative antibody data acquired using this method was not evaluated in this study, see antibody section below. Primary samples were then aliquoted and stored at −80°C until later use or shipment on dry ice to Emory University for additional testing. For molecular testing, total nucleic acids were extracted from 200μL of sample on an EMAG instrument and eluted into 50μL of buffer. Samples were tested for Zika virus, chikungunya virus and DENV by real-time RT-PCR (rRT-PCR) using a validated and published multiplex assay (the ZCD assay) [60], and DENV serotype and viral load were determined with a published DENV multiplex assay [61, 62]. Both rRT-PCRs were performed as previously described [60–62]. Serologic testing was performed on acute-phase samples using two different methods. First, anti-DENV IgG and IgM were analyzed using commercial ELISA kits [Dengue ELISA IgG (G1018) and Dengue ELISA IgM Capture (M1018), Vircell Microbiologists, Granada, Spain] according to manufacturer recommendations (interpretation: IgM or IgG index >11 positive, 9–11 indeterminate, <9 negative). Second, a 5μL aliquot of serum from 139 participants with sufficient sample was tested in the pGOLD assay (Nirmidas Biotech, Inc, Palo Alto, CA), which is a multiplex serological assay for IgM and IgG against DENV (DENV-2 whole virus antigen) and ZIKV (NS1 antigen). The pGOLD assay was performed as previously described [59, 63]. In each well of the pGOLD slide, antigens are spotted in triplicate, and average signals are used during analysis. For IgG, the negative control signal was subtracted from the sample signal, and the difference was divided by the average signal of four IgG control spots included in each well. For IgM, a similar calculation was performed using the signal from a known anti-DENV IgM positive control sample included on each run. A positive threshold ratio of 0.1 was established for each isotype, which was ≥ 3 standard deviations above the mean of the negative control. Chymase and LBP levels were determined using commercial ELISA kits (G-Biosciences, St. Louis, MO, USA), following the manufacturer’s instructions. Complete blood counts and chemistries were performed at the clinical site at the discretion of the care team, and results were included if the sample was obtained within ±1 day of enrollment. ## Case definitions Dengue cases were defined as individuals who met inclusion criteria for the parent study and had 1) detectable DENV RNA in the ZCD and/or DENV multiplex rRT-PCR or 2) detection of DENV NS1 by rapid test. For a single participant with DWS-, dengue was defined based on clinical presentation and a strong epidemiologic during a large was of DENV-4 cases. ## Statistical analysis Basic statistical analyses were performed using Excel software (Microsoft, Redmond, WA). Comparisons between group means and medians were made by the ANOVA, Welch’s test, both pooled and non-pooled two sample t-tests, Mann-Whitney U test, and Kruskal Wallis tests. Comparisons of proportions were made using chi-squared tests or Fisher exact tests (if the expected number in each cell was <5). Graphs were prepared with GraphPad Prism version 9 (GraphPad, San Diego, CA). Crude associations, statistical analysis and modeling were performed using SAS version 9.4. To calculate odds ratios for SD, domain models were developed using demographic (age, gender, comorbidities) and laboratory variables (basic clinical laboratory results, DENV viral load, chymase and LBP). Models were evaluated using binomial logistic regression (DWS-/DWS+ vs. SD), and goodness of fit was evaluated using area under the receiver operating curve (AUROC). Significance was set at two-sided p-value ≤0.05 for all analyses. Comorbidities were defined as present or absent for all statistical analysis. ## Demographic and clinical information Of 145 participants in this study, 55 were categorized as DWS-, 67 as DWS+, and 23 as SD. Demographic data and DENV diagnostic test results are shown in Table 1 (binary categories) and S1 Table (three categories). Participants were enrolled primarily at Hospital Central of the Instituto de Previsión Social ($$n = 124$$), followed by Hospital Villa Elisa [15] and IICS-UNA [6]. Results for DWS- and DWS+ were not significantly different for most analyses performed in this study. As such, results are reported for analyses using the binary outcome of DWS-/DWS+ vs. SD, except where indicated. Data and analyses for the three individual categories are provided in the Supplemental Material. Table 1 \| Characteristica \| DWS-/DWS+ N = 122 \| SD N = 23 \| p-value \| \| --- \| --- \| --- \| --- \| \| Age, years, mean (st. dev.) \| 34 (18) \| 61 (19) \| <0.001 \| \| Gender, female \| 81 (66.4) \| 6 (26.1) \| <0.001 \| \| Comorbidities, ≥ 1b \| 34 (28.1) \| 16 (84.2) \| <0.001 \| \| Hypertension \| 21 (17.4) \| 11 (57.9) \| <0.001 \| \| Diabetes \| 7 (5.8) \| 7 (36.8) \| <0.001 \| \| Chronic kidney disease \| 0 (0) \| 3 (15.8) \| 0.002 \| \| Chronic heart disease \| 1 (0.8) \| 4 (21.1) \| 0.001 \| \| Cancer \| 2 (1.7) \| 0 (0) \| 1.00 \| \| Autoimmune disease \| 8 (6.6) \| 1 (5.3) \| 1.00 \| \| Other \| 10 (8.3) \| 5 (26.3) \| 0.033 \| \| Days of symptoms, mean (st. dev.) \| 3.9 (1.9) \| 4.8 (1.7) \| 0.033 \| \| Year of Collection \| \| \| 0.015 \| \| 2018 \| 14 (11.5) \| 4 (17.4) \| \| \| 2019 \| 42 (34.4) \| 1 (4.3) \| \| \| 2020 \| 66 (54.1) \| 18 (78.3) \| \| \| DENV rRT-PCR, positive \| 110 (90.2) \| 20 (90.9) \| 1.00 \| \| Serotype \| \| \| 0.45 \| \| DENV-1 \| 14 (12.7) \| 4 (20.0) \| \| \| DENV-2 \| 9 (8.2) \| 0 (0) \| \| \| DENV-4 \| 86 (78.2) \| 16 (80.0) \| \| \| Negative \| 1 (0.9) \| 0 (0) \| \| \| NS1, positive \| 77 (63.1) \| 21 (91.3) \| 0.010 \| SD cases were significantly older than non-SD cases and were significantly more likely to be male and have at least one comorbidity (Table 1). The presence of specific comorbidities also differed by population (Table 1). In logistic regression of these variables in relation to disease severity, age, gender, and comorbidities remained in the model and were predictors of severity with a strong goodness of fit (AUROC = 0.94; Table 2). In logistic regression, comorbidity was defined as a discrete variable. In addition, SD cases presented for care later in the course of illness than non-severe cases (Table 1), and more SD cases were included 2020, consistent with the large DENV-4 outbreak that occurred in Paraguay that year [64]. Table 2 \| Unnamed: 0 \| SD vs. DWS-/DWS+ \| SD vs. DWS-/DWS+.1 \| \| --- \| --- \| --- \| \| Characteristic \| OR \| 95% CI \| \| Age, years \| 1.07 \| 1.03, 1.11 \| \| Gender, female \| 0.20 \| 0.05, 0.77 \| \| Comorbidity, count \| 2.09 \| 1.26, 3.48 \| ## DENV testing One hundred forty-four of 145 dengue cases ($99.3\%$) tested positive by rRT-PCR, NS1 rapid test, or both; and only one case was included based epidemiologic criteria alone. Over $90\%$ of cases tested positive for DENV by rRT-PCR, and this did not differ between severity categories (Tables 1 and S1). The proportion of DWS-/DWS+ cases with detectable NS1 ($\frac{77}{122}$, $63.1\%$) was significantly lower than SD cases ($\frac{21}{23}$, $91.3\%$; $$p \leq 0.010$$). DENV-4 was the predominant type, present in $78.5\%$ of the typed samples overall ($\frac{102}{130}$). Infections by Zika and chikungunya viruses were not detected, nor were coinfections by two DENV serotypes. Acute-phase samples were tested with two serologic tests: the pGOLD assay for anti-DENV and anti-ZIKV IgM and IgG, and a commercial ELISA for anti-DENV IgM and IgG (Tables 3 and S2). The proportion of individuals with detectable anti-DENV IgM was significantly higher with the pGOLD assay ($p \leq 0.001$, S3 Table). Although a smaller proportion of SD cases had detectable anti-DENV IgM compared to DWS-/DWS+ cases by either method, this difference only reached significance for the pGOLD assay. Most participants had detectable anti-DENV IgG by either method: $\frac{120}{139}$ ($86.3\%$) in the pGOLD, $\frac{128}{145}$ ($88.3\%$) by commercial ELISA. The proportion of individuals with detectable anti-DENV IgG did not differ significantly by severity category (Tables 3 and S2) or test method ($$p \leq 0.07$$, S3 Table). Table 3 \| Serologic Test \| DWS-/DWS+a \| SDa \| p-value \| \| --- \| --- \| --- \| --- \| \| pGOLD b \| \| \| \| \| DENV IgM \| 71/119 (59.7) \| 7/20 (35.0) \| 0.04 \| \| DENV IgG \| 102/119 (85.7) \| 18/20 (90.0) \| 1.0 \| \| ZIKV IgM \| 5/119 (4.2) \| 0/20 (0) \| 1.0 \| \| ZIKV IgG \| 19/119 (16.0) \| 4/20 (20.0) \| 0.52 \| \| ELISA \| \| \| \| \| DENV IgM \| 40/122 (32.8) \| 5/23 (21.7) \| 0.4 \| \| DENV IgG \| 106/122 (86.9) \| 22/23 (95.7) \| 1.0 \| The pGOLD assay yields a quantitative result that correlates with DENV neutralizing titers (S1 Fig) [59]. In crude binary logistic regression, for every unit increase in anti-DENV IgG, the odds of SD increased by a factor of 2.54 ($95\%$ CI, 1.19–5.42). No interaction was observed between anti-DENV IgG and day post-symptom onset, which was not included in the final logistic regression. No association was found between quantitative anti-DENV IgM results and disease severity in crude binary logistic regression. ## Clinical laboratory data Mean values for most routine laboratory tests, LBP, and chymase differed significantly between DWS-/DWS+ and SD cases (Fig 1, S4 Table). Laboratory values were similar between DWS- and DWS+ cases except for platelet count, which demonstrated a stepwise decrease from DWS- to DWS+ to SD, and serum glutamic oxaloacetic transaminase (SGOT) and LBP, which increased across severity categories (S2 Fig, S5 Table). DENV viral load did not differ by severity category. Fig 1: A-I) Clinical laboratory test result distributions by disease severity. J-L) Potential markers of disease severity measured in the current study: J) lipopolysaccharide binding protein (LBP), K) chymase, and L) DENV viral load by disease category. Bars on all graphs represent mean and standard deviation. Labels on the graphs indicate the following: ns, not significant, p>0.05; , p≤0.05; , p≤0.01; , p≤0.001; **, p≤0.0001. Routine laboratory tests were obtained at the discretion of the clinical care team, and as a result, many participants were missing data, particularly for analytes in the metabolic panel (S4 and S5 Tables). Due to this fact, crude associations with SD were calculated for all variables by binomial regression (Table 4), and variables evaluated in the laboratory domain multivariable logistic regression were limited to lymphocyte percent, platelet count, hematocrit, LBP, and chymase. These analytes displayed crude associations with SD, had sufficient data points to maintain model strength, and were not collinear with each other (e.g., hemoglobin and hematocrit, neutrophil and lymphocyte percent). After evaluating these five variables, those with non-significant regression coefficients were removed (LBP and hematocrit). In the final binary logistic regression model, lymphocyte percent, platelet count, and chymase were found to be associated with SD with a very good model fit (AUROC statistic, 0.95; Table 5). ## Chymase and SD Mean chymase level was significantly higher among individuals with comorbidities (10.75, st. dev. 22.01) compared to those without (2.41, 9.99; $$p \leq 0.014$$). Notably, the single DWS- case with an elevated chymase level (Figs 1K and S1K) occurred in an individual with systemic lupus erythematosus. To evaluate for a potential interaction between chymase and comorbidities on the development of SD, logistic regression was performed including these two variables with comorbidities defined as a discrete variable counting the number of comorbidities each patient has. Interaction product terms were nonsignificant in binomial and multinomial models. Together, comorbidities had an OR of 3.17 (1.71, 5.89) for binomial logistic regression (controlling for chymase); chymase had an OR of 1.11 (1.05, 1.17) (controlling for comorbidities). This model had a strong goodness of fit (C statistic = 0.95). Anti-NS1 antibodies may modulate chymase release by mast cells in acute dengue. As anti-DENV antibodies detected in the pGOLD assay target whole viral antigen, interactions between chymase and antibodies directed against the NS1 protein of ZIKV were investigated for their association with SD. These antibodies are detected in the pGOLD assay and predominantly represent cross-reactive anti-DENV antibodies in this population [59]. There was no association between chymase level and the quantitative anti-ZIKV NS1 IgM or IgG by linear regression, and no interaction was observed between anti-ZIKV NS1 IgG and chymase in binomial linear regression for SD. However, anti-ZIKV NS1 IgM showed effect modification of chymase in binomial linear regression such that as IgM increased, the chymase OR increased as well. With no detectable anti-ZIKV NS1 IgM, the chymase OR was 1.10 (1.04, 1.17), whereas at the mean level of anti-ZIKV NS1 IgM (0.02 in this population), the chymase OR was 1.21 (1.09, 1.34; AUROC = 0.93). ## Discussion In a predominantly adult population of dengue cases in Paraguay, due to DENV-1, -2, and -4, multiple factors were associated with clinically severe dengue, including patient (age, gender, comorbidities), serologic (elevated anti-DENV IgG), and laboratory variables (low platelet count, relative lymphopenia, and elevated chymase). Factors identified in the current study are generally consistent with the published dengue literature [4]. Although clinically severe dengue often occurs among children [1, 3, 27], age among adults has been identified as a risk factor for poor outcomes, and in 2019, individuals 15–49 years of age accounted for more deaths and disability adjust life years lost than children [5, 20, 26, 49]. Adults are more likely to develop severe bleeding, and this may be more difficult to manage than plasma leakage that develops in children, for which judicious fluid replacement is often effective [3, 9, 11, 20, 26, 27, 49]. Comorbid illness, including poorly-controlled diabetes mellitus (hemoglobin A1c >$7\%$) and renal disease, have been associated with SD [19, 21], and hypertension has also been identified in certain studies [21]. Notably, in our population, multiple comorbidities were associated with SD, and these demonstrated a cumulative effect when evaluated as a discrete variable. A gender difference among clinically severe dengue has varied across studies [1, 9, 16, 20, 21]. In our population, $66.4\%$ of DWS-/DWS+ cases were female in comparison to only $26.1\%$ of SD cases, and this difference remained significant after controlling for age and comorbidities. Although dengue is often associated with leukopenia [3, 27, 65–67], SD cases in the current study had a mild leukocytosis with reduced lymphocyte percentage (and a resulting neutrophil predominance). Thrombocytopenia is a common finding in SD cases and was one of the few factors that demonstrated a stepwise change across disease severity categories (DWS-, DWS+, and SD) [1, 3, 20, 21, 27, 41, 65]. Chymase and LBP were evaluated as two markers of clinically severe dengue based on data from their use in South and Southeast Asia [4, 34, 35, 37, 38, 41, 45–47]. Both demonstrated a crude association with SD compared to DWS-/DWS+. Although LBP did not remain in the final laboratory domain model, it demonstrated a stepwise increase across the categories of severity, which may have limited power in this study to identify a significant difference in a binomial model. Chymase, along with tryptase and other mast cell degranulation factors, has been associated with clinically severe dengue in several publications [35–38, 68], and the current study confirmed this finding among dengue cases in Paraguay. As clinically severe dengue appears to be more common in Southeast Asia relative to the Americas [2, 69], it is important to study potential differences in pathophysiology between these regions and confirm markers of severity between populations. As markers of dengue severity, mast cell degranulation factors have demonstrated more consistent results that other potential markers such as chemokines and cytokines [34–36, 40, 70], tryptase may play a direct pathophysiologic role in endothelial permeability [68], and mast cells can be stabilized by available FDA-approved medications [35]. Chymase release from mast cells may be modulated by specific anti-DENV antibodies. In mice, pre-treatment with anti-DENV IgG increased chymase release in an FCγRIII-dependent manner [36], and anti-NS1 IgG blocked mast cell degranulation [45]. In the current study, we observed an interaction between chymase level and antibodies against the NS1 protein of ZIKV, a closely related flavivirus. Further evaluation of this interaction using an array of DENV NS1 proteins may delineate a mechanism of protection for anti-NS1 antibodies, which demonstrate epitope-specific protection or enhancement [71, 72]. Higher levels of anti-DENV IgG in the pGOLD multiplex serologic assay were also associated with SD in our study population. This is consistent with findings in secondary dengue cases, though this is difficult to determine with certainty in acute-phase samples [3, 73], and SD can occur in primary dengue, particularly among adults experiencing a first infection [74]. Quantitative anti-DENV IgG levels in the pGOLD assay correlate with DENV FRNT50, and we previously observed that higher levels are associated with hospitalization in dengue cases [59]. This finding was confirmed in the current study when applying more consistent criteria for clinically severe dengue [3]. However, simultaneous detection of anti-ZIKV NS1 IgG did not increase the risk for SD in contrast to our earlier findings [59]. Anti-DENV IgM detection in the pGOLD proved more sensitive than a commercial ELISA and demonstrated little cross-reactivity on the ZIKV NS1 antigen. Notably, interpretation of these results required the use of a control sample that previously tested positive for anti-DENV IgM, and inclusion of a calibrator with this assay would improve generalizability. DENV serum viral load was not associated with SD in this cross-sectional study. Viral load decreases rapidly over the first week post-symptom onset, and viral kinetics differ between primary and secondary dengue [15, 75–83]. It is therefore difficult to capture peak viremia in most clinical settings. With only a single data point for each patient in our study, the lack of association between viral load and SD is not unexpected, but this highlights a potential limitation of using viral load as a predictor of severity in clinical practice. Difficulties in studying predictors of clinically severe dengue stem from the low proportion of severe cases among all DENV infections, lack of rapid and accurate diagnostics, and variability in the definition of study endpoints [3, 8, 9, 13]. The current study relied principally on DENV rRT-PCR for diagnosis, with a subset of participants detected by NS1. As part of the parent study design, participants typically presented with fever, which may bias this group toward more severe cases [52, 84]. Nonetheless, seven factors were associated with clinically severe dengue: five of these are commonly available at the acute visit (age, gender, comorbidities, platelet count, and lymphocyte percentage) and chymase and anti-DENV IgG can be measured by ELISA. Study designs that enroll participants based on rapid antigen test results limit the sample size necessary to include enough severe cases, but this may bias the study population given the clinical performance of current rapid tests [3, 28, 52, 85, 86]. An improved antigen diagnostic in combination with a prognostic test may then increase DENV detection, identify individuals at high risk for SD, and facilitate future trials for clinically severe dengue. This study had several limitations. First, a single acute-phase sample was available for each participant. Samples were obtained at different timepoints in relation to the development of severe disease among the participants, such that the study was not designed to prospectively evaluate each marker as a predictor of clinically severe dengue. Second, although all available SD cases were included, the sample size was small, particularly for the detection of differences among factors with relatively narrow value ranges, such as quantitative pGOLD values. Third, routine labs were collected at the discretion of the care team, and as a result, not all participants had laboratory values within the correct time frame. This limited the variables included in the laboratory domain multivariable analysis. Although dengue is endemic in Paraguay, scarce studies evaluate severity markers in this population. 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--- title: Contactin-1 links autoimmune neuropathy and membranous glomerulonephritis authors: - Janev Fehmi - Alexander J. Davies - Marilina Antonelou - Stephen Keddie - Sonja Pikkupeura - Luis Querol - Emilien Delmont - Andrea Cortese - Diego Franciotta - Staffan Persson - Jonathan Barratt - Ruth Pepper - Filipa Farinha - Anisur Rahman - Diana Canetti - Janet A. Gilbertson - Nigel B. Rendell - Aleksandar Radunovic - Thomas Minton - Geraint Fuller - Sinead M. Murphy - Aisling S. Carr - Mary R. Reilly - Filip Eftimov - Luuk Wieske - Charlotte E. Teunissen - Ian S. D. Roberts - Neil Ashman - Alan D. Salama - Simon Rinaldi journal: PLOS ONE year: 2023 pmcid: PMC9997925 doi: 10.1371/journal.pone.0281156 license: CC BY 4.0 --- # Contactin-1 links autoimmune neuropathy and membranous glomerulonephritis ## Abstract Membranous glomerulonephritis (MGN) is a common cause of nephrotic syndrome in adults, mediated by glomerular antibody deposition to an increasing number of newly recognised antigens. Previous case reports have suggested an association between patients with anti-contactin-1 (CNTN1)-mediated neuropathies and MGN. In an observational study we investigated the pathobiology and extent of this potential cause of MGN by examining the association of antibodies against CNTN1 with the clinical features of a cohort of 468 patients with suspected immune-mediated neuropathies, 295 with idiopathic MGN, and 256 controls. Neuronal and glomerular binding of patient IgG, serum CNTN1 antibody and protein levels, as well as immune-complex deposition were determined. We identified 15 patients with immune-mediated neuropathy and concurrent nephrotic syndrome (biopsy proven MGN in $\frac{12}{12}$), and 4 patients with isolated MGN from an idiopathic MGN cohort, all seropositive for IgG4 CNTN1 antibodies. CNTN1-containing immune complexes were found in the renal glomeruli of patients with CNTN1 antibodies, but not in control kidneys. CNTN1 peptides were identified in glomeruli by mass spectroscopy. CNTN1 seropositive patients were largely resistant to first-line neuropathy treatments but achieved a good outcome with escalation therapies. Neurological and renal function improved in parallel with suppressed antibody titres. The reason for isolated MGN without clinical neuropathy is unclear. We show that CNTN1, found in peripheral nerves and kidney glomeruli, is a common target for autoantibody-mediated pathology and may account for between 1 and $2\%$ of idiopathic MGN cases. Greater awareness of this cross-system syndrome should facilitate earlier diagnosis and more timely use of effective treatment. ## Introduction Peripheral neuropathy and renal disease commonly co-occur. In some cases, neuropathy may be secondary to uraemia, micronutrient deficiencies or the imbalanced metabolic milieu of renal failure [1]. Other causes include diabetes, haematological disorders such as lymphoma or myeloma [2], and drugs or metabolites that are both nephro- and neuro-toxic [3, 4]. Genetic neuropathies, such as those associated with Fabry disease [5] and Charcot-Marie-Tooth dominant-intermediate type E [6], can also be complicated by proteinuria and progressive renal failure. Accurate identification of the underlying cause is crucial for guiding management. Membranous glomerulonephritis (MGN) is one of the most common causes of nephrotic syndrome in adults and is strongly associated with autoantibodies to kidney antigens [7–9]. Previous small series and case reports have suggested an association between nephrotic syndrome and inflammatory neuropathies, namely Guillain-Barré syndrome (GBS) or chronic inflammatory demyelinating polyneuropathy (CIDP). However, the mechanisms linking these conditions have remained unclear. More recently, this combined presentation has been described in some patients with nodal or paranodal antibodies [10–13]. It has been speculated that this is due to a common autoimmune process involving both the peripheral nerve and kidney. Here, we demonstrate that antibodies targeting contactin-1 (CNTN1), a neuronal membrane protein anchoring paranodal myelin to the underlying axon, mechanistically connect these pathologies, in the largest cohort to date and identify a distinct and treatable neuro-renal syndrome. In addition, we show that a small percentage of idiopathic MGN may be caused by anti-CNTN1 antibodies, without overt neuropathy, and confirm that CNTN1 peptides are expressed in the affected glomeruli while RNA expression has been demonstrated in normal glomeruli, adding CNTN1 to the list of other important MGN antigens. ## Patient cohort and samples From January 2015 to August 2019 we prospectively recruited patients attending the peripheral nerve clinic in the John Radcliffe Hospital (Oxford, UK) with either confirmed or suspected inflammatory neuropathy to an observational study (Research Ethics Committee approval number 14/SC/0280). These patients provided informed written consent. Serum samples from these patients, and patients with suspected inflammatory neuropathies, received by our laboratory for diagnostic testing between August 2017 and August 2019. were screened for antibodies against paranodal (CNTN1, contactin-associated protein 1—Caspr1, neurofascin 155—NF155) and nodal (NF$\frac{140}{186}$) antigens. To investigate whether CNTN1 antibodies might be more widely associated with nephrotic syndrome caused by idiopathic MGN itself, we examined 295 serum samples from patients with idiopathic membranous nephropathy, collected as part of the MRC Glomerulonephritis bank [14]. Serum samples from 70 patients with other antibody-mediated CNS neurological disorders, 20 with multiple sclerosis, 120 individuals without neurological disease, and 46 patients with lupus nephritis, including pure class V membranous lupus nephritis, were also obtained as controls (S1 Fig). Information that could identify individual participants was stored confidentially and only accessible at the time of data collection to treating physicians, or those with necessary ethical approval and Good Clinical Practice (GCP) training. Data was subsequently de-identified. ## Nodal/paranodal antibody testing and binding to peripheral nerve All sera were initially screened using a live, transiently transfected cell-based assay (CBA), as previously described with slight modification [12]. Positive results, as well as titre and IgG subclass, were confirmed using ELISA. ## Cell based assay (CBA) Human embryonic kidney 293T (HEK) cells were either co-transfected with human DNA plasmid constructs for CNTN1 and Caspr1, NF155, or NF186 followed by incubation with patient sera. Fluorescently tagged secondary and tertiary antibodies against human IgG or human IgG subclasses 1–4 were used to visualise cell membrane binding by an investigator blind to the sample identity. ## Myelinating co-cultures Sera were assessed for topographical binding of IgG using myelinated co-cultures, generated using sensory neurons derived from human induced pluripotent stem cells (iPSC), according to a previously published protocol [15, 16]. For immunolabelling, live co-cultures were incubated with patient sera then fixed prior to labelling with secondary antibodies against human IgG. ## Teased nerve fibres Single fibres from $4\%$ paraformaldehyde-fixed mouse sciatic nerve were teased on glass sides, air-dried and permeabilised with $100\%$ acetone (10 min at –20°C), and blocked with $5\%$ fish gelatin and $0.1\%$ triton-X in PBS. They were incubated overnight at 4°C with guinea pig contactin-associated protein 1 (Caspr1) antibodies (1:1000) to label the paranodes, and patient sera (1:200). Immunolabelling was visualised with fluorescent-conjugated secondary antibodies. ## Human kidney CNTN1 immunohistochemistry Tissue sections of kidney biopsies from patients 1, 2, 12 and 13 (S1 Table in S1 File), as well as healthy human kidney, were deparaffinised, rehydrated and underwent antigen retrieval by boiling in citrate buffer. After permeabilisation and blocking ($0.3\%$ triton-X 100, $10\%$ normal donkey serum in PBS), sections were incubated overnight at 4°C with goat anti-CNTN1 (1:800; 2.5 μg/ml) (AF904, R&D systems), followed by a biotinylated anti-rabbit secondary antibody. Endogenous peroxidase was quenched with $3\%$ hydrogen peroxide prior to labelling with a horseradish peroxidase-streptavidin complex (Vectorstain Elite ABC HRP kit, Vector, PK-6100) and developed with diaminobenzidine (DAB) reagent (SK-4100, Vector) followed by haematoxylin counterstain. Sections of healthy human cortex were used as positive control. ## Laser microdissection and proteomics Formalin-fixed paraffin embedded renal or human brain tissues, were laser micro-dissected and captured using the Leica LDM7 system, and analysed in a proteomics approach [17] by LC-MS/MS using a Thermo Scientific Q-Exactive Plus mass spectrometer. MS raw data were analysed by Mascot software (Matrix Science, London, UK) using the Swiss-Prot human database. ## Pre-adsorption of sera with CNTN1 protein Sera available locally (from patients 1–3 and 12, 13 and 15 S1 Table in S1 File) were pre-adsorbed at 1:100 dilution with 1μg of CNTN1 protein overnight at 4°C and reapplied for testing in the CBA and against myelinated co-cultures to confirm CNTN1 as the specific antibody target. ## Serum CNTN1 protein measurement Serum CNTN-1 protein levels were measured on the Luminex® platform according to the manufacturer’s instructions (Human Magnetic Luminex Assay, R&D systems) as previously described [18]. Samples were coded randomly and analysed in duplicate. Measurements with a coefficient of variation >$15\%$ and outliers were repeated. ## Serum immune complex precipitation Whole sera were treated with polyethylene glycol (PEG) 6000 to a final concentration of $2.5\%$ overnight at 4°C. Immune complexes were precipitated as previously described [19], by centrifugation at 2000g for 30 minutes at 4°C, resuspending pellets in 30μl of prewarmed PBS, and analysed using SDS-PAGE and western blotting. ## Statistics Statistical analysis was performed using Prism 8 (version 8.0.2). Statistical significance for contingency data was assessed by Fishers exact test. Differences between CNTN1 antibody titres in remission versus in active disease, and in serum CNTN1 levels in the presence or absence of nephrotic syndrome, were assessed by the Mann-Whitney test. A p-value of <0.05 was considered significant. ## Contactin-1 antibodies serologically link immune-mediated neuropathies to membranous glomerulonephritis causing nephrotic syndrome We prospectively screened 468 patients with suspected inflammatory neuropathies for antibodies against nodal and paranodal targets (CNTN1, Caspr1, neurofascin 155 –NF155, neurofascin 186 –NF186) using a live, cell-based assay (see methods online). Thirty-six ($7.7\%$) patients were seropositive for antibodies targeting one of the nodal/paranodal proteins. Ten patients ($2.1\%$ of the total cohort and $27.8\%$ of the seropositive group) were monospecific for CNTN1 antibodies (S1 Fig), and eight of these additionally had nephrotic syndrome. Among the 432 seronegative patients, 147 ($34\%$) underwent investigation of renal function; in contrast with the seropositive group, only $\frac{5}{147}$ ($3.4\%$) of these had features supporting a diagnosis of nephrotic syndrome; ($p \leq 0.001$, Fisher’s exact test, OR for nephrotic syndrome with CNTN1 antibodies versus seronegative patients = 113.6, $95\%$ CI 11.2 to 169.1). Two patients with antibodies against other nodal/paranodal targets were diagnosed with nephrotic syndrome. Subsequently, a further seven patients with CNTN1 antibodies, an immune-mediated neuropathy, and confirmed nephrotic syndrome or suspected nephrotic syndrome with MGN confirmed on biopsy, were retrospectively identified from international collaborating centres. IgG4 was the predominant subclass, except in four patients, where IgG1 was equally reactive (S2 Fig). Thus a total of 15 patients, summarised here (S1 Table in S1 File) had a CNTN1 antibody-associated neuropathy and nephropathy. We also tested for CNTN1 antibodies in 295 patients with idiopathic MGN without neuropathy. Four ($1.4\%$) were seropositive (S1 Fig). PLA2R antibody testing was performed where possible, and found to be negative in these four, and $\frac{10}{15}$ patients with CNTN1 antibodies, nephrotic syndrome and a neuropathy ($\frac{14}{19}$ in total)—the remaining sera unavailable for further testing. Testing for other newly described MGN antigens was not done due to lack of clinical assay availability. CNTN1 antibodies were not detected in 256 control sera (90 from patients with other neurological diseases, 46 from patients with lupus nephritis, 12 of which had a pure class V membranous lupus nephritis, and 120 from individuals with other non-neurological, non-renal diseases). ## CNTN1-containing immune complexes are found in the glomeruli of patients with CNTN1 antibodies Renal biopsies were performed in 12 of the 15 patients. In all cases biopsies were characteristic of membranous glomerulonephritis, with diffuse thickening of glomerular capillary walls, and basement membrane spikes and lucencies evident on silver stain. Where available, immunostaining revealed glomerular capillary wall IgG (Fig 1A) and complement C3 (not shown), with subepithelial electron dense deposits, representative of immune complexes, on electron microscopy (Fig 1B). Granular deposition of CNTN1 protein was confirmed along glomerular basement membranes by immunohistochemistry (Fig 1C and S5 Fig). Furthermore, laser dissection of glomeruli followed by LC-MS/MS proteomic analysis revealed a 10 amino acid peptide (p.617-626, ATSVALTWSR) in CNTN1-immunopositive renal tissue, which matched with recombinant CNTN1 protein (S6 Fig). Fig 1: Contactin-1 immunoreactivity in kidney immune deposits and peripheral nerve tissue.A) Immunofluorescence of IgG deposition along glomerular capillary wall in anti-CNTN1 positive MGN patient kidney (P12 from S1 Table in S1 File). B) Electron microscopy reveals immune complex deposits on the extracapillary side of the glomerular basement membrane, visible as electron dense material (white arrows). C) Diaminobenzidine immunohistochemistry for CNTN1 is observed along the basement membrane of glomeruli from the same anti-CNTN1 positive MGN patient. Higher magnification image of a glomerulus demonstrating enrichment of CNTN1 in deposits lining the thickened basement membrane, characteristic of primary MGN (inset, black arrows). Equivalent results were obtained from biopsies of three further patients (S5 Fig). D) No CNTN1 labelling was observed in biopsy sections from an anti-PLA2R-positive patient with MGN. Scale bars in C-D, 20 μm. E-G) Labelling of the paranode in teased fibres from mouse sciatic nerve with IgG (green) from pre-treatment anti-CNTN1-positive human sera (E) is abrogated post-treatment (F) and absent in healthy control sera (G). Bound human IgG co-localises with CASPR at the paranode (magenta). Scale bars, 10μm. H) Human IgG binding (green) to myelinated co-cultures treated with patient sera sampled before steroid treatment. Cultures were permeabilised with methanol prior to sera incubation. Note diffuse axonal pattern and paranodal localisation of human IgG (green) (arrowheads in inset). Myelin basic protein (MBP, red) indicates myelin, and neurofilament (NF200, blue) indicates axons. Scale bar, 25μm (5μm in inset) I) Loss of human IgG binding (green) to myelinated co-cultures treated with patient sera sampled after steroid treatment. Scale bar 50 μm. J, K) CNTN1-transfected HEK293 cells (CBA) treated with patient sera sampled before (J) and after (K) steroid treatment. Serum titration in CNTN1 CBA shows a fall of anti-CNTN1 titre from 1:6400 to negative subsequent to steroid treatment. Scale bars, 50 μm. In contrast, no CNTN1 staining was observed in glomeruli from healthy donor kidney tissue (not shown) or PLA2R-associated MGN patient kidney tissue (Fig 1D). However, CNTN1 mRNA was detectable in healthy human kidney by polymerase chain reaction (PCR), and low-level CNTN1-protein expression could be observed by western blot (S7 Fig). ## Low serum CNTN1 protein levels correlated with immune complex formation We have recently shown that serum levels of CNTN1 protein (sCNTN1) are lower in antibody-positive neuropathy patients compared with seronegative and healthy controls [18]. Consistent with this, we found sCNTN1 levels were significantly lower at presentation in CNTN1-antibody positive patients (median 1.02 ng/ml, range 0.02 to 36.37) compared to 25 randomly selected seronegative patients with CIDP (four of whom had concurrent nephrotic syndrome) (median 10.67 ng/ml, range 3.96 to 30.06) and 25 seronegative patients with nephrotic syndrome only (median 13.91 ng/ml, range 4.37 to 26.72) ($$p \leq 0.003$$ and $p \leq 0.001$, Kruskal-Wallis) (Fig 2A). Overall, there was a non-significant trend for sCNTN1 and CNTN1 antibody levels to be inversely correlated ($$p \leq 0.099$$, R2 = 0.1088, simple linear regression) (Fig 2B). Finally, using western blotting we observed CNTN1 within the PEG precipitates isolated from the serum of a patient with active renal and neurological disease (patient 14 in S1 Table in S1 File). CNTN1 was not detected within the PEG precipitates isolated from the serum of the same patient during remission, or in that of a healthy control donor (Fig 2C and S8A and S8B Fig) suggesting the association of CNTN1 with immune complexes at disease peak. Conversely, CNTN1 was undetectable in the soluble fraction of PEG-treated serum of P14 during active disease (S8A and S8B Fig), consistent with the low levels of serum CNTN1 in the other anti-CNTN1 antibody patient samples (Fig 2A). Fig 2: Fall is serum CNTN1 protein is associated with immune complex formation in active disease.A) Serum CNTN1 (sCNTN1) protein levels are significantly lower in CNTN1 antibody seropositive CIDP (n = 11; sera available for testing from 10 patients in prospective group and 1 in retrospective group) vs seronegative CIDP patients (n = 29), and those with nephrotic syndrome but without CIDP (n = 25). Red diamonds and black dots indicate patients with and without nephrotic syndrome, respectively. Although 9/11 patients from the CNTN1 Ab+ CIDP group developed MGN, at the time of testing for sCNTN1 only 8/11 had nephrotic syndrome. Kruskal-Wallis p<0.001 overall, with post-test pairwise comparisons (CNTN1 Ab+ vs. seronegative CIDP, p = 0.003, vs. nephrotic syndrome alone p<0.001, seronegative CIDP vs. nephrotic syndrome ns, p = 0.19). B) Overall, there was a non-significant trend for sCNTN1 and CNTN1 antibody levels to be negatively correlated (p = 0.099, R2 = 0.1088, simple linear regression). However in 5/8 patients where CNTN1 antibodies later became undetectable, sCNTN1 remained low. C) Western blot for CNTN1 in the insoluble fraction of polyethylene glycol (PEG) treated healthy control normal human serum (NHS) and serum of patient 14 at nadir of active disease compared to remission. Recombinant CNTN1 protein (0.003 μg) loading; 5μl immune complexes purified from 300μl of patient serum. A repeat blot reveals reciprocal CNTN1 bands detectable only from the soluble fraction of the NHS and P14 serum in remission, but not during nadir of active disease (5μl loaded). ## CNTN1 antibodies from seropositive patients target peripheral nerves and paranodes Patient serum showed IgG reactivity with teased mouse nerve fibres, colocalising with Caspr1 at the paranodes (Fig 1E–1G). IgG bound to unmyelinated axons in live human stem cell-derived sensory neuron co-cultures (Fig 1H), as well as paranodal regions of myelinated axons (Fig 1H, inset). Following immunosuppressive treatment which led to complete suppression of CNTN1 antibody titres on the transient transfection cell-based assay (Fig 1J and 1K), all IgG labelling was lost from neuronal axons (Fig 1I). Further, locally available sera from patients found to have CNTN1 antibodies, neuropathy and nephrotic syndrome were pre-adsorbed with recombinant CNTN1 protein and re-applied to live CBA or neuronal co-cultures (S3 and S4 Figs). Immunolabelling was markedly reduced for all pre-absorbed sera tested in both assays, confirming CNTN1 as the antigen for serum IgG binding in the live cell culture systems. ## Patients with CNTN1 antibodies have a distinct clinical syndrome The majority of patients with CNTN1 antibodies presented with a neuro-renal syndrome characterised by a rapidly progressive, disabling, sensory-motor neuropathy accompanied by typical nephrotic syndrome, with oedema, low serum albumin, and elevated levels of urinary proteinuria. Additional features, including pain, tremor and autonomic dysfunction, were common, and over one-quarter had a prior diagnosis of diabetes. A summary of clinical characteristics is provided in Table 1, and further detail in S1 Table in S1 File. Smaller numbers of patients had isolated kidney or nerve involvement. Table 1 \| Unnamed: 0 \| SUMMARY OF KEY CHARACTERISTICS \| SUMMARY OF KEY CHARACTERISTICS.1 \| SUMMARY OF KEY CHARACTERISTICS.2 \| \| --- \| --- \| --- \| --- \| \| \| Characteristic \| Characteristic \| Patients % (No.) \| \| CLINICAL \| Age (median, range) \| Age (median, range) \| 59 (39–79) \| \| CLINICAL \| Male \| Male \| 80 (12/15) \| \| CLINICAL \| Diabetes \| Diabetes \| 26.7 (4/15) \| \| CLINICAL \| Acute/Subacute onset \| Acute/Subacute onset \| 86.7 (13/15) \| \| CLINICAL \| Additional clinical features (inc ataxia, tremor, cranial nerve palsy, autonomic dysfunction and abdominal weakness) \| Additional clinical features (inc ataxia, tremor, cranial nerve palsy, autonomic dysfunction and abdominal weakness) \| 93.3 (14/15) \| \| CLINICAL \| Pain \| Pain \| 53.3 (8/15) \| \| CLINICAL \| Median nadir disability—mRS (range) \| Median nadir disability—mRS (range) \| 5 (2–6) \| \| INVESTIGATIONS \| CSF \| Median protein g/l (range) \| 1.99 (0.24–5.93) \| \| INVESTIGATIONS \| CSF \| Normal protein \| 21.4 (3/14) \| \| INVESTIGATIONS \| CSF \| WCC >5 \| 28.6 (4/14) \| \| INVESTIGATIONS \| Meets electrodiagnostic criteria for CIDP A \| Meets electrodiagnostic criteria for CIDP A \| 93.3 (14/15) \| \| INVESTIGATIONS \| Median serum albumin g/l (range) \| Median serum albumin g/l (range) \| 22 (11–36) \| \| INVESTIGATIONS \| Median urinary PCR mg/mmol (range) \| Median urinary PCR mg/mmol (range) \| 1032 (633–4638) \| \| INVESTIGATIONS \| Median urinary protein g/24hr (range) \| Median urinary protein g/24hr (range) \| 3.35 (2.2–10) \| \| INVESTIGATIONS \| Clinical criteria met for nephrotic syndromeB \| Clinical criteria met for nephrotic syndromeB \| 92.3 (12/13)C \| \| INVESTIGATIONS \| MGN on renal biopsy \| MGN on renal biopsy \| 100 (12/12) \| \| INVESTIGATIONS \| Complement (C3 or C1q) deposition noted \| Complement (C3 or C1q) deposition noted \| 58 (7/12) \| \| INVESTIGATIONS \| PLA2R negative (IHC or serum antibodies) \| PLA2R negative (IHC or serum antibodies) \| 100 (10/10) \| \| TREATMENT RESPONSE D \| First line therapy(Steroids +/-IVIg +/- PE) \| Responders \| 20 (3/15) \| \| TREATMENT RESPONSE D \| First line therapy(Steroids +/-IVIg +/- PE) \| Non-responders \| 66.7 (10/15) \| \| TREATMENT RESPONSE D \| Escalation therapyE(inc. RTX) \| Responders \| 73 (8/11) \| \| TREATMENT RESPONSE D \| Escalation therapyE(inc. RTX) \| Non-responders \| 27 (3/11) \| \| OUTCOME \| mRS (median, range) \| mRS (median, range) \| 1 (0–6) \| \| OUTCOME \| Complete remission (of both neuropathy/nephropathy) \| Complete remission (of both neuropathy/nephropathy) \| 31 (5/15) \| \| OUTCOME \| Significant improvement (≥ 2 points mRS) \| Significant improvement (≥ 2 points mRS) \| 38 (6/15) \| \| OUTCOME \| Minimal improvement/stabilisation (of both neuropathy/nephropathy) \| Minimal improvement/stabilisation (of both neuropathy/nephropathy) \| 6 (1/15) \| \| OUTCOME \| Death \| Death \| 25 (3/15) \| ## The nephropathy and neuropathy are immunotherapy responsive and improve in parallel with a reduction in CNTN1 antibody titres Overall, $75\%$ of patients achieved either complete remission, significant improvement, or stabilisation of their neuropathy and nephropathy in parallel. However, only $20\%$ ($\frac{3}{15}$) responded well to the first-line treatments typically used for inflammatory neuropathies (steroids, IVIg, plasmapheresis) (Table 1). Four different treatment modalities, ultimately including escalation immunotherapies such as rituximab (RTX), were typically required to achieve a response. Two patients (noticeably the eldest of the cohort) died from multi-organ or respiratory failure whilst on the intensive care unit. A third patient died due to complications from coronavirus disease-19 (COVID-19) following prior treatment with IVIg, steroids and rituximab. CNTN1 antibody titres were high prior to immunosuppressive treatment, and lower in remission of both neuropathy and nephropathy (Fig 3A) ($$p \leq 0.001$$). In all 6 patients for whom both pre- and post-treatment serum samples were available, anti-CNTN1 titres fell in parallel with a reduction on the CIDP Disease Activity Scale (CDAS, Fig 3B). Serial assessments in one patient showed that improvements in strength and disability paralleled the fall in CNTN1 antibody titres, and a fall in urinary protein levels followed shortly after (Fig 3C). Fig 3: CNTN1 antibody titres correlate with disease activity.A) CNTN1 antibody titres were significantly higher in active disease (on or pre-treatment) (n = 15), compared to remission off treatment) (n = 6, p = 0.001, Mann-Whitney). B) In all 6 patients for whom serial samples were available, CNTN1 antibody titres fell in parallel with a reduction in neuropathy disease activity (measured using the CIDP Disease Activity Scale–CDAS) and became undetectable in 5 patients who achieved treatment-free remission. C) In one patient, where this level of detail was available, serial assessments showed that improvements in strength (Medical Research Council sum score, MRC-SS), functional abilities (inflammatory neuropathy Rasch-built Overall Disability Scale, iR-ODS) and normalisation of renal function (Protein Creatinine Ratio, PCR) correlated with falling CNTN1 antibody titres. Grey arrows indicate IVIg administration (2g/kg over 5 days), and black arrows pulsed dexamethasone (40mg daily for 4 days, repeated after 4 weeks). ## Discussion This study identifies CNTN1 antibodies as the pathological link between immune-mediated neuropathies and nephrotic syndrome caused by MGN, providing a mechanistic explanation for numerous previous clinical observations [10–13, 21]. We also demonstrate a significant association between CNTN1 antibody titres and immunotherapy responsiveness in this neuro-renal syndrome, and establish CNTN1 as another new antigenic target in a small proportion of “idiopathic” membranous glomerulonephritis. A previous report found non-concordant presentation of nephrotic syndrome and neuropathy in a patient with anti-CNTN1 antibodies—separated by a year, implying that some patients may further develop the combined neuro-renal syndrome at a later stage [22]. Without treatment, up to half of patients with MGN develop end-stage renal failure [23]. In keeping with recent data, patients with CNTN1 antibodies were poorly responsive to the typical first line therapies used for inflammatory neuropathies [24]. However, escalation therapies substantially reduced disability overall, and the associated renal disease invariably improved in parallel, as has recently been described in one further case report [25]. Around $70\%$ of patients with idiopathic MGN have detectable serum IgG4 autoantibodies against the glomerular podocyte antigen phospholipase A2 receptor (PLA2R) [7]. As such, testing for PLA2R antibodies has transformed the management of MGN, in many cases potentially avoiding the need for diagnostic biopsy. Similarly, the early detection of CNTN1 antibodies may negate the need for further renal investigation in anti-PLA2R negative patients, and provide an opportunity for earlier, therapeutic intervention. In addition, falling anti-CNTN1 titres mirrored clinical improvement, suggesting their potential as a prognostic marker. Indeed, high titre PLA2R antibodies predict a worse prognosis, [26] with reduced titres found to precede the onset of remission [27]. As with anti-PLA2R MGN [28], we found the detection of serum CNTN1 antibodies often preceded the onset of proteinuria- confirming their utility as biomarkers. Renal biopsy still needs to be considered for seronegative patients in whom antibody-mediated nephrotic syndrome is suspected, as evidenced by the fact that we observed one patient to have glomerular CNTN1-immune-complex deposition (patient 2 in S5 Fig) despite having become CNTN1 seronegative (following steroid treatment) by the time of renal biopsy. We confirm the presence of CNTN1 in both renal and peripheral nerve tissue, and provide evidence that CNTN1 antibodies identify an aberrant immune response targeting both peripheral nerve and kidney. Our observation of low-level expression of CNTN1 protein in healthy kidney is supported by recent single cell transcriptomic profiling of human kidney, which identified CNTN1 in podocytes [29], while others have recently described CNTN1 expression in normal kidney glomeruli and co-localisation of CNTN1 and IgG4 in the glomerular basement membrane of an affected patient with MGN [30]. This suggests that, similar to what is seen in anti-PLA2R MGN, circulating CNTN1 antibodies bind directly to the antigen in situ, which is then shed and accumulates in the glomerular space [31]. Emerging data also reveals the presence of CNTN1 protein in serum, and show levels reflect antibody status and neurological disease activity [18]. In contrast to PLA2R, which is undetectable in the serum of either controls or anti-PLA2R MGN patients [7], we detected CNTN1 protein in the serum of healthy controls, as well as seronegative CIDP and other forms of nephrotic syndrome, but levels were significantly lower in CNTN1 antibody-positive patients (Fig 2). The detection of CNTN1 protein within the PEG-precipitated fraction of patient serum sampled during a period of high anti-CNTN1 antibody titre suggests that CNTN1 may exist as circulating immune complexes within the blood in CNTN1 antibody-positive patients. The lack of mesangial and/or capillary immune complexes in patient biopsies may argue against these being the cause of nephrotic syndrome [32], though it remains possible that smaller circulating immune complexes can cause a subepithelial pattern of deposition [33]. Our findings encourage a fresh look at the potential for deposition of circulating immune complexes in human renal disease [34]. Recent studies provide insight into the pathogenic mechanisms of CNTN1 antibody-mediated peripheral neuropathy [35, 36]. Why some CNTN1 antibody patients preferentially develop nerve or renal injury is unclear. One or other disease may simply remain sub-clinical. Alternatively, pre-existing kidney injury may facilitate subsequent immune-mediated glomerular pathology, as can be seen in mice, where CNTN1 expression is upregulated within the kidney by glomerular injury [37]. The prior diagnosis of diabetes in over a quarter of patients in our cohort may be relevant in this regard. Further, antibodies against glomerular antigen THSD7A can be associated with an increased risk of cancer-associated MGN [38]. Although CNTN1 has well established roles in association with numerous solid organ cancers [39], malignancy was not detected as a potential source of CNTN1 antigen in the 6 patients in our cohort who were radiologically screened (S1 Table in S1 File). However, it is possible some patients may have subsequently developed cancer following diagnosis, as has been previously described [40]. In summary, the improvement of neuropathy and renal disease following immunotherapy highlights the utility of CNTN1 antibodies in the identification of a clinically reversible disorder. ## References 1. Arnold R, Issar T, Krishnan A v, Pussell BA. Neurological complications in chronic kidney disease.. JRSM Cardiovasc Dis (2016.0) 5 204800401667768. DOI: 10.1177/2048004016677687 2. Pop-Busui R, Roberts L, Pennathur S, Kretzler M, Brosius FC, Feldman EL. 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--- title: 'Anticancer derivative of the natural alkaloid, theobromine, inhibiting EGFR protein: Computer-aided drug discovery approach' authors: - Ibrahim H. Eissa - Reda G. Yousef - Eslam B. Elkaeed - Aisha A. Alsfouk - Dalal Z. Husein - Ibrahim M. Ibrahim - Mohamed S. Alesawy - Hazem Elkady - Ahmed M. Metwaly journal: PLOS ONE year: 2023 pmcid: PMC9997933 doi: 10.1371/journal.pone.0282586 license: CC BY 4.0 --- # Anticancer derivative of the natural alkaloid, theobromine, inhibiting EGFR protein: Computer-aided drug discovery approach ## Abstract A new semisynthetic derivative of the natural alkaloid, theobromine, has been designed as a lead antiangiogenic compound targeting the EGFR protein. The designed compound is an (m-tolyl)acetamide theobromine derivative, (T-1-MTA). Molecular Docking studies have shown a great potential for T-1-MTA to bind to EGFR. MD studies (100 ns) verified the proposed binding. By MM-GBSA analysis, the exact binding with optimal energy of T-1-MTA was also identified. Then, DFT calculations were performed to identify the stability, reactivity, electrostatic potential, and total electron density of T-1-MTA. Furthermore, ADMET analysis indicated the T-1-MTA’s general likeness and safety. Accordingly, T-1-MTA has been synthesized to be examined in vitro. Intriguingly, T-1-MTA inhibited the EGFR protein with an IC50 value of 22.89 nM and demonstrated cytotoxic activities against the two cancer cell lines, A549, and HCT-116, with IC50 values of 22.49, and 24.97 μM, respectively. Interestingly, T-1-MTA’s IC50 against the normal cell lines, WI-38, was very high (55.14 μM) indicating high selectivity degrees of 2.4 and 2.2, respectively. Furthermore, the flow cytometry analysis of A549 treated with T-1-MTA showed significantly increased ratios of early apoptosis (from $0.07\%$ to $21.24\%$) as well as late apoptosis (from $0.73\%$ to $37.97\%$). ## Introduction The world health organization (WHO) anticipated that during the next few years, cancer will dominate all other causes of death [1]. Developing treatments that suppress the growth of cancer by interacting with specific molecular targets and damaging the cancer cells is a major concern for medicinal chemists that work on cancer therapy [2]. Increasing vascularity (angiogenesis) is a crucial process that increases tumor development, so anti-angiogenesis strategies were considered to be very effective in the treatment [3]. Also, it was confirmed that the angiogenesis and growth of cancer cells are driven by the epidermal growth factor receptors (EGFR) [4]. In response to EGFR overexpression, downstream signaling pathways stimulate cell proliferation, differentiation, and survival. In cancers, EGFR was found to be elevated and promoted several solid malignant tumors [5]. Numerous cancer types express lower survival rates when EGFR is expressed. Also, EGFR’s expression served as a powerful diagnostic and prognostic indicator for cancer [6]. In contrast, this overexpression allowed researchers to utilize the EGFR’s inhibition as an essential strategy in cancer treatment [7,8]. Anciently, natural products, especially plants, were the most vital bases of treatments [9,10]. Recently, one-third of the FDA-approved drugs from 1981–2014 have been derived from natural sources [11]. Anticancer drug discovery finds xanthines, and xanthine derivatives, to be interesting compounds that exhibit different antimutagenic properties against ovarian cancer [12], prostate cancer [13], breast cancer, and leukemia [14]. Theobromine, the famous natural alkaloid, was discovered in 1841, while the synthesis of theobromine was described in 1882 [15,16]. Theobromine is found primarily in Theobroma cacao, chocolate, and other foods including tea leaves [17]. Theobromine showed promising anti-cancer activity in vitro and in vivo through the inhibition of DNA synthesis in glioblastoma multiforme [18] and prevented lung cancer angiogenesis [19]. Interestingly, in ovarian cancer, theobromine inhibited the VEGF in vivo and in vitro [20]. By using semi-synthesis to produce analogs, we can discover more potent drugs, give repurposing opportunities, and develop novel bioactive compounds, enhance drug-likeness, and improve pharmacokinetics and pharmacodynamics [21]. In scientific society today, computer-aided drug discovery (CADD) is widely accepted as a means of applying theoretical ideas using computers and a set of techniques for investigating chemical problems and is used in the pharmaceutical industry to investigate how potential drugs interact with biomolecules [22–26]. Our team applied the CADD in molecular design and docking, computational toxicity and ADME [27,28], in addition to MD simulations [29,30]. ## Rationale Erlotinib I [31,32] and olmutinib II [33] are reported as EGFR inhibitors. Compounds III and IV are derivatives of 1H-pyrazolo[3,4-d]pyrimidine that showed excellent efficacy for inhibiting EGFR-TK at nono-molar doses [34,35]. Our team previously synthesized compound V (a thieno[2,3-d]pyrimidine derivative) that was promising anti-proliferative and EGFR inhibitor [36] (Fig 1). Fig 1: EGFR inhibitors’ pharmacophoric features. These compounds possess some pharmacophoric features of EGFR-TKIs. These features are a planar heterocyclic system, an NH spacer, a terminal hydrophobic head and a hydrophobic tail. The key roles of the above-mentioned structural moieties are to occupy the adenine binding pocket [37], interact with amino acid residues in the linker region [38], to be inserted in the hydrophobic region I [39], and to occupy the hydrophobic region II [40,41], respectively (Fig 1). In this work and as an extension of our previous efforts in the discovery of new anti-EGFR agents [36,42–44], compound V was used as a lead compound to reach a more promising anticancer agent targeting EGFR. Several chemical modifications were carried out at four positions. The first position is the planar heterocyclic system. We applied the ring variation strategy as the thieno[2,3-d] pyrimidine moiety was replaced by a xanthine derivative (3-methyl-3,7-dihydro-1H-purine-2,6-dione). The six hydrogen bond (HB) acceptors may facilitate the HB interaction in the adenine binding pocket. Chain extension strategy was applied in the liker region through the replacement of the NH-linker with acetamide moiety. The terminal hydrophobic head (3-iodobenzoic acid) of the lead compound was replaced by toluene moiety) via ring variation strategy. A simplification strategy was applied for the hydrophobic tail (cyclohexene) of the lead compound. It was replaced by methyl group at 7-posision of xanthine moiety (Fig 2). Fig 2: T-1-MTA’s design rationale. ## 2.1. Chemistry Scheme 1 depicts the synthetic pathway used in this study to produce target T-1-MTA. The potassium salt of 3,7-dimethyl-3,7-dihydro-1H-purine-2,6-dione 2 was first obtained by refluxing 3,7-dimethyl-3,7-dihydro-1H-purine-2,6-dione (theobromine, 1) with alcoholic KOH [45,46]. 2-Chloro-N-(m-tolyl)acetamide 4, as the key intermediate, was prepared from commercially available m-toluidine 3 with chloroacetylchloride in DMF using NaHCO3. When equimolar amounts of potassium 3,7-dimethyl-3,7-dihydro-1H-purine-2,6-dione 2 and 2-Chloro-N-(m-tolyl)acetamide 4 were refluxed in DMF containing a sufficient amount of potassium iodide as a catalyst, an expected final product T-1-MTA was attained. Scheme 1: Synthetic pathway of T-1-MTA. The 1H NMR spectrum of T-1-MTA showed singlet signal at δ = 8.07 for CH imidazole and multiplet signals ranging from δ 7.41 to 6.87 for aromatic protons besides remarkable singlet signals for the CH3 (of m-tolyl group) and CH2 groups at δ = 2.27 and 4.67, respectively. The IR spectrum of the same product revealed absorption bands at 1711, 1662 cm-1 corresponding to carbonyl groups and absorption bands at 3255 cm-1 corresponding to NH. Regarding the 13C NMR spectrum, four shielded signals appeared at 43.84, 33.66, 29.90, and 21.62 ppm corresponding to CH2 and the three CH3 groups, respectively. ## 2.2. Molecular docking The examined proteins’ X-ray structures (EGFRWT; PDB: 4HJO and EGFRT790M; PDB: 3W2O) were acquired from the Protein Data Bank (PDB, http://www.pdb.org). First, the docking protocol was verified for both wild and mutant EGFR and the RMSD results were 1.20 and 1.15 Å, respectively Fig 3. Fig 3: A: Validation of wild EGFR using erlotinib as co-crystallized ligand and B: Validation of mutant EGFR using TAK-285 as co-crystallized ligand. Erlotinib, as a native inhibitor for EGFRWT, revealed an affinity value of -20.50 kcal/mol. The binding pattern of erlotinib revealed a key HB with Met769 (2.11 A˚) in addition to four hydrophobic interactions (HI) in the adenine pocket and three HIs with Ala719 and Val702, and Lys721 in the hydrophobic pocket (Fig 4). TAK-285, as a native inhibitor for EGFRT790M, presented a binding energy of -7.20 kcal/mol. The binding pattern of TAK-285 revealed a key HB with Met793 (2.44 A˚) through the pyrimidine moiety in the adenine pocket. The later moieties (3-(trifluoromethyl)phenoxy and N-ethyl-3-hydroxy-3-methylbutanamide moieties) were fixed in the hydrophobic pocket via a network of HIs with Lys745, Ile759, Met790, Val726, and Ala743, and Leu844 (Fig 5). Fig 4: A: 3D and B: 2D close view of erlotinib EGFRWT. Fig 5: 3D and 2D close view of TAK-285 into EGFRT790M. Regarding the EGFRWT, a comparable affinity value to erlotinib was obtained by T-1-MTA (-20.45 kcal/mol). Additionally, it interacts with the EGFRWT active site similar to erlotinib and adopts the same orientation. Besides, the 3,7-dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purine arm formed a crucial HB with Met769 besides two HIs with Lue694 inside the adenine pocket. On the other side, five HIs with Leu764, Ala719, Val702, and Lys721 were achieved via the m-tolyl moiety in the conserved hydrophobic pocket. The methyl group at 7-posision of xanthine moiety failed to form HIs in the hydrophobic pocket II (Fig 6). Fig 6: A: Mapping Surface (MS), B: 3D, and C: 2D close view of T-1-MTA inside the EGFRWT. Regarding the EGFRT790M, T-1-MTA (binding energy of -6.95 kcal/mol.) was tacked onto the catalytic site similarly to the positive control, TAK-285. In the adenine pocket, six pi-pi bonds with Leu844, Ala743, and Met79 were accomplished through the 3,7-dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purine arm. Also, in the same region, a crucial HB with Met793 was observed. Additionally, in the hydrophobic pocket, the m-tolyl moiety was buried to form one electrostatic interaction with Lys745 Fig 7. Fig 7: A: MS, B: 3D, and C: 2D close view of T-1-MTA with EGFRT790M. ## 2.3. MD simulations The MD analyses obtained on a 100 ns production run showing an overall system stability. The RMSD plot (Fig 8A) showed a stable trend for the EGFR only and the EGFR_T-1-MTA complex that were represented as blue and green curves showings averages of 2.16 Å and 2.97 Å, respectively. Moreover, the RMSD of the T-1-MTA (red) showed three states during the whole trajectory. The first 10 ns show an average of 2.16 Å before spiking to an average of 9.43 Å for the next 30 ns. Moreover, the last 60 ns show a large stable average value of 17.72 Å. The reason for this increase in the RMSD values of the compound T-1-MTA is due to the translational movement of the compound T-1-MTA relative to the protein as shown in Fig 8G which compares between the positions of the ligand at 1.5 ns (green sticks), 29.5 ns (cyan sticks), 83.9 ns (magenta sticks), and 94 ns (yellow sticks). The RoG (Fig 8B), SASA and (Fig 8C) HB show a stable protein fluctuation with an average of 19.51 Å, and 15285 Å2, respectively. The change in HBs between the T-1-MTA and EGFR (Fig 8D) shows that there is, approximately, at least one HB formed during the first 40 ns and it increases to at least two bonds during the rest of the simulation. The amino acids’ fluctuation was depicted in the RMSF plot (Fig 8E) showing low values of fluctuation (less than 2 Å) excepting the free C-terminal and the loop region E842:Y845 reaching 7 Å, and 3.5 Å, respectively. During the simulation time, the distance between the center of mass of compound T-1-MTA and the center of mass of EGFR protein shows a similar trend to the RMSD values of the ligand (three states) (Fig 8F). It started with an average of 16.72 Å for the first 15 ns before slightly decreasing to an average of 14.02 Å for the next 25 ns (from 15 ns to 40 ns). Finally, the last 60 ns showed an average value of 11.87 Å showing a stable interaction (Fig 8G). Fig 8: MD measurements calculated for a 100 ns.A) RMSD, B) RoG, C) SASA, D) HBBs’ change between the T-1-MTA and EGFR, E) RMSF, F) Center of Mass distance between the compound T-1-MTA and EGFR, and G) shows the positions of the compound T-1-MTA at different snapshots of the trajectory. T-1-MTA is in stick representation while the protein at the same snapshots is in cartoon representation. ## 2.4. MM-GBSA studies The binding free energy of the EGFR_T-1-MTA complex was further analyzed deeply by the MM-GBSA analysis. As Fig 9 shows, the EGFR_T-1-MTA complex had a total binding energy of an average value of -18.88 kcal/Mol. The various forms of energy that contribute to binding of the EGFR_T-1-MTA complex were analyzed to be. Van Der Waals interaction, electrostatic interaction with average values of -30.31 kcal/Mol and-10.23 kcal/Mol, respictively. Moreover, we performed an energy- decomposition analysis as shown in Fig 10 to identify the amino acids that had the highest contribution to the binding (1 nm or better). L694 (-1.48 kcal/Mol), S696 (-1.56 kcal/Mol), and R817 (-1.9 kcal/Mol) are the amino acids that exhibited the best contributions (better or less than -1 kcal/Mol). Fig 9: Energetic components of EGFR-T-1-MTA complex.Bars represent the standard deviation. Fig 10: Binding energy decomposition of the EGFR_T-1-MTA complex. ## 2.5. Protein-Ligand Interaction Profiler (PLIP) studies After that, to obtain a representative frame for each cluster of the EGFR_T-1-MTA complex, the obtained trajectory was clustered. The elbow method was used to automatically choose the number of clusters, as described in the methodologies section, and this resulted in four clusters. The PLIP website was used to determine the number and types of interactions between T-1-MTA and EGFR for each cluster representative (Table 1). As can be seen, HIs have a similar overall number of interactions in all the clusters compared to the HBs (7 HIs vs. 6 HBs). Additionally, a.pse file was generated to understand the 3D conformations of T-1-MTA as well as its interaction against the EGFR (Fig 11). Fig 11: The variation (types and numbers) of interactions of the EGFR_T-1-MTA complex produced from PLIP.HB: Blue solid line, HI: Dashed grey line, green dashed lines: Pi-Stacking interaction, amino acids: Blue sticks, and T-1-MTA: Orange sticks. TABLE_PLACEHOLDER:Table 1 ## 2.6. DFT studies In an attempt to clarify the inhibitory activity of T-1-MTA, theoretical DFT studies have been explored. The conceptual DFT has been used for understanding the electronic structure of the prepared molecule to determine its structural features which has far-reaching consequences on the molecules’ reactivity. Hence, the DFT-based reactivity descriptors (global), frontier molecular orbital analysis (FMO), and surface potential maps have been investigated to explore the reactivity of the prepared compound. ## Geometry optimization The reactivity of T-1-MTA is mainly determined by its chemical structure, so the structure is fully optimized and computed using DFT. The single bond length N2-C14 is 1.4765 Å, whereas the C14-N2-C3 bond angle is 116.70971° as given in Fig 12 at the B3LYB/6-311G++(d,p) level. The computed ground total energy (TE) is -30470.0 eV whereas the dipole moment (Dm) value is 5.9956 Debye which indicated a strong ability of interaction within the chemical system. Fig 12: The optimized chemical structure of T-1-MTA. ## Frontier molecular orbital analysis (FMO) analysis Border molecular orbitals in a molecule play a vital role in the electric properties as the system with a smaller value of energy gap between the border orbitals (Egap = ELUMO-EHOMO) should be more reactive than one having a greater Egap. Fortunately, T-1-MTA reported a smaller Egap value, so the electronic movement between the border orbitals; LUMO and HOMO, could occur easily [47]. The nodal properties of HOMO-LUMO orbitals of the studied heterocyclic molecule in Fig 13 are presented and show the strong orbital overlap, delocalization, and the low number of nodal planes. Hard molecules have a high HOMO-LUMO gap, and soft molecules have a smaller HOMO-LUMO gap. The value of Egap is given in Fig 13 and indicated that T-1-MTA is considered soft and the electronic transition (HOMO-LUMO) within the molecule is easy [48]. The quantum chemical parameters such as ionization potential (IP) and electron affinity (EA) were calculated and listed in Table 2. Fig 13: FMO analysis of T-1-MTA. TABLE_PLACEHOLDER:Table 2 ## Global reactive indices and total density of state (TDOS) Based on the density functional theory (DFT) concept, global reactivity parameters are essential tools for comprehending the behavior of any chemical molecular structure. Such global reactivity indices depend on the value of Egap. In Table 2, the static global properties of T-1-MTA, namely the electrophilicity (ω), maximal charge acceptance (Nmax), energy change (ΔE), chemical potential (μ), global chemical softness (σ), global electronegativity (χ), global chemical hardness (η), and electron affinity (EA) of T-1-MTA are presented after calculating using Koopmans’ theory. The results in Table 2 indicated that T-1-MTA is treated as soft within the nucleophilicity and electrophilicity scales [49]. The density of states and the distribution function probability determined by the occupied states per unit volume are important to provide an accurate description best than frontier molecular orbitals. The TDOS spectrum of T-1-MTA in Fig 14 depicted that the highest electronic intensity is located in the occupied orbitals under the HOMO orbital. Also, the TDOS spectrum confirmed the narrow HOMO-LUMO gap. Fig 14: The TDOS spectrum of T-1-MTA. ## 2.6.4. Molecular surface potential maps Molecular electrostatic surface potential discovers the relationship between the electronic distribution over the molecule surface and its binding ability. The molecular electrostatic potential explains and predicts the noncovalent interactions. Also, it finds the positive and negative domains of the electrostatic potential with low and high electron densities, respectively. The quantitative electrostatic surface potential (ESP) and total electrostatic density (TED) maps of T-1-MTA are demonstrated in Fig 15 after analysis of the optimized ground-state geometry. It appears that there are red regions indicating the negative potential is localized over the electronegative atoms such as O. The positive potential domains (blue color) are localized on the hydrogen atoms of purine moiety. The areas with moderate electron density values are shown with yellow color and localized on the phenyl ring. It can be predicted that the positive region on the purine ring of T-1-MTA will interact strongly with the negative region of the target and the negative areas at oxygen atoms will form strong interactions with areas of positive potential at the target. Also, it can be predicted that there is a strong attraction between the most positive region of T-1-MTA and the negative region of the target. The most negative region located around the oxygen atoms can also form a strong interaction with the positive region of the target. This implies that the difference in the distribution of electronic charges could result in enhancing the inhibition reactivity of T-1-MTA towards EGFR. Fig 15: TED and ESP maps of T-1-MTA. ## 2.7. ADMET profiling study The approval of any new compound as a marketed drug is based on a pharmacokinetic evaluation in addition to its biological activity. So, analyzing the ADME properties of a compound at the early stages should keep the discovery process from being delayed [50]. Although ADMET studies in vitro can investigate the properties of the absorbent, distribution, metabolism, excretion, and toxicity of drugs, in silico studies are advantageous because of their ability of saving cost, time, effort in addition to the regulations restricting the use of animals [51]. Computing ADMET parameters using Discovery is used to determine the ADMET parameters for T-1-MTA against erlotinib. Interestingly, the obtained results of T-1-MTA comparing erlotinib (Fig 16 and Table 3) showed a high likeness degree as it was anticipated to have a low potential to pass the BBB. Additionally, hepatotoxicity (HT) and the inhibition of cytochrome P-450 (CYP2D6-I) were expected to be absent. Also, T-1-MTA levels of aqueos solubility (AS) and intestinal absorption (IA) were computed as good. Fig 16: Computational prediction of ADMET parameters for T-1-MTA and erlotinib. TABLE_PLACEHOLDER:Table 3 ## 2.8. In silico toxicity studies For a drug to be developed successfully, toxicity assessment at the early stages must be done in order to control the possibility of failure in the clinical stage [52]. The in silico approach to toxicity assessment is promising being accurate and avoiding ethical and resource constraints in the in vitro and in vivo phases of toxicity development [53]. In silico prediction of toxicity basically uses the structure-activity relationship (SAR)-predicting toxicity. In detail, the computer compares the chemical properties of the examined molecules against the structural properties of tens of thousands of compounds of reported safety or toxicity [54]. Employing the Discovery studio software, eight toxicity models were used to estimate T-1-MTA’s toxicity in comparison to erlotinib. Providentially, T-1-MTA expressed very good and safe values in the carried-out models (Table 4) Table 4 \| Comp. \| FDA Rodent Carcinogenicity(Rat- female) \| Carcinogenic Potency TD50(Mouse) 1 \| Ames Mutagenicity \| Rat Maximum Tolerated Dose(Feed) 2 \| Rat Oral LD50 2 \| Rat Chronic LOAEL2 \| Skin Irritancy \| Ocular Irritancy \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| T-1-MTA \| Non-Carcinogen \| 111.107 \| Non-Mutagen \| 0.018 \| 4.712 \| 0.02 \| Mild \| Mild \| \| erlotinib \| Non-Carcinogen \| 39.771 \| Non-Mutagen \| 0.083 \| 0.662 \| 0.036 \| Non-Irritant \| Mild \| ## 2.9.1. In vitro EGFR inhibition For the purpose of examining the design and the computational outcomes that clearly demonstrated T-1-MTA’s significant affinity for EGFR, T-1-MTA’s inhibitory ability was assessed in vitro against the EGFR protein (Fig 17). The obtained inhibition value (22.89 nM) was near to erlotinib’s value, and the resulting in vitro results confirmed T-1-MTA ’s suppressive potential. Fig 17: In vitro EGFR- inhibition potentialities of T-1-MTA (A) and erlotinib (B). ## 2.9.2 Cytotoxicity and safety In vitro cytotoxicity assessment was performed for T-1-MTA using compared to erlotinib as demonstrated in Table 5. The obtained IC50 values of T-1-MTA against A549 and HCT-116 malignant cells were 22.49 and 24.97 μM, respectively. T-1-MTA’s anticancer potential was close to that of erlotinib. Table 5 \| Comp. \| In vitro cytotoxicity IC50 (μM) a \| In vitro cytotoxicity IC50 (μM) a.1 \| In vitro cytotoxicity IC50 (μM) a.2 \| A549(SI) \| HCT-116 (SI) \| EGFRIC50 (nM) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Comp. \| A549 \| HCT-116 \| WI-38 \| A549(SI) \| HCT-116 (SI) \| EGFRIC50 (nM) \| \| T-1-MTA \| 22.49 \| 24.97 \| 55.14 \| 2.4 \| 2.2 \| 22.89 \| \| Erlotinib \| 6.73 \| 16.35 \| 31.17 \| 4.6 \| 1.9 \| 5.91 \| As a confirmation of the computed safety pattern of T-1-MTA and to explore its selectivity, T-1-MTA was tested against the W138 human normal cell line. T-1-MTA showed a high IC50 value of 55.14 μM as well as very high selectivity indexes (SI) of 2.4 and 2.2 against the two cancer cell lines, respectively (Fig 18). Fig 18: In vitro anti-proliferative and safety assessments of T-1-MTA and erlotinib. ## 2.9.3. Cell cycle analysis and apoptosis assay Firstly, the cell cycle phases of A549 after T-1-MTA’s treatment was analyzed by flow cytometry according to the reported method before [55,56]. A concentration of 22.49 μM of T-1-MTA was added to A549 cells for 72 h. Then, the cancer’s cell cycle was investigated. Interestingly, T-1-MTA decreased the percentage of A549 cells in the Sub-G1 and S phases from $0.75\%$ and $68.17\%$ to $0.36\%$ and $28.60\%$, respectively. Contraversly, in the G2/M phase, the A549 percent was significantly increased from 18.69 to 49.20 after T-1-MTA’s treatment (Table 6 and Fig 19). Fig 19: Flow cytometric analysis of cell cycle phases and apoptosis.(A) The representative histograms show the cell cycle distribution of control (A549), and cells treated with 22.49 μM (IC50 value) of T-1-MTA for 72h. (B) Flow cytometric charts of apoptosis in A549 cells exposed to T-1-MTA (22.49 μM) for 72 h. TABLE_PLACEHOLDER:Table 6 To verify the apoptotic effects of T-1-MTA, the apoptosis percentage in the A549 cells was examined by Annexin V and PI double stains after it was subjected of 22.49 μM of T-1-MTA for 72 h [57,58]. Interestingly, T-1-MTA reduced the viable cancer cell count. Comparing control, T-1-MTA induced higher ratio of apoptotic cells. Also, T-1-MTA caused increased the apoptotic cells’ percentage significantly in the early stage of apoptosis (from $0.07\%$ to $21.24\%$) as well as the late stage of apoptosis (from $0.73\%$ to $37.97\%$). Also, the necrosis percentage was elevated to be 1.78, compared to $0.04\%$ in the control cells (Fig 19 & Table 7). In conclusion, T-1-MTA successfully arrested the A549 cell cycle at the G2/M phase causing cytotoxic potentialities that may be connected to apoptosis. Table 7 \| Sample \| Viable a(Left Bottom) \| Apoptosis a \| Apoptosis a.1 \| Necrosis a(Left Top) \| \| --- \| --- \| --- \| --- \| --- \| \| Sample \| Viable a(Left Bottom) \| Early(Right Bottom) \| Late(Right Top) \| Necrosis a(Left Top) \| \| A549 \| 99.16 ± 0.05 \| 0.07 ± 0.01 \| 0.73 ± 0.07 \| 0.04 ± 0.02 \| \| T-1-MTA / A549 \| 39.01 ± 4.152 \| 21.24 ± 1.07** \| 37.97 ± 6.02* \| 1.78 ± 0.45 \| ## Conclusion According to the essential structural features of EGFR inhibitors, a new lead theobromine-derived candidate, T-1-MTA has been designed. An anti-EGFR potential of the T-1-MTA was showed by molecular docking and verified by six MD simulations (over an100 ns), three MM-GBSA, and three DFT studies. Likely, computational ADMET studies indicated a general drug-likeness and safety. The biological evaluation confirmed the in silico results as T-1-MTA showed EGFR inhibitory activity with IC50 value of 22.89 nM. In addition, it exerted cytotoxic properties against A549 and HCT-116 cell lines with IC50 values of 22.49 and 24.97 μM, respectively. Moreover, T-1-MTA showed high selectivity indices towards the tumor cells. Also, the apoptotic potential of T-1-MTA was confirmed by the flow cytometry analysis. The obtained in silico and in vitro outputs are considered a step in the way to finding a cure through more deep investigations and or chemical modifications. ## 4.1. Chemistry 4.1.1. All apparatus used in analyses of T-1-MTA were illustrated in the supplementary section (S1) in S1 Data detailed explanations. 4.1.2. Synthesis of T-1-MTA. 2-Chloro-N-(m-tolyl)acetamide 4 (0.001 mol, 0.21g) was added to a solution of the potassium 3,7-dimethyl-3,7-dihydro-1H-purine-2,6-dione 2 (0.001 mol, 0.25g) in DMF (10 mL), and the mixture was heated in a water bath for 8 h. After being poured onto ice water (200 mL), the reaction mixture was gently stirred for certain time. To afford T-1-MTA (Fig 20), the obtained ppt was filtered, water washed, and crystallized from methanol. Fig 20: T-1-MTA. Off-white crystal (yield, $80\%$); m. p. = 233–235°C; IR (KBr) ν cm-1: 3255, 3143 (NH), 3073 (CH aromatic), 2965, 2923 (CH aliphatic), 1711, 1662 (C = O); 1H NMR: δ 10.20 (s, 1H, NH), 8.07 (s, 1H, CH imidazole), 7.41 (s, 1H, Ar-H), 7.35 (d, $J = 8.1$ Hz, 1H, Ar-H), 7.19 (t, $J = 7.8$ Hz, 1H, Ar-H), 6.87 (d, $J = 7.5$ Hz, 1H, Ar-H), 4.67 (s, 2H, CH2), 3.89 (s, 3H, CH3 at position 7 of purine), 3.44 (s, 3H, at position 3 of purine), 2.27 (s, 3H, CH3 of methyl phenyl); 13C NMR: δ 166.10, 154.64, 151.36, 148.93, 143.67, 139.15, 138.45, 129.08, 124.50, 120.06, 116.67, 107.03, 43.84, 33.66, 29.90, 21.62. For C16H17N5O3 (327.34). ## 4.2. Docking studies Was operated for T-1-MTA by MOE2014 software. The supplementary section (S2) in S1 Data includes a detailed explanation. ## 4.3. MD simulations Was operated for T-1-MTA by the CHARMM-GUI web server and GROMACS 2021 [24,59]. The supplementary section (S3) in S1 Data includes a detailed explanation. ## 4.4. MM-GBSA Was operated for T-1-MTA by the Gmx_MMPBSA package [60]. The supplementary section (S4) in S1 Data includes a detailed explanation. ## 4.5. DFT Was operated for T-1-MTA by Gaussian 09 and GaussSum3.0 programs. The supplementary section (S5) in S1 Data includes a detailed explanation. ## 4.6. ADMET studies Was operated for T-1-MTA by Discovery Studio 4.0. The supplementary section (S6) in S1 Data includes a detailed explanation. ## 4.7. Toxicity studies Was operated for T-1-MTA by Discovery Studio 4.0. The supplementary section (S7) in S1 Data includes a detailed explanation. ## 4.8 In vitro EGFR inhibition Was operated for T-1-MTA by Human EGFR ELISA kit. The supplementary materials (S8) in S1 Data show a comprehensive explanation. ## 4.9. In vitro antiproliferative activity Was operated for T-1-MTA by MTT procedure. The supplementary materials (S9) in S1 Data show a comprehensive explanation. ## 4.10. Safety assay Was operated for T-1-MTA by MTT procedure utilizing W138 cell lines. The supplementary section (S10) in S1 Data includes a detailed explanation. ## 4.11. Cell cycle analysis and apoptosis Was operated for T-1-MTA flowcytometry analysis technique. The supplementary section (S11 and S12) in S1 Data includes a detailed explanation. ## References 1. Abd El-Mageed M.M., Eissa A.A., Farag A.E.-S., Osman E.E.A.. Design and synthesis of novel furan, furo [2, 3-d] pyrimidine and furo [3, 2-e][1, 2, 4] triazolo [1, 5-c] pyrimidine derivatives as potential VEGFR-2 inhibitors. Bioorganic Chemistry (2021) 105336. DOI: 10.1016/j.bioorg.2021.105336 2. Chaudhari P., Bari S., Surana S., Shirkhedkar A., Wakode S., Shelar S., et al S.. 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--- title: Study protocol for development of an options assessment toolkit (OAT) for national malaria programs in Asia Pacific to determine best combinations of vivax radical cure for their given contexts authors: - Manash Shrestha - Josselyn Neukom - Sanjaya Acharya - Muhammad Naeem Habib - Lyndes Wini - Tran Thanh Duong - Ngo Duc Thang - Karma Lhazeen - Kamala Thriemer - Caroline Anita Lynch journal: PLOS ONE year: 2023 pmcid: PMC9997949 doi: 10.1371/journal.pone.0280950 license: CC BY 4.0 --- # Study protocol for development of an options assessment toolkit (OAT) for national malaria programs in Asia Pacific to determine best combinations of vivax radical cure for their given contexts ## Abstract ### Introduction Recent advances in G6PD deficiency screening and treatment are rapidly changing the landscape of radical cure of vivax malaria available for National Malaria Programs (NMPs). While NMPs await the WHO’s global policy guidance on these advances, they will also need to consider different contextual factors related to the vivax burden, health system capacity, and resources available to support changes to their policies and practices. Therefore, we aim to develop an Options Assessment Toolkit (OAT) that enables NMPs to systematically determine optimal radical cure options for their given environments and potentially reduce decision-making delays. This protocol outlines the OAT development process. ### Methods Utilizing participatory research methods, the OAT will be developed in four phases where the NMPs and experts will have active roles in designing the research process and the toolkit. In the first phase, an essential list of epidemiological, health system, and political & economic factors will be identified. In the second phase, 2–3 NMPs will be consulted to determine the relative priority and measurability of these factors. These factors and their threshold criteria will be validated with experts using a modified e-Delphi approach. In addition, 4–5 scenarios representing country contexts in the Asia Pacific region will be developed to obtain the expert-recommended radical cure options for each scenario. In the third phase, additional components of OAT, such as policy evaluation criteria, latest information on new radical cure options, and others, will be finalized. The OAT will be pilot-tested with other Asia Pacific NMPs in the final phase. ### Ethics and dissemination Human Research Ethics Committee approval has been received from the Northern Territory, Department of Health, and Menzies School of Health Research (HREC Reference Number: 2022–4245). The OAT will be made available for the NMPs, introduced at the APMEN Vivax Working Group annual meeting, and reported in international journals. ## Introduction Effective radical cure of the dormant liver forms of Plasmodium vivax, preventing relapses, is critical for the elimination of malaria. As vivax endemic countries intensify their malaria control efforts, malaria decreases, however often P. vivax becomes the dominant parasite [1]. A recent analysis suggests that more than $85\%$ of acute episodes are caused by relapses and this proportion might even be higher in some regions e.g. the Greater Mekong Subregion [2, 3]. Currently, the World Health Organization’s (WHO’s) recommended radical cure is low dose primaquine (PQ) given for 14 days [4]. This relatively long treatment regimen is challenging and often results in poor health worker compliance and patient adherence [5, 6]. In addition, until recently, limited point-of-care (PoC) diagnostic options were available to identify patients with glucose-6-dehydrogenase (G6PD) deficiency at risk of drug-induced haemolysis. Recent advances in the tools available to tackle P. vivax relapses, including shorter and higher dose treatment regimens with primaquine (PQ) [7], single dose tafenoquine (TQ) [8, 9], and novel quantitative PoC G6PD tests (SD biosensor) are changing the landscape of tools becoming available to national malaria programs (NMPs) [10, 11]. Global policy recommendations addressing the use of new tools are expected from the WHO’s Global Malaria Program but will need to be adapted to country contexts. As a result, NMPs are faced with determining which, among these options, are best for their given contexts while accounting for their vivax and G6PD epidemiology, health system capacity, and political and economic factors. Ruwanpura et al. [ 2021] demonstrated that policy change processes in the Asia Pacific region are nebulous and opaque, taking up to three years in some cases to move from evidence availability to policy change [12]. Given most countries in the region aim to eliminate malaria by 2030, the possibility of doing that could be constrained by slow decision-making processes. Research has shown that having multiple options for decision-makers to choose among can delay decision-making (e.g. in HIV) [13]. A toolkit that enables NMPs to systematically determine which radical cure options are best for their given environments, has the potential to reduce decision-making delays and accelerate availability of new tools, thus reducing morbidity and mortality related to vivax malaria. Additionally, different NMPs in the Asia Pacific have identified needing support to decide the optimal combination of vivax radical cure options as a priority in the coming 1–2 years as different radical cure advances become available [14]. As such, this research and tool development responds to an immediate need identified by NMPs in the region. Irrespective, several similar toolkits exist for other diseases and medical areas that have facilitated decision making by disease programs and stakeholders in the past [15]. However, reviews of public health toolkits reveal that only around $10\%$ of the toolkits are designed for health decision makers/policy makers and the uptake of toolkits can vary highly [16, 17]. In addition, there are limited availability of specific details of toolkit development methods, especially for malaria. Therefore, the aim of this work is to develop and pilot an options assessment toolkit (OAT) that enables national decision-makers determine the optimal set of tools for better radical cure of vivax malaria. While the NMPs await WHO’s global recommendations, the OAT seeks to provide evidence-informed support to boost NMP’s technical capacity and assist in nationally-owned decision making. ## Study design The OAT will be developed through participatory research methods in a sequential multi-phase design (Fig 1), where NMPs and regional experts will be engaged and have active roles in the research process and jointly contribute to the OAT development. Using this method, we aim to ensure the research is relevant to NMP decision-making on vivax radical cure treatment and approaches [18]. The foundational premise of participatory research methods is the value placed on genuine and meaningful participation–thus, any meetings and discussions held with NMPs and experts will include adapted in-depth interviews (IDI), participatory group discussions, and Delphi process. The aim in adapting these approaches is to ensure that engagement with NMPs and regional experts empowers their “ability to speak up, to participate, to experience oneself and be experienced as a person with the right to express yourself and to have the expression valued by others” [19]. Fig 1: OAT development study design. ## Study population NMPs in the Asia Pacific region are the intended users of the OAT and co-developers of the tool. NMPs are the national level governmental officers in the respective Ministries of Health who are designated for malaria control and responses in the country. Along with the NMPs, the toolkit development process will also include consultations (e.g. Delphi process) with global/regional experts. For operational feasibility, experts will be defined as international or regionally recognized individuals with more than 10 years of professional experience and high-level expertise in malaria. Methodologists who have experience of developing similar public health toolkits will also be consulted separately to strengthen the methodology of OAT development. The participants in this toolkit development can be classified as shown in Table 1. Table 1 \| S.N. \| Participant Category \| Type of participants \| Sample size \| \| --- \| --- \| --- \| --- \| \| a \| National Malaria Program \| 1. Government officers \| Up to 10 participants per country \| \| a \| National Malaria Program \| 2. Department heads \| Up to 10 participants per country \| \| a \| National Malaria Program \| 3. Malaria case management specialists \| Up to 10 participants per country \| \| a \| National Malaria Program \| 4. Others (eg. key stakeholders from other health departments, FDA, provincial stakeholders, and Ministry of Finance, if needed) \| Up to 10 participants per country \| \| b \| Experts \| 1. Malaria technical experts \| At least 12 \| \| b \| Experts \| 2. Policy experts \| At least 12 \| \| b \| Experts \| 3. Malaria advocates \| At least 12 \| \| b \| Experts \| 4. Social scientists \| At least 12 \| \| b \| Experts \| 5. Public health specialists \| At least 12 \| \| c \| Methodologists \| 1. Toolkit developers \| Up to 2 \| \| c \| Methodologists \| 2. Experts of Delphi process \| Up to 2 \| ## Participant selection A selection process was undertaken to identify 2–3 potential NMPs from different parts of the Asia Pacific region to approach for inviting to the OAT development process using the criteria outlined in Table 2. Table 2 \| Inclusion Criteria \| \| --- \| \| 1. Diverse country conditions including: 1. Different vivax caseload levels and a mixture of control and elimination stages of the national malaria program. 2. At least 2 sub-regional geographies included–EMRO, SEARO, WPRO 3. Different blood-stage treatment guidelines for vivax—CQ and ACT \| \| 2. Strong enabling environment with 1. NMP commitment as defined by responsiveness to an introductory invite mail requesting participation in the OAT development 2. Availability of relevant data in country/neighboring countries for consideration in OAT for example, health facility surveys, relevant research material, qualitative studies on political will or processes for change in antimalarial policy 3. Research institution or CSO partner support \| \| Exclusion Criteria \| \| 1. Countries in the prevention of re-establishment stage will be consulted, but will not be included as focus countries. 2. NMPs not willing to participate voluntarily \| The experts will be selected using the criteria as shown in Table 3. Table 3 \| Inclusion Criteria \| \| --- \| \| 1. More than 10 years of professional experience in malaria research 2. High-level expertise in malaria demonstrated by research publications on vivax malaria and/or health system strengthening 3. Capacity and willingness to contribute to the exploration of radical cure options combinations for different scenarios 4. Ensuring gender balance and representation from different countries from the Asia Pacific region \| \| Exclusion Criteria \| \| 1. Not willing to participate voluntarily 2. Cannot provide sufficient time dedicated to the Delphi exercise \| Patient and public involvement. None ## Pre-development phase The definition of “toolkit” was adapted from Barac et al., as “the package of multiple resources that codify explicit knowledge, such as templates, guidelines, algorithms, summaries, and that are geared to knowledge sharing and/or facilitate change in policy and practice” [16]. In this preliminary phase, a literature review was conducted that scoped out the systematic reviews of public health toolkits and similar toolkits developed for health policy makers. The review indicated that the evidence base on health toolkits is limited, with only around $10\%$ of the toolkits being designed for health decision makers/policy makers [16]. Most of the toolkits did not have their contents well-described and only two-thirds of the toolkits ($\frac{26}{39}$) specifically indicated the clinical evidence, rationale or theoretical basis underlying the toolkit strategies [16, 20]. While the users of existing public health toolkits were highly satisfied with the toolkit, usefulness of individual tools varied and so did the uptake of these toolkits [16]. A further review of toolkits similar to the expected OAT revealed limited availability of specific details of toolkit development methods, especially for malaria (S1 Table). Nonetheless, learning from the processes of previous toolkit development has helped inform OAT development methodology [15], with a keen focus on ensuring that the OAT is useful for decision-makers. Additionally, a list of key epidemiological, health system, and political & economic variables was identified for consideration to include as variables in consultation with NMPs (Table 4). Table 4 \| Epidemiological factors \| \| --- \| \| Vivax malaria caseload • Geographic variations in vivax cases within the country • G6PD deficiency prevalence • Efficacy and Effectiveness of current radical cure treatment regimen • Vulnerable population at risk \| \| Health system factors \| \| • Access to radical cure treatment regimen • Coverage of current radical cure regimen • Healthcare worker adherence to guidelines • Patient adherence • Pharmacovigilance • Logistics and supply chain • Human resources • Quality of training and supervision to healthcare workers \| \| Political and economic factors \| \| • Antimalarial policy change processes • Political will for vivax elimination • Acceptance/interest/appetite for alternative solutions with different risk/benefits to current regimes • Ease of policy implementation • Administrative feasibility • Economic burden of vivax • Cost-effectiveness analysis of radical cure tools • Public spending for malaria • External donor funding • Income inequality \| ## Development phase The first set of tools of OAT will be developed in this phase. Methodologists ($$n = 1$$–2) will be consulted in this phase to refine the OAT methodology as needed. Availability of relevant national data as pre-identified in Table 4 will be explored from the NMPs, and an expert panel will be consulted to determine optimal combinations of available radical cure tools for various scenarios of Asia Pacific region. This phase will include the following steps: Development of baseline assessment/situational analysis template. The list of initial variables (Table 4) identified in the pre-development phase will be explored further by collating national literature, particularly the gray literature found in national reports, project data, monitoring and evaluation reports. Using the variables and national-level information, a template for assessing the NMP’s current situation of vivax radical cure will be developed. This template will be aided by guidance on how to source relevant data. Initial phase of NMP consultations. The selected NMPs will be consulted to determine measurability and priority of the key variables identified. The consultation will be conducted either through face-to-face meetings or in the form of online in-depth interviews using interview guides, considering data and literature available on national contextual factors. Other stakeholders such as key partners, drug authorities, provincial programs etc., may also be included in the consultations if identified by the NMPs. Where necessary, the in-country policy change process for malaria will be mapped and any key variable or evidence that is required for that process that should be included into the OAT will be identified. The outputs from the initial consultations will be used to draft a list of prioritized and measurable variables that should be considered when deciding on optimal vivax radial cure per scenario. Development of scenarios representing Asia Pacific region. After the NMP consultations, the findings will be used, in addition to available literature, to develop 4–5 scenarios representing different epidemiological, health system and political economic contexts for the Asia-Pacific region. The developed scenarios will be presented to the NMPs for their feedback with a particular focus on how well they can identify with the scenarios and whether there are any significant gaps. Revised scenarios will then be presented to the experts to match optimal radical cure tools for each scenario. Different contextual and health system scenarios will be beneficial for the NMPs to see what the experts think and visualize potential future scenarios as burden reduces in higher-burden countries. The different scenarios might also present an opportunity to assess resource requirements to support the use of different tools for each scenario and flag additional investments required to facilitate certain options e.g. strengthening capacity at peripheral levels, increasing budget allocation, etc. Expert panel consultation. A list of regional/global experts will be identified and approached for an online expert panel consultation [21]. Using a modified e-Delphi process [22], multiple rounds of consultations will be conducted with the experts to validate the factors in baseline assessment template, determine threshold criteria for these factors, and match optimal radical cure combinations to regional scenarios. While Delphi sample sizes depend more on group dynamics in reaching consensus than their statistical power, a minimum of 12 respondents is generally considered to be sufficient [23]. Therefore, we will invite 25 experts to the Delphi process to enroll at least 12, assuming the response rate to be around $50\%$. A detailed methodology for the Delphi is provided as supporting information (S1 File). Second phase of NMP consultations. The NMPs will be followed-up after the initial consultations to clarify their priorities and explore key factors for decision-making. Again, after receiving agreed scenario-matched optimal radical cure combinations from the experts, another round of consultations will be held with the NMPs to review and test the feasibility of the radical cure combinations provided by the experts. Any concerns or issues raised by the NMP that could lead to decision making delays for radical cure will be documented. ## Review and finalization phase In this phase, the final set of tools of the OAT will be developed as described below: OAT algorithm. Based on the discussions with NMP and experts, an OAT algorithm or decision tree will be constructed that will include different criteria for consideration for use with NMPs. The OAT algorithm will be reviewed by the experts before using it with NMPs. The experts will be consulted virtually for their comments on the OAT algorithm and if necessary, a virtual round-table discussion session will be held afterwards. Other tools. Other tools to support the OAT algorithm will also be developed that can include: ## Pilot testing phase In this final phase, the final OAT developed will be first tested with the co-developing NMPs and then introduced for feedback from a broader set of NMPs from the Asia Pacific: Test with co-developing NMPs. The participating NMPs will be consulted for testing the OAT. The sessions will be documented to understand NMPs perspectives on usefulness and ease of using OAT. Any modifications needed to improve the OAT will be made according to NMP feedback. Pilot OAT with all NMPs. The final OAT version will be piloted among all the NMPs at the Asia Pacific Malaria Elimination Network (APMEN) Vivax Working Group face-to-face annual meeting. We will go beyond introducing the OAT to work with all participating countries to develop a plan to use the OAT in their country at the meeting. If needed, the OAT will be further revised and presented for use with NMPs at APMEN TechTalks/mini-workshops. ## Data capture and analysis Throughout the process, interviews, meetings and discussions with NMPs and regional experts will be fully documented and transcribed for analysis. Data analysis will be done primarily with NMPs and to some extent with regional experts as part of the participative approach. Participants will read the data and synopses of various meetings and interviews, on the data collection process in advance. A facilitator will pose a question of interest. Pertinent to the tool development e.g. how important was this variable when thinking about choosing vivax radical cure tools in X area of your country’? This will be explored by participants. Individually, participants will brainstorm and write down what they see as the most significant themes. These themes will be shared with the full group and the facilitator to inform the discussion. New insights or understanding gained through this interaction will be captured and added to the data collection and learning process. ## Ethics and dissemination This protocol has been reviewed and approved by the Human Research Ethics Committee of the Northern Territory, Department of Health and Menzies School of Health Research (HREC Reference Number: 2022–4245). Informed consents will be obtained from all participants. Due to the low-risk nature of the research and mostly online consultations, verbal consent will be sought from the participants during the initial enrolment. The verbal consent will be documented by the team members using online survey forms. Consent will also be sought from the participant to use their information for research, reporting and data sharing purposes after personal identifiable information are removed. During the consent process, participants will be specifically informed about the estimated time (60 minutes) needed to complete each consultation session. All participants will be ≥ 18 years of age and offered a soft-copy of the information sheet. The information sheet includes contact information of study investigator and ethics committees. All data generated by the toolkit development process will be kept strictly confidential and accessible only to relevant authorized staff and only used for purposes related to this protocol. The final toolkit developed will be made available for use for the NMPs and presented at APMEN annual meeting, various international talk programs, and conferences. The development and findings of the project will be reported in relevant international journals following the COREQ (Checklist for reporting qualitative research) guidelines [24]. ## Discussion Toolkits can be an effective strategy for knowledge translation, ensuring the use of research evidence to inform healthcare decision-making [16]. However, only a few health toolkits have focused on public health policymakers and even fewer toolkits describe how they have been developed. In this protocol paper, we have delineated the multiphasic, collaborative, and systematic plan of developing the OAT which aims to assist the NMPs and national policymakers in assessing their options for radical cure of vivax and influencing policy. Our study design and participatory research methods provide the necessary scientific rigor that is often lacking in toolkit development [16, 18]. Consideration of both the technical factors (i.e., malaria epidemiology and health systems) and non-technical factors (i.e., political will, costs, and external influence), along with a policy evaluation criteria and other significant tools, make OAT a comprehensive toolkit for radical cure of vivax malaria. Furthermore, our qualitative research design allows flexibility during the iterative process of consultations with NMPs and experts. Importantly, collaborating with NMPs during the development process will help in the uptake and ownership of the final toolkit. In addition, findings from the Delphi process in this study will provide expert consensus on radical cure options for particular scenarios and threshold criteria for key factors such as efficacy of radical cure drugs, which has remained contentious until now. Linking evidence into policy is seldom a linear process. This process often gets undermined and mediated by personal values, ideologies, economic interests, and organizational practices [25]. Through our approach, where we bring researchers/experts and policy/program managers together and collaborate in this toolkit development, we hope to promote knowledge sharing and mutual understanding. Our approach also seeks to add value for the policy makers and malaria program managers who are the end users of the OAT through consultative dialogue and coproduction of insights [25]. Despite the strengths of this study, there are a few limitations in the toolkit design. First, while 4–5 scenarios will be developed in the OAT, it is possible that some individual country contexts of the Asia Pacific region may not be fully represented in those scenarios. Second, some larger malaria endemic countries in the Asia Pacific have a wide diversity of contexts at the sub-national level when it comes to vivax malaria. However, as the policies are made at the national level, separate radical cure options for the sub-national level may not be developed explicitly in the OAT. Third, the toolkit may be subject to change/revision as new data on the radical cure tools become available. Finally, it can be challenging for the NMPs to use the toolkit if it is too complex as they may not have enough time. This can be mitigated by applying the steps of the toolkit over a certain time, as opposed to one sitting and using the APMEN TechTalks and annual meeting to simplify the toolkit. In conclusion, this paper outlines the process of OAT development in detail to inform relevant stakeholders and assist other researchers developing similar toolkit/s in other settings for malaria or for other diseases. ## References 1. Price RN, Commons RJ, Battle KE, Thriemer K, Mendis K. Plasmodium vivax in the era of the shrinking P. falciparum map. Trends Parasitol (2020.0) 36 560-70. DOI: 10.1016/j.pt.2020.03.009 2. 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--- title: Polygenic risk scores for asthma and allergic disease associate with COVID-19 severity in 9/11 responders authors: - Monika A. Waszczuk - Olga Morozova - Elizabeth Lhuillier - Anna R. Docherty - Andrey A. Shabalin - Xiaohua Yang - Melissa A. Carr - Sean A. P. Clouston - Roman Kotov - Benjamin J. Luft journal: PLOS ONE year: 2023 pmcid: PMC9997960 doi: 10.1371/journal.pone.0282271 license: CC BY 4.0 --- # Polygenic risk scores for asthma and allergic disease associate with COVID-19 severity in 9/11 responders ## Abstract ### Background Genetic factors contribute to individual differences in the severity of coronavirus disease 2019 (COVID-19). A portion of genetic predisposition can be captured using polygenic risk scores (PRS). Relatively little is known about the associations between PRS and COVID-19 severity or post-acute COVID-19 in community-dwelling individuals. ### Methods Participants in this study were 983 World Trade Center responders infected for the first time with SARS-CoV-2 (mean age at infection = 56.06; $93.4\%$ male; $82.7\%$ European ancestry). Seventy-five ($7.6\%$) responders were in the severe COVID-19 category; 306 ($31.1\%$) reported at least one post-acute COVID-19 symptom at 4-week follow-up. Analyses were adjusted for population stratification and demographic covariates. ### Findings The asthma PRS was associated with severe COVID-19 category (odds ratio [OR] = 1.61, $95\%$ confidence interval: 1.17–2.21) and more severe COVID-19 symptomatology (β =.09, $$p \leq .01$$), independently of respiratory disease diagnosis. Severe COVID-19 category was also associated with the allergic disease PRS (OR = 1.97, [1.26–3.07]) and the PRS for COVID-19 hospitalization (OR = 1.35, [1.01–1.82]). PRS for coronary artery disease and type II diabetes were not associated with COVID-19 severity. ### Conclusion Recently developed polygenic biomarkers for asthma, allergic disease, and COVID-19 hospitalization capture some of the individual differences in severity and clinical course of COVID-19 illness in a community population. ## Introduction Coronavirus disease 2019 (COVID-19) is caused by an infection by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) and remains a pandemic in the US and globally [1]. COVID-19 invades the respiratory and other crucial systems in the body, varying widely in severity, from asymptomatic presentation to severe outcomes including hospitalization or death. Moreover, at least one-third of individuals with COVID-19 develop post-acute COVID-19 syndrome [2], which consists of residual respiratory, fatigue, central nervous system, and musculoskeletal symptoms that persist at least four weeks after initial symptom onset [3, 4]. Despite vaccination efforts, the potentially severe and chronic outcomes of COVID-19 necessitate a better characterization of individuals at elevated risk for clinically significant disease outcomes. Across a range of studies, COVID-19 severity was found to be associated with clinical risk factors such as older age, male sex, smoking, respiratory illness, cardiovascular disease, and immunocompromised status [5–7]. Moreover, a person’s own genetic factors critically affect individual differences in COVID-19 disease severity [8]. Large-scale, population-based genome-wide association studies (GWAS) indicate that multiple common genetic variants can contribute to COVID-19 symptom severity after infection [9]. Genes that emerged from GWAS most strongly associated with severe COVID-19 progression are involved in immune response and lung disease pathology. The individual variants associated with disease severity in the discovery GWAS have minor effects, but cumulatively they explain a substantial proportion of genetic variation in response to the virus. Common variants can be collapsed into a polygenic risk score (PRS) to improve the prediction of at-risk patients in an independent cohort. For each individual, the number of risk alleles carried at each variant (0, 1, or 2) can be summed and weighted by its effect size, resulting in a single score of each individual’s genetic vulnerability to COVID-19 severity. COVID-19 PRS have shown to be associated with increased risk of morbidity and mortality due to COVID-19 in several independent samples and across ancestries, with PRS outperforming individual genetic variants and providing information over and above established risk factors [10–13]. While the COVID-19 PRS was designed to capture all genes associated with the severity of COVID-19, the studies relied partially on the UK Biobank cohort, which has limited phenotypic information on COVID-19 disease and exposure status of controls, largely based on electronic medical records of laboratory test results and ICD-10 codes for COVID-19 and respiratory support. It is therefore plausible that other PRS capture genetic vulnerability to other conditions, such as asthma or coronary artery disease, that might also associate with COVID-19 symptom severity or COVID-19 clinical course, although empirical evidence is limited. For example, only one published study has reported no association between asthma PRS and severe COVID-19 [14], but no other studies to our knowledge have examined post-acute COVID-19 symptoms. Therefore, the present study translated genetic discoveries in COVID-19 host genetics to an independent, well-characterized patient population with a full range of COVID-19 severity. Responders to the World Trade Center (WTC) attacks on September 11, 2001 are a cohort with detailed prospective health data, including information on COVID-19 infection, symptom severity, and follow-up outcomes. Our team has previously demonstrated that younger and employed responders were at a higher risk of being infected with the SARS-CoV-2 virus [15]. Among those who experienced infection, a pre-existing obstructive airway disease diagnosis predicted an elevated risk for a more severe COVID-19 presentation [2]. The risk of developing post-acute COVID-19 sequelae was similarly elevated in responders with respiratory problems as well as heart disease, over and above the risk conferred by the COVID-19 severity itself. While respiratory and other chronic conditions are associated with toxic exposures during the $\frac{9}{11}$ disaster, some of the individual differences in post-exposure outcomes in $\frac{9}{11}$ responders can be attributed to genetic vulnerabilities [16, 17]. Relatively little work describing genetic risk to COVID-19 severity has been conducted in community individuals with a full range of symptom severity. The associations between PRS and a common long-term outcome of post-acute COVID-19 also remain unknown. Thus, building on our previous work in the large sample of WTC responders who were infected with SARS-CoV-2, we investigated whether polygenic vulnerabilities to COVID-19 hospitalization, as well as to asthma, related allergic diseases, coronary artery disease, and type II diabetes, were associated with COVID-19 severity and post-acute COVID-19 symptoms, after adjusting for established risk factors. We investigated genetic associations with a full range of COVID-19 symptoms, as well as genetic risk for the severe COVID-19, which has the highest clinical relevance as it includes hospitalized cases. Based on previous findings, we hypothesized that PRS for COVID-19 hospitalization would be significantly associated with COVID-19 severity across a range of symptoms, and would help identify the most severe COVID-19 group. We tentatively hypothesized that PRS for asthma, allergic disease, coronary artery disease, and type II diabetes would be associated with worse COVID-19 severity outcomes and that the polygenic associations would extend to development of post-acute COVID-19 sequelae. ## Participants and procedure Participants were 983 WTC responders enrolled in a prospective cohort called the WTC Health Program which was established in 2002 and independently monitors the health of more than 13,000 WTC first responders living on Long Island, NY, USA [18]. The analytic sample comprised of all genotyped participants from an established cohort of responders with the first instance of SARS-CoV-2 infection before January 2022, with the median infection date of December 2020 [2]. The mean age of the analytic sample at infection was 56.06, SD = 7.37, range = 39.79–89.04, $93.4\%$ of the sample was male, and $82.3\%$ had European ancestry. The analytic sample did not differ significantly from the total cohort of responders with SARS-CoV-2 infection ($$n = 1$$,280) on demographic and symptom variables (all p-values for independent sample t-tests and chi-square tests >0.05). The analytic sample included responders with a verified history of a positive polymerase chain reaction, antigenic, or antibody test for SARS-CoV-2 or positivity for antibodies to SARS-CoV-2 nucleocapsid protein according to medical or laboratory records ($$n = 587$$), as well as responders who reported a positive laboratory test for SARS-CoV-2, but for whom documented proof of the test results was not available ($$n = 396$$). The unverified cases did not differ significantly from verified cases on all study variables, except for lower COVID-19 severity (t(df) = 2.30 (847.60), $$p \leq .02$$, $d = .15$). Verifiable hospitalization records at local hospitals increased the likelihood of objectively verifying the most severe responders. Analyses adjusted for verification status. All cases represent a first instance of SARS-CoV-2 infection. Clinical information on initial and residual COVID-19 symptoms was collected irrespective of verification status through in-person questionnaires, surveys sent via email and text, electronic medical records obtained with the release of health information forms, and follow-up calls. Symptoms of COVID-19 in this study included respiratory symptoms (i.e., shortness of breath, chest pain, sore throat, congestion/runny nose, and wheezing), systemic symptoms (i.e., fever, fatigue, headache, chills, and weight loss), gastrointestinal symptoms (i.e., nausea, vomiting, and diarrhea), musculoskeletal symptoms (i.e., joint and muscle pain), central nervous system symptoms (i.e., dizziness, vertigo, loss of smell/taste, and brain fog), and psychiatric symptoms (i.e., anxiety, depression, and post-traumatic stress disorder [PTSD]). Blood samples for genotyping were drawn at the WTC Health Program clinic as a routine part of the monitoring examination from January 2012 to January 2019. The study was approved by the Institutional Reviewer Board of Stony Brook University, and all participants provided written informed consent. For more information on the study design, see Lhuillier, Yang [2]. ## COVID-19 severity Participants in the analytic sample were categorized into four severity groups according to their symptoms: asymptomatic ($$n = 92$$, $9.4\%$), mild ($$n = 378$$, $38.5\%$), moderate ($$n = 408$$, $41.5\%$), and severe ($$n = 75$$, $7.6\%$). This categorization was based on the NIH COVID-19 clinical spectrum updated October 2021 (National Institutes of Health, 2021). The asymptomatic category consisted of responders who reported a positive SARS-CoV-2 virologic test result without any symptoms associated with COVID-19. The mild category included responders with at least one symptom associated with COVID-19 but no shortness of breath or difficulty breathing. Moderate cases were in responders who reported shortness of breath and/or diagnosis of lower respiratory disease (pneumonia/bronchitis) during clinical assessment or imaging. These responders maintained oxygen saturation (SpO2)≥$94\%$ on room air at sea level. Mild and moderate cases were medically managed primarily at home, even if they initially visited a healthcare facility for medical treatment and/or testing. Severe cases included responders with SpO2<$93\%$ on room air, respiratory rate >30 breaths/min, heart rate greater than 100 beats per minute, acute respiratory distress syndrome, septic shock, cardiac dysfunction, or an exaggerated inflammatory response in addition to pulmonary disease, or severe illness causing cardiac, hepatic, renal, central nervous system, or thrombotic disease during COVID-19. Responders were also categorized as severe if they were admitted to the hospital, or received intensive care or mechanical ventilation, or if they eventually died from COVID-19. Two complimentary analytic variables were created: an ordinal COVID-19 severity variable on 1–4 scale corresponding to asymptomatic, mild, moderate, and severe symptoms, and a binary COVID-19 severe category variable, with asymptomatic, mild, and moderate patients in one category, and severe patients in the second category. ## Post-acute COVID-19 symptoms Residual symptoms were defined as any COVID-19-related symptoms that lasted at least 4 weeks after symptom onset (Centers for Disease Control and Prevention, 2021b). Residual symptoms included respiratory problems (e.g., dyspnea, chest discomfort, cough, and fatigue), CNS symptoms (e.g., loss or reduction of smell/taste, mental fog, dizziness, and vertigo), and musculoskeletal complaints. In the analytic sample, $$n = 306$$ ($31.1\%$) participants reported at least one residual COVID-19 symptom. A binary analytic variable was created to compare participants with and without at least one residual symptoms. ## Covariates Electronic medical records updated on January 1, 2020, were used to obtain date of birth to calculate age at infection, obstructive airway disease and upper respiratory disease diagnoses, lifetime posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) diagnoses, and the most recent available measure of body mass index (BMI). Obstructive airway disease category includes diagnoses of asthma and bronchitis, upper respiratory disease includes chronic rhinitis and chronic sinusitis [18]. Respiratory diagnoses were determined based on systemic examination by a physician or nurse practitioner and included repeated pulmonary function tests, physical examination, and medical history. BMI scores were analyzed as continuous variable to maximize statistical power [19, 20]. Genetic data were used to obtain sex and ancestry information. ## Genetic data processing and polygenic risk scores (PRS) Genotyping of blood samples was performed at the Genomics Shared Resource at Roswell Park Cancer Institute, using the Infinium Global Screening Array (Illumina, San Diego, CA, USA), according to manufacturer protocols. Genotypes were imputed on the Michigan Imputation Server pipeline v1.2.4, using the Haplotype Reference Consortium reference panel [21]. Before imputation, the genotypes were filtered for ambiguous strand orientation, missingness rate>$5\%$ (by marker exclusion, then by individual), Hardy-Weinberg equilibrium violation ($p \leq 10$−6), and sex mismatch. After imputation, the single-nucleotide polymorphisms (SNPs) were excluded for imputation R2<0.5 and the average call rate below $90\%$. Genotype imputation was performed on 552,230 SNPs, resulting in 25,514,638 SNPs after quality control, which were used for matching to discovery GWAS variants for the final polygenic risk scoring. PRS were created using PRSice 2.0 [22]. PRS were created by aggregating genetic variants that emerged from the GWAS of the phenotype of interest, i.e. the GWAS discovery sample. Our primary analysis used a complete list of SNPs after clumping, and their corresponding weights from GWAS discovery samples (P-value threshold = 1) to incorporate more of the genome [23]. All analyses adjusted for the first ten genetic ancestry principal components (PCs). Primary analyses were conducted on participants of European ancestry due to the discovery GWAS being European; however, sensitivity analyses in the entire sample are also reported in the Supplementary Materials. PRS for asthma and allergic disease were based on two GWAS conducted on participants of European ancestry from the UK Biobank [24]. The asthma GWAS was conducted in 90,853 individuals, 14,085 physician-diagnosed asthma cases, and 76,768 controls without asthma or allergic diseases. Asthma is heterogeneous phenotype and cases with allergic, non-allergic, mixed, and other forms of asthma were included in GWAS. The allergic disease GWAS was conducted in 102,453 individuals, 25,685 with doctor-diagnosed hay fever, allergic rhinitis, eczema, or allergic asthma cases, and 76,768 controls without asthma or allergic disease. The coronary artery disease PRS was based on a GWAS meta-analysis conducted in 122,733 coronary artery disease cases and 424,528 controls from the UK Biobank and the CARDIoGRAMplusC4D Consortium [25]. Cases included patients with coronary artery disease diagnosis or treatment indicated in their electronic medical record or self-reported heart attack/myocardial infarction, coronary angioplasty, or heart bypass. The control group excluded participants who reported that their mother, father, or sibling suffered from heart disease. The type II diabetes PRS was based on a GWAS meta-analysis in 62,892 type II diabetes cases and 596,424 controls of European ancestry [26]. PRS for COVID-19 severity were based on summary statistics from two GWAS meta-analyses conducted by the COVID-19 Host Genetics Initiative [9] release 6, dated June 15, 2021. This initiative is an international collaboration spanning 61 studies from 24 countries. The first GWAS was performed in 2,085,803 individuals, contrasting 24,274 patients hospitalized due to COVID-19 with 2,061,529 population controls presumed to be COVID-19 negative (COVID-PRS hospitalized vs. population). The second GWAS was conducted in 87,671 patients with COVID-19, contrasting 14,480 hospitalized patients with 73,191 non-hospitalized patients (COVID-PRS hospitalized vs. not-hospitalized). ## Analytic approach Linear regression was used to test whether PRS is associated with ordinal COVID-19 severity. Logistic regression was used to test whether PRS is associated with the severe COVID-19 category classification (severe vs. moderate/mild/asymptomatic), and the presence of post-acute COVID-19 symptoms. Continuous variables were standardized prior to analysis by transforming them to Z scores with a mean of zero and standard deviation of one. Models were adjusted for the first ten principal components of the population structure, COVID-19 verification status, age, and sex. Models testing asthma and allergic disease PRS were further adjusted for obstructive airway disease and upper respiratory disease diagnoses to ensure that associations do not merely reflect respiratory illness status. A supplementary analysis further adjusting for PTSD and MDD diagnoses was conducted for asthma PRS, to test the potential role of mental health in the associations. Likewise, models testing PRS for coronary artery disease and type II diabetes further adjusted for BMI. Models testing associations with residual symptoms additionally adjusted for COVID-19 severity. Separate models were conducted for each PRS. The primary analyses were conducted in participants of European ancestry. Given that COVID-19 GWAS were completed using multi-ancestry participants, for sensitivity testing and inclusivity results are also reported for participants of all ancestries in Supplementary Materials. All analyses used nominal p-value threshold <.05 and were conducted in SPSS version 28 and R version 4.1.0. ## Results Descriptive statistics for the European ancestry sample are reported in Table 1. Many responders ($$n = 57$$, $7.0\%$) had severe COVID-19. In total, 247 ($30.4\%$) responders reported at least one residual COVID-19 symptom. Correlations between PRS are reported in S1 Table. Table 1 \| N total \| 813 \| \| --- \| --- \| \| N male (%) \| 772 (95.0%) \| \| Mean age at infection (SD), range \| 56.06 (7.37), 39.79–89.04 \| \| N OAD diagnosis (%) \| 312 (38.4%) \| \| N URD diagnosis (%) \| 525 (64.6%) \| \| N PTSD and/or MDD diagnosis (%) \| 124 (15.3%) \| \| Mean BMI (SD), range \| 31.34 (5.08),19.48–62.18 \| \| N COVID-19 severity (%) \| \| \| Asymptomatic \| 78 (9.6%) \| \| Mild \| 320 (39.4%) \| \| Moderate \| 332 (40.8%) \| \| Severe \| 57 (7.0%) \| \| Missing \| 26 (3.2%) \| \| N any residual symptoms \| 247 (30.4%) \| The PRS for asthma was significantly associated with COVID-19 severity in the European ancestry subsample (β =.09, $$p \leq .01$$), adjusting for co-occurring obstructive airway disease diagnoses and upper respiratory disease diagnoses, Table 2. Likewise, the PRS for asthma was associated with COVID-19 severe category (OR = 1.61, [1.17–2.21]). Allergic disease PRS showed a similar pattern, significantly associating the COVID-19 severe category (OR = 1.97, [1.26–3.07]). Associations with residual COVID-19 symptoms were not significant. There were no significant associations between coronary artery disease or type II diabetes PRS, and COVID-19 phenotypes (Table 3). The COVID-19 PRS based on the GWAS contrasting hospitalized cases vs. controls was significantly associated with the COVID-19 severe category in sample with European ancestry (OR = 1.35, [1.01–1.82]), Table 4. Conversely, the COVID-19 PRS based on the GWAS contrasting hospitalized cases vs. non-hospitalized cases did not reach significance in the European ancestry subsample. Moreover, none of the COVID-19 PRS were associated with the full range of COVID-19 severity, nor the presence of any residual symptoms. ## Sensitivity analyses Descriptive statistics for total sample with participants of all ancestries are reported in S2 Table. In the total sample, adjusting for respiratory disease diagnoses, the PRS for asthma was significantly associated with COVID-19 severity (β =.08, $$p \leq .01$$) and COVID-19 severe category (OR = 1.53, [1.15–2.03]), and the allergic disease PRS was associated with the COVID-19 severe category (OR = 1.86, [1.25–2.77]), S3 Table. There were no significant associations between coronary artery disease or type II diabetes PRS, and COVID-19 phenotypes, S4 Table. The COVID-19 PRS based on the GWAS contrasting hospitalized cases vs. controls was significantly associated with the COVID-19 severe category in the whole sample (OR = 1.42, [1.11–1.82]), S5 Table. Likewise, the COVID-19 PRS based on the GWAS contrasting hospitalized cases vs. non-hospitalized cases was also significantly associated with COVID-19 severe category in the total sample (OR = 1.33, [1.04–1.69]). Accounting for PTSD and MDD diagnoses did not alter associations between asthma PRS and COVID-19 outcomes, see S6 Table. ## Discussion The current study reports associations between recently developed polygenic biomarkers and COVID-19 severity in a community-based population of responders to the WTC disaster who had COVID-19. We found that the PRS for asthma, allergic diseases, and COVID-19 severity were elevated in the severe COVID-19 category responders.. Asthma PRS was additionally associated with COVID-19 symptomatology across the full spectrum of COVID-19 severity. No significant associations were observed for PRS for coronary artery disease and type II diabetes. Moreover, PRS were not associated with post-acute COVID-19 sequelae. These findings add to the growing body of evidence to suggest that existing polygenetic profiles could be informative about the risk for severe COVID-19 presentation, despite the small effect sizes. The current study is the first to report significant associations between asthma PRS and allergic disease PRS, and COVID-19 severity. A previous study in the UK Biobank found no association between asthma PRS and severe COVID-19 [14]. However, the direction of association was positive, and asthma diagnosis was significantly associated with COVID-19 severity (OR = 1.39), in line with epidemiological and clinical evidence [7]. In contrast to Zhu, Hasegawa [14], in our subsample with European ancestry, the PRS for asthma was associated with a 1.61-fold increase in the odds of classification into the most severe COVID-19 category, as well as with the full range of COVID-19 symptom severity, independently of respiratory diagnoses. Our focus on patients with a wide range of COVID-19 symptoms, in contrast with Zhu, Hasegawa [14] focus on hospitalized cases with severe COVID-19, might be a strength of the current study that explains different findings. Other potential explanations include demographic differences between the samples, data collection at different phases of the pandemic, and differences in sample sizes. Allergic disease PRS based on the related GWAS of cases with doctor-diagnosed hay fever, allergic rhinitis, eczema, or allergic asthma doubled the odds of classification into the most severe COVID-19 category in participants of European ancestry (OR = 1.97), adjusting for demographic covariates and respiratory diagnoses. Taken together, these results are consistent with the evidence for the role of immune function genes implicated in COVID-19 severity [8]. It is possible that asthma and allergic sensitization contribute to the pathobiology of severe COVID-19, such as expression of the SARS-CoV-2 receptor ACE2 [27, 28], and that asthma and allergic disease PRS capture these effects. Another possibility is that both asthma and atopic disease have a proclivity toward a TH2 immune response and thus may contribute to a TH1 or TH2 imbalance and a poorer outcome [29]. Finally, despite the substantial genetic correlation between asthma and mental health [30], responders’ history of PSTD and MDD did not explain the association between asthma PRS and COVID-19 severity. Contrary to our hypotheses, PRS for coronary artery disease and type II diabetes were not associated with any COVID-19 outcomes. Cardiovascular and other obesity-related disorders have previously been highlighted as risk factors for severe COVID-19 presentation and residual symptoms [7, 31], including in our sample [2]. However, our findings suggest that genetic vulnerability to these conditions might not contribute prominently to COVID-19 severity. A study found a significant association between BMI PRS and higher risk of severe COVID-19 outcomes such as hospitalization; however, the effect sizes were small (OR = 1.14) [32] and were below effect sizes the present study was powered to detect. Furthermore, the previous study did not find significant relationships between COVID-19 outcomes and PRS for BMI-adjusted waist-to-hip ratio and BMI-adjusted waist circumference, indicating an overall weak association between genetic vulnerability to high weight and COVID-19 severity, consistent with the current findings for PRS developed for coronary artery disease and type II diabetes. The PRS based on the largest GWAS to date comparing hospitalized COVID-19 cases to healthy controls [9] was significantly associated with a 1.35-fold increase in the odds of having had severe COVID-19. This finding agrees with previous studies based on large cohorts such as the UK Biobank which found that COVID-19 PRS were associated with severe COVID-19 outcomes [10–12]. For example, Nakanishi, Pigazzini [10] reported that COVID-19 PRS predicted death or severe respiratory failure (OR = 1.70). Similarly, Horowitz, Kosmicki [11] reported that a PRS based on the top six SNPs associated with COVID-19 predicted a 1.38-fold increased risk of hospitalization and a 1.58-fold increased risk of severe disease in European ancestry participants, with results replicated in other ancestries. Importantly, COVID-19 severity PRS were not associated with the full spectrum of COVID-19 symptoms in our population and did not associate with residual post-acute COVID-19 syndrome. This finding adds novel evidence that the COVID-19 PRS specifically captures genetic vulnerability to a severe response to SARS-CoV-2 infection. ## Strengths and limitations The strengths of the current study include a large sample with well-characterized COVID-19 symptomatology, follow-up information on post-acute COVID-19 sequelae, and application of PRS based on the largest discovery samples available for each disease. Nonetheless, we also note several limitations. First, although we demonstrated that genotyped patients did not differ significantly from non-genotyped patients, the specific inclusion and exclusion criteria might result in selection bias, contributing to the broader non-representativeness of the WTC cohort to the general population. The proportion of individuals with ancestry admixture in the WTC cohort is too small to allow genetic analyses in non-Northern European ancestries. Nonetheless, for transparency, we opted to report analyses in the total sample as well as the European ancestry subsample, and results are notably similar. In either sample, we were underpowered to detect very small associations. Relatedly, the WTC responder population is primarily male, and the number of females was insufficient to explore sex differences. Moreover, we were underpowered to stratify the sample by age, respiratory illness, or other characteristics related to COVID-19 severity. We have adjusted for these covariates in our analyses, but acknowledge that the findings arising from the current study may not generalize to other populations. Second, some participants were included based on infection information that was not independently verified by the study team. However, these participants reported testing positive for SARS-CoV-2 through one of the available testing methods and did not differ from verified participants on any characteristics except COVID-19 severity, which is due to verified records being available for hospitalized responders, suggesting that misclassification was likely to be minimal. The COVID-19 verification status was included as a covariate in all analyses. Third, different viral strains emerged and vaccination status changed over the course of this study. These factors can affect infection susceptibility and COVID-19 disease severity. For example, COVID-19 vaccines are very effective in lowering infection and disease severity [33, 34], which in turn could decrease the role of host genetics in COVID-19 outcomes, attenuating observed PRS associations. Moreover, in vaccinated individuals, PRS associations with COVID-19 severity could instead reflect vulnerability to vaccine breakthroughs. We did not have sufficient data to investigate these issues, which likely have contrasting effects on our results, and limit their comparability to studies conducted at different points in the pandemic. Future studies of PRS are needed to model complex interactions between these factors. Fifth, the current study used asthma PRS based on a GWAS of a broadly defined asthma. However, asthma is a complex and heterogeneous condition, with allergic and non-allergic asthmas demonstrating partially different genetic etiologies [24, 35, 36]. Accordingly, PRS for specific asthma subtypes based on allergy and age-at-onset have been shown to differentially associate with outcomes such as BMI [37]. 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--- title: 'Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach' authors: - Hamidreza Moradi - H. Timothy Bunnell - Bradley S. Price - Maryam Khodaverdi - Michael T. Vest - James Z. Porterfield - Alfred J. Anzalone - Susan L. Santangelo - Wesley Kimble - Jeremy Harper - William B. Hillegass - Sally L. Hodder journal: PLOS ONE year: 2023 pmcid: PMC9997963 doi: 10.1371/journal.pone.0282587 license: CC BY 4.0 --- # Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach ## Abstract ### Background The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19. ### Methods Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients’ outcome of death or discharge. Models leveraged the patients’ characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model’s final outcome prediction. ### Results Gradient Boosted Decision Tree classifiers present the highest prediction accuracy in identifying patient outcomes with area under the receiver operator characteristic curve of 0.90 and accuracy of 0.81 for the outcomes of death or sufficient improvement to be discharged. The resulting model predicts the treatment combinations of anticoagulants and steroids are associated with the highest probability of improvement, followed by combined anticoagulants and targeted antivirals. In contrast, monotherapies of single drugs, including use of anticoagulants without steroid or antivirals are associated with poorer outcomes. ### Conclusions This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. Analysis of the model’s components suggests benefit to treatment with combination of steroids, antivirals, and anticoagulant medication. The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies. ## Introduction At the time of this writing, 8,029 completed or ongoing clinical trials for COVID-19 have been listed in ClinicalTrials.gov [1]. A majority of these trials are prospective randomized controlled trials (RCTs) or similarly designed clinical trials. These approaches offer the benefit of directly comparing therapeutic arms and control groups and can minimize bias. Notably, RCTs necessarily have inclusion and exclusion criteria that can limit the generalizability of the conclusions drawn from them. Further, RCTs, due to economic interests, required sample sizes, and the relative complexity of factorial designs, are rarely designed to explicitly address the optimal therapeutic combination(s) as a function of severity of illness. Due to variable host-virus interactions, patients with SARS-CoV-2 infection may have a range of manifestations ranging from asymptomatic infection to critical illness [2]. Some studies of COVID-19 have described an initial viral stage of illness that can progress to a hyperinflammatory pulmonary stage, which can further evolve to a hypercoagulable phase or a late hyperinflammatory phase, as well as a chronic illness, referred to as the Post-Acute Sequelae of COVID-19 (PASC) or long-COVID [3]. Approaches to managing each of these phases are likely to require combinations of therapies directed at the respective underlying mechanisms and severity of illness. For instance, directly acting antiviral therapies would be anticipated to have the largest impact in the viral phase of the illness while anti-inflammatory treatments may be counterproductive as the body is mounting an antiviral immune response. By contrast, anti-inflammatory therapies would be expected to have the most beneficial effect in patients who have transitioned from the viral phase to a hyperinflammatory phase of illness. Antiviral agents may be less effective in this later phase. Especially in the early days of the pandemic, RCTs necessarily and largely evaluated individual therapeutic agents in critically ill patients, given the more favorable potential risk-benefit ratio. This approach may miss the impact of effective treatment combinations across the spectrum of COVID-19 illness severity. Treatments have largely been studied individually in RCTs; for example, RCTs demonstrated that steroids benefit most patients requiring oxygen therapy [4], and the antiviral drug, Remdesivir, has been used successfully during the viral phase [5]. While one study recently reported benefit from the combination of Remdesivir and dexamethasone [6], data are lacking on the optimal combination of therapies for individual patients at various stages of the illness. Review of patient outcomes from large, real-world data (RWD) sources offers the opportunity to assess the effect of therapies and their combinations not directly or adequately evaluated by RCTs, potentially augmenting our understanding of this increasingly complex therapeutic landscape [7]. Therefore, in this study, we explore the patient and treatment factors, particularly therapeutic agent combinations, associated with better outcomes using machine learning models (ML). The present study addresses key gaps in the extant literature by adopting ML models to evaluate the effect of therapeutic agents, singly and in combination, on patient outcomes using the large N3C cohort of patients. ## Overall setting and study design The National COVID Cohort Collaborative (N3C) is a high-granularity electronic health record (EHR) data repository containing harmonized, patient-level data from 72 sites across the United States (US). They are primarily tertiary care centers but also include data from health information exchanges and community hospitals. N3C data partners contribute data to N3C regularly. As of May 4, 2022 (Release 75), N3C contains data on more than 10 million patients, including more than 4.9 million COVID-19 SARS-CoV-2 infected persons. N3C design, data ingestion and harmonization, and sampling approach have been detailed previously [8, 9]. In brief, N3C contributing sites provide the central repository EHR data, including demographics, healthcare visits, vital signs, medications, laboratory results, and diagnoses which are then harmonized into the Observational Medical Outcomes Partnership (OMOP) common data model. Participating sites submit EHR data on all patients with a positive SARS-CoV-2 lab test (Polymerase Chain Reaction, Antigen, or Antibody) or a COVID-19 diagnosis and a demographically matched comparison group of SARS-CoV-2 uninfected persons (1:2 matching positive: negative). For this study, we modify the N3C COVID-19 positivity definition [10] to exclude those with antibody-only positive results after December 10, 2020, the date when vaccinations became publicly available in the US [11]. To account for differences in data availability at the site level, we excluded sites with low medication reporting (<2 standard deviations below mean reporting for all sites). This approach excluded 17 of the 72 sites in N3C at the time of our data extraction. Institutional Review Board (IRB) approval for this retrospective cohort study is obtained from the University of Mississippi Medical Center (IRB2020V0280, $\frac{3}{31}$/2021), Johns Hopkins University (IRB00249128, $\frac{9}{18}$/2020), Christiana Health (IRB604959, $\frac{5}{07}$/2021), West Virginia University (IRB2012192778, $\frac{12}{17}$/2020), University of Nebraska Medical Center (IRB050-21-EP, $\frac{2}{9}$/2021), Nemour’s Children’s Health (IRB1700991, $\frac{2}{17}$/2022), and Maine Medical Center (IRB1697848-2, $\frac{3}{5}$/2021). Further approval by the N3C Data Access Committee (RP-504BA5) is granted that operates under the authority of the National Institute of Health IRB with Johns Hopkins University School of Medicine serving as the central IRB. A limited dataset was available for this project, however, zip codes were not used for the analyses described in the paper. No informed consent was obtained as the study utilizes a limited dataset. ## Cohort identification For the purpose of this study, we selected COVID-positive patients with at least one day of hospitalization during the 28 days after their initial COVID-19 diagnosis. The cohort under study includes patients in the United States who tested positive for COVID-19 and were hospitalized between January 1, 2020, and July 1, 2021. Selection is then further limited to patients with an outcome of either death or discharge by the 28th day ($$n = 145$$,769) after COVID-19 diagnosis. Patients with any other outcome at the end of the 28-day period are not considered as they are still being treated, and our interest is limited to those who have completed treatments [12–14]. This selected cohort is hereafter referred to as patients with a stable outcome, as treatment duration is completed and the final outcome of either death or discharge has been achieved. Fig 1 presents the information flow diagram for the final cohort under the study. Fig 1: Information flow diagram for the cohort under the study. ## Data extraction Data were extracted on May 4, 2022 (N3C release 75) for the previously defined cohort with a stable outcome before July 1, 2021. The lag between the observation window cutoff and data extraction ensured that data from reporting sites was as complete as possible and placed the observation window before the rapid rise of the Delta variant. We developed concept sets for all conditions, drugs, and procedures used in this study, which include OMOP concept identifiers (derived from SNOMED CT, RxNorm, and other standardized vocabularies) contained with a patient’s EHR. Concept sets in use, available in Table 1, define computable phenotypes to programmatically identify patient health status at a point-in-time. All concept sets in use received review by three clinicians and one informatician during curation and implementation. Table 1 \| Class \| Medication \| Concept Sets \| \| --- \| --- \| --- \| \| Anticoagulants (Coag) \| Apixaban \| 259221776 \| \| Anticoagulants (Coag) \| Betrixaban \| 568693141 \| \| Anticoagulants (Coag) \| Dabigatran \| 23600781 \| \| Anticoagulants (Coag) \| Enoxaparin \| 858278110 \| \| Anticoagulants (Coag) \| Heparin \| 357794478 \| \| Anticoagulants (Coag) \| Rivaroxaban \| 544420473 \| \| Anticoagulants (Coag) \| Warfarin \| 441951686 \| \| Targeted Antivirals (ViralTrgt) \| Remdesivir \| 719693192 \| \| Targeted Antivirals (ViralTrgt) \| Nirmatrelvir/ritonavir (Paxlovid) \| 285332632 \| \| Targeted Antivirals (ViralTrgt) \| Molnupiravir \| 643666235 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Azithromycin \| 359938251 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Doxycycline \| 950251876 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Ciprofloxacin \| 369973585 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Moxifloxacin \| 609610642 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Gemifloxacin \| 382925247 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Delafloxacin \| 103404439 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Gatifloxacin \| 932126058 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Ofloxacin \| 931604126 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Norfloxacin \| 292248378 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Erythromycin \| 4697796 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Clarithromycin \| 4697796 \| \| Macrolide and Quinolone Antibiotics (BiotMQ) \| Levofloxacin \| 4697796 \| \| Spike Protein Monoclonal Antibodies (MonoSP) \| Bamlanivimab \| 804283782 \| \| Spike Protein Monoclonal Antibodies (MonoSP) \| Casirivimab/Imdevimab \| 204936358 \| \| Spike Protein Monoclonal Antibodies (MonoSP) \| Etesevimab \| 985547691 \| \| Spike Protein Monoclonal Antibodies (MonoSP) \| Sotrovimab \| 550646109 \| \| Spike Protein Monoclonal Antibodies (MonoSP) \| Tixagevimab/Cilgavimab \| 809722294 \| \| Spike Protein Monoclonal Antibodies (MonoSP) \| Bamlanivimab-Etesevimab combo \| String search \| \| Spike Protein Monoclonal Antibodies (MonoSP) \| Bebtelovimab \| String search \| \| Steroids Preparations (Ster) \| Dexamethasone \| 213873961 \| \| Steroids Preparations (Ster) \| Hydrocortisone \| 932266800, 422007021 \| \| Steroids Preparations (Ster) \| Methylprednisolone \| 640520004 \| \| Steroids Preparations (Ster) \| Prednisone \| 783588396 \| \| Monoclonal Antibody Immunomodulators (MonoI) \| Tocilizumab \| 276204116 \| \| Monoclonal Antibody Immunomodulators (MonoI) \| Baricitinib \| 394764748 \| \| Monoclonal Antibody Immunomodulators (MonoI) \| Tofacitinib \| 391595378 \| \| Monoclonal Antibody Immunomodulators (MonoI) \| Sarilumab \| 807728943 \| \| Unproven Antiviral Therapies (ViralUnp) \| Hydroxychloroquine \| 807281242 \| \| Unproven Antiviral Therapies (ViralUnp) \| Chloroquine \| 818210864 \| \| Unproven Antiviral Therapies (ViralUnp) \| Ivermectin \| 980395214 \| \| Unproven Antiviral Therapies (ViralUnp) \| Lopinavir/ritonavir \| 165611849 \| \| Unproven Antiviral Therapies (ViralUnp) \| Tenofovir \| 563211602, 568417090 \| \| Unproven Antiviral Therapies (ViralUnp) \| Interferon \| 359012050, 531467540 \| \| Miscellaneous (Misc) \| Vitamin D \| 689338842 \| \| Miscellaneous (Misc) \| Fluvoxamine \| 424477820 \| ## Feature engineering For the identified cohort, we have considered demographics, body mass index (BMI), comorbidities [15], treatment with pressors, the quarter of COVID-19 diagnosis, patient severity at the time of diagnosis, and prescribed treatments as input features for model development. To measure patient severity, we used an Ordinal Scale (OS) developed for use with EHR data [16]. Specifically, this was a 6-point ordinal scale assigned with odd integers from 1 to 11, devised explicitly for patients diagnosed with COVID-19 based on discrete EHR data elements. In this context, a level of 1 represents an outpatient or patient discharged from the hospital, level 3 indicates hospitalization, while being hospitalized on Oxygen or Mechanical Ventilator is an indicator of levels 5 and 7, respectively, with level 9 representing patients hospitalized on ECMO and level 11 representing death. Fig 2 shows the lookback period used for determining the patient’s comorbidities in green with a minimum of 2 years, while highlighted in blue are the considered treatments’ duration within up to 28 days after the diagnosis, followed by the recorded patient’s outcome as of the last day of treatment. Fig 2: Time windows for treatment, comorbidities, and outcome. Prescribed therapeutics on each day after the diagnosis were categorized and considered in eight distinct groups, defined as anticoagulants (Coag), steroid preparations (Ster), unproven antiviral therapies (ViralUnp), targeted antivirals (ViralTrgt), spike protein monoclonal antibodies (MonoSP), monoclonal antibody Immunomodulators (MonoI), macrolide and quinolone antibiotics (BiotMQ), and a miscellaneous treatments (Misc) category that included other treatments presumed to be administered for treatment of COVID-19. Medications in each category are shown in Table 1. The model considered the proportion of days on treatment combinations, any direct correlations between the treatment values and duration of treatment are removed, preventing the ML algorithm from leveraging this information directly for prediction. By using the proportion of days on treatment combinations, the modeling algorithm is forced to find the effect of different treatment distributions rather than attributing days on treatments to the outcome of interest. ## Modeling We implemented three models to predict the final patient outcomes at the end of the 28-day observation window. The first was a Gradient Boosted Decision Tree (GBDT) classifier based on an additive model that tunes a weak learner into a strong one by training on residuals in boosting rounds; GBDT combines the results of previous learners along the way, thus learning from the errors of previous iterations to improve accuracy [17]. Two Neural Network models were also implemented, the first was based on a Deep fully-connected Neural Network (DNN) with a self-attention mechanism to increase the attention of the model to key features. The second was a multi-layer Convolutional Neural Network (CNN), convolving over the features to provide levels of generalization and extract treatment patterns and their effects. For the CNN model, multiple convolution structures based on VGG-16 [18], Inception [19], and DenseNet [20] blocks were evaluated and results for the best model is reported. For the ML models, the input features considered as predictors of outcome are demographics, BMI, quarter of diagnosis, comorbidities, the severity of the patient at the time of diagnosis, being treated with pressors, and prescribed treatment combinations after diagnosis. Due to the sensitivity of ML models to hyper-parameters and to make the study repeatable, we used HyperOpt [21], an open-source Bayesian optimization library, to increase the model’s Area Under receiver operating Characteristic (AUC) curve by fine-tuning the parameters. Hyper-parameter tuning is performed on stratified random train, validation, and test splits of $60\%$, $20\%$, and $20\%$ respectively, with random over-sampling of the training dataset using the SMOTE [22] library to address the data imbalance in the training set. Then, given the discovered hyper-parameters, model evaluation is conducted by 5-fold cross-validation to report the models’ AUC and accuracy. ## Model interpretability Generally, machine learning models are considered black-box procedures, with limited insights and interpretability other than outcome prediction. However, recent years have seen many improvements in the ability to generate robust and interpretable insights from complex ML models [23]. Use of SHapley Additive exPlanation (SHAP) [24] values as an eXplainable Artificial Intelligence (XAI) algorithm can provide insightful interpretations of a complex machine learning model with high accuracy and robustness, similar to human interpretations. The generated SHAP values for input features of ML models can be used to characterize the effect of the inputs on the final model’s prediction. In this study, to communicate the effects of treatment combinations as features of patients’ outcomes, we first trained an accurate ML model on the patients’ data. Then, the trained model is utilized for generating the SHAP values of input features, providing insights into the features’ importance on the probability of a patient discharge prediction. For the analysis, a feature has a positive impact if the feature increases the probability of the discharge prediction, while the negative impact of a feature translates to a decrease in the probability of discharge prediction. After model hyper-parameter optimization, training, and evaluation, the model was retrained on the entire dataset, using the same parameters, to learn all existing interactions within the dataset. Then for SHAP value calculations, two required inputs are generated, background samples as a base of comparisons and input samples for evaluation of the effects. Following SHAP’s best practices for calculating the required background samples in large datasets, we applied the K-Nearest Neighbor clustering algorithm ($K = 50$) to patients in each class of outcome (death and discharge), providing us with a total of 100 cluster centroids to be used for the SHAP analysis. For input samples, we noticed, however, that in a highly imbalanced dataset, averaging the SHAP values for each feature to provide a holistic view of the effect can be biased by the class containing the larger sample size (which in this case was discharge), diminishing the impact of learned interactions within the smaller set (defined by death as the outcome). To overcome any unwanted effects that data imbalance may pose on the results, we used 1:1 matched sets of patients, matching on the demographics (age, sex, race, ethnicity), BMI, comorbidities (specified in Table 2, under comorbidities), quarter of the year, pressor status (presence or absence), and OS level at diagnosis as inputs for SHAP calculation, resulting in a more balanced set of patients, preserving the effects and discriminating factors learned from the smaller set. Table 2 \| Characteristics \| n = 145,769 \| \| --- \| --- \| \| Gender (%) \| \| \| Female \| 71,891 (49.3) \| \| Male \| 73,855 (50.7) \| \| Other/ Unknown \| <25 (0.0) \| \| Age (mean (SD)) \| 59.2 y (19.5) \| \| Race (%) \| \| \| Asian \| 5,305 (3.6) \| \| Black \| 32,467 (22.3) \| \| Native Haw./Pac. Islander \| 318 (0.2) \| \| White \| 75,658 (51.9) \| \| Other/ Unknown \| 30,883 (21.2) \| \| Ethnicity (%) \| \| \| Hispanic/Latino \| 29,419 (20.2) \| \| Not Hispanic/Latino \| 104,200 (71.5) \| \| Other/ Unknown \| 12,068 (8.3) \| \| OS at day 1 (%) \| \| \| OS 1—outpatient \| 35,328 (24.2) \| \| OS 3—hospitalized \| 94,601 (64.9) \| \| OS 5—hospitalized on Oxygen \| 9,326 (6.4) \| \| OS 7—hospitalized on Mechanical Ventilator \| 6,252 (4.3) \| \| OS 9—hospitalized on ECMO \| 262 (0.2) \| \| OS 11—death \| 0 (0) \| \| BMI (mean (SD)) \| 31.0 (7.1) \| \| Comorbidities (%) \| \| \| Hypertension \| 92,767 (63.6) \| \| Diabetes Mellitus \| 33,808 (23.2) \| \| Myocardial Infarction \| 19,197 (13.2) \| \| Congestive Heart Failure \| 31,629 (21.7) \| \| Peripheral Vascular Disease \| 24,011 (16.5) \| \| Stroke \| 24,168 (16.6) \| \| Dementia \| 13,037 (8.9) \| \| Chronic Pulmonary Disease \| 40,545 (27.8) \| \| Rheumatologic Disease \| 8,998 (6.2) \| \| Mild Liver Disease \| 15,014 (10.3) \| \| Severe Liver Disease \| 4,972 (3.4) \| \| Upper GI bleed \| 4,791 (3.3) \| \| Renal Disease \| 33,848 (23.2) \| \| Peptic Ulcer Disease \| 4,496 (3.1) \| \| Paralysis \| 5,116 (3.5) \| \| Cancer \| 14,397 (9.9) \| \| Diabetes with chronic complications \| 27,186 (18.7) \| \| Metastatic solid tumor \| 5,376 (3.7) \| \| HIV/AIDS \| 1,527 (1.0) \| \| Quarter of Diagnosis (%) \| \| \| Jan-Mar 2020 \| 6,956 (4.8) \| \| Apr-Jun 2020 \| 28,837 (19.8) \| \| Jul-Sep 2020 \| 17,111 (11.7) \| \| Oct-Dec 2020 \| 44,210 (30.3) \| \| Jan-Mar 2021 \| 33,468 (23.0) \| \| Apr-Jun 2021 \| 15,173 (10.4) \| \| Outcomes (%, IQR) \| \| \| Discharged \| 128,063 (87.9, 8) \| \| Death \| 17,706 (12.1, 13) \| ## Study population The dataset included 145,769 hospitalized patients (Table 2). Most patients (128,063; $87.9\%$) were discharged alive from the hospital within 28 days of COVID-19 diagnosis while the remaining 17,706 ($12.1\%$) were deceased. Although $24.2\%$ of patients were not hospitalized on day 1 of their diagnosis (OS level 1), they subsequently were hospitalized after day 1 as this study assessed only hospitalized patients. ## Prescribed treatment combinations Among single agent treatments, anticoagulants (Coag), steroids (Ster), and macrolide and quinolone antibiotics (BiotMQ) are the top three most commonly prescribed to patients; $22.7\%$ ($$n = 83$$,665), $6.5\%$ ($$n = 24$$,026), and $3.7\%$ ($$n = 13$$,625), respectively (Fig 3). The three most frequent treatment combinations prescribed were: 1) anticoagulants and steroids with unproven antivirals (ViraUnp) 2) anticoagulants and steroids, and 3) steroids with unproven antivirals (ViraUnp) (Fig 3). Fig 3: Top 10 prescribed treatments to patients. The top two prescribed treatments were also the treatments that patients received for the greatest number of days with $35.2\%$ for anticoagulants and $11.6\%$ for the combination therapy of anticoagulants with unproven antivirals (ViraUnp) and steroids. While steroids alone were the third most used therapeutic agent, patients spent roughly the same days on steroids in combination with anticoagulants ($5.9\%$) and on steroid single therapy alone ($6.8\%$). Fig 4 presents the top 10 therapeutics based on the cumulative number of days they were prescribed to patients. Fig 4: Top 10 treatments based on the number of days prescribed. ## Model accuracy Developing an ML model to leverage the aforementioned curated data and provide an accurate prediction, can be used not only as a predictive measure for taking therapeutic actions, but also as a means to evaluate the effect of patients’ characteristics and prescribed treatment combinations on the final patient outcomes. The devised models have been trained and evaluated using 5-fold cross-validation. Fig 5 shows the Receiver Operating Characteristic (ROC) curve, and accuracy of the models. Our results indicate that the Gradient Boosted Decision Tree (GBDT) classifier has superior (AUC = 0.90) and balanced accuracy ($81\%$ for both death and discharge classes) in identifying and discriminating patient outcomes compared to both Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models. Fig 5: ML models performance in predicting patient outcomes. ## Feature importance Given the accuracy and discriminative ability of the GBDT model, SHAP values were calculated to evaluate the impact of a feature on the model’s prediction. Specifically, positive SHAP values in this context indicate a positive impact on the predicted probability of classifying a patient as discharged while negative SHAP values indicate impact on the predicted probability of death. Fig 6 presents the top 10 features with the highest positive and negative impacts on the model’s predictive ability. It shows that six treatment combinations are among the top ten features with the highest positive impact underlining the importance of combination therapies; the steroid and anticoagulant combination provides the highest positive effect on model prediction. Monotherapies of both steroids and unproven antiviral therapies (ViralUnp) are ranked eighth and tenth after combination therapies. The other two features with high positive impact are COVID diagnosis in the first quarter of 2021 and OS severity level 1 (outpatient status) at the time of diagnosis. Among the features with the most negative impact, age is associated with the strongest negative impact on the model’s classification, followed by two of the single therapies: miscellaneous (Misc) and anticoagulants (Coag) alone. Among the comorbidities, renal disease (Renal), severe liver disease (LiverSevere), Myocardial Infarction (MI), and Congestive Heart Failure (CHF) are most highly associated with negative effects, in decreasing order of importance. In addition, OS levels 7 and 3 at the time of diagnosis are each associated with the negative outcome (death). Fig 6: Top 10 features with the highest impact on final model prediction. ## Discussion We developed accurate machine learning models with high accuracy for predicting death and discharge outcomes from COVID-19. By examing factors contributing to these predictions we can better understand the impact of treatment combinations on outcomes. Specifically, our findings suggest that combination therapy with different classes of drugs is more effective than therapy with only a single agent. These models also demonstrate that patient characteristics and comorbidities such as age, kidney, liver, heart disease, and severity of illness at diagnosis have a large impact on disease outcome, confirming previous literature [25–29]. Indeed, the models suggest that for COVID-19 outcomes, patient characteristics are not surprisingly often as influential as the treatments administered. Of note, pre-existing renal, liver, and heart diseases were strongly associated with poor prognosis. However, several combinations of treatments appear to be associated with better or worse outcomes. Specifically, our models support the efficacy of steroids, antiviral drugs, and anticoagulation while raising the possibility of harm from miscellaneous category therapies of vitamin D and fluvoxamine. Data from clinical trials of fluvoxamine in COVID-19 is mixed; however, our findings support guidelines recommending against its routine use at this time [30, 31]. Similarly, our negative findings regarding vitamin D are consistent with a clinical trial showing lack of efficacy of vitamin D in reducing the length of stay in hospitalized COVID-19 patients [32]. Steroids have a well established therapeutic benefit in COVID-19 patients requiring oxygen but have not been shown to benefit patients not requiring oxygen [33]. With electronic health record data, our models also observe the association between steroid treatment and higher likelihood of recovery among COVID-19 patients requiring oxygen therapy. COVID-19 is associated with micro and macrovascular thrombosis [34–36], and COVID-19 patients have high risk of thrombotic complications such as pulmonary embolism. Therefore, various doses of anticoagulation have been proposed as part of standard COVID-19 treatment. Intermediate dose anticoagulation in ICU patients failed to show benefit [37]. However, among hospitalized patients not requiring ICU care, full dose anticoagulation has been reported to have benefits [38, 39]. If and when to use higher dose anticoagulation remains controversial [40, 41]. Our models suggest the possibility of benefit from the addition of anticoagulation to steroids. The combination of two potentially beneficial therapies, steroids and anticoagulants, being associated with an increased likelihood of recovery may reinforce the need to consider therapeutic combinations when attempting to define the optimal treatment of COVID-19. The association with poor outcome of use of anticoagulants alone without steroids or antivirals is intriguing. Perhaps the use of anticoagulation alone is a marker for patients who were not treated aggressively for COVID-19 or who had comorbidities such as poorly controlled diabetes which might cause clinicians to withhold steroids or renal/hepatic failure that might give clinicians pause regarding the use of Remdesivir. However, this hypothesis cannot be tested in our dataset. Many experts have suggested that combining steroids with antivirals may be beneficial because of the potential immunosuppressive effect of steroids [42, 43]. We expected to see a benefit of the combination of steroids and antivirals with efficacy against COVID-19. However, the overall positive effect of steroids combined with antivirals of unproven efficacy was surprising. It may be that the decision to use the combination of steroids/antiviral drug before proven antiviral drugs were available may have been a marker of other aspects of care (for example, excellent supportive care such as proning) that may have been associated with better outcomes. Since many patients in the dataset were treated before the availability of proven antivirals with efficacy against COVID, this may have led to an association between use of unproven (and likely ineffective) antivirals and reduced mortality. Due to anti-inflammatory effects and proposed antiviral effects, macrolides were applied as possibly effective treatments for COVID-19 early in the pandemic [44]. Similarly, fluoroquinolone antibiotics were also suggested as COVID-19 treatments [45]. This was the rationale for including the antibiotics in our analysis. As enthusiasm for use of these medications for specific treatment of COVID-19 per se has declined, the positive associations found by our machine learning algorithms are perhaps unexpected. Severely ill COVID-19 patients are known to be at high risk of secondary infections [46]. It is possible that macrolides and quinolones treated secondary infections or prevented the development of such infections. Alternatively, the association between antibiotics treatment and improved outcome may be confounded by serving as a marker of more aggressive treatment. Further study of the mechanisms responsible for this association are needed. While this study demonstrates a generally applicable machine learning model (ML) approach to explore treatment factors, particularly therapeutic agent combinations for COVID-19, ML models have been successfully applied to other aspects of the COVID-19 pandemic. More specifically for COVID-19, ML models have been developed and validated to predict the outcomes of COVID-19 patients using metrics collected at the time of admission [47]. Another study using ML evaluated risk factors associated with increased mortality for COVID-19 patients [48]. ML has also been used to show the predictive effect of comorbidities and risk factors on progression of illness in COVID-19 patients [49, 50]. ML models generally demonstrate improved prediction of patient outcomes when compared to conventional statistical approaches [51–53]. Our study has several limitations. First, information on the doses of medications used is not available in the dataset. Similarly, the impact of steroid dose is unknown. However, the results of our study support the need for clinical trials to explore the efficacy of different doses of therapeutic combinations and single therapies. An additional limitation is that we have no knowledge as to why clinicians choose to administer or not administer certain therapeutic agents. Patients with treatment limitations, such as DNR orders, are more likely to die than those without such limitations [54]. It is possible that such care limitations or contraindications, especially early in the pandemic, influenced the decision to use or not use certain treatments. It is also possible that some patients were incidentally positive for COVID-19 but hospitalized for other serious illnesses, although this cannot be determined from the database. Another limitation of our study is the lack of full control over the diagnosis criteria that treating clinicians used and the possibility of false negatives or false positives, however, we followed the best practices provided by the NIH experts to define inclusion criteria for COVID-19 positivity. ## Conclusions Machine learning algorithms can predict mortality in hospitalized COVID-19 patients with a high degree of accuracy. Future work may allow use of such algorithms to identify high risk patients needing more aggressive therapies. In the meantime, our analyses of a large multicenter cohort of COVID-19 patients using machine learning algorithms supports use of steroids, anti-virals, and anticoagulant medications in combination. Further study is needed on the associations of macrolide and fluoroquinolone antibiotics with survival in COVID-19. In addition to the beneficial observed effects of specific treatments and, in particular, their combinations, patient characteristics such as age and comorbidities are strong predictors of increased likelihood of death as expected, perhaps serving as negative controls suggesting validity of the models. More generally, this study demonstrates use of a machine learning model (ML) approach to explore treatment factors, particularly therapeutic agent combinations, associated with outcomes across comorbidity profiles and initial severity of illness. It potentially provides useful evidence, particularly with regard to therapeutic combinations, to supplement evidence from RCTs. ## N3C Attribution The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave covid.cd2h.org/enclave and supported by NCATS U24 TR002306. This research was possible because of the patients whose information is included within the data from participating organizations (covid.cd2h.org/dtas) and the organizations and scientists (covid.cd2h.org/duas) who have contributed to the on-going development of this community resource. ## Individual Acknowledgments for Core Contributors We gratefully acknowledge contributions from the following N3C core teams: (Asterisks indicate leads) ## Data Partners with Released Data Advocate Health Care Network—UL1TR002389: The Institute for Translational Medicine (ITM) • Boston University Medical Campus—UL1TR001430: Boston University Clinical and Translational Science Institute • Brown University—U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Carilion Clinic—UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • Charleston Area Medical Center—U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) • Children’s Hospital Colorado—UL1TR002535: Colorado Clinical and Translational Sciences Institute • Columbia University Irving Medical Center—UL1TR001873: Irving Institute for Clinical and Translational Research • Duke University—UL1TR002553: Duke Clinical and Translational Science Institute • George Washington Children’s Research Institute—UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • George Washington University—UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • Indiana University School of Medicine—UL1TR002529: Indiana Clinical and Translational Science Institute • Johns Hopkins University—UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research • Loyola Medicine—Loyola University Medical Center • Loyola University Medical Center—UL1TR002389: The Institute for Translational Medicine (ITM) • Maine Medical Center—U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Massachusetts General Brigham—UL1TR002541: Harvard Catalyst • Mayo Clinic Rochester—UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) • Medical University of South Carolina—UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR) • Montefiore Medical Center—UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Nemours—U54GM104941: Delaware CTR ACCEL Program • NorthShore University HealthSystem—UL1TR002389: The Institute for Translational Medicine (ITM) • Northwestern University at Chicago—UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) • OCHIN—INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks • Oregon Health & Science University—UL1TR002369: Oregon Clinical and Translational Research Institute • Penn State Health Milton S. Hershey Medical Center—UL1TR002014: Penn State Clinical and Translational Science Institute • Rush University Medical Center—UL1TR002389: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey—UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University—U24TR002306 • The Ohio State University—UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo—UL1TR001412: Clinical and Translational Science Institute • The University of Chicago—UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa—UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. Miller School of Medicine—UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor—UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston—UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston—UL1TR001439: The Institute for Translational Sciences • The University of Utah—UL1TR002538: Uhealth Center for Clinical and Translational Science • Tufts Medical Center—UL1TR002544: Tufts Clinical and Translational Science Institute • Tulane University—UL1TR003096: Center for Clinical and Translational Science • University Medical Center New Orleans—U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • University of Alabama at Birmingham—UL1TR003096: Center for Clinical and Translational Science • University of Arkansas for Medical Sciences—UL1TR003107: UAMS Translational Research Institute • University of Cincinnati—UL1TR001425: Center for Clinical and Translational Science and Training • University of Colorado Denver, Anschutz Medical Campus—UL1TR002535: Colorado Clinical and Translational Sciences Institute • University of Illinois at Chicago—UL1TR002003: UIC Center for Clinical and Translational Science • University of Kansas Medical Center—UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • University of Kentucky—UL1TR001998: UK Center for Clinical and Translational Science • University of Massachusetts Medical School Worcester—UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) • University of Minnesota—UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center—U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center—U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill—UL1TR002489: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center—U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Rochester—UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California—UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont—U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • University of Virginia—UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • University of Washington—UL1TR002319: Institute of Translational Health Sciences • University of Wisconsin-Madison—UL1TR002373: UW Institute for Clinical and Translational Research • Vanderbilt University Medical Center—UL1TR002243: Vanderbilt Institute for Clinical and Translational Research • Virginia Commonwealth University—UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research • Wake Forest University Health Sciences—UL1TR001420: Wake Forest Clinical and Translational Science Institute • Washington University in St. Louis—UL1TR002345: Institute of Clinical and Translational Sciences • Weill Medical College of Cornell University—UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • West Virginia University—U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) ## Additional Data Partners Who Have Signed a DTA and Whose Data Submitted Icahn School of Medicine at Mount Sinai—UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler—UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis—UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine—UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, Los Angeles—UL1TR001881: UCLA Clinical Translational Science Institute • University of California, San Diego—UL1TR001442: Altman Clinical and Translational Research Institute • University of California, San Francisco—UL1TR001872: UCSF Clinical and Translational Science Institute ## Additional Data Partners Who Have Signed a DTA and Whose Data Release is Pending Arkansas Children’s Hospital—UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine—None (Voluntary) • Children’s Hospital of Philadelphia—UL1TR001878: Institute for Translational Medicine and Therapeutics • Cincinnati Children’s Hospital Medical Center—UL1TR001425: Center for Clinical and Translational Science and Training • Emory University—UL1TR002378: Georgia Clinical and Translational Science Alliance • HonorHealth—None (Voluntary) • Loyola University Chicago—UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin—UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute—UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth—None (Voluntary) • Montana State University—U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center—UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Ochsner Medical Center—U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute—UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research—None (Voluntary) • Stanford University—UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University—UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute—UL1TR002550: Scripps Research Translational Institute • University of Florida—UL1TR001427: UF Clinical and Translational Science Institute • University of New Mexico Health Sciences Center—UL1TR001449: University of New Mexico Clinical and Translational Science Center • University of Texas Health Science Center at San Antonio—UL1TR002645: Institute for Integration of Medicine and Science • Yale New Haven Hospital—UL1TR001863: Yale Center for Clinical Investigation The Rockefeller University—UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute—UL1TR002550: Scripps Research Translational Institute • University of Texas Health Science Center at San Antonio—UL1TR002645: Institute for Integration of Medicine and Science • The University of Texas Health Science Center at Houston—UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • NorthShore University HealthSystem—UL1TR002389: The Institute for Translational Medicine (ITM) • Yale New Haven Hospital—UL1TR001863: Yale Center for Clinical Investigation • Emory University—UL1TR002378: Georgia Clinical and Translational Science Alliance • Weill Medical College of Cornell University—UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • Montefiore Medical Center—UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Medical College of Wisconsin—UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • University of New Mexico Health Sciences Center—UL1TR001449: University of New Mexico Clinical and Translational Science Center • George Washington University—UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • Stanford University—UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • Regenstrief Institute—UL1TR002529: Indiana Clinical and Translational Science Institute • Cincinnati Children’s Hospital Medical Center—UL1TR001425: Center for Clinical and Translational Science and Training • Boston University Medical Campus—UL1TR001430: Boston University Clinical and Translational Science Institute • The State University of New York at Buffalo—UL1TR001412: Clinical and Translational Science Institute • Aurora Health Care—UL1TR002373: Wisconsin Network For Health Research • Brown University—U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Rutgers, The State University of New Jersey—UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Loyola University Chicago—UL1TR002389: The Institute for Translational Medicine (ITM) • #N/A—UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Children’s Hospital of Philadelphia—UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Kansas Medical Center—UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • Massachusetts General Brigham—UL1TR002541: Harvard Catalyst • Icahn School of Medicine at Mount Sinai—UL1TR001433: ConduITS Institute for Translational Sciences • Ochsner Medical Center—U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • HonorHealth—None (Voluntary) • University of California, Irvine—UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, San Diego—UL1TR001442: Altman Clinical and Translational Research Institute • University of California, Davis—UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, San Francisco—UL1TR001872: UCSF Clinical and Translational Science Institute • University of California, Los Angeles—UL1TR001881: UCLA Clinical Translational Science Institute • University of Vermont—U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Arkansas Children’s Hospital—UL1TR003107: UAMS Translational Research Institute. ## Review board approvals and consent to participate National Institute of Health’s (NIH) National COVID Cohort Collaborative (N3C) Data Utilization Request Approval committee approved the data utilization request of this project (RP-B3442B), which is approved under the authority of the National Institutes of Health Institutional Review Board and with Johns Hopkins University School of Medicine serving as a central institutional review board. The study protocol was reviewed by the University of Mississippi Medical Center (IRB2020V0280) and Johns Hopkins University’s (IRB00309495) IRBs. The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources. No informed consent was obtained because the study used a limited data set. ## Review board approval and consent National Institute of Health’s (NIH) National COVID Cohort Collaborative (N3C) Data Utilization Request (DUR) approval committee approved the data utilization request of this project (RP-504BA5). Each author’s home Institutional Review Board approved the study protocol (HM and WH # 2020V0280; TB #1700991; MK, BP, WK, and SH #2012192778; JA and JH #050-21-EP; SLS #1697848–2, MV #604959). 