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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7497ehp0113-00062615866775Mini-Monograph: BrevetoxinsInhalation Toxicity of Brevetoxin 3 in Rats Exposed for Twenty-Two Days Benson Janet M. 1Hahn Fletcher F. 1March Thomas H. 1McDonald Jacob D. 1Gomez Andrea P. 1Sopori Mohan J. 1Bourdelais Andrea J. 2Naar Jerome 2Zaias Julia 3Bossart Gregory D. 4Baden Daniel G. 21Lovelace Respiratory Research Institute, Albuquerque, New Mexico, USA;2Center for Marine Science Research, University of North Carolina at Wilmington, Wilmington, North Carolina, USA;3Department of Pathology, University of Miami, Miami, Florida, USA;4Division of Marine Mammal Research and Conservation, Harbor Branch Oceanographic Institution, Ft. Pierce, Florida, USAThis article is part of the mini-monograph “Aerosolized Florida Red Tide Toxins (Brevetoxins).”
Address correspondence to J.M. Benson, Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr. SE, Albuquerque, NM 87108 USA. Telephone (505) 348-9457. Fax: (505) 348-8567. E-mail:
[email protected] thank C. Elliott, A. Dison, S. Durr, D. Kracko, R. Langley, D. Meyer, D.C. Santistevan, J.C. Seagrave, G. Statom, and B. Tibbetts for their contributions to this study.
This research was supported by National Institute of Environmental Health Sciences (NIEHS) grant P01 ES 10594 and a Minority Supplement to the P01 also from the NIEHS.
The authors declare they have no competing financial interests.
5 2005 9 2 2005 113 5 626 631 2 8 2004 20 12 2004 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Brevetoxins are potent neurotoxins produced by the marine dinoflagellate Karenia brevis. Exposure to brevetoxins may occur during a K. brevis red tide when the compounds become aerosolized by wind and surf. This study assessed possible adverse health effects associated with inhalation exposure to brevetoxin 3, one of the major brevetoxins produced by K. brevis and present in aerosols collected along beaches affected by red tide. Male F344 rats were exposed to brevetoxin 3 at 0, 37, and 237 μg/m3 by nose-only inhalation 2 hr/day, 5 days/week for up to 22 exposure days. Estimated deposited brevetoxin 3 doses were 0.9 and 5.8 μg/kg/day for the low-and high-dose groups, respectively. Body weights of the high-dose group were significantly below control values. There were no clinical signs of toxicity. Terminal body weights of both low- and high-dose-group rats were significantly below control values. Minimal alveolar macrophage hyperplasia was observed in three of six and six of six of the low- and high-dose groups, respectively. No histopathologic lesions were observed in the nose, brain, liver, or bone marrow of any group. Reticulocyte numbers in whole blood were significantly increased in the high-dose group, and mean corpuscular volume showed a significant decreasing trend with increasing exposure concentration. Humoral-mediated immunity was suppressed in brevetoxin-exposed rats as indicated by significant reduction in splenic plaque-forming cells in both low- and high-dose-group rats compared with controls. Results indicate that the immune system is the primary target for toxicity in rats after repeated inhalation exposure to relatively high concentrations of brevetoxins.
brevetoxinimmunotoxicityinhalationneurotoxicityrats
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Blooms of the dinoflagellate Karenia brevis are responsible for what are commonly referred to as Florida red tides. K. brevis produces a series of potent neurotoxins known as brevetoxins (Baden 1989). The reader is referred to reviews by Fleming et al. (2005a) and Kirkpatrick et al. (2004) for discussion of Florida red tide exposures, effects, and implications for human health.
K. brevis red tides occur almost annually in the Gulf of Mexico and have increased in geographic distribution since the 1970s (Van Dolah 2000). Therefore, the possibility of repeated inhalation exposure for individuals working and living along affected beaches and waterways is increasing. Despite this, little is known about the possible systemic health effects associated with aerosolized brevetoxins beyond the obvious immediate upper respiratory tract irritation (Backer et al. 2003; Kirkpatrick et al. 2004). Bossart et al. (1998) reported respiratory tract inflammation and hemopathy in manatees dying during extensive 1996 K. brevis red tides. Immunohistochemical staining of tissues from these manatees indicated an accumulation of brevetoxins in tissue macrophages and lymphocytes, key players in humoral and cell-mediated immune responses. Although these effects in manatees occurred after weeks of exposure via several routes, the data suggest that the respiratory tract, hematopoietic, and immune systems might be targets for brevetoxin-induced toxicity after repeated exposure of humans to environmentally relevant airborne concentrations of brevetoxins.
In animal studies with inhaled brevetoxins, suppressed splenic antibody production was observed among Sprague-Dawley rats inhaling aerosols of crude K. brevis extract 4 hr/day for 1 and 4 weeks. No toxicity to the nervous, respiratory, or hematopoietic systems was noted (Benson et al. 2003). The extract contained primarily brevetoxins 2 and 3 but also contained brevenal, a newly identified compound in K. brevis having pharmacologic activity antagonistic to brevetoxin-induced neurotoxicity and bronchoconstrictor activities (Bourdelais et al. 2003; Abraham et al. 2005). Brevetoxin-induced suppression of splenic antibody production was confirmed in rats inhaling pure brevetoxin 3 at 500 μg/m3 for 0.5 hr and 2 hr/day for 5 consecutive days (Benson et al. 2004). Antibody production was suppressed by > 70% in the low-exposure (0.5-hr exposure/day) and high-exposure (2-hr exposure/day) groups. Small numbers of splenic and peribronchiolar lymphoid tissue macrophages stained positive for brevetoxin. No biochemical or histologic evidence of toxicity to the respiratory, nervous, or hematopoietic systems was found in the rats inhaling pure brevetoxin 3 for 5 days.
The purpose of the study reported here was to extend our investigation of the adverse systemic health effects associated with brevetoxin inhalation exposure, exclusive of acute respiratory tract irritation. Brevetoxin 3 was chosen for these studies because it is a major component of the brevetoxin mixture produced by K. brevis (Baden 1989) and of brevetoxin-containing aerosols measured along red tide affected beaches (Cheng et al. 2005). The exposure scenario was chosen to more closely mimic a 5 day/week occupational exposure than occasional recreational exposure, although daily exposure durations were much shorter (2 hr) than those expected for occupational exposures. The aerosol concentrations employed were 2–3 orders of magnitude higher than total brevetoxin concentrations measured to date along Florida beaches during red tides of mild to moderate intensity (Backer et al. 2003; Cheng et al. 2005; Pierce et al. 2003).
Materials and Methods
Chemicals
Brevetoxin 3 was isolated and purified from the Wilson clone of K. brevis at the Center for Marine Sciences, University of North Carolina, Wilmington. Unless otherwise specified, all other chemicals, including 3H-thymidine-5 (20–30 Ci/mmol; 95% radiochemically pure), were purchased from Sigma Chemical Company (St. Louis, MO).
Animals
Male F344/CrlBr rats, 6–7 weeks old when received from Charles River Laboratories (Wilmington, MA), were used. The rats were housed in polycarbonate cages with hardwood chip bedding. The animal rooms were maintained at 20–22°C with relative humidity at 20–50% and a 12-hr light cycle beginning at 0600 hr. Food [Harlan Teklad rodent diet (W), Madison, WI] and water were provided ad libitum. The rats were randomized by weight into exposure groups and weighed 230.3 ± 8.3 g (mean ± SD; n = 66) when exposures began. The rats were conditioned to the nose-only inhalation restraint tubes for 0.5, 1, and 2 hr before initiation of exposures. The study protocol was reviewed and approved by the Lovelace Respiratory Research Institute Institutional Animal Care and Use Committee.
Exposure System
The exposure system consisted of three, 36-port cylindrical nose-only inhalation chambers (InTox Products, Edgewood, NM), each supplied with a single nebulizer (Hospitak, Inc., Farmingdale, NY). Aerosols generated in the nebulizer were dried and diluted with supply air to achieve the desired chamber aerosol concentrations. Flow rate through the chamber was 10 L/min. Temperatures were monitored continuously with an acceptable range of 18–22°C. Chamber oxygen concentration was monitored continuously with an action level at ≤18%.
Aerosol Generation and Characterization
The aerosols used in this study were saline based, to mimic sodium chloride–based marine aerosols. Stock solutions containing 0.5 mg brevetoxin/mL were prepared in 100% ethanol. Generator solutions for the nebulizers supplying the low- and high-level exposure chambers were prepared daily by diluting the stock solutions with 0.9% saline to achieve a final concentration of 0.15 mg/mL. The generator solution for the control chamber was ethanol–saline. Target total exposure concentrations were 5 mg total aerosol/m3 for the low-level exposure chamber and 20 mg total aerosol/m3 for the control and high-level exposure chambers. Target exposure concentrations were achieved by operating the nebulizer at 2 psi for the low-level exposure chamber and 4 psi for the control and high-level exposure chambers. The total aerosol concentrations of the saline-based aerosols in the exposure atmospheres were determined gravimetrically. Aerosol was collected from the breathing zone of the animals at a flow rate of 2 L/min onto preweighed 25-mm Zefluor filters (SKC Gulf Coast, Houston, TX) at 0.5-hr intervals. The stability of the total aerosol concentration during the exposure was monitored using a TSI DustTrak real-time aerosol monitor (TSI Industries, Shoreview, MN). The particle size distributions [mass median aerodynamic diameter (MMAD) and geometric standard deviation (σg)] of the aerosols were determined using an eight-stage cascade impactor (InTox Products).
To directly quantitate brevetoxin concentration in the exposure atmosphere, selected filter samples were extracted with acetone (Fisher Scientific, Fairlawn, NJ), dried, and resuspended in methanol and water (50:50). Extracts were spiked with brevetoxin 2 as an internal standard. Samples were injected onto a high-pressure liquid chromatograph (10ADVP; Shimadzu Company, Kyoto, Japan) equipped with a 75 × 2-mm analytical column (Aqua 3 μm, Phenomenex USA, Torrance, CA). Brevetoxin was eluted using a methanol–water mobile phase containing 1 mM ammonium acetate. Eluant was directed into an electrospray mass spectrometer (API 365; Applied Biosystems, Foster City, CA). The mass spectrometer was monitored for ion pairs consisting of 897.590/725.404 (brevetoxin 3) and 895.559/877.550 (brevetoxin 2). Aerosol concentrations were confirmed by enzyme-linked immunosorbent assay (ELISA) analysis (Naar et al. 2002) performed on the above filter extracts.
To determine the concentration of ethanol vapor in the exposure atmosphere, a sample of the control chamber exposure atmosphere was collected into a Tedlar bag obtained using an SKC Vac-U Chamber (SKC Inc., Eighty Four, PA). A glass-fiber filter was placed in line to remove particles from the sample before it entered the sampling bag. The ethanol concentration in the sample was measured by gas chromatography with flame ionization using a Shimadzu Model GC-17A/FID (Shimadzu Scientific Instruments, Columbia, MD) equipped with a Restek Rtx-1 column (30-m × 0.32-mm × 5-μm film width; Restek Corporation, Bellefonte, PA). The chromatograph oven was operated isothermally at 50°C. The injector and detector temperatures were 200°C. A five-point ethanol vapor standard curve was over a concentration range of 1.16–9.4 g/m3 was used.
Experimental Design
Subgroup A rats (n = 6 per exposure level) were exposed 2 hr/day, 5 days/week for a total of 22 exposure days. End points specific to this group included gross and histopathology, hematology, serum chemistry, and evaluations of bronchoalveolar lavage fluid for indications of cytotoxicity [lactate dehydrogenase (LDH) activity] and inflammation (total protein, total and differential nucleated cell counts), and splenocyte proliferation in response to mitogen in vitro. Subgroup B rats (n = 10 per exposure level) were sacrificed after 5 and 22 days of exposure (n = 5 per sacrifice time) for quantitation of brevetoxin 3 or metabolites in liver, the organ expected to have the greatest dose (Benson et al. 1999; Cattet and Geraci 1993; Poli et al. 1990), and for evaluation of splenocyte antigen recognition in vitro after 22 days of exposure.
In Vivo Observations
All rats were observed for clinical signs of toxicity, especially neurotoxicity, after each day’s exposure. In addition, the rats were weighed, and detailed clinical observations were recorded weekly (Path Tox Software; Xybion, Cedar Knolls, NJ).
Necropsy
All rats were sacrificed by intraperitoneal injection of an overdose of Euthasol (Virbac AH Inc., Fort Worth, TX). Body weights were recorded for all rats at the 4-week (terminal) sacrifice. Blood was collected from subgroup A rats by cardiac puncture for evaluation of hematology and serum chemistry. Each subgroup A rat (n = 6) received a complete necropsy. Brain, lung, liver, kidney, and spleen weights were recorded. To obtain bronchoalveolar lavage fluid, the left lung bronchus was clamped, and the right lobes were lavaged twice with 4 mL physiologic saline. After the lavage, the right bronchus was clamped, and the left bronchus was unclamped. The left lobe was fixed via the trachea using 10% neutral-buffered formalin. The nose, brain, liver, kidney, femur with marrow, and spleen (half) were also fixed in 10% neutral-buffered formalin for histologic evaluation. Splenocytes were isolated from the remaining half of spleen for evaluation of their proliferative response to mitogen challenge in vitro.
Histopathology
The soft tissues were trimmed and embedded in paraffin. Tissues were sectioned at 5 μm and stained with hematoxylin and eosin. To more closely examine brain tissue for neuronal damage, three cross sections of brain (level of optic chiasma, caudal to mammillary bodies and caudal to transverse fibers of the pons) were stained with luxol fast blue-cresyl violet. These cross-sections contain profiles of the cerebellar cortex, hippocampus, and thalamus. The focus was on the neurons in these regions because of indications of neuronal damage and loss in these regions in mice inhaling brevetoxin 3 (Murray TF, unpublished results) and because of the defined nature of these groups of neurons.
Clinical Pathology
Hematologic evaluations on whole blood obtained at necropsy were performed using an Advia 120 Hematology System (Bayer, Terrytown, NJ). Clinical chemistry evaluations on serum were performed using a Hitachi 911 Automatic Analyzer (Roche Diagnostic Corp., Indianapolis, IN). Standards and reagents were purchased from Pointe Scientific (Lincoln Park, MI, and Diagnostic Chemical Ltd., Oxford, CT).
Lavage Fluid Analysis
The volume of fluid recovered in the first lavage wash was recorded. Nucleated cells were sedimented by centrifugation. The supernatant from the first lavage was analyzed for LDH and total protein using the Hitachi 911 Automatic Analyzer. The cell pellets from both washes from each animal were pooled and counted manually using a hemocytometer. Differential counts of nucleated cells were made on cytocentrifuge preparations stained with Kwik Diff (Shandon, Inc., Pittsburgh, PA).
The state of activation of the macrophages recovered in lavage fluid was determined using a zymosan-stimulated chemiluminescence assay (Benson et al. 2004; Hubbs et al. 2001). The assay was conducted on 2 × 105 alveolar macrophages per sample well in a total volume of 0.15 mL 1-piperazine ethane sulfonic acid, 4-(2-hydroxyethyl)-monosodium salt (HEPES) buffer.
Brevetoxin Concentration in Liver
Rats were sacrificed by intraperitoneal injection of Euthasol approximately 24 hr after exposure. The liver was removed, weighed, and frozen (−20°C) pending analysis. Thawed tissues were homogenized and 1-g aliquots of tissue homogenate were extracted three times with 4 mL acetone. The acetone extracts for each sample were analyzed for brevetoxin 3 or metabolites by ELISA as described by Naar et al. (2002).
Immune Responses
Antibody-forming cell response.
The immunoglobin M (IgM) antibody-forming cell response to the T-cell–dependent antigen sheep red blood cells (SRBCs) was assessed with a modified plaque-forming assay (Cunningham and Szenberg 1968). Subgroup B rats were immunized by tail vein injection (250 μL of a 15% suspension of SRBC in phosphate buffered saline). At 7 days postimmunization the rats were euthanized by intraperitoneal injection of an overdose of Euthasol. Cells were isolated from weighed portions of spleen, washed, and then resuspended in complete RPMI culture medium to a final concentration of 1 × 106 cells/mL. Analyses were performed as described previously (Benson et al. 2004).
Spleen lymphocyte proliferation.
The proliferative response of the spleen cells to the mitogen, concanavalin A (Con A) was assessed. Group A spleen cells (5 × 105/100 L complete RPMI medium) were incubated with 0.1, 0.3, and 1.0 μg Con A/50 μL, respectively, at 37°C in the presence of 5% CO2 for 54 hr. At that time, cells were pulsed with 0.5 μCi 3H-thymidine and incubated for an additional 18 hr. Cells were collected on filter paper using a cell harvester, and 3H activity (decays per minute) was determined by liquid scintillation spectrometry.
Statistical Analyses
Means, standard deviations, and standard errors for experimental parameters other than body and organ weights were calculated using Microsoft Excel software (Microsoft Corporation, Redmond, WA). One-way analysis of variance (ANOVA) was used to test if there was a statistically significant trend in the data (GraphPad Software, Inc., San Diego, CA). If group variances were not significantly different, the ANOVAs were performed with a Dunnett’s post-test to assess differences between the control and brevetoxin-exposure groups. If group variances were significantly different, the Kruskal-Wallis test was used coupled with Dunn’s post-test for comparisons of exposed versus the control groups.
Group mean body weight and organ weight data were tested for statistical significance using Path-Tox software. Bartlett’s test was used to establish the homogeneity of the data. If the data were homogeneous, significance was evaluated using a modified Dunnett’s test. If data were nonhomogeneous, a modified t-test was used. For all parameters, the criterion for significance was p < 0.05.
Results
Exposure atmosphere and estimated breve-toxin 3 respiratory tract deposition.
The characteristics of the exposure atmospheres are summarized in Table 1. Mean total aerosol concentrations for the control chamber, the low-brevetoxin chamber, and high-brevetoxin chamber were 21.6, 5, and 22.6 mg/m3, respectively. Mean brevetoxin 3 concentrations, determined by liquid chromatography–mass spectrometry (LC-MS) analysis of aerosol filter extracts for the low- and high-level groups, were 37 and 237 μg/m3, respectively. Brevetoxin concentrations obtained and by ELISA were similar to the values obtained by LC-MS. The particle size distribution indicated that the aerosol was highly respirable in rats. Assuming a minute volume of 0.25 L/min and total respiratory tract deposition of approximately 0.2 (for 1 μm particles; Schlesinger 1989), total respiratory tract deposition of brevetoxin 3 in a 2-hr period would be 0.22 and 1.4 μg/day (0.91 and 5.8 μg/kg/day) for the low- and high-exposure groups, respectively. The concentration of ethanol vapor in the control chamber exposure atmosphere was 5.9 g/m3. Because control rats displayed no clinical signs of toxicity and no biochemical or histologic change, this ethanol vapor concentration does not appear to be toxic under the conditions of this experiment.
Body weight gain and clinical signs of toxicity.
Mean group body weights of rats inhaling the high brevetoxin 3 concentration were significantly below control values 1 week after initiation of exposure with the effect persisting throughout the exposure period (Figure 1). No clinical signs of toxicity were observed at any time.
Terminal body and organ weights.
Terminal body weights were (mean ± SD, n = 6) 251 ± 9.20, 236 ± 9.49, and 227 ± 7.38 for the control, low-, and high-brevetoxin groups, respectively. The terminal body weights for the low- and high-exposure groups were significantly below the control values. Absolute liver weights for low- and high-exposure groups and absolute kidney weight for the high-exposure group were also significantly lower than their respective control values (data not shown). Percent organ to body weight data for liver and kidney indicate that the depression in liver and kidney weights was secondary to overall brevetoxin 3–induced body weight depression.
Histopathology.
The only lesion observed in the brevetoxin-exposed rats was alveolar macrophage hyperplasia of minimal severity. The incidences among the control, low-, and high-exposure subgroup A rats were 0 of 6, 3 of 6, and 6 of 6, respectively. The hyperplasia was characterized by a slight increase in the number of alveolar macrophages in the lung and occasional alveoli, where two to four were congregated (Figure 2). Macrophages were also found in the lumens of terminal bronchioles, an atypical location for these cells. The cytoplasm of the macrophages appeared normal and was not vacuolated, enlarged, or pigmented. No evidence of neuronal damage or loss was detected in sections of the hippocampus and cerebellar cortex. Occasional neurons or groups of neurons were crenated and darkly stained. However, there were no tissue changes associated with these cells such as edema, inflammatory cell infiltrates, macrophages, or staining alterations of the neutrophil indicating degeneration of nerve fibers. Therefore, these darkly stained neurons were interpreted as fixation artifacts.
Clinical pathology.
Brevetoxin inhalation had no significant effect on the total or differential white blood cell counts (Table 2). The numbers of reticulocytes were significantly increased in the high-exposure group, and there were decreasing trends in mean corpuscular volume (p < 0.042) and in the mean corpuscular hemoglobin concentration (p < 0.051). However, although there were slight decreases in the numbers of red blood cells, hematocrit, and hemoglobin with increasing exposure levels, these trends were not statistically significant.
Lavage fluid parameters.
The bronchoalveolar lavage fluid obtained from the right lungs of control rats contained (mean ± SD, n = 6) 159 ± 47 mIU of LDH activity, 0.41 ± 0.18 mg of total protein, and 4.67 ± 0.93 million total nucleated cells. Macrophages comprised ≥ 98% of the nucleated cell population. Results of the chemilumenescence assays indicated that brevetoxin inhalation did not affect baseline macrophage activity or the response of macrophages to particle (zymosan) stimulation (data not shown).
Brevetoxin concentration in liver.
The limit of detection of brevetoxin in the ELISA assay is 1 ng/mL for brevetoxin in buffer. Measured concentrations of brevetoxin and/or metabolites in low-exposure-group rats were not significantly different from control values after 5 and 22 days of exposure (Table 3). Brevetoxin concentrations in liver of high-exposure-group rats were significantly increased above background. Brevetoxin concentrations in high-exposure-group livers after 5 and 22 days of exposure were not significantly different, indicating that no accumulation of brevetoxin or its metabolites in liver occurs with repeated exposure. The reason for the relatively high background level in the control liver 11–12 ng/g liver is not known. However, because control animals were exposed in a chamber designated only for control animals, and all animals were housed individually, it is not likely that the background was due to an inadvertent exposure of the controls to brevetoxin.
Immune responses.
Repeated inhalation of brevetoxin 3 had no marked effect on spleen weights or numbers of lymphoid cells isolated from spleens (Table 4). Numbers of plaque-forming cells were depressed by > 60% in spleen cells after inhalation. The magnitude of suppression did not appear to be exposure concentration dependent. However, the high-exposure group’s large standard deviation makes it difficult to determine this with certainty.
Discussion
The purpose of this investigation was to evaluate the possible toxic effects associated with repeated inhalation of brevetoxin 3, one of the major brevetoxins produced by K. brevis. Because the occurrence and distribution of K. brevis–induced red tide has increased over time, there may be increased risk from brevetoxin-induced toxic effects, especially among individuals working and living along beaches affected by red tides. Few data are available on the effects of prolonged exposure to brevetoxins during red tides.
Rats in this study were exposed to a highly respirable, sodium chloride–based aerosol. The aerosol size (0.9–1.3 μm MMAD) is smaller than that measured along red tide–affected beaches (~ 6–12.2 μm MMAD) (Cheng et al. 2005). However, deposition of both the laboratory-generated brevetoxin-containing aerosols in rats and the larger-sized brevetoxin-containing environmental aerosols in humans is expected to occur primarily in the nasopharyngeal and, to a lesser extent, in the tracheobronchial and pulmonary regions (Schlesinger 1989). Regardless, brevetoxins are highly lipophilic and are likely absorbed from throughout the respiratory tract after aerosol deposition. After absorption, systemic distribution is expected to occur rapidly, with the liver expected to be the organ containing a high proportion of the deposited dose (Benson et al. 1999). We found significant concentrations of brevetoxin and/or its metabolites in the liver of rats after 5 and 22 days of exposure to only the high brevetoxin concentration (237 μg/m3). The concentration in liver did not increase with continued exposure between 5 and 22 days, suggesting that accumulation in tissue did not occur with repeated exposure.
We have found no evidence of inflammation in any organ, including the respiratory tract, in rats inhaling up to 237 μg/m3 for 2 hr/day for up to 22 days, resulting in an estimated deposition of 5.8 μg compound/kg body weight/day. These results are consistent with the lack of inflammatory responses in Sprague-Dawley rats inhaling K. brevis extract containing 50 or 200 μg brevetoxin/m3 for up to 4 weeks (Benson et al. 2003) or up to 500 μg/m3 for 5 days (Benson et al. 2004). The differences in inflammatory responses between the manatees and rats are likely a function of differences in overall brevetoxin exposure; however, there has been no systematic investigation on the relative sensitivities of marine mammals and rats to brevetoxins.
Although minimal macrophage hyperplasia was noted histologically in the lungs of brevetoxin 3–exposed rats, significant increases in macrophage numbers were not found on examination of bronchoalveolar lavage fluid. Activation of macrophages was not evident either morphologically or on examination of chemiluminescence with or without particle stimulation. Alveolar macrophage hyperplasia was not evident in rats inhaling 500 μg/m3 brevetoxin (Benson et al. 2004) for 5 days; therefore, it appears that prolonged brevetoxin inhalation may be necessary to induce minimal changes in macrophage numbers in rat lungs.
We found no evidence for neurotoxicity as evidenced by clinical signs or upon histopathologic examination of the rats. Lack of neurotoxicity, especially in the cerebellum, is notable, in light of the fact that brevetoxin localizes in rodent cerebellum after a single injection (Bourdelais et al. 2004) and is cytotoxic in rat cerebellar granular cells in vitro (Berman and Murray 1999). However, the relative dose of brevetoxin to brain is expected to be very low (< 1% of the initial body burden) compared with that distributed to carcass (primarily skeletal muscle; ~ 48%), gastrointestinal tract (~ 35%) and liver (~ 10%) based on results obtained after acute administration via the lung (Benson et al. 1999). Slow absorption of brevetoxin that occurs with an inhalation exposure compared with rapid uptake of a bolus dose may allow brevetoxin metabolism to less neurotoxic metabolites to occur, or allow time for uptake into tissue sinks such as skeletal muscle. For example, Poli et al. (1990) have demonstrated decreased neurotoxicity in rats upon slow infusion of brevetoxin 3 by injection compared with administration of a bolus dose.
There was some evidence that brevetoxin inhalation may affect the hematopoietic system. Reticulocyte numbers in blood were significantly increased in the brevetoxin high-dose group; however, there was no significant effect on erythrocyte count, hemoglobin concentration, or hematocrit; and there were no associated histologic changes in bone marrow. Decreasing trends in mean corpuscular hemoglobin volume and corpuscular hemoglobin concentration were consistent with the observed increase in reticulocyte counts. The no-effect level for the increases in reticulocyte counts in rats was 37 μg of brevetoxin/m3, a concentration orders of magnitude higher than the nanogram per cubic meter values measured to date along Florida beaches during red tide events (Cheng et al. 2005). Although our hematologic findings with brevetoxin 3 to some extent support the findings of hemolytic anemia in manatees exposed to high brevetoxin concentrations for long periods (Bossart et al. 1998), the implications for humans after exposure are limited.
Observed decreases in blood urea nitrogen and triglyceride levels in serum of high-exposure rats could be associated with reduced caloric intake, suggested by reduced body weights in this dose group for most of the exposure period.
Our findings of suppressed antibody cell production is consistent with our earlier findings in rats inhaling K. brevis extract for 4 weeks as well as pure brevetoxin 3 for 5 days (Benson et al. 2003, 2004) and reports of brevetoxin uptake by macrophage and lymphocytes in manatees (Bossart et al. 1998). This finding is probably the most important in relation to brevetoxin inhalation because we have yet to identify a no-effect level in rats. Therefore, it is possible that some immune suppression may occur at exposure concentrations encountered in the environment along red tide affected beaches.
The formation of IgM antibodies in the plaque-forming assay used requires the interaction of macrophages and B- and T-lymphocytes, so the functioning of any or all of these cells may have been affected by brevetoxin exposure (Luster et al. 1988). Although brevetoxins may have affected immune function by several mechanisms, the absence of histopathologic or changes in spleen weight or splenocyte numbers, changes in splenocyte profiles, or responses to mitogens suggest a mechanism independent of overt cytotoxicity. Cathepsin inhibition is one likely mechanism underlying brevetoxin-induced suppression of antibody production because a) alveolar macrophages, macrophages within bronchus associated lymphoid tissue, and spleen of brevetoxin-exposed rats stain positively for brevetoxin (Benson et al. 2003, 2004); b) brevetoxin 2 is a potent inhibitor of cathepsin L in vitro (Sudarsanam et al. 1992); and c) cathepsins located within macrophages and B-lymphocytes play an important role in the formation of antigenic determinants essential for both humoral and cell-mediated immune responses (Katunuma et al. 2003; Lewis et al. 1998; Riese and Chapman 2000). One example of an immunotoxicant that inhibits primary antibody production in vivo by suppressing splenic cathepsin activity is gallium arsenide (Lewis et al. 1998). No-effect levels for brevetoxin-induced suppression of antibody responses, effects on overall immune competence, and the role of cathepsin inhibition in these processes are the foci of future investigations.
Conclusions
Inhalation of 37 and 237 μg/m3 of brevetoxin 3 in a sodium chloride–based aerosol resulted in some significant reduction in body weight, minimal alveolar macrophage hyperplasia, and some alterations in hematologic parameters. No neurotoxic effects were found. The most significant effect of repeated brevetoxin 3 inhalation was the suppression of antibody production by splenic lymphocytes. This suppression has been seen after repeated inhalation of K. brevis extract for 4 weeks and with inhalation of brevetoxin 3 for 1 and 4 weeks in rats. These studies were conducted in young, healthy rats exposed for short periods of time each day, although to higher brevetoxin concentrations than expected to occur in the environment. Potential adverse health effects on the very young or aged laboratory animals remain to be determined. Therefore, much remains to be learned about the long-term health effects of repeated inhalation exposure to brevetoxins. The reader is referred to studies by Fleming et al. (2005b) and Abraham et al. (2005) for discussion of the effects of inhaled brevetoxins on respiratory function in asthmatic humans and in an animal model of allergy, respectively.
Figure 1 Effect of brevetoxin inhalation on body weight. Values are the mean of ≥ 14 values.
*Statistically different from control, ANOVA p < 0.05.
Figure 2 Increased numbers of alveolar macrophages in the alveoli of a rat exposed to high-brevetoxin concentration, a typical presentation. (A) Bar = 50 μm. (B) Bar = 25 μm. Arrows indicate macrophages.
Table 1 Exposure atmosphere characteristics.
Exposure group Total aerosol (mg/m3a) Brevetoxin 3 (μg/m3 by mass spectrometryb) Brevetoxin (μg/m3 by ELISAc) Particle size distribution [μm MMAD (σg)]
Vehicle control 21.6 ± 4.72 NAe NA 0.92 (2.12)
Low brevetoxin 3 5.06 ± 0.50 37.4 ± 10 44 ± 22 1.33 (2.05)
High brevetoxin 3 22.6 ± 1.45 237 ± 87 216 ± 56 1.12 (2.37)
NA, not applicable.
a Mean ± SD; n = 23.
b Mean ± SD; n = 10.
c Mean ± SD; n = 6–8.
Table 2 Effects of brevetoxin inhalation on selected hematology parameters (mean ± SD; n = 6).
Exposure group WBC (×103 cell/μL) RBC (×103cells/μL) Hgb (g/dL) Hct (%) MCV (fL) MCH (pg) Reticulocytes (cells/μL)
Vehicle control 2.75 ± 0.41 8.63 ± 0.34 15.03 ± 0.44 47.18 ± 2.49 54.7 ± 0.88 17.4 ± 1.19 169 ± 13.3
Low brevetoxin 3 3.35 ± 0.76 8.66 ± 0.39 15.12 ± 0.69 46.87 ± 1.76 54.2 ± 0.96 17.5 ± 0.14 194 ± 31.4
High brevetoxin 3 3.27 ± 0.43 8.49 ± 0.19 14.65 ± 0.31 45.47 ± 1.19 53.6 ± 0.69 17.3 ± 0.08 240 ± 13.8 *
Abbreviations: Hct, hematocrit; Hgb, hemoglobin; MCH, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; RBC, red blood cell count; WBC, white blood cell count.
* Mean significantly different from control (p ≤ 0.05).
Table 3 Concentrations of brevetoxin 3 or metabolites in rat liver (mean ± SD; n = 3–5).
5-Day sacrifice
22-Day sacrifice
Exposure group ng/g liver ng/liver ng/g liver ng/liver
Vehicle control 12.1 ± 1.28 106 ± 12.0 11.4 ± 0.44 93.0 ± 5.72
Low brevetoxin 3 16.3 ± 3.39 142 ± 29.8 13.7 ± 1.77 106 ± 18.7
High brevetoxin 3 22.7 ± 7.11 * 205 ± 64.9 * 29.9 ± 8.60 * 215 ± 63.2 *
* Mean significantly different from control (p ≤ 0.05).
Table 4 Effect of brevetoxin inhalation for 22 days on antibody-forming cell response (mean ± SD; n = 4–5).
Exposure group Spleen weight Lymphocytes × 106 Plaque-forming cells per 106 lymphocytes
Vehicle control 0.51 ± 0.04 114 ± 29 560 ± 164
Low brevetoxin 3 0.49 ± 0.02 126 ± 33 155 ± 87*
High brevetoxin 3 0.48 ± 0.03 124 ± 32 235 ± 227*
* Mean significantly different from control (p ≤ 0.05).
==== Refs
References
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Naar J Bourdelais A Tomas C Kubanek J Whitney PL Flewelling L 2002 A competitive ELISA to detect brevetoxins from Karenia brevis (formerly Gymnodinium breve ) in seawater, shellfish, and mammalian body fluid Environ Health Perspect 110 179 185 11836147
Pierce RH Henry MS Blum PC Lyons J Cheng YS Yazzie D 2003 Brevetoxin concentrations in marine aerosol: human exposure levels during a Karenia brevis harmful algal bloom Bull Environ Contam Toxicol 70 161 165 12478439
Poli MA Templeton CB Thompson WL Hewetson JF 1990 Distribution and elimination of brevetoxin-3 in rats Toxicon 28 903 910 2080516
Riese RJ Chapman HA 2000 Cathepsins and compartmentalization in antigen presentation Curr Opin Immunol 12 107 113 10679409
Schlesinger RB 1989. Deposition and clearance of inhaled particles. In: Concepts in Inhalation Toxicology (McClellan RO, Henderson RF, eds). Washington, DC:Taylor and Francis, 191–217.
Sudarsanam S Virca GD March CJ Srinivasan S 1992 An approach to computer-aided inhibitor design: application to cathepsin L J Comput Aided Mol Des 6 223 233 1517775
Van Dolah FM 2000 Marine algal toxins: origins, health effects, and their increased occurrence Environ Health Perspect 108 suppl 1 133 141 10698729
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7498ehp0113-00063215866776Mini-Monograph: BrevetoxinsEffects of Inhaled Brevetoxins in Allergic Airways: Toxin–Allergen Interactions and Pharmacologic Intervention Abraham William M. 1Bourdelais Andrea J. 2Ahmed Ashfaq 1Serebriakov Irakli 1Baden Daniel G. 21Division of Pulmonary and Critical Care Medicine, University of Miami at Mount Sinai Medical Center, Miami Beach, Florida, USA;2Center for Marine Science, University of North Carolina at Wilmington, Wilmington, North Carolina, USAAddress correspondence to W.M. Abraham, Department of Research, Mount Sinai Medical Center, 4300 Alton Rd., Miami Beach, FL 33140 USA. Telephone: (305) 674-2790. Fax: (305) 674-2790. E-mail:
[email protected] article is part of the mini-monograph “Aerosolized Florida Red Tide Toxins (Brevetoxins).”
This research was supported by National Institute of Environmental Health Sciences grant P01 ES 10594.
The authors declare they have no competing financial interests.
5 2005 10 2 2005 113 5 632 637 2 8 2004 13 1 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. During a Florida red tide, brevetoxins produced by the dinoflagellate Karenia brevis become aerosolized and cause airway symptoms in humans, especially in those with pre-existing airway disease (e.g., asthma). To understand these toxin-induced airway effects, we used sheep with airway hypersensitivity to Ascaris suum antigen as a surrogate for asthmatic patients and studied changes in pulmonary airflow resistance (RL) after inhalation challenge with lysed cultures of K. brevis (crude brevetoxins). Studies were done without and with clinically available drugs to determine which might prevent/reverse these effects. Crude brevetoxins (20 breaths at 100 pg/mL; n = 5) increased R
L 128 ± 6% (mean ± SE) over baseline. This bronchoconstriction was significantly reduced (% inhibition) after pretreatment with the glucocorticosteroid budesonide (49%), the β 2 adrenergic agent albuterol (71%), the anticholinergic agent atropine (58%), and the histamine H1-antagonist diphenhydramine (47%). The protection afforded by atropine and diphenhydramine suggests that both cholinergic (vagal) and H1-mediated pathways contribute to the bronchoconstriction. The response to cutaneous toxin injection was also histamine mediated. Thus, the airway and skin data support the hypothesis that toxin activates mast cells in vivo. Albuterol given immediately after toxin challenge rapidly reversed the bronchoconstriction. Toxin inhalation increased airway kinins, and the response to inhaled toxin was enhanced after allergen challenge. Both factors could contribute to the increased sensitivity of asthmatic patients to toxin exposure. We conclude that K. brevis aerosols are potent airway constrictors. Clinically available drugs may be used to prevent or provide therapeutic relief for affected individuals.
animal modelsasthmabrevetoxinbronchoconstrictionclinical therapies
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Florida red tide is a harmful algal bloom caused by the dinoflagellate Karenia brevis (previously Gymnodinium breve). K. brevis produces at least nine structurally related poly-ether brevetoxins (PbTxs; Baden 1989; Baden et al. 1995; Pierce and Kirkpatrick 2001), which are lipid-soluble, fused polyethers with molecular weights of approximately 900 Da. During red tide events, toxins released from disrupted organisms are concentrated in seawater droplets that subsequently can become aerosolized (Pierce et al. 1990, 2003). Onshore winds then carry these aerosols inland, where exposed individuals report both upper and lower airway symptoms, such as nonproductive cough, shortness of breath, rhinorrhea, and sneezing (Asai et al. 1982; Backer et al. 2003; Kirkpatrick et al. 2004). There is a suggestion that the frequency of these adverse respiratory events is increased in “susceptible populations,” that is, in those with pre-existing airway disease, as indicated from a clinical survey where 80% of patients with bronchial asthma were reportedly affected during a red tide event, with some having overt asthma attacks (Asai et al. 1982).
Although the data from these clinical surveys and our own field studies (Backer et al. 2003; Baden and Tomas 1988; Fleming et al. 2005; Kirkpatrick et al. 2004; Pierce et al. 1992, 2003) indicate that aerosolized toxins are respiratory irritants and that the effects may be more severe in asthmatics, there is a paucity of data examining the effects of aerosolized toxin in asthmatic airways under controlled conditions. To address this problem, we initially studied the airway responses to inhaled PbTxs in a sheep model of asthma (Abraham et al. 2005). This model shares many characteristics of the disease in humans, including the development of early airway responses (EAR) and late airway responses (LAR) and postantigen-induced airway hyper-responsiveness (AHR) after inhalation challenge with Ascaris suum antigen (Abraham 2000). The model also demonstrates nonspecific bronchial hyperresponsiveness to a variety of agents, and we have previously used it to study the pulmonary consequences of pollutant exposures (Abraham et al. 1980, 1981). Furthermore, the antigen-induced effects in this model can be ameliorated with the current armamentarium of clinically available asthma medications, including glucocorticosteroids, β 2 adrenergic agents, and leukotriene antagonists (Abraham 2000). Collectively, the characteristics of the model suggest that it can be used as a surrogate for patients with compromised airways to study the effects of inhaled toxin.
In our initial studies, allergic sheep that inhaled environmentally relevant concentrations (picogram per milliliter) of lysed cultures of K. brevis (i.e., crude brevetoxins, which contains all toxins and cell debris) or purified PbTx-2 or PbTx-3 developed significant bronchoconstriction (Abraham et al. 2005). The magnitude of the response was similar for the three toxins (Abraham et al. 2005). Previous in vitro studies using canine tracheal and human bronchial smooth muscle (Asai et al. 1982; Shimoda et al. 1988), demonstrated that brevetoxin-induced contractile effects can be blocked with atropine but not with a histamine antagonist, suggesting that toxin-induced constriction results from stimulation of parasympathetic postganglionic neurons. In contrast to the prior in vitro findings, however, our in vivo studies showed that protective effects were achieved with the mast cell stabilizer cromolyn sodium and the histamine H1-antagonist diphenhydramine. These data support a role for a histamine H1-mediated pathway contributing to the bronchoconstrictor response in vivo (Abraham et al. 2005). Because mast cells and basophils are the most prominent source of histamine in the airways, our findings suggest that toxin, either directly or indirectly, causes mast cell/basophil activation. Such a mechanism could in part explain the reported increased incidence of asthma attacks after natural red tide episodes.
Another factor that could play a role in asthma exacerbations is the generation of kinins in the airway. The pattern of airway responses and the pharmacologic profile seen with inhaled toxin (Abraham et al. 2005) are similar to those seen previously by us with inhaled bradykinin (Abraham et al. 1991). Increased airway kinin levels occur after exposure to a variety of noxious stimuli (Lauredo et al. 2003), including allergen, ozone, bacterial products, and metabisulfite (Forteza et al. 1994, 1999; Mansour et al. 1992), and are associated with bronchoconstriction, AHR, and lung neutrophilia; all of these responses are seen after inhalation challenge with PbTx-3 (Zaias et al. 2004).
The evidence suggesting that toxin stimulates mast cells/basophils and/or increases airway kinins heightens the importance of understanding toxin effects in allergic airways. Therefore, in this study we used allergic sheep to study further the effects of inhaled toxin in compromised airways. Specifically, we determined a) if toxin alone could induce airway responses similar to that seen with allergen, b) if preexposure to toxin could exacerbate allergen-induced responses, and c) the effects of clinically available drugs and experimental pharmacologic agents on toxin-induced airway constriction. In addition, we showed that toxin activates skin mast cells and so provide further support for the in vivo mast cell hypothesis.
Materials and Methods
Adult ewes (Quin Tindall, Okeechobee, FL) that were naturally sensitive to A. suum antigen and had demonstrated airway hypersensitivity to this antigen were used (Abraham 2000). The animals were conscious and restrained in a cart in an upright position with their heads immobilized for the described studies. Instrumentation was performed under local anesthesia. The study was conducted at Mount Sinai Medical Center under the approval of the Mount Sinai Medical Center Animal Research Committee.
Pulmonary resistance.
These methods have been reported in detail (Abraham et al. 1994, 2000, 2004). Briefly, a balloon catheter was advanced through one nostril into the lower esophagus, and the animals were intubated with a cuffed endotracheal tube through the other nostril. Animals remained intubated throughout the course of a particular experiment, but to avoid discomfort during these studies, the cuff of the endotracheal tube was inflated only during the measurements of pulmonary resistance (RL) and during delivery of nebulized agents. We have shown previously that this intubation procedure does not affect RL or airway responsiveness in these animals for periods of up to 9 hr (Russi et al. 1984). Pleural pressure was measured via the esophageal catheter. Lateral pressure in the trachea was measured with a side-hole catheter advanced through and positioned distal to the tip of the endotracheal tube. Transpulmonary pressure, the difference between tracheal and pleural pressure, was measured with a differential pressure transducer. To measure RL, the proximal end of the endotracheal tube was connected to a pneumotachograph, and the signals of flow and transpulmonary pressure were recorded on a computer. Respiratory volume was obtained by digital integration of the flow signal so that RL was calculated from the transpulmonary pressure and flow, at isovolumetric points. Analysis of 5–10 breaths was used for each determination of RL.
Agents.
Crude brevetoxins and purified PbTx-3 were obtained from the Center for Marine Science at the University of North Carolina at Wilmington. Crude brevetoxin was diluted in NH-15 buffer. PbTx-3 was first diluted in a small volume of Alkamuls (Emulphor EL-620: ethoxylated castor oil and water; Chemtec Chemical Co., Chatsworth, CA), followed by suspension in phosphate-buffered saline (PBS). Atropine sulfate injection (Baxter Health Care, Deerfield, IL) was given at a dose of 0.2 mg/kg, iv,; the histamine H1-antagonist diphenhydramine hydrochloride (Elkins-Sinn Inc., Cherry Hill, NJ) was diluted in PBS and given at a dose of 2 mg/kg, iv. Albuterol sulfate inhalation solution (2.5 mg/3 mL; Dey, Napa, CA) was given as an aerosol. A. suum extract (Greer Diagnostics, Lenoir, NC) was diluted with PBS to a concentration of 82,000 protein nitrogen units/mL and delivered as an aerosol (20 breaths/min). The following agents were all obtained from Sigma (St. Louis, MO): The bradykinin B2 receptor antagonist HOE-140 was diluted in PBS, and given as an aerosol (400 nM/kg); budesonide was first diluted in 1 mL ethanol and then in PBS to give a 1 mg/3 mL solution and was given as an aerosol; carbamylcholine (carbachol) was dissolved in PBS at concentrations of 0.25, 0.50, 1.0, 2.0, and 4.0% wt/vol and delivered as an aerosol. As reported previously in detail (Abraham et al. 1994, 2004; Scuri et al. 2002), we used a dosimeter-piston ventilator system with a Raindrop nebulizer (Nelcor Puritan Bennett, Carlsbad, CA) to deliver the aerosols directly into the endotracheal tube only during inspiration at a tidal volume of 500 mL and a rate of 20 breaths/minute.
Assessment of nonspecific airway responsiveness.
Airway responsiveness to carbachol was determined from cumulative concentration–response curves as previously described (Abraham et al. 1994, 2004; Scuri et al. 2002). RL was measured immediately after inhalation of PBS and within 5 min after each consecutive administration of 10 breaths of increasing concentrations of carbachol (0.25, 0.5, 1.0, 2.0, and 4.0% wt/vol PBS). The provocation test was discontinued when RL increased > 400% from the post-PBS value or after the highest carbachol concentration had been administered. The cumulative carbachol concentration (in breath units) that increased RL by 400% over the post-PBS value (PC400) was calculated by interpolation from the dose–response curve. One breath unit was defined as one breath of a 1% wt/vol carbachol aerosol solution. A decrease in the PC400 indicates the development of AHR.
Airway responsiveness to PbTx-3.
Baseline RL was measured, and then the sheep were challenged with 20 breaths of increasing concentrations of PbTx-3: 0.1, 0.3, 1, and 10 pg/mL of PbTx-3. RL was measured within 5 min after the delivery of each concentration of toxin.
Airway responses to toxin.
Baseline RL was measured, and then the sheep were challenged with 20 breaths of 100 pg/mL of crude brevetoxins. RL was measured immediately after challenge and then 15, 30, and 60 min after challenge. Responses to crude brevetoxins alone were compared with the responses obtained after treating the animals with the histamine H1-antagonist diphenhydramine, the anticholinergic agent atropine, the glucocorticosteroid budesonide, or the bradykinin B2 receptor antagonist HOE-140. All drugs were given 30 min before challenge. Repeat challenges were separated by a minimum of 48 hr. In a separate study, albuterol aerosol was given immediately after inhalation of crude brevetoxins to determine if the drug could reverse the toxin-induced bronchoconstriction. Drug doses chosen for these studies were based on our previous use of these compounds (Abraham 2000).
Allergen–PbTx-3 interaction.
To determine if multiple exposures to PbTx-3 affected antigen-induced responses, we challenged sheep in the morning with 20 breaths of 100 pg/mL PbTx-3 for 3 consecutive days and then on the fourth day challenged the animals with allergen to determine if the antigen-induced EAR, LAR, and AHR were affected. For these studies a baseline PC400 to carbachol was determined 1–3 days before the start of PbTx-3 exposures. One day after the last PbTx-3 exposure (day 4), baseline RL was measured, and then the sheep were challenged with antigen. R
L was measured immediately after, hourly for 1–6 hr after, and then on the half-hour from 6.5 to 8 hr after challenge. On the next day, the postantigen PC400 was determined. The results were compared with those obtained without PbTx-3 exposure in the same sheep.
To determine if allergen challenge affected the response to PbTx-3, a baseline PC400 to carbachol and a concentration–response curve to PbTx-3 were determined. One to 3 days later, baseline RL was measured, and then the sheep were challenged with antigen. RL was measured immediately after, hourly for 1–6 hr after, and then on the half-hour from 6.5 to 8 hr after challenge. On the next day, the postantigen response to PbTx-3 was determined. Two to 3 hr later when RL had returned to normal, the postantigen PC400 to carbachol was measured. The changes in the postchallenge compared with prechallenge responses to PbTx-3 and PC400 were assessed to determine if they were affected by the allergen provocation.
Cutaneous responses.
To further investigate the potential for a generalized H1-mediated response to toxin, we performed skin tests with crude brevetoxins and pure PbTx-3 to determine if they would induce a wheal response and if the response was histamine mediated (Lucio et al. 1992; Molinari et al. 1995). Immediate cutaneous responses (ICRs) were induced by intradermal injections of 0.05 mL of 5% wt/vol histamine, 100 pg/mL crude brevetoxins, or PbTx-3 solutions using insulin syringes with 28-gauge needles. These studies were repeated, but the sheep were treated with diphenhydramine (2 mg/kg, iv) 2 hr before intradermal challenge or with atropine (0.2 mg/kg, iv) 30 min before challenge.
Statistical analysis.
Overall effects of airway responses with and without pharmacologic intervention were analyzed with a multifactorial analysis of variance for repeated measures. If the null hypothesis was rejected, then Tukey’s post hoc test was used to determine the statistical significance of differences. In the event only two treatments were compared, a paired t-test was used. Analysis of the ICR was determined as described previously by our group (Molinari et al. 1995; Lucio et al. 1992). Wheal sizes were measured 20 and 60 min after injection of active test solutions. The surface area (mm2) of the wheal was determined by measuring the largest wheal diameter (D1) and its perpendicular (D2) and then calculating the surface area using the equation Π[(D1 + D2)/4]2. Results were compared by one-way analysis of variance followed by Tukey’s post hoc test if the null hypothesis was rejected. For all analyses, significance was accepted when p < 0.05 using a two-tailed analysis. Values in the text and figures are reported as mean ± SE.
Results
Airway responses to toxin with and without pharmacologic intervention.
Inhalation of crude brevetoxins resulted in an immediate bronchoconstriction (RL increased 128 ± 6% over baseline), which then resolved over the next 60 min (Figure 1). The constrictor effects of toxin were significantly reduced by pretreating the animals with the anticholinergic agent atropine (58% inhibition), the glucocorticosteroid budesonide (49% inhibition), the β 2 adrenergic agent albuterol (71% inhibition), and the histamine H1-antagonist diphenhydramine (47% inhibition). The reduced response in the presence of either diphenhydramine or atropine suggests that both cholinergic (vagal) and H1-mediated pathways play a role in the toxin-induced bronchoconstriction. It is important to note that we previously showed that the dose of atropine used in these studies does not block histamine-induced bronchoconstriction (Abraham et al. 2005). These results are consistent with histamine stimulation of H1 receptors on nerves. The protective effects of albuterol and budesonide are also consistent with the involvement of mast cells/basophils because both agents have been reported to reduce mediator release (Wanner et al. 1987). Although albuterol can inhibit mediator release, its primary action is to relax smooth muscle. As illustrated in Figure 2, giving albuterol immediately after toxin challenge causes a rapid reversal of the response. The dual action of albuterol may account for its increased efficacy when compared with diphenhydramine (Figure 1).
Figure 3 illustrates that HOE–140, a bradykinin B2 receptor antagonist, significantly reduces the toxin-mediated bronchoconstriction. The 34% protection was significant when compared with the response when the animals were untreated but is significantly less than that seen with atropine (58%). These data indicate that, as with other types of irritants, brevetoxin increases kinin levels in the airways and that this inflammatory mediator contributes to toxin-induced airway responses.
Cutaneous responses to toxin.
The in vivo airway data support a role for a histamine H1-mediated component in the bronchoconstrictor response. To further investigate the potential for a generalized H1-mediated event, we performed skin tests with crude brevetoxins and pure PbTx-3. Both crude brevetoxins and PbTx-3 induced an ICR, which was reduced when the animals were pretreated with the H1-antagonist diphenhydramine (Figure 4). As expected, atropine had no effect on the ICR (data not shown). Thus, the collective data obtained with inhaled and injected toxins support the hypothesis that histamine H1-mediated pathways contribute to the in vivo effects of toxin.
Allergen–toxin interactions.
Our results indicate that inhaled toxin induces histamine and kinin release in the airways. Because these mediators are key components of allergen-induced airway responses, we wanted to determine if toxin challenge would induce a LAR and AHR similar to that seen with allergen. We also wanted to determine if preexposure to toxin could accentuate the response to allergen or if allergen challenge affected the response to toxin. We found that although PbTx-3 (20 breaths of 100 pg/mL) caused an early bronchoconstriction, there was no subsequent LAR or AHR in sheep that demonstrated these responses to inhaled allergen (data not shown). To determine if toxin exposure would enhance the response to allergen and/or affect the postantigen-induced AHR, we exposed three sheep to PbTx-3 (20 breaths of 100 pg/mL) for 3 consecutive days and then, on the day 4, measured the response to allergen challenge. On the next day, we measured their PC400 to carbachol to determine if the postantigen-induced AHR was affected. Figure 5 shows that the 3-day exposure to PbTx-3 did not alter the airway responses to allergen (EAR, LAR, or AHR) in these animals when compared with the control allergen responses in these same sheep.
Although preexposure to PbTx-3 did not accentuate antigen-induced responses, allergen challenge did increase the sensitivity to PbTx-3. Figure 6 shows that the concentration–response curve for PbTx-3 was significantly enhanced 24-hr after allergen exposure in sheep that develop a LAR (138 ± 9%). The sheep were also hyperresponsive to carbachol at this time as evidenced by the decrease in the post challenge PC400 (15 ± 1 breath units) when compared with the prechallenge PC400 (28 ± 2 breath units, p < 0.05). These data suggest that, as seen with other irritants, the airways are more sensitive to toxin after an asthma exacerbation.
Discussion
The results of this study confirm and extend our previous work with brevetoxin aerosols (Abraham et al. 2005). We confirm that both cholinergic and histamine H1-mediated path-ways contribute to the bronchoconstrictor responses to toxin at concentrations 10–100 times greater than those used previously (Abraham et al. 2005). In addition, we identified standard asthma medications that protect against and/or reverse the constrictor effects of inhaled toxin. Our previous work showing that cromolyn sodium can block toxin-induced constrictor effects (Abraham et al. 2005) in conjunction with our current evidence that both the airway and cutaneous responses to toxin are histamine mediated, provides further support for the hypothesis that toxin activates mast cells in vivo. We show that kinins are released in the airways after toxin inhalation. This is a novel finding and, combined with the demonstration that the response to inhaled toxin is greater in inflamed airways, suggests that kinins could be contributing factors to the reported increased sensitivity of asthmatic patients to toxin exposure.
The methodology used in the airway provocation studies is designed to deliver toxin directly into the lung under controlled conditions. Although this technique does not mimic natural exposures because it bypasses the upper airways, it provides a reproducible controlled airway challenge system that eliminates dosing variability. This reproducibility is an important consideration when studying adverse lower airway effects and identifying protective pharmacologic agents. Given this, our findings should be applicable to humans experiencing lower airway symptoms during a red tide event.
Field studies indicate that humans with normal or healthy airways as well as those with compromised airways respond to a red tide event (Backer et al. 2003, 2005; Fleming et al. 2005; Kirkpatrick et al. 2004). Data from our previous study, using both allergic and nonallergic sheep, are consistent with these observations (Abraham et al. 2005). We found that the constrictor response to aerosolized brevetoxins was not limited to allergic animals. Normal (i.e., nonallergic) animals responded with a dose-dependent bronchoconstrictor response to inhaled toxin, and although there was tendency for the allergic animals to be more responsive, the difference was not significant. The similarity in responsiveness between the two groups was attributed to the fact that the allergic sheep had not seen allergen during the course of the studies and so the inflammatory status of their airways was similar to that of the nonallergic animals (Abraham et al. 2005). However, when the airways of allergic sheep are inflamed, as is the case after an antigen challenge (Abraham et al. 1994, 2000), the response to brevetoxin is enhanced (Figure 6). Thus, the response to toxin is not linked to the allergic status per se but is affected by the inflammatory status of the airways at the time of toxin exposure.
Even though toxin can elicit respiratory symptoms in both normal and compromised airways, a major aim of the present study was to identify asthma medications that could provide protective and/or therapeutic effects against toxin-induced bronchoconstriction. We used crude brevetoxins for these studies because we considered this aerosol more relevant to natural exposures experienced by persons living in coastal areas. To provide a more severe stimulus, higher concentrations of toxin were administered than used previously. The constrictor responses to PbTx-3 and crude brevetoxins at concentrations 10-to 100-fold lower were similar to those seen here and were inhibited by cromolyn sodium, atropine, and diphenhydramine (Abraham et al. 2005). Although atropine and diphenhydramine are not considered standard therapy for the treatment of asthma, the findings that these drugs blocked the responses to an increased toxin burden are important because they confirm that the protective effects seen previously were not dose dependent and that the same mechanisms are operative at higher toxin levels. Pretreatment with inhaled budesonide and albuterol provided significant protection against the toxin-induced bronchoconstriction. Acutely administered, the glucocorticosteroid budesonide is not a bronchodilator nor does it affect cholinergic responsiveness; therefore, its acute protective effects may be related to the drug’s ability to prevent mast cell activation in vivo (Denburg 1997; Kamada and Szefler 1995). This action would reduce the histaminergic component of the response. β 2 adrenergic agents, in addition to their bronchodilatory activity, can also reduce mediator release (Wanner et al. 1987); therefore, such a mechanism could contribute to and possibly explain the slightly enhanced protection afforded by albuterol (Figure 1). The bronchodilatory activity normally attributed to β 2 adrenergic agents is illustrated in Figure 2, where the albuterol is shown to reverse the toxin-induced bronchoconstriction. The latter effect is relevant to affected individuals that require rescue medication when exposed to aerosolized red tide toxins.
We previously reported that inhalation challenge with a variety of noxious agents results in increased tissue kallikrein activity and subsequent kinin generation in the airways. Increased kinin levels are associated with bronchoconstriction, inflammation, and AHR (Forteza et al. 1994, 1996; Scuri et al. 2000). Kinins also cause airway wall edema, which can influence the relaxation of the airways after a contractile stimulus (Wagner 1997). Asthmatic subjects are more sensitive to the effect of inhaled kinins than are normal individuals (Fuller et al. 1987). Inhaled bradykinin caused cough and retrosternal discomfort in both normal and asthmatic subjects, but caused bronchoconstriction only in the asthmatics (Fuller et al. 1987). Interestingly, this difference in kinin sensitivity could explain why Backer et al. 2003, 2005) were unable to demonstrate pulmonary function changes in normal subjects exposed to toxin even though they complained of chest discomfort, as opposed to Fleming et al. (2005), who were able to demonstrate pulmonary function changes in toxin-exposed asthmatics. Thus, kinin generation could be a contributing mechanism responsible for the heightened sensitivity of asthmatic subjects to toxin. Our data showing that the bradykinin B2 receptor antagonist HOE-140 significantly reduced the bronchoconstrictor response to inhaled toxin suggest that toxin stimulated increased kinin levels in the airway. These findings may be of greater consequence in terms of repeated exposures where increased kinins could contribute to inflammation and AHR (Zaias et al. 2004). Given the increased sensitivity of asthmatics to bradykinin (Fuller et al. 1987), our findings suggest that activation of the kinin pathway by brevetoxins could be another mechanism that contributes to more severe responses in patients with compromised airways.
In allergic sheep, the ICR to injected A. suum antigen is mast cell mediated, and antihistamines block this response (Ahmed et al. 1993; Lucio et al. 1992). We have used skin tests in combination with inhalation challenge tests to demonstrate that tryptase activates mast cells (Molinari et al. 1995, 1996). The data suggesting that inhaled toxin stimulates airway mast cells/basophils led us to test this hypothesis in the skin. Both crude brevetoxins and PbTx-3 induced ICRs, which were significantly reduced in the presence of diphenhydramine. These findings provide additional evidence that toxin activates mast cells in vivo. We should caution, however, that although the collective airway and skin data strongly argue in favor of mast cell hypothesis, final validation must be withheld until histamine measurements are obtained from lung and skin fluids. Nevertheless, if the findings in the airways and skin can be extrapolated to humans, then one might speculate that normal subjects, who would not generally have access to inhaled steroids and/or β 2 adrenergics, might obtain relief from toxin-induced irritation with antihistamines.
The collective data from the skin and the airways prompted a number of questions regarding toxin-allergen interactions. These questions were precipitated by reports of adverse respiratory events hours after the original toxin exposure in susceptible individuals. In an attempt to mimic this scenario in the laboratory, we determined if a single toxin exposure could stimulate the development of a LAR and AHR, similar to that seen with allergen. We were unable to demonstrate this effect. Similarly, we were unable to show that exposures for 3 consecutive days to PbTx-3 accentuated the response to allergen. These findings indicate that, although toxin elicits an acute bronchoconstriction, the stimulus and/ or signaling mechanisms are not the same as seen with allergen that can result in a LAR and AHR. Furthermore, under the conditions of the experimental protocol, repeated toxin exposure was an insufficient stimulus to prime the airways such that the response to allergen challenge was augmented. We did find that allergen challenge accentuated the response to toxin. The increased responsiveness occurred during the period when the airways are actively inflamed and when they demonstrate increased responsiveness to other constricting agents, as seen previously (Abraham et al. 1994, 2000) and here with carbachol.
Although the data in the present study were generated in an acute setting, there is a parallel to the more chronic situation that exists in asthmatics whose airways are chronically inflamed and have heightened sensitivity to a variety of nonspecific bronchoconstrictors. Thus, the contention that the airways of asthmatics may be more sensitive to inhaled toxins than those of persons with normal airways may depend, in part, on the status of airway inflammation at the time of exposure.
In summary, we have provided new data concerning the airway effects of inhaled brevetoxins. We have identified clinical drugs that may be useful in the prevention and treatment of toxin-induced bronchial responses. Confirmation of these results must await controlled clinical trials. Nevertheless, given the caveat that these findings are based on experimental data collected in animals, our results may be of benefit to individuals affected by aerosolized brevetoxins at environmentally relevant levels.
Figure 1 Effect of pharmacologic agents on breve-toxin-induced bronchoconstriction. Crude brevetoxins produced an immediate increase in RL, which then returned to baseline values within 1 hr. Pretreatment with atropine, budesonide, albuterol, and diphenhydramine all reduced the toxin-induced response. Values are mean ± SE for five sheep.
*p < 0.05 versus all others; #p < 0.05 albuterol versus diphenhydramine.
Figure 2 Reversal of crude brevetoxin–induced bronchoconstriction with albuterol. Values are mean ± SE for five sheep.
*p < 0.05 versus untreated.
Figure 3 Effect of the bradykinin B2 receptor antagonist HOE-140 on crude brevetoxin–induced bronchoconstriction. Animals treated with HOE-140 had a reduced response compared with that seen when the animals were untreated (Un). The response in the presence of HOE-140 was similar to that seen with diphenhydramine (Diphen) but less than that seen with atropine (Atr). Values are mean ± SE for five sheep.
*p < 0.05 versus untreated; #p < 0.05 versus HOE-140.
Figure 4 Effect of the histamine H1-antagonist diphenhydramine (H1A) on the ICRs to crude brevetoxins (PbTx) and PbTx-3. Values are mean ± SE for nine sheep.
*p < 0.05 versus untreated.
Figure 5 (A) The effect of 3 consecutive days of PbTx-3 exposure on antigen-induced EAR (early increase in RL, 0–4 hr) and LAR (late increase in RL, 4–8 hr; and (B) AHR to carbachol (indicated by the decreased PC400 24 hr after challenge). PbTx-3 exposure (20 breaths of 100 pg/mL) had no effect on any parameter. Values are mean ± SE for three sheep.
*p < 0.05 versus baseline.
Figure 6 Effect of allergen challenge on the response to PbTx-3. Animals were more sensitive (indicated by the leftward shift in the concentration–response curve) to inhaled PbTx-3 one day after antigen challenge (postantigen) compared with before challenge (preantigen). The animals also demonstrated AHR to carbachol at this time, as indicated by a fall in the PC400, as described in the text. Values are mean ± SE for five sheep.
*p < 0.05 versus preantigen.
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References
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Kamada AK Szefler SJ 2000. Glucocorticoids in asthma and rhinitis. In: Asthma and Rhinitis (Busse WW, Holgate ST, eds). Oxford:Blackwell Science Ltd, 1569–1581.
Kirkpatrick B Fleming LE Squicciarini D Backer LC Clark R Abraham W 2004 Literature review of Florida red tide: implications for human health effects Harmful Algae 3 99 115 20411030
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Pierce RH Henry MS Blum PC Lyons J Cheng YS Yazzie D 2003 Brevetoxin concentrations in marine aerosol: human exposure levels during a Karenia brevis harmful algal bloom Bull Environ Contam Toxicol 70 161 165 12478439
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Pierce RH Kirkpatrick GJ 2001 Innovative techniques for harmful algal toxin analysis Environ Toxicol Chem 20 107 114 11351396
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Scuri M Botvinnikova Y Lauredo IT Abraham WM 2002 Recombinant a1 -proteinase inhibitor blocks antigen- and mediator-induced airway responses in sheep J Appl Physiol 93 1900 1906 12433933
Scuri M Forteza R Lauredo I Sabater JR Botvinnikova Y Allegra L 2000 Inhaled porcine pancreatic elastase causes bronchoconstriction via a bradykinin-mediated mechanism J Appl Physiol 89 1397 1402 11007574
Shimoda T Krzanowski J Jr Nelson R Martin DF Polson J Duncan R 1988 In vitro red tide toxin effects on human bronchial smooth muscle J Allergy Clin Immunol 81 1187 1191 3379231
Wagner EM 1997 Effects of edema on small airway narrowing J Appl Physiol 83 784 791 9292464
Wanner A Ahmed T Abraham WM 1987. Drug actions on mediators. Drug therapy for asthma. In: Lung Biology and Health and Disease (Jenne JW, Murphy S, eds). New York:Marcel Dekker, 413–461.
Zaias J Botvinnikova Y Fleming LE Bossart GD Baden DG Abraham WM 2004 Aerosolized polyether brevetoxin (PbTx) causes airway hyperresponsiveness (AHR) and airway inflammation in both normal and allergic sheep [Abstract] Am J Respir Crit Care Med 169 A639
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7496ehp0113-00063815866777Mini-Monograph: BrevetoxinsCharacterization of Marine Aerosol for Assessment of Human Exposure to Brevetoxins Cheng Yung Sung 1Zhou Yue 1Irvin Clinton M. 1Pierce Richard H. 2Naar Jerome 3Backer Lorraine C. 4Fleming Lora E. 5Kirkpatrick Barbara 2Baden Dan G. 31Lovelace Respiratory Research Institute, Albuquerque, New Mexico, USA;2Mote Marine Laboratory, Sarasota, Florida, USA;3Center for Marine Science, University of North Carolina at Wilmington, Wilmington, North Carolina, USA;4Centers for Disease Control and Prevention, Atlanta, Georgia, USA;5National Institute of Environmental Health Sciences Marine and Freshwater Biomedical Science Center, University of Miami, Miami, Florida, USAAddress correspondence to Y.S. Cheng, Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr. SE, Albuquerque, NM 87108 USA. Telephone: (505) 348-9410. Fax: (505) 348-8567. E-mail:
[email protected] article is part of the mini-monograph “Aerosolized Florida Red Tide Toxins (Brevetoxins).”
We thank D. Kracko and J. McDonald for the liquid chromatography/mass spectrometry analysis.
This research was supported by the National Institute of Environmental Health Sciences (NIEHS) grant P01 ES 10594 and a Minority Supplement to the P01, also from the NIEHS, as well as by the Centers for Disease Control and Prevention, the Florida Harmful Bloom Taskforce, and the Florida Department of Health.
The authors declare they have no competing financial interests.
5 2005 9 2 2005 113 5 638 643 2 8 2004 20 12 2004 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Red tides in the Gulf of Mexico are commonly formed by the fish-killing dinoflagellate Karenia brevis, which produces nine potent polyether brevetoxins (PbTxs). Brevetoxins can be transferred from water to air in wind-powered white-capped waves. Inhalation exposure to marine aerosol containing brevetoxins causes respiratory symptoms. We describe detailed characterization of aerosols during an epidemiologic study of occupational exposure to Florida red tide aerosol in terms of its concentration, toxin profile, and particle size distribution. This information is essential in understanding its source, assessing exposure to people, and estimating dose of inhaled aerosols. Environmental sampling confirmed the presence of brevetoxins in water and air during a red tide exposure period (September 2001) and lack of significant toxin levels in the water and air during an unexposed period May 2002). Water samples collected during a red tide bloom in 2001 showed moderate-to-high concentrations of K. brevis cells and PbTxs. The daily mean PbTx concentration in water samples ranged from 8 to 28 μg/L from 7 to 11 September 2001; the daily mean PbTx concentration in air samples ranged from 1.3 to 27 ng/m3. The daily aerosol concentration on the beach can be related to PbTx concentration in water, wind speed, and wind direction. Personal samples confirmed human exposure to red tide aerosols. The particle size distribution showed a mean aerodynamic diameter in the size range of 6–12 μm, with deposits mainly in the upper airways. The deposition pattern correlated with the observed increase of upper airway symptoms in healthy lifeguards during the exposure periods.
brevetoxinexposure assessmentKarenia brevismarine aerosolparticle size distributionpersonal exposurered tide
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Red tides in the Gulf of Mexico are commonly formed by the fish-killing dinoflagellate Karenia brevis (formerly known as Gymnodinium breve). The organism produces as many as nine potent polyether brevetoxins called PbTxs and designated PbTx-1, PbTx-2, etc., that result in the death of a massive number of fish (Forrester et al. 1977), mammals (Bossart et al. 1998), and other marine species during red tide blooms. PbTxs can also concentrate in the tissues of shellfish that feed on dinoflagellates. People who eat these shell-fish may suffer from neurotoxic shellfish poisoning, a food poisoning syndrome that can cause severe gastrointestinal and neurologic symptoms.
Brevetoxins can also be transferred from water to air in the wind-powered white-capped waves during red tide episodes (Pierce et al. 1989a). Inhalation exposure to marine aerosol containing brevetoxins causes respiratory symptoms, including involuntary coughing and sneezing, watery eyes, rhinorrhea, a burning sensation in the throat and nose, and difficulty breathing (Backer et al. 2003; Cheng et al. 2005; Pierce et al. 2003). Red tides off Florida coastal waters are almost annual events. Historically, anecdotal reports and limited references to human symptoms in the literature (Music et al. 1973; Woodcock 1948) have consistently cited acute respiratory and eye irritations as typical responses from exposure to aerosolized PbTx. In addition, experimental work (Abraham et al. 2004; Asai et al. 1982; Baden et al. 1982) demonstrated that inhaled PbTx could cause bronchoconstriction and a smooth respiratory muscle response that could result in an asthma attack in susceptible individuals.
During two red tide events in Florida in 1999, PbTx levels in air and seawater were measured and personal interviews and pulmonary function tests were conducted on people before and after they visited Florida beaches (Backer et al. 2003). During moderate- and high-exposure periods, 36 and 80 ng/m3, respectively, of PbTx were detected in the air. Lower respiratory tract symptoms (e.g., tightness of chest, wheezing, shortness of breath) were reported by 8% of the people who had no or low exposure, 11% with moderate exposure, and 28% with high exposure; upper respiratory symptoms (eye and throat irritation, nasal congestion, cough) were also increased in the moderate- and high-exposure groups. Nasal-pharyngeal swabs were taken from people who experienced moderate or high exposure, and we found a mild inflammatory response in > 33% of these participants. This initial study indicated that brief recreational exposure to red tide aerosol by beach goers could be related to increased respiratory symptoms. We also found in the fall of 2000 in a red tide event near Corpus Christi, Texas, that low air concentrations of brevetoxins in the range of 3–4 ng/m3 could initiate symptoms in the upper respiratory tract, including cough, irritation in the throat, and itchy eyes (Cheng et al. 2005).
Red tide aerosols are marine aerosols containing aerosolized K. brevis fragments and associated bacteria (Pierce et al. 1989a). Environmental aerosol samples collected during red tides in Florida and North Carolina in 1987 (Pierce et al. 1989b) showed high concentrations of three PbTxs (PbTx-2, PbTx-3, and PbTx-5). Generation of red tide aerosol was also studied in the laboratory using cultures of K. brevis (Pierce et al. 1989a, 1989b). In general, similar PbTxs profiles were observed in the laboratory and the field.
Characterization of the red tide aerosols in terms of its concentration, chemical composition, toxin profile, and particle size distribution is essential in understanding the source, assessing human exposure, and estimating dose of inhaled aerosol. With additional information on local weather conditions and water samples in the bloom, one can also establish a relationship between the aerosol concentration and environmental conditions. We describe in this article a detailed characterization of aerosols and environmental conditions during an epidemiologic study of occupational exposure to Florida red tide aerosol.
Materials and Methods
Field sampling study.
The occupational exposure study was conducted in Sarasota, Florida. The experimental design had two 5-day study periods including an exposure period between 7 and 11 September 2001 during a red tide episode and a nonexposure period between 3 and 7 May 2002. The detailed study design for the epidemiologic study is described in this mini-monograph by Backer et al. (2005). Air samplers were set up along Siesta Beach and Lido Beach (Sarasota, Florida) to collect marine aerosols. High-volume air samplers (model G2000H; Andersen Instruments, Smyrna, GA) were placed near the water to collect large quantities of material for analysis of PbTx. Some samplers collected airborne particles in one filter substrate for total aerosol concentration, whereas other samplers housed a five-stage, high-volume cascade impactor (model SA235, Andersen Instruments) for total concentration as well as particle size distribution. Glass-fiber filters (20 cm × 25 cm) were used as the collection substrates (model EPM2000; Whatman International Ltd., Maidstone, UK) for the single-stage filter sampler. Cellulous filters (15 cm × 14 cm; filter paper 41, Whatman) were used for the five-stage cascade impactor. There were one impactor sampler and three filter samplers at Siesta and Lido beaches. The sampling time usually started between 0800 and 0830 hr and ended between 1530 and 1630 hr. We also ran one impactor sampler at night for a total of approximately 15 hr at Siesta Beach during the exposed period of 7–10 September 2001. The samples collected represented the time-integrated ambient concentration and particle size in the sampling area.
Seawater samples were collected in 1-L glass bottles 3 times each day (0830, 1200, and 1600 hr) from the surf zone adjacent to each air sampler location to provide an indication of changes in cell counts and toxin composition throughout each day. A 20-mL subsample was collected from each bottle and fixed with Utermohl’s solution for microscopic identification and enumeration of K. brevis cells. The remaining water sample was processed for brevetoxin analysis by liquid chromatography–electrospray ionization mass spectrometry (LC-MS) and for verification by enzyme-linked immunosorbent assay (ELISA).
Personal exposure.
Personal exposure was measured by the occupational study participants who wore a personal sampler (IOM inhalable dust sampler; SKC, Inc., Eighty Four, PA) connected to a battery-operated pump (Hi Flow Sampler; Gillian Instrument, Wayne, NJ). The sampler was placed at the lapel near the breathing zone. A 25-mm glass fiber filter (type A/E; Pall Life Science, Ann Arbor, MI) was used as the collection substrate. The sampling flow rate was 2-L/min controlled by a rotameter in the sampling pump.
Monitoring of weather conditions.
A portable, solar-powered weather station was deployed at Siesta Beach near the impactor sampler to provide wind speed and direction, temperature, and relative humidity (Complete Weather Station; Davis Instruments, Hayward, CA). It continued to monitor weather conditions during the sampling period, and the data were downloaded daily. The wind direction measured at Siesta Beach included 16 quadrants. We assigned arbitrary values for onshore wind (1 for W, WSW, SW, SSW, and S), offshore wind (0 for ESE, E, ENE, NE, NNE, and N), and partially onshore wind (0.5 for SSE and WNW, 0.3 for NW, 0.1 for NNW and SE). The daily average of the direction was then the mean value averaged over the sampling period; the standard deviation was a measure of the variability in wind direction measurement. The mean wind direction can also be viewed as the fraction of time the wind direction was onshore.
Analysis for air samples.
After collection, the high-volume impactor substrates were stored at −20°C. Filters were removed from the cold storage and equilibrated to room temperature before extraction. First, substrates were cut into several sections. About 3.0 cm2 was used for ELISA analysis for total concentration of brevetoxins and related compounds. About 90 cm2 of samples were extracted and analyzed by LC-MS.
The extraction and LC-MS techniques have been described in detail (Cheng et al. 2005). Briefly, the section of filter for LC-MS-MS analysis was extracted first by folding over and rolling it into a 15-mL polypropylene tube. Ten milliliters of acetone were added to the tube and the sample was vortexed for 20 sec, sonicated for 2 min, and then placed on a circular rotator (Roto-Torque, low speed #10; Cole-Parmer Instruments, Vernon Hills, IL) for 20 min. The 10-mL extract was then evaporated under a gentle stream of nitrogen to approximately 100 μL, vortexed for 5 sec to rehomogenize the extract, and recombined with a 50:50 methanol:purified water to the final analysis volume (typically 200 μL). The samples were then analyzed for brevetoxins by an LC-MS technique using a high-performance liquid chromatograph (SIL-DAD vp; Shimadzu Co., Kyoto, Japan) coupled with the API 365 MS/MS (Applied Biosystems Inc., Foster City, CA). We ran the instrument in multiple-response monitor mode and used the parent/daughter ion pairs of 867.5/849.5 (PbTx-1), 895.5/877.5 (PbTx-2), 897.5/725.4 (PbTx-3), 911.6/893.7 (PbTx-6), 899.6/863.9 (PbTx-9), and 657.4/273.2 (brevenal) to identify and quantify the PbTx components. The limit of quantitation for the analysis of the impactor sample was 0.01 ng/m3.
Filter samples from personal samples and small sections of each impactor substrate were analyzed by a competitive ELISA (Naar et al. 2002) based on the specific activity of the goat anti-PbTx antibody (Trainer and Baden 1991). The analysis provided a total amount of PbTxs but not the individual PbTxs. The limit of quantitation of the brevetoxins using the ELISA assay was 0.6 ng/sample. For the personal samples, this corresponded to about 1 ng/m3 for a sampling period of 300 min.
Analysis of water samples.
We extracted brevetoxins from the water samples by passing the seawater through a C18 solid-phase extraction disk under vacuum (Ansys Technologies, Inc., Lake Forest, CA) according to the procedure of Pierce et al. (2003). The C18 disks were then rinsed with reverse osmosis water to remove any remaining salts and eluted with methanol for LC-MS and ELISA analyses. We verified the extraction efficiency by recovery of standard toxins added to seawater samples and processed as described above.
Brevetoxin analyses of water samples were performed at the Mote Marine Laboratory by LC-MS using a ThermoFinnigan AqA LC-MS. The LC consisted of a SpectraSystems: LC pump P4000, autosampler AS3000, and degasser SCM1000 (Thermo Electron Co., Waltham, MA). Mass spectral detection was obtained using an AqA single quad system scanned from 204 to 1,216 AMU with AqA Max 40 V, and a scan rate of 1.1 scans/sec. All analysis was conducted using electrospray with the probe at 3 kV and 250°C. The column was a Phenomenex Security Guard C18 guard column with a Phenomenex Luna C18 5Fm 250 × 2 mm analytical column (Phenomenex USA, Torrance, CA). The solvent gradient was composed of acidified (0.3% acetic acid) acetonitrile (ACN)/H2O with initial 50:50 ACN/H2O to 95:5 ACN/H2O over 40 min.
Dose estimation of inhaled PbTxs.
The deposition pattern of the inhaled red tide aerosol in different regions of the respiratory tract can be estimated using the International Commission on Radiological Protection (ICRP) 66 lung model (ICRP 1994). The human respiratory tract is divided into three anatomical regions. The extrathoracic (ET) airway including the naso-oro–pharyngo–laryngeal region is the entry to the respiratory tract and the first defense against hazardous inhaled material. The tracheobronchial (TB) tree region includes the trachea and 16 generations of branching airways. Gas exchange takes place in the pulmonary region. Particles deposit in the lung by inertial impaction, sedimentation, diffusion, and electrostatic mechanisms.
Assuming a breathing rate of 25 L/min for light exercise of an adult male and measured particle size distribution, we calculated the deposition fractions of inhaled particles in the three regions of the human respiratory tract using LUDEP software (NRPB, Oxon, UK), which is based on the ICRP lung model (ICRP 1994).
Statistical analysis.
A paired t-test was used to determine whether differences of mass median aerodynamic diameter (MMAD) between the LC-MS and ELISA analysis were significant. A p-value < 0.05 was considered significant.
Results
Environmental conditions.
Table 1 summarizes the environmental data obtained for the two sampling periods during the 2001–2002 Occupational Exposure Study in Siesta Beach, Florida. The data showed that during the two sampling periods, the temperature and relative humidity were stable. In addition to temperature and relative humidity, the wind speed [in miles per hour (mph)] and wind direction were measured.
PbTx concentration in air and water.
Environmental sampling confirmed the presence of K. brevis in water and brevetoxins in the water and air during the red tide exposure period (September 2001), and the lack of significant toxin levels in the water and air during the unexposed period (May 2002) (Table 2). Surf water samples from the non-exposed May 2002 period contained low concentrations of brevetoxins, ranging from below the limit of quantitation (0.05 μg/L) to 0.3 μg/L for samples collected from 3 to 7 May 2002 in both Lido and Siesta beaches. Air concentrations were expressed as nanograms per cubic meter of PbTxs (LC-MS technique) and were the mean value of three filter and one impactor samples collected at Lido and Siesta beaches. During the nonexposure period, air samplers recovered trace amounts of brevetoxins from the air ranging below the detection limit of quantitation (< 0.01 ng/m3) to 1 ng/m3.
Water samples collected during a red tide bloom showed moderate-to-high concentrations of K. brevis cells and PbTxs. The daily mean total PbTx concentration and standard deviation in water samples ranged from 8 to 28 μg/L from 7 to 11 September 2001. The mean concentrations were between 18 and 28 μg/L for the first 2 days. The aerosol concentrations were between 1.9 and 12 ng/m3 at the Siesta Beach and 1.3 and 27 ng/m3 at the Lido Beach. Higher concentrations (> 8.6 ng/m3) were reported between 7 and 9 September 2001, in general corresponding to higher PbTx concentrations in water and onshore wind direction. On 10 and 11 September 2001, the wind directions were mostly offshore, resulting in lower air concentrations (< 6 ng/m3). We also analyzed evening samples collected on impactors at night between 7 and 10 September 2001. The air concentrations of PbTx were very low: from below limit of quantitation (0.01 ng/m3) to 0.32 ng/m3. Weather conditions were not monitored, nor were water samples taken at night.
The brevetoxin profiles in aerosol samples taken during the month of September 2001 at Lido Beach are shown in Figure 1. These data show that PbTx-2 and PbTx-3 were the major brevetoxin species present. Much lower concentrations of PbTx-1, PbTx-6 and PbTx-9 were also observed. Trace amounts of brevenal, a natural brevetoxin antagonist, were also detected in water and air samples. The brevenal was recently isolated from K. brevis culture by our colleagues at the University of North Carolina at Wilmington (Bourdelais et al. 2004) and was beneficial in preventing airway responses in the laboratory animal study (Abraham et al. 2004).
Particle size distribution.
Particle size distributions of red tide aerosol were estimated from impactor samples, which were analyzed by both the LC-MS and ELISA techniques. Figure 2A and B shows normalized size distributions on samples obtained 8 September 2001 at Siesta and Lido beaches, respectively. Our data showed that the particle size distributions as analyzed by both ELISA and LC-MS analysis were similar. The size distribution showed a range of particles from 0.5 to 20 μm with a single-size mode approximately 6–12 μm. As shown in Figure 2A and B, the particle size can be described with a lognormal size distribution:
where xo is the MMAD and σg is the geometric standard deviation (GSD). The best-fit curve using SigmaPlot software (Version 8.0; SPSS, Chicago, IL), as shown in Figure 2A and B, has an MMAD of 9.1 and 8.9 μm and a GSD of 1.85 and 1.76 for Siesta and Lido beaches, respectively, based on LC-MS analysis. The MMADs and GSDs of the fitted log-normal distribution of red tide aerosols are listed in Table 3. The MMAD obtained from LC-MS and ELISA analyses was not significantly different for both Siesta and Lido beaches. This particle size information was then used to estimate the dose of inhaled red tide aerosol in the human volunteers (see below). The concentration of PbTx was too low to estimate particle size distribution for samples obtained during the nonexposed period of May 2002.
Dose calculation.
The fractional deposition of inhaled red tide aerosol in the human respiratory tract was estimated based on the size measurements as listed in Table 3. A total deposition fraction of 76–90% was calculated, with the majority of aerosol deposited in the ET region or upper airway (75–84%), and small but not insignificant deposition (2–6%) in the lower airways (TB and pulmonary region). The inhaled red tide aerosol has high deposition efficiency in the respiratory tract; thus, the pattern of deposition would help to explain the observed respiratory symptoms. The dose rate of the deposited brevetoxins can be calculated from Table 3 for a breathing rate of 25 L/min and for a unit air concentration of 1 ng/m3 of PbTx, as shown in Table 4.
Correlation of PbTx concentration with environmental factors.
To understand the contribution of various parameters that may influence the air concentration of red tide aerosol, we analyzed further the data obtained at Siesta Beach where the weather information was measured during both the 2001 and 2002 sampling period. The total brevetoxin concentration obtained from the impactor sample was modeled as a function of water concentration, wind speed, and wind direction monitored near the impactor sampling site. The following empirical relationship was developed:
This empirical model indicates the relative importance of water concentration, wind speed, and wind direction on the red tide aerosol concentration. The four constants in this equation were obtained by a nonlinear regression procedure of SigmaPlot software. An R2 value of 0.99 indicates a very good fit between the model and the experimental data (Figure 3).
Personal samples.
During the occupational study conducted in 2001 and 2002, personal samples were worn by seven or eight researchers and by lifeguards working on the Siesta and Lido beaches for about 8 hr a day. All 39 personal samples taken during the sampling period of 3–7 May 2002 showed no detectable amount of PbTx. During the exposure period from 7 to 11 September 2001, a total of 39 personal samples (mean sampling time, 471 min) from both the Siesta and Lido beaches were analyzed. The exposure concentration of PbTx is shown in Figure 4. Our results indicated a large variability of personal samples within each sampling day. Comparison of PbTx concentration obtained from area samples taken with high-volume samplers showed that the personal samples indicated a lower exposure concentration (on the average about 50%) from those obtained from the area sample. However, the daily means of the exposure concentrations from the personal samples followed the concentrations obtained from the area samples. This may be attributable to different sampling techniques with much lower flow rate for personal samplers and the possibility that the subjects spent only part of the time in the exposure area. This was the case for both the researchers and lifeguards, who were in and out of the beach area during the study periods.
Discussion
In the pilot study of inhaled red tide aerosol (Backer et al. 2003; Cheng et al. 2005), we showed that during the red tide episodes, nanogram per cubic meter levels of brevetoxins in the environment could be related to the observed respiratory symptoms of exposed volunteers. It was the first evidence of health effects of measured exposure to red tide aerosol. This study was designed to compare occupational exposure during a red tide aerosol episode and during a nonexposure period. PbTx-2 and PbTx-3 were the most abundant species of brevetoxins in both water and air samples, similar to what we observed in the pilot studies (Cheng et al. 2005; Pierce et al. 2003). PbTx-2 and PbTx-3 were the major species produced by K. brevis. With improved LC-MS analysis, we detected PbTx-1, PbTx-6, and PbTx-9 at lower concentrations. We also identified trace amounts of a brevetoxin antagonist (brevenal) produced by K. brevis (Bourdelais et al. 2004) that appears to inhibit the effects of brevetoxins in animal models. Identification of specific brevetoxins and the brevenal antagonist compounds in marine aerosol would help explain why some red tide blooms appear to have greater respiratory effects on humans than others.
In addition to the LC-MS technique, we used an ELISA assay to analyze the same sample. The LC-MS was more specific in identifying and quantifying individual compounds. However, it requires standards for the known brevetoxins for analysis and may miss other material that has not been identified. The ELISA technique had high sensitivity but was not specific for individual brevetoxins. It detected a class of compounds reactive to the specific antibody. It required a small amount of sample and did not need elaborated extraction for the analysis. We showed that in both air and water samples the brevetoxin concentrations from ELISA analysis were higher than those of LC-MS-MS analysis (Cheng et al. 2005). This was reasonable because for LC-MS-MS analysis we only detect and quantify PbTx-1, PbTx-2, PbTx-3, PbTx-6, and PbTx-9, whereas ELISA analysis also detected other compounds that were reactive to the assay. This indicated that there are other components of red tide aerosol that remain to be identified. Furthermore, the particle size distributions of red tide aerosol obtained from both analyses were similar, indicating that the PbTxs and related compounds were from the same marine aerosol or from marine aerosol that was produced from the same process.
The ELISA technique was critical for the analysis of the personal samples because of very low amounts of material collected on the substrates. The sensitivity of the technique allowed us to determine the amounts of brevetoxin and related compounds on personal samples. The positive results on personal samples provided information on personal exposure levels of red tide aerosol. It also provided a marker for exposure, a direct indication of red tide aerosol exposure. The exposure levels of these samples showed lower concentrations than the red tide aerosol concentrations obtained from high-volume samples. This could be attributed to a difference of sampling technique (high-volume vs. low-volume sampling), but it was more likely because the volunteers wearing the personal samples only spent part of the time in the exposure area. A record of activities may be required in future studies to further assess personal exposure.
We observed in the pilot studies that the water concentration of PbTx and onshore wind were important for red tide exposure. In this study we had the weather data near the water and air sampling location at Siesta Beach. The temperature and humidity were stable during the sampling periods. The wind speed and wind direction were very variable among the 5-day sampling periods. The wind direction and speed changed during the 8-hr sampling day. The variability of wind speed and direction resulted in changes of daily mean concentration of PbTx in aerosol samples during the 5-day study periods. Thus, the exposure periods in September 2001 showed low concentrations of PbTx levels in the air on 10 and 11 September 2001 despite high concentrations of K. brevis and PbTx in the water. More frequent sampling during the same exposure day may be needed to reflect the time profile of red tide aerosol concentration.
The daily red tide aerosol concentration varied substantially during the 5-day study period because of the changes in environmental conditions. Our empirical model showed the relative importance of water concentration, wind speed, and wind direction on the aerosol concentration. Basically the red tide aerosol concentration increased with water concentration, wind speed, and fraction of the time that the wind was in an onshore direction. The wind direction appeared to have the greatest weight for the aerosol concentration, confirming our previous anecdotal observations. However, this is an empirical correlation and additional data sets are needed to validate and improve the correlation.
The particle size distribution showed that the red tide aerosol consisted of coarse particles with the mean aerodynamic diameter in the range of 6–12 μm, similar to what we observed in previous studies (Cheng et al. 2005). The red tide aerosol may be produced by a breakup of bubbles from white-capped waves (Pierce et al. 1989a). Measured size distributions of marine aerosols showed a bimodal distribution with a peak in the fine-particle mode (0.1–0.2 μm) and another peak in the coarse-particle mode (2–30 μm) (Fitzgerald 1991). The coarse mode constitutes about 90–95% of the total mass but only 5–10% of the total number of particles. The coarse-particle mass in clean marine air is mainly composed of sea salt, with a strong dependence on wind speed. The red tide aerosol appears to be a component of marine coarse particles that may be associated with the sea salts.
Inhaled red tide particles can deposit efficiently in the respiratory tract. The deposit pattern is seen predominantly in the nasal and oral airways (75–84%) and 2–6% in the lower airways. Based on the environmental sampling, the estimated deposited dose rates were between 1 and 31 ng/hr in the ET region and 0.07 and 1.0 ng/hr in the lower airway for the September 2001 study.
The PbTx concentrations observed during the red tide episodes (including during previous studies) ranged from 1 to 80 ng/m3. The estimated total deposited dose rate is small, between 1 and 100 ng/hr (picomole range). However, this small dose was sufficient to contribute to the observed respiratory symptoms in the study subjects. From the 2000 study, we observed that a dose rate of 4–5 ng/hr in the ET region was associated with observed upper airway symptoms (throat irritation, nasal irritation, itchy skin). In the same study, the estimated dose rate in the lung was 0.15–0.2 ng/hr, which was not sufficient to cause lower respiratory symptoms. The estimated dose rate in the ET region for the occupational study (1–31 ng/hr) was associated with upper respiratory symptoms including eye irritation, nasal congestion, throat irritation, and cough (Backer et al. 2005). This was consistent with our observation in Texas (Cheng et al. 2005) and supported the observation that a lower dose rate of 1–4 ng/hr in the upper airways could cause upper respiratory symptoms. The estimated dose rate in the low respiratory airways (0.07–1 ng/hr) was not sufficient to cause lower respiratory symptoms, particularly in normal nonasthmatic subjects.
Figure 1 PbTx profile in the impactor samples collected at Lido Beach in September 2001.
Figure 2 Particle size distribution of red tide aerosol based on LC-MS and ELISA analysis of impactor sample for (A) Siesta Beach on 8 September 2001 and (B) Lido Beach on 8 September 2001. Abbreviations: dae, aerodynamic diameter; M, PbTx mass collected on each stage; Mo, total mass of the impactor sample.
Figure 3 Comparison of PbTx concentrations of red tide aerosol observed in Siesta Beach and model calculations (Equation 2) for (A) September 2001 and (B) May 2002 sampling periods.
Figure 4 PbTx concentration (mean ± SD) as determined from area and personal samples in September 2001.
Table 1 Summarized data for environmental conditions in Siesta Beach, Florida (mean ± SD).
Date Temperature (°F) Humidity (%) Average wind speed (mph) Maximum wind speed (mph) Wind direction
7 Sept 2001 28.9 ± 0.9 69.5 ± 4.7 0.0 ± 0.0 0.1 ± 0.4 0.37 ± 0.55
8 Sept 2001 27.6 ± 0.6 75.0 ± 3.2 7.0 ± 2.8 10.9 ± 3.3 0.68 ± 0.30
9 Sept 2001 26.1 ± 0.7 81.5 ± 3.2 9.2 ± 3.5 13.3 ± 3.8 0.50 ± 0.47
10 Sept 2001 25.9 ± 0.7 86.3 ± 3.0 5.8 ± 2.2 9.3 ± 2.6 0.17 ± 0.36
11 Sept 2001 27.9 ± 1.9 NA 10.0 ± 2.0 16.7 ± 2.4 0.09 ± 0.16
3 May 2002 NA NA NA NA NA
4 May 2002 27.6 ± 0.6 NA 9.0 ± 1.8 12.2 ± 1.9 0.61 ± 0.43
5 May 2002 28.6 ± 0.8 65.8 ± 4.8 7.1 ± 3.0 10.1 ± 3.2 0.52 ± 0.42
6 May 2002 29.1 ± 2.7 56.9 ± 14.3 7.4 ± 1.9 13.2 ± 3.3 0.00 ± 0.02
7 May 2002 27.3 ± 1.2 69.8 ± 6.1 6.6 ± 0.8 9.3 ± 1.1 0.73 ± 0.42
NA, not applicable.
Table 2 Summarized air and water concentration of PbTxs by LC-MS method (mean ± SD).
Siesta Beach
Lido Beach
Date Water concentration (μg/L) Air concentration (ng/m3) Water concentration (μg/L) Air concentration (ng/m3)
7 Sept 2001 27.9 ± 14 7.53 ± 3.86 26 ± 16 26.90 ± 17.54
8 Sept 2001 18.9 ± 8 9.94 ± 6.41 18.3 ± 12.8 20.36 ± 27.16
9 Sept 2001 8.6 ± 3.7 11.89 ± 7.07 9.3 ± 6.6 17.43 ± 9.60
10 Sept 2001 10 ± 3.3 2.40 ± 2.64 13.8 ± 5 5.93 ± 7.26
11 Sept 2001 12.3 ± 2.3 1.90 ± 1.66 8.2 ± 2.4 1.32 ± 2.64
3 May 2002 0.04 ± 0.04 1.11 ± 0.48 < LOQ 0.08 ± 0.17
4 May 2002 0.3 ± 0.4 1.16 ± 0.17 < LOQ 0.08 ± 0.17
5 May 2002 < LOQ 0.05 ± 0.11 < LOQ 0.04 ± 0.09
6 May 2002 < LOQ < LOQ < LOQ < LOQ
7 May 2002 < LOQ 0.06 ± 0.14 < LOQ 0.03 ± 0.06
LOQ, limit of quantitation.
Table 3 Summary of particle size distribution.
Siesta Beach
Lido Beach
LC-MS
ELISA
LC-MS
ELISA
MMAD (μm) GSD MMAD (μm) GSD MMAD (μm) GSD MMAD (μm) GSD
7 Sept 2001 5.97 1.73 7.02 1.81 10.85 1.89 8.33 1.65
8 Sept 2001 9.06 1.85 9.69 1.87 8.90 1.76 8.90 1.56
9 Sept 2001 10.32 1.91 11.82 1.71 8.18 1.43 8.82 1.67
10 Sept 2001 12.21 1.77 9.61 1.98 10.73 1.78 8.04 1.68
11 Sept 2001 10.20 1.73 10.87 2.05 7.59 1.95 7.45 1.83
Mean ± SD 9.55 ± 2.30 1.80 ± 0.08 9.80 ± 1.80 1.88 ± 0.13 9.25 ± 1.48 1.76 ± 0.20 8.31 ± 0.59 1.68 ± 0.10
Table 4 Dose rate of PbTx (nanograms per hour) in the human respiratory tract based on aerosol concentration of 1 ng/m3 (mean ± SD).
Extrathoracic TB Pulmonary Total
Oct 2000 (Corpus Christi, TX) 1.25 ± 0.03 0.03 ± 0.01 0.02 ± 0.01 1.30 ± 0.04
Sept 2001 (Siesta Beach, FL) 1.18 ± 0.05 0.03 ± 0.01 0.02 ± 0.01 1.23 ± 0.08
Sept 2001 (Lido Beach, FL) 1.19 ± 0.05 0.03 ± 0.01 0.02 ± 0.01 1.24 ± 0.05
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References
Abraham WM Ahmed Bourdelais A Baden DG 2004. Effects of novel antagonists of polyether brevetoxin (PbTx)-induced bronchoconstriction in allergic sheep. In: Harmful Algae 2002 (Steidinger KA, Landsberg JH, Tomas CR, Vargo GA, eds). St. Petersburg, FL:Litho Service Inc; Florida Fish and Wildlife Conservation Commission, Florida Institute of Oceanography, and Intergovernmental Oceanographic Commission of UNESCO, 496–499.
Asai S Krzanowski JJ Anderson WH Martin DF Polson JB Lockey RF 1982 Effects of the toxin of red tide, Ptychodiscus brevis on canine tracheal smooth muscle: a possible new asthma-triggering mechanism J Allergy Clin Immunol 69 418 428 7200498
Backer LC Fleming LE Rowan A Cheng YS Benson JM Pierce RH 2003 Recreational exposure to aerosolized brevetoxins during Florida red tide events Harmful Algae 2 19 28
Backer LC Kirkpatrick B Fleming LE Cheng YS Pierce R Bean JA 2005 Occupational exposure to aerosolized brevetoxins during Florida red tide events: effects on a healthy worker population Environ Health Perspect 113 644 649 15866778
Baden DG Mende TJ Bikhazi G Leung I 1982 Bronchoconstriction caused by Florida red tide toxins Toxicon 20 929 932 6891120
Bossart GD Baden DG Ewing RY Roberts B Wright SD 1998 Brevetoxicosis in manatees (Trichechus manatus latirostris ) from 1996 epizootic: gross, histologic, and immunohistochemical features Toxicol Pathol 26 276 282 9547868
Bourdelais AJ Campbell S Jacocks H Naar J Wright JLC Carsi J 2004 Brevenal is a natural inhibitor of brevetoxins action in sodium channel receptor binding assays Cell Mol Neurobiol 24 553 563 15233378
Cheng YS Villareal TA Zhou Y Gao J Pierce RH Wetzel D 2005 Characterization of red tide aerosol on the Texas coast Harmful Algae 4 87 94 20352032
Fitzgerald JW 1991 Marine aerosols: a review Atm Environ 25A 533 545
Forrester DJ Gaskin JM White FH Thompson NP Quick JA Henderson GE 1977 An epizootic of waterfowl associated with a red tide episode in Florida J Wildlife Dis 13 160 167
ICRP (International Commission on Radiological Protection) 1994. Human Respiratory Tract Model for Radiological Protection. Publ 66. Annals of ICRP 24(1–3) London:Pergamon.
Music SI Howell JT Brumback CL 1973 Red tide, its public health implications J Florida Med Assoc 60 27 39
Naar J Bourdelais A Tomas C Kubanek J Whitney P Flewelling L 2002 A competitive ELISA to detect brevetoxins from Karenia brevis (formerly Gymnodinium breve ) in seawater, shellfish, and mammalian body fluid Environ Health Perspect 110 179 185 11836147
Pierce RH Henry MS Blum PC Lyons J Cheng YS Yazzie D 2003 Brevetoxin concentrations in marine aerosol: human exposure levels during a Karenia brevis harmful algal bloom Bull Environ Contam 70 161 165
Pierce RH Henry M Boggess S Rule A 1989a. Marine toxins in bubble-generated aerosols. In: Climate and Health Implications of Bubble-Mediated Sea-Air Exchange (Monahan EC, Van Patten MA, eds). Groton, CT:Connecticut Sea Grant College Program, 27–42.
Pierce RH Henry MS Proffitt S Hasbrouck PA 1989b. Red tide toxin (brevetoxin) enrichment in marine aerosol. In: Toxic Marine Phytoplankton (Graneli E, Sundstorm B, Edler L, Anderson DM, eds). New York:Elsevier Science, 397–402.
Trainer VL Baden DG 1991 An enzyme immunoassay for the detection of Florida red tide brevetoxins Toxicon 29 1387 1394 1814015
Woodcock AH 1948 Note on concerning human respiratory irritation associated with high concentrations of plankton and mass mortality of marine organisms Sears Found J Marine Res 7 56 62
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7502ehp0113-00064415866778Mini-Monograph: BrevetoxinsOccupational Exposure to Aerosolized Brevetoxins during Florida Red Tide Events: Effects on a Healthy Worker Population Backer Lorraine C. 1Kirkpatrick Barbara 2Fleming Lora E. 34Cheng Yung Sung 5Pierce Richard 3Bean Judy A. 6Clark Richard 7Johnson David 7Wanner Adam 4Tamer Robert 6Zhou Yue 5Baden Daniel G. 81National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA;2Mote Marine Laboratory, Sarasota, Florida, USA;3National Institute of Environmental Health Sciences Marine and Freshwater Biomedical Sciences Center, University of Miami School of Medicine, Miami, Florida, USA;4University of Miami School of Medicine, Miami, Florida, USA;5Inhalation Toxicology Laboratory, Lovelace Respiratory Research Institute, Albuquerque, New Mexico, USA;6Children’s Hospital Medical Center, Cincinnati, Ohio, USA;7Florida Department of Health, Tallahassee, Florida, USA;8Center for Marine Science Research, University of North Carolina at Wilmington, Wilmington, North Carolina, USAAddress correspondence to L.C. Backer, National Center for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford Highway NE, MS F-46, Chamblee, GA 30341 USA. Telephone: (770) 488-3426. Fax: (770) 488-3450. E-mail:
[email protected] article is part of the mini-monograph “Aerosolized Florida Red Tide Toxins (Brevetoxins).”
This study could not have been performed without the help of numerous investigators, including J. Horton, J. Howell, R. Sabogal, C. Bell (Centers for Disease Control and Prevention); D. Squicciarini, G. Van De Bogart (University of Miami National Institute of Environmental Health Sciences Center); R. Clark (Florida Department of Health); S. Campbell (University of North Carolina at Wilmington); T. Blum, M. Henry, C. Higham, G. Kirkpatrick, P. Stack, B. Turton (Mote Marine Laboratory); D.A. Kracko, J. McDonald, and C.M. Irvin (Lovelace Respiratory Research Institute). We also thank the Mote Marine Laboratory and the lifeguards of Sarasota and Manatee counties.
This research was supported by the Centers for Disease Control and Prevention, grant P01 ES 10594 from the National Institute of Environmental Health Sciences, and the Florida Department of Health.
The authors declare they have no competing financial interests.
5 2005 10 2 2005 113 5 644 649 2 8 2004 19 1 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Karenia brevis (formerly Gymnodinium breve) is a marine dinoflagellate responsible for red tides that form in the Gulf of Mexico. K. brevis produces brevetoxins, the potent toxins that cause neurotoxic shellfish poisoning. There is also limited information describing human health effects from environmental exposures to brevetoxins. Our objective was to examine the impact of inhaling aerosolized brevetoxins during red tide events on self-reported symptoms and pulmonary function. We recruited a group of 28 healthy lifeguards who are occupationally exposed to red tide toxins during their daily work-related activities. They performed spirometry tests and reported symptoms before and after their 8-hr shifts during a time when there was no red tide (unexposed period) and again when there was a red tide (exposed period). We also examined how mild exercise affected the reported symptoms and spirometry tests during unexposed and exposed periods with a subgroup of the same lifeguards. Environmental sampling (K. brevis cell concentrations in seawater and brevetoxin concentrations in seawater and air) was used to confirm unexposed/exposed status. Compared with unexposed periods, the group of lifeguards reported more upper respiratory symptoms during the exposed periods. We did not observe any impact of exposure to aerosolized brevetoxins, with or without mild exercise, on pulmonary function.
aerosolbrevetoxinsKarenia brevislifeguardspulmonary functionred tidespirometry
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Karenia brevis (formerly Gymnodinium breve) is a marine dinoflagellate responsible for red tides that form annually in the Gulf of Mexico. K. brevis produces brevetoxins, the potent toxins that cause neurotoxic shellfish poisoning. The human health effects from consuming shellfish with high concentrations of brevetoxins in their tissues have been well documented. However, there is very little information describing human health effects from environmental exposures. In 1948 Woodcock stated, “It is ironic that we know the least about the aspects of the Florida red tide problem that poses the greatest public health hazard in terms of number of people affected” (Hemmert 1974). In 1999 Kirkpatrick et al. (2001) conducted a pilot study of the impact of environmental exposures to Florida red tide during a red tide research cruise. Although the number of participants was small, two scientists (both < 30 years of age and with no reported underlying pulmonary disease) reported difficulty in obtaining a deep breath and had decreases in pulmonary function parameters on a day when K. brevis cell counts were measured at > 8 million cells/L and the wind speed was higher than on other cruise days. In addition to the reports of effects on healthy individuals, there is evidence that laboratory sheep with induced asthma and people with asthma are adversely affected from exposure to aerosolized brevetoxins (Abraham et al. 2005; Fleming et al. 2005).
A pilot study of recreational beachgoers (Backer et al. 2003) found significant increases in reported upper and lower respiratory symptoms but no significant differences in spirometry test parameters during the exposed periods (when there was a red tide) when compared with symptom and spirometry data collected during an unexposed period (when there was no red tide). However, a number of limitations were associated with the study; for example, the study participants were a convenience sample of people who came to the beach and included some individuals with underlying respiratory illnesses (i.e., chronic obstructive pulmonary disease and/or a history of smoking), and many participants reported that they had been exposed to airborne red tide toxins for up to a week before the study and believed their symptoms had decreased during that time.
To begin to address the limitations of earlier studies, we wanted to identify a group of healthy individuals who were occupationally exposed to aerosolized brevetoxins during red tide events. We identified a population of full-time lifeguards working along Florida’s gulf coast who were willing to participate in a study. This group was interested in the health effects from inhaling aerosolized brevetoxins because the beaches in these communities do not close during onshore red tides and the lifeguards are required to conduct their normal activities, including staying in the beach guard towers for approximately 6 hr during each shift.
In addition to their potential exposures to aerosolized brevetoxins while conducting their work activities, lifeguards engage in some form of vigorous exercise (e.g., running, swimming) each workday. Investigators have reported that strenuous exercise (exercise that causes mouth breathing) can induce reversible bronchospasm in asthmatic individuals (Kirkpatrick et al. 1982). Because brevetoxin also causes bronchospasm in laboratory animal studies (Wells et al. 1984), it is possible that exercising on the beach during a time when there is red tide aerosol blowing onshore is a risk factor for developing respiratory symptoms or changes in pulmonary function.
Our objective was to conduct an occupational epidemiologic study in healthy workers to evaluate the reported symptoms and respiratory effects (using spirometry) from exposure to aerosolized red tide toxins and conduct a pilot study to assess whether mild outdoor exercise during a red tide event affects pulmonary function test (PFT) results or the number of self-reported symptoms.
Materials and Methods
Study protocol.
The study protocol was approved by the institutional review boards of the Centers for Disease Control and Prevention (Atlanta, Georgia); the Florida Department of Health (Tallahassee, Florida); and the University of Miami (Miami, Florida).
Study population.
To be included in our study, an individual was required to be a full-time lifeguard working at one of the beaches in Sarasota or Manatee counties in Florida and at least 18 years of age. We recruited 28 full-time lifeguards who met our criteria and volunteered to be in the study. In general, these lifeguards are physically fit, participate in daily aerobic and weight training, and have little pulmonary disease.
Pulmonary function tests.
Spirometry tests were done using portable 8-L dry rolling–seal volume spirometers (OMI, Houston, TX) by personnel trained using the course developed by the National Institute for Occupational Safety and Health (NIOSH 1997). The spirometry values of interest were forced vital capacity (FVC), forced expiratory volume in 1 sec (FEV1), FEV1/FVC percentage, forced expiratory flow between 25 and 75% of total FVC (FEF25–75%), and peak expiratory flow (PEF). We used the reference values from OMIS98 Spirometry software (version 3.18.7; OMI, Houston, TX) and the interpretation recommendations from the American Thoracic Society (1991) to compute predicted spirometric values. All study participants had at least three reproducible spirograms before and after visiting the beach, and the best values from these three spirograms were used for data analysis (American Thoracic Society 1991). The data were considered adequate if they conformed to standard guidelines for the collection and interpretation of spirometry measurements (American Thoracic Society 1995).
Symptoms and respiratory effects study.
The lifeguards were interviewed using a questionnaire comprising questions about demographics and pulmonary health history. During a time when there was no red tide, we conducted pre- and postshift baseline PFTs and symptom surveys. The symptom survey included questions about upper respiratory symptoms (i.e., eye and throat irritation, nasal congestion, cough) and lower respiratory symptoms (i.e., chest tightness, wheezing, shortness of breath). The duration of the shift was 8 hr and included approximately 6 hr of exposure to marine aerosols. The pre- and postshift PFTs and symptom surveys were repeated during a time when there was a red tide.
Exercise pilot study.
Most of the lifeguards regularly run on the beach as part of their physical training. However, running as an activity to increase minute ventilation and mouth breathing was not appropriate for this study because the concentrations of brevetoxins in the air are not consistent along the shoreline. Instead, we used a Monark Ergomedic weight ergometer (model 874E; Wynne International, New Dundee, Ontario, Canada). The ergometers were placed in the surf zone adjacent to a lifeguard tower and near one of the high-volume air samplers used to monitor brevetoxin concentrations. During a time when there was no red tide, a subgroup of the lifeguards performed two sets of spirometry tests (5 min apart), rode the ergometer for 5 min at a constant workload (90 cal/watt), and then performed another set of two spirometry tests (immediately after exercise and 15 min after exercise) and reported their symptoms. During the exposed period, the same subgroup of lifeguards repeated the study activities except that, because the results from the two sets of spriometry tests done immediately after exercise and 15 min after exercise were similar, the lifeguards performed only one set of spirometry tests before and after exercising. In addition, during the exposed period, the study activities were performed before and after their shift to assess any changes in morning and afternoon environmental conditions.
Environmental monitoring.
During both studies, water samples were collected daily in 1-L glass bottles at 0830 hr, 1200 hr, and 1600 hr, from the surf zone adjacent to the study high-volume air sampler locations. A 20-mL subsample was taken from each bottle and fixed with Utermohl’s solution to provide K. brevis cell counts. The remaining water sample was transported to Mote Marine Laboratory and processed for liquid chromatography–mass spectrometry (LC-MS) analysis according to the procedure of Pierce et al. (2003).
In the laboratory brevetoxins were extracted by passing the water through a C-18 solid-phase extraction disk under vacuum (Ansys Technologies, Inc., Lake Forest, CA). The C-18 disks were then rinsed with reverse osmosis water to remove any remaining salts and eluted with methanol (Pierce et al. 2003). Brevetoxin analyses were performed by LC-MS using a ThermoFinnigan AqA high-performance liquid chromatograph–MS (Thermo Electron Corp., Manchester, UK). Mass spectral detection was obtained using an AqA single-quad system scanned from 204–1,216 AMU with AqA Max 40 V electrospray. The column was Phenomenex Luna C-18 5Fm 250 × 2 mm (Phenomenex, Torrance, CA); the solvent gradient was 0.3% acetic acid/H2O with initial 50:50 acetyl-nitrile (ACN)/H2O to 95:5 ACN/H2O over 30 min. The limit of detection (LOD) of the analysis for brevetoxins in seawater was 0.03 μg/L.
Air samples used to assess lifeguard exposure to brevetoxins in the air were collected using two instruments: high-volume air samplers and personal breathing zone samplers. Six high-volume air samplers (model TE-5000; Tisch Environmental, Inc., Village of Cleves, OH) with a single-stage filter were used; three were placed near the surf zone (about 25 m) approximately 100 m apart, and a second row of three was located approximately 50 m from the first row to provide an assessment of aerosolized toxin concentrations over time and space along the beach. The high-volume air samplers were fitted with a 20.32 × 25.4 cm glass-fiber filter (EPM2000; Whatman, Maidstone, UK). Filter samples were collected separately for morning and afternoon time periods (0830–1200 hr and 1230–1600 hr).
The traditional approach to individual occupational exposure assessment would be to have the lifeguards wear the personal samplers. However, there was concern that the personal samplers would interfere with emergency response activities or be destroyed by immersion in seawater. Instead, personal exposure was measured by placing personal samplers (IOM inhalable dust sampler; SKC, Inc., Eighty Four, PA) connected to a battery-operated pump (model 224-PCXR4; SKC, Inc.) on the lifeguard towers near the lifeguards’ breathing zones. A 25-mm glass-fiber filter (type A/E; Pall Life Science, Ann Arbor, MI) was used as the collection substrate. The sampling flow rate was 2 L/min controlled by a rotameter in the sampling pump.
Brevetoxins from the environmental and personal air samplers were recovered from the glass-fiber filters by extraction for 12 hr in acetone using a Soxhlet apparatus (Pierce et al. 2003). The extract was then transferred to vials using methanol for LC-MS and enzyme-linked immunosorbent assay (ELISA) analysis. Brevetoxin recovery from glass-fiber filters was verified by the addition of standard amounts of polyether brevetoxins PbTx-2 and PbTx-3 to each of three filters that were subsequently processed for LC-MS analyses.
A portable, self-contained weather station was used near the air sampling locations to monitor the air temperature, relative humidity, and wind speed and direction (Complete Weather Station; Davis Instruments, Hayward, CA). The weather station was solar powered, and the data were downloaded into a notebook computer.
Statistical analyses.
Descriptive and other statistical analyses were performed using SAS statistical software (version 8.03; SAS Institute Inc., Cary, NC). We used the paired t-test for continuous data (i.e., the PFT results) and McNemar’s test for categorical data (i.e., the symptom questionnaire data) (Kleinbaum et al. 1982). We compared the changes in spirometry results and self-reported symptoms that occurred over a shift on a day when there was a red tide with changes that occurred over a shift on a day when there was no red tide. We also examined the impact of exercise on spirometry tests and self-reported symptoms during a time when there was a red tide with the impact of exercise during a time when there was no red tide. Specifically, we examined the impact of exercise on spirometry tests and symptom surveys conducted before the shift and after the shift. Finally, we compared the preshift changes in test results and symptom reports with the postshift changes.
Results
The coastal environmental conditions present during the symptoms and respiratory effects study and the exercise pilot study are presented in Cheng et al. (2005b) (symptoms and respiratory effect study) and Table 1 (exercise study). The ambient temperatures were within typical ranges for the area during the symptoms and respiratory effects study; however, the ambient air temperatures were unusually low during both the exposed and unexposed periods for the exercise study. During the exposed period of the exercise study, onshore winds blowing from 10 to 25 km/hr provided a greater opportunity for exposure to aerosolized brevetoxins.
The environmental monitoring data, including K. brevis cell counts and brevetoxin levels in seawater samples and air samples during the symptoms and respiratory effects study and exercise pilot study, are presented in Table 2 (see also Pierce et al., in press). We found that the concentrations of K. brevis cells generally correlated with the concentrations of brevetoxins in seawater samples but did not correlate with concentrations of brevetoxins in the air. For example, on Lido Beach in September 2001, there were > 9 million K. brevis cells per liter on both 8 September and 10 September. The corresponding concentrations of brevetoxins in seawater samples were approximately 18 and 14 μg/L, respectively. However, the corresponding concentrations of brevetoxins in the air samples were approximately 20 and 6 ng/m3, respectively.
We found that the amount of brevetoxin in the air varied not only by time but also by geographic area (i.e., the specific beach where the samples were taken). For example, because there had been no reports of respiratory irritation or fish kills, we originally considered the May 2002 data collection period to be an unexposed period across all the beaches in our study. However, when the environmental sample analysis was completed for Nokomis Beach, the concentrations of K. brevis cells in seawater samples were low but above background levels (4,500 to > 36,000 cells/L), and there were measurable concentrations of brevetoxins in air samples on 2 of the 4 days on which data were collected. Thus, for this study, exposure status was determined separately for each individual lifeguard by day and by beach and was based on the air sample analyses.
There were 31 lifeguards eligible to be in the study; one declined to participate, and 30 were enrolled. We collected demographics and baseline spirometry data for 28 individuals (2 were lost to the study because they were called to military service). The demographics of the lifeguard study participants are presented in Table 3. Of the 28 lifeguards in our study population, 27 (96%) were white and 2 (7%) were female.
Baseline spirometry test results are presented in Table 4. As expected, the lifeguards are healthy with respect to lung function; that is, the PFT measurements were all at least 80% of the predicted values for the spirometry results based on reference values from OMIS98 Spirometry software, version 3.8.7, well above the minimum of 80% considered to be within the normal range. Also as expected, the measurements of lung function (FVC, FEV1, and PEF) were lower for female lifeguards than for male lifeguards.
On the basis of the environmental data for each beach on each day of our studies, we defined the unexposed period and two levels of exposed periods. The unexposed periods were days when there were no detectable levels of brevetoxins in air samples. The exposed days were defined as exposure level 1 (with detectable brevetoxin levels in air samples) and exposure level 2 (with brevetoxin levels > 10 ng/m3 in air samples). There were 17 lifeguards who worked a shift during a level 1 exposure and 13 who worked a shift during a level 2 exposure. There were 11 lifeguards who did not work a shift during an exposure period.
The results for self-reported symptoms for the symptom and respiratory effects study are presented in Table 5. Compared with the baseline data, there were significant increases in the reports of upper respiratory symptoms but not in the reports of lower respiratory symptoms during the periods of aerosolized brevetoxin exposure. In addition, there was a significant increase in self-reported headache in the exposure level 1 (any detectable brevetoxin in air samples) group.
The analyses of PFT results are presented in Table 6. We examined the changes in the individual test results during a shift (preshift data–postshift data). There were no significant changes in the PFT results during the unexposed period or during the exposure level 2 period. Compared with the unexposed period data, there were significant increases in FEV1 and PEF during the exposure level 1 period.
A subset of 11 lifeguards participated in the exercise pilot study to assess whether exercise during red tide events has an adverse impact on PFTs and/or the number of self-reported symptoms. When compared with the frequency of self-reported symptoms before exercising, there were no increases in the frequency of self-reported symptoms after exercising during the unexposed or the two exposed periods (data not shown).
During the unexposed period, and before their work shift, the lifeguards did two PFTs before exercising and two after exercising. The results from the two pre-exercise sessions were similar, and the results from the two postexercise sessions were similar (data not shown). There were no significant differences in PFT parameters when we compared the average pre-exercise results with the average postexercise results.
We also examined the changes in PFT values over the entire work shift (preshift and pre-exercise PFT results minus postshift and postexercise PFT values; data not shown). There were no significant changes in PFT parameter values during either the unexposed or the exposed periods.
Discussion
In this study we examined the impact of occupational exposure to aerosolized red tide toxins on a group of full-time lifeguards. As part of their job activities, the lifeguards are required to be on the beach in guard towers, even if an onshore red tide is present. In addition, they are required to participate in a fitness maintenance program that includes running on the beach, swimming, and lifting weights. The purpose of our study was to examine the impacts of the lifeguards’ exposures to aerosolized brevetoxins during their normal shift and whether the impacts would be modified by exercise. The two end points used to measure the impacts were self-reported symptoms and spirometry tests.
During study periods when the potential for exposure to aerosolized brevetoxins was verified by environmental monitoring, the lifeguards in our study experienced symptoms consistent with longstanding and common anecdotal complaints of upper respiratory irritation made by residents and beach visitors during previous Florida red tides. These results are also consistent with symptom reports made by recreational beachgoers during red tide events involving similar levels of exposure (up to 36 ng brevetoxins/m3 of air).
Compared with nonexposure periods, the healthy lifeguards in our study reported more upper airway but not lower airway discomfort during the red tide exposure periods. There were statistically significant effects on some spirometry test parameters during exposure to red tide, but the changes were small and not clinically significant. In addition, we did not observe significant changes in any spirometry test parameters when we compared the effects of mild exercise during a nonexposure period with effects observed during an exposure period. These findings suggest that occupational exposures to low levels of aerosolized brevetoxins are not a serious health threat to this population. However, the upper respiratory irritation and discomfort caused by inhaling aerosolized red tide toxins can be substantial. Although these symptoms can be relieved by eliminating exposure, the lifeguards cannot leave the beach. To address this issue, we plan to examine the efficacy of different types particle face masks to determine which types may provide relief for the lifeguards and others who may not be able to avoid exposure to aerosolized brevetoxins during red tides.
Work by Cheng et al. (2005a) indicates that the size distribution of aerosols collected during red tides primarily reflects larger particles that are deposited in the upper respiratory tract. However, they also reported that a small but biologically significant fraction of the inhaled red tide aerosol was deposited in the lower airways. Perhaps, as concentrations of brevetoxins in the air increase, the amount of brevetoxin present in the smaller respirable particles also increases, thus increasing the effective dose of brevetoxins to the lower airways. This would be consistent with the our findings in an earlier study in which recreational beachgoers (Backer et al. 2003) reported experiencing increased lower respiratory irritation (wheeze, chest tightness, shortness of breath) when there were higher concentrations (up to 93 ng/m3), but not when there were lower concentrations, of brevetoxins in the air (Backer et al. 2003). In the present study, although the lifeguards were exposed for a much longer period of time (~ 6 hr) than the beachgoers were (average, 71 min; Backer et al. 2003), they were not exposed to high concentrations of brevetoxins and did not experience lower respiratory irritation.
We anticipated that when the lifeguards exercised they would increase their ventilation and effectively increase their dose of brevetoxin. However, they did not report any lower respiratory irritation after exercising during exposure to low levels of brevetoxins, again suggesting that an increased concentration of brevetoxin in the smaller, respirable particles when aerosolized brevetoxin concentrations are higher may be important in eliciting a lower airway response.
From a public health perspective, we would like to predict when aerosolized brevetoxins associated with Florida red tides will be at concentrations that can affect people on the beach. One possible way to quickly predict the presence of aerosolized brevetoxins would be to quantify the number of cells in seawater samples and extrapolate to airborne brevetoxin concentrations. However, we have found that cell concentrations do not correlate well with brevetoxin concentrations found in air samples collected during the same time period. We also found that brevetoxin concentrations in air samples varied considerably over a fairly small geographic area (e.g., among the beaches in our study) and were dependent on wind direction and speed as well as the presence of brevetoxins in the seawater itself. Unfortunately, the currently validated method to assess brevetoxins in air is gas chromatography–MS analysis, which requires considerable expertise and time to conduct. A user-friendly short-term test, such as a competitive ELISA (Naar et al. 2002), could be used to test routinely collected air samples and provide a database for public health officials responsible for public health on Florida’s gulf coast beaches.
There are a number of potential limitations associated with this study. We recruited healthy workers for our study, and thus the results cannot be generalized to all populations because they include groups that may be at increased risk because of underlying respiratory disease or other characteristics (Fleming et al. 2005). Another limitation is using self-reported symptom data, which can suffer from reporting bias. However, the actual exposure status of individual study participants was not known at the time the symptom data were collected but was established only after the air and water analyses had been completed, making it less likely that study participants could influence study results. For example, during the May 2002 data collection period, we assumed that all the lifeguards were unexposed. However, we found that those working at one beach (Nokomis) were actually exposed to substantial levels of aerosolized brevetoxins on some days.
Another study limitation could be the use of spirometry tests to assess the impact of exposure because we could not guarantee that study participants were providing their maximum effort during the tests. However, using the American Thoracic Society standards, it is almost impossible to reproduce three spirograms within the guidelines without maximal effort, making spirometry an objective measure of lung function.
Conclusion
Anecdotal reports and some past studies have indicated that inhaling aerosolized brevetoxins associated with Florida red tides can cause respiratory irritation. This study has shown that when healthy lifeguards are occupationally exposed to low concentrations of brevetoxins in the air, they report upper airway irritation (i.e., eye irritation, nasal congestion, and cough) and headache. However, even when the lifeguards participated in mild exercise on the beach during a time when there were measurable levels of brevetoxins in the air, we did not detect changes in pulmonary function as measured using spirometry. Our results suggest that, for healthy people, exposure to low levels of brevetoxins in the air during Florida red tides is associated with temporary discomfort in the form of respiratory irritation but is not associated with acute adverse effects on pulmonary function. However, it would be appropriate to re-examine the health end points used in this study during periods of exposure to the higher levels of aerosolized brevetoxins (~ 100 ng/m3) that have been measured at Florida beaches.
Table 1 Coastal environmental conditions during the data collection periods for the exercise study.
Date of exercise study Temperature (°C) Humidity (%) Wind speed (km/hr) Wind direction (% onshore)a
Unexposed period
17 January 2003 12.2 ± 1.6 68 ± 5 25.6 ± 3.4 1
18 January 2003 8.3 ± 1.6 47 ± 5 10.9 ± 3.7 4
19 January 2003 13.3 ± 1.1 53 ± 7 12.4 ± 4.0 2
Exposed period
29 March 2003 24.4 ± 0.5 83 ± 4 10.5 ± 5.4 58
30 March 2003 18.9 ± 2.2 84 ± 6 24.9 ± 6.0 44
31 March 2003 12.8 ± 1.1 32 ± 12 22.7 ± 2.6 0
Values are mean ± SD unless otherwise specified.
a Percentage of time the wind was blowing onshore. See Cheng et al. (2005a) for details about wind direction.
Table 2 K. brevis cell counts and PbTx concentrations in seawater and air samples.
Beach Date No. of K. brevis cells in seawater samples (cells/L)a Brevetoxin levels in seawater samples (μg/L)b Brevetoxin levels in air samples (ng/m3)c
Symptoms and respiratory effects study
Siesta 3 May 2002 < LOD to 2,000 0.04 ± 0.4 1.11 ± 0.48
4 May 2002 < LOD to 2,000 0.3 ± 0.4 1.16 ± 0.17
5 May 2002 < LOD to 1,000 < LOD < LOD
6 May 2002 < LOD to 4,000 < LOD 0.05 ± 0.11
7 May 2002 < LOD < LOD 0.06 ± 0.14
7 September 2001 < LOD to 1,000 27.9 ± 14.0 7.53 ± 3.86
8 September 2001 < LOD 18.9 ± 8.0 9.94 ± 6.41
9 September 2001 < LOD 8.6 ± 3.7 11.89 ± 7.07
10 September 2001 388,500 ± 348,000 10.0 ± 3.3 2.40 ± 2.64
11 September 2001 240,800 ± 223,800 12.3 ± 2.3 1.90 ± 1.66
Lido 3 May 2002 < LOD < LOD 0.08 ± 0.17
4 May 2002 < LOD < LOD 0.08 ± 0.17
5 May 2002 < LOD < LOD 0.04 ± 0.09
6 May 2002 < LOD < LOD < LOD
7 May 2002 < LOD < LOD 0.03 ± 0.06
7 September 2001 12,100,000 ± 2,800,000 26.0 ± 16 26.90 ± 17.54
8 September 2001 9,410,000 ± 278,000 18.3 ± 12.8 20.36 ± 27.16
9 September 2001 799,000 ± 193,000 9.3 ± 6.6 17.43 ± 9.60
10 September 2001 1,496,000 ± 663,700 13.8 ± 5.0 5.93 ± 7.26
11 September 2001 79,399 ± 16,500 8.2 ± 2.4 1.32 ± 2.64
Nokomisd 3 May 2002 36,259 ± 22,100 2.1 ± 0.8 NA
4 May 2002 18,000 ± 20,000 1.1 ± 0.7 6.4 ± 0.1
5 May 2002 27,750 ± 11,200 0.6 ± 0.8 3.2 ± 2.0
6 May 2002 29,500 ± 38,000 1.3 ± 1.3 < LOD
7 May 2002 4,500 ± 3,100 0.1 ± 0.1 < LOD
7 September 2001 NA NA NA
8 September 2001 NA NA NA
9 September 2001 382,500 ± 180,312 9.36 ± 8.25 49.21
10 September 2001 608,500 ± 112,429 2.70 4.12
11 September 2001 82,000 ± 9899 NA 17.58
Coquinae 3 May 2002 < LOD < LOD < LOD
4 May 2002 < LOD to 1,000 < LOD < LOD
5 May 2002 < LOD < LOD < LOD
6 May 2002 < LOD < LOD < LOD
7 May 2002 < LOD < LOD < LOD
Exercise pilot study
Unexposed period
Siesta 17 January 2003 2,400 ± 1,400f < LOD < LOD
18 January 2003 < LOD < LOD < LOD
19 January 2003 < LOD < LOD < LOD
Exposed period
Siesta 29 March 2003 180,600 ± 131,100 3.44 ± 1.93 36.57 ± 17.51
30 March 2003 764,400 ± 263,700 14.01 ± 8.06 3.71 ± 2.63
31 March 2003 96,300 ± 86,400 3.31 ± 3.74 < LOD
NA, not analyzed. Data are from the unexposed (May 2002) and exposed periods (September 2001) for the pulmonary function study and the unexposed (January 2003) and exposed periods (March 2003) for the exercise study. The values are mean ± SD of results from two seawater samples, of results from three high-volume samplers at Siesta and Lido beaches, and of results from personal sampler measurements at Nokomis and Coquina beaches. The values are presented by the specific beach where the measurements were made and by date.
a The LOD for K. brevis cells in seawater samples was 1,000 cells/L. The range of K. brevis cell concentrations is provided when 50% or more of the samples were < LOD. The mean ± SD is reported when cell concentrations were > LOD.
b The LOD for brevetoxins in seawater samples was 0.05 μg/L.
c The LOD for total brevetoxins in air samples was 0.05 ng/m3 for the high-volume samplers and 1.0 ng/m3 for the personal samplers.
d The air sampling results from Nokomis Beach are the averages ± SDs from two personal samplers used in May 2002 and the value for one personal sampler hung on the outside of the lifeguard tower in September 2001.
e The air sampling results from Coquina Beach are from one personal sampler hung on the lifeguard tower in May 2002. The LOD for total brevetoxins in air samples was 1.0 ng/m3 for the personal samplers. Coquina Beach did not have an onshore red tide during September 2001.
f Mean ± SD of samples with ≥1,000 cells/L; 30% of samples were < LOD.
Table 3 Demographics of the lifeguards who were enrolled in the study (n = 28).
Characteristic No. (%)
Race
White 27 (96)
Asian/Pacific Islander 1 (4)
African American 0
American Indian, Alaska native 0
Sex
Female 2 (7)
Male 26 (93)
Mean age [years (range)] 35 (19–51)
Current smoker 0
Table 4 Baseline spirometry results for the lifeguards enrolled in the study (n = 26).
PFT parameter Mean ± SD Percent predicted ± SDa
Males only (n = 26)
FVC (L) 5.71 ± 0.96 97.8 ± 17.1
FEV1 (L) 4.29 ± 0.73 92.9 ± 19.0
FEV1/FVC (%) 75.25 ± 6.35 94.1 ± 7.9
FEF25–75% (L) 3.55 ± 0.99
Peak flow (L/sec) 10.53 ± 1.86
Females only (n = 2)
FVC (L) 4.16 ± 0.37 147.2 ± 16.3
FEV1 (L) 3.65 ± 0.78 136.6 ± 0.1
FEV1/FVC (%) 87.86 ± 5.90 93.6 ± 9.0
FEF25–75% (L) 4.12 ± 0.06
Peak flow (L/sec) 8.36 ± 2.75
For each lifeguard, we conducted spirometry tests in the morning before their shift, during a time when there was no red tide. For comparison, the estimated PFT values for a 180-pound adult male are FVC, 4.8 L; FEV1, 4.2 L; FEV1/FVC, > 70%; FEF25–75%, 4.5 L; peak flow, 9.5 L/sec (Scanlon et al. 1999).
a Percentage of predicted values as calculated by OMIS98 Spirometry software.
Table 5 Symptoms reported by study participants before and after going to the beach for the symptom and respiratory effects study.
Exposure period
Symptom Unexposed (n = 27) Exposure level 1 (n = 17)a Exposure level 2 (n = 13)b
Upper respiratory
Eye irritation 0 9 (52.9)* 7 (53.9)*
Nasal congestion 2 (8.7) 4 (23.5)* 3 (23.1)
Throat irritation 1 (4) 6 (35.3) 7 (53.8)*
Cough 1 (4) 9 (52.9)** 10 (76.9)**
Lower respiratory
Chest tightness 0 1 (5.9) 1 (7.7)
Wheezing 0 0 1 (7.7)
Shortness of breath 0 2 (11.8) 0
Other symptoms
Itchy skin 0 1 (5.9) 2 (15.4)
Headache 3 (12) 4 (23.5)* 1 (7.7)
Other 0 4 (23.5) 3 (23.1)
Screening symptom (not anticipated to be associated with aerosol exposure)
Diarrhea 0 0 0
Values are number (%) of lifeguards who did not report the symptom before being on the beach but did report the symptom after being on the beach. The level of exposure was determined by the concentration of brevetoxins in the air.
a Detectable concentrations of brevetoxin (PbTx-2 plus PbTx-3) in air samples.
b Brevetoxin (PbTx-2 plus PbTx-3) concentrations > 10 ng/m3. Statistically significant using McNemar’s test:
* p < 0.05;
** p < 0.01.
Table 6 Changes in PFT results in study participants before and after their shifts.
Exposure period
PFT parameter Unexposed (n = 28) Exposure level 1 (n = 17)a Exposure level 2 (n = 13)b
FVCc (L) 0.08 ± 0.15 0.00 ± 0.13 −0.02 ± 0.17
FEV1d (L) 0.07 ± 0.15 −0.03 ± 0.17* 0.03 ± 0.17
FEV1/FVC (%) 0.21 ± 3.41 −0.57 ± 2.05 0.63 ± 2.26
FEF25–75%e (L) 0.03 ± 0.35 −0.08 ± 0.44 0.17 ± 0.38
Peak flow (L/sec) 0.24 ± 0.74 −0.21 ± 0.70* −0.09 ± 0.69
Values are mean ± SD of the changes (preshift minus postshift) in the PFT parameters. The level of exposure was determined by the concentration of brevetoxins in the air.
a Detectable concentrations of brevetoxin (PbTx-2 plus PbTx-3) in air samples.
b Brevetoxin (PbTx-2 plus PbTx-3) concentrations > 10 ng/m3.
* Statistically significant from baseline values using a paired t-test: p < 0.05.
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NIOSH 1997. NIOSH Spirometry Training Guide. Morgantown, WV:National Institute for Occupational Safety and Health.
Pierce RH Henry MS Blum PC Hamel SL Kirkpatrick B Cheng Y-S In press. Brevetoxin composition in water and marine aerosol along a Florida beach: assessing potential human exposure to marine biotoxins. Harmful Algae.
Pierce RH Henry MS Blum PC Lyons J Cheng Y-S Yazzie D 2003 Brevetoxin concentrations in marine aerosol: human exposure levels during a K. brevis harmful algal bloom Bull Environ Contam Toxicol 70 161 165 12478439
Scanlon CL Wilkins RL Stoller JK 1999. Egan’s Fundamentals of Respiratory Care. 7th ed. St. Louis:Mosby, 391.
Wells JH Lerner MR Martin DF Strecher RA Lockey RF 1984 The effects of respiratory exposure to red tide toxin on airway resistance in conscious guinea pigs [Abstract] J Allergy Clin Immunol 73 128
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7500ehp0113-00065015866779Mini-Monograph: BrevetoxinsInitial Evaluation of the Effects of Aerosolized Florida Red Tide Toxins (Brevetoxins) in Persons with Asthma Fleming Lora E. 12Kirkpatrick Barbara 3Backer Lorraine C. 4Bean Judy A. 5Wanner Adam 2Dalpra Dana 3Tamer Robert 5Zaias Julia 12Cheng Yung Sung 6Pierce Richard 3Naar Jerome 7Abraham William 12Clark Richard 8Zhou Yue 6Henry Michael S. 3Johnson David 8Van De Bogart Gayl 1Bossart Gregory D. 19Harrington Mark 10Baden Daniel G. 71National Institute of Environmental Health Sciences Marine and Freshwater Biomedical Sciences Center, University of Miami Rosenstiel School of Marine and Atmospheric Sciences, Miami, Florida, USA;2University of Miami School of Medicine, Miami, Florida, USA;3Mote Marine Laboratory, Sarasota, Florida, USA;4National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA;5Children’s Hospital Medical Center and University of Cincinnati, Cincinnati, Ohio, USA;6Lovelace Respiratory Research Institute, Albuquerque, New Mexico, USA;7Center for Marine Science Research, University of North Carolina at Wilmington, Wilmington, North Carolina, USA;8Florida Department of Health, Tallahassee, Florida, USA;9Harbor Branch Oceanographic Institution, Fort Pierce, Florida, USA;10Twin Cities Hospital, Niceville, Florida, USAAddress correspondence to L.E. Fleming, c/o Department of Epidemiology and Public Health, University of Miami School of Medicine, 1801 NW 9th Ave., Highland Professional Building, Suite 200 (R 669), Miami, FL 33136 USA. Telephone: (305) 243-5912. Fax: (305) 243-3384. E-mail:
[email protected] article is part of the mini-monograph “Aerosolized Florida Red Tide Toxins (Brevetoxins).”
This study could not have been performed without the help of numerous volunteer investigators, including T.C. Fleming, C. Fleming, M. Johnson, W. Quirino, M. Friedman, D. Squicciarini, L. Pitman, and T. Pitman (University of Miami National Institute of Environmental Health Sciences Center); J. Horton, J. Howell, R. Sabogal, C. Bell (Centers for Disease Control and Prevention); P. Stack, G. Kirkpatrick, and C. Higham (Mote Marine Laboratory, Sarasota, FL). We also thank A. Weidner from the University of North Carolina at Wilmington for her help with the enzyme-linked immunosorbent assay analysis. Environmental monitoring was performed with help from S. Campbell (University of North Carolina at Wilmington); T. Blum, S. Hamel, B. Turton (Mote Marine Laboratory); D.A. Kracko, J. McDonald, and C.M. Irvin (Lovelace Respiratory Research Institute). We also thank the Mote Marine Laboratory, Sarasota County Parks and Recreation Department, the Coquina and Helmseley hotels, and all our volunteer participants and their families in Sarasota, Florida.
This research was supported by National Institute of Environmental Health Sciences (NIEHS) grant P01 ES 10594 and a Minority Supplement to the P01 also from the NIEHS, as well as by the Centers for Disease Control and Prevention, the Florida Harmful Bloom Taskforce, and the Florida Department of Health.
The authors declare they have no competing financial interests.
5 2005 10 2 2005 113 5 650 657 2 8 2004 19 1 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Florida red tides annually occur in the Gulf of Mexico, resulting from blooms of the marine dinoflagellate Karenia brevis. K. brevis produces highly potent natural polyether toxins, known as brevetoxins, that activate voltage-sensitive sodium channels. In experimental animals, brevetoxins cause significant bronchoconstriction. A study of persons who visited the beach recreationally found a significant increase in self-reported respiratory symptoms after exposure to aerosolized Florida red tides. Anecdotal reports indicate that persons with underlying respiratory diseases may be particularly susceptible to adverse health effects from these aerosolized toxins. Fifty-nine persons with physician-diagnosed asthma were evaluated for 1 hr before and after going to the beach on days with and without Florida red tide. Study participants were evaluated with a brief symptom questionnaire, nose and throat swabs, and spirometry approved by the National Institute for Occupational Safety and Health. Environmental monitoring, water and air sampling (i.e., K. brevis, brevetoxins, and particulate size distribution), and personal monitoring (for toxins) were performed. Brevetoxin concentrations were measured by liquid chromatography mass spectrometry, high-performance liquid chromatography, and a newly developed brevetoxin enzyme-linked immunosorbent assay. Participants were significantly more likely to report respiratory symptoms after Florida red tide exposure. Participants demonstrated small but statistically significant decreases in forced expiratory volume in 1 sec, forced expiratory flow between 25 and 75%, and peak expiratory flow after exposure, particularly those regularly using asthma medications. Similar evaluation during nonexposure periods did not significantly differ. This is the first study to show objectively measurable adverse health effects from exposure to aerosolized Florida red tide toxins in persons with asthma. Future studies will examine the possible chronic effects of these toxins among persons with asthma and other chronic respiratory impairment.
asthmabrevetoxinsCOPDharmful algal blooms (HABs)Karenia brevisred tidessensitive populationsspirometry
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Florida red tides annually occur in the Gulf of Mexico and result from blooms of the marine dinoflagellate Karenia brevis. K. brevis produces highly potent natural polyether toxins, known as brevetoxins, that activate voltage-sensitive sodium channels. Studies in experimental animals have shown that brevetoxins can cause upper and lower respiratory irritation. The two known clinical entities in humans that are associated with red tide toxins, first characterized in Florida, are acute gastroenteritis with neurologic symptoms after ingestion of contaminated shellfish [i.e., neurotoxic shellfish poisoning (NSP)] and an apparently reversible upper respiratory syndrome after inhalation of the Florida red tide aerosols (; Backer et al. 2003; Baden et al. 1995; Cheng et al. 2005a; Fleming et al. 2001 Fleming et al. 2005; Morris et al. 1991; Music et al. 1973; Pierce et al. 2003; Poli et al. 2000). Anecdotal reports and a prior study of recreational beachgoers have indicated that the inhalation of the aerosols associated with Florida red tide leads to both upper and lower respiratory symptoms, possibly with chronic health implications (Backer et al. 2003; Kirkpatrick et al. 2004).
Respiratory effects from exposure to Florida red tides or pure brevetoxins have been reported in experimental animals. Franz and LeClaire (1989) reported respiratory failure in less than 30 min in guinea pigs exposed intravenously to 0.016 ng/kg of brevetoxin 3. Benson et al. (1999) exposed 12-week-old male F344/Crl BR rats once to 6.6 μg/kg brevetoxin 3 through intratracheal instillation. The researchers concluded that the potential adverse health effects associated with inhaled brevetoxins could extend beyond the reportedly transient respiratory irritation to long-term impacts on asthma. Wells et al. (1984) reported increased airway resistance in Hartley guinea pigs when brevetoxin was inhaled as an aerosol or applied to the nares as nose drops. In unanesthestized asthmatic sheep, intra-tracheal instillation of picogram doses of brevetoxin 3 can cause a significant and rapid increase in airway resistance; this brevetoxin-induced bronchospasm can be effectively blocked by atropine, the mast cell–stabilizing agent cromolyn, the histamine H1 antagonist chlorpheniramine, and the β 2 agonist albuterol (Abraham et al. 2003, 2005; Baden et al. 2005; Benson et al. 2005; Singer et al. 1998). Thus, aerosolized brevetoxins appear to be potent respiratory toxins, involving both cholinergic and histamine-related mechanisms.
Multiple die-offs of marine mammals, particularly the endangered Florida manatee, have been reported in association with Florida red tide and brevetoxins (Bossart et al. 1998). These die-offs probably resulted from exposure to brevetoxins with prolonged inhalation of the red tide toxin aerosol and/or the ingestion of contaminated seawater over several weeks. Necropsies of the dead manatees revealed severe catarrhal rhinitis, pulmonary hemorrhage and edema, and nonsuppurative leptomeningitis, as well as possible chronic hemolytic anemia with multiorgan hemosiderosis and evidence of neurotoxicity.
Few reports have been published about possible adverse health effects associated with exposure to aerosolized red tide toxins in humans. After initial cases in Florida and Texas, Woodcock (1948) reported respiratory irritation during a severe red tide on the west coast of Florida in 1947. The exposure usually occurs on or near beaches during an active red tide bloom. Onshore winds and breaking surf result in the release of the toxins into the water and into the onshore aerosol (Pierce and Kirkpatrick 2001). Collection of marine aerosol along the gulf coast of Florida and the North Carolina Atlantic coast during natural red tide blooms showed that the aerosolized toxins were the same as those in the water and as those identified in the K. brevis cultures (Pierce et al. 1990). Aerosolized brevetoxin concentrations, particle size, and exposure level were measured in a recent Florida red tide episode in Texas associated with respiratory symptoms in humans (Cheng et al., 2005a; Pierce et al. 2003). The mass median aerodynamic diameters were 7–9 μm, a relatively large size for inhaled ambient particles.
In humans, inhalation of aerosolized red tide toxins reportedly results in conjunctival irritation, copious catarrhal exudates, rhinorrhea, nonproductive cough, and wheezing (Asai et al. 1982; Backer et al. 2003; Cheng et al., 2005a; Kirkpatrick et al. 2001, 2004; Music et al. 1973 ). The normal population can reportedly rapidly reverse the irritation and wheezing by leaving the beach area or entering an air-conditioned area (Baden 1983; Steidinger and Baden 1984). However, persons with asthma apparently are particularly susceptible to aerosolized Florida red tide and its toxins. In addition to anecdotal reporting, Asai et al. (1982) found that 80% of 15 persons with asthma exposed to red tide aerosol at the beach complained of subsequent asthma attacks. The possible susceptibility of persons with asthma to aerosolized brevetoxins is corroborated by recent investigations with an asthmatic sheep model evaluating the exposure of aerosolized red tide toxins, as discussed above (Abraham et al. 2003, 2005; Singer et al. 1998).
This report presents the initial evaluation of the exposures and effects of aerosolized Florida red tide toxins in persons with prior physician-diagnosed asthma during 1 hr of exposure to a Florida red tide and during 1 hr of nonexposure.
Materials and Methods
This study was part of the ongoing evaluation of the possible acute and chronic adverse health effects from exposure to aerosolized Florida red tide toxins (brevetoxins) by an interdisciplinary team of researchers from federal, state, private, and local organizations. These studies have received approval from the institutional review boards of the University of Miami School of Medicine, the Centers for Disease Control and Prevention, and the Florida Department of Health. The location for the study was Siesta Beach in Sarasota, Florida.
Environmental monitoring.
As described by Cheng et al. (2005b) and Pierce et al. (in press), a portable, self-contained weather station was used near the high-volume impactor sampling locations to monitor the air temperature, relative humidity, and wind speed and direction (Complete Weather Station; Davis Instruments, Hayward, CA). The weather station was solar powered, and the data were downloaded into a notebook computer.
Water samples were collected daily in 1-L glass bottles at 0800, 1200, and 1600 hr from the surf zone adjacent to the study’s high-volume air sampler locations. A 20-mL subsample was taken from each bottle and fixed with Utermohl’s solution to provide K. brevis cell counts. The remaining water sample was transported to Mote Marine Laboratory and processed for liquid chromatography–mass spectrometry (LC-MS) analysis according to the procedure of Pierce et al. (2003). A sub-sample of each extract was shipped to the University of North Carolina at Wilmington Center for Marine Research for brevetoxin analysis using a new brevetoxin enzyme-linked immunosorbent assay (ELISA) method.
In the laboratory, brevetoxins were extracted by passing the water through a C-18 solid-phase extraction disk under vacuum (Ansys Technologies, Inc., Lake Forest, CA). The C-18 disks then were rinsed with reverse osmosis water to remove any remaining salts and eluted with methanol (Pierce et al. 2003). Brevetoxin analyses were performed by LC-MS using a ThermoFinnigan AqA high-performance liquid chromatography–MS (HPLC-MS; Thermo Electron Corp., Manchester, UK). Mass spectral detection was obtained using an AqA single-quad system scanned from 204 to 1,216 AMU with AqA Max 40 V electrospray. The column was Phenomenex Luna C-18 5Fm 250 × 2 mm (Phenomenex, Torrance, CA); the solvent gradient was 0.3% acetic acid/H2O with initial 50:50 acetylnitrile (ACN)/H2O to 95:5 ACN/H2O over 30 min. The limit of detection (LOD) of the analysis for brevetoxins in seawater was 0.03 μg/L (Cheng et al. 2005b; Pierce et al. in press).
Air samples were collected using three different instruments: high-volume air samplers, high-volume air samplers equipped to capture aerosol particles by size, and personal breathing zone samplers. Brevetoxins were collected in air samples along Siesta Beach. Six high-volume air samplers (TE-5000; Tisch Environmental, Inc., Village of Cleaves, OH) with a single-stage filter were used; three were placed near the surf zone (about 25 m) approximately 100 m apart, and a second row of three was located approximately 50 m from the first row for assessment of aerosolized toxin concentrations over time and space along the beach. Two additional high-volume air samplers with five-stage impactors were deployed in the first row to obtain the distribution of toxin-containing aerosol particle size. Filter samples were collected separately for morning and afternoon time periods (0830–1200 hr and 1230–1600 hr). The impactor samplers were operated from morning to afternoon to collect enough material for analysis. Personal exposure was measured on volunteers wearing a personal sampler (IOM inhalable dust sampler; SKC, Inc., Eighty Four, PA) connected to a battery-operated pump (model 224-PCXR4; SKC, Inc.) while they were at the beach. The personal sampler was placed at the lapel near the breathing zone. A 25-mm glass-fiber filter (type A/E; Pall Life Science, Ann Arbor, MI) was used as the collection substrate. The sampling flow rate was 2 L/min controlled by a rotameter in the sampling pump (Cheng et al. 2005b; Pierce et al., in press).
The high-volume air samplers used to detect aerosolized brevetoxins were fitted with a 20.32 cm × 25.4 cm glass-fiber filter (EPM2000; Whatman, Maidstone, UK). Brevetoxins associated with marine aerosols were recovered from glass-fiber filters by extraction for 12 hr in acetone using a Soxhlet apparatus (Pierce et al. 2003). The extract was then transferred to vials using methanol for LC-MS and ELISA analysis. Brevetoxin recovery from glass-fiber filters was verified by the addition of standard amounts of brevetoxin 2 and brevetoxin 3 to each of three filters that were subsequently processed for LC-MS analyses. Cellulose filters from impactor samplers were cut in half and rolled into a 15-mL polypropylene tube (second half of filter was stored). Ten milliliters of acetone was added to the tube; then the sample was vortexed for 20-sec, sonicated for 2-min, and then placed on a circular rotator (Roto-Torque, low speed-10; Cole-Parmer Instruments, Vernon, IL) for 20-min. The 10 mL of extract then was evaporated under a gentle stream of nitrogen to approximately 100 μL, vortexed for 5 sec to rehomogenize the extract, and recombined with 50:50 methanol:purified water to the final analysis volume (typically 200 μL) (Cheng et al. 2005a; Pierce et al., in press). The samples then were analyzed for brevetoxins by an LC-MS technique using an HPLC (SIL-DAD vp; Shimadzu Co., Kyoto, Japan) coupled with the API 365 MS/MS (Applied Biosystems Inc., Foster City, CA) (Cheng et al. 2005a; Pierce et al., in press). The LOD for the analysis of impactor samples was 0.01 ng/m3.
Concentrations of brevetoxins on portions of the ambient air filters and the personal breathing zone filters, as well as the nasal and throat swabs, were also analyzed by a recently developed competitive ELISA (Naar et al. 2002). Air filters (personnel and aliquots of environmental air filters) were sonicated for 15 min in ELISA buffer (0.1 M phosphate-buffered saline, pH 7.4, 0.5% gelatin, 0.1% Tween 20). Nose and throat swabs were sonicated for 45 min in ELISA buffer. The sonicated material was analyzed directly according to the brevetoxin ELISA protocol (Naar et al. 2002). The limit of quantification of the brevetoxins using the ELISA was 0.6 ng/sample (air filter and/or nasal/throat swab).
Adverse human health effects.
Persons at least 12 years of age who reported a physician’s diagnosis of asthma were recruited as study participants in Sarasota, Florida, an area with a history of annual Florida red tides. All participants who gave informed consent to participate walked on the beach once during a Florida red tide and also walked once when no Florida red tide was present. Participants were instructed to maintain their daily regime for asthma control. They were asked to spend a minimum of 1 hr at the beach in areas where environmental monitoring was ongoing and were told they could leave the beach at any time if they felt uncomfortable or symptomatic and could freely use any personal medications. Study activities included the pre- and postexposure questionnaires, swab sampling, and spirometry, as well as carrying a personal air monitor while at the beach.
Questionnaires were administered upon enrollment and then before and after the participants visited the beach. The questionnaires collected information about demographics, baseline pulmonary health history, prior experience with Florida red tide, medications, potential confounders, and symptoms. In addition to asking about expected common respiratory symptoms, the questionnaire asked about diarrhea to detect overreporting bias, because diarrhea was not expected to be associated with exposure to aerosolized Florida red tide (vs. ingestion exposure resulting in NSP). For the purposes of analysis, age groups were evaluated as tertiles (< 18 years, 18–60 years, > 60 years). Geographic proximity of residence to the beach also was explored; proximity was defined as residence on a barrier island or along Sarasota Bay (i.e., within approximately 1 mile of the seashore). The investigators considered two surrogate measures of asthma severity: above and below the unexposed period study population mean forced expiratory volume in 1 sec (FEV1) and the use of asthma medications (predominantly β 2 agonists) within 12 hr before going to the beach. However, the mean FEV1 before the unexposed beach visit was not considered to be a good measure of disease severity and was not used: no untreated baseline was obtained before the unexposed beach visit, and in the study population, persons who used asthma medication within 12 hr before going to the beach, were more likely to have an FEV1 value greater than the mean FEV1 from the unexposed period (data not shown). Therefore, only the use of asthma medications within 12 hr before going to the beach was used as a surrogate for increased asthma severity.
Spirometry tests were performed using a portable OMI2000 10-L dry-rolling-seal volume spirometer (Occupational Marketing, Inc., Houston, TX) by personnel trained according to the standards of the National Institute for Occupational Safety and Health (NIOSH 1997). The spirometric values of interest were FEV1, forced vital capacity (FVC), forced expiratory flow between 25 and 75% (FEF25–75, peak expiratory flow (PEF), and FEV1/FVC percentage. For the purposes of this study, each participant served as his or her own spirometry control (i.e., pre-exposure/postexposure); all study participants had at least three reproducible spirograms before and after visiting the beach. The data were considered adequate if they conformed to standard guidelines for the collection and interpretation of spirometry measurements (American Thoracic Society 1991, 1995; Hankinson et al. 1999).
As an effect biomarker of inflammation, nose and throat swabs were collected from the study participants before and after they went to the beach. Samples were obtained by gently wiping the nose or throat with a cotton-tipped swab, smearing the material onto duplicate microscope slides, and fixing with a cytologic adhesive spray (Spray-Cyte; Becton Dickinson, Sparks, MD). One slide from each pair was stained using Diff Quik (Dade Behring Inc., Newark, DE) for cytologic evaluation of epithelial and inflammatory cells. The inflammatory response was characterized according to cellularity and the percentage of neutrophils and chronic inflammatory cells (e.g., macrophages, lymphocytes, and plasma cells). In addition, protein transudation and amount of fibrin present were evaluated because increasingly permeable cell membranes, a key event in the inflammatory process, lead to protein transudation as proteins leak out of the cells. As described above, brevetoxin levels also were analyzed by a newly developed brevetoxin ELISA (Naar et al. 2002) on the nasal and throat swabs as an exposure biomarker.
Statistics.
A study database was created in Microsoft Access. Descriptive and other statistical analyses were performed using SAS statistical software (version 8.03; SAS Institute Inc., Cary, NC). Statistical hypothesis testing was performed using the paired t-test for continuous data and the McNemar’s test for categorical data (Kleinbaum et al. 1982) to compare pre- and postexposure data. The number of persons reporting no symptom before going on the beach but reporting the particular symptom after exposure was compared with the number who reported no symptoms before and after their beach walk. Because this was an initial evaluation of the possible human adverse health effects of Florida red tide airborne toxin exposure, it was considered hypothesis generating. Therefore, multiple comparisons were not adjusted for in the level of statistical significance.
Results
The results contrast the environmental monitoring and human adverse health effects evaluations collected during 3 days when study participants were exposed to the Florida red tide aerosol (exposed sampling period, March 2003) with similar data collected during 3 days when no Florida red tide occurred (unexposed sampling period, January 2003).
Environmental data.
Environmental sampling confirmed the presence of K. brevis and brevetoxins in the water and air during the Florida red tide exposed period, and the lack of significant toxin and organism levels in the water and air during the Florida red tide unexposed evaluation (Table 1). Surf water samples from the unexposed period exhibited K. brevis cell counts ranging from none detected (< 1,000 cells/L) to a high of 6,000 cells/L on 17 January 2003, diminishing to < 1,000 cells/L on 18 January. During this time, water samples contained low concentrations of brevetoxins, ranging from none detected (< 0.05 μg/L) to 2.0 μg/L for samples collected on both days. High-volume air samplers recovered only trace amounts of brevetoxins from the air on 17 January, with none detected (< 0.5 ng/m3) on 18 January.
Water samples collected during the Florida red tide exposed period showed moderate to high concentrations of K. brevis cells (daily mean ± standard deviation) on the first day of the exposed study, 29 March 2003 (181,000 ± 131,000 cells/L). Cell concentrations increased on the second day of exposed study on 30 March (764,000 ± 264,000 cells/L) and remained high through 31 March morning (236,000 ± 69,000 cells/L), then diminishing rapidly by the 1200 hr sample collection (22,000 ± 11,000 cells/L). The mean total brevetoxin concentration in water samples on 29 March was 3.4 ± 1.9 μg/L throughout the day. Toxin concentrations in water were higher on 30 March, with a mean and standard deviation of 14 ± 8 μg/L, diminishing again to 3.3 ± 3.7 μg/L on 31 March.
In addition to temperature and relative humidity, wind speed and wind direction were measured. Both wind speed and wind direction are essential in the production and transport of the red tide aerosol to the beach. During the unexposed Florida red tide period, wind direction during the sampling was offshore. This and low brevetoxin concentration in water resulted in only trace or undetectable amounts of brevetoxin concentration in the air. During the first 2 days of the exposed Florida red tide period, the wind direction was partially onshore with medium to high concentrations of brevetoxins in water. These environmental conditions produced medium levels of brevetoxins in the air. On 31 March, the last day of the exposed Florida red tide period, the wind direction changed to offshore wind, and the air concentration of brevetoxins was much lower.
By LC-MS analyses, concentrations of brevetoxins in the air samples were found throughout the first day of the study, 29 March, with an overall daily mean and standard deviation of 37 ± 18 ng/m3. Samples collected near the surf did not differ from those collected 50 m up the beach, probably because of the strong winds rapidly dispersing the toxins, providing uniform exposure over the beach study area. Aerosolized brevetoxin concentrations in the ambient air diminished on 30 March to < 1/10th that observed on the first day of the exposed study, with none detected in air samples on 31 March, even though the cell counts and brevetoxin concentrations remained high in the water. This probably occurred because of a shift in wind direction from onshore to alongshore and offshore.
ELISA analyses detected no brevetoxins on either the swabs or the personal monitoring during the unexposed study period (January 2003). In the exposed study period (March 2003), brevetoxins were detected in seawater, environmental and personnel air monitoring filters, and nose swabs of some of the participants by ELISA. However, in initial experimental analysis, the presence of toxins in nose swabs did not appear to be simply correlated with the amount of toxin that participants were exposed to in their breathing zone (i.e., the toxin levels measured on personnel air monitoring filters). During the other 2 days of the Florida red tide exposed period, toxin levels in the sea spray at the beach were too low to be detected on air personnel filters and/or on nose swabs with the brevetoxin ELISA.
The particle size distribution from the impactor sample could be represented by a log-normal distribution. The mass median aerodynamic diameter was 6.54 ± 1.34 μm, with a geometric standard deviation of 1.73 ± 0.05 μm.
Adverse health effects.
Of the 130 persons initially enrolled in the study of sensitive sub-populations, 59 who had asthma participated in study activities during both an unexposed (January 2003) and an exposed (March 2003) study period evaluation. Their mean age was 35.8 ± 18.7 years (range, 12–69 years); most were white non-Hispanic women (Table 2). This population was relatively healthy, with very few current smokers [5 (8.5%)]; however, 11 (19.6%) had been hospitalized at least once in the past year for pulmonary reasons. Most (93.2%) of these participants reported variable use of asthma medications (predominantly β 2 agonists) and had experienced a Florida red tide with reported symptoms (82.3%).
During the unexposed period, the 59 participants who had asthma experienced neither significant respiratory impairment nor development of symptoms after being at the beach for 1 hr. The participants were significantly more likely to report symptoms and significantly more likely to have a measurable respiratory impairment on spirometry after going to the beach for 1 hr during a Florida red tide exposure (Table 3), although there was considerable variation in the respiratory function during the January 2003 unexposed period. The significant symptoms reported only during active exposure to Florida red tide included respiratory complaints of cough (p < 0.01), wheezing (p < 0.03), and chest tightness (p < 0.02), as well as throat (p < 0.02) and eye irritation (p < 0.01). Participants did not report diarrhea (p = 1.00). Statistically significant decreases in respiratory function during the Florida red tide exposed period were measured for the FEV1 (38.0 ± 118.0 mL; p < 0.02) and the FEF25–75 (95.0 ± 296.0 mL/sec; p < 0.02) with significant change from preexposure. No significant changes in the FVC or FEV1/FVC percentage were seen.
The association between symptoms and change in FEV1 during Florida red tide exposure was evaluated as a) greater or less than the mean change in FEV1 (i.e., preexposure minus postexposure FEV1) and b) as a positive change (i.e., preexposure > postexposure FEV1) vs. negative change (i.e., preexposure < post-exposure FEV1). Participants with a decrease in FEV1 after exposure to Florida red tide reported statistically significant increased coughing (p < 0.04) and borderline increased difficulty breathing (p < 0.06) and chest heaviness (p < 0.06). However, asthmatics with an increase in FEV1 after exposure to Florida red tide reported a statistically significant increased chest heaviness (p < 0.01) but no other respiratory symptoms. Of note, reporting a cough (not wheezing) after going to the beach was associated with statistically significant decreases in the PEF (p < 0.03) for asthmatics only during the Florida red tide exposed period.
Because the participants lived in an area with annual and often continuous Florida red tides, there was the possibility that their decreased respiratory function before the study beach walk existed because of some prior exposure (e.g., a prior Florida red tide exposure). This possibility was examined by comparing the pulmonary function before the beach walk during the unexposed time period with the pulmonary function before the beach walk during the Florida red tide exposed period. No significant differences were found between FEV1, FEF25–75, PEF, FVC, or FEV1/FVC values before the unexposed beach walk and those before the exposed beach walk. In fact, the mean pulmonary function was slightly better before the beach walk during the Florida red tide exposed period than it was before the unexposed period (data not shown).
Asthmatics who lived away (> 1 mile) from the seashore were significantly more likely to report respiratory symptoms after going to the beach during the Florida red tide exposed period, whereas those who lived close (i.e., within ≤1 mile) to the seashore had no statistically significant increase in reported symptoms. Asthmatics who lived far from the beach also were significantly more likely to experience a decrease in FEV1 (48.0 ± 122.3 mL; p < 0.01) and FEF25–75 (104.1 ± 254.7 mL/sec; p < 0.009) with significant change after beach exposure to Florida red tide. Respiratory function did not differ in asthmatics who lived close to the beach during the Florida red tide exposed period.
The day with the highest Florida red tide exposure by air environmental monitoring (29 March) was examined, although only 29 asthmatics were evaluated on that day. Reported symptoms and pulmonary function did not change significantly during their unexposed evaluations. During the Florida red tide exposed period, these participants reported significantly more cough (p < 0.02) after returning from the beach, and there was a statistically significant decrease in FEF25–75 (116.9 ± 272.9 mL/sec; p < 0.04).
Additional analyses determined whether other factors such as age, gender, and severity of illness were important with regard to reported symptoms and spirometry before and after going to the beach (data not shown). Only the 18- to 60-year-old participants had statistically significant increases in reported respiratory symptoms during the Florida red tide exposed period, but not in the unexposed period. Age was not associated with any significant changes in spirometry in the unexposed period, but during the Florida red tide exposed period, only asthmatics 18–60 years of age had statistically significant decreases in FEV1 (p < 0.04), FEF25–75 (p < 0.03), and PEF (p < 0.05) after going to the beach. No significant differences for the symptoms were reported by gender during the unexposed period; during the Florida red tide exposed period, female asthmatics reported significantly more respiratory symptoms after going to the beach. During the exposed period, there were statistically significant decreases in FEV1 (p < 0.009) and FEF25–75 (p < 0.05) for female asthmatics, as well as statistically significant decreases in FEV1 (p < 0.02) during the unexposed period; male asthmatics had no statistically significant changes during the Florida red tide exposed or unexposed periods.
To explore asthma severity, the surrogate measure was used to compare those asthmatics who did and did not use asthma medications within the 12 hr before going to the beach. More persons [42 (58%)] reported using asthma medication within 12 hr before going to the beach during the exposed to Florida red tide time period than during the unexposed time period [30 (42%)]. The use of asthma medications within 12 hr before going to the beach was associated with statistically significant increased report of cough (p < 0.004) and chest heaviness (p < 0.04) after going to the beach during the Florida red tide exposed period only. The reported use of asthma medications 12 hr before the beach walk was associated with no changes in the unexposed period, but with statistically significant changes in the FEV1 (45.0 ± 100.1 mL; p < 0.02), FEF25–75 (120.0 ± 299.3 mL/sec; p < 0.03), and PEF (160.0 ± 435.7 mL/sec; p < 0.04) during the Florida red tide exposed period. Asthmatic females [22/34 (65%)] were more likely to report using medication before going to the beach compared with males [10/25 (40%)]; female asthmatics using medications experienced statistically significant decreases in their pulmonary function during the Florida red tide exposed period, particularly FEV1 (78.0 ± 93.6 mL; p < 0.0008) and FEF25–75 (166.4 ± 423.7 mL/sec; p < 0.04). Asthmatics of both genders who lived far from the beach were more likely to report taking medication before going to the beach [25 of 44 (57%)] compared with those who lived close to the beach [7 of 15 (47%)]; these same asthmatics were more likely to experience statistically significant decreases in their pulmonary function during the Florida red tide exposed period, particularly FEV1 (57.6 ± 104.9 mL; p < 0.01) and FEF25–75 (132.0 ± 273.6 L/sec; p < 0.02). The seven asthmatics taking medication who lived near the beach experienced a statistically significant decrease in PEF (430.0 ± 394.0 mL; p < 0.03) during the Florida red tide exposure period.
Swabs.
The 59 asthmatics were sampled by nasal and throat swabs both when unexposed (January 2003) and exposed to Florida red tide (March 2003). The Florida red tide exposed swab data were compared with the unexposed swab data. Several parameters were evaluated (i.e., inflammation between pre- and post-beach walk samples, protein transudation, amount of fibrin present in the sample, and percentage of reactive cells). These parameters were all higher, although not statistically significant (chi square, 0.1 > p > 0.05), on the second exposed day (30 March) than throughout the unexposed period when no Florida red tide was present. Analyses among the 3 days of Florida red tide exposure indicated that the swab samples on the second exposed day (30 March) showed the greatest increase in inflammatory response within the day (i.e., postexposure samples contained a greater inflammation than preexposure sample); the first exposed day (29 March) was intermediate, and the last exposed day (31 March) had the least increase in inflammation. On the second exposed day, more (although again not statistically significant) protein and fibrin and a larger percentage of reactive epithelial cells were found (chi square, 0.1 > p > 0.05) in the samples than on the other days.
Discussion
This is the first study to demonstrate measurable adverse health effects, both in terms of reported symptoms and objectively measured respiratory decreases, from exposure to aerosolized Florida red tide toxins in persons with asthma. In addition, this study documents the water and air exposures to the aerosolized Florida red tide toxins associated with these adverse health effects. This study shows that just visiting the beach did not appear to adversely affect health for persons with asthma during periods of no red tide, despite relatively low temperatures and strong winds; in the past, these environmental factors have been associated with increased bronchoconstriction among asthmatics (Koh and Choi 2002). In fact, additional analysis of data collected on a subsequent unexposed warmer and more humid day in May 2004 (data not shown) illustrated that there was some effect of the cold temperatures in the January 2003 nonexposure period on the lung function of the asthmatics; however, the March 2003 Florida red tide toxin exposure caused substantial significant respiratory changes in the asthmatics.
In this preliminary study, the asthmatics who appeared to be at greatest risk for a statistically significant respiratory decrease after exposure to Florida red tide were those who chronically used medications. As discussed above, although precisely defining the asthma severity is not possible without an untreated unexposed baseline assessment of all the asthmatics, the most severe asthmatics in this study population are probably those who reported regular use of medications within 12 hr of going to the beach for the study. These more severe asthmatics had the most significant decreases in respiratory function during Florida red tide exposure among all the study participants. Furthermore, the effects of other possible factors (i.e., age, sex, and residence proximity to the shore) were less important when the data were stratified by use of medication. Therefore, the results suggest that the more severe asthmatics were the most sensitive to Florida red tide toxin exposure.
Preliminary work with an asthmatic sheep model has indicated that pretreatment with regularly used asthma medications (i.e., cromolyn, albuterol, and even antihistamines) minimize the respiratory depressive effects of brevetoxins and of Florida red tide aerosol. Therefore, the respiratory decreases in participants with more severe asthma in this study might have actually been much greater if they had not premedicated.
The brevetoxin dose to which the study participants were exposed was relatively low, with an average ambient air concentration on the beach even during the highest exposure day (29 March) of 36.67 ± 17.54 ng/m3. In a prior study of recreational beachgoers by these investigators, this was considered “moderate” exposure compared with a “high” exposure day with up to 108 ng/m3 of brevetoxin in the air (Backer et al. 2003, 2005). If an average adult at rest breathes in about 6 L of air per minute (Guyton 1981), then persons visiting the beaches during this study on the highest exposure day inhaled approximately 12.9 ng of brevetoxin each hour, or an inhaled dose of 0.18 ng/kg (assuming an average weight of 70 kg) each hour. Franz and LeClaire (1989) reported respiratory failure in < 3 min in guinea pigs exposed intravenously to 0.016 ng/kg brevetoxin 3, and Benson et al. (1999) exposed 12-week-old male F344/Crl BR rats to a single dose of 6.6 μg/kg brevetoxin 3 through intra-tracheal instillation resulted in systemic distribution of brevetoxin 3 lasting more than 7 days postexposure. Singer et al. (1998) and Abraham et al. (2003, 2005) have found that intratracheal administration of picogram doses of both brevetoxins and aerosolized Florida red tide samples caused significant respiratory depression in asthmatic sheep. Therefore, aerosolized Florida red tide and brevetoxins appear to be significant respiratory toxins at very low exposures in both humans and animals.
The physiologic impact of exposure to the Florida red tide aerosol depends on the mass and chemical characteristics of the inhaled particles. In a similar study of red tide aerosols conducted in Texas (Cheng et al. 2005a) and in the present study (Cheng et al. 2005b; Pierce et al., in press), the particles containing brevetoxin were 2.9–15 μm in mass median aerodynamic diameter. Inhaled particles of this size would be deposited primarily in the upper respiratory tract (Schlesinger 1985); subsequent respiratory irritation could result from the impact of the particles themselves and/or from the toxins associated with the particles. In the present study, the reported respiratory irritation and the measured decreases in respiratory function were caused by exposure to aerosolized Florida red tide and brevetoxins; in general, the study participants did not report symptoms or have measured respiratory decreases during nonexposure to Florida red tide, but they did when brevetoxins were measured in the environmental air sampling, as well as in their noses, using the newly developed ELISA for brevetoxins.
Possible chronic effects from exposure to aerosolized Florida red tide and brevetoxins may occur and should be evaluated. The swab inflammatory data suggest a possible delayed increased effect among the sensitive subpopulation after the beginning of red tide exposure. Anecdotally, several of the asthmatic participants have reported delayed effects after their Florida red tide beach exposures. This is further supported by another study (Quirino et al. 2004) using Florida Poison Information Center data to compare persons calling with Florida red tide–associated symptoms with unexposed control callers. Callers with Florida red tide exposure reported significantly more respiratory symptoms at the time of exposure, and a significantly longer duration of these symptoms (12.84 ± 25.35 days duration of symptoms compared with 2 ± 1.41 days for the unexposed callers). Also, callers exposed to Florida red tide were significantly more likely than unexposed callers to report seeking medical care (an elevated relative risk of 3.00; p < 0.025).
Study limitations and strengths.
This study has several limitations. First, exposure to aerosolized Florida red tide is difficult to assess. It is a natural event with significant variation over time and space caused by the ongoing effects of seawater concentrations, wind direction and speed, and other environmental factors. Furthermore, the aerosol is a mixture of seawater and salt, various brevetoxins, cellular particles, and other substances as yet to be defined. For example, the study investigators have discovered that K. brevis produces a natural inhibitor of brevetoxin, known as brevenol; brevenol was measured during the March 2003 study period on the environmental air samplers (Bourdelais et al. 2004; Cheng et al. 2005b; Pierce et al., in press; Purkerson-Parker et al. 2000). The exact constituents of Florida red tide aerosols and their individual and combined effects on humans and other animals need further evaluation.
This study took place in an area with annual Florida red tide exposure. Defining a complete unexposed period was not possible because there were K. brevis cells in the waters along the beach study site even during the “unexposed” period. Furthermore, the participants were all residents of this area, and many had a history of Florida red tide exposure. Therefore, these participants may have experienced intermittent Florida red tide aerosol exposure unmeasured by the investigators before, after, and during the two study periods. In addition, residents of this geographic region may have adapted to chronic Florida red tide aerosol exposure.
Techniques to measure human exposure and subsequent adverse health effects from exposure to aerosolized Florida red tide toxins are currently under development. The newly developed brevetoxin ELISA, as well as LC-MS and HPLC, were close to their LODs even during the Florida red tide exposure period; this was particularly true for the relatively low-flow personal air monitoring and for the swabs. The use of throat and nose swabs to evaluate inflammatory change is also under development. Nevertheless, these methodologies offer the possibility of quantitative and objective measurement with both exposure and effect biomarkers of brevetoxins in humans in the future, in addition to the more traditional exposure assessment (i.e., water, environmental air, personal air) and health effect assessment (i.e., questionnaire and spirometry).
In this initial study, the asthmatic participants were evaluated without Florida red tide exposure but not without the use of their medications. This is important for several reasons. Preliminary data using an asthmatic sheep animal model have shown that pretreatment with commonly used asthma medications (i.e., albuterol, atropine, cromolyn, and even antihistamines) can minimize the effects of brevetoxins and of Florida red tide aerosols on respiratory function (Abraham et al. 2005). Therefore, the ongoing use of asthma medications among the participants with more severe asthma may have decreased the physiologic effects of Florida red tide exposure in the most sensitive subpopulation.
In addition to the advantage of employing the objective effect measure of spirometry, this study used a methodology in which each subject served as his or her own control with regards to all effect measures, before and after 1 hr of beach exposure as well as during Florida red tide exposed and unexposed periods. This methodology has been used extensively looking at cross-shift and at longitudinal changes in respiratory function (Anees 2003; Chan-Yeung 2000; Eisen et al. 1997; Hnizdo et al. 1999; Skogstad et al. 2002; Waters et al. 2003) in occupational studies of respiratory toxins, as well as in environmental air pollution studies (Desqueyroux et al. 2002).
The strengths of this preliminary study of sensitive population and exposure to aerosolized Florida red tide outweigh its limitations. This study integrated extensive environmental assessment with evaluation of adverse human health effects. It is the first study to objectively measure both exposure and adverse health effects in a relatively large population of persons with underlying respiratory disease. Finally, although small, the objectively measured respiratory decreases were statistically significant and correlated with the both the environmental assessment and with the self-reported respiratory symptoms.
The investigators plan to evaluate the possible chronic effects of exposure to aerosolized Florida red tide toxins among the sensitive subpopulations. Not all persons with asthma may be equally sensitive to toxin exposures. In addition, evaluation of possible therapeutic interventions using animal models, as well as controlled trials in humans, needs to be explored.
Table 1 Environmental conditions and concentrations of brevetoxins in water and air samples during the unexposed and exposed sampling periods.
Predominant wind direction (% onshore) Brevetoxin
Date Temperature (°C) Humidity (%) Average wind speed (km/hr) K. brevis (cells/L) Seawater (μg/L) Air (ng/m3)
Unexposed
17 January 2003 12.2 ± 1.6 68 ± 5 25.6 ± 3.4 Offshore (1%) 2,400 ± 1400 < LOD < LOD
18 January 2003 8.3 ± 1.6 47 ± 5 10.9 ± 3.7 Offshore (4%) < LOD < LOD < LOD
19 January 2003 13.3 ± 1.1 53 ± 7 12.4 ± 4.0 Offshore (2%) < LOD < LOD < LOD
Exposed
29 March 2003 24.4 ± 0.5 83 ± 4 10.5 ± 5.4 Partly onshore (58%) 180,600 ± 131,000 3.44 ± 1.93 36.57 ± 17.51
30 March 2003 18.9 ± 2.2 84 ± 6 24.9 ± 6.0 Partly onshore (44%) 764,400 ± 263,700 14.01 ± 8.06 3.71 ± 2.63
31 March 2003 12.8 ± 1.1 32 ± 12 22.7 ± 2.6 Offshore (0%) 96,300 ± 86,400 3.31 ± 3.74 < LOD
Table 2 Demographics of 59 physician-diagnosed asthmatic study participants.a
Variable Asthmatics [n (%)]
n 59
Age ± SD (range in years) 35.8 ± 18.7 (12.0–69.0)
Female 34 (57.6%)
Race–ethnicity
White (%) 58 (98.3%)
Hispanic (%) 2 (3.4%)
Years with diagnosis ± SD 18.2 ± 14.9
Using asthma medications currentlyb 55 (93.2%)
Positive history of red tide symptoms with exposure 37 (82.2%)
Current smoker 5 (8.5%)
Number hospitalized in ≥ 1 in past year from respiratory causes 11 (19.6%)
a Based on baseline questionnaire information.
b Predominantly β 2 agonists.
Table 3 Self-reported symptoms and spirometry results for study participants preexposure and postexposure to beach.
No red tide exposure
Red tide exposure
Reported symptom Preexposure = no
Postexposure = yes (n) Pre- vs. post-difference significancea Preexposure = no
Postexposure = yes (n) Pre- vs. post-difference significancea
Respiratory
Cough 9 0.44 15 0.01
Wheezing 4 0.74 7 0.03
Shortness of breath 7 0.56 8 0.06
Chest tightness 8 0.25 17 0.002
Other
Throat irritation 5 0.56 12 0.02
Nasal congestion 6 0.76 12 0.25
Eye irritation 3 1.00 9 0.01
Headache 5 0.26 6 0.06
Itchy skin 1 0.32 1 0.56
Diarrhea 0 0 1 1.00
Spirometry value Prebeach mean ± SD Mean difference ± SD postexposure Significance (p-Value)b Prebeach mean ± SD Mean difference ± SD postexposure Significance (p-Value)b
FEV1 3.01 ± 0.88 L 21.0 ± 139.0 mL 0.24 3.03 ± 0.87 L 38.0 ± 118.0 mL 0.02
FVC 4.02 ± 1.07 L 2.0 ± 179.0 mL 0.93 4.04 ± 1.03 L 35.0 ± 176.0 mL 0.13
FEV1/FVC 75% ± 9% 0.6% ± 3% 0.09 75% ± 9% 0.3% ± 3% 0.48
FEF25–75 2.48 ± 1.19 L/sec 39.0 ± 332.0 mL/sec 0.36 2.53 ± 1.26 L/sec 95.0 ± 296.0 mL/sec 0.02
PEF 7.56 ± 2.02 L/sec 42.0 ± 693.0 mL/sec 0.64 7.59 ± 2.02 L/sec 81.0 ± 458.0 mL/sec 0.18
a McNemar’s test.
b Paired t-test.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0029015866747PerspectivesEditorialGuest Editorial: Health, Equity, and the Built Environment Frumkin Howard Rollins School of Public Health, Emory University, Atlanta, Georgia, E-mail:
[email protected] Frumkin is professor and chair of the Department of Environmental and Occupational Health at Emory University’s Rollins School of Public Health, and director of the Southeast Pediatric Environmental Health Specialty Unit. He is coauthor of Urban Sprawl and Public Health (Island Press 2004). His interests include health aspects of the built environment, environmental justice, and chemical toxicity.
The author declares he has no competing financial interests.
5 2005 113 5 A290 A291 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The modern era of environmental health dates from the publication of Silent Spring in 1962. In her classic book, Rachel Carson warned of the effects of pesticides on wildlife ecology, invoking a nightmarish die-off of songbirds in the book’s title. However, she also warned of human health effects, both acute and chronic, from liver damage to neurotoxicity to cancer (Carson 1962). In the ensuing decades, environmental health essentially became synonymous with the recognition and control of chemical exposures. Environmental health scientists were toxicologists and epidemiologists, specializing in pesticides, metals, solvents, asbestos, or persistent organic pollutants.
At least two paradigm shifts have revolutionized the field since Rachel Carson’s day. One occurred when environmental health encountered civil rights, forming the environmental justice movement. We are in the midst of the second, as environmental health reunites with architecture and urban planning.
The environmental justice movement coalesced around 1982, when a predominantly African-American community in Warren County, North Carolina, challenged a proposed polychlorinated biphenyl landfill as an act of “environmental racism” (Lee 1992). Early research by sociologist Robert Bullard (1983) found that hazardous waste sites were disproportionately located in African-American communities. Subsequent research documented racial disparities in other hazardous exposures such as industrial plants and bus depots (Bryant and Mohai 1992; Bullard 1990) and even in the enforcement of environmental laws (Lavelle and Coyle 1992).
The environmental justice movement has had a profound effect on environmentalism and on environmental health. It has focused attention on the needs of disenfranchised populations, especially poor people and people of color. In documenting that environmental hazards may target vulnerable populations, it helped draw attention to children, the elderly, people with disabilities, and other groups. It asserted a central role for community perspectives and placed grass-roots leadership at the heart of environmental health advocacy.
A second paradigm shift in environmental health has occurred in recent years: a broadening of focus from the chemical environment to the built environment. Many factors have contributed to this shift. Architectural changes following the oil shocks of the 1970s, especially the construction of “tight buildings,” were found to have health consequences. Rapid urbanization around the world and the sprawling expansion of cities in the United States (Frumkin et al. 2004) gave new meaning—and urgency—to the idea of “urban health.” The obesity epidemic in developed nations called attention to land use and transportation as determinants of physical activity (Saelens et al. 2003). The development of geographic information systems (GIS) facilitated spatial analysis of health problems. Because of these and other factors, environmental health is rediscovering its roots in geography and urban planning (Barton and Tsourou 2000; Corburn 2004).
Each of these trends—the environmental justice movement and the focus on the built environment—has helped transform the environmental health field. Significantly, the two are now converging, as described in this issue of EHP (Hood 2005). Disparities in the built environment can be identified in at least five arenas: housing, transportation, food, parks and green spaces, and squalor.
The nation faces a shortage of housing; housing is unaffordable for many poor families; and much of the available housing, especially rental stock, is substandard [Joint Center for Housing Studies (JCHS) 2004]. Substandard housing is clearly bad for health, posing risks that range from lead poisoning to respiratory disease to injuries (Bashir 2002; Krieger and Higgins 2002). Children who live in substandard housing, with such features as rat infestations, leaks, holes in walls and floors, and lack of heat, water, and/or functioning toilets, are at increased risk of emotional disorders (Sharfstein et al. 2001). On the other hand, good housing promotes health and well-being in many ways: providing shelter, serving as “the physical infrastructure for group life,” and providing a secure and rooted sense of home (Fullilove and Fullilove 2000). Poor people and people of color disproportionately reside in substandard housing, a pressing example of health inequities in the built environment.
The term “built environment” conjures images of places—buildings, neighborhoods, parks. But transportation infrastructure forms the connective tissue that links these places together and represents an integral part of the built environment. Equity concerns in transportation take at least two forms. First, certain elements of transportation infrastructure, such as highways and bus depots, are “locally undesirable land uses.” Poor people and people of color disproportionately live near these locations and suffer associated health consequences—the effects of diesel air pollution, noise, injury risks, and ugliness. Second, transportation systems that do not provide poor people with convenient, practical access to employment, medical care, and other necessities undermine their health in numerous ways (Bullard et al. 2004; Schweitzer and Valenzuela 2004). Perhaps most important, the spatial mismatch between where poor people live and where jobs are available, as well as the inability to get to good jobs (Stoll 2005), consigns people to ongoing poverty, a principal predictor of poor health.
There is increasing recognition that the built environment may affect what people eat. In poor neighborhoods where members of minority groups disproportionately live, junk food, soda, and cigarettes are readily available in small markets. Meanwhile, grocery stores that sell fresh foods are scarce and/or expensive (Morland et al. 2002a, 2002b); diabetics have a hard time finding appropriate foods (Horowitz et al. 2004); restaurants are unlikely to serve fresh fruits and vegetables (Edmonds et al. 2001); and liquor stores are common (LaVeist and Wallace 2000). These environmental factors matter; they help explain why people who live in poor neighborhoods eat less healthy diets (Morland et al. 2002a).
Parks and greenspaces represent critically important environmental amenities; contact with nature is highly valued (Kahn 1999), and it offers a range of health benefits (Frumkin 2001). In cities and towns, parks are the principal venue for regular public access to nature. Parks also offer settings for physical activity and social interaction. Racial and ethnic considerations arise in at least two ways. First, racial and ethnic groups vary in their preferences for park features and activities. For example, blacks tend to prefer recreational uses while whites tend to favor land conservation (Payne et al. 2002), and blacks prefer more highly structured and maintained parks, with more facilities, than do whites (Kaplan and Talbot 1988). These differences call for culturally sensitive park design (Rishbeth 2001). Second, members of minority groups in some cities may lack access to parks, trails, and other green spaces (Wolch et al. 2002). Also, a worrisome irony is that urban greenspace increases adjacent residential property values (Crompton 2001). Accordingly, efforts to enhance greenspace access in underprivileged areas of cities could have the unintended effect of raising property values and driving out lower-income residents.
The broken windows theory offers insight into public health. . . . Ultimately, healthy places . . . need to be well designed, well built, attractive, and functional for all people who live, work, learn, and play in them.
The corrosive effects of disorder and squalor in the environment have been widely recognized. Sociologist James Q. Wilson and criminologist George Kelling advanced the “broken windows theory” in 1982, suggesting that the environment sends powerful messages that regulate and release individual behavior: “If a broken window is unrepaired, all the windows will soon be broken. Broken windows are a signal that no one cares” (Wilson and Kelling 1982). Indeed, studies have suggested that sordid environments beget sordid behaviors (Sampson and Groves 1989).
The broken windows theory offers insight into public health. Cohen et al. (2000) found that after controlling for income, race, unemployment, and education, a high “broken windows index” (litter, graffiti, abandoned cars, and blighted housing) independently predicted neighborhood gonorrhea rates. Neighborhood of residence is an important predictor of mortality, an observation that cannot be fully explained by demographic, socioeconomic, lifestyle, and psychosocial factors (Shaw et al. 2000). Part of this effect may well be due to the disorder and squalor of the environment. Poor people and people of color are disproportionately exposed to “broken windows,” another example of a health inequity in the built environment.
In at least five arenas—housing, transportation, food, parks and green spaces, and squalor—environmental justice and the built environment intersect to affect the health of poor people and people of color. Environmental health professionals need to recognize both the scope of the problem and the many opportunities for effective interventions. As Hood (2005) points out, both technical tools (e.g., GIS) and inclusive processes (e.g., community-based participatory research and policy making) can contribute to solutions. Ultimately, healthy places need to be more than free of toxic exposures; they need to be well designed, well built, attractive, and functional for all people who live, work, learn, and play in them.
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References
Barton H Tsourou C 2000. Healthy Urban Planning. A WHO Guide to Planning for People. London:Spon Press.
Bashir SA 2002 Home is where the harm is: inadequate housing as a public health crisis Am J Public Health 92 5 733 738 11988437
Bryant B Mohai P eds. 1992. Race and the Incidence of Environmental Hazards. Boulder, CO:Westview Press.
Bullard RD 1983 Solid waste sites and the black Houston community Sociol Inquiry 53 273 288
Bullard RD 1990. Dumping in Dixie: Race, Class, and Environmental Quality. Boulder, CO:Westview Press.
Bullard RD Johnson GS Torres AO eds. 2004. Highway Robbery: Transportation Racism and New Routes to Equity. Cambridge, MA:South End Press.
Carson R 1962. Silent Spring. New York:Houghton Mifflin.
Cohen D Spear S Scribner R Kissinger P Mason K Wildgen J 2000 “Broken windows” and the risk of gonorrhea Am J Public Health 90 2 230 236 10667184
Corburn J 2004 Confronting the challenges in reconnecting urban planning and public health Am J Public Health 94 4 541 546 15053998
Crompton JL 2001 The impact of parks on property values: a review of the empirical evidence J Leis Res 33 1 1 31
Edmonds J Baranowski T Baranowski J Cullen KW Myres D 2001 Ecological and socioeconomic correlates of fruit, juice, and vegetable consumption among African-American boys Prev Med 32 476 481 11394951
Frumkin H 2001 Beyond toxicity: the greening of environmental health Am J Prev Med 20 47 53
Frumkin H Frank L Jackson RJ 2004. Urban Sprawl and Public Health. Washington, DC:Island Press.
Fullilove MT Fullilove RE 2000 What’s housing got to do with it? Am J Public Health 90 183 184 10667175
Hood E 2005 Dwelling disparities: how poor housing leads to poor health Environ Health Perspect 113 A310 A319 15866753
Horowitz CR Colson KA Hebert PL Lancaster K 2004 Barriers to buying healthy foods for people with diabetes: evidence of environmental disparities Am J Public Health 94 9 1549 1554 15333313
JCHS 2004. Joint Center for Housing Studies, Harvard University. State of the Nation’s Housing 2004. Cambridge, MA:Joint Center for Housing Studies. Available: http://www.jchs.harvard.edu/publications/markets/son2004.pdf [accessed 29 March 2005].
Kahn PH Jr 1999. The Human Relationship with Nature: Development and Culture. Cambridge, MA:MIT Press.
Kaplan R Talbot J 1988 Ethnicity and preference for natural settings: a review and recent findings Landsc Urban Plan 15 107 117
Krieger J Higgins DL 2002 Housing and health: time again for public health action Am J Public Health 92 5 758 768 11988443
LaVeist TA Wallace JM Jr 2000 Health risk and inequitable distribution of liquor stores in African American neighborhood Soc Sci Med 51 613 617 10868674
Lavelle M Coyle M 1992. Unequal protection: the racial divide in environmental law. Natl Law J (21 September):S1–S12.
Lee C 1992. Toxic waste and race in the United States. In: Race and the Incidence of Environmental Hazards (Bryant B, Mohai P, eds). Boulder CO:Westview Press, 10–27.
Morland K Wing S Diez Roux A 2002a The contextual effect of the local food environment on residents’ diet: the Atherosclerosis Risk in Communities study Am J Public Health 92 1761 1767 12406805
Morland K Wing S Diez Roux A Poole C 2002b Neighborhood characteristics associated with the location of food stores and food service places Am J Prev Med 22 23 29 11777675
Payne LL Mowen AJ Orsega-Smith E 2002 An examination of park preferences and behaviors among urban residents: the role of residential location, race, and age Leisure Sci 24 2 181 198
Rishbeth C 2001 Ethnic minority groups and the design of public open space: an inclusive landscape? Landscape Res 26 4 351 366
Saelens BE Sallis JF Frank LD 2003 Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures Ann Behav Med 25 2 80 91 12704009
Sampson RJ Groves WB 1989 Community structure and crime: testing social-disorganization theory Am J Sociol 94 774 802
Schweitzer L Valenzuela A Jr 2004 Environmental injustice and transportation: the claims and the evidence J Plann Lit 18 4 383 398
Sharfstein J Sandel M Kahn R Bauchner H 2001 Is child health at risk while families wait for housing vouchers? Am J Public Health 91 1191 1192 11499101
Shaw M David G Danny D Richard M Davey Smith G 2000 Increasing mortality differentials by residential area level of poverty: Britain 1981–1997 Soc Sci Med 51 151 153 10817478
Stoll MA 2005. Job Sprawl and the Spatial Mismatch between Blacks and Jobs. Washington, DC:Brookings Institution. Available: http://www.brookings.edu/metro/pubs/20050214_jobsprawl.htm [accessed 29 March 2005].
Thomson H Petticrew M Morrison D 2001 Health effects of housing improvement: systematic review of intervention studies Br Med J 323 187 190 11473906
Wilson JQ Kelling GL 1982. Broken windows: the police and neighborhood safety. Atlantic Monthly (March):29–38.
Wolch J Wilson JP Fehrenback J 2002. Parks and Park Funding in Los Angeles: An Equity Mapping Analysis. Los Angeles:Sustainable Cities Program and GIS Research Laboratory, University of Southern California. Available: http://www.usc.edu/dept/geography/ESPE/documents/publications_parks.pdf [accessed 29 March 2005].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0029215866748PerspectivesDirector's PerspectiveScientific Vision: Setting Forth a Strategy Schwartz David A. MDDirector, NIEHS, E-mail:
[email protected] 2005 113 5 A292 A292 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Environmental exposures may adversely affect those who are vulnerable temporally (age, developmental stage), spatially (geographic location), or by unique circumstance (comorbid disease, nutritional status, socioeconomic status, genetics). Understanding the complex relationship between endogenous and exogenous risks within populations and affected individuals, how environmental exposures affect human biology, and how this knowledge can be used to reduce morbidity and extend longevity is precisely the opportunity and challenge that faces the NIEHS. My vision for the NIEHS is to improve human health by increasing this understanding through support of research and professional development in the environmental sciences (toxicology, relevant basic science), environmental medicine, and environmental public health. In addition to understanding how environmental exposures affect human biology, the NIEHS needs to understand how this knowledge can be used to reduce morbidity and extend longevity.
Understanding the complex relationship between endogenous and exogenous risks within populations and affected individuals, how environmental exposures affect human biology, and how this knowledge can be used to reduce morbidity and extend longevity is precisely the opportunity and challenge that faces the NIEHS.
Because environmental exposures contribute substantially to the etiology of many common and complex human diseases, the NIEHS is in a unique position to focus on the interface between environmental exposures, vulnerable populations, human biology and genetics, and the common diseases that limit our longevity. In the postgenomic era of biomedical research, the NIEHS can take a leadership role in improving human health by investigating environmental toxicants to understand how genes work in biological systems, how genetic variants contribute to the development of disease, and why individuals with the same disease have very different clinical outcomes. Moreover, because of its focus and concentrated expertise, the NIEHS is uniquely poised to:
develop sensitive preclinical markers of exposure and biological response,
develop strategies to prevent disease in exposed and biologically responsive individuals and populations,
establish population-based cohorts to understand the impact of environmental exposures on human health,
understand how environmental exposures affect the course and prognosis of a medical condition, and
stimulate dialogue to advance our understanding of environmental health policy and ethical issues of environmental concern.
To achieve this vision, I will prioritize individual and programmatic efforts to understand the role of environmental exposures on human health and disease. This will be achieved through the following broad strategies:
development of interdisciplinary research opportunities that will focus on common, complex diseases with a substantial environmental component;
efforts to define the epidemiological and clinical significance of environmental exposures in high-risk populations, including those in the international community;
efforts to understand how genes and genetic variants interact with environmental stimuli to either preserve health or cause disease;
programmatic integration of basic findings in the environmental health sciences with populations of diseased patients, communities at the extremes of exposure and vulnerability, other academic medical centers, and industry;
study of environmental toxicants to understand basic mechanisms in human biology;
use of eukaryotic model systems (yeast, worms, zebrafish, fruitflies, rodents) to accelerate understanding of how environmental exposures affect human health;
support of the development of high-throughput in vitro and in vivo bioassays to establish reliable toxicity screens for potential toxicants;
efforts to strengthen and expand the next generation of environmental health scientists by creating research incentives to encourage basic scientists, epidemiologists, and physician–scientists to develop research careers in the environmental health sciences;
fostering of an integrated scientific approach that supports partnerships between the NIEHS and other NIH institutes, national and international research agencies, academia, industry, and community organizations to improve human health; and
support of programs for environmental scientists to work with ethicists and policy makers to fully consider the regulatory implications of our scientific advances in environmental health.
The NIEHS is a complex institute with a distinguished history, a clear purpose, and a dedicated constituency. To develop a plan for fulfilling our mission of improving human health, I will fully engage our dedicated scientific community in a process of strategic planning that will take place over the next year. I will work to involve a broad array of environmental health scientists engaged in toxicology, medicine, epidemiology, public health, basic biology, and genetics in this strategic planning process.
Our field is like no other—we are not limited by a biological system, a disease process, or an organ system. In fact, our scientific discipline represents the critical link between exposure and disease for many other fields of biomedical research. I fully believe that, working together, we can shape the future of the environmental health sciences and realize our critical role in understanding human disease, reducing morbidity, and extending longevity. Our success will be measured in the disease and suffering that we are able to prevent. I feel incredibly privileged to be working with you to meet this challenge, and look forward to your thoughts and comments.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00297PerspectivesErrataErrata 5 2005 113 5 A297 A297 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In Table 1 of “Estimating the Exposure–Response Relationships between Particulate Matter and Mortality within the APHEA Multicity Project” by Samoli et al. [
Environ Health Perspect 113:88–95 (2005)], the values for CVD deaths are incorrect. The corrected table is shown below. The authors apologize for the errors.
Bonner et al. would like to correct a factual error in “Occupational Exposure to Carbofuran and the Incidence of Cancer in the Agricultural Health Study” [
Environ Health Perspect 113:285–289 (2005)]. In the second paragraph of the introduction, the fourth sentence was incorrect. The two studies cited demonstrated that another carbamate pesticide, carbendazim, and not carbofuran, induced lymphoma. The sentence should read: “While two studies demonstrated that the carbamate pesticide carbendazim was able to induce lymphoma in Swiss mice (Borzsonyi and Pinter 1977; Borzsonyi et al. 1976), carcinogenicity of carbofuran was not evident in several 2-year dietary studies conducted on rats (Gupta 1994).”
Despite the error, the authors stand by the validity of the analysis and the interpretation of the results. The authors apologize for the error.
The March Focus article [“Great Lakes: Resource at Risk,” Environ Health Perspect 113:A164–A173 (2005)] stated that Dow Chemical released about 400 tons of mercury into Lake Superior from two chloralkali plants. In fact, these two plants were located in Sarnia, Ontario. Thus, the discharges were made into Lake Huron. EHP regrets the error.
Table 1 City descriptive data on the study period, population, exposure (PM10 and BS), outcome (daily number of deaths), and selected effect modifiers (region, mean temperature, mean NO2 over 24 hr, and directly standardized mortality rate).
No. of deaths per day
PM10 (μg/m3) percentile
BS (μg/m3) percentile
City Study period (month/year) Population (×1,000) Total CVD Respiratory 50th 90th 50th 90th Geographic region Mean temperature NO2 (24-hr) SDR
Athens 1/92–12/96 3,073 73 36 5 40a 59 64 122 South 18 74 784
Barcelona 1/91–12/96 1,644 40 16 4 60 95 39 64 South 16 69 740
Basel 1/90–12/95 360 9 4 1 28a 55 West 11 38 678
Bilbao 4/92–3/96 667 15 5 1 23 39 South 15 49 711
Birmingham 1/92–12/96 2,300 61 28 9 21 40 11 22 West 10 46 895
Budapest 1/92–12/95 1,931 80 40 3 40a 52 East 11 76 1,136
Cracow 1/90–12/96 746 18 10 0 54a 86 36 101 East 8 44 1,009
Dublin 1/90–12/96 482 13 6 2 10 26 West 10 — 940
Erfurt 1/91–12/95 216 6 — — 48 98 West 9 40 972
Geneva 1/90–12/95 317 6 2 0 33a 71 West 10 45 608
Helsinki 1/93–12/96 828 18 9 2 23a 49 West 6 33 915
Ljubljana 1/92–12/96 322 7 3 0 13 42 East 11 46 823
Lodz 1/90–12/96 828 30 17 1 30 77 East 8 39 1,231
London 1/92–12/96 6,905 169 71 29 25 46 11 22 West 12 61 851
Lyon 1/93–12/97 416 9 3 1 39 63 West 12 63 579
Madrid 1/92–12/95 3,012 61 22 6 33 59 South 15 70 636
Marseille 1/90–12/95 855 22 8 2 34 56 West 16 71 666
Milan 1/90–12/96 1,343 29 11 2 47a 88 West 14 94 632
Netherlands 1/90–9/95 15,400 342 140 29 34 67 63 122 West 10 43 757
Paris 1/92–12/96 6,700 124 38 9 22 46 21 45 West 12 53 644
Poznan 1/90–12/96 582 17 9 1 23 76 East 9 47 1,106
Prague 2/92–12/95 1,213 38 22 1 66 124 East 10 58 984
Rome 1/92–12/96 2,775 56 23 3 57a 81 South 17 88 585
Stockholm 1/94–12/96 1,126 30 15 3 14 27 West 8 26 666
Tel Aviv 1/93–12/96 1,141 27 12 2 43 75 South 20 70 430
Teplice 1/90–12/97 625 18 10 1 42 83 East 9 32 1,173
Torino 1/90–12/96 926 21 9 1 65a 129 West 14 76 724
Valencia 1/94–12/96 753 16 6 2 40 70 South 19 66 820
Wroclaw 1/90–12/96 643 15 9 1 33 97 East 9 27 970
Zurich 1/90–12/95 540 13 6 1 28a 54 West 11 40 666
Abbreviations: —, no data; CVD, cardiovascular deaths; SDR, directly standardized mortality rate. Mean temperature in degrees centigrade.
aPM10 were estimated using a regression model relating collocated PM10 measurements to the BS or total suspended particles.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0030015866751EnvironewsForumSustainable Development: Growing Green Communities Potera Carol 5 2005 113 5 A300 A300 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Advocates of green housing received a boost when the nonprofit Enterprise Foundation of Columbia, Maryland, announced that it plans to build 8,500 environmentally friendly, affordable homes through its Green Communities Initiative. Launched in September 2004, the Green Communities Initiative commits $550 million over five years to developers to construct housing units that promote health, conserve energy and natural resources, and are located near public transportation, jobs, social services, stores, and schools. The initiative is led by the Enterprise Foundation and the Natural Resources Defense Council, with the support of several other organizations.