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--- title: 'Addressing non-medical health-related social needs through a community-based lifestyle intervention during the COVID-19 pandemic: The Black Impact program' authors: - Joshua J. Joseph - Darrell M. Gray - Amaris Williams - Songzhu Zhao - Alicia McKoy - James B. Odei - Guy Brock - Dana Lavender - Daniel M. Walker - Saira Nawaz - Carrie Baker - Jenelle Hoseus - Tanikka Price - John Gregory - Timiya S. Nolan journal: PLOS ONE year: 2023 pmcid: PMC9997965 doi: 10.1371/journal.pone.0282103 license: CC BY 4.0 --- # Addressing non-medical health-related social needs through a community-based lifestyle intervention during the COVID-19 pandemic: The Black Impact program ## Abstract ### Background Non-medical health-related social needs (social needs) are major contributors to worse health outcomes and may have an adverse impact on cardiovascular risk factors and cardiovascular disease. The present study evaluated the effect of a closed-loop community-based pathway in reducing social needs among Black men in a lifestyle change program. ### Methods Black men ($$n = 70$$) from a large Midwestern city participated in Black Impact, a 24-week community-based team lifestyle change single-arm pilot trial adapted from the Diabetes Prevention Program and American Heart Association’s (AHA) Check, Change, Control Blood Pressure Self-Management Program, which incorporates AHA’s Life’s Simple 7 (LS7) framework. Participants were screened using the Centers for Medicare and Medicaid Services (CMS) Accountable Health Communities Health-Related Social Needs Screening Tool. Participants with affirmative responses were referred to a community hub pathway to address social needs. The primary outcome for this analysis is change in social needs based on the CMS social needs survey at 12 and 24 weeks using mixed effect logistic regressions with random intercepts for each participant. Change in a LS7 score (range 0–14) from baseline to 12 and 24 weeks was evaluated using a linear mixed-effects model stratified by baseline social needs. ### Results Among 70 participants, the mean age of participants was 52 ±10.5 years. The men were sociodemographically diverse, with annual income ranging from <$20,000 ($6\%$) to ≥$75,000 ($23\%$). Forty-three percent had a college degree or higher level of education, $73\%$ had private insurance, and $84\%$ were employed. At baseline $57\%$ of participants had at least one social need. Over 12 and 24 weeks, this was reduced to $37\%$ (OR 0.33, $95\%$CI: 0.13, 0.85) and $44\%$ (OR 0.50, $95\%$CI: 0.21, 1.16), respectively. There was no association of baseline social needs status with baseline LS7 score, and LS7 score improved over 12 and 24 weeks among men with and without social needs, with no evidence of a differential effect. ### Conclusions The Black Impact lifestyle change single-arm pilot program showed that a referral to a closed-loop community-based hub reduced social needs in Black men. We found no association of social needs with baseline or change in LS7 scores. Further evaluation of community-based strategies to advance the attainment of LS7 and address social needs among Black men in larger trials is warranted. ## Introduction Non-medical health-related social needs (social needs) are individual social and economic needs such as housing, food, transportation, and protection from violence [1]. Social needs are major contributors to worse health outcomes [2–4] and are estimated to impact up to 50–$60\%$ of health outcomes [5]. There is a strong body of evidence supporting social needs as a critical lever toward the achievement of health equity and the need to expand the healthcare sector’s purview beyond the traditional walls of a healthcare system [6]. Interventions to address social needs have been shown to: 1) improve processes (e.g. identification of social needs, referrals, and enrollment in community resources); and 2) lower cost and improve utilization (e.g. improved preventive care utilization, decreased length of stay and hospital readmissions) [7–10]. Evidence of health improvements after addressing social needs is mixed, with some studies showing an improvement in blood pressure, lipids, and fruit/vegetable consumption, while other studies did not show improvement in glycemic measures [7–10]. Emerging data shows that higher intervention dosage (number of contacts between the navigator and patient/participant) may be related to greater success of resource connections, with in-person contact being associated with the highest likelihood of success [11]. In the United States, the prevalence of social needs is higher among racial/ethnic minority groups, which impacts heart healthy behaviors. Black, Latino, and Filipino adults in the Kaiser Permanente Northern California integrated primary and specialty health care network were more likely than Whites to be in a lower income category and worry about their financial situation [12]. Cost-related reduced medication use was higher among Black individuals, and cost-related reduced fruit/vegetable consumption was higher among Black and Latino populations [12]. Racial/ethnic disparities in income were observed within similar levels of education [12]. In Black adults in the Jackson Heart Study, lower individual income, neighborhood socioeconomic status, and education were all significantly associated with lower American Heart Association (AHA) Life’s Simple 7 (LS7) scores (LS7 metrics include physical activity, diet, cholesterol, blood pressure, body mass index (BMI), smoking, and glycemia) [13]. In community-dwelling Black men participating in African American Male Wellness Walks, lower annual income (<$20,000 vs. ≥$75,000) and Medicare or no insurance vs. private insurance were associated with worse AHA cardiovascular health [14]. Unfortunately, interventions addressing social needs and cardiovascular risk factors have been limited in all populations, including racial/ethnic minority groups. This gap is troubling because in the United States, Black men have lower attainment of ≥ 5 AHA LS7 metrics compared to women and non-Hispanic White (White) populations [15]. Higher AHA LS7 scores are associated with lower risk of cardiovascular disease, type 2 diabetes (diabetes), cancer, and mortality among all races/ethnicities [16–19]. Thus, addressing social needs as one avenue to improve cardiovascular risk factors is critical given the widening racial disparities in preventable deaths from heart disease and stroke [20,21]. Our research group maintains a community-engaged and community-based focus founded in academic-community-government partnerships to advance health [22–24]. In a systematic review, we found no evidence of previous community-based participatory research (CBPR) approaches focused on LS7 in Black men [22]. Thus, using a CBPR approach with our community partner, The National African American Male Wellness Agency (AAMWA) and community members, we co-designed Black Impact, a 24-week CBPR study which improved LS7 attainment in Black men residing in a large Midwestern city [25]. Black Impact had 3 main components: 1) 24-week physical activity, nutrition and education intervention in Black men [25]; 2) Navigating participants without a primary care provider to establish care with a provider and improve patient activation; and 3) Addressing social needs that present barriers to wellness. The current report evaluates the baseline social needs screening, referral and outcomes and the impact on cardiovascular health scores. The study team hypothesized that: 1) participants’ social needs would improve over the course of the intervention; 2) participants with social needs would have worse cardiovascular health at baseline; and 3) baseline social needs would lower the magnitude of improvement in cardiovascular health scores. ## Study design and recruitment As has been described previously and is shown in Fig 1 [25], we enrolled Black men from the annual AAMWA walk/health fair with poor or average cardiovascular health (< 4 LS7 metrics in the ideal range). The inclusion criteria included: 1) Black men (self-report); 2) adult age 18 years or older; 3) poor or average cardiovascular health (< 4 LS7 metrics in the ideal range); English speaking; 5) lives in Metropolitan Columbus, Ohio area; 6) no healthcare provider-imposed limitations on physical activity; and 7) participant has no contraindications for a group setting. In February 2020, 100 Black men were enrolled in the pilot study and divided into 6 geographic-based teams by the study team [26]. The sample size was based on the number of participants needed to determine effect sizes for the primary outcome (50–100 participants) [26]. Due to COVID-19, the study was paused prior to initiation. In July 2020, the study began with 74 participants with programming through December 2020 [25]. The Black Impact programming phase was implemented over 24 weeks from July 2020 to December 2020. Twelve- and 24-week biometric health screenings occurred at study sites, and survey data were collected electronically via Research Electronic Data Capture (REDCap). The study was reviewed and approved by The Ohio State University Biomedical Sciences Institutional Review Board (Study ID: 2019H0302) and was retrospectively registered on ClinicalTrials.gov Identifier: NCT04787978 on March 9, 2021. The principal investigators were unaware of the necessity for clinical trial registration of pilot single-arm clinical trials at study commencement and confirm that future trials will be prospectively registered. All participants provided written informed consent. Fig 1: Consort 2010 flow diagram. ## Intervention The 24-week community-based lifestyle intervention focused on health education, physical activity and addressing social needs through screening and service coordination aimed to improve cardiovascular health among Black men. This single-arm pilot study was adapted from the Diabetes Prevention Program [27] and AHA Check, Change, Control programs, applying evidence-based strategies and stakeholder feedback [28]. Thus, participants were not randomized, and all received the entire intervention using a single-arm trial design [25,26]. Each participant was assigned to a health coach and grouped into six teams of 8–25 participants based on participant proximity to a central meeting location (e.g., Columbus Recreation and Parks recreation center). The Black Impact physical activity, nutrition, and education intervention has been described previously [25]. The Black Impact intervention was grounded in the social cognitive theory at the individual level, and used a multi-level framework consistent with the socioecological model (individual, interpersonal, organizational, community and policy). Our research team used the PETAL framework for CBPR: 1) prioritize health equity; 2) engage the community; 3) target health disparities; 4) act on the data; and 5) learn and improve [22–24,29]. As part of the community engagement and co-designing of the intervention, addressing social needs was determined to be a key component, consistent with the work of Kangovi and others [1]. At baseline, participants were screened for social needs using The Center for Medicare and Medicaid (CMS) Accountable Health Communities Health-Related Social Needs Screening Tool by the study team. The screening tool includes 26 questions addressing living situation, food security, transportation, utilities, safety, financial strain, employment, family and community support, education, physical activity, substance abuse, mental health, and disabilities [30,31]. Participants who screened positive for any social needs were referred to the Healthcare Collaborative of Greater Columbus Central (HCGC) Ohio Pathways Hub (Hub) using a secure web interface by Black Impact study staff. Black Impact participants were paired with a community health worker (CHW) from a care coordination agency (CCA) within the HCGC Hub. There were 13 CCAs employing over 30 CHWs participating in the HCGC Hub. CHWs served as partners, advocates, and coaches for their clients and worked to identify health needs and risks. The CHW contacted the study participants and conducted a comprehensive social needs screening assessment which aligned with the CMS screening tool but went into greater depth. Each risk or need was then translated into a pathway, with the CHW guiding participants through the appropriate care pathways, which were tracked in the Care Coordination Systems (CCS) secure data collection platform. CHWs were required to meet face-to-face with each participant monthly as well as have a second contact (phone, text, or email) per month. CHWs continued assisting participants in completing pathways and mitigating risks until participants’ needs were addressed. In the CCS secure data collection platform, referrals from the Black Impact program were flagged into a program designation and pertinent data was aggregated. HCGC reviewed the participants on an ongoing basis via a Checklist, Pathways, Tools (CPT) report and shared progress with Black Impact study staff. The report articulated all comprehensive risk assessments, any care pathways that were opened, completed successfully, or unsuccessfully and the reason why, and any tools utilized to help support client needs. The complete process is shown in Fig 2. Fig 2: Flow diagram for participants engaged in central ohio pathways hub. The model to address social needs noted above was derived from the validated Agency for Healthcare Research and Quality (AHRQ) Pathways Community Hub model [32]. This comprehensive, evidence-based approach leverages the known impact of care coordination facilitated by a Community Health Worker (CHW) to complete social needs-related screenings and referrals to address identified needs via coordination of health and social services across multiple community settings [33–36]. Notably, the Pathways Community Hub model facilitates cross-sector integration by supporting data sharing and aligning payment models through reimbursement for completed referrals to community-based organizations (CBOs) [37,38]. Specifically, the HCGC Hub model consists of three features [39]: [1] A regional coordination entity that employs CHWs to assess the medical and social needs of vulnerable patients and connect them to community resources; [2] the CHWs initiate a “care pathway,” a defined action plan that describes how patient needs will be addressed, which is then recorded and tracked in an electronic database (S1 Table); [3] completion of each care pathway is linked to payment from healthcare payers (Medicaid-managed care plans and other community partners) based on specific performance benchmarks. A financial contract is attached to each standardized care pathway; when a care pathway is completed, a CHW must confirm that a measurable outcome (e.g., patient has received food) is obtained in order for the agency to receive payment (Fig 2). ## Data collection and measures Biometric assessments were performed at baseline, 12 weeks, and 24 weeks. Data from participants included self-reported measures (sociodemographic and self-reported health history), survey data collected via REDCap, including the CMS Accountable Health Communities Health-Related Social Needs Screening Tool either onsite at the Recreation and Parks locations or at participant homes [30]. Biometric measurements, including blood pressure (mmHg), fasting cholesterol (mg/dl), fasting glucose (mg/dl), weight (lbs), and BMI were collected onsite at the Recreation and Parks locations and recorded in REDCap at each time point. The sociodemographic data included age, education, race, ethnicity, employment status, insurance status, and annual income. The self-reported health history included hypertension, diabetes, hyperlipidemia, and smoking status (I have never smoked, I currently smoke, I quit smoking > 1 year ago or I quit smoking ≤ 1 year ago), as well as medications for the aforementioned chronic conditions. The survey data included the Diet History Questionnaire (DHQ) III [40]. The DHQ-III nutrient and food group database is based on a compilation of national 24-hour dietary recall data from the National Health and Nutrition Examination Surveys (NHANES). Prior research has shown the questionnaire is valid and reliable [41–43]. In the current evaluation, we calculated physical activity minutes per week using the validated moderate physical activity 2-question physical activity questionnaire within the CMS screening tool [44]. Biometric screenings were performed by trained healthcare staff, including nurses and physicians. Blood pressure was checked via an automated oscillometric sphygmomanometer (Omron 5 series) with two measurements performed after the participants were seated for 5 minutes and averaged. Weight was measured using a zeroed and calibrated Omron Body Composition Monitor and Scale (Model: HBF-514C). Height was measured via a tape measurer. BMI was calculated by multiplying weight (lbs) by 703 and then dividing by height squared (inch2). Blood total cholesterol and glucose were measured in the fasting state using the Cardio Check Silver® (Polymer Technology, Inc., Heath, OH, USA) device. All participants received individual results at baseline, 12, and 24 weeks. ## Social needs outcome The main outcome in this analysis was change in social needs at 12 and 24-weeks compared to baseline. Social needs were coded as a “1” if any social need was identified on the CMS Accountable Health Communities Health-Related Social Needs Screening Tool [30,31] and “0” if none were identified. The social needs in the analysis included: 1) unstable or unsafe living situation; 2) food insecurity; 3) lack of transportation; 4) challenges with utilities; 5) physical safety; 6) financial strain; and 7) employment. The safety score was calculated from the 4 question HITS short domestic violence screening tool (a component of the CMS screening tool) with Likert scale answers from “Never” to “Frequently” with scores ranging from 1 (Never) to 5 (Frequently) [45]. A score of 11 or more when the numerical values for answers to questions 7–10 were added showed that the person might not be safe and was coded as a “1,” as has been validated in women and men [45,46]. ## Cardiovascular health outcomes The secondary outcome measure was change in LS7 cardiovascular health score (range 0–14) by baseline social needs. The LS7 cardiovascular health score was summed based on the individual LS7 metrics (glucose, cholesterol, blood pressure, BMI, physical activity, diet and smoking) categories of poor (0 points), intermediate [1] and ideal [2] cardiovascular health at baseline, 12 and 24 weeks (S2 Table), based on the AHA guidelines [47], as has been done previously [17,18,25]. Additionally, we developed a score using 6 components of the LS7 cardiovascular health score excluding diet (range 0–12) and 5 components excluding diet and physical activity (range 0–10). The 5 and 6 component scores were used in sensitivity analyses to confirm the robustness of the findings given that diet and physical activity were self-reported. ## Statistical analysis Descriptive statistics were performed for all variables, including mean (standard deviation [SD]) for continuous variables and frequencies and percentages for categorical variables. These characteristics have been compared between participants with and without social needs using two-sample t-test for continuous variables and chi-squared or fisher’s exact test for categorical variables. Mixed effect logistic regressions (generalized mixed models) with random intercepts for each participant were used to examine the change of social need from baseline to 24 weeks. Odds ratios (ORs) and corresponding $95\%$ confidence intervals (CIs) were reported. The models were sequentially adjusted for: 1) age and; 2) age and education. Additionally, change in LS7 cardiovascular health was calculated using linear mixed models with random intercepts for each participant stratified by baseline social needs status. Sensitivity analyses were performed where each instance of a social need from the first 12 questions of the CMS survey were counted at each time point and listed in S3 Table. If a participant had one of those listed social needs at a time point, they were considered to have a social need at that time point. A separate data set was created that included only those men who had a social need at any time point ($$n = 44$$). A spaghetti plot showing change in social needs among these participants per time point is presented in Fig 3. Statistical significance for all analyses was defined as two-sided alpha < 0.05. Statistical analyses were performed using SAS 9.4 (SAS Institute, Inc.; Cary, North Carolina, USA) and R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria). Fig 3: Non-medical health related social needs in black impact at 0, 12 and 24 Weeks.Lines represent the 44 participants that had social need(s) at some point during the study. Four had no social needs at baseline, then gained at least one social need as the study progressed. Forty had a social need at baseline. Thirteen of the 40 had no social needs by week 12, and one participant took until week 24 to resolve his social need(s). Six of the 13 that had social needs addressed by week 12 had regained social need(s) by week 24. The lines in the plot have random jitter added to allow individual participants to be distinguished. ## Results Seventy-four Black men participated in the intervention, and 70 are included in the current analysis (Fig 1). Baseline demographic characteristics of Black Impact participants are shown in Table 1. The mean age of participants was 52.0 years (SD 10.5). All participants had a high school degree or equivalent and $43\%$ had a college degree or higher level of education. The majority of participants were employed with private insurance ($84.3\%$ and $72.9\%$, respectively). The income of participants was heterogeneous, ranging from <$20,000 ($5.7\%$) to ≥$75,000 ($22.9\%$). LS7 cardiovascular health scores had a high proportion of participants in the poor range for blood pressure ($47.1\%$), glucose ($28.6\%$), body mass index ($54.3\%$), and diet ($40.0\%$). At baseline there was no difference in cardiovascular health scores stratified by social needs status (identified social need vs. not) using 5 (excluding diet and physical activity), 6 (excluding diet), or all 7 components of the cardiovascular health scores (Life’s Simple 7). Table 1 \| Baseline Characteristicsa,b \| Overall(N = 70) \| No SocialNeeds (N = 30) \| Social Needs(N = 40) \| p-value \| \| --- \| --- \| --- \| --- \| --- \| \| Age \| 52.0 (10.5) \| 54.9 (9.29) \| 49.8 (11.0) \| 0.046 \| \| Marital Status \| \| \| \| 0.091d \| \| Never Married \| 18 (25.7%) \| 4 (13.3%) \| 14 (35.0%) \| \| \| Married \| 37 (52.9%) \| 19 (63.3%) \| 18 (45.0%) \| \| \| Separated \| 1 (1.4%) \| 1 (3.3%) \| 0 (0%) \| \| \| Divorced \| 13 (18.6%) \| 5 (16.7%) \| 8 (20.0%) \| \| \| Widowed \| 1 (1.4%) \| 1 (3.3%) \| 0 (0%) \| \| \| Number of Children \| 3.03 (1.57) \| 3.30 (1.29) \| 2.83 (1.74) \| 0.213 \| \| Annual Income \| \| \| \| <0.001d \| \| <$20,000 \| 4 (5.7%) \| 0 (0%) \| 4 (10.0%) \| \| \| $20,000-$49,999 \| 19 (27.1%) \| 7 (23.3%) \| 12 (30.0%) \| \| \| $50,000-$74,999 \| 21 (30.0%) \| 5 (16.7%) \| 16 (40.0%) \| \| \| ≥$75,000 \| 16 (22.9%) \| 15 (50.0%) \| 1 (2.5%) \| \| \| Missing \| 10 (14.3%) \| 3 (10.0%) \| 7 (17.5%) \| \| \| Education \| \| \| \| 0.640d \| \| High School or equivalent \| 6 (8.6%) \| 2 (6.7%) \| 4 (10.0%) \| \| \| Vocational/Technical School (2 year) \| 7 (10.0%) \| 3 (10.0%) \| 4 (10.0%) \| \| \| Some College \| 27 (38.6%) \| 9 (30.0%) \| 18 (45.0%) \| \| \| College Graduate (4 year) \| 18 (25.7%) \| 9 (30.0%) \| 9 (22.5%) \| \| \| Master’s Degree (MS) \| 11 (15.7%) \| 6 (20.0%) \| 5 (12.5%) \| \| \| Professional Degree (MD,JD, etc.) \| 1 (1.4%) \| 1 (3.3%) \| 0 (0%) \| \| \| Employed \| \| \| \| 0.142 \| \| No \| 11 (15.7%) \| 2 (6.7%) \| 9 (22.5%) \| \| \| Yes \| 59 (84.3%) \| 28 (93.3%) \| 31 (77.5%) \| \| \| Health Insurance \| \| \| \| 0.418d \| \| Private insurance \| 51 (72.9%) \| 25 (83.3%) \| 26 (65.0%) \| \| \| Medicaid/Medicare \| 6 (8.6%) \| 2 (6.7%) \| 4 (10.0%) \| \| \| Military insurance \| 4 (5.7%) \| 1 (3.3%) \| 3 (7.5%) \| \| \| No insurance \| 9 (12.9%) \| 2 (6.7%) \| 7 (17.5%) \| \| \| Systolic Blood Pressure (mmHg) \| 139 (20.2) \| 140 (16.2) \| 139 (23.0) \| 0.845 \| \| Missing \| 1 (1.4%) \| 0 (0%) \| 1 (2.5%) \| \| \| Diastolic Blood Pressure (mmHg) \| 87.5 (13.4) \| 88.8 (13.0) \| 86.5 (13.9) \| 0.477 \| \| Missing \| 1 (1.4%) \| 0 (0%) \| 1 (2.5%) \| \| \| Blood Glucose (mg/dL) \| 123 (53.3) \| 124 (43.6) \| 122 (60.1) \| 0.865 \| \| Total Cholesterol (mg/dL) \| 159 (44.5) \| 163 (45.0) \| 157 (44.6) \| 0.593 \| \| Body Weight (lbs) \| 237 (65.2) \| 240 (59.9) \| 235 (69.5) \| 0.740 \| \| Body Mass Index (kg/m2) \| 33.1 (7.57) \| 33.8 (7.45) \| 32.6 (7.72) \| 0.519 \| \| Cholesterol Medications \| \| \| \| 0.461 \| \| No \| 51 (72.9%) \| 20 (66.7%) \| 31 (77.5%) \| \| \| Yes \| 19 (27.1%) \| 10 (33.3%) \| 9 (22.5%) \| \| \| Diabetes Medication \| \| \| \| 0.494 \| \| No \| 53 (75.7%) \| 21 (70.0%) \| 32 (80.0%) \| \| \| Yes \| 17 (24.3%) \| 9 (30.0%) \| 8 (20.0%) \| \| \| Anti-Hypertensive Medications \| \| \| \| 0.809 \| \| No \| 35 (50.0%) \| 14 (46.7%) \| 21 (52.5%) \| \| \| Yes \| 35 (50.0%) \| 16 (53.3%) \| 19 (47.5%) \| \| \| Life’s Simple 7 Score (0–14) c \| 7.48 (1.76) \| 7.54 (1.58) \| 7.44 (1.91) \| 0.834 \| \| Missing \| 10 (14.3%) \| 4 (13.3%) \| 6 (15.0%) \| \| \| Life’s Simple 6 Score (0–12) c \| 6.78 (1.75) \| 6.73 (1.60) \| 6.82 (1.88) \| 0.839 \| \| Missing \| 1 (1.4%) \| 0 (0%) \| 1 (2.5%) \| \| \| Life’s Simple 5 Score (0–10) c \| 5.33 (1.69) \| 5.20 (1.58) \| 5.44 (1.79) \| 0.570 \| \| Missing \| 1 (1.4%) \| 0 (0%) \| 1 (2.5%) \| \| \| LS7 Body Mass Index \| \| \| \| 0.202 \| \| Ideal \| 7 (10.0%) \| 1 (3.3%) \| 6 (15.0%) \| \| \| Intermediate \| 25 (35.7%) \| 13 (43.3%) \| 12 (30.0%) \| \| \| Poor \| 38 (54.3%) \| 16 (53.3%) \| 22 (55.0%) \| \| \| LS7 Physical Activity \| \| \| \| 0.567d \| \| Ideal \| 36 (51.4%) \| 17 (56.7%) \| 19 (47.5%) \| \| \| Intermediate \| 29 (41.4%) \| 12 (40.0%) \| 17 (42.5%) \| \| \| Poor \| 5 (7.1%) \| 1 (3.3%) \| 4 (10.0%) \| \| \| LS7 Blood Glucose \| \| \| \| 0.633 \| \| Ideal \| 18 (25.7%) \| 6 (20.0%) \| 12 (30.0%) \| \| \| Intermediate \| 32 (45.7%) \| 15 (50.0%) \| 17 (42.5%) \| \| \| Poor \| 20 (28.6%) \| 9 (30.0%) \| 11 (27.5%) \| \| \| LS7 Blood Pressure \| \| \| \| 0.332d \| \| Ideal \| 5 (7.1%) \| 1 (3.3%) \| 4 (10.0%) \| \| \| Intermediate \| 31 (44.3%) \| 12 (40.0%) \| 19 (47.5%) \| \| \| Poor \| 33 (47.1%) \| 17 (56.7%) \| 16 (40.0%) \| \| \| Missing \| 1 (1.4%) \| 0 (0%) \| 1 (2.5%) \| \| \| LS7 Smoking Status \| \| \| \| 0.125d \| \| Ideal \| 58 (82.9%) \| 28 (93.3%) \| 30 (75.0%) \| \| \| Intermediate \| 2 (2.9%) \| 0 (0%) \| 2 (5.0%) \| \| \| Poor \| 10 (14.3%) \| 2 (6.7%) \| 8 (20.0%) \| \| \| LS7 Cholesterol \| \| \| \| 0.929d \| \| Ideal \| 39 (55.7%) \| 16 (53.3%) \| 23 (57.5%) \| \| \| Intermediate \| 26 (37.1%) \| 12 (40.0%) \| 14 (35.0%) \| \| \| Poor \| 5 (7.1%) \| 2 (6.7%) \| 3 (7.5%) \| \| \| LS7 Diet \| \| \| \| 1.000d \| \| Ideal \| 1 (1.4%) \| 0 (0%) \| 1 (2.5%) \| \| \| Intermediate \| 32 (45.7%) \| 14 (46.7%) \| 18 (45.0%) \| \| \| Poor \| 28 (40.0%) \| 12 (40.0%) \| 16 (40.0%) \| \| \| Missing \| 9 (12.9%) \| 4 (13.3%) \| 5 (12.5%) \| \| Forty out of the 70 men ($57.1\%$) had an identified social need. Thirty-one of the 40 men were interested and referred to the HCGC Hub. Eight of the thirty-one men were enrolled into pathways including education, social services, medical referral, behavioral health, employment, and medical home pathways. The longitudinal change in social needs is shown in Table 2. The $57.1\%$ of participants with an identified social need at baseline decreased to $36.6\%$ and $44.2\%$ at 12 and 24 weeks, respectively. The odds of having a social need at week 12 and 24 were $67\%$ lower (OR 0.33, $95\%$ CI: 0.13, 0.85) and $50\%$ lower (OR 0.50, $95\%$ CI: 0.21, 1.16) than baseline. Given the study occurred during the COVID-19 pandemic we also evaluated social needs using 5 of the 7 social needs components excluding financial strain or employment. The results were similar with significant reductions in social needs at 12 weeks ($$p \leq 0.035$$) and a trend at 24 weeks ($$p \leq 0.13$$). Among the 7 individual social needs the majority showed trends in the direction of improvement except for employment with a numerically higher proportion (non-significant) of participants indicating a desire for “help finding work” or “help keeping work”. Table 2 \| Social Needs \| Intervention Week \| Participants \| Participants with Social Needs (n) \| Participants with Social Needs (%) \| Model 0—Unadjusted \| Model 0—Unadjusted.1 \| Model 0—Unadjusted.2 \| Model 1—Age \| Model 1—Age.1 \| Model 1—Age.2 \| Model 2 –Age & Education \| Model 2 –Age & Education.1 \| Model 2 –Age & Education.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Social Needs \| Intervention Week \| Participants \| Participants with Social Needs (n) \| Participants with Social Needs (%) \| Odds Ratio \| 95% CI \| p-value \| Odds Ratio \| 95% CI \| p-value \| Odds Ratio \| 95% CI \| p-value \| \| Non-medical health-related social needs* \| Baseline \| 70 \| 40 \| 57.14% \| \| \| \| \| \| \| \| \| \| \| Non-medical health-related social needs* \| week12–baseline \| 41 \| 15 \| 36.59% \| 0.32 \| (0.13, 0.81) \| 0.017 \| 0.33 \| (0.13, 0.82) \| 0.018 \| 0.33 \| (0.13, 0.85) \| 0.021 \| \| Non-medical health-related social needs* \| week24–baseline \| 52 \| 23 \| 44.23% \| 0.50 \| (0.22, 1.13) \| 0.096 \| 0.50 \| (0.22, 1.14) \| 0.100 \| 0.50 \| (0.21, 1.16) \| 0.104 \| \| Non-medical health-related social needs excluding Financial strain or Employment* \| Baseline \| 70 \| 27 \| 38.57% \| \| \| \| \| \| \| \| \| \| \| Non-medical health-related social needs excluding Financial strain or Employment* \| week12–baseline \| 41 \| 9 \| 21.95% \| 0.29 \| (0.10, 0.88) \| 0.030 \| 0.29 \| (0.10, 0.90) \| 0.032 \| 0.30 \| (0.10, 0.91) \| 0.035 \| \| Non-medical health-related social needs excluding Financial strain or Employment* \| week24–baseline \| 52 \| 15 \| 28.85% \| 0.47 \| (0.18, 1.23) \| 0.125 \| 0.47 \| (0.18, 1.25) \| 0.130 \| 0.47 \| (0.18, 1.25) \| 0.131 \| \| Living situation \| Baseline \| 70 \| 19 \| 27.14% \| \| \| \| \| \| \| \| \| \| \| Living situation \| week12–baseline \| 41 \| 7 \| 17.07% \| 0.44 \| (0.15, 1.32) \| 0.143 \| 0.43 \| (0.14, 1.30) \| 0.132 \| 0.42 \| (0.14, 1.30) \| 0.133 \| \| Living situation \| week24–baseline \| 52 \| 9 \| 17.31% \| 0.47 \| (0.17, 1.26) \| 0.131 \| 0.46 \| (0.17, 1.24) \| 0.123 \| 0.45 \| (0.17, 1.24) \| 0.122 \| \| Food Security \| Baseline \| 70 \| 10 \| 14.29% \| \| \| \| \| \| \| \| \| \| \| Food Security \| week12–baseline \| 41 \| 5 \| 12.20% \| 0.86 \| (0.23, 3.21) \| 0.817 \| 0.90 \| (0.24, 3.41) \| 0.874 \| 0.94 \| (0.24, 3.74) \| 0.935 \| \| Food Security \| week24–baseline \| 52 \| 7 \| 13.46% \| 0.80 \| (0.25, 2.63) \| 0.716 \| 0.82 \| (0.24, 2.72) \| 0.740 \| 0.81 \| (0.24, 2.79) \| 0.741 \| \| Transportation \| Baseline \| 70 \| 9 \| 12.86% \| \| \| \| \| \| \| \| \| \| \| Transportation \| week12–baseline \| 41 \| 3 \| 7.32% \| 0.35 \| (0.07, 1.74) \| 0.198 \| 0.34 \| (0.07, 1.70) \| 0.187 \| 0.33 \| (0.06, 1.72) \| 0.188 \| \| Transportation \| week24–baseline \| 52 \| 3 \| 5.77% \| 0.31 \| (0.06, 1.46) \| 0.137 \| 0.29 \| (0.06, 1.43) \| 0.129 \| 0.29 \| (0.06, 1.43) \| 0.127 \| \| Utilities \| Baseline \| 70 \| 10 \| 14.29% \| \| \| \| \| \| \| \| \| \| \| Utilities \| week12–baseline \| 41 \| 6 \| 14.63% \| 0.94 \| (0.24, 3.61) \| 0.923 \| 0.97 \| (0.25, 3.81) \| 0.969 \| 1.04 \| (0.25, 4.31) \| 0.952 \| \| Utilities \| week24–baseline \| 52 \| 8 \| 15.38% \| 0.92 \| (0.27, 3.17) \| 0.897 \| 0.94 \| (0.27, 3.31) \| 0.924 \| 0.95 \| (0.26, 3.47) \| 0.936 \| \| Safety \| Baseline \| 70 \| 1 \| 1.43% \| \| \| \| \| \| \| \| \| \| \| Safety \| week12–baseline \| 41 \| 0 \| 0% \| \| \| \| \| \| \| \| \| \| \| Safety \| week24–baseline \| 52 \| 1 \| 1.92% \| \| \| \| \| \| \| \| \| \| \| Financial strain \| Baseline \| 70 \| 22 \| 31.43% \| \| \| \| \| \| \| \| \| \| \| Financial strain \| week12–baseline \| 41 \| 7 \| 17.07% \| 0.37 \| (0.12, 1.08) \| 0.069 \| 0.36 \| (0.12, 1.07) \| 0.065 \| 0.36 \| (0.12, 1.10) \| 0.073 \| \| Financial strain \| week24–baseline \| 52 \| 15 \| 28.85% \| 0.77 \| (0.32, 1.87) \| 0.562 \| 0.76 \| (0.31, 1.85) \| 0.543 \| 0.76 \| (0.31, 1.88) \| 0.551 \| \| Employment \| Baseline \| 70 \| 12 \| 17.14% \| \| \| \| \| \| \| \| \| \| \| Employment \| week12–baseline \| 41 \| 10 \| 24.39% \| 1.54 \| (0.48, 4.98) \| 0.468 \| 1.56 \| (0.48, 5.08) \| 0.460 \| 1.72 \| (0.50, 5.92) \| 0.388 \| \| Employment \| week24–baseline \| 52 \| 14 \| 26.92% \| 1.92 \| (0.66, 5.58) \| 0.228 \| 1.95 \| (0.66, 5.70) \| 0.223 \| 2.10 \| (0.69, 6.36) \| 0.187 \| Table 3 shows the longitudinal change of cardiovascular health stratified by baseline social needs. Cardiovascular health scores improved by 0.94 points ($$p \leq 0.013$$) and 0.87 points ($$p \leq 0.022$$) in the group without and with social needs, respectively. No differential effect by baseline social needs status existed (interaction p-value for the interaction effect between social needs and time was $$p \leq 0.895$$). Table 3 \| Social Needs* (Yes/No) \| Intervention Week \| Number of Participants \| Estimate Model 0 \| 95% CI \| p-value \| Interaction Term \| Estimate Model 1 \| 95% CI.1 \| p-value.1 \| Interaction Term.1 \| Estimate Model 2 \| 95% CI.2 \| p-value.2 \| Interaction Term.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| Life’s Simple 7 (LS7) Cardiovascular Health (CVH) Scores \| \| No \| Week 1—Referent \| 26 \| 7.44 \| (6.75, 8.13) \| \| 0.898 \| 7.44 \| (6.74, 8.13) \| \| 0.891 \| 7.42 \| (6.73, 8.11) \| \| 0.895 \| \| No \| Week 12 vs.Week 01 \| 19 \| 0.69 \| (-0.06, 1.45) \| 0.070 \| \| 0.69 \| (-0.06, 1.44) \| 0.072 \| \| 0.70 \| (-0.05, 1.45) \| 0.069 \| \| \| No \| Week 24 vs.Week 01 \| 22 \| 0.93 \| (0.20, 1.66) \| 0.013 \| \| 0.93 \| (0.20, 1.66) \| 0.013 \| \| 0.94 \| (0.21, 1.67) \| 0.013 \| \| \| Yes \| Week 1 –Referent \| 34 \| 7.45 \| (6.82, 8.07) \| \| \| 7.44 \| (6.80, 8.07) \| \| \| 7.53 \| (6.89, 8.17) \| \| \| \| Yes \| Week 12 vs.Week 01 \| 12 \| 0.92 \| (0.06, 1.78) \| 0.037 \| \| 0.93 \| (0.06, 1.79) \| 0.036 \| \| 0.91 \| (0.04, 1.77) \| 0.040 \| \| \| Yes \| Week 24 vs. Week 01 \| 18 \| 0.89 \| (0.15, 1.63) \| 0.019 \| \| 0.90 \| (0.16, 1.64) \| 0.018 \| \| 0.87 \| (0.13, 1.61) \| 0.022 \| \| In sensitivity analyses among participants with social needs, among the 40 participants with social needs at baseline 13 had the social need resolved by week 12, but 4 of the 13 noted a new social need by week 24 (Fig 3). ## Discussion In this novel, 24-week CBPR lifestyle intervention that addressed social needs through screening and service coordination in Black men, a majority of men had social needs at baseline and were interested in addressing them through a referral program. Reductions in social needs were seen at 12 and 24 weeks. Social needs status at baseline was not associated with baseline cardiovascular health scores nor change in cardiovascular health scores at 12 and 24 weeks. To our knowledge this is the first study focused on addressing a complete suite of cardiovascular risk factors through a program that includes a lifestyle intervention, along with assessing and addressing social needs in Black men. ## Addressing social needs in a comprehensive intervention The referrals through the HCGC Hub model facilitated cross-sector integration between the Black Impact program and CHWs to address social needs [37,38]. The model also supported data sharing and aligned payment models through reimbursement for completed referrals to CBOs. Previous programs have addressed social needs through social workers, navigators, CHWs, advocates or referral-based programs in healthcare systems and other settings [7,48,49]. Programming solely focusing on addressing social needs without other components has been systematically reviewed [7]. The majority of interventions reported successfully identifying unmet social needs and referring to clinic and community resources [7]. There was wide heterogeneity in the uptake of the referrals, but generally studies show improvement in social needs [7]. Additionally, previous analyses of Pathways Community Hub models have shown that counties with broad networks of community-based services focused on care coordination to address the social needs of older adults had lower readmission rates and less avoidable nursing home care [50]. Likewise, a population-level analysis of cross-sector collaborations demonstrated lower mortality associated with cardiovascular disease, diabetes, and influenza [51]. The Black Impact study is the first study to our knowledge to address social needs as part of a comprehensive, community-based intervention founded in improving cardiovascular health in Black men. ## Social needs and life’s simple 7 Limited data exists on the impact of social needs on LS7. The study team hypothesized that participants with social needs would have worse cardiovascular health per the AHA LS7 measures at baseline. The hypothesis was not supported by our findings. The majority of the extant literature examines the relationship between socioeconomic status and LS7. In Black adults in the Jackson Heart Study, higher individual income, neighborhood socioeconomic status and education were associated with higher LS7 cardiovascular health scores, although sex-stratified findings were not reported and two-thirds of Jackson Heart Study participants are women [13]. Thus, it is unclear if these findings are consistent in Black men. In Black men participating in US AAMWA Walks there was no association of higher levels of education or employment status with six components (excluding diet) of the LS7 cardiovascular health scores, but were positive associations with income and insurance [14]. Caleyahetta et al. showed that a higher cumulative risk score summing four socioeconomic status measures (low family income, low education level, minority race, and single-living status) was associated with lower attainment of LS7 [52]. In terms of specific social needs, there are studies evaluating the relationship of social needs, most commonly housing, food insecurity, and financial stress with cardiovascular risk factors or chronic disease [53–55]. There are limited studies evaluating social needs and LS7. In a weight loss study in Louisiana (two-thirds Black participants), the mean LS7 total score was not significantly different by food security status at baseline [56]. Additionally, there was no association of food insecurity with adiposity in men [56]. Discordant with our study, food insecurity was associated with lower prevalence of “good” (LS7 ideal and intermediate vs. poor) cardiovascular health among majority White ($85\%$) individuals in Wisconsin [57]. Contrary to the authors hypotheses, food insecure individuals were more likely to have ideal blood pressure and total cholesterol [57]. Among middle age and older female health professional majority ($95\%$) White women, the number of financial stressors was associated with lower ideal cardiovascular health [58]. Lack of housing has been associated with higher cardiovascular disease risk, but there are no studies assessing housing security and/or quality and LS7, as most large cardiovascular cohorts and national data sources do not include individuals with significant housing insecurity or homelessness [59]. Thus, the findings of Black Impact showing no association of social needs and Life’s Simple 7 at baseline are an important contribution to the limited extant literature. The lack of association of social needs with LS7 in Black Impact may be due to multiple factors as previously noted by Azap et al. including “John Henryism”, allostatic load from multiple stresses including racism and discrimination, discrimination and bias in the healthcare setting leading to medical mistrust, and inequities in wealth, such that those without social needs, may still have difficulty attaining high levels of cardiovascular health [14,29]. Further studies addressing the role of social needs in LS7 cardiovascular health are a critical area of inquiry to discern the role and mechanisms of social needs in cardiovascular risk factors particularly among racial/ethnic minority sex groups. ## Social needs interventions and life’s simple 7 The authors hypothesized that even with addressing social needs as part of the Black Impact intervention, participants with baseline social needs would have less improvements in cardiovascular health scores. Importantly, in the study there was no difference in improvement in cardiovascular health scores across 12 and 24 weeks, suggesting that improvements in social needs and addressing cardiovascular health through physical activity and health education in a community-setting may be a potential strategy to advance cardiovascular health irrespective of social needs. There are limited studies to which to compare the Black Impact study, due to the novel nature of the community-based participatory research intervention in Black men. Interventions addressing social needs and cardiovascular risk factors have mostly focused on the healthcare setting. Overall, studies reporting health, utilization, or cost outcomes report mixed results [7]. A prominent study by Berkowitz, et al. used advocates in the healthcare setting to help individuals obtain resources across multiple social needs and showed reductions in blood pressure and cholesterol but not glycemia in individuals engaged in healthcare with cardiometabolic diseases [48]. Kangovi et al. studied goal setting vs. goal setting and a CHW to improve glycemia, blood pressure, obesity, or smoking in 302 individuals who were predominantly Black ($95\%$) and female ($75\%$) over 6-months [60]. While none of the individual categories were significantly different for goal setting vs. goal setting/CHW, there was a trend ($$p \leq 0.08$$) towards overall greater improvements in the outcomes (A1c, systolic blood pressure, BMI, or smoking) with the addition of the CHW [60]. The study team performed a follow-up study in 592 individuals with similar characteristics over 9 months and found no difference in changes in self-rated physical health, mental health nor a combination of A1c, systolic blood pressure, BMI, or smoking ($$p \leq 0.21$$) [61]. In a systematic review by Gottlieb et al, the authors found 81 studies where social needs were addressed as part of comprehensive interventions [7]. Only three of these studies had components of LS7 as outcomes and none had all 7 factors. Watt et al, performed a primary care based early childhood intervention in low-income Hispanic pregnant women. The program provided vouchers for fruits and vegetables from the local farmers’ market, nutrition classes, cooking classes, and lactation counseling. Women in the intervention compared to control had significant improvements in diet, exercise, and depression (p≤0.05) [62]. Loskutova et al, examined telephone-based nonprofessional patient navigation to promote linkages between the primary care provider and community programs in 179 patients with or at risk for diabetes. Two patient navigators provided services over the phone, including assessment of needs, barriers and limitations, motivational interviewing and a suggestion of 2 to 3 community programs with an average of 6 calls per patient. In pre-post analyses they showed a reduction in hemoglobin A1c ($7.8\%$ vs $7.2\%$, $$P \leq 0.001$$) in those with diabetes and improvement in patient self-efficacy. They found no change in fasting glucose, BMI, total cholesterol, low-density lipoprotein, high-density lipoprotein, or triglycerides [63]. At Intermountain Healthcare, a generalist model of chronic disease management was formulated, with care managers located within multi-payer primary care clinics collaborating with physicians, patients, and other members of a primary care team to improve patient outcomes. In patients with diabetes, they found a greater reduction in A1c compared to controls over 1 year ($8.0\%$ to $7.4\%$ [intervention] compared to $7.7\%$ to $7.5\%$ [control, $p \leq 0.001$]) [64]. In comparison, the nearly 1-point increase in cardiovascular health score from baseline to 24-weeks in Black Impact was a large improvement in cardiovascular health, considering a 1-point higher cardiovascular health score is associated with an $18\%$ and $19\%$ lower odds of stroke and myocardial infarction, respectively and an $11\%$ and $19\%$ lower risk of all-cause and cardiovascular mortality [25]. Additionally, there were improvements in individual components including body mass index, systolic blood pressure, fasting glucose, total cholesterol and dietary intake [25]. ## Strengths/Limitations The strengths of our study include: 1) a focus on an understudied population with large disparities in cardiovascular health and mortality; 2) community engagement framework for CBPR that recognized the importance of addressing social needs in Black Impact; 3) collaborations across a number of organizations to screen and address social needs through the evidenced-based Pathways Community Hub model; and 4) the use of trained health professionals using evidence-based approaches for biometric data collection. Despite these strengths, the study should be considered in light of some limitations. First, the study was not randomized due to: 1) no previous test of intervention feasibility and acceptability; and 2) concerns raised from community members in regards to not receiving a potentially beneficial intervention (albeit, novel and not previously tested). A second limitation is the lack of a control group. Our findings may be influenced by regression toward the mean, but this is unlikely given the difficulty in addressing social needs without a supportive system. The effect estimates generated from the study are being used to plan a powered, randomized, wait-list controlled intervention. Third, even with our sociodemographically diverse cohort, the Black Impact participants may not be representative of other populations of Black men. Fourth, only 8 of the 31 men referred to the HGCG Hub were enrolled in a pathway. For the 23 non-enrolled men they were given paper resources but were not interested in being enrolled in a pathway. In future iterations of Black Impact, the study team will consider performing further education with participants regarding the importance of addressing social needs and the benefit of enrolling in pathways. Additionally, we have discussed having the CHW come to the Black Impact study site to meet with participants and build relationships. Increased enrollment in pathways may lead to even greater reductions in social needs over the course of the intervention. Lastly, our study was performed during the COVID-19 pandemic and participants may have different social needs in a non-pandemic setting. Notably, the lack of worse cardiovascular health at baseline among participants with vs. without social needs may have been due to impacts of the COVID-19 pandemic increasing social needs and worsening cardiovascular health due to pandemic restrictions (e.g. closure of gyms, food shortages, etc.) and difficulty accessing preventive medical care. ## Conclusion To our knowledge this is the first study to show improvements in social needs as a component of a comprehensive lifestyle intervention in Black men. As part of the community engagement and co-design of the intervention using the PETAL framework for CBPR, addressing social needs was determined to be a key component. There was no evidence of baseline differences in LS7 by social needs, and the group with baseline social needs had similar improvements in cardiovascular health. In future iterations of Black Impact, it will be important to include an arm of the study that does not address social needs to determine the necessity of addressing social needs for LS7 improvement. Further research to evaluate how the dose and timing of addressing social needs may impact physical, mental health and quality of life outcomes is also warranted. Addressing social needs in Black men is an attainable goal through a multi-component intervention and may help individuals with social needs improve cardiovascular health. ## References 1. Martinez RM. Investing in Interventions That Address Non-Medical, Health-Related Social Needs: Proceedings of a Workshop.. (2019.0) 25544. DOI: 10.17226/25544 2. Dzau VJ, McClellan M, Duke University, Burke S. Vital Directions for Health and Health Care: Priorities from a National Academy of Medicine Initiative (2017.0) 7. DOI: 10.31478/201703e 3. 3HPOE.org—Social Determinants of Health Series: Housing. [cited 1 Jun 2021]. 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--- title: Protocol for a cluster randomized clinical trial of a mastery-climate motor skills intervention, Children’s Health Activity and Motor Program (CHAMP), on self-regulation in preschoolers authors: - Leah E. Robinson - Kara K. Palmer - Lu Wang - Katherine Q. Scott-Andrews - Katherine M. Chinn - Indica Sur - Carissa Wengrovius - Emily Meng - Sanne L. C. Veldman - Alison L. Miller journal: PLOS ONE year: 2023 pmcid: PMC9997967 doi: 10.1371/journal.pone.0282199 license: CC BY 4.0 --- # Protocol for a cluster randomized clinical trial of a mastery-climate motor skills intervention, Children’s Health Activity and Motor Program (CHAMP), on self-regulation in preschoolers ## Abstract ### Introduction Self-regulation (SR) is critical to healthy development in children, and intervention approaches (i.e., professional training, classroom-based curricula, parent-focused intervention) have shown to support or enhance SR. However, to our knowledge, none have tested whether changes in children’s SR across an intervention relate to changes in children’s health behavior and outcomes. This study, the Promoting Activity and Trajectories of Health (PATH) for Children-SR Study uses a cluster-randomized control trial to examine the immediate effects of a mastery-climate motor skills intervention on SR. Secondly, this study examines the associations between changes in SR and changes in children’s health behaviors (i.e., motor competence, physical activity, and perceived competence) and outcomes (i.e., body mass index and waist circumference) (ClinicalTrials.gov Identifier, NCT03189862). ### Methods and analysis The PATH—SR study will be a cluster-randomized clinical trial. A total of 120 children between the ages of 3.5 to 5 years of age will be randomized to a mastery-climate motor skills intervention ($$n = 70$$) or control ($$n = 50$$) condition. SR will be assessed using measures that evaluate cognitive SR (cognitive flexibility and working memory), behavioral SR (behavioral inhibition), and emotional SR (emotional regulation). Health behaviors will be assessed with motor skills, physical activity, and perceived competence (motor and physical) and health outcomes will be waist circumference and body mass index. SR, health behaviors, and health outcomes will be assessed before and after the intervention (pre-test and post-test). Given the randomization design, 70 children in the intervention group and 50 in the control group, we have $80\%$ power to detect an effect size of 0.52, at a Type I error level of 0.05. With the data collected, we will test the intervention effect on SR with a two-sample t-test comparing the intervention group and the control group. We will further evaluate the associations between changes in SR and changes in children’s health behaviors and health outcomes, using mixed effect regression models, with a random effect to account for within-subject correlations. The PATH-SR study addresses gaps in pediatric exercise science and child development research. Findings hold the potential to help shape public health and educational policies and interventions that support healthy development during the early years. ### Ethics and dissemination Ethical approval for this study was obtained through the Health Sciences and Behavioral Sciences Institutional Review Board, University of Michigan (HUM00133319). The PATH-SR study is funded by the National Institutes of Health Common Fund. Findings will be disseminated via print, online media, dissemination events and practitioner and/or research journals. ### Trial registration number ClinicalTrials.gov Identifier, NCT03189862. ## Introduction Self-regulation (SR) is a central area of research inquiry in child development. SR refers to the voluntary control of cognitive, emotional, and behavioral impulses in accordance with a long-term goal [1, 2], and it is required to sustain concentration and behavioral control while engaging in challenging tasks. SR is comprised of multiple interrelated processes that emerge rapidly across early childhood. These processes include cognitive skills that facilitate working memory, cognitive flexibility, and attention shifting (i.e., elements of executive function), behavioral skills that allow children to inhibit impulsive behaviors in favor of measured responses (i.e., behavioral inhibition), and emotional regulation skills that enable children to calm down from elevated or intense emotions [3]. The benefits of SR for long-term social, behavioral, and academic outcomes (e.g., academic success, school readiness, and classroom behavior) are well established [4, 5]. From a Science of Behavior Change (SOBC) perspective [6], SR is a behavioral mechanism that may underlie many different behaviors relevant for health and well-being; thus, enhancing SR early in life may have a translational, long-term impact on health [7, 8]. Due to the important role that SR has in the growth and development of children, there have been efforts to enhance SR in young children. Curriculum- or classroom-based approaches that use a combination of teachers’ professional training and classroom-based activities based on a defined curriculum, such as Head Start REDI [9] and Diamond’s Tools of the Mind [10] curricula, have shown to support social-emotional skills and problem-solving tactics. Parent-focused interventions that focus on routines and parent-child interactions [11] along with direct training to promote specific aspects of SR such as behavioral inhibition [12, 13] are also successful approaches to enhance SR. Despite some promising findings that SR can be promoted during childhood and adolescence [14] and SR-focused interventions can impact social emotional (REDI) and early academic skills [9], we know less about the effects of SR on other developmental domains. SR has been studied as a key mechanism of health behavior change in relation to a wide range of health outcomes in adults [6, 15] and has been proposed as a behavioral mechanism that may promote positive health outcomes such as weight status during childhood [8, 16–20]. SR is hypothesized to support health in multiple ways; including shaping an individual’s capacity to focus on long-term goals and aiding in stress reduction [21–26]. For example, SR can help individuals maintain a healthy weight by “tuning-out” external cues in the moment (e.g., advertisements about food), reducing unhealthy emotional coping behaviors (e.g., sedentary behavior or frequent/stress eating), and sustaining engagement in positive health behaviors (e.g., physical activity and exercise). Although most studies linking SR to health outcomes and behaviors have been in adults [27–30], some observational work in adolescents [31, 32] has linked poorer SR to increased sedentary behavior and decreased physical activity [30, 32]. Other cross-sectional [16, 20] and longitudinal [16, 18–20, 33] studies found that better SR among toddler-and preschool-aged children was associated with healthier weight status, and that early-life SR may have long-term health benefits for children [8], even into adulthood [26]. These established associations of SR with health behaviors and health outcomes, in conjunction with research suggesting that SR can be enhanced through intervention, demonstrates a need for more studies that: 1) test novel interventions to improve SR, and 2) test SR as a mechanism of behavior change in children and youth. Motor skills, goal-directed actions of the muscles that are categorized as gross and fine motor skills, are essential to children’s growth and development [34–37] From a SR perspective, learning motor skills may help individuals evoke, process, and regulate emotions [38–40]. Few studies have explored SR from a motor perspective in children. van der Fels, Te Wierike, Hartman, Elferink-Gemser, Smith and Visscher [38] conducted a systematic review that examined the relationship between cognitive and motor skills in typically developing children ages 4–16 years. van der Fels et al. concluded that there is insufficient evidence ‘for or against’ many correlations between motor and cognitive skills. However, the review found that fine motor skills, bilateral body coordination, and timed motor tasks demonstrated the strongest relationship to cognitive skills while balance, strength, and agility were less related. The authors speculate that the relationship might be because the former skills are more complex motor tasks and have a higher cognitive demand or load while the others require less cognitive demand. van der Fels et al’s review also demonstrated that stronger relationships between motor and cognitive skills are seen in pre-pubertal children compared to their counterparts. From an experimental standpoint, Becker, Miao, Duncan and McClelland [39] found that prekindergarten and kindergarten children’s visuomotor skills measured with the Beery visual-motor integration assessment were related to inhibitory control, working memory, and behavioral SR. There appears to be a link between motor and SR in children [38, 39] but the evidence is quite insufficient. Work from Becker, van der Fels, and Westendorp suggest that movement/motor skills interventions that used complex motor skills, like sequenced movement patterns or movement coordinated to rhythm, support higher order cognitive skills and tasks that could improve SR in young children. The Children’s Heath Activity Motor Program (CHAMP) is a mastery-climate motor skills intervention grounded in achievement goal theory [41–45]. CHAMP adheres to Epstein’s TARGET structure (Task, Authority, Recognition, Grouping, Evaluation, and Time) while teaching motor skills to young children and requires children to self-select, self-manage, self-evaluate, and self-direct themselves throughout the intervention setting [46]. Table 1 defines the foundational components of CHAMP and links to SR. These self-determined actions have the potential to support multiple aspects of SR by encouraging children to manage their emotions, focus attention, persist, plan and evaluate their actions while promoting motor skills and perceived motor competence [8, 40, 47, 48]. Robinson, Palmer and Bub [40] conducted a CHAMP efficacy trial and found that preschoolers in the CHAMP condition maintained delayed gratification (i.e., measured with the snack delay task) over time while those in the control group experienced a significant decrease in their scores. The efficacy trial also found that CHAMP led to significant improvement in preschoolers’ locomotor and ball skills [40]. This finding supports a connection between mastery climate motor skills interventions and SR and provides a rationale for using motor-based interventions to positively change SR in addition to health behaviors such as physical activity [36, 49–52] and motor skills [48, 53–55]. Table 1 \| TARGET Structure \| Use of TARGET Structure in CHAMP to Promote SR \| Example of Application \| \| --- \| --- \| --- \| \| Task: Provide a variety of tasks/activities that vary in difficulty \| Self-select from tasks/activities that vary in difficulty (create goals and strategies, plan and implement actions, make decisions, self-manage, self-monitor, and self-correct behavior) \| 3–4 motor skill stations included in the intervention each dayEach station will have at least three levels of difficulty.For example- catch station will include at least three different catchable items- scarfs (easy), yarn balls (medium), and tennis balls (difficult) \| \| Authority: Foster by allowing children to actively participate in the decision making process \| Self-manage and self-monitor behaviors (create goals and strategies, plan and implement actions, make decisions, self-manage, self-monitor, and self-correct behavior, manage emotions, understand and appropriately navigate social environments) \| The authority of CHAMP is shared between instructors and children.Instructors shared authority duties include: maintaining a safe learning environment, teaching motor skills, providing individualized feedback to children during the lesson, and encouraging children to engage in the daily stationsChild shared authority duties include: self-selecting how, when and where to engage in the motor skill practice, deciding who they want to play with, and creating and managing their own social and emotional environments within CHAMP. \| \| Recognition: Instructor and child recognize individual progress. Feedback is provided privately and individually. \| Self-monitor and evaluate own performance (self-monitor behaviors, self-reflection of progress, manage emotions, focus attention, persist on a task, understand and appropriately navigate social environments, collaborative efforts) \| Each child’s individual improvements are privately recognized by the instructor. \| \| Grouping: Focuses on grouping patterns Children are not grouped but given opportunity to self-select their engagement with others \| Self-select own engagement in task; give child ability to self-govern learning experience (plan actions and make decisions, self-monitor behavior, self-correct behaviors, manage emotions, appropriately navigate social environments, collaborative efforts) \| Children decide who they will navigate the intervention with. They can decide if they want to play in groups or individually. No children will be forced into groups of any kind. \| \| Evaluation: Determine progress based on self-norms not global norms \| Self-evaluate own performance (self-monitor behaviors, self-reflection of progress, manage emotions, focus attention) \| Gains accomplished in CHAMP are not benchmarked against external performance expectations. CHAMP instructors are not teaching children so that they can gain a higher percentile in a test, they are helping children evaluate their performance based on self-referenced standards. \| \| Time: Individualize pace of instruction and learning experience \| Self-direct own learning (plan actions and make decisions, self-monitor behaviors, self-correct behaviors, manage emotions) \| Children can self-pace through the multiple learning stations. Children can decide if they want to engagement on only one station or all the stations as well as can decide how long to stay any station. \| It is also well-established in the motor development literature that motor skills and perceived competence (i.e., how one perceived his or her own abilities in varying domains) are critical to multiple aspects of health (i.e., physical activity, cardiorespiratory fitness, muscular strength, muscular endurance, and a healthy weight status) [34]. For example, perceived motor competence (i.e., how one perceives his or her own motor performance) is a strong correlate of physical activity in children and youth [35, 56–61]. Barnett, Morgan, van Beurden and Beard [56] demonstrated the strong mediating role of perceived motor competence between motor skills and physical activity over the childhood years. While there is ample preliminary evidence that CHAMP directly promotes all these outcomes, motor skills [40, 47, 48, 62–67], physical activity [49, 67, 68], and perceived motor competence [47, 48, 63]; less is known regarding the effect of CHAMP on SR outcomes [40]. So, while there is preliminary evidence that CHAMP supports some SR skills in young children [40], additional research is needed. Further, SR is a multi-dimensional construct and additional research is needed to evaluate how CHAMP may impact various aspects of SR including cognitive skills, behavioral skills other than delayed gratification, and emotional regulation in young children. The body and brain work harmoniously together, and more studies are needed to investigate the role of movement-based interventions on SR and the secondary effect of SR on health outcomes. We propose a cluster randomized controlled trial (RCT) designed to enhance early SR, health behaviors (i.e., motor competence, perceived motor competence, and physical activity), and health outcomes (i.e., waist circumference and body mass index) in preschool-aged children. Specifically, we will examine the immediate (pre- to post-test) effects of CHAMP on SR, and associations between changes in SR and changes in health behaviors and outcomes. The specific aims and hypothesis of this study will be to: ## Study design The Promoting Activity and Trajectory of Health (PATH)–Self Regulation (SR) cluster RCT is a federally funded supplemental award from the National Institutes of Health (NIH) Common Fund which expands upon an ongoing RCT, A PATH (Promoting Activity and Trajectories of Health) for Children that is funded by the National Heart, Lung and Blood Institute (NHLBI; R01-HL-132979). The protocol paper for the PATH for Children has been published [69]. The Institutional Review Board at the University of Michigan approved the PATH-SR study (HUM00133319), and the RCT is registered in the Clinical Trials Registry NCT03189862. Informed written consent will be obtained from children’s parent/guardian(s) along with verbal assent from each child. The reporting of this research will follow the recommendations of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) [70]. Fig 1 depicts the SPIRIT diagram for the schedule of enrollment, interventions, and assessments and Fig 2 is the PATH-SR Study timeline for the 16-week intervention study during a preschool school-year. Fig 1: SPIRIT diagram for the schedule of enrollment, interventions, and assessments. Fig 2: PATH-SR study timeline. ## Study context The study will take place in two federally funded early learning centers located in the Midwestern United States. The centers provide free quality preschool programs to children who come from a household with an income that is at least $100\%$ below the federal poverty level. 120 children between the ages of 3.5 to 5 years of age will be randomized to a mastery-climate motor skills intervention ($$n = 70$$) or control ($$n = 50$$) condition. In center 1, 7 classrooms expressed interest in participating and in center 2, 6 classrooms were interested. Randomization will occur at the level of the classroom. Specifically, classrooms will be cluster-randomized to receive either the mastery-climate motor skills intervention (i.e., CHAMP; treatment) or the control condition (i.e., outdoor recess) using computer-generated random numbers. A total of 7 classes from both centers were assigned to CHAMP and the remaining 6 were assigned to the control. Unfortunately, due to constraints at the Head Start centers (i.e., uneven number of classrooms to recruit and teachers’ interest in participating), there was not an equal number of classes to distribute evenly between treatment and control. ## Inclusion/Exclusion criteria Preschoolers ≥ 3.5 to 5.11 years old are eligible to enroll and participate in this study. Children are ineligible if any of the following apply: exhibit characteristics or diagnosed with syndromes or diseases that would affect participation in the motor skills intervention and/or exhibit characteristics or had a previous diagnosis of any major illness, developmental, and/or physical disability since birth. If a child is deemed ineligible due to an above condition, but have parental consent, they will be able to participate in the treatment but no data will be collected on these individuals. ## Recruitment After receiving human subjects IRB approval, the following procedures will occur regarding participant recruitment and the informed consent process. Parent(s)/guardian(s) will receive an information letter from the Principal Investigator notifying them of the PATH-SR Study at the beginning of the school year. The letter will provide a brief description of the study along with a statement from the school administrators indicating that parent(s) are not obligated to participate. Members of the research team will be present during morning drop-offs and afternoon pick-ups to answer any questions from parents/guardians. All parents/guardians who return a consent form, regardless of whether they agreed to participate in the PATH-SR Study, will receive a one-time cash incentive of $5.00. In addition to parental consent, verbal assent will be obtained from each preschooler. Parents will receive reminder letters for each upcoming PATH-SR assessment and a developmental report of their child’s findings from each assessment (i.e., motor skills, physical activity, health outcomes, SR outcomes). Each center will be provided with aggregated data of the findings. ## Children’s Health Activity Motor Program (CHAMP) The intervention that will be used in this study will be the Children’s Health Activity Motor Program (CHAMP). CHAMP is an established motor skill intervention created using Achievement Goal Theory and delivered as a mastery-motivational climate. This approach encourages children to learn and develop new skills, increase their level of competence, and achieve a sense of motor skill mastery-based on their perceptions. Over 14 years of preliminary work supports the effectiveness of CHAMP in improving motor skill performance [40, 47, 48, 62–67], increasing physical activity [49, 67, 68], enhancing perceived physical/motor competence [47, 48, 63], and maintaining delay of gratification [40] in preschool- and/or school-age children. Due to the theoretical underpinnings and implementation of the intervention, CHAMP provides an innovative approach to potentially improve SR in young children. Table 2 provides an overview of the core tenants of CHAMP and details on the theoretical principles and intervention implementation (both instructor training, child engagement, and fidelity) that have the potential to support SR in this population are discussed below. Table 2 \| Core theoretical principles \| Grounded in Achievement Goal TheoryEncourage children to adopt a mastery-orientation through creating a mastery-motivational climate (e.g., implementing TARGET structure) \| \| --- \| --- \| \| Core constructs \| High-autonomy learning climate (multiple stations and multiple levels of difficulty within stations)Shared decision making between instructors and studentsMotor skill instruction using proper cue words, modeling, and developmentally appropriate pedagogy and activities \| \| Core instructional principles \| Shared decision making between instructors and studentsProvides motor skill instruction using proper cue words, modeling, and developmentally appropriate pedagogyRecognize individual levels of abilities and progressEvaluate student performance based on self-referenced not norm-referenced standards \| \| Core instructional practices/pedagogies of CHAMP instructors \| Set up CHAMP session with various level of difficulty within each of the 3–4 stationsIntroduce and teach each motor skill to whole group using proper cue words and modelingProvide individual recognition and evaluation during the autonomy-motor skills stationInstruction during autonomy-motor skill stations can range from verbal correction, modeling, to physical manipulation.All praise is delivered privately \| \| Expectations of child in navigating CHAMP \| To participate in large group activities during start and end of classTo self-navigate and engage in motor skill practice during autonomy-motor skill stationsTo create and curate self-selected peer groups \| ## Theoretical principles CHAMP is grounded in Achievement Goal Theory. This theory originates from educational psychology and focuses on the learners’ motivation to learn [41, 43, 44], since goals for learning influence intrinsic motivation. Achievement Goal Theory refers to the beliefs, attributions, and affect that contribute to one’s behaviors and represents how an individual approaches, engages, and responds to various activities [42, 43]. Individuals can take either a mastery- (task-) or performance- (ego-) orientation [42, 71]. Performance- (ego-) individuals focus on ensuring that their performance is successful and superior to others while mastery- (task-) individuals engage in learning for the sake of learning and are less threatened by failure. Mastery- (task-) oriented individuals often have higher intrinsic motivation [42, 43] exhibit an intrinsic interest in learning [44, 72] and have positive attitudes towards learning [72, 73]. Learning environments can be intentionally and purposefully structured to encourage learners to adopt a mastery- (task-) orientation to learning. These environment are called mastery-motivational climates and are created using Epstein’s TARGET structures [46] (see Table 1). Applying the TARGET structures to the learning environment redistributes the ownership to the learner and allows them to autonomy to navigate the intervention climate, make their own choices on how, where, and in what level of difficulty they engage in and practice new skills. In the case of CHAMP, allowing the child the autonomy to navigate the mastery-motivational climate fosters self-navigated engagement, self-selections of learning groups, and self-paced learning. These activities and expectations require a child to demonstrate and continually practice the cognitive, emotional and behavioral regulation skills that are hallmarks of effective SR (see Table 1). Therefore, even while the primary learning outcomes of CHAMP are motor skills and health behaviors, the theoretical principles (i.e., achievement-goal theory) and implementation (i.e., TARGET structures) applied during the program are likely to promote SR in children. ## Intervention session design The CHAMP motor skills intervention will be delivered across a dose of 2,155 minutes. The intervention will be completed 3 days per week across 19 weeks (i.e., 16 weeks of the CHAMP intervention plus 3 weeks in between for winter and spring breaks) in an academic school year. Each 45-minute CHAMP session will consist of three parts: (a) 3–5 min of introductory activity, (b) 35–38 min of motor skill instruction and practice delivered as a mastery-motivational climate, and (c) 3–5 min motor skill closure activity and review. Each CHAMP session will include 3–4 motor skill stations that will rotate across 15 different motor skills. The stations will all be designed using a “slanted rope effect” to allow a range of difficulty in practice and engagement in the stations that ranges from easy to difficult. All stations and the ability to manipulate difficulty of engagement within each stations will be taught to the children during the introductory activity, and then during 35–38 minutes of motor skill instructions and practice children will self-navigate through the activities. During this time, instructors move about the intervention and encourage children to continually engage in the intervention as well as will provide individualized feedback and instruction in accordance with the TARGET structures. Instructors provide feedback on motor skill performance using best pedagogical practices including cue words, modeling, and physical manipulation. For example, instructors may notice a child is not stepping contralaterally during throwing. They will recognize this individual performance and encourage the child to engage in better throwing performances by verbal prompts (e.g., “step with the other foot”), modeling (e.g., “watch me! Step like this”), external cues (e.g., sticker on the foot they should step forward with), or physical manipulation (e.g., picking up and moving the correct foot). Once the child changes their movement pattern and steps forward with the contralater foot, the instructor will evaluate this change in performance based on self-referenced norms and praise the child for their individual change in performance. While children and instructors share responsibility for the authority of the CHAMP sessions, children should engage in the session. During the introductory activity and wrap-up and review, children are asked to sit in a circle in the middle of the classroom and listen to the instructor explain the activities/stations of the day (introductory activity) or recap the daily activitys (wrap up and review). During the 35–38 min of motor skill instruction and practice delivered as a mastery-motivational climate children are granted autonomy to move around the space as they desire. Ultimately, where they engage, how long they engage, what level of difficulty they engage with, and their peer structures during practice are decided by the children with continued encouragement and re-direction from instructors. For example, a child could elect to spend the entire 35–38 minutes practicing their throws at a moderate level of difficulty by themselves. Alternatively, a child could elect to only spend 3 minutes at throwing at an easy level of difficulty then move to running with a group of three to four peers. Both examples constitute “successfully” engaging in CHAMP and require children to manage their emotions, focus attention, persist, plan, and evaluate their actions in order to improve their motor competence and perceived motor competence and enhance their engagement in PA [41, 42, 46]. All these behaviors are linked with SR which support the potential of CHAMP to improve SR in this population. ## Intervention instructors CHAMP will be implemented by two motor development researchers who are Ph.D. students. The lead instructor has 6 years of experience implementing the CHAMP intervention and was involved in the development of the program. The second instructor has a degree in physical education and a background in motor intervention implementation. Additional research personnel ($$n = 1$$–2) will be present to assist with other managerial tasks for the intervention (e.g., ensure that the cameras are recording, record attendance, equipment set-up and breakdown, collecting and returning of children to classroom, fidelity checks, etc.). All research personnel will undergo training before the start of the intervention. The training takes approximately 40 hours to complete. The training will include readings and discussion on (a) Achievement Goal Theory and mastery climates in general and in regards to movement interventions, (b) cue words best practices in motor skill instruction and feedback, and (c) best pedagogical practices for preschool-aged children. All lead instructors must undergo additional training whereby they will watch three previously recorded instructional sessions of the CHAMP intervention and discuss how achievement goal theory and the TARGET structures were implemented in the intervention. Lastly, each instructor will complete a mock CHAMP session and practice station set up, skill and CHAMP instruction, individual feedback and recognition on motor performance, and CHAMP closing. Both the reviewed videos and the mock CHAMP session will be completed under the direction of the lead author and creator of CHAMP (LER), and instructors have to demonstrate $100\%$ fidelity with CHAMP and TARGET structures prior to the start of the intervention. ## Intervention fidelity Fidelity checks on the TARGET structures and instruction will also be completed at every session to ensure the intervention adheres to the TARGET protocol. For the past 14 years, the following fidelity checks have been used to ensure the extent to which the CHAMP intervention is implemented as intended [40, 47–49, 62–68]. Daily checks will be completed to ensure the dose, adherence, quality of delivery, intervention alignment with core constructs and intervention protocol, and etc. All intervention sessions will be digitally recorded to enable future reviews of each session if needed. For each session, a research staff member (i.e., not the CHAMP instructors) will complete the following fidelity checks that address dose, instruction, and TARGET stuctures. For dose, the checks will record the start and finish of each session (i.e., to calculate the total minutes of the intervention session), the amount of time devoted to skill instruction and demonstration, and the amount of time children engaged in the practice of motor skills. Research staff members will evaluate the instruction provided during each CHAMP session to ensure that it aligns with the pre-determined CHAMP lesson plan. Specifically, the checks will ensure that clear instruction for each motor activity station are provided, the use of the provided critical cue words and that an accurate demonstration, ensure that instructors check for student understanding, compliance with the CHAMP lesson plan, and if modified a description of the deviation will be recorded. These instructional checks will also record the type of feedback (specific, corrective, and/or evaluative) provided to the child, along with the use of manual manipulation to aid motor skill learning. Finally, the TARGET structures will be used to ensure that there are three motor skills activity with 3–4 levels of task difficulty present (Task), children have the opportunity to independently choose their engagement in the session (Authority), feedback focuses on progress, effort, and improvement (Recognition and Evaluation), children have the option/choice to work in small group, with peers, or individually (Grouping), and lastly the 35–38 minutes of motor skill instruction and practice was self-paced based on the individual child’s level of engagement (Time). As noted in the intervention training section, two, PhD instructors will serve as the interventionist for this study and will be trained by the PI of the project. In addition to each session being digitally recorded, instructors will wear wireless microphones to aid in assessing the intervention fidelity. Instructors will receive feedback regarding their instruction weekly. Fidelity checks on the TARGET structures and instruction will also be completed at every session to ensure the intervention adheres to the TARGET protocol. ## Control condition The outdoor recess/free play program is the early learning centers’ current motor program for accreditation and will serve as the control condition for this study. Outdoor recess/free play will be implemented according to the existing procedures within the centers. Each class will receive two, 30–45 min outdoor recess (free play) periods each day. For this study, the control group will receive two, 30–45 min per day outdoor sessions, whereas the treatment (CHAMP) group will receive one, 30–45 min outdoor session each day after their nap, as the morning recess session was replaced with the CHAMP intervention on days the intervention will be implemented. The centers’ outdoor programs consist of outdoor free-play activities on a large playground area with a variety of play structures (e.g., swings, slides, ladders) that promote physical activity, gross motor skills, balance/stability, and movement skills. No planned instruction or activities will be provided to the preschoolers during outdoor recess. Classroom teachers and the research personnel will be asked to confirm that the daily outdoor recess sessions were completed with a check-off sheet. ## Measures Data will be collected by a trained research team. Outcome measures will be collected for all participants in both the treatment and control groups at pre-test (i.e., before the start of the intervention) and post-test (i.e., at the conclusion of the intervention). Pre-test measures will occur in September/October and post-tests will occur in late April/May. On average, we anticipate it will take three, 25–30-minute sessions across three days to complete all the assessments. This time was based on the allotted time provided by the preschools and the average time preschoolers’ tends to stay focused on these assessments from previous studies. All perceived motor competence data will be collected before children complete motor skill assessments. The order of completion will be as follows: Session 1: anthropometrics (e.g., height, weight, waist circumference; less than 5 minutes), perceived motor competence (5–8 minutes), and SR (17–20 minutes); Session 2 and 3: motor skills (30 minutes). Session 4: any measures that were not completed in Session 1–3, mainly motor competence and SR, was completed during Session 4 (30 minutes). If additional time was needed to complete the preschoolers’ assessment, the research team reached out to the classroom teachers directly to arrange for a time to complete the assessment(s). ## SR Measures SR will be assessed using a series of computer-based and interactional behavioral tasks, teacher reports, and observer reports. SR constructs measured will include cognitive SR (cognitive flexibility, working memory, attention shifting), behavioral SR (behavioral inhibition), and emotional SR (emotion regulation). Cognitive SR will be measured with cognitive flexibility and working memory using the Early Years Toolbox (EYT), a normed collection of iPad-based assessments for preschool-aged children [74]. All assessments will be administered on an iPad and data will be automatically stored in a secure online repository. The EYT is a developmentally sensitive measure of executive function in young children [74]. Cognitive flexibility will be assessed using the EYT “Boats & Rabbits” game. In this game, children will be presented with stimuli at a boat bifurcation and instructed to sort the stimuli into the correct castle at the end of each moat. Stimuli are sorted based on shape (rabbits vs. boats) or color (red vs. blue). The child is first asked to sort along one dimension (e.g., shape), then to switch and sort along the other dimension (e.g., color), then to short by either dimension depending on whether the stimulus is presented inside a black border. Thus, the game includes a total of three series: “pre-switch”, “post-switch”, and “border task”. Each series includes two practice trials and six test trials. Children will receive a point for each correctly sorted stimulus. Children must receive at least 5 points in both the pre- and post-switch series to progress into the border task series. The total number of correctly sorted stimuli after the switch (i.e., across the “switch” and “border task” series; range: 0–12) represents the ability of a child to flexibly shift attention and will be collected as the primary outcome variable indicating cognitive flexibility. Visual-spatial working memory will be assessed using the “Mr. Ant” game from the EYT. In this game, children are presented with a cartoon ant figure who “puts on” stickers on different parts of his body. Children will be required to remember the location of the stickers and put them back on Mr. Ant. In each trial, children will see Mr. Ant with stickers for 5 seconds, followed by a blank screen for 4 seconds. Then, they will see an image of Mr. Ant without stickers and will be verbally prompted to recall and place the stickers back on Mr. Ant. The game includes eight levels each with three trials. The task is progressive and ranges from one sticker presented in level 1 to nine stickers presented in level 8. Children must correctly place the stickers in at least one of the three trials to progress to the next level, and children advance through the game until they fail to get all three trials correct on the level. Both overall points and trial accuracy are recorded. Children receive 1 point for each level they complete with at least two correct trials and receive $\frac{1}{3}$ of a point when they only complete one correct trial. Total number of earned points will be summed across the assessment and trial accuracy will measured as the total number of correct trials completed across the assessment. Both the total number of earned points as well as accuracy will be recorded as primary outcome variables. Behavioral SR will be measured with the Head-Toes-Knees-Shoulders Task (HTKS) and observed SR. The HTKS is a developmentally appropriate measure to assess behavioral inhibition in young children [75–77]. The HTKS taps into three underlying and simultaneous mechanisms: [1] memory, [2] cognitive flexibility and [3] inhibition within a behavioral setting/outcome [76]. In the task, children will be instructed to touch the body part opposite to what the administrator says. For example, children are given the instruction “Touch your head” and must touch their toes. Children will have to remember the rules of the game, inhibit their initial reaction, and change their response to the opposite of the verbal instruction to successfully complete the tasks. The assessment includes three parts. Each part includes verbal instructions and between 4–6 practice trials preceding the 10 test trials. All practice and test trials will be scored from 0–2 points. Children receive a 2 if they successfully complete the trial, a 1 if they correct themselves during a trial, or a 0 if they fail to complete the trial correctly. Children only advance to the subsequent part if they receive at least 4 points during the test trials. The total number of points earned in test trials will serve as the primary outcome variable. All HTKS trials will be coded live. All coders will undergo a 5-hour training and establish inter-rater reliability of $90\%$ prior to the start of data collection. Observed SR will be assessed with the Child Behavior Assessment. This assessment will be completed by a member of the research team immediately following each data collection session and will be used to measure child engagement and compliance during SR testing. The goal of this overall behavior rating is to capture children’s global behavioral responses across a series of SR tasks. The Child Behavior Assessment included 10-items selected from the 28-item Preschool Self-Regulation Assessment Assessor Report [78]. Example questions include: “Lets examiner finish before starting task; does not interrupt” or “Child has difficulty waiting between tasks.” Scale items reflect child responses across tasks and are each rated on a scale from 0 indicating a low degree of SR with regard to the item (e.g., child impulsive throughout assessment, needed lots of boundary-setting) to 3 indicating a high degree of observed SR with regard to that item (e.g., child waits before pointing to materials, reaching for blocks), resulting in a score that reflects the degree to which the child consistently demonstrated self-regulation, across tasks. A single average scale score is recorded (Cronbach’s α = 0.71). Emotional SR will be assessed using both the Emotion Regulation Checklist [79]. The Emotion Regulation Checklist will be used to score children’s emotional regulation overall. This checklist is a valid and reliable 24-item questionnaire used to assess young children’s emotional regulation (Cronbach’s α =.83) and negative lability (α =.96; total scale score α = 0.89). Negative lability questions are scored so that a higher score reflects greater negative affect. Emotion regulation questions are scored so that a higher score reflects better emotional regulation. Negative lability and emotional regulation subscales are calculated to reflect average scores and will be the primary outcome variables. The Emotion Regulation Checklist will be completed by the classroom teachers. Classroom teachers will be paid for their services/role on this project as study reporters on child outcomes and will be trained in the administration of the Emotion Regulation Checklist. ## Health behaviors Motor Competence will be evaluated using process measures of motor skills at pre-test and post-test. The Test of Gross Motor Development-3rd edition (TGMD-3) assesses process measures of motor skills [80]. The TGMD-3 is a valid and reliable criterion-based assessment used to measure fundamental motor skills in children ages 3 to 10 years. It consists of six locomotor (run, jump, gallop, slide, hop, and skip; Cronbach’s α =.88) and seven ball skills (throw, catch, dribble, underhand throw, kick, one-handed forearm strike, and two-handed strike off a tee; Cronbach’s α =.93). Each motor skills is divided into 3–5 specific performance criteria and a child will receive a 1 if they perform the skill correct and a 0 if they fail to perform the criteria. Children will complete three trials for each skill; one practice trial and two scored trials. The TGMD-3 will be completed according to the test manual and procedures and children will receive a digital demonstration of the skills before the practice trial [81]. If the child did not understand the motor skill during the practice trial a second demonstration will be provided. The child wil then completed the two test trials. The identical verbal instructions will be provided in both the digital and live demonstration. The TGMD-3 assessments will be digitally recorded and coded by a motor development expert, who will serve as an external consultant to the project and is blind to the randomization. Raw scores for the two TGMD-3 subscales, locomotor (0–46) and ball skills (0–54), will be summed to derive the total score (0–100) that will be used for data analyses. Inter-rater reliability will be established between consultant/coder and two members of the research team on a random selection of $30\%$ of the assessments and will be completed every year. Physical Activity will be measured with ActiGraph accelerometers (model wGT3X-BT; Actigraph, Pensacola, FL, USA) secured by a hospital band on participants’ non-dominant wrist for one full week (i.e., 5 weekdays and 2 weekend days) at pre-test and post-test. The devices will be placed on the child during the school day and will be set to start recording at midnight. The devices will be removed after seven full days of recording. The devices will be set to collect data at 30 hz, the standard frequency used with accelerometers. Time spent in intensity categories will be based on vector magnitude minus the value of gravity (g) (i.e., (x2 + y2 + z2)$\frac{1}{2}$–1) referred to as ENMO (Euclidean norm minus one). The primary outcome will be minutes in MVPA per day but additional measures of physical activity will be analyzed based on the current physical activity recommendations [82, 83]. Hildebrand cut points will be applied to activity data [84, 85]. with MVPA defined as activity over 201 mg. To be considered valid wear, participants need to have at least 12 hours of valid accelerometry data per day for at least 4 days (3 weekdays and 1 weekend day) [86, 87]. Non-wear time is defined when either the standard deviation (SD) is less than 13 mg for two of the three axes or when the value range of each accelerometer axis is less than 150 mg, calculated for moving windows of 60 minutes with 15-minute increments [88]. The following steps will be taken to aid with device compliance 1) a letter to the parents which explains placement and provides a simple diagram, 2) physically show the parent and teachers how to place the accelerometer on the child if needed (both teachers and parents will be provided with spare bands), 3) text messages, phone calls, and flyers as prompts and reminders, 4) research staff will check the placement of accelerometers each day of data collection and 5) an incentive gift card ($10) upon the return of the device. Perceived competence will be assessed with the Harter and Pike Pictorial Scale of Perceived Competence and Social Acceptance—physical competence subscale [89, 90] and the Digital-Scale of Perceived Motor Competence [91, 92] at pre-test and post-test. The physical competence subscale of the Pictorial Scale of Perceived Competence and Social Acceptance measures children’s global perceived physical competence and consists of six items (swinging, climbing, tying shoes, running, hopping, skipping) that are presented in static pictures [89, 90]. Mean reliability coefficients (α) range from 0.66–0.89 and the reliability for the physical competence subscale is 0.66 [89, 90]. The appropriate scale (i.e., gender and ethnicity) will be used for each child. The Digital-Scale of Perceived Motor Competence is a digital-based assessment that measures perceived motor competence and allows individuals to see the entire motor skill executed as a video rather than a static picture [91]. The scale uses a similarly two part bifurcation selection process as the Pictorial Scale of Perceived Competence and Social Acceptance; however, the DSPMC uses an adult model and includes the fundamental motor skills of the TGMD-2. Face validity of the DSPMC has been established, and research supports the DSPMC has acceptable validity and reliability both preschool (α = 0.78; ICC = 0.84; $95\%$ CI = 0.76–0.89) [92] and elementary-aged children (α = 0.78; ICC 0.80; $95\%$ CI = 0.76–0.894) [91]. For both assessments, children [1] select the picture/video that is most like him or her (a competent/skilled or not competent/skilled) and [2] focus on the picture/video and indicate whether the picture/video was just a “little bit” or “a lot” like them. The range of scores for each item on the subscale is 1 (low competence) to 4 (high competence). Both assessments are established tools and standardized test protocols will be used [89, 91]. For analysis, these two measures will be examined separately since one is a measure of perceived physical competence and the other is a measure of perceived motor competence. ## Health outcomes Waist circumference and body mass index will be measured according to standard procedures [93, 94] at pre-test and post-test. Waist circumference will be measured with a non-elastic tape (Seca 201; Seca North America. Chino, CA. United States) at the umbilicus [95]. The measurement will be taken as the children completes a breath (i.e., exhales). Height will be measured to the nearest unit in bare feet with the child standing upright against a portable stadiometer (Charder HM200P PortStad; Taiwan R.O.C). Weight will be measured to the nearest unit with heavy clothes removed (i.e., wearing pants and shirt) using a portable electric weight scale (Seca 813; Seca North America. Chino, CA. United States). All scales will be calibrated before testing. BMI will be calculated based on age- and sex-specific CDC growth charts. BMI will be transformed into BMI z-scores for analyses. Inter- and intra-rater reliability of data collection staff will be assessed at baseline data collection and monitored throughout data collection. ## Overall analysis plans We will apply transformations to assure normality, run descriptive statistics, and assess potential covariates to include. While maximum effort will be made to retain all participants and minimize the amount of missing data, we anticipate there will be some data lost-to-follow-up and incomplete measures. Therefore, we will address missing data in the analysis plan by applying advanced statistical techniques, such as “multiple imputations” using PROC MI in SAS and IVEWARE SAS macro. Our overall approach will be to employ multivariate analysis to assess associations among key variables using the appropriate models based on the distribution of the data (i.e., normal, categorical data, count data, reaction time data). Since this is a cluster-randomized trial, adjusting the covariates (e.g., child sex, age, race/ethnicity) will aid in controlling additional unbalancesess due to the limited sample size. All analysis will be done using SAS 9.3 or R 4.1.0 [96–98]. Additionally, we will perform “intent to treat” analysis and assume all subjects comply to the assigned group. Findings/results from the study will also following the CONSORT guidelines. ## Power considerations When we calculate the sample size and power, we assumed the intra-cluster correlation is 0.3. The achievable power is calculated for a detectable effect size, which is the detectable mean difference standardized by the square root of sample variance. Given the randomization design with 70 children in the intervention group and 50 children in the control group, we have $80\%$ power to detect a difference in cognitive SR, behavioral SR, or emotional SR with an effect size of 0.52 and a Type I error level of 0.05. ## Specific analysis plan for Aim 1 Examine the immediate (pre- to post-test) intervention effects of CHAMP (compared to control participants) on cognitive SR (cognitive flexibility, working memory, attention shifting), behavioral SR (behavioral inhibition), and emotional SR (emotion regulation). The immediate post-intervention effect of CHAMP (compared to control participants) on each SR outcome variable will be evaluated at post-intervention. We will examine descriptive statistics for both pre-and post-intervention SR outcome variables for each group. The change in cognitive SR, behavioral SR, and emotional SR scores will be compared between the intervention (CHAMP) and control groups using regression models, adjusting for other confounding factors. We assume randomization will be successful and we will monitor throughout the trial. It is always good practice to monitor along the way and we will do so, but randomization may be imperfect as this is a randomized cluster trial taking place in the real word (i.e., Head Start setting). If randomization does not work this will lead to biased results and methodology, our planned analyses to adjust other variables will be considered. Random effects will be included in the model to accommodate for the potential within-cluster correlations due to nested classroom data. We anticipate that some children will have only partial adherence to the intervention (i.e., attend a subset of sessions), thus we will also conduct a dose-response analysis where dose corresponds to number of sessions. We will investigate the amount of attrition from pre-to post-intervention, and attempt to identify baseline (or pre-intervention) predictors of dropping out. The information will indicate possible bias in the change estimates. ## Specific analysis plan for Aim 2 Examine the associations between SR (cognitive SR, behavioral SR, and emotional SR) and changes in health behaviors (motor competence, perceived competence, physical activity) and health outcomes (body mass index, waist circumference). We will further evaluate the associations between changes in SR and changes in children’s health behaviors and outcomes, using regression models. More specifically, we will examine the strength of association between each SR variable and our outcomes of interest using bivariate analyses to compare change in SR to change in motor competence, perceived competence, and PA (using an alpha value of $p \leq .05$). To test whether the strength of association varies by intervention status, we will use multivariate regression models (controlling for covariates as needed) to examine the association of SR and outcomes in each group (CHAMP and control). Similarly, random effects will be included in the model to accommodate for the potential within-cluster correlations from nested classroom data. We will further apply Structural Equation Models (SEM) to evaluate whether the CHAMP intervention has a causal effect on children’s health outcomes mediated through SR. ## Data management Extreme care to ensure high-quality and secure data will be exercised. All data will be stored securely at the University of Michigan. All data will have only a numerical identifier so that individual respondents, except for video data, cannot be identified. All data will be reported as aggregate statistics and no individuals will be recognizable from the data reported. All data will be scanned for consistency, errors of omission, and appropriateness of the response, and $30\%$ of data will be checked by a blinded member of the research team. Once a coded and cleaned data file has been prepared, frequency distributions and descriptive statistics (means, standard deviations, and ranges) for each of the measured variables will be used for consistency checks and to verify the comparability of the groups. Logic check programs will be run to ensure that each data point falls within the expected range or corresponds to possible values in the codebook. These tracking system files will be maintained on a secure server at the University of Michigan. Data will be analyzed using SAS 9.3 or R 4.1.0 [96–99]. All members of the study team will be required to complete the web-based National Institutes of Health University of Michigan Responsible Conduct of Research Training Program. The investigative team will engage in ongoing data management training, data monitoring, and measurement training over the course of the investigation. Rewards and incentives will be incorporated after each assessment time point to aid in participant engagement. ## Discussion SR is an important domain of early child development that plays a foundational role in promoting well-being across the lifespan [4, 5], including emotional adjustment, social functioning, and educational achievement [9, 10]. Recently, motor skills have been linked to SR. Becker, Miao, Duncan and McClelland [39] found that fine motor skills were related to working memory and behavioral SR while Robinson, Palmer and Bub [40] later found that a mastery-based motor skills intervention helped maintained preschoolers’ delayed gratification, but the work in this area is limited. This study seeks to examine the immediate (pre- to post-test) intervention effects of a mastery-based motor skills intervention, CHAMP on child SR (cognitive SR, behavioral SR, and emotional SR). This study also seeks to examine associations between SR and changes in health behaviors (motor competence, perceived competence, physical activity) and health outcomes (waist circumference, body mass index). The research that explores SR from a movement perspective is relatively sparse. To our knowledge, no studies have tested the effects of a mastery climate, motor-based intervention on child SR measures. Findings from the study could potentially provide new knowledge as it relates to mastery-climate, motor-based intervention in early childhood settings and their contribution to the social and emotional development of young children along with the physical development. This proposed work is innovative in two ways. Specifically, this study will explore if a mastery-based motor skills intervention could enhance child SR. This intervention approach is not commonly used to promote SR in the child development literature. Secondly, this study will also study SR in the context of health behaviors and health outcomes. Interventions have shown to improve SR, but studies have not examined whether improving SR in children affects their health behaviors and/or health outcomes. This proposed study will expand the literature since little prior work has explored connections between SR and health constructs from a movement perspective. Additionally, most studies have established associations between motor interventions and outcomes to SR, but few have been experimental. Suppose this intervention proves to be effective in enhancing child SR. In that case, critical elements related to implementing a mastery-climate school-based motor skills intervention to promote SR will be identified. The new knowledge from this study could be used from an educational standpoint by classroom and/or physical education teachers as an intervention approach to promote motor skills and SR in young children. This study could also offer important insights into potential avenues for preventive interventions across a range of health behaviors. Eventually, the feasibility of disseminating and implementing the CHAMP intervention to support SR, health behaviors, and health outcomes could be scaled up to impact more children. ## References 1. Duckworth AL, Carlson SM. Self-regulation and school success. Self-regulation and autonomy: Social and developmental dimensions of human conduct (2013.0) 40 208 2. Raver CC. Placing emotional self‐regulation in sociocultural and socioeconomic contexts. Child development (2004.0) 75 346-353. DOI: 10.1111/j.1467-8624.2004.00676.x 3. Calkins SD, Fox NA. 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--- title: 'Association between acculturation and physician trust for internal migrants: A cross-sectional study in China' authors: - Enhong Dong - Ting Xu - Xiaoting Sun - Tao Wang - Yang Wang - Jiahua Shi journal: PLOS ONE year: 2023 pmcid: PMC9997971 doi: 10.1371/journal.pone.0280767 license: CC BY 4.0 --- # Association between acculturation and physician trust for internal migrants: A cross-sectional study in China ## Abstract ### Background Physician trust is a critical determinant of the physician–patient relationship and is necessary for an effective health system. Few studies have investigated the association between acculturation and physician trust. Thus, this study analyzed the association between acculturation and physician trust among internal migrants in China by using a cross-sectional research design. ### Methods Of the 2000 adult migrants selected using systematic sampling, 1330 participants were eligible. Among the eligible participants, $45.71\%$ were female, and the mean age was 28.50 years old (standard deviation = 9.03). Multiple logistic regression was employed. ### Results Our findings indicated that acculturation was significantly associated with physician trust among migrants. The length of stay (LOS), the ability of speaking Shanghainese, and the integration into daily life were identified as contributing factors for physician trust when controlling for all the covariates in the model. ### Conclusion We suggest that specific LOS-based targeted policies and culturally sensitive interventions can promote acculturation among Shanghai’s migrants and improve their physician trust. ## Background Physician trust refers to a patient’s optimistic acceptance of a vulnerable medical situation and belief that their physician is willing to care for the patient’s health and interests [1, 2]. Studies have revealed that physician trust is a strong predictor of health outcomes, such as patient satisfaction, health-seeking behaviors, continuity of care, and adherence to treatment [3, 4]. Physician trust facilitates patients’ access to health care and their disclosure of relevant information, which enables accurate and timely diagnoses [2], improves the self-reported health status of patients, and enhances the patient’s ability to manage chronic diseases [5]. Due to the information asymmetry between physicians and patients, patients’ trust in their physician is the basis of the physician–patient relationship [6]. The patient’s trust in their physician is the patient’s expectation that their physician will provide beneficial care and truthful information, regardless of the patient’s ability to monitor the physician [7]. Trust building is a key step in developing high-quality physician–patient interactions and relationships [8]. Distrustful patients are suspicious of their physician’s motivations and may behave aggressively. Many studies have identified that physician trust is a critical element of the physician–patient relationship and is necessary for building an effective health-care system [1, 4, 9]. Achieving high levels of physician trust and identifying sources of mistrust are key goals for health-care policy [4]. Acculturation is the process of cultural change and adaption or maladaptation that stems from contact with culturally different people, groups, and social influences [10]. Some scholars have applied immigration acculturation theory to internal migrants in China [11–13] because China hosts a large migratory population with massive intracultural differences between rural and urban regions [14, 15]. These differences are often demonstrated in terms of behaviors, psychology, interpersonal communication (e.g., language use) [16], and other social and cultural differences (e.g. food, clothing, customs, and social interactions). With the rapid increase in urbanization in China, many internal migrants who lived in a place other than their officially registered residence (hukou) for at least 6 months have gradually moved to urban areas in search of better educational and career opportunities. Despite many internal migrants’ strong desire to stay and become permanent residents in new cities, the adaptation and acculturation process is often long and stressful [12]. Due to economic and cultural discrepancies between urban and rural regions, migrants often experience social exclusion and prejudice when adjusting to city life. Furthermore, long-distance migration and structural barriers (e.g., hukou, health insurance, and perceived discrimination) can greatly increase the likelihood of cultural distance, which subsequently increases the chance of difficulties related to language, values, social networks, culture, and lifestyle [17, 18]. According to a report from the Xinhua News Agency in 2018, 260 million outpatient and emergency department visits and 4.4733 million discharged patients were recorded in Shanghai medical institutions, of which internal migrants accounted for approximately $30\%$ [19]. Although structural barriers have widened the existing gap between the restricted supply of and increased demand for health services, relationships between physicians and migrant patients have deteriorated in Shanghai; internal migrants were involved in approximately $60\%$ of violent events against physicians and physician–patient dispute cases in Shanghai in 2017 [20]. Many studies have supported that acculturation is a significant predictor of health service encounters, including physician trust and health care [21–24]. Therefore, to address the association of acculturation with physician trust, this article examines the association of acculturation with physician trust among migrant Shanghainese patients and the contributing acculturative factors. To identify the association between acculturation and physician trust among internal migrants, other variables must be controlled for. Therefore, we accounted for age; sex; educational level; type of insurance; and health profile, including outpatient visit frequency, number of chronic diseases, and self-reported health status [25–27], to identify the significance of the association between acculturation and physician trust. We constructed a conceptual framework based on immigration acculturation theory (Fig 1). Acculturation is often conceptualized as a process through which an immigrant adopts the language, customs, food, behaviors, and attitudes of the host culture [28]. Acculturation has been linked to physical and psychological outcomes, such as obesity [29], morbidity [30], mortality [31], satisfaction with urban life [32], psychological intentions (e.g., suicide intention) [33], and physician trust [34]. Although acculturation is positively associated with some physical health disorders, psychiatric disorders, and healthy behaviors among some populations [35], patient satisfaction and physician trust have also increased after acculturation among other populations [34, 36]. Many studies have indicated that immigrants’ reported trust in health-care providers increases with greater acculturation following immigration [25, 37, 38]. In other words, as migrants adjusted to their host environment, they reported better experiences during health visits and became more positive toward the quality of health care. Due to the uncertain nature of these findings, they have seldom been applied to preventive or intervention programs in physician–patient relationship management. Most studies examining the association between acculturation and physician trust among ethnocultural groups have examined Latin American and Asian patients; however, few studies have focused on internal Chinese migrants. Fig 1: Conceptual model based on immigration acculturation theory. By using the conceptual model, we hypothesized that physician trust tends to increase over time among internal migrants as LOS gradually increases (H1). Additionally, we expected that language proficiency is positively associated with physician trust. We separated H1 into three subhypotheses: Shanghainese speaking proficiency is positively associated with physician trust (H2a); Shanghainese listening ability is positively associated with physician trust (H2b); and Shanghainese use at home, work, and with friends is positively associated with physician trust (H2c). Finally, we hypothesized that lifestyle integration is positively associated with physician trust (H3). ## Participants The survey for this study was conducted at the Huangpu Physical Examination Center of Shanghai from June to September 2019. We used a systematic sampling method to select eligible participants. Participants were included in the study if they [1] were 16 years or older, [2] were not originally born in Shanghai but have been granted legal permanent or temporary residency permits by the Migrant Population Management Office for at least 6 months, and [3] visited a physician in Shanghai more than once in the past year. This study was conducted in compliance with the Shanghai University of Medicine and Health Sciences Institutional Review Board for the Protection of Human Subjects on March 5, 2019 (No:2019-gskyb-02-372424198012222511). All participants provided written informed consent. ## Measurements The data were collected using a self-developed questionnaire containing 27 questions regarding participants’ socio-demographics, health profiles (health-care use and health status), acculturation, and physician trust (see S1 Table and S1 File). Studies have measured acculturation by using a standardized scale, such as a Marin short acculturation scale [39]; an Acculturation Rating Scale for Mexican Americans-II (ARSMA-II); or proxy measures, including LOS- and language-based questions, to assess the level of acculturation. In particular, proxy measures have demonstrated a high correlation with other more frequently used standardized scales [40, 41]. Therefore, in this study, we measured acculturation through four proxies: LOS in Shanghai, dialect proficiency, dialect use, and lifestyle integration. LOS in Shanghai was self-reported and categorized into the following four groups: less than 1 year (referent), 1–5 years, 5–10 years, and 10 or more years. Dialect proficiency was measured using two items that required participants to rate their dialect speaking and listening abilities on a 4-point Likert scale with the endpoints of 1 (very well) and 4 (not at all). Dialect use was measured using three items regarding the choice of dialect during different occasions (i.e., at home, at work, and with friends). Lifestyle integration was measured using a Chinese version of the acculturation scale [42], which was revised using the existing acculturation scales developed by other scholars [43–45]. The scale included four questions regarding dietary habits, dressing, entertainment, and social customs. Each question was related to the degree of changes since moving to Shanghai and was rated on a 5-point Likert scale, with the endpoints of 1 (completely the same) and 5 (completely different). A score closer to 1 indicated little-to-no lifestyle integration or a lower acceptance of the host culture, and a score closer to 5 indicated high lifestyle integration or a higher acceptance of the host culture. Because life integration was measured ordinally, it was considered a binary variable (i.e., high vs. low) in this study for statistical simplicity. The cut-off point was set at a sample median used in another study [46]. Reliability was examined using Cronbach’s α. Cronbach’s α for the four items related to lifestyle integration was 0.870. Physician trust was the dependent variable in this study and was measured using a 11-item Chinese version scale for physician trust in patients, which was revised for evaluating Chinese patients and medical contexts [47]. The scale has been used in many studies to measure physician trust in the Chinese health-care context and has demonstrated good reliability and validity [48, 49]. In this study sample, Cronbach’s α for physician trust was 0.91. ## Statistical analysis Participant demographics and socioeconomic characteristics were summarized using descriptive analyses. Bivariate analyses were conducted to examine the association between acculturation variables and physician trust scores by performing a t test for continuous variables and a Chi-square test for categorical variables. Multivariate logistic regression models were performed to calculate the effects of acculturation on physician trust among migrants. Predictors that were significant at a liberal p value of <0.05 were retained as candidates for the multivariable model. The covariates in the regression models included age, sex, education level, type of insurance, outpatient visit frequency, and other confounders. We constructed and compared three models to identify a significant association between acculturation variables and physician trust. We used a directed acyclic graph to illustrate the relationship between acculturation and physician trust, including previously mentioned covariates. The covariates that we controlled for were chosen on the basis of findings in the literature [25–27] (S1 Fig). Three models were constructed using the manual forward selection procedure; sociodemographic variables (i.e., sex and insurance type) were significant in the bivariate analysis, and the health profile variables (i.e., outpatient visit frequency, number of chronic diseases, and self-reported health status) were sequentially added to the model. Specifically, Model 1 assessed the unadjusted association between acculturation variables and physician trust, Model 2 adjusted for the effect of sociodemographic variables, and Model 3 adjusted for both sociodemographic and health profile variables. Age and educational level were key confounders, which were also adjusted in the model irrespective of their p values. All analyses were conducted using Stata.15.0 (Stata, College Station, TX, USA). ## Descriptive analysis of participants Among 2000 participants, 1480 were eligible participants, 1390 participants (response rate of $93.92\%$) returned the questionnaires, and 1330 were included in the final analyses after excluding those with >$5\%$ missing data. The final sample included 1330 participants ($46\%$ women, Mage = 28.50 years, standard deviation = 9.03) who met the inclusion criteria. Of the 1330 participants, 1178 ($88.57\%$) reported a high level of physician trust in Shanghai. Additional characteristics of participants in analysis stratified by the level of physician trust are displayed in Table 1. Table 1 \| Variables \| n(%) \| Physician Trust \| Physician Trust.1 \| P value \| Unnamed: 5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variables \| n(%) \| High level n(%) \| Low level n(%) \| P value \| \| \| Variables \| n(%) \| Physician Trust(n = 1330) \| 1178 \| P value \| 152.0 \| \| Age \| 28.50±9.03 \| -- \| -- \| 0.093 \| \| \| Sex(n = 1330) \| \| \| \| \| \| \| Female \| 608(45.71) \| 524(39.40) \| 84(6.32) \| 0.012 \| \| \| Male \| 722(54.29) \| 654(49.17) \| 68(5.11) \| \| \| \| Educated level(n = 1330) \| \| \| \| \| \| \| Lower or equal to primary education \| 35(2.63) \| 30(2.26) \| 5(0.38) \| 0.200 \| \| \| Secondary education \| 984(73.98) \| 882(66.32) \| 102(7.67) \| \| \| \| College education \| 309(23.23) \| 264(19.85) \| 45(3.38) \| \| \| \| Higher or equal to graduated education \| 2(0.15) \| 2(0.15) \| 0(0.00) \| \| \| \| Marital type(n = 1330) \| \| \| \| \| \| \| Never married \| 767(57.67) \| 690(51.68) \| 77(5.79) \| 0.065 \| \| \| Married \| 563(42.33) \| 488(36.69) \| 75(5.64) \| \| \| \| Employed type(n = 1330) \| \| \| \| \| \| \| Unemployed \| 41(3.08) \| 40(3.01) \| 1(0.07) \| 0.185 \| \| \| Employed \| 1169(87.89) \| 1032(77.59) \| 137(10.30) \| \| \| \| Self-employed \| 120(9.02) \| 106(7.97) \| 14(1.05) \| \| \| \| Retired \| 0(0.00) \| 0(0.00) \| 0(0.00) \| \| \| \| Insured type(n = 1329) \| \| \| \| \| \| \| Uninsured \| 242(18.21) \| 225(16.93) \| 17(1.28) \| 0.040 \| \| \| NRCMI \| 407(30.62) \| 352(26.49) \| 55(4.14) \| \| \| \| UEBMI/URBMI \| 680(51.17) \| 600(45.15) \| 80(6.02) \| \| \| \| Annual income level(n = 1330) \| \| \| \| \| \| \| <50,000 RMB \| 429(32.26) \| 382(28.72) \| 47(3.53) \| 0.969 \| \| \| 50,000–100,000 RMB \| 596(44.81) \| 528(39.70) \| 68(5.11) \| \| \| \| 100,000–250,000 RMB \| 246(18.50) \| 216(16.24) \| 30(2.26) \| \| \| \| > = 250,000 RMB \| 59(4.44) \| 52(3.91) \| 7(0.53) \| \| \| \| Annual physical examination frequency (n = 1330) \| \| \| \| \| \| \| 0 time \| 131(9.85) \| 113(8.50) \| 18(1.35) \| 0.685 \| \| \| 1 time \| 958(72.03) \| 851(63.98) \| 107(8.05) \| \| \| \| 2 times \| 209(15.71) \| 187(14.06) \| 22(1.65) \| \| \| \| > = 3 times \| 32(2.41) \| 27(2.03) \| 5(0.38) \| \| \| \| Outpatient Visit frequency(n = 1330) \| \| \| \| 0.001 \| \| \| 1 time \| 550(41.35) \| 506(38.05) \| 44(3.31) \| \| \| \| 2 times \| 317(23.83) \| 281(21.13) \| 36(2.71) \| \| \| \| > = 3 times \| 463(34.81) \| 391(29.40) \| 72(5.41) \| \| \| \| District(n = 1330) \| \| \| \| 0.098 \| \| \| Metropolitan area \| 998(75.04) \| 894(67.22) \| 104(7.82) \| \| \| \| Fringe areas \| 209(15.71) \| 181(13.61) \| 28(2.11) \| \| \| \| Suburban areas \| 123(9.25) \| 103(7.74) \| 20(1.50) \| \| \| \| Number of chronic diseases (n = 1328) \| \| \| \| <0.001 \| \| \| 0 \| 1069(80.50) \| 961(72.36) \| 108(8.13) \| \| \| \| 1 \| 227(17.09) \| 193(14.53) \| 34(2.56) \| \| \| \| > = 2 \| 32(2.41) \| 22(1.66) \| 10(0.75) \| \| \| \| Number of major diseases (n = 1327) \| \| \| \| 0.925 \| \| \| 0 \| 1319(99.40) \| 1168(88.02) \| 151(11.38) \| \| \| \| 1 \| 8(0.60) \| 7(0.53) \| 1(0.08) \| \| \| \| > = 2 \| 0(0.00) \| 0(0.00) \| 0(0.00) \| \| \| \| Health status (n = 1329) \| \| \| \| \| \| \| High level \| 1201(90.37) \| 1088(81.87) \| 113(8.50) \| <0.001 \| \| \| Low level \| 128(9.63) \| 89(6.70) \| 39(2.93) \| \| \| \| Length of years in Shanghai (n = 1324) \| \| \| \| \| \| \| <1 year \| 267(20.17) \| 241(18.20) \| 26(1.96) \| 0.044 \| \| \| 1–5 years \| 492(37.16) \| 447(33.76) \| 45(3.40) \| \| \| \| 5–10 years \| 236(17.82) \| 201(15.18) \| 35(2.64) \| \| \| \| > = 10 years \| 329(24.85) \| 284(21.45) \| 45(3.40) \| \| \| \| Shanghainese speaking ability (n = 1329) \| \| \| \| 0.048 \| \| \| Not well \| 296(22.27) \| 265(19.94) \| 31(2.33) \| \| \| \| well \| 1033(77.73) \| 912(68.62) \| 121(9.10) \| \| \| \| Shanghainese listening ability (n = 1329) \| \| \| \| 0.045 \| \| \| Not well \| 658(49.51) \| 591(44.47) \| 67(5.04) \| \| \| \| well \| 671(50.49) \| 586(44.09) \| 85(6.40) \| \| \| \| Dialect use at home(n = 1330) \| \| \| \| 0.829 \| \| \| Shanghai dialect \| 40(3.01) \| 35(2.63) \| 5(0.38) \| \| \| \| Not Shanghai dialect \| 1290(96.99) \| 1143(85.94) \| 147(11.05) \| \| \| \| Dialect use at work(n = 1330) \| \| \| \| 0.756 \| \| \| Shanghai dialect \| 55(4.14) \| 48(3.61) \| 7(0.53) \| \| \| \| Not Shanghai dialect \| 1275(95.86) \| 1130(84.96) \| 145(10.90) \| \| \| \| Dialect use with friends(n = 1330) \| \| \| \| \| \| \| Shanghai dialect \| 43(3.23) \| 39(2.93) \| 4(0.30) \| 0.656 \| \| \| Not Shanghai dialect \| 1287(96.77) \| 1139(85.64) \| 148(11.13) \| \| \| \| Lifestyle integration \| \| \| \| 0.017 \| \| \| Low \| 158(11.90) \| 147(11.07) \| 11(0.83) \| \| \| \| High \| 610(45.93) \| 537(40.44) \| 73(5.50) \| \| \| ## Bivariate analysis Significant covariates identified in bivariate analyses were female sex, insurance type, and a high frequency of visiting physicians. A greater number of chronic diseases and worse self-reported health were associated with the less likelihood of reporting a high level of physician trust. However, a good health status was associated with a higher likelihood of reporting a high level of physician trust. No significant difference was observed in physician trust in analyses based on educational level, marital status, employment type, income level, physical examinatioFn frequency, number major diseases, district of residency. Significant acculturation variables associated with physician trust in bivariate analyses were LOS years in Shanghai, Shanghainese dialect speaking and listening, and lifestyle integration. Additional sociodemographic factors based on the level of physician trust are presented in Table 1. ## Logistic analysis In Model 1, we identified a positive association between LOS in the host region and physician trust (Table 2). Compared with the participants who lived in Shanghai for less than 1 year, those who lived in Shanghai for 1–5 years were 1.36 times more likely to have a higher level of physician trust (odds ratio [OR]: 1.36, $95\%$ confidence interval [CI]: 1.04–1.78). Moreover, those who lived in Shanghai for 5–10 years and 10 or more years were 1.78 and 2.70 times more likely to have a higher level of physician trust, respectively (OR: 1.78, $95\%$ CI: 1.66–3.97; OR: 2.70, $95\%$ CI: 2.58–4.93, respectively) compared with those who lived in Shanghai for less than 1 year. Moreover, those who reported high speaking and listening proficiency in Shanghainese were more likely to report higher physician trust than those who did not (OR: 1.56, $95\%$ CI: 1.35–2.04; OR: 1.74, $95\%$ CI: 1.50–2.11, respectively). Additionally, those who had a higher degree of lifestyle integration were more likely to report a higher level of physician trust compared with those who reported a lower degree of lifestyle integration (OR: 1.65, $95\%$ CI: 1.30–2.11). However, using Shanghainese at home, work, and with friends was not significantly associated with physician trust in Model 1. Table 2 \| Unnamed: 0 \| Model 1 ORa \| Model 2 ORb \| Model 3 ORc \| \| --- \| --- \| --- \| --- \| \| Acculturation variables \| Unadjusted# \| Adjusted for demographic and socio-economic covariates∫ \| Adjusted for additional health profile based on model 2† \| \| Length of years in Shanghai(n = 1324) \| \| \| \| \| <1 year \| 1.00 \| 1.00 \| 1.00 \| \| 1–5 years \| 1.36(1.04–1.78) \| 1.25(1.02–1.59) \| 1.23(1.01–1.49) \| \| 5–10 years \| 1.78(0.66–0.97) \| 1.69(0.58–0.87) \| 1.61(0.48–0.93) \| \| > = 10 years \| 2.70(0.58–0.93) \| 2.68(0.50–0.80) \| 2.59(0.33–0.84) \| \| Shanghainese speaking ability(n = 1329) \| \| \| \| \| Not well \| 1.00 \| 1.00 \| 1.00 \| \| Well \| 1.56(1.35–2.04) \| 1.55(1.33–2.06) ** \| 1.37(1.31–2.11) * \| \| Shanghainese listening ability(n = 1329) \| \| \| \| \| Not well \| 1.00 \| 1.00 \| 1.00 \| \| Well \| 1.74(1.50–2.11) \| 1.78(1.52–2.17) \| 1.68(0.82–2.47) \| \| Dialect use at home(n = 1330) \| \| \| \| \| Not Shanghai dialect \| 1.00 \| 1.00 \| 1.00 \| \| Shanghai dialect \| 0.65(0.20–2.13) \| 0.76(0.24–2.45) \| 0.71(0.22–2.32) \| \| Dialect use at work(n = 1330) \| \| \| \| \| Not Shanghai dialect \| 1.00 \| 1.00 \| 1.00 \| \| Shanghai dialect \| 0.83(0.32–2.15) \| 0.79(0.31–2.00) \| 0.74(0.28–1.96) \| \| Dialect use with friends(n = 1330) \| \| \| \| \| Not Shanghai dialect \| 1.00 \| 1.00 \| 1.00 \| \| Shanghai dialect \| 1.91(0.67–5.32) \| 1.79(0.55–5.85) \| 1.68(0.51–5.57) \| \| Lifestyle integration \| \| \| \| \| low \| 1.00 \| 1.00 \| 1.00 \| \| high \| 1.65(1.30–2.11) \| 1.62(1.26–2.17) \| 1.56(1.20–2.27)* \| In Model 2, we identified similar results as those in Model 1. First, compared with those who lived in Shanghai for less than 1 years, participants who lived in Shanghai for 1–5 years were 1.25 times more likely to report a higher level of physician trust (OR: 1.25, $95\%$ CI: 1.02–1.59), and those who lived in Shanghai for 5–10 years and 10 or more years were 1.69 and 2.68 times more likely to report a higher level of physician trust, respectively (OR: 1.69, $95\%$ CI: 1.58–3.87; OR: 2.68, $95\%$ CI: 2.50–4.80, respectively). Second, participants who reported high listening and speaking proficiency in Shanghainese also had a higher level of physician trust compared with their counterparts (OR: 1.55, $95\%$ CI: 1.33–2.06; OR: 1.78, $95\%$ CI: 1.52–2.17, respectively). Furthermore, those who had a higher degree of lifestyle integration were more likely to report a higher level of physician trust (OR: 1.62, $95\%$ CI: 1.26–2.17) compared with those who had a lower degree of lifestyle integration. However, using Shanghainese at home, work, and with friends was not significantly associated with physician trust in Model 2. In Model 3, when controlling for all the variables, we found that only LOS in Shanghai, high speaking proficiency of Shanghainese, and lifestyle integration were significantly associated with a high level of physician trust. However, the listening ability of Shanghainese was not a significant predictor. ## Discussion First, we found that LOS in Shanghai was associated with physician trust among Shanghai migrants, supporting H1. This finding is concordant with those of previous studies that LOS in host countries or regions was significantly associated with strong subjective assessment scores of health-care services among migrants [50, 51]. The most plausible explanation is that the duration of acculturation increased the reported health-related advantages among migrants, including physician trust [52]. For internal migrants, a migrant who has stayed longer in Shanghai may be more acculturated, experienced with navigating through the health care system, and able to communicate with health-care providers, all of which gradually improve trust in the health-care system and in physicians [34]. Accordingly, this effect may have caused the upward trend in physician trust among longer-term migrants in Shanghai. Second, Shanghainese proficiency and use were an effective measurement of acculturation, although they might not have indicated assimilation to unhealthy norms or lifestyles. For some migrants, higher Shanghainese proficiency and use may improve integration and acceptance into host culture [53, 54]. Some studies have argued that poor language proficiency and less frequent use may lead to discrimination against migrants and their exclusion from some aspects of the host society [54, 55]. However, our study suggested that, when considering all covariates, only Shanghainese speaking ability positively affected physician trust. Thus, only H2a was supported. Speaking proficiency rather than listening ability could establish more patient–physician language concordance and subsequently facilitate the assimilation of migrants into Shanghai’s local culture. For migrants in Shanghai, the physician–patient relationship was strengthened when migrants with a high proficiency of Shanghainese perceived themselves similar to their physicians in terms of beliefs, values, and language [56, 57]. Some studies have reported that perceived personal similarity is associated with higher levels of trust, satisfaction, and adherence [56–58]. Therefore, language proficiency may facilitate improvements to physician–patient relationships. Third, some lifestyle changes manifested in attitudes, beliefs, values, and behaviors, such as dietary habits, clothes, entertainment, and social customs. Acculturation is “the process by which an ethnic group, usually a minority, adopts the cultural patterns, including beliefs, religion, and language, of a dominant group” [32] and has been conceived as a dynamic process in which individuals gradually adjust to a new environment. For internal migrants in Shanghai, having a higher degree of lifestyle integration indicated that migrants were more likely to adapt to the new host environment and accept the host culture through cultural and behavioral assimilation. Lifestyle is an essential factor for acculturation among internal migrants [59]. Although adopting the mainstream lifestyle may negatively affect mental health by evoking stress [60, 61], lifestyle adjustments can promote cultural communication with local physicians and subsequently improve psychological adjustment [32]. Lifestyle adjustments also affected physician trust among migrants in Shanghai; lifestyle integration positively affected migrants’ medical visits, perceived quality of health-care services, and their relationship with physicians. Therefore, lifestyle integration was positively associated with physician trust, which supports H3. This finding is consistent with the results of other studies [38, 62], which indicated that cultural and behavioral assimilation can influence physician trust among internal migrants. This study has some limitations. First, this sample included only migrants who were granted legal permanent or temporary residency permits for 6 months or longer. Thus, migrants who did not obtain a legal permanent or temporary residency permit or had residency permits for less than 6 years were excluded. Moreover, participants were selected from one physical examination center, which prevented the generalizability of our findings. Further research has already been planned to evaluate patients from multiple centers. Second, the acculturation variables measured in this study may not have captured every aspect of acculturation. Acculturation is difficult to measure and has many aspects, such as values, interethnic interactions, cultural domains, participation, and identity, some of which are unobservable. Changes in acculturation can indicate how the original culture and new culture interact to produce new values, attitudes, and beliefs. In this study, we evaluated LOS, Shanghainese proficiency and use, and lifestyle integration to measure acculturation, which may have produced measurement bias. Therefore, using an alternative scale to measure the changes in psychological and behavioral acculturation, such as the Psychological–Behavioral Acculturation Scale [63], can ensure more robust results. Third, according to a study by Sakamoto [64], acculturation theory has some deficiencies. For example, acculturation theory mainly neglects structural issues that affect migrants experiencing unfamiliar cultures and may lead migrants to not being perceived as “sufficiently” assimilated. This can occur even if migrants experienced social exclusion or discrimination. Therefore, structural barriers, such as cultural distance [65], perceived discrimination [24], and hukou [66], should also be considered in future research. Fourth, the self-reported physician trust may have produced recall bias. For self-reported outcomes such as health-related quality of life, satisfaction and trust can vary depending on the event being recalled, time since the event, and the clinical and demographic characteristics of patients. The outcome measurement scores were always collected after patients sought health-care services, which may have caused recall bias that affected the actual trust scores. Therefore, physician trust should be supplemented with other measures to provide a broader understanding of the physician–patient relationship and health service quality. ## Contributions and policy implications The present study has several contributions. First, this study can inform the Shanghai government of targeted measures for rebuilding physician trust among migrants and improving the quality of health-care services from an acculturative perspective. Policies can target temporary and permanent residency, employment, and education for migrants and their children to facilitate integration and assimilation. Because long-term internal migrants demonstrated a higher level of physician trust compared with those who lived in Shanghai for less than 1 year, policies should target long-term migrants to enhance their physician trust. Additionally, our findings inform strategies for developing community-based intervention programs that address the needs of migrants who have lived in Shanghai for less than 1 year. Such policies can help health-care policymakers and practitioners to develop culturally sensitive interventions for providing medical care that targets Shanghai’s migrant community. Moreover, barriers between physicians and migrant patients can be broken down to improve physician trust and enhance the physician–patient relationship. Second, this study included several measures of acculturation. Our study presented a thorough examination of acculturation dynamics. By examining many aspects of acculturative progress in migration, we captured the specific acculturative factors that affect the migrants’ physician trust and identified nonsignificant factors. Third, we examined a large sample of migrants in a metropolitan city in China. These migrants routinely received physical examination in an authorized center that covered all regions of Shanghai, the second largest city in China. According to official statistics, approximately $15\%$ of internal migrants are concentrated in the four mega-cities of Beijing, Shanghai, Guangzhou, and Shenzhen. Although geographical, economic, and cultural differences exist among the four cities, the internal migrants in Shanghai, which has one of the largest proportions of migrants, exhibit common characteristics with internal migrants in the other three cities. For example, internal migrants mainly belong to interprovincial flows; are likely to pursue higher wages, better employment positions, and better educational opportunities for their children; and must adapt to the local cultural and competitive environment [67]. Therefore, the findings of this study may be more generalizable to the national-level rather than to specific ethnic groups or geographic regions. Further research with a longitudinal study design that focuses on physician trust and the aspects of acculturation is required. ## Conclusion This study established the association between acculturation and physician trust among internal migrants in Shanghai. LOS, Shanghainese speaking proficiency, and lifestyle integration were identified as contributing factors for physician trust when controlling for all acculturative variables. To improve physician trust among migrants in large cities in China, policies that target internal migrants based on their LOS and culturally sensitive interventions are required. Moreover, physician trust can be improved by fully considering the effects of acculturation in health-care settings, meeting the specific medical needs of internal migrants, and facilitating integration and assimilation into the host culture. ## References 1. Hall MA, Dugan E, Mishra ZAK. 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--- title: Hypothalamic TrkB.FL overexpression improves metabolic outcomes in the BTBR mouse model of autism authors: - Jacqueline M. Anderson - Amber A. Boardman - Rhiannon Bates - Xunchang Zou - Wei Huang - Lei Cao journal: PLOS ONE year: 2023 pmcid: PMC9997972 doi: 10.1371/journal.pone.0282566 license: CC BY 4.0 --- # Hypothalamic TrkB.FL overexpression improves metabolic outcomes in the BTBR mouse model of autism ## Abstract BTBR T+ Itpr3tf/J (BTBR) mice are used as a model of autism spectrum disorder (ASD), displaying similar behavioral and physiological deficits observed in patients with ASD. Our recent study found that implementation of an enriched environment (EE) in BTBR mice improved metabolic and behavioral outcomes. Brain-derived neurotrophic factor (Bdnf) and its receptor tropomyosin kinase receptor B (Ntrk2) were upregulated in the hypothalamus, hippocampus, and amygdala by implementing EE in BTBR mice, suggesting that BDNF-TrkB signaling plays a role in the EE-BTBR phenotype. Here, we used an adeno-associated virus (AAV) vector to overexpress the TrkB full-length (TrkB.FL) BDNF receptor in the BTBR mouse hypothalamus in order to assess whether hypothalamic BDNF-TrkB signaling is responsible for the improved metabolic and behavioral phenotypes associated with EE. Normal chow diet (NCD)-fed and high fat diet (HFD)-fed BTBR mice were randomized to receive either bilateral injections of AAV-TrkB.FL or AAV-YFP as control, and were subjected to metabolic and behavioral assessments up to 24 weeks post-injection. Both NCD and HFD TrkB.FL overexpressing mice displayed improved metabolic outcomes, characterized as reduced percent weight gain and increased energy expenditure. NCD TrkB.FL mice showed improved glycemic control, reduced adiposity, and increased lean mass. In NCD mice, TrkB.FL overexpression altered the ratio of TrkB.FL/TrkB.T1 protein expression and increased phosphorylation of PLCγ in the hypothalamus. TrkB.FL overexpression also upregulated expression of hypothalamic genes involved in energy regulation and altered expression of genes involved in thermogenesis, lipolysis, and energy expenditure in white adipose tissue and brown adipose tissue. In HFD mice, TrkB.FL overexpression increased phosphorylation of PLCγ. TrkB.FL overexpression in the hypothalamus did not improve behavioral deficits in either NCD or HFD mice. Together, these results suggest that enhancing hypothalamic TrkB.FL signaling improves metabolic health in BTBR mice. ## Introduction Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by deficits in social communication and social interaction and by repetitive and restricted patterns of behaviors, interests, and activities [1]. As of 2016, the prevalence of ASD in the United States is 1 in 54 children, with males being four times more likely than females to be diagnosed [2]. ASD is a very heterogeneous disorder, as individuals display varied combinations and severity of symptoms and comorbidities [1]. This heterogeneity makes it difficult to elucidate the underlying etiologies of ASD, but research suggests it likely involves a combination of genetics and environment influencing the developing brain [3]. Considering the environmental influence on the etiology of ASD and the use of environmental and sensory based therapies to treat ASD [4–6], investigating the effects of environment in an ASD-like murine model can help to elucidate the mechanisms behind ASD and ASD interventions. Past studies in our lab have extensively examined the effects of an enriched environment (EE) in a variety of mouse models of disease, including cancer and obesity. We have found that placing mice in an EE providing physical, social, and cognitive stimuli induces an anti-obesity, anti-cancer, and anxiolytic phenotype [7–10]. We have elucidated one mechanism behind these effects, termed the hypothalamic-sympathoneural-adipocyte (HSA) axis. Stimuli from the EE upregulate brain-derived neurotrophic factor (BDNF) expression in the hypothalamus, which elevates sympathetic tone preferentially to white adipose tissues (WAT) [7, 8]. As a result, the norepinephrine released from sympathetic nerve acts on β-adrenergic receptor on the adipocytes leading to profound adipose remodeling. These adipose phenotypic changes are in parallel but all driven by the HSA axis: decreased leptin expression and release contributing to an anti-tumor effect; increased levels of vascular endothelial growth factor (VEGF), which increases energy expenditure and leanness through inducing beige cells; increased PTEN expression contributing to the reduction of adipocyte size and the increase of lipolysis; increased interleukin 15 (IL-15) expression leading to induction of adipose resident natural killer (NK) cells. The HSA axis-driven adipose remodeling plays a critical role in mediating the anticancer and anti-obesity effects of EE [7, 8, 10–12]. Based on the beneficial effects we saw in models of obesity and cancer after EE intervention, we recently investigated the effects of EE in the BTBR T+ Itpr3tf/J (BTBR) murine model of ASD. We found that placing BTBR mice in an EE mitigated both metabolic and behavioral deficits. Enriched male BTBR mice displayed lower adiposity, increased lean mass, lower levels of circulating leptin, and improved glucose tolerance. Behavioral tests suggested that an EE decreased anxiety-like behavior and improved social affiliation. Gene expression of Bdnf was significantly upregulated, which was consistent with HSA axis activation. Gene expression of Ntrk2, encoding the BDNF receptor tropomyosin kinase receptor B (TrkB), was upregulated 3 to 6 folds in the hypothalamus, hippocampus, and amygdala of EE mice, an extent larger than Bdnf [13]. The BTBR mouse was originally bred for studies on insulin-resistance, diabetes-induced nephropathy and phenylketonuria [14]. BTBR mice have a genetic propensity to obesity and type II diabetes. They have a genetic variation that promotes insulin resistance and this results in severe diabetes when crossed with the ob mutation in the leptin gene (BTBR ob/ob). BTBR mice also have alleles that increase body weight and obesity as compared to C57Bl/6 mice [15]. BTBR mice have higher fasting insulin levels and are insulin resistant as compared to C57Bl/6 mice due to adipose tissue insulin resistance, but they retain hepatic insulin sensitivity [16]. Insulin stimulated glucose uptake in adipose tissue and muscle tissue are defective in BTBR mice. Several inflammation related genes are upregulated in the adipose tissue of BTBR mice, and mRNA levels of leptin in adipose tissue are higher in BTBR mice than in C57Bl/6 mice [17]. Proteomic and transcriptomic studies of the hippocampus and cortex have found aberrant expression of proteins and genes involved in neurodevelopment, connectivity maintenance and guidance, neurogenesis, and neuroprotection, as well as disruption of inter and intracellular signaling pathways. The altered expression of genes involved in neurodevelopment and connectivity leads to the unique neuroanatomy of BTBR mice, which includes agenesis of the corpus callosum and a reduction of the hippocampal commissure. In addition to abnormal gene/protein expression and neuroanatomy, studies have found that BTBR mice may display an excitatory/inhibitory neurotransmission imbalance. BTBR mice also show increased basal corticosterone levels, which may be due to dysfunctional regulation of the HPA axis. Additionally, BTBR mice display aberrant immune responses. Basal plasma levels of IgG, IgE, anti-brain antibodies (Abs), and proinflammatory cytokines are higher in BTBR mice than in B6 mice. Within the brain, there is a higher number of mast cells and an increased proportion of MHC class II-expressing microglia, which suggests ongoing neuroinflammation [14, 18]. Over a decade ago, researchers discovered that BTBR mice could be used as a model of ASD, as it displayed relevant key diagnostic symptoms of ASD—selectively reduced social approach, low reciprocal social interactions, impaired juvenile play, and repetitive behaviors [19]. Since this discovery, much research has focused on behavioral phenotype characterization and genetic profiling of the BTBR mouse [19, 20], but less is known about the neurobiological mechanisms underlying the phenotypes. Notably, one study found that feeding BTBR mice a high-fat diet exacerbates cognitive rigidity and social deficiency [21]. Children with ASD are often more likely to develop obesity than children with typical development [22, 23]. This highlights a need for understanding the relationship between metabolic and cognitive health in individuals with ASD. Our previous cancer and obesity studies identified BDNF as the key brain mediator for improved metabolic and immunity outcomes following EE, and our EE-BTBR study found that Bdnf and Ntrk2 were upregulated following EE. The actions of BDNF are mediated by two major TrkB isoforms—full length TrkB (TrkB.FL) and truncated TrkB (TrkB.T1) [24]. Therefore, we hypothesized that BDNF-TrkB signaling was integral to the phenotypic outcomes induced by EE in BTBR mice. The purpose of this current study was to investigate the role of hypothalamic TrkB signaling in metabolic and behavioral phenotypes for both a normal chow diet (NCD)-fed and a high-fat diet (HFD)-fed BTBR mouse model. ## Mice and diet Male BTBR T+ Itpr3tf/J (Jackson Laboratory #002282) mice were used to investigate the effects of adeno-associated virus (AAV) mediated hypothalamic TrkB.FL overexpression. Five cohorts of male mice were used—a long-term (24 wks), NCD ($11\%$ fat, caloric density 3.4 kcal/g, Teklad) fed cohort ($$n = 16$$); a long-term (23 wks), HFD ($60\%$ fat, caloric density 5.21 kcal/g, Research Diets, Inc. #D12492) fed cohort ($$n = 11$$); a short-term (4 wks) NCD fed cohort ($$n = 10$$); a short-term (4 wks) HFD fed cohort ($$n = 10$$), and a short-term C57BL/6 NCD fed cohort ($$n = 10$$). Ages of the mice were 15–17 weeks old (long-term NCD), 4–5 weeks old (long-term HFD), 4 weeks old (short-term NCD), 8–16 weeks old (short-term HFD), and 7–9 weeks old (short-term C57BL/6 NCD). For the HFD cohorts, mice were fed HFD for five weeks prior to AAV injections and maintained on the HFD for the duration of the studies. Weekly food consumption and body weights were recorded. One mouse from the short-term NCD cohort and one mouse from the short-term HFD cohort died during the study and were excluded from subsequent analysis. Experimental timelines for the long-term NCD and long-term HFD groups can be seen in Fig 1A and Fig 3A, respectively. All mice had ad libitum access to food and water. All mice were group housed (3–5 mice) in standard laboratory environment cages and housed in temperature (22–23°C) and humidity (30–$70\%$) controlled rooms under a 12:12 light:dark cycle. All animal experiments were approved by The Ohio State University Institutional Animal Care and Use Committee. Fig 1: Hypothalamic AAV-TrkB.FL overexpression decreases body weight gain and improves glucose tolerance in NCD BTBR mice.(A) Experimental timeline. (B) Body weight. (C) Percent body weight gain. (D) Relative food intake. (E) Glucose tolerance test (GTT) (F) GTT area under the curve (AUC). Data are means ±SEM. AAV-YFP: n = 8, AAV-TrkB.FL: n = 8. P<0.05, P<0.01, * P<0.001.* ## rAAV vector construction and packaging The rAAV plasmid contains an expression cassette consisting of the CMV enhancer and chicken β-actin (CBA) promoter, woodchuck post-transcriptional regulatory element (WPRE) and bovine growth hormone poly-A flanked by AAV2 inverted terminal repeats. cDNA of TrkB full length (TrkB FL, NM-001025074) was amplified by using a mouse clone of Ntrk2 (ORIGENE, MR226130) as a template through polymerase chain reaction (PCR), with EcoRI flanking at each side. Primers for TrkB.FL amplification are as follows: forward, 5’-AATTAAGAATTCATGTCGCCCTGGCTGA-3’ and reverse, 5’-AATATAGAATTCTTA CAGATCCTCTTCTGAGA-3’. PCR product of TrkB-FL was then cloned into EcoRI site of the rAAV plasmid. The insert sequence was confirmed by sequencing at the OSU core facility. Amplified TrkB-FL retains a Myc tag at C-terminal before stop codon. rAAV plasmids containing TrkB-FL or yellow fluorescence protein (YFP) were packaged into serotype AAV1 vectors. The details of generation of rAAV were described previously [25]. ## Stereotaxic surgery Mice were randomized to receive either bilateral injections of AAV-YFP or AAV-TrkB.FL to the hypothalamus. Mice were anaesthetized with a single dose of ketamine/xylazine (100 and 20 mg kg−1; i.p.) and secured via ear bars and incisor bar on a Kopf stereotaxic frame. A mid-line incision was made through the scalp to reveal the skull and two small holes were drilled into the skull with a dental drill above the injection sites (-1.2 AP, ±0.5 ML, -6.2 DV, mm from bregma). rAAV vectors (2.5 × 109 genomic particles per site) were injected bilaterally into the hypothalamus at a rate of 0.1 μl minute−1 using a 10 μl Hamilton syringe attached to Micro4 Micro Syringe Pump Controller (World Precision Instruments, Sarasota, FL). At the end of infusion, the syringe was slowly raised from the brain and the scalp was sutured. Animals were placed back into a clean cage and carefully monitored until recovery from anesthesia. ## Body composition For the long-term, NCD group, echoMRI was utilized to measure body composition of fat, lean, free water, and total water masses in live mice without anesthesia at 8 weeks post-injection (wpi). Body composition analysis was performed with an echoMRI 3-in-1 Analyzer at the Small Animal Imaging Core of The Dorothy M. Davis Heart & Lung Research Institute, The Ohio State University. ## Energy expenditure At 17 wpi (long-term NCD) and 12 wpi (long-term HFD), mice underwent indirect calorimetry using the Oxymax Comprehensive Lab Animal Monitoring System (CLAMS) (Columbus Instruments, Columbus, OH). Mice were singly housed with ad libitum access to food and water. Mice were acclimatized in the metabolic chambers for 18 hours, then behavior and physiological parameters (O2 consumption, CO2 production, respiratory exchange ratio, and physical activity) were recorded for 24 hours at room temperature. ## Glucose tolerance test A glucose tolerance test (GTT) was conducted at 22 wpi (long-term NCD) and 11 wpi (long-term HFD). Mice were fasted for 16 hours overnight, then injected with glucose solution intraperitoneally (1.0 mg glucose/kg body weight). Blood was collected from the tail at baseline, 15, 30, 60, 90, and 120 minutes post glucose injection. Blood glucose concentrations were measured with a portable glucose meter (Bayer Contour Next). ## Behavioral methods Methods for the behavioral assays can be viewed in S1 Text. ## Tissue harvest Mice were sacrificed at 24 wpi (long-term NCD), 23 wpi (long-term HFD), and 4 wpi (short-term BTBR and C57BL/6 NCD and HFD BTBR). Mice were anesthetized by isoflurane and decapitated. Brown adipose tissue (BAT), gonadal WAT (gWAT), inguinal WAT (iWAT), and retroperitoneal WAT (rWAT), and liver were collected and weighed from both long-term groups. Gastrocnemius and pancreas were also dissected and weighed from the long-term NCD group. Hypothalamus was dissected from all groups. Tissues were flash-frozen on dry ice and stored at -80° C until further analysis. ## Quantitative real-time PCR Total RNA was isolated from the hypothalamus, iWAT, gWAT, and BAT using the RNeasy Mini Kit plus RNase-free DNase treatment (Qiagen #74804). First-strand cDNA was generated using TaqMan Reverse Transcription Reagent (Applied Biosystems #N8080234). Quantitative real-time PCR was performed using Power SYBR Green PCR Master Mix (Applied Biosystems #A25742) on a StepOnePlus Real-Time PCR System (Applied Biosystems). Primer sequences can be viewed in S1 Table. Data were calibrated to endogenous control Hprt1 for hypothalamus and Actinb for adipose tissues and the relative gene expression was quantified using the 2 -ΔΔCT method [26]. ## Western blotting Hypothalamus, iWAT, gWAT, and BAT were homogenized in ice-cold Pierce RIPA buffer containing 1× Roche PhosSTOP and Calbiochem protease inhibitor cocktail III, then spun at 13,000 rpm for 15 min. Tissue lysates were separated by gradient gel (4–$20\%$, Mini-PROTEAN TGX, Bio-Rad) and transferred to a nitrocellulose membrane (Bio-Rad). Blots were incubated overnight at 4°C with primary antibodies listed in S2 Table. Blots were rinsed and incubated with HRP-conjugate secondary antibody (Bio-Rad). Chemiluminescence signal was detected and visualized by LI-COR Odyssey Fc imaging system (LI-COR Biotechnology). Quantification analysis was carried out with image studio software version 5.2 (LI-COR Biotechnology). Phosphorylated proteins were calibrated to their total protein levels and presented as ratio. TrkB.FL, TrkB.T1, Ras, and PTEN were normalized to reference proteins. ## Statistical analysis Data are expressed as mean ± SEM. GraphPad Prism 7 software (GraphPad, La Jolla, CA) was used to analyze our data, using Student’s t-tests. P ≤0.05 was considered statistically significant. Data were tested for normality using the Shapiro-Wilk test. If data violated assumptions of normality, either log2 transforms or Mann-Whitney tests were performed, and analysis was repeated. Welch’s correction was performed for data that violated assumptions of homogeneity of variance. Power analyses were conducted post-hoc. An ANOVA mixed effects model was used to analyze all longitudinal data, and Bonferroni’s test was used for corrections post-hoc. ## Hypothalamic TrkB.FL overexpression improves metabolic outcomes in BTBR mice fed on normal chow diet We firstly assessed effects of hypothalamic TrkB.FL overexpression in BTBR mice fed on NCD (Fig 1A). All outliers for all data have been included. Visualization to verify AAV injection location can be viewed in S1 Fig. AAV-TrkB.FL mice displayed reduced body weight gain (Fig 1B) and reduced percent body weight gain (Fig 1C) as compared to AAV-YFP mice. AAV-TrkB.FL mice consumed significantly more food relative to body weight than AAV-YFP mice ($$P \leq 0.01$$) (Fig 1D). At 22 wpi, mice were subjected to a GTT (Fig 1E). AAV-TrkB.FL mice displayed improved glycemic control compared to AAV-YFP mice as measured by an ANOVA mixed effects model ($$P \leq 0.0195$$) and area under the curve calculation ($$P \leq 0.0126$$) (Fig 1F). At 17 wpi, mice underwent indirect calorimetry using CLAMS. AAV-TrkB.FL animals showed significantly higher oxygen consumption compared to their AAV-YFP counterparts, measured by an ANOVA mixed effects model ($$P \leq 0.0418$$) and area under the curve calculation ($$P \leq 0.0403$$) (Fig 2A). The respiratory exchange ratio (RER) and physical activity was not significantly different between AAV-TrkB.FL and AAV-YFP animals (Fig 2B and 2C). An in vivo echoMRI was performed at 8 wpi to assess body composition. AAV-TrkB.FL mice displayed significantly decreased percent fat mass ($$P \leq 0.0086$$) and significantly increased percent lean mass as compared to AAV-YFP mice ($$P \leq 0.028$$) (Fig 2D). Fig 2: Hypothalamic TrkB.FL overexpression improves metabolic outcomes in NCD BTBR mice.(A) O2 consumption and AUC. (B) Respiratory exchange ratio (RER). (C) Ambulation. (D) Percent fat mass and percent lean mass, as measured by echoMRI. (E) Tissue weight (liver, inguinal white adipose tissue, gonadal white adipose tissue, retroperitoneal white adipose tissue, brown adipose tissue, pancreas, gastrocnemius) (F) Relative tissue weight, normalized to body weight. Data are means ±SEM. AAV-YFP: n = 8, AAV-TrkB.FL: n = 8. P<0.05, P<0.01, * P<0.001.* At sacrifice, tissue weights of iWAT, gWAT and BAT were significantly reduced ($$P \leq 0.0016$$, $$P \leq 0.027$$, $$P \leq 0.014$$) in the long-term NCD AAV-TrkB.FL mice as compared to AAV-YFP mice (Fig 2E). When normalized to body weight, iWAT mass was significantly reduced ($$P \leq 0.0022$$) and gastrocnemius mass was significantly increased ($$P \leq 0.0003$$) in the AAV-TrkB.FL animals (Fig 2F). ## Hypothalamic gene transfer of TrkB.FL improves metabolic outcomes in obese BTBR mice We next examined whether hypothalamic gene transfer of TrkB.FL alters obesity and associated metabolic dysfunction in obese BTBR mice. AAV-TrkB.FL and AAV-YFP mice had a significant weight difference at the start of HFD feeding and did not display significant differences in total weight gain over the course of the experiment (Fig 3B). Due to the significant difference in body mass at the beginning of the experiment, we calculated percent body weight gain and found a significantly reduced percent weight gain in the AAV-TrkB.FL injected mice as compared to the AAV-YFP injected mice (Fig 3C). AAV-TrkB.FL mice consumed significantly more food relative to body weight than AAV-YFP mice ($$P \leq 0.0018$$) (Fig 3D). At 11 wpi, mice were subjected to a GTT (Fig 3E) and showed no significant difference (Fig 3F). At 12 wpi, AAV-TrkB.FL animals showed no significant differences in VO2, RER, or ambulation as compared to their AAV-YFP counterparts (Fig 4A–4C). Fig 3: Hypothalamic AAV-TrkB.FL overexpression decreases body weight gain in HFD BTBR mice.(A) Experimental timeline. (B) Body weight. (C) Percent body weight gain. (D) Relative food intake. (E) Glucose tolerance test (GTT) (F) GTT area under the curve (AUC). Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 6. P<0.05, ** P<0.01.* Fig 4: Metabolic outcomes of hypothalamic AAV-TrkB.FL overexpression in HFD BTBR mice.(A) O2 consumption and AUC. (B) Respiratory exchange ratio (RER). (C) Ambulation. (D) Tissue weight (liver, inguinal white adipose tissue, gonadal white adipose tissue, retroperitoneal white adipose tissue, brown adipose tissue) (E) Relative tissue weight, normalized to body weight. Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 6. P<0.05, ** P<0.01.