The Denny Park Apartments, being built in Seattle, Washington, are a shining example of what can be achieved through the Green Communities Initiative. The project—the first recipient of funding through the Green Communities Initiative—is being built by the Low Income Housing Institute (LIHI), which develops and manages affordable housing units in Seattle. The six-story building will provide 50 units ranging from studios to three-bedroom apartments. The first tenants plan to move in by December 2005. Ten units will be reserved as transitional housing for homeless families.
The apartment building features numerous energy-saving features. It is located along an east–west axis to allow the units to capture more natural light through their oversized windows, reducing electricity bills. A central gas boiler will supply hot water and heat to all the units. “Gas is more efficient and less expensive than electricity in Seattle,” says architect Brian Sweeney, manager of development for LIHI. Moreover, hot-water heat makes people feel warmer at lower room temperatures than electric heat, according to Sweeney—people feel as warm at 65°F with hot-water heat as they do with drier electric heat set at 69–70°F. Ventilation fans will run continuously to reduce humidity and mold growth, a problem in Seattle’s moist climate.
The building is being constructed with sustainable building materials such as metal roofing and metal siding, which should last 50 years. These durable materials eliminate petroleum-based products such as traditional asphalt roofing shingles and oil-based exterior paint, which—in addition to their nonsustainable provenance—must be replaced every 10 years or so. The project is using caulks, paints, adhesives, and other construction materials with low levels of volatile organic compounds to ensure healthy indoor air. Carpets are made from recycled plastic products. Rainwater will be captured off the metal roof, purified by gravel filtration, and recycled to irrigate the landscaping, including a communal garden for the tenants.
Although green buildings currently can cost about 2% more to construct, the self-evident long-term energy and health benefits are passed on to tenants. “The things considered ‘green’ today are going to be part of any building project in the next ten to fifteen years,” predicts Sweeney.
Dana Bourland, senior program director at the Enterprise Foundation, says the foundation has received about 50 letters of inquiry from public housing administrators across the United States. Eight grants have been awarded for other projects in the Bronx, Boston, Chicago, and other cities, which are in various stages of development. The housing projects can consist of multi-family or single-family structures, but individuals cannot apply to build just one home. Most of the applicants are public housing offices and nonprofit groups seeking to improve their communities.
What distinguishes the Green Communities Initiative from other green housing programs? “We’re not interested in just one aspect like energy efficiency,” says Bourland. Each project has to meet “certain levels of greenness,” she says. Her group’s criteria include meeting standards for water conservation, healthy indoor air, use of environmentally friendly materials, good operations and management (for example, making sure gutters that collect rainwater for irrigation are kept free of leaves), and optimal location (for example, projects located within a quarter-mile of public transportation earn extra points toward meeting funding criteria). “Our goal is to transform the marketplace and shift the way we build to achieve health, environmental, and economic benefits in communities,” says Bourland.
Sustainable Development Features
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00303EnvironewsForumEHPnet: Community Environmental Health Resource Center Dooley Erin E. 5 2005 113 5 A303 A303 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The U.S. Environmental Protection Agency defines environmental justice as “the fair treatment of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies.” Among other goals, environmental justice activists work to better the living conditions of low-income communities, which often bear a disproportionate burden of environmental health hazards and the resulting health problems. One group working to improve housing for low-income communities around the nation is the Community Environmental Health Resource Center (CEHRC, pronounced “search”), based in Washington, DC. The CEHRC website at http://www.cehrc.org/ gives communities the tools to document housing deficiencies as well as to pursue corrective and preventive action. A number of the pages on the site are available in Spanish.
One section of the website is devoted to exposing health hazards in housing. This section provides discussion papers and other documents that describe why it is important for community members to become involved in identifying the hazards in their homes. There is also guidance to help advocates work effectively and responsibly with community residents. This guidance offers insights into how to avoid adverse outcomes (such as faulty repairs that exacerbate hazards), protect residents’ rights and privacy, and other ethical considerations.
At the core of the site is the Tools for Detecting Hazards section, which concentrates on five main health threats: lead, carbon monoxide, cockroaches, mold/moisture, and radon. For each threat the site provides background materials, step-by-step sampling instructions and checklists, decision guides to help determine whether testing is warranted in certain situations, and other materials. The lead segment includes specific information for various routes of exposure: dust, paint, soil, and water. The Tools section also provides thorough instructions in both English and Spanish for conducting a visual survey of a residence and preparing a visual survey report.
The How Communities Create Solutions section has information on how to actually enact change within a community. The Data as a Catalyst for Change portion discusses how data can be used to back up advocacy campaigns. It also provides a discussion paper on strategies for holding property owners and government agencies responsible, as well as an overview of models of social change to help organizations define their missions and goals. The Tools for Change page provides time-tested methods to create change through recruiting and training volunteers, accessing political resources, and fundraising. Other portions include information on tenants’ rights, case studies, and the basics of developing and understanding policy and legislation.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0030415866752EnvironewsNIEHS NewsHealth on the Banks of the Rio Grande Barrett Julia R. 5 2005 113 5 A304 A307 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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At the New Mexico Center for Environmental Health Sciences (NMCEHS), community concerns and relationships are as much at the center’s foundation as the latest research techniques. According to center director Scott Burchiel, the unique populations of New Mexico—including a high number of Hispanics and Native American tribes, pueblos, and sovereign nations—steer the center’s course. “One of the things we have tried to do is to determine what these populations’ concerns are for environmental health, because we want our center to be focused on environmental public health, and we want to work on basic research projects that address issues that people in this region are particularly interested in,” he says.
Based in Albuquerque as a partnership of The University of New Mexico Health Sciences Center and the Lovelace Respiratory Research Institute (LRRI), the NIEHS-funded center comprises major research cores in environmental cancer and oxidative stress, cardiovascular toxicology, environmental lung disease, and population health and epidemiology. The center also includes world-class facility cores (particularly for inhalation toxicology), a pilot project program to provide funding for small innovative research studies, and an unusually strong community outreach and education program (COEP).
Community input is essential, as is trust. “We have investigators who may have certain interests for certain kinds of projects and want to work with communities,” says Burchiel. “But when we go to the community, we find out that [the community has] a different agenda, different interests. We’ve been learning how to work with communities since we’ve had this environmental health center.” It’s a process that involves a lot of development of community relationships and trust relationships.
Burchiel says that investigating environmental health problems such as asthma requires a broad definition of environment. He says, “In our center, we define the environment quite broadly. It includes nutritional, social, behavioral, economic, and built environment components. . . . We have not only environmental epidemiologists [but] also social epidemiologists who help us look at social factors in assessing economic and poverty factors.”
The Heart (and Lungs) of the Matter
The center originated in 1998 with a three-year planning grant focused on environmental lung disease and Native Americans. Of particular interest was asthma incidence among Native American schoolchildren. At one pueblo, for example, 11.3% of children between ages 3 and 13 had been diagnosed with asthma, an incidence that was more than twice the national average for the same age group. Follow-up research focused on potential causes, including exposures to endotoxin and wood smoke, but Burchiel indicates that there are still no definitive conclusions.
Center investigators are also investigating asthma in an urban setting as well. The NMCEHS is currently pursuing an NIEHS Advanced Research Cooperation in Environmental Health grant with the University of Texas at El Paso to investigate factors that may affect asthma incidence among El Paso schoolchildren. Approximately 700,000 people reside in El Paso, and the neighboring Mexican city of Cuidad Juárez boosts the urban population to about 2 million. Old cars, unregulated trash burning, brick kilns, smelters, and heavy, slow-moving diesel-truck traffic crossing at the border and maquiladora industries along the U.S.–Mexican border contribute to significant air quality problems. Winter-time atmospheric inversions, which prevent air pollutants from circulating away from the cities, compound the problems. The grant project will draw on the LRRI’s expertise in environmental assessment and the university Health Sciences Center’s clinical expertise.
The inhalation toxicology facilities of the LRRI also support center investigations in environmental cardiology, a relatively new discipline. “One of the things that’s fairly interesting that’s come out in the last five to ten years is that environmental pollutants may be contributing to both the incidence as well as the severity and progression of cardiovascular disease in the United States,” says Mary Walker, director of the cardiovascular toxicology core. She notes that epidemiological air pollution studies, despite focusing on pulmonary disease and asthma, have yielded intriguing information about potential effects on the heart of particulate matter, ozone, carbon monoxide, and other pollutants.
Walker studies how such pollutants might affect cardiac birth defects as well as how fetal exposures might relate to cardiovascular disease in adulthood. A current project follows up on findings from an epidemiological study published in the January 2002 American Journal of Epidemiology. In that study, researchers from the University of California, Los Angeles, found fairly strong correlations between maternal air pollution exposure during the first trimester of pregnancy and children being born with overt heart defects. “This was something that I don’t think anyone would have predicted,” says Walker.
She and her colleagues are now collaborating with LRRI researchers to develop an animal model to test the observations that came out of that human epidemiology study. Their model uses diesel exhaust as the air pollutant of interest and also includes the biggest contributors to air pollution such as particulate matter, polycyclic aromatic hydrocarbons, and carbon monoxide.
“Our current hypothesis is that it’s actually the carbon monoxide component of the diesel exhaust that leads to a low-oxygen, or hypoxic, condition in the developing fetal heart,” says Walker, citing recent research at Case Western Reserve University. In that study, published in the March 2004 issue of Developmental Biology, researchers showed that normal septation, or formation of the aorta from the pulmonary artery, relies on hypoxia as a necessary transcriptional signal during heart development. “What we hypothesize is that the carbon monoxide component of the diesel exhaust is actually making that environment even more hypoxic and disrupting normal gene programming that should lead to normal septation of the aorta and pulmonary artery,” says Walker.
Walker’s team will look at factors such as expression of genes regulated by hypoxia in the developing heart and whether expression is altered in response to diesel exhaust exposure. She is also interested in subtle changes in the heart or in gene programming that may occur during fetal development and underlie disease in adulthood.
Kidney Toxicants in the Home
Environmental factors that may enhance disease progression is also a theme in research conducted on the high incidence of nondiabetic kidney disease among Native Americans in New Mexico. A genetic link is suspected; prior to the center’s formation, University of New Mexico researchers were already working with residents of a rural Native American community to identify the potential root cause or causes of the high incidence. Residents at this pueblo have a very high prevalence of kidney disease and a very high prevalence of diabetes, says Burchiel. Compared with other Native Americans, these pueblo residents experience kidney disease at more than a 5-fold higher incidence; compared with European Americans, incidence is more than 18-fold higher.
Although Native American tribes generally do not permit genetic research on their members for legal and cultural reasons, this pueblo is an exception. “The [participants] are very unusual in that they’ve been working with The University of New Mexico and a group of national investigators,” says Burchiel. The key reason for this unique participation is the high incidence of kidney disease—virtually every pueblo resident has a family member or knows someone who has died or is dying from this disease. The residents understand that there is likely a genetic component. Therefore, they have decided to take the unusual step of participating in genetic research.
With the formation of the NMCEHS, new avenues of research opened as investigators began considering gene–environment interactions in kidney disease. In considering potential environmental triggers, investigators began to focus on jewelry making—a common cottage industry in the Native American population—and whether people were being exposed to metals through their in-home jewelry production.
“Many of the metals found in jewelry making products are potentially toxic to the kidney,” says Melissa Gonzales, a member of the center’s population health and epidemiology core. This poses serious risks for a population that may already be genetically predisposed to develop kidney disease. In a preliminary study, Gonzales analyzed house dust samples for metal contaminants, including cadmium, nickel, copper, tin, lead, and silver. She found a relatively high concentration of metals in the dust in homes where jewelry was made compared to homes where no jewelry was made. The study also involved a survey and inventory of products used in jewelry making, which helped correlate the presence of metals with the work done in the home.
Gonzales explains that the main metal used in the residents’ jewelry making is sterling silver—an alloy of silver, copper, and nickel—but the inventory of related materials provided an explanation for unexpected findings such as boron, which is used in the form of powdered boric acid as a protective coating during the jewelry-making process. Researchers also provided study participants with information extracted from material safety data sheets on how to use the materials safely. The researchers are currently applying for a research grant based in part on the results of this study.
Naturally Occurring Threats
Naturally occurring substances fuel other populationwide environmental health concerns in New Mexico. In the U.S. Southwest, drinking water drawn from underground sources may have high concentrations of arsenic due to leaching of the mineral from surrounding rock over the course of millions of years. New Mexico has some of the highest drinking water concentrations of arsenic in the United States. Arsenic can induce cancers of the bladder, lung, skin, and other organs.
“Over the last five to ten years, there’s been an increased interest in arsenic research, partly because people realized arsenic is an important carcinogen, partly because of the political debate,” says Ke Jian Jim Liu, coleader of the environmental cancer and oxidative stress core. The political debate to which he refers centers on the U.S. Environmental Protection Agency’s new drinking water standard for arsenic of 10 parts per billion (ppb) or less by 2006. Most U.S. water supplies met the old standard of 50 ppb, but the new standard places some out of compliance. For example, according to the City of Albuquerque’s 2003 water quality report, some areas of the city had water containing up to 24 ppb of arsenic.
Arsenic is not a very strong carcinogen. However, exposure via drinking water is chronic and unavoidable, and Liu describes an additional risk factor due to the state’s high altitude and wealth of sunshine: “There have been reports that ultraviolet radiation and arsenic synergistically increase the rate of skin cancer development, so arsenic has become an even more important issue for the people in the Southwest.” A review article on this topic appeared in the August 2004 issue of Toxicology and Applied Pharmacology. No one is certain what the mechanism is for this synergistic action, although Liu hypothesizes that arsenic may inhibit repair of ultraviolet-induced DNA damage.
Several arsenic-related projects are under way at the center, and each aspect of arsenic research—neuroscience, toxicology, carcinogenesis, epidemiology—is represented by a different group that brings its own unique set of strengths to the topic. One group is conducting an epidemiological assessment of arsenic exposure and cancer rates among Southwesterners. Another group is focusing on potential neurological effects and has preliminary animal data that suggest a link between arsenic exposure and decreased cognitive abilities in infancy and early childhood. Liu’s own research focuses on identifying the arsenic-induced events that lead a normal cell to become cancerous. “Our hope is that by focusing on several areas this may lead to some interesting findings and potentially provide some insight on how arsenic induces skin cancer and some guidance on development of therapeutic interventions,” he says.
Another naturally occurring threat arises from New Mexico’s rich mineral resources. Uranium represents both naturally occurring and occupational exposures—as well as a keystone environmental justice issue. New Mexico boasts the largest open-pit uranium mine in the world, the Jackpile mine, along with many smaller mines. Much of the mining workforce has been drawn from Native American communities, and there is concern for both the workers’ health as well as potential widespread environmental contamination from abandoned mines.
A Partnership in Trust
Uranium exposure is one of the key issues addressed by community outreach and education efforts at the NMCEHS. COEP staff work closely with 20 chapters (local tribal government units) of the Navajo Nation to provide training to tribal members on environmental health, survey methodology, and water sampling. The goal of the training is to build the chapters’ capacity to understand and make informed decisions specifically about the effects of uranium exposure on kidney health. As this partnership has developed, the COEP has involved other center members—including biostatisticians, modelers, and epidemiologists—to support community-driven research on the relationship between kidney disease and uranium exposures.
The COEP is also beginning a similar effort with the Cheyenne River Sioux Tribe of South Dakota, at that tribe’s request. This effort will focus on building environmental health capacity to evaluate the impacts of mercury contamination of surface waters on tribal lands, as well as other environmental justice concerns.
The COEP also reaches out to local youth to show how they can be potent advocates for the environment. In February 2004, COEP staff worked with the South Valley Partners for Environmental Justice (a nonprofit partnership between Bernalillo County, the Rio Grande Community Development Corporation, and the COEP) and Amigos Bravos—Friends of the Wild Rivers to provide a three-part training course to staff at the local Indio-Hispano Academy of Agricultural Arts and Sciences. The academy provides a program in which at-risk youth (many of whom are referred by the courts as an alternative to juvenile detention) become involved in activities that focus on protecting and preserving the cultural and traditional agricultural lifestyles of the surrounding region. The partnership training taught the staff about the provisions of the Clean Water Act, surface and groundwater quality and hydrology, contaminant transport, basic toxicology, and potential surface water exposure pathways.
After hearing about the training from their mentors, seven academy students felt that the portion of the Rio Grande that passes through Albuquerque had been wrongly classified by the Environmental Protection Agency as “secondary contact,” meaning residents generally do not have direct bodily contact with the water. The students provided critical testimony to policy makers in Santa Fe during the New Mexico Water Quality Control Commission’s Triennial Review that spring, describing historical and current community uses of the acequias (irrigation waters from the Rio Grande) for farming, fishing, and swimming. They provided photo documentation of these uses.
The testimony resulted in the October 2004 decision by policy makers to raise the classification of that portion of the river to primary rather than secondary contact, meaning the water must be clean enough to permit activities involving direct contact with the water. This classification will result in more stringent standards and lower permissible levels of fecal coliform in the water.
A Community Resource
COEP director Johnnye Lewis describes the outreach program’s philosophy as building relationships and trust for the long term. “If we are doing our jobs,” she says, “communities can rely on us as a resource to help them answer their own questions by making environmental health research and decision making understandable and accessible.”
In truth, that philosophy extends to the entire center. “We’re really trying to work on—as we say in our mission statement—our regionally relevant environmental public health issues,” says Burchiel. As a measure of the center’s success and the trust it has built, surrounding communities have come to rely on center staff for answers to environmental health concerns. “Now, whenever there’s a question about environmental health, we get called,” says Burchiel. “That’s very gratifying.”
The lay of the land. A number of Native American pueblos make up part of the unique constituency served by the New Mexico Center for Environmental Health Sciences.
Risky business. In-home jewelry making, a common cottage industry among Native Americans, exposes community members to metals that may trigger a genetic predisposition to kidney disease.
Knowledge is power. Staff at the Indio-Hispano Academy of Agricultural Arts and Sciences (above) received training on the Clean Water Act, surface and groundwater quality and hydrology, contaminant transport, basic toxicology, and potential surface water exposure pathways. This new knowledge empowered academy youth to challenge the “secondary contact” designation assigned to the portion of the Rio Grande running through Albuquerque. The students convinced policy makers that this portion of the river actually requires the more stringent “primary contact” designation due to human uses of the river (left).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00307EnvironewsNIEHS NewsHeadliners: Breast Cancer: Repair of DNA Damage Differs Between Sisters With and Without Breast Cancer Phelps Jerry 5 2005 113 5 A307 A307 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Kennedy DO, Agrawal M, Shen J, Terry MB, Zhang FF, Senie RT, Motykiewicz G, Santella RM. 2005. DNA repair capacity of lymphoblastoid cell lines from sisters discordant for breast cancer. J Natl Cancer Inst 97:127–132.
Like many types of cancer, breast cancer results from complex interactions of genetic predisposition and environmental exposures. Individuals differ in both their unique genetic makeup and the exposures that they encounter throughout their lifetimes. Damage to DNA is known to be a critical early step in the development of cancer; unrepaired damage leads to alterations in cellular functions. Therefore, individual differences in DNA repair capacity may influence the risk of developing cancer.
NIEHS grantee Regina M. Santella and colleagues at Columbia University in New York recently tested this hypothesis by analyzing DNA repair capacity in pairs of sisters, one of whom had breast cancer and one of whom did not. The researchers used cell culture lines for 158 breast cancer patients and 154 control sisters obtained from the Metropolitan New York Registry of Breast Cancer Families.
The cell cultures were treated with benzo[a]pyrene diolepoxide (BPDE), a DNA-damaging agent, then either harvested immediately or washed and cultured for four hours to allow DNA repair. The team then measured the number of BPDE–DNA adducts to determine the extent of DNA damage and repair capacity.
The DNA repair capacity of the breast cancer patients proved to be significantly lower than that of their sisters without breast cancer. When the Columbia researchers divided DNA repair capacity into quartiles, they observed a threefold higher cancer risk among the women with the lowest repair capacity compared with those women with the highest capacity.
This study supports the theory that deficits in DNA repair capacity are associated with higher susceptibility of breast cancer development. The finding may point to a valuable marker to identify women who are at high risk for the disease, especially among families with high incidence of breast cancer. Further research is necessary to determine if therapeutic interventions could improve DNA repair capacity.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0030815884176EnvironewsNIEHS NewsBeyond the Bench: Mapping the Air in Public Schools Tillett Tanya 5 2005 113 5 A308 A308 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In the home, parents are the primary guardians of a child’s environmental health. However, once the child’s environment moves out of the home, identifying sources of protection can become difficult. The Community Outreach and Education Program (COEP) of Duke University’s NIEHS Environmental Health Sciences Research Center in Durham, North Carolina, has pledged to give children’s environmental health the attention it deserves and is proactively implementing projects to create preventive environmental interventions that protect children before they become sick.
One of the main questions the Duke COEP seeks to answer in its outreach activities is whether children are being exposed to environmental factors in school that contribute to asthma incidence. The Duke COEP staff have used geographic information system (GIS) technology in a number of applications to address this question, including a May 2004 assessment of the indoor air environment at nearby New Hope Elementary School.
For years, students and staff in the Orange County school system (of which New Hope Elementary is a part) had complained of respiratory problems as well as general discomfort with fluctuating indoor temperatures and a sense that the air in the school buildings was unhealthy. COEP staff were asked to evaluate one sample school in the system and offer recommendations. They selected New Hope Elementary for study, then went into the school and conducted an evaluation of every classroom.
The problems they observed included a high relative indoor humidity of around 75–78% (the ideal humidity is 35–40% when the outdoor temperature is 20°F or higher) and imbalances in the heating and cooling system (they observed temperature differences of as much as 5–10°F in separate classrooms in some instances). They also noticed problems exacerbated by failing insulation, such as mold spore development and air flow variations—some rooms would have a distinct breeze, while others had still, damp air.
Once they had gathered their data, the COEP staff created a GIS compilation to display the data and analyze the results. The advantage of using GIS technology to interpret information is that it visually integrates previously unlinked sets of information, facilitating the detection of relationships among them. In the case of New Hope Elementary, the GIS map let concerned parents, teachers, school administrators, and education policy makers see a visual representation of the spatial distribution of airflow irregularities and mold spores throughout the school.
Based on information gathered from the GIS map, the school administrators replaced carpeting throughout the school with tile floors and thoroughly cleaned the entire duct system, which significantly improved ventilation in the classrooms. “What’s exciting is that we were able to provide information, education, and outreach directly to the faculty and school governance council,” says Marie Lynn Miranda, director of the Duke COEP.
Another benefit of the study is the unique learning opportunity it will afford the New Hope Elementary students. All the data from the project will be turned over to the school’s fifth-grade science teachers for development into classroom lesson projects. COEP workers also plan to use the New Hope Elementary study as a model for future initiatives in childhood asthma prevention.
Mighty map. A GIS map of Hope Valley Elementary School allowed staff, parents, and administrators to see where there were unhealthful fluctuations in humidity (blue) and temperature (red). With this information in hand, the school was able to take appropriate corrective action.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0031015866753EnvironewsFocusDwelling Disparities: How Poor Housing Leads to Poor Health Hood Ernie 5 2005 113 5 A310 A317 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In recent years, environmental health science has broadened the scope of its inquiries, expanding its investigations beyond the effects of single pollutants on individuals to incorporate the entire panorama of external factors that may affect people’s health. Consideration of the health impacts of the built environment—the human-modified places where we live, work, play, shop, and more—has been a key element in the ongoing evolution of the field of environmental health.
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Substantial scientific evidence gained in the past decade has shown that various aspects of the built environment can have profound, directly measurable effects on both physical and mental health outcomes, particularly adding to the burden of illness among ethnic minority populations and low-income communities. Lack of sidewalks, bike paths, and recreational areas in some communities discourages physical activity and contributes to obesity; in those low-income areas that do have such amenities, the threat of crime keeps many people inside. Income segregation—the practice of housing the poor in discrete areas of a city—has also been linked with obesity and adverse mental health outcomes. Lack of a supermarket in a neighborhood limits residents’ access to healthy foods. Dilapidated housing is associated with exposures to lead, asthma triggers (such as mold, moisture, dust mites, and rodents), and mental health stressors such as violence and social isolation.
More recently, the field has begun to take an even wider-angle view, as investigators have begun using innovative new tools and approaches to explore the multifaceted interrelationships between the built environment and population-level health outcomes. It has become increasingly clear that the built environment not only directly impacts our health, but also factors in less direct, complex ways into the health of individuals residing in single-family homes, housing projects, blocks, neighborhoods, and entire cities.
Low-income and/or ethnic minority communities—already burdened with greater rates of disease, limited access to health care, and other health disparities—are also the populations living with the worst built environment conditions. Studies have shown that negative aspects of the built environment tend to interact with and magnify health disparities, compounding already distressing conditions.
Elucidating the associations between the built environment and health disparities has proven to be an enormous challenge to the scientific community, requiring the development of new research paradigms, hypotheses, and methodologies. Traditional studies have often lumped many important components of the built environment into a blanket socioeconomic status variable. But this approach makes it nearly impossible to tease out specific housing and community characteristics related to disease. So, although the traditional tools of environmental health science are still an important part of the mix, research endeavors in this area are now incorporating aspects of sociology, psychology, demography, urban planning, and architecture. Just as significantly, research efforts are reaching beyond the boundaries of the scientific community, embracing rapid translation of research into effective intervention and active collaboration with community members as central concepts in their research protocols.
Space: The Data Frontier
Spatial analysis is one of the new tools being applied in attempts to quantify the relationship between health disparities and the built environment. One spatial analysis tool, geographic information system (GIS) technology, has long been in use in other areas such as city planning, demography, and epidemiology, but is now allowing environmental and public health researchers to characterize local environments at finely resolved geographic scales. More complete, accurate, and comprehensive geocoded data on resources within communities are becoming increasingly available from a growing number of sources such as governmental agencies, law enforcement agencies, and marketing researchers. This allows researchers studying the built environment to look at communities in new ways.
“The use of GIS technology allows researchers to look at the density and proximity of goods, services, and community resources such as parks, youth clubs, fast food outlets, convenience stores, and other factors that might enhance or hinder health, in relationship to where people live and work,” says Marilyn Winkleby, an associate professor of medicine at the Stanford Prevention Research Center. “GIS provides the technology to spatially display, synthesize, and analyze data—it creates a dynamic visual understanding of people, places, and health.”
As part of a larger project examining neighborhood-level influences on mortality from all causes, Winkleby and her colleagues are preparing to publish results from two studies using GIS to link survey data with other information such as census data, health records, and site visits. Both studies suggest pathways by which disparities in the built environment can be related to disparities in health.
The first study showed that, of 82 neighborhoods studied in four northern/central California cities, the most deprived neighborhoods contained more places that sold alcohol than the least deprived neighborhoods, despite the fact that residents of the higher-SES neighborhoods were the most likely to be heavy drinkers. Winkleby cites this disproportionate clustering in low-income neighborhoods—with its documented sequelae of greater injuries and violence due to increased rates of youth drinking and driving, assault, and car crashes—as one example of why it’s important to examine the built environment in addition to individual risk factors when studying health disparities.
Interestingly, the other study found that another characteristic of the physical environment in the deprived neighborhoods influenced an individual risk factor. “Higher convenience store concentrations—whether measured by density, distance, or number of convenience stores within a one-mile radius of participants’ households—are significantly associated with higher levels of individual smoking,” says Winkleby.
Marie Lynn Miranda, director of the Children’s Environmental Health Initiative (CEHI) at Duke University, says the use of spatial analysis allows her center to more effectively quantify associations between the built environment and health outcomes. “You can create variables of interest and structure them according to what people tell you is the space of interest to them [such as individual homes or unique neighborhoods],” she says. In contrast, earlier studies often used traditional county, zip code, or census tract boundaries, which people often don’t pay attention to as they go about the business of their lives. Customizing study spaces “allows us to create and structure and understand data and influences in a way that is more directly linked with how people live their lives,” Miranda says.
Miranda’s research focuses on identifying interventions that will prevent harmful environmental exposures in children, rather than mitigating existing exposures that may have already affected children. Among several CEHI programs currently in progress, the farthest along is an initiative to prevent childhood lead exposure called Mapping for Prevention. The group has created GIS-based lead exposure maps for 36 North Carolina counties and several other sites around the country using census data, blood lead screening data, and county tax assessor data to identify high-risk areas for lead poisoning. “The notion here is to try to figure out the places that are likely to [contribute] to elevated blood lead levels in children,” says Miranda, “and go in there and do something about the housing stock before a child gets lead-poisoned.”
Once an exposure map has been developed, the researchers work with local health departments to selectively and proactively screen children for lead exposure, and to educate new parents who live in high-risk housing about how to prevent their children from being exposed. They also help housing departments identify ways to prioritize housing rehabilitation and lead abatement funding to address the housing at greatest risk for lead poisoning.
Miranda is convinced that the spatial component is vital: “The work we’re doing at this very highly resolved geographic scale is identifying the risk levels at the individual tax parcel unit, and it provides us with absolutely the most powerful tool to move from mitigation to prevention in childhood lead exposure.” [For more information on GIS projects at Duke, see “Mapping the Air in Public Schools,” p. A308 this issue.]
Drawing on Community Knowledge
One of the most significant recent developments in efforts to characterize and ameliorate built environment conditions associated with health disparities has been the growing movement toward community-based participatory research (CBPR), which has been largely pioneered and supported by the NIEHS through a variety of extramural grant programs. CBPR studies focus on gathering and disseminating scientific knowledge about the interrelationships between the physical and social environments and health, and to identify, evaluate, and implement potential interventions—all with a distinct emphasis on active collaboration with residents and other stakeholders within the communities being studied. These multidisciplinary projects generally draw from many resources to arrive at comprehensive understanding of the multifaceted dynamics at work in populations suffering health outcome disparities—including negative aspects of the built environment.
Detroit’s Healthy Environments Partnership is an example of a CBPR endeavor. Academic researchers from the University of Michigan are working directly with several community groups and health service providers to examine the contributions of the physical and social environments to both ethnic and socioeconomic disparities in risk factors for cardiovascular disease among the adult population of Detroit. Part of the partnership’s wide-ranging data collection involves assessing aspects of the built environment: housing conditions, sidewalk conditions, land use, concentrations of airborne particulate matter, and access to grocery stores, parks, and recreation areas.
Data have been gathered in three demographically diverse Detroit neighborhoods that were initially selected because they were anticipated to vary in their concentrations of airborne particulate matter. Air quality monitoring confirmed this differential exposure. The researchers also surveyed neighborhood residents, collected blood and saliva samples to assess physiological indicators of cardiovascular risk and stress, and sent observers into the neighborhoods to evaluate the built and social environments in each area.
Although the group is still analyzing the data, principal investigator Amy Schulz, a research associate professor of health behavior and health education at the University of Michigan School of Public Health, says they are seeing trends suggestive of variations in both cardiovascular disease risk factors and protective factors that play out for different racial and socioeconomic groups across areas of the city. For example, they have noted variations in dietary intakes of fruits and vegetables in population groups within the city, and intend to analyze whether conditions in the built environments of the neighborhoods can predict those variations.
Residents of the Detroit neighborhoods being studied have been involved at all stages of the project, including helping to design the survey as well as collect and analyze the data. “They are extremely knowledgeable about the research that has been conducted,” says Schulz, “and understand quite well the results that we are seeing and their implications for the community, because they have been so integrally involved every step of the way.”
Also, says Schulz, community involvement contributes to a very rich analysis whose results are more likely to lead to change. “It’s really important for us to have good information about the impact of the built environment and the social environment on health, but it’s clear that information alone is not going to create change,” she says. “Engaging community members in processes begins to build community mobilization efforts for change.”
Positive changes to the built environment that help reduce health disparities can and do emerge from these partnerships. One such success story is San Diego’s Environmental Health Coalition (EHC), a 25-year-old nonprofit organization, funded largely by a grant from the U.S. Department of Housing and Urban Development (HUD) through the Community Environmental Health Resource Center. This Washington, DC, group helps community organizations develop their capacity to document environmental health hazards in substandard housing and to pursue effective organizing and advocacy strategies for corrective and preventive action [for more information, see “Community Environmental Health Resource Center,” p. A303 this issue]. The EHC works with university researchers, government agencies, and community members to address toxic pollution, land use issues, and substandard housing conditions in San Diego’s low-income communities of color.
Much of that effort has focused on the Barrio Logan, a low-income Latino community plagued by poor air quality due to heavy diesel truck traffic from a major regional freeway dissecting the community, and a troubling mix of industries and residences in close proximity to one another. Armed with results of an informal community health survey that showed disturbing levels of asthma and other respiratory problems, the group convinced the California Air Resources Board to begin monitoring air quality in the community. Since then, says EHC research director Joy Williams, “We’ve gotten the city of San Diego to agree to reroute truck traffic around the community and not through it—one solution to the problem of diesel exhaust in the community. EHC and the community are also working on changes to land use and zoning that will reduce the number of warehouses and industries that generate diesel truck traffic in residential areas.”
The air monitoring also led to a direct intervention to help a family in distress. The group had identified a metal-plating shop on a residential street in Barrio Logan as a potential hazard, and asked for air samples to be collected in the immediate vicinity. Regulators previously had believed that an operation of that size that was basically in compliance would not pose much of a hazard. However, once they sampled the air, they found high levels of emissions such as chromium-6, a highly toxic air pollutant, at the houses next door and across the street, says Williams. It turned out that one family living next door had a son with poorly controlled asthma. Ultimately, the metal-plating shop was forced to close (although many businesses in similar circumstances simply relocate), reportedly leading directly to dramatic improvements in the boy’s health. The EHC continues to make such unhealthful mixed land uses a priority in its activities.
Housing Developments
Housing is perhaps the ultimate nexus between the built environment and health disparities, and it has been the focus of much recent research and intervention activity looking at new approaches to old problems. The intimate connection between housing and health has been well known for more than a century—Florence Nightingale once wrote, “The connection between health and the dwelling of the population is one of the most important that exists.” But today there is renewed interest in discovering the complex pathways connecting housing factors, neighborhood factors, social factors, adverse health outcomes, and disproportionate disease burden in poor and ethnic minority communities—particularly with respect to skyrocketing rates of chronic diseases such as asthma, obesity, and diabetes.
That renewed interest is being manifested at the national and international levels, as well as in the form of grassroots community action. In late 2004, the World Health Organization convened its 2nd International Housing and Health Symposium at Vilnius, Lithuania, a conference designed to review the existing scientific evidence on housing and health relationships, and assess needs for further research. In what may prove to be a development with wide-ranging global impact, the symposium generated the Vilnius Declaration, in which 250 scientists and officials representing 24 countries committed themselves “to taking action to ensure that health and environmental dimensions are placed at the core of all housing policies (from housing construction and rehabilitation plans, programmes and policies to the use of adequate building materials) and that healthy conditions are ensured and maintained in the existing housing stock.”
In January 2005, indoor environmental quality took center stage at the Surgeon General’s Workshop on Healthy Indoor Environment. The two-day gathering of more than 300 experts from government, academia, the building sciences industry, and public interest groups focused on increasing attention to the issue of indoor air pollution, with the surgeon general and other participants calling for action to improve the health of Americans by improving indoor environments.
At the local level, two of the many CBPR projects in progress around the nation demonstrate the multifaceted, collaborative approach being taken toward not only characterizing housing and health pathways, but designing, implementing, and evaluating interventions as well. Both have come about in response to the high prevalence of asthma in low-income urban communities, with a special focus on improving the health and housing conditions of public housing residents.
Boston’s Healthy Public Housing Initiative (HPHI) is a collaboration among public housing tenants’ right groups, the Committee for Boston Public Housing, the Peregrine Energy Group, and several area universities and city agencies. The collaboration has produced guidance for builders, architects, and others on ways to make both new construction and existing housing healthier. Participants are currently engaged in a four-year project to assess the effectiveness of HPHI asthma intervention programs in three public housing developments. The interventions include installation of air filters, purchase of new mattresses, heavy-duty cleaning, integrated pest management, family education on controlling asthma triggers, and installation of building systems upgrades and modifications.
The group published a study in the 7 December 2004 edition of the online journal Environmental Health: A Global Access Science Source reporting the results of a detailed baseline evaluation of 78 asthmatic children living in the three public housing developments. Among other findings, the study showed that many of the children, although they had access to primary care physicians, were not receiving care according to professional asthma management guidelines, which include recommended medications, monitoring practices and equipment, and other measures. Also, exposure to violence, which has been related to exacerbation of asthma symptoms, was a significant problem. In one development where a series of murders had taken place during the study period, 60% of the children were never allowed outside to play.
Add those factors to substandard housing, high concentrations of local ambient air pollution, and other negative aspects of neighborhood built environments, and it starts to become clear why the prevalence and incidence of asthma has risen so sharply and disproportionately among low-income minority urban children. The study report concluded, “Given the elevated prevalence of multiple risk factors, coordinated improvements in the social environment, the built environment, and in medical management would likely yield the greatest health benefits in this high-risk population.”
A CBPR project currently under way in Seattle known as the High Point Healthy Homes and Community Project is taking full advantage of a unique opportunity to simultaneously address built environment issues in the public housing context and gain useful knowledge about how comprehensive interventions can be used to improve the health and well-being of residents. The Seattle Housing Authority is in the process of reconstructing its public housing stock, to replace old, deteriorating structures with town homes. One of the sites being updated is High Point, formerly a 716-unit development, which is now being rebuilt as a 1,600-unit mixed-income community.
The High Point Healthy Homes and Community Project is taking a multilevel approach to designing a public housing development to be a healthy, sustainable community. Developers are thoughtfully addressing a range of considerations, from design issues such as layout, walkability, and watershed protection, to the use of construction materials and practices that enhance indoor environmental quality. This project of the local public health department, housing authority, social service providers, public housing residents, and the University of Washington, with funding from the NIEHS and HUD, is paying particular attention to the needs of families affected by asthma.
“At the individual housing unit level, we made an estimate of the number of families living in High Point affected by asthma, and are now building specially enhanced units which will minimize exposure to asthma triggers by improving the indoor environmental quality,” says James Krieger, principal investigator on the NIEHS-funded component of the project, who is an epidemiologist with the Seattle–King County Department of Public Health and a clinical associate professor of medicine and health services at the University of Washington. (Tim Takaro, also at the University of Washington, led the HUD-funded component.) Thirty-five “Breathe Easy” demonstration homes will feature hardwood flooring (instead of carpeting, which can outgas and cause respiratory problems) and enhanced ventilation systems, weatherization, and insulation to minimize humidity and moisture intrusion, costing an additional 3–4%, or roughly $5,000, more than the development’s standard units (which will also go well beyond building code requirements in several specifications).
Researchers will follow the families for a year before they move into the units to establish a baseline assessment of their asthma status, and then continue to follow them for a year after they move into their new homes, which are currently under construction (the first families are scheduled to move in in fall 2006). “This will be one of the first studies designed as an intervention of people moving into [better]-quality housing while having a health outcome that’s clearly measurable like asthma, to see what the health impact is,” says Krieger.
The Seattle Housing Authority and its organizational partner, Neighborhood House, have also actively sought residents’ input in the design of the new community. It is slated to include walking paths and trails, mini-parks and one larger park, a grocery store, a public library, and a community health center, in hopes that these amenities will provide a built environment more conducive to health and social interaction. A community-based education initiative is also under way, using trained teams of community residents called project action teams to teach their neighbors about basic principles of how to keep their homes and community healthy.
Krieger says they will conduct before-and-after community surveys assessing people’s physical activity, social cohesion, and other factors. “Hopefully, we’ll be able to empirically test the whole notion that a change in the built environment will change health behaviors and increase community cohesion and social capital,” he says. “There’s limited empiric data out there on that now.” [For information on another innovative Seattle housing project, see “Growing Green Communities,” p. A300 this issue.]
David Jacobs, a HUD housing expert, sees wide-ranging potential in projects that quantify such benefits. “If we are able to fully value those types of investments in houses that produce positive health outcomes,” he says, “then we can end the cost-shifting that causes both higher medical bills and higher housing costs. Right now, the benefits of health investments in housing or communities are largely hidden, with avoidable—and usually much higher—costs being absorbed by the medical care sector, after the harm has already been done.”
The Search for Solutions, Large and Small
Public health and urban planning, sectors that once were closely aligned, have drifted apart over the decades, evolving into professional specialties with too few opportunities to collaborate and little mutual influence, some say. Many practitioners seeking solutions to the festering problems of health disparities and built environment inequities see reconnection of the two fields as a critical goal.
Jason Corburn, codirector of the Center for Occupational and Environmental Health at Hunter College of the City University of New York, strongly advocates recoupling the fields so as to build health considerations into land use, zoning, community design, and other urban planning decisions that to a large extent shape the long-term nature of the built environment. “Too often we’re quantifying housing quality or some aspect of the built environment,” says Corburn, “but we’re not looking historically and at the present time at how these urban planning and land use decisions are being made—who’s involved, what are the processes, who has access to the information. Without that kind of political analysis of the public decision making behind it, I think we’re missing a big piece of the built environment–health disparities puzzle.”
Corburn has helped initiate and manage the development of a health impact assessment process in San Francisco, which is rezoning several neighborhoods in its Mission District. Corburn is working with the city’s public health department and a group of nearly 40 stakeholders including tenants’ rights organizations, business owners, and other community organizations. Their mission is first to define the community’s ideas of the physical and social characteristics of a healthy neighborhood, and then to construct a plan to incorporate those elements as distinct goals of the rezoning process. “The health impact assessment process in San Francisco is an experiment,” Corburn says, “but I think it’s very promising. This kind of approach holds the potential to reconstruct the boundaries of environmental health.”
Prevention Institute, a nonprofit group based in Oakland, California, works with governments, communities, and organizations to establish prevention-oriented health programs and policies. This institute is actively promoting increased participation by public health practitioners in the built environment decision-making process. To help foster greater understanding of the potential role public health can play in improving health outcomes and reducing disparities by altering the built environment, the group recently released a report, The Built Environment and Health: 11 Profiles of Neighborhood Transformation, that highlights local success stories across the country. The profiles include inner-city yard lead abatement efforts in Boston, separate successful drives to close nuisance liquor stores in south Los Angeles and open a grocery store in a deprived community in Rochester, New York, and programs that have brought amenities such as jogging paths, bike and walking trails, mural arts, and community gardens to other municipalities. All of the interventions have involved collaborations among community groups, local governments, and public health officials, and have been aimed at reducing health disparities and improving the health of residents of low-income communities.
One of the Prevention Institute’s projects to help communities address health disparities is called THRIVE (Community Tool for Health and Resilience in Vulnerable Environments). THRIVE is a toolkit designed to aid communities in comprehensively assessing their conditions along a scale of risk to resiliency. Executive director Larry Cohen illustrates the concept: “When we think about risk, a street that does not have sidewalks or a separate place for bicyclists is going to be far more dangerous than what we call a ‘complete street,’ which functions for public transportation, for automobiles, for pedestrians, and for bicyclists. Obviously, the first street is going to promote risk, with far more likelihood of automobile crashes and air quality health-related issues, and the second is going to promote resiliency, with more opportunities for physical activity. So a risk can be ameliorated and create a community which will be more resilient.”
The THRIVE toolkit describes 20 community factors within four interrelated clusters: built environment, social capital (which includes societal factors such as social cohesion and trust, civic engagement and participation, and broadly shared beliefs and standards of behavior), services and institutions, and structural factors, with a depiction of risk and resilience for each factor. “This is about looking broadly at the community and creating environments in communities where people want to live, where they can be healthy and safe,” says program manager Manal Aboelata. This can have tremendous impact on not just physical health but also mental health. The toolkit has been successfully pilot-tested in both rural and urban communities, and is currently awaiting final approval from federal funding agencies prior to being available nationally.
A Concrete Future?
Can this new paradigm within the environmental health sciences—with its multi-disciplinary, systems-level approach to interactions between the built environment and health disparities—actually help to solve some of these long-standing, large-scale problems? Even with the latest scientific knowledge and the enthusiastic participation of a multiplicity of stakeholders, can profound, lasting change realistically be expected to follow? Although there are many encouraging trends, the challenges are formidable and complex, and observers active in the field understand that their campaign will be long, with no assurance of victory.
Carlos Mendes de Leon, an epidemiologist at Rush University Medical Center in Chicago, is investigating the biological and environmental mechanisms by which socioeconomic deprivation leads to disability in older people. His comments on the potential for broad improvements at the societal scale are representative of those heard from many researchers who are determined to soldier on: “At this moment, it’s hard to see this kind of knowledge and understanding of the built environment and health disparities having very concrete ramifications for public policy and medical interventions,” he says.
“But at the same time,” he adds, “we still have to build up the knowledge base so that when conditions change in the broader political sphere, we can point to concrete evidence and concrete strategies that may make an effect.”
Yet there have been many important achievements that demonstrate the potential power of these ideas. For example, the number of both lead-poisoned children and houses with lead-based paint have been reduced over the past decade thanks to concerted action by policy makers and community members. Many diseases that still plague the developing world, such as typhoid and cholera, have been largely eradicated in the United States due in part to improvements in housing density, ventilation, and reliable community water supplies. Learning from these examples should be a first step toward restoring the connections between environmental health, housing, urban planning, and the built environment so that many of the diseases that still plague us today can also be wiped from our lives.
Home, bitter home? Tradition holds that home is a haven, where people are protected and nurtured. For many, however, home is a health hazard when factors such as poverty, environmental contamination, and poor design combine to cause or exacerbate disease.
New tech on the block. GIS technology can help public health officials pinpoint the worst environmental health hazards on a house-by-house basis. Here, researchers mapped expected lead exposures in Durham, North Carolina.
Envisioning a healthier community. The Environmental Health Coalition is working with Barrio Logan, a low-income Latino community in San Diego, California, to map out a plan for future land uses to help eradicate hazards and improve health.
It came from within. Some of the worst home health hazards, such as the black mold crawling across the ceiling of an apartment (left) and the cockroach droppings blanketing the floor behind a refrigerator (below), arise inside homes that are poorly maintained or designed. Indoor mold and cockroach antigens have both been associated with worsened asthma and other adverse health effects.
Making houses into healthy homes. The High Point Healthy Homes and Community Project in Seattle is employing a new paradigm for public housing: design with health and sustainability in mind.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0032015866754EnvironewsInnovationsOn Closer Inspection: Learning to Look at the Whole Home Environment Spivey Angela 5 2005 113 5 A320 A323 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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It’s supposed to be a harbor, a haven—it’s home. But some home environments can cause serious health problems. Moisture and molds can inflame asthma and allergies. Broken steps can cause a fall. A leaky oil furnace can produce deadly carbon monoxide. Cockroaches and mice can produce airborne allergens that trigger asthma episodes. A variety of health professionals and inspectors may enter a home for one reason or other, but few are equipped to spot all the possible ways a house can hurt its occupants. Now a new government partnership aims to change that.
To holistically address all the aspects of housing that affect health, the Centers for Disease Control and Prevention (CDC) and the U.S. Department of Housing and Urban Development have partnered to sponsor the National Healthy Homes Training Center and Network. Operated by the nonprofit National Center for Healthy Housing (NCHH), the network aims ultimately to change how city and state governments conduct public health activities related to housing.
The NCHH provides training tools and curricula, while the actual training is carried out by a nationwide network of universities including Eastern Kentucky University, The Johns Hopkins University, the University of Cincinnati, and the University of Washington. Rebecca Morley, executive director of the NCHH, hopes to expand the network soon to include other sites, such as Boston University and Boston Medical Center. “We made a conscious decision to use the already-trusted resources in the community,” Morley says. “By building on the existing infrastructure, we think we stand a lot better chance of bringing about change.”
Morley adds, “The role of the National Center for Healthy Housing is to anchor this network by providing training curricula and tools and serving as a repository for all the information about healthy housing that the partners might need to carry out the training and to promote healthy housing more broadly in their communities.” Part of that repository will be an electronic database of research information, assessment tools, and treatment protocols that will be available online in summer 2005.
So far the network has trained 25 workers through a 2004 pilot training session. This year, the goal is to expand training to at least four other training sites. By September 2006, the training will have reached nearly 600 public health nurses, home inspectors, weatherization inspectors, environmental health specialists, and others, Morley says.
A New Use for Resources
The idea that housing and health are intertwined is not new. As far back as the 1800s, the close connection between the two earned government attention in the United States because of high rates of infectious disease in overcrowded city slums, which often had inadequate sewage treatment and a lack of running water.
Jerry Hershovitz, associate director for program development at the CDC’s National Center for Environmental Health and the Agency for Toxic Substances and Disease Registry, says that as recently as the 1960s, several large cities actually trained their public health staff across multiple disciplines. But as budgets eroded, health departments tended to become much more focused on narrow issues, largely because funding has tended to target single-issue programs such as pest control or lead hazards. Training, as well, has tended to be narrowly focused, says Morley. As a result, the public health and housing fields have grown increasingly specialized.
But as public health problems that were once emergencies have become more manageable and the infrastructures developed to address them have matured, policy makers have begun to take a broader look at housing health, says Ellen Tohn, president of the Wayland, Massachusetts, environmental consulting firm ERT Associates. The NCHH itself, for example, was formerly the National Center for Lead-Safe Housing; it changed its name in 1999 to reflect its increasingly broader scope. Grassroots groups have also played a role in the growing focus on a holistic approach to housing and health. For example, New England’s nonprofit Asthma Regional Council has worked with the building industry to create detailed guidelines for construction practices that can reduce conditions that trigger asthma.
In the last 10 years, the CDC has begun to revisit a holistic approach to healthy housing. “We believe that it’s more cost-effective and more cost-efficient in the long run to take a comprehensive approach towards the problem,” Hershovitz says. “The impact of housing on health and safety has emerged as a major public health concern. The whole basis for the initiative was to encourage public health programs to address multiple housing deficiencies and hazards that affect the health and safety of residents.”
One-Touch System
Oftentimes, health departments receive funding for a single program to address a single issue. Individual workers will enter a house to investigate an isolated problem such as a child’s rat bite, peeling lead-based paint, or mold. But the training network is promoting a “one-touch” system in which one visit from a health or housing worker may trigger efforts to address multiple problems. With this one-touch system, anytime a health or housing worker enters a house, he or she will not only treat the problem that spurred the visit, but also will look for other housing problems that can threaten health, then refer the resident to someone who can fix the problem.
Such a holistic approach to healthy housing can make a real difference in people’s lives. For instance, in a pilot program in Philadelphia, environmental health workers working in rodent control carry carbon monoxide detectors and use them whenever they visit a house. “In the pilot area, they may have saved the lives of several people by doing that,” Hershovitz says.
Similarly, through a Boston pilot program called Breathe Easy at Home, any health care provider who treats someone with asthma and finds out there may be asthma triggers in the housing environment can make a special referral to the housing department of the Boston Public Health Commission. A specially trained inspector then inspects the patient’s home for conditions that exacerbate asthma and can cite violations and get such problems fixed. In one case, this program resulted in housing repairs that tenants had been requesting for a year, with significant reduction of a child’s asthma symptoms, Tohn says.
To bring more health departments nationwide on board with this one-touch model, the network organizers are counting on the frontline workers who actually visit people’s homes. “We’re trying to encourage each worker to think more broadly about what his or her role is,” says Morley. “When public health nurses walk into homes, immediately they focus on the people. We want to train all practitioners to look around the housing environment systematically to see what might be causing health problems.”
A Broad Use of Basics
The training sessions offered by the new network include basic background information on environmental public health, building science, and specific housing-related hazards. This approach will push workers to reach outside their disciplines. Most public health nurses, for example, have not been exposed to the basics of housing construction. The training will include basic information about such features as site grading, drainage, and ventilation, all of which can affect health by causing excess moisture and poor air quality in the home. While a public health nurse wouldn’t be expected to solve a ventilation problem, this training would make the nurse better able to see the signs of poor ventilation and know where to refer the client.
Likewise, although most housing inspectors are versed in their local building codes, they may not always think of all the ways they can use the codes to get the most important health hazards repaired. For example, the building codes of most U.S. cities do not explicitly mention mold as a violation, says Tohn. “But every large city has a clause that will allow you to cite mold—it could be cited as a public nuisance, or as a failure of a building system, or as chronic dampness and moisture.” The training teaches workers which housing problems are most dangerous to occupants’ health and how to apply local codes to correct those problems. Participants also learn basic assessment and treatment skills such as testing for carbon monoxide and identifying mold and its causes.
The heart of the training is teaching workers to think more broadly. Rather than thinking in such narrow categories as lead abatement or asthma treatment, workers are taught to visualize their jobs as ensuring that houses are clean, dry, pest-free, safe, comfortable, well-ventilated, free of contaminants, and well-maintained. Tohn, who helped develop the curriculum, calls this checklist a mantra that ideally all front-line workers would keep in mind when they visit a home for any reason.
Know Your Audience—And Your Colleagues
The training sessions also emphasize the importance of careful listening and observation. Casual conversation with residents can yield clues about where health threats might lurk in a home. “The training reminds workers to listen for those clues,” Tohn says. “Residents might say, ‘This is my brother-in-law. He’s living with us, but he hasn’t been able to work much.’ ‘Why?’ ‘He’s having trouble with asthma; he started having it after he moved in with us.’” Further conversation may reveal that the brother is sleeping in a room with poor ventilation or where there are cockroach or rodent droppings, any of which could trigger his asthma.
Workers should also be aware of how residents’ behaviors might be affecting the health environment of their homes. Terry Brennan, president of the New York–based building research and training firm Camroden Associates, points to the example of families who have immigrated to the United States from a tropical climate, who will try to recreate the high levels of indoor humidity they are accustomed to. “I find families who put pots of water on the stove and crank them up, and that gets the humidity level up to sixty or seventy percent inside the house,” he says. “But you can’t do that in, say, Minnesota; if it’s cold outside, you end up with condensation all over the walls and closets, which results in mold growth.”
It is not possible for the new network to train every frontline worker in the country, but Morley hopes this widespread training network will act as a catalyst. “The idea is for this training to become sustainable—we want it to become incorporated into the academic training process as well as into other existing training programs,” she says. “And we hope that the folks we train will go back to their departments and promote this systematic approach to housing.”
Another goal of the training sessions is to create connections among frontline workers in different fields. Even though a public health worker and a housing worker, for instance, are pursuing similar goals—including improving the health of housing residents—they often go about it very differently and rarely have a chance to meet. The sessions will therefore use small-group exercises to promote discussion among specialists from different fields.
Brennan, who helped conduct the pilot training, says that one of the most innovative aspects of the training—the way it brings housing workers and public health workers together in the same room—is also challenging because the curriculum must target people who have different sets of skills. But the two groups can learn from each other. “The public health folks know about our biggest health problems, what’s troubling us most now,” Brennan says. “The building folks know how buildings are made and how they fail.”
Taking It into the Field
The participants in the 2004 pilot session were enthusiastic about a more holistic approach to healthy housing, but they pointed out that to achieve real change, they would need the support of supervisors and higher-level administrators. In response, the training center is developing a video and other materials targeted to decision makers to pave the way for their staff to follow a holistic approach, Morley says.
Attendees also wanted more targeted information about which specific home hazards are most dangerous to health. “There’s a lot one can do to improve a home environment—how can we help the workers prioritize? In the revised training, we will try to be more focused in terms of addressing critical hazards,” Morley says.
One of the attendees, David Brosch, chief inspector for the City of Baltimore Weatherization Program, says that the training reinforced some of the ideas his department is trying to apply. “We’ve gotten fairly good at insulating houses, but now we’re concerned about making them too air-tight, which can cause moisture or indoor air-quality problems,” he says. Brosch tries to address indoor air quality by, for example, testing furnaces and other appliances that can create combustion by-products. The pilot training also gave Brosch some insight into areas where further training would be useful, such as the causes of mold.
Brosch adds that workers will need practical tools to apply the theories of healthy housing. He is excited about a software tool that the training network is developing for use as a comprehensive checklist and reporting tool when visiting homes. Joe E. Beck, a professor of environmental health at Eastern Kentucky University, is leading development of the Hazard Assessment and Reduction Program for Housing, software that can be loaded onto an electronic inspection tablet or personal digital assistant. Once complete, the program will prompt workers to check for specific health and safety risks related to housing, and will be able to generate fact sheets on numerous topics using a portable printer. So when the inspection is done, the worker will be able to provide the residents with a summary report of findings as well as information on how to address identified deficiencies and hazards.
The challenge is translating this broader policy focus into practical action. “How can we get [public health and housing workers] to really change what they do back home?” Tohn says. “The training by itself will not change that. We have to start opening people’s eyes to [the holistic approach to healthy housing] and challenge them to go back and create systems that offer this one-touch approach.”
The training network is part of a call for change; it is one way to inspire workers in public health, environmental health, and housing to transform the system by changing what they do every day. “We hope the training center and network will contribute greatly to a change in mind-set,” Hershovitz says. “There’s a lot to be done. And this network is a darn good first step.”
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Suggested Reading
Krieger J Higgins DL 2002. Housing and health: time again for public health action. Am J Public Health 92:758–768. Available: http://www.centerforhealthyhousing.org/Housing_and_Health.pdf
National Healthy Homes Training Center and Network homepage Available: http://www.centerforhealthyhousing.org/html/healthy_homestraining_center_s.html
Proscio T 2004. Healthy Housing, Healthy Families: Toward a National Agenda for Affordable Healthy Homes. Columbia, MD: The Enterprise Foundation. Available: http://www.centerforhealthyhousing.org/HH.12.17.04.pdf
Saegert SC Klitzman S Freudenberg N Cooperman-Mroczek J Nassar S 2003. Healthy housing: a structured review of published evaluations of US interventions to improve health by modifying housing in the United States, 1990–2001. Am J Public Health 93:1471–1477. Available: http://www.centerforhealthyhousing.org/Retrieving_the_Evidence.pdf
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00325EnvironewsScience SelectionsPoultry’s Persistence Problem: Drug-Resistant Campylobacter in Chicken Schmidt Charles W. 5 2005 113 5 A325 A325 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Mounting evidence suggests that the poultry industry’s use of antibiotics promotes antibiotic resistance among the foodborne bacteria that infect humans. One such bacterium is Campylobacter, a pathogen common to chicken products. Every year more than 1 million Americans develop Campylobacter-induced food poisoning from eating undercooked contaminated chicken. Resistant strains of Campylobacter are a growing public health threat, particularly among elderly and immunocompromised patients. This month, researchers from the Johns Hopkins Bloomberg School of Public Health provide evidence suggesting that chickens raised without antibiotics are less likely to carry antibiotic-resistant strains of Campylobacter
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The study focused on fluoroquinolones (FQs), a class of antimicrobials used to control the bacterium Escherichia coli in broiler chickens. Of the two FQs initially approved for use in poultry, Sara Flox WSP and Baytril, only the latter remains on the market. The Food and Drug Administration is seeking to repeal approval for Baytril due to concerns that it contributes to microbial resistance.
The authors collected chicken products from two “antibiotic-free” producers (Bell & Evans and Eberly Poultry) and two of the nation’s largest conventional producers (Tyson Foods and Perdue Farms). The conventional producers claimed to have stopped using FQs in February 2002. The authors began sampling chicken products one year later, in 2003. All samples were obtained from grocery stores in or near Baltimore, Maryland.
Chicken samples were processed using standard isolation techniques; however, at the final step, Campylobacter enrichments were streaked onto agar plates both with and without ciprofloxacin (a second-generation FQ used to treat human disease). The ciprofloxacin supplement enabled the authors to identify FQ-resistant Campylobacter isolates from among a mix of susceptible and resistant strains.
Campylobacter was detected on 84% of all the samples tested. FQ-resistant strains were detected on 17% using unsupplemented agar and on 40% using supplemented agar. Abstention from FQ use by poultry producers did not increase the likelihood of Campylobacter contamination. Moreover, conventional products were up to 460 times more likely to carry resistant strains than their antibiotic-free counterparts. Of particular interest is that FQ resistance in conventional products persisted for one year after cessation of industrial use.
Based on these findings, the authors suggest that even without antibiotics, resistant populations may remain prevalent over time. Persistence of these resistant populations may result from residual contamination in poultry houses, the authors suggest. For example, biofilms in water distribution systems can harbor Campylobacter and thus could serve as reservoirs for resistant populations. These findings suggest the need to further improve poultry house cleaning and disinfection, they write.
The authors say it is important to measure the prevalence of, and causes for, FQ-resistant strains in the food supply. To this end, they point out that supplemented agar may provide a much more sensitive tool than conventional methods for detecting resistant strains of the bacterium.
Chicken surprise. New data show that antibiotic-resistant Campylobacter (left) can persist in poultry populations—and products—long after producers stop using the drugs.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00329AnnouncementsNIEHS Extramural UpdateNanotechnology in the Environmental Health Sciences 5 2005 113 5 A329 A329 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Among the newest buzzwords in biomedical science is nanotechnology: small is big! The vision for nanotechnology has existed for many years—remember Isaac Asimov’s Fantastic Voyage—but the ability to manipulate individual atoms to engineer devices at the nanoscale is new. A number of benefits arise at the nanoscale, from the practical (reagent utilization and the ability to multiplex) to the emergence of new properties (optical and electrical). The intent here is to briefly outline some ways nanotechnology will impact the environmental health sciences.
Sensor Technologies
The benefits of nanotechnology make it ideal for sensor development, for environmental and biological monitoring as well as for linking exposure, disease, and susceptibility. Investigators are developing arrays for toxicants based on technologies such as ion channels or fluorescence-emitting nanoprobes. Similarly, nanomaterials are being used to investigate the mechanisms of disease etiology in vitro and in vivo.
Remediation Technologies
Nanomaterials offer two distinct advantages to remediation technologies: large surface-area-to-volume ratio and high chemical reactivity. This pays dividends for both catalysis (for example, with halogenated organics) and sequestration (for example, with radionuclides). One key issue needing to be resolved is how nanomaterials behave in the environment as they are used for site remediation.
Nanoparticle Toxicity
There are indications that exposure to certain nanomaterials may lead to adverse biological effects that appear to depend on the material’s chemical and physical properties. Nanoparticles will interact with biological systems; issues such as particle absorption and the contributions of surface geometry, chemistry, cellular uptake, and localization need to be examined to inform toxicity assessments for nanomaterials.
Long-Range Vision
Our ultimate “blue sky” goal is similar to the vision Asimov presented 40 years ago—to develop brilliant biocompatible nanoparticles to be used in vivo to detect exposure to a potential toxicant, to identify biological responses to that exposure and categorize them as compensatory or pathological, and to intervene to halt or reverse the development of disease.
Contact
David Balshaw, Phd |
[email protected]
Sally Tinkle. PhD |
[email protected]
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00330AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 5 2005 113 5 A330 A331 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Lung Response to Inhaled Highly Toxic Chemicals
The purpose of this program announcement (PA) is to investigate acute mucosal irritation in the upper and lower respiratory tract occurring after aerosol exposure to toxic chemicals with the goals to: 1) minimize initial injury promptly, 2) retard and ameliorate progressive mucosal irritation or inflammation, and 3) offer prophylaxis against pulmonary edema, if created by acute lung injury.
The National Heart, Lung, and Blood Institute (NHLBI) and the NIEHS are concerned about the U.S. population’s potential inhalational exposure to aerosolized harmful chemicals, possibly liberated as part of bioterrorism attacks against assembled groups of the civilian populace. Therefore, research is needed on how humans and relevant animal models respond to inhaled toxic chemicals. The goals of this PA are to develop better bio-protective therapies and to minimize respiratory injury and illness.
Many volatile toxic chemicals are produced and utilized in industry. Some of these are considered hazardous when they are inhaled in ambient air, introduced into food and water supplies, or make contact with body skin surfaces. Among toxic industrial materials that are considered highly hazardous are ammonia, chlorine, formaldehyde, hydrogen cyanine, fuming nitric acid, phosgene, and sulfur dioxide.
From a pulmonary perspective, inhalation exposure to some of these highly hazardous and irritative chemicals induces initial choking, inability to breathe deeply, and excessive output of secretions in the nose and throat from acute irritation. Other chemicals that have neurological effects—including such nerve agents as sarin, certain organophosphate-based pesticides, soman, and others—enter the body through absorption from the airways.
The NHLBI has a limited portfolio of existing research applicable to the respiratory exposures discussed. This PA will stimulate and build research against airborne chemical threats that affect the upper and lower respiratory tract, and will suggest potential therapy to prevent or limit development of pulmonary edema, which is a major complication of airway chemical irritation. Examples of research topics that are of interest include the following: 1) investigating mechanisms of chemical injury (including minimal threshold levels to establish injury) and subsequent effects at a cellular and molecular level causing airway inflammation or hypersensitivity; 2) identifying host responses to initial or immediate effects, and to long-term low-level exposure effects; 3) assessing systematic amount or dose of chemical absorbed from the airways; 4) developing preexposure preventive treatment or early use of antidotes; and 5) devising therapeutic strategies, especially if acute alveolar lung injury occurs and pulmonary edema ensues; specific therapies to prevent onset of pulmonary edema are sought. Development of physical protection (including facial masks and respirators) or environmental detectors for documenting exposure are not within the purview of this announcement.
The NIEHS encourages applications to study chemical exposures relating to civilian terrorism attack, industrial sabotage, or large-scale accidental exposure to toxic chemicals. Applications should focus on research that will develop or support development of treatment strategies that prevent or minimize respiratory track injury following exposure or that maximize repair of injured tissue. To be considered responsive to the NIEHS, the chemical exposure should be acute.
Multiple routes of chemical exposure (respiratory tract, skin, eye, digestive tract) are acceptable if injury resulting from the exposure is specific to the lung. Use of animal models and appropriate human biological specimens is encouraged. Examples of research topics for the NIEHS include but are not limited to the following: 1) the relationship between exposure, route of exposure, and absorbed dose to onset and magnitude of respiratory symptoms in a young, adult, and senior model; 2) cellular and molecular mechanisms of lung injury following acute chemical exposure, including induction of mucosal injury, pulmonary inflammation, acute alveolar injury, and pulmonary edema; 3) cellular and molecular mechanisms of lung tissue repair following acute chemical-induced lung injury; 4) development of postexposure strategies that prevent or minimize lung injury, including early use of antidotes; and 5) development of therapeutic strategies that promote lung tissue repair and that prevent or treat pulmonary edema.
This funding opportunity will use the NIH R01 award mechanism. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project. This funding opportunity uses just-in-time concepts. It also uses the modular as well as the nonmodular budget formats (see http://grants.nih.gov/grants/funding/modular/modular.htm). Specifically, if you are submitting an application with direct costs in each year of $250,000 or less, use the modular budget format described in the PHS 398 application instructions. Otherwise, follow the instructions for nonmodular research grant applications.
Applications must be prepared using the most current PHS 398 research grant application instructions and forms. The PHS 398 application instructions are available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance contact GrantsInfo at 301-435-0714 or by e-mailing
[email protected]. Applications must have a Dun & Bradstreet Data Universal Numbering System (DUNS) number as the universal identifier when applying for federal grants or cooperative agreements. This number can be obtained by calling 1-866-705-5711 or through the website at http://www.dnb.com/us/.
Applications must be mailed on or before the receipt date described at http://grants.nih.gov/grants/funding/submissionschedule.htm. The complete version of this PA is available online at http://grants.nih.gov/grants/guide/pa-files/PA-05-058.html.
Contact: Herbert Y. Reynolds, Division of Lung Diseases, NHLBI, 6701 Rockledge Dr, Two Rockledge Ctr, Ste 10018, MSC 7952, Bethesda, MD 20892 USA, 301-435-0218, fax: 301-480-3557, e-mail:
[email protected]; Sally S. Tinkle, Cellular, Organ and Systems Pathobiology Branch, Division of Extramural Research and Training, NIEHS, 111 TW Alexander Dr, PO Box 12233, MD EC-23, Research Triangle Park, NC 27709 USA, 919-541-5327, fax: 919-541-5064, e-mail:
[email protected]. Reference PA No. PA-05-058
In Utero Exposure to Bioactive Food Components and Mammary Cancer Risk
In utero exposures are important determinants of some cancers occurring in children and young adults. For example, exposure to ionizing radiation in utero promotes childhood leukemia, and maternal use of diethylstilbestrol during pregnancy has been linked to clear-cell adenocarcinoma of the vagina in these women’s daughters. In addition, maternal diets—specifically the consumption of vegetables, fruits and protein—are linked to decreased risk of childhood leukemia.
The prenatal period is critical in the development of the mammary gland. During this time, the mammary gland is in a largely undifferentiated state, making it particularly vulnerable to a host of environmental forces. Inappropriate nutritional status or exposure to environmental chemicals and the accompanied alteration in growth and endocrine homeostasis may permanently change the fetus’s structure, physiology, and metabolism, thereby predisposing it to various diseases in later life including mammary cancer.
Epidemiological studies suggest that altering the intrauterine nutritional status can increase mammary cancer risk. Failure of the materno-placental supply line to satisfy fetal nutrient requirements can result in a range of fetal adaptations and developmental changes. Birth weight is a gross surrogate marker for shifts in a host of metabolic processes. Many, but not all, studies reveal a positive relationship between increased birth weight and breast cancer risk. Likewise, other indicators of fetal size such as increased placental weight and birth length are positively correlated with breast cancer risk in the offspring. Recent studies suggest that birth weight is independent from neonatal growth patterns and the timing of puberty as a risk factor for breast cancer.
In addition to nutrition, the hormonal environment in the womb may play an important role in programming lifelong risk for breast cancer in female offspring. A reduction in circulating levels of estrogens and insulin-like growth factor 1 (IGF-1) and/or elevated levels of progesterone, androgens, human chorionic gonadotrophin, IGF-1 binding proteins 1 and 3, cortisol, and insulin have been associated with reduced risk. Such hormonal and growth factor changes are observed during preeclampsia. Maternal preeclampsia has been associated with a reduction in the female offspring’s later risk for breast cancer after adjustment for a variety of potential confounders.
Proliferation of primitive ductal structures in the newborn breast leads to branching and terminal end buds (TEBs). The expansion of TEBs represents an opportunity for malignant transformation because they contain pluripotent mammary stem cells. In fact, in utero exposures that bring about an increase in TEBs coincide with increased mammary carcinogenesis. Evidence exists that providing maternal diets that contain elevated amounts of n-6 polyunsaturated fatty acids (PUFAs) and genistein not only increased TEBs but also reduced the differentiation of TEBs to lobuloalveolar units. These diets also increased subsequent chemically induced mammary cancer in the offspring. In addition, prenatal exposures to environmental agents such as bisphenol A or dioxin results in alteration in the development of the mammary gland that may predispose to the development of cancers later in life. Some of this response may relate to changes in hormonal and growth factor status, including status of estrogen and IGF-1.
Greater estrogen exposure throughout a woman’s life has been identified as a major risk factor for the development of breast cancer. In utero exposures to the mammary gland can achieve concentrations 10–100 times the estrogen levels occurring later in life. Dietary factors, such as genistein and fat, that influence estrogen exposure to the fetus are related to subsequent cancer risk in several model systems. However, the response may not be totally explained by estradiol, because diets rich in n-3 fatty acids, when fed to pregnant rats, elevate this hormone but reduce mammary cancer incidence in the offspring.
It is possible that intrauterine exposure to other hormones or environmental hormone mimics or antagonists may also affect breast cancer susceptibility. Androgen exposure in utero may confer long-term protection against breast cancer by antagonizing the effects of estrogens on fetal breast ductal development. Dietary fatty acids, phytoestrogens, alcohol, and lycopene are among the various bioactive food components reported to influence androgen concentrations. Environmental agents with estrogenic agonist or antagonist activity may also alter gene expression during development, which may lead to functional deficits later in life that predispose one to cancer development. Thus there is the need for studies focusing on uncovering the mechanisms responsible for the protective and detrimental effects on breast cancer risk of exposure to bioactive food components and other environmental agents in utero. These studies should attempt to more comprehensively address the changes in all potentially relevant pregnancy hormones and growth factors.
Although the effects of in utero exposure to dietary components have been inadequately examined, considerable evidence exists for their ability to modify IGF-1 concentrations and mammary cancer susceptibility postnatally. Postnatal caloric restriction decreases IGF-1 and decreases mammary tumor growth and metastases. Furthermore, postnatal soy phytochemicals combined with green tea synergistically inhibited mammary tumor growth and depressed serum IGF-1 levels in mice. Future studies are warranted to determine whether in utero exposure to dietary manipulations that modulate IGF-1 expression will influence subsequent breast cancer risk.
Maternal nutritional status can also alter the epigenetic state of the fetal genome and imprint gene expression levels with lifelong consequences. Loss of imprinting is the silencing of active imprinted genes or the activation of silent imprinted genes, and is one of the most common epigenetic changes associated with the development of a wide variety of tumors. Several lines of evidence support the relationship between maternal nutrition and epigenetic changes in their offspring. Epigenetic changes may provide a molecular mechanism for the impact of maternal nutrition or environmental chemical exposures on postnatal disease susceptibility and deserves future research.
Investigators may choose from the full range of preclinical approaches. The use of genetically engineered animal models including transgenic or knockouts, such as those available through the Mouse Models of Human Cancer Consortium (MMHCC, http://emice.nci.nih.gov/), is encouraged. Studies that apply new high-throughput genomic, epigenomic, proteomic, and metabolomic technologies to determine how dietary and/or environmental chemical exposures in utero influence adult breast cancer susceptibility are encouraged.
This funding opportunity will use the NIH investigator-initiated research project grants (R01) and exploratory/developmental (R21) award mechanisms. Illustrative examples for the development of R01 or R21 applications include, but are not limited to, the following: 1) utilization of transgenic and knockout mouse models of human mammary cancer to identify molecular sites of action of bioactive food components in cancer prevention; 2) examination of the role of moderate caloric restriction in utero on hormone concentrations and mammary cancer prevention; 3) evaluation of synergistic effects of exposure to bioactive food components in utero and subsequent mammary cancer risk; 4) evaluation of imprinted genes after exposure to bioactive food components in utero and subsequent mammary cancer risk; 5) examination of the role of in utero exposures to environmental agents such at mycotoxins, heterocylic amines, bisphenol A, phthalates, and other agents with endocrine-like agonist or antagonist activity and subsequent mammary cancer risk; and 6) examination of the interaction of in utero exposures to bioactive food components and exposures to environmental agents in the etiology of breast cancer later in life.
No set-aside funds are available for this funding opportunity. Applicants may request up to 5 years of support for R01 awards with costs appropriately tailored to the proposed work. No limit is set on the costs requested by R01 applicants. An R21 applicant may request a project period of up to 2 years with a combined budget for direct costs of up to $275,000 for the 2-year period. Normally, no more than $200,000 may be requested in any single year.
Because the nature and scope of the proposed research will vary from application to application, it is anticipated that the size and duration of each award will also vary. Although the financial plans of the involved institutes and centers provide support for this program, awards pursuant to this funding opportunity are contingent upon the availability of funds and the receipt of a sufficient number of meritorious applications.
Applications must be prepared using the most current PHS 398 research grant application instructions and forms. The PHS 398 application instructions are available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance contact GrantsInfo at 301-435-0714 or by e-mailing
[email protected]. Applications must have a Dun & Bradstreet Data Universal Numbering System (DUNS) number as the universal identifier when applying for federal grants or cooperative agreements. This number can be obtained by calling 1-866-705-5711 or through the website at http://www.dnb.com/us/.
Applications must be received by the dates listed at http://grants.nih.gov/grants/funding/submissionschedule.htm. The complete version of this PA is available at http://grants.nih.gov/grants/guide/pa-files/PA-05-059.html.
Contact: Cindy D. Davis, Division of Cancer Prevention, National Cancer Institute, 6130 Executive Blvd, EPN Rm 3159, MSC 7328, Bethesda, MD 20892-7328 USA, 301-594-9692, fax: 301-480-3925, e-mail:
[email protected]; Mary Frances Picciano, Office of Dietary Supplements, 6100 Executive Blvd, Rm 3B01, Bethesda, MD 20892-7517 USA, 301-435-3608, e-mail:
[email protected]; Jerry Heindel, Cellular, Organs, and Systems Pathobiology Branch, Division of Extramural Research and Training, NIEHS, PO Box 12233, Research Triangle Park, NC, 27709 USA, 919-541-0781, fax: 919-541-5064, e-mail:
[email protected]. Reference PA No. PA-05-059
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0296a15866750PerspectivesCorrespondenceExposure to Environmental Tobacco Smoke and Cognitive Abilities Among U.S. Children and Adolescents Petersen Phillip Queensland Medical Laboratory and Queensland University of Technology, Rochedale, South Queensland, Australia, E-mail:
[email protected] author declares he has no competing financial interests.
Editor’s note: In accordance with journal policy, Yolton et al. were asked whether they wanted to respond to this letter, but they chose not to do so.
5 2005 113 5 A296 A296 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In the article “Exposure to Environmental Tobacco Smoke and Cognitive Abilities Among U.S. Children and Adolescents,” Yolton et al. (2005) stated that the data “indicate an inverse association between ETS exposure and cognitive deficits among children ….” They do not. They indicate an inverse association between ETS exposure and scores, but a direct association between ETS exposure and cognitive deficits.
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Reference
Yolton K Dietrich K Auinger P Lanphear BP Hornung R 2005 Exposure to environmental tobacco smoke and cognitive abilities among U.S. children and adolescents Environ Health Perspect 113:113 98 103 15626655
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0296b15866749PerspectivesCorrespondenceMaternal and Paternal Risk Factors for Hypospadias Bianca Sebastiano Ingegnosi Carmela Dipartimento Materno Infantile, Centro di Consulenza Genetica e di, Teratologia della Riproduzione, ARNAS, P.O. Garibaldi Nesima, Catania, Italy, E-mail:
[email protected] Giuseppe Dipartimento Materno Infantile, U.O. Ginecologia e Ostetricia, ARNAS, P.O. Garibaldi Nesima, Catania, ItalyThe authors declare they have no competing financial interests.
Editor’s note: In accordance with journal policy, Pierik et al. were asked whether they wanted to respond to this letter, but they chose not to do so.
5 2005 113 5 A296 A296 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Hypospadias is a common congenital anomaly, characterized by incomplete fusion of the urethral folds during fetal development, which results in the urethra opening on the ventral surface of the penis or on the scrotum. In their article, Pierik et al. (2004) proposed multiple possible maternal and paternal risk factors related to the development of isolated hypospadias, including genetic, endocrine, and environmental factors.
Concerns and alarms have been raised about the potential effects of endocrine disruptors, which include derivatives of poly-aromatic hydrocarbons and pesticides, on the developing male reproductive tract. We have recently published two case–control studies on risk factors and hypospadias, one on a possible association with maternal age (Bianca et al. 2005) and the other on the role of endocrine disruptors (Bianca et al. 2003).
In the first study (Bianca et al. 2005), we evaluated 415 newborns with isolated hypospadias and 812 controls. Our results suggest that an increased risk for hypospadias exists in women at the extremes of the age distribution (< 20 years and > 40 years; p = 0.000 and 0.026, respectively) relative to women in the middle of the distribution, with a mechanism probably related to hormonal disruption. It has been postulated that changes in concentrations of sex hormones during the fetal critical period of genital development (weeks 8–14), caused by endogenous or exogenous factors, may play a role in the development of hypospadias and that hypospadias could be associated with early malfunction of the placenta, resulting in decreased secretion of placental and fetal hormones that could in turn disturb fetal development (Akre et al. 1999). Mothers at the extremes of the age distribution may be more susceptible to this hormonal disruption. The association between hypospadias and maternal age, both for younger and older women, might be explained as a “defect in nature’s quality control” caused by a reduction of defensive maternal mechanisms that would be less efficient in the elimination of malformed fetuses in the mothers of hypospadias cases. This hypothesis may also apply to other birth defects where a relationship with maternal age has been demonstrated.
In the second study of 68 cases and 211 controls (Bianca et al. 2003), we identified a high incidence of hypospadias in two towns in southeastern Sicily, which have intense industrial (Augusta) and agricultural (Vittoria) activities. Our results showed an incidence that was 3.8 [95% confidence interval (CI), 2.16–6.14] and 2.3 times (95% CI, 1.48–3.43) higher, respectively, than expected (3.2 per 1,000 male live births in southeastern Sicily). The odds ratios for fathers’ job exposure alone were 5.5 (95% CI, 1.22–24.7) for working in an oil refinery in Augusta and 2.9 (95% CI, 1.01–8.55) for working in hothouses in Vittoria.
Pollutants in both areas include compounds with proven estrogenic activity and other chemicals: pesticides and herbicides and their metabolites; dieldrin, chlordane, and endosulfan; polychlorinated biphenyls and dioxins; bisphenols used in epoxy resins; and some phthalates used as plasticizers in a wide variety of applications, including polyvinyl compounds and alkyphenol ethoxylates. The direct cause–effect relationship between environmental pollutants, which act as endocrine disruptors, and the increased incidence of hypospadias in these areas is difficult to establish and demonstrate.
Our study (Bianca et al. 2003), suggested that exposure to large amounts of industrial and agricultural pollutants is sufficient to increase the risk of hypospadias. Other risk factors, such as reproductive history, may be involved in the etiology of hypospadias. Disturbances in sexual differentiation occur when endogenous and/or exogenous factors act to disrupt the metabolism of gonadal hormones during development.
Further epidemiologic and biologic studies are needed to explain, in a multi-factorial model, which factors (genetic and/or environmental) interact and influence the etiology of this congenital abnormality.
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References
Akre O Lipworth L Cnattingius S Sparen P Ekbom A 1999 Risk factor patterns for cryptorchidism and hypospadias Epidemiology 10 364 369 10401869
Bianca S Ingegnosi C Ettore G 2005 Maternal age and hypospadias Acta Obstet Gynecol Scand 84 410 15762979
Bianca S Li Volti G Caruso-Nicoletti M Ettore G Barone P Lupo L 2003 Elevated incidence of hypospadias in two Sicilian towns where exposure to industrial and agricultural pollutants is high Reprod Toxicol 17 539 545 14555191
Pierik FH Burdorf A Deddens JA Juttmann RE Weber RFA 2004 Maternal and paternal risk factors for cryptorchidism and hypospadias: a case–control study in newborn boys Environ Health Perspect 112 1570 1576 15531444
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0301aEnvironewsForumInnovative Technologies: Organic Solar Cells Manuel John 5 2005 113 5 A301 A301 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Photovoltaic cells that convert sunlight into electricity have been around for decades, yet their commercial use has been largely limited to applications where conventional electric power is difficult or impossible to provide, such as lighting of road signs and offshore buoys. The problem is primarily economic—although sunlight is free, the high cost of manufacturing traditional silicon-based solar cells has limited their penetration into markets where coal, nuclear, and other nonrenewable sources currently provide more economical energy. Researchers at the Georgia Institute of Technology have developed a new type of solar cell that may someday change that equation.
Bernard Kippelen, a professor in the Center for Organic Photonics and Electronics and the School of Electrical and Computer Engineering at Georgia Tech, is leading studies into the use of pentacene as a medium for converting sunlight to electricity. Pentacene, a compound of carbon and hydrogen, can form a crystalline film in which molecules assemble in an ordered pattern. This makes the compound more conducive to the flow of electricity than the disordered organic compounds that have been tested in the past for possible photovoltaic applications. Improved conductivity leads to higher efficiency, and if that quality can be combined with low cost of manufacture and ease of use, the material holds great promise.
In an article published in the 29 November 2004 issue of Applied Physics Letters, Kippelen and fellow research scientists Seunghyup Yoo and Benoit Domercq describe their tests of an organic film made of pentacene combined with a form of carbon known as C60. The organic layers and an electrode were sequentially deposited onto indium–tin oxide substrates. Broadband illumination was provided by a lamp, photocurrent was measured under varying light spectrums, and conversion efficiencies (the amount of light converted into electricity) were calculated.
The team was able to convert solar energy into electricity with 2.7% efficiency; in unpublished tests since then, they demonstrated power conversion efficiencies of 3.4%. Kippelen believes they will be able to reach 5% in the near future.
Commercial photovoltaic cells that employ silicon crystals are 12–15% efficient, but they are expensive to manufacture and run. Complete systems, including installation, produce electricity at a cost equivalent of 20–40¢ per kilowatt hour (depending upon scale of system and financing) versus 8–12¢ per kilowatt hour for electricity generated by conventional power plants. Kippelen says development of thin-film organic solar cells is not far enough along to estimate the costs of energy production. However, the thin-film cells could possibly be manufactured in a roll-to-toll process, significantly lowering their cost and narrowing the gap with fossil fuel–generated electricity.
Kippelen is confident that his product’s unique properties will allow it to be used in applications for which silicon cells are not appropriate. Whereas silicon cells are rigid and relatively thick at 100 microns across, thin-film organic solar cells are lightweight, flexible, and less than 1 micron thick. This could open up new markets for solar energy, perhaps powering small electronic devices such as radiofrequency identification tags, MP3 players, and laptop computers. Kippelen estimates that organic solar cells are at least five years away from residential applications but could find niche low-power applications within two years.
However, thin-film solar power will need to be deployed on a much larger scale if it is to significantly improve the environment. “Small electronic devices represent a miniscule part of total energy consumption,” says Tom Starrs, chairman of the American Solar Energy Society. “For any photovoltaic technology to make a significant contribution to global energy needs, it needs to be interconnected with the electrical grid, displacing power generated by coal, nuclear, and other nonrenewable sources of energy.”
Solar sensation. New thin-film technology offers promise in converting solar energy into electricity.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0301bEnvironewsForumThe Beat Dooley Erin E. 5 2005 113 5 A301 A303 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The New Face of Herbs
After a meteoric rise in sales in the 1990s, use of botanical dietary supplements appears to have plateaued, according to a study in the 14 February 2005 issue of Archives of Internal Medicine. Still, exposure may continue to increase as more multivitamins now include botanical ingredients such as the carotenoid antioxidants lutein and lycopene (which may protect against macular degeneration and cancer). The study authors, from Boston University, wrote that the latter finding suggests that herbal supplements are gaining wider acceptance as evidenced by their incorporation into mainstream medicine. Furthermore, multivitamins are now being marketed not just as a source of daily allowances of vitamins and minerals but also as a means for preventing chronic diseases.
Sunny Spanish Energy
Spain, known for its sunny climate, is turning that resource into energy. The Spanish Industry Minister has announced that as of 2005 any new or renovated buildings must be outfitted with solar panels. Spain’s government is seeking a 10-fold increase in the area of solar panels in use in Spain by 2010, and hopes to make the nation a leader in the use of renewable energy. The government has promised subsidies to help get the program off the ground, with details yet to be announced.
In making the announcement, the government stated that this measure could reduce greenhouse gases and save homeowners more than US$100 each year on fuel. According to additional government estimates, a single two-meter solar panel can cut a household’s yearly hot water bill by up to 70%.
A Fresh Wind Blows in Beijing
Everyone knows China is a big place with a big population. Now it can add having the world’s largest wind power project to its list of superlatives. The new power plant, located 60 miles outside Beijing, will generate 400 megawatts per day, enough to power 240,000–400,000 households. This nearly doubles the amount of wind-generated power that China currently has. China announced in 2004 that it hopes to generate 12% of its energy from renewable sources such as wind by 2020.
The plant was planned as a means to help relieve Beijing from some of the world’s worst air pollution. Some 70% of the country is affected by acid rain formed in part by emissions from the coal-burning power plants that China currently relies on. The country is also fraught with power outages as demand outstrips supply.
Butting Out of Bhutan
Known for its fierce protectiveness of its environment and culture, the Himalayan kingdom of Bhutan now has a unique place among the world’s nations—it is the first to impose a nationwide ban on public smoking and the sale of tobacco products. According to an act passed in July 2004 by the Royal National Assembly, selling tobacco products will result in a fine of US$225, a huge sum in this modest nation, and businesses caught in the act will lose their business licenses. Bhutanese bringing tobacco into the country from elsewhere will be charged a 100% tax and may smoke their tobacco only at home. Currently about 1% of Bhutanese are believed to smoke.
Federal Agencies Pledge Computer Stewardship
The U.S. government represents 7% of the world demand for computers and is expected to spend $60 billion this fiscal year on information technology needs. Now the White House and 11 federal departments and agencies have signed a memorandum of understanding to develop and promote common strategies for more sustainable management of government computer resources. The signatories will put their purchasing power toward increasing demand for more energy-efficient and environmentally sustainable equipment; promoting implementation of optimal life cycle management practices for electronic equipment; reducing the economic and environmental costs of federal electronic equipment; and promoting the market and infrastructure for the reuse, demanufacturing, and recycling of obsolete equipment.
Think Globally, Shop Locally
How much does that apple or carton of eggs really cost? A team of researchers from Britain’s University of Essex and City University tallied up the unaccounted environmental and transportation costs involved in bringing organic and conventionally grown produce to UK markets and published their calculations in volume 30, issue 1 (2005) of Food Policy. If Britons bought more organic produce and made their grocery trips by a means of transportation other than a car, the country would save more than US$7.5 million in impacts upon the environment. If all UK farms went organic, environmental costs would fall from almost US$3 billion to just over US$750 million. And if food came from within 12 miles of where it was consumed, environmental and congestion costs related to the transportation of food would fall from US$4.4 billion to US$440 million. Said coauthor Jules Petty, “The most political act we do on a daily basis is to eat, as our actions affect farms, landscapes, and food businesses.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0302aEnvironewsForumPolicy: Healthier Housing Ahead Korfmacher Katrina Smith 5 2005 113 5 A302 A302 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Community-based organizations around the country are working to promote healthy and affordable housing for all. Yet reduced government funding and increased competition for foundation support make it even harder to redress housing-based health disparities. In early March 2005, the Alliance for Healthy Homes convened nearly 50 leaders from community groups around the country to plan how to work together to build a national movement for healthy homes in the face of political and funding challenges.
Several groups shared recent local successes and strategies. Cleveland has passed a city ordinance that provides incentives for landlords to make housing lead-safe. In San Diego, the Environmental Health Coalition trained promotoras (community health promoters) to teach families and code inspectors about home environmental hazards; the coalition is also organizing citizens in support of a proposed lead-safe housing ordinance. The Boston Urban Asthma Coalition reported progress in promoting good air quality in schools, getting insurers to pay for asthma patient education, and referring asthma patients to city building inspectors for targeted housing code enforcement. These and other experiences provided fodder for workshops on enhancing code enforcement, strengthening ordinances, working with the media to communicate healthy homes issues, and funding housing improvements.
The Alliance for Healthy Homes presented a plan for combining forces with the National Center for Healthy Housing to integrate research, policy, and practice into practical solutions for communities. Community leaders also stressed the need for a separate “uncensored voice” to speak on behalf of low-income communities that suffer most severely from unhealthy housing. The advocates in attendance agreed to pursue forming a new network to serve as this voice for the people.
The meeting highlighted the need to build recognition of the importance of healthy housing and the political will to ensure it through new partnerships at many levels. Community groups can organize residents and promote local policy changes. Local and national groups concerned with education, health, criminal justice, economic development, and poverty can include healthy housing among their central objectives. Researchers can focus further attention on the health risks posed by substandard housing, test and evaluate practical solutions, and educate policy makers and the media.
At the federal policy level, the Alliance for Healthy Homes and the National Center for Healthy Housing are drafting federal healthy homes legislation to comprehensively address the current policy gaps. This legislation could provide the rallying point for community advocates, special interest groups, researchers, and others to move healthy and affordable housing to the central position it deserves on our national agenda.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0302bEnvironewsForumChildren’s Health: Sour News for Soy Formula? Barrett Julia R. 5 2005 113 5 A302 A302 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Naturally occurring phytoestrogens have been intensively studied for health effects in adults. However, studies of soy formula, which delivers high levels of phytoestrogens to infants, have not extended much beyond ensuring that babies are growing and developing normally. Still, soy formula has been considered a safe alternative to milk-based formulas for some 40 years. Recent studies from the University of Illinois at Urbana–Champaign now show that the soy phytoestrogen genistein can alter intestinal cell proliferation and migration, with unknown effects for infants fed soy formula.
“We are feeding infants soy formula as their sole source of nutrition for the first four to six months of life, a period of time when many systems are immature and undergoing development,” says Sharon Donovan, a professor of nutrition involved in both studies. It is known that infants metabolize genistein and can have some circulating level of the bioactive form.
But whether the effects are good, bad, or even measurable is unknown and fiercely debated. “Why [soy formula] has not received more research attention, I’m not sure,” says Retha Newbold, an NIEHS toxicologist who has investigated the developmental effects of genistein and other estrogenic substances for more than 25 years.
In the first Illinois study, published in the June 2004 Journal of Nutrition, researchers exposed human intestinal cells to varying doses of genistein and noted effects on cell numbers, DNA replication, apoptosis, and cell cycle. At low doses, genistein acted as a weak estrogen and stimulated cell growth; at high doses, the compound inhibited proliferation and altered cell cycle dynamics. This biphasic response correlates with how genistein is thought to exert its effects.
In the other study, published in the February 2005 Pediatric Research, 24 2-day-old piglets were divided into three dietary groups for eight days, receiving plain sow milk replacer or replacer with either a low or high dose of genistein. The high-dose piglets had circulating concentrations of genistein on par with those of soy formula–fed infants. At 10 days of age, there were no significant differences in weight gain, intestinal length or growth, nutrient uptake, or digestive enzyme activity among the piglets. However, there was a 50% decrease in intestinal cell proliferation and a 20% decrease in cell migration associated with the high genistein dose.
Donovan cautions that it’s premature to draw conclusions about negative or positive effects of infant soy formula. “This is what we see when we look at genistein alone,” she says, “but what happens when you look at a whole soy formula?”
More than 20 million American infants have been fed soy formula in the last 25 years, and their growth and development have been equal to that of infants fed milk-based formula, according to Thomas Badger, director and senior investigator at the Arkansas Children’s Nutrition Center in Little Rock. “If there have been no problems in more than twenty million people exposed to soy formula, then there is no human evidence of a problem,” says Badger.
Still, many researchers believe that more information is needed about the safety of infant soy formula. Phytoestrogen doses comparable to what infants receive through soy formula have been shown to cause cancer in some animal studies if given before puberty. “I think too little is known to conclude that soy formula is safe for the general infant population,” says Newbold.
Formula fitness? New findings on genistein raise questions about the safety of soy formula.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0324aEnvironewsScience SelectionsSeasick Lungs: How Airborne Algal Toxins Trigger Asthma Symptoms Freeman Kris 5 2005 113 5 A324 A324 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Adverse health effects from harmful algal blooms have most frequently been linked to eating fish or shellfish that have accumulated algal toxins. However, people have also suffered asthma-like symptoms after inhaling minute amounts of algal toxins that were aerosolized by waves. Now a research team uses an animal model to gain a better understanding of how exposure to airborne algal toxins causes these symptoms and whether available drugs can be used to prevent or relieve them [EHP
113:632–637].
The researchers focused on toxins produced by a subtropical species of dinoflagellate (Karenia brevis) that is a major component of a bloom known as Florida red tide. K. brevis produces at least nine types of brevetoxins. When ingested, brevetoxins cause neurotoxic shellfish poisoning, with symptoms that can include numbness, tingling, and gastrointestinal distress. Persons exposed to aerosolized brevetoxins may suffer shortness of breath, sneezing, and other allergy- and asthma-like symptoms. Persons with preexisting airway disease appear most likely to be affected.
To study airborne toxin exposure in a more controlled setting, the research team used a sheep model of asthma. The sheep model used is naturally sensitive to an antigen derived from the roundworm Ascaris suum, developing asthma-like symptoms (such as airway constriction) when exposed to this antigen. The sheep therefore can serve as surrogates for persons with asthma. To simulate environmental brevetoxin exposures, these allergic sheep were exposed to crude brevetoxins. These samples contained a variety of brevetoxin species (because multiple types are usually found in Florida red tide) as well as other parts of algal cells (because the toxin is usually released as the algae die and begin to decompose). In addition, the animals were also exposed to two types of purified brevetoxin, presumed to be the primary agent causing respiratory symptoms. In some studies the animals were treated before or after exposure with one of several clinically available medications.
Exposure to crude brevetoxins caused immediate bronchoconstriction in the sheep as evidenced by a twofold increase in airway constriction. This immediate bronchoconstriction was inhibited by 49% (compared with untreated animals) in animals that were pre-treated with budesonide, by 71% in animals pretreated with albuterol, by 58% in animals pretreated with atropine, and by 47% in animals pretreated with diphenhydramine. In addition, bronchoconstriction was quickly reversed in animals that had not been premedicated if they were dosed with albuterol immediately after exposure.
The fact that diphenhydramine, a histamine antagonist, reduced airway symptoms indicates that brevetoxins activate histamine-producing cells, such as mast cells and basophils. Further proof of the involvement of these cells was gained from studies where toxin was injected into the animals’ skin. In these skin tests, the reaction was up to 75% smaller if animals were pretreated with diphenhydramine.
The effectiveness of atropine, an anticholinergic agent, indicates that brevetoxins also activate neural pathways. The cholinergic pathway is involved in the regulation of the neurotransmitter acetylcholine, and is also activated by exposure to organophosphate pesticides.
The researchers also found that bronchoconstriction was reduced by 34% when animals were treated with HOE-140, a bradykinin β2 receptor antagonist. This response indicates that, in addition to raising histamine levels, exposure to brevetoxins also increases the level of bradykinin, a protein with effects similar to histamine that has also been linked to asthma symptoms. Asthmatics are more sensitive to bradykinin than are individuals with normal airways, and kinin levels are increased in inflamed airways. This may explain why the researchers found that animals whose airways were already inflamed responded more strongly to brevetoxins. Thus, the increased responsiveness to toxin in persons with preexisting airway disease may be linked to their underlying airway inflammation at the time of toxin exposure.
This research shows that brevetoxins are potent airway constrictors, triggering several physiological pathways. The research has also determined drugs that could mitigate symptoms and serve as rescue medications for persons with severe reactions to brevetoxins.
Waves of illness? Algal toxins from organisms such as Karenia brevis can be aerosolized in sea mist and breathed in by people. A mini-monograph in this issue examines the hazards they pose.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0324bEnvironewsScience SelectionsChallenging Assumptions about Lead and IQ: Effects Increase, Not Decrease, in Older Children Weinhold Bob 5 2005 113 5 A324 A325 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The concentration of lead in children’s blood peaks at about age 2 years and then declines as hand-to-mouth activity tends to drop off. Much of the practice and research concerning lead poisoning is based on the belief that the most damage is done by that peak. However, lead’s effects on IQ cannot be detected until about 4 or 5 years of age, when IQ becomes testable. Thus, researchers assume, if we wish to know the lowest level at which lead causes damage, we have to measure blood lead in 2-year-olds and follow them, and if we wish to prevent lead toxicity from occurring, we should focus on 2-year-olds. Both assumptions, and the outcomes they encourage, may be incorrect, concludes a U.S. research team after analyzing data from a study that began in 1994 [EHP
113:597–601].
The Treatment of Lead-Exposed Children study was initially designed to evaluate whether a drug called succimer, which lowers blood lead, would reduce or prevent the effects of lead on IQ. The 780 participating children, selected in approximately equal numbers from clinical centers in Baltimore, Cincinnati, Newark, and Philadelphia, were regularly tested for blood lead concentration and given IQ tests from about age 2 years to about age 7.5 years. About half the children had taken succimer, while the others had taken a placebo. The drug lowered the children’s blood lead concentrations, but the group given succimer did no better on IQ tests than the group given placebo.
In the current study, researchers used the earlier data to evaluate the strength of the association between IQ and blood lead at various ages, and whether blood lead at age 2 years affected IQ at ages 5 and 7 more than blood lead measured at the older ages. The team examined blood lead and intelligence data from ages 2, 5, and 7, as well as many other factors, such as race, sex, language spoken, caregiver’s IQ, and parent’s education, employment, and status as a single parent.
Contrary to most current thinking, which assumes that blood lead concentration at age 2 is the best predictor of IQ at ages 5 and 7, this team found that concurrent blood lead concentration had the strongest association with IQ, and the older the child, the stronger the association. This was true even though blood lead concentrations dropped progressively as the children aged. A few other studies had found somewhat similar results, but the researchers say the size of this study and the quality of its data reinforce the strength of the findings.
This study does have some drawbacks. For instance, the investigators had no data reflecting how much caregivers interacted with and stimulated the children, which can influence a child’s intelligence. In addition, the children selected for the original study weren’t representative of the population as a whole. For instance, their initial blood lead concentrations were higher than those of most U.S. children today, 77% of the children were black, 97% were receiving public assistance, and 72% lived in single-parent households.
Nonetheless, the team concludes that ongoing lead contamination has a significant effect on a child’s intelligence, emphasizing the importance of testing for and reducing lead contamination in the environment of children much older than typically targeted. They also say the findings, if they hold up and are accepted, would relax an important limitation on lead studies, allowing future efforts to include subjects who were not tested for lead at age 2.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0344a15866755AnnouncementsBook ReviewThe Bhopal Saga: Causes and Consequences of the World’s Largest Industrial Disaster Wood Carol S. Carol S. Wood is a toxicologist in the Life Sciences Division at Oak Ridge National Laboratory, Oak Ridge, Tennessee. She wrote the technical support documents on MIC and toluene diisocyanate for the National Advisory Committee on Acute Exposure Guideline Levels, which have been published by the National Academy Press. She also analyzes developmental, reproductive, and neurotoxicity data on a wide variety of pesticides.5 2005 113 5 A344 A344 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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By Ingrid Eckerman
Hyderabad, India:Universities Press, 2005. 283 pp. ISBN: 81-7371-515-7. Rs250
After 20 years, victims, health care workers, and governments are still trying to comprehend what has been called the world’s worst industrial accident. The Bhopal Saga is an attempt to bring order from the chaos of events before, during, and after the methyl isocyanate (MIC) release. Not a scientific analysis, the book summarizes events leading up to the accident of December 1984 and the relief work in the ensuing two decades.
Eckerman’s primary strength is her on-scene experience as a member of the International Medical Commission on Bhopal. She describes comprehensively the long-term health effects documented in the exposed population and suggests what might be done to improve health care for the victims. The paucity of data on certain end points, notably women’s reproductive health, childhood outcome, and cancer, is stressed. Also included is a summary of the positive and negative effects of various interim relief efforts on the population. Eckerman includes societal, economic, environmental, and political aspects that she considers imperative for integrated long-term health care.
The range of acute and chronic health effects in the exposed population is placed in terms of the toxicity of the probable major components of the gas cloud. Unfortunately, Eckerman relied on secondary sources for this information, although the database for most of the individual chemicals is robust and contains controlled experiments in both humans and animals. The primary literature would have informed her that beyond contact irritation, toxicity of the isocyanates varies widely. For example, MIC is not a sensitizer and causes systemic toxicity, whereas other isocyanates are potent sensitizers and local irritants only.
Nevertheless, the acute symptoms, delayed pulmonary effects, and chronic health problems are otherwise well correlated in the context of the known toxicities of the chemicals discussed. Both Union Carbide Corporation and the state and national Indian governments may deserve the constant accusations aimed at them in The Bhopal Saga, but this is almost a distraction. The known safety violations at the plant, the lack of education and training of workers, and the documented incidents of noncompliance before the leak speak for themselves; Eckerman even refers to the accident as a “massacre” or the “killings” in several places. The belief that Union Carbide is withholding data, especially the composition of the gas cloud, is speculation on her part. Current efforts by researchers in the United States to recreate the chemical reaction may soon answer the question of component gases. Although the issue is still debated in the medical community, Eckerman firmly believes that sodium thiosulfate should have been widely used as an antidote for the exposed victims: Eckerman concludes that sodium thiosulfate must have been necessary, not because of patient presentation, but because Union Carbide withdrew the initial recommendation for use of the cyanide antidote. Although it is probable that hydrogen cyanide was generated in the chemical reaction and was present in the gas cloud, cyanide has not been shown to be a breakdown product of MIC in human or animal systems. It is impossible to judge whether Union Carbide was basing its treatment recommendation on toxicity data or for some other reason.
A list of other accidents at Union Carbide plants worldwide contains a mistake (Table 10, p. 267). Large quantities of mercury were used in the 1950s and 1960s at a U.S. Department of Energy facility that was managed at the time by Union Carbide’s Nuclear Division. Eckerman incorrectly states that half of the workers involved were killed, when in fact no adverse health effects have been found. In addition, effects of released mercury on the environment and surrounding communities have been studied extensively.
Eckerman’s biases against industry and government can be forgiven when compensation was delayed for years, a reliable health care infrastructure is still not in place, environmental laws are not enforced, and worker safety appears compromised by lapses in oversight. Unfortunately, Eckerman offers no new solutions to the human rights issues specific to the Bhopal tragedy or to the world population in general.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0344b15866755AnnouncementsNew BooksNew Books 5 2005 113 5 A344 A344 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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An Introduction to Human Molecular Genetics: Mechanisms of Inherited Diseases, 2nd ed.
Jack K. Pasternak
Hoboken, NJ:John Wiley & Sons, 2005. 656 pp. ISBN: 0-471-47426-6, $83.95
CCN Proteins: A New Family of Cell Growth and Differentiation Regulators
Bernard Perbal, Masaharu Takigawa
Hackensack, NJ:World Scientific Publishing, 2005. 250 pp. ISBN: 1-86094-552-X, $80
Computation in Cells and Tissues: Perspectives and Tools of Thought
R. Paton, H. Bolouri, M. Holcombe, J.H. Parish, R. Tateson, eds.
New York:Springer-Verlag, 2004. 343 pp. ISBN: 3-540-00358-4, $69.95
Data Analysis and Visualization in Genomics and Proteomics
Francisco Azuaje, Joaquin Dopazo, eds.
Hoboken, NJ:John Wiley & Sons, 2005. 284 pp. ISBN: 0-470-09439-7, $150
Energy in the 21st Century
John R. Fanchi
Hackensack, NJ:World Scientific Publishing, 2005. 256 pp. ISBN: 981-256-185-4, $52
Environmental Economics for Non-Economists: Techniques and Policies for Sustainable Development, 2nd ed.
John Asafu-Adjaye
Hackensack, NJ:World Scientific Publishing, 2005. 392 pp. ISBN: 981-256-123-4, $58
Evolution in Four Dimensions: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life
Eva Jablonka, Marion J. Lamb
Cambridge, MA:MIT Press, 2005. 472 pp. ISBN: 0-262-10107-6, $34.95
Implications of Nanotechnology for Environmental Health Research
Lynn Goldman, Christine Coussens, eds.
Washington, DC:National Academies Press, 2005. 70 pp. ISBN: 0-309-09577-8, $18
Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems
Edward Keedwell, Ajit Narayanan
Hoboken, NJ:John Wiley & Sons, 2005. 256 pp. ISBN: 0-470-02175-6, $110
Medical Biomethods Handbook
John M. Walker, Ralph Rapley
Totowa, NJ:Humana Press, 2005. 656 pp. ISBN: 1-58829-288-6, $150
Molecular and Cellular Signaling
Martin Beckerman
New York:Springer-Verlag, 2005. 450 pp. ISBN: 0-387-22130-1, $99
Molecular Models of Life: Philosophical Papers on Molecular Biology
Sahotra Sarkar
Cambridge, MA:MIT Press, 2005. 352 pp. ISBN: 0-262-19512-7, $38
Mouse Phenotypes: A Handbook of Mutation Analysis
Virginia E. Papaioannou
Woodbury, NY:Cold Spring Harbor Laboratory Press, 2005. 235 pp. ISBN: 0-87969-640-0, $80
Protein Nanotechnology: Protocols, Instrumentation, and Applications
Tuan Vo-Dinh
Totowa, NJ:Humana Press, 2005. 480 pp. ISBN: 1-58829-310-6, $115
Selective Sweep
Dmitry Nurminsky
New York:Springer-Verlag, 2005. 130 pp. ISBN: 0-306-48235-5, $129
The Global Genome: Biotechnology, Politics, and Culture
Eugene Thacker
Cambridge, MA:MIT Press, 2005. 464 pp. ISBN: 0-262-20155-0, $39.95
The Inside Story: DNA to RNA to Protein
Jan Witkowski, ed.
Woodbury, NY:Cold Spring Harbor Laboratory Press, 2005. 220 pp. ISBN: 0-87969-750-4, $45
The Proteomics Protocols Handbook
John M. Walker
Totowa, NJ:Humana Press, 2005. 1,016 pp. ISBN: 1-58829-343-2, $175
XML for Bioinformatics
Ethan Cerami
New York:Springer-Verlag, 2005. 304 pp. ISBN: 0-387-23028-9, $69.95
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7634ehp0113-00065915929885ResearchReviewIntracellular Calcium Disturbances Induced by Arsenic and Its Methylated Derivatives in Relation to Genomic Damage and Apoptosis Induction Florea Ana-Maria 12Yamoah Ebenezer N. 2Dopp Elke 11Institute of Hygiene and Occupational Medicine, University Hospital, Essen, Germany2Department of Otolaryngology, Center for Neuroscience, University of California, Davis, California, USAAddress correspondence to E. Dopp, University of Duisburg-Essen, University Hospital Essen, Institute of Hygiene and Occupational Medicine, Hufelandstraße 55, 45122 Essen, Germany. Telephone: 0201-723-4578. Fax: 0201-723-4546. E-mail:
[email protected] authors declare they have no competing financial interests.
6 2005 10 2 2005 113 6 659 664 4 10 2004 9 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Arsenic and its methylated derivatives are contaminants of air, water, and food and are known as toxicants and carcinogens. Arsenic compounds are also being used as cancer chemotherapeutic agents. In humans, inorganic arsenic is metabolically methylated to mono-, di-, and trimethylated forms. Recent findings suggest that the methylation reactions represent a toxification rather than a detoxification pathway. In recent years, the correlation between arsenic exposure, cytotoxicity and genotoxicity, mutagenicity, and tumor promotion has been established, as well as the association of arsenic exposure with perturbation of physiologic processes, generation of reactive oxygen species, DNA damage, and apoptosis induction. Trivalent forms of arsenic have been found to induce apoptosis in several cellular systems with involvement of membrane-bound cell death receptors, activation of caspases, release of calcium stores, and changes of the intracellular glutathione level. It is well known that calcium ion deregulation plays a critical role in apoptotic cell death. A calcium increase in the nuclei might lead to toxic effects in the cell. In this review, we highlight the relationship between induced disturbances of calcium homeostasis, genomic damage, and apoptotic cell death caused by arsenic and its organic derivatives.
apoptosisarsenicgenomic damageintracellular calcium
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Arsenic and Its Derivatives as Potent Environmental Toxicants
Exposure to high levels of arsenic in drinking water has been recognized for many decades in some regions of the world, notably in China, India, and some countries in Central and South America. Millions of people are at risk of cancer and other diseases because of chronic arsenic exposure (National Research Council 1999, 2001).
General adverse health effects that are associated with human exposure to arsenicals include cardiovascular diseases, developmental abnormalities, neurologic and neurobehavioral disorders, diabetes, hearing loss, fibrosis of the liver and lung, hematologic disorders, blackfoot disease, and cancers (Abernathy et al. 1999; Sordo et al. 2001; Tchounwou et al. 1999). In humans, arsenic is known to cause cancer of the skin (in combination with ultraviolet irradiation; Rossman et al. 2004) and cancer of the lung, bladder, liver, and kidney (Abernathy et al. 1999; Kitchin 2001; Tchounwou et al. 1999). The principal proposed mechanisms of arsenic carcinogenicity are induction of chromosomal abnormalities, promotion, and oxidative stress (Kitchin 2001; Kitchin and Ahmad 2003). Also, chronic exposure to arsenic has been found to cause immunotoxicity and has been associated with the suppression of hematopoiesis (anemia and leukopenia; Cheng et al. 2004). In its inorganic form, arsenic is known to be cytotoxic and genotoxic in vivo and in vitro (for review, see Dopp et al. 2004a).
Inorganic arsenic is methylated via glutathione (GSH) conjugation to the pentavalent species: monomethylarsonic acid [MMA(V)], dimethylarsinic acid [DMA(V)], and tri-methylarsenic oxide [TMAO(V)] (Kitchin 2001; Sordo et al. 2001). This process requires the metabolic reduction of As(5+) to As(3+), and in this way, trivalent monomethylarsonous acid [MMA(III)], dimethylarsinous acid [DMA(III)], and trimethylarsine [TMA(III)] appear as metabolic products (Kitchin 2001; Kitchin and Ahmad 2003; Sordo et al. 2001) (Figure 1). Recent findings show that the trivalent methylated arsenic metabolites are highly toxic; DMA(III) has been shown to cause several genotoxic and/or clastogenic effects such as single-strand breaks, formation of apurinic/apyrimidinic sites, DNA and oxidative base damages, DNA–protein cross-links, chromosomal aberrations, and aneuploidy (Dopp et al. 2004b; Schwerdtle et al. 2003; Sordo et al. 2001). The genotoxic effects of arsenic and its methylated metabolites in vivo and in vitro, as well as the carcinogenic potencies of these substances, are discussed in detail by Dopp et al. (2004a), Florea et al. (2004), Patrick (2003), Hughes (2002), and Gebel (2001).
The major mechanisms in which toxic metallic entities may damage cells are direct binding to cellular molecules, induction of conformational changes, replacement of physiologic metals from their binding sites (Qian et al. 2003), or inhibition of DNA repair functions (Hartwig 1998). Thus, they may act as catalysts for the redox reactions that produce reactive oxygen species (ROSs). ROSs are capable of damaging a wide variety of cellular macromolecules, including DNA, lipids, and proteins. Finally, cellular signal transduction can be altered (e.g., activation of transcription factors, changes of gene expression); cell growth, proliferation, and differentiation can be promoted; and apoptosis leading to cell death or cancer development can be induced (Qian et al. 2003; Yang and Frenkel 2002).
In addition, Murphy et al. (1981) suggested a neurotoxic potential of arsenic after acute arsenic intoxication of human patients that caused a polyneuropathy with prolonged sensory and motor deficits. Namgung and Xia (2001) have shown that primary cultures of rat cerebellar neurons exposed to 5–15 μM sodium arsenite and 1–5 mM DMA(V) had reduced viability. These authors reported nuclear fragmentation, DNA degradation, and apoptosis induction in neuronal cells treated with sodium arsenite or DMA(V). They concluded that the neurotoxicity of arsenite might be caused by an activation of p38 and c-Jun N-terminal kinase 3 (JNK3) mitogen-activated protein kinases (MAPKs), which are involved in the apoptotic process.
The role of metallothionein (MT) in modifying DMA(V) genotoxicity was recently studied in MT-I/II null mice and in the corresponding wild-type mice by Jia et al. (2004). In this study, increased formation of 8-hydroxy-2′-deoxyguanosine was found together with elevated numbers of DNA strand breaks. The observed levels were significantly higher in MT-I/II null mice than in wild-type mice. Furthermore, the appearance of apoptotic cells was significantly higher in the urinary bladder epithelium of MT-I/II null mice than in dose-matched wild-type mice exposed to DMA(V) (Jia et al. 2004).
Genetic Damage and Apoptosis Induction by Arsenic Compounds
Arsenite is widely used as a chemotherapeutic agent for the treatment of several human diseases. Arsenic trioxide has been used as a mitochondria-targeting drug in acute promyelocytic leukemia (Jimi et al. 2004; Lau et al. 2004; Miller et al. 2002; Rojewski et al. 2004; Zhang et al. 1999). Thus, arsenite and arsenic trioxide are cytotoxic (Jimi et al. 2004; Lau et al. 2004) and are capable of triggering apoptosis (Akao et al. 2000; Cai et al. 2003; Iwama et al. 2001; Shen et al. 2000; Zhang et al. 1999). Cellular targets of arsenic trioxide action are presented in Figure 2. Arsenic facilitates profound cellular alterations, including induction of apoptosis, inhibition of proliferation, stimulation of differentiation, and inhibition of angiogenesis via numerous pathways. The biologic effects of arsenic (principally the trivalent forms, arsenite and arsenic trioxide) may be mediated by reactions with closely spaced cysteine residues on critical cell proteins.
The cytotoxic potential of arsenic trioxide leads to decreased mitochondrial membrane potential, fragmented DNA, and finally to apoptotic cell death. Additionally, apoptosis induced by arsenic is mediated by a mechanism involving intracellular GSH-reactive oxidation (Akao et al. 2000; Jimi et al. 2004; Zhang et al. 1999).
At the molecular level of the cellular response, arsenite is able to up-regulate or down-regulate several proteins involved in different physiologic and pathologic pathways. In rat lung epithelial cells treated with arsenite, 7 of 1,000 proteins changed expression levels significantly. The up-regulated proteins were mostly heat-shock proteins (HSPs) and anti-oxidative stress proteins, including HSP70, aldose reductase, heme oxygenase-1, HSP27, ferritin light chain, and alphaB-crystallin. The glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase, was down-regulated (Lau et al. 2004).
In addition, extracellular signal-regulated kinases ERK1 and ERK2 were completely inactivated, whereas p38 was found activated in human leukemia U937 cells treated with arsenic trioxide (As2O3). Experiments with transfected cells that expressed constitutively activated MAPK kinase MEK1 and a specific inhibitor of p38 have shown that inactivation of ERKs and activation of p38 might be associated with the induction of apoptosis by arsenic trioxide (Iwama et al. 2001). In contrast to the inactivation of ERKs and the activation of p38, activation of JNK by As2O3 appeared to protect cells against the induction of apoptosis. However, treatment of U937 cells with As2O3 also caused the Ca2+-dependent production of superoxide, intracellular acidification, and a decrease in the mitochondrial membrane potential at the early stages of apoptosis. These changes preceded the release of cytochrome c from mitochondria and the activation of caspase-3 (Figures 2 and 3) (Iwama et al. 2001; Miller et al. 2002).
Arsenic trioxide induces apoptosis in various cancer cells via complex mechanisms, which seem to be cell type dependent (Cai et al. 2003; Miller et al. 2002). Involvement of caspase 3 and caspase 8 was shown together with the down-regulation of Bcl-2 protein (Akao et al. 2000; Miller et al. 2002). A tight link between As2O3-induced apoptosis and mitotic arrest was recently shown by Cai et al. (2003), the latter being one of the common mechanism for As2O3-induced apoptosis in cancer cells. Arsenic can either enhance or reduce nitric oxide (NO) production, depending on the type of cell, the species, and dose of arsenical tested. The mechanisms of how arsenic increases or decreases NO production remain unclear (Gurr et al. 2003).
The Janus kinase (JAK)-signal transduction and activation of transcription (STAT) pathway is an essential cascade for mediating normal functions of different cytokines in the development of the hematopoietic and immune systems (Cheng et al. 2004). It has been suggested that arsenic-induced MAPK signal transduction leads to activation of transcription factors such as activator protein-1 (AP-1) and nuclear factor-κB (NFκB), which in turn alters gene expression (Yang and Frenkel 2002). This might be associated with the carcinogenicity of arsenic.
Ma et al. (1998) studied apoptosis in NB4 cells induced by sodium arsenite and arsenate using flow cytometry and DNA gel electrophoresis. The authors concluded that arsenite and arsenate induced apoptosis in NB4 cells by two different mechanisms: at low doses, arsenic might directly induce apoptosis through regulation of the cell cycle checkpoint, whereas at high doses it might directly induce apoptosis, but in this case Bcl-2 might not play an important role. Thus, the chemical valence of arsenic in a compound might be related to the efficiency of arsenical-induced apoptosis (Ma et al. 1998).
Woo et al. (2002) reported that HeLa cells underwent apoptosis in response to As2O3, accompanied by a decrease in mitochondrial membrane potential and caspase-3 activation. Overexpression of Bcl-2, however, prevented the dissipation of mitochondrial membrane potential, subsequently protecting the cells from As2O3-induced apoptosis. Arsenic trioxide increased the cellular content of ROSs, especially hydrogen peroxide, and the antioxidant N-acetyl-l-cysteine. Furthermore, incubation of the cells with catalase resulted in significant suppression of As2O3-induced apoptosis. The above results indicate that the induction of apoptosis in HeLa cells by arsenic trioxide include an early decrease in cellular mitochondrial membrane potential and an increase in ROS content, predominantly H2O2, followed by caspase-3 activation and DNA fragmentation (Miller et al. 2002; Woo et al. 2002).
For decades, arsenic has been considered a nongenotoxic carcinogen because it is only weakly active or, more often, completely inactive in bacterial and mammalian cell mutation assays. In recent studies, methylated metabolites of inorganic arsenic have been extensively investigated because of their high cytotoxic and genotoxic potential. Trivalent dimethylated arsenic, which can be produced by the metabolic reduction of DMA, has attracted considerable attention from the standpoint of arsenic carcinogenesis. Several groups have shown that DMA(III) is highly genotoxic compared with the pentavalent species and inorganic arsenic (e.g., Dopp et al. 2004b; Schwerdtle et al. 2003) (Figure 4).
Ochi et al. (1996) studied the induction of apoptosis caused by the methylated arsenic species. These authors showed that DMA(V) induces apoptosis in cultured human HL-60 cells at concentrations of 1–5 mM after an incubation period of 18 hr. On the other hand, Cohen et al. (2002) showed that in vivo administration of DMA(V) results in cytotoxicity with necrosis, followed by regenerative hyperplasia of the bladder epithelium. DMA(V) exerted differential antiproliferative and cytotoxic activity against leukemia and multiple myeloma cells, with no significant effect on normal progenitor cells (Duzkale et al. 2003).
In comparison with the trivalent inorganic arsenic form, therapeutic concentrations of As2O3 (1–2 μM) had dual effects on malignant lymphocytes: a) inhibition of growth through adenosine triphosphate (ATP) depletion and prolongation of cell cycle time, and b) induction of apoptosis (Zhu et al. 1999).
Zhang et al. (1999) suggested that the increase in intracellular Ca2+ is related to the sensitivity of human cells to As2O3 exposure, indicating that a critical intracellular Ca2+ signal transduction pathway could be involved in As2O3-mediated cell death.
The Toxicity of Arsenicals Is Related to Calcium Homeostasis Disturbances
In order to explore the early apoptotic signal messengers and the apoptotic pathway, the morphologic and functional changes of mitochondria were examined in a study by Shen et al. (2002b). The content of NO and free calcium ions (Ca2+) was measured over the course of apoptosis induction after exposure with As2O3 in esophageal carcinoma cells (SHEEC1). SHEEC1 cells were exposed to As2O3 (1, 3, and 5 μmol/L), and after 0, 2, 4, 8, 12, and 24 hr, the fluorescence intensity (FI) of rhodamine 123 (Rho123)-labeled cells was detected using a confocal laser scanning microscope for evaluation of the mitochondrial membrane potential. After adding arsenic trioxide, SHEEC1 cells showed characteristic morphologic and functional changes of mitochondria such as hyperplasia, disruption, and an accompanying decrease in transmembrane potential (FI of Rho123 decreased). The Ca2+ level increased immediately after adding As2O3, and the NO concentration increased in a step-wise manner up to 24 hr. At this time the cells appeared to have an apoptotic morphology. The results of Shen et al. (2002a, 2002b) suggest that by inducement of As2O3-increased Ca2+ and NO levels, the apoptotic signal messengers initiate the mitochondria-dependent apoptotic pathway.
In previous experiments (Florea 2004) we assessed inorganic Asi(III) and Asi(V), as well as MMA(V), DMA(V), and TMAO(V) (0.5 mM concentration) for early disturbances in calcium homeostasis in HeLa S3 cells within the first few seconds after application. If calcium homeostasis was disturbed, a drop in the fluorescence signal of the dye was recorded by confocal laser scanning microscopy. The drop was transient, and the signal returned rapidly to the initial level within 20 sec (Figures 5 and 6). These calcium signals might occur as active efflux from the cell to the exterior (energy consuming) or as deregulation of other ion transports. A mechanism via membrane receptor activation or membrane damage cannot be excluded (Florea 2004).
Recently, the original calcium hypothesis has been modified, taking into account that cell death is induced under experimental conditions not only by a rise in cytoplasmatic calcium but also when cytoplasmatic calcium activity drops below physiologic levels (Paschen 2003). Cellular stimulation can lead to activation of different signal transduction mechanisms, such as alterations of the cytoplasmatic levels of different ions. Cell alkalization slightly decreases the intracellular Ca2+ concentration due to an efflux of Ca2+ from the cell. Elevation of pH, however, increases Ca2+ either in the presence or absence of external Ca2+ (Cabado et al. 2000). In contrast to these findings, Kauppinen et al. (1989) reported a study involving cortical synaptosomes in the guinea pig. Cytosolic calcium drops were seen in this study in the absence of Ca2+ in the external solution and were related to an increased glucose utilization (Kauppinen et al. 1989). On the other hand, Buja et al. (1993) suggested that the initial modifications of cellular metabolism and calcium homeostasis may activate major pathways leading to a loss of membrane integrity by a) membrane phospholipid degradation, b) production of amphipathic lipids, c) damage of the cyto-skeleton, and d) generation of toxic oxygen species and free radicals.
Cellular Mechanisms of Intracellular Calcium Changes in Relation to Genetic Damage
Regulation of intercellular and intracellular signaling is fundamental for survival and death in biologic organisms; the systems that control ion movements across cell membranes are essential for cell survival. A deregulation of channels or pumps can cause events that lead to cell death. Apoptosis can be caused by loss of Ca2+ homeostatic control but can also be positively or negatively controlled by changes in Ca2+ distribution within intracellular compartments. It was shown that even non-disruptive changes in Ca2+ signaling could have adverse effects, including alterations in cell proliferation and differentiation, as well as in the modulation of apoptosis (Orrenius et al. 2003).
Cellular Ca2+ import through the plasma membrane occurs largely by receptor-operated and voltage-sensitive channels. Once inside the cell, Ca2+ can either interact with Ca2+-binding proteins or become sequestered to the endoplasmic reticulum (ER) or mitochondria, reaching millimolar levels. Ca2+ levels in the ER are regulated by Ca2+-ATPase pumps, inositol 1,4,5-trisphosphate (IP3) receptors, ryanodine receptors, and Ca2+-binding proteins (Orrenius et al. 2003). Thus, the mitochondrial permeability transition is involved in apoptotic cell death, in that it releases pro-apoptotic proteins from the mitochondria into the cytosol where, with the aid of cellular ATP, they complete the apoptotic cascade. The complexity of the regulation of Ca2+ inside the cell is probably because mitochondria are able to modulate the amplitude and shape of Ca2+ signals (Babcock et al. 1997). However, mitochondria contribute to both apoptotic and necrotic cell death (Nieminen 2003).
It was previously demonstrated by Lui et al. (2003) that tubules, in a vertical or horizontal orientation, extend deep inside the nucleus of HeLa cells. These extensions, together with the nuclear envelope and ER, physically form a spatial network. For Ca2+ signaling, the nuclear tubules provide a fast transport system to direct the release of IP3 and Ca2+ from the cytosol to the nucleus or vice versa. The lumen of the nuclear tubules contains many organelles, including mitochondria that move in and out of the nuclear tubules. To reduce Ca2+ overloading, mitochondria can take up a considerable amount of Ca2+ inside the nuclear tubules (Lui et al. 2003). Li et al. (1998) described that oscillations in cytosolic calcium at physiologic rates maximize gene expression depending on IP3. Spikes of cytosolic calcium were able to stimulate gene expression via the nuclear factor of activated T cells (Li et al. 1998).
Another study by Liu and Huang (1996) demonstrated that calcium ions are accumulated in the nuclei of Chinese hamster ovary (CHO)-K1 cells after arsenite treatment. These observed effects were related to disturbances in intracellular calcium homeostasis and with arsenite-induced cytotoxicity and micronucleus formation. A modulation of the calcium level within the nucleus might have toxic effects leading to DNA damage and/or inhibition of DNA repair function. Some authors have shown that micronucleus formation (expressing DNA damage) as well as induction of mitotic disturbances is strongly correlated with disturbances of calcium homeostasis (Dopp et al. 1999; Liu and Huang 1996; Xu et al. 2003). The correlation between DNA damage and calcium homeostasis disturbances was supported for Asi(III) when Liu and Huang (1997) observed an elevation of intracellular calcium after arsenite treatment. These authors showed that calcium ions play an essential role in arsenite-induced genotoxicity and concluded that arsenite exposure perturbs intracellular calcium homeostasis and activates protein kinase C activity in a dose-dependent manner.
Bucki and Gorski (2001) even suggested that the nucleoplasmic calcium concentrations ([Ca2+]n) may be regulated independently of that of cytosolic Ca2+. IP3 and cyclic ADP-ribose are the major factors responsible for Ca2+ release into the nucleus from the perinuclear space. [Ca2+]n is involved in the regulation of many events in the nucleus, such as gene expression, DNA replication, DNA repair, chromatin fragmentation in apoptosis, and modulation of an intranuclear contractile system. The importance of a precise cellular Ca2+-level regulation for an optimal DNA repair process was mentioned already by Gafter et al. (1997). Bugreev and Mazin (2004) showed that the human Rad51 protein, which plays a key role in homologous recombination and DNA repair, is dependent upon the intra-cellular calcium level. Arsenic and its methylated derivatives are able to modulate DNA repair processes (e.g., Andrew et al. 2003; Hartwig 1998; Hartwig et al. 2003) and gene expression (e.g., Elbekai and El-Kadi 2004; Wu et al. 2003). A possible correlation between inhibition of DNA repair function as well as changed gene expression profiles caused by arsenicals and disturbed intracellular calcium homeostasis requires further investigations.
Conclusions
Epidemiologic evidence suggests that exposure to inorganic arsenic causes cancer (e.g., National Research Council 1999). However, the mechanism of arsenic carcinogenesis is still unclear. A complicating factor receiving increasing attention is that arsenic is bio-methylated to form various metabolites. Methylated arsenic species are able to induce genomic damage as well as apoptosis in vivo and in vitro. Most research has been done with DMA(V) because it has neurotoxic effects and induces bladder cancer in rats and apoptosis in cultured human cells (Jia et al. 2004; Namgung and Zia 2001; Xie et al. 2004). The conjugation of DMA(V) with cellular GSH appears to be of mechanistic significance. More research is needed to determine the role of intracellular GSH and methylation in the toxicity of arsenicals in chronic arsenic poisoning or in cases where arsenicals are used as chemotherapeutics.
Several investigations have shown that DMA(V) exposure causes oxidative stress, DNA damage, and specific induction of apoptosis in target organs of arsenic carcinogenesis (Jia et al. 2004; Sakurai et al. 2004), which may be attributable to the mechanism(s) of arsenic-induced carcinogenesis in rodents. Compared with the pentavalent methylated arsenic species, the trivalent species are even more reactive and cause calcium homeostasis disturbances, oxidative stress, DNA damage, and apoptosis to a higher extent. The involvement of internal calcium stores, particularly mitochondria, can be assumed. This specific area requires further research. Also, more research should focus on the cellular effects of arsenic metabolites, which are generated inside the cell and may cause cellular damage at much lower concentrations than the inorganic arsenic species.
Many studies in the literature describe the effects of arsenite and arsenic trioxide on cellular targets, because these chemicals are or have been used as chemotherapeutic agents for the treatment of several human diseases. Apoptosis induction caused by As2O3 has been shown to be related with changes of the intracellular calcium concentration (e.g., Akao et al. 2000; Cai et al. 2003). The intracellular Ca2+ level increases immediately after adding As2O3. The initiation of the mitochondria-dependent apoptotic pathway was suggested (Iwama et al. 2001; Miller et al. 2002).
A precise cellular Ca2+-level regulation is also necessary for optimal DNA repair processes, DNA replication, and gene expression. Arsenicals are able to modulate these processes. A direct correlation between genotoxic effects caused by arsenicals and disturbances of intracellular calcium concentration is partially proven but requires further investigations.
Figure 1 Challenger (1945) mechanism for arsenic biomethylation. R = reduction, OM = oxidative methylation. [Copyright 2004 from “Environmental Distribution, Analysis and Toxicity of Organometal(loid) Compounds” by Dopp et al. (2004a). Reproduced by permission of Taylor & Francis Group, LLC. (http://www.taylorandfrancis.com).]
Figure 2 Cellular targets of arsenic trioxide action, with multiple pathways in malignant cells resulting in apoptosis or in the promotion of differentiation. Potential molecular targets for arsenic trioxide and arsenite are shown in gray. Abbreviations: AP1, activator protein-1; Apaf, apoptotic protease-activating factor; CK2, casein kinase; Co-A, coenzyme A; DAXX, death-associated protein; ER, estrogen receptor; FADH, flavin adenine dinucleotide; PARP, poly-(ADP-ribose)-polymerase; PML, promyelocytic leukemia. Modified from Miller et al. (2002) with permission from the American Association for Cancer Research.
Figure 3 Apoptosis induced by arsenic trioxide by way of changes in mitochondrial membrane potential and increased H2O2 in cells; this lowers the mitochondrial membrane potential, leading to the release of cytochrome c and the activation of the caspase pathway. Abbreviations: ψM, mitochondrial inner transmembrane potential; Apaf-1, apoptotic protease-activating factor 1; GPx, glutathione peroxidase 1; GS, glutathione. Modified from Miller et al. (2002) with permission from the American Association for Cancer Research.
Figure 4 Micronucleus formation in CHO cells after treatment of cells with (A) Asi(V), MMA(V), or DMA(V) and (B) Asi(III), MMA(III), or DMA(III). The cells were incubated with the arsenic species for 1 hr. Two thousand binucleated cells were evaluated for micronucleus induction in each case. Data from Dopp et al. (2004b).
p ≤ 0.01, and
p ≤ 0.001, Student t-test.
Figure 5 Intracellular calcium changes (relative intensity units measured by confocal laser scanning microscopy) in HeLa S3 cells after application of HEPES buffer (negative control). (A) Control. (B) Control and application of HEPES buffer (indicated by arrow). (C) Control after 1 min. (D) Control after 3 min. The incubation buffer did not modify the initial level of fluorescence intensity. No photo bleaching occurred. Data from Florea (2004).
Figure 6 Intracellular calcium changes (relative intensity units measured by confocal laser scanning microscopy) in HeLa S3 cells after application of 0.5 mM of different arsenic species (indicated by arrows). (A) Asi(III); (B) Asi(V); (C) MMA(V); (D) DMA(V); (E) TMAO. Note the drop in the fluorescence signal immediately after application (Florea 2004).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7648ehp0113-00066515929886ResearchArticlesFeasibility of Using Subject-Collected Dust Samples in Epidemiologic and Clinical Studies of Indoor Allergens Arbes Samuel J. Jr.1Sever Michelle 1Vaughn Ben 2Mehta Jigna 1Lynch Jeffrey T. 1Mitchell Herman 2Hoppin Jane A. 3Spencer Harvey L. 1Sandler Dale P. 3Zeldin Darryl C. 11Laboratory of Respiratory Biology, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA2Rho, Inc., Chapel Hill, North Carolina, USA3Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USAAddress correspondence to D.C. Zeldin, NIEHS/NIH, P.O. Box 12233, MD D2-01, Research Triangle Park, NC 27709 USA. Telephone: (919) 541-1169. Fax: (919) 541-4133. E-mail:
[email protected] acknowledge the assistance of photographer S. McCaw and graphic artist L. Wyrick (NIEHS Arts and Photography). We also thank A. Hodges (Rho Inc.) for her assistance with data management.
Funding for this study was provided by the Division of Intramural Research, NIEHS, NIH.
The authors declare they have no competing financial interests.
6 2005 14 3 2005 113 6 665 669 8 10 2004 14 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Studies of indoor allergen exposures are often limited by the cost and logistics of sending technicians to homes to collect dust. In this study we evaluated the feasibility of having subjects collect their own dust samples. The objectives were to compare allergen concentrations between subject- and technician-collected samples and to examine the sample return rate. Using a dust collection device and written instructions provided to them by mail, 102 subjects collected a combined dust sample from a bed and bedroom floor. Later the same day, a technician collected a side-by-side sample. Dust samples were weighed and analyzed for the cat allergen Fel d 1 and the dust mite allergen Der p 1. Fifty additional subjects who were enrolled by telephone were mailed dust collection packages and asked to return a dust sample and questionnaire by mail. A technician did not visit their homes. Correlations between subject- and technician-collected samples were strong for concentrations of Fel d 1 (r = 0.88) and Der p 1 (r = 0.87). With allergen concentrations dichotomized at lower limits of detection and clinically relevant thresholds, agreements between methodologies ranged from 91 to 98%. Although dust weights were correlated (r = 0.48, p < 0.001), subjects collected lighter samples. Among the group of 50 subjects, 46 returned a dust sample and completed questionnaire. The median number of days to receive a sample was 15. With some limitations, subject-collected dust sampling appears to be a valid and practical option for epidemiologic and clinical studies that report allergen concentration as a measure of exposure.
allergensenvironmentepidemiologysampling
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Epidemiologic and clinical studies typically estimate indoor allergen exposures by analyzing samples of settled dust collected at one or more sites within the home (Platts-Mills et al. 1997; Pope et al. 1993). One of the major limitations of these studies is the need for a technician to make home visits. Because home visits are expensive in terms of labor costs and pose logistical challenges for studies conducted in multiple or distant geographic areas, researchers often conduct studies with fewer subjects and with fewer repeated measurements than would be ideal. Undoubtedly, the limitations imposed by home visits is why only one national survey of indoor allergens has been conducted in the United States (Vojta et al. 2002).
One alternative would be to rely on questionnaire data alone to predict allergen levels; however, home characteristics “may not be sufficiently predictive for many clinical and epidemiologic purposes” (Chew et al. 1998). Another alternative would be to have study subjects collect and mail in their own dust samples. If the feasibility of such a methodology could be demonstrated, there would be many applications for its use, such as epidemiologic studies that examine the relationships between allergen exposures and disease, clinical studies in which repeated measurements of indoor allergens are required, and the national surveillance of indoor allergens.
The objective of this study was to evaluate the feasibility—in terms of validity and sample return rate—of having subjects collect their own dust samples. The methodology developed for this study was centered around a commercially available dust collection device that attaches easily to most vacuum-cleaner hoses. Validity was assessed by comparing allergen concentrations and dust weights between paired subject- and technician-collected dust samples. Sample return rate was assessed in another group of subjects who volunteered to collect a dust sample and return it by mail.
Materials and Methods
Study subjects.
This study was conducted in two phases. The recruitment goal was to enroll 100 subjects for phase I and 50 subjects for phase II. Eligibility criteria were the same for all subjects: reside within 50 miles of the National Institute of Environmental Health Sciences (NIEHS), be at least 21 years of age, speak and read English, and have access to a vacuum cleaner with an extension hose. Subjects, who were recruited through flyers posted at shopping centers, apartment complexes, and convenience stores, were enrolled by telephone. For their participation in the study, subjects were offered a gift certificate valued at $50. Subjects were informed at enrollment that study results would not be reported to them. The study, which was conducted from June 2003 to January 2004, was approved by the NIEHS Institutional Review Board.
Phase I.
The purpose of phase I was to quantitatively compare pairs of side-by-side dust samples, with one of each pair collected by the subject and the other by a trained technician. Subjects were mailed a dust collection package containing an introductory letter, a dust collection instruction card, a Mitest dust collector (Indoor Biotechnologies Inc., Charlottesville, VA), two 18 × 24 inch measuring templates, a self-administered questionnaire, and a postage-paid, pread-dressed mailing envelope. The Mitest dust collector is a commercially available plastic device that fits on the distal end of most vacuum cleaner hoses (Figure 1). The collector contains a 40-μm nylon mesh filter that traps vacuumed dust. Using the 10-step procedure described on the dust collection instruction card (Table 1; the instruction card also included illustrations), subjects collected a combined bed and bedroom floor sample.
On the same day, but after the subject had collected the sample, a technician visited the subject’s home and asked the subject to identify the areas sampled. Arrangements for this visit were made during the enrollment telephone call. The technician collected a sample adjacent to, but not overlapping, the area reportedly sampled by the subject. The technicians used the same protocol the subjects used, with three exceptions. First, the technicians used a Eureka Mighty-Mite, model 3685, type B vacuum cleaner for all sample collections (Eureka Company, Bloomington, IL). Second, technicians used the dust collection device employed in the National Survey of Lead and Allergens in Housing rather than the Mitest Dust Collector (Vojta et al. 2002). This device consisted of a 19 mm × 90 mm cellulose extraction thimble (Whatman International Ltd., Maidstone, UK) placed in the distal end of the vacuum’s extension tube and covered with a clean crevice tool. A rubber O-ring placed around the thimble created a seal between the thimble and the vacuum tube. Third, technicians hand-delivered dust samples to the NIEHS, whereas subjects mailed in their samples. Five NIEHS-trained technicians, using three Mighty-Mite vacuum cleaners, collected dust samples in phase I.
Phase II.
The purpose of phase II was to examine the sample return rate among a group of subjects who were enrolled by telephone and mailed a dust collection package. The introduction letter instructed the subjects to follow the steps on the dust collection instruction card, as described in Table 1. Study staff recorded the dates that dust collection packages were mailed out and the dates that dust samples were received at the NIEHS. A formal procedure for reminding subjects to return a sample was not written into the protocol. In general, the study manager telephoned a subject if he or she did not return a sample within a month.
Laboratory procedures.
The processing and analyses of the dust samples followed protocols used in the National Survey of Lead and Allergens in Housing (Vojta et al. 2002). In the NIEHS laboratory, subject- and technician-collected samples were stored at −20°C until processing. Phase I and II dust samples were sieved through 425-μm-pore grating and weighed. Phase I dust samples were extracted in phosphate-buffered saline and clarified by centrifugation. Supernatants were decanted and stored at −20°C. Concentrations of the cat allergen Fel d 1 and the dust mite allergen Der p 1 were measured with monoclonal-antibody–based, enzyme-linked immunosorbent assays (Chapman et al. 1987; de Blay et al. 1991). Subject- and technician-collected samples from the same home were always paired on the same microtiter plate. The lower limits of detection were 0.0032 μg allergen/g of dust for Fel d 1 and 0.010 μg/g for Der p 1. For statistical analyses, samples below the limit of detection were assigned the value of 0.5 times the lower limit of detection.
Statistical analyses.
All analyses, with the exception of calculating mean coefficients of variation, were conducted on the log10-transformed values of allergen concentrations and dust weights to stabilize variances. Paired t-tests were conducted to test whether the average difference between pairs of log-transformed values was different from zero. Pearson’s correlation coefficients and mean coefficients of variation that compared data between the two methods were calculated. Mean coefficients of variation were calculated from linear models that regressed the standard deviation of each data pair against the mean of each data pair. Regression equations that compared allergen concentrations and dust weights between methods were estimated using generalized linear models with technician values as the independent variable and subject values as the dependent variable. For comparisons between demographic groups, separate slopes were fit for each group and tested against each other. All analyses were conducted using SAS statistical software (SAS release 8.2; SAS Institute Inc., Cary, NC).
Of the 102 subjects enrolled in phase I, one subject collected a sample from a living room floor rather than a bed and bedroom floor and was dropped from statistical analyses. Although all subject-collected samples in phase I contained dust, two samples had only trace amounts of dust remaining after sieving, so those two samples could be neither weighed nor analyzed for allergens. However, for statistical comparisons of sample weights, those two samples were given the value of 0.0005 g (0.5 times the scale’s precision). Thus, 101 paired observations were available for statistical comparisons of dust weight, and 99 were available for statistical comparisons of Fel d 1 concentrations. Eleven samples with low dust weights were consumed in Fel d 1 laboratory analyses, which were given priority over Der p 1 laboratory analyses. Therefore, a total of 88 paired observations were available for Der p 1 statistical analyses.
Results
Characteristics of subjects.
The demographic characteristics of the 102 subjects enrolled in phase I and the 50 subjects enrolled in phase II are shown in Table 2. In each phase, most subjects were female. Almost all subjects were either white or black, with each of these racial groups being well represented in each phase. Only two phase I subjects and no phase II subjects were Hispanic. Approximately one-half of the subjects in each phase had college degrees. The average ages of phase I and II subjects, respectively, were 36.4 years and 39.0 years, with a wide range of ages represented.
Comparison of Fel d 1 concentrations.
The geometric mean concentrations (micrograms per gram) of Fel d 1 for subject- and technician-collected samples were 0.87 ± 0.251 (SE) and 0.94 ± 0.249, respectively. The average difference between paired values was not different from zero (p = 0.598). The correlation between subject- and technician-collected samples was very strong (r = 0.880, p < 0.001). As shown in the scatter plot in Figure 2, the regression line, with a slope of 0.95, is essentially undistinguishable from the reference line, which has a slope of 1.00. The mean coefficient of variation, which was calculated on the untransformed data, was 52 ± 2.2% (SE).
When Fel d 1 concentrations were dichotomized at the lower limit of detection (0.0032 μg/g) and the proposed thresholds for allergic sensitization (1 μg/g) and asthma symptoms among allergic patients (8 μg/g), agreements between subject- and technician-collected samples were 98.0, 90.9, and 98.0%, respectively (Custovic et al. 1998; Gelber et al. 1993).
Comparison of Der p 1 concentrations.
The geometric mean concentrations (micrograms per gram) of Der p 1 for subject- and technician-collected samples were 0.15 ± 0.039 (SE) and 0.13 ± 0.033, respectively. As with Fel d 1, the average difference in paired concentrations was not different from zero (p = 0.258), and the correlation between methodologies was very high (r = 0.868, p < 0.001). The regression line, with a slope of 0.88, closely approximates the reference line (Figure 3). The mean coefficient of variation was 93 ± 2.7%.
When Der p 1 concentrations were dichotomized at the lower limit of detection (0.010 μg/g) at the proposed thresholds for allergic sensitization (2 μg/g) and asthma symptoms among allergic individuals (10 μg/g), agreements between subject- and technician-collected samples were 90.9, 96.6, and 96.6%, respectively (Korsgaard 1998; Kuehr et al. 1994; Lau et al. 1989; Platts-Mills et al. 1987; Sporik et al. 1990).
Comparison of dust weights.
The correlation in dust weights between the two collection methods was significant (r = 0.481, p < 0.001); however, subjected-collected samples were lighter than technician-collected samples (paired t-test, p < 0.001). The geometric mean dust weights of subject- and technician-collected samples were 0.116 ± 0.018 g (SE) and 0.224 ± 0.022 g, respectively. The scatter plot and regression line in Figure 4 illustrate the tendency for subjects to collect lighter samples. Further analyses indicated that the slope of the regression line was not significantly modified by sex (p = 0.383), race (white vs. nonwhite, p = 0.562), education (any college vs. no college, p = 0.218), age (above median vs. at or below median, p = 0.200), size of bed (king or queen vs. smaller, p = 0.990), or whether the bed sheets had been washed within the previous 5 days (yes vs. no, p = 0.813). Although few hard-surfaced floors were sampled, the slope of the regression line was significantly modified by the type of floor surface (p = 0.004), with weights of the subject- and technician-collected samples being more similar if the floor was hard surfaced (slope = 1.16 ± 0.156; SE) rather than carpeted (slope = 0.69 ± 0.116). The slope of the regression line was also modified by the recency of cleaning the floor (p = 0.058), with weights being more similar between methods if the floor had not been cleaned within 5 days (slope = 0.94 ± 0.122) rather than within 5 days (slope = 0.71 ± 0.128). The mean coefficient of variation for the dust weight comparison was 66 ± 2.9%.
Sample return rate.
Of the 50 subjects who were enrolled in phase II, 46 returned a dust sample and a completed questionnaire by mail, giving a return rate of 92%. The average number of days for the NIEHS to receive a dust sample from a subject was 19.6 ± 2.28 (SE). The minimum, median, and maximum were 6, 15, and 88 days. With the outlier of 88 days excluded from the analysis, it took males longer on average than females to return a sample (22.8 vs. 15.7 days, p = 0.053); however, there were no significant differences by race (p = 0.217), age (p = 0.679), or education (p = 0.317). Demographic information was not available on the four subjects who did not return a questionnaire, but even had this information been available, there were too few subjects in this group to characterize.
The geometric mean dust weight of the phase II samples was 0.144 ± 0.041 g. Two samples had insufficient amounts of dust to weigh. Geometric mean dust weights for subject-collected samples did not significantly differ between phases I and II (two-sample t-test p = 0.468).
Discussion
This study provides evidence that subject-based sampling would be a valid option for measurements of indoor allergen concentrations. Correlations between subject- and technician-collected samples were very strong for concentrations of the two allergens tested, and percent agreements between the methodologies were very high when concentrations were categorized above and below clinically relevant thresholds. In addition, there was no evidence of systematic bias for comparisons of allergen concentrations, as evidenced by the regression slopes. Because the distributions of the untransformed concentrations and dust weights were highly skewed, as is the case in most allergen studies, mean coefficients of variation on the original scale were naturally high.
The correlation coefficients in this study were higher than those reported in a study that compared paired samples collected by a technician using the same vacuum cleaner (Wickens et al. 2004). In that study, which evaluated two somewhat similar dust collection devices, a technician collected side-by-side samples from longitudinal halves of 37 mattresses and duplicate samples from 37 floors (the sampled floor area was vacuumed twice) (Wickens et al. 2004). Pearson’s correlation coefficients for concentrations of Fel d 1 and Der p 1, respectively, were 0.76 and 0.67 for the bed and 0.82 and 0.58 for the floor (Wickens et al. 2004). Other studies have indicated that allergen concentrations vary between pairs of side-by-side bed and floor samples (Hirsch et al. 1998; Marks et al. 1995). This variation is due to random error, variation in laboratory assays, and the heterogeneity of allergen concentrations across surfaces.
For dust weight, subjects tended to collect lighter samples. One potential explanation is that subjects might not have been as thorough as the technicians in vacuuming the entire area within the template, especially when the floor surface was carpeted. We have observed that it is easier to move the dust collection device (regardless of type) across a hard surface than a carpeted surface, which may explain why dust weights were more similar when the sampled floor was hard surfaced. A second potential explanation is the difference in vacuum cleaners used by the subjects and the technicians. Technicians used the same make and model of vacuum cleaner throughout the study, and each of the three vacuum cleaners was new at the start of the study and had clean dust bags. Subjects used their own vacuum cleaners, which on average might have been less efficient than those used by the technicians. A third potential explanation is that the Mitest dust collector used by the subjects is less efficient in collecting dust than is the thimble device used by the technicians. The thimble device was used by the technicians because at the time of this study, we considered it to be the gold standard. However, our own side-by-side testing of these devices on six beds, eight carpeted floors, and six hard-surfaced floors did not reveal a significant difference in log-transformed dust weights across these 20 paired samples (data not shown; paired t-test, p = 0.475).
Whether differences in sample dust weights would affect the results of a study depends on whether the reported measure of exposure is allergen concentration or load. Studies typically report concentration (micrograms of allergen per gram of dust), which theoretically would be the same regardless of the amount of dust collected. However, for studies reporting allergen load, which is the product of the sample dust weight and allergen concentration, lower dust weights would result in lower allergen loads. Because the estimation of allergen load is very sensitive to the efficiencies of the vacuuming equipment and the dust collection device and to variations in vacuuming technique, we question, as have others (Wickens et al. 2004), whether load is a reliable measure of allergen exposure.
The major limitation to this subject-based sampling is that it requires subjects (or someone in their household) to have access to a vacuum cleaner, to be able to read and follow written instructions, and to be physically capable of completing the sampling procedure. Vacuum cleaner ownership is not universal, nor is literacy, and it is not likely that all subjects in a target population would be physically capable of performing the procedure. Therefore, this methodology would not be feasible for every study, especially for a study that targeted low-income, inner-city households or the very elderly. In epidemiologic surveys limited only to subjects who could carry out the procedure, people of low economic status and the very elderly might be underrepresented, which could reduce the generalizability of the results. The exclusion of these groups in clinical and case–control studies could also reduce the generalizability of results, although it would not be a source of bias as long as intervention (case) and control groups were limited by the same inclusion criteria. Depending on the study size and the number of subjects who could not perform the procedure themselves (or have a household member perform the procedure for them), researchers could combine subject- and technician-based sampling within the same study because the two methods give very similar results. Also, if a relatively small number of subjects did not have vacuum cleaners, the study could provide vacuum cleaners to those subjects. In fact, for small studies in which repeated sampling is needed through time, it would be more economical to provide vacuum cleaners to all subjects than to have technicians make repeated home visits. This would be especially true for studies that send out pairs of technicians to ensure their safety, as was the case in the National Cooperative Inner-City Asthma Study (Mitchell et al. 1997).
The sample return rate among the 50 phase II subjects who volunteered to collect a dust sample and return it by mail was quite good, especially when one considers that a formal call-back procedure was not in place. In general, the study manager telephoned a subject if his or her sample was not received at the NIEHS within a month. Among the 46 subjects who returned a sample and a completed questionnaire, only four were reminded by telephone, and each of them was contacted only once. The subjects who did not return a dust sample were contacted two or three times. Also, because allergen results were not reported to subjects, there was no direct benefit to subjects for their participation, other than the $50 they received. In studies of asthmatic or allergic subjects in which allergen levels would be reported to subjects, one might expect them to be even more motivated to comply with the dust collection protocol. It should be noted that phase II measured sample return rate as opposed to response rate. The subjects in phase II responded to advertising and agreed to participate in the study before the dust collection packages were mailed out, as would be the case in most clinical trials and environmental intervention studies. If the dust collection packages had been sent out to a random sample of the population without enrolling them beforehand, as is typically done in surveys, the response rate would likely have been much lower than the sample return rate reported in this study. In addition, this study did not test what the response rate or quality of the samples would have been if subjects had been asked to collect multiple samples over time, as would be the case in longitudinal studies, which typically experience some loss to follow-up.
One of the major benefits of having subjects collect their own samples is lower cost. We estimated a cost of $18 per dust collection package (Mitest dust collector, printed materials, templates, mailing envelopes, and postage). For technician-based sampling, there would typically be those same costs (technicians are often required to ship samples) plus the cost of training, equipment, labor, and travel. Even for a local study, the cost of having a technician collect a sample would be $75–100.
In conclusion, subject-collected dust sampling appears to be a valid method for measuring allergen concentrations in homes, and although it has some limitations, it should be considered a feasible option for many epidemiologic and clinical studies.
Figure 1 Example of a Mitest dust collector (Indoor Biotechnologies Inc.), along with its lids and dust filter, which subjects attached to their vacuums to collect a dust sample.
Figure 2 Scatter plot and regression line for the comparison of log-transformed Fel d 1 concentrations (μg/g) between subject- and technician-collected samples. The dashed line is the reference line, which has a slope of 1.0. [n = 99; r
2 = 0.77; y = −0.03 + 0.95(x)].
Figure 3 Scatter plot and regression line for the comparison of log-transformed Der p 1 concentrations (μg/g) between subject- and technician-collected samples [n = 88; r2 = 0.75; y = −0.04 + 0.88(x)]. The dashed line is the reference line, which has a slope of 1.0.
Figure 4 Scatter plot and regression line for the comparison of log-transformed dust weights (g) between subject- and technician-collected samples [n = 101; r
2 = 0.23; y = −0.44 + 0.76(x)]. The dashed line is the reference line, which has a slope of 1.0.
Table 1 The 10-step procedure described on the dust collection instruction card.
Bring to your bedroom: the dust collector and its cap, the two measuring squares, a watch or clock with second hand, and your vacuum cleaner.
Roll back the covers on your bed. Place one square on the bottom or fitted sheet. Place the other square on the floor beside the bed.
Place the dust collector on the end of your vacuum hose (it may fit loosely until the vacuum is turned on).
Prepare your watch or clock for timing. Vacuum the area within one square for exactly 2 min. Without turning the vacuum off, continue to step 5.
Vacuum the area within the other square for exactly 2 min.
While holding the collector up, turn the vacuum off. Push the cap firmly into the top of the collector.
Remove the collector from the hose and place it back into the Ziploc bag and close.
Complete the questionnaire.
Place the following items in the return mailing envelop: the Ziploc bag containing the dust collector and the completed questionnaire.
Place the return envelope in the U.S. mail within 12 hr.
The card given to subjects also included illustrations.
Table 2 Characteristics (frequencies) of subjects enrolled in phases I and II.
Characteristics Phase I (n = 102) Phase II (n = 50)
Demographic characteristics
Sex
Female 81 31
Male 21 15
Unknown 0 4
Race
White 55 21
Black 38 22
Asian 5 1
Native American 2 1
Pacific Islander 0 1
Unknown 2 4
Hispanic ethnicity
No 100 45
Yes 2 0
Unknown 0 5
Education
Some high school 3 3
High school 18 10
Some college 27 8
College degree 53 25
Unknown 1 4
Age (years)
Mean ± SE 36.4 ± 1.22 39.0 ± 1.69
Median 33.5 37.5
Minimum 17 23
Maximum 75 71
Sample-related characteristics
Type of vacuum used
Upright 75 35
Canister 10 7
Small hand-held 9 3
Central 1 0
Other 4 1
Unknown 3 4
Bed sampled
Twin/single 18 2
Double/full 31 11
Queen 39 19
King 13 13
Unknown 1 5
Floor covering sampled
Carpet 93 44
Hard surface 7 2
Carpet and hard surface 1 0
Unknown 1 4
Changed sheets within 5 days
No 60 35
Yes 41 9
Unknown 1 6
Cleaned floor within 5 days
No 53 33
Yes 48 13
Unknown 1 4
==== Refs
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Sporik R Holgate ST Platts-Mills TA Cogswell JJ 1990 Exposure to house-dust mite allergen (Der p 1) and the development of asthma in childhood. A prospective study N Engl J Med 323 502 507 2377175
Vojta PJ Friedman W Marker D Clickner R Rogers JW Viet S 2002 The first National Survey of Lead and Allergens in Housing: survey design and methods for the allergen component Environ Health Perspect 110 527 532 12003758
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7767ehp0113-00067015929887ResearchArticlesAssociation of Air Pollution with Increased Incidence of Ventricular Tachyarrhythmias Recorded by Implanted Cardioverter Defibrillators Dockery Douglas W. 12Luttmann-Gibson Heike 1Rich David Q. 1Link Mark S. 3Mittleman Murray A. 45Gold Diane R. 12Koutrakis Petros 1Schwartz Joel D. 12Verrier Richard L. 151Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA2Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA3New England Medical Center, Tufts University, Boston, Massachusetts, USA4Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA5Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USAAddress correspondence to D.W. Dockery, Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Landmark Center, Room 415D West, P.O. Box 15677, 401 Park Dr., Boston, MA 02215 USA. Telephone: (617) 384-8740. Fax: (617) 384-8745. E-mail:
[email protected] thank the data abstracters, including J. Baliff, C. Freed, C. Hu, R. Hulefeld, and L. McClelland.
The Health Effects Institute (grant 98-14) and the National Institute of Environmental Health Sciences (ES-09825 and ES-00002) funded this study. Particulate air pollution measurements were supported in part by the U.S. Environmental Protection Agency (grant R827353).
The authors declare they have no competing financial interests.
6 2005 18 2 2005 113 6 670 674 17 11 2004 14 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Epidemiologic studies have demonstrated a consistent link between sudden cardiac deaths and particulate air pollution. We used implanted cardioverter defibrillator (ICD) records of ventricular tachyarrhythmias to assess the role of air pollution as a trigger of these potentially life-threatening events. The study cohort consisted of 203 cardiac patients with ICD devices in the Boston metropolitan area who were followed for an average of 3.1 years between 1995 and 2002. Fine particle mass and gaseous air pollution plus temperature and relative humidity were measured on almost all days, and black carbon, sulfate, and particle number on a subset of days. Date, time, and intracardiac electrograms of ICD-detected arrhythmias were downloaded at the patients’ regular follow-up visits (about every 3 months). Ventricular tachyarrhythmias were identified by electrophysiologist review. Risk of ventricular arrhythmias associated with air pollution was estimated with logistic regression, adjusting for season, temperature, relative humidity, day of the week, patient, and a recent prior arrhythmia. We found increased risks of ventricular arrhythmias associated with 2-day mean exposure for all air pollutants considered, although these associations were not statistically significant. We found statistically significant associations between air pollution and ventricular arrhythmias for episodes within 3 days of a previous arrhythmia. The associations of ventricular tachyarrhythmias with fine particle mass, carbon monoxide, nitrogen dioxide, and black carbon suggest a link with motor vehicle pollutants. The associations with sulfate suggest a link with stationary fossil fuel combustion sources.
air pollutionarrhythmiasepidemiologyfibrillationheart arrest
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A large number of epidemiologic studies have found an association between short-term episodes of increased particulate air pollution and cardiovascular morbidity and mortality (Brook et al. 2004). Respirable particulate matter has been specifically implicated in the triggering of myocardial infarction (D’Ippoliti et al. 2003; Peters et al. 2001), arrhythmias (Peters et al. 2000), decompensation of heart failure patients (Morris and Naumova 1998; Schwartz and Morris 1995; Wellenius et al., in press), and the exacerbation of myocardial ischemia (Pekkanen et al. 2002; Wellenius et al. 2003). Particulate-related changes in autonomic nervous system activity, as assessed by heart rate variability, have been observed in both experimental animal studies (Godleski et al. 2000) and human panel studies (Creason et al. 2001; Gold et al. 2000; Liao et al. 1999, 2004; Pope et al. 1999), suggesting sympathetic activation or vagal suppression after particulate air pollution exposure. Such changes in autonomic tone may increase the risk of ventricular arrhythmias in vulnerable patients (Huikuri et al. 2001). Ventricular tachyarrhythmias, primarily ventricular tachycardia and ventricular fibrillation, are common precursors to sudden cardiac death (Bayes de Luna et al. 1989; Myerburg et al. 1992).
Implanted cardioverter defibrillators (ICDs) passively monitor for ventricular tachyarrhythmias that, if not terminated, could be life threatening. On detecting such an arrhythmia, the ICD can apply cardiac pacing or cardioverter shock to restore normal rhythms. The ICD also records the date and time of arrhythmias plus intracardiac electrograms immediately before and during these events. In a pilot study of 100 Boston area ICD patients with follow-up for up to 3 years, we found increased risk of an ICD therapeutic discharge on days after elevated air pollution concentrations (Peters et al. 2000). In this pilot study, we did not collect data on patient characteristics or medication. However, we did find stronger air pollution associations among patients with frequent ICD discharges.
This study was designed to confirm the pilot study observations. In a larger sample of ICD patients in Boston with longer follow-up, we identified ventricular tachyarrhythmias by review of ICD-recorded electrograms. We assessed the association between community air pollution and ventricular tachyarrhythmias using time-series methods. We also evaluated modification of the air pollution association by patient medical conditions, antiarrhythmic medications, and recent arrhythmias.
Materials and Methods
Arrhythmias.
We examined the effects of air pollution on incidence of tachyarrhythmias in ICD patients clinically followed between July 1995 and July 2002 at the Tufts New England Medical Center (Dockery et al., in press). The source population consisted of patients implanted with third-generation Guidant ICDs (Cardiac Pacemakers, Inc., St. Paul, MN) at the New England Medical Center Cardiac Electrophysiology and Pacemaker Laboratory between June 1995 and 31 December 1999. All patients met the American College of Cardiology and the American Heart Association guidelines for ICD implantation (Gregoratos et al. 1998). We excluded patients residing in ZIP codes > 40 km (25 miles) from the air monitoring site at the Harvard School of Public Health. Patient characteristics before implant (including age, sex, race and ethnicity, residential ZIP code, implant date, device model, diagnoses at implant, and physiologic measurements before implant) were abstracted from patient records. Prescribed medications were abstracted from clinical records at each follow-up visit.
Date, time, and intracardiac electrograms of all detected arrhythmias were downloaded from the ICD records collected at the patients’ regular clinical follow-up visits (on average, every 89 days). Patients contributed person-time to the follow-up between ICD implantation and their last clinical follow-up visit at the New England Medical Center before July 2002. We excluded the first 14 days after implantation, periods when the patient was a hospital inpatient, and periods between clinical visits when the patient was not followed up at the New England Medical Center. Subjects who died or who were lost to follow-up were censored at their last clinical follow-up.
The intracardiac electrograms for each ICD-detected arrhythmia were reviewed by an electrophysiologist (M.S.L.) blinded to air pollution levels. Ventricular tachyarrhythmias were identified based on atrial-ventricular dysynchrony, onset interval, stability, morphology of the tachycardia, and response to therapy. We excluded sinus tachycardias, arrhythmias originating outside the ventricle (e.g., atrial tachycardia, atrial fibrillation, atrial flutter, sinus tachycardia), and noise or over-sensing events. An episode day was defined as one or more ventricular arrhythmic events on a given calendar day.
Data collection and preliminary analyses have been described previously (Dockery et al., in press). The Harvard School of Public Health Human Studies Committee and the New England Medical Center Institutional Review Board approved this retrospective record review.
Air pollution.
Ambient concentrations of gaseous air pollutants were measured by the Massachusetts Department of Environmental Protection between 1995 and 2002 at six sites for ozone, nitrogen dioxide, and/or sulfur dioxide and four sites for carbon monoxide in the Boston metropolitan area. We calculated the average air pollution concentration across the reporting air pollution monitoring stations for each hour accounting for differences in the annual mean and the standardized deviations of each monitor (Schwartz 2000). The daily mean was then calculated from the 24-hr specific average concentrations across the monitors.
Fine particulate (< 2.5 μm aerodynamic diameter) matter (PM2.5) concentrations were measured (model 1400A tapered element oscillating microbalance; Rupprecht and Patashnick, East Greenbush, NY) at an ambient monitoring site in South Boston between 15 January 1995 and 19 January 1998 and at the Harvard School of Public Health starting on 17 March 1999. Particulate black carbon (BC) was measured (aethalometer model 8021; McGee Scientific, Berkeley, CA) at the South Boston site through 29 March 1997 and at the Harvard School of Public Health site starting on 15 October 1999. Daily particulate sulfate (SO4) was measured by ion chromatography (model 120; Dionex, Sunnyvale, CA) starting on 25 September 1999, and particle number (PN) by condensation particle counter (TSI Inc., Shoreview, MN) starting on 13 October 1999. We did not consider PM10 (particulate matter with a diameter < 10 μm), which was measured on a 1-in-6–day schedule.
The hourly surface observations from the National Weather Service at Logan Airport in East Boston were extracted from climatic records (Earth-Info, Inc., Boulder, CO). Daily minimum temperature and mean relative humidity were calculated from the hourly observations.
Statistical analyses.
Following the analytic methods used in the pilot study (Peters et al. 2000), we assessed the association of arrhythmias with air pollution using time-series methods. We merged the patient-specific record of days on study and ICD-detected ventricular arrhythmias with the daily mean air pollution and weather measurements. The association of arrhythmic episode-days with air pollution was analyzed by logistic regression using generalized estimating equations (Diggle 1988; Zeger et al. 1988) with random effects for patients, a linear trend, sine and cosine terms with periods of one, one-half, one-third, and one-quarter year, quadratic functions of minimum temperature and mean humidity, indicators for day of the week, and an indicator for a previous arrhythmia within 3 days.
We considered mean air pollution concentrations on the same day and lags of 1, 2, and 3 days. The lag structure of the data was estimated by evaluating each lag day (0 to 3) separately and jointly in an unconstrained distributed lag model (Pope and Schwartz 1996). We have found consistently elevated (although not statistically significant) risk estimates associated with air pollution concentrations on the day of (lag 0) and the day before (lag 1) the arrhythmia (Dockery et al., in press). Therefore, in this article we report only the effects of 2-day running mean air pollution concentrations.
We explored potential modification of the air pollution associations in multivariate logistic regression including interactions between air pollution and indicators of patient characteristics. Patients were stratified by reported ejection fraction before implantation (≤35% vs. > 35%), prior myocardial infarction, and the diagnosis of coronary artery disease before implantation (not sufficient numbers of patients for specific analyses of other cardiac diagnoses). We assessed modification of the air pollution associations by usual prescribed medications (reported at more than half of clinical follow-ups) grouped as beta-blockers, digoxin, and other antiarrhythmics (amiodarone, sotalol, mexilitine, and quinidine). The strongest predictor of a ventricular arrhythmia was an arrhythmia in the previous 3 days. Therefore, in addition to controlling for prior arrhythmias, we assessed the modification of the air pollution association by a prior ventricular arrhythmia.
We present odds ratios (ORs) and 95% confidence intervals (CIs) based on an interquartile range (25th percentile–75th percentile) increase in each air pollution concentration. p-Values are reported for the effects of air pollution and for the interactions of air pollution with posited effect modifiers. We characterize p-values < 0.05 as statistically significant, and p-values < 0.10 as marginally significant. For air pollutants and subgroups of events with statistically significant associations, we examined the risk of arrhythmias versus quintiles of air pollution concentration.
Results
Patient population.
A total of 307 patients had Guidant ICDs implanted at the New England Medical Center between June 1995 and the end of 1999. There were 203 patients followed up with residential ZIP codes within 40 km (25 miles) of the ambient air pollution monitoring site at the Harvard School of Public Health. These ICD patients had a total of 635 person-years (pyr) of follow-up or an average of 3.1 years per subject.
There were 933 ICD-detected tachyarrhythmias (separated by at least 60 min), of which 798 (86%) were ventricular (63 ventricular fibrillation, 25 nonsustained ventricular fibrillation, 622 ventricular tachycardia, and 88 nonsustained ventricular tachycardia). We restricted analysis to the 670 ventricular episode days (one or more ventricular arrhythmias on a calendar day), average of 1.06 episode days/pyr, among 84 (41%) patients.
Patients were predominantly men (75%) with an average age at implantation of 64 years (range, 19–90 years). The rate of ventricular episode days per person-year was higher among men (1.22/pyr) compared with women (0.62/pyr), and increased with age at implantation. Eighty-three percent of the patients were reported to be white, 3% African American, 5% Hispanic, 3% Asian, and 7% of undetermined or unknown race/ethnicity.
Among the patients reported to have had a myocardial infarction before ICD implantation, the rate of ventricular arrhythmias (1.73/pyr) was almost three times the rate among the patients without a prior myocardial infarction (0.61/pyr). The patients with low ejection fraction (≤35%) before implantation had a rate of ventricular episodes (1.48/pyr) approximately three times larger than that of patients with ejection fraction > 35% (0.45/pyr).
The most common preimplantation diagnosis was coronary artery disease (70%) followed by cardiomyopathy (36%). Nine patients (4%) were classified as having primary electrical disease, and four of these had ventricular arrhythmic events. Four patients (2%) had long QT syndrome (a congenital disorder characterized by prolongation of the QT interval on the electrocardiogram), but only one of these had an event during follow-up. Patients with coronary artery disease had the highest rates of detected ventricular arrhythmias (1.30/pyr) compared with those with other diagnoses (0.50/pyr).
Eighty-nine percent of these patients were prescribed antiarrhythmic medications. The rates of ventricular arrhythmic episode days was higher among those prescribed digoxin (1.68/pyr) or other antiarrhythmics (1.45/pyr) than among those prescribed beta-blockers (0.92/pyr) or no regular antiarrhythmic medication (0.88/pyr).
Approximately one-quarter (164) of the 670 ventricular arrhythmias followed a previous ventricular arrhythmia within 3 days. We found that having a prior arrhythmia (within 3 days) was a very strong predictor for a subsequent arrhythmia (OR = 7.2; 95% CI, 5.9–8.9).
Air pollution.
PM2.5 was measured on 2,005 (79%) of the follow-up days and BC on 1,533 (60%) days (Table 1). Particulate SO4 measurements were limited to 908 (36%) days, and PN to 772 (30%) days. Daily PM2.5 was strongly correlated with SO4 (Pearson correlation r = 0.74) and BC (r = 0.67), but only weakly correlated (r = –0.13) with PN.
The gaseous pollutants were measured on essentially all follow-up days (Table 1). Daily CO and NO2, both indicators of motor vehicle emissions, were highly correlated with each other (r = 0.61), positively correlated (r > 0.4) with BC, PM2.5, and SO2, but negatively correlated with O3.
Air pollution association.
We found positive associations between ventricular arrhythmic episode days and mean air pollution on the same and previous days, but none of these associations approached statistical significance (Table 2).
We did not find consistent increased susceptibility to the effects of air pollution on risk of ventricular arrhythmias based on patient characteristics. We found marginally significant (p < 0.10) interaction of the associations with CO with ejection fraction (stronger with low ejection fraction), preimplantation diagnosis of coronary artery disease (weaker with coronary artery disease), and prior myocardial infarction (weaker with prior myocardial infarction), and of the associations with NO2 with prior myocardial infarction (stronger with prior myocardial infarction). No other interactions approached statistical significance. We saw no evidence that any of the prescribed drugs modified the associations of ventricular arrhythmias with air pollution.
The interaction of a prior ventricular arrhythmia with air pollution was statistically significant for PM2.5, BC, NO2, SO2, and CO and marginally significant for SO4 (Table 3). For ventricular arrhythmias within 3 days of a prior event (Table 3), we found statistically significant positive associations with PM2.5, BC, NO2, CO, and SO2, marginally significant associations with SO4, but no associations with O3 or PN. For ventricular arrhythmias more than 3 days after a previous ventricular arrhythmia, we found no associations with any air pollutants (Table 3). We assessed the risk of ventricular arrhythmias stratified by a prior ventricular tachyarrhythmia versus quintiles of air pollution (Figure 1). We found generally increasing risk with increasing quintiles of PM2.5, BC, and CO and weaker suggestions of an exposure response with NO2, SO2, and O3.
Discussion
In this study of 203 New England Medical Center ICD patients living in the Boston metropolitan area with up to 7 years of follow-up, we found the risk of any ICD-detected ventricular tachyarrhythmia was positively but not significantly associated with increased exposure to air pollution on the days before the arrhythmia (Table 2). We found statistically significant associations of air pollution with increased risk of ventricular arrhythmias among patients with an arrhythmia within the previous 3 days. These findings suggest that air pollution may provoke ventricular tachyarrhythmias only in the presence of acutely predisposing conditions that increase ventricular electrical instability. We did not find consistent indications that the air pollution associations with ventricular arrhythmias were modified by indicators of chronically impaired cardiac function, including a prior myocardial infarction, a diagnosis of coronary artery disease, or an ejection fraction < 35%, or by prescribed antiarrhythmic medications.
These results are broadly consistent with those of previously published studies of air pollution associations with tachyarrhythmias leading to ICD therapeutic discharge. In this study, we found significantly increased risk of ventricular arrhythmias with PM2.5, BC, CO, NO2, and SO2 among patients with a recent rior ventricular arrhythmia. In the pilot study (Peters et al. 2000), ICD patients in Boston with frequent (> 10) discharges during follow-up had an exposure related increase in ICD discharge associated with PM2.5, BC, CO, and NO2.
A recent study assessed the association of air pollution in Vancouver, British Columbia, Canada, with ICD discharges among 50 patients with an average of 2.2 years of follow-up (Rich et al. 2004; Vedal et al. 2004). In crude analyses, the rate of ICD discharge increased with quartiles of NO2 and CO concentration on the same day (Vedal et al. 2004). However, there were no statistically significant positive associations with ICD discharge with NO2 or CO after adjusting for temporal patterns and numerous weather parameters. The lack of significant associations may be caused by overcontrol of some variables, as these investigators suggest.
Both of these previously reported studies (Peters et al. 2000; Vedal et al. 2004) focused on ICD therapeutic discharge without characterization or validation of the underlying arrhythmia. Of the almost 2,000 arrhythmias identified and recorded by the ICD devices in this study, 8% were classified as oversensing, 4% were sinus tachycardias, 18% were supra-ventricular arrhythmias, and 70% were ventricular arrhythmias. Thus, 30% of the ICD-detected arrhythmias were not the potentially life-threatening ventricular tachyarrhythmias defined as the primary outcome for this study.
An important question in these analyses is the appropriate exposure averaging time and the lag between exposure and cardiac arrhythmia. In the pilot study, we found associations with air pollutants 2 days before the arrhythmias and with the 5-day mean air pollution (Peters et al. 2000). In this study, ventricular arrhythmias were positively associated with ambient air pollution on the same and the previous calendar days. Temporality would require that air pollution exposure precede the arrhythmia. This temporal association is clearly true for associations with air pollution on the previous day, but mean air pollution on the same calendar day would include hours after as well as before the detected arrhythmia. Using the pollutant concentrations from the specific 24 hr preceding the arrhythmia would likely provide a better estimate of each subject’s exposure and allow investigation of exposures in the hours before the arrhythmia.
For these patients living in eastern Massachusetts, air pollution exposure was estimated based on a single or a small number of monitors in the Boston metropolitan area. This would lead to misclassification of air pollution exposure, but this misclassification would be independent of the risk for ventricular arrhythmias. Such nondifferential misclassification of exposure produces an attenuated estimate of associations (and larger CIs) in epidemiologic studies assuming linear associations. If these observations are true, then studies with improved estimation of subject specific air pollution exposures would be expected to find stronger, more statistically significant associations.
We found associations with CO, NO2, BC, and PM2.5. These four pollutants had high day-to-day correlations with each other and were strongly correlated with SO2. It would not be possible to differentiate the independent effects of these pollutants. Nevertheless, the associations with these specific pollutants are consistent with an effect from air pollution from motor vehicle sources.
Animal studies in Boston have suggested that changes in indicators of cardiac function are specifically associated with motor vehicle pollution (Clarke et al. 2000). Analysis of daily mortality in Boston and five other cities suggested that motor vehicle pollution was more strongly related to cardiovascular mortality than to respiratory mortality (Laden et al. 2000). Cardiovascular emergency department visits in Atlanta, Georgia, were significantly associated with these same markers of motor vehicle air pollution—NO2, CO, PM2.5, BC, and fine particle organic carbon (Metzger et al. 2004). For Atlanta emergency department visits for dysrhythmias, positive associations were found for these same motor vehicle pollutants, although these associations were not statistically significant because of the smaller number of events.
We cannot exclude the possible role of sulfur oxides, which are generally considered to be indicators of air pollution from power plants and other stationary fossil fuel combustion sources. In this analysis, we found associations of ventricular tachyarrhythmias in subjects with a recent event associated with SO2 (p = 0.013) and with SO4 (p = 0.06). The positive, marginally significant associations with SO4 are notable because SO4 data were available only on a limited number of days (37%) compared with SO2 and the other gaseous pollutants. Particulate SO4 concentrations in Boston largely reflect secondary particles formed during long-range transport. Gaseous SO2 concentrations reflect local sulfur emissions and were most highly correlated with motor vehicle pollutants.
A major advantage of the ICD data is the passive monitoring of cardiac tachyarrhythmias. Nevertheless, ICD-detected ventricular arrhythmias were rare events in this follow-up, and the small number of subjects with multiple ICD-detected arrhythmias is a limitation. These patients clearly represent a highly selected cohort of special interest, because their previous history of cardiovascular disease might make them particularly sensitive to the effects of air pollution episodes. The observed associations of ventricular tachyarrhythmias with particulate air pollution in these subjects are large compared with previous studies. In a mortality time-series analysis in Boston and five other cities (Schwartz et al. 1996), each increase of 10 μg/m3 in the 2-day mean PM2.5 was associated with a 2% increase in the risk of cardiovascular mortality. For Boston ICD patients (Table 2), the observed associations imply an 11% (95% CI, –9 to 35%) increased risk of potentially fatal ventricular arrhythmias when scaled to the same 10 μg/m3 in the 2-day mean PM2.5 concentrations. Thus, the ICD patients had a risk of potentially life-threatening ventricular tachyarrhythmias associated with fine particles that was more than five times the risk of cardiovascular death in the general population. Among those at the highest risk—those with a recent prior ventricular arrhythmia—the increased risk of a new ventricular tachyarrhythmia was 97% (95% CI, 46–165%) associated with each 10-μg/m3 increase in PM2.5.
Conclusions
We found that ventricular tachyarrhythmias among patients with ICDs increased with air pollution on the same and previous days, but these associations did not reach statistical significance. However, among patients with a recent tachyarrhythmia, the increased risk of a follow-up ventricular tachyarrhythmia associated with air pollution was large and statistically significant. These observations suggest that air pollution may act in combination with a cardiac electrical instability to increase the risk for ventricular tachyarrhythmias. Among such acutely vulnerable ICD patients, there was an exposure response with PM2.5, BC, NO2, CO, and SO2, which we interpret as indicators of mobile source pollution, and also evidence of an association with SO4, which we interpret as an indicator of power plant and other stationary fossil fuel combustion sources.
ICDs have proven to be highly effective in reducing the risk of death in patients with high risk of cardiac arrhythmias. The passive monitoring of arrhythmias by these devices has provided a rich resource for understanding the role of air pollution episodes as potential triggers of these events.
Correction
Some counts of observations (Tables 1 and 2) and the interquartile range of SO4 (Tables 1, 2, and 3) were for 1-day rather than 2-day mean in the original manuscript published online. They have been corrected here.
Figure 1 Relative odds and 95% CIs of ventricular arrhythmias versus quintiles of air pollution, ≤3 days of and > 3 days after, a previous arrhythmia: (A) PM2.5, (B) BC, (C) CO, (D) NO2, (E) SO2, (F) O3.
#p < 0.10;
*p < 0.05;
**p < 0.01.
Table 1 Distribution of the 2-day mean air pollutants averaged across multiple sites in Boston, and weather data: 11 July 1995 to 11 July 2002.
Percentile
Air pollutant No. 25th 50th 75th 95th
PM2.5 (μg/m3) 2,005 7.5 10.3 14.4 23.3
BC (μg/m3) 1,533 0.66 0.98 1.39 2.25
SO4 (μg/m3) 908 1.76 2.55 3.80 7.18
PN (103/cm3) 772 20.6 29.3 39.8 50.7
NO2 (ppb) 2,556 18.9 22.7 26.6 33.6
CO (ppm) 2,558 0.53 0.80 1.02 1.37
SO2 (ppb) 2,558 3.3 4.9 7.4 12.8
O3 (ppb) 2,548 15.7 22.9 31.1 42.1
Minimum temperature (°C) 2,553 0.6 7.2 14.4 20.6
Relative humidity (%) 2,549 56.7 69.0 81.5 94.3
Table 2 Estimated ORs (95% CIs) for an interquartile range increase in 2-day mean air pollution.
No. of days Interquartile range increase OR (95% CI) p-Value
PM2.5 2,005 6.9 μg/m3 1.08 (0.96–1.22) 0.21
BC 1,533 0.74 μg/m3 1.11 (0.95–1.28) 0.18
SO4 908 2.04 μg/m3 1.05 (0.92–1.20) 0.48
PN 772 19,120/cm3 1.14 (0.87–1.50) 0.35
NO2 2,556 7.7 ppb 1.07 (0.97–1.18) 0.19
CO 2,558 0.48 ppm 1.14 (0.95–1.29) 0.28
SO2 2,558 4.0 ppb 1.04 (0.94–1.14) 0.28
O3 2,548 15 ppb 1.09 (0.93–1.29) 0.28
Table 3 Association of interquartile range increase in 2-day mean air pollution with ventricular arrhythmias stratified by a recent arrhythmia (within 3 days).
Air pollutant (IQR increase) > 3 Days < 3 Days p-Value for interaction
PM2.5 (6.9 μg/m3) 0.98 (0.86–1.12) p = 0.73 1.60 (1.30–1.96) p < 0.001 < 0.001
BC (0.74 μg/m3) 1.02 (0.83–1.24) p = 0.86 1.74 (1.28–2.37) p < 0.001 0.003
SO4 (2.04 μg/m3) 1.03 (0.87–1.22) p = 0.73 1.19 (0.99–1.43) p = 0.060 0.066
PN (19,120/cm3) 1.17 (0.82–1.66) p = 0.38 1.11 (0.71–1.75) p = 0.65 0.86
NO2 (7.7 ppb) 1.02 (0.90–1.16) p = 0.78 1.34 (1.05–1.71) p = 0.018 0.050
CO (0.48 ppm) 1.04 (0.83–1.29) p = 0.75 1.65 (1.17–2.33) p = 0.005 0.016
SO2 (4 ppb) 0.98 (0.87–1.11) p = 0.78 1.30 (1.06–1.61) p = 0.013 0.006
O3 (15 ppb) 1.14 (0.92–1.40) p = 0.24 1.01 (0.76–1.35) p = 0.94 0.44
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7633ehp0113-00067515929888ResearchArticlesThe Environmental Estrogen Bisphenol A Inhibits Estradiol-Induced Hippocampal Synaptogenesis MacLusky Neil J. 1Hajszan Tibor 23Leranth Csaba 241Center for Neural Recovery and Rehabilitation Research, Helen Hayes Hospital, New York, New York, USA2Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine, New Haven, Connecticut, USA3Laboratory of Molecular Neurobiology, Biological Research Center, Hungarian Academy of Sciences, Szeged, Hungary4Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, USAAddress correspondence to N.J. MacLusky, Center for Neural Recovery and Rehabilitation Research, Helen Hayes Hospital, New York State Department of Health, Route 9W, West Haverstraw, NY 10993-1195 USA. Telephone: (845) 786-4810. Fax: (845) 786-4875. E-mail:
[email protected] thank K. Szigeti-Buck and G. Thomas for excellent technical assistance.
This work was supported by National Institutes of Health grants MH60858 and NS42644 (C.L.) and a Dow Chemical SPHERE award (N.J.M.).
N.J.M. received a Dow Chemical SPHERE award, an unrestricted award that provided support to the research laboratory but no direct compensation to the author. T.H. and C.L. declare they have no competing financial interests.
6 2005 24 2 2005 113 6 675 679 4 10 2004 24 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Bisphenol A (BPA) is an estrogenic chemical that is widely used in the manufacture of plastics and epoxy resins. Because BPA leaches out of plastic food and drink containers, as well as the BPA-containing plastics used in dental prostheses and sealants, considerable potential exists for human exposure to this compound. In this article we show that treatment of ovariectomized rats with BPA dose-dependently inhibits the estrogen-induced formation of dendritic spine synapses on pyramidal neurons in the CA1 area of the hippocampus. Significant inhibitory effects of BPA were observed at a dose of only 40 μg/kg, below the current U.S. Environmental Protection Agency reference daily limit for human exposure. Because synaptic remodeling has been postulated to contribute to the rapid effects of estrogen on hippocampus-dependent memory, these data suggest that environmental BPA exposure may interfere with the development and expression of normal sex differences in cognitive function, via inhibition of estrogen-dependent hippocampal synapse formation. It may also exacerbate the impairment of hippocampal function observed during normal aging, as endogenous estrogen production declines.
bisphenol ACA1estradiolhippocampusspine synapse density
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Natural and man-made chemicals in the environment can exert hormone mimetic or antagonist activity. Bisphenol A (BPA), a widely used chemical with mixed estrogen agonist/antagonist properties, is employed in the manufacture of plastics used in dental prostheses and sealants (Suzuki et al. 2000), in the linings of metal cans used to preserve foods (Kang et al. 2003), and in such items as baby bottles (Brede et al. 2003) and the clear plastic cages used in many research institutions to house laboratory animals (Howdeshell et al. 2003). The low affinity of BPA for the cell nuclear estrogen receptors ER-α and ER-β and weak bioactivity in standard tests of estrogenicity, such as the rat uterotrophic assay (Ashby 2001), have led to the conclusion that human BPA exposure is probably insufficient to elicit significant estrogenic responses [Degen et al. 2002; European Commission (EC) Scientific Committee on Food 2002; U.S. Environmental Protection Agency (EPA) 1993]. Whether the endocrine activity of BPA is accurately reflected in such tests, however, remains controversial, because of the potential for tissue and cell-specific estrogen effects (Safe et al. 2002). Of particular concern, several reports have indicated that BPA exposure inhibits sexual differentiation of nonreproductive behaviors, including maze learning behavior (Carr et al. 2003; Farabollini et al. 2002), at doses as much as 1,000-fold lower than those required for stimulation of uterine growth (Ashby 2001). The mechanisms underlying these low-dose effects remain unknown.
Sexual differentiation of the brain is believed to involve neurotrophic effects of estrogens, mediated at least in part via activation of kinase-dependent signaling cascades (Toran-Allerand et al. 1999). Kinase-associated neuroplastic responses to estrogen are also expressed in adulthood, in the cornu ammonis (CA) pyramidal neurons of the hippocampus (Bi et al. 2001; McEwen 2002). In adult female rats (Woolley and McEwen 1992) as well as nonhuman primates (Leranth et al. 2002), estradiol induces a rapid increase in CA1 pyramidal cell dendritic spine synapse density (PSSD), a response that has been postulated to involve intermediate activation of the mitogen-activated protein (MAP) kinase cascade (Bi et al. 2001). We reasoned that if BPA inhibits sexual differentiation of the rodent brain, there might also be significant interactions between estradiol and BPA with respect to the regulation of hippocampal CA1 PSSD. Consistent with this hypothesis, in rat hippocampal organotypic cultures, regulation of NMDA receptors, which are critical components of the mechanisms responsible for estrogen regulation of CA1 dendritic spine density (Woolley and McEwen 1994), has been reported to be sensitive to nanomolar concentrations of either 17β -estradiol (E2) or BPA (Sato et al. 2002). Therefore, in the present study, we examined the effects of estradiol and BPA, alone and in combination, on CA1 PSSD in adult ovariectomized (OVX) rats. Our results indicate that BPA does indeed have potent effects on the regulation of CA1 PSSD. However, the data demonstrate that, rather than inducing estrogen-like responses, BPA antagonizes the rapid inductive effects of estrogen on hippocampal PSSD.
Materials and Methods
Animals.
Experimental protocols were approved by the Institutional Animal Care and Use Committee of Yale University, where all studies using animals were performed. Adult female Sprague-Dawley rats (250–300 g; Charles River Laboratories, Wilmington, MA, USA) were used. The rats were ovariectomized under ketamine/xylazine/acepromazine anesthesia (3 mL/kg intramuscular injection of a cocktail containing 25 mg ketamine, 1.2 mg xylazine, and 0.03 mg acepromazine in 1 mL saline).
Morphologic studies.
One week after ovariectomy, animals were treated with estrogen, using groups of three animals per treatment condition. In the first PSSD study, 15 rats (five groups of three rats) were injected subcutaneously with either 17β -E2 (60 μg/kg; 12 rats) or the sesame oil vehicle (200 μL; three rats). Nine of the 12 estradiol-treated animals were treated simultaneously with increasing doses (40, 120, and 400 μg/kg) of BPA (> 99% purity; Sigma-Aldrich, St. Louis, MO, USA). In the second PSSD experiment, 12 rats were injected subcutaneously (three rats per treatment) with 17α -E2 (45 μg/kg), BPA (300 μg/kg), a combination of 17α -E2 (45 μg/kg) plus BPA (300 μg/kg), or the sesame oil vehicle (200 μL) alone. Thirty minutes after injection, animals were sacrificed under deep ether anesthesia by transcardial perfusion of heparinized saline followed by a fixative containing 4% paraformaldehyde and 0.1% glutaraldehyde in 0.1 M phosphate buffer (pH 7.35). The brains were removed and postfixed overnight in the same fixative without glutaraldehyde. The hippocampi were then dissected out, and 100 μm vibratome sections were cut perpendicular to the longitudinal axis of the hippocampus. The approximately 90 vibratome sections were divided into 10 subgroups using systematic random sampling and were flat-embedded in Araldite (Electron Microscopy Sciences, Fort Washington, PA, USA).
To correct for processing-induced changes in the volume of the tissue, we calculated a correction factor assuming that the treatments did not alter the total number of pyramidal cells. In all hippocampi, we examined six or seven disector pairs (pairs of adjacent 2-μm semi-thin sections mounted on slides and stained with toluidine blue). We calculated a pyramidal cell density value (D) using the formula D = N/sT, where N is the mean disector score across all sampling windows, T is the thickness of the sections (2 μm), and s is the length of the window. Based on these values, a dimensionless volume correction factor kv was introduced: kv = D/D1, where D1 is the mean cell density across the groups of hippocampi (Rusakov et al. 1997).
To exclude the possibility that alterations in PSSD might be a consequence of changes in the volume of reference, we used a subset of the vibratome sections for volume estimation of the stratum radiatum of CA1, using the Cavalieri’s principle (Gundersen and Jensen 1987). Areas of CA1 stratum radiatum were measured in each section using Scion Image software (Scion Corp., Frederick, MD, USA), and the total volume of CA1 stratum radiatum in each rat was estimated as
where T is the distance between the top of one sampled section and the top of the next section, and A(CA1SR)n is the measured area of CA1 stratum radiatum for each section.
Thereafter, serial ultrathin sections were cut from randomly sampled vibratome sections and collected on formvar-coated single-slot grids. Disector pairs of digitized electron micrographs (“reference” and “look-up”) were taken from adjacent ultrasections at a magnification of 11,000× in a Tecnai 12 transmission electron microscope (FEI Company, Hillsboro, OR, USA) furnished with an AMT Advantage 4.00 HR/HR-B CCD camera system (Hamamatsu Photonics, Hamamatsu, Japan), from an area located between the upper and middle third of the CA1 stratum radiatum (300–500 μm from the pyramidal cell layer; Leranth et al. 2004). Identical regions in adjacent sections were identified using landmarks such as myelinated fibers, large dendrites, or blood vessels that did not change significantly between neighboring sections. The investigator taking the electron micrographs was blinded to the treatment of individual animals. Areas occupied by potentially interfering structures such as blood vessels, large dendrites, or glial cells were subtracted from the measured fields. The digitized electron micrographs were printed out using a laser printer and coded. The code was not broken until the analysis was completed. Synapses were counted using a two-dimensional unbiased counting frame with an area of 79 μm2 superimposed on the electron microscopic prints. Only those spine synapses were counted that were present in the reference micrograph but not in the look-up micrograph, and vice versa. At least 10 disector pairs were photographed on each electron microscopic grid. With at least three grids (containing two adjacent ultrathin sections) prepared from each vibratome section (cut from three different regions of the hippocampus along its longitudinal septotemporal axis), each animal provided at least 3 × 3 × 10 × 2 = 180 neuropil fields for evaluation. The density of spine synapses in each animal was calculated as
where ∑Q(syn) is the total number of synapses sampled by the disector; 2 × 90 = 180 is the number of evaluated electron micrographs per animal; the section thickness t was measured by the method of Small’s smallest fold (Weibel 1979; average 0.075 μm); and 79 is the area of the counting frame in square micrometers. PSSD for each animal was calculated by dividing Nv(syn) by the volume correction factor kv.
Rat uterine weight assay.
To assess uterotrophic responses, a separate group of 12 rats (four groups of 3) was treated 1 week after ovariectomy with subcutaneous injections of 17β -E2 (60 μg/kg), BPA (400 μg/kg), or the combination of BPA (400 μg/kg) and 17β -E2 (60 μg/kg), daily for 3 days. Control animals received the sesame oil vehicle (200 μL/day) alone. Six hours after the last injection, the animals were sacrificed; their uteri were dissected free of adhering fat and connective tissue, drained of intraluminal fluid, and weighed.
Statistical analysis.
Results, in all cases, are presented as mean ± SD of observations from three animals per treatment group. We have verified that the use of three animals per treatment group provides sufficient statistical power to detect effects of the magnitude typically observed after steroid replacement, because of the precision obtained by analyzing large numbers of sections per animal. SDs for counting CA1 synapses in this laboratory are usually < 5% of mean PSSD. With an SD of 5% and sample sizes of three per group, a 15% change in mean PSSD can be detected with α = 0.05 and 80% power. In the present studies, SDs for measurement of PSSD were in most instances considerably < 5% of mean.
Data were analyzed statistically using Statview (SAS Institute, Cary, NC, USA) or SPSS for Windows (Systat Inc., Chicago, IL, USA). We used Bartlett’s test to verify homogeneity of variance. One- and two-way analysis of variance (ANOVA) and the Bonferroni-Dunn test were used for comparison of individual treatment groups. When only control versus treatment comparisons were considered, we used the Student t-test. Four-parameter least-squares regression analysis of the BPA dose–response data was performed using ALLFIT (DeLean et al. 1978).
Results
Treatment of OVX rats with 17β -E2 (60 μg/kg body weight) increased CA1 PSSD almost 2-fold (Figure 1A). This dose of 17β -E2 has previously been shown to induce a maximal PSSD response (MacLusky et al. 2005). Treatment with BPA did not further enhance hippocampal synapse formation but dose-dependently inhibited the effect of 17β -E2 (Figures 1B, 2). At a BPA dose of 400 μg/kg, the PSSD response to 17β -E2 was completely inhibited, compared with the CA1 PSSD in OVX vehicle-treated animals. Four-parameter least-squares regression analysis (DeLean et al. 1978) determined a median effective dose (ED50) of 117 μg/kg for BPA inhibition of the response to 17β -E2.
Increased uterine weight is a widely accepted bioassay for estrogen action (Ashby 2001). Therefore, we determined whether the dose of BPA (400 μg/kg) found to block induction of PSSD produced comparable inhibition of uterotrophic responses. Administration of the highest dose of BPA daily for 3 days only marginally inhibited the uterotrophic effect of 17β -E2 (Figure 3). These results are consistent with previous reports that BPA exerts weak antagonist effects on some uterine responses to estradiol at doses < 100 mg/kg, although it acts as an estrogen agonist at higher dose levels (Ashby 2001).
Like several other responses of neurons to estrogen (Green et al. 1997; Levin-Allerhand et al. 2002; Yu et al. 2004), PSSD is sensitive to both the 17α and 17β isomers of estradiol, the 17α isomer being considerably more potent as an inducer of CA1 spine synapses (MacLusky et al. 2005), despite the fact that it has very low uterotrophic activity (Lundeen et al. 1997). We therefore determined whether BPA also interferes with the synaptic effects of 17α -E2. Treatment with 17α -E2 at 45 μg/kg induced an increase in PSSD similar to that elicited by 60 μg/kg 17β -E2 (Figure 4; compare with Figure 1). Administration of 300 μg/kg BPA alone significantly reduced PSSD. The same dose of BPA also inhibited the increase in PSSD induced by 17α -E2 (Figure 4). The mean PSSD observed after treatment with the combination of BPA and 17α -E2 was not significantly different from that observed in OVX vehicle-injected controls.
In neither of the two PSSD studies was there any significant variation in the total volume of the CA1 stratum radiatum (Table 1), confirming the validity of the volume correction procedure used in calculating PSSD.
Discussion
Our data indicate that low-dose BPA exposure inhibits the rapid hippocampal synaptogenic response to estradiol. The minimum BPA dose required to elicit this effect is within the range of dose levels believed, until now, to have little or no significant biologic impact, even under conditions of long-term BPA exposure. In the United States, the current U.S. Environmental Protection Agency (EPA) maximum acceptable “reference” dose for chronic BPA ingestion is 50 μg/kg /day, calculated as 0.1% of the lowest observed adverse effect level (LOAEL) determined from toxicity studies (U.S. EPA 1993). The corresponding European Commission tolerable daily intake (TDI; 10 μg/kg/day) is based on the assumption of a 500-fold safety margin over the no observed effect level (NOEL) dose derived from three-generation rat reproductive toxicity trials (EC Scientific Committee on Food 2002). In OVX rats, our data indicate that a single BPA dose of 40 μg/kg, below the U.S. EPA reference dose and only 4-fold higher than the European Commission TDI safe daily limit, is sufficient to significantly impair the PSSD response to maximal 17β -E2 stimulation. Under conditions of low physiologic estrogen exposure, as is the case during prepubertal development as well as after reproductive senescence, considerably lower doses of BPA may be sufficient to interfere with the synaptogenic effects of the hormone. Circumstantial evidence supporting the view that the effects of BPA are not confined to rapid PSSD responses to estrogen administration is provided by the data for OVX rats. Even without estrogen treatment, in OVX rats BPA significantly reduced CA1 PSSD (Figure 4). Preliminary data from our laboratories indicate that the “baseline” PSSD observed in OVX rats includes a contribution from the phytoestrogens present in normal rat chow. Removal of these estrogens, by feeding with phytoestrogen-free chow, reduces CA1 PSSD to levels comparable with those observed in the present study after treatment of OVX animals with BPA (Leranth C, Hajszan T, MacLusky NJ, unpublished data).
Estradiol has important neurotrophic and neuroprotective functions in the brain, in addition to its role as a reproductive steroid (Nathan et al. 2004). A growing body of evidence indicates that estradiol is synthesized in the hippocampus (Hojo et al. 2004), providing a local source of estrogen onto which the effects of circulating levels of the hormone are superimposed. Because synapse formation in the hippocampus is believed to be involved in the mechanisms mediating the acquisition and retention of memory (Silva 2003), interference with estrogen action in the hippocampus could have serious long-term consequences. Deficiencies in gonadal steroid-induced stimulation of hippocampal synaptogenesis have been suggested to contribute to neuro-degenerative disorders and age-related cognitive impairment, for which women with low bioavailable circulating estradiol concentrations appear to be at enhanced risk (Gandy 2003; Yaffe et al. 2000). The ability of BPA to block the effects of estrogen on CA1 PSSD raises the possibility that chronic environmental exposure to BPA might interfere with estrogen effects on the development and function of the brain, inhibiting normal sex differences in nonreproductive behavior (Carr et al. 2003; Farabollini et al. 2002) as well as exacerbating the negative impact on the aging brain of declining gonadal hormone levels (Gandy 2003; Yaffe et al. 2000). Although it remains to be determined whether such effects have a significant impact on the health of human and animal populations exposed to BPA, the current exposure limits clearly do not provide a wide margin of safety in terms of the acute estrogen-dependent regulation of CA1 PSSD.