* At termination of the experiment 23 wpi, relative liver weight was significantly reduced ($$P \leq 0.0067$$) (Fig 4D) while relative BAT weight was significantly increased ($$P \leq 0.0011$$) in AAV-TrkB.FL mice as compared to AAV-YFP mice in the long-term HFD experiment (Fig 4E). ## Hypothalamic TrkB.FL overexpression does not improve behavioral deficits in BTBR mice In our previous study investigating the effects of enriched environment on BTBR metabolism and behavior, we found significant differences between groups for the open field test and three chambered sociability, so we repeated those tests in this study. To measure anxiety-like behavior and locomotion, an open field test was performed at 12 wpi (long-term NCD) and 22 wpi (long-term HFD). Total distance traveled, distance traveled in the periphery/distance traveled total ratio, and distance traveled in the center/distance traveled total ratio were measured. There was no significant difference between AAV-TrkB.FL and AAV-YFP mice for any measures, in both the long-term NCD (S2 Fig) and the long-term HFD group (S3A–S3C Fig). Another test to measure anxiety-like behavior is the novelty suppressed feeding test, which was performed at 18 wpi for the long-term HFD group. Mice were fasted overnight and then placed in a new cage with a piece of chow. Latency to feed and food consumed were measured. No significant differences were found between AAV-TrkB.FL and AAV-YFP mice for either measure (S3D and S3E Fig). In addition, long-term HFD mice were subjected to the third anxiety-like behavioral test, cold induced defecation test, at 18 wpi. Cold temperatures can induce stress in mice and stress increases defecation [27, 28]. We expected that an increase in number of fecal boli would reflect an increase in anxiety-like behavior. No significant differences were found between AAV-TrkB.FL and AAV-YFP mice (S3F Fig). For the long-term HFD experiment, the three-chambered sociability (3CS) test was conducted at 18 wpi. For the first phase, the time spent in the mouse-filled chamber, time spent in the center, and time spent in the empty chamber was recorded. The social affiliation index was calculated by taking the ratio of the time spent in the mouse chamber over the time spent in the empty chamber. No significant differences were found between AAV-TrkB.FL mice and AAV-YFP mice for any of these measures. For the second phase, the time spent in each chamber was recorded. The social novelty index was calculated by taking the ratio of time spent in the novel mouse chamber over the familiar mouse chamber. Again, there were no significant differences between AAV-TrkB.FL and AAV-YFP mice for any measures (S4 Fig). In ASD sometimes social deficits appear not as a lack of overall sociability but as inappropriate or indiscriminate approaches to strangers. The two parts of the three chamber sociability test allow us to test two distinct aspects of social behavior- 1) overall social approach and 2) social memory and novelty. The first part of the three chamber sociability test measured overall sociability, examining whether the mouse preferred an inanimate object to a mouse. We utilized the second part of the three chamber sociability test to measure social novelty preference. Generally, a C57Bl/6J mouse, when presented with both a familiar mouse and a novel mouse, will spend more time investigating the novel mouse due to the highly social nature of mice. In models of autism the mice spend less or equal time investigating the novel mouse, which is abnormal as wild-type mice have a preference for social novelty. This behavior could be analogous to social behavior in humans with ASD, where they may prefer to spend time with familiar people over new people or they approach strangers indiscriminately [29–31]. ## Hypothalamic TrkB.FL overexpression changes hypothalamic gene expression in NCD BTBR mice Real-time quantitative RT-PCR was used to profile hypothalamic gene expression in the long-term NCD experiment (Fig 5). AAV-TrkB.FL injected animals showed approximately 5-fold higher expression of Trk. FL compared to control mice ($$P \leq 0.0202$$) (Fig 5A) while the isoform TrkB.T1 was not different (Fig 5B). Genes involved in energy homeostasis and BDNF signaling were examined including Bdnf, Mc4r (encoding melanocortin-4 receptor), Vgf (encoding nerve growth factor inducible), Insr (encoding insulin receptor), Obrb (encoding long form leptin receptor), Crh (encoding corticotropin-releasing hormone), Npy (encoding neuropeptide Y), and Pomc (encoding proopiomelanocortin). Interestingly, both anorexigenic Pomc and orexigenic Npy were significantly upregulated in AAV-TrkB.FL injected mice ($$P \leq 0.0043$$, $$P \leq 0.0092$$) (Fig 5B). Fig 5: Hypothalamic AAV-TrkB.FL treatment alters hypothalamic gene expression in NCD BTBR mice.(A) Relative mRNA expression of TrkB.FL. (B) Relative mRNA expression. Data are means ±SEM. AAV-YFP: n = 8, AAV-TrkB.FL: n = 8. P<0.05, ** P<0.01.* Neuroinflammation is implicated in the aberrant behaviors of BTBR mice [32]. Accordingly, we examined a panel of immunomodulatory genes and microglial markers in the long-term NCD experiment. Overexpressing TrkB.FL significantly downregulated the expression of Il1b (encoding interleukin-1β) ($$P \leq 0.037$$) while upregulated Apoe (encoding apolipoprotein) ($$P \leq 0.039$$). It is reported that BTBR mice show an increased proportion of MHC class II (encoded by H2ab1)-expressing microglia compared to sociable strain C57BL/6 [32]. Hypothalamic H2ab1 and Tnfa (encoding tumor factor-α) expression showed a trend of downregulation in the AAV-TrkB.FL mice although not reaching significance ($$P \leq 0.089$$, $$P \leq 0.052$$). No changes were observed among Il33 (encoding interleukin-33), Ccl2 (encoding C-C motif chemokine ligand 2), or Cx3cr1 (encoding C-X3-C motif chemokine receptor 1) (Fig 5B). We profiled hypothalamic gene expression in the long-term HFD experiment including BDNF-TrkB relevant genes and the genes whose expression was altered by TrkB.FL overexpression in the long-term NCD experiment (Fig 6). TrkB.FL overexpression was confirmed in the HFD AAV-TrkB.FL injected mice but to a milder extent relative to the NCD experiment ($$P \leq 0.015$$) (Fig 6A, Fig 5A). There were no other significant differences in the long-term HFD group (Fig 6B). Fig 6: Hypothalamic AAV-TrkB.FL treatment alters hypothalamic gene expression in HFD BTBR mice.(A) Relative mRNA expression of TrkB.FL. (B) Relative mRNA expression. Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 6. P<0.01.* ## Hypothalamic TrkB.FL overexpression alters adipose gene expression in NCD BTBR mice Our previous studies have found that hypothalamic BDNF overexpression results in sympathoneural activation of adipose tissue and revealed adipose depot-dependent gene expression signatures consisting of the genes involved in thermogenesis, lipolysis, and energy metabolism [7, 8, 10–12]. Thus, we profiled gWAT, iWAT, and BAT gene expression in the long-term NCD experiment (Fig 7) targeting the gene signatures associated with hypothalamic BDNF overexpression. In gWAT, overexpression of TrkB.FL significantly upregulated gene expression of Adipoq (encoding adiponectin, C1Q and collagen domain containing, $$P \leq 0.0218$$), Hsl (encoding hormone sensitive lipase, $$P \leq 0.0332$$), Vegfa (encoding vascular endothelial growth factor A, $$P \leq 0.0249$$) and downregulated Lep (encoding leptin, $$P \leq 0.0178$$) (Fig 7A). Hypothalamic overexpressing TrKB.FL significantly upregulated Adrb3 (encoding adrenoceptor β3) expression in all adipose depots examined, gWAT ($$P \leq 0.0218$$), iWAT ($$P \leq 0.0226$$), and BAT ($$P \leq 0.0359$$). Ppargc1α (encoding PPARG coactivator 1 alpha) was upregulated in iWAT of TrkB.FL mice ($$P \leq 0.0095$$) (Fig 7B). In BAT, a cluster of thermogenic genes including Cidea (encoding cell death inducing DFFA like effector a, $$P \leq 0.0001$$), Elovl3 (encoding ELOVL fatty acid elongase 3, $$P \leq 0.0046$$), Prdm16 (encoding PR/SET domain 16, $$P \leq 0.0483$$), and Ucp1 (encoding uncoupling protein 1, $$P \leq 0.0396$$) were significantly upregulated in TrkB.FL injected mice (Fig 7C). Fig 7: Hypothalamic TrkB.FL overexpression regulates adipose gene expression in NCD BTBR mice.(A) Relative mRNA expression in gWAT. (B) Relative mRNA expression in iWAT. (C) Relative mRNA expression in BAT. Data are means ±SEM. AAV-YFP: n = 8, AAV-TrkB.FL: n = 8. P<0.05, P<0.01, * P<0.001.* ## Hypothalamic TrkB.FL gene transfer affects signaling mediators downstream of TrkB in BTBR mice The TrkB family includes at least three well-characterized TrkB isoforms through alternative RNA splicing. TrkB.FL is a receptor tyrosine kinase containing an extracellular ligand-binding domain, a single transmembrane domain, and a typical intracellular domain for tyrosine kinases. Two other isoforms (TrkB.T1 and TrkB.T2) are truncated for their intracellular kinase binding domain [33]. TrkB.FL expressed by our rAAV vector will not generate the truncated isoforms. Three canonical pathways are activated following TrkB receptor activation—MAPK/ERK, PI3K/AKT, and PLCγ [34]. Because we used all hypothalamic dissections from the long-term NCD experiment for qRT-PCR, we were unable to examine the signaling molecules. Hence, we carried out a short-term NCD experiment to determine which pathways were activated following TrkB.FL overexpression. Hypothalamic samples were collected by 4 weeks post AAV injection. Of note, no changes in body weight were observed in this short-term experiment (S5 Fig). Western blotting was performed to analyze ERK$\frac{1}{2}$, AKT, and PLCγ phosphorylation states, as well as protein levels of TrkB.FL, TrkB.T1, Ras, and PTEN. The presence of Myc, a tag to the transgene TrkB.FL carried by AAV vector, verified the transgene expression in the hypothalamus. TrkB.FL protein level was significantly elevated in AAV-TrkB.FL animals ($$P \leq 0.0002$$), confirming transduction by the AAV vector. The ratio of TrkB.FL/TrkB.T1 was significantly higher in AAV-TrkB.FL animals ($$P \leq 0.0109$$). TrkB.FL overexpression also significantly increased phospho-PLCγ levels ($$P \leq 0.034$$). There were no significant differences between AAV-TrkB.FL and AAV-YFP animals for TrkB.T1, phospho-AKT, phospho-ERK, Ras, or PTEN (Fig 8). Fig 8: Hypothalamic AAV-TrkB.FL overexpression affects signaling mediators downstream of TrkB in NCD BTBR mice.(A) Western blotting of right lobe of the hypothalamus. (B) Quantification of (A). Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 4. P<0.05, *** P<0.001.* To determine which pathways were activated following TrkB.FL overexpression in obese BTBR mice, we carried out a short-term HFD experiment. Hypothalamic samples were collected at 4 weeks post AAV injection. As in the short-term NCD experiment, no changes in body weight were observed for this experiment (S6 Fig). We performed Western blotting to analyze the same phosphorylation states and protein levels as in the short-term NCD experiment. The presence of Myc confirmed transgene expression in the hypothalamus. AAV-TrkB.FL animals showed a trend toward increased TrkB.FL ($$P \leq 0.10$$) and a trend toward an increased TrkB.FL/TrkB.T1 ratio ($$P \leq 0.11$$). TrkB.FL overexpression significantly increased phospho-PLCγ levels ($$P \leq 0.048$$). There were no significant differences between AAV-TrkB.FL and AAV-YFP animals for TrkB.T1, phospho-AKT, phospho-ERK, Ras, or PTEN (Fig 9). Fig 9: Hypothalamic AAV-TrkB.FL overexpression affects signaling mediators downstream of TrkB in HFD BTBR mice.(A) Western blotting of right lobe of the hypothalamus. (B) Quantification of (A). Data are means ±SEM. AAV-YFP: n = 4, AAV-TrkB.FL: n = 5. P<0.05.* ## Hypothalamic TrkB.FL gene transfer does not affects signaling mediators downstream of TrkB in C57BL/6 mice To examine hypothalamic signaling changes that occur following TrkB.FL overexpression in C57BL/6 mice of normal weight, we performed a short-term experiment mirroring the BTBR short-term experiments. Hypothalamic samples were collected 4 weeks post injection. There were no changes in body weight over the course of this experiment (S7 Fig). Myc expression verified TrkB.FLtransgene expression in the hypothalamus. Myc was not expressed in one TrkB.FL sample and was thus excluded from further analysis. As seen in the short-term NCD BTBR experiment, AAV-TrkB.FL mice showed significantly higher protein levels of TrkB.FL ($P \leq 0.0001$) and a significantly higher TrkB.FL/TrkB.T1 ratio as compared to AAV-YFP mice ($$P \leq 0.0015$$). However, overexpressing TrkB.FL in C57BL/6 mice did not alter phospho-AKT, phospho-ERK, Ras, PTEN, or phospho-PLCγ (Fig 10). Fig 10: Hypothalamic TrkB.FL gene transfer does not affect signaling mediators downstream of TrkB in C57BL/6 mice.(A) Western blotting of right lobe of the hypothalamus. (B) Quantification of (A). Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 4. P<0.05, P<0.01, ** P<0.0001.* ## Discussion In this study, we found that hypothalamic TrkB.FL gene transfer in BTBR mice improved metabolic outcomes, but not behavioral deficits. Both long-term NCD and long-term HFD AAV-TrkB.FL groups displayed lower percent body weight gain in the absence of decrease of food intake. Long-term NCD AAV-TrkB.FL animals showed increased leanness and decreased adiposity as well as improved glycemic control. The effects of TrkB.FL overexpression on metabolic outcomes were not as drastic in HFD-fed animals as in NCD-fed animals, and TrkB.FL NCD-fed mice displayed lower relative weight of iWAT while HFD-fed did not show a difference between AAV-YFP and AAV-TrkB.FL. BTBR animals show metabolic impairments at baseline [14]. It is possible that TrkB.FL overexpression was effective for improving metabolic outcomes in the BTBR mouse under normal feeding conditions, but introducing an additional metabolic disturbance in the form of a HFD was too much for the transgenic TrkB.FL to sufficiently mitigate. An alternative explanation could be that two animals in the HFD TrkB.FL group showed much higher protein levels of TrkB.FL than the other three animals. It is possible that the vector was more successful at transducing TrkB.FL in these animals and that these animals displayed greater changes than the other three animals, suggesting that TrkB.FL is crucially involved in the mechanism leading to improved metabolic outcomes but the administration of the AAV-TrkB.FL vector did not transduce neurons as successfully in three of the animals. Additionally, while the TrkB.FL protein level was elevated in the TrkB.FL HFD group, the elevation did not significantly alter the TrkB.FL/TrkB.T1 ratio as was observed in the TrkB.FL NCD group. TrkB.T1 can block the BDNF-TrkB.FL signaling cascade, so the lack of improved TrkB.FL/TrkB.T1 ratio could be limiting the effects of BDNF [35, 36]. An additional difference in outcomes between NCD and HFD mice was relative liver weight. NCD mice did not show a difference in relative liver weight between AAV-YFP and AAV-TrkB.FL while in HFD mice, TrkB.FL overexpression decreased relative liver weight. HFD has been shown to cause excessive fat deposition in the liver [37]. The HFD BTBR mice displayed both higher absolute liver weight and higher relative liver weight as compared to NCD BTBR mice. It is possible that in the HFD group, TrkB.FL overexpression helped to reduce potential excessive accumulation of lipids in the liver. In general, hypothalamic TrkB.FL overexpression resembles the metabolic outcomes induced by hypothalamic BDNF overexpression in C57BL/6 mice of normal weight as well as genetic obesity and DIO models on C57BL/6 background [7, 38–41]. For both BDNF and TrkB.FL gene transfer studies, AAV1 serotype vectors, largely neuronal-tropic, were used, and therefore transgenes were overexpressed primarily in the neurons. However, BDNF expressed from AAV1 vector can be secreted from the transduced neurons to act on other cell types within the hypothalamus or transport to extrahypothalamic area. In contrast, overexpressed TrkB.FL receptor remains on the transduced neurons, Hence, TrkB.FL overexpression data from the current study suggest hypothalamic neuronal BDNF-TrkB signaling is likely the main action mode for the metabolic benefits associated with either increasing the level of BDNF ligand or the level of TrkB receptor. BDNF can be expressed in different cell types such as microglia and astrocytes [42–44]. It is possible that BDNF is eliciting its effects on metabolism through neuronal BDNF-neuronal TrkB signaling, microglial BDNF-neuronal TrkB signaling, astrocytic BDNF-neuronal TrkB signaling, or a combination of several signaling mechanisms. One notable finding of the study is the downregulation of proinflammatory Il1b expression and alteration of the expression of some microglial-related genes in the hypothalamus of AAV-TrkB.FL injected mice. These molecular changes were observed in the long-term NCD study a few months after systemic metabolic effects occurred. We did not see these inflammatory changes in the long-term HFD study. Mice fed a high-fat diet display chronic hypothalamic inflammation [45, 46]. BDNF-TrkB is involved in regulating neuroinflammation [47, 48]. It is possible that in the long-term NCD study, upregulation of TrkB.FL was involved in mitigating neuroinflammation, but in the long-term HFD study perhaps the level of transgenic TrkB.FL was not sufficient to overcome the baseline neuroinflammation involved in HFD feeding. Additionally, we observed upregulation of Pomc and Npy in TrkB.FL NCD mice, but not in TrkB.FL HFD mice. Pomc and Npy expressing neurons act antagonistically to control energy homeostasis, and a disruption in this system is associated with obesity. Particularly, a HFD downregulates Pomc and Npy [49, 50]. Again, perhaps the level of transgenic TrkB.FL was not sufficient to overcome the baseline lower levels of Pomc and Npy involved in HFD feeding. New studies are required to investigate whether the potential modulation of neuroinflammation and/or microglial functions is a direct effect of hypothalamic neuronal BDNF-TrkB activation, or alternatively, a feedback of the systemic metabolic outcomes induced by hypothalamic TrkB.FL. Research points toward impaired neural circuit development as a potential cause of ASD. Due to the importance of neurotrophins in synaptic plasticity and neural growth and development, alterations in neurotrophin levels and their associated signaling pathways are one area of research into the pathophysiology of ASD in human subjects and in animal models of ASD [51]. Several studies have found a decrease in mRNA and protein levels of BDNF and its receptor TrkB in the hippocampus and cortex of BTBR mice [52–55]. One study found that the use of a TrkB receptor agonist, 7–8, DHF, in the BTBR mouse model reversed several aspects of social deficits [56]. Several studies have found an up-regulation of the Ras/Raf/Erk$\frac{1}{2}$ pathway in the hippocampus and prefrontal cortex of BTBR mice, which may contribute to the pathogenesis of ASD [57–59]. However, no research has examined these pathways in the hypothalamus of the BTBR mouse. BDNF-TrkB activation initiates three main signaling pathways—MAPK, PI3K, and PLCγ. Activation of TrkB at its Tyr490 and Tyr515 sites results in recruitment of GTPase Ras, which activates the MAPK/ERK pathway. MAPK/ERK pathway activation ultimately drives BDNF expression to regulate neuronal survival, differentiation, and synaptic plasticity [34]. Recruitment of Ras at the TrkB Tyr515 site also activates the PI3K/AKT pathway. Activation of this pathway regulates proteins that are essential for neuronal growth, differentiation, and survival [34]. Finally, TrkB phosphorylation at the Tyr816 residue activates the PLCγ pathway, which is important for survival, neurite outgrowth, and synaptic plasticity [34]. In addition, our previous EE-BTBR study indicated that Bdnf and its receptor Ntrk2 were both up-regulated, suggesting BDNF-TrkB signaling plays a role in mediating the EE-BTBR phenotype. Therefore, hypothalamic TrkB.FL overexpression in BTBR mice would also promote its downstream pathway signaling, thereby leading to the improved beneficial outcomes we saw in the EE-BTBR study. In the NCD group, strong Myc bands in the AAV-TrkB.FL group confirmed on-target transduction of the AAV vector (Fig 8A). There was a significant increase in TrkB.FL protein level in the AAV-TrkB.FL mice compared to the AAV-YFP mice. In BTBR mice, studies have found that TrkB levels are significantly lower in the hippocampus than in C57BL/6 mice [54], but no data is available regarding relative levels of TrkB in the hypothalamus. In this study, we assessed the ratio of TrkB.FL to TrkB.T1. TrkB.T1 is a truncated isoform of TrkB that does not contain an intracellular kinase domain, and therefore it does not activate the classical signaling pathways associated with TrkB.FL. TrkB.T1 can bind BDNF to prevent activation of the MAPK/ERK, PI3K/AKT, and PLCγ pathways by forming a heterodimer with TrkB.FL, acting as a dominant negative inhibitor of TrkB.FL. Increased expression of TrkB.T1 can thus prevent BDNF mediated cell survival and proliferation [60]. Our previous studies have indicated that hypothalamic overexpression of TrkB.T1 can block BDNF-TrkB signaling [7, 10]. Additionally, research has also found an association between neuropsychiatric and neurodegenerative disorders and altered levels of TrkB.FL and TrkB.T1 isoforms [24, 61, 62]. One study also showed that reducing TrkB.T1 levels in vivo in a Bdnf heterozygous knockout mouse partially rescued the obesity phenotype [63]. Here, TrkB.FL overexpression in BTBR mice increased the TrkB.FL/TrkB.T1 ratio and may contribute to the improved metabolic outcomes we saw. pPLCγ was significantly increased in the AAV-TrkB.FL group, leading us to believe that the pPLCγ pathway is important for the improved metabolic outcomes we saw in the NCD group. As previously mentioned, activation of the PLCγ pathway is important for synaptic plasticity. Over the course of the NCD experiment, AAV-TrkB.FL animals ate significantly more relative food (Fig 1D), but gained significantly less weight (Fig 1C). It is possible that overexpressing hypothalamic TrkB.FL activates the PLCγ pathway, leading to synaptic plasticity that increases energy expenditure, improving downstream metabolic outcomes. To investigate a mechanism downstream of hypothalamic TrkB.FL signaling that may explain the improved metabolic outcomes, we profiled a number of metabolism related genes in iWAT, gWAT, and BAT. WAT and BAT make up an organism’s adipose organ. WAT stores excess energy and acts as an endocrine organ, while BAT is metabolically active and dissipates energy as heat for thermoregulation. BAT dissipates energy as heat through thermogenesis, whereby uncoupling protein-1 (UCP1) mediates the process of uncoupling fatty acid oxidation from ATP production [64]. BAT is also involved in body weight regulation and contributes to energy expenditure. WAT can acquire brown fat characteristics in response to chronic cold exposure or prolonged B-adrenergic stimulation through a process termed “browning” or “beiging”, which is associated with increased energy expenditure and resistance against obesity [64–67]. Our previous studies have found a mechanism that links activation of hypothalamic BDNF to increased energy expenditure, termed the HSA axis. We discovered that upregulation of hypothalamic BDNF leads to increased sympathetic tone to WAT adipocytes via β-adrenergic signaling within the sympathetic nervous system (SNS). This increased sympathetic tone induces WAT “beiging”, a decrease of adipocyte leptin production and secretion, and an upregulation of adiponectin level, leading to increased energy expenditure and improved metabolic outcomes [7, 10, 38]. Moreover, environmental or genetic upregulation of hypothalamic BDNF prevents aging-related decline of BAT [38, 68]. Based on these previous studies we hypothesized that overexpression of TrkB.FL, similar to upregulation of BDNF, would activate the HSA axis leading to adipose remodeling and metabolic improvement. Adiponectin and leptin levels are both associated with adiposity, with adiponectin expression being reduced as adiposity is increased, and leptin being increased as adiposity is increased [69]. We found that in TrkB.FL overexpressing mice, gWAT Adipoq was upregulated and Lep was downregulated and iWAT Adipoq was upregulated. This is consistent with the reduction in body weight we observed, as well as our previous studies showing that activation of the HSA axis increases adiponectin and decreases leptin in WAT adipocytes. β-adrenergic signaling is important for the activation of BAT in response to cold and for the regulation of adiposity and it also plays an important role in inducing beiging of WAT [70, 71]. We found that in all three fat depots (iWAT, gWAT, BAT), TrkB.FL overexpression upregulated gene expression of Adrb3, suggesting increased sympathetic tone to the adipose tissues and possible induction of beiging in the WAT. Hsl was upregulated in gWAT of TrkB.FL overexpressing mice, suggesting increased lipolysis in WAT [72]. Our previous studies identified Vegfa as a key component of the HSA axis underlying the beiging effect of WAT, likely directly caused by the SNS stimulation to the WAT [10]. We found that TrkB.FL overexpression upregulated Vegfa expression in gWAT, suggesting beiging may be occurring in WAT. Ppargc1a encodes for PGC-1α, which has been shown to be an important modulator of UCP1 expression and thermogenesis in BAT and also plays an important role in the brown adipocyte differentiation process [73]. We found that Ppargc1a was upregulated in the iWAT of TrkB.FL overexpressing animals, which suggests beiging of WAT. Prdm16 controls a cell fate switch determining formation and function of brown adipocytes [74] and was upregulated in BAT of TrkB.FL mice. This was accompanied by upregulation of a BAT molecular signature, including Cidea, Elovl3, and Ucp1, which are all BAT selective markers and are positively regulated by Prdm16 [74]. Overall, the gene expression profile of iWAT, gWAT, and BAT demonstrates that hypothalamic TrkB.FL gene transfer induced adipose gene expression signature overlapped with hypothalamic BDNF gene transfer, which is known to increase energy expenditure and improves metabolic outcomes mediated by the HSA axis. TrkB.FL overexpression resulted in signaling changes in the HFD group similar to those in the NCD group. Just as in the NCD group, TrkB.FL overexpression in the HFD group significantly increased phospho-PLCy, while there were no significant differences for phospho-AKT, phospho-ERK, PTEN, or Ras. Thus, it seems that the PLCγ pathway is important for the improved metabolic outcomes in both NCD BTBR mice and HFD BTBR mice. In the AAV-TrkB.FL animals, increased TrkB.FL levels and the increased TrkB.FL/TrkB.T1 ratio trended toward significance, but did not reach the highly increased levels found in the NCD study. In C57BL/6 mice, a HFD has been found to decrease hippocampal TrkB activation and expression and reduce activation of hippocampal BDNF-TrkB signaling pathways [75, 76]. It’s possible that the HFD blunted the effects of the AAV vector mediated TrkB.FL overexpression. This is the first study examining the effects of a HFD on the hypothalamus of BTBR mice; more investigation is needed to understand the complex molecular and signaling interactions between diet and the BTBR model. To investigate whether the effects of TrkB.FL overexpression can be generalized to non-BTBR mice, we injected C57BL/6 mice with either TrkB.FL or YFP and examined protein expression of signaling mediators downstream of TrkB.FL. Similar to BTBR NCD mice, we found that TrkB.FL overexpression altered the ratio of TrkB.FL/TrkB.T1. In contrast to BTBR mice we found that TrkB.FL overexpression did not upregulate pPLCγ, suggesting strain-dependent response to TrkB.FL overexpression in mice of normal weight. We did not perform behavioral testing for C57Bl/6J mice in this study. The purpose of including C57Bl/6J mice was to compare the changes in TrkB.FL signaling pathways following TrkB.FL overexpression between C57Bl/6J mice and BTBR mice. Because C57Bl/6J mice do not have baseline deficits in behavior and sociability like BTBR mice we did not expect to see any significant changes in these groups following TrkB.FL overexpression. We have not overexpressed neuronal TrkB.FL in C57Bl/6 mice before, but we have overexpressed neuronal BDNF in the same area of the brain. We found that in C57Bl/6 mice fed a NCD, overexpressing BDNF led to weight loss; reduced adiposity; a decrease in serum leptin, insulin, cholesterol, triglycerides, and IGF-1 and an increase in adiponectin; and altered expression of metabolism related genes in the hypothalamus and adipose tissue [8, 39]. In C57Bl/6 mice fed a HFD to induce DIO, we found that overexpressing BDNF led to prevention of weight gain and weight loss; prevention of abdominal obesity; a decrease in serum leptin, insulin, cholesterol, triglycerides, IGF-1, and glucose and an increase in adiponectin; improved insulin sensitivity and glucose tolerance; prevention of liver steatosis; and altered expression of metabolism related genes in the hypothalamus and adipose tissue [7, 39]. In our previous EE-BTBR study, we found EE improved metabolic outcomes and several behavioral measures. Because of our additional prior studies showing that hypothalamic BDNF mediates the improved metabolism and cancer outcomes in previous obesity and cancer EE studies, we hypothesized that TrkB signaling was also responsible for the outcomes we saw in the BTBR EE study. In the current study, we found that hypothalamic TrkB.FL overexpression specifically in the hypothalamus of BTBR mice improved the metabolic outcomes, but not behavioral deficits. This suggests that there is an additional mechanism responsible for the EE-induced behavioral improvements in BTBR mice. Much of the research surrounding BTBR behavior has focused on characterizing the behavioral phenotypes [77, 78] or administering pharmacological or environmental interventions and assaying behavioral outcomes [79–82]. Less research has focused on the biological mechanisms underlying the behavioral outcomes. It seems likely that the behavioral phenotypes of BTBR mice are at least partially associated with deficits in the hippocampus or amygdala, as these are important brain regions for learning and memory [83] and emotion and motivation [84], respectively. Our previous study found that Ntrk2 was significantly upregulated by 3 to 6 folds in the hypothalamus, hippocampus, and amygdala of male mice upon EE exposure [13]. It is possible that the improved metabolic outcomes in our BTBR EE study were due to enhanced hypothalamic BDNF-TrkB signaling, as seen in our current study, and the improved behavioral outcomes might involve BDNF-TrkB signaling in other brain regions, such as the hippocampus and amygdala. Studies have found that feeding BTBR mice a HFD exacerbates autistic-like behaviors. One study found that feeding BTBR mice a HFD induced severe metabolic impairments including weight gain, increase in fat mass and decrease in lean mass, and decrease in energy expenditure. Mice fed with a HFD displayed enhanced cognitive rigidity and impaired social memory. Autism-like severity was associated with body weight and dopaminergic signaling in the hypothalamus [21]. Another study found that feeding BTBR mice a HFD led to an increase in body weight and induced fasting hyperglycemia and glucose intolerance. HFD feeding increased hyperactivity, rescued sociability but not social novelty in the three-chamber sociability test, and aggravated self-grooming repetitive behavior [85]. These studies suggest that in the BTBR model of ASD, metabolism and behavior may be linked. More research is needed to connect behavioral outcomes to the neurobiological mechanisms of the BTBR mouse. Age is another factor to investigate. Studies have found levels of BDNF and TrkB differ throughout the lifespan of BTBR mice, with fetal BTBR mice expressing significantly higher brain BDNF protein levels than fetal FVB/NJ mice [86] while aged BTBR mice expressing significantly lower hippocampal and cortical BDNF protein levels compared to age-matched C57BL/6 controls [53]. Future studies could investigate the differences in effectiveness of overexpressing hypothalamic TrkB.FL at different age points throughout the lifespan. Another path to examine is sex differences. Our previous EE-BTBR study found significant sex-dependent outcomes. EE induced more robust improvements in metabolic outcomes in male mice, with only modest improvements in females. It is worthy of noting that upregulation of Ntrk2 induced by EE was only observed in male mice [13], which promoted us to investigate TrkB.FL overexpression in male BTBR mice in the current study. However, BDNF-TrkB signaling should not be ignored in the females. In fact, there are also significant sex differences in prevalence and presentation of ASD in humans [87], so investigating the mechanisms underlying sex differences in BTBR mice and in people with ASD is an important area to investigate. Genetic manipulation of TrkB in specific brain regions could help to interrogate signaling pathways underlying the sex differences we found in our EE-BTBR study. Finally, to definitively confirm that hypothalamic BDNF-TrkB signaling is indeed critical for EE-induced metabolic benefits in BTBR mice, we can house BTBR mice in an EE while knocking out hypothalamic TrkB.FL to see if it blocks the metabolic improvements with similar strategies as in our previous studies [7, 8]. One drawback to this study are the age differences and the differences in time points of metabolic and behavioral measurements between the long-term NCD group and the long-term HFD group. As such, we were unable to directly compare these two groups. Age-matched experiments would have allowed us to examine exact differences in response to hypothalamic TrkB.FL overexpression between BTBR mice of a normal weight and obese BTBR mice and controlled for possible age effects. Unfortunately, COVID-19 restrictions placed limitations on the timings of the experiments and breeding availability. Due to these age differences we must note that direct comparisons or concrete conclusions cannot be made regarding the differences between the long-term NCD and long-term HFD groups. To date, this is the first study investigating hypothalamic BDNF-TrkB signaling pathways and their impact on metabolism and behavior in the BTBR mouse model of ASD or in a diet-induced obesity BTBR model. Dysregulated BDNF-TrkB signaling has been implicated in both ASD and in obesity, with dysfunction of the hypothalamus being critical in obesity, but the changes in signaling mediators downstream of hypothalamic TrkB in BTBR mice have not been studied sufficiently. 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--- title: 'Factors associated with undernutrition among pregnant women in Haramaya district, Eastern Ethiopia: A community-based study' authors: - Meseret Belete Fite - Abera Kenay Tura - Tesfaye Assebe Yadeta - Lemessa Oljira - Kedir Teji Roba journal: PLOS ONE year: 2023 pmcid: PMC9997975 doi: 10.1371/journal.pone.0282641 license: CC BY 4.0 --- # Factors associated with undernutrition among pregnant women in Haramaya district, Eastern Ethiopia: A community-based study ## Abstract ### Introduction Although undernutrition in pregnancy has continued to get global attention as pregnancy is considered a critical period in the life cycle owed to increase the metabolic and physiological demands, evidence is scarce on undernutrition and associated factors among pregnant women in eastern Ethiopia. Therefore, this study assessed the undernutrition and associated factors among pregnant women in Haramaya district, Eastern Ethiopia. ### Methods A community-based cross-sectional study was conducted among randomly selected pregnant women in Haramaya district, eastern Ethiopia. Data were collected through face-to-face interviews, anthropometric measurement, and hemoglobin analysis by trained research assistants. An adjusted Prevalence ratio (aPR), and a $95\%$ confidence interval (CI), were used to report associations. Poisson regression analysis model with a robust variance estimate identified variables associated with undernutrition. Data were double entered using Epi-data 3.1 and cleaned, coded, checked for missing and outliers, and analyzed using Stata 14 (College Station, Texas 77845 USA. Finally, the p-value <0.05 was the cut-off point for the significant association. ### Results A total of 448 pregnant women with a mean age of 25.68 (± 5.16) were included in the study. The prevalence of undernutrition among pregnant women was $47.9\%$ ($95\%$ CI: $43\%$-$53\%$). From the analysis, the undernutrition was more likely higher among respondents who had five or more family members (APR = 1.19; $95\%$ CI = 1.02–1.40), lower dietary diversity (APR = 1.58; $95\%$ CI = 1.13–2.21) and those who were anemic (APR = 4.27; $95\%$ CI = 3.17–5.76). ### Conclusion Nearly half of the pregnant women in study area were undernourished. High prevalence was found among women who had large family sizes, low dietary diversity and anemia during pregnancy. Improving dietary diversity, strengthening family planning services and giving special attention to pregnant women, supplementation of iron and folic acid, and early detection and treatment of anemia is essential to improve the high burden of undernutrition and the adverse effect on pregnant women and the fetus. ## Introduction Undernutrition refers to deficiency primarily of calories, and overall inadequate consumption of food and nutrients to provide an individual’s requirement to support good health [1]. Moreover, undernutrition occurred due to the double burden of increased demands during pregnancy and inadequate intake of food during pregnancy [2]. Undernutrition is a key contributor to maternal mortality and morbidity, and adverse birth outcomes [3]. Mid-upper arm circumference (MUAC) is a proper measure for screening undernutrition during pregnancy [4]. MUAC is a good indicator of the protein reserves of a body, and a thinner arm reflects wasted lean mass and most appropriate anthropometric measure to detect short-term changes in the nutritional status [5]. Worldwide, nearly about 462 million pregnant women had malnutrition [6]. In low-resource countries, undernutrition among pregnant women is continuing to increase unremarked, as the main predictor of adverse birth outcomes [7, 8]. The reports of studies indicate that vulnerability to undernutrition in utero is linked with impaired growth and development in childhood, short stature in adults, reduced academic achievement and decreased economic productivity [9, 10]. Although literature points to the association of maternal undernutrition with adverse birth outcomes, little is documented about the risk predictors that influence prenatal nutritional status. Prenatal undernutrition is unacceptably high in developing countries [11], and Africa is the utmost severely overwhelmed [12]. More than one-fifth of Ethiopian women are exposed to malnutrition during their pregnancy [13] and, the risk is $68\%$ higher among rural women compared to urban women [14]. Although Ethiopia has made a striding change in maternal health death over the last decades, undernutrition during pregnancy remains a significant public health issue with prevalence ranging from $14.4\%$ in Gonder [15] to $44.7\%$ in Gumay district [16]. Several studies indicated that factors including, maternal age, residency, literacy, marriage before 18 years old, ANC follow-up, meal frequency, meal skipping, and household food security [16–20], were associated with maternal undernutrition. However, these studies have documented that the level of magnitude undernutrition and associated risk factors among pregnant women vary across the agro-ecological setups [19]. Although the Ethiopian ministry of health has tried to implement health extension program strategies to reduce maternal undernutrition, studies indicate malnutrition among pregnant women persistently remains a serious public health problem in the country [15–20]. Moreover, evidence is scarce on undernutrition and associated factors among pregnant women in the Haramaya district. Therefore, this study assessed the undernutrition and associated factors among pregnant women in Haramaya District, Eastern Ethiopia. ## Description of the study area As a detailed description has been given elsewhere in the previous paper [21], the study was embedded into the Haramaya Health Demographic Surveillance and Health Research Centre (HDS-HRC), established in 2018. The HDS-HRC covers 12 rural kebeles (the lowest administrative unit in Ethiopia) out of 33 found in the district located approximately 500 KM from the capital city, Addis Ababa. Of 5252 pregnant women in the district during the study period, 2306 were followed by the HDS-HRC [22]. This study was conducted from January 5 to February 12, 2021 ## Study design and period A community-based cross-sectional study was conducted from January 5 to February 12, 2021. ## Source population and study population All pregnant women living in the district constituted the source population; whereas all pregnant women who lived in the selected kebeles for at least six months during the study period were the study population. ## Inclusion and exclusion criteria Participants were a part of pregnancy surveillance initiated in HDS-HRC. For the reason that dietary practice is affected by the local social and cultural values, all pregnant women who lived a minimum of six months in the district were involved in this study. However, all pregnant women with reported acute and chronic illnesses, seriously ill and unable to communicate during the study period were excluded ## Sample size determination and sampling procedures The sample size was determined using single and double population proportion formulas with their corresponding assumption, and the largest sample size was considered. As such, the sample was computed using the single population proportion formula with the following assumptions: $95\%$ confidence interval, the prevalence of undernutrition among pregnant women Gumay District, ($44.9\%$) [16], $5\%$ marginal error, and $10\%$ non-response rate; the final computed sample size was 419. However, since this study was part of a larger longitudinal study (a prospective cohort study aimed to assess neonates’ birth weight and the association with maternal iron status), the same 475 pregnant women were included. A detailed description has been given elsewhere in the previous papers [21, 23, 24]. ## Data collection and measurement Data were collected through face-to-face interviews, anthropometric measurement, and serum ferritin analysis by trained research assistants. The questionnaire contained data on socio-economic, obstetric, maternal perception, food consumption, dietary diversity, knowledge, attitude, and practices of pregnant women. In addition, mid-upper arm circumference (MUAC) and maternal height measurements were taken. The nutritional status of the pregnant women was measured with non-stretchable MUAC tape and the reading value was taken to the nearest 0.1-cm. All measurements were performed threefold and the average value of two concordant readings was considered as the ultimate value. Pregnant women with average MUAC measurements of less than 23 cm were categorized as having “undernutrition” otherwise normal [25, 26]. The questionnaire was initially prepared in English and translated to the local language (Afan Oromo) by individuals with good command of both languages. It was also pre-tested on $10\%$ of the samples in Kersa District before actual implementation. Women’s hemoglobin concentration (in g/dL) was measured at each study site by well-trained medical technologists using HemoCue® Hb 301 system, according to the manufacturer’s instructions (HemoCue AB Ängelholm Sweden) which is a gold standard for fieldwork. A prick was done on the tip of the middle finger after the site was cleaned with disinfectant. The first drop of blood was cleaned off and the second drop was collected to fill the microcuvette which is then placed in the cuvette holder of the device for measuring hemoglobin concentration. Hemoglobin values were adjusted for altitude as per the Center for Disease Prevention and Control (CDC) recommendation [27]. As the detailed description has been given elsewhere in a previous papers [23, 24], the formerly validated food frequency questionnaire (FFQ) containing 27 of the most common lists of food items consumed by the district community was used to assess the dietary diversity of the study participants [28–33]. The food items in the FFQ were grouped into ten food groups, including cereal, white roots and tubers, pulse and legumes, nuts and seeds, dark green leafy vegetables, other vitamin A-rich fruits and vegetables, meat, fish and poultry, dairy and dairy product, egg, other vegetables, and other fruits. The sum of each food group pregnant women consumed over seven days was calculated to analyze the dietary diversity scores (DDS) [32]. Furthermore, the dietary diversity score was converted into tertiles, with the highest tertile labeled as a "high dietary diversity score" whereas both lower tertiles combined were defined as a “low dietary diversity score". The food variety score (FVS) is the frequency of individual food items consumed during the reference period. Therefore, it was estimated by calculating each individual’s intake of the 27 food items over seven days. ## Data quality assurance Two training days were given for data collectors, laboratory professionals, and supervisors before the pre-test. The questionnaire pre-test was conducted on $10\%$ of the sampled pregnant women in a district that was not included in the main study; appropriate adjustments were made based on the results. Supervisors closely managed data collection, checking the data daily before entry. The investigators administered all data collection activities. In addition, laboratory analysis quality assurance was maintained and trained and experienced laboratory professionals strictly followed standard operating procedures for all parameters. ## Data processing and analysis Data were double entered using Epi-data 3.1. Data were cleaned, coded, checked for missing and outliers, and analyzed using Stata 14 (College Station, Texas 77845 USA). Frequencies, percentages, summary measures and tables were used to describe and present the descriptive information of respondents. The MUAC is a much simpler anthropometric measure than the BMI, as its use eliminates the need for expensive equipment, such as height charts and scales, and the need for calculations. It is also much easier to perform on a patient who is acutely unwell, bed bound or sedentary. Another important advantage of using MUAC is that there is minimal change in the MUAC during pregnancy, so it may be a better indicator of pre-pregnancy body fat and nutrition than the BMI. The outcome variable (undernutrition) was dichotomized as undernutrition (coded as 1) and normal (coded as 0). Poisson regression analysis models with a robust variance estimate were fitted to identify predictors of undernutrition. Next, the binary analysis variables with a $p \leq 0.25$ were entered into the adjusted log-binomial models. Results were presented using the crude prevalence ratio (CPR) and adjusted prevalence ratio (aPR). Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used to test for model fitness. The goodness-of-fit was assessed using the Pearson chi-square and deviance tests, with the statistical significance level at alpha = $5\%$. The explanatory variables were examined for multi-collinearity before taking them into the multivariable model using a correlation matrix for the regression coefficients, the standard errors, and the variance inflation factor value. The wealth index was employed to estimate the economic level of families. The wealth dispersion was generated by applying the principal component analysis (PCA). The index was calculated based on the ownership of latrines, agricultural land and size, selected household assets, livestock quantities, and source of drinking water, a total of 41 household variables. The previous paper [28] described nutritional knowledge and attitudes toward consumption of an iron-rich diet using the Likert scale applying the PCA; the factor scores were totaled and classified into tertiles. Women’s autonomy was evaluated using seven validated questions adopted from the Ethiopian Demographic Health Survey [34]. For each question, the response was coded as "one" when the decision was made by the woman alone or jointly with her husband, or "zero" otherwise. The detailed description has been given elsewhere in a previous papers [21, 23, 24]. ## Ethical consideration This study was conducted in agreement with the Declaration of Helsinki-Ethical principle for medical research involving human subjects [35]. The proposal was approved by the Institutional Health Research Ethics Review Committee (IHRERC) of the College of Health and Medical Sciences, Haramaya University (ref No: IHRERC/$\frac{266}{2020}$). Written informed consent was obtained from all participants and legally authorized representatives "of minors below 16 years of age and illiterates,” and confidentiality was maintained by excluding all personal identifiers ## Undernutrition Nutritional status of pregnant women measured by MUAC was labeled as under-nutrition when MUAC<23 cm, otherwise normal [25]. ## Anemia Anemia was defined as a Hemoglobin level of < 11.0 g/dl during the first or third trimester or <10.5 g/dl during the second [36]. ## Mid-upper arm circumference (MUAC) Is used as a measure of fat-free mass and a measurement of the circumference of the upper arm at the midpoint between the olecranon and acromion processes [26]. ## Nutritional knowledge Was measured through16 nutritional knowledge questions on the feature of nutrition needed in pregnancy and the score was computed by conducting PCA. Then composite was ranked into tertiles [29]. ## Educational status Respondents who had grade at least grade one education level were labeled as”formal”, whereas respondents those could able to read or write sentences were categorized as” Informal education” ## Socio-demographic characteristics Out of 475 eligible pregnant women, the study included 448, yielding a $94.3\%$ response rate. The mean age of the women was 25.68 (±5.16), ranging from 16 to 36. The majority of the respondents could not read or write ($73.88\%$), were housewives ($96.1\%$), farmers ($93\%$), and had a family size of 1–5 ($76.56\%$). Only $20.09\%$ were in the wealthiest quintiles (Table 1). Table 1 \| Variables \| Frequency(n) \| Percentage (%) \| \| --- \| --- \| --- \| \| Age (years) \| \| \| \| <18 \| 25 \| 5.58 \| \| 18–35 \| 400 \| 89.29 \| \| >35 \| 23 \| 5.13 \| \| Mean (± SD) \| 25.68 (± 5.16) \| \| \| Educational level of the woman \| \| \| \| Can’t read or write \| 331 \| 73.88 \| \| Read or write \| 26 \| 5.81 \| \| Formal education \| 91 \| 20.31 \| \| Educational level of husband \| \| 49(23.33) \| \| Can’t read or write \| 259 \| 57.81 \| \| Read or write \| 61 \| 13.62 \| \| Grade 1–8 \| 102 \| 22.77 \| \| Grade 9 and above \| 26 \| 5.8 \| \| Occupation of the woman \| \| \| \| Housewives \| 433 \| 96.65 \| \| Merchants \| 15 \| 3.65 \| \| Occupation of husband \| \| \| \| Farmers \| 420 \| 93.75 \| \| Daily labors \| 28 \| 6.25 \| \| Family size \| \| \| \| 1–5 \| 343 \| 76.56 \| \| ≥5 \| 105 \| 23.44 \| \| Agricultural land possession \| \| \| \| No \| 271 \| 60.49 \| \| Yes \| 177 \| 39.51 \| \| Wealth Index (Quintile) \| \| \| \| Poorest \| 90 \| 20.09 \| \| Poor \| 90 \| 20.09 \| \| Middle \| 89 \| 19.87 \| \| Rich \| 90 \| 20.09 \| \| Richest \| 89 \| 19.87 \| ## Anthropometric and nutritional status of respondents Among 448 respondents, $47.9\%$ ($95\%$ CI: $43\%$- $53\%$) were undernourished and $45.98\%$ were anemic. Of the total respondents, $29.46\%$, $37.50\%$, $24.8\%$, and $26.12\%$ of them had high dietary diversity, high food variety score, high consumption of ASFs, and > 4 meal frequency respectively, Table 2. Table 2 \| Variables \| Frequency(n) \| Percentage (%) \| \| --- \| --- \| --- \| \| Nutritional status \| \| \| \| Normal \| 233.0 \| 52.0 \| \| Undernutrition \| 215.0 \| 48.0 \| \| Anemia status \| \| \| \| Anemic \| 206.0 \| 45.98 \| \| Non-anemic \| 242.0 \| 54.02 \| \| Dietary diversity \| \| \| \| Low \| 316.0 \| 70.54 \| \| High \| 132.0 \| 29.46 \| \| Consumption of ASFs \| \| \| \| Low \| 337.0 \| 75.22 \| \| High \| 111.0 \| 24.78 \| \| Food Variety Sore (FVS) \| \| \| \| Low \| 280.0 \| 62.5 \| \| High \| 168.0 \| 37.5 \| \| Meal frequency \| \| \| \| < 4 \| 331.0 \| 73.88 \| \| ≥ 4 \| 117.0 \| 26.12 \| ## Factors associated with undernutrition In the bi-variable analysis, women’s educational level, stage of pregnancy, family size, dietary diversity, consumption of ASFs, skipping meals, anemia status, antenatal care, perceived confidence, food restriction, and khat chewing were found to be a candidate for multivariable analysis at $p \leq 0.25.$ Using the Poisson regression analysis model with a robust variance estimate, undernutrition was more likely higher among respondents who had more than five family members (APR = 1.19; $95\%$ CI = 1.02–1.40), low dietary diversity (APR = 1.58; $95\%$ CI = 1.13–2.21) and had anemia during pregnancy (APR = 4.27; $95\%$ CI = 3.17–5.76), Table 3. Table 3 \| Variables \| Undernutrition \| Undernutrition.1 \| CPR(95%CI) \| APR (95%CI) \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variables \| Yes \| No \| CPR(95%CI) \| APR (95%CI) \| P-value \| \| Variables \| (n = 215) \| (n = 233) \| CPR(95%CI) \| APR (95%CI) \| P-value \| \| Educational level of women \| Educational level of women \| Educational level of women \| Educational level of women \| Educational level of women \| Educational level of women \| \| Can’t read or write/Informal \| 177(82.33) \| 180(77.25) \| 1 \| 1 \| 0.334 \| \| Formal \| 38(17.67) \| 53(22.75) \| 0.73 (0.46,1.16) \| 1.12(0.89,1.41) \| 0.334 \| \| Stage of pregnancy \| \| \| \| \| \| \| First trimester \| 8(3.72) \| 11(4.72) \| 1 \| 1 \| 0.755 \| \| Second trimester \| 137(63.72) \| 159(68.24) \| 1.18 (0.46,3.03) \| 0.89 (0.74,1.30) \| 0.755 \| \| Third trimester \| 70(32.56) \| 63(27.04) \| 1.53 (0.58,4.04) \| 0.96 (0.72,1.27) \| 0.755 \| \| Family sizes \| Family sizes \| Family sizes \| Family sizes \| Family sizes \| Family sizes \| \| 1–5 \| 152(70.70) \| 191(81.97) \| 1 \| 1 \| 0.028* \| \| ≥5 \| 63(29.30) \| 42(18.03) \| 1.88 (1.21,2.94) \| 1.19 (1.02,1.40) \| 0.028* \| \| Dietary diversity \| Dietary diversity \| Dietary diversity \| Dietary diversity \| Dietary diversity \| Dietary diversity \| \| High \| 32(14.88) \| 100(42.92) \| 1 \| 1 \| \| \| Low \| 183(85.12) \| 133(57.08) \| 0.23 (0.147,0.37) \| 1.58 (1.13,2.21) \| 0.008* \| \| Consumption of ASFs \| \| \| \| \| \| \| low \| 182(84.65) \| 155(66.52) \| 1 \| 1 \| 0.777 \| \| High \| 33(15.35) \| 78(33.48) \| 2.39 (1.74, 3.28) \| 1.05 (0.75,1.46) \| 0.777 \| \| Skipping meals \| \| \| \| \| \| \| No \| 79(36.74) \| 83(35.62) \| 1 \| 1 \| 0.784 \| \| Yes \| 18(63.26) \| 150(64.38) \| 0.95 (0.65,1.40) \| 1.03 (0.85,1.24) \| 0.784 \| \| Anemia status \| \| \| \| \| \| \| Non-anemic \| 42(19.53) \| 200(85.84) \| 1 \| 1 \| < 0.001 \| \| Anemic \| 173(80.4) \| 133 (14.16) \| 4.84 (3.656.41) \| 4.27 (3.17,5.76) \| < 0.001 \| \| Antenatal care \| \| \| \| \| \| \| No \| 69(32.09) \| 95(40.77) \| 1 \| 1 \| 0.111 \| \| Yes \| 146(67.91) \| 138(59.23) \| 1.46(0.99,2.15) \| 1.14(0.97,1.33) \| 0.111 \| \| Perceived confidence \| \| \| \| \| \| \| No \| 168(78.14) \| 168 (72.10) \| 1 \| 1 \| 0.318 \| \| Yes \| 47(21.86) \| 65 (27.90) \| 0.72(0.47,1.11) \| 0.90 (0.73,1.11) \| 0.318 \| \| Food restriction \| \| \| \| \| \| \| No \| 133(61.86) \| 166 (71.24) \| 1 \| 1 \| 0.807 \| \| Yes \| 82(38.14) \| 67 (28.76) \| 1.53(1.03,2.27) \| 1.02(0.85,1.23) \| 0.807 \| \| Khat chewing \| \| \| \| \| \| \| No \| 73(33.95) \| 107 (45.92) \| 1 \| 1 \| 0.261 \| \| Yes \| 142(66.05) \| 126(54.08) \| 1.65 (1.13,2.42) \| 1.10(0.93,1.31) \| 0.261 \| ## Discussion Despite the encouraging improvement in maternal death in, undernutrition among pregnant women remains a public health issue in in Ethiopia [34]. In this study, we reported the prevalence of the undernutrition and associated factors among pregnant women in Haramaya district. We found that the prevalence of undernutrition among study participants was $47.9\%$ ($95\%$ CI: $43\%$-$53\%$) and was noted to be nearly half. Moreover, the risk factors of undernutrition were higher among women who had greater than five family sizes, low dietary diversity and were anemic. The present finding is comparably consistent with studies conducted in Northwest Tigray, Ethiopia [37] and South West Ethiopia [16]. However, the result of current is higher than studies carried out in Gambela, Ethiopia [19] and Eastern Ethiopia [13] and southern Ethiopia [38], Kenya [39], Sudan [40], and Nigeria [41]. The possible variation might be due to culturally diverse countries, thus it is not important to introduce direct correlation of the current the result with the findings of the studies carried out in different countries. Farmers in the Haramaya district have been growing khat for many years and are a major cash crop in this study area. Moreover, khat chewing amongst pregnant women is common in the district [22]. Therefore, the higher prevalence of undernutrition in this study setup might be due to increased nutritional demands in pregnancy and the decrement of dietary intake as of the effects amphetamine found in khat to reduced appetite [42]. On other hand, the difference in methods and measures used might contribute to the variations. In our study we used a community-based cross-sectional study design to assess the undernutrition and associated factors s among pregnant, whereas, some previous of the studies were carried out at the institutional level. In the present study, we observed that having low dietary diversity was independently associated with maternal undernutrition during pregnancy, which is in agreement with studies conducted in different parts of Ethiopia [19, 43, 44]. The inappropriate dietary practice among pregnant women was noted in this study [23]. This is might be due to maternal dietary habits, food taboos, and cultural beliefs that can affect nutrition during pregnancy and women do not consume additional meals during the pregnancy. Nutritious diets, essential nutrition services and optimal nutrition practices are essential to prevent all forms of malnutrition before and during pregnancy. Therefore, nutrition education and counseling of pregnant women is critical for every antenatal care and should be intensified. Anemia during pregnancy has maternal and perinatal various effects and it increase the risk of maternal and perinatal mortality [45, 46]. This study observed that pregnant women with anemia were more likely to be undernourished. The proportion of undernutrition was significantly more among anemic pregnant women compared to normal hemoglobin level pregnant women. This result is comparably in agreement with studies employed in Walayita Sodo town, Southern Ethiopia [47], in Gonder northwest Ethiopia [15], and India [48], which shows the risk of undernutrition tends to increase among anemic women. This could be due to the reality that anemic pregnant women have a greater risk of being inadequate in micronutrients and therefore more likely to be undernourished. The nether reason might be the fact that khat chewing is the most common in this study area and most of the women chew khat, which could decrease. Having a large family size was one of the determinants, which were independently associated with undernutrition during pregnancy. This study revealed that pregnant women who were from greater than five members of households were a greater prevalence of undernutrition, which is in line with studies conducted in different parts of Ethiopia [16, 19] and Western Nepal [20]. The result could be because, food insecurity is more common in households with large family sizes, women play a sacrificial role and are more vulnerable to being undernourished than other family members [49]. Large family sizes may lead to inadequate food intake. In Ethiopian culture, women habitually served their meals after all family members are addressed. Thus, pregnant women are more exposed to food insecurity and associated with inadequate nutrient intakes for two fundamental reasons. First, the physiological changes occur during pregnancy. Women’s nutrient needs increase during pregnancy and lactation. Maternal nutrient needs increase during pregnancy and breastfeeding, and when these needs are not met, it may contribute to wasting and fatigue. Second, women have a sociological vulnerability. Studies reveal that, during periods of decreased food supply, women expose to reduced consumption comparative to men. Furthermore, women are expected to decline their consumption to safe those of babies and small children [50]. Trained health workers and medical laboratory technologists collected and analyzed the socio-demographic data and blood samples. One strength of this study is the use of hemoglobin as an indicator of nutritional status, which is preferable to a community-based study. Various limitations need to be considered when interpreting our results. Since the study was cross-sectional, limiting the causal inference between under-nutrition and its correlates. ## Conclusion This study finding has shown that the prevalence of that undernutrition among pregnant women in Haramaya district is high. In addition, dietary diversity, family size, and anemia in pregnancy were identified as factors that hindered their maternal status. Therefore, it is important that nutrition education and counseling are given during each antenatal visit should be intensified. 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--- title: 'Policy and programmatic directions for the Lesotho tuberculosis programme: Findings of the national tuberculosis prevalence survey, 2019' authors: - R. Matji - L. Maama - G. Roscigno - M. Lerotholi - M. Agonafir - R. Sekibira - I. Law - M. Tadolini - N. Kak journal: PLOS ONE year: 2023 pmcid: PMC9997977 doi: 10.1371/journal.pone.0273245 license: CC BY 4.0 --- # Policy and programmatic directions for the Lesotho tuberculosis programme: Findings of the national tuberculosis prevalence survey, 2019 ## Abstract ### Introduction The Kingdom of Lesotho has one of the highest burdens of tuberculosis (TB) in the world. A national TB prevalence survey was conducted to estimate the prevalence of bacteriologically confirmed pulmonary TB disease among those ≥15 years of age in 2019. ### Method A multistage cluster-based cross-sectional survey where residents ≥15 years in 54 clusters sampled from across the country were eligible to participate. Survey participants were screened using a symptom screen questionnaire and digital chest X-ray (CXR). Respondents who acknowledged cough of any duration, fever, weight loss, night sweats and/or had any CXR abnormality in the lungs were asked to provide two spot sputum specimens. All sputum testing was conducted at the National TB Reference Laboratory (NTRL), where samples underwent Xpert MTB/RIF Ultra (1st sample) and MGIT culture (2nd sample). HIV counselling and testing was offered to all survey participants. TB cases were those with Mycobacterium tuberculosis complex-positive samples with culture; and where culture was not positive, Xpert MTB/RIF Ultra (Xpert Ultra) was positive with a CXR suggestive of active TB and no current or prior history of TB. ### Result A total of 39,902 individuals were enumerated, and of these, 26,857 ($67.3\%$) were eligible to participate; 21,719 ($80.9\%$) participated in the survey of which 8,599 ($40\%$) were males and 13,120 ($60\%$) were females. All 21,719 ($100\%$) survey participants underwent symptom screening and a total of 21,344 participants ($98.3\%$) had a CXR. Of the 7,584 ($34.9\%$) participants who were eligible for sputum examination, 4,190 ($55.2\%$) were eligible by CXR only, 1,455 ($19.2\%$) by symptom screening, 1,630 by both, and 309 by CXR exemption. A total of 6,780 ($89.4\%$) submitted two sputum specimens, and 311 ($4.1\%$) submitted one sample only. From the 21,719 survey participants, HIV counseling and testing was offered to 17,048, and 3,915 ($23.0\%$) were documented as HIV-positive. The survey identified 132 participants with bacteriologically confirmed pulmonary TB thus providing an estimated prevalence of 581 per 100,000 population ($95\%$ CI 466–696) for those ≥15 years in 2019. Using the survey results, TB incidence was re-estimated to be 654 per 100,000 ($95\%$ CI 406–959), which was comparable to the 2018 TB incidence rate of 611 per 100,000 ($95\%$ CI 395–872) reported by the World Health Organization (WHO). The highest TB burden was found in those ≥55 years and among men. The ratio of prevalence to case notification was estimated at 1.22. TB/HIV coinfection was identified in 39 ($29.6\%$) participants. Out of the 1,825 participants who reported a cough, $50\%$ of these participants, mostly men, did not seek care. Those who sought care predominantly went to the public health facilities. ### Conclusion The TB prevalence survey results confirmed that burden of TB and TB/HIV coinfection remains very high in Lesotho. Given that TB prevalence remains high, and there is a significant proportion of participants with confirmed TB that did not report TB suggestive symptoms. The National TB Programme will need to update its TB screening and treatment algorithms to achieve the End TB targets. A major focus will need to be placed on finding the “missing cases” i.e., undiagnosed or under-reported TB cases, or ensuring that not only TB symptomatic but also those who do not present with typical TB symptoms are promptly identified to reduce further onward transmission. ## Introduction The Kingdom of Lesotho, with a population of 2.2 million, is a small, landlocked mountainous country encircled by South Africa. Lesotho is classified as a lower-middle-income-country with 32 percent of the population living in urban/peri-urban areas and the rest in rural areas [1]. Most of the rural population is engaged in informal crop cultivation and animal husbandry and resides in scattered small communities making delivery of health care services a challenging task [2]. Lesotho is on the list of the world’s high-burden countries for both TB and HIV [3]. TB incidence increased from 827 per 100,000 ($95\%$ CI: 443–1330) in 2000 to a peak of 1,240 per 100,000 cases ($95\%$ CI: 449–2420) in 2008 [3]. This increase was largely attributed to the HIV epidemic but has steadily declined due to improved case detection, improvements in the health system’s diagnostic capacity and better access to care through the decentralization of health facilities. Nonetheless the burden of TB still remains high which is fueled by a very high burden of HIV. In 2020, the prevalence of HIV among those 15 years and above was $22.7\%$ corresponding to 324,000 people living with HIV [4]. National TB prevalence surveys help to estimate the burden of TB, particularly in countries where notification data obtained from national surveillance systems may not be accurate. Lesotho had never conducted a nationwide population-based TB prevalence survey. The country’s TB burden estimates were mainly based on routine surveillance data which informed WHO’s estimates. The overall objective of the first national TB prevalence survey was to enable the National TB Programme (NTP) to gain a better understanding of the burden of TB and to identify ways of improving TB management in the country by estimating the prevalence of bacteriologically confirmed pulmonary TB disease among those ≥15 years in 2019. A secondary survey objective was to describe the healthcare seeking behaviour of survey participants. A robust knowledge of the epidemic and its determinants would help the NTP in streamlining future strategies to better address the burden of TB in Lesotho and get closer to country targets to end TB. ## Survey design and population The national TB prevalence survey, using multi-stage cluster sampling with probability proportional to size (PPS), was conducted between March through November 2019 based on WHO guidelines [5]. No areas of Lesotho were excluded from the sampling frame. A sample size of 26,848 individuals across 54 clusters was estimated from the following parameters: a prior guess of bacteriologically confirmed TB prevalence of 736 per 100,000 population, an average cluster size of 500, a design effect of 2.33, a relative precision on $23\%$ and an expected participation rate of $85\%$. Clusters were allocated proportional to population size across 3 geographical stratum: rural [29], urban [21], peri-urban [4] (Fig 1). The survey population consisted of adults 15 years or more who had lived and slept in the households of selected clusters for at least two weeks prior to screening at that site. Fig 1: Lesotho national tuberculosis prevalence survey map.Black lines delineate provinces and red dots represent households of the enumerated survey population. ## Survey procedures There were three survey teams and at any given time two were in the field, while the third team was preparing for its next two clusters. Each cluster was surveyed by one team over a continuous seven-day period. The first few days of operation involved door-to-door household visits, enumerating all individuals living in the selected area, and invited those who were at least 15 years of age and resident for 14 days before the survey census. At a centrally located area in the cluster, several stations were set up to conduct the following: registration; interview; digital chest X-ray (Innomed units fitted with the Samsung Detector panels and Sedecal Dragon 5kW Digital X-ray units), clinician review, sputum collection and HIV testing. All adult participants were asked to sign an informed consent form while those between 15 and 17 had to provide assent and consent from an adult or guardian. All survey participants were screened with a questionnaire (i.e., a cough of any duration, fever, unexplained weight loss in the last one month or night sweats) and a digital chest X-ray (CXR). Participants who reported any of the four symptoms and/or had CXR findings suggestive of TB were referred to the laboratory station to produce two spot sputum samples collected in sterile Falcon tubes at least 60 minutes apart. Participants who declined CXR were also asked to provide two sputum samples irrespective of whether they had TB symptoms or not. A radiologist based in South Africa read all received abnormal CXRs and approximately $11\%$ of normal CXRs (sampled by the digital system) for quality assurance using a standardized assessment form. ## Laboratory procedures All survey participants were offered HIV testing directly at they survey site after consenting (Alere™ Determine and UniGold™ Recombigen HIV test kits). All participants who tested HIV-positive were referred to the nearest health facility for enrollment on care, and those tested HIV-negative were counseled about HIV infection prevention. All sputum samples were transported using a longstanding partner called “Riders for Health” (https://www.riders.org) in cool boxes to the National TB Reference Laboratory (NTRL) in Maseru. Spot 1 sample was tested with Xpert MTB/RIF Ultra (Xpert Ultra). Xpert Ultra trace results were classified as Xpert Ultra negative. Spot 2 sample was first decontaminated and processed for BD BACTEC MGIT 960 (one tube). A culture was only reported negative if there was no growth after eight weeks. For the positive cultures, identification of Mycobacterium tuberculosis complex was based on presumptive phenotypic appearance of colonies in the medium and confirmed using TB antigen test SD Bioline®. ## Survey TB definitions Cases were defined in two ways: 1) All cultures with MTB-positive results (regardless of Xpert Ultra results), or 2) (a) Xpert Ultra positive (excluding trace), and (b) culture not TB (i.e., culture-negative, nontuberculous mycobacteria, contaminated, or not done), and (c) CXR suggestive of active TB as determined by three central readers, and (d) no history of TB (past or current). ## Data collection and analysis Data were directly captured using tablets and laptops with a secure web-based application (RedCap 8.7.1). The data capture system had built in algorithms to validate data in real time as it was being captured. The central data management team cleaned and merged all data from the field, laboratory and radiology teams. Digital CXR images were stored in a cloud-based server. Data were analysed using STATA MP Version 16.1 (Stata Corp. Station TX, USA). Best-practice analytical methods to estimate TB prevalence (i.e. number of survey participants with TB per 100,000 population) were used to account for cluster sampling, non-participation, and missing data [6]. Three logistic regression models were conducted: [1] cluster-level analysis, [2] individual-level analysis, and [3] estimation with inverse probability weighting and with multiple value imputation, and the survey population was standardized to the 2016 national population. Only model 3 is presented in this paper. As a proxy indicator of case detection, prevalence to case notification ratios (P:N) were estimated by comparing the age- and sex-specific TB prevalence to TB case notification rates for new and relapse TB cases for the same age groups and sex as reported by the NTP in 2019 [7]. ## Ethical clearance and consent The study protocol was reviewed and approved by the Lesotho Research and Ethics Committee (Reference number: ID23-2017, June 29, 2018). The data collection team followed strict ethical norms and sought informed consent or assent from each survey participant. During the enrolment, each participant was provided with information about the survey, benefits to the individual participant and the society. Opportunities were given to participants to ask questions and assured that their participation was voluntary, and that refusal will not affect any potential benefit accrued to the participant. Only participants who gave written consent were enrolled for the survey. Those participants who could not read or write, the consent form was translated in the local language. If a verbal consent was given, thumb print was taken in the presence of a witness for such participants. All participants with a positive laboratory test result (TB and HIV) were managed in accordance with national treatment guidelines. ## Survey participants During the enumeration of 54 clusters, 39,902 individuals were recorded from 15,279 households. Of these, 26,857 ($67.3\%$) were eligible to participate in the survey, of whom 21,719 ($80.9\%$) participated in the survey (Fig 2). Participation was higher among women than men ($84.2\%$ vs $76.2\%$) and increased with age (Fig 3). Fig 2: Flow diagram of the national TB prevalence survey of Lesotho.CXR, chest X-ray. Panel review (Xpert-positive alone (no positive culture results) with a CXR suggestive of active TB disease and no current or past history of TB). ** These 15 participants also did not have a history of TB (current or past).* Fig 3: Age and sex distribution of eligible and participant populations. ## Screening Among the 21,719 study participants screened, 3085 ($14.2\%$) screened positive by interview by acknowledging at least one of the screening symptoms (Table 1): 1825 ($8.4\%$) reported having a cough, 1,423 ($6.6\%$) had weight loss, 907 ($4.2\%$) had night sweats and 845 ($3.9\%$) had fever. Symptoms were reported more frequently among men than women and progressively increased with age. Table 1 \| Unnamed: 0 \| Total screened participants \| Total screened positive \| Total screened positive.1 \| Interview positive only \| Interview positive only.1 \| CXR positive only \| CXR positive only.1 \| Both interview and CXR positive \| Both interview and CXR positive.1 \| CXR exempt and with no symptoms \| CXR exempt and with no symptoms.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| No. \| No. \| % \| No. \| % \| No. \| % \| No. \| % \| No. \| % \| \| Total \| 21719 \| 7584 \| 34.9 \| 1455 \| 6.7 \| 4190 \| 19.3 \| 1630 \| 7.5 \| 309 \| 1.4 \| \| Sex \| \| \| \| \| \| \| \| \| \| \| \| \| Female \| 13122 \| 3903 \| 29.7 \| 879 \| 6.7 \| 2050 \| 15.6 \| 739 \| 5.6 \| 235 \| 1.8 \| \| Male \| 8597 \| 3681 \| 42.8 \| 576 \| 6.7 \| 2140 \| 24.9 \| 891 \| 10.4 \| 74 \| 0.9 \| \| Age group (years) \| \| \| \| \| \| \| \| \| \| \| \| \| 15–24 \| 5695 \| 921 \| 16.2 \| 305 \| 5.4 \| 445 \| 7.8 \| 84 \| 1.5 \| 87 \| 1.5 \| \| 25–34 \| 4039 \| 1029 \| 25.5 \| 303 \| 7.5 \| 512 \| 12.7 \| 152 \| 3.8 \| 62 \| 1.5 \| \| 35–44 \| 3436 \| 1140 \| 33.2 \| 270 \| 7.9 \| 615 \| 17.9 \| 221 \| 6.4 \| 34 \| 1.0 \| \| 45–54 \| 2580 \| 1065 \| 41.3 \| 202 \| 7.8 \| 608 \| 23.6 \| 243 \| 9.4 \| 12 \| 0.5 \| \| 55–64 \| 2710 \| 1391 \| 51.3 \| 185 \| 6.8 \| 821 \| 30.3 \| 366 \| 13.5 \| 19 \| 0.7 \| \| ≥65 \| 3259 \| 2038 \| 62.5 \| 190 \| 5.8 \| 1189 \| 36.5 \| 564 \| 17.3 \| 95 \| 2.9 \| \| Strata \| \| \| \| \| \| \| \| \| \| \| \| \| Rural \| 12490 \| 4430 \| 35.5 \| 709 \| 5.7 \| 2502 \| 20.0 \| 1001 \| 8.0 \| 218 \| 1.7 \| \| Urban \| 8230 \| 2613 \| 31.7 \| 634 \| 7.7 \| 1439 \| 17.5 \| 462 \| 5.6 \| 78 \| 0.9 \| \| Peri-urban \| 999 \| 541 \| 54.2 \| 112 \| 11.2 \| 249 \| 24.9 \| 167 \| 16.7 \| 13 \| 1.3 \| A total of 21,344 ($98.3\%$) participants were screened by CXR in the field. Of the 21,344 participants, 14,937 ($70.0\%$) had a normal CXR, 5,820 ($27.3\%$) had an abnormality in the lung field suggestive of TB, and 587 ($2.8\%$) had an abnormal CXR that was not suggestive of TB. The abnormal CXR findings suggestive of TB were higher in men ($35.2\%$) than women ($21.2\%$) and increased with age with those ≥65 years having the highest percentage of abnormal CXRs ($53.8\%$). There were 375 participants who were CXR exempt. Of the 21,719 participants, 7,584 ($34.9\%$) screened positive and were eligible to submit sputum samples: 1,455 ($19.2\%$) by symptoms only, 4,190 ($55.5\%$) by CXR only, 1,630 ($21.5\%$) by both symptoms and CXR, and 309 ($4.1\%$) by CXR exemption (and no symptoms). Of all participants, there were a total of 285 ($1.3\%$) who were currently on TB treatment, 1943 ($8.9\%$) with a past history of TB treatment, and 62 ($0.3\%$) had both. ## Laboratory examination results Of the 7,584 participants eligible for sputum collection, 7,091 ($93.5\%$) submitted at least one sputum sample, 6,780 ($89.4\%$) gave two samples, 311 ($4.1\%$) submitted only one sample, and 493 ($6.5\%$) did not submit any samples. Of 7,584 participants, 6,945 had a valid Xpert Ultra result. Of these, 6,730 ($96.9\%$) tests were negative, 144 ($2.1\%$) were positive (grade other than trace), and 71 ($1.0\%$) were trace positive. Of 7,584 eligible participants, 6,941 had a culture result: 6,305 ($90.9\%$) tests were culture negative, 108 ($1.6\%$) were culture positive for Mycobacterium tuberculosis, 340 ($4.9\%$) were contaminated, and 188 ($2.7\%$) were nontuberculous mycobacteria. Of the 180 laboratory positive results, 72 ($40\%$) were detected by both methods, 72 ($40\%$) were positive by Xpert Ultra alone, while 36 ($20\%$) were culture positive for M.tb but negative or trace by Xpert Ultra. Assuming culture to be the reference standard, 66 out of 144 ($46\%$) Xpert Ultra-positives (excluding trace) were culture-negative (Table 2). When comparing Xpert Ultra with culture, culture positivity increased with Xpert Ultra grade, and most culture positives had a low grade Xpert Ultra result. Table 2 \| Unnamed: 0 \| Culture results \| Culture results.1 \| Culture results.2 \| Culture results.3 \| Culture results.4 \| Culture results.5 \| Culture results.6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Xpert MTB/RIF Ultra results \| Culture-positive for Mycobacterium tuberculosis \| Culture-negative for Mycobacterium tuberculosis \| Non-tuberculous mycobacteria \| Contaminated \| Not done \| Rejected \| Total \| \| Xpert MTB/RIF Ultra positive \| 72 \| 66 \| 0 \| 6 \| 0 \| 0 \| 144 \| \| High \| 10 \| 2 \| 0 \| 0 \| 0 \| 0 \| 12 \| \| Medium \| 11 \| 6 \| 0 \| 0 \| 0 \| 0 \| 17 \| \| Low \| 36 \| 29 \| 0 \| 3 \| 0 \| 0 \| 68 \| \| Very low \| 15 \| 29 \| 0 \| 3 \| 0 \| 0 \| 47 \| \| Xpert MTB/RIF Ultra negative \| 22 \| 6182 \| 187 \| 332 \| 6 \| 1 \| 6730 \| \| Trace* \| 14 \| 54 \| 1 \| 2 \| 0 \| 0 \| 71 \| \| Not done \| 0 \| 3 \| 0 \| 0 \| 635 \| 1 \| 639 \| \| Total \| 108 \| 6305 \| 188 \| 340 \| 641 \| 2 \| 7584 \| The sensitivity and specificity of symptom screening, assuming culture was the reference standard, was $35.2\%$ ($95\%$ CI: 26.2–45.9) and $59.9\%$ ($95\%$ CI: 58.7–$61.1\%$), respectively. The sensitivity and specificity of CXR screening, assuming culture was the reference standard, was $93.5\%$ ($95\%$ CI: 87.1–97.4) and $21.2\%$ ($95\%$ CI: 20.2–22.2), respectively. Assuming culture was the reference standard (and trace was negative), the specificity of Xpert Ultra among those with no history of TB was $99.3\%$ ($95\%$ CI: 99.1–99.5). This was lower among those with a history of TB (current or past history of TB): $97.3\%$ ($95\%$ CI 96.1–98.0). ## Participants identified with TB There were a total of 180 participants with either a positive result by culture or Xpert Ultra. There were 108 culture TB positive (of which 72 were also positive by Xpert Ultra). Of the remaining 72 participants who were Xpert Ultra-positive alone, 33 were excluded as cases because they had a current or past history of TB, and the other 39 CXRs were re-examined by a three-person panel of which 24 were found to have CXRs suggestive of active TB disease. Therefore, a total of 132 participants had bacteriologically confirmed pulmonary TB based on the survey case definition: 36 ($27.3\%$) by culture alone, 72 ($54.6\%$) were positive by Xpert Ultra and culture; and 24 ($18.2\%$) by Xpert Ultra alone (no positive culture results) with a CXR suggestive of active TB disease and no current or past history of TB. A total of 6 survey participants were identified via Xpert Ultra as rifampicin resistant. Yield was highest among those detected via CXR screening which identified 125 participants with TB ($94.7\%$), followed by symptom screening which identified 49 others ($37.1\%$) (Table 3). CXR alone identified 78 participants with TB, symptoms alone identified 2, and 5 ($3.8\%$) others were CXR exempt and reported no symptoms. Of the 49 symptomatic participants with TB, 42 ($86\%$) reported a cough, 24 ($49\%$) reported weight loss, 16 ($33\%$) reported fever and 15 ($31\%$) reported night sweats. Table 3 \| Screening category \| Screen positive participants \| Participants with TB \| Participants with TB.1 \| Participants with TB.2 \| Participants with TB.3 \| Participants with TB.4 \| Participants with TB.5 \| Participants with TB.6 \| Participants with TB.7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Screening category \| Screen positive participants \| Total \| % \| HIV-positive* \| % \| HIV-negative \| % \| HIV-unknown \| % \| \| Symptoms only \| 1455 \| 2 \| 0.14 \| 0 \| 0 \| 2 \| 100 \| 0 \| 0 \| \| Abnormal CXR only \| 4190 \| 78 \| 1.9 \| 23 \| 29 \| 38 \| 49 \| 17 \| 22 \| \| Symptoms and abnormal CXR \| 1630 \| 47 \| 2.9 \| 15 \| 32 \| 27 \| 57 \| 5 \| 11 \| \| CXR exempt with no symptoms \| 309 \| 5 \| 1.6 \| 1 \| 20 \| 4 \| 80 \| 0 \| 0 \| \| Total \| 7584 \| 132 \| 1.7 \| 39 \| 30 \| 71 \| 54 \| 22 \| 17 \| ## HIV status A total of 16,092 ($74\%$) of the participants knew and were willing to inform their HIV status, and of those, 3862 ($18\%$) self-reported being HIV-positive. Complete HIV testing data were not available from the first 10 clusters as testing was initially conducted by an independent NGO that did not align with survey requirements. The survey team took over direct HIV testing thereafter. Of 17,031 participants tested for HIV in the survey, 3,915 ($23.0\%$) were positive (of these 3,505 self-reported as positive). In total, HIV status was known for 17,915 participants: 4,048 ($22.6\%$) were HIV-positive (of these 133 self-reported only and 3,915 tested in the survey). Of the 110 survey participants with known HIV status, 39 ($29.5\%$) were HIV-positive and 71 ($64.5\%$) were HIV-negative. More women than men were co-infected (17 participants, $48.6\%$ vs. 22 participants, $31.9\%$), and highest in the 25–44-year age groups (20 participants, $66.7\%$). The proportion of symptomatic survey participants by HIV status was similar (coinfected: $38.5\%$, $\frac{15}{39}$; HIV-negative: $40.8\%$, $\frac{29}{71}$) (Table 3). Of the 39 co-infected people, 30 already knew they were HIV-positive prior to the survey and were not currently being treated for TB. ## Estimated prevalence of bacteriologically confirmed pulmonary TB The weighted and adjusted prevalence of bacteriologically confirmed pulmonary TB among those≥15 years was 581 per 100,000 ($95\%$ CI: 466–696) (Table 4). Estimated prevalence was significantly higher in men than women and increased with age with notable peaks in the 35–44 and ≥65 years age groups. Prevalence was 2.5 times higher in those co-infected with HIV, compared with those who were HIV-negative. There was no difference in prevalence between geographical strata. The prevalence-to-notification (P:N) ratio was 1.22 overall, greater than 1.5 in those ≥55 years age groups, and higher for men than for women (Table 4). Table 4 \| Category \| Number of survey participants with TB \| Sex ratio (Male:Female) \| Estimated prevalence, cases per 100,000 population (95% CI) \| TB case notification rate per 100,000 in 2018, new and relapsed cases \| Prevalence to case notification ratio \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| All \| 132.0 \| 2.0 \| 581 (466–696) \| 476 \| 1.22 \| \| Sex \| \| \| \| \| \| \| Female \| 44.0 \| \| 327 (218–435) \| 331 \| 0.99 \| \| Male \| 88.0 \| \| 849 (642–1,057) \| 628 \| 1.35 \| \| Age \| \| \| \| \| \| \| 15–24 \| 7.0 \| 2.5 \| 142 (26–258) \| 127 \| 1.11 \| \| 25–34 \| 12.0 \| 1.4 \| 331 (147–516) \| 425 \| 0.78 \| \| 35–44 \| 25.0 \| 1.8 \| 892 (516–1,268) \| 619 \| 1.44 \| \| 45–54 \| 13.0 \| 3.3 \| 593 (267–920) \| 664 \| 0.89 \| \| 55–64 \| 28.0 \| 3.0 \| 1,211 (795–1,626) \| 808 \| 1.50 \| \| ≥65 \| 47.0 \| 1.6 \| 1,661 (1,143–2,178) \| 1061 \| 1.57 \| \| Strata \| \| \| \| \| \| \| Rural \| 85.0 \| 2.1 \| 670 (491–848) \| - \| - \| \| Urban \| 40.0 \| 1.7 \| 453 (310–595) \| - \| - \| \| Peri-urban \| 7.0 \| 2.5 \| 531 (329–732) \| - \| - \| \| HIV status* \| \| \| \| - \| - \| \| HIV-positive \| 39.0 \| 1.3 \| 1,272 (870–1,673) \| - \| - \| \| HIV-negative \| 65.0 \| 2.4 \| 504 (364–643) \| - \| - \| ## Health-seeking behaviour Of 1,825 participants who reported a cough, 916 ($50.2\%$) did not seek care for it; men (544, $59.4\%$), those living in rural areas (502, $54.8\%$), and those ≥65 years (180, $19.7\%$) were least likely to seek care. The most common reasons given for not seeking care were ignorance ($30\%$), not recognized as illness ($24\%$), and self-treatment ($27\%$); of note, high costs only accounted for $8.2\%$ of participants. More people with HIV and cough sought care than those without HIV and cough ($54.9\%$ vs $47.3\%$). Survey participants who did seek care for their symptoms primarily went to public health facilities ($86\%$), followed by pharmacies especially in urban areas ($5.8\%$), nongovernmental organizations ($4.4\%$) then private health facilities ($3.7\%$). Of the 49 participants with TB who had a cough, 19 ($39\%$) did not seek care. Of these 19, 9 ($47\%$) self-treated, 9 ($47\%$) did not recognize it as an illness or ignored it. Of the 30 participants with TB who had a cough and had sought care, 26 ($87\%$) visited a public health facility (and only 1 of the 26 were on current TB treatment at the time of the survey), and pharmacies and private practitioners accounted for the remainder. ## Discussion Lesotho is in WHO’s global list of high-burden countries for TB and HIV-associated TB, and the prevalence survey further confirms this [3]. The estimated prevalence of bacteriologically confirmed pulmonary TB in those aged ≥15 years, 581 per 100,000 population ($95\%$ CI: 466–696), is comparable to recent surveys conducted in neighbouring countries with also high co-infection burdens: Eswatini (352 per 100,000 population $95\%$ CI: 264–440), Mozambique (334, 252–416) South Africa (852, 679–1026), and Zambia (638, 502–774) [8–10]. Based on these survey data, post-survey incidence estimates were revised to 654 per 100 000 ($95\%$ CI: 406–959) by WHO; making it one of the highest in the world [11]. It is comparable to the pre-survey estimate of 2018: 611 per 100,000 ($95\%$ CI 395–872) [12]. Prevalence increased with age with a notable peak of more than $1\%$ in those greater than 55 years of age, and a secondary peak in the working age group of 35–44-year-olds. This bimodal distribution suggests that TB in Lesotho is marked by active transmission and suggests an epidemic driven by both HIV in the younger age groups and reactivation in older ones. Over the past decade, Southern Africa have seen large programmatic investments in HIV with concomitant declines in TB incidence. Between 2010 and 2017, Lesotho had an average annual rate of decline of $7\%$, with notifications falling at a similar rate during that time period [3]. Latest estimates from the 2020 population-based HIV impact assessment estimates that $90.1\%$ of adults living with HIV knew their status, $96.9\%$ of adults who were aware of their HIV status who were on ART, and $91.5\%$ of adults who were on ART had viral load suppression [4]. Nonetheless, despite these great gains, the burden of TB/HIV still remains incredibly high. It is of note that there were 30 co-infected people who were not diagnosed with TB prior to the survey; assuming they were in HIV care, this presents missed opportunities for TB screening and diagnosis. Needless to say, that the proportion of people with TB who are not infected with HIV is also high. Given the high ART coverage, PLHIV likely had more opportunities to access and engage with the health system, and therefore were more likely to have any TB suggestive symptoms detected and investigated, as evidenced by the higher TB/HIV co-infection rate reported by the NTP in 2019 ($62\%$) compared to the survey findings ($30\%$) [3]. Furthermore, more PLHIV in the survey sought care for their symptoms than those without HIV, therefore, greater effort is also required to identify those with TB who are HIV-negative. Like many other prevalence surveys, we also observed significant gender disparity in TB burden and in health-seeking behaviour related to TB care, showing greater reluctance among men to seek care when sick [13]. Among the survey’s confirmed participants with TB, the majority of those with symptoms who did not seek treatment were men. Combined with the findings that men had a burden more than twice that of females (especially in young and older men) and a high ratio of prevalence to notification in men, it highlights that Lesotho needs to identify social and structural determinants (e.g. mining, smoking, alcohol, undernutrition) that will assist in developing specific approaches to remove barriers and stigmas to case finding, increase equitable access to care, reduce delays in diagnosis, and improve the retention to effective management of TB among men. Overall, the survey identified a gap between TB prevalence and cases routinely notified to the NTP be it through underdiagnosis or underreporting. Notably, men had a lower proportion of being detected and reported with TB than women. Other gaps include those in the 35-to-44-year age group, and those aged above 55 years. The national programme will need to develop targeted strategies to reach men in general, but to also identify where people in these age groups are generally seeking care. Although we highlight the limitations and barriers of certain subgroups seeking care, another critical factor is the level of service quality especially in public health sectors. The survey identified more than $80\%$ of symptomatic participants diagnosed with TB that had visited a public health facility. An examination of the cascade of care is required to identify why most of these people, especially men, were not diagnosed [14]. The survey also showed that a significant proportion of TB participants did not report classical TB symptoms. Most were identified due to an abnormal screening CXR ($\frac{83}{132}$, $63\%$), therefore a large number of TB prevalent cases would have missed detection by using only the 4-symptom screen. Considering that Lesotho uses symptom screening for identification of those with presumptive TB, this survey highlights that excluding CXR screening potentially misses a large proportion of TB cases, reinforcing the urgent need for the local adaptation of recent recommendations by WHO for routine CXR use as a sensitive TB screening tool in active case-finding [15]. This issue is similar to other TB prevalence surveys where a substantial proportion of identified people with TB are subclinical i.e., bacteriologically confirmed for TB but do not report screening symptoms, and potentially driving transmission [16]. One of the survey limitations relates to the performance characteristics of Xpert Ultra when used for active case finding. As shown in various reviews, assuming culture is the reference standard in this survey, specificity of Xpert Ultra was lower in those with a history of TB than those without [17, 18]. Therefore, the survey case definition was quite conservative in restricting cases to only those with enough evidence to minimize the test’s effect i.e., Xpert Ultra-positive with no culture confirmation, no history of TB and a CXR suggestive of active TB disease. Given the historical cumulative burden of TB in the country (and others in the region), this will continue to affect the use of Xpert Ultra in active case finding projects. This does not assume culture was perfect either. Given the challenges of geography, transportation of specimens under cold chain may impact culture performance especially given the paucibacillary nature of specimens collected via active case finding. The contamination rate was only $4.9\%$ suggesting potential harsh decontamination of specimens. Therefore, it may be possible that some Xpert Ultra-positive only results could have had a confirmatory culture e.g., those with a high or medium Xpert Ultra grade. Despite the diagnostic challenges, estimated prevalence, albeit based on a conservative case definition, was still inextricably high. The other major limitation was not knowing the HIV status for all survey participants from the first 10 clusters. HIV results were not available at the time of analysis because initially we relied on a Government partner to provide HIV results, but that strategy did not work. Assuming that the proportion of PLHIV were the same for all clusters, estimates of TB/HIV prevalence were conservative and still very high. A repeat TB prevalence survey (implemented after 8–10 years) could be an excellent source of information to show gaps in disease detection and burden trends over time. However, prevalence surveys provide sufficient data to allow estimates of the burden at the national or provincial levels, but they do not provide enough data for decision making at districts or levels below. With this and other limitations in mind, including the huge cost of a single TB prevalence survey, it is critical to strengthen the national TB surveillance system and use different approaches to identify gaps in disease detection and reporting (e.g. inventory studies, capture-recapture studies, etc.) [ 19]. ## Conclusions The Lesotho TB prevalence survey is one of the first where both Xpert Ultra and culture were used in parallel to establish TB prevalence at the national level. It is clear that Lesotho continues to have a very high burden of TB with many undetected cases in the community, and that HIV remains a major driver of the TB epidemic. The national TB programme will need to work closely with HIV programme in order to optimize screening and diagnostic opportunities; develop strategies to increase healthcare seeking especially among those with symptoms; and undertake greater utilization of chest X-ray facilities (with concomitant training of more in-country radiologists) for active case finding activities. The other key challenges relate to gender disparity, community knowledge of TB, and access to quality diagnostics and human resources for faster and more effective case finding, patient-centered treatment and support. ## References 1. 1Bureau of Statistics, Lesotho [Available from: https://www.bos.gov.ls]. 2. UNDP. Lesotho Country Analysis, Working Document Final Draft (2017.0) 3. 3World Health Organization. Global Tuberculosis Report. Geneva, Switzerland; 2021.. Global Tuberculosis Report (2021.0) 4. ICAP. The Lesotho Population-based HIV Impact Assessment (2020.0) 5. 5World Health Organization. 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--- title: 'In-hospital stress and patient outcomes: A systematic review and meta-analysis' authors: - Daniel M. Ford - Luke Budworth - Rebecca Lawton - Elizabeth A. Teale - Daryl B. O’Connor journal: PLOS ONE year: 2023 pmcid: PMC9997980 doi: 10.1371/journal.pone.0282789 license: CC BY 4.0 --- # In-hospital stress and patient outcomes: A systematic review and meta-analysis ## Abstract ### Background Hospital inpatients are exposed to high levels of stress during hospitalisation that may increase susceptibility to major adverse health events post-hospitalisation (known as post-hospital syndrome). However, the existing evidence base has not been reviewed and the magnitude of this relationship remains unknown. Therefore, the aim of the current systematic review and meta-analysis was to: 1) synthesise existing evidence and to determine the strength of the relationship between in-hospital stress and patient outcomes, and 2) determine if this relationship differs between (i) in-hospital vs post-hospital outcomes, and (ii) subjective vs objective outcome measures. ### Methods A systematic search of MEDLINE, EMBASE, PsychINFO, CINAHL, and Web of Science from inception to February 2023 was conducted. Included studies reported a measure of perceived and appraised stress while in hospital, and at least one patient outcome. A random-effects model was generated to pool correlations (Pearson’s r), followed by sub-group and sensitivity analyses. The study protocol was preregistered on PROSPERO (CRD42021237017). ### Results A total of 10 studies, comprising 16 effects and 1,832 patients, satisfied the eligibility criteria and were included. A small-to-medium association was found: as in-hospital stress increased, patient outcomes deteriorated ($r = 0.19$; $95\%$ CI: 0.12–0.26; I2 = 63.6; $p \leq 0.001$). This association was significantly stronger for (i) in-hospital versus post-hospital outcomes, and (ii) subjective versus objective outcome measures. Sensitivity analyses indicated that our findings were robust. ### Conclusions Higher levels of psychological stress experienced by hospital inpatients are associated with poorer patient outcomes. However, more high-quality, larger scale studies are required to better understand the association between in-hospital stressors and adverse outcomes. ## Introduction Psychological stress is known to adversely influence health and wellbeing by causing negative changes in mental health outcomes and multiple physiological processes [1]. More specifically, stress has been shown to play a detrimental role in immune system dysfunction [2, 3], cardiovascular disease, coronary heart disease, and stroke [4]. In response to stressful encounters (‘stressors’), the body veers from its homeostatic state, adjusting physiological parameters and releasing endocrinological mediators such as cortisol (the so-called “stress hormone”). This process of adapting is necessary for survival and is known as allostasis (“remaining stable by being variable” [see 5]). However, with prolonged exposure to stress, the body experiences excessive “wear and tear” from an inefficient management of stress mediators; a concept known as allostatic load [1, 6]. When this load becomes too great, the body experiences deleterious effects; a concept known as allostatic overload [7; see 8]. Allostatic overload is theorised to be the cause of post-hospital syndrome (PHS); an acquired period of generalised vulnerability to adverse events (e.g., post-operative wound infection) following hospitalisation [9]. Indeed, in some prominent conditions, only a third of all post-discharge readmissions (a proxy for poor post-hospital outcomes) were the same as that of the index admission [10]. This is even lower still for some conditions: the 30-day readmission for patients hospitalised due to acute myocardial infarction is approximately one in six [11], where only $10\%$ of those readmissions were for a subsequent myocardial infarction [12]. Consequently, Krumholz [9] suggests that we should view the post-discharge period as a generalised syndrome of physiological impairment, rather than a routine recovery specific to the initial ailment. More recently, the theorised, causal relationship between allostatic overload and PHS has been elaborated on by Goldwater and colleagues [13]. These authors have outlined several “hospital-related stressors” that are likely catalysts of allostatic overload: sleep disruption, malnourishment and dehydration, mobility restriction, and pain. However, this list is by-no-means comprehensive, there exists an unknown (and likely vast) number of these stressors, for example: loss of control [14], mental distress [15], equipment visibility [16], lack of light and nature [17], and, perhaps the most salient of all, relationships with staff [18, 19]. The combination of these stressors may make for an unpleasant experience for inpatients in their already vulnerable states [e.g., 20]. Indeed, it follows that, if stress causes deleterious effects, and if hospital stays expose patients to an assortment of stressors, then hospitalisation may be contributing to these adverse patient outcomes (this is the essence of PHS). Previous research has characterised hospitalisation as a traumatic event [e.g., 21, 22], even resembling interrogation [23], and has recorded that patient-reported hospital experiences are potentially associated with patient outcomes [22, 24–27]. In fact, regardless of stress, hospitalisation may be damaging for patients (particularly older adults), being a likely risk factor for cognitive decline [28, 29], functional decline [30–32], decompensated frailty [33], and new iatrogenic disability [34, 35]. Therefore, taken together, there is an immediate need for us to improve our understanding of in-hospital stress, and its effects on in-hospital and post-hospital patient outcomes. At present, the literature has not identified the strength of the relationship between in-hospital stress and patient outcomes. The current systematic review and meta-analysis will aim to do this by synthesising the existing evidence base of studies that have investigated the relationship between in-hospital stress–whereby stress is perceived and appraised by the patient during their hospital stay–and an in-hospital and/or post-hospital patient outcome. ## Research aims The current review aims to synthesise the existing evidence base to determine the strength of the relationship between in-hospital stress and patient outcomes–broad definitions of these two variables are offered below. Secondary aims are to uncover whether the magnitude of this relationship differs between groups of outcomes: (i) in-hospital vs post-hospital, (ii) subjective (patient-reported) vs objective, and (iii) by study quality. ## In-hospital stress O’Connor and Ferguson [36] describe three approaches that have been used in studying stress: the stimulus-based approach; the response-based approach; and the psychological interactional-appraisal approach. The latter is also known as the transactional model approach and has been defined as “a particular relationship between the person and the environment that is appraised by the person as taxing or exceeding his or her resources and endangering his or her well-being” [37 p. 19]. This appraisal is postulated to have two dimensions: a primary and secondary appraisal [37]. A primary appraisal evaluates the risks, demands, or challenges of a situation, while a secondary appraisal evaluates the availability of perceived resources and whether anything can be done to alter the outcome of the situation. Therefore, should two persons experience the same noxious event one person may appraise the situation as stressful (depending on the extent to which they perceive that they can meet its demands), while the other may not. Moreover, central to the transactional model approach is the notion that stress is a psychological construct that only arises when there is a mismatch between primary and secondary appraisal. Therefore, in the current review, in keeping with this approach, we will include any measure of stress that is perceived or appraised by a patient during their hospital stay. ## Patient outcomes Outcomes following hospitalisation are varied; individual, specialty measures alone are not sufficient to gauge a patient’s recovery. In their call for standardised patient outcomes, Porter and colleagues [38] postulate that patients are most concerned with the health status achieved, time, complications, suffering involved, and sustainability of benefits. For this reason, the current review will conduct a holistic approach to measuring hospital-related outcomes, under the umbrella term of patient outcomes. These outcomes will be sorted into two categories: subjective (e.g., self-rated, such as quality of life or pain) and objective (e.g., patient records, such as length of stay or readmission). ## Methods The current review adhered to the PRISMA guidelines for the reporting of systematic reviews and meta-analyses [39]. ## Eligibility criteria Eligible studies were quantitative and included a measure of both: (i) in-hospital stress, whereby psychological stress was perceived and appraised by the patient during their hospital stay, and (ii) in-hospital and/or post-hospital patient outcome(s). Distress, measures of stress that did not include a perceived appraisal (e.g., cortisol levels), and studies focussing exclusively on participants with a psychiatric disorder (e.g., PTSD) were not included. Patient outcomes included clinical assessments, Patient-Reported Outcome Measures (PROMs; as defined by the Cochrane Handbook, Chapter 18 [40]), and patient records denoting quality of care (e.g., length of stay and readmission). Patient satisfaction was also included in the current review, as it has been included in previous systematic reviews measuring patient outcomes [e.g., 41–43], as well as the patient-reported outcomes chapter of the Cochrane Handbook, cited above. Routine in-hospital assessments (e.g., heart rate, body temperature, etc.), however, were not considered patient outcomes for the purpose of this review, as they are more likely to be markers of poor health, rather than an ailment in themselves. Similarly, Patient-Reported Experience Measure (PREM [e.g., 44]) were not included. Participants in the eligible studies were adults (18 years or older) that were hospital inpatients at the time in-hospital stress was measured. If the period spanned by the stress measure (e.g., “indicate how much each item has applied to you over the past week”) covered more of the pre-hospital period than in-hospital period, then it was not included. ## Search strategy Five databases were searched from inception to present: Medline, Embase, PsycINFO, CINAHL, and Web of Science. The search was first conducted on 5th July 2021 and updated on 2nd February 2023; and was limited by (i) English language, (ii) human studies, (iii) adults (18+ years), and (iv) peer-reviewed articles. All titles and abstracts were screened by the first author (D.F.), $20\%$ of which were independently screened again (L.B.); any discrepancies were resolved via discussion. This process was repeated for full texts, with a third reviewer (D.OC or R.L) consulted where there was ambiguity or lack of agreement. Details of the protocol for this review were preregistered on PROSPERO (CRD42021237017), which can be accessed at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=237017. ## Search terms The method of formulating search terms was adapted from the PICO framework [45] as shown in Table 1. Indexing terms were adapted as necessary for use in the databases searched (see Appendix A in S1 File for a full list of search terms for each database). Table 1 \| Population: \| Adult inpatients \| \| --- \| --- \| \| Intervention (Exposure): \| In-hospital stress \| \| Comparison: \| Not applicable \| \| Outcome: \| Patient outcomes \| Outcome search terms were informed by several recently published systematic reviews measuring patient outcomes and using the same databases as the current review [e.g., 41–43]. These were amalgamated after the removal of unwanted terms: i.e., terms specific to these systematic reviews (e.g., “medication system errors”) and those pertaining to routine in-hospital assessments (e.g., “blood pressure”). Post-hospital syndrome was considered a principal term to include as an outcome, and so was added to each search as a keyword. ## Data extraction Data was extracted by D.F. and comprised: author, year, study design, recruitment method, country, sample size, age, sex, reason for treatment, length of stay, number of previous hospital stays, measure of in-hospital stress (including time frame of stress experienced, e.g., “in the past month”), and patient outcome measure (including length of follow-up). Where multiple patient outcome measures were present in a study, discussion between three of the reviewers (D.F., D.OC. R.L.) took place to determine which measure(s) was (were) most appropriate to include. For experimental studies, only control data was used. Where pre- and post-hospitalisation patient outcome measures were recorded, post-measures were chosen as these were more representative of the hospital period. In-hospital patient outcomes measured at the same time as in-hospital stress were not included, as the nature of the causal relationship was unclear (e.g., pain measure in study by Volicer [46]). ## Quality assessment The Effective Public Health Practice Project (EPHPP) quality assessment tool for quantitative studies was employed. This tool was chosen over others as it is more appropriate for observational studies, while other options (e.g., Cochrane Risk of Bias tool) are more appropriate for randomised controlled trials. Each study was assessed on its design, method, and analysis, which informed an overall rating of the paper as "strong", "moderate", or "weak". Papers deemed as "weak" were not excluded from the overall analysis; rather, a subgroup analysis was conducted comparing the magnitude of association in these papers against those rated as “strong” or “moderate”. All eligible studies were assessed by D.F. and L.B. using the chosen tool (See Appendix B in S1 File for individual assessment scores). ## Data analysis Each study identified for inclusion in the review was inspected for research design, country, sample, stress measure, and patient outcome(s); these data were extracted and systematically recorded. Meta-analyses were conducted using R Studio (version 4.1.3) [47] (all packages and code used are included in Appendix C in S1 File), employing random-effects modelling via the metafor package [48]. As we expected most of the eligible studies to employ a correlational design, we chose Pearson’s r as the pooled effect size metric (using Fisher z to r back-transformation method), where $r = 0.10$, 0.30, and 0.50 were considered small, medium, and large, respectively [49]. Unadjusted correlations were chosen over adjusted if both were provided in the paper. Where other statistics were reported, r was estimated using the Campbell Collaboration Effect Size Calculator [50]. Three sub-group analyses were planned a priori to address the secondary research questions. Sub-groups were split by (i) strong and medium vs weak quality, (ii) in-hospital vs post-hospital outcomes, and (iii) subjective vs objective outcome measures. A meta-regression calculated whether the pooled effects of these sub-groups were significantly different. Meta-regression was also used to explore whether age and sex were significant covariates of the relationship between in-hospital stress and patient outcomes. Heterogeneity was assessed with Cochran’s Q statistic and related I2 statistic. Funnel plots were generated, and Egger’s regression [51] was calculated to test for asymmetry, which assessed the risk of small study bias: an indicator of publication bias [52]. A selection model [53] was also calculated to directly assess the risk of publication bias. All analyses were subject to leave-one-out sensitivity analyses [54] to observe how each study influenced the overall model. Any studies indicated as disproportionately influencing the model were excluded, with reason offered as to why the result of the study in question may be inaccurate. ## Results Initial systematic searching yielded 2,227 records, plus three records identified through Google Scholar during the scoping review and feasibility stage, before the formal database search commenced. Following the PRISMA screening process guidelines [39], 10 studies remained for inclusion in the systematic review [46, 55–63], comprising 1,832 participants (Fig 1). All 10 studies were also suitable for meta-analysis; some studies did not record data for all variables we wished to extract (e.g., length of stay; see Table 2). There was $100\%$ agreement between the two authors screening (D.F. and L.B.) on which studies to include and their quality assessment scores. Fig 1: PRISMA flow diagram presenting an overview of the selection process. TABLE_PLACEHOLDER:Table 2 Studies were conducted in the following countries: four in the United States [46, 55, 58, 61], two in Australia [62, 63], one in Greece [59], one in India [57], one in Iran [56], and one in Turkey [60]. Studies were of varied design: four cross-sectional [55, 56, 59, 60], three cohort [46, 62, 63], one cohort analytic [58], one controlled clinical trial [57], and one randomised controlled trial [61]. All studies used convenience sampling, recruiting sample sizes of 91 to 535 across nine cohorts. However, it is important to note, only five of these 10 studies sought explicitly to address our research question [46, 55, 59–61]; while the other five studies assessed stress while in hospital, though this was not the main aim of the study. A variety of scales were used to measure stress while in hospital: three studies used the Depression, Anxiety and Stress Scale (DASS [64]), two studies used the Hospital Stress Rating Scale (HSRS [19]), one used the Perceived Stress Scale (PSS [65]), one used the Stress Arousal Checklist (SACL [66]), one used the Intensive Care Unit Environmental Stressor Scale (ICUESS [67]), one used a single-item interview question, and one used a three-item questionnaire. Within the 10 studies, there were 16 patient outcomes. Similarly, these measures were varied; three measured length of hospital stay, two measured satisfaction of care (rated 1–10 in study by Ahmadi [55]; ENCS [68]), two related to subjective health (rated 1–100 in study by Karademas [59]; Recovery Inventory [69]), two to quality of life (EQ-5D; [70]; WHOQOL-BREF [71]), two were self-rated pain measures (using numeric pain rating scales), two were incidence of readmission, one focussed on return to usual activities (rated 0–5 in study by Volicer [46]), one reported incidence of atrial fibrillation, and one used a spinal cord independence measure (SCIM [72]). An unadjusted correlation was attained for each of the outcomes with their respective stress measures, with all 16 effects reporting in their predicted directions; nine of which reached statistical significance. As all effect sizes presented in their predicted directions (adverse outcomes correlated positively with stress; beneficial outcomes correlated negatively), it was possible to group both adverse and beneficial patient outcomes, temporarily ignoring the direction of the effect and focussing only on the magnitude. The random-effects model revealed a medium-sized, significant relationship between in-hospital stress and patient outcomes ($r = 0.27$; $95\%$ confidence interval [CI], 0.12–0.41; $$n = 1$$,832; $p \leq 0.001$), with considerable heterogeneity (I2 = $92.7\%$, $p \leq 0.001$). However, one effect size was identified as an influential outlier, disproportionately influencing heterogeneity [74], and so was excluded from the remainder of the meta-analysis. The outlier was identified as quality of life in the study by Chalageri and colleagues [57], which was a near-perfect correlation ($r = 0.84$). We suspect that this is due to the two correlated measures quantifying similar constructs. The remaining 15 correlations were suitable to be included in the full meta-analysis. The second random-effects model (Fig 2) revealed a small-to-medium, statistically significant relationship ($r = 0.19$; $95\%$ CI: 0.12–0.26; $$n = 1$$,832; $p \leq 0.001$), with moderate heterogeneity (I2 = 63.6, $p \leq 0.01$). As meta-analyses assume effect size independence, the use of robust variance estimation (RVE [75]) was necessary to account for within-subject statistical dependencies of studies that reported multiple outcomes. No notable differences were identified between the RVE model ($r = 0.19$; $95\%$ CI: 0.09–0.30; $p \leq 0.01$) and unadjusted model (Fig 2), indicating that effect size dependencies were not disproportionately influencing the model. Fig 2: In-hospital stress on patient outcomes: A forest plot of correlation coefficients within the included studies. ## Sub-group analyses Three pairs of models were produced, two of which addressed the secondary research questions, and the other observing the effect of study quality (Table 3). A statistically significant difference was reported for both of the relationships between in-hospital stress and (i) in-hospital versus post-hospital patient outcomes, and (ii) subjective verses objective outcome measures. In-hospital patient outcomes correlated more strongly with in-hospital stress than did those measured post-hospital (QM = 4.23, $$p \leq 0.04$$). Similarly, the effect was larger for subjectively measured patient outcomes than those measured objectively (QM = 10.77, $p \leq 0.001$). However, no significant difference was found in the effect sizes reported in strong and moderate studies versus weaker ones (QM = 2.19, $$p \leq 0.14$$). Table 3 \| Sub-groups of patient outcomes \| Correlation (0 < r < 1) \| 95% CI \| Number of studies \| p value \| Heterogeneity \| Heterogeneity.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Sub-groups of patient outcomes \| Correlation (0 < r < 1) \| 95% CI \| Number of studies \| p value \| I 2 \| p \| \| In-hospital \| 0.25 \| [0.15; 0.34] \| 10 \| < 0.001 \| 54% \| 0.02 \| \| Post-hospital \| 0.13 \| [0.06; 0.19] \| 5 \| < 0.001 \| 46% \| 0.13 \| \| QM = 4.23, p = 0.04 \| QM = 4.23, p = 0.04 \| QM = 4.23, p = 0.04 \| QM = 4.23, p = 0.04 \| QM = 4.23, p = 0.04 \| QM = 4.23, p = 0.04 \| QM = 4.23, p = 0.04 \| \| Subjective \| 0.27 \| [0.19; 0.36] \| 8 \| < 0.001 \| 60% \| 0.02 \| \| Objective \| 0.08 \| [0.02; 0.14] \| 7 \| < 0.01 \| 0% \| 0.78 \| \| QM = 10.77, p < 0.001 \| QM = 10.77, p < 0.001 \| QM = 10.77, p < 0.001 \| QM = 10.77, p < 0.001 \| QM = 10.77, p < 0.001 \| QM = 10.77, p < 0.001 \| QM = 10.77, p < 0.001 \| \| Strong/Moderate \| 0.14 \| [0.07; 0.21] \| 6 \| < 0.001 \| 46% \| 0.09 \| \| Weak \| 0.24 \| [0.14; 0.34] \| 9 \| < 0.001 \| 61% \| < 0.01 \| A meta-regression was conducted to determine whether age and sex influenced the correlation between in-hospital stress and patient outcomes. Neither sex (β = -0.0003, $$p \leq 0.86$$) nor age (β = -0.008, $$p \leq 0.08$$) were identified as significant covariates. However, all but one of the studies reported mean age within a restricted range, between 52.0–68.0 years, and so this estimate may be inaccurate due to a lack of statistical power. It was not possible to test if length of stay (or other similar variables, such as number of previous hospital stays) was a significant covariate, as not enough studies reported this value, and some studies included length of stay as an outcome. ## Sensitivity analyses The presence of publication bias was investigated. Egger’s regression test was not statistically significant ($$p \leq 0.176$$), suggesting that there was no presence of small-study bias. However, a funnel plot of standard errors (Fig 3) showed that three studies may be missing to the left of the mean; this was supported by a Trim and Fill analysis [76], which shifted the x-intercept to the left by 0.041 (i.e., the pooled effect size decreased from: $r = 0.191$ to $r = 0.150$). Fig 3: Funnel plot (left) with trim and fill applied (right). A selection model was calculated to directly address publication bias by giving more weight to effect sizes that were not statistically significant. A Likelihood Ratio test was then conducted, which indicated that the selection model ($r = 0.20$; $95\%$ CI, 0.10–0.30; $$n = 1$$,832; $p \leq 0.001$) was not significantly different to the unadjusted model, suggesting that there was no evidence of publication bias (X2 = 0.054, $$p \leq 0.816$$). Systematically removing one of the 15 study correlations at a time, via leave-one-out analysis, indicated that no single effect size was disproportionately contributing to the model. The pooled effect size ranged from: $r = 0.173$ (-0.018) to $r = 0.205$ (+0.014), with each model remaining significant ($p \leq 0.001$). ## Discussion The current review synthesised findings from 10 diverse studies that reported a measure of in-hospital stress and at least one patient outcome. A statistically significant association was identified between the two variables, consistent with previous systematic reviews observing the association between stress and health outcomes–including wound healing [77], cardiovascular disease [78], and poorer health outcomes generally [8, 79, 80]. However, the current systematic review is the first of its kind to look at patients’ psychological stress specific to the in-hospital period; where the stressors are more numerous, and the body more vulnerable. In these unadjusted analyses, a small-to-medium negative association was found, suggesting that as in-hospital stress increased, patient outcomes deteriorated, though no inference about causality can be made. The association was significantly stronger for subjective than objective outcome measures. This difference may be due to sources of information bias within the subjective measures, such as self-report bias and confirmation bias [81]. Indeed, these biases may also be compounded by common method variance [82]. Additionally, the observed differences are likely, in part, a result of the disparate nature of the two groups of measures. Subjective and objective measures in these studies tended to assess different types of outcomes; while subjective measures pertained to more complex and dynamic outcomes such as quality of life and subjective health, objective measures pertained more to outcomes associated with healthcare resource use such as length of stay and readmission. Nevertheless, the association between in-hospital stress and patient outcomes, albeit small, gives credence to Goldwater and colleagues’ [13] theory that hospital-related allostatic overload may be a plausible aetiology of PHS [9]. Similarly, the association was significantly stronger for in-hospital patient outcomes than those measured post-hospital. Patients assessed in the post-hospital period are no longer exposed to in-hospital stressors, and so may not be experiencing the effects of PHS as acutely as their in-hospital counterparts as time has elapsed since the initial stressor exposure. However, other explanations for this difference in strength are the presence of case-mix (i.e., the differing types of patients treated) and the possibility that in-hospital stress is acting as a proxy for other associated and unmeasured confounding variables within the included studies. This may then be aggregated, again, by the disparate nature of the measures used to assess patient outcomes at the in-hospital versus post-hospital periods. Meta-regression identified that neither sex nor age were statistically significant covariates; although, it is important to note that statistical power was too weak to draw any concrete conclusions. Other potential covariates, such as length of stay and number of previous hospital stays, were similarly not calculated on account of the limited number of studies. Previous literature would suggest that age is a significant covariate, where the association between stress and health increases with age. In their recent systematic review, Guidi and colleagues [8] outlined that allostatic overload, in older adults, is associated with frailty [83], cognitive and physical decline [e.g., 84–86], delirium [87], and risk of mortality [88]. Therefore, it is important that the role of age, in the context of in-hospital stress and patient outcome relations, is further investigated. The results of the current systematic review and meta-analysis indicate that patient outcomes may be, in part, a function of the stress experienced by patients during their hospital stay. Should this relationship be investigated further, and causality is shown to be likely, emphasis should be placed on the need for (i) an increased focus on reducing the need for hospital admissions and (ii) greater attention to reducing the stress experienced by patients during their hospital stay. These actions must be culturally sensitive, and address healthcare at the individual and system levels [89]. If causation were to be established, reducing in-hospital stress could be a cost-effective strategy for healthcare providers, given the association with longer stays and readmissions. The first logical step in this process would be to identify the specific aspects of hospitalisation that cause patients the most stress, such as the hospital-related stressors outlined by Goldwater and colleagues [13]. With this knowledge, appropriate policies and interventions can be implemented to reduce in-hospital stress, which may then lead to less adverse patient outcomes. ## Strengths and limitations Our findings must be interpreted within the context of the limited academic literature. Consequently, the current review included relatively few articles, and reported on a variety of patient conditions, in-hospital stress measures, and patient outcome measures, which complicated attempts to make fair comparisons between studies. For example, incidence of atrial fibrillation (Tully et al., 2011) is an unusual outcome measure, and unlike any of the other outcomes included. Despite this, heterogeneity values were only moderate, leave-one-out analysis identified no statistical outliers, and every association within the included studies presented in their predicted directions–as in-hospital stress increased: beneficial patient outcomes (e.g., physical status, quality of life, etc.) deteriorated; and adverse patient outcomes (e.g., pain, readmission, etc.) increased. Within the included studies, only half sought to address the research question of the current review, while the remaining articles were not specific to stress attributable to hospitalisation (though they did measure stress during the patients’ hospital stays). Further, most of the included studies were deemed of weak quality, and only one was deemed strong. Most of the studies were cross-sectional and utilised either a correlational or non-randomised cohort design. Samples within the included studies were also limited in their ability to represent the wider population; all studies employed convenience sampling, most of which were limited to one-to-two wards within a single hospital. Evidently, more high-quality studies are essential to draw conclusions with sufficient confidence; these studies would ideally be large-scale, longitudinal, and randomised, using an agreed upon measure (e.g., HSRS) across multiple wards and hospitals. Finally, despite only including studies where in-hospital stress was measured before the patient outcome, the presence of bidirectional relationships is entirely conceivable. For example, a patient may have had high levels of pain at the beginning of their hospital stay–at the time stress was measured–which would likely inflate the stress score, this pain would then be measured later, and assumed to be high due to an inflated stress score. In essence, in-hospital stress could be argued to be, at least in part, a proxy measure for a host of (undoubtedly stressful) factors that are antecedents to poorer patient outcomes, or even patient outcomes themselves. This ambiguity could have been partially accounted for by controlling for potential confounding variables (e.g., severity of illness), of which, few of the included studies measured. Alternatively, the design of more randomised controlled trials attempting to reduce in-hospital stress and measure patient outcomes [e.g., 61]. ## Conclusion This systematic review and meta-analysis found a small-to-medium relationship between in-hospital stress and a variety of patient outcomes. The association was stronger for in-hospital than post-hospital outcomes, and subjective than objective outcome measures. Our findings are comparable to other systematic reviews exploring the relationship between stress and health outcomes. 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--- title: Chronic kidney disease, preoperative use of antispasmodics and lower resected prostate volume ratios are risk factors for postoperative use of adrenergic Alpha-blockers and antispasmodics authors: - Chen-Hsun Hsueh - Li-Wen Chang - Kun-Yuan Chiu - Sheng-Chun Hung - Jun-Peng Chen - Jian-Ri Li journal: PLOS ONE year: 2023 pmcid: PMC9997983 doi: 10.1371/journal.pone.0282745 license: CC BY 4.0 --- # Chronic kidney disease, preoperative use of antispasmodics and lower resected prostate volume ratios are risk factors for postoperative use of adrenergic Alpha-blockers and antispasmodics ## Abstract ### Objectives Transurethral resection of prostate (TURP) and laser prostate surgery are common surgeries for benign prostate hyperplasia (BPH). We conducted an investigation using hospital database to evaluate the clinical factors associated with post-operative usage of alpha-blockers and antispasmodics. ### Methods This study was conducted using retrospective clinical data from the hospital database, which contained newly diagnosed BPH patients between January 2007 and December 2012 who subsequently received prostate surgery. The study end-point was the use of alpha-blockers or antispasmodics for at least 3 months duration after 1 month of surgery. The exclusion criteria was prostate cancer diagnosed before or after the surgery, recent transurethral surgeries, history of open prostatectomy, and history of spinal cord injury. Clinical parameters, including age, body mass index, preoperative prostate specific antigen value, comorbidities, preoperative usage of alpha-blockers, anstispasmodics and 5-alpha reductase inhibitors, surgical methods, resected prostate volume ratios, and preoperative urine flow test results, were evaluated. ### Results A total of 250 patients receiving prostate surgery in the database and confirmed pathologically benign were included. There was significant association between chronic kidney disease (CKD) and the usage of alpha-blockers after prostate surgery (OR = 1.93, $95\%$ CI 1.04–3.56, $$p \leq 0.036$$). Postoperative antispasmodics usage was significantly associated with preoperative usage of antispasmodics (OR = 2.33, $95\%$ CI 1.02–5.36, $$p \leq 0.046$$) and resected prostate volume ratio (OR = 0.12, $95\%$ CI 0.02–0.63, $$p \leq 0.013$$). ### Conclusions BPH patients with underlying CKD were more likely to require alpha-blockers after surgery. In the meantime, BPH patients who required antispasmodics before surgery and who received lower prostate volume resection ratio were more liable to antispasmodics after prostate surgery. ## Introduction Benign prostate hyperplasia (BPH) is a common problem in elder patients, with a prevalence of over $50\%$ in male population aged over 50 years [1]. BPH may lead to prostate enlargement that obstructs bladder neck, which further causes lower urinary tract symptoms (LUTS). Symptomatic LUTS can be manifested as storage symptoms and voiding symptoms, and may result in decreased quality of life as well as various complications [2]. Treatment of BPH-induced LUTS is indicated to relieve symptoms and prevent complications. The treatment begins with lifestyle modification and medical treatment, and may proceed to surgical intervention if initial treatments fail. Most patients present with general improved LUTS symptoms after surgery [3,4]. However, some patients still require medical treatment, especially adrenergic alpha-blockers and antispasmodics, after surgery due to recurrent BPH or persistent LUTS symptoms [5,6]. Obviously, there may be some different characteristics in this patient group that hinder the effects of surgical interventions. Further investigation of this patient group is essential to provide better treatment suggestion in advance. In this study, we aim to identify BPH patients who require adrenergic alpha-blockers and antispasmodics for at least three months after receiving surgery using preoperative and perioperative data. ## Database Our database was a web-based system that recorded clinical information of patients receiving medical care in Taichung Veterans General Hospital (VGHTC) since January 2000. It included diagnosis, operation notes, results of examinations from outpatient departments, emergency departments and hospitalizations, as well as records of prescribed medicine. The patient data was de-identified before our analysis for privacy concern. ## Patient selection and treatments Patients who were newly diagnosed BPH between January 2007 and December 2012, and subsequently received prostate surgery were included. The ICD code for BPH was ICD-9-CM 600. The exclusion criteria included prostate cancer diagnosed before or after surgery, transurethral incision or resection within a year before surgery, history of open prostatectomy, and history of spinal cord injury. History of prostate cancer was identified by medical records, preoperative transurethral biopsy results, and biopsy retrieved peri-operatively. All patients included were followed up to December 2021 for last hospital visit or death. ## Study process This study was conducted using retrospective clinical data from VGHTC database, and was approved by institutional review board with No. CE21221A. The study endpoints included the usage of adrenergic alpha-blockers and antispasmodics after surgery for at least three months. Most patients returned back to the outpatient clinic one week after discharge and were followed up every three months afterward. The cut-off of minimum three months was identical to previous study, and was determined based on the fact that some patients may require short-term medications for LUTS [6]. The adrenergic alpha-blockers and antispasmodics prescribed within a month postoperatively were neglected since these medicines prescribed then may be for operation-related symptom relief. Data collected were classified into preoperative data and perioperative data. Preoperative data included age, body mass index (BMI), prostate specific antigen (PSA), comorbidities (hypertension, ICD9 401–405, ICD10 I10-I16; diabetes mellitus [DM], ICD9 250, ICD 10 E08-E13; ischemic heart disease, ICD9 410–414, ICD10 I20-I25; cerebrovascular disease, ICD9 430–438, ICD I60-I69; hyperlipidemia, ICD9 272, ICD10 E78; chronic obstructive pulmonary disease [COPD], ICD9 490–496, ICD10 J40-J47; peripheral vascular disease, ICD9 440–449, ICD10 I70-I79; chronic kidney disease [CKD], ICD9 585, ICD10 N18; sleep disorder, ICD9 327, ICD10 G47; gout ICD9 274, ICD10, M10), history of prostate surgery, urodynamic testing results, and the usage of adrenergic alpha-blockers, 5-alpha reductase inhibitors (5-ARIs), and antispasmodics before surgery. The age and BMI data was collected at time of operation. PSA level was collected within a year before surgery. Urodynamic testing included maximum flow rate, average flow rate, voided volume and post-void residual volume, and was performed within a year preoperatively. The adrenergic alpha-blockers, 5-ARIs and antispasmodics were coded using anatomical therapeutic chemical classification. The adrenergic alpha-blockers and 5-ARIs included Doxazosin 2mg and 4mg (C02CA04), Alfuzosin 10mg (G04CA01), Tamsulosin 0.2mg and 0.4mg (G04CA02), Terazosin 1mg and 2mg (G04CA03), Silodosin 4mg (G04CA04), Tamsulosin plus Dutasteride 0.4mg/0.5mg (G04CA52), Dutasteride 0.5mg (G04CB02), Finasteride 5mg (G04CB01). The antispasmodics included Oxybutynin 5mg (G04BD04), Tolterodine 2mg and 4mg (G04BD07), Solifenacin 5mg (G04BD08), Trospium 10mg (G04BD09), and Mirabegron 25mg and 50mg (G04BD12). Usage of adrenergic alpha-blockers, 5-ARIs and antispasmodics preoperatively were only adopted if the usage duration last at least three months. On the contrary, perioperative data included surgical methods and resected prostate volume to preoperative prostate volume ratios. Surgical methods contained TURP, which included monopolar TURP and bipolar TURP, and laser procedure, which included laser enucleation of prostate and photoselective vaporization. Resected prostate weight to preoperative prostate volume ratios were calculated using resected prostate weight, which was based on final histopathology weight, and preoperative prostate volume, which was measured by trans-abdominal ultrasound performed within a year before surgery. ## Statistical analysis Data in this study were assessed using IBM SPSS version 22.0 (International Business Machines Corp, New York, USA). Logistic regression was performed to evaluate the impact of individual factor on postoperative medications. $P \leq 0.05$ was considered to demonstrate statistical significance, and all P values were 2-sided in this study. ## Results There were 341 patients in our database that were newly diagnosed BPH within January 2007 to December 2012, and further received prostate surgery. Seventy patients with prostate cancer diagnosed before or after surgery were excluded. In addition, 16 patients receiving transurethral incision within a year before surgery, and 5 patients with history of open prostatectomy were also excluded. Eventually, there were 250 patients enrolled in this study. Seven patients ($2.8\%$) were loss to follow up. The median follow-up duration was 5.3 years. The baseline characteristics were listed in Table 1. There were 146 ($58.4\%$) patients with hypertension, 78 ($31.2\%$) patients with DM, 59 ($23.6\%$) patients with COPD, and 61 ($24.4\%$) patients with CKD. Among all 61 CKD patients, 29 patients ($47.5\%$) had DM. As for preoperative medical history, $56.8\%$ patients had received adrenergic alpha-blockers for at least three months, while $22.4\%$ patients had undergone 5-ARIs, $11.6\%$ patients had undergone antispasmodics, and $11.2\%$ patients had undergone a combination therapy of adrenergic alpha-blockers and antispasmodics. As for endpoints, 108 ($43.2\%$) patients received postoperative adrenergic alpha-blockers, and 58 ($23.2\%$) patients received postoperative antispasmodics. In addition, 36 ($14.4\%$) patients received postoperative medications of both adrenergic alpha-blockers and antispasmodics. The median time to first use of postoperative adrenergic alpha-blockers was 2.28 months (IQR 1.36–10.52), and the median time to first use of postoperative antispasmodics was 1.90 months (IQR 1.27–4.88). Table 1 \| Characteristics \| Patients (n = 250) \| \| --- \| --- \| \| Age (year) \| 71.14 ± 9.26 \| \| Body mass index \| 24.83 ± 3.29 \| \| Prostate specific antigen level (ng/mL) \| 6.62 ± 7.36 \| \| Comorbidities \| Comorbidities \| \| Hypertension, n(%) \| 146 (58.4%) \| \| Diabetes mellitus, n(%) \| 78 (31.2%) \| \| Ischemic heart disease, n(%) \| 63 (25.2%) \| \| Cerebrovascular disease, n(%) \| 59 (23.6%) \| \| Hyperlipidemia, n(%) \| 67 (26.8%) \| \| Chronic obstructive pulmonary disease, n(%) \| 59 (23.6%) \| \| Peripheral vascular disease, n(%) \| 21 (8.4%) \| \| Chronic kidney disease, n(%) \| 61 (24.4%) \| \| Sleeping disorder, n(%) \| 12 (4.8%) \| \| Gout, n(%) \| 32 (12.8%) \| \| Medical history \| Medical history \| \| History of adrenergic alpha-blockers for over 3months preoperatively \| 142 (56.8%) \| \| History of 5-alpha reductase inhibitors for over 3months preoperatively \| 56 (22.4%) \| \| History of antispasmodics for over 3 monthspreoperatively \| 29 (11.6%) \| \| History of combination therapy of adrenergicalpha-blockers and antispasmodics for over 3months preoperatively \| 28 (11.2%) \| \| History of prostate surgery \| 13 (5.2%) \| \| Preoperative urodynamic study \| Preoperative urodynamic study \| \| Maximum flow rate (ml/sec) \| 9.18 ± 4.65 \| \| Average flow rate (ml/sec) \| 3.83 ± 2.16 \| \| Post-void residual volume \| 104.65 ± 109.30 \| \| Voided volume \| 180.41 ± 110.97 \| \| Perioperative factors \| Perioperative factors \| \| Surgical intervention with TURP \| 140 (56%) \| \| Surgical intervention with laser procedure \| 110 (44%) \| \| Resected prostate volume to preoperative prostatevolume ratio \| 0.34 ± 0.20 \| \| Endpoints \| Endpoints \| \| Patients receiving postoperative adrenergic alpha-blockers \| 108 (43.2%) \| \| Patients receiving postoperative antispasmodics \| 58 (23.2%) \| \| Patients receiving both adrenergic alpha-blockers and antispasmodics postoperatively \| 36 (14.4%) \| \| Median time to first use of postoperative adrenergic alpha-blockers (months) \| 2.28 (IQR 1.36–10.52) \| \| Median time to first use of postoperative antispasmodics (months) \| 1.90 (IQR 1.27–4.88) \| In Table 2, we identified the following as risk factors for postoperative adrenergic alpha-blockers usage by univariate analysis: hypertension (OR 1.84, $$P \leq .021$$), COPD (OR 1.96, $$P \leq .025$$) and CKD (OR 2.34, $$P \leq .005$$). Multivariate analysis further disclosed the CKD (OR 1.93, $$P \leq .036$$) as the only independent risk factor for postoperative adrenergic alpha-blockers. Table 2 \| Unnamed: 0 \| Univariate \| Univariate.1 \| Univariate.2 \| Multivariate \| Multivariate.1 \| Multivariate.2 \| Multivariate.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| OR \| 95%CI \| pvalue \| OR \| 95%CI \| 95%CI \| pvalue \| \| Age \| 1.00 \| (0.98–1.03) \| 0.767 \| \| \| \| \| \| Body mass index \| 1.07 \| (0.99–1.15) \| 0.096 \| \| \| \| \| \| Prostate specific antigen level \| 1.00 \| (0.97–1.04) \| 0.780 \| \| \| \| \| \| Comorbidities \| \| \| \| \| \| \| \| \| Hypertension, n(%) \| 1.84 \| (1.09–3.09) \| 0.021* \| 1.54 \| (0.90–2.65) \| (0.90–2.65) \| 0.114 \| \| Diabetes mellitus, n(%) \| 1.61 \| (0.94–2.76) \| 0.083 \| \| \| \| \| \| Ischemic heart disease, n(%) \| 1.64 \| (0.92–2.92) \| 0.090 \| \| \| \| \| \| Cerebrovascular disease, n(%) \| 1.64 \| (0.91–2.95) \| 0.099 \| \| \| \| \| \| Hyperlipidemia, n(%) \| 1.19 \| (0.68–2.08) \| 0.554 \| \| \| \| \| \| Chronic obstructive pulmonary disease, n(%) \| 1.96 \| (1.09–3.54) \| 0.025* \| 1.66 \| (0.90–3.06) \| (0.90–3.06) \| 0.102 \| \| Peripheral vascular disease, n(%) \| 2.87 \| (1.12–7.39) \| 0.029 \| \| \| \| \| \| Chronic kidney disease, n(%) \| 2.34 \| (1.30–4.22) \| 0.005** \| 1.93 \| (1.04–3.56) \| (1.04–3.56) \| 0.036* \| \| Sleeping disorder, n(%) \| 0.64 \| (0.19–2.20) \| 0.483 \| \| \| \| \| \| Gout, n(%) \| 1.03 \| (0.49–2.17) \| 0.946 \| \| \| \| \| \| Medical history \| \| \| \| \| \| \| \| \| History of adrenergic alpha-blockers for over3 months preoperatively \| 1.56 \| (0.94–2.60) \| 0.087 \| \| \| \| \| \| History of 5-alpha reductase inhibitors forover 3 months preoperatively \| 0.81 \| (0.44–1.49) \| 0.502 \| \| \| \| \| \| History of antispasmodics for over 3months preoperatively \| 1.26 \| (0.58–2.74) \| 0.558 \| \| \| \| \| \| History of prostate surgery \| 1.13 \| (0.37–3.48) \| 0.825 \| \| \| \| \| \| Preoperative urodynamic study \| \| \| \| \| \| \| \| \| Maximum flow rate \| 1.00 \| (0.95–1.05) \| 0.939 \| \| \| \| \| \| Average flow rate \| 1.05 \| (0.93–1.17) \| 0.440 \| \| \| \| \| \| Post-void residual volume \| 1.00 \| (1.00–1.00) \| 0.372 \| \| \| \| \| \| Voided volume \| 1.00 \| (1.00–1.00) \| 0.311 \| \| \| \| \| \| Perioperative factors \| \| \| \| \| \| \| \| \| Surgical intervention with laser procedure \| 0.79 \| (0.48–1.31) \| 0.366 \| \| \| \| \| \| Resected prostate volume to preoperativeprostate volume ratio \| 0.78 \| (0.22–2.71) \| 0.694 \| \| \| \| \| In Table 3, univariate analysis revealed the following risk factors for postoperative antispasmodics usage: preoperative usage of antispasmodics (OR 2.69, $$P \leq .016$$) and surgical intervention with laser procedure (OR 1.97, $$P \leq .025$$). In addition, the resected prostate weight to preoperative prostate volume ratio (OR 0.08, $$P \leq .003$$) was disclosed as a protective factor against postoperative antispasmodic medications. Multivariate analysis further disclosed preoperative antispasmodics usage (OR 2.33, $$P \leq .046$$) as a risk factor for postoperative antispasmodic medications, whereas resected prostate weight to preoperative prostate volume ratio (OR 0.12, $$P \leq .013$$) was shown to be a protective factor. Table 3 \| Unnamed: 0 \| Univariate \| Univariate.1 \| Univariate.2 \| Multivariate \| Multivariate.1 \| Multivariate.2 \| Multivariate.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| OR \| 95%CI \| pvalue \| OR \| 95%CI \| 95%CI \| p value \| \| Age \| 0.99 \| (0.96–1.03) \| 0.754 \| \| \| \| \| \| Body mass index \| 1.04 \| (0.95–1.13) \| 0.431 \| \| \| \| \| \| Prostate specific antigen level \| 0.98 \| (0.93–1.03) \| 0.384 \| \| \| \| \| \| Comorbidities \| \| \| \| \| \| \| \| \| Hypertension, n(%) \| 1.34 \| (0.73–2.46) \| 0.343 \| \| \| \| \| \| Diabetes mellitus, n(%) \| 1.34 \| (0.72–2.50) \| 0.349 \| \| \| \| \| \| Ischemic heart disease, n(%) \| 0.93 \| (0.47–1.84) \| 0.832 \| \| \| \| \| \| Cerebrovascular disease, n(%) \| 1.48 \| (0.76–2.87) \| 0.244 \| \| \| \| \| \| Hyperlipidemia, n(%) \| 0.94 \| (0.48–1.83) \| 0.854 \| \| \| \| \| \| Chronic obstructive pulmonary disease, n(%) \| 1.85 \| (0.97–3.54) \| 0.063 \| \| \| \| \| \| Peripheral vascular disease, n(%) \| 1.04 \| (0.36–2.97) \| 0.945 \| \| \| \| \| \| Chronic kidney disease, n(%) \| 1.94 \| (1.02–3.68) \| 0.043 \| \| \| \| \| \| Sleeping disorder, n(%) \| 1.11 \| (0.29–4.24) \| 0.880 \| \| \| \| \| \| Gout, n(%) \| 1.12 \| (0.47–2.65) \| 0.796 \| \| \| \| \| \| Medical history \| \| \| \| \| \| \| \| \| History of adrenergic alpha-blockers for over3 months preoperatively \| 1.46 \| (0.80–2.68) \| 0.221 \| \| \| \| \| \| History of 5-alpha reductase inhibitors forover 3 months preoperatively \| 1.14 \| (0.57–2.27) \| 0.717 \| \| \| \| \| \| History of antispasmodics for over 3months preoperatively \| 2.69 \| (1.20–6.02) \| 0.016* \| 2.33 \| (1.02–5.36) \| (1.02–5.36) \| 0.046* \| \| History of prostate surgery \| 0.26 \| (0.03–2.07) \| 0.204 \| \| \| \| \| \| Preoperative urodynamic study \| \| \| \| \| \| \| \| \| Maximum flow rate \| 1.03 \| (0.97–1.09) \| 0.379 \| \| \| \| \| \| Average flow rate \| 1.04 \| (0.92–1.19) \| 0.524 \| \| \| \| \| \| Post-void residual volume \| 1.00 \| (1.00–1.00) \| 0.375 \| \| \| \| \| \| Voided volume \| 1.00 \| (1.00–1.00) \| 0.123 \| \| \| \| \| \| Perioperative factors \| \| \| \| \| \| \| \| \| Surgical intervention with laser procedure \| 1.97 \| (1.09–3.57) \| 0.025* \| 1.83 \| (0.99–3.38) \| (0.99–3.38) \| 0.054 \| \| Resected prostate volume to preoperativeprostate volume ratio \| 0.08 \| (0.02–0.42) \| 0.003** \| 0.12 \| (0.02–0.63) \| (0.02–0.63) \| 0.013* \| The time to first use of adrenergic alpha-blockers and antispasmodics after surgery were recorded and demonstrated in Fig 1. The duration of medications usage with respect to the initiating time was demonstrated in Fig 2. Fig 1: Interval of adrenergic alpha-blockers and medications for OAB initiating time after surgery. Fig 2: Average adrenergic alpha-blockers and antispasmodics using duration with respect to medicine initiating times. ## Discussion Our study showed several observational data which can assist clinical practices in BPH patients. First, the prostate surgery rate among newly diagnosed BPH patients was $4.88\%$ ($\frac{250}{5122}$) in our database during a mean follow-up duration of 5.3 years. Second, we found that BPH patients having comorbid CKD were more liable to using adrenergic alpha-blockers after surgery. Third, patients undergoing antispasmodic medications for at least three months before surgery were more likely to initiate OAB medications postoperatively. Fourth, resected higher prostate volume ratio reduces the usage of antispasmodics postoperatively. In BPH, the hyperplastic volume of prostate is mainly composed of stromal elements. Approximately half of the stromal elements were smooth muscle, which contracts when the alpha 1 adrenoceptors it contains are activated [7]. The contraction of prostatic smooth muscle blocks bladder outlet, and may further lead to bladder dysfunction, which results in LUTS symptoms [8,9]. Adrenergic alpha-blockers act by antagonizing those alpha 1 adrenoceptors, and further alleviates LUTS symptoms. In our study, CKD was associated with increased usage of postoperative adrenergic alpha-blockers usage. However, the underlying rationale remains unclear. Previous studies discovered association between LUTS symptoms and CKD [10,11]. It was postulated vascular diseases and DM that frequently accompanied CKD may cause bladder dysfunction, which further leads to LUTS symptoms [12]. Atherosclerosis may further result in inflammation and oxidative stress of the bladder that facilitates the development of detrusor overactivity. These comorbidities of CKD do not disappear after prostate surgery, and may altogether lead to persistent LUTS symptoms. This may explain why patients with CKD are more likely to undergo adrenergic alpha-blockers after surgery. Based on the clinical practice, the prescription of adrenergic alpha-blockers was based on either LUTS or patient’s preference. However, a recent study revealed that adrenergic alpha-blockers may be associated with higher risk for kidney disease progression in CKD patients, which should be aware in clinical conditions [13]. In order to recognize precise overactive bladder (OAB) patients, we used antispasmodics prescription to identify the target subjects instead of using ICD codes. OAB may be caused by BPH-induced bladder outlet obstruction (BOO). The coexistence of BPH-induced BOO and OAB increases the complexity for treatment [14]. Even worse, the OAB symptoms may persist after relief of BOO [9,15]. This is compatible with our study finding that $23.2\%$ of patients who underwent prostate surgery receive medications for OAB postoperatively. Medications for OAB were antispasmodics, which include beta-3 adrenergic agonists, antimuscarinics, anticholinergics, and some tricyclic antidepressants that act to relax detrusor muscles [16,17]. In this study, we found patients who use antispasmodic medications preoperatively were more liable to antispasmodics usage after surgery. The finding was compatible to previous studies, which revealed that history of anticholinergics therapy was associated with continued medical therapy for LUTS after prostatic surgery [6,18–20]. Patients have to be informed the potential need of postoperative medications so as to avoid unnecessary expectations. Univariate analysis showed association between surgical methods and postoperative antispasmodics usage, while multivariate analysis revealed no correlation. The surgical methods in this study contain TURP, which includes monopolar TURP and bipolar TURP, and laser procedure, which includes laser enucleation of prostate and photoselective vaporization. Previous studies suggested laser procedures were comparable to TURP in surgical and functional outcomes [21–23]. However, laser procedures were shown to be associated with higher postoperative usage of antispasmodic medications as compared to TURP in a study [18]. The difference between our study and previous studies is that our study included laser enucleation, while previous study did not. This put the effect of laser enucleation on postoperative antispasmodic medications usage into request for further investigation. The lower resected prostate weight to preoperative volume ratio was found to be an independent risk factor for postoperative antispasmodic medications usage in our study. One study investigated the relationship between residual prostate volume and postoperative clinical outcomes using LUTS evaluation questionnaire and urinary flow rate, and negative correlation was shown [24]. Another study revealed weak relationship between resected prostate volume and early symptom relief after surgery [25]. The results of our studies were compatible with previous studies, but instead of using symptom improvement as evaluation metrics, we measured the usage of antispasmodic medications. International Prostate Symptom Score (IPSS) was considered an adequate tool to evaluate storage symptoms. However, the retrospective setting of this study and real-world practice showed massive missing data in this category. In addition, we also believe the use of antispasmodics provides a more practical information for urologists to evaluate whether patients may require postoperative medications or not during surgery. We analyzed the time to first use of medications after surgery in this study, which showed most medications were initiated within a year postoperatively. The median time to first use of postoperative adrenergic alpha-blockers and antispasmodics were 2.28 months and 1.90 months respectively in our study. Previous study from Canada showed a median time to first use of adrenergic alpha-blockers and antispasmodics after TURP to be 1.25 years and 0.69 years respectively, which was longer than our study [20]. They excluded the prescription of medications within the first 90 days after TURP, which was longer than the interval of a month set in our study. This may explain the difference in time to first use of postoperative medications in our studies. In addition, medical availability between different countries may also be a cause of the difference. Previous studies proposed that prostatic surgery destroys prostatic and bladder neck urothelium and submucosal tissues, which may result in denervation of the afferent neurons. This further hinders the neurons from initiating detrusor contraction that causes OAB symptoms [26,27]. The proposed mechanism may explain why approximately half of the patients with OAB symptoms benefit from surgery in short term follow up [28,29]. However, it could not explain why some of the patients in our study still require medications 1–2 months after surgery, as the neurons responsible for OAB symptoms were damaged during surgery, and could not recover within that period. For those patients with persistent OAB symptoms after surgery, further investigation should be performed. Besides, it seemed that the number of patients needing post operative antispasmodic exceeded that in the pre-operative ($23.2\%$ vs $11.6\%$). This result was concordant with the trend presented by Campbell et al. in 2019 in which that storage problems were most difficult to be relieved by TURP. Furthermore, the utilization of antispasmodics after TURP increased over time. These might explain why the number of patients undergoing postoperative antispasmodic exceeded than those in the preoperative [20]. Patients initiating antispasmodics over a year after surgery were shown to undergo treatment with a shorter duration, as shown in Fig 2. A possible explanation is that patients initiating antispasmodics within a year postoperatively were more likely to be influenced by preoperative and perioperative factors. However, patients initiating antispasmodics over a year after surgery were more likely to be associated with new-onset LUTS symptoms. Patients in this group tend to suffer from poor compliance to antispasmodics due to its side effects, and thus presented with a shorter medication duration [30]. On the contrary, patients who initiated adrenergic alpha-blockers and antispasmodics within 2–6 months postoperatively tend to undergo medical treatments longer. Relatively short duration of postoperative adrenergic alpha-blocker usage in patients starting at 6–12 months after surgery was noted. This may be biased by relatively fewer patients in this group, which only accounted for $7.41\%$ of all patients receiving postoperative adrenergic alpha-blockers. In addition, the follow-up time of postoperative medications usage may also be confined and biased by the relatively short follow-up time of our study, which a median of 5.3 years was obtained. This study discloses the factors associated with the medications usage after operation for patients with BPH. However, there are some limitations. Firstly, some data were unable to obtain due to the retrospective nature of this study such as IPSS. Postoperative urodynamic tests and cystoscopy were not regularly performed in these patients. This may result in the lack of objective measurements of symptom improvement after surgery. Furthermore, urethral stricture related to surgery may not be completely identified. However, these patients were followed up in VGHTC, and further examinations may be performed after evaluation of urologists. Secondly, the severity and the duration of the comorbidities were not included in this study. In fact, longer diabetes duration was associated with OAB in diabetic patients [31]. In addition, patients with CKD progressed to end stage renal disease often present with OAB symptoms [32]. Further classifying comorbidities based on severity and duration may shed more light on future studies. Thirdly, some of the symptomatic patients may refuse medications after surgery, while some patients with minimal residual symptoms may insist on medications. The patient preference of medical treatments may result in bias in this study. Perioperative factors may influence the outcome of the surgeries, and further affect postoperative medications. These factors include surgical techniques, experience of the surgeon and resected prostate regions. In this study, laser enucleation and photoselective vaporization were categorized in laser intervention. These two techniques present with similar surgical outcome and functional outcome [33,34]. However, the difference in the technique selection may still lead to bias in medication usage. Patients may receive medical treatment from other hospitals, where the medical records were not included in this study. This may result in bias that we did not record all the medical treatments these patients underwent. To deal with this problem, data collection through Taiwan’s National Health Insurance Research Database may provide more thorough medical records for further research. ## Conclusion BPH patients with CKD are more likely to undergo adrenergic alpha-blockers after prostate surgery. In the meantime, patients who underwent antispasmodic medications before surgery are more liable to receive antispasmodics postoperatively. In addition, patients who received greater resected prostate weight to preoperative prostate volume ratio are less likely to undergo antispasmodic medications after surgery. For patients who undergo medications after prostate surgery, most of them started medications within a year postoperatively. ## References 1. Berry SJ, Coffey DS, Walsh PC, Ewing LL. The development of human benign prostatic hyperplasia with age. J Urol (1984) 132 474-9. DOI: 10.1016/s0022-5347(17)49698-4 2. O’Leary MP. LUTS, ED, QOL: alphabet soup or real concerns to aging men?. 