The mechanisms responsible for BPA’s effects remain unknown. BPA interacts with a number of hormone receptor systems, including androgen and thyroid receptors as well as ER-α and ER-β (Moriyama et al. 2002; Wetherill et al. 2002; Zoeller et al. 2005), providing several potential pathways through which BPA could interfere with hippocampal synaptogenesis. Recent work has demonstrated that BPA and 17β -E2 are equipotent activators of CREB phosphorylation in pancreatic islet cells, a response mediated via a “nonclassical” membrane ER (Quesada et al. 2002). Although the in vitro equilibrium binding affinities of ER-α and ER-β for BPA are low (Kuiper et al. 1998a), this does not preclude the possibility that BPA could act via these ERs as well because rapid membrane ER-mediated responses may not reflect equilibrium binding affinity. ER-α and ER-β both partially localize to the plasma membrane, where they mediate activation of kinase-dependent signaling pathways. Induction of these rapid kinase-mediated mechanisms exhibits a different pharmacologic specificity than do nuclear receptor–activated responses. Thus, activation of extracellular-signal–regulated kinase (ERK) phosphorylation in rat-2 cells transfected with ER-α or ER-β is equally sensitive to 17α -E2 and 17β -E2 (Wade et al. 2001), despite the large difference that exists between these steroids in nuclear ER-α and ER-β equilibrium binding affinity (Kuiper et al. 1998b) and uterotrophic activity (Lundeen et al. 1997). Studies in bone cells and ER-transfected HeLa cells suggest that rapid membrane receptor–activated responses to estrogen have a much broader ligand specificity than do slower nuclear receptor–mediated transcriptional effects because ER ligand association rates tend to have a much more relaxed structural specificity than do dissociation rates. Therefore, ligands that are incapable of forming stable nuclear ER complexes because they dissociate rapidly from the receptor may, nonetheless, modulate membrane ER-mediated effects (Kousteni et al. 2001).
Circumstantial evidence points to a role for nonclassical receptor mechanisms in the hippocampal response to estrogen. Effects of 17β -E2 on CA1 dendritic structure are accompanied by increased ERK phosphorylation (Bi et al. 2001), as well as changes in the distribution of the phosphorylated form of the serine-threonine kinase Akt in CA1 pyramidal cell dendrites (Znamensky et al. 2003). The fact that 17α -E2 and 17β -E2 both induce an increase in PSSD is consistent with the hypothesis that membrane-associated ERs may mediate rapid estrogen activation of CA1 spine synapse formation (Wade et al. 2001). That the rapid actions of estradiol on CA1 PSSD involve nonclassical ER systems is also suggested by recent data from this laboratory demonstrating that short-term induction of CA1 spine synapses requires relatively high circulating 17β -E2 concentrations (MacLusky et al. 2005). The effects of BPA on rapid estrogen induction of CA1 PSSD may reflect interference, directly or indirectly, with this putative novel estrogen response pathway. Such a hypothesis would be consistent with recent studies in Mytilus that have demonstrated marked inhibition of p38 MAP kinase phosphorylation by low concentrations of BPA, a response diametrically opposite to that of estradiol (Canesi et al. 2004, 2005). A critical experiment for future studies will be to determine whether the effects on hippocampal PSSD of sustained physiologic circulating levels of 17β -E2 (Woolley and McEwen 1992), which may involve a greater contribution from nuclear ER-α and/or ER-β, are similarly affected by low-dose BPA exposure.
In summary, these data demonstrate that the environmental estrogen BPA inhibits estrogen-activated hippocampal spine synapse formation. Because hippocampal spine synapses are believed to be involved in the mechanisms responsible for the formation of memory (Silva 2003), these observations raise concerns regarding the potential impact of low-dose continuous BPA exposure on cognitive development and function. In addition, they further emphasize the dangers inherent in reliance on only one or a few nuclear ER-dependent tests as a basis for environmental estrogen risk assessment (Safe et al. 2002). There may be other compounds in the environment—natural and man-made—that, like 17α -E2 and BPA, exert potent effects on neural estrogen response mechanisms, even though their reported affinities for ER-α and ER-β are low. If so, current screening methods for the evaluation of putative estrogen-like “endocrine disruptors” (U.S. EPA 1998) may underestimate the potential risk of exposure to such compounds.
Figure 1 BPA inhibits the effect of 17β -E2 on CA1 PSSD. (A) At 30 min after 17β -E2 injection, PSSD increased by almost 100%. (B) The PSSD response to 17β -E2 is inhibited in a dose-dependent manner by coadministration of BPA. Data in all cases represent mean ± SD of independent observations from three rats at each dose level. In the case of the 120-μg/kg dose, the bars indicating SD are so close to the mean that they are partially obscured by the symbol. The line through the points represents the four-parameter logistic best-fit regression analysis of the data. The ED50 for BPA inhibition of the 17β -estradiol–induced increase in PSSD, calculated from the four-parameter curve fit, is 117 μg/kg.
*Significantly different from oil-injected controls (p < 0.001, Student t-test).
**Significantly different from PSSD in animals treated with 17β -E2 alone (Bonferroni-Dunn post hoc test, p < 0.05).
Figure 2 High-power electron micrographs taken from the CA1 stratum radiatum of rats treated with either (A) 60 μg/kg 17β -E2 or (B) 60 μg/kg 17β -E2 + 400 μg/kg BPA. Arrows indicate spine synapses. Bar = 500 nm.
Figure 3 BPA administration only slightly impairs the uterotrophic response to 17β -E2. Two-way ANOVA: 17β -E2 effect, F = 301.2, df 1,8, p < 0.0001; BPA effect, F = 11.1, df 1,8, p = 0.01; 17β -E2 × BPA interaction, F = 0.29, df 1,8, p > 0.6.
Figure 4 BPA inhibits the effects of 17α -E2 on CA1 PSSD. In the absence of BPA, 17α -E2 induced an increase in synapse density of 0.463 synapses/μm3, a 65% increase above the mean synapse density in vehicle-injected controls. In the presence of BPA, the effect of the estrogen was reduced to an increase of 0.192 synapses/μm3, 39% above the level observed in animals treated with BPA alone. Two-way ANOVA: 17α -E2 effect, F = 237.3, df 1,8, p < 0.0001; BPA effect, F = 292.8, df 1,8, p < 0.0001; 17α -E2 × BPA interaction, F = 40.8, df 1,8, p = 0.0002.
*Significantly higher PSSD than in vehicle-treated rats, without BPA (Student t-test, p < 0.05).
**Significant inhibitory effect of BPA compared with animals given the same vehicle or 17α -E2 without BPA (Bonferroni-Dunn post hoc test, p < 0.05).
Table 1 Effect of 17β -E2 or 17α -E2 with or without BPA on total CA1 stratum radiatum volume (mean ± SD).
Treatment CA1 stratum radiatum volume (mm3)
17β -E2
Vehicle control 3.84 ± 0.15
60 μg/kg 17β -E2 3.99 ± 0.26
60 μg/kg 17β -E2 + 40 μg/kg BPA 3.96 ± 0.16
60 μg/kg 17β -E2 + 120 μg/kg BPA 4.05 ± 0.49
60 μg/kg 17β -E2 + 400 μg/kg BPA 3.93 ± 0.43
17α -E2
Vehicle control 3.97 ± 0.42
45 μg/kg 17α -E2 3.97 ± 0.39
300 μg/kg BPA 3.97 ± 0.37
45 μg/kg 17α -E2 + 300 μg/kg BPA 3.77 ± 0.19
The volume of CA1 stratum radiatum was measured in the animals used for spine synapse counting (n = 3 animals per group). For animals treated with 17β -E2 ± BPA, synapse densities are shown in Figure 1; one-way ANOVA: F = 0.162, df 4,10, p > 0.95. For animals treated with 17α -E2 ± BPA, synapse densities are shown in Figure 4; one-way ANOVA: F = 0.241, df 3,8, p > 0.85.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7837ehp0113-00068015929889ResearchArticlesIn Vitro Inhibition of Human Hepatic and cDNA-Expressed Sulfotransferase Activity with 3-Hydroxybenzo[a]pyrene by Polychlorobiphenylols Wang Li-Quan 1Lehmler Hans-Joachim 2Robertson Larry W. 2Falany Charles N. 3James Margaret O. 11Department of Medicinal Chemistry, University of Florida, Gainesville, Florida, USA;2Department of Occupational and Environmental Health, College of Public Health, University of Iowa, Iowa City, Iowa, USA;3Department of Pharmacology and Toxicology, University of Alabama, Birmingham, Alabama, USAAddress correspondence to M.O. James, Department of Medicinal Chemistry, Room P6-20B, 1600 SW Archer Rd., University of Florida, Gainesville, FL 32610-0485 USA. Telephone: (352) 846-1952. Fax: (352) 846-1972. E-mail:
[email protected] thank F.P. Guengerich for providing samples of human liver and W. Farmerie for providing access to instruments for polymerase chain reaction amplification.
This study was supported by grants P42 ES07375 and P42 ES07380 from the National Institute of Environmental Health Sciences, National Institutes of Health (NIH), and grant GM38954 from the National Institute of General Medical Sciences, NIH.
This article reflects the authors’ views and not any official views of NIH.
The authors declare they have no competing financial interests.
6 2005 24 2 2005 113 6 680 687 8 12 2004 24 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Sulfonation is a major phase II biotransformation reaction. In this study, we found that several polychlorobiphenylols (OH-PCBs) inhibited the sulfonation of 3-hydroxybenzo[a]pyrene (3-OH-BaP) by human liver cytosol and some cDNA-expressed sulfotransferases. At concentrations > 0.15 μM, 3-OH-BaP inhibited its own sulfonation in cytosol fractions that were genotyped for SULT1A1 variants, as well as with expressed SULT1A1*1, SULT1A1*2, and SULT1E1, but not with SULT1A3 or SULT1B1. The inhibition fit a two-substrate kinetic model. We examined the effects of OH-PCBs on the sulfonation of 0.1 or 1.0 μM 3-OH-BaP, noninhibitory and inhibitory substrate concentrations, respectively. At the lower 3-OH-BaP concentration, OH-PCBs with a 3-chloro-4-hydroxy substitution pattern were more potent inhibitors of cytosolic sulfotransferase activity [with concentrations that produced 50% inhibition (IC50) between 0.33 and 1.1 μM] than were OH-PCBs with a 3,5-dichloro-4-hydroxy substitution pattern, which had IC50 values from 1.3 to 6.7 μM. We found similar results with expressed SULT1A1*1 and SULT1A1*2. The OH-PCBs were considerably less potent inhibitors when assay tubes contained 1.0 μM 3-OH-BaP. The inhibition mechanism was noncompetitive, and our results suggested that the OH-PCBs competed with 3-OH-BaP at an inhibitory site on the enzyme. The OH-PCBs tested inhibited sulfonation of 3-OH-BaP by SULT1E1, but the order of inhibitory potency was different than for SULT1A1. SULT1E1 inhibitory potency correlated with the dihedral angle of the OH-PCBs. The OH-PCBs tested were generally poor inhibitors of SULT1A3- and SULT1B1-dependent activity with 3-OH-BaP. These findings demonstrate an interaction between potentially toxic hydroxylated metabolites of PCBs and polycyclic aromatic hydrocarbons, which could result in reduced clearance by sulfonation.
3-hydroxy-benzo[a]pyrenehuman liver cytosolinhibition of sulfonationpolychlorobiphenylolsSULT1A1*1SULT1A1*2SULT1E1
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Polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) are two classes of environmentally prevalent pollutants. PAHs are formed through the combustion of fossil fuels and the burning of organic materials (Dipple 1985). PCBs were first produced industrially in the middle of the last century for their desirable dielectric properties (Erickson 2001) and remain in the environment because of their continued use, because of their release from waste sites, and because many congeners are slowly degraded. The more lipophilic PAHs and PCBs are often found in the same environmental samples, such as soils and sediments, and are bio-transformed in animals by similar pathways (James 2001).
Of the PAHs, benzo[a]pyrene (BaP) is a well-studied chemical carcinogen, which is metabolized by cytochrome P-450 (CYP) to a variety of products (Dipple 1985). These include 3-hydroxybenzo[a]pyrene (3-OH-BaP), a major metabolite of BaP in humans and animals, which has estrogenic properties and binds to hemoglobin (Charles et al. 2000; Sugihara and James 2003). Hydroxylated PAH metabolites such as 3-OH-BaP are substrates for glucuronidation and sulfonation, catalyzed by one or more of the UDP-glucuronosyltransferases and 3′-phosphoadenosine 5′-phosphosulfate (PAPS)-dependent sulfotransferases (SULTs), respectively (James et al. 2001). Sulfonation is considered a detoxification pathway for 3-OH-BaP.
PCBs have several metabolites of toxicologic importance, including the polychlorobiphenylols (OH-PCBs), which are formed in vivo from CYP-dependent mono-oxygenation of PCBs (James 2001). Although they are slightly more hydrophilic than are the parent PCBs, several OH-PCBs are eliminated slowly (Klasson-Wehler et al. 1993). People who are highly exposed to PCBs through the diet typically have OH-PCBs in their blood, some bound to plasma proteins (Guvenius et al. 2003; Sandau et al. 2000). Several OH-PCB congeners interact with components of the endocrine system, potentially interfering with thyroid hormone and estrogen function (Lans et al. 1993; Safe 1994; Schuur et al. 1998). Although the OH-PCBs have low affinities for both α and β estrogen receptors, some OH-PCBs are strikingly potent inhibitors of human estrogen sulfotransferase (SULT1E1), with sub-nanomolar concentrations that produced 50% inhibition (IC50) (Kester et al. 2000). This suggests that OH-PCBs may be indirectly estrogenic by increasing estradiol bioavailability in target tissues. As well as possibly causing toxicity by inhibiting the sulfonation of hormones, several OH-PCBs inhibited the sulfonation and glucuronidation of the PAH metabolite 3-OH-BaP in channel catfish intestine (van den Hurk et al. 2002).
Sulfonation is an important phase II conjugation pathway for the detoxification of xenobiotics as well as the modulation of endogenous compounds such as thyroid hormones, steroids, and neurotransmitters (Coughtrie et al. 1998). One or more members of a superfamily of cytosolic SULT enzymes catalyze these reactions (Blanchard et al. 2004). SULT1A1, SULT1B1, and SULT1E1 are the major phenol sulfotransferases expressed in human liver, with SULT1A1 (also known as ST1A3) found at the highest concentration (Honma et al. 2002). SULT1A3 is expressed in the gut but is present in very low concentrations in adult human liver (Richard et al. 2001). Genetic polymorphisms are known for SULT1A: a G638→A transition leading to an Arg213→His exchange in the protein was observed with a frequency of 33.2% in Caucasian subjects, 8% in Chinese, and 29.4% in African Americans (Carlini et al. 2001). SULT1A1*His (SULT1A1*2) was a less thermostable protein than SULT1A1*Arg (SULT1A1*1), and some authors have reported that the SULT1A1*2 variant is less catalytically active (Ozawa et al. 1998; Raftogianis et al. 1997).
Because people are frequently coexposed to PAHs and PCBs, we wished to determine if OH-PCBs would inhibit 3-OH-BaP sulfonation in human liver (HL) cytosol and, if so, whether the inhibition was isozyme selective. We used cDNA-expressed human SULT1A1*1, -1A1*2, -1A3, -1B1, and 1E1 isozymes, which we expected would use 3-OH-BaP as substrate. We genotyped the HL cytosol fractions used in this study, with respect to the common SULT1A1 polymorphic variants, to examine the possibility that OH-PCBs would affect their activity differently. These studies were conducted with a series of predominantly para-OH-PCBs.
Materials and Methods
Materials.
The structures of the OH-PCBs used in this study are shown in Figure 1. In naming these OH-PCBs, we followed the recommendation of Maervoet et al. (2004) to name them as metabolites of PCBs, referring back to the Ballschmiter and Zell numbering system for PCBs (Ballschmiter and Zell 1980). The 6′-OH-CB35 (A1), 4′-OH-CB35 (B1), 4′-OH-CB36 (B2), 4′-OH-CB79 (C1), and 4-OH-CB36 (C2) were synthesized by Suzuki coupling as described previously (Bauer et al. 1995; Lehmler and Robertson 2001). We verified the structures of each of these OH-PCBs by 1H and 13C-nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, and gas chromatography–mass spectrometry (GC-MS). We found that each OH-PCB was > 99% pure by GC-MS analysis (Mass Spectrometry Facility, University of Kentucky, Lexington, KY), combustion analysis (Atlantic Microlab, Atlanta, GA), and thin-layer chromatography. The 4′-OH-CB69 (B3), 4′-OH-CB106 (B4), 4′-OH-CB112 (B5), 4′-OH-CB121 (C3), 4′-OH-CB159 (C4), 4′-OH-CB165 (C5), and 4′-OH-CB72 (C6) were purchased from AccuStandard (New Haven, CT). S.S. Singer (University of Dayton, Dayton, OH) supplied the PAPS. We purchased 35S-PAPS, 3.05 μCi/nmol (99.1% pure), from PerkinElmer Life Science (Boston, MA). Benzo[a]pyrene-3-sulfate (BaP-3-SO4) and 3-hydroxybenzo[a]pyrene (3-OH-BaP) were purchased from the NCI Chemical Carcinogen Reference Standard Repository (Midwest Research Institute, Kansas City, MO). We obtained HaeII from Fisher Scientific (Atlanta, GA) and Taq DNA polym-erase, along with other polymerase chain reaction (PCR) reagents, from Promega (Madison, WI). Integrated DNA Technologies (Coralville, IA) supplied primers for use in genotyping. We purchased the highest available grade of other reagents from Fisher Scientific (Atlanta, GA) and Sigma Chemical Company (St. Louis, MO).
Physicochemical properties of the OH-PCBs.
We calculated the structural characteristics of dihedral angle, molecular volume, molecular surface area, pKa, log P, and log D at pH 7.0 with MM2* using GB/SA water solvent continuum as implemented by MacroModel 5.0 (Schrödinger, Portland, OR) and described previously by Tampal et al. (2002).
Cytosolic preparations.
F.P. Guengerich (Vanderbilt University) kindly donated the samples of human liver, which were procured from organ donors (Guengerich 1995). We prepared liver cytosolic fractions from four livers by standard methods and stored aliquots at −80°C until use (Wang et al. 2004). We used three or four of these cytosol fractions in each experiment.
SULT1A1 genotype determination.
We used a genomic DNA isolation kit (EASY-DNA; InVitrogen, Carlsbad, CA) to extract genomic DNA from samples of the individual human livers used in this study. We used a published method to detect the SULT1A1 polymorphism status of each liver (Nowell et al. 2000; Ozawa et al. 1998). Amplified DNA fragments were digested with HaeII, and the fragments were resolved on 2% (weight/volume) agarose gels. Fragments from individuals homozygous for SULT1A1*1 exhibited two bands, visualized by ultraviolet transillumination, whereas DNA fragments from individuals homozygous for SULT1A1*2 were not cleaved by this enzyme and exhibited one band.
Expression and partial purification of SULT isozymes.
The expression of human SULT1A1*1, SULT1A3, SULT1B1, and SULT1E1 in Escherichia coli has been described previously (Dajani et al. 1998; Wang et al. 1998). We grew E. coli cells containing the respective sulfotransferase genes as described previously (Falany et al. 1990, 1994), and prepared 100,000g supernatant fractions for use in inhibition studies and for partial purification of the SULT enzymes. We purchased expressed SULT1A1*2 cytosolic extract from PanVera (Madison, WI) and used it as supplied.
The 100,000g supernatant fractions of SULT1A1*1, SULT1A3, SULT1B1, and SULT1E1 were partially purified by chromatographic methods (Falany et al. 1990, 1994). After the last step, a 3′-phosphoadenosine 5′-phosphate (PAP)-agarose affinity column, we dialyzed the fractions eluted with PAP with three changes of buffer to remove PAP before the assay of SULT activity with 3-OH-BaP as substrate. We analyzed active fractions by SDS-PAGE (Laemmli 1970) to assess the purity of each SULT enzyme. We stained the gels with Coomassie R-250 reagent and determined the percentage of protein present as each respective SULT enzyme by scanning densitometry.
Kinetic analysis of 3-OH-BaP sulfonation.
We determined SULT activity with 3-OH-BaP as substrate by a fluorimetric assay of BaP-3-SO4 product formation, as described previously (Wang et al. 2004). We ensured that the formation of BaP-3-SO4 was linear for time and protein and did not exceed 10% of the added 3-OH-BaP with each of the enzyme sources used. Duplicate tubes were prepared for each incubation condition. We examined the kinetics of sulfonation in three liver cytosol fractions by systematically varying the concentration of 3-OH-BaP or PAPS. When the variable substrate was 3-OH-BaP, we used 12 concentrations in the range from 0.035 to 2.00 μM, and the concentration of PAPS was kept constant at 10 μM. When we varied PAPS, we used 7 concentrations from 0.157 to 10.0 μM and kept the concentration of 3-OH-BaP constant at 0.100 μM.
We determined the kinetic parameters for 3-OH-BaP sulfonation by partially purified preparations of the cDNA-expressed SULT isozymes under incubation conditions similar to those used for liver cytosol. For SULT1A1*1 and -1A1*2, we used seven substrate concentrations in the range from 5 to 100 nM; for SULT1E1 we used six 3-OH-BaP concentrations from 15.6 to 1,000 nM; and for SULT1A3 and 1B1 we used seven concentrations of 3-OH-BaP from 0.25 to 5.0 μM.
Inhibition of SULT activity by OH-PCBs.
To assess inhibition of 3-OH-BaP SULT activity, we prepared stock solutions of OH-PCBs in dimethyl sulfoxide (DMSO) and added aliquots to incubation mixtures such that the final concentration of OH-PCB was in the range of 0.01–200 μM and the DMSO concentration did not exceed 0.5% (vol/vol). For each OH-PCB, we examined the concentration dependence of inhibition with three liver cytosol fractions, as well as with cytosol fractions from the E. coli expressing SULT1A1*1, SULT1A3, SULT1B1, and SULT1E1, and the purchased Sf-9 cytosol fraction (PanVera, Madison, WI) containing SULT1A1*2. For studies with HL cytosol, SULT1A1*1, and SULT1E1, we examined two concentrations of 3-OH-BaP, 0.1 μM and 1.0 μM. For studies with SULT1A1*2, we examined only 0.1 μM 3-OH-BaP, a concentration that did not elicit substrate inhibition. For studies with SULT1B1, we used only 1.0 μM 3-OH-BaP because this enzyme had very low activity at 0.1 μM 3-OH-BaP and did not exhibit substrate inhibition. Examination of the effect of 50 μM concentrations of several OH-PCBs on the activity of SULT1A3, measured with 1.0 μM 3-OH-BaP, revealed little inhibition, so no further concentrations were studied.
Kinetics of inhibition.
To study the type of inhibition produced by OH-PCBs, we used 4′-OH-CB112 (B5) as a model inhibitor. We prepared four sets of assay tubes containing HL cytosol and varying amounts of 3-OH-BaP from 35 to 150 nM: one set (control) contained no 4′-OH-CB112; the other sets contained 0.25 μM, 0.5 μM, or 1.0 μM 4′-OH-CB112.
Data analysis.
We calculated the enzyme kinetic parameters from studies with variable concentrations of 3-OH-BaP using nonlinear regression analysis and GraphPad 4.0 software (GraphPad Software, San Diego, CA). We selected the built-in Michaelis-Menten equation for most analyses. Where we found evidence of 3-OH-BaP substrate inhibition, we fit the data into an equation derived from a two-substrate model (Zhang et al. 1998):
This equation denoted the constant for binding of the first substrate (S) molecule as Km and the second substrate molecule as Ki. V1 is the maximum rate for the noninhibitory substrate concentration range, and V2 is the minimum rate in the inhibitory substrate concentration range
We calculated the effects of OH-PCBs on 3-OH-BaP SULT activity as percentage inhibition compared with the controls without an inhibitor. We obtained IC50 values by fitting log OH-PCB concentration and percent control activity to a sigmoidal curve. We examined the relationships between IC50 and physicochemical properties of the OH-PCBs by linear correlation analysis. We calculated the inhibitory constant (Ki) from the kinetic studies with 4′-OH-CB112 by means of Dixon plots and plots of Km/Vmax against inhibitor concentration (Cornish-Bowden 1995).
Results
SULT1A1 genotype of the liver donors.
We found that the HL cytosols used were from individuals with different SULT1A1 genotypes, as determined by PCR amplification of the region of the SULT1A gene flanking the polymorphic base pair. The G to A mutation in SULT1A1 removed the restriction site for the endonuclease HaeII. As shown in Figure 2, an individual homozygous for the SULT1A1*2 allele did not have the HaeII restriction site, and the PCR product was not cleaved (lane 1). The PCR product from the individual homozygous for SULT1A1*1 showed complete cleavage by HaeII, generating two fragments of approximately 100 and 181 bp (lane 3). Enzymatic digestion of the PCR product from the heterozygote (SULT1A1*1/*2) generated one band of 281 bp and the two fragments of 100 and 181 bp (lane 2). Thus, the individual liver designated HL 1 was homozygous for the SULT1A1*1 allele, HL 2 was heterozygous, and HL 3 was homozygous for the SULT1A1*2 allele.
Sulfonation of 3-OH-BaP by HL cytosol and expressed human SULT isoforms.
Initial studies of the sulfonation of 3-OH-BaP by HL cytosol revealed that concentrations of 3-OH-BaP > 0.15 μM resulted in a decrease in activity. To find a saturating concentration of PAPS, we conducted incubations in the presence of 0.1 μM 3-OH-BaP and varying concentrations of PAPS. The data fit the Michaelis-Menten equation, with an apparent Km of 0.56 ± 0.09 μM and a Vmax of 48 ± 2 pmol/min/mg protein (mean ± SD; n = 3). The dependence of activity upon PAPS concentration in expressed human SULT1A1*2, in the presence of 0.1 μM 3-OH-BaP, also followed Michaelis-Menten kinetics. The apparent Km was 0.32 μM, and Vmax was 684 pmol/min/mg protein. As shown in Figure 3, cytosol and the expressed enzyme were saturated by a PAPS concentration of 10 μM, and we used this concentration in subsequent studies.
We conducted detailed studies of the effect of a range of 3-OH-BaP concentrations up to 2 μM on reaction rates with HL cytosol and expressed human SULT1A1*2. We obtained preliminary estimates of the kinetic constants Km and V1 by fitting the initial rates of sulfonation at concentrations < 0.15 μM 3-OH-BaP to the Michaelis-Menten equation. We then obtained the values of Ki and V2 through constraining Km using the equation of Zhang et al. (1998). We also analyzed data by constraining V1, but a better fit was found when constraining Km. Figure 4A shows how the data fit this equation for three individual HL cytosols. Kinetic studies with expressed SULT1A1*2 revealed substrate inhibition with the single enzyme (Figure 4B). Table 1 shows values for Km, Ki, V1, and V2 for each HL cytosol and the expressed SULT1A1*2. The expressed enzyme showed a lower value for Km (0.022 μM) and Ki (0.16 μM) than did any of the HL cytosols.
Table 2 shows the results of kinetic studies with the other expressed human enzymes. The values shown in Table 2 are from substrate concentration ranges in which the data fit the Michaelis-Menten equation. SULT1A1*1 and SULT1E1 showed substrate inhibition at concentrations of 3-OH-BaP > 0.15 μM, but detailed kinetic analyses at inhibitory concentrations was not conducted with these expressed enzymes. We found that SULT1A1*1 had an apparent Km (0.018 μM) similar to that found with SULT1A1*2 (0.022 μM). SULT1E1 also had high affinity for 3-OH-BaP, with an apparent Km of 0.05 μM. SULT1A3 and SULT1B1 did not exhibit substrate inhibition over a concentration range up to 5 μM and showed much higher apparent Km values for 3-OH-BaP. These expressed enzyme preparations were partially purified, and SDS-PAGE showed they contained different percentages of the respective SULT enzymes (Table 2). The values shown for Vmax were corrected for the percentage of each respective SULT isoform in the partially purified enzyme preparation.
Inhibition of 3-OH-BaP sulfonation by OH-PCBs with HL cytosol.
The 4-OH-PCBs with one (B group) or two (C group) flanking chlorine substituents inhibited HL cytosolic 3-OH-BaP sulfotransferase activity in a concentration-dependent manner. Figure 5A shows inhibition curves from selected OH-PCBs in the presence of 0.1 μM 3-OH-BaP, and Figure 5B shows the same compounds studied with 1.0 μM 3-OH-BaP. Table 3 presents the IC50 values of 3-OH-BaP sulfotransferase activity with all the tested compounds, each at two concentrations of 3-OH-BaP. Compounds B1–B5 with the 3-chloro-4-hydroxy substitution pattern were potent inhibitors, with IC50 values ranging from 0.33 to 1.08 μM, when activity was measured with 0.1 μM 3-OH-BaP. The OH-PCBs with two chlorine atoms flanking the hydroxy group (C1–C6) were less potent inhibitors under these conditions (IC50, 1.31–6.71 μM; Table 3). The single 6-OH-PCB studied, A1, was a very weak inhibitor, with an IC50 of > 100 μM (Figure 5). When activity was measured with 1 μM 3-OH-BaP, a concentration at which substrate inhibition occurred, the measured IC50 values showed lower inhibitory potencies for all OH-PCBs, but especially so for the C group compounds, whose IC50 values ranged from 3 to 58.7 μM (Table 3).
Inhibition of 3-OH-BaP sulfonation by OH-PCBs with cDNA-expressed SULTs.
For SULT1A1*1, Figure 6A shows inhibition curves with selected OH-PCBs using 0.1 μM 3-OH-BaP, whereas Figure 6B shows results with a substrate concentration of 1.0 μM 3-OH-BaP. We found that 6′-OH-CB35 (A1) was a poor inhibitor of 3-OH-BaP sulfonation under both conditions of substrate concentration. When using 0.1 μM 3-OH-BaP, type B compounds (B1–B5) showed IC50 values ranging from 0.77 to 1.31 μM, whereas type C compounds (C1–C6) exhibited IC50 from 2.16 to 6.65 μM (Table 3). When using 1.0 μM 3-OH-BaP, the inhibitory potencies of the OH-PCBs were dramatically reduced. The IC50 values for type B OH-PCBs were reduced to 10.3–67.5 μM, and for type C OH-PCBs were 33.8 to > 100 μM (Table 3).
For SULT1A1*2, the IC50 of 6′-OH-CB35 (A1) was > 100 μM, as shown in Table 3. At 0.1 μM 3-OH-BaP, the IC50 ranged from 0.54 to 1.48 μM for type B (B1–B5) compounds and from 1.67 to 6.52 μM for type C compounds (C1–C6). When using 1.0 μM 3-OH-BaP, the OH-PCB IC50 was approximately 5 μM for type B (B1–B5) compounds and 50 μM for type C (C1–C6) compounds (data not shown).
As shown in Figure 7, expressed SULT1A3 was not inhibited or was weakly inhibited by OH-PCBs when 3-OH-BaP was used at the noninhibitory concentration of 1.0 μM. Addition of 50 μM concentrations of compounds 6′-OH-CB35 (A1), 4′-OH-CB69 (B3), 4′-OH-CB106 (B4), 4′-OH-CB112 (B5), 4′-OH-CB121 (C3), 4′-OH-CB165 (C5), and 4′-OH-CB72 (C6) did not inhibit the sulfonation of 3-OH-BaP. Compounds 4′-OH-CB35 (B1), 4′-OH-CB36 (B2), 4′-OH-CB79 (C1), and 4′-OH-CB159 (C4) showed 2–20% inhibition at 50 μM, and 4-OH-CB36 (C2) produced 43% inhibition. Because SULT1A3 activity was poorly inhibited by 50 μM concentrations, we did not examine a range of concentrations of OH-PCBs.
Expressed SULT1B1 showed a quite different inhibitory interaction with OH-PCBs, compared with SULT1A1*1, SULT1A1*2, SULT1A3, and SULT1E1, in that 6′-OH-CB35 (A1) was a quite potent inhibitor (IC50, 4.72 μM) of 3-OH-BaP sulfonation (Table 3). Compounds B1 (4′-OH-CB35) and B4 (4′-OH-CB106) showed IC50 values of 16.76 and 17.45 μM, respectively. The other type B and type C OH-PCBs were weak inhibitors.
For SULT1E1, compound A1 (6′-OH-CB35) was a poor inhibitor of 3-OH-BaP sulfonation at either of the substrate concentrations studied (Table 3). When using 0.1 μM 3-OH-BaP, OH-PCBs with no or one ortho-substituted chlorine (B1, B2, B4, C1, C2, C4, and C6) were potent inhibitors of 3-OH-BaP sulfonation, with IC50 values between 0.24 and 1.3 μM (Table 3). The OH-PCBs with two ortho-substituted chlorine atoms (B3, B5, C3, and C5) were less potent inhibitors, with IC50 values of 4.87–7.98 μM (Table 3). When we used 1.0 μM 3-OH-BaP as substrate, there was a 3- to 5-fold reduction in inhibitory potency, and the order of potency remained as it was with 0.1 μM 3-OH-BaP.
Structure–activity relationships.
For HL cytosol, expressed SULT1A1*1, SULT1A1*2, and SULT1E1, we investigated the relationship between inhibitory potency, measured at 0.1 μM 3-OH-BaP, and each of several physicochemical properties of the 4-OH-PCBs. For HL cytosol, SULT1A1*1, and SULT1A1*2, we found no significant correlation between dihedral angle, molecular surface area, molecular surface volume, log P, log D at pH 7.0, or pKa. The IC50 values with SULT1E1 showed a significant (p < 0.001) linear correlation with dihedral angle, as shown in Figure 8. No other significant correlations were found.
Kinetics of 3-OH-BaP sulfotransferase inhibition by 4′-OH-CB112.
We investigated the type of inhibition of 3-OH-BaP sulfonation using HL cytosol. Figure 9A shows that 4′-OH-CB112 (B5) reduced sulfotransferase activities at all the tested 3-OH-BaP concentrations in a concentration-dependent manner. The kinetic constants showed a steady reduction in Vmax with increasing concentration of 4′-OH-CB112, but little change in Km, indicating a noncompetitive type of inhibition (Table 4). Figure 9B shows a plot of Km/Vmax versus the concentration of 4′-OH-CB112, which indicated a Ki value for 4′-OH-CB112 of 0.52 ± 0.14 μM.
Discussion
The major human metabolite of BaP, 3-OH-BaP, was very readily sulfonated in HL cytosol, especially at concentrations < 0.15 μM. We observed substrate inhibition in HL cytosol and with SULT1A1 and SULT1E1, but not with SULT1A3 or SULT1B1. We studied the kinetics of substrate inhibition in liver cytosol and SULT1A1*2 and found that they fit a two-substrate model proposed for the sulfonation of estradiol by SULT1E1. This model suggested that SULT1E1 could bind two molecules of estradiol per molecule of enzyme, one at a preferred site for sulfonation and the other at an allosteric site associated with substrate inhibition (Zhang et al. 1998). Our results suggest a similar scenario for the interaction of 3-OH-BaP with SULT in HL cytosol and SULT1A1. The Km values for each of the three tested HL cytosol fractions (48–51 nM), SULT1A1*2 (22 nM), and SULT1A1*1 (18 nM) were low, indicating that 3-OH-BaP has a very high affinity for human SULT1A1. The Ki values were about 10-fold higher. The 3-OH-BaP was not, however, specific for SULT1A1 but was a substrate for the other human phenol sulfotransferases studied. In particular SULT1E1 showed a high affinity for 3-OH-BaP, with a Km of 50 nM. A related compound, 1-hydroxypyrene, also had a very low Km with SULT1A1 (8 nM) and SULT1E1 (21 nM) but a higher Km with SULT1A3 (0.8 μM) (Ma et al. 2003). When we calculated 3-OH-BaP clearance values (Vmax/Km) for the four partially purified SULT isoforms, the highest value was found for SULT1A1*1 (Table 2). Thus, 3-OH-BaP was a selective but not specific substrate for SULT1A1. Other investigators showed that the SULT1B1 protein content in liver cytosol was about one-fourth that of SULT1A1 (Honma et al. 2002). The present study showed that expressed SULT1B1 had a 40-fold higher Km value (2.0 μM) than found in HL cytosol (0.05 μM), so it is not likely to contribute much to HL cytosolic sulfonation of 3-OH-BaP at 0.1 μM substrate concentration (Table 2). Although SULT1A3 had activity with 3-OH-BaP, it is expressed at very low levels in the adult liver (Richard et al. 2001) and is unlikely to contribute much to 3-OH-BaP sulfonation in human liver. Because Km values for 3-OH-BaP in HL cytosol were similar to those of purified SULT1A1 and SULT1E1, and others have shown that SULT1A1 is expressed in liver at approximately 14-fold higher concentrations than SULT1E1 (Honma et al. 2002), we conclude that the observed activity with 3-OH-BaP in HL cytosol is catalyzed largely by SULT1A1. Differing structural features for inhibition of SULT1A1 and SULT1E1 by OH-PCBs further support our conclusion that, in HL cytosol, activity with 3-OH-BaP is due primarily to SULT1A1. By chance, the three HL cytosol fractions we used in these studies were from individuals with different SULT1A1 genotypes. One was SULT1A1*1 homozygous, a second was heterozygous for SULT1A1*1/*2, and the third was SULT1A1*2 homozygous. Kinetic analysis showed little difference among the three cytosol fractions for V1, which was 121 pmol/min/mg for the homozygous SULT1A1*1 liver and 94 pmol/min/mg protein for the SULT1A1*2 liver (Table 1); however, the small size of our sample precludes a more detailed analysis of genotype effects on 3-OH-BaP sulfonation activities.
In previous studies, we showed that OH-PCBs inhibited 3-OH-BaP sulfonation in catfish intestinal cytosol (van den Hurk et al. 2002) and that a compound structurally related to OH-PCBs, 2,4,4′-trichloro-2′-hydroxydiphenyl ether (triclosan), inhibited sulfonation and glucuronidation of 3-OH-BaP and other substrates in HL cytosol and with SULT1A1, SULT1B1, and SULT1E1 (Wang et al. 2004). Here we demonstrated that a set of 4-OH-PCBs inhibited SULT activity with 3-OH-BaP, the major metabolite of another pollutant chemical, BaP, in HL cytosol as well as with cDNA-expressed SULTs. In HL cytosol, all the 4-OH-PCBs examined inhibited the sulfonation of 3-OH-BaP. Under incubation conditions in which the 3-OH-BaP substrate did not cause substrate inhibition (0.1 μM 3-OH-BaP), compounds with one chlorine atom adjacent to the OH group (B1–B5) were more potent inhibitors of sulfonation than were compounds in type C, with chlorine atoms flank-ing the OH group on each side. We observed very similar results for potency of inhibition and order of inhibitory potency with all three liver cytosol fractions and the two allelic variants of expressed SULT1A1. When incubated with 1.0 μM 3-OH-BaP, a concentration that produced substrate inhibition in liver cytosol and with both SULT1A1 variants, the OH-PCBs were considerably less potent inhibitors in cytosol and even more so with the expressed SULT1A1*1 and SULT1A1*2 enzymes (Table 3 and data not shown). The effect of substrate concentration on the inhibitory potency of the OH-PCBs suggested the possibility that the OH-PCBs competed with the 3-OH-BaP for an inhibitory site of the SULT1A1 protein. Gamage et al. (2003) reported that SULT1A1*2 could accommodate two molecules of the xenobiotic model substrate p-nitrophenol in the active site. They proposed that substrate inhibition at high concentrations of p-nitrophenol was due to impeded catalysis when both binding sites were occupied. The active site of SULT1A1 appears to be plastic enough to accept a wide range of hydrophobic phenolic compounds (Gamage et al. 2003) and may be able to accommodate two molecules of 3-OH-BaP, leading to substrate inhibition, or one molecule of 3-OH-BaP and one molecule of OH-PCB, resulting in the OH-PCB inhibiting 3-OH-BaP sulfonation. The kinetic studies with 4′-OH-CB112 (B5) showed that the mechanism of inhibition was noncompetitive. This result could fit the scenario for inhibition discussed above but does not suggest direct competition of the OH-PCB for binding to the active site in an orientation that favors sulfonation. Whatever the mechanism of inhibition, the loss in inhibitory potency of OH-PCB when assays were conducted with 1.0 μM 3-OH-BaP suggested that the enzyme favored binding of 3-OH-BaP over binding of OH-PCB, and this was especially true for type C OH-PCBs, which showed a greater loss in potency than did the type B compounds. These findings suggest that OH-PCBs are likely to be poor substrates for sulfonation, but this has not been studied in human liver.
We could not discern any other clear relationship of inhibitory potency with structural features or with physicochemical properties of the OH-PCBs in this relatively small series of compounds, with cytosol or the two expressed SULT1A1 enzymes. The small size of the series of compounds studied and the lack of ready availability of a systematic series of 4-OH-PCBs prevent further analysis of structure–potency relationships at this time.
Of the other expressed enzymes studied, only SULT1E1 exhibited potent inhibition by the 4-hydroxylated PCBs. The structure–inhibitory potency requirement for SULT1E1 was very different from that for HL cytosol, SULT1A1*1, or 1A1*2, where type B compounds were more potent inhibitors than were type C OH-PCBs. With SULT1E1, OH-PCBs with no or one ortho-substituted chlorine were more potent as inhibitors of 3-OH-BaP sulfonation than were those with two ortho-substituted chlorine atoms. Substituted biphenyls with less than one ortho substituent preferentially adopt coplanar conformation of the two phenyl rings, whereas those with two or more ortho substituent atoms take on non-coplanar conformations. We found a significant linear correlation between inhibitory potency and calculated solution dihedral angles (Figure 8, Table 3). Similarly, Kester et al. (2000) found that the best OH-PCB inhibitors of estrogen sulfonation (IC50 values < 5 nM) did not have chlorine substituents at the 2- or the 6-position. Shevtsov et al. (2003) later showed that 4,4′-di-OH-CB80 (4,4′-di-OH-3,3′,5,5′-tetrachlorobiphenyl) did not bind the SULT1E1 in a planar conformation, but rather with a 30° twist between the phenyl rings. We found that the four OH-PCBs with solution dihedral angles of 38° were more potent inhibitors than were those with larger dihedral angles. Although it is possible that interaction with the protein could alter the conformation of the OH-PCBs, resulting in a different dihedral angle for the enzyme-bound OH-PCB, our results show that lack of ortho substituents is associated with higher inhibitory potency for a xenobiotic SULT1E1 substrate, 3-OH-BaP.
SULT1A3 metabolized 3-OH-BaP with a very high Vmax, although its preferred substrates are reported to be catecholamines and other monocyclic phenols containing hydrogen bond donors (Dajani et al. 1998). Interestingly, 50 μM OH-PCBs caused little or no inhibition of this enzyme, thereby showing that the inhibitory interaction was enzyme selective. SULT1B1, the thyroid hormone sulfotransferase, catalyzed the sulfonation of 3-OH-BaP; however, OH-PCBs that were potent inhibitors of SULT1A1 were only weak inhibitors of the SULT1B1-catalyzed reaction. In contrast to results with the other enzymes, compound A1 (6′-OH-CB35) was a fairly potent inhibitor of SULT1B1 (Table 3). Previously, ortho-, meta-, and para-hydroxylated PCBs were found to inhibit thyroid hormone sulfonation (Schuur et al. 1998). The meta-hydroxylated PCB, 3-OH-2,3′,4,4′,5-pentachlorobiphenyl (3-OH-CB118), was the most potent inhibitor of thyroid hormone sulfonation in male rat liver cytosol, followed by two para-hydroxylated PCBs. The ortho-hydroxylated PCB had the lowest potency among the four OH-PCBs studied. However, with 3-OH-BaP as substrate, the ortho-OH-PCB, 6′-OH-CB35, was a more potent inhibitor than were those with para-OH groups, which suggested that the inhibitory interaction with SULT1B1 was substrate dependent.
Because several OH-PCBs have been detected in human blood and are presumably also present in liver and other tissues, it is important to understand their biologic activities. Some OH-PCBs interact with components of thyroid hormone and estrogen hormone systems (Kester et al. 2000; Klasson-Wehler et al. 1993; Schuur et al. 1998; Sinjari and Darnerud 1998). Our finding that OH-PCBs inhibited the sulfonation of 3-OH-BaP in HL suggests another aspect of the toxicology of OH-PCBs. The interaction with phenol sulfotransferase may be of toxicologic importance because sulfonation is a major pathway of xenobiotic biotransformation (Glatt 2002). Sulfonation is particularly important at low concentrations of hydroxylated xenobiotics, such as may be encountered from environmental exposure to pollutants that require CYP-dependent biotransformation to introduce a hydroxyl group before their elimination. Formation of sulfate conjugates of phenolic xenobiotics usually decreases their toxicity, so inhibition of this pathway may lead to prolonged exposure to the parent compound, a shift to an alternative phase II conjugation pathway, glucuronidation, or to further CYP-dependent metabolism. Both 3-OH-BaP and BaP-3-glucuronide bind to hemoglobin (Sugihara and James 2003), a potentially toxic interaction. Further CYP-dependent biotransformation of 3-OH-BaP may lead to more toxic metabolites such as 3-OH-BaP-7,8-dihydrodiol-9,10-oxide (Glatt et al. 1987; Ribeiro et al. 1986). On the other hand, xenobiotics that are activated by sulfonation, such as 2-hydroxyamino-1-methyl-6-phenylimidazo[4,5-b]pyridine (Ozawa et al. 1998), may be rendered less toxic in the presence of inhibitors of sulfonation.
Our findings may be placed in the context of the structures of OH-PCBs that have been reported in human blood. All OH-PCB metabolites identified in blood have the hydroxy group in a para- or meta-position, with chlorine atoms on vicinal carbon atoms (Hovander et al. 2002; Sandau et al. 2000, 2002; Sjodin et al. 2000). The para-OH-PCBs found in blood are likely to fall into the type C OH-PCBs examined in this study. Although these were generally less potent as inhibitors of SULT1A1 than the type B OH-PCBs, it is possible that the concentrations of these OH-PCBs may reach inhibitory levels in tissues of highly exposed people or animals. Sjodin et al. (2000) reported total measured OH-PCB concentrations of up to 6 μM in blood lipids, whereas Sandau et al. (2000) reported whole blood concentrations up to 30 nM. Tissue concentrations have not been reported but they may be higher than blood levels. Type B OH-PCBs with the 3-chloro-4-hydroxy substitution pattern do not appear to be persistent in blood; however, of the 209 PCB congeners, 19 have a 3-chloro substitution in one of the phenyl rings, which can be biotransformed to type B OH-PCBs. If type B OH-PCBs are formed in people, their high potency as inhibitors of 3-OH-BaP sulfonation may cause increased toxicity in people who are coexposed to PAH and PCBs.
Conclusion
We found that several OH-PCBs, especially those with a 3-chloro-4-hydroxy substitution pattern in the phenolic ring, inhibited the sulfonation of 3-OH-BaP in cytosol and with SULT1A1 at submicromolar concentrations. Some OH-PCBs with no or one ortho chlorine were potent inhibitors of 3-OH-BaP sulfonation with SULT1E1. SULT1B1- and SULT1A3-catalyzed sulfonation of 3-OH-BaP was less sensitive to inhibition by OH-PCBs. The inhibitory interaction of OH-PCBs with SULT1A1 and SULT1E1 may have consequences for the biotransformation and toxicity of phenolic xenobiotics.
Figure 1 Structures of the hydroxylated PCBs used in this study. Type A, hydroxy without a flanking chlorine atom; type B, para-hydroxy with one flanking chlorine atom; type C, para-hydroxy with two flanking chlorine atoms.
Figure 2 Detection of SULT1A1*1/*2 alleles by restriction fragment length polymorphism analysis. Lane M, marker; lane 1, SULT1A1*2/*2 homozygous; lane 2, SULT1A1*1/*2 heterozygous; lane 3, SULT1A1*1/*1 homozygous. Specific PCR products were generated and digested with HaeII as described in “Materials and Methods.”
Figure 3 Rates of sulfonation of 3-OH-BaP (0.1 μM) in the presence of varying concentration of PAPS (0.125–10 μM) in HL cytosol (A) and cytosol of SULT1A1*2 (B). Data in (A) are given as the mean ± SD of three experiments.
Figure 4 Partial substrate inhibition by 3-OH-BaP in HL cytosol from three individuals (A) and SULT1A1*2 (B).
Figure 5 Inhibition of 3-OH-BaP sulfotransferase in HL cytosol by OH-PCBs. (A) 0.1 μM 3-OH-BaP. (B) 1.0 μM 3-OH-BaP. 3-OH-BaP sulfotransferase activity is given as percentage of control. Data given are the mean ± SD of three experiments. Structures of the tested OH-PCBs are shown in Figure 1.
Figure 6 Inhibition of 3-OH-BaP sulfotransferase in SULT1A1*1 by OH-PCBs. (A) 0.1 μM 3-OH-BaP. (B) 1.0 μM 3-OH-BaP. 3-OH-BaP sulfotransferase activity is given as percentage of control. Data given are the mean ± SD of three experiments. Structures of the tested OH-PCBs tested are shown in Figure 1.
Figure 7 Inhibition of 3-OH-BaP sulfotransferase activity with SULT1A3 by OH-PCBs shown in Figure 1, each at 50 μM.
Figure 8 Correlation of IC50 values (μM) for type B and type C 4-OH-PCBs with dihedral angles in the presence of SULT1E1. The regression line was significantly different from zero (p < 0.001), and the goodness of fit (r2) was 0.73 for the positive correlation of SULT1E1 IC50 values with a dihedral angle.
Figure 9 Effect of 4′-OH-CB112 on the kinetics of sulfotransferase with 3-OH-BaP in HL cytosol. (A) Saturation curves, with each point representing the mean of data from three livers; the kinetic parameters are summarized in Table 4. (B) Plots of apparent Km/Vmax versus the concentration of 3-OH-BaP for calculation of Ki value. Data given are the mean ± SD of three experiments.
Table 1 Kinetic parameters for 3-OH-BaP sulfonation by human livers and SULT1A1*2.
Km (μM) Ki (μM) V1 (pmol/min/mg protein) V2 (pmol/min/mg protein) R2
HL 1 cytosol 0.048 0.915 121 2 0.95
HL 2 cytosol 0.051 0.534 93.0 38.50 0.956
HL 3 cytosol 0.048 0.460 94.4 31.0 0.958
Mean ± SD 0.049 ± 0.01 0.636 ± 0.244 102 ± 15.8 28.3 ± 19.3
SULT1A1*2 0.022 0.160 4,400 290 0.953
Kinetic analysis was performed using a two-substrate model as described in “Materials and Methods.” HL 1 cytosol was homozygous for SULT1A1*1, HL 2 cytosol was heterozygous as SULT1A1*1/*2, and HL 3 cytosol was homozygous for SULT1A1*2.
Table 2 Apparent kinetic constants for cDNA-expressed sulfotransferase with 3-OH-BaP as substrate.
SULT Km (μM) Vmax (nmol/min/mg protein) Vmax/Km (mL/min/mg protein)
SULT1A1*1 0.018 6.89 383
SULT1A3 2.90 333.3 115
SULT1B1 2.00 9.70 4.9
SULT1E1 0.05 8.35 167
Partially purified SULT isoforms were used for these studies. Vmax was calculated from the mg/mL of the partially purified preparation and corrected by the percentage of protein estimated to be SULT, from SDS-PAGE: 44.0% for SULT1A1*1, 39.0% for SULT1A3, 80.3% for SULT1B1, and 65.0% for SULT1E1.
Table 3 In vitro inhibition of 3-OH-BaP sulfotransferase activity by the tested OH-PCBs using HL cytosol and cDNA-expressed sulfotransferases at 0.1 and 1.0 μM substrate concentration.a
IC50 (μM)
0.1 μM 3-OH-BaP
1.0 μM 3-OH-BaP
Compound no. Compound Log D at pH 7.0 Dihedral angle (º) HL cytosol SULT1A1*1 SULT1A1*2 SULT1E1 HL cytosol SULT1A1*1 SULT1B1 SULT1E1
A1 6′-OH-CB35 4.7 50 > 100 > 100 > 100 ~100 > 100 > 100 4.72 > 100
B1 4′-OH-CB35 4.7 38 0.33 ± 0.02 0.77 0.55 0.24 0.96 ± 0.30 25.2 16.8 1.02
B2 4′-OH-CB36 4.8 38 0.67 ± 0.12 1.31 0.94 0.45 1.05 ± 0.39 28.0 37.0 1.89
B3 4′-OH-CB69 5.1 72 0.91 ± 0.09 1.16 1.31 4.87 1.50 ± 0.32 67.5 > 100 30.8
B4 4′-OH-CB106 5.2 60 0.37 ± 0.04 1.07 1.06 1.18 2.61 ± 0.67 10.3 17.4 6.97
B5 4′-OH-CB112 5.2 78 1.08 ± 0.12 1.17 1.48 5.35 4.22 ± 1.03 42.5 86.5 23.2
C1 4′-OH-CB79 4.5 38 6.71 ± 0.91 6.65 4.57 0.50 58.7 ± 13.9 59.8 39.9 1.32
C2 4-OH-CB36 4.2 38 2.30 ± 0.45 3.09 3.05 0.41 35.9 ± 1.47 > 100 47.5 1.65
C3 4′-OH-CB121 4.7 72 3.95 ± 0.23 8.15 6.52 7.98 44.6 ± 6.42 99.5 > 100 16.7
C4 4′-OH-CB159 4.7 78 1.31 ± 0.14 2.16 1.67 1.30 38.4 ± 15.2 34.1 > 100 3.55
C5 4′-OH-CB165 4.6 78 2.87 ± 0.09 2.58 2.59 6.96 47.3 ± 10.2 54.8 > 100 21.4
C6 4′-OH-CB72 4.5 57 1.72 ± 0.21 2.21 2.03 0.57 3.05 ± 0.41 33.8 > 100 2.28
a Values for HL cytosol are the means ± SDs of three livers, tested in duplicate; results for expressed SULT enzymes are the means of duplicate determinations.
Table 4 Apparent kinetic constants for 3-OH-BaP sulfotransferase activity in HL cytosol in the presence and absence of 4′-OH-CB112 (B5).
B5 (μM) Km (μM) Vmax (pmol/min/mg protein)
0 0.045 ± 0.02 A 69.1 ± 8.0 B
0.25 0.043 ± 0.02 A 56.3 ± 6.0 B
0.5 0.045 ± 0.01 A 41.2 ± 3.3 C
1.0 0.066 ± 0.02 A 33.3 ± 3.2 D
Values for liver cytosol are mean ± SD (n = 3), except for studies with 0.25 μM B5, where two livers were used. Different letters indicate values that are significantly different from each other (p < 0.05).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7786ehp0113-00068815929890ResearchArticlesClimate Factors Influencing Coccidioidomycosis Seasonality and Outbreaks Comrie Andrew C. Department of Geography and Regional Development, University of Arizona, Tucson, Arizona, USAAddress correspondence to A.C. Comrie, Department of Geography and Regional Development, University of Arizona, 409 Harvill Building, Box #2, Tucson, AZ 85721-0076 USA. Telephone: (520) 621-1585. Fax: (520) 621-2889. E-mail:
[email protected] assistance of J. Tabor for data acquisition, B. Bonanno for preliminary analyses, and K. Kolivras and J. Galgiani for comments is gratefully acknowledged.
Partial funding of the preliminary analyses was provided by the Arizona Disease Control Research Commission.
The author declares he has no competing financial interests.
6 2005 3 3 2005 113 6 688 692 23 11 2004 3 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Although broad links between climatic factors and coccidioidomycosis have been established, the identification of simple and robust relationships linking climatic controls to seasonal timing and outbreaks of the disease has remained elusive. Using an adaptive data-oriented method for estimating date of exposure, in this article I analyze hypotheses linking climate and dust to fungal growth and dispersion, and evaluate their respective roles for Pima County, Arizona. Results confirm a strong bimodal disease seasonality that was suspected but not previously seen in reported data. Dispersion-related conditions are important predictors of coccidioidomycosis incidence during fall, winter, and the arid foresummer. However, precipitation during the normally arid foresummer 1.5–2 years before the season of exposure is the dominant predictor of the disease in all seasons, accounting for half of the overall variance. Cross-validated models combining antecedent and concurrent conditions explain 80% of the variance in coccidioidomycosis incidence.
climateCoccidioidescoccidioidomycosisenvironmentmeteorologic factorsrainseasonal variationsouthwestern United Statesweather
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Coccidioidomycosis, or valley fever, is caused by inhalation of spores from Coccidioides immitis and Coccidioides posadasii. These dimorphic soil fungi are endemic to the deserts of the southwestern United States, Mexico, and elsewhere in Central and South America (Fisher et al. 2002; Kolivras et al. 2001). Although approximately 60% of people infected with the disease are asymptomatic, others experience mild influenza-like symptoms, and a small percentage experience severe effects and sometimes death resulting from dissemination of the disease to other parts of the body (Kolivras et al. 2001). Those at greatest risk for coccidioidomycosis infection include immunocompromised patients, young children, the elderly, and members of several ethnic minorities in the United States (Kolivras et al. 2001; Pappagianis 1988). In Arizona alone, > 2,000 cases per year have been reported (Komatsu et al. 2003), and the incidence of coccidioidomycosis is greater than that for other emerging infectious diseases in the region such as West Nile virus [Centers for Disease Control and Prevention (CDC) 2004a]. The number of Arizona cases is likely to exceed 3,000 by the end of 2004 (CDC 2004b).
Environmental conditions appear to have an important impact on coccidioidomycosis incidence. The current Arizona coccidioidomycosis epidemic has been linked to climate conditions (Kolivras and Comrie 2003; Komatsu et al. 2003; Park et al. 2005), whereas California experienced an epidemic in the 1990s that was possibly linked to drought conditions (Jinadu 1995). Initial links between climate conditions and the disease were identified several decades ago (Hugenholtz 1957; Maddy 1965). It is only recently that further details on climate and coccidioidomycosis have been published (Kolivras and Comrie 2003; Komatsu et al. 2003). These studies identified associations linking climate and other factors to seasonal patterns of coccidioidomycosis and to interannual variability and trends in the disease. Significant variables included drought indices, lagged precipitation, temperature, wind speed, and dust during the preceding 1 or more years. The relationships to coccidioidomycosis were quite complex, however, perhaps because of disease data issues outlined below. In this article I aim to identify simple and robust relationships linking climatic controls to seasonal timing and outbreaks of the disease, which until now have remained elusive and poorly understood. Important public health opportunities exist if environmental factors controlling coccidioidomycosis outbreaks and trends can be better comprehended, including the timing and degree of mitigation efforts such as soil treatment and the development of an advance warning system for public health management.
Part of the reason for the current state of knowledge has been the lack of high-quality disease data series. In fact, a major challenge to understanding more about the links between climate and infectious disease continues to be the difficulty in obtaining regular time series of disease data (National Research Council 2001). This is especially true for coccidioidomycosis with respect to data on Coccidioides in the soil or atmosphere. The current environmental detection method using laboratory mice is expensive and time-consuming, and although there is ongoing research into more rapid detection techniques (e.g., using polymerase chain reaction analysis to detect the fungus in soil samples), it will be several years before time series of such data become available. In the absence of suitable data on the environmental variability of the fungus itself, there is a need to exploit epidemiologic data in different ways to better identify the role of environmental controlling factors such as climate. Thus, for now, disease incidence data offer the best (and only) available multiyear time series for comparison with climatic conditions.
The use of human disease data to study potential relationships to climate conditions introduces numerous methodologic and analytical issues related to collection and reporting. Incidence data do not provide a homogeneous time series because of changes in reporting requirements, changes in population demographics, and the introduction of new diagnostic tests. In addition, the reported data necessarily contain imprecise estimations of disease onset dates because of various factors including patient recall, incorrect or delayed diagnoses caused by displacement of diagnoses during the respiratory disease season, and the variability in disease incubation and onset of symptoms from case to case.
If these data issues can be dealt with at least partially, the research challenge in using human incidence data is to understand the second- or third-order connections between the soil fungus and reported cases of the disease. There are essentially two hypothesized parts to the role of climate (Kolivras and Comrie 2003) that need to be evaluated. First, existing Coccidioides spores present in dry soil need increased soil moisture (via precipitation) to grow the fungus, followed by a dry period during which fungal hyphae desiccate and form spores. Second, wind or other disturbance is required to disperse the spores for inhalation by a host. The relative roles of these climate factors in the seasonality and outbreaks of coccidioidomycosis are not clearly understood. My principal goals in this article are therefore to analyze the postulated climate and dust relationships to fungal growth and dispersion and evaluate their respective roles.
Two subquestions are also considered. First, southern Arizona has a bimodal annual precipitation pattern with one peak in summer and one in winter (Sheppard et al. 2002), but county-level coccidioidomycosis reports in the past have not clearly reflected an associated bimodal coccidioidomycosis pattern (Kolivras and Comrie 2003). Yet early work and a study using student health service data have noted such a pattern (Hugenholtz 1957; Kerrick et al. 1985). Thus, in this article I examine whether recent county-level reports can shed light on the existence of a bimodal incidence pattern in reported data. Second, in evaluating climatic controls on coccidioidomycosis, the critical date is the date of exposure (spore inhalation) rather than the case report date. A method is required that incorporates this lag as well as the changes in coccidioidomycosis reporting characteristics over time. This article presents such an adaptive data-oriented method for estimating date of exposure.
Materials and Methods
Tucson and the surrounding areas of Pima County in Arizona are highly endemic for coccidioidomycosis (Kolivras et al. 2001). Pima County coccidioidomycosis case data were obtained from the Arizona Department of Health Services (Phoenix, Arizona) for the period 1992–2003. Reporting was voluntary at the beginning of this period (Ampel et al. 1998), although the data continuity and quality are good relative to previous decades (Kolivras and Comrie 2003). The disease became nationally notifiable in 1995 and reporting by laboratories became mandatory at the state level in 1997 (Komatsu et al. 2003). Although the number of reported cases initially appeared to increase as a result, this effect appears to have been minor because incidence continued to grow in an ongoing epidemic (Komatsu et al. 2003).
Pima County annual mid-year population data were obtained from the U.S. Census Bureau (2004). Environmental data were obtained for the greater Tucson urban area, which contains > 90% of the county population. Both precipitation and dust are good potential predictors of coccidioidomycosis (Kolivras and Comrie 2003; Komatsu et al. 2003). Monthly precipitation data for all five available sites in the Tucson area were obtained from the Western Regional Climate Center (2004) for 1988–2003. In conjunction with the incidence data, the precipitation data enable evaluation of hypothesized soil-moisture–fungal-growth relationships. Ambient concentrations of atmospheric particulate matter with a diameter < 10 μm (PM10) were obtained from the Pima County Department of Environmental Quality (2004) for the five stations with data from 1991–2003. The PM10 data are a direct measure of airborne dust, and because this size threshold includes the typical spore size, these data should be proportionally related to the hypothesized windblown spore concentrations. Precipitation and PM10 values were averaged across sites to provide a single time series of the areawide mean for each.
With regard to analyzing the hypothesized climatic controls on coccidioidomycosis, the most relevant information to extract from the incidence data is the date that each patient most likely inhaled the fungal spore (i.e., exposure date). The coccidioidomycosis incidence data include three possibly useful dates to approximate exposure date: estimated date of onset of symptoms (“onset date”), diagnosis date, and report date (although many cases do not have all three dates recorded). Onset date is potentially the most useful of the three, but it is only available for about one-third of the cases, and that proportion varies considerably over time. Ideally, the onset date accounts for some of the variable lag between exposure and reporting; although it is imprecise, it is likely the most accurate index of exposure date. Conversely, the diagnosis date is more precise but the exposure-to-diagnosis lag, which varies from case to case and is longer than the exposure-to-onset lag, has to be estimated in some way. Diagnosis dates are available for most cases. Report dates are, de facto, available for all cases, but they are the most lagged in time from the exposure date; exposure-to-report lags therefore display the greatest variability and are least likely to provide useful links to climate.
Exploration of the various lags and dates indicated no consistent bias or pattern that could be satisfactorily corrected via simple adjustments, such as an overall mean onset-to-diagnosis delay. Instead, the mean onset-to-diagnosis and onset-to-report lag times were calculated for each individual month in the record (rather than averaged across the entire time series). These temporally adaptive empirical lags were smoothed with a 3-month moving average, centered on the middle month, and then used to estimate exposure dates. For cases with an onset date, the exposure date was estimated to be 14 days earlier to allow for the incubation period (Kolivras and Comrie 2003); for cases without an onset date but with a diagnosis date, the exposure date was estimated to occur earlier by the number of days for that month-specific onset-to-diagnosis lag plus 14 days; for cases with only a report date, the exposure date was estimated to occur earlier by the number of days for that month-specific onset-to-report lag plus 14 days. For example, a case reported on 24 November 2003 might have a diagnosis date of 24 July 2003 and no onset date. Based on the mean of other reports with onset dates in November 2003 (actually the October through December 2003 mean), the onset–diagnosis lag is 10 days, so this case would be estimated to have had an onset date of July 14, and thus an estimated exposure 14 days before, on 30 June.
There were 3,283 cases in the data set; 3,181 of these had diagnosis dates, but only 1,089 had onset dates. The proportion of the latter each month and the length of lag for either varied inconsistently over time, necessitating this set of temporally adaptive adjustments. Onset–diagnosis lags had a mean of 12.6, a median of 11.5, a standard deviation of 5.9, a minimum of 2, and a maximum of 32 days; onset–report lags had respective values of 43.0, 44.0, 19.1, 8, and 99 days. Monthly case totals based on estimated exposure were computed and converted to incidence rates per 100,000 of population using linearly interpolated monthly population estimates.
To analyze the lagged relationships and the relative climatologic significance of different times of year, the data were grouped into seasons. Seasonal analyses are advantageous for several reasons: a) they are a useful way of dividing the year into alternating wet and dry periods, b) they facilitate identification of recurring times of the year that are important, c) seasonal aggregation avoids the monthly variability that characterizes the region and leads to overly complex analyses, and d) it is analytically and conceptually simpler to compute and understand seasonal lag relationships. In the southwestern United States, seasons are defined principally by precipitation rather than the thermally based spring, summer, fall, and winter sequence typical of middle-latitude locations (Sheppard et al. 2002). Seasonal groupings are widely used for similar kinds of climate analyses (Crimmins and Comrie 2004). Seasons were defined by monthly sequences that captured the predominant seasonal maxima and minima for each variable.
Stepwise regression of the 1992–2003 seasonal data was used to model coccidioidomycosis rates from concurrent PM10 (hypothetically related to spore dispersion and therefore exposure) and concurrent and lagged antecedent precipitation (hypothetically related to fungal growth). Previous work has shown that the relevant climate conditions may be different for each coccidioidomycosis season (Kolivras and Comrie 2003), and therefore each season was modeled separately. Models were cross-validated on independent data points using a leave-one-out jackknife method. Because coccidioidomycosis reporting before 1997 may not have been consistent, the same modeling analysis was run on a subset of the data covering just the improved reporting period from 1997 through 2003 for confirmatory purposes.
Results
Application of the estimated exposure date methodology resulted in a time series of coccidioidomycosis incidence, as defined above. An annual plot shows the epidemic in recent years, which coincides with an ongoing regional drought as well as variability in PM10 (Figure 1). The 2003 decrease may end up being less pronounced after some reports recorded later in 2004 (unavailable at the time these study data were acquired) are estimated to have been 2003 exposures. Analysis of similar data for the Phoenix area attributed the increase in coccidioidomycosis to climate-related factors (Komatsu et al. 2003).
Average monthly coccidioidomycosis rates based on estimated exposure dates display obvious seasonal behavior (Figure 2), but with greater clarity than in previous studies. A bimodal pattern with peaks in June–July and October–November is apparent, along with relatively lower incidence in August–September and February–March. PM10 concentrations follow an inverse relationship with soil moisture, falling during wet periods and rising during dry periods (Figure 2). Monthly coccidioidomycosis rates are largely consistent with the hypothesis of increased dust exposure leading to increased disease incidence. On the average at least, the less dusty months of the year coincide with lower coccidioidomycosis exposure rates, and elevated rates coincide with or follow the dustier months. Although it is tempting to draw a similar first-order inverse connection between precipitation and incidence at the overall mean monthly level, it is important to recall that this is likely valid for the immediate dust-inhibiting role of rainfall (precipitation has a strong negative correlation with dust) but not likely for its antecedent fungal growth and desiccation role. Thus, although a wet–dry precipitation sequence occurs during the several months before each of the annual coccidioidomycosis peaks on average, closer examination shows that the amount of precipitation and the matching responses as well as the time lags for each are inconsistent. This underlines the importance of investigating the role of antecedent moisture at time scales longer than a season or year.
The monthly averages presented in Figure 2 enabled the definition of seasonal groupings centered on the periods of maxima and minima. Coccidioidomycosis seasons for estimated exposure dates consist of a winter decrease that occurs January through April, a foresummer peak that is seen May through July, a monsoon decrease that takes place in August and September, and a fall peak that is experienced October through December. The same seasons were used for monthly PM10 concentrations because they had similar periods of maxima and minima, and because they needed to match the coccidioidomycosis seasons for analysis. For precipitation, the winter peak occurs between December and March, followed by the driest time of the year during the arid foresummer from April through June. The monsoon is the most distinctive aspect of the region’s climate, bringing rainfall during July, August, and September, after which conditions become dryer in a brief fall during October and November (Crimmins and Comrie 2004). Because precipitation is hypothesized to affect fungal growth months or years before the exposure date, it is not necessary to have precipitation seasons exactly match the monthly groupings for the other variables. Thus, for example, it is more meaningful to use July through September for monsoon precipitation and relate that seasonal peak to coccidioidomycosis in subsequent seasons. For simplicity, the names of the seasons are kept the same across all variables.
Adjusted R2 values for the four seasonal models and standardized (β) coefficients for the variables found to be significant in each model are shown in Table 1. All four models explained significantly high to very high proportions of the variance in coccidioidomycosis rates. It is notable that the strongest relationships do not occur simply in a wet–dry sequence in the season immediately before a rise in coccidioidomycosis rates. A remarkable result is the positive role of precipitation during the arid foresummer for coccidioidomycosis occurring in all subsequent seasons up to 2 years later. One implication is that precipitation during this hottest and driest part of the year (April through June), as opposed to other wetter seasons, is most favorable for Coccidioides growth in the environment. This is typically a time of soil desiccation and vegetation dormancy, so the ability to grow opportunistically in the foresummer may be a competitive advantage of Coccidioides over other soil biota. A second implication is that fungal spores produced after a wet period in the foresummer may accumulate in the soil and remain viable for several years. Consistent with this hypothesis, monsoonal precipitation does not appear in any model within a 3-year lag, and in only one at 4 years.
Ambient dust levels, as an index of potential spore dispersion, are positively associated with concurrent coccidioidomycosis rates in winter and the foresummer. Dust is not a useful predictor of coccidioidomycosis rates during the monsoon or the fall. Yet wetter conditions in fall appear to decrease concurrent coccidioidomycosis rates and in the winter immediately after, presumably via dispersion inhibition due to greater soil moisture.
The analysis was repeated on the more reliable 1997–2003 data period to check for consistency. This step reduced the modeled n from 12 to 7, which decreased statistical reliability, and therefore detailed results are not shown. Nonetheless, although the full set of significant variables differed for each model, the results from the shorter period showed some similarities with the longer period. Those variables that were significant in both the full-period and the later-period models are noted by asterisks in Table 1. Both sets of models have in common the foresummer precipitation 1 or 2 years before the predicted coccidioidomycosis season, as well as concurrent fall precipitation for fall coccidioidomycosis incidence.
The overall time series of observed and predicted seasonal coccidioidomycosis incidence (for the full period) is shown in Figure 3. The combined predictions of all four multivariate seasonal models are in close agreement with observations, with an overall cross-validated R2 of 0.80, and a cross-validated mean absolute error of 0.53 cases per 100,000, or about 19% of the mean incidence. The proportions of model-oriented (systematic) error versus data-oriented (unsystematic) error were 14 and 86%, respectively (Comrie 1997), implying that the model is well specified and that noisy data are responsible for most of the error. To further isolate the role of the foresummer, antecedent foresummer precipitation alone was regressed on coccidioidomycosis incidence in fall, winter, foresummer, and the monsoon in the relevant period 1.5–2 years later. The resulting cross-validated R2 between observations and combined predictions of all four antecedent foresummer-based models was 0.27.
Discussion
The development of a method to estimate Coccidioides spore exposure date from coccidioidomycosis incidence data has enabled the production of a relatively homogeneous time series. This approach reveals a strong bimodal seasonality of the disease in Pima County, Arizona, consistent with earlier findings based on other data (Hugenholtz 1957; Kerrick et al. 1985), a pattern that until now was not clearly seen in the regular reported data. On average, peaks in exposure to the fungal spores occur in June–July and in October–November, consistent with the drier and dustier months of the year. Fewer exposures occur in February–March and August–September, consistent with the timing of the wetter and less dusty months.
Multivariate models of the incidence data series indicate that concurrent dispersion conditions are important during fall (via precipitation) and in winter and the arid foresummer (via PM10). However, the most striking result of this study is the dominant role of precipitation during the normally arid foresummer 1.5–2 years before the season of exposure. Even when considered alone, April–June precipitation accounts for more than one-quarter of the overall variance in subsequent seasonal coccidioidomycosis incidence. When other antecedent and concurrent seasonal conditions are included as predictors, the combined seasonal models explain a significant and large proportion of the variance in coccidioidomycosis incidence. The model is relatively simple in structure compared with other studies (Kolivras and Comrie 2003; Komatsu et al. 2003). The model uses only lagged seasonal precipitation and concurrent seasonal dust and precipitation, yet it clearly captures both the seasonality and the trends in the incidence data. The bulk of the error is associated with noise in the data, so future improvements to the model are likely to result from improved data and a longer length of record with a larger model n.
An improved understanding of the climatic factors behind outbreaks of coccidioidomycosis will enable better timing of environmental sampling for Coccidioides and any related mitigation efforts, separation of environmental factors from population and other factors affecting outbreaks, and the potential for development of an advance warning system before an outbreak. The results of this work provide strong support for the two hypothesized relationships between climate and coccidioidomycosis, namely, fungal growth in the longer term and spore dispersion and exposure in the short term. Furthermore, the relative simplicity and strength of these results relative to earlier studies (Kolivras and Comrie 2003; Komatsu et al. 2003) lend considerable confidence to the potential for the development of an operational disease forecast model. The ability to define a critical event, such as precipitation during the foresummer, might enable mitigation procedures immediately after the event as well as provide a useful public health tool with an 18-month lead time on expected incidence of coccidioidomycosis. Future work will need to evaluate how specific these findings are to southern Arizona versus other regions in which C. posadasii is also endemic, and whether similar relationships also apply to C. immitis in California. It will also be valuable to test how a more complex model (Komatsu et al. 2003) and this simpler model compare against data from other locations and over time.
Figure 1 Annual coccidioidomycosis incidence based on estimated exposure date for Pima County, Arizona, with total annual precipitation and mean annual PM10 concentrations across sites in the Tucson region.
Figure 2 Mean monthly coccidioidomycosis incidence in Pima County, Arizona, based on estimated exposure date, with mean monthly precipitation and mean monthly PM10 concentrations, 1992–2003.
Figure 3 Observed coccidioidomycosis incidence in Pima County, Arizona, and predicted incidence from the cross-validated seasonal models, based on estimated exposure date.
Table 1 Model performance and standardized (β) coefficients for the four seasonal regression models predicting coccidioidomycosis rates from concurrent PM10 and antecedent precipitation, 1992–2003 (significance in parentheses).
Measure Foresummer Monsoon Fall Winter
Performance
Adjusted R2 0.98 (≤ 0.001) 0.60 (0.006) 0.61 (0.006) 0.95 (≤ 0.001)
Cross-validated R2 0.95 (≤ 0.001) 0.66 (0.001) 0.66 (0.001) 0.74 (≤ 0.001)
Dust
PM10 0.75 (≤ 0.001) 0.44 (≤ 0.001)
Precipitationa
Winter-0 N/Ab N/A N/A
Fall-0 N/A N/A −0.49* (0.029) −0.36 (0.004)
Monsoon-0 N/A
Foresummer-0 0.47 (≤ 0.001) 0.49 (≤ 0.001)
Winter-1 0.20 (0.023) −0.33 (0.004)
Fall-1 −0.26 (0.030)
Monsoon-1
Foresummer-1 0.45 (0.044) 0.73* (0.004) 0.56* (≤ 0.001)
Winter-2
Fall-2
Monsoon-2
Foresummer-2 1.36* (≤ 0.001) 0.64* (0.008)
Winter-3
Fall-3
Monsoon-3
Foresummer-3
Winter-4
Fall-4 N/A
Monsoon-4 −0.93 (≤ 0.001) N/A N/A
Foresummer-4 N/A N/A N/A
a For precipitation variables, Fall-0 denotes the concurrent fall, Winter-4 denotes the winter occurring 4 years earlier, and so on, ordered from most to least recent.
b Seasons falling before or after the period including the concurrent season through 4 years earlier are marked as not applicable (N/A).
* Model variables that were also present in a 1997–2003 subset analysis, signifying those variables that were significant in both the full-period and the later-period models.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7609ehp0113-00069315929891ResearchArticlesSocioeconomic and Racial Disparities in Cancer Risk from Air Toxics in Maryland Apelberg Benjamin J. 1Buckley Timothy J. 2White Ronald H. 131Department of Epidemiology,2Department of Environmental Health Sciences, and3Risk Sciences and Public Policy Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USAAddress correspondence to T.J. Buckley, Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe St., Room E6614, Baltimore, MD 21205 USA. Telephone: (410) 614-5750. Fax: (410) 955-9334. E-mail:
[email protected] thank J. Samet for his valuable comments on the manuscript.
T.J.B. acknowledges support from the National Institute of Environmental Health Sciences Johns Hopkins Center for Urban Environmental Health (PES 003819) and a grant from the Maryland Cigarette Restitution Fund made to the Sidney Kimmel Comprehensive Cancer Center at the Johns Hopkins Medical Institutions.
The authors declare they have no competing financial interests.
6 2005 14 3 2005 113 6 693 699 27 9 2004 14 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We linked risk estimates from the U.S. Environmental Protection Agency’s National Air Toxics Assessment (NATA) to racial and socioeconomic characteristics of census tracts in Maryland (2000 Census) to evaluate disparities in estimated cancer risk from exposure to air toxics by emission source category. In Maryland, the average estimated cancer risk across census tracts was highest from on-road sources (50% of total risk from nonbackground sources), followed by nonroad (25%), area (23%), and major sources (< 1%). Census tracts in the highest quartile defined by the fraction of African-American residents were three times more likely to be high risk (> 90th percentile of risk) than those in the lowest quartile (95% confidence interval, 2.0–5.0). Conversely, risk decreased as the proportion of whites increased (p < 0.001). Census tracts in the lowest quartile of socioeconomic position, as measured by various indicators, were 10–100 times more likely to be high risk than those in the highest quartile. We observed substantial risk disparities for on-road, area, and nonroad sources by socioeconomic measure and on-road and area sources by race. There was considerably less evidence of risk disparities from major source emissions. We found a statistically significant interaction between race and income, suggesting a stronger relationship between race and risk at lower incomes. This research demonstrates the utility of NATA for assessing regional environmental justice, identifies an environmental justice concern in Maryland, and suggests that on-road sources may be appropriate targets for policies intended to reduce the disproportionate environmental health burden among economically disadvantaged and minority populations.
air toxicscancerdisparityenvironmental justiceexposureincomeNATArace
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Environmental justice is a term used to describe the movement concerned with inequities in the distribution of adverse environmental and health consequences of industrial activities and environmental policies [U.S. Environmental Protection Agency (EPA) 2004a]. The movement grew from early observations that a seemingly unequal burden of pollution fell on disenfranchised and disadvantaged communities, often characterized by lower incomes and high proportions of minorities (Brown 1995). With the issuance of Presidential Executive Order 12898 in 1994, achieving “environmental justice” was integrated into the missions of all federal agencies (Clinton 1994). The U.S. EPA defines environmental justice to mean that “no group of people, including a racial, ethnic, or a socioeconomic group” should be disproportionately affected by “industrial, municipal, and commercial operations or the execution of federal, state, local, and tribal programs and policies” (U.S. EPA 2004a).
There is ample evidence that minority and low-income communities bear a disproportionate burden of exposure to many environmental contaminants (Brown 1995; Institute of Medicine 1999), including air pollution (Samet et al. 2001; Schweitzer and Valenzuela 2004). The availability of nationwide ambient monitoring for the criteria air pollutants (carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter, and sulfur dioxide) makes assessment of exposure and risk in disadvantaged and minority communities particularly feasible. However, considerably less is known about the distribution of exposure to and risk from the wide range of hazardous air pollutants (HAPs; also known as “air toxics”) identified by Congress in the Clean Air Act Amendments (1990), because nationwide ambient monitoring is not possible because of the sheer number of pollutants and their diverse chemical properties (Caldwell et al. 1998; Morello-Frosch et al. 2000; Woodruff et al. 1998).
In the early 1990s, the U.S. EPA undertook the Cumulative Exposure Project (CEP) with the goal of modeling annual ambient air concentrations of 148 air toxics and their associated risk (Rosenbaum et al. 1999; Woodruff et al. 1998). A recent analysis of modeled national estimates suggests that ambient concentrations of HAPs exceed benchmark risk levels for cancer and non-cancer end points in many areas of the country (Caldwell et al. 1998; Woodruff et al. 1998, 2000). Furthermore, several recent studies have documented a disproportionate burden of air toxics exposure and/or risk falling on minority and low-income populations. These studies have included varying sources of exposure, including high traffic density (Green et al. 2004; Gunier et al. 2003), location of Toxics Release Inventory (TRI) and other treatment, storage, and disposal facilities (Morello-Frosch et al. 2002; Pastor et al. 2001; Perlin et al. 2001), and modeled estimates from the U.S. EPA’s CEP (Lopez 2002; Morello-Frosch et al. 2002). Although these results suggest that mobile sources and large point sources are likely contributors to exposure disparities, none of these studies examined the relative contribution of different source categories in a particular region to estimated risk disparities.
To address this data gap, we examined the U.S. EPA’s 1996 National Air Toxics Assessment (NATA) (U.S. EPA 2002a) in Maryland along with U.S. Census 2000 data (Maryland Department of Planning 2004) to describe the relationship between tract-level socioeconomic and racial characteristics and estimated cancer risk from exposure to air toxics. Because the NATA estimates are source specific, we are able to examine the emission source(s) driving risk disparities and, for socioeconomic characteristics, the sensitivity of this relationship to the measure used to define socioeconomic position. We use Maryland as a case study because of the high cancer rates in the state compared with national averages. For 2000, Maryland’s rate of 48.6 per 10,000 was significantly higher than the national average of 47.3 per 10,000 (Maryland Department of Health and Mental Hygiene 2003). In addition to the elevated cancer rates observed, Maryland ranked 12th among all states in estimated mean risk from cancer-causing air pollutants, based on the U.S. EPA’s 1996 NATA estimates (U.S. EPA 2002a). In this analysis, we investigate whether this apparent excess cancer risk falls disproportionately on economically disadvantaged and/or minority communities, and whether particular sources are primarily associated with these health risks and should be targeted for emissions reductions to help achieve environmental justice.
Materials and Methods
We examined whether racial and economic disparities in estimated cancer risk from air toxics exist in the state of Maryland, and whether such disparities arise from particular emission source categories. To do so, we obtained modeled cancer risk estimates from the U.S. EPA’s NATA (U.S. EPA 2002a) and linked them to socioeconomic and racial characteristics from the 2000 U.S. Census (Maryland Department of Planning 2004) for all census tracts in the state of Maryland. We chose the census tract as the unit of analysis to examine the relationship between a community’s economic and racial makeup and risk from exposure to air toxics. Further, the tract is the smallest unit for which estimated cancer risks are available.
U.S. EPA’s NATA.
The NATA and its predecessor the CEP provide an established means for using source emission data to derive estimates of ambient air toxin exposure (Rosenbaum et al. 1999) and its associated cancer risk (Caldwell et al. 1998; Woodruff et al. 1998, 2000). We downloaded the NATA cancer risk estimates at the census tract level (U.S. EPA 2002a) and extracted results for Maryland. The U.S. EPA’s most recent national-scale air toxics assessment was conducted for 1996 and estimates the annual aggregate cancer risk for 29 chemicals (U.S. EPA 2004b). The methods used to generate census tract–level estimates of risk are described in detail by the U.S. EPA (U.S. EPA 2004b). In brief, NATA combines source emission data (i.e., TRI data, databases from the U.S. EPA’s Maximum Achievable Control Technology program, and emissions estimates for mobile and area sources) with meteorology (wind speed and direction) in a Gaussian dispersion model [Assessment System for Population Exposure Nationwide (ASPEN)] that accounts for atmospheric decay to provide an estimate of the annual ambient air toxin concentration (U.S. EPA 2003). Estimates of ambient concentrations from ASPEN are then included in an inhalation model called the Hazardous Air Pollution Exposure Model 4 (HAPEM4). This model incorporates activity patterns that may influence personal exposure to ambient pollutants.
From these concentration estimates, NATA further estimates cancer risk by applying inhalation unit risk factors according to U.S. EPA standard methods (U.S. EPA 1992
U.S. EPA 2004b). For cancer, even though the type (e.g., liver, blood, lung) and weight of evidence (e.g., known, suspected, or possible) varied by chemical, aggregate risk was estimated as the sum of individual chemical risks. The cancer risk estimates are considered by the U.S. EPA to be “upper-bound” estimates—“a plausible upper limit to the true probability that an individual will contract cancer over a 70 year lifetime as a result of a given hazard (such as exposure to a toxic chemical)” (U.S. EPA 2002c).
The following emission source categories are included in the inventory and subsequent assessment (U.S. EPA 2002c): a) Major emissions sources were “stationary facilities that emit or have the potential to emit 10 tons of any one toxic air pollutant or 25 tons of more than one toxic air pollutant per year” (e.g., electric utility power plants, oil refineries). b) Area and other emissions sources were “sources that generally have smaller emissions on an individual basis than ‘major sources’ and are often too small or ubiquitous in nature to be inventoried as individual sources”; this may include smaller facilities (e.g., dry cleaning facilities, gas station/automobile repair) or other sources such as wildfires. c) On-road mobile sources were “vehicles found on roads or highways,” and d) nonroad mobile sources were “mobile sources not found on roads and highways (e.g., airplanes, trains, lawn mowers, construction vehicles, farm machinery).” In addition, background concentrations are estimated, which represent exposure from “natural sources, persistence in the environment of past years’ emissions and long-range transport from distant sources.”
Linking NATA risk estimates with census data.
We obtained U.S. Census 2000 data for the state of Maryland from the Maryland State Data Center (Maryland Department of Planning 2004). The choice of socioeconomic measures was guided by Krieger et al. (1997) and encompasses indicators of income, wealth, poverty, and education. In particular, we extracted the following year 2000 census tract level data: median household income in 1999 (US$), per capita income in 1999 (US$), percentage of households owner occupied, percentage of households with public assistance income for 1999, percentage living below the poverty level in 1999, and percentage of the population ≥25 years of age without a high school diploma. Additionally, we examined the percentage of the population composed of whites, African Americans, and Hispanics, where the percentages are based on those who consider themselves “white only” or “African-American only.”
NATA cancer risk estimates were calculated for the year 1996 and use 1990 census tracts. In the 2000 Census, several changes were made to census tract boundaries. The U.S. Census Bureau provides a set of census tract relationship files that link the 1990 and 2000 census tracts (U.S. Census Bureau 2003). We downloaded this file for Maryland, which contains the proportion of the population in a given year 2000 census tract coming from redefined 1990 census tracts.
To link NATA risk estimates among 1990 census tracts with 2000 census tracts, we identified the NATA cancer risk estimates for the 1990 census tracts and constructed weighted averages of risk for the 2000 census tracts, based on the 2000 population proportions as follows:
CR00 is the cancer risk in the year 2000 census tract, CR90,i is the cancer risk in the year 1990 census tract i, Pi is the proportion of the 2000 census tract population coming from 1990 census tract i, and n is the number of 1990 census tracts at least partially contained in the 2000 census tract. This calculation was performed for all source categories (total, major, area, on-road, nonroad, and background).
Statistical analysis.
We downloaded NATA data (U.S. EPA 2002a) and racial/socioeconomic data from the U.S. Census 2000 (Maryland Department of Planning 2004) as Excel spreadsheets and the census relationship file for Maryland (U.S. Census Bureau 2003) as a text file, which we imported into Excel. Data linking and data management were performed in SAS (SAS Institute Inc., Cary, NC), and statistical analyses were performed in STATA (StataCorp, College Station, TX). We initially treated cancer risk as a continuous variable and explored the relationships between median household income, per capita income, and race and tract-level cancer risk estimates. We used a linear regression model to estimate the average change in estimated cancer risk associated with changes in income and racial distribution. The Breusch-Pagan/Cook-Weisberg test was used to identify the presence of heteroskedasticity (“hettest” in STATA 8.0), in which case robust SEs were used. Multivariate models included race as a linear predictor and income as a quadratic term or indicator variable (quartiles). We also included interaction terms in multivariate models to look for the presence of effect modification between income and race on estimated cancer risk.
We then divided census tracts into quartiles defined by each of the socioeconomic and racial characteristics. We calculated the proportion of census tracts in each quartile that were “high risk,” defined as greater than the 90th percentile of cancer risk among all Maryland tracts. We used Pearson’s chi-square tests to test for differences in proportions across quartiles. We also estimated relative risks (RRs) and 95% confidence intervals (CIs) for being high risk across quartiles of socioeconomic and racial characteristics. This analysis was performed for each of the socioeconomic indicators and race for all source categories.
Results
Four census tracts in the NATA file, consisting of 88 individuals, were excluded because the corresponding tracts were not present in the 2000 census relationship file (U.S. Census Bureau 2003). Two additional tracts were excluded because they had a population size of zero. Finally, we excluded five tracts whose entire population was housed in “group quarters” because no median household income measure was available and these tracts were not informative with respect to the hypothesis under study.
Table 1 presents the distribution of racial and socioeconomic characteristics among Maryland census tracts in 2000, along with estimated cancer risk from air toxics. Considerable variability exists in the distributions of socioeconomic and most racial indicators among Maryland census tracts. However, little variability was observed for the percentage of Hispanic residents because most tracts had few Hispanics. For example, in 75% of the census tracts, < 4% of the residents identified themselves as Hispanic. The correlation between socioeconomic and racial characteristics is shown in Table 2.
The cancer risk estimates shown in Table 1 were derived from population-weighted averages of the 1996 NATA estimates, as described above. The average estimated cancer risk from all sources was 5.8 × 10−5, suggesting a greater than one in a million lifetime excess cancer risk. In fact, the lowest cancer risk estimate among the census tracts was 2.3 × 10−5, 20 times higher than this commonly used regulatory threshold (Clean Air Act Amendments 1990). Among source contributions, on-road sources provide the greatest contribution to cancer risk (on average, 50% of total risk from nonbackground sources), followed by nonroad (25%) and area sources (23%). By comparison, major sources contribute significantly less to the overall cancer risk burden (< 1%).
We examined the relationships between risk from all sources and household income and per capita income using scatter plots. The trend in risk as a function of income was similar for the two indicators, so only median household income is shown here (Figure 1A). As shown in Figure 1A, the relationship between risk and income differs by level of income. Below a median household income of $50,000, an estimated decrease in risk of 1.2 × 10−5 was associated with each $10,000 increase in income (p < 0.001). Above $50,000, there was no statistically significant association between median household income and estimated cancer risk at the census tract level (β = 2.9 × 10−7 per $10,000; p = 0.11). An analysis by race showed an average decrease in estimated cancer risk of 2.6 × 10−4 for every 10% increase in the percentage of whites living in a census tract (p < 0.001). Conversely, an increase in risk of the same magnitude (2.6 × 10−4) was observed for a 10% increase in the percentage of African Americans (p < 0.001; Figure 1B). No significant association was observed between Hispanic ethnicity and total risk.
We then examined the relationship between quartiles of the various socioeconomic indicators and race and the probability of a tract being high risk (defined as greater than the 90th percentile of risk; Table 3). If there were no relationship between racial and socioeconomic characteristics and risk, then the proportion of high-risk tracts should be similar among quartiles. We did not find this to be the case. For example, census tracts with the highest proportion of whites were one-third as likely to be high risk compared with the lowest quartile (95% CI, 0.17–0.45). Conversely, tracts in the highest quartile defined by proportion of African Americans were three times as likely to be high risk compared with the lowest quartile (95% CI, 2.0–5.2). Census tracts with higher proportions of Hispanics were less likely to be high risk; however, the small range in the proportion of Hispanics living in a census tract limits interpretation of these results. For this reason, Hispanic ethnicity was not analyzed further.
The disparities observed were even greater when stratifying by income and education levels. For example, census tracts in the lowest quartile of median household income were 100 times more likely to be high risk than were those in the highest quartile (95% CI, 14–715). Furthermore, an increasing trend in the percentage of high-risk tracts was observed from the highest to the lowest quartile of median household income (0.3, 1.0, 5.6, and 33% for the fourth, third, second, and first quartile, respectively). Similar results were observed for other socioeconomic indicators (Table 3), although the magnitude differed by indicator used. For per capita income, the percentage of high-risk tracts increased from 2.6 to 29% from the highest to lowest quartile (RR = 1.0, 2.1, and 11 comparing the third, second, and first quartiles with the fourth). For the remaining indicators, trends in the RR of being high risk were apparent from highest to lowest levels of socioeconomic position (proportion owner occupied: RR = 3.3, 14, 22; proportion below poverty: RR = 2.0, 18, and 100; proportion without a high school diploma: RR = 1.0, 4.0, and 34; proportion with public assistance income: RR = 0.7, 3.3, and 15).
An examination of socioeconomic disparities in cancer risk by emission source category revealed significant disparities for on-road, area, and nonroad sources. Given the correlation between different socioeconomic indicators (Table 2), we focus here on the results for median household income. Figure 2A shows the percentage of census tracts defined as high risk from each source category by quartile of median household income. For on-road, area, and nonroad sources, census tracts in the lowest quartile of median household income were 51 (95% CI, 13–206), 101 (95% CI, 14–722), and 17 (95% CI, 6.4–47) times more likely than the highest quartile to be high risk. Furthermore, the proportion of high-risk tracts monotonically decreased with increasing income. Similar trends were observed when using other socioeconomic indicators, although the magnitude varied. For example, the RRs for highest versus lowest quartiles of per capita income was 8.0 (95% CI, 4.4–15) for on-road sources, 12 (95% CI, 5.7–23) for area sources, and 4.7 (95% CI, 2.7–8.2) for nonroad sources. Comparatively less evidence of a socioeconomic disparity was observed for cancer risk from major sources. For major sources, the magnitude of the difference in cancer risk between the highest and lowest quartiles of the various socioeconomic indicators ranged from 0.9- to 2.8-fold.
Similarly, the strongest racial disparities in estimated cancer risk were observed among on-road and area sources. Figure 2B shows the percentage of high-risk census tracts from each source category by quartile of proportion of African Americans in the population. Significant differences in the proportions were observed for on-road (RR = 6.2; 95% CI, 3.5–11 comparing highest with lowest quartile) and area sources (RR = 3.0; 95% CI, 2.0–4.7 comparing highest with lowest quartile). In contrast, for major sources, a statistically significant reduction in the proportion of high-risk tracts was observed as the proportion of African Americans residing in a census tract increased. Opposite effects were observed for quartiles defined by the proportion of white residents (data not shown). Finally, we oberved no significant differences among quartiles defined by the proportion of white residents for risk from nonroad sources.
To examine the joint effects of race and income on estimated cancer risk, we ran a linear regression model, with interaction terms, of estimated cancer risk on median household income and percentage of African Americans. We found evidence of an interaction between the effects of income and race on risk (p < 0.001). Specifically, the strongest association between race and risk was observed in the lowest quartile of median household income (Figure 3A). In this quartile, a 10% increase in the percentage of African Americans in the tract was associated with an average increase in risk of 3.4 × 10−4. By contrast, in the highest quartile of income (Figure 3D), we observed a slight but statistically significant reduction in risk with increasing percentage of African Americans. Because the strongest disparities in cancer risk were observed from area and on-road sources, we performed a similar analysis using estimated risk from these sources. Once again, interaction terms were statistically significant (p < 0.001), with a stronger effect of race on risk at lower incomes.
Discussion
In this analysis, we characterized the relationship between estimated cancer risk from air toxics and socioeconomic and racial characteristics at the census tract level in Maryland. We found strong and consistent associations between socioeconomic and racial characteristics of census tracts and estimated cancer risk from air toxics. Census tracts were more likely to be characterized as high risk as the level of socioeconomic disadvantage (as measured by several indicators) increased, the proportion of white residents decreased, and the proportion of African-American residents increased. In general, risk declined as the proportion of Hispanic residents increased; however, there were relatively few tracts with a large proportion of Hispanic residents. Although income, education, and race were all significantly associated with estimated cancer risk, the magnitude of disparities observed was more pronounced for income and education compared with race.
Economic and racial disparities in estimated cancer risk were not uniformly observed for all emission source categories. Significant disparities among tracts defined by income and education level were observed for area, on-road, and nonroad sources. For these sources, census tracts in the lowest quartiles of median household income were 15- to 100-fold more likely to be high risk than those in the highest quartile of income. For tracts defined by racial distribution, statistically significant disparities were observed only for area and on-road sources. Conversely, risk from major sources was more evenly distributed among census tracts defined by income and education. In contrast to the other source categories, for major sources, census tracts with an increasing fraction of whites and a decreasing fraction of African-American residents yielded an increased risk. However, because high risk was defined as the top 10% of risk and major sources were a small contribution to overall risk, the impact of this association may have minimal public health relevance.
In a recent analysis of results from the U.S. EPA’s CEP, Morello-Frosch et al. (2002) reported that mobile sources drive cancer risk from air toxics in southern California, whereas area and point sources are drivers of air toxics exposure. Although we did not examine source contributions to air toxics exposure, our risk findings were consistent; that is, on-road sources were the greatest contributor to cancer risk among census tracts in Maryland, followed by nonroad sources (Table 1). The difference in source contributions to estimated exposure and cancer risk may be due to a lack of cancer potency data for compounds released from point sources, emissions of more potent carcinogenic compounds from mobile sources, and/or a greater likelihood for personal exposure from mobile sources (Morello-Frosch et al. 2000).
In examining race and income concurrently, Morello-Frosch et al. (2001) reported a relatively consistent disparity in population-weighted individual cancer risk between racial/ethnic groups across income strata in southern California. This differs from our results, which use the census tract as the unit of observation. We found little evidence of a disparity in risk, at higher incomes, between tracts with large differences in racial makeup. It is not clear whether the different inferences regarding the joint effects of race and income reflect differences in methodology or variation in source and demographic characteristics between the two study regions.
In our analysis, on-road sources were significantly associated with the racial and socioeconomic characteristics of census tracts in Maryland. The finding of a potential disparity in cancer risk from on-road sources is not surprising, given the likelihood for poorer neighborhoods to be in the midst of high-traffic congested areas. Gunier et al. (2003) studied the relationship between traffic density and socioeconomic level and race in California. They found that the census block groups in the lowest quartile of median family income were more likely to have high traffic density than were the highest quartile. Furthermore, the inverse relationship between median income and traffic density was observed for all race/ethnicities except whites (Gunier et al. 2003). In another recent study of traffic exposure and public school locations in California, Green et al. (2004) reported that schools located near high-traffic areas were more likely to be “economically disadvantaged” and “nonwhite.” Therefore, the results of this study are supported by a growing body of evidence indicating that low-income and minority populations are more likely to reside and attend school near sources of on-road pollution, and that the relationship between income and exposure may differ by race.
One unexpected finding was the lack of a consistent association between risk from major sources and tract-level income characteristics. Recent studies have documented racial and economic disparities in the location of TRI and other treatment, storage, and/or disposal facilities (Morello-Frosch et al. 2002; Pastor et al. 2001, 2002; Perlin et al. 2001). The potential for long-range transport of air pollutants from major point sources may attenuate any disparities in cancer risk that would be expected on the basis of disparities in the location of treatment, storage, and disposal facilities. It has also been suggested (Morello-Frosch et al. 2002; Pastor et al. 2001) that the relationship between income and exposure from major point sources may have an inverted U-shape. The areas with the lowest income have little exposure because of lack of economic and industrial development, and areas with the highest income have little exposure because of increased mobility and political will. Under this scenario, the burden of exposure would fall on low- to middle-income working-class populations (Morello-Frosch et al. 2002; Pastor et al. 2001). We observed no suggestion of such a U-shaped pattern (data not shown).
There are several limitations to the NATA analysis, some of which reflect inherent limitations in the risk assessment process (U.S. EPA 2002b). The cancer risk assessment was limited to 29 air toxics with sufficient emission and risk estimate data; therefore, the cancer risk estimates are not a comprehensive assessment of all air toxics of concern. As mentioned above, diesel exhaust was excluded from the cancer risk estimation because of the lack of EPA consensus on a cancer risk estimate. This would have implications for overall risk from on-road and nonroad sources and, likely, the magnitude of disparity observed. Further, threshold reporting of emissions from major point source databases such as TRI may have underestimated risk from these sources.
The U.S. EPA’s analysis focuses only on inhalation exposure from air toxics, omitting exposure from other pathways (e.g., dermal and ingestion). To the extent that these other pathways contribute to risk, cancer risk estimates would be underestimated. Furthermore, several studies have reported that modeled and measured outdoor levels of volatile organic compounds (VOCs) underestimate indoor concentrations and personal exposures. A recent study conducted in several south Baltimore communities concluded that personal exposures tend to be higher relative to parallel measurements made indoors and outdoors. For many VOCs, indoor concentrations dominated exposure; however, the authors reported that compounds associated with vehicle emissions were found to have similar indoor and outdoor concentrations (Payne-Sturges et al. 2004). In a similar study in three Minnesota communities, personal exposure to VOCs was consistently higher than indoor and outdoor concentrations (Sexton et al. 2004). Thus, cancer risk estimates based on personal monitoring would likely be higher than those based on estimated outdoor concentrations. However, even with the imprecision in exposure and risk estimates, the NATA results should provide a good indication of the relative levels of source emissions among communities. Results from the present study indicate that HAP source emissions are higher among minority and economically disadvantaged communities.
An additional source of uncertainty arises from the comparison of 1996 risk estimates to racial and socioeconomic measures from 2000 census tracts. Significant emission reductions have taken place since the mid-1990s as a result of federal, state, and local efforts, thereby affecting the magnitude of cancer risk (U.S. EPA 2002b). It is unlikely, however, that significant changes in all of the socioeconomic measures evaluated would have occurred in such a short time frame, so this analysis can be seen as an estimate of the relationship between racial and socioeconomic characteristics and estimated cancer risk from air toxics as of the mid-1990s. Furthermore, for on-road mobile sources, it is likely that the observed risk and disparity have increased in proportion to increases in vehicle miles traveled and the proportion of less fuel-efficient sports utility vehicles (de Abrantes et al. 2004; Schmitz et al. 2000; U.S. EPA 2000).
In conclusion, these results provide evidence that cancer risk associated with air toxics exposure, particularly from on-road and area sources, disproportionately falls onto socioeconomically disadvantaged and African-American communities. This research also highlights the potential for confounding by socioeconomic status when examining the long-term health effects of traffic-related pollutants, because lower socioeconomic status is associated with a host of adverse health effects that may or may not be mediated through the effects of air pollution. Additional analyses should be performed nationwide to examine whether similar relationships exist across different regions of the country and which compounds are the primary determinants of this risk disparity. Furthermore, future research should explore the complex interactions between race and income on risk from air toxics exposure. In the interim, these data, along with prior literature on the health effects associated with residing in close proximity to high traffic density, suggest that efforts to reduce the disproportional health risk burden falling on lower income and minority populations should include policies targeting emissions from on-road vehicle sources.
Figure 1 Relationship of estimated cancer risk from air toxics with median household income (A) and proportion of African-American residents (B) among Maryland census tracts, 2000. Line represents a lowess smoothing function.
Figure 2 Percentage of high-risk tracts by emission source category and quartile (Q) of median household income (A) and proportion African-American residents (B). High risk is defined > the 90th percentile of risk from each source among Maryland census tracts. Error bars represent SE.
*p < 0.05;
**p < 0.01 using Pearson’s chi-square test to compare each quartile within a source category to the first.
Figure 3 Risk from all sources as a function of the proportion of African-American residents in the first (A, lowest), second (B), third (C), and fourth (D, highest) quartiles of median household income. Line represents a lowess smoothing function.
Table 1 Distribution of demographic characteristics and estimated cancer risk from air toxics among Maryland census tracts, 2000 (n = 1,210 tracts).
Percentile
Characteristic Mean 5th 25th 50th 75th 95th
Median household income (US$)a 55,010 21,188 38,577 50,910 67,339 99,531
Per capita income (US$)a 24,952 11,679 18,135 23,247 28,671 45,589
Percent owner-occupied homes 68 20 52 74 87 95
Percent with public assistance incomea 2.8 0 0.7 1.6 3.2 11
Percent below poverty levela 10 1.4 3.4 6.3 13 33
Percent without a high school diploma 18 3.3 9.1 16 25 42
Percent white 63 1.9 38 75 90 97
Percent African American 30 0.8 4.4 15 47 96
Percent Hispanic 4.1 0.5 1.1 1.8 3.7 17
Cancer risk
All sourcesb 5.8 × 10−5 2.8 × 10−5 4.4 × 10−5 5.3 × 10−5 6.5 × 10−5 1.1 × 10−4
Major sources 2.8 × 10−7 4.0 × 10−8 1.5 × 10−7 2.7 × 10−7 3.4 × 10−7 5.8 × 10−7
Area sources 8.8 × 10−6 2.4 × 10−6 5.2 × 10−6 7.2 × 10−6 1.0 × 10−5 2.2 × 10−5
On-road sources 1.9 × 10−5 3.5 × 10−6 1.2 × 10−5 1.6 × 10−5 2.2 × 10−5 4.5 × 10−5
Nonroad sources 9.6 × 10−6 1.6 × 10−6 6.2 × 10−6 9.3 × 10−6 1.2 × 10−5 2.0 × 10−5
a Estimates are for 1999.
b Includes background sources.
Table 2 Correlation between census tract level demographic characteristics in Maryland, 2000 (n = 1,210 tracts).
Percent white Percent African American Percent Hispanic Median household income Per capita income Percent owner occupied Percent below poverty level Percent with public assistance Percent without high school diploma
Percent white 1.0000
Percent African American −0.9705 1.0000
Percent Hispanic −0.1632 −0.0300 1.0000
Median household income 0.3449 −0.3909 0.0089 1.0000
Per capita income 0.3879 −0.4241 −0.0391 0.8568 1.0000
Percent owner occupied 0.4820 −0.4413 −0.2209 0.6210 0.4470 1.0000
Percent below poverty level −0.4698 0.4954 −0.0170 −0.6240 −0.5400 −0.6061 1.0000
Percent with public assistance −0.5167 0.5612 −0.0781 −0.5391 −0.4859 −0.4778 0.7866 1.0000
Percent without high school diploma −0.3626 0.4013 0.1001 −0.6880 −0.6731 −0.4192 0.7023 0.6889 1.0000
Table 3 Percentage of high-risk tracts and RRs by quartile of demographic measure in Maryland, 2000.
Census tract measure Percent high riska RR (95% CI)
Median household income
Quartile 1 33 100 (14–715)
Quartile 2 5.6 17 (2.3–127)
Quartile 3 1.0 3.0 (0.3–29)
Quartile 4 0.3 —
Per capita income
Quartile 1 29 11 (5.5–22)
Quartile 2 5.6 2.1 (0.9–4.9)
Quartile 3 2.7 1.0 (0.4–2.6)
Quartile 4 2.6 —
Percent owner occupied
Quartile 1 22 22 (6.9–68)
Quartile 2 14 14 (4.5–46)
Quartile 3 3.3 3.3 (0.9–12)
Quartile 4 1.0 —
Percent with public assistance income
Quartile 1 2.0 —
Quartile 2 1.3 0.7 (0.2–2.3)
Quartile 3 6.6 3.3 (1.4–8.2)
Quartile 4 30 15 (6.7–34)
Percent below poverty level
Quartile 1 0.3 —
Quartile 2 0.7 2.0 (0.2–22)
Quartile 3 6.0 18 (2.4–134)
Quartile 4 33 100 (14–710)
Percent age ≥ 25 without a high school diploma
Quartile 1 1.0 —
Quartile 2 1.0 1.0 (0.2–4.9)
Quartile 3 4.0 4.0 (1.1–14)
Quartile 4 34 34 (11–107)
Percent white
Quartile 1 22 —
Quartile 2 6.9 0.32 (0.20–0.51)
Quartile 3 5.6 0.26 (0.16–0.44)
Quartile 4 5.9 0.28 (0.17–0.45)
Percent African American
Quartile 1 6.6 —
Quartile 2 5.3 0.8 (0.4–1.5)
Quartile 3 6.6 1.0 (0.6–1.8)
Quartile 4 22 3.2 (2.0–5.2)
Percent Hispanic
Quartile 1 17 —
Quartile 2 8.3 0.48 (0.31–0.75)
Quartile 3 8.0 0.46 (0.29–0.73)
Quartile 4 6.6 0.38 (0.23–0.63)
Quartile 1 is lowest; quartile 4 is highest.
a > the 90th percentile of risk among Maryland census tracts.
==== Refs
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Woodruff TJ Axelrad DA Caldwell J Morello-Frosch R Rosenbaum A 1998 Public health implications of 1990 air toxics concentrations across the United States Environ Health Perspect 106 245 251 9518474
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7509ehp0113-00070015929892ResearchArticlesPerinatal Exposure to Low Levels of the Environmental Antiandrogen Vinclozolin Alters Sex-Differentiated Social Play and Sexual Behaviors in the Rat Colbert Nathan K.W. Pelletier Nicole C. Cote Joyce M. Concannon John B. Jurdak Nicole A. Minott Sara B. Markowski Vincent P. Maine Center for Toxicology and Environmental Health, University of Southern Maine, Portland, Maine, USAAddress correspondence to V.P. Markowski, Department of Psychology, University of Southern Maine, 96 Falmouth St., 178 Science Building, Portland, ME 04104-9300 USA. Telephone: (207) 228-8174. Fax: (207) 228-8057. E-mail:
[email protected] thank W.D. Thompson for his assistance with the statistical analyses and S.E. Frankel for his efforts proofreading the manuscript.
This study was supported by a research grant from the Bioscience Research Institute of Southern Maine to V.P.M.
The authors declare they have no competing financial interests.
6 2005 16 3 2005 113 6 700 707 18 8 2004 15 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In this study we examined the effects of exposure to the antiandrogenic fungicide vinclozolin (Vz) on the development of two sex-differentiated behaviors that are organized by the perinatal actions of androgens. Pregnant Long-Evans rats were administered a daily oral dose of 0, 1.5, 3, 6, or 12 mg/kg Vz from the 14th day of gestation through postnatal day (PND)3. The social play behavior of juvenile offspring was examined on PND22 and again on PND34 during play sessions with a same-sex littermate. After they reached adulthood, the male offspring were examined with the ex copula penile reflex procedure to assess erectile function. Vz did not produce any gross maternal or neonatal toxicity, nor did it reduce the anogenital distance in male pups. We observed no effects of Vz on play behavior on PND22. However, the 12-mg/kg Vz dose significantly increased play behavior in the male offspring on PND34 compared with controls. The most dramatic increases were seen with the nape contact and pounce behavior components of play. The Vz effect was more pronounced in male than in female offspring. As adults, male offspring showed a significant reduction of erections at all dose levels during the ex copula penile reflex tests. The 12-mg/kg dose was also associated with an increase in seminal emissions. These effects demonstrate that perinatal Vz disrupts the development of androgen-mediated behavioral functions at exposure levels that do not produce obvious structural changes or weight reductions in androgen-sensitive reproductive organs.
antiandrogenpenile reflexesprenatal exposureratsocial playvinclozolin
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Fungicides are applied to many foods to control plant diseases such as Sclerotinis sclerotiorum (white mold) and Botrytis cinerea (gray mold) (Papadopoulou-Mourkidou 1991). After application, fungicides have been shown to volatilize and circulate through air and water and on untreated foods, increasing their distribution (Baumeister et al. 2002). Consumers cannot readily reduce their exposure because fungicides are not removed from fresh produce by rinsing with tap water (Krol et al. 2000), and commercial processing increases their concentrations (Will and Kruger 1999). Fungicides are also widely used on golf courses, industrial landscapes, lawn turf, and ornamental plants, where they can enter water supplies in contaminated runoff (Haith and Rossi 2003).
The dicarboximide fungicide vinclozolin (Vz) is used in a number of commercial formulations to treat fruits and vegetables such as lettuce, snap beans, canola, and grapes [U.S. Environmental Protection Agency (EPA) 2003]. Vz belongs to a group of environmental endocrine disruptors known as the anti-androgens. These compounds share a common, clearly defined hormone-receptor–mediated mechanism of action. Vz is biotransformed into at least two active metabolites that bind competitively to the human, monkey, and rat androgen receptor (Kelce and Monosson 1995; Kelce et al. 1994). Exposure to antiandrogens during development could have serious effects on sexual development. They are already recognized as one of the factors responsible for the recent increase of hypospadias, a male reproductive disorder where the urethral opening is on the ventral surface of the penis (Baskin et al. 2001; Davis et al. 1998; Egeland et al. 1994; Jensen et al. 1995; Sharpe and Skakkebaek 1993).
To date, most investigations have focused on the impact of the environmental anti-androgens on the development of androgen-sensitive male reproductive organs. For example, adult male rats have reduced anogenital distances (AGDs), reduced seminal vesicle and ventral prostate weights, and lower epididymal sperm counts after perinatal exposure to Vz (Gray et al. 1994; Hellwig et al. 2000). Vz is not the only fungicide that acts as an environmental antiandrogen. Procymidone and iprodione are fungicides that are structurally similar to Vz, and they produce a nearly identical profile of effects on the reproductive system (Gray et al. 1999b; Ostby et al. 1999). All three fungicides have the same final metabolite. However, the doses of Vz that have been shown to affect the weights of reproductive organs in animal studies are quite high, often 5–10 times the U.S.EPA’s lowest observed adverse effect level (LOAEL). High doses are associated with measures of gross toxicity such as lowered body weights and increased mortality rate due to granulomas and bladder stones (Gray et al. 1994; Hellwig et al. 2000). Thus, although effects such as reductions in rat reproductive organ weights demonstrate that environmental antiandrogens can produce long-term effects after perinatal exposure, they do not necessarily represent the most sensitive end points. Investigations of the functional effects of low-level environmental antiandrogen exposure are needed to complement the high-dose studies and place organ deficits into the larger context of male reproductive health.
One of the questions examined in the present study was whether much lower levels of Vz during the perinatal period affect reproduction via a disruption of male copulatory behavior. An androgen-sensitive neuro-muscular system that is critical for normal male copulatory behavior is the levator ani and bulbocavernosus (BC) skeletal muscles and their motor neuron control centers in the lumbar spinal cord [the spinal nucleus of the BC (SNB)]. In rats, contraction of the levator ani and BC muscles, as well as vascular mechanisms, produces penile erections (Hart and Melese-D’Hospital 1983; Leipheimer and Sachs 1993; Sachs 1982). The sex-specific development of the BC/SNB system is organized during the perinatal period by the non-aromatizable androgen dihydrotestosterone (DHT) (Hart 1979; Thomas et al. 1982). In developing males, the presence of DHT reduces motor neuron death in the SNB and promotes retention of the BC (Mills and Sengelaub 1993). In the adult male, there are two to three times more SNB motor neurons than in females (Sengelaub et al. 1989). However, environmental antiandrogen exposure can disrupt the development of the SNB/BC system. Vz exposure during the perinatal (Wolf et al. 2000, 2004) or peripubertal period (Monosson et al. 1999) significantly reduces the weight of the BC and levator ani muscles in adult males. Other antiandrogens such as procymidone, prochloraz, and linuron also affect the development of the BC muscle (Lambright et al. 2000; Ostby et al. 1999; Vinggaard et al. 2002).
What are the functional implications of an underweight BC muscle that has been affected by Vz exposure? Gray et al. (1994) have shown that adult male rats exposed to perinatal Vz will mount sexually receptive females but are unable to achieve vaginal penetration, suggesting that there is an underlying erectile dysfunction. Other environmental antiandrogens, such as p,p′-dichlorodiphenyldichloroethylene (p,p′-DDE), have already been shown to reduce erectile functions in rats (Brien et al. 2000). Female rats can detect subtle behavioral deficits and prefer to copulate with healthy, dominant males (McClintock et al. 1982). Antiandrogens could therefore affect the reproductive success of a wide range of animal species by altering male copulatory behavior. For instance, female guppies prefer males with high rates of sexual display, and Vz exposure has been shown to significantly reduce male guppy courtship display (Baatrup and Junge 2001; Bayley et al. 2002).
Most functional investigations of environmental endocrine disruptors have focused on the effects of perinatal exposure in adult offspring and have ignored the developmental trajectory of the effects of antiandrogen exposure. Juvenile play is a sexually dimorphic behavior that is an important precursor to adult sexual behavior (Pellis et al. 1992) and dominance relationships (Pellis and Pellis 1992). Males typically engage in more bouts of play and perform more behaviors during bouts than females (Thor and Holloway 1983). Even though they are prominent at different times in the life span, juvenile play and copulation are interconnected. During play, rats perform numerous crawl-over behaviors with same-sex partners. There is a shift of interest in male pups in their preferred play partners during the postnatal day (PND)36–40 period, from male to female (Meaney and Stewart 1981). Older, sexually mature but naive males will perform crawl-overs with females until they achieve a mount with a successful vaginal intromission. After the first intromission, mounting behavior increases and crawl-overs decrease. Adult males that do not have the opportunity to experience normal play during development show excessive play components but little normal copulatory behavior in the presence of sexually receptive females (Gerall et al. 1967; Goldfoot 1977; Gruendel and Arnold 1960; Hard and Larsson 1968). Thus, early environmental factors can affect important neonatal or juvenile social interactions, culminating in aberrant behaviors in adulthood (Dunlap et al. 1978).
The neural mechanisms for play are potential targets for environmental antiandrogens because they are organized in part by androgens during the perinatal period. Administration of the androgen receptor antagonist flutamide during the first 10 postnatal days demasculinizes male rat play behavior (Meaney et al. 1983). Other manipulations such as prenatal protein deprivation (Almeida et al. 1996), perinatal genistein exposure (Flynn et al. 2000), prenatal polychlorinated biphenyl (PCB) exposure (Vreugdenhil et al. 2002), or maternal stress during gestation can also demasculinize play behavior (Ward and Stehm 1991). Recently there has been some interest in the effects of perinatal Vz exposure on social play behavior in juvenile subjects. Hotchkiss et al. (2003) exposed neonatal rat pups to 200 mg/kg Vz on PND2 and PND3. This acute, high-dose exposure had long-term consequences for male rats. Juveniles performed significantly fewer chase and pin behaviors during play sessions with a same-sex partner. Female pups were not exposed to Vz in this study, although it is well known that female play can also be altered by neuro-endocrine manipulations (Hines 2003; Nordenstrom et al. 2002; Servin et al. 2003). One study that used a chronic, low-level dietary exposure to Vz found that female rats were more sensitive than males (Flynn et al. 2001).
In the present study, pregnant rats were exposed to low doses of Vz through the last third of gestation and for several days after parturition. Play behavior was examined in juvenile male and female offspring. Erectile function in adult males was assessed using the ex copula penile reflex procedure.
Materials and Methods
Breeding and exposure.
Long-Evans hooded rats (Harlan, Indianapolis, IN) were allowed to acclimate to the University of Southern Maine Vivarium quarters for 2 weeks before breeding. All rats were fed standard pellet chow (Teklad Global 18% Protein Rodent Diet; Harlan Teklad, Madison, WI) ad libitum and were maintained on a 12-hr light/12-hr dark cycle in a barrier facility room with an ambient temperature of 68 ± 2°F and 40–60% humidity.
Groups of three females were housed with stud males, and vaginal smears were examined each morning for the presence of sperm. We regarded a sperm-positive smear as gestation day (GD)0. Pregnant rat dams were placed individually into polycarbonate shoebox cages and assigned to an exposure condition according to a randomized block design. Each block consisted of five groups: 0, 1.5, 3, 6, or 12 mg Vz/kg maternal body weight. Vz (Crescent Chemical Co. Inc., Islandia, NY) was dissolved in corn oil, and the appropriate volume (~ 0.5–1.5 mL) was administered via gavage from GD14 through PND3 to coincide with the period of sexual differentiation in the rat (Miller et al. 1988). Vz was not administered on the day of parturition (PND0). We chose the doses in order to examine a range below the U.S.EPA’s LOAEL of 11.5 mg/kg/day (U.S. EPA 2000a). The adverse developmental event that is associated with the LOAEL is the retention of nipples and areolas in immature male offspring.
We recorded maternal body weights daily during the gestational period. Cages were inspected each morning and afternoon for the presence of litters. Litter size, sex distribution, pup weights, and AGDs were recorded on PND1 and every 4 days thereafter. Using a randomized procedure, litters were culled to eight offspring on PND4, maintaining equivalent sex distributions when possible. After weaning on PND21, offspring were housed with same-sex littermates in plastic cages with filter bonnets. All animal procedures complied with approved institutional animal care protocols and in accordance with National Institutes of Health guidelines (Institute of Laboratory Animal Resources 1996). Animal care and welfare were supervised by a veterinarian and a Registered Laboratory Animal Technologist certified by the American Association of Laboratory Animal Science.
The exposure and rearing procedure yielded a total of 51 viable litters (Table 1). From this cohort, we assigned 11, 11, 8, and 6 pairs of same-sex littermates from the 0-, 3-, 6-, and 12-mg/kg groups, respectively, to the play procedure. Only those litters with at least 2 male offspring and 2 female offspring were assigned to this procedure. The litter was always considered the unit of analysis, and only 1 male and 1 female pair per litter was assigned to the play procedure. For the penile reflex procedure, 11, 7, 11, 10, and 6 male offspring from the 0-, 1.5-, 3-, 6-, and 12-mg/kg groups were assigned, respectively.
Play behavior.
We randomly selected two male and two female pups from each litter. Within each same-sex pair, one animal was designated as the “target” and the other animal served as partner. Before data collection, target and partner animals were marked with a nontoxic marker for identification; they were then separated from their littermates. Twenty-four hours later, the target animal and their same-sex partner were placed together for 10 min in a glass aquarium (12 in. wide ×24 in. long × 12 in. high) with clean cage bedding. We filmed their interactions under dim red light with a Canon XL1s digital video camera (Canon, Inc., Lake Success, NY) interfaced to an iMac computer running iMovie software (Apple Computer, Inc., Cupertino, CA). All testing was conducted during the middle of the dark phase of the light/dark cycle. No other animals were present in the room during filming.
We examined play behavior on PND22 and again on PND34 in the same animals. The assessment ages were chosen in order to examine play immediately after weaning on PND21 and immediately before the decline in same-sex play in male rats, which begins during PND36–40 (Meaney and Stewart 1981).
A pair of trained observers later viewed the films using QuickTime software. (Apple Computer Inc.) Observers tabulated the frequency and distribution of the following five behaviors: nape attack (the snout of the target animal makes contact with the nape area of the partner animal; this behavior occurs frequently and often initiates a bout of play behavior); pounce (the target animal lunges forward with its forepaws extended and makes contact with the partner animal); pin (the target animal is positioned on top of the partner animal with its forepaws placed on the partner; the partner animal lies on its back, fully exposing its ventral surface to the target animal); wrestle (the target and partner animal roll and tumble with each other); and mount (a component of the adult male copulatory pattern where the target animal approaches the partner animal from the rear, clasps its flanks, and mounts).
Penile reflex.
In rats, reflexive penile erections and movements can be observed if the penile sheath is retracted with light pressure directed at the base of the penis (Hart and Melese-D’Hospital 1983; Sachs and Garinello 1978). Penile reflexes in the rat consist of erections (tumescence followed by detumescence), cupping (the end of the erect glans penis flares out), and flipping (rapid dorso-flexion of the erect penis). Erections serve to extend the penis beyond the penile sheath, a function that is necessary to achieve vaginal penetration during copulation. Penile flipping serves to stretch the vaginal wall and cupping serves to collect coagulating semen and seal the seminal plug against the cervix.
We conducted all erection tests during the middle of the dark phase. Tests lasted for 20 min after the first response or for 15 min in the absence of responses. During each test, animals were restrained in a supine position with their head and upper torso positioned in a darkened, ventilated tube (8.5 × 5.5 × 20 cm) fastened to a plastic base. The darkened tube is anxiolytic, and rats rapidly habituate to brief periods of restraint. The penile sheath was retracted and held in place (Hart and Melese-D’Hospital 1983; Sachs and Garinello 1978). Typically, clusters of penile erections and dorsoflexions (movements or “flips”) begin spontaneously within 5–10 min after sheath retraction.
Trained observers recorded the frequency and time distribution of three gradations of erections: E1, reddening and distension of glans; E2, tumescence of the base and tip of glans; and E3, intense erection accompanied by cupping of the tip of glans (Eaton et al. 1991; Hull et al. 1991; Warner et al. 1991). Penile movements, seminal emissions, latency to the first reflex, and the number of response clusters were also determined. We defined a response cluster as any display of responses separated by ≥ 15 sec. Seminal emissions were defined as the expulsion of seminal fluid followed by a coagulating plug.
Statistical methods.
For the play procedure, the five behaviors were summed and analyzed as total play behaviors per session with repeated-measures analysis of variance (ANOVA). The individual behaviors were also analyzed separately. The litter always served as the statistical unit of analysis, with the exposure level as a between-litter factor and sex and PND as within-litter factors. In cases where there was a significant main effect or interaction involving the exposure factor, Duncan’s probe tests were used to make pairwise comparisons. We considered p < 0.05 statistically significant.
For the penile reflex procedure, we tested each animal on 2 consecutive days, and the data from the two sessions were averaged before analysis. If an animal was inactive during one of the sessions, that session was dropped. If an animal was inactive during both sessions, that animal was dropped from the analysis. Only 2 of 47 animals were inactive during both sessions. The averaged behavior was analyzed with one-way ANOVA according to exposure level.
Because the penile erection data showed a clear dose–response relationship with evidence that even the lowest dose of Vz disrupted the behavior, we further examined the data with Benchmark Dose Modeling Software (BMDS; version 1.3.2; U.S.EPA National Center for Environmental Assessment, Washington, DC). BMDS is a useful alternative to the no observed adverse effect level (NOAEL) approach because it uses the entire dose–response relationship and does not involve extrapolations far below experimental observations. We used the BMDS continuous model to calculate benchmark doses that represented the model-estimated control mean, minus proportional deviations equivalent to a 10% (ED10) or 1% (ED01) decrement in behavior. BMDS also provided a 95% lower bound that can be divided by a standard uncertainty factor to calculate a reference dose or generate a margin of exposure.
Results
Maternal and postpartum data.
We found no evidence of gross maternal or neonatal toxicity (Table 1), nor were there any exposure-related changes in maternal body weight, pup body weight, or AGD (males, Table 2; females, Table 3). The percentage of male pups that possessed at least one visible areola on PND12 (control, 18%; 1.5 mg/kg, 50%; 3 mg/kg, 44%; 6 mg/kg, 45%; 12 mg/kg, 33%) was not significant (p = 0.367).
Play behavior.
For this procedure, the primary dependent variable was the total number of play behaviors per session. Although we found no significant main effects of the exposure, sex, or PND factors on total play behaviors, there was a significant exposure × PND interaction [F(1,21) = 7.72, p = 0.01]. We also examined the total number of behaviors separately for PND22 and PND34. There was a significant effect of sex on PND22 [males > females; F(1,21) = 9.91, p < 0.01] and a significant effect of exposure on PND34 [F(1,37) = 16.38, p < 0.001]. Probe tests revealed that the male 12-mg/kg and 6-mg/kg Vz groups produced significantly more play behaviors than the did controls on PND34 (Figure 1A). There were no differences between the female exposure groups (Figure 1B).
Nape contact and pounce variables made the greatest contribution to the significant exposure-related effects on total play behaviors. For nape contacts, there was a significant exposure × PND interaction [F (1,21) = 5.51, p = 0.03]. We also examined the number of nape contacts separately for PND22 and PND34. As with the total play behavior variable, there was a significant main effect of sex on PND22 [males > females; F(1,21) = 11.13, p < 0.01] and a significant main effect of exposure on PND34 [F(1,37) = 16.09, p < 0.001]. Probe tests indicated that the male 12-mg/kg Vz group produced significantly more nape contacts than did the 0- and 3-mg/kg groups on PND34 (Figure 2). For the pounce variable, there was a significant main effect of exposure [F(1,21) = 6.44, p = 0.02]. Data were averaged across sex and age, and probe tests indicated that the 12-mg/kg group pounced more than did controls (Figure 3). There were no exposure-related differences for pin, wrestle, or mount behaviors.
Penile reflex.
We found a significant exposure-related decline in total erections per session [F(4,40) = 4.62, p < 0.01; Figure 4] as each of the Vz groups produced significantly fewer erections than controls. The decline in total erections was due primarily to a dose-related decline of E1 or low-intensity erections [F(4,40) = 10.07, p < 0.01] as well as the number of reflex clusters per session [F(4,40) = 3.23, p = 0.02; Figure 5]. The latency to the first penile reflex and the frequency of E2 and E3 responses were not significantly different. Surprisingly, there was a significant increase in seminal emissions [F(4,40) = 7.37, p < 0.01; Figure 6] as the 12-mg/kg group expelled more often than did any of the other groups. This effect was unanticipated because rats do not usually emit seminal fluid during the ex copula procedure.
Benchmark dose modeling.
We performed benchmark dose calculations on the total erections per session and the total play behavior in the male offspring on PND34. These two variables were selected because they are the best overall measures of erectile function and play, and consequently, they generalize more readily to humans. A polynomial model provided the best description of the erection data. The corresponding ED10 benchmark for erections was 1.23 mg/kg with a 95% lower bound of 0.84 mg/kg (Figure 7). The ED01 benchmark was 0.11 mg/kg with a lower bound of 0.08 mg/kg. The linear model provided the best description of the play data. The ED10 associated with total play in the male offspring on PND34 was 1.33 mg/kg with a lower bound of 0.77 mg/kg (Figure 8). The ED01 for total play was 0.13 mg/kg with a lower bound of 0.08 mg/kg.
Discussion
The results of this study clearly demonstrate that social and reproductive behaviors in the rat are disrupted by exposure to low doses of Vz during the perinatal period. Maternal doses of 12 mg/kg, administered from GD14 through PND3, were associated with a significant increase in social play behavior in PND34 offspring. We observed no Vz-mediated differences on PND22, indicating that the effect emerged as offspring matured. The increased play on PND34 was more pronounced in males than in females. In adulthood, male offspring produced significantly fewer penile erections, an effect that was even more sensitive than play behavior because a decrease was noted after maternal doses as low as 1.5 mg/kg.
Although other researchers have reported that high doses of Vz (200 mg/kg) administered to rat pups on PND2 and PND3 reduced play behavior (Hotchkiss et al. 2003), this study is the first to describe play behavior effects near the LOAEL of 11.5 mg/kg/day (U.S. EPA 2000a). Although we did not observe nipple and areola retention in immature male offspring, visual inspection of the data suggests that there was a dose-related trend.
The play behavior procedure used in the present study was more sensitive to low-dose effects than those used in previous investigations, possibly because of methodologic differences. In the present study we examined nape contact, pounce, pin, and wrestle, as well as mount behaviors, whereas previous studies examined only pin (Flynn et al. 2001) or pin and chase behaviors (Hotchkiss et al. 2003). Nape contact, a behavior that often initiates a play bout, was greatly affected by perinatal Vz, and this component was not examined in previous studies. Although we hypothesized that perinatal Vz would demasculinize male offspring and lead to a reduction of play behavior, we actually observed a dose-related increase in play. Exposure to other developmental toxicants such as prenatal morphine (Hol et al. 1996; Niesink et al. 1999), mycotoxins (Ferguson et al. 1997), or phytoestrogens (Flynn et al. 2000) has been associated with increased play, and, as mentioned above, social hyperactivity in juvenile rats is linked to aberrant sexual behavior in adults (Gerall et al. 1967).
In the present study we also examined play behavior at two different time points, an approach that detected the apparently transient or age-specific effect of perinatal Vz. In rats, the ontogeny of play is characterized by an inverted U-shaped function that peaks between PND32 and PND40 (Panskeep 1980; Spear and Brake 1983; Ward and Stehm 1991). Male behaviors peak earlier, during PND26–35, with female behaviors peaking during PND36–40 (Meaney and Stewart 1981). The increased play observed in the PND34 exposed males could be interpreted as a developmental delay. Peripubertal exposure to higher doses of Vz has been shown to delay the age of preputial separation, which is a milestone of puberty in the male rat (Monosson et al. 1999). Typically, as male rats age, they show an increasing preference for female versus male partners, a shift that was not observed in the juvenile Vz males. The behavior of the exposed males actually resembled the female offspring, who performed more play on PND34 than on PND22. Future studies should examine additional time points to better characterize the age-dependent nature of the effects of Vz on play. Alternatively, the increased play in the exposed offspring could be due to greater sensitivity to social isolation. Because social deprivation is often viewed as a means of increasing play motivation, this hypothesis could be explored in future studies that compare play after different periods of deprivation. In normal juvenile rats, play solicitation increases after longer periods of deprivation (Thor and Holloway 1983).
Finally, it might be the case that we found significant effects at lower doses in the present study because the offspring were exposed during gestation and the neonatal period via maternal dosing with the gavage procedure. Although many play behavior studies have focused on the role of androgens during the neonatal period, perhaps because of the ease of working with newborn versus fetal rats, the available evidence suggests that the critical period for the differentiation of play begins late in gestation and continues through PND10 (Meaney et al. 1983; Ward and Stehm 1991). Data on the effects of prenatal morphine suggest that the onset of the critical period for play is GD16 (Niesink et al. 1999).
A survey of the developmental toxicology literature indicates that the reduction of erections measured in the present study is one of the most sensitive outcomes observed to date in a perinatal Vz study. Earlier work found that reduced AGD in male neonates and nipple retention occurred after exposure to maternal doses as low as 3.125 mg/kg, whereas at least 50 mg/kg was required to affect ventral prostate weight and increase the incidence of hypospadias (Gray et al. 1999a). Although behavior analysis was not an objective in these earlier investigations, during copulation, exposed males mounted but were unable to achieve vaginal penetration (Gray et al. 1994). In adult male rats, a number of manipulations can produce similar effects, including castration (Leipheimer and Sachs 1993), lesions of the medial preoptic area (Everitt 1990), or microinjection of dopamine antagonist drugs (Pfaus and Phillips 1991). In developing males, prenatal exposure to antiestrogens (Matuszczyk and Larsson 1995) also appears to impair copulatory performance without disturbing sexual motivation. All of these procedures produce structural or functional changes in the erectile system (Hull et al. 1992; Monaghan et al. 1993; Warner et al. 1991).
The Vz-exposed males showed a selective reduction of low-intensity (E1) erections. In this regard, the exposed offspring resemble males castrated as adults, which also show an early reduction of E1 erections (Leipheimer and Sachs 1993). The behavior of the exposed offspring is also reminiscent of male rats that have been administered serotonin receptor agonists (Mas et al. 1985) or agents that block the synthesis of nitric oxide (Hull et al. 1994). Both of these treatments reduce erections and increase seminal emissions. Perhaps the most parsimonious explanation of the differential regulation of penile erections versus seminal emissions has been offered by Hull and others. In a series of drug microinjection studies, this group has demonstrated that pharmacologic stimulation of dopamine D2 receptors in the medial preoptic area decreases the frequency of erections while increasing seminal emissions (Bazzett et al. 1991). Stimulation of D2 receptors in the paraventricular nucleus also facilitates seminal emission (Eaton et al. 1991; Pehek et al. 1989). On the other hand, stimulation of D1 receptors in the medial preoptic area has the opposite effect and occurs at much lower doses (Hull et al. 1992). Because the functional integrity of dopamine systems in this part of the brain is maintained by circulating testosterone (Du and Hull 1999), an environmental antiandrogen such as Vz might disrupt the development of these complex interactions.
As mentioned above, animal studies indicate that fetal males are far more sensitive to environmental antiandrogens than adults. Results from maternal stress studies shed some light on the likely developmental mechanisms affected by environmental antiandrogens. Maternal stress during the last week of pregnancy lowers the surge of plasma testosterone that is normally present in male rat fetuses during GD18 and GD19 (Ward and Weisz 1984). Attenuation of the GD18–19 surge is associated with impaired sexual behavior in adulthood (Dunlap et al. 1978; Ward and Reed 1985). This testosterone surge also exerts an organizational effect on the muscle and spinal cord mechanisms that control penile erections in adulthood (Grisham et al. 1991). Perinatal androgens serve to rescue SNB motor neurons from programmed death (Sengelaub et al. 1989), a process that could be blocked by an antiandrogen like Vz. In the present study, animals were exposed to Vz from GD14 through PND3 in order to compare our results with previous perinatal Vz investigations. However, because differentiation of spinal cord motor neurons continues until PND10 (Mills and Sengelaub 1993) and the weight of the adult BC muscle is the most sensitive to Vz exposure during the GD16–17 period (Wolf et al. 2000), it is likely that the toxic window for Vz on erectile function spans the GD16–PND10 period. Thus, it appears that the critical periods for masculinization of erectile function and play behavior in the male rat are the same. As of yet, no one has examined the effects of environmental antiandrogen exposure during this entire perinatal sensitive period. It may be the case that social play and erectile functions are responsive to even lower doses of Vz, if an exposure were to span the GD16–PND10 period.
No previous studies explicitly link Vz to human erectile dysfunction. However, Vz and other antiandrogenic fungicides are used in agriculture, they may be responsible for the recently noted link between pesticide exposure and erectile dysfunction in otherwise healthy men. Specifically, pesticide-exposed men had a significantly higher incidence of complete impotence, showing little to no change from baseline flaccidity on measures of penile rigidity, tumescence, frequency, and duration (Oliva et al. 2002). Occupational exposure to stilbene has also been associated with an increase in self-reported impotence and decreased libido (Quinn et al. 1990; Whelan et al. 1996). Stilbene is a component of textile finishing agents and detergents, and it is structurally similar to the synthetic estrogen diethylstilbestrol. Both of these clinical studies examined the effects of exposure during adulthood. The long-term effects in men after perinatal exposure are unknown.
It is estimated that children 1–6 years of age are exposed to 0.167 mg Vz/kg body weight/day (U.S. EPA 2000b). Given this chronic exposure estimate, a 2-year-old boy who weighs 13 kg [Centers for Disease Control and Prevention (CDC) 2000] would consume an average of 2.17 mg Vz/day, whereas a 6-year-old with a body weight of 21 kg would consume an average of 3.51 mg Vz/day. Both of these estimated daily intakes exceed the ED10 benchmark doses associated with altered juvenile play behavior and erectile dysfunction in our animal model. Typically, the U.S. EPA would divide the 95% lower bound by 100 to calculate a reference dose. If this practice were applied to the juvenile play behavior or erectile data, the average daily intake of Vz would exceed the reference doses based on these data by more than two orders of magnitude. It should also be pointed out that humans are exposed to multiple compounds on a chronic basis, whereas this study examined only Vz, which was administered during a limited period of development. The cumulative effects of chronic exposure to multiple compounds and their metabolites are unknown. Lastly, our benchmark doses should be interpreted as conservative estimates because they are based on maternal doses. The actual amount of Vz and/or metabolite that entered our fetal or neonatal subjects is unknown, although the level was certainly lower than the applied maternal dose.
In conclusion, the results of this study demonstrate that the effects of perinatal exposure to an environmental endocrine disruptor can be observed throughout the life span, provided that age-appropriate, sex-specific end points are examined. Low doses of Vz administered during the GD14–PND3 period significantly increased social play behavior in juvenile male rat offspring. These results are interesting in light of recent findings in humans that higher prenatal levels of PCBs have been associated with less masculinized play behavior in Dutch schoolboys and more masculinized play in schoolgirls (Vreugdenhil et al. 2002). Even lower doses of Vz reduced the erectile response in adult male offspring. Because men who work with agricultural chemicals are more likely to experience erectile dysfunction (Oliva et al. 2002), it is quite possible that some of the relevant agrochemicals are antiandrogenic fungicides.
Correction
The AGD data for control females were incorrect in Table 3 of the original manuscript published online, but they have been corrected here.
Figure 1 Mean ± SEM total number of play behaviors per session on PND22 and PND34 for male offspring (A) and their female littermates (B). Males exposed to 6 mg/kg or 12 mg/kg Vz performed significantly more behaviors on PND34 than did same-sex controls; females were not significantly different on either day.
Figure 2 Mean ± SEM number of nape contacts per session on PND22 and PND34 for male offspring (A) and their female littermates (B). The male 12-mg/kg Vz group performed significantly more nape contacts on PND34 than did controls or the 3-mg/kg group.
Figure 3 Mean ± SEM number of pounces per session. Data have been averaged across sex and postnatal age groups. The 12-mg/kg Vz group produced pounced significantly more than did the control group.
*p < 0.05.
Figure 4 Mean ± SEM total number of erections during the ex copula penile reflex procedure. All four of the Vz exposure groups produced significantly fewer erections than did the control group.
*p < 0.01.
Figure 5 Mean ± SEM total number of reflex clusters during the ex copula penile reflex procedure. A cluster of reflexes is defined as a rapid sequence, where each behavior occurs within 15 sec of the previous behavior. The 3-, 6-, and 12-mg/kg Vz groups produced significantly fewer clusters of reflexes than did the control group.
*p < 0.01.
Figure 6 Mean ± SEM total number of seminal emissions during the ex copula penile reflex procedure. The 12-mg/kg Vz group produced significantly more emissions than did the control group.
*p = 0.02.
Figure 7 Polynomial model for ED10 benchmark dose (BMD) value and 95% lower confidence level (BMDL) for the total erections per session variable.
Figure 8 Linear model for ED10 benchmark dose (BMD) value and 95% lower confidence level (BMDL) for total play behavior in the male offspring on PND34.
Table 1 Mean values for parturition end points for control and Vz-exposed rats.
Pups per litter
Exposure group Sperm-positive females assigned to groupa Sperm-positive females that delivered a litter Gestation length (days) Male Female Postnatal mortality
Control 14 12 22.1 6.3 6.3 1
Vz
1.5 mg/kg 8 7 22.0 4.2 5.7 3
3 mg/kg 16 13 21.9 6.1 6.3 3
6 mg/kg 15 13 22.2 4.7 5.2 2
12 mg/kg 7 7 22.3 6.1 7.1 7b
a The number of sperm-positive females differs because several females did not copulate or the sperm plug was not detected.
b Five pups were lost from one litter and two from a second litter.
Table 2 Mean ± SEM body weight (g) and AGD (mm) for male pups.
No. of litters Exposure group End point PND1 PND4 PND8 PND12 PND16 PND20
12 Control Body weight 6.9 ± 0.25 10.4 ± 0.52 18.4 ± 0.96 28.3 ± 1.08 36.5 ± 1.34 48.4 ± 1.98
AGD 3.6 ± 0.39 4.7 ± 0.19 6.7 ± 0.18 9.1 ± 0.29 12.0 ± 0.23 16.5 ± 0.46
7 1.5 mg/kg Body weight 6.6 ± 0.47 10.4 ± 0.75 16.6 ± 1.6 25.5 ± 1.83 33.3 ± 2.33 43.6 ± 3.28
AGD 3.4 ± 0.15 4.4 ± 0.17 6.1 ± 0.52 8.5 ± 0.57 12.4 ± 0.87 15.8 ± 1.25
13 3 mg/kg Body weight 6.7 ± 0.17 10.0 ± 0.25 17.7 ± 0.61 26.9 ± 0.53 35.2 ± 0.67 45.9 ± 0.88
AGD 3.7 ± 1.0 4.5 ± 0.14 6.5 ± 0.14 9.0 ± 0.25 12.4 ± 0.23 16.3 ± 0.48
13 6 mg/kg Body weight 7.4 ± 0.27 11.0 ± 0.54 19.2 ± 0.79 28.4 ± 0.74 37.7 ± 1.18 47.5 ± 1.27
AGD 3.5 ± 0.11 4.8 ± 0.16 6.8 ± 0.20 9.0 ± 0.24 12.0 ± 0.30 16.0 ± 0.47
7 12 mg/kg Body weight 6.9 ± 0.35 9.6 ± 0.57 19.2 ± 1.01 27.8 ± 1.20 36.7 ± 1.72 47.5 ± 2.79
AGD 3.7 ± 0.10 4.7 ± 0.11 6.7 ± 0.11 8.7 ± 0.30 12.7 ± 0.54 15.4 ± 0.41
Table 3 Mean ± SEM body weight (g) and AGD (mm) for female pups.
No. if litters Exposure group End point PND1 PND4 PND8 PND12 PND16 PND20
12 Control Body weight 6.6 ± 0.24 9.8 ± 0.51 17.7 ± 0.96 27.3 ± 1.18 35.1 ± 1.37 46.2 ± 1.89
AGD 2.2 ± 0.04 2.2 ± 0.04 2.7 ± 0.10 4.3 ± 0.16 6.5 ± 0.23 8.5 ± 0.21
7 1.5 mg/kg Body weight 6.7 ± 0.31 10.6 ± 0.67 17.9 ± 0.69 27.2 ± 0.52 35.2 ± 0.43 45.8 ± 1.25
AGD 2.1 ± 0.09 2.1 ± 0.09 2.6 ± 0.03 4.2 ± 0.12 6.3 ± 0.22 8.9 ± 0.16
13 3 mg/kg Body weight 6.4 ± 0.17 9.6 ± 0.26 17.4 ± 0.66 26.3 ± 0.66 34.2 ± 0.79 44.3 ± 1.06
AGD 2.2 ± 0.04 2.2 ± 0.04 2.7 ± 0.09 4.1 ± 0.14 6.3 ± 0.21 9.0 ± 0.25
13 6 mg/kg Body weight 6.8 ± 0.21 10.3 ± 0.45 18.0 ± 0.77 26.9 ± 0.57 34.4 ± 0.76 44.9 ± 1.02
AGD 2.2 ± 0.12 2.2 ± 0.12 2.8 ± 0.07 4.4 ± 0.13 6.5 ± 0.15 8.8 ± 0.19
7 12 mg/kg Body weight 6.3 ± 0.41 9.0 ± 0.69 17.1 ± 1.22 25.2 ± 1.56 33.8 ± 2.21 43.8 ± 3.31
AGD 2.2 ± 0.09 2.2 ± 0.09 2.9 ± 0.13 4.2 ± 0.13 6.2 ± 0.18 9.1 ± 0.34
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7783ehp0113-00070815929893ResearchArticlesSustained Exposure to the Widely Used Herbicide Atrazine: Altered Function and Loss of Neurons in Brain Monoamine Systems Rodriguez Veronica M. Thiruchelvam Mona Cory-Slechta Deborah A. Environmental and Occupational Health Sciences Institute, and Department of Environmental and Occupational Medicine, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey, USAAddress correspondence to D.A. Cory-Slechta, Environmental and Occupational Health Sciences Institute, 170 Frelinghuysen Rd., Piscataway, NJ 08854 USA. Telephone: (732) 445-0205. Fax: (732) 445-0131. E-mail:
[email protected] express our appreciation to R. Reeves and M. Virgolini for their input.
This work was supported by grant ES10791 from the National Institute of Environmental Health Sciences.
The authors declare they have no competing financial interests.
6 2005 24 2 2005 113 6 708 715 22 11 2004 24 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The widespread use of atrazine (ATR) and its persistence in the environment have resulted in documented human exposure. Alterations in hypothalamic catecholamines have been suggested as the mechanistic basis of the toxicity of ATR to hormonal systems in females and the reproductive tract in males. Because multiple catecholamine systems are present in the brain, however, ATR could have far broader effects than are currently understood. Catecholaminergic systems such as the two major long-length dopaminergic tracts of the central nervous system play key roles in mediating a wide array of critical behavioral functions. In this study we examined the hypothesis that ATR would adversely affect these brain dopaminergic systems. Male rats chronically exposed to 5 or 10 mg/kg ATR in the diet for 6 months exhibited persistent hyperactivity and altered behavioral responsivity to amphetamine. Moreover, when measured 2 weeks after the end of exposure, the levels of various monoamines and the numbers of tyrosine hydroxylase-positive (TH+) and -negative (TH−) cells measured using unbiased stereology were reduced in both dopaminergic tracts. Acute exposures to 100 or 200 mg/kg ATR given intraperitoneally to evaluate potential mechanisms reduced both basal and potassium-evoked striatal dopamine release. Collectively, these studies demonstrate that ATR can produce neurotoxicity in dopaminergic systems that are critical to the mediation of movement as well as cognition and executive function. Therefore, ATR may be an environmental risk factor contributing to dopaminergic system disorders, underscoring the need for further investigation of its mechanism(s) of action and corresponding assessment of its associated human health risks.
atrazinedopaminehypothalamuslocomotor activitymicrodialysisprefrontal cortexstriatumsubstantia nigraunbiased stereology
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Atrazine (ATR; 2-chloro-4-ethylamino-6-isopropylamino-s-triazine), a chlorinated member of the family of s-substituted triazines, is one of the most widely employed herbicides in the world, with an estimated 76.4 million pounds used annually in the United States alone. It acts to suppress photosynthesis by inhibiting electron transfer at the reducing site of chloroplast complex II (Eldridge et al. 1999). Although it has limited solubility in water, ATR is frequently detected in ground and surface waters in agricultural regions (Colborn and Short 1999). Studies also reveal that ATR can be transported into the home, presumably tracked by soil (Lioy et al. 2000).
Human exposure has been confirmed (Adgate et al. 2001; Clayton et al. 2003), and, in fact, approximately 60% of the U.S. population is exposed to ATR (Birnbaum and Fenton 2003). Recent reports indicate that acute dietary exposures range from 0.234 to 0.857 μg/kg/day, and corresponding figures for chronic dietary exposure are 0.046 to 0.286 μg/kg/day, considering all commodities with U.S. Environmental Protection Agency (EPA) tolerances and drinking water (Gammon et al. 2005). Occupational exposure to ATR, as measured in mixer-loader-tender applicators, was reported to be approximately 2.8 mg ATR/day of work, with an absorbed dose of 1.8–6.1 μg/kg/day based on a 5.6% dermal absorption rate (Gammon et al. 2005). An earlier study of manufacturing workers reported a total ATR exposure of 10–700 μmol (~ 2.157–151.004 mg) per work shift (Catenacci et al. 1993).
The understanding of the potential of ATR to serve as a contributing factor to human disease and dysfunction is currently extremely limited. Epidemiologic studies have linked environmental and/or occupational ATR exposure to increased mortality (Sathiakumar et al. 1996), and to non-Hodgkin’s lymphoma (MacLennan et al. 2003; Sathiakumar and Delzell 1997).
In experimental models, however, a growing experimental literature documents deleterious hormonal and reproductive system effects of ATR. In rodents, reported effects include reductions in testosterone levels; increases in tri-iodothyronine (Friedmann 2002; Stoker et al. 2000, 2002); suppression of immune function (Rooney et al. 2003), of luteinizing hormone (LH), and of prolactin surges (Cooper et al. 2000); the appearance of mammary gland tumors; a disruption of regular ovarian cycles; and the induction of pseudopregnancies (Cooper et al. 1996; Laws et al. 2000).
The effects of ATR on ovarian function in female rats have been ascribed to changes in function of catecholamines in the hypothalamus, specifically decreases in norepinephrine (NE) and increases in dopamine (DA) in this region (Cooper et al. 1998). In correspondence with this observation, in vitro studies in PC12 cells show concentration-dependent decreases in intracellular DA after exposure to 12.5–200 μM ATR for 6, 12, 18, and 24 hr and decreases in NE release and intracellular NE concentrations after exposures to 100 and 200 μM ATR for 12, 18, and 24 hr (Das et al. 2000, 2003). In addition, reductions in the expression of DA β-hydroxylase [but not of tyrosine hydroxylase (TH)] were observed. The inhibitory effects of ATR on intracellular NE content and NE release, but not on DA intracellular content, were reversed when PC12 cells were co-incubated with ATR and agents known to enhance transcription, phosphorylation, or activity of TH and DA β-hydroxylase, such as 8-bromo-cAMP, forskolin, or dexamethasone (Das et al. 2003). These findings suggest that ATR could disrupt catecholamine metabolism by altering its biosynthetic enzymes.
The fact that ATR can adversely affect hypothalamic catecholamine systems has notable implications because such effects would be unlikely to be restricted to this particular region, but could affect brain catecholamine systems more generally and thus affect pathways critical to the control of movement (nigrostriatal dopaminergic systems) and of complex cognitive functions (mesocorticolimbic dopaminergic systems). If so, then ATR exposures may also serve as a risk factor for neurodegenerative diseases and/or dysfunctions associated with these systems, which include Parkinson’s disease, schizophrenia, and attention deficit disorder, among others (Crossman 2000; Epstein et al. 1999; Viggiano et al. 2003). Indeed, epidemiologic studies have linked pesticides to an increased odds ratio for Parkinson’s disease (Breysse et al. 2002), and various pesticides that affect catecholaminergic systems have been shown to produce characteristics of Parkinson’s disease in experimental models (Betarbet et al. 2000; Reeves et al. 2003; Thiruchelvam et al. 2000b).
The potential for neurotoxic effects of ATR in vivo, however, particularly chronic effects, has received almost no experimental attention. Oral exposure of rats to 1,000 mg/kg ATR for 4–11 days decreased rearing in the open field (Ugazio et al. 1991), whereas acute exposure of rats to 100 mg/kg decreased spontaneous Purkinje cell firing rate and cerebellar potentials evoked by electrical stimulation (Podda et al. 1997).
The objective of the present study was to evaluate the potential for sustained low-level ATR exposure to affect two critical catecholamine pathways of the brain: the nigrostriatal DA pathway, involved in the mediation of movement (Crossman 2000), and the mesocorticolimbic DA pathway critical to complex cognitive functions (Clark et al. 2004; Remy and Samson 2003). For this purpose, we evaluated locomotor activity across the course of exposure, whereas monoamine levels in striatum, prefrontal cortex, nucleus accumbens, and hypothalamus and stereologic cell counts of TH-positive (TH+) and TH-negative (TH−) cells in the midbrain were evaluated 2 weeks after cessation of exposure. Further, this study sought to determine mechanisms by which any changes in dopaminergic function in these pathways might be produced by examining the acute effects of ATR on striatal DA release using microdialysis.
Materials and Methods
Chronic ATR Exposure
Subjects, exposure, and experimental design.
Thirty male Long-Evans rats purchased from Taconic Farms (Germantown, NY) were housed individually in plastic cages in a temperature- and humidity-controlled vivarium room with a 12-hr dark/light cycle (lights on 0600 hr). Food intake was restricted to maintain body weights at 300 g, and water was available ad libitum during the entire experiment. In our experience, this protocol sustains health and viability to a greater degree than does ad libitum feeding. At 9 months of age, exposure to 0, 5, or 10 mg/kg ATR mixed in food was initiated with continuation of ad libitum access to distilled drinking water. These doses of ATR were chosen based on reports for the rat of an oral median lethal dose (LD50) of 1,869 mg/kg (U.S. EPA 2001), a no observed adverse effect level (NOAEL) of 3.3 mg/kg/day, and a lowest observed adverse effect level (LOAEL) of 34.5 mg/kg/day for this route of administration measured as body weight loss. A chronic dietary NOAEL of 1.8 mg/kg/day and LOAEL of 3.65 mg/kg/day were also reported (U.S. EPA 2001). We recorded body weights and food consumption periodically over the entire duration of the experiment. All procedures were carried out in accord with National Institutes of Health and University of Medicine and Dentistry of New Jersey Animal Use and Care Committee Guidelines (Institute of Laboratory Animal Resources 1996). The experimental design is summarized in Figure 1A.
We recorded locomotor activity at 2, 3, and 6 months of ATR exposure and 2 weeks after cessation of exposure. At the 2-month time point, we measured locomotor activity on 3 consecutive days, with animals receiving an intraperitoneal (ip) injection of saline 5 min before the session during the first 2 days, and an injection of d-amphetamine sulfate (1 mg/kg) on day 3. Only a single locomotor activity session was carried out at the 3-and 6-month time points and at 2 weeks after the termination of ATR exposure. Locomotor activity was recorded during the light phase (from 0900 hr to 1300 hr) of the light/dark cycle using methods described below.
Two weeks after cessation of ATR exposure, rats were sacrificed by decapitation, brains were removed, and hypothalamus, prefrontal cortex, nucleus accumbens, and striatum were dissected on ice and frozen for HPLC analysis. The remaining tissue was postfixed in 4% paraformaldehyde for immunohistochemistry and stereologic counts.
Locomotor activity measurement.
Each rat was individually placed in an automated locomotor activity chamber equipped with infrared photobeams (Opto-Varimex Minor; Columbus Instruments International Corporation, Columbus, OH). Horizontal, vertical, and ambulatory activities were simultaneously measured and data were collected over the course of a 45-min session.
Measurement of monoamine levels.
Tissues were sonicated in 0.1N perchloric acid and centrifuged. Supernatants were stored at −80°C until analyzed for monoamine content. Pellets were digested in 0.5 M sodium hydroxide for measurements of protein concentration using reagents from Bio-Rad (Hercules, CA).
We measured monoamines and their metabolites using HPLC with electrochemical detection as described elsewhere (Thiruchelvam et al. 2000a). Briefly, a Waters pump 515 plus autosampler (Waters Corporation, Milford, MA) was joined to a chromatographic column (Alltech Associates Inc, Deerfield, IL). The amperometric potential was set at 600 mV relative to the silver/silver chloride, and the sensitivity of the detector was set at 100 ρA (microdialysates) or 1 ηA (tissue samples). The mobile phase was an isocratic 0.1 M monobasic phosphate solution containing 0.5 mM sodium octyl sulfate, 0.03 mM EDTA, and 12–14% vol/vol methanol. Results generated by these determinations were analyzed with the Empower Pro program (Empower Software, Waters Corporation) and are expressed in picograms per milliliter of microdialysate or nanograms per milligram of protein of tissue. DA turnover was expressed as the ratio of dihydroxyphenylacetic acid (DOPAC) to DA.
Tyrosine hydroxylase immunohistochemistry.
Five randomly selected paraformaldehyde-fixed brains from each treatment group were cut into 30-μm sections, collected in cryoprotectant, and stored at −20°C for immunolabeling studies. Sections were rinsed with 0.1 M phosphate buffer (PB), blocked with 10% normal goat serum for nonspecific binding, and incubated in TH primary antibody (Chemicon, Tamecula, CA) for 48 hr at a dilution of 1:3,500 in PB with 0.3% Triton X-100 and 10% normal goat serum. Sections were then incubated with a secondary antibody 1:200 (Vector Laboratories Inc., Burlingame, CA) overnight. Sections were washed and incubated with avidinbiotin solution from Vectastain ABC reagents (Vector Laboratories) for 1 hr and developed in 3–3′-diaminobenzidine tetrachloride and H2O2 in 0.05 M Tris buffer. Sections were counterstained with cresyl violet after TH staining. We counted total numbers of TH+ and Nissl-stained neurons (TH−) in substantia nigra pars compacta (SNpc) and the ventral tegmental area (VTA) using the optical fractionator method as described below.
Stereologic analysis.
After delineation of the SNpc and VTA at low magnification (4× objective), one side of every fourth section from the entire midbrain region was sampled at higher magnification (100× objective) using the stereology module of the Stereo Investigator imaging program (MicroBrightField Inc., Williston, VT) with an Olympus Provis microscope (Olympus America, Melville, NY). We used the optical fractionator method, an unbiased quantitative technique, for counting TH+ (TH+ and cresyl violet+ neurons) and TH− (cresyl violet+ only) cells. Criteria for TH+ and TH− neurons were determined as previously described (Barlow et al. 2004; Thiruchelvam et al. 2004). We determined the mean thickness by measuring two fields from five sections per sample, and the entire depth of field was sampled, ignoring the upper and lower 0.5 μm. All samples were evaluated by one experimenter without knowledge of treatment status.
Chemicals.
ATR at 98% purity was purchased from Chem Services Inc. (West Chester, PA). Reagents for microdialysis, HPLC analysis, methylcellulose, and cresyl violet were purchased from Sigma (St. Louis, MO).
Acute ATR Exposure
Subjects, exposure, and experimental design.
Thirty male Long Evans rats weighing between 270 and 320 g purchased from Taconic Farms were habituated to constant standard laboratory conditions of humidity, temperature, and dark/light cycle (lights on 0600 hr) as described above. As shown in Figure 1B, we used microdialysis to evaluate changes in striatal DA release after acute intraperitoneal (ip) exposures to ATR in sessions lasting 7 hr.
Surgery.
After a habituation period of at least 1 week, rats were anesthetized with pentobarbital (30–40 mg/kg ip) and every 30 min thereafter received an injection of atropine sulfate (0.3 mg/kg ip) to avoid respiratory failure during the cannula implantation. Once anesthetized (assessed by absence of corneal reflex), the rat was placed in a stereotaxic apparatus (Kopf Instruments, Tujunga, CA), the skull was exposed, and a hole was drilled for placement of a guide cannula (MD-2250; Bioanalytical Systems Inc., West Lafayette, IN) over the right striatum, using stereotaxic coordinates (anterior-posterior, +1.0 mm, medio-lateral, −2.0 mm with reference to bregma, dorso-ventral, −3.4 mm from flat skull) according to the atlas of Paxinos and Watson (1986). The cannula was fixed to the skull with anchor screws and acrylic cement. After surgery, rats were individually housed for a recovery period of 5–7 days with food restricted to keep body weight at 300 g and water was available ad libitum.
Microdialysis.
A probe of concentric design (MD-2262, tip 2 mm; Bioanalytical Systems, Inc.) was inserted into the guide cannula. The dialysis probe was continuously perfused at a flow rate of 2.5 μL/min through a liquid swivel from an automated system (Bioanalytical Systems Inc.) with a physiologic Ringer’s solution containing 147 mM NaCl, 4.0 mM KCl, 1.2 mM CaCl2, and 1 mM MgCl2, pH 6.0–6.5. Sample collection occurred every 30 min. The first hour of sampling was discarded to avoid erroneous data due to probe insertion. After three baseline samples, rats received an ip injection of vehicle (1% methyl-cellulose) or ATR (100 or 200 mg/kg), and five subsequent samples of perfusate were collected. In order to probe characteristics of DA release, a high potassium solution (91 mM NaCl, 60 mM KCl, 1.2 mM CaCl2, and 1 mM MgCl2, pH 6.0–6.5) replaced the normal Ringer’s solution, and two samples were collected under these conditions. Normal Ringer’s solution was subsequently restored, and two additional samples of perfusate were collected. Collection vials contained 3.75 μL 0.1 M HClO4 solution. Collected samples were immediately frozen at −80°C until monoamine quantification by HPLC as described above.
Histology.
At the completion of micro-dialysis sampling, rats were overdosed with sodium pentobarbital and transcardially perfused with an isotonic saline solution followed by 10% formaldehyde. Brains were postfixed in 10% formalin overnight and then transferred to 30% sucrose. Brains were sectioned in 50 μm coronal slices, mounted, stained with cresyl violet, and coverslipped. Cannula placement for the microdialysis study was confirmed under microscopic analyses.
Statistical Analyses
We analyzed total locomotor activity counts, body weight, and food consumption using repeated-measures analysis of variance (RMANOVA; treatment × time) followed by post hoc tests as appropriate. Responsivity to d-amphetamine and changes in neurotransmitter levels in various brain regions were evaluated by one-way ANOVA with post hoc assessments in the event of main effects of treatment. To provide a more conservative analysis of changes in cell counts, because counts in both regions were derived from the same animals (brains), RMANOVA was carried out based on changes in TH+ and TH− cells in both SN and VTA (but not total counts because that was the sum of the TH+ and TH− cells), followed by post hoc testing as appropriate. We evaluated the effects of ATR on microdialysis by RMANOVAs with treatment and time as factors, followed by post hoc evaluation in the case of main effects or interactions. In all cases, statistical significance was defined as p ≤ 0.05.
Results
Chronic ATR Exposure
Gross effects of treatment.
No treatment-related changes in body weight or food consumption were detected at any point during the course of the exposure (data not shown), nor did any other signs of overt toxicity manifest at any point.
Locomotor activity.
In contrast to the other time points of measurement, the assessment of locomotor activity after 2 months of ATR exposure actually involved three sessions, the first two of which were preceded by an ip injection of saline and the third by 1 mg/kg d-amphetamine sulfate. No treatment-related changes in locomotor activity were seen in either of the sessions preceded by saline. However, in the third session, the administration of d-amphetamine increased locomotor activity of all three groups relative to levels of activity after saline administration [session 2; F(2,26) = 3.63, p < 0.041]. Additionally, these increases were modified by ATR treatment in that the 10-mg/kg dose further enhanced locomotor activity by an additional 70% (Figure 2A) relative to the increases in the 0- and 5-mg/kg groups, as confirmed in post hoc analyses.
At the remaining time points of measurement, single locomotor activity sessions were carried out in the absence of drug administration. Under these conditions, after 3 months of ATR exposure, we found pronounced increases in locomotor activity again at the 10-mg/kg dose of ATR [F(2,26) = 3.62, p = 0.041], with levels of horizontal activity that exceeded those of controls and the 5-mg/kg group by approximately 50% (Figure 2B). These treatment-related effects were also evident in the measurement of locomotor activity at the 6-month time point [F(2,24) = 3.45, p = 0.048] and again when measured 2 weeks after the termination of ATR treatment [F(2,24) = 4.42, p = 0.024], when levels remained at 40% above control.
Changes in monoamine levels.
Measured 2 weeks after the termination of ATR exposure, changes in DA content (Figure 3A) were significant in striatum [F(2,23) = 3.61, p = 0.044] as well as in frontal cortex [F(2,21) = 3.82, p = 0.039]. Statistical analysis confirmed decreased levels of DA (~ 20%) in relation to treatment in striatum, with post hoc assessments indicating efficacy at the 10-mg/kg ATR dose with a similar but nonsignificant trend at 5 mg/kg. In contrast, levels of DA were increased in prefrontal cortex in an inverse U-shaped fashion, with post hoc assessment confirming a significant increase at 5 mg/kg (by 30–40%), with levels declining back toward control values at 10 mg/kg. Both doses of ATR reduced serotonin (5-HT) levels in hypothalamus [Figure 3B; F(2,21) = 5.19, p = 0.015] by 10–15%. Chronic ATR exposure also decreased levels of NE in frontal cortex [Figure 3C; F(2,21) = 3.84, p = 0.038], with post hoc assessments showing the effect with the 10-mg/kg dose producing reductions of approximately 15–20%. Although a trend toward increases in NE in nucleus accumbens was suggested, it was associated with significant variability and therefore not statistically significant.
We observed no changes in levels of the metabolites of either 5-HT (5-hydroxyindole acetic acid) or DA [DOPAC, homovanillic acid (HVA)] or DA turnover (DOPAC:DA) in any region.
Unbiased stereologic counts of cells in the midbrain.
Changes in numbers of cells in the regions of the cell bodies of the two major DA pathways are shown in Figure 4 for a sample of five randomly selected animals from each treatment group; numbers are shown for TH+, TH−, and total cells in SNpc and for corresponding data for the VTA. Because these regions were from the same brains, we performed a more conservative statistical analysis based on RMANOVA to examine the impact of treatment on numbers of cells using counts of TH+ and TH− from each region (not including total counts). That analysis confirmed a significant main effect of treatment [F(2,36) = 5.53, p = 0.02] and no interaction of treatment by region, indicating that cell loss occurred in both regions and, moreover, in both TH+ and TH− cells. These effects were primarily attributable to the 10-mg/kg dose of ATR, as confirmed in subsequent post hoc tests; the mean reductions in cell numbers ranged from 9 to 13% in the 10-mg/kg dose group, whereas those in the 5-mg/kg group ranged from 0 to 3%.
Acute ATR Exposure
That systemic administration of ATR can indeed directly affect brain dopaminergic systems was further confirmed in microdialysis experiments. The impact of acute ip administration of ATR (100 or 200 mg/kg) on levels of DA in striatum, as assessed via microdialysis, is presented in Figure 5A. Acutely, ATR significantly decreased basal DA release, as shown in the inset in Figure 5A [main effects: treatment, F(2,19) = 4.88, p = 0.02; sampling time: F(9,18) = 26.77, p < 0.0001; treatment by time interaction: F(18,171) = 2.77, p = 0.0003]. Post hoc tests confirmed decreases as measured at 90, 120, 150, and 180 min postadministration of ATR. By 150 min, the decrements averaged approximately 40% and were seen in both the 100-mg/kg and the 200-mg/kg treatment groups.
We also observed a dose-dependent decrease in DA release when the system was challenged with 60 mM high potassium solution for 60 min [F(2,19) = 3.717, p = 0.0434]. Although the control group showed a 1,256% increase from baseline in response to potassium (210 min time point), corresponding values for the 100-mg/kg and 200-mg/kg ATR groups were 729 and 427%, respectively, from baseline. After high potassium perfusion, the system was flushed again with normal Ringer’s solution; levels of DA declined in all groups, and no treatment-related differences were evident during the remaining 60 min of sampling. The increase in DA seen in the first sample (240 min time point) after high potassium infusion was due to dead volume of the microdialysis sample collection system.
Analysis of striatal DOPAC levels in the dialysates revealed only a significant effect of sampling time [F(9,18) = 13.735, p < 0.0001]. One-way ANOVA at each time point did not show any difference among groups in DOPAC concentration during the course of the experiment (Figure 5B). Similarly, analysis of HVA levels showed a significant effect of sampling time [F(9,18) = 6.074, p < 0.0001] but no effect of group or group × sampling time interaction (Figure 5C).
Administration of vehicle (1% methyl-cellulose) or 100 mg/kg ATR did not result in acute observable effects in these rats, but some rats injected with 200 mg/kg ATR exhibited hypoactivity during the first 2 hr after injection, after which levels appeared normal. Histologic analysis confirmed that cannula placement was appropriately located in dorsal striatum for all rats.
Discussion
The present study demonstrates that sustained low-level ATR exposure in diet can adversely affect both major long-length dopaminergic tracts of the central nervous system, resulting in persistent increases in locomotor activity, alterations in responsivity to the indirect DA agonist amphetamine, changes in monoamine levels, and, ultimately, loss of neurons in the midbrain. Thus, adverse effects of ATR are not restricted to endocrine and reproductive systems or to hypothalamic regions of brain. The effects observed here cannot be ascribed to acute toxicity because the half-life of ATR in tissue ranges from 31.3 to 38.6 hr, and 95% of the ATR administered is excreted within 7 days of dosing, whereas changes in monoamines and numbers of neurons were measured 2 weeks posttreatment. Moreover, the doses used here did not produce any changes in body weight or food consumption or any signs of overt toxicity.
The observations of protracted changes in neurotransmitter levels coupled with neuronal loss have particular significance, given the critical roles of the nigrostriatal and mesocorticolimbic dopaminergic systems in controlling fine motor behavior and complex cognitive function, respectively (Clark et al. 2004; Crossman 2000; Remy and Samson 2003). Dysfunctions of dopaminergic systems include Parkinson’s disease, schizophrenia, attention deficit disorder, and learning and memory impairments. Collectively, the present findings raise the possibility that ATR exposure could be a contributory risk factor for such disorders.
Chronic ATR exposure caused cell loss not only to TH+ immunoreactive cells but also to TH− cells in the VTA and SNpc. The non-dopaminergic neuronal subpopulation in these regions includes GABAergic (Deniau et al. 1978), calbindin (Gerfen et al. 1985), and cholecystokinin-like immunoreactive neurons (Seroogy and Fallon 1989). The lack of selectivity of effects makes it likely that ATR will exhibit neurotoxicity, including cytotoxicity to other neuronal populations in other brain regions, although other regions were not examined in the present study. Additionally, ATR may exert neurotoxic effects on other cell types of the brain as well, such as glial cells. The specificity and mechanism(s) of ATR effects within the central nervous system remain to be determined, and such assessments are clearly warranted based on the findings presented here.
Chronic ATR increased locomotor activity, an effect present after 3 months of exposure, persisted for 6 months and was still evident even 2 weeks after cessation of exposure. Moreover, rats treated for 2 months with 10 mg/kg ATR exhibited an enhanced locomotor activity response to a d-amphetamine challenge. Amphetamine is known to promote the release of DA and a decrease in its re-uptake into the presynaptic terminal (Cooper et al. 2003). Thus, the increases in locomotor activity could reflect ATR-induced up-regulation of striatal DA receptors, as might be expected to occur in response to the corresponding reduction in basal DA levels (Figure 3A) or DA release produced by ATR (Figure 5A). Placement in a novel environment such as the locomotor activity chamber could increase DA, activating up-regulated DA receptors and thereby producing hyperactivity (Badiani et al. 1998), a hypothesis in agreement with the increases in locomotor activity induced by amphetamine sulfate (Mao et al. 2001).
The locomotor hyperactivity observed here differs from findings of a previous study in which 1,000 mg/kg ATR administered for 4–11 days decreased rearing in the open field (Ugazio et al. 1991). Such decreases could reflect acute toxicity of a high dose of ATR because the chemical was administered immediately before the behavioral evaluation in that study, coupled with a decline in DA release that would accompany its administration and be expected to reduce activity levels, as was observed.
The reductions noted here in levels of DA, NE, and 5-HT observed, respectively, in striatum, prefrontal cortex, and hypothalamus at the 10-mg/kg ATR dose could be due to inhibitory effects on synthesis in these monoamine pathways. Precursors of DA and NE (tyrosine) and of 5-HT (tryptophan) undergo the same hydroxylation process via TH or tryptophan hydroxylase, respectively. Both enzymes are pteridin-dependent aromatic amino acid hydroxylases and are highly homologous, reflecting a common evolutionary origin from a single genetic locus (Cooper et al. 2003). In an in vitro study using PC12 cells in which NE and DA were decreased by ATR, the NE effect was reversed when cells were co-incubated with agents known to enhance transcription and phosphorylation of dopamine β-hydroxylase and TH (Das et al. 2003), consistent with the possibility that ATR may have inhibitory effects on these enzymes.
Previous studies have reported changes in hypothalamic DA and/or NE levels after acute ATR administration at 100 mg/kg by gavage to male rats (Cooper et al. 1998). We did not observe such changes in the chronic exposure paradigm used here, a difference that could reflect initiation of compensatory mechanisms to maintain a constant production of these neurotransmitters under conditions of chronic exposure. Alternatively, catecholamine levels were determined in specific hypothalamic nuclei in that study, whereas here we examined the hypothalamus in its entirety, thus possibly diluting any regional changes (Cooper et al. 1998).
Chronic ATR exposure did decrease hypothalamic 5-HT levels, effects consistent with its known alterations of neuroendocrine systems, including the release of LH and prolactin. Serotonergic neurons from the dorsal and medial raphe nuclei project to hypothalamus, activating the hypothalamo–pituitary–adenocortical (HPA) and the hypothalamo–pituitary–gonadal (HPG) axes in the rat (Fuller 1996; Jorgensen et al. 1998). Agents that disrupt 5-HT transmission are known to alter the HPA and HPG axes (Fuller 1996; Fuller and Snoddy 1990). Furthermore, selective degeneration of the midbrain dorsal and ventromedial region of the hypothalamus induced by 5,7-dihydroxytryptamine reduces LH levels (van de Kar et al. 1980). Taken together, it can be inferred that reductions in hypothalamic 5-HT resulting from ATR could affect both the HPA and HPG axes and thereby alter other organ systems of the body with which these systems interact.
DA and NE alterations in prefrontal cortex are also notable given the critical role of this structure in mediating executive function, including working memory (Dreher et al. 2002). Dysfunction of this region is also involved in cognitive deficits, altered stress responsivity, hyperactivity disorder, and schizophrenia (Mostofsky et al. 2002; Tam and Roth 1997; Viggiano et al. 2003). An increase in cortical DA levels, such as observed at 5 mg/kg, could be due to an increase in DA synthesis, decreased degradation, or altered re-uptake. It is worth noting that autoreceptors on DA terminals in the prefrontal cortex regulate release but not synthesis of DA (Cooper et al. 2003), which may explain why augmented DA concentrations in this region are not corrected after ATR exposure.
Findings from the microdialysis component of these experiments are consistent with such an assertion and show alterations in the dynamics of DA in striatum after acute ATR treatment. A dose-dependent decrease in striatal DA release as observed here would normally trigger compensatory mechanisms such as decreased re-uptake rate and increased production and release of DA. As is evident from Figure 5, none of these compensatory mechanisms appears to be operative, at least within the time frame encompassed by these experiments.
The observed decline in both basal and stimulated DA release could have several explanations. First, it could reflect a generalized inhibition of DA synthesis, given the absence of group differences at the end of the experiment. DA is distributed mainly in two functional presynaptic compartments, a cytoplasmic pool and a vesicular pool. Potassium-induced release is Ca2+ dependent and occurs from the vesicular pool (Du et al. 1999), which is the newly synthesized pool (Lamensdorf et al. 1996). Another possibility is that ATR decreases the firing rate of striatal and/or SN neurons, decreasing DA release. Additionally, TH exists in two kinetic forms, with differential affinities for tetrahydrobiopterin (cofactor for TH). The proportion of TH in the high-affinity state appears to be a function of neuronal firing rate (Cooper et al. 2003). A dose of 100 mg/kg ATR decreased cerebellar cell firing rate after 60 and 90 min, with rates returning to normal by 180 min; this inhibitory effect lasted up to 180 min after exposure to 200 mg/kg ATR (Podda et al. 1997). ATR could also be acting on ionotropic GABA receptors. Binding of RO15-4513 (an inverse agonist of the GABAA receptor benzodiazepine site) was inhibited when cortical membranes were incubated with ATR (Shafer et al. 1999). This would increase the influx of chloride ions, leading to hyperpolarization of cells, preventing depolarization that would, in turn, decrease DA release.
No changes in levels of the DA metabolites DOPAC and HVA were observed in the microdialysis component of this study, although DA levels were altered. DA is converted to DOPAC intraneuronally after re-uptake, whereas extraneuronal DA is converted to HVA by the enzymes catechol-O-methyltransferase and monoamine oxidase. The lack of change in DOPAC and HVA could reflect the relatively modest nature of the changes in DA, leaving a sufficiently high concentration of DA in the intracellular space to maintain constant levels of DOPAC and HVA production. Further, DA re-uptake was not impaired. The decreases in DOPAC and reductions in extracellular DOPAC and HVA after potassium stimulation across sampling time in the microdialysis component of this study agree with results of other such studies (Holson et al. 1998; Robinson and Camp 1991; Stanford et al. 2000; Westerink and Tuinte 1986) and may reflect initial damage caused by the probe insertion, which recovers after several hours. The decrease in DOPAC levels that occurs with the increase in extracellular DA after potassium perfusion is thought to reflect a decrease in intracellular DA metabolism by monoamine oxidase (Camarero et al. 2002).
At the present time, the health-related impacts that ATR exposures may exert in human populations remain unknown. Assessments of occupational exposures have been limited, and systematic studies of environmental exposures have not been undertaken. Although the doses of ATR used in these studies are higher than those estimated for human exposures, additional considerations must be applied to such comparisons. First, data projecting human exposure are always limited because they are dependent upon when exposures occurred relative to the time of measurement and do not provide measurement of the tissue of interest (i.e., the brain) in such cases. Second, the doses used here are low for the experimental species (rat), being consistent with previously reported NOAEL and LOAEL levels. Furthermore, even at these levels, the doses may not have been as high as administered because probably not all the ATR ingested would have been absorbed; a portion of it could have been easily eliminated through the feces, suggesting that lower actually absorbed doses could underlie the deleterious effects observed in this study.
In summary, the collective findings from this study demonstrate that ATR may have broad effects on brain monoamine systems and thereby influence a wide range of behavioral functions. Clearly, additional studies are needed to unravel the targets of ATR and the mechanism(s) of its effects, as well as the ultimate human health consequences of such exposures for behavioral and/or neurologic dysfunctions.
Figure 1 Experimental designs for the chronic ATR exposure component of the study (A) and for acute ATR exposure for microdialysis studies (B). Abbreviations: MC, methylcellulose; SNpc, substantia nigra pars compacta.
Figure 2 Horizontal locomotor activity (group mean as percentage of control ± SEM). (A) Effect of a 1-mg/kg dose of amphetamine sulfate administration on locomotor activity after 2 months of ATR exposure. (B) Spontaneous locomotor activity measured at 3 months and 6 months of ATR exposure, and 2 weeks after cessation of ATR exposure. RMANOVA was followed by Fisher’s post hoc test. Absolute values (total counts ± SEM) for control animals are 8094.44 ± 1822.95 (amphetamine sulfate challenge after 2 months of ATR exposure); 3860.56 ± 703.66 (3 months of ATR exposure); 3168.60 ± 550.36 (6 months of ATR exposure), and 4257.00 ± 588.45 (2 weeks after cessation of ATR). TX, treatment.
*Significantly different from control group, p < 0.05 (n = 8–10 rats per treatment group).
Figure 3 Levels of DA (A), 5-HT (B), and NE (C; presented as the group mean as a percentage of control ± SEM) in striatum, prefrontal cortex, nucleus accumbens, and hypothalamus 2 weeks after cessation of ATR exposure (6 months). One-way ANOVAs for each region were followed by Fisher’s post hoc test. Absolute values (ng/mg protein ± SEM) for control animals are, for DA: 122.25 ± 8.54 (striatum), 2.83 ± 0.21 (prefrontal cortex), 6.09 ± 0.55 (hypothalamus), 78.70 ± 7.12 (nucleus accumbens); for 5-HT: 2.46 ± 0.27 (striatum), 15.45 ± 1.31 (prefrontal cortex), 14.44 ± 0.46 (hypothalamus), 8.22 ± 0.68 (nucleus accumbens); and for NE: 11.44 ± 0.77 (prefrontal cortex), 34.65 ± 1.67 (hypothalamus), 1.63 ± 0.14 (nucleus accumbens).
*Significantly different from control group, p < 0.05 (n = 7–10 rats per treatment group).
Figure 4 Numbers of TH+, TH−, or total cells in SNpc (top row) or in VTA (bottom row) measured using unbiased stereology and determined 2 weeks after termination of ATR treatment. Each point represents the value for an individual animal, with n = 5 randomly selected per treatment group counted. Black bars represent group medians.
Figure 5 Time course (group mean as percentage of basal release ± SEM) of striatal release of DA (A; baseline DA release shown in inset), DOPAC (B), and HVA (C) over the course of microdialysis. Microdialysates were collected every 30 min; high potassium infusion (60 mM K+) lasted 1 hr, after which normal Ringer’s solution was restored for 1 hr longer. The presence of response in the first sample after high potassium was due to the dead volume of the microdialysis sample collection system. +100-mg and *200-mg groups significantly different from control group, p < 0.05 (n = 7–8 rats per treatment group).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7728ehp0113-00071615929894ResearchArticlesDifferential Effects of Glyphosate and Roundup on Human Placental Cells and Aromatase Richard Sophie Moslemi Safa Sipahutar Herbert Benachour Nora Seralini Gilles-Eric Laboratoire de Biochimie et Biologie Moleculaire, USC-INCRA, Université de Caen, Caen, FranceAddress correspondence to G.-E. Seralini, Laboratoire de Biochimie, EA2608-USC INRA, IBFA, Université de Caen, Esplanade de la Paix, 14032 Caen, France. Telephone: 33-0-2-31-56-54-89. Fax: 33-0-2-31-56-53-20. E-mail:
[email protected] thank M.-J. Simon for technical assistance, and F. Baudoin for secretarial assistance.
For financial support we thank the Quality and Sustainable Development Department of Carrefour Group, La Fondation pour une Terre Humaine, CRII-GEN, Ad.Gene laboratory, La Ligue Nationale contre le Cancer, Comité du Calvados. We acnowledge student grants from the Ligue contre le Cancer (Comité du Calvados) (S.R.); Société Française d’Exportation des Resources Educatives (H.S.); the Human Earth Foundation and Fondation Denis Guichard (N.B.).
The authors declare they have no competing financial interests.
6 2005 25 2 2005 113 6 716 720 5 11 2004 24 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Roundup is a glyphosate-based herbicide used worldwide, including on most genetically modified plants that have been designed to tolerate it. Its residues may thus enter the food chain, and glyphosate is found as a contaminant in rivers. Some agricultural workers using glyphosate have pregnancy problems, but its mechanism of action in mammals is questioned. Here we show that glyphosate is toxic to human placental JEG3 cells within 18 hr with concentrations lower than those found with agricultural use, and this effect increases with concentration and time or in the presence of Roundup adjuvants. Surprisingly, Roundup is always more toxic than its active ingredient. We tested the effects of glyphosate and Roundup at lower nontoxic concentrations on aromatase, the enzyme responsible for estrogen synthesis. The glyphosate-based herbicide disrupts aromatase activity and mRNA levels and interacts with the active site of the purified enzyme, but the effects of glyphosate are facilitated by the Roundup formulation in microsomes or in cell culture. We conclude that endocrine and toxic effects of Roundup, not just glyphosate, can be observed in mammals. We suggest that the presence of Roundup adjuvants enhances glyphosate bioavailability and/or bioaccumulation.
adjuvantsaromataseendocrine disruptionglyphosateherbicidehuman JEG3 cellsplacentareductaseRoundupxenobiotic
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Glyphosate is known as the active ingredient of the broad-spectrum herbicide Roundup; it inhibits the shikimic acid pathway that is important for plant protein synthesis (Schonbrunn et al. 2001), but it has also been shown to modulate plant cytochrome P450 (Lamb et al. 1998). Glyphosate is believed to be rather specific and less toxic to the ecosystem than are other pesticides; transgenic plants tolerant to this compound have even been developed following this argument (Vollenhofer et al. 1999; Williams et al. 2000). However, mammals and humans may be exposed to herbicide residues by agricultural practices (Acquavella et al. 2004) or when they enter the food chain (Takahashi et al. 2001); glyphosate is also found as a contaminant in rivers (Cox 1998). Roundup contains acid glyphosate and adjuvants such as polyethoxylated tallowamine (Cox 1998). Its adjuvants are generally considered dilutants for regulatory purposes. Although some agricultural workers using glyphosate-based herbicides are reported to have pregnancy problems (Savitz et al. 1997), glyphosate’s mechanism of action in mammals is still questioned, and it may have several enzymatic effects (Daruich et al. 2001; Williams et al. 2000). It has also been recently shown to disrupt the animal cell cycle in urchin eggs (Marc et al. 2002) and even the post-transcriptional expression of the steroidogenic acute regulatory protein (StAR) in mouse testicular Leydig cells (Walsh et al. 2000).
In this study we tested glyphosate and Roundup toxicity on human placental JEG3 cells and also evaluated its possible capacity to act as an endocrine disruptor, as do other pesticides (Nativelle-Serpentini et al. 2003), by measuring their effects at nontoxic levels on aromatase, a mammalian cytochrome P450 enzyme crucial for sex steroid hormone synthesis. The cytochrome P450 superfamily includes numerous proteins able to metabolize xenobiotics (Nelson 1998). The enzyme aromatase is composed of the product of the CYP19 gene (Bulun et al. 2003) and the associated nicotinamide adenosine dinucleotide phosphate (NADPH)-dependent reductase, and is responsible for the irreversible conversion of androgens into estrogens. It is considered a limiting factor involved in estrogen synthesis and thus in physiologic functions, including female and male gametogenesis (Carreau 2001), reproduction, sex differentiation, and even bone growth. It is also pharmacologically controlled in the treatment of estrogen-dependent cancers (Seralini and Moslemi 2001).
The direct action of glyphosate on aromatase could explain some effects on reproduction observed in vivo, at least in part; thus, we also tested glyphosate and Roundup directly on aromatase present in microsomes from human placenta and equine testis, a tissue known to be aromatase-rich (Lemazurier et al. 2001). We also purified aromatase from equine testis to assess the specificity of the interaction within the active site in this very well-characterized mammalian model (Auvray et al. 1998).
Materials and Methods
Chemicals.
N-(Phosphonomethyl)glycine (glyphosate) was purchased from Sigma-Aldrich (Saint Quentin Fallavier, France), and the pesticide Roundup (containing 360 g/L acid glyphosate; Monsanto, Anvers, Belgium) was from a commercial source. A 2% solution of Roundup and an equivalent solution of glyphosate were prepared in Eagle’s modified minimum essential medium (EMEM; Abcys, Paris, France), and the pH of glyphosate solution was adjusted to the pH of the 2% Roundup solution (~ pH 5.8). Successive dilutions were then obtained with serum-free EMEM. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) was obtained from Sigma-Aldrich. It was prepared as a 5-mg/mL stock solution in phosphate-buffered saline, filtered through a 0.22-μm filter before use, and diluted to 1 mg/mL in serum-free EMEM. The polyclonal rabbit antibody directed against estrone (E1) was purchased from PARIS company (Compiègne, France). Tritiated E1 ([2,4,6,7-3H]-E1, 95 Ci/mmol, 3.52 TBq) was from DuPont NEN (Les Ulis, France).
Cell line.
The human choriocarcinoma-derived placental cell line (ref JEG3, ECACC 92120308) was provided by CERDIC (Sophia-Antipolis, France). Cells were grown in phenol red–free EMEM containing 2 mM glutamine, 1% nonessential amino acids, 100 U/mL antibiotics (mix of penicillin, streptomycin, and fungizone), 1 mM sodium pyruvate, and 10% fetal calf serum (Biowhittaker, Gagny, France). Fifty thousand cells per well were grown to 80% confluence in 24-well plates, washed with serum-free EMEM, and then exposed to various concentrations of Roundup or the equivalent concentrations of glyphosate in serum-free EMEM for 1 hr or 18 hr or in serum-containing medium for longer exposures.
MTT assay.
We used this enzymatic test, based on the cleavage of MTT into a blue-colored product (formazan) by mitochondrial enzyme succinate dehydrogenase (Mossman 1983), to evaluate JEG3 cell viability exposed to Roundup or glyphosate during various times. Cells were washed with serum-free EMEM and incubated with 250 μL MTT per well. The plates were incubated for 3 hr at 37°C, and 250 μL of 0.04 N-hydrochloric acid–containing isopropanol solution was added to each well. The plates were vigorously shaken in order to solubilize the blue formazan crystals formed. The optical density was measured using a spectrophotometer (Stratagene, Strasbourg, France) at 560 nm for test and 640 nm for reference.
Measurement of aromatase activity in vitro by radioimmunoassay.
The conversion of androstenedione to E1 by the aromatase complex was measured in cell supernatants by radioimmunoassay (RIA) as previously described (Nativelle-Serpentini et al. 2003). JEG3 cells exposed to Roundup or glyphosate were washed with serum-free EMEM and incubated for 90 min with 200 nM androstenedione at 37°C in 5% CO2. The reaction was stopped by placing the plates on ice for 5 min, and supernatants were extracted by adding 10 volumes of diethyl ether. The extraction efficiency, evaluated by adding radio-labeled E1, was 60 ± 3%. The rabbit E1 antibody was prepared according to the manufacturer’s instructions. The sensitivity of the RIA was 10 pg/mL. Intra- and interassay coefficients of variation were 4 and 6%, respectively. The aromatase activity was expressed in relation to the protein concentration that was evaluated in cell extracts using bovine serum albumin as standard (Bradford 1976).
RNA extraction and quantification.
Total RNA was isolated from JEG3 cells using the guanidium/phenol/chloroform method (Chomczynski and Sacchi 1987). RNA samples were treated with DNase I at 37°C for 30 min to remove genomic DNA. Then DNase I was inactivated at 65°C for 10 min.
Total RNA (1 μg) was reverse-transcribed (RT) using 100 U M-MLV-RT (Moloney murine leukemia virus reverse transcriptase) at 42°C for 45 min in the presence of 0.5 μg 18-mer oligo(dT), 500 μM of each dNTP, 50 mM Tris-HCl (pH 8.3), 75 mM KCl, 3 mM MgCl2, 10 mM dithiothreitol (DTT), and 6 U RNasin in a total volume of 25 μL. The absence of DNA contamination in the RNA samples was checked in controls without M-MLV-RT.
For each run, a master mix was prepared with 1 × SYBR Green buffer containing 5 mM MgCl2; 200 mM dATP, dCTP, and dGTP; 400 mM dUTP; 1.25 U AmpliTaq Gold DNA polymerase (Applied Biosystems, Courtaboeuf, France); and 300 nM of each primer: EXIIc sense primer, 5′ TGA GGT CAA GGA ACA CAA GA 3′, exon II specific (positions 9–28); and EXIII antisense primer, 5′ ATC CAC AGG AAT CTG CCG TG 3′, for exon III (positions 211–230) (Corbin et al. 1988). Five microliters of each diluted RT sample were added to 20 μL of the polymerase chain reaction (PCR) master mix. The thermal cycling conditions consisted of an initial denaturation step at 95°C for 10 min and 40 cycles at 95°C for 15 sec and 60°C for 1 min. We also quantified the transcripts of the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as an endogenous control to normalize each sample using sense and antisense primers, 5′ CCA TCA CCA TCT TCC AGG AGC 3′ (positions 278–298) and 5′ GGA TGA TGT TCT GGA GAG CC 3′ (positions 663–682), respectively (Tokunaga et al. 1987). All PCR reactions were performed using an ABI Prism 7000 Sequence Detection System (Applied Biosystems).
Preparation of microsomes.
Microsomal fractions (endoplasmic reticulum) were obtained from full-term placentas of young healthy and nonsmoking women (Centre Hospitalier Régional de Caen, France) and equine testis by differential centrifugations (Moslemi et al. 1997). Briefly, tissues were washed with 0.5 M KCl, homogenized in 50 mM phosphate buffer (pH 7.4) containing 0.25 M sucrose and 1 mM DTT, and centrifuged at 20,000g. The supernatant was then ultracentrifuged at 100,000g, and the final pellet was washed twice, dissolved in the same buffer containing 20% glycerol, and stored at −70°C until use. All steps of the preparation were carried out at 4°C.
Measurement of microsomal aromatase activity.
Microsomal aromatase activity was evaluated by tritiated water release from radio-labeled substrate [1β-3H]-androstenedione as previously described (Moslemi et al. 1993, 1997). This method is based on the stereo-specific release of 1β-hydrogen from the androstenedione substrate, which forms tritiated water during aromatization (Dintinger et al. 1989; Thompson and Siiteri 1974). Human placental microsomes (50 μg protein) were incubated with radiolabeled androstenedione (100 pmol/tube) at 37°C for 15 min, in the presence or absence of various concentrations of Roundup or glyphosate in 1 mL total volume of 50 mM Tris-maleate buffer (pH 7.4). The reaction was started by adding 100 μL of 0.6 mM H+-NADPH and stopped with 1.5 mL chloroform, and then centrifuged at 2,700g at 4°C for 5 min. After adding 0.5 mL 7% charcoal/1.5% dextran T-70 solution into the preparation, the centrifugation was repeated for 10 min. Aromatase activity was determined by measuring the radioactivity of the 0.5 mL aqueous phase. The kinetic parameters were determined by incubating equine testicular microsomes (2 μg protein) with various concentrations of radiolabeled androstenedione in the presence of various concentrations of Roundup in 0.5 mL of H+-NADPH containing Tris-maleate buffer (pH 7.4) at 25°C for 3 min.
Purification of aromatase moieties.
Reductase was obtained after chromatographic separation, by ω-aminohexyl-Sepharose 4B and adenosine 2′,5′-diphosphate agarose, respectively; hydrophobic interaction; and affinity columns (Vibet et al. 1990). The cytochrome P450 aromatase was purified from equine microsomes, after its separation from reductase, by successive chromatographic steps: concanavalin A-Sepharose 4B affinity column, diethyl amino ethyl-Sepharose CL-6B ion exchange, and hydroxyapatite-Sepharose 4B adsorption/partition columns (Moslemi et al. 1997). Protein concentration was determined as previously described (Bradford 1976).
Measurement of reductase activity.
Reductase activity was determined by the measurement of the increasing absorbance of the preparation, corresponding to the reduction of the cytochrome C in the presence of H+-NADPH (Vibet et al. 1990) at 550 nm for 2 min at 37°C using a Kontron-Uvikon 860 spectrophotometer (Kontron Instruments S.A., St. Quentin Yvelines, France). The pH of the preparation was adjusted when adjusted to 7.4 by adding an appropriate volume of 10 N NaOH. After equilibration, the reaction was started by adding cytochrome C.
Spectral studies.
The absorbance of purified equine aromatase in the presence or absence of glyphosate or Roundup was recorded from 375 to 475 nm with a spectrophotometer as previously described (Moslemi and Seralini 1997). Briefly, absorption spectra of 362 μg aromatase protein in 1.5 mL 50 mM Tris-maleate containing 2 μM androstenedione were recorded during incubation at 37°C, after adding 0.0046% glyphosate or 0.1% Roundup. The spectra of aromatase with glyphosate or Roundup alone were subtracted from the incubation spectrum.
Statistical analysis.
All data are presented as the mean ± SE. The experiments were repeated three times in triplicate unless otherwise indicated. Statistically significant differences were determined by a Student t-test using significance levels of 0.01 and 0.05.
Results
Cell viability.
The recommended agricultural use for Roundup is 1–2% in water, so we tested its effect on human placental JEG3 cell viability at concentrations of up to 2% after 18-, 24-, or 48-hr exposures in serum-containing medium, by the MTT assay in conditions previously described (Nativelle-Serpentini et al. 2003), compared with glyphosate. The Roundup dilutions and equivalent quantities of glyphosate were adjusted to the same pH to facilitate the comparisons. The toxicity increased with time (8-fold at 0.8% between 24 and 48 hr), and the median lethal dose (LD50) was approximately 1.8 times lower for Roundup (0.7%) than for glyphosate (Figure 1). This difference was even visible after 1 hr of incubation in serum-free medium (Figure 2A) and increased 3-fold after 18 hr of incubation (Figure 2B). Acidity of the 2% Roundup or glyphosate solution (pH 5.80 ± 0.08 instead of pH 7.91 ± 0.16) reduced cell viability only 23% after 18 hr, and thus could not alone explain the 90% reduction of cell viability observed at this concentration. When only 0.1% Roundup was added to glyphosate, bringing small amounts of the adjuvants to the solution, the cell viability was diminished significantly (Figure 2B).
Aromatase activity in cell culture.
We measured aromatase activity after incubation of cells in the presence of nontoxic concentrations of Roundup or glyphosate, by RIA of E1 formed from 200 nM androstenedione, as previously described (Nativelle-Serpentini et al. 2003). As shown in Figure 3A, after 1 hr of incubation, the estrogen synthesis was enhanced by about 40% but only with Roundup. After 18 hr of incubation, we noted a clear inhibition of aromatase activity in vitro, with a median inhibiting concentration (IC50) of 0.04% again with Roundup only. This inhibition of aromatase activity is, at least in part, assumed to be an effect on aromatase gene expression, because mRNA levels were decreased (Figure 3B). Glyphosate was inefficient alone in these conditions. But it inhibited aromatase activity with minute dilutions of Roundup, bringing adjuvants in the solution (Figure 4).
Aromatase activity in microsomes.
We evaluated microsomal aromatase activity by tritiated water release from the radiolabeled substrate (Dintinger et al. 1989; Thompson and Siiteri 1974) in human (Figure 5) and equine microsomes. Aromatase inhibition by Roundup was equivalent in these two mammalian models. The IC50 was 0.6% for Roundup in these conditions and more than three times greater for glyphosate. The kinetic parameters were determined by incubating equine testicular microsomes with various concentrations of radiolabeled androstenedione and Roundup. The inhibition constant Ki (0.6%) showed a competitive inhibition (Figure 6A).
Enzymatic activity of purified enzymes.
We further purified the enzyme moieties from the aromatase-rich equine testis, giving better yields than placenta. The incubation with the herbicide demonstrated a direct interaction of glyphosate within the active site. We obtained spectral interactions between Roundup or glyphosate and the active site of the purified cytochrome P450 aromatase by measuring the absorbance of the preparations from 375 to 475 nm. A type II spectrum was observed (Figure 6B); it was characteristic of an interaction between a nitrogen atom of the molecule and the heme iron of the cytochrome. In addition, we tested the effect of the herbicide on the ubiquitous moiety of the aromatase, which is the electron donor reductase. NADPH-dependent reductase activity was determined by the measurement of the increasing absorbance of the preparation, corresponding to the reduction of the cytochrome C. Reductase is also directly affected after purification and incubation with Roundup, but to a lesser extent (IC50 5%) than the cytochrome P450 aromatase responsible for steroid binding and catalysis (Figure 7).
Discussion
This study demonstrates that Roundup reduces JEG3 cell viability at least twice more efficiently than glyphosate. This effect increased with time and was obtained with concentrations of Roundup 10 times lower than that of the agricultural use. The presence of serum buffers the toxic effect of the herbicide. It is generally recognized that serum proteins can bind to chemicals and reduce their availability to cells. Seibert et al. (2002) have shown that the presence of albumin influences the cytotoxicity of compounds. Moreover, the lack of growth factors in serum-free medium, for instance, could also play a role in this phenomenon. In our experiments, the incubation in serum-free medium was interesting to optimize the visible effects of the compounds in the shortest time. These were also observed anyway after 48 hr in the presence of serum. The physiologic significance of these effects can be questioned, in regard to the concentration used. However, the time of exposure to pollutants may be longer in vivo, and here in vitro we observed that long times of exposure allowed low concentrations to present toxic effects. This phenomenon could be caused by metabolism, genomic action, and/or bioaccumulation of some products of Roundup. For instance, Peluso et al. (1998) demonstrated the formation of covalent links between DNA and some Roundup adjuvants. Their genotoxicity or toxicity was also noticed (Lioi et al. 1998; Mitchell et al. 1987; Vigfusson and Vyse 1980). Even though absorbed Roundup is excreted rapidly from the body, usually in feces (Brewster et al. 1991; Williams et al. 2000), a part may be retained or conjugated with other compounds that can stimulate biochemical and physiologic responses. The bioaccumulation of some of its residues may be hypothesized. For example, the harmful effect of glyphosate on semen quality after 6 weeks of post-treatment period in rabbits (Yousef et al. 1995) may be considered an indication of its retention and conjugation in the body, helped by Roundup adjuvants.
Additionally, in this work Roundup presents a differential time effect at nontoxic levels on aromatase activity of JEG3 cells; this phenomenon was already observed with other xenobiotics such as lindane and bisphenol A (Nativelle-Serpentini et al. 2003). The 40% rise in aromatase activity after 1 hr of incubation is perhaps caused by an increase of the membrane fluidity and androgenic substrate bioavailability in a first step provoked by adjuvants. By contrast, once well entered into cells, Roundup always reduced aromatase activity. Furthermore, this was associated with the decrease of CYP19 mRNAs. Walsh et al. (2000) showed that Roundup preferentially diminished the expression of StAR mRNA by decreasing at least the rate of gene transcription.
The direct inhibition of aromatase activity by Roundup was verified in human and equine microsomes, two mammalian aromatase models that we have precisely characterized, in order to understand the active site configuration of this membrane-bound cytochrome P450 (Auvray et al. 1998; Moslemi and Seralini 1997; Seralini et al. 2003). Contrary to results obtained in cells, glyphosate had an inhibitory effect on aromatase activity in human and equine microsomes, but four times lower than the effects of Roundup. Moreover, Roundup inhibited aromatase better in cells than in microsomes (IC50 values, 0.04 and 0.6%, respectively). This could be explained by the difference in incubation duration (18 hr vs. 15 min) inducing metabolism and genomic action. Glyphosate penetration through the cell membrane and subsequent intracellular action appeared in our work to be greatly facilitated by adjuvants, as in plants (Haefs et al. 2002) or in animal cells, where it can act at the level of cycle regulation (Marc et al. 2002). Indeed, in this work, minute dilutions of Roundup bringing adjuvants to cells allowed the aromatase inhibitory effect of glyphosate as well as cytotoxic effects.
Moreover, the presence of Roundup in the incubation medium resulted not only in the decrease of the activity of the cytochrome P450 aromatase, but also to a lesser extent in a partial inhibition of its associated reductase. This is confirmed by kinetic and spectral studies that showed that Roundup inhibits the enzyme at the active site level in a competitive manner. Furthermore, our spectral study shows a type II spectrum for purified equine aromatase in the presence of glyphosate or Roundup at the saturating concentration of androstenedione. After androstenedione elimination, Roundup induces a type I spectrum. A type II spectrum with minimal absorbance at 390 nm and maximal absorbance at 420 nm is considered specific for an interaction between a nitrogen atom of the molecule and the heme iron of the cytochrome, whereas a type I spectrum (inverted absorbance) is observed when this type of interaction is absent. Androstenedione, a natural hormone, thus appears to facilitate pesticide access to the active site of the enzyme. However, this occurs more easily with glyphosate directly in contact with the solubilized enzyme than with Roundup, because less concentration of the former was needed to produce the same spectrum.
Conclusion
Our studies show that glyphosate acts as a disruptor of mammalian cytochrome P450 aromatase activity from concentrations 100 times lower than the recommended use in agriculture; this is noticeable on human placental cells after only 18 hr, and it can also affect aromatase gene expression. It also partially disrupts the ubiquitous reductase activity but at higher concentrations. Its effects are allowed and amplified by at least 0.02% of the adjuvants present in Roundup, known to facilitate cell penetration, and this should be carefully taken into account in pesticide evaluation. The dilution of glyphosate in Roundup formulation may multiply its endocrine effect. Roundup may be thus considered as a potential endocrine disruptor. Moreover, at higher doses still below the classical agricultural dilutions, its toxicity on placental cells could induce some reproduction problems.
Figure 1 Effects of Roundup (A) and equivalent quantities of glyphosate (B) on JEG3 placental cell viability in a serum-containing medium. This was evaluated by the MTT assay, the results are presented as percentages compared with nontreated cells. Cells were incubated with increasing concentrations of Roundup or equivalent concentrations of glyphosate for 18, 24, or 48 hr (n = 9). The LD50 is indicated by a dashed line. Error bars indicate SE.
*p < 0.05;
**p < 0.01.
Figure 2 Effects of Roundup and equivalent quantities of glyphosate on JEG3 placental cell viability in serum-free medium. The incubation was for 1 hr (A) or 18 hr (B). The addition of 0.02 or 0.1% Roundup shows adjuvant effects (n = 9). Error bars indicate SE.
**p < 0.01.
Figure 3 Effects of Roundup and equivalent quantities of glyphosate on JEG3 aromatase activity and mRNA levels in a serum-free medium. Aromatase activity (A) was obtained with nontoxic concentrations of Roundup or glyphosate for 1 and 18 hr (n = 9). (B) Cytochrome P450 aromatase mRNA levels, normalized with GAPDH levels, in the presence of Roundup or glyphosate after 18 hr (n = 4). Error bars indicate SE.
**p < 0.01.
Figure 4 Combined effects of glyphosate and minute levels of Roundup on JEG3 aromatase activity in serum-free medium (n = 9). It was obtained at non-toxic concentrations after 18 hr exposure.
**p < 0.01.
Figure 5 Effects of Roundup and equivalent quantities of glyphosate on microsomal aromatase activity. Human placental microsomes were incubated with Roundup or glyphosate at 37°C for 15 min (n = 9). The IC50 is indicated by a dashed line. Similar results were obtained with equine testicular microsomes. Error bars indicate SE.
**p < 0.01.
Figure 6 Kinetic and spectral studies of aromatase in the presence of Roundup or glyphosate. (A) Lineweaver-Burk representation of equine testicular microsomal aromatase activity in the presence of Roundup at 25°C with radiolabeled androstenedione. Comparable results were obtained with human placental microsomes (n = 9). (B) Spectral analysis of interactions between the active site of purified equine cytochrome P450 aromatase and 0.1% Roundup (left) or 0.0045% glyphosate (right). Type II spectra were obtained with Roundup or glyphosate in the presence of 2 μM androstenedione, and a type I spectrum was obtained in its absence. The results are representative of three experiments. Error bars indicate SE.
Figure 7 Effect of Roundup on reductase activity. Activity of purified equine reductase was measured in the presence of increasing concentrations of Roundup in nonadjusted or adjusted pH (7.4) medium for 15 min at 37°C (n = 9). Error bars indicate SE.
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Moslemi S Vibet A Papadopoulus V Camoin L Gaillard JL 1997 Purification and characterization of equine testicular cytochrome P-450 aromatase: comparison with the human enzyme Comp Biochem Physiol 118B 217 227
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7598ehp0113-00072115929895ResearchArticlesAccurate Prediction of the Response of Freshwater Fish to a Mixture of Estrogenic Chemicals Brian Jayne V. 1Harris Catherine A. 1Scholze Martin 2Backhaus Thomas 3Booy Petra 4Lamoree Marja 4Pojana Giulio 5Jonkers Niels 5Runnalls Tamsin 1Bonfà Angela 5Marcomini Antonio 5Sumpter John P. 11Institute for the Environment, Brunel University, Uxbridge, Middlesex, United Kingdom2Centre for Toxicology, School of Pharmacy, London, United Kingdom3Department of Biology and Chemistry, University of Bremen, Bremen, Germany4Institute for Environmental Studies, Vrije Universiteit, The Netherlands5Department of Environmental Sciences, University of Venice, Venice, ItalyAddress correspondence to J.V. Brian, Institute for the Environment, Brunel University, Uxbridge, Middlesex, UB8 3PH, United Kingdom. Telephone: 44-1895-266-264. Fax: 44-1895-269-761. E-mail:
[email protected] research presented here is part of the ACE (Analysing combination effects of mixtures of estrogenic chemicals in marine and freshwater organisms) project, which is funded by the European Commission under the 5th Framework Programme (contract EVK1-2001-00091).
The authors declare they have no competing financial interests.
6 2005 14 3 2005 113 6 721 728 23 9 2004 14 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Existing environmental risk assessment procedures are limited in their ability to evaluate the combined effects of chemical mixtures. We investigated the implications of this by analyzing the combined effects of a multicomponent mixture of five estrogenic chemicals using vitellogenin induction in male fathead minnows as an end point. The mixture consisted of estradiol, ethynylestradiol, nonylphenol, octylphenol, and bisphenol A. We determined concentration–response curves for each of the chemicals individually. The chemicals were then combined at equipotent concentrations and the mixture tested using fixed-ratio design. The effects of the mixture were compared with those predicted by the model of concentration addition using biomathematical methods, which revealed that there was no deviation between the observed and predicted effects of the mixture. These findings demonstrate that estrogenic chemicals have the capacity to act together in an additive manner and that their combined effects can be accurately predicted by concentration addition. We also explored the potential for mixture effects at low concentrations by exposing the fish to each chemical at one-fifth of its median effective concentration (EC50). Individually, the chemicals did not induce a significant response, although their combined effects were consistent with the predictions of concentration addition. This demonstrates the potential for estrogenic chemicals to act additively at environmentally relevant concentrations. These findings highlight the potential for existing environmental risk assessment procedures to underestimate the hazard posed by mixtures of chemicals that act via a similar mode of action, thereby leading to erroneous conclusions of absence of risk.
concentration additionestrogenestrogen mimicfathead minnowmixture effectsPimephales promelasprediction
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Many environmental contaminants are capable of disrupting endocrine function in humans and wildlife. This phenomenon has been associated with reduced fecundity, reproductive failure, and population-level effects in a variety of aquatic organisms (Jobling et al. 2002; Matthiessen and Gibbs 1998; Nash et al. 2004). This highlights the urgent need to develop accurate methods of assessing the risk that these chemicals pose. Current methods usually focus on the assessment of single chemicals. This is in clear contrast to real-world exposure situations, which are generally to mixtures of endocrine-disrupting chemicals, many of which act via a common mode of action. This means that the overall risk posed in real exposure situations may be greater than that expected on the basis of the effects assessment of the individual mixture components, due to the potential for combined effects. Concerns over the ecological significance of these effects were heightened in the late 1990s after reports of spectacular synergisms between binary mixtures of estrogenic pesticides in vitro (Arnold et al. 1996). These results were subsequently withdrawn because of issues of reproducibility, leading many to question the overall significance of mixtures (Kortenkamp and Alterburger 1999). However, the issue has continued to attract interest in view of the fact that many of the estrogenic effects reported in the literature exceed expectations based on chemical-by-chemical assessments. A notable example of this is the discrepancy between the widespread distribution of reproductive abnormalities in wild fish populations relative to the low concentrations of estrogenic chemicals to which they are exposed (Jobling et al. 1998; van Aerle et al. 2001).
Many of the chemicals identified as endocrine disruptors are known to mediate their effects by binding with the estrogen receptor (Payne et al. 2000). Estrogenic chemicals include both the natural and synthetic steroidal estrogens, as well as a wide range of synthetic chemicals that mimic the actions of endogenous estrogen. The potencies of these different types of chemical vary over several orders of magnitude. For example, the steroidal estrogens, such as 17β-estradiol (E2) and 17α-ethynylestradiol (EE2), are capable of exerting estrogenic effects on fish when present in the water in the low nanograms per liter range (Thorpe et al. 2003). These chemicals pose a significant environmental risk, having been detected in effluents that discharge into rivers at concentrations that are individually capable of inducing a significant effect (Desbrow et al. 1998). In contrast, chemicals that mimic the actions of estrogen, such as the alkylphenols, exhibit much lower potencies and rarely occur at concentrations that are individually effective in the environment (Desbrow et al. 1998). Hence, the individual assessment of the hazard posed by these chemicals indicates a negligible risk. However, this approach does not account for the potential for endocrine disruptors to act in combination. This may lead to the underestimation of hazards that exist in real exposure situations, resulting in erroneous conclusions of absence of risk.
Increasing recognition of these shortcomings has prompted considerable efforts to investigate the combined effects of estrogenic chemicals (e.g., Ashby et al. 1997; Soto et al. 1994). However, many of these studies have been hampered by inadequate theoretical foundations on which to base the expected effects of mixtures of chemicals that exhibit nonlinear concentration–response curves (Kortenkamp and Altenburger 1999). More recently, however, the pharmacological concept of concentration addition (CA) has been applied to the assessment of estrogenic mixtures. This concept is based on the assumption that the components of the mixture act in a similar manner, such that replacing one or more chemicals totally, or in part, with the other mixture components can produce the same overall effect. The overall effect of the mixture can therefore be described quantitatively using a mathematical model, based on the concentration and potency of the individual mixture components (Bödeker et al. 1992). This means that potential hazards can be predicted from basic information about the components of the mixture and its composition (number and concentration of chemicals present), thereby negating the need for mixture testing. A number of studies have attempted to validate this concept by comparing the effects of the mixture with those expected on the basis of additivity. This has involved the single-substance testing of the individual mixture components in order to gain information for the modeling of mixture effects. The predictions made can then be tested experimentally. This approach has been used extensively in aquatic toxicology to demonstrate the validity of CA as a means of predicting the toxicity of multicomponent mixtures of similarly acting compounds in various assays with fish, daphnia, algae, and bacteria (e.g., Altenburger et al. 2000; Backhaus et al. 2000, 2004; Faust et al. 2001; Hermens et al. 1984a, 1984b; Könemann 1981).
There is considerable evidence that CA may also be used to predict the effects of mixtures of estrogenic chemicals. The validity of this approach has been demonstrated in vitro, using assays such as the yeast estrogenicity screen (YES) and the human breast cancer cell proliferation assay (E-SCREEN) (Payne et al. 2000, 2001; Rajapakse et al. 2002; Silva et al. 2002). Such studies have revealed the capacity for the components of the mixture to contribute to the overall effect by acting in relation to their potency, even at low-effect concentrations. For example, Silva et al. (2002) combined eight estrogenic chemicals at low-effect concentrations and demonstrated that the effects of this mixture were consistent with the predictions of CA. This highlights the capacity for these chemicals to act in combination, even when the individual components of the mixture are present at concentrations below the threshold of statistically detectable effects. This has become known as the “something from nothing” phenomenon (Silva et al. 2002).
In light of this in vitro evidence, there is now an urgent need to assess whether these mixture effects also occur in higher life forms, which reflect the net effects of complex chains of events involving the uptake, distribution, and metabolism of test agents until they reach their target sites. The induction of the egg yolk protein vitellogenin (VTG) is an established in vivo assay for analyzing estrogenic effects in fish. This protein is normally induced in the livers of female fish in response to stimulation by endogenous estrogen. However, it can be induced in both male and female fish exposed to extremely low concentrations of estrogenic chemicals (Sumpter and Jobling 1995). Although a causal relationship has not been established, a number of studies have demonstrated that VTG induction is associated with effects at higher levels of biological organization (e.g., Harries et al. 2000). It therefore offers a sensitive and integrated measure of estrogenic activity, which is relevant to the assessment of environmental risk. Recent evidence indicates that the induction of VTG can be used to assess the joint action of binary mixtures of estrogenic chemicals in vivo (Thorpe et al. 2001, 2003). Here, we have applied this assay to the analysis of multi-component mixture effects.
The aim of this study was to investigate the predictability of the combined effects of five estrogenic chemicals on VTG induction in the fathead minnow (Pimephales promelas). We used CA as a concept on which to base the expectation of additivity. We tested the predictive power of CA by analyzing the estrogenic effect of each mixture component individually. Information on their potency was then used to make predictions, which were then tested by comparison with the observed mixture effects. Mixture effects at low-effect concentrations of the individual components were also investigated to analyze the applicability of CA under environmentally realistic conditions. All studies were conducted using an optimal experimental design that minimized the number of test organisms. Quality checks of the exposure conditions were conducted using analytical chemistry.
The work described in this article contributes to our current understanding of the combined effects of multicomponent mixtures of estrogenic chemicals at higher levels of biological complexity, as well as aiding in the development of methods that can be applied to the analysis of mixtures. Hence, the findings are of considerable relevance to the assessment of environmental risk.
Materials and Methods
Test organisms.
A stock of fathead minnows was obtained from Osage Catfisheries (Osage Beach, MO, USA). These fish, and their offspring, were used to conduct 14 independent exposure studies. Before exposure, stock fish were held in communal holding tanks with a recirculating water supply. The exposure studies were conducted in 30-L glass aquaria (0.6 m × 0.3 m × 0.3 m), which were supplied with a continuous flow of water. The analysis of VTG induction focused on the responses of male fish. However, an equal number of females were included in each experiment to reduce the level of aggression between males. During the exposure, the fish were fed twice daily: once with frozen brine shrimp and once with flaked fish food. The photoperiod was maintained at 16-hr light/8-hr dark with 20-min dawn and dusk transition periods.
Test chemicals.
We investigated the activity of five estrogenic chemicals. These were selected on the basis of previous reports of their presence in the environment and because of their likely association with intersexuality in wild fish (Desbrow et al. 1998). They included the natural steroidal estrogen E2, the synthetic steroidal estrogen EE2, and the estrogen-mimicking compounds 4-tert-nonylphenol (NP), 4-tert-octylphenol (OP), and bisphenol A (BPA). Stocks of E2 (98% purity), EE2 (98% purity), OP (97% purity), and BPA (99% purity) were purchased from Sigma Aldrich (Dorset, UK). NP (99% purity) was obtained from ACROS Organics (Leicestershire, UK). All chemicals were dissolved in HPLC-grade dimethylformamide (DMF) supplied by BDH Laboratory Supplies (Dorset, UK).
Water supply and test apparatus.
We applied stock solutions to the tanks using a Watson-Marlow 205U multichannel peristaltic pump using silicon tubing (Watson Marlow, Falmouth, Cornwall, UK). Solutions were delivered at a rate of 0.02 mL/min into mixing vessels, which were supplied with dechlorinated water that had been heated to 25°C. Water entered the mixing vessels at a flow rate of 300 mL/min, resulting in a 1:15,000 dilution of the stock solution. The diluted stock solution then flowed into the tanks at a rate of 18 L/hr, which resulted in one complete water change every 100 min. Dissolved oxygen and water temperature were recorded daily, and the functioning of the delivery system was monitored throughout the study.
Delivery of the test chemical commenced 1 week before the start of each exposure. During this equilibration period, the fish were acclimatized to the experimental conditions in an identical set of undosed tanks. After 7 days, the fish were transferred into the tanks containing the chemical or chemicals, where they were maintained under exposure conditions for a period of 2 weeks. Three control tanks were run alongside each exposure. Two of these were negative controls (NCs), consisting of one undosed tank that received water only [water control (WC)] and one tank that was dosed with DMF [solvent control (SC)]. A positive control (PC) was also included in each study. The PC tank was dosed with EE2 at a concentration of 10 ng/L, which has previously been found to induce a maximum VTG response (Panter et al. 2002).
Analytical chemistry.
We determined exposure concentrations at three different time points during each experiment. We collected the first set of water samples after 1 week of dosing, immediately before the addition of the fish. The second set was taken 1 week after this, and the third set was taken after the final week, on the day that the exposure was terminated. Water samples were collected in solvent rinsed glass bottles. If the sample was to be analyzed for the presence of steroid estrogens, the bottles were silylated before use. The water samples were then analyzed according to the nature of the chemical in question, using one of the four following analytical techniques.
Water samples containing EE2 were extracted onto preconditioned solid-phase C18 cartridges. Extracts were eluted into methanol, which was removed under a stream of nitrogen. The extracts were then resuspended in ethanol, and the EE2 concentration was determined using an established radioimmunoassay technique (Lange et al. 2001). Samples containing E2 also underwent solid-phase extraction (SPE) on a DVB Speedisk (Baker, Deventer, The Netherlands). After cleanup of the extracts with C18 cartridges, derivitization of E2 was carried out using silyl reagents before analysis using gas chromatography combined with ion trap detection (adapted from Belfroid et al. 1999; Houtman et al. 2004). Samples containing BPA underwent SPE analogous to the E2 procedure, after which the extracts were analyzed using HPLC coupled to diode array detection. This was carried out under isocratic elution conditions with methanol/water (60/40, vol/vol) (adapted from Belfroid et al. 1999). For the analysis of water samples containing NP and OP, the extraction step was omitted. After the addition of acetonitrile (5%), large sample volumes (300–800 μL) were injected onto a reversed-phase HPLC column, which was coupled with an ion trap mass spectrometer via an electrospray interface for on-column enrichment. Analytes were eluted using a fast gradient (Pojana et al. 2004).
Experimental design.
We determined the complete concentration–response curve of each chemical in the test system in order to provide the information necessary to generate the prediction. The successful comparison of observed and predicted mixture effects was dependent upon the quality of these data. In order to generate a prediction of low uncertainty, that is, high accuracy and precision, it was necessary to minimize the chance of unknown systematic shifts in VTG sensitivity for each chemical within the study time that could result in a biased prediction (inaccuracy), and determine the concentration-effect information of each compound with a certain precision in order to maintain a given statistical variability of the prediction (precision). We achieved this by repeating each exposure at least once after a given time lag. Data from repeated studies were then pooled.
Slight differences in the absolute VTG levels between studies were accounted for by standardizing the absolute effects scale to relative effects of between 0 and 1. The mean VTG concentration in the fish from the NC (SC) and the PC tanks were used as the minimum and maximum responses, respectively. This scaling was carried out after the VTG effects data were log10 transformed, such that a median effective concentration (EC50) corresponds to the concentration that produces a log10-transformed VTG induction, which is median in relation to the NCs and PC.
The aim of the single-chemical exposures was to produce the data necessary to predict the median effect concentration of the mixture without exceeding a given level of statistical uncertainty (the 95% confidence limits for the predicted EC50 were set at a maximum of ± 0.2 on the log10-transformed concentration scale). This relied on the premise that there was average effect data variability, determined on the basis of historical data sets produced under similar test conditions, and it required that the concentration range tested provided sufficient information on the VTG response curve. This information was based on results of the repeated preliminary exposure studies, each of which included six different concentrations, to which four male and four female fish were exposed.
In order to compare the mixture effects with the predictions of CA for a wide range of different VTG levels in the final mixture experiment, we used a “fixed-ratio” mixture design: a master stock was prepared, containing each of the chemicals at their EC50 concentrations. This was diluted to give a range of mixture concentrations of 100, 50, 30, 20, 10, and 5%, which corresponded with relative VTG responses between 0 and 100%, according to the CA expectations. Fish were exposed to this dilution series in two independent studies using the same methods and design as employed in the individual exposure studies. The concentration–response to the mixture was then determined and related to the effects predicted by CA.
In order to directly relate the effects of the compounds to the observed mixture effects, we performed a second mixture experiment. The design of this experiment involved the parallel testing of each chemical, both individually and in combination. Only one concentration of each chemical was tested. This approach aimed to investigate the potential for mixture effects to occur at low-effect concentrations of the components, that is, at concentrations that would not, individually, induce a statistical significant effect (Silva et al. 2002). The low-effect concentrations adopted were based on the EC50 of each chemical divided by 5. According to the principles of CA, it was predicted that this mixture would induce a 50% level of effect.
Fish sampling and analysis of plasma VTG.
At the end of each exposure, the fish were sacrificed by overdosing with MS222 (Sigma Aldrich). The length and weight of each individual were recorded before bleeding. Blood samples were collected from the caudal peduncle using heparinized capillary tubes (Hawksley and Sons Ltd., Sussex, UK). These were centrifuged at 4,000g for 5 min. Plasma was then drawn off and stored at −20°C until required for analysis. Plasma VTG concentrations were determined using a carp-VTG enzyme-linked immunosorbent assay (ELISA) validated for the measurement of VTG in fathead minnows (Tyler et al. 1996).
Mathematical modeling and statistical analysis.
We determined concentration–response curves for each of the five chemicals and for the mixture using pooled data from the repeated exposures. To account for the intra- and interexperimental variability associated with this nested data scenario, we used the generalized nonlinear mixed modeling approach in which both fixed and random effects are permitted to have a nonlinear relationship with the effect end point (Vonesh and Chinchilli 1997). As random effect, a shift parameter was included in the nonlinear regression model, which accounts for a shift of the whole curve based on the log10-transformed concentration scale. Furthermore, a best-fit approach was adopted: three different regression models (probit, logit, and Weibull) were fitted independently to the same pooled data set, and the best fit was selected on the basis of statistical criteria (Scholze et al. 2001). This approach was implemented using the NLMIXED function of the SAS statistical software package (SAS Institute, Cary, USA).
The expected concentration–response relationship of the mixture was calculated using CA, which is represented by Equation 1:
where ECxMix is the concentration of the mixture that induces an overall effect x, ECxi is the concentration of the ith chemical in an n-component mixture required to induce the same magnitude of effect, and pi is the proportion of the ith component in the mixture (Backhaus et al. 2000). Hence, in addition to information regarding the exact composition of the mixture, knowledge of identical effect concentrations (ECx values) of the mixture components is all that is required to predict an ECx value for the mixture. CA was used to predict ECx values for the mixture in steps of 1% for effect levels from 10% up to 95%. These values were then connected using straight lines to give a graphical representation of the predicted curve. All predicted effect concentrations are estimates and are therefore subject to stochastic variability, which meant that the predicted effect concentration of the mixture also had to include a measure of statistical uncertainty. This was achieved using the bootstrap method (Efron and Tibshirani 1993), which enabled 95% confidence limits to be derived for the mean predicted effect.
Results
Analytical determination of exposure concentrations.
Because of occasional technical problems that were encountered with the analysis of the water samples, we did not obtain full sets of reliable data for each chemical in all exposures. Inconsistencies between data sets for some chemicals created problems when plotting the concentration–response data by effectively shifting the position of the curve along the x-axis, thereby increasing the variability associated with the biological response. This reduced the accuracy and precision of the effect model for the chemicals concerned. In contrast, when the biological data (the VTG response) were plotted against the nominal concentrations, it proved to be highly reproducible. This strongly suggested that the occasional differences between nominal and measured concentrations were artifactual. For this reason, the concentration–response analyses were based on nominal, as opposed to measured, exposure concentrations.
The problems encountered with the chemical analyses were subsequently resolved, and good agreement between the nominal and actual exposure concentrations of each chemical was obtained in the mixture experiments. This is demonstrated in Table 1, which shows the measured concentration of all of the mixture components on the first day of each of the mixture experiments. These values were between 100 and 166% and 66 and 128% of the nominal value for EE2 and E2, and 64 and 128%, 50 and 110%, and 55 and 105% of nominal value for NP, OP, and BPA, respectively. Hence, the extent of the deviation from nominal concentrations did not vary consistently between chemicals, despite the differences between their exposure concentrations. The mean measured concentration of each chemical remained fairly constant over time: the measured concentrations of EE2, E2, NP, OP, and BPA 1 week and 2 weeks after the start of the exposure were an average of 99 and 77%, 89 and 92%, 92 and 96%, 84 and 97%, and 92 and 86% of those measured at the start of the exposure, respectively. Hence, the analytical data generally confirm that the exposure conditions were similar and reproducible for each of the chemicals used.
Biological effects data.
All exposure studies ran to completion. The rate of mortality did not differ between treatments, which indicated that the chemicals tested were not acutely toxic and that the fish were not unduly stressed. The baseline concentrations of VTG determined for control males and females were consistent with the literature (Harries et al. 2000; Panter et al. 1998; Tyler et al. 1996), and there were no significant differences between the VTG levels of WC and SC fish in any of the exposures. Clear concentration–response curves could be determined for male fish in response to each of the single chemicals as well as to the mixture. In contrast, female VTG levels exhibited extensive variability, depending on their stage in the spawning cycle (data not shown). For this reason, only the data from male fish were suitable for inclusion in the analyses.
Concentration–response analysis for individual chemicals.
The analysis of the concentration–response data for each chemical was based on data pooled from at least two independent exposure studies. In the case of OP and BPA, a third smaller-scale study was conducted. This was necessary because the first two exposures did not cover the full extent of the VTG response curve. In general, data from repeated studies showed excellent agreement, although there was some disparity between the positions of the curves for EE2 and, to a lesser extent, E2. This is likely to reflect the increased potential for error when working in the nanograms per liter concentration range. These findings support the need to base the prediction of mixture effects on more than one set of data using the means of repeated and pooled data sets.
Each of the chemicals tested induced VTG in a concentration-dependent manner. Figure 1 shows the concentration–response data for each chemical and their estimated regression curves. The corresponding best-fit models with estimated parameters are given in Table 2, together with the estimated EC50 values and the confidence limits, which were always below the planned tolerance benchmark of ± 0.2 on the log10-transformed concentration scale. It was possible to determine the 100% effect (relative to the PC) for each chemical, and the lowest tested concentration did not provoke effects significantly different from the untreated controls. This allowed the estimation of full concentration–response curves without needing to extrapolate to untested effect levels. Figure 2 shows the concentration–response curves for each chemical plotted on the same concentration scale, thus highlighting the magnitude of variations in potency. EE2 was the most potent chemical tested, with an EC50 of 0.9 ng/L, which was between 25 and 30 times more potent than E2. The EC50 of the natural steroid E2 was 25 ng/L. NP and OP were 280 and 1,800 times less potent than E2, with EC50 values of 7 and 45 μg/L, respectively. BPA was the least potent chemical tested, with an EC50 of 150 μg/L. This was 6,000 times less potent than E2.
Concentration–response analysis for the mixture.
The VTG response induced by the mixture is shown in Figure 3, together with the line of best fit and the curve predicted by CA. The variability associated with the best-fit estimate is shown in Table 2. A concentration–response curve was evident, and there was excellent agreement between the results of the two independent exposures. The pooled data sets provide sufficient information for EC estimates of low statistical uncertainty and thus a good basis for the comparative assessment of observed and predicted mixture effects. The comparison of the observed VTG response and the corresponding regression fit with the prediction curve yielded excellent agreement, independently of the effect level. No statistical deviation could be detected, with the prediction lying within the narrow 95% confidence limits along the full length of the curve. These findings provide evidence that estrogenic chemicals act in an additive manner in vivo and that their effects can be predicted accurately using CA.
Mixture effects at low-effect concentrations.
The results of the investigation into mixture effects for compounds at low-effect concentrations are shown in Figure 4. Analysis of the data revealed that, individually, each of the chemicals failed to provoke a response that was statistically different from that of the controls at a concentration that was equivalent to one-fifth of their EC50. In contrast, when fish were exposed to the same dose of all five chemicals in combination, VTG was significantly induced. In line with the first experiment, there was good agreement between the observed effect of the mixture and the prediction of CA, with the prediction falling within the confidence limits of the observed effects. This confirms that the combined action of estrogenic chemicals does not deviate from additivity even in the low-effect concentration range.
Discussion
Exposure concentrations.
The decision to determine the concentration–response relationships on the basis of nominal, as opposed to measured, exposure concentrations was made in order to overcome problems that were initially encountered with the analytical chemistry (discussed above). In theory, the measured concentrations should provide a more accurate reflection of the exposure conditions, because they account for experimental errors that may have arisen because of inaccuracies in the preparation of stock solutions and/or the dosing of tanks. As a result, the measured concentrations should provide the basis for the mathematical modeling of mixture effects. However, if problems occur when measuring the exposure concentrations, these can add more variability than they remove. This, in turn, reduces rather than improves the accuracy of the prediction. Hence, in the absence of a full set of reliable measured concentrations, it was more accurate to base the mathematical model on the nominal values.
This approach did not appear to reduce the reproducibility of the concentration–response analysis of NP, OP, and BPA. In contrast, the agreement between the concentration–response curves determined for E2 and EE2 in each of the repeated exposures was slightly reduced when the VTG response data were plotted against nominal, as opposed to measured, concentrations. However, these differences were marginal. This indicates that the nominal values provided a reliable indication of the real exposure concentrations and validates their use in the concentration–response analyses. This approach may not have used the chemical analytical data to their full potential. However, the determination of exposure concentrations was extremely useful in confirming the accuracy of the dosing system. Without this, it would not have been possible to validate the methods employed.
Single-substance effects.
Despite the plethora of published data describing the potency of the chemicals tested in this study, comparisons between studies are complicated by apparent differences between the species tested, the end points analyzed, and the assay systems used. However, comparable studies involving the analysis of VTG induction in male fathead minnows exposed to estrogenic chemicals under flow-through conditions have yielded results that are consistent with the effects reported here. For example, Panter et al. (1998) reported the induction of VTG in response to between 32 and 100 ng/L of E2 after a 3-week exposure, which is in the same order of magnitude as the potency observed in this study. EE2 has previously been found to induce VTG at concentrations between 0.1 and 1 ng/L (Pawlowski et al. 2004). This is consistent with the EC50 of 0.9 ng/L reported here. The potency of NP is also consistent with previous evidence that this chemical is effective at concentrations between 1 and 10 μg/L in fathead minnows after a 2- to 3-week exposure (Harries et al. 2000; Pickford et al. 2003). Studies by Sohoni et al. (2001) suggest that BPA is less potent, although the effects reported were of a similar order of magnitude as those observed in this study. Concentration–response data from comparable studies on the test species were not available for OP.
Differences between the relative potencies of each of the compounds tested in this study are also described in the literature. These data are reviewed in Table 3, which reflects the differences in the potency of each of the chemicals tested. The potency of each chemical relative to E2 also varied extensively between studies. The cause of this variability is unknown, but is likely to reflect differences between the exposure systems, the concentrations tested, and the effect levels used to determine potency. Differences in species sensitivity may have also influenced the patterns observed.
Mixture effects.
The results of the first mixture experiment demonstrate that mixtures of estrogenic chemicals have the capacity to act in combination and that their effects can be accurately predicted on the basis of the concentration–response curves of the individual mixture components according to the principles of CA. The predictions were in close agreement with the observed effects across the entire range of effects. Thus, we can conclude that the combined effect of the mixture does not deviate from additivity. This is consistent with the a priori assumption of this concept, which is dependent upon the components of the mixture acting via a common mechanism to contribute to the overall mixture effect. Although the validity of this concept has been demonstrated for estrogenic chemicals in assays involving unicellular organisms and mammalian cells (Payne et al. 2000, 2001; Rajapakse et al. 2002; Silva et al. 2002), these results provide the first evidence that the principles of CA hold true for multicomponent mixtures of estrogenic chemicals at higher organizational levels, despite the increased biological complexity of the assay system and the greater potential for toxicokinetic effects.
Similar additive effects have previously been reported in response to binary mixtures of estrogenic chemicals in vivo. Thorpe et al. (2001) investigated the effects of two-component mixtures on VTG induction in rainbow trout. Concentration–response curves were determined for fixed-ratio binary mixtures of E2 and NP (1:1,000) and of E2 and methoxychlor (MXC; 1:1,000), and these were related to the predictions of CA. The mixture of E2 and NP induced effects that were in agreement with the predictions of CA across the entire range of concentrations tested. In contrast, the mixture of E2 and MXC induced effects that were less than additive. This was attributed to the fact that MXC may act via a mechanism different from that of E2 and NP. Nevertheless, the effects observed provide strong evidence of the capacity for mixtures of similarly acting chemicals to behave in an additive manner according to the principles of CA. However, this conclusion was not confirmed in a subsequent investigation into the combined effects of E2 and EE2 (Thorpe et al. 2003). The effects of this mixture were consistent with CA at low-effect concentrations, but a divergence occurred with increasing effect level, with the predicted effects exceeding those that were observed. This was attributed to the limitations of the experimental design rather than being the result of a real deviation from additivity (Thorpe et al. 2003).
The problems encountered by Thorpe et al. (2003) were attributed to the fact that only three concentrations of the mixture were tested. This may have reduced the accuracy of the concentration–response relationship. An additional problem arose because of difficulties in defining the maximum response to the individual test compounds, as well as the maximum response predicted by CA. These difficulties were overcome in the present study by testing a wider range of mixture concentrations and by standardizing the response across exposures according to the minimum and maximum response of the controls. The accuracy with which these methods allowed the effects of the mixture to be predicted undoubtedly reflects the power of the mathematical modeling and statistical analyses. It also demonstrates the capacity for the VTG induction assay to produce high-quality, reproducible data for analyzing the mixture response.
Low-dose implications.
The additive nature of the combined effects observed in the first mixture experiment demonstrates that all components contribute to the overall effect of a mixture. This implies that the overall effects will always exceed the highest individual effect of the mixture components. By this line of reasoning, low-effect concentrations of the individual components may give rise to considerable mixture effects. This phenomenon is of particular importance for the environmental hazard assessment of chemicals because it indicates that concentrations of chemicals that show no effect when applied singly may provoke substantial effects when acting in combination. The second mixture experiment investigated whether these theoretical conclusions from the CA concept also hold true in the real world, by analyzing the combined effect of the mixture components when they were present at low, noneffective concentrations. Even under these circumstances, a highly significant mixture effect of more than 50% was observed. These in vivo results were consistent with the “something from nothing” effects reported by Silva et al. (2002), which were produced using in vitro techniques.
More recently, the potential for estrogenic mixture effects at low concentrations has been explored in vivo using an assay based on an increase in rat uterotrophic weight (Tinwell and Ashby 2004). Concentrations that individually induced low effects were determined for seven estrogenic chemicals. Equipotent concentrations were tested, both individually and in combination, at various concentrations. The highest concentration of the mixture induced a significant increase in uterine weight in relation to the effects produced by the individual chemicals (although this difference was marginal). At 5- and 10-fold dilutions, few of the individual chemicals induced a significant response, and at a 50-fold dilution, no significant responses were observed. However, the same dilutions of the mixture were found to induce a significant response, thereby demonstrating the potential for mixture effects, even when the effects of each individual chemical cannot be detected. Although these findings were not related to expectations based on additivity, they are in perfect agreement with the results of the present study. This provides strong evidence of the capacity for estrogenic chemicals to act in combination at higher levels of biological organization, even at the type of low-effect concentrations encountered in the environment.
Regulatory context.
Our findings in this study, combined with those of Tinwell and Ashby (2004), highlight the limitations of existing approaches to environmental (and human) risk assessment when considering the hazard posed by mixtures of endocrine-disrupting chemicals. Estrogenic chemicals, such as the alkylphenols, which are generally present in the environment as mixtures and at concentrations below those required to individually induce an effect, may therefore add to the overall risk when present with other chemicals that act via a similar mechanism. The failure to account for the combined effects of these chemicals will undoubtedly lead to the underestimation of potential hazards and hence erroneous conclusions regarding the risk that they pose. In demonstrating the inadequacy of the chemical-by-chemical approach to risk assessment, these findings represent a significant step toward achieving a more realistic means of assessing the environmental hazard posed by estrogenic chemicals. In addition to their regulatory implications, these findings indicate that CA may be a valuable tool for predicting the hazard posed by this type of mixture.
Research needs.
It is important to recognize that CA can be applied only when the mixture is completely defined in terms of the number of chemicals present and the mixture ratio. A predictive risk assessment of combination effects will therefore depend heavily on the generation of robust tools for analyzing the type of mixtures that occur in real exposure situations. It should also be acknowledged that the scope of these findings is limited to the assessment of chemicals that act via the same mechanism to induce a common effect. The next major challenge will be to consider the endocrine-disrupting effects of mixtures of chemicals that act via different modes of action, or that have both agonistic and antagonistic effects. Potential interactions with non-endocrine-active compounds, such as solvents and surfactants, should also be considered, along with the influence of additional stresses incurred via changes in the environment and organismal physiology. Although the task of integrating this body of knowledge into hazard assessment procedures presents a formidable challenge, these improvements will be essential in ensuring the adequate protection of wildlife populations and human health.
Figure 1 Pooled concentration–response data and best-fit regression curves for each of the individual mixture components. (A) EE2. (B) E2. (C) NP. (D) OP. (E) BPA. Each point represents the VTG response of one fish, with each color representing an independent exposure study. The solid line represents the best-fit curve, and the dashed lines represent the 95% confidence interval.
Figure 2 Best-fit regression curves for the individual mixture components plotted on the same concentration scale.
Figure 3 Comparison between the observed and CA-predicted mixture effects of five estrogenic chemicals in the male fathead minnow. Each point represents the VTG response of one fish, with each color representing an independent exposure study. The solid black line represents the best-fit of the observed effect data, and the solid red line represents the CA prediction. Dashed lines represent the 95% confidence intervals. The predicted effect of the mixture falls within the 95% confidence interval of the observed data across the entire dose–response curve.
Figure 4 Mixture effects at low-effect concentrations (one-fifth of EC50) of five estrogenic chemicals. Error bars indicate SEM. Individual concentrations were 0.12 ng/L EE2, 5 ng/L E2, 1.4 μg/L NP, 9 μg/L OP, and 30 μg/L BPA. The mixture treatment contained all five chemicals at the aforementioned concentrations, resulting in an overall mixture concentration of 40.4 μg/L. Analysis of variance detected a significant difference between treatments (F6,19 = 4.05, p < 0.01). Post hoc tests revealed no difference between the response of fish exposed to each of the chemicals individually and that of the control fish. In contrast, the mixture elicited a response that was significantly different from that of the controls.
Table 1 Nominal and measured exposure concentrations at the beginning of each mixture experiment.
EE2 (ng/L)
E2 (ng/L)
NP (μg/L)
OP (μg/L)
BPA (μg/L)
Concentration (mixture dilution) Nominal Measured Nominal Measured Nominal Measured Nominal Measured Nominal Measured
First mixture experiment
10.1 mg/L (5%) 0.03 0.03, 0.05 1.25 < 0.8, 1.3 0.35 0.4, 0.7 2.25 1.5, 2.4 7.5 4.1, 6.1
20.2 mg/L (10%) 0.06 0.07, 0.08 2.5 < 1.5, 2.6 0.7 0.7, 0.8 4.5 2.5, 5.1 15 9.6, 12
40.4 mg/L (20%) 0.12 0.14, 0.19 5 3.9, 4.9 1.4 0.9, 1.4 9 4.5, 8.2 30 19, 22
60.6 mg/L (30%) 0.18 0.23, 0.23 7.5 6.2, 9.0 2.1 2.3, 2.0 13.5 11, 12 45 43, 32
101 mg/L (50%) 0.3 0.31, 0.42 12.5 13, 16 3.5 3.5, 2.8 22.5 20, 14 75 79, 41
202 mg/L (100%) 0.6 0.6, 1.0 25 25, 28 7 7.1, 5.5 45 35, 32 150 150, 110
Second mixture experiment
40.4 mg/L (20%) 0.12 0.13 5 6 1.4 1.8 9 9.4 30 20
The measured values given for the first mixture experiment represent the concentrations determined during two independent exposure studies.
Table 2 VTG induction by the individual compounds and the mixture.
Concentration–response function
Compound Modela β̂1 β̂2 σ̂2between exp EC50 (95% CI)
EE2 Probit 5.03 1.65 0.29 0.0009 (0.0005–0.001)
E2 Probit 3.75 2.33 0.11 0.025 (0.020–0.029)
NP Logit −7.10 8.40 < 106 7.02 (6.05–8.56)
OP Weibull −6.37 3.57 < 106 48.2 (36.2–58.0)
BPA Probit −5.61 2.55 0.06 158 (119–205)
Mixture
Observed Weibull −6.61 3.71 < 106 48.0 (40.9–61.4)
Predicted CA — — — 44.3 (38.6–47.1)
CI, confidence interval. β̂1 and β̂2 are statistical estimates of model parameters; 95% CIs are approximate confidence intervals for effect concentrations given in μg/L; σ̂2 between exp is the statistical estimate for variance between experiments; and EC50 values are in relation to the NCs and PC, calculated from the given concentration–response function (rounded values).
a Concentration–response functions as defined by Scholze et al. (2001).
Table 3 Relative potencies previously reported for the five mixture components in terms of VTG induction.
Test organism Sex Exposure system Exposure duration (days) Effect level EE2 E2 NP OP BPA
Roach (Rutilus rutilus)a Male Flow-through 21 LOEC — 1 — 1,000 —
Rainbow trout (Oncorhynchus mykiss)a Male Flow-through 21 LOEC — 1 — 100 —
Zebrafish (Danio rerio)b Male Flow-through 8 LOEC 0.06 1 — — —
Sheepshead minnow (Cyprinodon variegatus)c Male Flow-through 16 LOEC 0.53 1 50 — —
Killifish (Fundulus heteroclitis)d Male Injection 8 LOEC — 1 20 200 100
Rainbow trout (Oncorhynchus mykiss)e Female (juvenile) Flow-through 14 EC50 0.04–0.09 1 1,000 — —
Zebrafish (Danio rerio)f Male Semistatic 21 LOEC > 0.25 1 25,000 5,000 50,000
Rainbow trout (Oncorhynchus mykiss)f Juvenile Semistatic 21 LOEC > 0.25 1 5,000 1,500 50,000
Fathead minnow (Pimephales promelas)g Male Flow-through 14 EC50 0.036 1 280 1,800 6,000
LOEC, lowest observed effect concentration. These data are scaled relative to the E2 potency observed in each study.
a Routledge et al. (1998).
b Rose et al. (2002).
c Folmar et al. (2003).
d Pait and Nelson (2003).
e Thorpe et al. (2001).
f Van den Belt et al. (2003).
g Present study.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7791ehp0113-00072915929896ResearchArticlesEmbedded Weapons-Grade Tungsten Alloy Shrapnel Rapidly Induces Metastatic High-Grade Rhabdomyosarcomas in F344 Rats Kalinich John F. 1Emond Christy A. 1Dalton Thomas K. 1Mog Steven R. 2Coleman Gary D. 3Kordell Jessica E. 1Miller Alexandra C. 1McClain David E. 11Heavy Metals Research Team and2Veterinary Sciences Department, Armed Forces Radiobiology Research Institute, Bethesda, Maryland, USA3Division of Veterinary Pathology, Walter Reed Army Institute of Research, Silver Spring, Maryland, USAAddress correspondence to J. F. Kalinich, Heavy Metals Research Team, AFRRI, 8901 Wisconsin Ave., Bethesda, MD 20889-5603 USA. Telephone: (301) 295-9242. Fax: (301) 295-0292. E-mail:
[email protected] work was supported in part by U.S. Army Medical Research and Materiel Command grant DAMD17-01-1-0821.
The views and opinions expressed in this report are strictly those of the authors and should not be construed as official U.S. Department of Defense policy.
The authors declare they have no competing financial interests.
6 2005 15 2 2005 113 6 729 734 24 11 2004 14 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Continuing concern regarding the potential health and environmental effects of depleted uranium and lead has resulted in many countries adding tungsten alloy (WA)-based munitions to their battlefield arsenals as replacements for these metals. Because the alloys used in many munitions are relatively recent additions to the list of militarily relevant metals, very little is known about the health effects of these metals after internalization as embedded shrapnel. Previous work in this laboratory developed a rodent model system that mimicked shrapnel loads seen in wounded personnel from the 1991 Persian Gulf War. In the present study, we used that system and male F344 rats, implanted intramuscularly with pellets (1 mm × 2 mm cylinders) of weapons-grade WA, to simulate shrapnel wounds. Rats were implanted with 4 (low dose) or 20 pellets (high dose) of WA. Tantalum (20 pellets) and nickel (20 pellets) served as negative and positive controls, respectively. The high-dose WA-implanted rats (n = 46) developed extremely aggressive tumors surrounding the pellets within 4–5 months after implantation. The low-dose WA-implanted rats (n = 46) and nickel-implanted rats (n = 36) also developed tumors surrounding the pellets but at a slower rate. Rats implanted with tantalum (n = 46), an inert control metal, did not develop tumors. Tumor yield was 100% in both the low- and high-dose WA groups. The tumors, characterized as high-grade pleomorphic rhabdomyosarcomas by histopathology and immunohistochemical examination, rapidly metastasized to the lung and necessitated euthanasia of the animal. Significant hematologic changes, indicative of polycythemia, were also observed in the high-dose WA-implanted rats. These changes were apparent as early as 1 month postimplantation in the high-dose WA rats, well before any overt signs of tumor development. These results point out the need for further studies investigating the health effects of tungsten and tungsten-based alloys.
cobaltembedded fragmentnickelratrhabdomyosarcomatungstentungsten alloy
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Tungsten has been used for many years in a variety of applications. Combining the hard, brittle tungsten metal with various other metals, including nickel and cobalt, produces tungsten alloys (WAs) with specific characteristics, some of which are of interest to the military. Recently, WAs have replaced lead in some small-caliber ammunition (the “green bullet”) [Oak Ridge National Laboratory (ORNL) 1998] and depleted uranium (DU) in kinetic-energy penetrators (ORNL 1996). Based on a small number of studies, prevailing theory is that elemental tungsten or insoluble tungsten compounds have only limited toxicity (Leggett 1997). For example, tungsten coils implanted into the subclavian artery of rabbits rapidly degrade, leading to elevated serum tungsten levels as early as 15 min after implantation. However, after 4 months, no signs of local or systemic toxicity were observed (Peuster et al. 2003). Studies on health effects of Ni and Co are more numerous. Intramuscular injections (28 mg) of soluble metallic Ni or Co result in formation of rhabdomyosarcomas at the injection site. With Ni, 100% of injected rats develop a tumor within 41 weeks (Heath and Daniel 1964), whereas administration of Co results in tumor formation in 40% of the rats with a latency period of 71 weeks (Heath 1954, 1956). However, intramuscular implantation of rods or pellets composed of various Ni or Co alloys used in orthopedic prosthetics results in no excessive tumor formation (Gaechter et al. 1977; Sunderman 1989). A variety of other Ni compounds, including nickel subsulfide, nickel oxide, and nickel monosulfide, have been tested for carcinogenic potential via intramuscular administration (Gilman 1962; Sunderman and Maenza 1976; Sunderman et al. 1977). Tumors (rhabdomyosarcoma and fibrosarcoma) were found in many cases at the injection site, with tumor yield dependent on solubility and concentration of the administered compound. It has been postulated that the yield of localized tumors is inversely related to the rate of solubilization of the Ni-containing compound (Kasprzak et al. 1983). This hypothesis does not appear to hold for Co compounds (Lison et al. 2001).
Metal alloys present additional problems when investigating health effects. The various metals comprising the alloy, as well as the method of production, can all factor into the overall health effect observed upon exposure. Investigations on hard-metal disease have shown that either tungsten carbide or Co alone has limited toxicity on lung tissue (Lasfargues et al. 1992). However, when combined, the tungsten carbide/cobalt mixture acts synergistically to increase the observed toxicity. It is not known whether this is due to the combined toxicity of the tungsten carbide/cobalt mixture or to an increase in the bioavailability of the known toxicant, Co (Lison and Lauwerys 1997). In vitro studies investigating malignant transformation of immortalized human cells by mixtures of tungsten, Ni, and Co suggest a synergistic effect that greatly exceeds the effects of the metals individually (Miller et al. 2001, 2002).
Advancements in metallurgy have led the military of many nations to replace DU in some armor-penetrating munitions and lead in small-caliber ammunition with various alloys of tungsten. One motivation for such a replacement is widespread public concern about the health and environmental impact of continued use of these metals. However, to our knowledge, none of these militarily relevant WAs has been tested for potential health effects, especially as embedded shrapnel. There is a growing list of health concerns related to tungsten exposure. Although a definitive link has not been established, several cancer clusters in the United States are associated with elevated levels of tungsten in the environment. Those findings, along with the results presented in this article, raise questions about the possible consequences of tungsten exposure. More important, these results raise extremely serious concerns over the potential health effects of WA-based munitions currently being used as nontoxic alternatives to lead and DU.
Materials and Methods
Rodents.
Male F344 rats (6 weeks of age; Harlan, Frederick, MD) were maintained in a facility accredited by the Association of Assessment and Accreditation of Laboratory Animal Care in accordance with the Guide for the Care and Use of Laboratory Animals (Institute of Laboratory Animal Resources 1996). All procedures, including euthanasia criteria (Tomasovic et al. 1988), were approved by the Armed Forces Radiobiology Research Institute’s (AFRRI) Animal Care and Use Committee. Upon arrival, animals were screened for common rodent pathogens. Rats were pair-housed in plastic microisolator cages with hardwood chips for bedding and fed a certified NTP-2000 (Quality Lab Products, Elkridge, MD) diet (Rao 1996) with acidified water provided ad libitum. Animals were on a 12-hr light/dark cycle with no twilight and were weighed weekly.
Pellets.
All metal pellets were cylinders 1 mm in diameter and 2 mm in length. Nickel (99.995% metallic Ni) and tantalum (99.95% Ta) pellets were purchased from Alfa Aesar (Ward Hill, MA). WA pellets were fabricated by Aerojet Ordnance Tennessee (Jonesborough, TN) using standard kinetic energy penetrator production processes. An average WA pellet weighed 27.5 mg and consisted of 91.1% tungsten, 6.0% Ni, and 2.9% Co. Ni and Ta pellets weighed 14 mg and 27 mg, respectively. Before implantation surgery, all pellets were cleaned and chemically sterilized (Pellmar et al. 1999).
Pellet-implantation surgery.
A rodent model system (AFRRI 1996), originally developed to mimic DU shrapnel loads seen in wounded personnel from the 1991 Persian Gulf War, was used to investigate the health effects of retained WA shrapnel. All rats were implanted with a total of 20 pellets split evenly between each hind leg. Experimental groups included Ta (negative control, 20 Ta pellets), low-dose WA (4 WA pellets and 16 Ta pellets), high-dose WA (20 WA pellets), and Ni (positive control, 20 Ni pellets). Tantalum was used as a negative implantation control because it is considered inert and has been used in human prostheses (Hockley et al. 1990; Johansson et al. 1990). Nickel, a known carcinogen, was used as a positive control (Costa and Klein 1999; Kasprzak et al. 2003). Rats were implanted at 9 weeks of age. For the pellet implantation procedure, anesthesia was induced by continuous administration of isoflurane using an open circuit system with a scavenger/recapture system. All surgery was done using aseptic techniques. After the surgical sites were clipped and cleansed with Betadine, an incision was made through the skin to expose the gastrocnemius muscle. Pellets were implanted in the muscle, spaced approximately 1.5 mm apart on the lateral side of each leg. The incision was closed with sutures and tissue adhesive. Rats were closely monitored after surgery until they were ambulatory. An analgesic (buprenorphine hydrochloride; Reckitt and Colman, Hull, UK) was administered preoperatively and then as needed postoperatively. The surgical sites were examined daily for signs of inflammation, infection, and local metal toxicity.
Experimental groups.
Our pellet implantation groups included Ta (negative control), WA (both a low- and high-dose group), and Ni (positive control). The original euthanasia time points were to be 1, 3, 6, 12, 18, and 24 months; however, because of the rapid tumor development, no WA- or Ni-implanted rat survived much past 6 months post-implantation. Final survival data therefore included rats originally assigned to the 12-, 18-, and 24-month experimental groups, whose animals died earlier than those designated time points. This resulted in group sizes of n = 46 for the Ta and both WA groups, and n = 36 for the Ni group. Hematologic assessments were conducted on the separate 1-, 3-, and 6-month WA implantation groups.
Pathology.
At various times postimplantation or when moribund, rats were euthanized by isoflurane overdose. A complete gross pathology examination was conducted, noting any abnormalities, and tissues were collected for analysis. Weights of representative tissues, including spleen, thymus, testes, kidney, and liver, were determined and normalized to body weight. Tissues for histopathology were fixed in buffered formalin, processed and embedded in paraffin, cut at 5–6 μm, mounted, and stained with hematoxylin and eosin (H&E). Immunohistochemical analysis was conducted on 5-μm-thick sections of formalin-fixed, paraffinized tissue. After deparaffination and rehydration, nonspecific binding was blocked with Power Block (Biogenex, San Ramon, CA). The tissue was then reacted with prediluted rabbit anti-desmin polyclonal antibody (Biogenex) and treated with biotinylated secondary anti-rabbit antibody (Biogenex). After blocking with hydrogen peroxide, the tissue sections were labeled with peroxidase-conjugated streptavidin (Biogenex) and aminoethyl carbazole (AEC; Biogenex) was used as a chromogen. Slides were then counter-stained with hematoxylin and mounted.
Hematology.
At euthanasia, we obtained blood for hematologic assessments from the abdominal aorta of isoflurane-anesthetized rats using a heparinized needle and sample tubes containing EDTA (Becton-Dickinson, Franklin Lakes, NJ). We determined white and red blood cell counts; hemoglobin; hematocrit; mean corpuscular volume, hemoglobin, and hemoglobin concentration; red cell distribution width; platelet counts and volume; and neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts with a Bayer Advia 120 Hematology Analyzer (Bayer Diagnostics, Terrytown, NY).
Results
All rats tolerated the pellet implantation procedure with no apparent adverse effects. The incision sites were examined daily; no rat showed any signs of infection from the surgery, or any discomfort postoperatively. Body weights were recorded weekly. Once they had recovered from the surgical procedure, all rats gained weight at equivalent rates. However, in the first week after the pellet implantation surgery, the rate of weight gain by the Ta and low-dose WA rats was slower than normal, and high-dose WA and Ni rats lost weight. This was followed by large weight gains in postimplantation week 2 in all experimental groups. There were no statistical differences in rate of body weight gain between any of the groups throughout the remaining experimental period. As previously reported, the implantation and retention of cylindrical metal pellets (1 mm ×2 mm) had no effect on locomotive abilities in rats (AFRRI 1996; Pellmar et al. 1999), nor did we observe any such difficulties in this study.
At approximately 16–20 weeks post-implantation, we began to observe tumors at the pellet implantation sites in the WA and Ni rats. In some high-dose WA animals, palpable tumors were apparent as early as 14 weeks postimplantation. Tumors developed rapidly in WA-implanted animals. The tumors were aggressive and fast growing, necessitating euthanasia of the animals several weeks later. On the basis of previously published literature (Heath and Daniel 1964), we expected the Ni-implanted positive control rats to develop tumors at the implantation site, but the speed at which the tumors developed was surprising: approximately 5 months after implantation. Figure 1 shows the percentage of surviving animals as a function of time after pellet implantation. Rats implanted with Ta pellets (n = 46) survived well beyond 12 months with no apparent health problems. All rats in the high- and low-WA and the Ni groups developed tumors and were euthanized upon becoming moribund. Rats in the high-dose WA group (n = 46) survived the least amount of time (mean survival time ± SD = 21.8 ± 2.1 weeks). Nickel-implanted animals (n = 36) and the low-dose WA group (n = 46) survived slightly longer, with mean (± SD) survival times of 25.4 ± 2.1 and 27.0 ± 4.6 weeks, respectively. The mean survival time of the high-dose WA animals was significantly shorter than that of the low-dose WA- or Ni-implanted animals [analysis of variance (ANOVA) followed by Dunnett’s test, p < 0.05]. The mean survival times of the low-dose WA- and the Ni-implanted animals were not statistically different from each other. The results reported here are part of a larger study that also investigated the health effect of embedded DU fragments. We did not observe tumor formation in the DU-implanted rats (Kalinich JF, Miller AC, McClain DE, unpublished data).
Upon euthanasia, the animals underwent necropsy, and tissue samples were taken for various analyses. Figure 2 shows the appearance of the hind limb of rats implanted with Ta (Figure 2A) or WA (Figure 2C) for 26 and 23 weeks, respectively, before surgical removal of the implanted pellets. The gross anatomy of the Ta-implanted leg is normal, whereas in the WA leg the tumor is clearly visible. Upon dissection, no obvious abnormalities were observed in the Ta-implanted animals, and the pellets could be easily removed (Figure 2B). However, in the WA-implanted animals, the pellets were surrounded by tumor (Figure 2D). In many cases, the interior of the tumor had become necrotic and/or hemorrhagic. Similar tumors were found for both WA- and Ni-implanted animals. In low-dose WA animals, tumors were found surrounding the WA pellets only. No tumors were found surrounding implanted Ta pellets. Implanted WA pellets rapidly oxidized and had a slightly eroded appearance. Ta pellets did not have an eroded appearance even after implantation for 6 months. However, despite their appearance, the WA pellets lost < 5% of their mass over this time.
Tumor tissue was histopathologically examined and characterized. Figure 3A shows the neoplastic cells surrounding the site of the implanted WA pellet. These cells infiltrated preexisting skeletal muscle fibers. Fibers that became isolated by this process degenerated and demonstrated a loss of cross-striations and internalization of nuclei (Figure 3B,C). Neoplastic cells were pleomorphic with marked anisocytosis and anisokaryosis (Figure 3D). In addition, an extremely high mitotic rate was observed in these cells, and bizarre mitoses were present. Immunohistochemical staining was used to determine the origin of these neoplastic cells. The cells were strongly positive for desmin (Figure 3E,F), suggesting a skeletal muscle origin.
In the WA-implanted animals, the tumors had metastasized to the lung. None of the Ni-implanted animals showed signs of lung metastases, although some exhibited endogenous histiocytic lipid pneumonia not seen in the WA animals. Figure 4A shows numerous metastatic foci in the lungs of a high-dose WA rat. These multiple masses obscure > 50% of the lung surface and up to 90% in the latter stages of development. Figure 4B shows a photomicrograph of these pulmonary metastases. Apparent is the multifocal, vascular orientation of these neoplasms. There are neoplastic cells surrounding the arterioles and bronchioles, expanding the alveolar septae, and replacing alveolar spaces. These neoplastic cells have a high mitotic rate and are often seen surrounding or occluding arterioles (Figure 4C). Figure 4D shows that the metastatic neoplastic cells, as well as vascular and airway smooth muscle, are strongly positive for the muscle marker desmin.
Selected hematologic and organ weight parameters for euthanized rats are shown in Table 1. The Ta data were obtained from rats implanted with Ta pellets for 6 months. The data for the remaining groups were obtained at the time the rats became moribund because of tumor development. No significant differences in organ/body weight ratios were seen for the low-dose WA- or Ni-implanted animals compared with Ta-implanted control rats. However, high-dose WA-implanted rats showed significantly higher spleen:body weight ratios compared with control rats. In addition, thymus:body weight ratios were decreased in the high-dose WA rats. Because the spleen and thymus are integral components of the immune system, these changes suggest that embedded WA, at certain levels, may be immunotoxic. The kidney:body weight ratio for high-dose WA rats was also significantly higher than that of Ta-implanted rats. High-dose WA rats euthanized 1 and 3 months after pellet implantation also exhibited significantly elevated spleen:body weight ratios compared with the appropriate Ta-implanted control rats (Tables 2 and 3). Thymus:body weight ratios, however, were not significantly different. At 3 months post-implantation, the kidney:body weight ratio in high-dose WA rats was significantly higher than that in Ta rats, but it was significantly lower at 1 month postimplantation. There were no 1- and 3-month Ni-implanted groups.
WA-implanted animals had significant changes in a number of hematologic parameters. Rats implanted with 20 WA pellets exhibited significant increases in white blood cell counts, red blood cell counts, hemoglobin, and hematocrit levels compared with Ta control rats, whereas rats implanted with 20 Ni pellets had significant decreases in red blood cell counts, hemoglobin, and hematocrit levels (Table 1). Hematologic parameters from low-dose WA rats were not statistically different from controls. Statistically significant increases in red blood counts, hemoglobin, and hematocrit levels were observed in high-dose WA animals as early as 1 month after pellet implantation and persisted throughout the experimental period (Tables 2 and 3). In addition, there were statistically significant increases in the numbers of neutrophils, lymphocytes, monocytes, and eosinophils present in high-dose WA animals. Low-dose WA animals had elevated neutrophil, lymphocyte, and monocyte numbers at 3 months post-implantation, but only the neutrophil numbers were statistically different from the controls at the 5–6 month euthanasia point. The Ni-implanted animals had significantly lower lymphocyte counts than the controls. All other parameters were statistically identical to the controls. These results suggest there is a dose-dependent perturbation in many hematology parameters as a result of an increasing WA pellet number.
Discussion
Tungsten-based alloys are currently being used as replacements for DU in kinetic-energy penetrators and for lead in small-caliber ammunition. However, the health effects of these unique alloys have not been investigated, especially in the case of embedded fragments such as shrapnel wounds. In this study, using male F344 rats and a system designed to investigate the effects of embedded metal fragments (AFRRI 1996), we have shown the embedded weapons-grade WA (91.1% W, 6.0% Ni, 2.9% Co) results in rapid tumor formation at the implantation site in 100% of the rats. The rate of tumor formation correlates with pellet number. Ni-implanted rats also develop tumors at the implantation site, although not as rapidly as seen with WA. Histopathologic and immunohistochemical data support a diagnosis of a pleomorphic rhabdomyosarcoma for both the WA- and Ni-induced leg tumors (Altmannsberger et al. 1985).
Rats implanted with 20 WA pellets (high-dose WA) showed significantly increased spleen:body weight ratios compared with Ta control rats. Low-dose WA rats (four WA pellets) also exhibited increased spleen:body weight ratios, but these increases were not statistically significant (ANOVA followed by Dunnett’s test). Values for Ni-implanted rats were identical to control rats. The spleen changes observed in the high-dose WA rats were apparent as early as 1 month after pellet implantation. Once again, low-dose WA rats showed increased, but not statistically significant, spleen:body weight ratios. With the exception of the spleen, the only other organ:body weight perturbations were seen in high-dose WA rats and included a decrease in thymus:body weight ratio at approximately 5 months and changes in kidney:body weight ratios. The 1-month kidney:body weight ratio for high-dose WA rats was significantly lower than control. However, from 3 months on, these ratios were significantly higher than control. It is possible that the lower kidney weights at 1 month postimplantation represent a toxic response to the heavy metals from the implanted pellets, but by 3 months and later, the kidney has begun to respond in a different manner. Although there were no gross abnormalities of the kidney at necropsy, we continue to investigate this observation.
A variety of hematologic changes were observed in WA- and Ni-implanted rats. Ni-implanted rats showed a significant decrease in red blood cells, hemoglobin, and hematocrit at the time of morbidity, indicating possible Ni-induced anemia. For low-dose WA rats the hematologic changes, including significant increases in red blood cells, white blood cells, hemoglobin, hematocrit, neutrophils, lymphocytes, and monocytes, peaked at 3 months postimplantation and returned to normal by 5–6 months. High-dose WA rats demonstrated the same changes observed in low-dose WA rats, but they occurred much more rapidly (as early as 1 month postimplantation) and persisted throughout the life of the animal. The splenomegaly and hematologic changes observed in these rats are suggestive of polycythemia. Cobalt has been used experimentally to induce polycythemia in rats (Endoh et al. 2000; Rakusan et al. 2001), although the concentration required is far greater than found in the WA pellets. In addition, the speed at which these hematologic changes occurred in the high-dose WA rats was also surprising. These results suggest a dose-dependent perturbation in many hematology parameters as a result of an increasing WA pellet number.
The search for munitions that are considered environmentally friendly yet still retain their military effectiveness has led to the appearance of many unique alloys on the modern battlefield. Often, decisions on the health consequences of exposure (inhalation, ingestion, wound contamination, etc.) to these specific alloys are based on studies that investigated only one specific metal of the alloy rather than the particular alloy in question. Tungsten-based munitions are a recent addition to many countries’ arsenals, primarily in response to the continuing concerns regarding the potential environmental and health effects of DU in kinetic-energy penetrators and of lead in small-caliber ammunition. For years, exposure to tungsten was thought to be of little consequence to health. In fact, tungsten is occasionally found as a minor component in some of the various alloys used to produce medical implant devices such as artificial hips and knees. The tungsten concentration in these alloys ranges from 5% to 15%. Because the alloy used in WA munitions usually contains > 90% tungsten, along with smaller amounts of other metals, it was also assumed that exposure to these alloys would present little or no health risk. As we have shown here, this is not the case in our rodent model. Embedded WA pellets not only resulted in aggressive, metastatic, pleomorphic rhabdomyosarcomas, but also caused significant hematopoietic changes well before the carcinogenic effect was observed. It seems unlikely that these adverse health effects can be attributed solely to the small amounts of Ni and/or Co present in the alloy. The tumors induced by the 100% Ni implants occurred later than those induced by the alloys containing 6% Ni. However, recent in vitro studies have demonstrated a synergistic effect in terms of damage when tungsten is present with these metals (Miller et al. 2001, 2002).
The mechanism of the effects reported here with embedded WA pellets remains unclear. Despite the fact that the smooth and impermeable surface of the pellets represent characteristics known capable of inducing foreign-body or solid-state carcinogenesis (Bates and Klein 1966; Brand et al. 1975), this process is unlikely to have occurred in our experiments because implanted Ta pellets of an identical geometry and surface resulted in no tumor formation. One possibility is that free-radical reactions at the interface of the pellet and tissue could result in damage leading to carcinogenesis. Recently, the role of tungsten in human health and disease has come under increased scrutiny. Environmental testing of the leukemia cluster around Fallon, Nevada, in the United States showed slightly elevated levels of several heavy metals including uranium and Co but significantly elevated levels of tungsten [Centers for Disease Control and Prevention (CDC) 2003]. Although no definitive link between elevated tungsten levels and cancer has been established, because of the uncertainty surrounding this issue, the U.S. National Toxicology Program recently added tungsten to their list of compounds to be assessed for adverse health effects. Further study of the health effect of tungsten and WAs is clearly indicated.
Figure 1 Survival times of pellet-implanted rats.
Figure 2 Effect of implanted WA pellets on F344 rats. (A) Gross appearance of Ta-implanted hind leg. (B) Dissected area around implanted Ta pellet (arrow indicates pellet). (C) Gross appearance of WA-implanted hind leg with tumor(s). (D) Dissected area around implanted WA pellet with tumor surrounding pellet (arrow indicates pellet). Bar = 2 cm.
Figure 3 Histopathologic examination of leg tumor surrounding WA pellet. (A) H&E-stained section of leg tumor from F344 rat showing WA pellet hole (P); bar = 500 μm. (B) H&E-stained tumor section showing neoplastic infiltration of preexisting muscle fibers (MF); bar = 200 μm. (C) H&E-stained tumor section showing neoplastic cells with numerous mitoses (arrows) and bizarre mitotic figures (BZ); bar = 100 μm. (D) H&E-stained tumor section showing pleomorphic cell (arrow); bar = 100 μm. (E) Desmin staining of leg tumor showing neoplastic cells (NC) and muscle fibers (MF); bar = 500 μm. (F) Desmin staining of neoplastic cells; bar = 50 μm.
Figure 4 Lung metastases from WA-implanted F344 rats. (A) Gross appearance of pulmonary metastases from WA-implanted rat (arrows indicate metastatic foci); bar = 1 cm. (B) H&E-stained section of pulmonary metastases (arrows); bar = 1 mm. (C) H&E-stained section of an occluded pulmonary arteriole [arrow indicates vascular smooth muscle wall (AW)] showing neoplastic cells with numerous mitoses (arrows); bar = 50 μm. (D) Desmin staining of pulmonary metastases (arrows); bar = 500 μm.
Table 1 Selected hematologic and organ weight parameters (mean ± SEM) for euthanized rats.
Ta WA (low) WA (high) Ni
White blood cells (103/μL) 3.19 ± 0.24 3.95 ± 0.43 4.56 ± 0.29* 2.56 ± 0.20
Red blood cells (106/μL) 8.32 ± 0.09 8.03 ± 0.19 10.10 ± 0.07** 7.46 ± 0.13**
Hemoglobin (g/dL) 14.50 ± 0.13 13.90 ± 0.36 16.46 ± 0.30** 12.95 ± 0.23**
Hematocrit (%) 41.77 ± 0.53 40.38 ± 0.96 50.18 ± 0.39** 38.12 ± 0.77**
MCV (fL) 50.22 ± 0.16 50.26 ± 0.28 49.71 ± 0.16 51.08 ± 0.66
MCH (pg) 17.46 ± 0.15 17.31 ± 0.13 16.30 ± 0.28** 17.35 ± 0.08
MCHC (g/dL) 34.77 ± 0.36 34.46 ± 0.32 32.81 ± 0.62** 34.05 ± 0.50
RDW (%) 12.54 ± 0.09 13.07 ± 0.11** 13.77 ± 0.09** 13.04 ± 0.16*
Platelets (103/μL) 562.00 ± 14.72 542.05 ± 14.27 467.50 ± 17.57** 487.18 ± 26.10*
MPV (fL) 9.93 ± 0.69 8.64 ± 0.52 10.13 ± 0.62 8.97 ± 0.52
Neutrophils (103/μL) 0.79 ± 0.05 1.03 ± 0.09* 1.31 ± 0.12** 0.78 ± 0.09
Lymphocytes (103/μL) 2.21 ± 0.18 2.42 ± 0.17 2.95 ± 0.23* 1.63 ± 0.12*
Monocytes (103/μL) 0.07 ± 0.01 0.09 ± 0.02 0.13 ± 0.02* 0.05 ± 0.01
Eosinophils (103/μL) 0.08 ± 0.01 0.08 ± 0.01 0.12 ± 0.01** 0.06 ± 0.01
Basophils (103/μL) 0.02 ± 0.00 0.03 ± 0.00 0.03 ± 0.00 0.02 ± 0.00
Spleen (mg/g bw) 2.18 ± 0.10 2.30 ± 0.08 2.60 ± 0.06** 2.17 ± 0.05
Thymus (mg/g bw) 0.86 ± 0.03 0.76 ± 0.04 0.70 ± 0.04* 0.74 ± 0.07
Liver (mg/g bw) 29.21 ± 0.28 29.39 ± 0.24 28.77 ± 0.35 29.52 ± 0.39
Kidney (mg/g bw) 5.13 ± 0.06 5.13 ± 0.06 5.36 ± 0.05* 5.15 ± 0.08
Testes (mg/g bw) 7.31 ± 0.07 7.20 ± 0.08 7.40 ± 0.10 7.21 ± 0.14
Abbreviations: bw, body weight; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MPV, mean platelet volume; RDW, red blood cell distribution width. Data represent mean ± SEM of 20 observations (10 for Ni group).
* p < 0.05, and
** p < 0.01 compared with the Ta control group by one-way ANOVA followed by Dunnett’s test for group mean comparisons.
Table 2 Selected hematologic and organ weight parameters (mean ± SEM) for rats implanted with metal pellets for 3 months.
Ta WA (low) WA (high)
White blood cells (103/μL) 2.88 ± 0.20 4.06 ± 0.14** 4.01 ± 0.21**
Red blood cells (106/μL) 7.48 ± 0.06 8.48 ± 0.15* 9.10 ± 0.70**
Hemoglobin (g/dL) 12.90 ± 0.09 15.48 ± 0.35* 17.29 ± 0.15**
Hematocrit (%) 38.10 ± 0.27 42.14 ± 0.73* 44.79 ± 0.62**
MCV (fL) 50.96 ± 0.45 49.70 ± 0.09 48.87 ± 0.39
MCH (pg) 17.26 ± 0.12 18.27 ± 0.17 17.65 ± 0.12
MCHC (g/dL) 33.84 ± 0.35 36.71 ± 0.31** 35.89 ± 0.31**
RDW (%) 12.82 ± 0.33 12.68 ± 0.12 13.61 ± 0.09**
Platelets (103/μL) 513.20 ± 38.36 585.11 ± 35.87 568.29 ± 8.82
MPV (fL) 9.58 ± 1.13 9.14 ± 0.59 11.74 ± 0.51
Neutrophils (103/μL) 0.62 ± 0.04 0.79 ± 0.03* 0.91 ± 0.08*
Lymphocytes (103/μL) 2.10 ± 0.16 3.06 ± 0.14* 2.82 ± 0.17*
Monocytes (103/μL) 0.04 ± 0.01 0.07 ± 0.01* 0.08 ± 0.01*
Eosinophils (103/μL) 0.09 ± 0.01 0.09 ± 0.01 0.09 ± 0.01
Basophils (103/μL) 0.01 ± 0.00 0.01 ± 0.00 0.02 ± 0.00
Spleen (mg/g bw) 2.07 ± 0.03 2.16 ± 0.03 2.50 ± 0.03**
Thymus (mg/g bw) 0.73 ± 0.03 0.84 ± 0.03 0.70 ± 0.04
Liver (mg/g bw) 30.58 ± 0.33 31.00 ± 0.33 30.27 ± 0.31
Kidney (mg/g bw) 5.43 ± 0.06 5.73 ± 0.23 5.76 ± 0.04**
Testes (mg/g bw) 8.34 ± 0.12 8.21 ± 0.46 8.42 ± 0.18
Abbreviations: bw, body weight; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MPV, mean platelet volume; RDW, red blood cell distribution width. Data represent mean ± SEM of 15 observations.
* p < 0.05, and
** p < 0.01 compared with the age-matched Ta control group by one-way ANOVA followed by Dunnett’s test for group mean comparisons.
Table 3 Selected hematologic and organ weight parameters (mean ± SEM) for rats implanted with metal pellets for 1 month.
Ta WA (low) WA (high)
White blood cells (103/μL) 3.86 ± 0.20 3.81 ± 0.14 3.86 ± 0.21
Red blood cells (106/μL) 7.84 ± 0.08 7.74 ± 0.07 8.50 ± 0.07**
Hemoglobin (g/dL) 13.65 ± 0.15 14.81 ± 0.16 15.84 ± 0.14**
Hematocrit (%) 40.15 ± 0.42 39.66 ± 0.50 43.29 ± 0.35**
MCV (fL) 51.20 ± 0.14 51.22 ± 0.31 50.98 ± 0.19
MCH (pg) 17.41 ± 0.05 19.12 ± 0.09 18.64 ± 0.19**
MCHC (g/dL) 34.01 ± 0.12 37.37 ± 0.29 36.56 ± 0.41**
RDW (%) 12.21 ± 0.11 12.69 ± 0.11 14.18 ± 0.18**
Platelets (103/μL) 646.50 ± 18.76 641.00 ± 17.97 756.20 ± 43.48*
MPV (fL) 7.91 ± 0.40 8.56 ± 0.39 9.90 ± 0.55*
Neutrophils (103/μL) 0.65 ± 0.04 0.79 ± 0.05 0.81 ± 0.04**
Lymphocytes (103/μL) 3.04 ± 0.18 2.85 ± 0.13 2.90 ± 0.18
Monocytes (103/μL) 0.06 ± 0.00 0.06 ± 0.01 0.07 ± 0.00
Eosinophils (103/μL) 0.07 ± 0.01 0.08 ± 0.01 0.05 ± 0.00*
Basophils (103/μL) 0.02 ± 0.00 0.02 ± 0.00 0.01 ± 0.00
Spleen (mg/g bw) 2.37 ± 0.06 2.42 ± 0.05 2.73 ± 0.04**
Thymus (mg/g bw) 1.07 ± 0.03 1.14 ± 0.04 1.06 ± 0.03
Liver ((mg/g bw) 34.47 ± 0.26 34.31 ± 0.22 34.18 ± 0.61
Kidney (mg/g bw) 6.17 ± 0.08 6.06 ± 0.06 5.91 ± 0.05*
Testes (mg/g bw) 10.10 ± 0.16 9.86 ± 0.13 9.98 ± 0.11
Abbreviations: bw, body weight; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MPV, mean platelet volume; RDW, red blood cell distribution width. Data represent mean ± SEM of 15 observations.
* p < 0.05, and
** p < 0.01 compared with the age-matched Ta control group by one-way ANOVA followed by Dunnett’s test for group mean comparisons.
==== Refs
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Heath JC Daniel MR 1964 The production of malignant tumors by nickel in the rat Br J Cancer 18 261 264 14189681
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Miller AC Mog S McKinney L Luo L Allen J Xu J 2001 Neoplastic transformation of human osteoblast cells to the tumorigenic phenotype by heavy-metal tungsten-alloy metals: induction of genotoxic effects Carcinogenesis 22 115 125 11159749
Miller AC Xu J Prasanna PGS Page N 2002 Potential late health effects of the heavy metals, depleted uranium and tungsten, used in armor piercing munitions: comparison of neoplastic transformation and genotoxicity using the known carcinogen nickel Mil Med 167 120 122 11873492
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Sunderman FW Jr Maenza RM 1976 Comparisons of carcinogenicities of nickel compounds in rats Res Commun Chem Pathol Pharmacol 14 319 330 940963
Sunderman FW Jr Maenza RM Alpass PR Mitchell JM Damjanov I Goldbalatt PJ 1977 Carcinogenicity of nickel subsulfide in Fischer rats and Syrian hamsters after administration by various routes Adv Exp Med Biol 91 57 67 605854
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7636ehp0113-00073515929897ResearchArticlesEffects of Ambient Ozone Exposure on Mail Carriers’ Peak Expiratory Flow Rates Chan Chang-Chuan Wu Tsung-Huan Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei, TaiwanAddress correspondence to C.-C. Chan, Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Room 1447, 1st Section, No. 1 Ren-ai Rd., Taipei 100, Taiwan. Telephone/Fax: 886-2-2322-2362. E-mail:
[email protected] authors declare they have no competing financial interests.
6 2005 14 3 2005 113 6 735 738 5 10 2004 14 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The extent to which occupational exposure to ozone in ambient air can affect lung function remains unclear. We conducted a panel study in 43 mail carriers by measuring their peak expiratory flow rates (PEFRs) twice daily for 6 weeks in 2001. The daily exposure of each mail carrier to O3, particulate matter < 10 μm in aerodynamic diameter (PM10), and nitrogen dioxide was estimated by one air monitoring station in the center of the mail carrier’s delivery area. Hourly concentrations of air pollutants during their exposure periods were 6–96 ppb for O3, 11–249 μg/m3 for PM10, and 14–92 ppb for NO2. Linear mixed-effects models were used to estimate the association between air pollution exposures and PEFR after adjusting for subject’s sex, age, and disease status and for temperature and humidity. We found that night PEFR and the deviation in night PEFR were significantly decreased in association with 8-hr O3 exposures with a lag 0–2 days and by daily maximum O3 exposures with a lag of 0–1 day in our multipollutant models. By contrast, neither PM10 nor NO2 was associated with a PEFR reduction. Daily 8-hr mean concentrations of O3 had greater reduction effects on PEFR than did daily maximum concentrations. For a 10-ppb increase in the 8-hr average O3 concentration, the night PEFR was decreased by 0.54% for a 0-day lag, 0.69% for a 1-day lag, and 0.52% for a 2-day lag. We found that an acute lung function reduction occurs in mail carriers exposed to O3 concentrations below current ambient air quality standards and occupational exposure limits.
deviationlung functionmail carrierozone exposurepeak expiratory flow rate
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Epidemiologic evidence suggests that exposures to short-term ambient ozone are associated with consistent and reversible decrements in lung function among children (Burnett et al. 2001; Chen et al. 1999; Hoppe et al. 2003; Jalaludin et al. 2000), the elderly (Hoppe et al. 1995, 2003), and people with a history of respiratory diseases (Hoppe et al. 1995, 2003; Jorres et al. 1996; Kehrl et al. 1999). Recent studies also found that exposures to O3 are related to healthy adults’ decreases in lung function, such as forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and peak expiratory flow rate (PEFR) (Kinney and Lippmann 2000; Korrick et al. 1998; Naeher et al. 1999; Spektor et al. 1988). These effects usually occur at ambient O3 concentrations between 30 and 80 ppb during high O3 hours between 0900 and 1700 hr. Such O3 concentrations are lower than the U.S. ambient air quality standards for O3, which are an 8-hr average at 80 ppb and a 1-hr maximum at 120 ppb, and below the permissible exposure level for workers promulgated by the U.S. Occupational Safety and Health Administration (2004), which is an 8-hr time-weighted average of 100 ppb. Incidentally, the exposure duration between 0900 and 1700 hr described in previous studies happens to be the time when most mail carriers travel door to door to deliver mail and packages in Taiwan. Daytime ambient O3 concentrations these mail carriers experience, therefore, are expected to be very close to their occupational exposures. Because potential health effects due to this particular exposure scenario have not been reported before, we conducted this study to assess whether exposure to O3 at concentrations below current permissible levels will reduce mail carriers’ lung function.
Materials and Methods
Study population.
The study group consisted of 43 mail carriers who were randomly selected from 215 full-time mail carriers working in a main post office of Taichung City, Taiwan. To cover a service area of approximately 10 km2 and a half million residents, these mail carriers use either motorcycles or bicycles to deliver mail from 0900 to 1700 hr daily on preassigned delivery routes. A face-to-face questionnaire survey was performed in advance in September 2001 to obtain data from each mail carrier, including age; height; weight; smoking status; disease history of doctor-diagnosed asthma, bronchitis, and pneumonia; and incense burning and environmental tobacco smoke (ETS) exposures at home. Our field study took place from 14 November to 31 December 2001. The Institutional Review Board of National Taiwan University College of Public Health approved the research protocol, and written informed consent was obtained from each participant.
Lung function measurement.
We chose PEFR as the outcome variable for lung function because it is highly correlated with FEV1 in clinical diagnosis (Nowak et al. 1982) and widely used in epidemiology studies (Jalaludin et al. 2000; Krzyzanowski et al. 1992; Naeher et al. 1999; Peters et al. 1999). Each mail carrier was provided with a Midget peak expiratory flow meter (Medget Quan-ding Inc., Taipei City, Taiwan) to measure morning PEFR after awakening and night PEFR between 1000 and 1200 hr daily. Each mail carrier was trained to take three consecutive PEFR readings in the standing position in each measurement. The PEFR measurement was considered valid when the variation of three consecutive readings was < 10%. The best value of three readings was selected for use in further analysis. Our PEFR measurements were conducted between 14 November and 31 December 2001. The PEFR data of the first 3 days were used solely to validate our study subjects’ PEFR measuring technique and were not used in further data analyses. A daily maximum PEFR and daily deviation of PEFR for both morning and night PEFR data were used as outcome variables in our statistical models. Daily deviation of PEFR was defined as the difference between the daily highest PEFR reading and the 6-week average PEFR calculated according to the methods of Pope and Dockery (1992). We present here only the findings of night PEFR to keep our results as concise and informative as possible.
Monitoring of ambient air pollutants.
To estimate the daily exposure of each mail carrier to air pollutants, we abstracted hourly air pollution levels of O3, particulate matter < 10 μm in aerodynamic diameter (PM10), and nitrogen dioxide from one air monitoring station in the center of each mail carrier’s delivery area according to their daily working hours. The air monitoring station operated in Taichung City, Taiwan, by the Taiwan Environmental Protection Administration (2005) also provided hourly meteorologic data on wind direction, wind speed, temperature, dew point, and precipitation. The locations of the air monitoring station and post office in this study are shown in Figure 1. The environmental data were not used in further data analyses if there were > 20% of hourly values missing in a single day. The 8-hr average and maximum values for O3, NO2, and PM10 between 0900 and 1700 hr were calculated from the data obtained from this monitoring station to represent each subject’s daily exposures to air pollutants. We also summarized meteorologic variables of temperature and relative humidity for the same time segments.
Statistical methods.
We used a two-step statistical model to estimate the association between PEFR and O3 exposures. Multiple linear regressions (MLR) without air pollutants were first used to screen key PEFR-related personal covariates with a p-value < 0.25 for further analyses according to the methods of Peters et al. (1999) and Krzyzanowski et al. (1992). In the second step, linear mixed-effects models were used to estimate the pollution effects on PEFR adjusting for personal and meteorologic variables. Such mixed-effects models have the advantage of adjusting for invariant variables by fixed-effects models and accounting for individual differences by random-effects models. We treated subject’s sex; age; body mass index; history of diagnosed respiratory disease; smoking status; air pollutants O3, PM10, and NO2; ambient temperature; and relative humidity as fixed effects and each subject as a random effect in the mixed-effects models. Each of the three air pollutants considered was first put into the linear mixed-effects models separately as single-pollutant models. All of the three pollutants were then jointly put into the linear mixed-effects models as multipollutant models. Air pollution levels with 0- to 3-day lags were used to estimate the time course of pollution effects. Statistical analyses were performed using general additive procedures in the S-PLUS 2000 program (MathSoft Inc., Cambridge, MA, USA). Model selection was based on minimizing Akaike’s information criterion (Akaike 1974).
Results
Study population.
As shown in Table 1, there were 39 (91%) males and 4 females (9%) among the 43 mail carriers who participated in the study. The average age was 39 years, and employment duration averaged 13 years. Fifteen (35%) male mail carriers were current smokers. Only a few subjects had a history of doctor-diagnosed respiratory diseases, such as asthma (n = 0), bronchitis (n = 2), and pneumonia (n = 1). Among 43 mail carriers, 15 (35%) were exposed to incense-burning smoke and 9 (21%) were exposed to ETS in their homes.
Levels of ambient air pollutants and meteorologic parameters.
Table 2 summarizes the O3, PM10, and NO2 concentrations, temperature, and relative humidity. The 8-hr average concentrations (mean ± SD) of air pollutants during the study period were 36 ± 12 ppb for O3, 75 ± 38 μg/m3 for PM10, and 30 ± 10 ppb for NO2. The meteorologic conditions were generally mild during the study period with an 8-hr daytime temperature of 19 ± 3°C and a relative humidity of 72 ± 7%. Hourly concentrations of air pollutants in the exposure period were 6–96 ppb for O3, 11–249 μg/m3 for PM10, and 14–92 ppb for NO2 during mail carriers’ exposure periods. Pearson correlation coefficients among air pollutants and meteorologic parameters are shown in Table 3. The O3 level was not significantly correlated with the other two pollutants, but PM10 was highly correlated with NO2 (r = 0.85). Temperature was also moderately correlated with relative humidity (r = 0.46).
PEFR parameters, and O3.
We included sex, age, disease history, temperature, relative humidity and smoking status in the mixed-effects models because our MLR models without air pollutants found that these covariates were associated with PEFR. By contrast, covariates of incense burning and ETS were not included in our second-step models because they were not significantly associated with PEFR. Table 4 lists the results of single-pollutant mixed-effects models separately for O3, PM10, and NO2. Only O3 was consistently associated with decreases in night PEFR and the deviation in night PEFR among these three air pollutants. The night PEFR of the mail carriers was significantly reduced in association with 8-hr average O3 concentrations with 0- to 2-day lags and maximum O3 concentrations during exposure periods with 0- to 1-day lags. The deviation in night PEFR was reduced in association with both 8-hr and maximum O3 concentrations with 0- to 2-day lags. Instead of consistent correlation between O3 and PEFR, we found NO2 effects on both night PEFR and night PEFR deviation at the 2-day lag only, and no PM10 effects on either night PEFR or night PEFR deviation.
We then put O3, PM10, and NO2 with 0- to 3-day lags in the multipollutant mixed-effects models to estimate the pollution effects on decrease in PEFR by adjusting co-pollutants and key meteorologic factors. We found that O3 was associated with PEFR after adjusting for PM10, NO2, and other covariates. By contrast, PEFR reduction was not associated with either PM10 or NO2 in the multipollutant models. As shown in Figure 2A, night PEFR and deviation in night PEFR were significantly decreased by O3 exposures up to a 2-day lag after adjusting for co-pollutants and key personal covariates. Night PEFR was decreased by 0.54% for 0-day lag, 0.69% for 1-day lag, and 0.52% for 2-day lag. Compared with 8-hr O3, 1-hr O3 had comparatively less effect on decreasing night PEFR, which was 0.36% for 0-day lag and 0.44% for 1-day lag. As shown in Figure 2B, the effect of O3 exposure on the deviation in night PEFR had the same time course as its effects on night PEFR. However, the effects of O3 exposure on the deviation in night PEFR were smaller compared with its effects on night PEFR for the same time lag. Our multipollutant mixed-effects models thus showed that ambient 8-hr O3 concentrations had greater and longer effects on decreasing PEFR than did maximum O3 concentrations during exposure periods. No other covariate except ambient temperature was significantly related to night PEFR and the deviation in night PEFR in our multipollutant mixed-effects models. In addition, subjects’ disease history, including asthma, bronchitis, and pneumonia, had a negative but statistically insignificant influence on PEFR in our multipollutant mixed-effects models. We also found similar O3 effects on morning PEFR deviation but not morning PEFR in our multipollutant mixed-effects models (data not shown).
Discussion
This is the first study to demonstrate that there are effects of occupational O3 exposures lagged 0–2 days on reducing mail carriers’ lung function. Such effects can be detected by using either PEFR or PEFR deviation as an indicator of lung function. After occupational exposures during daytime work, night PEFR measurements seem to be more sensitive to O3 exposures than are morning PEFR measurements. Because none of our study subject’s daily O3 exposure exceeded the hourly standard of 120 ppb, our study supports previous findings from studies in the United States and Canada of a dose–response relationship between lung function change and O3 exposure at relatively low daytime ambient concentrations for healthy adults. Exercising healthy adults in New York City (USA) who were exposed to < 80 ppb O3 were reported to have a 0.55-L/min decrease in their PEFR per 1 ppb O3 (Spektor et al. 1988); healthy women exposed to 8-hr O3 at 54 ppb in Connecticut and Virginia (USA) were reported to have a 0.083-L/min/ppb decrease in their PEFR per 1 ppb O3 (Naeher et al. 1999); farm workers in Fraser Valley (Canada) who were exposed to a 1-hr daily maximum O3 of 40 ppb were reported to have 3.3-mL and 4.7-mL decreases in their FEV1.0 and FVC, respectively, per 1 ppb O3 (Brauer et al. 1996). A similar dose–response relationship between O3 and PEFR reduction was also reported in some European studies. Male cyclists in the Netherlands who were exposed to < 60 ppb O3 were reported to have 0.57-L/min decreases in PEFR per 1 ppb O3 (Brunekreef et al. 1994); healthy workers and athletes in Germany who were exposed to < 80 ppb O3 were also reported to have decrements in their FEV1 (Hoppe et al. 1995). Our study also further confirmed that time-weighted O3 exposures had greater effects on decreasing lung function than did daily peak concentrations as reported in previous studies (Castillejos et al. 1992; Jalaludin et al. 2000).
Several limitations in our study should be noted. First, the personal O3 exposures of mail carriers were not directly measured in this study but were represented by ambient monitoring data. However, the use of fixed-site monitoring data to represent personal O3 exposures was not expected to bias our results because the delivery areas of each mail carrier were located within 5 km of the fixed-site monitoring station in this study, and previous studies have shown relatively high spatial representativeness of ambient O3 measurements in similar urban environments (Chan and Hwang 1996; Romieu et al. 1998). The lack of personal exposure data could misclassify mail carriers’ actual O3 exposures. It has been reported that exposures misclassification can produce biases in both directions for outcomes with multiple risk factors and where exposures are correlated (Zeger et al. 2000; Zeka and Schwartz 2004). Therefore, we cannot entirely rule out the effects of PM10 and NO2 on reducing mail carriers’ PEFR in this study. PM10 does not distribute throughout an air shed as thoroughly as O3, and its use may have introduced more exposure misclassification for that pollutant. This may partially explain the lack of an observed effect on PEFR by relatively high acute PM10 exposures in this study. Another potential confounding factor of our findings was that some unmeasured air pollutants, such PM2.5 and volatile organic compounds from tailpipe emissions, could also have been responsible for lowering lung function rather than O3 alone in our study.
Despite these limitations, our data generally support the finding that a lung function reduction occurred among mail carriers exposed to daily O3 concentrations below current ambient air quality standards and occupational exposure limits. O3 is a strong oxidant that can induce pulmonary function impairment at low levels via several toxicologic mechanisms. For example, O3 can trigger the neutral receptors of the airway by inducing lipid peroxidation and the production of cycloxygenase (Hazucha et al. 1996) or increase respiratory allergy or reduce resistance to respiratory tract infections by suppressing TH1 cells in the immune system (Van Loveren et al. 1996). More recently, O3 exposure was found to induce mild and moderate respiratory response among children in Taipei by causing DNA breaks and impairing pulmonary cells (Cheng et al. 2003). Because O3 pollution is still widespread in major metropolitan areas worldwide, more studies are needed to elucidate clinical significance of O3 effects on lung function at low exposure levels, especially for susceptible populations.
Figure 1 Map of Taichung City.
Figure 2 Percent changes in night PEFR (A) and night PEFR deviation (B) per 10 ppb for 8-hr O3 and maximum O3. Error bars indicate mean ± SD.
Table 1 Basic characteristics of 43 mail carriers participating in the study (PEFR measurement period from 17 November through 31 December 2001).
Characteristic Male Female Total
No. of subjects (%) 39 (91) 4 (9) 43
Age [years (mean ± SD)] 38.1 ± 9.6 39.7 ± 4.4 39 ± 8
Work [years (mean ± SD)] 12.2 ± 6.7 11.3 ± 0.5 13 ± 6
Height [cm (mean ± SD)] 169.0 ± 4.9 160.4 ± 8.4 167.9 ± 5.5
Weight [kg (mean ± SD)] 66.8 ± 9.6 62.8 ± 5.3 65.8 ± 7.1
Disease history
Asthma [n (%)] 0 (0) 0 (0) 0 (0)
Bronchitis [n (%)] 2 (5) 0 (0) 2 (5)
Pneumonia [n (%)] 1 (3) 0 (0) 1 (2)
Smoking status
Current smoker [n (%)] 15 (38) 0 (0) 15 (35)
Nonsmoker [n (%)] 24 (57) 4 (100) 28 (60)
ETS at home [n (%)] 9 (23) 0 (0) 9 (21)
Incense burning at home [n (%)] 13 (33) 2 (50) 15 (35)
No. of PEFR measurements 986 87 1,073
Table 2 Summarized statistics for air pollutants and meteorologic data during the study period (14 November through 31 December 2001).
Variable No. Mean ± SD Minimum Maximum
8-hr average during exposure periodsa
O3 (ppb) 44 35.6 ± 12.1 7.6 65.1
PM10 (μg/m3) 43 74.7 ± 37.9 19.1 213.8
NO2 (ppb) 43 30.0 ± 10.1 17.3 65.9
Temperature (°C) 45 19.1 ± 3.4 12.2 24.2
Relative humidity (%) 45 71.5 ± 6.6 59.0 88.0
Maximum during exposure periods
O3 (ppb) 44 52.6 ± 18.8 5.6 95.5
PM10 (μg/m3) 43 106.8 ± 44.8 11.4 249.0
NO2 (ppb) 43 52.9 ± 21.8 14.0 91.6
a Mail carriers’ exposure periods are about 8 hr between 0900 and 1700 hr every working day.
Table 3 Pearson correlation coefficients for air pollutants and meteorologic data during the study period (14 November through 31 December 2001).
Pearson correlation coefficients O3 PM10 NO2 Temperature Relative humidity
O3 1.000
PM10 0.211 1.000
NO2 0.093 0.854** 1.000
Temperature 0.010 0.402** 0.353* 1.000
Relative humidity −0.413** 0.088 −0.063 0.460** 1.000
* p < 0.05;
** p < 0.01.
Table 4 Regression coefficients (95% CIs) of individual pollutants on PEFR estimated by single-pollutant linear mixed-effects models.
8-hr average for exposure period
Hourly maximum for 8-hr exposure period
O3 PM10 NO2 O3 PM10 NO2
Night PEFR
Lag 0 −0.33* (−0.44 to −0.18) 0.02 (−0.03 to 0.07) 0.09 (−0.06 to 0.23) −0.20* (−0.26 to −0.08) −0.01 (−0.03 to 0.06) −0.01 (−0.09 to 0.05)
Lag 1 −0.38** (−0.50 to −0.22) 0.04 (−0.03 to 0.06) 0.19 (0.04 to 0.34) −0.22* (−0.26 to −0.08) 0.01 (−0.04 to 0.04) 0.08 (−0.02 to 0.15)
Lag 2 −0.32* (−0.42 to −0.15) −0.04 (−0.10 to −0.01) −0.26 (−0.46 to −0.10) −0.17 (−0.23 to −0.04) −0.05 (−0.05 to 0.01) −0.18* (−0.27 to −0.10)
Lag 3 −0.22 (−0.34 to −0.05) 0.02 (−0.01 to 0.07) 0.08 (−0.11 to 0.25) −0.09 (−0.17 to 0.00) −0.02 (−0.06 to 0.01) 0.08 (−0.02 to 0.17)
Night PEFR deviation
Lag 0 −0.32* (−0.43 to −0.18) −0.00 (−0.04 to 0.04) 0.11 (−0.03 to 0.25) −0.19* (−0.27 to −0.11) −0.02 (−0.05 to 0.02) −0.01 (−0.08 to 0.06)
Lag 1 −0.38** (−0.51 to −0.26) 0.02 (−0.03 to 0.06) 0.17 (0.02 to 0.32) −0.20* (−0.29 to −0.12) −0.02 (−0.05 to 0.02) 0.06 (−0.01 to 0.13)
Lag 2 −0.32* (−0.44 to −0.19) −0.07 (−0.12 to −0.03) −0.26 (−0.41 to −0.11) −0.16* (−0.25 to −0.08) −0.04 (−0.07 to 0.00) −0.18* (−0.25 to −0.11)
Lag 3 −0.22 (−0.35 to −0.09) 0.01 (−0.04 to 0.05) 0.06 (−0.10 to 0.22) −0.11 (−0.20 to −0.03) −0.01 (−0.04 to 0.02) 0.07 (0.00 to 0.15)
* p < 0.05;
** p < 0.01.
==== Refs
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Van Loveren H Steerenberg PA Garssen J Van Bree L 1996 Interaction of environmental chemicals with respiratory sensitization Toxicol Lett 86 163 167 8711768
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7694ehp0113-00073915929898ResearchArticlesExposures among Pregnant Women near the World Trade Center Site on 11 September 2001 Wolff Mary S. 1Teitelbaum Susan L. 1Lioy Paul J. 2Santella Regina M. 3Wang Richard Y. 4Jones Robert L. 4Caldwell Kathleen L. 4Sjödin Andreas 4Turner Wayman E. 4Li Wei 2Georgopoulos Panos 2Berkowitz Gertrud S. 11Mount Sinai School of Medicine, New York, New York, USA2Environmental and Occupational Health Sciences Institute, Robert Wood Johnson Medical School–University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey, USA3Mailman School of Public Health, Columbia University, New York, New York, USA4Centers for Disease Control and Prevention, Atlanta, Georgia, USAAddress correspondence to M.S. Wolff, Mount Sinai School of Medicine, Box 1057, 1 Gustave L. Levy Place, New York, NY 10029 USA. Telephone: (212) 241-6173. Fax: (212) 996-0407. E-mail:
[email protected] thank L.L. Needham, R. Callan, Z. Liu, J. Golub, K. Nichols, K. Yamada, L. Spellman, R. Osborne, C. Dodson, and P.B. Olive for their generous and valuable contributions to this study.
This research was supported by National Institute of Environmental Health Sciences (NIEHS) grants P42ES07384 and P30ES09089 and by the September 11th Fund created by the New York Community Trust and United Way of New York City. In addition, the Centers for Disease Control and Prevention provided support for laboratory measurements of metals, organochlorines, and polybrominated diphenylethers. Support from the Environmental and Occupational Health Sciences Institute for the exposure characterization was derived from a U.S. Environmental Protection Agency University Partnership (CR827033) and an NIEHS Center Grant supplement (ES05022-1551).
The authors declare they have no competing financial interests.
6 2005 10 2 2005 113 6 739 748 25 10 2004 27 1 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We have characterized environmental exposures among 187 women who were pregnant, were at or near the World Trade Center (WTC) on or soon after 11 September 2001, and are enrolled in a prospective cohort study of health effects. Exposures were assessed by estimating time spent in five zones around the WTC and by developing an exposure index (EI) based on plume reconstruction modeling. The daily reconstructed dust levels were correlated with levels of particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5; r = 0.68) or PM10 (r = 0.73–0.93) reported from 26 September through 8 October 2001 at four of six sites near the WTC whose data we examined. Biomarkers were measured in a subset. Most (71%) of these women were located within eight blocks of the WTC at 0900 hr on 11 September, and 12 women were in one of the two WTC towers. Daily EIs were determined to be highest immediately after 11 September and became much lower but remained highly variable over the next 4 weeks. The weekly summary EI was associated strongly with women’s perception of air quality from week 2 to week 4 after the collapse (p < 0.0001). The highest levels of polycyclic aromatic hydrocarbon–deoxyribonucleic acid (PAH-DNA) adducts were seen among women whose blood was collected sooner after 11 September, but levels showed no significant associations with EI or other potential WTC exposure sources. Lead and cobalt in urine were weakly correlated with ∑EI, but not among samples collected closest to 11 September. Plasma OC levels were low. The median polychlorinated biphenyl level (sum of congeners 118, 138, 153, 180) was 84 ng/g lipid and had a nonsignificant positive association with ∑EI (p > 0.05). 1,2,3,4,6,7,8-Heptachlorodibenzodioxin levels (median, 30 pg/g lipid) were similar to levels reported in WTC-exposed firefighters but were not associated with EI. This report indicates intense bystander exposure after the WTC collapse and provides information about nonoccupational exposures among a vulnerable population of pregnant women.
dustexposure indexmetalPAHparticulatePBDEPCBpolychlorinated dibenzodioxinspolychlorinated dibenzofuranspregnantWTCWTC plume
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Depending on the World Trade Center (WTC) source characteristics and the date after 11 September 2001, emissions from the WTC site during September 2001 through early 2002 affected lower Manhattan as well as immediately adjacent areas in Brooklyn and New Jersey. Environmental and personal air monitoring, dust analyses, and biomonitoring have identified that dust and air particulates and fibers, alkaline dust, and polycyclic aromatic hydrocarbons (PAHs) were greatly elevated in the first days after 11 September, with some contaminants remaining above background for 1–3 months (Lioy et al. 2002, in press; McGee et al. 2003; Swartz et al. 2003). Higher atmospheric levels, compared with contemporaneous or pre-11 September measures in mid-Manhattan, have been found for a number of chemicals, including volatile hydrocarbons (benzene, xylene, tetrachloro-ethylene), metals [lead (Pb), antimony (Sb)], and semi- and nonvolatile chlorinated hydrocarbons [e.g., polychlorinated dibenzofurans (PCDF)] (Lioy et al. 2002; Offenberg et al. 2003). Individual exposures have been assessed among firefighters and construction workers who were working close to the site on and after 11 September (Edelman et al. 2003). However, bystander exposures have not been described, although adverse health effects have been reported (Trout et al. 2002). Air-monitoring data for both particulate mass (e.g., Figure 1) and PAHs (Landrigan et al. 2004; Thurston et al. 2003) showed elevations within the first month after 11 September, with intermittent excursions after that time ascribed to meterologic conditions as well as operational changes at the site (Dalton 2003).
We have characterized and estimated WTC-related exposures among 187 women who were pregnant and were at or near the WTC on 11 September or within the subsequent 3 weeks. A preliminary report on birth outcomes in this group has been published (Berkowitz et al. 2003b).
Materials and Methods
In 2002, we established a prospective epidemiologic study of 187 women who were pregnant and located within or near the WTC on or about 11 September. The great majority of the women recruited to this study were self-referred on the basis of extensive media publicity surrounding our investigation. Additional participants were located by sending letters to nearly 3,000 obstetricians in the greater New York City area, distributing flyers in lower Manhattan, and advertising in local newspapers. Eligibility criteria were pregnancy on 11 September 2001 or shortly thereafter and presence in one of five “exposure” zones at or near the WTC at 0900 hr (n = 170) on that day or within the next 3 weeks (n = 17). The five exposure zones were a) the area including and surrounding the WTC site bordered by Murray Street (north), Nassau Street (east), the Battery (south), and the Hudson River (west); b) the area of Manhattan south of Chambers Street excluding zone 1; c) south of Canal Street and north of Chambers Street; d) Brooklyn Heights; and e) the easternmost part of New Jersey across the Hudson River from the WTC (Figure 2). The women were evenly distributed (28–34%) for trimester of pregnancy on 11 September.
We used questionnaires and a time–activity log to help characterize exposures from 11 September through 8 October 2001. The questionnaire included items on socio-demographic characteristics; medical and pregnancy history; employment; potential home, work, and food exposures to PAHs (i.e., grilled foods and cigarette smoke); evacuation on 11 September through the dust and debris; perception of air quality (PAQ) after 11 September; and presence of WTC dust in the home. The time–activity log recorded all the time spent indoors and outdoors at any distinct street address in one of five zones around the WTC for 4 weeks after 11 September (Figure 2). These zones were defined by five different areas at increasing distances from WTC, which represented likely high exposures to emissions and dust depending on the location and intensity of the WTC plume (emissions) and dust impact areas. The time spent outside any of the five zones was not recorded but could be computed by subtraction. In separate analyses, no differences in exposure patterns were seen between indoors and outdoors, so we used total time spent in the exposure analyses. The time–activity logs were geocoded using Arc GIS software (ESRI, Inc., Redlands, CA) based on each street address and daily time spent within these zones.
Beginning in February 2002, we obtained blood and urine specimens from 178 women, 55 of whom were in the third trimester of pregnancy (the remainder had delivered), to determine levels of PAH-DNA adducts and other biomarkers of exposure. For 160 samples, DNA was successfully obtained from mononuclear cells by standard RNase and proteinase K treatment and phenol/chloroform extraction. Mononuclear cells have higher adducts than do granulocytes (Jahnke et al. 1990; Savela and Hemminki 1991), and thus this method should allow detection of adducts in women who were most heavily exposed to WTC fire emissions or dust. The half-life of PAH-DNA adducts in total white blood cells has been reported to be 3–5 months (Mooney et al. 1995), and the half-life of adducts in mononuclear cells should be no shorter than in total white blood cells. PAH-DNA adducts were measured by a competitive ELISA with chemiluminescence end-point detection (Divi et al. 2002). Results were the average of triplicate measurements. For 7% of samples with sufficient DNA, samples were reassayed. Repeat analysis of three positive controls resulted in a coefficient of variation of 22%. Samples with inhibition < 20% were considered nondetectable. The limit of detection (LOD) was approximately 40 adducts per million nucleotides (40 apmn), based on 20% inhibition and 10 μg DNA assayed per well. PAH-DNA adducts were categorized as below the LOD, < 60 apmn (median of detectable adducts), ≥60 apmn, and > 100 apmn. This upper category was derived from a plot showing a marked shift in the cumulative distribution at this level (> 92%).
In a randomly selected subset of 100 women, the Centers for Disease Control and Prevention (CDC) determined that these women had metals in urine and blood and organochlorines [OCs; included 40 polychlorinated biphenyls (PCBs), 7 polychlorinated dibenzodioxins (PCDDs), 10 PCDFs, and 8 polybrominated diphenylethers (PBDEs) in plasma. Random selection was done by assigning a random number obtained using the SAS function (SAS Institute, Inc., Cary, NC) to all 187 women, sorting on the random number, and choosing the first 100 women. Metals were measured in whole blood and urine using published methods (Date and Gray 1989; Guo and Baasner 1993; Nixon et al. 1999; Paschal et al. 1998). OCs and PBDEs were analyzed using high-resolution gas chromatography/isotope-dilution high-resolution mass spectrometry (Patterson et al. 1991; Sjödin et al. 2004). Serum total lipids were calculated using the values for triglycerides and cholesterol assayed with an enzymatic method (Akins et al. 1989). Urinary creatinine was assayed using a kit from Sigma (St. Louis, MO). The median was 0.43 g/L, and the maximum 1.9 g/L; two values were < 0.01 g/L. Urine metals are presented uncorrected to allow comparison with previously reported data, but adjustment for creatinine was done in multivariate models by including it as a covariate.
Limits of detection for each biomarker are given in each table in “Results.” For metals and PBDE, laboratory values were censored at the LOD. In parametric analyses, we substituted the value LOD/√2 for unreported metal levels. Because we focused on OCs with levels above the LOD in > 50% of the samples, univariate analyses of OC concentrations [four PCBs (∑PCB4), two PCDDs, and two PCDFs] changed very little whether LOD values were assigned a censored value (LOD/√2) or the actual value was reported. However, the measures of central tendency for concentrations of ∑PCB4/total PCBs were influenced by choice of surrogate value for the LOD. Few samples had ∑PCB4 congeners below the LOD, but many samples had values below the LOD for other PCBs. As a result, the ∑PCB4: total PCB ratio decreased artificially when a surrogate value (LOD/√2) was used. The mean ∑PCB4: total PCB ratio increased by 40% and the median by 50% when using LOD/√2 rather than zero or the actual value below the LOD. Therefore, we used the actual values (which were all positive) or the lowest positive value (to avoid zeroes) of congeners for the sum of PCBs (see discussion and citations in Berkowitz et al. 2003a; Fitzgerald et al. 2004). We could not use this approach for PBDEs because the lowest positive value was usually higher than the lowest reported LOD value. Therefore, because individual LOD values were given, we used the median LOD of all samples and substituted the median LOD/√2 for LOD samples in parametric analyses.
A daily dust exposure index (EI) was estimated for each woman. The EI each day was derived by reconstruction of the post-11 September WTC plume. The Computational Chemodynamics Laboratory (a research unit of the Exposure Measurement and Assessment Division of the Environmental and Occupational Health Sciences Institute, University of Medicine and Dentistry New Jersey) developed simulations of the plume that was generated by the collapse of the WTC buildings and the subsequent fires at the site (Huber et al. 2004).
The plume reconstruction was obtained by generating a set of simulations that were the result of employing the Regional Atmospheric Modeling System (RAMS)/Hybrid Particle and Concentration Transport (HYPACT) model, which is employed as part of the Modeling Environment for Total Risk (MENTOR) developed by the Environmental and Occupational Health Sciences Institute. The RAMS/HYPACT model was used to reconstruct the atmospheric dispersion of “generic” particulate matter (PM) emitted from the location of the WTC. This simulation employed a triple-nested modeling domain of 4 × 4 km (grid 1), 1 × 1 km (grid 2), and 250 × 250 m (grid 3) resolutions. The RAMS/HYPACT results were averaged over time to produce 8-hr concentrations for five zones across lower Manhattan, Brooklyn, and New Jersey. A source decay factor was incorporated into each 8-hr concentration average estimate, corresponding to a sigmoidal weakening function. This was done to simulate the decrease in the intensity of the fires and other emission sources during the days after 11 September.
The RAMS/HYPACT model accounts for advection, convection, and dispersion of emissions after the collapse of the towers, but is not constructed to model the initial wave of dust and debris. A geographic information system (GIS)-based database developed by the U.S. Environmental Protection Agency [EPA; U.S. Geological Survey (USGS) 2001] from overflight photos and satellite images provides approximate boundaries of dust/debris, allowing characterization of the extent of the area of deposition from the collapse of the WTC buildings (U.S. EPA 2002; USGS 2001). The GIS data were superimposed on the zones of concern to determine the relative impact of the dust and debris wave for use in the development of the EI for 11 September. The available images show that the dust/debris field completely covered zones 1 and 2 from 11 through 13 September 2001. During this same time period, zone 3 was also heavily covered to an area within 300 feet of zone 2 boundaries. Most remaining suspended dust was settled and/or washed away after a rain event on 14 September. For zone 1, zone 2 and the first 300 feet within zone 3 (nearest the zone 2 boundary), the airborne concentrations from resuspended dust/debris on 11 through 13 September were estimated to be approximately equal to two-thirds of the maximum plume concentration. In the remainder of zone 3, this estimate was one-third of the maximum plume concentration (in each case, the maximum plume concentration is the maximum of the 8-hr average). These weighting factors were derived from visual inspection/interpretation of the available images of the WTC plume and debris and the relative intensity of plume and dust.
Reflecting the RAMS/HYPACT model start time of 0900 Eastern Daylight Savings Time, only two 8-hr averages exist for 11 September. Therefore, the total number of hours per person was set to be ≤16 for 11 September. Because the time–activity diary for an individual does not include the specific hours spent in a particular zone in a particular day, the data sets cannot be used to directly estimate exposure based on the time of day. As a result, the daily average plume density in each zone was combined with the time–activity diary entries (i.e., time spent in each zone) to develop an average EI.
The EI accounts for time spent both indoors and outdoors. Various indoor/outdoor (I/O) concentration penetration factors have been reported and used for microenvironmental models. These values typically range from 0.05 to 0.9 (Burke et al. 2001; Thornburg et al. 2001; Vette et al. 2001). To account for the type of building ventilation system (residential/nonresidential) and a particle size range, an I:O ratio value of 0.15 was used.
The EI was calculated for 187 women for 11 September and each of the 27 days immediately thereafter. Because the exact emission amount resulting from the WTC collapse is unknown, a hypothetical unit pollutant release rate was used in the RAMS/HYPACT simulation. The simulation with a unit release provides the relative “normalized” concentration of airborne contaminants at different locations. These calculations result in a dimensionless EI. Because the values are relative, identical EIs on different days may not represent exactly equivalent daily exposures. However, the potential misclassification was small enough to justify their summation. This was shown by the correlation of the daily median EI with EPA PM monitoring data at four sites (see Results). In addition, for most women, their exposure sums were dominated by the value on 11 September or on a few other days with very high readings, because of the logarithmic nature of the EI distribution. Therefore, for the purposes of estimating total exposure, the indoor and outdoor EIs have been summed over certain time periods, with ∑EI being the 28-day (4-week) sum. If additional monitoring data become available from ongoing data analyses, the current EIs can be analyzed to determine if there is reason to improve the estimates and more accurately calibrate the EIs.
Statistical analyses were conducted using Microsoft (Redmond, WA) Excel and SAS PC (version 9; SAS Institute Inc.). The EIs were explored as continuous variables [daily values, weekly sums, and 28-day total (∑EI) for each woman] and as quantiles (dichotomized at the median). The significance of associations among categorical variables was assessed by chi-square analysis, using the Fisher exact test and the Mantel-Haenszel test for trend where appropriate. Because the exposure variables were not normally distributed, nonparametric methods (Wilcoxon rank-sum test and Kruskal-Wallis test) were used to test for differences in the medians between groups. Spearman correlations (rS) were used to examine associations between continuous variables. In addition, we used multivariate analyses to predict biomarker levels of those analytes that demonstrated any association with ∑EI [Pb, mercury, octachlorodibenzodioxin (OCDD), ∑PCB4]. For these models, the log-transformed biomarker level was the dependent variable, and the independent variables were EI (dichotomized below or above the median ∑EI) and factors that might potentially affect selected biomarkers (age, body mass index, pregnancy at blood draw, breast-feeding, smoking, race, and fish intake). We used the general linear models program (PROC GLM) in SAS with the LSMEANS option to determine whether the biomarker level differed significantly by ∑EI (below vs. above the median).
Results
Within this cohort of pregnant women, most participants were located north and southeast of the WTC in zones 1–3 at 0900 hr on 11 September (Figure 2). Most women (133 of 187, 71%) were located within eight blocks (zones 1 and 2), including 12 women who were in a WTC tower or the complex underneath. The locations of participants at 0900 hr on 11 September were 40.6% in zone 1, 30.5% in zone 2, 17.6% in zone 3, 1.6% in zone 4, 0.5% in zone 5, and 9.1% who were not there but who reentered the area within the succeeding weeks.
Regardless of their exact location at 0900 hr on 11 September, most women either lived or worked in zones 1–5 before 11 September (33 women both lived and worked there, 38 lived there, and 113 worked there; 3 neither worked nor lived there). Therefore, although we do not have data on average time spent in these zones before 11 September, it is likely to be much higher than the durations reported in the 4 weeks after 11 September. As shown in Figure 3A, women reported spending little time in zones 1–3 immediately after 11 September, with time spent gradually increasing thereafter, with notable dips on the weekends. During the first week after 11 September, 37% of women spent some time in zones 1–3, and the percentage rose over the next 3 weeks (42, 61, and 70%). During the month after 11 September, women spent more time in zones 2 and 3 than in zone 1, which was quite inaccessible at this time. The average duration in zone 3 was 1.2 hr/day in the week immediately after 11 September, rising gradually to 3.3 hr/day for the third work week (Monday through Friday) after 11 September (mean hours per day by zone are shown in Figure 3A); time spent in zones 1 and 2 was similar in later weeks but lower than in zone 3 in the early weeks. Over the same period, average time in zone 1 was 0.2 hr/day in week 1 and 1.2 hr/day in week 3. Patterns of time spent in zones 1–5 did not differ materially with regard to whether women lived and/or worked in lower Manhattan.
In contrast to the increasing time spent in zones 1–3, the relative dust concentrations within the five zones declined rapidly during the 28 days after 11 September (Figure 3B). However, marked fluctuations in the dust and emission levels existed, consistent with changing construction, traffic, and weather patterns over this period (Dalton 2003; Landrigan et al. 2004). The individual EIs in Figure 3C were computed by combining individual-level data of the type shown in Figures 3A and B. The daily EIs exhibit a steep decline during the 3 days after 11 September, lower values during the weekends after that, and a slight shift toward increasing levels (but well below those of the first 3 days) in weeks 3 and 4, consistent with the reported time spent in zones close to the WTC in Figure 3A. The daily relative dust levels in zone 1 (Figure 3B) were correlated with PM concentrations at four stations close to the WTC that monitored PM after 22 September 2001 (Columbia University 2003) [Chambers Street: PM ≤10 μm in aerodynamic diameter (PM10), r = 0.91; PM ≤2.5 μm in aerodynamic diameter (PM2.5), r = 0.93; 290 West Broadway: PM10, r = 0.73; West Broadway–Park Place: PM2.5, r = 0.68; n = 7–15 per site]. Using the models of reported PM data tested for performance against daily EI (Figure 3B), we estimated the daily median PM2.5 or PM10 levels in zone 1 by extrapolation to have been > 1,000 μg/m3 on 11 September and > 100 μg/m3 from 12 September though 16 September 2001 in some locations (data not shown).
Sociodemographic factors did not differ by the ∑EI (28-day total) among participants (Table 1). The cohort has a mean age of 35 years and is largely white, married, highly educated, and nonsmoking. Women who both worked and lived in lower Manhattan on 11 September (n = 33) had a higher ∑EI than did women who only worked (n = 113), only lived (n = 38), or spent time for other reasons in one of the zones (n = 3) (Table 1). Answers to exposure-related questions about when and where women lived were strongly correlated with ∑EI, as expected from the congruity of these questions with data in the time–activity log (selected exposures are presented in Table 1). Notably, evacuation through the immediate area on 11 September led to a higher ∑EI. The median day’s EI for 11 September was 3.2 among 85 women who evacuated through the WTC debris; the 11 September EI was higher among women who remained in the debris for ≥40 min (median EI, 3.7 on 11 September; n = 40) than among those who stayed for 1–39 min (median EI, 2.6 for 11 September; n = 45) or not at all (median EI, 1.3; n = 102; p < 0.0001). Women who wore dust masks during cleaning also had higher EIs, although the reported presence of dust in the home was not associated with EI.
The pattern of EI versus time as shown in Figure 3C is explored in relation to PAQ in Table 2. Here, in week 1, the 7-day EI sum was not related to PAQ, and this was probably because few women reported spending much time in the area during week 1 (39 of 185, 21%). During weeks 2–4 after 11 September, a strong association was observed between PAQ and weekly EI sum, because women who worked or lived in lower Manhattan returned to the area (Table 2). The association between PAQ and ∑EI existed for all women in weeks 2–4 and among just those women who reported spending most of their time in zones 1–5 (i.e., 49% in week 2, 69% in week 3, 77% in week 4; n = 39, 92, 127, and 143, respectively).
PAH-DNA adducts were measured in 160 women. Most were nondetectable (88 of 160, 55%); the median of detectable values was 60 apmn. Eleven women had levels higher than 100 apmn, and nine of these women had blood drawn in February or March 2002 (Table 3), a time interval within the reported clearance times of PAH-DNA adducts from lymphocytes (Mooney et al. 1995). Samples collected in February/March had a significantly greater number of detectable PAH adducts (46 apmn; 64%) and a higher median value (46.7 apmn) compared with later samples (26 apmn, 30%; median, 20.0 apmn; p < 0.0001; Table 3). There were no consistent associations between PAH adducts and EI, when considered overall or by various temporal windows of blood draw and EI weekly sum. Neither ∑EI nor PAH adducts were associated with dietary intake of foods that may contain PAHs (e.g., broiled meat) or smoking, questions included in the questionnaire for this purpose.
Thirteen urinary metals as well as Pb, cadmium, and Hg in blood were measured in a subset of 100 women. After comparing the percentage detected and the median values of the urinary levels in our data with data from earlier reports (CDC 2003; Edelman et al. 2003), in Table 4 we present findings on urinary Sb, Cd, Pb, and uranium and blood Pb and Cd, for which levels were reported among firefighters exposed at the WTC (Edelman et al. 2003). We also included urinary cobalt because levels were weakly correlated with ∑EI. We included blood Hg because levels in the women were higher than in the National Health and Examination Study (NHANES) data and because Hg was reported as a possible WTC site contaminant (Edelman et al. 2003). None of the other urinary metals was associated with ∑EI, either among all women or among the February–March biospecimen collections. Moreover, the observed correlations with ∑EI, which were significant among 100 urine samples (rS = 0.20 for Co and 0.21 for Pb; p < 0.05), were not significant among the February–March biospecimens (rS = 0.13 and 0.12, respectively; p > 0.3; n = 44). The median values of four urine metal concentrations in Table 4 were lower than those reported in WTC-exposed or control firefighters (urinary Co was not reported). The medians of Co, Cd, and uranium were lower than among females in the NHANES data (CDC 2003), whereas urinary Pb was higher (Table 4). Blood Cd was significantly correlated with ∑EI (rS = 0.29, p = 0.01), but the comparison with ∑EI above and below the median was not significant for any of these (Kruskal-Wallis test). No significant associations of blood metals with exposure were seen among the February–March biospecimens.
Selected organohalogen compounds (40 PCBs, 7 PCDDs, 10 PCDFs, 8 PBDEs) were measured in blood plasma in the same randomly selected subset of 100 women as were the metals. Data on specific PCBs, PCDFs PCDDs, and PBDEs presented in Table 5 were chosen because they were detected in > 50% of our samples, they were reported in WTC debris (PCBs, pentachlorodibenzofuran, PBDEs; Litten et al. 2003; Offenberg et al. 2003), or they were elevated in serum among firefighters (Edelman et al. 2003). PCB and OCDD levels were not significantly higher among women whose ∑EIs were above the median, and none of the other OCs or PBDEs differed significantly with respect to median ∑EI. In comparison with other reports, the median heptachlorodibenzodioxin level (30 pg/g) was similar to the adjusted geometric mean reported in exposed firefighters (28 pg/g lipid; Edelman et al. 2003). Heptachlorodibenzofuran levels were higher in exposed fire-fighters, but no levels were reported. 2,3,4,7,8-Pentachlorodibenzofuran was below the LOD in 59% of our sample. Two other OCs were detectable in > 50% of the women, including 1,2,3,6,7,8-hexachlorodibenzodioxin (median, 22 pg/g) and OCDD (median, 224 pg/g); levels were not reported for the firefighters. There were no significant associations between PBDEs and ∑EI.
We performed multivariate analyses to evaluate the association of analytes that showed some pattern of association with ∑EI, by computing the geometric means of the biomarker (dichotomized above vs. below the ∑EI median) adjusted for age, nonwhite race, being currently pregnant, breast-feeding, body mass index, and smoking. We also adjusted models for PCBs and Hg for fish intake. We found no significant differences in the adjusted geometric means with regard to dichotomized ∑EI for PCBs, OCDDs, PBDEs, Pb (blood and urine), Cd, or Hg among all 100 women tested or among the 44 women who donated blood samples in February–March 2002.
Discussion
The predominant air pollutant at the WTC was dust composed of large-size particles (> 10 μm) (Lioy et al. 2002). Air monitoring conducted in the vicinity of the WTC after 25 September 2001 revealed elevated levels of PM10 and PM2.5 as close as six blocks northeast of the site (Figure 1). Daily PM2.5 levels measured five blocks east of the site from 14 September through 16 October 2001 were 15–60 μg/m3 (Landrigan et al. 2004). At the end of September 2001, when more air monitoring had commenced, median levels of PM2.5 were > 60 μg/m3 (one to two blocks away). At Chambers Street (six blocks north-northwest), the median PM10 level was 43 μg/m3 and the median PM2.5 level was 10 μg/m3 in October 2001. At 290 Broadway (six blocks northeast), the median PM2.5 level was 22 μg/m3 in late September (n = 4 days) and 15 μg/m3 in October, whereas the median PM10 levels were 32 and 30 μg/m3, respectively, during those time periods. However, daily excursions of PM10 and PM2.5 levels near or higher than 100 μg/m3 occurred periodically, depending on site operations, weather, and traffic (Columbia University 2003; Dalton 2003; Vette et al. 2001). Some distance away at Canal Street (15 blocks or about 0.7 miles north), levels were lower (median PM2.5, 14.3 μg/m3 in September, 12.5 μg/m3 in October 2001). At Public School 64 (about 50 blocks northeast), PM2.5 levels were 11.2 μg/m3 in September and 13.8 μg/m3 in October 2001 (median), which is similar to annual data at Public School 64 (13.5 μg/m3 in 2001, 12.1 μg/m3 in 2002). Women in our cohort in zone 1 were probably exposed to > 100 μg/m3 of particulates on 11 through 12 September, based on our extrapolation of levels of PM2.5 and PM10 from the daily EI correlations with the air monitoring data. The U.S. EPA 24-hr standard is 65 μg/m3 for PM2.5 and 150 μg/m3 for PM10 (U.S. EPA 1997).
As the dust exposures in lower Manhattan gradually diminished in weeks 1–4 after 11 September, women gradually returned to that area for longer time periods. Therefore, fluctuation in the daily EIs (Figure 3) reflects personal activity patterns (which increased) as well as well-known changes in pollution sources and pollutant levels in lower Manhattan during this period (Dalton 2003). Consistent with the temporal decline in particulate levels, the median daily EI in our population decreased by 10- to 100-fold over the first few days (Figure 3C). After 14 September 2001, the median of nonzero reconstructed daily relative dust levels remained < 0.01 (the nominal detection limit) (Figure 3B). The significant association of EI with questionnaire data on exposure and air quality indicates that personal recollection may be a reasonable way to estimate a person’s relative exposure intensity in lower Manhattan after 11 September. For the events of 11 September, these results are reasonable because the magnitude of the changes in pollutant levels by day and by geographic location was large. Therefore, individual exposure to WTC contaminants can be ranked by assessing perceived air pollution as well as information from time–activity logs and GIS-based exposure modeling.
Uncertainties exist in our exposure estimates, including the recall data and the plume reconstruction. We used recall information to derive the GIS time–activity variables and to obtain data on PAQ from the exposed women. Plume reconstruction was used to calculate the GIS dust exposure levels. There were no air monitoring data during the first 24 hr after the events of 11 September, and the plume dust level estimates for the GIS were evaluated for precision during that time using plume density derived from satellite photographs.
The highest levels of PAH-DNA adducts were found in women whose blood was collected closest to the date of 11 September, but no association was found with ∑EI. PAH exposure around the WTC is known to have been significant (Landrigan et al. 2004). Extremely high levels of PAHs were found in settled dust (Lioy et al. 2002; Offenberg et al. 2003), and estimated air levels were > 10 ng/m3 in the PM2.5 fraction soon after the incident compared with < 1 in normal urban air and in the plume later on (Pleil et al. 2004).
Our data for each woman’s EI and zone locations were available for the day of the event through 8 October 2001, the time when peak exposures occurred around the WTC. PM and PAH levels were highest during the first 36 hr after 11 September (Offenberg et al. 2003). However, airborne PM and PAH levels were elevated for some time after 11 September (Pleil et al. 2004; Swartz et al. 2003). Therefore, PM and PAH exposures were greater in weeks 3 and 4 than in week 2, because women spent more time in the affected area as time went on. This pattern may reflect seasonality of PAH emissions, as recent New York City personal air monitoring data suggest (Perera et al. 2004). However, other studies that have found higher PAH levels in winter observed marked differences only in highly polluted areas (Perera et al. 1992; Topinka et al. 2000). With urban or tobacco smoke exposures, the PAH-DNA adducts were only slightly higher in winter than in summer (Georgiadis et al. 2001).
Metals were reported to be elevated at the WTC site debris by some but not all investigators. Blood Pb and urine Sb were higher in exposed than in control firefighters, whereas urine Cd was higher in more heavily exposed than in less heavily exposed workers (Edelman et al. 2003). When adjusted for potential confounders, we found no significant associations between metal biomarkers and EI or timing of the blood draw closer to 11 September. Metal levels other than blood Hg in these women were not higher than among firefighters or among females in NHANES. Moreover, the adjusted geometric mean for blood Hg was identical to that in the NHANES females (0.9 μg/L; Table 4) and in women of reproductive age in NHANES (1.0 μg/L; Schober et al. 2003). Although these comparisons are helpful in understanding our exposure data, they should be interpreted with caution because the three groups are very different demographically and geographically.
Organochlorines were abundant at the WTC site and in runoff from the site (Lioy et al. 2002; Litten et al. 2003; Offenberg et al. 2003), and the compounds found at the highest levels in environmental samples were those consistent with combustion products. Similarly, elevated levels of heptachlorodibenzodioxin and heptachlorodibenzofuran were reported in the most highly exposed firefighters examined (Edelman et al. 2003). In our study group, there were no significant associations between OCs and EI. The PCB levels are quite similar to levels reported in several recent studies, especially considering the differences in the populations and the laboratories performing the analyses. For example, our median ∑PCB4 level (83 ng/g lipid) is lower than that in a New York City cohort of pregnant women who were younger (median, 151 ng/g lipid; mean age, 25 years; Wolff et al. 2005). Another northern New York cohort had similar levels [geometric mean, 1.2 μg/L; estimated to be 150 ng/g lipid based on 8 g/L plasma lipids in pregnant women (Longnecker et al. 2003); mean age, 26 years; Fitzgerald et al. 2004]. Finally, levels are similar to those found in two breast milk pools from California and North Carolina, collected in 2002–2003 from mothers approximately 30 years of age (Wang and Needham 2003).
Levels of 2,3,4,7,8-pentachlorodibenzofuran were unusually high in the WTC outfall (Litten et al. 2003); this compound was not detectable in 59% of our women, although it has usually been found in breast milk at levels equal or higher than those of 1,2,3,6,7,8-hexachlorodibenzodioxin or 1,2,3,4,6,7,8-heptachlorodibenzodioxin/furan (Focant et al. 2002; Glynn et al. 2001; Yang et al. 2002). Other detected dioxins and dibenzofurans found by us (1,2,3,6,7,8-hexachlorodibenzodioxin, 1,2,3,4,6,7,8-heptachlorodibenzodioxin/furan, and OCDD) are also widely detected and are the highest detected congeners in breast milk (Focant et al. 2002; Glynn et al. 2001; Schaum et al. 2003; Schecter et al. 2002). Their relative proportions (i.e., OCDD > 1,2,3,4,6,7,8-heptachlorodibenzodioxin > 1,2,3,6,7,8-hexachlorodibenzodioxin) are similar to those among the WTC women in our cohort, even where levels were much higher (Yang et al. 2002). Furthermore, the proportions are similar to those reported from California and North Carolina 30-year-old mothers (Wang and Needham 2003), although median levels in our mothers were higher (e.g., OCDD from California, 42 pg/g lipid; from North Carolina, 84.6 pg/g lipid). This would not be attributable to age or other factors, given the effect sizes in our multivariate models (data not shown). PBDEs in our samples were somewhat lower than those reported in two milk pools (Wang and Needham 2004), but the proportions were very similar.
Our data show only a weak association of OCs with WTC-derived exposure in the month after 11 September. Levels are similar to those in firefighters with demonstrated elevations, and levels are higher than those reported in recent national data (Centers for Disease Control and Prevention 2003). Therefore, some of these compounds might have been absorbed by bystanders and firefighters in lower Manhattan after 11 September. However, the levels might not be high enough to show relationships with exposure because the WTC increment over existing body burden is not detectable. For example, if the native body burden is 20 pg/g lipid, a pregnant woman would have 600 ng total in 30 kg of adipose and circulating lipid. To see an increase in level, an absorption of 120 ng would be required, based on 20% precision in the measurements (laboratory and population variation). Our population was in varying stages of pregnancy and after delivery at time of blood collection, which greatly complicates interpretation of biomarker data, including metals and persistent organic compounds.
In summary, we were able to successfully incorporate results of a WTC plume reconstruction model into predicted exposures and doses of airborne emissions among these women who were near the WTC immediately after 11 September. The results provided daily estimates of exposure by using both geographic dust concentrations and individual activity patterns to predict individual EIs among 187 women for 28 days. Women experienced very high dust exposures; as a result of being close to the WTC in September 2001, the PM exposures were likely > 100 μg/m3 for women exposed immediately after 11 September. Higher PAH-DNA adducts were found in blood samples collected closer to 11 September, and personal PAQ was associated with relative dust levels estimated from plume reconstruction (EI).
Correction
The values listed in “Materials and Methods” for the sample (165 women, 33 of whom were in the third trimester) were incorrect in the online version. Also, in Table 3, the values in the “Total” row appeared under the wrong columns. These have all been corrected here.
Figure 1 U.S. EPA particulate air monitoring approximately six blocks northeast of WTC (290 Broadway) from 25 September through 26 March 2002. PM10, n = 169 days; PM2.5, n = 181 days. Data from Columbia University (2003) and U.S. EPA (2003).
Figure 2 (A) WTC diary study zones over ZIP codes. The five exposure zones are described in “Materials and Methods.” (B) Start point for 166 women study participants who were in zones 1–3 at 0900 hr on 11 September 2001. WTC is the blank trapezoid just south of Vesey Street.
Figure 3 Duration and levels of exposure among 187 women participants from 11 September 2001 through 9 October 2001. SS, Saturday and Sunday; weekend dates were 15–16, 22–23, and 29–30 September and 6–7 October 2001. (A) Average time spent by women in zones 1–3 (indoor and outdoor combined). Average time spent in zones 4–5 (not shown) was < 0.7 hr/day on any day. (B) Reconstructed relative intensity of dust exposure in each zone for the same time period, derived from plume reconstruction. Values less than 0.01 were considered below the limit of reliable estimate. (C) Individual daily EI values for all women (n = 187). EIs for each woman are individual values computed from relative intensity (B) and time spent at all street addresses within zones 1–5 over this time period (A). The solid line in C connects the median of nonzero EI values for each day. The numbers at the bottom of C are the n values of women whose EI was zero on this day.
Table 1 Selected demographic factors and activity patterns among 187 participants in the Mount Sinai WTC pregnancy study by ∑EI derived from plume reconstruction data.
∑EI <median (n) ∑EI ≥ median (n) Total (n) p-Value
Interview age (years)
< 25 1 1 2
25–29 19 8 27
30–34 30 43 73
35–39 34 28 62
≥40 9 14 23 0.12a
Race
White 70 64 134
Black or African American 9 11 20
Asian 2 6 8
Hispanic 5 8 13
Other (mixed) 7 5 12 0.48
Marital status
Married 83 83 166
Living with the baby’s father 7 7 14
Never married/separated/divorced 3 4 7 0.93
Education
Some high school/high school 4 5 9
Some college 12 12 24
Bachelor’s degree (grades 13–16) 31 27 58
Some graduate school (grades ≥17) 4 5 9
Master’s degree 25 23 48
Doctoral degree (e.g., JD, MD, PhD) 17 22 39 0.33a
Smoking at time of blood draw
No 87 90 177
Yes 6 4 10 0.50
Worked or lived in lower Manhattan before 11 September
Worked near WTC 59 54 113
Lived near WTC 22 16 38
Lived and worked near WTC 9 24 33 0.019*
Neither worked nor lived near WTC 3 0 3 0.009**
Evacuation through debris on 11 September
Did not evacuate through debris 66 36 102
1–39 min 20 25 45
≥40 min 7 33 40 < 0.0001
Dust in home?
Yes 25 28 53
No 7 10 17 0.67
Dust removal method
Wet cloth 4 4 8
Both wet cloth and wet mop 14 20 34
Don’t know 7 5 12
None of the above 2 3 5 0.76
Did you wear a dust mask while cleaning?
Yes 10 24 34
No 21 11 32 0.003
The median ∑EI for 187 women was 2.66 (relative dust exposure). p-Values are for chi-square.
a Test for trend.
* p = 0.019; chi-square test of the three categories of women who worked or lived near the WTC.
** p = 0.009; all four categories, using Fisher’s exact test.
Table 2 EI (dust intensity) in relation to PAQ score in lower Manhattan, by week after 11 September.
Week 1 (EI median = 2.45)
Week 2 (EI median = 0.016)
Week 3 (EI median = 0.13)
Week 4 (EI median = 0.11)
PAQ score in lower Manhattan (PAQ range, 1–8; n = 185) No. < median No. ≥ median % ≥ median Total No. < median No. ≥ median % ≥ median Total No. < median No. ≥ median % ≥ median Total No. < median No. ≥ median median % ≥ Total
Women who spent most of their time in zones 1–5 (n = 185)
Very hazy/smoky to dense/visible haze/smoke with bad smell during some/most of the time (PAQ 3–4) 12 16 57 28 10 62 86 72 25 64 72 89 28 56 67 84
None to rarely visibly hazy or smoky with none to occasional bad smell (PAQ 1–2) 3 8 73 11 7 13 65 20 15 23 61 38 24 35 59 59
Those who did not spend most of the week in zones 1–5 (PAQ not applicable) 77 69 47 146 75 18 19 93 52 6 10 58 40 2 5 42
p-Valuea 0.19 < 0.0001 < 0.0001 < 0.0001
Week 1 (EI median = 2.45)
Week 2 (EI median = 0.016)
Week 3 (EI median = 0.13)
Week 4 (EI median = 0.11)
Women who spent most of their time in zones 1–5 during the given week after 11 September
No. 39 92 127 143
Mean PAQ score [sum of week’s air (mean ± SD) quality at work and/or home] 3.3 ± 1.5 3.4 ± 1.2 3.1 ± 1.1 2.9 ± 1.2
Mean ∑EI for week (mean ± SD) 3.92 ± 3.39 0.086 ± 0.15 0.22 ± 0.20 0.22 ± 0.20
Spearman correlation of ∑EI with PAQ score (rS) 0.074, p = 0.66 0.288, p = 0.005 0.219, p = 0.013 0.308, p = 0.0002
Women who worked or lived in zones 1–5 on 11 September including those who did not spend much time therein the 4 weeks after 11 September (n)
No. 185 185 185 185
∑EI for week (mean ± SD) 2.87 ± 2.54 0.051 ± 0.115 0.16 ± 0.19 0.18 ± 0.20
Median 2.45 0.016 0.13 0.11
Two women who did not live or work were not present in zones 1–5 on 11 September were excluded from these analyses for this table.
a p-Values are for chi-square and were identical for the Mantel-Haenszel (trend test). In the 4-week analyses for PAQ versus weekly EI, the p-values for the two groups with PAQ values 1–2 and 3–4 (2 × 2 tables) were 0.37 (n = 39), 0.031 (n = 92), 0.21 (n = 127), and 0.37 (n = 143).
Table 3 PAH-DNA adducts among 160 mothers in the Mount Sinai WTC pregnancy study.
Biospecimen collection times
Specimens collected Feb–Marcha
All mothers
PAH-DNA (apmn) No. (%) April–October 2002 No. (%) Feb–March 2002 No. (%) < Median ∑EI No. (%) ≥ Median ∑EI No. (%)
ND 88 (55) 62 (70) 26 (36) 11 (15) 15 (21)
< 60 36 (22) 14 (16) 22 (31) 14 (20) 8 (11)
60–100 25 (16) 10 (11) 15 (22) 6 (8) 9 (12)
> 100 11 (7) 2 (2) 9 (11) 5 (7) 4 (6)
Total 160 88* 72* 36 36
ND, not detected.
a By ∑EI (median = 3.30).
* Distributions are significantly different, chi-square p < 0.001.
Table 4 Blood and urinary biomarkers for metals among 100 mothers in the Mount Sinai WTC pregnancy study.
February–March 2002 biospecimens
All mothers tested (n = 100)
(n = 44)
(n = 44)a WTC exposed firefightersb
Median % ≥ LOD Median % ≥ LOD < Median ∑EI ≥ Median ∑EI Control Exposed NHANESc median (females)
Urinary metals (μg/L)d
Pb 0.82 94 0.78 98 0.75 0.89 1.0 1.2 0.60
Co 0.32 96 0.38 100 0.35 0.40 NS 0.41
Cd 0.22 98 0.20 98 0.24 0.19 0.38e 0.32 0.34
Sb < LOD 27 < LOD 34 < LOD < LOD 0.16* 0.20* 0.12
U < LOD 53 0.0055 52 0.0055 < LOD 0.0075f 0.0061 0.006
Blood metals (μg/L)g
Pb 17 100 16 100 16 16 19* 28* 13
Cd 0.30 71 0.30 55 0.30 0.22 0.3
Hg (total) 3.2 100 2.2 100 2.4 2.2 “Not higher” 0.9
Abbreviations: GM, geometric mean; NS, not significant; U, uranium. Metals were tested in a random subset of 100 women, of whom 44 gave specimens from February through March 2002; the remainder were sampled after this time.
a Median < or ≥ median ∑EI (3.62); metals were not significantly different in this analysis for values < versus ≥ EI with all or with 44 samples, both median tests (Kruskal-Wallis) and trend (Mantel-Haenszel). There were 22 samples each in the groups with values < versus ≥ median ∑EI.
b Edelman et al. 2003; GM adjusted.
c CDC 2003.
d LODs for metals in urine were as follows (μg/L): Pb, 0.3; Co, 0.08; Sb, 0.07; Cd, 0.06; U, 0.005.
e Urinary Cd was higher in more heavily exposed than in less heavily exposed firefighters, although all-exposed firefighters were not higher than control.
f Urine uranium levels were higher in more heavily exposed than in less heavily exposed firefighters, although this difference was not statistically significant.
g LODs for metals in blood were as follows (μg/L): Pb, 3; Cd 0.2; total Hg, 0.2. Metals were not significantly different in this analysis for values < versus ≥ EI with all 100 or with 44 samples collected in February through March 2002, both median tests (Kruskal-Wallis) and trend.
* p-Value < 0.05.
Table 5 Plasma OC and PBDE levels among 100 mothers in the Mount Sinai WTC pregnancy study.
All mothers (n = 100)
February–March 2002 biospecimens (n = 44)
By ∑EI < or ≥ median (2.66) (n = 100)
WTC exposed firefightersa
Median % ≥ LOD Median % ≥ LOD < Median ∑EI ≥ Median ∑EI Control Exposed NHANESb median (females)
Serum OCsc
∑PCB4 (ng/g lipid)d 84 94 82 93 81 92 —e < LOD
2,3,4,7,8-PentaCDF (pg/g lipid) LOD (4.0)f 41 < LOD 50 < LOD < LOD < LOD (4.8)
1,2,3,6,7,8-HexaCDDg (pg/g lipid) 22 80 22 89 21 22 < LOD (7.5)
1,2,3,4,6,7,8-HeptaCDDh (pg/g lipid; n = 99) 30 97 24 95 31 29 19* 28* < LOD (24.7)
1,2,3,4,6,7,8-HeptaCDFi (pg/g lipid) 7.2 79 6.2 84 7.6 6.7 —*j < LOD (5.2)
OCDDk (pg/g lipid) 224 95 214 98 206 236 < LOD (145)
Serum PBDEs
BDE-28l (ng/g lipid) 0.65 59 0.38 50 0.72 0.55
BDE-47m (ng/g lipid) 9.7 96 8.7 93 9.1 10.2
BDE-99n (ng/g lipid) 1.5 70 1.1 61 1.5 1.5
BDE-100o (ng/g lipid) 1.8 96 1.5 95 1.5 1.9
BDE-153p (ng/g lipid) 1.8 98 2.1 100 1.7 2.2
Abbreviations: GM, geometric mean. Organochlorines were tested in a random subset of 100 women, of whom 44 gave specimens in February through March 2002; the remainder were sampled thereafter.
a Edelman et al. 2003; GM adjusted.
b CDC 2003.
c OC and PBDE levels did not differ significantly by date of collection or by ∑EI. p-Values (Kruskal-Wallis) were all > 0.2 by median ∑EI; the Spearman correlation coefficients were not statistically significant (p > 0.05). Multivariate analyses predicting the OC/PBDE levels by the median ∑EI adjusting for covariates did not change the significance of the associations. There were 50 samples each in the groups with values < versus ≥ median ∑EI.
d Median of ∑PCB4 without lipid-correction was 0.46 μg/kg. LOD for ∑PCB4 was 29 ng/g lipid.
e Not different from controls; values not reported by Edelman et al. (2003).
f Median of all LOD values; range of LODs was 1.8–11 pg/g.
g LOD, 6.2 pg/g lipid (median of LODs for all such individuals; range, 3.1–18 pg/g).
h LOD, 7.2 pg/g; range, 3.9–20.
i LOD, 4.9 pg/g; range, 2.6–14.
j 1,2,3,4,6,7,8-HeptaCDF in the firefighter study (Edelman et al. 2003) was more prevalent among all-exposed versus controls (rates not given here); adjusted odds ratio, 3.5; 95% confidence interval, 1.4–9.0 for exposed (Edelman et al. 2003).
k LOD, 78 pg/g; range, 36–240.
l LOD, 0.4 ng/g lipid (median of LODs and limits of quantitation for all such individuals; range, 0.3–0.7 ng/g lipid).
m LOD, 2.0 ng/g; range, 1–4.
n LOD, 1.0 ng/g; range, 0.4–3.
o LOD, 0.5 ng/g; range, 0.5–6 (n = 3).
p LOD, 0.4 ng/g; range, 0.3–5 (n = 2).
* p-Value < 0.05.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7596ehp0113-00074915929899ResearchArticlesLead Exposure Inhibits Fracture Healing and Is Associated with Increased Chondrogenesis, Delay in Cartilage Mineralization, and a Decrease in Osteoprogenitor Frequency Carmouche Jonathan J. 1Puzas J. Edward 1Zhang Xinping 1Tiyapatanaputi Prarop 1Cory-Slechta Deborah A. 2Gelein Robert 2Zuscik Michael 1Rosier Randy N. 1Boyce Brendan F. O’Keefe Regis J. 1Schwarz Edward M. 11Center for Musculoskeletal Research, University of Rochester Medical Center and2Department of Environmental Medicine, University of Rochester, School of Medicine and Dentistry, Rochester, New York, USAAddress correspondence to E.M. Schwarz, Center for Musculoskeletal Research, University of Rochester Medical Center, 601 Elmwood Ave., Box 665, Rochester, NY 14642 USA. Telephone: (585) 275-3063. Fax: (585) 756-4721. E-mail: Edward_ [email protected] thank J. Harvey and B. Stroyer for assistance with histology.
This work was supported by Public Health Service grants NIH PO1 ES011854 and NIH P30 ES01247.
The authors declare they have no competing financial interests.
6 2005 14 3 2005 113 6 749 755 21 9 2004 14 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Lead exposure continues to be a significant public health problem. In addition to acute toxicity, Pb has an extremely long half-life in bone. Individuals with past exposure develop increased blood Pb levels during periods of high bone turnover or resorption. Pb is known to affect osteoblasts, osteoclasts, and chondrocytes and has been associated with osteoporosis. However, its effects on skeletal repair have not been studied. We exposed C57/B6 mice to various concentrations of Pb acetate in their drinking water to achieve environmentally relevant blood Pb levels, measured by atomic absorption. After exposure for 6 weeks, each mouse underwent closed tibia fracture. Radiographs were followed and histologic analysis was performed at 7, 14, and 21 days. In mice exposed to low Pb concentrations, fracture healing was characterized by a delay in bridging cartilage formation, decreased collagen type II and type X expression at 7 days, a 5-fold increase in cartilage formation at day 14 associated with delayed maturation and calcification, and a persistence of cartilage at day 21. Fibrous nonunions at 21 days were prevalent in mice receiving very high Pb exposures. Pb significantly inhibited ex vivo bone nodule formation but had no effect on osteoclasts isolated from Pb-exposed animals. No significant effects on osteoclast number or activity were observed. We conclude that Pb delays fracture healing at environmentally relevant doses and induces fibrous nonunions at higher doses by inhibiting the progression of endochondral ossification.
endochondral ossificationfracture healingleadosteoblastosteoclastPbPb toxicity
==== Body
Despite stringent governmental regulations, lead exposure continues to be a major public health problem, with blood levels being elevated in a bimodal distribution predominantly affecting young children 1–5 years of age and individuals > 50 years of age (Pirkle et al. 1998). The Centers for Disease Control and Prevention’s (CDC’s) Childhood Lead Surveillance Program monitors blood Pb at the state and local levels. Mean national blood Pb levels have decreased dramatically over the past 30 years, as documented by the third National Health and Nutrition Examination Survey, Phase 2 (NHANES, 1991–1994) (CDC 2000; Pirkle et al. 1998). According to NHANES data from 1999 and CBLS data from 1996–1998, despite the decreases in mean national blood Pb among children, levels remain elevated in specific areas, affecting mostly low-income, urban children and those living in older housing (CDC 2000).
In addition to children, many adults maintain elevated blood levels due to past occupational or environmental exposure. With the reduction of Pb in fuel and soldered cans as well as increased awareness and vigilance, acute environmental Pb exposure has decreased dramatically (Pirkle et al. 1998). However, Pb becomes sequestered in the skeleton, incorporated into hydroxyapatite crystals during calcification, and remains there until the bone is resorbed or remodeled (Wittmers et al. 1988).
Elevated blood Pb levels, particularly perimenopausal, may have a causative role in the pathogenesis of the costly metabolic bone disease osteoporosis. Blood Pb levels increase during periods of high bone turnover such as menopause (Silbergeld et al. 1988, 1993). Additionally, the aging process itself has been shown to increase the release of Pb from the skeleton by cadaveric analysis as well as by experimental study (Barnes et al. 1999). In vivo models have demonstrated a decrease in bone density with Pb exposure (Escribano et al. 1997; Gruber et al. 1997). In addition, multiple reports in humans and animals support a role for Pb in osteopenia (Berlin et al. 1995; Silbergeld et al. 1988). The decrease in bone quality may not only cause individuals to cross a fracture threshold earlier, but, we hypothesize, it may also impede normal fracture healing.
It is widely appreciated that the role of the skeleton in Pb toxicokinetics is greater than that of a reservoir. Several authors have described the inverse relationship between elevated blood Pb levels and skeletal development, chest circumference, and stature (Pearl and Boxt 1980; Schwartz et al. 1986; Shukla et al. 1989). The effects on adults are more subtle. Previously, we identified chondrocytes as important targets of Pb toxicity (Puzas et al. 1992) and demonstrated that Pb suppresses the expression of phenotypic markers in growth plate chondrocytes (Hicks et al. 1996). More recently, we have shown that Pb alters the effects of parathyroid-hormone–related peptide, transforming growth factor-β (TGF-β), activator protein-1, and nuclear factor-κB signaling in chondrocytes (Zuscik et al. 2002). Histomorphometric studies have demonstrated a significant Pb-associated decrease in length of rat femoral growth plate cartilage (Gonzales-Riola et al. 1997). Together, these data suggest an inhibitory effect on endochondral ossification (Gonzales-Riola et al. 1997; Hicks et al. 1996).
Several authors have demonstrated adverse effects of Pb on both bone formation and resorption mediated by cellular pathways affecting osteoblasts and osteoclasts (Dowd et al. 1990; Long et al. 1990; Miyahara et al. 1994; Pounds et al. 1991; Schanne et al. 1989, 1990). Osteoblasts are known targets of Pb toxicity from in vitro studies with ROS 17/2.8 cells, which demonstrated suppression of alkaline phosphatase, type I collagen, and osteocalcin (Long et al. 1990). In addition, circulating levels of osteocalcin, which serve as markers of osteoblast activity and regulators of bone formation and remodeling (Ducy et al. 1996), are decreased in Pb-intoxicated children (Markowitz et al. 1988). The mechanism by which these effects occur remains unclear.
Fracture healing is a complex orchestration of several cell types. It is unique in that healing occurs by reformation of bone rather than scar tissue. Fracture healing involves primary recruitment, proliferation, and differentiation of both osteoprogenitors (osteoblast progenitors), for intramembranous ossification, and undifferentiated mesenchymal cells, for endochondral bone formation (Barnes et al. 1999; Einhorn 1998). Fracture healing is an established means of studying the formation and repair of skeletal elements in vivo as well as the crucial signaling pathways involved (Zhang et al. 2002).
Given the known effects on cells involved in bone formation and remodeling and the absence of literature on Pb in skeletal repair, the purpose of the present study was to evaluate the effects of elevated blood Pb levels on fracture healing. We employed an established murine tibia fracture model (Bonnarens and Einhorn 1984; Einhorn 1998) to characterize the effects of Pb on skeletal repair.
Materials and Methods
Pb exposure and whole blood Pb level determination.
All Pb solutions were made using Pb acetate (Gibco, Grand Island, NY) dissolved in distilled water. All mice had unrestricted access to the normal rodent diet supplied by the vivarium staff, and drinking water was replenished at least one time per week by the investigative staff to ensure continuous Pb exposure. All Pb waste solutions were disposed of appropriately. Mice were housed in groups of ≤5 animals per microisolator cage in the vivarium and maintained on a 12-hr light/dark cycle. All procedures were carried out in accordance with the regulations of and following the approval of the University of Rochester Animal Use and Care Committee.
Female C57/Bl6 mice (n = 32) at 6 weeks of age were divided into eight groups of four mice each. Each group was exposed to one of the following doses: 0, 55, 230, 580, 1,160, 1,750, 2,300, or 5,800 ppm Pb in the drinking water. This preliminary exposure was carried out to determine the whole-blood Pb (BPb) concentrations needed to approximate environmentally relevant levels of human exposure. This cohort is referred to as group A. Pb exposures of 0, 55, and 230 ppm Pb in the drinking water were selected as most environmentally relevant. A second cohort (group B) of female C57/Bl6 mice (n = 54) was divided into three groups of 18 animals and housed under conditions as outlined above. All experiments related to the analysis of the fracture healing process were performed with group B except one, which documented chronic nonunions in mice exposed to 2,300 ppm Pb.
Before Pb exposure, 100 μL whole blood samples were collected for baseline BPb level measurement. Samples were collected, using the saphenous vein technique (Hem et al. 1998), into 100 μL nitric acid–washed volumetric capillary tubes (VWR, Buffalo Grove, IL). Each whole-blood sample was then immediately diluted 1:10 with a matrix modifier solution containing 0.2% NH4H2PO4, 0.5% Triton X-100, and 0.2% HNO3 (Parsons 1993). Whole blood was collected for BPb analysis at 3-week intervals.
BPb levels were determined using a Perkin-Elmer A Analyst 600 atomic absorption spectrophotometer equipped with longitudinal Zeeman background correction and a transverse heated graphite furnace (PerkinElmer Life and Analytical Sciences, Boston, MA). This method was adapted by Parsons from the Department of Environmental Health and Toxicology, SUNY at Albany (Parsons 1993). All instruments, plastic, and glassware were periodically tested for Pb contamination by atomic absorption throughout these experiments. In addition, the accuracy of our atomic absorption technique was verified using Standard Reference Material 1486 bone meal (National Institute of Standards and Technology, Gaithersburg, MD).
Bone Pb determination.
After 6 weeks, four animals from each of the 0, 55, 230, 2,300, and 5,800 ppm Pb groups were sacrificed. The femora and tibiae were removed, the epiphyses and soft tissues were discarded, and the bone marrow was flushed out with a 25-gauge 5/8-inch needle (Becton-Dickinson, Franklin Lakes, NJ). The remaining diaphy-seal bones were washed with phosphate-buffered saline (PBS) to remove all marrow elements and blood cells. The bones were then processed as described previously (Parsons 1993). Briefly, cortical bones were dried in a vacuum at 60°C overnight. They were then weighed dry in Teflon digestion vials (Savillex, Minnetonka, MN). Bones were wet washed with 5 mL ultrapure nitric acid 67–70% (SeaStar Chemicals, British Columbia, Canada) in the Teflon vials and taken to dryness after refluxing for 3 hr. The bone ash was then resuspended in 1 mL HNO3, and the total volume was brought to 10 mL with distilled water and analyzed.
Bone marrow osteoblast differentiation.
Group B animals were sacrificed after 6 weeks of Pb exposure. The femora and unfractured tibiae were excised, and the soft tissues were removed. Primary bone marrow cells were isolated and prepared as described previously (Franzoso et al. 1998; Zhang et al. 2002). Bone marrow cells were cultured in α-minimal essential medium (α-MEM) with 10% fetal bovine serum (FBS; Hyclone Laboratories, Logan, UT) and 1% penicillin/streptomycin (Gibco) for 3 days. On the 4th day, nonadherent cells were washed off. The cells were then cultured in complete osteoblast medium of α-MEM with 10% FBS, 50 μM ascorbic acid, 1% penicillin/streptomycin, and 5 mM β-glycerophosphate. The cells were plated at 2 × 106 cells per 6-cm plate for nodule formation assays, and medium was changed every 3 days. After 17 days in culture, triplicate cultures were fixed with 10% formalin, rinsed in distilled water, and stained by von Kossa’s method (Sigma Chemical Co., St. Louis, MO). Total nodular area was quantified by histomorphometry.
Osteoclast precursor isolation.
Osteoclast progenitor cells (OCPs) were prepared from splenocytes as described previously (Schwarz et al. 1997). Animals were sacrificed after 6 weeks. Spleens were removed aseptically and placed in 10 mL Dulbecco’s Modified Eagle Medium (DMEM; Gibco). The organs were homogenized and washed through a wire mesh with 10 mL DMEM supplemented with 10% FBS and 1% penicillin/streptomycin. The cells were spun at 1,600 rpm for 5 min and then resuspended in 1 mL α-MEM, after lysing erythrocytes with cold ammonium chloride.
Osteoclastogenesis.
Splenocytes were seeded at 1.75 × 105 cells/well in a 96-well plate in α-MEM supplemented with macrophage-colony–stimulating factor (M-CSF; 30 ng/mL) and receptor activator nuclear factor κB ligand (RANKL; 100 ng/mL). Fifty percent medium was added the next day, and medium was changed every other day thereafter. Cultures were incubated at 37°C for 6–7 days and then fixed and stained for tartrate-resistant acid phosphatase (TRAP) using the leukocyte acid phosphatase kit (Sigma). The number of TRAP+ multinucleated cells was then counted to quantify osteoclast formation as described previously (Zhang et al. 2001).
Flow cytometry.
Surface protein staining was performed on splenocytes. After erythrocyte lysis, a single-cell suspension was incubated in DMEM with 10% FBS. Cells were harvested in PBS containing 5 mM EDTA and stained for CD11b with biotin-labeled antibodies, as described previously (Li et al. 2004). Data were acquired using a FACScalibur cytometer and analyzed with Cell Quest software (Beckton Dickenson, Bedford, MA).
Macrophage colony-forming assay.
The in vitro colony-forming assay was performed as described previously (Franzoso et al. 1997). Freshly isolated splenocytes were plated at 105 cells/mL in a 35-mm dish. Splenocytes were cultured in methylcellulose-based medium (StemCell Technologies, Vancouver, British Columbia, Canada) supplemented with M-CSF (30 ng/mL) for 12 days. Individual colonies, defined as > 40 cells, were then quantified under an inverted microscope. The total number of colonies represents the original number of monocyte/macrophage and osteoclast precursors (Schwarz et al. 1997).
Bone resorption assay.
Splenocytes were seeded at 1.75 × 105 cells/well on sterile 4 mm × 4 mm bovine femoral cortical bone wafers. Cells were cultured in α-MEM with 10% FBS supplemented with M-CSF (30 ng/mL), RANKL (100 ng/mL), 1% l-glutamine, 1% penicillin/streptomycin, and 1% nonessential amino acids (Gibco). Fifty percent medium was added the next day, and medium was changed every other day thereafter. After 12 days, the wafers were scraped, dried, stained with toluidine blue, and examined under a 40× objective. Wafer images were captured, and resorption pits on the wafer surface were traced to determine the total pitted area using Osteometrics software (Osteometrics, Atlanta, GA) as described previously (Childs et al. 2001).
Fracture.
After 6 weeks of Pb exposure, mice were anesthetized by intraperitoneal injection of 100 mg/kg ketamine HCl and 15 mg/kg xylazine. After adequate sedation, the surgical site was prepared with 70% ethanol and an incision was made about the left knee. A 25-gauge 5/8-inch needle was inserted lateral to the patellar tendon and into the tibial marrow space. This needle was then removed and a 0.25-mm diameter insect pin (Fine Science Tools, Foster City, CA) was placed into the tibia. The pin was trimmed proximally at the level of the tibial plateau. The wound was then closed with 4–0 nylon sutures. The left tibiae were placed in a modification of the guillotine three point-bending device described by Bonnarens and Einhorn (1984) and Hiltunen et al. (1993). The tibial diaphyses were fractured, without trauma to the overlying skin, using a force exerted by a 540 g weight dropped 16.5 cm. Radiographs characterized the fracture and confirmed intramedullary fixation. Radiographs were obtained using a Faxitron cabinet X-ray system (Faxitron, Wheeling, IL). The radiographic exam was repeated at 7-day intervals to follow healing and confirm maintenance of fixation.
Histology.
Fractured tibiae were harvested at 7, 14, and 21 days. The tissues were fixed in 10% formalin for 24 hr and decalcified in 10% EDTA at pH 7.2 for 2 weeks. Samples were then processed, embedded in paraffin, and cut in 3-μm sagittal sections. Three contiguous sections (100 μm apart) for each specimen were stained with Alcian blue and counterstained with hematoxylin, orange G, and eosin (ABH/OG) as described previously (Zhang et al. 2002). Additional contiguous sections were stained for TRAP activity using naphthol AS-BI phosphate and counter-stained with hematoxylin (Sigma).
Fracture histomorphometry.
Quantitative analysis was performed on the sagittal ABH/OG- and TRAP-stained sections by histomorphometric analysis using Osteometrics software. Bone and cartilage formation was quantified on ABH/OG-stained slides by outlining the perimeter of the fracture callus under the 2× objective. Areas of new woven bone and cartilage were then traced. This procedure was repeated until the entire fracture callus had been evaluated. The areas of woven bone, cartilage, and total fracture callus were obtained directly in square millimeters from the software. The amount of mesenchymal tissue was then calculated by subtracting bone and cartilage area from the total callus area. Bone, cartilage, and mesenchymal tissue areas each were expressed as a percentage of total fracture callus. Osteoclast quantification was performed on the TRAP-stained slides. Using the 10× objective, the perimeter of fracture callus was outlined and individual TRAP positive osteoclasts were identified and counted. The entire fracture callus was evaluated. The number of TRAP+ osteoclasts was then expressed per total area of fracture callus.
In situ hybridization.
Plasmids corresponding to osteocalcin, collagen type II (Col II), and collagen type X (Col X) were used to synthesize 35S-labeled sense and anti-sense riboprobes as described previously (Ferguson et al. 1999; Zhang et al. 2002). Cut sections were incubated in hybridization buffer (50% formamide, 0.3 M NaCl, 20 mM Tris HCl, 5 mM EDTA, 10% dextran sulfate, 0.02% Ficoll, 0.02% bovine serum albumin, 0.02% polyvinyl pyrrolidone, and 0.5 mg/mL yeast RNA) containing each riboprobe at 10,000 cpm/μL, and hybridization was performed at 55°C overnight. Nonspecifically bound probe was hydrolyzed with RNase A (20 μg/μL) and washed at high stringency at 55°C with 2× salt sodium citrate/50% formamide (Ferguson et al. 1999). Emulsion-dipped slides were exposed to beta emissions for 14 days.
Statistics.
Data are expressed as mean ± SEM. Statistical significance was determined using the Student t-test and analysis of variance (ANOVA) where appropriate. A value of p ≤0.05 was accepted as statistically significant.
Results
Pb exposure and whole-blood/bone Pb level determination.
We established a reproducible murine Pb dosing protocol by introducing various concentrations of Pb into the drinking water of group A animals and determining BPb levels, similar to our rat exposure protocol (Cory-Slechta et al. 1997). We noted that BPb levels are quite variable after short-term exposure (1–2 weeks), whereas treatments from 3 to 6 weeks resulted in stable levels (Figure 1). To assure that this treatment resulted in soft and hard tissue exposure, we determined the Pb content in blood and bone (Table 1). These data indicate that oral dosing leads to environmentally relevant exposures (10–40 μg/dL) and that BPb measurements faithfully reflect organ exposure in vivo. Additionally, mice appear to be very tolerant to these Pb exposures. No gross physical or behavioral changes were noted in animals with BPb levels three to four times greater than a lethal human exposure, in preliminary experiments. There was no statistically significant difference by ANOVA testing in body weight among the various dose groups in unpublished preliminary experiments (data not shown).
Pb inhibits fracture healing.
By 14 days after fracture, all animals showed radiographic evidence of fracture callus formation (Figure 2). However, radiographs of Pb-exposed animals demonstrated a marked increase in radiolucency within the fracture callus compared with controls (Figure 2D–F). At 21 days, all groups showed evidence of remodeling of the fracture callus with no remarkable difference in the three groups (Figure 2G–I).
Consistent with our radiographic findings, histologic analysis revealed no remarkable differences at 7 and 21 days because all fracture sites consisted of undifferentiated mesenchyme and fibrocartilage at the early time point and remodeling bone at the latter time point (Figure 3). In our day 14 untreated animals, we see the normal pattern of newly formed bone through a cartilage intermediate, as evidenced by the areas of Alcian blue–stained cartilage throughout the callus (Figure 3D). However, in the 14-day Pb-treated groups, immature cartilage accounts for the large radiolucency identified by X ray (Figure 3E–F). Histomorphometry of the day 14 fracture calluses showed a significant delay in endochondral ossification because the Pb-treated mice had a 4- to 5-fold increase in unmineralized cartilage with a commensurate decrease in bone (Figure 4). Interestingly, there was a nonlinear response to the effects of Pb on bone tissue because a similar effect was seen in both the 55 and 230 ppm treatment groups.
In situ hybridization and TRAP staining in the 14-day fracture group helped assess phenotypic gene expression and quantify osteoclasts in the fracture callus, respectively (Figure 5). Robust expression of Col II, decreased expression of Col X, and absence of both the mature osteoblast marker osteocalcin and TRAP+ osteoclasts were noted in Pb-treated animals. This confirmed the prevalence of immature cartilage in the fracture callus. Importantly, gene expression outside of this immature cartilage was indistinguishable from untreated controls (Figure 5A–I), as was osteoclast number (Figures 4 and 5J–L). Thus, low Pb exposures did not completely inhibit any process of fracture healing. Rather, Pb delayed endochondral ossification.
A cohort of group A mice (n = 4) were exposed to 2,300 ppm Pb and analyzed radiographically and histologically. In contrast to the lower-dose treatment groups (group B), the radiolucency in the day-14 X rays was not accompanied by surrounding fracture callus (Figure 6A). Furthermore, histology failed to identify evidence of endochondral ossification at the fracture site. Day 21 specimens confirmed fibrous nonunions in 75% of the group A 2,300 ppm animals (Figure 6B–C). Thus, Pb can completely inhibit fracture healing at very high doses.
The direct effects of Pb on chondrocytes (Zuscik et al. 2002), osteoclasts (Bonucci et al. 1983), and osteoblasts (Klein and Wiren 1993; Long et al. 1990; Pounds et al. 1991) have been previously documented. Our experiments on cells isolated from Pb-exposed animals focus on the mechanism by which Pb inhibits fracture healing by determining its effects on progenitor cells. When osteoprogenitor cell differentiation was examined using a bone nodule formation assay, von Kossa staining revealed that Pb-treated animals produced significantly fewer nodules at both the 55 ppm and 230 ppm dosing regimens (Figure 7). Because there was no Pb exposure in the culture medium during osteoblast differentiation, we interpret these results as a reduction in the total number of osteoprogenitor cells.
We found no effect on OCP number or function from in vivo Pb exposure. Flow cytometry analysis on splenocytes to determine the CD11b+ OCP frequency showed no significant Pb effects on the number of OCPs (Figure 8A). By culturing these cells with RANKL and M-CSF, we were also able to show that their potential to form osteoclasts (Figure 8B–C) and resorb bone (Figure 8D) was not affected by in vivo Pb exposure. These results are consistent with the fracture callus histology and indicate that in vivo Pb has differential effects on mesenchymal versus myeloid progenitors.
Discussion
Toxicity due to Pb exposure remains a major public health concern and presents a broad spectrum of pathologies in children and adults. Over the last decade, our group has focused on the effects of Pb on bone and cartilage and its potential role in osteoporosis (Campbell et al. 2004; Puzas et al. 2004). Because the clinical manifestation of osteoporosis is fracture and because fracture healing is a proven model for examining cellular and molecular aspects of skeletal repair, we evaluated the effects of in vivo Pb exposure on callus formation, maturation, and remodeling. In addition to osteoporosis, Pb exposure is known to contribute to dental caries (Moss et al. 1999) and complications of skeletal growth in children (Kafourou et al. 1997) via poorly defined mechanisms. As such, elucidation of the Pb effects on angio-genesis, stem cell recruitment, endochondral ossification, osteoclastogenesis, and bone remodeling during fracture healing could also have implications for these other human conditions. Additionally, individuals who have high-injury-risk occupations and are concurrently exposed to Pb may also be susceptible to fracture nonunions.
Our first aim was to develop a reproducible dosing regimen that could achieve stable BPb levels over time. For this study we chose < 3, 15, and 40 μg/dL in whole blood as our targets of human background, intermediate, and high Pb exposures, respectively. We found a tight correlation between the drinking water concentration and BPb levels (Table 1) that was similar to our observations in rats (Cory-Slechta et al. 1997). We verified the incorporation of Pb into hard and soft tissues (Figure 1B), which revealed similar concentrations to those previously reported in bone (Pounds et al. 1991). Of interest, no gross toxic effects of very high BPb exposures (~ 400 μg/dL) were noted. Because this is three to four times the lethal dose in humans, future studies using murine models with human extrapolation may need to consider higher dosing.
In this study, remarkable effects of Pb on fracture healing were clearly apparent, even at the lowest dose (Figures 2–5). The phenotype can be described as an increase in chondrogenesis and delay in endochondral maturation, vascular invasion, and resorption. Although fracture healing is a highly ordered biologic process that requires precise temporal and spatial regulation, it is known that various compensatory mechanisms have evolved to rescue healing under adverse conditions. In our experience with various drug treatments and genetically deficient strains, essentially all closed stable fractures heal in mice (Flick et al. 2003; Zhang et al. 2002). Thus, the finding that the profound Pb-induced phenotype on day 14 is completely resolved by day 21 is not surprising. However, the presence of fibrous nonunions in three out of four mice with very high Pb exposure is remarkable given that all other mice studied (n > 100) healed. Because it is well known that mice have an extremely robust ability to heal fractures, we speculate that mice healed fractures at Pb exposures at which humans could not.
Although the precise mechanism by which Pb inhibits fracture-healing remains a topic for future investigation, the phenotype is very reproducible and is somewhat reminiscent of phenotypes described in other mouse models. The increased chondrogenesis observed is similar to that seen in the fracture callus of parathyroid hormone and prostaglandin-treated mice, indicating that Pb may be an agonist of protein kinase A signaling in chondrocytes, as predicted in our in vitro studies (Zuscik et al. 2002). The literature provides three potential explanations for the persistence of cartilage in the fracture callus. The first is a defect in chondrocyte apoptosis, as seen in mice with defective tumor necrosis factor receptors (Gerstenfeld et al. 2001). The second is a defect in vascular invasion of the cartilage, as seen in the matrix metalloproteinase-9 knockout mice (Colnot et al. 2003). The third is the inhibition of mesenchymal stem cell differentiation into osteoprogenitors, as seen in mice deficient in cyclooxygenase-2 (Zhang et al. 2002), which is required for callus mineralization. Evidence for this mechanism is supported by our finding that Pb-treated animals have a significant decrease in osteoprogenitor frequency (Figure 7). An additional possibility is inhibition of osteoclastic resorption and remodeling of the fracture callus, as seen in mice with defective M-CSF and RANKL signaling. However, our findings that in situ osteoclast numbers (Figures 4, 5) and OCP frequency (Figure 8) are unaffected by in vivo Pb exposure renders this possibility less likely.
The overall clinical significance of Pb inhibition of fracture healing relates to persons with osteoporosis. We have argued that because of the high environmental Pb exposures from the 1940s to 1960s, women currently going through menopause are at an additional risk of osteoporosis (Puzas et al. 2004). It is now well recognized that factors released from bone during resorption, such as TGF-β, can act on cells in the bone marrow to induce the production of osteoclastic stimulating factors or to inhibit osteoblastic new bone formation (Evans et al. 1989; Yin et al. 1999). As a consequence of the high bone turnover, which would release Pb from its inactive state in bone hydroxyapatite crystals, an additional imbalance of bone resorption over formation would occur from Pb’s preferential toxic effects on osteoblasts. Our results indicate that osteoporotic, Pb-exposed patients may sustain a fragility fracture earlier and heal their fractures at a slower rate compared with non-Pb-exposed osteoporotic individuals. Future investigations into the molecular mechanisms of Pb effects on osteoporosis and fracture healing are warranted.
Figure 1 In vivo Pb exposures in group A mice (n = 4/group). Mice were exposed continuously to Pb in drinking water for the indicated time; see “Materials and Methods” for details. Error bars indicate SE.
Figure 2 Radiographic analysis of the Pb effects on fracture healing in group B mice (n = 6/group). Mice were continuously exposed to Pb in drinking water for 6 weeks, with X rays from representative mice taken at the indicated time after fracture. See “Materials and Methods” for details. Arrows indicate the radiolucency in the day-14 fracture callus of Pb-treated mice (E, F), which is absent in the unexposed animals (D).
Figure 3 Histologic analysis of the Pb effects on fracture healing in group B mice continuously exposed to Pb in drinking water for 6 weeks (n = 6/group). See “Materials and Methods” for details. ABH/OG histology sections are shown at 10× magnification. Note the large amount of Alcian blue–stained cartilage in the day-14 fracture callus of Pb-treated mice (E, F). The immature fracture callus has less cartilage and exhibits a more advanced stage of remodeling in the unexposed animals (D).
Figure 4 Histomorphometry of the fracture callus of group B mice continuously exposed to Pb in drinking water for 6 weeks (n = 6/group). See “Materials and Methods” for details. No significant differences were found between the groups at 7 and 21 days (data not shown) or between the amount of fibrotic tissue (A) and osteoclast numbers (D) between the groups. However, Pb significantly increased the amount of cartilage (B) and decreased the amount of bone (C) present in the day-14 fracture calluses of exposed mice. Error bars indicate SE.
*p < 0.05 determined using ANOVA.
Figure 5 Inhibition of cartilage maturation in day-14 fracture callus of group B mice continuously exposed to Pb in drinking water for 6 weeks (n = 6/group). Histology sections parallel to those presented in Figure 3D–F used for in situ hybridization to radiolabeled antisense probes for Col II (A–C), Col X (D–F), or osteocalcin (OC; G–I) or stained for TRAP (J–L). There is increased Col II signal in the middle of the Pb-exposed fracture callus (B, C) compared with controls, and there is an absence of Col X (E, F), osteocalcin (H, I), and TRAP (K, L) signal in this same region.
Figure 6 Pb exposure in group A mice (n = 4) exposed to 2,300 ppm Pb for 6 weeks, fractured, and assessed for skeletal repair. See “Materials and Methods” for details. A day-14 X ray from a representative mouse demonstrates the limited radiographic healing (arrow) in these mice (A). ABH/OG-stained histology section of day 21 fracture callus from a representative mouse at 10× (B) and 40× (C) magnification confirms the presence of fibrotic tissue between the fractured ends of the tibia and the complete absence of endochondral bone formation. These findings indicate a fibrous nonunion.
Figure 7 Inhibition of osteoprogenitor cells in group B mice (n = 6/group) continuously exposed to Pb in drinking water for 6 weeks. See “Materials and Methods” for details. (A) Representative photographs of the von Kossa–stained plates. (B) Bone nodules in these plates quantified as the percentage of nodule area as described in “Materials and Methods.”
*p < 0.05 determined using ANOVA.
Figure 8 Lack of effects on osteoclast precursors after in vivo Pb exposure. M1, gate used to distinguish between CD11b-positive and -negative cells on the FACScalibur cytometer. Splenocytes from group B mice (n = 6/group) were used to determine the CD11b+ OCP frequency by flow cytometry analysis (A) or cultured in M-CSF and RANKL to form osteoclasts on tissue culture plates (B, C) or on cortical bone wafers (D). We observed no significant differences in OCP frequency (A), TRAP-stained osteoclast morphology at 10× magnification (B), TRAP+ multinucleated osteoclast formation (C), or bone-resorbing potential (D) between groups.
Table 1 In vivo Pb bone exposures.
Pb in drinking water (ppm) μg Pb/g dry bone
0 0.08 ± 0.01
55 33.3 ± 2.80
230 117.3 ± 11.13
2,300 472.88 ± 61.98
5,800 682.41 ± 142.75
n = 4/treatment group. Data presented are mean ±SD.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7595ehp0113-00075615929900ResearchArticlesHospitalization Rates for Coronary Heart Disease in Relation to Residence Near Areas Contaminated with Persistent Organic Pollutants and Other Pollutants Sergeev Alexander V. *Carpenter David O. Institute for Health and the Environment, University at Albany, Rensselaer, New York, USAAddress correspondence to D.O. Carpenter, Institute for Health and the Environment, University at Albany, One University Place, A217, Rensselaer, NY 12144-3456 USA. Telephone: (518) 525-2660. Fax: (518) 525-2665. E-mail:
[email protected]*Current address: Hospital Therapy Department, Smolensk State Medical Academy, Krupskoy St., 28, Smolensk, 214019 Russia.
We thank L. Le, I. Scherbatykh, and R. Huang for help with the figure and data analysis.
This work was supported by the Edmund S. Muskie/Freedom Support Act Graduate Fellowship Program and the Fogarty International Center, National Institutes of Health (TW00636 to D.O.C.).
The authors declare they have no competing financial interests.
6 2005 14 3 2005 113 6 756 761 20 9 2004 14 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Exposure to environmental pollutants may contribute to the development of coronary heart disease (CHD). We determined the ZIP codes containing or abutting each of the approximately 900 hazardous waste sites in New York and identified the major contaminants in each. Three categories of ZIP codes were then distinguished: those containing or abutting sites contaminated with persistent organic pollutants (POPs), those containing only other types of wastes (“other waste”), and those not containing any identified hazardous waste site (“clean”). Effects of residence in each of these ZIP codes on CHD and acute myocardial infarction (AMI) hospital discharge rates were assessed with a negative binomial model, adjusting for age, sex, race, income, and health insurance coverage. Patients living in ZIP codes contaminated with POPs had a statistically significant 15.0% elevation in CHD hospital discharge rates and a 20.0% elevation in AMI discharge rates compared with clean ZIP codes. In neither of the comparisons were rates in other-waste sites significantly greater than in clean sites. In a subset of POP ZIP codes along the Hudson River, where average income is higher and there is less smoking, better diet, and more exercise, the rate of hospitalization for CHD was 35.8% greater and for AMI 39.1% greater than in clean sites. Although the cross-sectional design of the study prevents definite conclusions on causal inference, the results indirectly support the hypothesis that living near a POP-contaminated site constitutes a risk of exposure and of development of CHD and AMI.
acute myocardial infarctionhazardous waste siteshospitalizationpersistent organic pollutantsSuperfund sites
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Coronary heart disease (CHD) is the most common, serious, chronic, life-threatening illness in the United States, affecting more than 11 million people (Selwyn and Braunwald 2001). The major risk factors for CHD (smoking, hypertension, dyslipidemia, diabetes mellitus, male sex, and older age) are well known. However, environmental exposures may also contribute to the risk of development of CHD. Air pollution with particulate matter results in an elevated risk for cardiovascular mortality and an increase in hospitalization rates for cardiovascular diseases (Brook et al. 2004). In the Kuopio Ischemic Heart Disease Risk Factor Study, Salonen et al. (1991, 1992) found evidence that copper and iron are risk factors for heart disease and later demonstrated that higher dietary intake of mercury from non-fatty freshwater fish was associated not only with an increased risk of acute myocardial infarction (AMI) but also with CHD mortality, cardiovascular disease mortality, and any cause mortality among eastern Finnish men (Salonen et al. 1995); the researchers suggested that the mechanism occurs through promotion of lipid peroxidation. Guallar et al. (2002) also found a direct association between toenail mercury concentration and risk of myocardial infarction. Arsenic exposure is associated with peripheral vascular disease (Tseng et al. 1996). Occupational exposure to dioxins/furans and polychlorinated biphenyls (PCBs) has been correlated with an excess incidence of CHD (Calvert et al. 1998; Flesch-Janys et al. 1995; Gustavsson and Hogstedt 1997; Hay and Tarrel 1997; Vena et al. 1998).
Several reports have indicated elevations in rates of various diseases (Goldberg et al. 1995; Hall et al. 1996) and birth defects (Croen et al. 1997; Geschwind et al. 1992) among individuals living near hazardous waste sites. New York has a large number of relatively well-characterized hazardous waste sites, known by location and by the major contaminants present at each site. New York also has an excellent statewide system of reporting diseases diagnosed in every hospitalized patient in state-regulated hospitals, which was available to us on the basis of the ZIP code of residence of each patient for the years 1993–2000. We have matched these data sets to investigate the frequency of hospitalization diagnosis for various forms of CHD with residence in ZIP codes that either contain, or do not contain, hazardous waste sites with persistent organic pollutants (POPs) or other wastes.
Fat-soluble POPs, such as PCBs, dioxins/furans, and persistent pesticides such as dichlorodiphenyltrichloroethane (DDT) are of special concern because of their persistence in both the human body and the environment. This class of compounds bioconcentrate in food and can be ingested, inhaled, or absorbed through the skin. These compounds alter normal functioning of the immune, nervous, and endocrine systems and are carcinogenic (Carpenter 1998; Lucier 1991; Vine et al. 2000). In previous studies, we demonstrated elevated hospitalization for thyroid diseases, diseases of the genital tract, and endometriosis among hospitalized women residing within 15 miles of three areas of concern (AOCs) in western New York (Carpenter et al. 2001) and when comparing all ZIP codes containing or abutting POP-contaminated sites with either “other waste” ZIP codes (ZIP codes containing a hazardous waste site but not one containing POPs) or “clean” ZIP codes (ZIP codes that do not contain an identified hazardous waste site) (Carpenter et al. 2003). Kudyakov et al. (2004) reported a similar elevation in risk of infectious respiratory diseases and chronic bronchitis in POP-contaminated ZIP codes. Residential proximity to PCB-contaminated waste sites is associated with increased risk of giving birth to a low-birth-weight infant, especially if it is a male child (Baibergenova et al. 2003).
There is evidence in animals that PCBs induce lipogenic enzymes (Boll et al. 1998), that dioxin-like PCBs cause direct damage to endothelial cells (Toborek et al. 1995), and that workers occupationally exposed to dioxins and PCBs have elevated serum lipids (Calvert et al. 1996; Stehr-Green et al. 1986). Because elevated serum lipids are a major risk factor for CHD, we hypothesized that residency in proximity to POP sites may be associated with increased use of CHD hospital care services. The objective of our study was to examine the potential effect of residential exposure to POPs and non-POP contaminants on hospitalization rates for patients with AMI and other forms of CHD.
Materials and Methods
We conducted a cross-sectional study of hospital discharge rates. Data on hospital discharges were obtained from the New York Statewide Planning and Research Cooperative System (SPARCS; New York State Department of Health, Albany, NY), which is an administrative database. Like other administrative databases, it contains formalized information derived/abstracted from data sets such as clinical charts. We used SPARCS data from 1993–2000, with some 2.5 million hospital discharge records collected annually. Up to 15 diagnoses and 15 procedures, coded according to the International Classification of Diseases, 9th Revision (ICD-9; 1992), are recorded for every patient discharged from any hospital in New York State, except for federal hospitals. The SPARCS data set does not include data from New York City, which maintains its own hospitalization data. For this reason and because New York City differs from the rest of the state in many other ways, we excluded New York City from this study. Data were collected on all the hospital discharges that contained any of the ICD-9 codes for CHD [410.0–414.9 (ischemic heart disease) and 429.7 (sequelae of myocardial infarction, not elsewhere classified)]. The primary outcome variable was the CHD hospital discharge rate calculated as a number of hospital discharges of patients with a diagnosis of CHD divided by the total population residing in a given area (ZIP code). We analyzed separately data for ICD-9 code 410 (AMI), because AMI is more likely to be a primary diagnosis and is less likely to recur in a single individual.
Every SPARCS record contains information on patient’s age, sex, race, source of payment (including the type of health insurance coverage), and the ZIP code of their residence. We used this information to adjust for potential confounders. Income was employed as a proxy measure of socioeconomic status (SES), which is another potential confounder in our study. Data on income (median household income on the ZIP code level) were obtained from the 2000 U.S. Census (U.S. Census Bureau 2000).
The ZIP codes were classified as POPs, other waste, or clean, depending on whether they contained or abutted contaminated sites, where POPs were considered to be PCBs, dioxins/furans, or chlorinated and persistent pesticides. We considered the hazardous waste sites in New York identified by the U.S. Environmental Protection Agency (EPA) (87 National Priority List sites) (U.S. EPA 2003), the New York State Department of Environmental Conservation (NYSDEC) (865 state Superfund sites) (NYSDEC 2004b), and the International Joint Commission (six AOCs) (U.S. EPA 2004), and for each site identified the ZIP code(s) containing or abutting the site and the contaminants listed as being those of primary concern by these agencies. Detailed information on the federal and state Superfund sites, including amounts of contaminants they contain, insofar as that is known, is available on the U.S. EPA website (U.S. EPA 2005β); for the National Priorities List sites on the NYSDEC website (NYSDEC 2004a), and for the AOCs from a different U.S. EPA website (U.S. EPA 2004). Exclusive of New York City, there were 196 POP ZIP codes, 222 ZIP codes with other-waste sites, and 996 clean ZIP codes. Figure 1 is a map of New York State in which the ZIP codes with POP sites are shown in red, and those with other waste are shown in yellow. We analyzed separately the 78 ZIP codes in the PCB-contaminated portion of the Hudson River (200 miles from Hudson Falls to Manhattan), a National Priorities List site, because we have previously reported, using data from the Behavioral Risk Factor Surveillance System (BRFSS), that in those counties abutting the contaminated portion of the Hudson there is less smoking, more consumption of fruit and vegetables, and more frequent exercise than in the rest of the state (Kudyakov et al. 2004, their table 3). In addition, the average family income is greater in the ZIP codes along the Hudson River (Kudyakov et al. 2004, their table 2). Thus analysis of this subset of POP sites allows for partial control over the other major risk factors for CHD and AMI. In addition, we attempted to control for the adverse cardiovascular effects of particulate air pollution by use of the reported mean daily levels of 2.5 μm particulates as reported by the NYSDEC from its network of 43 air monitoring stations, with data reported on the NYSDEC website (NYSDEC 2004b) and Environmental Defense (2004). Effects of living in contaminated sites on the CHD and AMI hospital discharge rates were assessed using a negative binomial model. All analyses were performed using SAS statistical software (version 8.2; SAS Institute Inc., Cary, NC). GENMOD procedure in SAS was used to perform negative binomial regression.
Results
Table 1 shows the demographics of the populations in each of the three categories of ZIP code for the period 1993–2000. Living in a ZIP code containing or abutting a POP hazardous waste site was associated with a 15% increased frequency of discharge diagnosis of CHD compared with living in a clean ZIP code (Table 2). Living in a ZIP code with other waste was also associated with a 4% elevated CHD diagnosis, but this was not statistically significant. As expected, CHD discharge rates were higher in men than in women and increased with age. Compared with CHD patients with median household income of < $30,312, those with higher income had lower hospital discharge rates. Coverage by Medicare (the U.S. government program for health care for the elderly) or comprehensive private health insurance, such as Blue Cross, was associated with lower use of in-patient hospital care services compared with Medicaid coverage (the U.S. government program for health care for the poor) or absence of insurance (self-pay). Hospitalization rates differed across racial/ethnic groups. Asians and Pacific Islanders had lower rates than did Caucasians, African Americans, and Native Americans.
Chronic forms of CHD would be expected to be coded often in the SPARCS database as one of the 14 “other diagnoses” (along with other comorbidities) rather than as the “principal diagnosis.” This may result in a bias problem. Nonsevere comorbidities that do not require treatment during the hospital stay may be undercoded, resulting in underestimation of the association between CHD hospitalization rates and exposure (bias toward null). But the relatively high prevalence of CHD in the general population makes it quite a common comorbidity. Higher hospitalization rates for any disease caused by the contaminants of interest would result in higher CHD prevalence among hospitalized patients, resulting in overestimation of the association between pollution and hospital care use by CHD patients.
To control for these possible biases, we analyzed the association between the most severe form of CHD (AMI) and ZIP code of residence. AMI is a very serious and often life-threatening disease, and it is less likely to be a comorbidity. Table 3 shows hospital discharge rates for AMI in relation to ZIP code of residence and adjusting for known covariates with negative binomial regression. After adjustment for the confounders, those residing in the areas contaminated with POPs have a significantly greater number of AMI hospitalizations. Residency in a POP-contaminated ZIP code is associated with a 20.0% increase in AMI hospital discharge rates compared with clean sites. The 7% elevation in AMI hospitalization rate in other-waste ZIP codes was not statistically significant.
Although other factors associated with AMI hospital discharge rates were not of primary interest for this study, they can be used for quality control purposes and thus merit careful examination. Male sex and older age are well-known risk factors for AMI and other forms of CHD, and more frequent hospitalizations should be expected in these population groups. Consequently, male sex and older age should be associated with higher hospital discharge rates for AMI. So adequacy of the model describing association between any exposure and AMI hospital discharge rates can be questioned if the model fails to indicate the contribution of sex and age.
The hospital discharge rate for AMI among males was about twice that among females, and it increased with age (Table 3). Compared with the lowest income category, those with higher household income had lower hospital discharge rates. Inequality in health insurance coverage is associated with some difference in hospital care use. Those covered by Medicaid or not insured (self-pay) had lower hospital discharge rates compared with those covered by comprehensive health insurance or by Medicare. Caucasians, African Americans, and Native Americans have higher discharge rates than Asians and Pacific Islanders. These results indirectly support the plausibility of the model.
There are other important risk factors for CHD, especially smoking, diet, and exercise. Information at an individual level for these risk factors is not available in our data sets. However, by use of BRFSS, we have county-level information, as reported previously (Kudyakov et al. 2004). In the counties along the 200 miles of the Hudson River that are contaminated with PCBs, there is less smoking, more frequent exercise, and more consumption of fruits and vegetables than in the rest of the upstate region. Tables 4 and 5 show the rates of hospitalization of residents in the 78 ZIP codes that abut the contaminated portion of the Hudson River for CHD and AMI, respectively. Despite living a healthier life style, residents along the Hudson River were 35.8% more likely to be discharged with a diagnosis of CHD, and 39.1% more likely to be discharged with a diagnosis of AMI. These results are highly significant.
Particulate air pollution is well documented to be an important risk factor for CHD and AMI. It is difficult to control for local differences in air pollution in an ecologic study such as this, but we have used what information is available from the air monitoring stations operated by the NYSDEC. Of the 43 stations in New York, 20 are outside of New York City, but in only 16 is regular monitoring of 2.5-μm particulates obtained (six in POP ZIP codes, seven in clean ZIP codes, and three in other-waste ZIP codes). The mean 24-hr 2.5-μm particulate levels reported were 11.5 μg/m3 (range, 7.7–13.7) in the POP ZIP codes, 11.1 μg/m3 (range, 10.5–12.2) in the other-waste ZIP codes, and 11.2 μg/m3 (range, 9.5–12.4) in the clean ZIP codes. Although the number of ZIP codes for which mean particulate information is available is small, the information that can be obtained does not suggest that air pollution is a major confounder.
Discussion
The results of this study are consistent with the hypothesis that exposure to certain environmental contaminants increases the risk of development of CHD and AMI. Those persons residing in POP-contaminated ZIP codes have significantly higher rates of diagnosis of CHD and/or AMI on hospital discharge than do those living in noncontaminated areas. Residency in areas contaminated with other waste is also associated with an elevation in hospital discharge rates, but this relationship did not reach the traditionally used significance level of α = 0.05.
Others have reported health effects of living near hazardous waste sites [reviewed by Vrijheid (2000)]. Health Canada demonstrated a statistically significant elevation, relative to the rest of Ontario, in standardized morbidity ratios for a number of different diseases, including reproductive dysfunction, respiratory and gastrointestinal disease, and diabetes, in Canadian AOCs, highly contaminated sites along the Great Lakes as defined by the International Joint Commission (Elliott SJ et al. 2001). Congenital malformations have been found more commonly in residents living near hazardous waste sites in a number of studies (Croen et al. 1997; Dolk et al. 1998; Geschwind et al. 1992; Orr et al. 2002), whereas others have reported elevated incidence of low birth weight (Elliott P et al. 2001) and end-stage renal disease (Hall et al. 1996). Low birth weight is important in two regards: It is a known risk factor for the development of CHD later in life (Barker et al. 2002), and it has been demonstrated to occur more frequently in infants born to women occupationally exposed to PCBs (Taylor et al. 1989). Using New York birth registry data, we have previously demonstrated an elevation in rates of low-birth-weight infants (especially males) in residents of PCB ZIP codes after adjustment for other factors, including mother’s age, race, weight, height, and incidence of smoking (Baibergenova et al. 2003). Using SPARCS data, we demonstrated statistically significant elevation of hospitalization rates for respiratory infectious diseases, especially diseases such as chronic bronchitis (Kudyakov et al. 2004). The fact that we have demonstrated relationships between residence near waste sites and various diseases using two independent registries adds support to the hypothesis that living near such sites constitutes a risk of exposure and disease.
The observations reported in this investigation raise two important questions: What is the mechanism(s) involved, and what is the route(s) of exposure? Exposure to PCBs and dioxins is known to increase atherogenic serum lipid levels in both animals (Lind et al. 2004; Lovati et al. 1984) and humans (Calvert et al. 1996; Chase et al. 1982; Hu et al. 2003). Most likely, these actions are secondary to gene and enzyme induction in the liver resulting from exposure to POPs, which are difficult to metabolize (Boll et al. 1998). In addition, these contaminants cause direct damage to endothelial cells via oxidative stress (Choi et al. 2003; Hennig et al. 2002; Slim et al. 1999). The combination of an elevation in serum lipids with damage to endothelial cells would be expected to increase the risk of cardiovascular disease.
With regard to the route of exposure, the present and our previous observations (Baibergenova et al. 2003; Carpenter et al. 2001, 2003; Kudyakov et al. 2004) are most consistent with air transport of contaminates, both in vapor phase and particulate bound, being the major route of spread, with inhalation of these semivolatile compounds a major (and underappreciated) route of exposure. Ingestion is usually considered to be the most important route of exposure to POPs, but what people eat is not defined by the ZIP code in which they live. Consumption of contaminated fish is an important route of exposure to PCBs and dioxins, but even persons who engage in sports fishing are not defined by ZIP code of residence. There is increasing evidence that although PCBs and other POPs are not as volatile as some other organic pollutants, they are present in air at high levels around contaminated sites (Hermanson et al. 2004), and they can be absorbed from air and cause biologic effects in animals (Casey et al. 1999; Imsilp et al. 2005). POPs also bind to particulates, which can spread to nearby residences by air currents and can be either breathed in or unintentionally ingested. Although exposure to some of the contaminants at other-waste sites, especially some metals, is also associated with an increased risk of CHD, the fact that these compounds are not very volatile may explain why we did not find a statistically significant relationship between residence in ZIP codes containing these contaminants and CHD.
Several important confounders could explain these observations, particularly SES and behavioral risk factors. Harmful behavioral patterns and unfavorable environmental exposures associated with development of diseases have higher prevalence among lower social classes. Woodward and Boffetta (1997) report differences in exposure to environmental factors among different social classes. People of lower SES are more likely than those of higher SES to reside near polluted sites. New sources of pollution are likely to be placed in poorer neighborhoods, and populations of low SES tend to migrate to such disadvantaged areas for economic reasons (Woodward and Boffetta 1997). However, our results are adjusted for the median household income. Income is a commonly used proxy measure of SES (Berkman and Macintyre 1997; Krieger et al. 1997; Moss and Krieger 1995). Thus, an unequal distribution of socially disadvantaged population as a possible explanation of elevation in hospitalization rates for CHD in residents of POP ZIP codes is unlikely. Our results also control for age, race, and type of health insurance. In the subset of POP sites along the Hudson River, the average family income is higher than in the rest of New York State, yet rates of hospitalization for CHD and AMI are even more elevated than in the rest of the POP sites.
Health insurance coverage is also related to SES (Blackstock et al. 2002; Bradley et al. 2002; Rogers et al. 2000; Schneider et al. 2002). The effect of health insurance on mortality (lacking insurance is associated with higher mortality) is comparable with effects of education and income (Franks et al. 1993). For CHD, patients covered by the most comprehensive types of insurance (e.g., Blue Cross) and Medicare had lower rates than did those covered by Medicaid or not insured (self-pay). The opposite was observed for AMI patients (except for the subgroup of the Hudson River area residents): Those covered by the most comprehensive types of insurance and Medicare had higher rates. This probably reflects the fact that the CHD and AMI populations are different, with CHD being more heterogeneous and including AMI. Many of the chronic forms of CHD are not emergencies, and patients with good insurance are more likely to seek help and be diagnosed in the early stages of the disease. As a result, their chances for progression of CHD to more severe forms, such as AMI, are lower. In contrast, those covered by Medicaid or not insured are less likely to seek health care for conditions that are not emergencies. As a result, they present at hospitals with AMI.
Fine particulate air pollution is another well-documented risk factor for cardiovascular disease (Laden et al. 2000). Although there is no information on particulate levels in every ZIP code, the information available through the network of air monitoring stations in New York does not indicate that fine particulate levels explain the results obtained. However, additional study of air pollution as a possible confounder is warranted.