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--- title: 'Person-centred care in the Dutch primary care setting: Refinement of middle-range theory by patients and professionals' authors: - Anam Ahmed - Maria E. T. C. van den Muijsenbergh - Hubertus J. M. Vrijhoef journal: PLOS ONE year: 2023 pmcid: PMC9997984 doi: 10.1371/journal.pone.0282802 license: CC BY 4.0 --- # Person-centred care in the Dutch primary care setting: Refinement of middle-range theory by patients and professionals ## Abstract In a previous rapid realist review (RRR) of international literature insight was provided into how, why, and under what circumstances person-centred care (PCC) in primary care works (or not) among others for people with low health literacy skills and for people with a diverse ethnic and socioeconomic background, by establishing a middle-range programme theory (PT), which describes the relationship between context items, mechanisms, and outcomes. Since the application of PCC in primary care in the Dutch setting is expected to differ from other countries, the objective of this study is to validate the items (face validity) resulting from the RRR for the Dutch setting by assessing consensus on the relevance of items. Four focus group discussions with patient representatives and patients with limited health literacy skills ($$n = 14$$), and primary care professionals ($$n = 11$$) were held partly combined with a Delphi-study. Items were added to refine the middle-range PT for the Dutch primary care setting. These items indicated that in order to optimally align care to the patient tailored supporting material that is developed together with the target group is important, next to providing tailored communication. Healthcare providers (HCPs) and patients need to have a shared vision and set up goals and action plans together. HCPs should stimulate patient’s self-efficacy, need to be aware of the patient’s (social) circumstances and work in a culturally sensitive way. Better integration between information and communications technology systems, flexible payment models, and patients access to documents, and recorded consultations should be in place. This may result in better alignment of care to the needs of patients, improved accessibility to care, improved patient’s self-efficacy, and improved health-related quality of life. On the long-term higher cost-effectiveness and a higher quality of healthcare can be realised. In conclusion, this study shows that for PCC to be effective in Dutch primary care, the PT based on international literature was refined by leaving out items and adding new items for which insufficient or sufficient consensus, respectively, was found. ## Introduction Healthcare systems are gradually transforming from biomedically-oriented systems towards more person-centred care (PCC) oriented systems [1, 2]. To understand and adequately address a person’s health problem(s) and experience of illness, having a disease-oriented perspective alone is not sufficient [3, 4]. Worldwide, person-centredness has gained more recognition over the years and is considered a core element of high-quality healthcare [5–7]. Driving factors behind this recognition are the growing and changing demand for care, more technological possibilities, and the rising healthcare costs [8]. When PCC addresses also non-medical causes of and solutions for physical distress, it could reduce costs of more expensive (hospital-based) medical specialist care. A core element of PCC is to create a partnership between the healthcare professional and the care recipient, in which the unique needs and beliefs of the latter are the starting point for the provision of care [9]. PCC is considered a core value of primary care [10, 11]. In the Netherlands general practitioners (GPs) have a central role in the healthcare system. As GPs are the first contact point for individuals experiencing health problems and an increasing number of patients with complex care needs ending up in primary care, it is especially important for GPs to provide appropriate support by applying a holistic and person-centred approach that contributes to the overall well-being of individuals [12]. The Dutch healthcare system is recognised for its well-developed primary healthcare [13, 14]. Important elements for this are GPs acting as gatekeepers for specialist care and hence the gradual accessibility of secondary medical specialistic care. The assumption behind this is that a well-functioning primary care setting takes over the care demand as much as possible, which otherwise would end up in the more expensive secondary care. The implementation of practice nurses in Dutch GP practices has increased the interdisciplinary character of care [15]. In addition to the gatekeeping function, empanelment is also considered an important component for building or strengthening primary care [16]. Literature advocating PCC is widespread [17] and the experiences gained with PCC in primary care in the Netherlands are increasingly shared, often in terms of best practices, barriers to implementation and conditions for success [18]. However, despite the conceptual attractiveness of PCC, in daily practice PCC remains poorly understood and implemented [19]. A previously published rapid realist review (RRR) of international literature aimed to provide insight into the question for whom, how and why PCC in primary care does (not) work under what circumstances [20]. The resulting middle-range programme theory (PT) (Fig 1) demonstrated that healthcare providers (HCPs) should be trained and equipped with the knowledge and skills to communicate effectively (i.e., in easy-to-understand words, emphatically, checking whether the patient understands everything, listening attentively) tailored to the wishes, needs and possibilities of the patient, which may lead to higher satisfaction of patients, informal caregivers, and/or healthcare professionals. This way patients will be more involved in their care process and in the shared decision-making process, which may result in improved concordance, and an improved treatment approach. A respectful and empathic attitude of the HCP plays an important role in establishing a strong therapeutic relationship and improved health (system) outcomes. Together with a good accessibility of care for patients, setting up a personalised care planning with all involved parties may positively affect the self-management skills of patients. Good collaboration within the team and between different domains is desirable to ensure good care coordination. Fig 1: Middle-range PT from the RRR [20]. However, since the application of PCC in primary care in the Dutch setting is expected to differ from primary care in other countries, it is deemed relevant to assess the relevance of the obtained items from the international RRR for the Dutch setting. In doing so, the active involvement of experts from the field is of great importance, both for providing input and for translating theoretical insights into suggestions for daily practice [21]. Moreover, PCC should also take into account diversity in age, gender, socio-economic status, education, migration background, (multi)morbidity as well as personal preferences and needs [22]. For example, approximately $25\%$ of the Dutch population has a migration background [23], more than $18\%$ are low-literate [24], and $30\%$ have insufficient or limited health literacy skills [25]. People from these groups often have poorer health, partly because the care provided insufficiently match their needs and possibilities. Existing treatment protocols and standards of care are largely based on scientific evidence usually obtained from study participants outside these groups and therefore do not or only partially apply to these groups [26]. The objective of this study is to validate the items (face validity) resulting from the international RRR for the Dutch setting by assessing consensus on the relevance of the items among different stakeholders. ## Patient and public involvement This study was commissioned by the National Health Care Institute, who, amongst others, encourages good healthcare by helping all parties involved to continually improve healthcare quality. This study is part of a larger study for which a steering committee was established. The ten members of the steering committee were purposively selected based on their expertise in the PCC or primary care field and were primary care practitioners, senior researchers, medical specialists, policy makers, patient’s representatives (specifically concerning patients with limited (health-)literacy and a migrant background) (see Acknowledgements). Several meetings with the steering committee were held during the study (February 2018, December 2018, April 2019, December 2019). These meetings were held with the objective to provide feedback and guidance on the methods, the interpretation of (interim) results, and providing overall advice regarding the research. Stakeholder perspectives were considered when testing and refining the PT derived from the RRR. Members of the steering committee were asked to discuss, and to indicate if the identified items on context, mechanisms and outcomes in the literature match with what they see in Dutch practice. ## Programme theory One of the key elements in doing realist research is to establish a PT. A PT explains what mechanisms will generate the outcomes and what features of the context will affect whether or not those mechanisms operate [27, 28]. Context items refer to wider external factors, and mechanisms are considered enablers, underlying entities, processes, structures, reasoning, choices, or collective beliefs). The interaction between context and mechanisms lead to outcomes (intended and unintended). In the international RRR we established a middle-range PT (see Introduction and Fig 1), which we aimed to refine based on the findings of this study in the Dutch setting. ## Study design In this qualitative study, four focus group discussions (FGDs) were held with the objective to encourage group interaction between participants and to explore and clarify individual and shared perspectives [29]. FGD 3 and 4 were combined with a Delphi-study. The four FGDs were held with different stakeholders to validate the findings from the international RRR for the Dutch setting. A FGD lasted approximately 90 minutes. All FGDs were held at a neutral place that participants already knew (i.e., at a research organisation), and where they felt comfortable. Participants of FGD 1 and 2 were patient representatives and patients with limited health literacy skills. Participants of FGD 3 and 4 were various primary care professionals. Due to the different target groups, a target group-specific approach was used. The different approaches are explained in more detail below. ## Recruitment Participants of FGD 1 and 2 were recruited through purposive sampling. Adult participants were approached using trusted network organisations. These organisations are the Network of Organisations of Older Migrants (NOOM), which focus on diverse groups of migrant older people in the Netherlands, and the ABC foundation, a volunteer organisation for low-literate people throughout the Netherlands. During the recruitment process maximum variation in gender, age, ethnic background, educational level and level of health literacy was aimed to achieve. FGD 1 and 2 were led by a researcher [AA] and another moderator experienced in leading FGDs with people with low (health) literacy skills [NHvR]. FGD 1 and 2 took place in August 2018. Participants of FGD 3 and 4 were various primary care professionals, members of care organisations, policy makers, and researchers. Participants of FGD 3 and 4 were recruited (purposive sampling) through the expert network of the researchers of this project, aiming for variation in gender, age, professional background, and experience with person-centred care. To be included in the FGD, participants needed to have scientific (research) experience and/or practical work experience in a professional or service organisation regarding person-centred care in primary care. FGD 3 and 4 were led by two researchers [AA and HJMV or MvdM]. FGD 3 and 4 took place in December 2018. ## Data collection For FGD 1 and 2 an open-ended semi-structured topic guide was used by the moderators, which was compiled based on the context items, mechanism, and outcome variables from the RRR (Fig 1). Only patient-related items were included and were presented in the form of simple formulated questions during the FGDs (Fig 2 and S1 File). Participants could also ask other questions and/or share their own story or experiences. This facilitated the researchers to collect additional data. Participatory learning and action (PLA) techniques were applied to facilitate equal input from participants, thereby stimulating the active participation of participants. PLA is a form of participatory research, which emphasizes the need for stakeholders’ active engagement across the full range of research activities, including data generation and data analysis, and is specifically suitable for meaningful involvement of stakeholders with limited power or skills [30, 31]. Fig 2: Overview of participants, data collection, and data analysis. Field notes were made during the FGDs. In FGD 3 and 4 validation of the CMO-items by participants took place by means of an e-Delphi questionnaire (S2 File) and a FGD during the second round (Fig 2). The Delphi technique is a widely used research method, which consists of several rounds of data collection to capture and structure the knowledge and opinions of a panel of participants on a topic in which they have expertise [32]. Field notes were made during the FGDs. ## Delphi round 1 Participants received a web link to an online version of the questionnaire in SurveyMonkey (version 2018). The questionnaire started with an introduction of the study and its objectives, the structure of the questionnaire, and the definitions of the constructs: context, mechanisms, programme-activities, and outcomes. The questionnaire continued with six general questions regarding gender, age, highest level of education, current job position, number of years working within the position, and number of years of experience with PCC. The questionnaire contained another 63 questions related to CMO-data derived from the RRR. Experts were asked to assess the relevance on a 9-point Likert scale (1 = very irrelevant to 9 = very relevant) of PCC-related items in primary care in the Netherlands of context items ($$n = 30$$), mechanisms ($$n = 19$$), and outcomes ($$n = 14$$) identified in the RRR. The questionnaire ended with two open questions, namely possible additions to the stated context items, mechanisms, and/or outcomes based on personal experiences, and participants were asked if they had any additional comments/suggestions about the questionnaire. The answers of the participants were completely anonymised. The respondents were given a total of two weeks to complete the questionnaire. ## FGD (second round) Before the second round of the Delphi questionnaire was completed, a FGD was held (Fig 2). The aim of this FGD was to discuss the context items, mechanisms and outcomes for which insufficient consensus/dissensus was found in round 1. During this FGD, the group results from the first Delphi round were provided, including 1) the median assessment results and interquartile range (IQR) on each item), 2) the level of (insufficient) consensus between the participants and, whether consensus was achieved [32, 33]. The IQR is the difference between the 3rd and 1st quartile in which $50\%$ of core values lie [34] and also shows the degree of convergence of the answers [35–38]. The items, for which dissensus was found, were presented and discussed during the FGD to give insight into the level of (dis)agreement between experts in the first round and to generate additional insights about the specific item(s). Providing feedback on the level of group agreement reached, influences achieving the level of consensus subsequently [39]. Misinterpretation on item(s) needed to be clarified. ## Delphi round 2 An online version of the questionnaire was sent including the context items, mechanisms, and outcomes for which no consensus was found in round one [33]. The questionnaire started with the same general questions as round 1. Then, participants were asked to indicate the degree of relevance of context items, mechanisms and outcomes for PCC in primary care in the Netherlands on the same 9-point Likert scale. At the end of the questionnaire, participants had the possibility to add items that were not included in the questionnaire and could also provide general comments/suggestions on the questionnaire. For round 2, the respondents were given a total of two weeks to complete the questionnaire. ## Data analysis All FGDs were audio-taped and transcribed verbatim manually. Using thematic analysis techniques [40], text segments were assigned a code if they related to a specific theme/topic, using an inductive, iterative process. Categories with similar content were investigated for inter-relationships, and further refined. Half of the data was coded independently by two researchers [AA, MvdM] to maximise credibility and trustworthiness [40]. Any differences in code application were resolved by discussion with a third researcher [HJMV]. Data were analysed both descriptively and exploratively. For the Delphi rounds in FGD 3 and 4 a 9-point Likert scale (1 = very irrelevant to 9 = very relevant) was used to indicate the degree of relevance of the CMO-items. To collect data from participants in a most sensitive matter, use was made of a 9-point Likert scale. For analysis, data were recorded into: irrelevant (1–3), equivocal (4–6) and relevant (7–9). Recoding enabled us to assess consensus on these meaningful levels and hence derive recommendations for improvement. To determine the level of consensus within the Delphi panel, many studies use a predetermined level of consensus among the experts [41]. However, the literature does not describe a standard threshold for reaching consensus [42], with thresholds for consensus varying from 55–$100\%$ [43]. In this study the level of consensus was $75\%$ or more [42, 44, 45], with the condition that less than $15\%$ of participants scored in the opposite range of that scale namely the 1–3 range [46, 47]. All items with scores in the 4–6 range and without consensus, were presented again to the Delphi panel in round 2. Respondents’ overall consensus on each context, mechanism, and outcome was analysed based on the median of the group’s scores. The analysis was performed in MS Excel 2018. Consensus on items being found relevant by FGD 1 and 2 and/or FGD 3 and 4, remained part of the PT or were added to the PT. Consensus on items being irrelevant or no consensus on items were removed from the PT. ## Trustworthiness This study largely complies with the COnsolidated criteria for REporting Qualitative research (COREQ) Checklist, a checklist for explicit and comprehensive reporting of qualitative studies (in-depth interviews and focus groups) [48]. To increase the credibility of this study multiple FGDs were held, multiple stakeholders’ perspectives were included, and triangulation of data collection methods took place. Regarding transferability, sampling strategies, detailed descriptions of participants, a description of the topic list, and the procedure of methods were included. With respect to confirmability, (interim) results were presented to the commissioner of this study and the steering committee of this study. Regarding dependability, multiple authors independently coded the transcripts, interpretation of the results took place individually by multiple authors, and participants quotations were included to accurately report their perspectives. ## Ethics As this study does not involve patients or study subjects, according to the Dutch Medical Research in Human Subjects Act (WMO) in the Netherlands, an ethical approval was not needed. However, all participants provided their (verbal) consent and participation in the survey was anonymous. ## FGD 1 and 2 with patient representatives FGD 1 and 2 consisted of a total of 14 participants. In Table 1 the participants’ characteristics are shown. Participants who were not originally born in the Netherlands have been in the Netherlands for on average of 44 years (SD: 11.4 years). Table 1 \| Characteristic \| Unnamed: 1 \| FGD 1 and 2 (n = 14) \| FGD 3 and 4 (n = 11) \| \| --- \| --- \| --- \| --- \| \| Gender (%) \| Female \| 36 \| 45 \| \| \| Male \| 64 \| 55 \| \| Age (years) \| Average (SD) \| 66 (9.7) \| 50.1 (10.2) \| \| Highest level of education (%)# \| Elementary education \| 57 \| - \| \| \| Intermediate vocational education \| 21 \| - \| \| \| Bachelor \| 7 \| 27 \| \| \| Master \| 14 \| 45 \| \| \| PhD \| - \| 27 \| \| Background (%) \| Research/academic \| - \| 36 \| \| \| Healthcare provider \| - \| 36 \| \| \| Other \| 100* \| 27^ \| \| Years of experience \| Average (SD) \| \| 13.6 (11.1) \| All context items, mechanisms, and outcomes presented to participants were found relevant for PCC in primary care in the Netherlands. This concerns the context items: patients having social support (networks), a good collaboration between HCPs, patient education being provided, sufficient time during consultation, setting up a personalised care planning, and making use of e-health options. The mechanisms deemed relevant for PCC in primary care in the Netherlands are HCPs providing effective communication (including listen attentively), HCPs having a holistic approach, HCPs showing respect and having an open, friendly, and empathic attitude, patients having an active role in their care process, establishing a therapeutic relationship, self-management support, and shared decision-making. The outcomes considered relevant concerned health outcomes, patient involvement, satisfaction of the patient, therapy concordance, self-management skills, and an improved treatment approach. On the items below participants had additional comments next to them being considered relevant. The participants reflected on these items based on their own experience, indicating that they are relevant for PCC in primary care, but not always carried out properly in practice. ## Communication According to the participants, HCPs did not (yet) adapt their communication sufficiently to the needs and wishes of the patients. Participants stated that “in the communication by the care provider more attention should be paid to diversity” (P1 and P2). One participant expressed that “communication is extremely important when you visit the GP. Often older migrants cannot communicate well in Dutch, but they do know what they want to ask in their own language. They often bring their son or daughter to the GP together with them to ask questions [related to medical health of patient]” (P1). In addition, the use of aids (pictures, attributes, etc.) during the consultation could support communication, which is currently very limited done. Also, patients often had difficulties understanding health information and medical terms, while most of them did not indicate this. This is particularly the case for low-literate people and migrants, who had difficulty with the (Dutch) language and were therefore limited in their communication options. One participant mentioned that "people still don’t have the guts to say they are illiterate, and that’s just because of the shame associated with it" (P3). Reinforcing patients’ language skills and using interpreters can improve communication. ## Consultation time An important barrier of PCC in primary care according to the participants was the consultation time with the GP, which is too short to actually explain their problem. A participant mentioned that: “In my own GP practice, I am experiencing the third generation of GPs, I noticed that doctors have less and less time. The consultation really just takes 10 minutes, so you can just ask one question. If you have more questions and your time is up, you will be cut off. It becomes very clear that there is no time left” (P4). Patients often felt unheard or misunderstood, because there was insufficient time during the consultation to discuss all relevant matters or to explain everything properly. As a result, the HCP was also unable to provide adequate support based on the patient’s context and to discuss any underlying problems. Participants said: “I would like that he [the GP] gives extra time to people who have difficulties with reading and writing. He [the GP] has knowledge in the medical field, but he should also know which patient have difficulties with reading and writing. Also, it should be pointed out what the rules and regulations are here in the Netherlands compared to other countries [regarding time]” (P5). Patients making a double appointment with the GP could be helpful. Moreover, patients at home writing down points to discuss as preparation of the consultation could contribute to a more efficient use of consultation time. One participant stated that “healthcare is commercialising in such a way that everything is expressed in Euros. The GP would like to take half an hour herself [for the consultation], but the health insurer, which is focused on the money, plays a very important role here. And it’s getting worse, I feel. Sufficient time and attention for the patient are the building blocks of a relationship of trust, and this is at odds with the available time" (P4). ## Shared decision-making Participants experienced that shared decision-making in practice was not conducted properly. Partly because of the short consultation time, the pros and cons of different treatment options were not always explained well by the HCP. Some participants stated that due to insufficient insight of patients into the disease and treatment options, as well as the expectation that the HCP is the expert in the medical field, this resulted in both parties being reluctant to make shared decisions. Therefore, the choice of HCP often played a decisive role. The wishes and preferences of the patient often remained underexposed. Overall, participants mentioned that “I really like it when a GP asks you if you want to do something [which is part of care process] and whether you agree [with a treatment plan]” (P6). ## Collaboration between HCPs The collaboration between HCPs (e.g., between practice nurse and GP or HCPs between primary and secondary care) could be improved. Participants often experienced that the different HCPs involved in the care process were not always well informed. As a result, patients often had to repeat their story, at the expense of the limited time available. For example, (electronic) information transfer often fell short and relevant (medical) documents were insufficiently shared. The HCPs involved also often gave different advices, which led to confusion among patients. Better coordination between HCPs of the agreements and advices made, is necessary to provide PCC. ## Active role patient In certain groups, such as people with low health literacy skills, patients often lacked confidence to ask questions to the HCPs and take an active role for the benefit of their health. This was partly because patients assigned a high status to the GP and placed him/her on a pedestal. These patients often did not want to bother the GP with their questions. In addition, they did not indicate by themselves that they had low (health) literacy skills because of past unfortunate events (e.g., bullying, bad experiences with HCPs ‘not knowing who the patient is’). The patient was also rarely asked by the HCP whether they had low (health) literacy skills, with the result that the HCP had insufficient knowledge about the patient’s background. As one participant stated: “it would be good if the GP knew the background of the patient and what to consider. It is very important that the doctor knows what is going on behind the person in front of him/her” (P7). Solutions for patients having an active role could be to schedule an intake interview for every new patient in the practice; inform other involved HCPs of important characteristics of the patient (e.g., low literacy); give sufficient room to patients to ask questions, check whether patients have asked all their questions and whether they have understood the answers. On the other hand, patients can go into the consultation better prepared by writing down their discussion points and questions in advance. ## FGD 3 and 4 with care professionals A total of 18 experts received the invitation to participate in the FGDs, of which eleven experts agreed. In Table 1 the characteristics of the participants are shown. ## Quantitative description of consensus level In round 1 consensus was achieved for 46 items out of a total 63 items ($73\%$) among experts. All items were found relevant for the Dutch setting with the overall median lying in the 7–9 range. On 18 out of the 30 context items consensus was found ($60\%$), 17 out of 19 mechanisms ($89\%$), and 11 out of 14 outcomes ($79\%$) (Fig 3). On 17 items dissensus was found with a panel median in the 4–6 point range (3 items) and 7–9 point range (14 items). These items were included in round 2. Fig 3: Level of consensus found in round 1 and 2. In the second round, consensus was achieved among experts for 6 out of 17 items ($35\%$), of which 4 out of 12 context items ($33\%$), 1 out of 2 mechanisms ($50\%$), and 1 out of 3 outcomes ($33\%$). The overall median was in the 7–9 range. For 11 items, the relevance remained undecided. The overall median was in the 4–6 range (5 items) and in the 7–9 range (4 items), 2 items equally fell in the 4–6 range and 7–9 range. After both rounds, for 52 items out of 63 items ($83\%$) consensus was found with all items being considered relevant. ## Qualitative description of context items, mechanisms, and outcomes The outcomes on every context item, mechanism, and outcome of the first and second Delphi round are shown in S3 and S4 Files respectively. The items from round 1 that were found to be equivocal, were included in the second round. ## Context items Based on both rounds, context items that were considered relevant for PCC in primary care in the Netherlands on macro-level were shifting the focus from a disease- and complaint-oriented approach to a more holistic approach, using evidence-based guidelines, foreseeing in sufficient capacity and time for patients during consultation, offering (more) space and resources to HCPs to experiment, and having flexible payment systems. Participants believed that “experimenting in its broadest sense should be taken into account to improve PCC towards patients” (P10, P13, P18). “ For example, if you have patients with a chronic conditions and you want them to take more control of their health themselves, and as a care provider you have learned a new conversation technique to be applied during consultation in which you approach the person openly and let him/her decide for themselves what they want to change [in their care process], then you have to have the space to try out the new technique, practice with it, and to improve it” (P16). On an organisational (meso) level, experts found that improving accessibility (e.g., to healthcare organisations, to documents, recorded consultations), having a good collaboration between HCPs and having a shared vision, having a supportive policy in place which strengthens the quality of PCC especially concerning low health literacy, and better integration between information and communications technology (ICT) systems are relevant items. Of the latter a participant mentioned: “Better integration between ICT systems promotes cooperation, care is then better coordinated and it becomes more person-centred. Now everyone works according their own way” (P12). On an individual (micro) level HCPs having PCC skills (e.g., regarding communication, shared decision-making, providing culturally sensitive care) possibly through training or acquired during their medical education was found relevant. In addition, HCPs providing patient education, patients having social support (networks), and patients being involved in organising care was considered relevant. A participant mentioned that “HCPs setting goals and making action plans is also very relevant, because often patients don’t know this by themselves. They often have questions during the consultation, and when the care provider reaches the bottom layer of those questions, you discover why the patient finds that important. Also, other things that are important for the patient emerge” (P10). After two rounds, a lack of agreement on the relevance of some items for PCC in primary care in the Netherlands was observed, such as the application and efficient use of ICT and e-health initiatives. “ The information in e-health applications needs to be in line with what the healthcare provider says. Only if the information is in line and explained well, it will reinforce each other, otherwise it will lose its function.” ( P13) “E-health applications may not work for low-literate people or non-native speakers. Moreover, there are also people that are digitally illiterate” (P14). There was also dissensus on the item having sufficient male and female HCPs per practice, as participants found that “there are people who would like to have a male or a female care provider, it’s nice that people have that choice. But whether you choose a male or female doctor, they both have to provide PCC, regardless of their gender” (P15). Some participants believed that providing better administrative support for HCPs might positively influence PCC, but is not considered relevant to provide PCC. “ Providing better administrative support for caregivers can reduce administrative barriers to increase working in a person-centred way. The [consultation] time you can spend on a patient is already limited, so if you can spend less time on administrative things such as electronically saving or capturing what has been discussed with the patient such as setting the goals, you have more time to provide PCC to the patient. But it is not a precondition to provide good PCC and therefore, not relevant” (P16). Regarding the item preparation of consultation by patient it was mentioned that “the preparation of a consultation by the patient is not by definition relevant for the provision of person-centred care by the care provider” (P9). “ It is nice if a patient prepares a consultation, it can be very helpful. The question is also whether each patient can prepare the consultation, whether he/she is competent enough to do so. Someone who actively thinks about his/her health makes the conversation easier, but it is not a condition for the provision of PCC, that is the task of the care provider” (P8). About the item patients having a high/low socioeconomic status (SES), some mentioned that “having a high or low SES is not relevant for providing PCC. Most of the time it does require more effort to provide PCC to people with a low SES. But providing care to people with a high SES, such as expats, can also be challenging, as they are not familiar with the systems [in the country], but are highly educated at the same time. SES is not decisive for PCC” (P12, P15). Dissensus was also found on the items setting up a personalised care planning and, HCPs stimulating patient empowerment. ## Mechanisms On meso-level experts found a focus on care coordination and achieving effective collaboration between patient and HCP(s) relevant. On micro-level, it is key that HCPs provide effective communication (e.g., simplifying treatment strategies and information for patients, encouraging patients to ask questions), have an open and empathic attitude, are aware of the patient’s social circumstances, have a holistic focus, respecting the wishes and preferences of patients, applying shared decision-making together with patients, provide self-management support, and establishing a therapeutic relationship. Also, the involvement of patients and their family/informal caregivers in the care process was found relevant. There was no agreement on the relevance concerning HCPs stimulating self-monitoring by patients. It was mentioned that “It is important that the patient can monitor his own medical condition. However, a person with low health literacy skills with for example severe rheumatism may need someone else to monitor him/her. Stimulating by the care provider is important, but you have to take into account what someone is able to do. I don’t think everyone can and will monitor their own health. It is beneficial for those who can” (P11). ## Outcomes The following outcomes were considered relevant for PCC in primary care: an improved treatment approach with a more accurate intensity of support provided, higher therapy concordance, increased patient involvement, improved (psychological) health outcomes, improved health-related quality of life (HRQoL), higher satisfaction of patient, informal caregiver and/or HCP(s), improved relationship between patient and HCP(s), more accessible care, higher quality of care, and a higher cost-effectiveness of healthcare. One participant mentioned: “Intensity of the support provided by the HCP is very important as an outcome. You could consider it as a success factor of PCC, it is tailored support to the patient” (P12). No consensus was found on the items self-management skills of patients and health system outcomes (reduced use of healthcare system, less referrals, less follow-up examinations, reduced emergency department visits, reduced hospital (re)admissions) for PCC in primary care in the Netherlands. ## Additional items In addition to the items identified in the literature, the participants stated several other items, such as caregivers having more pleasure in their job as an outcome. To enhance (the focus on) PCC in primary care for low health literacy skills groups, the expertise of professionals who are familiar with working and treating these groups from diverse backgrounds could be used (i.e., peer education). Another item mentioned was that when involving patients in their care process, the responsibilities of the patient and HCP need to be clearly defined. ## Refined programme theory Based on the results of the FGDs, the middle-range PT derived from the international RRR has been refined for the Dutch setting (Fig 4). In this refined PT the context items (C), mechanisms (M), and outcomes (O) that have been added, are underlined. The non-underlined items were already included in the middle-range PT. Fig 4: Refined PT by FGDs. The refined PT demonstrated that to provide a better intensity of support to the patient (O) and optimally align care to the patient (O), it is necessary that HCPs are equipped with the knowledge and skills and are trained and educated (C) to have a holistic focus (M) taking into account the diversity aspect (C), instead of a biomedical, disease-oriented approach (C). Communication (M) tailored to the needs and health literacy skills of the patient plays an important role, just as tailor-made supporting material (C) being available for patients. By developing these together with the target group (C), it is more likely these will match the target group and contribute to realising a more active role of the patient (and their families) in the care process (M, O), and in the shared decision-making process (M). To communicate effectively (M), HCPs should be provided with sufficient time and space (C), also to become aware of the patient’s (social) circumstances (C), discuss the wishes and preferences of patients (M), and work in a culturally competent way (C). As a result, a higher satisfaction of patient, informal caregivers and/or HCP(s) (O) can be achieved and the PCC treatment approach (O) can be improved. If several HCPs are involved in the care process, good collaboration within the team (C) and between different domains (C) is desirable to ensure good care coordination (M). These elements can be stimulated by including them in the policy of (care) organisations, wherein attention is also paid to people with low health literacy skills (C). HCPs having an open, respectful, and empathic attitude (M) plays an important role in establishing a strong therapeutic relationship (M). Patient’s social support networks (C) also help to improve the patients’ (psychological) health (O). In addition, better integration between ICT systems (C), offering e-health options and access to documents, recorded consultations (C), play a key role in a more accessible care (O). Flexible payment models (C) could facilitate PCC in primary care (O). Next to providing patient education (C), HCPs should provide self-management support to patients (M), stimulating patient’s self-management skills (O), self-efficacy (O) and therapy concordance (O). When goals and action plans are set up together during personalised care planning (C), HCPs and patients have a shared vision (C), the patient has more confidence to ask questions (C) about the treatment (possibilities), and has more insight into the importance of his/her treatment (M), this may lead to improved HRQoL (O). On the long-term, higher cost-effectiveness of healthcare (O) and a higher quality of care (O) can be accomplished. ## Principal findings In this study the middle-range PT from the international RRR was refined for PCC in primary care in the Netherlands by assessing the level of consensus on the relevance of items derived from the RRR by means of FGDs and a Delphi-panel. Based on the FGDs, several items have been added to refine the PT. The context items that were added concern HCPs being aware of the patient’s (social) circumstances, working in a culturally competent way, HCPs and patients having a shared vision and setting up goals and action plans together, patients having more confidence to ask questions, providing tailor-made supporting material, developing supporting material and tools together with the target group, a better integration between ICT systems, providing patient access to documents and recorded consultations, and flexible payment models being in place. No mechanisms were added. Outcomes that were added include better alignment of care to the patient, having accessible care, improving the patient’s self-efficacy, improving HRQoL, higher cost-effectiveness of healthcare, and a higher quality of care. One item was excluded from the middle-range PT to refine the PT as not all FGDs found this item relevant for PCC in primary care in the Dutch setting, namely improved health system outcomes (outcome). This study makes clear that sufficient attention needs to be paid to the complex interplay of the context items, mechanisms and outcomes concerning PCC in primary care in the Netherlands. Bypassing this complexity will most likely not lead to the desired effectiveness of PCC in primary care. The use of all items in their mutual coherence is necessary to truly realise PCC. ## Strengths and limitations One of the strengths of this study is the use of the combination of FGDs and the Delphi method. The participation of both–the often thought of as hard to reach—patients with low (health) literacy levels and primary care professionals increase the face validity of the results of this study. A possible limitation concerns the limited number of FGDs. It is suggested to conduct two to three FGDs to capture $80\%$ of themes, and three to six groups for $90\%$ of themes [49]. However, data saturation seemed to be reached as in the second and fourth FGD no new items were mentioned than in the first and third FGD. Also, there were no specific inclusion criteria for participants of FGD 1 and 2. These participants were recruited through convenience sampling. A third limitation to be considered is that the group moderators of FGD 3 and 4 were not impartial to the study. Nevertheless, they only moderated the discussion and did not share their own opinions. ## Comparison to previous studies Consistent with our refined PT, studies have found that in order to deliver effective PCC the patient wishes, needs, and abilities need to be taken into account to align care to the patient [50, 51]. Also, HCPs should stimulate patients to set and achieve their own treatment goals, and access to care should be optimised [50]. The importance of providing tailored supporting materials, culturally competent working, and self-efficacy of the patient has also been reported [51–53]. Individualised care plans, physical comfort at GP practice, and providing patients emotional support were also mentioned, but not found in our study [50]. ## Implications for practice and research Given the complexity of the interplay of all items, it is recommended for healthcare organisations to develop and implement an all-encompassing approach and to divide the approach into phases, to make it manageable. During the first phase (initiation) HCPs need to acquire relevant knowledge and skills through education and training. Patients need to be aware of their role in their care process and that they have social support networks. In the second phase (decision & adoption) adjustments regarding the healthcare system, policy-making, financing issues, integration between ICT systems, and creating sufficient experimental space, time and resources are made concrete. In the third phase (execution) the focus is on the implementation of a good collaboration between HCPs, the provision of self-management support, patient education, shared decision-making, whereby information and communication should be simplified. In the fourth phase (monitoring & evaluation) it is necessary to gain insight into (unexpected) problems and challenges, to find out to what extent the intended results/effectiveness are being achieved and to meet the needs for resources. With respect to further research, it is recommended to assess how and to what extent the items have been collectively implemented and to evaluate how effective PCC is in practice, for whom, how and why. Also, items on which dissensus was found need to further examined why they were found irrelevant for the Dutch setting. Our understanding of PCC is likely to increase (faster) when applying realist research iteratively and in different settings. ## Conclusion This study shows that for PCC to be effective in primary care, the complex interplay of context, mechanisms, and outcomes deemed relevant to a setting must be met. Added items to refine the PT for the Dutch primary care setting indicated that to optimally align care to the patient, next to tailored communication, also tailored supporting material that is developed together with the target group is key. HCPs and patients need to have a shared vision and set up goals and action plans together. HCPs should stimulate patient’s self-efficacy, need to be aware of the patient’s (social) circumstances and work in a culturally sensitive way. Better integration between ICT-systems, flexible payment models, and patients access to documents, recorded consultations should be in place. 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--- title: 'APSified OCT-angiography analysis: Macula vessel density in healthy eyes during office hours' authors: - Meike Müller - Julia Schottenhamml - Sami Hosari - Bettina Hohberger - Christian Y. Mardin journal: PLOS ONE year: 2023 pmcid: PMC9997993 doi: 10.1371/journal.pone.0282827 license: CC BY 4.0 --- # APSified OCT-angiography analysis: Macula vessel density in healthy eyes during office hours ## Abstract ### Purpose Optical coherence tomography angiography (OCT-A) can visualize retinal capillary microcirculation non-invasively. In order to investigate potential factors influencing OCT-A diagnostics, the aim of the present study was to determine circadian changes in macular vessel density (VD) in healthy adults during office hours, considering axial length (AL) and subfoveal choroidal thickness (CT). ### Methods In the prospective study 30 eyes of 30 healthy subjects (mean age 28.7 ± 11.8, range 19–60 years) were recruited who underwent repeated measurements of AL, subfoveal CT and three-layer macula VD (superficial vascular plexus (SVP), intermediate capillary plexus (ICP) and deep capillary plexus (DCP)) on a single day at three predetermined timepoints (9 AM, 3 PM, and 9 PM). For better intra- and interindividual scan comparability, the new Anatomic Positioning System function (APS, part of Glaucoma Module Premium Edition [GMPE], Heidelberg Engineering, Germany) allowing analysis of identical retinal areas, was used for quantitative OCT-A analysis. ### Results Overall mean macula VD was unchanged during office hours in SVP, ICP and DCP, respectively ($p \leq 0.05$). In addition, AL and CT showed no statistically significant changes over time ($p \leq 0.05$). Rather, a large interindividual variance of VD with different peak time was observed. Contrary to the overall data, sectorial VD changed in dependency of office hours in all layers with an increase of VD in SVP between 9 AM and 9 PM ($$p \leq 0.003$$), in ICP between 3 PM and 9 PM ($$p \leq 0.000$$), in DCP between 9 AM and 9 PM ($$p \leq 0.048$$), and 3 PM and 9 PM ($$p \leq 0.000$$), respectively. ### Conclusion Overall mean macula VD, subfoveal CT and AL tended not to show statistically significant changes over time in this cohort, whereas a regional analysis of VD did. Therefore, a circadian influence on capillary microcirculation should be kept in mind. Moreover, the results highlight the importance of a more detailed analysis of VD in different sectors and different vascular layers. In addition, the pattern of diurnal variation could vary inter-individually, thus a patient-specific fluctuation pattern would need to be considered when evaluating these parameters in clinical practice. ## Introduction Optical coherence tomography angiography (OCT-A) is a new non-invasive imaging technique, offering a novel diagnostic option by generating high-quality, three-dimensional images of the macular and peripapillary regions at capillary level [1,2]. In contrast to fundus fluorescein angiography (FA), the former gold standard to visualize retinochoroidal blood flow, OCT-A is based on detecting the reflection behavior of moving erythrocytes in a static environment and therefore no longer requires dye injection for vascular imaging as used in FA. In addition, FA is not able to distinguish between single retinal vascular layers [1,3]. OCT-A technology, which has been shown to have high correlation with anatomical structures [4], enables multi-layer analysis of vessel density (VD). Recent developments have made it possible to visualize three retinal sublayers with OCT-A: a superficial vascular plexus (SVP), intermediate capillary plexus (ICP) and deep capillary plexus (DCP) [5]. Since the macula with its complex microvasculature plays an important role in the pathogenesis of many ocular diseases and a reduction of its perfusion can be considered as an early glaucoma marker [6], a more detailed understanding of its regulation is the focus of clinical interest. In particular, when comparing longitudinal OCT-A data to study disease progression or to evaluate a therapeutic success, it is important to distinguish physiologic from pathologic factors that may influence alterations of VD, such as time of day. Circadian variations have already been described for parameters like axial length (AL), choroidal thickness (CT) or intraocular pressure (IOP) [7–11]. However, very little is known to date about diurnal fluctuations in retinal perfusion visualized by OCT-A, and so far there are only few published data [12–18]. To the best of our knowledge, the present study is the first to investigate potential circadian overall and sectorial macula VD changes of not only two but three retinal layers (including ICP) in healthy adult eyes during a time span from 9 AM to 9 PM. Additionally, axial length (AL) and subfoveal choroidal thickness (CT) changes were investigated. ## Participants and study design The present study was designed as a prospective control study. 30 eyes of 30 healthy adults were evaluated (7 male, 23 female). Mean age was 28.7 ± 11.8 years with a range of 19–60 years (women: 28.8 ± 11.4; men: 28.4 ± 13.0). According to the spherical equivalent refraction (SER), 15 emmetropic subjects (SER: +0.75 to -0.75 DS, mean: -0.13 ± 0.2 DS), 13 myopic (SER: ≤ -1.00 DS, mean: -3.46 ± 1.8 DS) and two hyperopic (SER: ≥ + 1.00 DS, mean +1.375 ± 0 DS) eyes were analyzed. A complete standardized ophthalmologic screening, including slit-lamp biomicroscopy, fundoscopy and Goldman applanation tonometry, was performed on each subject. The presence of any eye disease and previous ophthalmic surgery or laser treatment were exclusion criteria and IOP had to be within the normal range. The study protocol was approved by the local ethic committee of Erlangen and was performed in accordance with the tenets of the Declaration of Helsinki. Informed written consent, approved by the ethic committee of Erlangen, was obtained from all study participants. ## Image acquisition All measurements were performed on each subject without prior pupil dilation at three consecutive sessions (9 AM, 3 PM and 9 PM) within one day. An interval of six hours had to be between each measurement. One eye of each subject was chosen randomly before the first acquisition. To avoid fluctuations in blood pressure and heart rate, the measurements were taken each time after a waiting period of ten minutes in a sitting position and the participants were prohibited to consume caffeine prior to each visit. Each session included a high-resolution three-layer en face OCT-A scan of macula region (including SVP, ICP and DCP) and a high-speed OCT scan with enhanced depth imaging mode (EDI) to determine subfoveal CT, both by Heidelberg Spectralis II OCT (Heidelberg Engineering, Heidelberg, Germany). In addition, AL was measured with IOL master 500 (Carl Zeiss Meditec AG, Jena, Germany). All OCT-A scans were recorded on a 2.9 x 2.9 mm2 window with a 15° x 15° angle and a lateral resolution of 5.7 μm/pixel. Subfoveal CT was measured manually with a vertical distance between the hyperreflective line of Bruch’s membrane and the choroid-scleral interface. ## Erlangen Angio-Tool and Anatomic Positioning System After all OCT-A scans were manually checked for artefacts, shadows and correct segmentation, macula data was exported from the clinical database and then imported into the prototype SP-X1902 software (Heidelberg Engineering, Heidelberg, Germany). The Anatomic Positioning System function (APS, part of Glaucoma Module Premium Edition [GMPE], Heidelberg Engineering, Germany) allows each scan to be aligned to the patient´s individual Fovea to Bruch’s Membrane Opening (FoBMOC) axis for better intra- and interindividual scan comparability. Integration of APS information was also implemented into the Erlangen Angio-Tool (EA-Tool) version 2.0, coded in Matlab (The MathWorks, Inc., R2017b). In addition to the APS information, the macular en face OCT-A images of SVP, ICP and DCP were imported into the EA-Tool and analyzed separately for each scan. Overall and sectorial macula VD (12 sectors s1-s12 á 30°) were analyzed for SVP, ICP and DCP, respectively. The analyzed region of the scan size was 6.10 mm2. ## Statistical analysis SPSS version 28 (IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp.) was used for the statistical analysis and p-values less than 0.05 were considered to be statistically significant. Demographic information (age and gender) was available and used as covariates to correct for them. Moreover, interaction terms were firstly incorporated into the statistical model. After a first run, statistically not significant variables and interactions were removed and the model was run again without them. For all experiments, a linear mixed model with type III sum of squares was used with the daytime as predictor variable to examine circadian changes in VD as well as AL and CT. A random intercept was included into the model to account for a clustering in the patients. The daytime was set as a repeated measure with a compound symmetry covariance structure. For the sectorial VD analysis, the sectors were additionally added as a repeated measure with the same covariance structure. Pairwise comparisons were computed based on the estimated marginal means and were adjusted with Bonferroni to account for multiple comparisons. Macula VD, AL and CT data were presented as mean and standard deviation (SD). ## Macula OCT-A vessel density Overall mean VD ± SD over time was 30.77 ± 1.6 (SVP), 22.97 ± 1.5 (ICP) and 24.44 ± 2.2 (DCP), respectively. A linear mixed model analysis with pairwise comparisons (9 AM to 3 PM, 9 AM to 9 PM and 3 PM to 9 PM) revealed no statistically significant changes in overall mean VD between the time points for all retinal layers ($p \leq 0.05$). Table 1 presents overall mean VD of SVP, ICP, and DCP at the corresponding time of measurement and their respective p-values. Table 1 \| Unnamed: 0 \| 9 AMmean VD ± SD \| 3 PMmean VD ± SD \| 9 PMmean VD ± SD \| p \| p.1 \| p.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| 9 AMmean VD ± SD \| 3 PMmean VD ± SD \| 9 PMmean VD ± SD \| 9 AM–3 PM \| 9 AM–9 PM \| 3 PM–9 PM \| \| SVP \| 30.59 ± 1.7 \| 30.75 ± 1.7 \| 30.97 ± 1.4 \| 1.00 \| 0.19 \| 0.76 \| \| ICP \| 22.96 ± 1.6 \| 22.72 ± 1.7 \| 23.22 ± 1.2 \| 0.88 \| 0.80 \| 0.10 \| \| DCP \| 24.39 ± 2.2 \| 24.17 ± 2.2 \| 24.76 ± 2.1 \| 1.00 \| 0.57 \| 0.13 \| While overall mean VD remained constant, it was found that the individual circadian VD curves differed from subject to subject. No uniform fluctuation pattern but a high interindividual variance with different peak times was determined (Fig 1). Fig 1: Circadian vessel density curves showing patient-specific fluctuations with different peak times.Overall vessel density (VD) curves are presented for superficial vascular plexus (SVP), intermediate capillary plexus (ICP) and deep capillary plexus (DCP) at 9 AM, 3 PM and 9 PM. The dashed line corresponds to the respective mean values. Contrary to overall data, sectorial VD analysis revealed statistically significant changes between the selected time points in all sectors. With pairwise comparisons of linear mixed model analyses, statistically significant changes with an increase of VD in all sectors in SVP were observed between the 9 AM and 9 PM measurement ($$p \leq 0.003$$). For ICP, VD increased statistically significant in all sectors from the 3 PM to 9 PM measurement ($$p \leq 0.000$$). Analysis of VD data of DCP, a statistically significant increase between the 9 AM and 9 PM measurement ($$p \leq 0.048$$) and 3 PM and 9 PM ($$p \leq 0.000$$) was observed, respectively. All p-values from pairwise comparisons are shown in Table 2. Table 2 \| Unnamed: 0 \| 9 AM– 9 PM \| 9 AM– 3 PM \| 3 PM– 9 PM \| \| --- \| --- \| --- \| --- \| \| SVP \| 0.003 \| 0.517 \| 0.177 \| \| ICP \| 0.084 \| 0.112 \| 0.0 \| \| DCP \| 0.048 \| 0.456 \| 0.0 \| In addition, VD varied between all sectors: pairwise comparisons of linear mixed model analyses revealed multiple statistically significant differences of VD between the sectors for each layer and for each measurement time ($p \leq 0.05$). Mean sectorial VD ± SD (sector 1–12) of each layer at the corresponding time of measurement are shown in Table 3. Table 3 \| Unnamed: 0 \| SVP \| SVP.1 \| SVP.2 \| ICP \| ICP.1 \| ICP.2 \| DCP \| DCP.1 \| DCP.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| mean VD ± SD \| mean VD ± SD \| mean VD ± SD \| mean VD ± SD \| mean VD ± SD \| mean VD ± SD \| mean VD ± SD \| mean VD ± SD \| mean VD ± SD \| \| \| 9 AM \| 3 PM \| 9 PM \| 9 AM \| 3 PM \| 9 PM \| 9 AM \| 3 PM \| 9 PM \| \| s1 \| 31.19±2.6 \| 31.50±2.1 \| 31.52±2.1 \| 22.64±2.4 \| 22.25±2.7 \| 22.58±2.3 \| 24.71±3.3 \| 24.36±3.8 \| 25.11±3.3 \| \| s2 \| 30.34±2.4 \| 30.57±2.5 \| 30.80±2.3 \| 22.84±2.4 \| 22.17±2.7 \| 22.55±2.5 \| 24.61±3.0 \| 23.94±3.7 \| 24.64±3.3 \| \| s3 \| 30.00±2.3 \| 30.01±3.4 \| 30.32±2.4 \| 24.07±2.3 \| 23.43±3.2 \| 24.16±1.8 \| 24.93±3.4 \| 24.25±4.1 \| 25.20±2.6 \| \| s4 \| 30.62±2.2 \| 30.53±2.5 \| 30.70±2.3 \| 23.87±2.2 \| 23.58±2.6 \| 24.10±1.7 \| 25.27±3.0 \| 24.98±3.2 \| 25.47±2.4 \| \| s5 \| 30.80±1.9 \| 31.21±2.2 \| 31.58±1.8 \| 22.40±2.2 \| 22.54±2.5 \| 23.08±1.9 \| 23.85±2.6 \| 24.23±2.9 \| 24.59±3.0 \| \| s6 \| 31.31±3.1 \| 31.73±2.0 \| 32.05±1.7 \| 21.94±2.6 \| 22.52±2.0 \| 22.95±1.9 \| 23.95±3.2 \| 24.67±2.9 \| 24.75±2.9 \| \| s7 \| 31.93±2.3 \| 32.06±1.7 \| 32.17±1.7 \| 22.21±2.4 \| 22.31±2.4 \| 22.90±1.8 \| 23.91±3.0 \| 24.02±3.0 \| 24.67±2.8 \| \| s8 \| 31.06±1.9 \| 31.16±1.9 \| 31.34±1.7 \| 22.42±2.1 \| 22.64±2.1 \| 23.10±1.6 \| 23.23±2.9 \| 23.86±2.4 \| 24.37±2.6 \| \| s9 \| 29.82±1.6 \| 29.62±2.4 \| 29.82±1.7 \| 23.76±1.4 \| 23.30±1.9 \| 23.94±1.9 \| 24.55±2.9 \| 24.23±2.6 \| 24.29±3.0 \| \| s10 \| 28.37±2.2 \| 28.57±2.5 \| 28.83±1.7 \| 23.88±1.7 \| 22.99±2.5 \| 23.78±1.4 \| 24.33±2.3 \| 23.41±2.9 \| 24.14±2.7 \| \| s11 \| 30.19±2.2 \| 30.39±1.9 \| 30.71±1.7 \| 23.02±1.8 \| 22.56±1.7 \| 22.93±1.6 \| 24.57±2.4 \| 23.83±2.7 \| 24.84±2.6 \| \| s12 \| 31.47±2.7 \| 31.69±2.4 \| 31.82±2.1 \| 22.52±2.5 \| 22.40±2.4 \| 22.53±2.0 \| 24.79±3.0 \| 24.31±3.5 \| 25.00±2.9 \| ## Axial length and subfoveal choroidal thickness Mean AL ± SD was 23.96 ± 0.79 mm (range 23.67–24.26 mm) and mean subfoveal CT ± SD was 332.99 ± 78.1 μm (range 303.15–362.99 μm) over time. Linear mixed model analyses with pairwise comparisons (9 AM to 3 PM, 9 AM to 9 PM and 3 PM to 9 PM) were done to reveal diurnal changes. Both AL and CT showed no statistically significant changes between the measurement times (AL: $$p \leq 1.00$$; CT: $$p \leq 1.00$$), yet a wide SD of the values was observed. Table 4 provides an overview of mean AL ± SD and subfoveal CT ± SD at the corresponding time of measurement together with the respective p-values. Table 4 \| Unnamed: 0 \| 9 AMmean ± SD \| 3 PMmean ± SD \| 9 PMmean ± SD \| p \| \| --- \| --- \| --- \| --- \| --- \| \| AL \| 23.963 ± 0.80 \| 23.964 ± 0.80 \| 23.963 ± 0.80 \| 1.0 \| \| CT \| 332.83 ± 77.9 \| 332.0 ± 79.6 \| 334.13 ± 79.2 \| 1.0 \| ## Discussion OCT-A technology enables three-dimensional multi-layer imaging of retinochoroidal vascular structures in detail [1,2]. With the capability to generate high-resolution scans efficiently and non-invasively, OCT-A is becoming increasingly important in the diagnosis of ocular vascular diseases [19]. For proper use, physiological factors or drugs affecting retinochoroidal blood flow are clinically meaningful in order to be reliably distinguished from real pathological changes, especially when evaluating longitudinal OCT-A data. Yet, the effect of physical exertion, age, sex, smoking and mydriatic agents on retinal perfusion have been described [20–23]. This study aimed to investigate circadian variations of retinal macula VD measured with OCT-A in healthy adults for a better understanding of the complex functioning and control of capillary structures in the retina. Additionally, AL and CT changes were investigated. Overall mean VD did not change significantly between 9 AM to 9 PM for SVP, ICP and DCP in the present cohort. AL and CT showed no statistically significant diurnal variations either. A uniform fluctuation pattern of overall mean VD of all subjects was not detectable, but rather a hint of patient-specific fluctuation with high interindividual variance and different peak times. Contrary to overall VD, a sectorial analysis revealed statistically significant changes in all layers, showing a trend of VD to increase over the day. For SVP, a statistically significant increase of VD was seen between the 9 AM measurement and the 9 PM measurement ($$p \leq 0.003$$), for ICP between 3 PM and 9 PM ($$p \leq 0.000$$) and for DCP between 9 AM and 9 PM ($$p \leq 0.048$$) as well as 3 PM and 9 PM ($$p \leq 0.000$$), respectively. The first step for correct scan comparability resulting in high diagnostic value, is precise data generation and analysis. Since there are different OCT-A devices and no standardized analysis software is available so far, different perfusion values can be generated. Moreover, high scan quality is required as it affects longitudinal changes of VD. Increasing scan quality has been shown to correlate with increasing peripapillary and macula VD [12,24]. In the present study all scans were performed with en face OCT-A module by Heidelberg Spectralis II OCT. It provides detailed visualization of fine capillary vascular networks within three retinochoroidal layers (SVP, ICP and DCP) with high lateral and axial resolution. By using TruTrack Active Eye Tracking® and a projection artefact removal (PAR) algorithm, which uses information from the superficial vascular complex to remove artefacts and shadows out of underlying layers, microvascular structures can be imaged with high precision. In addition with the semi-automated EA-Tool, which enables quantification of VD with a high level of reliability and reproducibility [25], even smallest capillary changes can be displayed. Furthermore, all measurements require a reliable point-to-point comparison to avoid variations in VD due to site-specific variations. So far, all scans of a single patient can be aligned to the baseline scan using the standard follow-up mode to eliminate influence of head tilt and eye rotation. For a more accurate scan comparison not only within a subject but also between different subjects, it must be considered that interindividual anatomic variations exist and may affect sectorial analysis. The new APS tool enables improved analysis of identical retinal areas by defining two anatomical landmarks, the fovea (Fo) and the Bruch’s membrane opening center (BMOC) on an optical nerve head (ONH) circle scan. Afterwards, the OCT-A scan can be re-positioned according to the FoBMOC axis by aligning the B-scan axis parallel to the FoBMOC axis (Fig 2). Fig 2: OCT-A scan window of macula region without (green box) and after (blue box) aligning according to the fovea (Fo) to Bruch’s membrane opening center (BMOC) axis (FoBMOC axis). Up to now little is known about diurnal changes of retinal perfusion. In this publication, no statistically significant overall mean VD changes were found during predetermined office hours between 9 AM to 9 PM. Consistent with these findings, previous studies have described a constant retinal blood flow in healthy subjects throughout the day [13–16]. Odabaş et al. found no statistically significant changes in VD for superficial and deep retinal layer (except for superior zone of the deep retinal layer) between 9 AM and 3 PM [13]. Lin et al., who repeated their scan sessions between 9 AM and 5 PM on a separate day to achieve highly reliable results, could not detect significant circadian variations either [15]. Similar results of a constant sublayer and full retinal perfusion measured at 7 AM and 4 PM on a single day were achieved by Rommel et al. Interestingly, the authors found out that retinal blood flow was unaffected by mean arterial pressure and IOP which has led them to assume that the insensitivity may be caused by autoregulatory mechanisms [16]. While this effect may account for the retina, the choriocapillaris has found to behave differently. Significant diurnal changes of choroidal microperfusion were found in healthy individuals [26–28] and also in patients with idiopathic epiretinal membrane [29]. Moreover, previous studies have shown that choroidal blood flow, unlike retinal blood flow, depends on changes in systemic pressure [28,30,31]. Yet, the exact mechanisms are not totally understood and further investigations are necessary. While vascular structures in the choroid were found to be rich of autonomic vasoactive nerve endings, adrenergic fibers in the retina were detected between inner nuclear and inner plexiform layers only in areas until the lamina cribrosa [32,33]. Blood flow in peripheral retinal areas is thought to be subject to autoregulation, which is mediated by an interaction of myogenic and metabolic factors through a release of vasoactive substances [33–35]. Changes in perfusion pressure or metabolic demand of the tissue cause an adjustment of the vascular tone of the resistance vessels by influencing arteriolar smooth muscle cells and capillary pericytes [35]. While this mechanism works well in healthy eyes, there is evidence that autoregulatory capacity is impaired in patients with ocular diseases such as diabetic retinopathy [36] and glaucoma [12,14,17]. Mansouri et al. reported diurnal variations of peripapillary and macula VD with a small but mostly not significant increase throughout their scan sessions [12]. Baek et al. showed significantly greater changes of peripapillary and macula VD as well as IOP and mean ocular-perfusion pressure in eyes with primary open-angle glaucoma than in healthy ones [14]. Müller et al. found a statistically significant increase in macula VD for deep layer and a relationship between VD and mean arterial pressure as well as heart rate [17]. Thus, based on the assumption of disturbed blood flow regulation in those patients, repeated OCT-A measurements need to be performed every time under the same conditions and at the same time of day. In contrast to overall mean VD, this study showed statistically significant differences in all layers in regional analysis of VD by subdivision into 12 sectors in the present cohort. A possible reason for this could be the large spread of the individual (as seen in Fig 1) as well as the sectorial (as seen in Table 3) values around the mean. Accordingly, a calculation of overall mean VD would not represent the actual conditions. Therefore, a subdivision into sectors as well as vascular layers might be considered for analysis of VD. Previous studies have reported that AL and CT exhibit circadian fluctuations in healthy subjects [8,9,11,37–39]. Using partial coherence interferometry (PCI), Stone et al. proved a significant diurnal variation of AL with a maximum ad midday [39]. Brown et al., who also used PCI, were the first to show diurnal variations of CT and described a trend of AL and CT to fluctuate in antiphase [37]. Chakraborty et al. also showed nearly antiphasic variations in mean AL and mean CT [8]. Controversially to the findings of Tan et al., Usui et al. and Lee et al., who reported a significant decrease in CT during the day (maximum in the morning and minimum in the evening) [9,11,38], Chakraborty et al. found the choroid to be thinnest in the morning and thicker at night [8]. A possible reason could be that their measurements were performed with an optical biometer which may not be comparable to OCT. Furthermore, it was determined that both myopes and participants with longer AL and thinner CT had a significantly lower pattern of diurnal variation with lower amplitude [9,38]. In contrast, the present study did not detect significant diurnal changes between 9 AM and 9 PM. But since almost half of all participants in this study were myopic (13 of 30), previous findings of a lower fluctuation pattern in myopes may explain why no variations were detected. Furthermore, no uniform fluctuation pattern but a wide SD of the values was observed. This finding is consistent with study data from Pollithy et al., who also found no significant circadian CT changes with Heidelberg Spectralis OCT, but individually different fluctuation patterns within a 24-hour period [40]. Another study by Osmanbasoglu et al., which also used the Heidelberg Spectralis OCT, could not detect significant mean CT changes in healthy eyes between 9 AM and 4 PM either [41]. A possible reason for the differing results could be a mix of factors, including differences between devices or time span of the measurements, which did not include night times, while others covered 24 hours or even longer. As previously described by Pollithy et al. [ 40], no uniform increase or decrease in the values of all subjects was observed in the present study. However, the statements of most of the authors are based on calculated mean values at the respective time points [8,9,11,38], so interindividual differences could not be taken into account. This study is not without limitations. The selected timepoints did not contain night times, so the results do not show whole diurnal fluctuation. Moreover, only one OCT-A device was used, but as mentioned above, VD might differ between devices. Advantage of a small scan size area of 2.9x2.9 mm2 is generating high-resolution OCT-A images, but as a result, a large retinal area remains unclear. Furthermore, the cohort of the present study is small, thus the results of this paper should be seen as trend, which has to be validated in studies with larger patients’ cohorts. Therefore, further studies with larger patient database are necessary. In addition, systemic factors such as IOP, blood pressure or hydration were not recorded, so their influence on diurnal retinal blood flow could not be investigated. This may be interesting when assuming that retinal VD might be subject to any autoregulation. Finally, it remains to be seen whether peripapillary region shows circadian fluctuations as macula was at the center of interest in this study. ## Conclusion As a non-invasive tool for imaging and evaluating capillary retinochoroidal blood flow, OCT-A expands diagnostic spectrum. This is the first study evaluating diurnal fluctuations of three-layer retinal macular VD in healthy adult eyes. Although overall macula VD, CT and AL showed no significant changes during office hours, regional differences and an interindividual pattern of diurnal fluctuation were observed and should be considered in clinical routine. 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--- title: The vascular function effects of adding exenatide or meal insulin to basal insulin therapy in early type 2 diabetes authors: - Ravi Retnakaran - Jiajie Pu - Chang Ye - Alexandra Emery - Caroline K. Kramer - Bernard Zinman journal: Cardiovascular Diabetology year: 2023 pmcid: PMC9998007 doi: 10.1186/s12933-023-01781-z license: CC BY 4.0 --- # The vascular function effects of adding exenatide or meal insulin to basal insulin therapy in early type 2 diabetes ## Abstract ### Objective Basal insulin glargine has a neutral effect on cardiovascular risk in type 2 diabetes (T2DM). In practice, basal insulin is often paired with a glucagon-like peptide-1 receptor agonist (GLP1-RA) or meal insulin; however, the cardiovascular implications of these combinations have not been fully elucidated. In this context, we sought to evaluate the vascular function effects of adding the GLP1-RA exenatide or meal insulin lispro to basal glargine therapy in early T2DM. ### Methods In this 20-week trial, adults with T2DM of < 7-years duration were randomized to 8-weeks treatment with (i) insulin glargine (Glar), (ii) glargine + thrice-daily lispro (Glar/Lispro), or (iii) glargine + twice-daily exenatide (Glar/Exenatide), followed by 12-weeks washout. At baseline, 8-weeks, and washout, fasting endothelial function was assessed with reactive hyperemia index (RHI) measurement by peripheral arterial tonometry. ### Results At baseline, there were no differences in blood pressure (BP), heart rate (HR) or RHI between participants randomized to Glar ($$n = 24$$), Glar/Lispro ($$n = 24$$), and Glar/Exenatide ($$n = 25$$). At 8-weeks, Glar/Exenatide decreased systolic BP (mean − 8.1 mmHg [$95\%$CI − 13.9 to − 2.4], $$p \leq 0.008$$) and diastolic BP (mean − 5.1 mmHg [− 9.0 to − 1.3], $$p \leq 0.012$$) compared to baseline, with no significant changes in HR or RHI. Notably, baseline-adjusted RHI (mean ± SE) did not differ between the groups at 8-weeks (Glar 2.07 ± 0.10; Glar/Lispro 2.00 ± 0.10; Glar/Exenatide 1.81 ± 0.10; $$p \leq 0.19$$), nor did baseline-adjusted BP or HR. There were no differences between the groups in baseline-adjusted RHI, BP or HR after 12-weeks washout. ### Conclusion Adding either exenatide or lispro to basal insulin therapy does not appear to affect fasting endothelial function in early T2DM. Trial Registration: ClinicalTrials. Gov NCT02194595. ## Introduction The 2008 Food and Drug Administration (FDA) guidance requiring ascertainment of the cardiovascular safety of new glucose-lowering medications has launched an era in which the vascular implications of diabetes therapies are fundamental considerations in the management of type 2 diabetes (T2DM)[1, 2]. In this regard, glargine is unique amongst older (i.e. pre-2008) diabetes therapies in having been the subject of a dedicated cardiovascular outcome trial that confirmed its neutral impact on vascular risk [3]. In current clinical practice, basal insulin is often complemented with either a glucagon-like peptide-1 receptor agonist (GLP1-RA) or meal insulin [4–6]. However, in the absence of assessment with a dedicated cardiovascular outcome trial, the vascular implications of these combinations are less certain and have generally been inferred from post-hoc analyses of previous trials evaluating their components [7, 8]. In the recently-reported PREserVing Beta-cell Function in Type 2 Diabetes with Exenatide And InsuLin (PREVAIL) Trial, we demonstrated that 3 glargine-based regimens (glargine alone, glargine with thrice daily meal insulin lispro, and glargine with twice daily administration of the GLP1-RA exenatide) had similar effects on both pancreatic beta-cell function after 8 weeks of treatment (primary outcome) and rates of diabetes remission after 12-weeks washout [9]. Recognizing the opportunity to also evaluate the vascular impact of these therapies, the design of the trial included serial assessment of endothelial function at baseline, after the 8-week intervention, and after washout. Thus, in this context, we now report the vascular function effects of adding exenatide or lispro to basal insulin therapy in early T2DM. ## Methods PREVAIL was a 20-week, open-label, parallel-arm trial wherein patients aged 30–80 years with T2DM of ≤ 7 years duration were randomized (1:1:1) to 8-weeks treatment with insulin glargine, glargine + thrice-daily lispro, or glargine + twice-daily exenatide, followed by 12-weeks washout. The study protocol and primary outcome have been described in detail previously [9]. The study was approved by the Mount Sinai Hospital Research Ethics Board and registered at ClinicalTrials. Gov (NCT02194595). All participants provided written informed consent. While the primary and metabolic outcomes have been reported recently [9], the current report presents the pre-specified ancillary outcome of endothelial function and associated vascular measures. ## Intervention The study intervention has been described in detail [9]. In brief, participants were randomly assigned by computer-generated random allocation sequence to 8-weeks treatment with one of the following regimens:(I)Glargine, administered at bedtime, with doses titrated to target fasting glucose ≤ 5.3 mmol/l;(II)Glargine at bedtime with thrice-daily pre-meal lispro, with doses titrated to target fasting glucose ≤ 5.3 mmol/l and 2-h postprandial glucose < 8 mmol/l;(III)Glargine at bedtime with twice-daily exenatide before breakfast and dinner at doses of 5 ug twice daily for the first 4 weeks, followed by 10ug twice daily for the next 4 weeks (glargine titrated to target fasting glucose ≤ 5.3 mmol/l). The assigned intervention was stopped at 8-weeks, after which participants entered a 12-week washout during which they were advised to follow healthy lifestyle practices for managing T2DM [9]. ## Vascular measures At each of the study visits at baseline, 4-weeks, 8-weeks and washout, heart rate and blood pressure were measured in a seated position, after 10 min of rest, with an automated blood pressure monitor. Two measurements were performed 10 min apart, with the average recorded. At the study visits at baseline, 8-weeks and washout, endothelial function was assessed by peripheral arterial tonometry (PAT) with the Endo-PAT2000 device (Itamar Medical Inc, Framingham, MA). This assessment was performed after overnight fasting (with glargine held that night). On the morning of the testing, meal insulin and exenatide were not administered because of the intention to assess the primary outcome of the trial (beta-cell function) on oral glucose tolerance test (OGTT) in the absence of these medications [9]. Because it was done prior to the OGTT at the study visits, PAT assessment was performed in the fasting state. For this assessment, PAT finger probes were placed on the distal index fingers of supine participants to enable measurement of baseline pulse amplitude. The measurement was then repeated at 30 s intervals for 4 min following deflation of an occluding blood pressure cuff that was placed on the proximal forearm at 60 mm Hg above systolic blood pressure for 5 min. Pulse amplitude data was recorded electronically and analyzed by the Endo-PAT digital signal processing algorithm, which calculated the Reactive Hyperemia Index (RHI) from the ratio of the average post-deflation pulse amplitude to the baseline pulse amplitude, divided by the corresponding ratio in the contralateral control finger (i.e. this hand did not face an occluding blood pressure cuff). Lower RHI has been shown to predict atherosclerotic coronary artery disease [10, 11]. The COVID-19 pandemic prevented completion of the Endo-PAT portion of the protocol in the full study population of 102 participants, first by preventing some in-person visits and later because of unavailability of the PAT finger probes. Accordingly, Endo-PAT assessment was completed in 73 participants, yielding the study population for the current analysis. ## Statistical analyses Statistical analyses were conducted with R 4.2.1 and on an intention-to-treat basis. Two-tailed P values < 0.05 were considered statistically significant. Continuous variables were tested for normality of distribution, and natural log transformation of skewed variables were conducted where necessary. Characteristics of the study arms at baseline were compared by Analysis of Variance (normally distributed variables) or Kruskal–Wallis test (skewed variables), or either Chi-Square test or Fischer exact test (categorical variables) (Table 1). Paired t-tests were conducted to assess changes in vascular measures from baseline to 8-weeks within each treatment arm (Table 2). The longitudinal changes in vascular measures from baseline to 8-weeks were compared between the 3 treatment groups by generalized estimating equation (GEE) model, wherein the treatment effect and time effect were examined. Vascular outcomes after intervention and after 3-month washout were compared between the groups by Analysis of Covariance (ANCOVA) with adjustment for their baseline measurements (Table 3).Table 1Baseline characteristics of the 3 treatment groups: (i) glargine, (ii) glargine + lispro, and (iii) glargine + exenatideGlargineGlargineGlargine + lispro + exenatide($$n = 24$$)($$n = 24$$)($$n = 25$$)pAge (years)56.4 (9.5)60.6 (7.1)55.5 (8.9)0.09Sex (% male)14 [58]10 [42]17 [68]0.17Ethnicity0.40 White n(%)15 (62.5)16 (66.7)18 [72] South Asian n(%)2 (8.3)1 (4.2)4 [16] Other n(%)7 (29.2)7 (24.1)3 [12]Duration of diabetes (years)3.5 (2.1)4.1 (2.0)3.4 (2.0)0.43DM medications before study0.91 Lifestyle only n(%)3 (12.5)2 (8.3)5 [20] Metformin n(%)17 (70.8)16 (66.7)15 [60] DPP-4 inhibitor n(%)1 (4.2)0 [0]0 [0] SGLT-2 inhibitor n(%)0 [0]0 [0]1 [4] Sulfonylurea n(%)0 [0]1 (4.2)0 [0] Metformin + DPP-4 inhibitor n(%)3 (12.5)4 (16.7)3 [12] Metformin + SGLT-2 inhibitor n(%)0 [0]1 (4.2)1 [4] Metformin + Sulfonylurea n(%)0 [0]0 [0]0 [0]Vascular complications Retinopathy n(%)0 [0]0 [0]0 [0]* Albuminuria n(%)1 [4]1 [4]1 [4]1.00 Neuropathy n(%)1 [4]2 [8]2 [8]1.00 Cardiovascular disease n(%)1 [4]2 [8]1 [4]0.84Vascular Risk Factors Hypertension n(%)10 [42]19 [79]12 [48]0.02 Hypercholesterolemia n(%)12 [52]14 [58]16 [64]0.69 Current smoking n(%)2 [14]3 [18]3 [13]1.00Cardioprotective medications ACE inhibitor/ARB n(%)9 [38]16 [67]12 [48]0.12 Statin n(%)14 [58]15 [62]17 [68]0.78 Aspirin n(%)4 [17]7 [29]3 [12]0.32Body mass index (kg/m2)32.2 (7.0)31.2 (5.7)30.9 (5.8)0.75Waist circumference (cm)108 (15.6)104 (13.7)105 (12.5)0.59Baseline A1c (%)6.6 (0.7)6.3 (0.7)6.6 (0.7)0.34Vascular measures Systolic blood pressure (mmHg)133 (18.7)135 (15.2)137 (15.5)0.69 Diastolic blood pressure (mmHg)82 (9.7)81 (9.0)84 (10.7)0.56 Heart rate (beats per min)71 (13.0)72 (11.8)69 (9.5)0.62 RHI1.88 (0.41)1.83 (0.46)1.83 (0.32)0.86Continuous variables are presented as mean followed by standard deviation in parentheses (if normal distribution) or median followed by interquartile range (if skewed distribution). Categorical variables are presented as proportionsTable 2Changes in vascular measures from baseline to 8-weeks in response to the interventionsVascular measuresGlargineGlargine + LisproGlargine + Exenatidemean$95\%$CIPmean$95\%$CIPmean$95\%$CIPSystolic BP (mmHg)− 3.7(− 10.2 to 2.7)0.24− 2.3(− 9.0 to 4.5)0.50− 8.1(− 13.9 to − 2.4)0.008Diastolic BP (mmHg)− 3.7(− 7.9 to − 0.5)0.08− 0.5(− 3.9 to 2.8)0.74− 5.1(− 9.0 to − 1.3)0.012Heart rate (bpm)0.0(− 3.8 to 3.9)0.990.2(− 4.5 to 4.9)0.920.7(− 2.3 to 3.8)0.62RHI0.2(− 0.04 to 0.44)0.100.17(− 0.14 to 0.47)0.27− 0.03(− 0.14 to 0.08)0.58P-values reflect comparison between measurement at 8-weeks and measurement at baselineTable 3Baseline-adjusted vascular outcomes after intervention and after 3-month washoutGlargineGlargineGlarginep + Lispro + ExenatideVascular outcomes after 8-week intervention Baseline-adjusted systolic BP at 8-weeks (mmHg)130 (2.7)133 (2.7)128 (2.6)0.38 Baseline-adjusted diastolic BP at 8-weeks (mmHg)79 (1.7)81 (1.7)78 (1.7)0.34 Baseline-adjusted heart rate at 8-weeks (bpm)71 (1.6)72 (1.7)71 (1.6)0.91 Baseline-adjusted RHI at 8-weeks2.07 (0.10)2.00 (0.10)1.81 (0.10)0.19Vascular outcomes after 3-month washout Baseline-adjusted systolic BP at 20-weeks (mmHg)133 (2.1)132 (2.0)128 (1.9)0.25 Baseline-adjusted diastolic BP at 20-weeks (mmHg)83 (1.6)81 (1.5)79 (1.5)0.28 Baseline-adjusted heart rate at 20-weeks (bpm)70 (1.6)72 (1.6)69 (1.5)0.53 Baseline-adjusted RHI at 20-weeks1.74 (0.10)1.87 (0.09)1.96 (0.09)0.25Data are presented as adjusted mean (SE) ## Results Table 1 shows the baseline characteristics of the study participants randomized to (i) insulin glargine alone (Glar; $$n = 24$$), (ii) glargine + pre-prandial lispro (Glar/Lispro; $$n = 24$$), and (iii) glargine + twice-daily exenatide (Glar/Exenatide, $$n = 25$$), respectively. There were no significant differences between the groups in clinical, metabolic or vascular measures, apart from a higher prevalence of hypertension in the Glar/Lispro group ($$p \leq 0.02$$). Of note, there were no differences between the groups in blood pressure (BP), heart rate and RHI (Table 1). Figure 1 shows the similar improvement in mean fasting capillary glucose in the 3 groups across the 8 weeks of intervention. As in the overall trial [9], baseline-adjusted A1c at 8-weeks was lowest in Glar/Exenatide followed by Glar/Lispro and Glar (mean $5.79\%$ vs $5.85\%$ vs $6.16\%$; $$p \leq 0.0002$$), as was baseline-adjusted BMI (mean 30.9 vs 31.6 vs 32.1 kg/m2; $p \leq 0.0001$). After 8-weeks of therapy, Glar/Exenatide decreased systolic BP (mean − 8.1 mmHg [$95\%$CI − 13.9 to − 2.4], $$p \leq 0.008$$) and diastolic BP (mean -5.1 mmHg [-9.0,-1.3], $$p \leq 0.012$$), while there no significant BP changes in the Glar and Gar/Lispro groups (Table 2). There were no significant changes in heart rate and RHI between baseline and 8-weeks in any of the 3 groups (Table 2). Importantly, as shown in Fig. 2, there were no differences in (A) systolic BP, (B) diastolic BP, (C) heart rate or (D) RHI between the 3 groups at either 4-weeks or 8-weeks, nor were there differences between the groups in the changes over time in these measures during the 8-week intervention (systolic BP: $$p \leq 0.92$$; diastolic BP: $$p \leq 0.97$$; heart rate: $$p \leq 0.54$$; RHI: $$p \leq 0.22$$). Indeed, at 8-weeks, the pre-specified ancillary outcome of baseline-adjusted RHI (mean ± SE) did not differ between the groups (Glar 2.07 ± 0.10; Glar/Lispro 2.00 ± 0.10; Glar/Exenatide 1.81 ± 0.10; $$p \leq 0.19$$), nor did baseline-adjusted BP or heart rate (Table 3). Similarly, at the washout visit at 20-weeks, the baseline-adjusted vascular measures also did not differ between the groups (Table 3).Fig. 1Mean fasting capillary glucose on self-monitoring during each week of therapy in the 3 treatment arms (data are presented as mean with standard error)Fig. 2Vascular measures in each of the treatment arms during the trial: (Panel A) systolic blood pressure; (Panel B) diastolic blood pressure; (Panel C) heart rate; and (Panel D) reactive hyperemia index (RHI) ## Discussion In this study, 8-weeks of glargine/exenatide combination therapy decreased BP in patients with T2DM of modest duration, without inducing significant changes in heart rate or RHI. Notably, there were no differences in baseline-adjusted RHI between glargine alone, glargine + lispro and glargine + exenatide after the 8-week intervention or after subsequent 12-weeks washout. These data thus suggest that adding either exenatide or lispro to basal insulin therapy does not affect endothelial function in early T2DM. For determining the cardiovascular risk implications of a glucose-lowering regimen, the optimal evaluation would be provided by a dedicated cardiovascular outcome trial. Though such trials have shown neutral effects of both glargine [3] and exenatide once weekly [12] on cardiovascular risk, combination therapy has not been prospectively assessed in this way. The knowledge gap arising from the absence of such a trial is underscored by work from Ceriello et al. demonstrating that the simultaneous infusion of insulin and GLP-1 acutely improves endothelial function in patients with T2DM more than either therapy on its own [13]. These data thus raise the question of how chronic basal insulin/GLP1-RA combination therapy may impact vascular function. To date, there has been limited investigation of this question. In a recent study of 34 glargine-treated patients with T2DM with mean A1c ~ $8.0\%$ at baseline, the addition of either once daily lixisenatide or insulin glulisine (administered only before breakfast) for 8-weeks was found to yield similar effects on systemic hemodynamic measures in the fasted state (including BP, HR, stroke volume, cardiac index, systemic vascular resistance index, and normalized augmentation index) [14]. The current study builds upon that report, but with salient design differences in (i) evaluating more than twice as many participants ($$n = 73$$) with well-controlled T2DM, (ii) providing prandial insulin at all 3 meals in the lispro group, and (iii) including a third arm with glargine alone. With this design, we show that adding either twice-daily exenatide or thrice-daily lispro to glargine therapy for 8-weeks did not affect RHI, BP or HR. Our data suggest that, in the setting of basal insulin therapy, the postprandial metabolic coverage provided by exenatide and lispro does not improve endothelial function. However, this interpretation should be coupled with certain caveats. First, with well-controlled T2DM of modest duration and relatively high rates of adoption of cardioprotective medications (renin-angiotensin system blockers, statins), it is possible that this study population may not have had sufficiently advanced vascular disease for demonstration of a therapeutic effect on endothelial function or had insufficient dysfunction for such a demonstration at this sample size. Indeed, at baseline, the mean RHI values in each group (ranging from 1.83 to 1.88) exceeded the threshold that typically reflects abnormal function (< 1.67). Second, since RHI was assessed in only 73 of the 102 participants, it is possible that there was insufficient statistical power to detect differences in this measure. Third, the absence of differences in RHI under fasting conditions (as measured herein) may not exclude differences in endothelial function in the postprandial state. Of note, previous studies have shown that a single dose of exenatide can improve RHI when the latter is measured after a meal challenge [15, 16]. Also, while lower RHI has been shown to predict atherosclerotic coronary artery disease [10, 11], it remains to be established whether improvement in RHI in response to intervention reduces future risk of cardiovascular events. Another consideration is that, in current practice, long-acting GLP1-RAs have surpassed short-acting formulations in popularity (both with and without basal insulin). Interestingly, a recent study in 112 individuals with T2DM showed that the long-acting GLP1-RA dulaglutide (which has been previously shown to reduce the risk of major adverse cardiovascular events in T2DM [17]) improved RHI after 9-months of treatment but that this effect was not apparent at 3-months [18]. Accordingly, in the current study, the absence of an impact on RHI after 8-weeks may not rule out the possibility of beneficial effects on vascular function emerging with longer duration of therapy. In conclusion, 8-weeks of glargine/exenatide combination therapy decreased BP, without significant changes in heart rate or RHI. Importantly, RHI at fasting did not differ between glargine alone, glargine + lispro, and glargine + exenatide either after 8-weeks of treatment or after subsequent washout. 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--- title: Lack of adipocyte IP3R1 reduces diet-induced obesity and greatly improves whole-body glucose homeostasis authors: - Xin Zhang - Lu Wang - Yubo Wang - Linjuan He - Doudou Xu - Enfa Yan - Jianxin Guo - Chenghong Ma - Pengguang Zhang - Jingdong Yin journal: Cell Death Discovery year: 2023 pmcid: PMC9998023 doi: 10.1038/s41420-023-01389-y license: CC BY 4.0 --- # Lack of adipocyte IP3R1 reduces diet-induced obesity and greatly improves whole-body glucose homeostasis ## Abstract The normal function of skeletal muscle and adipose tissue ensures whole-body glucose homeostasis. Ca2+ release channel inositol 1,4,5-trisphosphate receptor 1 (IP3R1) plays a vital role in regulating diet-induced obesity and disorders, but its functions in peripheral tissue regulating glucose homeostasis remain unexplored. In this study, mice with Ip3r1 specific knockout in skeletal muscle or adipocytes were used for investigating the mediatory role of IP3R1 on whole-body glucose homeostasis under normal or high-fat diet. We reported that IP3R1 expression levels were increased in the white adipose tissue and skeletal muscle of diet-induced obese mice. Ip3r1 knockout in skeletal muscle improved glucose tolerance and insulin sensitivity of mice on a normal chow diet, but worsened insulin resistance in diet-induced obese mice. These changes were associated with the reduced muscle weight and compromised Akt signaling activation. Importantly, Ip3r1 deletion in adipocytes protected mice from diet-induced obesity and glucose intolerance, mainly due to the enhanced lipolysis and AMPK signaling pathway in the visceral fat. In conclusion, our study demonstrates that IP3R1 in skeletal muscle and adipocytes exerts divergent effects on systemic glucose homeostasis, and characterizes adipocyte IP3R1 as a promising target for treating obesity and type 2 diabetes. ## Introduction The increasing prevalence of obesity and type 2 diabetes worldwide has led to increased complications of cardiovascular diseases, hypertension, fatty liver disease, and cancer [1, 2]. With the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), obesity and related metabolic disorders accelerate severe COVID-19 [3]. Obesity-induced ‘unhealthy’ white adipose tissue (WAT) displays a pro-inflammatory state and enhanced fibrosis and hypoxia, leading to the onset and progression of type 2 diabetes [4], while numerous studies also showed no association between adipose inflammation and metabolic dysfunction or insulin action [5, 6]. Furthermore, skeletal muscle contributes to approximately one-third of postprandial glucose disposal [7, 8], and its diminished response to insulin is characteristic of type 2 diabetes. Therefore, studies are urgent to clarify factors that modulate WAT and skeletal muscle functions in controlling glucose homeostasis, especially in the development of obesity. Calcium is a critical second messenger regulating gene expression, protein synthesis, muscle contraction, and metabolism [9]. Accumulating evidence has identified disturbing calcium signaling as emerging factor involved in the insulin resistance development [10, 11]. IP3Rs are ubiquitous ligand-gated Ca2+ release channels located on the membrane of endoplasmic reticulum (ER). Their important roles have been recognized for neurological, immunological, cardiovascular, and neoplastic human diseases by identifying specific mutations [12]. Strikingly, IP3R1 heterozygous mutant mice were susceptible to diet-induced glucose intolerance and insulin resistance [13]. The reduction of IP3R1-mediated ER-mitochondria Ca2+ transfer conduces to the development of complications of type 2 diabetes, including diabetic cardiomyopathy and hepatic insulin resistance [14, 15]. Growing evidence has highlighted the association between IP3R1 expression level and functions of skeletal muscle and adipose tissue. For example, IP3R1 expression was reduced in the skeletal muscle of aged mice, and its inhibition was detrimental to muscle regeneration [16]. Regarding WAT, up-regulation of CD36 in preadipocytes was reported to induce lipid accumulation and inflammation through activating IP3R1 [17]. Of particular importance is that muscle dysfunction and inflammation account for the progression of insulin resistance [18, 19]. Thereby, it can be anticipated that the contribution of IP3R1 to glucose homeostasis may be different in skeletal muscle and WAT. However, it remains obscure whether and how IP3R1 affects whole-body glucose homeostasis by balancing the functions in different tissues, especially in WAT and skeletal muscle. In this study, we observed the abnormal regulation of IP3R1 in the diet-associated insulin resistance. We generated two mouse models lacking IP3R1 selectively in skeletal muscle or adipocytes to decipher its role in maintaining whole-body glucose homeostasis and identified adipocyte IP3R1 as a potential therapeutic target to promote metabolic health during over-nutrition. ## Identification of IP3R1 as a regulator of diet-induced insulin resistance To investigate the potential role of IP3R1 in diet-induced obesity and metabolic disorders, C57BL/6 mice received a regular chow diet (CD) or a high-fat diet (HFD) for 8 wk. HFD dramatically increased the body weight of mice, elevated the mass of epididymal WAT (eWAT, visceral fat) and inguinal WAT (iWAT, subcutaneous fat), declined the mass of tibialis anterior (TA) and gastrocnemius (GAS), and also induced the expansion of adipocyte size (Fig. 1A–C). Furthermore, HFD led to metabolic disorders, including compromised glucose tolerance (Fig. 1D) and insulin sensitivity (Fig. 1E), and increased blood glucose levels under fasting condition (Fig. 1F). Notably, the HFD significantly increased Ip3r1 expression in iWAT ($P \leq 0.05$), and tended to increase Ip3r1 expression in TA ($$P \leq 0.09$$), while did not alter Ip3r1 expression in eWAT and GAS compared with the control (Fig. 1H). We also found a significant increase in IP3R1 protein level in GAS ($P \leq 0.01$), but its phosphorylation on Tyr353 was significantly decreased in GAS ($P \leq 0.01$) and tended to decrease in TA ($$P \leq 0.06$$) in HFD group (Fig. 1I–L). Therefore, HFD increased IP3R1 expression but decreased its activation by tyrosine phosphorylation especially in the skeletal muscle. Fig. 1Metabolic analysis and IP3R1 expression of C57BL/6 mice on chow diet (CD) or high-fat diet (HFD).A Body weight of mice maintained on CD or HFD for 8 wk ($$n = 15$$). B Tissue weight (eWAT, iWAT, TA and GAS) percentage of body weight ($$n = 6$$-7). C Representative HE-stained sections of iWAT and eWAT from mice fed CD or HFD ($$n = 6$$-7). Scale bar = 50 μm. D GTT (2 g/kg glucose, i.p.) ( $$n = 10$$). E ITT (1 U/kg insulin, i.p.) ( $$n = 10$$). F, G Blood glucose and plasma insulin levels under fasting and fed states ($$n = 9$$-10 for fasting state, $$n = 6$$ for fed state). H Relative mRNA expression levels of Ip3r1 in adipose tissues (iWAT and eWAT) and skeletal muscles (TA and GAS) ($$n = 6$$). I–L Western blot analysis of p-IP3R1 (Y353) and IP3R1 in TA and GAS lysates. Quantification of p-IP3R1 (Y353)/IP3R1 and IP3R1 was determined by ImageJ software ($$n = 6$$). Data were shown as means ± SEM. * $P \leq 0.05$, *$P \leq 0.01.$ A–C, F–L: two-tailed unpaired Student’s t test; D and E: two-way ANOVA followed by Bonferroni’s post hoc test. ## Generation of skeletal muscle-specific Ip3r1 knockout mice Since skeletal muscle accounts for ~$30\%$ of postprandial glucose disposal, we aimed to investigate whether Ip3r1 knockout in skeletal muscle affects glucose homeostasis. qPCR results revealed a significantly diminished Ip3r1 level in TA and GAS of Ip3r1MKO mice ($P \leq 0.01$), while Ip3r1 expression was unaltered in other tissues (Fig. 2A). Further, Ip3r1 knockout reduced Ip3r2 expression in the GAS but not TA ($P \leq 0.05$, Fig. 2B). No changes were observed in Ip3r3 expression levels in TA and GAS between genotypes (Fig. 2C).Fig. 2Ip3r1 deletion in skeletal muscle leads to lower muscle weight. A mRNA expression levels of Ip3r1 in skeletal muscle (TA and GAS), adipose tissues (eWAT and iWAT), heart, liver, spleen, lung and kidney ($$n = 6$$). B, C Relative mRNA expression levels of Ip3r2 and Ip3r3 in TA and GAS isolated from WT and Ip3r1MKO mice ($$n = 5$$). D, E Body weight curve of WT and Ip3r1MKO female and male mice ($$n = 10$$). F Fat mass and (G) lean mass of adult WT and Ip3r1MKO female and male mice measured by the nuclear magnetic resonance system ($$n = 4$$–6). H, I Representative images of size and TA, EDL, GAS and Sol muscles in adult WT and Ip3r1MKO mice. J Muscle weight of adult WT and Ip3r1MKO mice ($$n = 5$$). K H&E staining of TA muscles and (L) frequency histogram of fiber cross-sectional area ($$n = 6$$). Scale bar = 100 µm. All data were analyzed by two-tailed unpaired Student’s t test and presented as means ± SEM. $P \leq 0.05$, *$P \leq 0.01.$ Ip3r1MKO mice grew slowly during postnatal growth, resulting in lighter body weights from week 4 or week 6 for female or male mice, respectively (Fig. 2D, E). MRI scanning demonstrated that Ip3r1 conditional loss in skeletal muscle reduced lean mass of male and female mice significantly ($P \leq 0.05$), and tended to reduce the fat mass of male mice ($$P \leq 0.06$$) (Fig. 2F, G). Congruously, Ip3r1MKO mice exhibited a smaller body size and decreased weight of TA ($P \leq 0.05$) and GAS ($$P \leq 0.09$$) at 8-week-old (Fig. 2H–J). Interestingly, we found that Ip3r1 loss in skeletal muscle tended to increase the proportion of small muscle fiber (0–800 µm2, $$P \leq 0.06$$), while significantly decreased the proportion of large muscle fiber (1200–1600 µm2, $P \leq 0.05$, Fig. 2K, L). The shift of muscle fiber size distribution also evidenced that Ip3r1 loss in skeletal muscle led to decreased muscle mass. ## Metabolic analysis of Ip3r1MKO mice To access the metabolic roles of IP3R1 in skeletal muscle, WT and Ip3r1MKO mice were first maintained on a regular chow diet. Results revealed that Ip3r1MKO mice exhibited improved glucose tolerance and insulin sensitivity at 8-week-old, concomitant with the decreased blood glucose level in the fasting state ($$P \leq 0.06$$), although plasma insulin and C-peptide levels were similar between the two groups (Fig. 3A–E).Fig. 3Role of IP3R1 in skeletal muscle in controlling systemic glucose homeostasis of mice maintained on chow diet (CD) or high-fat diet (HFD).A GTT (2 g/kg glucose, i.p.), B ITT (1U/kg insulin, i.p.), C blood glucose levels, D plasma insulin levels, and E plasma C-peptide levels after 8 wk of CD feeding ($$n = 7$$–10). F Growth curve of body weight during the 8 wk of HFD feeding ($$n = 10$$-11). G Muscle weight, H muscle weight percentage, I GTT (2 g/kg glucose, i.p.), J ITT (1U/kg insulin, i.p.), K blood glucose levels, L plasma insulin levels, and M plasma C-peptide levels after 8 wk of HFD feeding. $$n = 6$$–9 for G and H, $$n = 10$$-11 for I and J, and $$n = 10$$ for K–M. All Data are presented as means ± SEM. $P \leq 0.05$, *$P \leq 0.01.$ A, B, I, J: two-way ANOVA followed by Bonferroni’s post hoc test; C–H, K–M: two-tailed unpaired Student’s t test. WT and Ip3r1MKO mice at 4 wk of age were then challenged with HFD for 8 wk. Ip3r1MKO mice recorded fewer body weights than WT mice (Fig. 3F), partly due to the lower muscle weight (Fig. 3G, H). Next, to determine the effect of skeletal muscle-specific Ip3r1 deletion on HFD-induced metabolic deficits, a series of metabolic tests were performed. Ip3r1 deficiency in the skeletal muscle caused a worsening of HFD-induced insulin resistance (Fig. 3J), along with a significant elevation in blood glucose levels and plasma insulin levels in the fasting state (Fig. 3K, L). Meanwhile, the two groups of mice showed similar glucose tolerance (Fig. 3I) and C-peptide level in plasma (Fig. 3M). Further, the expression levels of genes related to skeletal muscle fiber-type (Myh7, Myh2 and Myh4) and mitochondrial function (Pgc1α, Ndufs1, Ndufv2, and Cycs) were not changed in TA and GAS (Supplementary Fig. S1A, B), while Atp5a1 level was significantly increased in the TA of Ip3r1MKO mice ($P \leq 0.05$, Supplementary Fig. S1A). AMPK signaling, the hub of metabolic control, was also detected. Ip3r1 specific knockout in skeletal muscle significantly increased the phosphorylation of AMPK in TA ($P \leq 0.01$, Supplementary Fig. S1C, D) but not GAS muscle (Supplementary Fig. S1E, F), while WT and Ip3r1MKO mice showed similar phosphorylation levels of ACC in TA and GAS muscle (Supplementary Fig. S1C–F). These data suggest different roles of skeletal muscle IP3R1 in maintaining glucose homeostasis under normal energy and energy-excess conditions. ## IP3R1 regulates the IR-Akt-GSK3β axis in skeletal muscle Insulin exerts a fundamental role in glucose homeostasis through Akt signaling pathway. Hence, we hypothesized that Ip3r1 deficiency in skeletal muscle might affect insulin-stimulated Akt activation when mice were exposed to excessive energy intake. To this end, WT and Ip3r1MKO mice were fed an HFD for 8 wk and intraperitoneally injected with insulin (1 U/kg body weight). After 10 min, TA and GAS from mice under basal and insulin-stimulated states were sampled. Under the basal state, except for the phosphorylation of IRβ tyrosine residue, the phosphorylation of IRβ, Akt, and Akt target GSK3β in TA and GAS was not changed between the groups (Fig. 4). As expected, the insulin-stimulated p-Akt (S473) ($$P \leq 0.05$$) and p-Akt (T308) ($P \leq 0.05$) levels were blunted in the TA of Ip3r1MKO mice (Fig. 4A–E). Similarly, the insulin-induced p-Akt (T308) ($P \leq 0.05$) and p-GSK3β (S9) ($P \leq 0.05$) levels were also reduced in the GAS of Ip3r1MKO mice, while the p-IRβ (Y1146) level was markedly increased ($P \leq 0.05$) (Fig. 4F–J).Fig. 4Mice with Ip3r1 knockout in skeletal muscle show significantly reduced IR-Akt-GSK3β axis when maintained on a high-fat diet. Western blot analysis of p-IRβ (Y1146), p-Akt (S473), p-Akt (T308), p-GSK3β (S9), IRβ, Akt, and GSK3β in response to insulin (1 U/kg for 10 min) in (A–E) TA and (F–J) GAS lysates. Quantification of p-IRβ (Y1146)/IRβ, p-Akt (S473)/Akt, p-Akt (T308) /Akt, and p-GSK3β (S9)/GSK3β was determined by ImageJ software ($$n = 3$$). All data were analyzed by two-tailed unpaired Student’s t test and presented as means ± SEM. $P \leq 0.05.$ ## Ip3r1 deficiency in adipocytes is protected from HFD-induced obesity and metabolic dysfunction Ip3r1FKO mice were generated to access the metabolic roles of adipocyte IP3R1. Ip3r1 mRNA expression levels were significantly decreased in the eWAT and iWAT of Ip3r1FKO mice ($P \leq 0.05$), and remained unaltered in other metabolically active tissues (Supplementary Fig. S2A). Further, Ip3r2 and Ip3r3 expression levels in iWAT and eWAT of Ip3r1FKO mice remained indistinguishable from those of WT mice (Supplementary Fig. S2B, C). When fed the regular chow diet, WT and Ip3r1FKO mice showed similar body weight, body composition, and insulin sensitivity (Supplementary Fig. S2D–H, J), but improved glucose tolerance was observed in Ip3r1FKO mice (Supplementary Fig. S2I). Next, to address the potential role of adipocyte IP3R1 in diet-induced metabolic dysregulation, WT and Ip3r1FKO mice were maintained on the HFD for 8 wk from 8 wk of age. Ip3r1FKO mice gained less body weight than WT mice (Fig. 5A), mainly due to a significant reduction in fat mass but not lean mass as revealed by MRI scanning and anatomy data (Fig. 5B–E). Consistently, smaller body size, adipose tissue size (eWAT and iWAT), and adipocyte size were also observed in Ip3r1FKO mice (Fig. 5F–H). A strikingly improved glucose tolerance and a reduction in blood glucose, plasma insulin, total cholesterol, LDL and VLDL levels were observed in IP3R1FKO mice compared with those in WT mice (Fig. 5I, K, L, M, O, P). Loss of adipocyte Ip3r1 had no significant effect on plasma HDL, total triglycerides, NEFA and leptin levels (Fig. 5N, Q–S). Furthermore, mRNA expression levels of Leptin were significantly decreased in the eWAT and iWAT of Ip3r1FKO mice ($P \leq 0.01$, Fig. 6A, C). Hsl expression was also significantly increased in eWAT ($P \leq 0.01$, Fig. 6A), indicating the enhanced adipokinetic action in eWAT. Expression levels of several genes encoding for the components of carnitine shuttle (Cpt1a, Cpt1b, Slc25a20 and Cpt2) and involved in β-oxidation (Acadl, Acadm, Acads and Hadh) were not affected (Fig. 6B, D). Therefore, a lack of IP3R1 in adipocytes could combat the development of diet-induced obesity, insulin resistance and dyslipidemia. The lack of Ip3r1 in adipocytes on whole-body energy homeostasis was further examined. The two groups of mice maintained on the HFD showed no difference in physical activity, O2 consumption, CO2 production, EE and RER (Supplementary Fig. S3B–J). The food intake of Ip3r1FKO mice was significantly decreased compared with that of WT mice (Supplementary Fig. S3A).Fig. 5Ip3r1 specific deletion in adipocytes protects from HFD-induced obesity and metabolic disorders. A Body weight measurement of mice maintained on HFD ($$n = 8$$–15). B Fat mass and (C) lean mass analyzed by the nuclear magnetic resonance system ($$n = 8$$–15). D Tissue mass (eWAT, iWAT, BAT, TA, and GAS) and (E) percentage after 8 wk on HFD ($$n = 8$$–10). F Representative images of WT and Ip3r1FKO mice (8 wk on HFD). G Representative images of adipose tissues (eWAT, iWAT, and BAT) isolated from WT and Ip3r1FKO mice (8 wk on HFD). H Representative images of H&E stained sections of eWAT and iWAT. Scale bar = 20 µm. I GTT (2 g/kg glucose, i.p.) ( $$n = 8$$–12). J ITT (1U/kg insulin, i.p.) ( $$n = 8$$–12). K Blood glucose levels and (L–S) circulating plasma levels of (L) insulin, (M) total cholesterol, (N) HDL, (O) LDL, (P) VLDL, (Q) total triglycerides, (R) NEFA, and (S) leptin in WT and Ip3r1FKO mice ($$n = 8$$–15). All Data were presented as means ± SEM. * $P \leq 0.05$, $P \leq 0.01.$ A–H, K–S: two-tailed unpaired Student’s t test; I and J: two-way ANOVA followed by Bonferroni’s post hoc test. Fig. 6Expression profile of genes related to fatty acid metabolism in white adipose tissues. Relative mRNA expression levels of genes related to lipid synthesis, carnitine shuttle, and β-oxidation in (A, B) eWAT and (C, D) iWAT isolated from WT and Ip3r1FKO mice maintained on a high-fat diet for 8 wk ($$n = 5$$). All Data were analyzed by two-tailed unpaired Student’s t test and presented as means ± SEM. $P \leq 0.01.$ ## Ip3r1FKO mice showed no altered inflammation on HFD The development of obesity and insulin resistance may be correlated with enhanced peripheral inflammation. Thus, we investigated whether knockout of Ip3r1 in adipocytes affected peripheral inflammation. F$\frac{4}{80}$ stained eWAT and iWAT sections from HFD mice were analyzed, and adipose tissues from WT and Ip3r1FKO mice showed similar infiltration of pro-inflammatory immune cells (Supplementary Fig. S4A). Circulating levels of pro-inflammatory cytokines, including IL4, IL6, IFNγ and MCP1, and anti-inflammatory cytokine IL10 showed no significant differences, and plasma resistin levels tended to increase in Ip3r1FKO mice (Supplementary Fig. S4B–G). In agreement with plasma cytokine levels, expression levels of Mcp1, Il6, Tnfα, Ifnγ, Mip1α and Mip1β did not significantly change (Supplementary Fig. S4H, I). The mRNA expression levels of macrophage markers (F$\frac{4}{80}$ and Cd68) were also analyzed. Confusingly, F$\frac{4}{80}$expression was significantly increased in eWAT of Ip3r1FKO mice ($P \leq 0.05$, Fig. S4H). These experiments reveal that adipocyte-specific knockout of Ip3r1 has little impact on obesity-associated inflammation. ## IP3R1 regulates AMPK signaling in white adipose tissues To further understand the role of adipocyte IP3R1 in regulating metabolic homeostasis, AMPK signaling was measured by western blotting. Following feeding with HFD for 8 wk, WT and Ip3r1FKO mice showed similar phosphorylation levels of AMPK and ACC in iWAT (Fig. 7A–C). Strikingly, eWAT from Ip3r1FKO mice showed increased levels of p-AMPK (T172) and decreased levels of p-ACC (S79) compared to WT mice (Fig. 7D–F). Thus, IP3R1 activates AMPK signaling in visceral fat but not subcutaneous fat in mice offered HFD.Fig. 7Ip3r1FKO mice showed activated AMPK signaling in white adipose tissues. Western blot analysis of p-AMPK (T172), p-ACC (S79), AMPK, and ACC in (A–C) iWAT and (D–F) eWAT lysates. Quantification of p-AMPK (T172)/AMPK and p-ACC (S79)/ACC was determined by ImageJ software ($$n = 6$$). Data were analyzed by two-tailed unpaired Student’s t test and presented as means ± SEM. * $P \leq 0.05.$ ## Discussion Obesity is one of the main risk factors for developing type 2 diabetes. Maintaining Ca2+ homeostasis is critical for the function of metabolic organs [11, 20]. The imbalance of Ca2+ also leads to ectopic adipocyte accumulation [21] and obesity-related brown adipose tissue whitening [22]. IP3Rs are key players controlling Ca2+ release from ER to cytoplasm or mitochondria. In the current study, we systematically analyzed the role of IP3R1 in glucose homeostasis by making skeletal muscle- or adipocyte-specific knockout mice. Results demonstrated that skeletal muscle Ip3r1 deletion improved systemic glucose metabolism of mice fed a regular diet but impaired insulin sensitivity in obese mice. Significantly, loss of Ip3r1 in mature adipocytes enhanced lipolysis and AMPK signaling, especially in the visceral fat, contributing to the improved glucose tolerance in obese mice. An important observation was the increased IP3R1 expression level in the WAT of diet-induced obese mice, revealing the inverse correlation between IP3R1 expression in WAT and glucose homeostasis. Consequently, the result may suggest a dominant contribution by adipose tissue IP3R1 to systemic glucose metabolism in the condition of diet-induced obesity. Here, we show that Ip3r1 deletion in skeletal muscle improved glucose tolerance and insulin sensitivity of mice fed regular diets but accelerated insulin resistance in obese mice, which was confirmed by the decreased insulin-mediated Akt signaling in skeletal muscle. Insulin-mediated activation of Akt is central to glucose disposal in mammals [23, 24] and insulin-stimulated Akt phosphorylation is decreased in the skeletal muscle of individuals with insulin resistance [23]. It should be noted that spare insulin receptors are present in metabolic tissues [25]. Normal glucose and insulin tolerance were maintained in mice with heterozygous loss of IR, and Akt signaling was not impaired in skeletal muscle upon IR deficiency [26], suggesting the non-linearity between IR and Akt activity, which could also account for the paradoxical lack of change or increase in IRβ phosphorylation with a corresponding decline in Akt phosphorylation in this study. Notably, Ip3r1 deletion in skeletal muscle resulted in muscle loss but not weight loss in obese mice, which was in alignment with recent studies that IP3R1 knockdown or inhibition repressed myoblast differentiation [27]. Insulin resistance and muscle loss often coincide in individuals with type 2 diabetes [28], and individuals with low muscle mass have a higher prevalence of metabolic syndrome [29, 30]. Therefore, HFD-induced insulin resistance may be further aggravated by the lower muscle mass in Ip3r1MKO mice, but the causal relationship between muscle mass and the development of type 2 diabetes remains to be elucidated [31]. Feeding mice an HFD elevated IP3R1 expression in WAT, revealing its potential role in obesity-associated adipocyte pathophysiology. In this study, Ip3r1 deficiency in adipocytes prevented HFD-induced obesity and adipocyte hypertrophy. In response to over-nutrition, WAT expands by increasing the size of pre-existing adipocytes (hypertrophy) or generating new adipocytes (hyperplasia). In contrast with adipocyte hyperplasia, adipocyte hypertrophy is correlated with pathological WAT remodeling and leads to a deterioration of systemic metabolic health [32], which accounts for the improved glucose tolerance in Ip3r1FKO mice. Although Ca2+ signal pathways are present in human preadipocytes [33], the role of IP3R1 in adipocyte hyperplasia remains unclear yet. Additionally, WAT dysregulation often causes local and systemic inflammation [4]. However, no reduction was observed in plasma cytokine levels in Ip3r1FKO obese mice, suggesting that IP3R1 activation does not lead to obesity-associated inflammation. Herein, enhanced lipolysis in WAT, evidenced by the increased Hsl expression in eWAT and decreased total triglyceride, LDL and VLDL levels, may contribute to the reduced adiposity. Visceral WAT accumulation is associated with the risk of insulin resistance, whereas subcutaneous WAT expansion is protective due to the differences in location and adipocyte heterogeneity [34]. Therefore, IP3R1 is of great interest for future development as a target to combat the pathological expansion of visceral WAT. In tissues such as WAT, AMPK is a master regulator of energy metabolism. Mice lacking AMPK in adipocytes are susceptible to diet-induced glucose intolerance [35]. Short-term AMPK activation in the liver reduced blood glucose levels and induced fatty acid utilization in adipose tissue [36]. Together, these studies strongly demonstrate that increased AMPK activity in the visceral WAT after Ip3r1 knockout in adipocytes improves whole-body metabolism. Another interesting observation was the reduced food intake and Leptin mRNA expression in eWAT and iWAT of Ip3r1FKO mice. Although partial leptin deficiency was reported to reduce food intake and protect mice from diet-induced obesity and metabolic disorders [37], it should also be noted that plasma leptin levels remained unchanged in WT and Ip3r1FKO mice. Future studies still need to address the exact mechanism behind the beneficial effect of Ip3r1 deficiency in adipocytes. Anyway, this study strongly suggests that IP3R1 antagonists exclusively targeting adipose tissue may be beneficial for the treatment of obesity and type 2 diabetes. Available antagonists, such as Xestospongins and 2-APB, exhibits low specificity or low receptor affinity, and the development of new antagonists of IP3R1 is also challenging due to the ~$70\%$ homology of three IP3R subtypes [38]. Considering the structural heterogeneity of IP3Rs in the presence of different activators [39], the structural basis of gating IP3R1 should be thoroughly deciphered first. Additionally, combined pharmacophore and grid-independent molecular descriptors (GRIND) analysis can be applied to screen potential antagonists against IP3R1 as described previously [40]. Some limitations of this study should also be noted. As Ip3r1 deficiency was achieved in skeletal muscle and adipocytes, we can’t confirm it at the protein level due to its low expression. The understanding of IP3R1 in adipocyte precursors (APs) is still limited. AP-specific Ip3r1 knockout mice could be generated by mating Ip3r1-floxed mice with Pdgfrα-Cre mice to address this issue. Besides, although Ip3r1 deficiency in adipocytes enhanced AMPK signaling in the visceral fat, β-oxidation of fatty acids was not influenced. A comprehensive analysis is needed to identify the metabolic landscape regulated by IP3R1. In addition, Ip3r1 knockout in adipocytes reduced the food intake of obese mice, and we cannot rule out the possibility that the reduced food intake led to lipolysis. Collectively, our study reveals that IP3R1 in skeletal muscle and adipocytes exerts divergent effects on obesity and obesity-related metabolic disorders and provides a rational basis for developing adipocyte IP3R1 as a promising target for the treatment of obesity and type 2 diabetes. ## Animal model All mice used in this study were C57BL/6 background. To obtain mice with a conditional knockout allele of Ip3r1, exon 3 was selected as conditional knockout (cKO) region and flanked by loxp sites (referred to as floxed) using gene targeting in C57BL/6 embryonic stem (ES) cells and built by Cyagen Biosciences (Guangzhou, China). The procedure of knockout mice generation in this study was similar to that described previously [41], including construction of targeting vector, electroporation of ES cells, G418 selection, identification of homologous recombined ES cells, and generation of Ip3r1f/f mice. To generate mice lacking IP3R1 selectively in skeletal muscle, IP3R1-floxed mice were crossed with Myf5-cre mice (007893, Jackson Laboratory) expressing recombinase under the control of Myf5 promoter. Mice used for experiments were Ip3r1f/f (wild type, WT) and Myf5-cre+/-Ip3r1f/f (Ip3r1MKO) mice. To generate mice lacking IP3R1 selectively in adipocytes, we crossed IP3R1-floxed mice with Adipoq-cre mice expressing recombinase under the control of Adiponectin promoter. Adipoq-cre mice were purchased from the Jackson Laboratory (J010803). Mice used for experiments were Ip3r1f/f (WT) and Adipoq-cre+/−Ip3r1f/f (Ip3r1FKO) mice. ## Mouse maintenance and diet-induced obesity Mice were kept in a temperature-controlled environment (23 ± 2 °C) and had free access to food and water under a 12 h/12 h light/dark cycle. Except for obesity, the mice were in generally good health. To induce obesity, 4 or 8-week-old male mice were fed a high-fat diet ($60\%$ kcal fat, H10060, Huafukang Bioscience, Beijing, China) for 8 week, and mice received a chow diet ($10\%$ kcal fat, H10010, Huafukang Bioscience, Beijing, China) were served as control. Adult male mice were used in this study unless specified. Genotype- and sex-matched mice were randomly assigned to experimental groups mitigating the cage effect and no blinding was done in this study. ## Glucose and insulin tolerance tests Glucose tolerance test (GTT) and insulin tolerance test (ITT) were carried out in the morning after a 6 h of fasting. After determining the fasted blood glucose levels, mice were intraperitoneally injected with glucose (2 g/kg body weight) or insulin (1 U/kg body weight) for GTT and ITT, respectively. At 15, 30, 60, 90 and 120 min after injection, blood was collected from the tail tip and glucose concentrations were determined using contour portable glucometer (Sinocare, Changsha, China). All GTT and ITT were performed on adult mice that were more than 8-week-old. To ensure that stress was minimized prior to and during these tests, experimental mice were handled at least once every week after weaning [42]. ## Plasma metabolic profiling and cytokine levels Mice at the fed state or fasted for 12 h were sacrificed, and whole blood was collected from retrobulbar venous plexus and centrifuged for 10 min at 12,000 × g to obtain plasma. Plasma glucose, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), very low-density lipoprotein (VLDL), and total triglycerides were measured using commercial kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). ELISA kits (Sinoukbio, Beijing, China) were used to measure plasma insulin, C-peptide, non-esterified fatty acid (NEFA), leptin, IL (interleukin) 4, IL6, IL10, resistin, interferon γ (IFNγ) and monocyte chemotactic protein-1 (MCP1) following the manufacturer’s instruction. ## Body composition analysis Body composition, including whole-body fat mass and lean mass, were analyzed by a nuclear magnetic resonance system (Body Composition Analyzer QMR06-090H, Niumag Corporation, Shanghai, China). ## Indirect calorimetry Energy expenditure (O2 consumption/CO2 production), locomotor activity, and food intake were determined by metabolic cages using an Oxylet system (Columbus Instruments, Columbus, USA). Mice were individually housed in metabolic chambers with food and tap water ad libitum. The sampling interval for each cage was 3 min, with repetition every 27 min. Oxygen consumption (VO2), carbon dioxide production (VCO2), and spontaneous motor activity were measured over three consecutive days. Expiratory exchange ratio (RER) was calculated by VCO2/VO2. ## Histology of skeletal muscle and adipose tissues All tissues analyzed in this study were collected in the fasted state. Different skeletal muscle and adipose tissues were dissected from mice and quickly fixed in $4\%$ paraformaldehyde. Samples embedded in paraffin were sectioned into transverse sections with a thickness of 5 μm, followed by haematoxylin and eosin (H&E) staining. Muscle fiber cross-sectional area was determined by Adobe Photoshop (CS6 version, Adobe Systems Inc., San Jose, USA). Fixed adipose tissues were stained for F$\frac{4}{80}$ using standard immunohistochemistry methods as described previously [43]. ## RNA extraction and qPCR Total RNA was isolated from frozen tissues or organs using RNAiso Plus (Takara Biomedical Technology, 9108, Beijing, China) and reverse-transcribed into cDNA using a PrimeScript RT reagent kit with gDNA Eraser (Takara Biomedical Technology, RR047A, Beijing, China). SYBR Green-based qPCR was performed in a qTOWER 2.2 thermocycler (Analytik Jena, Jena, Germany). The mRNA expression levels of target genes were normalized to that of Gapdh. The primer sequences for qPCR were listed in Supplementary Table S1. ## Western blot assay Skeletal muscle or adipose tissues were lysed in RIPA lysis buffer (Huaxingbio, Beijing, China) with a protease inhibitor cocktail (Roche, Basel, Switzerland). Approximately 60 μg of total protein was resolved on 8–$10\%$ SDS-PAGE gels and transferred to polyvinylidene fluoride membranes (Millipore, Boston, USA). Membranes were blocked in TBS containing $5\%$ (w/v) bovine serum albumin at room temperature for 1 h and then incubated against primary antibodies (Supplementary Table S2) at 4 °C overnight. Blots were developed using DyLight 800-labeled secondary antibodies, detected with the Odyssey Clx (4647 Superior Street, LI-COR Biotechnology, Lincoln, NE) and quantified by ImageJ software (National Institutes of Health, Bethesda, USA). ## Statistics All data were analyzed in SPSS software (IBM SPSS Statistics 23) and presented as means ± SEM. The number of mouse samples per group was 3–15. The exact sample size for each experimental group/condition was given as a number in the figure legends. Normal distribution of populations at 0.05 level was calculated using Shapiro–Wilk Test. 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--- title: Alterations in intestinal microbiota and metabolites in individuals with Down syndrome and their correlation with inflammation and behavior disorders in mice authors: - Shaoli Cai - Jinxin Lin - Zhaolong Li - Songnian Liu - Zhihua Feng - Yangfan Zhang - Yanding Zhang - Jianzhong Huang - Qi Chen journal: Frontiers in Microbiology year: 2023 pmcid: PMC9998045 doi: 10.3389/fmicb.2023.1016872 license: CC BY 4.0 --- # Alterations in intestinal microbiota and metabolites in individuals with Down syndrome and their correlation with inflammation and behavior disorders in mice ## Abstract The intestinal microbiota and fecal metabolome have been shown to play a vital role in human health, and can be affected by genetic and environmental factors. We found that individuals with Down syndrome (DS) had abnormal serum cytokine levels indicative of a pro-inflammatory environment. We investigated whether these individuals also had alterations in the intestinal microbiome. High-throughput sequencing of bacterial 16S rRNA gene in fecal samples from 17 individuals with DS and 23 non-DS volunteers revealed a significantly higher abundance of Prevotella, Escherichia/Shigella, Catenibacterium, and Allisonella in individuals with DS, which was positively associated with the levels of pro-inflammatory cytokines. GC-TOF-MS-based fecal metabolomics identified 35 biomarkers (21 up-regulated metabolites and 14 down-regulated metabolites) that were altered in the microbiome of individuals with DS. Metabolic pathway enrichment analyses of these biomarkers showed a characteristic pattern in DS that included changes in valine, leucine, and isoleucine biosynthesis and degradation; synthesis and degradation of ketone bodies; glyoxylate and dicarboxylate metabolism; tyrosine metabolism; lysine degradation; and the citrate cycle. Treatment of mice with fecal bacteria from individuals with DS or Prevotella copri significantly altered behaviors often seen in individuals with DS, such as depression-associated behavior and impairment of motor function. These studies suggest that changes in intestinal microbiota and the fecal metabolome are correlated with chronic inflammation and behavior disorders associated with DS. ## Introduction Down syndrome (DS), a genetic disease caused by trisomy of chromosome 21, was first described by Doctor John Langdon Down in 1866, and is characterized by abnormal brain development, including the reduction of total brain volume, specifically in the cortical, hippocampal, and cerebellar areas (Zigman, 2013). DS occurs in approximately 1–2 per 1,000 live births across the world (Parker et al., 2010; Marlow et al., 2021; Osborne et al., 2021), with a prevalence of $\frac{1}{800}$ worldwide, $\frac{1}{500}$ in the United States, and $\frac{1.4}{1}$,000 in China (Xiao et al., 2022). Approximately $50\%$ of babies with DS die in utero; the survival rate of infants with DS is approximately $90\%$ (Best et al., 2018). Due to numerous impairments, individuals with DS may suffer a low quality of life and require costly, life-long care; it has been estimated that DS is associated with a financial burden of US$60,000 (Bull, 2020). Since chromosome 21 encodes many genes, the course of DS is polygenic and complex. Individuals with DS are at high risk of congenital heart disease, developmental delay, leukemia, obesity, obstructive sleep apnea, and abnormal myelopoiesis and inflammatory responses (Khalid-Raja and Tzifa, 2016). Many studies have reported immune system impairments in DS, with high levels of pro-inflammatory and low levels of anti-inflammatory cytokine production (Huggard et al., 2020). Suppression of pro-inflammatory cytokines in individuals with DS may reduce the risk of comorbid conditions and improve quality of life. Intestinal microbiota are a colonizing flora of microorganisms in the human body, comprising anaerobic bacteria, protozoa, fungi, and archaeas. Among these, bacteria are the most prominent microorganism, with 150-fold more genes than the human genome (Qin et al., 2010). Bacteria within the microbiome secrete a range of enzymes that modulate diverse host functions, such as metabolism of indigestible carbohydrates, production of vitamins, reproduction and differentiation of the intestinal epithelium, regulation of the immune system, and maintenance of intestinal homeostasis (Zimmermann et al., 2019; Casertano et al., 2021), which help hosts adapt various environments (Xiong, 2022). Clinical evidence has shown that the composition of intestinal microbiota is different between patients with various diseases and the healthy population (Bäumler and Sperandio, 2016). For example, gut microbial alterations are associated with cognitive impairment and Aβ load in older adults (Wu et al., 2022), and the composition of the gut microbiota of patents with Alzheimer’s disease (AD) differs from that of healthy controls at the taxonomic level. Perturbation of intestinal microbiota composition has also been linked to accelerated development of inflammation in people suffering from memory impairment (Wang et al., 2021). Metabonomics is the study of changes in metabolism in response to pathophysiological states or exogenous substances. For example, the composition of serum saturated fatty acids and unsaturated fatty acids was shown to vary between individuals with AD and healthy volunteers (Wang et al., 2012), and analysis of the urinary metabolome revealed differences in metabolites between patients with interstitial cystitis and healthy people (Kind et al., 2016). Interestingly, many studies have suggested that changes in intestinal microbiota composition are associated with alterations in host metabolites in the gut (Guo et al., 2020). The current study explored the production of pro-inflammatory cytokines, intestinal microbiota, and fecal metabolites in individuals with DS and non-DS volunteers using multiparameter flow cytometry analysis, high-throughput sequencing of the intestinal microbiome, and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based fecal metabolomics. The analysis revealed differences in the intestinal microbiota of individuals with DS and non-DS volunteers, a correlation between gut microbial flora/fecal metabolite markers and pro-inflammatory cytokines in DS individuals, and association of the DS microbiome with DS-related behaviors in mice. These studies may provide insights into DS pathophysiology and inform the development of new therapeutics for individuals with DS. ## Study participants A total of 40 participants were recruited: 23 non-DS volunteers (HC) and 17 individuals with DS (Supplementary Table 1). Participants were recruited from the Fuzhou Second Social Welfare Home (Fuzhou, China). All the DS individuals had a congenital cognitive delay, while all non-DS volunteers had a physical disability. Ethical approval for this study was provided by the NHS Health Research Authority (REC reference: 15/SW/0354) and informed consent was obtained from all volunteers or their legal guardians prior to enrollment in the study. ## Sample collection All samples were collected from participants on the same day (03. 23. 2017). Whole blood was collected by venipuncture after a 12 h fast and stored at 25°C for 0.5 h. Samples were centrifuged at 3,000 g for 20 min to collect the serum, which was stored at −80°C until use. Fecal samples were collected in sterile urine containers and stored at −80°C until use. ## Measurement of cytokine biomarkers in serum Serum interleukin-9 (IL-9), interleukin-1β (IL-1β), macrophage inflammatory protein-1α (MIP-1α), angiogenin, granulocyte colony-stimulating factor (G-CSF), interleukin-1α (IL-1α), monocyte chemoattractant protein 1 (MCP-1), macrophage inflammatory protein-1β (MIP-1β), immunoglobulin E (IgE), interleukin-6 (IL-6), fractalkine, interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α), monokine induced by interferon-gamma (MIG), rantes, and granzyme B were analyzed by multiparameter flow cytometry using the corresponding antibodies from BD Biosciences (East Rutherford, NJ, United States) and the BD FACSymphony™A5 (BD Biosciences). ## Intestinal microbiota analysis Genomic DNA was isolated from frozen fecal samples using a DNA isolation kit (MoBio, Carlsbad, CA, United States) and quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States). 16S rRNA genes (V3-V4 region) were PCR amplified using the forward primer 341F (5′-ACTCCTACGGGRSGCAGCAG-3′) and reverse primer 806R (5′-GGACTACVVGGGTATCTAATC-3′), purified using Agencourt AMPure magnetic purification beads (Beckman Coulter, Brea, CA, United States), and run on $2\%$ agarose gel. High-throughput sequencing of the PCR products was carried out on the PacBio RS II platform and analyzed at Ruiyi Biotechnology Co., Ltd. (Hangzhou, China). Raw data from high-throughput sequencing were demultiplexed and quality filtered using the QIIME2 platform, then used to assemble operational taxonomic units (OTUs) to define species, genus, or class of bacterial communities by UCLUST algorithm (an exceptionally fast sequence clustering program for nucleotide and protein sequences) with a threshold of $97\%$. Principal component analysis (PCA) plots were generated using R software (v 4.1.2). STAMP (Ver. 2.1.3) software was applied to identify intestinal microbial phylotypes. ## Fecal sample preparation for metabolomic analysis Fecal samples (100 mg) were mixed with 500 μL of extract solution (methanol:chloroform = 3:1) and 20 μL of internal standard (L-2-chlorophenylalanine), followed by ultrasonic treatment on ice. The mixture was centrifuged at 13,000 rpm for 20 min at 4°C. The supernatant (400 μL) was transferred to an Eppendorf tube, dried in a vacuum concentrator at 30°C, and dissolved in 80 μL of methoxyamine hydrochloride (in pyridine, 20 mg/mL). Samples were placed at 75°C for 30 min, then 100 μL of pyridine and N,O,-bis-(trimethylsilyl) trifluoroacetamide (BSTFA) (containing $1\%$ trimethylchlorosilane, v/v) was added and the samples were incubated at 70°C for 90 min. After cooling to 25°C, the samples were mixed well with 8 μL of fatty acid methyl esters (FAME)s and used for GC-TOF-MS analysis. ## Metabolomic analysis with GC-TOF-MS GC-TOF-MS analysis was carried out using a gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer. The system utilizes a DB-5 MS capillary column coated with $5\%$ diphenyl cross-linked with $95\%$ dimethylpolysiloxane (30 m × 250 μm × 0.25 μm, J&W Scientific, United States). An analyte (1 μL) was injected in splitless mode. Helium was used as the carrier gas, the front inlet purge flow was 3 mL/min, and the gas flow rate through the column was 20 mL/min. The initial temperature was kept at 50°C for 1 min, then raised to 310°C at a rate of 10°C/min and kept for 9 min at 310°C. The injection, transfer line, and ion source temperatures were 280, 270, and 220°C, respectively. The energy was −70 eV in electron impact mode. The mass spectrometry data were acquired in full-scan mode with the m/z range of 50–500 at a rate of 20 spectra/s after a solvent delay of 312 s. The raw data from GC-TOF-MS were processed using Progenesis software (v 3.0) for peak detection, filtering, denoising, alignment, and standardization. The resulting normalized data were evaluated by multivariate statistical analyses using R software (v 4.1.2), including PCA, partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Potential biomarkers were selected based on the VIP (variable importance plots) value and p values were determined by two-tailed t-test. A VIp value more than 1.0 and P value less than 0.05 were considered statistically significant. Heatmaps were constructed based on the area of potential biomarkers using a random forest algorithm with TBtools software. Pathway enrichment analysis of the potential biomarkers was performed using the website MetaboAnalyst1 based on the KEGG database.2 Correlation between the potential biomarkers and pro- and anti-inflammatory cytokines was determined using R software (v4.1.2) and Cytoscape (v3.9.0). ## Animals and behavioral studies Twenty-four germ-free (GF) C57BL/6 mice (6 weeks of age) were supplied by the Chinese Academy of Medical Sciences (Beijing, China) and used for behavioral testing following treatment with fecal bacteria derived from individuals with DS and non-DS volunteers, as well as a preparation of Prevotella copri. All animal experimental procedures adhered to guidelines approved by the Chinese Academy of Medical Sciences [Permit No. SYXK (Beijing)-2018–0019]. Mice were housed in a standard room at 22 ± 1°C with a humidity of 55 ± $5\%$, under a 12 h light/dark cycle at the Chinese Academy of Medical Sciences Animal Facility. After acclimation for 7 days, the mice were randomly divided into four groups of six mice and received the following treatments twice a week for 42 days. [ 1] The control group was treated with intragastric gavage of 100 μL phosphate-buffered saline. [ 2] The HC group was treated with intragastric gavage of 100 μL of fecal bacteria pooled from 23 non-DS volunteers. [ 3] The DS group was treated with intragastric gavage of 100 μL of fecal bacteria pooled from 17 participants with DS. [ 4] The Prevotella group was treated with intragastric gavage of 100 μL of Prevotella copri (108 CFU). After 42 days of treatment, the mice were underwent a series of behavior tests including sucrose preference test, open field test, and forced swimming test, as described below. All mice were then anesthetized with isoflurane and blood samples were collected by heart puncture after a 12-h fast following the accomplishment of all behavior tests. ## Sucrose preference test Depression is often associated with DS (Thom et al., 2021). The sucrose preference test was used to measure anhedonia-like symptoms (indicating depression-like behavior) over a 48-h period. Animals were acclimated to two identical water bottles on their cages for 3 days. Then, each mouse was given two bottles (A: water and B: $2\%$ sucrose solution). To avoid a locational bias, we changed the position of bottles A and B every 12 h. The amount of the sucrose solution or water consumed was measured by weighing the bottles before and after the test. The percentage of sucrose preference was calculated as an index of depression-like behavior. ## Open field test Basic motor skills, movement disorders, and abnormal gait and posture are correlated with cognitive limitations and abnormal sensorimotor integration found in DS (Carvalho and Vasconcelos, 2011). Therefore, the open field test was performed to assess locomotor activity and exploratory behavior of mice. Briefly, mice were placed individually in 40 cm × 60 cm × 50 cm box with the floor divided into 25 smaller rectangular units. The total time spent in the central zone and total distance crossed by each mouse were recorded every 5 min for 30 consecutive days. Between each test, the apparatus was cleaned with $75\%$ ethanol solution to eliminate possible odors left by other mice. ## Forced swimming test The forced swimming test is a non-invasive behavioral test often used for evaluation of depression in rodents (Can et al., 2012). Therefore, forced swimming tests were carried out using a previously reported protocol (Hirano et al., 2009). Each mouse was placed into a transparent cylinder (20 cm × 20 cm × 40 cm) filled with water (depth of water: 30 cm and temperature: 25 ± 1°C). Immobility time was recorded during the last 4 min of the 6-min testing period. ## Serum biochemical analysis Blood samples collected from the mice were placed at room temperature for 2 h then centrifuged (6,000 rpm, 25°C, 15 min). Serum C-reactive protein (CRP), lipopolysaccharide (LPS), and corticosterone levels were measured using commercially available kits according to the manufacturer’s instructions (Baiaolaibo Technology Co., Beijing, China). ## Statistical analysis Experimental results are expressed as the mean ± SEM using Prism 7.0 (GraphPad Software, San Diego, CA, United States). Statistical analysis was carried out using one-way analysis of variance (ANOVA), followed by Tukey test using SPSS 20.0 (IBM, Chicago, IL, United States) unless otherwise indicated. Statistically significant differences are indicated as $p \leq 0.05$, and $p \leq 0.01.$ In the meantime, we used LDA Effect Size (LEfSe) analysis and rank sum test to analyze the differences between groups and to find out the species that had significant differences between groups. For the analysis results of the rank sum test, we performed False Discovery Rate (FDR) correction on the value of p, and selected the different species according to the corrected value of p. ## Cytokine levels are shifted in individuals with DS Chronic inflammatory conditions and autoimmunity are common features of DS, with previous studies reporting high levels of circulating pro-inflammatory cytokines and low levels of anti-inflammatory cytokines. Therefore, we examined the serum levels of IL-9, IL-1β, IL-1α, MCP-1, MIP-1β, IgE, IL-6, TNF-α, MIG, rantes, granzyme B, angiogenin MIP-1α, G-CSF, fractalkine, and IL-8 in individuals with DS and non-DS volunteers. As shown in Figure 1, the levels of serum IL-9, IL-1β, IL-1α, MCP-1, MIP-1β, IgE, IL-6, TNF-α, MIG, rantes, and granzyme B were all significantly increased in the DS group compared with the non-DS group ($p \leq 0.05$). The level of serum angiogenin was significantly reduced in the DS group compared with the non-DS group ($p \leq 0.05$). No significant differences were seen in the serum levels of MIP-1α, G-CSF, fractalkine, and IL-8 between the DS group and the non-DS group ($p \leq 0.05$). These results indicate the presence of a distinct pro-inflammatory cytokine profile in individuals with DS. Figure 1:* Pro-inflammatory and anti-inflammatory cytokines in individuals with DS and non-DS volunteers (HC). Serum levels of IL-9, IL-1β, MIP-1α, angiogenin, G-CSF, IL-1α, MCP-1, MIP-1β, IgE, IL-6, fractalkine, IL-8, TNF-α, MIG, rantes, and granzyme B were analyzed by multiparameter flow cytometry. p < 0.05 and *p < 0.01 when comparing DS with the non-DS group. ## Composition and diversity of intestinal microbiota are altered in individuals with DS The intestinal microbiota play a vital role in the inflammatory response. Thus, we examined the composition of intestinal microbiota in the non-DS and DS groups using high-throughput sequencing analysis. A PCA biplot of the intestinal microbiota structure revealed an apparent separation between the non-DS and DS groups (Figure 2A). The first principal component (PC1) and the second principal component (PC2) accounted for 57 and $11\%$ of the total variation, respectively, i.e., the intestinal microbiota in the non-DS group was mainly distributed in the second and third quadrants, whereas the intestinal microbiota in the DS group was mainly distributed in the first and fourth quadrants. As shown in Figure 2B, there were a total of 651 OTUs in the non-DS and DS groups, with each OTU representing a single species. Among these, 360 OTUs overlapped in the non-DS and DS groups. The DS group exhibited 161 unique OTUs, whereas the non-DS group displayed 130 unique OTUs. Thus, individuals with DS had significantly higher fecal microbial OTUs compared with non-DS volunteers. As seen in Figure 2C, the distribution of fecal flora was relatively concentrated in the non-DS group, and relatively scattered in the DS group, indicating that the individual difference ratio in fecal flora in non-DS volunteers was small, with greater variation among individuals with DS. The fecal flora of the DS group tended to move away from that of the non-DS group, indicating variations in microbiome abnormality characteristics that may be linked to variations in DS severity. In summary, the intestinal flora of individuals with DS was significantly different from that of non-DS volunteers, with clear differences in intestinal microbiota diversity. Figure 2: Differences in the intestinal microbiota composition and diversity in the non-DS and DS groups. 16S rRNA sequencing data was used to assemble operational taxonomic units (OTUs). (A) PCA plot of intestinal flora. (B) Venn diagram of intestinal microbiota. (C) Analyses of intestinal flora based on OTUs. (D–G) Alpha diversity was used to assess the flora diversity and distribution, including the Chao1 index (D), observed species index (E), Shannon index (F), and Simpson index (G). A significant difference (p < 0.01) in the observes species of intestinal microbiota was seen in individuals with DS and non-DS volunteers. HC, non-DS volunteers (or non-DS group); DS, individuals with DS (or DS group). Next, we conducted alpha diversity analyses to further assess the species diversity and distribution in the two study groups, calculating the Chao1 index, observed species index, Shannon index, Simpson index, and PD whole Tree index. The alpha diversity index of each sample was calculated using QIIME software, and a corresponding dilution curve was generated. The Chao algorithm was used to estimate the OTU index (the total number of species) in the flora. As shown in Supplementary Table 2, compared with the non-DS group, the Chao value of intestinal bacteria in individuals with DS was higher, indicating that the overall number of intestinal microorganisms in individuals with DS was more abundant. The intestinal flora diversity of individuals with DS (Simpson = 0.8447) was slightly higher than that of the non-DS group (Simpson = 0.8067), but the difference was not significant (Supplementary Table 2). The observed species and Shannon index in the DS group were significantly higher than that in the non-DS group ($p \leq 0.01$; Figures 2E,F); however, there was no significant difference in Chao1 and the Simpson index between the non-DS and DS groups ($p \leq 0.05$; Figures 2D,G). Taxon-based analysis at the phylum level revealed differences in specific bacterial phylotypes between the non-DS and DS groups (Figures 3A,B). Firmicutes and Bacteroidetes are the primary phyla in gut, and their ratio is often associated with host health status. We found that the ratio of Firmicutes to Bacteroidetes was significantly elevated in the DS group compared with the non-DS group ($p \leq 0.01$). At the family level, Prevotellaceae was most abundant in the feces of individuals with DS, followed by Ruminococcaceae, Veillonellaceae, Lachnospiraceae, and Bacteroidaceae, suggesting that Prevotellaceae may be a characteristic intestinal bacteria of individuals with DS at the family level (Supplementary Figure 1). In contrast, Bacteroidaceae was the most abundant in the feces of non-DS volunteers, followed by Prevotellaceae, Lachnospiraceae, Ruminococcaceae, and Acidaminococcaceae. Therefore, Bacteroidaceae may be the characteristic intestinal bacteria in the non-DS group at the family level (Supplementary Figure 1). Figure 3: Taxon-based analysis of the microbiome in the DS and non-DS groups. (A) A significant difference was observed (p < 0.01) in the ratio of Firmicutes and Bacteroidetes in the intestinal microbiota of the non-DS and DS groups. (B) The classification and abundance of intestinal microbiota from individuals with DS and non-DS volunteers. Different colors represent classification levels, and the size of the circle represents the relative abundance of the classification. The number below the taxonomic name indicates the relative percentage abundance. HC, non-DS volunteers (or non-DS group); DS, individuals with DS (or DS group). At the genus level, the fecal microbiome of individuals with DS was enriched in Prevotella, Bacteroides, Faecalibacterium, Alloprevotella, Megasphaera, and Dialister (Figure 4A). By comparison, the fecal microbiome of the non-DS group was enriched for Bacteroides, Prevotella, Megasphaera, Phascolarctobacterium, Faecalibacterium, Roseburia, and Lachnospiracea incertae sedis. The relative abundance of 20 genera was significantly different between individuals with DS and non-DS volunteers (Figures 4B,C). The relative abundance of Bacteroides, Anaerostipes, Paraprevotella, Bilophila, Asaccharobacter, Parasutterella, Roseburia, Clostridium XVIII, Alistipes, and Clostridium XIVb was significantly lower in the DS group compared with the non-DS group. The relative abundance of Prevotella, Allisonella, Alloprevotella, Erysipelotrichaceae incertae sedis, Oribacterium, Dialister, Escherichia/Shigella, Catenibacterium, Mitsuokella, Succinivibrio, and Howardella was significantly higher in the DS group. Therefore, Prevotella and Bacteroides emerged as the characteristic bacteria of individuals with DS and non-DS volunteers, respectively. These data offer potential microbiome-based biomarkers of DS that may be linked to gut and overall health. Figure 4: Differences in microbial composition between individuals with DS (DS) and non-DS volunteers (HC) at the genus level. (A) Mean proportion (left) and difference in mean proportion (right) of each genus present in the microbiome of DS and non-DS groups. (B) Fecal microbial richness of each DS and non-DS volunteer at the genus level. (C) Mean composition and richness of fecal microbiota in individuals with DS and non-DS volunteers. According to the flora classification, among the top 20 genera, we set the abundance difference and evolutionary relationship of dominant species in single or multiple samples against the entire classification system (Figure 3B). This analysis confirmed Prevotella as the dominant bacterium in individuals with DS, with a relative abundance of $35.04\%$, while Bacteroides was the dominant bacteria in non-DS volunteers, with a relative abundance of $29.56\%$. Both Prevotella and Bacteroides belong to Bacteroidetes (plylum), Bacteroidia (class), and Bacteroides (order). Bacteroides can be further divided into Prevotellaceae (family), Prevotella (genus) and Bacteroidaceae (family), Bacteroides (genus). This suggests that that the intestines of individuals with DS may provide a preferred environment for Prevotella rather than Bacteroides, and that the relative level of order Bacteroides bacteria may be a key marker of DS linked to severity. We next used hierarchical clustering to reveal the arrangement of groups (clusters) of similar bacteria within the intestinal flora in the DS and non-DS groups. The top 20 genera were used for a comparative analysis. As seen in Supplementary Figure 2, at the genus level, the fecal flora of individuals with DS and non-DS people separated into two clusters with a few exceptions, suggesting distinct differences in the fecal microbiota between the two study groups. As shown in Supplementary Figure 3, a heatmap was generated to represent the relative abundance of each genus by color gradient and cluster the data according to the similarity of species or sample abundance. This analysis showed a similar result: Prevotella is the dominant bacterium in fecal samples of individuals with DS, whereas Bacteroides is the dominant bacteria in the non-DS group. Of note, in the heatmap of species distribution, some samples in one group were clustered outside another group. Nonetheless, there were significant differences in the expression of dominant bacteria between the two groups, with a clear shift in the composition of intestinal microbiota in individuals with DS. In addition, we used linear discriminant analysis effect size (LEfSe) analysis to compare the two groups and identify any subgroups with significant differences in bacterial abundance at the order, family, and genus levels. As shown in Supplementary Figure 4, this analysis further confirmed differences between the two groups, with Prevotella, Prevotellaceae, and Veillonellaceae identified as the dominant bacterial flora in individuals with DS, and Bacteroides, Bacteroidaceae, and Lachnospiracea as the dominant bacterial flora in the non-DS group. ## Differential abundance of fecal metabolites in individuals with DS The alteration of intestinal microbiota can lead to the changes in fecal metabolomics. The analytical tools used for detecting the fecal metabolites include GC-TOF-MS, nuclear magnetic resonance (NMR), and ultra-performance liquid chromatography-mass spectrometry (LC–MS) (Chen et al., 2020). Among these tools, we selected GC-TOF-MS due to its complete database, fast scanning rate, its high efficiency, and sensitivity (Ma et al., 2011). To explore the connection between intestinal microbiota and fecal metabolomics in individuals with DS, we performed fecal metabolomics-based GC-TOF-MS analyses followed by PCA, PLS-DA, and OPLS-DA analyses and compared fecal metabolic profiles between the non-DS and DS groups. PCA score scatter plot and volcano plot were used to illustrate differences in all metabolites in all samples (Supplementary Figure 5). A total of 35 substances were detected with highly significant differences between the two study groups (Supplementary Table 3). Compared with non-DS volunteers, 21 fecal metabolites were significantly higher in individuals with DS, indicated by the red section of the table (ID 1–21). A total of 14 fecal metabolites were significantly lower in individuals with DS compared to non-DS volunteers, indicated by the blue section of the table (ID 22–35). As shown in Figure 5A, the metabolic spectra of two groups were separated by the PCA score plots, suggesting a significant difference in fecal metabolite profiles between the non-DS and DS groups. PLS-DA and OPLS-DA analyses of the fecal metabolome further illustrated significant separation between the non-DS group and DS group (Figures 5B,C). As illustrated in Figure 5D, the fecal metabolites in the load map located far from the center point were regarded as significantly changed (increased or decreased) in individuals with DS compared with non-DS volunteers, and were marked as potential biomarkers. As shown in Supplementary Table 3, a total of 35 potential biomarkers differed in feces between the non-DS and DS groups, including 21 metabolites that were increased and 14 that were decreased in the DS group compared with the non-DS group. As shown in Figure 5F, metabolites that were higher in the DS group than in the non-DS group included amino acids (serine, isoleucine, valine, alanine, phenylalanine, norvaline, cycloserine, and alanine); acids (phenylacetic acid, pipecolinic acid, glutaric acid, oxalacetic acid, 3-hydroxybutyric acid, and oxamic acid); amines (indole-3-acetamide, tyramine); alcohols (benzyl alcohol, and 4-methyl-5-thiazolethanol); and others (methionine sulfoxide, glutaraldehyde, and farnesal). Metabolites that were lower in the DS group than in the non-DS group included fatty acids (arachidic acid, behenic acid, alpha-tocopherol, elaidic acid, and 1-monopalmitin); amino acids (3-cyanoalanine, alpha-aminoadipic acid, and asparagine); carbohydrates (ribonic acid, cellobiose, N-acetyl-D-galactosamine, and 4-aminobutyric acid); and others (DL-dihydrosphingosine, gamma-lactone, and conduritol epoxide). Metabolic pathway enrichment analyses of these potential biomarkers (Figure 5E) suggested that valine, leucine, and isoleucine biosynthesis; synthesis and degradation of ketone bodies; valine, leucine, and isoleucine degradation; aminoacyl-tRNA biosynthesis; phenylalanine metabolism; glyoxylate and dicarboxylate metabolism; tyrosine metabolism; lysine degradation; citrate cycle (TCA cycle); and alanine, aspartate, and glutamate metabolism were different between the DS and non-DS groups. Figure 5: Differences in fecal metabolomic profiles and pathways in individuals with DS and non-DS volunteers. (A) Fecal metabolomic profiling by GC-TOF-MS. PCA score plot for the non-DS and DS groups. (B) PLS-DA score plot for the non-DS and DS groups. (C) OPLS-DA score plot for the non-DS and DS groups. (D) S-loading plot based on the OPLS-DA analysis of fecal metabolomics. (E) Heatmap of the relative abundance of significantly different metabolites (VIP > 1.0 and p < 0.05) between the non-DS (HC) and DS groups. (F) Metabolic pathway impact prediction based on the KEGG online database. The -ln(p) values from the pathway enrichment analysis are indicated on the horizontal axis, and the impact values are indicated on the vertical axis. HC, non-DS volunteer (or non-DS group); DS, individual with DS (or DS group). ## Cytokine levels are correlate with key microbial phylotypes and fecal metabolites To further address whether the observed differences in intestinal microbiota and the fecal metabolome in the DS and non-DS groups are linked to differences in their cytokine profiles, we performed a heatmap and network study. The correlation between cytokines and key microbiota phylotypes is illustrated in Figures 6A,B. Genus Oribacterium, Alloprevotella, Catenibacterium, Allisonella, Prevotella, Erysipelotrichaceae incertae sedis, Howardella, Mitsuokella, Succinivibrio, Dialister, and Escherichia/Shigella were positively associated with the levels of serum IL-1α, MIG, TNF-α, granzyme B, MCP-1, rantes, IL-1β, IL-9, fractalkine, IL-8, MIP-1β, IgE, and IL-6, and negatively associated with the levels of serum angiogenin, MCP-1α, and G-CSF. However, genus Bilophila, Alistipes, Parasutterella, Roseburia, Paraprevotella, Anaerostipes, and Clostridium XVIII were negatively associated with the levels of serum IL-1α, MIG, TNF-α, granzyme B, MCP-1, rantes, IL-1β, IL-9, fractalkine, IL-8, MIP-1β, IgE, and IL-6, and positively associated with the levels of serum angiogenin, MCP-1α, and G-CSF. Intestinal microbiota-derived metabolites are reported to accelerate or prevent inflammation. Therefore, we used Spearman’s correlation analysis to further uncover the potential correlation between fecal metabolites and cytokines in individuals with DS (Figures 6C,D). Indole-3-acetamide 2, methionine sulfoxide 2, oxamic acid, serine 1, norvaline, phenylalanine1, beta-alanine 2, farnesal 1, glutaric acid, oxamic acid, tyramine, 3-hydroxybutyric acid, 4-methyl-5-thiazolethanol, phenylacetic acid, pipecolinic acid, benzyl alcohol, alanine 1, glutaraldehyde 2, isoleucine, cycloserine, and valine were positively associated with the levels of serum IL-1α, MIG, TNF-α, granzyme B, MCP-1, rantes, IL-1β, IL-9, fractalkine, IL-8, MIP-1β, IgE, and IL-6, and negatively associated with the levels of serum angiogenin, MCP-1α, and G-CSF. In contrast, 1-monopalmitin, alpha-aminoadipic acid, 3-cyanoalanine, behenic acid, DL-dihydrosphingosine 1, asparagine 3, alpha-tocopherol, ribonic acid, gamma-lactone, N-Acetyl-D-galactosamine 1, arachidic acid, cellobiose 2, 4-aminobutyric acid 1, elaidic acid, and conduritol b epoxide 2 were negatively associated with the levels of serum IL-1α, MIG, TNF-α, granzyme B, MCP-1, rantes, IL-1β, IL-9, fractalkine, IL-8, MIP-1β, IgE, and IL-6, but were positively associated with the levels of serum angiogenin, MCP-1α, and G-CSF. Figure 6: Correlation analyses of intestinal bacterial flora, metabolites, and cytokine levels in DS and non-DS groups. (A) Heatmap of Spearman’s correlation between the key intestinal bacterial phylotypes and cytokine levels. Red indicates a positive correlation and blue indicates a negative correlation. (B) Visualization of the correlation network based on partial correlation between the key intestinal bacterial phylotypes (teal) and cytokines (pink). Red lines indicate a positive correlation and blue lines indicate a negative correlation. The thicker the line, the stronger the correlation. (C) Heatmap of Spearman’s correlation between the significantly different metabolites and cytokine levels. Red indicates a positive correlation and blue indicates a negative correlation. (D) Visualization of the correlation network based on partial correlation between the significantly different metabolites (green) and cytokines (purple). Red lines indicate a positive correlation and blue lines indicate a negative correlation. The thicker the line, the stronger the correlation. HC, non-DS volunteers (or non-DS group); DS, individuals with DS (or DS group). ## Influence of key microbial phylotypes on serum CRP levels and DS-related behaviors in germ-free mice Previous studies have suggested that brain function and behavior can be affected by the gut flora. Therefore, we treated germ-free mice with the gut microbiota of non-DS volunteers or individuals with DS and a selected microbial species, Prevotella copri, to assess the effect on specific behaviors in mice. We selected Prevotella copri based on our consistent finding that the microbiome of individuals with DS is highly enriched in Prevotella bacteria compared to the microbiome of non-DS volunteers. Compared with the untreated control group, mice treated with fecal bacteria derived from individuals with DS exhibited reduced sucrose preference, reduced total distance moved and time in center of the open field test, and increased immobility time in the forced swimming test (all $p \leq 0.01$) (Figures 7A–D). In contrast, mice treated with feces derived from non-DS volunteers showed an increase in activity in the open field tests compared to controls ($p \leq 0.01$), an effect that needs to be further studied in the future. Prevotella copri treatment led to a significant reduction in sucrose preference, total distance moved, and time in center, and a significant increase in the immobility time, similar to the effect of the DS gut microbiota (all $p \leq 0.01$). The distance moved and time in center of mice in the Prevotella group were slightly lower than that of mice in the DS gut microbiota-treated group. These data suggest that the higher abundance of Prevotella copri in the microbiome of individuals with DS may play a role in modulating behaviors associated with DS. Figure 7: Behavioral effects of intestinal microbiota derived from non-DS volunteers or individuals with DS and Prevotella copri in germ-free mice. (A–D) Behavior of mice treated with intestinal microbiota derived from non-DS volunteers or individuals with DS and Prevotella copri in sucrose preference, open field, and forced swimming tests. Mice treated with intestinal microbiota derived from individuals with DS or Prevotella copri exhibited reduced sucrose preference (A), reduced total distance traveled (open field test) (B), reduced time in center (open field test) (C), and increased immobility time (forced swimming test) (D). (E–G) Effects of intestinal microbiota derived from non-DS volunteers or individuals with DS and Prevotella copri on serum CRP (A), LPS (B), and corticosterone (C) levels. Each group contained 6 mice. The data are expressed as the mean ± SEM from one-way ANOVA followed by a post hoc test and Student’s t-test. p < 0.05, and *p < 0.01. Serum collected after the behavior tests was used to measure the levels of several microbial metabolites shown to be enriched in individuals with DS. LPS can attack multiple organs to promote inflammation and is used to establish animal models of cognitive impairment (Zhao et al., 2022). CRP is regarded as a marker of inflammation and corticosterone can maintain homeostasis through significant regulation of inflammation (Reeder and Kramer, 2005; Belda et al., 2020). Serum CRP, LPS, and corticosterone levels were significantly increased in mice treated with the intestinal microbiome derived from individuals with DS, suggesting that fecal bacteria play a role in DS (Figures 7E–G). Unexpectedly, we found an increase in serum LPS in the mice treated with the microbiome derived from non-DS volunteers, whereas CRP and corticosterone levels were not affected, a finding which needs to be addressed in the future. Prevotella copri treatment also significantly elevated serum CRP, LPS, and corticosterone levels in mice. These data suggest that changes in these metabolites may be correlated with specific behaviors in individuals with DS. ## Discussion Down syndrome is one of the most common genetic causes of intellectual disability, with comorbid conditions that can reduce the quality of life and elevate the financial burden of care. Accumulating evidence suggests that intestinal microbiota play a vital role in various diseases and conditions and may serve as useful biomarkers of associated physiologic and behavioral changes (Choi et al., 2021). In this study, we found that serum pro-inflammatory cytokines, intestinal microbiota composition and diversity, and fecal metabolites were significantly different in individuals with DS compared to a group of volunteers without DS. Dysbiosis of intestinal microbiota occurs frequently in individuals with DS and has been regarded as a contributor to comorbid conditions associated with DS (Ren et al., 2022). Previous studies have suggested that the reduction of microbiota diversity, as indicated by a reduction in the Shannon index and increase in the Simpson index at the OTU level, is associated with various neurodevelopmental and neurodegenerative disorders including autism, Alzheimer’s disease, and Parkinson’s disease (Harach et al., 2015; Hu et al., 2016; Mangiola et al., 2016). In the present study, we observed significantly different species diversity (per the Shannon index) in individuals with DS compared with non-DS volunteers, which may be associated with cognitive impairment in individuals with DS as suggested by others (Ren et al., 2022). Specifically, we found that the Firmicutes/Bacteroidetes ratio at the phylum level is significantly elevated in individuals with DS. This observation is consistent with clinical evidence suggesting that the alteration of intestinal microbiota in individuals with DS influences cognitive function by shifting the status of the microglia, such as through an increase in Firmicutes abundance (Jimenez et al., 2008). A high Firmicutes/Bacteroidetes ratio has also been reported in disorders of glycolipid metabolism, which may underlie developmental delays in individuals with DS (Grigor’eva, 2020). In addition, an increase in the Firmicutes/Bacteroidetes ratio has been linked to inflammation in patients with primary biliary cholangitis (Han et al., 2022). Therefore, the Firmicutes/Bacteroidetes ratio may be useful as a diagnostic biomarker for nutritional malabsorption in individuals with DS. At the genus level, we found an abundance of Prevotella in the microbiome of the DS group compared to that of the non-DS group, where Bacteroides accounted for the largest proportion. These data are different from those in a previous report suggesting a higher abundance of Parasporobacterium and Sutterella in DS (Biagi et al., 2014). The discrepancy may be due to differences in demographic or lifestyle factors in study cohorts or differences in experimental conditions. We predict that even under the same living environment, the intestinal microbiota of those with DS and those without might be different. A larger sample size needs to be studied in the future. Nevertheless, it has been shown that eating patterns may regulate the gut microbiome by altering the use of nutrients (Wang and Kasper, 2014; Jiang et al., 2017). Both Prevotella and Bacteroides have been suggested as biomarkers for diet or disease (Gorvitovskaia et al., 2016). Bacteroides is well adapted to use of a large number of dietary polysaccharides and host-derived polysaccharides (such as mucus) (Martens et al., 2008). In the genome of Bacteroides, polysaccharide utilization sites have been widely expanded, and each seems to be dedicated to the utilization of specific categories of carbohydrates (Sonnenburg et al., 2010). The significant enrichment of Bacteroides in non-DS volunteers compared to individuals with DS in our study suggests a difference in carbohydrate intake between the two groups. Prevotella copri has previously been reported to induce insulin resistance and increase the risk of cardiovascular disease (Pedersen et al., 2016). Prevotella copri encodes superoxide reductase and adenosine phosphate phosphoryl sulfate reductase, both of which can enhance its resistance to ROS produced by inflammation, promote its proliferation in the inflammatory environment, and intensify inflammation (Scher et al., 2013). Bacteroides lacks these two enzymes (Scher et al., 2013), and its growth is inhibited and its abundance decreases in the inflammatory environment. The overexpression of SOD-1 gene in the 22.2–22.3 region of the long arm of chromosome 21 in individuals with DS (Donato, 2001; Netto et al., 2004) likely disturbs the steady-state biochemical balance of intracellular reactive oxygen species and increases the content of intracellular ROS, favoring enrichment of Prevotella. We also found that Roseburia, Anaerostipes, and Parasutterella were reduced in individuals with DS compared to non-DS volunteers. Roseburia plays a vital role in the decomposition of polysaccharides to offer short-chain fatty acids to the host, enhance the body’s immune function, and regulate glycolipid metabolism (Du et al., 2020; Furuta et al., 2021). Anaerostipes maintains gut homeostasis through production of butyrate, the primary source of bacterial energy (Bui et al., 2014). Parasutterella serves as a core member of the intestinal microbiota, and its abundance is positively associated with the levels of short-chain fatty acids (SCFAs), which promote the secretion of IL-10 by regulating G-protein coupled receptor 43 (GPR43), a receptor for short-chain free fatty acids that is involved in the inflammatory response and regulation of lipid plasma levels (Sun et al., 2018; Mei et al., 2020). On the contrary, the relative abundance of Escherichia/Shigella and Catenibacterium was significantly higher in the DS group. Escherichia/Shigella are the mammalian bacillary dysentery pathogens, which result in diarrhea, fever, urinary tract infections, and pneumonia (Sun et al., 2019). Catenibacterium is Gram-positive bacteria, and its abundance is positively associated with serum biochemical parameters related to obesity and metabolic syndrome (Gallardo-Becerra et al., 2020). In addition, Allisonella genera are related to a pro-inflammatory phenotype (Aranaz et al., 2021). We found that increased serum pro-inflammatory cytokines was associated with a profile shift of gut microbiota in individuals with DS. Persistent and high-intensity inflammation in the intestines of individuals with DS may be related to their shift of microbiota composition. Therefore, enrichment of these microbial flora may be useful as diagnostic biomarkers or therapeutic targets for reducing gut inflammation in individuals with DS. Since the fecal metabolome is closely related to the composition of intestinal microbiota, we performed GC-TOF-MS–based metabolomics analyses and found significant differences in fecal metabolites between individuals with DS and non-DS volunteers. Specifically, fecal behenic acid, α-tocopherol, DL- dihydrosphingosine, and aminobutyric acid levels were all significantly reduced in individuals with DS, while norvaline, glutaraldehyde, and glutaric acid were significantly increased compared to the non-DS group. Behenic acid is a saturated, very long-chain fatty acid that is beneficial for ameliorating postprandial inflammation by regulating serum IL-6, LPS, CRP, and insulin (da Silva et al., 2019). α-Tocopherol is a lipid-soluble antioxidant that is easily absorbed by the intestine, and plays a vital role in preventing oxidative damage to polyunsaturated fatty acids (Minehira-Castelli et al., 2006). Oral administration of DL-dihydrosphingosine inhibits the development of inflammation by protecting against TNF-α–induced cytotoxicity (Meyer and de Groot, 2003). Aminobutyric acid is an amino acid that ameliorates abnormal glycolipid metabolism in streptozotocin-induced diabetic mice (Abdelazez et al., 2022). Norvaline is a non-proteinogenic amino acid that destroys mitochondrial morphology and function by reducing cell viability at low concentrations (Samardzic and Rodgers, 2019). Glutaraldehyde is a five-carbon dialdehyde that is irritating to skin, eyes, nose, lungs, and increases the rates of cutaneous (Zissu et al., 1998). Glutaric acid, a water-soluble dicarboxylic acid, is one of the main metabolites of Oscillibacter. Glutaric acid stimulates the secretion of pro-inflammatory cytokines (Schmidt and Ferger, 2004; Athankar et al., 2017). Together, the observed differences in fecal metabolites in individuals with DS are linked not only to the metabolism dysfunction but also activation of the immune response and inflammation. It has been previously reported the serum pro-inflammatory cytokines (including IL-2, IL-6, IL-8, IL-18, IL-1α, IL-1β, and TNF-α) are significantly increased and anti-inflammatory cytokines (including G-CSF) are significantly reduced in individuals with DS (Huggard et al., 2020; Martini et al., 2022). Consistent with these studies, we observed higher amounts of pro-inflammatory cytokines (including IL-1α, IL-1β, IL-6 and TNF-α) and lower anti-inflammatory cytokines (including G-CSF and angiogenin) in individuals with DS compared to non-DS volunteers. IL-1α is a canonical alarmin that induces neutrophil influx and activation, monocyte recruitment, prostaglandin synthesis, T- and B-cell activation and cytokine production by triggering the IL-1R, and results in the rapid recruitment of inflammatory cells (Batista et al., 2020). IL-1β is known as a key pro-inflammatory mediator that stimulates the NF-κB pathway (Karnam et al., 2020; Guo et al., 2021). In addition, increases in MCP-1 secretion are strongly associated with IL-1β and can activate multiple inflammatory pathways leading to the secretion of IL-6, TNF-α, and G-CSF (Huang et al., 2022). IL-6 is a well-established inflammation-promoting cytokine secreted by a variety of cell types (Mauer et al., 2015; Lin et al., 2021). Abnormal levels of TNF-α disturb the immune system by activating TNF receptors and up-regulating the downstream pathways and regulatory molecules, including NF-κB, MAPKs, caspases, and ROS/RNS (Blaser et al., 2016). In contrast, a high concentration of G-CSF is beneficial for promoting the survival, proliferation, differentiation, and function of granulocyte precursors (Cai et al., 2017). Furthermore, angiogenin has been reported to control bacterial overgrowth and maintain the integrity of the intestinal barrier (Chiang, 2017). We also found significant differences in glycolytic metabolic pathways between individuals with DS and non-DS volunteers. Parallel to the enrichment of Prevotella in individuals with DS, Prevotella copri intervention significantly increased the serum levels of CRP, LPS, and corticosterone in mice, which are all involved in inflammation. LPS can attack multiple organs, including liver, kidney, lung, and brain, to promote inflammation and is widely used to establish animal models of cognitive impairment, such as Parkinson’s disease (Zhao et al., 2022). CRP is regarded as a marker of inflammation that is strongly associated with the expression of adhesion molecules and chemokines in human endothelial cells. Corticosterone is the end product of the hypothalamic pituitary adrenal axis response to stress; it supports metabolic activities requiring energy to overcome environmental challenges and maintains homeostasis through significant regulation of inflammation (Reeder and Kramer, 2005; Belda et al., 2020). Therefore, there is a strong correlation between the observed DS-specific patterns of microbiota, metabolites, and inflammation. Intestinal microbes and the brain interact with each other, and coordinate processes involved in health and disease (Ding et al., 2019). The gut–brain axis is a complex two-way system that uses various communication pathways, including endocrine, immune, neurotransmitter system, vagus nerve, and metabolites, such as SCFAs, branched chain amino acids, and peptidoglycans. In our studies on the effect of gut microbiota on behavior in mice, we found that the intestinal microbiota derived from individuals with DS and Prevotella copri could significantly change sugar water preference, autonomous activity, and depressive behavior. For rodents, the body’s receptors for sugar mainly exist in the oral cavity, which increase the body’s desire for sugar after being stimulated, and this desire is further enhanced in the intestinal tract (Sclafani and Ackroff, 2012). A low level of sugar water preference is often used as an indicator for depression (Lamers et al., 2019). In this study, the preference for sugar water was significantly lower in mice treated with intestinal flora from individuals with DS of Prevotella copri compared to untreated controls. In addition, we used open field test to evaluate the exploration ability and anxiety of experimental animals (Sheppard et al., 2022). We found that the movement distance and distance from the center of the open field were lower for mice transplanted with flora from individuals with DS and Prevotella copri, suggesting multiple systemic disorders related to basic motor skills, movement disorders, and abnormal gait and posture (Horvat et al., 2010), which are correlated with cognitive limitations, biomechanical defects, neurological defects, abnormal sensorimotor integration, and/or impaired somatosensory system found in individuals with DS (Carvalho and Vasconcelos, 2011). These data are consistent with the role of Prevotella in inflammatory diseases and its association with the development of cognitive impairment reported previously (Dillon et al., 2014; Wen et al., 2017). Therefore, we speculate that chronic inflammation and developmental delay in individuals with DS may be associated with the intestinal microbiota composition. ## Conclusion The present study shows abnormal levels of serum cytokines, disordered intestinal microbial composition and diversity, and dysregulation of fecal metabolites in individuals with DS compared to non-DS volunteers. Since intestinal microbiota play a harmful role in the development of inflammation, intestinal microbiota and fecal metabolites-based biomarkers may provide potential targets for diagnosing and treating chronic inflammation in individuals with DS. This concept needs further clinical validation and mechanistic studies, in particular, a deeper exploration of the relationship between the intestinal microbiota and fecal metabolites and the levels of inflammation in individuals with DS. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: NCBI – PRJNA870642. ## Ethics statement The animal study was reviewed and approved by the Chinese Academy of Medical Sciences [Permit No. SYXK (Beijing)-2018-0019]. ## Author contributions SC, QC, YandingZ, and JH conceived and designed the experiments. SC, JL, ZL, SL, ZF, and YangfanZ performed the experiments and analyzed the data. SC and QC wrote the manuscript. All authors contributed to the article and approved the submitted version. ## Funding This work was supported by the Natural Science Foundation of Fujian Province, China (Grant Nos. 2017J01621 and 2021J01202). ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1016872/full#supplementary-material ## References 1. 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--- title: Disease-specific quality of life in patients with diabetic neuropathy authors: - Mushabab Alghamdi - Lukman F. Owolabi - Bappa Adamu - Magaji G. Taura - Abubakar Jibo - Mohammed Almansour - Saeed N. Alaklabi - Mohammed A. Alghamdi - Isa A. Imam - Reda Abdelrazak - Ahmad Rafaat - Muktar H. Aliyu journal: Saudi Medical Journal year: 2022 pmcid: PMC9998049 doi: 10.15537/smj.2022.43.4.20210861 license: CC BY 4.0 --- # Disease-specific quality of life in patients with diabetic neuropathy ## Body Diabetes mellitus (DM) is one of the most prevalent chronic diseases and is a growing global health concern. Worldwide, it is expected that the number of people living with DM will increase dramatically, reaching more than 500 million by 2035. 1 The tremendous increase in the prevalence of this disease will be accompanied by an increase in chronic diabetic microvascular and macrovascular complications.2 Diabetic neuropathy (DN), one of the most prevalent complications of DM, significantly impacts the quality of life and increases the risk of early death in patients with DM. 2 Diabetic neuropathy, a microvascular complication of DM, is attributed to chronic hyperglycemia and is described as the presence of peripheral nerve dysfunction in a patient with DM after exclusion of other causes. The heterogeneous sequelae of DM affect different parts of the nervous system and cause diverse clinical manifestations. 3 Diabetic neuropathy may cause secondary complications, such as Charcot arthropathy, foot ulcer, and lower limb amputation. 4 Diabetic neuropathy often has an insidious onset and is asymptomatic in approximately $50\%$ and symptomatic with sensory manifestations in approximately $20\%$ of affected individuals. 5 Symptoms of DN may include pain, paresthesia, numbness, hyperesthesia, and gait imbalance, with the potential for foot ulcer and amputation. The evaluation of health-related quality of life (HRQoL) is increasingly valued as a vital arm of the assessment of chronic diseases and their complications. 6 The existing evidence has revealed a negative impact of DM and its complications, including DN, on people’s HRQoL and psychosocial and physical well-being. 7 Previous studies have reported that changes in the physical domain of HRQoL in patients with DN are associated with patient’s age and body mass index (BMI). Poor mental HRQoL has been associated with female gender, smoking, duration of DM, and BMI. The poor HRQoL observed in patients with DN has been attributed to factors such as pain, mood, unhealthy mental state, disturbed sleep, and impaired daily activities.8,9 The need to be cognizant of the broad impact of DN in patients with DM beyond the specific disease process and the importance of assessing patient-reported outcomes such as HRQoL have been increasingly recognized in medical practice. In addition to the impact that poor HRQoL has on the well-being of patients and their relatives, it plays a role in how successful the medical management of DM and its complications will be; thus, an assessment of HRQoL in every patient with DM and DN is necessary. Unlike other DM complications, the impact of DN on HRQoL has not been extensively studied using a disease-specific HRQoL tool, particularly in Saudi Arabia, where DM is prevalent. 10 Data on the HRQoL of patients with DN are critical in DN treatment centers to optimize the surveying of perceived health problems, screening and monitoring of psychosocial issues, and performance of outcome measures, medical audits, and cost-utility analyses. 6 We hypothesized that there would be notable differences in HRQoL, as measured by a disease-specific HRQoL tool, between DM patients with DN (D+DN), DM patients without DN (D-DN), and healthy participants. The study aimed to compare HRQoL in a cohort of Saudi patients comprising D+DN, D-DN, and healthy participants to evaluate the factors associated with poor HRQoL in patients with D+DN. ## Abstract ### Objectives: To compare health-related quality of life (HRQoL) among patients with diabetes mellitus (DM) and diabetic neuropathy (DN) (D+N) with patients with DM without DN (D-DN) and healthy participants. To evaluate factors associated with poor HRQoL in patients with DN. ### Methods: This study included 306 participants residing in Bisha, Saudi Arabia. Patients with DM were screened for DN using the Michigan Neuropathy Screening Instrument. Neuropathy severity, disability and HRQoL were determined using the Neuropathy Severity Scale (NSS), the Neuropathy Disability Score (NDS), and the Norfolk Quality of Life-Diabetic Neuropathy (QOL-DN) tool, respectively. Nerve conduction studies (NCSs) were also performed. ### Results: The D+DN group had poorer overall and domain HRQoL scores compared to the D-DN group ($p \leq 0.001$). There was a strong correlation between overall HRQoL score and both NDS and NSS scores in the D+DN group (ρ= −0.71 and $p \leq 0.0001$; ρ= −0.81 and $p \leq 0.0001$, respectively). There was also a significant difference in all mean HRQoL domain scores between D+DN participants with normal and abnormal NCS. Physical inactivity ($$p \leq 0.043$$), duration of DM ($p \leq 0.0001$), abnormal NCS, NSS ($p \leq 0.0001$), and NDS ($p \leq 0.0001$) predicted HRQoL in the D+DN group. ### Conclusion: D+DN participants had a worse HRQoL compared with D-DN and healthy counterparts. NDS, NNS, physical inactivity, abnormal NCS, and duration of DM independently predicted poor HRQoL in D+DN participants. ## Methods This cross-sectional study included 306 participants recruited from the diabetes, endocrinology, and metabolic diseases center of a medical clinic, medical outpatient, and electrophysiology laboratory from December to June 2021 at King Abdullah Hospital (KAH), Bisha, Saudi Arabia. The required sample size was calculated using the STATA software package (STATA, Inc., version 16, Texas 77845-4512. USA) with the following requisite parameters: statistical power (α) = 0.8, statistical significance (α) = 0.05, effect size = 5 value points, and expected standard deviation (SD =10 points using a balanced study design. The minimum sample size was determined to be 92 subjects per group. In order to reduce the influence of unknown variables on the sample size calculation, the sample size was increased to 102 participants in each group. The participants were consecutively recruited and included 102 patients with DM (type I and II) and DN, 102 patients with DM (type I and II) without DN (based on the presence or absence of peripheral neuropathy during screening), and 102 apparently healthy controls from the general population. The inclusion criteria were all participants were adult (>18 years of age) and were matched for age and gender using nearest neighbor matching method.11 Participants were excluded from the study if they had a mental or cognitive illness, were hospitalized at the time of the study, or were undergoing treatment for cancer. Screening for DN was performed using the Michigan Neuropathy Screening Instrument (MNSI), which is widely used for the evaluation of distal symmetrical peripheral neuropathy in DM. Patients with abnormal test results, defined as ≥4 abnormal items on the MNSI questionnaire, were considered to have DN and were included in the D+DN group. 12 The severity of neuropathy was determined in the D+DN group using the Neuropathy Severity Scale (NSS), which evaluates neuropathic symptoms such as burning, cramping, aching, fatigue, tingling sensations or numbness, and nocturnal exacerbation. Disability due to neuropathy was assessed using the Neuropathy Disability Score (NDS), which comprises measurements of ankle reflex, temperature, pinprick sensation, vibration sense tested with a 128-Hz tuning fork, and monofilament test. A detailed medical history, followed by somatic and neurological status, was obtained from all participants. While obtaining medical history, we specifically focused on demographic data and comorbidities that were important for this study. The participants’ medical records were also reviewed for the presence of comorbidities, DM-related complications, lipid profiles, hemoglobin A1c (HbA1c), sociodemographic characteristics, duration of DM in years, medication prescription, and drug adherence. The variables of interest were derived from a literature search. Search for relevant previous studies was performed using related MESH terminologies and relevant search engines (PubMed, Google, Google scholar, and Endnote). A disease-specific HRQoL tool was administered to all participants. The study was carried out in Bisha, KSA between November 2020 and June 2021. ## Measuring health-related quality of life Health-related quality of life was measured using the Norfolk Quality of Life-Diabetic Neuropathy (Norfolk QOL-DN) instrument, which has been established as a reliable outcome measure across multiple populations and is sensitive to both small- and large-fiber abnormalities. 12 The Norfolk QoL-DN instrument is a comprehensive and validated 47-item questionnaire designed to capture the entire spectrum of DN related to small fiber, large fiber, and autonomic neuropathy. 13 It comprises 2 sets of questions: queries related to symptoms experienced by the diabetic patient and those related to the impact of the diabetic patient’s neuropathy on activities of daily living (ADLs). The questions in the instrument are categorized into 6 exploratory domains, including total quality of life, symptoms, ADLs, physical functioning/large fiber, small fiber, and autonomic neuropathy. 5,14 The Norfolk QOL-DN has a fairly good reliability profile, with a Cronbach alpha of 0.60–0.80 for all 3 clinical groups, 0.74-0.86 for patients with DM and DN, 0.63-0.80 for patients with DM without DN, and 0.62-0.79 for healthy controls. 5 Regarding the Norfolk QOL-DN scoring algorithm adopted in this study, all items in the symptom domain (items 1-7) were assigned a score of either 1 or 0, indicating the presence or absence of the indicated symptoms. With the exception of items 31 and 32, the other items were scored based on a 5-point Likert scale (0-4, “No Problem” to “Severe Problem”). For item 31, “Poor” was assigned a score of 2, “Fair” a score of 1, “Good” a score of 0, “Very Good” a score of –1, and “Excellent” a score of –2. Item 32 was also scored on a scale of −2 to 2, with −2 indicating “Much Better,” −1 indicating “Somewhat Better,” 0 indicating “About the Same,” 1 indicating “Somewhat Worse,” and 2 indicating “Much Worse.” The algorithm used for the summation of scores was as follows: total quality of life, Σ [1-7, 8-35]; physical functioning/large fiber, Σ [8, 11, 13-15, 24, 27-35]; ADLs, Σ [12, 22, 23, 25, 26]; symptoms, Σ [1-7, 9]; small fiber, Σ [10, 16, 17, 18]; and autonomic, Σ [19, 20, 21]. Scores were calculated without weighting and were reported as the sum of the integers of the listed questionnaire items. 15 A higher score on the Norfolk QOL-DN denotes a poorer quality of life. ## Electrophysiological study Nerve conduction studies (NCS) were conducted on consenting patients in the D+DN group using the Natus Nicolet Viking Quest electromyography machine. Median (mixed), ulnar (mixed), and radial nerves in the upper limbs and tibial (motor), peroneal (motor), superficial peroneal (sensory), and sural (sensory) nerves in the lower limbs were assessed. An abnormal NCS result was determined based on local NCS reference values in the electromyography laboratory. ## Other definitions Diabetes mellitus: diabetes mellitus was defined as the fulfillment of one of the following conditions: a) a self‐reported diagnosis by a health professional, b) the use of glucose‐lowering medications, and c) HbA1c >$6.5\%$ or fasting blood glucose ≥ 126 mg/dL.16 Diabetic neuropathy (outcome variable): diabetic neuropathy was defined as a score of >4 on the MNSI questionnaire and confirmed with electrophysiological evidence of neuropathy on NCS. 12 Dyslipidemia (covariate): dyslipidemia was defined as the presence of one of the following: total cholesterol ≥5.2 mmol/L, high-density lipoprotein ≤1.3 mmol/L, low-density lipoprotein ≥3.4 mmol/L, or triglycerides ≥1.7 mmol/L based on the National Cholesterol Education Program Adult Treatment Panel III guidelines, or current use of cholesterol-lowering drugs. 17 Sociodemographic characteristics (covariates): Gender was dichotomized as male or female. Level of education was classified as educated (formal Western or Islamic) and not educated. Lifestyle characteristics (covariates): History of stroke or transient ischemic attack (TIA) was defined as any self-reported participant history of stroke or TIA. Physical inactivity was based on the average number of hours of physical activity performed per day (including working and leisure activities), and classified as either “no” if respondents were not involved in moderate or strenuous exercise for ≥4 hours per week, or “yes” for all other quantities of physical activity. 