Our study is not free from limitations above and beyond the usual limitations of ecologic investigations (Morgenstern 1982). The SPARCS database contains information on discharges from state-regulated hospitals only. Data from federally regulated hospitals, including those operated under auspices of the Veterans Administration, are not available from the SPARCS database. Also, there is no information in SPARCS on how many times a patient was hospitalized, which prevents us from drawing conclusions on CHD incidence. Measurement of CHD incidence would provide stronger support of a cause–effect relationship between exposure to the pollutants and CHD development than only rates of CHD diagnosis on hospitalization. This is somewhat less of a problem with AMI, because repeated hospitalizations of the same person (i.e., hospitalizations for subsequent myocardial infarction) do not happen often. So to some degree AMI incidence can be approximated by hospitalization rate. A major limitation is that ZIP code of residence is a very crude measure of exposure. ZIP codes are of varying size, and we have not controlled for the location of the waste site within the ZIP code, because we have only the ZIP code of patient residence, not street address. However, if anything, these limitations would be expected to result in an underestimation of the true relationship. Although we have to some extent controlled for behavioral confounders through use of BRFSS data and air pollution through use of data from the network of monitoring stations in New York, neither of these data sets has information at the level of every ZIP code.
In summary, we determined that residency in POP-contaminated sites is associated with increased rates of hospitalization for CHD and AMI. Although the cross-sectional design of the study prevents us from making definitive conclusions on causal inference, the results support the hypothesis that exposure to PCBs, dioxins/furans, and/or persistent pesticides as a result of living near a hazardous waste site results in an elevated risk of CHD.
Figure 1 Map of New York State showing the locations of POP sites in red and other-waste sites in yellow. Map prepared by Rick Crowsey, Crowsey Incorporated.
Table 3 Hospital discharge rates for AMI in New York (other than New York City) as a function of residence near hazardous waste sites, sex, age, income, types of health insurance, and race.
Parameter Variable name OR (95% CI)
Contamination (vs. clean site)
POPs POP 1.200 (1.034–1.393)
Other waste OTH 1.074 (0.927–1.244)
Sex (vs. female) MALE 2.014 (1.781–2.279)
Age [years (vs. 25–34)] AGE
35–44 5.560 (4.347–7.111)
45–54 16.794 (13.237–21.306)
55–64 37.375 (29.424–47.480)
65–74 59.710 (45.943–77.603)
≥ 75 104.199 (79.464–133.647)
Median household income range [quartiles (vs. < $30,312.50)] INCOME
$30,312.50–35,714.50 0.940 (0.791–1.116)
$35,715–47,348.50 0.900 (0.759–1.068)
> $47,349 0.673 (0.568–0.797)
Health insurance coverage (vs. Medicaid/self-pay)
Most comprehensive types of health insurance (Blue Cross, worker’s compensation, other government, insurance company, HMO, no-fault, self-insured) BLUEX 1.680 (1.440–1.961)
Medicare MCARE 2.058 (1.716–2.469)
Race (vs. Asian/Pacific Islander)
Caucasian CAU 1.372 (1.149–1.639)
African American AA 1.538 (1.283–1.843)
Native American NA 1.620 (1.307–2.008)
Abbreviations: CI, confidence interval; HMO, health maintenance organization; OR, odds ratio.
Table 2 Hospital discharge rates for CHD in New York (other than New York City) as a function of residence near hazardous waste sites, sex, age, income, type of health insurance, and race.
Parameter Variable name OR (95% CI)
Contamination (vs. clean site)
POPs POP 1.150 (1.029–1.286)
Other waste OTH 1.041 (0.919–1.180)
Male sex MALE 1.726 (1.573–1.893)
Age [years (vs. 25–34)] AGE
35–44 6.510 (5.422–7.818)
45–54 28.786 (24.059–34.446)
55–64 81.191 (76.836–97.164)
65–74 180.098 (148.725–218.066)
≥ 75 301.992 (249.236–365.951)
Median household income range [quartiles (vs. < $30,312.50)] INCOME
$30,312.50–35,714.50 0.915 (0.802–1.043)
$35,715–47,348.50 0.831 (0.729–0.947)
>$47,349 0.658 (0.577–0.750)
Health insurance coverage (vs. Medicaid/self-pay)
Most comprehensive types of health insurance (Blue Cross, worker’s compensation, other government, insurance company, HMO, no-fault, self-insured) BLUEX 0.525 (0.467–0.590)
Medicare MCARE 0.602 (0.532–0.682)
Race (vs. Asian/Pacific Islander)
Caucasian CAU 1.713 (1.503–1.952)
African American AA 1.992 (1.741–2.279)
Native American NA 1.556 (1.339–1.808)
Abbreviations: CI, confidence interval; HMO, health maintenance organization; OR, odds ratio.
Table 1 Sociodemographic characteristics of the study population by categories of ZIP codes of residence: number (%) of person-year hospitalizations for 993–2000.
Characteristic POP Other waste Clean
Total population (n = 56,078,068) 14,385,988 (25.65) 16,093,148 (28.70) 25,598,932 (45.65)
Sex
Male 6,740,660 (46.86) 7,586,200 (47.14) 12,190,288 (47.62)
Female 7,645,328 (53.14) 8,506,948 (52.86) 13,408,644 (52.38)
Age (years)
25–34 3,268,864 (22.72) 3,557,760 (22.11) 5,479,460 (21.41)
35–44 3,413,468 (23.73) 3,830,688 (23.80) 6,162,816 (24.07)
45–54 2,595,680 (18.04) 3,102,428 (19.28) 5,130,716 (20.04)
55–64 1,932,692 (13.43) 2,262,196 (14.06) 3,596,760 (14.05)
65–74 1,716,204 (11.93) 1,828,000 (11.36) 2,860,344 (11.17)
≥ 75 1,459,080 (10.14) 1,512,076 (9.40) 2,368,836 (9.24)
Race
Caucasian 12,889,508 (89.60) 14,402,696 (89.50) 23,591,292 (92.16)
African American 1,155,728 (8.03) 1,303,800 (8.10) 1,375,224 (5.37)
Native American 56,552 (0.39) 43,616 (0.27) 72,124 (0.28)
Asian/Pacific Islander 284,200 (1.98) 343,036 (2.13) 560,292 (2.19)
Median household income range (quartiles)
< $30,312.50 3,021,896 (21.01) 2,313,260 (14.37) 3,038,588 (11.87)
$30,312.50–35,714.50 3,136,328 (21.80) 2,301,088 (14.30) 3,547,376 (13.86)
$35,715–47,348.50 4,340,476 (30.17) 3,413,996 (21.21) 4,693,148 (18.33)
>$47,349 3,887,288 (27.02) 8,064,804 (50.11) 14,319,820 (55.94)
Table 4 Hospital discharge rates for CHD in a subset of POP sites along the Hudson River compared with clean ZIP codes in all New York except New York City.
Parameter Variable name OR (95% CI)
Contamination (vs. clean site)
POPs POP 1.358 (1.185–1.557)
Sex (vs. female) MALE 1.669 (1.500–1.857)
Age [years (vs. 25–34)] AGE
35–44 6.442 (5.216–7.955)
45–54 27.741 (22.560–34.110)
55–64 78.783 (64.065–96.883)
65–74 180.170 (144.373–224.820)
≥ 75 297.020 (237.817–370.962)
Median household income range [quartiles (vs. < $30,312.50)] INCOME
$30,312.50–35,714.50 0.882 (0.757–1.027)
$35,715–47,348.50 0.833 (0.714–0.971)
> $47,349 0.624 (0.535–0.727)
Health insurance coverage (vs. Medicaid/self-pay)
Most comprehensive types of health insurance (Blue Cross, worker’s compensation, other government, insurance company, HMO, no-fault, self-insured) BLUEX 0.542 (0.473–0.621)
Medicare MCARE 0.618 (0.535–0.713)
Race (vs. Asian/Pacific Islander)
Caucasian CAU 1.566 (1.343–1.826)
African American AA 1.873 (1.600–2.193)
Native American NA 1.529 (1.282–1.824)
Abbreviations: CI, confidence interval; HMO, health maintenance organization; OR, odds ratio.
Table 5 Hospital discharge rates for AMI in a subset of POP ZIP codes along the Hudson River compared with clean and other-waste ZIP codes in all New York except New York City.
Parameter Variable name OR (95% CI)
Contamination (vs. clean site)
POPs POP 1.391 (1.185–1.632)
Sex (vs. female) MALE 2.038 (1.802–2.306)
Age [years (vs. 25–34)] AGE
35–44 5.772 (4.491–7.418)
45–54 18.601 (14.603–23.696)
55–64 43.645 (34.257–55.606)
65–74 90.432 (69.860–117.062)
≥ 75 163.973 (125.952–213.471)
Median household income range [quartiles (vs. < $30,312.50)] INCOME
$30,312.50–35,714.50 0.960 (0.805–1.145)
$35,715–47,348.50 1.056 (0.882–1.264)
> $47,349 0.672 (0.565–0.799)
Health insurance coverage (vs. Medicaid/self-pay)
Most comprehensive types of health insurance (Blue Cross, worker’s compensation, other government, insurance company, HMO, no-fault, self-insured) BLUEX 0.602 (0.516–0.703)
Medicare MCARE 0.585 (0.496–0.690)
Race (vs. Asian/Pacific Islander)
Caucasian CAU 1.269 (1.062–1.518)
African American AA 1.259 (1.048–1.513)
Native American NA 1.481 (1.173–1.869)
Abbreviations: CI, confidence interval; HMO, health maintenance organization; OR, odds ratio.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7545ehp0113-00076215929901Environmental MedicineCase ReportChronic Neuropsychological Sequelae of Cholinesterase Inhibitors in the Absence of Structural Brain Damage: Two Cases of Acute Poisoning Roldán-Tapia Lola 1Leyva Antonia 2Laynez Francisco 2Santed Fernando Sánchez 11Departamento de Neurociencia y Ciencias de la Salud, Universidad de Almería, Almería, Spain;2Hospital de Poniente, Almería, SpainAddress correspondence to F. Sánchez Santed, Departamento de Neurociencia y Ciencias de la Salud, Universidad de Almería, La Cañada s/n 04120, Almería, Spain. Telephone: 34-950015159. Fax: 34-950015473. E-mail:
[email protected] thank the Neuropsychological Research Group of the University of La Laguna (Spain) for computed tomography and magnetic resonance imaging management and D. Fuldauer and S.P. Smith for revising the English-language text.
This research was supported by research grants PM 96-0102 (Ministerio de Educación y Ciencia) and PM 99-0146 (Ministerio de Ciencia y Tecnología).
The authors declare they have no competing financial interests.
6 2005 10 2 2005 113 6 762 766 3 9 2004 10 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Here we describe two cases of carbamate poisoning. Patients AMF and PVM were accidentally poisoned by cholinesterase inhibitors. The medical diagnosis in both cases was overcholinergic syndrome, as demonstrated by exposure to cholinesterase inhibitors. The widespread use of cholinesterase inhibitors, especially as pesticides, produces a great number of human poisoning events annually. The main known neurotoxic effect of these substances is cholinesterase inhibition, which causes cholinergic overstimulation. Once AMF and PVM had recovered from acute intoxication, they were subjected to extensive neuropsychological evaluation 3 and 12 months after the poisoning event. These assessments point to a cognitive deficit in attention, memory, perceptual, and motor domains 3 months after intoxication. One year later these sequelae remained, even though the brain magnetic resonance imaging (MRI) and computed tomography (CT) scans were interpreted as being within normal limits. We present these cases as examples of neuropsychological profiles of long-term sequelae related to acute poisoning by cholinesterase inhibitor pesticides and show the usefulness of neuropsychological assessment in detecting central nervous system dysfunction in the absence of biochemical or structural markers.
central nervous system dysfunctionlong-term sequelaeneuropsychological profilepesticide poisoning
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Poisoning events and chronic exposure to cholinesterase inhibitors, organophosphates (OPs), and carbamates have traditionally been associated with neurotoxic consequences, such as poor neurobehavioral performance in some cognitive domains such as information processing and memory (Abou-Donia 2003; Wesseling et al. 2002; Yokoyama et al. 1998) or delayed neuropathy induced by certain OPs (Eyer 1995; Jamal 1997). The following two cases show neurocognitive deficits after different types of poisoning events by cholinesterase inhibitors (carbamates and OPs): the first case (AMF) was due to accidental ingestion of a carbamate compound, and the second (PVM) was a greenhouse worker with a history of repeated poisonings while working with OPs, carbamates, or both together. Both patients received emergency-room care for overcholinergic syndrome by cholinesterase inhibitors. Three and 12 months later, cholinesterase levels and neuropsychological performance were assessed following protocols proposed for neurotoxicology evaluation (Fiedler 1996; White and Proctor 1995).
Case 1
In January 1998, AMF, a 55-year-old right-handed female, was attended at the emergency room in the Hospital de Poniente (Almería, Spain) after accidentally drinking a glass of methomyl, a carbamate pesticide. This compound had been prepared by her son and kept in the refrigerator in a bottle for later use in the greenhouse. AMF drank a glass, thinking it was a refreshment. Half an hour later, she was taken to the hospital by her husband, suffering from physical discomfort. Upon the arrival at the emergency room, she presented a Glasgow Coma Scale (GCS) of 15, and her symptoms included perspiration, tremors, myosis, respiratory problems, sialorrhea, and vomiting. The patient did not present hypoxia, coma, or loss of consciousness [butyrylcholinesterase (BuChE) levels shown in Figure 1]. She was treated in the hospital with gastric lavage, activated charcoal, cathartics, and antiemetics. She was released 1 week later without further medical treatment. No neurological or physical disturbances were observed. Once at home, she reported slowness, subtle disorientation, and attention and memory problems in daily activities, such as recalling telephone numbers or cooking.
A year later in a medical follow-up visit, she reported that she felt well: Orientation and speed had improved, as had competency in routine abilities such as cooking, but memory problems remained. No other physical problems were recorded. The brain computed tomography (CT) done at this time was considered normal.
Summary of neuropsychological functioning.
AMF underwent a broad-based clinical neuropsychological examination in April 1998 and April 1999. For some of the tests [Logical Memory test and Rey Auditory Verbal Learning tests (Rey 1964)], an alternative form was used in the second evaluation, to avoid effects of learning and practice. During the interview, AMF reported a low level of education, not having finished obligatory primary school. Her primary role was as a housewife, managing money and the home, raising her children, and sometimes helping in agricultural work. Her estimated IQ was 95 (Bilbao-Bilbao and Seisdedos 2004), and her corrected score on the Mini Mental State Examination (Lobo et al. 1979) was 28, indicating the absence of dementia. She adequately performed tests of single-word reading, basic written arithmetic, and semantic knowledge. Her writing skills were not evaluated, except for her name. Each outcome was evaluated in relation to her educational level, sex, estimated IQ, and age, bearing in mind that the variable “education” has an influence on an individual’s overall neuropsychological performance.
In the first assessment, her performance was below expectation for her estimated pre-morbid IQ in the domains of attention, memory, motor skill, and constructional abilities (Table 1). A minimal depression was also registered.
Case 2
PVM, a 26-year-old right-handed male, was most recently poisoned (February 1998) by a mixture of pesticides [carbamate (methomyl) and pyrethroid (cypermethrin)] while spraying in a greenhouse without any personal safety equipment. PVM attended the emergency room at the Hospital de Poniente (Almería, Spain) because of cephalalgia, abdominal pain, and vomiting. His GCS was 15 (BuChE levels shown in Figure 1). He was given a 2-week prescription for atropine (1.2 mg) and antiemetics and was released the next day. He had been poisoned with OPs and carbamates six times previously (all due to the absence of personal safety equipment), with three of them documented. The first took place in June 1996 while he was spraying with methomyl; upon arrival at the hospital, his symptoms were dizziness and perspiration. The second happened in September 1996 while he was working with a mixture of methomyl and chlorpyrifos (OP); in addition to cholinesterase inhibition, the patient showed tremors, perspiration, respiratory problems, sialorrhea, and vomiting. The third documented poisoning event happened in December 1996 when he was spraying with a mixture of methomyl and chlorpyrifos; his symptoms were vomiting, myosis, abdominal pain, perspiration, and respiratory problems. In these four events, the medical diagnosis was overcholinergic syndrome. No coma, hypoxia, or convulsions were recorded in any of these events. In all cases, treatment in the hospital was gastric lavage and atropine, and the poisonings were resolved in < 24 hr.
PVM underwent cognitive testing in May 1998 and May 1999. During the first interview, he reported mnesic problems, such as remembering telephone numbers and the events of the previous day, but complained of no physical symptoms. He had completed secondary school and had been working in agriculture for 10 years. His estimated IQ was 105, and he obtained a score of 30 in the Mini Mental State Examination. Each outcome was evaluated in relation to his educational level, estimated IQ, sex, and age. Tests of single-word reading, writing skills, basic mental, and written arithmetic and semantic knowledge were completed correctly. Physical and neurological assessment did not show any alteration at the time of the neuropsychological testing. He did not receive pharmacological treatment.
During the second assessment in May 1999, he reported that he continued working in the greenhouse. No further poisoning events had been recorded during this time. The magnetic resonance image (MRI) taken a year after the last poisoning event did not reveal any evidence of brain injury. He reported no physical complaints.
Summary of neuropsychological functioning.
In 1998, PVM’s performance was below expectation for estimated premorbid abilities in the domains of motor skills and short and long-term memory. Deficits at the level of learning new information were detected on several tasks, but he showed forgetting of information over a delay only in visuospatial tasks. His outcomes reflected slowness in the copying of a complex figure or the fulfillment of a complex attentional task (Table 1).
When comparing his 1999 performance with that of 1998, the speed of processing appeared to improve, although probably due to a practice effect. Yet, disturbances remained in the short- and long-term logical memory, as well as in visual memory, and in motor tasks such as alternation and coordination, which implies programming and motor regulation injury. His score for depression was in the normal range, whereas the Taylor Anxiety Scale score (Taylor 1953) showed a subtle increase, although still in normal range. During the assessment sessions, the patient was cooperative but very worried about the possibility of sequelae.
Discussion
Cholinesterase inhibitors, OPs, and carbamates are powerful insecticides widely used in agriculture. However, they are acutely toxic to humans and may cause poisoning as a result of exposure in the workplace, or as accidental or suicidal events (Fengsheng 2000). This is the case in the intensive agriculture industry in southern Spain, where a large number of intoxications have been documented. In only the first half of 2000, 49 occupational poisoning events were recorded [Sociedad Española de Sanidad Ambiental y Asociación Española de Toxicología (SESA/AET) 2000]. The toxic compounds that produced those events were insecticides, mainly OPs (59%), followed by carbamates (34%) and organochlorides (10%).
The main neurotoxic reaction after absorption of cholinesterase inhibitors is acute cholinergic syndrome due to the inhibition of the acetylcholinesterase (AChE) enzyme, which is reversible in case of the carbamates and irreversible in case of OPs. This inhibition leads to an accumulation of acetylcholine (ACh) at synapses, causing overstimulation and subsequent disruption of transmission of impulses in the central, peripheral, and autonomic nervous systems (Martín-Rubí et al. 1995; Storm et al. 2000). Symptoms in patients who experience cholinesterase inhibitor poisonings may include dry mouth, fasciculation, tremor, agitation, ataxia, weakness (Steenland 1996), tension, anxiety, irritability, restlessness, and headaches (Stephens et al. 1995). Once the cholinergic imbalance has been corrected, many of the symptoms usually disappear.
Several studies have shown the existence of both short- and long-term neuropsychological symptoms after acute intoxication by pesticides, mainly OPs. The first publications of acute effects reported neurocognitive sequelae, anxiety, irritability, insomnia (Tabershaw and Cooper 1966), loss of memory (Holmes and Gaon 1956), and reactions similar to schizophrenia and depressive symptoms (Gershon and Shaw 1961). The first controlled study assessing workers who suffered acute poisoning from cholinesterase inhibitor compounds and/or organochlorides was reported by Savage et al. (1988). The poisoned group showed deteriorated intellectual functioning, academic skills, abstraction, reasoning, motor skills, and sensitivity to social stress. No significant difference between poisoned subjects and controls was found on audiometric tests, ophthalmic tests, electroencephalograms, or clinical serum and blood biochemistry evaluations (Savage et al. 1988). Later, Rosenstock et al. (1991) reported a retrospective study in which 36 workers were tested, all of whom had suffered acute OP intoxication 1–3 years earlier. Lifetime work experience data (years worked, other toxics) and recent exposure were controlled. Rosenstock et al. (1991) found evidence of brain damage with impairment of short-term memory, attention, sequencing and problem solving, visuospatial cognition, and depression. A third study compared an OP-poisoned group with control subjects matched in sex, age, and educational level (Steenland et al. 1996). Cholinesterase inhibition was also registered for the poisoned group. Tests included a neurological physical examination, and nerve conduction, vibrotactile sensitivity, neurobehavioral, and postural sway tests. The results pointed to neurocognitive deficits and disturbed peripheral nerve function. The poisoned group had poor scores in neuropsychological tests, such as sustained attention, and showed confusion and tension in the mood scales (Steenland et al. 1996).
In another study, Yokoyama et al. (1998) assessed neuropsychological sequelae in a small group of people who had suffered acute sarin poisoning in a terrorist attack 6–8 months earlier. The authors assessed serum cholinesterase activity in patients on the day of poisoning, controlling for age, education level, alcohol consumption, and smoking status. Deficits in psychomotor performance and posttraumatic disorders, together with disturbances in brain-evoked potentials, were found.
In a recent study carried out in Costa Rica, Wesseling et al. (2002) compared neuro-behavioral performance between two groups of farmers with previous acute intoxications by OP or carbamates. Plasma cholinesterase was assessed for each group of subjects. Two years later, the patients showed long-term sequelae: deficits in visuo- and psychomotor tasks. Performance of the OP-poisoned subjects was worse than that of the farmers who had been poisoned by carbamates.
Although the dysfunctions found are different depending on the task, the type of pesticide, and the severity of poisoning, the three studies (Rosenstock et al. 1991; Wesseling et al. 2002; Yokoyama et al. 1998) have a common profile of deteriorated intellectual functioning, academic abilities, distress, motor skills, posttraumatic stress, confusion, and tension, as well as self-reported symptoms of depression, irritability, and confusion.
The pattern of cognitive deficit that we report here is generally quite typical of the pattern of deficits reported after acute poisoning events. AMF and PVM displayed decreases in the speed of processing, visuospatial functioning, and short-term visual and logical memory deficits. Programming motor activity was also damaged, and minor anxiety was recorded. In both cases, neuropsychological dysfunction remained a year later in the absence of biochemical abnormality or structural brain damage, as shown by CT scan and MRI, in line with several previous reports (Jamal 1997).
It could be argued that the results obtained by AMF on the Beck Depression Inventory (Beck et al. 1961) may explain some of the deficits found. Some authors (Buckelew and Hannay 1986; Gass and Daniel 1990; Meyers and Meyers 1996) suggest that high degrees of depression or anxiety can produce alterations in neuropsychological performance (including some of the tests we used, e.g., Rey-Osterrieth Complex Figure (ROCF), Trail-Making Test B, block design; Table 1). AMF suffered a minimal degree of depression that had mostly disappeared a year later, whereas neurobehavioral deficits remained; thus, it seems that affective deregulation cannot account for our results for AMF.
Repeated administrations of the same test can produce practice effects, especially in tests of verbal memory (Benedict and Zgaljardic 1998; Theisen et al. 1998). To avoid this learning effect, alternative forms of Logical Memory and Rey Auditory Verbal Learning (RAVL) tests were used for retesting both patients. Nevertheless, very small increases occurred in the second administration of the nonverbal tests, such as the score on the time to copy and the recall of the complex figure. In general, the subtle improvement shown in the second assessment indicates a recovery effect, which reinforces the idea that neither education nor poor intellectual abilities fully explain the deficits in neuropsychological performance.
Finally, the last poisoning event suffered by PVM was due to a mixture of carbamate and pyrethroid. Although pyrethroid has been considered among the safest classes of insecticides available (Dorman and Beasley 1991), there have been few reports of systemic poisoning in humans by pyrethroid insecticides. In a review on this topic, Müller-Mohnssen (1999) reported that the symptoms after an acute intoxication (burning sensation of eyes and face, painful irritation of respiratory mucosa, vertigo, and disturbed consciousness) and the period of latency and symptoms of subacute intoxication (tingling, sensation of burning, and sensibility disorder) are dissimilar to those reported after OP and/or carbamate poisoning events, and dissimilar to those presented by PVM upon arrival at the hospital. In fact, it is difficult to attribute the neuropsychological deficit to pyrethroid intoxication when PVM’s diagnosis was overcholinergic syndrome by cholinesterase inhibitors, corroborated by the symptomology presented, which was similar to that previously recorded after intoxications with cholinesterase inhibitors, and the certainty of contact with the carbamate methomyl.
The pharmacological changes in the cholinergic system could be related to the cognitive deficits found. The mechanism of this neurotoxic effect is uncertain. Bushnell et al. (1995) demonstrated a phenomenon of tolerance to repeated doses of chlorpyrifos (OPs) in rats due to the synaptic adaptation of muscarinic receptors (down-regulation). However, this adaptation has a functional cost: The rats showed cognitive deficits, even when the cholinesterase level had returned to normal.
The inhibition of brain AChE by carbamates affects different subtypes of neuronal nicotinic receptors, independently of AChE inhibition. This implies that neuronal nicotinic receptors are additional targets for some carbamate pesticides and that these receptors may contribute to carbamate pesticide toxicology, especially after long-term exposure (Smulders et al. 2003).
Perhaps the underlying mechanism should be sought in the role of the cholinesterase enzyme. A current hypothesis proposes a compensatory mechanism with functional consequences, whereby cholinesterase activity increases after the poisoning events, which is explained by cholinesterase being quickly synthesized in response to brain hyperexcitability after cholinesterase inhibitor poisoning events and after stress (Meshorer et al. 2002). AChE inhibition causes an increase in ACh, activating pre- and postsynaptic cholinergic receptors. There is an immediate transcriptional regulation of genes coding for AChE, choline acetyltransferase (ChAT), and vesicle ACh transporter (VAChT), which reduces the expression of ChAT and VAChT mRNA, increasing the AChE mRNA (Grisaru et al. 1999).
This may be the case for PVM, who after six poisoning events had very high levels of plasma cholinesterase. Indeed, a year later, both AMF and PVM showed above-normal BuChE (Figure 1). BuChE, also called pseudo-cholinesterase or plasma cholinesterase, is an enzyme genetically different from AChE, although they share some important functions, such as ACh hydrolysis (Darvesh et al. 2003). Individual susceptibility to cholinesterase inhibitor compounds is due, in part, to individual genetic variations of this enzyme (Fontoura-da-Silva et al. 1996). Nevertheless, it is widely used as a biomarker of both exposure to cholinesterase inhibitors and recovery from acute intoxications. A cholinergic crisis, together with reduced levels of plasma BuChE activity, leads to the diagnosis of overcholinergic syndrome (Martín-Rubí et al. 1995). Research in progress by our group points to a strong correlation between the number of poisoning events and plasmatic cholinesterase level, which is higher in subjects who have undergone several poisoning events (Roldán-Tapia et al. 2000).
The underlying mechanism might be found in the noncatalytic functions of cholinesterases. Current studies support the idea of a trophic function of the G1 form, and possibly the G4 form, of this enzyme in the central nervous system and neuromuscular junction during development (Andres et al. 1997, 1998; Darvesh et al. 2003). The toxic effect of the synthesis of different forms of the enzyme is unknown but might explain functional damage in the central nervous system. In this regard, it has been shown that an overexpression of AChE in transgenic mice produces progressive neurochemical, neuromorphological, and neurocognitive alteration, at least in spatial memory in adult mice (Andres et al. 1997, 1998; Beeri et al. 1997, 1998). These studies lead us to believe that changes in enzymatic level may produce a pathological dysfunction that explains the neuropsychological deficits found after poisoning by cholinesterase inhibitors such as OP compounds and carbamates (Kaufer et al. 1998).
Conclusions
Pesticide poisoning is a serious health problem that affects the general population, specifically people working with these compounds. Pesticides are designed to kill, reduce, or repel insects, fungi, and other organisms that can threaten public health. However, when improperly used or stored, these chemical agents can also harm humans. Key risks are cancer, birth defects, and damage to the nervous system and to the functioning of the endocrine system. Pesticides are known to cause millions of acute poisoning cases per year, of which at least 1 million require hospitalization. Between one and three agricultural workers per every 100 worldwide suffer from acute pesticide poisoning [United Nations Environment Programme (UNEP) 2004]. The contribution of pesticides to chronic diseases, on the other hand, is unknown. Tackling the risks of pesticide exposure and poisoning requires comprehensive strategies. These strategies should be designed at the local level and supported regionally, nationally, and internationally. They should include research activities on how to develop effective economic and legal instruments. In addition, they should ensure that the public is informed, that health conditions are monitored and, where necessary, that treatment programs are established.
At the clinical level, our findings, together with previous research, provide evidence that cholinesterase inhibitor poisoning has both short- and long-term neuropsychological sequelae, through cholinesterase inhibition and/or other unknown mechanisms, demonstrating the utility of neuropsychological examinations in detecting secondary central nervous system dysfunction after pesticide intoxication. Follow-up studies, controlling for the type and amount of pesticides, AChE level, different forms of the enzyme, and more specific neurobehavioral tasks, will be needed to demonstrate a possible effect of these substances on the central nervous system.
Figure 1 Plasmatic cholinesterase levels (BuChE) of both patients immediately after poisoning and at 3 months and 1 year later, as well as enzymatic activity levels of PVM’s previous intoxications in 1996. The two dashed lines indicate the normal range.
Table 1 Test scores of patients AMF and PVM on tests of general cognitive abilities and emotional state, and interpretation according to accepted cutoff points.
AMF
PVM
Test 3 months 1 year Cutoff norms 3 months 1 year Cutoff norms
Attention
Digit span WAIS backwarda 3 3 4.1 ± 0.6b,c 4 5 4.30 ± 1.11
Stroop test NA NA 52 52 < 50th percentiled
Letter cancellation A 3 errors 2 errors 0 errorse 0 errors 0 errors 0 errorse
Trail-Making A NA NA 40 sec 42 sec Test 18.5 ± 5.1c
Retest 16.2 ± 5.0
Trail-Making B NA NA 90 sec 92 sec Test 41.6 ± 11.4c
Retest 34.0 ± 12.7
Digit symbol WAIS 5 7 > 10 12 12 > 10
Reasoning
Picture completion WAIS 7 8 > 10 12 12 > 10
Similarities WAIS 4 6 > 10 14 12 > 10
Memory
Digit span WAIS forward 4 4 4.9 ± 0.8c 5 5 5.98 ± 1.12c
RAVL test (trial 1–5)f 2, 5, 6, 8, 8 3, 5, 7, 8, 8 Means 4.86, 6.81
8.66, 9.40, 9.71c 4, 6, 10, 11, 13 4, 6, 9, 12, 13 Means 7.4, 10.5, 12.2, 13.0, 13.4c
RAVL test after delay 5 6 7.81 ± 3.7 11 11 12.1 ± 2.8
Logical Memory A, immediate recall (WMS)g 5 5 13.0 ± 2.3h 10 7 13.8 ± 6.0h
Logcal Memory A after delay 5 4 10.2 ± 4.0 8 6 13.0 ± 5.0
ROCF immediate recall 9 10 14.45 ± 5.3e 11.5 12 18.88 ± 6.1e
ROCF delayed recall 9.5 10 13.4 ± 6.0e 12 12.5 20.00 ± 6.4e
BVRTi 9 11 12.8 13 12 13.7
Visuospatial/visual motor
ROCF copy quality 10 10 30.5 ± 4.7c 25 28 33.0 ± 2.8c
ROCF copy time 5 min 20 sec 4 min 30 sec < 3 minj 3 min 56 sec 3 min 50 sec > 3 minj
BVRFTh 26 27 Mean 31.00k 28 28 Mean 31.00k
Astereognosis 5 5 Mean 5e 5 5 Mean 5e
Poppelreuter’s test 10 10 9 ± 1e 10 10 9 ± 1e
Block design WAIS 5 6 > 10 12 12 > 10
Ideomotor praxis 0 errors 0 errors 0 errorsl 0 errors 0 errors 0 errorsl
Ideational praxis 0 errors 0 errors 0 errorsl 0 errors 0 errors 0 errorsl
Reciprocal inhibition 0 errors 0 errors 0 errorse 3 errors 1 error 0 mistakese
Motor alternate Notm Not Correcte Not Not Correcte
Motor coordination Notn Not Correct Correct Correct Correct
Rhythm reproduction 4 6 9 ± 2e 4 5 10 ± 2e
Language
Boston Naming Testo 45 43 49.2 ± 5.6c 54 55 57.8 ± 2.1c
Emotional status
Beck Depression Inventory 14 10 10–15: minimal depression 6 6 0–9 normal range
Taylor Anxiety Scalep 33 21 16–45 18 20 14–45
Abbreviations: BVRFT, Benton Visual Recognition Form Test; BVRT, Benton Visual Retention Test; NA, not available; RAVL, Rey Auditory Verbal Learning; ROCF, Rey-Osterrieth Complex Figure; WAIS, Wechsler Adult Intelligence Scale; WMS, Wechsler Memory Scale.
a Weschler (1993).
b Mean ± SD.
c Mitrushina et al. (1999).
d Golden (1994).
e Lezak (1995).
f Rey (1964).
g Weschler (1997).
h Spreen and Strauss (1998).
i Benton et al. (1994).
j Rey (1997).
k Rey and Sivan (1995).
l Strub and Black (1988).
m The same sequence of alternation in two essays of seven trials each.
n The movement was not correctly performed in either of the trials.
o Goodglass and Kaplan (1990).
p Taylor (1953).
==== Refs
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Wesseling C Keifer M Ahlbom A McConeel R Moon J Rosenstock L 2002 Long-term neurobehavioral effects of mild poisoning with organophosphate and N-metyl carbamate pesticides among banana workers Int J Occup Health 8 27 34
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7727ehp0113-00076715929902Environmental MedicineGrand RoundsHypersensitivity Pneumonitis Associated with Environmental Mycobacteria Beckett William 1Kallay Michael 1Sood Akshay 2Zuo Zhengfa 3Milton Donald 31Pulmonary and Critical Care Division, Occupational Medicine Program and Finger Lakes Occupational Health Services, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA;2Division of Pulmonary and Critical Care Medicine, Southern Illinois University School of Medicine, Springfield, Illinois, USA;3Harvard School of Public Health, Brookline, Massachusetts, USAAddress correspondence to W. Beckett, University of Rochester School of Medicine and Dentistry, Environmental Medicine, Room 4-5702, Box EHSC, 601 Elmwood Ave., Rochester, NY 14642 USA. Telephone: (585) 273-4964. Fax: (585) 256-2591. E-mail:
[email protected] work was supported in part by the National Institute of Environmental Health Sciences (grant P30 ES01247) and the New York State Network of Occupational Health Clinics, New York State Department of Health.
The authors declare they have no competing financial interests.
6 2005 10 2 2005 113 6 767 770 4 11 2004 10 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. A previously healthy man working as a machine operator in an automotive factory developed respiratory symptoms. Medical evaluation showed abnormal pulmonary function tests, a lung biopsy showed hypersensitivity pneumonitis, and his illness was traced to his work environment. His physician asked the employer to remove him from exposure to metalworking fluids. Symptoms reoccurred when he was later reexposed to metalworking fluids, and further permanent decrement in his lung function occurred. Investigation of his workplace showed that five of six large reservoirs of metalworking fluids (cutting oils) grew Mycobacterium chelonae (or Mycobacterium immunogenum), an organism previously associated with outbreaks of hypersensitivity pneumonitis in automaking factories. His lung function remained stable after complete removal from exposure. The employer, metalworking fluid supplier, union, and the National Institute for Occupational Safety and Health were notified of this sentinel health event. No further cases have been documented in this workplace.
atypical mycobacteriaextrinsic allergic alveolitishot tubhypersensitivity pneumonitismetalworking fluidsMycobacterium chelonaeMycobacterium immunogenumnontuberculous mycobacteriaoccupational lung disease
==== Body
Case Presentation
A 57-year-old nonsmoking auto-parts machine operator presented in 1995 because of shortness of breath on exertion, cough, fatigue, and chest congestion. In his job he operated a machine that cut metal parts using a semi-synthetic metalworking fluid (Figure 1) that was collected and recycled through large tanks holding > 1,000 gal fluid. A chest radiograph showed a generalized increase in interstitial markings. He was treated with empiric antibiotics on two occasions. Later, his treating physician suspected occupational asthma due to exposure to oil mist, and asked the employer to remove him from exposure to metalworking fluids. A trial of bronchodilator medications was not effective in improving his symptoms, which were worse after work. Spirometry was performed by the factory’s medical department just before and after a 5-day work week; no change in spirometry was noted. A measurement of total metalworking fluid aerosol done in the patient’s work area showed that the mass of aerosol was 0.42 mg/m3 of air sampled, which was below the recommended limit of a recent advisory committee.
When the physician’s recommendations to remove the patient from all metalworking fluids was not followed and symptoms persisted, the patient was referred to a pulmonary specialist for further testing. Pulmonary function tests showed a reduced diffusing capacity of 67% predicted with oxygen desaturation on ambulation (Table 1), and a carbachol challenge (a test for airway hyperreactivity in asthma) was negative. Bronchial alveolar lavage showed 90% lymphocytes and 10% macrophages in alveolar lining fluid, with negative smear and culture for acid fast bacilli (mycobacteria) and fungi. A transbronchial lung biopsy (Figure 2) showed interstitial chronic inflammation and collections of epithelioid cells suggestive of granulomas with negative stains for acid-fast bacilli and fungus and, on review, diagnostic of hypersensitivity pneumonitis. Several years later, testing of the preserved tissue block by polymerase chain reaction was negative for sequences found in Mycobacterium chelonae. The patient’s treating pulmonologist suspected that the hypersenstivitiy pneumonitis was due to bacteria growing in the metalworking fluid. Serum-precipitating antibodies to a standard panel of nine substances, including bacteria, several fungi, and pigeon serum, were all negative. The pulmonologist gave the patient a brief note for his employer restricting exposure to metalworking fluids; the company physician misinterpreted the message as indicating that the patient had chronic obstructive pulmonary disease made worse by metalworking fluid exposures, and changed his work location but did not fully restrict him from exposure to metalworking fluids.
No specific interventions were made in the workplace with regard to the metalworking fluids, although a plantwide program of reduction of fluid aerosol exposures for all workers was already in progress. Several months later the patient had an uncomplicated myocardial infarction, and after 3 months returned to work with continued exposure to metalworking fluids. Three years later, in 2000, he noted daily nasal congestion associated with work, and worsening dyspnea on exertion. His pulmonologist repeated lung function tests, which showed a further decline in diffusing capacity to 44% predicted (Table 1), and a thin-section computed tomography (CT) scan of the chest (Figure 3) showed “ground glass” opacities indicating interstitial lung disease and mild bronchiectasis. A visit to the patient’s residence by a treating physician trained in occupational and environmental medicine did not reveal any exposures suggestive of contributing to his hypersensitivity pneumonitis.
With the assistance of the county health department, samples of metalworking fluid were obtained for culture from the large reservoir supplying metalworking fluids to the patient’s work area. Standard bacterial and fungal counts were below the level of detection of 10 organisms/mL, unusually low for industrial metalworking fluids, which are usually contaminated by microorganisms. Stain of the centrifuged fluid pellet for acid-fast bacilli was qualitatively “very high,” and culture grew 1.6 × 105 mycobacteria/mL, which were identified as M. chelonae. This mycobacterium, although similar to the M. chelonaeabscessus group, has been proposed as a new species, Mycobacterium immunogenum (Brown-Elliott and Wallace 2002). Additional, separate fluid specimens were sent to another laboratory, which cultured and identified the same organism. Samples of fluid from five reservoirs, a blank of “virgin” metalworking fluid, and a tap water control were for a third time tested and showed > 2,500 mycobacteria/mL, with single-stranded conformational polymorphism analysis showing M. chelonae subtype M. immunogenum in the used fluid samples, and none in the virgin fluid or tap water. Endotoxin, the active agent in the walls of gram-negative bacteria, was measured in the five samples from the five reservoirs at from 2.4 × 102 to 2.5 × 104 endotoxin units per milliliter of fluid by the Limulus assay. Based on these findings, the patient was removed completely from exposure to metalworking fluids.
The treating occupational physician scheduled a meeting with the plant occupational physician, industrial hygienists, and the contracting supplier of the metalworking fluids to recommend a) a survey of symptoms and chest X rays of workers exposed to metalworking fluids to identify any additional cases and b) testing of all metalworking fluid reservoirs in the facility for mycobacteria. In addition, the disease occurrence was reported to the Division of Respiratory Disease Studies of the National Institute for Occupational Safety and Health (NIOSH) and the New York State Health Department Occupational Lung Disease registry.
Discussion
Metalworking fluids are widely used where metal is cut, drilled, milled, or otherwise shaped with cutting tools, to remove heat from both the machine tool and the product being made and to lubricate the parts, remove metal debris, and inhibit metal corrosion. Hypersensitivity pneumonitis is a serious environmental immunologic lung disease in which recurrent exposures to inhaled antigens lead to immunologic sensitization with a predominantly cell-mediated lung response. Subsequent exposures then cause an inflammatory response in the lung that can produce symptoms of dyspnea, cough, and wheeze; fever and elevated blood white count; and transient lung infiltrates and hypoxemia. Persistent disease can cause permanent loss of lung function and even death. Many patients develop disease from exposures associated with work, although exposure to biologic aerosols from home can also cause disease (Apostolakos et al. 2001; Kawai et al. 1984; Wright et al. 1999).
Hypersensitivity pneumonitis was first described in dairy farmers exposed to aerosol from stored, moldy hay containing mixed microorganisms. The list of inhaled substances or mixtures known to cause this condition has grown over the years (Patel et al. 2001); most (but not all) causative agents are biologic materials, including proteins from pigeons and other domestic birds. Blood tests for serum precipitating antibodies to a panel of approximately 10 common causes of hypersensitivity pneumonitis are available from commercial laboratories. However, disease may occur from exposure to substances not included in these panels. In addition, exposure may result in asymptomatic sensitization. Use of precipitating antibodies in diagnosis of hypersensitivity pneumonitis is limited by these factors.
Metalworking fluids may be pure petroleum oils (“straight oils”), emulsions of petroleum in a water base (semisynthetic fluids), or emulsions of synthetic oils in water (synthetic fluids). Because they contain biologically available carbon (in the form of lipids) and water, water-based metalworking fluids routinely sustain microbial growth, but excess growth degrades the fluids and leads to loss of usefulness. Thus, standard use of these metalworking fluids in industry often includes routine testing for bacteria counts (without identification of all organisms) and the use of microbicides with the objective of suppressing, although not necessarily sterilizing, microbial growth.
A variety of respiratory illnesses have been reported to be associated with occupational inhalation of metalworking fluids, including bronchitis, asthma, and lipoid pneumonia (Cullen et al. 1981; Kennedy et al. 1989; Leigh and Hargreave 1999), and their toxicology has recently been reviewed (Gordon 2004). Currently there is no specific Occupational Safety and Health Administration (OSHA) standard for metalworking fluids, although guidance in prevention of health hazards is provided in an NIOSH criteria document [Centers for Disease Control and Prevention (CDC) 1998]. An advisory panel appointed by OSHA recommended a new permissible exposure limit of 0.4 mg/m3 thoracic particulate and 0.5 mg/m3 total particulate (OSHA 1999), based in large part on the NIOSH criteria document. However, at present, this recommendation has not been the subject of rule making. Hypersensitivity pneumonitis associated with metalworking fluids was first described in 1995 (Bernstein et al. 1995). Since then, numerous outbreaks have been described, associated with inhalation of aerosols of water-containing metalworking fluids (reviewed in Kreiss and Cox-Gaenser 1997). Prevention efforts have focused on reduction of inhalation exposures by workplace modifications that reduce generation of aerosols or improve dilution and ventilation of workplace air, and one follow-up study has documented successful remediation (Bracker et al. 2003).
More recently, outbreaks of this condition have been found in workplaces with metalworking fluids containing nontuberculous mycobacteria (CDC 2002; Kreiss and Cox-Gaenser 1997), most frequently M. immunogenum. Detection of these mycobacteria requires special laboratory culture and identification techniques that are not included in routine microbiologic testing of industrial metalworking fluids, such that their identification requires knowledge of their potential for growth and the ability to perform special testing.
During recent years, association of hypersensitivity pneumonitis disease with a different species, Mycobacterium avium complex (MAC), from hot tubs, whirlpool baths, and spas has also been identified, sometimes referred to as “hot tub lung” (Capelluti et al. 2003; Grimes et al. 2001; Rickman et al. 2002; Scully et al. 1997). In these hot water bathing tubs, water may be agitated by powerful jets of air or water that produce bubbles and hence aerosols of water droplets. MAC grows well in the high water temperature of the indoor hot tub. The combination of MAC organisms’ growth and jet aerosolization and subsequent inhalation of large amounts of MAC presumably leads to the development of this disease. Hot tub lung appears to be hypersensitivity pneumonitis to MAC aerosol rather than a direct infection of the lung, although this subject is still a matter of debate (Aksamit 2003; Embil et al. 1997). Interestingly, there have been no documented cases of hot tub lung with outdoor hot tubs.
In hot tub lung, pulmonary function tests were mainly restrictive with occasional obstruction (Anonymous 2000; Kahana and Kay 1997; Khoor et al. 2001; Mangione et al. 2001; Mery and Horan 2002; Rihawi et al. 2004). Chest radiography shows diffuse infiltrates, and high-resolution CT of the chest shows ground glass opacities and micronodules (Pham et al. 2003). Sputum culture was positive for MAC in about 70% of the patients; transbronchial biopsy and bronchoalveolar lavage cultures increased the yield further (Anonymous 2000; Kahana and Kay 1997; Khoor et al. 2001; Mangione et al. 2001; Mery and Horan 2002). Hot tub water usually grows MAC. The histopathologic findings reveal discrete nonnecrotizing granulomas with centrilobular and bronchiolocentric distribution. The granulomas described in hot tub lung were more exuberant and well formed than those seen in typical cases of hypersensitivity pneumonitis from other causes.
There is no standard approach to treatment of hot tub lung. Case reports describe significant improvement with removal from exposure to the hot tubs. Oral corticosteroids, antimycobacterial therapy, or both have also been used. The expected course of this disease after the above measures is recovery without relapse. Measures proposed as being helpful in prevention include better ventilation of the hot tub room, frequent cleaning of the hot tub, frequent change of hot tub water, and use of disinfectants such as chloramines, bromine, and ultraviolet light. These measures are similar to those usually proposed for prevention of hypersensitivity due to exposure to myco-bacteria in metalworking fluids.
Conclusions
Environmental mycobacteria have been associated with a serious lung condition, hypersensitivity pneumonitis, when inhaled as part of liquid droplet aerosols generated from large volumes of liquids serving as a culture medium. These organisms are found commonly in nature and are able to grow in sufficient quantities to cause disease. The case reported here involved an occupational source of such an exposure (aerosolized metalworking fluid in a machining environment), although aerosols containing mycobacteria have been described in other settings as well (aerosolized water from hot tubs). For this reason, specific investigation of sources of aerosols in the work or home environment of patients with this condition should consider the growth of mycobacteria as one of the potential sources of disease. As with other causes of hypersensitivity pneumonitis, removal from exposure and remediation of exposure are the first approaches to treatment.
Figure 1 In a process similar to that used by the patient, metalworking fluid (milky appearance) is flowed over auto parts to reduce friction and cool metal tools. As fluids are sprayed over metal parts, a visible aerosol is formed that can be breathed by operators of the machinery unless specific control measures are instituted. Fluids are recycled from large holding tanks. The presence of carbon and water in fluids permits growth of microorganisms, including mycobacteria.
Figure 2 Transbronchial biopsy specimen of the patient’s lung showing marked alveolar inflammation and cell proliferation with the presence of inflammatory and epithelioid cells.
Figure 3 Thin-section CT scan of the chest showing ground glass opacities in the lung parenchyma, indicating interstitial inflammation and/or fibrosis.
The University of Rochester Southern Illinois University The Harvard School of Public Health
Table 1 Patient’s pulmonary function laboratory data.
O2 saturation (%)
Date FEV1 (%) FVC (%) DLCO (%) Rest Exercise Notes
June 1985 2.70 (88) 3.0 (70) — — — Preplacement work exam before onset of symptoms
January 1996 2.77 (94) 3.47 (95) — — — After onset of symptoms; spirometry before the work week
January 1996 2.98 (101) 3.40 (93) — — — After shift at end of work week
September 1997 — — — 96 96 —
January 1998 — — (67) — — —
April 2000 2.52 (89) 3.14 (89) 9.8 (44) 92 89 More symptomatic
June 2000 1.86 (60) 2.55 (65) — — — —
April 2004 2.42 (89) 3.15 (92) 11.5 (45) — — Symptoms stable
Abbreviations: —, not measured; DLCO, diffusing capacity for carbon monoxide (percent predicted); FEV1, forced expiratory volume in 1 sec in liters (percent predicted); FVC, forced vital capacity in liters (percent predicted).
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References
Anonymous 2000 Case records of the Massachusetts General Hospital. Weekly clinicopathological exercises. Case 27-2000. A 61-year-old with rapidly progressive dyspnea N Engl J Med 34 642 649
Aksamit TR 2003 Hot tub lung: infection, inflammation, or both? Semin Respir Infect 18 33 39 12652452
Apostolakos M Rossmoore H Beckett W 2001 Hypersensitivity pneumonitis from ordinary residential exposures Environ Health Perspect 109 979 981 11673130
Bernstein D Lummus Z Santilli G Siskosky J Bernstein I 1995 Machine operator’s lung. A hypersensitivity pneumonitis disorder associated with exposure to metal working fluid aerosols Chest 108 636 641 7656609
Bracker A Storey E Yang C Hodgson MJ 2003 An outbreak of hypersensitivity pneumonitis at a metalworking plant: a longitudinal assessment of interventional effectiveness Appl Occup Environ Hyg 18 96 108 12519684
Brown-Elliott BA Wallace RJ Jr 2002 Clinical and taxonomic status of pathogenic nonpigmented or late-pigmenting rapidly growing mycobacteria Clin Microbiol Rev 15 716 746 12364376
Cappelluti E Fraire AE Schaefer OP 2003 A case of “hot tub lung” due to Mycobacterium avium complex in an immunocompetent host Arch Intern Med 163 845 848 12695276
CDC 1998. Criteria for a Recommended Standard: Occupational Exposure to Metalworking Fluids. NIOSH 98-102. Atlanta, GA:Centers for Disease Control and Prevention.
CDC 2002 Respiratory illness in workers exposed to metalworking fluid contaminated with nontuberculous mycobacteria—Ohio, 2001 MMWR Morbid Mortal Wkly Rep 51 349 352
Cullen MR Balmes JR Robins JM Smith GJW 1981 Lipoid pneumonia caused by oil mist exposure from a steel rolling tandem mill Am J Indust Med 2 51 58
Embil J Warren P Yakrus M Stark R Corne S Forrest D 1997 Pulmonary illness associated with exposure to Mycobacteriumavium complex in hot tub water. Hypersensitivity pneumonitis or infection? Chest 111 813 816 9118726
Gordon T 2004 Metalworking fluid—the toxicity of a complex mixture J Toxicol Environ Health A 67 209 219 14681076
Grimes M Cole T Fowler A 2001 Obstructive granulomatous bronchiolitis due to Mycobacterium avium complex in an immunocompetent man Respiration 68 411 415 11464091
Kahana LM Kay JM 1997 Pneumonitis due to Mycobacterium avium complex in hot tub water: infection or hypersensitivity? Chest 112 1713 1714 9404787
Kawai T Tamura M Murao M 1984 Summer-type hypersensitivity pneumonitis: a unique disease in Japan Chest 85 311 317 6697785
Kennedy SM Greaves IA Kriebel D Eisen EA Smith TJ Woskie SR 1989 Acute pulmonary responses among automobile workers exposed to aerosols of machining fluids Am J Indust Med 15 627 641
Khoor A Leslie KO Tazelaar HD Helmers RA Colby TV 2001 Diffuse pulmonary disease caused by nontuberculous mycobacteria in immunocompetent people (hot tub lung) Am J Clin Pathol 115 755 762 11345841
Kreiss K Cox-Gaenser J 1997 Metalworking fluid-associated hypersensitivity pneumonitis: a workshop summary Am J Indust Med 32 423 432
Leigh R Hargreave FE 1999 Occupational neutrophilic asthma Can Respir J 6 194 196 10322102
Mangione E Huitt G Lenaway D 2001 Nontuberculous mycobacterial disease following hot tub exposure Emerg Infect Dis 7 1039 1042 11747738
Mery A Horan R 2002 Hot tub-related Mycobacterium avium intracellulare pneumonitis Allergy Asthma Proc 23 271 273 12221898
OSHA 1999. Final Report of the OSHA Metalworking Fluids Standards Advisory Committee. Washington, DC:Occupational Safety & Health Administration. Available: http://www.osha.gov/SLTC/metalworkingfluids/mwf_finalreport.html [accessed 1 April 2005].
Patel AM Ryu JH Reed CE 2001 Hypersensitivity pneumonitis: current concepts and future questions J Aller Clin Immunol 108 661 701
Pham R Vydareny K Gal A 2003 High-resolution computed tomography appearance of pulmonary Mycobacterium avium complex infection after exposure to hot tub: case of hot-tub lung J Thorac Imaging 18 48 52 12544748
Rickman O Ryu J Fidler M Kalra S 2002 Hypersensitivity pneumonitis associated with Mycobacterium avium complex and hot tub use Mayo Clin Proc 77 1233 1237 12440560
Rihawi M Kulkarni P Sherba L 2004. Interstitial Lung Disease in a Hot Tub User. General Occupational Lung Disease Clinical Cases. New York:American Thoracic Society. Available: http://www.thoracic.org/assemblies/eoh/cases/jan04.asp [accessed accessed 1 April 2005].
Scully RE Mark EJ McNeely WF Ebeling SH Phillips LD 1997 Case records of the Massachusetts General Hospital. Weekly clinicopathological exercises. Case 20-1997. A 74-year-old man with progressive cough, dyspnea, and pleural thickening N Engl J Med 336 1895 1903 9197219
Wright RS Dyer Z Liebhaber MI Kell DL Harber P 1999 Hypersensitivity pneumonitis from Pezizia domiciliana . A case of El Niño lung Am J Respir Crit Care Med 160 1758 1761 10556152
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7666ehp0113-00077115929884Environmental MedicineGrand RoundsOccupational Bladder Cancer in a 4,4′-Methylenebis(2-chloroaniline) (MBOCA)-Exposed Worker Liu Chiu-Shong 1Liou Saou-Hsing 23Loh Ching-Hui 4Yu Yi-Chun 5Uang Shi-Nian 5Shih Tung-Sheng 5Chen Hong-I 61Department of Family Medicine, China Medical University, Taichung, Taiwan, Republic of China;2Department of Public Health, National Defense Medical Center, Nei-Hu, Taipei, Taiwan, Republic of China;3Division of Environmental Health and Occupational Medicine, National Health Research Institutes, Kaohsiung, Taiwan, Republic of China;4Department of Family Medicine and Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Nei-Hu, Taipei, Taiwan, Republic of China;5Institute of Occupational Safety and Health, Council of Labor Affairs, Shi-Jr, Taipei, Taiwan, Republic of China;6Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Nei-Hu, Taipei, Taiwan, Republic of ChinaAddress correspondence to H.-I. Chen, Division of Urology, Department of Surgery, Tri-Service General Hospital, 325 Chen-Kung Rd., Section 2, Nei-Hu, Taipei, 114 Taiwan ROC. Telephone: 886-2-8792-3100. Fax: 886-2-87924814. E-mail:
[email protected] study was supported in part by the Taiwan National Health Research Institutes (NHRI-92A1-EOPP12-1).
The authors declare they have no competing financial interests.
6 2005 25 2 2005 113 6 771 774 18 10 2004 24 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. A 52-year-old male chemical worker was admitted to the hospital with a history of paroxysmal microscopic hematuria for about 2 years and nocturia with gross hematuria about five times per night for 2 months. He was a nonsmoker and denied a history of any other bladder carcinogen exposure except for occasional pesticide application during agricultural work. Intravenous urogram imaging showed a mass occupying half of the bladder capacity. Cystoscopy revealed a mass over the left dome of the bladder. Cystoscopic biopsy revealed a grade 3 invasive transitional cell carcinoma with marked necrosis. From 1987 until hospital admission in 2001, the patient had worked in a company that produced the 4,4′-methylenebis(2-chloroaniline) (MBOCA) curing agent. He did not wear any personal protective equipment during work. Ambient air MBOCA levels in the purification process area (0.23–0.41 mg/m3) exceeded the U.S. Occupational Safety and Health Administration’s permissible exposure level. Urinary MBOCA levels (267.9–15701.1 μg/g creatinine) far exceeded the California Occupational Safety and Health Administration’s reference value of 100 μg/L. This patient worked in the purification process with occupational exposure to MBOCA for 14 years. According to the environmental and biologic monitoring data and latency period, and excluding other potential bladder carcinogen exposure, this worker was diagnosed as having occupational bladder cancer due to high exposure to MBOCA through inhalation or dermal absorption in the purification area. This case finding supports that MBOCA is a potential human carcinogen. Safe use of skin-protective equipment and respirators is required to prevent workers from MBOCA exposure.
bladder cancerMBOCAtransitional cell carcinoma
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Incidence and mortality rates of bladder cancer vary about 10-fold worldwide. The highest rates are found in North America and Europe, and the rates are low in many parts of Asia (Engel et al. 2002). In Taiwan, the incidence and mortality rates of bladder cancer (per 100,000) in 2000 were 8.93 and 3.00 for males and 3.87 and 1.36 for females, respectively [Taiwan Department of Health (DOH) 2004]. Transitional cell carcinomas account for about 95% of bladder neoplasms. The remaining 5% consist of squamous cell carcinomas, adenocarcinomas, and others (Engel et al. 2002). Cigarette smoking and occupational exposures are well-documented risk factors for bladder cancer. Other known or suspected risk factors for bladder cancer include race, sex, age, lifestyle, chlorination by-products and arsenic in drinking water, ionizing radiation, bladder infection, high consumption of phenacetin-containing analgesics, and hair dyes (Engel et al. 2002). Several genetic susceptibility factors have been found to be related to bladder cancer (Engel et al. 2002).
Bladder cancer is associated with a number of occupational exposures. The first such association was observed in 1895 (Rehn 1895), and subsequent research among dyestuff workers identified the aromatic amines benzidine and 2-naphthylamine as bladder carcinogens (Case et al. 1954). Several other aromatic amines and related compounds have also been identified as suspected human bladder carcinogens, including 1-naphthylamine, 4-aminobiphenyl, 4-chloro-o-toluidine, o-toluidine, and 4,4′-methylenedianiline [Engel et al. 2002; International Agency for Research on Cancer (IARC) 1987]. Excess risk of bladder cancer has also been observed among rubber workers; painters; truck, bus, and taxi drivers; aluminum workers; and leather workers (Engel et al. 2002). It has been estimated that these occupational exposures are responsible for 18% of bladder cancer cases. As little as 2 years’ exposure may be sufficient to increase the risk, but the time between exposure and subsequent cancer may be as long as 45 years (Goroll et al. 2000).
4,4′-Methylenebis(2-chloroaniline) (MBOCA) is used as a curing agent in industries that primarily produce castable polyurethane parts; thus, occupational exposure may occur during the manufacturing processes in these industries [Agency for Toxic Substances and Disease Registry (ATSDR) 1994]. Workers may inhale MBOCA in small particles in the air or absorb it through the skin during contact with MBOCA dust or vapor. Acute exposure to high levels of MBOCA may cause eye and skin irritation in humans (Hosein and Van Roosmalen 1978). Intermediate and chronic exposure to MBOCA may lead to tumors of the urinary bladder (ATSDR 1994; Kommineni et al. 1979; Russfield et al. 1975; Stula et al. 1975, 1978). In a U.S. retrospective bladder cancer incidence study, 385 of 532 workers ever exposed to MBOCA from 1968 to 1979 and 20 workers who were first employed in 1980 and 1981 participated in a urine screening test (Ward et al. 1990). Workers were exposed to MBOCA for a median employment period of 3.2 months (between 1968 and 1981). Cystoscopy revealed a papillary cell tumor in one worker, and low-grade papillary transitional cell tumors of the urinary bladder were diagnosed in 2 of the remaining 200 workers examined by cystoscopy (Ward et al. 1990). The U.S. Department of Health and Human Services has determined that MBOCA may reasonably be anticipated to be a carcinogen (ATSDR 1994). IARC has determined that MBOCA is probably carcinogenic to humans (category 2A; IARC 1993). The U.S. Environmental Protection Agency has determined that MBOCA is a probable human carcinogen (category 2A; ATSDR 1994). In the Report on Carcinogens, Eleventh Edition [National Toxicology Program (NTP) 2005], the NTP reported that MBOCA may reasonably be anticipated to be a human carcinogen. In this article we report a sentinel case of transitional cell carcinoma of the urinary bladder diagnosed in an MBOCA-manufacturing factory in Taiwan.
Case Presentation
Case report.
A 52-year-old male chemical worker was admitted to the hospital with a history of paroxysmal microscopic hematuria for about 2 years and nocturia with gross hematuria about five times per night for 2 months. He was a nonsmoker and denied taking any medication. He did not use hair dye or reside in an area with endemic blackfoot disease (arsenic intoxication). Social alcohol drinking was noted.
Microscopic hematuria was noted in the periodic health examinations for about 2 years, but the patient ignored it. Two months before admission, he developed nocturia about five times per night. Paroxysmal painless hematuria was also noted. Gross hematuria accompanied by lower abdominal distress occurred 2 weeks before admission. His body weight had decreased from 75 kg to 72 kg over the previous 3 months. He visited the hospital, and an intravenous urogram (IVU) showed a mass occupying half of the bladder capacity. Cystoscopy revealed a mass over the left dome area of the bladder. Cystoscopic biopsy specimens revealed a grade 3/3 invasive transitional cell carcinoma with marked necrosis. Radical cystectomy with ileal conduit combined with radiotherapy was performed because the bladder tissue showed lymphovascular permeation with lymph node metastasis. The worker is still on medical leave because of disease.
Occupational history.
The manufacturing of MBOCA includes the reaction, neutralization, washing, purification, and packing processes. Briefly, o-chloroaniline is reacted with sulfuric acid and formaldehyde under the catalyst of stannous chloride for 3–4 hr. The MBOCA products are then neutralized with an alkali, sodium hydroxide, at a high temperature. After washing and purification, the solid MBOCA is cut into pellets. Although most of the processes are done in closed systems and are automatic, leakage of products from pipes and tanks has been reported. The work schedule in the factory where our patient worked has three 8-hr shifts, and three or four workers in each shift are assigned to each manufacturing line.
The patient was a farmer until he began working at this factory. He worked in the purification area of this MBOCA-producing company for 14 years (1987–2001), and he did not wear any personal protective equipment during work. He denied working for any chemical company other than his present company. He occasionally applied pesticides during agricultural work.
Environmental monitoring data.
An area sampling program to monitor MBOCA levels in the work environment of this MBOCA manufacturing factory was initiated by the Taiwan Institute of Occupational Safety and Health (IOSH), Council of Labor Affairs (IOSH 2003a). The U.S. Occupational Safety and Health Administration’s (OSHA) Analytic Method 24 was adopted in this study (OSHA 1981). Briefly, an impinger filled with 0.1N HCl was used for sampling. The sampling rate was 1 L/min. The sampling time was > 6 hr or > 100 L air. The solution was then analyzed with high-performance liquid chromatography (HPLC) with a 254 nm ultraviolet (UV) detector. The detection limit was 0.056 μg/mL. A quality assurance and quality control program was implemented during the sampling and analysis procedures. All quality tests were shown to be adequate (IOSH 2003a).
Two consecutive days’ air samples were collected in the MBOCA-manufacturing factory, in the areas where the reaction, neutralization, washing, purification, and cutting/packing processes took place. The concentration of MBOCA was highest in the purification area (0.23–0.41 mg/m3, n = 2), followed by the washing area (< 0.02–0.08 mg/m3, n = 7) and the neutralization area (< 0.05–0.06 mg/m3, n = 2) (IOSH 2003a). The concentrations of MBOCA were within OSHA permissible exposure level (0.22 mg/m3) except for the purification area, where levels exceeded permissible levels.
Biological monitoring data.
In addition to environmental monitoring, the Taiwan IOSH also collected workers’ urine to monitor MBOCA concentrations. The U.S. National Institute for Occupational Safety and Health (NIOSH) analytic method 8302 (NIOSH 1994) and the method used by Robert et al. (1999) were adopted in this study. Briefly, sulfamic acid (10 g/L) was used as urine preservative, and urine was neutralized by sodium hydroxide (0.05 g/mL). After cleaning with the Extrelut NT3 column (Merck, Darmstadt, Germany) and evaporating with methanol, the concentrated urine was analyzed by HPLC with a 254 nm UV detector. The limit of detection was 9.54 μg/L.
Urine from 10 workers in this MBOCA-manufacturing company was analyzed. The total urine MBOCA concentrations ranged from 267.9 to 15701.1 μg/g creatinine, with a mean of 5,544 μg/g creatinine (IOSH 2003b). All the urine MBOCA concentrations far exceeded the California Occupational Safety and Health Administration (Cal-OSHA) reference value of 100 μg/L (Robert et al. 1999).
Discussion
Although the production of MBOCA in the United States ceased in 1982, MBOCA is still manufactured in other countries. It is also widely used, primarily in industries producing castable polyurethane elastomers (ATSDR 1994). Therefore, the health impact of MBOCA is still of concern in occupational settings in many countries. In this article we report the first known case of MBOCA-related occupational bladder cancer in Taiwan, which led to the establishment of recommended exposure levels of MBOCA in the workplace. This case report supports the conclusion that MBOCA is a potential human carcinogen.
Environmental monitoring of MBOCA levels in ambient air performed in the present study showed that the concentration was high in the purification process area (0.23–0.41 mg/m3) and exceeded the OSHA permissible exposure level (0.22 mg/m3) and NIOSH’s recommended exposure level of MBOCA (3 mg/m3 as a 10-hr time-weighted average) (Ward et al. 1990). The production quantity of MBOCA at this patient’s plant was about 1,500 tons/year, which was much higher than the 184–580 tons/year reported by Ward et al. (1990). The air levels of MBOCA in this plant were also higher than those in a polyurethane elastomer factory using MBOCA as a curing agent (Clapp et al. 1991). No airborne MBOCA was detected in another polyurethane elastomer factory (Clapp et al. 1991). In another study, personal sampling of the breathing zone of workers for 6–7 hr every other day found levels ranging from 0.002 to 0.0089 mg/m3 (Ichikawa et al. 1990).
In addition to exposure to high air concentrations of MBOCA, workers in the patient’s plant also showed high levels of MBOCA in urine. The urine MBOCA concentrations ranged from 267.9 to 15701.1 μg/g creatinine with a mean of 5,544 μg/g creatinine. The urine MBOCA concentrations of all 10 workers far exceeded the Cal-OSHA reference value of 100 μg/L (Robert et al. 1999). Urine samples obtained by Ward et al. (1990) from plant workers several months after production ceased also showed detectable MBOCA levels that ranged as high as 50,000 μg/L. Osorio et al. (1990) reported a urine MBOCA level of 1,700 μg/L 4 hr postexposure in a worker who was accidentally sprayed with molten MBOCA, but no acute symptoms or other laboratory abnormalities were observed. The urine MBOCA levels in the MBOCA-manufacturing factory workers were much higher than in workers at a polyurethane elastomer factory using MBOCA as a curing agent. Clapp et al. (1991) reported concentrations for mixers in the polyurethane elastomer factory ranging from 5 μg/L to > 100 μg/L urine (average of 61.9 μg/L), whereas concentrations for molders were considerably lower (nondetectable to 50 μg/L urine; average of 14.8 μg/L). Workers in a plant that used MBOCA had urine concentrations of MBOCA ranging from 13 to 458 μg/L (mean 145 μg/L; Keeslar 1986). Another study of 150 workers with industrial exposure to MBOCA in 19 factories showed that, at the end of the work shift, excretion levels of MBOCA ranged from < 0.5 μg/L to 1,600 μg/L, with the highest average urine concentrations (600 μg/L) in workers directly involved in MBOCA manufacture or use (Ducos et al. 1985). In a NIOSH study of urine samples from mixers and molders in a polyurethane elastomer factory (NIOSH 1986), the average urine MBOCA level during the week was approximately 30 μg/L for a mixer over a 5-day work week; the level dropped to 8.9 μg/L over the weekend. However, in Australia, monitoring of workers at seven facilities that used MBOCA in the manufacture of polyurethane polymers showed that the average MBOCA levels in the urine of the workers dropped from 29,600 to 10,400 μg/L within 8–9 months after the implementation of an exposure prevention program (Wan et al. 1989).
The bladder cancer patient reported here worked in the purification process with high exposure to MBOCA for 14 years. He did not wear any personal protective equipment during work. Workers may inhale small particles of MBOCA in the air or absorb the agent through the skin if they come into contact with MBOCA dust or vapor (Chin et al. 1983; Cocker et al. 1988, 1990; Ichikawa et al. 1990; NIOSH 1986). According to the environmental monitoring data for the patient’s factory, he may have been exposed to high concentrations of MBOCA through inhalation or dermal absorption.
Limited epidemiologic studies have examined the incidence of cancer in workers exposed to MBOCA, and three cases of bladder cancer have been reported to be associated with MBOCA exposure. Ward et al. (1990) screened 385 MBOCA-exposed workers and revealed a papillary tumor in one worker after cystoscopy; two low-grade papillary transitional cell carcinomas of the urinary bladder were diagnosed in two of the remaining 200 workers examined by cystoscopy. Two of the men with bladder cancer in Ward et al.’s study (Ward et al. 1988) were younger than 30 years of age. The interval between the time of first exposure and the initiation in that study averaged 11.5 years. The latency period in our case was 14 years, which was compatible with that reported by Ward et al. (1988). The present study adds evidence to the potential carcinogenicity of MBOCA. Human epidemiologic findings are supported by results obtained in dogs (Stula et al. 1978). Results in other animal species also support the conclusion that MBOCA is a potential carcinogen (ATSDR 1994). In addition to the bladder, other target organs include the lung, liver, breast, and Zymbal’s gland in rats (Kommineni et al. 1979; Russfield et al. 1975; Stula et al. 1975) and the lung, liver, and vascular system in mice (Russfield et al. 1975). No adequate epidemiologic studies of MBOCA had been conducted. The present case supports the conclusions from other studies that MBOCA is potentially carcinogenic to humans as well as animals, but it does not substitute for the gap between epidemiologic evidence and animal studies.
Conclusion
This patient was a nonsmoker. Because no other potential risk factors of bladder cancer could be found, occupation-related cancer was strongly suspected. This case finding supports the conclusion that MBOCA is a potential human carcinogen. In most cases, dermal absorption is the most important occupational exposure pathway. Workplace monitoring by air sampling has been reported to be useless because contamination may occur in the absence of observable air levels (Clapp et al. 1991). Biological monitoring of urine MBOCA concentration is a useful method to assess percutaneous MBOCA absorption. Because MBOCA is a potential human carcinogen, it is important to reduce exposure primarily through engineering ventilation controls. However, safe use of skin protective equipment and respirators is required to prevent MBOCA exposure. In addition, appropriate clearance of spills, work training, air monitoring, and periodic health examinations are recommended (ATSDR 1994; Clapp et al. 1991).
China Medical University, National Defense Medical Center, National Health Research Institutes, Institute of Occupational Safety and Health
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Taiwan DOH 2004. Annual Report of Cancer Registry. Taipei, Taiwan:Department of Health.
Wan KC Dare BR Street NR 1989 Biomedical surveillance of workers exposed to 4,4′-methylene-bis-(2-chloroaniline) (MBOCA) in Perth, Western Australia J R Soc Health 109 159 165 2509701
Ward E Halperin W Thun M Grossman HB Fink B Koss L 1988 Bladder tumors in two young males occupationally exposed to MBOCA Am J Ind Med 14 267 272 3189344
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7780ehp0113-00077515929903Children’s HealthArticlesDevelopmentally Restricted Genetic Determinants of Human Arsenic Metabolism: Association between Urinary Methylated Arsenic and CYT19 Polymorphisms in Children Meza Maria Mercedes 1*Yu Lizhi 2*Rodriguez Yelitza Y. 2Guild Mischa 2Thompson David 2Gandolfi A. Jay 3Klimecki Walter T. 21Department of Natural Resources, Sonora Institute of Technology (ITSON), Ciudad Obregon, Sonora, Mexico;2Arizona Respiratory Center, and3Department of Pharmacology and Toxicology, University of Arizona, Tucson, Arizona, USAAddress correspondence to W.T. Klimecki, Arizona Respiratory Center, University of Arizona, P.O. Box 245030, Tucson, AZ 85724 USA. Telephone: (520) 626-7470. Fax: (520) 626-6970. E-mail:
[email protected]*These authors contributed equally to the manuscript.
We thank S. Guerra for constructive discussions of this study.
This research was supported by National Institute of Environmental Health Sciences grant 04940. M.M.M. is supported by the Sonora Institute of Technology (ITSON).
The authors declare they have no competing financial interests.
6 2005 22 3 2005 113 6 775 781 22 11 2004 22 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We report the results of a screen for genetic association with urinary arsenic metabolite levels in three arsenic metabolism candidate genes, PNP, GSTO, and CYT19, in 135 arsenic-exposed subjects from the Yaqui Valley in Sonora, Mexico, who were exposed to drinking water concentrations ranging from 5.5 to 43.3 ppb. We chose 23 polymorphic sites to test in the arsenic-exposed population. Initial phenotypes evaluated included the ratio of urinary inorganic arsenic(III) to inorganic arsenic(V) and the ratio of urinary dimethylarsenic(V) to monomethylarsenic(V) (D:M). In the initial association screening, three polymorphic sites in the CYT19 gene were significantly associated with D:M ratios in the total population. Subsequent analysis of this association revealed that the association signal for the entire population was actually caused by an extremely strong association in only the children (7–11 years of age) between CYT19 genotype and D:M levels. With children removed from the analysis, no significant genetic association was observed in adults (18–79 years). The existence of a strong, developmentally regulated genetic association between CYT19 and arsenic metabolism carries import for both arsenic pharmacogenetics and arsenic toxicology, as well as for public health and governmental regulatory officials.
arsenic metabolismCYT19genetic associationGSTOpharmacogeneticsPNPpolymorphismSNP
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Scientific effort focused on the characterization of the mechanisms of arsenic-induced human toxicity is currently proceeding along several lines of research, including characterization of the spectrum of disease manifestations in human arsenicism, discovery of the proximal biochemical targets of arsenic toxicity, and the elucidation of the gene products involved in the complex biotransformation of arsenic (Karagas et al. 2004; Thomas et al. 2004). Understanding the biotransformation of arsenic is essential to a complete understanding of human arsenicism—a point that is underscored by the wealth of arsenic toxicology literature suggesting that the various chemical forms of arsenic in the human biotransformation scheme can have markedly different toxic potencies and spectra of biologic targets (Mass et al. 2001; Styblo et al. 2000, 2002). This relationship between the production and clearance of various chemical forms of arsenic and the particular manifestation of arsenic toxicity in humans could explain both the variability in measured phenotypes relating to arsenic metabolism in exposed human populations and the long-standing observation of individual variability in arsenic toxicity among relatively homogeneously exposed human populations (Rahman et al. 2003; Watanabe et al. 2001). If the metabolism of arsenic follows similar parameters as the metabolism of a number of xenobiotics, genetic variation in metabolic pathway members could be a determinant of individual variation in metabolism and concomitant toxicity (Hein et al. 1992; May 1994). For some time now hypotheses have been advanced that invoke genetic determinants of individual variability in arsenic metabolism, based on the observed variation in urinary arsenic metabolic profiles in humans, and on interspecies and intraspecies strain differences in animal models (Styblo et al. 1999; Thomas et al. 2001; Vahter 2000). More recently, the case for genetic determinants of variability in human arsenic metabolism was strengthened by Chung et al. (2002), who found a stronger correlation in arsenic methylation-related phenotypes among siblings than among genetically unrelated individuals.
To date, the arsenic literature specifically supports three genes as being involved in arsenic biotransformation: purine nucleoside phosphorylase (PNP), glutathione-S-transferase omega (GSTO), and arsenic(III) methyltransferase (CYT19) (Lin et al. 2002; Radabaugh et al. 2002; Zakharyan et al. 2001). (Recently the HUGO-recognized name for CYT19 has been designated AS3MT.) Some but not all reports show that PNP is capable of functioning in the reduction of arsenate to arsenite (Nemeti et al. 2003; Radabaugh et al. 2002). GSTO is capable of the reduction of monomethylarsenic(V) [MMA(V)] to monomethyl-arsenic(III) (Nemeti and Gregus 2004). CYT19 was initially characterized as an arsenic methyltransferase in rodents that is capable of the methylation of inorganic arsenic to its monomethyl form, and of monomethylarsenic to its dimethylarsenic form (Lin et al. 2002). In rodents, CYT19, in the presence of the proper system of reducing equivalents, has been proposed to be capable of the entire gamut of arsenic biotransformations that begin with arsenite and end with dimethylarsenic(V) [DMA(V)] (Thomas et al. 2004). These studies have provided the identity of candidate genes and the basis for beginning the genetic association studies necessary to test the hypotheses of the existence of genetic determinants of interindividual variability of arsenic metabolism. A second prerequisite for genetic association studies is a catalog of the variable positions within the candidate genes, preferably ascertained in an ethnically relevant population. Collectively, several studies have produced these catalogs in various ethnic populations for GSTO (Marnell et al. 2003; Whitbread et al. 2003; Yu et al. 2003). In addition, catalogs of polymorphic sites in PNP have been published for European and indigenous Americans (Yu et al. 2003). Despite the availability of these catalogs, only one genetic association study has been reported (Marnell et al. 2003).
To address the need for genetic association studies aimed at testing the hypothesis of the existence of genetic determinants of interindividual variability in human arsenic metabolism, we used existing polymorphism catalogs for GSTO and PNP, produced a resequencing-derived catalog of polymorphisms in CYT19 (no such resequencing-based catalog was publicly available), and tested 23 polymorphic sites within these three genes in a population of arsenic-exposed subjects from the Yaqui Valley area of Sonora, Mexico, who had been phenotyped for the levels of urinary metabolites of arsenic. We performed subsequent genetic association analysis to screen for the presence of statistically significant effects of genotypes on arsenic metabolism, as indirectly reflected by urinary arsenic metabolite levels.
Materials and Methods
Description of study subjects.
A total of 144 subjects were included in the study, ranging in age from 7 to 79 years, from several towns in the Yaqui Valley of Sonora, Mexico. Subjects were recruited in 2004 by contact through local health care officials, after attending an informational meeting in their hometowns. All subjects were in good health (self-reported and by physical examination) and free from any skin lesions suggestive of arsenic toxicity. Although we did not track the characteristics of individuals who declined to be in the study, participation rate was estimated to be > 90% of the individuals who attended the informational meetings. Before field collection, we decided to collect two age groups, an adult group ≥ 18 years of age and a child group between 7 and 11 years of age, with the lower age limit based on the logistics of sample collection and the upper age limit chosen to avoid confounding effects of puberty in the child group. The towns (and arsenic content of their drinking water) were Esperanza (each of two wells measured multiple times; combined mean ± SD, 43.3 ± 8.4 μg As/L), Cocorit (one well, 19.33 ± 0.8 μg As/L), Pueblo Yaqui (two wells, combined mean, 9.65 ± 0.23 μg As/L), Colonia Allende (one well, 5.5 ± 0.20 μg As/L), and Campo 47 (one well, 5.5 ± 0.07 μg As/L). Subjects reported that their sole source of drinking water was well water, although we did not independently validate this. Of the study population, 86 individuals were unrelated to any other study participants, 36 subjects consisted of duos of one parent and one child of that parent, 9 subjects consisted of trios of one parent and two children, 9 subjects consisted of trios of two parents and one child, and 4 subjects consisted of pairs of siblings. All subjects gave their informed consent, as approved by the Human Subjects Committee of the University of Arizona and the Ministry of Public Health of Sonora State. Participants were asked to exclude seafood from their diet for 3 days before sample collection. Physical data and data on health status, cigarette smoking, dietary habits, and other variables were obtained by questionnaire and physical examination. Subjects agreed to donate peripheral blood or buccal cells and urine samples for DNA isolation and arsenic determination, respectively. We failed to collect urine or tissue from five subjects, resulting in a total usable study population of 139 individuals.
Tissue collection: blood samples.
Blood was collected by venous puncture. Personnel from the Ministry of Public Health withdrew 5 mL of blood from each subject into Vacutainers containing anticoagulant. The samples were transported on ice to the Sonora Institute of Technology, where they were kept at 4°C for up to 3 days before the DNA extraction was performed.
Tissue collection: buccal samples.
Subjects vigorously rinsed their mouth twice, each time with 25 mL commercial mouthwash, and then discharged the rinse into a 50 mL conical tube. The tubes were stored at 4°C until the DNA extraction was performed.
DNA isolation: blood samples.
Genomic DNA was extracted from whole blood samples using the Blood and Cell Culture Midi Kit (QIAGEN, Valencia, CA, USA).
DNA isolation: buccal samples.
Tubes containing buccal cells were centrifuged at 4,200 rpm for 10 min, washed with sodium chloride solution, and centrifuged again at 4,200 rpm for 10 min. The cell pellet was then processed for DNA isolation using the QIAamp Mini kit (QIAGEN).
Urine collection.
First morning void urine samples were obtained in 100-mL polypro-pylene bottles and kept on ice. Within 6 hr, cooled samples were taken to the Sonora Institute of Technology and kept frozen at −40°C. The accumulated samples were then shipped on dry ice to the University of Arizona, where the samples were stored at −80°C until the analysis was performed.
Total arsenic analysis.
Urine samples were digested with nitric acid using a microwave oven, following published protocols (Francesconi et al. 2002). Freeze-dried urine reference material for trace elements (Clinchek-control; RECIPE, Munich, Germany), containing arsenic at a level of 68 μg As/L, was used for quality control and to validate the assay. Analysis of this standard by inductively coupled plasma/mass spectrometry (ICP/MS) yielded a range of 64.6–68.9 μg As/L with a range of recoveries of 94.9–101.3%.
Arsenic speciation analysis.
Frozen samples were thawed at room temperature and diluted 2-fold using Milli-Q water and filtered with a 0.45 μm filter before injection (Mandal et al. 2001). The HPLC system consisted of an Agilent 1100 HPLC with a reverse-phase C18 column (Prodigy 3 μm ODS, 150 × 4.60 mm2; Phenomenex, Torrance, CA, USA), with an Agilent 7500 ICP/MS used as a detector for the analysis of arsenic species {(arsenic(III) [As(III)], arsenic(V) [As(V)], MMA(V), and DMA(V)}. The detection limits, quality control, precision, and accuracy of this analytical method were assessed. The detection limits were 0.42–1.08 μg/L for arsenic compounds. The precision was estimated 10 times with a solution containing approximately 10 times the detection limit concentrations, yielding percentages of relative standard deviation of 1.14–4.00%. Accuracy values were calculated by spiking standard compounds of all the species that were studied [10 μg/L As(III), As(V), and MMA(V); 20 μg/L DMA(V)] in urine samples. The recovery of the added compounds was 96–108%. Similar recovery experiments used reference urine samples (Institut National de Santéé Publique Québec, Québec, Canada), containing As(III), MMA(V), and DMA(V). Analyses of these standards yielded recoveries between 80 and 109%.
Resequencing: resequencing subjects.
Anonymous DNA samples from healthy individuals of self-reported ancestry were obtained from the Coriell Institute (Camden, NJ, USA). We studied 22 samples from individuals of European ancestry (EA), and 24 samples from individuals of indigenous American ancestry (IA). EA individuals were selected from unrelated Centre d’Etudes du Polymorphisme Humain (CEPH; Paris, France) samples. The geographic origin of the IA samples consisted of five samples from Peru, nine samples from Mexico, one sample from Ecuador, and nine samples from Brazil. One DNA sample isolated from a chimpanzee was also included in the study. These samples are commercially available; sample identification and ordering information are available from the authors.
Resequencing: PCR amplification.
Genomic sequence for CYT19 was accessed from the Human Genome Browser (University of California, Santa Cruz 2004). Because of the large genomic size of human CYT19, we used a sampling strategy in determining the subset of the 32.4-kilobase (kb) genomic region to resequence. Included in the resequencing were approximately 2 kb 5′’ to the first exon, 1 kb 3′’ to the last exon, all exons, including at least 100 bases of the exon intron junction, and roughly evenly spaced regions of intron sequence. Within this strategy a total of 12.2 kb of the genomic region were resequenced. Polymerase chain reaction (PCR) amplicons were designed such that each amplicon was approximately 900 base pairs (bp) in length, and consecutive amplicons overlapped each other by approximately 200 bp. PCR reactions contained 20 ng genomic DNA, 1 pmol of each primer, 0.2 U Taq polymerase (platinum Taq; Invitrogen, Carlsbad, CA, USA), and 0.1 μM dNTPs in a total volume of 10 μL. Specific reaction conditions, including primer sequences, are available from the authors.
Resequencing: direct PCR sequencing.
PCR amplicons were prepared for cycle sequencing by diluting them with water using a dilution range of 1:3 to 1:6, depending on the reaction yield, as determined by agarose gel electrophoresis. Cycle sequencing reactions were assembled using 0.4 μL of cycle sequencing premix (BigDye V3.0; Applied Biosystems, Foster City, CA, USA), 1 pmol of sequencing primer, 1.8 μL 5× sequencing dilution buffer and 5 μL of PCR product in a final volume of 10 μL. Cycle sequencing reactions were purified using DNA-affinity magnetic beads (Agencourt Biosciences, Beverly, MA, USA). Purified sequencing reactions were electrophoretically analyzed using a DNA Analyzer 3730xl (Applied Biosystems).
Resequencing: polymorphism identification and analysis.
Sequence chromatograms were processed for base calling and assembly using the Phred, Phrap and Consed suite of software programs (Ewing et al. 1998; Gordon et al. 1998). Initial polymorphism tagging was performed using Polyphred, with a minimum sequence quality of phred 25 (Nickerson et al. 1997). Potential polymorphic sites that were initially identified by Polyphred were individually confirmed by visual inspection of sequence traces. A criterion of this visual inspection–confirmation was that the polymorphism must have been observed in multiple chromatograms from singleton polymorphisms (polymorphisms occurring in only one subject) or in multiple subjects. For these confirmed polymorphic sites, each genotype for each subject was also confirmed by visual inspection of chromatograms. Polymorphic sites and associated subject-identified genotypes were automatically output to a relational database for further analysis, which included the automated generation of ethnicity-specific genotype frequencies, allele frequencies, and goodness-of-fit tests for Hardy-Weinberg equilibrium. Haplotypes were inferred using a Gibbs-sampling algorithm as implemented in the PHASE software program (Stephens and Donnelly 2003; Stephens et al. 2001). Because the accuracy of statistically inferred haplotypes has been shown to increase with increasing haplotype frequency, we used polymorphisms with a minimum frequency (minor allele frequency; MAF) of 0.10 to define relatively common haplotypes (Tishkoff et al. 2000). Pairwise linkage disequilibrium (LD) was calculated as r2, a measure of the product-moment correlation coefficient (Devlin and Risch 1995).
Resequencing: annotation of polymorphic sites.
Each gene was annotated graphically using the Artemis software program (Rutherford et al. 2000). Annotations included exon location, protein coding exon subset, reading frame, and polymorphism site. Coding region polymorphisms were evaluated for codon changes resulting from polymorphisms and the predicted effect on amino acid sequence.
Genotyping: selection of polymorphic sites for genetic association analysis.
From the ethnicity-specific catalogs of polymorphic sites, a tagging strategy based on bins of polymorphic sites that exceeded 10% minor allele frequency and a within-bin LD (r2) exceeding 0.7 was employed, using publicly available software as recently described (Carlson et al. 2004). Because we did not know in advance the extent to which the population from Sonora would be accurately modeled in polymorphic site frequency and in LD by the EA and IA resequencing populations, tagging polymorphisms were ascertained independently in the IA and the EA populations. Tagging sites that were shared by the EA and IA groups were identified wherever possible; however, one tagging site for each bin for each group was included in the final analysis. Some nonsynonymous polymorphic sites at < 10% frequency were combined with the set of bin-tagging sites to be used in association testing.
Genotyping: Taqman genotyping.
All polymorphic sites selected for genetic association testing were submitted for assay development to the “Assay by Design” service (Applied Biosystems). Sites that were successfully developed through this system were genotyped using the 5′-exonuclease–based Taqman assay. Reaction mixtures, consisting of 1× Taqman universal PCR master mix, 1× assay reagent (Applied Biosystems), and template (genomic DNA at 15 ng or water blank) were robotically assembled. Total reaction volume was 5 μL. Plates were sealed with optical sealing film and subjected to thermal cycling in PCR 9700 thermal cyclers (Applied Biosystems) at 95°C for 15 min, followed by 40 cycles of 92°C for 15 sec and 60°C for 1 min. Following thermal-cycling, plates were assayed for fluorescence using a 7900HT sequence detection system (Applied Biosystems). Assignment of genotypes was based on the ratio of reporter fluorescence to passive dye standard and performed automatically using the “auto-clustering” feature of the allelic discrimination software (SDS, version 2.0; Applied Biosystems).
Genotyping: sequencing-based genotyping.
Polymorphic sites that could not be successfully designed into Taqman assays were genotyped by conventional DNA resequencing, as described above. Trained analysts scored chromatograms from each subject at the polymorphic site for the three possible genotypes.
Genotyping: quality control.
The 22 EA samples used in the initial resequencing that produced the polymorphism catalogs were included in all genotyping samples sets, including Taqman and resequencing-based genotyping. Genotyping results from Taqman assays in the 22 EA DNA samples were compared with genotypes that were derived from resequencing in the same samples. Resequencing-based genotyping included the 22 EA subjects that were included in the original resequencing project in which the polymorphism was first identified. Deviation from Hardy-Weinberg equilibrium at each site was tested using goodness of fit chi-square or Fisher exact test. Nonassignment rates were also calculated at each site tested.
Statistical analyses.
All statistical analyses were performed using SPSS software (version 12; SPSS, Chicago, IL, USA). Evaluation of the polymorphisms for genetic association with urinary arsenic metabolites involved calculating three derivative phenotypes: the ratio of inorganic As(III) to inorganic As(V) (3:5), the ratio of DMA(V) to MMA(V) (D:M), and the ratio of inorganic As(III) to MMA(V) (3:M). All three variables were transformed by a natural log conversion to approximate a normal distribution. Two of the phenotypes, 3:5 and D:M, were used in the initial screen for genetic association between each variable and each polymorphic site. Genotypes were recoded to conform to an analysis of a dominant genetic effect, in which the major homozygotes were assigned one categorical variable value and a second categorical variable value was assigned to the combined heterozygotes and minor homozygotes. For a given phenotype, mean values of each genotypic group were tested for difference using t-tests (two-tailed) for variables that did not deviate significantly from a normal distribution. Mann-Whitney and Wilcoxon tests were used to compare the means of variables that demonstrated a significant (p < 0.05) departure from a normal distribution, based on Kolmogorov-Smirnov or Shapiro-Wilk tests. The initial genetic association screening involved testing 23 polymorphic sites in two variables. To correct for multiple testing, a Bonferroni correction was applied to adjust each level of significance value for 46 tests. Because the presence of closely related subjects violates the assumption of the independence of samples, parents of the children that were analyzed in this study as well as one randomly selected member of each of the five sibling pairs in the study were removed from the statistical tests for difference of means between genotype groups.
We evaluated the relationship between other factors such as age, sex, daily arsenic dose (estimated by the product of arsenic well water concentration by self-reported average daily water consumption volume divided by body weight), and D:M ratio using a stepwise linear regression model, with log-transformed D:M ratio as the dependent variable and genotype, age, sex, and log-transformed daily arsenic dose as independent variables. The entry and removal criteria of the model were probabilities of F-values < 0.05 for entry and > 0.10 for removal. We evaluated the difference between genotype effects in adults versus children using a general linear model in a univariate analysis that included log D:M ratio as the dependent variable, with genotype and child/adult status as fixed factors, together with an interaction term for these two factors.
Results
Resequencing of CYT19.
In total, 12.2 kb of the genomic region that includes CYT19 were resequenced. The polymorphic sites discovered in this effort, their frequencies and genomic contexts are displayed graphically in Figure 1. We have previously published the resequencing results for GSTO and PNP (Yu et al. 2003). The consensus sequence and the location and sequence context of all polymorphic sites in PNP, CYT19, and GSTO have been submitted to Genbank (accession numbers AY817667, AY817668, and AY817669, respectively) and to dbSNP (www.ncbi.nlm.nih.gov/SNP, submitter handle KLIMECKI_LAB). Because the northern Mexican population represents an admixture between IA and EA, resequencing in all genes was done in separate EA and IA populations, to obtain the largest set of sites that could be expected to be polymorphic in the subjects from Sonora. These catalogs of sites served as the basis for the selection of polymorphic sites to test for genetic association with urinary arsenic metabolite profiles. The final set of polymorphic sites selected for genetic association testing consisted of a total of 23 polymorphic sites, 5 in GSTO, 8 in PNP, and 10 in CYT19.
Genotyping results.
DNA from 139 subjects was genotyped at the 23 selected sites. Initial analyses of the data involved examining the call rate on a site and a sample basis. Four DNA samples were excluded from association analysis because they failed to generate genotypes for at least 6 of the 23 sites tested. Within the remaining 135 subjects, genotype assignment rates for all sites were acceptable, with nonassignment (individual reactions in which genotypes could not be called) rates for the 23 sites ranging from 0 to 5.2%, with a mean ± SD of 2.0 ± 1.5%. No sites demonstrated statistically significant deviation from Hardy-Weinberg equilibrium. In addition to the 135 samples from the Sonoran subjects, the 22 EA samples used in the initial resequencing project that initially identified all polymorphic sites were used in all genotyping assays to allow a check of concordance. All Taqman assays were completely concordant with resequencing data. All resequencing-derived genotypes scored in the genotyping phase were completely concordant with the genotypes that were assigned in the initial resequencing of the genes.
Genetic association analysis.
Table 1 summarizes characteristics, including urinary arsenic species distributions, of the study subjects. The results of the screening of these three genes for association with the 3:5 and D:M phenotypes are shown in Table 2. Testing of differences of means between the genotype groups for 3:5 used the Mann-Whitney and Wilcoxon tests because the log-transformed data did not conform to a normal distribution. Testing of means in the D:M variable used two-tailed t-tests. After Bonferroni correction was applied for 46 tests, three polymorphic sites in CYT19 (2393, 7388, and 3058) were significantly associated with D:M levels in this population. To further characterize the association between CYT19 DNA sequence and urinary D:M ratio, we tested the influence of three literature-validated potential covariates with D:M ratio, age, sex, and daily arsenic dose, together with the genotype at site 30585, which had the strongest association with D:M ratio (Chowdhury et al. 2003; Hopenhayn-Rich et al. 1996a, 1996b). This characterization was performed using a stepwise linear regression model, incorporating log-converted D:M ratio as the dependent variable, and CYT19 30585 genotype, age, sex, and log-converted daily arsenic dose (micrograms arsenic per kilogram body weight) as independent variables. In the final model, the only factors included were CYT19 30585 genotype and age, both of which were highly significant (p < 0.001; data not shown). We then reexamined the data in light of the strong effect of age on the relationship between CYT19 genotype and D:M ratio. The distribution of age in this population consisted of a distinct group of children (n = 45) whose age ranged from 7 to 11 years. A second group consisted of adults whose ages ranged from 18 to 79 years. Given our observation of a strong age and genotype effect in D:M values, as well as studies that have reported that arsenic metabolism as measured by D:M ratio may be developmentally regulated, we stratified the population into children and adults, and tested the CYT19 genetic association with D:M ratio separately within each group. Figure 2 shows plots of the 95% confidence intervals (CIs) of the mean log-converted D:M ratio by genotype, together with the significance values for t-tests comparing the genotype-grouped means, for the three CYT19 polymorphic sites, analyzed separately for adults and children. Although no statistically significant genetic association is seen in adults, a highly significant genetic association is observed in children. Using a univariate general linear model approach to evaluate the significance of the difference between the response of children and adults, we tested age (as a dichotomous variable, child, or adult) and CYT19 30585 genotype as fixed factors against D:M ratio as a dependent variable. The interaction term between age and CYT19 site 30585 genotype was highly significant (p = 0.0004, unadjusted for multiple comparisons), suggesting that the effect of CYT19 genotype at site 30585 relative to D:M value is different in children from that in adults. As indicated above, the parents of the children were removed from the adult group to allow statistical testing that assumes sample independence. Nevertheless, when the mean D:M ratio by CYT19 30585 genotype group is compared only between children and their parents, a similar child-specific effect of the variant allele on D:M ratio is observed (data not shown). Because the D:M phenotype has a self-evident relationship with the second arsenic methylation, we created a phenotypic variable to explore the first arsenic methylation step, the 3:M ratio. Figure 3 shows the genetic association testing in this phenotype for site 30585, in which a statistically significant difference between genotypes is observed in children.
We performed LD and haplotype analysis to further characterize the occurrence of multiple polymorphism sites in strong association with D:M ratio. Pairwise LD analysis demonstrated that all three CYT19 sites are in significant LD, with r
2 = 0.56 for sites 2393 and 30585 and r2 = 0.94 for sites 7388 and 30585. Genotypes for each child, for all 10 CYT19 sites, were input into the Gibbs sampling-based haplotype analysis program PHASE (Stephens and Donnelly 2003; Stephens et al. 2001). The haplotypes inferred from this analysis were filtered to remove any haplotype predicted to occur on only one chromosome, because of the low confidence of these rare predictions. Five haplotypes were predicted to occur on two or more chromosomes. The variant alleles at sites 7388 and 30585 only occur together, and only on one haplotype. The variant allele at site 2393 occurs on two haplotypes, one of which contains the variant alleles for sites 7388 and 30585.
Discussion
To our knowledge this is the most comprehensive genetic association study of arsenic metabolism to date. We observed an extremely strong association between the DNA sequence of CYT19 and D:M ratios, an association that remained highly significant even after conservative multiple testing correction. This association was confined to the children in the study. Although the finding of a genetically and developmentally restricted association with arsenic metabolism was unexpected, the presence of a developmentally restricted component in the metabolism of arsenic has been documented (Chowdhury et al. 2003; Kurttio et al. 1998). Our observations were consistent with these reports, in that we observed an overall higher D:M ratio in children. In the study by Kurttio et al. (1998), a random effects analysis found a highly significant effect of age within the 7- to 13-year age group on DMA levels in the urine, with children of this age group having higher DMA levels. It is noteworthy that in the report by Chowdhury et al. (2003), a graph depicting age-specific D:M ratio demonstrates both a pronounced peak in D:M ratio and considerable interindividual variability, within an age range that is similar to that of the children in our study. It is possible that genetic variation in CYT19 also explains the variability of the children’s D:M ratio values in that study.
Figure 2 also shows that the allele frequency of the positively associated polymorphic sites in CYT19 differed between children and adults. Despite the fact that among the 23 sites examined no statistically significant difference in allele frequency between children and adults was observed (data not shown), it is possible that the allele frequency difference between children and adults may reflect real differences in the extent of admixture between children and adults. In a preliminary exploration of this, we compared the difference in allele frequency between EA and IA persons at each of the 23 polymorphic sites from the resequencing data against the allele frequency difference between children and adults from Mexico at the same sites and found a statistically significant negative correlation (data not shown), suggesting that a participant selection bias favored more IA ancestry children, more EA adults, or both. If this admixture bias is real, two potential ramifications relative to our observation of a child-specific effect must be considered. The first is that we have simply tested a marker for IA background that has nothing to do with this particular biochemical process. We feel that this is unlikely, given the magnitude of the effect and the relatively tight distribution of D:M ratios for children and adults within a scenario that would predict that the children are only marginally more “indigenous American” than the adults. Alternatively, admixture bias could possibly underlie a different LD structure in the children than in the adults. In this scenario the effect in D:M ratio that we observed would not be child specific, but rather an LD-specific effect due to our tested markers indirectly “tagging” for another marker, specifically in children. Although this scenario is possible, analysis of the pairwise LD in the existing data from the Mexican population does not support larger blocks of LD in the children compared with the adults. Even if child-specific LD was involved in the child-specific genetic association, only the location of the causal polymorphism would be potentially changed, not the underlying biologic significance of the genetic association. Although not precluding it, our data do not provide strong support for an LD-specific effect in children. Another potential confounding factor is the sex distribution between children, in which there was a similar fraction of males and females, and the adult group, which was skewed toward females. Although it is possible that this could have biased the results in the adult group away from a genetic association, we think that the marginal difference in sex composition between children and adults is unlikely to explain the difference between the sizable effect seen in children and lack of observed effect in adults.
One explanation for the presence of an overall developmental association to the phenotype, concurrent with a developmental association between genotype and phenotype, is that the same biochemical process is involved with both observations. If the widely observed developmental association between age and D:M ratio is caused by the regulation of CYT19 expression, and the effect of the CYT19 polymorphisms that were tested in this study occurred through modulation of CYT19 expression, then a developmentally influenced genetic association would not be unexpected. Notwithstanding the potential that these polymorphisms may be important in adults who are arsenic-exposed under different conditions, this study provides clear evidence that in children exposed to low doses of arsenic in drinking water there exist significant genetic determinants that are strongly associated with the distribution of urinary arsenic metabolites, measured by two variables, D:M ratio and 3:M ratio. It is reasonable to generalize this to a genetic association with altered arsenic metabolism itself in children. However, because we did not measure all known metabolic intermediate species of arsenic, a more specific assignment of genetic association to particular metabolic steps awaits more detailed studies.
The actual genetic variation or variations that are the causative source of the phenotypic difference cannot be determined from the existing data. The fact that the strength of the genetic association is proportional to the LD values with the most highly associated site, 30585, raises the possibility that the source of the association signal is a cluster of polymorphic sites that is marked by site 30585. Likewise, the fact that the sites with the two strongest association signals, 7398 and 30585, perfectly define a single haplotype (they only occur together, and only on one haplotype) suggests that an effect owing to this haplotype cannot be excluded as the source of the association signal. Ultimately, resequencing the affected subjects will be required to identify the set of polymorphisms that was tagged by sites 7398 and 30585 in the association study subjects. A frequent focus of genetic association studies is nonsynonymous polymorphisms. Resequencing CYT19 revealed only one non-synonymous polymorphism, a methionine-to-threonine substitution at residue 287, located at genomic site 9456. In both the EA and IA resequencing populations, this site was in perfect LD with CYT19 site 5207, which we tested; we failed to detect any significant genetic association with any phenotypes. Thus, our data do not support altered amino acid sequence as the cause of the genetic association between CYT19 and arsenic metabolism.
If, as has been well characterized in the arsenic toxicology literature, arsenic metabolism can govern the creation and removal of extremely toxic arsenic species, then these findings may suggest that a particular subset of exposed children may have increased susceptibility to arsenic toxicity by virtue of metabolism that is skewed toward enhanced accumulation of toxic species. Furthermore, the reemergence of arsenic compounds as contemporary pharmaceutical agents, most recently in the treatment of cancers, expands the translational scope of this research to potentially include human pharmacogenetics of arsenic-based therapy (Evens et al. 2004; Raza et al. 2004; Rousselot et al. 2004). In particular, this study should be considered in light of the current use of arsenical compounds in the treatment of cancer in children (Calleja and Warrell 2000; George et al. 2004; Ravindranath et al. 2004).
Conclusion
We report a strong genetic association between polymorphisms of CYT19 and D:M ratio in Mexican children but not in Mexican adults. Because the drinking water concentrations of arsenic in this study represent a range that includes a large number of children throughout the world, including North America as well as Central and South America, the public health and regulatory implications of this study are significant. Follow-up studies should be directed at replicating this finding and at a finer dissection of the entire arsenic metabolic pathway. Ideally, these studies will shed light on the general applicability of this finding to similar situations, as well as those involving differing exposure levels and differing genetic backgrounds of exposed individuals.
Figure 1 Summary of frequency and gene context of polymorphisms discovered in CYT19 in EA (Europe) and IA (America) ancestry subjects. ID column indicates the polymorphism identification number relative to the location in the consensus sequence, with the first base of the consensus numbered 1. ATG offset column indicates the polymorphism location relative to the first base “A” of the ATG methionine initiation codon. Freq % columns are the minor allele frequency, graphically displayed in the column to the right. SNP, single nucleotide polymorphism.
Figure 2 D:M ratio, stratified by genotype and age group at three CYT19 polymorphic positions in unrelated Mexican subjects. Log-transformed D:M ratio for each group is shown as geometric mean, with error bars delineating the 95% CI of the geometric mean values. Genotype groups are depicted on the abscissa. p-Values (unadjusted for multiple comparisons) are from a two-tailed t-test comparing the geometric means of the genotype groups, and shown only when p < 0.05. Graphs are presented for CYT19 sites 2393, 7388, and 30585.
Figure 3 3:M ratio, stratified by genotype at CYT19 site 30585 in the group of children analyzed in Figure 2. 3:M ratio for each genotype group is shown as geometric mean, with error bars delineating the 95% CI of the geometric mean values. Genotype groups are depicted on the abscissa. p-Value (unadjusted for multiple comparisons) = 0.023, two-tailed t-test comparing the geometric means of the genotype groups.
Table 1 Characteristics of and urinary arsenic species distribution [μg/L; geometric mean (95% CI)] within all subjects.
No. Percent male Age As(III) As(V) MMA(V) DMA(V)
Adult 90 29 38.6 7.4 (5.9–9.3) 1.6 (1.4–4.9) 3.6 (3.1–4.2) 22.6 (19.7–25.9)
Child 46 56 9.1 6.3 (4.4–9.2) 1.7 (1.4–2.0) 2.1 (1.5–2.7) 20.7 (15.8–27.0)
Table 2 Significance (p-values) of two-tailed t-tests for differences in mean phenotype values between genotype groups, corrected for multiple testing.
Gene Site D:M ratio
GSTO 1859 NS
GSTO −1242 NS
GSTO 5711 NS
GSTO 8102 NS
GSTO 8147 NS
PNP −1626 NS
PNP −1545 NS
PNP 567 NS
PNP 2934 NS
PNP 3746 NS
PNP 5837 NS
PNP 6760 NS
PNP 7821 NS
CYT19 −400 NS
CYT19 −262 NS
CYT19 49 NS
CYT19 2393 0.024
CYT19 5207 NS
CYT19 7388 0.008
CYT19 7588 NS
CYT19 8597 NS
CYT19 20984 NS
CYT19 30585 0.003
NS, not statistically significant at p < 0.05. The 3:5 ratio was not significant for any gene studied.
==== Refs
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Zakharyan RA Sampayo-Reyes A Healy SM Tsaprailis G Board PG Liebler DC 2001 Human monomethylarsonic acid (MMA(V)) reductase is a member of the glutathione-S -transferase superfamily Chem Res Toxicol 14 1051 1057 11511179
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7652ehp0113-00078215929904Children’s HealthArticlesEnvironmental Health Assessment of Deltamethrin in a Malarious Area of Mexico: Environmental Persistence, Toxicokinetics, and Genotoxicity in Exposed Children Ortiz-Pérez María D. 1Torres-Dosal Arturo 2Batres Lilia E. 2López-Guzmán Olga D. 2Grimaldo M. 1Carranza C. 3Pérez-Maldonado Iván N. 2Martínez Flavio 1Pérez-Urizar José 3Díaz-Barriga Fernando 21Facultad de Medicina,2Unidad Pediátrica Ambiental de la Facultad de Medicina, and3Facultad de Ciencias Químicas, Universidad Autónoma, San Luis Potosí, MéxicoAddress correspondence to F. Díaz-Barriga, Unidad Pediátrica Ambiental, Facultad de Medicina, Avenida Venustiano Carranza 2405, 78210 San Luis Potosí, SLP México. Telephone and fax: 52-444-826-2354. E-mail:
[email protected] work was supported by a grant of the North American Commission for Environmental Cooperation. The work described in the manuscript was conducted in accordance with national and institutional guidelines for the protection of human subjects.
The authors declare they have no competing financial interests.
6 2005 25 2 2005 113 6 782 786 11 10 2004 24 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We reported previously that children are exposed to deltamethrin in malarious areas. In the present work we explored the levels of this insecticide in soil samples and also obtained relevant toxico-kinetic data of deltamethrin in exposed children. Results show that, after spraying, indoor levels of deltamethrin in soil samples were higher than outdoor levels. The mean half-life estimated with these data was 15.5 days for outdoor samples and 15.4 days for indoor samples. Children’s exposure to deltamethrin was assessed using as biomarkers the urinary concentrations of the metabolites 3-phenoxybenzoic acid (3-PBA) and cis-3-(2,2-dibromovinyl)-2,2-dimethylcyclopropane-1-carboxylic acid (Br2CA). The mean level of both biomarkers reached a peak within the first 24 hr postexposure; 6 months after the initial exposure, urinary levels of 3-PBA and Br2CA were found at levels observed before exposure. Approximately 91% of the total 3-PBA or Br2CA was excreted during the first 3 days after exposure. Therefore, we estimated a half-life for this period, the values for 3-PBA and Br2CA being almost identical (13.5 vs. 14.5 hr). Finally, considering reports about the genotoxicity of deltamethrin, we assessed DNA damage in children before and 24 hr after indoor spraying of deltamethrin; we found no differences in the comet assay end points. In conclusion, we observed exposure to deltamethrin in children, but we did not find any relationship between soil concentrations of deltamethrin and urinary levels of the metabolites. At least for genotoxicity, the exposed children appeared not to be at risk.
children’s healthdeltamethringenotoxicitypyrethroidssoil pollutiontoxicokinetics
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Indoor spraying of insecticides has been the main strategy for malaria vector control in Mexico. Until the year 2000, DDT (dichloro-diphenyltricholoroethane) was used in this program; since then, deltamethrin has been the insecticide selected for indoor spraying of dwellings in malarious areas (Chanon et al. 2003). Deltamethrin is one of the insecticides recommended by the World Health Organization (WHO) for indoor spraying (WHO 2001) and is one of the insecticides being used for treatment of mosquito nets (Barlow et al. 2001). To maintain coverage in Africa alone, 50 million nets a year are needed (WHO 2003); thus, we can conclude that numerous individuals are being exposed to deltamethrin. However, the number of exposed individuals is larger if we consider that this pyrethroid is also used in agriculture and in household insecticides (EXTOXNET 2003).
Deltamethrin is a type II pyrethroid insecticide that kills insects on contact and through digestion (EXTOXNET 2003). Although deltamethrin appears to be the most persistent pyrethroid in commercial use, laboratory and field studies suggest that this insecticide degrades faster than many of the persistent organochlorides, including DDT. For example, after initial application of 1 mg/kg deltamethrin in a mineral soil, 52% of that concentration was recovered after an 8-week incubation [Chapman and Harris 1981; Chapman et al. 1981; International Programme on Chemical Safety (IPCS) 1990]. However, in an organic soil the percentage of recovery increased to 74% (Chapman and Harris 1981; Chapman et al. 1981; IPCS 1990).
Ingestion of treated soil particles and contact with sprayed surfaces can be important pathways of exposure for children living in dwellings exposed to deltamethrin. However, inhalation of particles can also be relevant if children enter the sprayed room just after deltamethrin application. Absorption of pyrethroids through lungs, gastrointestinal tract, and skin has been observed in humans [Agency for Toxic Substances and Disease Registry (ATSDR) 2001]. It appears that pyrethroids are rapidly absorbed after inhalation, based on the appearance of urinary metabolites within 30 min of exposure (ATSDR 2001). In turn, oral absorption seems to be more important than dermal. For example, up to 63% of the administered dose was recovered in male volunteers after oral exposure to a type II pyrethroid (Eadsforth et al. 1998; Woollen et al. 1992), whereas only 1.8% of the same pyrethroid applied to volunteers was absorbed through skin (Woollen et al. 1992). Absorption, distribution, and excretion have been studied in three male human volunteers given 14C-radiolabeled deltamethrin as a single 3.0-mg dose orally (IPCS 1990). Plasma concentrations were maximal 1–2 hr after administration with an apparent elimination half-life in plasma of 10–11.5 hr (IPCS 1990). Over 5 days, 10–26% of the dose was eliminated via feces and 51–59% via urine; 90% of urinary excretion was within the first 24 hr (IPCS 1990). Urinary half-life of 10–13.5 hr was consistent with plasma half-life (IPCS 1990).
Although acute effects of deltamethrin, including nervous system effects and allergic reactions, have been described in exposed populations, limited information regarding chronic effects in humans is available (ATSDR 2001; EXTOXNET 2003; IPCS 1990). Therefore, two interesting in vitro effects—deltamethrin-elicited neuronal apoptosis, possibly mediated by nitric oxide synthase (Wu et al. 2003), and deltamethrin-induced DNA damage, as revealed by the comet assay (Villarini et al. 1998)—merit more studies in exposed populations. In such studies, measurement of urinary metabolites may serve as a useful marker of exposure. In fact, for delta-methrin, 3-phenoxybenzoic acid (3-PBA) and cis-3-(2,2-dibromovinyl)-2,2-dimethylcyclo-propane-1-carboxylic acid (Br2CA) have been used as biomarkers either in urban populations (Heudorf and Angerer 2001) or in exposed workers (Tuomainen et al. 1996).
Many of the effects caused by deltamethrin have been reported in exposed workers; however, considering that the capability of children for detoxification of pyrethroid compounds through metabolic pathways may be different from that of adults, and taking into account that this difference could result in an increased distribution of unmetabolized pyrethroids to the central nervous system (ATSDR 2001), it is important to study the exposure of children to this insecticide. We have previously reported that, in malarious areas, children are exposed to deltamethrin (Yáñez et al. 2002). In that study, we suggested, as a working hypothesis, that children are exposed either by ingesting contaminated soil or by having contact with sprayed areas (in the tropics children normally play close to the walls because these are the areas most protected from the sun). In the present work, we explored this hypothesis further, and we also include relevant data with regard to the toxicokinetics of deltamethrin in exposed children.
Materials and Methods
Population.
Participants were unpaid volunteers selected from four malarious communities located in the state of San Luis Potosí, Mexico. Children were selected from those living in 16 sprayed dwellings. Twenty-four girls and eight boys, 3–12 years of age, were studied. All the children lived at their same address for the duration of this study. After informed consent was obtained, a questionnaire was administered and urine samples were taken. The questionnaire registered socio-demographic characteristics, occupation of parents, and food habits. Deltamethrin was sprayed on the walls and ceilings of residences as a wettable powder (2.5%) at a dose of 25 mg/m2. Urine samples were collected in sealable plastic bottles and stored in the deep freezer until analysis. The samples were obtained at day 0 (previous to exposure to deltamethrin spraying) and at days 1, 2, 3, 7, 15, 30, 45, and 180 after exposure. During the first 2 days, seven samples were collected approximately 6 hr apart. On other days, only first void samples were obtained.
Environmental samples.
All of the 16 dwellings had dirt floors. Surface soil samples indoors and outdoors (1–3 cm) were collected in aluminum foil. Two composite samples (one indoor and one outdoor) were obtained from each dwelling. Composite samples were generated with four individual samples collected in each corner of the main room (the one in which the children sleep and play). Samples were obtained at day 0 (previous to the spraying) and at days 1, 8, 15, 30, 45, 60, and 180 after spraying the room with deltamethrin. Samples were transported to the laboratory, dried at 50°C, sieved, and kept under refrigeration (4°C) until analysis.
Urinary 3-PBA and Br2CA analysis.
We quantified 3-PBA and and Br2CA following the method described by Angerer and Ritter (1997). Under our conditions, the method detection limits were 0.58 μg/L for 3-PBA and 0.185 μg/L for Br2CA. The repeatability precision was 5.5% and 6.13% for 3-PBA and Br2CA, respectively. Intralaboratory reproducibility was 1.43% for 3-PBA and 2.58% for Br2CA. Recoveries averaged 117%.
Deltamethrin analysis in soil.
Soil samples (1 g) were microwave extracted in 15 mL acetone:hexane (1:1) using Mars 5-MES 1000 (CEM Corporation, Matthews, NC, USA) equipment. The microwave conditions were as follows: power, 100%; extraction temperature, 100°C; extraction time, 30 min. After the extraction, samples were filtered and evaporated close to 0.1 mL, using a gentle stream of nitrogen. Analyses were performed by gas chromatography using a Hewlett-Packard model 6890 chromatograph (Agilent Technologies, Palo Alto, CA, USA) with an autosampler and a split/splitless injector operating in the splitless mode. The inlet purge off time was 2 min. The operating temperature for the injector was 280°C. Separation was carried out on an HP-5 column (5% phenyl-methylpolysiloxane; Hewlett Packard, Agilent Technologies), 60 m × 0.25 mm inner diameter, 0.25 μm film thickness. Column temperatures were as follows: initially 150°C held for 2 min, raised at a rate of 15°C/min to 300°C, and held at this temperature for 20 min. The transfer line temperature was maintained at 290°C. Helium was used as the carrier gas at a linear velocity of 1.1 mL/min. Injection volume was 2.0 μL. The quantitative analysis of deltamethrin was performed by selected ion monitoring, using mass spectrometry (HP 5973 mass spectrometer; Hewlett-Packard, Agilent Technologies). The characteristic ions were 181 and 253. Under these conditions and using data generated by seven replicates near the lowest concentration attainable at the calibration curve, the method detection limit for deltamethrin was 0.2 mg/kg and the quantification limit was 0.63 mg/kg. The between-assay variation co-efficient was 7 ± 2%. Recovery averaged 85 ± 4.4%. The linear range used for this determination was 0.6 at 15 mg/kg. For the half-life estimation of deltamethrin in soil, we followed a first-order degradation model as described by Hill (1983).
DNA damage.
We evaluated this parameter using the comet assay following the method reported by Singh et al. (1988). Details of the method have been described previously (Yáñez et al. 2004). The blood samples were obtained in 28 children at day 0 (before the deltamethrin was sprayed) and 24 hr after spraying.
Toxicokinetic parameters.
Individual time courses of urinary 3-PBA and Br2CA rate of excretion, expressed as creatinine-corrected values, as well as cumulative curves of metabolite excretion, were constructed. We estimated peak rate excretion, percentage of urinary excreted metabolites, and apparent half-life (by regression analysis of log-transformed) by non-compartmental methods, using the software WinNonLin Pro R.2.1 (Pharsight, Mountain View, CA, USA).
Statistical analysis.
Data were transformed logarithmically to adjust to a normal distribution. A paired t-test, pairing the indoor and outdoor levels at each location, was used to assess the significance of indoor versus outdoor soil deltamethrin concentrations. Differences in half-life of deltamethrin in surface soil samples were analyzed using analysis of variance followed by a comparison analysis using the Tukey procedure. A p < 0.05 value was considered to be statistically significant. A t-test was used to studied differences between 3-PBA and Br2CA in urinary concentrations, half-life, and cumulative excreted concentration (CEC). Analysis of paired Student t-test was used to test differences in the comet assay. The correlation analyses were done with log-transformed data. For all statistical analyses, we used JMP IN software (version 5.0.1.2; SAS Institute, Inc., Cary, NC, USA).
Results
We quantified the levels of deltamethrin in surface soil samples in four communities: Tancuime, El Chuche, El Naranjal, and El Topo. These samples were obtained before and after spraying the insecticide. Results showed that indoor levels were higher than outdoor levels (p < 0.001) (Table 1). The maximum concentration in both environments (outdoors and indoors) was registered between 8 and 15 days after spraying (Table 1, Figure 1). Background levels were recovered on different days in the four communities studied in this work, but at 60–90 days after the application, deltamethrin levels were close to the background levels in all the communities (Table 1). The mean half-lives estimated with these data were 15.5 days for outdoor samples and 15.4 days for indoor samples; however, different half-lives were estimated in the four communities (Table 2). Moreover, although in general the differences in deltamethrin half-lives in soil between indoor and outdoor environments were not statistically significant, it is worth noting that the organic carbon content outdoors (3.1% ± 1.2) was significantly higher than that indoors (2.2% ± 0.8, p < 0.05). No differences among communities were found in soil carbon content.
Discussion
Deltamethrin is being used for the control of malaria in different countries, and indoor spraying may make soil contamination a pathway of exposure for children. In this work, the highest concentration found in soil was 8.9 mg/kg (corresponding to an indoor sample collected in the community of El Naranjal; data not shown), a concentration 32 times lower than the environmental guideline for soil calculated using 0.01 mg/kg/day as an acceptable daily intake for deltamethrin (WHO 2002), 10 kg of body weight, and 350 mg/day of soil ingestion (Díaz-Barriga et al. 1997). Thus, for the concentrations of deltamethrin sprayed in the communities studied in this work, soil ingestion might not be an important pathway of exposure for children.
Indoor deltamethrin soil levels were higher than outdoor concentrations; however, half-lives were very similar. Taking into account that photolysis and biodegradation have been reported in soils treated with deltamethrin, a higher degradation rate for outdoor soils would be expected. However, a possible explanation for this result may be that the content of organic carbon outdoors was higher than indoors. It has been reported that 8 days after treatment, more deltamethrin was recovered from an organic soil than from a sandy soil (Chapman and Harris 1981). The interaction of deltamethrin with soil would decrease the bioavailability and thus the biodegradation of this insecticide.
Although a half-life of 6.8 weeks has been reported for deltamethrin in soil under field conditions, a half-life of 4.8 weeks was reported for indoor experiments (Hill 1983). In this study, we report half-lives of 2.22 weeks (outdoors) and 2.20 weeks (indoors). However, the two studies cannot be compared, because in our study meteorologic conditions (e.g., ambient temperature, humidity, rain) were not controlled during the 6 months of the study. Also, in the present work, different half-lives were found among communities; however, considering the low number of soil samples studied in some of them, it is not possible to postulate further conclusions.
Taking into account that deltamethrin was sprayed in homes, it was important to follow the exposure in children by analyzing urinary metabolites of this insecticide. Different studies have shown that 3-PBA and Br2CA can be used as biomarkers of exposure for deltamethrin (Heudorf and Angerer 2001; Tuomainen et al. 1996). In our study, urinary concentrations of both metabolites increased after exposure (Table 3), and a significant correlation was found between them. However, the concentrations of Br2CA were higher than those of 3-PBA. Although data for humans are limited, in animals the metabolism of deltamethrin includes oxidative attacks at several sites, and conjugation reactions to produce a complex array of primary and secondary water-soluble metabolites (ATSDR 2001; IPCS 1990). Our results indicate that the metabolic pathway of 3-PBA may include more degradation steps than does that of Br2CA. Therefore, and taking into account that 3-PBA is not specific for deltamethrin exposure (Heudorf and Angerer 2001), we recommend using Br2CA as a biomarker of exposure to deltamethrin.
Few studies have reported exposure to deltamethrin in children (Heudorf and Angerer 2001; Heudorf et al. 2004; Yáñez et al. 2002). Our results in this study (urinary concentration of 3-PBA) are similar to those reported by our group in the state of Oaxaca 2–3 days after exposure (Yáñez et al. 2002). Together, both studies show that children living in malarious areas where deltamethrin is sprayed for vector control are more exposed than are children in the general population (Heudorf and Angerer 2001; Heudorf et al. 2004; Yáñez et al. 2002).
Taking into consideration that 91% of the total 3-PBA or Br2CA was excreted during the first 3 days after exposure, an apparent half-life for this period was estimated. The results for 3-PBA (13.5 hr) and Br2CA (14.5 hr) were in the range of what was reported in three young male human volunteers who received a single dose of 3 mg of 14C-deltamethrin. In that study, the apparent half-life of urinary excretion was 10.0–13.5 hr, and 90% of this radioactivity was excreted during the 24 hr after absorption (IPCS 1990).
In our study, considering the concentration of metabolites in urine, the main exposure takes place during the first 3 days after spraying; however, detection of metabolites was possible until 45 days after spraying. The presence of metabolites in all this period may reflect a constant exposure (i.e., due to the presence of deltamethrin in soil). Thus, a second apparent half-life was estimated for the 7- to 45-day postexposure period. In contrast to the initial half-life (estimated for the first 3 days postexposure), in this second half-life we observed a significant difference between the results obtained with 3-PBA and Br2CA; the second half-life of 3-PBA (288 hr) was higher than that estimated with Br2CA levels (197 hr) (Table 4). This result would reflect that the metabolic pathways for the two metabolites are different.
We did not find any correlation between the half-lives with sex or age. However, we did observe a significant inverse correlation between the first half-life and the CEC. Considering that CEC is an indicator of the magnitude of exposure, this result is important because it implies that the metabolism of deltamethrin in humans may be autoinducible. In rats, evidence of development of tolerance on repeated dosing with deltamethrin suggests that the compound induces its own metabolism (Barlow et al. 2001).
We also found an inverse correlation of CEC with age. This result indicates that exposure is related to time at home. Thus, for a risk reduction program, it would be important to identify those pathways of exposure in the home environment. In this regard, three routes of exposure are important for deltamethrin: inhalation, ingestion (in this case of soil particles), and dermal absorption. Considering that the maximum concentration of deltamethrin in soil was not observed until 8–15 hr post-application (Table 1), that deltamethrin content in soils remains almost constant during the first month after spraying (Table 1), and that urinary metabolites content increases from the first day but that it abruptly decreases by day 15 postspraying, the soil pathway could be dismissed as the most important pathway. In contrast, it has been reported that the concentrations of cypermethrin (another halogenated type II pyrethroid), detected in indoor air of vacant dormitory rooms after its application for cockroach control, were 18.2, 8.5, and 3.0 μg/m3 at 0, 7, and 28 days postapplication, respectively (Wright et al. 1993). Assuming that these data can be taken as a good example for deltamethrin, we can argue that inhalation may be an important pathway of exposure during the first days postspraying. In regard to dermal absorption, we have to take into account that, in general, the absorption of pyrethroids through skin is limited; for example, it was estimated that 1.8% of the applied dose was absorbed in volunteers after dermal application of cypermethrin (Woollen et al. 1992). In conclusion, inhalation during the first hours or days postapplication may be considered the main pathway of exposure, leaving soil ingestion and dermal exposure as secondary routes.
Diet can be excluded as a source of delta-methrin in the studied population because the urinary levels of the metabolites found in all children before spraying were lower than the detection limit of the analytical method.
Children in this study were exposed to deltamethrin; thus, we decided to assess DNA damage in them, before and after exposure. Results were negative because no differences were observed. In the literature, DNA damage elicited by deltamethrin has been reported in human peripheral blood leukocytes treated in vitro; however, the minimum significant dose of the insecticide in that study was 100 μg/mL (Villarini et al. 1998). Therefore, we can speculate that deltamethrin blood concentration of the studied children was < 100 μg/mL.
Deltamethrin has been related to a variety of dermal and neurologic symptoms, and although in this work we did not investigate these symptoms in detail, in talking to the parents we learned that no effects in children were related to this insecticide. However, in other malarious communities of Mexico (Pérez-Maldonado I, Díaz-Barriga F, unpublished observations), the children complain about redness, burning sensation, and itching. In this regard, it is important to take into account that, on the basis of human biomonitoring data, it has been difficult to relate exposure with effects. For example, dermal or neurologic symptoms in sprayers exposed to deltamethrin have showed no significant correlation with urinary metabolites excretion (He et al. 1991; Zhang et al. 1991).
In this study, we demonstrated that the health risk for children exposed to delta-methrin in malarious areas can be reduced if precautions are taken, at least during the first 24 hr after spraying. For example, the access of children to sprayed areas has to be limited; furthermore, no foodstuffs should remain in the area during spraying or until 24 hr after. Cooking can be done only after the first day and just after cleaning all the cooking areas (e.g., tables, chairs, rustic oven). It is important to remember that ambient conditions may modify the exposure (i.e., increased volatilization of deltamethrin); furthermore, keeping children’s behavior under observation is a good practice to control soil ingestion and dermal contact with sprayed areas. It is important to consider that although the levels and the effects described in this article may suggest a minimum risk for children, in other communities the situation could be different; therefore, it is important to institute surveillance programs in those communities treated with pesticides. Moreover, studies are needed to assess the exposure to deltamethrin when other formulations are used. For example, a 25% water-dispersible granule formulation of deltamethrin has been recommended by WHO for use for indoor residual spray in malaria vector control programs (WHO 2002). Finally, exposure assessment programs are imperative for communities where a pyrethroid is used for the control of malaria and an organophosphate is used for the control of dengue. An interaction between these types of insecticides has been reported in the literature (Ortíz et al. 1995).
Figure 1 Outdoor and indoor time course of delta-methrin surface soil levels: mean concentration of all the sampled soils. Deltamethrin was sprayed on day 1.
Figure 2 Time course of the logarithm of the amount to be excreted showing the biexponential elimination kinetics of deltamethrin metabolite Br2CA in all children. Abbreviations: Aex, delta-methrin’s metabolite excreted at a particular time; Aexα, total of deltamethrin’s metabolite excreted during entire period of sampling. The mean ± SE are shown for each day.
Table 1 Deltamethrin mean levels (mg/kg) in surface soil samples before and after spraying.
Days No. Outdoor Indoor
Tancuime 0 9 0.4 0.6
1 9 1.4 2.2
8 9 3.0 3.7
15 9 1.7 2.6
30 9 1.0 2.0
45 9 0.9 1.6
60 9 0.6 0.8
180 9 0.3 0.4
El Naranjal 0 3 0.4 1.4
1 3 1.7 4.4
8 3 0.8 3.3
15 3 3.2 7.0
30 3 2.4 4.4
45 2 1.6 4.4
60 2 1.3 1.8
90 2 0.6 0.6
180 2 0.4 0.6
El Chuche 0 3 0.3 0.4
1 3 0.8 1.2
8 3 0.6 0.8
15 3 1.2 2.4
30 3 1.2 2.0
45 3 1.1 1.1
60 3 0.4 0.9
90 3 0.5 0.5
180 3 0.5 0.6
El Topo 0 1 0.3 0.3
1 1 3.4 1.1
8 1 4.4 4.3
15 1 3.3 3.2
30 1 3.9 4.4
45 1 1.9 1.2
60 1 3.3 1.6
90 1 1.7 0.3
180 1 0.3 0.3
Indoor versus outdoor p < 0.001.
Table 2 Half-life of deltamethrin in surface soil samples (days).
Community No. Mean ± SD Range
Tancuime
Outdoor 9 13.4 ± 1.7 10.9–16.4
Indoor 9 13.2 ± 2.6 9.4–17.5
El Chuche
Outdoor 3 25.2 ± 6.0 20.2–31.9
Indoor 3 21.5 ± 6.3 16.2–28.5
El Naranjal
Outdoor 2 18.4 ± 2.1 16.8–19.9
Indoor 2 17.1 ± 2.2 15.5–18.6
El Topo
Outdoor 1 15.1 —
Indoor 1 14.3 —
Total
Outdoor 15 15.5 ± 5.5 10.9–31.9
Indoor 15 15.4 ± 4.6 9.4–28.5
Indoor, no differences among communities. Half-lives of outdoor samples between El Naranjal and Tancuime versus El Chuche are significantly different, p < 0.05.
Table 3 Urinary deltamethrin metabolites in children living in sprayed residences.
3-PBA
Br2CA
Days No. Mean Range Mean Range
Tancuime 0.00 22 ND — ND —
0.25 22 15.5 3–50 24.0 ND–93
0.50 22 26.9 5–71 50.0 6–134
0.75 22 35.2 9–135 83.5 20–226
1.00 22 34.8 5–148 44.1 3–200
1.33 22 22.5 6–95 63.0 8–350
1.66 22 17.5 ND–109 65.0 ND–275
2.00 22 27.3 4–106 60.0 15–368
3.00 22 8.1 ND–20 28.7 ND–150
7.00 22 4.6 ND–30 12.4 3–45
15.00 22 1.5 ND–6 6.0 ND–23
30.00 22 1.9 ND–4 9.3 ND–27
45.00 22 3.5 ND–10 6.3 ND–43
180.00 22 ND — ND —
El Naranjal 0.00 3 ND — ND —
0.25 3 25.1 19–37 90.4 70–122
0.50 3 56.3 40–71 120 28–271
0.75 2 37.8 29–46 136 55–217
1.00 1 25.8 — 11.1 —
1.33 3 31.5 17–41 55.6 22–80
1.66 2 17.5 13–22 93.8 35–182
2.00 3 27.7 20–35 42.1 24–71
3.00 2 5.7 2–12 2.4 ND–6
7.00 2 5.8 5–7 25.6 24–26
15.00 2 1.0 ND–3 3.6 ND–4
30.00 2 3.6 3.6 16.9 15–18
45.00 2 7.4 3–11 ND —
180.00 2 ND — ND —
El Chuche 0.00 5 ND — ND —
0.25 5 11.8 4–26 17.6 3–50
0.50 5 13.0 5–20 27.0 10–56
0.75 5 21.3 13–31 57.2 30–95
1.00 5 21.8 13–37 19.0 9–34
1.33 5 20.1 9–27 50.0 14–140
1.66 5 7.4 ND–20 46.0 ND–180
2.00 5 16.9 11–22 31.4 19–59
3.00 5 7.4 ND–26 20.1 ND–43
7.00 5 5.2 3–8.2 13.4 6–21
15.00 5 1.8 ND–5 4.4 ND–11
30.00 5 1.8 ND–4.3 8.6 1–10
45.00 5 3.0 ND–7 7.1 ND–18
180.00 5 ND — ND —
El Topo 0.00 2 ND — ND —
0.25 2 5.8 4–7 7.0 5–9
0.50 2 7.6 5–10 11.0 9–13
0.75 2 9.9 8–11 31.2 20–43
1.00 2 10.6 8–13 32.0 16–49
1.33 2 9.8 8–12 24.0 13–35
1.66 2 4.2 3–5 18.6 4–34
2.00 2 12.7 8–17 40.5 36–45
3.00 2 6.1 5–7 10.8 4–17
7.00 2 5.5 4–7 10.3 10–11
15.00 2 1.5 1–2 7.3 4–10.6
30.00 2 2.5 2–3 5.0 1–9
45.00 1 4 — 14.3 —
180.00 2 ND — ND —
ND, not detectable. The analyses were done before and after spraying (days). Results are μg/g creatinine. Using a paired t-test, differences between both metabolites were found to be significant (p < 0.05) when using the information of all the children in the study.
Table 4 Toxicokinetic parameters in children exposed to deltamethrin.
3-PBA Br2CA
T1/2 (first 3 days) 13.5 ± 3.7 14.5 ± 4.0
T1/2 (7–45 days) 288.8 ± 98.9* 197.5 ± 78.8
CEC (μg/g creatinine) 172.1 ± 98.3* 409.7 ± 254.2
T1/2, half-life (hours). Values are arithmetic mean ± SD.
* p < 0.05.
Table 5 DNA damage in children exposed to delta-methrin.
End point Sample Mean ± SD
Tail moment Before spraying 9.1 ± 3.2
24 hr after spraying 9.1 ± 3.0
Tail length Before spraying 43.2 ± 12.7
24 hr after spraying 39.2 ± 9.6
DNA damage was assessed by the comet assay, n = 28.
==== Refs
References
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Chapman RA Harris CR 1981 Persistence of four pyrethroid insecticides in a mineral and organic soil J Environ Sci Health B16 605 615
Chapman RA Tu CM Harris CR Cole C 1981 Persistence of five pyrethroid insecticides in sterile, and natural, mineral and organic soil Bull Environ Contam Toxicol 26 513 519 7236909
Díaz-Barriga F Batres L Calderón J Lugo A Galvao L Lara I 1997 The El Paso smelter twenty years later: residual impact on Mexican children Environ Res 74 11 16 9339209
Eadsforth CV Bragt PC van Sittert NJ 1998 Human dose-excretion studies with pyrethroid insecticides cypermethrin and alphacypermethrin. Relevance for biological monitoring Xenobiotica 18 603 614 3400277
EXTOXNET 2003. Deltamethrin. Corvallis, OR:Oregon State University Extension Toxicology Network. Available: http://extoxnet.orst.edu/ghindex.html [accessed 9 February 2005].
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Heudorf U Angerer J 2001 Metabolites of pyrethroid insecticides in urine specimens: current exposure in an urban population in Germany Environ Health Perspect 109 213 217 11333180
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Singh NP McCoy MT Tice RR Schneider EL 1988 A simple technique for quantitation of low levels of DNA damage in individual cells Exp Cell Res 175 184 191 3345800
Tuomainen A Kangas J Liesivuori J Manninen A 1996 Biological monitoring of deltamethrin exposure in greenhouses Int Arch Occup Environ Health 69 62 64 9017436
Villarini M Moretti M Pasquini R Scassellati-Sforzolini G Fatigoni C Marcarelli M 1998 In vitro genotoxic effects of the insecticide deltamethrin in human peripheral blood leukocytes: DNA damage (“comet” assay) in relation to the induction of sister-chromatid exchanges and micronuclei. Toxicology 130 129 139
WHO 2001. WHO Recommended Insecticides for Indoor Residual Spraying against Malaria Vectors. Geneva:World Health Organization.
WHO 2002. Report of the Sixth WHOPES Working Group Meeting. Review of Deltamethrin 25% wg & wp and agnique mmf. Communicable Disease Control, Prevention and Eradication; WHO Pesticide Evaluation Scheme. Geneva:World Health Organization.
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Wu A Li L Liu Y 2003 Deltamethrin induces apoptotic cell death in cultured cerebral cortical neurons Toxicol Appl Pharmacol 187 50 57 12628584
Yáñez L Borja-Aburto VH Rojas E de la Fuente H González-Amaro R Gómez H 2004 DDT induces DNA damage in blood cells. Studies in vitro and in women chronically exposed to this insecticide Environ Res 94 18 24 14643282
Yáñez L Ortiz-Pérez D Batres LE Borja-Aburto VH Díaz-Barriga F 2002 Levels of dichlorodiphenyltrichloroethane and deltamethrin in humans and environmental samples in malarious areas of Mexico Environ Res 88 174 181 12051795
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7707ehp0113-00078715929905Children’s HealthArticlesMaternal Exposure to Occupational Solvents and Childhood Leukemia Infante-Rivard Claire 1Siemiatycki Jack 2Lakhani Ramzan 3Nadon Louise 31Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, Montréal, Québec, Canada;2Département de Médecine sociale et préventive, Université de Montréal, Montréal, Québec, Canada;3Institut National de la Recherche Scientifique–Institut Armand-Frappier, Laval, Québec, CanadaAddress correspondence to C. Infante-Rivard, Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, 1130 Pine Ave. West, Montréal, Québec, Canada H3A 1A3. Telephone: (514) 398-4231. Fax: (514) 398-7435. E-mail:
[email protected] project was supported by grants from the National Health and Welfare Research and Development Program, Leukemia Research Fund of Canada, Canadian Institutes for Health Research, Fonds de la recherche en Santé du Québec, and the Laboratory Center for Disease Control at Health Canada.
The authors declare they have no competing financial interests.
6 2005 3 3 2005 113 6 787 792 29 10 2004 3 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Many organic solvents are considered probable carcinogens. We carried out a population-based case–control study including 790 incident cases of childhood acute lymphoblastic leukemia and as many healthy controls, matched on age and sex. Maternal occupational exposure to solvents before and during pregnancy was estimated using the expert method, which involves chemists coding each individual’s job for specific contaminants. Home exposure to solvents was also evaluated. The frequency of exposure to specific agents or mixtures was generally low. Results were generally similar for the period ranging from 2 years before pregnancy up to birth and for the pregnancy period alone. For the former period, the odds ratio (OR), adjusted for maternal age and sex, for any exposure to all solvents together was 1.11 [95% confidence interval (CI), 0.88–1.40]. Increased risks were observed for specific exposures, such as to 1,1,1-trichloroethane (OR = 7.55; 95% CI, 0.92–61.97), toluene (OR = 1.88; 95% CI, 1.01–3.47), and mineral spirits (OR = 1.82; 95% CI, 1.05–3.14). There were stronger indications of moderately increased risks associated with exposure to alkanes (C5–C17; OR = 1.78; 95% CI, 1.11–2.86) and mononuclear aromatic hydrocarbons (OR = 1.64; 95% CI, 1.12–2.41). Risk did not increase with increasing exposure, except for alkanes, where a significant trend (p = 0.04) was observed. Home exposure was not associated with increased risk. Using an elaborate exposure coding method, this study shows that maternal exposure to solvents in the workplace does not seem to play a major role in childhood leukemia.
acute lymphoblastic leukemiachildchildhood leukemiamaternal occupational exposuresolvents
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Acute lymphoblastic leukemia (ALL) is the most frequent form of cancer in children (National Cancer Institute of Canada 2004). At this time, there is only limited knowledge and evidence on environmental or other risk factors contributing to the incidence of ALL. Some convincing data show that ALL can arise in utero because characteristic chromosome translocations that generate chimeric fusion genes unique for each patient’s leukemic clone are found at birth (Greaves 2002); therefore, the pregnancy and periconceptional periods are exposure windows of primary interest to study risk factors that could be involved in childhood ALL.
Fetal exposure to chemical agents is likely to come primarily from maternal exposure at work. Among chemical agents with carcinogenic potential and to which a substantial proportion of workers are likely to be exposed are organic solvents. In a multisite case–control study of cancer patients in Montréal, Canada, an estimated 40% of men had been occupationally exposed to at least one solvent over the course of their work careers (Siemiatycki 1991). A solvent is any substance capable of dissolving another substance to form a uniformly dispersed mixture or solution (Stacey 1993). In industrial processes, water, a polar solvent, is often incapable of dissolving a large number of substances, and therefore organic liquids are used. The expressions “industrial solvents” and “organic solvents” are conventionally applied to these organic liquids (Stacey 1993).
Previous studies assessing parental occupational exposures for ALL have not always evaluated maternal exposures or have done so in studies of small size where a substantial proportion of mothers were homemakers, often leading to few usable results (Colt and Blair 1998). More recent and larger studies have included an assessment of maternal exposures; one reported “solvents” as an exposure category (Schuz et al. 2000), whereas another reported results on specific solvents or groups of solvents; but in both studies, exposure assignment seems to have been directly based on parents’ self-reporting (Shu et al. 1999). Recently, McKinney at al. (2003) used parental self-report and a group of professionals to assign exposure to “occupational groups” (a mixture of occupations, industries, and groups of agents). Overall, results from previous studies that reported on solvents and leukemia have not been consistent. We conducted a case–control study of childhood leukemia using the expertise of trained chemists to determine maternal exposure to occupational solvents.
Materials and Methods
Case ascertainment.
Details of the study have been described elsewhere (Infante-Rivard 2003; Infante-Rivard et al. 2000, 2001). Briefly, cases of ALL diagnosed between 1980 and 2000 in the province of Québec, Canada, were recruited from tertiary care centers designated by governmental policy to hospitalize and treat children with cancer in the province. Tracing cases from these hospitals is equivalent to population-based ascertainment. Between 1980 and 1993 cases 0–9 years of age at diagnosis were recruited for study; between 1994 and 2000 case selection included those up to 14 years of age at diagnosis. A case was determined to have ALL (International Classification of Diseases, 9th Revision, code 204.0) (World Health Organization 1975) on the basis of a clinical diagnosis by an oncologist or a hematologist. Because cancer care is covered under a universal health plan, we believe that a negligible number of children, if any, were treated outside the province.
Control selection.
Population-based controls (one per case) were matched on sex and age at the time of diagnosis (calendar date) and thus were concurrently selected. From 1980 to 1993, the population-based controls were chosen from family allowance files (Régie des Rentes du Québec, Québec, Canada). The family allowance is a government stipend awarded to all families with children living legally in Canada. This source of data was the most complete census of children for the study years. According to the expected distribution of cases based on matching criteria, a list of 10 potential controls was randomly chosen from the lists. Between 1994 and 2000, we used the provincial universal health insurance files (Régie de l’Assurance Maladie du Québec, Québec, Canada) as a source for controls, which is an equivalently complete census of children. It was a better source of data for that time period because family allowances were more often directly deposited in the mother’s bank account, which meant that the home address was no longer available in the file. We proceeded the same way to obtain potential controls.
Study participants.
Children who were adopted, who lived in foster families, whose families spoke neither French nor English, who did not reside in Canada, or whose parents were both unavailable for interview were excluded. We identified 848 eligible cases, and interviewed the parents of 790 (93.1%); of 916 eligible controls, 790 parents were interviewed (86.2%). The reasons for nonparticipation were confidential telephone number, refusal to participate, or inability to trace the family. The study was approved by each hospital’s institutional review board as well as by the provincial agency regulating access to public databases with nominal information. We requested that the parents return a signed informed consent form for the interview.
Data collection.
Soon after the anticipated reception of a letter introducing participants to the general purpose of the study, trained interviewers contacted the parents to schedule an appointment for the interview, which was administered by telephone using structured questionnaires. Questionnaires were reviewed as they came in, and feedback was given regularly to interviewers. One questionnaire addressed general risk factors and potential confounders. To assess maternal occupational risk factors, the procedure was as follows: A complete job history was obtained from the mother for the period ranging from 18 years of age to the end of pregnancy. This information included the job title and dates on this job, and the type of industry and its name and address. For each job held by the mother from 2 years before pregnancy and up to birth of the index child, a semistructured questionnaire was used to probe the details of each job; as previously described (Goldberg et al. 2001), the information collected included the company’s activities; raw materials used; machines used; goods produced; responsibilities for machine maintenance; type of room or building in which the woman worked; activities of workmates; presence of gases, fumes, dusts, biocides, oils, solvents, and ionizing and nonionizing radiation sources; use of area or personnel protective equipment; and a detailed open-ended description of the woman’s typical activities at work. In addition, for frequent job titles and/or jobs with a significant potential for occupational exposures (e.g., nurse, sewing machine operator, hairdresser, waitress, cook, textile dry cleaner, knitting and weaving operator), a specialized questionnaire was also administered that probed more deeply into the specific tasks, the time spent at them, specific exposures related to these tasks, and the environment in which they were conducted.
Exposure coding.
Exposure coding was carried out by a team of chemists and industrial hygienists who have many years of experience in exposure assessment in community-based case–control studies. They first assigned each occupation to standard Canadian industrial titles (at the three-digit level) and job titles (at the seven-digit level) (Statistics Canada 1980, 1992). The next step was to determine whether there was or was not exposure to specific solvents or chemical mixtures with solvents (listed in Table 1; discussed further below); the complete list of chemicals that were coded includes > 300 items, but the focus here is on solvents because these were the chemicals of primary interest of the study. The strategy to code exposures from individual job histories is termed the “expert method” in the occupational epidemiology literature (Teschke et al. 2002) and has been described previously (Gérin et al. 1985; Siemiatycki et al. 1987). Briefly, experienced chemists use all the available information provided by the study subject, information accumulated from coding exposures for thousands of jobs held in the same geographical area (albeit for men) (Siemiatycki 1991), and their personal knowledge or consultants’ knowledge of the industries. Chemists were blind to the case/control status.
Home exposure to solvents.
The general questionnaire included items to assess exposure to solvents in the home: hobbies, such as model building, furniture stripping, and types of art work; activities carried out in and around the home with the potential for similar exposure, such as electronic and motor vehicle repair; and painting in the home. For each question, we asked who carried out the activity and during what time period, specified as 1 year before pregnancy, during pregnancy, and from birth to the reference date.
Statistical analysis.
Two time periods were defined: from 2 years before pregnancy up to birth, and during the specific pregnancy period. We used conditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Each agent, mixture, and family was analyzed in a separate model, and the analyses were adjusted for maternal age and level of schooling. Results are first presented contrasting any exposure with no exposure. For the exposure period ranging from 2 years before pregnancy up to birth, we repeated the analysis contrasting “any exposure” with “no exposure” but this time taking into account the chemist’s confidence factor in coding. In the latter analysis, if the chemist had coded the exposure as “possible” (vs. “probable” and “definite”), exposure was assigned to the “no exposure” category. For the same time window, we also conducted an analysis with three levels of exposure: level 0 (baseline), no exposure (defined as none coded or exposure coded with a “possible” confidence); level 1, some exposure (exposure resulting in concentration × frequency < 4), and level 2, greater exposure (concentration × frequency ≥4). Finally, we used a model that includes all specific agents and mixtures. We analyzed residential exposure to solvents in the household as never/ever for each question.
Results
The distribution of sociodemographic characteristics between cases and controls was quite similar (Table 2). More than 99% of mothers in both the case and control groups answered their own questionnaires. The proportion of women with gainful employment in both groups was almost equal (Table 3). Control mothers had an average of 1.33 jobs over the study period, whereas case mothers had an average of 1.29 jobs. Thirteen job titles among the 15 most frequently held jobs were similar between case and control mothers. There were more sewing machine operators and cosmetologists among case mothers than among control mothers.
Discussion
The distributions of jobs between case and control mothers were remarkably similar, with a few exceptions, the main one being that there were more sewing machine operators in the case group. This was previously reported for the study subjects included between 1980 and 1993 (Infante-Rivard and Deadman 2003). Despite the fact that prevalence of any exposure to unspecific solvents as a whole was substantial, that to specific agents or mixtures was low and even lower at the highest levels for these agents. For the “solvents” category, cases and controls had a similar exposure prevalences. Among the specific agents and mixtures to which mothers were exposed before or during pregnancy and that were associated with increased risk of ALL were 1,1,1-trichloroethane, toluene, mineral spirits, leaded gasoline, and possibly methylene chloride and methyl ethyl ketone. Among the chemical families, there were stronger indications of increased risk for alkanes (C5–C17) and mononuclear aromatic hydrocarbons.
In a recent review on organic solvents and cancer, Lynge et al. (1997) reported that there is some evidence for an increased risk of cancer with toluene, 1,1,1-trichloroethane, and methylene chloride, although none is classified yet as a carcinogen by any regulatory agency. The U.S. National Institute for Occupational Safety and Health (NIOSH 1978) issued a bulletin on chloroethanes stating that 1,1,1-trichloroethane should be treated in the work-place with caution because of its structural similarity to other chloroethanes shown to be carcinogenic in animals. Mineral spirits are refined petroleum solvents that include varnish makers’ and painters’ naphtha, Stoddard solvent, and white spirits (Siemiatycki 1991). They are largely composed of saturated hydrocarbons (or alkanes, which in liquid form are C5–C17), but also include a small proportion of benzene. Although benzene is a recognized leukemogenic agent [International Agency for Research on Cancer (IARC) 1987], there is little information on the carcinogenicity of mineral spirits as such; a Swedish study reported an increased risk of acute leukemia among painters (Lindquist et al. 1987), but painters may be exposed to a greater extent to other solvents such as toluene and xylene. Leaded gasoline, another mixture that showed indications of increasing risk, is a mixture of hydrocarbons used as fuel for automobiles; it also contains benzene and toluene. A fairly consistent link between fathers with occupations in motor-vehicle–related occupations and childhood leukemia has been reported (Colt and Blair 1998); however, such occupations involve exposure to a variety of chemicals, including polycyclic aromatic hydrocarbons such as benzo[a]pyrene, which is considered a probable or suspected carcinogenic agent by regulating agencies (IARC 1983).
Results from studies on maternal occupational exposures and childhood ALL published between 1980 and 1997 (Feingold et al. 1992; Gold et al. 1982; Hemminki et al. 1981; Lowengart et al. 1987; Magnani et al. 1990; McKinney et al. 1987; Olsen et al. 1991; Shu et al. 1988; Van Steensel-Moll et al. 1985) have been reviewed before (Colt and Blair 1998). In all these studies except that by Feingold et al. (1992), exposure was defined as having an occupation or belonging to an exposed industry, or occasionally, exposure to a specific agent was reported, as determined by maternal reporting. This strategy was also used in a more recent study from Germany (Schuz et al. 2000) not included in the review. On the other hand, Feingold et al. (1992) used a general job-exposure matrix to determine exposure to a list of specific agents; unfortunately, this study was very small (~ 60 cases of ALL and 60 controls), and results were inconclusive. In a more recent and large study from the United States (Shu et al. 1999), mothers reported specific exposures as well as the approximate length of time spent being exposed to a particular agent. No additional strategy to code exposure involving chemists or similar experts is explicitly described, so it is assumed that the self-reported exposures were used as such. Finally, in another recently published and large study, this time from the United Kingdom (McKinney et al. 2003), occupations and industries were coded according to standard classifications; in addition, a panel of experts, including a hygienist, created 31 occupational groups that were said to be homogeneous for specific exposures. The occupational groups used in the analysis include job titles (e.g., leather workers), sectors of activity (e.g., agriculture), and agents such as solvents and hydrocarbons (skin/epidermal or inhaled particulate). The method to create the groups is not detailed in the report. Because of the different ways to classify exposures and the multiple classifications used (even for job titles or industries, each study using their respective national classification), results are difficult to compare. However, in the three more recent studies (McKinney et al. 2003; Schuz et al. 2000; Shu et al. 1999), an explicit “solvents” category was used, and results are as follows: Schuz et al. (2000) report an OR of 1.2 (95% CI , 0.9–1.7) for exposure to solvents during periconception and a similar result during pregnancy. Shu et al. (1999) report an OR of 1.8 (95% CI, 1.3–2.5) for exposure to “possible organic solvents” during preconception (2 years before conception) and a similar result during pregnancy. McKinney et al. (2003) report an OR associated with exposure to solvents at periconception of 1.0 (95% CI, 0.66–1.51); however, exposure to “dermal hydrocarbons” had an OR of 2.16 (95% CI, 1.16–4.02).
The present study uses the most detailed and elaborate exposure assessment method in comparison with previous studies. The expert method used here has been found to have good validity (Fritschi et al. 2003). Its steps have been clearly detailed (Siemiatycki 1991), and results using this method to uncover carcinogens in community-based case–control studies have been abundantly published (Aronson et al. 1996; Parent et al. 2000). In this study, all specific agents associated with increased risk have also been associated with increased risks of cancer in previous studies. Although results for some specific agents or families indicated increased risk, this was not as clear for the general category “solvents.” The families of alkanes (C5–C17) and mono-nuclear aromatic hydrocarbons are ones that many previous studies have tried to capture by using the hydrocarbon-related occupations (Lowengart et al. 1987; McKinney et al. 2003; Olsen et al. 1991; Shu et al. 1988, 1999; Van Steensel-Moll et al. 1985). The result by McKinney et al. (2003) showing a substantial increase in risk associated with periconception dermal exposure to hydrocarbons is consistent with our own. Overall, our results on alkanes and mononuclear aromatic hydrocarbons are consistent with and reinforce previous results. However, except for alkanes, it was disconcerting to find no indication of an exposure–response relationship, an observation also reported by Shu et al. (1999).
A study reported results on home exposure to solvents and childhood ALL (Freedman et al. 2001). Only artwork at a frequency of more than four times a month was associated with an increased risk of ALL. We did not measure frequency of exposure for home solvents, but none of the ORs in our study indicated any increase in risk for the ever-exposed category.
In comparison with previous studies on parental occupational exposures and childhood ALL, this is the third largest study in terms of number of cases. Nevertheless, power is still an issue. With respect to potential biases often affecting case–control studies, in this study selection bias was unlikely: participation rates for cases and especially for controls were markedly higher (by ~ 20%) than in any of the other large studies cited. However, although the exposure assignment method used in this study seems more refined than in previous studies, it is safe to say that nondifferential misclassification of exposures affected the results and reduced our ability to uncover significant findings.
In conclusion, this study used an exposure assignment method that is among the best available for community-based case–control studies of cancer. The results gave more specific indications than previous studies and point to an increase in ALL risk associated with maternal exposure to occupational alkanes and mononuclear aromatic hydrocarbons. Nevertheless, the results are still somewhat uncertain. From a public health point of view, it was reassuring to observe that, as in all previous studies, maternal exposures were most often rare and occurred at low levels; this, of course, makes the task of uncovering effects more difficult. However, this fact should not deter us from a continued search in this direction because prenatal exposure to carcinogens as risk factors for childhood leukemia makes biologic sense, and even low levels during this period could be damaging. We are thus challenged to develop more sensitive methods to ascertain parental occupational exposures. Adding a genetic susceptibility perspective could also enhance our ability to uncover the susceptible dyads (mother and child).
Table 1 Matrix of specific chemicals, complex mixtures of chemicals, and chemical families used in the analysis.
Chemical familiesb
Codea 1 2 3 4 5 6 7
Specific chemicals
Methanol 232 XXc
Ethanol 233 XX
Isopropanol 234 XX
Ethylene glycol 235 XX
Carbon tetrachloride 237 XX
Chloroform 238 XX
Methylene chloride 239 XX
1,1,1-Trichloroethane 240 XX
Trichloroethylene 242 XX
Perchloroethylene 243 XX
Ethylene dichloride 300 XX
Acetone 248 XX
Methyl ethyl ketone 304 XX
Benzene 252 XX
Toluene 253 XX
Xylene 254 XX
Ethyl acetate 302 XX
Diethyl ether 250
Turpentine 280
Carbon disulfide 266
Butyl cellosolve 306
Mixtures
Mineral spirits post-1970d 202 Xe X
Mineral spirits pre-1970d 203 X X
Leaded gasoline 191 X X
Unleaded gasoline 299 X X
Aviation gasoline 190 X X
Kerosene 195 X X
a These codes were used by Siemiatycki (1991) to catalogue and define the various substances, and they can thus be used to easily find additional information on these chemicals in that reference.
b Chemical families: 1, alkanes (C5–C17); 2, aliphatic alcohols; 3, chlorinated alkanes; 4, chlorinated alkenes; 5, aliphatic ketones; 6, mononuclear aromatic hydrocarbons; 7, aliphatic esters.
c XX signifies that the agent listed to the left is a member of the chemical family indicated at the top.
d Before 1970, mineral spirits contained relatively higher amounts of benzene, toluene, and xylene due to ignorance of their toxic effects.
e X signifies that the agent listed to the left contains components that are members of the chemical family indicated at the top.
Table 2 Demographic characteristics [no. (%)] of ALL cases and controls.
Cases Cases (n = 790) Population controls (n = 790)
Mother’s education
None or primary school 34 (4.3) 25 (3.2)
Secondary school 437 (55.3) 436 (55.2)
College or university 319 (40.4) 328 (41.6)
Mother’s age at child’s birth
< 35 721 (91.3) 743 (94.0)
≥35 69 (8.7) 47 (6.0)
Family income at diagnosis (Can$)
≥40,000 312 (39.9) 309 (40.2)
10,000–39,000 427 (54.7) 422 (54.9)
< 10,000 42 (5.4) 38 (4.9)
Table 3 Distribution of job titles among mothers of ALL cases and population controls during the period ranging from 2 years before pregnancy up to birth of the index child.
Cases Controls
No. not working (%) 178 (22.5) 173 (21.9)
No. working 612 617
No. of jobs held (average per person) 792 (1.29) 820 (1.33)
Job titles by order of frequency (highest to lowest)
Secretary Secretary
Clerk (general office) Clerk (general office)
Sewing machine operator Waitress
Waitress Nurse (general duty)
Cashier (clerical) Cashier (clerical)
Nurse (general duty) Teller
Cosmetologist Sales clerk
Sales clerk Elementary school teacher
Teller Sewing machine operator
Elementary school teacher Cashier (customer service)
Baby sitter Cosmetologist
Receptionist Receptionist
Computer operator Baby sitter
Accountant clerk Accountant clerk
Nurse’s aide Counterwoman (cafeteria)
Table 4 Adjusteda OR (95% CI) and ratio of discordant pairs (RDP) for maternal exposure to solvents.
2 years before pregnancy up to birth
During pregnancy
ORb (95% CI) RDP OR (95% CI) RDP
Specific chemicals
Methanol 0.77 (0.41–1.47) 17:22 0.78 (0.39–1.55) 15:19
Ethanol 1.22 (0.66–2.25) 23:19 1.06 (0.55–2.03) 19:18
Isopropanol 0.96 (0.71–1.29) 85:89 0.95 (0.69–1.31) 73:78
Chloroform 0.25 (0.05–1.17) 2:8 0.25 (0.05–1.17) 2:8
Methylene chloride 1.34 (0.54–3.34) 11:8 1.25 (0.46–3.35) 9:7
1,1,1-Trichloroethane 7.55 (0.92–61.97) 7:1 4.07 (0.45–36.7) 4:1
Perchloroethylene 0.96 (0.41–2.25) 11:11 0.84 (0.30–2.34) 7:8
Acetone 1.05 (0.53–2.08) 17:16 1.13 (0.52–2.44) 14:12
Methyl ethyl ketone — 4:0 — 4:0
Benzene 0.82 (0.22–3.06) 4:5 1.39 (0.31–6.25) 4:3
Toluene 1.88 (1.01–3.47) 29:16 2.25 (1.02–4.95) 20:9
Diethyl ether 0.50 (0.17–1.48) 5:15 0.63 (0.20–1.93) 5:8
Turpentine 1.76 (0.42–7.42) 5:3 1.76 (0.42–7.42) 5:3
Mixtures
Mineral spirits, post-1970 1.82 (1.05–3.14) 37:20 1.66 (0.86–3.22) 24:14
Minerals spirits, pre-1970 — 5:0 — 4:0
Leaded gasoline 5.09 (0.59–43.65) 5:1 4.14 (0.46–37.16) 4:1
Unleaded gasoline 0.90 (0.30–2.71) 6:7 0.83 (0.22–3.10) 4:5
Chemical familiesc
Alkanes (C5–C17) 1.78 (1.11–2.86) 48:27 1.72 (0.98–3.03) 33:19
Aliphatic alcohols 0.90 (0.68–1.18) 97:108 0.89 (0.66–1.20) 84:95
Chlorinated alkanes 1.33 (0.68–2.61) 20:15 1.05 (0.50–2.19) 15:14
Chlorinated alkenes 0.97 (0.43–2.17) 12:12 0.86 (0.33–2.25) 8:9
Aliphatic ketones 1.30 (0.68–2.50) 21:16 1.46 (0.70–3.03) 18:12
MAH 1.64 (1.12–2.41) 70:43 1.68 (1.06–2.67) 49:29
Solventsd 1.09 (0.87–1.38) 154:141 1.00 (0.78–1.28) 125:125
MAH, mononuclear aromatic hydrocarbons.
a Adjusted for maternal age and level of schooling; specific chemicals or mixtures with fewer than four exposed mothers are not shown.
b Odds ratio (95% CI) for any exposure; baseline is no exposure.
c Chemical families regroup specific chemicals that belong to a family and mixtures that have components belonging to it.
d Includes all specific chemicals and mixtures in the table.
Table 5 Adjusted ORsa (95% CIs), versus possible/no exposure, for levels of maternal exposure to solvents during the 2 years before pregnancy up to birth.
Probable/definite Level 1b Level 2c
Specific chemicals
Methanol 0.81 (0.43–1.55) 0.81 (0.38–1.70) 0.82 (0.25–2.77)
Ethanol 1.11 (0.59–2.08) 1.44 (0.61–3.39) 0.81 (0.32–2.07)
Isopropanol 0.97 (0.72–1.31) 0.92 (0.65–1.32) 1.09 (0.65–1.84)
Chloroform 0.16 (0.02–1.36) 0.30 (0.03–2.90) —
Methylene chloride 3.22 (0.88–11.73) 4.68 (0.55–40.20) 2.49 (0.48–12.81)
Perchloroethylene 0.87 (0.35–2.18) 0.95 (0.35–2.55) 0.55 (0.05–6.34)
Acetone 1.11 (0.54–2.29) 0.95 (0.39–2.28) 1.55 (0.43–5.51)
Benzene 0.77 (0.17–3.48) — 1.47 (0.25–8.85)
Toluene 1.98 (1.06–3.72) 3.19 (1.43–7.12) 0.68 (0.18–22.05)
Diethyl ether 0.63 (0.20–1.94) 0.67 (0.19–2.41) 0.51 (0.04–5.59)
Turpentine 1.76 (0.42–7.42) 1.64 (0.27–9.92) 2.00 (0.18–22.05)
Mixtures
Mineral spirits, post-1970 1.74 (0.99–3.06) 1.60 (0.86–2.98) 2.50 (0.66–9.46)
Chemical familiesd
Alkanes (C5–C17)* 1.78 (1.09–2.91) 1.56 (0.91–2.67) 3.39 (0.94–12.21)
Aliphatic alcohols 0.91 (0.69–1.20) 0.89 (0.64–1.23) 0.95 (0.60–1.51)
Chlorinated alkanes 2.00 (0.90–4.47) 2.18 (0.67–7.10) 1.86 (0.62–5.57)
Chlorinated alkenes 0.89 (0.37–2.11) 1.07 (0.41–2.80) 0.35 (0.03–3.53)
Aliphatic ketones 1.40 (0.71–2.77) 1.24 (0.54–2.84) 1.80 (0.52–6.17)
MAH 1.67 (1.13–2.48) 1.82 (1.15–2.87) 1.32 (0.62–2.80)
Solventse 1.11 (0.88–1.40) 1.11 (0.85–1.46) 1.11 (0.75–1.63)
MAH, mononuclear aromatic hydrocarbons.
a Adjusted for maternal age and level of schooling.
b Defined as concentration × frequency < 4; baseline is possible or no exposure.
c Defined as concentration × frequency ≥4.
d Chemical families regroup specific chemicals that belong to a family and mixtures that have components belonging to it.
e Includes all specific chemicals and mixtures in the table.
* p-Value for trend = 0.04.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7759ehp0113-00079315929906Children's HealthArticlesSeasonality and Children’s Blood Lead Levels: Developing a Predictive Model Using Climatic Variables and Blood Lead Data from Indianapolis, Indiana, Syracuse, New York, and New Orleans, Louisiana (USA) Laidlaw Mark A.S. 1Mielke Howard W. 2Filippelli Gabriel M. 1Johnson David L. 3Gonzales Christopher R. 21School of Population Health, University of Western Australia, Crawley, Western Australia;2Department of Basic Pharmaceutical Sciences, College of Pharmacy, Xavier University of Louisiana, New Orleans, Louisiana, USA;3Department of Chemistry, State University of New York, College of Environmental Science and Forestry, Syracuse, New York, USAAddress correspondence to M.A.S. Laidlaw, School of Population Health, M431, 35 Stirling Hwy, University of Western Australia, Crawley, Western Australia 6009. Telephone: 6488-1260. Fax: 6488-1188. E-mail:
[email protected]. Griffin of the Louisiana Childhood Lead Poisoning Prevention Program, Office of Public Health, supplied the New Orleans blood lead data set; the Marion County Health Department made available the Marion County (Indianapolis) blood lead data; and the Onondaga County Health Department provided the child blood lead database for Syracuse. D. Bivin suggested the use of monthly dummy variables to increase modeling significance, and Y. Fan of the National Oceanic and Atmospheric Administration provided soil moisture data.
The authors declare they have no competing financial interests.
6 2005 24 2 2005 113 6 793 800 15 11 2004 24 2 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. On a community basis, urban soil contains a potentially large reservoir of accumulated lead. This study was undertaken to explore the temporal relationship between pediatric blood lead (BPb), weather, soil moisture, and dust in Indianapolis, Indiana; Syracuse, New York; and New Orleans, Louisiana. The Indianapolis, Syracuse, and New Orleans pediatric BPb data were obtained from databases of 15,969, 14,467, and 2,295 screenings, respectively, collected between December 1999 and November 2002, January 1994 and March 1998, and January 1998 and May 2003, respectively. These average monthly child BPb levels were regressed against several independent variables: average monthly soil moisture, particulate matter < 10 μm in diameter (PM10), wind speed, and temperature. Of temporal variation in urban children’s BPb, 87% in Indianapolis (R2 = 0.87, p = 0.0004), 61% in Syracuse (R2 = 0.61, p = 0.0012), and 59% in New Orleans (R2 = 0.59, p = 0.0000078) are explained by these variables. A conceptual model of urban Pb poisoning is suggested: When temperature is high and evapotranspiration maximized, soil moisture decreases and soil dust is deposited. Under these combined weather conditions, Pb-enriched PM10 dust disperses in the urban environment and causes elevated Pb dust loading. Thus, seasonal variation of children’s Pb exposure is probably caused by inhalation and ingestion of Pb brought about by the effect of weather on soils and the resulting fluctuation in Pb loading.
climatelead dustlead exposure seasonalitymodelingPM10soil leadsoil moisture
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Lead poisoning causes permanent neurologic, developmental, and behavioral disorders, particularly in children. The identification and removal of new sources of human exposure to Pb over the past several decades have significantly reduced the percentage of Pb-poisoned children in the United States, although one remaining population that has not seen this improvement is urban children, particularly from minority groups (Macey et al. 2001) or families with low socioeconomic standing (Mielke et al. 1999). Although some of this continued Pb poisoning is due to remaining point sources (e.g., paint dust from poorly maintained homes), it appears that a significant additional source of Pb contamination is from soil (Filippelli et al. 2005; Mielke et al. 1983)—the legacy of 100 years of Pb use in cities linked to multiple sources (e.g., leaded gasoline, leaded paint, smelters). Recent work has suggested that seasonal increases in children’s blood Pb (BPb) levels relate to exposure via activity, that is, summer days of outdoor play and open windows and doors leading to increased contact with Pb-contaminated soils (Haley and Talbot 2004; Mielke and Reagan 1998; Yiin et al. 2000). Here we suggest an additional possibility—that higher children’s BPb levels may be related to a combination of weather, soil moisture, and wind that effectively remobilizes and makes more bioavailable the diffuse soil Pb. This process may exacerbate this usual summertime behavioral link, with added impacts on urban children’s health.
Urban Pb.
In the 1970s, the assumed source of soil Pb contamination was Pb-based house paint (Ter Haar and Aronow 1974). An early study of garden soils conducted in metropolitan Baltimore, Maryland, raised questions about that assumption. Soil around Baltimore’s inner-city buildings, predominantly unpainted brick, exhibited the highest amounts of Pb, and soils outside of the inner city, where buildings were commonly constructed with Pb-based paint on wood siding, contained comparatively low amounts of Pb, suggesting that Pb-based house paint could not account for the observed pattern of soil Pb (Mielke et al. 1983). Similarly, the same pattern was also found in Ottawa, Canada (Ericson and Mishra 1990). The quantity and distribution of soil Pb have been studied in numerous places: cities in Minnesota (Mielke et al. 1984/85); New Orleans, Louisiana (Mielke 1994 ); Milwaukee County, Wisconsin (Brinkmann 1994); Washington, DC (Elhelu et al. 1995); Indianapolis, Indiana (Filippelli et al. 2005; Laidlaw 2001); Syracuse, New York (Johnson and Bretsch 2002); Oslo, Norway (Tijhuis et al. 2002); and Ibadan, Nigeria (Sridhar et al. 2000). All these cities exhibited the same distance decay characteristic of high soil Pb contamination in the inner city and decreasing contamination toward the outer parts of the city as initially identified in garden soils of Baltimore (Mielke et al. 1983). Further, similarities in this distance decay pattern of soil Pb supports the idea that Pb-based house paint was not the sole source contributing to these observed differences.
Sources of Pb.
Except for storage batteries, paint and gasoline additives were the two major high-volume products containing Pb; about the same quantity of Pb, 5 to 6 million metric tons, was used to manufacture each (Mielke and Reagan 1998). Lead-based house paint sales were phased out in 1978 in response to the Lead Paint Poison Prevention Act (Tong 1990). The major processes that now release Pb-based house paint into the soil are deterioration and especially disturbance of old Pb-based paint by power sanding (Mielke et al. 2001).
In the United States, motor vehicles used gasoline containing tetramethyl and tetraethyl Pb additives from the 1920s to 1986. By the 1950s, Pb additives were contained in virtually all grades of gasoline. By 1986, when leaded gasoline was banned, 5 to 6 million metric tons of Pb had been used as a gasoline additive, and about 75% of this Pb was released into the atmosphere (Chaney and Mielke 1986; Mielke and Reagan 1998). Thus, an estimated 4 to 5 million tons of Pb has been deposited into the U.S. environment by way of gasoline-fueled motor vehicles (Mielke 1994). Accumulation of soil Pb created by leaded gasoline is proportional to highway traffic flow (Mielke et al. 1997).
Pb content and Pb loading of urban soils.
A critical aspect of Pb accumulated in soils is the relationship between Pb content and Pb loading. Studies in Minnesota and Louisiana examined the issue of Pb loading of the soil (Mielke 1993; Mielke et al. 1992). In large cities of Minnesota and Louisiana, the median soil Pb for various site types measured from 6.0 to 32.25 g/m2, and the top 0.025 mm contained 6,000–32,250 μg Pb/m2 (557–2,996 μg Pb/ft2) (Mielke 1993). When one compares this Pb loading rate with the U.S. Department of Housing and Urban Development’s guideline of 40 μg Pb/ft2 [U.S. Department of Housing and Urban Development 1999; U.S. Environmental Protection Agency (EPA) 2001] for interior floors, it becomes evident that soil is an enormous reservoir of Pb dust. Because of the low mobility of Pb in soil, all of the Pb that accumulates on the surface layer of the soil is retained within the top 20 cm (Laidlaw 2001; Mielke et al. 1983). The half-life of Pb in surface soils has been estimated to be approximately 700 years, so without corrective action, Pb dust will persist for many generations (Semlali et al. 2004). The persistence of the Pb burden that has accumulated in soil has significant long-term public health implications (Lejano and Ericson 2005).
Anthropogenic soil Pb speciation and bioavailability.
Pb deposited by human activity onto and retained by surface soils has been added to the relatively small quantities of Pb naturally occurring in the soil. This anthropogenic Pb is generally speciated in the highly bioavailable carbonate, iron, and manganese hydroxide soil fractions, whereas the Pb in natural soils is speciated in the residual, or nonbioavailable fractions (Chlopecka et al. 1996; Lee 1997). Therefore, dust originating from urban soils contaminated by anthropogenic Pb is more toxic than naturally occurring Pb dust. Lead is associated with the smallest particles, the clay grain size fraction in urban soils (Dong et al. 1984); therefore, Pb in dust originating from urban soils is more potent and concentrated than would be expected from simple measurements of the Pb content of the soil (Young et al. 2002).
Bioavailability is indicated by an isotope study of BPb and soil Pb. Each source of Pb has an isotopic signature that is unique to a particular mine. When this characteristic of Pb was first described, most manufacturers began interchanging Pb mining sources, and the effect was to scramble the isotope signatures and render Pb isotopes essentially useless for source identification. The former Soviet Union, however, did not scramble Pb sources that were used in gasoline. Armenia eliminated the use of leaded gasoline before 1997 (Kurkjian and Flegal 2003). The half-life of BPb is about 30 days and is therefore cleared from the blood in a matter of months. If Pb exposure continues, then BPb remains elevated. A study conducted in Yerevan, Armenia, 2 years after the elimination of leaded gasoline indicated that the soil Pb from previous gasoline Pb emissions persisted as a route of exposure for adults (Kurkjian and Flegal 2003). The Pb isotopes of the BPb of adults and the Pb isotopes in contaminated soils were identical, and this provided strong evidence that prior leaded gasoline emissions persist and are highly bioavailable as a route of exposure (Kurkjian and Flegal 2003).
Seasonal changes in BPb concentration.
Average monthly BPb of children from urban areas tends to increase significantly in summer months (Billick et al. 1979; Blatt and Weinberger 1993; Haley and Talbot 2004; Hayes et al. 1994; Hunter 1977; Hwang and Wang 1990; Johnson and Bretsch 2002; Johnson et al. 1996; Kimbrough et al. 1994; Marrero et al. 1983; Mielke and Reagan 1998; Rabinowitz and Needleman 1982; Rothenberg et al. 1996; Stark et al. 1980; U.S. EPA 1995, 1996; Yiin et al. 2000). Summertime increases of children’s BPb were so prominent over many years in Syracuse, New York, that researchers concluded that the phenomenon was probably caused by the interaction between climate and soils (Johnson and Bretsch 2002; Johnson et al. 1996). The purpose of this study is to test the hypothesis that children’s exposure as measured by BPb is associated with climate and soil factors affecting Pb dust flux in three cities: Indianapolis, Indiana; Syracuse, New York; and New Orleans, Louisiana. Figure 1 presents a map illustrating the locations of the three cities.
Materials and Methods
This study differs from previous studies because it uses environmental variables as predictors of children’s BPb concentrations, which does not appear to have been attempted before using an ecologic study design. The U.S. EPA studies in Milwaukee (U.S. EPA 1996) and Boston (U.S. EPA 1995) attempted to model BPb using sinusoidal functions; however, it appears that multiple linear regression using climate and soil moisture variables may be more robust due the high percentage of variation explained in the model (up to 87%). The U.S. EPA models did not attempt to use environmental variables to predict BPb concentrations; however, both studies suggested that Pb from the environment might be causing the child BPb seasonality.
This study’s design is described as an analytic time-trend ecologic study (Morgenstern 1998). In ecologic studies, the unit of analysis is the group rather than the individual. The ecologic unit of analysis in this study is the group of children within the city limits of each city who have had their BPb measured. An ecologic design was selected because it is neither practical nor ethical to draw blood from large groups of children on a monthly basis over a long period. One potential limitation of ecologic studies is known as the ecologic fallacy (Morgenstern 1998): the failure of expected ecologic effect estimates to reflect biologic effects at the individual level. However, the biologic plausibility of the associations found at the ecologic level in this study have been found at the individual level in smaller studies (Aschengrau et al. 1994; Lanphear et al. 2003; Maisonet et al. 1997; Mielke and Reagan 1998; Sheldrake and Stifelman 2003). This supports the biologic plausibility of the suggested model. Another facet of this study is that it uses empiric data from three cities that differ in geographic location and climate. Syracuse (latitude 43° N and longitude 76° W) has a cold continental climate; Indianapolis (latitude 40° N and longitude 86° W) is located in the middle continent region; and, New Orleans (30° N latitude and 90° W longitude) has a southerly and warm Gulf Coast climate. The relationship between BPb, weather, and soil moisture is thus studied in geographically and hence climatically diverse locations.
Data Sources
The independent variables—average monthly soil moisture, particulate matter < 10 μm (PM10), wind speed, and temperature—were obtained from state or federal government data sources. Blood Pb databases for each city were obtained from local or state governmental sources as follows.
Indianapolis.
In Indianapolis, Indiana, BPb data for 15,944 children were obtained from the Marion County Health Department (personal communication). Nearly 15% of the children listed in the Indianapolis database were < 1 year (n = 2,320), 20% were 1–2 years (n = 3,202), 13% were 2–3 years (n = 2,078), 19% were 3–4 years (n = 3,050), 22% were 4–5 years (n = 3,476), and 11% were ≥5 years of age (n = 1,820). The BPb measurements were collected using the venous method. PM10 data were obtained from the Indiana Department of Environmental Management air monitoring station located at 3302 Englist Avenue (personal communication). Soil moisture data were obtained (personal communication) from actual field measurements of the top 6 inches of soil at Illinois Water Survey soil moisture monitoring site number 81 located near Champaign, Illinois, which is approximately 110 miles west of Indianapolis (Hollinger and Isard 1994). Wind speed and temperature data were obtained from the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC 2004).
Syracuse.
In Syracuse, New York, child BPb data were obtained from the Onondaga County Health Department (personal communication). The child BPb screenings for the Syracuse BPb data set were collected from within the city limits of Syracuse. Approximately 90% of child BPb screenings were obtained from passive sources such as county clinics or physicians, and 10% of the screenings were collected from a mobile bus that screened children at locations including day care centers, prekindergarten centers, and Head Start centers. The bus schedule started in May and ended in September, operating full time in June, July, and August. The bus traveled to different locations each summer period. The bus sampling strategy typically targeted areas that had high percentages of BPb concentrations greater than 20 μg/dL. The child BPb screenings were conducted through a combination of capillary and venous methods. The BPb analysis was completed by laboratories certified by the New York State Department of Health. PM10 data were obtained from the New York State Department of Environmental Conservation’s Solvay High School air monitoring site located on Gertrude Avenue (personal communication). Soil moisture data were obtained from NOAA (Fan Y, personal communication), and wind speed and temperature data were obtained from the NCDC (2004).
New Orleans.
In New Orleans, Louisiana, the child BPb data were obtained from the Louisiana Childhood Lead Poisoning Prevention Program Office of Public Health (personal communication). The screenings for the New Orleans BPb data set were collected from within the city limits. No known geographic or temporal sampling bias was reported. Eighty-four percent of the screenings originated from private providers such as pediatric clinics, physicians, and family practice physicians. Approximately 70% of the children whose BPb was screened were eligible or enrolled in Medicaid. The BPb levels were analyzed primarily by the following laboratories: Labcorp (Burlington, NC), Tamarac (Centennial, CO), Medtox (St. Paul, MN), Quest Diagnostics (Metairie, LA), and ARUP Laboratories (Salt Lake City, UT). The screening procedures were not reported to have changed between January 1998 and May 2003. The data set used in this study was screened for children that had blood drawn using the venous method. The PM10 data were obtained from the Louisiana Department of Environmental Quality (personal communication). The soil moisture data were obtained from NOAA (Fan Y, personal communication), and the wind speed and temperature data were obtained from the NCDC (2004).
Statistical Analysis
We computed the average BPb concentration in each city using the child BPb measurements for each month. The outcome variable, children’s average monthly city BPb concentration for each city, was regressed against the independent variables average monthly soil moisture, PM10, wind speed, and temperature; interaction variables; and monthly dummy variables using backward elimination procedures. The independent variables temperature, PM10, and soil moisture were computed as the arithmetic mean, whereas the wind speed was computed as the median. Each model’s entry and criteria were 0.10 and 0.15, respectively. Backward variable elimination enters all of the variables in the block in a single step and then removes them one at a time based on removal criteria. Spearman’s rank correlation coefficient was used to assess the association between variables. Statistical analysis was performed using SPSS (version 11.5; SPSS Inc., Chicago, IL).
The Durbin-Watson (DW) and Lagrange multiplier (LM) statistics were calculated to assess the presence of serial autocorrelation. The LM was calculated by regressing the residuals of a model versus the same residuals shifted backward one value relative to the other residuals. The LM statistic is computed by multiplying the R2 value of this regression by the number of values in the regression. The DW statistic was calculated using SPSS.
Results
For each city, we calculated Spearman’s rank correlation coefficient matrices for the variables soil moisture, wind speed, PM10, and temperature; interaction variables; and monthly dummy variables (M1 to M11). In Indianapolis, Syracuse, and New Orleans, BPb concentration and soil moisture exhibited inverse correlations of –0.41, –0.75, –0.47, respectively. The correlations are presented in Tables 1–3.
Regression results: Indianapolis.
The time period of the regression consisted of 36 months between December 1999 and November 2002. The dependent variables for the first model consisted of the average monthly child BPb from a data set of 15,969 children. This model was run using backward elimination procedures.
This model indicates that the variables or interaction variables including soil moisture, wind speed, PM10, temperature, and the monthly dummy variables for March, April, June, July, August, and September explain 87% of the variation in the response variable, monthly average child BPb concentration (R2 = 0.87, p = 0.0004). The DW and LM statistics indicate that the model did not display serial autocorrelation (DW = 1.73, LM = 0.24). Figure 2 presents a chart of the average monthly child BPb concentration for the entire data set versus the predicted child BPb concentration.
The model regression coefficients indicate that the seven predictors with p-values less than 0.05 are temperature (p = 0.00093), wind speed (p = 0.00093), the interaction between temperature and wind (p = 0.002), soil moisture (p = 0.006), the interaction between soil moisture and temperature (p = 0.0076), the interaction between wind and soil moisture (p = 0.011), and the interaction between wind and PM10 (p = 0.016).
Regression results: Syracuse.
The time period of the regression consisted of 51 months from January 1994 through March 1998. The use of a mobile clinic to screen children in Syracuse in high-risk areas may have biased the high aggregate monthly average during the months of May through September. However, starting with the 1996 data, the universal screening requirement of the New York State Department of Health (2002) went into effect; subsequently, higher screening rates and more random sampling were apparent in the results. The dependent variables consisted of the average monthly child BPb concentration of a data set of 14,467 children from Syracuse. The model was run using backward elimination procedures. The time-series difference method was used to correct for serial autocorrelation.
This model indicates that the variables or interaction variables including soil moisture, wind speed, PM10, temperature, and the monthly dummy variables for January, March, April, May explained 61% of the variation in the response variable, monthly average child BPb concentration (R2 = 0.61, p = 0.0012). The DW and LM statistics indicate that the model did not display serial correlation (DW = 2.05, LM = 0.049). Figure 3 presents a chart of the average monthly child BPb concentration versus the predicted child BPb concentration.
The model regression coefficients indicate that the four predictors with p-values less than 0.05 are the interaction between temperature and PM10 (p = 0.0004), PM10 (p = 0.0047), wind speed (p = 0.029), and the interaction between soil moisture and temperature (p = 0.042).
Regression results: New Orleans.
The time period of the regression consisted of 65 months from January 1998 through May 2003. The dependent variable is the average monthly blood level of a data set of 2,295 children. This model was run using backward elimination procedures.
The model indicates that the variables soil moisture, wind speed, PM10, temperature, several interaction variables, and the monthly dummy variables for January, February, March, April, July, and October explained 59% of the variation in the response variable, monthly average child BPb concentration (R2 = 0.59, p = 0.0000078). The DW and LM statistics indicated that the model did not display serial autocorrelation (DW = 1.71, LM = 0.85). Figure 4 presents a chart of the average monthly child BPb concentration versus the predicted child BPb concentration.
The model regression coefficients indicate that the predictors with p-values < 0.05 are PM10 (p = 0.00003), the interaction between PM10 and wind (p = 0.00005), the interaction between PM10 and temperature (p = 0.0006), and soil moisture (p = 0.006). A summary of the statistics from the three cities is presented in Table 4.
Discussion
Soil moisture and soil suspension.
Numerous studies have demonstrated that soil moisture concentration is a significant control of dust (PM10) suspension and loading (Chen et al. 1996; Clausitzner and Singer 1996, 2000; Cornelis and Gabriels 2003; Nickovic et al. 2001). Soil moisture is a predictor of wind erosion because soil moisture contributes to bind particles together (McKenna-Neuman and Nickling 1989). Soil particles will become deflated when destabilizing forces such as drag, lift, and aerodynamic forces become greater than stabilizing forces such as particle weight and interparticle binding forces (Iverson et al. 1976).
The threshold shear velocity of a particle is the wind velocity required to deflate (suspend) a particle in the atmosphere (Cornelis and Gabriels 2003). Most models that predict wet threshold shear velocity (utw) of a particle take the form
where ut is the threshold shear velocity under dry conditions. The function f (moisture) is a function of the surface moisture expressed in terms of moisture content w (kilogram per kilogram) or capillary potential (Pascal) (Cornelis and Gabriels 2003).
Most models of the threshold shear velocity predict a rise in deflation threshold with increasing moisture content (Cornelis and Gabriels 2003).
With decreasing soil matrix potential from a dry soil, the utw will increase exponentially until a soil matrix potential of –1.5 MPa occurs, at which no soil deflation takes place. The matrix potential (ψ) has been found to be a function of temperature (T ), air humidity (e/es), molar volume of water (Vw; 0.0224 m3/mol), and the universal gas constant (R; 8.3145 J/mol K) (Edlefson and Anderson 1943):
These equations suggest that when temperature is high and soil moisture is low in the summertime, soils are susceptible to deflation. The modeling approach used in this study may have successfully explained the temporal variation in BPb because the matrix potential variables soil moisture (volumetric water content) and temperature were incorporated, which permits prediction of when soils are susceptible to dust emission. The variables PM10 and atmospheric Pb represent the end product of dust generation, and the variable wind speed may contribute to the explanation of the variance because of its effect on the PM10 (dust) deposition rate. Essentially, the high R2 values suggest that these variables predict temporal dust generation and exposure of children to Pb from dust in the environment.
The regression models indicate that environmental variables from outside the home, adjusted for seasonality, such as soil moisture, PM10, temperature, and wind speed, are significant predictors (p < 0.05) of children’s seasonal BPb fluctuations. This suggests that the Pb controlling the seasonal fluctuations originates from the outdoor environment. In the three cities studied here, the urban soils are highly contaminated by Pb (Filippelli et al. 2005; Johnson and Bretsch 2002; Laidlaw 2001; Mielke 1994; Mielke et al. 1999). Thus, there is an abundant source of Pb in urban soils that could be suspended, resulting in elevated Pb dust loading rates during certain weather conditions.
The hypothesis that urban soils are being resuspended into the atmosphere is also supported by the literature that indicates a strong relationship between the suspension of surface soils and atmospheric particulates. In Bakersfield, California, 74% of PM10 from July through September 1988 was composed of geologically originated materials (Young et al. 2002). One study estimated that street dust was composed of approximately 76% soil materials (Hopke et al. 1980), and another study estimates that soil contributes between 57 and 90% of road dust (Hunt et al. 1993). Finally, 43% of Pb emissions in the South Coast Air Basin in California resulted from the resuspension of soil and road dust (Lankey et al. 1998).
Blood Pb seasonality.
Blood Pb seasonality suggests that Pb exposure varies over time. Thus, those who postulate that Pb-based paint is the primary source of Pb exposure also theorize that dust generation from Pb paint is somehow related to accelerated flaking from painted surfaces during summer months. Some have suggested that the opening and closing of windows painted with Pb paint may produce seasonal exposure to Pb dust (Haley and Talbot 2004). However, this study indicates that soil moisture, PM10, wind speed, and temperature fluctuations, adjusted for each other, are very strongly associated with children’s BPb levels. If these variables were noncausally associated with BPb fluctuations, and Pb paint was the source of the seasonality, this would imply that the opening and shutting of doors was associated with soil moisture, PM10, wind speed, and temperature. This appears to be counterintuitive because the opening and shutting of windows is likely temperature dependent and not dependent on soil moisture, PM10, and median wind speed.
We propose that BPb seasonality results from three or four exposure routes: First, children are likely seasonally exposed to elevated dust Pb loading on interior and exterior surfaces via hand-to-mouth processes, and the elevated Pb loading likely results from seasonal high Pb loading rates due to suspension of urban soils. Second, children are also exposed to direct ingestion of urban Pb-contaminated soil during warmer months. Third, it is possible that children are being seasonally exposed to Pb particles derived from the seasonal opening and shutting of windows painted with Pb paint. Fourth, children are exposed through inhalation to elevated atmospheric dust Pb concentrations resulting from seasonal soil suspension.
In addition, on the basis of the relationship between BPb, high temperatures, low soil moisture, and PM10 found in this study, we infer that arid climates with major urban areas and a long-term historical use of Pb in petroleum will experience high sustained rates of Pb loading that originate from Pb dust in soils. We also expect a more prolonged exposure when compared with colder climate areas, resulting in a muting of seasonality trends. These regions may include areas such as Los Angeles, California (USA), Mexico City and Tijuana, Mexico; arid areas of China, Pakistan, and India; and Nigeria, Saudi Arabia, and Cairo, Egypt. Furthermore, the aridity may exacerbate Pb exposure and childhood poisoning, particularly in emerging economies, where leaded gasoline is still in use (Nriagu et al. 1996; Sridhar et al. 2000). Soil Pb may be an important exposure variable in these environments, possibly overwhelming exposure to other sources of Pb.
Soil Pb and exposure.
A growing body of research supports the conclusion that urban soils contribute significantly to child BPb poisoning (Mielke and Reagan 1998). Several ecologic studies have found associations between urban soil Pb concentrations and children’s BPb concentrations. A significant logarithmic relationship was reported between soil concentration (> 3,000 sampling points) and child BPb in New Orleans by census tract (R2 > 0.65) (Mielke et al. 1997). An independent study found a similar relationship in Syracuse (R2 > 0.65) (Johnson and Bretsch 2002). Both these studies show that, non-temporally, soil accounts for a significant amount of the variation in BPb on a spatial basis. A study of urban dusts and soils in Britain (Culbard et al. 1988) found that soil and outdoor and indoor dusts were the most significant predictor variables in the regression model used to explain children’s BPb levels. The study also found that Pb in interior paint was not a strong independent variable in the final stepwise regression analysis used to explain children’s blood levels (Culbard et al. 1988). A pooled study of 12 epidemiologic studies found that dust Pb loading and soil Pb concentration were the two most significant predictors of children’s BPb levels (Lanphear et al. 1998). In Bunker Hill, Idaho, structural equation modeling indicated that 40–50% of the BPb is from house dust, whereas approximately 30% was from community-wide soils and 30% from the yard at the home and the immediate neighborhood (von Lindern et al. 2003). In Tijuana, Mexico, several studies have found associations between soil Pb and children’s BPb levels (Ericson and Gonzalez 2003; Gonzalez et al. 2002).
The epidemiology literature has also indicated that the removal of Pb-contaminated soil results in significant reductions in child BPb concentration and supports the causal spatial relationship between soil Pb and BPb that has been found in the ecologic studies (Johnson and Bretsch 2002; Mielke et al. 1997, 1999). Soil Pb abatement resulted in a 2.25–2.70 μg/dL reduction in BPb levels when a randomized trial of soil abatement was conducted (Malcoe et al. 2002). Logistic regression indicated that soil Pb > 165 mg/kg was independently associated with BPb concentrations > 10 μg/dL [odds ratio (OR) = 4.1; 95% confidence interval (CI), 1.3–12.4]. Yard soil removal resulted in a 3-fold reduction in the child BPb concentrations of children in the Silver Valley of Idaho, located near the Bunker Hill Superfund site, and reduced the dust Pb levels inside the homes (Sheldrake and Stifelman 2003). Removal of soil from children’s yards reduced the children’s BPb when compared with controls (OR = 0.28; 95% CI, 0.08–0.92) (Maisonet et al. 1997). A study conducted on the effect of soil removal on child BPb concentrations at homes where the soil Pb concentration was greater than 500 mg/kg showed a statistically significant difference between BPb concentrations in homes in which soil was removed versus those where contaminated soil was not removed (p < 0.05) (Lanphear et al. 2003).
Integrated Exposure Uptake Biokinetic Model for Lead in Children.
The degree to which the proposed exposure hypotheses result in reasonable predictions for BPb levels can be examined with the U.S. EPA’s Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK) (U.S. EPA 1994). To develop an exposure regime for the IEUBK model input, we note that indoor residential dusts generally show Pb concentrations about two times higher, on average, than the corresponding outdoor soils, although these results may have been influenced by fine particulate automotive Pb emissions (Clark et al. 1988; Rabinowitz et al. 1985; Thornton et al. 1990). More recent data are available as summaries from 299 residential locations in eight different Idaho communities for the Human Health Risk Assessment for the Coeur d’Alene Basin showed an average enrichment factor of 1.6 for Pb concentration in carpet dusts compared with outdoor soils (TerraGraphics and URS Greiner 2001; von Lindern et al. 2003). Studies also indicate that dust Pb loading was 1.2 times higher in spring and fall than in winter and that in summer the loading was 1.6 times higher than in winter (Yiin et al. 2000). Model predictions were obtained by specifying seasonal dust concentration differences that would result in the observed dust Pb loading differences. Using the IEUBK default values for Pb in air, water, food, and soil, soil and dust ingestion rates, and with soil representing 45% of the combined soil and dust ingestion, dust Pb concentration was specified as 333 ppm or 550 ppm. For children 1–2 years of age, this increased BPb values from 5.5 μg/dL (winter) to 7.0 μg/dL (summer). When the soil ingestion was specified as 25% of the combined soil/dust intake, the predictions ranged from 5.8 μg/dL (winter) to 7.8 μg/dL (summer).
A more realistic exposure regime for Syracuse might specify a soil Pb concentration of 150 mg/kg, with a winter–summer range of 200–320 mg/kg for the indoor dusts. If the ingested soils are limited to 10% of the total ingestion for combined soils and dusts, the predicted BPb values range from 4.5 to 5.9 μg/dL. This corroborates the range of observations for the 1997–1998 monitoring (Figure 3). Mean observed BPb values for recent Indianapolis data are lower than the 1994–1998 results in Syracuse, so IEUBK model input values would have to be lower to replicate the observations. If one assumes soil Pb is 100 mg/kg and ingestion of soil represents 10% of the total soil/ dust ingestion, and the dust concentration varies from 100 to 180 mg/kg and is associated with a bioavailability of 40% instead of the 30% default value, predicted BPb values range from 3.6 to 4.9 μg/dL. This shows reasonable agreement with the observations (Figure 2).
These exploratory uses of the IEUBK are meant only to indicate the types of concentration values and changes in physiochemical parameters that might provide a mechanistic explanation for the correlations observed in this work. A temporal structure in BPb levels can be modeled by the multicompartmental biokinetic IEUBK model using a wide variety of ingestion rates, soil and dust concentration values, and bioavailability parameters. Because we posit the seasonal variations in PM10, it is not unreasonable to consider changes in model default parameters for bioavailability. Small particle size is known to increase Pb uptake from particles (Rieuwerts and Farago 1995; Steele et al. 1990; Wixson and Davies 1994). The potential influence of soil resuspension processes in modulating BPb levels needs careful examination, and future studies should incorporate detailed monitoring for temporal resolution of suspended Pb per volume of air, seasonal influences on residential dust Pb loading and concentration, and measures of Pb bioavailability. Further discussion about the many influences that the natural environment has on public health may be found in Selinus et al. (2005).
Conclusion
A conceptual model of child BPb seasonal Pb poisoning is suggested. Lead from multiple sources has accumulated in soils of urban environments. The seasonal resuspension of Pb-contaminated soil in urban atmospheres appears to be controlled by soil moisture and climate fluctuations. This study indicates that higher urban atmospheric Pb loading rates are experienced during periods of low soil moisture and within areas of Pb-contaminated surface soils. Children and adults living in urban areas where surface soils are contaminated with Pb may become exposed through indoor and outdoor inhalation of Pb dust and ingestion of Pb deposited within homes and outdoor surfaces. Because resuspension of Pb from contaminated soil appears to be driving seasonal child BPb fluctuations, concomitantly, we suggest that Pb-contaminated soil in and of itself may be the primary driving mechanism of child BPb poisoning in the urban environment.
Figure 1 Map showing the locations of Indianapolis, Indiana; Syracuse, New York; and New Orleans, Louisiana.
Figure 2 Actual monthly average BPb versus predicted monthly average BPb in Indianapolis, Indiana, for a 36-month period between December 1999 and November 2002 (n = 15,969, R2 = 0.87, p = 0.0004, DW = 1.71, LM = 0.85).
Figure 3 Actual monthly average BPb versus predicted monthly average BPb in Syracuse, New York, for a 51-month period between January 1994 and March 1998 (n = 14,467, R2 = 0.61, p = 0.0012, DW = 2.05, LM = 0.049).
Figure 4 Actual monthly average BPb versus predicted monthly average BPb in New Orleans, Louisiana, for a 65-month period between January 1998 and May 2003 (n = 2,295, R2 = 0.59, p = 0.0000078, DW = 1.71, LM = 0.85).
Table 1 Indianapolis: Spearman’s correlation matrix (December 1998 to November 2002).
SM W PM10 Temp
BPb all −0.41 −0.36 0.16 0.20
SM 0.55 −0.13 −0.66
W −0.18 −0.65
PM10 0.19
Abbreviations: SM, soil moisture; Temp, temperature; W, wind speed.
Table 2 Syracuse: Spearman’s correlation matrix (January 1994 to March 1998).
SM W PM10 Temp
BPb all −0.75 −0.28 0.10 0.43
SM 0.61 −0.19 −0.57
W −0.48 −0.66
PM10 0.49
Abbreviations: SM, soil moisture; Temp, temperature; W, wind speed.
Table 3 New Orleans: Spearman’s correlation matrix (January 1998 to May 2003).
SM W PM10 Temp
BPb all −0.47 0.03 −0.05 −0.16
SM 0.18 −0.12 −0.13
W −0.24 −0.48
PM10 0.59
Abbreviations: SM, soil moisture; Temp, temperature; W, wind speed.
Table 4 Multiple linear regression modeling results: all three cities.
City R2 df F-value p-Value SE DW LM Month No. Time-series transform
Indianapolis 0.87 16 6.43 0.0004 0.39 1.73 0.24 36 15,969 No transform
Syracuse 0.61 15 3.52 0.0012 0.51 2.05 0.049 51 14,467 Difference
New Orleans 0.59 13 5.33 < 0.00001 3.58 1.71 0.85 65 2,295 No transform
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7635ehp0113-00080115929907ToxicogenomicsArticlesDiscovery of Novel Biomarkers by Microarray Analysis of Peripheral Blood Mononuclear Cell Gene Expression in Benzene-Exposed Workers Forrest Matthew S. 1Lan Qing 2Hubbard Alan E. 1Zhang Luoping 1Vermeulen Roel 2Zhao Xin 1Li Guilan 3Wu Yen-Ying 1Shen Min 2Yin Songnian 3Chanock Stephen J. 2Rothman Nathaniel 2Smith Martyn T. 11School of Public Health, University of California, Berkeley, California, USA;2Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA;3National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, ChinaAddress correspondence to M.T. Smith, School of Public Health, 140 Warren Hall, University of California, Berkeley, CA 94720-7360 USA. Telephone: (510) 642-8770. Fax: (510) 642-0427. E-mail:
[email protected] thank H. Asahara for assistance with the Affymetrix GeneChip hybridizations.
This work was supported by National Institutes of Health grants RO1ES006721 and P30ES001896 to M.T.S.
The authors declare competing financial interests. M.T.S. has received consulting and expert testimony fees from law firms representing both plaintiffs and defendants in cases involving exposure to benzene. G.L. has received funds from the American Petroleum Institute for consulting on benzene-related health research.
6 2005 14 3 2005 113 6 801 807 5 10 2004 14 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Benzene is an industrial chemical and component of gasoline that is an established cause of leukemia. To better understand the risk benzene poses, we examined the effect of benzene exposure on peripheral blood mononuclear cell (PBMC) gene expression in a population of shoe-factory workers with well-characterized occupational exposures using microarrays and real-time polymerase chain reaction (PCR). PBMC RNA was stabilized in the field and analyzed using a comprehensive human array, the U133A/B Affymetrix GeneChip set. A matched analysis of six exposed–control pairs was performed. A combination of robust multiarray analysis and ordering of genes using paired t-statistics, along with bootstrapping to control for a 5% familywise error rate, was used to identify differentially expressed genes in a global analysis. This resulted in a set of 29 known genes being identified that were highly likely to be differentially expressed. We also repeated these analyses on a smaller subset of 508 cytokine probe sets and found that the expression of 19 known cytokine genes was significantly different between the exposed and the control subjects. Six genes were selected for confirmation by real-time PCR, and of these, CXCL16, ZNF331, JUN, and PF4 were the most significantly affected by benzene exposure, a finding that was confirmed in a larger data set from 28 subjects. The altered expression was not caused by changes in the makeup of the PBMC fraction. Thus, microarray analysis along with real-time PCR confirmation reveals that altered expressions of CXCL16, ZNF331, JUN, and PF4 are potential biomarkers of benzene exposure.
Affymetrixbenzenebiomarkersbloodexpression profilingleukemialymphocytemicroarraymolecular epidemiologyoccupational exposurereal-time PCR
==== Body
Benzene is an important industrial chemical (> 2 billion gallons produced annually in the United States) and component of gasoline (Gist and Burg 1997). Its toxic effects on the blood and bone marrow include leukopenia, pancytopenia, and aplastic anemia, and it is also an established cause of human leukemia (Snyder 2002). However, the mechanisms of benzene-induced hematotoxicity and leukemo-genesis remain unclear, as does the risk benzene poses at low levels of exposure (Krewski et al. 2000). To shed further light on these mechanisms and better understand the risk benzene poses, we examined the effects of benzene exposure on peripheral blood mononuclear cell (PBMC) gene expression in a population of shoe-factory workers with well-characterized occupational exposures to benzene using cDNA microarrays and real-time polymerase chain reaction (PCR).
Microarrays use immobilized cDNA or oligonucleotide probes to simultaneously monitor the expression of thousands of genes and obtain a view of global gene expression (i.e., a view of all mRNA transcripts expressed by a cell is known as the transcriptome) (Staudt 2003; Staudt and Brown 2000) and are becoming increasingly used in toxicology (Hamadeh et al. 2002; Waters et al. 2003). They have also been used recently to investigate variation in gene expression in the peripheral blood leukocytes of normal individuals (Whitney et al. 2003). We hypothesized that microarrays could identify changes in gene expression that could be used as new biomarkers of exposure and early effect for benzene and provide information on mechanisms of benzene toxicity.
One potential problem with using microarrays in epidemiologic studies is that mRNA is unstable (Thach et al. 2003). Most epidemiologic studies that have collected biologic samples have not collected material that contains stabilized RNA for analysis. Here, we have overcome this problem by performing the first step of RNA isolation in the field and stabilizing the RNA for later analysis. We have analyzed this RNA from selected subjects using a comprehensive and standardized human array, the U133A/B Affymetrix GeneChip set (Iacobuzio-Donahue et al. 2003). U133A and U133B chips together contain almost 45,000 probe sets, representing > 39,000 unique transcripts derived from approximately 33,000 well-substantiated human genes, allowing investigators to obtain a global view of gene expression.
We performed a proof-of-principle study in which we examined global gene expression in a small number of well-matched exposed–control subject pairs. Genes with differential expression were then ranked and selected for further examination using several forms of statistical analysis. We also specifically examined the expression of all cytokine genes on the array under the a priori hypothesis that these key genes involved in immune function are likely to be altered by benzene exposure (Aoyama 1986). We then attempted to confirm the array findings for the leading differentially expressed genes using real-time PCR, which is thought to be more accurate than microarray analysis but can be used only to investigate a few genes at a time (Etienne et al. 2004). Once these genes were confirmed in the paired analysis, we examined their expression in a larger number of benzene-exposed subjects and controls. The overall goal is to provide potential gene markers of exposure and early effect for benzene and to produce mechanistic insight into how benzene affects the body, especially the immune system and lymphocyte function.
Materials and Methods
Study subjects and exposure assessment.
We studied workers exposed to benzene in two shoe manufacturing factories and unexposed controls from three clothes manufacturing factories in the same region of Tianjin, China. The study was approved by institutional review boards at all institutions. Participation was voluntary, written informed consent was obtained, and the participation rate was approximately 95%.
An initial group of six workers was selected from among the more highly exposed workers (mean benzene ± SD = 47.3 ± 24.3 ppm), and six controls were frequency-matched to these subjects on the basis of age and sex. Mean age was 33.7 ± 7.1 years for the six exposed workers and 31 ± 6.7 years for the controls. Four pairs were male and the other two female.
Before phlebotomy, individual benzene and toluene exposure was monitored by each wearing an organic vapor passive monitor badge as previously described (Vermeulen et al. 2004). Personal full-shift air monitoring was conducted about every month over a 3- to 4-month period before biologic sample collection. Benzene and toluene were not detected in air samples from the control factories.
Each subject was given a physical exam by a study physician. A questionnaire was administered that requested detailed information on occupation, environmental exposures to solvents and pesticides, past and current tobacco and alcohol use, past and current medical history including recent infections, diagnostic and therapeutic ionizing radiation exposure, medication use, family history, and a food frequency questionnaire developed for use in northern China.
Biologic sample collection.
Peripheral blood, buccal cells, and urine were collected from each subject at the beginning of the workday around 0900 hr and were processed within 6 hr of collection. White blood cell differential counts and the levels of natural killer (NK) cells, B lymphocytes, CD4+ and CD8+ T lymphocytes were determined. The PBMC fraction, consisting of lymphocytes, monocytes, and some platelets, was isolated in the field using Ficoll-Paque (Amersham, Piscataway, NJ). One to five million PBMCs were added to 1 mL RLT buffer (Qiagen, Valencia, CA) containing 1% β-mercaptoethanol to preserve RNA in the cells. RNA that is frozen in this buffer at −80°C is highly stable.
RNA isolation, amplification, and hybridization.
We isolated total RNA using RNeasy mini kits (Qiagen) according to manufacturer instructions and quantified using a SmartSpec 3000 (Bio-Rad, Hercules, CA). Only samples with an A260/A280 between 1.7 and 2.2 were considered suitable for use. Samples were prepared according to the GeneChip Eukaryotic Small Sample Target Labeling Assay Version II (Affymetrix 2003a), with the exception that the GeneChip Sample Cleanup Module (Affymetrix, Santa Clara, CA) was used and not ethanol precipitation. Total RNA (100 ng) was amplified for each sample, with 400 ng of first-round cRNA used for the second round of cDNA synthesis. Second-round cRNA (15 μg) was fragmented in 30 μL of 1× fragmentation buffer. Hybridization cocktails were made as described in the GeneChip Expression Analysis Technical Manual (Affymetrix 2003b) and hybridized to U133A chips at 60 rpm, 45°C. After 16 hr, the hybridization cocktails were removed, added back to the unused hybridization cocktails, and stored at −80°C. GeneChips were stained with streptavidin–phycoerythrin using the EukGE-WS2 protocol (Affymetrix 2003b). GeneChips were scanned twice using a GeneChip Scanner GA 2500 (Affymetrix). Frozen hybridization cocktails were heated to 65°C for 5 min and then applied to U133B chips as described for U133A chips [of the 45,000 probe sets, only 100 (which can be used for normalization) are found on both chips, so this “recycling” of hybridization cocktail should not affect the results]. Chips were then analyzed as described below.
Chip normalization.
To allow comparison, all chips were scaled to a target intensity of 500 based on all probe sets on each chip. Samples were run blind so that exposure status was unknown and designated as being either group A or B. Group A chips were used as the baselines when analyzing chips from group B.
Statistical analysis to identify differentially expressed genes.
Robust multiarray analysis (RMA) (Irizarry et al. 2003) was used to analyze the data produced by the chips. Two RMA analyses of the GeneChip data were performed. First, we performed a global gene analysis that looked at all genes on both chips simultaneously. Probe sets for which expression was significantly different between exposed and unexposed individuals were identified using a standard paired t-test, and a recently developed bootstrapping technique to provide a critical value adjusted to provide a 5% familywise error rate (FWER), the standard value used in the literature. The bootstrapping technique can provide a more accurate (and less conservative) FWER than standard methods (e.g., Bonferroni’s adjustment) (Pollard and Van der Laan 2003). As Dudoit et al. (2004) noted, bootstrapping resampling techniques can be used to directly model the joint distribution of the null test statistics so that the dependence of genes is implicitly factored in when determining error rates for different cutoffs. The main advantage of this technique over others such as Bonferroni’s is that it can provide accurate control of error rates even when gene expressions on the same chip are statistically dependent (in this case, Bonferroni is often very conservative). However, the theory developed for the technique is asymptotic, and its performance can be less than optimal with very small sample sizes (due to random sampling of matched array pairs with replacement causing an excessive number of ties in some samples).
RMA (normalization, background correction, and calculation of expression) was applied to all genes and all chips simultaneously. We then performed a targeted analysis of cytokine genes by applying the multiple testing procedures to this subset after RMA processing was completed. This was based on the a priori hypothesis that cytokines involved in the immune response should be affected by benzene exposure because of its known immunotoxicity, and we thus derived more power to select differentially expressed cytokine genes by limiting the analysis only to this subset. The subset of 508 cytokine probe sets represented on the U133 chips was identified using NetAffx (http://www.affymetrix.com/analysis/index.affx) and the key word “cytokine.”
Real-time PCR confirmation using TaqMan.
Total RNA (100 ng) was converted to cDNA using the SuperScript First-Strand Synthesis System for reverse-transcriptase PCR (Invitrogen, Carlsbad, CA) using oligo dT primers according to the manufacturer’s instructions. This cDNA was used to confirm GeneChip findings using TaqMan Gene Expression Assays (TMGEAs; Applied Biosystems, Foster City, CA). Assays were run in quadruplicate with 1× TaqMan Master Mix (Applied Biosystems), 1× assay mix, and 50 ng of cDNA in each 25-μL reaction for six genes plus TATA box binding protein (TBP) as an endogenous control (32 reactions/sample). Reactions were run on Applied Biosystems ABI PRISM 7700 Sequence Detection System as follows: 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec, 60°C for 1 min. The 12 samples that had been run on chips were run in exposed–unexposed pairs to reduce experimental variability. The mean baseline (TBP) threshold cycle (Ct) was subtracted from the mean Ct for the other six assays to normalize results. These were then compared between exposed and unexposed sample pairs. Assays used were as follows: TBP (endogenous control), Hs99999910_m1; CXCL16, Hs00222859_m1; IL4R, Hs00166237_m1; JUN, Hs00277190_s1; PF4, Hs00427220_g1; PTPRE, Hs00369944_m1; ZNF331, Hs00367929_m1.
Results
Differential global gene expression in the exposed–control matched pairs.
PBMC RNA from six matched pairs of subjects (one exposed and one age- and sex-matched control in each pair) was analyzed on Affymetrix GeneChips. RMA analysis of the data using paired t-statistics, bootstrapping, and a 5% FWER indicated that 2,129 probe sets were significantly different in people exposed to high levels of benzene compared with matched unexposed subjects. Expression of 964 of these probe sets was decreased, and 1,165 were increased. Table 1 shows the top 25 up-regulated probe sets identified on the basis of the lowest p-values, and Table 2 shows the top 25 down-regulated probe sets. A number of the probe sets identified in the top 50 were expressed sequence tags (ESTs) or coded only for hypothetical proteins. Twenty-nine probe sets corresponded to genes coding for known proteins. Of these, the gene for HSPA1A was the most strongly down-regulated (−66%) (Table 1), and that for CREM was the most strongly up-regulated (+145%) (Table 2). The significance of this latter finding is unclear because CREM has very low expression in lymphocytes. Other genes of note that were up-regulated were ZNF331, PTPRE, toll-like receptor 2, the chemokine CXCL16, and CD44 antigen (Table 1). Note that CD44 and ZNF331 are present on both A and B chips and so they are listed twice in Table 1, but both have similar p-values and expression ratios on the A and B chips, providing a good quality control check. Other genes of interest that were significantly down-regulated include the oncogene JUN, MAP4, and CALM1 (Table 2).
Differential cytokine gene expression in the exposed–control matched pairs.
RMA analysis of the subgroup of 508 cytokine probe sets on the chip indicated that the expression of 19 cytokine genes was significantly different between the exposed and control subjects (Table 3). IFNGR1, IL6R, CCNT2, PBEF1, and PPP1CB were identified by two probe sets, so 28 identification numbers (IDs) are listed in Table 3. The 19 differentially expressed cytokine genes were also identified in the global analysis, but only a few had p-values low enough to be listed in Tables 1 and 2. However, several had high ratios of differential expression between exposed and controls, with PBEF1, IFNGR1, and CXCL16 being increased around 100% (Table 3). Interestingly, six of the up-regulated genes were receptors for interleukins 2, 4, 6, 10, and 11 and interferon gamma, the latter being the most strongly down-regulated cytokine gene. PF4 was the second most significantly down-regulated gene (Table 3).
Confirmation by real-time PCR.
Four genes—CXCL16, JUN, PTPRE, and ZNF331—were chosen from the global analysis and two genes—L4R, PF4—from the cytokine subset for further study and confirmation by real-time PCR. We selected the global analysis genes for further study by first removing ESTs, hypothetical proteins, and genes with low levels of expression. From the remaining genes, we used magnitude and direction of change and availability of TMGEAs at the time of this analysis to decide which to confirm by real-time PCR. Using these three parameters, we chose three of the most significantly up-regulated genes and one strongly down-regulated gene (JUN) for confirmation. IL4R and PF4 were chosen for confirmation from the cytokine subset because they were, respectively, the most significantly up-regulated and down-regulated cytokine genes for which TMGEAs were available at the time of this analysis.
Real-time PCR of RNA from the six exposed–control pairs tested by GeneChips confirmed that CXCL16 and ZNF331 were consistently up-regulated in exposed individuals (mean increases of 103% and 113%, respectively) and that JUN and PF4 were consistently down-regulated in exposed individuals (mean decreases of 81% and 58%, respectively) when compared with unexposed individuals (Figure 1). These differences in expression are very similar to those found by GeneChip analysis (Table 1). Results for IL4R and PTPRE were less concordant, with increases in some pairs and decreases in others (Figure 1).
Effect of benzene exposure on the expression of the differentially expressed genes.
Having shown 100% concordance for CXCL16, ZNF331, JUN, and PF4 between array and real-time data in six matched pairs of benzene-exposed workers and controls, we studied their expression using real-time PCR in a larger set of exposed workers and matched controls (Table 4). RNA from the PBMCs of 13 highly exposed subjects (mean benzene = 43.7 ± 23.9 ppm) and 15 controls was examined (the original six matched pairs of subjects included). The exposed and unexposed subjects were matched on the basis of gender (p = 0.7), age (p = 0.48), current smoking status, and recent infections (Table 4). We also tested the effect of each covariate, and none negatively confounded (i.e., weakened) the impact of benzene exposure on any of the end points. In this larger data set, CXCL16 and ZNF331 were again shown to be very significantly up-regulated, and JUN and PF4 significantly down-regulated (Table 4). Thus, CXCL16, ZNF331, JUN, and PF4 are four genes clearly identified as being differentially expressed after benzene exposure.
Lack of potential confounding by changes in lymphocyte subsets.
It is well established that benzene lowers peripheral blood lymphocyte counts (Qu et al. 2002; Rothman et al. 1996), and certain lymphocyte subsets may be more sensitive to benzene’s effects than are others. This raises a concern that our findings could be explained in part by a different distribution of lymphocyte subset populations in workers exposed to benzene compared with controls. To address this potential confounding, we first evaluated the distribution of all measured cell populations that comprise the PBMCs from which mRNA was isolated (Table 5). Total mononuclear cells (i.e., monocytes, CD4+ T, CD8+ T, CD19+ B), lymphocytes, and CD56 (NK) cells were significantly decreased in exposed workers compared with controls (p = 0.0052; Table 5). Further, the percentage of total mononuclear cells composed of B cells (i.e., B-cell mononuclear percentage) in the exposed workers was significantly less than that in controls (p = 0.0061), whereas the CD8+ T-cell mononuclear percentage was significantly increased (p = 0.0096). Using linear regression, we determined that the proportion of the mononuclear cell fraction made up by each of the five cell types had no impact on expression of CXCL16, ZNF331, JUN, and PF4. Further, the strength and direction of the association between benzene exposure and gene expression were only minimally changed after adjusting for both CD8+ T-cell and CD19+ B-cell mononuclear cell number and percentages.
Discussion
To our knowledge, this is the first molecular epidemiologic study to use whole-genome Affymetrix GeneChips for in vivo studies of the effects of a specific chemical exposure in humans. A limited number of earlier studies have looked at selected subsets of genes (Wu et al. 2003) or at the effects of smoking (Lampe et al. 2004), but none has examined differences in expression in the transcriptome in the context of benzene exposure. Using a relatively small sample size of six matched pairs of exposed and control subjects, we have been able to identify differentially expressed genes in the PBMC of benzene-exposed individuals that could be confirmed and measured by real-time PCR.
A global analysis of 45,000 probe sets, representing approximately 33,000 well-substantiated human genes, was performed on the GeneChips using stabilized PBMC RNA collected in the field in China as part of a large molecular epidemiology study of benzene-exposed workers (Vermeulen et al. 2004). Although the results will differ based on both the type of processing (e.g., RMA) and adjustment for multiple testing (e.g., FWER with bootstrapping), our results showed that, in the six pairs examined, a potentially large number (> 2,100) of probe sets were (statistically) differentially expressed in the benzene-exposed subjects compared with the control, unexposed subjects. Because the accuracy of this bootstrapping technique is based on asymptotic theory, a 5% FWER is not guaranteed, and thus the statistical results should not be the only criterion for identifying genes for more detailed study. However, by ranking the differentially expressed probe sets identified in the global gene analysis by unadjusted p-value, we were able to identify the top 50 that were highly likely to be differentially expressed. We chose four of the known genes from this list for confirmation by real-time PCR. We also increased our probability of finding genes altered by benzene exposure by performing an analysis of the limited subset of 508 cytokine probe sets on the GeneChips. The equivalent analysis of this subgroup indicated that the expression of 19 cytokine genes was significantly different between the exposed and control subjects, and two of these were chosen for confirmation by real-time PCR.
Genes were chosen for real-time PCR confirmation based on the availability of TMGEA, fold change of expression, and expected copy number. Genes thought to be expressed at very low levels were avoided because these have much higher relative measurement error rates (Novak et al. 2002) and because availability of RNA for confirmation was limited. The six genes chosen for further study were CXCL16, JUN, PTPRE, ZNF331, IL4R, and PF4. TBP was chosen as the endogenous control gene because recent research suggests it is well suited for real-time PCR investigations of lymphocytes (Lossos et al. 2003) and it is on a different chromosome (6q) from the other genes investigated. This means that gross genetic events (chromosome duplications, deletions, etc.), which may alter the copy number of the genes investigated, will not affect the control gene.
GeneChip findings in the six pairs of subjects for ZNF331, CXCL16, PF4, and JUN were all shown to be concordant with real-time PCR data. For IL4R and PTPRE there was less consistency between the GeneChip findings and those by real-time PCR in the six pairs (Figure 1). Low copy number can be ruled out as an explanation for the discrepancies between GeneChip and real-time PCR findings for IL4R and PTPRE because they were detected at levels similar to those of the other four genes. One possible explanation is that the Affymetrix probes for these genes are at the 3′ end of transcripts whereas the probes for the IL4R and PTPRE TMGEAs span exons farther upstream. The different target sequences might explain the discrepancies found in relative expression. Results of real-time PCR using the second-round cRNA used for hybridization to the GeneChips were not significantly different from those described in Figure 1 (data not shown). This suggests that differences were not caused by use of the Small Sample Target Labeling Assay Version II protocol.
In a larger set of 28 study subjects, all four concordant genes were shown to be consistently altered by benzene exposure: CXCL16 and ZNF331 were up-regulated, whereas JUN and PF4 were down-regulated. Alteration in the expression of any of the four genes could be a consequence of upstream events. However, it is also possible that germline variation in one or more regulatory regions of these four genes could be particularly susceptible to the effects of benzene exposure. Further studies are needed to investigate genetic variation across each of these genes to determine if one or more variants could be functionally important in benzene exposure. The identification of interactions between genetic variants and benzene’s effects could lead to further insights into the mechanisms associated with benzene-induced leukemia and other hematologic diseases.
CXCL16 is also known as SR-PSOX or CXCLG16 and maps to chromosome 17p13. It encodes chemokine (C-X-C motif) ligand 16, a scavenger receptor that mediates adhesion and phagocytosis of both Gram-negative and Gram-positive bacteria. This facilitates the uptake of various pathogens and chemotaxis of T cell and NK T cells by antigen-presenting cells through its chemokine domain (Shimaoka et al. 2003). ZNF331 is a member of the Krüppel-related family of zinc finger proteins that contain a Krüppel associated box (KRAB) domain and is likely a transcriptional repressor (Meiboom et al. 2003). The ZNF331 gene lies close to a frequent breakpoint region of follicular thyroid adenomas (Meiboom et al. 2003), but the question of why benzene should so markedly affect ZNF331 expression remains unclear at present.
The JUN, FOS, MAF, and A TF subfamilies are dimeric, basic region–leucine zipper proteins that make up the AP-1 transcription factor. AP-1 transcription factors (Shaulian and Karin 2002) are involved in both the induction and prevention of apoptosis, depending on tissue type (Shaulian and Karin 2002). As part of AP-1, JUN is primarily a positive regulator of proliferation. JUN-deficient fibroblasts have marked proliferative defects in vitro (Schreiber et al. 1999; Wisdom et al. 1999), and proliferation of JUN-deficient hepatocytes is severely impaired during liver regeneration in vivo (Bakiri et al. 2000; Behrens et al. 2002; Schreiber et al. 1999). In mouse erythroleukemia and fibroblast cells, inhibition of fos and jun has demonstrated their requirement for proliferation and cell-cycle progression (Shaulian and Karin 2001). The lower levels of JUN could thus be indicative that the PBMCs of benzene-exposed individuals are not proliferating or progressing through the cell cycle as quickly as those of nonexposed individuals.
PF4, also known as CXCL4, is a polypeptide constituent of platelet alpha granules that is released during platelet aggregation and inhibits heparin-mediated reactions. PF4 has been shown to have numerous other biologic properties, including inhibiting endothelial cell proliferation, migration, and angiogenesis (Gupta and Singh 1994; Maione et al. 1990; Niewiarowski et al. 1976) and inhibiting T-cell function by down-modulating cell proliferation and cytokine release (Fleischer et al. 2002). PF4 is expressed exclusively in platelets, megakaryocytes, and their precursors (Doi et al. 1987), and its down-regulation in benzene-exposed workers probably reflects decreased expression in platelets or progenitor cells because they are present in the PBMC fraction.
We explored whether increased and decreased expression of these genes after benzene exposure was a reflection an alteration in the subset make up of the PBMC population. Although benzene exposure did cause changes in the subset makeup of the PBMC fraction (Table 5), these changed proportions had no impact on expression of CXCL16, ZNF331, JUN, and PF4, and the strength and direction of the association between benzene exposure and gene expression was minimally changed after adjusting for both CD8+ T-cell and B-cell mononuclear cell counts and percentages. Thus, the altered expression was not likely to be caused by changes in the make up of the PBMC fraction. Unfortunately, the subjects studied here were selected to have a high level of benzene exposure to make this initial exploratory effort as efficient as possible by maximizing the contrast between the exposed workers and controls. Consequently, the exposure range was too narrow to be able to detect a dose–response relationship among exposed workers, which was not a goal of this study. In the future, we plan to analyze substantially more samples selected from workers with a wide range of benzene exposure to allow us to construct a detailed model of the dose–response relationship.
Generating relative expression using RMA combined with a bootstrapping method for controlling the FWER appears to be an effective way to identify genes associated with chemical exposure. The relative expression of a subset of six genes (all selected as statistically differentially expressed from GeneChips) were confirmed by real-time PCR in either all or most of the six exposed–unexposed pairs analyzed and in a larger data set from 28 subjects. There was also remarkable consistency between the real-time data and the differential expression ratios calculated by RMA for at least four of these genes. Larger data sets will be needed if we are to characterize a pattern of gene expression related to benzene exposure using machine-learning algorithms. However, we did attempt to explore gene ontology with the program EASE (Expression Analysis Systematic Explorer; Hosack et al. 2003) using the current data set and found that immune response genes gave the largest number of significant population hits, supporting our decision to analyze cytokine genes as a subset.
In conclusion, we have shown that microarray analysis can be a good tool for discovering genes of potential mechanistic interest or biomarkers of exposure and early effect in molecular epidemiologic studies of populations exposed to potential carcinogens. Further, only small numbers of paired study subjects are required to identify differentially expressed genes, making such studies cost-effective. The small-sample protocol used here also limits the amount of high-quality RNA required, meaning that archived samples, stored by partial isolation and stabilization of the RNA in the field, are amenable to analysis. These studies therefore provide a model for biomarker discovery in chemically exposed human populations, although with lower exposed populations it may be necessary to study more subject pairs, with 15 pairs probably being ideal. Because the price of global gene expression arrays is decreasing, such studies are becoming more feasible.
Figure 1 Comparison of GeneChip microarray and real-time PCR data in the six matched exposed–control pairs for the up-regulated genes (A) ZNF331, (B) CXCL16, (C) IL4R, and (D) PTPRE and the down-regulated genes (E) JUN and (F) PF4: ratios of gene expression in exposed versus control pairs as measured by real-time PCR and microarray.
Table 1 List of top 25 probe sets up-regulated by benzene exposure identified on U133 chips.a
Probe set ID Gene symbol Location Gene description p-Value Ratio (GM)
207630_s_at CREM 10p12.1–p11.1 cAMP responsive element modulator 3.97 × 10−4 2.45
221840_at PTPRE 10q26 protein tyrosine phosphatase receptor type Eb 7.77 × 10−4 2.07
219228_at ZNF331 19q13.3–q13.4 zinc finger protein 331b 4.49 × 10−4 2.02
204924_at TLR2 4q32 toll-like receptor 2 5.09 × 10−4 2.01
227613_at ZNF331 zinc finger protein 331 3.73 × 10−4 1.97
223454_at CXCL16 17p13 chemokine (C-X-C motif) ligand 16b 3.93 × 10−4 1.96
228962_at PDE4D phosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila) 7.11 × 10−4 1.85
214696_at MGC14376 17p13.3 hypothetical protein MGC14376 1.71 × 10−4 1.78
210732_s_at LGALS8 1q42–q43 lectin, galactoside-binding, soluble, 8 (galectin 8) 5.49 × 10−4 1.76
212371_at PNAS-4 CGI-146 protein 7.61 × 10−4 1.7
225390_s_at KLF13 Krüppel-like factor 13 4.34 × 10−4 1.69
227645_at P101-PI3K 17p13.1 phosphoinositide-3-kinase, regulatory subunit, polypeptide p101 4.71 × 10−4 1.66
226652_at USP3 ubiquitin specific protease 3 5.48 × 10−4 1.64
221641_s_at ACATE2 Xp22.13 likely ortholog of mouse acyl-coenzyme A thioesterase 2, mitochondrial 6.01 × 10−4 1.63
202055_at KPNA1 karyopherin alpha 1 (importin alpha 5) 2.89 × 10−4 1.61
226743_at FLJ34922 17q12 hypothetical protein FLJ34922 7.42 × 10−4 1.6
228393_s_at ZNF302 zinc finger protein 302 6.64 × 10−4 1.58
225120_AT PURB purine-rich element binding protein B 3.90 × 10−4 1.58
218515_at C21orf66 21q21.3 chromosome 21 open reading frame 66 5.80 × 10−4 1.56
202224_at CRK v-crk sarcoma virus CT10 oncogene homolog (avian) 6.45 × 10−5 1.55
200614_at CLTC 17q11–qter clathrin, heavy polypeptide (Hc) 6.33 × 10−4 1.55
212014_x_at CD44 11p13 CD44 antigen (homing function and Indian blood group system) 3.08 × 10−4 1.54
223461_at TBC1D7 6p23 TBC1 domain family, member 7 6.75 × 10−4 1.51
209835_x_at CD44 11p13 CD44 antigen (homing function and Indian blood group system) 2.69 × 10−4 1.51
213315_x_at LOC91966 Xq28 hypothetical protein LOC91966 7.54 × 10−4 1.49
GM, geometric mean.
a Top 25 up-regulated probe sets were selected by RMA on the basis of p-value and then ranked according to expression ratio. Gene annotations are from NetAffx (http://www.affymetrix.com/analysis/index.affx).
b Genes chosen for further analysis by real-time PCR.
Table 2 List of top 25 probe sets down-regulated by benzene exposure identified on U133 chips.a
Probe set ID Gene symbol Location Gene description p-Value Ratio (GM)
200800_s_at HSPA1A 6p21.3 heat shock 70 kDa protein 1A 4.38 × 10−4 0.34
242904_x_at MGC8721 8p12 hypothetical protein MGC8721 4.42 × 10−4 0.43
213281_at JUN 1p32–p31 v-jun sarcoma virus 17 oncogene homolog (avian)b 6.56 × 10−4 0.51
229264_at FLJ39739 FLJ39739 protein (M. musculus) S00030 neurofilament triplet M protein, mouse 3.06 × 10−4 0.56
237510_at MYNN 3q26.31 myoneurin 2.17 × 10−4 0.59
229054_at FLJ39739 FLJ39739 protein 6.64 × 10−4 0.59
202732_at PKIG 20q12–q13.1 protein kinase (cAMP-dependent, catalytic) inhibitor gamma 3.24 × 10−4 0.65
229872_s_at KIAA0493 8q24.13 Homo sapiens cDNA FLJ39739 fis, clone SMINT2016440 1.48 × 10−4 0.67
230574_at Homo sapiens transcribed sequences 1.13 × 10−4 0.7
224495_at MGC10744 17p13.1 hypothetical protein MGC10744 8.17 × 10−4 0.73
243_g_at MAP4 3p21 microtubule-associated protein 4 1.41 × 10−4 0.75
244741_s_at MGC9913 19q13.43 hypothetical protein MGC9913 4.67 × 10−4 0.76
221419_s_at 6.82 × 10−4 0.77
219503_s_at FLJ11036 3p25.1 hypothetical protein FLJ11036 1.21 × 10−4 0.77
240406_at USP16 21q22.11 ubiquitin specific protease 16 4.19 × 10−4 0.8
241749_at 9q31.1 similar to RIKEN cDNA 2310039E09 2.96 × 10−4 0.81
228932_at Homo sapiens transcribed sequence with moderate similarity to protein sp:P39194 (H. sapiens) ALU7_HUMAN Alu subfamily SQ sequence contamination warning entry 4.08 × 10−4 0.82
227667_at Homo sapiens transcribed sequence with weak similarity to protein pir:B36298 (H. sapiens) B36298 proline-rich protein PRB3S (cys)—human (fragment) 1.56 × 10−4 0.82
239063_at Homo sapiens cDNA FLJ39803 fis, clone SPLEN2007794 3.51 × 10−5 0.83
236509_at Homo sapiens transcribed sequences 4.61 × 10−4 0.83
200655_s_at CALM1 14q24–q31 calmodulin 1 (phosphorylase kinase, delta) 1.23 × 10−4 0.83
221384_at UCP1 4q28–q31 uncoupling protein 1 (mitochondrial, proton carrier) 9.86E-06 0.84
203834_s_at TGOLN2 2p11.2 trans-Golgi network protein 2 5.44 × 10−4 0.85
225122_at RNF31 14q11.2 ring finger protein 31 1.87 × 10−4 0.86
229975_at Homo sapiens transcribed sequence with weak similarity to protein ref:NP_060312.1 8.19 × 10−4 0.86
GM, geometric mean.
a Top 25 down-regulated probe sets were selected by RMA on the basis of p-value and then ranked in the table according to expression ratio. Gene annotations are from NetAffx (http://www.affymetrix.com/analysis/index.affx).
b JUN was chosen for further analysis by real-time PCR.
Table 3 List of cytokine probe sets identified by U133 GeneChips from the cytokine subset that were significantly different in benzene-exposed and unexposed individuals.a
Probe set ID Gene symbol Gene description p-Value Ratio (GM)
243296_at PBEF pre-B-cell colony-enhancing factor 0.0275 2.17
211676_s_at IFNGR1 interferon gamma receptor 1 0.0037 2.16
223454_at CXCL16 chemokine (C-X-C motif) ligand 16b 0.0004 1.96
217738_at PBEF pre-B-cell colony-enhancing factor 0.0206 1.93
201408_at PPP1CB protein phosphatase 1, catalytic subunit, beta isoform 0.0017 1.72
213743_at CCNT2 cyclin T2 0.0080 1.68
226333_at IL6R interleukin 6 receptor 0.0042 1.57
209827_s_at IL16 interleukin 16 (lymphocyte chemoattractant factor) 0.0025 1.53
203233_at IL4R interleukin 4 receptor 0.0009 1.49
228222_at PPP1CB protein phosphatase 1, catalytic subunit, beta isoform 0.0206 1.43
205945_at IL6R interleukin 6 receptor 0.0269 1.4
204912_at IL10RA interleukin 10 receptor, alpha 0.0044 1.39
205291_at IL2RB interleukin 2 receptor, beta 0.0113 1.39
224914_s_at CIP29 cytokine induced protein 29 kDa 0.0115 1.38
202727_s_at IFNGR1 interferon gamma receptor 1 0.0146 1.32
204773_at IL11RA interleukin 11 receptor, alpha 0.0111 1.23
204645_at CCNT2 cyclin T2 0.0149 1.22
223961_s_at CISH cytokine inducible SH2-containing protein 0.0244 1.16
206359_at SOCS3 suppressor of cytokine signaling 3 0.0229 1.08
Down-regulated gene expression
210354_at IFNG interferon, gamma 0.0149 0.35
206390_x_at PF4 platelet factor 4 [chemokine (C-X-C motif) ligand 4]b 0.0108 0.62
209767_s_at GP1BB glycoprotein Ib (platelet), beta polypeptide 0.0204 0.77
201896_s_at DDA3 differential display and activated by p53 0.0232 0.8
242254_at Homo sapiens transcribed sequence with moderate similarity to protein ref:NP_071431.1 (H. sapiens) cytokine receptor-like factor 2; cytokine receptor CRL2 precursor 0.0187 0.84
213258_at TFPI tissue factor pathway inhibitor (lipoprotein-associated coagulation inhibitor) 0.0124 0.85
244848_at CDNA FLJ31075 fis, clone HSYRA2001484 0.0282 0.88
235889_at Homo sapiens transcribed sequence with moderate similarity to protein ref:NP_060312.1 (H. sapiens) hypothetical protein FLJ20489 0.0131 0.89
243438_at PDE7B phosphodiesterase 7B 0.0019 0.91
GM, geometric mean.
a Gene annotations are from NetAffx (http://www.affymetrix.com/analysis/index.affx).
b Gene chosen for further analysis by real-time PCR.
Table 4 Gene expression measured by real-time PCR in the larger set of 28 benzene-exposed workers and controls.
Characteristics Control (n = 15) Exposed (n = 13) p-Valuea p-Valueb (Unadjusted)
Sex
Male 8 (53%) 6 (46%)
Female 7 (47%) 7 (54%)
Age (years)
Mean ± SD 32.5 ± 8.8 35.1 ± 6.1
Median 33 36
Current smoking
Yes 5 (33%) 4 (31%)
No 10 (67%) 9 (69%)
Recent infection
Yes 1 (7%) 1 (8%)
No 14 (93%) 12 (92%)
Benzene exposure
Air level (ppm) < 0.04 43.66 ± 23.87
Urine level (μg/L) 0.36 ± 0.51 778.70 ± 1433.02
Up-regulated genes
CXCL16 2.37 ± 0.62c 3.66 ± 0.54 0.00001 < 0.0001
2.55d 3.9
ZNF331 1.50 ± 0.70 3.00 ± 0.91 0.00001 < 0.0001
1.61 2.92
Down-regulated genes
JUN 6.29 ± 1.00 4.94 ± 1.26 0.0037 0.0038
6.57 4.66
PF4 6.05 ± 0.90 5.46 ± 1.35 0.052 0.012e
6.24 5.18
a By Wilcoxon exact test.
b Analyzed by linear regression. Results are unadjusted because age, sex, current smoking, recent infections, and alcohol use did not weaken the effect of benzene exposure on any gene transcript.
c Data presented as mean ± SD.
d Data presented as median.
e One subject with an extreme outlier value was excluded from the regression analysis.
Table 5 Subsets of lymphocytes and monocytes in the PBMC fraction of the 28 subjects comprising the larger study population.
Cell types Control (n = 15) Exposed (n = 13) p-Valuea
Mononucleocytes/L 2240 ± 540b 1704 ± 393 0.0052
Monocytes/mononucleocytes (%) 8.24 ± 2.43 8.53 ± 2.06 0.56
CD4+ T cells/mononucleocytes (%) 34.08 ± 7.95 33.01 ± 5.62 0.86
CD8+ T cells/mononucleocytes (%) 25.32 ± 6.98 33.15 ± 7.13 0.0096
CD19 cells/mononucleocytes (%) 10.14 ± 2.27 6.79 ± 5.00 0.0061
CD56 cells/mononucleocytes (%) 22.22 ± 10.42 18.52 ± 4.20 0.62
a By Wilcoxon exact test.
b Data presented as mean ± SD.
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Affymetrix 2003b. GeneChip Expression Analysis Technical Manual. Santa Clara, CA:Affymetrix. Available: http://www.affymetrix.com/support/technical/manual/expression_manual.affx [accessed 16 January 2003].
Aoyama K 1986 Effects of benzene inhalation on lymphocyte subpopulations and immune response in mice Toxicol Appl Pharmacol 85 92 101 2941900
Bakiri L Lallemand D Bossy-Wetzel E Yaniv M 2000 Cell cycle-dependent variations in c-Jun and JunB phosphorylation: a role in the control of cyclin D1 expression EMBO J 19 2056 2068 10790372
Behrens A Sibilia M David JP Mohle-Steinlein U Tronche F Schutz G 2002 Impaired postnatal hepatocyte proliferation and liver regeneration in mice lacking c-jun in the liver EMBO J 21 1782 1790 11927562
Doi T Greenberg SM Rosenberg RD 1987 Structure of the rat platelet factor 4 gene: a marker for megakaryocyte differentiation Mol Cell Biol 7 898 904 3821732
Dudoit S van der Laan MJ Pollard KS 2004. Multiple testing. Part I. Single-step procedures for control of general type I error rates. Stat Appl Genet Mol Biol 3(1):Article 13. Available: http://www.bepress.com/sagmb/vol3/iss1/art13/ [accessed 2 September 2004].
Etienne W Meyer MH Peppers J Meyer RA Jr 2004 Comparison of mRNA gene expression by RT-PCR and DNA microarray Biotechniques 36 618 626 15088380
Fleischer J Grage-Griebenow E Kasper B Heine H Ernst M Brandt E 2002 Platelet factor 4 inhibits proliferation and cytokine release of activated human T cells J Immunol 169 770 777 12097379
Gist GL Burg JR 1997 Benzene—a review of the literature from a health effects perspective Toxicol Ind Health 13 661 714 9399416
Gupta SK Singh JP 1994 Inhibition of endothelial cell proliferation by platelet factor-4 involves a unique action on S phase progression J Cell Biol 127 1121 1127 7962072
Hamadeh HK Amin RP Paules RS Afshari CA 2002 An overview of toxicogenomics Curr Issues Mol Biol 4 45 56 11931569
Hosack DA Dennis G Jr Sherman BT Lane HC Lempicki RA 2003 Identifying biological themes within lists of genes with EASE Genome Biol 4 10 R70 R70.8 14519205
Iacobuzio-Donahue CA Ashfaq R Maitra A Adsay NV Shen-Ong GL Berg K 2003 Highly expressed genes in pancreatic ductal adenocarcinomas: a comprehensive characterization and comparison of the transcription profiles obtained from three major technologies Cancer Res 63 8614 8622 14695172
Irizarry RA Bolstad BM Collin F Cope LM Hobbs B Speed TP 2003 Summaries of Affymetrix GeneChip probe level data Nucleic Acids Res 31 e15 12582260
Krewski D Snyder R Beatty P Granville G Meek B Sonawane B 2000 Assessing the health risks of benzene: a report on the benzene state-of-the-science workshop J Toxicol Environ Health A 61 307 338 11086936
Lampe JW Stepaniants SB Mao M Radich JP Dai H Linsley PS 2004 Signatures of environmental exposures using peripheral leukocyte gene expression: tobacco smoke Cancer Epidemiol Biomarkers Prev 13 445 453 15006922
Lossos IS Czerwinski DK Wechser MA Levy R 2003 Optimization of quantitative real-time RT-PCR parameters for the study of lymphoid malignancies Leukemia 17 789 795 12682639
Maione TE Gray GS Petro J Hunt AJ Donner AL Bauer SI 1990 Inhibition of angiogenesis by recombinant human platelet factor-4 and related peptides Science 247 77 79 1688470
Meiboom M Murua Escobar H Pentimalli F Fusco A Belge G Bullerdiek J 2003 A 3.4-kbp transcript of ZNF331 is solely expressed in follicular thyroid adenomas Cytogenet Genome Res 101 2 113 117 14610350
Niewiarowski S Lowery CT Hawiger J Millman M Timmons S 1976 Immunoassay of human platelet factor 4(PF4, anti-heparin factor) by radial immunodiffusion J Lab Clin Med 87 720 733 818325
Novak JP Sladek R Hudson TJ 2002 Characterization of variability in large-scale gene expression data: implications for study design Genomics 79 104 113 11827463
Pollard KS Van der Laan MJ 2003. Resampling-based Multiple Testing: Asymptotic Control of Type I Error and Applications to Gene Expression Data. Division of Biostatistics Working Paper 121. Berkeley, CA:University of California Berkeley. Available: http://www.bepress.com/ucbiostat/paper121 [accessed 2 September 2004].
Qu Q Shore R Li G Jin X Chen LC Cohen B 2002 Hematological changes among Chinese workers with a broad range of benzene exposures Am J Ind Med 42 275 285 12271475
Rothman N Li GL Dosemeci M Bechtold WE Marti GE Wang YZ 1996 Hematotoxocity among Chinese workers heavily exposed to benzene Am J Ind Med 29 236 246 8833776
Schreiber M Kolbus A Piu F Szabowski A Mohle-Steinlein U Tian J 1999 Control of cell cycle progression by c-Jun is p53 dependent Genes Dev 13 607 619 10072388
Shaulian E Karin M 2001 AP-1 in cell proliferation and survival Oncogene 20 2390 2400 11402335
Shaulian E Karin M 2002 AP-1 as a regulator of cell life and death Nat Cell Biol 4 E131 E136 11988758
Shimaoka T Nakayama T Kume N Takahashi S Yamaguchi J Minami M 2003 Cutting edge: SR-PSOX/CXC chemokine ligand 16 mediates bacterial phagocytosis by APCs through its chemokine domain J Immunol 171 1647 1651 12902461
Snyder R 2002 Benzene and leukemia Crit Rev Toxicol 32 155 210 12071572
Staudt LM 2003 Molecular diagnosis of the hematologic cancers N Engl J Med 348 1777 1785 12724484
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Thach DC Lin B Walter E Kruzelock R Rowley RK Tibbetts C 2003 Assessment of two methods for handling blood in collection tubes with RNA stabilizing agent for surveillance of gene expression profiles with high density microarrays J Immunol Methods 283 269 279 14659918
Vermeulen R Li G Lan Q Dosemeci M Rappaport SM Bohong X 2004 Detailed exposure assessment for a molecular epidemiology study of benzene in two shoe factories in China Ann Occup Hyg 48 105 116 14990432
Waters MD Olden K Tennant RW 2003 Toxicogenomic approach for assessing toxicant-related disease Mutat Res 544 415 424 14644344
Whitney AR Diehn M Popper SJ Alizadeh AA Boldrick JC Relman DA 2003 Individuality and variation in gene expression patterns in human blood Proc Natl Acad Sci USA 100 1896 1901 12578971
Wisdom R Johnson RS Moore C 1999 c-Jun regulates cell cycle progression and apoptosis by distinct mechanisms EMBO J 18 188 197 9878062
Wu MM Chiou HY Ho IC Chen CJ Lee TC 2003 Gene expression of inflammatory molecules in circulating lymphocytes from arsenic-exposed human subjects Environ Health Perspect 111 1429 1438 12928151
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0036015929873PerspectivesEditorialGuest Editorial: Reducing Arsenic Exposure from Drinking Water: Different Settings Call for Different Approaches Graziano Joseph H. van Geen Alexander Columbia University, New York, New York, E-mail:
[email protected];
[email protected] Graziano is associate dean for research and professor of environmental health sciences at the Mailman School of Public Health at Columbia University. He is also professor of pharmacology at Columbia’s College of Physicians and Surgeons, and director of the Columbia University Superfund Basic Research Program. His research is focused on the health effects of exposure to metals.
Alexander van Geen is a geochemist at the Lamont-Doherty Earth Observatory of Columbia University and associate director of Columbia University’s Superfund Basic Research Program. He studies the cycling of trace elements in natural and perturbed environments, particularly redox-sensitive processes affecting metals and metalloids.
The authors declare that they have a competing financial interest, in that Columbia University has filed for two patents regarding an improved field test for arsenic.
6 2005 113 6 A360 A361 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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On 1 January 2006, a new U.S. drinking water standard of 10 μg arsenic/L will come into effect [U.S. Environmental Protection Agency (EPA) 2001a). We strongly support the U.S. EPA’s decision to lower the allowable limit of As in drinking water from 50 μg/L to 10 μg/L because it promises to reduce the risk of an array of adverse health outcomes attributable to As exposure, notably various cancers and cardiovascular and neurologic diseases.
Throughout the United States, but particularly in the northeastern and southwestern states, where drinking water sources are most likely to exceed the 10 μg/L limit, public agencies responsible for water quality are preparing for the arrival of the new standard in a variety of ways. In 2001, the U.S. EPA estimated that the arsenic content of water provided by roughly 5% of U.S. community water supplies exceeded 10 μg/L (U.S. EPA 2001b); in these cases, the introduction of water-treatment facilities will be required to bring systems into compliance. Although this will be expensive, the ever-increasing evidence that waterborne arsenic is a menace to public health—including new findings that it impacts children’s intellectual functioning (Wasserman et al. 2004)—warrants the cost.
A significant segment of the U.S. population at risk, however, relies on individual household wells for their drinking water. Groundwater studies conducted by the U.S. Geological Survey (Focazio et al. 1999) imply that nearly 8% of domestic wells exceed the new As standard. Here, the responsibility for water treatment lies with the homeowner. Simple over-the-counter filtration systems are not effective for removing As from tap water. Rather, more elaborate technologies costing several thousand dollars (e.g., reverse osmosis systems) are required. For those who can afford it, the cost of installing such systems to protect family health is small, but for those who are economically disadvantaged, a water treatment system to remove As (and other potentially harmful elements) may not be a high priority. To help alleviate the situation, testing of household water for As should become part of the building-inspection process that preceeds the sale of a home, allowing for the cost for water treatment to be factored into the transaction.
In comparison to the situation in Bangladesh and other developing nations, the U.S. problem is small and readily solvable. Although estimates vary, perhaps as many 100 million rural inhabitants of Bangladesh and other affected South Asian countries drink untreated well water with As concentrations that can exceed the Bangladesh standard of 50 μg/L by more than an order of magnitude. A single visit to a severely affected region of Bangladesh can be a life-altering experience, as the skin lesions associated with the consumption of As-contaminated water are evident, even in young children. When one realizes that skin lesions are but a visible manifestation of a wider syndrome that damages multiple internal organ systems, the magnitude of the arsenic problem becomes even more unsettling.
The extent of the problem, coupled with the relative economic plight of the country, drives home the need for a more significant response by developed nations—and the donor community—to assist Bangladesh as it works toward achieving a safe water supply. Despite continued efforts by the government of Bangladesh, scientists, industry, and other governmental and nongovernmental organizations, large-scale removal of arsenic from groundwater or human pathogens from surface water appears to be an exceedingly difficult objective to achieve in the near future.
A temporary solution appears to be at hand in thousands of affected villages, but residents are often not aware of it. Deeper aquifers are typically low in As. Over the past several years, the World Bank–sponsored Bangladesh Arsenic Mitigation Water Supply Project (BAMWSP) has conducted a massive field-testing campaign for arsenic of over 5 million wells across the most affected half of the country (BAMWSP 2005). By and large, these results have been accurate and probably already have led many households to switch from their As-contaminated well to a neighboring low-As well (van Geen et al. 2002). The testing campaign, however, did not address the needs of the many households that could not switch to a safe well because of geographic or social constraints.
The BAMWSP data could also be useful by guiding the installation of community wells to those deeper aquifers that are low in As. In collaboration with scientists from Bangladesh, research conducted by a number of international groups has shown that extraction of drinking water from such aquifers (but not large-volume pumping for irrigation water, which could lead to contamination of the deeper aquifers) is feasible and likely to be sustainable in a majority of villages in Bangladesh [British Geological Survey/Department of Public Health Engineering (BGS/DPHE) 2001; Zheng et al., in press]. The use of community wells that tap these deeper aquifers has been extensive in 50 villages of Araihazar upazila, where health, Earth, and social scientists of Columbia University have been conducting basic research with support from the Superfund Basic Research Program (van Geen et al. 2003).
The valuable BAMWSP arsenic data, which have been compiled with information about well location and depth, should be used in a concerted effort to target aquifers for the installation of community wells across a larger portion of Bangladesh. Although coupling the installation of these community wells to complex piped-water supply systems, as currently favored by the World Bank, should be a longer-term goal, it may slow the process in the short term.
In the significant number of villages where the BAMWSP data do not unambiguously identify a safe depth, exploratory drilling will be needed (Gelman et al. 2004; van Geen et al. 2004). A team supported by the Earth Institute at Columbia University is piloting a cell phone–based system to provide access to the BAMWSP database from any village in Bangladesh and to update the database as new wells are installed. This approach will allow communities to determine the local depth of low-As aquifers and empower them to make an informed decision concerning the eventual placement of a safe community well. All who are involved in As mitigation should make available and advertise, at the village level, local testing for As.
Of the 6,000 wells within a 25-km2 area that we tested in 2000–2001, roughly 1,000 had been replaced privately by 2004, partly in response to the previous test results (van Geen et al. 2005); this phenomenon is apparently very widespread. Sadly, these new wells had been installed blindly, and the groundwater pumped from half of the new wells still contained > 50 μg/L As. The spatial variability of As concentrations in Bangladesh ground-water complicates the prediction of the As content of water from a particular well but also provides an opportunity for mitigation in that safe aquifers can be targeted to provide the vast majority of households access to safe water.
==== Refs
References
BAMWSP 2005. Bangladesh Arsenic Mitigation Water Supply Project Homepage. Available: http://www.bamwsp.org [accessed 3 May 2005].
BGS/DPHE 2001. Arsenic Contamination of Groundwater in Bangladesh, Vol 2, Final Report (Kinniburgh DG, Smedley PL, eds). BGS Technical Report WC/00/19. Keyworth, UK:British Geological Survey. Available: http://www.bgs.ac.uk/arsenic/bangladesh/reports.htm [accessed 3 May 2005].
Focazio MJ Welch AH Watkins SA Helsel DR Horn MA 1999. A retrospective analysis on the occurrence of arsenic in ground-water resources of the United States and limitations in drinking-water-supply characterizations. Water-Resources Investigations Report 99-4279. Reston, VA:U.S. Geological Survey. Available: http://water.usgs.gov/nawqa/trace/pubs/wrir-99-4279/index.html [accessed 3 May 2005].
Gelman A Trevisani M Lu H van Geen A 2004 Direct data manipulation for local decision analysis, as applied to the problem of arsenic in drinking water from tube wells in Bangladesh Risk Anal 24 1597 1612 15660615
U.S. EPA 2001a. Technical Fact Sheet: Final Rule for Arsenic in Drinking Water. EPA-815-F-00-016. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/safewater/ars/ars_rule_techfactsheet.html [accessed 9 May 2005].
U.S. EPA 2001b. Arsenic Rule Benefit Analysis: An SAB review. EPA-SAB-EC-01-008. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/sab/pdf/ec01008.pdf [accessed 3 May 2005].
van Geen A Ahmed KM Seddique AA Shamsudduha M 2003 Community wells to mitigate the current arsenic crisis in Bangladesh Bull WHO 82 632 638 14710504
van Geen A Ahsan H Horneman AH Dhar RK Zheng Y Hussain I 2002 Promotion of well-switching to mitigate the current arsenic crisis in Bangladesh Bull WHO 81 732 737 12378292
van Geen A Cheng Z Seddique AA Hoque MA Gelman A Graziano JH 2005 Reliability of a commercial kit to test groundwater for arsenic in Bangladesh Environ Sci Technol 39 1 299 303 15667109
van Geen A Protus T Cheng Z Horneman A Seddique AA Hoque MA 2004 Testing groundwater for arsenic in Bangladesh before installing a well Environ Sci Technol 38 24 6783 6789 15669339
Wasserman GA Liu X Parvez F Ahsan H Factor-Litvak P van Geen A 2004 Water arsenic exposure and children's intellectual function in Araihazar, Bangladesh Environ Health Perspect 112 1329 1333 15345348
Zheng Y van Geen M Stute R Dhar Z Mo Z Cheng A In press. Geochemical and hydrogeological contrasts between shallow and deeper aquifers in two villages of Araihazar, Bangladesh: Implications for deeper aquifers as drinking water sources. Geochim Cosmochim Acta.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0036215929874PerspectivesDirector's PerspectiveNIEHS Priorities: The Process of Strategic Planning Newton Sheila PhDDirector, Office of Science Policy and Planning, NIEHSSchwartz David A. MDDirector, NIEHS E-mail:
[email protected] 2005 113 6 A362 A362 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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As we work to move the NIEHS forward, it is important that we critically consider how our research can have the greatest impact on public health. In last month’s Director’s Perspective, I identified my overarching vision for the NIEHS: to improve human health by elucidating the complex relationship between endogenous and exogenous risks within populations and affected individuals, how environmental exposures affect human biology, and how this knowledge can be used to reduce morbidity and extend longevity. Fundamental to this vision is an emphasis on human health and disease. Examples of previous research areas supported by the NIEHS that have had a profound effect on human health include:
the link between methylmercury and neurodevelopmental deficits in the fetus;
the role of aflatoxin in the development of hepatocellular carcinoma;
the importance of manganese in neurodegenerative disorders;
the adverse developmental, cognitive, cardiovascular, and renal effects of lead;
the role of polluted air in asthma attacks, and in deaths from chronic obstructive pulmonary disease and cardiovascular disease in the elderly;
the contribution of paraoxonase to pesticide toxicity;
the identification of biomarkers of beryllium-induced lung disease; and
the effect of nitrous oxide on fertility.
If further advances are to be made—and I believe that the environmental health sciences are uniquely poised to profoundly impact the development and progression of disease and substantially improve public health—I am convinced that additional strategic planning is needed to efficiently and effectively focus our resources.
Over the past decades, the NIEHS has periodically conducted outside reviews and assessments of its research directions and priorities, with the objective of setting priorities, readjusting its course, and establishing new goals. The NIEHS has benefited greatly from the input of the nation’s top scientists and experts in environmental health in this process. For instance, the 1970 report Man’s Health and the Environment was the result of a task force convened in 1969 by then–NIEHS director Paul Kotin. This document guided some of the early developments of the institute including the development of the environmental health sciences centers. A 1976 report, Human Health and the Environment, comprised the input of over 80 scientists and resulted in a comprehensive outline of research needs including focus on specific pollutants of air, food, and water. A 1984 task force report also titled Human Health and the Environment focused on how to bring new developments and tools in the biological sciences to bear upon issues in environmental health. In 2000, the NIEHS Strategic Vision document outlined the priorities and research objectives of the institute. This document emphasized the importance of disease prevention and placed high priority on efforts targeted towards populations with specific vulnerabilities to environmental insults (minorities, women, and children). It also articulated priorities in new technology development that ultimately matured into what is now termed “toxicogenomics.”
It is timely, then, that we are now embarking on the development of a strategic plan to consider future programmatic development at the NIEHS. Given our anticipated limited resources for the foreseeable future, it is absolutely crucial that we establish our priorities and develop a plan to support the best science that will have the greatest impact on human health.
It is absolutely crucial that we establish our priorities and develop a plan to support the best science that will have the greatest impact on human health.
Throughout the strategic planning process, we will be fully committed to an inclusive approach that draws opinions from a broad array of stakeholders and focuses on critical opportunities in environmental health. To begin the process, we will solicit input from NIEHS-supported investigators and interested stakeholders through the Federal Register and the NIEHS web-site (http://www.niehs.nih.gov/). We plan to focus this survey on questions that are critical to the NIEHS:
What are the disease processes and public health concerns that are relevant to environmental health sciences?
How can environmental health sciences be used to understand how biological systems work, why some individuals are more susceptible to disease, or why individuals with the same disease have very different clinical outcomes?
What are the major opportunities and challenges in global environmental health?
What are the critical exposures that need further investigation?
What are the critical needs in training the next generation of scientists in environmental health?
What technology or structural changes are needed to fundamentally advance environmental health sciences?
Responses to these questions will be compiled and analyzed. Then the topics will be discussed in detail by a Strategic Planning Group that will meet sometime this fall. I anticipate that the Strategic Planning Group will comprise approximately 100 individuals, including NIEHS-supported scientists as well as nonscientist stakeholders. The Strategic Planning Group will produce a brief document that outlines the goals and objectives that are thought to warrant the greatest scientific and programmatic attention over the next several years. Once developed, this draft document will be vetted for public comment on the NIEHS website, in the Federal Register, and through our advisory councils before a final document is compiled.
In the end, I would like a strategic plan that clearly articulates the goals and objectives that will guide our growth over the next five years. While I am reluctant to commit to a timeline, ideally I would like to have this document completed by early 2006.
I believe that the strategic plan for the next phase of development at the NIEHS will strengthen our institute’s relationship with its constituent communities and create a clear path to meeting the challenges that lie before us. We have unparalleled opportunities to achieve groundbreaking advances in science and to translate these advances into measurable improvements in human health. I am depending on all of you to contribute to the development of the next strategic plan for the NIEHS.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0036515929876PerspectivesCorrespondenceGlyphosate Results Revisited Farmer Donna R. Product Safety Center, Monsanto Company, St. Louis, Missouri, E-mail:
[email protected] Timothy L. Boston University School of Public Health, Boston University, Boston, MassachusettsAcquavella John F. Product Safety Center, Retired, Monsanto Company, St. Louis, MissouriD.R.F. is a current employee of Monsanto, J.F.A. is retired from Monsanto, and T.L.L. works as a consultant to Monsanto.
6 2005 113 6 A365 A366 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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With respect to the recent article by De Roos et al. (2005), we would like to a) comment on the authors’ incomplete genotoxicity review, which is inconsistent with conclusions reached by regulatory agencies; b) estimate the likely range of systemic doses and margins of exposure for farmers based on comprehensive glyphosate biomonitoring data published in 2004; and c) request further evaluation of confounding and selection bias in their analyses for multiple myeloma.
In their discussion of genotoxicity, De Roos et al. focused on selected studies that conflict with the weight of evidence for glyphosate and Roundup brand (Monsanto Company, St. Louis, MO) agricultural herbicides containing glyphosate. They cited Williams et al. (2000) regarding the lack of a carcinogenic effect in rodent feeding studies with glyphosate but neglected to cite the extensive genotoxicity review in the same article in which Williams et al. concluded that Roundup and its components do not pose a risk for heritable or somatic mutations. This conclusion is in agreement with findings by the U.S. Environmental Protection Agency (U.S. EPA 1993), the World Health Organization (WHO 1994), the European Commission (2002), and regulatory agencies worldwide. None of the studies cited by De Roos et al. (2005) as presumptive evidence of genotoxicity were conducted under Good Laboratory Practices or according to international guidelines. Additionally, many of these studies used toxic dose levels and/or irrelevant routes of exposure.
When evaluating epidemiologic findings, it can be helpful to compare the range of likely exposure levels to the exposure levels of toxicologic significance (Acquavella et al. 2003). The cancer no-effect levels for glyphosate, based on rat and mouse lifetime feeding studies, are 1,000 and 1,500 mg/kg/day, respectively (Williams et al. 2000). Acquavella et al. (2004) reported results of a biomonitoring study in which 48 farmers collected all of their urine over 5 consecutive days (before, during, and for 3 days after a glyphosate application). In this study, the maximum systemic dose resulting from application of glyphosate to areas as large as 400 acres was 0.004 mg/kg. The geometric mean systemic dose was 0.0001 mg/kg. Accordingly, in the worst-case situation, if a farmer made a similar application every day for a lifetime, the systemic dose would be at least 250,000-fold lower than the cancer no-effect level in rodents. Indeed, this very large margin of exposure combined with the lack of evidence for genotoxicity must be factored into an assessment of biologic plausibility.
Finally, De Roos et al.’s Table 2 (De Roos et al. 2005) shows an age-adjusted relative risk (RR) of 1.1 [95% confidence interval (CI), 0.5–2.4] associating multiple myeloma and ever-use of glyphosate. The RR adjusted for selected demographic and lifestyle variables was 2.6 (95% CI, 0.7–9.4). The factors that account for the difference in these RRs are not well explained. Given the weak associations between the covariates and ever-use of glyphosate and the weak or nonexistent relation between these variables and risk of multiple myeloma, it is unlikely that the change in RR from 1.1 to 2.6 is attributable to confounding. The authors mention that only 75% of eligible subjects were included in the fully adjusted analysis and that this reduction in analytic sample size was due to the exclusion of subjects that were missing covariate data. Further, De Roos et al. (2005) did not find an association in the complete data set without adjustment for covariates (RR = 1.1), but they did find a positive association in the restricted data set without adjustment for covariates. The difference in association due simply to restricting the data set to those with covariate information was not quantified, although such quantification would help the reader understand what proportion of the change from 1.1 to 2.6 was attributable to adjustment for candidate confounders and what proportion was due to selection of subjects with more complete data. An analysis stratified by each covariate individually should have allowed the investigators to identify covariates for which missing data and/or adjustment made the biggest impact on the estimated RR. The identity of these covariates would help the reader weigh the potential for confounding versus selection bias to explain the change in RR from 1.1 to 2.6. Given that only 32 cases of multiple myeloma were observed and as few as 19 cases were included in some of the analyses, the authors should have explored the potential for the analysis of sparse data to result in estimates biased away from the null (e.g., see Greenland et al. 2000 for an example involving conditional logistic regression).
==== Refs
References
Acquavella JF Alexander BH Mandel JS Gustin C Baker B Chapman P 2004 Glyphosate biomonitoring for farmer-applicators and their families: results from the Farm Family Exposure Study Environ Health Perspect 112 321 326 14998747
Acquavella JF Doe J Tomenson J Chester G Cowell J Bloemen L 2003 Epidemiologic studies of occupational pesticide exposure and cancer: regulatory risk assessments and biologic plausibility Ann Epidemiol 13 1 7 12547479
DeRoos AJ Blair A Rusiecki JA Hoppin JA Svec M Dosemeci M 2005 Cancer incidence among glyphosate-exposed pesticide applicators in the Agricultural Health Study Environ Health Perspect 113 49 54 10.1289/ehp.7340.15626647
European Commission 2002. Report for the Active Substance Glyphosate. Available: http://europa.eu.int/comm/food/plant/protection/evaluation/existactive/list1_glyphosate_en.pdf [accessed 25 January 2002].
Greenland S Schwartzbaum JA Finkle WD 2000 Problems due to small samples and sparse data in conditional logistic regression Am J Epidemiol 151 531 539 10707923
U.S. EPA 1993. Glyphosate Reregistration Eligibility Decision (RED). EPA-738-R-93-014. Washington, DC:U.S. Environmental Protection Agency.
Williams GM Kroes R Munro IC 2000 Safety evaluation and risk assessment of the herbicide Roundup and its active ingredient, glyphosate, for humans Regul Toxicol Pharmacol 31 117 165 10854122
WHO 1994. Glyphosate. Environmental Health Criteria 159. Geneva:World Health Organization.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00366PerspectivesCorrespondenceGlyphosate Results Revisited: De Roos et al. Respond De Roos Anneclaire J. Svec Megan A. Program in Epidemiology, Fred Hutchinson Cancer Research Center and the Department of Epidemiology, University of Washington, Seattle, Washington, E-mail:
[email protected] Aaron Rusiecki Jennifer A. Dosemeci Mustafa Alavanja Michael C. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MarylandHoppin Jane A. Sandler Dale P. Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North CarolinaThe authors declare they have no competing financial interests.
6 2005 113 6 A366 A367 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The reaction of Farmer et al. regarding our article on glyphosate exposure and cancer incidence in the Agricultural Health Study (AHS) (De Roos et al. 2005) is difficult to understand given the tentative nature of our conclusions. For the most part, we found no associations with the cancers we studied, and to quote from our abstract,
Glyphosate exposure was not associated with cancer incidence overall or with most of the cancer subtypes we studied. There was a suggested association with multiple myeloma incidence that should be followed up as more cases occur in the AHS.
Despite the fact that we believe our presentation of the data was quite fair and included a lengthy discussion of possible biases affecting our results, several comments by Farmer et al. necessitate a response.
Farmer et al. had several criticisms of our review of the genotoxicity literature (De Roos et al. 2005). Although the discussion of the toxicity studies is interesting, these studies only serve as background information in our article; the epidemiologic associations between glyphosate exposure and cancer incidence we observed are the empirical result of our investigation. Criticisms of our reference to the genotoxicity literature do not, of course, alter the human data we presented. We stated in our article the conclusion of the U.S. Environmental Protection Agency (U.S. EPA 1993) and the World Health Organization (1994) that glyphosate is not mutagenic, but because that conclusion focused on the active ingredient, glyphosate, and not formulated products such as Roundup (Monsanto Company, St. Louis, MO), we also cited several studies which show potentially greater toxic effects of Roundup than glyphosate. Our article (De Roos et al. 2005) does not purport to be a comprehensive review of the toxicology literature, and because of space limitations imposed by the journal, we did not discuss several studies showing potentially toxic effects of several glyphosate-based pesticide products through disruption of cell-cycle control mechanisms, which may be relevant for cancer as well as noncancer health outcomes (Marc et al. 2002, 2004).
The fact the some of the studies we cited did not use Good Laboratory Practices is irrelevant, because this system is used primarily in analytical chemistry and contract laboratories for routine support of pesticide regulation, and is not required by any of the principal funding agencies for research studies. Studies that are submitted to the U.S. EPA to support applications for licensing pesticides are required to meet specified guidelines for record keeping, data reporting, and protocol development. These Good Laboratory Practices provide some assurance that regulators can rely on the data they review and give them the ability to perform audits as needed. Investigators who perform studies for research purposes are not required to follow these structured practices, but many may do so. Furthermore, it does not follow that work done in labs that do not strictly adhere to the U.S. EPA’s testing and reporting requirements follow “bad” laboratory practices. Quality assurance for research studies is provided by the peer-review process and by replication. This is analogous to the distinction between clinical laboratory tests performed in the context of human research and tests performed for diagnostic purposes. In order for these tests to be covered by insurers, they must be performed in laboratories approved by the Clinical Laboratory Improvement Amendments (CLIA 2005). CLIA approval assures that the test results are valid but does not address the underlying science that led to the development of the test.
In their letter, Farmer et al. used exposure information from a study by Acquavella et al. (2004) in which biomonitoring of farmers who applied glyphosate was used to determine a maximum dose calculation. The dose thresholds Farmer et al. cite as relevant for carcinogenicity are from mouse and rat models in which the active ingredient, glyphosate, was tested in feeding studies (Williams et al. 2000). Lower relevant doses may apply for Roundup and other formulated products containing glyphosate, or for glyphosate products used in combination with other active ingredients. In addition, epidemiology can provide direct information on the question of what happens in humans from more relevant routes of exposure.
Some questions were raised about the possible associations we observed between glyphosate and multiple myeloma concerning the discrepancy between the age-adjusted relative risk of 1.1 [95% confidence interval (CI), 0.5–2.4] and the relative risk adjusted for selected demographic and lifestyle variables of 2.6 (95% CI, 0.7–9.4) (De Roos et al. 2005). Farmer et al. question whether the discrepancy may be due to confounding or the selection of subjects into the more restricted analysis. This is plausible, and we discussed these issues at length in our article. The association only appeared within the subgroup with complete data on all the covariates; even without any adjustment, there was a 2-fold increased risk of multiple myeloma associated with glyphosate use among the smaller subgroup with covariate data. We acknowledged that this could be due to selection bias, effect modification, or confounding within this subgroup. We would point out, however, that confounding can be both positive and negative. The type of analysis suggested by Farmer et al., in which the data are stratified by each covariate individually in order to identify covariates for which missing data and/or adjustment made the biggest impact on the estimated relative risk, would be unreliable for such a small number of cases. Each estimate would be subject to small sample bias (Greenland 2000), which was cited by Farmer et al. as an issue with our overall estimate for myeloma. The most reliable approach will be to reanalyze the data after more cases accumulate, both to assess whether the association with myeloma persists and to further evaluate confounding and selection bias using a larger case group to support analyses. Following up initial observations with more comprehensive epidemiologic data from the AHS has been our plan since the inception of the study.
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References
Aquavella JF Alexander BH Mandel JS Gustin C Baker B Chapman P 2004 Glyphosate biomonitoring for farmers and their families: results from the Farm Family Exposure Study Environ Health Perspect 112 321 326 14998747
CLIA 2005. Clinical Laboratory Improvement Amendments Homepage. Available: http://www.cms.hhs.gov/clia/ [accessed 27 April 2005].
De Roos AJ Blair A Rusiecki J Hoppin JA Svec M Dosemeci M 2005 Cancer incidence among glyphosate-exposed pesticide applicators in the Agricultural Health Study cohort Environ Health Perspect 113 49 54 15626647
Greenland S Schwartzbaum JA Finkle WD 2000 Problems due to small samples and sparse data in conditional logistic regression Am J Epidemiol 151 531 539 10707923
Marc J Mulner-Lorillon O Durand G Bellé R 2002 Pesticide Roundup provokes cell division dysfunction at the level of CDK1/cyclin B activation Chem Res Toxicol 15 326 331 11896679
Marc J Mulner-Lorillon O Bellé R 2004 Glyphosate-based pesticides affect cell cycle regulation Biol Cell 96 245 249 15182708
U.S. EPA 1993. Reregistration Eligibility Decision (RED). Glyphosate. EPA-738-R-93-014. Washington, DC:U.S. Environmental Protection Agency.
Williams GM Kroes R Munro IC 2000 Safety evaluation and risk assessment of the herbicide Roundup and its active ingredient, glyphosate, for humans Regul Toxicol Pharmacol 31 117 165 10854122
World Health Organization 1994. Glyphosate. Environmental Health Criteria 159. Geneva:World Health Organization.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00368AnnouncementsErratumErratum 6 2005 113 6 A368 A368 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In Figures 1, 2, and 3 of “Altered Profiles of Spontaneous Novelty Seeking, Impulsive Behavior, and Response to d-Amphetamine in Rats Perinatally Exposed to Bisphenol A” by Adriani et al. [
Environ Health Perspect 111:395–401 (2003)], results for oil controls and bisphenol A (BPA)-treated rats were labeled incorrectly. The corrected figures are shown below. EHP apologizes for the errors.
Figure 1 (A,B) Mean (± SE) percentage of time spent in the novel compartment by subjects of both sexes on testing day (experiment 1). (C,D) Mean (± SE) activity rate, measured as number of line crossings per minute, shown by subjects of both sexes in the novel compartment on testing day. During the pretreatment period (days 1–3), subjects were familiarized to one compartment. On testing day, animals were placed in the familiar compartment. After 5 min, a partition was removed and subjects were allowed free access to a novel compartment of the apparatus for a 24-min session.
*p < 0.05 in comparisons between BPA and control perinatal treatments (n = 9).
Figure 2 Mean (± SE) choice (%) of the large reinforcer, demanded by nose poking at the LAD hole, shown by rats during the test for impulsivity (experiment 2). These data reveal that, as the length of the delay increased, animals increased demanding the small but immediate reinforcement and decreased demanding the larger but delayed one. A shift to the right of the whole curve (i.e., a profile of reduced impulsivity) was evident in BPA-exposed rats compared with controls. In the absence of significant differences, data from the two sexes were collapsed (n = 18).
Figure 3 Mean (± SE) frequency of inadequate responding at the IAS hole (i.e., nose poking during the length of the delay, when it was without any consequence) shown by rats during the test for impulsivity (experiment 2). These data reveal that, when animals were waiting for the delivery of the large reinforcer, they failed to rest and were demanding the immediate one. A clear-cut demasculinization in the restlessness profile was evident.
*p < 0.05 in multiple comparisons between BPA and control perinatal treatments (n = 9).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0037015929877EnvironewsForumLivestock Issues: On Hens and Needles Washam Cynthia 6 2005 113 6 A370 A370 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Asian governments alarmed at the unprecedented spread of the deadly H5N1 avian influenza virus are seeking relief in a controversial vaccination program. The Thai government announced in February 2005 that it would join China and Indonesia in vaccinating select healthy ducks and chickens. Vietnam also is considering a vaccination program.
Vaccinations can lessen the risk of influenza by reducing the birds’ chances of infection and minimizing the amount of virus shed through nasal secretions and feces by those that do become infected. But vaccinated chickens can still become infected while showing no symptoms of disease (chickens that have not been vaccinated typically die within 48 hours of infection). For that reason, many countries—including Japan, one of Thailand’s biggest poultry markets—ban imports of vaccinated chickens. Countries therefore usually vaccinate poultry against influenza only as a last resort.
“The concern is that if a vaccine is used, it will be harder to identify the virus,” says epidemiologist Mark Katz of the Centers for Disease Control and Prevention, “and it’s not a guarantee that vaccination will completely eliminate the shedding of virus.”
Asian farmers, though, are running out of options. Mass culling has done little to stem the epidemic. More than 120 million chickens in Vietnam, Thailand, and China died or were destroyed during a three-month period early in 2004. A 2 September 2004 article in Nature says many Thai farmers are turning to ineffective black-market vaccines to avoid killing their birds. But black-market vaccines can contain viruses that have not been properly inactivated, and may spur the evolution of even more dangerous strains.
Moreover, the virus poses the serious threat of sparking a worldwide human pandemic. H5N1 is highly virulent in humans, with a death rate of more than 60%. What’s kept the virus in check among humans so far is its inability to spread readily from person to person. Fewer than 10 of the 79 confirmed human cases are thought to have resulted for person-to-person contact—most victims handled infected poultry. Scientists believe, though, that H5N1 could mutate into a strain that spreads as easily among humans as the common cold.
“If you put less virus back into the environment, there’s less chance of transmission,” says David Swayne, laboratory director at the U.S. Department of Agriculture’s Southeast Poultry Research Laboratory. “The negatives of vaccination are small if it’s used properly.”
The best prospects for containing avian flu come from using vaccines in conjunction with rigorous surveillance, quarantines, escape-proof poultry coops, and disinfection of poultry handlers and their equipment. Through much of Southeast Asia, though, low budgets and a weak infrastructure hinder such commonsense measures. Millions of peasants, each raising a dozen chickens in their backyard, are simply beyond the reach of government efforts.
Yet another barrier to stemming the epidemic is the reluctance that developing countries have to reporting news that could hurt their economies. Mainland China, where scientists believe the virus first emerged before 1997, acknowledged avian flu for the first time only in 2004, after outbreaks were reported in several neighboring countries. Chinese scholars later admitted in an article published 16 February 2004 in Newsweek International that the virus was rampant in several provinces as early as 2001.
H5N1 has become so entrenched in some regions of Southeast Asia that it has now established a permanent ecological niche in poultry, according to a January 2005 World Health Organization report, Avian Influenza: Assessing the Pandemic Threat. “The chance of complete eradication in the near future is very unlikely,” Swayne says.
Still, Swayne sees reason for hope. In a 7 March 2005 review he wrote for the International Society for Infectious Disease, he deemed the commonly used inactivated AI vaccine effective, along with two new vaccines developed for use in Chinese poultry. And government-sponsored vaccination programs such as those in China and Thailand reduce the risk of farmers using black-market vaccines.
Juan Lubroth, a veterinarian specializing in infectious diseases with the Food and Agriculture Organization of the United Nations, agrees with Swayne that progress is being made in the fight against H5N1, albeit slowly. “I think we’ll have a few years to deal with this virus,” Lubroth says. “But during that time, I think we’ll strengthen the veterinary structure in Asia. Ultimately, it will be good for the production of other livestock.”
Preventive medicine? Several Asian governments have begun vaccinating healthy poultry in hopes of averting the spread of avian flu, but some scientists have concerns about the effectiveness of such programs.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00373EnvironewsForumEHPnet: West Bengal & Bangladesh Arsenic Crisis Information Centre Dooley Erin E. 6 2005 113 6 A373 A373 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Bangladesh and the neighboring Indian state of West Bengal are the site of what has been called the largest mass poisoning in history: millions of people here are drinking water that is heavily contaminated with arsenic. Researchers, engineers, and others who wish to learn more about this public health crisis can access the latest information and research at the West Bengal & Bangladesh Arsenic Crisis Information Centre, located at http://bicn.com/acic/. The page is a service of the Bangladesh International Community News website.
The homepage of the site features a color-coded map showing the levels of arsenic contamination across the region. More than half of Bangladesh’s 10 million drinking water tubewells are contaminated with arsenic in concentrations exceeding World Health Organization guidelines. The United Nations Development Programme estimates that 20,000 people may die of arsenic-related disease each year; however, the numbers are hard to calculate because of the long time it takes for some cancers to emerge. If caught early enough, arsenic poisoning can be reversed with safe drinking water, nutritious foods, and time—three things most people of the region have little of.
The fully searchable site comprises pages of links to news and research articles, data sets, and online forums. Arsenic-crisis and WaterForum are Yahoo group forums that are available to anyone with access to the Internet. Participants may discuss, among other topics, arsenic geochemistry, remediation options, health effects, and related groundwater and surface water issues.
Other pages are devoted to reports, project documents, reference materials, and organizations and individuals from around the world who are involved in the arsenic crisis or related work. For example, a team at Harvard and the Massachusetts Institute of Technology is working to consolidate arsenic data, study the hydro-geochemistry of groundwater, and identify feasible, effective water treatment options for villagers, among other projects.
One page, Water Treatment & Alternative Supplies, specifically links to information on a variety of water treatment projects. Included here are sections on arsenic removal technology verification projects, removal technology providers and projects, alternative water supply technology providers and projects, and field test kits and other measurement-related resources. On another page, Health Effects & Medical Info, visitors will find suggestions for a homemade ointment to ease the suffering of the cracked palms and feet that can accompany chronic arsenic poisoning.
Visitors can also subscribe to Arsenic Crisis News through the site. This free newsletter covers such topics as arsenic geochemistry, water treatment technologies, epidemiology, disease mechanisms, and medical treatments. The site provides a list of arsenic-related conferences as well as a bibliography of books and other media, along with ordering information for these resources.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0037415929878EnvironewsNIEHS NewsColumbia Program Digs Deeper into Arsenic Dilemma Mead M. Nathaniel 6 2005 113 6 A374 A377 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The German idiom verschlimmbesserung refers to an intervention that is made with the best intentions to solve a problem but ends up worsening the situation or creating new problems. Past efforts to improve the drinking water supply of Bangladesh are a classic example. Three decades ago, the people of this poverty-stricken country got their drinking water primarily from surface sources, which were often contaminated with fecal pathogens that caused diarrhea, cholera, typhoid, and other life-threatening diseases. In 1971, the United Nations Children’s Fund launched a campaign to drill hand-pumped tubewells into the Ganges Delta alluvium. Hailed as a public health coup, the effort led to a plummet in waterborne microbial diseases throughout Bangladesh. But in the mid-1990s, local physicians noted a steep increase in the incidence of arsenicosis and other arsenic-related diseases—a trend subsequently linked to the drinking of tubewell water naturally rich in the metalloid element.
Now scientists at Columbia University have devised an approach that they believe could become part of an overall strategy to resolve the arsenic crisis in Bangladesh. Spearheaded by Alexander van Geen, a senior research scientist at the Lamont-Doherty Earth Observatory, Habibul Ahsan, an associate professor of epidemiology, and Joseph Graziano, associate dean of research at the Mailman School of Public Health, their proposed strategy includes the installation and monitoring of deep community wells in affected villages throughout Bangladesh. There is also a strong emphasis on training and organizing villagers at the community level to secure a source of safe water that is tailored to local needs.
The proposed strategy grew out of field work and a well survey conducted through the university’s NIEHS Superfund Basic Research Program, of which Graziano is director. The program was established in 2000 to study the bioavailability, health effects, and geochemistry of arsenic and lead. Projects to date have included bioavailability and geochemistry studies at four Superfund sites, epidemiologic and geochemistry studies of arsenic in drinking water in Bangladesh, and the development of practical remediation strategies for arsenic in wastewater and drinking water.
Public Health Crisis
Today, 97% of the Bangladesh population drinks water from an estimated 10 million tubewells, according to research by van Geen and colleagues published in volume 81, issue 9 (2003) of the Bulletin of the World Health Organization. The contamination is most common in tubewells drawing groundwater from 10- to 100-meter depths in much of southern Bangladesh and, to a lesser extent, in northern areas along the Ganges River. A survey published in 2001 by the Bangladeshi Department of Public Health Engineering (DPHE) and the British Geological Survey (BGS) mapped out the variable extent to which different upazilas (subdistrict administrative units) were affected. The surveyors concluded that 35 million Bangladeshis are exposed to groundwater arsenic at concentrations exceeding the country’s standard of 50 micrograms per liter (μg/L), and 57 million are exposed to concentrations exceeding the World Health Organization (WHO) standard of 10 μg/L.
Prior to the inception of their investigations into chronic arsenic exposure in Bangladesh, the Columbia research team faced ethical review boards in both the United States and Bangladesh. “The Bangladesh committee on ethics in human research argued vehemently that we couldn’t just study the situation; we had to do something to reduce the population’s exposure to arsenic,” says Graziano. “This echoed the views of the institutional review board here in the States, and it has been our central credo ever since. Whatever our research findings might be, if we failed to lower the population’s exposure, we would fail, period.”
Digging Deeper
In the spring of 2000, Graziano, van Geen, Ahsan, and others began a pilot study in Araihazar upazila, an area with a wide range of arsenic exposure. They used questionnaires to collect information on household water usage, awareness of arsenic-related risks, and preferences for remedial options should a well turn out to be unsafe. They used handheld Global Positioning System (GPS) receivers to map the location of each well. A few months later, the team began recruiting residents of this region into a prospective cohort study, and urine arsenic data were obtained for 12,000 residents, or about 17% of the study area’s population.
Nearly half the wells gave water with an arsenic content exceeding the national standard of 50 μg/L. Moreover, the team found that the distribution of arsenic in this region showed a high degree of spatial variability and was therefore difficult to predict. The proportion of safe wells varied greatly from one village to the next. But the high resolution of the GPS-based mapping enabled the Columbia team to clearly discern patterns in the spatial variability between the wells.
“We quickly realized that this spatial variability in high- and low-arsenic wells was our most valuable finding,” says van Geen. “Even though the highly variable nature of the [arsenic] distribution would complicate the intervention, it was clear that this high degree of spatial variability also presented an opportunity for remediation that needed to be more fully explored. For mitigation purposes, it seemed more important to know the proportion of unsafe wells in a particular village and [the variation of arsenic toxicity relative to depth] because this would be very different from the average value obtained for the entire upazila.”
Close to 90% of the Araihazar inhabitants, on average, were found to live within 100 meters of a safe well. Another promising finding was that 65% of households with an unsafe well had responded to the Columbia team’s testing and information dissemination by switching their usage to a nearby safe well. Within a few months of the testing, the mean urinary arsenic concentration among the 12,000 villagers tested previously had already significantly declined.
In 2001, the Columbia group installed seven community wells in villages where nearly all of the wells gave water with arsenic in excess of 50 μg/L. Two years later, 79% of study residents living within 150 meters of these community wells had switched to the community wells for drinking and cooking water. Their average urinary arsenic concentration dropped over this period from 204 to 91 μg/L, a figure approaching the 70 μg/L seen in residents of the same region who drank from low-arsenic wells.
Eventually the Columbia team charted the spatial scale of arsenic variability in 6,000 tubewells within a 25-square-kilometer area. In a report published in volume 80, issue 9 (2002) of the Bulletin of the World Health Organization, they concluded that well switching—the practice of sharing the safe wells that were frequently interspersed with unsafe wells—might be a viable temporary option in this area and possibly throughout Bangladesh. But they also cited anecdotal evidence suggesting that many villagers would not continue to fetch their water from a relatively distant well, especially one owned by another household (the vast majority of the wells surveyed were privately owned). In all likelihood, then, well switching was only a short-term solution, and one that might prove difficult to implement on a national scale.
In Search of a Long-Term Solution
One long-term solution seemed to reside in the deep groundwater aquifers—those sometimes beyond the reach of tubewells—which were consistently low in arsenic. These aquifers, formed probably 40,000-plus years ago, offer a promising source of drinking water for the long term because no treatment and little maintenance is required for the wells, according to van Geen. “The geology of Bangladesh is now understood well enough to guarantee that a three-hundred-meter well in many parts of the country will tap into a low-arsenic aquifer, and in many parts of the country lesser depths will be equally safe,” he says. Fortunately, he adds, the vast majority of rural households reside within drilling distance of aquifers that are consistently low in arsenic.
The Columbia strategy focuses on creating a network of trained village workers, with an emphasis on community-based decision making. One person in each affected village would be provided with and trained to use a field kit for measuring arsenic, a handheld GPS receiver to determine each well’s position, and a handheld computer to enter field data. Groups of approximately 20 workers from different villages would then communicate this information digitally to a supervisor, who would be linked by wireless phone to a national support center to submit data for quality control and analysis. These collaborators would apply a simple decision tree to help village residents develop their plan for obtaining safe water, enabling them to determine the optimal depth and location of up to five deep community wells per village.
The private sector would likely play the major role in construction, maintenance, and possibly operation of the wells. The manpower and technical capability would come from well-drilling companies in Bangladesh and other countries. Nongovernmental organizations would serve as facilitators, building links between communities, local government, and local businesses. Funding would have to come from the government.
Graziano and van Geen predict that 100,000 wells could be installed for less than US$100 million (less than $1 per Bangladeshi citizen) and would enable the vast majority of households to be within a short walking distance of a safe community well. “If one considers the total costs, it is not a big price to pay, given the many benefits that would accrue to the public health and economy,” says Mushtaque Chowdhury, deputy executive director of the nongovernmental Bangladesh Rural Advancement Committee.
Moreover, a huge part of the plan’s cost-effectiveness derives from its target-specific nature. The Columbia team has demonstrated that tailoring the installation of community wells to local conditions halved the cost relative to what it would have been under a policy of blanket installations to a 300-meter depth. The cost would be reduced to a third if only the highest-risk areas are addressed initially, says Ahmed.
“By carefully considering the local geology, taking into account the variability in depth at which low-arsenic groundwater occurs, and precisely mapping this variability, the Columbia strategy enables us to pinpoint safe aquifers for the installation of community wells,” says Kazi Matin Ahmed, a professor of geology at the University of Dhaka and coauthor of the landmark BGS/DPHE report. “This is a more promising option than treating high-arsenic groundwater or microbe-contaminated surface water. The plan also emphasizes continued monitoring, which is currently lacking for the water supply wells in Bangladesh.” Ahmed advocates a national water quality surveillance program that would monitor levels not only of arsenic, but of lead, manganese, organic pollutants, and other contaminants of concern as well.
According to Graziano, the deep well strategy would easily achieve the WHO standard of 10 μg/L—an important point, given his team’s research in adults and children as well as other recent epidemiologic evidence indicating that the 50 μg/L guideline is not adequate to protect public health. Graziano asserts that the Bangladeshi standard reflects a somewhat arbitrary threshold, and that, over the long term, the WHO guideline should be followed whenever possible.
One Plan of Many?
The Columbia plan has its fair share of critics, such as Harvard physics professor Richard Wilson, who says the approach overlooks many of the mitigation options proposed at the International Workshop on Arsenic Mitigation, organized by the WHO for the Bangladeshi government and held in Dhaka in January 2002. “Several alternative ways of obtaining pure water were suggested, each one appropriate for a different area, each with its advantages and disadvantages, and several now being successfully implemented,” says Wilson.
Wilson adds that all aspects of the Columbia strategy—or any other strategy—will require intensive community education and involvement, as well as continuous monitoring at the national level. This is the hardest problem, he says, and the one that has delayed all efforts so far and could make the strategy difficult to execute.
Wilson urges instead choosing from the list of alternative mitigation options developed at the January 2002 meeting, which include the use of dug wells, rainwater collection, and simple arsenic-removing techniques such as filtration at the household level.
Although these alternative strategies are technically feasible, Graziano and van Geen feel that most of them will never work over the long term in the dollar-a-day economy of Bangladesh. “Home filters require attention every day and will by definition run out of capacity at some point,” says Graziano. Rainwater collection, he notes, does appear to be a viable mitigation option in certain parts of the country and could therefore be considered an alternative or complement to the deep community well strategy.
Wilson voices another concern as well: that careless implementation of any strategy can be dangerous. “For example,” he says, “deep wells . . . [if] carelessly installed and not monitored, can lead to cross-contamination in some areas. If the deep aquifer becomes contaminated by the arsenic-laden aquifer, the strategy will have failed.”
Other experts disagree, to a point. “There is little danger for leakage along the outside of the well casing,” says Martin Stute, director of the Columbia program’s Research Core Laboratory for Hydrogeology. “The fine sediments of the Ganges–Brahmaputra Delta are closing in around any well installations very quickly. Indeed, the potential problem lies in breakage of the casing in the shallow aquifer due to shifting sediments.” Stute says periodic arsenic measurements can detect this problem, and if it occurs, the well should be filled with clays and redrilled. “However,” he says, “monitoring data collected so far indicate that this process is very unlikely to happen.”
In the meantime, education remains the key to change. In 2003, Columbia launched its Building Capacity to Reduce Arsenicosis in Bangladesh training program with funds from the John E. Fogarty International Center. Under this program, Bangladeshi pre- and postdoctoral students who are accepted into Columbia receive funding for two years of health, social, and Earth sciences training in the United States, then two years of field training in Bangladesh. Columbia faculty have also traveled to Bangladesh to conduct several short courses in environmental health, GIS technology, and geochemistry. Most recently, faculty have agreed to participate in the teaching of courses at a new school of public health in Dhaka.
Above all, Graziano says, any mitigation approach should be designed to rapidly reach the largest number of affected people. Only time and further research will tell whether the Columbia approach—or any other approach—can be effectively put into action in Bangladesh.
The reason for the research. Habibul Ahsan (left, in blue shirt), Joseph Graziano (center left, in glasses), and Paul Brandt-Rauf (center right, in glasses) of the Mailman School of Public Health meet with residents of Araihazar, Bangladesh.
High-tech search for low arsenic. The Columbia project used GIS mapping (above) to survey arsenic concentrations in wells around Araihazar, Bangladesh. Safe wells are thusly labeled (right), and residents are encouraged to use them for drinking and cooking water.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0037615945148EnvironewsNIEHS NewsBeyond the Bench: Oasis of Fun at Mount Desert Island Tillett Tanya 6 2005 113 6 A376 A377 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Kids, summer, and water are three things that just naturally go together. The Community Outreach and Education Program (COEP) of the Center for Membrane Toxicity Studies, housed at Mount Desert Island Biological Laboratory in Bar Harbor, Maine, is using its open-air Myers Marine Aquarium Visitors Center to maximize this combination and get kids excited about learning during the summer months. The Center for Membrane Toxicity Studies, one of the NIEHS Marine and Freshwater Biomedical Sciences Centers, educates children about the environment and their own role in keeping it healthy.
For about five years, the COEP has provided environmental health enrichment to the surrounding community through the Myers Marine Aquarium Visitors Center. Aquarium staff provide weekly programs in the summer for local and tourist visitors to learn about the center’s research. In a typical tour, visitors learn about water pollutants and how they affect our health and environment, and are also given information on water quality management.
Because school-aged children are the target audience, the message delivery is kept simple and fun. The aquarium has several tanks that allow for viewing and touching of the lab’s research species and other sea life from the Gulf of Maine. Cartoon-emblazoned posters illustrate center findings on how pollutants are processed in the body and describe in nonspecialist language our own responsibility in safeguarding the environment we live in. As part of the tour, center staff describe their research projects and allow visitors to view specimens through microscopes.
“When we talk about environmental stewardship, I am always amazed by the reaction of the children,” says Jeri Bowers, the COEP associate director. “They love to get involved and to feel like they are playing an important role in maintaining a healthy environment; they take it very seriously. Our hope is that we can capture that enthusiasm and interest while they’re young—and influence their behavior well into adulthood.”
The aquarium attracts large numbers of student groups. One particularly memorable visit was a May 2004 tour by a group of 40 children with Almström syndrome, a rare hereditary disorder that can affect multiple organ systems and cause blindness, hearing impairment, type 2 diabetes mellitus, heart failure, and liver disease. The visit was arranged by researchers at the nearby Jackson Laboratory, who discovered the gene for Almström syndrome and provide their own outreach to children affected by the illness. Bowers says the tactile component of the aquarium experience was especially important and beneficial for these children, most of whom can’t see or hear.
Along with the strong community outreach it offers through the aquarium and labs, Mount Desert Island also has exciting environmental health research in progress that staff hope will someday become part of the children’s experience. Earlier this year, the lab set up a website (http://ctd.mdibl.org/) for the Comparative Toxicogenomics Database, a data system being created to provide centralized data on diverse organisms to scientists worldwide. The lab has also received NIH approval to sequence the genome of the skate, data that will eventually be entered into the Comparative Toxico-genomics Database. One of the future goals of the researchers is to incorporate the electronic data into the visitors program so that children can access the database on computers set up as part of the aquarium tour.
Diving in. Children learn about the marine environment and their role in keeping it healthy at the Myers Marine Aquarium Visitors Center.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00377EnvironewsNIEHS NewsHeadliners: Skin Cancer: Linking Toenail Arsenic Content to Cutaneous Melanoma Phelps Jerry 6 2005 113 6 A377 A377 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Beane Freeman LE, Dennis LK, Lynch CF, Thorne PS, Just CL. 2004. Toenail arsenic content and cutaneous melanoma in Iowa. Am J Epidemiol 160:679–687.
Although exposure to arsenic has been associated with increased risk of non-melanoma skin cancer, little research has been conducted on possible links between arsenic and cutaneous melanoma. Now NIEHS grantees Laura E. Beane Freeman of the National Cancer Institute and the University of Iowa and Peter Thorne of the University of Iowa have joined with their colleagues to perform what may be the first epidemiologic study to investigate the association between cutaneous melanoma and environmental arsenic exposure.
Cutaneous malignant melanoma incidence is increasing in the United States, and its annual percentage increase is one of the highest for all cancers. This deadly cancer also has the lowest survival rate of all skin cancers. In 2003, approximately 54,200 cases were diagnosed and 7,600 deaths were attributed to cutaneous melanoma in the United States.
The study was conducted in Iowa, where some areas have high drinking water concentrations of arsenic. The researchers identified participants through the population-based Iowa Cancer Registry. They selected 368 white Iowans aged 40 and older who had been diagnosed with cutaneous melanoma and 373 controls who had been diagnosed with colorectal cancer. Colorectal cancer was chosen as a control because it is a common cancer with no known link to arsenic. Study participants completed a survey and submitted toenail clippings for arsenic analysis.
The researchers found about a twofold increased risk of melanoma for participants with elevated toenail arsenic concentrations. Risk of melanoma with increasing toenail arsenic content was almost seven times greater for those reporting an earlier skin cancer diagnosis. Participants with the highest toenail arsenic levels were more likely to use private wells as compared to those with the lowest arsenic levels. Private well use is a known risk factor for arsenic exposure because wells are not held to the same testing requirements as public water supplies.
The observed higher correlation in people with a prior skin cancer diagnosis lends further support to a causal association between arsenic and cutaneous melanoma. The authors speculate that this finding may not have been previously reported because similar studies have been conducted primarily in Asian populations, who have a much lower risk of melanoma than Caucasians. The authors further suggest that genetic factors such as skin color may modify the effect of arsenic on melanoma risk. Further research must be conducted to confirm these results and clarify the link between arsenic exposure and development of melanoma.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0037815929879EnvironewsFocusArsenic: In Search of an Antidote to a Global Poison Mead M. Nathaniel 6 2005 113 6 A378 A386 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Arsenic. No other element has such a complex and variegated past. As early as 500 B.C. the ancients knew about arsenic, whose name comes from the Greek word for potent. Through the centuries, this “king of poisons” was a common means of homicide. And yet, arsenic’s image has not always been so morbid. People in the Middle Ages wore arsenic amulets around their necks to ward off the bubonic plague, and women in Victorian times applied arsenic compounds to their faces to whiten their complexions. Hippocrates, the father of western medicine, recorded arsenic’s usefulness as a topical remedy for skin ulcers.
Today, arsenic compounds are still used for pharmaceutical purposes. Arsenic trioxide is known for its use in the treatment of acute promyelocytic leukemia in patients who are unresponsive to, or have relapsed from, certain chemotherapy agents. Research published in the 1 April 2005 issue of the Journal of Clinical Oncology suggests that arsenic trioxide may have therapeutic uses in other malignancies as well, and that it may be used in combination with other chemotherapy drugs to expand their benefits.
And yet, no toxicologist would deny that chronic arsenic exposure places people at risk for a host of adverse health effects, from skin and internal cancers (of the bladder, kidney, liver, lung, colon, uterus, prostate, and stomach) to diabetes mellitus and vascular, reproductive, developmental, and neurological effects. Studies have shown arsenic to be a potent endocrine disruptor, altering hormone-mediated cell signaling at extremely low concentrations.
Joshua Hamilton, program director of the Dartmouth College Superfund Basic Research Program, and colleagues published papers on the latter topic in the March 2001 issue of EHP and the August 2004 issue of Chemical Research in Toxicology. “We demonstrated this with the glucocorticoid receptor and subsequently showed that arsenic has similar effects on all five steroid receptors,” says Hamilton. “Furthermore, we recently found similar effects on other members of the nuclear receptor signaling family, including retinoic acid and thyroid hormone receptors.” Since these receptors are central to so many biological processes, Hamilton suggests that this may be an important way by which chronic arsenic exposure contributes to so many malignancies as well as nonmalignant diseases.
The noncancer effects of arsenic arise from both acute and chronic exposures. Among those symptoms linked with acute exposure to arsenic-laced well water (typically containing more than 1,200 micrograms per liter [μg/L]) are abdominal pain, vomiting, diarrhea, muscular weakness and cramping, pain to the extremities, erythematous skin eruptions, and swelling of the eyelids, feet, and hands. A progressive deterioration in the motor and sensory responses may also result, finally leading to shock and death.
The effects of chronic arsenic poisoning (also called arsenicosis) are more complex. Aside from cancer, these chronic effects include atherosclerosis, diabetes, hypertension, anemia, liver disorders, kidney damage, headache, confusion, peripheral neuropathy, and a variety of skin lesions, notably hyperkeratosis, or thickening of the skin, and both hypo- and hyperpigmentation.
Skin lesions are the most common outward sign of chronic arsenic exposure, though many dermatologic symptoms are thought to be mediated by nutritional factors. Studies conducted in Taiwan, India, and Bangladesh have linked high-arsenic well water with the incidence of both skin lesions and diabetes in a dose-responsive pattern. One recent population study in West Bengal, India, published in the March 2003 issue of Epidemiology, showed that the lowest peak arsenic ingested by a confirmed case of arsenic-induced skin lesions was 115 μg/L.
Among children, chronic arsenic exposure has also been reported to cause adverse effects on the digestive, respiratory, cardiovascular, and nervous systems. An article in the September 2004 issue of EHP reported intellectual impairment occurring when arsenic in drinking water exceeded 50 μg/L.
There is evidence that arsenic-exposed people who are predisposed to noncancerous skin lesions may be more vulnerable to other cancers. “During our long field experience in West Bengal and Bangladesh we observed that those who are suffering from severe keratosis appear more likely to develop cancer later on,” says Dipankar Chakraborti, director of the School of Environmental Studies at Jadavpur University in Calcutta. “Not only skin cancer but internal cancers also may arise in people who show such noncancerous lesions.”
The lung, too, seems to be a major site of action of ingested arsenic. “Lung cancer is the main cause of arsenic-related death,” says Allan Smith, director of the Arsenic Health Effects Research Program at the School of Public Health, University of California, Berkeley. “But we’re also seeing many noncancer [lung] effects, such as a tenfold [increase in the] rate of bronchiectasis in people with skin lesions in India.”
The prevalence and incidence of these noncancer manifestations of arsenic exposure is highly variable from one country to the next. For example, whereas skin pigmentation and hyperkeratosis are common indicators of arsenic exposure in Taiwan, it may be more common in India to see respiratory stress, polyneuropathy, and peripheral vascular disease linked with habitual ingestion of high-arsenic drinking water. This topic remains a very active area for epidemiologic research.
A World Exposed
Globally, millions of people are at risk for the adverse effects of arsenic exposure. The majority of harmful arsenic exposure comes from drinking water from wells drilled through arsenic-bearing sediments. Drinking water contains primarily inorganic arsenic, which is more acutely toxic than the organic form. The other major sources of arsenic exposure are through food, soil, and air. For most people, in fact, the primary exposure to arsenic comes from food, but dietary arsenic includes primarily organic forms, which are relatively nontoxic and contribute little, if any, to the overall risk associated with exposure. (Unless otherwise indicated, all mentions of arsenic in the remainder of this article refer to the inorganic form.)
Rebecca Calderon, chief of the Epidemiology and Biomarker Branch at the Environmental Protection Agency (EPA) National Health and Environmental Effects Research Laboratory, says that preparing foods in arsenic-containing water increases the arsenic content by 10–30% for most foods, and by 200–250% for beans and grains, which absorb cooking water. Moreover, arsenic-laced irrigation water can substantially increase the arsenic content of rice and vegetables, as recently shown in several studies in Southeast Asia, including a February 2005 Chemosphere report on West Bengal crops and soil.
Soil- and waterborne arsenic does not readily permeate the skin, though soil can be a key source of exposure in young children who show significant hand-to-mouth activity. People are also exposed on a more sporadic basis through a hodgepodge of human activities, such as the burning of fossil fuels, waste incineration, smelting of ores, pesticide and herbicide use, coal burning, semiconductor production, and other manufacturing processes. The public health impact of these exposures is largely unknown as the epidemiologic focus has been on exposure via drinking water.
For most U.S. citizens who are on piped water systems, drinking water is not a major source for arsenic exposure. Nonetheless, in certain areas in the West, Midwest, Southwest, and Northeast, people drinking well water may be exposed to arsenic levels ranging from 50 to 90 μg/L, well above the EPA’s guideline of 10 μg/L. To date, no statistically significant relationships have been found between arsenic exposure and cancer in these areas.
The situation in Bangladesh and West Bengal is radically different: arsenic exposure through drinking naturally contaminated groundwater is widespread and often excessive. This situation began in the 1970s, when the United Nations Children’s Fund, in response to epidemics of cholera, dysentery, and other waterborne infectious diseases, spearheaded an effort to switch the region’s population from drinking surface waters to groundwater. Millions of tube-wells were drilled into arsenic-rich sediments; as a result, in many of these wells arsenic levels reach 500–1,000 μg/L and even higher.
Field studies have shown that many people living in a vast geological zone known as the Ganga-Meghna-Brahmaputra plain are being exposed to high arsenic levels in the water. A large portion of this plain, an area totaling 500,000 square kilometers and spanning all of Bangladesh and most of India, shows significant groundwater arsenic contamination, putting more than 500 million people at risk of chronic arsenic poisoning, says Chakraborti. He published these alarming estimates in the June 2004 issue of the Journal of Environment Monitoring. With 80% of Bangladeshis estimated to be at risk of arsenic-related diseases, the World Health Organization (WHO) has labeled this “the worst mass poisoning in history.”
Large areas of China also face severe arsenic exposure from groundwater contamination, with more than 3 million people affected, based on estimates in the August 2004 issue of Toxicology and Applied Pharmacology. In Shanxi Province alone, an estimated 900,000 people are at risk of arsenicosis. Among the investigated villages in Shanxi, an average of 52% of wells give water containing arsenic concentrations higher than 50 μg/L, according to a recent report from the School of Public Health at China Medical University in Shenyang.
A unique type of exposure, resulting from the burning of arsenic-rich coal, is found in Guizhou Province, an area of endemic arsenicosis. Guizhou inhabitants commonly use this coal for cooking, heating, and drying their dietary staples of corn and hot peppers. The coal is burned in open stoves without chimneys, resulting in contamination of both the indoor air and the foods being prepared. At this time, arsenicosis is known to affect eight provinces, but most of China has not been studied, and new endemic areas are continuously emerging. Reports on arsenicosis in China actually preceded those from Bangladesh and India, but have been overlooked due to limited scientific exchange and publication.
Other countries with arsenic-rich groundwaters include Argentina, Chile, Mexico, Cambodia, Vietnam, Thailand, Nepal, and Ghana. In the Obuasi area of Ghana, arsenic contamination of food and water has been linked with gold-mining activities. Much of the gold in the Obuasi mines is locked in pyrite and arsenopyrite, both associated with arsenic and sulfur. The extraction of the gold results in the release of airborne particles that include large concentrations of arsenic. At least 10% of Ghana’s rural borehole wells have arsenic concentrations exceeding 10 μg/L. In the Terai region of Nepal, inhabited by half the country’s total population, hundreds of thousands of shallow tubewells have been installed by various agencies, and groundwater is the primary source of drinking water. According to Chakraborti, around 500,000 people in Terai are at risk of arsenic poisoning from drinking this water, and up to 1 in 20 people may show skin lesions indicative of arsenicosis.
The Arsenic–Cancer Equation
Today, researchers around the world are racing against the clock to unravel the secrets of arsenic’s workings, including how it influences the cancer process and thereby increases cancer risk. Although inorganic arsenic is generally held to be more acutely toxic, some researchers argue that the organic metabolites of arsenic may be the ultimate carcinogens. One of these metabolites, DMA, has been shown in rodents to induce bladder cancer and to promote tumor growth in several other organs. A review article focusing on induced disturbances of calcium homeostasis, genomic damage, and apoptotic cell death caused by arsenic and its organic metabolites appears in the June 2005 issue of EHP.
There is general agreement that arsenic does not directly interact with DNA, and that its toxic effects occur through indirect alteration of gene expression, such as via the perturbation of DNA methylation, inhibition of DNA repair, oxidative stress, and altered modulation of signal transduction pathways. Many of these mechanisms are overlapping, interdependent, and heavily influenced by factors in the cellular environment. For example, arsenic promotes both oxidative stress and impaired DNA repair, and yet both of these effects tend to amplify mutation rates, thus increasing the likelihood of cancer.
Another indirect mechanism is the influence of growth-stimulating chemicals or cytokines generated in response to arsenic exposure. Dori Germolec, a research scientist at the NIEHS Laboratory of Molecular Toxicology, has been approaching the arsenic question from the standpoint of cytokine biology. “Arsenic alters the production of inflammatory cytokines and does so persistently over time,” Germolec says. “These effects on cytokines seem to relate to its effects on the skin. Arsenic seems to stimulate progenitor cells that could ultimately be responsible for tumor formation. This is just one of a number of mechanisms that has biological plausibility.” Research published in the April 2004 issue of EHP by Toby Rossman, an environmental science professor and program director of the Molecular and Genetic Toxicology Program at New York University, has demonstrated similar relationships in animal models as well as in cultured human cells.
Studies of differences in arsenic metabolism between individuals have led to further insights—and further questions. The importance of individual arsenic metabolites in terms of cancer induction is still being determined. All of the human populations studied thus far have been found to methylate inorganic arsenic, but the patterns of arsenic metabolites in urine show substantial interindividual variation. Within any given population, individuals differ in the quantity and distribution of the various metabolites of arsenic excreted by the kidney. If some happen to excrete more of the carcinogenic metabolites or are unable to metabolize arsenic efficiently, they may be more vulnerable to cancer. This variation may be affected by a variety of factors, including dose level, route(s) of exposure, diet, and the particular type of arsenic to which the individual is exposed. Polymorphisms in genes that code for the enzymes important in metabolism, such as arsenic methyltransferase, have also been implicated as accounting for some of this variability.
No one yet knows how this interindividual variation in arsenic metabolism actually affects cancer risk. “This is a difficult question since when you deal with the carcinogenicity of inorganic arsenic you are dealing with six or more distinct [metabolites],” says H. Vasken Aposhian, a molecular and cell biology professor at the University of Arizona. Aposhian is involved in studies in New England, Mongolia, Romania, Mexico, and Kazakhstan to identify unique or abnormal arsenic urine profiles in people who develop cancer in areas of high arsenic exposure. Once studies reveal which of these metabolites are promoters and/or carcinogens, it will be possible to better answer the riddle of interindividual variation in vulnerability to arsenic-induced effects.
The metabolite MMAIII presently is one of the leading candidates as a potential cancer inducer. If MMAIII turns out to be carcinogenic, an increased or decreased amount in the urine might prove useful as a marker for potential future arsenic-mediated cancer.
In time, the identification of reliable exposure markers could help identify groups that may be more susceptible to cancer at the levels of arsenic exposure typically found in the United States (less than 50 μg/L). At the present time, the carcinogenic risk of such exposures is unclear. Biomarkers would provide a more detailed picture of individual arsenic exposure and how the body is responding to that exposure. “The low-dose extrapolations used for risk assessment purposes may be subject to error in part because they are based more on ecologic data than on individual measures of exposure,” says Margaret Karagas, an epidemiology professor at Dartmouth College. “Use of relevant markers in human tissue samples eventually may help us sort out the risk at lower levels of exposure.”
One practical biological marker identified by Karagas is toenail clippings, with arsenic content measured via instrumental neutron activation analysis. Using this measure, she and her colleagues reported in the June 2004 issue of Cancer Causes & Control on a case–control study in New Hampshire suggesting an increased cancer risk associated with moderate arsenic exposure, but only in smokers.
An Emerging Consensus: Arsenic Does Not Act Alone
Studies such as Karagas’s point to the growing recognition that arsenic does not always operate alone. Rather, arsenic appears to work with other factors to promote cancer, at least at some target sites. “Animal models indicate it takes a promoter or some genotoxic carcinogen to get arsenic to produce skin cancers,” says Michael Waalkes, section chief of the Inorganic Carcinogenesis Section at the National Cancer Institute Laboratory of Comparative Carcinogenesis, housed at the NIEHS. “When you always see this kind of cotreatment effect, it makes it harder to nail down the precise contribution of arsenic to the final tumor.”
The classic cofactor in this regard may be tobacco smoke. “There is mounting evidence of a malignant synergy between smoking and arsenic,” says Smith. “Smokers are at an increased risk from arsenic in drinking water and appear to comprise a susceptible subpopulation.” A study by Smith and colleagues, published in the November 2000 issue of Epidemiology, found that the relative risk of lung cancer for Chileans who smoked and had high arsenic in their water was 32 times that of nonsmokers with low arsenic concentrations in their water. In contrast, the lung cancer risk of smokers without arsenic in their water was about 6 times that of nonsmokers. Similar findings have come from studies in Taiwan and New Hampshire.
Other cofactors are also gaining attention. Rossman’s group was among the first to hypothesize that arsenic requires a carcinogenic partner—in their April 2004 EHP article and another in the 1 August 2004 issue of Toxicology and Applied Pharmacology, they reported finding that arsenic plus ultraviolet (UV) radiation exposure led to a dose-related increase in skin cancers in mice compared with mice exposed to UV light alone. The tumors in mice treated with arsenite plus UV light also appeared earlier and were larger and more invasive than those in mice exposed to UV light alone. At the 2004 Third International Conference on Comparative Physiology and Biochemistry, Rossman reported that selenium deficiency also enhanced the carcinogenic effects of arsenic.
Such insights may carry over to the epidemiological realm. In Bangladesh and West Bengal, for example, the most likely cofactors for arsenicosis include malnutrition (with resulting deficiency of selenium and other nutrients that can affect arsenic metabolism) and agricultural activities that lead to frequent sun exposure. Not only does selenium seem to help protect against the toxic effects of chronic arsenic exposure, but high levels of chronic arsenic ingestion from well water may accelerate the excretion of selenium, according to research published in the 5 May 2004 issue of Science of the Total Environment.
“We need to find out whether Bangladesh and other poverty-stricken countries with arsenic-tainted groundwater may benefit by this relatively cheap strategy of supplementing the diet with selenium,” says Floyd Frost, an epidemiologist at the Lovelace Respiratory Research Institute in Albuquerque. “We need solutions that are cheap and doable. If you’re in Bangladesh, there just isn’t much money for expensive mitigation strategies.”
However, Smith notes that he and colleagues found only modest increased risks in West Bengal with some dietary deficiencies. He and others contend that the top priority should be to reduce arsenic exposure. Other approaches being explored include rainwater collection, novel filtration systems, chelation, and deep community wells, as well as the use of antioxidants, methionine (an amino acid), and other dietary supplements that may limit arsenic’s toxicity. [For more information on remediation strategies, see “Columbia Center Digs Deeper into Arsenic Dilemma” and “Metal Attraction: An Ironclad Solution to Arsenic Contamination?” p. A374 and A398 this issue.]
A Special Population: The Very Young
Infants and children are deemed to be more susceptible than adults to the adverse effects of arsenic and other toxic substances. Chakraborti has observed that arsenical skin lesions show up sooner in children than they do in adults. If the child’s nutrition is poor, outward signs of arsenic toxicity manifest even sooner and at less extreme levels of exposure. An additional concern is the potential for increased sensitivity of children to arsenic-associated neuropsychological effects such as reduced verbal IQ scores, as reported in the September 2004 issue of EHP.
Chakraborti speculates that infants and children may be intrinsically more susceptible than adults due to differences in metabolism, a view supported by some preliminary studies. “In one of our studies on an arsenic-affected population in Bangladesh, we found that the second step in arsenic metabolic pathways is more active in exposed children in comparison with exposed adults,” he says. In the June 2005 issue of EHP, Maria Mercedes Meza and colleagues identified a developmentally restricted component of arsenic metabolism, a genetic association with urinary arsenic metabolites that applied only to children.
Complicating this scenario is the special threat posed by in utero exposure to arsenic. One of the concerns here is that low-level exposures may have a greater impact if experienced in utero than if experienced in childhood or adulthood. Waalkes and his colleagues were the first to identify the transplacental carcinogenic potential of arsenic. They duplicated this finding in several rodent studies, reported in the 1 August 2004 issue of Toxicology and Applied Pharmacology and the 20 May 2004 issue of Toxicology.
“The critical window of exposure for mice equates to about the middle three months of pregnancy in humans,” says Waalkes. “This could lead to a fifty percent increase in the risk of hepatocellular carcinoma for adults. This is a reproducible phenomenon, and it has alarming implications for in utero exposures in humans.” The first half of fetal development is a period of very high sensitivity because of a high rate of cell proliferation, cell differentiation, and gene imprinting, all of which, when disrupted, can lead to carcinogenesis.
Smith’s studies of bladder and lung cancers also have indicated that there may be a long latency—40 years or more for these cancers—from arsenic exposure to the manifestation of malignant disease. For example, he has found very high lung cancer risks in Chilean adults who were exposed as children or in utero. He notes that it is critically important to study large populations with significant and well-documented arsenic exposure. Smith says Chile has the best-documented exposure in the world.
“In any country where people are exposed to high levels of arsenic, if nothing else is done, they should focus on protecting pregnant women, providing them with low-arsenic water,” says Waalkes. “That would be my top priority if I could advise the governments of those countries on what to do.”
How Much Protection Is Enough?
Although the effects of severe arsenic contamination are well established, there is much debate about the risk associated with chronic ingestion of drinking water that contains arsenic levels lower than regulatory standards. The WHO adopted a standard of 10 μg/L in 1993. Bangladesh and many other developing countries use a guideline of 50 μg/L. Beginning in January 2006 the maximum contaminant level for inorganic arsenic permitted in U.S. drinking water will be 10 μg/L, although scientists still debate this standard.
Part of the uncertainty regarding the 10 μg/L standard stems from the absence of epidemiologic data to help determine the exact shape of the dose–response curve, particularly at exposures under 10 μg/L. Cancer risks at these levels of exposure may be about 1 in 300 people, according to the National Research Council report Arsenic in Drinking Water: 2001 Update. However, says Smith, epidemiology will never prove such risks are real. He points to the fact that large numbers of studies throughout the world were required to eventually demonstrate that nonsmokers married to smokers had an increased risk of lung cancer, even though such risk involves about 1 in 100 persons.
Still, some argue that different study designs and larger sampling will, in time, provide adequate data to answer the question of whether there is a level of arsenic exposure below which health effects do not develop. In the interim, the precautionary principle holds sway; policy makers assume that the burden of proof for potentially harmful actions or policies rests on the assurance of safety, and that when there are threats of cancer or other serious diseases, scientific uncertainty must be resolved in favor of prevention.
Acceptance of the limitations of epidemiologic research in detecting the risk associated with low-level exposures lies at the very heart of this principle. “It is possible that the effects may be nonlinear, with certain extremely low levels of arsenic exposure posing no excess risk,” says Karagas. “In epidemiologic studies, however, it is important to distinguish between ‘no effect’ and ‘inability to detect an effect’ due to various methodological limitations.”
There is also, she says, a critical need for further data on other health outcomes and in potentially susceptible subgroups such as pregnant women and children, and those particularly at risk due to genetic or lifestyle factors. By studying the whole population but not susceptible subgroups, scientists may be missing key pieces to the arsenic puzzle.
Hamilton concurs but emphasizes a more mechanism-based rationale. He theorizes that arsenic at different doses may act by different mechanisms, perhaps producing different patterns of disease. For example, the patterns of disease in areas such as Bangladesh that have high and endemic arsenic contamination may be quite different than the patterns seen at the lower doses encountered elsewhere. “We have observed an almost completely nonoverlapping pattern of gene expression changes with a low versus a high dose of arsenic, almost as if they were two different agents,” says Hamilton.
“At the lower, noncytotoxic dose,” he explains, “we saw an approximately equal number of genes that were increased as were decreased, whereas at the higher, cytotoxic dose, virtually all of the significant changes involved activation of genes.” Most of the genes in the latter case were members of stress response and apoptosis pathways. Taken together with Hamilton’s studies of the endocrine-disrupting effects of low to moderate arsenic levels, this indicates the importance of examining arsenic at doses that are directly relevant to the end point of interest.
On the Threshold of a New Understanding
A major challenge for future research is the issue of linking genetic polymorphisms with arsenic-related disease susceptibility. “Since arsenic metabolism seems to be a key to the carcinogenic process, sorting out these polymorphisms will be important, but this is extremely difficult to do,” says Julian Preston, director of the EPA’s Environmental Carcinogenesis Division and a member of the committee that produced Arsenic in Drinking Water: 2001 Update. “You need to see a very strong association between a particular polymorphism and the cancer end point in order to establish a link.” To date, a few polymorphisms have been identified in an indigenous population in Chile that may confer protection against the carcinogenic effects of arsenic exposure, but the findings are only suggestive.
Given that humans appear to be substantially more sensitive than experimental animals to arsenic-induced cancers, more epidemiologic research will be needed to assess the effects of early-life exposures for child as well as adulthood cancers. “Humans remain the most sensitive species when it comes to understanding the toxicity of arsenic,” says Calderon. “Despite several attempts to use rodents and other animal species, those assays and experiments have had limited success in explaining what appears to be a rather unique response on the part of Homo sapiens to arsenic. This represents a unique challenge, and perhaps the keys reside in emerging areas of genomics, proteomics, or molecular epidemiology.” Childhood exposure to arsenic has emerged as a potential regulatory concern.
Arsenic contamination of drinking water is among the most awesome environmental health challenges of our time. With hundreds of millions of people affected in Southeast Asia and elsewhere, the need for effective arsenic mitigation strategies has never been greater. Thus the focus is moving beyond exposure to include those physiologic variables that may mediate the effects of exposure and that correlate with adverse effects in humans.
Exposures associated with arsenic due to cooking and agricultural activities (including herbicide and pesticide use) should be explored, along with the identification and control of other carcinogenic compounds that may act as cocarcinogens. Such efforts could, in time, result in profound public health benefits and alleviate a great deal of suffering.
For people living in areas where arsenic exposure is less extreme, the question of whether arsenic is safe below a certain dosage level remains central. Many scientists assert that only biological data based on measurements of the variation in human metabolic responses to arsenic will resolve the low-dose controversy. Such data will pave the way for developing biologically based dose–response models that should greatly enhance our understanding of arsenic’s carcinogenic potential. Only with persistent inquiry and innovative investigation will the elemental mystery of arsenic be solved.
Arsenic concentrations in Bangladeshi tubewells
Good intentions gone awry. Villagers drill a tubewell in Bangladesh (left). Encouraged as a solution to pathogenic contamination of surface waters, such wells have resulted in exposure of millions to arsenic, leading to the need for alternative water sources (above).
Coal catastrophe. Cyclists on their way to work in Guizhou Province, China, pass through smoke pouring out of a coal-burning cooking stove. Exposure to the arsenic-rich coal burned in this region has resulted in endemic arsenicosis.
Fool’s gold? Gold mining in areas of Ghana such as the Ashanti Goldfields in Obuasi results in the release of airborne arsenic particles that also have been linked to food and water contamination.
Arsenic concentrations across the United States . . .
. . . and in New Hampshire
Special victims. New information indicates that children metabolize arsenic differently than adults, and provides compelling reason to further study the effects of the element in vulnerable populations.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0038815929880EnvironewsFocusFrom Point B To Point A: Applying Toxicogenomics to Biological Inference Freeman Kris 6 2005 113 6 A388 A393 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The data points in an integrated toxicogenomics experiment with microarray, proteomics, and metabolomics data are virtually innumerable. With thousands—or tens of thousands—of data points for each sample in each type of analysis, the complexity and the sheer amount of data multiply fast. As teams of statisticians, bioinformaticists, and biologists work to interpret this complexity, they must also ensure that each data point is valid and can be integrated with other data from the same or different types of experiments. Underlying this detailed exercise are two expansive goals. One is to identify markers of toxic exposures or disease. The other is to understand the biological processes underlying disease. The latter is called biological inference—the highly iterative process of inferring cause-and-effect relationships from toxicogenomics data, using computation efforts linked to mathematics.
Efforts to identify markers of exposure are concerned primarily with discerning patterns in output from microarray, proteomics, and metabolomics technology. These patterns can be characterized as molecular fingerprints and can be extremely useful in diagnosing levels of exposure, even though researchers may not understand exactly why particular patterns appear. In contrast, biological inference is concerned with an understanding of how patterns in genomics data actually translate into the details of gene transcription, protein creation, and metabolism.
By studying associations among the expression of genes, proteins, and metabolites, researchers try to identify genes of influence, many of which act as hubs of metabolic networks, affecting many other genes. Transient hubs, those that act briefly as a cell changes state, can be especially difficult to find and analyze. A goal of special importance to toxicogenomics is to distinguish endogenous pathways—those involved in the cell’s normal chores of metabolism and reproduction—from exogenous pathways triggered by exposures to drugs or toxicants. The ultimate goal is to follow a pathway from the expression of genes through the creation and modification of proteins and metabolites, as well as all the associated gene–gene, protein–protein, and metabolite–metabolite interactions in between.
Anchor Management
Inferring biological pathways requires research teams to mine and interpret vast quantities of genomics data. Interpretation strategies include grouping or clustering data to find patterns as well as use of statistical methods to filter data on genes with the strongest signals or those expressed in concert. One key technique in biological inference is phenotypic anchoring, using known biological information to interpret signals, or uncharacterized data, from genomics experiments. These signals indicate the presence of molecules (such as mRNA or proteins in a given range of molecular weights) and can take various forms, depending on the type of technology used. For example, in microarray experiments signals take the form of fluorescence generated by the bonding of strands of mRNA to the microarray slide. In some studies, these genomics data are compared to data from traditional toxicology tests performed on the same samples. In others, the team integrates what is already known or suspected about biological pathways based on past studies.
Examples of research using phenotypic anchoring can be found in acetaminophen studies sponsored by the NIEHS National Center for Toxicogenomics (NCT). In this research, microarray data were compared to those from traditional toxicology tests, which both aided in interpretation of the microarray data and led to the development of new knowledge and understanding about the toxicity of this commonly used drug.
For example, in one study published in the July 2004 issue of Toxicological Sciences, data from microarrays and traditional toxicity tests confirmed previous results from other labs showing that toxic doses of acetaminophen deplete adenosine triphosphate (ATP; molecules that store cellular energy) and damage mitochondria, the organelles that produce ATP. In addition, microarray data revealed other exposure effects that traditional tests had missed. For example, liver cells begin to express genes consistent with cellular energy loss at doses too low to cause the kind of cell damage that can be detected by histopathology and other traditional methods.
The microarray data also provided information on a possible new signature of acetaminophen toxicity involving the metal-lothionein gene and several others, which may be involved in the liver’s antioxidant defense system. “We didn’t previously know that those genes were involved in acetaminophen toxicity, but it fits into the biological story of cell defense mechanisms,” says Alexandra Heinloth, a research scientist with the NCT and lead author of the 2004 paper.
A different type of phenotypic anchoring was used in a study of pathways linked to inflammation. Microarray experiments with mouse strains that exhibited both high and low levels of response to inhaled lipopolysaccharide compounds (which trigger immune responses) identified about 500 genes that were responsive in at least one of the strains. Researchers from Duke University, The Institute for Genomic Research, and George Washington University, including John Quackenbush, now a professor in biostatistics and computational biology at the Dana-Farber Cancer Institute and the Harvard School of Public Health, used two independent methods to filter the results of these microarray experiments, to prioritize genes for future study.
In the first method, the team identified 30 genes whose expression levels best distinguished the low- and high-responding mice. In the second method, they used quantitative trait locus (QTL) analysis to find regions genetically linked to the strength of the lipopolysaccharide-induced response. When the researchers compared their 500-gene list to the QTL regions, they found a set of 28 that were both differentially expressed and genetically linked to the observed phenotypes. There was no overlap among the genes identified by these two methods.
What people can find across many arrays are patterns suggesting coregulation. If you look across hundreds of arrays and find that expression of two genes [moves] up and down together, that’s highly suggestive of interactive behavior. It’s not so much biological modeling as it is finding associations that are suggestive of biological interactions.
–Terry Speed
University of California, Berkeley
In a report of the study published in the June 2004 issue of Genomics, the researchers acknowledge that they may miss genes with important roles with these filtering methods. They argue, however, that their approaches provided an objective way to obtain a small number of high-priority genes for future functional studies.
Beyond Microarrays
Although researchers aim to eventually link pathways from expression to metabolism, genomics research thus far has focused on microarrays because this technology is more standardized and far more widely available than methods for analyzing proteins (primarily mass spectrometry) and metabolites (mass spectrometry and nuclear magnetic resonance). Although array-like assays for proteins have been developed, some using antibodies as tags, these technologies are still relatively exploratory and limited in scope, says Terry Speed, a professor in the Department of Statistics at the University of California, Berkeley.
But microarray data have serious limitations when it comes to biological inference. Although they can show associations, such data can rarely indicate cause and effect. “What people can find across many arrays are patterns suggesting coregulation,” says Speed. “If you look across hundreds of arrays and find that expression of two genes [moves] up and down together, that’s highly suggestive of interactive behavior. It’s not so much biological modeling as it is finding associations that are suggestive of biological interactions.” One of the greatest limitations of microarray data reflects the underlying biology: the expression of mRNAs doesn’t always translate into proteins because silencing RNAs and other mechanisms can block the translation process.
Sample sizes in most of the current systems biology experiments are not adequate to infer the kinds of complex networks that are the goal of such studies.
– Gary Churchill
Jackson Laboratory
One way to bridge the gap between gene expression and protein creation is to assess the proteins in a cell through proteomics analysis. Another is to gain a better understanding of the “transcriptome” (also called the “RNAome”)—the expression of all regulatory elements operating to regulate the expression, stability, and translation of transcripts (strands of RNA) in the cell. “To make genotype–phenotype correlations, you need to have a complete catalogue of transcripts that are expressed at each locus where such genotype–phenotype correlations are to be made,” says Thomas Gingeras, vice president of biological science at Affymetrix.
Gingeras and other researchers at Affymetrix and the National Cancer Institute have studied the RNAome with microarrays containing probes for the entire nonrepetitive sequences, not just the coding regions, of 10 human chromosomes. Data from these arrays have demonstrated that RNA activity is extraordinarily varied and complex. Although researchers have been able to identify the roles of many sequences, such as ribosomal and protein-coding RNAs, they have had to classify a substantial number of the newly discovered transcripts as TUFs (transcripts of unknown function).
Sequences for TUFs are equally complicated. “Curiously enough, many of these transcripts are sitting in the middle of genes, overlapped on both the sense and antisense strands of coding sequences,” says Gingeras. The sense, or template, strand of DNA is the one that is copied or transcripted. The authors speculate that the RNAs correlating to antisense strands may be cRNA copies, created in somewhat the same way as the cDNA copies of RNA used in microarrays.
Another challenge in all types of genomics experiments is detecting signals from molecules expressed at low levels. Quantities of mRNAs and proteins in a sample can vary by a ratio of 1 million to 1. Some of these low-expression molecules could be critical triggers to biological cascades, but may be lost in the signal-to-noise ratio.
Proteomics technologies are making progress in detecting low-expression molecules through more sophisticated sorting technologies such as SELDI-TOF (a method that selects only a subset of proteins from a given sample for analysis). To detect low-expression transcripts and quantify the number of mRNAs in a given sample, researchers studying gene expression turn to methods such as RT-PCR (which can involve the use of controls and fluorescent markers to quantify the amount of a molecule produced during polymerase chain reaction) and SAGE (which involves marking each transcript with a unique tag and then linking and sequencing the combined transcripts to count the number of times each tag occurs).
Analysis Issues
As great as the challenges are in developing technology to detect and identify transcripts, proteins, and metabolites, the difficulties in analyzing the resulting data may be even greater. As research teams plan their experiments, they must chose from a bewildering and ever-changing assortment of statistical methods for data analysis. Speed says no one protocol will work for all experiments: “Usually there will be one method that will be preferred and often several that will be acceptable. All of them have their strengths and weaknesses.”
Part of the difficulty relates to the current nature of genomics experiments. Whereas traditional statistics methods are based on the assumption that a study will have far more samples than data points per sample, genomics experiments usually involve the inverse situation: a few dozen samples, with tens of thousands of data points per sample. New statistical methods are being developed to deal with the peculiarities of genomics data. Simultaneously, other teams are working to ensure that the data to be analyzed are valid by addressing issues such as differences in experimental technologies and laboratory procedures, and revisiting the effects of sample size.
Recent studies have shown that increased standardization of microarray platforms has greatly reduced the influence of platform type on results. In a study published in the May 2005 issue of Nature Methods, Quackenbush and colleagues found that differences in microarray platforms (oligonucleotide versus spotted cDNA) did indeed affect results. However, these differences were eclipsed by a very high correlation between the platforms in expression changes caused by varying exposures to angiotensin II, a potent peptide that causes blood vessels to constrict. “The question we asked was, does the biology or the platform dominate?” says Quackenbush. “For more than ninety percent of genes for which we could make a reasonable comparison, we found that biology dominated platform.”
The bad news is that variations in protocols (for RNA labeling, hybridization, and microarray processing), statistical methods for data acquisition and normalization, and other “lab effects” can still significantly impact microarray data. This was the finding of two other studies also published in the May 2005 issue of Nature Methods, one led by Rafael Irizarry, an associate professor in the Department of Biostatistics at The Johns Hopkins University, and another by researchers with the NIEHS Toxicogenomics Research Consortium (TRC). Both studies compared the results of microarray analysis of identical samples performed at multiple laboratories, and both found that, with care, results can be comparable across labs. However, “you have to pay close attention to how you do things. You have to standardize your protocols from lab to lab,” says Katherine Kerr, a coauthor of the TRC study and the director of the Bio-informatics and Biostatistics Facility Core at the University of Washington–NIEHS Center for Ecogenetics and Environmental Health.
The design for most microarray experiments calls for three to five samples per treatment condition. However, some statisticians say that sample size must be increased to generate the statistical power necessary to infer biological pathways. “Sample sizes in most of the current systems biology experiments are not adequate to infer the kinds of complex networks that are the goal of such studies,” says Gary Churchill, a staff scientist at the nonprofit Jackson Laboratory in Bar Harbor, Maine.
Churchill is a cofounder of the Collaborative Cross, a project to develop a panel of 1,000 new and genetically diverse mouse strains. The mice are descendants of just eight parent strains, minimizing the need for genotyping, and are being bred for maximum genetic variation, allowing for a plethora of diverse yet controlled strains. Not all studies will require 1,000 mice, Churchill says, although some will. The resource is being generated to cover a wide range of needs.
Statistics isn’t about the formulas, how to crunch numbers. It’s about the concepts. It’s about how to quantify uncertainty, about how to take data and turn it into knowledge.
– Gary Churchill
Jackson Laboratory
Interpretation Is Key
Before teams can begin analyzing data, they must be confident that they are interpreting the raw signals accurately. For microarray data, this involves summarizing the fluorescence data for each spot—which are generated by the individual mRNAs linked to the probes—into a single value for each gene. “The hardest challenge is removing the component of intensity that is due to background noise,” says Irizarry. Some background noise—extraneous signals that can be confused with the signals being observed—can be caused by mismatches in the attachment of mRNA to the chip.
Another potential issue, says Irizarry, is that some of the 25–base pair probes used on oligonucleotide chips can be “stickier” than others—that is, more likely to attract mRNA. “If one gene is represented by a sequence that’s sticky, it will collect more than a probe that is less sticky,” he says. As a result, results from such a probe may reflect the chemistry of cDNA–mRNA bonding more than the biology of the sample.
Once values have been established for each gene in an array, the signals need to be normalized across the array. Equalizing fluorescence signals in two-color arrays is the most basic type of normalization. If equal amounts of two samples were hybridized to an array, then the total fluorescent signal from each sample should also be equivalent. If one is uniformly higher, a statistician can adjust the fluorescence values to better represent the relationship between the samples. Several more-involved processes may also be used to normalize data.
According to the TRC study, normalization procedures seem to increase the accuracy of microarray data. But there still remain lots of unanswered questions about normalization formulas, as well as the algorithms used to analyze genomics data, says Kerr. “The data look better when we’re done with [normalization],” she says. “But we don’t know if we’ve really made a correction.”
After normalization, researchers can take a basic count of changes in gene expression. Churchill calls this “making lists.” He explains: “You measure microarray data from normal tissue and from diseased tissue and . . . generate lists of genes that are up or down. The problem now is how we take those lists and turn them into biological sense.”
Turning Data into Sense
The next step for many teams is to shorten the list. They may focus on genes with the strongest or most closely correlated changes in expression. Often this process is informed by preexisting information about gene function. But care must be taken. Setting filters that are too tight can cause an analysis to ignore genes of importance, while setting parameters too broadly can cause false positives.
That is why different groups can have difficulty replicating results on microarray experiments, says Greg Carr, a research fellow working in product safety at Procter and Gamble. If the statistical power—or probability of detecting genes of significance—is set relatively low, say 10%, one lab may pick up on some of the low-power genes and a second lab may pick up on others, but no one lab is likely to detect them all, he says.
We need to simplify to understand complexity, to start off with the exploration and understanding of simple systems. If you try to look at the complexity first off, you’ll never really unravel it.
– Kenneth Ramos
University of Louisville
Another approach sometimes used with or instead of data filtering is to cluster, or group, data according to similarities in expression patterns. Methods include hierarchical or “Eisen” clustering, which produces a tree-like structure; κ-means clustering, which produces line graphs; and principal components analysis, which produces a three-dimensional array that can be rotated. Clustering methods are usually exploratory and, Speed says, don’t provide an answer to a well-defined question; they can only show associations among genes. However, they can provide effective ways to organize data. Scientists then have to infer cause and effect.
Various types of modeling algorithms can aid in this inference process. One of the simplest models, Boolean networks, can capture multivariate gene relationships that can be inferred from measurement data. Ilya Shmulevich, an associate professor at the Institute for Systems Biology in Seattle, has worked to increase the flexibility of Boolean network modeling through the development of probabilistic Boolean networks. These networks allow for multiple functional possibilities for each gene, mimicking underlying biological and measurement uncertainty, says Shmulevich. He and his colleagues have applied probabilistic Boolean network analysis to gene expression data from studies of melanomas and gliomas.
When using most modeling methods, “you have to put in a rather limited set of genes and then learn something about that set,” says Speed. The art of assigning genes to modeling programs and deciding how to filter the results of other genomics data draws heavily on preexisting knowledge about the systems in general. Researchers comb through the scientific literature or databases of genetics, proteomics, and metabolomics data. However, database mining can only take researchers so far. Only 60–65% of human genes have been adequately annotated as to function, says Raymond Tennant, director of the NCT. This makes it difficult to infer the function of genes that haven’t yet been annotated.
The Chemical Effects in Biological Systems Knowledgebase, set to become publicly available in late 2005, will provide access to microarray data for about 140 reference compounds, and comprehensive data sets on about 10 hepatotoxicants, including acetaminophen. “The database will also include reference information on the biological effects of chemicals and other agents, and pathways related to their mechanism of action,” says Michael Waters, NCT assistant director for database development.
Speaking the Language of Inference
Beyond having data in hand, biological inference requires a wide range of skills and expertise. “There’s a need for transdisciplinary efforts—notice I said transdisciplinary rather than interdisciplinary,” says Kenneth Ramos, chair of the Department of Biochemistry and Molecular Biology at the University of Louisville and EHP’s toxicogenomics editor. “We need people who speak more than one language, a new type of scientist. We’ve become so specialized that it is difficult to cross disciplines with fluidity. I think that research in biological inference will demand that ability. Classically trained biologists will need to reengage their appreciation and understanding of mathematics in order to begin to tackle some of these questions.”
Biologists may also need to have a better understanding of, and possibly training in, statistics. “Taking Stats 101 isn’t necessarily going to imbue the concepts you need,” says Churchill. “Statistics isn’t about the formulas, how to crunch numbers. It’s about the concepts. It’s about how to quantify uncertainty, about how to take data and turn it into knowledge.”
“It would be beautiful if you had that [statistical] training when you start in the field,” says Heinloth. “But if you include enough statisticians and bioinformaticists on your team as equal members, you can have that covered.” In Heinloth’s research group, statisticians are involved in experiments from the beginning. “There’s hardly any decision in a study that’s made by only one person,” she says. However, she notes that this collaborative process is not “science by committee.” As experiments are designed and implemented, team members weigh in only in their areas of expertise.
As researchers delve into this enormous quantity of data, they are confronted with the limits of human cognitive ability. It is not possible for a single individual to fully comprehend the astonishing complexity of metabolism in even a single cell type. So as researchers develop new technologies and new statistical tools for genomics research, and for inferring the ramifications of the data they uncover, they also need to find new ways to approach the limits of their own understanding.
“We need to simplify to understand complexity, to start off with the exploration and understanding of simple systems. If you try to look at the complexity first off, you’ll never really unravel it,” says Ramos. There’s a lot of potential for error in starting with simple systems, he adds. But it’s a way to start, to gradually build up to more complex models.
“As we accumulate more data, we’re understanding how limited our understanding is and how much more there is to discover,” says Churchill. “That can be discouraging from a diagnostic sense, but it’s wonderful to have a new universe opening up in front of you.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0039415929881EnvironewsSpheres of InfluenceBy Order of the Court: Environmental Cleanup in India Sharma Dinesh C. 6 2005 113 6 A394 A397 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Metal scrap from New York’s World Trade Center towers. Live missiles and mortar shells from Iraq and Somalia. Used lead-acid batteries from Canada. Aging oil tankers and military carriers from Europe. This is just a little of the imported waste found in scrap yards and hazardous stockpiles across India. That’s in addition to large amounts of toxic wastes generated and dumped by local industries every day. Many industries dump sludge and effluents laden with heavy metals and persistent organic compounds in open areas, in rivers, and around residential areas, in gross violation of national laws. At some places, toxic dumps have contaminated soil and groundwater for decades, making communities around them sick. Industry’s near-total disregard for laws relating to hazardous waste, coupled with apathy and inaction by state agencies, has made the situation grim.
Today, more than 13,000 licensed industries generate about 4.4 million metric tons of hazardous waste every year, according to estimates from the Indian Ministry of Environment and Forests (MEF). This doesn’t include small-scale businesses such as backyard smelters. According to the ministry, the five states of Maharashtra, Gujarat, Tamil Nadu Karnataka and Andhra Pradesh generate about 80% of the waste in India. Unsound practices have caused widespread degradation of the environment and adverse health impacts on Indian communities and industrial workers.
Now, however, in a significant gesture, the Indian Supreme Court has taken up the challenge of forcing polluters and states to clean up these hazards. Helping the court in this task is a monitoring panel of scientists and concerned citizens.
A Heightening of Awareness
Economic liberalization policies in the past 20 years or so have led to rapid growth in Indian industries. The production of petrochemicals, pesticides, pharmaceuticals, textiles, dyes, fertilizers, leather products, paint, and chlor-alkali has grown significantly. These industries produce wastes containing heavy metals, cyanides, pesticides, complex aromatic compounds (such as polychlorinated biphenyls), and other toxics. Several toxic waste hot spots—such as the industrial belt of Vapi and Vadodara in Gujarat, Thane-Belapur in Maharashtra, and Patancheru-Bollarm in Andhra Pradesh—developed in this period.
At the same time, India woke up to the dangerous realities of industrial hazards after the Bhopal disaster in 1984. The government enacted the Environment Act in 1986; under this legislation, the Hazardous Waste Rules were formulated in 1989.
These rules required each industry generating hazardous waste to obtain authorization from its state pollution control board. Boards, in turn, could issue authorization only after verifying that the industry had the facilities, technical capability, and equipment to safely handle hazardous waste. Industries were to deposit their hazardous waste in disposal sites set up by state governments and specifically designed to receive different kinds of waste. Significantly, the rules permitted the import of hazardous waste for processing or reuse as raw material.
But followup action such as creating secured landfills has come slowly. Fifteen states were given funds to identify landfill sites, but none were opened until 1997. Likewise, although India joined the Basel Convention in 1992, the nation’s hazardous waste rules were brought into compliance with convention stipulations only in 2000.
In the absence of disposal mechanisms permitted under the rules industries either stored wastes onsite or dumped them in the open. Temporary storage—permitted for 90 days under the 1989 rules—became permanent. It was hazardous waste anarchy.
In 1995, in response to a petition by a New Delhi–based nongovernmental organization (NGO) now known as the Research Foundation for Science, Technology, and Ecology, the Supreme Court asked relevant agencies for information on the amount of hazardous waste imported and generated domestically, as well as how it was being disposed of. But the state pollution control boards were not collecting data properly, so for two years, the MEF and the Central Pollution Control Board (CPCB; the entity that oversees the state boards) had no authentic data to provide. So the court convened a panel to investigate and make recommendations. Known as the High Powered Committee on Management of Hazardous Wastes (HPC), this panel submitted its final report in 2001.
A Legacy of Toxic Wastes
The committee’s findings were grim. “Most industries used the opportunity [presented by the delay in constructing disposal sites] to discharge their hazardous waste in illegal dump sites outside industrial estates, along roadsides, in low-lying areas, along with municipal wastes, or even in river and canal pits,” observed the 2001 HPC report. The report further noted that “the authorities appeared to have ignored several warnings, reports, investigations, and studies that highlighted zones of ecological degradation due to indiscriminate dumping and disposal of hazardous waste.” The committee reported the existence of 80 illegal dumps in Andhra Pradesh and Gujarat alone. Satellite imagery is now being used to locate and confirm the extent of wastes strewn throughout the Thane region of Maharashtra.
Meanwhile, other findings began coming to light. In the Gorwa industrial area of the city of Vadodara, Hema Chemicals had been dumping 77,000 metric tons of highly carcinogenic hexavalent chromium waste over the past 20 years or so, according to the Gujarat Pollution Control Board. A 2001 study by the National Institute of Occupational Health of Ahmedabad, Health Surveillance of Workers Exposed to Chromium in a Chemical Industry, revealed blood chromium levels in exposed Hema employees to be more than twice as high as control subjects. No systematic studies have been carried out on nearby communities, but a local NGO, Paryavaran Surksha Samiti, claims that blood chromium levels in area residents also are high.
In a 1997 report, Groundwater Quality in Kanpur, Status Sources and Control Measures, the CPCB reported chromium concentrations 124–258 times higher than the Indian permissible limit in areas polluted by tanneries and companies making basic chrome sulphate. They also found high levels of several other contaminants such as mercury, arsenic, chloride, and lead. Although the polluted water is not fit even for irrigation, people continue to drink it as alternate supplies are not available, says Rakesh Jaiswal, executive secretary of the NGO Eco-Friends Society in Kanpur. The CPCB study found people blending chromium-rich sludge with coal ash to make a binding material for building. The contaminated sludge has also been used in road construction.
Ship-breaking is another source of toxicants. Ship-breaking activity at Alang-Sosiya has resulted in wastes containing heavy metals and petroleum hydrocarbons. For many years, most of the wastes have been dumped on the coast or burned in the open. Studies by the Central Salt and Marine Chemicals Research Institute have shown that wastes accumulate in the soil first and then migrate incrementally to the tidal zone, the subtidal zone, and finally to deep seawaters and into sediments. High levels of trace metals such as cobalt, nickel, copper, lead, and cadmium have been found in sediments. No formal studies have been done in Alang-Sosiya, but anecdotal evidence indicates sea creatures are dying off as a result of the pollution.
Still other threats are posed by lead. Field studies in Karnataka and Gujarat—conducted by the University of Cincinnati Department of Environmental Health and the National Referral Centre for Lead Poisoning in India, Bangalore—have indicated abnormally high environmental lead levels near lead smelters, lead-acid battery assembly units, service centers, and electronic soldering units. Thuppil Venkatesh, head of the referral center, says soils near battery dismantling and smelting units had lead levels up to 100,000 parts per million. Due to the pressure of growing urbanization, he says, people continue to live near such units.
Recycling of imported waste is a legal business in India, with all kinds of waste—from discarded electronics to cow dung—coming from more than 100 countries. Lists of allowable items have been developed (although companies have taken advantage of lax implementation at ports). Metal scrap, including dead ammunition, can be imported, but preshipment inspection was mandatory only for imports from war zones. However, when live missiles and bombs exploded in scrap yards near Delhi last year, killing 14 workers, the government changed the rules and made pre-shipment inspection necessary for all scrap imports. Old computers and other electronics waste is being sent to India for recycling under the aegis of charitable donations. Discarded lead-acid batteries can be imported only by licensed recyclers using safe technologies, but these batteries find their way into the unlicensed informal recycling market. The government finds it difficult to ban waste imports altogether because waste recycling provides employment to a large number of people.
Judicial Activism
In recent years, in the face of these and other findings, the Supreme Court has spurred major environmental actions such as relocating polluting industries out of Delhi and replacing diesel with compressed natural gas in public transport. In so doing, the court has expanded the scope of “the right to life”—a concept enshrined in the constitution of India—to include the right to a clean and healthy environment.
“It is not as if the court is encroaching upon territories of legislature or [government]—it is only protecting citizens’ rights guaranteed under the constitution and various laws like the Environment Act,” points out Sanjay Parikh, a Supreme Court attorney representing the public interest in the hazardous waste case. “If the state does not fulfill its legal and constitutional obligations, then the court can direct it to do so.”
Under the Indian constitution, Parikh says, the Supreme Court’s directives are to be treated as law until the government enacts suitable legislation or changes existing regulations. This often happens in response to petitions filed by individuals or groups, but may also be initiated by the Supreme Court itself. At times, even informal complaints written on postcards and sent to the court have been treated as petitions, and proceedings initiated.
In the case of hazardous waste, the Supreme Court intervened because the government had signed the Basel Convention but failed to change the rules to check the import of hazardous waste. Parikh says it was the court’s intervention that led to regulatory mechanisms and procedures for the import, transport, storage, recycling, and final disposal of hazardous waste. The Supreme Court’s October 2003 final judgment on the 1995 petition set a detailed timetable for such actions as amending various sets of rules; reviewing lists of hazardous waste; setting up testing laboratories at ports to verify the content of declared hazardous waste; construction of secured landfills and treatment, storage, and disposal facilities (TSDFs); closure of industries violating rules; and disclosure of such information to communities.
As a result of the court’s intervention and the HPC’s recommendations, the MEF amended the 1989 rules in 2000 and 2003 to make them more stringent. Categorization of waste produced by different industrial processes as well as from imports has been further refined. A new list of 29 categories of hazardous waste completely banned for import and export has been added. The roles of different agencies have been clearly demarcated. New sets of rules for recycling and handling of used lead-acid batteries and plastic waste have also been codified.
Another set of amendments currently under way will introduce new measures such as punitive action for illegal imports and the allowance of re-exports after 30 days if imported wastes are in contravention of rules. (Currently, wastes cannot be re-exported after 30 days, so many exporters simply dump them at ports.) The list of banned wastes also is being further scrutinized.
Committee Effort
In a rare gesture, the Supreme Court also constituted a committee to oversee implementation of its 2003 judgment (followup is usually left to the party receiving the judgment). The Supreme Court Monitoring Committee (SCMC) reports quarterly to the court on progress being made toward each of the points in the timetable.
The SCMC’s work has brought more hazardous waste skeletons to light. One example is Travancore Titanium Products at Thiruvananthapuram, found to have been operating close to a beach for several years without an effluent treatment plant. “Effluent of pH less than one and temperature more than fifty degrees centigrade is being discharged into the open sea in violation of every conceivable law,” the committee noted in its March 2005 report.
The SCMC ordered the unit to close, but the company was granted a stay in the Kerala High Court, giving it until 2006 to set up an effluent treatment plant. The SCMC has pleaded to the Supreme Court that such stays granted by high courts prevent the committee from carrying out its mandate. On 9 May 2005 the Supreme Court directed that no high court or any other authority shall interfere with directions of the Supreme Court given in the October 2003 judgment.
The committee has ordered the closure of several other industries and is applying the “polluter pays” principle for cleaning up the mess. The amount of hexavalent chromium waste dumped by Hema and two other companies—Golden Chemicals and Tamil Nadu Chromates—has been estimated to be 250,000 metric tons. The SCMC called chromium pollution by Hema Chemicals a case of “deliberate poisoning of communities with toxic wastes, contaminating water, soil, and air.” Hema has been asked to pay about US$3.9 million for remediation of the surrounding area. In another instance, the SCMC has directed costs of mercury decontamination to be recovered from Hindustan Lever, which owned a thermometer factory at Kodaikanal. In Bhopal, action groups have demanded that Dow Chemical—with whom Union Carbide merged in 2001—pay for the cleanup of toxic dumps at the closed Union Carbide plant. But the SCMC has not taken a final view on this.
Other Progress
In Kanpur, the CPCB is partnering, of its own volition, with a consortium of Indian research institutes and New York’s nonprofit Blacksmith Institute for remediation of groundwater polluted by hexavalent chromium. “We will chart migration pathways of pollutants that have traveled both vertically and horizontally over all these years, using mathematical modeling. This could offer a model for cleaning up similar groundwater pollution sites and will help communities access clean water,” says R.K. Singh, a CPCB scientist.
Twenty-one ship-breaking units in Alang-Sosiya have been closed, and citations have been issued to 11 others for improper handling of wastes. A TSDF site for ship-breaking waste has been identified. Meanwhile, wastes are being transported to another facility in Ahmedabad.
In line with the Supreme Court judgment and the HPC report, the SCMC says ship-breaking activity can continue with proper safeguards such as decontamination in the exporting country itself. Other stakeholders disagree with this decision, however. “In our view, ships that come [into India] with waste oil, asbestos, poly-chlorinated biphenyls, and radioactive material are a violation of the Basel Convention,” says Ramapati Kumar, toxics campaigner of Greenpeace India.
In Gujarat, 13 industries have set up their own TSDFs, while 6 other facilities have been set up for use by clusters of industries. Battery manufacturers have started buying back their used products, to avoid their going to backyard smelters. Large users of batteries such as railways now auction their old batteries only to registered recyclers.
“We see some signs of change like establishment of secured landfills and TSDFs in states that were earlier refusing to do so, display of information on hazardous waste at factory gates, initiation of projects to clean up dumps, and stern action to close down violators,” says Gopalkrishnan Thyagarajan, chairman of the SCMC. Overall, he says, there is greater acceptance by industry of the committee’s authority, and industry’s attitude is changing. At many places, local watchdog panels have been set up with scientists, prominent citizens, NGO representatives, and pollution control officials as members.
Public reaction to court actions has been very favorable, at least over the long term. The phaseout of diesel vehicles from Delhi is a case in point. Bus owners cursed the courts at first, and the common people, too, suffered through the growing pains of the transition period. But today the average city dweller is thankful to the Supreme Court for its role in improving Delhi’s air quality.
An Active Future for the Court?
Despite all these efforts, the overall scenario remains serious. A large number of units are still operating without authorization in several states, and illegal dumping of wastes continues in Maharashtra, Tamil Nadu, Gujarat, and Delhi, the SCMC told the Supreme Court in its March 2005 quarterly report. The committee has asked state pollution control boards to hire detective agencies and encourage whistle-blowers to report such practices.
Preparation of inventories of hazardous waste generated and illegal dumps is running behind schedule. In the absence of reliable inventories of wastes, few efforts are made to use tools such as environmental impact assessments, risk assessments, or health impact assessments for addressing hazardous waste problems, says Suneel Pandey, a fellow at the Centre for Environmental Studies at The Energy and Resources Institute of New Delhi. “Although the government recognizes the localized nature of hazardous waste generators, and large dumps are being identified, we need to quantify and characterize the volume of waste residues,” he says. “Also, since the growth of the industrial sector is dynamic, there is a need to constantly upgrade this waste inventory in order to develop suitable management strategies.”
Necessary rules and regulations have gradually been put in place for the import, handling, transport, and safe disposal of hazardous waste, but central and state pollution control boards tasked with implementing them remain weak. “The need is to strengthen these boards and the existing institutional base so that enforcement can be made sustainable. After all, a monitoring committee can’t have an endless tenure,” says K.P. Nyati, head of the environment management division of the Confederation of Indian Industry.
Also, the rules do not provide any incentive to industry for waste reduction or minimization. So companies are reluctant to adopt such measures, says Pandey. Moreover, he says, in the absence of standards for the cleanup of contaminated sites and limits for the disposal of wastes on land, polluters are not legally bound to clean up a site unless ordered by judicial intervention to do so.
For the foreseeable future, it seems likely that such judicial intervention will continue. The active role played by the Indian judiciary in the past two decades has redefined its place in India’s society. Courts are increasingly being viewed not just as a mechanism to settle disputes, but as a platform to protect citizens’ rights and to undo wrongs committed by the government. In a move that other governments will certainly take note of, the Indian Supreme Court has taken a keen interest in environment-related matters, and its judgments have impacted society at large.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0039815929882EnvironewsInnovationsMetal Attraction: An Ironclad Solution to Arsenic Contamination? Frazer Lance 6 2005 113 6 A398 A401 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Inorganic arsenic—the more acutely toxic form of this metalloid element—contaminates drinking water supplies around the world. In the United States, the most serious arsenic contamination occurs in the West, Midwest, Southwest, and Northeast; as many as 20 million people—many getting their water from unregulated private wells—may be exposed to excess arsenic in their drinking water. In Bangladesh, it’s estimated that as many as 40 million people may be suffering from arsenic poisoning; contaminated drinking water is also a problem in many other countries, including Argentina, China, Chile, Ghana, Hungary, India, and Mexico.
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There are several methods for removing inorganic arsenic from water. Many take advantage of the strong bond that forms between arsenic and iron. Now Littleton, Colorado–based ADA Technologies, through funding from the NIEHS Superfund Basic Research Program, the U.S. Air Force, the U.S. Environmental Protection Agency (EPA), and the state of Colorado, has gone a step farther in capitalizing on that characteristic with a new class of amended silicate sorbents that remove even more arsenic from water, and do it more easily and more cheaply. ADA is also working with researchers at Virginia Polytechnic Institute and State University and Old Dominion University to study the interaction between arsenic species and iron oxide–based media, and is collaborating with other partners to develop low-cost approaches for quantifying the concentration of arsenic in drinking water.
A Host of Health Risks
Most environmental arsenic occurs naturally, appearing in deposits of minerals and ores including arsenopyrite, enargite, and proustite. A smaller but still significant source of arsenic exposure is anthropogenic. Inorganic arsenic as well as various arsenical compounds have been used in agricultural chemicals and wood preservatives, in the glass industry, and in the production of lead shot. Elsewhere, emissions from coal-burning power plants are a significant source of arsenic exposure.
However, the majority of toxic exposure comes from drinking water contaminated with naturally occuring arsenic. Chronic arsenic ingestion through drinking water is known to cause skin cancer, and has been linked to an increased risk for cancers of the bladder, lung, kidney, liver, colon, stomach, uterus, and prostate. Arsenic has also been associated with cardiac, pulmonary, and artery diseases, diabetes mellitus, and neurological, developmental, and reproductive problems.
In the United States, a revised drinking water standard for arsenic of 10 micrograms per liter (μg/L) is set to take effect in January 2006, but there is substantial concern that this level is still too high for public safety. In many other countries, allowable levels are even higher. With millions of people around the world facing potential adverse health effects from this contaminant, the need for effective, affordable ways to remove arsenic from drinking water is critical.
Ridding Water of Arsenic
Arsenic is generally found in two inorganic forms in nature—arsenate and arsenite. Arsenate is present as a negatively charged ion at typical drinking water pH (roughly 6.5–8.5), whereas arsenite is neutral in the same pH range. Many treatment methods rely on a negative arsenic charge, so they tend to be more successful at capturing arsenate.
One such method is ion exchange, which uses polystyrene-based resins containing positively charged sites to remove negatively charged species. Besides being effective only with arsenate, sulfate ions are removed preferentially to arsenic, so if large amounts of sulfate are present, those ions will tie up the bonding sites, leaving fewer available for arsenic to bond with. Activated alumina is a filter medium that will remove a variety of contaminants, including fluoride, arsenic (both arsenite and arsenate), and selenium, but it requires periodic cleaning with an appropriate regenerant such as alum or caustic in order to remain effective. Activated alumina also is effective only across a very narrow pH range (6 to 7).
Granular ferric oxide is an iron-based adsorbent that can capture both arsenate and arsenite, but in general, it functions best at or below a pH of 7, and both phosphates and silicates can interfere with its action. A fourth method, a coagulation/filtration process, uses a ferric chloride liquid and an oxidizing agent such as sodium hypochlorite to create insoluble ferric hydroxide. Arsenic adsorbs readily onto the solids, but workers must store and handle corrosive ferric oxide and oxidant solutions.
The ADA formulation takes a slightly different approach. The basic ingredient is an iron oxide known as akageneite. According to Craig Turchi, ADA program manager for the arsenic project, the company focused on an iron oxide because iron tends to form very strong, stable bonds with arsenic. Additionally, Turchi explains, oxides tend to be among the most stable substances in nature, and many of them tend to accumulate the substances with which they come into contact, including many contaminants. “Iron oxide is a molecule with hydroxyl groups,” he says. “And we know that, from a chemical perspective, arsenic behaves like a hydroxyl in some ways.”
One of the key advantages to ADA’s approach is that the akageneite is coated onto an inert silicate substrate. In comparison, most other approaches involve pure iron oxide granules. Turchi says, “Our tests have shown we can get the same capacity as our competitors, but with much less iron. And that cuts the cost from around four dollars per pound down to around two dollars per pound.” Additionally, because ADA’s akageneite particles are on the nanoscale, they can be dispersed far more efficiently in water undergoing treatment. The ADA formulation also is notable for its ability to remove both arsenite and arsenate, and its effectiveness at a wider pH range of 6.5–8.5.
A Super Sorbent
The ADA formulation has been shown to reduce arsenic contamination as high as 1,000 μg/L to 10 μg/L in as little as 30 minutes. “The capacity of the adsorbent increases with the concentration of arsenic in the water,” Turchi says. “This behavior is typical of most adsorbents.” Capacity varies somewhat with the water content, says Turchi, but generally ADA’s material removes about 2 milligrams of arsenic per gram of sorbent at a concentration of 50 μg/L, and about 40 milligrams of arsenic per gram of sorbent at 1,000 μg/L.
There is a catch, though—as the water gets cleaner and more bonding sites are taken up by arsenic, it becomes harder and harder to remove the last bits of arsenic. Turchi says it probably is not possible to remove all the arsenic from water with processes such as this. “It comes down to the efficiency of an equilibrium process,” he says. “Arsenic has an affinity to stay in water, as well as an affinity to attach to materials like iron. You’ll get diminishing returns until these affinities balance at some point.”
Turchi says users will eventually be able to choose from either a solubilized form of the sorbent that can be sprinkled into contaminated water, circulated, and then filtered off or allowed to settle out, or a pelletized form for use in a packed-bed approach. A packed bed consists of layers of adsorbent material. Contaminated water is poured in the top, and purified water is collected at the bottom after seeping through the material. “In the ideal case you periodically add new sorbent to the clean water end and remove the arsenic-saturated sorbent from the other end, so that you get the maximum use of your sorbent,” Turchi says.
Once the binding sites on the iron oxide have been used up, the material could be reused by acidifying it to break the arsenic–iron bonds, then filtering off the arsenic. But Turchi says economics—and the logistics of dealing with the arsenic-laden, high-pH waste—will probably dictate disposal of the used adsorbent. The spent material has passed EPA tests to determine the likelihood of contaminants leaching out of landfills into the surrounding water supply, so Turchi says the sorbent can simply be disposed of in a regular landfill.
Turchi says ADA’s sorbent system has the benefits of being both robust and simple: no moving parts, and little training required. “I think that makes our system more appropriate for smaller-scale uses,” he says. “Typically, in a large municipal facility, you’d run the water through the filtration system, but a small village [in a developing nation] probably doesn’t have a water treatment facility.”
ADA and collaborator Kinetico Incorporated, a water treatment system engineering company, are planning a field test involving a packed column of the amended silicate for summer 2005 at a facility in New Mexico, where the ADA formulation will be tested against two commercially available competitors. The formulation also underwent earlier field tests at two Colorado sites.
Questions of Stability
Though not familiar with ADA’s work specifically, Joshua Hamilton, director of the Center for Environmental Health Sciences at Dartmouth College and the Dartmouth Toxic Metals Research Program, says, “I know a lot of people are working with iron oxides, and it appears to be a very fruitful area. Iron oxides and arsenic exhibit tight bonding properties, and oxides are relatively cheap materials. All of these are pluses—high efficiency, low maintenance, low cost, and easily renewable.”
Still, says Hamilton, there are a few aspects of the ADA approach that may be cause for concern. For one, he says, “I think that assuming you can safely put it in a conventional landfill might be overstating the strength of the bond.” He explains that a lot of arsenic is found in granitic deposits throughout his home state of New Hampshire. “We’re seeing, under normal environmental circumstances, the mobilization of a good deal of arsenic out of materials that are basically compounds of arsenic and iron,” he says. “And it should also be taken into account that there’s a lot of interesting and somewhat unpredictable chemistry that goes on in landfills.” He points out that the environmental conditions that allow organics to remain contained are quite different from those that allow arsenic to be contained. “So if you focus on arsenic,” he says, “you could end up releasing organics into the environment and vice versa.”
Turchi agrees that the issue of long-term sorbent stability in landfills needs to be addressed. He explains that the EPA leach tests examine the stability of landfilled contaminants under acidic conditions, because most metals leach off of sorbents under acidic conditions. “Research indicates that arsenic could conceivably leach off the media under alkaline conditions, so a different test may be required,” he says.
Michael Harbut, chief of the Center for Occupational and Environmental Medicine at Wayne State University, points to another concern: “I’d [like] to see enough studies to show me the substance that resulted when the arsenic bonded didn’t have toxic properties of its own—inhalational studies, cardiac trend studies, the whole suite. Then we’d move on to worry about bond strength in the environment.” Harbut has studied low-level arsenic poisoning for many years and has lobbied for stricter water thresholds and broader testing.
Hamilton raises a third concern: the fact that even $2 per pound could prove to be an insurmountable barrier in many countries. With comparable technologies costing $4–8 per pound, ADA’s price does seem a bargain. But with the annual per capita income in Bangladesh, for example, hovering around US$360, the outlay of even $2 per pound of sorbent (enough to remediate about 800,000 liters of water) could well prove prohibitively high.
“One factor that could mitigate that cost would be to make the sorbent material in the country where it will be used,” says Turchi. “Using less expensive labor and avoiding the costs of transportation could lower the cost significantly.”
In the meantime, even affluent countries such as the United States are still searching for an effective, affordable response to arsenic. “You can put a reverse osmosis filter on your sink at a cost of six to eight hundred dollars,” Harbut says. “That’s not much to some, but for too many in this country, that’s just more than they can afford. We need, as a society, to fund these systems for those who can’t afford them.” Municipal water systems appear to have the technology to address the arsenic issue, but private well owners, as well as developing nations whose populations are scattered far beyond the reach of any centralized system, need a fast, safe, reliable system for removing arsenic from water. Further testing will tell if ADA’s amended silicate technology can provide one answer.
Other Arrows in the Arsenic Arsenal
Many other researchers are seeking the magic combination of a cheap and effective arsenic remediation process. All too often, if a treatment process is effective, it’s not cheap, and if it’s cheap, it’s not effective. A couple of other new ideas being tested might, like the ADA strategy, meet both goals.
Ashok Gadgil, a researcher at Lawrence Berkeley National Laboratory, is working with a by-product of coal burning called bottom ash. Bottom ash (which differs from fly ash in that the former contains no heavy metals) is an ultrafine substance, with particles one-tenth to one-hundredth the width of a human hair. Gadgil and his team coat the ash particles with ferric hydroxide, which in turn bonds powerfully with available arsenic. Initial laboratory tests indicate the substance can reduce arsenic concentrations from 2,400 μg/L to only 10 μg/L within an hour.
Gadgil envisions loading this material into a teabag-sized filter to go in a water jug, providing a Bangladeshi family of six with a day’s safe drinking water. Costs, he estimates, might run around 30¢ per person per year. Gadgil is also testing the material for possible use in a water treatment system for small U.S. municipal water treatment facilities.
On the opposite coast, an engineering team under the direction of Massachusetts Institute of Technology engineering professor Susan Murcott has hit on the idea of a filtration system utilizing layers of sand, brick chips, gravel, and iron nails. Once again, the strong attraction between arsenic and iron comes into play, as tests indicate arsenic contamination can be reduced to 10 μg/L within an hour. Cost of the initial system is about US$16 per year.
Only time—and much more testing—will tell whether these approaches or any of the others being developed around the world will meet all of the criteria of simplicity, reliability, and ease of use. One additional incentive to find such an approach is the new Grainger Challenge Prize for Sustainability. The National Academy of Engineering is offering this $1 million prize to help solve the massive public health problem of arsenic contamination. The prize will be awarded to an individual or group for the design and creation of a workable, sustainable, economical point-of-use water treatment system for arsenic-contaminated groundwater in Bangladesh, India, Nepal, and other developing countries. The first Grainger Challenge prize will be awarded in February 2007.
The stuff of sorbency. ADA’s amended silicate sorbent comes in two formulations, one more suitable for a packed-bed approach (top) and a finer version that can be sprinkled into water, then filtered off (bottom).
Juggling strategies. Simple arsenic mitigation methods that can be used at home are one target of ongoing research.
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Suggested Reading
Hussain MD Haque MA Islam MM Hossen MA 2001. Approaches for removal of arsenic from tubewell water for drinking purpose. In: Ahmed MF, Ali MA, Adeel Z, eds. Technologies for Arsenic Removal from Drinking Water. Tokyo; Dhaka: The United Nations University; Bangladesh University of Engineering and Technology; 69–75. Available: http://www.unu.edu/env/Arsenic/Hussain.pdf
Mushak P 2000. Arsenic and Old Laws: A Scientific and Public Health Analysis of Arsenic Occurrence in Drinking Water, Its Health Effects, and EPA’s Outdated Arsenic Tap Water Standard. New York: Natural Resources Defense Council. Available: http://www.nrdc.org/water/drinking/arsenic/aolinx.asp
Robins RG Nishimura T Singh P 2001. Removal of arsenic from drinking water by precipitation, adsorption or cementation. In: Ahmed MF, Ali MA, Adeel Z, eds. Technologies for Arsenic Removal from Drinking Water. Tokyo; Dhaka: The United Nations University; Bangladesh University of Engineering and Technology; 31–42. Available: http://www.unu.edu/env/Arsenic/Robins.pdf
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00402EnvironewsScience SelectionsA Safer Mosquito Treatment?: Minimizing Deltamethrin Risks to Children Tenenbaum David J. 6 2005 113 6 A402 A402 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Indoor spraying to control disease-carrying mosquitoes is the strategy of choice in Mexico’s effort to reduce malaria. When Mexico discontinued the use of DDT for this purpose in 2000, the pyrethrum-derived compound deltamethrin became the primary pesticide in the battle against mosquitoes. To find out how deltamethrin is distributed, absorbed, and excreted, and how it affects human DNA, researchers at the Universidad Autónoma in San Luis Potosí, Mexico, tracked 32 Mexican children aged 3–12 years before and after their homes were sprayed with deltamethrin [EHP
113:782–786]. Their findings suggest that with appropriate precautions, health risks to children exposed to deltamethrin can be minimized.
Deltamethrin is recommended by the World Health Organization for application to walls and mosquito nets, and is also used for other in-home insect control, and for agriculture. But during in vitro tests, deltamethrin has caused chromosome damage, which can be a precursor to cancer. There have also been published reports of neurotoxicity in exposed humans.
The children in the current study lived in four villages in the state of San Luis Potosí. The researchers sampled the soil of the homes’ dirt floors and measured metabolites of deltamethrin in the children’s urine at several time points in the 180 days after spraying. The urine metabolites served as biomarkers for systemic deltamethrin uptake.
The researchers also took blood samples from 28 children before spraying and then 24 hours afterwards, and looked for chromosome breaks using the comet assay. In this assay, cells are broken apart to remove proteins, and the DNA is allowed to unwind. When the DNA undergoes gel electrophoresis (separation in an electric field), DNA fragments move away, and damage is measured by counting the fragments that have migrated.
Half of the deltamethrin degraded in indoor soil within 2.2 weeks. Indoor soil levels peaked above 2 parts per million 8 days after spraying, and declined to about 0.5 parts per million at 180 days. The highest urine metabolite concentrations appeared within 24 hours of spraying, and 91% of the metabolites were excreted within 3 days. Metabolite concentrations had returned to undetectable prespraying levels after 180 days. The results of the comet assay were statistically identical between prespraying and postspraying blood samples, indicating no DNA damage resulting from exposure.
Although peaks in urine biomarkers did not correlate with those of deltamethrin measured in the dirt floors—allowing the researchers to dismiss soil ingestion as the most important pathway of exposure—the study did confirm that children in treated houses had higher levels of deltamethrin metabolites than children in the general population, as measured in previous studies. Other studies have shown limited absorption of pyrethroids through the skin. The researchers therefore suggest that inhalation in the first hours or days after spraying is the most important pathway of deltamethrin exposure for children.
The researchers conclude that children may be protected by keeping them out of sprayed areas for one day, and then cleaning cooking surfaces and utensils before use. In addition, children should be monitored to minimize soil ingestion or contact with sprayed walls.
Taking the health bite out of mosquito fighters. Some simple precautions can protect children from the pesticide deltamethrin sprayed inside homes.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00404EnvironewsScience SelectionsThe Arsenic Differential: Metabolism Varies Between Children and Adults Adler Tina 6 2005 113 6 A404 A404 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Worldwide, millions of people drink water contaminated with arsenic, but not everyone who drinks contaminated water has the same severity of effects. It has long been speculated that this differing susceptibility to the adverse health effects of arsenic may be due to differences in the way people metabolize the element. Now researchers in Mexico and Arizona are finding that age may play a role as well: children may metabolize arsenic differently than adults, even if they share similar genetic traits [EHP 113:775–781].
When arsenic is metabolized, it takes on new chemical identities that vary widely in their toxicity. Enzymes attach a methyl group to arsenic and convert it first to monomethylarsenic and then to dimethylarsenic. During this process, arsenic also changes its valence (the configuration of its electrons), which can affect its toxicity. The way a person metabolizes arsenic is reflected in a pattern of relative concentrations of arsenic metabolites in urine.
In the current study, researchers investigated urinary arsenic patterns of healthy residents of the Yaqui Valley in Sonora, Mexico. Participants fell into two age groups: children aged 7–11 and adults aged 18–79. The arsenic concentrations in the participants’ water were between 5.5 and 43.3 parts per billion, a range found in many parts of the world. The researchers cataloged polymorphisms in a gene known to be involved in arsenic metabolism, arsenic (III) methyltransferase (CYT19), and used existing catalogs of polymorphisms in two others, purine nucleoside phosphorylase and glutathione-S-transferase omega. Then they tested the participants’ urine to see how a subset of 23 of these polymorphisms related to urinary arsenic levels.
Among the children, polymorphisms on CYT19 were strongly associated with a particular pattern of metabolites: a high ratio of dimethylarsenic to monomethylarsenic. Indeed, children with three polymorphic sites on CYT19 were much more likely than any other participants to have the high ratio. No statistically significant association was seen in the adults—children and adults could have the same polymorphisms on their CYT19 gene, yet have different ratios of dimethylarsenic to monomethylarsenic in their urine. However, even children with nonvariant CYT19 had a higher ratio of dimethylarsenic to monomethylarsenic than adults.
Finding the association between the polymorphisms and urinary arsenic patterns only in children indicates that the association may be developmentally regulated, the researchers suspect. The CYT19 gene may turn on during a certain developmental stage and be more or less active at different ages in a way that depends on a person’s DNA sequence.
Whereas this study examined healthy subjects, planned follow-up studies will include individuals suffering from arsenic-related health effects to see if there is a relationship between their health effects, their urinary arsenic patterns, and their DNA sequences. The findings from such research may prove particularly important as new clinical uses for arsenic compounds are emerging in the area of cancer treatment, where differences in metabolism and toxicity are important to oncologists and their patients.
New data on big and little dippers. Research in Mexico reveals differences in the way adults and children metabolize arsenic from drinking water.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00405AnnouncementsNIEHS Extramural UpdateAutism and the Environment? 6 2005 113 6 A405 A405 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders that emerge before 3 years of age and are characterized by impairments in social and communicative skills and the presence of stereotyped and repetitive behaviors and interests. The prevalence of ASD appears to have increased dramatically within the last decade. Intensive community-based surveys estimate that as many as 6 of 1,000 school-age children are affected. Although part of the increase can be attributed to changes in diagnosis and greater public awareness, there is concern that increased exposure to toxic environmental agents during critical periods of brain development may play a role.
Much of the existing data used to implicate environmental agents in ASD is limited by methodological shortcomings and has not addressed the issue of gene–environment interactions. In recognition of the public health importance of understanding autism and the lack of reliable data that bear on potential environmental etiologies, the NIEHS has taken steps to support research in this area. The largest effort has been through the NIEHS/EPA Centers for Children’s Environmental Health and Disease Prevention Research (http://www.niehs.nih.gov/translat/children/children.htm), where two centers focus on autism.
These centers, located at the University of California, Davis, and the University of Medicine and Dentistry of New Jersey, are conducting multidisciplinary studies to identify environmental and genetic risk factors in autism. Strong partnerships have been formed between community advocacy groups and center investigators and have been used to develop and refine the studies to be conducted. Ongoing projects include epidemiologic and clinical investigations of risk factors and the development of animal and cellular models to examine the interaction of candidate neurotoxicants with signaling pathways and molecules that have been implicated in autism.
In addition to these centers, the NIEHS works collaboratively with other NIH institutes to support broader autism initiatives and activities. As one example, Program Announcement (PA) 04-085, Research on Autism and Autism Spectrum Disorders (http://grants.nih.gov/grants/guide/pa-files/PA-04-085.html) describes the broad scope of autism research topics of interest to the NIH. The NIEHS encourages applications with a primary focus on environmental factors that may influence autism risk or phenotypic expression. Other topics may be suitable for support from other participating institutes.
Contact
Cindy Lawler, PhD |
[email protected]
Participants and their families at UC Davis M.I.N.D. Institute annual holiday party.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00406AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 6 2005 113 6 A406 A407 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Japan Society for the Promotion of Science: Opportunities for U.S. Scientists in Japan
The Japan Society for the Promotion of Science (JSPS) conducts fellowship programs for foreign researchers to promote international cooperation in and mutual understanding through scientific research in Japan. These programs provide opportunities for U.S. researchers to conduct cooperative research under their host researchers. Research applications are accepted at the John E. Fogarty International Center of the National Institutes of Health, which acts as a nominating authority for JSPS programs. As the eligibility requirements and application procedures are different for each fellowship, the following information describing eligibility and application procedures should be reviewed before submitting an application.
JSPS Invitation Fellowships.
The JSPS conducts short- and long-term programs under the Invitation Fellowship Program, funded by a subsidy from the Japanese government. The fellowship allows scientists employed at designated Japanese research institutions and laboratories to invite fellow researchers from the United States to Japan to participate in cooperative activities. These visits presuppose the existence of contacts between scientists in Japan and other countries, a condition considered favorable to the promotion of future scientific cooperation and exchange. Application deadline: 31 October 2005.
JSPS Postdoctoral Fellowships.
The JSPS conducts short- and long-term Postdoctoral Fellowships for Foreign Researchers. These fellowships were established to assist promising and highly qualified young researchers wishing to conduct research in Japan. The program is aimed at providing opportunities for such researchers to, under the guidance of their hosts, conduct cooperative research with leading research groups in Japanese universities and other institutions, thereby permitting them to advance their own research while stimulating Japanese academic circles—particularly young Japanese researchers—through close collaboration in scientific activities. The program’s intention is also for such collaboration to advance scientific research in the counterpart countries. Postdoctoral Fellowship (short-term) application deadline: 31 October 2005. Postdoctoral Fellowship (long-term) application deadline: 30 June 2005.
Contact: Maria “Mili” Ferreira, Division of International Training and Research, Fogarty International Center, NIH, 31 Center Dr, Bldg 31, Rm B2C39, MSC 2220, Bethesda, MD 20892-2220 USA, 301-594-9778, fax: 301-402-0779, e-mail:
[email protected].
Arsenic Research: The Grainger Challenge Prize for Sustainability
The National Academy of Engineering (NAE), supported by The Grainger Foundation, has established the Grainger Challenge Prize for Sustainability. The primary purpose of this “inducement prize” is to accelerate the development and dissemination of technologies to enhance social and environmental sustainability for the benefit of current and future generations. A complementary goal of the prize competition is to increase awareness among the U.S. engineering community of the importance of designing and engineering for sustainability, particularly in an international context, and to encourage and showcase efforts by U.S. engineers to bring sustainable technologies to the marketplace and promote green design philosophies. The recipient(s) of the prize will be awarded US$1 million.
The specific goal of this competition, which may be followed by future prize competitions in like amounts for comparable goals, will be the development of a community- or household-scale water treatment system to remove arsenic from the contaminated groundwater found in many developing countries. The system must have a low life-cycle cost; must be technically robust, reliable, maintainable, socially acceptable, and affordable; must be manufactured and serviced in a developing country; and must not degrade other water quality characteristics.
Arsenic contamination has affected millions of people, primarily in rural Bangladesh, as well as in eastern India, Nepal, and several other countries. In Bangladesh, the arsenic is an unintended consequence of an aggressive international program to control the spread of cholera spread through surface waters by drilling thousands of tubewells. Unfortunately, the tubewells tapped into aquifers containing hundreds of parts per billion (ppb) of naturally occurring arsenic, usually within 100 meters of the surface.
Efforts to solve this problem have been under way for a decade, but no single solution has been implemented on a large scale. Field and laboratory tests have been conducted on technologies to determine if they are affordable, robust, and meet World Health Organization (WHO) water quality standards for a treatment system that can be used either in individual homes or at the community level. The intent of the NAE/Grainger Foundation competition is to encourage the U.S. engineering community to become engaged in finding a solution to this specific challenge.
Technical Performance.
The successful technology should address the potable water needs of a rural community of approximately 1,000 residents (roughly 200–300 families). Daily per capita potable water demand is assumed to be 7.5 liters as recommended by the WHO. Such a community might be served by a community-level system, with water piped to convenient distribution points or into homes, or by hand-operated tubewell pumps spread throughout the community. Arsenic can be removed and controlled either at a central community plant or at the household level.
In typical villages, women and children usually fetch water. Therefore a treatment process will depend largely on women’s and children’s ability to use the system easily and conveniently. However, community-level systems could be run by a trained operator. Contestants may submit proposals for either type of system.
As the reference standard of contamination, specially formulated test water will be used with an arsenic concentration of 300 ppb. This level of contamination is considered to be in the midrange in severely affected areas. The system must reduce this concentration to 10 ppb—presently considered the most acceptable standard by the U.S. Environmental Protection Agency and the WHO—without causing deterioration in other water quality characteristics such as taste and odor, or resulting in an increase in fecal coliforms or other contaminants. The exact formulation of the test water and the complete testing protocol will be made available by the NAE. Contestants may assume that electricity is available for community-level systems because rural areas in many developing countries do have electric power. However, a point-of-use system that does not require electricity will also be considered. If electricity is required, the full life-cycle cost of providing it in all locations must be factored into the economic feasibility estimate.
Economic Performance/Scalability.
On a per-person basis, the winner will maximize the volume per unit time while minimizing the cost and arsenic content. Because 10 ppb is the WHO standard, no extra credit will be given for attaining lower concentrations. Obviously, this metric allows for some tradeoffs. However, the other constraints described in the Technical Performance section above must not be compromised.
All system components should be capable of being manufactured and serviced locally at the village level. Contestants must develop a business plan to demonstrate how this might be done in a specific rural environment and must demonstrate the potential for scale-up to thousands of units. Contestant must also have a plan for disseminating the technology through a nongovernmental organization, the private market, or government, and for covering all costs. All technology developed by the contestants will remain their property. The NAE will hold all information conveyed to it by the contestants in confidence, except that the winning contestant must agree to disclose the winning technology at the time of announcement of the award in February 2007. It is the sole responsibility of all contestants to apply for such patent protection as they deem necessary.
Social Acceptability.
Social acceptability must be demonstrated either through carefully documented field experience and/or by drawing upon existing research in social/behavioral science relevant to the challenge. Each contestant must submit a credible field monitoring plan describing how well the system is expected to work, and over what period of time and at what level of maintenance. The plan should also pinpoint expected operational problems and explain how they might be handled.
Judging.
Entries will be judged by a panel of NAE members appointed by the NAE Council. Panel members will be prominent members of the engineering, scientific, industrial, government, educational, and environmental communities. The panel will base its selection of a winner on the criteria outlined in the three requirements sections above with the assistance of specialists familiar with the arsenic problem.
Under the conditions of the NAE/Grainger Foundation Prize Agreement, the Grainger Prize is available only to a U.S. citizen or team of U.S. citizens, who must be living at the time of notification of selection as prize recipients in the United States. All team members must agree and stipulate in writing in advance how they wish the prize money to be distributed. Such stipulation, once made, shall be irrevocable and unchangeable. The NAE accepts no responsibility for adjudicating disputes of any kind among team members. All expenses incurred by the contestants in pursuit of the prize are their responsibility. For the most up-to-date announcement, see http://www.nae.edu/nae/grainger.nsf/weblinks/NAEW-68HUZT?OpenDocument.
Contact: Jack Fritz, NAE, 500 Fifth Street NW, Washington DC 20001 USA, 202-334-2491, e-mail:
[email protected].
Global Research Initiative Program, Social Science
As part of its global health initiative, the John E. Fogarty International Center (FIC) of the National Institutes of Health (NIH), in partnership with the National Eye Institute (NEI), the National Heart, Lung, and Blood Institute (NHLBI), the National Institute on Drug Abuse (NIDA), the NIEHS, the National Institute of General Medical Sciences (NIGMS), the Office of Behavioral and Social Sciences Research (OBSSR), the Office of Dietary Supplements (ODS), and the Office of Research on Women’s Health (ORWH), invites applications from current and former NIH-supported foreign research trainees to compete for funds that will support their research efforts upon return to their home countries.
As junior scientists complete training programs in the United States, many find it difficult to secure the support needed to continue their research projects and careers in their home countries. This Global Research Initiative Program (GRIP) provides the opportunity for junior foreign scientists to compete for such funds through a peer-reviewed process. This is a critical adjunct in the continuation of promising independent research careers that will be of benefit to the investigators’ home countries and the world at large. Women and underrepresented minority scientists in their countries are especially encouraged to apply for these reentry grants. Project proposals should be geared toward the research interests of the applicant and focus on high-priority health and health care problems in the investigator’s home country that also carry global importance, and are of interest to the collaborating institutes, centers, and offices.
In order to be eligible, foreign scientists must meet at least one of the following criteria: 1) at least two years of research training experience under an FIC-supported training grant; 2) one year of such training experience coupled with one year of significant, well-documented mentored research experience; 3) one year of the NIDA INVEST Fellowship plus at least one additional year of mentored research (http://www.drugabuse.gov/International/HHHRF.html); 4) at least two years of research training experience through the NIH intramural Visiting Fellows Program; 5) one year of training through an f5 international fellowship program and one subsequent year of mentored research; 6) be a recipient of a Long-Term Fellowship award through the Human Frontier Science Program, who comes from a low- or middle-income country, and who has spent at least two years in research training; or 7) at least one year of training in the United States and one additional year of significantly mentored research, in the United States or abroad, leading to a completed master’s degree or doctoral degree, at least partially funded through an FIC research training program, with preapproval by the program director.
It is expected that research topics will be diverse. Please refer to the full program announcement at http://grants.nih.gov/grants/guide/pa-files/PAR-05-082.html for more information on specific research topics of interest. All research must be performed in accordance with NIH and U.S. government regulations regarding the responsible conduct of research. This program precludes the support of research involving enrollment in pilot studies for clinical trials or the actual support of clinical trials since the resources and infrastructure to support and oversee such trials generally exceed the resources available under this award mechanism.
Evaluation of the program will occur on an ongoing basis. Because this is a program to move research trainees to the status of independent investigator, there are several outcomes to be measured: 1) development of laboratory capabilities or research projects; 2) training of other potential researchers; 3) publications in local as well as international peer-reviewed journals; 4) participation in workshops, seminars, and international conferences; 5) collaborations with past mentors, as well as with other researchers; and 6) attraction of funding from other sources.
This funding opportunity will use the R01 award mechanism. An applicant can request up to two modules of $25,000 each or total direct costs of $50,000 per year, plus facilities and administrative costs to a maximum of 8% for a foreign institution. Applications may have a project period of no less than three years and no more than five years. Because an investigator can receive a maximum of five years of support under the GRIP program, and this specific GRIP award is not renewable, any future application will be considered to be an unsolicited competing application based on this project and will compete with all investigator-initiated applications submitted to NIH through the Center for Scientific Review.
Applications must be prepared using the PHS 398 research grant application instructions and forms. Applications must have a Dun and Bradstreet (D&B) Data Universal Numbering System number as the universal identifier when applying for federal grants or cooperative agreements. The D&B number can be obtained by calling -866-705-5711 or online at http://www.dnb.com/us/. For further assistance contact GrantsInfo by calling 301-435-0714 (telecommunications for the hearing impaired: TTY 301-451-0088) or by e-mail:
[email protected].
The letters of intent receipt dates for this PAR are 22 August 2005; 21 August 2006, and 21 August 2007, with the application receipt dates 21 September 2005; 21 September 2006; and 21 September 2007. The earliest anticipated start date for these awards is July of the year following the receipt date.
Contact: Aron Primack, Division of International Training and Research, FIC, Bldg 31, Rm B2C39, 31 Center Dr, MSC 2220, Bethesda, MD 20892-2220 USA, 301-496-4596, fax: 301-402-0779, e-mail:
[email protected]; Chyren Hunter, Retinal Neurosciences and Oculomotor Systems Program, Division of Extramural Research, NEI, 5635 Fishers Ln, MSC 9300, Ste 1300, Bethesda, MD 20892-9300 USA, 301-451-2020, fax: 301-402-0528, e-mail:
[email protected]; Ruth Johnsson Hegyeli, Office of the Director, NHLBI, NIH, 31 Center Dr, Rm 4A07, Bethesda, MD 20892-2490 USA, 301-496-5375, fax: 301-496-2734, e-mail:
[email protected]; Steven Gust, International Programs, NIDA, 6001 Executive Blvd, Rm 5-274, Bethesda, MD 20892-9581 USA, 301-443-6480, fax: 301-443-9127, e-mail:
[email protected]; Dennis Lang, Division of Extramural Research and Training, NIEHS, PO Box 12233, MD EC-20, Research Triangle Park, NC 27709 USA, 919-541-7729, fax: 919-541-2583, e-mail:
[email protected]; Ann A. Hagan, NIGMS, 45 Center Dr, Rm 2AN24H, Bethesda, MD 20892-6200 USA, 301-594-4499, fax: 301-480-1852, e-mail:
[email protected]; Virginia Cain, Office of the Director, OBSSR, Bldg 1, Rm 256, 1 Center Dr, Bethesda, MD 20892 USA, 301-402-1146, fax: 301-402-1150, e-mail:
[email protected]; Mary Frances Picciano, ODS, 6100 Executive Blvd, Ste 3B01, Bethesda, MD 20892-7517 USA, 301-435-3608, fax: 301-480-1845, e-mail:
[email protected]; Lisa Begg, Office of the Director, ORWH, 1 Center Dr, Rm 201, Bethesda, MD 20892 USA, 301-402-1770, fax: 301-402-1798, e-mail:
[email protected]. Reference: PAR-05-082
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0364a15929875PerspectivesCorrespondenceArsenic on the Hands of Children Kissel John C. Department of Occupational and Environmental Health Sciences, University of Washington, Seattle, Washington, E-mail:
[email protected] author declares he has no competing financial interests.
6 2005 113 6 A364 A364 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Kwon et al. (2004) reported significantly elevated dislodgeable soluble arsenic loads on (one or both?) hands of children following play on structures treated with chromated copper arsenate (CCA) but then concluded that the observed difference is unimportant:
With a safe conservative assumption that all the arsenic on children’s hands is ingested, the measured value is below the estimated average daily intake of inorganic arsenic from water and food ….
However, Kwon et al.’s analysis is not conservative for at least two reasons. First, it is likely that they substantially underestimated arsenic on hands. Kwon et al. reported, but apparently did not actually measure, total arsenic on hands. They washed hands, filtered the wash water, and measured soluble arsenic in the filtrate. Insoluble residue was measured as dry mass gain on the filters. They then estimated insoluble arsenic on hands as the product of the average arsenic concentration in playground sand samples (not solids recovered from hands) and filter dry weight gain. I did not arrive at this conclusion because the procedures are clearly described in the paper but because a) there is no discussion of extraction of filters and b) the ratios of minimum, mean, median, and maximum “sand arsenic” on hands to minimum, mean, median, and maximum sand mass recovered from hands are nearly constant and equivalent (in all cases but one) to the mean concentration reported for each playground.
This procedure could easily give a very poor estimate of insoluble arsenic on hands because unfractionated 0- to 6-in sand samples are likely to be a poor surrogate for adherent particles. The filter residue from the hand-wash water probably contained at least some wood particles with much higher arsenic concentrations and lower densities than the playground sand. Hemond and Solo-Gabriele (2004) reviewed studies in which (typically adult) human hands were used to deliberately wipe CCA-treated lumber and reported much higher arsenic residues on hands than found by Kwon et al. (2004). One obvious potential explanation is that the arsenic concentration in material dislodged from CCA-treated wood (Nico et al. 2004) can easily be 1,000-fold higher than the 2–3 ppm found by Kwon et al. in playground sand.
Second, the observed loads that Kwon et al. (2004) reported may be greatly influenced by the very activity they wish to assess. That is, mass recoverable at any given time reflects net accumulation and does not include material already ingested. Consider the following simplified model of mass accumulation on hands:
where A = area (in square centimeters), L = load (in milligrams per square centimeter), G = net gain in the absence of ingestion (addition minus losses other than ingestion; in milligrams per hour), and king = a first order rate constant describing ingestion (per hour).
At steady state,
and
Assuming reasonable efficiency of washing, Kwon et al. (2004) provided a measure of the product of the two variables on the left hand side (for soluble arsenic). They have not measured either of the variables on the right hand side. In the absence of knowledge of king, they guessed. Because an infinite number of paired values of G and king can be selected to match the available data, large values of king are not excluded. Hence any reassuring conclusion based on this work is a reflection of the assumed rate at which hand residues are orally harvested and not of the reported measurements.
Kwon et al. (2004) further concluded that
Most of the arsenic on children’s hands is water soluble and is readily washed off with water. We recommend that children wash their hands after playing to reduce their potential exposure to arsenic.
Again, this conclusion is not supported by evidence presented in the article. To evaluate efficiency of washing, some measure of the initial mass present is required. Kwon et al. measured removable soluble arsenic and estimated removable insoluble arsenic. They did not measure or estimate either soluble or insoluble arsenic remaining on the hands. Because insoluble arsenic bound to soil or wood is likely to be at least partially removed mechanically by washing regardless of solubilization, washing is probably a good strategy. However, that argument is merely logical rather than empirical and could have been made in the absence of Kwon et al.’s experiments.
Kwon et al. (2004) stated that the purpose of their study was to provide “direct measurement of arsenic levels on the hands of children in contact with … CCA-treated wood ….” Given that arsenic is amenable to biomonitoring via urine, comparable urine samples from children who do and do not play on CCA-treated structures are what is most needed. Then perhaps we would be able to stop guessing about ingestion rates.
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References
Hemond HF Solo-Gabriele HM 2004 Children’s exposure to arsenic from CCA-treated wooden decks and playground structures Risk Anal 24 1 51 64 15028000
Kwon E Zhang H Wang Z Jhangri GS Lu X Fok N 2004 Arsenic on the hands of children after playing in playgrounds Environ Health Perspect 112 1375 1380 15471728
Nico PS Fendorf SE Lowney YW Holm SE Ruby MV 2004 Chemical structure of arsenic and chromium in CCA-treated wood: implications of environmental weathering Environ Sci Technol 38 19 5253 5260 15506225
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0364b15929875PerspectivesCorrespondenceArsenic on the Hands of Children: Wang et al. Respond Wang Zhongwen Kwon Elena Zhang Hongquan Jhangri Gian S. Lu Xiufen Li Xing-Fang Le X. Chris Department of Public Health Sciences, University of Alberta, Edmonton, Alberta, Canada, E-mail:
[email protected] Nelson Environmental Health, Capital Health, Edmonton, Alberta, CanadaGabos Stephan Health Surveillance Branch, Alberta Health and Wellness, Edmonton, Alberta, CanadaThe authors declare they have no competing financial interests.
6 2005 113 6 A364 A365 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In our study of arsenic on children’s hands (Kwon et al. 2004), we measured arsenic in water samples in which participating children washed both hands after playing on selected playgrounds. The hand-washing water was filtered, and the soluble arsenic concentration in the filtrate was determined by inductively coupled plasma mass spectrometry. In response to Kissel’s comment that we did not measure insoluble arsenic, we analyzed the arsenic levels in the insoluble residue collected on the filter and summarized the unpublished data here. Results from the analysis of 64 samples from the CCA playgrounds and another 63 samples from the non-CCA playgrounds are available upon request. The total amount of arsenic in the insoluble residue collected in the hand-washing water of 64 children from the eight CCA playgrounds was 418 ± 601 ng (mean ±SD), compared to 172 ± 278 ng in the hand-washing water of 63 children from the eight non-CCA playgrounds. The total arsenic collected in the hand-washing water (insoluble arsenic on the filter plus water-soluble arsenic in the filtrate) was 934 ± 940 ng for the CCA playground and 265 ± 311 ng for the non-CCA playgrounds. The maximum amount of total arsenic collected from children’s hands was 4,743 ng (4.7 μg). This is compared with the 3.9 μg that we reported previously (Kwon et al. 2004).
To provide a perspective of relative contribution of this amount of arsenic to the overall exposure to arsenic, in our article (Kwon et al. 2004) we included references for the average daily dietary ingestion of total arsenic:
38 μg (15 μg for children 1–4 years of age) for Canada (Dabeka et al. 1993), 62 μg for the United States (Gartrell et al. [1985]), 89 μg for the United Kingdom (Food Additives and Contaminants Committee 1984), 55 μg for New Zealand (Dick et al. 1978), and 160–280 μg for Japan (Tsuda et al. 1995). A range of arsenic species that have different toxicities may be present in food (Le et al. 2004). Estimated daily dietary intake of inorganic arsenic was 8.3–14 μg in the United States (Yost et al. 1998), 4.8–12.7 μg in Canada (Yost et al. 1998), and 15–211 μg in Taiwan (Schoof et al. 1998).
We did not monitor children’s hand-to-mouth activity because this behavior has already been documented in the literature (Reed et al. 1999; Tulve et al. 2002). Our intent was to provide direct measurements of the amount of arsenic on children’s hands. We recognize the importance of these other studies, as we pointed out in our “Conclusions” (Kwon et al. 2004):
The results—along with other information, such as the frequency and habit of hand-to-mouth activity, efficiency of transfer of arsenic from hands to mouth, and repeated contact of hands with CCA-treated wood surface after hand-to-mouth activity—are useful for assessing children’s exposure to arsenic.
We have measured arsenic in sequential hand-washings and found that most arsenic was present in the first hand-washing (unpublished data). Results of arsenic in hand-washings of three children before and after playing on a CCA playground are available upon request. The amount of arsenic in the second washing was < 10% of that in the first washing, suggesting that the arsenic on children’s hands is readily washed off with water. Therefore, we conclude that children should “wash their hands after playing to reduce their potential exposure to arsenic” (Kwon et al. 2004).
Biomonitoring of arsenic species in urine samples from children who play on CCA-treated structures and children who do not could be useful if the ingestion of arsenic from dietary sources would not be a major confounder.
==== Refs
References
Dabeka RW McKenzie AD Lacroix GM Cleroux C Bowe S Graham RA 1993 Survey of arsenic in total diet food composites and estimation of the dietary intake of arsenic by Canadian adults and children J AOAC Int 76 14 25 8448438
Dick GL Hughes JT Mitchell JW Davidson F 1978 Survey of trace elements and pesticide residues in the New Zealand diet. 1. Trace element content NZ J Sci 21 57 69
Food Additives and Contaminants Committee 1984. Report on the Review of the Arsenic in Food Regulations. Ministry of Agriculture, Fisheries and Foods, FAC/REP/39. London:Her Majesty’s Stationery Office.
Gartrell MJ Craun JC Podrebarac DS Gunderson EL 1985 Pesticides, selected elements, and other chemicals in adult total diet samples, October 1979-September 1980 J Assoc Off Anal Chem 68 1184 1197 4086442
Kwon E Zhang H Wang Z Lu X Jhangri GS Fok N 2004 Arsenic on the hands of children after playing in playgrounds Environ Health Perspect 112 1375 1380 15471728
Le XC Lu X Li X-F 2004 Arsenic speciation Anal Chem 76 26A 33A
Reed KJ Jimenez M Freeman NCG Lioy PJ 1999 Quantification of children’s hand and mouthing activities through a videotaping methodology J Expo Anal Environ Epidemiol 9 5 513 520 10554153
Schoof RA Yost LJ Crecelius E Irgolic K Goessler W Guo HR 1998 Dietary arsenic intake in Taiwanese districts with elevated arsenic in drinking water Human Ecol Risk Assess 4 117 135
Tulve NS Suggs JC McCurdy T Cohen Hubal EA Moya J 2002 Frequency of mouthing behavior in young children J Expo Anal Environ Epidemiol 12 259 264 12087432
Tsuda T Inoue T Kojima M Aoki S 1995 Market basket and duplicate portion estimation of dietary intakes of cadmium, mercury, arsenic, copper, manganese, and zinc by Japanese adults J AOAC Int 78 1363 1368 8664571
Yost LJ Schoof RA Aucoin R 1998 Intake of inorganic arsenic in the North American diet Hum Ecol Risk Assess 4 137 152
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0371aEnvironewsForumInfectious Disease: ExPECting the Worst Phillips Melissa Lee 6 2005 113 6 A371 A371 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Dramatic media reports have alerted the public to the dangers of foodborne pathogens such as Salmonella and Escherichia coli O157:H7. But less-publicized microbes may soon become serious public health threats as well. Two foodborne bacteria, extraintestinal pathogenic E. coli (ExPEC) and antimicrobial-resistant Campylobacter, are becoming more prevalent, according to studies published in the 1 April 2005 Journal of Infectious Diseases.
ExPEC causes millions of urinary tract infections and an estimated 36,000 sepsis deaths each year in the United States alone, and untold numbers globally. ExPEC can live in the gut, but—unlike other classes of E. coli—causes infection only if it travels to other parts of the body. ExPEC can live in the intestine for weeks without causing symptoms before inducing illness elsewhere in the body, says Kirk Smith, supervisor of the Foodborne, Vectorborne, and Zoonotic Disease Unit of the Minnesota Department of Health. This delay in onset can create the illusion that an infection is caused by something other than a foodborne pathogen.
In the first Journal of Infectious Diseases paper, Smith and colleagues at the University of Minnesota–Twin Cities report their analysis of E. coli contamination in foods they bought at 10 Minneapolis–St. Paul markets between 2001 and 2003. They found E. coli in 24% of the 1,648 items sampled, including 92% of poultry items, 69% of beef and pork items, and 9% of ready-to-eat foods such as produce, cheeses, and delicatessen items. Almost half of the E. coli found in poultry products was ExPEC; about one-fifth of the E. coli from beef and pork and a small percentage of that from ready-to-eat foods was ExPEC.
The number of E. coli organisms found in each food sample was relatively low, says Sita Tatini, a professor emeritus of food science and nutrition at the University of Minnesota and senior author of the paper. However, ExPEC’s virulence factors—the properties that permit it to infect tissue—allow even a small number of bacteria to cause disease, Tatini says.
The scientists also found that 94% of poultry samples contaminated with E. coli contained a strain that was resistant to at least one antibiotic. They isolated resistant strains from 85% of E. coli–contaminated beef and pork and from 27% of E. coli–contaminated ready-to-eat items.
The second paper focused on drug-resistant strains of Campylobacter. Kåre Mølbak, director of the Department of Epidemiology at the Statens Serum Institut in Copenhagen, examined the clinical effects of human infection with Campylobacter strains resistant to quinolones and erythromycin.