18 Smoking status implied tobacco use and was classified as “never” (if the participant had never used any type of tobacco product) and “ever” (if the participant smoked cigarettes or used any type of tobacco product in the past 12 months). 15 Alcohol use was defined as “never” when the participant had never consumed any form of alcoholic drink and “ever” when the participant had consumed any form of alcoholic drink previously or in the last 30 days. 19 Anthropometric measurements: measurement of height (cm) and weight (kg) was performed using a standard standiometer and weight scale, respectively, and used to calculate BMI (weight in kg divided by the square of height in meters) based on the World Health Organization guidelines. 20 The study was approved by the ethics committee of the University of Bisha Medical College (UBCOM-RELOC Reg No: H-06-BH-087). The study was carried out in accordance with the principles of the Helsinki declaration. ## Data analysis Data analysis was performed using the STATA software package (STATA, Inc., version 16, College Station, TX: StataCorp LLC). We assessed the missingness of missing-at-random data for HRQoL and other variables by eliminating them using the deletion method. Descriptive statistics were expressed as mean±SD in the case of parametric data, such as age, and median with interquartile range (IQR) for non-parametric data, such as NSS and NDS scores. Categorical data, such as sociodemographic characteristics, drug compliance, illness history, symptoms, and NCS findings, were expressed with proportions. The association between 2 or more categorical variables was assessed using chi-squared and Fisher’s exact tests. Comparisons of 2 groups (D+DN with D-DN or healthy participants) based on HRQoL scores were performed using the independent t-test and reported as effect size (such as, mean difference) and $95\%$ confidence interval (CI). Comparisons between 3 groups (D+DN, D-DN, and healthy participants) were carried out using analysis of variance with a post hoc Bonferroni test. Correlation of the participants’ HRQoL scores with NDS and NSS scores (not normally distributed) was evaluated using Spearman’s correlation (ρ). Multivariate analysis was carried out to determine factors that predict DN HRQoL, their coefficient, and $95\%$ confidence intervals (CI) for factors associated with DN in patients with DM. We included new and previously researched covariates in the regression. A significant association between or among variables was declared if the p-value was <0.05. ## Sociodemographic characteristics of the participants A total of 306 participants were recruited for the study. The participants were separately and matched for age and gender in three groups (D-N, D+N, and healthy participants from the source population) with 102 participants in each group. Forty-seven ($46.1\%$) of the participants in each group were female, $94.1\%$ were married, and $83.3\%$ had formal education (Western or Islamic). All the participants were Saudi nationals. The mean age of the participants was 54.1 ± 12.8 years. There was no significant difference among the groups in terms of age ($$p \leq 0.957$$) and gender ($$p \leq 1.0$$). Table 1 presents the distribution of the participants based on sociodemographic characteristics. Table 1 \| Variables \| Participants \| Participants.1 \| Participants.2 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| \| Variables \| DM with DN \| DM without DN \| Healthy control \| P-value \| \| Age \| 54.1±12.8 \| 54.1±12.8 \| 53.5±13.1 \| 0.9752 \| \| Gender \| \| \| \| \| \| Male \| 53 \| 53 \| 53 \| 1.000 \| \| Female \| 49 \| 49 \| 49 \| 1.000 \| \| Occupation \| \| \| \| \| \| Private \| 55 \| 9 \| 1 \| 0.001 \| \| Public \| 22 \| 93 \| 101 \| 0.001 \| \| Others \| 25 \| 0 \| 0 \| 0.001 \| \| Marital status \| \| \| \| \| \| Married \| 99 \| 93 \| 89 \| 0.036 \| \| Single \| 3 \| 9 \| 13 \| 0.036 \| \| Education \| \| \| \| \| \| Educated \| 88 \| 82 \| 73 \| 0.033 \| \| Not educated \| 14 \| 20 \| 29 \| 0.033 \| \| Financial satisfaction \| Financial satisfaction \| \| \| \| \| Satisfied \| 92 \| 98 \| 89 \| 0.080 \| \| Not satisfied \| 10 \| 4 \| 13 \| 0.080 \| \| Smoking \| \| \| \| \| \| Yes \| 14 \| 11 \| 17 \| 0.475 \| \| No \| 88 \| 91 \| 85 \| 0.475 \| ## Diabetes-related characteristics of the participants Diabetes mellitus was diagnosed in 204 participants (with and without DN). The median duration of diabetes was 6 years (IQR, 7 years). Of the patients with DM, 189 ($92.7\%$) reported adherence to their diabetes medications. The mean BMI of patients with DM was 33.7 ± 5.2 kg/cm2 and their mean HbA1c was $7.5\%$ ± $0.96\%$. Among the patients with DM, 18 ($8.8\%$) had a history of nephropathy, 19 ($9.3\%$) had a history of transient ischemic attack (TIA), 18 ($8.8\%$) had a history of retinopathy, 97 ($47.8\%$) had systemic hypertension, 6 ($2.9\%$) had ulcers, 51 ($25\%$) had some form of urinary incontinence, and 115 ($56.4\%$) had dyslipidemia. Nerve conduction studies (NCS) were performed on 82 patients in the D+DN group; of these, 73 ($89\%$) had abnormal NCS findings. There was no difference between the D+DN and the D-DN groups in the presence of nephropathy ($$p \leq 0.082$$), TIA ($$p \leq 1.000$$), retinopathy ($$p \leq 0.082$$), hypertension ($$p \leq 0.942$$), dyslipidemia ($$p \leq 0.778$$), foot ulcers ($$p \leq 0.407$$), urinary incontinence ($$p \leq 0.628$$), diabetic medication adherence ($$p \leq 0.7885$$), and physical inactivity ($$p \leq 0.390$$; Table 2). Table 2 \| Diabetes –related characteristics \| No/Total \| OR \| 95%CI of OR \| P -value \| \| --- \| --- \| --- \| --- \| --- \| \| Nephropathy \| \| \| \| \| \| Present \| 13/102 \| 2.8 \| 0.90, 10.52 \| 0.048 \| \| Absent \| 5/102 \| 2.8 \| 0.90, 10.52 \| 0.048 \| \| TIA \| \| \| \| \| \| Present \| 10/102 \| 1.1 \| 0.39,3.28 \| 0.81 \| \| Absent \| 9/102 \| 1.1 \| 0.39,3.28 \| 0.81 \| \| Retinopathy \| \| \| \| \| \| Present \| 13/102 \| 2.8 \| 0.90, 10.52 \| 0.048 \| \| Absent \| 5/102 \| 2.8 \| 0.90, 10.52 \| 0.048 \| \| Hypertension \| \| \| \| \| \| Present \| 48/102 \| 0.96 \| 0 .54, 1.73 \| 0.889 \| \| Absent \| 49/102 \| 0.96 \| 0 .54, 1.73 \| 0.889 \| \| Dyslipidemia \| \| \| \| \| \| Present \| 59/102 \| 1.1 \| 0.62,2.04 \| 0.672 \| \| Absent \| 56/102 \| 1.1 \| 0.62,2.04 \| 0.672 \| \| Foot ulcers \| \| \| \| \| \| Present \| 4/102 \| 2.0 \| 0.28, 22.96 \| 0.407 \| \| Absent \| 2/102 \| 2.0 \| 0.28, 22.96 \| 0.407 \| \| Urinary incontinence \| Urinary incontinence \| \| \| \| \| Present \| 24/102 \| 0.9 \| 0.43,1.69 \| 0.628 \| \| Absent \| 27/102 \| 0.9 \| 0.43,1.69 \| 0.628 \| \| Adherence to medications \| Adherence to medications \| \| \| \| \| Present \| 94/102 \| 0.9 \| 0.26, 2.86 \| 0.786 \| \| Absent \| 95/102 \| 0.9 \| 0.26, 2.86 \| 0.786 \| \| Physical inactivity \| Physical inactivity \| \| \| \| \| Present \| 94/102 \| 0.6 \| 0.15, 2.19 \| 0.39 \| \| Absent \| 95/102 \| 0.6 \| 0.15, 2.19 \| 0.39 \| Health-related quality of lifein patients with diabetes with neuropathy compared with age- and gender-matched healthy participants The patients with D+DN had poorer HRQoL mean scores compared with apparently healthy participants, both overall ($p \leq 0.001$) and across all domains of the Norfolk QOL-DN, including the physical functioning/large fiber ($p \leq 0.001$), ADLs ($p \leq 0.001$), symptoms ($p \leq 0.001$), small fiber ($p \leq 0.001$), and autonomic ($p \leq 0.001$) domains (Table 3). Table 3 \| HRQoLdomains \| Mean±SD \| MD \| 95%CI of MD \| P -value \| \| --- \| --- \| --- \| --- \| --- \| \| DM with DN versus DM without DN \| \| \| \| \| \| Physical functioning \| \| \| \| \| \| DM with DN \| 31.6 ± 11.2 \| 26.47 \| 24.00, 28.96 \| <0.001 \| \| DM without DN \| 5.2 ± 6.2 \| 26.47 \| 24.00, 28.96 \| <0.001 \| \| ADLS \| \| \| \| \| \| DM with DN \| 10.2 ± 6.2 \| 10.0 \| 8.77, 11.23 \| <0.001 \| \| DM without DN \| 0.2 ± 0.9 \| 10.0 \| 8.77, 11.23 \| <0.001 \| \| Symptoms \| \| \| \| \| \| DM with DN \| 11.8 ± 4.9 \| 9.81 \| 8.80, 10.83 \| <0.001 \| \| DM without DN \| 2.0 ± 1.8 \| 9.81 \| 8.80, 10.83 \| <0.001 \| \| Small fibers \| \| \| \| \| \| DM with DN \| 9.9 ± 4.1 \| 9.28 \| 8.38,10.17 \| <0.001 \| \| DM without DN \| 0.6 ± 1.9 \| 9.28 \| 8.38,10.17 \| <0.001 \| \| Autonomic \| \| \| \| \| \| DM with DN \| 7.2 ± 3.5 \| 6.81 \| 6.10,7.53 \| <0.001 \| \| DM without DN \| 0.4 ± 1.2 \| 6.81 \| 6.10,7.53 \| <0.001 \| \| Overall \| \| \| \| \| \| DM with DN \| 70.8 ± 24.8 \| 62.37 \| 57.21,67.53 \| <0.001 \| \| DM without DN \| 8.4 ± 9.2 \| 62.37 \| 57.21,67.53 \| <0.001 \| \| DM with DN versus healthy population \| \| \| \| \| \| Physical functioning \| \| \| \| \| \| DM with DN \| 31.6 ± 11.2 \| 31.61 \| 29.42, 33.79 \| <0.001 \| \| Healthy population \| 0.01 ± 0.02 \| 31.61 \| 29.42, 33.79 \| <0.001 \| \| ADLS \| \| \| \| \| \| DM with DN \| 10.2 ± 6.2 \| 10.23 \| 9.01, 11.44 \| <0.001 \| \| Healthy population \| 0.095 ± 0 \| 10.23 \| 9.01, 11.44 \| <0.001 \| \| Symptoms \| \| \| \| \| \| DM with DN \| 11.8 ± 4.9 \| 9.24 \| 8.12,10.35 \| <0.001 \| \| Healthy population \| 2.6 ± 2.9 \| 9.24 \| 8.12,10.35 \| <0.001 \| \| Small fibers \| \| \| \| \| \| DM with DN \| 9.9 ± 4.1 \| 9.91 \| 9.10, 0.72 \| <0.001 \| \| Healthy population \| 0.1 ± 0.02 \| 9.91 \| 9.10, 0.72 \| <0.001 \| \| Autonomic \| \| \| \| \| \| DM with DN \| 70 ± 3.5 \| 7.21 \| 6.53, 7.89 \| <0.001 \| \| Healthy population \| 0.1 ± 0.01 \| 7.21 \| 6.53, 7.89 \| <0.001 \| \| Overall \| \| \| \| \| \| DM with DN \| 70.8 ± 32 \| 70.79 \| 65.96, 75.63 \| <0.001 \| \| Healthy population \| 0.1 ± 0.03 \| 70.79 \| 65.96, 75.63 \| <0.001 \| Multiple comparisons of the 3 groups using analysis of variance and post hoc Bonferroni testing confirmed a significant difference among the 3 groups and between each combination of 2 groups across all HRQoL domains ($p \leq 0.001$). Health-related quality of life of patients with diabetes and neuropathy compared with age- and gender-matched patients with diabetes without neuropathy Using the Norfolk QOL-DN, which comprises 5 domains and one overall HRQoL mean score, the D+DN group had significantly poorer HRQoL scores compared to the D-DN group in both the overall HRQoL score ($p \leq 0.001$) and across all domains: physical functioning/large fiber ($p \leq 0.001$), activities of daily living (ADLs) ($p \leq 0.001$), symptoms ($p \leq 0.001$), small fiber ($p \leq 0.001$), and autonomic domains ($p \leq 0.001$; Table 3). In the D+DN group, there was a strong correlation between NDS score and overall HRQoL score on the Norfolk QOL-DN (Spearman’s ρ= −0.71, $p \leq 0.001$). Similarly, a strong correlation was found between NSS score and overall HRQoL score on the Norfolk QOL-DN (Spearman’s ρ= −0.81, $p \leq 0.001$; Table 4). Summary statistics of the correlation matrix of BMI, duration of DM, HbA1C, and age with HRQoL score are also shown in Table 4. Table 4 \| DN-QOL \| DN-QOL.1 \| DN-QOL.2 \| DN-QOL.3 \| DN-QOL.4 \| DN-QOL.5 \| DN-QOL.6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variables \| Total \| Physical functioning \| ADL \| Symptoms \| Small fibers \| Autonomic \| \| NDS \| ρ=0.71 p<0.001 \| ρ=0.64 p<0.001 \| ρ=0.60 p<0.001 \| ρ=-0.36 p=0.002 \| ρ=0.62 p<0.001 \| ρ=0.55 p<0.001 \| \| NSS \| ρ= 0.81 p<0.001 \| ρ=-0.79 p<0.001 \| ρ=0.85 p<0.001 \| ρ= 0.68 p<0.001 \| ρ=0.80p<0.001 \| ρ 0.80p<0.001 \| \| Body mass index \| ρ=0.10 p=0.093 \| ρ= 0.09 p=0.119 \| ρ=0.16 p=0.005 \| ρ=-0.13 p=0.029 \| ρ=0.11 p-=0.061 \| ρ=0.15 p<0.008 \| \| Duration of DM \| ρ=0.82 p<0.001 \| ρ=0.79 p<0.001 \| ρ=0.77 p<0.001 \| ρ= 0.75 p<0.001 \| ρ=0.76 p<0.001 \| ρ=0.73 p<0.001 \| \| Hemoglobin A1C \| ρ=0.30 p<0.001 \| ρ=0.30 p<0.001 \| ρ=0.30 p<0.001 \| ρ= 0.22 p=0.001 \| ρ=0.27 p=0.001 \| ρ=0.26 p<0.001 \| \| Age \| ρ=0.008 p=0.896 \| ρ=0.01 p=0.9317 \| ρ=0.01 p=0.936 \| ρ= 0.06 p=0.291 \| ρ=0.02 p=0.735 \| ρ=0.05 p=0.399 \| ## Relationship between electrophysiology study findings and HRQoL With the exception of the symptoms domain of the Norfolk QOL-DN, there were significant differences in mean HRQoL scores between participants with normal and abnormal NCS results (Table 5). Table 5* \| NCS(Abnormal=73 Normal=9) \| Mean \| MD \| 95%CI of MD \| P-value \| \| --- \| --- \| --- \| --- \| --- \| \| All \| \| \| \| \| \| Abnormal \| 78.89 (23.4) \| 19.45 \| 3.01, 35.88 \| 0.011 \| \| Normal \| 59.44 (23.3) \| 19.45 \| 3.01, 35.88 \| 0.011 \| \| Physical functioning \| Physical functioning \| \| \| \| \| Abnormal \| 35.14 (10.6) \| 9.69 \| 2.27, 8.56 \| 0.005 \| \| Normal \| 25.44 (9.7) \| 9.69 \| 2.27, 8.56 \| 0.005 \| \| ADLS \| \| \| \| \| \| Abnormal \| 12.19 (5.9) \| 4.41 \| 0.31, 17.07 \| 0.019 \| \| Normal \| 7.78 (6.0) \| 4.41 \| 0.31, 17.07 \| 0.019 \| \| Symptoms \| \| \| \| \| \| Abnormal \| 12.12 (5.9) \| -0.76 \| -4.18, 2.65 \| 0.672 \| \| Normal \| 12.89 (3.4) \| -0.76 \| -4.18, 2.65 \| 0.672 \| \| Small fibers \| \| \| \| \| \| Abnormal \| 11.23 (3.6) \| 3.68 \| 1.08, 6.27 \| 0.003 \| \| Normal \| 7.56 (4.1) \| 3.68 \| 1.08, 6.27 \| 0.003 \| \| Autonomic \| \| \| \| \| \| Abnormal \| 8.21 (3.1) \| \| \| \| \| Normal \| 5.78 (3.0) \| 2.43 \| 0.23, 4.62 \| 0.015 \| ## Predictors of HRQoL in patients with diabetes and neuropathy On regression analysis, physical inactivity ($$p \leq 0.043$$), duration of DM ($p \leq 0.001$), abnormal NCS, NDS ($p \leq 0.001$), and NSS ($p \leq 0.001$) predicted HRQoL in patients in the D+DN group (Table 6). The regression model was a good fit of the data, F[21,44]=13.53, $p \leq 0.001$, and our independent variables explained $86.6\%$ of the variability in the overall HRQoL (R2=0.866). Table 6 \| Variable \| Coefficient \| SE \| t \| 95%CI \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Age \| -0.27 \| 0.12 \| -0.22 \| -0.28, 0.22 \| 0.827 \| \| Gender \| -0.34 \| 2.72 \| -0.12 \| -5.79,5.11 \| 0.902 \| \| Education \| -7.67 \| 6.68 \| -1.15 \| -21.04,5.70 \| 0.256 \| \| Marital status \| -18.09 \| 12.35 \| -1.46 \| -42.82,6.63 \| 0.148 \| \| Body mass index \| 0.23 \| 0.28 \| 0.82 \| -0.33,0.78 \| 0.413 \| \| Hemoglobin A1c \| -1.24 \| 1.61 \| -0.77 \| -4.46,1.98 \| 0.444 \| \| Smoking \| 12.9 \| 17.51 \| 0.74 \| -22.14,47.95 \| 0.464 \| \| Physical inactivity \| -14.53 \| 7.01 \| -2.07 \| -28.56, -0.50 \| 0.043* \| \| TIA \| -10.57 \| 9.3 \| -1.14 \| -29.18,8.03 \| 0.260 \| \| Retinopathy \| -3.33 \| 14.98 \| -0.22 \| -33.32,26.66 \| 0.825 \| \| Financial satisfaction \| 0.47 \| 9.72 \| 0.05 \| -18.99,19.92 \| 0.962 \| \| Ulcers \| -2.79 \| 6.45 \| -0.43 \| -15.70,10.13 \| 0.668 \| \| Urinary incontinence \| -4.34 \| 3.61 \| -1.2 \| -11.56, 2.88 \| 0.234 \| \| Adherence \| -4.18 \| 5.32 \| -0.79 \| -14.82,6.47 \| 0.435 \| \| Duration of DM \| 2.08 \| 0.42 \| 4.93 \| 1.24, 2.93 \| 0.001* \| \| Neuropathy disability score \| -6.1 \| 1.38 \| -4.44 \| -8.86-3.35 \| 0.001* \| \| Neuropathy symptoms score \| -1.71 \| 0.85 \| -2.02 \| -3.40, -0.01 \| 0.048* \| \| Dyslipidemia \| 1.46 \| 2.89 \| 0.5 \| -4.32,7.24 \| 0.616 \| \| NCS \| 13.09 \| 5.24 \| 2.5 \| 2.32,23.86 \| 0.019 \| ## Discussion The present study compared HRQoL findings among D+DN, D-DN, and apparently healthy participants. Our results indicated a significant difference between D+DN and D-DN and between D+DN and healthy individuals in overall HRQoL scores and across the 5 domains of HRQoL on the Norfolk QOL-DN. This result is consistent with previous studies that focused on both DN pain specifically and DN in general.21-23 Our finding suggests that the perceptions of patients with DM and DN of their physical, mental, sexual, cognitive, self-perception, and social aspects of their lives and overall well-being are worse than the perceptions of both persons with DM without DN and apparently healthy individuals drawn from the same population. Given that the 3 groups in the study were well-matched for age and gender, and as our results showed no significant between-group differences in the other background characteristics such as hypertension, cardiovascular events, dyslipidemia, smoking, urinary issues, adherence to medications, physical inactivity, and foot ulcers, confounding factors were unlikely to influence the HRQoL assessment outcomes. However, it is possible that the effect of DN on HRQoL transcends the impact of the pain component of neuropathy, as previous studies have shown that DN in patients with DM independently affects the physical and mental components of HRQoL even after controlling for pain and pain severity. 24 We observed a reduction in perceived physical functioning in the D+DN group. Physical functioning is a serious public health concern given that mobility impairments, such as compromised walking speeds and difficulty with safely negotiating the physical environment may impact an individual’s socioeconomic and mental well-being. Although common symptoms of DN, including numbness, paraesthesia, pain, and tingling are treatable, the adverse impact of DN on physical functioning may be experienced even before overt clinical manifestations occur.25,26 Hence, prevention of these complications and their associated impacts on physical function is critical. Consistent with previous reports, this study found a significant correlation between NSS, NDS, and HRQoL scores in patients in the D+DN group. 21,27,28 This result could be a reflection of the high levels of pain, paraesthesia, numbness, and cramping often reported by patients with DM and DN, which have an undeniable impact on their physical and mental well-being. In addition, in agreement with previous studies reporting that prolonged disease duration is a significant factor related to poor HRQoL in patients with DM, 22,24,29,30 we found a significant correlation between duration of DM and HRQoL. Given the absence of a correlation between patient age and HRQoL, the present study has shown that the duration of DM is more consequential than age in determining HRQoL in patients with DM and DN. This finding increases the importance of prevention, as people who are diagnosed at a younger age will have a longer duration of illness over their lifetime and thus a higher risk of poorer HRQoL. Our results confirm the reports from previous studies that revealed a relationship between patients’ HbA1C and HRQoL.31,33 This finding also corroborates the results of a longitudinal study that showed that a reduction in hyperglycemic symptoms resulted in an improved HRQOL.34 One of the targets of DM management is to improve glycemic control and reduce the risk of diabetes-associated complications, which, in turn, can improve DM patients’ HRQoL. 33 To attain this target, there needs to be unwavering cooperation between patients and their attending physicians. Intuitively, patients’ compliance can be expected to improve when their HRQoL improves as a consequence of treatment adherence. Nonetheless, it should be noted that there have also been studies assessing the relationship between HRQoL and HbA1C or glycemic control that reported contrary findings in patients with type 2 DM. 35 In our study, the presence of abnormal NCS findings was also significantly associated with impaired HRQoL in patients with DM and DN. Our results demonstrated that 5 of the covariates considered-NDS, NSS, NCS result, physical inactivity, and duration of DM-independently predicted overall HRQoL. In previous studies, duration of DM has been reported to be an important predictor of HRQoL, 22 although there is a paucity of data on the relationship between NDS or NSS and HRQoL. While the aforementioned covariates were the most important predictors of impaired HRQoL in the D+DN group as determined by a disease-specific HRQoL measurement tool, in other studies employing different HRQoL tools with varying psychometric properties, MNSI, HbA1c, mental fatigue, depression, treatment, female gender, diabetic complications, non-diabetic comorbidities, and coronary artery disease have been found to be important predictors of HRQoL status.2,22 Although some previous studies have reported that BMI negatively influenced HRQoL scores,our study results, like many others,did not show a significant relationship between BMI and HRQoL. 2,22,27,29,36 Of note, the strength of association between neuropathy or neuropathic pain and HRQoL was found to be dependent on both the type of HRQoL tool employed (such as, whether a disease-specific or generic HRQoL tool was used) and the HRQoL domains investigated. 28,37 The Norfolk QOL-DN tool utilized in this study is reliable across different populations, with a high sensitivity profile for both small- and large-fiber nerve impairment. 13 As demonstrated in the present study, DN has a significant negative effect on HRQoL, as the condition limits physical activity and interferes with ADLs. Therefore, we recommend that clinicians include regular assessment of HRQoL in their management plan for patients with DN. In addition, preventive strategies coupled with patient education should be considered key factors in the prevention and alleviation of morbidity and mortality rates. ## Implications of our findings and future research We report a significant impact of DN on the HRQoL of participants in this study. Therefore, clinicians need to develop strategies and intervention programs aimed at promoting the health status of patients with DM from the time of diagnosis to preclude the progression of the disease and its complications. Doing so is critical given the undesirable effect of DN on quality of life and its negative impact on available therapeutic options. Evidence-based factors that are strongly associated with poor HRQoL will also be integral tools in the early identification of patients who may require intensive intervention as well as psychotherapy. We suggest the use of a disease-specific HRQoL tool to gather important information regarding patients’ perceptions of their health, which may not be fully explained by the patients’ objective health status. This additional information may aid physicians in engaging patients in discussions concerning the impact of complications of DM, such as DN, on the overall disease outcome and achieving optimum disease control and patient well-being. Future operational research can include larger samples drawn from multiple clinical sites to improve on the generalizability of our findings. Future studies can also explore potential interventions to generate the evidence-basis for effective measures to improve HRQoL especially those targeting persons with poor HRQoL scores. A major study strength of the present study is the use of a disease-specific HRQoL instrument, which has been advocated for in previous studies and that employed a generic HRQoL outcome measure. 22,28 In addition, the present study explored the relationship between electrophysiological findings and HRQoL in a cohort of diabetic patients with DN. ## Study limitations First, this study was limited by its use of laboratory parameters that were obtained from medical records and therefore prone to the potential of incomplete data. This limitation, however, was mitigated by excluding records with missing data. Second, the study was cross-sectional, and as such, interpretation of causal relationships should be made with caution. Regardless of these limitations, this study constitutes one of the most comprehensive efforts to compare HRQoL scores in patients with DM and DN with age- and gender-matched patients with DM without DN and healthy participants. 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--- title: COVID-19 in pregnant women authors: - Şeyhmus Tunç - Mehmet Rifat Göklü - Süleyman Cemil Oğlak journal: Saudi Medical Journal year: 2022 pmcid: PMC9998050 doi: 10.15537/smj.2022.43.4.20210904 license: CC BY 4.0 --- # COVID-19 in pregnant women ## Body The first case of pneumonia of an unknown origin was identified in Wuhan, the capital of Hubei province, China, in December 2019. 1,2 The World Health Organization defined this disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) and declared a pandemic in 2020 as a result of its rapid spread. 3,4 As of September 2021, the total number of recorded cases of Coronavirus disease 2019 worldwide has approached 250 million and that of deaths has exceeded 4.5 million. 5 Coronavirus disease 2019 mainly manifests as a pulmonary disease with flu-like symptoms such as fever, cough, shortness of breath, fatigue, and headache. 6 Severe acute respiratory syndrome coronavirus 2 infection may be asymptomatic or cause critical illness that canresult in pneumonia and respiratory failure. 7 Neurological, renal, hepatic, gastrointestinal, thromboembolic, cardiac, endocrine, and dermatological symptoms may be present during the disease course. 8 Pregnancy is a unique immunological situation that is modulated since the immune system is affected by signals generating from the placenta. 9-14 Previous studies indicated compelling evidence that pregnant women are at a greater risk of severe disease and mortality from viral infections, notably during pandemics and particularly those involved in the respiratory system. 9 During pregnancy, a 9.5-$25\%$ decrease occurs in the functional residual capacity. Moreover, oxygen use increases by $21\%$ due to physiological hyperventilation. 15 Also, changes in the nasal mucosa induced by progesterone tend to facilitate the retention of the virus in the upper respiratory tract, thus making it difficult for the host immunity to remove it. 16 The disease causes destruction, inflammation, and hemorrhage in type 1 and 2 pneumocytes through the angiotensin-converting enzyme-2 (ACE-2) receptor in the lungs. 17,18 Compared with nonpregnant women, pregnant women show a 2-fold increase in the expression of ACE-2 receptors. 19 Thus, pregnant women are at a higher risk of severe illness and death than nonpregnant women; the former group also has a higher need for admission in the intensive care unit (ICU) and oxygen support than the latter. 20 Coronavirus disease 2019 in pregnant women is a popular research topic. The present study aimed to investigate the association between the hospitalization rates, symptoms, and laboratory parameters of pregnant women diagnosed with COVID-19 and the week of gestation and determine the symptoms or laboratory parameters that can predict the need for possible ICU admission. ## Abstract ### Objectives: To investigate the association between the hospitalization rates, symptoms, and laboratory parameters of pregnant women diagnosed with coronavirus disease 2019 (COVID-19) and the gestational week, and determine their symptoms or laboratory parameters predictive of the need for possible admission in the intensive care unit (ICU). ### Methods: We retrospectively analyzed the symptoms, laboratory parameters, and treatment modalities of 175 pregnant women with COVID-19 who were admitted to a tertiary referral hospital between March 2020 and March 2021 and investigated their association with pregnancy trimesters. ### Results: The COVID-19-related hospitalization rates in the first trimester was $24.1\%$, second trimesters was $36\%$, and third trimester was $57.3\%$. Cough and shortness of breath were significantly higher in the pregnant women in their third trimester than those in the first 2 trimesters ($$p \leq 0.042$$ and $$p \leq 0.026$$, respectively). No significant relationship was found between pregnancy trimesters and the need for ICU admission. Shortness of breath at the first admission increased the need for ICU by 6.95 times, and a 1 unit increase in C-reactive protein (CRP) level increased the risk of ICU by 1.003 times. ### Conclusion: The presence of respiratory symptoms and the need for hospitalization increased significantly with later trimesters in pregnant women with COVID-19. The presence of shortness of breath or high CRP level at the time of admission could predict the need for ICU admission. ## Methods This retrospective study examined the clinical and laboratory findings and treatment modalities of 175 pregnant women diagnosed with COVID-19 who were admitted Diyarbakır Gazi Yaşargil Training and Research Hospital, Turkey, which was a tertiary referral hospital, between March 2020 and March 2021. Ethical approval for the study was obtained from the hospital’s ethical committee. The study was carried out in accordance with the Declaration of Helsinki. This study included pregnant women diagnosed with COVID-19 and followed up at our hospital (outpatient or inpatient).Patient data were retrieved from the hospital’s archive system and patient files. Severe acute respiratory syndrome coronavirus 2 nucleic acid was detected in all pregnant women by real-time upper respiratory tract specimen polymerase chain reaction (PCR). Pregnant women with COVID-19 experienced clinical evaluation of vital signs, laboratory analysis, and radiologic chest assessment at admission. A chest x-ray or computed tomography (CT) was performed for pneumonia diagnosis. All patients signed the informed consent before chest x-ray ($$n = 175$$) and CT examination ($$n = 56$$). During the x-ray and CT examination their pelvis and lower abdomen were covered with a lead blanket. Each participant in the study cohort was an independent sample. All participants have enrolled in the study according to their findings at the time of admission (specimen date). Based on the national COVID-19 guideline of the Turkish Ministry of Health, pregnant women with pneumonia were divided into 2 groups, namely, mild-moderate (respiratory rate <30/minute (min); SpO2 level >$90\%$ in ambient air; and bilateral diffuse, <$50\%$ lung involvement on imaging) and severe (fever, muscle/joint pains, cough, and sore throat; tachypnea ≥30/min; SpO2 level ≤$90\%$ in ambient air; and bilateral diffuse pneumonia findings on chest x-ray or tomography) disease. 21 Pregnant women with mild-moderate pneumonia who did not need oxygen therapy were followed up as outpatients. Pregnant women with mild to moderate pneumonia requiring oxygen therapy and those with severe pneumonia were hospitalized. Clinical and laboratory parameters obtained at the first admission were used in our study. The clinical parameters were examined in terms of fever, cough, shortness of breath, headache, loss of smell, diarrhea, and myalgia. The laboratory parameters were examined in terms of white blood cell (WBC) counts, lymphocyte count, neutrophil count, hemoglobin, hematocrit, platelet, glucose, urea, creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), D-dimer, C-reactive protein (CRP), ferritin, and procalcitonin. Owing to the risk of influencing the clinical and laboratory parameters, COVID-19-positive pregnant women with comorbidities such as diabetes, hypertension, coronary artery disease, and asthma as well as those hospitalized because of obstetric reasons were not included in this study. ## Statistical analysis The data analysis was performed using the Statistical Package for the Social Sciences, version 26 (IBM Corp., Chicago, IL, USA). The data used here were tested for violations of assumptions of parametric tests, such as using the Levene test for homogeneity of variances and Shapiro Wilk test with Q-Q plots for normality. So, to identify differences between independent 2 groups, Mann-Whitney U or Student’s t-test were used. For the continuous variables which were compared among more than 2 independent groups; Kruskal Wallis or One Way ANOVA test was used with post hoc multi comparison tests to identify the groups that make the difference. The Chi-square or Fisher’s exact test were used to compare groups among the categories of variables. Data were presented as mean ± standard deviation and (median-range) values and as numbers with relevant percentages. To define risk factors of outcome variables, multiple logistic regression analysis and adjusted odds ratios with their confidence intervals were calculated. All covariates with missing data in less than $20\%$ of observations and a p-value of <0.05 in univariate testing were considered for inclusion in the final multiple regression model and retained if the p-value was <0.05. Highly collinear covariates (defined as correlation coefficient >0.5) were not included together in the final multivariate model. The goodness of model fit was assessed by Hosmer-Lemeshow test. Whether the CRP variable has diagnostic power to determine ICU need, recdeiver operating characteristic analysis was used and the Youden index was calculated to determine cut-off value. A p-value of <0.05 was considered statistically significant for all statistical processes. ## Results In our study cohort, COVID-19 patients were not vaccinated against the SARS-CoV-2. The hospitalization rate was $45.7\%$ after including all pregnant women in the study. This rate increased in later gestational weeks. Outpatient follow-up was proportionally higher in patients in their first and second trimesters. The hospitalization rate in patients in their third trimester was higher than that in the outpatient groups. Cough was the most common ($68\%$) symptom among the cohort. Other symptoms included high fever ($48.5\%$), shortness of breath ($48.5\%$), headache ($33\%$), myalgia ($32\%$), diarrhea ($10\%$), and the loss of smell and taste ($8\%$). Comparisons of clinical symptoms among the groups based on trimester indicated that compared with the patients in their first 2 trimesters, the presence of cough and shortness of breath due to COVID-19 was significantly higher in those in their third trimester (Table 1). Table 1 \| Symptoms \| First trimester \| First trimester.1 \| First trimester.2 \| First trimester.3 \| Second trimester \| Second trimester.1 \| Second trimester.2 \| Third trimester \| Third trimester.1 \| Third trimester.2 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Symptoms \| \| n \| Row (%) \| Column (%) \| n \| Row (%) \| Column, (%) \| n \| Row (%) \| Column (%) \| P-value \| \| Outpatient follow-up \| \| 22 \| 23.2 \| 75.9 \| 32 \| 33.7 \| 64.00 \| 41 \| 43.2 \| 42.7 \| 0.002 \| \| Hospitalization \| \| 7 \| 8.6 \| 24.1 \| 18 \| 22.5 \| 36.0 \| 55 \| 68.8 \| 57.3 \| 0.002 \| \| Fever \| − \| 15 \| 16.8 \| 51.7 \| 28 \| 31.1 \| 56.0 \| 47 \| 52.2 \| 49.0 \| 0.721 \| \| Fever \| + \| 14 \| 16.5 \| 48.3 \| 22 \| 25.9 \| 44.0 \| 49 \| 57.7 \| 51.0 \| 0.721 \| \| Cough \| − \| 12 \| 21.4 \| 41.4 \| 21 \| 37.5 \| 42.0 \| 23 \| 41.1 \| 24.0 \| 0.042 \| \| Cough \| + \| 17 \| 14.3 \| 58.6 \| 29 \| 24.4 \| 58.0 \| 73 \| 61.3 \| 76.0 \| 0.042 \| \| Shortness of breath \| − \| 16 \| 17.8 \| 55.2 \| 33 \| 36.7 \| 66.0 \| 41 \| 45.6 \| 42.7 \| 0.026 \| \| Shortness of breath \| + \| 13 \| 15.3 \| 44.8 \| 17 \| 20.0 \| 34.0 \| 55 \| 64.7 \| 57.3 \| 0.026 \| \| Headache \| − \| 17 \| 14.5 \| 58.6 \| 30 \| 25.6 \| 60.0 \| 70 \| 59.8 \| 72.9 \| 0.187 \| \| Headache \| + \| 12 \| 20.7 \| 41.4 \| 20 \| 34.7 \| 40.0 \| 26 \| 44.8 \| 27.1 \| 0.187 \| \| Loss of smell and taste \| − \| 28 \| 17.4 \| 96.6 \| 45 \| 27.9 \| 90.0 \| 88 \| 54.7 \| 91.7 \| 0.640* \| \| Loss of smell and taste \| + \| 1 \| 7.1 \| 3.5 \| 5 \| 35.7 \| 10.0 \| 8 \| 57.1 \| 8.3 \| 0.640* \| \| Diarrhea \| − \| 26 \| 16.5 \| 89.7 \| 44 \| 27.9 \| 88.0 \| 88 \| 55.7 \| 91.7 \| 0.731* \| \| Diarrhea \| + \| 3 \| 17.7 \| 10.3 \| 6 \| 35.3 \| 12.0 \| 8 \| 47.1 \| 8.3 \| 0.731* \| \| Myalgia \| − \| 15 \| 12.6 \| 51.7 \| 38 \| 31.9 \| 76.0 \| 66 \| 55.5 \| 68.8 \| 0.081 \| \| Myalgia \| + \| 14 \| 25.0 \| 48.3 \| 12 \| 21.4 \| 24.0 \| 30 \| 53.6 \| 31.3 \| 0.081 \| \| Intensive care unit admission \| − \| 29 \| 17.3 \| 100.0 \| 47 \| 28.0 \| 94.0 \| 92 \| 54.8 \| 95.8 \| 0.432* \| \| Intensive care unit admission \| + \| 0 \| 0.0 \| 0.0 \| 3 \| 42.9 \| 6.0 \| 4 \| 57.1 \| 4.2 \| 0.432* \| \| Severe illness \| − \| 29 \| 17.6 \| 100.0 \| 46 \| 27.9 \| 92.0 \| 90 \| 54.5 \| 93.8 \| 0.369* \| \| Severe illness \| + \| 0 \| 0.0 \| 0.0 \| 4 \| 40.0 \| 8.0 \| 6 \| 60.0 \| 6.3 \| 0.369* \| The evaluation of the selected blood parameters in the cohort indicated that $56.5\%$ had lymphocytopenia, $35.4\%$ had an elevated CRP level, $32\%$ had an elevated LDH level, $30\%$ had an elevated ALT level, $24\%$ had an elevated D-dimer level, $22\%$ had leukocytosis, and $12.5\%$ had an elevated AST level. Comparisons among the trimester groups based on laboratory parameters also indicated that the leukocyte count increased in all 3 trimesters and the rate of increase was the highest in the third trimester. The decrease in lymphocyte counts was observed in all 3 trimesters and lymphocytopenia intensified in the later trimester. Varying degrees of increase in the serum levels of AST, ALT, LDH, D-dimer, and CRP were detected with progressing trimester (Table 2). Table 2 \| Laboratory findings (reference ranges)* \| First trimester (n=29) \| Second trimester (n=50) \| Third trimester (n =96) \| \| --- \| --- \| --- \| --- \| \| Leucocytes (10 3 /mm 3 reference range) \| (5.7–13.6) \| (5.6–14.8) \| (5.9–16.9) \| \| Increased \| 6 (21) \| 7 (14) \| 26 (27) \| \| Decreased \| 2 (7) \| - \| - \| \| Lymphocytes (%; reference range) \| (19–26) \| (16–26) \| (16–21) \| \| Increased \| 1 (4) \| - \| - \| \| Decreased \| 11 (37) \| 28 (56) \| 60 (62) \| \| Aspartate transaminase (U/L; reference range) \| (3–23) \| (3–33) \| (4–32) \| \| Increased \| - \| 7 (14) \| 15 (16) \| \| Alanine transaminase (U/L; reference range) \| (3–30) \| (2–33) \| (2–25) \| \| Increased \| 1 (4) \| 6 (12) \| 7 (7) \| \| Lactate dehydrogenase (U/L; reference range) \| (78–433) \| (80–447) \| (82–524) \| \| Increased \| 4(14) \| 14(28) \| 38 (40) \| \| D-dimer (μg/L; reference range) \| (500–950) \| (320–1290) \| (130–1700) \| \| Increased \| - \| 4 (8) \| 38 (34) \| \| C-reactive protein (mg/L; reference range) \| (0.2–3.0) \| (0.4–20.3) \| (0.4–8.1) \| \| Increased \| 9 (31) \| 11 (22) \| 42 (44) \| Comparisons among the trimester groups in terms of laboratory parameters indicated that D-dimer and procalciton in levels varied across trimesters. A significant increase was noted in the median value of the D-dimer level with progressing trimester. Although the median values of the procalciton in level were similar in the first 2 trimesters, these were significantly higher in the third trimester. The medians that accounted for the difference are explained using letter indices in Table 3. Table 3 \| Unnamed: 0 \| First trimester \| First trimester.1 \| Second trimester \| Second trimester.1 \| Third trimester \| Third trimester.1 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Mean ± SD \| Median–Range \| Mean ± SD \| Median–Range \| Mean ± SD \| Median–Range \| P-value \| \| Age \| 32.43 ± 5.74 \| 34–17 \| 31 ± 5.81 \| 30.5–20 \| 29.81 ± 6.66 \| 28–29 \| 0.526* \| \| White blood cells \| 6.27 ± 1.38 \| 6.3–3.8 \| 7.51 ± 2.58 \| 6.95–7.9 \| 8.96 ± 3.33 \| 7.9–13.1 \| 0.444 \| \| Lymphocyte% \| 25.03 ± 6.19 \| 24.3–16.4 \| 20.69 ± 7.49 \| 19.55–27.2 \| 18.89 ± 6.8 \| 17.8–29.5 \| 0.079* \| \| Neutrophil% \| 65.5 ± 7.94 \| 66.1–21.4 \| 72.97 ± 8.34 \| 73.75–30.2 \| 75.03 ± 7.71 \| 75–34.2 \| 0.096 \| \| Hemoglobin \| 12.39 ± 1.77 \| 13.3–4.1 \| 11.87 ± 1.26 \| 12.3–5 \| 11.17 ± 1.65 \| 11.2–7.7 \| 0.093 \| \| Hematocrit \| 38.64 ± 4.38 \| 40.6–10.8 \| 36.84 ± 3.36 \| 38.15–12.6 \| 35.49 ± 4.53 \| 35.3–22.2 \| 0.093 \| \| Platelet \| 229.29 ± 40.33 \| 230–118 \| 243.11 ± 93.17 \| 215.5–399 \| 236.98 ± 69.64 \| 238–353 \| 0.476 \| \| Glucose \| 82.5 ± 10.32 \| 79–26 \| 94.06 ± 30.44 \| 87–134 \| 85.13 ± 24.36 \| 77–119 \| 0.288 \| \| Creatinine \| 0.59 ± 0.09 \| 0.6–0.2 \| 0.49 ± 0.07 \| 0.5–0.3 \| 0.53 ± 0.08 \| 0.5–0.3 \| 0.072 \| \| Aspartate transaminase \| 19.71 ± 7.52 \| 20–24 \| 44.29 ± 60.57 \| 20–194 \| 36 ± 53.05 \| 22–356 \| 0.749 \| \| Alanine transaminase \| 19.43 ± 13.94 \| 14–39 \| 38.65 ± 57.27 \| 16–204 \| 27.76 ± 55 \| 15–401 \| 0.535 \| \| Lactate dehydrogenase \| 209.67 ± 51 \| 213–124 \| 222.76 ± 57.67 \| 219–237 \| 266.67 ± 104.59 \| 246–470 \| 0.512 \| \| D-dimer \| 211.2 ± 70.34 \| 197–187a \| 288.94 ± 123.2 \| 268.5–404b \| 953.9 ± 1202.71 \| 525–6213c \| <0.001 \| \| C-reactive protein \| 18.33 ± 18.9 \| 11–42.5 \| 26.02 ± 33.76 \| 12.9–130 \| 23.98 ± 30.91 \| 11.8–119 \| 0.962 \| \| Ferritin \| 75.5 ± 91.1 \| 24.5–211 \| 105.44 ± 105.55 \| 81–431 \| 66.7 ± 91.94 \| 27.5–452 \| 0.068 \| \| Procalcitonin \| 0.04 ± 0.03 \| 0.02–0.07a \| 0.11 ± 0.21 \| 0.04–0.76a \| 0.13 ± 0.18 \| 0.07–0.9b \| 0.020 \| Of the cohort, $5.7\%$ ($$n = 10$$) had severe disease. Among the pregnant women diagnosed with COVID-19, $70\%$ ($$n = 7$$) with severe disease needed ICU admission. No significant difference was observed in the rates of ICU admission due to COVID-19 among the trimester groups. The presence of dyspnea was significantly high in patients who needed ICU admission regardless of their trimester (Table 4). Table 4 \| Parameters \| Unnamed: 1 \| ICU admission (−) \| ICU admission (−).1 \| ICU admission (+) \| ICU admission (+).1 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Parameters \| \| Row (%) \| Column (%) \| Row (%) \| Column (%) \| P-value \| \| Trimester \| First \| 100.0 \| 17.26 \| 0.0 \| 0.0 \| 0.432* \| \| Trimester \| Second \| 94.0 \| 27.98 \| 6.0 \| 42.9 \| 0.432* \| \| Trimester \| Third \| 95.8 \| 54.76 \| 4.2 \| 57.1 \| 0.432* \| \| Fever \| − \| 94.4 \| 50.60 \| 5.2 \| 71.4 \| 0.445* \| \| Fever \| + \| 97.6 \| 49.40 \| 2.4 \| 28.6 \| 0.445* \| \| Cough \| − \| 98.2 \| 32.70 \| 1.8 \| 14.3 \| 0,432* \| \| Cough \| + \| 95.0 \| 67.30 \| 5.0 \| 85.7 \| 0,432* \| \| Shortness of breath \| − \| 100.0 \| 53.60 \| 0.0 \| 0.0 \| 0.006* \| \| Shortness of breath \| + \| 91.8 \| 46.40 \| 8.2 \| 100.0 \| 0.006* \| \| Headache \| − \| 94.0 \| 65.50 \| 6.0 \| 100.0 \| 0.097* \| \| Headache \| + \| 100.0 \| 34.50 \| 0.0 \| 0.0 \| 0.097* \| \| Loss of smell and taste \| − \| 95.7 \| 91.70 \| 4.3 \| 100.0 \| 0.426 \| \| Loss of smell and taste \| + \| 100.0 \| 8.30 \| 0.0 \| 0.0 \| 0.426 \| \| Diarrhea \| − \| 95.6 \| 89.90 \| 4.4 \| 100.0 \| 0.376 \| \| Diarrhea \| + \| 100.0 \| 10.10 \| 0.0 \| 0.0 \| 0.376 \| \| Myalgia \| − \| 94.1 \| 66.70 \| 5.9 \| 100.0 \| 0.098* \| \| Myalgia \| + \| 100.0 \| 33.30 \| 0.0 \| 0.0 \| 0.098* \| Independent of the trimester, CRP and procalciton in levels were significantly higher in patients who needed ICU admission (Table 5). Table 5 \| Characteristics \| ICU admission (−) \| ICU admission (−).1 \| ICU admission (+) \| ICU admission (+).1 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristics \| Mean ± SD \| Median–Range \| Mean ± SD \| Median–Range \| P-value \| \| Age, years \| 28.89 ± 6.06 \| 28–30 \| 32.43 ± 7.46 \| 34–22 \| 0.154* \| \| Gestational week at admission \| 25.4 ± 10.05 \| 27–34 \| 29.43 ± 8.94 \| 33–23 \| 0.284* \| \| White blood cells \| 8.57 ± 3.16 \| 7.9–14.1 \| 6.83 ± 2.47 \| 5.8–6.8 \| 0.084 \| \| Lymphocyte \| 19.49 ± 7.78 \| 18.4–43.6 \| 19.86 ± 6.06 \| 20.7–16.7 \| 0.810* \| \| Neutrophil \| 73.23 ± 10.21 \| 74.2–80.19 \| 74.81 ± 7.06 \| 72.7–20.2 \| 0.818* \| \| Hemoglobin \| 11.54 ± 1.55 \| 11.7–7.8 \| 11.49 ± 1.15 \| 11.5–3 \| 0.887 \| \| Hematocrit \| 39.27 ± 33.51 \| 36.5–378.5 \| 35.83 ± 3.23 \| 36.2–8.9 \| 0.698 \| \| Platelet \| 237.02 ± 71.28 \| 230–477 \| 224 ± 34.26 \| 230–96 \| 0.722 \| \| Glucose \| 90.95 ± 28.13 \| 84–201 \| 106 ± 29.92 \| 108–79 \| 0.155 \| \| Urea \| 14.07 ± 5.22 \| 13–26 \| 15.29 ± 4.96 \| 16–12 \| 0.476* \| \| Creatinine \| 0.52 ± 0.08 \| 0.5–0.4 \| 0.54 ± 0.05 \| 0.5–0.1 \| 0.351* \| \| Aspartate transaminase \| 28.26 ± 41.4 \| 19–359 \| 56.29 ± 68.09 \| 34–192 \| 0.064 \| \| Alanine transaminase \| 23.65 ± 41.8 \| 15–401 \| 44.29 ± 73.84 \| 16–203 \| 0.439 \| \| Lactate dehydrogenase \| 245.19 ± 94.83 \| 224.5–484 \| 295 ± 81.17 \| 300–228 \| 0.070 \| \| D-dimer \| 736.84 ± 1049.08 \| 413.5–6277 \| 769.57 ± 986.89 \| 411–2763 \| 0.939 \| \| C-reactive protein \| 18.4 ± 24.4 \| 11.2–130 \| 62.3 ± 46.69 \| 89.3–107.2 \| 0.010 \| \| Ferritin \| 66.99 ± 89.2 \| 30–452 \| 137.86 ± 113.5 \| 137–321 \| 0.081 \| \| Procalcitonin \| 0.11 ± 0.18 \| 0.05–0.9 \| 0.2 ± 0.15 \| 0.21–0.42 \| 0.041 \| \| Vitamin D \| 15.25 ± 9.48 \| 12.55–32.2 \| 18.2 ± 5.94 \| 18.2–8.4 \| 0.732 \| After adjusting the variables, including maternal age and gestational week, multiple logistic regression analysis was performed using the clinical symptoms and laboratory parameters of the pregnant women who needed ICU admission at the time of hospitalization. Table 6 presents the results obtained based on the variables of shortness of breath, and CRP level, which were significant. The presence of shortness of breath increased the need for ICU admission by 6.95 times, and an increase of 1 unit in the level of CRP increased the risk of ICU admission by 1.003 times. Table 6 \| Unnamed: 0 \| Beta coefficient \| Standard error \| P-value \| OR \| 95% CI for OR \| 95% CI for OR.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Beta coefficient \| Standard error \| P-value \| OR \| Lower \| Upper \| \| Constant \| −4.359 \| 1.205 \| 0.000 \| 0.013 \| \| \| \| C-reactive protein \| 0.031 \| 0.012 \| 0.009 \| 1.031 \| 1.008 \| 1.056 \| \| Shortness of breath (+) \| 1.939 \| 1.081 \| 0.073 \| 6.949 \| 0.835 \| 57.841 \| As a result of the ROC analysis (Figure 1), it was understood that the CRP variable is a parameter that can be used to determine ICU admission ($$p \leq 0.010$$). For this case, the cut-off value calculated according to the Youden index was found to be 77.2 mg/dL (sensitivity=$57.1\%$, and specificity=$96.6\%$). Figure 1: - Receiver operating characteristic curve for serum C-reactive protein value for predicting the requirement of intensive care unit. AUC: under area the curve ## Discussion The present study revealed that the need for ICU admission due to COVID-19 increased with progressing trimester. Although the majority of the pregnant women in their first 2 trimesters were followed-up as outpatients, the majority of those in their third trimester were followed-up as inpatients. A significant increase was observed in the cough and shortness of breath symptoms in the pregnant women in their third trimester. However, no significant difference was detected in the laboratory parameters among the trimester groups. Also, the gestational week did not have any significant effect on the need for ICU admission. Shortness of breath and high CRP levels at the time of admission was significantly associated with the need for subsequent ICU admission. According to Centers for Disease Control and Prevention data of January-June 2020, $31.5\%$ of pregnant women were hospitalized for COVID-19. The reported rate was higher than that reported in nonpregnant women ($5.8\%$). 22 Zambrano et al 20 reported that pregnant women with COVID-19 had an increased risk of severe disease, need for ICU admission, and mortality compared with nonpregnant women. The higher rate of hospitalization due to COVID-19 than the normal population might be associated with the increased load on the cardiopulmonary system and the suppressed immune system during pregnancy. In the present study, the rate of hospitalization during pregnancy was $45.7\%$, which was well above that reported in the literature and for the normal population. The hospitalization rates increase with the progressing gestational week. Of the patients in their third trimester, $68.7\%$ needed hospitalization. This may be associated with the increase in oxygen demand with the gestational week, physiological hyperventilation, and aggravation of dyspnea. Mohr-Sasson et al 23 reported that cough was the most common symptom in $68\%$ of pregnant women with COVID-19. Other symptoms included high fever in $48.5\%$ of the pregnant women, shortness of breath in $48.5\%$, myalgia in $32\%$, diarrhea in $10\%$, and the loss of smell and taste in $8\%$. In our study, comparisons among the trimester groups indicated that the presence of respiratory symptoms (such, cough and shortness of breath) associated with COVID-19 was significantly higher among patients in their third trimester than those in their first 2 trimesters. As mentioned above, this can be explained by the fact that the physiological changes related to pregnancy become more pronounced with the progressing gestational week. These outcomes also led to an increase in hospitalization rates. Our study revealed no significant association between other symptoms and trimester. In a meta-analysis by Diriba et al, 24 $28.4\%$ of the pregnant women with COVID-19had leukocytosis, $63\%$ hadlymphocytopenia, and $55.9\%$ of the patients had high CRP levels. Their study underlined lymphocytopenia as the most frequently reported laboratory finding. Mohr-Sasson et al 23 reported the presence of lymphocytopenia in $45.5\%$ of pregnant patients. The relative lymphocyte count to that of WBCs was significantly reduced in the pregnant group compared with the nonpregnant women ($$p \leq 0.003$$). Consistent with the literature, in our study, lymphocytopenia was noted in $56.5\%$ of patients, and an elevated CRP level in $35.4\%$ of patients. According to a study on pregnant women with COVID-19, the need for ICU admission of the mother or fetus due to COVID-19 was $31.3\%$ and the maternal mortality rate was $2.7\%$. 24 In the present study, the rate of severe patients was $5.7\%$, and the rate of ICU admission was $4\%$. These values were consistent with the literature. There was no significant difference between the trimesters by the need for ICU admission. A review of all groups included in our study indicated a significant association between CRP elevation at the time of admission or especially the complaint of shortness of breath and subsequent ICU admission. It was determined that the shortness of breath increased the risk of ICU admission by 6.95 times. Respiratory symptoms are the most common cause of COVID-19-related emergency department admissions in pregnancy. 23 Pregnant women who have adapted to the physiological dyspnea secondary to cardiopulmonary changes can initially tolerate the increased oxygen demand due to COVID-19, and therefore, it may take time before the respiratory symptoms become evident. Based on the foregoing assumptions, it can be suggested that pregnant women present to the emergency department late and have a more severe disease at the time of admission than the normal population. ## Study limitations This study has been designed retrospectively. Moreover, our study group comprised of pregnant women, and we did not include a control group in this study. Our study examined the parameters at the time of hospitalization and the laboratory parameters and clinical symptoms during the course of hospital stay were not included. The strength of our study is a relatively large sample size. Also,to the best of our knowledge, there are few studies in the literature examining the association between COVID-19 and pregnancy trimesters. In conclusion, the symptoms of dyspnea and cough increased significantly and the need for hospitalization increased with the progressing gestational week. Pregnancy-related physiological changes may have worsened the symptoms of COVID-19. We found a significant association between shortness of breath and higher CRP level at the first admission and subsequent need for ICU admission regardless of trimester. Therefore, we highly suggest that pregnant women be evaluated regarding shortness of breath and CRP levels at the first admission. 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--- title: The effect of fetal gender on the biochemical markers of the first-trimester screening authors: - Hakan Cokmez - Simge Tezel Yozgat journal: Saudi Medical Journal year: 2022 pmcid: PMC9998053 doi: 10.15537/smj.2022.43.4.20210906 license: CC BY 4.0 --- # The effect of fetal gender on the biochemical markers of the first-trimester screening ## Body Currently, first-trimester screening tests are routinely recommended for every pregnant woman between the 11th and 14th gestational week to determine the risk of trisomy 15, 18, and 21. The biochemical markers of the first trimester screening test are pregnancy-related plasma protein A (PAPP-A) and beta-human chorionic gonadotropin (β-hCG). Pregnancy-related plasma protein A is a metalloproteinase whose concentration increases due to the expansion of the placenta at the end of the first trimester, breaks down insulin-like growth factor-binding proteins, and is synthesized by the trophoblasts. 1,2 Beta-human chorionic gonadotropin is a hormone with a glycoprotein structure and is synthesized in the placenta by the syncytiotrophoblasts 3 and ensures continued progesterone synthesis from the corpus luteum by binding to luteinizing hormone receptors in the first trimester of pregnancy. Multiple studies that investigated the relationship between poor pregnancy results and biochemical markers of the screening test reported that maternal blood levels of PAPP-A and β-hCG can be used for early detection of placental complications that may occur in the succeeding weeks as well as fetal trisomies. 4-7 Since both PAPP-A and β-hCG are synthesized in the placenta, they are common products of maternal and fetal cells. The role of the fetus in placental development raises the question of whether PAPP-A and β-hCG levels are affected by the fetal gender in ischemic placental diseases, which include mall for gestational age (SGA) newborn, hypertension above $\frac{140}{90}$ mmHg that was not present before the pregnancy, and non-traumatic placental ablation. 8-10 Some studies have demonstrated that both perinatal morbidity and mortality and maternal PAPP-A and β-hCG levels are affected by the fetal gender. 11-14 Additionally, other studies have reported that preeclampsia and primary cesarean section rates in pregnant women with male fetuses are higher compared with pregnant women with female fetuses. 15,16 The aim of this study was to investigate the effects of fetal gender on maternal serum PAPP-A and free β-hCG levels. ## Abstract ### Objectives: To determine the effects of fetal gender on the maternal levels of first-trimester screening biochemical markers, such pregnancy-related plasma protein A (PAPP-A) and beta-human chorionic gonadotropin (β-hCG). ### Methods: In this retrospective study, we assessed 267 cases of singleton pregnancies, who underwent first trimester screening tests and delivered between January 2016 and January 2019 at our hospital. Multiple of median (MoM) levels of PAPP-A and free β-hCG, and the neonatal genders according to the birth records were compared and analyzed. Additionally, patients with small for gestational age (SGA) newborns, preeclampsia, and placental ablation, called ischemic placental diseases, were classified into a separate group and their PAPP-A and free β-hCG MoM values and fetal genders were compared. ### Results: There was no significant relationship between the mean values of PAPP-A (1.07±0.6) and free β-hCG (1.23±1.14) and the fetal gender (males: 137, $51.3\%$; females: 130, $48.7\%$), respectively ($$p \leq 0.833$$; $$p \leq 0.075$$). In 41 cases ($15.4\%$) with ischemic placental disease, free β-hCG values was significantly higher in the fetal females (19 cases; $46.3\%$) than males (22 cases; $53.7\%$), (1.53±1.02 and 0.77±0.53, respectively), ($$p \leq 0.004$$). ### Conclusion: Pregnancy-related plasma protein A and free β-hCG values were not affected by the fetal gender. However, the significant relationship observed between free β-hCG MoM levels and fetal gender in patients with ischemic placental diseases suggests the need for larger studies on this topic. ## Methods In this retrospective cohort study, 267 cases who underwent delivery between January 2016 and January 2019 in a single tertiary center were included. The study was carried out in accordance with the 1964 Helsinki Declaration, revised in 2013. Due to the retrospective design of the study and the anonymous data used in the analyses, informed consent was not obtained from the patients. Ethics committee approval was obtained from our institutional committee before initiating the study (#$\frac{475}{2019}$). Data regarding the demographics, pregnancy results, neonatal gender, and ultiple of median (MoM) levels of the biochemical parameters were obtained from the hospital records. During the study period, PAPP-A and free β-hCG levels were assessed using Siemens IMMULITE® 2000 XPi immunoassay system (Siemens Healthcare GmbH, Erlangen, Federal Republic of Germany). For MoM calculations, Siemens PRISCA prenatal risk calculation system was used during the study period. Singleton pregnancies in which both the first trimester screening test and delivery (gestation >24 weeks) were performed at our institution in adult women of >18 and <39 years of age were included in the study. Adolescents and pregnant women with advanced age (>39 years) were excluded due to their higher risk of obstetric complications because age could be a confounding factor. Additionally, pregnant women with chronic diseases (type 2 diabetes and autoimmune and chronic cardio-vascular diseases), pregnancies that had required assisted reproductive techniques, and pregnant women who were smokers were also excluded. The subjects included in the study were classified according to the neonatal gender and were compared using the calculated mean values of PAPP-A and free β-hCG MoM levels in the first trimester screening test. Additionally, the calculated mean values of PAPP-A and free β-hCG MoM levels in pregnant women with ischemic placental diseases were also compared with the neonatal gender. The gestational age was calculated based on the fetal head-breech length measured on ultrasound in the first trimester. Additionally, the calculated mean values of PAPP-A and free β-hCG MoM levels in pregnant women with ischemic placental diseases were also compared with the neonatal gender. Preeclampsia, SGA, and non-traumatic ablation are obstetrical complications about the placenta. Uteroplacental ischemia may be a factor that is responsible for these 3 conditions. These conditions are called ischemic placental disease as described in the literature. 10 In this study, preeclampsia was defined as systolic blood pressure of 140 mm Hg or more or diastolic blood pressure of 90 mm Hg or more after 20 weeks of gestation with proteinuria (protein/creatinine ratio of 0.3 mg/dL or more or dipstick reading of 2+), SGA was defined as birth weight below the 10th percentile of the birth-weight-for-gestational-age reference curve, ablation was determined as vaginal bleeding, uterine tenderness, fetal distress and accompanied by postpartum retroplacental clots on the placenta. 10 Continuous numerical data were expressed as mean ± standard deviation, while intermittent numerical data were expressed as numbers, and nominal data were expressed as numbers and percentages. The distribution of continuous data was determined using Kolmogorov–Smirnov test. In the comparative analysis of continuous data, student’s t-test was used in cases of normal distribution, and Mann–Whitney U test was used in cases of non-normal distribution. All analyses were performed using SPSS version 20.0 (IBM Inc., Armonk, NY, USA). Results with $p \leq 0.05$ were considered statistically significant. ## Results Overall, 267 patients with a mean age of 28.5±6.1 years were included. Of them, 59 ($22.1\%$) were nulliparous and 208 ($77.9\%$) were multiparous. Additionally, 61 ($22.8\%$) patients had past history of at least one abortion. The pregnancies included in the study ended by cesarean section in 122 ($45.7\%$) patients and vaginal delivery in 145 ($54.3\%$) patients. Stillbirth was observed in 2 ($0.7\%$) cases. Post-delivery, 137 ($51.3\%$) neonates were males and 130 ($48.7\%$) were females. The average birth weight of the neonates was 3359.2±406.3 grams. The mean PAPP-A MoM level was 1.07±0.6; the mean free β-hCG MoM level was 1.23±1.14. The comparisons of serum PAPP-A and free β-hCG MoM levels and birth weights according to the neonatal gender are summarized in Table 1. Table 1 \| Data* \| Female \| Male \| P-value \| \| --- \| --- \| --- \| --- \| \| PAPP-A (MoM) \| 1.07±0.63 \| 1.07±0.62 \| 0.833† \| \| Free β-hCG (MoM) \| 1.35±2.07 \| 1.12±1.08 \| 0.075† \| \| Birth weight, grams \| 3244.8±417.4 \| 3372.5±386.7 \| 0.010‡ \| Overall, 41 ($15.4\%$) patients had encountered at least one of ischemic placental diseases during their pregnancy (Table 2). The comparisons of serum levels of PAPP-A and free β-hCG MoM in these cases according to the neonatal genders are summarized in Table 3. In addition, the comparison of patients with or without placental ischemic disease according to the neonatal genders are presented in Table 4. ## Discussion There was no significant relationship between the mean values of PAPP-A and free β-hCG and the fetal gender. The most striking observation in our study was that in patients who encountered at least one of the ischemic placental diseases during the pregnancy, the mean MoM level of free β-hCG was significantly different between the female and male fetal genders. We also investigated the effect of fetal gender on the relationship between ischemic placental diseases and the first trimester screening biochemical parameters PAPP-A and free β-hCG. Spencer et al, 17 in their investigation of the changes in first-trimester screening parameters according to fetal gender in 2923 pregnant women, reported that PAPP-A and free β-hCG MoM levels were higher by 10-$15\%$ in women with female fetuses compared with those with male fetuses. Hence, they stated that this may result in a 1-$2\%$ change in the detection of trisomy 21 detection in female fetuses. Cowans et al, 18 in their investigation of the changes in first trimester screening parameters according to fetal gender in 56,024 pregnant women, reported that the mean free β-hCG MoM level was $14.7\%$ higher in women with female fetuses compared with those with male fetuses. Both studies stated the uncertainty in correcting the values of the first trimester screening test according to fetal gender and their results on the clinical efficacy since the determination of fetal gender is difficult in the first trimester. 17,18 Ischemic placental diseases mostly occur in the last trimester and the fetal gender can be detected before this trimester more easily. Therefore, the determined fetal gender before the third trimester and first trimester biochemical parameters may be used for predicting the risk of ischemic placental disease. However, there is no cut-off value for these biochemical parameters in the literature yet. Thus, in terms of predicting the risk of ischemic placental disease, studies should be carried out to identify a cut-off value, determined according to the fetal gender, for first trimester biochemical parameters. In studies that investigated changes in first trimester screening test parameters according to fetal gender, some reported that free β-hCG MoM level and PAPP-A MoM level were higher in female fetuses. 13,18-20 In our study, we did not find a significant relationship between both PAPP-A and free β-hCG MoM levels and the fetal gender. However, in the group of patients with at least one of the ischemic placental diseases, we found the free β-hCG MoM levels to be higher in women with female fetuses than those with male fetuses. Intrauterine growth retardation is more common in female fetuses. 21 In some studies that did not include fetal gender classification, high free β-hCG MoM level in the first trimester screening test was found to be associated with low birth weight according to the week of gestation and placental ablation. 6,7 In their study Khalil and Alzahra 15 investigated the association of poor pregnancy outcomes and male-bearing gravids in 29,140 patients, they confirmed the effect of a male fetus on the existence of preeclampsia in their study population. In our study, we did not find a significant relationship between fetal gender and ischemic placental disease. ## Study limitations A limitation of this study is the loss of data because the first trimester screening test reports before 2017 were not uploaded to the system. Furthermore, our neonatal intensive care unit was closed during the study period, which resulted in a mismatch between the number of outpatient patients and the number of deliveries. Another limitation is the limited number of patients included in the study. Additionally, the single-centered and retrospective design includes inherent weaknesses of the design. The distribution of ischemic placental disease in our study was not suitable for statistical analysis. In addition, this study has included cases with more than one ischemic placental disease. Multicenter studies with large sample sizes on patients with placental ischemic disease may overcome this problem. The strength of our study is that it investigates the effect of fetal gender on the first trimester screening biochemical parameters PAPP-A and β-hCG in the context of ischemic placental diseases. In conclusion, we found that, in patients who suffer at least one of the ischemic placental diseases during pregnancy, the mean MoM levels of free β-hCG were significantly different between female and male fetuses. Therefore, we believe that further studies are required to investigate the relationship between fetal gender and free β-hCG MoM levels to contribute to the understanding of the pathogenesis and to determine a cut-off value according to fetal gender in ischemic placental diseases. ## References 1. Smith GCS.. First trimester origins of fetal growth impairment. Semin Perinatol (2004) 28 41-50. PMID: 15058901 2. Ong CY, Liao AW, Spencer K, Munim S, Nicolaides KH.. First trimester maternal serum free beta human chorionic gonadotropin and pregnancy associated plasma protein A as predictors of pregnancy complications. BJOG (2000) 107 1265-1270. PMID: 11028579 3. Cole LA.. Biological functions of hCG and hCG-related molecules. Reprod Biol Endocrinol (2010) 8 102. PMID: 20735820 4. 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--- title: The relationship between sleep quality and menopausal symptoms among postmenopausal women in Saudi Arabia authors: - Enas M. Abdelaziz - Nadia B. Elsharkawy - Sayeda M. Mohamed journal: Saudi Medical Journal year: 2022 pmcid: PMC9998056 doi: 10.15537/smj.2022.43.4.20210682 license: CC BY 4.0 --- # The relationship between sleep quality and menopausal symptoms among postmenopausal women in Saudi Arabia ## Body Menopause is a natural physiologic event for women in midlife, defined as the permanent cessation of menses for at least one year of amenorrhea after the final menstruation. This occurs due to the aging of the ovaries leading to decrease secretions of estrogen and progesterone and not including factors such as chemotherapy, gland disorders, and hysterectomy. 1 With increased life expectancy, women can spend more than one-third of their lives in the postmenopausal state. 2,3 It is estimated that 1.2 billion women will be menopausal by 2030, with 47 million new additions each year globally. 4,5 Women’s menopause can be influenced by genetic, nutritional, environmental, and psychological factors. 6,7 During menopause, women may encounter declining physical well-being and climacteric symptoms, including vaginal dryness, hot flashes, sweating, nervousness, stress, mood swings, poor concentration, difficulty with memory, and sleeplessness. 2 The onset of menopause varies across countries, but the average age is around 50-52 years and most Saudi women reach menopause between the ages of 51-55. 7 Change in estrogen levels cause irregularities in the menstrual cycle and is considered the first sign of menopause. As estrogen deprivations increase, major somatic and psychological problems originate that influence a woman’s well-being. Early somatic indicators of estrogen deprivation are hot flashes, sweating, headache, and sleep disorders. In contrast, late manifestations are mood swings, cardiovascular disease, osteoporosis, urogenital changes, fatigue, decreased sexual desire, stress incontinence, depressed mood, crying, concentration difficulties, and poor memory. 8 Sleep plays an essential role in the well-being of an individual. It is a vital physiological process that affects physical, neurological, and psychological functions. 9 Sleep disorder is the most prevalent and clinically prominent symptom observed during menopause and among the elderly. It is linked to unfavorable health outcomes, such as exhaustion, poor daytime function, and increased visits to healthcare providers. 10 Numerous researches have sought to find out the causes of sleep disturbances and have discovered that hormonal changes, somatic symptoms, natural aging, and stressful life events can all affect sleep quality. 10,11 Aging, obesity, hypertension, smoking, and a lack of physical activity have all been associated with sleep disturbances.12 The prevalence of sleep disturbances affects 39-$47\%$ of perimenopausal women and 35-$60\%$ of postmenopausal women. These rates are concerning and may require intervention by health care providers. 13-15 Poor sleep has various negative consequences, including poor physical, psychological, cognitive, and social outcomes.15 Sleep duration strongly influences health, and various studies support the association between sleep problems such as sleeplessness, sleep disturbances, night arousals, excessive daytime sleepiness, apnea, depression, and hot flashes with menopause in women. 13 Moreover, insufficient sleep has been linked to an increased tendency to gain weight and ultimately, develop diabetes, osteoporosis, and increased fracture risk. 16,17 Thus, health problems related to the postmenopausal period are essential public health concerns in women at the transitional period of life. Health care providers, such as nurses, dietitians, midwives, and doctors must be sensitive and responsive to a woman’s needs during this stage of life. They should provide guidelines and design health education programs that emphasize adopting healthy and active lifestyles, including weight reduction, physical activity, a healthy diet, maintaining regular medical follow-up to improve the general well-being, and minimize the severity of menopause symptoms. The increased number of postmenopausal women raising concerns regarding their health and sleep. In addition, only few studies have researched the topic in Saudi Arabia and there is limited literature available from the Arab countries. This present study examines the association between sleep quality and menopausal symptoms in Saudi Arabian postmenopausal women. ## Abstract ### Objectives: To assess sleep quality and examine its relationship with menopausal symptoms among Saudi postmenopausal women. ### Methods: We carried out a cross-sectional study of 410 postmenopausal women, aged 50-60 years, visiting Prince Mutaib bin Abdulaziz Hospital, Maternity and Children Hospital, and primary health care clinics, Sakaka, Jouf, Saudi Arabia. The menopause rating scale (MRS) was used to assess menopause symptoms and severity, while the Pittsburgh sleep quality index (PSQI) was used to assess sleep quality. ### Results: The participants’ age was 53.04±4.15 years, their mean age at natural menopause was 49.14±3.07, and the meantime since their menopause was 6.50±3.84 years. The PSQI total mean score was 6.10±4.17, classified into good versus poor sleepers; $65.4\%$ scored ≤5, and $34.6\%$ scored >5. The Mann-Whitney analysis revealed that somatic and urogenital symptoms, and total MRS score were associated with poor sleep quality ($p \leq 0.001$). ### Conclusion: The study findings revealed that more than one-third of Saudi postmenopausal women had poor sleep quality. ## Methods A cross-sectional study of postmenopausal women attending or accompanying patients visiting outpatient clinics at Prince Mutaib bin Abdulaziz Hospital, Maternity and Children Hospital, and primary healthcare clinics, Sakaka, Jouf, Saudi Arabia, between January and April 2021, were selected based on personal interview for the study. The inclusion criteria included all postmenopausal women between 50-60 years old, having menopause for at least one year and voluntarily ready to participate in the study. The exclusion criteria included women who received psychiatric drugs, hormone replacement therapy, undergoing hysterectomy, or having any acute or chronic surgical conditions, cancer, and cognitive impairments, or physical handicap. A sample of 373 was calculated using Roasoft sample size calculator. 18 The required sample size was computed using a total population size of 12.704 women aged 50-60 years in Sakaka, Jouf, Saudi Arabia, $5\%$ margin of error, $95\%$ confidence interval, and $50\%$ response rate. A large convenience sample of 410 postmenopausal women were recruited to adjust to the dropout rate. The researchers interviewed the eligible participants using a face-to-face structured interview. The study questionnaire was self-administered, and only if the participant could not read or write, the researcher completed the questionnaire based on the participant’s response. To achieve the study objectives, a 3-structure sectioned and validated questionnaire was used. The questionnaire was administered in the Arabic language to verify that the items were understood by the participants. Before the data collection process, the structure and clarity of this Arabic version were piloted with 40 Saudi postmenopausal women, and no changes to the questionnaire were recommended and the pilot study data were excluded. Demographic characteristics included information on the participants’ age, education, occupation, marital status, smoking habit, physical exercise, age at menopause onset, time since the menopause onset, having chronic illnesses, and parity were collected. In this section, each participant’s height and weight were measured during the interview to assess their body mass index (BMI) that was calculated by dividing body weight (kg) by height squared (m). Body mass index was classified into 4 groups based on World Health Organization cut-off points: underweight (<18.5), healthy weight (18.5-24.9), overweight (25-29.9), and obese (≥30). 19 Menopausal rating scale (MRS) is a self-reported standardized Likert scale covering 11 items related to aging symptoms or complaints and was developed by Schneider et al. 20 Menopausal rating scale was translated from English into simple, understandable Arabic language, which is appropriate for Arab culture by Sweed et al 21 and was used in the present study. The MRS was categorized into the following 3 subscales: psychological symptoms (4 items that included depressive mood, irritability, anxiety, physical, and mental exhaustion), somatic symptoms (4 items that included sweating/hot flashes, sleep problems, heart discomfort, and joint and muscular discomforts), and urogenital symptoms (3 items that included bladder problems, sexual problems, and dryness of vagina). Each item was scored on a 5-point Likert scale ranging from 0 (no symptoms) to 4 (very severe symptoms). The total score was calculated by adding all the points from each item. The scores ranged from 0 (asymptomatic) to 44, indicating the highest level of complaint and reliability (0.87). 1 The severity classification summation scores were none [0-4], mild [5-8], moderate [9-16], severe and very severe (≥17). Menopausal rating scale validity and reliability were preserved in the Arabic version; with $90\%$ test-retest agreement. 21 In this study, Cronbach’s alpha was 0.81 indicating good reliability. Pittsburgh sleep quality index (PSQI) is an efficient self-reporting scale for measuring subjective sleep quality and sleep patterns, developed by Buysse et al. 22 An Arabic version is available. 23 The PSQI distinguishes between “poor” and “good” sleeper by evaluating different aspects of sleep using 7 components and 19 items. Responses were scored on a scale of 0-3. Whereas, 3 represented the adverse extreme of the Likert scale. The researchers added the sum of 7 components to calculate the global PSQI score, which ranged from 0-21; a score of >5 denoted a “poor” sleeper, while a cut-off point of ≤5 indicates a “good” sleeper. Thus, a score of 5-7 indicated the need for medical assessment; 8-14 recommended the need for care and medical treatment, and 14-21 suggested a serious sleeping problem. The Arabic version of the PSQI was tested with 35 Arabic bilinguals, and the documented internal consistency reliability was borderline acceptable (Cronbach’s alpha 0.65). 23 The scale had good internal reliability in this study (Cronbach’s alpha of 0.83). The Local Committee of Bioethics at Jouf University, Saudi Arabia approved the study protocol (no.: 03-03-42) in accordance with the Helsinki Declaration principles. The study was described to the director and nurses’ supervisors at the hospital and clinics for approval to carry out the study and facilitate the data collection process. The study’s purpose, design, and benefits were explained to the participants, and written informed consents were collected before they were asked to complete the questionnaire. The participants were informed that the study was voluntary and that they had a right to withdraw anytime. Code numbers were created for each participant, and confidentiality of data was maintained. ## Statistical analysis Statistical Package for the Social Sciences for Windows, version 20.0 (IBM Corp., Armonk, NY, USA) was used for all statistical analyses. Cronbach’s alpha was used to determine reliability. The Kolmogorov-Smirnov test was used to verify the normality of the distribution. Frequencies and percentages were calculated for categorical variables; means and standard deviations were measured for continuous variables. Mann-Whitney test was used to compare between 2 categories. Kruskal-Wallis test was used to compare between more than 2 categories and post hoc (Dunn’s multiple comparisons test) for pairwise comparisons. The relationship between MRS scores and PSQI scores was explored by Pearson’s correlation coefficient. A p-value of <0.05 was considered significant. ## Results The characteristics of the 410 postmenopausal Saudi women are presented in Table 1. Their mean age was 53.04±4.15 years. Their mean age at natural menopause was 49.14±3.07, and the meantime since their menopause was 6.50±3.84. Most of the participants were married ($86.8\%$) and housewives ($74.9\%$). The BMI was 29.09±5.62 kg/m2, and $77.3\%$ were overweight and obese. Aprroximately $70.2\%$ could not read and write and have a primary level of education. The mean number of children was 5.11±1.62. Most participants ($60.7\%$) had chronic illness and $96.8\%$ did not smoke. Hot flashes and sweating were reported by $53.4\%$ and $28.8\%$ of the participants that varied from once to more than 3 times per week. Pairwise comparisons of frequency of hot flashes using Dunn’s post-hoc test indicated that one time per week hot flashes were observed to be significantly higher than not repeated ($$p \leq 0.002$$). The post-hoc tests also indicated that poor sleep quality was seen more among the younger participants (50-52 years) in the early years of the postmenopausal period than other groups ($p \leq 0.001$), women who had menopause duration of 5-10 years had poor sleep quality than other groups ($p \leq 0.001$). The obese participants ($37.1\%$), had poor sleep quality than normal and overweight ($p \leq 0.001$; Table 1). The mean night sleeping time was 6.20±1.40, indicating short sleep duration, and the total mean scores of PSQI was 6.10±4.17. In categorizing good versus poor sleepers, 268 ($65.4\%$) participants reported good sleep quality and had global scores of ≤5, whereas 142 ($34.6\%$) reported poor sleep quality (PSQI score of >5). Table 1 \| Characteristics \| Total \| Sleep quality \| Sleep quality.1 \| Sleep quality.2 \| Sleep quality.3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristics \| n (%) \| Mean±SD \| Median \| Test of sig. \| P-value \| \| Age (years), mean±SD \| 53.04±3.15 \| \| \| \| \| \| 50-52 \| 202 (49.3) \| 7.28±4.28 \| 5.04.03.0 \| H=24.779* \| <0.001* \| \| 53-55 \| 128 (31.2) \| 6.97±4.38 \| \| \| \| \| 56-60 \| 80 (19.5) \| 5.08±3.75 \| \| \| \| \| Time since menopause onset, mean±SD \| 6.50±3.84 \| \| \| \| \| \| <5 years \| 181 (44.1) \| 4.85±3.46 \| 3.0 \| \| \| \| 5-10 years \| 153 (37.3) \| 7.86±4.54 \| 10.0 \| H=27.713* \| <0.001* \| \| ≥10 years \| 76 (18.5) \| 6.71±4.32 \| 5.0 \| \| \| \| Education \| Education \| Education \| Education \| Education \| Education \| \| Cannot read and write/primary \| 288 (70.2) \| 5.78±4.02 \| 4.0 \| \| \| \| Intermediate/secondary \| 73 (17.8) \| 6.25±4.26 \| 4.0 \| H=1.350 \| 0.509 \| \| University/Master - PhD \| 49 (12.0) \| 7.43±4.42 \| 10.0 \| \| \| \| BMI, mean±SD \| 29.09±5.62 \| \| \| \| \| \| Underweight \| 15 (3.7) \| 5.93±4.22 \| 4.0 \| \| \| \| Normal - health weigh \| 78 (19.0) \| 4.69±3.23 \| 3.5 \| H=19.682* \| <0.001* \| \| Overweight \| 165 (40.2) \| 5.80±4.24 \| 4.0 \| \| \| \| Obese \| 152 (37.1) \| 7.16±4.29 \| 5.0 \| \| \| \| Parity, mean±SD \| 5.11±1.62 \| \| \| \| \| \| 0 \| 9 (2.2) \| 10.33±4.00 \| 11.0 \| \| \| \| 1-4 \| 136 (33.2)2 \| 5.86±3.97 \| 4.0 \| H=9.338* \| 0.009* \| \| ≥5 \| 65 (64.6) \| 6.57±4.72 \| 4.0 \| \| \| \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| \| Single \| 9 (2.2) \| 9.44±2.74 \| 10.0 \| \| \| \| Married \| 356 (86.8) \| 6.08±4.22 \| 4.0 \| H=5.785 \| 0.055 \| \| Widowed or divorced \| 45 (11.0) \| 5.56±3.75 \| 4.0 \| \| \| \| Occupation \| Occupation \| Occupation \| Occupation \| Occupation \| Occupation \| \| Housewife \| 307 (74.9) \| 5.93±4.07 \| 4.0 \| \| \| \| Employee \| 63 (15.4) \| 6.67±4.58 \| 4.0 \| H=1.350 \| 0.509 \| \| Retired \| 40 (9.8) \| 6.45±4.31 \| 4.5 \| \| \| \| Smoking \| Smoking \| Smoking \| Smoking \| Smoking \| Smoking \| \| Yes \| 13 (3.2) \| 6.62±4.57 \| 4.0 \| \| \| \| No \| 397 (96.8) \| 6.08±4.16 \| 4.0 \| U=2376.5 \| 0.624 \| \| Exercise \| Exercise \| Exercise \| Exercise \| Exercise \| Exercise \| \| Yes \| 98 (23.9) \| 6.05±4.14 \| 3.0 \| \| \| \| No \| 312 (76.1) \| 6.24±4.29 \| 4.0 \| U=490.5 \| 0.316 \| \| Having chronic illnesses \| Having chronic illnesses \| Having chronic illnesses \| Having chronic illnesses \| Having chronic illnesses \| Having chronic illnesses \| \| Yes \| 249 (60.7) \| 6.71±4.32 \| 5.0 \| \| \| \| No \| 161 (39.3) \| 5.15±3.75 \| 3.0 \| U=15655.0* \| <0.001* \| \| Frequency of sweating \| Frequency of sweating \| Frequency of sweating \| Frequency of sweating \| Frequency of sweating \| Frequency of sweating \| \| Not been repeated \| 292 (71.2) \| 5.96±4.10 \| 4.0 \| \| \| \| Once a week \| 59 (14.4) \| 5.76±4.02 \| 4.0 \| H=7.720 \| 0.052 \| \| Twice a week \| 45 (11.0) \| 6.42±4.09 \| 5.0 \| \| \| \| 3 times a week or more \| 14 (3.4) \| 9.29±5.58 \| 11.0 \| \| \| \| Frequency of hot flashes \| Frequency of hot flashes \| Frequency of hot flashes \| Frequency of hot flashes \| Frequency of hot flashes \| Frequency of hot flashes \| \| Not been repeated \| 191 (46.6) \| 4.48±3.39 \| 3.0 \| \| \| \| Once a week \| 136 (33.2) \| 5.90±3.93 \| 4.0 \| H=83.673* \| <0.001* \| \| Twice a week \| 37 (9.0) \| 10.27±2.78 \| 11.0 \| \| \| \| 3 times a week or more \| 46 (11.2) \| 10.02±3.97 \| 11.0 \| \| \| Table 2 shows the total MRS and the subscale score according to poor and good sleep quality. Somatic ($p \leq 0.001$), urogenital symptoms ($p \leq 0.001$), and total mean MRS score ($p \leq 0.001$) were associated with poor sleep quality. Several somatic symptoms including hot flashes and sweating, sleep problems, and joint and muscular discomfort, and urogenital symptoms including bladder problems, vaginal dryness, and sexual problems were significantly related to poor quality of sleep. Psychological symptoms were not associated with poor sleep quality ($$p \leq 0.095$$). Table 2 \| Menopausal symptoms \| Sleep quality \| Sleep quality.1 \| Sleep quality.2 \| Sleep quality.3 \| U \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Menopausal symptoms \| Poor >5 (n=142) \| Poor >5 (n=142) \| Good ≤5 (n=268) \| Good ≤5 (n=268) \| U \| P-value \| \| Menopausal symptoms \| Mean±SD \| Median \| Mean±SD \| Median \| U \| P-value \| \| Somatic symptoms \| 4.77±2.87 \| 5.0 \| 3.44±2.56 \| 3.0 \| 13264.0* \| <0.001* \| \| Hot flashes, sweating \| 1.58±1.17 \| 2.0 \| 1.01±1.06 \| 1.0 \| 13666.5* \| <0.001* \| \| Heart discomfort \| 0.66±0.86 \| 0.0 \| 0.56±0.79 \| 0.0 \| 17942.0 \| 0.279 \| \| Sleep problems \| 1.19±1.0 \| 1.0 \| 0.87±0.89 \| 1.0 \| 15666.0* \| 0.002* \| \| Joint and muscular discomfort \| 1.34±1.12 \| 1.0 \| 1.0±1.07 \| 1.0 \| 15671.5* \| 0.002* \| \| Psychological symptoms \| 4.0±3.48 \| 4.0 \| 3.40±3.45 \| 3.0 \| 17108.0 \| 0.084 \| \| Depressive mood \| 0.90±1.01 \| 1.0 \| 0.77±1.01 \| 0.0 \| 17509.0 \| 0.148 \| \| Irritability \| 1.23±1.14 \| 1.0 \| 1.03±1.12 \| 1.0 \| 17190.0 \| 0.087 \| \| Anxiety \| 0.63±1.0 \| 0.0 \| 0.67±0.93 \| 0.0 \| 18156.5 \| 0.383 \| \| Physical and mental exhaustion \| 1.22±1.10 \| 0.0 \| 0.94±1.07 \| 0.0 \| 16190.0* \| 0.009* \| \| Urogenital score \| 4.88±2.37 \| 5.0 \| 3.93±2.64 \| 4.0 \| 14943.5* \| <0.001* \| \| Bladder problems \| 1.87±0.89 \| 2.0 \| 1.60±1.04 \| 1.0 \| 15921.0* \| 0.004* \| \| Vaginal dryness \| 1.53±1.0 \| 2.0 \| 1.29±1.23 \| 1.0 \| 16649.5* \| 0.031* \| \| Sexual problems \| 1.49±1.14 \| 1.0 \| 1.04±1.14 \| 1.0 \| 14756.0* \| <0.001* \| \| Total \| 13.75±6.77 \| 13.0 \| 10.71±6.52 \| 9.0 \| 13379.5* \| <0.001* \| Pearson correlation coefficient revealed significant weak correlations with the total PSQI score, including the total MRS score ($r = 0.210$), the somatic symptoms ($r = 0.228$), and a very weak correlation with urogenital symptoms ($r = 0.177$), suggesting the worse menopausal symptoms, the poor sleep quality ($p \leq 0.001$; Table 3). Table 3 \| Menopausal symptoms \| Total sleep \| Total sleep.1 \| \| --- \| --- \| --- \| \| Menopausal symptoms \| r \| P-value \| \| Somatic symptoms \| 0.228* \| <0.001* \| \| Psychological symptoms \| 0.082 \| 0.098 \| \| Urogenital symptoms \| 0.177* \| <0.001* \| \| MRS total scale \| 0.210* \| <0.001* \| ## Discussion The study results showed that most participants ($65.4\%$) had good sleep quality, which may be related to the good living condition, high economic status, and high quality of medical services in Saudi Arabia. However, approximately one-third ($34.6\%$) had poor sleep quality that necessitated medical attention. This finding was consistent with Kim et al 24 who reported that $30.2\%$ of South Korean postmenopausal women had poor sleep quality. A Canadian longitudinal study by Zolfaghari et al 25 showed that $32.4\%$ of women expressed poor sleep satisfaction, and Creasy et al 26 revealed that $35\%$ of postmenopausal women had short sleep duration of ≤6 hours per night in the United States. While Valencia et al13 found that nearly half ($46.7\%$) of Argentinian women had poor sleep quality. Middle-aged Chinese women had experienced sleep disturbances with a total PSQI score of 8.58±4.37. 27 In an Iranian study, $56.3\%$ of postmenopausal women were identified as poor sleepers. 28 A naturally postmenopausal women among Shanghai residents, China, had poor sleep quality by $12.5\%$. 29 The variations in the results could be attributed to biological, psychosocial, socioeconomic, cultural, and racial/ethnic factors. Moreover, women are more likely to have disturbances of sleep due to estrogen declining during menopause, making them more sensitive to negative emotional information. 30 Poor sleep quality may increase the risk of cardiovascular diseases, hypertension, obesity, diabetes mellitus, increase healthcare usage, depression, distress, and low quality of life.13 This study results were in the same alignment with previous studies indicating a significant association between poor sleep quality and sociodemographic variables ($p \leq 0.001$). Creasy et al26 and Blümel et al 32 stated that an inactive lifestyle had been linked with insomnia in postmenopausal women. Wu et al29 showed that chronic disease was linked with sleep disturbance in the middle-aged and elderly. Zhang et al 33 found that the symptoms of menopause were more prominent during early years of postmenopause. The study findings revealed a statistically significant association between somatic and urogenital symptoms with poor sleep quality ($p \leq 0.001$). Previous studies have supported these findings which draw a correlation between menopausal symptoms and sleep disturbances.11,12,24,31,32 It has been proposed that menopause may have no negative impact on sleep quality and there were other causes for poor sleep quality among postmenopausal such as vasomotor symptoms, estrogen reduction, and the process of aging. 34 Women with a low level of education or who were uneducated were more susceptible to experiencing poor quality of sleep. Therefore, the present study suggests that a higher educational level positively influences sleep quality. Educated women are less complaining and are more worried on their physical well-being. They intend to seek answers to their health problems, whether through serious research or with the assistance of specialists, and they more often have easier access to healthcare strategies. This finding was consistent with Kim et al24 who reported that with increased education level in middle-aged women, the sleep difficulties decreased. Furthermore, there was a link between poor sleep quality and BMI as $40.2\%$ of all participants were overweight, and $49.3\%$ with poor sleep quality were obese with a mean BMI of 30.4±5.87 kg/m2. In postmenopausal women, sleep disturbances are caused by higher BMI and abdominal obesity while increasing obstructive sleep apnea.3,24,32 Conversely, Zagalaz et al12 disagreed with the link between high BMI and poor sleep quality. ## Study limitations The use of a convenience sample and the inability to conclude cause and effect due to the nature of the cross-sectional research design. In addition, we could not rule out the possibility of other intervening effects of the aging influencing the quality of sleep. Furthermore, the study focused on women from a specific geographic location, it cannot be generalized to the whole Saudi middle-aged women since they do not share the same characteristics as the sample population. Self-reported questionnaires were also used to assess sleep quality and menopausal symptoms, implying the requirement for an objective approach like polysomnography. In light of this, there is a need to develop effective management strategies to reduce menopausal symptoms and other related factors that may improve sleep quality. There should be more awareness on the importance of education and having a healthy lifestyle. Further interventional studies need to be carried out to establish effective measures to improve sleep quality. The effect of obesity and physical exercise on sleep quality and menopausal symptoms among Saudi women must be examined. A longitudinal study is crucial to assess menopausal symptoms effect on sleep quality among Saudi women, and large-scale national clinical studies are recommended in the future. In conclusion, more than one-third of Saudi postmenopausal women had poor sleep quality, which needs medical attention. Poor sleep quality seems to be related to somatic and urogenital symptoms. In addition, factors such as uneducated or lower-educated women, obesity, and no physical exercise influence sleep quality among Saudi postmenopausal women. ## References 1. Malik E, Sheoran P, Siddiqui A.. Health-promoting behaviors and menopausal symptoms: an interventional study in rural India. J Midlife Health (2018) 9 200-206. PMID: 30692816 2. Kamal NN, Seedhom AE.. Quality of life among postmenopausal women in rural Minia, Egypt. East Mediterr Health J (2017) 23 527-533. PMID: 29105043 3. Naufel MF, Frange C, Andersen ML, Girão MJBC, Tufik S, Beraldi Ribeiro E. 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--- title: Side-effects of COVID-19 vaccines among the Saudi population authors: - Ebtehaj S. Almughais - Ali H. Alharbi - Hadi A. Aldarwish - Areeb F. Alshammari - Razan S. Alsuhaymi - Jumanah A. Almuaili - Atheer M. Alanizy journal: Saudi Medical Journal year: 2022 pmcid: PMC9998061 doi: 10.15537/smj.2022.43.4.20210905 license: CC BY 4.0 --- # Side-effects of COVID-19 vaccines among the Saudi population ## Body Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-sense single-stranded RNA virus (+ssRNA) which is the cause of the current Coronavirus Disease 2019 (COVID-19) pandemic. SARS-CoV-2 was first identified in Wuhan City, China, in late 2019. Thereafter, it quickly spread around the planet, with ~14 million active cases and ~582,000 deaths recorded as of July 2020. 1 Therefore, there has been an urgent international demand for the scientific community to develop an effective vaccine. In September 2020, the World Health Organization declared the launch of several COVID-19 vaccines. 2 On December 31, 2020, the mRNA vaccine produced by Pfizer, and on February 15, 2021, the ChAdOx1 nCoV19 vaccine produced by AstraZeneca Oxford, were approved for emergency use. 3 Research found that the AstraZeneca was $70\%$ effective and Pfizer vaccine was $95\%$ effective. 4,5 Soon after the vaccines were developed, Saudi Arabia took the initiative and promptly provided these 2 vaccines to the public over 3 phases. Phase one was targeted towards individuals over 65 years of age and front-line health care workers (HCWs). Phase 2 targeted individuals over 50 years of age and other health care practitioners. Last, phase 3 targeted all citizens and residents in Saudi Arabia. 6 A major obstacle in managing the COVID-19 pandemic is vaccination hesitancy (example, unwillingness to get vaccinated). Previous studies in Qatar found that $20\%$ and Kuwait $26.2\%$ expressed vaccine hesitancy. 7,8 Researchers see this as a significant public health challenge, which is fueled by misleading and inaccurate information on vaccine safety and efficacy. 9 In general, many concerns, questions, and arguments were raised regarding the COVID-19 vaccine program by the general population of Saudi Arabia, regarding how safe the approved vaccines were. There is however limited data and literature concerning each vaccine’s side-effects, along with the influence of demographic factors such as age, gender, smoking, and comorbidities. Therefore, the objective of the present study was to investigate the safety and adverse effects of the Pfizer and Oxford-AstraZeneca vaccines among Saudis who had received one of them. ## Abstract ### Objectives: To measure and assess the side-effects of Pfizer/BioNTech and AstraZeneca vaccines on residents of Saudi Arabia, as well as provide a database that gives insight into the relative safety of these 2 COVID-19 vaccines. ### Methods: A community-based cross-sectional study was conducted to determine the side-effects of the two COVID-19 vaccines. The study was initiated on the 5th of June 2021 at Hail University, Hail, Saudi Arabia. The information was collected through an online survey designed on Google forms. The questionnaire was pre-tested for validity, with all information carefully reviewed. ### Results: The study included 2,530 participants from different regions of Saudi Arabia, with a mean age of 26.9 ± 12.4 years old. The most common vaccine among the study group was Pfizer, which $73.8\%$ of the population were provided; the remaining $26.2\%$ received the AstraZeneca vaccine. Regarding the Pfizer vaccine, the common systemic side-effects followed the first dose, included headaches, followed by muscle pain, fever, and joint pain. Those who had the AstraZeneca vaccine reported a few more side-effects. For example, during the first dose fever was reported as the most common side-effect, followed by headache, muscle pain and fatigue. ### Conclusion: The present study confirmed that vaccine side-effects are more frequently reported by smokers and those who received the AstraZeneca vaccine. Further studies are needed to acquire a better understanding of the association between risk factors and the experiencing of post-vaccine side-effects. ## Methods A community-based cross-sectional study was approved by the Research Ethics Committee, University of Hail, Hail, Saudi Arabia (No H-2021-177, dated $\frac{20}{9}$/2021). The study was carried out to determine the vaccine side-effects from June to September 2021. The information was collected through an online survey designed on Google forms, which was written in Arabic and distributed to participants via 2 social media platforms (namely, WhatsApp and Twitter). Participation was voluntary and anonymous. Participants’ information was kept confidential according to Google’s privacy policy. The first page of the survey included a description of study, along with a statement regarding informed consent. The principles of the Declaration of Helsinki were followed to insure the rights of the human participants. By using the formula ss=(Z2×p×q)/c2 the optimal sample size for conducting this study was determined to be 384 participants from each province (central, northern, southern, eastern, western). Where ss=sample size, $Z = 1.96$, $$p \leq 0.5$$, q=(1-p) =0.5, c=sampling error of $5\%$. In total, 2,530 respondents participated. Inclusion criteria included participants aged 18 years or over, who had received either the Pfizer or Oxford-AstraZeneca vaccine and willing to participants in the study. Respondent below the age of 18 and incomplete submissions were excluded. ## Development and application of the questionnaire Questionnaires were developed after undertaking a literature review. Many post-vaccination side-effects were identified and covered in this study. Information was collected regarding participants’ demographic data, including their age, gender, height, and weight. Furthermore, assessments were made of their past medical history and general health status prior to vaccination. The prevalence of infection rates among the vaccinated population and their intention to take the second dose was also recorded. The second section of the study then considered the side-effects associated with the 2 COVID-19 vaccines under investigation. These were divided into general side-effects such as headaches, fatigue, and fever, along with local side-effects such as pain, tenderness, and swelling. Participants’ consent was secured before they completed the questionnaire. ## Statistical analysis Data was collected, reviewed and then inputted into the Statistical Package for Social Sciences for Windows, version 21 (IBM Corp., Armonk, NY, USA). All statistical methods used were two tailed, with an alpha level of 0.05 considering significance if the p-value was <0.05. Descriptive analysis was performed by prescribing frequency distribution and percentages for study variables, including respondents’ personal data, history of COVID-19 infection, vaccination data, and post-vaccination side-effects. Cross tabulation showing the distribution of participants’ post-vaccination side-effects by their bio-demographic data, medical data, history of COID-19 infection, and vaccine type, was conducted via a Pearson Chi-square test for significance and exact probability due to the small frequency distribution. ## Results The study included 2,530 participants from different regions of Saudi Arabia, 751 from the Western region, 508 from the Northern region, 467 from the Eastern region, 427 from the Southern region, and 377 from the Central region, with a mean age of 26.9±12.4 years old (Table 1). Table 1 \| Bio-demographic data \| n \| (%) \| \| --- \| --- \| --- \| \| Age in years \| Age in years \| Age in years \| \| 18-25 \| 1212 \| (47.9) \| \| 26-35 \| 703 \| (27.8) \| \| 36-50 \| 497 \| (19.6) \| \| 51-60 \| 87 \| (3.4) \| \| >60 \| 31 \| (1.2) \| \| Gender \| Gender \| Gender \| \| Male \| 1037 \| (41.0) \| \| Female \| 1493 \| (59.0) \| \| Educational level \| Educational level \| Educational level \| \| Primary or less \| 26 \| (1.0) \| \| Intermediate / secondary \| 567 \| (22.4) \| \| University \| 1937 \| (76.6) \| \| Work \| Work \| Work \| \| Healthcare workers \| 677 \| (26.8) \| \| Others \| 1853 \| (73.2) \| \| Body mass index \| Body mass index \| Body mass index \| \| Underweight \| 170 \| (6.7) \| \| Normal \| 1224 \| (48.4) \| \| Overweight \| 680 \| (26.9) \| \| Obese \| 279 \| (11.0) \| \| Morbid obesity \| 177 \| (7.0) \| \| Smoking \| Smoking \| Smoking \| \| Yes \| 412 \| (16.3) \| \| No \| 2118 \| (83.7) \| \| Chronic health problems \| Chronic health problems \| Chronic health problems \| \| Yes \| 372 \| (14.7) \| \| No \| 2158 \| (85.3) \| \| Had any type of allergy \| Had any type of allergy \| Had any type of allergy \| \| Yes \| 366 \| (14.5) \| \| No \| 2164 \| (85.5) \| Regarding the participants’ COVID-19 infection history, approximately $19\%$ of the study participants had previously had a positive test for COVID-19. The most taken vaccine among the study group was the Pfizer vaccine, which $73.8\%$ of the participants received. Among those who had received a single dose, $62.3\%$ agreed to have the second dose. $94.1\%$ of the vaccinated respondents had no COVID-19 infection after the vaccine. Most participants ($87.5\%$, 2213) had at least one of the reported post-vaccination side-effects (Table 2). Table 2 \| COVID-19 infection and vaccination data \| n \| (%) \| \| --- \| --- \| --- \| \| Previously had positive test for COVID-19 \| Previously had positive test for COVID-19 \| Previously had positive test for COVID-19 \| \| Yes \| 488 \| (19.3) \| \| No \| 2042 \| (80.7) \| \| Type of vaccine \| Type of vaccine \| Type of vaccine \| \| Pfizer \| 1868 \| (73.8) \| \| AstraZeneca \| 662 \| (26.2) \| \| Agree to have second dose of COVID-19 vaccine \| Agree to have second dose of COVID-19 vaccine \| Agree to have second dose of COVID-19 vaccine \| \| Yes \| 1575 \| (62.3) \| \| No \| 435 \| (17.2) \| \| Had both doses \| 520 \| (20.6) \| \| Infected with COVID-19 after vaccination \| Infected with COVID-19 after vaccination \| Infected with COVID-19 after vaccination \| \| After 1st dose \| 132 \| (5.2) \| \| After 2nd dose \| 18 \| (0.7) \| \| No \| 2380 \| (94.1) \| \| Post-vaccination side-effects \| Post-vaccination side-effects \| Post-vaccination side-effects \| \| Yes \| 2213 \| (87.5) \| \| No \| 317 \| (12.5) \| A wide spectrum of post vaccination side-effects among the Saudi population were studied. Table 3 reveals the frequency of these side-effects. Regarding the Pfizer vaccine, the most reported systemic side-effects came after the first dose. These were headache ($34.7\%$) followed by muscle pain ($31.7\%$). The least reported systemic side-effects were nausea ($10.2\%$) and diarrhea ($6.7\%$). Considering localized side-effects, the most reported were local injection pain with touch ($70\%$). Axillary lymphadenopathy was only reported among $4.2\%$ of the participants. Compared with the Pfizer vaccine, those who had the AstraZeneca vaccine, $62.5\%$ of participants complained of fever following the first dose, followed by headache ($55.9\%$). The least reported side-effect was nausea ($21.5\%$). Regarding the local side-effects, $68.3\%$ of the participants complained of local injection pain. The least reported local side-effect was axillary lymphadenopathy $5\%$. Table 3 \| Type of vaccine \| Side effects \| None \| None.1 \| After 1st dose \| After 1st dose.1 \| After 2nd dose \| After 2nd dose.1 \| After both doses \| After both doses.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Type of vaccine \| Side effects \| n \| (%) \| n \| (%) \| n \| (%) \| n \| (%) \| \| Pfizer \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| \| Pfizer \| Headache \| 1108 \| (59.3) \| 648 \| (34.7) \| 60 \| (3.2) \| 52 \| (2.8) \| \| Pfizer \| Fatigue \| 1433 \| (76.7) \| 352 \| (18.8) \| 57 \| (3.1) \| 26 \| (1.4) \| \| Pfizer \| Fever \| 1367 \| (73.2) \| 360 \| (19.3) \| 106 \| (5.7) \| 35 \| (1.9) \| \| Pfizer \| Chills and tremors \| 1635 \| (87.5) \| 177 \| (9.5) \| 41 \| (2.2) \| 15 \| (0.8) \| \| Pfizer \| Joint pain \| 1456 \| (77.9) \| 326 \| (17.5) \| 51 \| (2.7) \| 35 \| (1.9) \| \| Pfizer \| Muscle pain \| 1142 \| (61.1) \| 592 \| (31.7) \| 70 \| (3.7) \| 64 \| (3.4) \| \| Pfizer \| Diarrhea \| 1742 \| (93.3) \| 103 \| (5.5) \| 21 \| (1.1) \| 2 \| (0.1) \| \| Pfizer \| Nausea \| 1676 \| (89.7) \| 154 \| (8.2) \| 27 \| (1.4) \| 11 \| (0.6) \| \| Pfizer \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| \| Pfizer \| Local pain \| 888 \| (47.5) \| 826 \| (44.2) \| 45 \| (2.4) \| 109 \| (5.8) \| \| Pfizer \| Local edema \| 1428 \| (76.4) \| 365 \| (19.5) \| 32 \| (1.7) \| 43 \| (2.3) \| \| Pfizer \| Local pain with touch \| 560 \| (30.0) \| 1091 \| (58.4) \| 56 \| (3.0) \| 161 \| (8.6) \| \| Pfizer \| Itching \| 1652 \| (88.4) \| 185 \| (9.9) \| 20 \| (1.1) \| 11 \| (0.6) \| \| Pfizer \| Axiliary lymphadenopathy \| 1790 \| (95.8) \| 56 \| (3.0) \| 18 \| (1.0) \| 4 \| (0.2) \| \| Pfizer \| Local redness \| 1561 \| (83.6) \| 255 \| (13.7) \| 27 \| (1.4) \| 25 \| (1.3) \| \| Pfizer \| Local bruising \| 1714 \| (91.8) \| 128 \| (6.9) \| 16 \| (0.9) \| 10 \| (0.5) \| \| Pfizer \| Local hotness \| 1378 \| (73.8) \| 409 \| (21.9) \| 31 \| (1.7) \| 50 \| (2.7) \| \| AstraZeneca \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| Systemic side effects \| \| AstraZeneca \| Headache \| 272 \| (41.1) \| 370 \| (55.9) \| 8 \| (1.2) \| 12 \| (1.8) \| \| AstraZeneca \| Fatigue \| 367 \| (55.4) \| 267 \| (40.3) \| 9 \| (1.4) \| 19 \| (2.9) \| \| AstraZeneca \| Fever \| 218 \| (32.9) \| 414 \| (62.5) \| 7 \| (1.1) \| 23 \| (3.5) \| \| AstraZeneca \| Chills and tremors \| 386 \| (58.3) \| 257 \| (38.8) \| 6 \| (0.9) \| 13 \| (2.0) \| \| AstraZeneca \| Joint pain \| 394 \| (59.5) \| 246 \| (37.2) \| 11 \| (1.7) \| 11 \| (1.7) \| \| AstraZeneca \| Muscle pain \| 312 \| (47.1) \| 328 \| (49.5) \| 10 \| (1.5) \| 12 \| (1.8) \| \| AstraZeneca \| Diarrhea \| 582 \| (87.9) \| 70 \| (10.6) \| 7 \| (1.1) \| 3 \| (0.5) \| \| AstraZeneca \| Nausea \| 513 \| (77.5) \| 142 \| (21.5) \| 3 \| (0.5) \| 4 \| (0.6) \| \| AstraZeneca \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| Local SE \| \| AstraZeneca \| Local pain \| 284 \| (42.9) \| 343 \| (51.8) \| 5 \| (0.8) \| 30 \| (4.5) \| \| AstraZeneca \| Local edema \| 491 \| (74.2) \| 158 \| (23.9) \| 6 \| (0.9) \| 7 \| (1.1) \| \| AstraZeneca \| Local pain with touch \| 166 \| (25.1) \| 452 \| (68.3) \| 8 \| (1.2) \| 36 \| (5.4) \| \| AstraZeneca \| Itching \| 568 \| (85.8) \| 87 \| (13.1) \| 4 \| (0.6) \| 3 \| (0.5) \| \| AstraZeneca \| Axillary lymphadenopathy \| 629 \| (95.0) \| 28 \| (4.2) \| 3 \| (0.5) \| 2 \| (0.3) \| \| AstraZeneca \| Local redness \| 551 \| (83.2) \| 100 \| (15.1) \| 4 \| (0.6) \| 7 \| (1.1) \| \| AstraZeneca \| Local bruising \| 585 \| (88.4) \| 67 \| (10.1) \| 5 \| (0.8) \| 5 \| (0.8) \| \| AstraZeneca \| Local hotness \| 466 \| (70.4) \| 179 \| (27.0) \| 3 \| (0.5) \| 14 \| (2.1) \| Table 4 highlights the determinants of developing post-vaccination side-effects among the studied Saudis population. Approximately $88.5\%$ of participants aged 51 years or older had side-effects after vaccination, compared with $83.1\%$ of those aged 35 to 50 years old, with a recorded statistical significance of $$p \leq 0.017.$$ Approximately $93.2\%$ of smokers had post-vaccine side-effects compared with $86.4\%$ of non-smokers ($$p \leq 0.001$$). Side-effects after vaccination were also reported by $88.7\%$ of persons with no history of COVID-19 infection, in comparison with $82.2\%$ among those who had a history of COVID-19 infection ($$p \leq 0.001$$). In addition, $94.3\%$ of participants who had the AstraZeneca vaccine reported post-vaccination side-effects, in comparison with $85.1\%$ of those who had the Pfizer vaccine ($$p \leq 0.001$$). Table 4 \| Factors \| Yes \| Yes.1 \| No \| No.1 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Factors \| n \| (%) \| n \| (%) \| P-value \| \| Age in years \| Age in years \| Age in years \| Age in years \| Age in years \| Age in years \| \| 18-25 \| 1081 \| (89.2) \| 131 \| (10.8) \| 0.017* \| \| 26-35 \| 615 \| (87.5) \| 88 \| (12.5) \| 0.017* \| \| 36-50 \| 413 \| (83.1) \| 84 \| (16.9) \| 0.017* \| \| 51-60 \| 77 \| (88.5) \| 10 \| (11.5) \| 0.017* \| \| >60 \| 27 \| (87.1) \| 4 \| (12.9) \| 0.017* \| \| Gender \| Gender \| Gender \| Gender \| Gender \| Gender \| \| Male \| 897 \| (86.5) \| 140 \| (13.5) \| 0.219 \| \| Female \| 1316 \| (88.1) \| 177 \| (11.9) \| 0.219 \| \| Nationality \| Nationality \| Nationality \| Nationality \| Nationality \| Nationality \| \| Saudi \| 2097 \| (87.2) \| 309 \| (12.8) \| 0.036* \| \| Non-Saudi \| 116 \| (93.5) \| 8 \| (6.5) \| 0.036* \| \| Body mass index \| Body mass index \| Body mass index \| Body mass index \| Body mass index \| Body mass index \| \| Non obese \| 1211 \| (86.9) \| 183 \| (13.1) \| 0.314 \| \| Overweight / obese \| 1002 \| (88.2) \| 134 \| (11.8) \| 0.314 \| \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| \| Below university \| 456 \| (76.9) \| 137 \| (23.1) \| 0.001* \| \| University \| 1757 \| (90.7) \| 180 \| (9.3) \| 0.001* \| \| Work \| Work \| Work \| Work \| Work \| Work \| \| HCWs \| 606 \| (89.5) \| 71 \| (10.5) \| .061 \| \| Others \| 1607 \| (86.7) \| 246 \| (13.3) \| .061 \| \| Smoking \| Smoking \| Smoking \| Smoking \| Smoking \| Smoking \| \| Yes \| 384 \| (93.2) \| 28 \| (6.8) \| 0.001* \| \| No \| 1829 \| (86.4) \| 289 \| (13.6) \| 0.001* \| \| Chronic health problems \| Chronic health problems \| Chronic health problems \| Chronic health problems \| Chronic health problems \| Chronic health problems \| \| Yes \| 332 \| (89.2) \| 40 \| (10.8) \| 0.262 \| \| No \| 1881 \| (87.2) \| 277 \| (12.8) \| 0.262 \| \| Had any type of allergy \| Had any type of allergy \| Had any type of allergy \| Had any type of allergy \| Had any type of allergy \| Had any type of allergy \| \| Yes \| 325 \| (88.8) \| 41 \| (11.2) \| 0.407 \| \| No \| 1888 \| (87.2) \| 276 \| (12.8) \| 0.407 \| \| Previously had positive test for COVID-19 \| Previously had positive test for COVID-19 \| Previously had positive test for COVID-19 \| Previously had positive test for COVID-19 \| Previously had positive test for COVID-19 \| Previously had positive test for COVID-19 \| \| Yes \| 401 \| (82.2) \| 87 \| (17.8) \| 0.001* \| \| No \| 1812 \| (88.7) \| 230 \| (11.3) \| 0.001* \| \| Type of vaccine \| Type of vaccine \| Type of vaccine \| Type of vaccine \| Type of vaccine \| Type of vaccine \| \| Pfizer \| 1589 \| (85.1) \| 279 \| (14.9) \| 0.001* \| \| AstraZeneca \| 624 \| (94.3) \| 38 \| (5.7) \| 0.001* \| \| Infected with covid-19 after vaccination \| Infected with covid-19 after vaccination \| Infected with covid-19 after vaccination \| Infected with covid-19 after vaccination \| Infected with covid-19 after vaccination \| Infected with covid-19 after vaccination \| \| After 1st dose \| 121 \| (91.7) \| 11 \| (8.3) \| 0.288# \| \| After 2nd dose \| 15 \| (83.3) \| 3 \| (16.7) \| 0.288# \| \| No \| 2077 \| (87.3) \| 303 \| (12.7) \| 0.288# \| The factors mentioned in Table 5 were the most significant predictors for post-vaccination side-effects. Those who were female, overweight, non-Saudi, university educated, smokers, who have a chronic disease or took the AstraZeneca vaccine had more chance of developing post-vaccine side-effects (OR>1). On the other hand, those who were older or who previously had a positive test for COVID-19 had less risk than the others (OR<1). Table 5 \| Factor \| P-value \| OR A \| 95% CI for OR \| 95% CI for OR.1 \| \| --- \| --- \| --- \| --- \| --- \| \| Factor \| P-value \| OR A \| Lower \| Upper \| \| Female \| 0.004* \| 1.50 \| 1.10 \| 1.90 \| \| Old age \| 0.007* \| 0.80 \| 0.70 \| 0.90 \| \| Non-Saudi \| 0.048* \| 2.00 \| 1.00 \| 4.30 \| \| University education \| 0.001* \| 2.90 \| 2.30 \| 3.80 \| \| Smokers \| 0.001* \| 2.60 \| 1.70 \| 3.90 \| \| Have chronic health problems \| 0.027* \| 1.60 \| 1.10 \| 2.30 \| \| Previously had positive test for COVID-19 \| 0.001* \| 0.60 \| 0.40 \| 0.80 \| \| AstraZeneca vaccine \| 0.001* \| 2.80 \| 1.90 \| 4.00 \| ## Discussion Since vaccine production began, people have expressed concerns on the dangers and risks of administering them. This study was therefore carried out to assess the side-effects of the Pfizer and AstraZeneca vaccines among vaccinated Saudi populations. Out of 2530 participants, 2213 ($87.5\%$) had at least one of the reported side-effects, while 317 ($12.5\%$) reported no side-effects. The most common systemic side-effects found in this study were headache, muscle pain, fever, fatigue, and joint pain. These results are similar to other studies conducted by Riad et al 10 and Zhu et al. 11 Local injection pain was the most reported local side-effect. Similar data was also reported in a recent study. 12-14 Data analysis identified several adjusted determinants for developing side-effects. These were: younger age, female, smokers, comorbidity, history of COVID-19 infection, and receiving the AstraZeneca vaccine. The survey was distributed online. This can result in sampling biases regarding age, as older people are less likely to have internet access or be computer literate. Moreover, other studies have also found that older people (>55 years) were less likely to develop side-effects. 15-18 This finding could be interpreted in terms of the immune system response. Immune systems are more efficient and stronger in younger people. Since the immune system can produce cytokines post-vaccination, which could have an inflammatory effect on blood vessels, muscles, and other tissues, this may therefore explain the prevalence of the development of side-effects in younger people more than in the elderly. 16 However, in contrast to the assumption that the older you are, the less likely you are to developed side-effects, El Shitany et al 19 found that Saudi people aged 60 and over had a significantly higher frequency of developing local side-effects, particularly pain in the injection site area ($80.8\%$ versus $68.6\%$: significant=0.0056). Furthermore, $50\%$ of female participants were more prone to develop post-vaccine side-effects compared with males (adjusted odds ratio [AOR] 1.5,$95\%$confidence interval [CI]: 1.1-1.9, $$p \leq 0.004$$). This finding was also observed by Menni et al, 20 for both Pfizer and AstraZeneca vaccines. Moreover, many other studies also observed this association. 19,21 This is likely because COVID-19 vaccines work by stimulating the immune system, which can have more pronounced effects on females due to gender-based differences in immune response, as seen in vaccines such as bacille Calmette-Guerin, measles, mumps, and rubella, and Yellow fever vaccine along with many others. 22,23 Watanabe et al 24 measured antibody titers in smokers who received the Pfizer vaccine and found serum antibody titer concentrations were significantly lower when compared with expected values. In this study, smoking displayed a significant relation with developing more post-vaccine side-effects. $16.3\%$ of study participants were smokers; $93.2\%$ of them developed side-effects. Smokers have lower antibody titer concentrations, which can explain the increase of post-vaccine side-effects in this group. Moreover, chronic health problems also showed a positive association with developing side-effects (AOR 1.6,$95\%$CI: 1.1-2.3, $$p \leq 0.027$$). Furthermore, most other studies have also identified comorbidities as a significant factor. 25 This can be due to the complicated and multifactorial nature of chronic diseases. For instance, in one study, obesity and hypertension were associated with lower antibody titer concentration 3 weeks after receiving the Pfizer vaccine. 24 On the other hand, no significant association between high BMI and post-vaccine side-effects was found in this study. The present study showed that people with COVID-19 history had lower odds of adverse effects after COVID-19 vaccination. On the other hand, most of the previous studies showed that prior COVID-19 infection had been associated with increase the risk of vaccination side effects. For instance, population studies in Iraq and the United Kingdom found that individuals with evidence of past SARS-CoV-2 infection were also more likely to have adverse effects to both vaccines than those without evidence of past infection. 20,26 Although there is no clear explanation, previous research examined the antibody responses in 109 people. A total of 68 patients had never had COVID-19, whereas 41 had previously tested positive. The research indicated that people who had a history of COVID-19 infection had higher antibody concentrations than those who had never been infected. 27-29 Concerning the type of vaccine, the AstraZeneca vaccine had more frequently reported side-effects than the Pfizer vaccine. Other studies have also found this. 30-33 This finding is consistent with the claim that the mRNA vaccine has fewer side-effects than other types of vaccine. According to the clinical trials, there were no serious systemic side-effects after administration of mRNA vaccine, only headache and fatigue. Fever was noticed after the second dose in less than $16\%$ of participants, which supports the view that mRNA vaccines are safe and have fewer side-effects. 34 When comparing the first dose and the second dose of the vaccine, Hatem et al 32 also found that side-effects are usually more pronounced after the first dose, as also highlighted by Riad et al. 28 However, according to the Centers for Disease Control and Prevention (CDC), as well as other studies, side-effects can be more intense after the second dose. 35,5,19 The CDC report that people with a history of allergic reactions should be vaccinated with great caution. Furthermore, people with a history of severe allergic reactions, such as anaphylaxis, should not receive the vaccine at this stage. 36 However, in this study, it was found that a history of an allergic reaction was not a significant factor in developing side-effects. This study also found that heatlhcare workers (HCWs) were not more likely to develop side-effects. On the other hand, another recent study among HCWs found a wide range of post-vaccination symptoms, most of which were not life-threatening. 12 ## Study limitations The study was reliant on self-reports by participants, snowballing sampling method was utilized which can lead to a biased study population. The main limitation of this study is that during the data collection period, the Saudi government only allowed persons who were over 60 years old, HCWs, and those with a few selected medical conditions, to take the second dose of the vaccine, However, now every age group can take the second dose. Unfortunately, this cannot be reflected in our study and the second dose side-effects cannot therefore be sufficiently represented. Furthermore, a randomized control study could be better at detecting any significant relationships between risk factors and developing post-vaccinated side-effects. In conclusion, this study despite the high prevalence of side-effects after vaccination among participants, this study concluded that most post-vaccination side-effects are typical symptoms which are also found with other vaccines. The most common side-effects in both vaccines were headache, muscle pain, injection site pain, and local pain with touch, with these side-effects reported especially after the first dose. More frequent side-effects were reported by smokers and those who received the AstraZeneca vaccine. This study therefore provides a database to inform people on the possibility of developing side-effects based on their gender, age, and the type of vaccine which is administered. 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--- title: 'Prevalence of overweight, obesity, and associated factors among healthcare workers in the Gaza Strip, Palestine: A cross-sectional study' authors: - Joma Younis - Hong Jiang - Yahui Fan - Lina Wang - Zhaofang Li - Majed Jebril - Mei Ma - Le Ma - Mao Ma - Zhaozhao Hui journal: Frontiers in Public Health year: 2023 pmcid: PMC9998069 doi: 10.3389/fpubh.2023.1129797 license: CC BY 4.0 --- # Prevalence of overweight, obesity, and associated factors among healthcare workers in the Gaza Strip, Palestine: A cross-sectional study ## Abstract ### Background Overweight and obesity are multifactorial conditions that are prevalent in developing and developed countries. They are emerging as a significant public health concern among healthcare workers (HCWs). We aimed to estimate the prevalence of overweight and obesity and their associated factors among HCWs in the Gaza Strip. ### Methods A cross-sectional study was conducted to recruit 1,850 HCWs aged 22 years and older. Interviews were carried out to collect sociodemographic information, nutritional information, and physical activity. Anthropometric measurements [height, weight, and waist circumference] were conducted with the HCWs. The body mass index was computed to determine the prevalence of overweight and obesity. Chi-square, t-test, and one-way ANOVA were used to compare the variables, and logistic regression was used to examine the associated factors of overweight and obesity. ### Results The combined prevalence of overweight and obesity among HCWs was $65\%$. The result of logistic regression showed the risk of being overweight and obesity increased within the age group of 40–49 years (OR = 3.20; $95\%$ CI: 2.37–4.32; $P \leq 0.001$). Male participants had more risk of obesity than female participants (OR = 1.77; $95\%$ CI: 1.45–2.15). Married participants had a significantly higher risk of being overweight and obese (OR = 2.52; $95\%$ CI: 2.05–3.28; $$P \leq 0.001$$). Increased monthly income was significantly associated with the risk of being overweight and obese (OR = 2.16; $95\%$ CI: 1.22–3.83; $$P \leq 0.008$$). In addition, hypertension (OR = 2.49; $95\%$ CI: 1.65–3.78; $P \leq 0.001$) and type 2 diabetes (OR = 2.42; $95\%$ CI: 1.21–4.85; $$P \leq 0.012$$) were associated with overweight and obesity. Finally, a family history of NCDs was associated with overweight and obesity (OR = 1.69; $95\%$ CI: 1.38–2.07; $P \leq 0.001$). ### Conclusion This study showed a high prevalence of overweight and obesity among HCWs. Age, monthly income, marital status, known hypertension, type 2 diabetes, and eating habits were associated with the prevalence of overweight and obesity compared to other variables that were not associated with overweight and obesity such as profession, vegetables, fruit consumption, and physical activity. Urgent action is needed to tackle overweight and obesity among HCWs. ## Introduction Around the world, 650 million adults, 340 million adolescents, and 39 million children are obese. This estimation is still rising. According to the WHO, 167 million adults and children will experience deteriorating health by 2025 as a result of being overweight or obese [1]. Obesity is a significant contributor to poor health, including type 2 diabetes, cardiovascular disease, cancer, decreased life expectancy, and mortality [2], which could impact all classes of communities. Most body systems, including the endocrine, gastrointestinal, neurological, and cardiovascular systems, are severely impacted by overweight and obesity, increasing a person's risk of contracting infectious diseases [3]. Obesity-related complications increase the number of morbidities that need to be managed by the declining number of healthcare professionals [4]. Healthcare workers (HCWs) who have direct contact with patients and often influence their behaviors ought to have a healthy physique to show a good role model in front of patients, and this revealed the importance of a healthy physique toward disease prevention [5]. Overweight and obese HCWs might have difficulty counseling the patients even if the patients clinically state with weight increase [6], which could be one of the barriers that can affect the patient consultation due to the same health issue. Obesity and overweight rates are rapidly rising in developed and developing countries, including Palestine [7]. Overweight [body mass index (BMI) of 25.0–29.9 kg/m2] and obesity (BMI ≥ 30 kg/m2) increases the risk of non-communicable diseases (NCDs) [8]. A meta-analysis study in Middle East countries found that the prevalence of obesity and overweight was 21.17 and $33.14\%$, respectively [9]. A recent survey conducted in Palestine concluded that the prevalence of overweight and obesity is 23.6 and $19.5\%$ in the Gaza Strip and $26.1\%$ in the West Bank [10]. Due to the Israeli siege, people in Gaza live in unfavorable and dangerous conditions. The political events there, where Palestinian refugees, in particular, and Palestinian lives, in general, are concerned, have contributed to widespread unemployment, malnutrition, food insecurity, a lack of income, and a continuous decline in the quality of care for all patient categories [11, 12]. These factors undoubtedly could have an impact on people with NCDs. Healthcare workers are exposed to a wide range of difficulties that prevent the provision of health services as required, including lack of human resources, lack of medical supplies, workload, and financial burden, and these factors may negatively affect the health of health sector workers and lead to the development of work-related illness [13]. Understanding the viewpoints of healthcare professionals is crucial before creating a successful healthcare intervention [14]. A healthy society depends on the efforts of an important set of professionals, the HCWs [15]. Unfortunately, little health research has been done on managing NCDs. Previous studies revealed that sedentary jobs, long periods of sitting, and shift work all greatly raise the risk of obesity [16]. Healthcare providers include certified medical personnel (e.g., doctors, nurses, medical scientists, pharmacists, and technicians) and non-clinical support staff (e.g., the administrative class) [17]. Because of their specialized training, healthcare professionals are supposed to have a high level of knowledge and awareness of their health condition and the effects of lifestyle changes on their health [18, 19]. In addition, HCWs are responsible for promoting appropriate lifestyle changes that affect disease prevention and serve as role models for the general population by leading healthy lifestyles [20]. Therefore, few studies have been conducted to evaluate overweight, obesity, and the risk factors linked to these conditions among Palestinian HCWs working in the Gaza Strip. Because they are overlooked in research studies while being identified as high-risk populations, this study was done to evaluate the prevalence of overweight and obesity and its associated factors among HCWs. ## Study design and setting A cross-sectional study was conducted from Feb to May 2020 in the Gaza Strip, Palestine, among a representative sample of Palestinian HCWs (physicians, nurses, paramedics, and non-medical) who work in hospitals and the primary healthcare (PHCs) of the ministry of health. Gaza Strip is divided into five smaller governorates, which include North Gaza, Gaza City, Mid Zone, Khan Younis, and Rafah. The whole number of hospitals was 10 and PHCs was 51 [21]. The study sample was distributed according to the number of hospitals and PHCs in each governorate. ## Sampling methods and participants The consistency and completeness of all 1,900 responses were rigorously checked. The final analysis had 1,850 responses after 50 were excluded since they were considered incomplete or inconsistent. The participants were selected by multistage stratified random sampling. First, we dived the population into five governorates, and second, we selected two hospitals and three PHCs for each governorate. Considering the population distribution in each area, we have increased the sample size to be representative of the overall HCWs in the Gaza Strip. The sample size was determined using the formula n = Z2 P (1–P)/d2 at $95\%$ CI [22]. ## Study criteria Our inclusion criteria were: All HCWs working at the ministry of health in the Gaza Strip, with at least 1 year of experience, aged 22 years and older. Pregnant and lactating women, in addition to those workers under an unemployment program, were excluded. ## Data collection Using a self-constructed face-to-face interview questionnaire, data were collected with multistage stratified random sampling. The data included detailed demographic and socioeconomic information (sex, marital status, educational levels, workplace, experience, monthly income, etc.), lifestyle involved (sleep duration, workload, work routine, and physical activity [measured by the International Physical Activity Questionnaire, IPAQ-short version]) [23]; the health profile involved [dietary patterns adapted from questions used in the food frequency questionnaire [24, 25]], menstrual cycle, family history of the disease, medical records, etc. After analyzing the literature on the subject, the questionnaire was well-prepared. The questionnaire's validity was tested by sending the completed questionnaire and a cover letter explaining the study's goal to 10 experts in various health professions (associated professors, hospital directors, managers of health departments, and academic teachers) who were asked to comment on the questionnaire. From the original English edition, all questions were translated into Arabic (forward and English and Arabic mother-tongue speakers performed backward translations). The study's research team consisted of five workers, physicians, and nurses, who collected the questionnaires by filling out the printed sheets. Expert nurses measured each participant's anthropometric data by using standard protocols [26, 27], and height and weight were measured using stadiometers and weighing scales, respectively [26]. A measuring tape was positioned 1 cm below the umbilicus and at the iliac crest to measure the circumferences of the waist and hips, respectively [27]. The body mass index (BMI) formula (kg/m2) = Weight (kg)/Height squared (m2) [28] was used to calculate the BMI. We defined obesity according to WHO criteria; where underweight people had a BMI of <18.5 kg/m2, normal weight had a BMI of 18.5–24.9 kg/m2, overweight had a BMI of 25.0–29.9 kg/m2, and obese were over 30.0 kg/m2 [29]. The blood pressure (BP) values were taken with a sphygmomanometer. After participants had rested in a sitting position for at least 10 min, experienced nurses took two measurements on the right arm at a properly sized cuffed 1-min interval, with the arm supported at heart level and feet flat on the floor [30]. ## Ethical considerations All subjects signed consent forms before participating in the study. The ethical committee of Xi'an Jiao tong University Health Science Center approved the study, which was carried out following the Declaration of Helsinki. The Palestinian Health Research Committee at the Directorate General of Human Resources Development, Ministry of Health, Gaza (PHRC/HC/$\frac{663}{19}$) approved the protocol. The data were analyzed in an anonymous and non-linked manner, with no participant names being used. Moreover, there are no physical risks as there is no intervention such as blood sampling during the study. ## Statistical analysis SPSS V.26 (Statistical package of social science) was used to carry out all data analyses. Continuous variables were represented by mean values and standard deviations (SD), while categorical variables were described by frequency and percentage. For categorical and continuous variables, chi-square and t-tests were used. If three or more groups were studied, one-way variance analysis (ANOVA) was used to compare demographic characteristics between groups. The odds ratio (OR) and $95\%$ confidence interval (CI) of overweight and obesity were calculated by using univariate logistic regression, with the predictors associated (gender, age, marital status, work experience, monthly income, known hypertension, type 2 diabetes, and family history of NCDs). The statistical significance was set as a two-sided level, $P \leq 0.05.$ ## Sociodemographic characteristics of the HCWs A total of 1,850 HCWs were included in this study, 1,146 ($61.9\%$) were male participants, 704 ($38.1\%$) were female participants, and most of the participants were in the age group (30–39) with $49.3\%$. About $78.1\%$ of participants were married, while $68.6\%$ had a first degree (Bachelor's). The HCWs included 226 physicians ($12.2\%$), 1,208 nurses ($65.3\%$), 334 paramedics ($18.1\%$), and 82 non-medical ($4.4\%$). Most of the participants ($33.8\%$) had work experience of 10–15 years. The average monthly income of most participants was less than (2,000 NIS) per month at $61.8\%$. The means anthropometric measurements for the participants were as follow: height 170.39 ± (8.86) cm, weight 78.63 ± (14.92) Kg, BMI 27.09 ± (4.77) Kg/m2, waist circumference 107.14 ± (11.63) cm, hip circumference 99.83 ± (7.02) cm, systolic blood pressure (SBP) 118.16 ± (10.13) mmHg, and diastolic blood pressure (DBP) 73.04 ± (10.01) mmHg. Moreover, $65\%$ of the participants were overweight or obese. BMI had significant associations with gender, age, marital status, profession, experience, monthly income, and anthropometric measurements (weight, height, waist circumference, hip circumference, SBP, and DBP) ($P \leq 0.05$; Table 1). Table 1 \| Variable \| All \| Underweight \| Normal \| Overweight \| Obese \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| n =1,850 \| n = 18 (1.0%) \| n = 630 (34.0%) \| n = 727 (39.3%) \| n = 475 (25.7%) \| \| \| Gender \| Gender \| Gender \| Gender \| Gender \| Gender \| Gender \| \| Male \| 1146 \| 8 (0.7) \| 336 (29.3) \| 490 (42.8) \| 312 (27.2) \| <0.001a \| \| Female \| 704 \| 10 (1.4) \| 294 (41.7) \| 237 (33.7) \| 163 (23.2) \| \| \| Age group (years) \| Age group (years) \| Age group (years) \| Age group (years) \| Age group (years) \| Age group (years) \| Age group (years) \| \| 22–29 \| 334 \| 2 (0.6) \| 184 (55.1) \| 98 (29.3) \| 50 (15.0) \| <0.001a \| \| 30–39 \| 912 \| 10 (1.1) \| 304 (33.3) \| 390 (42.8) \| 208 (22.8) \| \| \| 40–49 \| 440 \| 6 (1.4) \| 118 (26.8) \| 184 (41.8) \| 132 (30.0) \| \| \| 50–61 \| 164 \| - \| 24 (14.6) \| 55 (33.6) \| 85 (51.8) \| \| \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| \| Unmarried \| 406 \| 4 (1.0) \| 201 (49.5) \| 135 (33.2) \| 66 (16.3) \| <0.001a \| \| Married \| 1444 \| 14 (1.0) \| 429 (29.7) \| 592 (41.0) \| 409 (28.3) \| \| \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| Educational level \| \| Diploma \| 354 \| 5 (1.4) \| 129 (36.5) \| 142 (40.1) \| 78 (22.0) \| 0.258a \| \| Bachelor \| 1269 \| 12 (0.9) \| 433 (34.1) \| 498 (39.3) \| 326 (25.7) \| \| \| Post-graduated \| 227 \| 1 (0.4) \| 68 (30.0) \| 87 (38.3) \| 71 (31.3) \| \| \| Profession \| Profession \| Profession \| Profession \| Profession \| Profession \| Profession \| \| Physician \| 226 \| 4 (1.8) \| 68 (30.1) \| 94 (41.6) \| 60 (26.5) \| 0.033a \| \| Nurse \| 1208 \| 6 (0.5) \| 414 (34.3) \| 483 (40.0) \| 305 (25.2) \| \| \| Paramedics \| 334 \| 8 (2.4) \| 112 (33.5) \| 122 (36.5) \| 92 (27.5) \| \| \| Non-medical \| 82 \| – \| 36 (43.9) \| 28 (34.1) \| 18 (22.0) \| \| \| Experience (years) \| Experience (years) \| Experience (years) \| Experience (years) \| Experience (years) \| Experience (years) \| Experience (years) \| \| <5 \| 381 \| 4 (1.0) \| 192 (50.4) \| 132 (34.7) \| 53 (13.9) \| <0.001a \| \| 5–10 \| 575 \| 6 (1.0) \| 192 (33.4) \| 238 (41.4) \| 139 (24.2) \| \| \| 10–15 \| 626 \| 6 (1.0) \| 190 (30.3) \| 258 (41.2) \| 172 (27.5) \| \| \| >15 \| 268 \| 2 (0.8) \| 56 (20.9) \| 99 (36.9) \| 111 (41.4) \| \| \| Monthly income (NIS) \| Monthly income (NIS) \| Monthly income (NIS) \| Monthly income (NIS) \| Monthly income (NIS) \| Monthly income (NIS) \| Monthly income (NIS) \| \| <2,000 \| 1144 \| 14 (1.2) \| 432 (37.8) \| 453 (39.6) \| 245 (21.4) \| <0.001a \| \| >2,000 \| 706 \| 4 (0.6) \| 198 (28.0) \| 274 (38.8) \| 230 (32.6) \| \| \| \| Mean (SD) \| Mean (SD) \| Mean (SD) \| Mean (SD) \| Mean (SD) \| \| \| Height \| 170.39 (8.86) \| 170.44 (10.39) \| 170.05 (8.97) \| 171.46 (8.95) \| 169.19 (8.33) \| <0.001b \| \| Weight \| 78.63 (14.92) \| 52.67 (6.41) \| 65.36 (8.45) \| 80.28 (8.61) \| 94.69 (11.68) \| <0.001b \| \| BMI \| 27.09 (4.77) \| 18.07 (0.37) \| 22.53 (1.64) \| 27.24 (1.24) \| 33.25 (3.78) \| <0.001b \| \| WC \| 107.14 (11.63) \| 82.22 (2.41) \| 97.25 (5.58) \| 110.51 (9.96) \| 116.03 (9.20) \| <0.001b \| \| HC \| 99.83 (7.02) \| 82.72 (4.53) \| 95.61 (5.08) \| 100.32 (5.29) \| 105.30 (7.02) \| <0.001b \| \| SBP \| 118.16 (10.13) \| 113.33 (8.40) \| 115.72 (8.76) \| 118.51 (10.02) \| 121.04 (11.18) \| <0.001b \| \| DBP \| 73.04 (10.01) \| 67.78 (9.42) \| 70.40 (9.44) \| 73.55 (9.48) \| 75.94 (1.61) \| <0.001b \| ## Prevalence of overweight and obesity among HCWs Based on gender, the prevalence of overweight and obesity among HCWs was $43.4\%$ for male participants and $21.6\%$ for female participants. Moreover, the total prevalence in both genders was $65\%$ for the whole study participants and $35\%$ had normal weight. There is a statistical association between gender and BMI ($P \leq 0.05$; Table 1). The prevalence of overweight and obesity among HCWs stratified by age group also offers the most age group at risk of overweight and obesity between 30 and 39 years with $32.32\%$, followed by 40 and 49 years with $17.09\%$, and the lowest prevalence for the age group of 50–61 years with $7.56\%$. There is a statistically significant association between age and BMI ($P \leq 0.05$; Table 1). ## Lifestyle factors of the healthcare workers and BMI Approximately $18.9\%$ of the study participants were smokers, and $65.5\%$ had low physical activity. The consumption of fruits <3 days/week was $92.9\%$, and the consumption among overweight and obese, as well as for vegetables, was $96.6\%$, <3 days/week. However, there is no relationship between fruit/vegetable consumption and the prevalence of overweight, and obesity. While $91.6\%$ of the participants ate three meals daily, $8.9\%$ ate more than three times per week out of home, and $65.2\%$ ate with more than five people. The prevalence of hypertension was $8.4\%$, type 2 diabetes was $2.9\%$, and family history of NCDs among participants was $67.6\%$. There are statistically significant associations between BMI and the number of meals, the number of meals eaten outdoors, the total number of members who ate together, known hypertension, type 2 diabetes, and family history of NCDs ($P \leq 0.05$; Table 2). Table 2 \| Variable \| All \| Underweight \| Normal \| Overweight \| Obese \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| n = 1,850 \| N = 18 (1.0%) \| n = 630 (34.0%) \| n = 727 (39.3%) \| n = 475 (25.7%) \| \| \| Smoking \| Smoking \| Smoking \| Smoking \| Smoking \| Smoking \| Smoking \| \| Yes \| 350 \| 2 (0.6) \| 100 (28.6) \| 152 (43.4) \| 96 (27.4) \| \| \| No \| 1500 \| 16 (1.1) \| 530 (35.3) \| 575 (38.3) \| 379 (25.3) \| \| \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| \| Low \| 1211 \| 12 (1.0) \| 421 (34.8) \| 456 (37.6) \| 322 (26.6) \| 0.353 \| \| Moderate \| 540 \| 5 (0.9) \| 174 (32.2) \| 236 (43.7) \| 125 (23.2) \| \| \| Hight \| 99 \| 1 (1.0) \| 35 (35.4) \| 35 (35.4) \| 28 (28.2) \| \| \| Fruits consumption in a week \| Fruits consumption in a week \| Fruits consumption in a week \| Fruits consumption in a week \| Fruits consumption in a week \| Fruits consumption in a week \| Fruits consumption in a week \| \| ≤3 days \| 1718 \| 18 (1.0) \| 584 (34.0) \| 677 (39.4) \| 439 (25.6) \| 0.654 \| \| >3 days \| 132 \| – \| 46 (34.8) \| 50 (37.9) \| 36 (27.3) \| \| \| Vegetable consumption in a week \| Vegetable consumption in a week \| Vegetable consumption in a week \| Vegetable consumption in a week \| Vegetable consumption in a week \| Vegetable consumption in a week \| Vegetable consumption in a week \| \| ≤3 days \| 1788 \| 18 (1.0) \| 604 (33.8) \| 707 (39.5) \| 459 (25.7) \| 0.457 \| \| >3 days \| 62 \| – \| 26 (41.9) \| 20 (32.3) \| 16 (25.8) \| \| \| Number of meals (day) \| Number of meals (day) \| Number of meals (day) \| Number of meals (day) \| Number of meals (day) \| Number of meals (day) \| Number of meals (day) \| \| ≤3 \| 1694 \| 16 (0.9) \| 570 (33.7) \| 685 (40.4) \| 423 (25.0) \| 0.009 \| \| >3 \| 156 \| 2 (1.3) \| 60 (38.5) \| 42 (26.9) \| 52 (33.3) \| \| \| How many days do you usually eat at home every week? \| How many days do you usually eat at home every week? \| How many days do you usually eat at home every week? \| How many days do you usually eat at home every week? \| How many days do you usually eat at home every week? \| How many days do you usually eat at home every week? \| How many days do you usually eat at home every week? \| \| ≤3 \| 164 \| 4 (2.5) \| 44 (26.8) \| 62 (37.8) \| 54 (32.9) \| 0.015 \| \| >3 \| 1686 \| 14 (0.8) \| 586 (34.8) \| 665 (39.4) \| 421 (25.0) \| \| \| How many people do you usually eat together? \| How many people do you usually eat together? \| How many people do you usually eat together? \| How many people do you usually eat together? \| How many people do you usually eat together? \| How many people do you usually eat together? \| How many people do you usually eat together? \| \| ≤5 \| 1206 \| 12 (1.0) \| 428 (35.5) \| 498 (41.3) \| 268 (22.2) \| <0.001 \| \| >5 \| 644 \| 6 (0.9) \| 202 (31.4) \| 229 (35.6) \| 207 (32.1) \| \| \| Known hypertension \| Known hypertension \| Known hypertension \| Known hypertension \| Known hypertension \| Known hypertension \| Known hypertension \| \| Yes \| 155 \| – \| 29 (18.7) \| 55 (35.5) \| 71 (45.8) \| \| \| No \| 1695 \| 18 (1.1) \| 601 (35.5) \| 672 (39.6) \| 404 (23.8) \| \| \| Known type 2 diabetes \| Known type 2 diabetes \| Known type 2 diabetes \| Known type 2 diabetes \| Known type 2 diabetes \| Known type 2 diabetes \| Known type 2 diabetes \| \| No \| 1796 \| 15 (0.8) \| 623 (34.7) \| 705 (39.3) \| 453 (25.2) \| <0.001 \| \| Yes \| 54 \| 3 (5.6) \| 7 (13.0) \| 22 (40.7) \| 22 (40.7) \| \| \| Family history of NCDs \| Family history of NCDs \| Family history of NCDs \| Family history of NCDs \| Family history of NCDs \| Family history of NCDs \| Family history of NCDs \| \| Yes \| 1250 \| 8 (0.6) \| 380 (30.4) \| 505 (40.4) \| 357 (28.6) \| \| \| No \| 600 \| 10 (1.7) \| 250 (41.7) \| 222 (37.0) \| 118 (19.6) \| \| The prevalence of overweight and obesity among HCWs was $65\%$. Approximately $21.6\%$ of them were female participants and $43.4\%$ were male participants. The most age group of overweight and obesity was 30–39 years with $32.3\%$. Moreover, nurses were the most HCWs who had overweight and obese, with $42.6\%$. In addition, the prevalence of type 2 diabetes and hypertension was higher in a group of overweight and obese, thus, there is a statistically significant association between BMI and type 2 diabetes and hypertension ($P \leq 0.05$; Table 2). ## Association between overweight, obesity, and associated risk factors Univariate analysis using logistic analysis showed the significant predictors associated (gender, age, marital status, work experience, monthly income, known hypertension, type 2 diabetes, and family history of NCDs) with overweight and obesity among HCWs. The odds ratio of overweight and obesity increased 1.77 times for male participants than female participants ($95\%$ CI: 1.45–2.15). This relationship was statistically significant ($P \leq 0.001$). Moreover, the age group (40–49) was associated with overweight and obesity by almost three times (OR = 3.20; $95\%$ CI: 2.37–4.32; $P \leq 0.001$). The OR of overweight and obesity among married participants was two times greater than that of unmarried participants (OR = 2.30; $95\%$ CI: 1.84–2.88; $P \leq 0.001$). Doctors were associated with an increase in overweight and obesity than non-medical professions by 1.67 times (OR = 1.67; $95\%$ CI: 0.99–2.81). This relationship was not statistically significant ($$P \leq 0.051$$). The work experience increased overweight and obesity by 3.8 times for more than 15 years (OR = 3.83; $95\%$ CI: 2.69–5.46; $P \leq 0.001$). Furthermore, the increase in monthly income was associated with overweight and obesity by 1.59 times (OR = 1.59; $95\%$ CI: 1.30–1.95; $P \leq 0.001$). Hypertension was strongly associated with overweight and obesity (OR = 2.49; $95\%$ CI: 1.65–3.78; $P \leq 0.001$) as well as overweight and obesity was associated with type 2 diabetes (OR = 2.42; $95\%$ CI: 1.21–4.85; $$P \leq 0.012$$). Finally, a family history of NCDs was associated with overweight and obesity (OR = 1.69; $95\%$ CI: 1.38–2.07; $P \leq 0.001$; Table 3). Table 3 \| Variables \| Regression coefficient B \| OR (95% CI) \| Wald statistics (df) \| P-value \| \| --- \| --- \| --- \| --- \| --- \| \| Gender \| Gender \| Gender \| Gender \| Gender \| \| Male \| 0.572 \| 1.77 (1.45–2.15) \| 32.90 \| <0.001 \| \| Female \| \| 1 \| \| \| \| Age group \| Age group \| Age group \| Age group \| Age group \| \| 22–29 \| \| 1 \| \| \| \| 30–39 \| 0.873 \| 2.393 (1.85–3.09) \| 44.83 \| <0.001 \| \| 40–49 \| 1.164 \| 3.203 (2.37–4.32) \| 57.99 \| <0.001 \| \| 50–61 \| 1.992 \| 7.331 (4.51–11.89) \| 65.12 \| <0.001 \| \| Marital status \| Marital status \| Marital status \| Marital status \| Marital status \| \| Unmarried \| \| 1 \| \| \| \| Married \| 0.835 \| 2.305 (1.84–2.88) \| 53.17 \| <0.001 \| \| Profession \| Profession \| Profession \| Profession \| Profession \| \| Physician \| 0.515 \| 1.674 (0.99–2.81) \| 3.80 \| 0.051 \| \| Nurse \| 0.384 \| 1.468 (0.93–2.30) \| 2.77 \| 0.096 \| \| Paramedics \| 0.333 \| 1.396 (0.85–2.27) \| 1.77 \| 0.182 \| \| Non-medical \| \| 1 \| \| \| \| Experience (years) \| Experience (years) \| Experience (years) \| Experience (years) \| Experience (years) \| \| <5 \| \| 1 \| \| \| \| 5–10 \| 0.702 \| 2.017 (1.54–2.62) \| 27.04 \| <0.001 \| \| 10–15 \| 0.843 \| 2.324 (1.78–3.02) \| 39.66 \| <0.001 \| \| >15 \| 1.340 \| 3.836 (2.69–5.46) \| 55.59 \| <0.001 \| \| Monthly income (NIS) \| Monthly income (NIS) \| Monthly income (NIS) \| Monthly income (NIS) \| Monthly income (NIS) \| \| <2,000 \| \| 1 \| \| \| \| >2,000 \| 0.466 \| 1.594 (1.303–1.951) \| 20.50 \| <0.001 \| \| Number of meals (day) \| Number of meals (day) \| Number of meals (day) \| Number of meals (day) \| Number of meals (day) \| \| ≤3 \| \| 1 \| \| \| \| >3 \| −0.044 \| 0.957 (0.83–1.09) \| 0.42 \| 0.515 \| \| How many people do you usually eat together? \| How many people do you usually eat together? \| How many people do you usually eat together? \| How many people do you usually eat together? \| How many people do you usually eat together? \| \| ≤5 \| \| 1 \| \| \| \| >5 \| 0.186 \| 1.204 (0.98–1.47) \| 3.23 \| 0.072 \| \| Known hypertension \| Known hypertension \| Known hypertension \| Known hypertension \| Known hypertension \| \| No \| \| 1 \| \| \| \| Yes \| 0.916 \| 2.499 (1.65–3.78) \| 18.66 \| <0.001 \| \| Type 2 diabetes \| Type 2 diabetes \| Type 2 diabetes \| Type 2 diabetes \| Type 2 diabetes \| \| No \| \| 1 \| \| \| \| Yes \| 0.885 \| 2.424 (1.21–4.85) \| 6.26 \| 0.012 \| \| Family history of NCDs \| Family history of NCDs \| Family history of NCDs \| Family history of NCDs \| Family history of NCDs \| \| NO \| \| 1 \| \| \| \| YES \| 0.530 \| 1.699 (1.38–2.07) \| 26.68 \| <0.001 \| ## Discussion Obesity among HCWs is an important issue as it impacts the morbidity of HCWs. In the present study, the overall prevalence of overweight and obesity among HCWs was $65\%$ ($39.3\%$ overweight and $25.7\%$ obesity). These findings are comparable with the meta-analysis in the Middle Eastern countries, which reported the prevalence of overweight and obesity were 33.14 and $21.17\%$, respectively [9]. Moreover, a previous study conducted in the Gaza Strip said the prevalence of obesity was $19.5\%$ [10]. The current research has found a higher prevalence of overweight and obesity among HCWs than the prevalence of overweight and obesity reported in the previous study. When compared to the overall adult population, healthcare workers have a higher prevalence of obesity. This may be because they are more susceptible to the disease due to irregular and extended work hours, poor food, and workplace stress. There is a significant association between the elevation of the anthropometric measure (Height, weight, WC, HC, SBP, and DBP) and being overweight or obese. It was also noted that the participant's elevation in anthropometric measurements had a significant positive association with being overweight and obese. The higher prevalence of overweight and obesity among HCWs might be influenced by sedentary behaviors, which might be contributed to the work environments and the socioeconomic status that may encourage the adaption of less physical activity and eating habits [31, 32]. One of the contributing factors may be eating a poor diet while watching TV, especially sugary snacks. Other researchers have discovered a link between screen time and sugary, high-energy snacks [33]. Due to the work environment, the HCW's low frequency of intake of meals, low intake of fruits and vegetables, and low physical activity increased overweight and obesity. Further research is needed to investigate dietary habits and socioeconomic status and their association with overweight and obesity among HCWs. Furthermore, the prevalence of overweight and obesity increased with age. Similar results were reported by Low et al. [ 34], Addo et al. [ 35], Kishawi et al. [ 36], and Firouzbakht et al. [ 37], that the prevalence of overweight and obesity increased as age increased. This places a future load of illness on the medical workforce. In addition, the previous study found that the estimated peak increase in the prevalence of overweight and obesity as age increased was 40–50 years in developing countries while it was 50–60 years in developed countries [34], which is in line with the current study. The peak increase in the prevalence of obesity with age was 30–39 years. In the present study, the peak age is lower than in the previous study. Most of the studies confirmed the association between age increasing and susceptibility to non-communicable diseases and overweight and obesity as one of the risk factors of illness. Overweight and obesity are associated with increased age due to decreased physical activity, routine daily activities, comorbidities, and dietary habits. Therefore, the high ratio of youth and the predominance of the HCWs category found in this study represent the actual age and job category distribution among HCWs in the Gaza Strip. In addition, the findings from this study show that married participants had a higher prevalence of overweight and obesity than single participants. The results support the findings reported by Dagen et al. [ 38] and Tzotzas et al. [ 39] that married adults had a higher prevalence of being overweight and obese than single adults and hypothesized that the increase in BMI among married couples is due to the increased social support, along with regularly eating dense food that increases the risk of being overweight or obese [39]. Our results revealed that the prevalence of overweight and obese increased among patients who had hypertension and type 2 diabetes. In addition, participants with a positive family history of NCDs had a higher prevalence of overweight and obesity than those without a family history of NCDs, in which similar findings have been found in previous studies (40–43). Similar to the findings of our investigation, numerous studies have shown that nurses had a higher risk of obesity than workers in other occupations. In their academic careers in Scotland and England [44, 45], it is covered that the risk of obesity was lower for different HCW job categories than for nurses. Unfortunately, Hegde et al. in their studies in Tamil Nadu, India, found results that contradicted our study, suggesting that the burden of obesity was greater among physicians than nurses [46]. All the studies mentioned earlier agree that different job categories were associated with obesity among HCWs. However, the aforementioned studies did not share comparable sociodemographic populations to that of the Gaza Strip. Most studies among HCWs did not specifically analyze different types of HCW occupations and obesity. Hazmi et al. [ 47] only mentioned the overall prevalence of obesity without further analysis according to job category, while Mustafa et al. [ 48] and Ramli [49] only divided the occupations of HCWs into professional vs. ancillary jobs. In contrast, our study is the first in the Gaza Strip to work with HCWs and determine the prevalence of overweight and obesity and classify HCW occupations into four main groups, doctors, nurses, paramedical, and non-medical categories, with the prevalence of overweight and obesity. Thus, we know nurses are at a greater risk of becoming obese than doctors and other job categories in the Gaza Strip. Our findings reported that eating behavior was significantly associated with increased overweight and obesity, thus, the participants <3 meals per day had increased the prevalence of overweight and obesity in other groups (Table 2). Incomparable to previous studies, eating behavior was reported as one of the leading factors in the development of overweight and obesity with carried gender and age differences (50–53). In addition, no local studies reported an association between eating behavior and the prevalence of obesity. In addition, physical inactivity is identified as a risk factor for overweight and obesity [54, 55]. However, the current study revealed that all healthcare workers with low or moderate activity had no significant association between physical activity and overweight and obesity. A similar result was reported by Firouzbakht et al. [ 37] and El Kishawi et al. [ 36]. These previous studies recognized the low physical activity due to the working shift of HCWs, limited availability of exercise facilities, and workload. However, the current study showed significant predictors associated with an increase in the prevalence of overweight and obesity among HCWs. These associated factors included gender, age, marital status, monthly income, known hypertension, type 2 diabetes, family history of NCDs, and elevation of anthropometric measurements. On the other hand, the current study did not find any significant associations between educational level, smoking status, physical activity, and fruit and vegetable consumption. ## Strengths and limitations of the study This study has several strengths. The first study estimated the prevalence of overweight and obesity among HCWs in the Gaza Strip. This study defined the associated factors with an increase in the prevalence of overweight and obesity; therefore, this study will be a baseline for subsequent studies among HCWs. There are some limitations in the study that need to be considered. First, cross-sectional data do not explore the causal pathways that underlie the reported association. Second, no availability of previous studies about the prevalence of overweight and obesity to interpret our results. Third, recall bias is also possible by using food frequency. Fourth, the majority of our participants were nurses compared with other professions. ## Conclusion This study showed a high prevalence of overweight and obesity among HCWs. Age, monthly income, marital status, known hypertension, type 2 diabetes, and eating habits were associated with the prevalence of overweight and obesity. These findings appear to show an emerging problem in HCWs. A wellness program should be developed by decision-makers throughout mass-level educational awareness campaigns for HCWs to prevent and manage the modifiable risk factors that increase overweight or obesity. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by Palestinian Health Research Committee at the Directorate General of Human Resources Development, Ministry of Health, Gaza (PHRC/HC/$\frac{663}{19}$). The patients/participants provided their written informed consent to participate in this study. ## Author contributions LM, JY, and MaM generated the idea for the study and formulated a research plan. JY wrote the original draft preparation and reviewed and edited. MJ, HJ, YF, LW, ZL, ZH, and MeM revised the manuscript and interpreted the data and editing. LM and MaM supervised the study. All authors acquired, analyzed, and interpreted the data. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Utkirzhonovna N. Prevalence of the main risk factors for overweight and obesity in young people. Res J Trauma Disabil Stud. (2022) 1 14-25 2. 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--- title: Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations authors: - Chiara Auwerx - Marie C Sadler - Tristan Woh - Alexandre Reymond - Zoltán Kutalik - Eleonora Porcu journal: eLife year: 2023 pmcid: PMC9998083 doi: 10.7554/eLife.81097 license: CC BY 4.0 --- # Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations ## Abstract Despite the success of genome-wide association studies (GWASs) in identifying genetic variants associated with complex traits, understanding the mechanisms behind these statistical associations remains challenging. Several methods that integrate methylation, gene expression, and protein quantitative trait loci (QTLs) with GWAS data to determine their causal role in the path from genotype to phenotype have been proposed. Here, we developed and applied a multi-omics Mendelian randomization (MR) framework to study how metabolites mediate the effect of gene expression on complex traits. We identified 216 transcript-metabolite-trait causal triplets involving 26 medically relevant phenotypes. Among these associations, $58\%$ were missed by classical transcriptome-wide MR, which only uses gene expression and GWAS data. This allowed the identification of biologically relevant pathways, such as between ANKH and calcium levels mediated by citrate levels and SLC6A12 and serum creatinine through modulation of the levels of the renal osmolyte betaine. We show that the signals missed by transcriptome-wide MR are found, thanks to the increase in power conferred by integrating multiple omics layer. Simulation analyses show that with larger molecular QTL studies and in case of mediated effects, our multi-omics MR framework outperforms classical MR approaches designed to detect causal relationships between single molecular traits and complex phenotypes. ## Introduction Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) associated with a wide range of complex traits (MacArthur et al., 2017; Visscher et al., 2017). However, the path from GWAS to biology is not straightforward as most SNPs implicated by GWASs reside in non-coding regions of the genome (MacArthur et al., 2017) and do not directly inform on the functional mechanism through which variants exert their effect on phenotypes. GWASs have been performed on gene expression (Võsa et al., 2021), DNA methylation (Min et al., 2021), protein (Sun et al., 2018), and metabolite (Shin et al., 2014; Lotta et al., 2021) levels, identifying genetic variants influencing molecular traits, commonly referred to as molecular quantitative trait loci (molQTLs). The large overlap between complex and molecular trait-associated variants suggests that integrating these data can help interpreting GWAS loci (Vandiedonck, 2018; Taylor et al., 2019; Ongen et al., 2017). Advances in the field of transcriptomics make gene expression the best studied molecular phenotype, thanks to the presence of large expression QTL (eQTL) studies (e.g., eQTLGen Consortium [Võsa et al., 2021] N>30,000). Availability of these datasets fostered the development of summary statistic-based statistical approaches aiming at identifying associations between transcripts and complex traits (Zhu et al., 2016; Porcu et al., 2019; Hormozdiari et al., 2014; Gusev et al., 2016), prioritizing genes from known GWAS loci for functional follow-up, and inferring the directionality of these relations (Porcu et al., 2019; Porcu et al., 2021b). However, the cascade of events that mediates the effect of genetic variants on complex traits involves more than one molecular trait. Although approaches used for gene expression can be extended to other molecular data, investigating whether these molecular traits reside along the same causal pathway remains under-explored and only recently have studies applied colocalization and Mendelian randomization (MR) to methylation, gene expression, and protein levels data (Giambartolomei et al., 2018; Wu et al., 2018; Gleason et al., 2020; Sadler et al., 2022) and to a lesser extent to metabolic QTLs (mQTL) (Yin et al., 2022). Metabolites are often the final products of cellular regulatory processes and the most proximal omic layer to complex phenotypes. Their levels could thus represent the ultimate response of biological systems to genetic and environmental changes. For instance, the metabolic status of organisms reflects disease progression, as metabolic disturbances can often be observed several years prior to the symptomatic phase (Shah et al., 2010; Wang et al., 2011; Sabatine et al., 2005). Therefore, using metabolomics to identify early-stage biomarkers of complex phenotypes, such as prediabetes and COVID-19 susceptibility, has gained increased interest (Wang-Sattler et al., 2012; Julkunen et al., 2021). While two-sample MR approaches using metabolites as single exposure have revealed biomarkers for several diseases (Qian et al., 2021; Lord et al., 2021; Porcu et al., 2021a), these analyses focused on the prediction of disease risk rather than on deciphering the mechanisms of discovered associations. Integrating transcriptomics with metabolomics data can provide insights into how metabolites are regulated, elucidating targetable functional mechanisms. Here, we develop a framework based on established MR methodology that hypothesizes a mediating role of the metabolome in the transcript-to-phenotype axis, with the primary exposure being defined as an upstream omic layer, namely gene expression. Specifically, our integrative MR analysis combines summary-level multi-omics (i.e., GWAS, eQTL, and mQTL) data to compute the indirect effect of gene expression on complex traits mediated by metabolites in three steps (Figure 1). First, we map the transcriptome to the metabolome by identifying causal associations between transcripts and metabolites. Next, we screen metabolites for downstream causal effects on 28 complex phenotypes, resulting in the identification of gene expression → metabolite → phenotype cascades (Figure 1A). In parallel, we prioritize trait-associated genes by testing the association of transcripts with phenotypes (Figure 1B). Third, for transcripts found to causally influence either a metabolite (A) or a complex phenotype (B), we test whether the identified target genes exert their effect on the phenotype through the metabolite using multivariable MR (MVMR; Figure 1C). Finally, we carried out extensive power analyses to determine under which conditions the mediation analysis (Figure 1C) outperforms the conventional exposure-outcome MR framework (Figure 1B). Figure 1.: Workflow overview.(A) Estimation of the causal transcript-to-metabolite and metabolite-to-phenotype effects through univariable Mendelian randomization (MR). (B) Estimation of the causal transcript-to-phenotype effects through univariable transcriptome-wide MR (TWMR). (C) Estimation of the direct (i.e., not mediated by the metabolites) and mediated effect of transcripts on phenotypes through multivariable MR (MVMR) by accounting for mediation through the metabolome. ## Mapping the transcriptome onto the metabolome We applied univariable MR to identify metabolites whose levels are causally influenced by transcript levels in whole blood (Figure 1A). Summary statistics for cis-eQTLs stem from the eQTLGen Consortium meta-analysis of 19,942 transcripts in 31,684 individuals (Võsa et al., 2021), while summary statistics for mQTLs originate from a meta-analysis of 453 metabolites in 7824 individuals from two independent European cohorts: TwinsUK ($$n = 6056$$) and KORA ($$n = 1768$$) (Shin et al., 2014). After selecting SNPs included in both the eQTL and mQTL studies, our analysis was restricted to 7884 transcripts with ≥3 instrumental variables (IVs) (see Methods, Figure 1—figure supplement 1A) and 242 metabolites with an identifier in the Human Metabolome Database (HMDB) (Wishart et al., 2022) (see Methods, Supplementary file 1a). By testing each gene for association with the 242 metabolites, we detected 96 genes whose transcript levels causally impacted 75 metabolites, resulting in 133 unique transcript-metabolite associations (FDR $5\%$ considering all 1,907,690 instrumentable gene-metabolite pairs; Supplementary file 1b). Most involved genes ($86\%$; $\frac{83}{96}$) were causally influencing the level of a single metabolite, with some notable exceptions acting as mQTL hubs, such as TMEM258 and FADS2 both affecting the same 11 metabolites, followed by FADS1 affecting a subset of six metabolites. While only 5 ($3.8\%$) of the 133 associations were reported in HMDB, an automated literature review (see Methods) identified a match for 22 ($16.5\%$) of the identified transcript-metabolite pairs (Supplementary file 1b). ## Mapping the metabolome onto complex phenotypes Univariable metabolome-wide MR (MWMR; Figure 1A) was used to identify causal relationships between 48 metabolites with ≥3 IVs (Figure 1—figure supplement 1B) and 28 complex phenotypes. The latter include a wide range of anthropometric traits, cardiovascular assessments, and blood biomarkers, whose summary statistics originate from the UK Biobank (UKB) (Bycroft et al., 2018;Supplementary file 1c). Overall, 34 metabolites were associated with at least one phenotype (FDR $5\%$ considering all 1344 metabolite-phenotype pairs), resulting in 132 unique metabolite-phenotype associations (Supplementary file 1d). ## Mapping the transcriptome onto complex phenotypes We applied univariable transcriptome-wide MR (TWMR [Porcu et al., 2019] Figure 1B) to identify associations between expression levels of 10,435 transcripts from the eQTLGen Consortium with ≥3 IVs (Figure 1—figure supplement 1C) measured in both exposure and outcome datasets and the same 28 UKB phenotypes described in the previous section (Supplementary file 1c). In total, 5140 transcripts associated with at least one phenotype (FDR $5\%$ considering all 292,170 gene-phenotype pairs) resulting in 13,141 unique transcript-phenotype associations (Supplementary file 1e). ## Mapping metabolome-mediated effects of the transcriptome onto complex phenotypes The mapping of putative causal effects performed in the previous steps provides the opportunity to infer the mediating role of the metabolome in biological processes leading to transcript-phenotype associations. We combined the 133 transcript-metabolite (FDR ≤$5\%$) and 132 metabolite-trait (FDR ≤$5\%$) associations to pinpoint 216 transcript-metabolite-phenotype causal triplets (FDR = 1–0.952 = $9.75\%$) (Supplementary file 1f). Among the 37 triplets for which the transcript and metabolite had previously been linked through automated literature review, none remained after incorporating a third term for the phenotype in the search and manually removing abstracts for which the search terms were used in an erroneous context. Relaxing the search criteria by omitting the metabolite term, $\frac{13}{37}$ ($35\%$) triplets returned at least one match for the gene-trait association. For each of these 216 putative mechanisms, an MVMR approach to compute the direct effect of gene expression on the phenotype was applied (see Methods; Figure 1C; Supplementary file 1f). Regressing direct effects (αd) on total effects (αTP) and accounting for regression dilution bias (see Methods; Figure 2A), it was estimated that $77\%$ [$95\%$ CI: 70–$85\%$] of the transcript effect on the phenotype was direct and thus not mediated by the metabolites (Figure 2B). Figure 2.: Direct and mediated effects.(A) Graphical representation of the multivariable Mendelian randomization (MVMR) framework for mediation analysis: DNA represents genetic instrumental variables (IVs) chosen to be directly associated with either the exposure (transcript; βeQTL) or the mediator (metabolite; βmQTL) through summary statistics. The effect of these IVs on the outcome (phenotype; βGWAS) originates from genome-wide association studies (GWASs) summary statistics. Total effects αTP of transcripts on phenotypes are estimated by transcriptome-wide Mendelian randomization (TWMR), while direct effects αd are estimated by MVMR. Total effects αTP are assumed to equal the sum of the direct αd and indirect αi (i.e., mediated) effects, the two former being depicted in B. (B) Direct (αd ; y-axis) and total (αTP ; x-axis) effects for the 216 transcript-metabolite-trait causal triplets. The dashed line represents the identity, while the purple line represents the regression line with a shaded 95% confidence interval. Data related to Figure 2 panel B are available in Figure 2—source data 1. Figure 2—source data 1.Direct and mediated effects.Total (αTP ; transcript-to-phenotype effect) and direct (αd ; direct_effect) effects for the 216 transcript-metabolite-trait causal triplets involving the listed transcript (Gene_ID), metabolite (Shin_ID), and complex phenotype. This file relates to Figure 2B. ## Molecular mechanisms of genotype-to-phenotype associations Dissecting causal triplets allows gaining mechanistc insights into biological pathways linking genes to phenotypes. For instance, expression of TMEM258 [MIM: 617615], FADS1 [MIM: 606148], and FADS2 [MIM: 606149], all mapping to a region on chromosome 11 (Figure 3A), were found to influence a total of 17 complex phenotypes through modulation of 1-arachidonoylglycerophosphocholine (LPC(20:4); HMDB0010395; αTMEM258→LPC(20:4)=−1.02; $$P \leq 8.0$$×10–81; αFADS1→LPC(20:4)=−0.39; $$P \leq 4.6$$×10-15; αFADS2→LPC(20:4)=−0.63; $$P \leq 5.1$$×10–62), 1-arachidonoylglycerophosphoethanolamine (LPE(20:4);HMDB0011517; αTMEM258→LPE(20:4)=−0.68; $$P \leq 1.1$$×10–37; αFADS1→LPE(20:4)=−0.30; $$P \leq 1.4$$×10–07; αFADS2→LPE(20:4)=−0.37; $$P \leq 1.2$$×10–18), and 1-arachidonoylglycerophosphoinositol (LPI(20:4); HMDB0061690; αTMEM258→LPI(20:4)=−0.51; $$P \leq 8.2$$×10–18; αFADS2→LPI(20:4)=−0.28; $$P \leq 6.3$$×10–16) levels (Figure 3B–C). These results align with the known pleiotropy of the region (i.e., >6000 associations reported in the GWAS Catalog as of May 2022). Interestingly, involved metabolites are complex lipids synthesized from arachidonic acid, a product of the rate-limiting enzymes encoded by FADS1 and FADS2 (Figure 3B). Recently, polymorphisms affecting the expression of these genes were shown to associate with the levels of over 50 complex lipids, including the ones identified by our study (Reynolds et al., 2020). Overall, this example illustrates how our method can capture meaningful biological associations and shed light on underlying molecular pathways of pleiotropy. Figure 3.: Molecular pleiotropy at the FADS locus.