By accessing the Danish government’s national registry of patient admissions and discharges, Mølbak and his colleagues were able to track the outcomes of about 3,500 people who were diagnosed with Campylobacter infections between 1996 and 2000. Within 30 days of infection, patients with quinolone-resistant infections were more than six times as likely as patients infected with susceptible strains to die or suffer an invasive illness such as meningitis, abscess, pancreatitis, or hepatitis. Within 90 days of infection, patients with erythromycin-resistant infections were more than five times as likely to die or to be diagnosed with an invasive illness.
Antibiotic overuse by people is just one reason why we’re now seeing more antibiotic-resistant microbes, says Wondwossen Abebe Gebreyes, an assistant professor of food safety and molecular epidemiology at North Carolina State University in Raleigh. Farmers in many countries use antibiotics not only to treat or prevent infection but also to promote growth of healthy animals. Fluoroquinolones have been used in human medicine since the 1980s, but it was not until farmers began to use them to treat animal infection in the 1990s that resistant bacterial strains appeared. In some countries, quinolone-resistant Campylobacter species are now more common than quinolone-susceptible strains.
In Denmark, the prevalence of Campylobacter species resistant to macrolide-class antibiotics such as erythromycin has dropped since 1998, when all growth promoters, including macrolides, were banned from use in livestock. “That’s really good news,” says Mølbak, “because that suggests that if you change the policy—for example, improve hygiene and management practices rather than give the animals antibiotics—then you are able to reverse the situation.” Use of fluoroquinolones is limited but not banned in Danish livestock.
Indeed, in most countries, antibiotic use on farms is on the rise, and so is antibiotic-resistant bacterial infection in humans, says Martin Blaser, chair of medicine at New York University and president-elect of the Infectious Diseases Society of America. Resistance has “been recognized as a cost of antibiotic use for more than fifty years,” Blaser says. “As a society, we’re using more and more [antibiotics], so it’s not surprising that resistance is growing.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0371bEnvironewsForumThe Beat Dooley Erin E. 6 2005 113 6 A371 A373 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Seaweed for Safety
Researchers from Oregon State University and Northeastern University have found that certain red seaweeds including Portieria hornemannii and Acrosiphonia coalita can detoxify organic pollutants such as TNT and polycyclic aromatic hydrocarbons 5–10 times faster than any known terrestrial plant. TNT is found at the sites of sunken warships, while polycyclic aromatic hydrocarbons are emitted from watercraft. The scientists, who presented their research at the 2005 annual meeting of the American Association for the Advancement of Science, see their discovery possibly playing a role in seafood safety, with marine seaweeds being planted around aquaculture beds to protect oysters, clams, and other bioaccumulators from contamination.
Nations’ Environmental Efforts Ranked
In January 2005 the second Environmental Sustainability Index was released, ranking 146 nations on their environmental stewardship efforts. Prepared by researchers at Yale and Columbia, the index is based on 75 measures, including hazardous waste generation, pesticide consumption, participation in international environmental agreements, and carbon emissions. The first index, which came out in 2002, was a wakeup call for countries, inspiring some to improve their performance. South Korea, for example, moved up 13 spots between the first and second indexes.
Finland, Norway, and Uruguay took the top three positions, while the United States ranked 45th. The lowest-ranking country was North Korea. The report cited a significant correlation between higher ranking and countries having open political systems and effective governments.
Sustainable Wildcrafting in Nepal
In Nepal, approximately 15,000 tons of medicinal plants are collected for export each year by villagers who often receive less than a living wage for their work and are encouraged by unscrupulous buyers to strip plant supplies. A coalition of Nepalese and U.S. product buyers, advocacy groups, and donors was set up in 2002 to promote sustainable collection among villagers and responsible buying among western purchasers, with certification as one incentive. These efforts are paying off: in January 2005 the Federation of Community Forestry Users, Nepal, received certification from the Rainforest Alliance for its handmade paper and herbal products. The federation’s members manage community forests by sustainable principles and supply wildcrafted ingredients to the international herbal, medicinal, and natural products industries.
Obesity Cuts Longevity
The surge of obesity, especially among children and adolescents, could shorten life expectancy in the United States by 2–5 years, reversing the steady rise in longevity of the past two centuries, says a data analysis published in the 17 March 2005 New England Journal of Medicine. The predictions are based on data from the Third National Health and Nutrition Examination Survey and previously published reports on estimated years of life lost from obesity. Current trends indicate that rates of obesity will continue to rise, and that ever-younger age groups will be affected. The surge in obesity has already triggered a sharp rise in type 2 (“adult-onset”) diabetes mellitus in children.
EU Holds Firm on REACH
On 4 April 2005 the European Commission announced its plan to introduce its controversial Registration, Evaluation, and Authorisation of Chemicals (REACH) policy for consideration by the European Parliament. The policy calls for the chemical industry to provide safety data for 30,000 chemicals. Business leaders, citing costs for testing, had urged the commission to require rigorous testing only for the 4,000–5,000 chemicals that pose the greatest risks. An impact study published 27 April 2005 did confirm that the policy could be pricey for businesses. But EU environment commissioner Stavros Dimas said he is convinced that the plan strikes the proper balance between protecting environmental and human health and protecting business interests. REACH was first proposed in October 2003 [see “REACHing for Chemical Safety,” EHP 111:A766–A769 (2003)]. A final decision is expected in early 2006.
CFCs: A Dying Breed
China and Venezuela have pledged to phase out the use and production of chlorofluorocarbons (CFCs) by the end of 2007, two years earlier than required by the Montréal Protocol on Substances that Deplete the Ozone Layer. A total of US$26.5 million from the protocol’s Multilateral Fund has been allocated to finance the phaseouts. With these pledges, production of more than 100,000 tons of CFCs will be eliminated each year, and the use of CFCs in developing countries will end. China is the world’s largest producer and consumer of CFCs, which are used as coolants, solvents, and propellants.
Protection against further degradation of the ozone layer should prevent millions of cases of skin cancer and cataracts resulting from harmful ultraviolet rays reaching the Earth’s surface. The phaseout also means fewer emissions contributing to global warming.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0372aEnvironewsForumAsthma: A Gut Reaction to Antibiotics Potera Carol 6 2005 113 6 A372 A372 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Is the explosive rise in asthma and allergies being seen especially in children partially related to antibiotic use? Epidemiologic studies have found strong connections between antibiotic treatment and the later development of asthma and allergies. Yet, until recently, no studies had looked at how the two are linked. Now researchers at the University of Michigan in Ann Arbor have created a mouse model that offers clues to the mechanism behind the association.
Immunologist Gary Huffnagle and colleagues are the first to demonstrate in a mouse model that the disruption of beneficial intestinal bacteria by antibiotics allows yeast to take hold and flourish. They developed their mouse model specifically to study the relationship between antibiotic use and allergies. When mice inhale fungal spores known to trigger allergies in people, the allergic reaction is more potent in mice with an overgrowth of yeast in their guts.
In their studies, the Michigan researchers first treat mice for several days with the broad-spectrum antibiotic cefoperazone to destroy the gut flora. Then the mice are fed Candida albicans, a yeast that commonly lives in people. “This represents the clinical scenario of getting a yeast infection after taking antibiotics,” says Huffnagle. Next, the mice are exposed nasally to spores of the mold Aspergillus fumigatus (a major indoor contaminant) and to egg white protein.
Results are showing that both allergens produce significant increases in inflammation-related white blood cells in the lungs of the mice, and they elevate blood levels of key markers of allergic reactions, including IgE, interleukin-5, and interleukin-13. Mice not treated with antibiotics show much milder reactions to the allergens. The team’s latest report appears in the January 2005 issue of Infection and Immunity. Future work with the model will investigate the actions of other antibiotics (such as amoxicillin) and allergens (such as pollen and dust mites).
How do changes in gut flora influence respiratory allergies? The answer likely involves oral tolerance, Huffnagle theorizes. Upon ingestion of allergens, the oral mucosa generate regulatory T cells, which circulate to the respiratory tract, where they suppress allergic reactions. “We live in a dirty world, and we swallow mold spores, pollen, dust, and other allergens constantly,” says Huffnagle. These oral allergens trigger immune responses that instruct the rest of the body to be more tolerant of allergens so allergic reactions don’t occur. Moreover, other studies have indicated that mice lacking gut flora cannot generate oral tolerance. When the gut flora are restored, oral tolerance returns.
Huffnagle plans to evaluate over-the-counter probiotics—concentrated supplements of beneficial bacteria—to identify which, if any, work best for replenishing gut flora. “[Probiotics are a] relatively new concept, and there’s not a lot of precedent for their use now,” says infectious disease specialist Bruce Klein of the University of Wisconsin–Madison. If future studies show that probiotics do replace flora, Klein adds, physicians may be inclined to recommend their use. Eating yogurt with live cultures also remains a good way to replenish gut flora following a course of antibiotics.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0372bEnvironewsForumNeurology: Triple Threat Activates Neurons Potera Carol 6 2005 113 6 A372 A372 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Scientists from the Marine Biological Laboratory in Woods Hole, Massachusetts, have reported on a potentially sinister synergy, showing that a combination of three common pollutants—bromoform, chloroform, and tetrachloroethylene—alters nerve cell development, whereas the toxicants alone or in pairs do not. The discovery is an intriguing first step toward understanding whether this trio of pollutants is linked to neurological disorders such as autism.
Carol Reinisch, an expert in chemical-induced neurotoxicity, had read in the scientific literature about the contamination of municipal drinking water in Brick Township, New Jersey, and its possible connection to higher autism rates in local children. Chemical wastes dumped at the town’s landfill over the years had contaminated nearby wells with bromoform, chloroform, and tetrachloroethylene, and in 1983, the U.S. Environmental Protection Agency declared the landfill a Superfund site. In the 1990s, autism rates in the town started rising, and researchers from the Centers for Disease Control and the Agency for Toxic Substances and Disease Registry began to investigate in 1998. Although the incidence of autism was twice the national average, the federal scientists concluded in 2000 that the levels of individual well water contaminants were too low to adversely impact children’s health.
Reinisch wondered whether the synergistic effect of the chemicals would tell a different story. Her lab was already using a surf clam (Spisula solidissima) embryo model to assess how polychlorinated biphenyls affect embryonic neuronal development. The transparency of the embryos and the fact that most basic molecular processes involved in early development are conserved across species make the surf clam a good model for such studies. She and her colleagues began studying the three well-water contaminants in combination.
When tested alone or in pairs, the toxicants produced no significant changes, even at levels 1,000 times those in the mixture. But the trio acted synergistically to upregulate a regulatory subunit of cAMP-dependent protein kinase, a ubiquitous protein involved in neurologic pathways and a key regulator of neuronal growth in the clam embryo model. The clam embryos also showed increased cilia movement. The study appears in the January 2005 issue of Environmental Toxicology and Pharmacology.
“The fact that several events are speeded up is abnormal,” says Reinisch. Coauthor Jill Kreiling, a developmental biologist, adds, “We found something unusual going on neurologically, but we cannot say this is causing autism.”
Now the team is testing the mixture in zebrafish embryos, and their preliminary results parallel those for clam embryos. They hope others will undertake experiments in mice, rats, and higher mammals in order to confirm the association.
Studying mixtures of toxicants yields a more accurate picture of how contaminants work in the environment. “Most risk assessments look at single chemicals acting on single target organs with single outcomes, but that’s not the way [exposures] work in nature,” says Nigel Fields, a research program manager at the Environmental Protection Agency, which funded Reinisch’s project.
Brain teaser. The embryo of the surf clam yields intriguing clues to a potential neuronal threat.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0403aEnvironewsScience SelectionsNo Magic Bullet: Tungsten Alloy Munitions Pose Unforeseen Threat Schmidt Charles W. 6 2005 113 6 A403 A403 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In response to concerns about the human and environmental health effects of materials used to produce munitions, countries including the United States have begun replacing some lead- and depleted uranium–based munitions with alternatives made of a tungsten alloy. But this solution may not be the “magic bullet” it was once envisioned to be. Researchers from the Armed Forces Radiobiology Research Institute and the Walter Reed Army Institute of Research now report that weapons-grade tungsten alloy produces aggressive metastatic tumors when surgically implanted into the muscles of rats [EHP
113:729–734]. These findings raise new questions about the possible consequences of tungsten exposure, and undermine the view that tungsten alloy is a nontoxic alternative to depleted uranium and lead.
In the study, male F344 rats were implanted with pellets in each hind leg, an exposure protocol that mimicked shrapnel wounds received in the field. The rats were split into four treatment groups: a negative control implanted with 10 pellets of tantalum (an inert metal), a positive control implanted with 10 pellets of nickel (a known carcinogen), a high-dose group implanted with 10 pellets of tungsten alloy, and a low-dose group implanted with 4 pellets of tungsten alloy and 16 pellets of tantalum. The alloy used in this research was the same as that used in weapons: 91.1% tungsten, 6.0% nickel, and 2.9% cobalt.
By 6 months after implantation all the rats in the high-dose, low-dose, and positive control groups had developed leg tumors. None of the rats in the negative control developed tumors, and all survived beyond 12 months with no apparent health effects. All remaining rats were sacrificed at 24 months.
At sacrifice, blood samples were assessed for a range of hematologic parameters. The high-dose group exhibited statistically significant increases in levels of white blood cells, red blood cells, hemoglobin, and hematocrit as compared to the low-dose and control groups.
The rats also underwent a pathology exam, and tissues were collected for histology. Whereas the tantalum pellets in the low-dose group were surrounded by normal tissue, all of the tungsten alloy and nickel pellets were surrounded by tumors. Tumors in the tungsten alloy–treated animals metastasized to the lung. Histology further indicated that tungsten alloy pellets were surrounded by invasive neoplastic cells that had infiltrated into skeletal muscle tissue. No metastasis was observed in the positive controls.
Organ measurements identified significant increases in both spleen and thymus body-to-weight ratios in the high-dose group only. Both these organs are components of the immune system, leading the authors to suggest that embedded tungsten alloy may be immunotoxic at certain concentrations.
The authors write that the amounts of cobalt (a suspected human carcinogen) and nickel in the tungsten alloy material likely were too small to produce the effects seen in the two groups implanted with the alloy. However, they do cite recent evidence indicating that the combination of these metals may produce synergistic effects. The biological mechanism by which embedded tungsten alloy produces tumors is unclear, they add, and warrants further study.
“Better” bullets? New data show that tungsten alloy, used in munitions in hopes it would be an environmentally friendlier alternative to lead and depleted uranium, causes tumors in animals.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0403bEnvironewsScience SelectionsRoundup Revelation: Weed Killer Adjuvants May Boost Toxicity Bonn Dorothy 6 2005 113 6 A403 A404 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Although the glyphosate-based herbicide Roundup is generally thought to be less toxic to the ecosystem than other pesticides, concerns about its effects on human reproduction persist. In a study in Ontario, Canada, exposure of male farmers to glyphosate-based herbicides was associated with an increase in miscarriage and premature birth in farm families. Seeking an explanation for these pregnancy-related problems, researchers at France’s Université de Caen investigated the effects of the full Roundup formulation and glyphosate alone on cultured human placental cells [EHP
113:716–720]. The herbicide, they found, killed the cells at concentrations far below those used in agricultural practice. Surprisingly, they also found that Roundup was at least twice as toxic as glyphosate alone.
Virtually all previous testing of Roundup for long-term health damage has been done on glyphosate rather than on the full herbicide formulation, of which glyphosate makes up only around 40%. The remainder consists of inactive ingredients including adjuvants, chemicals that are added to improve the performance of the active ingredient. Roundup’s main adjuvant is the surfactant polyethoxylated tallowamine, which helps glyphosate penetrate plant cells.
The Roundup concentration recommended for agricultural use is 1–2% in water. The authors incubated placental cells with various concentrations of Roundup (up to 2.0%) or equivalent concentrations of glyphosate. The viability of the cells was measured after 18, 24, and 48 hours. No one is sure how Roundup interferes with reproduction, so the team also tested whether it, like other pesticides, would disrupt the activity of aromatase (an enzyme that regulates estrogen synthesis) in placental cells. Aromatase activity was measured after 1 hour and 18 hours.
The researchers found that a 2.0% concentration of Roundup and an equivalent concentration of glyphosate killed 90% of the cultured cells after 18 hours’ incubation. The median lethal dose for Roundup (0.7%) was nearly half that for glyphosate, meaning Roundup was nearly twice as toxic as the single chemical alone. Further, the viability of cells exposed to glyphosate was considerably reduced when even minute dilutions of Roundup were added.
After an hour’s incubation with Roundup, estrogen synthesis in placental cells (as shown by aromatase activity) was enhanced by about 40%. After 18 hours, however, synthesis was inhibited, perhaps reflecting an effect on aromatase gene expression. This effect was not seen with glyphosate alone.
The study showed that the effect of Roundup on cell viability increased with time and was obtained with concentrations of the formulation 10 times lower than those recommended for agricultural use. Roundup also disrupted aromatase activity at concentrations 100 times lower than those used in agriculture. The researchers suspect that the adjuvants used in Roundup enhance the bioavailability and/or bioaccumulation of glyphosate.
How these findings translate into activity of Roundup in the human body is hard to say. The French researchers point out that serum proteins can bind to chemicals and reduce their availability—and therefore their toxicity—to cells. Nevertheless, the authors conclude that the demonstrated toxicity of Roundup, even at concentrations below those in agricultural use, could contribute to some reproduction problems.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0422a15929883AnnouncementsBook ReviewEnvironmental Health, Third Edition Greenberg Michael Michael Greenberg is professor and associate dean of the faculty at the E.J. Bloustein School of Planning and Public Policy, Rutgers University. His research and teaching focus on environmental health policy; much of it is on clean-up and reuse of contaminated sites in distressed neighborhoods.6 2005 113 6 A422 A422 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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By Dade W. Moeller
Cambridge, MA:Harvard University Press, 2005. 606 pp. ISBN: 0-674-01494-4, $65 cloth
At least once a week, I need a brief account of an environmental health area that falls outside my specialty. At those times, I usually am more likely to find what I need in Moeller’s second edition (1997) than in any of my other reference books. This third edition maintains the strengths of the two earlier editions: brief and clear presentations and broad coverage of environmental health.
This edition contains 20 chapters. It begins with an overview and traditional chapters on toxicology and epidemiology. These three are followed by reviews of water, air, and solid waste. About halfway through the 600-page book, the chapters begin to incorporate topics that some environmental health volumes do not cover, such as rodents and insects, food, injury control, environmental economics and law, risk assessment, standards and monitoring, energy, and disasters. A sizeable portion of this material is new or updated for this third edition. For example, you will find new or redone sections on indoor air quality, environmental justice, endangered species, multiple chemical sensitivities, electromagnetic radiation, disasters, and ergonomics. In other words, this book is environmental health in the broadest sense of the word: It is not just pollution control. At the end of each chapter, the author adds an interesting “general outlook” section that summarizes his views of the future.
Two of the most useful chapters are those on electromagnetic radiation and environmental law. The former begins with a description of the electromagnetic spectrum and uses that as a springboard to differentiate among risks associated with different forms of radiation. The chapter briefly reviews highly amplified risks about automobile traffic radar and cell phones and about electromagnetic fields from overhead lines. It also presents useful sections about radon, medical and dental applications, and nuclear materials from electricity-generating stations and nuclear weapons testing. The overall goal is to help readers understand that not all radiation is to be equally dreaded and that some of the most feared radioactive elements are less dangerous than their less feared counterparts. The chapter on environmental law is helpful because the author tries hard to be comprehensive. Moeller offers lists of laws, brief comments on what the laws do and do not accomplish, the trajectory of laws during the last century, and their implementation. This is followed immediately by a chapter on environmental standards that is a good sequel to the law chapter.
Every textbook has limitations. The most obvious is that it takes so long to write one that some of the material is bound to be out of date by the time the book is published. Hence, I would like to have seen more about globalization, pollution prevention, damages to natural resources, terrorism, and life-cycle costs than this edition offers. The author does not ignore these; they are mentioned here and there in the book, but not to the extent needed.
If I have a problem with the book, it is the failure to present a separate chapter on risk perception. I realize that environmental scientists want to operate on the premise that decisions are made using rational thinking processes based on scientific evidence. Unfortunately, as a person who has been studying environmental policy for over 30 years, I say unequivocally that risk perception, trust, and mental models of risk have a major impact on environmental health policies. A chapter needs to be added that includes why people dread some hazards (nuclear weapons, terrorism, handguns) and so their risk is amplified, whereas other hazards are much more tolerable to the public and to elected officials (tobacco, alcohol). Despite this critique, I really like this book, I use it myself, and I will use it in my senior-level undergraduate environmental health course.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0422b15929883AnnouncementsNew BooksNew Books 6 2005 113 6 A422 A422 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Asbestos and Fire
Rachel Maines
Piscataway, NJ:Rutgers University Press, 2005. 288 pp. ISBN: 0-8135-3575-1, $34.95
Chemical Genomics: Reviews and Protocols
Edward D. Zanders
Totowa, NJ:Humana Press, Inc., 2005. 300 pp. ISBN: 1-58829-399-8, $110
Computational Genetics and Genomics: Tools for Understanding Disease
Gary Peltz
Totowa, NJ:Humana Press, Inc., 2005. 286 pp. ISBN: 1-58829-187-1, $125
Epigenetics
Bruce Stillman, ed.
Woodbury, NY:Cold Spring Harbor Laboratory Press, 2005. 520 pp. ISBN: 0-87969-729-6, $295
Essential Mathematics and Statistics for Science
Graham Currell
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 344 pp. ISBN: 0-470-02228-0, $115.50
EU Environmental Law
Maria Lee
Oxford, UK:Hart Publishing, 2005. 270 pp. ISBN: 1-84113-410-4, $50
Handbook of Genome Research: Genomics, Proteomics, Metabolomics, Bioinformatics, Ethical and Legal Issues
Christoph W. Sensen
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 614 pp. ISBN: 3-527-31348-6, $355
Health Effects of Transport-Related Air Pollution
M. Krzyzanowski, B. Kuna-Dibbert, J. Schneider
Geneva:World Health Organization Press, 2005. 275 pp. ISBN: 92-890-1373-7, $54
Microarrays in Clinical Diagnostics
Thomas O. Joos, Paolo Fortina
Totowa, NJ:Humana Press, Inc., 2005. 300 pp. ISBN: 1-58829-394-7, $135
National Environmental Accounting: Bridging the Gap Between Ecology and Economy
Joy E. Hecht
Washington, DC:RFF Press, 2005. 240 pp. ISBN: 1-891853-93-7, $60
Pharmacogenomics: Methods and Applications
Federico Innocenti
Totowa, NJ:Humana Press, Inc., 2005. 250 pp. ISBN: 1-58829-440-4, $125
Proteomics in Cancer Research
Daniel C. Liebler, Emanuel F. Petricoin, Lance A. Liotta
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 202 pp. ISBN: 0-471-44476-6, $150
Rational Choice and Judgment: Decision Analysis for the Decider
Rex Brown
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 245 pp. ISBN: 0-471-20237-1, $79.95
Sprawl Costs: Economic Impacts of Unchecked Development
Robert Burchell, Anthony Downs, Sahan Mukherji
Washington, DC:Island Press, 2005. 224 pp. ISBN: 1-55963-530-4, $18.75
Sustainable Energy: Choosing among Options
Jefferson W. Tester, Elisabeth M. Drake, Michael J. Driscoll, Michael W. Golay, William A. Peters
Cambridge, MA:MIT Press, 2005. 872 pp. ISBN: 0-262-20153-4, $78
The Evolution of the Genome
T. Gregory
Burlington, MA:Elsevier, 2005. 768 pp. ISBN: 0-12-301463-8, $69.95
The Oncogenomics Handbook
William J. LaRochelle, Richard A. Shimkets
Totowa, NJ:Humana Press, Inc., 2005. 752 pp. ISBN: 1-58829-425-0, $195
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Totowa, NJ:Humana Press, Inc., 2005. 1,016 pp. ISBN: 1-58829-343-2, $175
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Water Quality Hazards and Dispersion of Pollutants
Wlodzimierz Czernuszenko, Pawel Rowinski
New York:Springer-Verlag, 2005. 250 pp. ISBN: 0-387-23321-0, $139
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7716ehp0113-00080916002366CommentariesVinyl Chloride: A Case Study of Data Suppression and Misrepresentation Sass Jennifer Beth 1Castleman Barry 2Wallinga David 31Natural Resources Defense Council, Washington, DC, USA2Environmental Consultant, Garrett Park, Maryland, USA3Institute for Agriculture and Trade Policy, Minneapolis, Minnesota, USAAddress correspondence to J.B. Sass, Natural Resources Defense Council, 1200 New York Ave. NW, Suite 400, Washington, DC 20005 USA. Telephone: (202) 289-6868. Fax: (202) 289-1060. E-mail:
[email protected] gratefully acknowledge funding for this work from the Beldon Fund.
J.B.S and D.W. are employed by environmental nonprofit organizations with an interest in ensuring that regulations of toxic chemicals are as health protective as feasible. B.C. is an independent consultant in toxic substances control and has no competing financial interests regarding the subject matter of this paper.
7 2005 24 3 2005 113 7 809 812 1 11 2004 24 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. When the U.S. Environmental Protection Agency (EPA) finalized its 2000 update of the toxicological effects of vinyl chloride (VC), it was concerned with two issues: the classification of VC as a carcinogen and the numerical estimate of its potency. In this commentary we describe how the U.S. EPA review of VC toxicology, which was drafted with substantial input from the chemical industry, weakened safeguards on both points. First, the assessment downplays risks from all cancer sites other than the liver. Second, the estimate of cancer potency was reduced 10-fold from values previously used for environmental decision making, a finding that reduces the cost and extent of pollution reduction and cleanup measures. We suggest that this assessment reflects discredited scientific practices and recommend that the U.S. EPA reverse its trend toward ever-increasing collaborations with the regulated industries when generating scientific reviews and risk assessments.
angiosarcomacancercorporateEPAindustryIRISpolyvinyl chloridePVCU.S. Environmental Protection Agencyvinyl chloride
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Short History of Vinyl Chloride Regulation
Vinyl chloride (VC) is manufactured exclusively for polymerization into polyvinyl chloride (PVC), a plastic used in construction, packaging, electrical, and transportation industries; in household products such as flooring, water piping, videodiscs, and credit cards; and in medical products such as disposable intravenous bags, tubing, and bedpans. Global PVC production in 2002 was nearly 59 billion pounds (27 million metric tons), valued at approximately US$19 billion, with an average annual growth rate of 3% since 1997 (Linak and Yagi 2003). Approximately 15 billion pounds (7 million metric tons) of PVC was manufactured in the United States and Canada in 2002, primarily for domestic use (Linak and Yagi 2003). Pollution sources include production and fabrication, incineration, and landfills.
The first experimental evidence of VC carcinogenicity was reported in 1969 (Viola PL, unpublished data). Additional data were published in 1971 (Viola et al. 1971), followed in 1974–1975 by disclosure of rare liver cancers in workers (Creech and Johnson 1974; Creech and Makk 1975; Maltoni 1974, 1975; Maltoni et al. 1974). Upon release of these data, the U.S. Occupational Safety and Health Administration (OSHA) issued a notice effective April 1975 that VC and PVC production plants must reduce time-weighted average workplace exposure levels from 500 ppm to 1 ppm, to provide adequate worker protection (OSHA 1975).
When OSHA issued the new exposure limit of 1 ppm, industry spokespeople issued dire predictions of job loss and plant closures. However, in < 2 years virtually all U.S. manufacturing plants were able to meet the new standard while still maintaining rapid growth of sales volume. This was accomplished largely through better containment of unpolymerized VC monomer and improved exposure monitoring (OSHA 1975).
Early Suppression of Evidence of Liver Damage
Industry leaders privately acknowledged that the existing limit of 500 ppm was excessive long before the OSHA standard (OSHA 1975). In 1959, internal industry experiments had revealed micropathology in rabbit livers after repeat exposures to 200 ppm VC monomer (Markowitz and Rosner 2002), causing Dow Chemical toxicologist V.K. Rowe (1959) to admit privately to his counterpart at B.F. Goodrich:
We feel quite confident … that 500 ppm is going to produce rather appreciable injury when inhaled 7 hours a day, five days a week, for an extended period. As you can appreciate, this opinion is not ready for dissemination yet and I would appreciate it if you would hold it in confidence but use it as you see fit in your own operations.
VC and PVC manufacturers also delayed public release of findings of liver angiosarcoma in VC-exposed rodents by Cesare Maltoni (Markowitz and Tosner 2002). In late 1972, the industry was briefed on Maltoni’s report of primary cancers of both liver and kidneys at exposures as low as 250 ppm, half the 500 ppm allowable exposure limit for workers. However, in a meeting with government officials 8 months later in the summer of 1973, industry representatives avoided any mention of Maltoni’s findings (Markowitz and Rosner 2002). The public learned of the deadly hazards of VC only in early 1974 through newspaper reports of the deaths of three workers in a B.F. Goodrich vinyl plant in Louisville, Kentucky (Creech and Johnson 1974). Like Maltoni’s experimental animals, the workers had liver angiosarcoma.
Evidence of Nonliver Cancer
In addition to evidence of liver cancer, starting in the 1970s the industry’s own studies described excess cancers in nonliver sites, including the respiratory system and the brain (Tabershaw and Gaffey 1974). In a 1976 interoffice memo, Mitchell Zavon, a physician with Ethyl Corporation, acknowledged that
At present, the epidemiological work has amply demonstrated an association between high exposures to VCM [vinyl chloride monomer] and an increase in angiosarcoma of the liver, brain and lung tumors. (Zavon 1976)
A scientific review by the International Agency for Research on Cancer (IARC 1979) found that
Vinyl chloride is a human carcinogen. Its target organs are the liver, brain, lung and haemo-lymphopoietic system … there is no evidence that there is an exposure level below which no increased risk of cancer would occur in humans.
A second IARC review in 1987 supported the previous evaluation, citing more recent data that, in addition to angiosarcoma of the liver, VC caused hepatocellular carcinoma, brain tumors, lung tumors, and malignancies of the lymphatic and hematopoietic system (IARC 1987).
After the IARC evaluation, the industry commissioned British epidemiologist Richard Doll to review the previously published VC epidemiology. Doll combined data from four studies finding an aggregated excess risk of brain cancer [29 observed vs. 19.54 expected, standardized mortality ratio (SMR) = 148; confidence limits were not reported]; he reported this as “not statistically significant” and “nothing to suggest that they are occupational in origin” (Doll 1988). Doll (1988) downplayed risk of cancer in all sites other than liver, concluding that
[T]he mortality of the exposed men, other than that due to angiosarcoma of the liver, is typical of the normally healthy industrial worker—that is not to say that no other hazard exists, but that the effect of any other hazard is small.
Doll did not acknowledge funding sources in his article (Doll 1988), but in a legal deposition taken in a toxic tort case brought by a worker dying of brain cancer, Doll testified for the defendants that his 1988 report was conducted “on behalf of the Chemical Manufacturers Association” for which Doll received 12,000 British pounds (~ US$21,000) as “a donation to a charity in recompense” for his work (Doll 2000). The charity Doll selected was the Green College at Oxford, of which Doll is the founder and first warden (president).
Evidence of VC-associated brain cancer continued to accumulate after 1988. A 1991 Chemical Manufacturers Association (CMA)–sponsored follow-up study by Wong et al. (1991) reported significant excess deaths from cancer of the brain and central nervous system [23 observed vs. 12.76 expected death; SMR = 180; 95% confidence interval (CI), 114–271]. Wong et al. (1991) concluded that “this update confirms the excess in cancer of the brain and [central nervous system].” In addition, they reported significant excess deaths from cancer of the liver and biliary tract combined (37 observed vs. 6 expected deaths; SMR = 641; 95% CI, 450–884), from liver cancer excluding angiosarcoma (15 observed vs. 3.0 expected deaths; SMR = 500, significant at 1% level), and from biliary tract cancer excluding angiosarcoma (7 observed vs. 2.7 expected deaths; SMR = 259, significant at 5% level).
Two years later, in a highly unusual reversal, two of the original four authors published a retraction, saying “we conclude that our finding of an excess of brain cancer among U.S. vinyl chloride workers reported earlier was not likely related to the chemical” (Wong and Whorton 1993). The Houston Chronicle described the retraction and the uses made of it:
Wong hadn’t received permission from the study’s sponsor, the Chemical Manufacturers Association, to publish his data—data that could be used against the industry in lawsuits, that might alarm workers and attract regulators. The unauthorized publication provoked members of the CMA’s Vinyl Chloride Panel and touched off a months-long effort to persuade Wong to recant, documents show. Although Wong denies that he was pressured, he changed his story on vinyl chloride, declaring that the apparent excess of brain cancer deaths among workers might well be the result of “diagnostic bias”—better reporting and diagnosis of the disease in industry than in the general population …. Reprints of the Wong and Shah letters were distributed among the chemical companies and their attorneys. They are still cited by defendants in brain cancer cases, and are used to reassure workers about the safety of vinyl chloride and polyvinyl chloride. (Morris 1998)
In 2000, for the fourth time, an industry-sponsored study of VC epidemiology found an excess of brain cancer among exposed workers (Doll 1988; Mundt et al. 2000; Tabershaw and Gaffey 1974; Wong et al. 1991). Mundt et al. (2000) reported an increase in brain cancer among exposed workers (SMR = 142; 95% CI, 100–197), with mortality from brain cancer showing the largest excess for study subjects with the longest work history, based on 22 deaths (SMR = 177; 95% CI, 111–268). Nonetheless, Mundt et al. (2000) concluded that the “risk of mortality from brain cancer has attenuated, but its relationship with exposure to vinyl chloride remains unclear.”
U.S. EPA Reassessment of VC Toxicology
Many of the U.S. Environmental Protection Agency (EPA) assessments of regulated chemicals are publicly available on its database, the Integrated Risk Information System (IRIS), which contains U.S. “EPA scientific consensus positions on potential human health effects from environmental contaminants” (U.S. EPA 1996). Although not a legal regulatory standard per se, such information is used by regulators at the state and federal level and by others worldwide in combination with exposure data to set cleanup standards and various exposure standards for air, water, soil, and food (Phibbs 2002). The widespread use of IRIS assessments is demonstrated by the fact that the database receives more than half a million visits monthly, from > 50 countries (IRIS 2005).
In 1994, the CMA’s Vinyl Chloride Panel initiated plans to work with the U.S. EPA on its IRIS assessment of VC. H.C. Shah, the industry panel manager, confirmed that the U.S. EPA “expressed an interest in working with industry to develop a scientifically-sound vinyl chloride risk assessment” (Shah 1994a, 1994b). At the meeting, CMA-sponsored scientists made presentations to the U.S. EPA on both the CMA-sponsored epidemiology and a prepublication risk model (Reitz and Gargas 1994; Shah 1994a, 1994b). The model, a physiologically based pharmacokinetic (PBPK) model, was designed to quantitatively express the relationship between external exposure to VC and internal dose at the liver, taking into account absorption, distribution, metabolism, and elimination of VC and its metabolites.
Although internal documents demonstrate that the U.S. EPA and the VC industry had been in joint discussions on an updated IRIS assessment of VC since 1994 (Shah 1994a, 1994b), it was not until 1996 that the U.S. EPA issued a public notice inviting submissions of technical information for VC and 10 other industrial chemicals to be assessed for the IRIS database (U.S. EPA 1996).
U.S. EPA Standard Based on Overall Risk of Liver Cancer, Not Overall Cancer Risk
As noted above, as early as 1994 the VC industry had been promoting PBPK models for use by the U.S. EPA in its VC assessment. Two such models were presented to the U.S. EPA for its VC risk assessment. The models predicted that VC was 150-fold less (Reitz and Gargas 1994; Reitz et al. 1996) and 80-fold less (Clewell et al. 1995, 2001) potent as a carcinogen than values used at the time for environmental decision making, implying that pollution and cleanup standards could be weakened significantly. The final IRIS assessment relied on the Clewell model (Clewell et al. 1995, 2001), but with adjustments such that VC was estimated by the U.S. EPA to be 10-fold less potent as a carcinogen. Although the model was developed using only liver angiosarcoma tumor data, cancer estimates for the U.S. EPA assessment were revised to include all liver tumors but exclude all nonliver tumors (U.S. EPA 2000a). Because exposure was not adequately characterized in the epidemiology studies, the U.S. EPA cancer potency estimates were based on animal bioassay data.
Both models were designed to model only VC’s effects on the liver, despite scientific consensus that it is a multisite carcinogen in humans and experimental animals (Byren et al. 1976; Cooper 1981; Drew et al. 1983; Feron et al. 1979; Hagmar et al. 1990; IARC 1979, 1987; Infante 1981; Maltoni and Lefemine 1975; Maltoni et al. 1981; Monson et al. 1974; Mundt et al. 2000; Smulevich et al. 1988; Tabershaw and Gaffey 1974; Wagoner et al. 1980; Waxweiler et al. 1976, 1981; Weber et al. 1981; Wong and Whorton 1993; Wong et al. 1991; Wu et al. 1989).
VC administered orally or by inhalation to mice, rats, and hamsters produced tumors in the mammary gland (Feron et al. 1981; Hong et al. 1981; IARC 1987), leading Clewell et al. (1995) to suggest that
it seems reasonable that the evidence of increased mammary tumor incidence from VC should be considered at least qualitatively during risk management decisions regarding potential human VC exposure.
In its May 1999 draft VC assessment, the U.S. EPA had proposed to apply a protective 3-fold factor to adjust for VC’s possible induction of nonliver tumors (U.S. EPA 1999a). However, in a letter to the U.S. EPA, chemical manufacturers protested that
[T]he available epidemiological evidence does not support an association between vinyl chloride exposure and human cancer except angiosarcoma of the liver. The ill-advised three-fold uncertainty factor introduced by EPA to account for possible tumor induction at such sites can therefore be eliminated. (Price 1999)
In response, the U.S. EPA final VC assessment completely eliminated the protective factor it had originally included (U.S. EPA 2000a). In the same letter to the U.S. EPA, chemical manufacturers disputed the U.S. EPA statement that there is “suggestive epidemiological evidence that cancer of the brain, lung, and lymphopoietic system are associated with exposure,” saying it “should be deleted from the final review” (Price 1999). The U.S. EPA complied (U.S. EPA 2000a).
The U.S. EPA assessment’s exclusion of risks to organs other than liver is striking. The U.S. EPA justifies this approach on two grounds: first, relying on the conclusions of Richard Doll that evidence for induction of nonliver tumors is weak (Doll 1988); and second, suggesting that the liver is the most sensitive end point and therefore regulatory standards protective of liver cancer would adequately protect all other sites from cancer risk (U.S. EPA 2000b). However, this limited view precludes the U.S. EPA from developing a standard based on an assessment of the total cancer risk to all organs from VC exposure, as required by U.S. EPA guidelines for calculating carcinogenic risk (U.S. EPA 1999b, 2005).
Downplaying risk to nonliver cancer sites leaves the public and exposed workers inadequately informed of the health threat posed by exposure to VC-containing products, processes, and pollution. Medical professionals are less likely to suspect a link to VC exposures in patients with nonliver cancers, and thus causal links are more likely to be overlooked. Downplaying of nonliver cancer risks by the U.S. EPA may also have important implications in litigation of compensation cases, because claims for cancers at sites other than the liver are vigorously disputed in the courts.
Peer Review Reflects Industry Participation
The U.S. EPA’s external peer review process is intended to ensure that a scientifically credible assessment is produced. However, at least 7 of the 19 external peer reviewers of the VC assessment were chemical industry employees and consultants, 4 were government representatives, and none represented unions or public interest groups (U.S. EPA 2000b). This committee accepted the assertion by the U.S. EPA that human exposure limits based on liver cancer would be sufficiently protective against cancer developing in other tissues. The committee rejected the use of any protective adjustment factor to account for the possibility of nonliver cancer risk (U.S. EPA 2000aa). As noted above, the final assessment made no adjustments for the possibility of cancer at nonliver sites.
The final VC assessment currently posted on the IRIS database (U.S. EPA 2000b) assigns a cancer risk from VC inhalation (8.8 × 10−6 risk per μg/m3; an excess of 8.8 cases per 1 million people exposed over a lifetime to an average of 1 μg/m3 VC) that is about 10-fold lower than the previous assessment (8.4 × 10−5 risk per μg/m3; an excess of 84 cases per 1 million people exposed over a lifetime to an average of 1 μg/m3 VC). As a result, allowable pollution levels may increase by 10-fold.
The Trend to Incorporate Industry Participation in U.S. EPA Scientific Assessments
For some of the most widespread and toxic chemicals under regulation, the manufacturers are generating much of the data (often unpublished) used for risk assessment and are working closely with the U.S. EPA to evaluate available data and produce risk assessments. Unfortunately, the efforts of the regulated industries often outweigh the ability of the public, unions, and public interest groups to participate in developing regulations. In a 2002 interview, Paul Gilman, at that time the science adviser to U.S. EPA Administrator Whitman, expressed dissatisfaction with the industry submissions for IRIS:
[I]t is taking staff as much or more time to work with the outside parties as it does to develop in-house toxicological reviews, Gilman said. To date, the process has not saved the time or resources it was designed to save. (Phibbs 2002)
Nonetheless, in late August 2004, the U.S. EPA announced changes to its pesticide review process “that would give industry officials greater input in the science behind its risk reviews … in an effort to reduce the agency’s review times” (Inside EPA 2004). The trend toward increasing industry participation allows corporate interests with products under regulation to more effectively recommend acceptable limits of public exposure to their own products and wastes, while placing an unrealistic burden on the U.S. EPA scientists and the public to provide adequate peer review and oversight. Public confidence is undermined when commercial interests, instead of scientific evaluations, shape public health policy.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7720ehp0113-00081316002367CommentariesPesticide Testing on Human Subjects: Weighing Benefits and Risks Resnik David B. 1Portier Christopher 21Office of the Scientific Director, and2National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USAAddress correspondence to D.B. Resnik, NIEHS/NIH, P.O. Box 12233, Mail Drop NH-06, Research Triangle Park, NC 27709 USA. Telephone: (919) 541-5658. Fax: (919) 541-3659. E-mail:
[email protected] thank P. Blackshear, W. Schrader, W. Stokes, and E. Zeiger for helpful comments and discussions.
The ideas and opinions in this article are the authors’ personal views and do not represent the views of the NIEHS, the NIH, or the U.S. government.
The authors declare they have no competing financial interests.
7 2005 16 3 2005 113 7 813 817 3 11 2004 16 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In the debate surrounding testing pesticides on human subjects, two distinct positions have emerged. The first position holds that pesticide experiments on human subjects should be allowed, but only under stringent scientific and ethical standards. The second position asserts that these experiments should never be allowed. In this article, we evaluate what we consider to be the strongest argument for the second position—namely, that the benefits of the experiments are not significant enough to justify the risks posed to healthy subjects. We challenge this argument by examining the benefits and risks of testing pesticides on human subjects. We argue that a study that intentionally exposes humans subjects to pesticides should be permitted if a) the knowledge gained from the study is expected to promote human health; b) the knowledge cannot be reasonably obtained by other means; c) the study is not expected to cause serious or irreversible harm to the subjects; and d) appropriate safeguards are in place to minimize harm to the subjects.
Environmental Protection AgencyethicsFood Quality Protection Acthuman subjects researchpesticide testing
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Background
Although private companies have tested pesticides on human subjects since the 1960s, the public debate about the ethics of such experiments began to simmer in 1998, when the Environmental Working Group (EWG) released a report titled The English Patients: Human Experiments and Pesticide Policy 1998. According to the report, the companies exposed volunteers to various insecticides to determine safety levels for exposure to these compounds. One of the experiments mentioned in the report involved the oral administration of dichlorvos to 53 subjects. Another experiment administered orange juice laced with aldicard to 47 subjects (EWG 1998). The media soon reported other pesticide experiments conducted elsewhere. In one experiment conducted by Novartis, managers for the company ingested diazinon. Experiments conducted by Novartis and Dow AgroSciences each used 60 paid volunteers (Gorovitz and Robertson 2000). In a study sponsored by Dow AgroSciences, dozens of college-age volunteers were paid $460 to swallow a pill containing chlorpyrifos, a roach poison (Shogren 2001).
The EWG report recommended that the U.S. Environmental Protection Agency (EPA) conduct a comprehensive review of its human research policies and issue a moratorium on the acceptance of data derived from privately funded (or third party) human experiments. In 2000, the U.S. EPA announced that it would not accept any pesticide data derived from privately funded toxicology research on human subjects until the ethical and regulatory issues were resolved (Lockwood 2004). In 2001, the U.S. EPA asked the National Research Council (NRC) to examine these issues; the U.S. EPA issued an Advance Notice of Proposed Rule-making in May 2003, before the NRC had completed its report (U.S. EPA 2003). In the notice, the U.S. EPA requested public comments on many different issues concerning industry-funded human studies submitted to the agency. The agency did not unconditionally endorse applicability of the Common Rule [Department of Health and Human Services (DHHS) 2001] to those studies, even though it has adopted the Common Rule for U.S. EPA–sponsored research (Silbergeld et al. 2004).
In February 2004, the NRC issued its report. It recommended that privately funded human dosing experiments for U.S. EPA regulatory purposes can be conducted only if they meet strict scientific and ethical standards and provide a public health or environmental benefit. It also recommended that the Common Rule should also apply to such research (NRC 2004). The NRC recommended that institutional review boards (IRBs) should review all proposed experiments to determine whether they meet appropriate scientific and ethical standards and that the U.S. EPA should establish a special review board to oversee these types of experiments. The NRC also stated that the U.S. EPA should not accept data from previous experiments, which it said did not meet scientific and ethical standards (NRC 2004).
On 3 November 2004, the U.S. EPA released a draft of a proposed plan for human testing. In the proposed plan, the U.S. EPA announced that it would evaluate data from industry-sponsored studies on a case-by-case basis “applying statutory requirements, the Common Rule, and high ethical standards as a guide, until such time as this practice is replaced by a rulemaking” (U.S. EPA 2004a, p. 6664). As soon as the U.S. EPA made this announcement, some commentators faulted the proposed plan for lack of consistency and enforceability (Associated Press 2004). However, the plan has helped clarify the U.S. EPA’s position on human testing by signaling its commitment to adhering to the Common Rule for all human experiments. The U.S. EPA plans to issue guidance for third-party researchers for adherence to the Common Rule and develop a final rule by 2006.
A variety of laws, including the Federal Food, Drug and Cosmetic Act (1999), the Federal Insecticide, Fungicide and Rodenticide Act (1964), and the Toxic Substances Control Act (1999) grant the U.S. EPA authority to regulate human exposures to environmental toxins in the United States, including pesticide residues on foods and in food additives. The U.S. EPA establishes safety levels for exposure to pesticides through a process known as pesticide registration (U.S. EPA 2004b). Before a manufacturer can sell a pesticide, it must register it with the U.S. EPA. In registering a pesticide, the U.S. EPA determines allowable human exposures of the pesticide, based on data submitted by pesticide manufacturers and federal agencies, as well as its own research. In arriving at an acceptable exposure, the U.S. EPA considers exposures from different sources, such as agricultural work and ingestion of food with traces of pesticides, as well as cumulative exposures (NRC 2004). Users of the pesticide, such as farmers and applicators, are required to comply with the U.S. EPA’s requirements for allowable human exposures.
The Food Quality Protection Act (FQPA), which President Clinton signed in 1996, amended existing laws pertaining to the U.S. EPA. Before the FQPA, the U.S. EPA regulated allowable pesticide exposure in food based on the no observable adverse effect level (NOAEL) in animal studies. After establishing a NOAEL in animals (usually rodents), the U.S. EPA would usually add a 10-fold interspecies safety factor to allow for differences between animals and humans, and a 10-fold intraspecies safety factor to account for variation in sensitivities among humans. Thus, the allowable exposure in human beings would usually be no more than 1% of the NOAEL exposure. The FQPA mandated an additional 10-fold increase in safety to account for variations between adults and children when there are no data to support a smaller safety factor. Therefore, under the FQPA, many chemicals would have an allowable exposure of no more than 0.1% of the NOAEL in animals. This change in the allowable exposure would have a significant impact not only on pesticide companies but also on agriculture, which depends heavily on pesticides. In implementing the law, the U.S. EPA has focused on 40 different organophosphates, which have been used to kill insects for many years.
Faced with higher safety standards for a variety of chemicals, some pesticide companies decided to conduct experiments on human subjects to produce data that they hoped would convince the U.S. EPA to lower the interspecies safety factor. From 1996 to 2004, the U.S. EPA received 20 studies from private companies providing human dosing data on pesticide toxicity (U.S. EPA 2004a). Thus, a law that was intended to provide additional safety protection for children had the unintended effect of encouraging some companies to test toxic compounds on human beings to avoid the regulatory impact of the law.
In the public debate surrounding pesticide testing on human subjects, two distinct positions have crystallized (Robertson and Gorovitz 2000). The first position, adopted by the NRC and others (NRC 2004; Oleskey et al. 2004), holds that pesticide testing on human subjects can be conducted, but only under the most stringent scientific and ethical standards, such as favorable benefit–risk ratios, informed consent, equitable subject selection, risk minimization, valid study design, and scientific necessity. The second position, adopted by environmental and public health interest groups, maintains that these experiments should be prohibited (Children’s Environmental Health Network 1999; EWG 1998; Sharav 2003).
In this commentary, we evaluate what we consider to be the strongest argument for prohibiting any testing of pesticides on human subjects—namely, that the benefits of the experiments are not significant enough to justify the risks posed to healthy subjects. We challenge this argument by exploring the benefits of pesticide testing for human health, discussing the scientific necessity of some experiments, and proposing ways to reduce the risks to subjects. We are not commenting on the studies that have been conducted. We accept Lockwood’s (2004) analysis that at least six of the human dosing studies submitted to the U.S. EPA were scientifically and ethically flawed. We are concerned here with the broader question of whether any type of experiment that intentionally exposes human subjects to pesticides can meet scientific and ethical standards.
Benefits versus Risks in Research
One of the most important principles of ethical research is that the risks to the subjects must be justified by virtue of the benefits to the subject and to society (Emanuel et al. 2000; Levine 1988; Nuremberg Code 1949; World Medical Association 2000). The Common Rule codifies this principle: “Risks to subjects are reasonable in relation to anticipated benefits, if any, to subjects, and the importance of the knowledge that may reasonably be expected to result” [Common Rule (DHHS 2001)]. If the benefits of testing pesticides on human subjects do not outweigh the risks, then these experiments should not be conducted.
To determine whether the benefits of an experiment outweigh its risks, one must consider both sides of the benefit–risk ratio. In the experiments we are considering here, the subjects would be healthy individuals who would not stand to benefit medically or psychologically from participation. They may benefit economically from participation, but most agencies and commentators hold that it is not ethically appropriate to consider a financial incentive to participate in an experiment as a potential benefit in calculating the benefit–risk ratio [Food and Drug Administration (FDA) 1998; NIH 2004]. Because the subjects do not stand to benefit from the experiments, the benefits of these experiments hinge on their potential benefits to society, which are based on the value of the knowledge produced.
Social Value
The principle that human experiments should have some redeeming social value has been an essential principle in human experimentation since the adoption of the Nuremberg Code (1949). Opponents of the pesticide experiments have argued that these experiments do not have any significant benefits for society. According to the EWG (1998, p. 13), “the degree to which society as a whole benefits from the use of specific pesticides, and pesticides generally, is the subject of heated debate. It is not obvious that these debatable social benefits alone would justify experimental risks to humans.” Richard Wiles, vice president for research for the EWG, also challenges the social benefits of the research: “This is not research designed to find a cure for a disease or to generate a new scientific advance” (Kamenetsky 2003, p. 1).
Even though the disputed experiments would not be designed to diagnose, treat, or prevent a disease, they could yield knowledge about the toxic effects of pesticides on humans, which could promote human health (NRC 2004). First, the knowledge obtained from the experiments could be used by the U.S. EPA to impose stricter safety standards on the chemicals under investigation. In some situations, a more than 10-fold interspecies safety factor may be required to protect the general human population or susceptible subpopulations (Cranor 1997). For this outcome to happen, it is important that the experiments have sufficient statistical power to demonstrate that a greater (or less) than 10-fold interspecies safety factor is needed for a particular chemical. Because pesticide companies, like drug companies, would have a strong financial motive for not reporting unfavorable results, steps should be taken to ensure that they do not suppress such findings (Angell 2004). All data from such studies submitted to the U.S. EPA should be publicly available within a reasonable time after completion of the studies.
Second, knowledge about how pesticides affect human beings can be useful in addressing human health issues outside of the U.S. EPA’s regulatory authority. People are exposed to pesticides in variety of different contexts, such as exposure from vehicles and clothing; exposure in public places that use pesticides; and exposure in the air, soil, and water. Knowledge about how pesticides affect human beings could be useful in taking measures to reduce pesticide exposure in areas that lie beyond the U.S. EPA’s domain and could encourage Congress to adopt new legislation to protect the public from pesticides.
Third, the proposed experiments may contribute to our understanding of the usefulness of animal models in toxicology testing because they would allow researchers to compare human and animal data. In toxicology research, scientists draw conclusions about the impacts of chemical on human health based on experiments in animals. For example, chemicals may be classified as carcinogens if they cause cancer in laboratory animals. Although animal models play an essential role in all toxicology testing, they do have some limitations due to differences in genetics, anatomy, and physiology between humans and different animal species (Brent 2004; Swanson et al. 2004). Understanding limitations of animal models may contribute to human health by improving our knowledge of the toxic effects of chemicals in human beings and contributing to effective regulation of pesticides, pharmaceuticals, and other compounds.
A critic of the studies might admit that there are some potential benefits from testing pesticides on human subjects, yet still maintain that the benefits are not great enough. One might argue that the benefits must be at least as great as the potential benefits of research that exposes healthy subjects to an equivalent amount of risk, such as Phase I clinical trials of new pharmaceuticals. We address this objection more fully below, when we evaluate the risks of human pesticide testing. At this juncture, however, we would like to point out that new drugs are not always beneficial, and that some cause a greater deal of harm, as demonstrated by Merck’s decision to withdraw Vioxx from the market (Miller 2005). In deciding whether to approve a new drug, the Food and Drug Administration weighs benefits and risks of the drug. If the risks are high, then the benefits must also be high. If the risks are low, then the benefits do not have to be as high. We argue below that the risks of some types of pesticide experiments, if implemented and monitored properly, can be low enough to justify the use of human subjects.
Scientific Necessity
If the knowledge produced by pesticide experiments has some social value, the benefits of the experiments will not outweigh the risks if the knowledge can be obtained by other means. One of the key principles of research ethics is that human beings should not be used in experiments if those experiments are not scientifically necessary (Emanuel et al. 2000; Nuremberg Code 1949). If an experiment is not scientifically necessary, then the risks of the experiment outweigh the benefits of the experiment (Levine 1988). Critics of pesticide testing on human subjects hold that there is no need to conduct these experiments because scientists can obtain adequate data from experiments on animals, as well as studies on human beings that do not involve controlled experiments, such as epidemiologic or field studies (EWG 1998).
Without a doubt, epidemiologic studies and field studies can provide useful information about the effects of pesticides on human health. For example, an epidemiologic study by Kato et al. (2004) examined 376 cases and 463 controls from a cancer registry to determine whether pesticide exposure increases the risk of non-Hodgkin lymphoma (NHL) in women. The study found that women who worked on a farm where pesticides were used for at least 10 years had twice the risk of NHL in relation to a comparable group of women who did have this pesticide exposure. A similar epidemiologic study conducted by McDuffie et al. (2001) examined 517 cases and 1,506 controls of Canadian men from a variety of occupations. The study concluded that NHL is associated with several different pesticides. A field study conducted by Aprea et al. (1997) measured pesticides in the urine of agricultural workers 1, 5, and 11 days after exposure to pesticides during vine spraying and leaf thinning. The study compared the agricultural workers to a control group of 46 people who did not have the same exposure. Aprea et al. (1997) found that pesticide excretion was positively correlated with pesticide exposure, with the peak pesticide excretion the night after exposure. Coronado et al. (2004) performed a similar type of study, using a random sample of agricultural workers and their children. They measured pesticide residues and pesticide excretion in urine.
Although these studies and others like them provide scientists, clinicians, public health practitioners, and regulators with important knowledge, they have some limitations. First, they have many different uncontrolled variables that can confound data analysis and interpretation. In all of these studies, subjects were exposed to more than one type of pesticide as well as to many other types of potentially toxic chemicals. Exposures also were not uniform. The subjects had variations in diet, tobacco use, environmental temperature, water intake, alcohol use, and other factors that can affect health. Although epidemiologic and field studies can establish patterns and correlations, they cannot adequately prove causation. Kato et al. (2004) were careful to point out that their study showed the pesticides increase the risk of NHL but do not cause the disease. The randomized, controlled clinical trial is the gold standard for proving causation in clinical research (Sackett et al. 1997). Controlled trials also offer the best data concerning the effects of pesticides in humans.
Second, to conduct epidemiologic or field studies of pesticides, the products must already be on the market because one cannot measure natural exposures to a chemical that people are not using. Thus, epidemiologic and field studies do not provide regulators or clinicians with any information about a pesticide before its introduction. It would often be important to have better information about a pesticide before human populations are exposed to that pesticide, because this information could help promote human health and safety. Although the U.S. EPA examines animal data before making decisions about new compounds, the agency could also benefit from having access to human data.
The NRC (2004) recommended that three types of experiments on human beings could provide information not obtainable by other methods or means: a) pharmacokinetic (PK) studies, which are designed to elucidate how pesticides are absorbed, metabolized, and eliminated by the human body; b) pharmacodynamic (PD) studies, which are designed to elucidate how pesticides affect human physiology via their action on biomarkers; and c) studies that examine the psychological and behavioral effects of pesticides, such as nausea, dizziness, fatigue, or headache. According to the NRC (2004), the first two types of studies could be conducted at very low doses that would pose very low risks to subjects. The third type of study poses risks to human subjects, which can be minimized through proper population selection and protocol design, according to the NRC (2004).
We disagree somewhat with the NRC on these issues. For all these types of studies, it is possible to develop field studies, like the one conducted by Aprea et al. (1997), that are ethically less troubling than an intentional dosing study. One can take advantage of the fact that people expose themselves to pesticides to design experiments that measure the effects of pesticides on human beings. For example, carefully assessing blood concentrations before field entry by agricultural workers, followed by multiple time-point blood concentrations on leaving the field, could be used to determine overall absorption and elimination kinetics. Matching data from this type of study with PD measurements could eliminate the need for a clinical study that intentionally exposes individuals to pesticides. Although this type of study has many of the methodologic difficulties associated with classical epidemiology studies, such as confounding variables and bias, and some additional medical concerns, such as conducting the research in the field rather than in a clinical setting, it creates less of an ethical problem than an intentional dosing study because the subjects are already exposed to pesticides in their daily lives. These studies would pose few additional risks to subjects beyond those that they would already face in their environment.
Using field studies to obtain pesticide data has an important limitation, however: They do not provide information about pesticides that are not being used at all or that are not being used frequently enough to obtain reliable data. For the method to work, one must be able to recruit enough subjects to obtain reliable and statistically significant data. If one wants to obtain human data on a pesticide that is not being used at all or that is being used infrequently, one must intentionally expose human subjects to the chemical. Thus, we believe there are good reasons to conduct studies on pesticides that have not been introduced to the market or are not being used frequently enough to obtain reliable data from field studies. Only these types of intentional dosing studies are scientifically necessary.
Risk and Safety
If the experiments have social value and are scientifically necessary, they will still not be ethical unless the risks are low enough to yield a favorable benefit–risk ratio. The benefits of the experiments, though significant, are probably not as high as the benefits of a clinical study on a new medical therapy. Could the risks be kept low enough that the benefits would outweigh them? To address this question, it is important to understand the dosing regimen of the studies and compare it to the dosing regimen used in Phase I trials on healthy subjects. We realize that the comparison to Phase I drug trials is not completely apt, because pesticides will not be used to diagnose, treat, or prevent human diseases. However, we make the comparison as a way of understanding aspects of the studies related to toxic chemical exposures.
A Phase I study occurs after extensive animal testing to determine whether the drug is safe enough to test on human subjects. The goal of a Phase I trial of a new drug is to determine its safety for human use. Phase I studies usually are conducted on healthy volunteers, although some Phase I studies are conducted on very ill subjects, such as patients with advanced cancer. Phase I studies follow a dose-escalation regimen designed to determine the maximum tolerable dose (MTD). The MTD for a particular subject is the dose at which the drug causes toxicity or at which the subject experiences intolerable symptoms, such as nausea, pain, or difficulty breathing. The pesticide experiments that we have been discussing would be designed not to measure the MTD for a chemical, but to measure the NOAEL (i.e., the level of exposure to the chemical at which the subject has no observable adverse effects). To measure the NOAEL, the experiments escalate the exposure level until some predefined effect is observed, such as an effect on a biomarker, specific levels of the chemical in the subject’s blood or urine, or symptoms such as nausea, dizziness, or headache. The adverse effects could be measured by giving the subjects very low exposures and then stopping the escalation as soon an adverse effect is observed.
Would these types of experiments be safe enough? The NRC (2004) concluded that studies to measure NOAELs for pesticides would probably be at least as safe as studies designed to measure MTDs for drugs. One might argue that short-term risks of exposing people to low levels of pesticide would be lower than the risks of exposing people to toxic levels of drugs, since an observable adverse effect is safer than toxicity. But what about the long-term effects of pesticide experiments? Unfortunately, we are not aware of any data on the long-term risks of intentionally exposing human subjects to low doses of pesticides for a short period of time. However, data from other types of studies indicate that there could be some significant long-term risks of limited exposures to pesticides because pesticides can induce mutations that cause cancer and may have adverse impacts on the neuromuscular, cardiovascular, and endocrine systems (NRC 2004). To minimize long-term risks from intentionally exposing human subjects to pesticides, we recommend that human subjects should not be exposed to pesticides that are known carcinogens or that are known to cause permanent damage to human tissues or organs in low doses.
We agree with the NRC (2004) that pesticide experiments on human subjects should not be conducted if the pesticides are expected to cause serious or irreversible harm to human subjects. The experiments can be conducted only if the harms they are expected to produce are not serious and are reversible. For example, the presence of a pesticide in the blood or urine is an effect that is not serious and is reversible because the body will continue to eliminate the pesticide. Tissue or organ damage, however, might be serious or irreversible. We also think that the burden of proof should be on the researchers to prove that a proposed study is not expected to produce effects that are serious or irreversible. IRBs should assume that intentionally exposing human subjects to even small doses of pesticides may produce serious or irreversible effects, unless the researchers produce evidence to the contrary.
To minimize all of the risks from the experiments discussed herein, we recommend the following safety measures, most of which have also been endorsed by the NRC (2004):
The experiments should take place in a clinical setting, supervised by medical personnel.
Subjects should be carefully selected and monitored.
The studies should exclude subjects who are pregnant, are unhealthy, or have significant pesticide exposures in their daily lives.
Extensive animal testing should take place to determine exposure levels that are not likely to cause any serious or permanent damage to subjects.
Escalation of exposure levels should proceed cautiously and stop as soon as a well-defined, observable adverse effect is detected or as soon as the expected maximum human exposure in food, water, or the environment is achieved.
Independent data and safety monitoring boards (DSMBs) should be established to monitor risks to subjects and protect them from harm.
Researchers should have a clear definition of an “adverse event” and immediately report adverse events to the IRB, the DSMB, research sponsors, and the U.S. EPA.
Subjects should be fully informed of the risks of participation.
Conclusion
The strongest argument against any pesticide testing on human subjects is that the benefits of the research do not outweigh the risks. [In our supplemental material (http://ehp.niehs.nih.gov/members/2005/7720/suppl.pdf), we evaluate three other arguments against testing pesticides on human subjects.] In this article, we have attempted to rebut this argument by showing that in some types of studies, the benefits would outweigh the risks. Such studies must meet at least four stringent conditions [the supplemental material (http://ehp.niehs.nih.gov/members/2005/7720/suppl.pdf) contains a more complete list]:
The knowledge gained from the study is expected to promote human health.
The knowledge cannot be reasonably obtained by other means.
The study is not expected to cause serious or irreversible harm to the subjects.
Appropriate safeguards are in place to minimize harm to the subjects.
Because we think that some of the experiments discussed in this article could meet these conditions, we do not support a ban on experiments that intentionally expose human subjects to pesticides, and we support the U.S. EPA’s decision to move forward with rule making and guidance in this area.
Supplemental material is available online (http://ehp.niehs.nih.gov/members/2005/7720/suppl.pdf)
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7732ehp0113-00081816002368CommentariesUshering in the New Toxicology: Toxicogenomics and the Public Interest Balbus John M. Environmental Defense, Washington, DC, USAAddress correspondence to J.M. Balbus, Environmental Defense, 1875 Connecticut Ave. NW, #600, Washington, DC 20009 USA. Telephone: (202) 572-3316. Fax: (202) 234-6049. E-mail:
[email protected] thank D. Triola, who assisted with the interviews described in this article. In addition, E. Silbergeld, G. Lucier, and W. Farland provided critical guidance to the project from which this article is derived.
This work was partially supported by a grant from the Beldon Foundation. Opinions expressed are solely those of the author.
The author is employed by the non-profit organization Environmental Defense.
7 2005 24 3 2005 113 7 818 822 5 11 2004 24 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. New scientific tools spawned by the genomics revolution promise to improve our ability to identify causative factors in human diseases. But as these new tools elucidate the complex interactions between chemical toxins and biologic systems, the strain on traditional ways of understanding toxic effects grows. Despite major advances in the science and technology of these new toxicogenomics tools, scientific and political complexities threaten to delay the use of toxicogenomics to further the public interest or—worse—to advance its use initially to weaken the regulation and safety of widely used chemicals. To gain further insight into the scientific and political landscape of the new toxicology, we interviewed 27 experts from a variety of disciplines and sectors. Interviewees expressed widespread agreement that the new toxicology promises a significant increase in the amount of information available on toxic effects of chemicals. But the interviews show that the promise of the new toxicology will be realized only if technical and political obstacles can be overcome. Although scientific rigor is necessary for the new toxicology to move forward, the scientific and public-interest communities must ensure that inappropriate definitions of rigor, as well as proprietary interests, do not create unnecessary barriers to more effective public health protection.
bioinformaticscomputational toxicologymetabolomicsmetabonomicsmicroarrayspredictive toxicologyproteomicstoxicogenomicstranscriptomics
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New scientific tools spawned by the genomics revolution promise to improve our ability to identify causative factors in human diseases. These tools are expected to allow more rapid screening of chemicals for toxic effects and to provide mechanistic insight into a greater range and earlier stage of adverse outcomes associated with chemical exposures. Greater reliance on computer-based models has already brought remarkable advances in our ability to predict disease progression. For example, Petricoin and Liotta (2003) and Petricoin et al. (2002) have demonstrated a diagnostic screening test based on serum protein patterns for early detection of ovarian cancer. But the reliance on computers may make it more difficult for scientists trained in traditional toxicology to integrate this new knowledge into existing paradigms. In addition to barriers within the scientific community, the emergence of these new technologies is taking place in a political context that involves a variety of stakeholders with separate agendas. Thus, despite major advances in the science and technology of these new toxicogenomics tools, these scientific and political complexities threaten to delay the use of toxicogenomics to further the public interest or—worse—to advance its use initially to weaken the regulation and safety of widely used chemicals. In this article we highlight three important issues in the development of toxicogenomics and then report on a series of expert interviews that give additional insights into these and other critical questions.
Replace, Augment, or Refine?
How will toxicogenomics be developed and incorporated into testing and regulatory regimes? The often-stated promise of toxicogenomics techniques is that they will improve the screening of chemicals for toxicity by being faster, cheaper, more accurate, and more comprehensive than existing methods. But this promise is likely to be realized only after a period of relatively expensive and deliberate test validation and generation of the massive reference databases needed to make rigorous conclusions about test results. Without proper study design, appropriate use of controls, and multidisciplinary development of standardized methods, acceptance of new screening tests will be slow. In the interim, the power of toxicogenomics is likely to be applied piecemeal, with specific and often proprietary toxicogenomics assays developed and applied primarily to address the problems faced by regulated industries.
The pharmaceutical industry has been capitalizing on the strengths of toxicogenomics to screen compounds for potential toxicity. Large pharmaceutical companies have invested in enormous databases of genomic responses to known toxins and complex pattern recognition software programs to screen test data against these reference compounds (Hood 2003). This reflects the enormous savings to the pharmaceutical industry from early identification of the potential toxicity of a new product as well as the fact that the drug development process has a significant backstop to toxicogenomics screening in the form of extensive required clinical testing. This backstop allows screening for a limited set of toxic end points; early identification of drugs causing the limited set of toxicities is still cost-effective, whereas the subsequent rigorous clinical testing helps protect the public by detecting other types of toxicity.
The chemical industry, on the other hand, does not face any specific regulatory testing requirements for new products. The Toxic Substances Control Act of 1976 (TSCA) (1976) stipulates that all known toxicity information be disclosed in a premanufacture notice, but it does not require that any specific testing be performed before manufacture and marketing of a new chemical product. Thus, the chemical industry has less of a regulatory incentive to prescreen chemicals under development for potential toxicity. Although the stated public positions of the American Chemistry Council (ACC) and the Chemical Industry Institute of Toxicology Centers for Health Research (CIIT) describe a desire to broaden the understanding of potential health risks of chemical products, the regulatory context and incentive structures are leading to a focus on revising risk assessments of existing products that the industry believes overestimate true risks (e.g., Greenlee et al. 2003). Thus, the CIIT is applying toxicogenomics and systems biology approaches to compounds such as formaldehyde and chloroform in order to more fully elucidate cancer mechanisms and potentially demonstrate nonlinearity of cancer dose responses. The CIIT asserts that systems biology will provide a “valuable payback” by reducing uncertainty and that reduction in uncertainty will affect environmental standard compliance costs, presumably by allowing more relaxed exposure standards. The ACC’s Long-Range Research Initiative, in response to recent funding cuts, has redirected its academic program away from exploratory toxicogenomics and systems biology work but is continuing to fund the CIIT to perform more focused work on existing chemicals (MacKenzie 2004). For now, the stronger incentive for the chemical industry to use toxicogenomics to improve mechanistic understanding and reduce uncertainty factors rather than to develop new, more powerful screening tools suggests that the public need for better screening tools will have to be met elsewhere.
Phenotypic Anchoring: Scientific Rigor or Scientific Shackling?
With the massive number of data points now being monitored in gene and protein expression assays, some stakeholders are concerned that changes in gene expression or protein levels will be mistakenly interpreted as related to adverse effects when in fact they are benign. Within the chemical industry, some are calling for linking toxicogenomics results to traditional toxicology tests (e.g., Henry 2002). The implication is that changes in gene expression should not be considered adverse unless they can be directly related to outcomes observed in traditional toxicology tests.
Although, on the face of it, this may seem like a reasonable way to inject rigor into a complicated process, placing overly strict limitations on interpretation of gene expression data ensures that only the end points now assessed by traditional toxicology tests will be the end points considered in the future. One of the greatest promises of toxicogenomics is the ability to assess toxicity more comprehensively, picking up more subtle changes than may be detected by histopathology or other traditional detection methods. Tying the interpretation of toxicogenomics testing so strictly to traditional toxicity tests would keep this promise from being realized because information gained could be no more comprehensive than current test methods. Such scientific shackling of toxicogenomics approaches must be prevented if the new technologies are going to provide their maximal benefits.
Toxicogenomics Meets Toxicogenetics
Can the study of the toxic effects of chemicals be separated from the study of the genetic mechanisms underlying variations in individual susceptibilities to those chemicals? In the pharmaceutical arena, the observation that a new drug for small cell lung cancer works miraculously, but only for around 10% of those affected, demonstrates the power to identify critical variations in genetics and raises the ethical issue of dividing populations into those who benefit from new technology and those who do not (Lynch et al. 2004). Molecular epidemiology studies similarly show that people with particular genotypes are at increased risk of adverse health outcomes from toxic exposures. Various metabolic polymorphisms have been shown to convey small differences in risks for environmental exposures (Kelada et al. 2003); more extreme examples, whereby rare genotypes or combinations of genotypes convey greatly increased risks, are certainly possible. Current regulatory regimes inconsistently address interindividual differences in susceptibility. The Clean Air Act of 1990 (1990) (and the Safe Drinking Water Act Amendments of 1996 (1996) require explicit consideration of sensitive subpopulations in setting standards. TSCA, on the other hand, is silent on the subject of individual susceptibility. Inadequate ethical and societal frameworks for addressing susceptibility may lead to public backlash against generating toxicogenetic information or, worse, inappropriate or discriminatory use of such information.
To gain further insight into the scientific and political landscape of the new toxicology, we interviewed 27 experts from a variety of disciplines and sectors. Their views on the current status of the different fields within toxicogenomics, where they are going, and what the barriers are to fulfilling their potential provide an opportunity to compare and contrast different stakeholder viewpoints and suggest roles for the public-interest science community in promoting beneficial applications of toxicogenomics.
Materials and Methods
Interviewees were selected to represent five different sectors involved in toxicogenomics: government researchers, government regulators, academic researchers, private-sector scientists, and public-interest scientists. Of 34 experts initially identified for interviews, two declined, saying they were not deeply enough involved in the field to speak about it, and five could not be reached. The author and one assistant conducted the interviews by telephone, using a structured set of questions as a guideline. We recorded and took notes during the interviews and have complete transcripts of each interview, except for four that were not fully transcribed because of problems with recording.
We analyzed the transcripts by thoroughly reviewing the text and identifying important statements. Although the categories of responses were partly determined by the specific questions, the answers often overlapped with other issues. The categories of response we used were a) general challenges to chemical regulation and safety, b) impacts of toxicogenomics on chemical regulation and safety, c) applications closest to implementation, d) applications furthest from implementation, e) barriers to implementation, f ) use of animals in testing, and g) actions that should be taken by the environmental public-interest community.
Comments were assigned to one of these categories and put into a separate database to facilitate comparison of the responses. The responses then were grouped according to the respondent’s sector.
Results
Question 1—What is the greatest challenge facing chemical regulation and safety?
Most respondents mentioned long-standing limitations of traditional toxicologic testing, specifically the difficulties associated with using high doses in animals to study low-dose effects in humans. In addition, many of the respondents mentioned the inability of traditional toxicologic testing to address issues of mixtures and to differentiate easily between genotoxic and nongenotoxic carcinogens. Finally, many respondents referred to the fact that the large number of chemicals used in commerce precludes obtaining enough data to assess their risks, at least using current testing methods.
Several respondents pointed out other challenges of the current system. One respondent described the greatest challenge as persuading public-health professionals to recognize (at least in that person’s opinion) that the toxicity of common pharmaceuticals greatly exceeded the toxicity of environmental chemicals and that more efforts should be made to reduce the public-health burden of adverse drug reactions. Another felt that the need to overcome the differing paradigms for cancer versus noncancer end points was a significant problem. Another respondent pointed to the lack of good exposure data and generally poor risk communication as problems.
Many respondents spoke about the same uncertainties from different perspectives. One academic talked about reaching the right balance in chemical regulation (presumably balancing need to protect public health with needs to allow commerce to continue), whereas some in academic research and the private sector were more concerned that the current use of uncertainty factors was unscientific and overly protective.
Question 2—Will the impacts of this new technology on chemical regulation and safety be positive or negative?
Most of the respondents felt that the development of new technology would be positive because more information about biologic effects of chemicals would allow more effective controls. Many said the most positive impact would be that toxicogenomics could help avoid the need for dose and species extrapolation and reduce the use of uncertainty factors. One government researcher referred specifically to obtaining information about controversial beneficial effects of low-dose exposures to compounds that are toxic at a higher dose. Several also mentioned that toxicogenomics methods might provide insights into whether mixtures of chemicals produce additive, synergistic, or antagonistic effects.
Several perspectives regarding potential scientific benefits emerged. One respondent pointed out that compared with traditional toxicology tests, which are generally limited in the number of end points that can be assessed, toxicogenomics assays have a greater ability to measure multiple dose–response curves for multiple end points and multiple points in time. This reinforces that much more information will be available from a given experiment, with both positive consequences (better understanding of actual biologic responses) and negative ones (difficulties of handling and interpreting massively increased amounts of data). Two respondents from industry pointed to the benefit of greater confidence in the safety (and lowered liability risk) of products. Concerning potential negative impacts, several respondents, including those within the industry sector, expressed concern that the technologies were being forced too soon. Negative consequences included misinterpreting the data and making regulatory decisions before there was sufficient scientific certainty.
Question 3—What applications are closest to implementation?
Several applications and types of information were cited as feasible in the near term. Indeed, respondents noted several applications that were presently under way and had been used to generate data already published. Many felt that gene and protein arrays would yield useful information about mechanisms and/or modes of action in the near future. A few qualified this by limiting the near-term applications to those related to mechanisms of action that are already fairly well characterized, especially those that are mediated with known receptors.
Many respondents, especially those in the pharmaceutical industry, mentioned the ability to screen compounds for some types of toxicity early in the drug development process. An interesting area of divergence was whether these same techniques could be applied to chemicals in general. Several respondents from the academic and government research sectors felt that the ability to obtain hazard-identification information from gene expression assays, at least for some types of hazards, was close at hand. In the private sector, some felt that such ability was also close at hand, but others felt that lack of reproducibility of assays prevented any effective use of toxicogenomics data. For some respondents, differences of opinion about the feasibility of toxicogenomics for chemical screening were related to different concepts of what that entailed. Those that had in mind screening for recognized types of toxicity on chemicals with some well-characterized structural analogs generally felt that such screening was close at hand. Respondents who felt that screening was further in the future generally conceptualized such screening as involving completely unknown chemicals and screening them comprehensively for any type of toxicity.
Many respondents felt that the ability to understand the importance of polymorphisms for susceptibility to toxic compounds was near. Different respondents, however, often had different applications in mind when speaking of susceptibility. Some were referring to the ability to understand and predict an individual’s adverse reactions to pharmaceuticals, whereas others were referring to identifying susceptibility factors for toxic exposures in populations and individuals. There was a further distinction between the ability to identify specific genetic susceptibility factors for adverse outcomes from specific drugs or toxic exposures and the ability to use a more comprehensive genetic profile to either tailor pharmaceutical interventions or characterize a person’s overall susceptibility to a variety of toxic exposures. Although many respondents believed genetic susceptibility to be a near-term prospect, those who were referring to the more comprehensive application of genetic susceptibility factors felt that this application would not be available for many years. Sector affiliation did not correlate with the views on this topic, with private and public sector respondents represented on both sides of the disagreement.
Question 4—What applications are furthest from implementation?
Many respondents considered the use of toxicogenomics data quantitatively within risk assessments to be far from implementation. Reasons for this included the technical difficulty of getting the assays to provide reliable, reproducible, quantitative dose–response information, as well as the political and social difficulties of convincing regulatory agencies to change their practices. The ability to analyze complex mixtures and long-term low-dose effects was also considered unlikely to be realized soon.
A number of respondents placed the ability to model complex biologic systems as being at least 5–10 years away. This theme encompassed the potential to link gene expression changes to specific biologic pathways that includes higher levels of biologic organization, from protein expression through pathology, as well as the promise of in silico modeling and the ability to test chemicals for toxicity without the use of animals. No respondents felt that these promises of toxicogenomics were near. Two individuals mentioned related concepts that were far in the future: the ability to generate complete proteomic profiles and the ability to use the entire genome in gene expression microarrays.
Finally, metabonomics and metabolomics were singled out as approaches not yet ready for full implementation. The respondents noted that the technologies to make comprehensive analyses of metabolites were in relatively early stages of development, and that therefore the ability to standardize and analyze the data from these types of experiments was just now developing. The inability to perform comprehensive metabolite assays was related to the difficulty of developing complex biologic systems models.
Several concepts or applications were mentioned by only one or two respondents. One respondent opined that the ability to intervene clinically to address early signs of toxicologic insult was far off. Other one-time mentions included interspecies extrapolation and understanding the mechanisms of neurotoxicity.
Question 5—What are the main barriers to implementation?
Respondents offered numerous answers to this question. Their responses could be separated into those that were primarily technical, relating either to the development of the laboratory technology itself or to the analysis and interpretation of data, and those that were primarily sociopolitical, including barriers within the community of scientists and regulators developing toxicogenomics as well as barriers within society at large.
One frequently mentioned technical barrier was the high cost of generating the data necessary to understand, standardize, and validate toxicogenomics results and methods. There was some disagreement as to whether the government was investing sufficient financial resources in toxicogenomics research to meet this need, but there was considerable agreement that a massive amount of data needs to be generated in the early stages of implementation to provide the understanding and context necessary for reliably interpreting individual experiments.
Related to this, many respondents noted that the amount of data generated by the toxicogenomics experiments was itself a barrier to the development and standardization of methods. Many mentioned that the methods, software, and hardware necessary to handle the massive amounts of data were being developed but were relatively new to most molecular biologists. In addition to the sheer quantity of data generated by toxicogenomics assays, many respondents also expressed concerns that the quality of data was a barrier. Respondents expressed concern about appropriate study design, the reproducibility of results across different laboratories and the different assays and reagents used.
A critical, frequently mentioned technical barrier was the ability to separate important changes from background “noise.” Different aspects of this issue include the need to distinguish between important signals of a much smaller magnitude than some benign changes in gene and protein expression that might occur simultaneously. In addition, changes are being monitored in tens of thousands of genes and hundreds of proteins at once, increasing the statistical probability of many false positives and false negatives. The difficulty in identifying true responses has underlined the particular importance of negative controls in toxicogenomics research. These challenges make the central task of defining significant changes in gene and protein expression extremely complicated.
In addition to these technical barriers, the respondents mentioned numerous sociopolitical barriers. Some of these were intertwined with the technical details of toxicogenomics and had to do with scientific culture and the nature of the research and regulatory institutions involved. For example, one frequently mentioned barrier is the difficulty in achieving the effective multidisciplinary collaboration needed to develop toxicogenomics effectively and appropriately. This problem is not unique to toxicogenomics and has always been a problem for environmental health and safety, but certain aspects of toxicogenomics were highlighted as posing special challenges because of the need for multidisciplinary work. For example, several respondents noted that the quantity and complexity of data analysis requires more effective collaboration between molecular biologists and computational biologists. Some respondents addressed this issue by requiring training molecular biologists to learn the programming that is necessary to analyze their own data; others emphasized multi-disciplinary input into study design to avoid asking the wrong questions and failing to generate the types of data needed to answer specific questions.
Difficulty of persuading the scientific community to change its methods for approaching problems was a second sociopolitical barrier. Several respondents pointed out that toxicologists tend to be conservative, to adhere to “tried and true” laboratory methods (i.e., traditional animal tests), and to be very slow in adopting new methods. A few respondents noted that this is especially true for regulatory toxicologists employed in government. A philosophical challenge constituted a more complex barrier. Several respondents spoke of moving from a paradigm of reductionism, in which scientists focus intently on one gene or one mechanism to solve problems, to a broader paradigm of complex biologic systems. Several stated that in making this paradigm shift, scientists would need to abandon the comfort of feeling that they understand in detail what changes are occurring in a gene or other component of a cell and instead rely on computerized analysis of changes in thousands of genes and hundreds of proteins. Both the resistance to change and the philosophical shift required were common themes in the interviews.
Turning from the scientific community to society at large, many respondents voiced concerns that societal forces would hinder the implementation and use of toxicogenomics advances. The most frequently mentioned was the concern that the development of societal mechanisms for addressing the ethical, legal, and social aspects of toxicogenomics (and toxicogenetics) was lagging behind the technologic advances. This was mentioned in two very different contexts. Many respondents expressed concern that privacy concerns and mistrust among the general public would impede the development of methods to determine and understand susceptibility. Others pointed to the lack of legal and regulatory mechanisms that allow the increased use of toxicogenomics data in regulatory matters. Several maintained that without clarity and some form of protection from premature regulatory use, industries would be reluctant to start generating and providing toxicogenomics data in new chemical or drug applications. They pointed out that the U.S. Food and Drug Administration was well ahead of the U.S. Environmental Protection Agency (EPA) and had developed a safe-harbor system that was indeed facilitating the development of genomics and proteomics data in new drug applications. Several respondents mentioned the related concept of incentives, noting that the chemical industry had little incentive to spend money to develop toxicogenomics data, that the pharmaceutical industry had little incentive to share databases, and that the health care industry in general had little incentive to make a large investment in research that would lead to prevention rather than therapeutics.
Many respondents’ comments were more appropriately categorized as ways of reducing barriers rather than identifying the barriers themselves. One respondent observed that the private sector, with its greater flexibility and focus, might be better suited to foster multi-disciplinarity. Several comments addressed steps the government needed to take, specifically the U.S. EPA. Suggestions included the need to invest more in data generation now that methods for gene arrays have been established and the need to train and develop U.S. EPA staff to be able to understand and use these data better. One respondent focused on the need to fund work that would better integrate data generation and the bioinformatics work needed to interpret the data. Last, several respondents emphasized the need for better educating toxicologists and scientists (and recruiting scientists already familiar with toxicogenomics to do that educating) as well as educating the general public before toxicogenomics is applied more widely.
Question 6—How will the development of toxicogenomics affect the use of animals for testing?
There was a moderate amount of disagreement about how the development of toxicogenomics would affect the use of animals in testing in both the short and the long term. Most respondents felt that the use of animals would not decrease over the short term; some believed that their use would not be much affected; and others predicted an increase, perhaps sizable. Over the long term, more respondents felt that overall use of animals would decrease, although some predicted that the use of animals in testing would still be necessary, even as toxicogenomics methods supplied ever-greater amounts of information. In general, most respondents agreed that the promise of toxicogenomics to reduce the use of animals in testing would not be realized in the near term, and none felt that in vitro toxicogenomics methods would ever completely replace animals in toxicologic testing.
Question 7—What are appropriate roles for the environmental public-interest community?
The most common response to this question was some form of public education. Many respondents felt that a critical role of the environmental public-interest community was to provide reliable information to the public about the benefits and limitations of toxicogenomics. Many respondents, particularly those from the academic and private sectors, emphasized the need to “paint an even picture” and to avoid overselling the potential of toxicogenomics. Respondents from the nonprofit sector emphasized the need to promote development of toxicogenomics applications as a means of improving our ability to detect toxicity and regulate toxic chemicals.
The second most common response involved science advocacy, either advocating for adequate funding or ensuring that the interpretation of results would be both scientifically rigorous and in the public interest. Some respondents mentioned the need to persuade government agencies to conduct the extensive basic science research required at this stage, whereas others stressed the need to engage policymakers in setting research agendas and ensuring that basic science research served policy needs. Many respondents emphasized the need for the public-interest community to be represented by scientists familiar with the technology and science issues so that the public-interest community could be part of the discussions between government agencies and industry and could play a watchdog role in those discussions. This was true for both the science-monitoring and the regulatory-monitoring themes. Several respondents underscored this by pointing to the need to provide a counterpoint to industry’s influence on the process of determining how toxicogenomics information would be incorporated into risk assessment and regulatory processes.
Finally, many respondents mentioned the need for a brokering function among industry interests, academics, and government staff, particularly with regard to the ethical, legal, and social implications of toxicogenomics. Several respondents cited the need to convene a workshop on this topic, led by public-interest groups.
Discussion
Interviewees expressed widespread agreement that the new toxicology promises a significant increase in the amount of information available on toxic effects of chemicals. Nearly all respondents felt this would ultimately make it easier to identify and predict which chemicals cause adverse effects at environmentally relevant doses. And nearly all welcomed this improved ability because they were frustrated by the limitations of current toxicologic test batteries. For some, the main frustration was the limited ability of current toxicologic test batteries to assess subtle forms of toxicity that may occur at low doses; for others, the main source of frustration was the sense that extrapolations across dose and species leads to overly cautious standard setting. It is critical that discussion of implementing the new toxicology not get bogged down in a false dichotomy of whether the new will replace the old. Instead, the focus should be on how scientists and regulators can obtain the most information from these techniques now and how best to incorporate results from the new techniques in a progressive fashion that builds the experience necessary to make full use of this additional knowledge in the future.
The interviews show that the promise of the new toxicology will be realized only if a set of obstacles can be overcome. Respondents differed as to which obstacles were the most important and how quickly they might be overcome. Many of the obstacles identified were technical in nature, whereas others were political in nature. Public-interest scientists have a role to play in overcoming both types of obstacles. Advocacy for increased research resources will help address technical obstacles. Sociopolitical obstacles must be addressed through public and policymaker education and engagement in committees and stakeholder processes.
The most commonly identified technical needs were refining computational methods to be able to analyze vast, complex data sets, generating sufficient data to be able to train predictive toxicology models, and developing higher throughput assays for proteins and metabolites. Validation of assays and development of standardized data reporting frameworks were mentioned by some as significant obstacles, but other experts felt that such significant progress had been made in those areas that they were no longer major problems, at least for genomic data.
A recurrent sociopolitical obstacle mentioned by experts from many sectors was the inherent inertia of current toxicologic practices. This related not only to the reluctance of toxicologists and regulators to educate themselves about new toxicologic methods but also the reluctance of scientists to rely more heavily on computational analyses of complex patterns of responses that cannot easily be understood in terms of known mechanisms and pathways. This conflict has been played out in the literature in the context of clinical diagnostics and will likely become more prominent in debates about types of toxicogenomics data that can be used in regulatory settings (Diamandis et al. 2003). Along with phenotypic anchoring to existing end points, it is essential that the use of toxicogenomics data in a screening or regulatory setting not depend exclusively on achieving complete understanding of the functional implications of individual gene, protein, or metabolite changes. Although scientific rigor is necessary for the new toxicology to move forward, the scientific and public-interest communities must ensure that calls for rigor are not part of a strategy of foot-dragging and stalling.
The issue of proprietary databases was controversial. Several government and academic respondents maintained that information produced by biotechnology companies is often their sole commodity and thus there are strong disincentives to full sharing of new and useful data. Most respondents from the private sector downplayed this limitation, saying that the private sector was contributing significantly to open international databases and that the amount of data withheld for proprietary reasons was small. Landmark initiatives such as the Chemical Effects in Biological Systems knowledge base (http://cebs.niehs.nih.gov/) sponsored by the National Institute of Environmental Health Sciences will be limited in their value if only a subset of developed data are submitted to it. Clearly, this is an issue that must be monitored by both the public and the public-interest sectors.
The public-interest community has a critical role in helping guide the application of this powerful new science. As with all new technologies, societal risks may accompany societal benefits. Toxicogenomics promises to increase knowledge of biologic mechanisms and reduce toxicologic uncertainty. However, public-interest groups must engage in the science-policy process to ensure that needless barriers are not erected, that shortcuts are not taken, and that public-health protection and an individual’s privacy are not compromised. To engage effectively, public-interest groups must increase their capability in this new, exciting, and complex toxicology.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7339ehp0113-00082316002369ReviewsNanotoxicology: An Emerging Discipline Evolving from Studies of Ultrafine Particles Oberdörster Günter 1Oberdörster Eva 2Oberdörster Jan 31Department of Environmental Medicine, University of Rochester, Rochester, New York, USA2Department of Biology, Southern Methodist University, Dallas, Texas, USA3Toxicology Department, Bayer CropScience, Research Triangle Park, North Carolina, USAAddress correspondence to G. Oberdörster, University of Rochester, Department of Environmental Medicine, 575 Elmwood Ave., MRBx Building, Box 850, Rochester, NY 14642 USA. Telephone: (585) 275-3804. Fax: (585) 256-2631. E-mail:
[email protected] views expressed by the authors are their own and do not necessarily reflect those of the EPA.
J. Oberdörster is an employee of Bayer CropScience. The other authors declare they have no competing financial interests.
7 2005 22 3 2005 113 7 823 839 18 6 2004 22 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Although humans have been exposed to airborne nanosized particles (NSPs; < 100 nm) throughout their evolutionary stages, such exposure has increased dramatically over the last century due to anthropogenic sources. The rapidly developing field of nanotechnology is likely to become yet another source through inhalation, ingestion, skin uptake, and injection of engineered nanomaterials. Information about safety and potential hazards is urgently needed. Results of older bio-kinetic studies with NSPs and newer epidemiologic and toxicologic studies with airborne ultrafine particles can be viewed as the basis for the expanding field of nanotoxicology, which can be defined as safety evaluation of engineered nanostructures and nanodevices. Collectively, some emerging concepts of nanotoxicology can be identified from the results of these studies. When inhaled, specific sizes of NSPs are efficiently deposited by diffusional mechanisms in all regions of the respiratory tract. The small size facilitates uptake into cells and transcytosis across epithelial and endothelial cells into the blood and lymph circulation to reach potentially sensitive target sites such as bone marrow, lymph nodes, spleen, and heart. Access to the central nervous system and ganglia via translocation along axons and dendrites of neurons has also been observed. NSPs penetrating the skin distribute via uptake into lymphatic channels. Endocytosis and biokinetics are largely dependent on NSP surface chemistry (coating) and in vivo surface modifications. The greater surface area per mass compared with larger-sized particles of the same chemistry renders NSPs more active biologically. This activity includes a potential for inflammatory and pro-oxidant, but also antioxidant, activity, which can explain early findings showing mixed results in terms of toxicity of NSPs to environmentally relevant species. Evidence of mitochondrial distribution and oxidative stress response after NSP endocytosis points to a need for basic research on their interactions with subcellular structures. Additional considerations for assessing safety of engineered NSPs include careful selections of appropriate and relevant doses/concentrations, the likelihood of increased effects in a compromised organism, and also the benefits of possible desirable effects. An interdisciplinary team approach (e.g., toxicology, materials science, medicine, molecular biology, and bioinformatics, to name a few) is mandatory for nanotoxicology research to arrive at an appropriate risk assessment.
biokineticscentral nervous systemengineered nanomaterialsenvironmental healthhuman healthnanosized particlesrespiratory tractrisk assessmentskinultrafine particles
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Exposures to airborne nanosized particles (NSPs; < 100 nm) have been experienced by humans throughout their evolutionary stages, but it is only with the advent of the industrial revolution that such exposures have increased dramatically because of anthropogenic sources such as internal combustion engines, power plants, and many other sources of thermo-degradation. The rapidly developing field of nanotechnology is likely to become yet another source for human exposures to NSPs—engineered nanoparticles (NPs)—by different routes: inhalation (respiratory tract), ingestion [gastrointestinal (GI) tract], dermal (skin), and injection (blood circulation). Table 1 summarizes some of the natural and anthropogenic sources of NSPs, the latter divided into unintentional and intentional sources.
Biologically based or naturally occurring molecules that are found inside organisms since the beginning of life can serve as model nanosized materials. For example, biogenic magnetite is a naturally occurring NSP that occurs in many species ranging from bacteria to protozoa to animals (Blakemore 1975; Kirschvink et al. 2001). Biogenic magnetite has even been found in brains of humans (Dunn et al. 1995; Kirschvink et al. 1992; Schultheiss-Grassi et al. 1999) and has been associated with neurodegenerative diseases (Dobson 2001; Hautot et al. 2003). A biologic model of coated nanomaterials can be found in ferritin, which is an approximately 12-nm-large iron storage protein that contains 5- to 7-nm-sized hydrous ferric oxide phosphate inside a protective protein shell (Donlin et al. 1998). Nanosized materials, including fullerenes, occur naturally from combustion processes such as forest fires and volcanoes.
Obvious differences between unintentional and intentional anthropogenic NSPs are the polydispersed and chemically complex nature (elemental, soluble, and volatile carbon compounds; soluble and poorly soluble inorganics; Cyrys et al. 2003; Hughes et al. 1998) of the former, in contrast to the monodisperse and precise chemically engineered characteristics and solid form of the latter, generated in gas or liquid phase [National Nanotechnology Initiative (NNI) 2004]. However, despite these differences, the same toxicologic principles are likely to apply for NPs, because not only size but also a number of other particle parameters determine their biologic activity. The multitude of interactions of these factors has yet to be assessed, and in this article we attempt to summarize our present knowledge.
NSPs are variably called ultrafine particles (UFPs) by toxicologists [U.S. Environmental Protection Agency (EPA) 2004], Aitken mode and nucleation mode particles by atmospheric scientists [Kulmala 2004; National Research Council (NRC) 1983], and engineered nanostructured materials by materials scientists (NNI 2004). Figure 1 depicts the range of sizes of airborne ambient particulate matter, including the nucleation-mode, Aitken-mode, accumulation-mode, and coarse-mode particles. Ambient particles < 0.1 μm, defined as UFPs in the toxicologic literature, consist of transient nuclei or Aitken nuclei (NRC 1983). More recently, even smaller particles in the nucleation mode with peak diameters around 4 nm have been observed (McMurry and Woo 2002). The distinction between NSPs generated by internal combustion engines and NPs becomes further clouded by the finding of nanotubes in diesel exhaust (Evelyn et al. 2003). The use of the term “nano” in this review reflects only particle size and not chemical composition. For the purposes of this review, we use the following terms: “Nanosized particle” (NSP) includes all engineered and ambient nanosized spherical particles < 100 nm. “Engineered nanoparticle” (NP) includes only spherical NSPs specifically engineered in the laboratory; other engineered nanosized structures will be labeled according to their shape, for example, nanotubes, nanofibers, nanowires, nanorings, and so on. “Ultrafine particle” (UFP) includes ambient and laboratory-generated NSPs that are not produced in a controlled, engineered way.
Table 2 shows the tremendous differences in particle number concentrations and particle surface areas for particles of the four ambient modes, assuming an airborne concentration of 10 μg/m3 of unit density particles of each size. The extraordinarily high number concentrations of NSPs per given mass will likely be of toxicologic significance when these particles interact with cells and subcellular components. Likewise, their increased surface area per unit mass can be toxicologically important if other characteristics such as surface chemistry and bulk chemistry are the same. Although the mass of UFPs in ambient air is very low, approaching only 0.5–2 μg/m3 at background levels (Hughes et al. 1998), it can increase several-fold during high pollution episodes or on highways (Brand et al. 1991; Shi et al. 2001; Zhu et al. 2002).
Physicochemical characteristics as determinants of biologic activity.
The small size and corresponding large specific surface area of solid NSPs (Table 2) confer specific properties to them, for example, making them desirable as catalysts for chemical reactions. The importance of surface area becomes evident when considering that surface atoms or molecules play a dominant role in determining bulk properties (Amato 1989); the ratio of surface to total atoms or molecules increases exponentially with decreasing particle size (Figure 2). Increased surface reactivity predicts that NSPs exhibit greater biologic activity per given mass compared with larger particles, should they be taken up into living organisms and provided they are solid rather than solute particles. This increased biologic activity can be either positive and desirable (e.g., antioxidant activity, carrier capacity for therapeutics, penetration of cellular barriers for drug delivery) or negative and undesirable (e.g., toxicity, induction of oxidative stress or of cellular dysfunction), or a mix of both. Not only may adverse effects be induced, but interactions of NSPs with cells and subcellular structures and their biokinetics are likely to be very different from those of larger-sized particles. For example, more than 60 years ago virologists described the translocation of 30- to 50-nm-sized virus particles along axons and dendrites of neurons and across epithelia (Howe and Bodian 1940), whereas first reports about increased inflammatory activity and epithelial translocation of man-made 20- and 30-nm solid particles appeared only more recently (Ferin et al. 1990; Oberdörster et al. 1990).
The characteristic biokinetic behaviors of NPs are attractive qualities for promising applications in medicine as diagnostic and therapeutic devices and as tools to investigate and understand molecular processes and structures in living cells (Akerman et al. 2002; Foley et al. 2002; Kreuter 2001; Li et al. 2003). For example, targeted drug delivery to tissues that are difficult to reach [e.g., central nervous system (CNS)], NPs for the fight against cancer, intra-vascular nanosensor and nanorobotic devices, and diagnostic and imaging procedures are presently under development. The discipline of nanomedicine—defined as medical application of nanotechnology and related research—has arisen to design, test, and optimize these applications so that they can eventually be used routinely by physicians (Freitas 1999).
However, in apparent stark contrast to the many efforts aimed at exploiting desirable properties of NPs for improving human health are the limited attempts to evaluate potential undesirable effects of NPs when administered intentionally for medicinal purposes, or after unintentional exposure during manufacture or processing for industrial applications. The same properties that make NPs so attractive for development in nanomedicine and for specific industrial processes could also prove deleterious when NPs interact with cells. Thus, evaluating the safety of NPs should be of highest priority given their expected worldwide distribution for industrial applications and the likelihood of human exposure, directly or through release into the environment (air, water, soil). Nanotoxicology—an emerging discipline that can be defined as “science of engineered nanodevices and nanostructures that deals with their effects in living organisms”—is gaining increased attention. Nanotoxicology research not only will provide data for safety evaluation of engineered nanostructures and devices but also will help to advance the field of nanomedicine by providing information about their undesirable properties and means to avoid them.
Human exposure to nanosized materials.
In addition to natural and anthropogenic sources of UFPs in the ambient air, certain workplace conditions also generate NSPs that can reach much higher exposure concentrations, up to several hundred micrograms per cubic meter, than is typically found at ambient levels. In contrast to airborne UFP exposures occurring via inhalation at the workplace and from ambient air, not much is known about levels of exposure via different routes for NPs, whether it is by direct human exposure or indirectly through contamination of the environment. For example, are there or will there be significant exposures to airborne singlet engineered carbon nanotubes or C60 fullerenes? First measurements at a model workplace gave only very low concentrations, < 50 μg/m3, and these were most likely in the form of aggregates (Maynard et al. 2004). However, even very low concentrations of nanosized materials in the air represent very high particle number concentrations, as is well known from measurements of ambient UFPs (Hughes et al. 1998). For example, a low concentration of 10 μg/m3 of unit density 20-nm particles translates into > 1 × 106 particles/cm3 (Table 2). Inhalation may be the major route of exposure for NPs, yet ingestion and dermal exposures also need to be considered during manufacture, use, and disposal of engineered nanomaterials, and specific biomedical applications for diagnostic and therapeutic purposes will require intravenous, subcutaneous, or intramuscular administration (Table l). It can be assumed, however, that the toxicology of NPs can build on the experience and data already present in the toxicology literature of ambient UFPs. [Additional details provided in Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf).]
Manufactured nanomaterials in the environment.
Manufactured nanomaterials are likely to enter the environment for several reasons. Some are and others will be produced by the ton, and some of any material produced in such mass quantities is likely to reach the environment from manufacturing effluent or from spillage during shipping and handling. They are being used in personal-care products such as cosmetics and sunscreens and can therefore enter the environment on a continual basis from washing off of consumer products (Daughton and Ternes 1999). They are being used in electronics, tires, fuel cells, and many other products, and it is unknown whether some of these materials may leak out or be worn off over the period of use. They are also being used in disposable materials such as filters and electronics and may therefore reach the environment through landfills and other methods of disposal.
Scientists have also found ways of using nanomaterials in remediation. Although many of these are still in testing stages (Chitose et al. 2003; Jaques and Kim 2000; Joo et al. 2004; Nagaveni et al. 2004; Nghiem et al. 2004; Tungittiplakorn et al. 2004), dozens of sites have already been injected with various nanomaterials, including nano-iron (Mach 2004). Testing to determine the safety of these NPs used for remediation to environmentally relevant species has not yet been done. Although most people are concerned with effects on large wildlife, the basis of many food chains depends on the benthic and soil flora and fauna, which could be dramatically affected by such NP injections. In addition, as noted by Lecoanet et al. (2004), nanosized materials may not migrate through soils at rapid enough rates to be valuable in remediation. Future laboratory and field trials will help clarify the line between remediation and contamination [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Toxicology of Airborne UFPs
In recent years, interest in potential effects of exposure to airborne UFPs has increased considerably, and studies have shown that they can contribute to adverse health effects in the respiratory tract as well as in extrapulmonary organs. Results on direct effects of ambient and model UFPs have been reported from epidemiologic studies and controlled clinical studies in humans, inhalation/instillation studies in rodents, or in vitro cell culture systems. For example, several epidemiologic studies have found associations of ambient UFPs with adverse respiratory and cardiovascular effects resulting in morbidity and mortality in susceptible parts of the population (Pekkanen et al. 1997; Penttinen et al. 2001; Peters et al. 1997a, 1997b; von Klot et al. 2002; Wichmann et al. 2002), whereas other epidemiologic studies have not seen such associations (Pekkanen et al. 1997; Tiittanen et al. 1999). Controlled clinical studies evaluated deposition and effects of laboratory-generated UFPs. High deposition efficiencies in the total respiratory tract of healthy subjects were found, and deposition was even greater in subjects with asthma or chronic obstructive pulmonary disease. In addition, effects on the cardiovascular system, including blood markers of coagulation and systemic inflammation and pulmonary diffusion capacity, were observed after controlled exposures to carbonaceous UFPs (Anderson et al. 1990; Brown et al. 2002; Chalupa et al. 2004; Henneberger et al., 2005; Jaques and Kim 2000; Pekkanen et al. 2002; Pietropaoli et al. 2004; Wichmann et al. 2000).
Studies in animals using laboratory-generated model UFPs or ambient UFPs showed that UFPs consistently induced mild yet significant pulmonary inflammatory responses as well as effects in extrapulmonary organs. Animal inhalation studies included the use of different susceptibility models in rodents, with analysis of lung lavage parameters and lung histopathology, effects on the blood coagulation cascade, and translocation studies to extra-pulmonary tissues (Elder et al. 2000, 2002, 2004; Ferin et al. 1991; Ferin and Oberdörster 1992; Kreyling et al. 2002; Li et al. 1999; Nemmar et al. 1999, 2002a, 2002b, 2003; Oberdörster et al. 1992a, 1995, 2000, 2002, 2004; Semmler et al. 2004; Zhou et al. 2003).
In vitro studies using different cell systems showed varying degrees of proinflammatory-and oxidative-stress–related cellular responses after dosing with laboratory-generated or filter-collected ambient UFPs (Brown et al. 2000, 2001; Li et al. 2003). Collectively, the in vitro results have identified oxidative-stress–related changes of gene expression and cell signaling pathways as underlying mechanisms of UFP effects, as well as a role of transition metals and certain organic compounds on combustion-generated UFPs (Figure 3). These can alter cell signaling pathways, including Ca2+ signaling and cytokine signaling (e.g., interleukin-8) (Donaldson et al. 2002; Donaldson and Stone 2003). Effects were on a mass basis greater for model UFPs than for fine particles, whereas effects for ambient UFP cellular responses were sometimes greater and sometimes less than those of fine and coarse particles. The interpretation of the in vitro studies is oftentimes difficult because particles of different chemical compositions were used, target cells were different, and duration, end points, and generally high dose levels also differed. Results from high doses in particular should be viewed with caution if they are orders of magnitude higher than predicted from relevant ambient exposures (see “Exposure dose–response considerations,” below). [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf).]
Concepts of Nanotoxicology
Laboratory rodent studies.
With respect to potential health effects of NSPs, two examples should serve to illustrate a) that NSPs have a higher inflammatory potential per given mass than do larger particles, provided they are chemically the same, and b) that NSPs generated under certain occupational conditions can elicit severe acute lung injury.
The first example involves studies with ultrafine and fine titanium dioxide (TiO2) particles, which showed that ultrafine anatase TiO2 (20 nm), when instilled intratracheally into rats and mice, induced a much greater pulmonary-inflammatory neutrophil response (determined by lung lavage 24 hr after dosing) than did fine anatase TiO2 (250 nm) when both types of particles were instilled at the same mass dose (Figure 4A,C). However, when the instilled dose was expressed as particle surface area, it became obvious that the neutrophil response in the lung for both ultrafine and fine TiO2 fitted the same dose–response curve (Figure 4B,D), suggesting that particle surface area for particles of different sizes but of the same chemistry, such as TiO2, is a better dosemetric than is particle mass or particle number (Oberdörster G 2000). Moreover, normalizing the particle surface dose to lung weight shows excellent agreement of the inflammatory response in both rats and mice [Figure S-2 in Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)]. The better fit of dose–response relationships by expressing the dose as surface area rather than mass when describing toxicologic effects of inhaled solid particles of different sizes has been pointed out repeatedly, especially when particles of different size ranges—nano to fine—were studied (Brown et al. 2001; Donaldson et al. 1998, 2002; Driscoll 1996; Oberdörster and Yu 1990; Oberdörster et al. 1992a; Tran et al. 1998, 2000) [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Particle chemistry, and specifically surface chemistry, plays a decisive role in addition to particle size, as shown in the second example: exposure of rats to polytetrafluoroethylene (PTFE) fume. PTFE fume (generated by heating PTFE) has long been known to be of high acute toxicity to birds and mammals, including humans (Cavagna et al. 1961; Coleman et al. 1968; Griffith et al. 1973; Nuttall et al. 1964; Waritz and Kwon 1968). Analysis of these fumes revealed the nanosized nature of the particles generated by heating PTFE to about 480°C, with a count median diameter (CMD) of 18 nm. They were highly toxic to rats, causing severe acute lung injury with high mortality within 4 hr after a 15-min inhalation exposure to 50 μg/m3 (Oberdörster et al. 1995). This short exposure resulted in an estimated deposited dose in the alveolar regions of only approximately 60 ng. In humans, acute lung injury, known as polymer fume fever, can result from exposures to PTFE fumes (Auclair et al. 1983; Goldstein et al. 1987; Lee et al. 1997; Williams et al. 1974; Woo et al. 2001). Additional rat studies showed that the gas phase alone was not acutely toxic and that aging of the PTFE fume particles for 3 min increased their particle size to > 100 nm because of accumulation, which resulted in a loss of toxicity (Johnston et al. 2000). However, it is most likely that changes in particle surface chemistry during the aging period contributed to this loss of toxicity, and that this is not just an effect of the accumulated larger particle size [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
These examples seem to represent the extremes of NSPs in terms of pulmonary toxicity, one (TiO2) being rather benign yet still inducing significantly greater inflammatory effects on a mass basis than fine particles of the same chemical makeup, the other (PTFE fumes) inducing very high acute toxicity, possibly related to reactive groups on the large surface per unit mass.
Engineered nanomaterials can have very different shapes, for example, spheres, fibers, tubes, rings, and planes. Toxicologic studies of spherical and fibrous particles have well established that natural (e.g., asbestos) and man-made (e.g., biopersistent vitreous) fibers are associated with increased risks of pulmonary fibrosis and cancer after prolonged exposures [Greim et al. 2001; International Agency for Research on Cancer (IARC) 2002]. Critical parameters are the three Ds: dose, dimension, and durability of the fibers. Fibers are defined as elongated structures with a diameter-to-length ratio (aspect ratio) of 1:3 or greater and with a length of > 5 μm and diameter ≤3 μm [World Health Organization (WHO) 1985]. Carbon nanotubes have aspect ratios of up to ≥100, and length can exceed 5 μm with diameters ranging from 0.7 to 1.5 nm for single-walled nanotubes, and 2 to 50 nm for multiwalled nanotubes. Results from three studies using intratracheal dosing of carbon nanotubes in rodents indicate significant acute inflammatory pulmonary effects that either subsided in rats (Warheit et al. 2004) or were more persistent in mice (Lam et al. 2004; Shvedova et al. 2004b). Administered doses were very high, ranging from 1 to 5 mg/kg in rats; in mice doses ranged from 3.3 to 16.6 mg/kg (Lam et al. 2004) or somewhat lower, from 0.3 to 1.3 mg/kg (Shvedova et al. 2004a). Granuloma formation as a normal foreign body response of the lung to high doses of a persistent particulate material was a consistent finding in these studies. Metal impurities (e.g., iron) from the nanotube generation process may also have contributed to the observed effects. Although these in vivo first studies revealed high acute effects, including mortality, this was explained by the large doses of the instilled highly aggregated nanotubes that caused death by obstructing the airways and should not be considered a nanotube effect per se (Warheit et al. 2004). In vitro studies with carbon nanotubes also reported significant effects. Dosing keratinocytes and bronchial epithelial cells in vitro with single-walled carbon nanotubes (SWNTs) resulted in oxidative stress, as evidenced by the formation of free radicals, accumulation of peroxidative products, and depletion of cell antioxidants (Shvedova et al. 2004a, 2004b). Multiwalled carbon nanotubes (MWNTs) showed pro-inflammatory effects and were internalized in keratinocytes (Monteiro-Riviere et al. 2005). Again, the relatively high doses applied in these studies need to be considered when discussing the relevancy of these findings for in vivo exposures. A most recent study in macrophages comparing SWNTs and MWNTs with C60 fullerenes found a cytotoxicity ranking on a mass basis in the order SWNT > MWNT > C60. Profound cytotoxicity (mitochondrial function, cell morphology, phagocytic function) was seen for SWNTs, even at a low concentration of 0.38 μg/cm2. The possible contribution of metal impurities of the nanotubes still needs to be assessed. Therefore, whether the generally recognized principles of fiber toxicology apply to these nanofiber structures needs still to be determined (Huczko et al. 2001).
Future studies should be designed to investigate both effects and also the fate of nanotubes after deposition in the respiratory tract, preferentially by inhalation using well-dispersed (singlet) airborne nanotubes. In order to design the studies using appropriate dosing, it is necessary to assess the likelihood and degree of human exposure. It is of utmost importance to characterize human exposures in terms of the physicochemical nature, the aggregation state, and concentration (number, mass, surface area) of engineered nanomaterials and perform animal and in vitro studies accordingly. If using direct instillation into the lower respiratory tract, a large range of doses, which include expected realistic exposures of appropriately prepared samples, needs to be considered [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Ecotoxicologic studies.
Studies have been carried out to date on only a few species that have been accepted by regulatory agencies as models for defining ecotoxicologic effects. Tests with uncoated, water-soluble, colloidal fullerenes (nC60) show that the 48-hr LC50 (median lethal concentration) in Daphnia magna is 800 ppb (Oberdörster E 2004b), using standard U.S. EPA protocols (U.S. EPA 1994). In largemouth bass (Micropterus salmoides), although no mortality was seen, lipid peroxidation in the brain and glutathione depletion in the gill were observed after exposure to 0.5 ppm nC60 for 48 hr (Oberdörster E 2004a). There are several hypotheses as to how lipid damage may have occurred in the brain, including direct redox activity by fullerenes reaching the brain via circulation or axonal translocation (see also “Disposition of NSPs in the respiratory tract,” below) and dissolving into the lipid-rich brain tissue; oxyradical production by microglia; or reactive fullerene metabolites may be produced by cytochrome P450 metabolism. Initial follow-up studies using suppressive subtractive hybridization of pooled control fish versus pooled 0.5-ppm–exposed fish liver mRNA were also performed. Proteins related to immune responses and tissue repair were up-regulated, and several proteins related to homeostatic control and immune control were down-regulated. A cytochrome P450 (CYP2K4) involved in lipid metabolism was up-regulated [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)]. In addition to these biochemical and molecular-level changes in fish, bactericidal properties of fullerenes have also been reported and are being explored as potential new antimicrobial agents (Yamakoshi et al. 2003). Engineered nanomaterials used as antimicrobials may shift microbial communities if they are released into the environment via effluents. As we know from anthropogenic endocrine-disrupting compounds, interference of signaling between nitrogen-fixing bacteria and their plant hosts could be extremely harmful both ecologically and economically in terms of crop production (Fox et al. 2001).
Aqueous fullerenes and coated SWNTs are stable in salt solutions (Cheng et al. 2004; Warheit et al. 2004), cell culture media (Lu et al. 2004; Sayes et al. 2004), reconstituted hard water, and MilliQ water (Dieckmann et al. 2003; Oberdörster E 2004a, 2004b). NSPs will tend to sorb onto sediment and soil particles and be immobilized because of their high surface area:mass ratio (Lecoanet and Wiesner 2004). Biologic transport would occur from ingested sediments, and one would expect movement of nanomaterials through the food chain (Figure 5).
To make engineered nanomaterials more biocompatible, both surface coatings and covalent surface modifications have been incorporated. Some studies have shown that both the surface coating and the covalent modifications can be weathered either by exposure to the oxygen in air or by ultraviolet (UV) irradiation for 1–4 hr (Derfus et al. 2004; Rancan et al. 2002). Therefore, although coatings and surface modifications may be critically important in drug-delivery devices, the likelihood of weathering under environmental conditions makes it important to study toxicity under UV and air exposure conditions. Even coatings used in drug delivery of NPs may not be bio-persistent or could be metabolized to expose the core NP material [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Reactive oxygen species mechanisms of NSP toxicity.
Both in vivo and in vitro, NSPs of various chemistries have been shown to create reactive oxygen species (ROS). ROS production has been found in NPs as diverse as C60 fullerenes, SWNTs, quantum dots, and UFPs, especially under concomitant exposure to light, UV, or transition metals (Brown et al. 2000, 2001; Derfus et al. 2004; Joo et al. 2004; Li et al. 2003; Nagaveni et al. 2004; Oberdörster E 2004a; Rancan et al. 2002; Sayes et al. 2004; Shvedova et al. 2004a, 2004b; Wilson et al. 2002; Yamakoshi et al. 2003). It has been demonstrated that NSPs of various sizes and various chemical compositions preferentially mobilize to mitochondria (de Lorenzo 1970; Foley et al. 2002; Gopinath et al. 1978; Li et al. 2003; Rodoslav et al. 2003). Because mitochondria are redox active organelles, there is a likelihood of altering ROS production and thereby overloading or interfering with antioxidant defenses (Figure 3).
Figure 6 diagrams some of the antioxidant defense systems that occur in animals, and possible areas where NSPs may create oxyradicals. The C60 fullerene is shown as a model NP producing superoxide, as has been shown by Yamakoshi et al. (2003). The exact mechanism by which each of these diverse NPs cause ROS is not yet fully understood, but suggested mechanisms include a) photo excitation of fullerenes and SWNTs, causing intersystem crossing to create free electrons; b) metabolism of NPs to create redox active intermediates, especially if metabolism is via cytochrome P450s; and c) inflammation responses in vivo that may cause oxyradical release by macrophages. Other mechanisms will likely emerge as studies on NP toxicity continue.
The small size and respective large specific surface area of NPs, like those of ambient airborne UFPs, give them unique properties with respect to a potential to cause adverse effects. Certainly, as shown in studies with UFPs, chemical composition and other particle parameters are additional important effect modifiers. Results from these studies will therefore serve as a basis for future studies in the field of nanotoxicology, for example, the propensity of NSPs to translocate across cell layers and along neuronal pathways (see “Disposition of NSPs in the respiratory tract” below).
Exposure dose–response considerations.
A careful evaluation of exposure–dose–response relationships is critical to the toxicologic assessment of NSPs. This includes not only questions about the dosemetric—mass, number, or surface of the particles, as discussed above—but most important, also the relevance of dose levels. For example, it is tempting, and a continual practice, to dose primary cells or cell lines in vitro with very high doses without any consideration or discussion of realistic in vivo exposures; for instance, 100 μg NSPs/mL culture medium—labeled as a low dose—is extremely high and is unlikely to be encountered in vivo. Likewise, intratracheal instillations of several hundred micrograms into a rat does not resemble a relevant in vivo inhalation exposure; both dose and dose rate cause high bolus dose artifacts. Although such studies may be used in a first proof-of-principle approach, it is mandatory to follow up and validate results using orders of magnitude lower concentrations resembling realistic in vivo exposures, including worst-case scenarios. The 500-year-old phrase “the dose makes the poison” can also be paraphrased as “the dose makes the mechanism.” The mechanistic pathways that operate at low realistic doses are likely to be different from those operating at very high doses when the cell’s or organism’s defenses are overwhelmed.
Therefore, in vivo and in vitro studies will provide useful data on the toxicity and mode of action of NSPs only if justifiable concentrations/doses are considered when designing such studies. This approach is particularly important for the proper identification of the dose–response curve. When data are generated only at high concentrations/doses, it is difficult to determine whether the dose–response curve in question is best described by a linear (no threshold), supralinear, threshold, or hormetic model (Figure 7). Study designs should include doses that most closely reflect the expected exposure levels. A critical gap that urgently needs to be filled in this context is the complete lack of data for human or environmental exposure levels of NSPs. Furthermore, some knowledge about the biokinetics of NSPs is required in order to estimate appropriate doses. Do specific NPs reach certain target sites? If so, what are the doses, dose rates, and their persistence? Further, although it may be tempting to extrapolate from in vitro results to an in vivo risk assessment, it is important to keep in mind that in vitro tests are most useful in providing information on mechanistic processes and in elucidating mechanisms/mode of actions suggested by studies in whole animals. A combination of in vitro and in vivo studies with relevant dose levels will be most useful in identifying the potential hazards of NPs, and a thorough discussion and justification of selected dose levels should be mandatory.
Portals of Entry and Target Tissues
Most of the toxicity research on NSPs in vivo has been carried out in mammalian systems, with a focus on respiratory system exposures for testing the hypothesis that airborne UFPs cause significant health effects. With respect to NPs, other exposure routes, such as skin and GI tract, also need to be considered as potential portals of entry. Portal-of-entry–specific defense mechanisms protect the mammalian organism from harmful materials. However, these defenses may not always be as effective for NSPs, as is discussed below.
Respiratory Tract
In order to appreciate what dose the organism receives when airborne particles are inhaled, information about their deposition as well as their subsequent fate is needed. Here we focus on the fate of inhaled nanosized materials both within the respiratory tract itself and translocated out of the respiratory tract. There are significant differences between NSPs and larger particles regarding their behavior during deposition and clearance in the respiratory tract [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Efficient deposition of inhaled NSPs.
The main mechanism for deposition of inhaled NSPs in the respiratory tract is diffusion due to displacement when they collide with air molecules. Other deposition mechanisms of importance for larger particles, such as inertial impaction, gravitational settling, and interception, do not contribute to NSP deposition, and electrostatic precipitation occurs only in cases where NSPs carry significant electric charges. Figure 8 shows the fractional deposition of inhaled particles in the nasopharyngeal, tracheobronchial, and alveolar regions of the human respiratory tract under conditions of nose breathing during rest, based on a predictive mathematical model (International Commission on Radiological Protection 1994). These predictions apply to particles that are inhaled as singlet particles of a given size and not as aggregates; the latter obviously will have larger particle size and different deposition site. In each of the three regions of the respiratory tract, significant amounts of a certain size of NSPs (1–100 nm) are deposited. For example, 90% of inhaled 1-nm particles are deposited in the nasopharyngeal compartment, only approximately 10% in the tracheobronchial region, and essentially none in the alveolar region. On the other hand, 5-nm particles show about equal deposition of approximately 30% of the inhaled particles in all three regions; 20-nm particles have the highest deposition efficiency in the alveolar region (~ 50%), whereas in tracheobronchial and nasopharyngeal regions this particle size deposits with approximately 15% efficiency. These different deposition efficiencies should have consequences for potential effects induced by inhaled NSPs of different sizes as well as for their disposition to extrapulmonary organs, as discussed further below.
Disposition of NSPs in the respiratory tract.
In the preceding section we summarized data demonstrating that inhaled NSPs of different sizes can target all three regions of the respiratory tract. Several defense mechanisms exist throughout the respiratory tract aimed at keeping the mucosal surfaces free from cell debris and particles deposited by inhalation. Several reviews describe the well-known classic clearance mechanisms and pathways for deposited particles (Kreyling and Scheuch 2000; Schlesinger et al. 1997; U.S. EPA 2004), so here we only briefly mention those mechanisms and point out specific differences that exist with respect to inhaled NSPs [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Once deposited, NSPs—in contrast to larger-sized particles—appear to translocate readily to extrapulmonary sites and reach other target organs by different transfer routes and mechanisms. One involves transcytosis across epithelia of the respiratory tract into the interstitium and access to the blood circulation directly or via lymphatics, resulting in distribution throughout the body. The other is a not generally recognized mechanism that appears to be distinct for NSPs and that involves their uptake by sensory nerve endings embedded in airway epithelia, followed by axonal translocation to ganglionic and CNS structures.
Classical clearance pathways.
The clearance of deposited particles in the respiratory tract is basically due to two processes (Table 3): a) physical translocation of particles by different mechanisms and b) chemical clearance processes. Leaching refers to loss of elements from a particle matrix (e.g., loss of sodium from asbestos fibers due to dissolution in intra-or extracellular milieu). Chemical dissolution is directed at biosoluble particles or components of particles that are either lipid soluble or soluble in intracellular and extracellular fluids. Solutes and soluble components can then undergo absorption and diffusion or binding to proteins and other subcellular structures and may be eventually cleared into blood and lymphatic circulation. Chemical clearance for biosoluble materials can happen at any location within the three regions of the respiratory tract, although to different degrees, depending on local extracellular and intracellular conditions (pH). In contrast, a number of diverse processes involving physical translocation of inhaled particles exist in the respiratory tract and are different in the three regions. Figure 9 summarizes these clearance processes for solid particles. As discussed further below, some of them show significant particle-size–dependent differences, making them uniquely effective for a certain particle size but very inefficient for other sizes.
The most prevalent mechanism for solid particle clearance in the alveolar region is mediated by alveolar macrophages, through phagocytosis of deposited particles. The success of macrophage–particle encounter appears to be facilitated by chemotactic attraction of alveolar macrophages to the site of particle deposition (Warheit et al. 1988). The chemotactic signal is most likely complement protein 5a (C5a), derived from activation of the complement cascade from serum proteins present on the alveolar surface (Warheit et al. 1986; Warheit and Hartsky 1993). This is followed by gradual movement of the macrophages with internalized particles toward the mucociliary escalator. The retention half-time of solid particles in the alveolar region based on this clearance mechanism is about 70 days in rats and up to 700 days in humans. The efficacy of this clearance mechanism depends highly on the efficiency of alveolar macrophages to “sense” deposited particles, move to the site of their deposition, and then phagocytize them. This process of phagocytosis of deposited particles takes place within a few hours, so by 6–12 hr after deposition essentially all of the particles are phagocytized by alveolar macrophages, to be cleared subsequently by the slow alveolar clearance mentioned above. However, it appears that there are significant particle-size–dependent differences in the cascade of events leading to effective alveolar macrophage-mediated clearance.
Figure 10 displays results of several studies in which rats were exposed to different-sized particles (for the 3- and 10-μm particles, 10-μg and 40-μg polystyrene beads, respectively, were instilled intratracheally) (Kreyling et al. 2002; Oberdörster et al. 1992b, 2000; Semmler et al. 2004). Twenty-four hours later, the lungs of the animals were lavaged repeatedly, retrieving about 80% of the total macrophages as determined in earlier lavage experiments (Ferin et al. 1991). As shown in Figure 10, approximately 80% of 0.5-, 3-, and 10-μm particles could be retrieved with the macrophages, whereas only approximately 20% of nanosized 15–20-nm and 80-nm particles could be lavaged with the macrophages. In effect, approximately 80% of the UFPs were retained in the lavaged lung after exhaustive lavage, whereas approximately 20% of the larger particles > 0.5 μm remained in the lavaged lung. This indicates that NSPs either were in epithelial cells or had further translocated to the interstitium [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Epithelial translocation.
Because of the apparent inefficiency of alveolar macrophage phagocytosis of NSPs, one might expect that these particles interact instead with epithelial cells. Indeed, results from several studies show that NSPs deposited in the respiratory tract readily gain access to epithelial and interstitial sites. This was also shown in studies with ultrafine PTFE fumes: shortly after a 15-min exposure, the fluorine-containing particles could be found in interstitial and submucosal sites of the conducting airways as well as in the interstitium of the lung periphery close to the pleura (Oberdörster G 2000). Such interstitial translocation represents a shift in target site away from the alveolar space to the interstitium, potentially causing direct particle-induced effects there.
In a study evaluating the pulmonary inflammatory response of TiO2 particles, ranging from NP TiO2 to pigment-grade TiO2 (12–250 nm), a surprising finding was that, 24 hr after intratracheal instillation of different doses, higher doses induced a lower effect (Oberdörster et al. 1992a). This was explained by the additional finding that at the higher doses (expressed as particle surface area) of the nanosized TiO2, ≥50% had reached the pulmonary interstitium, causing a shift of the inflammatory cell response from the alveolar space to the interstitium [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)]. The smaller particle size of 12 and 20 nm versus 220 and 250 nm also means that the administered particle number was more than three orders of magnitude higher for the NSPs, a factor that seems to be an important determinant for particle translocation across the alveolar epithelium, as are the delivered total dose and the dose rate (Ferin et al. 1992). Because interstitial translocation of fine particles across the alveolar epithelium is more prominent in larger species (dogs, nonhuman primates) than in rodents (Kreyling and Scheuch 2000; Nikula et al. 1997), it is reasonable to assume that the high translocation of NSPs observed in rats occurs in humans as well [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Translocation to the circulatory system.
Once the particles have reached pulmonary interstitial sites, uptake into the blood circulation, in addition to lymphatic pathways, can occur; again, this pathway is dependent on particle size, favoring NSPs. Berry et al. (1977) were the first to describe translocation of NSPs across the alveolar epithelium using intratracheal instillations of 30-nm gold particles in rats. Within 30 min postexposure, they found large amounts of these particles in platelets of pulmonary capillaries; the researchers suggested that this is an elimination pathway for inhaled particles that is significant for transporting the smallest air pollutant particles—in particular, particles of tobacco smoke—to distant organs. They also hypothesized that this “might predispose to platelet aggregation with formation of microthrombi atheromatous plaques” (Berry et al. 1977).
Since then, a number of studies with different particle types have confirmed the existence of this translocation pathway, as summarized in Table 4. Collectively, these studies indicate that particle size and surface chemistry (coating), and possibly charge, govern translocation across epithelial and endothelial cell layers. In particular, the studies summarized by Mehta et al. (2004) and those performed by Heckel et al. (2004) using intravenous administration of albumin-coated gold nanoparticles in rodents demonstrated receptor-mediated transcytosis (albumin-binding proteins) via caveolae (Figure 11). These 50–100 nm vesicles, first described by Simionescu et al. (1975), form from indentations of the plasmalemma and are coated with the caveolin-1 protein. Albumin, as the most abundant protein in plasma and interstitium, appears to facilitate NP endocytosis, as does lecithin, a phospholipid: even 240-nm polystyrene particles translocated across the alveolo-capillary barrier when coated with lecithin, whereas uncoated particles did not (Kato et al. 2003). The presence of both albumin and phospholipids in alveolar epithelial lining fluid may, therefore, be important constituents for facilitated epithelial cell uptake of NSPs after deposition in the alveolar space.
Rejman et al. (2004) reviewed a number of different endocytic pathways for internalization of a variety of substances, including phagocytosis, macropinocytosis, clathrin-mediated endocytosis, and caveolae-mediated endocytosis. They found in nonphagocytic cells in vitro that internalization via clathrin-coated pits prevailed for latex microspheres < 200 nm, whereas with increasing size up to 500 nm, caveolae became the predominant pathway. However, as shown in Table 4, surface coating of NSPs with albumin clearly causes even the smallest particles to be internalized via caveolae. The presence of caveolae on cells differs: they are abundant in lung capillaries and alveolar type l cells but not in brain capillaries (Gumbleton 2001). In the lung, during inspiratory expansion and expiratory contraction of the alveolar walls, caveolae with openings around 40 nm disappear and reappear, forming vesicles that are thought to function as transport pathways across the cells for macromolecules (Patton 1996). Knowledge from virology about cell entry of biologic NSPs (viruses) via clathrin-coated pits and caveolae mechanisms should also be considered (Smith and Helenius 2004) and can shed light on the mechanism by which engineered NPs may enter cells and interact with subcellular structures.
Evidence in humans for the translocation of inhaled NSPs into the blood circulation is ambiguous, with one study showing rapid appearance in the blood and significant accumulation of label in the liver of humans inhaling 99Tc-labeled 20-nm carbon particles (Nemmar et al. 2002a), whereas another study using the same labeled particles reported no such accumulation (Brown et al. 2002). Taking into consideration all of the evidence from animal and human studies for alveolar translocation of NSPs, it is likely that this pathway also exists in humans; however, the extent of extrapulmonary translocation is highly dependent on particle surface characteristics/chemistry, in addition to particle size. Translocation to the blood circulation could provide a mechanism for a direct particle effect on the cardiovascular system as an explanation for epidemiologic findings of cardiovascular effects associated with inhaled ambient UFPs (Pekkanen et al. 2002; Wichmann et al. 2000) and for results of clinical studies showing vascular responses to inhaled elemental carbon UFPs (Pietropaoli et al. 2004). In addition to direct alveolar translocation of NSPs, cardiovascular effects may also be the corollary of a sequence of events starting with particle-induced alveolar inflammation initiating a systemic acute phase response with changes in blood coagulability and resulting in cardiovascular effects (Seaton et al. 1995).
Once NSPs have translocated to the blood circulation, they can be distributed throughout the body. The liver is the major distribution site via uptake by Kupffer cells, followed by the spleen as another organ of the reticulo-endothelial system, although coating with polyethylene glycol (PEG) almost completely prevents hepatic and splenic localization so that other organs can be targeted (Akerman et al. 2002). Distribution to heart, kidney, and immune-modulating organs (spleen, bone marrow) has been reported. For example, several types of NPs, ranging from 10 to 240 nm, localized to a significant degree in bone marrow after intravenous injection into mice (Table 5). Such target specificity may be extremely valuable for drug delivery; for example, drug delivery to the CNS via blood-borne NPs requires NP surface modifications in order to facilitate translocation across the tight blood–brain barrier via specific receptors (e.g., apolipoprotein coating for LDL-receptor–mediated endocytosis in brain capillaries) (Kreuter 2001, 2004; Kreuter et al. 2002). Such highly desirable properties of NPs must be carefully weighed against potential adverse cellular responses of targeted NP drug delivery, and a rigorous toxicologic assessment is mandatory [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Neuronal uptake and translocation.
A translocation pathway for solid particles in the respiratory tract involving neuronal axons is apparently specific for NSPs. Respective studies are summarized in Table 6. This pathway was described > 60 years ago, yet it has received little or no attention from toxicologists. This pathway, shown in Figure 9 for the nasal and tracheobronchial regions, comprises sensory nerve endings of the olfactory and the trigeminus nerves and an intricate network of sensory nerve endings in the tracheobronchial region. These early studies concerned a large series of studies with 30-nm polio virus intranasally instilled into chimpanzees and rhesus monkeys (Bodian and Howe 1941a, 1941b; Howe and Bodian 1940). Their studies revealed that the olfactory nerve and olfactory bulbs are, indeed, portals of entry to the CNS for intranasally instilled nanosized polio virus particles, which could subsequently be recovered from the olfactory bulbs. The close proximity of nasal olfactory mucosa and olfactory bulb requires only a short distance to be covered by neuronal transport (Figure 12). Bodian and Howe (1941b) determined the transport velocity of the virus in the axoplasm of axons to be 2.4 mm/hr, which is very well in agreement with neuronal transport velocities measured later by Adams and Bray (1983) for solid particles (up to 500 nm) directly microinjected into giant axons of crabs, and by de Lorenzo (1970) for silver-coated colloidal gold (50 nm) in squirrel monkeys.
The de Lorenzo (1970) study demonstrated in squirrel monkeys that intranasally instilled silver-coated colloidal gold particles (50 nm) translocated anterogradely in the axons of the olfactory nerves to the olfactory bulbs. The 50-nm gold particles even crossed synapses in the olfactory glomerulus to reach mitral cell dendrites within 1 hr after intranasal instillation. An interesting finding in this study—and important for potential adverse effects—was that the NSPs in the olfactory bulb were no longer freely distributed in the cytoplasm but were preferentially located in mitochondria (see also “Reactive oxygen species mechanisms of NSP toxicity,” above).
Newer studies indicated that this translocation pathway is also operational for inhaled NSPs. Inhalation of elemental 13C UFPs (CMD = 35 nm) resulted in a significant increase of 13C in the olfactory bulb on day 1, which increased further throughout day 7 post-exposure (Oberdörster et al. 2004). Results of another inhalation study with solid nanosized (CMD = 30 nm) manganese oxide (MnO2) particles in rats showed after a 12-day exposure a more than 3.5-fold significant increase of Mn in the olfactory bulb, compared with only a doubling of Mn in the lung. When one nostril was occluded during a 6-hr exposure, Mn accumulation in the olfactory bulb was restricted to the side of the open nostril only (Figure 13) (Feikert et al. 2004). This result contrasts with 15-day inhalation of larger-sized MnO2 particles in rats (1.3 and 18 μm mass median aerodynamic diameter) where no significant increases in olfactory Mn was found (Fechter et al. 2002). This was to be expected given that the individual axons of the fila olfactoria (forming the olfactory nerve) are only 100–200 nm in diameter (de Lorenzo 1957; Plattig 1989).
Collectively, these studies point out that the olfactory nerve pathway should also be considered a portal of entry to the CNS for humans under conditions of environmental and occupational exposures to airborne NSPs. However, there are important differences between rodents and humans. The olfactory mucosa of the human nose comprises only 5% of the total nasal mucosal surface as opposed to 50% in rats—which in addition are obligatory nose breathers (Table 7). One can argue that the olfactory route may therefore be an important transfer route to the CNS for inhaled NSPs in animals with a well-developed olfaction system, yet at the same time its importance for humans with a more rudimentary olfactory system can be questioned. However, estimates using a predictive particle deposition model and data from Table 7 show that concentrations of 20-nm translocated particles in the human olfactory bulb can, indeed, be 1.6–10 times greater than in rats [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Translocation into deeper brain structures may possibly occur as well, as shown in rats for soluble Mn (Gianutsos et al. 1997), but requires further confirmatory studies with respect to solid NSPs. Further evidence for movement of NSPs along axons and dendrites in humans is provided by knowledge accumulated by virologists who have long understood the movement of human meningitis virus through olfactory and trigeminal neurons and, similarly, herpes virus movement up and down the trigeminal neuron to trigger outbreaks of herpes cold sores in humans (Kennedy and Chaudhuri 2002; Terasaki et al. 1997).
There are additional neuronal translocation pathways for solid NSPs via the trigeminus nerve and tracheobronchial sensory nerves (Table 6). A study by Hunter and Dey (1998) in rats demonstrated the translocation of intranasally instilled rhodamine-labeled microspheres (20–200 nm) to the trigeminal ganglion inside the cranium via uptake into the ophthalmic and maxillary branches of the trigeminus nerve that supplies sensory nerve endings throughout the nasal mucosa. In another study, Hunter and Undem (1999) instilled the same microparticles intratracheally into guinea pigs; they found neuronal translocation of these solid microparticles to the ganglion nodosum in the neck area that is networked into the vagal system. This finding may be relevant for ambient UFPs because it can be hypothesized that cardiovascular effects associated with ambient particles in epidemiologic studies (Utell et al. 2002) are in part due to direct effects of translocated UFPs on the autonomic nervous system via sensory nerves in the respiratory tract.
In the context of potential CNS effects of air pollution, including ambient UFPs, two recent studies with exposures of mice to concentrated ambient fine particles and UFPs should be mentioned. Campbell et al. (2005) and Veronesi et al. (in press) found significant increases of tumor necrosis factor-α or decreases in dopaminergic neurons, supporting the hypothesis of ambient PM causing neuro-degenerative disease. A study by Calderon-Garcidueñas et al. (2002) may also point to an interesting link between air pollution and CNS effects: these authors described significant inflammatory or neurodegenerative changes in the olfactory mucosa, olfactory bulb, and cortical and subcortical brain structures in dogs from a heavily polluted area in Mexico City, whereas these changes were not seen in dogs from a less-polluted rural control city. However, whether direct effects of airborne UFPs are the cause of these effects remains to be determined.
Although the existence of neuronal translocation of NSPs has been well established, size alone is only one particle parameter governing this process. Surface characteristics of NSPs (chemistry, charge, shape, aggregation) are essential determinants as well, and it should not be assumed that all NSPs, when inhaled, will be distributed by the mechanism described here. It should be kept in mind, however, that the unique biokinetic behavior of NSPs—cellular endocytosis, transcytosis, neuronal and circulatory translocation and distribution—which makes them desirable for medical therapeutic or diagnostic applications—may be associated with potential toxicity. For example, NP-facilitated drug delivery to the CNS raises the question of the fate of NPs after their translocation to specific cell types or to subcellular structures in the brain. For example, does mitochondrial localization induce oxidative stress? How persistent is the coating or the core of the NPs? A respective safety evaluation is key [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf)].
Exposure via GI Tract and Skin
NSPs cleared from the respiratory tract via the mucociliary escalator can subsequently be ingested into the GI tract. Alternatively, nanomaterials can be ingested directly, for example, if contained in food or water or if used in cosmetics or as drugs or drug delivery devices. Only a few studies have investigated the uptake and disposition of nanomaterials by the GI tract, and most have shown that NSPs pass through the GI tract and are eliminated rapidly. In rats dosed orally with radiolabeled functionalized C60 fullerenes, water solubilized using PEG and albumin (18 kBq in 100 μL), 98% were cleared in the feces within 48 hr, whereas the rest was eliminated via urine, indicating some uptake into the blood circulation (Yamago et al. 1995). In contrast, in this same study, 90% of the same radiolabeled fullerenes administered intravenously (9.6 kBq in ~ 50 μL or 14–18 kBq in 215 μL) were retained after 1 week, with most (73–80%, depending on time course) found in the liver. Studies by Kreyling and colleagues (Kreyling et al. 2002; Semmler et al. 2004) using ultrafine 192Ir did not show significant uptake in the GI tract, whereas earlier studies with larger TiO2 particles (150–500 nm) found uptake into the blood and movement to the liver (Böckmann et al. 2000; Jani et al. 1994). Likely there are differences in GI tract uptake dependent on both particle surface chemistry and particle size. Indeed, after oral dosing in rats, Jani et al. (1990) found a particle-size–dependent uptake of polystyrene particles (ranging from 50 to 3,000 nm) by the GI mucosa. This uptake (6.6% of the administered 50 nm, 5.8% of the 100 nm NSP, 0.8% of 1 μm particles, and 0% for 3 μm particles) was mainly via the Peyer's patches with translocation into the mesenteric lymph and then to systemic organs (i.e., liver, spleen, blood, bone marrow, and kidney).
A potentially important uptake route is through dermal exposure. The epidermis, consisting of the outer horny layer (stratum corneum), the prickle cell layer (stratum spinosum), and basal cell layer (stratum basale), forms a very tight protective layer for the underlying dermis (Figure 14). The dermis has a rich supply of blood and tissue macrophages, lymph vessels, dendritic cells (Langerhans, also in stratum spinosum of epidermis), and five different types of sensory nerve endings. Broken skin represents a readily available portal of entry even for larger (0.5–7 μm) particles, as evidenced by reports about accumulation of large amounts of soil particles in inguinal lymph nodes of people who often run or walk barefoot; this can be associated with elephantiatic lymphedema (podoconiosis; Corachan et al. 1988; Blundell et al. 1989). Tinkle et al. (2003) hypothesized that unbroken skin when flexed—as in wrist movements—would make the epidermis permeable for NSPs. They demonstrated in a proof-of-concept experiment that, indeed, flexing the skin, but not flat skin, resulted in penetration of even 1 μm fluorescent beads to the dermis. The follow-up question about access of particles in the dermis to the circulation is answered by the aforementioned reports of podoconiosis, that is, uptake into the lymphatic system and regional lymph nodes. Subsequent translocation of NSPs beyond lymph nodes to the blood circulation is likely to occur as well, as shown in studies with small asbestos fibers (Oberdörster et al. 1988).
Newer studies by Kim et al. (2004) in mice and pigs with intradermally injected near-infrared quantum dots confirmed that NPs, once in the dermis, will localize to regional lymph nodes, which makes these particles very useful for in vivo imaging. Likely transport mechanisms to the lymph nodes are skin macrophages and dendritic (Langerhans) cells (Ohl et al. 2004; Sato et al. 1998); this raises a question about potential modulation of immune responses, after interaction of these NP-containing macrophages and dendritic cells with T lymphocytes. For example, Chen et al. (1998) were able to raise antibodies in mice specific for C60 after intraperitoneal injections of C60 conjugated to thyroglobulin and serum albumin. Clearly, research is needed to determine whether and under what conditions NPs can be recognized by the immune system, following any route of uptake into the organism.
Another question relates to the potential of sensory skin nerves to take up and translocate NPs. Given that this mechanism has been demonstrated for the nasal and tracheobronchial regions of the respiratory tract, how likely is this to occur in the dermis layer of the skin with its dense supply of different types of sensory nerves? It may be conceivable, considering data on neuronal uptake and translocation of NSPs after intramuscular injection. For example, nanosized ferritin and iron-dextran, after injection into the tongue of mice, labeled the neurons of the hypoglossal nuclei, and injection of both of these NSPs into facial muscles of mice also resulted in synaptic uptake; cationized ferritin was also detected in cell bodies of facial neurons, indicating that electrical charge is of importance for incorporation into axons and axonal transport (Arvidson 1994; Malmgren et al. 1978; Olsson and Kristensson 1981). Other studies using intramuscular injection of ferritin (~ 112 nm), iron-dextran (11 or 21 nm), and gold protein (20–25 nm) NSPs also showed rapid penetration through the basal lamina into the synaptic clef of the neuromuscular junction, but this was restricted to only the smaller nanoparticles, implying that there may be a size-dependent penetration of the basal lamina with a threshold somewhere between 10 and 20 nm (Oldfors and Fardeau 1983).
Neuronal transport of NSPs along sensory skin nerves is well established for herpes virus. After passing through the skin—especially broken skin—the viruses are transported retrogradely along dendrites of sensory neurons to the dorsal root ganglion, where they remain dormant until a stress situation triggers antero-grade translocation along the dendrites back to the skin (Kennedy and Chaudhuri 2002; Terasaki et al. 1997). Future studies need to determine whether and to what degree such translocation along sensory skin neurons also occurs with NPs penetrating the epidermis.
Risk Assessment
The lack of toxicology data on engineered NPs does not allow for adequate risk assessment. Because of this, some may even believe that engineered NPs are so risky that they call for a precautionary halt in NP-related research. However, the precautionary principle should not be used to stop research related to nanotechnology and NPs. Instead, we should strive for a sound balance between further development of nanotechnology and the necessary research to identify potential hazards in order to develop a scientifically defensible database for the purpose of risk assessment. To be able to do this, a basic knowledge about mammalian and ecotoxicologic profiles of NPs is necessary, rather than attempting to assess NP risks based on some popular science fiction literature. Most important, sufficient resources should be allocated by governmental agencies and industries to be able to perform a scientifically based risk assessment and then establish justifiable procedures for risk management. The data needed for this risk assessment should be determined a priori so that limited resources can be used efficiently to develop useful and well-planned studies.
At this point, governmental regulation is not possible, given the lack of needed information on which to base such regulations. However, academia, industry, and regulatory governmental agencies should seriously consider the view that NPs have new and unique biologic properties and that the potential risks of NPs are not the same as those of the bulk material of the same chemistry. Assigning a unique identifier to nanosized materials would indicate that the toxicology profile of the material in question may not be the same as the bulk material. Toxicologic tests and the resulting database would provide information for material safety data sheets for NPs as well as a basis for potential NP risk assessments and risk management. Obviously, this approach may not be appropriate for all NPs, for example, when embedded in a matrix, and the feasibility of this proposed strategy needs to be thoroughly discussed and considered. For discussing this, and for developing and deciding upon a reasonable battery of tests for toxicologic profiling, it would be very useful to convene international multidisciplinary workshops of experts from industry, academia, and regulatory agencies (including material scientists, chemists, chemical engineers, toxicologists, physicians, regulators, statisticians, and others) to establish an NP classification scheme and testing guidelines. A multidisciplinary and multinational collaborative team approach is critical. Respective efforts have been initiated nationally by the American National Standards Institute (ANSI 2004) and internationally by the International Council on Nanotechnology (ICON 2004) as well as the International Organization for Standardization (Geneva, Switzerland).
Because many regulatory agencies do not consider a nanotechnologically manufactured substance different from the conventional substance, the manufacture and use of nanotechnology products are currently not specifically regulated. Typically, nanosized substances are treated as variations of the technical material or existing formulation and thus do not require a separate registration. A main reason for producing a nanosize form of a registered substance, however, is that conversion of a substance to a nanoparticle imparts new properties to the substance (e.g., enhanced mechanical, electrical, optical, catalytic, biologic activity). Thus, as stated above, although the toxicology of the base material may be well defined, the toxicity of the nanosize form of the substance may be dramatically different from its parent form. As a result, new toxicology data on the nanosize form of a substance is likely to result in a different hazard assessment for the NPs. Figure 15 shows a risk assessment/risk management paradigm that points out different steps and data required for this process.
As described in the preceding sections, the difference in toxicologic profile of NPs compared with its parent form is due to not only its intrinsic chemical properties but also to a large degree to its differing kinetics in vivo. Although larger particles may not enter the CNS, the potential exists for inhaled NSPs to be translocated to the CNS via the axons of sensory neurons in the upper respiratory tract. Furthermore, although the toxicity per unit mass of a particular substance may vary depending on the nano versus larger form, it will be important to take into account not only new biologic activities but also potential new target organs and routes of exposure. To what degree does the nanoform of a substance have enhanced dermal penetration, or increased systemic uptake via the lung or GI tract? What determines how many nanoparticles that enter the systemic circulation will distribute throughout the body, reach the bone marrow, cross the blood–brain barrier, cross the placenta to affect the developing offspring, or sequester effectively in the liver? Do nanoparticles released into the environment affect species that are important in food chain dynamics? What are the long-term consequences of exposure to nanoparticles? Changes in toxicity profile and new target organs can be expected, and it will then be necessary to establish new risk assessments for nanoparticles in addition to the bulk material. Currently there exists a paucity of data to effectively address these questions, but it will be important to determine whether there exist common modes of action/behavior of NPs to establish baseline assumptions for use in risk assessments.
The use of nanotechnology products will likely increase dramatically over the next decade. In fact, nanomaterials are already being used in applications ranging from burn and wound dressings to dental-bonding agents to sunscreens and cosmetics to fuel cells, tires, optics, clothing, and electronics. Although currently there exists little public awareness of nanotechnology in everyday life (e.g., stain-free clothing), it would be prudent to examine and address environmental and human health concerns before the widespread adoption of nanotechnology. Both the societal benefits and potential risks of nanotechnology should be evaluated and clearly communicated to the general public and regulators. This type of open communication and risk/benefit evaluation will avoid the pitfalls encountered with genetically modified organisms recently experienced in the field of biotechnology. In that instance, the benefits of the emerging field of biotechnology were not communicated effectively before the introduction of the technology. As the public’s awareness of this new technology grew, regulators and producers of biotechnology failed to effectively acknowledge public concerns that genetically modified organisms could adversely affect ecosystem balance. As a result, the public support of genetically modified organisms, particularly in the European Union, is low. For nanomaterial producers, it will be important to demonstrate that what they may perceive as a new and potentially harmless form of a familiar material has, indeed, an acceptable risk profile. If such proactive steps are not taken, nanomaterials may be regarded as dangerous by the public and regulators, which could lead to inappropriate categorization and unnecessarily burdensome regulations. Such action (or inaction on the side of producers), in turn, could result in significant barriers to commercialization and the widespread acceptance of otherwise useful nanotechnology materials.
Summary and Outlook
Research on ambient UFPs has laid the foundation for the emerging field of nanotoxicology, with the goal of studying the biokinetics and the potential of engineered nanomaterials (particles, tubes, shells, quantum dots, etc.) to cause adverse effects. Major differences between ambient UFPs and NPs are the polydisperse nature of the former versus the monodisperse size of the latter, and particle morphology, oftentimes a branched structure from combustion particles versus spherical form of NPs, although other shapes (tubes, wires, rings, planes) are also manufactured. In addition, combustion-derived volatile organic compounds and inorganic constituents (e.g., metals, nitrates, sulfates) of different solubilities on UFPs predict differences in the toxicologic profile between UFPs and NPs. However, as far as the insoluble particle is concerned, concepts of NSPs kinetics, including cell interactions, will most likely be the same for UFPs and NPs (Figure 16).
The introduction of nanostructured materials for biomedical and electronics applications opens tremendous opportunities for biomedical applications as therapeutic and diagnostic tools as well as in the fields of engineering, electronics, optics, consumer products, alternative energy, soil/water remediation, and others. However, very little is yet known about their potential to cause adverse effects or humoral immune responses once they are introduced into the organism—unintentionally or intentionally. Nanomedicine products will be well tested before introduction into the marketplace. However, for the manufacturers of most current nanotechnology products, regulations requiring nanomaterial-specific data on toxicity before introduction into the marketplace are an evolving area and presently under discussion (Bergeson and Auerbach 2004; Foresight and Governance Project 2003). During a product’s life cycle (manufacture, use, disposal), it is probable that nanomaterials will enter the environment, and currently there is no unified plan to examine ecotoxicologic effects of NPs. In addition, the stability of coatings and covalent surface modifications need to be determined both in ecologic settings and in vivo. [Supplemental Material available online (http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf).]
Results of older biokinetic studies and some new toxicology studies with NSPs (mostly ambient UFPs) can be viewed as the basis for the expanding field of nanotoxicology. These studies showed that the greater surface area per mass renders NSPs more active biologically than larger-sized particles of the same chemistry, and that particle surface area and number appear to be better predictors for NSPs-induced inflammatory and oxidative stress responses. The following emerging concepts of nanotoxicology can be identified from these studies:
The biokinetics of NSPs are different from larger particles. When inhaled, they are efficiently deposited in all regions of the respiratory tract; they evade specific defense mechanisms; and they can translocate out of the respiratory tract via different pathways and mechanisms (endocytosis and transcytosis). When in contact with skin, there is evidence of penetration to the dermis followed by translocation via lymph to regional lymph nodes. A possible uptake into sensory nerves needs to be investigated. When ingested, systemic uptake via lymph into the organism can occur, but most are excreted via feces. When in blood circulation, they can distribute throughout the organism, and they are taken up into liver, spleen, bone marrow, heart, and other organs. In general, translocation rates are largely unknown; they are probably very low but are likely to change in a compromised/diseased state.
The biologic activity and biokinetics are dependent on many parameters: size, shape, chemistry, crystallinity, surface properties (area, porosity, charge, surface modifications, weathering of coating), agglomeration state, biopersistence, and dose. These parameters are likely to modify responses and cell interactions, such as a greater inflammatory potential than larger particles per given mass, translocation across epithelia from portal of entry to other organs, translocation along axons and dendrites of neurons, induction of oxidative stress, pro-oxidant and antioxidant activity of NSPs in environmentally relevant species, binding to proteins and receptors, and localization in mitochondria.
The principles of cellular and organismal interactions discussed in this article should be applicable for both ambient UFPs and NPs, even if the latter are coated with a bio-compatible material. Knowledge about the bio-persistence of this coating is as essential as is knowledge about the bioavailability of the core material that could have intrinsic toxic properties, for example, semiconductor metal compounds in sub-10-nm quantum dots consisting of cadmium and lead compounds. The very small size of these materials makes them available to the same translocation processes described here for polydisperse NSPs, possibly even in a more efficient way because of their uniform size. When studying biologic/toxicologic effects, new processes of interactions with subcellular structures (e.g., microtubuli, mitochondria) will likely be discovered. The diversity of engineered nanomaterials and of the potential effects represents major challenges and research needs for nanotoxicology, including also the need for assessing human exposure during manufacture and use. The goal to exploit positive aspects of engineered nanomaterials and avoid potential toxic effects can best be achieved through a multidisciplinary team effort involving researchers in toxicology, materials science, medicine, molecular biology, bioinformatics, and their subspecialties.
Correction
The authors found additional information on GI tract uptake of NSPs that was not in the original manuscript published online. This information has been included here in “Exposure via GI Tract and Skin.”
Supplemental Material is available online at http://ehp.niehs.nih.gov/members/2005/7339/supplemental.pdf
We thank J. Havalack for excellent assistance in preparing the manuscript.
This work was supported in part by the U.S. Environmental Protection Agency (EPA) STAR Program grant R827354, National Institute of Environmental Health Sciences grant ESO1247, U.S. Department of Defense MURI grant FA9550-04-1-430, and the National Science Foundation (SGER) BES-0427262.
Figure 1 Idealized size distribution of traffic-related particulate matter (U.S. EPA 2004). Dp, particle diameter. The four polydisperse modes of traffic-related ambient particulate matter span approximately four orders of magnitude from < 1 nm to > 10 μm. Nucleation- and Aitken-mode particles are defined as UFPs (< approximately 100 nm). Source-dependent chemical composition is not well controlled and varies considerably. In contrast, NPs (1–100 nm) have well-controlled chemistry and are generally monodispersed.
Figure 2 Surface molecules as a function of particle size. Surface molecules increase exponentially when particle size decreases < 100 nm, reflecting the importance of surface area for increased chemical and biologic activity of NSPs. The increased biologic activity can be positive and desirable (e.g., antioxidant activity, carrier capacity for therapeutics, penetration of cellular barriers), negative and undesirable (e.g., toxicity, induction of oxidative stress or of cellular dysfunction), or a mix of both. Figure courtesy of H. Fissan (personal communication).
Figure 3 Hypothetical cellular interaction of NSPs (adapted from Donaldson and Tran 2002). EGFR, epidermal growth factor receptor. Inflammation and oxidative stress can be mediated by several primary pathways: a) the particle surface causes oxidative stress resulting in increased intracellular calcium and gene activation; b) transition metals released from particles result in oxidative stress, increased intracellular calcium, and gene activation; c) cell surface receptors are activated by transition metals released from particles, resulting in subsequent gene activation; or d) intracellular distribution of NSPs to mitochondria generates oxidative stress.
Figure 4 Percentage of neutrophils in lung lavage of rats (A,B) and mice (C,D) as indicators of inflammation 24 hr after intratracheal instillation of different mass doses of 20-nm and 250-nm TiO2 particles in rats and mice. (A,C) The steeper dose response of nanosized TiO2 is obvious when the dose is expressed as mass. (B,D) The same dose response relationship as in (A,C) but with dose expressed as particle surface area; this indicates that particle surface area seems to be a more appropriate dosemetric for comparing effects of different-sized particles, provided they are of the same chemical structure (anatase TiO2 in this case). Data show mean ± SD.
Figure 5 Routes of exposure, uptake, distribution, and degradation of NSPs in the environment. Solid lines indicate routes that have been demonstrated in the laboratory or field or that are currently in use (remediation). Magenta lettering indicates possible degradation routes, and blue lettering indicates possible sinks and sources of NSPs.
Figure 6 NPs have been shown to release oxyradicals [pictured here is the mechanism of C60 as determined by Yamakoshi et al. (2003)], which can interact with the antioxidant defense system. Abbreviations: GPx, glutathione peroxidase; GSH, reduced glutathione; GSSG, oxidized glutathione; ISC, intersystem crossing; R, any organic molecule; SOD, superoxide dismutase. In addition to fullerenes, metals such as cadmium, iron, or nickel quantum dots, or iron from SWNT manufacturing, could also act in Fenton-type reactions. Phase II biotransformation, ascorbic acid, vitamin E, beta carotene, and other interactions are not shown.
Figure 7 Some basic shapes of exposure–response or dose–response relationships. Abbreviations: H, hormetic (biphasic); L, linear (no threshold); S, supralinear; T, threshold. Prerequisites for establishing these relationships for NSPs from in vitro or in vivo studies include a sufficient number of data points, that is, over a wide range of exposure concentrations or doses; knowledge about exposure levels; and information about correlation of exposure with doses at the organismal or cellular level (an exposure is not a dose). Dose–response curves of different shapes can be extrapolated when only response data at high dose levels (indicated by dashed oval) are available. Lack of data in the low—oftentimes the most relevant—dose range can result in severe misinterpretation if a threshold or even a hormetic response is present. Consideration also needs to be given to the likelihood that the shape or slope of exposure–dose–response relationships change for susceptible parts of the population.
Figure 8 Predicted fractional deposition of inhaled particles in the nasopharyngeal, tracheobronchial, and alveolar region of the human respiratory tract during nose breathing. Based on data from the International Commission on Radiological Protection (1994). Drawing courtesy of J. Harkema.
Figure 9 Pathways of particle clearance (disposition) in and out of the respiratory tract. There are significant differences between NSPs and larger particles for some of these pathways (see “Disposition of NSPs in the respiratory tract”). Drawing courtesy of J. Harkema.
Figure 10 In vivo retention of inhaled nanosized and larger particles in alveolar macrophages (A) and in exhaustively lavaged lungs (epithelial and interstitial retention; B) 24 hr postexposure. The alveolar macrophage is the most important defense mechanism in the alveolar region for fine and coarse particles, yet inhaled singlet NSPs are not efficiently phagocytized by alveolar macrophages.
Figure 11 Different forms of caveolae and cellular tight junctions function as translocation mechanisms across cell layers. Depending on particle surface chemistry, NSPs have been shown to transcytose across alveolar type I epithelial cells and capillary endothelial cells (Table 4), but not via cellular tight junctions in the healthy state (A). However, in a compromised or disease state (e.g., endotoxin exposure; B) translocation across widened tight junction occurs as well (Heckel et al. 2004). This indicates that assessing potential effects of NSPs in the compromised state is an important component of nanotoxicology. Adapted from Cohen et al. (2004).
Figure 12 Close proximity of olfactory mucosa to olfactory bulb of the CNS. Inhaled NSP[s], especially below 10 nm, deposit efficiently on the olfactory mucosa by diffusion, similar to airborne “smell” molecules which deposit in this area of olfactory dendritic cilia. Subsequent uptake and translocation of solid NSP[s] along axons of the olfactory nerve has been demonstrated in non-human primates and rodents. Surface chemistry of the particles may influence their neuronal translocation. Copyright © the McGraw-Hill Companies, Inc. Reproduced from Widmaier et al. (2004) with permission from McGraw-Hill.
Figure 13 Occlusion of the right nostril of rats during 6-hr inhalation of nanosized MnO2 particles (~ 30 nm CMD, ~ 450 μg/m3) resulted in accumulation of Mn only in the left olfactory bulb only at 24 hr after dosing. Data are mean ± SD. Data from Feikert et al. (2004).
Figure 14 The epidermis represents a tight barrier against NSP penetration. Quantitatively, dermal translocation will therefore be minimal or nonexistent under normal conditions but increases in areas of skin flexing (Tinkle et al. 2003) and broken skin. Once in the dermis, lymphatic uptake is a major translocation route, likely facilitated by uptake in dendritic cells (epidermis) and macrophages; other potential pathways may include the dense networks of blood circulation and sensory nerves in the dermis. Adapted from Essential Day Spa (2005) with permission from www.essentialdayspa.com.
Figure 15 Risk assessment (NRC 1983) and risk management paradigm for NPs. Risk assessment requires answers to the following questions: Do NPs have adverse effects? What are the dose–response relationships? What are occupational/environmental levels in different media? What is the calculated risk? Once a risk is determined, a risk management decision can be established, including exposure standards and regulations and efforts for effective risk communication. Modified from Oberdörster (1994).
Figure 16 Biokinetics of NSPs. PNS, peripheral nervous system. Although many uptake and translocation routes have been demonstrated, others still are hypothetical and need to be investigated. Translocation rates are largely unknown, as are accumulation and retention in critical target sites and their underlying mechanisms. These, as well as potential adverse effects, largely depend on physicochemical characteristics of the surface and core of NSPs. Both qualitative and quantitative changes in NSP biokinetics in a diseased or compromised organism also need to be considered.
Table 1 UFPs/NPs (< 100 nm), natural and anthropogenic sources.
Anthropogenic
Natural Unintentional Intentional (NPs)
Gas-to-particle conversions Internal combustion engines Controlled size and shape, designed for functionality
Forest fires Power plants
Volcanoes (hot lava) Incinerators Metals, semiconductors, metal oxides, carbon, polymers
Viruses Jet engines
Biogenic magnetite: magnetotactic Metal fumes (smelting, welding, etc.) Nanospheres, -wires, -needles, -tubes, -shells, -rings, -platelets
bacteria protoctists, mollusks, arthropods, fish, birds Polymer fumes
human brain, meteorite (?) Other fumes Untreated, coated (nanotechnology applied to many products: cosmetics, medical, fabrics, electronics, optics, displays, etc.)
Ferritin (12.5 nm) Heated surfaces
Microparticles (< 100 nm; activated cells) Frying, broiling, grilling
Electric motors
Table 2 Particle number and particle surface area per 10 μg/m3 airborne particles.
Particle diameter (μm) Particle no. (cm–3) Particle surface area (μm2/cm3)
5 153,000,000 12,000
20 2,400,000 3,016
250 1,200 240
5,000 0.15 12
Table 3 Clearance mechanisms for inhaled solid particles in the respiratory tract.
Physical clearance processes (translocation)
Mucociliary movement (nasal, tracheobronchial)
Macrophage phagocytosis (tracheobronchial, alveolar)
Epithelial endocytosis (nasal, tracheobronchial, alveolar)
Interstitial translocation (tracheobronchial, alveolar)
Lymphatic drainage (tracheobronchial)
Blood circulation (tracheobronchial, alveolar)
Sensory neurons (nasal, tracheobronchial)
Chemical clearance processesa
Dissolution
Leaching
Protein binding
a Nasal, tracheobronchial, and alveolar regions.
Table 4 Particle size and surface chemistry-related alveolar–capillary translocation.
Particle size (nm) Type Translocation Localization/effect Reference
5–20 Gold, albumin coated Yes Via caveolae Mehta et al. 2004
8 Gold, albumin coated Yes Via “vesicles” König et al. 1993
8 Gold, albumin coated Yes Via caveolae Heckel et al. 2004
18 Iridium Yesa Extrapulmonary organs Kreyling et al. 2002
30 Gold Yes Platelet? Berry et al. 1977
35 Carbon Yes Liver Oberdörster et al. 2002
60 Polystyreneb Yes Thrombus, early Nemmar et al. 2002b
Silva et al., in press
60 Polystyrene ? No thrombus Nemmar et al. 2002b
80 Iridium Yesa Extrapulmonary organs Kreyling et al. 2002
240 Polystyrene, lecithin Yes Monocytes Kato et al. 2003
240 Polystyrene, uncoated No Kato et al. 2003
400 Polystyrene No Thrombus, late Nemmar et al. 2003
?, unknown.
a Minimal.
b Indirect evidence.
Table 5 Translocation of NSPs in the blood circulation to bone marrow in mice.
Particle size Type Finding Reference
~10 nm PEG quantum dots Fast appearance of quantum dots in liver, spleen, lymph nodes, and bone marrow (mouse) Ballou et al. 2004
< 220 nm Metallo-fullerene Highest accumulation in bone marrow after liver; continued increase in bone marrow but decrease in liver (mouse) Cagle et al. 1999
90–250 nm HSA-coated polylactic acid nanoparticles Significant accumulation in bone marrow, second to liver (rat) Bazile et al. 1992
240 nm Polystyrene (nonbiodegradable) polylisohexylcyonacrylate (biodegradable) Rapid passage through endothelium in bone marrow, uptake by phagocytizing cells in tissue (mouse) Gibaud et al. 1996, 1998, 1994
HSA, human serum albumin.
Table 6 Studies of neuronal translocation of UFPs from respiratory tract.
Reference Study
Bodian and Howe 1941 Olfactory axonal transport of polio virus (30 nm) after intranasal instillation in chimpanzee; transport velocity, 2.4 mm/hr
de Lorenzo 1970 Olfactory axonal transport of 50 nm silver-coated gold after intranasal instillation in squirrel monkey; transport velocity, 2.5 mm/hr
Hunter and Dey 1998 Retrograde tracing of trigeminal neurons from nasal epithelium with microspheres
Hunter and Undem 1999 Rhodamine-labeled microspheres (20–200 nm) translocation via sensory nerves of TB region to ganglion nodosum in hamster after intratracheal instillation
Oberdörster et al. 2004 13C particles (CMD ~ 36 nm) in olfactory bulb after whole-body inhalation exposure in rats
TB, tracheobronchial.
Table 7 Rat versus human nasal and olfactory parameters.
Measure Rat Human
Breathing mode Obligatory nose Nasal/oronasal
Area of nasal mucosa ~16 cm3 ~105 cm2
Area of olfactory mucosa (% total mucosa) ~8 cm3 (50) ~5.25 cm2 (5)
Percent nasal airflow going to olfactory mucosa ~15 ~10
Weight of olfactory bulb ~85 ng ~168 ng
Based on Keyhani et al. (1997), Kimbell et al. (1997), and Turetsky et al. (2003).
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7651ehp0113-00084016002370ReviewsPersonalized Exposure Assessment: Promising Approaches for Human Environmental Health Research Weis Brenda K. 1Balshaw David 1Barr John R. 2Brown David 3Ellisman Mark 4Lioy Paul 5Omenn Gilbert 6Potter John D. 7Smith Martyn T. 8Sohn Lydia 9Suk William A. 1Sumner Susan 10Swenberg James 11Walt David R. 12Watkins Simon 13Thompson Claudia 1Wilson Samuel H. 31Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA2Centers for Disease Control and Prevention, Atlanta, Georgia, USA3Office of the Director, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA4Department of Neuroscience, University of California at San Diego, La Jolla, California, USA5Environmental and Occupational Health Sciences Institute, University of Medicine and Dentistry of New Jersey at Rutgers University, Piscataway, New Jersey, USA6Schools of Medicine and Public Health, University of Michigan, Ann Arbor, Michigan, USA7Fred Hutchinson Cancer Research Center, Seattle, Washington, USA8School of Public Health and9Department of Mechanical Engineering, University of California at Berkeley, Berkeley, California, USA10Research Triangle Institute, Research Triangle Park, North Carolina, USA11Department of Environmental Science and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA12Department of Chemistry, Tufts University, Medford, Massachusetts, USA13 Department of Cell Biology and Physiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USAAddress correspondence to B.K. Weis, NIEHS/DERT, P.O. Box 12233, MD EC-27, 111 TW Alexander Dr., Research Triangle Park, NC 27709 USA Telephone: (919) 541-4964. Fax: (919) 541-4937. E-mail:
[email protected] thank A.P. Sassaman for her careful review of the manuscript.
The authors declare they have no competing financial interests.
7 2005 3 3 2005 113 7 840 848 9 10 2004 3 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. New technologies and methods for assessing human exposure to chemicals, dietary and lifestyle factors, infectious agents, and other stressors provide an opportunity to extend the range of human health investigations and advance our understanding of the relationship between environmental exposure and disease. An ad hoc Committee on Environmental Exposure Technology Development was convened to identify new technologies and methods for deriving personalized exposure measurements for application to environmental health studies. The committee identified a “toolbox” of methods for measuring external (environmental) and internal (biologic) exposure and assessing human behaviors that influence the likelihood of exposure to environmental agents. The methods use environmental sensors, geographic information systems, biologic sensors, toxicogenomics, and body burden (biologic) measurements. We discuss each of the methods in relation to current use in human health research; specific gaps in the development, validation, and application of the methods are highlighted. We also present a conceptual framework for moving these technologies into use and acceptance by the scientific community. The framework focuses on understanding complex human diseases using an integrated approach to exposure assessment to define particular exposure–disease relationships and the interaction of genetic and environmental factors in disease occurrence. Improved methods for exposure assessment will result in better means of monitoring and targeting intervention and prevention programs.
body burdenepidemiologyexposureexposure assessmentexposure technologygeographic information systemsGISsensors
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Well-designed epidemiologic studies are the desired approach for defining the relationships between environmental exposures and human disease. This is partly because human health studies provide the research framework for addressing issues of individual susceptibility to exposure and disease. Furthermore, they are much richer in relevant information than is simple extrapolation from laboratory studies with nonhuman models. This is underscored in recent articles highlighting the importance of designing studies in which interactions between the environment and genetics can be examined to address important health outcomes (Collins 2004; Potter 2004). There is wide agreement in the scientific community that diseases that contribute the greatest public health burden to society result from complex interactions between genetic and environmental factors, such as chemical pollutants, nutrition, lifestyle, infectious agents, and stress (Doll and Peto 1981; Hemminki et al. 2001; World Cancer Research Fund Panel 1997). Environmental factors are an attractive target for disease prevention, especially when susceptible subgroups within the population can be identified. The lack of accurate, quantitative measures of exposure, and information about their relationship to one another and to disease, is the greatest source of uncertainty in epidemiologic studies, limiting the power of such studies to enable definitive conclusions about the association between exposure and disease. New technologies are available for improving exposure assessment in human health investigations and can be exploited in environmental health research, creating a public health strategy for guiding health research and for translating basic research findings into effective prevention, intervention, and treatment efforts.
Exposure Assessment Methods
The cornerstone of exposure assessment in epidemiologic studies is the development of the exposure metric, the estimate of exposure for each individual of the study population. Ideally, the metric is developed independently for each individual using an actual measurement of exposure that can be validated (Schulte and Perera 1993). Typically, the exposure metric is based on the concentration of specific chemicals, their metabolites, or reaction products in a biologic sample such as blood, urine, or saliva. Obtaining an actual exposure measurement may not be practical when the exposure has occurred in the past and can no longer be detected in a biologic sample. In these instances, the metric is usually developed from environmental monitoring data and chemical transport and fate models using assumptions about the activity patterns and age-specific variables that predict exposure in relation to frequency, duration, and route of entry into the body [Lioy 1990; U.S. Environmental Protection Agency (EPA) 1989). Thus, a theoretical construct is developed for estimating potential exposure to hypothetical or actual individuals of the study population. Uncertainty is an obvious concern when the exposure assessment is derived from a theoretical rather than an evidence-based construct.
Epidemiology relies on inference of associations between exposure and response variables. Typically, the measurements of response in epidemiologic studies reflect late-stage end points of morbidity, mortality, body weight decrease, tumor development, and tissue pathology (Bocchetta and Carbone 2004; Maier et al 2004). Defining risk at a late stage in the disease process provides little opportunity to intervene and redirect the outcome. It is clearly more desirable to identify early changes in biologic processes that can serve as predictive markers of exposure, of early effect, or of susceptibility [Committee on Biological Markers of the National Research Council (NRC) 1987]. These components of the proposed continuum between exposure and disease have been described in a number of reports (Lioy 1990; Maier et al. 2004; NRC 1987; Omenn 2002; Perera and Weinstein 1982; Pesch et al. 2004; Schulte 1989; Waters and Fostel 2004). It is important to remember that the distinctions between exposure and response in this continuum are arbitrary. As scientists, we are not able to measure dynamic biologic processes in real time; instead, we must rely on static measurements made at a single or multiple time points. Thus, some markers may represent the event of interest, or may be the event itself, or may be a predictor of the event (Goldstein 1995; Schulte and Perera 1993). This point is illustrated in Figure 1 using data from several human studies evaluating genetic and biologic markers of susceptibility, biologically effective dose, and carcinogenesis from exposure to polycyclic aromatic hydrocarbons (PAHs) in tobacco smoke and urban air (Mooney et al. 2005; Perera et al. 2002, 2004; Poirier and Beland 1992; Rundle et al. 2000; Tang et al. 1995; Veglia et al. 2003). Collectively, the findings illustrate that genetic and biologic markers reflect different biologic events in the overall exposure–disease continuum. The events do not necessarily reflect linearity, although they may be related, nor do they conform to the conventional boundaries for exposure and response. Appropriately, the assessment of exposure and risk should focus on understanding the biologic processes of human disease by defining markers that represent and can link events, both genetic and environmental, in the exposure–disease relationship. In this context, there are multiple approaches for defining the relationship between exposure and disease; some of these will be based on qualitative data that are not readily amenable to conventional dose–response analysis, and each will require a specialized assessment and validation strategy (Rebbeck et al. 2004a; Schulte and Perera 1993).
The biologic response to environmental exposure occurs as a result of complex interactions between multiple genetic, environmental, and behavioral factors, highlighting the importance of defining markers of genetic variation that confer differential functional significance in target cellular pathways. Several programs supported by the National Institute of Environmental Health Sciences (NIEHS), such as the Environmental Genome Project and Children’s Environmental Health Sciences Centers, focus on identifying genetic determinants of susceptibility and the role of gene–environment interactions in disease (Gilliland et al. 2002a, 2002b; Lan et al. 2004; Rollinson et al. 2004; Skibola et al. 2004). For example, a cross-sectional study of benzene-exposed shoe workers (Lan et al. 2004) identified two genetic variants in key metabolizing enzymes, myeloperoxidase and NAD(P)H:quinone oxidoreductase, that influence susceptibility to benzene hematotoxicity, showing a strong gene–dose effect that persisted in workers exposed to benzene at very low levels (< 1 ppm). The findings are particularly robust in relation to previous occupational studies of benzene-induced hematotoxicity because, in this study, personalized exposure monitoring was conducted over a 16-month period and individual air monitoring was linked to specific end points of toxicity. The study highlights the use of personalized exposure and genetic information to define individual susceptibility.
Despite recent advances in genetic susceptibility studies, challenges remain in defining the functional significance of genetic variants and their interaction with environmental factors in biologic systems. In general, health researchers lack reliable, high-throughput, and cost-effective approaches to measure early changes in biologic processes, particularly at the molecular level. The information can provide insight into the biologic and health significance of genetic variants in human populations. Even when information about early molecular and cellular events is available, it is often difficult to interpret in relation to a point of departure from normal physiologic adaptive response to adverse response. At present, the usual information available generally provides limited insight into the relationship between exposure and health risk in individuals or populations.
An ad hoc Committee on Environmental Exposure Technology Development met several times during the summer of 2004 to identify new technologies and methods for improving exposure assessment in human health research. The committee identified a “toolbox” of methods that can be used alone or in combinations to provide information about individual exposure for a variety of exposure scenarios (Lioy 1995). Certain methods within the toolbox, such as environmental sensors and geographic information systems (GIS), can be used to derive information about external environmental exposures and the personal activity patterns that influence the magnitude, frequency, duration, and pathways of exposure. Other methods, such as biologic sensors, toxicogenomics, and body burden assays, can be used to derive measurements of internal biologic exposure. Linking the data sets across multiple scales provides an integrated view of exposure that is needed to define complex exposure–disease relationships and the interplay between genes, environmental factors, and behavior in disease occurrence (Lioy 1995; Maier et al. 2004). We discuss each of these methods in relation to current applicability to human health studies; we also identify specific gaps in the development, validation, and application of the methods. We identified specific activities as first-generation (first 5 years) and second-generation (5–10 years) priorities for moving these technologies into full use and acceptance by the scientific community (Table 1). Finally, the committee developed a conceptual framework for integrating these new technologies in human health studies (Figure 2). The strategy identifies elements of the study design and implementation where new approaches to human-exposure assessment can be incorporated. Implementation of the strategy focuses on common complex human diseases, such as asthma and respiratory disease, neurodegenerative disease, and cancer, each of which represents a significant public health burden to society. Adopting a disease-first approach to exposure assessment allows researchers to take full advantage of new scientific approaches that are currently available in order to advance current knowledge about important diseases (Wilson and Suk, in press). In addition, for each of these outcomes there is substantial evidence of genetic and environmental risk, providing a logical basis for focusing new health research, prevention, and intervention efforts.
Environmental Exposure Methods
Environmental sensors.
Environment-sensing devices provide exposure information on a variety of scales, including macro-level exposures in the ambient environment such as from industrial effluents; microscale exposures in the household, workplace, and personal environment; and nanoscale exposures at the points of human contact. Over the past 15 years, there has been significant progress in the development of sensors for monitoring a variety of chemical and biologic agents in the ambient and personal environment (Cui et al. 2001; Georgieva et al. 2002; Haruyama 2003; Svitel et al. 2001). Macroscale technologies such as laser-based and infrared-radiation–based sensors are currently being used for assessing population exposures to sulfur and nitric oxides in industrial-stack effluents. Other microscale sensors, including personal dosimeters, are being used to monitor levels of carbon dioxide, carbon monoxide, volatile organic compounds, pesticides, and PAHs in the workplace, household, and personal environment. The benefits of using microscale over macroscale devices for environmental agents include increased sensitivity for a range of compound classes, decreased volumes of analytic reagents, and increased throughput and potential for multiplexing.
New technologies to assess environmental exposures at the personal level are being conceived, but development is moving slowly. Recent efforts have focused on automated “lab-on-a-chip” sensing devices for detecting environmental agents (Hood et al. 2004). The devices deliver nanoscale volumes of sample to a detection element based on electricity, fluorescence, affinity, or cell function. The sensors can be very small, inexpensive, and easy to use and offer the potential for continuous monitoring on a global or microscale, making them ideal tools for monitoring personal exposure to individual compounds and mixtures.
The use of environmental sensors for large human studies will remain elusive until important issues related to sample handling and control, comprehensiveness of analytic probes, and validation of results are adequately addressed. Targeted efforts by academic researchers and industry are needed, in the short term, to develop sensing devices to accurately and reliably measure high-priority environmental agents, including chemical and biologic agents and complex mixtures, in small-scale projects that have the potential for scalability. This remains an unmet need that was identified in the NRC report on exposure assessment (NRC 1991). Some efforts could build on existing technological developments of countermeasures against bioweapons based on bacteria, viruses, and toxins (Brown 2004). Personal monitors could include global-positioning systems so that the changes in exposure could be tracked as an individual moves through the environment. In aggregate, this could enable researchers to develop data sets that allow mapping and modeling of patterns of exposures across communities.
GIS technology.
GIS technology is now an increasingly popular tool for developing the exposure metric in epidemiologic studies (Beyea and Hatch 1999; Jarup 2004; Nuckols et al. 2004). The technology is used to create distinct electronic data displays or layers that can be linked spatially and temporally using mathematical modeling systems or tools. The data displays are created by piecing together available environmental, population, and land-use data sets. Many of the available data sets have been generated by government agencies for purposes of national surveillance and environmental regulation. For example, the U.S. EPA and the U.S. Geological Survey (USGS) monitor ambient environmental contaminant levels, and the U.S. Census Bureau collects information about population demographics and land-use patterns. The data sets can provide information about the sources and concentrations of environmental agents at specific monitor points, the nature of the immediate neighborhood, and the characteristics of the study population. The environmental data sets tend to cover very broad geographic scales, such as fixed monitoring stations and contaminant release points for national air and water quality assessment programs (U.S. EPA 2004). Although the data are useful for defining trends in regional environmental quality and assessing compliance with environmental regulations, the scale and resolution may not be appropriate for estimating personal exposures in human studies (Nuckols et al. 2004). Efforts to create more comprehensive environmental data displays and mapping systems that may be used in human health research are under way. For example, the USGS, in collaboration with the NIEHS, has launched the Environmental Mercury Mapping, Modeling and Analysis (EMMMA) website for visualizing the distribution of mercury across geographic, temporal, and biologic scales (EMMMA 2004). The system uses GIS technology to link distinct data sets on mercury levels in environmental media (soil, stream sediments) and multiple fish species and tissues. Web-based mapping tools are used to analyze the mercury data and generate local and national maps of mercury distribution that can be used in human research.
A number of epidemiologic studies have used GIS technology to assess environmental exposures. In many of the early studies, GIS was used to define the study population and to develop the exposure surrogate based on the proximity of the study population to the contaminant source. More recently, GIS has been used in conjunction with computer-based geographic models and analysis tools for predicting contaminant transport and fate, extrapolating between data points to identify potential pathways and routes of exposure, and defining the temporal and spatial distribution of exposure across the study population (Beyea and Hatch 1999; Georgopoulos et al. 1997; Jarup 2004). For example, GIS-based approaches have been used for developing individual metrics for exposure to pesticides, drinking water contaminants, and air pollutants such as nitric oxide, sulfur dioxide, and particulates (Bellander et al. 2001; Kunzli et al. 2005; Nuckols et al. 2004). Only very recently, researchers have been considering the use of GIS to derive personal exposure estimates by linking information about personal activity and behavioral patterns with environmental data (Hellstrom et al. 2004; Jarup 2004; Nuckols et al. 2004). Although no applications of GIS in epidemiologic studies have been reported, several researchers have used GIS with global positioning systems (GPS) to define activity patterns that could conceivably be linked to environmental data for exposure assessment (Elgethun et al. 2003; Nuckols et al. 2004; Phillips et al. 2001). In two studies, a GPS data recorder was used to obtain time–location data that could be linked with information about environmental exposure to pesticides in children (Elgethun et al. 2003) and used to validate exposure information collected by personal diaries (Phillips et al. 2001). A recent report used GIS technology to develop a household-level priority model for childhood lead poisoning in North Carolina (Miranda and Dolinoy 2005). The model linked household location and age with blood lead level and demographics to define potentially at-risk individuals and subpopulations. The model could be expanded to include personal activity, health outcome, and genetic information. Establishing temporal and spatial linkages could be used to assess potential exposure–disease relationships and define genetically susceptible individuals (Miranda and Dolinoy 2005). Information about activity patterns could be collected from study participants using biologic sensing devices and GPS units. Using GIS to spatially integrate personal behavior patterns with environmental data will enable researchers to develop hypotheses about exposure–disease relationships that can be tested in focused studies of potentially at-risk individuals or subgroups of the population. Smaller, focused studies provide an opportunity to use more costly and exploratory technologies, such as environmental or biologic sensors and toxicogenomics, to develop more personalized measures of environmental exposure. In general, environmental or biologic measurements with tight coefficients of technical variation but large ranges of real variation will be most informative. If the environmental or biologic variation is small or small in relation to measurement error, the measurement will have little utility for exposure estimation.
Biologic Exposure Methods
The greatest impediment to conducting environmental epidemiologic studies is the lack of accurate, quantitative measures of exposure at points of human contact and within the organism. Technologies based on biologic sensors, toxicogenomics, and body burden (biologic) measurements may be useful in human studies to address these critical information gaps.
Biologic sensors.
A common limitation in exposure assessment is the lack of information about patterns of physical activity and behavior that affect the likelihood of exposure, the frequency and duration of exposure, and the uptake and distribution of environmental agents in the body. Personal dosimetry devices are able to measure individual variables related to activity such as motion, temperature, pressure, energy use, respiratory function, and heart rate (Balbatun et al. 2003; Cao et al. 2004; Jianrong et al. 2004; Kalinowski et al. 2004; Koo et al. 2004; Miljanic et al. 2003; Mo and Smart 2004; Salimi et al. 2004). Many of the devices have been applied successfully in clinical, radiologic, and other occupational and laboratory settings. For application to epidemiology, the technologies require reengineering to combine them into a single small device or set of portable devices that provide readouts that can be integrated over space and time. Wireless tracking devices, global positioning systems, and videography can be incorporated into the sensors, allowing researchers to follow enrolled study participants as they move around. Biologic sensors, some of which may contain tracking devices, are being considered for use by the military in homeland defense (Center for Nanoscale Science and Technology 2004) but have not been integrated into epidemiology study designs. Inclusion of tracking devices in personal-dosimetry tools will enable researchers to link data about external and internal exposures with personal activity patterns. Establishing such linkages provides a basis for developing models for predicting personal biologic exposure based on external measurements that are more readily available in epidemiologic studies.
Biosensors are devices that contain a biologic sensing agent, such as an enzyme, antibody, or microorganism, to detect the presence of a specific analyte in the body. Detection of the analyte produces a biologic change that is converted by a transducer component into a measurable output, such as an electrical or optical signal (Mo and Smart 2004). Biologic sensors hold great promise for improving exposure assessment because many such devices provide rapid, accurate, and quantitative detection and monitoring of a variety of exposures, including mixtures, at a personal level (Bayley and Cremer 2001; Cao et al. 2002; Cui et al. 2001; Culha et al. 2004; Fehr et al. 2005; Haruyama 2003; Jianrong et al. 2004; Mehrvar and Abdi 2004; Salimi et al. 2004; Vo-Dinh 2002).
There has been a renaissance in biologic sensor development over the last several decades. Nanoscale technologies are being proposed for use in medical and basic-research applications (National Cancer Institute 2004; www.biosensors-congress.elsevier.com
2004). Miniature sensors with micro- and even nano-scale dimensions are currently being developed, with many technologies available for laboratory- and research-based measurements. New electrochemical and optical sensors, based on such techniques as surface plasmon resonance, surface enhanced Raman spectroscopy, fluorescence, and microelectrodes, offer promise for personalizing exposure assessment. Engineered materials such as polymers, smart materials, nano- and microstructured materials, and affinity-based reagents such as aptamers, phage-display libraries, and single-chain antibodies have been employed for sensing and offer opportunities for providing integrated analyses for environmental epidemiologic studies. Because sensors have the potential to measure continuously, they provide many opportunities for improving our ability to reduce uncertainties in characterizing human exposure. However, continuous monitoring will yield massive data sets that will require sophisticated database structures and query capabilities, as well as new biostatistical tools for analysis and integration. Consequently, studies incorporating biologic sensing technologies will require appropriate computational tools and support (Porod et al. 2004). With their potentially small dimensions, sensors can be integrated into networks to provide global sensing networks in which continuous spatiotemporal monitoring is achieved. Network development is a complex undertaking, however, and will not be feasible in the near future. Nonetheless, activities needed to develop the networks can be readily piggybacked onto current efforts to address homeland security and public health infrastructure (Brown 2004).
Toxicogenomics for defining molecular signatures.
Toxicogenomics is a broad field that seeks to define, on a global basis, the levels, activities, regulation, and interaction of genes, mRNA transcripts (transcriptomics), proteins (proteomics), and metabolites (metabolomics) in a biologic sample or system. The molecular signature is derived from the complete data set of mRNA, protein, or metabolite signals from a biologic sample using data-reduction approaches. A useful signature is composed of an ensemble of markers that allows exposures or states to be distinguished with high sensitivity and high specificity (Pepe et al. 2001).
Toxicogenomics methods are evolving at different rates, largely because of trends in scientific discovery, available funding, and ease of platform development and validation. Overall, the experimental methods are approachable, although improvements are still needed to increase sample throughput, quantification, and information yield per sample and to decrease costs. Toxicogenomics-based methods are widely used in laboratory settings to develop biomarkers of exposure, early biologic response, and susceptibility. The approaches have been used for classifying exposures to a variety of chemicals and drugs, for example, hydrazine, 2-bromoethanamine, lead acetate, cadmium, and acetaminophen based on mechanism of action and dose; they have been used for classifying health outcomes for cardiovascular disease and cancer based on disease status and severity (Brindle et al. 2002; Chevalier 2004; Choi et al. 2004; Chung et al. 2002; Coen et al. 2003; Griffin et al. 2001; Hamadeh et al. 2002; Holleman et al. 2004; Holmes et al. 2000, 2001; Kimura et al. 2004; Petricoin et al. 2004a, 2004b; Posadas et al. 2004; Robertson et al. 2000; Tallman et al. 2004; Troyer et al. 2004). Toxicogenomics approaches have been used for predicting the mechanism of action of toxicants and drugs (Gavanagh et al. 2000; German et al. 2003; Phelps et al. 2002; Toraason et al. 2004) and for characterizing the functional significance of genetic polymorphisms (Raamsdonk et al. 2001; Suarez-Merino et al. 2005). The primary basis of classification and discovery in these studies is the molecular signature. The signature itself provides little information about the underlying mechanisms of biologic response. However, once the discriminating elements of the molecular signature are identified, biologic function can be inferred by mapping components to known biologic pathways and verifying functionality in follow-up studies.
Misclassification of exposure and outcome is an important source of bias in epidemiologic studies, and most study designs provide little opportunity to focus on biologic mechanisms underlying the exposure–disease relationship. Toxicogenomics-based methods are being increasingly used in epidemiology and clinical studies, but inclusion is sporadic, primarily due to the lack of readily obtainable, stable, and abundant sample material. Despite the enormous promise of toxicogenomics for advancing our understanding of the relationship between environmental exposure and disease, the challenge has been, and will continue to be, the development of genetic and biologic markers that are predictive of adverse health outcomes in both experimental and human studies.
Toxicogenomics provides an opportunity to move beyond traditional approaches to exposure assessment based on one chemical agent in one environmental medium (air, water, soil) at a time, to a more realistic view of exposure involving multiple exposures, at potentially low environmental concentrations, and multiple biologic response pathways. This comprehensive view of exposure is needed to define complex exposure–disease relationships and the interactions between genetic and environmental factors in human disease. Achieving this goal will require new information and sophisticated modeling capabilities to annotate the components of biologic pathways and to describe their interactions under normal homeostatic conditions and after perturbation by environmental agents. Model development is time consuming, however, and requires a critical mass of appropriate data from human and experimental laboratory studies to be collected and organized. In the short term, molecular signature studies conducted using laboratory animals and human cells lines will be useful for guiding the interpretation of exposure data from epidemiologic studies.
Background levels of expression and variability for mRNA transcripts, proteins, and metabolites in human tissues are currently not known but must be defined if toxicogenomics methods are to be used to assess personal exposures in epidemiologic studies. Expression levels are expected to vary widely because of differences in diet, lifestyle, health status, and genetic predisposition. In addition, expression changes due to low-level environmental exposures may not be distinguishable from baseline, given this inherent variability. Developing background measures of expression for mRNA transcripts, proteins, and metabolites in biosamples will take time and require a critical mass of data. Equally important is the need for technology platforms that produce reliable, quantitative, and stable measurements over time. Standards are needed to assess the quality and reliability of data and to facilitate data sharing and compilation across multiple studies using a variety of technology platforms. Efforts to develop data and technology standards for transcriptomics, proteomics, and metabolomics are under way (Ball et al. 2004; Freeman 2004; Henry 2004; Kaiser 2002; Lindon et al. 2003; Omenn 2004; Orchard et al. 2004; Weis 2005). Preliminary findings suggest that data reproducibility across laboratories is highest when standardized data reporting requirements, technology platforms, and experimental protocols are used (Lindon et al. 2003; Weis 2005; Yauk et al. 2004). These are important considerations for designing epidemiologic studies to ensure that meta-analyses and inferences can be made across other populations.
Body burden (biologic) measurements.
Assays are available to measure individual body burden or internal dose for a variety of environmental agents, including heavy metals, phthalates, and organic compounds [Barr et al. 2003; Marin et al. 2004; Metcalf and Orloff 2004; National Health and Nutrition Examination Survey (NHANES) 2005; Pirkle et al. 1995]. Most of the assays are based on the quantification of chemicals, their metabolites, or reaction products in blood and urine samples. For most chemicals, the assays produce reliable, quantitative measurements. However, there are several limitations of the available assays. Many assays lack the sensitivity and specificity needed to detect the broad range of compounds present in biologic samples, and many assays have not been validated at background levels in the environment. Most assays can accommodate only moderate sample throughput. Sample collection for blood is invasive and requires clinical supervision and informed consent, which can limit sample collection from infants and children. Modern analytic methods have increased sensitivity, thus requiring smaller sample volumes, which are easier to obtain. Biologic measurements of body burden are difficult to interpret in relation to the biologically effective dose and to early biologic response, but they can provide helpful links to associated health outcome. Population-based surveys such as NHANES, a program of the Centers for Disease Control and Prevention (CDC), provide information about the distribution of many environmental chemicals in the U.S. population (NHANES 2005). Such population estimates are useful for benchmarking measurements of individual exposure in epidemiologic studies but are not designed to provide information about the relative contribution of multiple dietary and lifestyle factors, and the impact of genetic variability and stress on susceptibility. These components of exposure are critical to the understanding of dose in the context of the likelihood of adverse effects and the need for intervention.
A number of innovative, sensitive, and specific methods for measuring internal dose, including biologically effective dose, are currently under development. New methods based on traditional technologies such as chromatography and mass spectrometry are being implemented in the CDC biomonitoring program (Barr et al. 2003). The methods are being used in basic research to identify exposure biomarkers for environmental chemicals based on DNA and protein adducts (Barr et al. 2003; Chen et al. 1996; Perera et al. 2004; Swenberg 2004). Quantification of DNA and protein biomarkers also provides information about the role of genetic polymorphisms in exposure susceptibility for important environmental compounds such as PAHs (Perera et al. 2004; Swenberg 2004). The methods for biologic measurement offer a wide range of sample collection matrices, including hair, saliva, and tissue, and are capable of detecting a myriad of compounds in a single sample. Continued development of these methods is needed to solve problems in sample collection and analysis, sample throughput, and instrument sensitivity.
Additional research is needed to define the functional significance of DNA and protein adducts in human disease processes. Adduct formation occurs naturally at a high frequency in the human genome, making it difficult to define the relative contribution of environmental stressors to overall genomic alteration and to assign a predictive value to specific adduct formation for the risk of developing human disease. Studies are needed to link biologic measurements of body burden or adduct formation to environmental concentrations of exposure and to early biologic responses that are predictive of adverse outcome. Establishing such quantitative links will enable researchers to develop more accurate models for predicting internal dose based on external environmental concentrations and for predicting disease risk based on internal dose. With such improvements, biologic measurements will become an invaluable source of information for personalizing exposure assessment in human health studies.
A Strategy for the Future
Clearly, a continuum exists between biologic markers of exposure and effect. Each step along the way is an opportunity for prevention through elimination or minimization of exposure (Goldstein 1995) or, in the case of beneficial exposures such as some dietary constituents, augmentation. Realistically, single markers may never provide a definitive answer linking environmental exposure to disease because disease processes involve complex interactions among genes, environmental factors, and behavior. What is needed is a combination of genetic and biologic markers linking critical events in the exposure–disease continuum and a toolbox of methods to derive them. The toolbox should include new experimental technologies, and bioinformatics and statistical tools (Molidor et al. 2003; Rebbeck et al. 2004a), to assess the contribution of multiple genetic variants in multiple biologic pathways and health end points.
As an ad hoc Committee on Environmental Exposure Technology Development, we identified a toolbox of promising new methods, and improvements to existing methods, to personalize exposure assessment in human health research (Table 1). Specific activities needed to enhance technology development for exposure assessment are identified as first generation and second generation. Highest priority is given to activities that a) address needed improvements that are readily identifiable and achievable and fill critical gaps in knowledge and b) generate information that is high quality, reliable, and stable over time. The toolbox is intended to facilitate technology applications in exposure assessment in the public and private sectors. It is clear that application of new technologies will require multidisciplinary teams of exposure analysts, epidemiologists, clinicians, molecular biologists, toxicologists, statisticians, and bioinformaticians because the new approaches cannot be applied successfully by any one discipline independently.
We developed a conceptual framework for integrating these new technologies in human health research. The framework focuses on common complex human diseases, such as asthma and respiratory disease, neuro-degenerative disease, and cancer, for each of which there is substantial evidence of genetic and environmental risk and each of which represents a significant public health burden. Environmental agents can be used to understand the disease processes by defining the interaction between genes and environmental factors in susceptible populations (Schwartz et al. 2004). This framework combines human and laboratory studies and incorporates new technologies, as appropriate, to answer the biologic questions of interest. For example, recent work by Kiechl et al. (2002) used a prospective population-based survey approach to identify important genetic variants in the toll-like receptor 4 (tlr4) that confer differential susceptibility to airway inflammatory response from inhaled bacterial lipopolysaccharides (LPS). As a follow-up, quantitative trait locus (QTL) analysis and microarray-based gene expression analysis were combined in a study of genetic recombinant inbred mice strains with differential susceptibility to inhaled LPS to identify target genes (n = 28), in addition to tlr4, that may have a causal or modifier role in the innate response to LPS (Cook et al. 2004). Functional genomics approaches can then be used to assess the biologic significance of the target genes and their protein products in biologic pathways of response.
The framework identifies aspects of the study design and implementation where new approaches to exposure assessment can be incorporated to identify genetic variants of susceptibility, link genotype and phenotype data for targeted diseases and exposures, and assess the functional significance of targeted gene variants and their interactions with environmental factors. These aspects of the study design and implementation are presented in Figure 2 and discussed briefly here.
Identification of priority diseases, plausible environmental factors, genetic determinants, pathways, and model systems.
This identification can be accomplished by reviewing the available scientific literature and publicly available databases of environmental, health, and genetic information. Many of the available data sources are maintained by federal agencies such as the National Institutes of Health (NIH), the U.S. EPA, and the CDC. In addition, academic institutions, hospitals and health care facilities, and industries have developed surveillance programs for specific exposure and health indices in targeted populations. A variety of data sources, such as the GeneSNPs database (Environmental Genome Project 2005) and the International HapMap Project (2005), can be used to identify target gene polymorphisms in human and animal populations. Biologic pathway mapping systems can provide insight into potential biologic processes and research targets for priority diseases. Workshops and meetings can be convened for brainstorming and establishing research priorities. Participants would include representatives from government agencies, academia, and industry who are responsible for environmental and health surveillance, and other scientific experts in exposure assessment, molecular epidemiology, clinical medicine, toxicogenomics, public health, toxicology, and bioinformatics. Workshop participants could define priority diseases and data sources for plausible environmental exposures and genetic susceptibility that are readily available or feasible to obtain.
Identification of target study populations.
Given the exploratory nature of many new exposure assessment technologies, it is not practical to apply them in all human health studies. One approach to study population selection is to identify existing, well-designed and controlled studies that could benefit from the inclusion of new data to improve the exposure assessment aspect of the study. The NIEHS supports a number of environmental health studies focusing on identifying genetic and environmental risk factors and gene–environment interactions in asthma and respiratory disease, neurodegenerative disease, and cancers. Other NIH institutes, the CDC, the U.S. EPA, and other agencies have ongoing studies that may be appropriate. The NHANES program periodically seeks recommendations of new assays for its studies. Highest priority should be given to studies with clearly defined disease outcomes, quantifiable environmental exposures that may be plausibly related to the disease, and an accessible study population. Inclusion of new exposure assessment technologies into these ongoing studies, in particular to derive personalized exposure measurements for individuals or subpopulations at greatest risk of exposure or disease, provides a cost-effective approach to explore the practicality of their implementation and the usefulness of the data they generate. Specific study populations or subpopulations for which body burden measurements, personal monitoring data, and tissue repositories are available or can be readily obtained are particularly attractive candidates.
In addition, new study populations can be identified using global screening tools such as GIS-based technologies to identify specific sub-populations with unusually high rates of the disease or potentially elevated exposures for the disease. The NIEHS and U.S. EPA support a number of investigators who are currently using GIS-based approaches as part of ongoing research projects (Kunzli et al. 2005; Miranda and Dolinoy 2005). Application of GIS-based technologies, together with information about personal activity patterns for the study participants, can be used to identify and target specific subpopulations for in-depth personalized assessment of exposure.
Determinants of genetic variability and susceptibility.