(A) Genome browser (GRCh37/hg19) view of the genomic region on chromosome 11 encompassing TMEM258, FADS1, and FADS2 (red). (B) Diagram of the mediation signals detected for TMEM258, FADS1, and FADS2. Two of the implicated genes encode enzymes involved in arachidonic synthesis (purple). Involved genes impact 17 phenotypes (pink) through alteration of the levels of three metabolites, 1-arachidonoylglycerophosphocholine (LPC(20:4)), 1-arachidonoylglycerophosphoethanolamine (LPE(20:4)), and 1-arachidonoylglycerophosphoinositol (LPI(20:4)) whose structure is depicted (orange). (C) Network of the 65 transcript-metabolite-trait causal triplets involving TMEM258, FADS1, and FADS2. Nodes represent genes (purple), metabolites (orange), or phenotypes (pink). Edges indicate the direction of the effects estimated through univariable Mendelian randomization. Width of edges is proportional to effect size and color indicates if the effect is positive (red) or negative (blue). ## Power analysis Importantly, only $42\%$ ($\frac{90}{216}$) of the causal triplets showed a significant total transcript-to-phenotype effect (i.e., estimated by TWMR), suggesting that the method lacks power under current settings. To characterize the parameter regime where the power to detect indirect effects is larger than it is for total effects, we performed simulations using different settings for the mediated effect. In each scenario we evaluated 500 transcripts and 80 metabolites and varied two parameters characterizing the mediation: Transcripts were simulated with $6\%$ heritability (i.e., median h2 in the eQTLGen data) and a causal effect of 0.035 (i.e., ~$65\%$ of power in TWMR at α=0.05) on a phenotype. Each scenario was simulated 10 times and results were averaged to assess the mean difference in power (see Methods). Simulations show that with current sample sizes (i.e., NGWAS=300,000, NeQTL=32,000, and NmQTL=8000), when αMP>αTM (i.e., σ<1), TWMR has increased power to detect significant transcript-to-phenotype associations, especially when ρ>0 (i.e., direct and total effect have the same direction (Figure 4A)). However, for all 216 causal triplets, we observed σ>1 (Figure 4—figure supplement 1). Under this condition, and assuming that the total effect of the transcript on the phenotype is dominated by the effect mediated by the metabolite (i.e., ρ<0.5 and ρ>1.5), TWMR had less power than the approach identifying mediators (Figure 4A), confirming that significant associations were missed by TWMR due to power issues related to the proportion of mediated effect. Figure 4.: Power comparison between transcriptome-wide Mendelian randomization (TWMR) and multivariable Mendelian randomization (MVMR).Heatmap showing the difference in statistical power between TWMR and mediation analysis through MVMR at current (A; N=8000) and realistic future (B; N=90,000) metabolic quantitative trait loci (mQTL) dataset sample sizes. The x-axis shows the proportion (ρ) of direct (αd) to total (αTP) effect (i.e., effect not mediated by the metabolite) ranging from –2 to 2, arrows indicating increasing proportion of direct effect. The y-axis shows the ratio (σ) between the transcript-to-metabolite (αTM) and the metabolite-to-phenotype (αMP) effects, ranging from 0.1 to 10. Red vs. gray indicates higher power for TWMR vs. mediation analysis, respectively, while white represents equal power between the two approaches. Data related to Figure 4 panels A and B are available in Figure 4—source data 1 and Figure 4—source data 2, respectively. Figure 4—source data 1.Difference in statistical power between transcriptome-wide Mendelian randomization (TWMR) and mediation analysis at N = 8000 metabolic quantitative trait locus (mQTL) dataset sample size.Each cell represents the mean difference in power between TWMR and mediation analysis for a given scenario across 10 simulations. Rows reflect decreasing ratio between transcript-to-metabolite and metabolite-to-phenotype effects from 10 to 0.1 (sigma). Columns reflect increasing proportion of direct to total effect from –2 to 2 (rho). This file relates to Figure 4A. Figure 4—source data 2.Difference in statistical power between transcriptome-wide Mendelian randomization (TWMR) and mediation analysis at N = 90,000 metabolic quantitative trait locus (mQTL) dataset sample size.Each cell represents the mean difference in power between TWMR and mediation analysis for a given scenario across 10 simulations. Rows reflect decreasing ratio between transcript-to-metabolite and metabolite-to-phenotype effects from 10 to 0.1 (sigma). Columns reflect increasing proportion of direct to total effect from –2 to 2 (rho). This file relates to Figure 4A. Repeating the simulations with an mQTL sample size of 90,000, nearing state-of-the-art sample sizes (Lotta et al., 2021), leads to a strong shift in the above-described trends (Figure 4B). Specifically, when the effect of the transcript on the phenotype is dominated by the effect mediated by the metabolite (ρ<0.3 and ρ>1.7), mediation analysis has more power than TWMR when σ>0.2. For larger proportions of direct effect, TWMR has increased power the more σ differs from 1. In line with the increased power of mediation analysis with larger mQTL datasets, the gain in power of mediation analysis over TWMR decreases with decreasing mQTL dataset sample sizes (ranging between $$n = 1000$$ and $$n = 4000$$; Figure 4—figure supplement 2), indicating that our approach is dependent on large sample sizes to reach its full potential. ## Identifying new genotype-to-phenotype associations The 126 triplets that were not identified through TWMR due to power issues represent putative new causal relations. This is well illustrated by a proof-of concept example involving ANKH [MIM: 605145] and calcium levels, for which 48 publications were identified through automated literature review (Supplementary file 1f). While the TWMR effect of ANKH expression on calcium levels was not significant (αANKH→calcium=-0.02; $$P \leq 0.03$$), ANKH expression decreased citrate levels (αANKH→citrate=-0.30; $$P \leq 2.2$$×10–06), which itself increased serum calcium levels (αcitrate→calcium=0.07; $$P \leq 6.5$$×10–0). Mutations in ANKH have been associated with several rare mineralization disorders [MIM: 123000, 118600] (Williams, 2016) due to the gene encoding a transmembrane protein that channels inorganic pyrophosphate to the extracellular matrix, where at low concentrations it inhibits mineralization (Ho et al., 2000). Recently, a study proposed that ANKH instead exports ATP to the extracellular space (where it is then rapidly converted to inorganic pyrophosphate), along with citrate (Szeri et al., 2020). Citrate has a high binding affinity for calcium and influences its bioavailability by complexing calcium-phosphate during extracellular matrix mineralization and releasing calcium during bone resorption (Granchi et al., 2019). Together, our data support the role of ANKH in calcium homeostasis through regulation of citrate levels, connecting previously established independent links into a causal triad. In another example, SLC6A12 [MIM: 603080], which encodes the betaine/GABA transporter-1, involved in betaine and GABA uptake (Borden et al., 1995), was identified as a negative regulator of betaine (αSLC6A12→betaine=-0.37; $$P \leq 8.2$$×10–08). While blood betaine levels negatively impacted serum creatinine levels (αbetaine→creatinine=-0.06; $$P \leq 1.7$$×10–07), the effect of SLC6A12 expression on creatinine was not significant (αSLC6A12→creatinine=0.02; $$P \leq 1.5$$×10–03). This observation is particularly interesting given that betaine acts as a protective renal osmolyte whose plasma and kidney tissue concentration were found to be downregulated in renal ischemia/reperfusion injury (Jouret et al., 2016; Wei et al., 2014) and whose urine levels have been proposed as a biomarker for chronic kidney disease progression (Gil et al., 2018). As both renal conditions are commonly monitored through serum creatinine levels, our data support the critical role of osmolyte homeostasis in renal health. ## Discussion In this study, we combined MR approaches integrating eQTL, mQTL, and GWAS summary statistics to explore the role of the metabolome in mediating the effect of the transcriptome on complex phenotypes. Applied to 28 medically relevant traits, our approach revealed 216 causal transcript-metabolite-phenotype triplets. Our automated literature review indicates that while some detected associations were previously reported, a large fraction, especially among the triplets, appears to be novel. It should be noted that the number of previously reported associations is likely underestimated as our approach does not account for all synonyms of a given feature and requires the terms to appear in the title or abstract of the publication. This makes it more likely for hypothesis-driven studies, inherently biased toward well-studied genes and metabolites, to be identified. Conversely, high-throughput, hypothesis-free studies that report the given association in a supplemental table are likely to be missed. Furthermore, due to its automated nature, our search is context-blind, so that some of the identified studies might report negative results, associations only under specific conditions (e.g., different organisms, experimental settings), or usage of the search term with a different meaning. To attenuate the latter, we also performed manual review of the retained abstracts for transcript-to-phenotype searches. While flawed, this rough estimate of the amount of existing evidence supporting our findings can be interpreted in combination with other lines of evidence. For instance, among the 90 signals that were also identified through TWMR, $93\%$ showed a directionally concordant effect between the transcript-to-phenotype, transcript-to-metabolite, and metabolite-to-phenotype estimates (i.e., sign of product of the transcript-to-metabolite and metabolite-to-phenotype effects agrees with the sign of the transcript-to-phenotype effect). In these situations, dissection of causal effects provides clues as to the molecular mechanism through which involved genes modify complex phenotypes. This information is particularly valuable to identify key molecular mediators of highly pleiotropic genetic regions, such as the TMEM258/FADS1/FADS2 locus (Figure 3). While transcript levels of these genes affected eleven metabolites, three complex lipids were highlighted as strong molecular mediators of the transcript-to-phenotype effects. Strikingly, $58\%$ of the 216 causal transcript-metabolite-phenotype triplets were missed by TWMR – an approach that only considers gene expression and GWAS data. We highlight two novel but biologically plausible mechanisms linking ANKH to calcium levels through modulation of citrate and SLC6A12 to serum creatinine levels through regulation of the renal osmolyte betaine. Simulation analyses showed that these signals were likely missed by TWMR due to lack of power, as mediation analysis is better suited to detect associations with a low direct to total effect proportion and stronger transcript-to-metabolite than metabolite-to-phenotype effect. Promisingly, our simulations showed that mediation analysis becomes increasingly powerful over a wider range of parameter settings as the sample size of the mediator QTL study increases, highlighting the importance of generating large and publicly available molQTL datasets that can help to unravel functional gene-to-phenotype mechanisms. As illustrated through the selected examples, a large fraction of detected mediations involves genes encoding metabolic enzymes or transporters/channels, with an enrichment for ‘secondary active transmembrane transporter activity’, for example (GO:0015291; FDR=0.021; background: 7884 genes with ≥3 IVs assessed through TWMR; STRING database). Matching the finding that the most likely effector genes of mQTLs are enriched for pathway-relevant enzymes and transporters (Smith et al., 2022), these results are not surprising given that the proteins encoded by these genes directly interact with metabolites, making it more likely that the effect of changes in their expression is mediated by metabolites. While our method is well suited to detect such effects, interpretation of discovered mediations is limited by the lack of spatial resolution of the mQTL data. Access to metabolite concentrations in different cellular compartments (e.g., extracellular space, cytosol, mitochondrial matrix, etc.) would generate more fine-tuned mechanistic hypotheses that consider the directionality of metabolite fluxes. The observation that $77\%$ of the transcript’s effect on the phenotype is not mediated by metabolites suggests that either true direct effects are frequent or that other unassessed metabolites or molecular layers (e.g., proteins, post-translational modifications, etc.) play a crucial role in mediation. It is to note that in the presence of unmeasured mediators or measured mediators without genetic instruments, our mediation estimates are lower bounds of the total existing mediation. In addition, unmeasured mediators sharing genetic instruments with the measured ones can modify result interpretation as some of the observed mediators may simply be correlates of the true underlying mediators. While this is a limitation of all MR methods, metabolic networks may harbor particularly large number of genetically correlated metabolite species. Similarly, owing to linkage disequilibrium and regulatory variants affecting multiple genes, transcripts from adjacent genes might appear to be involved in the same signals, as exemplified with the TMEM258/FADS1/FADS2 locus (Figure 3). While literature supports the role of the FADS genes, one cannot exclude a role for TMEM258, nor disentangle the specific function of FADS1 and FADS2. Thanks to the flexibility of the proposed framework, we expect that in the future and upon availability of ever larger and more diverse datasets, our method could be applied to estimate the relative contribution of currently unassessed mediators in translating genotypic cascades. Another consideration is that complex phenotypes can have a stronger impact on gene expression than the opposite (Porcu et al., 2021b). Due to the lack of genome-wide trans-eQTL association summary statistics, our method does not investigate reverse causality on metabolites and gene expression, nor the role of metabolites as regulators of gene expression. Metabolites might also integrate the effect of several transcripts (i.e., multiple transcripts causally impact the levels of the same metabolite) before affecting complex phenotypes (Supplementary file 1g) or multiple metabolites may jointly mediate the impact of a single transcript. Modelling the latter phenomenon, which is beyond the scope of our current work, requires the development of structural equation models accounting for such effects and will eventually lead to a more comprehensive modelling of causal relations in complex biological networks, nuancing the interpretation of the molecular mechanisms shaping complex traits. In conclusion, we developed a modular MR framework that has increased power over classical MR approaches to detect causal transcript-to-phenotype relationships when these are mediated by alteration of metabolite levels and is likely to become increasingly powerful upon release of larger molQTL datasets. ## Univariable MR analyses TWMR and MWMR (Porcu et al., 2019) were used to estimate the causal effects of transcript and metabolite levels (exposure) on various outcomes. For each transcript/metabolite, using inverse-variance weighted (IVW) method for summary statistics (Burgess et al., 2013), the causal effect of the molecular traits on the outcome was defined as[1]α^=(β′C−1β)−1(β′C−1γ) Here, β is a vector of length n containing the standardized effect size of n independent SNPs on the gene/metabolite, derived from eQTL/mQTL studies, with β` being the transpose of β. γ is a vector of length n containing the standardized effect size of each SNP on the outcome. C is the pairwise LD matrix between the n SNPs. The standardized effect sizes for molecular and outcome GWASs were obtained from Z-score of summary statistics standardized by the square root of the sample size to be on the same standard deviation scale. IVs were selected as autosomal, non-strand ambiguous, independent (r2 <0.01), and significant (PeQTL<1.8×10−05 / PmQTL<1.0×10−07) eQTL/mQTLs available in the UK10K reference panel (Huang et al., 2015) using PLINK (v1.9) (Chang et al., 2015). As retained SNPs are independent, we used the identity matrix to approximate C. SNPs with larger effects on the outcome than on the exposure were removed, as these potentially indicate violation of the MR assumptions (i.e., likely reverse causality and/or confounding). The variance of α can be calculated approximately by the Delta method[2]var(α^)= (∂α^∂β)2∗var(β^)+(∂α^∂γ)2∗var(γ^)+(∂α^∂β)∗(∂α^∂γ)∗cov(β^,γ^) where cov(β,γ) is 0 if β and γ are estimated from independent samples. The causal effect Z-statistic for transcript/metabolite i was defined as α^iSE(α^i), where SE(α^i)=var(α^)i,i. The IVW method provides an unbiased estimate under the assumption that all genetic variants are valid IVs, that is, all three MR assumption hold. However, the third assumption (no pleiotropy) is easily violated, leading to inaccurate estimates when horizontal pleiotropy occurs (Verbanck et al., 2018). To test for the presence of pleiotropy, we used Cochran’s Q test (Bowden et al., 2015; Burgess et al., 2017) to assess whether there were significant differences between the MR effects of an instrument (i.e., αβi) and the estimated effect of that instrument on phenotype/metabolite levels (γi). We defined[3]di=γi− αβi and its variance as[4]var(di)=var(γi)+(βi)2∗var(α)+ var(γi)∗(α)2+ var(βi)∗var(α) Next, the deviation of each SNP was tested using the test statistic[5]Ti=di2var(di) ∼ χ12 When $p \leq 0.05$, the SNP with largest \| di \| was removed and the test was repeated. ## Mediation analysis through MVMR analyses An MVMR approach was used to dissect the total causal effect of transcript levels on phenotypes (αTP) into a direct (αd) and indirect (αi) effect measured through a metabolite. Through inclusion of a metabolite and its associated genetic variants (r2 <0.01, pmQTL<1 × 10–07), the direct effect of gene expression on a phenotype can be estimated using a multivariable regression model (Burgess et al., 2013) as the first element of[6]α^=(B′C−1B)−1(B′C−1γ) where B is a matrix with two columns containing the standardized effect sizes of n IVs on transcript levels in the first column and on the metabolite levels in the second column, γ is a vector of length n containing the standardized effect size of each SNP on the phenotype, and C is the pairwise LD matrix between the n SNPs. The proportion of direct effect (ρ) is calculated by regressing direct effects (αd) on total effects (αTP) and then correcting for regression dilution bias:[7]ρcorrected= ρ1−∑(SE(αTP))2∑αTP2 ## Omics and traits summary statistics Expression QTL data originated from the eQTLGen Consortium (Võsa et al., 2021) ($$n = 31$$,684), which includes cis-eQTLs (<1 Mb from gene center, two-cohort filter) for 19,250 transcripts (16,934 with at least one significant cis-eQTL at FDR <0.05 corresponding to $p \leq 1.8$ × 10–05). mQTL data originate from Shin et al., 2014, which used ultra-high performance liquid chromatography-tandem mass spectrometry to measure 486 whole blood metabolites in 7824 European individuals. Association analyses were carried out on ~2.1 million SNPs and are available for 453 metabolites at the Metabolomics GWAS Server (http://metabolomics.helmholtz-muenchen.de/gwas/). Among these metabolites, 242 were manually annotated with the HMDB identifiers (Supplementary file 1a) and used in this study. GWAS summary statistics for 28 outcome traits measured in the UKB originate from the Neale Lab (http://www.nealelab.is/uk-biobank/). Protein interactions with metabolites were downloaded from HMDB v5.0 (https://hmdb.ca/downloads/) and were used to annotate transcript-metabolites associations. ## Automated literature review An automated literature review of all transcript-metabolite associations (Supplementary file 1b) was conducted in PubMed (September 20, 2022) following the scheme:[8](Gene[Title/Abstract]) AND ((MetHMDB[Title/Abstract]) OR (MetShin[Title/Abstract])) With Gene being the name of the gene whose transcript is involved in the association, MetHMDB the involved metabolite’s common name on HMDB, and MetShin the involved metabolite’s name as reported in Shin et al., 2014. Returned PubMed identifiers were retrieved (Supplementary file 1b). For transcript-metabolite associations involved in a causal triplet and for which the transcript-metabolites returned at least one publication (Supplementary file 1f), the search was extend by (i) adding an additional search term for the trait (i.e., AND (trait[Title/Abstract])) and (ii) substituting the metabolite term for the trait term. Returned PubMed identifiers were retrieved and corresponding abstracts were manually curated to exclude abstracts in which the search terms were used in a meaning other than the intended one (Supplementary file 1f). ## Simulation analyses Simulation analyses were conducted to assess the gain in power upon inclusion of metabolomics data in the MR framework. In the simulated scenario, a transcript has an effect on a phenotype mediated by a metabolite. Two parameters were allowed to vary: the proportion (ρ) of direct effect (i.e., effect not mediated by the metabolite) and the ratio (σ) between the effect of the transcript on the metabolite (αTM) and of the metabolite on the phenotype (αMP). Other parameters were fixed, including the heritability of the transcript at hT2=0.06 (corresponding to the median h2 in the eQTLGen data), the number of IVs NIVs at 6 (corresponding to the median number of IVs used in TWMR analyses). Effect sizes βeQTL are from a normal distribution βeQTL∼N(0,hT2NIVs). The causal effect of the transcript on the phenotype (αTP) was fixed to 0.035, which results in ∼$65\%$ power to detect a significant effect with TWMR. These quantities allowed to define βGWAS as βGWAS= αTP* βeQTL+ εP, where εP∼N(0,1NGWAS) with NGWAS=300,000 to reflect the sample size of UKB GWASs. The same vector of βeQTL was used to define βmQTL and estimate the causal effect of the transcript on the metabolite. βmQTL was defined as βmQTL=αTM∗βeQTL+εM, where εM∼N(0,1NmQTL) and NmQTL=8000 to reflect the sample size of the mQTL study used in this work. Simulations were also performed at NmQTL=90,000, to reflect sample size of potential future studies and NmQTL=1000, NmQTL=2000 and NmQTL=4000, to compare the two approaches’ power were the developed framework to be applied on existing smaller mQTL datasets. The total effect αTP can be expressed as αTP=αTMαMP+ αdirect, where αdirect represents the direct effect of the transcript on the phenotype and αTMαMP is the indirect effect mediated by the metabolite. Equivalently, αTMαMP=αTP(1-ρ) where ρ=αdirectαTP. To assess the ratio between the effect of the transcript on the metabolite and the effect of the metabolite on the phenotype (i.e., σ=αTM/αMP), αTM can be expressed as αTM=αTP∗(1−ρ)∗σ. Similarly, to estimate the effect of the metabolite on the phenotype, a metabolite with heritability hM2=0.04 (corresponding to the median of h2 in the KORA +TwinsUK mQTL data) and NIVs=5 (corresponding to the median number of IVs used in MWMR analyses) is considered. Effect size βmQTL are from a normal distribution βmQTL∼N(0,hM2NIVs). These quantities allowed to define βGWAS as βGWAS= αTP1-ρ/σ βmQTL+ εP, where εP∼N(0,1NGWAS). Ranging ρ and σ from –2 to 2 and from 0.1 and 10, respectively, we run each simulation for 500 transcripts measuring 80 metabolites at each run and performed TWMR and MWMR starting from above-described βeQTL, βmQTL, and βGWAS. For each MR analysis the power to detect a significant association as well as the difference in power between TWMR and the mediation analyses (i.e., powerTP- powerTMpowerMP) was calculated. Each specific scenario was repeated 10 times and the average difference in power across simulation was plotted as a heatmap. ## Data and code availability All data used in this study are publicly available. GWAS summary statistics for outcome traits measured in the UKB originate from the Neale Lab (http://www.nealelab.is/uk-biobank/). eQTL data originated from the eQTLGen Consortium (https://www.eqtlgen.org) and was published in Võsa et al., 2021. mQTL data originate from Shin et al., 2014, and are available at the Metabolomics GWAS Server (http://metabolomics.helmholtz-muenchen.de/gwas/). The HMDB was used to annotate metabolites and the v5.0 release from November 9, 2021, of the ‘All proteins’ file was downloaded to extract transcript-metabolite interactions (https://hmdb.ca/downloads). PubMed was used for the automated literature review (https://pubmed.ncbi.nlm.nih.gov). The UCSC Genome Browser (https://genome.ucsc.edu/) was used to visualize the FADS locus, while the GWAS Catalog was used to assess the number of reported GWAS signals in the region (https://www.ebi.ac.uk/gwas/). The STRING database was used for the enrichment analysis (https://string-db.org/). Produced data is available as Supplementary file 1 and Source Data. Code used to perform analyses is freely available at https://github.com/eleporcu/Gene_Metab_Pheno; (Porcu, 2022 copy archived at swh:1:rev:c6bff8d094e369ff0d399751fc85fcd5ea250134). ## Funding Information This paper was supported by the following grants: ## Data availability All data used in this study are publicly available. GWAS summary statistics for outcome traits measured in the UK Biobank originate from the Neale Lab (http://www.nealelab.is/uk-biobank/). eQTL data originated from the eQTLGen Consortium (https://www.eqtlgen.org) and was published in Vosa et al., 2021 [3]. mQTL data originate from Shin et al. 2014 [6], and are available at the Metabolomics GWAS Server (http://metabolomics.helmholtz-muenchen.de/gwas/). The Human Metabolome Database (HMDB) was used to annotate metabolites and the v5.0 release from 2021-11-09 of the "All proteins" file was downloaded to extract transcript-metabolite interactions (https://hmdb.ca/downloads). PubMed was used for the automated literature review (https://pubmed.ncbi.nlm.nih.gov). The UCSC Genome Browser (https://genome.ucsc.edu/) was used to visualize the FADS locus, while the GWAS Catalog was used to assess the number of reported GWAS signals in the region (https://www.ebi.ac.uk/gwas/). 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--- title: Orai3 and Orai1 mediate CRAC channel function and metabolic reprogramming in B cells authors: - Scott M Emrich - Ryan E Yoast - Xuexin Zhang - Adam J Fike - Yin-Hu Wang - Kristen N Bricker - Anthony Y Tao - Ping Xin - Vonn Walter - Martin T Johnson - Trayambak Pathak - Adam C Straub - Stefan Feske - Ziaur SM Rahman - Mohamed Trebak journal: eLife year: 2023 pmcid: PMC9998091 doi: 10.7554/eLife.84708 license: CC BY 4.0 --- # Orai3 and Orai1 mediate CRAC channel function and metabolic reprogramming in B cells ## Abstract The essential role of store-operated Ca2+ entry (SOCE) through Ca2+ release-activated Ca2+ (CRAC) channels in T cells is well established. In contrast, the contribution of individual Orai isoforms to SOCE and their downstream signaling functions in B cells are poorly understood. Here, we demonstrate changes in the expression of Orai isoforms in response to B cell activation. We show that both Orai3 and Orai1 mediate native CRAC channels in B cells. The combined loss of Orai1 and Orai3, but not Orai3 alone, impairs SOCE, proliferation and survival, nuclear factor of activated T cells (NFAT) activation, mitochondrial respiration, glycolysis, and the metabolic reprogramming of primary B cells in response to antigenic stimulation. Nevertheless, the combined deletion of Orai1 and Orai3 in B cells did not compromise humoral immunity to influenza A virus infection in mice, suggesting that other in vivo co-stimulatory signals can overcome the requirement of BCR-mediated CRAC channel function in B cells. Our results shed important new light on the physiological roles of Orai1 and Orai3 proteins in SOCE and the effector functions of B lymphocytes. ## Introduction Calcium (Ca2+) is an essential regulator of immune cell function (Prakriya and Lewis, 2015; Trebak and Kinet, 2019). Crosslinking of immunoreceptors like the T cell receptor (TCR) or B cell receptor (BCR) triggers a robust elevation in intracellular Ca2+ concentrations through the release of endoplasmic reticulum (ER) Ca2+ stores and a concomitant influx of Ca2+ from the extracellular space (Baba and Kurosaki, 2016; King and Freedman, 2009). In lymphocytes, Ca2+ entry across the plasma membrane (PM) is predominately achieved through CRAC channels, which constitute the ubiquitous SOCE (Prakriya and Lewis, 2015; Trebak and Kinet, 2019; Trebak and Putney, 2017; Vaeth et al., 2020). Stimulation of immunoreceptors coupled to phospholipase C isoforms results in the production of the secondary messenger inositol-1,4,5-trisphosphate (IP3), which triggers the release of ER Ca2+ through the activation of ER-resident IP3 receptors. A reduction in ER Ca2+ concentrations is sensed by stromal interaction molecule 1 (STIM1) and its homolog STIM2, resulting in a conformational change and clustering in ER-PM junctions where STIM molecules interact with and activate PM hexameric Orai channels (Orai1-3) to mediate SOCE (Lunz et al., 2019). Ca2+ entry through CRAC channels regulates immune cell function through a host of Ca2+-sensitive transcription factors including the NFAT, nuclear factor κB (NF-κB), c-Myc, and mTORC1 (Berry et al., 2018; Trebak and Kinet, 2019; Vaeth and Feske, 2018; Vaeth et al., 2020; Vaeth et al., 2017a). The coordination of these master transcriptional regulators is indispensable for innate and adaptive immune cell effector function including entry into the cell cycle, clonal expansion, cytokine secretion, differentiation, and antibody production (Shaw and Feske, 2012; Trebak and Kinet, 2019; Vaeth et al., 2020). Orai1 has long been established as a central component of the native CRAC channel in all cell types studied. Although all three Orai isoforms are ubiquitously expressed across tissue types and can form functional CRAC channels when ectopically expressed, only recently have Orai2 and Orai3 emerged as regulators of native CRAC channel function (Emrich et al., 2022b; Tsvilovskyy et al., 2018; Vaeth et al., 2017b; Yoast et al., 2020a). Patients with inherited loss-of-function (LoF) mutations in Orai1 (e.g. R91W mutation) develop a CRAC channelopathy with symptoms including combined immunodeficiency, ectodermal dysplasia, muscular hypotonia, and autoimmunity (Feske et al., 2006; Lian et al., 2018; McCarl et al., 2009). While these patients display relatively normal frequencies of most immune cell populations, they are highly susceptible to reoccurring viral, bacterial, and fungal infections due to defects in T cell expansion, cytokine secretion, and metabolism (Vaeth et al., 2020). There are currently no reported patient mutations in Orai2 or Orai3 genes and the role these channels play within the immune system had largely been unclear. Interestingly, recent studies utilizing global Orai2 knockout mice have demonstrated that loss of Orai2 leads to enhanced SOCE and corresponding CRAC currents in bone marrow-derived macrophages, dendritic cells, T cells, enamel cells, and mast cells (Eckstein et al., 2019; Tsvilovskyy et al., 2018; Vaeth et al., 2017b), suggesting that Orai2 is a negative regulator of CRAC channel activity. Combined knockout in mice of both Orai1 and Orai2 in T cells led to a near ablation of SOCE and impaired humoral immunity, while ectopic expression of pore-dead mutants of Orai1 (E106Q) or Orai2 (E80Q) into individual Orai1-/- or Orai2-/- T cells blocked native SOCE (Vaeth et al., 2017b). Similarly, the generation of HEK293 cell lines lacking each Orai isoform individually and in combination resulted in altered Ca2+ oscillation profiles, CRAC currents, and NFAT isoform activation (Emrich et al., 2022b; Emrich et al., 2021; Yoast et al., 2020a; Yoast et al., 2020b). These data provide evidence that Orai2 and Orai3 exert negative regulatory effects on native CRAC channels possibly by forming heteromeric CRAC channels with Orai1. In contrast to T cells, much less is known regarding the role of SOCE in B lymphocytes. Early landmark work investigating B cell-specific STIM knockout mice established that STIM1 mediates the vast majority of SOCE in B cells, while only the combined deletion of both STIM1 and STIM2 resulted in substantial impairments in B cell survival, proliferation, and NFAT-dependent IL-10 secretion (Matsumoto et al., 2011). Unexpectedly, mice with STIM1/STIM2-deficient B cells show normal humoral immune responses to immunization with both T cell-dependent and independent antigens. These and other studies suggest that the severe immunodeficiency observed in CRAC channelopathy patients is due to impaired T cell responses (Matsumoto et al., 2011; Vaeth et al., 2016). Investigation of Orai1-/- mice on the mixed Institute for Cancer Research (ICR) background and Orai1R93W knock-in mice (the equivalent of human R91W mutation) demonstrated that SOCE is significantly attenuated in B cells (Gwack et al., 2008; McCarl et al., 2010). However, SOCE in B cells is only partially reduced with the loss of Orai1, suggesting that Orai2 and/or Orai3 mediate the remaining SOCE in B cell populations. A recent study showed that the deletion of Orai3 in mice does not affect B cell function and humoral immune responses (Wang et al., 2022), suggesting other Orai homologs compensate for the loss of Orai3. Thus, the contribution of each Orai isoform to native CRAC channel function and downstream signaling in B cells has remained obscure. In this study, we investigated the contributions of Orai isoforms to SOCE and its downstream signaling in B cells through multiple CRISPR/Cas9 knockout B cell lines and novel B cell-specific Orai knockout mice. Our findings demonstrate that the expression of each Orai isoform is dynamically regulated in response to B cell activation and that the magnitude of SOCE is unique among effector B cell populations. We show that the deletion of Orai1 alone does not alter BCR-evoked cytosolic Ca2+ oscillations, proliferation, and development. Unexpectedly, we show that Orai3 is involved in regulating B cell SOCE and downstream signaling functions. Deletion of both Orai1 and Orai3 strongly inhibits SOCE and hampers mitochondrial metabolism in B cells. Transcriptome and metabolomic analysis uncovered key signaling pathways that are regulated by SOCE and the phosphatase calcineurin for the efficient transition of B cells from a quiescent to a metabolically active state. Surprisingly, humoral immunity to influenza A virus infection of mice with B cell-specific deletion of both Orai1 and Orai3 was unaltered, suggesting that alternative Ca2+-independent signaling pathways activated in vivo through co-stimulatory receptors can overcome the loss of CRAC channel activity in B cells. Our data elucidate the role of SOCE in B cell functions and show that CRAC channel function in B lymphocytes is mediated by both Orai1 and Orai3. This knowledge is important for the potential targeting of CRAC channels in specific lymphocyte subsets in immune and inflammatory diseases. ## Orai channel isoforms are dynamically regulated in response to B cell activation Throughout their lifespan, B lymphocytes must undergo dramatic periods of metabolic adaptation whereby naïve, metabolically quiescent cells prepare to clonally expand and differentiate into effector populations (Akkaya and Pierce, 2019; Boothby and Rickert, 2017). Induction of these diverse metabolic programs is driven by downstream BCR-mediated Ca2+ signals in combination with Ca2+-independent costimulatory signals including CD40 activation and/or stimulation with various toll-like receptor (TLR) ligands (Akkaya et al., 2018; Baba and Kurosaki, 2016; Berry et al., 2020). Thus, using Seahorse assays we first evaluated how activation of wild-type mouse primary splenic B cells (isolated as described in methods) regulates oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) as indicators of oxidative phosphorylation (OXPHOS) and glycolysis, respectively (Figure 1A and B). Naïve primary B cells from mouse spleens were isolated by negative selection and stimulated for 24 hr under five different conditions as follows: [1] control unstimulated cells (Unstim), [2] stimulation with anti-IgM antibodies (IgM) to activate the BCR, [3] stimulation with anti-CD40 (CD40), [4] stimulation with lipopolysaccharides (LPS), and [5] co-stimulation with anti-IgM and anti-CD40. B cell stimulation for 24 hr led to a robust increase in basal OCR, except for B cells stimulated with anti-CD40 alone where the increase in basal OCR was not statistically significant (Figure 1A and C). Figure 1B shows that B cells stimulated with either anti-IgM, anti-IgM +anti-CD40, or LPS become highly energetic and upregulate both OXPHOS and glycolytic pathways. However, B cells stimulated with anti-CD40 alone remain metabolically quiescent like control non-stimulated B cells (Figure 1B). We used the protonophore trifluoromethoxy carbonylcyanide phenylhydrazone (FCCP) to dissipate the mitochondrial membrane potential and calculate the maximal respiratory capacity of B cells. Consistent with results obtained for basal OCR, stimulated B cells showed a robust increase in maximal respiratory capacity except for cells stimulated with anti-CD40 alone (Figure 1D). Both total mitochondrial content and membrane potential measured with MitoTracker Green and TMRE staining, respectively, were substantially increased following anti-IgM, anti-IgM +anti-CD40, or LPS stimulation while both parameters were only slightly increased with stimulation with anti-CD40 alone (Figure 1E and F). While previous studies suggested that Orai1 regulates the majority of SOCE in primary B cells (Gwack et al., 2008; McCarl et al., 2010), the expression of different Orai isoforms both at rest and upon B cell activation is unknown. Utilizing the same five experimental conditions described above, we analyzed the mRNA expression of Orai1, Orai2, and Orai3 following 24 hr of stimulation. Stimulation with either anti-IgM or anti-IgM +anti-CD40 dramatically increased Orai1 expression, while the magnitude of this change following anti-CD40 or LPS stimulation was smaller and did not reach statistical significance (Figure 1G). Interestingly, stimulation conditions that strongly increased mitochondrial respiration (anti-IgM, anti-IgM +anti-CD40, LPS) all significantly reduced expression of Orai2 while stimulation with anti-CD40 alone had no significant effect on Orai2 expression (Figure 1H). The expression of Orai3 has significantly increased only with anti-IgM +anti-CD40 or LPS stimulation, but not with anti-IgM or anti-CD40 alone (Figure 1I). Figure 1.: B cell activation dynamically regulates Orai channel expression.(A) Measurement of oxygen consumption rate (OCR) in primary B lymphocytes following 24 hr stimulation with anti-IgM (20 μg/mL), anti-CD40 (10 μg/mL), anti-IgM +anti-CD40, or LPS (10 μg/mL) using the Seahorse Mito Stress Test (n=3 biological replicates). (B) Energy map of maximal OCR and extracellular acidification rate (ECAR) following the addition of the protonophore carbonyl cyanide p-trifluoromethoxyphenylhydrazone (FCCP). (C, D) Quantification of basal (C) and maximal (D) respiration from Seahorse traces in (A) (One-way ANOVA with multiple comparisons to Unstimulated). (E, F) Measurement of (E) total mitochondrial content with the fluorescent dye MitoTracker Green and (F) mitochondrial membrane potential with TMRE following 24 hr stimulation (n=3 biological replicates; One-way ANOVA with multiple comparisons to Unstimulated). (G–I) Quantitative RT-PCR of (G) Orai1, (H) Orai2, and (I) Orai3 mRNA following 24 hr of stimulation with the stimuli indicated (n=3 biological replicates; one-way ANOVA with multiple comparisons to Unstimulated). All scatter plots and Seahorse traces are presented as mean ± SEM. For all figures, p<0.05; p<0.01; p<0.001; **p<0.0001; ns, not significant. Figure 1—source data 1.Source data for Figure 1. ## Both Orai3 and Orai1 mediate SOCE in A20 B lymphoblasts Robust activation of primary B cells with anti-IgM/anti-CD40 co-stimulation resulted in upregulation of both Orai1 and Orai3 mRNA with downregulation of Orai2 (Figure 1G–I). We, therefore, reasoned that B cells may sustain long-term cytosolic Ca2+ signals through Orai1 and/or Orai3, while downregulation of Orai2 might relieve the inhibition of native CRAC channels, as recently demonstrated for activated T cells (Vaeth et al., 2017b). To gain insights into the molecular composition of CRAC channels in B cells, we first developed an in vitro system utilizing the mouse A20 B lymphoblast cell line to generate single and double Orai1 and Orai3 knockout cell lines with CRISPR/Cas9 technology. Two guide RNA (gRNA) sequences were utilized to cut at the beginning and end of the coding region of mouse Orai1 and Orai3 (mOrai1 and mOrai3), effectively excising the entirety of the genomic DNA for each Orai gene (Figure 2A). We generated multiple A20 clones that were lacking Orai1 and Orai3 individually and in combination and these knockout clones were validated through genomic DNA sequencing (Source data 1) and the absence of Orai1 or Orai3 transcripts by qPCR (Figure 2B–D). We measured SOCE in A20 cells after passive store depletion with thapsigargin, an inhibitor of the sarcoplasmic/endoplasmic reticulum ATPase (SERCA). A20 cells lacking Orai1 demonstrated a large reduction in maximal SOCE by ~$62\%$, while in cells lacking Orai3 SOCE was reduced by ~$28\%$ (Figure 2E and F). Importantly, the combined deletion of both Orai1 and Orai3 caused a near abrogation of SOCE by ~$91\%$ (Figure 2E and F). Furthermore, measurements of cytosolic Ca2+ oscillations induced by anti-IgG stimulation demonstrated that the oscillation frequency was substantially reduced with combined Orai1/Orai3 knockout, while only slightly reduced with the loss of either Orai1 or Orai3 individually (Figure 2G–K). These data suggest that both Orai3 and Orai1 were involved in optimal CRAC channel function in A20 B lymphoblasts. These data are consistent with the function of Orai channels in other cell types. Indeed, we recently utilized a series of single, double, and triple Orai CRISPR/Cas9 knockout HEK293 cell lines and showed that SOCE, but not Orai1, is required for agonist-induced Ca2+ oscillations (Yoast et al., 2020b). Under conditions of physiological agonist stimulation that causes modest ER store depletion while eliciting Ca2+ oscillations, Orai2 and Orai3 were sufficient to maintain cytosolic Ca2+ oscillations, while having relatively minor contributions to SOCE induced by maximal store depletion (Yoast et al., 2020b). Figure 2.: Orai1 and Orai3 mediate the bulk of store-operated Ca2+ entry (SOCE) in A20 B lymphoblasts.(A) Cartoon schematic of the two gRNA CRISPR strategies we used to excise mouse Orai1 and Orai3 genes. (B–D) Quantitative RT-PCR of (B) Orai1, (C) Orai2, and (D) Orai3 mRNA in A20 Orai CRISPR clones (n=3 biological replicates). (E) Measurement of SOCE with Fura2 upon store depletion with 2 µM thapsigargin in 0 mM Ca2+ followed by re-addition of 2 mM Ca2+ to the external bath solution. (F) Quantification of peak SOCE in (E) (from left to right n=99, 100, 89, and 98 cells; Kruskal-Wallis test with multiple comparisons to WT A20). (G–J) Representative Ca2+ oscillation traces from 5 cells/condition measured with Fura2 upon stimulation with 10 μg/mL anti-IgG antibodies at 60 s (indicated by arrows) in the presence of 2 mM external Ca2+. (K) Quantification of total oscillations in 9 min from (G–J) (from left to right n=76, 79, 79, and 78 cells; Kruskal-Wallis test with multiple comparisons to WT A20). All scatter plots are presented as mean ± SEM. For all figures, p<0.05; p<0.01; *p<0.0001; ns, not significant. Figure 2—source data 1.Source data for Figure 2. ## Orai1 is dispensable for cytosolic Ca2+ oscillations in primary B cells To determine the contribution of Orai1-mediated Ca2+ signals to primary B cell function, we generated B cell-specific Orai1 knockout (Orai1fl/fl Mb1-Cre/+) mice (Figure 3). Compared to Orai1fl/fl controls, the average surface expression of Orai1 was significantly reduced on B220+ B cells from Orai1fl/fl Mb1-Cre/+ mice by~$70\%$ (Figure 3A and B). The residual signal of ~$30\%$ above FMO (Figure 3A) is likely the result of the non-specific binding of the Orai1 antibody by its variable region to other Orai homologs (Orai2, Orai3), which partially share the peptide epitope recognized by the anti-Orai1 antibody. This residual Orai1 antibody signal is consistent with previous studies showing that Cre-mediated deletion of Orai1fl/fl alleles is very efficient in other cell types including T cells (Kaufmann et al., 2016), and neuronal progenitor cells (Somasundaram et al., 2014). Indeed, other lines of evidence indicate that Orai1 is deleted in B cells of Orai1fl/fl Mb1-Cre/+ mice: our qPCR data show that B cells from Orai1fl/fl Mb1-Cre/+ mice showed a near abrogation of Orai1 mRNA (to ~$3\%$ of control) compared to Mb1-Cre/+ control mice (Figure 3C) without any significant change in Orai2 (Figure 3D) or Orai3 (Figure 3E) mRNA expression. Further, Ca2+ measurements demonstrated that SOCE is reduced by ~$69\%$ in B cells isolated from Orai1fl/fl Mb1-Cre/+ mice (Figure 3F and G). We measured cytosolic Ca2+ oscillations in response to anti-IgM stimulation in the presence of 1 mM extracellular Ca2+. In agreement with previous reports and consistent with our data with A20 B lymphoblasts (Figure 2G and H), Orai1-deficient B cells display a comparable frequency of agonist-induced Ca2+ oscillations as control B cells (Figure 3H–J). Thus, while Orai1 knockout significantly decreased SOCE in primary B cells, it is dispensable for maintaining Ca2+ oscillations in response to physiological agonist stimulation. In control Mb1-Cre/+ mice, Orai1 expression was comparable between B220+ B cells and CD8+ T cells, with CD4+ T cells showing a slightly lower signal (Figure 3—figure supplement 1A, B). After 24 hour stimulation of control B cells under the five conditions described above, Orai1-deficient B cells showed no apparent defects in activation as the expression of MHC-II was comparable to control B cells (Figure 3—figure supplement 1C–E), while expression of CD86 was slightly reduced with anti-IgM stimulation or anti-IgM +anti-CD40 co-stimulation (Figure 3—figure supplement 1F–H). Figure 3.: Orai1 is dispensable for BCR-induced Ca2+ oscillations in primary B cells.(A) Representative flow cytometry histogram of B cells isolated from Orai1fl/fl and Orai1fl/fl Mb1-Cre/+ mice. Splenocytes from naïve Orai1fl/fl and Orai1fl/fl Mb1-Cre/+ mice were fixed, permeabilized, and stained with rabbit anti-Orai1 polyclonal antibody (YZ6856, epitope: human ORAI1#275–291 intra-cellular C-terminal, cross-reacts with the mouse). The numbers inside the panel represent the mean fluorescence intensity (MFI) for the Orai1 antibody staining for each sample. (B) Quantification of Orai1 MFI minus fluorescence minus one (FMO) in B cells is shown. (n=4 biological replicates; unpaired T-test). (C–E) Quantitative RT-PCR of (C) Orai1, (D) Orai2, and (E) Orai3 mRNA in isolated B cells (n=6 biological replicates for each; Mann-Whitney test). (F) Measurement of SOCE with Fura2 upon store depletion with 2 µM thapsigargin in 0 mM Ca2+ followed by re-addition of 1 mM Ca2+ to the external bath solution. (G) Quantification of peak store-operated Ca2+ entry (SOCE) in (F) n=169 and 178 cells; Mann-Whitney test. (H–I) Representative Ca2+ oscillation traces from 5 cells/condition measured with Fura2 upon stimulation with 20 μg/mL anti-IgM antibodies at 1 min (indicated by arrows) in the presence of 1 mM external Ca2+. (J) Quantification of total oscillations in 9 min from (I, J) (n=97 and 111 cells; Mann-Whitney test). All scatter plots are presented as mean ± SEM. For all figures, p<0.01; **p<0.0001; ns, not significant. Figure 3—source data 1.Source Data for Figure 3. ## Loss of Orai1 does not alter B cell development While patients and mouse models with loss of function (LoF) mutations in Orai1 present with severe immunodeficiency due to impaired T cell function, the development of most immune cell populations is largely unaltered, suggesting SOCE is dispensable for initial immune cell selection and development (Gwack et al., 2008; Lacruz and Feske, 2015; McCarl et al., 2010). We evaluated B cell development within the bone marrow and spleen of Orai1fl/fl Mb1-Cre/+ mice (Figure 4). In agreement with previous reports that investigated B cell development in global Orai1 deficient mice, the development of early B cell progenitors in fractions A-F within the bone marrow were unaltered in Orai1fl/fl Mb1-Cre/+ mice (Figure 4A–C). Similarly, we observed no significant differences in peripheral transitional type 1 (T1), T2, and T3 immature B cells in the spleen (Figure 4D and F). Mature B cell populations in the spleen are predominately follicular B cells with a smaller fraction of marginal zone B cells, and these ratios were largely comparable between control and Orai1fl/fl Mb1-Cre/+ mice (Figure 4E and F). Figure 4.: Orai1 is dispensable for B cell development.(A–B) Flow cytometric analysis of bone marrow populations for B cell fractions A (B220+CD43+HSA−BP-1−), B (B220+CD43+HSA+BP-1−), C (B220+CD43+HSA+BP-1+), D (B220+CD43−IgM−CD93+), E (B220+CD43−IgM+CD93+), and F (B220+CD43−IgM+CD93−). (C) Quantification of bone marrow populations in (A, B) (n=7 and six biological replicates; Mann-Whitney test). (D) Flow cytometric analysis of isolated populations in the spleen for B cell developmental stages T1 (B220+AA4.1+CD23−IgM+), T2 (B220+AA4.1+CD23+IgM+), and T3 (B220+AA4.1+CD23+IgM−). (E) Flow cytometric analysis of isolated populations in the spleen for marginal zone (MZ) B cells (B220+CD93−CD23−IgM+) and follicular (FO) B cells (B220+CD93−CD23+IgM+). (F) Quantification of splenic populations in (D, E) (n=7 and six biological replicates; Mann-Whitney test). All scatter plots are presented as mean ± SEM. Figure 4—source data 1.Source data for Figure 4. ## Both Orai3 and Orai1 synergistically contribute to SOCE in primary B cells To determine whether Orai3 regulates Ca2+ signals and function of primary B cells, we generated B cell-specific Orai3 knockout mice (Orai3fl/fl Mb1-Cre/+) and B cell-specific double Orai1/Orai3 knockout mice (Orai1/Orai3fl/fl Mb1-Cre/+). Primary B cells isolated from spleens of Orai1, Orai3, and Orai1/Orai3 knockout mice showed near complete ablation of their respective Orai isoform mRNA (to ~0.3–$4\%$ of control) with no compensatory changes in Orai2 mRNA expression (Figure 5A). As documented above (Figure 3F and G), depletion of ER Ca2+ stores with thapsigargin demonstrated that SOCE was significantly reduced in Orai1 knockout B cells (by ~$69\%$), and this remaining Ca2+ entry was further reduced in Orai1/Orai3 double knockout B cells (by ~$83\%$; Figure 5B and C). However, SOCE in single Orai3 knockout B cells was ~$102\%$ of control, which was not significantly different from control B cells. Figure 5.: Orai1 and Orai3 synergistically mediate store-operated Ca2+ entry (SOCE) in primary B cells.(A) Quantitative RT-PCR of Orai1, Orai2, and Orai3 mRNA in negatively isolated B cells from B cell-specific Orai knockout mice (n=3 biological replicates per genotype). (B) Measurement of SOCE in naïve B cells with Fura2 upon store depletion with 2 µM thapsigargin in 0 mM Ca2+ followed by re-addition of 1 mM Ca2+ to the external bath solution. Subsequently, SOCE was inhibited with the addition of 50 µM 2-APB at 13 min followed by 5 µM Gd3+ at 18 min. (C) Quantification of peak SOCE in (B) (from left to right n=200, 200, 199, and 149 cells; Kruskal-Wallis test with multiple comparisons to Mb1-Cre/+). (D) Quantification of the rate of 2-APB inhibition from 13 to 18 min. (E) Quantitative RT-PCR of Orai1, Orai2, and Orai3 mRNA in negatively isolated B cells from wild-type and Orai2-/- mice (n=3 and six biological replicates). (F) Measurement of SOCE in naïve B cells with Fura2 from wild-type and Orai2-/- mice. (G) Quantification of peak SOCE in (F) (n=147 and 240 cells; Mann-Whitney test). All scatter plots are presented as mean ± SEM. For all figures, p<0.001; *p<0.0001; ns, not significant. Figure 5—source data 1.Source data for Figure 5. Orai channel isoforms demonstrate distinct pharmacological profiles and sensitivities to various CRAC channel modifiers (Bird and Putney, 2018; Zhang et al., 2020). One of the most extensively utilized CRAC channel modifiers is 2-aminoethyl diphenyl borate (2-APB) (Prakriya and Lewis, 2001), which at high (25–50 µM) concentrations strongly inhibits Orai1, partially inhibits Orai2, and potentiates Orai3 channel activity (DeHaven et al., 2008; Zhang et al., 2008; Zhang et al., 2020). After allowing Ca2+ entry to the plateau, 50 µM 2-APB was added, followed by the addition of 5 µM gadolinium (Gd3+) which potently blocks all Orai isoforms (Yoast et al., 2020b; Zhang et al., 2020). In wild-type B cells, 2-APB led to a gradual inhibition of SOCE over the course of 5 min, which completely returned to baseline following the addition of Gd3+. Interestingly, the remaining SOCE in Orai1 knockout B cells showed essentially no inhibition by 2-APB but was strongly inhibited by Gd3+. The lack of effect of 2-APB is likely due to the residual SOCE mediated by the remaining Orai3 isoform, which is resistant to inhibition by 2-APB. Furthermore, SOCE in Orai3 knockout B cells was inhibited at a significantly faster rate by 2-APB compared to wild-type B cells and was not further inhibited by Gd3+ (Figure 5B and D). The small amount of SOCE remaining in Orai1/Orai3 double knockout cells was reduced to baseline following the addition of 2-APB. This remaining SOCE in Orai1/Orai3 double knockout B cells prompted us to measure SOCE in B cells isolated from global Orai2 knockout mice (Orai2-/-). B cells from Orai2-/- mice showed complete loss of Orai2 mRNA with no apparent compensation in Orai1 or Orai3 mRNA (Figure 5E). SOCE stimulated by thapsigargin in B cells from Orai2-/- mice was enhanced by comparison to B cells from wild-type littermate controls (Figure 5F and G), suggesting that, as was shown in T cells (Vaeth et al., 2017b), Orai2 is also a negative regulator of SOCE in B cells. Thus, these experiments indicate that the remaining SOCE in B cells from Orai3/Orai1 double knockout mice is most likely mediated by Orai2. Given the modulation of Orai channel isoforms in response to B cell activation, we performed Ca2+ imaging recordings, similar to those in Figure 5, on primary B cells that were first activated for 48 hr with anti-IgM +anti-CD40 (Figure 5—figure supplement 1A–C). While similar trends were observed in experiments with naïve primary B cells, several exceptions were notable. SOCE in Orai3 knockout B cells was reduced compared to control cells, like data in A20 Orai knockout cell lines (Figure 5—figure supplement 1A, B). Differences in inhibition by 50 µM 2-APB also became less pronounced between control and Orai3 knockout cells (Figure 5—figure supplement 1A–C). Collectively, these data demonstrate that Orai3 contributes to SOCE in activated B cells. ## SOCE is an essential regulator of NFAT activation in B cells Ca2+ entry through CRAC channels is critical for the activation of multiple NFAT isoforms (Prakriya and Lewis, 2015; Trebak and Kinet, 2019; Vaeth and Feske, 2018). NFAT nuclear translocation mostly requires Ca2+ entry through native Orai1, while native Orai2 and Orai3 caused marginal NFAT activation in cells lacking Orai1 despite mediating a Ca2+ signal (Yoast et al., 2020b). To determine the role of Orai1 in regulating NFAT1 activation in B cells, primary B cells from control Mb1-Cre/+ and Orai1fl/fl Mb1-Cre/+ mice were stimulated with anti-IgM antibodies and native NFAT1 nuclear translocation was evaluated using ImageStream analysis (Figure 6A–C). In unstimulated B cells, colocalization of endogenous NFAT1 with nuclear DAPI staining was relatively low, and this colocalization increased three-fold following anti-IgM stimulation (Figure 6A–C). However, this anti-IgM mediated NFAT1 translocation was significantly reduced in B cells from Orai1fl/fl Mb1-Cre/+ mice (Figure 6B and C). Figure 6.: Orai1 is a regulator of nuclear factor of activated T cells (NFAT) activation in naïve and activated B cells.(A) Representative Imagestream images following intracellular staining for NFAT1 and DAPI in naïve B cells from Mb1-Cre/+ mice before and after 20 μg/mL anti-IgM stimulation for 15 min. Merge image indicates similarity score co-localization between NFAT1/DAPI. (B) Histograms of NFAT1/DAPI similarity scores before (black trace) and after (red trace) anti-IgM stimulation in naïve B cells from Mb1-Cre/+ and Orai1fl/fl Mb1-Cre/+ mice. (C) Quantification of similarity scores following anti-IgM stimulation in (B) (n=3 biological replicates for each; unpaired T-test). (D) Western blot analysis of NFAT1 and α-tubulin in naïve B cells isolated from Mb1-Cre/+ and Orai1fl/fl Mb1-Cre/+ mice. B cells were left unstimulated or treated with 2 µM thapsigargin (Tg) for 15 min before harvesting. (E) Quantification of NFAT1 dephosphorylation in (D) (n=4 biological replicates for each; Mann-Whitney test). (F) Western blot analysis of NFAT1 and α-tubulin in B cells stimulated for 48 hr with anti-IgM +anti-CD40. (G) Quantification of NFAT1 dephosphorylation in (F) (n=3 biological replicates for each; Mann-Whitney test). All scatter plots are presented as mean ± SEM. For all figures, p<0.05; p<0.01; *p<0.0001. Figure 6—source data 1.Source data for Figure 6 including labeled blots for NFAT1 and α-tubulin from Figure 6D and Figure 6F. Figure 6—source data 2.Panel D and F-Raw unedited uncropped blots for NFAT1 and α-tubulin from Figure 6. We used another complimentary biochemical protocol to assess the nuclear translocation of native NFAT1 in response to stimulation with thapsigargin. After B cell stimulation, proteins were harvested and processed for Western blotting using a specific NFAT1 antibody (Figure 6D–G). In unstimulated samples, NFAT1 appears as a single band corresponding to its highly phosphorylated, non-activated state (Figure 6D and F). Stimulation of naïve B cells with thapsigargin, which completely empties ER stores and maximally activates SOCE, led to a complete shift of the phosphorylated single band into lower molecular weight bands (Figure 6D and E, left) and this shift in NFAT1 molecular weight was inhibited by $43.6\%$ in Orai1-deficient B cells (Figure 6D and E, right). Given the dynamic expression of each Orai isoform following B cell activation (Figure 1G–I), we also evaluated NFAT1 activation in B cells stimulated for 48 hr with anti-IgM +anti-CD40. While thapsigargin stimulation also led to a complete shift in NFAT1 molecular weight in activated control B cells, this shift was reduced by $78\%$ in activated B cells from Orai1fl/fl Mb1-Cre/+ mice (Figure 6F and G). Similar results were obtained when we analyzed NFAT2 isoform dephosphorylation in naïve B cells (Figure 6—figure supplement 1A). Furthermore, when naïve B cells were stimulated with anti-IgM (instead of thapsigargin), we observed similar trends of NFAT1 and NFAT2 dephosphorylation, although this dephosphorylation was not as robust as with thapsigargin (Figure 6—figure supplement 1A). Nevertheless, this anti-IgM mediated dephosphorylation of NFAT1 and NFAT2 was inhibited in B cells from Orai1fl/fl Mb1-Cre/+ mice (Figure 6—figure supplement 1A). Our findings that B cells from Orai1fl/fl Mb1-Cre/+ mice have maintained BCR-induced Ca2+ oscillations with defects in NFAT activation is in agreement with previous studies in HEK293 cells showing that NFAT activation depends mostly on native Orai1 (Yoast et al., 2020b). In these studies, intact Ca2+ oscillations in Orai1 knockout cells were mediated by Orai$\frac{2}{3}$, yet NFAT nuclear translocation was largely reduced (Yoast et al., 2020b). We also evaluated NFAT1 activation in response to thapsigargin in both naïve B cells (Figure 6—figure supplement 1B) and activated B cells (48 hr with anti-IgM +anti-CD40; Figure 6—figure supplement 1C) isolated from either Orai3fl/fl Mb1-Cre/+ or Orai1/Orai3fl/fl Mb1-Cre/+ mice. Loss of Orai3 alone from naïve B cells had no effect on NFAT1 activation, while naïve B cells isolated from double Orai1/Orai3 knockout cells showed reductions in NFAT1 activation comparable to B cell isolated from single Orai1 knockout mice (Figure 6—figure supplement 1B). Importantly, this impairment of NFAT1 activation was further exacerbated in activated B cells isolated from Orai1 knockout and Orai1/Orai3 double knockout mice (Figure 6—figure supplement 1C). Collectively, these data reveal that Orai1 plays a more prominent role in the activation of NFAT isoforms in activated B cells compared to naïve cells. ## The combined deletion of Orai3 and Orai1 inhibits B cell proliferation BCR-induced Ca2+ signals that are sustained through SOCE are critical for the activation of gene programs that regulate proliferation and apoptosis (Berry et al., 2020; Matsumoto et al., 2011). Interestingly, suboptimal proliferation and survival of B cells in response to BCR stimulation can largely be rescued through the addition of secondary co-stimulatory signals (e.g. CD40 or TLR stimulation) (Berry et al., 2020; Matsumoto et al., 2011; Tang et al., 2017). To understand how Ca2+ signals downstream of Orai isoforms regulate B cell expansion, primary B cells were labeled with carboxyfluorescein diacetate succinimidyl ester (CFSE) and cell divisions tracked in response to multiple conditions of stimulation (Figure 7A). BCR stimulation with anti-IgM alone resulted in multiple rounds of cell division in most wild-type cells ($87.7\%$) with $28.6\%$ of viable cells at 72 hr post-stimulation (Figure 7B and C, black). B cell viability at 72 hr was dramatically increased to $67.4\%$ when cells were co-stimulated with anti-IgM +anti-CD40, with an increase in the number of cells undergoing several cycles of cell division. Following anti-IgM stimulation Orai1-deficient B cells showed a similar percentage of proliferating cells ($83.1\%$) as controls, with a moderate reduction in cell viability to $20.9\%$ (Figure 7B and C, red). While anti-IgM mediated proliferation of Orai3-deficient B cells was comparable to controls ($90.3\%$), their viability was higher, at $34.8\%$ (Figure 7B and C, blue). The combined deletion of Orai1 and Orai3 resulted in a substantial reduction in the percentage of both viable ($10\%$) and proliferating ($60.8\%$) cells in response to anti-IgM stimulation (Figure 7B and C, teal). These defects in survival and proliferation of Orai1/Orai3-deficient B cells were partially rescued when cells were co-stimulated with anti-IgM +anti-CD40, albeit to a lesser extent than control and single Orai knockout B cells. Similar percentages of viability and proliferation were observed in all experimental groups when B cells were stimulated with either anti-CD40 or LPS, suggesting that activation of the toll-like receptor 4 (TLR4) signaling pathway is able to compensate for the loss of Orai1/Orai3. We also analyzed the proliferation and survival of B cells isolated from Orai2-/- mice and their wildtype littermates exposed to the same stimuli. We found only marginal or no effects of Orai2 knockout on B cell proliferation and survival (Figure 7—figure supplement 1A, B). Furthermore, we performed RNA sequencing on B cells isolated from control Mb1-Cre/+ and Orai3/Orai1fl/fl Mb1-Cre/+ mice after stimulation with anti-IgM for 24 hr (Source data 2) and differential expression analysis was performed with edgeR (Source data 3). Based on the edgeR output, GSEA software was applied to perform, we performed pathway analyses using gene set enrichment analysis (GSEA) (Source data 4). In agreement with our proliferation and survival data, GSEA showed significant downregulation of pathways that govern survival and cell cycle progression in Orai1/Orai3-deficient B cells (Figure 7D and E). Together, these findings reveal that only loss of both Orai1 and Orai3 affects B cell survival and proliferation in response to anti-IgM stimulation while single knockout of either Orai1 or Orai3 had marginal effects. Figure 7.: Orai1 and Orai3 regulate B cell proliferation and survival.(A) Measurement of B cell proliferation by tracking cabroxyfluorescein diacetate succinimidyl ester (CFSE) dilution. B lymphocytes from control, Orai1, Orai3, and Orai1/Orai3 knockout mice were loaded with CFSE (3 μM) and stimulated with anti-IgM (20 μg/mL), anti-CD40 (10 μg/mL), anti-IgM +anti-CD40, or LPS (10 μg/mL). CFSE dilution was determined 72 hr after stimulation for all conditions. (B) Quantification of the percentage of proliferating cells for each condition in (A) (from left to right n=9, 8, 4, and 4 biological replicates; one-way ANOVA with multiple comparisons to Mb1-Cre/+). CFSE dilution gate is drawn relative to unstimulated controls. (C) Quantification of the percentage of viable cells for each condition in (A) as determined by a Live/Dead viability dye (from left to right n=9, 8, 4, and 4 biological replicates; one-way ANOVA with multiple comparisons to Mb1-Cre/+). (D) Top KEGG pathways showing differential expression from RNA-sequencing analysis of B cells from Mb1-Cre/+ vs Orai1/Orai3fl/fl Mb1-Cre/+ mice stimulated for 24 hr with anti-IgM (20 μg/mL) (n=3 biological replicates for each). (E) Gene set enrichment analysis (GSEA) plots show enrichment statistics (ES) in the Hallmark G2M Checkpoint, E2F Targets, and Cell Cycle gene sets. Large positive ES values suggest activation of these pathways. Normalized enrichment score (NES) values are used to assess statistical significance, and the results for these gene sets are highly significant. All scatter plots are presented as mean ± SEM. For all figures, p<0.01; p<0.001; *p<0.0001; ns, not significant. Figure 7—source data 1.Source data for Figure 7. ## The combined deletion of Orai3 and Orai1 inhibits B cell metabolism Metabolic reprogramming results in dramatically enhanced OXPHOS and remodeling of the mitochondrial network (Akkaya et al., 2018; Waters et al., 2018). Indeed, GSEA of B cells stimulated for 24 hr with anti-IgM identified the Oxidative Phosphorylation gene set as one of the most highly enriched pathways relative to unstimulated controls (Figure 8A). Additionally, anti-IgM stimulated B cells to demonstrate a significant increase in the total number of mitochondria per cell compared to naïve B cells as determined by transmission electron microscopy (TEM; Figure 8B and C). However, this increase in mitochondrial mass triggered by B cell activation was not affected by the depletion of Orai1 from B cells, as determined by Mitotracker Green staining (Figure 8D). We measured OCR and ECAR in B cells isolated from control and B cell-specific Orai knockout mice after B cell stimulation with anti-IgM for 24 hr (Figure 8E). Anti-IgM stimulation of wild-type B cells led to a robust increase in both OCR and ECAR (Figure 8F). Furthermore, Anti-IgM stimulation led to an increase in both basal and maximal respiration of wild-type B cells (Figure 8E–H). The increase in OCR and ECAR following BCR activation was significantly blunted in Orai1-deficient B cells, and further reduced in Orai1/Orai3-deficient B cells (Figure 8E–H). Interestingly, GSEA analysis showed that anti-IgM mediated enrichment of the Oxidative Phosphorylation gene set and Myc targets gene set were specifically inhibited in B cells from Orai1/Orai3fl/fl Mb1-Cre/+ mice, but not in B cells from Orai1fl/fl Mb1-Cre/+ mice (Figure 8—source data 1). However, the glycolysis gene set was inhibited in B cells from both Orai1/Orai3fl/fl Mb1-Cre/+ mice and Orai1fl/fl Mb1-Cre/+ mice (Figure 8—source data 1). Loss of Orai3 alone only partially reduced glycolytic flux and basal respiration but did not affect maximal respiration (Figure 8E–H). Previous research has shown that loss of SOCE in the chicken DT40 B cell line impaired mitochondrial metabolism by reducing CREB-mediated expression of the mitochondrial calcium uniporter (MCU) (Shanmughapriya et al., 2015). We observed no differences in CREB phosphorylation on Ser133 (a surrogate for CREB activation) or MCU expression in primary Orai1-deficient B cells from Orai1fl/fl Mb1-Cre/+ mice (Figure 8—figure supplement 1A). Likewise, we observed no differences in MCU expression in mouse A20 B cells lacking either Orai1, Orai3, or both Orai1 and Orai3 (Figure 8—figure supplement 1B). Both naïve and activated (24 hr with anti-IgM) B cells from Orai1fl/fl Mb1-Cre/+ mice showed no obvious changes in the expression of different components of the electron transport chain (Figure 8—figure supplement 1C, D). Figure 8.: Orai1 and Orai3 regulate B cell mitochondrial respiration.(A) Gene set enrichment analysis (GSEA) of the KEGG Oxidative Phosphorylation gene set in B cells stimulated for 24 hr with anti-IgM (20 μg/mL) relative to unstimulated controls. (B) Representative transmission electron microscopy (TEM) images of B cells from Mb1-Cre/+ mice. Shown are naïve, unstimulated B cells (top) and B cells stimulated for 24 hr with anti-IgM (bottom). (C) Quantification of total mitochondria per cell in unstimulated B cells and B cells stimulated for 24 hr with anti-IgM (n=25 for each; Mann-Whitney test). (D) Measurement of total mitochondrial content with MitoTracker Green in B cells from Mb1-Cre/+ and Orai1fl/fl Mb1-Cre/+ mice following 24 hr stimulation (n=3 biological replicates for each; Mann-Whitney test). (E) Measurement of oxygen consumption rate (OCR) in primary B lymphocytes following 24 hr stimulation with anti-IgM (20 μg/mL) using the Seahorse Mito Stress Test. (F) Energy map of maximal oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) following carbonyl cyanide p-trifluoromethoxyphenylhydrazone (FCCP) addition. (G, H) Quantification of basal (G) and maximal (H) respiration from Seahorse traces in (E) (n=4 biological replicates for each genotype; one-way ANOVA with multiple comparisons to Mb1-Cre/+). All scatter plots and Seahorse traces are presented as mean ± SEM. For all figures, p<0.01; **p<0.0001; ns, not significant. Figure 8—source data 1.Source data for GSEA summary statistics for Figure 8. Figure 8—source data 2.Source data for Figure 8. To further investigate how Orai-mediated Ca2+ signaling regulates B cell metabolism, we profiled polar metabolites utilizing liquid chromatography followed by mass spectrometry in control and Orai1/Orai3-deficient B cells (Figure 9). Isolated B cells from each genotype were either unstimulated or stimulated for 24 hr with anti-IgM alone, anti-IgM +anti-CD40, anti-IgM +the calcineurin inhibitor FK506, or anti-IgM +the CRAC channel inhibitor GSK-7975A. We utilized GSK-7975A as we recently demonstrated that this compound inhibited all Orai isoforms compared to differential effects on Orai isoforms with other common SOCE inhibitors like Synta66 (Zhang et al., 2020). Stimulation of control B cells with anti-IgM led to a significant increase of glycolytic and TCA cycle metabolites along with most non-essential amino acids. This effect was further enhanced with anti-IgM +anti-CD40 co-stimulation (Figure 9A; Figure 9—figure supplement 1A). Inclusion of FK506 or GSK-7975A strongly blunted the effects of anti-IgM activation as overall metabolic profiles remained like those of unstimulated cells (Figure 10A). Importantly, this upregulation in polar metabolites upon B cell activation was significantly reduced in Orai1/Orai3-deficient B cells with either anti-IgM stimulation or anti-IgM +anti-CD40 co-stimulation (Figure 9B and C; Figure 9—figure supplement 1B). Of note, upregulation of most polar metabolites from Orai1/Orai3 knockout B cells co-stimulated with anti-IgM +anti-CD40 typically reached levels comparable to those of control B cells stimulated with anti-IgM alone. The inhibitory effects of FK506 and GSK-7975A on the metabolite status of wildtype B cells were comparable to those of Orai1/Orai3-deficient B cells (Figure 9A, Figure 9—figure supplement 1A). Collectively, these results reveal that SOCE mediated through Orai1/Orai3 channels contributes to the metabolic reprogramming of B lymphocytes. Figure 9.: Orai1/Orai3-mediated SOCE-calcineurin-NFAT pathway regulates B cell metabolism.(A) Analysis of polar metabolites in B cells from Mb1-Cre/+ mice utilizing liquid chromatography followed by mass spectrometry. B cells were either unstimulated or stimulated for 24 hr with anti-IgM, anti-IgM +anti-CD40, anti-IgM with 1 µM FK506, or anti-IgM with 10 µM GSK-7975A. (B, C). Heat maps of statistically significant polar metabolites in B cells from Mb1-Cre/+ and Orai1/Orai3fl/fl Mb1-Cre/+ mice following (B) 24 hr anti-IgM stimulation or (C) 24 hr anti-IgM +anti-CD40 stimulation. (n=3 biological replicates for each condition). Figure 9—source data 1.Source data for Figure 9. Figure 10.: Deletion of Orai1 and Orai3 in B cells does not compromise immunity to influenza A virus (IAV).(A) Experimental outline. Littermate controls and Orai1fl/flOrai3fl/fl Mb1-Cre/+ mice have infected intranasally with 1x105 TCID50 of the x31 H3N2 strain of influenza A virus. Serum was collected on days 9 and 14, and mice were sacrificed on day 14 for analysis. (B) Representative H&E stains of lung sections. Scale bar: 500 µm. (C) Alveolar volume fraction of 10 mice per cohort. (D) Representative flow cytometry plots of B cells isolated from mediastinal lymph nodes (med LN). (E) Summary of the frequencies (%) and total cell numbers of B220–CD138+ plasma cells and Fas+GL-7+ GC B cells shown in panel D from 10 mice per cohort. (F) Representative flow cytometry plots of plasma cells isolated from the bone marrow of five IAV-infected mice per cohort. (G) Representative flow cytometry plots (left) and summary (right) of the frequencies of class-switched IgG1+ GC B cells in med LN of five IAV-infected mice per cohort. (H) IAV-specific IgM and IgG levels in the serum of 10 mice per cohort were measured on days 9 and 14. Panels B-H show the results of two independent experiments. Statistical analysis by unpaired Student’s t-test: p<0.001, p<0.01, p<0.05. Figure 10—source data 1.Source data for Figure 10. ## Immunity to influenza A virus is intact in mice with B cell-specific deletion of Orai1 and Orai3 Given the important role of Orai1 and Orai3 for multiple B cell functions in vitro, we hypothesized that deletion of Orai1 and Orai3 affects the B cell cytokine profile and compromises immune responses to influenza A virus (IAV) infection. Profiling of B cell cytokines and cytokine receptors by RNA-sequencing of B cells from Orai1fl/flOrai3fl/fl Mb1-Cre/+ and littermate control mice stimulated ex vivo with anti-IgM showed no obvious differences between the two groups (Figure 10—figure supplement 1C–D). To evaluate the role of Orai1/Orai3 for B cell function and humoral immunity in vivo, we infected Orai1fl/flOrai3fl/fl Mb1-Cre/+ mice and Orai1fl/flOrai3fl/fl littermate controls intranasally with a single dose of the laboratory strain A/HK/x31 (Hkx31, H3N2) (Thomas et al., 2006) and analyzed immune responses 14 days later (Figure 10A). All infected WT and Orai$\frac{1}{3}$-deficient mice survived and experienced a similar reduction in body weight over the two week-course of infection (not shown). Lung histology at day 14 post-infection (p.i.) showed comparable pulmonary inflammation and alveolar volume in Orai$\frac{1}{3}$-deficient and littermate control mice (Figure 10B and C). To directly investigate the effects of Orai1 and Orai3 on humoral immunity during IAV infection, we analyzed total B cells, germinal center (GC) B cells, and plasma cells in mice at day 14 p.i. The frequencies and numbers of B220–CD138+ plasma cells and B220+Fas+GL-7+ GC B cells were similar in the mediastinal lymph nodes of WT and Orai$\frac{1}{3}$-deficient mice (Figure 10D and E). Moreover, the numbers of CD138+ plasma cells in the bone marrow of infected Orai1fl/flOrai3fl/fl Mb1-Cre/+ mice were comparable to those in control littermate mice (Figure 10F). We next investigated if deletion of Orai1 and Orai3 in B cells affects their ability to induce class switch recombination. The percentages of class-switched IgG1+ cells (and those of IgM–IgG– non-switched B cells) among GC B cells, however, were comparable in control and Orai$\frac{1}{3}$-deficient mice (Figure 10G). Consistent with these results, we found normal levels of IAV-specific IgM and class-switched IgG antibodies in the serum (Figure 10H). Taken together, our data show that B cell-specific deletion of Orai1 and Orai3 does not significantly impair immune responses to influenza A virus infection. ## Discussion Given the essential role of B lymphocytes in driving humoral immunity against foreign pathogens, a comprehensive understanding of the molecular pathways that govern their development, differentiation, and effector functions is critical for future targeted therapies. One of the earliest signaling events upon crosslinking of the BCR is a biphasic increase in intracellular Ca2+ concentrations (Baba and Kurosaki, 2016). Early landmark studies established that multiple Ca2+ dependent transcription factors display unique activation requirements by relying on either ER Ca2+ release (e.g. JNK, NF-κB) or sustained Ca2+ signals driven by SOCE (NFAT) (Dolmetsch et al., 1997; Healy et al., 1998). While the SOCE-calcineurin-NFAT pathway is well established in the context of B cell effector function, recent reports have shed light on the mechanisms by which SOCE also regulates NF-κB activation and its downstream target genes (Berry et al., 2020; Berry et al., 2018). These two BCR-activated signaling pathways are sustained by Ca2+ entry through CRAC channels and synergize with one another to activate a series of Ca2+-regulated checkpoints that determine B cell survival, entry into the cell cycle, and proliferation (Akkaya et al., 2018; Berry et al., 2020). Unlike recent findings that have established Orai1 and Orai2 as the major Orai isoforms mediating SOCE in T cells (Vaeth et al., 2017b), the relative contributions of Orai isoforms to the native CRAC channel in B cells remained, until now, unclear. Our results herein establish that B cell activation through BCR stimulation alone or co-stimulation with secondary signals like CD40 or TLR ligands significantly enhanced metabolic activity. Concurrently, these stimulation conditions drive dynamic changes in the expression of each Orai isoform. Interestingly, we observe that robust B cell activation with BCR and CD40 co-stimulation results in upregulation of both Orai1 and Orai3, and downregulation of Orai2. However, we do not see any apparent increase in SOCE in activated B cells versus naive B cells, suggesting that Orai1 and Orai3 upregulation in activated B cells helps maintain the magnitude of SOCE, commensurate with the enhanced volume of activated B cells. While Orai1 has previously been shown to contribute to the majority of SOCE in B cells under conditions of maximal ER Ca2+ depletion (Gwack et al., 2008; McCarl et al., 2010), we show using B-cell specific Orai1 knockout mice that Orai1 is dispensable for maintaining cytosolic Ca2+ oscillations in response to BCR crosslinking. In agreement with our previous results in HEK293 cells (Yoast et al., 2020b), we also show that Orai1 is an essential regulator of NFAT1 and NFAT2 isoforms in B cells and that its role becomes more prominent for NFAT induction in activated B cells compared to naïve unstimulated B cells. By generating CRISPR/Cas9 B cell lines and B-cell specific knockout mice lacking Orai1 or Orai3 individually and in combination, we found that both Orai1 and Orai3 contribute to the native CRAC channels in B cells. However, the oligomeric state of native CRAC channels in B cells and whether Orai1 and Orai3 form homomeric or heteromeric assemblies or both in primary B cells remains an open question. The combined loss of Orai1 and Orai3, but not either isoform alone, led to a significant reduction in both B cell proliferation and survival. The original study by Gwack et al showed a partial inhibition of proliferation in B cells from Orai1-/- mice. While we observe only a marginal inhibition of proliferation in B cells from Orai1fl/fl Mb1-Cre/+ mice and a moderate reduction in the viability of these cells. The reason for the difference between our results on B cell proliferation and those of Gwack et al., 2008 is unknown. However, one potential explanation could be that in B cells from Orai1-/- mice in Gwack et al., 2008 Orai1 is deleted from early B cell development in mice on a mixed ICR background (note that Orai1-/- mice on the C57BL/6 background show perinatal lethality) whereas in our Orai1fl/fl Mb1-Cre/+ mice, Orai1 is deleted later in B cell development in mice that are on a pure C57Bl/6 background. Another potential explanation could be the complete deletion of the Orai1 gene in the studies of Gwack et al., 2008 vs the $97\%$ reduction in mRNA expression we observe in B cells from Orai1fl/fl Mb1-Cre/+ mice. We show that SOCE is important for the metabolic reprogramming of B cells by regulating mitochondrial metabolism and the flux of polar metabolites in response to B cell activation. This shift in metabolic profiles in response to B cell activation could be neutralized by inhibition of either calcineurin or CRAC channels with FK506 or GSK-7975A, respectively, suggesting that this metabolic flux is driven through SOCE and NFAT-dependent mechanisms. Our data demonstrate that both Orai1 and Orai3 contribute to CRAC channel activity and to shaping cytosolic Ca2+ signaling in B cells. While our data has provided evidence for the contribution of Orai3 to CRAC channels in naïve B cells, this contribution was only apparent within the context of double Orai1 and Orai3 knockout. Our data with B cell-specific Orai3 knockout is consistent with a recent study reporting a lack of SOCE inhibition in B cells, T cells, and macrophages from Orai3 global knockout mice (Wang et al., 2022). Our data and the study of Wang et al., 2022, showed that Orai3 is highly expressed in B cells compared to other immune cells like T cells. Nevertheless, Orai3 knockout, on its own, has no measurable contribution to SOCE in naïve B cells while moderately reducing SOCE in activated B cells. This reduced SOCE in activated B cells from Orai3 knockout mice is consistent with the reduced basal respiration of B cells stimulated with anti-IgM for 24 hr. This could also explain why the double knockout of Orai1 and Orai3, which inhibits SOCE more dramatically, has a larger effect on mitochondrial respiration and metabolomic reprogramming of B cells. Alternatively, it is possible that Orai3 couples to alternative cytosolic signaling pathways that synergize with SOCE-mediated Ca2+ influx. Orai3 could mediate its effects on mitochondrial respiration through the store-independent Ca2+ influx pathway activated by arachidonic acid or its metabolite, leukotrieneC4 (Thompson et al., 2013; Zhang et al., 2015; Zhang et al., 2013; Zhang et al., 2018; Zhang et al., 2014). Earlier studies investigating human effector T cells demonstrated that Orai3 becomes upregulated in response to oxidative stress, which may act as a potential mechanism to maintain a threshold of Ca2+ influx and T cell function in various inflammatory conditions (Bogeski et al., 2010). In support of this model, new findings have demonstrated that Orai3 expression is increased in CD4+ T cells from patients with rheumatoid arthritis and psoriatic arthritis and that silencing of Orai3 reduces tissue inflammation in a human synovium adoptive transfer model (Ye et al., 2021). Interestingly, these effects were proposed to be through the store-independent function of Orai3 (Thompson et al., 2013; Zhang et al., 2015; Zhang et al., 2013; Zhang et al., 2018; Zhang et al., 2014). However, a recent study showed that Orai3 deficient mice have no altered susceptibility in a model of collagen-induced arthritis (Wang et al., 2022), challenging a role for Orai3, on its own, in the immune system. The situation in T cells is different as the combined loss of Orai1 and Orai2 in murine T cells led to a near complete ablation of SOCE and substantially impaired T cell function (Vaeth et al., 2017b). Whether Orai3 contributes to the small, residual amount of SOCE in Orai1/Orai2-deficient T cells and/or mediates store-independent signaling functions in murine T and B cells is unknown. Furthermore, differences in Orai channel contributions are apparent between mice and humans, as SOCE and CRAC currents are completely inhibited in T cells from patients with LoF mutations in Orai1, while SOCE is only partially inhibited in Orai1-deficient murine T cells (Feske et al., 2006; Lian et al., 2018; McCarl et al., 2009; Vig et al., 2008). Curiously, B cells from Orai1 LoF mutation patients still retain residual SOCE, suggesting that another Orai isoform may contribute to SOCE in human B cells than in T cells (Feske et al., 2001), in agreement with our data herein on mouse B cells. Thus, the composition of native CRAC channels among lymphocytes appears cell-type and context-specific. While SOCE is substantially reduced in Orai1/Orai3-deficient B cells, defects in their ability to proliferate and survive in response to antigenic stimulation are not as severe as the phenotype observed in STIM1/STIM2-deficient B cells (Berry et al., 2020; Matsumoto et al., 2011). Our data establish additivity between Orai1 and Orai3 channels in controlling B cell survival and proliferation, but we cannot exclude a potential role for Orai2 in the regulation of SOCE in B cells. Quite the opposite, we show that Orai2 is functional in B cells by documenting that B cells from Orai2-/- mice have enhanced SOCE. This suggests that Orai2 acts as a negative regulator of SOCE in B cells as was shown for T cells (Vaeth et al., 2017b), mast cells (Tsvilovskyy et al., 2018), and HEK293 cells (Yoast et al., 2020b), and that the remaining SOCE activity in B cells from Orai1/Orai3 double knockout mice is likely mediated by Orai2. This is consistent with our results showing that the SOCE inhibitor GSK-7975A inhibits the upregulation of polar metabolites by B cells, in response to anti-IgM +anti-CD40 stimulation, to a greater extent than Orai1/Orai3 double knockout. Therefore, the development of Orai triple knockout mice and additional Orai knockout pairs is needed to further clarify this issue. Similarly, how SOCE and the composition of the native CRAC channel vary among different B cell subsets and during pathological conditions is completely unknown. Indeed, earlier studies have demonstrated that effector populations like germinal center B cells display unique metabolic and Ca2+ signaling requirements (Khalil et al., 2012; Luo et al., 2018). Our data also suggest that the expression of each Orai isoform is dynamic in response to B cell activation, similar to the case of naïve vs effector T cells (Vaeth et al., 2017b). Considering the clear effects of B cell-specific deletion of Orai1 and Orai3 on SOCE, NFAT activation, metabolic reprogramming, survival, and proliferation, it was surprising that humoral immunity to influenza A virus (IAV) infection was unaffected in Orai1fl/flOrai3fl/fl Mb1-Cre/+ mice. This raises the question regarding the role of Orai1, Orai3, and SOCE in general for B cell function and humoral immunity in vivo. Our results are, however, consistent with those from B cell-specific deletion of Stim1 and Stim2 genes, which was shown to result in abolished SOCE causing impaired expression of two key anti-apoptotic genes and blunted activation of the mTORC1 and c-Myc metabolic signaling pathways, resulting in decreased B cell survival and proliferation (Berry et al., 2020). While this study did not investigate the effects of Stim1/Stim2 deletion on B cell function in vivo, Baba and colleagues had reported normal B cell development as well as T-dependent and T-independent antibody responses following immunization of mice with B cell-specific deletion of Stim1 and Stim2 genes (Matsumoto et al., 2011). This was surprising, because Stim1/Stim2 deletion almost completely abolished thapsigargin and anti-IgM induced SOCE, and strongly impaired B cell survival and proliferation in vitro (Matsumoto et al., 2011). A potential explanation for the normal humoral immune responses in Orai1fl/flOrai3fl/fl Mb1-Cre/+ and Stim1fl/flStim2fl/fl Mb1-Cre/+ mice is provided by the fact that, in contrast to anti-IgM stimulation, stimulation of Stim1/Stim2-deficient B cells via CD40 or TLR4 results in normal B cell survival and proliferation (Matsumoto et al., 2011). When combined with anti-IgM stimulation, CD40 was able to rescue the proliferation defect of Stim1/Stim2-deficient B cells. A similar rescue of defective B cell proliferation by CD40 and TLR9 agonists in Stim1/Stim2-deficient B cells was reported by Berry at al. ( Berry et al., 2020). Our data with Orai1/Orai3-deficient B cells show only a partial rescue of B cell proliferation by anti-IgM and anti-CD40 co-stimulation compared to anti-IgM alone. We did, however, observe normal B cell survival and proliferation in Orai1/Orai3-deficient B cells stimulated with the TLR4 agonist LPS. Collectively, these data demonstrate that co-stimulatory pathways such as CD40, TLR4, and TLR9 can bypass the requirement for BCR-induced SOCE to induce B cell activation. IAV is a dsRNA virus that is detected by TLR7 in plasmacytoid dendritic cells and B cells (Iwasaki and Pillai, 2014). TLR7 is widely expressed in B cells and antibody responses to IAV were shown to depend on TLR7 (Geeraedts et al., 2008). Although we did not investigate if TLR7 stimulation with synthetic agonists such as imiquimod can rescue the proliferation and survival defects of Orai1/Orai3-deficient B cells in vitro, we speculate that it would, given the fact that TLR7 and TLR4, whose activation by LPS rescues the function of Stim1/Stim2 and Orai1/Orai3-deficient B cells, signal through the same Myd88-IRAK4-TRAF6 pathway to activate NF-κB and other transcription factors (Duan et al., 2022). Activation of TLR7 and potentially other costimulatory pathways, in Orai1/Orai3-deficient B cells is, therefore, a likely explanation for their normal humoral immune response to IAV in vivo. Although Ca2+ and SOCE are clearly important for B cell function, B cells have developed other Ca2+-independent means to ensure their proper activation. Future studies with Orai1fl/flOrai3fl/fl Mb1-Cre/+ mice might reveal indispensable in vivo contributions of SOCE to B cell populations such as memory B cells or to immune disease in other in vivo models such as the experimental autoimmune encephalomyelitis (EAE) mouse model. Taken together, our results uncover additive functions of Orai3 and Orai1 in B cell Ca2+ signaling that regulates NFAT activation, metabolism, survival, and proliferation of B cells. ## Mice All animal experiments were carried out in compliance with the Institutional Animal Care and use Committee (IACUC) guidelines of the Pennsylvania State University College of Medicine. All mice were housed under specific pathogen-free conditions and experiments were performed in accordance with protocols approved by the IACUC of the Pennsylvania State University College of Medicine. Mb1-Cre mice (Hobeika et al., 2006) were obtained from The Jackson Laboratory (strain#: 020505; https://www.jax.org/strain/020505). Orai1fl/fl mice (Ahuja et al., 2017) were obtained from Dr. Paul Worley (Johns Hopkins University). Orai3fl/fl mice were generated by our laboratory through the MMRC at the University of California Davis. A trapping cassette was generated including ‘SA-βgeo-pA’ (splice acceptor-beta-geo-polyA) flanked by Flp-recombinase target ‘FRT’ sites, followed by a critical Orai3 coding exon flanked by Cre-recombinase target ‘loxP’ sites. This cassette was inserted within an intron upstream of the Orai3 critical exon, where it tags the Orai3 gene with the lacZ reporter. This creates a constitutive null Orai3 mutation in the target Orai3 gene through efficient splicing to the reporter cassette resulting in the truncation of the endogenous transcript. Mice carrying this allele were bred with FLP deleter C57BL/6 N mice to generate the Orai3fl/fl mouse. All experiments were performed with 8–12 week-old age and sex-matched mice. ## Cell culture Parental A20 cells were purchased from ATCC (Catalog # TIB-208) and cultured in RPMI 1640 with L-glutamine supplemented with $10\%$ fetal bovine serum and 1 X Antibiotic-Antimycotic and were routinely tested for lack of mycoplasma contamination (Emrich et al., 2022a). Naïve splenic B lymphocytes were purified by negative selection using the EasySep Mouse B Cell Isolation Kit (STEMCELL Technologies). Primary B cells from each transgenic mouse line were cultured in RPMI 1640 with L-glutamine supplemented with $10\%$ fetal bovine serum, 1 X GlutaMAX, 1 X Penicillin-Streptomycin Solution, sodium pyruvate (1 mM), 2-ME (5 uM), and HEPES (10 mM). Primary lymphocytes were stimulated with F(ab')₂ Fragment Goat Anti-Mouse IgM antibody (Jackson ImmunoResearch, 20 µg/mL), anti-mouse CD40 antibody (BioXCell, 10 µg/mL), or LPS (Sigma-Aldrich, 10 µg/mL). All cell lines and primary lymphocytes were cultured at 37 °C in a humidified incubator with $5\%$ CO2. ## Generation of A20 Orai CRISPR/Cas9 knockout cells For the generation of A20 Orai knockout clones, we used a similar strategy to our previous studies (Emrich et al., 2021; Yoast et al., 2021; Yoast et al., 2020b; Zhang et al., 2019). Briefly, two gRNAs targeting the beginning and end of the mouse Orai1 or Orai3 coding region were cloned into two fluorescent vectors (pSpCas9(BB)–2A-GFP and pU6-(BbsI)_CBh-Cas9-T2AmCherry; Addgene). For mOrai1 knockout, the gRNA sequences are the following: mOrai1n: 5’-GCCTTCGGATCCGGTGCGTC-3’; mOrai1c: 5’-CACAGGCCGTCCTCCGGACT-3’. For mOrai3 knockout, the gRNA sequences are the following: mOrai3n: 5’- GCGTCCGTAACTGTTCCCGC-3’; mOrai3c: 5- GAAGGAGGTCTGTCGATCCC-3’. A20 cells were electroporated with both N- and C-terminal gRNA combinations with an Amaxa Nucleofector II and single cells with high GFP and mCherry expression were sorted at one cell per well into 96-well plates 24 hr after transfection using a FACS Aria SORP Cell Sorter. Individual clones were obtained and genomic DNA was tested using primers targeting the N- and C-terminal gRNA cut sites to resolve wild-type and knockout PCR products. Knockout PCR products were cloned into pSC-B-amp/kan with the StrataClone Blunt PCR Cloning Kit (Agilent) and Sanger sequenced to determine the exact deletion. Knockout clones were also confirmed for the absence of mRNA with qRT-PCR and functionally through Ca2+ imaging experiments. ## Fluorescence imaging A20 cell lines and primary B cells were seeded onto poly-L-lysine (Sigma-Aldrich) coated coverslips. Coverslips were mounted in Attofluor cell chambers (Thermo Scientific) and loaded with 2 µM Fura-2AM (Molecular Probes) in a HEPES-buffered saline solution (HBSS) containing 120 mM NaCl, 5.4 mM KCl, 0.8 mM MgCl2, 1 mM CaCl2, 20 mM HEPES, 10 mM D-glucose, at pH 7.4 for 30 min at room temperature. Following Fura-2 loading, cells were washed three times with HBSS and mounted on a Leica DMi8 fluorescence microscope. Fura-2 fluorescence was measured every 2 s by excitation at 340 nm and 380 nm using a fast shutter wheel and the emission at 510 nm was collected through a 40 X fluorescence objective. Fluorescence data was collected from individual cells on a pixel-by-pixel basis and processed using Leica Application Suite X. All cytosolic Ca2+ concentrations are presented as the ratio of F340/F380. ## Flow cytometry Spleens were processed into single-cell suspensions and stained using the following antibodies: B220-BV605 (RA3-6B2, Biolegend), CD86-PE/Cy5 (GL-1, Biolegend), MHC-II-PE/Cy7 (M$\frac{5}{114.15.2}$, Biolegend), Orai1 rabbit polyclonal (Gwack et al., 2008; McCarl et al., 2009), CD3e-PE (145–2 C11, BD Biosciences), CD8a-V500 (53–6.7, BD Biosciences), CD4-BB700 (RM4-5, BD Biosciences), APC–anti-CD24 (HSA; M$\frac{1}{69}$), FITC–anti-CD23 (B3B4), PE–anti-IgM (eB121-15F9, eBioscience), APC–anti-CD93 (AA4.1, eBioscience), PE–Cy5-streptavidin (Biolegend), and Pacific blue–anti-B220 (RA3-6B2, Biolegend). All staining was performed in FACS buffer (DPBS, $2\%$ FBS, 1 mM EDTA) for 30 min at 4 °C. Prior to surface staining, all cells were stained with eBioscience Fixable Viability Dye eFluor 780 (Thermo Fisher) and anti-CD$\frac{16}{32}$ Fc block (2.4G2, Tonbo Biosciences). Measurement of mitochondrial content was performed using MitoTracker Green (Molecular Probes) and mitochondrial membrane potential was performed using TMRE (Molecular Probes). For all CFSE dilution experiments, primary B lymphocytes were stained with 3 µM CFSE (C1157, Thermo Fischer) in PBS with $5\%$ FBS for 5 min at room temperature, followed by the addition of FBS and two washes in PBS before resuspension in complete RPMI media. CFSE dilution of labeled cells was assessed 72 hr after plating and stimulation. All flow cytometry data were collected on a BD LSR II flow cytometer using FACSDiva software (BD Biosciences) and analyzed with FlowJo 9.9.6 software (Tree Star). ## Analysis of NFAT nuclear translocation Primary B cells were purified using the EasySep Mouse B Cell Isolation Kit (STEMCELL Technologies) and stimulated with anti-IgM (20 µg/mL) for 15 min at room temperature in complete RPMI media. Cells were fixed and permeabilized using the Foxp3 /Transcription Factor Staining Buffer Set (eBioscience) following the manufacturer’s instructions. Cells were stained in 1 X permeabilization buffer for 1 hr at room temperature with NFAT1-Alexa Fluor 488 (D43B1, Cell Signaling Technology). Nuclei were stained with DAPI prior to acquisition on an Amnis ImageStream X Mark II Imaging Flow Cytometer and analyzed with IDEAS software using the ‘‘Nuclear Localization’’ feature (EMD Millipore). ## Western blot analysis A20 and primary B lymphocytes were harvested from the culture and lysed for 10 min in RIPA buffer (150 mM NaCl, $1.0\%$ IGEPAL CA-630, $0.5\%$ sodium deoxycholate, $0.1\%$ SDS, 50 mM Tris, pH 8.0; Sigma) containing 1 X Halt protease/phosphatase inhibitors (Thermo Scientific). Following lysis, samples were clarified by centrifugation at 15,000 × g for 10 min at 4°C. Supernatants were collected and protein concentration was determined using the Pierce Rapid Gold BCA Protein Assay Kit (Thermo Scientific). Equal concentrations of protein extract were loaded into 4–$12\%$ NuPAGE BisTris gels (Life Technologies) and transferred to PVDF membranes utilizing the Transblot Turbo Transfer System (Bio-Rad). Membranes were blocked for 1 hr at room temperature in Odyssey Blocking Buffer in TBS (LI-COR) and incubated overnight at 4°C with primary antibody. The following antibodies and dilutions were used: MCU (1:2000; 14997 S, Cell Signaling Technology), GAPDH (1:5000; MAB374, Sigma), Total OXPHOS Rodent Antibody Cocktail (1:1000, ab110413, Abcam), NFAT1 (1:1000, 4389 S, Cell Signaling Technology), NFAT2 (1:1000, 8032 S, Cell Signaling Technology), α-Tubulin (1:5000, 3873 S, Cell Signaling Technology), phospho-CREB (Ser133, 1:1000, 9198 S, Cell Signaling Technology), and CREB (1:1000, 9104 S, Cell Signaling Technology). Membranes were washed with TBST and incubated for 1 hr at room temperature with the following secondary antibodies: IRDye 680RD goat anti-mouse (1:10,000 LI-COR) or IRDye 800RD donkey anti-rabbit (1:10,000 LI-COR). Membranes were imaged on an Odyssey CLx Imaging System (LI-COR) and analysis was performed in Image Studio Lite version 5.2 (LI-COR) and ImageJ. ## Quantitative RT-PCR Total mRNA was isolated from A20 cells and primary B lymphocytes using an RNeasy Mini Kit (Qiagen) following the manufacturer’s instructions. RNA concentrations were measured using a NanoDrop 2000 (Thermo Scientific) and 1 µg of DNAse I treated RNA was used with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). cDNA was amplified on a QuantStudio 3 Real-Time PCR System (Applied Biosystems) using PowerUp SYBR Green Master Mix (Applied Biosystems). PCR amplification was performed by initial activation for 2 min at 50°C, followed by a 95°C 2 min melt step. The initial melt steps were then followed by 40 cycles of 95°C for 15 s, 60°C for 15 s, and 72°C for 30 s. Data were analyzed with the instrument software v1.3.1 (Applied Biosystems) and analysis of each target was carried out using the comparative Ct method. ## Seahorse extracellular flux analysis Oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) were measured using an XFe24 Extracellular Flux Analyzer (Seahorse Bioscience). A20 cells (0.8 × 106 per well) and primary B lymphocytes (2 × 106 per well) were resuspended in XF DMEM pH 7.4 media supplemented with 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose (Mito Stress Test) and plated in poly-L-lysine coated microchamber wells. For the Mito Stress Test, 1.5 μM oligomycin, 2 μM FCCP, and 0.5 μM antimycin/rotenone were utilized. Data were analyzed using the Agilent Seahorse Wave Software and normalized to total protein context per well using the Pierce Rapid Gold BCA Protein Assay Kit (Thermo Scientific). ## Transmission electron microscopy Primary B cells (unstimulated or 24 hr IgM stimulated) were seeded onto poly-L-lysine coated cell culture dishes and fixed with $1\%$ glutaraldehyde in 0.1 M sodium phosphate buffer, pH 7.3. After fixation, the cells were washed with 100 mM Tris (pH 7.2) and 160 mM sucrose for 30 mins. The cells were washed twice with phosphate buffer (150 mM NaCl, 5 mM KCl, 10 mM Na3PO4, pH 7.3) for 30 min, followed by treatment with $1\%$ OsO4 in 140 mM Na3PO4 (pH 7.3) for 1 hr. The cells were washed twice with water and stained with saturated uranyl acetate for 1 hr, dehydrated in ethanol, and embedded in Epon (Electron Microscopy Sciences, Hatfield, PA). Roughly 60 nm sections were cut and stained with uranyl acetate and lead nitrate. The stained grids were analyzed using a Philips CM-12 electron microscope (FEI; Eindhoven, The Netherlands) and photographed with a Gatan Erlangshen ES1000W digital camera (Model 785, 4 k 3 2.7 k; Gatan, Pleasanton, CA). ## RNA sequencing and differential expression analysis Total mRNA was isolated from primary B lymphocytes using an RNeasy Mini Kit (Qiagen) following the manufacturer’s instructions. RNA concentrations were quantitated using a NanoDrop 2000 (Thermo Scientific) and library preparation was performed by Novogene. A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext Ultra TM RNA Library Prep Kit for Illumina (NEB, USA) following the manufacturer’s recommendations, and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First-strand Synthesis Reaction Buffer (5 X). First-strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure was ligated to prepare for hybridization. To select cDNA fragments of preferentially 150~200 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 µl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95°C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. Lastly, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina NovaSeq 6000 Platform (Illumina, San Diego, CA, USA) using a paired-end 150 run (2 × 150 bases). ## Differential expression analysis Ensembl gene identifiers were converted to gene symbols, and identifiers with duplicated or missing gene symbols were removed from the analysis. Exploratory analyses were performed on the gene-level read count data, and lowly expressed genes were removed from the data set. The EDASeq R package (Risso et al., 2011) was used to create a SeqExpressionSet object based on the read counts, then upper quantile normalization was applied. The RUVSeq R package (Risso et al., 2011) was then applied using $k = 1$ and a set of 14 mouse housekeeping genes identified by Ho and Patrizi, 2021 to identify factors of unwanted variation that were included as a covariate in the differential expression analysis performed with edgeR (McCarthy et al., 2012; Robinson et al., 2010). Differentially expressed genes were chosen based on a false discovery rate threshold of q<0.05. R 4.0.5 was used for all analyses (https://www.R-project.org). ## Gene set enrichment analysis (GSEA) Based on the edgeR output, GSEA software was applied to perform pathway analyses (Mootha et al., 2003; Subramanian et al., 2005). The edgeR output includes likelihood ratio (LR) test statistics, p-values, and false discovery rate q-values, as well as log fold change (logFC) values based on the expression values of each gene in the comparison groups of interest. Because the LR statistics are non-negative, their values alone cannot distinguish up- and down-regulated genes, as is required for a GSEA pre-ranked analysis. Thus, we computed a signed version of the LR statistic that was defined to be the product of LR statistic times and the sign of the logFC, thereby enabling us to rank the genes according to both the statistical significance and the direction of the expression differences. A GSEA pre-ranked analysis was performed using the ranked genes and gene sets available at the Molecular Signature Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). After the biomaRt R package (Durinck et al., 2005; Durinck et al., 2009) was used to convert mouse gene symbols to human gene symbols. Statistical significance was assessed at the q<0.05 level. ## Metabolite extraction A metabolite extraction was carried out on each sample based on a previously described method (Pacold et al., 2016). An approximate cell count (5 × 106 cells for all samples) of the samples was used to scale the metabolite extraction to a ratio of 1 × 106 cells/1 mL extraction solution [$80\%$ LCMS grade methanol (aq) with 500 nM labeled amino acid internal standard (Cambridge Isotope Laboratories, Inc, Cat No. MSK-A2-1.2)]. The lysis was carried out in two steps as follows. First, 1 mL of freezing extraction solution was added to the tubes containing each pellet. That suspension was transferred to tubes along with zirconium disruption beads (0.5 mm, RPI) and homogenized for 5 min at 4 °C in a BeadBlaster with a 30 s on/30 s off pattern. The resulting lysate was then diluted to a fixed concentration of 1 × 106 cells/1 mL in a new tube with disruption beads in that same buffer and then re-homogenized in the same way as above. The homogenate was centrifuged at 21,000 × g for 3 min, and 450 μL of the supernatant volume was transferred to a 1.5 mL microfuge tube for speed vacuum concentration, no heating. The dry extracts were resolubilized in 50 μL of LCMS grade water, sonicated in a water bath for 2 min, centrifuged as above, and transferred to a glass insert for analysis. ## LC-MS/MS methodology Samples were subjected to an LC-MS/MS analysis to detect and quantify known peaks. The LC column was a Millipore ZIC-pHILIC (2.1 × 150 mm, 5 μm) coupled to a Dionex Ultimate 3000 system and the column oven temperature was set to 25 °C for the gradient elution. A flow rate of 100 μL/min was used with the following buffers; (A) 10 mM ammonium carbonate in water, pH 9.0, and (B) neat acetonitrile. The gradient profile was as follows; 80–$20\%$ B (0–30 min), 20–$80\%$ B (30–31 min), 80–$80\%$ B (31–42 min). Injection volume was set to 2 μL for all analyses (42 min total run time per injection). MS analyses were carried out by coupling the LC system to a Thermo Q Exactive HF mass spectrometer operating in heated electrospray ionization mode (HESI). Method duration was 30 min with a polarity switching data-dependent Top five method for both positive and negative modes. Spray voltage for both positive and negative modes was 3.5 kV and the capillary temperature was set to 320 °C with a sheath gas rate of 35, aux gas of 10, and max spray current of 100 μA. The full MS scan for both polarities utilized 120,000 resolution with an AGC target of 3 × 106 and a maximum IT of 100ms, and the scan range was from 67 to 1000 m/z. Tandem MS spectra for both positive and negative modes used a resolution of 15,000, AGC target of 1 × 105, maximum IT of 50 ms, isolation window of 0.4 m/z, isolation offset of 0.1 m/z, fixed first mass of 50 m/z, and 3-way multiplexed normalized collision energies (nCE) of 10, 35, 80. The minimum AGC target was 1 × 104 with an intensity threshold of 2 × 105. All data were acquired in profile mode. ## Data analysis Metabolomics data were processed with an in-house pipeline for statistical analyses and plots were generated using a variety of custom Python code and R libraries including: heatmap, MetaboAnalystR, and manhattanly. Peak height intensities were extracted based on the established accurate mass and retention time for each metabolite as adapted from the Whitehead Institute and verified with authentic standards and/or high-resolution MS/MS manually curated against the NIST14MS/MS and METLIN spectral libraries. The theoretical m/z of the metabolite molecular ion was used with a±10 ppm mass tolerance window, and a±0.2 min peak apex retention time tolerance within the expected elution window (1–2 min). To account for sample-to-sample variance in the estimated cell counts, a sum-normalization step was carried out on a per-column (sample) basis. Detected metabolite intensities in a given sample were summed, and a percentage intensity was calculated for each metabolite (custom Rscript available from NYU Metabolomics Laboratory at https://med.nyu.edu/research/scientific-cores-shared-resources/metabolomics-laboratory, contact the Laboratory Director Dr. Drew Jones at: [email protected]). The median mass accuracy vs the theoretical m/z for the library was −0.7 ppm ($$n = 90$$ detected metabolites). Median retention time range (time between earliest and latest eluting sample for a given metabolite) was 0.23 min (30 min LCMS method). A signal-to-noise ratio (S/N) of 3 X was used compared to blank controls throughout the sequence to report detection, with a floor of 10,000 (arbitrary units). Labeled amino acid internal standards in each sample were used to assess instrument performance (median CV=$5\%$). ## Influenza A virus (IAV) infection Orai1/Orai3fl/fl Mb1-Cre/+ and Orai1/Orai3fl/fl (control) mice were anesthetized with isoflurane and infected intranasally (i.n.) with 105 TCID50 of the laboratory strain A/HK/x31 (x31-IAV) of the influenza A virus subtype H3N2. Lungs were isolated for histology. Mediastinal lymph nodes and bone marrow were used to prepare single-cell suspensions followed by flow cytometric analysis. Serum was harvested for analyzing virus-specific antibody titers. ## Histology Lungs were fixed with $4\%$ paraformaldehyde in PBS, embedded in paraffin, and cut at 5 μm. Sample slides were stained with hematoxylin and eosin (H&E) using standard methods. Images were acquired using an SCN400 slide scanner (Leica), viewed with Omero Slidepath (Open Microscope Environment). The total alveolar volume fraction was determined using the following procedure: (i) regions representing empty space were acquired by setting a color threshold in ImageJ (Schneider et al., 2012); (ii) to calculate the total alveolar volume fraction, the area of empty space was divided by the total lung area using MATLAB (v2018a). ## ELISA To measure X31-specific antibodies, half-area ELISA plates (Corning, Cat#3690) were coated with heat-inactivated x31-IAV overnight at 4 °C. ELISA plates were blocked with $20\%$ FBS in PBS at 37 °C for 1 hr followed by washing three times with wash buffer ($0.05\%$ v/v Tween-20 in PBS). Plates were incubated with gradient dilutions of serum from control and Orai1/Orai3fl/fl Mb1-Cre/+ mice for 1 hr at 37 °C followed by incubation with alkaline phosphatase (AP) goat anti-rabbit IgG secondary antibody (SouthernBiotech, Cat#1030–04), goat anti-rabbit IgM (SouthernBiotech, Cat#1021–04) at room temperature for 2 hr. After the addition of substrate solution, absorption was measured at 405 nm using a Flexstation three plate reader (Molecular Devices). ## Cell preparation of mediastinal lymph nodes and bone marrow Single-cell suspensions of mediastinal lymph nodes (medLNs) and bone marrow were grounded and passed through 70 µm cell strainer (BD, 22-363-548). Cells were treated with ACK buffer for 3 min and then washed and spun at 800 g for 5 min, resuspended in RPMI medium plus $2\%$ fetal bovine serum, and stained with antibodies as described below. Cells isolated from medLNs and bone marrow were counted with trypan blue, washed, and prepared for flow cytometry analysis in PBS containing $2\%$ FBS and 2 mM EDTA. For surface staining, cells were stained with fluorescently labeled antibodies at 4°C for 15 min in the dark, followed by Live/Dead Blue (Invitrogen, Cat# L23105) staining following the manufacturer’s instructions. Samples were acquired on an LSR Fortessa (BD Biosciences) and analyzed using FlowJo software (TreeStar, versions 9.3.2 and 10.5.3.). The list of antibodies used is as follows: B220- BV510 (RA3-6B2, Biolegend), CD138-PE (281–2, Biolegend), CD95-BV421 (Jo2, BD Bioscience), GL-7-Alexa-Fluor647 (GL7, Biolegend), CD4-APC-Cy7 (GK1.5, Biolegend), and CD38-PE-Cy7 (T10, Biolegend). ## Statistics All statistical tests were conducted using GraphPad Prism 9 and data was presented as mean ± SEM. When comparing two groups the Student’s t-test was used. If greater than two groups were compared, then a One-way analysis of variance was used. For all results with normally distributed data, parametric statistical tests were used. When data were not normally distributed, non-parametric statistical tests were used. Data normality was determined using Prism 9.0. Biological replicates were defined as primary cells isolated from an individual mouse from their respective genotype. 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--- title: 'Intermittent Fasting: Benefits, Side Effects, Quality of Life, and Knowledge of the Saudi Population' journal: Cureus year: 2023 pmcid: PMC9998115 doi: 10.7759/cureus.34722 license: CC BY 3.0 --- # Intermittent Fasting: Benefits, Side Effects, Quality of Life, and Knowledge of the Saudi Population ## Abstract Introduction Intermittent fasting (IF) is an eating pattern that alternates between periods of fasting and eating. IF has shown many benefits for people who are obese and are trying to lose weight and attain a healthy lifestyle. The aim of our study was to evaluate the efficacy of IF and how it can be used as a daily lifestyle as well as to measure the knowledge of the IF diet among the Saudi population about its benefits, side effects, and life quality. Method For this retrospective cross-sectional study, data about the common side effects, benefits, and the measurement of the quality of life were collected by a survey distributed using Google Forms. Microsoft Excel was used for the data analysis, with the data and results being mainly expressed as numbers and percentages. Results Among the 147 individuals practicing the IF plan who were surveyed, male participants were more than females ($53.7\%$ vs $46.3\%$). The highest percentage of respondents were in the age group 18-35 years old, and 88 individuals ($59.9\%$) had a high body mass index (BMI). Duration of fasting varied from less than a month to three months in $70.8\%$, and $71.4\%$ of participants had undertaken IF several times. Side effects were headache ($61.3\%$), lethargy ($68\%$), mood swings ($57.8\%$), and lastly dizziness and polyuria ($55.8\%$ and $46.2\%$, respectively). Slightly more females expressed happiness than males ($86.8\%$ vs $83.6\%$). Conclusion The IF diet is an efficient dietary plan for those aiming at a weight loss journey over a short duration, ranging from less than a month up to three months. Minimal side effects were found during fasting, being of different intensities, which did not need surgical or medical treatment. All in all, most of our respondents were pleased with their experience and saw excellent weight loss results using the IF diet. ## Introduction Fasting is a practice followed by our ancestors for thousands of years; it has several aspects and is of different types such as for religious, cultural, or health reasons. Muslims fast during the month of Ramadan for a specific period during the day, which is similar to intermittent fasting (IF) [1]. This type of diet is based on an eating pattern that alternates between periods of fasting and eating. IF on the other hand mainly focuses on the timing of the meal, yet the quality of the food is also considered a factor, since losing weight is the primary goal. It commonly consists of a daily fast for 16 hours, a 24-hour fast on alternate days, or a fast two days per week on non-consecutive days. These types of diets aren't only used for obese people trying to lose weight, but also for people with cardiovascular risk factors and type 2 diabetes risk factors [2]. Obesity is a major medical problem that can lead to other significant risks such as metabolic diseases, mainly type 2 diabetes, hypertension, and stroke, and gastrointestinal tract diseases such as the fatty liver. According to WHO, obesity has nearly tripled between 1975 and 2016, with $39\%$ of adults aged 18 years and over being overweight in 2016, and $13\%$ obese [3]. Preventable obesity is still considered a major problem around the world. IF provides many benefits for the obese and those trying to lose weight and attain a healthy lifestyle. It can reduce body fat and inflammation, and improve glucose metabolism [4]. Indeed, although IF is used for weight loss, ADF (alternate day fasting), which is a type of IF, has shown benefits in non-obese people also by lowering triacylglycerol, C reactive protein, and leptin, while increasing low-density lipoprotein particle size and adiponectin concentrations [5]. As much as IF is filled with benefits, there are mild side effects that can happen during fasting, which do not generally require medical or surgical treatment. Possible side effects can include dizziness, nausea, insomnia, headache, weakness, etc. [ 6]. Another study has shown low blood sugar (hypoglycemia) to be a side effect of IF [7]. The aim of our cross-sectional study was to observe the effectiveness of IF throughout the Saudi population. Furthermore, we wanted to evaluate behavioral characteristics and experiences of the IF diet from people's viewpoints on the side effects, benefits, and life quality. ## Materials and methods Study design and period A retrospective cross-sectional study was carried out among individuals who had or are currently following the IF. In the four-week preparation period, we created our study title, reviewed the literature to gather information on typical side effects and how to measure the quality of life, and created a questionnaire. In the following four weeks, we completed our research, which included looking for the target group on social media, creating a Google Form for the survey, and testing the form by using a small sample of the findings to make sure they presented as we planned. We collected and examined our data for a further four weeks before writing our report. Inclusion and exclusion criteria The study includes people who have ever practiced or are currently practicing IF and who can speak the Arabic language. The target population is in Saudi Arabia in all regions of the kingdom. The initial assessment questions excluded participants who had never engaged in IF. The sample size was calculated automatically using Google Forms. A total of 300 people were registered in the Google Form; 147 of them had practiced IF, and 153 were excluded. Data collection tool We developed a questionnaire in Arabic to collect data about IF attitudes and quality of life (questionnaire in the Appendices section). The questionnaire was developed to meet the health-related quality of life (HRQOL) measures, which were translated into Arabic to be suitable for the participant's understanding of the question in our survey. The Centers for Disease Control and Prevention (CDC) HRQOL-4 measures had acceptable test-retest reliability and strong internal validity, which has been used by the CDC and its partners, for tracking population health status and HRQOL measures in states and communities [8]. The standard four-item set of healthy days’ core questions was developed by the CDC. The questionnaire consists of the following four main parts: (i) sociodemographic data, (ii) side effects assessment, (iii) participants' attitude toward IF assessment, and (iv) participants' quality of life assessment. Data collection technique We conducted the survey in an electronic self-assessment format using Google Forms. We targeted IF groups on social media in Saudi Arabia for a time interval of four weeks through messages and direct contact with the group administrator and ensured that only people who practiced IF participated in the survey. One of the first questions in the survey was “Have you tried Intermittent fasting?” If the answer was no, then the participant is excluded from the survey. The data obtained from the survey were reviewed and automatically copied into a personal computer. Data entry and analysis The data collected using the questionnaire was rearranged in Microsoft Excel data sheets. The data was mainly expressed as numbers and percentages. We used a chi-square test to evaluate participants’ perceived happiness, which also indicated the p-value for statistical significance. We also used a pie chart that displays the change in the participants’ body weight following their adoption of IF and to seek the perceived happiness of the participants' IF experience. Ethical considerations This study was approved by the Bioethics Committee for Scientific and Medical Research at the University of Jeddah (Approval number UJ-REC-069). Individual consent was required prior to data collection, and it was stated on the questionnaire's front page that completing it signified consent to participate in the study. All information was kept private and was solely utilized for scientific studies. Furthermore, we made sure that parental consent was given to participants less than 18 years old. ## Results A total of 147 respondents who practiced the IF diet plan participated to explore their IF experience and to determine its consequences on their overall health. Table 1 shows the respondent characteristics of gender, age, and body mass index (BMI). Male participants were more than the females ($53.7\%$ vs $46.3\%$), and $75.5\%$ were in the age group of 18-35 years. According to the BMI ranges they were derived from CDC, $38.1\%$ of the participants were within the normal weight category, with $35.4\%$ being overweight, and $23.1\%$ obese. Table 1 \| Characteristics \| Characteristics.1 \| Number \| Percentage \| \| --- \| --- \| --- \| --- \| \| Gender \| Male \| 79.0 \| 53.70% \| \| Gender \| Female \| 68.0 \| 46.30% \| \| Age categories \| <18 years \| 4.0 \| 2.40% \| \| Age categories \| 18-35 years \| 111.0 \| 75.50% \| \| Age categories \| 36-55 years \| 27.0 \| 18.40% \| \| Age categories \| 56-70 years \| 5.0 \| 3.40% \| \| Age categories \| > 70 years \| \| \| \| BMI categories \| Within Normal (18.5-24.9) \| 56.0 \| 38.1% \| \| BMI categories \| Overweight (25-29.9) \| 52.0 \| 35.40% \| \| BMI categories \| Obesity (30-39.9) \| 34.0 \| 23.1% \| \| BMI categories \| Morbid Obesity (≥40) \| 2.0 \| 1.40% \| \| BMI categories \| underweight (<18.5) \| 3.0 \| 2.00% \| As seen in Table 2, most of the respondent individuals ($70.8\%$) practiced the IF diet for a duration ranging from less than a month to three months, while $29.3\%$ of the individuals practiced the diet from more than three months to six months. According to the frequency, $28.6\%$ were for the first time experiencing IF, and for the second time was $29.2\%$. Lastly, $34\%$ have experienced IF more than three times. During the IF diet plan, close to two-thirds of the respondents ($67.3\%$) were performing physical exercise and $32.7\%$ didn't perform any physical exercise. Table 2 \| Practices during Intermittent fasting \| Practices during Intermittent fasting.1 \| Number \| Percentage \| \| --- \| --- \| --- \| --- \| \| Duration \| <1 month \| 47 \| 32% \| \| Duration \| 1 month \| 36 \| 24.50% \| \| Duration \| > 1 month-3 months \| 21 \| 14.30% \| \| Duration \| >3 month-6 months \| 43 \| 29.20% \| \| Frequency \| Once \| 42 \| 28.60% \| \| Frequency \| Twice \| 43 \| 29.20% \| \| Frequency \| 3 times \| 12 \| 8.20% \| \| Frequency \| >3 times \| 50 \| 34% \| \| Regular Performance of Physical Exercise \| Yes \| 99 \| 67.30% \| \| Regular Performance of Physical Exercise \| No \| 48 \| 32.70% \| Figure 1 displays the weight changes in a sample who practiced IF throughout their lifetime so far, of whom 67 respondents ($45.6\%$) reported weight loss varying from 1 kg to less than 3 kg. Of these, 49 respondents ($33.3\%$) lost 3-5 kg and 19 ($12.9\%$) respondents lost 5-10 kg; four respondents ($2.7\%$) reported losing 10-15 kg, whereas only two respondents ($1.4\%$) reported losing more than 15 kg. Lastly, six respondents ($4.1\%$) reported weight gain. Figure 1: Change in weight as a response to intermittent fasting We asked responders about any symptoms they may have felt while following the IF diet allowed researchers to gauge the overall effectiveness of the diet, particularly within the first month (Table 3). As shown, the most frequently occurring symptoms were headache (mild $36.1\%$, moderate $17.7\%$, and severe $7.5\%$), lethargy (mild $27.9\%$, moderate $23.1\%$, and severe $17\%$), mood swings (mild $31.3\%$, moderate $16.3\%$, and severe $10.2\%$), and dizziness with a total of 55.8, and lastly, polyuria among $46.2\%$ respondents. Table 3 \| Symptoms \| Mild \| Moderate \| Severe \| Not Sure \| Not at All \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Headache \| 53 (36.1%) \| 26 (17.7%) \| 11 (7.5%) \| 17 (11.6%) \| 40 (27.2%) \| \| Mood swings \| 46 (31.3%) \| 24 (16.3%) \| 15 (10.2%) \| 19 (12.9%) \| 43 (29.3%) \| \| Palpitations \| 24 (16.3%) \| 18 (12.2%) \| 11 (7.5%) \| 16 (10.9%) \| 78 (53.1%) \| \| Fever \| 10 (6.8%) \| 4 (2.7%) \| 4 (2.7%) \| 10 (6.8%) \| 119 (81%) \| \| Flu \| 18 (12.2%) \| 3 (2%) \| 7 (4.8%) \| 11 (7.5%) \| 108 (73.5%) \| \| Low Blood Sugar \| 18 (12.2%) \| 15 (10.2%) \| 9 (6.1%) \| 26 (17.7%) \| 79 (53.7%) \| \| Lethargy \| 41 (27.9%) \| 34 (23.1%) \| 25 (17%) \| 4 (2.7%) \| 43 (29.3%) \| \| Constipation \| 29 (19.7%) \| 19 (12.9%) \| 9 (6.1%) \| 15 (10.2%) \| 75 (51%) \| \| Dizziness \| 41 (27.9%) \| 25 (17%) \| 16 (10.9%) \| 9 (6.1%) \| 56 (38.1%) \| \| Vomiting \| 16 (10.9%) \| 6 (4.1%) \| 4 (2.7%) \| 8 (5.4%) \| 113 (76.9%) \| \| Dehydration \| 27 (18.4%) \| 18 (12.2%) \| 5 (3.4%) \| 8 (5.4%) \| 89 (60.5%) \| \| Bloating \| 17 (11.6%) \| 10 (6.8%) \| 9 (6.1%) \| 15 (10.2%) \| 96 (65.3%) \| \| Polyuria \| 29 (19.7%) \| 25 (17%) \| 14 (9.5%) \| 15 (10.2%) \| 64 (43.5%) \| Figure 2 demonstrates a pie-chart of perceived happiness for the IF experience of our respondents, with $85\%$ of them pleased about adopting the IF diet and $15\%$ of them weren't. Figure 2: Perceived happiness regarding intermittent fasting According to gender in Table 4, females were happier than males ($86.8\%$ vs $83.6\%$), and when it came to the age groups we found that the most perceived happiness was in the age group 36-55 years old ($92.6\%$). In the BMI category happiness perception among obese respondents had the highest percentage ($88.2\%$). These differences showed no statistical significance ($p \leq 0.05$). Table 4 \| Variables \| Variables.1 \| Yes \| Yes.1 \| No \| No.1 \| X^2 \| P-Value \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variables \| Variables \| Number \| % \| Number \| % \| X^2 \| P-Value \| \| Gender \| Male \| 66 \| 83.6% \| 13 \| 16.4% \| 0.297 \| 0.585 \| \| Gender \| Female \| 59 \| 86.8% \| 9 \| 13.2% \| 0.297 \| 0.585 \| \| Age \| <18 Years \| 3 \| 75% \| 1 \| 25% \| 2.074 \| 0.149 \| \| Age \| 18-35 Years \| 91 \| 82% \| 20 \| 18 \| 2.074 \| 0.149 \| \| Age \| 36-55 Years \| 25 \| 92.6% \| 2 \| 7.4% \| 2.074 \| 0.149 \| \| Age \| 56-70 Years \| 4 \| 80% \| 1 \| 20% \| 2.074 \| 0.149 \| \| Age \| > 70 Years \| \| \| \| \| 2.074 \| 0.149 \| \| BMI categories \| Within Normal \| 49 \| 87.5% \| 7 \| 12.5% \| 3.489 \| 0.061 \| \| BMI categories \| Overweight \| 43 \| 82.7% \| 9 \| 17.3% \| 3.489 \| 0.061 \| \| BMI categories \| Obesity \| 30 \| 88.2% \| 4 \| 11.7% \| 3.489 \| 0.061 \| \| BMI categories \| Morbid Obesity \| 1 \| 50% \| 1 \| 50% \| 3.489 \| 0.061 \| \| BMI categories \| Underweight \| 2 \| 66.6% \| 1 \| 33.3% \| 3.489 \| 0.061 \| ## Discussion IF has shown an increase in popularity as a method for weight loss and health optimization, and previous research has found promising results for its utility [10]. The current study collected data on IF to perceive its efficacy, side effects, and benefits, as well as the measurement of the resulting quality of life among the Saudi population adopting IF. The findings of our study showed that the number of males was slightly higher than the females by $7.4\%$, while another study has shown that female participants tend to be a majority, and also practice IF longer than males [1]. When it comes to age category, most of our respondents were between the age of 18 and 35, which makes sense since this age group is adolescents/adults who have greater concern about their weight than any other age category. The relationship between weight loss and young adults was studied by Jessica Gokee LaRose et al., who stated that people <35 years old have a higher chance of gaining weight than older adults [11]. Most of our respondents had a high BMI; a combination of overweight ($35.4\%$), obese ($23.1\%$), and morbidly obese ($1.4\%$). Among the respondents, $59.9\%$ had a high BMI and $38.1\%$ were within the normal weight, with the remaining $2\%$ underweight. So, most of these participants practiced IF to lose weight and reduce their BMI number. A study by Maha H Alhussain et al. in Saudi Arabia compared the usage of IF for different purposes and found that almost half of the respondents ($47.59\%$) were trying to lose weight [12]. When it came to the duration, people who practiced the IF diet for less than a month and up to three months constituted $70.8\%$, while those who did it for a long term (>3-6 months) were $29.3\%$. Frequency-wise, the percentages were relatively similar to one another; the people who practiced it once were $28.6\%$, twice $29.3\%$, and thrice were only $8.2\%$ making it the least, whereas those adopting IF more than three times were $34\%$, accounting for the largest percentage. Moreover, our statistics show that $67.3\%$ of the people who adopted the IF diet also performed physical exercise, whereas only $32.7\%$ practiced IF without any physical exercise. People felt that exercising while fasting isn’t a great option since you aren’t eating, yet a study has shown that exercising while you are on your fast increases adipose tissue lipolysis and peripheral fat oxidation, both of which act as energy producers for the body [13]. Alexandra Ferreira Vieira found that those who did moderate aerobic workouts during the fasting state showed a significant increase in fat oxidation [14]. A total of $94.5\%$ of our respondents have lost weight (ranging from 1 kg up to 10 kg), and $4.1\%$ had a significant decrease in weight (greater than or equal to 10 kg). Only six of our participants gained weight during their IF, which might be due to the quality of the food they chose to eat. Lora E. Burke et al. carried out a systematic review of 22 studies with self-monitoring; 15 out of the 22 studies involved dietary self-monitoring. Within these 22 studies, they found out there was more weight loss among people who self-monitored than among the less frequent self-monitoring group [15]. In spite of the fact that IF may be used by many people to cleanse their bodies or for weight loss, yet the diet does come with side effects, which you may experience within the first month of your fast. The most frequent side effects were headache, dizziness, polyuria, mood swings, and lastly lethargy. All of these symptoms had different intensity levels, from mild to severe. Headache is a common side effect seen in fasting in general, which is mainly due to hypoglycemia, and it is characterized as a diffuse and non-pulsatile headache. The headache is of mild to moderate intensity, it happens during the fasting period, and when you fast at least for 8 hours [16]. Lastly, $85\%$ of respondents were happy with their results, and only $15\%$ weren’t happy with their IF experience. As mentioned before, this may be due to their nutritional lifestyle routine or because they did not achieve the desired results. The study has some limitations. First, it was distributed over social media groups with a special interest in the IF diet, thereby possibly exaggerating the positive attitude toward this diet. Second, the study's retrospective questions may have been influenced by how participants overall felt and how they remembered their symptoms. ## Conclusions The majority of our participants tried the IF diet ranging from a month up to three months, suggesting that this type of diet is a short-term solution for people trying to lose weight. Almost all of the participants lost weight. Side effects were reported in our results, with a variation of intensities (mild, moderate, severe), particularly within the first month. 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--- title: 'Comparison of maternal and fetal health outcomes in the pandemic period of covid-19 with the same last year duration in health centers of second largest city of Iran: A population-based cohort study' authors: - Neda Davaryari - Saeed Davaryar - Adele Azarshab - Mohammad Moein Vakilzadeh - Veda Vakili - Zahra Moazzeni journal: Heliyon year: 2023 pmcid: PMC9998123 doi: 10.1016/j.heliyon.2023.e14439 license: CC BY 4.0 --- # Comparison of maternal and fetal health outcomes in the pandemic period of covid-19 with the same last year duration in health centers of second largest city of Iran: A population-based cohort study ## Abstract ### Objective The exact link between COVID-19 pandemic and different adverse outcomes of pregnancy remains unclear. Plus, large-scale research is lacking. In the present study, we aimed to compare the maternal and fetal health outcomes during the COVID-19 pandemic with the same last year duration in Iran. ### Design Two retrospective cohorts (pre-COVID-19 and during COVID-19) were studied. The pre-COVID-19 cohort include pregnant women who had given birth between January 1, 2019 and December 31, 2019. The COVID-19 cohort, who had given birth between January 1, 2020 and December 31, 2020. The characteristics of pregnant women before COVID-19 and during COVID-19 pandemic were compared with Fisher's exact test. Univariate and multivariate log-binomial regression models were used to determine the risk ratios of the impacts of the COVID-19 pandemic on adverse pregnancy outcomes. ### Results Among 128968 women showed that women who had given birth during the pandemic were more likely to be of young age, lower rates of alcohol consumption and smoking, lower weight gain, and higher rates of using synthetic milk for feeding neonates ($P \leq 0.05$). Also, the risks of preterm labor were high (cOR $95\%$ CI, 1.13 to 1.31; $p \leq 0.01$) and the risk of caesarian were low (cOR $95\%$ CI, 0.95 0.92 to 0.98; $p \leq 0.01$) among pregnant women who gave birth during the COVID-19 pandemic compared with those who gave birth before the pandemic. ### Conclusions In summary, we found that during the COVID-19 pandemic there were the higher risks of preterm labor and lower risk of caesarean among pregnant women. ## Introduction At the end of 2019, the discovery of a new coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), was reported and linked to a series of pneumonia cases in Wuhan, China. The virus spread throughout the country rapidly, with several other countries following soon afterwards. The World Health Organization (WHO) named the disease Coronavirus disease 2019 (COVID-19), and declared it a global health emergency in March 2020 [1,2]. The risk of COVID-19 infection in pregnant women appears to be the same as in general population; however, it is speculated that immunological changes associated with pregnancy might leave the mother more susceptible to viral infections and their complications [3]. Indeed, some studies have demonstrated that COVID-19 infections are more severe in the final months of pregnancy [4,5]. In addition, a number of studies have indicated higher rates of adverse pregnancy outcomes in women with COVID-19, including preterm birth, cesarean section, and perinatal death [4,6,7]. COVID-19 pandemic has also seen numerous lockdown measures implemented occasionally in many countries, which could potentially disrupt maternal and neonatal health services and lead to adverse health outcomes [8]. As such, the exact link between COVID-19 pandemic and different adverse outcomes of pregnancy remain unclear, and more studies are needed to better elucidate the impacts of COVID-19 pandemic on pregnancy outcomes. Plus, large-scale research is lacking. In the present study, we aimed to compare the maternal and fetal health outcomes during the COVID-19 pandemic with the same last year duration in Iran. ## Study settings In Iran, maternity care is state-funded and easily accessible for everyone. Pregnant women are closely followed by health care providers in designated health centers, run under the supervision of the regional medical university, for the duration of pregnancy and a few weeks after giving birth. Their data, including past medical histories, pregnancy status and pregnancy outcomes are recorded in detail by health care staff in an electronic archive. This study was approved by the Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.REC.1399.160). ## Study design and population Two retrospective cohorts (pre-COVID-19 and during COVID-19) were studied. The pre-COVID-19 cohort consisted of 63618 pregnant women who had given birth between January 1, 2019 and December 31, 2019 (approximately one year before the beginning of the pandemic). There were 65350 pregnant women in the COVID-19 cohort, who had given birth between January 1, 2020 and December 31, 2020 (approximately one year after the beginning of the pandemic). ## Data collection Data were collected from the electronic information system of health centers of Mashhad University of Medical Sciences. These data included related demographic characteristics (age, education, and occupation), past medical & social histories, pregnancy relate data (gravidity, parity, history of previous delivery problems), and health status (body mass index (BMI) before pregnancy, gestational weight gain (GWG)). Collected maternal outcomes of pregnancy were gestational diabetes, gestational hypertension, delivery mode, hospitalization, blood transfusion, and postpartum preeclampsia. Evaluated fetal pregnancy outcomes included preterm birth, low birth weight and macrosomia. ## Statistical analyses The characteristics of pregnant women before COVID-19 and during COVID-19 pandemic were compared with Fisher's exact test. Univariate and multivariate log-binomial regression models were used to determine the crude risk ratios (cRRs) and adjusted risk ratios (aRRs) of the effects of the COVID-19 pandemic on poor pregnancy outcomes. Sensitivity analysis was done by fitting various models to survey the robustness of the estimation. Three models were fitted: the first model (A) was unadjusted; the second model (B) was adjusted for demographic characteristics (age, education, and occupation) and the third model (C) was based on model B, with further adjustments for pregnancy conditions (gravidity, parity, history of abortion), BMI before pregnancy, GWG, past medical and social history, history of low birth weight delivery, history of stillbirth, and history of delivery with congenital disorder. All analyses were performed using Statistical Package for the Social Sciences (SPSS) software version 26. A p value < 0.05 was considered statistically significant. ## Results A total of 128968 pregnant women were included in this study, with a mean age of 30.56 (±6.71, SD). $71.9\%$ were unemployed and $74.4\%$ had a bachelor's degree or less. Characteristics and pregnancy histories of the study population are provided in Table 1 and Table 2 respectively. Pregnant women who experienced the COVID-19 pandemic were more likely to be younger ($30.1\%$ compared to $25.3\%$ in the pre-COVID-19 group), employed ($28.7\%$ compared to $25.9\%$ in the pre-COVID-19 group), and to have received a primary school education or less ($21.4\%$ compared to $20.8\%$ in the pre-COVID-19 group) when compared to women in the pre-pandemic group. Also, pregnant women during the COVID-19 pandemic were more likely to have a smaller number of gravidities, but a higher number of parities, compared with women in the pre-COVID-19 pandemic group. Regarding the pregnancy history, compared with women in the pre-COVID-19 pandemic group, pregnant women during the COVID-19 pandemic were more likely to have a positive history of low-birth-weight delivery ($5.6\%$ vs $6.1\%$, respectively), but were less likely to have a history of delivery with congenital disorder ($0.9\%$ vs $0.7\%$, respectively). There were not significant differences between cases in the pre-COVID-19 pandemic group and cases during the COVID-19 regarding the history of Diabetes Miletus and hypertension (all $p \leq 0.05$), but the rates of other chronic diseases were higher in COVID-19 pandemic group ($5.3\%$ vs $6.2\%$). Evaluating the social history of women showed that the rate of alcohol consumption and smoking were both significantly lower in pregnant women during the COVID-19 pandemic, compared with women in the pre-COVID-19 pandemic group ($p \leq 0.01$, and $$p \leq 0.002$$ respectively), but there were no significant differences in rates of addiction ($$p \leq 0.279$$). Weight gain during pregnancy was lower during the COVID-19 pandemic compared with women in the pre-COVID-19 pandemic group ($63.8\%$ vs $65.5\%$). Feeding neonates with synthetic milk was more common during COVID-19 pandemic, compared with pre-COVID-19 pandemic group ($$p \leq 0.001$$). Also, there was a significant difference between BMI index before pregnancy, There were no significant differences in other characteristics between the two groups (all $p \leq 0.05$).Table 1Characteristics of 128968 pregnant women before and during the COVID-19 pandemic. Table 1ItemsN/mean (SD)Pre-COVID-19 (N, %; mean, SD)COVID-19 (N, %; mean, SD)P valueMaternal age (years)30.56 (6.71)31.09 (6.59)30.04 (6.78)<0.01Maternal age (years)≤242598911012 (17.3)14977 (22.9)<0.0125–356731533445 (52.6)33870 (51.8)≥353566419161 (30.1)16503 (25.3)OccupationUnemployed94,75647521 (74.1)47235 (71.3)<0.01employed3561116576 (25.9)19035 (28.7)EducationPrimary school or less2778813481 (20.8)14307 (21.4)0.001Junior high school2989814692 (22.7)15206 (22.7)Senior high school4033320161 (31.1)20172 (30.1)Undergraduate or above3372216488 (25.4)17234 (25.8)Past medical historychronic disease69703252 (5.3)3718 (6.2)<0.01Diabetes Miletus624286 (0.5)56061 (0.6)0.213ahypertension605288 (0.5)318 (0.6)0.684aSocial historyAlcohol consumption9075 (0.1)15 (0.0)<0.01aAddiction16889 (0.2)79 (0.1)0.281aSmoker1126601 (1.1)525 (0.9)0.002aGestational weight gainAppropriate6282929721 (65.5)33108 (63.8)<0.01Inappropriate3439315647 (34.5)18746 (36.2)BMI before pregnancy, kg/m2Underweight (18.5)70583312 (6.2)3746 (6.7)0.002Normal (18.5–24.9)5196825429 (47.6)26539 (47.7)Overweight (25–29.9)3401016827 (31.5)17183 (30.9)Obese [30]160317847 (14.7)8184 (14.7)aFisher exact test. Table 2Pregnancy history of 128968 pregnant women before and during the COVID-19 pandemic. Table 2ItemsN/mean (SD)Pre-COVID-19 (N, %; mean, SD)COVID-19 (N, %; mean, SD)P valueGravidity2.52 (1.37)2.54 (1.36)2.51 (1.37)0.001Gravidity12740312867 (23.6)14536 (25.5)<0.0123629218043 (33.2)18249 [32]≥34776723501 (43.2)24266 (42.5)parity2.07 (1.13)2.05 (1.11)2.08 (1.14)<0.01Parity14609723117 (35.7)22980 (34.5)<0.0124742924182 (36.3)23247 (35.9)≥33774918311 (28.3)19438 (29.2)Positive History forLow-birth-weight delivery48952310 (5.6)2585 (6.1)0.001aabortion3225515886 (24.5)16369 (24.5)0.856Stillbirth1250634 (1.6)616 (1.5)0.459adelivery with congenital disorder642356 (0.9)286 (0.7)0.003aGestational weight gainAppropriate6282929721 (65.5)33108 (63.8)<0.01Inappropriate3439315647 (34.5)18746 (36.2)Child feedingMothers milk12529861950 [97]63348 (96.7)0.001Mothers and synthetic milk33741616 (2.5)1758 (2.7)Synthetic milk968301 (0.5)397 (0.6)aFisher exact test. Assessing the pregnancy outcomes demonstrated that the prevalence of caesarean sections was lower during the COVID-19 pandemic period compared with women prior to the pandemic ($42.0\%$ vs $41.3\%$; $$p \leq 0.007$$). During the COVID-19 pandemic period, the occurrence of post-partum preeclampsia was greater compared to women before the pandemic, with a difference of $0.2\%$ ($1.1\%$ versus $0.9\%$), which was found to be statistically significant ($$p \leq 0.045$$). However, there were no significant differences in other pregnancy outcomes between the two periods. ( $p \leq 0.05$, Table 3). The prevalence of preterm birth and macrosomia were higher during the COVID-19 pandemic period compared with cases prior to the pandemic ($4.2\%$ vs $4.7\%$, $p \leq 0.01$; and $4.3\%$ vs $4.6\%$, respectively; $$p \leq 0.008$$). However, prevalence of low birth weight was not significantly different during the COVID-19 pandemic compared with the pre-pandemic period ($$p \leq 0.868$$).Table 3Pregnancy outcomes before and during the COVID-19 pandemic. Table 3outcomesPre-COVID-19 (N, %; mean, SD)COVID-19 (N, %; mean, SD)P valueAdverse maternal outcomesGestational diabetes1707 (3.8)1911 (4.0)0.180aGestational hypertension607 (0.9)594 (0.9)0.354aCaesarean section25771 [42]24649 (41.3)0.007hospitalization5893 (9.1)5963 (8.9)0.256aBlood transfusion77 (0.1)89 (0.1)0.485aPostpartum preeclampsia574 (0.9)622 (1.1)0.045aAdverse fetal outcomesPreterm birth2695 (4.2)3142 (4.7)<0.01aLow birth weight4934 (7.6)5077 (7.6)0.868aMacrosomia2784 (4.3)3058 (4.6)0.015aaFisher exact test. In our log-binomial regression models (Table 4), although risk for Caesarean section in univariate model was 0.97 times ($95\%$ CI, 0.92 to 0.98; $$p \leq 0.003$$) during the COVID-19 pandemic compared with pre-COVID-19, but after adjusting for all confounding factors, the risk was not significantly difference ($$p \leq 0.467$$). The risk of macrosomia during the COVID-19 pandemic compared with pre-COVID-19 cases was increased by 1.08 times ($95\%$ CI, 1.01 to 1.16; $$p \leq 0.023$$) when adjusting for age, occupation and education (Table 3). The risk of premature birth was increased by 1.2 times in all three models ($95\%$ CI, 1.10 to 1.31; $p \leq 0.01$).Table 4The influence of the COVID-19 pandemic on pregnancy outcomes. Table 4OutcomesModel AModel BModel CcOR ($95\%$ CI)P valueaOR ($95\%$ CI)P valueaOR ($95\%$ CI)P valueAdverse maternal outcomesGestational diabetes0.97 (0.89–1.05)0.4751.04 (0.95–1.12)0.3441.02 (0.94–1.11)0.545Gestational hypertension0.88 (0.75–1.02)0.1090.94 (0.81–1.10)0.5030.88 (0.75–1.03)0.115Caesarean section0.95 (0.92–0.98)0.0030.99 (0.96–1.02)0.9030.98 (0.95–1.02)0.467hospitalization1.00 (0.95–1.05)0.9000.98 (0.93–1.04)0.6080.97 (0.92–1.03)0.393Blood transfusion1.41 (0.91–2.19)0.1211.43 (0.92–2.23)0.1061.41 (0.90–2.19)0.129Postpartum preeclampsia0.96 (0.82–1.12)0.6310.99 (0.84–1.16)0.9260.99 (0.84–1.16)0.908Adverse fetal outcomesPreterm birth1.13 (1.13–1.31)<0.011.15 (1.15–1.33)<0.011.20 (1.10–1.31)<0.01Low birth weight1.02 (0.96–1.08)0.3681.04 (0.98–1.10)0.1931.03 (0.97–1.09)0.193Macrosomia1.07 (0.99–1.14)0.0571.08 (1.01–1.16)0.0231.06 (0.99–1.14)0.088Model A: a univariate model without controlling for any confounding factors. Model B: controls for demographic characteristics (age, occupation and education).Model C: based on model B, supplemented to control for gravidity, parity, history of abortion, BMI before pregnancy, gestational weight gain, history of chronic disease, History of Low-birth-weight delivery, History of Stillbirth, History of delivery with congenital disorder, history of Diabetes Miletus, history of hypertension, Alcohol consumption, Addiction and smoking).aOR, adjusted odds ratio; BMI, body mass index; cOR, crude odds ratio. ## Summary of the findings Results of this cohort study on 128968 women with a focus on secondary impacts of the COVID-19 pandemic on pregnancy outcomes showed that women who had given birth during the pandemic were more likely to be of young age, employed, less educated, have a smaller number of gravidity and higher number of parity, have a positive history of Low-birth-weight, less likely to have a history of delivery with congenital disorder, have higher rates of other chronic diseases, lower rates of alcohol consumption and smoking, lower weight gain, and higher rates of using synthetic milk for feeding neonates. There were the higher risks of preterm labor ($13\%$ up to $15\%$) and lower risk of caesarian ($5\%$), among pregnant women who gave birth during the COVID-19 pandemic compared with those who gave birth before the pandemic. ## Strengths and limitations The strengths of this study include its cohort-study design, large sample size, and use of a variety variable to detect the impacts of the COVID-19 pandemic on pregnancy outcomes. Moreover, log-binomial regression models were utilized to assess the effect of either a policy intervention or a natural alteration. This multicenter cohort study was carried out in a major city of Iran. Nevertheless, there were certain limitations to our study. Firstly, it was a retrospective investigation. Moreover, we lacked access to data beyond the scope of pregnancy outcomes. ## Comparison with other studies Although there are conflicting reports of pregnancy outcomes during the COVID-19, but to our knowledge, this is the initial multicenter cohort study in Iran that concentrates on the secondary consequences of the COVID-19 pandemic on pregnancy outcomes. Some studies have demonstrated higher rates of gestational diabetes, hypertension, premature rupture of membrane, and admission to intensive care unit during the pandemic, compared with pre-COVID-19 period [[9], [10], [11], [12]]; while other studies have not demonstrated any such changes. A systematic review and meta-analysis by Chmielewska et al. did not find any significant increase in the prevalence of gestational diabetes, and hypertensive disorders of pregnancy during COVID-19 pandemic [13]. Yang et al. conducted a study where they reported a decrease in the unadjusted likelihood of preterm birth during the COVID-19 pandemic period in comparison to the pre-pandemic period [14]. A recent study by Du et al. found that there was a heightened chance of premature rupture of membranes and fetal distress during the COVID-19 pandemic. However, no significant connections were detected between the pandemic and other pregnancy outcomes [15]. One of the conflicting issues is the impact of COVID-19 pandemic on rates of preterm birth (PTB). In a systematic review and meta-analysis conducted by Yang et al., which analyzed the impact of COVID-19 on pregnancy and neonatal outcomes across 43 studies comprising of 986,466 women during the pandemic period and 8,716,000 women in the pre-pandemic period, it was found that the unadjusted odds of preterm birth (PTB) were lower during the pandemic period as compared to the pre-pandemic period (pooled unadjusted odds ratio [OR] 0.95, $95\%$ confidence interval [CI] 0.93–0.98). However, when adjusted estimates were taken into consideration, which were reported by five of the studies with different factors adjusted, the pooled analysis did not reveal any significant differences in the odds of PTB during the pandemic period [14]. In the present study unadjusted odds ratio of preterm birth during COVID-19 pandemic was 1.13 ($95\%$ CI 1.13–1.31); also, the risk of premature birth was increased significantly even in adjusted models ($95\%$ CI, 1.10 to 1.31; $p \leq 0.01$). Also, the evaluation of the Yang review in birthweight of studies, shows that There was no difference in the odds of low birth weight, very low birthweight, or extremely low birth weight during COVID-19 pandemic [14]. In our study, we evaluate birth weight in two categories including macrosomia and low birth weight. Significant odds ratio was noted for macrosomia when adjusted by mother's age, occupation, and education. But other crude or adjusted models of odds ratio for low birth weight and macrosomia were not different between pre COVID-19 period compare with during COVID-19 period. In retrospective study on 2503 pregnant women in a hospital in Tehran, Iran; Ranjbar et al. showed that the rate of preterm birth and low births weight had decreased during COVID-19 pandemic period [16]. Although their study was performed just in single center and did not evaluate the odds ratio of the pregnancy outcomes, but it was the only similar study that had been performed in the same region as of the current study. In Du M et al. study, the prevalence of caesarean sections among pregnant women during the COVID-19-pandemic was increased. Also, they found that there was a greater proportion of women aged ≥35 years in the COVID-19 cohort [15]. In the present study, the crude odds ratio of caesarean sections had decreased only in unadjusted model had significant (cOR 0.95, $95\%$ CI 0.92–0.98; $$p \leq 0.003$$) particularly those in the early stages of pregnancy, were studied during the COVID-19 pandemic period. This might be related to the recent incentive policies of childbearing in Iran. Ceulemans et al. study on impact of the COVID-19 pandemic on breastfeeding, shows that COVID-19 pandemic has not affected breastfeeding practices, nor breastfeeding cessation [17]. In our study, feeding neonates with synthetic milk was more common during COVID-19 pandemic compared with pre-COVID-19 pandemic group. Lebel and her collogues study on prevalence of Alcohol and substance use in pregnancy during the COVID-19 pandemic demonstrated that alcohol and substance use among pregnant Women during the pandemic was lower or comparable with overall rates of previous estimates in other samples [18]. Although in our study the number of pregnant women who drank alcohol or smoked were low both before and during COVID-19 pandemic based on self-reports, but the rates of alcohol drinking and smoking were significantly lower during pandemic ($p \leq 0.01$, and $$p \leq 0.002$$ respectively). Also, in our study there were no significant differences in addiction before and during the COVID-19 pandemic ($$p \leq 0.281$$). In a systematic review and meta-analysis of Chmielewska and coauthors on 40 studies, the results showed no significant effects of COVID-19 pandemic on prevalence of maternal gestational diabetes, and hypertensive disorders of pregnancy [13]. In the present study, crude and adjusted odds ratio of both gestational diabetes and gestational hypertension did not significantly differ during COVID-19 pandemic compared with pre COVID-19 pandemic ($p \leq 0.05$). The pandemic process negatively affected the daily routine and lead to sedentary life style. The sedentary lifestyle associated with obesity and get weight. In addition to, the studies indicated that obesity independently increased the risk of fetal loss [19]. However, in our study the stillbirth rates were shown as similar between the periods of study. It could be explained by the short period duration of study which could demonstrated the associated results later. ## Conclusions In summary, we found that there were more pregnant women who had given birth during the pandemic were more likely to have lower weight gain, lower rates of alcohol consumption and smoking, and. Also, there were the higher risks of preterm labor and lower risk of caesarian among pregnant women who gave birth during the COVID-19 pandemic compared with those who gave birth before the pandemic. ## Author contribution statement Zahra Moazzeni: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data. Neda Davaryari: Conceived and designed the experiments; Performed the experiments; Wrote the paper. Saeed Davaryar: Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Adele Azarshab: Performed the experiments; Wrote the paper. Mohammad Moein Vakilzadeh: Analyzed and interpreted the data; Wrote the paper. Veda Vakili: Contributed reagents, materials, analysis tools or data; Wrote the paper. ## Funding statement Neda Davaryari was supported by Mashhad University of Medical Sciences, Mashhad, Iran [code: 990124]. ## Data availability statement Data will be made available on request. ## Declaration of interest's statement The authors declare no competing interests. ## References 1. Organization W.H.. (2020) 2. Huang C.. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet* (2020) 395 497-506. PMID: 31986264 3. Akhtar H.. COVID-19 (SARS-CoV-2) infection in pregnancy: a systematic review. Gynecol. Obstet. Invest. (2020) 85 295-306. PMID: 32728006 4. Knight M.. Characteristics and outcomes of pregnant women admitted to hospital with confirmed SARS-CoV-2 infection in UK: national population based cohort study. BMJ (2020) 369 m2107. PMID: 32513659 5. Mullins E.. 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--- title: Utilization of Hypolipidemic Drugs, Patterns, and Factors Affecting Dyslipidemia Among Type 2 Diabetes Mellitus at a Tertiary Care Teaching Hospital in South India journal: Cureus year: 2023 pmcid: PMC9998133 doi: 10.7759/cureus.34748 license: CC BY 3.0 --- # Utilization of Hypolipidemic Drugs, Patterns, and Factors Affecting Dyslipidemia Among Type 2 Diabetes Mellitus at a Tertiary Care Teaching Hospital in South India ## Abstract Background The prevalence of dyslipidemia is higher in type 2 diabetes mellitus (T2DM) and hypolipidemic drugs like statins are effective for the primary and secondary prevention of cardiovascular events. Most of the patients with type 2 diabetes have a mixed type of dyslipidemia. This study aimed to evaluate the utilization of hypolipidemic drugs, patterns, and factors affecting dyslipidemia in T2DM participants. Methods This cross-sectional observational study was approved by the institutional ethics committee (IEC) of the Vydehi Institute of Medical Sciences and Research Center. It was conducted for a period of one year from July 2021 to June 2022. Participants with T2DM visiting the Department of General Medicine and Endocrinology were enrolled after obtaining informed consent. Demographic details, medication history, and laboratory data were recorded in case report form and statistical measures were applied. Results Out of 237 participants enrolled in the study, the predominance ($$n = 133$$, $56\%$) was males. The mean age of the study population was 47.92±9.17 years, and the mean duration of diabetes was 6.8±5.3 years. Out of the total participants, 164 ($69\%$) had deranged lipid profiles. Out of them, 129 ($78.65\%$) were on hypolipidemic drugs. Regarding drug utilization, 122 ($94.6\%$) received statins either rosuvastatin ($54\%$) or atorvastatin ($40\%$). In the deranged lipid profiles pattern, $24\%$ [58] participants had one abnormal lipid parameter and the majority $70\%$ [166] had combined lipid profile abnormality. Factors like increased BMI were significantly associated with dyslipidemia ($$p \leq 0.004$$). Utilization of hypolipidemic drugs was also significantly associated with the control of dyslipidemia ($p \leq 0.001$). It was observed that participants who were not on lipid-lowering drugs had 5.38 times more chance of dyslipidemia (OR=5.38; CI=2.82-10.28; $p \leq 0.001$). Conclusion A high prevalence of dyslipidemia was observed among diabetic patients. Statins were the most prescribed drug in the study. BMI and lack of pharmacotherapy were found to have a statistically significant association with dyslipidemia in diabetic patients. ## Introduction Diabetic dyslipidemia is characterized by elevated plasma triglyceride levels, low high-density lipoprotein cholesterol (HDL-C) levels, and an increase in several small dense forms of low-density lipoprotein (LDL) particles which are highly atherogenic [1]. According to ICMR guidelines for the management of type 2 diabetes mellitus (T2DM), targets for lipid control in individuals are total cholesterol less than 200 mg/dL, HDL cholesterol more than 40 mg/dL for men, and more than 50 mg/dL for women, LDL cholesterol less than 100 mg/dL, triglycerides less than 150 mg/dL, respectively [2]. Dyslipidemia and hypertension are definite risk factors for atherosclerotic cardiovascular disease (ASCVD). ASCVD is also defined as coronary heart disease (CHD), cerebrovascular disease, or peripheral arterial disease and is the leading cause of morbidity and mortality in diabetics [3]. Besides antidiabetic drugs, lifestyle modification and hypolipidemic drugs are effective [3,4]. Statin is one such medication that has demonstrated efficacy in extensive studies and is advised in current guidelines for individuals with T2DM. In both primary and secondary prevention, statin showed a consistent reduction in mortality and major adverse cardiovascular events by (Risk ratio=0.80; $95\%$ CI=0.77-0.83) for every one mmol/L (38.7 mg/dL) decrease in LDL-C [5]. American Diabetes Association (ADA) published medical care standards, including recommendations for starting lipid-lowering therapy and prescribing moderate-intensity statins for those with no additional risk factors. High-intensity statins are recommended for those with either CVD risk factors or overt CVD with consideration of 10-year ASCVD risk [3]. The American College of Cardiology/American Heart Association (ACC/AHA) clinical practice guidelines advise prescribing moderate-intensity statins to patients aged 40 to 75 who have T2DM and those with LDL-C more than 70 mg/dL without considering 10-year ASCVD risk [6]. According to ADA high-intensity statin therapy like atorvastatin (40-80mg) and rosuvastatin (20-40mg) lowers LDL cholesterol by $50\%$ and moderate-intensity statin therapy like atorvastatin (10-20mg), rosuvastatin (5-10mg), simvastatin (20-40mg), pravastatin (40-80mg), lovastatin (40mg), fluvastatin extended-release (80mg), pitavastatin (1-4mg) lowers LDL cholesterol by $30\%$-$49\%$ [3]. Previous studies on diabetic dyslipidemia in India found that dyslipidemia was more in females and the age group 45-54 years. They also concluded that the majority $44.2\%$ of participants suffer from a mixed type of dyslipidemia [7,8]. They also found that in India the prevalence of dyslipidemia is about $86\%$ [8]. Only a few studies were conducted in the southern part of India. This current study focuses on statin utilization in people with diabetic dyslipidemia. ## Materials and methods The present study was a cross-sectional observational study. The study was conducted by the Department of Pharmacology in collaboration with the Department of Endocrinology and the Department of General Medicine at Vydehi Institute of Medical Sciences and Research Center, Bengaluru. The duration of the study was one year from July 2021 to June 2022. The study was conducted after receiving approval from the institutional ethics committee, under the registration number VIEC/PG/APP/$\frac{015}{2020}$-21. Participants were screened for inclusion and exclusion criteria. Both male and female patients with type 2 diabetes irrespective of lipid-lowering therapy between 18 and 60 years attending the OPD and consenting to the study were included. The exclusion criteria included type 1 diabetes mellitus patients, gestational diabetes mellitus, and inpatients. The study objectives and process were explained to the participants or their relatives in their language. Participants were asked to read and sign an informed consent form. The demographic details, medical history, personal history, medication history, and laboratory data of all participants included in the study were recorded on Case Report Form (CRF). Participants were divided into two groups based on lipid profile level. Group one was participants with high lipid levels than the normal range, and group two was participants with low lipid levels than the normal range. Drug utilization was evaluated. Several factors like age, gender, body mass index (BMI), duration of disease, comorbidities, drug utilization of hypolipidemic drugs, family history, and HbA1c were compared between the two groups. Finally, all details from the CRF form were recorded on an excel sheet and statistical measures were applied. The following biological references were considered low lipid levels. Total cholesterol less than 200 mg/dL, triglycerides less than 150 mg/dL, HDL cholesterol more than 40 mg/dL, LDL cholesterol less than 100 mg/dL, VLDL between 2 and 30 mg/dL. Any one or more than one lipid parameter higher than the normal lipid range was considered high lipid levels. The sample size was calculated considering a confidence interval of $95\%$, precision of $5\%$, and power of study at $80\%$. Data collected were entered into a Microsoft office excel sheet. Demographic details were subjected to descriptive statistical analysis and were expressed as mean SD and percentages. Drug utilization patterns were analyzed and expressed in frequencies and percentages. The categorical variable was compared using the chi-square test and odds ratio. Association between all categorical variables and lipid profile was observed and a P-value of less than or equal to 0.05 was considered statistically significant. ## Results Demographic details The total number of participants in the study was 237. Out of 237 participants, $56\%$ [133] were males. Out of the males, $72\%$ [96] were found to have high lipid levels. The mean age (± SD) of the participants was 47.91 ± 9.17 years. Most participants were in the age group of more than 30 years. The mean BMI (± SD) was 25.27 ± 4.08 kg/m2. Most participants had a family history of T2DM. The mean duration (± SD) of the disease was 6.8 ± 5.3 years. Most participants had a duration of disease of less than six years. And in the same category, $75\%$ [91] were found to have dyslipidemia. The mean HbA1c (± SD) was 8.9 ± $2.35\%$. The results are presented in Table 1. Table 1 \| Variables \| Category \| Mean ± SD \| n (%) \| Dyslipidemia \| Dyslipidemia.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variables \| Category \| Mean ± SD \| n (%) \| High lipid levels n (%) \| Low lipid levels n (%) \| \| Sex \| Male \| - \| 133 (56) \| 96 (72) \| 37 (28) \| \| Sex \| Female \| - \| 104 (44) \| 68 (65) \| 36 (35) \| \| Age \| < 30 years \| 47.91 ± 9.17 \| 7 (3) \| 7 (100) \| 0 (0) \| \| Age \| > 30 years \| 47.91 ± 9.17 \| 230 (97) \| 157 (68) \| 73 (32) \| \| Occupation \| Merchants \| - \| 51 (22) \| 40 (78) \| 11 (22) \| \| Occupation \| Farmer \| - \| 20 (8) \| 15 (75) \| 5 (25) \| \| Occupation \| Employee \| - \| 67 (28) \| 42 (63) \| 25 (37) \| \| Occupation \| Homemakers \| - \| 99 (42) \| 67 (68) \| 32 (32) \| \| BMI \| < 24.9 kg/m2 \| 25.27 ± 4.08 \| 106 (45) \| 62 (58) \| 44 (42) \| \| BMI \| 25-29.9 kg/m2 \| 25.27 ± 4.08 \| 102 (43) \| 81 (79) \| 21 (21) \| \| BMI \| ≥ 30 kg/m2 \| 25.27 ± 4.08 \| 29 (12) \| 21 (72) \| 8 (28) \| \| Family history of diabetes mellitus \| Yes \| - \| 137 (58) \| 92 (67) \| 45 (33) \| \| Family history of diabetes mellitus \| No \| - \| 100 (42) \| 72 (72) \| 28 (28) \| \| Duration of diabetes mellitus \| < 6 years \| 6.84 ± 5.30 \| 122 (51) \| 91 (75) \| 31 (25) \| \| Duration of diabetes mellitus \| 6-10 years \| 6.84 ± 5.30 \| 67 (28) \| 44 (66) \| 23 (34) \| \| Duration of diabetes mellitus \| > 10 years \| 6.84 ± 5.30 \| 48 (21) \| 29 (60) \| 19 (40) \| \| HbA1c \| ≤ 7% \| 8.9 ± 2.3 \| 61 (26) \| 43 (70) \| 18 (30) \| \| HbA1c \| >7% \| 8.9 ± 2.3 \| 176 (74) \| 121 (69) \| 55 (31) \| \| Hypertension \| Yes \| - \| 100 (42) \| 71 (71) \| 29 (29) \| \| Hypertension \| No \| - \| 137 (58) \| 93 (68) \| 44 (32) \| \| Hypolipidemic drug utilization \| Yes \| - \| 129 (54) \| 108 (84) \| 21 (16) \| \| Hypolipidemic drug utilization \| No \| - \| 108 (46) \| 56 (52) \| 52 (48) \| Hypolipidemic drugs utilization Out of 237 participants, $69\%$ [164] participants were found to have high lipid levels. Out of them, $78.65\%$ [129] received hypolipidemic drugs. A total of 129 generic drugs were prescribed. Most of the participants $94.6\%$ [122] received HMG-CoA reductase inhibitors, statins. In fixed dose combination (FDC), rosuvastatin and fenofibrate was the common prescription. The drug utilization pattern is presented in Figure 1. Figure 1: Frequency of hypolipidemic drugs prescribedFDC= Fixed Dose Combination, PPARs= Peroxisome proliferator-activated receptors Patterns of dyslipidemia Regarding dyslipidemia, high lipid level was observed in $69\%$ [164] participants, isolated in $24\%$ [58] of participants and combined abnormality in $70\%$ [166]. After analyzing dyslipidemia components low level of HDL-C was found in $35\%$ [82], hypertriglyceridemia in $44\%$ [104], elevated level of LDL in $60\%$ [142], and hypercholesterolemia in $26\%$ [61] participants, respectively. The details are presented in Table 3. Table 2 \| Lipid abnormality \| Lipid abnormality.1 \| n (%) \| \| --- \| --- \| --- \| \| Isolated \| Low HDL \| 18 (7.59) \| \| \| High TC \| 0 (0) \| \| \| High LDL \| 31 (13) \| \| \| High TG \| 9 (3.79) \| \| Combined \| Low HDL & High LDL \| 15 (6.3) \| \| \| Low HDL & High TG \| 14 (5.9) \| \| \| Low HDL, High LDL & High TG \| 21 (8.8) \| \| \| Low HDL, High LDL & High TC \| 1 (0.42) \| \| \| Low HDL & High TC \| 14 (5.9) \| \| \| High LDL & High TG \| 15 (6.3) \| \| \| High TG & High TC \| 1 (0.42) \| \| \| High LDL, High TC & High TG \| 32 (13.5) \| \| \| Low HDL, High LDL, High TC & High TG \| 13 (5.4) \| \| Combined & Isolated \| Low HDL \| 82 (35) \| \| \| High TG \| 104 (44) \| \| \| High LDL \| 142 (60) \| \| \| High TC \| 61 (26) \| Factors affecting dyslipidemia The factors like gender, age, occupation, hypertension, family history of diabetes, and duration of disease, HbA1c had no statistically significant association with dyslipidemia. The participants with hypertension had 1.2 times more chance of having dyslipidemia (OR=1.2; CI=0.60-2.38; $$p \leq 0.59$$). The participants with a family history of T2DM had 1.17 times more chance of having dyslipidemia (OR=1.17; CI=0.62-2.20; $$p \leq 0.62$$). Regarding BMI and dyslipidemia statistical significance was seen ($$p \leq 0.004$$). It was seen that participants in the overweight and obese category had 1.07 times more chance of having dyslipidemia than those in the normal weight category (OR=1.07; CI=0.98-1.16; $$p \leq 0.92$$). Regarding drug utilization and dyslipidemia statistical significance was observed ($p \leq 0.001$). It was observed that participants who were not on lipid-lowering medication had 5.38 times more chance of having a derranged lipid profile (OR=5.38; CI=2.82-10.28; $p \leq 0.001$). The results are presented in Tables 3, 4. ## Discussion The purpose of this study was to evaluate the prevalence of dyslipidemia and the utilization of hypolipidemic drugs use among participants suffering from T2DM. In this study, $56\%$ [133] of the participants were males, and high lipid levels were present in $72\%$ [96] of males. Comparable results were observed in a prior study by Bekele et al. where $53.6\%$ [120] participants were males and $70\%$ [84] had high lipid levels [9]. In this study, $97\%$ [230] of participants were older than 30 years of age, and $68\%$ [157] of them had high lipid levels. Comparable results were observed by Bekele et al. where the majority of participants $66.5\%$ [149] were older than 30 years and out of that $43.8\%$ [98] had dyslipidemia. Kassahun Haile et al. where most participants $86.3\%$ [214] were older than 30 years out of which $72.4\%$ [155] had dyslipidemia [9,10]. The mean BMI in this study was 25.27 ± 4.08 kg/m2. The majority of participants $45\%$ [106] had a BMI of less than 24.9 kg/m2 and $58\%$ [62] of them had high lipid levels. Equivalent results were observed by Bekele et al. where $58\%$ [130] participants were in a normal BMI category and $29.5\%$ [66] had dyslipidemia [9]. These results contrasted with the study by Kebede et al. where $56\%$ [183] were in the overweight category out of which $59\%$ [113] had dyslipidemia [11]. This may be because due to different ethnicity. The majority of participants $51\%$ [122] had a duration of disease of fewer than six years and among them $75\%$ [91] had dyslipidemia. Similar results were observed by Hyassat et al. [ 12]. In the present study, $54\%$ [129] of participants received hypolipidemic medications. Out of which $94.6\%$ [122] of participants received statins, and atorvastatin accounted for $40\%$ [52] prescriptions. Like Jayaram's study [13], Gupta et al. also found statins were prescribed in the majority of participants, and atorvastatin was mostly prescribed medication [14]. Similar results were observed in Patel's [15] and Adhikari's studies where atorvastatin was the most prescribed medication [16]. In this study, $69\%$ [164] of the individuals had elevated lipid levels. Among them, $24\%$ [58] of participants had one abnormal lipid parameter and $70\%$ [166] had several abnormal lipid parameters. Borle et al. discovered that $28\%$ [14] of participants exhibited isolated lipid profile abnormalities, while $58\%$ [29] had combined and mixed lipid profile abnormalities [8]. According to Hyassat et al., $62.48\%$ [493] of participants had combined and mixed deranged lipid profiles and $27.8\%$ [220] had isolated lipid profile abnormality [12]. A study by Sarfraz et al. also observed that $34\%$ [68] participants had isolated and $66\%$ [132] had combined and mixed dyslipidemia [17]. In the present study, low level of HDL-C was found in $35\%$ [82], hypertriglyceridemia in $44\%$ [104], elevated levels of LDL in $60\%$ [142] and hypercholesterolemia were found in $26\%$ [61], respectively. Comparable results were observed by Dayakar et al. [ 18]. Gender, age, occupation, hypertension, family history of diabetes, and disease duration exhibited no statistically significant association with the incidence of dyslipidemia in the present study. Also, no statistical significance was found between hypertension and abnormal lipid profiles ($$p \leq 0.60$$). Participants with hypertension had a 1.2 times greater likelihood of having high lipid levels (OR=1.2; CI=0.60-2.38; $$p \leq 0.59$$). In contrast to a study conducted by Bekele et al. where hypertension had 1.3 times more chance of having dyslipidemia (AOR=1.331; CI=0.436-4.062; $$p \leq 0.016$$), and Haile et al. found that participants with hypertension had 2.65 times more chance of having dyslipidemia (AOR-2.65; CI-1.4-4.9; $$p \leq 0.01$$) [9,10]. A family history of type 2 diabetes in this study did not statistically significantly correlate with abnormal lipid profiles ($$p \leq 0.42$$). The odds of having abnormal lipid profiles were also 1.17 times higher in persons with a family history (OR=1.17; CI=0.62-2.20; $$p \leq 0.62$$). Bekele et al. reported similar findings, finding that participants with a family history had a 2.1 times greater likelihood of having high lipid levels (AOR=2.1; CI=0.454-9.877; $$p \leq 0.339$$) [9]. HbA1c exhibited no statistically significant connection with abnormal lipid profiles in this study ($$p \leq 0.79$$). Participants with poor glycemic control were more likely to have high lipid levels (OR=1.00; CI=0.89-1.14; $$p \leq 0.98$$). Kebede et al. found that patients with poor glycemic control were 1.3 times more likely to have abnormal lipid profiles (OR=1.3; CI=0.8-1.9; $p \leq 0.05$) [11]. BMI was shown to be strongly linked with abnormal lipid profiles in this study ($$p \leq 0.004$$). It was also observed that those who were overweight or obese had 1.07 times greater likelihood of having high lipid levels (OR=1.07; CI=0.98-1.16; $$p \leq 0.92$$). A similar substantial correlation was observed by Kebede et al., where obese participants had 3.5 times the risk of having disordered lipid profiles (OR=3.5; CI=1.6-7.9; p 0.001). Ahmmed et al. found that participants who were overweight or obese had a 2.08 times greater likelihood of having high lipid levels (OR=2.08; CI=1.73-2.23; p 0.001) [11,19]. In the present study, drug use was found to have a statistically significant relationship with dyslipidemia (p 0.001). Participants who were not using cholesterol-lowering medication had a 5.38 times greater likelihood of having abnormal lipid profiles than those who were (OR=5.38; CI=2.82-10.28; p 0.001). There is insufficient evidence or previous research to support the findings. Limitations of the study In this study the sample size was small. Participants in this study belonged to a small geographical area and were from a single centre. High-risk participants from the cardiology department were not included. Confounding factors such as poor glycemic control and concurrent primary hyperlipidemias that act independently of T2DM were not evaluated. ## Conclusions In this study, a high prevalence of increased lipid levels was observed among diabetic patients. 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--- title: Red cell distribution width and mortality in older patients with frailty in the emergency department authors: - Janne Alakare - Kirsi Kemp - Timo Strandberg - Maaret Castrén - Jukka Tolonen - Veli-Pekka Harjola journal: BMC Emergency Medicine year: 2023 pmcid: PMC9998144 doi: 10.1186/s12873-023-00801-1 license: CC BY 4.0 --- # Red cell distribution width and mortality in older patients with frailty in the emergency department ## Abstract ### Background The red cell distribution width (RDW) reflects the degree of heterogeneity of red blood cells. Elevated RDW is associated both with frailty and with increased mortality in hospital-admitted patients. In this study we evaluate whether high RDW values are associated with mortality in older emergency department (ED) patients with frailty, and if the association is independent of the degree of frailty. ### Methods We included ED patients with the following criteria: ≥ 75 years of age, Clinical Frailty Scale (CFS) score of 4 to 8, and RDW % measured within 48 h of ED admission. Patients were allocated to six classes by their RDW value: ≤ $13\%$, $14\%$, $15\%$, $16\%$, $17\%$, and ≥ $18\%$. The outcome was death within 30 days of ED admission. Crude and adjusted odds ratios (OR) with $95\%$ confidence intervals (CI) for a one-class increase in RDW for 30-day mortality were calculated via binary logistic regression analysis. Age, gender and CFS score were considered as potential confounders. ### Results A total of 1407 patients ($61.2\%$ female), were included. The median age was 85 with an inter-quartile range (IQR) of 80–89, median CFS score 6 (IQR: 5–7), and median RDW 14 (IQR: 13–16). Of the included patients, $71.9\%$ were admitted to hospital wards. A total of 85 patients ($6.0\%$) died during the 30-day follow-up. Mortality rate was associated with RDW increase (p for trend <.001). Crude OR for a one-class increase in RDW for 30-day mortality was 1.32 ($95\%$ CI: 1.17–1.50, $p \leq .001$). When adjusted for age, gender and CFS-score, OR of mortality for one-class RDW increase was still 1.32 ($95\%$ CI: 1.16–1.50, $p \leq .001$). ### Conclusion Higher RDW values had a significant association with increased 30-day mortality risk in frail older adults in the ED, and this risk was independent of degree of frailty. RDW is a readily available biomarker for most ED patients. It might be beneficial to include it in risk stratification of older frail ED patients to identify those who could benefit from further diagnostic assessment, targeted interventions, and care planning. ## Background Red cell distribution width (RDW) is a measure reflecting the degree of heterogeneity of red blood cell size. RDW is calculated by dividing the standard deviation of red blood cell volumes by the mean corpuscular volume (MCV), usually expressed as a percentage value. RDW has traditionally been used for differential diagnosis of anaemia, but, subsequently, RDW elevation has been found to associate with higher short- and long-term increased mortality, both in the general population and in patients with many specific conditions [1–5], such as infections and sepsis [6–8], liver cirrhosis [9, 10], diabetic ketoacidosis [11], trauma [12, 13], acute pancreatitis [14], cardiac diseases [8, 15–19], pulmonary embolism [20], COVID-19, and acute respiratory failure [21–23]. In hospitals, high RDW has been shown to predict poor prognosis among general patients [24], among surgical patients [25], and among patients with critical illness [26, 27]. Older patients in emergency departments (EDs) and hospital wards have an increased risk of mortality if their RDW is elevated [8, 12, 28–30]. Although several mechanisms for this association have been presented [1, 2, 31], defined mechanisms for the association of elevated RDW and increased mortality have not yet been established. Frailty syndrome, an ageing-related state of vulnerability due to decline in physiological reserves and functions [32], is usually defined either as a clinical phenotype or by calculating accumulated deficits such as diseases, physical and cognitive impairments, psychosocial risks, and geriatric syndromes [33–35]. Frailty is an independent predictor of mortality in patients admitted to emergency departments and hospital wards [36–38]. Elevated RDW has been shown to associate with frailty besides increased mortality of older ED patients [39–41]. Because frailty is related with both increased mortality of older ED patients and elevated RDW, frailty may be a confounder explaining increased mortality of older patients with elevated RDW. We studied whether elevated RDW is a risk predictor in older patients with frailty in the ED, and how frailty stage affects the association between elevated RDW and mortality. ## Methods This study is a secondary analysis of an observational cohort study in frail older ED patients that was performed in an ED of a teaching hospital in Finland. In the primary study we included patients who were ≥ 75 years of age, had a score between 4 to 9 on the 9-point Clinical Frailty Scale (CFS) [34], and were registered residents of the hospital’s service area. ED visit data were collected between December 11th, 2018 and June 7th, 2019. The included patients were followed up from electronic health records. Methods for the primary study have been described in detail in our previous article [42]. The clinical laboratory service of the ED routinely gives RDW values (% value as integer) for all blood counts tested. Besides the clinical laboratory service, the ED has point-of-care testing equipment available, which does not provide RDW values. Point-of care testing is typically preferred, if more extensive laboratory testing is not anticipated based on patient’s chief complaint or condition. For the secondary analysis conducted here, those patient visits from the primary study who had the CFS score 4–8 and had RDW tested 0–48 h after ED admission were included. If more than one blood count was drawn from a patient within 48 h of ED admission, the result of the first laboratory test was used for the analysis. Patients who had a CFS score of 9 were excluded because such patients are defined as having a short life expectancy < 6 months, but otherwise not living with severe frailty. Nonparametric baseline data were presented with interquartile ranges (IQR). The outcome measure was 30-day mortality. Patients were allocated to six classes based on their RDW value: ≤ $13\%$, $14\%$, $15\%$, $16\%$, $17\%$, and ≥ $18\%$. We used same cut-off values as a recent study to enable comparison of our results in frail ED patients to general older adult ED patient population [42]. Mortality rate was calculated for each class. The Cochran–Armitage test for trend was used to test the statistical significance of the trend of increasing mortality with higher RDW values. Crude and adjusted ORs with $95\%$ confidence intervals (CI) of a one-class increase in RDW for 30-day mortality were calculated. Univariate and multivariate models of binary logistic regression analysis were used for crude and adjusted ORs, respectively. Age, sex, and CFS score were considered as potential confounders and were included in the analysis. As a sensitivity analysis to assess if categorisation of the RDW values has impact on the results, we performed a regression analysis with RDW as continuous variable. We also performed a sensitivity analysis with haemoglobin as a potential confounder, because haemoglobin level is directly related to red blood cells, like RDW is, and may be associated with mortality. From clinical perspective, we were interested whether RDW is independent of vital parameters. The National Early Warning Score 2 (NEWS2), a widely used prognostic score based on common vital signs, was included in the baseline data for our previous study [42]. We performed an additional testing by adjusting with the NEWS2 besides other potential confounders used in the regression analysis. A p value of < 0.05 was considered statistically significant. GraphPad Prism software, version 9.4.1 (Graphpad Software LCC) was used for the Cochran–Armitage test. SPSS software, version 28 (IBM) was used for all other statistical analyses. The primary study which this secondary analysis was based on, was registered at ClinicalTrials.gov on December 20th, 2018, identifier NCT03783234. ## Results A total of 1407 ($61.2\%$ female) patient visits were included after excluding 294 visits for patients who either had no blood count drawn within 48 h of ED admission or had only point-of-care blood count testing without RDW-values, as well as four cases for patients with dual peak RDW values (due to previous red cell transfusions), and seven cases for patients with a CFS score of 9. Patient characteristics for the analytical sample are presented in Table 1: median age was 85 (IQR: 80–89), median CFS was 6 (IQR: 5–7), and median RDW % was 14 (IQR: 13–16, range: 12–28). Distribution of the RDW % values in the analytical sample are presented in Fig. 1. Of the included patients, 1011 ($71.9\%$) were admitted to hospital wards. Table 1Patient characteristicsN1407Agemedian (IQR)85 (80–89)Femalen (%)861 (61.2)Hospital admissionn (%)1011 (71.9)CFSmedian (IQR)6 (5–7) CFS: 4n (%)282 (20.0) CFS: 5–6718 (51.0) CFS: 7–8407 (28.9)RDW %Median (IQR)14 (13–16) RDW ≤ $13\%$n (%)412 (29.3) RDW $14\%$358 (25.4) RDW $15\%$262 (18.6) RDW $16\%$149 (10.6) RDW $17\%$80 (5.7) RDW ≥ $18\%$146 (10.4)Abbreviations: IQR Interquartile range, CFS Clinical Frailty Scale, RDW Red cell distribution widthFig. 1Distribution of RDW % in included patients. Normal distribution marked by black curve. Abbreviation: RDW, red cell distribution width Follow-up data for 30-day mortality were available for all ED visits. A total of 85 of 1407 ($6.0\%$) of included patients with RDW value available died during the 30-day follow-up. Within 30 days of ED admission, mortality rates were as follows: $\frac{9}{412}$ ($2.2\%$) of patients in the RDW ≤ $13\%$ group, $\frac{19}{358}$ ($5.3\%$) in the RDW $14\%$ group, $\frac{21}{262}$ ($8.0\%$) in the RDW $15\%$ group, $\frac{14}{149}$ ($9.4\%$) in the RDW $16\%$ group, $\frac{5}{80}$ ($6.3\%$) in the RDW $17\%$ group, and $\frac{17}{146}$ ($11.6\%$) in the RDW ≥ 18 group. Mortality rate was significantly higher with an increase in RDW (p for trend < 0.001). Mortality rates are presented in Fig. 2. For comparison, 30-day mortality of excluded patients who had no RDW value available was $\frac{8}{298}$ ($2.7\%$).Fig. 230-day mortality rates in each red cell distribution width category. The Cochran–Armitage test for trend was used to test the statistical significance of the trend. Abbreviation: RDW, red cell distribution width Crude OR of a one-class increase in RDW for 30-day mortality was 1.32 ($95\%$ CI: 1.17–1.50, $p \leq 0.001$). When adjusted for age, sex and CFS score, OR of a one-class increase was still 1.32 ($95\%$ CI: 1.16–1.50, $p \leq 0.001$). Crude and adjusted odds ratios are presented in Table 2.Table 2Crude and adjusted odds ratios for 30-day mortalityOR, crude ($95\%$ CI)p for crude OROR, adjusted ($95\%$ CI)p for adjusted ORRDW a1.32 (1.17–1.50) <.0011.32 (1.16–1.50) <.001CFS b1.47 (1.22–1.78) <.0011.43 (1.18–1.73) <.001Age c1.03 (1.00–1.07).0761.04 (1.00–1.08).082Female0.73 (0.47–1.14).1690.67 (0.42–1.05).083Binary logistic regression was used for the analysis. RDW class, CFS, age, and sex were included in the analysis for adjusted odds ratiosAbbreviations: RDW red cell distribution width, CFS Clinical Frailty Scale, OR odds ratio, CI confidence intervalaone-class increasebone-point increasecone-year increase In the sensitivity analysis with RDW as a continuous variable the significance of the results were not changed. Crude and adjusted ORs of $1\%$-unit increase of RDW for 30-day mortality were: 1.15 ($95\%$ CI: 1.06–1.24, $p \leq 0.001$), and 1.15 ($95\%$ CI: 1.07–1.25, $p \leq 0.001$), respectively. The absolute OR values were expectedly lower as scale increased from 6 categorical steps to 16 steps in %-units (range of RDW, 12–28). When haemoglobin level was added as a potential confounder, the adjusted OR of one-class increase in RDW was 1.34 ($95\%$ CI: 1.17–1.54, $p \leq 0.001$), without significant change in results. When NEWS2 was added as a confounder, the adjusted OR of one-class increase of RDW for 30-day mortality was slightly lower than without it, but still significant: 1.27 ($95\%$: 1.11–1.47, $p \leq 0.001$). ## Discussion In this study, increasing RDW was associated with higher 30-day mortality in frail older ED patients. The association remained significant when adjusted for age, gender and CFS score. This study shows that the association of higher RDW value and increased mortality applies to the frail older population in an acute care setting. The association is independent of CFS score. The mortality rate increase is similar to those rates shown in a recent large cohort study of general hospital-admitted older patients [29], supporting the hypothesis that RDW is independent of frailty as a risk predictor. A small notch in mortality rate was noted in the group of patients with RDW of $17\%$. However, since the total trend was statistically significant, we interpret this dip to be variation due to limited sample size. Many mechanisms, both short- and long-term, have been suggested for the association of elevated RDW and increased mortality. Impaired erythropoiesis and shortened red cell survival due to organ dysfunction, metabolic imbalances, and inflammatory reactions may cause alterations in red cell volumes. Oxidative stress and suppression of the erythrocyte lineage due to alterations in neutrophil and thrombocyte production during inflammation in acute conditions are potential contributors. Other possible causes of higher RDW include poor nutrition and erythrocyte fragmentation [1, 2, 31]. In addition, direct causality of high RDW and impaired intravascular haemodynamics, especially with vascular pathologies, has been presented [31]. Telomere shortening may be a link for poor outcomes in older vulnerable patients, as this is associated with both MCV variation and ageing-related all-cause mortality [43, 44]. Association of RDW increase with mortality was slightly lower, when NEWS2 was added as a potential confounder, which supports that elevated RDW is reflecting both short-, and long-term clinical deterioration. RDW is a readily available biomarker for most ED patients. In clinical practice, RDW may be overlooked as a marker when clinical state and risks are assessed. Including RDW in patient assessment could lead to better high-risk feature identification, better targeting of further diagnostic work-up, effective interventions, and individualized advanced care planning. Current risk-assessment methods, including ED triage systems, have limited performance, especially in older adults [45–47]. Machine-learning systems are promising tools for objective and more accurate risk assessment in emergency care, and may help in identifying patients who would benefit from targeted interventions [48–50]. In this study we have considered RDW as a general predictor. As higher RDW predicts poor outcomes in numerous different conditions, RDW may be associated with other predictive biomarkers in different specific conditions. Independence of RDW in multivariable predictive models could be studied preferably with machine learning methods with large data sets, as many predictive variables, including haemoglobin, white blood cell, and platelet counts, may have nonlinear associations. Kim et al. stated in their article that RDW value should be included in risk stratification strategies for hospitalized older patients [29]. Based on earlier studies and our results, we agree with those authors and suggest that RDW should also be included in risk stratification of frail patients in emergency departments and hospital wards. Older patients often have non-specific presentations in the ED, and vital signs are less reliable for detecting early clinical deterioration in older patients [51–53]. Therefore, older patients, with or without frailty, could be one patient group that would benefit in particular from more comprehensive deep-learning risk-assessment methods. It may be favourable to include RDW among other variables when such artificial intelligence models are studied. The strengths of this study include the prospectively collected patient data from a clinical setting, that is representative for the frail older ED patient population. Frailty status was assessed systematically with the CFS during ED admission. Baseline and outcome data were available for all patients included. The study has some limitations. The analysis of this study was based on data collected in a previous prospective study. In this study, $82\%$ of patients who met the eligibility criteria, had an RDW value available. The included patients had higher mortality than patients who had no laboratory testing, or only point-of-care testing available. Chief complaints or acute disease severity were not included in the analyses, but we assume that those patients without blood tests taken were more likely visiting the ED for simple, low-acuity complaints, and therefore the results may not be representative for low-acuity patients. ## Conclusion Higher RDW values were significantly associated with increased 30-day mortality in frail older adults in the ED. In this study, RDW was independent of frailty state as a risk predictor. RDW is a readily available parameter for most ED patients who have laboratory tests. It might be beneficial to include RDW in risk stratification of older frail ED patients in order to identify patients at high risk of adverse outcomes who could benefit from further diagnostic assessment, targeted interventions, and care planning. ## References 1. Salvagno GL, Sanchis-Gomar F, Picanza A, Lippi G. Red blood cell distribution width: a simple parameter with multiple clinical applications. 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--- title: Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network authors: - Hameedur Rahman - Tanvir Fatima Naik Bukht - Rozilawati Ahmad - Ahmad Almadhor - Abdul Rehman Javed journal: Computational Intelligence and Neuroscience year: 2023 pmcid: PMC9998154 doi: 10.1155/2023/7717712 license: CC BY 4.0 --- # Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network ## Abstract Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of $93\%$, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms. ## 1. Introduction The predominant cause of cancer-related deaths among women globally is breast cancer [1, 2]. According to a report by the World Health Organization's (WHO) cancer research institute, the International Agency for Research on Cancer, in 2018 globally, 17.1 million breast cancer cases were reported. The number of cases is predicted to increase to double the amount by 2025 [3]. Breast cancer is a highly invasive tumor that primarily affects women [4]. The high death rate among women makes it the second deadliest malignancy after lung cancer [5, 6]. A study by the National Institute of Cancer in China found 1.67 million breast cancer and 522,000 deaths cases from 2008 to 2012 [7]. Despite extensive efforts from medical professionals and researchers, a definitive method for treating breast cancer has yet to be established and reliable evidence for its prevention remains elusive [8–11]. Some components of breast cancer tissues are highly malignant and pose a severe danger to patients' lives as they can spread to other vital organs [12–15]. The growth of mammary cells can lead to tumors in women. Tumors are classified as benign or malignant based on the area, size, and location, using the BI-RAD scores [16, 17]. Benign tumors are not life-threatening and can be treated through medication to prevent further growth [17, 18]. Malignant tumors, on the other hand, can spread to other parts of the body via the lymphatic system or blood, making them much more dangerous [19–22]. This uncontrolled cell proliferation in the breast leads to the formation of malignant tumors, which can only be treated through surgery or radiation therapy [23, 24]. Early detection of breast cancer is crucial for accurate diagnosis and analysis, and many researchers are turning to biomedical imaging to aid specialist radiologists. Various methods such as MRI, mammography, and ultrasound are utilized to identify breast carcinoma [25, 26]. However, the large volume of images challenges radiologists in identifying potential cancerous areas. Therefore, an efficient automated method is needed, and computer-aided diagnostic (CAD) systems are being utilized in aiding radiologists in detecting cancerous breast tumors [27, 28]. Increasingly, deep learning techniques are being applied to medical imaging to develop automated computer-aided diagnosis (CAD) systems [29–35]. Deep learning is considered the most effective method for detecting and classifying medical images [29, 30]. With these techniques, the mammogram image's significant low to high-level hierarchical features can be directly extracted, making deep learning the most reliable medical imaging method [29]. Several CAD systems based on deep learning have been developed for breast lesions detection, which outperforms traditional systems [36]. Accurate detection of breast lesions is crucial for improving breast cancer diagnosis [29, 31]. However, detecting these lesions can be challenging due to their varying texture, shape, position, and size. Deep learning and image processing methods have been proposed to overcome the limitations of conventional technology, which cannot perform automated identification [29]. The final stage in the CAD model is the classification of breast lesions into benign or malignant, which is important in assessing the correctness of the diagnostic [30]. Currently employed methods for detecting breast cancer are slow, costly, and require extra efforts to run the radiology equipment. Accurately detecting breast cancer automatically from an image processing perspective is not easy. Hence, early diagnosis and proper treatment are deemed crucial. Therefore, an efficient screening system and automation are necessary for breast cancer detection due to the following reasons [12]: incorrect diagnoses and predictions, tumors appearing in low contrast areas, unreliable human diagnoses, overburdening of radiologists, human error in diagnosis, need for large training data to avoid overfitting in deep learning algorithms, high computational complexity, and longer processing time for accurate tumor identification. A novel breast cancer detecting system is proposed with an improved architecture that integrates deep convolutional neural network (DCNN) and breast mammogram images to address previous drawbacks mentioned above. The proposed system intends to divide breast tumors into benign and malignant categories. The system's performance is evaluated and compared with existing classification systems using a public mammographic image dataset named INbreast. The new system includes transfer learning to fine-tune the pretrained DCNN and detailed results from experiments on the INbreast dataset. The system's performance is evaluated using the following metrics: AUC, specificity, accuracy, sensitivity, and F-1 score. The rest of the paper is organized as follows. Section 2 presents the related work. Section 3 provides the proposed approach for breast cancer detection. Section 4 presents experimental analysis, results and comparison with existing work. Section 5 concludes this paper and presents future work. ## 2. Related Work Breast cancer diagnosis in modern medical procedures often involves using mammography images [37]. A summary of recently developed systems for breast cancer diagnosis using mammogram images is presented in this section. Structured support vector machine (SSVM) and conditional random field (CRF) are two structured prediction techniques proposed in [38] to classify mass mammograms. Both approaches used potential functions based on deep convolution and belief networks. The results demonstrated that the CRF method outperformed the SSVM method in training and inference time. Authors in [29] utilized four-fold cross-validation on X-ray mammograms from the INbreast dataset to estimate a full-resolution convolutional network (FrCN). It resulted in an F1 score of $99.24\%$, an accuracy of $95.96\%$, and a Matthews correlation coefficient (MCC) of $98.96\%$. In another study [39], the BDR-CNN-GCN approach was proposed by combining a graph-convolutional network (GCN) with a basic 8-layer CNN that includes batch normalization and dropout layers. The final BDR-CNN-GCN model was formed by integrating the two-layer GCN with the CNN. This method was tested using the MIAS dataset, and successful results were obtained with a $96.10\%$ accuracy level. Authors in [40] proposed modifying the YOLOv5 network for identifying and classifying breast cancers, with the algorithm run using specific parameter values. The modified YOLOv5 was compared with a faster RCNN and YOLOv3, achieving an accuracy of $96.50\%$ and an MCC value of $93.50\%$. The diverse features (DFeBCD) method was proposed by [41], which classified mammograms into two categories normal and abnormal. They used two classifiers, an emotion learning-inspired integrated classifier (ELiEC) and SVM, with the IRMA mammography dataset. The ELiEC classifier outperformed SVM, achieving an accuracy rate of $80.30\%$. In [30], a deep-CNN model that utilized transfer learning (TL) was introduced to prevent overfitting when working with small datasets. DDSM, MIAS, BCDR, and INbreast were used to assess its performance. INbreast dataset achieved an accuracy of $95.5\%$, the DDSM dataset achieved an accuracy level of $97.35\%$, and the BCDR database achieved a $96.67\%$ accuracy level. Authors in [42] for extracting features from breast mammograms utilized lifting wavelet transform (LWT). Feature vectors' size was reduced using linear discriminant analysis (LDA) and principal component analysis (PCA). The classification was performed using the moth flame optimization and extreme learning machine (ELM) approach with MIAS and DDSM and datasets, achieving accuracy of $95.80\%$ and $98.76\%$, respectively. In addition, researchers have also trained the CNN Inception-v3 model on 316 images, resulting in a sensitivity of 0.88, specificity of 0.87, and an AUC of 0.946 [43]. Furthermore, in [44], a CNN and TL classification method was proposed to evaluate the performance of eight fine-tuned pretrained models. Authors in [45] presented a hybrid classification model using Mobilenet, ResNet50, and Alexnet with an accuracy level of $95.6\%$. In [46], four different CNN architectures (VGG19, InceptionV3, ResNet50, and VGG16) were utilized for model training using 5000 images, while prediction models were evaluated on 1007 images. Authors in [47] utilized alpha, geostatistics, and diversity analyses forms in their proposed breast cancer detection method. They employed the SVM classifier on MIAS and DDSM databases, which resulted in a detection accuracy level of $96.30\%$. The SVM classifier and gray level co-occurrence matrix (GLCM) were employed by [48] for detecting breast cancer abnormalities in the MIAS data set. Their method achieved an accuracy of $93.88\%$ and surpassed the performance of the k-nearest neighbour (kNN) algorithm. Authors in [49] used AlexNet and SVM to enhance classification accuracy with data augmentation techniques. The method achieved $71.01\%$ accuracy, which increased to $87.2\%$ with SVM and was evaluated on DDSM and CBIS-DDSM datasets. A DenseNet deep learning framework extracted image features and classified cancerous and benign cells by feeding them into a fully connected (FC) layer. The effectiveness of this technique was evaluated by adjusting the hyperparameters [50]. An algorithm named DICNN was developed by Irfan et al. [ 51], which uses a dilated semantic segmentation network and morphological operation. Combining these feature vectors with SVM classification yielded an accuracy of $98.9\%$. Although prior breast cancer detection and classification systems have improved information extraction, several issues still need attention, such as low contrast in tumor location, high memory complexity, long processing time, and the need for a large amount of training data for deep learning approaches. In response to these problems, we propose a new approach to breast cancer detection and classification, which will be discussed in detail in the following section. ## 3. Methodology In this section, the processes used for implementing our proposed scheme are described in depth. The system consists of the following steps: [1] image enhancement, [2] image segmentation, [3] feature extraction and the selection, and [4] feature classification. The proposed system is illustrated in Figure 1. ## 3.1. Dataset This study used a digital breast X-ray database named INbreast to implement the proposed CAD approach. The INbreast dataset is a public database that contains more recent FFDM images. It typically has an image size of 3328 × 4084 pixels. It contains 115 patients' cases along with 410 mammograms with both craniocaudal (CC) view and a mediolateral oblique (MLO) view. Of these 115 patients, 90 had mammograms taken of both breasts, totaling 360 images, while the other 25 had only two mammograms taken each. In total, 410 mammograms were produced from 115 patients, including cases of normal, benign, and malignant breasts. 107 cases with breast lesions were used from the MLO and CC views for evaluation purposes. ## 3.2. Convolutional Neural Network This subsection will examine the fundamental structure of all convolutional neural network (CNN) architectures. CNNs are deep neural networks used for image recognition and classification. In recent years, CNNs have become a crucial tool in image analysis, especially for identifying faces, text, and medical imaging. CNNs have a long history of success in image classification and segmentation, first developed in 1989. CNNs replicate the human brain's visual information processing by incorporating layers of “neurons” that only respond to their local surroundings. These networks can understand the topological aspects of an image through a combination of convolutional, pooling, and fully connected (FC) layers. The architecture of a CNN is shown in Figure 2. ## 3.2.1. Convolutional Layers The convolutional layers are assembled into feature maps based on local connections and weight distribution principles. A filter bank, a group of weights, connects neurons in a feature map to corresponding local regions in the preceding layer. Each feature map uses a different filter bank, and all the units in the map share the same filter row. This weight distribution and local connection help reduce the number of parameters by utilizing the close relationship between neighboring pixels and location-independent image features. The output of the weights is then sent to an activation function, such as ReLU or Sigmoid. This activation function enables the nonlinear transformation of the input data, which is necessary for the following processing stages. ## 3.2.2. Pooling Layer As illustrated in Figure 2, the pooling layer follows the convolution layer and uses subsampling to integrate the features from the convolutional layer into a single layer semantically. This layer's primary objective is to decrease the size of the image by combining pixels into one value while preserving its features. In this layer, typical operations include max as well as main pooling. ## 3.2.3. Fully Connected Layer The last layer in CNN is the dense classification layer, which is responsible for determining the category of input data based on extracted features from CNN. The number of units in the FC layer is the same as the number of different classifications (categories). ## 3.3. Proposed Workflow This section provides the proposed workflow for breast cancer diagnosis using a deep convolutional neural network. ## 3.3.1. Image Enhancement Image enhancement refers to increasing contrast and suppressing noise in mammogram images to assist radiologists in detecting breast abnormalities. Various image enhancement methods exist, including adaptive contrast enhancement (AHE). AHE improves the local contrast and reveals more image details, making it a helpful technique for enhancing both natural and medical images [52]. However, it may also result in considerable noise. In this paper, we utilized the contrast-limited adaptive histogram equalization (CLAHE) technique, a form of AHE, to enhance image contrast [52]. A drawback of AHE is that it can over-enhance the images due to the integration process [49]. To mitigate this issue, CLAHE is used as it limits the local histogram by setting a clip level, thus controlling contrast enhancement. Figure 3 illustrates an image enhanced by the CLAHE algorithm. Furthermore, CLAHE algorithm steps are given as follows [53]:Split image into equal-sized contextual regions. Apply histogram equalization to all contextual regions. Limit the histogram to the level of the clip. Reallocate the clipped values in the histogram. Obtain enhanced pixel value through histogram integration. ## 3.4. Image Segmentation Image segmentation involves dividing an image into regions with similar characteristics and features. Segmentation aims to simplify the image for easier analysis [54]. Popular image segmentation techniques include edge detection, partial differential equation (PDE), fuzzy theory, artificial neural network (ANN), region-based segmentation, and thresholding. ## 3.4.1. Thresholding Method One of the simplest image segmentation methods is the thresholding method [55, 56]. The pixels of the image are split according to their intensity level. The global threshold is the most commonly used thresholding technique [57]. It is accomplished by setting a threshold value (T) constant throughout the image. The output image is derived from the original image based on the threshold value. ## 3.4.2. Region-Based Segmentation Methods It is a simple approach compared to other methods, as it involves dividing an image into different sections based on predetermined. Compared to others, it is a straightforward method because it entails separating an image into different sections based on predetermined criteria [58]. There are two primary kinds of region-based segmentation: [1] region splitting and merging and [2] region growing. Region growing allows the removal of a region from an image using defined criteria, such as intensity. It involves selecting a starting seed point. It is important to note that unlike region growing, region splitting and merging work on the entire image [59]. In the present study extracting the region of interest (ROI) involves using both thresholding and region-based techniques. The tumor in the INbreast dataset samples cites moreira2012inbreast is labeled by a white bounding box, as shown in Figure 4. For extracting ROI, the tumor region is first determined by setting a threshold value based on the white color pixels in the image. The threshold for all images is determined to be 80 after several attempts, independent of tumor size. After identifying the greatest area inside this threshold within the image, the tumor is automatically cropped. Figure 4 shows ROI extracted using threshold and region-based methods. The method for extracting ROI can be summarized in four steps:Thresholding the grayscale mammogram image to create a binary image. Labelling and counting the binary image objects, then retaining only the largest one, which is the tumor, as defined by the white bounding box. Assign the largest area within the threshold value to “1” and the rest a value of “0.”Multiply binary image with original mammogram image for obtaining final ROI without including other parts of breast or artifacts. ## 3.4.3. Feature Extraction and Selection Numerous methods exist for feature extraction. Due to their exceptional performance, deep convolutional neural networks (DCNN) garnered significant interest in recent years. Consequently, the DCNN is utilized in this paper. ## 3.4.4. Deep Convolutional Neural Network The success of DCNN in image classification and analysis has been documented in various studies [60, 61]. Convolutional neural networks (CNNs) are composed of multiple trainable stages that culminate in a supervised classifier and feature maps [62]. Three primary types of layers are employed to build CNN structures: convolutional, pooling, and fully connected (FC) layers [63]. The ResNet50 CNN classification model categorizes breast cancer as benign or malignant in this work. ## 3.4.5. Feature Learning through Transfer Learning Machine learning has various feature learning methods (FL), allowing a system to automatically identify the representations required for feature detection, prediction, or classification from a preprocessed dataset [64]. This implies that the machine can learn and use the features to perform tasks such as classification or prediction. In deep learning, FL can be accomplished by constructing a complete CNN to train and test image datasets or adjust a pretrained CNN for classification or prediction on a new image dataset, referred to as transfer learning. In deep learning, transfer learning (TL) is a widely-used technique that enables the utilization of a pretrained network for new prediction or classification tasks. This is achieved by adjusting the parameters of the pretrained network with randomly initialized weights for the new task. TL typically results in faster training than starting from scratch and is considered an optimization that saves time and improves performance, as stated in [65]. For this purpose, transfer learning is utilized to fine-tune ResNet50 CNN. This involves using pretrained weights from the ImageNet dataset [66] for retraining after preprocessing the collected dataset. The network parameters and hyperparameters are optimized during this process. ## 3.4.6. Classification The features are taken from ResNet-50 and processed via a fully connected (FC) layer with a $40\%$ dropout rate to avoid overfitting [67, 68]. This layer is then activated with the rectifying function, ReLU. All negative values are set to zero in the input matrix, while other remains unchanged. The use of ReLU leads to faster and more reliable convergence than a sigmoid activation function during training deep networks [69]. The output layer comprises a sigmoid function (binary classifier) to provide class probabilities. The sigmoid function normalizes the input into two outcomes, i.e., malignant vs. benign [70]. ## 4. Evaluation and Results The proposed deep convolutional neural network for mammogram imaging undergoes examination and validation in this section. Information about benchmark datasets, assessment metrics, and comparisons to other leading techniques are also covered. ## 4.1. Image Acquisition Process The proposed system's performance is evaluated using digitized mammogram images from the INbreast dataset [71]. The database is used to demonstrate the efficiency and reliability of the proposed method for identifying breast cancer. INbreast dataset includes 336 mammogram images, with 269 abnormal and 69 normal images, where 220 are benign and 49 malignant cases. Tables 1 and 2 show the distribution of mammography images. ## 4.2. Metrics of Performance The purpose of cross-validation is to improve efficiency, validate performance, and assess the results from the dataset. To assess the classification efficiency of the proposed method, multiple metrics are utilized such as confusion matrix, accuracy, sensitivity, specificity, error rate, F1 score, and area under the curve (AUC). All these metrics act as benchmark values for comparing the proposed method against previous algorithms [72]. These measurements are defined as follows. ## 4.2.1. Confusion Matrix Confusion matrix represents the performance of a classifier in the form of a table. In ML, it is also known as an error matrix. The image regions are labeled positive or negative based on the data type. The classifier's decision can be correct (true) or incorrect (false). This results in four outcomes: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). Correct decisions are represented along the diagonal of the confusion matrix. ## 4.2.2. Accuracy Accuracy characterizes the suitable labeled images for benign, normal, and malignant mammograms. The accuracy of the process is computed as follows:[1]Accuracy=TP+TNTP+FP+TN+FN. TP accurately represents positive examples; TN addresses classified negative examples; TN means incorrectly classified examples as accurately classified; and FN indicates accurately classified examples as the wrong sample. ## 4.2.3. Specificity The chances that the test will correctly recognize the patient who has the disease is shown in the following equation:[2]Specificity=TNTN+FP. ## 4.2.4. Sensitivity The chance that the test will correctly recognize a patient with the disease is shown in equation:[3]Sensitivity=TPTP+FN. ## 4.2.5. F1 Score It is a weighted average of precision and recall used for assessing the classifier's performance. It considers both false positives and negatives in its calculation, as shown in the following equation:[4]F1score=2∗Precision∗RecallPrecision+Recall. ## 4.2.6. Area Under the Curve (AUC) AUC is the classifier's ability to distinguish between benign, normal, and malignant mammograms. ## 5. Results and Discussion For this study, a subset is taken from the INbreast dataset, and each sample is increased to four images. During the experiment, $60\%$ images were used for training, and the remaining $40\%$ were used for testing. The samples were first subjected to enhancement and segmentation according to the procedures described in the “Methodology” section. Afterward, features were extracted from the samples using a CNN. Finally, all the samples were classified using ResNet-50. The proposed DCNN method categorizes mammogram images of breast tumors into benign or malignant. A dataset named INbreast is used for experimentation. Table 3 displays the classification accuracy achieved by the proposed ResNet-50 method across the INbreast database. From the INbreast dataset, 132 benign and 29 malignant image samples were selected for training, and 20 malignant and 88 benign for testing. The resulting accuracy is $93\%$. The proposed ResNet-50 approach is also compared quantitatively with previously existing algorithms. The study's results revealed that the presented approach outperformed these algorithms with high accuracy, specificity, F1 score values, and sensitivity. As shown in Table 3, the proposed approach demonstrated improved results on the INbreast database with an accuracy of $93.0\%$, specificity of $93.86\%$, and sensitivity of $93.83\%$. It outperforms other methods in terms of accuracy. Although the accuracy achieved by [16] is slightly higher at $91.0\%$, the proposed approach still exhibits the best performance compared to the other methods. Compared to existing methods, the proposed approach enhances breast cancer detection and classification performance. It can potentially be used for real-time evaluation and to support radiologists in automating the analysis of mammogram images. However, performance may vary when the same method is applied to different datasets due to factors such as background noise, lighting conditions, occlusion, overfitting, and the nature of the method. The performance of the presented approach is also evaluated using the confusion matrix and ROC curves. Figure 5 illustrates the confusion matrix on the INbreast data set. AUC, a crucial statistical metric in the ROC curve, is computed using the INbreast data set. Metric in the ROC curve is calculated INbreast data set. ROC curves were constructed based on true positive rate (sensitivity) and false positive rate (1-specificity) rates, controlled by the threshold of the obtained probability maps. Figure 6 shows the ROC curve graph. Table 4 presents our proposed system's results of breast cancer detection. The proposed approach achieved an F1 score and AUC of $93.03\%$ and $93.02\%$, respectively, on the INbreast database. In recent years, breast cancer detection and classification applications have gained widespread use in the medical field, making the diagnostic process more accurate [76, 77]. The goal of the proposed method is to enhance clinical diagnosis by enhancing the detection of breast cancer. The opinions of two medical specialists were gathered based on the accuracy level generated by our proposed algorithm. These experts expressed their appreciation for the improved results of ResNet-50 compared to other approaches. To sum it up, the proposed approach enhances performance compared to other methods and can be utilized for real-time evaluations along with helping radiologists automate the evaluation of mammograms. ## 6. Conclusion The proposed system aimed to detect malignant breast masses and classify benign and malignant tissues in mammograms. A novel computer-aided detection (CAD) system is proposed, which involves thresholding and region-based segmentation techniques. A region-based method with a threshold of 80 determines the largest area included in this threshold. A deep convolutional neural network (DCNN) is utilized during feature extraction. Specifically, the ResNet-50 is retrained to classify the mammograms into two classes (malignant or benign), and its parameters were modified to classify breast mammograms. The proposed approach is applied to the INbreast database to evaluate its performance of the proposed approach. The proposed method achieved an accuracy of $93.0\%$, specificity of $93.86\%$, AUC of $93.02\%$, a sensitivity of $93.83\%$, and an F1 score of $93.03\%$, which are extremely satisfying results. The proposed method surpasses the detection and classification of mammograms, delivering more precise results and improved visual outcomes compared to other systems. The proposed system efficiently detects and classifies malignant breast masses with reduced computation time and produced satisfactory results. Alternative networks, such as deep convolutional networks (VGG) and AlexNet architecture, will be proposed for future development. In the future, we intend to extend this work by collecting large datasets on breast cancer in different age intervals to detect cancer in its early stages. ## Data Availability The (Breast Cancer Diagnosis) data used to support the findings of this study are included within the article. ## Conflicts of Interest The authors declare that they have no conflicts of interest. ## References 1. Xian M., Zhang Y., Cheng H.-Da, Xu F., Zhang B., Ding J.. Automatic breast ultrasound image segmentation: a survey. (2018) 79 340-355. 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--- title: Electroacupuncture Zusanli (ST36) Relieves Somatic Pain in Colitis Rats by Inhibiting Dorsal Root Ganglion Sympathetic-Sensory Coupling and Neurogenic Inflammation authors: - Yi-li Wang - Hai-yan Zhu - Xi-qian Lv - Xing-ying Ren - Ying-chun Peng - Jin-yu Qu - Xue-fang Shen - Ran Sun - Meng-lu Xiao - Hong Zhang - Zhao-hui Chen - Peng Cong journal: Neural Plasticity year: 2023 pmcid: PMC9998159 doi: 10.1155/2023/9303419 license: CC BY 4.0 --- # Electroacupuncture Zusanli (ST36) Relieves Somatic Pain in Colitis Rats by Inhibiting Dorsal Root Ganglion Sympathetic-Sensory Coupling and Neurogenic Inflammation ## Abstract Referred somatic pain triggered by hyperalgesia is common in patients with inflammatory bowel disease (IBD). It was reported that sprouting of sympathetic nerve fibers into the dorsal root ganglion (DGR) and neurogenic inflammation were related to neuropathic pain, the excitability of neurons, and afferents. The purpose of the study was to explore the potential and mechanism of electroacupuncture (EA) at Zusanli (ST36) for the intervention of colon inflammation and hyperalgesia. Sprague-Dawley (SD) was randomly divided into four groups, including control, model, EA, and sham-EA. Our results showed EA treatment significantly attenuated dextran sulfate sodium- (DSS-) induced colorectal lesions and inflammatory cytokine secretion, such as TNF-α, IL-1β, PGE2, and IL-6. EA also inhibited mechanical and thermal pain hypersensitivities of colitis rats. Importantly, EA effectively abrogated the promotion effect of DSS on ipsilateral lumbar 6 (L6) DRG sympathetic-sensory coupling, manifested as the sprouting of tyrosine hydroxylase- (TH-) positive sympathetic fibers into sensory neurons and colocalization of and calcitonin gene-related peptide (CGRP). Furthermore, EA at Zusanli (ST36) activated neurogenic inflammation, characterized by decreased expression of substance P (SP), hyaluronic acid (HA), bradykinin (BK), and prostacyclin (PGI2) in colitis rat skin tissues corresponding to the L6 DRG. Mechanically, EA treatment reduced the activation of the TRPV1/CGRP, ERK, and TLR4 signaling pathways in L6 DRG of colitis rats. Taken together, we presumed that EA treatment improved colon inflammation and hyperalgesia, potentially by suppressing the sprouting of sympathetic nerve fibers into the L6 DGR and neurogenic inflammation via deactivating the TRPV1/CGRP, ERK, and TLR4 signaling pathways. ## 1. Introduction Inflammatory bowel disease (IBD) is a chronic, life-threatening inflammatory disease affecting the gastroenterological system, which includes Crohn's disease (CD) and ulcerative colitis (UC) [1, 2]. The etiology and the pathogenesis of inflammatory bowel diseases (IBD) are still not completely understood [3]. Studies have confirmed that hyperalgesia caused by IBD was closely related to the hypersensitivity response triggered by sympathetic-sensory coupling in the skin-dorsal root ganglion (DRG) [4, 5]. A previous study illustrated that sympathetic nerve sprouting in DRG was observed in a model of colitis [6]. On the other hand, neurogenic inflammation was the important mechanism of referred pain caused by visceral lesions on body surfaces [7]. The activation and release of mediators, such as substance P (SP) from unmyelinated afferent nerve endings caused venule vasodilation and increased permeability, which led to inflammatory reactions such as plasma extravasation and edema [8, 9]. Thus, regulation of sympathetic-sensory coupling and neurogenic inflammation provides a new idea for the treatment of visceral hyperalgesia. It has been well established in traditional Chinese medicine that acupoints which have a diagnostic and curative effect on diseases are an important position for the correlation between the meridian and visceral organs [10, 11]. The acupoints are the paresthesias in corresponding parts of the body surface through neurogenic involved responses in the pathological process of the body [12]. Recently, study reported that neurogenic inflammatory sites were found on the dorsal trunk cutaneous of the liver injury rat model, which was matched with locations of acupoints [13]. Meanwhile, electroacupuncture at neurogenic spots reduced bile duct ligation-induced liver injury [13]. Similarly, our previous study found that colitis rats exhibited secondary hyperalgesia, accompanied by sensitization phenomenon in the Zusanli (ST36) acupoints. Electroacupuncture on Zusanli (ST36) acupoints significantly reduced colon lesions and relieved somatic referred pain of colitis rats [14]. However, the underlying mechanism of electroacupuncture at Zusanli (ST36) relieving colitis and pain hypersensitivity has not been studied. In this study, we explored whether electroacupuncture at Zusanli (ST36) inhibits DRG sympathetic-sensory coupling and surface neurogenic inflammatory response, thereby reducing colon lesions and eliminating referred pain in a colitis rat model. Furthermore, we also investigated the molecular mechanisms of electroacupuncture at Zusanli (ST36) in the treatment of colitis by regulating the DRG sympathetic-sensory coupling and neurogenic inflammation. ## 2.1. Animal Treatment All experiments were approved according to the Ethics Committee the Institutional Animal Welfare and Use Committee of the Institute of Acupuncture-Moxibustion, China Academy of Chinese Medicine (no. 20170313). 32 Sprague-Dawley (SD) male rats (SPF grade, 12 weeks, 180-200 g) were purchased from Chengdu Dossy Experimental Animals Co., Ltd. (Chengdu, Sichuan; SCXY (Chuan) 2020-034). The feeding environment was 23 ± 1°C, relative humidity 50 ± $5\%$, and light/darkness for 12 h circulation. SD rats are allowed to eat and drink freely. The SD rats were randomly divided into 4 groups ($$n = 8$$), namely, the control group, colitis model group, Zusanli electroacupuncture (Zusanli-EA) group, and sham electroacupuncture (sham-EA) group. For the colitis model group, rats were gavaged with $5\%$ (w/v) dextran sulfate sodium (DSS) saline solution (MP Biomedicals, Santa Ana, California, USA) for 4 days (50 mL/d) as previously described [15, 16]. The status of rats was monitored using the disease activity index (DAI) [17]. Meanwhile, the control group rats were gavaged an equal volume of saline solution. For the Zusanli-EA group, electroacupuncture was immediately performed under isoflurane inhalation anesthesia after modeling. Rats received electroacupuncture treatment at Zusanli acupoint (ST36, bilateral) using 1.0-inch filigree needles (0.25 mm × 13 mm, Huatuo Brand, depth of about 7 mm). An electroacupuncture treatment device (G6805-2A, Huatuo Brand) was from Suzhou Medical Appliance Factory, China. The electroacupuncture parameter is a dilute wave with $\frac{2}{100}$ Hz, the intensity of 1 mA, and performed for 15 minutes, once a day, for 21 consecutive days as previously described [18]. For the sham-EA group, rats were anesthetized by inhalation of 3–$4\%$ isoflurane and then received sham electroacupuncture with a pragmatic placebo needle on sham acupoints. The neurological function and DAI score of rats were tested per week. At the end of the 3-week administration, all rats were anesthetized with $1\%$ sodium pentobarbital (50 mg/kg) and euthanized. The colon tissues, serum, ipsilateral lumbar 6 (L6) dorsal root ganglia (DRG), and nearby skin were removed and kept at -80°C for subsequent analysis. The flow of subjects through the experimental procedure is described in Figure 1(a). ## 2.2. Measurement of Thermal Sensitivity A BME-410C thermal stimulation meter (Tianjin Berne Technology Co., Ltd., Tianjin, China) was used to detect thermal withdrawal latency (TWL) of rat hind paws according to published methods [19]. Briefly, the rats were placed in a hot plate instrument (52°C), and the paw-withdrawal latency is defined as the time since the foot touches the hot plate instrument until the hind paw licking (s). ## 2.3. Measurement of Mechanical Sensitivity A BME-404 electrical mechanical analgesia tester (Institute of Biomedical Engineering Chinese Academy of Medical Sciences, Tianjin, China) was used to measure the mechanical withdrawal threshold (MWT) of the rat hind paw. Stainless steel filaments (0.6 mm in diameter) were employed to stimulate the plantar surface of the left hind paw with pressure. When a retracted paw response occurs, the force (g) was automatically recorded. ## 2.4. Hematoxylin and Eosin (H&E) Stain The colon tissues were collected and fixed in $4\%$ paraformaldehyde overnight, processed, and embedded in paraffin. The tissue sections were stained with hematoxylin and eosin (H&E) to observe the degree of the lesion and inflammatory cell infiltration under 10x and 400x magnification optical microscope (Olympus BH2, Tokyo, Japan). ## 2.5. Transmission Electron Microscopy (TEM) The pathological changes in colon tissues were observed by TEM. Briefly, colon tissues were fixed with $3\%$ glutaraldehyde for 15 min and postfixed with $1\%$ osmium tetroxide for 2 h at 4°C. The colon tissues were then incubated with propanone for 2 h and embedded with Ep812 resin. The blocks were sliced with a Leica EM UC7, and sample sections were stained with uranium acetate-lead citrate. A JEM-1400Flash transmission electron microscopy (JEOL; Tokyo, Japan) was used to examine. ## 2.6. Immunohistochemistry (IHC) Stain Paraffin blocks were sectioned at 4 μm. IHC stain was performed to detect the 5-hydroxytryptamine (5-HT) expression in skin tissue corresponding to the L6 DRG of mice according to instructions of the IHC protocol. 5-HT antibody was purchased from Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd. (BS-1126R, Beijing, China; $\frac{1}{100}$). IHC images were captured using a digital trinocular camera microscope camera system (BA400Digital, Motic Instruments, Inc., Baltimore, MD, USA). Image quantification was performed using Halo software (Indica Labs; Albuquerque, NM). ## 2.7. Immunofluorescence (IF) Stain Paraffin sections of L6 DRG were dewaxed and hydrated. The sections were incubated in QuickBlock™ Blocking Buffer (Beyotime Biotechnology, Jiangsu, China, P0260) for 30 min at room temperature. Then, the sections were incubated with calcitonin gene-related peptide (CGRP) antibody (bs-0791R, Beijing Bosen Biological Technology Co., Ltd. Beijing, China, $\frac{1}{100}$), tyrosine hydroxylase (TH) antibody (ab129991, Abcam; $\frac{1}{100}$), or NeuN (ab129991, Abcam; $\frac{1}{100}$) antibody at 4°C overnight and washed 3 times with phosphate-buffered saline (PBS, ZLI-9062, Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd., Beijing, China). Then, DAPI (ZLI-9557; Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd., Beijing, China) was added dropwise into the sections for 5 min. The staining was observed under a fluorescence microscope Olyvia (Olympus, Tokyo, Japan) at 100x and 400x magnifications. Image processing was conducted with Image J (National Institutes of Health, Bethesda, MA, USA). ## 2.8. Western Blot Analysis L6 DRG tissues were treated using RIPA buffer (Signaling Technology, Inc.). The concentration of protein was determined by a BCA kit (Sigma-Aldrich; Merck KGaA). Total protein (30 μg/sample) was separated via $10\%$ SDS-PAGE. And then, the separated proteins are transferred to nitrocellulose membranes. The membranes were blocked with $5\%$ nonfat dried milk overnight at 4°C and incubated with corresponding protein antibodies. Then, the membranes were washed with Tris-buffered saline/$0.1\%$ Tween (TBST) and incubated for 1.5 hours with an HRP Goat anti-Rabbit IgG. The bands were visualized using the ECL system (Affinity Biosciences, Cincinnati, Ohio, USA), and β-actin was used as an internal control. The net optical density was measured using Quantity One software (Bio-Rad). Antibody information used for Western blot analysis is shown in Table 1. ## 2.9. Detection of the Levels of Inflammatory Cytokines in Serum and Pain-Causing Substances in Skin Tissues The levels of interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, prostaglandin E2 (PGE2), and IL-10 in rat serum and substance P (SP), hyaluronic acid (HA), bradykinin (BK), and prostacyclin (PGI2) in rat skin tissues (corresponding to the L6 DRG) were examined by quantizing enzyme-linked immunosorbent assay (ELISA) kit (ZhuoCai Biological Technology, China) based on the manufacturer's instructions. The absorbance of wells was measured with a microplate reader (SpectraMAX Plus384, USA) at 450 nm wavelength to calculate the sample concentration. ## 2.10. Statistical Analysis The data were represented as means ± standard deviation. Statistical analysis was performed using SPSS 20.0 (IBM Corp.). One-way analysis of variance (ANOVA) with Tukey's post hoc test and Student's t-test was used for statistical analysis. Differences with a $P \leq 0.05$ were considered to indicate statistically significant. ## 3.1. EA Attenuated Colitis Severity and Somatic Hyperalgesia in DSS-Induced Colitis Rats DAI score was undertaken weekly to evaluate the progression of colitis. As compared with the control, the DAI score was significantly increased on day 7 after DSS treatment (Figure 1(b)). EA treatment continuously reduced the DAI score. On days 11 and 14, the DAI score was significantly decreased compared with the sham-EA group (Figure 1(b)). Meanwhile, colitis rats displayed lower TWL and MWT on days 11 and 14, which were dramatically corrected by EA treatment (Figures 1(c) and 1(d)). These results indicated that the therapeutic effect of EA at Zusanli in the early stage of colitis in rats is significant. Furthermore, the results of the H&E stain showed that model group colonic mucosa was severely damaged, with a high number of infiltrating inflammatory cells in comparison to the control group (Figure 2(a)). It was found that the EA group exhibited significantly reduced mucosal injury and infiltration of inflammatory cells compared with the sham-EA group (Figure 2(a)). Meanwhile, we also used TEM to observe the pathological changes of colon tissues. As compared with the control, colitis rats exhibited severe colonic injury marked by severe damage appeared including transmural infiltration of inflammatory cells, necrosis, and destruction of crypts, massive transmural inflammatory cell infiltration, and thickening of the colonic wall (Figure 2(b)). EA treatment significantly improved DSS-induced damage to colon tissues compared with the sham-EA group (Figure 2(b)). These data suggested that EA treatment alleviated DSS-induced colonic injury and hypersensitivity in colitis rats. ## 3.2. EA Attenuated Sympathetic-Sensory Coupling of L6 DRG in DSS-Induced Colitis Rats Compared with the control group, the distribution of TH-positive sympathetic fibers in the DRG was increased at 21 d after DSS induction (Figures 3(a) and 3(b)). Meanwhile, the TH-positive sympathetic fibers wrapped around sensory neurons to form sympathetic-sensory coupling (Figures 3(a) and 3(b)). EA treatment decreased the expression of TH and inhibited sympathetic fibers from wrapping around sensory neurons (Figure 3(a)). In addition, sensory nerve fibers were labeled with CGRP, which was a factor in neurogenic inflammatory responses. As shown in Figure 3(b), the density of CGRP-positive sensory nerve fibers and TH-positive sympathetic fibers were both increased in L6 DRG of colitis rats (Figures 3(c) and 3(d)). Compared with the sham-EA group, EA treatment suppressed sympathetic nerve sprouting in L6 DRG of colitis rats (Figures 3(c) and 3(d)). These findings confirmed that DSS induction promoted the sprouting of sympathetic nerve fibers which corresponded to the increased sympathetic nerve activity. EA intervention ameliorated these changes. ## 3.3. EA Decreased Neurogenic Inflammatory on Body Surfaces in DSS-Induced Colitis Rats To evaluate DSS-associated neurogenic inflammatory on body surfaces, we performed an ELISA assay. Three weeks after DSS induction, the expression levels of neurogenic inflammatory response-related inflammatory and pain-causing substances SP, HA, BK, and PGI2 in skin tissues were significantly increased compared with the control group (Figure 4(a)). EA treatment decreased the expression of SP, HA, BK, and PGI2 compared with the sham-EA group (Figure 4(a)). Meanwhile, the secretion of proinflammatory factors IL-1β, IL-6, TNF-α, and PGE2 was enhanced, as well as the levels of anti-inflammatory factor IL-10 were reduced in the serum of colitis rats, which was all reversed by EA treatment (Figure 4(b)). IHC stain showed that DSS induction promoted neurotransmitter 5-HT expression in skin tissues, which was also eliminated by EA treatment (Figures 4(c) and 4(d)). Meanwhile, TH was reported to be a key enzyme that regulates neurotransmitters in nerve cells [20]. The increased TH expression induced by DSS was dramatically blocked by EA treatment in L6 DRG of colitis rats (Figures 4(e) and 4(f)). ## 3.4. EA Inhibited TRPV1/CGRP, ERK, and TLR4 Signaling Pathways in DSS-Induced Colitis Rats To elucidate the underlying cellular mechanism of EA stimulation, we analyzed whether EA at ST36 augmented TRPV1/CGRP, ERK, and TLR4 signaling pathway activation, which were known to be a crucial role in the neurogenic inflammatory. We found that DSS induction resulted in the activation of TRPV1/CGRP, ERK, and TLR4 signaling pathways, characterized by the increased expression of TRPV1, CGRP, MEK, p-MEK, CREB, p-CREB, TLR4, IRF3, p-IRF3, P-65, and p-P65. EA stimulation significantly inhibited this process, whereas EA at nonacupoint did not produce a significant improvement (Figures 5(a)–5(j)). ## 4. Discussion It is well known that referred somatic pain was caused by activation of primary nociceptive afferents by visceral lesions stimuli [21]. Referred somatic pain was accompanied by secondary hyperalgesia, reflex muscle spasms, deep tenderness, and autonomic hyperactivity [22, 23]. Studies have found that the sympathetic nervous system was closely related to hyperalgesia due to visceral lesions [24–26]. Sympathetic nerve sprouts in the DRG by coupling around neurons to form sympathetic-sensory coupling were found in animal models of pathological conditions. Referred somatic pain and peak sympathetic sprouting were observed in the neuropathic pain model of the cuff and spared nerve injury (SNI) in the sciatic territory [27]. A previous study found that nerve growth factor (NGF) released from the sprouted sympathetic fibers in the synovial membrane and upper dermis contributed to the pain-related behavior associated with arthritis [28]. It has been shown that jaw pain due to myocardial ischemia could be explained by the convergence of cardiac visceral afferent fibers with spinothalamic tract (STT) neurons [29]. In a rat model of trinitrobenzene sulfonic acid- (TNBS-) induced colitis, sympathetic nerve fiber sprout was found in the DRG of the lumbosacral segment (L6, S1), manifested by tyrosine hydroxylase- (TH-) positive nerve fibers increased [30]. Dextran sulfate sodium (DSS) treatment caused mechanical hypersensitivity in the abdominal and facial skin of colitis mice by increasing TRPA1 expression in cultured DRG neurons and selectively enhanced currents evoked by the TRPA1 agonist [31]. Importantly, in the current study, we detected that DSS induced colon tissue lesions and somatic hyperalgesia in rats. Moreover, DSS treatment increased sprouting of sympathetic fibers in L6 DRG into the sensory ganglia. On the other hand, neurogenic inflammation was an important part of the pathogenesis of referred somatic pain. Primary afferent nociceptive neurons released neuropeptides to the periphery, leading to mast cell degranulation and the release of biologically active substances that produce pain and/or inflammation such as CGRP, SP, 5-HT, HA, BK, and PGI2, which induced neurogenic inflammation characterized by vasodilatation, protein extravasation, and leukocyte migration [32, 33]. In the inflammatory pain model, PGI2 was involved in pain transmission at the spinal cord [34]. 5-HT participated in the mediation of joint pain in experimental arthritis by exciting and sensitizing the medial articular afferent nerve [35]. A previous report indicated that the blockade of receptor channels such as TRPV1 and TRPA1 on nociceptive sensory neurons was shown to attenuate experimental colitis by suppressing the release of GRP and SP [36]. Furthermore, HA sensitized the nociceptor TRPV1 in mouse nociceptive dorsal root ganglion neurons and was known to contribute to relieving visceral hypersensitivity, symptoms, and abdominal pain in IBD patients [37]. Our results demonstrated that DSS colitis upregulated SP, HA, BK, PGI2, and 5-HT expression in the skin and increased TRPV1, TRPA1, and TH in L6 DRG, implying that DSS promoted surface neurogenic inflammation evoked by sympathetic-sensory coupling in skin-DRG. EA, as a traditional therapeutic method, has been used to treat IBD and hyperalgesia in China. EA treatment at Zusanli (ST36) attenuated the macroscopic damage and the myeloperoxidase activity of colonic samples [38]. Furthermore, EA at Zusanli (ST36) and Guanyuan (CV4) activated microglia in hippocampus CA1 and CA3 regions of DSS-induced colitis mice [39]. Interestingly, EA at Zusanli (ST36) and Shangjuxu (ST37) significantly reduced the severity of colonic inflammation, as well as the visceral hypersensitivity and referral hind paw hyperalgesia in colitis rats by increasing [40]. EA at Zusanli (ST36) eliminated the expression and activation of mast cells and improved visceral hypersensitivity in experimental colitis [41]. The mechanism may be via inhibiting of NGF/TrkA/TRPV1 peripheral afferent pathway triggered by the mast cells [41]. We found that EA at Zusanli (ST36) improved the pathological state of the colon tissues and referred somatic pain in a colitis rat model, which was related to the inhibiting of sensory-sympathetic coupling in L6 DRG and neurogenic inflammation in the skin. As is well known, TLR4 initiated downstream genes such as NF-KB and IRF3 and activated the expression of inflammatory factors such as IL-1β, IL-6, and TNF-a, thereby inducing inflammatory responses and pain-related hypersensitivity. Increased expression of TLR4, p-p65, TNF-α, and IL-1β in (L4/L5) DRGs was observed in a postoperative pain model [42]. Importantly, TNBS treatment enhanced TLR4 and TRPV1 coexpression in primary afferents including the trigeminal sensory neurons and DGR neurons of colitis mice [43]. Additionally, suppressing the synthesis of ERK in DRG has proven effective to alleviate hyperalgesia. The deactivation of the MEK/ERK pathway in the DRG of chronic constriction injury rats alleviated neuropathic pain development [44]. H2O2-induced hyperalgesia was related to increased phosphorylation of ERK in neurons of DRG [45]. A recent study found that in a rat model of colitis, the activation of ERK5 mediated BDNF upregulation in the DRG primary afferent neurons [46]. In this research, we demonstrated that DSS induction activated TRPV1/CGRP, ERK, and TLR4 signaling pathways, which were significantly offset by EA at Zusanli (ST36). In conclusion, EA at Zusanli (ST36) relieved hyperalgesia induced by colitis via the inhibition of surface neurogenic inflammation and sympathetic sprouting into the DRG, which were mediated by TRPV1/CGRP, ERK, and TLR4 signaling pathway deactivation. EA at Zusanli (ST36) may be an effective treatment for referred somatic pain in UC patients. ## Data Availability The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request. ## Conflicts of Interest The authors declare that they have no competing interests. ## Authors' Contributions YL W, HY Z, and P C conceived and designed the experiments. YL W, HY Z, XQ L, XY R, YC P, JY Q, XF S, and R S performed the experiments. YL W, HY Z, ML X, H Z, and ZH C analyzed the data. 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--- title: Dietary Level of the Omega-3 Fatty Acids EPA and DHA Influence the Flesh Pigmentation in Atlantic Salmon authors: - T. Ytrestøyl - M. Bou - C. Dimitriou - G. M. Berge - T.-K. Østbye - B. Ruyter journal: Aquaculture Nutrition year: 2023 pmcid: PMC9998164 doi: 10.1155/2023/5528942 license: CC BY 4.0 --- # Dietary Level of the Omega-3 Fatty Acids EPA and DHA Influence the Flesh Pigmentation in Atlantic Salmon ## Abstract Atlantic salmon with a start weight of 53 g were fed diets with different levels of EPA and DHA or a diet with 1: 1 EPA+DHA ($0\%$, $1.0\%$, and $2.0\%$ of the diet). At 400 g, all fish groups were mixed and equally distributed in new tanks and fed three diets with $0.2\%$, $1.0\%$, or $1.7\%$ of EPA+DHA. At 1200 g, the fish were transferred to seawater pens where they were fed the same three diets until they reached a slaughter size of 3.5 kg. The fillet concentration of astaxanthin and its metabolite idoxanthin was analysed before transfer to seawater pens at 1200 g and at slaughter. The fatty acid composition in the fillet was also analysed at the same time points. Salmon fed low levels of EPA and DHA had lower fillet astaxanthin concentration and higher metabolic conversion of astaxanthin to idoxanthin compared to salmon fed higher dietary levels of EPA and/or DHA. DHA had a more positive effect on fillet astaxanthin concentrations than EPA. There were positive correlations between fillet DHA, EPA, sum N-3 fatty acids, and fillet astaxanthin concentration. A negative correlation was found between the concentration of N-6 fatty acids in the fillet and the astaxanthin concentration. ## 1. Introduction Feeds for Norwegian farmed salmon have gone through major changes in composition in the last decades, from essentially a marine based diet in the early 1990s to a diet with $70\%$ plant ingredients today [1, 2]. One consequence of this shift in ingredients is reduced levels of the healthy N-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in the diet. Deficiency of these important fatty acids may have serious consequences for the salmon, and requirements for N-3 polyunsaturated fatty acids (PUFA) have been reported to range from 0.5 to $2.7\%$ of the diet, depending on the species, life stage, and experimental conditions ([3–9]). However, it is important that studies on requirements not only assess fish growth and survival but also other important criteria such as fish health and fillet quality. The pink flesh color in salmon is important for consumer acceptance. The color intensity is determined by the deposition of keto-carotenoids such as astaxanthin in the muscle, but the basal biological mechanisms that regulate the deposition of astaxanthin in the salmon muscle have not been studied in detail. In particular, the effects of interactions between astaxanthin and other dietary components such as N-3 fatty acids, vitamin A, and other antioxidants on pigmentation are needed. Most of the existing studies are done on diet formulations with a higher content of marine ingredients than what is used in commercial farming today. Some studies report no significant effects of replacing fish oil with various plant oils on astaxanthin deposition in the muscle [10–14]. Other studies have found a positive effect of increasing the levels of N-3 PUFA in feed for Atlantic salmon on sensory-evaluated color tone [15] and fillet astaxanthin concentration [16]. Long-term studies with Atlantic salmon have shown a negative effect on flesh astaxanthin concentrations of feeding diets with low levels of N-3 fatty acids [17]. Thus, high PUFA oils may have a positive effect on astaxanthin utilization. The mechanisms are not known, and most of the studies mentioned above only measured flesh color and not digestibility or metabolism of astaxanthin, both of which may affect fillet astaxanthin deposition. The muscle retention of astaxanthin varies with season and life stage but is typically between 5–$10\%$ whereas the digestibility of astaxanthin is often around $40\%$ [16, 18, 19]. Thus, most of the digested astaxanthin is metabolized and/or excreted, possibly through several different pathways. Carotenoids are potent antioxidants due to their chemical structures with a long carbon chain of unconjugated double bounds quenching singlet oxygen and free radicals. Carotenoids like astaxanthin are also metabolized into diverse metabolites and cleavage products [20] that may have important biological functions of which many are linked to lipid metabolism [21]. Astaxanthin is a precursor of vitamin A in salmon and other fish [22–24], and a key gene in the regulation of this pathway in mammals is β,β-carotene-15,15′-monooxygenase (BCO1) [25, 26]. The expression of this enzyme is regulated by several dietary factors in mammals, including the content of EPA and DHA, N-6 fatty acids, vitamin A, and carotenoid content [27, 28]. Salmonids also have a reductive metabolism of ketocarotenoids [24, 29, 30]. Idoxanthin (3,3′,4′-trihydroxy-β,β-carotene-4-one) is the first metabolite in this reductive pathway of astaxanthin, and the concentration of idoxanthin and other reductive metabolites are influenced by life stage, genotype, and environmental conditions [16, 19, 30–32]. In the present study, diets with different concentrations of EPA and DHA or combinations of EPA and DHA were fed to Atlantic salmon from 53 g up to 3.5 kg, and the effects on fillet concentration of astaxanthin and its metabolite idoxanthin were measured. The fatty acid composition of the fillet was also measured at 1.2 and 3.5 kg and correlations were made between fillet astaxanthin, idoxanthin, and fatty acid compositions. A transcriptome analysis of the intestine was performed at 3.5 kg of genes related to oxidative stress and inflammation and carotenoid metabolism. ## 2.1. Experimental Design Individually tagged (PIT-tags, passive integrated transponder; Biosonic) Atlantic salmon (S. Salar) with a mean initial weight of 52.8 g and adapted to seawater were fed two dietary levels of EPA, DHA, or a 1: 1 mixture of EPA and DHA ($1.0\%$ and $2.0\%$ of the diet in each dietary group) in duplicate tanks. One group was fed a diet without EPA and DHA ($0\%$, control). This resulted in a total of 7 diets, with the same content of protein (46.6–$47.0\%$), fat (24.6–$25.9\%$), energy (22.1–22.6 MJ/kg), and astaxanthin (50 mg/kg) that the fish were fed until they were 400 g (period 1). The basal test diet from 52.8 to 400 g was without fish meal and fish oil but carefully formulated to meet the nutritional requirements for amino acids. EPA and DHA were added in the form of concentrates. The formulation and chemical composition of the diets and a detailed description of the experimental conditions are given in Bou et al., [ 3]. The fish were kept under continuous light (L:D 24: 0) in indoor seawater tanks (1 m3) with flow-through seawater (33 g L−1 salinity). Temperature and oxygen were measured daily. The temperature varied between 6.3°C and 13.8°C (mean temperature 10.0°C), and the oxygen saturation level was over $85\%$. From 400 g, fish from all diet groups were evenly distributed into 9 tanks (3m3) with flow-through seawater on land (period 2A) and fed experimental diets containing three different levels of EPA + DHA (0.2, 1.0, and $1.7\%$) in triplicate until they reached approximately 1200 g. Feeds were produced as 4-, 5-, and 7-mm pellets according to fish size. The diets in the seawater period 2A in tanks on land were different in their formulation. The $0.2\%$ EPA + DHA diet was fishmeal- and fish oil-free, and the main oil source was a mixture of poultry, rapeseed, and linseed oil (50: 30: 20; v:v:v), and the main protein source was poultry meal. The $1.0\%$ EPA + DHA diet was also fishmeal-free, but the main source of EPA and DHA was fish oil. The composition of the $1.7\%$ EPA+DHA diet resembled a commercial diet with respect to the content of fishmeal and fish oil. The diets contained around $32.8\%$ fat, $36.5\%$ protein, and 50 mg astaxanthin per kg. All diets were produced by Biomar. The feeding trials in period 1 from 50 to 400 g and period 2A from 400 to 1200 g were conducted at the Nofima Research Station in Sunndalsøra, Norway. When the fish were 1200 g, they were transported by truck to sea cages at LetSea Aquaculture Research Station in Dønna, Norway (period 2B, pens in seawater). The fish were fed diets with the same levels of EPA and DHA ($0.2\%$, $1.0\%$, and $1.7\%$) as in tanks on land until they reached a weight of 3.5 kg. Diet concentrations of fatty acids in diets fed until 400 g (period 1), sea water period in tanks on land (period 2A), and sea cage period 2B are given in Table 1. A detailed description of diet formulation and fatty acid composition is found in Bou et al., [ 4]. During period 2B in sea cages, the fish were treated for sea lice 4 times (25 April, 10 July, 20 August, and 6 November) by providing chemical bath treatment with azamethiphos (0.2 mL/m3 for 35 min; Trident Vet, Neptune Pharma) and deltamethrin (0.3 mL/m3 for 30 min; ALPHA MAX, PHARMAQ). Further details on feeds and experimental conditions in seawater are found in Bou el al., [ 4]. ## 2.2. Sampling and Analysis of Carotenoids The effects of dietary EPA and DHA concentrations on flesh pigmentation were evaluated when the fish were 1200 g and 3.5 kg. At 1.2 and 3.5 kg, the fillet (right side) was sampled from 30 fish per diet treatment and stored on −80°C for later analysis of carotenoid and fatty acid composition as described below and in Bou et al., [ 3]. Fish were killed by an overdose of the anesthetic metacain (MS-222; 0.08 g/l), and individual weights and lengths were recorded. The skin and bone of the fillet were removed before the fillet was homogenized, and the carotenoids extracted using a 1: 1: 3 mixture of distilled water, methanol (containing 500 ppm butylated hydroxytoluene), and chloroform as described by Bjerkeng et al., [ 33]. A Spherisorb S5CN-4800 nitrile column (Hichrom Ltd., Theale, Berkshire, UK) was used to determine the amount of astaxanthin and idoxanthin in the samples using a mobile phase with $20\%$ (v/v) acetone in n-hexane+ (HPLC system I). An external standard of all-E-astaxanthin (Hoffmann-La Roche, Basel, Switzerland) with known concentration was prepared to establish a response line, and sample concentrations were calculated using peak areas from the chromatograms. The concentration of astaxanthin standards were determined spectrophotometrically in n-hexane containing $4.5\%$ (v/v) CHCl3. Standards of idoxanthin 3′, 4′-cis and trans glycolic isomers were prepared according to Aas et al. [ 29]. The retention times (RT) for the 3′, 4′-cis and 3′, 4′-trans glycolic isomers of idoxanthin were ca. 7.6 and 9.3 min, respectively. ## 2.3. Fillet Colour Measurements In instrumental tristimulus colour analysis (CIE L∗a∗b∗), CIE [1986] was performed on the fillets when the fish was 1200 g (30 fillets per diet treatment). The measurements were done on the dorsal muscle posterior to the dorsal fin, under the adipose fin, and in the tail on each fillet, using a Minolta Chroma Meter CR-300 (Minolta, Osaka, Japan). Measurements were made directly on the fillets, and the measuring head was rotated 90o between duplicate measurements per position, and means of six recordings per fish were used for data analysis. Visual color assessment using a Roche SalmoFan (Hoffmann-La Roche, Basel, Switzerland) was done on of the same fillets at the same positions. The color was evaluated using a scale between 20 and 34 (20 represents a low degree of pigmentation and 34 represents a highly pigmented fillet). ## 2.4. RNA Isolation, cDNA Synthesis, and Quantification of Transcript Levels by Real-Time Quantitative PCR Total RNA was isolated from the intestine at the final sampling (3.5 kg) using a PureLink Pro 96 RNA Purification Kit (Invitrogen), according to the manufacturer's instructions. RNA was treated with PureLink DNaseI (Invitrogen) to remove any contaminating DNA. RNA concentration was measured using a NanoDrop® ND-1000 spectrophotometer (NanoDrop Technologies). Reverse transcription of 1 μg total RNA into complementary DNA (cDNA) was carried out using a TaqMan® Reverse Transcription Reagents kit (Applied Biosystems) according to the manufacturer's protocol in a 20-μl reaction volume. PCR primers (Table 2) were designed using Vector NTI (Invitrogen) and synthesized by Invitrogen. The efficiency was checked from 10-fold serial dilutions of cDNA for each primer pair. Real-time PCR was performed in a LightCycler 480 Instrument (Roche Applied Science). The PCR master mix consisted of 0.5 μl forward and 0.5 μl reverse primer (0.5 μM final concentrations), 4 μl of a 1: 10 dilution of cDNA and 5 μl LightCycler 480 SYBR® Green I Master (Roche Applied Science). All samples were analysed in duplicate with a non-template control for each gene. The reaction was performed by incubating the samples at 95°C for 5 min, forty-five cycles of 95°C for 15 s and 60°C for 15 s, and 72°C for 15 s for denaturation, annealing, and extension, respectively. The specificity of PCR amplification was confirmed by melting curve analysis (95°C for 5 s and 65°C for 1 min, and a continuous temperature ramp (0.11°C/s) from 65 to 97°C). Eukaryotic translation initiation factor 3 (etif3), RNA polymerase 2 (rpol2), and elongation factor 1-alpha (ef1a) were evaluated as reference genes, and it was found that the latter was the most stable. Relative expression levels of mRNA transcripts were calculated using the -ΔΔCt method using ef1a as the reference gene [34]. ## 2.5. Statistical Analysis Diet effects on fillet astaxanthin and idoxanthin concentration and color were analysed by ANOVA in SAS jmp with diet in period 1 (50–400 g) and period 2 (400–3500 g) as fixed factors. P values <0.05 were considered significant differences and $P \leq 0.1$ were considered a trend. Linear correlations were made between fillet content of astaxanthin, idoxanthin, content of EPA, DHA, sum N-3, N-6, and saturated (N-0) fatty acids. Correlation analyses were done in SAS jmp. Principal component analysis (PCA) was performed using muscle concentrations of astaxanthin, idoxanthin, and fatty acid composition (content of EPA, DHA, sum N-0, N-3, N-6, and N-9) as input variables using the software Unscrambler® X, version 10.3 (CAMO). ## 3.1. Fatty Acid Composition of the Fillet The fatty acid composition of the fillets at 1.2 and 3.5 kg are shown in Tables 3 and 4, respectively. The fatty acid composition of the fillet reflected the dietary fatty acid composition and showed a 2.2-fold increase in percentage of EPA, a 1.7-fold increase in percentage of DHA and a 1.6-fold decrease in percentage of N-6 fatty acids as EPA + DHA in the diets increased from $0.2\%$ to $1.7\%$ in the diets. When looking into prediet effects in these two sizes of fish, fish fed increasing dietary levels of EPA and DHA in period 1 had significantly higher muscle levels of these fatty acids when they reached 1.2 kg (Table S1), although they had been fed the same diets since they were 400 g. The effects of the prediets were still significantly present at the final sampling, when the fish reached 3.5 kg, with fish fed higher content of EPA+DHA during the early life stage showing higher levels of these FA regardless of the main diet received during period 2 (Table S2). ## 3.2. Carotenoid Concentrations and Fillet Color When the salmon was 1200 g, the major effects on fillet astaxanthin and idoxanthin concentrations were due to the diet fed during period 2B (400–1200 g) ($P \leq 0.0001$, Figure 1). However, there were also some significant effects of the diet used in period 1 (50–400 g) on muscle carotenoid concentration ($P \leq 0.05$). Fish that had been fed a diet without EPA and DHA from 50 to 400 g ($0\%$) had lower astaxanthin concentration in the fillet, compared to fish fed diets containing EPA and DHA (Figure 1(a)). Fish fed diets with DHA in period 1 had the highest astaxanthin concentrations. There was no significant effect of dietary content of EPA and DHA in period 1 on fillet idoxanthin concentration at 1200 g in fish fed $1\%$ EPA+DHA or $1.7\%$ EPA+DHA in period 2B (Figure 1(b)). In salmon fed diets with low EPA and DHA concentration in period 2 there was a tendency for higher idoxanthin concentrations in fillets of salmon fed low levels of EPA and DHA in period 1 ($$P \leq 0.09$$, Figure 1(b)). However, the major effect on fillet astaxanthin concentration at 1200 g was the diet concentration of EPA and DHA in period 2B (400–1200 g). Salmon fed the diet with $0.2\%$ EPA+DHA (low) had the lowest fillet astaxanthin concentration with a mean of 1.38 mg/kg when all diet groups from period 1 are pooled (Figure 2(a)). Salmon fed the $1.0\%$ and the $1.7\%$ diets in period 2A had 1.79 and 2.75 mg astaxanthin per kg, respectively, when all diets in period 1 were pooled. The differences in fillet astaxanthin were also reflected in fillet visual color measured by SalmoFan scores and Minolta chroma, its redness and yellowness (Figures 2(b) and 2(c)). Fillets from salmon fed diets containing $0.2\%$ EPA and DHA from 400 to 1200 g were less colored than fillets from fish fed the diets containing $1.0\%$ or $1.7\%$ EPA and DHA ($P \leq 0.0001$). However, the diet provided to the fish during period 1 (50–400 g) did not have a significant effect on fillet color of 1200 g salmon measured as Salmofan score, chroma, redness, and yellowness assessed by Minolta. There was a significant effect of diet in period 2A on the concentration of idoxanthin in the fillet ($P \leq 0.0001$). Salmon fed the commercial control diet ($1.7\%$ EPA+DHA) had lower idoxanthin concentration compared to salmon fed the 0.2 and $1.0\%$ diets (Figure 2(a)). Idoxanthin amounted $14\%$ of the total carotenoid content in fillet of salmon fed the $1.7\%$ control diet (all diets in period 1 pooled) whereas the idoxanthin concentration was around $30\%$ of total carotenoids in the 0.2 and $1.0\%$ diets (pooled mean for all diets period 1). There were significant negative correlations between fillet concentrations of EPA, DHA, sum N-9 fatty acids, and % idoxanthin of total carotenoids ($P \leq 0.0001$, R2 = 0.27, 0.25 and 0.25, respectively). Correlations between fillet astaxanthin and fatty acid composition showed a positive correlation between fillet DHA and EPA concentration and astaxanthin concentration ($P \leq 0.0001$, R2 = 0.43 and 0.46, respectively) and a negative correlation with N-6 fatty acids ($P \leq 0.01$, R2 = 0.13). PCA analysis showed no clear pattern related to diet in period 1. The diet in period 2 was responsible for the differences observed (Figure 3). There was no significant effect of diet in period 1 on fish bodyweight at 400 g. There was however an effect of diet in period 2A on the bodyweight; the fish fed the commercial control diet with a $1.7\%$ of EPA+DHA were 1.3 kg; the fish fed with a$1\%$ of EPA+DHA diet were 1.2 kg; and the fish fed with a low EPA + DHA diet were 1.1 kg when the fish were transferred to pens in seawater ($P \leq 0.0001$). Linear regressions between fish weight and fillet astaxanthin and idoxanthin at 1200 g were made to test whether fish weight had an effect on fillet color. For the 0.2 and $1.0\%$ diet, there was no significant relationship between fish weight and fillet color, astaxanthin, or idoxanthin concentration. For the commercial control diet ($1.7\%$ EPA+DHA) there was a significant positive correlation between bodyweight and fillet astaxanthin, chroma, redness and yellowness ($P \leq 0.01$), with bodyweight explaining $9\%$ of the total variation in fillet colour parameters and $12\%$ of the variation in fillet astaxanthin concentration. There were no significant differences in bodyweight between diets at the final sampling when the salmon weighed around 3.5 kg. There was however a clear positive effect of a higher dietary concentration of omega-3 fatty acids on fillet astaxanthin concentration. The effect of diet in period 1 on fillet astaxanthin or idoxanthin concentrations was no longer significant. Data from the dietary groups in period 1 is therefore pooled in Figure 2(d). The fillet astaxanthin concentration was significantly affected by diet fatty acid composition in period 2 A and B, between 400 and 3.5 kg ($P \leq 0.001$). Salmon fed the $0.2\%$ diet had the lowest fillet astaxanthin concentration and fish fed the commercial control diet with a $1.7\%$ EPA+DHA had the highest fillet astaxanthin concentration. The concentration of idoxanthin in the fillet at 3.5 kg was also affected by diet (Figure 2(d)). As in the 1200 g fish, it was the highest in the $1\%$ diet and lowest in the commercial control diet ($P \leq 0.001$). There was a significant linear correlation between fillet content of EPA and DHA ($P \leq 0.0001$, $y = 0.81$x + 0.75, R2 = 0.64). Positive correlations between fillet concentrations of astaxanthin and content of EPA and DHA were found ($P \leq 0.0001$) explaining around $25\%$ of the variation in fillet astaxanthin. There were also positive correlations between sum of N-3 and N-0 fatty acids and fillet astaxanthin concentration ($P \leq 0.0001$, R2 = 0.26 and 0.28, respectively) whereas a weaker negative correlation with sum N-6 fatty acids in fillet was found ($P \leq 0.001$, R2 = 0.11). For idoxanthin in fillet (% of total carotenoids) weak negative correlations were found with concentration of DHA in fillet ($P \leq 0.05$, R2 = 0.06) and sum N-0 in fillet ($P \leq 0.01$, R2 = 0.09). ## 3.3. Gene Expression in Intestine The expression of the β-carotene oxygenases bmco1, bmco1-like, and bmco2-c in the intestine was measured at the final sampling at 3.5 kg. There were no significant effects of diet in periods 1 or 2 on the expression on the three carotenoid oxygenase genes (Table 5). The diet treatments in period 1 is therefore not shown, but instead pooled within diet treatment in period 2 (Figures 4(a)–4(c)). Overall, salmon fed $1\%$ shoved the numerically the highest expression of all three genes in the intestine, but the differences were not statistically significant due to large variation between individuals. At the final sampling, expression of genes involved in cellular defense mechanisms against oxidative stress and inflammation were also measured in the intestine. The expression of cyclooxygenase 2 (cox2), a gene involved in synthesis of proinflammatory eicosanoids, was elevated in fish fed the $1\%$ diet compared to the two other diets (Figure 4(d)). There was also an effect of diet from 50 to 400 g, where increasing levels of EPA and DHA in early life stages down regulated the expression of cox2 (Figure S1). The expression of the gene coding for nuclear factor erythroid 2-related factor 2 (nrf-2) involved in regulation of genes protecting against oxidative damage was upregulated in fish fed $1.0\%$ EPA+DHA compared to in salmon fed diets with a 0.2 and $1.7\%$ of EPA+DHA (Figure 4(e)). Reduced expression of the transcription factor NF-κB was found in diets with a 0.2 and $1.0\%$ EPA + DHA compared to the salmon fed with a $1.7\%$ diet (Figure 4(f)). ## 4. Discussion The reduction in dietary concentration of the important omega-3 fatty acids EPA and DHA in later years due to the replacement of marine ingredients with plant ingredients may have consequences for salmon health and quality. Requirements have often been assessed based on growth effects in trials of short duration under experimental conditions without stress, and many of the trials have been done on juveniles in freshwater. However, requirements cannot be based only on growth effects, but also have to consider fish health, welfare, and quality [3, 4]. Requirements for large salmon under challenging conditions in seawater are also needed to ensure fish health and the quality of the final product. Bou et al. [ 4] found that $1\%$ EPA+DHA in the Atlantic salmon diet, a level previously regarded as sufficient, was too low to maintain fish health under demanding environmental conditions in sea cages. Low EPA+DHA resulted in increased mortality during delousing at high water temperatures, increased fat content in the liver, intestine, and viscera, reduced intervertebral space, and caused mid-intestinal hyper-vacuolization. EPA and DHA in the different tissue membrane phospholipids were typically replaced by pro-inflammatory N-6 fatty acids. In the present study, negative effects of low levels of EPA and DHA on flesh pigmentation and a positive correlation between muscle concentrations of astaxanthin and N-3 fatty acids were found, whereas a negative correlation between astaxanthin concentration and muscle content of N-6 fatty acids was observed. Long chain omega-3 fatty acids may improve pigmentation through several mechanisms. Fatty acid composition affects the solubility and transfer of carotenoids into the aqueous phase of carotenoids in triglyceride emulsions [35]. Differences in the transfer efficiency of astaxanthin into intestinal micelles may explain the effects of some dietary oils observed on astaxanthin deposition. Saturated fatty acids have been shown to be negative for the digestibility of astaxanthin at low temperatures [36], but not at higher temperatures. Studies on interactions between PUFAs and temperature on astaxanthin digestibility in vivo are however, lacking in salmon, and the digestibility was not measured in the present study. In the present study, the diet raw material composition was identical until the fish were 400 g, but from 400 g until 3.5 kg, the diets differed in the content of fish oil and fish meal [4]. The effects seen in diet in period 1 can thus be attributed to the differences in EPA and DHA concentration, but in period 2, the effects could also potentially be a result of the different fatty acid composition, in particular of the N-6 fatty acid content which was higher in the $0.2\%$ diet and lowest in the $1.7\%$ diet [4]. The fatty acid concentration of the fillet reflected that of the diet, but the fatty acid concentration at slaughter was still significantly affected by diet EPA and DHA content in Period 1 from 50 to 400 g. Although there was no longer a significant effect of diet in period 1 on fillet astaxanthin at slaughter, a stronger positive correlation between fillet astaxanthin and EPA and DHA content was found at slaughter than the negative correlation found between astaxanthin and N-6 fatty acids in the fillet at slaughter. Some other studies support the findings in the present study. In a study on Atlantic salmon, Waagbø et al. [ 17] replaced $64\%$ of the dietary fish oil with vegetable oil and reduced the fishmeal content in a full-scale seawater production. Reduced growth and feed efficiency were observed with decreasing fishmeal inclusion levels. When the low marine diets were boosted with a South American omega-3 fish oil three months prior to slaughter, fillet color and astaxanthin content improved significantly. The average astaxanthin concentration was 5.0 mg kg−1 in Norwegian Quality Cut (NQC) samples from all fish groups taken after one year of feeding, with no difference between the dietary groups. After an additional 5 months, the average astaxanthin content in NQC samples was still 5.5 mg kg−1 for fish groups fed the control low-marine diets, but 6.2 mg kg−1 in the fish groups fed the omega-3- boosting diets. However, there were no significant differences between diets in visual color measured by SalmoFan and Minolta. Some earlier studies have also shown positive effects of high-PUFA oils on flesh pigmentation. Atlantic salmon fed diets with high Peruvian PUFA oil deposited $13\%$ more carotenoids in the fillet than fish fed diets supplemented with herring oil, and a positive linear relationship was found between final fillet idoxanthin concentration and total saturated fatty acids in supplement oils [16]. Fillets of Atlantic salmon fed diets containing $29\%$ Peruvian, high-PUFA fish oil contained $41\%$ more carotenoids (canthaxanthin and astaxanthin combined) than fillets of salmon fed a diet with $29\%$ soybean oil ([37, 38]). Since most of the studies that have examined the effect of diet fatty acid composition on fillet color have replaced fish oils or fish meal with different plant oils and protein sources, it is not possible to say if the effect on pigmentation is due to a higher content of PUFA in the diet or a lower content of N-6 fatty acids. The N-6 fatty acids are known to promote the formation of pro-inflammatory and pro-aggregatory eicosanoids, whereas the N-3 fatty acids have the opposite effects. Carotenoids such as astaxanthin are potent antioxidants due to their chemical structure with a long carbon chain of unconjugated double bounds quenching singlet oxygen and free radicals [21, 39]. Higher oxidative stress levels in the salmon could thus result in more astaxanthin being oxidized in the salmon and lead to reduced astaxanthin levels in the muscle. Low EPA+DHA levels also increased fat deposition in the liver, intestine, and viscera and caused mid-intestinal hyper-vacuolization in the 0.2 and $1\%$ EPA+DHA groups [4]. There were also indications of a higher oxidative stress level in the intestine of salmon fed the $1.0\%$ diet. The transcription factor nrf2 (NF-E2-related factor 2) is a potent transcriptional activator and plays a central role in inducible expression of many cytoprotective genes in response to oxidative stress [40, 41]. The expression of nrf-2 in the intestine was higher in fish fed $1.0\%$ EPA+DHA than in fish fed $0.2\%$ EPA+DHA, while in fish fed, the control diet had intermediate expression. Salmon fed the diet with a $1\%$ of EPA+DHA also had the highest expression of cyclooxygenase 2 (cox2), a gene involved in the synthesis of proinflammatory eicosanoids. The nuclear factor-kappa B (NF-κB) is a transcription factor that plays a key role in the control of genes involved in inflammation, cell proliferation, and apoptosis and is activated in response to inflammatory stimuli and environmental stressors [42]. Reduced expression of nf-κb was found in the intestine of salmon fed diets with 0.2 and $1.0\%$ EPA+DHA compared to the control diet with $1.7\%$ EPA and DHA which could suggest less response in these groups. However, the regulation of expression nf-κb in mammals is shown to be quite complex and also mediated through feed-back regulation [43]. Little information is available on the regulation of this pathway in salmon, but a negative feedback mechanism could explain lower expression of this gene in salmon fed diets with lower levels of EPA and DHA. Carotenoids are transformed into diverse metabolites and cleavage products that may have important biological functions including regulation of expression of genes involved in cell metabolism and defense against oxidative damage [20, 25]. In mammals, carotenoids have been shown to lower blood concentrations of inflammation markers and pro-inflammatory cytokines, improve insulin sensitivity, and reduce obesity [44, 45]. Many of these biological effects of carotenoids in mammals are linked to the activity of carotenoid oxygenases that cleaves β-carotene as the first step in retinol synthesis [26]. Two distinct forms of this enzyme are found in mammals: beta-carotene 15,15′-monooxygenase (BCMO1), and beta-carotene-9,10-dioxygenase 2 (BCDO2). The Atlantic salmon beta-carotene oxygenase gene family contains 5 members, three bco2, and two bcmo1 paralogs (bcmo1 and bcmo1 like) that have tissue-specific expression, with the highest expression in the liver and intestine and lowest in the muscle [46]. The regulation of the carotenoid oxygenases has been studied in mammals, but little is so far known for fish. The presence of a PPARγ response element in the upstream of BCMO1 promoter, may explain the connection between carotenoid and lipid metabolism in mammals [47], but if this is also the case in salmon is not known. Dietary factors, including type and amount of dietary fat have been shown to influence the activity of BCMO1 in mammals [28]. Unsaturated N-3 fatty acids were shown to enhance β,β-carotene 15,15′-dioxygenase activity in rat intestine [27, 48]. However, in the present study, there were not found any clear effects of dietary content of N-3 fatty acids on gene expression of carotenoid oxidases in the intestine. Overall, the $1\%$ diet shoved the highest expression of all three genes, but the differences were not statistically significant due to the large variation between individuals. ## 5. Conclusions Low levels of EPA and DHA in the diet were negative for salmon flesh pigmentation, both in a tank environment and in seawater pens. The fillet concentration in all treatments was quite low for a salmon of 3.5 kg. The fish in this trial had several delousing operations during the seawater period that led to elevated mortality when the water temperature was high. There is currently little information on how these essential nutrients interact and how stress may affect the dietary requirements necessary for optimal health and quality of the salmon. Handling in connection with delousing have become more frequent in commercial salmon farming in recent years, and it has also become more challenging to obtain the sufficient flesh color. Although not providing a casual explanation, the present study shows a connection between low dietary EPA and DHA levels and reduced flesh color in salmon. ## Data Availability The data used to support the findings of this study are included within the article. ## Ethical Approval The feeding trial followed the Norwegian guidelines for research animals and was approved by the Norwegian Food Safety Authority. ## Conflicts of Interest There are no conflicts of interests for this manuscript. ## Authors' Contributions The design of the trial was performed by BR and GMB. Writing and statistical analysis of data were performed by TY, MB, and BR. Analysis of gene expression was performed by TKØ and MB. Measurements of visual flesh color were worked by CD. Analysis of fatty acids was in charged by MB and CD. All the authors have read and approved the final version of the manuscript. ## References 1. Aas T. S., Åsgård T., Ytrestøyl T.. 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--- title: 'Women’s experiences and views on early breastfeeding during the COVID-19 pandemic in Norway: quantitative and qualitative findings from the IMAgiNE EURO study' authors: - Eline Skirnisdottir Vik - Sigrun Kongslien - Ingvild Hersoug Nedberg - Ilaria Mariani - Emanuelle Pessa Valente - Benedetta Covi - Marzia Lazzerini journal: International Breastfeeding Journal year: 2023 pmcid: PMC9998246 doi: 10.1186/s13006-023-00553-5 license: CC BY 4.0 --- # Women’s experiences and views on early breastfeeding during the COVID-19 pandemic in Norway: quantitative and qualitative findings from the IMAgiNE EURO study ## Abstract ### Background Little is known about women’s experience of care and views on early breastfeeding during the COVID-19 pandemic in Norway. ### Methods Women ($$n = 2922$$) who gave birth in a facility in Norway between March 2020 and June 2021 were invited to answer an online questionnaire based on World Health Organization (WHO) Standard-based quality measures, exploring their experiences of care and views on early breastfeeding during the COVID-19 pandemic. To examine associations between year of birth [2020, 2021] and early breastfeeding-related factors, we estimated odds ratios (ORs) with $95\%$ confidence intervals (CIs) using multiple logistic regression. Qualitative data were analysed using Systematic Text Condensation. ### Results Compared to the first year of the pandemic [2020], women who gave birth in 2021 reported higher odds of experiencing adequate breastfeeding support (adjOR 1.79; $95\%$ CI 1.35, 2.38), immediate attention from healthcare providers when needed (adjOR 1.89; $95\%$ CI 1.49, 2.39), clear communication from healthcare providers (adjOR 1.76; $95\%$ CI 1.39, 2.22), being allowed companion of choice (adjOR 1.47; $95\%$ CI 1.21, 1.79), adequate visiting hours for partner (adjOR 1.35; $95\%$ CI 1.09, 1.68), adequate number of healthcare providers (adjOR 1.24; $95\%$ CI 1.02, 1.52), and adequate professionalism of the healthcare providers (adjOR 1.65; $95\%$ CI 1.32, 2.08). Compared to 2020, in 2021 we found no difference in skin-to-skin contact, early breastfeeding, exclusive breastfeeding at discharge, adequate number of women per room, or women’s satisfaction. In their comments, women described understaffed postnatal wards, early discharge and highlighted the importance of breastfeeding support, and concerns about long-term consequences such as postpartum depression. ### Conclusions In the second year of the pandemic, WHO Standard-based quality measures related to breastfeeding improved for women giving birth in Norway compared to the first year of the pandemic. Women’s general satisfaction with care during COVID-19 did however not improve significantly from 2020 to 2021. Compared to pre-pandemic data, our findings suggest an initial decrease in exclusive breastfeeding at discharge during the COVID-19 pandemic in Norway with little difference comparing 2020 versus 2021. Our findings should alert researchers, policy makers and clinicians in postnatal care services to improve future practices. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13006-023-00553-5. ## Background The COVID-19 pandemic has affected maternal and perinatal healthcare worldwide [1, 2]. New and continuously shifting government regulations had a large impact on the organization of hospital care, including restrictions that may have affected breastfeeding [3, 4]. Both the World Health Organization (WHO) and the International Confederation of Midwives (ICM) called the attention of the international community to women’s and newborn’s rights during the COVID-19 pandemic [5, 6]. However, a reduction in exclusive breastfeeding initiation during the first wave of the pandemic was observed [3, 7]. Further, European hospitals have differed in their interpretation of guidance around breastfeeding policies during the pandemic, with some advising formula feeding although no national or international guidelines recommended discontinuation of breastfeeding [8]. Breastfed children are less likely to die of infections [9], less likely to become overweight [10] and are less prone to diabetes and allergy later in life [11], while their intelligence quotients can be higher [12, 13]. Women who breastfeed have a reduced risk of a range of physical and emotional health problems, such as ovarian and breast cancers, postpartum depression and maternal excess of stress [14]. The multiple benefits of breastfeeding likely outweigh possible risk factors related to SARS-CoV-2 infection and should therefore be encouraged [15, 16]. In addition, while SARS-CoV-2 transmission from mother to baby via breastmilk seems to be unlikely, milk produced by infected mothers has been found to be a source of anti-SARS-CoV-2 IgA and IgG valuable for the baby’s health [16]. This study is part of the IMAgiNE EURO project, a multi-country survey conducted in 18 countries in the WHO European Region to collect views of women on the quality of maternal and newborn healthcare during the COVID-19 pandemic [17]. Similar to what occurred in other European countries, in order to prevent the spread of COVID-19, since 12 March 2020 the Norwegian government introduced strict social distancing regulations with major impacts on society [18]. During the COVID-19 pandemic in Norway, an increased use of formula, a reduction in exclusive breastfeeding and reduction in the average length of breastfeeding were documented [4]. However, little is known about women’s experience of care and views on early breastfeeding during the COVID-19 pandemic in Norway. More evidence on this topic may be of interest for researchers, policy makers and clinicians and support further improvements in the quality of maternal and newborn care. The aim of this study was to investigate quality of care at facility level, and women’s experiences and views on early breastfeeding practices during different phases of the COVID-19 pandemic in Norway. ## Study design Details of the multi-country IMAgiNE EURO project are reported elsewhere [17, 19]. In the current mixed-method cross-sectional study, both quantitative and qualitative data were analysed to investigate women’ experiences and views on early breastfeeding during the pandemic. To strengthen the reporting of quantitative data, a STROBE (Strengthening the reporting of observational studies in epidemiology) checklist was used (see Additional File 1). Likewise, we used the COREQ Checklist (COnsolidated criteria for REporting Qualitative research Checklist) in reporting our qualitative data (see Additional File 2). ## Setting In Norway, approximately 53,000 babies are born each year [20]. Maternity care is part of the public healthcare system and is built on the principle of free and equal access for all regardless of factors such as ethnicity or social background [21, 22]. Prior to the COVID-19 pandemic, Norway reported one of the highest rates of exclusive breastfeeding at 6 months of age in Europe [23]. Breastfeeding is promoted in a national action plan launched in 2017 for a healthier diet [24] and national surveys published in 2020 showed that $78\%$ of babies in Norway were breastfed at 6 months [25] and $48\%$ at 12 months [26]. By National law, parents in Norway are entitled to 49 weeks paid parental leave [27]. Additionally, a nursing mother returning to work is entitled to 30 minutes time off, which may be taken twice daily or as a reduction in working hours by up to 1 hour per day, to promote breastfeeding [28]. During the COVID-19 pandemic, in Norway the risk of maternal hospitalization due to COVID-19 disease in pregnancy was low [29]. However, maternity wards changed their practices, companion of choice often encountered restrictions in participation of care [30], and women were on average discharged from hospital earlier than in preceding years [20]. Further, during the pandemic women who gave birth in Norway have described feelings of loneliness and isolation in relation to antenatal care, when arriving at the hospital for labor, in cases of induction of labor, and at the postnatal ward [30]. This could result in a lack of maternal support which is crucial for the establishment of exclusive breastfeeding, especially during the hospital stay in the early postpartum period [3]. Women remained isolated from their social network after discharge due to strict social distancing regulations [18]. While several restrictions remained in healthcare services in Norway in 2021, the first year of the pandemic [2020] was characterized by more uncertainties both for new families and healthcare workers. ## Participants Data were collected with a voluntary anonymous online survey, open to women ≥18 years-of-age who gave birth at a facility in the WHO European Region between 1 March 2020 and 30 June 2021 ($$n = 34$$,391). Data were cleaned as previously reported according to standard operating procedures [17]. Briefly, suspected duplicates and cases missing $20\%$ or more answers on the 40 key quality measures and five key socio-demographic variables (i.e., date of birth, age, education, parity, whether the women gave birth in the same country where she was born) were excluded. The current study includes responses given by women who gave birth in Norway in the same period ($$n = 2922$$). For the purposes of this study, twin or multiple births ($$n = 31$$), and infants admitted to the Neonatal Intensive Care Unit (NICU) or Special Care Baby Unit (SCBU) ($$n = 291$$) were excluded from the analysis. Further exclusion of cases can be seen in Fig. 1.Fig. 1Flowchart of the derivation of the study sample. Women who gave birth in Europe during the COVID-19 pandemic (March 2020 to July 2021) 1Percentage of missing data for each woman was calculated over mandatory questions ($$n = 45$$) ## Data collection Data were collected with an online questionnaire (see Additional File 3). The questionnaire was developed and validated by an international team of experts [17]. The questionnaire was built on WHO standards of improving quality of maternal and newborn care (QMNC) ($$n = 30$$) [31] and also included questions relevant due to the COVID-19 pandemic ($$n = 10$$). The wording on education levels was agreed among partners during a Delphi exercise including 26 experts from 11 countries of the WHO European Region [17, 19]. The last question in the questionnaire was an open-ended question with no word limit to collect input and suggestions from mothers. The open-ended question was phrased as follows: Do you have any suggestions to improve quality of care provided at the facility where you gave birth or to improve this questionnaire? The online survey was made available in 22 languages and actively promoted though social media (i.e., Facebook groups, such as pregnancy due date groups, migrant groups in Norway and a group for breastfeeding mothers) in Norway by researcher ESV. Project partners in the WHO European Region promoted the survey through social media, organizational websites, local networks and Non-Governmental Organizations by project partners [17]. Women participated in the study in their preferred language regardless of which country they gave birth in. Data were collected through an online survey using REDCap 8.5.21 (© 2021 Vanderbilt University). ## Data analysis Both quantitative and qualitative data were analysed. In the current study, the analysis of quantitative data included 13 out of the original 40 quality measures in the full survey. All variables ($$n = 3$$) explicitly describing breastfeeding and infant feeding were based on WHO’s standards for improving QMNC in health facilities [31]. In addition, qualitative data were extracted from the final open-ended question collecting women’s suggestions on how to improve quality of care. Both quantitative and qualitative variables are described in the following text. ## Analysis of quantitative data We used descriptive statistics to summarize quantitative data, reported as frequencies and percentages. Differences of sample characteristics by year of birth [2020, 2021] were tested with a Chi-square test or a Fisher exact test. To examine associations between year of birth and early breastfeeding-related factors ($$n = 13$$), we estimated crude and adjusted odds ratios (adjORs) with $95\%$ confidence intervals (CIs) using logistic regression. The main exposure was year of birth [2020, 2021]. Outcome variables were dichotomized variables related to early breastfeeding and included the following: opportunity to have skin-to-skin within the first hour after birth (yes, no); early breastfeeding within the first hour after giving birth when applicable (yes, no); adequate breastfeeding support (yes, no); exclusive breastfeeding at discharge from hospital (yes, no); immediate attention by healthcare providers when needed (yes, no); clear communication from healthcare providers (yes, no); allowed companion of choice (yes, no); adequate number of women per room (yes, no); adequate visiting hours for companion of choice (yes, no); adequate number of healthcare providers (yes, no); adequate professionalism of the healthcare providers (yes, no); reduction in QMNC due to COVID-19 (yes, no); and reduction in their general satisfaction due to COVID-19 pandemic (yes, no). When the option “partially” was available, such answers were categorized as “no”. Other variables included in the model were: women born in Norway (yes, no, missing); answered the survey in other language than Norwegian (yes, no); age range (18–24, 25–30, 31–35, 36–39, ≥ 40); educational level (None, Elementary school, Junior High school, High School, University degree, Postgraduate degree / Master / Doctorate or higher); parity (1, > 1); birth mode (vaginal spontaneous, instrumental vaginal birth, Cesarean section). A two-tailed P-value < 0.05 was considered statistically significant. Statistical analyses were performed using Stata version 14 (Stata Corporation, College Station, TX, USA) and R version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria). ## Analysis of qualitative data Systematic Text Condensation (STC) was used to analyse data related to breastfeeding from the open-ended question. Comments including variations on the words breastfeeding and milk / formula, suckle / latch, and / or breast were included in the qualitative analysis. STC was conducted using the following four step method: 1) We became familiar with the data and identified preliminary themes (i.e., partner, information, understaffed ward, consequences of poor breastfeeding support, positive descriptions of breastfeeding support, and poor competence); 2) Meaning units describing women’s experiences and views on early experiences with breastfeeding during the COVID-19 pandemic in Norway were sorted into code groups (i.e., postnatal wards, partner’s role, and consequences of poor breastfeeding support); 3) Code groups were sorted into subgroups, and meaning units in each subgroup were summarized and condensed; and 4) The condensates from Step 3 formed the basis for our final analytic text [32]. In line with the method and guidelines for reporting qualitative research, we used direct quotations to elucidate the findings [32]. Initially, qualitative data were analysed by one researcher (ESV) using NVivo (NVivo Release 1.6.1 [4830], 1999-2022 QSR International). In Step 3, preliminary results were discussed with two more researcher, SK and IHN, who both had access to the raw data. ESV, SK and IHN read and agreed on the final analytic text. ## Results The study sample included answers from 2922 births, 2425 ($83.0\%$) in 2020 and 497 ($17.0\%$) in 2021 were included (Fig. 1). Characteristics of the study sample are shown in Table 1. Both in 2020 and 2021, few women had an immigrant background ($$n = 204$$) or answered the survey in a different language than Norwegian ($$n = 19$$). In both 2020 and 2021, most women were 25 to 35 years old; nearly four out of five had either a university or postgraduate degree; the number of instrumental births was similar ($12.4\%$ vs. $11.5\%$). When compared to 2021, the numbers of first-time mothers were statistically significant higher in 2020 ($58.4\%$ vs. $53.1\%$, $$P \leq 0.030$$); Cesarean sections were slightly but not statistically significant more frequent ($14.3\%$ vs. $11.3\%$); and the number of women born outside Norway and vaginal spontaneous births were slightly but not statistically significantly lower ($7.2\%$ vs. 5.8 and $73.3\%$ vs. $77.3\%$, respectively).Table 1Characteristics of the study sampleYear of birth2020 $$n = 24252021$$ $$n = 497$$ P-value Women born in Norway Yes2249 (92.7)468 (94.2)0.258 No175 (7.2)29 (5.8)0.271 Missing1 (0.0)0 (0.0)> 0.99 Answered the survey in a language other than Norwegian Yes17 (0.7)2 (0.4)0.654 No2408 (99.3)495 (99.6)0.654 Age range 18–24147 (6.1)32 (6.4)0.750 25–301059 (43.7)237 (47.7)0.101 31–35910 (37.5)184 (37.0)0.833 36–39249 (10.3)37 (7.4)0.054 ≥ 4060 (2.5)7 (1.4)0.148Educational levela None1 (0.0)0 (0.0)> 0.99 Elementary school0 (0.0)0 (0.0)– Junior High school37 (1.5)7 (1.4)0.845 High School498 (20.5)101 (20.3)0.914 University degree1244 (51.3)259 (52.1)0.741 Postgraduate degree / Master / Doctorate or higher645 (26.6)130 (26.2)0.839 Parity 11416 (58.4)264 (53.1)0.030 > 11009 (41.6)233 (46.9)0.030 Birth mode Vaginal spontaneous1778 (73.3)384 (77.3)0.068 Instrumental vaginal birth301 (12.4)57 (11.5)0.559 Cesarean section346 (14.3)56 (11.3)0.077 aWording on education levels was agreed among partners during the Delphi exercise Associations between year of birth [2020, 2021] and factors related to early breastfeeding ($$n = 13$$ factors) are shown in Table 2. When compared to the first year of the pandemic [2020], women who gave birth in 2021 reported higher odds of adequate breastfeeding support (adjOR 1.79; $95\%$ CI 1.35, 2.38); immediate attention when needed (adjOR 1.89; $95\%$ CI 1.49, 2.39); clear communication from healthcare providers (adjOR 1.76; $95\%$ CI 1.39, 2.22); allowed companion of choice (adjOR 1.47; $95\%$ CI 1.21, 1.79); adequate visiting hours for partner and / or relatives (adjOR 1.35; $95\%$ CI 1.09, 1.68); adequate number of healthcare providers (adjOR 1.24; $95\%$ CI 1.02, 1.52); and adequate professionalism of the healthcare providers (adjOR 1.65; $95\%$ CI 1.32, 2.08). When compared to the first year of the pandemic [2020], women who gave birth in 2021 reported lower odds of reduction in QMNC due to COVID-19 pandemic (adjOR 0.73; $95\%$ CI 0.60, 0.89). We found no difference by year of birth; in having skin-to-skin contact with the baby in the first hour after giving birth; early breastfeeding; exclusive breastfeeding at discharge; adequate number of women per room; or reduction in their general satisfaction due to COVID-19.Table 2Results of the logistic regression analysisa Factors related to early breastfeedingn cases (yes)n women (total)Crude OR ($95\%$ CI)Adjusted ORb ($95\%$ CI) Opportunity to have skin-to-skin contact in the first hour after giving birth (when applicable, i.e., in absence of maternal or neonatal health problems) 2020214822701.001.00 20214604801.31 (0.81, 2.12)1.07 (0.59, 1.92) Early breastfeeding (when applicable, i.e., in absence of maternal or neonatal health problems) 2020196822331.001.00 20214304691.48 (1.04, 2.11)1.32 (0.88, 1.96) Adequate breastfeeding support 2020190224251.001.00 20214334971.86 (1.40, 2.46)1.79 (1.35, 2.38) Exclusive breastfeeding at discharge 2020186824251.001.00 20214024971.26 (0.99, 1.61)1.14 (0.89, 1.46) Immediate attention when needed 2020163624251.001.00 20213974971.91 (1.51, 2.42)1.89 (1.49, 2.39) Clear communication from healthcare providers 2020165724251.001.00 20213954971.79 (1.42, 2.27)1.76 (1.39, 2.22) Allowed companion of choice 202096224251.001.00 20212464971.49 (1.23, 1.71)1.47 (1.21, 1.79) Adequate number of women per room 2020181224251.001.00 20213564970.85 (0.69, 1.06)0.84 (0.67, 1.04) Adequate visiting hours for partner and / or relatives 202055224251.001.00 20211414971.34 (1.08, 1.67)1.35 (1.09, 1.68) Adequate number of healthcare providers 202083024251.001.00 20211984971.27 (1.04, 1.55)1.24 (1.02, 1.52) Adequate professionalism of the healthcare providers 2020159424251.001.00 20213804971.69 (1.35, 2.12)1.65 (1.32, 2.08) Reduction in the QMNC due to COVID-19 2020163124251.001.00 20212944970.71 (0.58, 0.86)0.73 (0.60, 0.89) Reduction in their general satisfaction due to COVID-19 (among women who reported a reduction in the QMNC due to COVID-19) 2020144216311.001.00 20212642941.15 (0.77, 1.73)1.13 (0.75, 1.70) aOR are calculated with 2020 year of birth as reference category bAnalyses were adjusted for the following variables: born in Norway (yes, no, missing), responded to a non-Norwegian survey (yes, no), age range (18–24, 25–30, 31–35, 36–39, ≥ 40), educational level (None, Elementary school, Junior high school, High school, University degree, Postgraduate degree / Master / Doctorate or higher), parity (1, > 1), birth mode (Vaginal spontaneous, Instrumental vaginal birth, Cesarean section). Wording on education levels agreed among partners during the Delphi exerciseAbbreviations: COVID-19 Corona virus disease of 2019, QMNC Quality of maternal and newborn care ## Findings on qualitative data Of the 2922 women who responded, 1021 ($34.9\%$) provided a free-text comment. Of the 1021 free-text comments, 88 comments ($8.6\%$) were directly related to breastfeeding. There were eight non-Norwegian language comments (i.e., four comments in English, three in Swedish and one in German; none of which were related to breastfeeding). The following themes were identified during analysis of the open-ended question: 1) Understaffed postnatal wards, 2) Early discharge and a lack of professional support, 3) The importance of breastfeeding support from companion of choice, and 4) Long term consequences. ## Understaffed postnatal wards Women in the study described understaffed postnatal wards. Positive characterizations of healthcare providers included words such as being nice and skilled, often followed by descriptions of how the wards were understaffed and unavailable to the women. One woman who pointed out that understaffed postnatal wards may be a common problem, put it like this: “The breastfeeding guidance was extremely poor and divergent... A large part of the staff was nice, but it was very clear that they were understaffed. For all I know, understaffing [of postnatal wards] is a common problem.” ( Norwegian woman No. 1746). ## Early discharge and a lack of professional support Among negative narratives, several mentioned that they would have liked to stay for longer at the postnatal ward, to learn how to breastfeed and feel safe before being discharged. Some related early discharge due to COVID-19 restrictions and explained how they had to be discharged early to lessen the risk of spreading the virus. Others disclosed voluntary early discharge due to COVID-19 restrictions or a lack of help at the hospital. Most birth narratives contained positive language, while descriptions of the immediate postpartum period tended to be more negative in nature. Women in the study highlighted a need for improved breastfeeding support and continuity of care. Most women described intentions to breastfeed as a means of providing optimal nutrition and to bond with the newborn. Some women described being advised to watch videos or use the internet for breastfeeding support, while others described healthcare providers as stressed or heavy-handed when asking for breastfeeding support. Women reported feeling like a burden, being forgotten, or feeling like bad mothers. Some described how they felt pushed to breastfeed by healthcare providers and not being offered help if they for different reasons preferred to abstain from breastfeeding. Others described how healthcare providers fed the baby with formula without information or consent. Some women questioned the healthcare providers competence in breastfeeding and the quality of handovers between hospital shifts. One woman who initially planned to breastfeed, who ended up not breastfeeding, described it like this: “The staff often said, ‘I’ll get someone to help you’, but it never happened. It happened several times that I had to wait more than half an hour to get help. With a screaming newborn that I was unable to breastfeed, it felt absolutely horrific. The staff would come, but then had to run again halfway through a sentence.” ( Norwegian woman No. 552). ## The importance of breastfeeding support from companion of choice While some women described how partners were allowed to stay at the postnatal ward, other women described how their companion of choice was not allowed to visit her and baby at the postnatal ward due to COVID-19 restrictions. Some women discharged themselves early to be reunited with their partner. Having a partner present during the postnatal period was identified as a supportive factor for breastfeeding. This was reported to have positive impacts on the emotional well-being of the mother, as well as providing practical assistance and reducing the workload on healthcare providers. One woman who preferred her partner to stay at the postnatal ward put it like this: “It was a great burden that my partner was not allowed to be there. Having children and breastfeeding is a family project, and not something I had volunteered to do alone.” ( Norwegian woman No. 3135). Others were pleased with the calm atmosphere that followed the strict visitor restrictions, including restrictions related to the partner. A calm atmosphere was described as positive for breastfeeding. ## Long term consequences Women expressed how family centered care was lacking, with possible negative consequences for breastfeeding, bonding, and becoming a family. Further, fearing postpartum depression was mentioned by several, and some stated that their experiences made them fear having more children in the future. One woman who believes she suffers from a postpartum depression put it in the following way: “... I experienced the first days of my daughter’s life as very traumatic. Due to COVID-19 restrictions, we also did not receive sufficient help and guidance (including breastfeeding support). Due to the lack of help, I have struggled with what I believe is a postpartum depression.” ( Norwegian woman No. 506). ## Discussion When compared to women who gave birth in Norway during the first year of the COVID-19 pandemic, women who gave birth the following year were more likely to experience adequate breastfeeding support; immediate attention when needed; clear communication from healthcare providers; being allowed a companion of choice; adequate visiting hours for partner and / or relatives; adequate number of healthcare providers; and adequate professionalism of the healthcare providers. Compared to 2020, in 2021, the women also experienced lower odds of reduction in QMNC due to COVID-19 pandemic. When comparing factors related to breastfeeding in 2020 vs. 2021, we found no difference in the opportunity to have skin-to-skin contact with the baby in the first hour after giving birth; early breastfeeding; exclusive breastfeeding at discharge; adequate number of women per room; or reduction in their general satisfaction due to COVID-19. In comments related to breastfeeding (2020 and 2021), women described understaffed postnatal wards; early discharge and highlighted the importance of breastfeeding support from healthcare providers and companion of choice; and concerns about long-term consequences such as postpartum depression. Many factors contribute to successful breastfeeding in the early postpartum period, and we found a range of breastfeeding-related factors were improved for women giving birth in Norway in 2021, compared to the first year of the COVID-19 pandemic [2020]. One UK study from the COVID-19 pandemic reported that face-to-face breastfeeding support was reduced during the pandemic and some women struggled to get breastfeeding support, while others found strict regulations positive because of increased time at home, less pressure and fewer visitors [33]. In the current study, answers to the open-ended question revealed some women were pleased with the calm atmosphere that followed the strict visitor restrictions. Our findings are in line with the UK study and suggest that the COVID-19 pandemic affected women’s breastfeeding experiences differently. When comparing data from 2020 with data from 2021, we found little difference in early skin-to-skin contact, early breastfeeding within the first hour after giving birth or exclusive breastfeeding at discharge. Women’s satisfaction with the number of women per room stayed constant over the study period and women’s general satisfaction with care due to COVID-19 did not improve significantly from 2020 to 2021. One Italian study including 204 mothers and babies in the early stage of the COVID-19 pandemic (9 March to 8 May 2020) found a decrease in exclusively breastfeeding in the studied population [3]. Consistent with our findings, one study including 821 women who gave birth in Norway in the spring of 2020 found great reliance on breast-milk substitutes, which may imply that fewer women in Norway were exclusively breastfeeding during the initial phase of the COVID-19 pandemic [4]. Findings of a quantitative study including 3642 women giving birth in Norway during the pandemic adds support that one in three women experienced being discharged early due to COVID-19 related factors [34]. In our data, one in four women ($23.2\%$) who underwent labor in Norway in the study period reported not exclusively breastfeeding at discharge [17]. In the current study, when comparing data from 2020 with data from 2021, we found no difference in early breastfeeding or exclusive breastfeeding at discharge. In 2020, a nation-wide Norwegian report showed that $97\%$ of babies born in Norway in 2018 were breastfed before postpartum discharge [25]. To our knowledge, updated national data on exclusive breastfeeding at discharge has not been published. However, early discharge may be one explanation for why fewer women exclusively breastfeed their babies early in the pandemic [4]. The lack of improvement in exclusive breastfeeding during the study period should alert policy makers in postnatal care services to implement specific quality improvement actions. To better understand the reasons for a lack of improvement in exclusive breastfeeding at discharge and no change in women’s general satisfaction with care due to COVID-19 from 2020 to 2021, future studies with other designs are needed. In comments related to breastfeeding, the open-ended question in our survey gave information on understaffed postnatal wards, the importance of breastfeeding support from healthcare professionals and companion of choice, and a concern for long term consequences, such as postpartum depression, due to insufficient breastfeeding support during the pandemic. Our findings related to understaffed postnatal wards and the importance of partner are supported by a qualitative study exploring women’s experiences with giving birth in Norway during the pandemic [30]. Further, studies from Norway and the UK support the concern for the occurrence of postpartum depression, as they found an increase in maternal depression and anxiety postpartum, during the COVID-19 pandemic [34, 35]. A Norwegian national report on parental experience of QMNC published before the COVID-19 pandemic [2018] found that new parents in Norway were well satisfied with the care given on labor wards, however, postnatal care scored lower than other areas [36]. Findings in the Norwegian report suggest that our results related to QMNC in Norway during the first year of the Covid-19 pandemic cannot be attributed to the pandemic alone and must therefore be interpreted with caution. The qualitative findings in the current study support the concerns arising from the quantitative data, such as those related to women experiencing inadequate breastfeeding support, lack of attention when needed, not being allowed companion of choice, or a low number of healthcare providers. ## Strengths and limitations It may be seen as a limitation to the study that changes in local and national COVID-19 regulations over time were not accounted for. Because we only included data from women who gave birth during the pandemic, comparison with pre-pandemic data must be made with caution. It may be seen as a strength that the study includes both quantitative and qualitative data (i.e., triangulation or mixed-methods), an approach which provides a more comprehensive picture of the results than either method could do alone [37]. The qualitative data is not suitable for quantification, and comparison between the free-text responses given in 2020 vs. 2021 is therefore not included. The study used standard procedures and indicators, and allowed for future rounds of data collection, and comparison over time and settings. Open-ended questions can provide crucial information that closed-ended questions cannot deliver [38, 39]. Women themselves chose whether to answer the open-ended question or not, thus these questions were not subject to systematic measurements [38, 39]. Therefore, we did not analyse the open-ended questions for 2020 and 2021 separately. Due to self-administration, open-ended questions may cause selection bias in those responding [39]; women who were satisfied with breastfeeding support may be less likely to provide comments related to breastfeeding. The results from the open-ended questions should therefore be interpreted with caution. We acknowledge that the online survey lacked important information on the sample, such as more information on maternal and newborn clinical characteristics which may be relevant for the interpretation of the results [17]. Caution is necessary when comparing the current study’s $7.0\%$ response rate for migrant women with national data indicating that $28.9\%$ of women who gave birth in Norway in 2020 and 2021 were born outside the country [20]. Women who experienced vaginal birth, planned or emergency Cesarean sections were all included in analysis, however, experiences related to early breastfeeding may differ between these groups due to several factors. Causality cannot be drawn from this cross-sectional study [40]. ## Recommendation for research and for policies Our study provides critical information for researchers, policy makers and clinicians on the need for continuous surveillance of national breastfeeding rates and for improving postnatal care services and breastfeeding support in Norway and similar settings. This study highlights the importance of promoting continuity of care and evidence-based interventions, such as inclusion of companion of choice in postnatal wards. To improve women’s general satisfaction with postpartum care, adequate staffing for breastfeeding support must be made available to all new mothers. ## Conclusions In the second year of the COVID-19 pandemic, several but not all breastfeeding-related factors improved for women giving birth in Norway compared to the first year of the pandemic. 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--- title: 'Effects of eating together online on autonomic nervous system functions: a randomized, open-label, controlled preliminary study among healthy volunteers' authors: - Hideaki Hasuo - Nahoko Kusaka - Mutsuo Sano - Kenji Kanbara - Tomoki Kitawaki - Hiroko Sakuma - Tomoya Sakazaki - Kohei Yoshida - Hisaharu Shizuma - Hideo Araki - Motoyuki Suzuki - Satoshi Nishiguchi - Masaki Shuzo - Gaku Masuda - Kei Shimonishi - Kazuaki Kondo - Hirotada Ueda - Yuichi Nakamura journal: BioPsychoSocial Medicine year: 2023 pmcid: PMC9998259 doi: 10.1186/s13030-023-00263-8 license: CC BY 4.0 --- # Effects of eating together online on autonomic nervous system functions: a randomized, open-label, controlled preliminary study among healthy volunteers ## Abstract ### Background Eating alone has been significantly associated with psychological distress. However, there is no research that evaluates the effects or relation of eating together online to autonomic nervous system functions. ### Methods This is a randomized, open-label, controlled, pilot study conducted among healthy volunteers. Participants were randomized into either an eating together online group or an eating-alone group. The effect of eating together on autonomic nervous functions was evaluated and compared with that of the control (eating alone). The primary endpoint was the change in the standard deviation of the normal-to-normal interval (SDNN) scores among heart rate variabilities (HRV) before and after eating. Physiological synchrony was investigated based on changes in the SDNN scores. ### Results A total of 31 women and 25 men (mean age, 36.6 [SD = 9.9] years) were included in the study. In the comparison between the aforementioned groups, two-way analysis of variance revealed interactions between time and group on SDNN scores. SDNN scores in the eating together online group increased in the first and second halves of eating time (F[1,216], $P \leq 0.001$ and F[1,216], $$P \leq 0.022$$). Moreover, high correlations were observed in the changes in each pair before and during the first half of eating time as well as before and during the second half of eating time ($r = 0.642$, $$P \leq 0.013$$ and $r = 0.579$, $$P \leq 0.030$$). These were statistically significantly higher than those in the eating-alone group ($$P \leq 0.005$$ and $$P \leq 0.040$$). ### Conclusions The experience of eating together online increased HRV during eating. Variations in pairs were correlated and may have induced physiological synchrony. ### Trial registration The University Hospital Medical Information Network Clinical Trials Registry, UMIN000045161. Registered September 1, 2021. https://center6.umin.ac.jp/cgi-open-bin/icdr/ctr_view.cgi?recptno=R000051592. ## Background Diet is important not only for nutritional and health aspects but also because it constitutes an essential part of daily social interactions [1]. Dietary environment can affect health from various biopsychosocial aspects. Solitary eating has been related to the development of depressive symptoms, increased mortality, and/or reduced diet quality and intake [2–5]. Recently, social isolation owing to the COVID-19 pandemic has resulted in increased solitary eating, which has been noted to be associated with psychological distress [6, 7]. Additionally, a related cohort study showed that a greater degree of unhappiness was associated with a greater proportion of eating alone [1]. Eating together has been reported to increase food intake and to improve taste through social associations with other people [8, 9]. The social facilitation of eating is defined as the promotion of an individual’s activities, such as an increase in food intake, by the presence of other people while eating [9]. Eating together is also influenced by social modeling [10], in which one’s eating behavior influences others and vice versa. As a factor affected by social interactions with other people, the feeling of relaxation enhanced by communication while eating together is important [8, 10]. Apparently, eating together increases subjective wellbeing and provides a sense of relaxation [11, 12]. In recent years, eating together online has become popular following the evolution of video conversation technologies. This phenomenon, termed digital commensality, can circumvent environmental constraints to increase the maintenance and enhancement of health from the biopsychosocial aspect of those who have been eating alone [13]. Moreover, it has been reported that eating together online can be perceived by participants as “just alone but together,” with increased food intake and reduced loneliness [14]. The results of this report suggest that eating together, even online, may stimulate social interactions. However, this has not been specifically demonstrated. Furthermore, no study has investigated the effects of eating together online on feelings of relaxation, energy or loneliness on autonomic nervous functions, which is an objective evaluation of relaxation. This study thus aimed to address these research gaps and hypothesized that eating together online affects autonomic nervous function by social interaction through a variety of factors, including feelings of relaxation, energy, and loneliness. It has been reported that relaxation, such as by hypnosis, or intense loneliness reduces heart rate variability (HRV) at rest; reduced autonomic function can be predicted by HRV [15, 16]. It has also been reported that hand gripping between a patient with cancer and their family caregiver positively affects each other’s HRV [17]. The association or interdependency of physiological activities between two people is referred to as physiological synchrony [18]. Quantitation using maximal cross-correlation or cross-correlation with local slopes has been reported to be effective for the assessment of physiological synchrony using HRV [19, 20]. However, no method has been established yet. We thus further hypothesized that people eating together online would favorably affect each other’s autonomic functions, which would further provoke physiological synchrony. ## Objective This study aims to evaluate among healthy volunteers the effect on HRV of eating together online in comparison with persons eating alone. ## Study design This is a randomized, open-label, controlled, preliminary study conducted among healthy volunteers who worked at Kansai Medical University in Osaka, Japan. The study was approved by the Medical Ethics Committee of Kansai Medical University (reference number: 2021167) and was performed in accordance with the Declaration of Helsinki (as revised in 2013). Written informed consent was obtained from all study participants before the commencement of the study procedure. The study was registered with the University Hospital Medical Information Network Clinical Trials Registry (approval number: UMIN000045161) on September 1, 2021. This study was conducted from January to April 2022. ## Study participants The study participants were healthy volunteers, defined as “normal” persons who had no significant medical conditions or histories and no difficulty in their daily lives. They were employees at Kansai Medical University who responded to our post on the volunteer recruitment bulletin board at the university. The exclusion criteria included [1] currently taking medication or seeking medical care and [2] having neurological or mental disorders such as cognitive dysfunction and being unable to communicate. Participants were excluded if they met the diagnostic criteria for neurological or mental disorders according to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders [21], confirmed by two psychosomatic physicians. ## Study procedures Figure 1 summarizes the study regimen. Participants were randomized into either an eating together online (pairing) group or a control (eating alone) group by a computer using the minimization method at a 1:1 ratio. Each participant was informed of their allocated group after randomization. The participants were not allowed to change their groups during the study period. Concurrently, the investigators participating in the study were also informed of their designated groups. The study staff in charge of the statistical analysis concealed the results of the randomization. The participants’ names were also kept anonymous. The data were collected in interview rooms by the clinicians responsible for the study. Each investigator interviewed and assessed the participants at the beginning of the study. Fig. 1Flowchart of the study procedures. UMACL, UWIST Mood Adjective Checklist; HRV, Heart rate variability The study participants completed a self-report questionnaire, the UWIST Mood Adjective Checklist (UMACL), at the beginning and end of the study period. Each study subject attached a special electrode pad connected to the HRV measurement device (myBeat WHS-1; Union Tool Co., Tokyo, Japan) to their chest. The participants ate a snack for 10 min and took breaks for 5 min each before and after eating. The 10 min eating time was divided into first and second halves. The data evaluated using the HRV were continuously recorded for 20 min. The HRV scores for each session were calculated based on the mean of the 5-min HRV record. We used HRV analysis software (Kubios HRV version 3.1; Kubios Oy, Kuopio, Finland), which is highly reliable for short-term recording [22]. The snacks were commercial cookies. The participants eating together online ate the snacks while looking at their partner on the computer screen, during which conversation was allowed. The participants in the eating-alone group ate the snack while looking at an offline black screen, during which conversation was not allowed; however, soliloquy was allowed. During the 20-min eating time, including snack time and the short breaks, participants remained in their allocated eating rooms. ## UWIST mood adjective checklist The Japanese version of the UMACL was used to evaluate the mood of comfort [23]. The original checklist, developed by Matthews et al., was created based on dimension theory, making it possible to assess arousal levels [24]. The scale has two subscales that can be used to evaluate energetic arousal (10 items; vigorous vs. tired: coefficient α = 0.79) and tense arousal (10 items; nervous vs. relaxed: coefficient α = 0.76) [25]. High energetic arousal represents active and happy, whereas low tense arousal represents calm and quiet. Participants were asked to respond on a 4-point Likert scale. In a previous study, the mean energetic arousal was 24.4 (standard deviation [SD]: 0.5) for males and 24.4 (SD: 0.4) for females, and the mean tense arousal was 18.5 (SD: 0.4) for males and 17.56 (SD: 0.3) for females [25]. ## Heart rate variability and standard deviation of the normal-to-normal interval HRV, the fluctuation of heartbeat intervals measured using an electrocardiogram, is used to evaluate autonomic nerve activities [26, 27]. HRV tends to be lower in a person with anxiety or depression. However, it is relative rather than absolute; therefore, it is not directly compared among individuals. Standard deviation of the normal- to- normal interval (SDNN) is the quantification of HRV to further compare it among individuals. Particularly, SDNN is the standard deviation of the R-R intervals of the heartbeat in a certain time duration and is obtained via time-domain analysis. SDNN was used to evaluate cardiovascular compatibility. SDNN includes all the different types of variations and represents total variability [28]. It assesses the flexibility of the autonomic nervous system and the balance of sympathetic and parasympathetic nervous systems, with an increase in SDNN reflecting the stability of these systems [16, 29]. The grand mean of SDNN scores among resting adults is 50 mseconds [29]. ## Endpoints The primary endpoint of this study was the change in the SDNN score before and during eating. The key secondary endpoints were the amount of cookie intake, change in UMACL score, and correlation coefficient of pairs in change in SDNN score. ## Sample size estimation This is a preliminary study conducted among healthy volunteers to evaluate the effects of eating together online on autonomic nervous system functions. To the best of our knowledge, no similarly designed studies have been conducted previously. Therefore, we recruited as many study volunteers as possible. ## Statistical analysis Continuous data are summarized as means with SD, and discrete data are presented as the number of subjects (n) and their frequencies (%), as appropriate. Pearson’s chi-square test was used to evaluate discrete data, including age, sex, and mutual relationships. An unpaired two-sided t-test was used to compare mean age. Changes in UMACL scores (before and after eating) were analyzed using one-way repeated-measures analysis of variance (ANOVA). To compare the change in the UMACL and SDNN scores between the two groups, two-way repeated measures ANOVA with fixed effects of time points and groups was used. Moreover, the variable effect of subjects was used to examine the time course changes of these scores. In ANOVA, multiple comparisons were corrected using Bonferroni’s method. Lastly, after calculating the correlation coefficients of pairs in the change in SDNN scores, we performed Fisher’s z-transformation on the correlation coefficients followed by the z-test statistic. The last UMACL and SDNN scores of participants who withdrew from the study before completion were used for analysis. A significance level of alpha < 0.05 was used for statistical analysis. Statistical analyses were conducted using SPSS version 18.0 J for Macintosh (SPSS Inc., Chicago, IL, USA). Only the z-test statistic was calculated based on web links and references without using SPSS [30, 31]. ## Clinical demographic characteristics A total of 56 healthy volunteers were randomized into either the eating together online group ($$n = 28$$) or the eating-alone group ($$n = 28$$) or 28 pairs ($100.0\%$) and completed the study. Table 1 presents the demographic and clinical characteristics of the participants. The mean age was 36.6 years (SD: 10.1), and 25 were male and 31 female. The mutual relationships of the participants included 47 work colleagues and nine friends. There were no group differences in age, sex, or mutual relationship. Table 1Demographic and mutual relationships of the study participantsEating together online groupEating-alone group($$n = 28$$)($$n = 28$$) P-valueAge (year), mean (SD)37.1(10.3)36.1(9.9)0.711Sex, n (%) Male11(39.3)14(50.0)0.296 Female17(60.7)14(50.0)Mutual relationships Work colleague23(82.1)24(85.7)0.500 Friend5(17.9)4(14.3) SD Standard deviation ## Changes in SDNN scores and between-group comparisons Figure 2 shows the changes in the SDNN scores between the pre-eating break and during eating or the break after eating. During the pre-eating break, the SDNN scores were 32.8 (SD: 10.8) and 32.2 (SD: 12.7) for the eating together online and eating-alone groups, respectively. The SDNN scores for the eating together online group were significantly higher during eating than during the pre-eating break, rather than thereafter (first half of eating; $P \leq 0.001$, second half of eating; $P \leq 0.001$, rest after eating; $$P \leq 0.071$$). The SDNN scores for the eating-alone group were higher during and after eating than during the pre-eating break. However, the difference was insignificant (first half of eating; $$P \leq 0.708$$, second half of eating; $$P \leq 0.093$$, rest after eating; $$P \leq 0.556$$).Fig. 2Changes in SDNN between the pre-eating break and during or break after eating. SDNN, standard deviation of the normal-to-normal interval A two-way ANOVA showed an interaction between time and group in the change in SDNN score (F[3,216], $$P \leq 0.037$$). The change in SDNN score of the eating together online group was significantly higher than that of the eating-alone group before and during the first half of eating, and before as well as during the second half of eating (F[1,216], $P \leq 0.001$ and F[1,216], $$P \leq 0.022$$). However, the difference did not differ for before and after the breaks (F[1,216)]; $$P \leq 0.287$$). ## Amount of cookie intake The amount of cookie intake was 4.2 (SD: 2.6) for the online eating together group and 3.5 (SD: 2.5) for the eating-alone group ($$P \leq 0.310$$). ## Changes and between-group comparisons of scores Table 2 shows the change in the UMACL scores and comparisons between the groups. The mean energetic arousal scores at the beginning of the study were 30.0 (SD: 1.2) for male and 31.3 (SD: 0.5) for female participants, while those for mean tense arousal were 26.9 (SD: 0.5) and 26.3 (SD: 0.3), respectively. The energetic arousal score was higher after eating in the eating together online group and differed between the groups ($$P \leq 0.036$$). The tense arousal score was lower after eating in both the groups, with no difference between the groups ($$P \leq 0.199$$).Table 2Changes and between-group comparisons of UMACL scoresEating together online groupEating-alone group P-valueBefore eatingAfter eating P-valueBefore eatingAfter eating P-valuemeanSDmeanSDmeanSDmeanSDEnergetic Arousal score30.53.732.43.40.00130.73.429.93.70.2840.036Tense Arousal score26.81.524.61.70.00126.71.225.91.50.0340.199 SD Standard deviation, UMACL UWIST Mood Adjective Checklist ## The correlation coefficients of pairs in changes in SDNN scores Figure 3 shows a plot of changes in the SDNN scores for each pair. Comparing the correlation coefficients of both groups revealed that the eating together online group had higher correlations of pairs in changes both before and during the first half of eating and before and during the second half of eating; these differed significantly compared with the eating-alone group ($$P \leq 0.005$$ and $$P \leq 0.040$$). Before the break and during the first half of eating in the eating together online group, 10 out of 14 pairs showed positive changes in the SDNN score. However, two pairs showed negative changes. Before the break and during the second half of eating in the eating together online group, 13 out of the 14 pairs showed positive changes in the SDNN score. However, one pair showed negative changes. Nevertheless, the two groups differed insignificantly before and after the breaks ($$P \leq 0.405$$).Fig. 3The correlation coefficients of pairs (r) in changes in SDNN scores. SDNN, standard deviation of the normal-to-normal interval ## Discussion To the best of our knowledge, this is the first study to examine the effects of eating together online on reciprocal HRV using objective parameters. The first important finding in this study is that eating together online significantly increased HRV during eating, compared with eating alone, implying that eating together online enhanced autonomic functions during eating [26, 27]. This is a favorable outcome for people who are not able to eat together, such as those who are admitted to facilities or hospitals or physically separated from their families. During the recent COVID-19 pandemic, hospitalized patients with cancer have been prohibited from receiving visitors. Consequently, more than half of them reported feeling lonely [32]; further encouragement of eating together online is desirable. The UMACL reported that eating together online resulted in significantly higher energetic arousal postprandial than pre-snacking, indicating that vigorous arousal may affect autonomic function. Tense arousal decreased in both groups, and there was no significant difference. The mean tense arousal score, a subscale of UMACL, in this study was significantly higher than that in a previous study [25], and it is possible that significant differences between before and after the study were difficult to obtain. SDNN assesses the flexibility of the autonomic nervous system and the balance of sympathetic and parasympathetic nervous systems [16, 29]; hence, it is likely to be affected by various factors such as energetic arousal, tense arousal, and loneliness. Another study reported that significant loneliness reduces HRV [16]. Therefore, our result regarding increased HRV following eating together online may indicate alleviated loneliness associated with the presence of other people. Apparently, autonomic signals, such as electrocardiogram and electrodermal activity, were synchronized with each other because the strangers were in the same space without direct communication [33]. Future studies using self-administered questionnaires of loneliness will be useful for investigating whether visual presence in the same space, even online, reduces loneliness. The second important finding of this study is that the pre- and intra-snack correlations of SDNN changes when eating as a pair online were statistically significantly stronger compared with those eating alone. This suggests that eating together online may positively affect HRV through social interaction with other people. Notably, during the second half of eating in the eating together online group, the SDNN changes were synchronized in all pairs, including one pair whose SDNN scores were both negative. This suggests that the physiological synchrony of eating together online may have increased as the diet progressed. Methods to assess physiological synchrony in eating together have not yet been established. Therefore, we implemented a new method to visually clarify that physiological synchrony of change in SDNN occurs in each pair only in the eating together online group. Although the design of our study does not identify the cause of this effect on social interactions, energetic arousal may have contributed to the outcome of the UMACL.Apparently, pairing with a person with a poor relationship is more likely to increase physiological synchrony as assessed by HRV than pairing with a friend [19]. The report stated that this may be because if relationships with others were poor, participants tried to increase affinity to establish social affiliation. We believe that increasing affinity may also increase energy arousal. More than $80\%$ of the participants in this study were work colleagues; however, poor relationships may have influenced social interactions and energetic arousal. Lastly, unlike previous studies [8, 9], there was no difference in ingestion between the two groups in our study. Previous studies state that the increased ingestion for eating together online groups is attributed to choosing and eating more types of food owing to social interactions with other people [8]. However, this study was one type of cookie only. This study has two limitations. The first is that in the eating-alone group, the element of conversation was missing in the assessment of its effects on autonomic nervous system functioning during eating. Future studies creating an eating together online group that does not talk would be able to reduce this bias. Second, the results of the study cannot be generalized because it was conducted among relatively young, healthy volunteers who were work colleagues employed by the same institution. Sociodemographic characteristics such as being prone to eating alone, male sex, older age, and unemployment are listed [1]. 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--- title: 'Motivating non-physician health workers to reduce the behavioral risk factors of non-communicable diseases in the community: a field trial study' authors: - Mehran Asadi-Aliabadi - Seyed M. Karimi - Fariba Mirbaha-Hashemi - Arash Tehrani-Banihashemi - Leila Janani - Ebrahim Babaee - Marzieh Nojomi - Maziar Moradi-Lakeh journal: Archives of Public Health year: 2023 pmcid: PMC9998263 doi: 10.1186/s13690-023-01047-w license: CC BY 4.0 --- # Motivating non-physician health workers to reduce the behavioral risk factors of non-communicable diseases in the community: a field trial study ## Abstract ### Background Non-communicable diseases behavioral risk factors can be improved if effective interventions are designed considering the health system’s capabilities and local resources. This study evaluated the effectiveness of interventions that aimed at increasing non-physician community health workers’ motivation in reducing non-communicable diseases behavioral risk factors in the community. ### Methods A randomized field trial study was conducted in 32 community health centers in 4 Iranian districts after a baseline population survey on the status of NCDs of 30–70-year-old individuals ($$n = 1225$$). The interventions were performed to improve insufficient physical activity, insufficient fruit consumption, insufficient vegetable consumption, high salt intake, and tobacco use. Four intervention packages were implemented in 24 community health centers; the other 8 centers were used as control groups. The non-physician community health workers performed the interventions. The packages additively included goal-setting, evidence-based education, operational planning, and incentive payments. A second survey was conducted 1 year after the start of the interventions to identify the effects on an independent random sample of 30–70-year-old individuals ($$n = 1221$$). Difference-in-difference method was used to quantify the interventions’ effects. ### Results The average age of participants in both surveys was about 49 years. Also, about half of the participants were female, and about $43\%$ were illiterate or had a primary school education. The interventions had statistically significant effects only on decreasing the prevalence of insufficient physical activity. The package with all the intervention components decreased the odds of insufficient physical activity to 0.24 ($95\%$ CI, 0.08, 0.72). The package with operational planning but no performance-based financing did not change the odds of insufficient physical activity. ### Conclusions This study highlighted the importance of components, design, and implementation details of interventions intended to reduce NCDs behavioral risk factors. Some risk factors, such as insufficient physical activity, seem more easily modifiable with limited low-cost interventions in a one-year horizon. However, risk factors related to healthy food consumption and tobacco use need more extensive interventions. ### Trial registration This trial was registered on the Iranian Registry of Clinical Trials (IRCT20081205001488N2) on 3 June 2018 (https://en.irct.ir/trial/774). ### Supplementary Information The online version contains supplementary material available at 10.1186/s13690-023-01047-w. ## Background Non-communicable diseases (NCDs) are the leading cause of death worldwide, as they accounted for $74\%$ of total deaths and $85\%$ of premature deaths in low- and middle-income countries (LMICs) in 2019 [1, 2]. In 2015, the United Nations (UN) acknowledged that the increase in the burden of NCDs is a major threat to its Sustainable Development Goals (SDGs) for the twenty-first century and targeted to reduce NDCs’ premature deaths to one-third by 2030 [3]. In Iran, NCD mortality increased from 49 to $82\%$ from 1990 to 2017 [4]. In 2017, the loss of 7.0 million years of life was attributed to NDCs, indicating a $98\%$ increase from 1990. Also, 15.0 million years were lived with disability due to NDCs, showing a $48\%$ increase from 1990 [5, 6]. Structural social changes such as rapid urbanization, globalization, and population aging have accelerated the prevalence of NCDs [7]. Changes in social structures are usually followed by lifestyle changes and increased prevalence of behavioral risk factors such as unhealthy diet, physical inactivity, tobacco smoking, and alcohol use [8]. Behavioral risk factors precede the development of metabolic risk factors (e.g., raised blood pressure, overweight/obesity, and increased blood cholesterol) and can then further develop into NCDs [9]. According to the World Health Organization (WHO), 1.6 million preventable deaths per year are caused by physical inactivity, 7.2 million by tobacco use, 4.1 million by excess salt/sodium intake, and 3.9 million by inadequate fruit and vegetable consumption [10, 11]. These estimates indicate the importance of behavioral risk factors as preventable contributors to the development of NCDs [10]. In 2019, $16.5\%$ of Iran’s deaths were attributed to an unhealthy diet, $14.1\%$ to tobacco use, $4.4\%$ to inadequate physical activity, and $1.2\%$ to excess salt intake. The corresponding attributable DALYs to the risk factors were 7.5, 9.2, 1.9, and $0.6\%$, respectively [1]. Therefore, effective investments in curtailing NCDs risk factors can save and improve lives and will be economically justifiable if at-risk people are identified in the early stages [12]. Many cost-effective intervention methods to control NCDs have been introduced worldwide. However, the main challenge is implementing them in LMICs, which typically face a limited skilled workforce, financial resources, and community participation [13, 14]. Efforts to control the increasing rate of NCDs in Iran intensified in 2014 by launching an adaptation of the WHO’s Package of Essential, IraPEN [15]. However, the mismatch of typical training provided to health care workers with the extent of NCDs in the community, insufficient documentation to determine the existing NCDs’ status, inconsistency between training and practice, and instability of financial resources have obstructed the successful implementation of the package [16, 17]. In this study, a field trial was devised and carried out to address the above shortcomings. A set of intervention packages were designed to encourage reducing NCDs behavioral risk factors and implemented by non-physician community health workers (NPHWs) in a group of randomly selected community health centers (CHCs)—locally called Health Houses—in four Iranian districts. The NPHWs were informed about the status of NCDs behavioral risk factors in their catchment areas and encouraged to meet a set of goals to reduce the risk factors. Then, they randomly received a combination of different interventions: evidence-based training, an action plan to reduce the risk factors based on the NCDs status in their catchment areas, and incentive payments based on their performance in achieving the goals. The trial was conducted to understand the effects of these interventions on NCDs risk factors in the studied population. ## Study sample Four districts (equivalent to counties) were selected for the field trial. One of the districts was the no-intervention district, and the other three were intervention districts. In each selected district, four urban and four rural CHCs were selected. In each of the 32 selected CHCs, a baseline survey was conducted on 30–70-year-old residents of its catchment area to understand the existing status of NCDs risk factors. The survey was administered from June to September 2018 using a Persian translation of an adapted WHO stepwise approach to surveillance (STEPS) questionnaires [18]. Then, four different intervention packages were randomly assigned to the selected urban or rural CHCs in the intervention districts. In any intervention district, one urban and one rural CHC received intervention package A, one urban and one rural CHC received intervention package B, and so on (Fig. 1). The intervention period was 12 months, after which the second survey was conducted on the same age population in the 32 CHCs to assess the impacts of the interventions from September to November 2019.Fig. 1CONSORT flow diagram. Note: Analysed based on population in first and second surveys. CHCs, Community Health Centers; UCHCs, Urban Community Health Centers; RCHCs, Rural Community Health Centers. a These three universities are located in three provinces of Tehran, Semnan, and Bushehr in Iran. One of the universities’ health care systems’ key tasks is providing primary health services to the covered population. b Shahriar, Dashtestan, and Damghan. c Garmsar For each survey, a random sample of the population in the catchment area of each studied CHC was drawn. The sample size for each of the surveys was set to be 320 in each district; it was stratified by urban/rural CHCs, sex and age groups (30–39, 40–49, 50–59 and 60–69 years); 4 urban and 4 rural CHCs from each district were selected (totally, 12 urban and 12 rural in the intervention districts, in addition to the 4 urban and 4 rural in the “no intervention” district). So, the planned total sample size for each round of survey was 1280 [(12 + 12 + 4 + 4) * 40]. If we look at this number based on the intervention packages, the sample size was 240 for each of the intervention packages 1 to 4, and 320 for the no-intervention group [(4* 240) + 320 = 1280]. The sample size of 240 for each intervention group was enough to find a one-third decrease in the prevalence of $34\%$ (based on a primary estimate for prevalence of physical inactivity) with an Alpha of 0.05 and a power of $80\%$. We used a higher sample size for the no-intervention group (320 versus 240) both to increase the power and to use a similar sampling protocole in all districts. As explained in the CONSORT flow diagram (Fig. 1), the final sample size in the 1st and 2nd surveys were 1226 and 1221 individuals, respectively. Since the cluster sample sizes were not proportional to the catchment areas’ population, sampling weights were used in all analyses. All interviews were in-person. If a household was not available at the first reach, it was contacted by the research team up to three times on three successive days to perform the interview. In each household, only one male and one female member from each of the following age group strata were interviewed: 30–39, 40–49, 50–59, and 60–70 years. If more than one male or female from an age group were living in a household, one of them was randomly selected and interviewed. A necessary inclusion criterion was informed consent by interviewees. While the sampling method was similar in both surveys, the selected participants were not necessarily the same. Although the survey participants were expected to be among the target groups of the interventions, they were not necessarily the ones who received the intervention directly. According to the country’s Integrated Health Record System—called the SIB system locally [19]—on average, $20\%$ of the catchment area population visit their local CHC in any given quarter. The country’s NPHWs’ reach, however, was expected to be much more than CHC’s direct utilization rate because, in addition to those who refer to them, they are responsible for improving the health of the entire population assigned to them. Based on how CHCs are structured and organized in the country, if individuals in a catchment area do not visit CHCs and demand health care, the NPHWs must reach out to them, encourage visits, or at least monitor their health remotely over the phone to make sure they are ceceiving health care somewhere else (such as the provate sector). This active follow-up method, especially in rural areas, has led to significant health improvements in the country, for example, remarkable decreases in maternal and childhood mortality and communicable diseases over several decades (Barzegar and Djazayeri 1981 [20], Rahbar and Ahmadi 2015 [21], and Keshvari et al. 2016 [22]). This study was built on the same infrastructures and intended to extend the NPHWs’ experiences regarding maternal and child health and communicable disease to non-communicable diseases. Based on the approved protocol, the trial was planned to continue for 24 months, with a third survey at the end of the study. However, it was terminated prematurely after 12 months to comply with the country’s COVID-19 social distancing protocols. The selected districts were Shahriar (population = 744,210), Dashtestan (population = 252,047), Damghan (population = 94,190), and Garmsar (population = 77,421) (Additional file 1). The districts’ populations are based on the country’s 2016 census [23]. A simple randomization method was used to select four urban and four rural CHCs in each district and to assign the intervention packages to the selected CHCs. Detailed explanations of the inclusion criteria for the districts, CHCs, and participants were explained in the protocol [24]. NPHWs implemented four intervention packages after receiving extensive training. Physicians were not the target group of this trial because they were undergoing a separate incentive payment scheme [25]. The CONSORT checklist can be found in Additional file 2 [26]. ## The interventions and intervention packages An intervention package in this study included the first, the first two, the first three, or all of the following interventions:The first intervention (target-setting): Short-term targets (e.g., decrease in tobacco use and salt consumption) were set based on the preliminary results obtained from the baseline survey and the national goals to control NCDs behavioral risk factors. The national goals were to reduce insufficient physical activity, insufficient fruit and vegetable, salt intake, and tobacco use by 20, 30, 30, and $30\%$, respectively, until 2025 [27, 28]. Specific quarterly and yearly targets for NCDs risk factors are reported in Additional file 3. Meetings were held with the NPHWs of the selected CHCs, and they were informed about the status of NCDs behavioral risk factors in their catchment area population and the national goals to reduce them. Also, the proposed targets were presented to them, and they were encouraged to work through achieving them. The research team did not go beyond providing information on the status of NCDs in the catchment areas and national NDC goals, setting the goals mentioned above, and encouraging the NPHWs to achieve them. The second intervention (evidence-based education): The research team set up a 16-hour workshop for the NPHWs’ of the CHCs that received this intervention. The workshops were merely informational, during which the adverse health effects of overconsumption of salt, underconsumption of fruit and vegetable, insufficient physical activity, and tobacco use were extensively discussed. The trainees were provided with the related informational brochures as well. In addition, the team used Disease Control Priorities, 3rd edition [28, 29], and the Iranian version of the WHO package of essential NCDs (PEN) [30] to prepare a review summary of the effectiveness of the interventions aimed at decreasing the risk of NCDs in LMICs. The review also included success stories in other countries and methods of selecting, planning, and implementing cost-effective interventions. For the review. The third intervention (operational planning): The research team coordinated with NPHWs and the local health experts to collaboratively devise operational plans for the selected CHCs in a 12-hour workshop. The major component of the NPHWs’ action plans was periodic (biweekly or monthly) educational sessions for the covered population on the causes and detrimental health effects of NDCs and practical methods to decrease the risk of NDCs (such as increasing physical activities, adjusting the diet, reading food products’ nutrition label, decreasing the consumption of salt, canned and fast food, and the use of tobacco products). Healthy lifestyle (especially in regards to movement and diet) and smoking reduction counseling was also offered to the CHC visitors. Other action plan items were organizing weekly public walking events, setting up group activities (such as painting, reading, and board games), and coordinating with government-owned sports facilities to provide free hours to the public (specifically, three two-hour sessions a week). The focus of the action plan was different from one CHC to another based on the finding of the baseline survey at the CHC’s catchment area. For example, if the body mass index was particularly high in a catchment area, more frequent educational sessions were set on the risk of obesity and the importance of physical activity, and more public walking events were organized by the NPHWs. To support the action plans’ execution, the team allocated a supportive budget for the devised operational plans. The maximum supportive budget was 60 million Rials—equivalent to 556 United States dollars, USD, based on the exchange rate of 107,832 Rial/USD at the time of study [31]. The budget could be spent on purchasing equipment for the CHC (for example, digital blood pressure sphygmomanometers, body weight scales, and height measuring devices) and materials for educational sessions, group sports, and non-sport events. The fourth intervention (performance-based financing or PBF): NPHWs of the selected CHCs received incentive payments. The payments were calculated at the CHC level, and paid to all NPHWs of that CHC per the pre-defined targets every 3 months. The average level of achievement of each center to its 3-month targets for eight different NCD behavioral and metabolic risk factors was quantified. The CHCs were then classified based on the percentage of their achievements into one of the following four groups: < 25, 25–49.99, 50–62.49, and $62.5\%$ or more. These groups, respectively, received no incentive, one-third, two-thirds, and full incentive. The full monthly incentive was $10\%$ of the average monthly salary of a typical NPHW in the studied districts, which was determined to be approximately 25 million Rial (or 232 USD). Therefore, the maximum monthly incentive payment was approximately 23 USD. No payment was delayed because they were made directly to the NPHWs’ bank account immediately after each assessment. Intervention package A included only the first intervention, goal setting. Intervention package B included the first two interventions: goal setting and evidence-based training. Intervention package C included the first two interventions plus an action plan. In intervention package D, PBF was added to other interventions (Table 1). CHCs that received the intervention packages A, B, C, and D were also called intervention groups A, B, C, and D, respectively, in this study. The no-intervention district (Garmsar district) received neither of the interventions. Table 1Assignment of interventions to CHCs inside each of the three treatment districtsIntervention PackageUrban/Rural SeparationIntervention:Target-SettingEvidence-Based EducationOperational PlanningPerformance-Based FinancingA1 Rural, 1 UrbanYesNoNoNoB1 Rural, 1 UrbanYesYesNoNoC1 Rural, 1 UrbanYesYesYesNoD1 Rural, 1 UrbanYesYesYesYes Every two to four weeks, the implementation status of the interventions was reviewed and checked by the district and province supervisors selected by the research team. Also, reports on the interventions were received by the research team every quarter. The reports contained performance reviews per the set goals. Moreover, they included detailed accounts of the activities conducted at the CHCs that received an action plan. The key components of the reports were activities’ dates and type of the activity and the number of participants. ## Statistical analysis This study’s objective was to compare NCDs behavioral risk factors before and after the interventions and identify effective interventions. NCDs behavioral risk factors analyzed in this study were zero-one indicators of insufficient physical activity, insufficient fruit consumption, insufficient vegetable consumption, high salt intake, and tobacco use. Not meeting the WHO recommendations on physical activity (Metabolic Equivalent of Task, MET, less than 600 METs per week) was defined as insufficient physical activity [32]. The WHO’s recommendations were used to determine insufficient fruit (less than two medium-sized fruits, such as two medium apples or half a cup of nuts, in the last 24 hours) and vegetable consumption (less than three cups of raw leafy vegetables or one and a half cups of cooked or chopped vegetables in the last 24 hours) [33–35]. A person was identified as a high salt consumer if the person always or often added salt or salt additives to the food [36]. Current tobacco smoking was defined as the use of any tobacco products, including cigarettes, cigars, or pipes, on a daily or non-daily basis in the last 30 days [37]. The prevalence of each NCDs risk factor in the baseline and second surveys was calculated in populations assigned with each intervention package. Then, the difference in the prevalence rates between the two surveys was calculated. For the more formal analysis of the effect of the designed intervention packages, the difference-in-difference (DID) design was employed. The following equation shows the linear specification of the DID design: 1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Y}_{ic t}=\alpha +\beta {IntPackage}_{ic}+\gamma {Post}_{it}+\rho \left({IntPackage}_{ic}\times {Post}_{it}\right)+\theta {CHC}_{it}+\delta {X}_{ic t}+{\varepsilon}_{ic t}$$\end{document}Yict=α+βIntPackageic+γPostit+ρIntPackageic×Postit+θCHCit+δXict+εictwhere i indicates a surveyed individual, c indicates the community health center to which the individual is affiliated, and t indicates the survey year. The dependent variable, Y, is a binary variable that indicates one of the NCDs behavioral risk factors for the individual. The variable IntPackage is a categorical variable with five values (0, 1, 2, 3, and 4), indicating the intervention package assigned to the CHC where the individual receives health services (Table 1). The value 0 was assigned to individuals surveyed in the no-intervention district. Therefore, four β s were estimated. Estimations of βA, βB, βC, and βD provide an adjusted comparison of the average level of dependent variable at the catchment areas that received intervention packages A, B, C, and D, respectively, to that in the non-intervention district. The variable Post indicates the survey year. It takes the value 0 if the individual was surveyed before the implementation of the interventions [2018] and 1 if surveyed after the trial [2019]. The variable CHC is a community health center indicator (1, 2, …, 32), as any surveyed individual is affiliated with a specific CHC. This variable accounts for the influence of all unobservable/unmeasurable CHC-specific confounders that might not change over the study period (e.g., health care resources in the community, attitudes towards using modern medicine versus traditional practices, average distance from the CHCs, and overall weather patterns). The variable X is a vector of socioeconomic factors including age, sex, marital status (in three categories: never married, married, divorced or widowed), the level of education (in four categories: illiterate or primary, secondary, high school, and some college), labor market status (in six categories: public wage and salary job, private wage and salary job, self-employed, homemaker, retired, and unemployed), health insurance status, and homeownership status. The coefficients of interest in this specification are ρs (i.e., ρA, ρB, ρC, and ρD) which show the effect of the intervention packages versus no intervention among those surveyed after the intervention. Given the binary nature of the outcome variables in this study, logistic models were used in fitting Eq. 1. Odds ratios were calculated, representing the change in the odds of the dependent variable being equal to 1 due to one unit change in either of the terms on the right-hand side of Eq. 1. Equation 2 is the logistic transformation of Eq. 1. 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathit{\ln}\left(\frac{p_{Y_{ic t}}}{1-{p}_{Y_{ic t}}}\right)=\alpha +\beta {IntPackage}_{ic}+\gamma {Post}_{it}+\rho \left({IntPackage}_{ic}\times {Post}_{it}\right)+\theta {CHC}_{it}+\delta {X}_{ic t}+{\epsilon}_{ic t}$$\end{document}lnpYict1-pYict=α+βIntPackageic+γPostit+ρIntPackageic×Postit+θCHCit+δXict+ϵictwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}_{Y_{ict}}$$\end{document}pYict is the probability of the dependent variable Yict being reported as 1. To account for the possibility that the NCDs behavioral risk factors (the Ys) may not be independently distributed within the population covered by each community health center, hence the estimated standard error be artificially low, standard errors were clustered at the CHC level [38]. Also, a sampling weight was assigned to the surveyed individuals. Sampling weights were calculated as the inverse of the ratio of sampled individuals characterized by sex and age group relative to the number of individuals in the respective sex and age group (namely, 30–39, 40–49, 50–59, and 60–70) in the population. For a specific individual, the ratio was the multiplication of two shares: [1] the share of surveyed individuals of the same sex and age group in the corresponding catchment area’s population, and [2] the share of individuals of the same sex and age group in the corresponding district in the country’s population. The shares were separately calculated for urban and rural areas. The estimations were done with and without adjusting for the socioeconomic factors (X) to assess the extent of any potential observable bias in the selection of CHCs and the assignment of intervention packages. The statistical package used for analyses was STATA 14.0 (Stata, Inc., College Station, Texas). ## Ethical issues/statement This study has been approved by the national committee on ethics in medical research (code: IR.NIMAD.REC.1396.084) as well as our institutional review board (code: IR.IUMS.REC.1395.1057613). Written informed consent has been obtained from study participants. ## Results A total of 2446 people aged 30–70 years participated in the two surveys, 1225 individuals in the first and 1221 in the second survey. The socioeconomic characteristics of survey participants in both rounds were strikingly similar. For example, the mean age and sex ratio of the participants in the two surveys were very similar (about 49 years and about $50\%$ women). Approximately $43\%$ had illiterate or primary school education, while about $11\%$ had a college education. About $5\%$ were employed, 24–$27\%$ were self-employed, 93–$95\%$ were insured, and 84–$85\%$ were homeowners (Table 2).Table 2Demographic and economic characteristics of participants in each group in the two surveysSocioeconomic FactorsTotaln = 2446Intervention Package/GroupAn = 445Bn = 463Cn = 465Dn = 450Nonen = 623Survey 1n = 1225Survey 2n = 1221Survey 1n = 225Survey 2n = 220Survey 1n = 232Survey 2n = 231Survey 1n = 239Survey 2n = 226Survey 1n = 224Survey 2n = 226Survey1n = 305Survey 2n = 318Mean Age (Standard Deviation)49.3 (0.33)49.4 (0.32)49.5 (0.76)49.6 (0.74)49.3 (0.73)49.3 (0.76)49.9 (0.73)49.1 (0.74)48.9 (0.78)49.1 (0.73)48.8 (0.65)49.7 (0.63)p-value0.8310.968Sex: Male (%)611 (49.9)606 (49.6)112 (49.8)107 (48.6)117 (50.4)117 (50.6)113 (47.3)107 (47.4)110 (49.1)117 (51.8)159 (52.1)158 (49.7) Female (%)614 (50.1)615 (50.4)113 (50.2)113 (51.4)115 (49.6)114 (49.4)126 (52.7)119 (52.6)114 (50.9)109 (48.2)146 (47.9)160 (50.3)p-value0.8530.899Education: Illiterate or Primary School (%)497 (43.5)517 (42.5)105 (50.0)122 (55.5)109 (49.1)116 (50.4)117 (52.5)121 (53.8)88 (47.3)83 (36.7)78 (25.8)75 (23.7) Secondary School (%)158 (13.8)196 (16.1)19 (9.0)35 (15.9)39 (17.6)39 (17.0)20 (9.0)30 (13.3)27 (14.5)44 (19.5)53 (17.6)48 (15.1) High School (%)365 (31.9)368 (30.2)65 (31.0)43 (19.6)58 (26.1)64 (27.8)68 (30.5)52 (23.1)45 (24.2)70 (31.0)129 (42.7)139 (43.9) Some College (%)123 (10.8)137 (11.2)21 (10.0)2 (9.0)16 (7.2)11 (4.8)18 (8.0)22 (9.8)26 (14.0)29 (12.8)42 (13.9)55 (17.3)p-value0.001>0.001>Marital Status: Never Married (%)82 (7.0)84 (6.9)12 (5.7)7 (3.2)9 (4.0)13 (5.7)18 (8.1)12 (5.4)19 (9.0)22 (9.7)24 (7.9)30 (9.4) Married (%)996 (85.4)1039 (85.4)187 (89.5)198 (90.4)190 (86.0)194 (84.7)187 (84.2)198 (88.4)176 (83.0)185 (81.9)256 (84.8)264 (83.0) Divorced/Widowed (%)88 (7.6)93 (7.7)10 (4.8)14 (6.4)22 (10.0)22 (9.6)17 (7.7)14 (6.2)17 (8.0)19 (8.4)22 (7.3)24 (7.6)p-value0.4560.581Job: Public Wage and Salary (%)101 (8.7)75 (6.2)13 (6.3)8 (3.6)18 (8.1)14 (6.1)16 (7.2)15 (6.8)20 (9.4)13 (5.8)34 (11.4)25 (7.9) Private Wage and Salary (%)104 (9.0)92 (7.6)21 (10.1)31 (14.0)21 (9.5)15 (6.5)28 (12.7)21 (9.6)12 (5.7)18 (8.1)22 (7.4)7 (2.2) Self-Employed (%)316 (27.2)292 (24.1)53 (25.5)46 (20.8)54 (24.3)56 (24.6)32 (14.5)49 (22.3)86 (40.6)58 (26.0)91 (30.5)83 (26.1) Homemaker (%)491 (42.3)563 (46.5)89 (42.8)103 (47.1)98 (44.1)110 (47.8)110 (49.8)105 (47.7)73 (34.4)102 (45.7)121 (40.6)143 (45.0) Retired (%)91 (7.8)132 (10.9)20 (9.6)24 (10.9)19 (8.6)24 (10.4)22 (10.0)19 (8.6)12 (5.7)25 (11.2)18 (6.0)40 (12.6) Unemployed (%)58 (5.0)57 (4.7)12 (5.8)8 (3.6)12 (5.4)11 (4.8)13 (5.9)11 (5.0)9 (4.25)7 (3.1)12 (4.0)20 (6.2)p-value0.0310.307Health Insurance: Insured (%)1035 (92.6)1152 (94.7)203 (96.2)209 (95.4)209 (94.6)205 (90.3)171 (96.1)210 (92.9)164 (78.8)215 (95.1)288 (96.0)313 (98.4) Uninsured (%)83 (7.4)64 (5.3)8 (3.8)10 (4.6)12 (5.4)22 (9.7)7 (3.9)16 (7.1)44 (21.2)11 (4.9)12 (4.0)5 (1.6)p-value0.001>0.001Homeownership: Yes (%)898 (83.8)1027 (85.1)183 (87.6)171 (79.2)170 (86.3)209 (92.9)164 (92.7)192 (86.1)135 (70.7)175 (77.4)246 (82.8)280 (88.3) No (%)173 (16.2)180 (14.9)26 (12.4)45 (20.8)27 (13.7)16 (7.1)13 (7.3)31 (13.9)56 (29.3)51 (22.6)51 (17.2)37 (11.7)p-value0.001>0.001> There was no significant difference in the average age and sex of survey participants from catchment areas of no-intervention and intervention districts. However, the share of participants with primary or no education in the non-intervention district (25.8 and $23.7\%$ in the first and second surveys) was much lower than that in the intervention districts (between 36.5 and $55.5\%$). Consequently, there were more participants with a high school or a college education in the non-intervention than in intervention districts. In addition, the share of high school-educated participants who were potentially exposed to intervention package A decreased from $31.0\%$ in survey 1 to $19.6\%$ in survey 2, an $11.4\%$ decrease; the share of illiterate or primary school-educated participants who were potentially exposed to intervention package D decreased from $47.3\%$ in survey 1 to $36.7\%$ in survey 2, an $11.6\%$ decrease. Most surveyed individuals were married, regardless of the intervention packages assigned to their pertinent CHCs: the share of married participants was between 81.9 and $90.4\%$ across intervention groups and surveys. Also, the majority of participants were either homemakers (between 34.4 and $49.8\%$) or self-employed (between 14.5 and $40.6\%$), had health insurance (between 78.8 and $98.4\%$), and were homeowners (between 70.7 and $92.9\%$) (Table 2). Given the observed variations in the characteristics, they were used in adjusting the estimated effects of the intervention packages. The crude comparison of the levels of NCDs behavioral risk factors in each intervention group before and after the interventions showed the largest decrease in insufficient physical activity in intervention group D with a $29\%$ decrease ($95\%$ Confidence Interval: $20\%$, $38\%$), then in intervention groups B and A with $25\%$ ($95\%$ CI: $17\%$, $34\%$) and $15\%$ ($95\%$ CI: $6\%$, $24\%$) decreases, respectively. The observed level of physical activity did not change in the control group during the study period. A large and consistent increase in fruit consumption was also observed in all intervention and non-intervention groups. We suspect the increase in fruit consumption was largely attributable to the strikingly lower relative prices for fruits during the study period (Additional file 4). No consistent and statistically significant pattern of increase or decrease in fruit and vegetable consumption, high salt intake, and tobacco use was observed in the studied groups (Table 3).Table 3The difference in NCDs behavioral risk factors between the two surveysNCDs Risk FactorsIntervention Package a / GroupFirst Survey (%)Second Survey (%)Difference (%) ($95\%$ Confidence Interval)Insufficient Physical ActivityA4025−15 (−24, −6)B4924−25 (−34, −17)C4140−1 (−11, 8)D5324−29 (−38, −20)None39401 (−7, 8)Insufficient Fruit ConsumptionA6657−9 (−19, 0)B5953−6 (−1, 3)C6851−17 (−27, −8)D7259−13 (−22, −4)None7356−17 (−25, −10)Insufficient Vegetable ConsumptionA32342 (−6, 11)B33396 (− 2, 15)C3736− 1 (− 11, 9)D40477 (− 2, 16)None3837− 1 (−9, 7)High Salt IntakeA1513−2 (−9, 5)B19223 (−4, 11)C51510 [4, 16]D19223 (−5, 11)None1513−2 (− 8, 3)Current Tobacco UseA16204 (− 3, 11)B2019− 1 (− 8, 7)C16226 (− 2, 12)D14140.3 (− 6, 7)None1811−7 (− 13, 1)NCDs Non-communicable diseasesa Intervention package A included target-setting. Intervention package B included A plus evidence-based education. Intervention package C included B plus operational planning. Intervention package D included plus performance-based financing The results of the statistical analyses largely confirmed the results of the crude comparisons. Using the non-intervention district as the reference group and employing a DID research design (Eq. 2), no improvement in fruit and vegetable consumption, high salt intake, and tobacco use was estimated. However, fairly consistent improvements in the level of physical activity in all intervention groups were observed compared to the non-intervention group. Among the intervention packages, B and D resulted in decreasing insufficient physical activity. Specifically, the unadjusted estimations showed that the odds of reporting insufficient physical activity decreased to 0.32 ($95\%$ CI: 0.11, 0.88) among the surveyed individuals covered by CHCs that received the intervention package B, and to 0.28 ($95\%$ CI: 0.10, 0.75) among those covered by CHCs that received the intervention package D. These results were confirmed after adjusting for socioeconomic factors: 0.27 ($95\%$ CI: 0.09, 0.85) decrease in the odds of insufficient physical activity in intervention group B, 0.24 ($95\%$ CI: 0.08, 0.72) in intervention group D (Table 4). In other words, the package with all intervention components (which added the provision of an action plan and incentive payments to the previous two) decreased the likelihood of insufficient physical activity by $76\%$ ($95\%$ CI: $28\%$, $92\%$).Table 4The estimated effects of intervention packages on the NCDs behavioral risk factorsNCDs Risk FactorIntervention Package aUnadjustedAdjusted for Socioeconomic FactorsOdds Ratio ($95\%$ CI)p-valueOdds Ratio ($95\%$ CI)p-valueInsufficient Physical ActivityA0.49 (0.20, 1.22)0.120.56 (0.21, 1.51)0.25B0.32 (0.11, 0.88)0.020.27 (0.09, 0.85)0.02C0.91 (0.25, 3.40)0.890.81 (0.17, 3.76)0.78D0.28 (0.10, 0.75)0.010.24 (0.08, 0.72)0.01NoneReference GroupInsufficient Fruit ConsumptionA1.44 (0.35, 5.95)0.611.52 (0.27, 8.62)0.63B1.70 (0.45, 6.35)0.431.94 (0.38, 9.84)0.42C1.04 (0.23, 4.72)0.950.99 (0.13, 7.32)0.99D1.19 (0.35, 4.03)0.781.42 (0.31, 6.61)0.64NoneReference GroupInsufficient Vegetable ConsumptionA1.17 (0.37, 3.71)0.791.13 (0.26, 4.89)0.86B1.39 (0.53, 3.62)0.511.71 (0.64, 4.54)0.28C1.00 (0.34, 2.91)0.990.70 (0.19, 2.62)0.59D1.39 (0.44, 4.42)0.571.71 (0.46, 6.43)0.42NoneReference GroupHigh Salt IntakeA1.01 (0.33, 3.11)0.981.04 (0.32, 3.38)0.94B1.45 (0.51, 4.12)0.481.23 (0.41, 3.70)0.71C3.92 (1.05, 14.63)0.044.35 (0.80, 23.47)0.08D1.43 (0.37, 5.56)0.611.38 (0.36, 5.33)0.64NoneReference GroupCurrent Tobacco UseA2.37 (1.09, 5.18)0.031.87 (0.83, 4.23)0.13B1.73 (0.65, 4.59)0.271.47 (0.50, 4.29)0.48C2.54 (0.84, 7.66)0.091.55 (0.56, 4.29)0.40D1.82 (0.81, 4.12)0.151.24 (0.44, 3.54)0.68NoneReference GroupBoldface indicates statistical significance ($p \leq 0.05$)NCDs Non-communicable diseasesa Intervention package A included target-setting. Intervention package B included A plus evidence-based education. Intervention package C included B plus operational planning. Intervention package D included plus performance-based financing ## Discussion The frontline CHCs in rural areas were piloted in the 1970s and expanded rapidly to a nationwide network in the early 1980s. Each CHC has at least one female and one male community health worker. Most of them are NPHWs, usually selected from local people and trained in a specific practical program designed by the country’s Ministry of Health. Their focus has been providing primary health care services, including health education, promotion of food supply and proper nutrition, advocacy for and monitoring of water safety and basic sanitation, maternal and child health, immunization according to the national immunization program, prevention and control of locally important diseases, treatment of common diseases and injuries, and provision of essential medications [20]. In the past two decades, they have been more involved in managing NCDs risk factors, and there is indirect evidence of the success of their role in this area [39]). The functions of the urban CHCs are close to their rural counterparts. Each urban CHC has at least one NPHW for every 2500 individuals in the catchment area. The average coverage of rural and urban CHCs is currently 1200 and 12,500 individuals, respectively. In either case, urban or rural, people covered by a CHC cannot use the services of other CHCs. In rural areas, especially in remote areas, utilization of CHC services is usually high. In urban areas and especially in large cities, there are several alternatives, usually more specialized, in public and private sectors for all kinds of health services but with a higher out-of-pocket cost. Also, active follow-up of the target population for health services in urban centers is not as usual and orderly as in rural centers [40]. This trial used the aforementioned community health foundation to examine the effectiveness of four interventional packages designed to improve four major NCDs behavioral risk factors, namely, insufficient physical activity, insufficient fruit and vegetable consumption, high salt consumption, and current tobacco use. NPHWs implemented the interventions at a randomly selected number of CHCs in four Iranian districts. The most basic intervention package included target-setting for NPHWs. Evidence-based education, operational planning, and PBF for NPHWs were added to target-setting in other packages. Improvements were observed in only one NCDs behavioral risk factor, insufficient physical activity. The most effective intervention package included all four interventions. In this study, incentives were paid to enhance the achievement of public health goals. The finding is in line with the result of previous studies that have shown increasing the motivation of NPHWs can pave the way for achieving predetermined goals in areas where access to physicians is limited [41, 42]. Our findings accord with some review studies which shown that interventions in primary health care can significantly improve physical activity compared to other NCDs behavioral risk factors, even 6 to 12 months after intervention [43, 44]. A randomized controlled trial (RCT) in the United Kingdom showed that motivational interviewing of primary care patients by NPHWs improved physical activity levels assessed 12 months after the intervention [45]. Other RCTs also showed interventions with multiple lifestyle improvement components can increase physical activity [46–48]. This study’s measured improvements in physical activity can be attributed to the provision of regular physical activity programs by NPHWs to the CHC visitors, NPHWs’ active involvement in physical activity initiatives, and encouraging local councils to waive recreational areas’ and sports facilities’ usage fees for group activities supported by CHCs. The finding that the addition of PBF to other interventions made the results stronger indicates the importance of incentive payments and their role in promoting NPHWs’ efforts. A similar study in San Diego, California, USA, included direct involvement of health workers in public exercises and group walking over 12 months and measured a noticeable increase in physical activity in the community [49, 50]. Other studies have also shown the effectiveness of community-based physical activity interventions [51, 52]. An irregularity was found in the results for physical activity. Adding more components to the intervention package was expected to make it more effective. Accordingly, adding evidence-based training to goal-setting made the effect larger and statistically significant. However, adding an action plan to the intervention package without PBF eradicated the effect of goal-setting and evidence-based training. Adding PBF to the package that included goal-setting, evidence-based training, and an action plan, on the other hand, significantly increased the magnitude of the effect. Therefore, an action plan without incentive payment appeared to be ineffective. This irregularity may be an important result of this study. Anecdotal evidence collected by the authors showed that some NPHWs of CHCs who received the intervention package with and without incentive payments (packages C and D) might be communicating during the study period, although the research team set up workshops for different interventions at different weeks and advised district health care authorities to keep the intervention assignments to the CHCs in their district confidential. Hence, no incentive payment to NPHWs who implemented intervention package C might have acted as a disincentive, perhaps because they were expected to do the same amount of work as NPHWs who implemented intervention package D. Comparing the effects on physical activity of intervention packages B and D, we found no discernable difference: the adjusted odds ratios for intervention packages B and D were 0.27 ($95\%$ CI: 0.09, 0.85) and 0.24 ($95\%$ CI: 0.08, 0.72), respectively. This is another unexpected result, indicating that a package with embedded financial incentives (D) did not improve the results over a package without the incentives (B). This finding highlights the importance of providing evidence-based training to the participating NPHWs. The goal of increasing fruit and vegetable consumption may be achievable by spending more time and holding counseling sessions, as other studies have shown [53, 54]. On the other hand, failure to achieve this goal might be due to the high inflation rate in the country during the study period, especially the sharp increase in food, fruit, and vegetable prices because of a new round of international economic sanctions on the country from 2018 [55]. Virtually, the average cost of food in 2019 was estimated to be 3.6 times more than that in 2017 [56]. Factors influencing failure to achieve the desired effect of interventions in the consumption of fruit and vegetable have been reported before. Poor nutritional knowledge [57], the role of media in advertising food with low nutritional value [58], and the high relative price of fruits [59] are posed as the main factors. Measuring no improvement in reducing high salt intake in this study is consistent with the contemporary consumption habits in the country and the challenges of changing them. For example, a WHO report indicated that in 2012, salt consumption in Iran was twice the recommended level [60, 61]. Evidence has shown that changing behavior and modifying consumption patterns require comprehensive and extensive programs [62]. Other studies have shown that changing the habit of salt consumption is less probable only with educational and information campaigns [63, 64]. One potential challenge in reducing salt intake in rural Iran may be the presence of traditional healers who disseminate rumors mixed with religious stories about the benefits of using salt, salt stone, and sea salt, even for people with hypertension [65]. No measured effect of this study’s interventions on tobacco use was expected as influencing tobacco use habits faces easy access and low price challenges. The smoking economics literature shows that policies that increase the price of tobacco products (through increased sales taxes, for example) are most influential in decreasing tobacco use [66–70]. Such macro-level policies were out of this study’s scope. This study has several limitations. First, only short-term effects were measured because individuals in the intervention CHCs were not followed up. The trial was initially designed for 24 months [24]. Nonetheless, in compliance with limitations imposed by the emergence of the COVID-19 pandemic and the required social distancing policies, the study was stopped prematurely. Similar studies with longer periods of intervention might be necessary to measure longer-term effects on NCDs behavioral risk factors. Second, the survey participants were not necessarily the ones who were directly affected by the interventions. Although the influence of a typical CHC’s activities in the country is expected to go beyond direct visits, the measured effects may only reflect the lower bounds of the actual effects on the treated individuals. Third, participating NPHWs’ relocation to serve in other CHCs was out of the researchers’ control. Nonetheless, no relocation took place between the selected intervention CHCs. The research team traced all relocations monthly and provided the replacing NPHWs with the same training. Fourth, implementing simultaneous national research projects, such as the High Blood Pressure Campaign, was not under the researchers’ control [71]. However, such factors are expected to affect all intervention and non-intervention groups similarly. The fifth is a general limitation of studies that target NPHWs. Although we used training workshops to mprove NPHWs’ knowledge about non-communicable diseases, and developing their skills in applying the trial’s designed interventions, we do not believe these efforts would replace formal academic or occupational training or employing more skilled health workers. This might be one of the reasons for not measuring an effect on most behavioral risk factors in this study. ## Conclusion Paying incentives to NPHWs, along with other interventions, could be considered a useful means of improving physical activity in the community. This study could not be continued because of COVID-19. Longer studies are needed to identify the long-term effects of such interventions. ## Supplementary Information Additional file 1. Districts of intervention and no intervention in IRPONT study. Additional file 2. CONSORT checklist. Additional file 3. Specific quarterly and yearly targets for NCDs risk factors. Additional file 4. 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--- title: 'Prevalence, incidence, and treatment of anaemia in patients with non-dialysis-dependent chronic kidney disease: findings from a retrospective real-world study in Italy' authors: - Roberto Minutolo - Giuseppe Grandaliano - Paolo Di Rienzo - Robert Snijder - Luca Degli Esposti - Valentina Perrone - Lora Todorova journal: Journal of Nephrology year: 2022 pmcid: PMC9998309 doi: 10.1007/s40620-022-01475-x license: CC BY 4.0 --- # Prevalence, incidence, and treatment of anaemia in patients with non-dialysis-dependent chronic kidney disease: findings from a retrospective real-world study in Italy ## Abstract ### Background Limited data are available on the epidemiology and clinical management of anaemia in patients with non-dialysis-dependent chronic kidney disease (NDD-CKD). ### Methods This retrospective observational study was based on records from databases of five Local Health Units across Italy. Adults with reported NDD-CKD stage 3a–5 between 1 January 2014 and 31 December 2016 were identified. Annual prevalence and incidence of anaemia (age- and sex-standardised) and clinical management (erythropoiesis-stimulating agents [ESAs], intravenous [IV] iron, and blood transfusions) were evaluated. Eligibility for ESAs was defined by ≥ 2 records of Hb < 10 g/dL, or < 11 g/dL over 6 months. ### Results Overall, 101,143 individuals with NDD-CKD (3a–5) recorded between 2014 and 2016 were identified, of whom 40,020 ($39.6\%$) were anaemic. Prevalence of anaemia was $33.8\%$ in 2016 and incidence of anaemia was stable (11.4–$12.4\%$) from 2014 to 2016. Prevalence and incidence of anaemia increased with CKD stage. Among eligible patients, $12.8\%$ with Hb < 11 g/dL and $15.5\%$ with Hb < 10 g/dL received ESAs, and the proportion treated increased with CKD stage. Among ESA-treated patients with at least 2 years of follow up, $18.4\%$ and $19.3\%$ received IV iron in the Hb < 11 and < 10 g/dL groups, respectively, and $16.5\%$ and $19.4\%$ received blood transfusions. Corresponding proportions for the overall anaemic cohort were $9.0\%$ and $11.3\%$, respectively. ### Conclusions Anaemia is a significant issue in patients with NDD-CKD. Low rates of ESA treatment indicate a potential treatment gap and suggest that anaemia may not be adequately controlled in many patients. ### Supplementary Information The online version contains supplementary material available at 10.1007/s40620-022-01475-x. ## Introduction Chronic kidney disease (CKD) has an estimated global prevalence of 11–$13\%$ and is a growing public health issue worldwide [1]. Data from the 2008–2012 Cardiovascular risk profile in Renal patients of the Italian Health Examination Survey (CARHES), which examined Italian residents aged 35–79 years, showed a CKD prevalence of about $7\%$, with early stages (1 and 2) accounting for nearly $60\%$ of cases [2]. Anaemia is a common complication in patients with CKD, and its prevalence increases with advancing CKD stage [3, 4]. The main cause of anaemia associated with CKD is reduced production of erythropoietin by the failing kidneys as a result of impaired oxygen-sensing mechanisms in CKD [5]. Anaemia is associated with CKD progression and other adverse outcomes, including major adverse cardiovascular events, hospitalisation, and all-cause mortality [3]. Iron supplementation and erythropoiesis-stimulating agents (ESAs) are the mainstays of treatment for anaemia in CKD [6–8]. Recent data from the prospective, multinational Chronic Kidney Disease Outcomes and Practice Patterns Study (CKDopps) on the management of anaemia in patients with non-dialysis-dependent CKD (NDD-CKD) under nephrologist care in Brazil, France, Germany, and the USA, highlighted that anaemia monitoring and treatment are suboptimal, with many patients untreated despite guideline-based indications to treat [9, 10]. With respect to Italy, data on the prevalence and management of anaemia of NDD-CKD are limited. In a prospective cohort study of adults with NDD-CKD stage 3–5 attending 25 renal clinics in Italy in 2003, the prevalence of mild anaemia was $41.3\%$ at the first visit [11]. Another prospective study in 19 outpatient renal clinics across Italy reported an unexpectedly high prevalence of anaemia (severe 18.0–$19.3\%$, mild 43.2–$44.0\%$) among patients with NDD-CKD over a 6-month period, which was linked to clinical inertia in initiating anaemia treatment [12]. Therefore, this observational study was designed to evaluate the prevalence of NDD-CKD (stage 3a–5), in addition to the prevalence and incidence of anaemia of NDD-CKD (stage 3a–5), and the therapeutic management of this complication in Italian clinical practice. ## Materials and methods This was a retrospective cohort study involving adult patients (aged ≥ 18 years) with reported NDD-CKD stage 3a–5 between 1 January 2014 and 31 December 2016 (the inclusion period). Records were extracted from administrative (demographic, hospitalisation, pharmaceutical, and outpatient specialist services) and laboratory databases of five Local Health Units (LHUs) across Italy (full details provided in Online Resource 1). The Ethics Committee of each participating LHU was notified of this study and provided approval (reference numbers: Prot. N AslVC.Farm.19.02, Prot. 96/SegCE/2019, Prot. N. 0,219,118, Prot. N. 03, and Prot. CEUR-2020-Os-029 [Online Resource 1]). NDD-CKD (3a–5) and respective stages were defined as detailed in Online Resource 2. Anaemia was defined as a haemoglobin (Hb) value below the cut-off specified by Kidney Disease Improving Global Outcomes (KDIGO) guidelines (< 13 g/dL in males and < 12 g/dL in females) [7]. Patients were grouped into three cohorts: NDD-CKD (3a–5) cohort, anaemia of NDD-CKD (3a–5) cohort, and anaemia of NDD-CKD (3a–5) ESA-treated cohort (referred to hereafter as NDD-CKD, anaemic, and ESA-treated cohorts, respectively). The three patient cohorts had distinct index dates during the study period (Fig. 1). Patients were excluded if 1) they were on dialysis at or before the CKD index date; 2) they had a cancer diagnosis ≤ 5 years before the CKD index date; 3) the characterisation (‘look-back’) period was < 1 year; 4) the anaemia index date was ≥ 1 month before the CKD index date (incident anaemic patients); or 5) the ESA treatment index date was ≥ 1 month prior to the anaemia index date (incident ESA-treated patients). For each cohort, the index date marked the beginning of the follow-up period (Fig. 1). An identification period of ≥ 1 year before the respective index dates was used to characterise patients in terms of comorbidities (see Online Resource 2 for details) and previous treatment for the management of anaemia. The follow-up period was 2 years after the respective index date, or from index date until death, dialysis, transplant, or exiting the database, whichever occurred first. Fig. 1Study design. aFor the NDD−CKD cohort (CKD index date), date of first diagnosis of CKD stage 3a–5 or earliest record of eGFR <60 mL/min/1.73 m2 within the inclusion period; for the anaemic cohort (anaemia of CKD index date): date of earliest second of two consecutive tests (within 3 months of each other) reporting Hb <13 g/dL (males) or <12 g/dL (females) within the inclusion period; for the ESA−treated cohort (ESA treatment index date), date of first ESA prescription within the inclusion period. bOr until death, dialysis, transplant, or exiting the database (whichever occurred first). CKD chronic kidney disease; eGFR estimated glomerular filtration rate; ESA erythropoiesis−stimulating agent; Hb haemoglobin; NDD−CKD non−dialysis dependent chronic kidney disease The objectives of this study were to: 1) estimate the annual prevalence of NDD-CKD; 2) estimate the annual prevalence and incidence of anaemia among patients with NDD-CKD; and 3) evaluate the management of anaemia in patients with NDD-CKD, including treatment with ESAs (frequency and types of ESAs used), intravenous iron, and blood transfusions. Incident and prevalent patients within the three cohorts are defined in Online Resource 3. Results were age- and sex-standardised according to Italian census data from 1 January of the year following the inclusion date. Prevalence was defined as the presence of NDD-CKD or anaemia of NDD-CKD at 31 December of the year. Incident cases were defined as newly diagnosed NDD-CKD or anaemia of NDD-CKD during the year of interest. Eligibility for ESA treatment was defined using two Hb thresholds: ≥ 2 records of Hb < 10 g/dL over 6 months; and ≥ 2 records of Hb < 11 g/dL over 6 months. The Hb < 10 g/dL threshold is recommended by international guidelines [7], while the Hb < 11 g/dL threshold is supported by the Italian Society of Nephrology [13] and defined in the Italian Therapeutic Plan for ESA prescription [14, 15]. Patients with a record of ESA prescription and ≥ 2 records of Hb < 10 or < 11 g/dL over 6 months were categorised as the ESA-treated cohort (referred to hereafter as Hb < 10 g/dL ESA-treated cohort and Hb < 11 g/dL ESA-treated cohort). ESAs were sub-categorised as short-acting (epoetin alfa/beta) or long-acting (darbepoetin alfa, methoxy polyethylene glycol-epoetin beta). Autoimmune diseases were defined by ICD-9-CM codes 696, 714, 720, 555, and 556 (see Online Resource 2 for further details). ICD-9-CM 250.0x (diabetes mellitus without mention of complication) and ATC A10 (drugs used in diabetes) codes were used as a proxy for diabetes. Data analysis was primarily descriptive. Statistical significance was determined for binary variables using the Cochran–Armitage test, continuous variables using linear regression, and categorical variables using a Cochran–Mantel–Haenszel test. Analyses were reported by CKD stage as well as for the overall sample. All analyses were performed using STATA SE, version 12.0 (StataCorp LLC, College Station, TX, USA). End-stage renal disease (ESRD) risk assessment was determined using the Fine and Gray proportional sub-distribution hazards model, with death as a competing risk factor. Risk of death was determined using the Cox proportional hazards model. Covariates for each included: CKD stage, age, sex, cardiovascular disease, hypertension, diabetes mellitus, and autoimmune diseases. ## Disposition of patients The five LHU databases included records for just over 1.5 million inhabitants, which corresponds to about $3\%$ of the entire adult population of Italy [16]. A total of 101,143 individuals with NDD-CKD were included in the analysis, of whom 40,020 ($39.6\%$) were anaemic (Fig. 2).Fig. 2Patient flowchart. aAnaemia of NDD-CKD (3a–5) cohort. bAnaemia of NDD-CKD (3a–5) ESA-treated cohort (eligibility criterion for ESA treatment: ≥ 2 records of Hb < 11 g/dL over a 6-month period). cAnaemia of NDD-CKD (3a–5) ESA-treated cohort (eligibility criterion for ESA treatment: ≥ 2 records of Hb < 10 g/dL over a 6-month period). ESA erythropoiesis-stimulating agent; Hb haemoglobin; LHU local health unit; NDD-CKD non-dialysis dependent chronic kidney disease ## Patient demographics and characteristics at index date Over $70\%$ of NDD-CKD patients had CKD stage 3a (Table 1). Mean age was 76.1 years and the majority of patients (> $90\%$) were aged ≥ 60 years. Approximately half of male patients were 60–79 years of age ($50.7\%$), and approximately half of female patients were ≥ 80 years of age ($50.2\%$) (Online Resource 4). No clear trends were observed for mean age across the CKD stages in the anaemic and ESA-treated cohorts (Online Resource 5 and 6). The proportion of males ranged between 39 and $48\%$ across the CKD stages in the NDD-CKD cohort (Table 1). Of those patients with NDD-CKD, $43.3\%$ of males and $37.0\%$ of females were included in the anaemic cohort (Online Resource 4).Table 1Patient demographics for the NDD-CKD (3a–5) cohort at index dateCKD stageOverallp valued3a3b45N71,63820,32071891996101,143–Age, mean (SD)74.7 (12.4)79.7 (11.2)80.0 (12.1)75.1 (14.5)76.1 (12.4) < 0.001Age in years, n (%): 18–39948 (1.3)139 (0.7)68 (0.9)40 (2.0)1,195 (1.2) < 0.001 40–596948 (9.7)1036 (5.1)439 (6.1)245 (12.3)8668 (8.6) 60–7935,632 (49.7)7203 (35.4)2284 (31.8)788 (39.5)45,907 (45.4) ≥ 8028,100 (39.2)11,941 (58.8)4397 (61.2)920 (46.1)45,358 (44.8)Male, n (%)29,608 (41.3)7939 (39.1)2845 (39.6)954 (47.8)41,346 (40.9) < 0.001Included in anaemic cohorta, n (%)23,626 (33.0)10,181 (50.1)4682 (65.1)1531 (76.7)40,020 (39.6)–Included in Hb < 11 g/dL ESA-treated cohortb, n956 (1.3)851 (4.2)946 (13.2)485 (24.3)3238 (3.2)–Included in Hb < 10 g/dL ESA-treated cohortc, n832 (1.2)715 (3.5)771 (10.7)424 (21.2)2742 (2.7)–Anaemia was defined as a Hb value below the cut-off specified by Kidney Disease Improving Global Outcomes (KDIGO) guidelines (< 13 g/dL in males and < 12 g/dL in females) [7]CKD chronic kidney disease; ESA erythropoiesis-stimulating agent; Hb haemoglobin; NDD-CKD non-dialysis dependent chronic kidney disease; SD standard deviationaAnaemia of NDD-CKD (3a–5) cohortbAnaemia of NDD-CKD (3a–5) ESA-treated cohort (eligibility criterion for ESA treatment: ≥ 2 records of Hb < 11 g/dL over a 6-month period)cAnaemia of NDD-CKD (3a–5) ESA-treated cohort (eligibility criterion for ESA treatment: ≥ 2 records of Hb < 10 g/dL over a 6-month period)dResult of a statistical test to identify the existence of a trend in the ordered CKD stages In general, rates of comorbidities tended to be slightly higher in the anaemic cohort than in the NDD-CKD cohort (Table 2 and Online Resource 7 and 8). Cardiovascular disease, hypertension, diabetes mellitus, autoimmune diseases, and myelodysplastic syndrome were detected in $23.5\%$, $89.9\%$, $32.4\%$, $2.1\%$, and $0.8\%$ of patients with anaemia, respectively, without any apparent trends across the CKD stages (Table 2). The proportions of patients in the anaemic cohort who had received previous treatments for anaemia (ESAs, iron, blood transfusion) showed an upward trend with CKD progression (Table 2). Clinical characteristics of patients with anaemia of NDD-CKD stratified by sex can be found in Online Resource 9.Table 2Clinical characteristics of patients with anaemia of NDD-CKD (3a–5) at index dateCKD stageOverallp valuea3a3b45N23,62610,1814682153140,020–CV disease, n (%)5321 (22.5)2548 (25.0)1216 (26.0)328 (21.4)9413 (23.5) < 0.001Hypertension, n (%)20,827 (88.2)9457 (92.9)4322 (92.3)1366 (89.2)35,972 (89.9) < 0.001Diabetes mellitus, n (%)7341 (31.1)3451 (33.9)1660 (35.5)502 (32.8)12,954 (32.4) < 0.001Autoimmune diseases, n (%)513 (2.2)211 (2.1)97 (2.1)30 (2.0)851 (2.1)0.886Myelodysplastic syndrome, n (%)196 (0.8)84 (0.8)49 (1.0)9 (0.6)338 (0.8)0.416Previous ESA treatment, n (%)389 (1.6)410 (4.0)567 (12.1)396 (25.9)1762 (4.4) < 0.001Previous iron therapy (IV or oral), n (%)211 (0.9)175 (1.7)200 (4.3)136 (8.9)722 (1.8) < 0.001Previous blood transfusion, n (%)51 (0.2)32 (0.3)38 (0.8)10 (0.7)131 (0.3) < 0.001CKD chronic kidney disease; CV cardiovascular; ESA erythropoiesis-stimulating agent; IV intravenousaResult of a statistical test to identify the existence of a trend in the ordered CKD stages ## Prevalence of NDD-CKD In 2016, the standardised prevalence of NDD-CKD was $5.6\%$ (Table 3). CKD stage 3a was the most common ($4.2\%$), while the prevalence of stages 3b–5 ranged from 0.1–$1.0\%$. Overall, there was a numerically higher proportion of female ($6.3\%$) than male ($4.7\%$) patients with NDD-CKD in 2016.Table 3Prevalence of NDD-CKD (3a–5) and anaemia of NDD-CKD (3a–5) in 2016CKD stageOverall3a3b45Prevalence of NDD-CKD (3a–5) in the LHU patient population 2016Numerator (adult patients diagnosed)62,68315,2214486123583,625 2016Denominator (adult LHU patient population)1,507,3911,507,3911,507,3911,507,3911,507,391 2016Prevalence$4.2\%$$1.0\%$$0.3\%$$0.1\%$$5.5\%$ 2016Standardised prevalencea$4.2\%$$1.0\%$$0.3\%$$0.1\%$$5.6\%$ 2016Standardised prevalence, male$3.5\%$$0.8\%$$0.3\%$$0.1\%$$4.7\%$ 2016Standardised prevalence, female$4.7\%$$1.2\%$$0.3\%$$0.1\%$$6.3\%$Prevalence of anaemia among patients with NDD-CKD (3a–5) 2016Numerator (patients diagnosed with anaemia)17,6816779282697328,259 2016Denominator (NDD-CKD patients by CKD stage)62,68315,2214486123583,625 2016Prevalence$28.2\%$$44.5\%$$63.0\%$$78.8\%$$33.8\%$ 2016Standardised prevalencea$28.2\%$$44.6\%$$63.1\%$$78.9\%$$33.8\%$ 2016Standardised prevalence, male$31.2\%$$48.5\%$$66.7\%$$81.1\%$$37.0\%$ 2016Standardised prevalence, female$26.2\%$$42.2\%$$60.6\%$$76.5\%$$31.7\%$Bold italic text emphasizes the key data discussed in the body of the text rather than specific differencesCKD chronic kidney disease; LHU Local Health Unit; NDD-CKD non-dialysis dependent chronic kidney diseaseaPrevalence at 31 December of each year was age- and sex-standardised using census data from 1 January of the following year ## Prevalence and incidence of anaemia of NDD-CKD Standardised prevalence of anaemia among patients with NDD-CKD in 2016 was $33.8\%$ (Table 3) and increased with CKD stage, rising from $28.2\%$ among patients with stage 3a to $78.9\%$ among those with stage 5. There was a numerically higher proportion of male ($37.0\%$) than female ($31.7\%$) patients with anaemia of NDD-CKD. Overall, the standardised incidence of anaemia of NDD-CKD was $12.4\%$, $12.3\%$, and $11.4\%$ in 2014, 2015, and 2016, respectively, and increased with CKD stage within each year (Table 4). For all years, overall standardised incidence of anaemia of CKD was numerically higher for males than females. Table 4Annual incidence of anaemia among patients with NDD-CKD (3a–5) (2014–2016)YearIncidence of anaemiaCKD stageOverall3a3b452014Numerator (patients newly diagnosed with anaemia)a4174212597427775502014Denominator (NDD-CKD patients by CKD stage)42,35213,1904472111861,1322014Incidence$9.9\%$$16.1\%$$21.8\%$$24.8\%$$12.4\%$2014Standardised incidenceb$9.9\%$$16.1\%$$21.8\%$$24.8\%$$12.4\%$2014Standardised incidence, male$11.2\%$$17.9\%$$23.4\%$$23.6\%$$13.7\%$2014Standardised incidence, female$9.0\%$$15.1\%$$20.8\%$$25.9\%$$11.5\%$2015Numerator (patients newly diagnosed with anaemia)a5077207880724882102015Denominator (NDD-CKD patients by CKD stage)50,13112,339350388666,8592015Incidence$10.1\%$$16.8\%$$23.0\%$$28.0\%$$12.3\%$2015Standardised incidenceb$10.1\%$$16.9\%$$23.1\%$$28.0\%$$12.3\%$2015Standardised incidence, male$11.1\%$$18.3\%$$24.3\%$$26.6\%$$13.3\%$2015Standardised incidence, female$9.5\%$$16.0\%$$22.3\%$$29.6\%$$11.7\%$2016Numerator (patients newly diagnosed with anaemia)a4901182370222576512016Denominator (NDD-CKD patients by CKD stage)52,94510,788261168767,0312016Incidence$9.3\%$$16.9\%$$26.9\%$$32.8\%$$11.4\%$2016Standardised incidenceb$9.3\%$$16.9\%$$26.9\%$$32.8\%$$11.4\%$2016Standardised incidence, male$10.3\%$$18.6\%$$29.6\%$$30.1\%$$12.5\%$2016Standardised incidence, female$8.6\%$$15.9\%$$25.1\%$$36.1\%$$10.7\%$p valuec < 0.0010.174 < 0.0010.001 < 0.001Bold italic text emphasizes the key data discussed in the body of the text rather than specific differencesCKD chronic kidney disease; NDD-CKD non-dialysis dependent chronic kidney diseaseaIncident cases were defined as newly diagnosed NDD-CKD during the year of interestbAnnual incidence was age- and sex-standardised using census data from 1 January of the following yearcp value refers to the comparison of standardised incidence value among different years ## Eligibility for ESAs and ESA treatment Using the Hb < 11 g/dL criterion to determine ESA eligibility, 25,360 patients were considered eligible for ESA treatment, of whom 3238 were prescribed ESAs. Using the Hb < 10 g/dL criterion, 17,703 patients were considered eligible for ESA treatment, and 2742 were prescribed ESAs (Fig. 2). Over the whole study period (2014–2016), the overall proportion of patients with anaemia of NDD-CKD who were eligible for ESA treatment increased with CKD stage, regardless of the eligibility criterion used, or sex (Table 5; Online Resource 10). The proportion of ESA-eligible patients was $63.4\%$ using the Hb < 11 g/dL threshold ($57.6\%$ for male and $68.0\%$ for female patients), and $44.2\%$ using the Hb < 10 g/dL threshold ($40.7\%$ for male and $47.1\%$ for female patients). There was an upward trend across the CKD stages for the proportion of eligible patients who were treated with ESAs. The proportion of eligible patients treated with ESAs was $12.8\%$ when the Hb threshold was < 11 g/dL, ranging from $6.7\%$ for patients in stage 3a to $39.8\%$ for those in stage 5. Using the stricter eligibility criterion of Hb < 10 g/dL, a slightly higher proportion of eligible patients were treated with ESAs overall ($15.5\%$) and for each CKD stage. Table 5Proportion of patients with anaemia of NDD-CKD (3a–5) eligible for ESA treatment, and treated with ESAs (overall study period, 2014–2016)CKD stageOverallp valuea3a3b45Number of patients with anaemia of NDD-CKD (3a–5)23,62610,1814682153140,020–Eligibility criterion ≥ 2 records of Hb < 11 g/dL over a 6-month period Eligible for ESA, n (%)14,203 (60.1)6592 (64.7)3347 (71.5)1218 (79.6)25,360 (63.4) < 0.001 Eligible patients treated with ESAs, n/N (%)$\frac{956}{14}$,203 (6.7)$\frac{851}{6592}$ (12.9)$\frac{946}{3347}$ (28.3)$\frac{485}{1218}$ (39.8)$\frac{3238}{25}$,360 (12.8) < 0.001Eligibility criterion ≥ 2 records of Hb < 10 g/dL over a 6-month period Eligible for ESA, n (%)9675 (41.0)4612 (45.3)2442 (52.2)974 (63.6)17,703 (44.2) < 0.001 Eligible patients treated with ESAs, n/N (%)$\frac{832}{9675}$ (8.6)$\frac{715}{4612}$ (15.5)$\frac{771}{2442}$ (31.6)$\frac{424}{974}$ (43.5)$\frac{2742}{17}$,703 (15.5) < 0.001CKD chronic kidney disease; ESA erythropoiesis-stimulating agent; Hb haemoglobin; NDD-CKD non-dialysis dependent chronic kidney diseaseaResult of statistical test to identify the existence of a trend in the ordered CKD stages Overall, $59.5\%$ ($\frac{1927}{3238}$) of patients in the Hb < 11 g/dL ESA-treated cohort received short-acting ESAs and $49.3\%$ ($\frac{1597}{3238}$) received long-acting ESAs. The corresponding proportions for the Hb < 10 g/dL ESA-treated cohort were $62.0\%$ ($\frac{1699}{2742}$) and $47.4\%$ ($\frac{1300}{2742}$), respectively. Some patients switched between the two ESA types, receiving both long- and short-acting ESAs during the study period. In the Hb < 11 g/dL ESA-treated cohort, the proportion of patients receiving short-acting ESAs decreased with CKD stage from $69.6\%$ to $54.0\%$, in favour of long-acting ESAs (the proportion of patients increased from $36.1\%$ for stage 3a to $63.7\%$ for stage 5). A similar pattern was observed for the Hb < 10 g/dL ESA-treated cohort. ## Iron supplementation and blood transfusions (patients with at least 2 years of follow up) Intravenous (IV) iron infusions were received by $9.0\%$ of patients in the anaemic cohort, and $18.4\%$ and $19.3\%$ of patients in the Hb < 11 g/dL ESA-treated and Hb < 10 g/dL ESA-treated cohorts, respectively (Table 6). Data for patients who received oral iron during the follow-up period were not consistently available in the study databases, and thus were not analysed here. Blood transfusions were received by $11.3\%$, $16.5\%$, and $19.4\%$ of patients in the anaemic cohort, Hb < 11 g/dL ESA-treated cohort, and Hb < 10 g/dL ESA-treated cohort, respectively. Table 6Proportions of patients in the anaemia of NDD-CKD (3a–5) and ESA-treated cohorts with at least 2 years of follow up who received intravenous iron supplementation and blood transfusionsCKD stageOverallp valuee3a3b45Intravenous irona, n/N (%) Anaemic cohortb$\frac{1564}{16}$,509 (9.5)$\frac{472}{6336}$ (7.4)$\frac{196}{2528}$ (7.8)$\frac{108}{658}$ (16.4)$\frac{2340}{26}$,031 (9.0) < 0.001 Hb < 11 g/dL ESA-treated cohortc$\frac{127}{590}$ (21.5)$\frac{67}{547}$ (12.2)$\frac{82}{515}$ (15.9)$\frac{64}{192}$ (33.3)$\frac{340}{1844}$ (18.4) < 0.001 Hb < 10 g/dL ESA-treated cohortd$\frac{113}{495}$ (22.8)$\frac{56}{439}$ (12.8)$\frac{68}{392}$ (17.3)$\frac{49}{155}$ (31.6)$\frac{286}{1481}$ (19.3) < 0.001Blood transfusion, n/N (%) Anaemic cohortb$\frac{1803}{16}$,509 (10.9)$\frac{728}{6336}$ (11.5)$\frac{334}{2528}$ (13.2)$\frac{81}{658}$ (12.3)$\frac{2946}{26}$,031 (11.3)0.026 Hb < 11 g/dL ESA-treated cohortc$\frac{123}{590}$ (20.8)$\frac{83}{547}$ (15.2)$\frac{79}{515}$ (15.3)$\frac{19}{192}$ (9.9)$\frac{304}{1844}$ (16.5)0.014 Hb < 10 g/dL ESA-treated cohortd$\frac{118}{495}$ (23.8)$\frac{79}{439}$ (18.0)$\frac{72}{392}$ (18.4)$\frac{19}{155}$ (12.3)$\frac{288}{1481}$ (19.4)0.054Percentages calculated using the proportion of patients with at least 2 years of follow up in each cohortAnaemia was defined as a Hb value below the cut-off specified by Kidney Disease Improving Global Outcomes (KDIGO) guidelines (< 13 g/dL in males and < 12 g/dL in females) [7]CKD, chronic kidney disease; ESA, erythropoiesis-stimulating agent; Hb, haemoglobin; NDD-CKD, non-dialysis dependent chronic kidney diseaseaNo data were available for oral iron therapybAnaemia of NDD-CKD (3a–5) cohortcAnaemia of NDD-CKD (3a–5) ESA-treated cohort (eligibility criterion for ESA treatment: ≥ 2 records of Hb < 11 g/dL over a 6-month period)dAnaemia of NDD-CKD (3a–5) ESA-treated cohort (eligibility criterion for ESA treatment: ≥ 2 records of Hb < 10 g/dL over a 6-month period)e Result of statistical test to identify the existence of a trend in the ordered CKD stages ## Risk of end stage renal disease and death The sub-distribution model showed that CKD stage, age, sex, hypertension, diabetes mellitus, and anaemia had a significant effect on the development of ESRD in patients with CKD ($p \leq 0.001$ for all; Online Resource 11). In particular, anaemia was associated with a fourfold higher risk of ESRD (sub-distribution hazard ratio 4.00, confidence interval [CI]: 2.75–5.81; $p \leq 0.001$). Similarly, we found an increased risk of death in CKD patients with anaemia (hazard ratio 2.24, CI: 2.17–2.31; $p \leq 0.001$) compared to patients without anaemia. ## Discussion To date, this is the largest real-world study on anaemia of NDD-CKD in Italy. Starting with records for approximately 1.5 million inhabitants from a pool of five areas geographically distributed across Italy, 101,143 patients with NDD-CKD were identified. Anaemia of NDD-CKD was characterised in terms of epidemiology, demographics, clinical characteristics, and management. The overall standardised prevalence of NDD-CKD in 2016 was $5.6\%$. Previous Italian studies describing prevalence rates for CKD have not reported data on anaemia, or focused on NDD-CKD, and used different patient inclusion criteria; therefore, those findings are not directly comparable with the results from this research [2, 17]. In this study, the standardised prevalence rate in 2016 for anaemia among patients with NDD-CKD was $33.8\%$, which differs from rates (41–$62\%$) reported by other Italian studies enrolling patients regularly followed in renal clinics [11, 12]. Different prevalence rates reported by studies are expected given the variations in study populations and methodology. Patients with NDD-CKD were predominantly female in the present study. This is consistent with previous studies, which report that the prevalence of CKD was higher in female patients, despite increased severity and risk of progression of CKD in male patients [18]. In the present study, the prevalence of anaemia among patients with NDD-CKD was numerically higher in males than females, which conflicts with findings from previous studies that females were more likely to develop anaemia of CKD than males [3, 19]. This may be a consequence of the higher Hb cut-off value in male patients compared to females (< 13 g/dL versus < 12 g/dL) for the definition of anaemia in the present study. As a result, the Hb cut-off value might be attained at an earlier stage of disease in male than female patients [20]. Furthermore, comorbidities with the potential to induce anaemia (e.g., CV disease and diabetes) were reported in higher proportions of male patients than female patients in this study. Standardised prevalence and incidence of anaemia among patients with NDD-CKD increased through CKD stages, suggesting a greater need for anaemia management in patients with more advanced CKD. Trends in the standardized incidence of anaemia among patients with NDD-CKD from 2014 to 2016, overall and by CKD stage, were variable; particularly for stage 3a where anaemia was less frequent. Anaemia is known to become more common as CKD progresses, and may be associated with increased risks of cardiovascular disease, hospitalisation, cognitive impairment, and death [6]. In the current study, hypertension was the most frequent comorbidity, with rates of $83.8\%$ to $96.4\%$ across the three cohorts, and without a clear relationship with CKD stage. These observations are in line with other studies in patients with NDD-CKD, in which about 80–$87\%$ of patients had hypertension [21, 22]. Similarly, no trend across CKD stages was observed for the prevalence of diabetes mellitus, a clinical condition more frequently associated with new-onset anaemia [23]. The present study also showed that a diagnosis of anaemia in patients with CKD was associated with a higher risk of ESRD, and of death, independently of risk factors related to adverse outcomes in this patient population [11, 22, 24, 25]. Among anaemic patients, we applied two different cut-offs of Hb concentration (≥ 2 records of either Hb < 10 or < 11 g/dL over 6 months) in estimating ESA eligibility to reflect international guidelines and clinical practice in Italy, respectively. This ensured that the data would be relevant worldwide and not just in Italy. Requiring ≥ 2 records over 6 months rather than a single record reduced the chance that a reduction in Hb may have had a temporary cause such as inflammation, infection, bleeding, or surgery [26]. Based on the two eligibility criteria, $44.2\%$ (Hb < 10 g/dL) and $63.4\%$ (Hb < 11 g/dL) of anaemic patients were eligible for ESA treatment. Regardless of the Hb cut-off, the proportion of eligible patients actually treated with ESAs was low (≤ $15.5\%$), although the proportion treated increased across the CKD stages (from < $10\%$ in stage 3a to approximately $40\%$ in stage 5). These results suggest that patients who required treatment for anaemia were inadequately treated when ESAs could have been prescribed. Therapeutic inertia has been reported previously in the context of nephrology care, with $34\%$ of NDD-CKD patients not receiving ESAs despite having anaemia over a 6-month observation period [12]. This phenomenon has been confirmed recently by the CKDopps analysis, which showed that a large proportion of patients with anaemia of NDD-CKD did not receive anaemia medication within 1 year [10]. However, it would be an oversimplification to conclude from the present study that anaemia was undertreated. Indeed, international guidelines recommend that the decision to treat with ESAs is not based on Hb level alone, but should also consider symptoms related to anaemia, prior cardiovascular history (e.g., stroke), active or past history of malignancy, rate of decrease of Hb concentration, prior response to iron therapy, the risk for transfusion, and risks related to ESA therapy [7, 26]. Therefore, the large proportion of untreated patients may reflect the need for healthcare professionals to understand the underlying causes of anaemia before prescribing ESAs, while also reducing the burden of cost of inappropriate prescriptions on the National Health Service. In addition, this large untreated patient population may rely on a sub-optimal healthcare service, which may not refer all patients to the appropriate specialist, such as a nephrologist, to receive treatment. Of note, the present analysis was based on ESAs prescribed to patients in the outpatient setting, and ESA use during hospitalisation was not captured. However, ESAs prescribed during hospitalisation continue to be given after discharge, and therefore the absence of hospital records of ESA use is unlikely to have affected the study findings. The proportion of anaemic patients who received IV iron infusions in the present study ($9.0\%$) was generally consistent with data reported in other studies. A study using data from the CKDopps found that IV iron was received by $12\%$, $9\%$, $33\%$, and $8\%$ of patients with anaemia of CKD stages 3a–5 in Brazil, France, Germany, and the USA, respectively [9]. Another study conducted in Italy found that IV iron was prescribed to approximately $3\%$ of NDD-CKD patients receiving iron supplements [12]. A challenge remains in the estimation of the proportion of patients receiving oral iron therapy. Oral iron supplements are likely to be the predominant form of iron therapy in patients with NDD-CKD, but their over-the-counter availability makes their use more difficult to track. As oral iron data were not readily available in the databases used for the current study, these data were not analysed for the follow-up period. An original finding of this study was the rate of blood transfusions in the NDD-CKD population. Studies from the USA have reported a rising prevalence of blood transfusions in the non-dialysis population, which has been linked with a concomitant and marked reduction in the use of ESAs [27]. This greater use of blood transfusions than ESAs or IV iron is believed to have been triggered by policy changes, including drug reimbursement, and additional FDA ‘black box’ warnings related to the potential adverse effects of ESAs [27]. Indeed, in one US study, the proportion of NDD-CKD patients aged 66–85 years with anaemia who received blood transfusions ($22.2\%$) was found to be almost double the proportion observed in anaemic patients ($11.3\%$) in the present study [27]. This finding could be explained by differences between the US and Italian healthcare systems. Of note, in this study the use of blood transfusions decreased with advancing CKD stage in parallel with increasing ESA prescriptions. This trend was independent of the severity of anaemia (either Hb < 10 g/dL or < 11 g/dL). The ‘real-world’ setting and large sample size are major strengths of this study. Using population-based data, a ‘snapshot’ has been provided of the management of anaemia in more than 100,000 patients with NDD-CKD in Italy, a population for whom these epidemiologic data were lacking. This study, however, has limitations. We cannot exclude that other haemoglobinopathies (in particular thalassaemia) have been classified as renal anaemia in our study, although the advanced age of our study population strongly limits the potential impact of thalassaemia on our estimates. Also, the inclusion of patients with anaemia from non-CKD-related causes in this study may have resulted in a slight overestimation of the number of patients eligible for ESAs, and the occurrence of clinical inertia. Frequently, the use of large administrative databases to investigate a specific clinical question can be associated with limited suitable data, thus reducing cohort size [28]. The databases captured only direct medical healthcare resource use reimbursed by the Italian National Health Service. In patients without ICD-9-CM codes for CKD, only one creatinine value was used to define the presence of CKD, whereas international guidelines require at least two pathological creatinine values or other markers of kidney damage more than 3 months apart [29]. This discrepancy may have resulted in a partial overestimation of CKD cases. A proxy (e.g., specific drugs, hospitalisations) was used for comorbidities, such as diabetes, which may have introduced error in estimating their prevalence. The length of look-back period can also impact estimates of prevalence and incidence: a shorter look-back period can potentially lead to the overestimation of an incidence rate due to the misclassification of prevalent and recurrent cases as incident cases [30]. The minimum look-back period for inclusion in this study was 1 year but could have been up to 8 years depending on the individual patient’s inclusion date; however, the length of look-back period was not analysed for this study. Other limitations include the evaluation of drug treatments based on prescriptions only, and therefore ESA prescriptions could have been related to conditions other than anaemia of CKD. Patients with a cancer diagnosis were excluded from the study in order to reduce this potential bias, as ESAs could be prescribed to these patients and therefore could have influenced the results for CKD and anaemia. Eligibility for ESA treatment was based on Hb levels alone. As other decision criteria for ESA treatment recommended by the guidelines, such as symptoms of anaemia and prior cardiovascular history, were not recorded in the LHU databases, it is not possible to comment on the extent to which patients with anaemia may have been undertreated [7, 26]. The iron supplementation data were limited to IV iron. Lastly, it is unknown to what extent the data for Italy from the current study may be generalised to other countries given the differences in healthcare systems. In conclusion, this study demonstrated that anaemia is a significant issue in patients with NDD-CKD, and the incidence increases with CKD stage. The proportion of patients eligible for ESA treatment who actually received ESAs was relatively low, indicating a potential treatment gap, and suggesting that anaemia may not be adequately controlled. There appears to be an unmet need to remedy the apparent clinical inertia and improve the diagnosis and treatment of anaemia of NDD-CKD in Italy. However, this study also highlights the careful choices made by healthcare professionals when prescribing ESAs, accounting for both the underlying conditions of the patients, and the cost of inappropriate ESA prescription to the Italian National Health Service. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 87 kb) ## References 1. Hill NR, Fatoba ST, Oke JL. 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--- title: 'Maternal factors associated with iron deficiency without anaemia in early pregnancy: ECLIPSES study' authors: - Lucía Iglesias-Vázquez - Mercedes Gimeno - Pilar Coronel - Ida Henriette Caspersen - Josep Basora - Victoria Arija journal: Annals of Hematology year: 2023 pmcid: PMC9998312 doi: 10.1007/s00277-023-05123-7 license: CC BY 4.0 --- # Maternal factors associated with iron deficiency without anaemia in early pregnancy: ECLIPSES study ## Abstract Several population-specific genetic, sociodemographic, and maternal lifestyle factors are related to iron status in early pregnancy, and their identification would allow preventive actions to be taken. The study aimed to identify maternal factors associated with iron deficiency (ID) in early pregnancy in non-anaemic pregnant women from a European Mediterranean country. Cross-sectional study using the initial population of the ECLIPSES study performed in non-anaemic pregnant women before gestational week 12. Serum ferritin (SF) and haemoglobin concentrations were measured to evaluate iron status, and ID was defined as SF < 15 µg/L. Several sociodemographic and lifestyle data were recorded and used as covariates in the multivariate-adjusted regression models. Out of the 791 participants, $13.9\%$ had ID in early pregnancy. Underweight (OR 3.70, $95\%$CI 1.22, 15.53) and parity (1 child: OR 2.03, $95\%$CI 1.06, 3.88; ≥ 2 children: OR 6.96, $95\%$CI 3.09, 15.69) increased the odds of ID, while a high intake of total meat (≥ 108.57 g/day: OR 0.37, $95\%$CI 0.15, 0.87), red/processed meat (≥ 74.29 g/day: OR 0.70, $95\%$CI 0.35, 0.98), protein (≥ 65.05 g/day: OR 0.85, $95\%$CI 0.30, 0.99), and dietary iron (≥ 8.58 mg/day: OR 0.58, $95\%$CI 0.35, 0.94) protected against it. Smoking was also associated with a reduction in ID odds (OR 0.34, $95\%$CI 0.12, 0.99). Baseline BMI, parity, smoking, and diet are associated with ID in early pregnancy in non-anaemic women. Pregnancy planning policies should focus on women at higher risk of ID, such as those who are underweight, multiparous, or following vegetarian diets. This clinical trial was registered at www.clinicaltrialsregister.eu as EudraCT number 2012–005,480-28 and at www.clinicaltrials.gov with identification number NCT03196882. ### Supplementary Information The online version contains supplementary material available at 10.1007/s00277-023-05123-7. ## Introduction Maternal iron status during pregnancy is a public health concern given the high prevalence of anaemia and iron deficiency (ID) during the gestational period. Estimates indicate that around $25\%$ of pregnant women worldwide suffer from anaemia, mainly caused by ID [1]. Anaemia poses significant problems for maternal and child health [2–4], so haemoglobin levels are routinely monitored to detect it preventively. However, there is a high percentage of pregnant women presenting ID without anaemia who are not diagnosed and monitored in daily clinical practice, as serum ferritin (SF) is not routinely measured [5, 6]. In Europe, 10–$33\%$ of pregnant women have ID [7], such as in our study where we have previously observed $14\%$ of participants with ID in early pregnancy [8]. It is therefore of great importance among childbearing women to maintain an adequate iron balance, given the role of iron in multiple physiological processes that take place during pregnancy. Wide evidence supports that prenatal iron imbalances also in absence of anaemia can be detrimental to mother and child. In this regard, previous studies have found that not only anaemia but also ID during the gestational period is associated with preeclampsia, premature births, and even miscarriages [4, 9], as well as physical and cognitive developmental delays in children in postnatal life [3, 10–13]. It is worth mentioning that maternal iron levels in early pregnancy by themselves strongly influence the progression of iron stores during gestation and a woman’s iron status at the end of pregnancy. Indeed, in the ECLIPSES study, we already found a positive association of iron-related biomarker concentrations between the first and third trimester of gestation [8]. Previous studies reached similar findings when assessing the correlation of iron status between early and late pregnancy [14, 15]. Several studies in developed countries have identified some maternal factors that influence the initial iron status of pregnant women, such as sociodemographic, genetic, and lifestyle characteristics [16–19]. However, not all factors that are related have always been analysed together. Many of the risk factors for ID are population-specific, and it is therefore important to disentangle which maternal characteristics place women at risk for ID in each population group. In this regard, few studies have assessed factors associated with iron status in non-anaemic pregnant women. This study aimed to identify maternal factors associated with ID in early pregnancy in a sample of non-anaemic pregnant women, although with a moderate prevalence of ID, from a European Mediterranean country. ## Study design and population The present work included the baseline population of the ECLIPSES study, a community-based randomized controlled trial (RCT) conducted in the province of Tarragona (Catalonia, Spain) [20], before starting the intervention. Briefly, the participants were recruited by midwives in their primary care centres during the routine obstetrical visits prior to gestational week (GW) 12. The main inclusion criteria were over 18 years old and not having anaemia (haemoglobin [Hb] ≥ 110 g/L). Women who had taken > 10 mg iron daily during the 3 months prior to GW12 were excluded. ## Outcome The study outcome was the iron status of women in early pregnancy and its associated factors. For this, concentrations of iron-related biomarkers (Hb and SF) were measured. Since having anaemia was an exclusion criterion, only ID defined as SF < 15 µg/L according to the WHO guidelines was considered [21]. ## Data collection The research staff recorded the sociodemographic and lifestyle data of the participants during a personal interview using specific questionnaires, including maternal age, baseline body mass index (BMI), smoking habit, ethnicity, parity, pregnancy planning, and use of hormonal contraceptives. The educational level and occupational status of women and their partners were also registered. The family’s socioeconomic status (SES) was calculated from the sociodemographic data of participants and their partners, including educational level and occupational status. Dietary assessment was done using a short food frequency questionnaire (FFQ) validated in our population [22]. Food groups assessed included total meat, red and processed meat, fish, fruits, vegetables, legumes, and dairy products as grams per day (g/day). From this information, energy intake (kcal/day) and nutrients (g/day or mg/day) were calculated using the REGAL (Répertoire Général des Aliments) food composition table [23], complemented by a Spanish food composition table [24]. As for the nutrient intake, protein, fibre, vitamin C, calcium, and dietary iron were assessed. Detailed information is available in Aparicio et al. [ 25]. Information from the FFQ allowed us to calculate the percentage of adherence to the Mediterranean diet, considered a high–quality dietary pattern [25, 26]. Extended information on data collection can be found elsewhere [8, 20]. Blood samples were taken on GW12 to perform blood and genetic tests. Haematological parameters (Hb and MCV), some specific biochemical markers (SF and C-reactive protein [CRP]), and genetic mutations of the HFE gene (C282Y, H63D and S65C) were performed. ## Statistical analyses Continuous variables (mean and SD) were described using Student’s t-test and ANOVA test, while the chi-squared test was used for categorical variables (percentages). Natural logarithm (Ln) transformation was applied to normalize the distribution of SF, increasing the validity of analyses, and using the median and interquartile ranges (IQR). Multivariate regression models (multiple linear regressions and logistic regressions) were used to assess the effect of different prenatal predictors on maternal iron status in early pregnancy. The regression models were adjusted for a wide range of potential confounders, described in the bivariate analyses, including age (< 25 years, 25–35 years, and > 35 years), baseline BMI (underweight, BMI < 18.5; normal weight, BMI 18.5–24.9; overweight, BMI 25–29.9; and obesity, BMI ≥ 30), smoking habit (yes or not), SES (low or middle-high), ethnicity (Caucasian, Latin American, Arab, and Black), parity (primiparous, 1 child, or ≥ 2 children), pregnancy planning (yes or no), use of hormonal contraceptives (yes or no), HFE genotype (WT/WT, C282Y/WT, H63D carrier, and S65C carrier), and dietary intake expressed as quartiles. Daily dietary consumption of food groups (total meat, red and processed meat, fish, fruits, vegetables, legumes, and dairy products) and nutrients (protein, fibre, vitamin C, calcium, and iron) were separately included in the regression models to avoid over-adjustment. Daily energy intake as kcal was included in both models. Given that SF can raise in infectious or inflammatory processes, the regression model for SF concentration was additionally adjusted for CRP levels. All statistical analyses were performed using SPSS (version 25.0 for Windows; SPSS Inc., Chicago, IL, USA) and statistical significance was set at $p \leq 0.05.$ ## Results The study included 791 pregnant women, with a median age of 31 (17–46) years and a median gestational age of 12 weeks at the assessment. Sociodemographic and lifestyle characteristics were presented in Table 1. Regarding the body mass index (BMI), near of $60\%$ of participants had normal weight and more than $40\%$ had excess weight, including overweight and obesity. Most of them were Caucasian ($80.4\%$) and belonged to a middle SES ($67\%$), almost $18\%$ were smokers at the recruitment, and $18.3\%$ reported having used hormonal contraceptives before getting pregnant. For $50.4\%$ of the participants, this was their first pregnancy, and $80\%$ had planned to become pregnant. As for the HFE genotype, $33.1\%$ had some mutation, the H63D/WT ($26.1\%$) and C282Y/WT ($3.5\%$) being the most represented genotypes. The less represented genotypes were considered together, leaving the following categories: “WT/WT,” “C282Y/WT,” “H63D carrier” (including C282Y/H63D, H63D/ H63D), and “S65C carrier” (including H63D/S65C, S65C/S65C). In relation to educational level, almost $30\%$ accounted for higher or vocational education, while less than $5\%$ reported having unfinished primary school. Median dietary iron intake was 8 mg/day, and adherence to the Mediterranean diet was high in almost all the participants in the study. A strong statistically significant correlation (Spearman rho 0.915, $p \leq 0.001$) was found between energy (kcal) and iron intake. Compared to women of normal weight, underweight women reported lower intakes of energy (1827.96 and 1724.57 kcal/day, respectively) and iron (7.97 and 7.40 mg/day, respectively), although the difference was not statistically significant (data not shown).Table 1Baseline characteristics of the population in the study ($$n = 791$$)Age, years31 [17–46]Maternal ethnic origin < 2514.7 Caucasian80.4 25–3563.3 Latin American10.7 > 3522.0 Arab6.3Gestational age, weeks12 [8–12] Black2.0 Baseline BMI25.05 (4.50) Asian0.6 Underweight1.6Education Normal weight57.8 Unfinished primary school4.6 Overweight26.4 Primary school28.4 Obesity14.2 Secondary school38.3Smoking habit (yes)17.8 Higher/vocational education28.7 Use of hormonal contraceptives (yes)18.3Familiar SES Parity Low16.2 Primiparous50.4 Middle67.0 1 child37.6 High16.8 ≥ 2 children11.9Food intakePregnancy planning (yes)80.0 Total meat (g/d)92 [0–314] HFE genotype Red and processed meat (g/d)56 [0–239] WT/WT66.9 Fish (g/d)45 [0–179] H63D/WT26.1 Fruits (g/d)244 [0–929] C282Y/WT3.5 Vegetables (g/d)78 [0–441] S65C/WT1.3 Legumes (g/d)15 [0–60] C282Y/H63D1.0 Dairy products (g/d)294 [0–1530] H63D/H63D1.0Energy (kcal/d)1788 [853–4290] H63D/S65C0.2 Nutrient intake S65C/S65C0.2 Protein (g/d)56 [10–167] C282Y/ C282Y0.0 Fibre (g/d)13 [3–35] C282Y/ S65C0.0 Vitamin C (mg/d)77 [3–280] Calcium (mg/d)663 [55–2123] Iron (mg/d)8 [2–24]Data are expressed as mean (SD), median [min–max] and %BMI body mass index, WT wild type, SES socioeconomic status Concentrations of SF and Hb, as well as the percentage of women with ID in early pregnancy, were described according to genetic, sociodemographic and lifestyle characteristics (Supplementary Table 1) and by quartiles of dietary intake (Supplementary Table 2). From the overall sample, $13.9\%$ had ID in early pregnancy. Both SF and Hb concentrations increased across increasing BMI categories. In addition, smokers and primiparous women showed higher SF concentrations and a lower percentage of ID than their counterparts early in pregnancy. As for dietary intake, SF concentrations increased progressively across quartiles of total meat and iron intake, whereas a U-shaped distribution was observed for red and processed meat intake, and an inverse U-shaped for calcium intake by quartiles. Multivariate adjusted analyses showed the effect of prenatal sociodemographic and lifestyle factors on SF and Hb concentrations, and ID at GW12 (Table 2). Parity and being underweight were negatively associated with maternal Hb and SF concentrations, whereas a high intake of total meat (≥ 108.57 g/day), and red and processed meat (≥ 74.29 g/day) increased them in early pregnancy. Additionally, young maternal age (< 25 years) reduced and smoking increased SF concentrations at GW12 but did not show any effect on Hb levels. In relation to ID, smoking, and a high intake of total meat, and red and processed meat, reduced its odds by $66\%$, $63\%$ and $30\%$, respectively. Consistent with the observed effect on SF levels, underweight and non-parous women were more likely to suffer from ID in early pregnancy compared to their peers. As for dietary intake, complementarily, when the models were adjusted for daily nutrient intake instead of food groups consumption, a high intake of protein (≥ 65.05 g/day) and iron (≥ 8.58 mg/day) was positively associated with Hb and SF concentrations in early pregnancy, as well as with a reduction in the percentage of ID.Table 2Associations between early pregnancy concentrations of haemoglobin and serum ferritin and iron deficiency, and predictor variablesHaemoglobin (g/L)Serum ferritin (µg/L)ID (SF < 15 µg/L)β ($95\%$CI)OR ($95\%$CI)Age (years)a < 25 − 0.28 (− 2.68, 2.11) − 0.24 (− 0.45, − 0.03)1.71 (0.70, 4.18) 25–350.00 (Ref.)0.00 (Ref.)1.00 (Ref.) > 351.62 (− 0.16, 3.40) − 0.14 (− 0.30, 0.02)1.06 (0.56, 2.03)Smoking No0.00 (Ref.)0.00 (Ref.)1.00 (Ref.) Yes1.17 (− 0.94, 3.27)0.08 (0.10, 0.27)0.34 (0.12, 0.99)Baseline BMI (kg/m2) < 18.5 − 0.70 (− 2.59, − 0.05) − 0.35 (− 0.90, − 0.20)3.70 (1.22, 15.53) 18.5–24.90.00 (Ref.)0.00 (Ref.)1.00 (Ref.) 25–29.90.89 (− 0.92, 2.71) − 0.04 (− 0.21, 0.12)0.85 (0.43, 1.67) ≥ 301.81 (− 0.44, 4.06)0.02 (− 0.19, 0.22)0.80 (0.33, 1.91)Parity Primiparous0.00 (Ref.)0.00 (Ref.)1.00 (Ref.) 1 child − 2.33 (− 3.99, − 0.68)* − 0.17 (− 0.31, − 0.02)2.03 (1.06, 3.88) ≥ 2 children − 2.76 (− 4.78, − 0.46)* − 0.48 (− 0.71, − 0.25)6.96 (3.09, 15.69)Total meat intake (g/d) < 68.570.00 (Ref.)0.00 (Ref.)1.00 (Ref.) 68.57–91.730.66 (− 1.65, 2.97)0.12 (− 0.08, 0.31)0.53 (0.25, 1.12) 91.74–108.560.06 (− 2.14, 2.26)0.12 (− 0.07, 0.30)0.47 (0.22, 1.02) ≥ 108.572.48 (0.25, 4.72)0.20 (0.15, 0.33)0.37 (0.15, 0.87)Red and processed meat (g/d) < 37.140.00 (Ref.)0.00 (Ref.)1.00 (Ref.) 37.14–55.990.75 (− 1.62, 3.13)0.04 (− 0.17, 0.24)0.42 (0.14, 1.28) 56.00–74.280.30 (− 2.62, 2.03)0.01 (− 0.19, 0.21)0.55 (0.24, 1.29) ≥ 74.291.02 (0.65, 3.91)0.16 (0.08, 0.41)0.70 (0.35, 0.98)Age (years)b < 25 − 0.28 (− 2.23, 2.78) − 0.24 (− 0.45, − 0.03)1.62 (0.66, 4.01) 25–350.00 (Ref.)0.00 (Ref.)1.00 (Ref.) > 350.86 (− 1.00, 2.72) − 0.14 (− 0.30, 0.02)1.11 (0.57, 2.14)Smoking No0.00 (Ref.)0.00 (Ref.)1.00 (Ref.) Yes0.82 (− 1.37, 3.02)0.08 (0.10, 0.27)0.33 (0.11, 0.98)Parity Primiparous0.00 (Ref.)0.00 (Ref.)1.00 (Ref.) 1 child − 2.30 (− 4.03, − 0.58) − 0.17 (− 0.31, − 0.02)2.20 (1.13, 4.28) ≥ 2 children − 1.23 (− 3.96, 1.50) − 0.48 (− 0.71, − 0.25)7.39 (3.10, 17.59)Protein (g/d) < 48.100.00 (Ref.)0.00 (Ref.)1.00 (Ref.) 48.10–55.920.34 (− 0.85, 1.07)0.12 (− 0.09, 0.33)0.84 (0.31, 2.27) 55.93–65.040.85 (0.52, 2.01)0.19 (0.01, 0.37)0.70 (0.22, 0.96)* ≥ 65.051.06 (0.87, 2.65)0.25 (0.10, 0.31)0.85 (0.20, 0.99)Iron (mg/d) < 6.310.00 (Ref.)0.00 (Ref.)1.00 (Ref.) 6.31–7.670.28 (− 0.45, 1.65)0.06 (− 0.20, 0.15)0.89 (0.64, 1.98) 7.68–8.570.94 (− 0.67, 1,96)0.26 (− 0.39, 0.42)0.62 (0.50, 1.32) ≥ 8.581.13 (0.21, 2.98)0.30 (0.12, 0.73)0.58 (0.35, 0.94)ID iron deficiency, SF serum ferritin, BMI body mass indexStatistically significant differences compared to the reference groupaAdjusted for age (< 25, 25–35, > 35), baseline BMI (< 18.5, 18.5–24.9, 25–29.9, ≥ 30), smoking habit (yes/no), SES (low, middle-high), ethnicity (Caucasian, Latin American, Arab, Black), parity (primiparous, 1 child, ≥ 2 children), pregnancy planning (yes/no), use of hormonal contraceptives (yes/no), HFE genotype (WT/WT, C282Y/WT, H63D carrier, S65C carrier), and daily dietary intake (kcal, total meat, red and processed meat, fish, fruits, vegetables, legumes, dairy products)bAdjusted for age (< 25, 25–35, > 35), baseline BMI (< 18.5, 18.5–24.9, 25–29.9, ≥ 30), smoking habit (yes/no), SES (low, middle-high), ethnicity (Caucasian, Latin American, Arab, Black), parity (primiparous, 1 child, ≥ 2 children), pregnancy planning (yes/no), use of hormonal contraceptives (yes/no), HFE genotype (WT/WT, C282Y/WT, H63D carrier, S65C carrier), and daily dietary intake (kcal, protein, fibre, vitamin C, calcium, iron) ## Discussion This study contributes to the identification of some maternal sociodemographic and lifestyle factors associated with the risk of developing ID in early pregnancy in a sample of non-anaemic pregnant women from northeastern Spain, with the aim of preventing this deficiency, which is so common during pregnancy, and to avoid its negative consequences. From all the analysed maternal biological, sociodemographic, and lifestyle conditions, the most relevant predictor of iron status in early pregnancy identified in this work was multiparity, showing almost 7 times higher odds of ID than primiparous women. That has already been repeatedly reported from around the world [16, 17, 27, 28] and the main explanation is that the high iron cost of pregnancy puts multiparous women at risk of not recovering iron stores from one pregnancy to the next one [29]. Another important predictor of ID in early pregnancy was being underweight, in accordance with previous findings [16, 18, 30, 31]. This association would be indirectly reflecting the effect of poor nutritional status, with low food and nutrient intake. Additionally, underweight women in our study reported lower energy and iron intake than those of normal weight, reinforcing the proposed argument. Other of the studied maternal factors showed a minor impact on the women’s iron status. This is the case of age which, although previous studies have associated younger age with a higher risk of ID [17, 32], being a young mother led to a decrease in SF concentration but did not influence the likelihood of ID in our case. As for the smoking habit, smokers usually show higher levels of SF than non-smokers [19] and, therefore, apparently decrease the odds of ID, as has been found in the present study. According to scientific evidence, cigarette smoking disrupts iron homeostasis inducing a systemic accumulation [19, 33, 34], which leads to the detection of an increase in iron reserves. This study also assessed dietary intake and its relationship with iron levels. Thus, it was expected that a higher daily intake of total, red and processed meat, as well as protein and iron, would increase both Hb and SF concentrations in early pregnancy, protecting against ID. However, a concern arising from our results is the low iron intake that women reported (median: 8 mg/day), with only 8 participants ($1\%$) meeting the DRI (16 mg/day), and $35.5\%$ not even reaching the EAR (7 mg/day) that EFSA indicates for pregnant women [35]. However, according to a recent review [36], that is not an isolated problem, but the dietary iron intake of most pregnant women in Europe is* well below the recommendations. Finally, it has to be stated that, contrary to expected, no effect was found of mutated HFE genotypes on iron-related biomarker concentrations or ID in early pregnancy. Despite HFE mutations being common in the European population, they are less frequent in Southern Europe [37, 38]. Especially, the variant with high clinical penetrance, HFE C282Y homozygous [39, 40], is absent in our study population which may preclude observing some influence of HFE mutations on women’s iron status. A high percentage of women with ID in early pregnancy show no signs of anaemia and their low iron stores go undetected and untreated since Hb is often the only biomarker measured for assessing iron status in routine practice [5, 6]. It is worth mentioning that Hb is the biomarker that is altered the latest when iron status is assessed, as it is only altered when iron stores are already nil and erythropoietic synthesis is unable to synthesise Hb in the required amount. We must therefore consider Hb to be an ineffective biomarker for the prevention of ID during pregnancy, where a decrease in iron levels as pregnancy progresses is the norm unless preventive iron supplementation is carried out. Therefore, some women, even if they have normal Hb levels, may have a high chance of developing ID and anaemia later in pregnancy, with negative consequences for their health and that of their baby. Otherwise, measurement of SF concentrations, which is not common in clinical practice, would provide valuable information on iron stores, including incipient iron deficiency states. The concentration of SF is internationally recognized as a very robust biomarker for assessing iron reserves; when low, there is no possibility of false positives for ID and, although it is true that it may increase in presence of infectious/inflammatory processes, the analyses in our study were adjusted for CRP concentration to account for acute infections. It must be said that deciphering which factors are associated with ID in early pregnancy does not mean that iron stores should not continue to be monitored throughout pregnancy. However, it has been observed that early iron status is highly correlated with iron status during and at the end of gestation. Therefore, it is in the early stages of pregnancy, and even periconceptionally, that effective preventive actions can be considered, such as increased promotion of healthier lifestyles from pregnancy planning services and increased monitoring of non-modifiable characteristics where appropriate. Thus, knowing which maternal conditions or characteristics may affect maternal iron stores would allow obstetricians and midwives to focus public health actions on the target population for early prevention of ID, helping women to achieve the best possible iron status. The strengths of the present work include [1] the extensive data collection carried out, which covered many sociodemographic and lifestyle characteristics of the participants, including diet and toxic habits; [2] SF levels were adjusted for CRP concentrations in multivariate analyses allowing control for acute inflammatory processes at the time of measurements that could bias the results. However, some limitations must be considered when interpreting these findings. First, the observational design of the study may influence the external validity of the results. On the assumption that the sample includes only non-anaemic women in early pregnancy, the current findings cannot be extrapolated to other populations. Second, information on recent blood donations, which would reduce iron reserves, was not available. Third, dietary assessment using questionnaires is susceptible to misreporting bias; however, given that women with low BMI in our study did not over-report food intake, as is often the case, we believe that potential misreporting bias would not greatly influence our results. Finally, information about interpregnancy interval was not available, which could have allowed further interpretation of parity as a predictor of iron status. ## Conclusion Since Hb concentration is very often the only biomarker of iron status used in clinical practice, pregnant women with anaemia are treated with iron supplementation, but those with ID without anaemia remain under-diagnosed and untreated, so estimating SF concentration in early pregnancy and its associated factors is of great importance for early prevention of ID. Multiparity and being underweight are strong predisposing factors of maternal ID in early pregnancy in non-anaemic women. A diet high in meat, protein, and iron reduce the likelihood of starting pregnancy with ID. 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--- title: 'Diuretic dose trajectories in dilated cardiomyopathy: prognostic implications' authors: - Vincenzo Nuzzi - Antonio Cannatà - Pierpaolo Pellicori - Paolo Manca - Davide Stolfo - Caterina Gregorio - Giulia Barbati - Daniel I. Bromage - Theresa McDonagh - John G. F. Cleland - Marco Merlo - Gianfranco Sinagra journal: Clinical Research in Cardiology year: 2022 pmcid: PMC9998319 doi: 10.1007/s00392-022-02126-8 license: CC BY 4.0 --- # Diuretic dose trajectories in dilated cardiomyopathy: prognostic implications ## Abstract ### Background For patients with heart failure, prescription of loop diuretics (LD) and of higher doses are associated with an adverse prognosis. We investigated LD dose trajectories and their associations with outcomes in patients with dilated cardiomyopathy (DCM). ### Methods Associations between outcomes and both furosemide-equivalent dose (FED) at enrolment and change in FED in the subsequent 24 months were evaluated. According to FED trajectory, patients were classified as (i) dose↑ (FED increase by ≥ $50\%$ or newly initiated); (ii) dose↓ (FED decrease by ≥ $50\%$); (iii) stable dose (change in FED by < $50\%$); and (iv) never-users. The primary outcome was all-cause-death/heart transplantation/ventricular-assist-device/heart failure hospitalization. The secondary outcome was all-cause-death/heart transplantation/ventricular-assist-device. ### Results Of 1,131 patients enrolled, 738 ($65\%$) were prescribed LD at baseline. Baseline FED was independently associated with outcome (HR per 20 mg increase: 1.12 [$95\%$ CI 1.04–1.22], $$p \leq 0.003$$). Of the 908 with information on FED within 24 months from enrolment, $31\%$ were never-users; $29\%$ were dose↓; $26\%$ were stable dose and $14\%$ were dose↑. In adjusted models, compared to never-users, stable dose had a higher risk of the primary outcome (HR 2.42 [$95\%$ CI 1.19–4.93], $$p \leq 0.015$$), while dose↑ had the worst prognosis (HR 2.76 [$95\%$ CI 1.27–6.03], $$p \leq 0.011$$). Results were similar for the secondary outcome. Compared to patients who remained on LD, discontinuation of LD (143, $24\%$) was associated with an improved outcome (HR 0.43 [$95\%$ CI 0.28–0.65], $p \leq 0.001$). ### Conclusions In patients with DCM, LD use and increasing FED are powerful markers of adverse outcomes. Patients who never receive LD have an excellent prognosis. ### Graphical abstract Among 1131 DCM patients $65\%$ received loop diuretics at enrolment (upper left side). The bar chart on the upper right side shows the categorization in never-users/ dose↓/stable dose/ dose↑ over 24 months of follow-up. At the bottom is reported on the left side of each panel (observation period) the trajectory of LD dose in the four groups (left panel) and in patients who have their LD suspended vs those who continue LD (right panel) in the first two years. On the right side of each panel is shown the incidence of primary outcomes during the subsequent follow-up in the subgroups (outcome assessment) ### Supplementary Information The online version contains supplementary material available at 10.1007/s00392-022-02126-8. ## Introduction Loop diuretics (LD) are a cornerstone of treatment for heart failure (HF) to manage symptoms and signs of congestion, but their effect on long-term outcomes is controversial [1–3]. Observational studies suggest that higher doses of LD are associated with worse outcomes, perhaps because LD use and dose reflect the severity of congestion and cardiac dysfunction [4, 5]. However, LD might also accelerate disease progression by reducing the chance to up-titrate neurohormonal drugs, progressively worsen renal function and induce hypovolemia with consequent iatrogenic hypotension [6, 7]. So far, only a randomized trial including only 40 patients evaluated the effects of LD reduction in stable outpatients with chronic HF [8]. Current guidelines recommend using the lowest dose of LD to control signs and symptoms of congestion [1, 9], but the management of congestion is difficult [10]. Diuretic requirements may decline as disease-modifying therapies are introduced [11, 12] or might increase if the underlying disease progresses or complications, such as atrial fibrillation (AF), develop. In a sub-analysis of the CHAMPION trial, it has been proven that the most frequent therapy adjustment in chronic HF regards the LD dose, more frequently in terms of increased dose, suggesting that selected patients may require LD therapy intensification. Clinical experience and randomized trials suggest that discontinuing LD in carefully selected patients is feasible, safe and well tolerated [13]. On the other hand, suspending LD therapy can also cause a rapid and substantial increase in congestion in other patients [14]. Moreover, under-utilization of LD may lead to persistent congestion which might have adverse effects on cardiac remodelling and prognosis [9, 10]. Dilated cardiomyopathy (DCM) is a common cause of left ventricular ejection fraction (LVEF) impairment leading to HF with reduced (HFrEF) or mildly reduced LVEF. Recovery of LVEF is more common in patients with DCM than HFrEF due to other causes. [ 15]. LD therapy may no longer be required if cardiac function and congestion improve substantially, although the clinical course of DCM may be influenced by several other factors, such as the development of left bundle branch block (LBBB), AF and renal dysfunction and may also be truncated by sudden death [11]. Regular follow-up, with effective implementation of medical and device therapy, generally improves cardiac function and survival [11]. However, the trajectories of LD prescription in DCM were not previously explored and, therefore, the clinical associations and the prognostic implications of LD dose changes are still unknown. The longitudinal evolution of clinical and instrumental findings, alongside the LD dose prescription changes, might provide relevant insights into the pathophysiology linking diuretics and outcomes. In the present study, we investigated the long-term trajectories in the dose of LD in patients with DCM, and their associations with cardiac function and prognosis. ## Study population Consecutive outpatients with DCM, enrolled in the Trieste Heart Muscle Disease Registry between January 1st 1990 and March 7th 2019 who had a complete clinical and echocardiographic evaluation at baseline, including information on LD dose, were included in this study [16, 17]. Patients who had at least one additional clinical assessment performed within 24 (± 4) months after enrolment were considered for further analyses on the diuretic trajectory. Patients with significant coronary artery disease (> $50\%$ stenosis of an epicardial coronary artery, ruled out by coronary angiography or computed tomography), a history of significant systemic hypertension (i.e. blood pressure > $\frac{140}{90}$ mmHg), primary valve disease, active myocarditis, high alcohol intake (≥ 80 g/day [≥ 46 units per week], for at least 5 years), tachycardia-induced or peripartum cardiomyopathy, congenital heart disease or history of any advanced extra-cardiac disease (i.e. terminal cancer) associated with poor short-term prognosis were excluded [18]. Daily furosemide equivalent dose (FED) was calculated by multiplying doses of torasemide by 4 and doses of bumetanide by 40 [1, 19]. The FED was recorded at each visit, whenever possible. All beta-blockers and renin–angiotensin system inhibitors were converted into bisoprolol- and ramipril-equivalent doses, respectively (Supplementary Table S1). Patients were classified according to the LD dose trajectory observed in the first 24 months of follow-up as follows: (i) dose↑, if LD dose was increased by at least $50\%$ compared to baseline or the patient was initiated on an LD; (ii) dose↓, if LD dose decreased by at least $50\%$ or if LD was withdrawn; (iii) never-users, if patients never received LD therapy; and (iv) stable dose, all remaining patients. For patients with multiple visits during the first 2 years, the last visit available before the 2 years of follow-up was considered to define the LD dose trajectory. Twelve patients went through both an increase and a decrease of at least $50\%$ in LD dose within the first 24 months and were not included in the analysis on follow-up diuretic trajectory. The study was approved by the institutional ethical boards and complied with the Declaration of Helsinki. All the patients provided written informed consent. Altogether 1156 patients with DCM were enrolled; of these, 25 did not have information on the use of LD at baseline and were excluded from the present analysis (consort diagram for this study is reported in Supplementary Fig. S1). This analysis included 1131 patients. At enrolment, 738 ($65\%$) patients were receiving a LD and the median FED was 25 (IQR 25–50) mg/day; 508 ($45\%$) received 1–40 mg of FED daily, 171 ($15\%$) received 41–80 mg of FED/day and 59 ($5\%$) received higher doses (Supplementary Table S2). Compared to never-users, those taking doses of LD > 80 mg/day were older, more likely to be men and to have AF and more severe symptoms (New York Heart Association class III/IV). They also had a lower LVEF, a larger left atrium (LA), were more likely to have a restrictive filling pattern (RFP) and were more likely to have moderate or severe MR. ## Echocardiography and genetic test Echocardiograms were recorded on digital media storage at the institutions’ echocardiographic core laboratories and analysed offline by experienced echocardiographers, blinded to patient clinical information and outcomes, according to the latest available recommendations [20]. Details are reported in the supplementary text. A subgroup of patients underwent next-generation sequencing, as previously described [20]. We classified patients in Titin-truncating variant-related DCM vs other forms of DCM, on the basis of the specific natural history of Titin-truncating variant-related DCMs [21, 22]. Additional details are provided as supplementary material. ## Study outcomes The primary outcome was a composite of all-cause mortality, heart transplantation, durable ventricular-assist device implantation and HF hospitalization (ACD/HTx/VAD/HFH). The secondary outcome was a harder outcome including only all-cause mortality, heart transplantation and durable ventricular-assist device implantation (ACD/HTx/VAD). Information about outcomes was obtained from official reports and hospital discharge letters; for patients coming from other regions, information regarding outcomes were collected by direct contact with patients, their families or general practitioners, from the regional healthcare data warehouse and death registers. The trend of clinical and echocardiographic parameters in the four groups of patients divided according to the LD temporal trajectories was analysed in order to study the association between LD dose change and longitudinal functional parameters at follow-up (i.e. LVEF, left atrium dimension, New York Heart Association class, mitral regurgitation (MR), heart rate, beta-blockers and renin–angiotensin system inhibitors dose). No patient was lost to follow-up concerning outcomes. For this analysis, the last day of follow-up was the 7th of April 2021. ## Statistical analysis Descriptive statistics are reported as mean and standard deviation, median and interquartile range [IQR], or counts and percentages, as appropriate. Comparisons of continuous variables were performed with cross-sectional comparisons between groups by the one-way ANOVA test, or the non-parametric Mann–Whitney U-test, when appropriate. The chi-square or Fisher exact tests were used for the comparisons of categorical variables. The trajectory of FED during the follow-up was illustrated using a smoothed conditional means analysis with penalized cubic regression splines. Most modifications of FED occurred within 24 months (Fig. 1) and consequently, the last available FED assessment in this period was used to define LD trajectory. Survival curves for the outcome measure were estimated using the Kaplan–Meier estimator and they were compared using the Log-Rank Test. Univariate and multivariable Cox regression models were fitted for the primary and secondary outcomes. For analyses of the relationship between baseline FED and outcomes, the baseline values for other variables were included in the multivariable models and the time was measured from enrolment. For analyses of the association between LD trajectories and outcomes the LD dose trajectory was treated as a time-depending variable; baseline was set at the 24th month of follow-up. Multivariable models were also taken from this assessment and the results were adjusted for variables recorded at 24 months visit. The same method was used for the assessment of the association between LD withdrawal and outcome. When there was evidence of non-linear effects between a continuous predictor and the primary outcome, the former was modelled using restricted cubic spline analyses with degrees of freedom. The variables considered in the multivariable model for adjustment were derived from univariate analysis. Variables with a statistical significance < 0.05 on univariate analysis were included in multivariable models. Baseline variables associated with diuretic withdrawal at 24-month follow-up were identified by univariate and multivariable Cox regression models. In the sensitivity analysis for the outcome, 19 patients with non-fatal events (i.e. HFH) that occurred between baseline and 2-year follow-up were excluded. The Cumulative Incidence Function was used to show the probability of LD withdrawal during follow-up, taking into account the competing risk of death. All statistical analyses were performed with IBM-SPSS (New York) version 25 and R statistical package version 3.6.2 (R Foundation, Vienna, Austria), with libraries “survival”, “ggplot2” and “splines”. Fig. 1Association of furosemide-equivalent dose at enrolment and primary outcome. Reference value was set at a FED of 20 mg/day (vertical dotted line). HR for the primary outcome is adjusted for age, sex, enrolment period, systolic blood pressure, AF, NYHA III or IV, LVEF, LVEDVI, LAESD, moderate–severe MR, restrictive filling pattern, MRA and ICD implantation ## Prognostic role of baseline loop diuretic dose Higher baseline FED, even after adjusting for potential confounders, was associated with the primary outcome in a linear fashion (HR per 20 mg increase: 1.12 [$95\%$ CI 1.04–1.22], $$p \leq 0.003$$) (Fig. 1). ## Follow-up evolution of loop diuretic prescription During the first 2 years of follow-up (23 [14–26] months), 908 ($80\%$) patients had at least one other clinical assessment with information on LD dose. Among them, 129 ($14\%$) patients were classified as dose↑, 234 ($26\%$) as stable dose, 263 ($29\%$) as dose↓ and 282 ($31\%$) as never-users. Among dose↑, 99 ($77\%$) had their LD dose increased by at least $50\%$, and 30 ($23\%$) were newly initiated on LD therapy (Fig. 2A). Compared to other patients, never-users were younger, were less likely to have severe symptoms, AF or LBBB, had a higher LVEF, a smaller LA, and were less likely to have moderate–severe MR or an RFP (Table 1).Fig. 2Changes, or lack of, in prescriptions of loop diuretics during the first 24 months of follow-up (908 patients) (A). Smoothed mean analysis showing the mean FED evolution during a long-term follow-up according to the trajectory in the first 24 months in the overall population (B). In green are represented never-users patients, in blue dose↓ patients, in brown stable dose patients and in red dose↑ patients; in grey are depicted the confidence intervals. The dotted lines represent the 24-month follow-up timeTable 1Baseline characteristics of the study population with at least a second assessment of loop diuretic dose within 24 months with which to define diuretic trajectory (follow-up population)908 patientsNDose↑ (129, $14\%$)NStable dose (234, $26\%$)NDose↓ (263, $29\%$)NNever-users (282, $31\%$)p-valueClinical evaluationAge, years12953 (39–63)23454 (46–63)26353 (44–62)28244 (34–56)0.002Men, no. (%) 12987 [67]234157 [67]263186 [71]282204 [72]0.547Disease duration, months1207 (1–48)2155 (1–19)2433 (1–14)22812 (3–44) < 0.001Enrollment decade, no (%) 1990–200012948 [37]23466 [28]26391 [35]282102 [36]0.507 2000–201043 [33]85 [36]92 [35]90 [32] 2010–202038 [30]83 [36]80 [30]90 [32] Heart rate, bpm12271 (60–85)22474 (65–86)25175 (64–90)27469 (60–76)0.063AF, no. % 12510 [8]21637 [17]24633 [13]25914 [5]0.004SBP, mmHg125120 (110–136)231120 (110–140)257120 (115–140)275120 (115–140)0.298NYHA III or IV, no. % 12343 [35]22472 [32]24083 [35]2720 [0] < 0.001 LBBB, no. % 12654 [43]23285 [37]26070 [27]27869 [25] < 0.001Creatinine, mmol/l11295 (82–106)20390 (80–106)24491 (80–106)20988 (77–97)0.035Sodium, mEq/l81140 (138–142)147139 (138–142)186140 (138–141)168141 (138–142)0.055Genetic testTTNtv346 [18]6713 [19]7018 [26]10115 [15]0.360Genetic negative/other variants3428 [82]6754 [81]7052 [74]10186 [85]–EchocardiographyLVEF, %12929 (22–34)23428 (23–34)26328 (22–34) 28238 (32–44)0.001LVEDVI, ml/m212997 (78–123)23491 (78–117)26393 (76–118)28277 (68–93) < 0.001LAESD, mm11743 (38–48)24842 (37–48)21743 (38–49)26237 (32–42) < 0.001Moderate or severe MR, no. (%) 12655 [44]233111 [50]255118 [46]26635 [13] < 0.001RFP, no. (%) 10235 [34]20459 [29]17057 [34]24021 [9] < 0.001Medications and device therapyACE-I or ARB or ARNI, no. (%) 129128 [99]234232 [99]263262 [100]282257 [91] < 0.001Ramipril dose equivalent, mg1275 (2.5–10)2325 (2.5–10)2625 (3.75–10)2745 (2.5–7.3)0.031Beta-blockers, no. (%) 129112 [87]234217 [93]263247 [94]282248 [88]0.026Bisoprolol dose equivalent, mg992.5 (1.25–6.25)1972.5 (2.5–5)2283.75 (2.5–7.5)2333.75 (2.5–7.5)0.080MRA, no. (%) 12963 [49]234153 [65]263164 [62]28230 [11] < 0.001Ivabradine, no. % 1294 [3]23410 [4]2635 [2]2825 [2]0.271Diuretics, no. (%) 12999 [77]234234 [100]263263 [100]2820 [0] < 0.001Furosemide dose equivalent, mg12925 (0–25)23425 (25–50)26325 (25–50)2820 (0–0) < 0.001CRT, no. % 12924 [19]23448 [21]26327 [10]28227 [10] < 0.001ICD, no. % 12948 [37]23481 [35]26373 [28]28271 [25]0.025AF atrial fibrillation, SBP systolic blood pressure, NYHA New York Heart Association, LBBB left bundle branch block, TTNtv Titin truncating variant, LVEF left ventricular ejection fraction, LVEDVI left ventricular end-diastolic volume index, LAESD left atrial end-systolic diameter, MR mitral regurgitation, RFP restrictive filling pattern, ACE-i angiotensin-converting enzyme–inhibitors, ARB angiotensin receptor blockers, ARNI angiotensin receptor neprilysin inhibitors, MRA mineralocorticoid receptors antagonists, CRT cardiac resynchronization therapy, ICD implantable cardioverter-defibrillatorp-values are estimated by the χ2 test for categorical variables; continuous variables are estimated by Student’s t-testIn bold are reported variables with significant differences among groups ## Loop diuretic dose trajectories, longitudinal echocardiographic assessment and genetic information Substantial changes in FED were more frequent in the first 2 years of follow-up (Fig. 2B). On multivariable analysis, baseline factors associated with a reduction in FED were higher LVEF (HR per $1\%$ 1.02 [$95\%$ CI 1.01–1.04], $$p \leq 0.006$$) and lower BMI (HR per kg/m2 0.97 [$95\%$ CI 0.94–0.99], $$p \leq 0.022$$) (Supplementary Table S3). Of the 908 patients with at least two clinical assessments, 751 ($83\%$) had an echocardiography paired with the LD dose reassessment. Substantial improvement in LVEF was observed for all patient subgroups; however, the proportion of patients who normalized their LVEF (e.g. LVEF ≥ $50\%$) during the first 2 years of follow-up was greater in dose↓ and never-users ($27\%$ and $23\%$, respectively) compared to dose↑ and stable dose ($5\%$ and $10\%$, respectively). Importantly, only dose↓ had a smaller LA compared to the initial echocardiographic assessment, which was associated with a reduction in moderate-severe MR (that declined from 35 to $10\%$). In contrast, the prevalence of moderate–severe MR at follow-up remained ≥ $40\%$ for those who intensified LD therapy. Few patients classified as dose↓ or stable dose had severe symptoms on follow-up assessment; on the other hand, $41\%$ of those with dose↑ remained severely symptomatic. Overall, doses of other treatments for HF increased during follow-up, except for beta-blockers among the dose↑, with a consequent higher heart rate compared to never users (Fig. 3).Fig. 3Baseline and 24-month follow-up assessment of the main echocardiographic characteristics, NYHA functional class and medical therapy in the four groups. 751 patients with available information on the diuretic trajectory and a paired echocardiographic at follow-up are included. p-values for the repeated measures are calculated with Mann–Whitney U-test. Ramipril equivalent dose includes the conversion of angiotensin converter enzyme inhibitors, angiotensin receptor antagonists and angiotensin neprilysin inhibitor. * p-value vs never-users < 0.05; °p-value vs dose < ↓0.05; ^p-value vs stable dose < 0.05 Finally, we observed that, despite a similar dose of FED at enrolment for patients with and without TTNtv, the LD dose was reduced over the first 2 years of follow-up only for those with TTNtv (Supplementary Fig. S2). ## Association of loop diuretics trajectories with outcome Over a median follow-up of 122 (IQR 62–195) months, 412 ($36\%$) patients met the study primary endpoint (248 deaths, 95 heart transplantations, 14 VAD, 115 HFH). Patients who had FED increased during follow-up had the worst outcome, while the risk was lowest for never-users ($p \leq 0.001$) (Fig. 4). The results were consistent setting the FED re-evaluation at 6- and 12-month follow-up (Supplementary Fig. S3). In adjusted models, compared to never-users patients (reference), stable dose patients had a more than the two-fold greater risk of ACD/HTx/VAD/HFH (HR 2.42 [$95\%$ CI 1.19–4.93], $$p \leq 0.015$$), while dose↑ had the poorest outcome (HR 2.76 [$95\%$ CI 1.27–6.03], $$p \leq 0.011$$) (Table 2). Similar results were observed for the secondary composite outcome (Supplementary Fig. S4). In sensitivity analysis, associations of LD trajectories with outcomes did not change when patients with non-fatal events (i.e. HFH, 20 patients) prior to the follow-up evaluation were excluded (Supplementary Fig. S5A, B).Fig. 4Kaplan–Meier analysis for the primary outcome according to the loop diuretics trajectory during the first 24 months. On the left side (observation period) is shown the trajectory of the FED in the four groups in the first 2 years. On the right side is shown the incidence of primary outcome during the subsequent follow-up in the subgroups (outcome assessment). HFH occurring before the follow-up visit was not considered. In green are represented never-users patients, in blue dose↓ patients, in brown stable dose patients and in red dose↑ patients. In the table are shown the unadjusted (left table) and adjusted (right table) HR, CI and p-values. Details regarding the multivariable model are reported in the textTable 2Time-depending univariate and multivariable analysis for primary outcome (ACD/HTx/VAD/HFH) considering the trajectory of diuretic dose over 24 monthsUnivariate, HR ($95\%$ CI), p-valueMultivariable, HR ($95\%$ CI), p-valueClinical evaluationAge, per year1.020 (1.012–1.028), $p \leq 0.0011.003$ (0.988–1.019), $$p \leq 0.670$$BMI per Kg/m20.991 (0.966–1.016), $$p \leq 0.462$$Men1.426 (1.111–1.832), $$p \leq 0.0051.950$$ (1.134–3.352), $$p \leq 0.016$$Disease duration per month1.002 (0.998–1.005), $$p \leq 0.326$$Enrollment decade (compared to 1990–2000)ReferenceReference 2000–20100.713 (0.547–0.929), $$p \leq 0.0120.984$$ (0.518–1.871), $$p \leq 0.961$$ 2010–20200.466 (0.306–0.710), $p \leq 0.0010.429$ (0.199–0.922), $$p \leq 0.030$$Heart rate per bpm2.889 (1.939–4.303), $p \leq 0.0011.000$ (0.977–1.025), $$p \leq 0.972$$AF1.825 (1.362–2.446), $p \leq 0.0011.287$ (0.536–3.087), $$p \leq 0.572$$SBP per mmHg0.992 (0.984–0.999), $$p \leq 0.0330.998$$ (0.987–1.009), $$p \leq 0.742$$NYHA III or IV4.387 (3.214–5.989), $p \leq 0.0011.136$ (0.328–3.930), $$p \leq 0.840$$LBBB1.184 (0.943–1.488), $$p \leq 0.146$$Creatinine per mmol/l1.001 (0.999–1.002), $$p \leq 0.222$$Diuretics trajectory (compared to never-users group)ReferenceReference Dose↓1.639 (1.118–2.263), $$p \leq 0.0031.554$$ (0.878–2.750), $$p \leq 0.130$$ Stable dose2.885 (2.114–3.936), $p \leq 0.0012.423$ (1.190–4.932), $$p \leq 0.015$$ Dose↑4.060 (2.905–5.675), $p \leq 0.0012.764$ (1.266–6.031), $$p \leq 0.011$$EchocardiographyLVEF per %0.944 (0.933–0.955), $p \leq 0.0010.971$ (0.950–0.992), $$p \leq 0.008$$IVS per mm0.980 (0.927–1.035), $$p \leq 0.468$$LAESD per mm1.069 (1.051–1.087), $p \leq 0.0011.045$ (1.011–1.080), $$p \leq 0.008$$Moderate or severe MR2.443 (1.861–3.208), $p \leq 0.0012.254$ (1.427–3.559), $p \leq 0.001$RFP2.757 (1.858–4.091), $p \leq 0.0011.602$ (0.872–2.944), $$p \leq 0.129$$Medications and device therapy ACE-i/ARB/ARNI1.185 (0.663–2.117), $$p \leq 0.567$$ Ramipril/ARNI dose equivalent per mg1.009 (0.989–1.028), $$p \leq 0.387$$ Beta-blockers0.986 (0.651–1.492), $$p \leq 0.945$$ Bisoprolol dose equivalent per mg0.978 (0.948–1.009), $$p \leq 0.163$$ MRA1.846 (1.422–2.396), $p \leq 0.0011.011$ (0.564–1.810), $$p \leq 0.972$$ Ivabradine1.874 (0.826–4.253), $$p \leq 0.133$$ Furosemide dose equivalent per 20 mg1.127 (1.106–1.150), $p \leq 0.0010.980$ (0.871–1.103), $$p \leq 0.705$$ CRT1.126 (0.838–1.513), $$p \leq 0.432$$ ICD1.290 (1.031–1.614), $$p \leq 0.0261.045$$ (0.692–1.578), $$p \leq 0.833$$All the variables are measured at 24 monthsBM body mass index, AF atrial fibrillation, SBP systolic blood pressure, NYHA New York Heart Association, LBBB left bundle branch block, LVEF left ventricular ejection fraction, IVS interventricular septum, LAESD left atrial end-systolic diameter, MR mitral regurgitation, RFP restrictive filling pattern, ACE-i angiotensin-converting enzyme–inhibitors, ARB angiotensin receptor blockers, ARNI angiotensin receptor neprilysin inhibitors, MRA mineralocorticoid receptors antagonists, CRT cardiac resynchronization therapy, ICD implantable cardioverter-defibrillatorIn bold are reported variables with significant association with the outcome at multivariable analysis ## Loop diuretics therapy withdrawal Of the 608 patients who were taking LD at baseline, 143 ($24\%$) had their LD stopped within 24 months; a further 49 ($8\%$) patients had LD withdrawn during longer follow-up (Supplementary Fig. S6). Compared to those who had their LD withdrawn, those who continued LD were older, had a lower LVEF, a larger LA and were more likely to have moderate–severe MR, RFP and AF (Supplementary Table S4). On multivariable analysis, younger age, absence of moderate–severe MR and higher doses of beta-blockers were independently associated with stopping LD therapy within 24 months (Supplementary Table S5). LD therapy withdrawal within 24 months was associated with a reduced risk of the primary (HR 0.37 [$95\%$ CI 0.29–0.48], $p \leq 0.001$) and secondary (HR 0.34 [$95\%$ CI 0.26–0.45], $p \leq 0.001$) outcome (Supplementary Fig. S7). ## Discussion In a large cohort of patients with DCM managed at a national tertiary-care center, use, higher daily dose and increasing the dose of LD all identified a greater risk of adverse outcomes. For patients who were not prescribed or discontinued LD, the prognosis was excellent (Graphic abstract). Changes in LD dose were more frequent during the 24 months and these modifications were associated with consistent changes in cardiac structure and function. For the subset of patients with genetic sequencing, only those with TTNtv-truncating variant-related DCM reduced their doses of diuretics during follow-up, perhaps suggesting a greater response to anti-HF therapy. The principal therapeutic goals of managing DCM are prolonging life, control of symptoms, maintenance or improvement in quality of life and reduction in disability and morbidity. These goals can be achieved by implementing guideline-recommended pharmacological, device therapy, diuretics, and advices on life style [1, 18]. In our cohort, more than one-third of patients were not receiving LD at enrolment, which is perhaps a slightly higher proportion than other registries that enrolled outpatients with more diverse etiologies of HF [4, 11]. Several reasons may explain these differences. For instance, family screening programs for DCM patients might have allowed early diagnosis and initiation of HF treatment in our patients, when LV dysfunction was still asymptomatic; alternatively, a diagnosis of DCM may have been triggered by an arrhythmic event rather than symptoms or signs of congestion. Finally, patients with DCM might be characterized by a lower propensity to develop fluid overload compared to other forms of HF [23]. The association between LD use and higher doses with poor prognosis in ambulatory patients with HF is well known [4, 5], even among those with mild symptoms. In a post-hoc analysis of the EMPHASIS-HF trial, the use of LD was more strongly associated with prognosis than a history of HF hospitalization or plasma natriuretic peptide concentrations [24]. An association between the use of LD and adverse cardiovascular outcomes has been reported even in the absence of a diagnosis of HF for patients with type II diabetes and for those with AF. It is possible that the diagnosis of HF is often missed because signs and symptoms of congestion are masked by the use of LD [25]. The relationship between changes in LD treatment and prognosis in patients with HF has received less attention. Using the European Society of Cardiology Heart Failure Long-Term (ESC-HF-LT) Registry, Kapelios and colleagues showed that $16\%$ of patients with all-etiologies HF had their LD dose increased and only $8\%$ had it decreased; while in our DCM cohort, up to $29\%$ of patients underwent LD down-titration [26]. They also found that LD dose escalation was associated with higher mortality but those with a reduction in LD dose had a similar outcome to those maintained on a stable dose. However, these authors used different criteria to define dose↓ or dose↑ (i.e. any change from baseline, respectively) and only included patients who were receiving an LD at enrolment. The ReBIC-1 randomized trial demonstrated the feasibility of LD withdrawal in carefully selected outpatients with HFrEF, but did not provide information regarding long-term outcomes [13]. While neurohormonal antagonists should be continued for DCM lifelong to prevent clinical deterioration, [27], our findings suggest that when LD can be withdrawn without worsening congestion the subsequent risk of adverse cardiovascular events is low. Indeed, in this retrospective DCM population, when the treating physician considered it safe to reduce or even withdraw LD, the subsequent risk of adverse outcomes was significantly reduced. On the other hand, patients that may benefit from LD dose reduction have to be carefully selected, as in $40\%$ of our cohort the LD dose was increased or maintained stable to avoid congestion and increased filling pressure. The most likely explanation for the association between LD and prognosis is that patients with more severe cardiac and renal dysfunction also have more evidence of congestion, requiring treatment with LD to control symptoms and signs. Our results, showing the more favourable evolution of clinical and echocardiographic data in patients reducing their LD dose, support this hypothesis. Thus, LD are associated with a worse prognosis, which might be much worse for such patients if they were not used [5]. However, it is also possible that LD impair renal function and cause hypotension that reduces the ability to titrate other treatments for HF [6]. The analyses on the longitudinal trend of clinical and echocardiographic data according to the LD dose trajectories represent a novel result. We observed that patients who did not receive LD had better LV function and smaller ventricular and atrial volumes. Conversely, patients in whom LD were increased were less likely to show favourable cardiac reverse remodelling at follow-up. A dilated and dysfunctional LV will increase the severity of MR and LA dilation; eventually, this will lead to pulmonary and systemic venous hypertension and congestion requiring LD therapy. Further prospective studies are required to clarify if clinical and echocardiographic evolution might guide LD therapy in patients with DCM. Interestingly, for the subset of patients with genetic characterization, reduction in LD appeared more common in those with TTNtv-related DCM, perhaps suggesting a better response to HF treatment [28]. This finding should be confirmed in other cohorts. Currently, prognostic stratification for DCM is mostly based on the severity of LV systolic function on imaging, assessment of fibrosis by cardiac MRI, biomarkers, cardiopulmonary exercise testing or genetic studies [23, 29, 30]. Information on the use and dose of LD is readily available to healthcare providers. Our study demonstrates that LD use and dose are powerful prognostic markers of clinical outcomes; patients with DCM who do not take LD have an excellent prognosis and can be reassured. They should avoid interventions that are associated with risk or substantial costs and should be enrolled less in trials investigating the effects of new treatments on morbidity and mortality because there is little prospect that they will benefit but they might experience serious adverse effects. Patients prescribed with LD, especially at higher or increasing doses, are at much higher risk and require further investigation to identify remediable causes and should be included in randomized trials of new interventions. Altogether, these results support the notion that LD should be used as a pharmaco-epidemiological marker of underlying congestion, cardiac dysfunction, and adverse prognosis in patients with DCM. ## Limitations The long enrolment period may have introduced a bias, as a treatment for HF has improved in the past few decades. However, in our centre, all patients with DMC and LV dysfunction received comprehensive treatment with anti-neurohormonal therapies since 1990 [31], and we adjusted for the enrolment period in our multivariable models. We report all-cause mortality; the incidence of non-cardiovascular death is low in younger patients with DCM [16]. We did not attempt to investigate possible differences between different types of LD because $99\%$ of patients enrolled in this study were treated with furosemide. The FED change cut-off chosen to classify patients was defined according to the clinical significance of an increase of at least $50\%$ in FED. Information on natriuretic peptides, genetic testing, cardiac magnetic resonance and clinical congestion was not systematically available. Finally, the retrospective nature allowed us to find prognostic associations as we could not exclude the presence of potential unmeasured confounders. ## Conclusion For patients with DCM, LD use and increasing dose are associated with a poor prognosis and adverse cardiac remodelling. Conversely, in selected patients not receiving LD or being able to stop them is associated with a greater likelihood that ventricular function will recover and a good prognosis. LD use and dose should be included in prognostic models for DCM and considered as inclusion criteria in clinical trials when their objective is to reduce morbidity and mortality. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 813 KB) ## References 1. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M. 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--- title: Genome-wide identification, expression profile and evolutionary relationships of TPS genes in the neotropical fruit tree species Psidium cattleyanum authors: - Drielli Canal - Frank Lino Guzman Escudero - Luiza Alves Mendes - Marcia Flores da Silva Ferreira - Andreia Carina Turchetto-Zolet journal: Scientific Reports year: 2023 pmcid: PMC9998390 doi: 10.1038/s41598-023-31061-5 license: CC BY 4.0 --- # Genome-wide identification, expression profile and evolutionary relationships of TPS genes in the neotropical fruit tree species Psidium cattleyanum ## Abstract Terpenoids are essential for plant growth, development, defense, and adaptation mechanisms. Psidium cattleyanum (Myrtaceae) is a fleshy fruit tree species endemics from Atlantic Forest, known for its pleasant fragrance and sweet taste, attributed to terpenoids in its leaves and fruits. In this study, we conducted genome-wide identification, evolutionary and expression analyses of the terpene synthase gene (TPS) family in P. cattleyanum red guava (var. cattleyanum), and yellow guava (var. lucidum Hort.) morphotypes. We identified 32 full-length TPS in red guava (RedTPS) and 30 in yellow guava (YlwTPS). We showed different expression patterns of TPS paralogous in the two morphotypes, suggesting the existence of distinct gene regulation mechanisms and their influence on the final essential oil content in both morphotypes. Moreover, the oil profile of red guava was dominated by 1,8-cineole and linalool and yellow guava was enriched in α-pinene, coincident in proportion to TPS-b1 genes, which encode enzymes that produce cyclic monoterpenes, suggesting a lineage-specific subfamily expansion of this family. Finally, we identified amino acid residues near the catalytic center and functional areas under positive selection. Our findings provide valuable insights into the terpene biosynthesis in a Neotropical Myrtaceae species and their potential involvement in adaptation mechanisms. ## Introduction Psidium cattleyanum Sabine (Myrtaceae), commonly known as araçá, cattley guava, strawberry guava, and cherry guava, is a fleshy fruit belonging to the Neotropical Myrteae tribe (Myrtaceae). The species is native to the Atlantic Forest, where it has readily adapted to a variety of climates, is associated with wet forests across the tropics1, occurs in areas under stress conditions2,3, and is considered among the worst invasive species4,5. The genus Psidium is rich in essential oils6,7, stored in the leaf secretory cavities8–10, and traditionally used for extraction, with inexpensive resources and potential uses in the pharmaceutical and medicine industries2,11. These essential oils regulate environmental processes and ecological interactions between organisms, such as defense against herbivores and pathogens11,12, protection against abiotic environments13,14 and attraction of pollinators, especially in neotropical species with fleshy berries that serve as a food source15,16. Psidium cattleyanum species is divided into two morphotypes. The red guava (P. cattleyanum Sabine var. cattleianum) and yellow guava (P. cattleyanum Sabine var. lucidum Hort). The ripe fruits of red and yellow guava present red and yellow epicarps, respectively17. They also exhibit differences in antioxidant activity and phenolic content18, leaf morphology, and phytochemistry, size, and habit19–21. Previous studies have also detected considerably different oil profiles of yellow and red guava, which was attributed to differences in isolation techniques or the area of collection22–25. However, the genetic and evolutionary factors that can induce modifications in the secondary metabolism of the plant in the two morphotypes of P. cattleyanum remain largely unknown. Therefore, the study of genes of the biosynthetic pathway of these compounds in this species is highly relevant. Because Myrtaceae species exhibit the highest concentrations and functional versatility of foliar terpenes among plants, significant efforts have been made to investigate the molecular mechanisms determining the structural diversity of terpene synthase (TPS) genes in this family. However, to the best of our knowledge, there are still no studies of these genes in Neotropical Myrtaceae species. The TPS family catalyzes the cyclization and rearrangement of geranyl diphosphate (GPP) or its cis-isomer neryl diphosphate (NPP) into monoterpenes (C10) and trans-geranyl diphosphate (GGPP) into diterpenes (C20) in the plastidic 2C-metil-D-eritritol-4-fosfato (MEP) pathway. In addition, farnesyl diphosphate (FPP) is converted into sesquiterpenes (C15) and triterpenes (C30) via the mevalonate (MVA) pathway in the cytosol, endoplasmic reticulum, and peroxisomes26–29. TPS controls not only the terpene chemodiversity present in plants but is also responsible for the unique composition of each taxon30. Recent studies have revealed that, among those with dried capsular fruits, the species of the Eucalypteae tribe, including Eucalyptus grandis, E. globulus, and Corymbia citriodora, contain the largest number of complete TPS genes reported in eudicotyledons (70, 69, and 89 complete genes, respectively). This is due to the key role of terpenes in defense over their long lifespans26,29,31. Terpene synthase genes have also been identified in Melaleuca alternifolia and Leptospermum scoparium, with 37 and 49 putative TPS genes, respectively32,33. The oil profile patterns in foliar terpenes across this species, which are common in forest woodlands, are the monoterpenes α-pinene and 1,8-cineole. Instead, fleshy-fruited species from Myrtaceae family have low foliar 1,8-cineole concentrations, with a greater diversity of abundant foliar sesquiterpenes34. In the present study, we aimed to conduct a comprehensive genome-wide analysis of TPS genes in P. cattleyanum to gain insights into the underlying mechanisms responsible for the differences in terpenoid biosynthesis and in the essential oil profiles in two morphotypes. Based on genomic and transcriptomic data, we identified the TPS gene repertoire and revealed its expression pattern in two P. cathleyanum morphotypes. We also examined the expansion and diversification of the TPS gene family among the Myrtaceae species. Finally, we investigated key amino acids using positive selection analysis to understand their effects on product specificity and consequently explain the chemical variability of the essential oil compounds. Our findings provide a foundation for deciphering TPS biosynthesis in P. cattleyanum and diversification of the two morphotypes. This knowledge will contribute to further studies on natural populations and the evolution of the Myrteae tribe, providing evidence of the successful distribution and adaptation of these species. ## Genome-wide identification of putative terpene synthases We performed a genome-wide sequence homology search to identify the complete repertoire of TPS genes across the Psidium cattleyanum morphotype genomes. The genomes were assembled separately for comparison. Based on the conservation of hidden Markov model (HMM) profiles and BLAST searches, we identified 110 loci in the red genome (RedTPS) (Supplementary Table S1) and 106 loci in the yellow genome (YlwTPS) (Supplementary Table S2). Three RedTPS and seven YlwTPS sequences were excluded from further analysis due to the presence of premature stop codons, lack of both C and N terminal domains, presence of less than three exons, and one gene that presented 38 exons likely to be pseudogenes, partial genes, or assembly errors (Supplementary Table S3). In YlwTPS, 28 lost the C-terminal domain (PF03936), 45 lost the N-terminal domain (PF01397), and 33 of them contained two domains. In RedTPS, 27 lost the PF03936 domain, 49 lost the PF01397 domain, and 34 of them contained two domains (Supplementary Tables S1, S2). Of the remaining TPS gene models, only 32 RedTPS and 30 YlwTPS were classified as full-length putative loci coding genes (Supplementary Tables S1, S2). The number of TPS may be underrepresented due to incomplete sequences or atypical gene structures obtained and in part due to draft genome assembly. To identify putative orthologs between the two morphotypes, we created a sequence percentage identity matrix (Supplementary Table S4), and genes containing the top hits are shown (Supplementary Table S5). Only four TPS partial genes had identical sequences in the two genomes (Pca_red_g91813 and Pca_ylw_g91315; Pca_red_g71488 and Pca_ylw_g60043; Pca_red_g21900 and Pca_ylw_g23854; Pca_red_g46186 and Pca_ylw_g20612). In addition, sequence identity among TPS genes between the two morphotypes was considerably lower, with only nine full length genes having greater than $90\%$ amino acid identity (Supplementary Table S5). However, comparing partial and full genes, only 23 showed > $90\%$ identity. The number increased when comparing only partial genes, where 29 genes showed > $90\%$ identity (Supplementary Table S5). Most TPS genes of subfamilies TPS-a, TPS-b, and TPS-g contained six to nine exons (Fig. 1a), with exceptions (Supplementary Tables S1, S2). Genes from the remaining subfamilies, TPS-c, TPS-e, and TPS-f, contained 7–14 exons (Fig. 2A). Moreover, only one full YlwTPS (Pca_ylw_g56204) and four RedTTPS (Pca_red_g44464, Pca_red_g43593, Pca_red_g28651, and Pca_red_g25997) lacked the highly conserved aspartate-rich motif “DDXXD” (Supplementary Tables S1, S2). The TPS-c subfamily is present in land plants and is characterized by the “DXDD” motif but not the “DDXXD” motif in their proteins, which was detected in only one RedTPS and two full YlwTPS26. The second motif in the C-terminal domain, “NSE/DTE”, is less conserved in TPS and presents the variation “(L,I) × (D,N,G)D(F,I,L) × (S,T,G,A)xxxE”. Figure 1Phylogeny and gene structure of TPS from secondary metabolism. ( a) Conserved domains in TPS genes and their consensus sequences from P. cattleyanum. ( b) Phylogenetic tree of the Tps-a, Tps-b and Tps-g subfamilies from P. cattleyanum genome and characterized representative TPS from other Myrtaceae species. This tree was constructed through maximum likelihood analysis comparing the red and yellow morphotypes (Pca_red and Pca_ylw), C. citriodora subsp. variegata (Cci), E. grandis (Egr), E. globulus (Egl), M. alternifolia (Mal) and A. thaliana (Ath). Functional characterized terpene synthases are written in bold. Bootstrap values supported by < $60\%$ are noted by number. A few species from TPS-c clade were used as the outgroup. Figure 2Phylogeny and gene structure of the TPS from primary metabolism. ( a) Conserved domains in TPS genes and their consensus sequences from P. cattleyanum. ( b) Phylogenetic tree of the Tps-c, Tps-e and Tps-f subfamilies from P. cattleyanum genome and representative TPS from other Myrtaceae species. This tree was constructed through maximum likelihood methods comparing the red and yellow morphotypes (Pca_red and Pca_ylw), C. citriodora subsp. variegata (Cci), E. grandis (Egr), E. globulus (Egl), M. alternifolia (Mal) and A. thaliana (Ath). Functional characterized terpene synthases are written in bold. Bootstrap values supported by > $60\%$ are noted by number. A few species from TPS-a clade were used as the outgroup. In the clade corresponding to the TPS-b subfamily monoterpene synthases, using different algorithm predictors, we found that only five full RedTPS and three full YlwTPS have an N-terminal transit peptide required for plastidial targeting (Supplementary Table S6). We identified seven RedTPS and five YlwTPS with the N-terminal domain containing an “RRX8W" motif. In addition to these motifs, there is a highly conserved arginine-rich “RXR” motif. The TPS-g (Pca_ylw_g32591; Pca_red_g25997) subfamily is closely related to TPS-b; however, it lacks the conserved “R(R)X8W” motif in its encoded proteins, and its members may function in producing acyclic mono-, sesqui-, and diterpene products26. ## Molecular evolutionary analysis To accurately classify the members of the P. cattleyanum TPS gene family based on sequence relatedness as well as functional assessments, we first collected 164 sequences of full-length TPS genes (containing the two TPS domains and having sequence lengths greater than 200 amino acids) from previous studies of species functionally characterized A. thaliana and E. grandis (Myrtaceae family) (Supplementary Fig. S2). The topology of the phylogenetic tree allowed us to divide TPSs into subfamilies belonging to secondary metabolism, clustered with subfamily TPS-a, which produces sesquiterpenes (C15) with 14 RedTPS and 12 YlwTPS (Table 1, Fig. 3), and TPS-b, which encodes enzymes that produce monoterpenes with 14 RedTPS and 14 YlwTPS. Only one TPS-g gene was found in each morphotype, which predominantly produced acyclic mono-, sesqui-, and diterpenes (Table 2, Fig. 1b). In the cluster representing primary metabolism, a single gene, TPS-c, which produces diterpenes (C20), was found in P. cattleyanum red morphotype, while two were found in the yellow morphotype (Table 2; Fig. 2B). In the TPS-e/f subfamily, which produces mono-, sesqui-, and diterpenes, a single gene was found in the yellow morphotype, whereas two were found in the red morphotype. Our analysis including other Myrtaceae TPS genes showed that all TPS proteins identified in this study clustered into monophyletic-specific clades related to the subfamilies. The TPS-a and TPS-b subfamilies were the most expanded, accounting for approximately $80\%$ of the total TPS full length genes identified (Fig. 3).Table 1Chemical constituents of leaf oil from red and yellow morphotypes of Psidium cattleyanum. NCompoundaRIbContent (%)cClassificationdRedYellow1α-Pinene93010.035.4MH2β-Pinene972–3.2MH3β–Myrcene991–9.5MH41,8-Cineole102859.522.4MO5β-Ocimene10392.9–MH6γ-Terpinene10584.1–MH7Linalool11009.63.7MO8α-Terpineol11895.62.7MO9β-Caryophyllene14142.52.7SH10Nerolidol15632.4–SO11Caryophyllene oxide1579–6.6SO12Viridiflorol1598–2.2SO13Aromadendrene epoxide1633–2.5SOTotal96.690.9aMajor compounds listed in the elution order using Rtx®-5MS column.bRI: Retention index determined by the normalization of retention times with respect to an n-alkane mixture (C7–C40)87–89.cCompounds with a relative area of > $2\%$ were identified.dTerpenic classification: oxygenated monoterpene (MO), hydrogenated sesquiterpene (SH), oxygenated sesquiterpene (SO).Figure 3Proportion of TPS gene subfamilies found in Myrtaceae species. The number of genes in each subfamily relative to the total number of genes indicates the proportion of TPS genes. Psidium cattleyanum had the highest proportion of TPS-b1 genes (~ $40\%$).Table 2Numbers of TPS genes in Myrtaceae species. SpeciesTotal TPS gene modelsPutative full lengthFull lengthabcde/fghPsidium cattleyanum red11032141410210Psidium cattleyanum yellow10630121420110Corymbia citriodora12784383310390Melaleuca alternifolia3737141410440Eucalyptus globulus14310344372010100Eucalyptus grandis17270381510880Leptospermum scoparium49239710420 Site model selection analyses indicate sites that evolve under positive selection fit the data significantly better than the respective null models (M8 vs. M7: LRT = 14.46, df = 2, $$p \leq 0.001$$), however, the posterior probability was low ($p \leq 0.55$) (Supplementary Table S9). Therefore, positive selection may only occur during specific stages of evolution or in particular branches, we tested a branch-specific model to detect positive selection in the three clades formed in the TPS-b subfamily, which were fixed as foreground branches. Clade 1 contained only TPS-b1 genes from Eucalyptus and Psidium species. Clade 2 contained only TPSb-1 genes from Psidium species. A third clade contained some genes from Populus, Vitis, and Eucalyptus which were grouped with three pinene synthase genes from Psidium and classified as the only TPS-b2 genes. The one-ratio branch model indicated an overall purifying selection for TPS evolution (ω mean values smaller than 1.0). We also investigated selective pressure using the branch site model according to the likelihood ratio tests (LRT) and comparisons of clade 1 ($$p \leq 0.26$$), clade 2 ($p \leq 0.05$), and clade 3 ($p \leq 0.05$), indicating that some sites were statistically significant (Supplementary Table S12). However, only five residues were strongly identified to be under positive selection in clade 2, located in the N-terminal portion of TPSb-1 genes of Psidium, including residue 121, with an aspartate (D) and the alteration to a leucine (L) in the foreground branches, and residue 124 with the most commonly found lysine (K), arginine (R), or tryptofan (W) and its alteration to alanine (A) in foreground branches. We also detected residues 222 with a cysteine (C) and alteration to valine (V) or leucine (L) in the foreground branches, and site 279 with a threonine (T) or isoleucine (I) that presented an alteration to cysteine (C) or tyrosine (Y) in the foreground branches, around “RDR” and “DDXXD” motifs in the C-terminal portion (Fig. 4). Clades 1 and 3 show the residuals with weak signs of positive selection. Figure 4Positive selected sites in TPS-b1 branch including only Psidium genes. ( a) The pinene synthase sequence Pca_red_g24428 representing clade 2 as foreground clade. ( b) The linalool synthase sequence Pca_red_g28382 represents clade 2 as foreground clade. Amino acids that were identified on positive selection (red circles) are demonstrated on the protein sequence of these representative species corresponding to the sites in each alignment presented on Supplementary Table S12. Also, the representation of mainly motifs of the entrance of the active site (yellow square, circles, and triangle) represented for the “DDXXD”, “NSE/DTE” and “RXR” domains. ## Global and differential expression analysis associated with the terpene biosynthesis To gain more insight into the TPS biosynthetic pathway, global and differential expression profiles were evaluated on TPS genes from RNA samples extracted from its leaves and compared in two morphotypes. After library construction, illumina sequencing, and assembly, approximately 84 and 86 million paired end reads already cleaned were generated for yellow and red morphotypes, respectively. Looking at total gene expression across the two morphotypes, approximately $30\%$ of TPS genes were expressed in leaves (transcripts anchored in 35 genes in the red genome and transcripts anchored in 30 genes in the yellow genome). Genes that showed some expression patterns fell into five clades (Supplementary Tables S7, S8). We found 17 full-length and 18 partial TPS genes with evidence of expression in the red genome and 13 full-length and 18 partial TPS genes in the yellow genome. A heat map showing differential gene expression using DESeq2 based on \|log2Fold Change \|≥ 1 and FDR < 0.05 in the red and yellow morphotypes, with two biological replicates in leaves, is shown in Fig. 4. As the two genomes were assembled separately and belonged to the same species, two heatmaps were generated, anchoring all transcripts in the red genome (Fig. 5A) and all transcripts in the yellow genome (Fig. 5B). Therefore, statistical analysis can be performed and then compared. Among these, 19 gene sequences were upregulated in the red morphotype, with only 10 full TPS genes (Fig. 5C; Supplementary Tables S7, S8). In the yellow morphotype, 32 TPS genes were upregulated, but only 14 were full TPS. A total of 12 TPS genes showed the same expression pattern between the two transcriptome comparisons and > $90\%$ of identity, indicating that the same gene was found in the different genome assemblies. Figure 5Differential expression of terpene synthase genes of P. cattleyanum leaves. ( a) Red morphotype genome. ( b) Yellow morphotype genome. The blue colored cells indicate the Log2 transformed FPKM (unit of fragments per kb of exon per million mapped reads) with no expression and value zero in this tissue (down-regulated), and red color indicates a higher percentage of total expression for a given gene (up-regulated). Squares represent full-length genes and black dots represent partial genes. ( c) Veen Diagram representing the up-regulated unique transcripts common between the morphotypes. ## Terpenoid profling in Psidium cattleyanum leaves The leaves of Psidium cattleyanum were examined for chemical compositions of the volatile terpene compounds, to investigate the genetic influence on the chemical variations of the oil content between the two morphotypes. The content of each terpenoid was calculated as a percentage of the total essential oil using gas chromatography with a flame ionization detector (GC-FID) and gas chromatography coupled to mass spectrometry (GC–MS) approaches. Thirteen compounds were identified, and the most abundant monoterpenes in both morphotypes were 1,8-cineole, α-pinene, linalool, and α-terpineol (Table 1; Supplementary Fig. S1A). Although these compounds were commonly found, they showed significant quantitative variation. For example, the α-pinene showed a large difference of $35.4\%$ in yellow and only $10.0\%$ in red morphotype; 1,8-cineole showed a difference of $59.5\%$ in the red and $22.4\%$ in yellow morphotype; whereas linalool showed a difference of $9.6\%$ and $3.7\%$ in the red and yellow morphotypes, respectively. In addition to quantitative variations, the plants used also showed qualitative variations in the chemical composition of their essential oils. The hydrogenated monoterpenes β-ocimene ($2.9\%$) and γ-terpinene ($4.1\%$) were observed only in red morphotype essential oil, and the oxygenated sesquiterpene nerolidol ($2.4\%$) (Supplementary Fig. S1B). The hydrogenated monoterpenes β-pinene ($3.2\%$) and β-myrcene ($9.5\%$) were observed only in yellow morphotype, and the oxygenated sesquiterpenes caryophyllene oxide ($6.6\%$), aromadendrene epoxide ($2.5\%$), and viridiflorol ($2.2\%$) (Supplementary Fig. S1C). ## Discussion The Myrtaceae family is recognized for its great potential to produce volatile oils of economic interest35. The identification of photochemical profiles of some species combined with genomic studies, revealing a high diversity of TPS genes that control the synthesis pathways of these compounds and are responsible for the various biological activities of essential oils28,29,31,36. In this study, the TPS family has been characterized in Psidium cattleyanum, a fleshy-fruited species from the Myrtaceae family, for the first time at the genomic and transcriptomic levels. It reveals a low number of putative functional full-length TPS genes (32 RedTPS and 30 YlwTPS) required for this species associated with wet forests across the neotropics, when compared with the woody-fruited species (Table 2) from open forest and woodland, such as Eucalypteae tribe, including Eucalyptus grandis (70 full length TPS), Eucalyptus globulus (103 full length TPS), and Corymbia citriodora (84 full length TPS), all species with the diversity center in the Asia and Oceania37. These species are predicted to defend their leaves much more strongly. Moreover, the relatively long lifespan of eucalyptus (well over 200 years)33 compared to Psidium (approximately 40 years)38, may drive further gene diversification as the need to adapt to long-term environmental changes. These results imply that evolutionary forces have acted differently upon lineages since they diverged from their most recent common ancestor more than 70 million years ago1,39. Partial genes might be considered non-functional, even though some of their incomplete sequences could have resulted from poor sequencing techniques. Still, the redundancy of TPS genes has been observed in many other plants, e.g., in grape (Vitis vinifera) there are 152 TPS-like genes, but only 62 full length TPS, with two domain structures40 where tandem duplication rates for both domains (~ $90\%$) are the main mechanisms for family expansion. In E. grandis there were 70 full-length TPS, but seven had only the PF01397 domain where gene losses were mostly related to tandem duplications ($71.4\%$) and less related to segmental duplication ($3.9\%$) events, and 22 TPS with only the PF03936 domain more related to tandem duplication ($71.7\%$) and fewer segmental duplication events ($4.3\%$)41. We observed the same pattern in Psidium cattleyanum, where 28 RedTPS and 27 YlwTPS had only the PF01397 domain and 48 RedTPS and 45 YlwTPS had only the PF03936 domain (Supplementary Table S9). These data suggest that domain loss has been a common event in plants during the evolution of the TPS gene family, with the loss of the PF01397 domain being more frequent in the Myrtaceae family and plants in general than the loss of the PF03936 domain41. The functionality of these single domain-containing TPS is not yet known, but more investigation on regulatory mechanisms, expansion history, and evolutionary advantage of the domains separately should provide a comprehensive view of the impact of partial genes in the diversification of TPS in plants26. Transcriptome examination revealed that out of 32 full-length RedTPS, 10 genes were upregulated in the red morphotype. Among the 30 full-length YlwTPS, only 14 genes were upregulated in the yellow morphotype. This demonstrates that the differential expression patterns in the two morphotypes can also contribute to the final terpene content in the leaves (Fig. 5). The high abundance of transcripts in this study (FPKM, Fragments Per Kilobase of exon per Million reads) from the TPS-a and TPS-b1 subfamilies in the transcriptome indicated their involvement in the formation of mono and sesquiterpenoid volatiles in leaves. A comparison of the essential oil composition revealed the presence of oxygenated monoterpenes on leaves of P. cattleyanum, where the major compound was α-pinene ($35.4\%$) in yellow morphotype and the 1,8-cineole (59,$5\%$) and linalool ($9.6\%$) in the red morphotype. This variation in the essential oil of P. cattleyanum morphotypes have also been previously described in native species in southern Brazil24,25. In cultivated plants of P. cattleyanum in different parts of the world, previous studies have identified the chemical composition with β-caryophyllene, a hydrocarbon sesquiterpene, as the main component7,23,25,42–45, which was also found in smaller amounts in both morphotypes in this work. The variations found between the two morphotypes in this study reflect a genetic and evolutionary origin. The identification of chemotype phenotypes (qualitative variability in foliar essential oil composition) within a single species has already been reported among different varieties or ecotypes of other species46–49, mainly when a significant shift in the relative concentrations involved more similar compounds, such as cineole and pinene50. The yellow morphotype tends to be found at slightly lower elevations than the red morphotype17,21, this could reflect environmental adaptation48 and also in the terpenes plasticity51. In the TPS-a subfamily that encodes only sesqui-TPSs found in both eudicot and monocot plants40, phylogenetic analysis revealed two YlwTPS (Pca_ylw_g29958 and Pca_ylw_g20359) closely related to RtTPS3 (AXY92168)52 and in the same branch of the gene EgranTPS038 (Euc_Eucgr_J01451) of E. grandis. In addition, four RedTPS (Pca_red_g40189, Pca_red_g58727, Pca_red_g34404, and Pca_red_g61229) were found in the same branch as RtTPS4 (AXY92169)52; both belong to a branch of the betacaryophylene synthase (BS) (Fig. 6).Figure 6A schematic view of putative terpene synthase genes involved in α-pinene, 1,8-cineol, linalool and β-caryophyllene biosynthesis in P. cattleyanum red and yellow morphotypes. DXP 1-deoxylulose 5-phosphate, DMAPP dimethylallyl diphosphate, IPP isopentenil diphosphate, GPPS geranyl diphosphate synthase, GPP geranyl diphosphate, FPPS farnesyl diphosphate synthase, FPP farnesyl diphosphate, ER endoplasmic reticulum, MEP methyl erythritol 4-phosphate pathway, MVA mevalonate pathway. The monophyletic TPS-b subfamily is divided into two groups. The TPS-b1 clade contains putative cyclic monoterpene synthases, with transit peptides positioned upstream of the “RRX8W” motif and therefore has a high probability of localizing in the plastids29. The subfamily had the highest number of full-length genes ($40\%$) as a proportion of the total number of TPS genes compared to Melaleuca alternifolia ($32.4\%$) and Populus trichocarpa ($31.2\%$) (Fig. 3). The high proportions of the TPS-b1 subfamily could be indicative of rapid ongoing evolution and lineage-specific gene family expansion of this subfamily in warm subtropical habitats, particularly for protection from damage caused by rapid temperature fluctuations53,54. Some terpenes can act by selecting the defense of antimicrobial secondary metabolites such as cyclic monoterpenes32. This suggests that subfamilies of TPS-b1 expansion might be related to species or ecotype diversification, enabling quick adaptation in response to environmental changes. The other TPS-b subfamily contains putative isoprene/ocimene (C5, C10) synthases, described as TPS-b232 and has few genes in Psidium cattleyanum ($6.6\%$ in yellow and $3.1\%$ in red morphotype) as a proportion of the total number of TPS genes compared with Eucalyptus globulus ($9.4\%$) and Melaleuca alternifolia ($5.4\%$) (Fig. 3)29,32,55. However, when including genes functionally characterized in the TPS-b2 clade, the relationships among Psidium genes were not entirely congruent because we detected these three genes (Pca_ylw_g54543, Pca_red_g66899, and Pca_ylw_g73225) positioned in the same clade as Rhodomyrtus tomentosa RtTPS1 (AXY92166), characterized as pinene synthase (PS), a cyclic terpene52, despite the high support (bootstrap value of 97) in the same branch of acyclic EglobTPS106, functionally characterized as isoprene synthase29 (Fig. 1b). Including more TPS from Myrtaceae species from neotropics and functionally characterizing the Psidium genes should clarify their role and division within the clade TPS-b. Two other genes (Pca_ylw_g32667 and Pca_red_g24428) clustered together with RtTPS2 (AXY92167) and EpTPS1 (MK873024) in the TPS-b1 branch and were related to pinene synthase (Fig. 6). Moreover, four genes (Pca_red_g44464, Pca_red_g28382; Pca_ylw_g3537, Pca_ylw_g40677) were in the same branch as EpTPS2 and EpTPS3 from Eucalyptus polybractea belonging to the CS TPS-b1 clade56, and one gene from E. grandis monoterpene synthase (XP_010046521), which has similarity ($95\%$ amino acid identity) to CS that produces 1,8-cineole an in vitro assay using GPP as substrate57. Analysis of transcript abundance showed that the gene Pca_red_g28382 was highly expressed in leaves. The dominant product of many characterized CS enzymes is 1,8-cineole; however, they also produce small amounts of limonene, β-myrcene, sabinene, β-pinene, α-pinene, and α-terpineol. This group of compounds synthesized by CS is known as the ‘cineole cassette’, which has been reported in many plants58,59. Therefore, as multiple TPS genes are often expressed in the same tissue and many of these TPS’ have overlapping ranges of products, it is not easy to identify the action of individual TPS enzymes on the profile of the terpene observed in that tissue60. Other expressed TPS-b genes Pca_ylw_g54543, Pca_red_g66899, and Pca_ylw_g73225 were positioned in the same clade as RtTPS1 of Rhodomyrtus tomentosa, and Pca_red_g24428 grouped with RtTPS2. Previous studies have shown the in vitro activity of RtTPS1 and RtTPS2, which mainly produce (+)-α-pinene and (+)-β-pinene with GPP, whereas RtTPS1 is also active with FPP, producing β-caryophyllene, along with a smaller amount of α-humulene53. This suggests that, depending on their expression profile or subcellular location, the enzymatic products of these TPS present in leaves can contribute to the different terpene mixtures found in the essential oil. We also detected the expression of four genes (Pca_red_g44464, Pca_red_g28382; Pca_ylw_g3537, Pca_ylw_g40677) in the same branch of EpTPS2 and EpTPS3, belonging to the PS TPS-b clade. There is a large diversity of Myrtaceae species, with α-pinene and 1,8-cineole being the dominant compounds in the leaves. The reaction cascade that leads to these two compounds includes the same carbocation intermediate, γ-terpinyl cation49. There is evidence to show that the amino acid changes induced through site-directed mutagenesis can result in a different ratio of particular terpenes produced47,61,62 and in natural systems, this might lead to different dominant compounds, such as the a-terpineol synthases of many species, which are the only characterized terpene synthases that are not 1,8-cineole synthases, but produce significant amounts of 1,8-cineole59. The TPS-g subfamily has two subclades encoding TPS’ without the “R(R)X8W” motif, which facilitates isomerization of the geranyl cation in the linalyl cation. This subfamily is closely related to the TPS-b subfamily, and its members may function with the prevalence of acyclic monoterpene products. We also identified two genes from Psidium (Pca_red_g25997 and Pca_ylw_g32591) in the same branch as the functionally characterized PS of EgranTPS10129 (Egr_EucgrE03562; Fig. 6). We screened TPS genes to identify the LS based on functionally characterized enzymes from other plant species. Phylogenetic analysis demonstrated that only two genes (Pca_ylw_g14698 and Pca_red_g69489) are closely related to LS from the rosids Clarkia breweri (Cbr_AAD1984), Oenothera arizonica (Oca_AAD1984), and Clarkia concinna (Cco_AAD1983). They fall into the TPS-f synthase classification, proposed to be the most ancient, and could have been due to a relatively recent common ancestor, copalyl diphosphate synthase (CPS)63, as evidenced by the sequence conservation of this region in the N-terminus of the protein (Fig. 6). In this study, LS gene expression was not observed in leaves. Monoterpene synthases of this subfamily are responsible for the conversion of GDP into the bulk of monoterpenes found in vegetative organs, whereas the subfamilies TPS-f and TPS-g are thought to be exclusively active in flowers, likely having a primary function in attracting insect pollinators36,64. In addition, other genes could be expressed when directly involved in plant defense against herbivores by attracting predators65 or by directly driving herbivores away66. Depending on the extent to which gene function is affected, single-base substitutions may result in changes in terpene composition and profile, and if upstream pathway elements are involved, even in terpene concentrations46,67. To infer whether selection acted on the TPS-b subfamily, we used several statistical tests to compare clades on the phylogenetic tree. Codon substitution patterns with a maximum likelihood approach implementing a branch-site model indicated positive selection acting on a specific TPS-b1 branch, including some pinene and cineole synthase genes and other non-functionally assigned genes. In particular, some positively selected sites are located in the N-terminal region, which controls substrate specificity. It is interesting to note that residue 224 contains an arginine (R) in the PS genes (Fig. 4A), whereas we observed an alteration to a tryptophan (W) residue in the CS genes (Fig. 4B). Conserved arginines close to the diphosphate moiety stabilize the evolving negative charges68. The tryptophan residue contributes to stabilization of the cation and deprotonation of the substrate69. In addition, the positively selected residues 222 and 279 were located around the aspartate-rich motif (“DDXXD”) in the C-terminal half, which is important for the coordination of divalent ion(s), water molecules, and stabilization of the active site70–72. These results illustrate the importance of these residues to product spectrum of TPS genes, mainly in this case of PS and LS, that have the same carbocation intermediate, thereby differing in their profiles46. Future studies should investigate in detail how the active site promotes discrimination from other potential substrates. Analysis of this type of data could be used to better understand the diversity of terpene synthases and the role of different terpenes in mediating ecological interactions34. Several biological and pharmacological activities have been reported for pinene, cineol, and linalool, including anti-inflammatory and antinociceptive properties11,73–75, anticancer61,76,77, antifungal78,79, antidiabetic80, antioxidant, antimicrobial77,81,82, antidepressive and neuroprotective77, allelopathic83, antibacterial, and insecticidal activity84,85. The high content of these compounds in the volatile oils of these species suggests that they could constitute an alternative commercial source of this compound86. ## Conclusion In this study we identified putative TPS genes responsible for the formation of predominant essential oil compounds in Psidium cattleyanum. The chemotypic variability found in the red and yellow morphotypes confirm our hypothesis about the complex and polymorphic nature of the genes encoding the key enzymes regulating compound production and suggest adaptive genetic plasticity of the two morphotypes. The TPS-b clade has undergone substantial expansion compared to other subfamilies and includes some positively selected amino acid residues, evidence the monoterpene synthase genes are important for adaptation to Psidium at different niches. The present study provides the first insight into the genetic basis of TPS in P. cattleyanum morphotypes, gaining insights about the biodiversity in the Atlantic rainforest for further ecological genetic studies in the genus. ## Plant materials Young leaf samples of the yellow and red morphotypes were grown on the same open ground plot (in two 5-m long rows per cultivar) at the Federal University of Rio Grande do Sul (Porto Alegre, Brazil). The plants were 20–25 years old during the sampling year [2020]. The leaves were washed with distilled water, frozen, and stored at -18 °C until extraction of volatile compounds, immediately frozen in liquid nitrogen and stored at − 80 °C for further RNA extraction. ## Chromatographic profile of the essential oils We collected volatiles from the leaves of the two morphotypes under the same growth conditions and ambient temperature, in biological triplicates. Approximately 100 g of dry leaves from the two morphotypes, were extracted with 1000 mL of reverse osmosis water using a Clevenger apparatus87, following four hours of extraction by hydro-distillation. Samples of the essential oils extracted from the leaves were analyzed using gas chromatography with a flame ionization detector (GC-FID) (Shimadzu GC-2010 Plus) and gas chromatography coupled to mass spectrometry (GC–MS) (Shimadzu GCMS-QP2010 SE). We conducted the analyses according to the following conditions: helium (He) as the carrier gas for both detectors, with the flow and linear speeds of 2.80 mL min−1 and 50.8 cm s−1 (GC-FID), and 1.98 mL min−1 and 50.9 cm s−1 (GC–MS), respectively; injection port temperature of 220 °C with a split ratio of 1:30; fused silica capillary column (30 m × 0.25 mm); stationary phase Rtx®-5MS (0.25 μm film thickness); oven with an initial temperature of 40 °C, maintained for 3 min, then gradually increased by 3 °C min−1 until 180 °C, where it remained for 10 min (total analysis time: 59.67 min); and FID and MS detector temperature of 240 °C and 200 °C, respectively49. The used samples were taken from the vials in 1 μL of a solution containing $3\%$ essential oil dissolved in hexane with 0.1 mol L−1 dimethylacetamide (DMA; external standard for reproducibility control). The GC–MS analyses were performed using electron impact equipment with an impact energy of 70 eV, scanning speed of 1000, scanning interval of 0.50 fragments s−1, and fragments detected from 29 to 400 (m/z). The GC-FID analyses were carried out in a flame formed by H2 and atmospheric air at a temperature of 300 °C. Flow rates of 40 mL min−1 and 400 mL min−1 were used for H2 and air, respectively. Identification of the compounds in the essential oils was accomplished by comparing the obtained mass spectra with those available in the spectral library database (Wiley 7, NIST 05, and NIST 05 s) and retention indices (RI). To calculate the RIs, we used a mixture of saturated alkanes C7–C40 (Supelco-USA) and adjusted retention time of each compound, obtained by GC-FID. The values calculated for each compound were compared with those reported in literature88–90. We calculated the relative percentage of each compound in the essential oil using the ratio between the integral area of the peaks and the total area of all sample constituents obtained via GC-FID analyses. The compounds with a relative area above $2\%$ were identified and considered predominant if above $10\%$. ## Terpene synthase gene identification and annotation Initially, we used two terpene synthase-specific domains, PF01397 and PF03936, which represent respectively the N-terminal and C-terminal domains of TPS from the Pfam database (http://pfam.xfam.org/) 91, as queries to search for terpene synthase homolog genes in the P. cattleyanum yellow and red morphotypes predicted genes from their genomes (unpublished data). We analysed each morphotype separately using HMMER version 3.192. We also performed a local BLASTP search for TPS genes in the P. cattleyanum reference genome based on functionally characterized genes93,94. We created a preliminary list of putative TPS genes based on hits with a high similarity (e-value < 1e − 05). To better understand the structural sequence features of each gene, we used the open reading frame (ORF) Finder of NCBI (http://www.ncbi.nlm.nih.gov/orffinder/) to identify the ORFs for each sequence recovered. Gene structure was determined using the Gene Structure Display Server (GSDS; http://gsds.cbi.pku.edu.cn) 95. We confirmed the presence of functional domains based on the translation of gene sequences identified in Simple Modular Architecture Research Tool (SMART)96. Moreover, several algorithms were used to predict a putative transit peptide for chloroplast targeting in the N-terminal sequence upstream of the RRX8W motif (ChloroP 1.197, TargetP v.1.0198, PCLR 0.999). To determine the sequence diversity between the two morphotypes, a complete set of pairwise comparisons of protein sequences was performed using Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/). ## Phylogenetic reconstruction In this study, we first used terpene synthase protein sequences from fully sequenced genomes of A. thaliana100 and E. grandis29, to classify the putative genes found in P. cattleyanum according to the previous classification in the subfamilies TPS-a,-b,-c,-e/f, and -g by sequence similarity26. To examine the evolutionary history of TPS genes, a second analysis including more species (E. grandis, E. globulus, A. thaliana, P. trichocarpa, V. vinifera, C. citriodora, and M. alternifolia) was carried out. We generated a tree with TPS sequences related to primary metabolism (subfamilies -c, -e, and -f) with a total of 45 sequences and a second tree related to secondary metabolism (subfamilies a, b, g) including 360 sequences29,32,55. The functionally characterized pinene (RtTPS1 and RtTPS2 accession number AXY92166 and AXY92167, respectively) and caryophyllene synthases (RtTPS3 and RtTPS4 accession numbers AXY92168 and AXY92169) from Rhodomyrtus tomentosa52, pinene synthase (EpTPS1 accession number MK873024) and 1,8-cineole synthases (EpTPS2 and EpTPS3 accession numbers MK873025 and QCQ05478) from Eucalyptus polybractea56, beta cayophyllene synthase (Eucgr. J01451) from E. grandis29, myrcene synthase from Antirrhium majus (AAO41727)101, two isoprene synthase genes from E. globulus (EglobTPS106), E. grandis (Eucgr. K00881)29 and five linalool synthases from Oenothera californica (AAD19841)63, Clarkia breweri (AAD19840), Clarkia concinna (AAD19839), and Fragaria x ananassa (CAD57106)102 were also included in the phylogenetic analysis to assess the homology of known TPS to Psidium genes. For each dataset used to construct the trees, we first aligned the amino acid sequences of putative TPS genes using ClustalW implemented within MEGA v7.0 software package103. Due to high levels of variation and variable exon counts between taxa, we trimmed the alignment using Gblocks104 with the following parameters: smaller final blocks, gap positions within the final blocks, and less strict flanking positions. We used the maximum-likelihood method implemented in PhyML v2.4.4105 online web server106 to perform the phylogenetic analysis. The JTT + G + F was the best-fit substitution model selected with ModelGenerator for protein analyses107. The confidence values in the tree topology were assessed by running 100 bootstrap replicates. Trees were visualized using Figtree v1.4.4108. ## Molecular evolutionary analysis involving TPS-b To understand the molecular evolution at the amino acid level and the intensity of natural selection acting on metabolism in a specific clade, we used a tree based on codon alignment produced by the maximum-likelihood method using the software EasyCodeML109. We retrieved Coding Sequencing (CDS) sequences from TPS-b genes from A. thaliana, E. grandis, P. cattleyanum, V. vinifera and P. trichocarpa species in Phytozome v11 (http://phytozome.jgi.doe.gov/; last accessed November 2020), to use in positive selection analysis. The dataset included 76 sequences and 389 amino acids from five species. We performed statistical analysis using the CodeML program in PAML version 4.9 software using the site, branch, and branch-site models110, implemented in EasyCodeML109. Parameter estimates (ω) and likelihood scores111 were calculated for the three pairs of models. These were M0 (one-ratio, assuming a constant ω ratio for all coding sites) vs. M3 (discrete, allowed for three discrete classes of ω within the gene), M1a (nearly neutral, allowed for two classes of ω sites: negative sites with ω0 < 1 estimated from our data and neutral sites with ω1 = 1) vs. M2a (positive selection, added a third class with ω2 possibly > 1 estimated from our data), and M7 (beta, a null model in which ω was assumed to be beta-distributed among sites) vs. M8 (beta and ω, an alternative selection model that allowed an extra category of positively selected sites)112. A series of branch models and branch site models were tested: the one-ratio model for all lineages and the two-ratio model, where the original enzyme functional evolution occurred. The branch-site model assumes that the branches in the phylogeny are divided into the foreground (the one of interest for which positive selection is expected) and background (those not expected to exhibit positive selection). Likelihood ratio tests (LRT) were conducted to determine which model measured the statistical significance of the data. The twice the log likelihood difference between each pair of models (2ΔL) follows a chi-square distribution with the number of degrees of freedom equal to the difference in the number of free parameters, resulting in a p-value for this113. A significantly higher likelihood of the alternative model compared to the null model suggests positive selection. Positive sites with high posterior probabilities (> 0.95) were obtained using empirical Bayes analysis. If ω > 1, then there is a positive selection on some branches or sites, but the positive selection sites may occur in very short episodes or on only a few sites during the evolution of duplicated genes; ω < 1 suggests a purifying selection (selective constraints), and ω = 1 indicates neutral evolution. Finally, naive empirical Bayes (NEB) approaches were used to calculate the posterior probabilities that a site comes from the site class with ω > 1112. The selected sites and images of protein topology were predicted using Protter114. ## Transcriptome analysis For expression analysis, we used the published RNA-Seq dataset from leaves for the yellow and red morphotypes of P. cattleyanum115. To verify the quality of reads and the presence of Illumina adaptors, we used the FastQC software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Based on these data, we used the Trim Galore software (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) to eliminate read strings with a quality below 30 and adapter sequences. Two replicates from the red morphotype and two from the yellow morphotype, corresponding to four RNAseq libraries, were aligned on the draft genome assembly of each morphotype (unpublished data) using TopHat2116. The read count tables mapped to each gene were generated using the featureCounts module of the Subread software117, from the bam anchor files generated by TopHat2. The criteria used to create the counting tables were as follows: fragments (pairs of reads) were counted instead of individual reads, pairs of reads anchored on different chromosomes or anchoring on identical chromosomes but on different strands were not considered, and neither were the reads anchored in multiple places in the genome. We used the DESeq2 package version 1.36118 to perform statistical analysis and identify differential expression. We analyzed the counting tables using a false discovery rate (FDR) of 0.05, log2 fold change ≥ ± 1119 and separated them into a group formed by the “up-regulated” genes and another formed by the “down-regulated” genes. As the genome of each morphotype was assembled separately and corresponded to the same evaluated species in question, we performed two independent comparative transcriptomic analyses: a comparison of morphotype red leaf against yellow leaf anchoring in red morphotype genome (i) and in yellow genome (ii). We evaluated the differential expression considering each gene found in each morphotype, and were able to detect more genes under differential gene expression (DGE), considering that some gene copies were detected only in one of the reference genomes. ## Ethical standards The yellow and red morphotypes of Psidium cattleyanum were sampled originally as part of the project “Genomics and Transcriptomics Analysis of Psidium cattleyanum Sabine (Myrtaceae)”. The studied samples were collected in full compliance with specific federal permits issued by the approved by the Brazilian Ministry of Environment (MMA) and the Chico Mendes Institute for Biodiversity Conservation (ICMBio), and approved by the Biodiversity Information and Authorization System (SISBIO 43338-2) and National System for Governance of Genetic Heritage and Associated Traditional Knowledge (SisGen A7B0331). The studied plants are kept in an ex situ collection at the Federal University of Rio Grande do Sul (UFRGS). Exsiccates will be deposited in the ICN herbarium of UFRGS. As official authorities in Brazil reported, the species used in this study are not endangered or protected in the Rio Grande do Sul State, where the sampling occurred. ## Supplementary Information Supplementary Figures. Supplementary Tables. The online version contains supplementary material available at 10.1038/s41598-023-31061-5. ## References 1. Biffin E. 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