Genotyping can be applied to human studies to identify genetic variants that may predispose individuals to environmental exposure or disease. Genetic linkage and association studies have been used to identify potential susceptibility genes for a number of outcomes, including asthma and chronic obstructive pulmonary disease (Meyers et al 2004; Sharma et al. 2004), inflammatory response to inhaled bacterial pathogens and atherogenesis (Kiechl et al. 2002), acute myeloid leukemia (Rollinson et al. 2004), non-Hodgkin lymphoma (Skibola et al. 2004), and lung cancer (Liu et al. 2004). Some of these studies involved genotyping families to define disease-related genes that co-segregate with DNA markers (Meyers et al. 2004; Sharma et al. 2004). Well-established familial cohorts are an excellent resource for conducting gene discovery studies, especially for complex disorders where disease subtypes (e.g., type I vs. type II diabetes, breast cancer) can be discriminated within families (Merikangas 2003). Both family and twin studies have been useful for determining the relative contribution of genetic and environmental factors in disease occurrence, although the findings may not be generalizable to other populations. Population-based studies are useful for identifying the distribution of newly identified polymorphisms in the population, in particular, susceptibility genes that have low population frequencies. Knowledge of population genetic structure may provide insight into the functional relevance of a genetic variant on a disease trait (Rebbeck et al. 2004a). Public databases containing information about single nucleotide polymorphisms (SNPs) in human populations can be used to identify target gene variants for further study. Once genetic susceptibility genes are identified, other approaches, such as nested case–control studies for specific susceptibility genotypes, may help define environmental factors that contribute to disease risk.
Targeted exposure assessment.
Targeted exposure assessment is needed to identify valid genetic and biologic markers, determine the functional significance of genetic variants, and describe gene–environment interactions in disease. New technologies can be used to define markers of external environmental exposure based on human activity patterns and personal monitoring, and markers of internal biologic dose and response based on body burden measurement, sensors, and toxicogenomics. For many complex diseases, environmental risk factors are not known; application of new approaches provides an opportunity to identify important environmental and behavioral risk factors for disease. Exposure information generated using new approaches should be considered complementary to information collected by study questionnaire or survey, in particular regarding occupational, dietary, and lifestyle factors. To the extent possible, quantitative linkages between environmental data and personal exposure measurements should be established as a basis for developing predictive models for exposure assessment. Integrating data from these new approaches into the study design and data analysis phases will require appropriate rigor of data and sample collection and validation that is intrinsic to the best epidemiologic and clinical research. In addition, improvements in analytic, statistical, and bioinformatics tools will be needed to support the integration of molecular, clinical, and epidemiologic data in human studies (Molidor et al. 2003).
Concurrent studies in appropriate animal models and primary human cell cultures should be considered for developing and validating genetic and biologic markers, establishing the functional significance of candidate genetic variants, and gaining mechanistic insight into gene–environmental interactions in human disease. Many model organisms are not as genetically diverse as are humans but have orthologous genes and biologic pathways that are represented in humans. Comparative studies in model systems with shared genes, functions, and pathways provide the greatest opportunity to define biologically relevant responses to environmental exposures and the impact of genetic variation on that response in humans (Schwartz et al. 2004).
New technologies for personalizing exposure assessment will benefit the scientific and regulatory community by providing range-finding and sensitivity matrices for specific methods, developing baseline data on important environmental factors, and improving the results of exposure-model simulations. Efforts to address genetic or genomic variation alone will have little value in personalizing human exposure assessment unless there are effective linkages with information about environmental and behavioral variables that affect the likelihood of exposure and risk. Therefore, future studies will require that personal genetic information be linked with estimated or measured personal exposure data, while ensuring that individual privacy is protected.
Correction
In the last paragraph of “Exposure Assessment Methods,” the authors added information about the benefit of adopting a disease-first approach to exposure assessment.
Figure 1 A schematic representation of markers of exposure, response, and susceptibility in the exposure–disease continuum: an example for PAHs and cancer. CYP2A6, cytochrome P4502A6 gene; ETS, environmental tobacco smoke; GSTM1, glutathione S-transferase M1 gene; PAHs, polycyclic aromatic hydrocarbons; Arrows indicate predictability of each marker for exposure or disease in the exposure–disease continuum. Adapted from NRC (1987). PAHs in ETS and urban air are a marker for exposure source. GSTM1 null genotype and blood PAH–DNA adducts are independent markers of cancer case status (disease) but have a multiplicative effect in combination (Perera et al. 2002; Tang et al. 1995). GSTM1 null genotype is a predictor of tissue PAH–DNA adducts, which are a marker for altered function (Perera et al. 2002; Rundle et al. 2000; Tang et al. 1995). CYP2A6 variant is a marker for increased internal dose of nicotine and protective effect on cancer development (Spitz et al. 2005). Plasma cotinine is a marker for internal exposure to ETS but is not correlated with blood PAH–DNA adducts (Mooney et al. 2005). Blood PAH adducts are a marker for PAH/ETS exposure, internal dose, biologically effective dose, early biologic response, and cancer (Mooney et al. 2005; Perera et al. 2002, 2004; Poirier and Beland 1992; Veglia et al. 2003; Whyatt et al. 1998). Tissue PAH–DNA adducts are a marker for altered function and cancer (Rundle et al. 2000).
Figure 2 Conceptual strategy for integration of new exposure assessment technologies in human environmental health research.
Table 1 A toolbox of promising exposure assessment technologies and activities for integration in human environmental health research.
Technology First-generation activities Second-generation activities
All technologies Identify priority diseases, plausible environmental exposure factors (including dietary and lifestyle factors, infectious agents), genetic determinants, biologic pathways, and model systems Develop background ranges and study population distribution of parameters for priority environmental exposures, response parameters, and genetic variants
Identify and review available scientific literature and databases in government, academia, and industry
Convene a workshop of experts to establish research priorities.
Environmental sensors Develop and validate in vitro sensors for detecting and quantifying priority environmental exposures Develop multiplexed sensors for continuous monitoring of priority environmental exposures
Develop analytic tools and approaches to link environmental data across multiple scales, from macroenvironmental to personal Develop integrated sensor networks
GIS technology Select priority environmental and population data sets and develop GIS displays Initiate studies using environmental and biologic sensors and other exposure assessment methods to generate GIS displays for individualized exposure assessment in targeted studies
Develop and apply modeling and mapping tools to link environmental and personal exposure data to identify at-risk populations
Biologic sensors Develop wearable personal sensors for monitoring activity patterns Develop deployable in vivo (microscale and nanoscale) sensors for monitoring biologic responses to priority exposures
Develop data management and analytic to support biologic sensing devices
Develop in vitro diagnostic sensors for monitoring early biologic responses to priority environmental factors Develop sensor networks
Toxicogenomicsa Select preferred technology platforms Conduct human and animal studies to validate molecular signatures as markers of exposure, response and susceptibility, and define biologic response pathways for priority exposures and responses
Develop data and technology standards
Develop improved methods of sample preparation and analysis (throughput)
Initiate human and animal studies to develop molecular signatures as markers of exposure, response and susceptibility, and define disease processes
Body burden assays Develop and apply assays to quantify priority exposures in biologic samples Develop and apply new methods to assess biologically effective doses for priority exposures and mixtures
Improve methods of sample preparation and analysis Conduct studies to link body burden with biologically effective dose and environ- mental levels for priority exposures
Improve sample matrix selection, and assay sensitivity and selectivity
New methods, and improvements to existing methods, to personalize exposure assessment in human health research. Specific activities needed to enhance technology development for exposure assessment are identified as first generation (0–5 years from today) and second generation (5–10 years from today).
a Refers to global analysis of genes, gene expression transcripts (transcriptomics), proteins (proteomics), and metabolites (metabolomics).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7657ehp0113-00084916002371ResearchChanges in Levels of Nerve Growth Factor in Nasal Secretions after Capsaicin Inhalation in Patients with Airway Symptoms from Scents and Chemicals Millqvist Eva 1Ternesten-Hasséus Ewa 1Ståhl Arne 1Bende Mats 21Asthma and Allergy Research Group, Department of Respiratory Medicine and Allergy, ahlgrenska University Hospital, Göteborg, Sweden2Allergy Centre, Central Hospital, Skövde, SwedenAddress correspondence to E. Millqvist, Asthma and Allergy Research Group, Department of Respiratory Medicine and Allergy, Sahlgrenska University Hospital, S-413 45 Göteborg, Sweden. Telephone: 46-31-3423635. Fax: 46-31-417824. E-mail:
[email protected] thank S. Rak for valuable discussions and E. Carlsson for skillful laboratory assistance.
This study was supported by grants from the Vårdal Foundation, the Regional Health Care Authority of West Sweden, and the Swedish Heart and Lung Foundation.
The authors declare they have no competing financial interests.
7 2005 17 3 2005 113 7 849 852 13 10 2004 17 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Patients complaining of upper and lower airway symptoms caused by scents and chemicals have previously been shown to have increased cough sensitivity to inhaled capsaicin, but the precise mechanisms behind this reaction are unknown. Hypothesizing that a neurochemical alteration related to sensory hyperreactivity (SHR) of the airway mucosa occurs, we measured levels of nerve growth factor (NGF) in nasal lavage fluid (NAL) before and after capsaicin inhalation provocations and related the capsaicin cough sensitivity to the NGF levels. Thirteen patients with SHR and 14 control subjects were provoked with capsaicin inhalation at three different doses. We measured NGF in NAL before and after provocation and recorded cough and capsaicin-induced symptoms. All subjects demonstrated a dose-dependent cough response to capsaicin inhalation, with a more pronounced effect in patients than in controls. Basal levels of NGF were significantly lower in the patient group than in the control subjects (p < 0.01). After capsaicin provocation, the patients showed a significant increase in NGF (p < 0.01), which was related to capsaicin cough sensitivity. The findings demonstrate that, in patients with airway symptoms induced by scents and chemicals, SHR is real and measurable, demonstrating a pathophysiology in the airways of these patients compared to healthy subjects.
airway symptomschemical intolerancemultiple chemical sensitivitynasal lavage fluidnerve growth factorsensory hyperreactivity
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According to several epidemiologic studies, chemical intolerance is a common problem (Baldwin et al. 1997; Bell et al. 1993; Caress and Steinemann 2003; Meggs et al. 1996), with various kinds of elicited symptoms. Nevertheless, the pathophysiology behind the symptoms is unknown. A comprehensive term for chemical intolerance is “multiple chemical sensitivity” (MCS), which is associated with symptoms in more than one organ after the sufferer has been exposed to very low concentrations of chemical substances (Cullen 1987). Because no objective investigations have, until now, confirmed a diagnosis of MCS, the disorder has been rejected as an established organic disease (American Academy of Allergy 1999; Gots 1995; Staudenmayer 2001).
A subgroup of patients with airway complaints, reporting cough and other airway symptoms from scents and chemicals, had an increased capsaicin cough sensitivity (Millqvist et al. 1998; Ternesten-Hasseus et al. 2002b). This capsaicin-induced cough could be blocked by inhaled anesthesia, and it was suggested that sensory nerves are involved in the pathophysiology (Millqvist 2000). Furthermore, elevated levels of sensory nerve-mediated tachykinin substance P were found in nasal lavage fluid (NAL) of patients with chronic cough and increased capsaicin cough sensitivity (Cho et al. 2003). Consequently, we previously proposed a mechanism of increased sensitivity of the afferent nerves in patients with airway symptoms induced by scents and chemicals and suggested using the term “sensory hyperreactivity” (SHR; Millqvist et al. 1998).
Capsaicin, the ingredient that produces the heat in hot chiles, is a well-known cough-inducing agent when inhaled (Fuller et al. 1985; Karlsson et al. 1988; Midgren et al. 1992). Capsaicin stimulates unmyelinated, afferent sensory C-fibers. The capsaicin receptor, vanilloid receptor 1 (VR1), has been identified (Caterina et al. 1997). This receptor responds not only to capsaicin but may also participate in the detection of noxious thermal and chemical stimuli in vivo (Caterina et al. 1999; Tominaga and Julius 2000). In the skin, VR1 is essential to selective modalities of pain sensation and to tissue injury-induced thermal hyperalgesia (Caterina et al. 2000). There is also a link between pain and hyperalgesia, on the one hand, and neurotrophins and capsaicin, on the other (Anand 1995, 2004; Shu and Mendell 199b, 2001). Increased levels of serum nerve growth factor (NGF) have been reported in patients with MCS, and the levels rose 1.5-fold after provocation with irritating fumes (Kimata 2004).
Neurotrophins are a group of four similar proteins, which are essential for survival and axonal outgrowth and development of sensory neurons, including capsaicin-sensitive fibers (Chun and Patterson 1977; Levi-Montalcini 1987; Lewin and Barde 1996; Shu and Mendell 1999a, 2001). In the airways, the most important neurotrophin is believed to be NGF, but because it is derived primarily from nonneuronal sources, its role in patients with SHR is uncertain. Hypothesizing that a neurochemical alteration takes place in patients with SHR, we measured NGF levels in nasal secretions before and after capsaicin inhalation provocations and related the levels to capsaicin cough sensitivity.
Materials and Methods
The study included 13 nonsmoking patients, 8 women and 5 men, 30–63 years of age (average age 50 years). They were referred to an asthma and allergy outpatient clinic because of symptoms suggestive of asthma or allergy, but, during the workup, this could not be verified. All participants had normal lung function [forced expiratory volume during 1 sec (FEV1) > 75% of predicted values], negative metacholine tests, and negative skin-prick tests (using a standard panel of 11 allergens). None demonstrated spirometric reversibility or variability in pulmonary function. Five patients regularly used β2-agonists and/or inhalation corticosteroids, but with no tangible effect. All patients had tried β2-agonists and inhalation corticosteroids, but 8 had stopped taking medication completely because of its lack of effect. None had a history of gastroesophageal reflux or heart disease.
The patients were screened using a questionnaire on airway symptoms and on symptoms in response to scents and chemicals. All had a history of at least 2 years of coughing and pronounced upper and lower airway symptoms induced by scents and chemicals. The patients had positive reactions to a capsaicin inhalation test, administered according to the method described by Johansson et al. (2002). They were diagnosed as having SHR as an explanation for their airway symptoms, in accordance with the guiding principles in this article, which recommend this diagnosis for patients with a combination of pronounced airway sensitivity to chemicals and increased capsaicin cough sensitivity.
The control group consisted of 14 healthy, nonsmoking subjects 29–69 years of age (average age 50 years), composed of 10 women and 4 men. None had a history of respiratory difficulties, asthma, or allergy (except for 1 person with an isolated positive reaction to birch), and none was on regular medication. Informed consent was obtained from all subjects at the start of the investigation, which was approved by the Hospital Ethics Committee in Gothenburg, Sweden.
We performed capsaicin inhalation provocation in accordance with previous studies (Millqvist et al. 1998; Ternesten-Hasseus et al. 2002b). One milliliter of saline was nebulized (Pariboy 36; Paulritzau Pari-Werk, Starnbergam-See, Germany) and inhaled to completion over 6 min to induce coughing, followed by 4 min rest. The patients did not use a nose clip during the provocations. We counted the number of coughs for 10 min from the onset of provocation and recorded them on tape. Thereafter, we carried out provocations in the same way, using increasing concentrations of capsaicin: 0.4, 2, and 10 μM in a 1-ml solution. We measured FEV1 with a spirometer (Vitalograph, Buckingham, UK) before and after each provocation and recorded the best of two trials. Following each provocation, we registered symptoms by scoring them on a scale of 0–3 (0 = no symptoms, and 1 = mild, 2 = moderate, and 3 = severe). Eleven main symptoms were analyzed: breathing problems, nasal irritation, throat irritation, hoarseness, chest pain/pressure, phlegm, eye irritation, dizziness, headache, fatigue, and sweating. The selection of symptoms was based on previous experience (Ternesten-Hasseus et al. 2002b).
Collection of NAL and determination of nerve growth factor.
A 5-mL aliquot of room-tempered saline was instilled into the right nostril of the subjects before and directly after capsaicin provocation. The subject’s head was tilted back and after approximately 10 sec the head was raised and NAL expelled into a collecting basin, measured for volume, and then transferred into conical tubes. We measured the returned volume and centrifuged it at 400g for 10 min at 4°C. The supernatant was then frozen at −80°C. We measured concentrations of NGF in supernatant samples of pre- and postcapsaicin challenge NAL using an enzyme-linked immunosorbent assay (ELISA; Duo Set ELISA; R&D Systems, Abingdon, UK). Nerve growth factor in standard and samples was bound to an anti-NGF mouse monoclonal antibody coated onto a BD Falcon 96-well microplate (BD Biosciences, Bedford, MA, USA). After washing, we added biotinylated goat antihuman NGF and allowed it to bind to the NGF. After another wash, we added peroxidase-labeled streptavidin and allowed it to bind to the biotin on the goat antibodies. After a final wash, tetramethylbenzidine was added as an enzyme substrate. We added sulfuric acid as a stop solution and determined the amount of NGF by optical density using a microplate reader set at 450 nm (VMax microplate reader; Molecular Devices, Sunnyvale, CA, USA). We used recombinant human NGF as standard, ranging from 15.6 to 2,000 ng/L. The assay had a lower detection limit of 2 pg/mL. To test the specificity of the assay, we performed double ELISA measurements of NGF on each specimen.
Statistics.
We analyzed the results obtained with different doses in the two groups using the Mann-Whitney U-test for nonpaired data and the Wilcoxon signed-rank test for paired data. Data are presented as means and 95% confidence intervals (CIs), and a p-value of < 0.05 was taken as statistically significant.
Results
Outcome of the capsaicin provocations.
Both patient and control groups demonstrated a dose-dependent response to capsaicin inhalation. In the patient group, the mean number of coughs was 21 (95% CI, 0–44), 58 (95% CI, 34–73) and 86 (95% CI, 74–98) for the three doses, respectively. The corresponding values for the control group were 6 (95% CI, 0–13), 17 (95% CI, 12–23), and 37 (95% CI, 26–48). With each dose of capsaicin, the patient group coughed significantly more than the control group (p < 0.01 for the 0.4 μM dose and p < 0.001 for both 2 and 10 μM doses).
In addition to increased coughing in patients, capsaicin provocations induced other symptoms, the most common being throat irritation, heavy breathing, eye irritation, phlegm, and rhinorrohea. The control subjects had no, or only a few, symptoms induced by capsaicin inhalation. The mean FEV1 in the patient group was 99% (95% CI, 90–108) of that predicted before capsaicin inhalation provocations, and no significant differences were found after the provocations.
Nerve growth factor in NAL.
The double ELISA measurements of NGF, performed on each specimen, were constant, without significant differences in the outcome of the results from capsaicin provocations. Basal levels of NGF were significantly lower (p < 0.02) in the patient group: their mean value was 13.8 pg/mL (95% CI, 6.3–21.4) compared with control subjects, who had mean values of 25.1 pg/mL (95% CI, 18.9–31.2). After capsaicin inhalation, there was an increase in NGF in the patient group (mean increase after provocation: 11.1 pg/mL; 95% CI, 3.1–19.1; p < 0.01) but not among control subjects, who displayed a decrease in NGF (mean decrease after provocation: 3.2 pg/mL; 95% CI, −1.6 to 8.1). The change in NGF levels after capsaicin provocation differed significantly between the two groups (p < 0.005; Figure 1).
In the patient group, there was a statistically significant correlation between the number of coughs after the highest inhaled capsaicin dose (10 μM) and the change in NGF levels in NAL after provocation (p < 0.01, r = 0.7; Figure 2). After the highest inhaled capsaicin concentration, each of the symptom scores for throat irritation, phlegm, and rhinorrohea correlated significantly (p < 0.004, r = 0.8; p < 0.04, r = 0.6; and p < 0.04, r = 0.8, respectively) with the change in NGF levels in NAL after provocation.
Discussion
The main finding of this study is that patients with SHR do have enhanced cough sensitivity to inhaled capsaicin, which correlates to a small but significant increase in NGF in NAL after capsaicin provocation. This indicates a neurochemical imbalance of the respiratory system in patients with SHR. Because there are similarities in symptoms and capsaicin sensitivity between patients with SHR and patients with MCS (Ternesten-Hasseus et al. 2002a), the groups probably overlap each other.
Compared with patients with asthma and allergy, the levels and increase of NGF are discrete and emphasize the discrepancy between these two conditions, though these groups have similar airway symptoms and are often confused. Recent studies have shown an interplay between NGF and airway inflammation. Increased levels of NGF have been found in the serum, bronchial tissue, and bronchoalveolar fluid of patients with allergy and asthma (Bonini et al. 1996; Noga et al. 2001, 2003; Olgart Hoglund et al. 2002). In patients with allergic rhinitis, levels of NGF in NAL were increased after allergen provocation compared with control subjects (Sanico et al. 2000).
Among the patients in our study, the lower basal NGF levels in NAL may seem surprising, but this finding underlines that patients with SHR do not have mucosal inflammation, which is probably the main source of high airway NGF levels in asthma and allergy sufferers. However, in this study, the NGF reaction induced by capsaicin was evident among patients and may be derived from hyperreactive nerve endings. The nervous system of patients with SHR may have an ability to overrespond to noxious stimuli with NGF production, which, in the long run, is followed by depletion of basal neurotrophin levels. Because the original source of NGF is unknown, one possibility is that the measured levels of NGF reflect plasma levels or levels in the lower airways. In control subjects, levels of NGF were unchanged, with a tendency toward lower values after capsaicin provocation. This indicates a stable system in the control group, where, after two nasal lavages, some of the NGF had been rinsed out.
After capsaicin challenge, the symptom scores for rhinitis had a strong, but unsurprising, correlation with an increase in NGF, because the nasal mucosa may have produced the factor being analyzed. However, the mechanisms behind the reaction are unclear: it could be reflex mediated; a small amount of capsaicin could have reached the nose from the pharynx; or some of the nebulized solution could have been dispersed and inhaled through the nose because the patients did not use a nose clip.
The NAL technique involves dilution of the expelled nasal secretions. We did not estimate the dilution factor, which is a possible source of error in the measured NGF. Most patients complained of rhinorrohea after capsaicin provocation, which may have contributed to a further dilution of NAL in patient group compared to controls. However, after provocation, there was an increase in NGF per milliliter of NAL and also a correlation with a high rhinorrohea score in the patient group; therefore, the dilution effect should not invalidate the results. Because of the dilution of NAL, the absolute NGF values must be regarded as uncertain and conclusions could be drawn only from group levels. In future research, albumin levels should also be measured to compensate for the dilutional effect. Another source of error for measured NGF may have been the influence of medication in the patient group. Five of the patients were on regular medication with β2 agonists or inhaled steroids, both of which are known to suppress NGF production (Amann and Schuligoi 2004; Noga et al. 2001).
Previously, an abnormality of the sensory nervous system in the airways was suggested in the pathophysiology of patients with symptoms induced by chemicals (Meggs 1995). Recently, Kimata (2004) recorded increased plasma levels of substance P, vasoactive intestinal peptide, and NGF in patients with self-reported MCS, which support this theory. This theory is also corroborated by the findings of augmented cough sensitivity to inhaled capsaicin (Johansson et al. 2002; Millqvist 2000; Millqvist et al. 1998, 2000; Ternesten-Hasseus et al. 2002b). Increased levels of substance P were found in NAL of patients with chronic cough but without bronchial hyper-responsiveness; there was also increased capsaicin cough sensitivity compared with controls (Cho et al. 2003). The conditions of chronic cough and airway sensitivity to scents and chemicals are closely related. Many patients with SHR complain of daily problems with cough (Millqvist et al. 1998, 2000; Ternesten-Hasseus et al. 2002b). However, it is not known how often patients with a diagnosis of chronic cough are also intolerant to scents and chemicals. In both conditions, there is no well-defined explanation, but neurochemical findings allude to analogous mechanisms.
In animals, NGF acutely conditions the response to capsaicin, suggesting that NGF may be important in sensitizing the response of sensory neurons and may play a role in pain and hyperalgesia (Anand 1995, 2004; Shu and Mendell 1999b, 2001). Initial interpretation of the findings in the present study suggests that an alteration of neurochemical balance in the airways is related to SHR, resulting in cough and other airway symptoms. We hypothesize that a type of mucosal hyperalgesia has occurred in patients with SHR. The upper and lower airways could mirror each other by trigeminal and vagal reflexes—the two main sensory nerves of the airways. Neuropeptides released from the lower airways may also induce symptoms in the other end of the airway system and vice versa. The term “united airways” is often used with reference to asthma and allergy, but it is probably, more or less, suited to most conditions in the airways. A sensory hyperreactivity after contact with chemical stimuli, in concentrations normally regarded as harmless, may result in symptoms mimicking those usually elicited by noxious stimuli and resulting in symptoms in both the upper and the lower airways. Other neurochemical factors, such as neuropeptides, will need to be analyzed in the future.
Figure 1 Box plot of NGF levels in 13 patients with airway symptoms induced by scents and chemicals and 14 control subjects before and after inhalation provocation with three concentrations of capsaicin. See “Materials and Methods” for details. The horizontal line in the center of each box is the median. The top and bottom of the box represent the 25th and 75th percentiles, and whiskers indicate the 10th and 90th percentiles. Circles are individual maximum and minimum data points.
Figure 2 Correlation between change in NGF after provocation with three concentrations of capsaicin and number of coughs after inhalation of the highest dose of capsaicin (10 μM). r = 0.7.
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Ternesten-Hasseus E Bende M Millqvist E 2002a Increased capsaicin cough sensitivity in patients with multiple chemical sensitivity J Occup Environ Med 44 1012 1017 12448352
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7640ehp0113-00085316002372ResearchLipid Adjustment in the Analysis of Environmental Contaminants and Human Health Risks Schisterman Enrique F. 1Whitcomb Brian W. 1Buck Louis Germaine M. 1Louis Thomas A. 21Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Rockville, Maryland, USA2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USAAddress correspondence to E.F. Schisterman, Epidemiology Branch, Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, 6100 Executive Blvd., Room 7B03, Rockville, MD 20852 USA. Telephone: (301) 435-6893. Fax: (301) 402-2084. E-mail:
[email protected] authors declare they have no competing financial interests.
7 2005 17 3 2005 113 7 853 857 6 10 2004 17 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The literature on exposure to lipophilic agents such as polychlorinated biphenyls (PCBs) is conflicting, posing challenges for the interpretation of potential human health risks. Laboratory variation in quantifying PCBs may account for some of the conflicting study results. For example, for quantification purposes, blood is often used as a proxy for adipose tissue, which makes it necessary to model serum lipids when assessing health risks of PCBs. Using a simulation study, we evaluated four statistical models (unadjusted, standardized, adjusted, and two-stage) for the analysis of PCB exposure, serum lipids, and health outcome risk (breast cancer). We applied eight candidate true causal scenarios, depicted by directed acyclic graphs, to illustrate the ramifications of misspecification of underlying assumptions when interpreting results. Statistical models that deviated from underlying causal assumptions generated biased results. Lipid standardization, or the division of serum concentrations by serum lipids, was observed to be highly prone to bias. We conclude that investigators must consider biology, biologic medium (e.g., nonfasting blood samples), laboratory measurement, and other underlying modeling assumptions when devising a statistical plan for assessing health outcomes in relation to environmental exposures.
causal modelingdirected acyclic graphsorganochlorinespolychlorinated biphenylsrisk estimationserum lipids
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Persistent lipophilic xenobiotics pose particular methodologic challenges when assessing potential human health risks. The human health effects literature on exposure to lipophilic agents such as organochlorines (OCs) is equivocal, impairing our ability to quantify risks (Calle et al. 2002; Hunter et al. 1997; Laden et al. 2001a, 2001b). For example, Wolff and colleagues (Wolff 1985; Wolff and Toniolo 1995; Wolff et al. 1993, 2000) found an increased odds ratio for breast cancer for the highest quintile of wet-weight dichlorodiphenyl-dichloroethylene (DDE) and polychlorinated biphenyls (PCBs; expressed as nanograms analyte per milliliter serum) when compared with the lowest quintile, whereas Laden et al. (2001a, 2001b) found no association when concentrations of DDE and PCBs were standardized for serum triglycerides and cholesterol. No association was reported for PCBs and risk of breast cancer when expressing concentrations either as wet weight or lipid standardization values (Helzlsouer et al. 1999).
Varying laboratory practices for expressing PCB concentrations may in part account for the equivocal findings for human health end points. Serum PCB concentrations, as with other lipophilic xenobiotics, are dependent on serum lipid concentrations (Eyster et al. 1983; Guo et al. 1987). Under certain circumstances an equilibrium is reached, and information regarding serum PCB levels and serum lipid levels may be predictive of PCB body burden (Brown and Lawton 1984). If serum lipids indeed act in this manner, higher serum lipid levels should correspond to higher serum PCB concentrations (Calvert et al. 1996). However, serum OC concentrations and lipids are affected postprandially and need to be considered in relation to quantity and timing of food consumption (Phillips et al. 1989). When it is not possible to collect adipose tissue, serum samples are frequently used. However, serum (or plasma) introduces methodologic challenges with regard to lipids when estimating health risks, particularly when nonfasting samples are used (Whitcomb et al. 2005). Collection of fasting samples can hamper the feasibility of epidemiologic research and may adversely impact study participation. Nonfasting samples require further attention to serum lipids (Brown and Lawton 1984; Brown et al. 1994; Eyster et al. 1983).
Our limited understanding of the true relation between serum and adipose tissue concentrations of lipophilic xenobiotics in relation to serum lipids and particular health outcomes makes model specification difficult (Calvert et al. 1996; Mussalo-Rauhamaa 1991). Investigators typically express measurements on a wet-weight basis or per unit volume of serum or as lipid-standardized values, where the concentration is divided by serum lipids.
Lipid standardization may be useful for comparing exposure concentrations across tissue specimens or across study populations by expressing PCB concentrations per gram of fat (Morgan and Roan 1970). Use of lipid weight (PCB per unit of serum lipids) as opposed to wet weight (PCB per unit of serum) has been advocated for the measurement of persistent lipophilic chemicals (Brown and Lawton 1984), especially if one assumes body burden equilibrium. Other approaches reported in the literature include the use of a log-linear model with serum lipids included as a separate term in the regression equation (Moysich et al. 1998). Other investigators have conducted two-stage analyses wherein serum lipids are regressed on serum PCB concentrations with the residuals entered as an individual risk factor (Hunter et al. 1997).
The issue of how best to model the relation among serum PCBs, lipids, and health outcomes remains an understudied area critical for the assessment of health effects. Here we demonstrate the impact of model (mis) specification and its effect on the interpretation of study findings. We used directed acyclic graphs (DAGs) to define a causal framework among exposure, lipids, and health outcome and values for parameters as informed by the literature (Hernan et al. 2002; Robins et al. 2000). Using DAGs to supply a causal framework and parameter values informed by the literature, we present the results of a simulation study. These results identify the best statistical model for each circumstance and the bias produced by a mismatch between the DAG and the statistical analysis.
Materials and Methods
Statistical models and DAGs.
Optimal modeling of the statistical relations among serum PCBs, serum lipids, and health outcomes requires positing an underlying causal model that reflects the following considerations: a) biologic plausibility; b) laboratory capability for quantifying compounds and lipids; c) underlying statistical assumptions (e.g., error structure); and d) other relevant study covariates (e.g., known and potential confounders). To focus on bias, we assume perfect laboratory measurement of PCBs and the absence of unmeasured confounding.
We depict each scenario via a simple DAG that shows relations but does not dictate a specific statistical model (i.e., mean and error structures). A single-headed arrow represents a causal relation between the ancestor (tail) and the descendant (head). A dashed line represents a noncausal association between two variables, suggesting a shared ancestor that may or may not have been measured; the absence of an arrow signifies no relation.
The true causal structure relating PCBs and serum lipids depends on the outcome under study. Investigators typically have insufficient biologic information to specify the correct analytic model, often resulting in analytic strategies based on unverified assumptions. For example, research indicates a possible causal effect of PCBs on serum lipid levels (Hennig et al. 2005; Langer et al. 2003). Additionally, lipid levels have been suggested to affect breast cancer risk (Atalay et al. 2004), but their impact on other health end points has received limited attention. For our purposes in this study, our scenarios, hypothetical “causal truths,” are based on the literature and their relation to frequently used statistical models.
Statistical models.
We investigated four statistical models (unadjusted, standardized, adjusted, and two-stage) for the analysis of hypothesized PCB exposure, serum lipids, and a health outcome (breast cancer), along with eight plausible DAGs for each model to illustrate the choices facing investigators. For illustrative purposes, all models assume that there are no unmeasured confounders. For all models, P = Pr(Y = 1|X, SL), where Y is a dichotomous dependent variable representing the presence/absence of the disease; X = PCB; and SL = serum lipids.
Unadjusted model.
The unadjusted model is equivalent to the use of wet-weight values when estimating the effect of an exposure such as PCBs on a health outcome without further consideration of serum lipids.
Accordingly, this model is suitable for use when it is reasonable to assume that serum lipids are not a confounder. This assumption holds true regardless of the relation between lipids and the outcome. Inclusion or exclusion of lipids as an adjustor may affect model fit, but it will not impact PCB exposure/response estimates. Four DAGs, shown in Figure 1, are appropriately evaluated by use of the unadjusted statistical model. Figure 1A reflects a scenario that will result in an unbiased risk estimate as serum lipids are assumed to be unrelated to PCB levels. Use of this model for Figure 1B yields optimal estimates, if serum lipids are unrelated to both PCBs and the outcome.
An unadjusted model is also appropriate for Figure 1C, where PCBs are assumed to have an indirect effect via serum lipids; adjustment for a variable in the causal pathway may introduce an undesirable bias when estimating direct effects (Greenland 1996, 2003; Greenland and Morgenstern 2001).
In Figure 1D, PCBs are assumed to affect both serum lipids and the outcome, creating a spurious association (Robins et al. 2000). Here, only an unadjusted model is appropriate for risk estimation. Because they vary with PCBs, adjustment for serum lipids is tantamount to partial adjustment for the exposure itself.
Standardized model.
The lipid-standardized model is one way to account for the effect of serum lipids on serum PCB levels. This model is used frequently and is conceptually similar to use of the body mass index (BMI; weight in kilograms divided by the squared height in meters) to adjust weight for height in measuring adiposity.
The power, m in Equation 2 is a factor that generalizes the relation of PCBs and serum lipids. Due to measurement error in the quantification of lipids, use of Equation 2 when Figure 1A holds can result in biased estimates. If Figure 1B holds, estimates will be affected by a scaling issue, as the beta coefficient is that for the log of the ratio of PCB to lipids. If the true relations follow Figure 1 (C or D), then use of Equation 2 will adjust, albeit incompletely, for the exposure of interest, as in both Figure 1C and D, PCBs determine the variance of serum lipids. Figure 1C depicts a causal relation between both PCBs and serum lipids with the outcome, and a noncausal association between PCBs and serum lipids resulting from a common ancestor, A. Use of the standardization model will be valid for this situation only if the standardization completely accounts for the association between PCB and serum lipids. Otherwise, use of this model will result in biased estimates.
Figure 1F is modeled similarly to Figure 1D in that the relation between PCBs and lipids is due to a common cause, A. In this scenario, the standardized model again suffers from a scale issue. All other models will produce unbiased estimates, but precision of the estimate may vary depending on several factors, including measurement error. The potential error associated with the measurement of serum lipids can exceed that for the analyte itself (Needham and Wang 2002) and is an important source of bias.
Figure 1G represents two possible circumstances in which serum PCBs are causally related or correlated with the true exposure/outcome association. If the relation between serum and adipose concentration levels of PCBs is governed by serum lipid levels, then standardization may allow use of one as a proxy for the other.
Adjusted model.
In the adjusted model, there is an assumption that PCBs are not standardized for serum lipids, reflecting the absence of an association between lipids and the study outcome. Note that the standardized model is a member of the family of adjusted models.
When comparing the lipid component in the standardized model [ln(X) − m × ln(SL)] with the lipid term of the adjusted model [β4 ln(SL)], equivalent results are produced in that β4 is forced to be equal to –m. If m is set equal to 1, PCBs are divided by serum lipids, as is the case with the standardized model. However, the adjusted model is more flexible than the standardized model and, in general, is applicable under the same set of assumptions.
For Figure 1A, the adjusted model will produce unbiased estimates without regard for the degree of standardization, while the standardized model is conditional on standardization being sufficient. The adjusted model will yield unbiased estimates for Figure 1A, B, D, and F. For Figure 1C, E and H, the adjusted model will yield biased estimates because the adjustment is performed for a variable in the causal pathway; for Figure 1H this bias is to estimates of the total effect due to its partitioning into direct and indirect.
Two-stage model.
The two-stage model includes the effects of PCBs and serum lipids on the outcome:
Implications of the two-stage model arise from its relation to the adjusted model. Both the intercept and the beta coefficient in the two-stage model are simple functions of the parameters from the adjusted model and the regression of serum lipids on log PCBs. The coefficient for the residual term, R, is precisely that of the adjusted model’s lipids term:
Use of the two-stage model for Figure 1A will result in estimates similar to those produced by the adjusted model, because there is no assumption about an association between PCBs and serum lipids. Therefore, the residuals will be equivalent to the lipid term in the model. The two-stage model may also be used to represent Figure 1F, with an important caveat that the risk estimates now have a different interpretation in that they separate the PCB effect from the lipid effect on the outcome. In some circumstances, the two-stage model will generate unbiased risk estimates for Figure 1B, although they will be inefficient. Similarly, the model may yield unbiased risk estimates for Figure 1C although confounding is not addressed.
The two-stage model is appropriate when it is important to distinguish direct and indirect effects of PCBs (Figure 1H). In this scenario, the effect of serum lipids is an indirect effect via PCBs; their inclusion introduces bias as is the case for the standardized model where assumptions of causality may not be clearly delineated.
Simulations.
In addition to showing causality in a statistical model, each DAG can be used to guide model selection. We conducted a simulation study to evaluate the utility of various models for various scenarios depicted by DAGs. We used the causal structures they define, assigned lognormal distributions for PCB and serum lipids, and assumed a binomial outcome variable Y with Pr(Y = 1 | PCB, serum lipids). For example, in Figure 1H PCB causes disease Y and affects serum lipid (which in turn also affects disease); these associations motivate the model:
The log odds [logit(P(X, SL)] equals an intercept (α0), the prevalence among the unexposed, plus the factor, β1+β2γ, by which PCB affects the probability of the event. There is no serum lipid term, denoting that there is no linear influence of serum lipid levels.
In Figure 1, the assumptive role of serum lipids is variously a) an independent cause, b) a dependent cause, c) an independent noncause, d) a dependent noncause, and e) a modifier. A represents an unmeasured variable that is an ancestor to both PCB and serum lipids (e.g., fish consumption) that may result in confounding (Hernan 2001, Hernan et al. 2002).
Additionally, we assessed the effects of serum lipid measurement error [ɛ~N(0, σe2)] with different values of σe2 and the relation between PCB and serum lipids by varying the strength of their linear relation, α, from the linear regression, SL = α0 + αX.
In these quantitative representations of the DAGs, it is clear that magnitude of effects, error, and bias will be functions of the values chosen for the parameters. We set the independent effect of PCB as a constant (βlnPCB = 0.6 in the logistic regression model), with approximate values taken from the literature (Wolff and Toniolo 1995). In our unpublished data, we observed a significant linear relation between total serum PCBs and serum lipids with a regression coefficient value of approximately 0.3. The values provided for the strength of the linear relation between PCB, and serum lipids represented a very weak association (α = 0.01) to a strong association (α = 2.0).
Results
Table 1 displays the bias and mean square error for estimates that result from the four statistical models given the underlying causal truths for σe2 = 1, and α = 0.3. For Figure 1A, which represents PCB and SL as independent causes of the outcome, all models except the standardized produce minimally biased estimates. The standardized model results in a biased underestimate of the PCB effect on outcome. When SL is completely extraneous, as in Figure 1B, bias occurs similar to the previous situation. Figure 1C depicts the effect of PCB acting strictly through SL and is estimated unbiasedly by the two-stage approach. The unadjusted model produces minimal bias. Adjustment for SL results in a large underestimate of effect, as does standardization, though underestimates resulting from standardization are substantially greater (351%). When SL is affected by PCB but does not directly influence the outcome (Figure 1D), standardization is the only modeling approach with substantial bias, underestimating the true effect by nearly 80%, whereas the other models are within 1% of the true effect. In the confounded case, (Figure 1E), only the adjusted model performed well. Lack of adjustment failed to address the confounding by SL, and standardization was not a sufficient method to account for this confounder. In adjusting for serum lipids via the residuals, the two-stage model misattributes the association between PCB and SL as a causal link and results in biased estimates of the effect of interest—the total effect of PCB on risk. Figure 1F represents a noncausal correlation between PCB and SL and, as for Figure 1A, B, and D, produced biased underestimates using the standardized model. Figure 1G is unique among the DAGs in that it posits that serum levels of PCB are dependent on levels in adipose, which are in turn causally related to the outcome. In this situation, standardization functioned optimally; the adjusted model produced similarly unbiased estimates, while neither the unadjusted nor two-stage model worked well. Figure 1H represents a direct and indirect causal link of PCB with outcome. The relation was modeled well by the unadjusted (which estimates total effect) and the two-stage (which separates total into estimated direct and indirect) approaches. Adjustment resulted in a small amount of bias, and standardization produced the most biased estimates in this scenario.
The foregoing results indicate that the standardized and the adjusted models should be compared. With the exception of Figure 1G, the adjusted model produces smaller bias than the standardized model. However, even under conditions ideally suited for the standardized model (Figure 1G: adipose PCB causes both serum PCB per serum lipids and the outcome), the adjusted model yielded a nearly identically unbiased estimate. The two-stage model produced results similar to those of the unadjusted model, though less biased, for Figure 1C, for which serum lipids are in the causal pathway of PCBs and outcome.
Measurement error.
To address the potential for measurement error accompanying quantification of serum lipids, an error term with mean 0 and variance σe2 was added to the simulated distribution of serum lipids. Figures 2–4 display bias as a function of this measurement error at 4 values of α for each of the models (unadjusted, standardized, adjusted, and two-stage). Bias as a function of σe2 followed three distinct patterns among the eight DAGs. Figure 2 displays the pattern for Figure 1A, B, D, and F; with increasing measurement error, bias was stable for the unadjusted, adjusted, and two-stage models, staying close to zero. For the standardized model the relation between bias and σe2 was more complicated; bias increased with measurement error when the relation between PCB and lipids was weak, but at the highest value of α evaluated, bias decreased with measurement error. The value of σe2 at the inflection point varied from 0.5 for Figure 1F to 3.0 for Figure 1A.
Figure 3 displays the pattern of bias observed when Figure 1C, E, and H depict the truth. Similar to pattern 1, bias for the standardized model varied in a nonlinear manner, increasing for all values of α but the highest (α = 2). The adjusted and two-stage models were essentially robust to measurement error; however, both the unadjusted and adjusted did not always produce unbiased estimates of parameters for all underlying DAGs, especially at different levels of α. A stronger linear relation between PCB and lipids resulted in greater bias in the adjusted model. Bias of estimates produced by the unadjusted model varied slightly with σe2; for Figure 1C and H bias increased slightly with increasing measurement error (from 0 to 0.1 for 8, from 0 to 0.2 for 3). Increasing measurement error in Figure 1E reduced bias as the strength of the noncausal relation between PCBs and serum lipids was altered by the variance in serum lipids.
Figure 4 displays bias for the four models under the conditions represented by Figure 1G. Both the standardized and adjusted models produced unbiased estimates robust to measurement error, whereas the unadjusted and two-stage models produced biased estimates that were equally prone to measurement error. Changes in the strength of the linear relation between PCB and lipids did not affect bias for any of the four models in this scenario.
Discussion
We have described and evaluated four statistical models (unadjusted, standardized, adjusted, and two-stage) commonly used to assess the effects of lipophilic environmental contaminants on human health when relying on blood specimens for quantifying toxicant concentrations. Our simulations show that each statistical model has minimal bias for at least the causal truth for which it is ideally suited. Although most models performed well under all but one causal scenario, the standardized model produced large biases for most of the evaluated DAGs. The adjusted model produced only a small bias even for the DAG for which standardization is optimal.
We evaluated basic causal scenarios; the eight DAGs we considered included only two to four factors. When additional factors impact levels of both PCB and serum lipids as well as health outcome risk, the evaluation will be more complex, and the trade-off between statistical efficiency and robustness will be more important. Although the adjusted model produced consistently unbiased estimates, there are circumstances where adjustment (or stratification) is inappropriate and should be avoided. For example, adjustment for a collider (an effect of two or more other variables in the graph) has been demonstrated to bias estimators of effect (Greenland and Brumback 2002; Hernan et al. 2002). Additionally, factors that share a common cause will appear correlated in strata of that common cause. Given an alleged relation between PCB and serum lipids, their adjustment might generate spurious associations if an unmeasured factor is related to both serum lipid levels and the outcome.
A discussion of causality, particularly when regarding estimation of direct and indirect causes, necessitates consideration of counter-factuals. Consistent estimation of a direct or indirect effect require at minimum the absence of unmeasured confounding as well as the assumptions of consistency and the existence of a direct effect (Cole and Hernan 2002; Robins 2003). Estimation of causal effects and their relations to DAGs is intimately tied to the notion of counterfactuals. In reality, when a factor impacts an outcome through both direct and indirect pathways, we cannot observe the direct effect in absence of the indirect effect, and vice versa; their estimation depends on counterfactual comparisons (Robins 2003). A general counterfactual model has been proposed that permits the estimation of total and direct effects of fixed and time-varying exposures in longitudinal studies whether randomized or observational in design (Robins et al. 2000). However, a more detailed discussion is beyond the scope of this paper.
Findings from our simulations demonstrate that statistical models failing to uphold underlying assumptions about causality lead to biased results with implications for the interpretation of effects of exposures on human health end points. We speculate that equivocal findings may arise, at least in part, from the varying laboratory and analytic approaches for specifying serum lipids when using nonfasting blood specimens to estimate risk. Investigators must remember to consider biology, biologic medium, and laboratory methodology when specifying a statistical model and its underlying assumptions appropriate for study.
Correction
Equation 4 was incorrect in the manuscript originally published online but has been corrected here.
Figure 1 Causal scenarios for relations among PCB, serum lipids (SL), and outcome (Y). (A) PCB and SL are marginally dependent conditional on Y; serum PCB (S-PCB) causes Y, and SL causes Y. (B) PCB as cause of Y; S-PCB causes Y, independent of SL. (C) PCB and Y are marginally dependent on and blocked by SL; S-PCB causes SL, which causes Y. (D) Y and SL are marginally dependent and blocked by serum PCB; S-PCB causes Y and SL. (E) PCB and SL are marginally dependent conditional on both the shared ancestor variable, A, and Y. An unmeasured variable, A, causes both S-PCB and SL, each of which independently causes the outcome; this is the traditional situation of confounding, with SL acting as a confounder of the relation between serum PCBs, PCBs, and Y. (F) PCB and SL are marginally dependent on the ancestor, A; SL and Y are marginally dependent on A and, thus effectively, on PCB. S-PCB and SL are caused by A, but only PCB is causally related to Y. (G) PCB per unit SL and Y are marginally dependent conditional on adipose tissue PCB. Adipose tissue PCB (A-PCB) causes serum PCB per unit serum lipid and causes Y; PCB and outcome are correlated rather than directly causally related. (H) Blocked and unblocked path. Y is both directly caused by PCB and marginally dependent conditional on SL; S-PCB causes Y, as well as SL, which causes Y.
Figure 2 Comparison of bias for standardization versus all other models as a function of measurement error of serum lipids and strength of linear association of PCB with serum lipids for Figure 1A, B, D, and F. Bias for the standardized model was systematically centered on −0.60 (100% underestimation). As measurement error increased, the impact of the strength of the relation between PCB and serum lipid was reduced. None of the other models were sensitive to measurement error under any conditions of the PCB–serum lipid relation. The vertical line at σe2 = 1 signifies the level used for Table 1.
Figure 3 Bias as a function of measurement error of serum lipids and strength of linear association of PCB with serum lipids for Figure 1C, E, and H. The vertical line at σe2 = 1 signifies the level used for Table 1.
Figure 4 Bias as a function of measurement error of serum lipids and strength of linear association of PCB with serum lipids for Figure 1G. For this causal diagram, the standardized and adjusted models track together and are robust to both measurement error and the strength of the linear relation between PCB and serum lipid. The unadjusted and two-stage models also track together and are somewhat affected by increasing measurement error, although not to changes in the strength of the relation between PCB and serum lipid. The vertical line at σe2 = 1 signifies the level used for Table 1.
Table 1 Percent bias of estimates of effect of PCBs on outcome for evaluated statistical models.
Percent bias (MSE)a
DAG (Figure 1) Unadjusted Standardized Adjusted Two-stage
A 1.2 (1.26) –51.3 (10.3) 1.8 (1.28) 1.8 (1.28)
B –0.8 (1.34) –75.9 (21.1) –0.7 (1.35) –0.7 (1.33)
C –15.4 (2.78) –351.3 (161.1) –99.4 (1.59) 1.1 (2.78)
D 0.4 (1.14) –79.8 (23.3) 0.8 (1.17) 0.5 (1.14)
E 24.0 (3.37) –128.8 (60.3) 0.1 (1.39) 27.2 (3.37)
F –0.4 (1.29) –85.0 (26.4) –0.1 (1.41) –0.3 (1.29)
G –86.3 (27.0) –1.0 (1.51) –1.0 (1.51) –85.9 (27.0)
H –11.2 (1.75) –128.3 (59.7) –25.4 (3.65) –8.7 (1.75)
Serum lipid measurement error distributed normally with mean 0, variance 1; α (strength of linear relation between log PCB and log serum lipids) = 0.3; 500 repetitions; n = 1,000.
a Mean square error multiplied by 100 for illustration (shown in parentheses).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7820ehp0113-00085816002373ResearchSerum Dioxin Concentrations and Age at Menopause Eskenazi Brenda 1Warner Marcella 1Marks Amy R. 1Samuels Steven 12Gerthoux Pier ario 3Vercellini Paolo 4Olive David L. 5Needham Larry 6Patterson Donald G. Jr.6Mocarelli Paolo 31School of Public Health, University of California at Berkeley, Berkeley, California, USA2School of Public Health, University at Albany, Albany, New York, USA3Department of Laboratory Medicine, University of Milano-Bicocca, School of Medicine, Hospital of Desio, Desio-Milano, Italy4Department of Obstetrics and Gynecology, Mangiagalli Hospital, University of Milan, Milan, Italy5Department of Obstetrics and Gynecology, University of Wisconsin Medical School, Madison, Wisconsin, USA6Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USAAddress correspondence to B. Eskenazi, School of Public Health, University of California, 140 Warren Hall, Berkeley, CA 94720-7360 USA. Telephone: (510) 642-3496. Fax: (510) 642-9083. E-mail:
[email protected] thank S. Casalini for coordinating data collection at the Hospital of Desio and W. Turner from the Centers for Disease Control and Prevention for serum TCDD measurements.
This work was supported by the following grants: R01 ES07171 and F06 TW02075-01 from the National Institutes of Health, R82471 from the U.S. Environmental Protection Agency, EA-M1977 from the Endometriosis Association, 2P30-ESO01896-17 from the National Institute of Environmental Health Sciences, and 2896 from Regione Lombardia and Fondazione Lombardia Ambiente, Milan, Italy.
The authors declare they have no competing financial interests.
7 2005 24 3 2005 113 7 858 862 2 12 2004 24 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. 2,3,7,8-Tetrachlorobenzo-p-dioxin (TCDD), a halogenated compound that binds the aryl hydrocarbon receptor, is a by-product of numerous industrial processes including waste incineration. Studies in rats and monkeys suggest that TCDD may affect ovarian function. We examined the relationship of TCDD and age at menopause in a population of women residing near Seveso, Italy, in 1976, at the time of a chemical plant explosion. We included 616 of the women who participated 20 years later in the Seveso Women’s Health Study. All women were premenopausal at the time of the explosion, had TCDD levels measured in serum collected soon after the explosion, and were ≥ 35 years of age at interview. Using proportional hazards modeling, we found a 6% nonsignificant increase in risk of early menopause with a 10-fold increase in serum TCDD. When TCDD levels were categorized, compared with women in the lowest quintile (< 20.4 ppt), women in quintile 2 (20.4–34.2 ppt) had a hazard ratio (HR) of 1.1 (p = 0.77), quintile 3 (34.3–54.1 ppt) had an HR of 1.4 (p = 0.14), quintile 4 (54.2–118 ppt) had an HR of 1.6 (p = 0.10), and quintile 5 (> 118 ppt) had an HR of 1.1 (p = 0.82) for risk of earlier menopause. The trend toward earlier menopause across the first four quintiles is statistically significant (p = 0.04). These results suggest a nonmonotonic dose-related association with increasing risk of earlier menopause up to about 100 ppt TCDD, but not above.
2,3,7,8-tetrachlorodibenzo-p-dioxinCox proportional hazardsdioxinendocrine disruptorsmenopauseSevesoTCDD
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2,3,7,8-Tetrachlorobenzo-p-dioxin (TCDD), a halogenated compound that binds and activates the aryl hydrocarbon receptor, is a byproduct of numerous industrial processes including waste incineration (Zook and Rappe 1994). Dioxin, a known human carcinogen [International Agency for Research on Cancer (IARC) 1997], is also thought to disturb the reproductive and endocrine systems (Birnbaum and Tuomisto 2000).
Studies in rats and monkeys suggest that TCDD may affect ovarian function directly or indirectly via the pituitary (Gao et al. 2000; Li et al. 1995a; Moran et al. 2001). In utero and lactational TCDD exposure in rats has been associated with reduced ovarian weight and decreased number of corpus lutea and pre-antral and antral follicles (Heimler et al. 1998). Postnatal TCDD exposure in rats has been related to reduced ovarian weight gain, ovulation rate, and number of follicles as well as inhibition of follicular rupture (Gao et al. 1999; Roby 2000), morphologic changes in the ovary, and altered cyclicity with disruption of the estrous cycle (Kociba et al. 1976; Li et al. 1995a, 1995b; Roby 2000; Son et al. 1999). Although TCDD does not increase apoptosis of follicles (Heimler et al. 1998), it slows follicular maturation (Mattison 1980; Silbergeld and Mattison 1987). Postnatal TCDD exposure in monkeys has been associated with decreases in serum estradiol and progesterone, leading to anovulation in some cases (Allen et al. 1979; Barsotti et al. 1979). In a study of mature female macaques, a single TCDD dose (below the maximum in the current study) led to long-term effects on ovarian function (Moran et al. 2001).
Menopause, the cessation of menstruation, is thought to be caused by a loss of primordial ovarian follicles, resulting in the decline in estradiol production and the concomitant increase in circulating concentrations of follicle-stimulating hormone (FSH) (Lopez et al. 2000). The age of onset of menopause is believed to reflect the rate of atrophy of the ovarian follicles. Alterations in age at menopause can have important health implications because women with early menopause are at higher risk for osteoporosis, cardiovascular disease, and reproductive cancers (Bradsher and McKinlay 2000; Karagas et al. 2000).
There is limited epidemiologic evidence that endocrine-disrupting chemicals affect the natural timing of menopause. Data from a case–control study of breast cancer in North Carolina (USA) show that women with serum dichlorodiphenyldichloroethene (DDE) levels in the upper 10th percentile had an earlier onset of natural menopause than did women with levels below the median [hazard ratio (HR) = 1.4], but poly-chlorinated biphenyl (PCB) levels were not related to age at menopause (Cooper et al. 2002). Women from the Yu-cheng, China, cohort who ingested cooking oil contaminated with PCBs and polychlorinated dibenzofurans did not differ from unexposed women in their mean age at menopause or in the percentage of women who had experienced menopause (Yu et al. 2000). Among Michigan (USA) women who had been exposed to polybrominated biphenyls (PBBs) and PCBs in 1973, no association was found between serum concentrations of PBBs or PCBs and time to menopause (Blanck et al. 2004). Amenorrhea was noted in a case report of a 30-year-old Austrian woman with extremely high levels of serum TCDD (144,000 ppt) (Geusau et al. 2001). Additional evidence for the potential effects of TCDD on the menstrual cycle is our earlier report of longer menstrual cycles in exposed women who were premenarcheal at the time of exposure (Eskenazi et al. 2002).
In this study, we examine the relationship of TCDD and age of onset of natural menopause in a population of women residing near Seveso, Italy, in 1976, at the time of a chemical plant explosion. These women were exposed to the highest levels of TCDD known in residential populations (Mocarelli et al. 1988).
Materials and Methods
Study population.
The Seveso Women’s Health Study (SWHS) is the first comprehensive epidemiologic study of the reproductive health of a female population exposed to TCDD. Women eligible for SWHS were 1 month to 40 years of age in 1976, had resided in one of the most highly contaminated areas (zone A or B), and had adequate stored sera collected soon after the explosion (Eskenazi et al. 2000). Recruitment took place from March 1996 through July 1998. Of 1,271 eligible women, 17 could not be contacted, and 33 had died or were too ill to participate. Of the 1,221 women contacted, 981 (80%) agreed to participate. For this analysis, we included the 616 women who had not reached natural or surgical menopause before 10 July 1976, the date of the explosion, and who were at least 35 years of age at the time of interview.
Procedure.
Details of the study are presented elsewhere (Eskenazi et al. 2000). Briefly, after informed consent was obtained, women were interviewed by a trained nurse-interviewer who was blind to TCDD level and residence of the woman. Information was collected during the interview about demographic characteristics, personal habits, and occupational, menstrual, reproductive, and medical histories. Subsequently, women were asked to undergo a gynecologic examination with a transvaginal ultrasound and to complete a menstrual cycle diary for 3 months. Medical records were abstracted for all gynecologic treatments or conditions.
Serum TCDD laboratory analyses.
Details of serum sample selection are presented elsewhere (Eskenazi et al. 2000). The TCDD concentration in these samples was measured by high-resolution mass spectrometry methods (Patterson et al. 1987). Values are reported on a lipid-weight basis in parts per trillion by dividing TCDD on a whole-weight basis by total serum lipid content, estimated from measurements of triglycerides and cholesterol (Akins et al. 1989).
We measured TCDD in sera collected between 1976 and 1977 for 564 women, between 1978 and 1982 for 28 women, and between 1996 and 1997 for 24 women whose earlier samples had insufficient volume. For nondetectable values (n = 71), a serum TCDD level equal to one-half the detection limit was assigned (Hornung and Reed 1990). For women with detectable post-1977 TCDD measurements (≥10 ppt), the TCDD exposure level was back-extrapolated to 1976 using the first-order kinetic model (Pirkle et al. 1989) for women who were > 16 years of age in 1976 (n = 40) or the Filser model (Kreuzer et al. 1997) otherwise (n = 1). For the 7 women with post-1977 measures whose TCDD levels were < 10 ppt, measured values were used. The study median serum sample weight was 0.65 g, and the median limit of detection was 18.8 ppt, lipid-adjusted.
Definitions of menopause.
Each woman was categorized by menopausal status using the following definitions: premenopause, if the woman was still menstruating or if she had amenorrhea due to pregnancy or lactation at the time of interview with evidence of subsequent menstruation from the menstrual diary or exam; natural menopause, if the woman had ≥12 months of amenorrhea not due to other obvious causes such as pregnancy, lactation, and medical conditions [World Health Organization (WHO) Scientific Group 1996); surgical menopause, if the woman had a medical-record–confirmed hysterectomy and/or a unilateral or bilateral oophorectomy; impending menopause, if the woman menstruated within 12 months, but not in the 2 months before interview or exam, whichever was most recent, and if her amenorrhea could not be explained by pregnancy, lactation, or other medical conditions; perimenopause, if the woman did not menstruate within the last 2 months before interview or exam but either gave evidence of subsequent menstruation in her menstrual diary or on ultrasound exam her endometrial lining was classified as secretory, indicating ovulation and impending menses, or if the woman reported cycles becoming less predictable (either irregular or longer) in the previous 2–5 years (a woman was not classified as perimenopausal if she reported a return to a regular cycle, if there was evidence only for a single irregular cycle, or if the irregularity was attributable to another cause); and other menopausal status, if the woman’s menopausal status could not be determined because of current oral contraceptive (OC) or other hormone use (including hormone replacement therapy) or previous chemotherapy for cancer.
Statistical analyses.
We considered serum TCDD levels both a continuous (log10 TCDD) and a categorical variable based on quintiles of serum levels near the time of the explosion (1, < 20.4 ppt; 2, 20.4–34.2 ppt; 3, 34.3–54.1 ppt; 4, 54.2–118.0 ppt; 5, > 118.0 ppt). To evaluate the relationship between serum TCDD and age at natural menopause, we performed Cox proportional hazards analyses with the robust method of calculating the variance-covariance matrix (Lin and Wei 1989). The Cox model assesses effects on age-specific probabilities of reaching natural menopause by the relative hazard, or HR, the ratio of probabilities computed for each categorized level of exposure versus the reference group (< 20.4 ppt) or for the effect of a 10-fold increase in TCDD (log10 TCDD). Scaled Schoenfeld residuals were generated for the final multivariate model and used to test the proportional hazard assumption (i.e., that the HR is proportional over time) (Grambsch and Therneau 1994). Analyses were performed using STATA (release 8.0; Stata Corp., College Station, TX, USA). All p-values are two-tailed.
Covariates were considered for the multivariate Cox proportional hazards analysis if they had been reported in previous literature to be related to age at menopause. Covariates were kept in the multivariate model if they were statistically significant (p < 0.10) or if they changed the regression coefficient for TCDD exposure by > 10%. We considered the following as potential covariates: current body mass index (BMI), premenopausal smoking history, education, marital status, current physical activity, age at menarche, parity, OC use, and history of heart disease. We also created a variable for premenopausal history of medical conditions that could potentially be related to age at menopause, including type 1 diabetes (n = 1), rheumatoid arthritis (n = 4), radiation for cancer (n = 4), epilepsy (n = 2), hyperthyroid (n = 10), and untreated hypothyroid (n = 2) (Dorman et al. 2001; Klein et al. 2001; Steinkampf 1990). In addition to controlling for these conditions with an indicator variable, we repeated our models excluding women with these conditions (n = 23). The results were similar; therefore, we present only the results including the women with other medical conditions. One covariate (education) was found to violate the proportional hazards assumption (p = 0.06); therefore, estimates stratified by education were obtained.
For women who met the definition of natural menopause, age (in years) at last menstrual period was assigned as their age at menopause. Surgically menopausal women were censored at the age at which they had surgery. Premenopausal and perimenopausal women were censored at their age at interview. Women using OCs or other hormones and those with a history of chemotherapy were censored at the age at which they began use or treatment. Each woman was entered into the analysis at 35 years of age, before which natural menopause was unlikely to occur. Twenty-seven women (4%) were censored before 35 years of age because of surgical menopause (n = 7), OC use (n = 19), or other hormone use (n = 1).
We reran the final models including as menopausal the 13 women in the impending menopause group who may have been menopausal but had not yet reached the definition of natural menopause (12 months of amenorrhea). To assess the possibility that TCDD exposure is associated with conditions that would lead to surgical menopause or that it is associated with a longer menopausal transition, we also reran the final models with a redefined outcome including surgical menopause, perimenopause, impending menopause, and natural menopause.
The final models were also rerun considering alternative TCDD exposure scenarios including cumulative exposure dose (area under the curve measured in parts per trillion-years) and estimated exposure dose at time of failure or censorship (parts per trillion). These doses were estimated for each year of exposure (time dependent) following the first-order kinetic model assuming a half-life of 9 years for TCDD (Pirkle et al. 1989). Ages at risk before the explosion were assigned to the “unexposed” category. We also reran the final models excluding the 24 women for whom it was necessary to estimate TCDD exposure by back-extrapolation from TCDD levels measured in serum collected in 1996.
Results
Characteristics of the 616 women in the analysis are presented in Table 1 for all women and by menopausal category. The mean (± SD) age at interview of the 616 women was 47.8 ± 8.1 years and ranged from 35 to 63 years. A total of 260 women (42.2%) were in pre-menopause, 169 women (27.4%) were in natural menopause, 83 women (13.5%) were in surgical menopause, 13 women (2.1%) were in impending menopause, 33 (5.4%) were in perimenopause, and 58 (9.4%) were assigned other status [current OC use (n = 39), other hormone use (n = 17), chemotherapy (n = 2)]. The mean (± SD) age at menopause for those in natural menopause (n = 169) was 49.2 ± 3.7 years (median, 49; range, 39–57), which was older than those in surgical menopause (42.7 ± 6.2 years; median, 43; range, 22–52).
All women were Caucasian, about half had less than the required amount of education, about 40% were overweight or obese (≥25 kg/m2), two-thirds had never smoked, about half had ever used OCs, and nearly all had been married and were parous (Table 1). Compared with premenopausal women, natural menopausal women were less educated and more likely to be overweight or obese, to be nonsmokers, to have used OCs for a shorter period of time, and to have had more pregnancies.
For each menopausal category, the median lipid-adjusted serum TCDD level and inter-quartile range (IQR) are presented in Table 2. Overall, the median lipid-adjusted serum TCDD level for the 616 women was 43.7 ppt (IQR, 24–95 ppt; range, 2.5–6,320 ppt). For premenopausal women, the median serum TCDD level was 43.6 ppt (IQR, 21–91 ppt), and for naturally menopausal women the median was 45.8 ppt (IQR, 28–100 ppt). Serum TCDD levels did not vary significantly across the menopausal categories (analysis of variance for log10 TCDD, p = 0.87).
In Cox proportional hazards modeling, the unadjusted HR associated with a 10-fold increase in TCDD (log10 TCDD) was 1.02 [95% confidence interval (CI), 0.8–1.3; test for trend, p = 0.89] (Table 3). That is, there was a 2% nonsignificant increase in risk of onset of menopause with a 10-fold increase in TCDD (e.g., from 10 to 100 ppt). After controlling for education, parity, duration of OC use, and “other medical conditions,” the association with log10 TCDD remained nonsignificant (HR = 1.06; 95% CI, 0.8–1.4). However, when a square term in log10 TCDD was added to the continuous variable model, it was statistically significant, suggesting a curvature in the dose–response curve (results not shown).
When serum TCDD levels were categorized into quintiles, risk of earlier menopause trended upward in the first four quintiles but not in the highest quintile in the unadjusted (Table 3) and adjusted models (Figure 1). After adjusting for covariates, relative to women with TCDD levels in the lowest quintile (< 20.4 ppt), women with TCDD levels in quintile 2 (20.4–34.2 ppt) had a 10% increase in hazard of natural menopause (adjusted HR = 1.1; 95% CI, 0.7–1.8; p = 0.77), women with TCDD levels in quintile 3 (34.3–54.1 ppt) had a 40% increase in hazard of natural menopause (adjusted HR = 1.4; 95% CI, 0.9–2.3; p = 0.14), and women with TCDD levels in quintile 4 (54.2–118 ppt) had a 60% increase in hazard of natural menopause (adjusted HR = 1.6; 95% CI, 0.9–2.6; p = 0.10). Women in the highest quintile (5, > 118 ppt), however, had only a 10% increase in hazard of earlier natural menopause (adjusted HR = 1.1; 95% CI, 0.6–1.9; p = 0.82). Although no increasing trend of earlier natural menopause was observed across the five quintiles (p = 0.44), a significant trend to earlier natural menopause across the first four quintiles was found (p = 0.04). Furthermore, when we excluded the 24 women who had back-extrapolated TCDD levels from 1996, the association is strengthened. Compared with women in the lowest quintile (< 20.4 ppt), women in quintile 2 (20.4–34.2 ppt) had an HR of 1.2 (p = 0.5); quintile 3 (34.3–54.1 ppt) had an HR of 1.6 (p = 0.08); quintile 4 (54.2–118 ppt) had an HR of 1.7 (p = 0.05); and quintile 5 (> 118 ppt) had an HR of 1.2 (p = 0.5) for risk of earlier menopause. The trend toward earlier menopause across the first four quintiles is statistically significant (p = 0.02).
The results did not change when women in the impending menopause category were classified as menopausal in the analysis (data not shown). Similar results were found when women in surgical menopause and peri-menopause were also combined with natural and impending menopause as one outcome (data not shown).
When TCDD exposure was extrapolated to the time of failure or censorship, the results were no different. Cumulative TCDD exposure (parts per trillion-years), however, was not related to age at onset of menopause (adjusted HR = 1.02; 95% CI, 0.8–1.3).
In the final models described above, nulliparity was associated with earlier natural menopause (adjusted HR = 1.9; 95% CI, 1.1–3.4), and history of OCs for at least 5 years was associated with later natural menopause (adjusted HR = 0.5; 95% CI, 0.3–1.1). We observed a nonsignificant earlier natural menopause for women who were current smokers (adjusted HR = 1.2; 95% CI, 0.8–1.7). BMI, however, was not associated with age at natural menopause.
Discussion
The results of this study of women residing in Seveso, Italy, in 1976, at the time of a chemical plant explosion that resulted in very high levels of TCDD exposure, suggest a nonmonotonic dose-related association of TCDD levels in sera collected near the time of exposure with earlier onset of natural menopause; the trend for increasing risk is observed with TCDD levels up to about 100 ppt, but not above. Our finding is supported by the earlier mean age of menopause observed in our study (49.2 ± 3.7 years) relative to that (mean = 50.9 years) reported in an Italian clinic-based study of > 4,300 menopausal women during the same time period (1995–1997) (Meschia et al. 2000). It is also earlier than the mean age of 49.9 years reported contemporaneously for menopausal women from another unexposed province in the Lombardia region (Celentano et al. 2003).
To our knowledge, no previous epidemiologic studies have examined the relation of TCDD exposure and age at menopause. However, amenorrhea was observed in a case report of an Austrian woman with extremely high levels of serum TCDD (144,000 ppt) (Geusau et al. 2001). Our findings are also consistent with findings from a case–control study of breast cancer in women residing in North Carolina (Cooper et al. 2002). In that study, investigators did not find a relationship between age at menopause with serum levels of total PCBs (including dioxin-like and non-dioxin-like PCBs) but did find an elevated risk (HR = 1.4) for earlier menopause in women with serum levels in the top decile of DDE compared with women with levels below the median. However, the mechanism of action for TCDD is not the same as for DDE (Mizuyachi et al. 2002), and the effects of TCDD may differ depending on the estrogen-target material (Chaffin et al. 1996).
The potential impact of TCDD exposure on age at menopause is biologically plausible, as animal studies indicate (Kociba et al. 1976; Li et al. 1995a, 1995b; Roby 2000; Son et al. 1999). In a rat model, a serum estradiol concentration 8–10 times higher than normal was needed to overcome TCDD-blocked ovulation, including restoration of the luteinizing hormone and FSH surges. This suggests that the hypothalamic–pituitary axis may be less sensitive to estrogen in TCDD-treated animals (Gao et al. 2001).
If TCDD exposure induces earlier menopause, it is unlikely to occur via oocyte apoptosis. Recent data in mice suggest that TCDD does not induce Bax gene expression in oocytes, which is necessary for the oocyte loss related to premature ovarian failure (Matikainen et al. 2001). Although this relation remains to be examined in human cells, the findings on Bax activation would suggest that TCDD exposure may not cause premature ovarian failure (J. Tilly, personal communication).
We observed an inverted U-shaped relationship between TCDD serum levels and earlier menopause. An inverted U-shaped dose response has been hypothesized by Kohn and Melnick (2002) as a plausible outcome with endocrine-disrupting chemicals. Myers et al. (2003) hypothesized that at lower “physiologic” doses a chemical may mimic a hormone, but at higher doses the toxic effect of the chemical may overwhelm the stimulatory or inhibitory effects. Empirical data from animals exposed to a variety of estrogenic xenobiotics (Rubin et al. 2001; vom Saal et al. 1995) support this theory, although only one prior study of TCDD (Markowski et al. 2001) has demonstrated non-monotonic effects (i.e., of in utero exposure on adult weight of offspring). The present results as well as those in animals suggest a reevaluation of the presumed monotonic dose–response relationships with exposure to endocrine-disrupting chemicals that are typically tested in statistical modeling of epidemiologic data.
This study has some limitations. One limitation is the retrospective recall of age of natural menopause. However, previous studies have reported moderately high reliability and accuracy based on interview (Colditz et al. 1987). Further, in the women with surgical menopause, the reported age at menopause was similar to the age recorded in the medical record. In addition, we augmented our classification using ultrasound, menstrual diary and medical record information. We also counted women who had evidence of impending menopause as menopausal, and saw a similar pattern of results.
Although smoking has been associated with earlier menopause in a number of studies (Brambilla and McKinlay 1989; Brett and Cooper 2003; Bromberger et al. 1997; Cooper et al. 2002; Gold et al. 2001; Meschia et al. 2000; Palmer et al. 2003; Sowers and La Pietra 1995; van Noord et al. 1997; Willett et al. 1983), we did not observe a significant relationship in the present study of a TCDD-exposed population. This lack of association may be due to the paucity of heavy smokers, or possibly related to an interaction between different ligand-activated receptor pathways (Klinge et al. 2000). Another reason for the lack of association may be that we defined smoking status as that at interview. Smoking status at the time of the outcome (if it occurred before the interview) may have been different.
Another limitation of the study is that the lowest TCDD exposure group (≤20.4 ppt) experienced relatively high serum levels compared with the contemporary levels we have reported for this area (~ 2 ppt) (Warner et al. 2004). Also, although the explosion resulted in exposure specifically to TCDD, pooled serum samples collected in 1976 from females who resided in the unexposed area showed substantial background exposure to other polychlorinated dibenzo-p-dioxins and PCBs during this time period [90 ppt dioxin toxic equivalents (TEQ), on average, for this age group) (Eskenazi et al. 2004)]. Therefore, individuals with TCDD levels < 20 ppt might still have had substantial dioxin TEQ exposure. Because we could consider only TCDD in this study, our results may underestimate an effect due to dioxin TEQ exposure.
An advantage of this study is that we were able to measure TCDD levels in individual serum samples collected near the time of exposure, and there was a wide range of exposure. For the few women whose samples were of inadequate volume, we used serum collected between 1996 and 1997. If we exclude these women, the relation is strengthened. We have examined multiple exposure scenarios including exposure soon after the explosion as well as exposure extrapolated to each age at risk.
In summary, we observed a nonmonotonic dose–response relationship between serum TCDD levels and age of onset of natural menopause. The women in this study experienced substantial TCDD exposure during the postpubertal–adult developmental period. Animal evidence suggests that in utero and lactational TCDD exposure may have significant effects on ovarian follicles (Heimler et al. 1998); therefore, continued follow-up of the younger women in the SWHS cohort as well as the female offspring of the SWHS cohort is essential.
Figure 1 Serum TCDD quintiles and age at natural menopause: HRs and 95% CIs, SWHS, Italy, 1996–1998 (n = 616), adjusted for education, parity, duration of OC use, and other medical conditions. Test for trend across quintiles: quintiles 1–5: p = 0.44; quintiles 1–4: p = 0.04.
Table 1 Distribution of select characteristics [n (%)] by menopausal status, SWHS, Italy, 1996–1998 (n = 616).
Characteristic All womena Premenopausalb Natural menopauseb Surgical menopauseb Impending menopauseb Perimenopauseb Otherb
Menopausal status 616 (100) 260 (42.2) 169 (27.4) 83 (13.5) 13 (2.1) 33 (5.4) 58 (9.4)
Age at interview [years (mean ± SD)] 47.8 ± 8.1 41.9 ± 4.7 56.6 ± 3.7 52.1 ± 6.4 51.5 ± 2.8 47.1 ± 3.2 41.8 ± 6.6
Education
Less than required 341 (55) 85 (25) 139 (41) 59 (17) 11 (3) 26 (8) 21 (6)
Required/university 275 (45) 175 (64) 30 (11) 24 (9) 2 (1) 7 (3) 37 (13)
Current BMI (kg/m2)
< 18.5 13 (2) 8 (62) 3 (23) 0 (0) 0 (0) 1 (8) 1 (8)
18.5–24.9 353 (57) 170 (48) 80 (23) 37 (10) 6 (2) 18 (5) 42 (12)
25.0–29.9 180 (29) 63 (35) 60 (33) 31 (17) 6 (3) 9 (5) 11 (6)
≥30 70 (11) 19 (27) 26 (37) 15 (21) 1 (1) 5 (7) 4 (6)
Cigarette smoking
Never 419 (68) 155 (37) 130 (31) 66 (16) 10 (2) 25 (6) 33 (8)
Former 88 (14) 47 (53) 16 (18) 9 (10) 0 (0) 3 (3) 13 (15)
Current 109 (18) 58 (53) 23 (21) 8 (7) 3 (3) 5 (5) 12 (11)
Total OC use (years)
0 332 (54) 111 (33) 132 (40) 53 (16) 6 (2) 18 (5) 12 (4)
< 1–5 184 (30) 106 (58) 30 (16) 21 (11) 2 (1) 11 (6) 14 (8)
≥5 100 (16) 43 (43) 7 (7) 9 (9) 5 (5) 4 (4) 32 (32)
Ever married
No 16 (3) 11 (69) 2 (13) 1 (6) 0 (0) 1 (6) 1 (6)
Yes 600 (97) 249 (42) 167 (28) 82 (14) 13 (2) 32 (5) 57 (10)
Parous
No 49 (8) 32 (65) 8 (16) 5 (10) 0 (0) 1 (2) 3 (6)
Yes 567 (92) 228 (40) 161 (28) 78 (14) 13 (2) 32 (6) 55 (10)
a No. (%) of column.
b No. (%) of row.
Table 2 Distribution of serum TCDD levels near the time of explosion by menopausal category at interview, SWHS, Italy, 1996–1998 (n = 616).
Category No. (%) Serum TCDD [median ppt (IQR)]
Premenopause 260 (42.2) 43.6 (21–91)
Natural menopause 169 (27.4) 45.8 (28–100)
Surgical menopause 83 (13.5) 43.4 (28–98)
Impending menopause 13 (2.1) 43.8 (24–105)
Perimenopause 33 (5.4) 36.5 (22–85)
Other 58 (9.4) 39.6 (17–85)
Total 616 (100.0) 43.7 (24–95)
Table 3 Serum TCDD levels, percentage with natural menopause, and unadjusted HRs (95% CIs) for onset of menopause, SWHS, Italy, 1996–1998 (n = 616).
TCDD (ppt) nmp/ntot (%) HR (95% CI)
Continuous
log10 TCDD 169/616 (27) 1.02 (0.8–1.3)
Quintiles
< 20.4 24/123 (20) 1.0 (reference)
20.4–34.2 35/123 (28) 1.1 (0.7–1.8)
34.3–54.1 41/123 (33) 1.4 (0.9–2.3)
54.2–118 37/124 (30) 1.6 (1.0–2.7)
> 118 32/123 (26) 1.0 (0.6–1.8)
nmp, number of women who reached natural menopause; ntot, total number of women.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7141ehp0113-00086316002374ResearchInfluence of Tap Water Quality and Household Water Use Activities on Indoor Air and Internal Dose Levels of Trihalomethanes Nuckols John R. 1Ashley David L. 2Lyu Christopher 3Gordon Sydney M. 4Hinckley Alison F. 1Singer Philip 51Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA2Emergency Response and Air Toxicants Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, USA3Battelle, Centers for Public Health Research and Evaluation, Durham, North Carolina, USA4Battelle, Columbus, Ohio, USA5Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, North Carolina, USAAddress correspondence to J.R. Nuckols, Environmental Health Advanced Systems Laboratory, Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523-1681 USA. Telephone: (970) 491-7295. Fax: (970) 491-2940. E-mail:
[email protected] thank C. Wilkes for his assistance in the design and implementation of the field study; M. Brinkman, M. Holdren, and W. Keigley (Battelle) for air and breath sample analysis; R. Dietz (Brookhaven National Laboratory) for assisting in tracer analysis; B. Blount, M. Bonin, L. Silva, M. Smith, and C. Dodson (CDC) for assisting in blood analysis; E. DePaz (University of North Carolina at Chapel Hill) for water analysis support; and our nurses and field data collection staff for their hard work.
Funding for this project and manuscript preparation was provided in part by the National Center for Environmental Health (CDC), the American Water Works Association Research Foundation, and the National Institutes of Health, National Cancer Institute, Occupational and Environmental Epidemiology Branch.
The authors declare they have no competing financial interests.
7 2005 24 3 2005 113 7 863 870 31 3 2004 24 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Individual exposure to trihalomethanes (THMs) in tap water can occur through ingestion, inhalation, or dermal exposure. Studies indicate that activities associated with inhaled or dermal exposure routes result in a greater increase in blood THM concentration than does ingestion. We measured blood and exhaled air concentrations of THM as biomarkers of exposure to participants conducting 14 common household water use activities, including ingestion of hot and cold tap water beverages, showering, clothes washing, hand washing, bathing, dish washing, and indirect shower exposure. We conducted our study at a single residence in each of two water utility service areas, one with relatively high and the other low total THM in the residence tap water. To maintain a consistent exposure environment for seven participants, we controlled water use activities, exposure time, air exchange, water flow and temperature, and nonstudy THM sources to the indoor air. We collected reference samples for water supply and air (pre–water use activity), as well as tap water and ambient air samples. We collected blood samples before and after each activity and exhaled breath samples at baseline and postactivity. All hot water use activities yielded a 2-fold increase in blood or breath THM concentrations for at least one individual. The greatest observed increase in blood and exhaled breath THM concentration in any participant was due to showering (direct and indirect), bathing, and hand dishwashing. Average increase in blood THM concentration ranged from 57 to 358 pg/mL due to these activities. More research is needed to determine whether acute and frequent exposures to THM at these concentrations have public health implications. Further research is also needed in designing epidemiologic studies that minimize data collection burden yet maximize accuracy in classification of dermal and inhalation THM exposure during hot water use activities.
biomarkerschlorinationdisinfection by-productsexposuretrihalomethanewater use
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Trihalomethanes (THMs) are a by-product of water chlorination, arising from the reaction between natural organic matter in the source water and chlorine used for disinfection. There are four primary species of THM: chloroform (CHCl3), bromodichloromethane (CHBrCl2), dibromochloromethane (CHBr2Cl), and bromoform (CHBr3). The speciation of the THM depends on raw water quality and treatment characteristics (Miles et al. 2002). The U.S. Environmental Protection Agency (EPA) has established a maximum contaminant level of 0.08 mg/L for the total THM (TTHM) because of increased evidence of adverse health effects linked to these compounds (U.S. EPA 1998). Researchers have found an association between elevated levels of THM and adverse health outcomes, including cancer (Cantor et al. 1978, 1987, 1998; Hildesheim et al. 1997; King and Marrett 1996; McGeehin et al. 1993) and adverse reproductive outcomes (Aschengrau et al. 1989, 1993; Bove et al. 1995; Gallagher et al. 1998; Klotz and Pyrch 1999; Waller et al. 1998). Exposure assessments for most of these studies were based on reported levels of TTHM in the water distribution system serving the participants’ residences, and in some cases reconstructing study participants’ water consumption histories.
Exposure to THM through routes other than ingestion has been demonstrated as a significant component of the overall exposure matrix. In controlled experiments, Weisel et al. (1992) and Xu and Weisel (2005) reported elevated breath concentrations of CHCl3 due to showering. In a later field study of 33 subjects using public water supplies in New Jersey with relatively low THM concentrations, Weisel et al. (1999) determined that timing of sampling postshower exhaled air was important in order to capture a high correlation to water concentration. Critical time frames reported by their study were 20 min for CHCl3 and CHBrCl2 and 5 min for CHBr2Cl and CHBr3.
Weisel and Jo (1996) demonstrated that dermal contact is an important route of exposure for CHCl3, reporting higher exhaled air concentrations from this route than from inhalation due to showering and bathing. Gordon et al. (1998) also reported elevated CHCl3 concentrations in exhaled breath from subjects who breathed clean air while bathing in waters ranging in temperature from 30 to 40°C (86–104°F). For these dermal-only exposures, they reported that for similar levels of CHCl3 in the bath water, much higher levels of the compound in exhaled air were measured from an individual taking a 40°C bath compared with the same individual taking a 30 or 35°C bath.
Studies have demonstrated that exposure to THM results in significant increases in blood THM concentrations. Backer et al. (2000) reported increases in blood CHCl3, CHBrCl2, and CHBr2Cl compared with pre-activity blood levels in groups of approximately 10 individuals each due to showering, bathing, and consuming 1 L of cold tap water for a 10-min period. They found that increases in blood concentrations of these THMs from showering or bathing were significantly greater than the increases from drinking 1 L of water. Pegram et al. (2002) reported maximum blood concentrations of CHBrCl2 ranging from 0.4 to 4 ng/mL due to ingestion versus 39–170 ng/mL due to dermal contact with water containing the same concentration of CHBrCl2. They also reported that blood CHBrCl2 levels returned much more rapidly to baseline after ingestion (4 hr) as opposed to after dermal exposure (24 hr). Lynberg et al. (2001) measured THM in pre- and postshower blood samples from 25 participants in each of two water utility service areas. They reported significant intersite differences in both tap water samples and blood THM levels, as well as significant increases in blood THM levels for all participants due to the showering event. Miles et al. (2002) further analyzed the data from the field study and found that although showering activity shifted the THM distribution in the blood toward that found in the corresponding tap water (including concentration), there was no significant correlation between blood concentration and tap water concentration.
Household water uses other than showering and bathing have not been evaluated in terms of potential exposure to THM. In this study, we determined the relative contributions of showering and bathing, along with 12 other water use activities, to THM exposure in a household environment. The purpose of this article is to provide a description of the methods used in our study and a summary of the results. The findings are relevant to the design and implementation of epidemiologic studies concerning exposure to volatile water supply contaminants.
Materials and Methods
Study location/participants.
We conducted our study at a single residence in each of two sites: one in North Carolina (NC site) and the other in Texas (TX site). The floor plans for the study residences at the NC site and TX site were almost identical. Both were three-bedroom/two-bathroom, one-story, ranch-style houses (about 111.5 m2 or 1,200 ft2 total floor space). Both residences had central heating, ventilation, and air conditioning (HVAC) systems, and both had electric water heaters. Each residence was served by a public water distribution system. The study was conducted 5 August through 17 September 2002 in North Carolina and 13 October through 6 November 2002 in Texas. We treated the data as representative of a water supply with relatively high (NC) and relatively low (TX) THM concentrations, predominated by chlorinated THM species.
We planned for and recruited seven participants by advertising in local media and distributing study flyers on local college campuses. We used a standardized questionnaire to screen applicants for the following eligibility criteria (acceptable range given in parentheses): age (18–35 years), body mass index (22–24), tobacco smoking (nonsmoker only), alcohol consumption (average < 2 drinks/day), and swimming activity (< 4 days/week). We also excluded applicants who reported asthma or other breathing problems, high blood pressure or hypertension, a history of problems associated with blood draws, regularly taking any medications for any health conditions, or any condition that would prevent them from conducting the water use activities prescribed by our study. The final study group was composed of three males and one female at the NC site, and one male and two females at the TX site. The age range for participants in our study was 21–30 years. Two of the male participants at the NC site reported their race as African American. All other participants reported their race as Caucasian.
Data collection.
Before the introduction of participants, we prepared the study residence for data collection and analysis. Only one of the bathrooms in each residence was used as the study bathroom. Approximately 30 min before the first activity began each day, the second bathroom door was shut and the vent fan turned on. To prevent and account for contribution of THMs to household air, the use of the second bathroom during the study activities was minimized as much as possible and was documented. The showerhead in the study bathroom of each residence was replaced with a custom showerhead designed to maintain consistent flow. This showerhead was connected to a remote water sampling apparatus designed to minimize loss of volatile THM. The apparatus was used to collect water samples from the showerhead and the shower stall drain. The thermostat for the HVAC in each house was set at 75°F, and the HVAC fan was set to the “on” position during the entire study period. The exhaust fan in the study bathroom was not turned on at any time during the study. At each study site, we conducted airflow and tracer gas studies to characterize the house-to-environment air exchange rates and bathroom-to-house air flow rates and to identify the optimal locations for collecting household air samples during the THM exposure study (Dietz and Cote 1982).
We collected data THM exposure data over a 2-day period for each study participant. The second day of the study typically occurred approximately 1 week after the first. On each day, the participant performed a set of prescribed water use activities while we collected pre- and postactivity samples of air, water, blood, and exhaled breath. These activities are listed in Table 1. Between events on the participation day, the participant was required to remain in the residence. We designed the sampling regimen so that activities expected to result in the largest increase in internal dose levels were spaced at estimated time intervals sufficient to allow blood THM concentrations to return as much as possible to preexposure levels before the next water use activity. For some activities we collected concurrent air and/or water samples, as well as exhaled breath samples. Water temperature was measured during each activity.
To reduce the likelihood of inadvertent THM exposure, each participant arrived at the study residence the night before his/her scheduled day of data collection and slept in the study residence. Upon arrival, the participant completed a questionnaire to provide information on demographics, water use and consumption in the past 48 hr, and exposure to chemicals that might be confounding factors in the study. These data were collected primarily to screen for water or chlorinated compound use (e.g., swimming) that could interfere with our premise that early morning blood concentrations could represent a “baseline” for each individual. The subjects were instructed to wear swimsuits for the showering and bathing components of the study.
Over the study period, we measured the flow of water to each study house using a water meter data logger (Meter Master model 100EL; F.S. Brainard Company, Burlington, NJ). These data were collected primarily for modeling purposes and will be discussed in a separate report. We measured ambient and indoor temperatures and relative humidity using electronic thermometers. We controlled and standardized the water temperature for each study activity.
Water samples.
We collected 21 water samples over the 2-day period. These samples were either associated with a water use activity or collected from a cold-water tap over the course of each exposure day to establish “baseline” THM concentrations (TTHM and each of four species). We collected and analyzed duplicates of each sample. All water samples were collected using headspace-free 40-mL acid-washed glass vials. Immediately after collection, ammonium sulfate was added to the sample in order to quench residual chlorine and prevent further THM formation. We measured and recorded the temperature of the tap water for each sample. Sample containers were refrigerated and packed into coolers with ice packs and shipped by overnight express courier to the University of North Carolina at Chapel Hill for analysis using gas chromatography.
Air samples.
We collected air samples to determine the levels of THM (TTHM and each of the four species) in the air associated with each activity. Thirteen samples were collected over the 2-day study period for each participant. We collected a “baseline” sample each day before any water use activity. The air samples were collected using precleaned and evacuated SUMMA polished 6-L stainless steel canisters (Scientific Instrumentation Specialists, Moscow, ID, and Biospherics, Hillsboro, OR). We collected “grab” samples by opening the canister valve and allowing air to flow into the canister until atmospheric pressure equilibrium was attained (≤ 1 min). We shipped exposed canisters by overnight express courier to Battelle Memorial Institute (Columbus, OH) for analysis. Samples were analyzed by automated gas chromatography/mass spectrometry (GC/MS) using a modified version of U.S. EPA Method TO-14 (Winberry et al. 1990).
Blood samples.
We collected blood samples from each participant in order to examine the levels of THM (TTHM and each of four species) associated with each water use activity. Vacutainers (Becton, Dickinson & Co., Franklin Lakes, NJ) were prepared by heating, restoration of vacuum, and resterilization in order to eliminate background contamination from the blood collection device (Cardinali et al. 1995). We collected samples approximately 5 min before and after each activity, using a multisample adapter (venous catheter). Additional blood samples were collected 30 min after the shower and bath activities. The catheter remained in the participant for the duration of each day of the study, approximately 12 hr. We collected a total of 26 10-mL blood samples from each participant over the course of the 2-day study, 14 on day 1 and 12 on day 2. After collection, each blood sample was refrigerated and packed into coolers with ice packs, and at the end of each day shipped by overnight express courier to the Volatile Organics Laboratory at the Centers for Disease Control and Prevention (CDC; Atlanta, GA). We analyzed THM in the blood samples using a variation of the standardized method reported by Ashley et al. (1992). This method includes spiking 3-mL blood samples with isotopically labeled standards, extracting with solid-phase micro-extraction, and analysis by GC followed by high-resolution magnetic-sector MS. We quantified blood THM concentrations using calibration curves generated from dilutions of pure samples of each THM species. Blanks and quality control materials were analyzed with each analytical run. Detection limits were in the parts per quadrillion range, allowing the quantification of most samples even at background levels.
Breath samples.
We collected breath samples using a self-administered procedure in which the subject exhaled alveolar air directly into an evacuated single breath canister (Pleil and Lindstrom 1995). For this study we used 1-L Silcosteel stainless steel canisters (Entech, Simi Valley, CA) fitted with a short Teflon tube that served as a disposable mouthpiece. We instructed the subject to begin sample collection near the end of a normal resting tidal breath in order to provide what is mostly alveolar breath. We collected a total of 15 breath samples from each subject over the 2-day study period. Baseline measurements were obtained once per day before all activities began. Samples were shipped at the end of each day by overnight express courier to Battelle for THM analysis (TTHM and each of four species), which was carried out by the same automated GC/MS procedure used for air samples.
Data analysis.
We calculated summary statistics (mean, SD, median, range) for measured THM species in water, air, blood, and exhaled breath samples and for measurements of temperature in the water samples and in the ambient air during activities. We calculated relative exposure, defined as the ratio between pre- and postactivity blood concentration and between exhaled breath concentrations, for each participant and activity. We plotted the data and examined for natural break points. Based on this procedure, we established a cut-point of 2-fold deviation from baseline concentrations as indicators of meaningful increase or decrease in these biologic marker concentrations. We established similar criteria of ± 20% for the ratio of activity-related water concentrations to baseline (cold tap) water sample concentrations, and a 5-fold deviation in the ratio of activity-related air concentrations to baseline. Our approach is similar to that suggested by the American Chemical Society Committee on Environmental Improvement (ACSCEI) to determine whether increases in biologic concentrations are meaningful when comparing environmental chemistry data (ACSCEI 1980). The ACSCEI suggested an increase of at least three times the SD of the smallest (baseline) concentration in making this determination. Our approach is generally more conservative.
We used a repeated measures design of the general linear model (Ott and Longnecker 2001) to test for statistically significant inter-site, interparticipant, and temporal differences in measured water temperature and concentrations of THM in water. We used two-factor experiments with repeated measures on one factor (order of activity or baseline measurements as a proxy for time), and α = 0.05 level of significance, to conduct these analyses.
Results
Water supply temperature and THM concentration.
Figure 1 provides a summary of the median and range of concentrations of THM measured in baseline (cold tap) and water samples from each water use activity that resulted in at least a 2-fold increase in biologic markers of exposure for at least one participant. It also includes the median water temperature for each sample type. The only activity that did not meet the criteria for inclusion in Figure 1 was ingestion of a cold tap water beverage.
Baseline THM concentrations in the tap water were much higher at the NC site for TTHM, ranging from 113 to 212 μg/L compared with a range of 12–53 μg/L at the TX site. Although these concentrations did change over the course of the day, the difference between concentration for any THM by time of day was not statistically significant across the study population (p = 0.07–0.65). THM concentrations in the activity-associated water were also much higher at the NC site compared with the TX site.
Most ratios of THM concentration in activity-associated water to concentration in baseline (cold tap water) samples were near or below 1.0. Only the ratio for CHCl3 for the showering event at the NC site exceeded our criteria of a 20% increase as being meaningful. At the NC site, median ratios of activity to baseline concentration for several THM species and activities were at least 20% less than 1.0, including CHBrCl2 in showering and bathing, and CHBr2Cl in showering and hand dish-washing. At the TX site, we did not observe a deviation of > 20% in ratios of THM concentration in activity and baseline water samples at the group or individual level, except for one participant whose water for the shower and for hand dish washing had a ratio of 3.2 for CHBr2Cl and 3.3 for TTHM, respectively.
We found that activity-associated water temperatures for most activities in Figure 1 were much higher than the temperature of the corresponding baseline water sample, with the exception of the automatic clothes washing activity. Median temperatures of the baseline (cold tap) water samples were very similar, with a difference of < 2°C for any activity between the two study sites. The intersite differences in the median water temperature were < 1°C for most activities. We found no statistically significant correlation between water temperature and THM concentration, with the exception of CHBrCl2 at the NC site (p = 0.02).
Air temperature and THM concentration.
Table 2 provides a summary of median and range of concentrations of THM measured in baseline samples (before any water use activities) and in ambient air samples for each water use activity that resulted in at least a 2-fold increase in biologic markers of exposure for at least one participant. It also includes the median and range in air temperature for each sample type. The only activity that did not meet the criteria for inclusion in Table 2 was ingestion of a cold tap water beverage.
At both study sites, we observed a > 5-fold increase in the ratio of activity ambient air to baseline THM concentration for all THM compounds other than CHBr3 for participants as a group due to showering and indirect shower exposure, and due to the bathing activity (except CHBr2Cl). The air TTHM concentration during showering increased by 70% across individuals at the NC site and by 38% at the TX site (data not shown). We observed a 4- to 11-fold (median = 7) increase in ambient air TTHM concentration due to the hand washing activity across participants at the NC site. This increase was primarily due to a corresponding increase in CHCl3 concentration. We also observed large increases in ambient air CHCl3 due to the automatic clothes washing with bleach (median increase > 9-fold) and the hand dish washing (median > 5-fold) activities across participants at the TX site. For most of the other water use activities listed in Table 2, we observed a slight to moderate increase in ambient air THM concentration at both sites (median increase < 2.5-fold).
For the activities listed in Table 2, median temperatures of the baseline ambient air samples were equal for day 1 and within 0.7°C for day 2. Median temperatures of ambient air during the water use activities were within 5% of baseline at both sites, except for the clothes washing II activity at the TX site. For that activity, the median air temperature was 27°C (81°F) compared with a median baseline temperature of 23°C (73°F).
Markers of exposure: blood and exhaled air THM.
Table 3 provides a summary of median and range of concentrations of THM measured in blood samples collected 5 min before and after each water-related activity by study site. At both sites, there was a > 2-fold increase in blood concentrations for all participants and all THM species except CHBr3 due to the showering and bathing activities. Increases as a result of showering were 5- to 15-fold in participants at the NC site and approximately 5-fold at the TX site. Increases as a result of the bathing activity were 3- to 6-fold in participants at the NC site and 3- to 19-fold at the TX site. Hand dish washing resulted in a 2- to 8-fold increase in blood THM concentrations (except CHBr3) in two of the three participants at the Texas site. Increases of 3-fold in concentrations of CHBrCl2 and CHBr2Cl were observed in the other participant. Hand dish washing resulted in a < 2-fold increase in blood THM concentrations in three of the four participants at the NC site.
The average preshower blood TTHM concentrations at the NC and TX sites were 47 and 19 pg/mL, respectively. The average increases in blood TTHM due to showering at the sites were 358 and 79 pg/mL, respectively. We observed similar preactivity average blood TTHM concentrations for bathing and hand dish washing (except one participant at the TX site). The average increases in concentration for bathing were 164 and 118 pg/mL at the NC and TX sites, respectively. The average increases in concentration for hand dish washing were 98 and 57 pg/mL, respectively, but there was a high degree of interparticipant variation at both sites. Increases in blood THM for the other activities were generally < 20 pg/mL and highly varied.
Table 4 provides a summary of the median and range of concentrations of THM in exhaled breath samples collected before all water use activities (baseline) and during or after activities by study site. The baseline exhaled breath THM concentrations were very similar between the two sites for all THM species except CHCl3, which was consistently higher at the NC site. Baseline CHBrCl2 concentration in one NC participant was 9 μg/m3, but this was inconsistent with all other baseline measurements at the NC site, which ranged from below the detection limit (0.8) to 4.6 μg/m3.
We found a > 2-fold increase in the median exhaled breath concentrations of TTHM across participants as a group due to bathing (both study sites) and showering (NC) activities, and an almost 2-fold increase due to showering at the TX site. These increases in TTHM were primarily due to increases in CHCl3 concentration. Similar increases in median exhaled breath concentrations of CHCl3 were also observed due to hand dish washing activities at both sites, the automatic dish washing activity at the TX site, and the automatic clothes washing with bleach activity at the NC site. Across individual participants, increases in exhaled breath TTHM concentrations due to showering ranged from 3- to 6-fold at the NC site and were approximately 2-fold at the TX site. Individual increases due to bathing ranged from 3- to 6-fold at the NC site and 3- to 19-fold at the Texas site. Individual increases due to hand dish washing ranged from approximately 1.5- to 2.5-fold at both sites, except for one outlier at the NC site with a measured decrease of 0.5-fold. This outlier had no influence on any of the reported results. We observed a 2-fold or better increase in the exhaled breath concentration of at least one THM compound in at least one study participant due to each of the other water use activities, with the exception of hand washing and indirect shower exposure.
Discussion
We measured blood and exhaled air concentrations of THM as biomarkers of exposure to participants conducting 14 common household water use activities (Table 1). We found that the showering (10 min) and bathing (20 min) activities consistently resulted in at least 2-fold increases in median blood and exhaled breath TTHM across two study groups, regardless of whether the study site was characterized by high (NC site median = 136 μg/L) or low (TX site median = 38 μg/L) TTHM in the residential water supply. This magnitude of increase was observed for all THM species except CHBr3 in the blood samples, but only for CHCl3 in the exhaled breath samples. We also observed > 2-fold increases in median exhaled breath concentrations of CHCl3 at both sites and in blood CHCl3 and TTHM in two of the three participants at the TX site for the hand dish washing activities. There was no activity without a 2-fold increase in concentration in any biomarker of exposure for at least one THM and one individual.
The greatest observed increase in blood and exhaled breath THM concentration in any participant was due to showering and bathing. The average increases in blood TTHM due to showering were 358 and 79 pg/mL at the NC and TX sites, respectively. Average increases due to bathing were 164 and 118 pg/mL, and those due to hand dish washing were 98 and 57 pg/mL, respectively. However, we observed a high degree of interparticipant variation in the increase due to hand dish washing at both sites. Increases in blood TTHM concentration due to other activities were < 20 pg/mL and were also highly variable. More human-based research is needed to determine whether acute and frequent exposures to THM at these concentrations have public health implications.
The results of our study are consistent with findings of other studies for which shower water and pre- and postshower blood THM concentrations have been reported. Table 5 presents a summary of shower water and participant blood (pre- and postshower) THM concentrations for two studies in addition to ours. If we group the shower water concentrations of CHBrCl2 for the five study sites described in Table 5 into three categories: 6, 11–14, and 33 μg/L, the corresponding median blood CHBrCl2 concentrations reported for these groups are 19, 28–43, and 93 pg/mL after showering for 10 min. These findings indicate a dose response between concentration in the source water and blood. Similar correspondence between shower water and postshower blood CHBr2Cl and CHCl3 concentrations were observed across the five study sites, as well as for source water and postbathing THM concentrations reported for our study and by Backer et al. (2000). Lynberg et al. (2001) did not conduct a bathing analysis.
Our observations are also consistent with results reported in other residential studies of exposures to disinfected tap water in which air and exhaled breath samples were analyzed for THM. Table 6 summarizes results of during-shower air THM concentrations from three studies (Egorov et al. 2003; Kerger et al. 2000; May et al. 1995) in addition to ours. THM concentrations of exhaled breath from participants during showering were also reported by Egorov et al. (2003). In all cases reported in Table 6, the air concentrations during showers showed the same decreasing trend of CHCl3 > CHBrCl2 > CHBr2Cl, which was consistent with their relative concentrations in the source water of each respective study.
When we adjust for variation in THM water concentrations across the studies by taking the ratios of the shower air to source water concentrations, this ratio is approximately 2.2 and 2.4 μg/m3 per microgram per liter water for the “high” and “low” sites in our study, respectively. In comparison, we obtained a ratio of 1.7 from the May et al. (1995) and Egorov et al. (2003) data and a ratio of 3.5 from the Kerger et al. (2000) data. The differences in ratios between these studies could be due to a variety of factors known to affect THM transfer coefficients from water to air that we did not take into account in this comparison. These factors include water temperature and flow rate, shower duration, volume of shower enclosure, air exchange rates, and showerhead type. Available published studies on the measurement of THM concentrations in exhaled breath are sparse. Table 6 summarizes our results for the “high” and “low” sites along with values presented by Egorov et al. (2003) from their study of exposures to tap water disinfection by-products in a Russian city. In each case, the data for the three THMs listed show a corresponding gradient, high to low, between the during-shower air concentrations and the postshower exhaled breath concentrations. However, both our “high” site and “low” site concentrations for breath CHCl3 are significantly lower than the value reported by Egorov et al. (2003) despite the relatively close agreement between air concentrations at our “high” site and their value (Table 6). A reason for the observed differences could be the time when the samples were taken after exposure ended (Gordon et al. 1998; Weisel et al. 1999; Xu and Weisel 2005). In our study, breath samples were taken 5 min after exposure ceased; Egorov et al. (2003) collected breath samples within 1 min after subjects completed their showering activity.
We observed changes in baseline (cold tap water) THM concentrations over the course of each study day. However, the difference between baseline concentration for any THM by time of day was not statistically significant across the study population (p = 0.07–0.65). We also observed a high degree of variation between tap water THM concentrations over the period of study, especially at the NC site. For example, at this site water samples were collected 7 different days over the period of approximately 43 days; the range in TTHM concentrations in the samples collected at 0800 hr on each of those days was 139–200 μg/L (average = 169 μg/L), and the maximum CHBrCl2 was 63 μg/L (range, 23–63 μg/L). The THM levels in our samples were much different from the average concentrations reported by the utility that provides water to our NC study site. For example, the utility reported an annual average TTHM concentration of 76.7 μg/L (range, 28–145 μg/L) and a maximum CHBrCl2 concentration of 17 μg/L (range, 5–17 μg/L) for the year in which our study was conducted. These findings are important in terms of exposure assessment for epidemiologic studies concerning THM, because they indicate that although “snapshot” measurements of THM on a given day can be representative of levels for water use activities on that day, they may not be representative of THM in a specific residential water supply over a longer period of time.
The results of the present study support the findings of other studies that blood THM concentrations in response to equal or equivalent THM exposure appear to be higher in some individuals. At each of our study sites, we observed a large difference in relative increase in THM blood levels by one of the study participants in response to exposure by showering in waters with approximately the same THM concentration and temperature. We also observed differences in response for the same individual to exposure from hand dishwashing. Although our sample size is very small, these findings lend support to similar patterns reported by Backer et al. (2000) and Lynberg et al. (2001). Backer et al. (2000) suggested that such differentiation in response may be the result of differences in individuals’ abilities to metabolize THM. A number of metabolic enzymes exist in polymorphic form. For example, some THM are substrates for glutathione S-transferase theta-1 (GSTT1)–mediated glutathione conjugation reactions (Landi et al. 1999). Among Caucasian populations, about 17–18% of people are null for this gene. Another candidate enzyme is CYP2E1, which has a demonstrated role in metabolism of THM (Allis et al. 2001; Constan et al. 1999). Further research is needed to understand the implication of these findings in terms of design of epidemiology studies concerning THMs.
Our findings in the present study have important ramifications for exposure assessment in epidemiologic studies concerning THMs. The study confirms that showering and bathing activities are important sources of THM exposure. It provides evidence that hand dishwashing, indirect shower exposure, and other hot water use activities could also be important sources but need more study. Water temperature, THM concentration, and duration of use have been demonstrated to be important variables for quantifying THM exposure during showering and bathing (Giardino and Andelman 1996; Keating et al. 1997; Kerger et al. 2000; Wilkes et al. 2004). Water temperature was not correlated to water THM concentration in the present study. It is well established that THM concentrations of water in residential water heaters are generally much higher than in tap water from the utility distribution system, and we observed much higher temperatures in activity-associated water compared with baseline (cold tap) samples. However, we observed THM concentration ratios (TTHM and all species) near or below 1.0 between these water samples for most all activities. THM concentrations in air samples collected in association with these water use activities were all significantly elevated, indicating that THMs formed by heating of the water supply were volatile. For example, showering and indirect shower exposure median air concentrations were 318 and 142 μg/m3 compared with a baseline of 4 and 3 μg/m3, respectively at our NC site (Table 2). The fact that the ratios of the shower air to source water concentrations for the “high” and “low” sites were about equal (2.2 and 2.4) in our study indicates that estimates of air THM concentrations associated with specific hot water use activities may be possible if accurate THM water concentrations are known.
Weisel and Chen (1994) observed a doubling of CHCl3 concentration and a 50% increase in CHBrCl2 and CHBr2Cl in water heated to 65°C that contained 0.7–0.8 mg/L total chlorine residual. They reported that most of this increase occurred within 0.5 hr and was essentially complete within 1 hr. If THM concentrations do “plateau” in a residential water heater, obtaining measurements of temperature and THM concentration in separate hot and cold water samples during an epidemiology study could simplify exposure assessment. The temperature measurements could be used to estimate potential range of dermal exposure. Gordon et al. (1998) reported a strong effect of bath water temperature on dermal absorption of CHCl3, and it is likely this effect would hold for other hot water uses with dermal contact. Likewise, it might be possible to estimate air THM concentrations for specific water use activities based on the hot and cold water THM concentration. These results could be used in conjunction with air to water THM concentration ratios to construct “confidence intervals” for predictions of air THM concentrations from specific water use activities. A limitation to this approach is that these ratios can vary by activity as a function of room volume, ventilation, and other factors. For example, in our study intersite differences in these factors were minimized for the shower activity, and the ratios were near equal (2.2 and 2.4). However, the average air to water CHCl3 concentration ratios for the bathing activities, which were measured in the bathroom rather than shower stall, were 0.7 at our NC site and 1.2 at the TX site. The intersite difference in ratios for the bathing activity was due to a difference in bathroom volume. More research is needed to determine if standardized air to water THM concentration ratios for hot water activities related to significant THM exposure can be developed and applied in the context of an epidemiologic study.
The results of the present study clearly indicate that epidemiology studies concerning THMs need to consider hot water use activities as important exposure events. Further research is needed in designing epidemiologic studies that minimize data collection burden yet maximize accuracy in classification of dermal and inhalation THM exposure during these activities.
Figure 1 on a graph, that in the samples collected as baseline for the activity. All concentrations are rounded to nearest integer for presentation purposes. The concentration scales used vary by study site and THM compound.
Table 1 Description of water use activities and duration over the course of the study.
Time Water use activity Duration (min)
Day 1
2100a Participant arrives at the study house and sleeps there overnight
0800 Baseline measurements: ambient household air, tap water, blood THM 6.0
0820 Breakfast, including preparation and consumption of a hot beverage from tap water (0.25 L) 25.0
1000 Hot water showerb 13.0c
1300 Lunch, including drinking 0.5 L of cold tap water 30.0
1500 Automatic clothes washing (clothes washer)d 50.0
1730 Hand washinge 0.5
1800 Supper, including consumption of bottled water (no specified volume)f 45.0
1900 Automatic dish washing, open dishwasher at end of cycle 50.0
2100 Participant departs study house
Day 2 (1 week after day 1)
2100a Participant arrives at the study house and sleeps there overnight
0800 Baseline measurements: ambient household air, tap water, blood THM 6.0
0820 Breakfast, including consumption of a cold beverage prepared from tap water (0.25 L) 25.0
1000 Hot water bathb 23.0g
1300 Lunch, including consumption of bottled water (no specified volume)f 30.0
1400 Automatic clothes washing, adding bleach during the wash cycle 50.0
(clothes washer II)d
1600 Hand washing of dishesh 10.0
1800 Supper, including consumption of bottled water (no specified volume)f 45.0
1900 Sitting in room adjacent to the study bathroom and a shower event, opening bathroom door at end of the eventi 13.0
2100 Participant departs study house
a Evening before day of study; arrival between 2100 and 2300 hr allowed.
b No cleaning products such as soap or shampoo were used by the participant; subjects wore swimsuits.
c Participant in shower stall or bath for 10 min, followed by 3 min in study bathroom with door closed for changing clothes.
d Participant did not stay in same room as water use device.
e No cleaning products such as soap were used by the participant.
f Bottled water was tested and confirmed to have no THM species present.
g Filling time from 1000 to 1006 hr, maintained constant (6 min) for each participant; this was sufficient volume to submerge the torso and legs; participant stayed in the tub from 1006 to 1020 hr (14 min), followed by 3 min in study bathroom with door closed for changing clothes; subjects wore swimsuits.
h Detergent (Dawn Ultra; Procter & Gamble, Cincinnati, OH) was used.
i Termed “indirect shower exposure.”
Table 2 Median temperature and concentration of THM in air (μg/m3) for baseline and activities with at least a 2-fold increase in blood concentration for at least one participant.
Air temp (°C)
CHCl3 CHBrCl2 CHBr2Cl
CHBr3 TTHM
Activity NC site TX site NC site TX site NC site TX site NC site TX site NC site TX site NC site TX site
Baseline day 1 24 (22–24) 24 (23–25) 4 (2–10) 2 (1–2) 3 (BDL–7) 2 (2–3) BDL (—) BDL (—) BDL (—) BDL (—) 8 (5–19) 5 (5–7)
Hot beverage 24 (24–25) 23 (23–24) 7 (3–10) 2 (2–2) 2 (1–4) 3 (2–3) BDL (—) BDL (—) BDL (—) BDL (—) 10 (6–16) 6 (6–7)
Shower 25 (24–32) 24 (20–28) 318 (219–351) 67 (50–70) 54 (31–68) 23 (20–25) 9 (4–13) 4 (3–6) BDL (—) BDL (—) 384 (255–431) 95 (74–102)
Clothes washer 24 (24–27) 27 (25–27) 21 (7–25) 4 (2–5) 7 (BDL–8) 2 (0.7–3) BDL (BDL–2) BDL (—) BDL (—) BDL (—) 31 (9–34) 4 (2–5)
Hand washing 24 (22–27) 23 (22–23) 49 (19–85) 3 (3–5) 10 (3–13) 2 (1.3–2.3) 2 (BDL–2) BDL (—) BDL (—) BDL (—) 62 (23–101) 6 (6–9)
Automatic dishwasher 24 (24–25) 25 (24–26) 8 (4–12) 5 (4–5) 2 (BDL–3) 3 (3–3) BDL (—) BDL (—) BDL (—) BDL (—) 11 (6–18) 9 (9–10)
Baseline day 2 24 (23–24) 23 (21–24) 3 (2–4) 1 (0.8–2) 1 (BDL–1) 1 (1–3) BDL (—) BDL (—) BDL (—) BDL (—) 6 (4–7) 4 (4–7)
Bath 24 (22–24) 23 (21–24) 71 (49–98) 14 (8–61) 12 (9–14) 7 (4–15) 2 (1–3) 1.4 (BDL–2) BDL (—) BDL (—) 88 (60–112) 24 (13–79)
Clothes washer II 24 (24–25) 27 (27–28) 9 (8–33) 9 (4–13) 2 (1–5) 2 (0.9–3) BDL (—) BDL (—) BDL (—) BDL (—) 12 (11–39) 14 (6–17)
Hand dish washing 24 (24–25) 24 (24–28) 8 (6–17) 5 (3–9) 2 (1–4) 1 (1–5) BDL (—) BDL (—) BDL (—) BDL (—) 11 (9–23) 8 (6–15)
Indirect shower exposure 24 (22–25) 24 (22–24) 142 (117–370) 75 (63–86) 30 (20–114) 27 (25–29) 7 (3–11) 5 (3–7) BDL (—) BDL (—) 176 (151–495) 108 (100–115)
BDL, below detection limit (detection limits are 0.5 μg/m3 for CHCl3, 0.7 μg/m3 for CHBrCl2, 0.8 μg/m3 for CHBr2Cl, and 1.0 μg/m3 for CHBr3). Values shown in parentheses are ranges; ranges are not included if all samples were at or below detection.
Table 3 Median THM concentration in blood (pg/mL) approximately 5 min before and after water use activities.
CHCl3 CHBrCl2 CHBr2Cl
CHBr3 TTHM
NC site
TX site
NC site
TX site
NC site
TX site
NC site
TX site
NC site
TX site
Activity Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post
Hot bev 40 (34–44) 31 (30–36) 19 (8–22) 13 (9–16) 9 (6–17) 8 (5–15) 4 (4–8) 3 (3–9) 2 (1–5) 2 (0.8–5) 2 (1–4) 1 (1–4) 0.6 (0.5–1) 0.6 (0.5–1) 0.5 (0.5–0.7) 0.6 (0.5–0.8) 52 (41–64) 44 (36–52) 28 (14–32) 21 (13–26)
Shower 26 (23–83) 290 (262–374) 13 (11–13) 63 (56–66) 6 (3–8) 93 (64–95) 4 (3–7) 28 (26–31) 1 (0.6–3) 13 (12–18) 1 (0.9–3) 6 (6–10) 0.7 (0.5–1) 0.8 (0.5–1) 0.5 (0.5–0.6) 0.7 (0.6–1) 34 (31–90) 399 (338–482) 18 (16–23) 97 (88–108)
Lunch w/water 51 (38–99) 45 (43–54) 37 (18–44) 41 (33–41) 11 (9–14) 12 (9–13) 6 (5–12) 7 (5–9) 2 (2–3) 3 (2–3) 2 (1–5) 2 (1–4) 0.6 (0.5–1) 0.6 (0.5–1) 0.6 (0.5–0.8) 0.6 (0.6–0.7) 66 (51–110) 59 (57–70) 45 (25–62) 48 (47–51)
Clothes washer I 32 (30–44) 52 (51–166) 27 (19–43) 35 (19–45) 7 (5–9) 12 (8–14) 5 (4–9) 5 (2–8) 2 (1–2) 2 (1–3) 2 (1–4) 2 (0.8–4) 0.6 (0.5–0.9) 0.5 (0.5–0.8) 0.6 (0.5–0.6) 0.5 (0.5–0.7) 43 (39–50) 67 (66–175) 35 (25–56) 42 (22–58)
Hand washing 36 (27–48) 48 (34–51) 23 (17–33) 19 (11–43) 9 (5–10) 11 (6–13) 4 (3–8) 5 (3–8) 2 (0.8–2) 2 (0.9–3) 1 (0.9–3) 1 (0.8–3) 0.5 (0.5–0.6) 0.6 (0.5–0.6) 0.6 (0.5–0.7) 0.6 (0.5–0.6) 47 (33–61) 61 (41–65) 29 (21–39) 25 (15–31)
Auto dishwasher 32 (22–36) 38 (30–43) 17 (14–43) 29 (17–39) 8 (4–9) 9 (6–11) 4 (3–4) 4 (4–4) 2 (0.7–2) 2 (0.8–3) 1 (0.9–5) 1 (1–3) 0.6 (0.6–0.6) 0.5 (0.5–0.6) 0.6 (0.5–1) 0.5 (0.5–0.5) 42 (27–47) 49 (37–56) 21 (20–62) 40 (22–45)
Cold bev 30 (24–95) 40 (29–56) 21 (20–50) 24 (16–85) 7 (3–47) 6 (5–24) 5 (4–8) 4 (3–9) 2 (0.5–17) 2 (0.8–9) 1 (1.0–3) 1 (0.6–3) 0.6 (0.5–0.9) 0.5 (0.5–0.8) 0.5 (0.5–0.6) 0.5 (0.5–0.5) 39 (29–161) 48 (37–88) 27 (26–62) 36 (21–89)
Bath 37 (27–40) 161 (125–188) 12 (8–22) 54 (48–156) 5 (5–14) 41 (40–43) 3 (2–7) 36 (26–65) 1 (1–5) 10 (6–13) 1 (0.5–3) 10 (8–11) 0.6 (0.5–0.9) 0.7 (0.5–1) 0.5 (0.5–0.5) 1 (0.5–1) 44 (35–60) 212 (181–234) 16 (12–32) 101 (83–231)
Clothes washer II 33 (22–44) 52 (38–61) 22 (12–39) 17 (—)a 5 (5–12) 8 (8–14) 8 (4–8) 5 (5–8) 2 (0.8–3) 2 (1–4) 2 (0.9–3) 2 (1–2) 0.5 (0.5–0.8) 0.6 (0.5–1) 0.5 (0.5–0.5) 0.5 (0.5–0.6) 44 (30–50) 66 (50–72) 34 (18–50) 17 (7–24)
Hand dish washing 43 (39–48) 73 (41–285) 33 (9–41) 42 (25–97) 7 (5–15) 19 (8–63) 4 (3–9) 12 (7–66) 2 (0.7–4) 6 (2–11) 1 (0.5–3) 3 (1.1–18.1) 0.6 (0.5–1) 0.6 (0.5–0.7) 0.6 (0.5–0.6) 0.7 (0.5–2) 56 (45–60) 99 (52–359) 38 (13–53) 58 (33–183)
Indirect shower exposure 35 (28–43) 50 (45–59) 52 (15–52) 19 (12–61) 6 (5–11) 10 (6–15) 5 (3–9) 6 (3–9) 1 (1–4) 2 (0.8–4) 1 (0.6–3) 2 (0.6–3) 0.6 (0.5–0.6) 0.5 (0.5–1) 0.5 (0.5–0.5) 0.5 (0.5–0.6) 45 (36–53) 63 (53–70) 53 (21–57) 23 (19–73)
Abbreviations: Auto, automatic; bev, beverage; w/, with. Values shown in parentheses are ranges.
a One participant with blood concentration of 17 pg/mL.
Table 4 Median and range of THM concentrations (μg/m3) in exhaled air: baseline and post-water activity by study site.
CHCl3 CHBrCl2 CHBr2Cl
CHBr3 TTHM
Activity NC site TX site NC site TX site NC site TX site NC site TX site NC site TX site
Baseline day 1 5 (2–6) 1 (1–2) 2 (BDL–5) 2 (2–3) BDL (—) BDL (—) BDL (—) BDL (—) 9 (4–13) 6 (5–6)
Hot beverage 4 (2–5) 2 (0.8–5) 2 (BDL–5) 3 (1–4) BDL (—) BDL (—) BDL (—) BDL (—) 7 (5–14) 7 (6–8)
Shower 24 (16–51) 6 (5–8) 6 (2–8) 3 (3–4) BDL (—) BDL (—) BDL (—) BDL (—) 28 (26–61) 11 (9–14)
Clothes washer 11 (3–17) 1 (0.7–2) 3 (BDL–6) 1 (BDL–2) BDL (—) BDL (—) BDL (—) BDL (—) 15 (6–25) 4 (4–5)
Hand washing 6 (3–11) 1 (0.9–1) 2 (BDL–2) 2 (1–5) BDL (—) BDL (—) BDL (—) BDL (—) 9 (5–15) 5 (4–12)
Automatic dishwasher 4 (2–4) 3 (3–4) 1 (BDL–2) 2 (2–2) BDL (—) BDL (—) BDL (—) BDL (—) 7 (5–15) 5 (4–12)
Baseline day 2 5 (2–12) 1 (BDL–2) 2 (1–9) 0.7 (BDL–2) BDL (—) BDL (—) BDL (—) BDL (—) 9 (6–15) 4 (3–6)
Bath 15 (11–22) 7 (4–9) 3 (1–4) 3 (3–3) BDL (—) BDL (—) BDL (—) BDL (—) 20 (14–26) 13 (9–13)
Clothes washer II 12 (6–13) 2 (2–3.5) 2 (1–8) 2 (1–2) BDL (—) BDL (—) BDL (—) BDL (—) 16 (9–46) 6 (5–7)
Hand dish washing 14 (5–18) 3 (3–4) 2 (BDL–3) 2 (1–5) BDL (—) BDL (—) BDL (—) BDL (—) 18 (7–22) 7 (6–11)
Indirect shower exposure 5 (2–8) 2 (1–2) 0.8 (BDL–2) 2 (2–2) BDL (—) BDL (—) BDL (—) BDL (—) 8 (4–11) 6 (5–6)
BDL, below detection limit (detection limits are 0.5 μg/m3 for CHCl3, 0.7 μg/m3 for CHBrCl2, 0.8 μg/m3 for CHBr2Cl, and 1.0 μg/m3 for CHBr3). Values shown in parentheses are ranges. Ranges are not included if all samples were at or below detection.
Table 5 Comparison of median shower water and pre- and postshower blood THM concentrations for participants in three studies.
Shower water concentration (μg/L)
Postshower blood concentration (pg/mL)a Ratio of post- to preshower blood concentration
Backer et al. 2000 Lynberg et al. 2001 Present study
Backer et al. 2000 Lynberg et al. 2001 Present study
Backer et al. 2000 Lynberg et al. 2001 Present study
THM compoundb High site Low site High site Low site High site Low site High site Low site High site Low site High site Low site
CHCl3 28 85 8 148 28 120 280 57 290 63 4 3 7 2 2
CHBrCl2 6 14 12 33 11 21 38 43 93 28 4 3 4 3 3
CHBr2Cl 1 14 2 6 2 5 41 6 13 6 5 3 3 2 3
n = 11 in Backer et al. (2000); n = 25 at each site of Lynberg et al. (2001); and n = 4 and 3 at the high (NC) and low (TX) sites, respectively, in the present study.
a Approximately 10 min postshower.
b CHBr3 was above, below, or near detection limit in water source at four of five sites and thus was not comparable.
Table 6 Comparison of THM concentrations in source water, during-shower air, and postshower breath concentrations in this and other published studies.
Source water concentration (μg/L)
During-shower air concentration (μg/m3)a Postshower breath concentration (μg/m3)b
THM compound May et al. 1995c Kerger et al. 2000 Egorov et al. 2003 Present study
May et al. 1995c Kerger et al. 2000 Egorov et al. 2003 Present study
May et al. 1995c Kerger et al. 2000 Egorov et al. 2003 Present study
High site Low site High site Low site High site Low site
CHCl3 51 47 198 148 28 84 165 330 318 67 — — 110 24 6
CHBrCl2 17 42 7 33 11 24 80 8 54 23 — — 1 6 3
CHBr2Cl 6 31 1 6 2 ND 16 ND 9 4 — — ND 1 1
ND, not determined. Kerger et al. (2000) and Egorov et al. (2003) reported mean concentrations; May et al. (1995) reported median concentrations; we report median concentrations from Tables 2, 4, and 5. n = 44 in May et al. study; n = 20 for source water and n = 12 for shower air in Kerger et al. study; n = 14 for source water, n = 35 for shower air, and n = 9 for exhaled breath in Egorov et al. study; n = 4 and 3 for source water, shower air, and exhaled breath at the high (NC) and low (TX) sites, respectively, in the present study. In water source, CHBr3 was near or below limit of detection at most sites; in air samples, CHBr2Cl and CHBr3 were below limits of detection in Egorov et al. and May et al. studies; in breath samples, CHBr2Cl and CHBr3 were below limits of detection in Egorov et al. and the present study.
a Shower duration: May et al. reported 10 min; Kerger et al. reported 6.8 min and 12 min; Egorov et al. reported 15–20 min; we report 10 min.
b Breath sample collection time: Egorov et al. reported ≤ 1 min postexposure; we report 5 min postexposure.
c Median values for CHCl3, CHBrCl2, and CHBr2Cl for source water and shower air estimated from plots in May et al.
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Wilkes CR Nuckols JR Koontz MD 2004. Evaluating Alternative Data Gathering Methods for Exposure Assessment of Disinfection By-product. Report No. 2831. Denver, CO:American Water Works Association.
Winberry WT JrMurphy NT Riggin RM 1990. Methods for Determination of Toxic Organic Compounds in Air: EPA Methods. Park Ridge, NJ:Noyes Data Corporation.
Xu X Weisel CP 2005 Human respiratory uptake of chloroform and haloketones during showering J Expo Anal Environ Epidemiol 15 6 16 15138448
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7793ehp0113-00087116002375ResearchPolychlorinated Biphenyls Disturb Differentiation of Normal Human Neural Progenitor Cells: Clue for Involvement of Thyroid Hormone Receptors Fritsche Ellen 1Cline Jason E. 1Nguyen Ngoc-Ha 2Scanlan Thomas S. 2Abel Josef 11Group of Toxicology, Institut für umweltmedizinische Forschung gGmbH an der Heinrich-Heine Universität, Düsseldorf, Germany2Departments of Pharmaceutical Chemistry and Cellular and Molecular Pharmacology, University of California-San Francisco, San Francisco, California, USAAddress correspondence to E. Fritsche, Institut für umweltmedizinische Forschung, Auf’m Hennekamp 50, 40225 Düsseldorf, Germany. Telephone 49-211-3389203. Fax: 49-211-3190910. E-mail:
[email protected] thank U. Krämer for her help with the statistics.
This work was supported by the Bundesministerium für Umwelt (BMU B1), and by a grant from the U.S. National Institutes of Health (DK52798).
The authors declare they have no competing financial interests.
7 2005 18 4 2005 113 7 871 876 25 11 2004 18 4 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Polychlorinated biphenyls (PCBs) are ubiquitous environmental chemicals that accumulate in adipose tissues over the food chain. Epidemiologic studies have indicated that PCBs influence brain development. Children who are exposed to PCBs during development suffer from neuropsychologic deficits such as a lower full-scale IQ (intelligence quotient), reduced visual recognition memory, and attention and motor deficits. The mechanisms leading to these effects are not fully understood. It has been speculated that PCBs may affect brain development by interfering with thyroid hormone (TH) signaling. Because most of the data are from animal studies, we established a model using primary normal human neural progenitor (NHNP) cells to determine if PCBs interfere with TH-dependent neural differentiation. NHNP cells differentiate into neurons, astrocytes, and oligodendrocytes in culture, and they express a variety of drug metabolism enzymes and nuclear receptors. Like triiodothyronine (T3), treatment with the mono-ortho-substituted PCB-118 (2,3′,4,4′,5-pentachlorobiphenyl; 0.01–1 μM) leads to a dose-dependent increase of oligodendrocyte formation. This effect was congener specific, because the coplanar PCB-126 (3,3′,4,4′,5-pentachlorobiphenyl) had no effect. Similar to the T3 response, the PCB-mediated effect on oligodendrocyte formation was blocked by retinoic acid and the thyroid hormone receptor antagonist NH-3. These results suggest that PCB-118 mimics T3 action via the TH pathway.
NH-3NHNP cellsoligodendrocytePCBretinoic acidthyroid hormone receptors
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Polychlorinated biphenyls (PCBs) are anthropogenic industrial chemicals, the production of which was banned in the 1970s because of their presumed carcinogenicity (Chana et al. 2002). However, these chemicals are still present in the food chain; they accumulate in animal and human tissues and are among the most abundant persistent organic pollutants found in humans (DeKoning and Karmaus 2000; Kim et al. 2004). Depending on their degree of chlorination, they are metabolized to their hydroxy- and/or sulfur-containing metabolites (Haraguchi et al. 1997). PCBs can cross the placenta, and infants are exposed via contaminated breast milk (DeKoning and Karmaus 2000).
Epidemiologic studies have indicated that PCBs influence brain development (reviewed by Schantz et al. 2003). Children who are exposed during development exhibit neuropsychologic deficits such as lower full-scale IQ (intelligence quotient), reduced visual recognition memory, and attention and motor deficits (Ayotte et al. 2003; Darvill et al. 2000; Huisman et al. 1995a, 1995b; Osius et al. 1999; Walkowiak et al. 2001). Results from studies in rodents supported these findings (Berger et al. 2001; Lilienthal et al. 1990; Roegge et al. 2000; Widholm et al. 2001). PCBs decrease circulating levels of thyroxine (T4) in animals (Brouwer et al. 1998; Gauger et al. 2004; Meerts et al. 2002). The neuropsychologic findings in offspring after developmental exposure to PCBs overlap with those described for maternal thyroid insufficiency (Haddow et al. 1999; Morreale et al. 2000; Pop et al. 1999). However, exposure at doses that lower serum thyroid hormone (TH) did not always produce signs of hypothyroidism [e.g., no elevation in TSH (Barter and Klaassen 1992; Kolaja and Klaassen 1998), no lowering of body weight of rat pups (Zoeller et al. 2000), and acceleration of eye opening in rat pups that can also be caused by high levels of TH (Goldey et al. 1995)].
Epidemiologic studies do not uniformly find an association between PCBs and thyroid homeostasis. A negative correlation between circulating levels of TH and PCB exposure and a positive correlation between the TH-regulating hormone thyrotropin (TSH) and PCB exposure have been observed (Osius et al. 1999; Schell et al. 2002). Others found no association between PCB exposure and disturbances of the TH pathway. This may be due to comparing combined high-and low-exposure groups to the reference group. Nevertheless, all observed hormone levels in these epidemiologic studies were within the normal range (Hagmar 2003) [i.e., accidental exposure to PCBs was not associated with overt hypothyroidism (Nagayama et al. 2001)].
Because there is no clear relationship between PCB exposure, blood TH levels, and symptoms of hypothyroidism in animals or in humans, several investigators have speculated that PCBs may affect brain development by directly interfering with TH signaling (McKinney and Waller 1998; Porterfield 2000; Porterfield and Hendry 1998). Dowling and Zoeller (2000) showed that RC3/neurogranin expression in the fetal rat brain is controlled by TH of maternal origin. This laboratory also demonstrated that the technical PCB mixture Aroclor 1254 regulated the TH-dependent genes myelin basic protein and RC3/neurogranin in a TH-like manner in animals (Zoeller et al. 2000). Thus, despite the anti-thyroid effect of PCBs on serum TH, they seem to act like TH at the cellular level.
On the basis of these findings and because no one has critically tested the hypothesis that PCBs can influence developmental events in the human brain, we asked two questions: a) Do PCBs have a TH agonistic/antagonistic effect on human neural development? and b) Which mechanisms are involved? For these purposes we established the model of normal human neural progenitor (NHNP) cells (Brannen and Sugaya 2000), which allow us to study the effect of these environmental chemicals on neural differentiation. Most studies on the effects of PCBs use Aroclor, which consists of many different PCB congeners. Rather than deal with a heterogeneous group, we chose two different specific PCB congeners: PCB-118, a compound with weak dioxin-like activity, and PCB-126, a congener with strong dioxin-like properties (van den Berg et al. 1998). We applied the single congener approach to identify specific PCB involvement in the disturbance of neural differentiation.
Materials and Methods
Chemicals.
Triiodothyronine (T3; Sigma-Aldrich, München, Germany) was diluted in ethanol at a concentration of 300 mM. Ortho-substituted PCB-118 (2,3′,4,4′,5-pentachlorobiphenyl), coplanar PCB-126 (3,3′,4,4′,5-pentachlorobiphenyl (both from Ökometric GmbH, Bayreuth, Germany), all-trans-retinoic acid (RA; Sigma-Aldrich) and the TH antagonist NH-3 (Nguyen et al. 2002) were diluted in DMSO (Sigma-Aldrich) at stock concentrations of 1.53, 1.59, 10, and 10 mM, respectively. Benzo(a)pyrene (BAP; Sigma-Aldrich) was diluted in tetrahydrofuran (10 mM).
Cell culture and treatment.
NHNP cells were purchased from Cambrex BioScience (Verviers, Belgium) and cultured as neurospheres in NPMM (Neural Progenitor Maintenance Medium; Cambrex BioScience) at 37°C with 5% CO2. Medium was changed every 2–3 days. Upon significant growth (0.7-mm diameter), spheres were chopped with a McIlwaine tissue chopper as previously described (Svendsen et al. 1998); the resultant cubes formed new spheres within hours and were named according to increasing passage after each chopping event (passages 1–7).
For treatment of neurospheres, chemicals were diluted in NPMM to the following final concentrations: 30 nM T3; 0.01 μM, 0.1 μM and 1 μM PCB-118 and PCB-126; 10 μM BAP; 1 μM each RA and NH-3; and 0.065% DMSO. We treated 3–10 spheres with a diameter of approximately 0.4 mm each for 7 days before plating for differentiation. Spheres were treated with each chemical alone or with a cotreatment containing PCB-118 and either NH-3 or RA for 1 week. Differentiation of NHNP cells was initiated by growth factor withdrawal and plating onto poly-d-lysine coated chamber slides (BD Biosciences, Erembodegem, Belgium). Neurospheres were plated in a defined medium consisting of Dulbecco modified Eagle medium (DMEM)/F12 (3:1) supplemented with N2 (Invitrogen GmbH, Karlsruhe, Germany). After differentiating for 2 days, cells were fixed in 4% paraformaldehyde for 30 min and stored in phosphate-buffered saline (PBS) at 4°C until immunostaining was performed.
Immunocytochemistry.
Fixed slides were washed two times for 5 min each in PBS. Slides were incubated with the following primary antibodies: a) double staining beta(III)tubulin 1:100 and glial fibrillary acidic protein (GFAP) 1:1,000 (both from Sigma-Aldrich) in PBS containing 0.3% Triton X-100, or b) mouse antioligodendrocyte marker O4 1:15 (Chemicon, Temecula, CA, USA) in PBS with 10% goat serum for 1 hr at 37°C followed by three 10-min washes with PBS. We used fluorescein isothiocyanate (FITC)- and/or Rhodamine Red-coupled secondary antibodies (1:100 each; Jackson ImmunoResearch, Dianova GmbH, Hamburg, Germany) for detection by incubating slides for 30 min at 37°C, followed by three 10-min washes with PBS. In the third wash, we added 0.1 μg/mL Hoechst for nuclear staining. After brief drying, slides were mounted with Vectashield Mounting Medium (Vector Laboratories, Burlingame, CA, USA), covered with cover glass, and sealed with nail polish.
Slides were examined using a fluorescent microscope (Olympus, Hamburg, Germany), and photographs were taken with a ColorView XS digital camera (Olympus). We determined the number of O4-positive oligodendrocytes for each individual sphere by manual counting.
Statistical analysis.
The counts were approximately lognormally distributed. Therefore, we used the geometric mean and the standard deviation of the geometric mean. The t-test was performed after logarithmic transformation of the values, and each treatment was compared to its respective control. The inhibition values were not logarithmically transformed.
RNA preparation and reverse transcription polymerase chain reaction.
Total RNA was prepared from 10–15 pooled untreated and undifferentiated spheres (passages 0–2) using the Absolutely RNA Microprep Kit (Stratagene, La Jolla, CA, USA). Reverse transcription polymerase chain reaction (RT-PCR) was performed as previously described (Döhr et al. 1995). Sequences and annealing temperatures of the PCR primers are listed in Table 1. Fragments were separated on a 3% agarose gel containing ethidium bromide and visualized under ultraviolet light. We used a 100-bp marker (peqlab, Erlangen, Germany) to estimate the appropriate sizes of the PCR fragments.
Results
Cultivation and molecular characterization of NHNP cells.
Neurospheres were successfully kept in suspension culture over several months. When they exceeded 0.7 mm in diameter, they were passaged by chopping into 0.3-mm cubes. This passaging was performed up to seven times during the lifespan of the NHNP cells. Plating of spheres onto poly-d-lysine–coated chamber slides under withdrawal of growth factors resulted in quick radial outgrowth and differentiation of the cells (Figure 1). After immunostaining, the differentiated cells were identified as neurons, astrocytes, and oligodendrocytes (Figure 2). Furthermore, neurons seem to form a neuronal network.
To determine molecular characterization of NHNP cells we performed RT-PCRs of cell type–specific genes throughout the first three passages. We could identify typical gene products for the three different cell lineages in undifferentiated neurospheres: neuron specific enolase (NSE) for neurons, GFAP for astrocytes (Figure 3), and proteolipid protein with its splicing variant dm20 (data not shown) for oligodendrocytes. Finding these cell-specific markers in undifferentiated cells implies that specific cell fate is determined before plating and differentiation of cells.
To ascertain if NHNP cells are suitable for neurotoxicologic studies, we characterized them for their expression of genes playing a role in xenobiotic metabolism. The results obtained from undifferentiated neurospheres are shown in Figure 3. NHNP cells express the aryl hydrocarbon receptor (AhR) and the AhR repressor (AhRR), which represent central proteins in the regulation of AhR battery genes. Concerning phase 1 enzymes, we could detect gene products for cytochrome P450 (CYP)1A1, CYP1B1, and CYP2D6, whereas CYP2A6, CYP2B6, CYP2C9, CYP2C19, and CYP3A4 were not expressed. With regard to phase 2 enzymes, NHNP cells do express glutathione S-transferase (GST)M1 and GSTT1, but are abundant for UDP-glucuronosyltransferase (UGT)1A6. Hence, NHNP cells have the ability to metabolize xenobiotics.
Our objective was to investigate endocrine disruption of TH homeostasis in NHNP cells; thus we studied the expression of genes coding for thyroid hormone receptors (TR), retinoid acid (RAR), and retinoid X receptors (RXR), which are crucial molecules in hormone signal transduction. Undifferentiated NHNP cells express TRα1, β1, and β2, as well as RARα and β and RXRα, β, and γ. Therefore they represent a suitable cell model for investigating thyroid hormone disruption.
Effects of T3 and PCBs on NHNP cells.
Our initial goal was to investigate the mechanisms leading to disturbance of human brain development in a human in vitro model. Because disruption of thyroid hormone signaling is suspected to be involved in impairment of intellectual development by PCBs (reviewed by Zoeller and Crofton 2000) and because the timing of oligodendrocyte development seems to be dependent on TH (reviewed by Konig and Moura 2002), we investigated the occurrence of oligodendrocytes during differentiation of NHNP cells. Therefore, undifferentiated neurospheres were treated with 30 nM T3 for 1 week. After 2 additional days of differentiation, we found a significant increase in the number of oligodendrocytes formed compared to the medium controls (Figure 4). Treating neurospheres with PCB-118 for 1 week also led to an increase in oligodendrocyte formation, whereas PCB-126 had no effect. It is noteworthy that the solvent DMSO shows some intrinsic effect in this system (Figure 4). Thus, PCB-118 seems to have a TH-like effect in NHNP cells.
Antagonism of T3 effects with RA and NH-3.
To determine whether the TH-like effect of PCB-118 is mediated by TH receptors, we cotreated NHNP cells with 30 nM T3, 1 μM PCB-118, 1 μM RA, and 1 μM NH-3, or in combination. After 1 week, we counted the number of oligodendrocytes in the neurospheres. Both RA and NH-3 treatment prohibited the formation of oligodendrocytes by T3 and PCB-118 while having no intrinsic activity themselves (Figure 5). These results support the conclusion that PCB-118 acts by interfering with the TR complex.
Discussion
It is now generally accepted that developmental exposure to drugs or chemicals can have adverse effects on the structure or function of the nervous system. Identification of such substances resulted mainly from epidemiologic data and animal studies. It is important to develop in vitro approaches because, in some cases, severe species differences can exist (Harry et al. 1998; Tilson 1996). In this article, we characterize an in vitro human neural model. To demonstrate the toxicologic usefulness of this model, we have shown the effects of two different PCB congeners on neural development. Although the ability of PCB congeners to induce cytochrome P450 enzymes has been intensively studied in rats (Parkinson et al. 1983), AhR-dependent toxic equivalency factors were revised at an expert meeting organized by the World Health Organization (van den Berg et al. 1998). In this report, van den Berg et al. (1998) described PCB-118 as a compound with weak dioxin-like activity and PCB-126 as a congener with strong dioxin-like properties. The present findings demonstrate that an individual PCB congener known to widely contaminate human populations can alter the course of neural differentiation in primary NHNP cells. This effect was restricted to PCB-118, which has weak dioxin-like activity, and was not observed following treatment with PCB-126, a dioxin-like congener, despite the fact that these cells express the dioxin receptor (AhR). Moreover, the effect of PCB exposure on oligodendrocyte differentiation was similar to the effect of T3 and could be blocked by the T3 antagonist NH-3. Therefore, these findings suggest that nondioxin-like PCB congeners such as PCB-118 may directly interfere with TH signaling in the developing human brain, altering the course of neural differentiation and potentially accounting for the observation that exposure to PCBs is linked to cognitive deficits in the human population.
We are the first to establish a human primary cell model for investigating endocrine disruption in neural development. NHNP cells, which have the ability to differentiate into the three major cell types of the human brain—neurons, astrocytes, and oligodendrocytes (Figure 2)—formed the basis of this model. The number of oligodendrocytes was relatively low, with approximately 30% of the differentiated cells being neurons and approximately 70% appearing as astrocytes (data not shown). Other laboratories have reported a distinct distribution pattern of neurons and glia cells in human neurospheres (Buc-Caron 1995; Caldwell et al. 2001; Kanemura et al. 2002; Messina et al. 2003; Piper et al. 2001). These differences may be due to culture conditions, ages of the embryos/fetuses, or the brain areas from which the cells were prepared. Nevertheless, the low abundance of oligodendrocytes in NPHH cells provides a very sensitive system to identify agents that induce their differentiation.
Two important features of our in vitro model support their use in studies of chemical exposure on neurodevelopment: their xenobiotic metabolic capacity and their TH signal transduction machinery. mRNA analyses reveal that NHNP cells express a variety of phase 1 and phase 2 enzymes (Figure 3), which indicates that the cell may be capable of xenobiotic metabolism. This is important because the parent PCB congeners may be metabolized before developing toxicity (James 2001). In regard to the expression pattern of phase 1 and phase 2 enzymes, no data are available for the developing human brain. However, in adult brain, the expression of CYPs differs partially from NHNP cells (Nishimura et al. 2003); we did not identify CYP2A6 or CYP3A4 expression in NHNP cells, but adult brain exhibits a relatively high abundance of these enzymes compared with CYP1A1 expression. In contrast, neurospheres expressed CYP1A1, CYP1B1, and CYP2D6. These enzymes are also present in adult brain (Nishimura et al. 2003). Furthermore, NHNP cells express phase 2 enzymes; GSTM1 and GSTT1 were present in NHNP cells and were found in human brain tissue as well (Sherratt et al. 1997). To the contrary, human adult brain, but not NHNP cells, expressed UGT1A6 (King et al. 1999). Because of the abundance of phase 1 and phase 2 enzymes, we consider NHNP cells to be a suitable toxicologic model for studying the effects of xenobiotics on the human developing nervous system.
TH and RA are fundamental for brain development (reviewed by Bernal et al. 2003 and by McCaffery et al. 2003). They exert their actions through nuclear hormone receptors (i.e., TR, RAR, and RXR). An important premise for investigating endocrine disruption of the thyroid hormone system by PCB is expression of the involved receptors; TRα1, β1, and β2, as well as all RAR and RXR isoforms, with exception of RARg, were present in NHNP cells. This is in agreement with the distribution of these receptors in adult rodent brains (Zetterstrom et al. 1999). TR mRNA and protein was also detected in human fetal brain (Bernal and Pekonen 1984; Kilby et al. 2000).
In the present study, we found that the mono-ortho-substituted PCB-118, as well as TH, leads to an increased formation of oligodendrocytes in NHNP cells. The development of oligodendrocytes, which are the myelin producing cells in the central nervous system, is dependent on TH, which aids proliferation and survival of oligodendrocyte pre-progenitor cells (Barres et al. 1994; Ben Hur et al. 1998; Schoonover et al. 2004). The importance of TH for oligodendrocyte formation was further confirmed in hypothyroid animals exhibiting fewer numbers of oligodendrocytes than control animals (Ahlgren et al. 1997).
PCBs have been observed to have an intrinsic TH-like effect: rat pups exposed to Aroclor 1254 opened their eyes at an earlier time point, an effect that is elicited with an excess of T4 (Brosvic et al. 2002; Goldey et al. 1995). In addition, in pregnant animals Aroclor treatment led to an increased expression of TH-dependent genes such as RC3/neurogranin and myelin basic protein in fetal brains (Zoeller et al. 2000), although PCB can cause a decrease of serum TH levels (Gauger et al. 2004; Meerts et al. 2002; Morse et al. 1993, 1996). Most studies performed on the effects of PCBs used Aroclor, technical mixtures of PCBs containing planar and nonplanar congeners. Because of the heterogeneity of these mixtures, we decided to apply a single congener approach with two different pentachlorbiphenyls that have weak and strong dioxin-like activities, respectively. Our results show for the first time that PCB-118 exerts a TH-like effect on a cellular level in primary human cells by increasing the number of oligodendrocytes (Figure 4).
In our study of the molecular mechanism of PCB effects on oligodendrocytes, we investigated the TH-like effect of PCB-118 and whether it is mediated through the TH receptor complex. Therefore, we performed the experiments in the presence of the specific TR antagonist NH-3. NH-3 binds to the ligand-binding domain of the TRs, with selectivity for TRβ over TRα, leading to a conformational change of the receptor with release of TR corepressors. Unlike TH, NH-3 prohibits the subsequent recruitment of TR coactivators. Specificity of TRβ inhibition was shown in vitro and in vivo (Lim et al. 2002; Nguyen et al. 2002). In the presence of NH-3 the formation of oligodendrocytes by TH and PCB-118 was blocked (Figure 5A), which may indicate that the TRβ complex is involved in PCB-118–mediated effects on oligodendrocyte differentiation. Because Gauger et al. (2004) showed that a large variety of PCBs, including PCB-118, and their metabolites do not competitively bind to TR, we speculate that the TH-like effect of PCB-118 on neural differentiation is due to facilitation of coactivator binding.
In another approach to investigate whether PCB-118 acts through the TR complex, we cotreated NHNP cells with RA. As shown in Figure 5B, RA anticipated oligodendrocyte formation induced by TH or PCB-118 treatment. RA binds to the RAR receptor, which shares its heterodimerization partner RXR with several other nuclear receptors including TR (reviewed by Rowe 1997). Therefore, we suggest that antagonism of TH or PCB-118 by RA is caused by competition over RXR. A similar antagonism of TH by RA has been described by Davis and Lazar (1992), and it has been hypothesized that participation of RXR in other activation pathways may modify the cellular response to TH (Sarlieve et al. 2004).
Regarding the metabolic capacity of these progenitor cells, we cannot exclude that the observed induction of oligodendrocytes by PCB-118 is a result of PCB metabolites rather than the parent substance, and further experiments are needed. However, the observed effect is congener specific because PCB-126 did not increase oligodendrocytes in NHNP cells. PCB-126 is a coplanar biphenyl that activates the AhR, whereas PCB-118 is mono-ortho substituted and exerts only weak AhR agonist activity (Hestermann et al. 2000). The inability of BAP, a classical AhR agonist, to induce oligodendrocyte formation in NHNP cells (data not shown) supports the suggestion that the AhR is not involved in the disturbance of neural differentiation.
In summary, we developed a primary human in vitro model for investigating endocrine disruption of neural development. We identified the mono-ortho-substituted PCB-118 as a TH disrupter on human neural development because it induced oligodendrocyte formation in NHNP cells. In contrast, PCB-126, a coplanar AhR ligand, showed no hormone-like activity. The effects seen after PCB-118 treatment seem to be mediated through the TR complex because they can be antagonized by the TR antagonist NH-3 and by RA. The precise molecular mechanisms require further elucidation.
Figure 1 Neurosphere plated on poly-d-lysine–coated slides showing differentiation and radial outgrowth of cells out of the sphere after 4 days in culture. Phase contrast image. Bar = 200 μm.
Figure 2 Immunocytochemical staining of differentiated NHNP cells. (A) β(III)Tubulin-positive neurons (green) and GFAP-positive astrocytes (red); nuclei stained with Hoechst. (B) O4-positive oligodendrocyte.
Figure 3 Expression patterns (RT-PCR) of different drug-metabolizing enzymes (CYPs, GSTs, UGT), neural markers (NSE, GFAP), and nuclear receptors (TRs, RARs, RXRs) during passaging of undifferentiated NHNP cells (P0–P2). RT-PCR was performed as previously described (Döhr et al. 1995). Respective primer sequences are given in Table 1. [The unspecific bands in some samples may be caused by the high cycle numbers (40) needed for detection of specific gene products due to the small amount of RNA obtained from each sample.]
Figure 4 Induction of O4-positive (+) oligodendrocytes per sphere (geometric mean and SD) by T3 or PCB-118. Photographs show typical results of treatments (bars = 100 μm). Neurospheres were treated with T3 or PCBs as described in “Materials and Methods.” Values represent typical representatives of three independent experiments.
*p < 0.05, and **p < 0.01 by t-test.
Figure 5 Antagonism of T3- or PCB-118-induced oligodendrocyte formation by (A) NH-3 and (B) RA. See “Materials and Methods” for details. Inhibitions are shown as a percentage of T3 or PCB-118 controls, respectively. Values represent typical representatives of three independent experiments.
Table 1 Sequences of oligonucleotides used to perform RT-PCRs with NHNP cells as shown in Figure 1.
Gene Sequences Size (bp) Annealing temperature (°C) Reference
β-Actin FW CCCCAGGCACCAGGGCGTGAT
RW GGTCATCTTCTCGCGGTTGGCCTTGGGGT 263 60 Ihm et al. 2002
NSE FW CCCACTGATCCTTCCCGATACAT
RW CCGATCTGGTTGACCTTGAGCA 254 60 Kukekov et al. 1999
GFAP FW GATCAACTCACCGCCAACAGC
RW CTCCTCCTCCAGCGACTCAATCT 206 60 Kukekov et al. 1999
PLP/dm20 FW CCATGCCTTCCAGTATGTCATC
RW GTGGTCCAGGTGTTGAAGTAAATGT 354 PLP
249 dm20 59 Kukekov et al. 1999
CYP1A1 FW TAGACACTGATCTGGCTGCAG
RW GGGAAGGCTCCATCAGCATC 146 60 Omiecinski et al. 1990
CYP1B1 FW AACGTCATGAGTGCCGTGTGT
RW GGCCGGTACGTTCTCCAAATC 360 63 Sutter et al. 1994
CYP2A6 FW CAGCTGAACACAGAGCAGATGTACA
RW CGCTCCCCGTTGCTGAATA 227 60 Yengi et al. 2003
CYP2B6 FW CATTCTTCCGGGGATATGGTG
RW CCTCATAGTGGTCACAGAGAATCG 83 60 Yengi et al. 2003
CYP2C9 FW GAGGAGTTTTCTGGAAGAGGCAT
RW CAAAATTCCGCAGCGTCAT 130 60 Yengi et al. 2003
CYP2C19 FW GAGGAGTTTTCTGGAAGAGGCC
RW CATTGCTGAAAACGATTCCAAA 76 60 Yengi et al. 2003
CYP2D6 FW CTTTCTGCGCGAGGTGCT
RW TGGGTCAGGAAAGCCTTTTG 96 60 Yengi et al. 2003
CYP3A4 FW TCTCATCCCAGACTTGGCCA
RW CATGTGAATGGGTTCCATATAGATAGA 85 60 Yengi et al. 2003
UGT1A6 FW TCCTGGCTGAGTATTTGGGCC
RW GTTCGCAAGATTCGATGGTCG 562 59 Strassburg et al. 1997
GSTM1 FW GAACTCCCTGAAAAGCTAAAGCT
RW GTTGGGCTCAAATATACGGTGG 132 60 Ko et al. 2000
GSTT1 FW TTCCTTACTGGTCCTCACATCTC
RW TCCCAGCTCACCGGATCAT 262 60 Ko et al. 2000
TRα1 FW CCCTGAAAACCAGCATGTCAG
RW TTCTTCTGGATTGTGCGGC 150 68 Silva et al. 2002
TRβ1 FW AAGTGCCCAGACCTTCCAAA
RW AAAGAAACCCTTGCAGCCTTC 150 68 Silva et al. 2002
TRβ2 FW GGGCTGGAGAATGCATGCGTAGACT
RW ATTCACTGCCCAGGCCTGTTCCATA 239 68 Gittoes et al. 1997
RAR-α FW ACCCCCTCTACCCCGCATCTACAAG
RW CATGCCCACTTCAAAGCACTTCTGC 226 60 Kimura et al. 2002
RAR-β FW ATTCCAGTGCTGACCATCGAGTCC
RW CCTGTTTCTGTGTCATCCATTTCC 349 62 Kimura et al. 2002
RAR-γ FW TACCACTATGGGGTCAGC
RW CCGGTCATTTCGCACAGCT 195 60 Kimura et al. 2002
RXR-α FW TTCGCTAAGCTCTTGCTC
RW ATAAGGAAGGTGTCAATGGG 113 58 Kimura et al. 2002
RXR-β FW GAAGCTCAGGCAAACACTAC
RW TGCAGTCTTTGTTGTCCC 111 58 Kimura et al. 2002
RXR-γ FW GCAGTTCAGAGGACATCAAGCC
RW GCCTCACTCTCAGCTCGCTCTC 352 62 Kimura et al. 2002
GenBank accession no.a/position in sequence
AhR FW TGGTCTCCCCCAGACAGTAG
RW TTCATTGCCAGAAAACCAGA 132 60 BC070080/1113-1244
AhRR FW CAGTTACCTCCGGGTGAAGA
RW CCAGAGCAAAGCCATTAAGA 161 60 NM_020731/269-429
Abbreviations: AhR, arylhydrocarbon receptor; AhRR, AhR repressor; CYP, cytochrome P450; FW, forward primer; GST, glutathione S-transferase; NSE, neuron specific enolase; PLP, proteolipid protein; RAR, retinoic acid receptor; RW, reverse primer; RXR, retinoic x receptor; UGT, UDP glucuronosyltransferase; TR, thyroid hormone receptor.
a GenBank (2005).
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7645ehp0113-00087716002376ResearchEnvironmental MedicineNeurologic Symptoms in Licensed Private Pesticide Applicators in the Agricultural Health Study Kamel Freya 1Engel Lawrence S. 2Gladen Beth C. 1Hoppin Jane A. 1Alavanja Michael C. R. 3Sandler Dale P. 11National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA2Memorial Sloan-Kettering Cancer Center, New York, New York, USA3National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, Maryland, USAAddress correspondence to F. Kamel, Epidemiology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr., Room A360, Research Triangle Park, NC 27709 USA. Telephone: (919) 541-1581. Fax: (919) 541-2511. E-mail:
[email protected] thank M. Shepherd and M. Richards for data analysis; the Iowa (C. Lynch, N. Logsden-Sackett, P. Gillette, and E. Heywood) and North Carolina (C. Knott, M. Pennybacker, and J. Herrington) field stations for conducting the Agricultural Health Study; and W. Boyes and K. Thomas for thoughtful comments on the manuscript.
The authors declare they have no competing financial interests.
7 2005 15 4 2005 113 7 877 882 7 10 2004 14 4 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Exposure to high levels of many pesticides has both acute and long-term neurologic consequences, but little is known about the neurotoxicity of chronic exposure to moderate levels of pesticides. We analyzed cross-sectional data from 18,782 white male licensed private pesticide applicators enrolled in the Agricultural Health Study in 1993–1997. Applicators provided information on lifetime pesticide use and 23 neurologic symptoms typically associated with pesticide intoxication. An indicator of more symptoms (≥10 vs. < 10) during the year before enrollment was associated with cumulative lifetime days of insecticide use: odds ratios (95% confidence intervals) were 1.64 (1.36–1.97) for 1–50 days, 1.89 (1.58–2.25) for 51–500 days, and 2.50 (2.00–3.13) for > 500 days, compared with never users. A modest association for fumigants [> 50 days, 1.50 (1.24–1.81)] and weaker relationships for herbicides [> 500 days, 1.32 (0.99–1.75)] and fungicides [> 50 days, 1.23 (1.00–1.50)] were observed. Pesticide use within the year before enrollment was not associated with symptom count. Only associations with insecticides and fumigants persisted when all four pesticide groups were examined simultaneously. Among chemical classes of insecticides, associations were strongest for organophosphates and organochlorines. Associations with cumulative exposure persisted after excluding individuals who had a history of pesticide poisoning or had experienced an event involving high personal pesticide exposure. These results suggest that self-reported neurologic symptoms are associated with cumulative exposure to moderate levels of fumigants and organophosphate and organochlorine insecticides, regardless of recent exposure or history of poisoning.
fumigantsinsecticidesneurologic symptomsorganochlorinesorganophosphatespesticide applicatorspesticides
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Pesticides are used extensively throughout the world. In the United States, > 18,000 products are licensed for use, and annual use of pesticides for crops, homes, schools, parks, and forests exceeds 2 billion pounds [U.S. Environmental Protection Agency (EPA) 2002]. Neurologic dysfunction is the best-documented health effect of pesticide exposure. High-level exposure has both acute and long-term neurologic effects, and adverse effects have been reported for most types of pesticides, including organophosphate, carbamate, organochlorine, and pyrethroid insecticides, herbicides, fungicides, and fumigants. Organophosphates have been studied in greatest detail. Acute organophosphate poisoning can involve a wide range of both central and peripheral neurologic symptoms (Ecobichon 1996; Keifer and Mahurin 1997). Effects of organophosphate poisoning may persist long after the acute response is resolved; sequelae include increased neurologic symptoms, deficits in neurobehavioral performance, decreased vibration sensitivity, and impaired nerve conduction (Kamel and Hoppin 2004). Effects continue up to 10 years after poisoning (Savage et al. 1988), suggesting permanent residual damage. Even less severe poisoning can have long-term consequences (Wesseling et al. 2002).
Questions remain concerning the neurologic effects of moderate pesticide exposure. Most studies show effects on cognitive and psychomotor neurobehavioral function in chronically exposed individuals without a history of poisoning, although clinical measures of peripheral nerve function like vibration sensitivity and nerve conduction are not generally affected (Kamel and Hoppin 2004). Increases in both central and peripheral neurologic symptoms are also found in many studies of moderate exposure (Kamel and Hoppin 2004). Increased symptom prevalence may provide early evidence of neurologic dysfunction, before clinically measurable signs are evident. Unresolved issues regarding the relationship of pesticide exposure to symptom prevalence include the relative importance of acute and chronic exposure, of pesticide poisoning or high-exposure events compared with chronic moderate exposure, and of pesticides other than organophosphates.
The Agricultural Health Study (AHS) is a large cohort study of licensed pesticide applicators and their spouses (Alavanja et al. 1996, 1999a). Questionnaires completed by applicators at enrollment provided information on neurologic symptoms during the prior year as well as detailed information on lifetime pesticide use and exposure. We used this information for a cross-sectional analysis of the relationship of symptoms to several measures of pesticide exposure.
Materials and Methods
Population and questionnaires.
The AHS cohort was recruited in 1993–1997 (Alavanja et al. 1996, 1999a). In Iowa and North Carolina, licenses for restricted-use pesticides must be renewed every 3 years. Individuals applying for new or renewed licenses were invited to enroll in the study. Approximately 52,400 private applicators (mostly farmers) participated, 82% of those eligible. At enrollment, participants completed a self-administered questionnaire that collected information on demographic characteristics, lifestyle, medical history, and pesticide use. A supplemental questionnaire, completed at home by 44% of the enrolled private applicators, collected additional information in these categories as well as information on neurologic symptoms. Applicators who returned the supplemental questionnaire were similar to those who did not in most respects, including prevalence of symptoms and pesticide exposure (Tarone et al. 1997). Together the two questionnaires collected information on frequency and duration of use of 50 specific pesticides as well as on high-pesticide-exposure events, medical visits for pesticide-related illness, and pesticide poisoning. All information on exposures and disease states was taken from these self-reports. Symptom questions were based on an established questionnaire, Q16, previously used to evaluate effects of occupational exposure to neurotoxicants (Lundberg et al. 1997). The questionnaires are available on the AHS website (AHS 2004); the symptom questions are in section 8, Medical History, in the Farmer Applicator Questionnaire.
The present analysis was restricted to applicators with complete information on symptoms—89% of those who returned the supplemental questionnaire. Applicators with incomplete symptom information (n = 2,603) were slightly older, less well educated, and more likely to live in North Carolina, compared with the analysis group, but the median number of symptoms experienced at least once was the same. Many of the excluded applicators omitted information for only one or two symptoms (n = 1,651, 63% of those excluded). Including them in the analysis by imputing either a positive or negative response for the missing symptoms did not substantively change results. To reduce heterogeneity, the analysis was further restricted to white men 18–75 years of age, who comprised 92% of those otherwise eligible. Characteristics of the 18,782 applicators included in the analysis are presented in Table 1.
The institutional review boards of the National Institutes of Health, the University of Iowa (Iowa field station), and Battelle (North Carolina field station) approved the AHS. The study was explained to potential participants, who indicated consent by returning questionnaires.
Data analysis.
Table 2 shows frequencies of 23 symptoms during the year before enrollment, in the categories originally reported. Most of our analyses focused on these symptoms as a group. We created two measures reflecting the number of symptoms experienced at least once in the year before enrollment: a continuous variable, “number of symptoms” [median (interquartile range) = 4 (1–8)], and a dichotomous variable, “many symptoms,” that compared the 20% of applicators who experienced ≥10 symptoms (“cases”) with the remaining 80% who experienced < 10 symptoms (“controls”). The distribution of number of symptoms was not greatly affected by omitting the two most common and nonspecific symptoms, headache and fatigue. We also considered the 23 symptoms individually, dichotomizing the frequency of each symptom so that 5–15% of the population was in the positive (“case”) category (Table 2).
We considered several pesticide exposure measures, listed in Table 3. Applicators reported years of use of any pesticide (in five categories) and days per year (in seven categories). We then calculated lifetime days of use by multiplying duration (quantified as the central value of the reported category for years of use) by frequency (central value of the reported category for days per year) and then categorizing in quartiles. A high-exposure event was one causing “unusually high personal exposure” (Alavanja et al. 1999b). Internal exposure was defined as inhalation or ingestion of pesticide. Pesticides were categorized by function or mode of use, as herbicides, insecticides, fungicides, or fumigants (Table 3). Insecticides were further categorized by chemical class. Cumulative lifetime days for pesticide groups were calculated by multiplying duration by frequency of use for each pesticide in the group, summing over all pesticides in the group, and categorizing into three or four levels. We further categorized by whether or not any pesticides in the group were used in the year before enrollment (Table 3).
We analyzed the data using linear regression for number of symptoms and logistic regression for the dichotomous outcomes. Analyses were performed in SAS (version 8.2; SAS Institute Inc., Cary, NC) using AHS phase I data (Prerelease 9/02; AHS 2004). We adjusted for age, state, education, smoking, and alcohol use, with variables categorized as in Table 1. Information on age and state was available for all applicators. For applicators (< 3%) who were missing data on education, cigarette smoking, or alcohol use, we imputed the median value for their state and age category. All outcome measures had similar relationships to covariates. Final models included two-way interactions for age × state, age × education, age × smoking, age × drinking, state × education, state × smoking, and state × drinking.
Results
In multivariate analyses, symptom prevalence was greater among applicators who were from Iowa, had more than a high school education, smoked more, and drank more. There was a strong monotonic inverse association of symptoms with age: Applicators 66–75 years of age had 2.2 fewer symptoms and were half as likely to have ≥10 symptoms than did applicators 18–30 years of age.
Both measures of symptom count—having many (≥10) symptoms and total number of symptoms—were associated with overall pesticide use (Table 3). Applicators in the highest quartile of lifetime days of pesticide use were 1.2 times as likely to experience ≥10 symptoms and averaged 0.6 more symptoms in the year preceding enrollment, compared with the lowest quartile, with a weak dose–response relation across quartiles. Individuals with a history of pesticide poisoning experienced more symptoms than did those without such a history (Table 3). Applicators who had ever sought medical attention for pesticide-related illness also experienced more symptoms than those who had not (Table 3), even when individuals with a history of pesticide poisoning were excluded (doctor visit: 2.1 times more likely to have ≥10 symptoms and 1.8 more symptoms). Experiencing a high-exposure event was associated with an increased symptom count, particularly when internal exposure (inhalation or ingestion of pesticide) was involved (Table 3). Again, similar results were seen when individuals with a history of pesticide poisoning were excluded (risk estimates for internal exposure were unchanged).
Pesticides were grouped in functional categories, and cumulative use (lifetime days) and recent use (in the year before enrollment) were evaluated simultaneously. Cumulative use of insecticides was associated with both measures of symptom count, with a pronounced dose response (Table 3). Weaker associations of symptoms with cumulative herbicide, fungicide, and fumigant use were observed. After accounting for cumulative use, recent use was not associated with an increased symptom count for any pesticide category (Table 3). Associations with cumulative use were similar in models not including recent use (data not shown). When cumulative use in all four functional categories was assessed simultaneously, associations with insecticides and fumigants were only slightly reduced, but relationships with herbicides and fungicides were no longer present; results were similar whether or not recent use was included in the models (data not shown).
Insecticides were further categorized by chemical class, with cumulative and recent use evaluated simultaneously. Greater symptom count was associated with cumulative use in all four chemical classes of insecticides (Table 3). Associations were strongest for organophosphates and organochlorines; dose response for cumulative use was evident for all classes except pyrethroids. After accounting for cumulative use, recent use was not associated with symptom count for any chemical class (Table 3). Associations with cumulative use were similar in models not including recent use (data not shown). When cumulative use in all four chemical classes was considered simultaneously, associations with organophosphates, organochlorines, and carbamates were still present, although slightly attenuated, but the relationship with pyrethroids was present only for number of symptoms and not for many symptoms; Results were similar whether or not recent use was included in the models (data not shown).
Associations with cumulative use of pesticides in functional or chemical groups were not affected by excluding individuals with diagnosed pesticide poisoning (n = 363) or those who had experienced a high-exposure event (n = 2,688). In models including cumulative use of all four functional categories of pesticides, applicators in the highest category of insecticide use were 2.2–2.4 times as likely to experience ≥10 symptoms and averaged 2.0–2.1 additional symptoms, regardless of whether or not individuals with pesticide poisoning, a high-exposure event, or either were excluded from the analysis. In models including cumulative use in all four chemical classes of insecticides, applicators in the highest category of organophosphate use were 1.6–1.7 times as likely to experience ≥10 symptoms and averaged 1.0–1.1 additional symptoms, regardless of exclusions. Associations of symptoms with cumulative pesticide use were also not affected by excluding individuals with self-reported neurologic disease (n = 498), depression (n = 849), stroke (n = 113), head injury (n = 2,307), myocardial infarction (n = 518), or diabetes (n = 635); these were similar in Iowa and North Carolina and were not affected by adjusting for occupational exposure to solvents or metals on or off the farm (data not shown).
Specific symptoms were also related to pesticide use. Symptoms were associated with use of any pesticide, any insecticide, organophosphates, organochlorines, or fumigants (Table 4). Pesticide poisoning, pesticide-related medical visits, and high-exposure events were also associated with increases in specific symptoms but did not alter the observed associations with pesticide use (data not shown). For any particular exposure measure, there was little variation in the magnitude of the associations among symptoms. Similar results were found when symptoms were grouped in categories defined a priori to reflect particular aspects of neurologic function, including affect, cognition, autonomic function, motor function, and vision (data not shown).
Discussion
In this study we found that increased symptom count was associated with cumulative lifetime use of pesticides, particularly insecticides and fumigants. Increased symptom count was also associated with a history of pesticide poisoning or events involving high personal pesticide exposure. Significantly, however, associations with cumulative use persisted even after excluding individuals with a history of pesticide poisoning or high exposure events. Recent pesticide use, within the year before reporting symptoms, was not related to symptom count after accounting for cumulative exposure, and adjustment for recent use did not affect the association of cumulative use with symptom count.
Most previous studies of pesticides and neurologic symptoms have focused on organophosphates. Farm workers (Gomes et al. 1998; Strong et al. 2004), greenhouse workers (Bazylewicz-Walczak et al. 1999), and factory workers (Bellin and Chow 1974) exposed to organophosphates reported more symptoms than unexposed workers. Farmers and farm workers who applied organophosphates had higher symptom prevalence than did nonapplicators (London et al. 1998; Ohayo-Mitoko et al. 2000; Smit et al. 2003), as did commercial termiticide applicators (Steenland et al. 2000) and sheep dippers (Pilkington et al. 2001). Other studies have used the Profile of Mood States or other scales to evaluate changes in mood, finding higher levels of tension, anxiety, anger, and depression in workers exposed to organophosphates (Bazylewicz-Walczak et al. 1999; Levin et al. 1976; Steenland et al. 2000; Stokes et al. 1995). Most (Bellin and Chow 1974; Gomes et al. 1998; Leng and Lewalter 1999; Ohayo-Mitoko et al. 2000) although not all (Ciesielski et al. 1994; Lee et al. 2003) studies found increased symptom prevalence associated with inhibition of erythrocyte acetylcholinesterase activity, a bio-marker of recent organophosphate exposure.
Although poisoning by high exposures to organochlorines, fungicides, and fumigants as well as organophosphates is well documented, and carbamates, pyrethroids, and herbicides are also neurotoxic (Ecobichon 1996; Keifer and Mahurin 1997), questions remain concerning the effects of moderate exposure to pesticides other than organophosphates. One study of moderate exposure found that dichlorodiphenyltrichoroethane (DDT) was associated with increased symptom prevalence (van Wendel de Joode et al. 2001), as did one study of fumigants (Anger et al. 1986), but not another (Calvert et al. 1998). We found that symptom count was related to all classes of pesticides examined, although associations with herbicides and fungicides appeared to be due to confounding by insecticide use. Organophosphate, carbamate, and organochlorine insecticides were independently associated with increased risk. The relative neurotoxicity of specific chemicals or chemical classes may differ for acute high-level and chronic moderate exposure. For example, the stronger effects that we observed for organochlorines may be related to their long biologic half-lives (Ecobichon 1996).
Few previous studies were able to distinguish between effects of acute and chronic exposure because the two are often correlated. Two studies with sufficient information to make the distinction found that in farmworkers who applied pesticides increased symptom prevalence was associated with acute but not chronic exposure (London et al. 1998; Ohayo-Mitoko et al. 2000). In contrast, our results suggest that at moderate levels cumulative lifetime exposure has a greater impact on symptom prevalence than exposure during the year before reporting symptoms. This disparity may be due to the higher level of exposure experienced by farm workers compared with licensed applicators.
The role of pesticide poisoning in the apparent effects of cumulative use is still a question. We confirmed previous reports that a history of pesticide poisoning is associated with increased symptom prevalence (Kamel and Hoppin 2004). A notable finding in our study is that a history of events involving high personal pesticide exposure conferred equally great risk, even in the absence of diagnosed poisoning. Some studies have not differentiated exposed individuals with a history of pesticide poisoning from those without. Two studies that excluded poisoned individuals found no relationship of moderate organophosphate exposure to symptom prevalence (Ames et al. 1995; Fiedler et al. 1997), although a study of DDT that excluded poisoned individuals did find an association (van Wendel de Joode et al. 2001). We found dose-related associations of symptom count to cumulative exposure to all insecticides, organophosphates, and organochlorines whether or not we excluded individuals with a history of pesticide poisoning or those who had experienced high-exposure events, indicating that moderate exposure itself is associated with increased risk.
Our findings were similar regardless of whether we considered summary measures of all symptoms, individual symptoms, or symptom groups defined a priori. These results are consistent with previous studies suggesting that moderate pesticide exposure is associated with a wide range of symptoms, reflecting cognitive, sensory, and motor dysfunction and affecting both the central and peripheral nervous systems (Kamel and Hoppin 2004). Pesticide exposure may be associated with some fundamental disorder, such as depression or neurologic disease, which then influences the experience or perhaps the reporting of multiple symptoms. Similarly, confounding by head injury, which was related to pesticide exposure in the AHS cohort, might explain some of the increase in symptoms. However, our findings were not affected by excluding individuals with depression, neurologic disease, or head injury. The earliest manifestation of neurotoxicity after moderate pesticide exposure may in fact be an increase in many symptoms, not restricted to particular aspects of neurologic function. A similar increase in a wide range of symptoms is associated with solvent exposure in mild cases of chronic solvent-related toxic encephalopathy (van der Hoek et al. 2000; White and Proctor 1997).
Confounding by demographic factors does not appear to explain our results. There was a strong inverse association of symptom prevalence with age. The basis of this association is unclear; it may represent participants’ understanding of the symptom questions or reporting proclivities rather than a real relationship. Other explanations are possible. Symptomatic individuals may have left farming at an early age and thus never entered our cohort, representing a type of healthy worker effect. Younger applicators used more pesticides in the year before enrollment, the period for which symptom prevalence was reported; however, adjusting for recent use did not affect associations with cumulative use. In any case, because symptom prevalence decreased and cumulative exposure increased with age, confounding by age or age-related conditions like heart disease or diabetes cannot explain the positive associations we observed with cumulative exposure; moreover, excluding individuals with the latter conditions did not affect our results. This point is particularly important in interpreting results for organochlorine pesticides. Secular trends in use mean that older applicators are more likely to have used these chemicals, but this cannot account for the association with symptoms because age was inversely related to symptom count. We adjusted for other potential confounders, including education, so these are also unlikely to account for our results. We had no information on personality traits that may have affected symptom reporting, and so could not adjust for these, but they are unlikely to have covaried with exposure, particularly in a way that could account for the dose–response relationships we observed.
Potential bias is also a concern. The present analysis was based on the subset of private applicators who completed the take-home questionnaire. Although these are only 44% of the private applicators enrolled in the AHS, they are clearly representative of the cohort as a whole: Applicators who did or did not complete the take-home questionnaire were similar for every lifestyle or demographic characteristic except age; for health outcomes, including experience of pesticide-related health symptoms; and for farm characteristics and tasks and a variety of measures of pesticide exposure (Tarone et al. 1997). These results mitigate concerns regarding selection bias. Because symptoms were self-reported, another concern is potential recall or reporting bias. However, the fact that only some pesticides were associated with symptoms suggests that recall bias does not account for our findings. Risks associated with insecticide exposure were dose-related, further suggesting that bias does not explain our results. Moreover, our findings are biologically plausible, because we found the greatest risk for insecticides, which are designed to be neurotoxicants.
An important strength of our study is its large size. Further, because farming practices are considerably different in Iowa and North Carolina, the AHS cohort represents a diverse farming population (Alavanja et al. 1996, 1999a). We used internal comparisons of more and less exposed individuals from the same population, thereby reducing potential confounding. The primary strength of the study is, however, the availability of detailed exposure information. Although the present analysis is limited by its cross-sectional design, data on symptoms and pesticide exposure were collected in separate portions of the questionnaires, some completed at different times, minimizing potential bias. Exposure data were reported by the applicators themselves, but farmers in general and AHS cohort members in particular report pesticide use reliably (Blair et al. 2002; Hoppin et al. 2002).
In conclusion, we found that prevalence of neurologic symptoms was associated with cumulative lifetime exposure to pesticides, particularly organophosphate and organochlorine insecticides and fumigants. These associations were present in individuals with no history of pesticide poisoning or high exposure events and were independent of recent exposure. Thus, they are likely due to chronic moderate exposure. Although the neurotoxicity of high-level exposure is accepted, more attention to the risks associated with moderate exposure may be required.
Table 1 Characteristics of licensed pesticide applicators enrolled in the AHS 1993–1997 (n = 18,782).a
Characteristic Percent
Age (years)
18–30 8
31–35 9
36–40 13
41–45 14
46–50 12
51–55 12
56–60 12
61–65 10
66–75 10
State
Iowa 70
North Carolina 30
Educationb
≤High school 56
> High school 44
Cigarette smoking (lifetime pack-years)b
0 57
> 0–30 35
> 30 8
Alcohol use (any consumption in last year)b
No 34
Yes 66
a The analysis was restricted to white men who provided a response for each of 23 neurologic symptoms.
b Missing data (< 3%) were imputed on an age- and state-specific basis for education, cigarette smoking, and alcohol use, using medians of the relevant strata.
Table 2 Percentage of study participants experiencing the indicated frequency of neurologic symptoms in the year before enrollment, among licensed pesticide applicators enrolled in the AHS 1993–1997 (n = 18,782).a
Symptom Never Once a year Once a month Once a week > Once a week
Headache 32 18 36 10* 4*
Fatigue 42 16 24 10 9*
Tension 48 16 22 9* 6*
Insomnia 57 10 19 8* 7*
Irritability 63 13 16 5* 3*
Dizziness 72 19 7* 1* 1*
Numbness in hands or feet 73 9 10 4* 5*
Depression 73 12 10* 3* 2*
Nausea 73 24 3* < 1* < 1*
Absentmindedness 76 7 9 4* 4*
Difficulty concentrating 80 7 7* 3* 2*
Loss of appetite 82 11 5* 1* < 1*
Excessive sweating 83 8 5* 2* 2*
Twitches in arms or legs 83 7 6* 2* 2*
Fast heart rate 85 7 5* 2* 1*
Weakness in arms or legs 85 6 5* 1* 2*
Poor balance 88 7 3* 1* 1*
Poor night vision 88 3 3* 1* 4*
Tremor in hands 89 5* 3* 1* 2*
Blurred/double vision 90 5* 3* 1* 1*
Changes in smell or taste 94 4* 1* < 1* < 1*
Difficulty speaking 96 2* 1* 1* 1*
Loss of consciousness 98 1* < 1* < 1* < 1*
a Symptoms are listed in order of decreasing frequency. Symptom distributions were dichotomized so that the positive category included between 5 and 15% of participants.
*Responses included in the positive (“case”) category.
Table 3 Association of summary measures of neurologic symptom prevalence with pesticide use and exposure among licensed pesticide applicators enrolled in the AHS 1993–1997 (n = 18,782).
Exposure Case (%) Control (%) Many symptomsa [OR (95% CI)] No. symptomsb [β(SE)]
Use of any pesticide
Cumulative lifetime days of use
0–64 23 25 1.00 (referent) 0 (referent)
65–200 22 21 1.16 (1.04–1.30) 0.45 (0.10)
201–396 27 26 1.14 (1.02–1.26) 0.47 (0.10)
397–7,000 28 27 1.20 (1.08–1.33) 0.60 (0.10)
Ever diagnosed with pesticide poisoning
No 96 98 1.00 (referent) 0 (referent)
Yes 4 2 2.46 (1.97–3.08) 2.58 (0.25)
Ever received medical attention for pesticide-related illness
No 89 95 1.00 (referent) 0 (referent)
Saw doctor only 9 4 2.25 (1.95–2.59) 2.07 (0.16)
Hospitalized 2 1 1.98 (1.43–2.74) 1.76 (0.35)
Ever had an event involving high personal exposure
No event 76 88 1.00 (referent) 0 (referent)
Yes, no internal exposurec 13 8 1.80 (1.60–2.02) 1.68 (0.12)
Yes, with internal exposurec 11 4 3.04 (2.65–3.49) 3.08 (0.15)
Cumulative pesticide use, functional categories
Insecticides (lifetime days, without or with use in past year)
0 days 5 9 1.00 (referent) 0 (referent)
1–50 days, no use in past year 14 15 1.64 (1.36–1.97) 1.25 (0.15)
1–50 days, used in past year 8 10 1.34 (1.09–1.63) 0.90 (0.17)
51–500 days, no use in past year 22 22 1.89 (1.58–2.25) 1.67 (0.14)
51–500 days, used in past year 32 31 1.82 (1.53–2.15) 1.72 (0.14)
> 500 days, no use in past year 6 5 2.50 (2.00–3.13) 2.34 (0.20)
> 500 days, used in past year 12 9 2.50 (2.06–3.03) 2.43 (0.17)
Herbicides (lifetime days, without or with use in past year)
0 days 2 3 1.00 (referent) 0 (referent)
1–50 days, no use in past year 5 5 1.04 (0.77–1.40) 0.57 (0.27)
1–50 days, used in past year 5 6 0.85 (0.63–1.15) 0.28 (0.26)
51–500 days, no use in past year 13 13 1.16 (0.89–1.52) 0.79 (0.24)
51–500 days, used in past year 36 36 1.11 (0.86–1.43) 0.80 (0.23)
> 500 days, no use in past year 7 7 1.32 (0.99–1.75) 1.10 (0.26)
> 500 days, used in past year 32 30 1.27 (0.98–1.64) 1.25 (0.23)
Fungicides (lifetime days, without or with use in past year)
0 days 69 71 1.00 (referent) 0 (referent)
1–50 days, no use in past year 12 10 1.31 (1.16–1.48) 0.94 (0.12)
1–50 days, used in past year 9 8 1.21 (1.05–1.39) 0.60 (0.13)
> 50 days, no use in past year 4 4 1.23 (1.00–1.50) 0.60 (0.19)
> 50 days, used in past year 8 7 1.24 (1.05–1.45) 0.67 (0.15)
Fumigants (lifetime days, without or with use in past year)
0 days 78 81 1.00 (referent) 0 (referent)
1–50 days, no use in past year 13 11 1.48 (1.31–1.66) 1.06 (0.11)
1–50 days, used in past year 3 3 1.16 (0.90–1.48) 0.19 (0.23)
> 50 days, no use in past year 5 4 1.50 (1.24–1.81) 0.99 (0.18)
> 50 days, used in past year 2 2 1.29 (0.98–1.71) 0.56 (0.27)
Cumulative insecticide use, chemical classes
Organophosphates (lifetime days, without or with use in past year)
0 days 9 14 1.00 (referent) 0 (referent)
1–50 days, no use in past year 20 21 1.39 (1.20–1.60) 1.00 (0.12)
1–50 days, used in past year 11 12 1.25 (1.06–1.46) 0.80 (0.14)
51–500 days, no use in past year 21 20 1.63 (1.41–1.88) 1.36 (0.12)
51–500 days, used in past year 29 27 1.59 (1.39–1.83) 1.47 (0.12)
> 500 days, no use in past year 4 3 2.16 (1.71–2.73) 1.91 (0.23)
> 500 days, used in past year 6 4 2.23 (1.84–2.71) 2.08 (0.19)
Organochlorines (lifetime days, without or with use in past year)
0 days 51 56 1.00 (referent) 0 (referent)
1–50 days, no use in past year 29 27 1.60 (1.45–1.75) 1.24 (0.09)
1–50 days, used in past year 1 1 1.04 (0.61–1.74) 0.74 (0.48)
> 50 days, no use in past year 18 16 2.00 (1.78–2.23) 1.76 (0.10)
> 50 days, used in past year 1 1 2.23 (1.51–3.31) 1.74 (0.42)
Carbamates (lifetime days, without or with use in past year)
0 days 40 45 1.00 (referent) 0 (referent)
1–50 days, no use in past year 29 26 1.42 (1.30–1.56) 1.02 (0.09)
1–50 days, used in past year 5 6 1.18 (1.00–1.40) 0.57 (0.16)
> 50 days, no use in past year 15 14 1.55 (1.38–1.75) 1.22 (0.11)
> 50 days, used in past year 10 10 1.48 (1.27–1.71) 1.15 (0.14)
Pyrethroids (lifetime days, without or with use in past year)
0 days 72 78 1.00 (referent) 0 (referent)
1–50 days, no use in past year 13 10 1.31 (1.17–1.47) 0.80 (0.12)
1–50 days, used in past year 6 5 1.16 (0.99–1.37) 0.61 (0.16)
> 50 days, no use in past year 4 3 1.24 (1.09–1.58) 0.86 (0.19)
> 50 days, used in past year 5 3 1.31 (1.09–1.58) 1.09 (0.19)
a Proportion of applicators who experienced ≥10 versus < 10 specific symptoms in the year before enrollment. Results are expressed as odds ratio (OR) with 95% confidence interval (95% CI) from logistic regressions adjusted for age, state, education, cigarette smoking, and alcohol use.
b Number of specific symptoms experienced at least once in the year before enrollment, used as a continuous variable. Results are expressed as estimates (SE) from linear regressions adjusted for age, state, education, cigarette smoking, and alcohol use.
c A high-pesticide-exposure event involving internal exposure was defined as one involving inhalation or ingestion of pesticide.
Table 4 Associations of specific neurologic symptoms with pesticide exposure among licensed pesticide applicators enrolled in the AHS 1993–1997 (n = 18,782).
Any pesticide All insecticides Organophosphates Organochlorines Fumigants
Headache 1.25* 1.80* 1.62* 1.31* 1.10
Fatigue 0.99 2.29* 2.34* 1.60* 1.10
Tension 1.22* 1.98* 1.82* 1.55* 1.30*
Insomnia 1.24* 1.78* 1.70* 1.56* 1.21
Irritability 1.16* 2.33* 1.93* 1.57* 1.46*
Dizziness 1.26* 2.34* 1.77* 1.66* 1.38*
Numbness in hands or feet 1.18* 2.64* 2.67* 1.75* 1.25*
Depression 1.19* 2.30* 2.09* 1.68* 1.31*
Nausea 1.11 1.93* 1.88* 1.59* 1.37
Absentmindedness 1.12 2.57* 2.13* 1.75* 1.24*
Difficulty concentrating 1.15* 2.27* 2.07* 1.84* 1.36*
Loss of appetite 1.14 1.80* 1.76* 1.51* 1.22
Excessive sweating 1.12 2.03* 1.84* 1.70* 1.10
Twitches in arms or legs 1.16* 2.65* 2.34* 1.68* 1.43*
Fast heart rate 1.22* 2.19* 1.95* 1.74* 1.23
Weakness in arms or legs 1.01 1.84* 1.82* 1.77* 1.03
Poor balance 1.20* 2.46* 1.77* 1.95* 1.40*
Poor night vision 1.24* 2.06* 1.85* 1.62* 1.38*
Tremor in hands 1.26* 2.20* 2.00* 1.68* 1.17
Blurred/double vision 1.25* 2.08* 1.87* 1.75* 1.29*
Changes in smell or taste 1.61* 2.11* 1.83* 2.12* 1.57*
Difficulty speaking 1.30* 2.25* 1.94* 1.97* 1.07
Loss of consciousness 1.49* 1.41 1.26 1.76* 1.34
Table entries are odds ratios for experiencing the symptom with high frequency compared with low frequency, using cut points shown in Table 2. Odds ratios were calculated by logistic regression models with adjustment for age, state, education, cigarette smoking, and alcohol use. Estimates are for the highest category of lifetime days of use of the indicated pesticide groups.
*Estimates for which the 95% confidence interval excluded 1.00.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7737ehp0113-00088316002377ResearchEnvironmental MedicineAir Pollution and ST-Segment Depression in Elderly Subjects Gold Diane R. 12Litonjua Augusto A. 1Zanobetti Antonella 2Coull Brent A. 3Schwartz Joel 2MacCallum Gail 4Verrier Richard L. 5Nearing Bruce D. 5Canner Marina J. 1Suh Helen 6Stone Peter H. 41Channing Laboratory, Brigham and Women’s Hospital, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA2Environmental Epidemiology Program, Department of Environmental Health, and3Environmental Statistics Program, Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA4Cardiology Division, Brigham and Women’s Hospital, Department of Medicine, Boston, Massachusetts, USA5Division of Cardiology, Beth Israel Deaconess Medical Center, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA6Environmental Science and Engineering Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USAAddress correspondence to D.R. Gold, Channing Laboratory, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave., Boston, MA 02215 USA. Telephone: (617) 525-2738. Fax: (617) 525-0950. E-mail:
[email protected] work was supported in part by National Institutes of Health grant 5 P01 ES09825, U.S. Environmental Protection Agency (EPA) Cooperative Agreement CR821762, EPA 826780-01-0, and EPA R827353-01-0.
The authors declare they have no competing financial interests.
7 2005 14 3 2005 113 7 883 887 8 11 2004 14 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Increased levels of daily ambient particle pollution have been associated with increased risk of cardiovascular morbidity. Black carbon (BC) is a measure of the traffic-related component of particles. We investigated associations between ambient pollution and ST-segment levels in a repeated-measures study including 269 observations on 24 active Boston residents 61–88 years of age, each observed up to 12 times from June through September 1999. The protocol involved continuous Holter electrocardiogram monitoring including 5 min of rest, 5 min of standing, 5 min of exercise outdoors, 5 min of recovery, and 20 cycles of paced breathing. Pollution-associated ST-depression was estimated for a 10th- to 90th-percentile change in BC. We calculated the average ST-segment level, referenced to the P-R isoelectric values, for each portion of the protocol. The mean BC level in the previous 12 hr, and the BC level 5 hr before testing, predicted ST-segment depression in most portions of the protocol, but the effect was strongest in the postexercise periods. During post-exercise rest, an elevated BC level was associated with −0.1 mm ST-segment depression (p = 0.02 for 12-hr mean BC; p = 0.001 for 5-hr BC) in continuous models. Elevated BC also predicted increased risk of ST-segment depression ≥0.5 mm among those with at least one episode of that level of ST-segment depression. Carbon monoxide was not a confounder of this association. ST-segment depression, possibly representing myocardial ischemia or inflammation, is associated with increased exposure to particles whose predominant source is traffic.
air pollutioncardiologyelderlyparticlesST-segment depressiontraffic
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Numerous studies have demonstrated associations of acute increases in particle levels with increased risk of cardiac morbidity and mortality (Pope et al. 1995). Efforts have been directed toward understanding mechanisms for these associations. Canine studies showing increased risk of myocardial ischemia (Wellenius et al. 2003) and a chamber study showing decreased brachial artery diameter with particle exposure (Brook et al. 2002) have provided supportive evidence for particle-induced ischemia as a potential mechanism. Both carbon monoxide and particle mass < 2.5 μg/m3 (PM2.5) were associated with increased risk of ST-segment depression during repeated submaximal exercise tests among subjects with coronary heart disease in 45 adults with stable coronary heart disease in Helsinki, Finland (Pekkanen et al. 2002); PM2.5 was believed to be the primary source of this association, but because of correlation with CO, the authors reported that independent effects were difficult to separate. Black carbon (BC) may be a more precise measure than PM2.5 of the portion of particle mass related to traffic (Laden et al. 2000). We examined whether there were independent associations of the ambient traffic-associated pollutants, BC and CO, with ST-segment depression before and after submaximal exercise in a community-based repeated-measures study of elderly adults from Boston, Massachusetts.
Materials and Methods
Study design and protocol.
We recruited a panel of elderly subjects living at or near an apartment complex located within 1 km of a central site monitoring station. A baseline screening questionnaire was administered regarding medications, pulmonary and cardiac symptoms, and smoking history. A resting 12-lead electrocardiogram (ECG) was performed. Exclusion criteria included unstable angina, atrial flutter, atrial fibrillation, or paced rhythm. Each subject was assigned a day of the week and a time of day for weekly testing, with the goal of 12 weekly visits during the summer of 1999. Each week, participants were administered a brief questionnaire regarding chest pain, medication changes, and whether medications had been taken that morning. Continuous Holter monitoring with electrodes in a modified V5 and aVF position was performed using the Marquette Seer Digital Recorder (Marquette Inc., Milwaukee, WI). The protocol (Gold et al. 2000) consisted of a) 5 min rest, b) 5 min standing, c) 5 min exercise outdoors (if the participant felt able, a standard walk was performed, involving one climb up a slight incline), d) 4 min supine recovery, or e) 3 min 20 sec slow, paced breathing (for each of 20 respiratory cycles, the participant was asked to breathe in for 5 sec and then out for 5 sec, coached by a technician).
Processing of Holter recordings.
The digital Holter recordings were downloaded to a MARS Ultra 60 playback system (Marquette Inc.) for analysis. ST-segments were evaluated for the average value for each portion of the protocol and for possible ischemia, defined as reversible horizontal or downsloping ST-segment depression ≥0.5 mm, a level associated with adverse cardiac risk in patients with acute coronary syndrome (Cannon et al. 1997). Recordings were visually scanned by an experienced analyst to censor artifacts. Custom algorithms were created to calculate the average “ST-segment level” or value, referenced to the P-R isoelectric values, for each portion of the protocol. Separately, each candidate episode of reversible ST-segment deviation was evaluated as possibly representing ischemia, by using real-time ECG strips examined by an experienced analyst and physician blinded to air pollution status. A table of J-point values, ST-segment values, ST-segment slope, and heart rate was printed for each candidate episode beginning 10 min before each episode and ending 10 min after the resolution of each episode. The ST-segment value 60 msec after the J-point was used to define the ST-segment deviation and the ST-segment slope.
Air pollution measurements.
Air pollution measurements (PM2.5, BC, CO) were collected at a central site within 0.5 km of the residences of the subjects, which were on the same busy street trafficked by diesel-powered buses and trucks as well as cars of commuters. Measurements of sulfur dioxide, ozone, and nitrogen dioxide were obtained from state monitoring sites in Boston. Continuous PM2.5 was measured using a tapered element oscillating microbalance (TEOM; model 1400A; Rupprecht and Patashnick, Albany, NY). The TEOM sample filter is heated to 50°C, leading to season-specific temperature-related loss of semivolatile mass. Season-specific calibration factors were used to correct for the losses of mass (Allen et al. 1997). The calibration factors were obtained by regressing continuous PM2.5 concentrations averaged over 24-hr periods on the corresponding collocated integrated 24-hr Harvard Impactor (Air Diagnostics Environmental Inc., Harrison, ME, USA) low-volume Teflon filter gravimetric measurements.
In the summer in Boston, BC measurements are surrogates for carbonaceous particles, components of PM2.5, many of which derive from traffic (local or transported). BC data from this instrument, using the internal empirically determined conversion factor, have correlated well with elemental carbon (Hansen and Rosen 1984). BC was measured using a model AE-14 aethalometer (Magee Scientific Inc., Berkeley, CA). CO was measured continuously with a gas analyzer (model 48; ThermoEnvironmental, Franklin, MA) using a U.S. Environmental Protection Agency (EPA) reference method (Automated Reference Method: RFCA-0981-054).
Statistical analyses.
For each portion of the protocol, we analyzed the effect of pollution on between-visit, within-subject changes in mean ST-segment level. A standard model for analyzing repeated measures on the same individual is the linear mixed model, which accounts for residual correlation among observations taken on the same subject by including normally distributed random intercepts and pollutant slopes in a linear regression model. Descriptive statistics for ST-segment values, however, revealed skewness in the subjects’ baseline values, making the normality assumption on the random intercepts untenable. As a result, we used two alternative approaches to analyzing the data from each portion of the protocol. First, treating ST-segment level as a continuous outcome, we used a conditional linear mixed model (Verbeke and Molenberghs 2000), which estimates the within-subject effect of a pollutant after conditioning out each subject’s baseline value. This corresponds to putting subject into the linear model as a fixed effect, while specifying the linear slope of pollutant as a random effect (Verbeke and Molenberghs 2000).
The Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air (ULTRA) study has demonstrated the importance of selecting a vulnerable population when seeking to investigate whether pollution influences ECG changes consistent with ischemia (Pekkanen et al. 2002). Although we did not, as in the ULTRA study, have a cohort selected for coronary artery disease, our aim was to evaluate particle pollution effects on elderly individuals with a tendency to develop ST-segment depression, with some ECG evidence for vulnerability to the outcome of interest. Therefore, a priori, for each part of the protocol for analyses treating ST-segment level as a continuous outcome, we included only vulnerable subjects, defined as those whose mean ST-segment values for that part of the protocol were negative at least two times during the study (23 of 28 study participants). Analyses were repeated including all study participants to assess the sensitivity of results to the exclusion criteria and to the presence of outliers.
In addition to analyses evaluating ST-segment level as a continuous outcome, we analyzed the binary response “ST-segment depression ≥0.5 mm,” defined as a mean ST-segment level for a given portion of the protocol of at least −0.5 mm (i.e., mean ST-segment level ≤−0.5 mm compared with ST-segment level > −0.5 mm). This definition differed from that of classic ischemia in that it did not require within-test or within-portion of the protocol reversibility. For this secondary analysis, we fit a logistic regression model with random intercepts to data from those subjects having at least one response of each type (depressed and nondepressed ST-segment) during that particular protocol (13 of 28 study participants contributed data to at least one portion of the protocol).
Twenty-four study participants with 269 observations were included in analyses either with continuous or with binary (dichotomous) ST-segment outcomes. We had sufficient observations to evaluate the effects of between-test increases in pollution levels on between-test depression in the mean ST-level for each portion of the protocol. However, we were unable to assess the effect of between-test changes in pollution on the risk of within-test reversible ST-segment depression that fit criteria for ischemia because of the rarity and lack of variability of such events. During the study, only 5 of 28 study participants had ischemic ECG events (defined above as within-test reversible horizontal or down-sloping ST-segment depression ≥0.5 mm).
Each regression model included an indicator variable for each subject, pollutant concentration, a cubic effect of the mean of the current hour temperature, and a linear trend of time. Other confounders considered included day of week and time of day, which were both highly correlated with the subject indicator variables and were thus dropped from the model. Separate models were fit using lags of 1–24 hr, as well as previous 12 and 24 hr moving averages, of pollution concentration. Finally, models containing multiple pollutant concentration as predictors were fit to account for confounding due to moderate to high correlations among different pollutant concentrations. Multiple lags and moving averages were evaluated to select the best lag structure for temperature and each individual pollutant, and models reflect these evaluations. All statistical analyses were performed using the SAS statistical software package (SAS Institute Inc., Cary, NC). The conditional linear mixed models were fit using PROC MIXED, whereas the logistic mixed models were fit using PROC NLMIXED (SAS Institute Inc.).
Estimates of the effects of BC were scaled to the difference between the 10th and the 90th percentile in levels for the appropriate lag or mean value of BC.
Results
The median age of the population was 73, and many participants had cardiac risk factors (e.g., history of hypertension, prior smoking) or coronary artery disease (Table 1). As expected, mean heart rate rose during exercise and returned to baseline at rest (Table 2) during the 269 tests for the 24 participants included in analyses. Simultaneously, median ST-segment level was lower during and immediately after exercise than at first rest. ST-segment depression was rare in the modified aVF lead, and all subsequent analyses are based on findings in the modified V5 lead, the lead that most consistently identifies myocardial ischemia when it is present (Lanza et al. 1994). Air pollution levels were only modestly elevated, and maximum levels for U.S. EPA criteria pollutants were all below accepted or proposed National Air Quality Standards (Table 3). CO levels never exceeded 2 ppm. BC levels rose early in the morning and were at their peak between 0600 and 0900 hr.
Individual hourly lag models showed consistent negative associations of ST-segment level with increased BC for the first 12 hr before testing (Figure 1), but with waning effects after 12 hr. The strongest association between BC and ST-segment level was for the 5-hr lagged value of BC (Table 4). For each portion of the protocol in the continuous models, higher 5-hr BC predicted lower between-test mean ST-segment levels. There was also a consistent effect of the mean of the BC levels during the 12 hr before testing on between-test ST-segment depression. Higher BC levels were also associated with lower between-test ST-segment levels, when averaged (for each individual, for each testing session) over all portions of the protocol (12-hr mean BC: estimated overall ST-segment change = −0.08 mm; p = 0.03; 5-hr BC: estimated change = −0.10 mm; p = 0.004), suggesting a pollution effect sustained throughout the protocol. Although they were also consistently negative, associations of ST-segment depression with the mean of BC during the 24-hr before testing were weaker, and the BC levels 2 days before testing had no association with ST-segment depression. There was no effect of air pollution on changes in ST-segment level from the rest to exercise or from the exercise to recovery portions of the protocol. The effects of BC on ST-segment depression were not modified by medication use, diagnosis of coronary artery disease, hypertension, sex, or ethnicity.
For the smaller group who had at least 0.5 mm depression at one or more visits, increases in BC were associated with an elevated risk of ST-segment depression ≥0.5 mm, although confidence in the estimates was limited by the smaller numbers of observations (Table 4). The largest estimated risk occurred during the rest period immediately after exercise, when there was a 10.4-fold risk [95% confidence interval (CI), 1.3–83.0] of having between-test ST-segment depression ≥0.5 mm. Although CO was associated with ST-segment depression in single-pollutant models, in multiple-pollution models only BC remained associated with ST-segment depression (Table 5).
Discussion
In elderly subjects, we found that increases in levels of ambient BC in the 12 hr before testing were associated with between-week depression in the mean ST-segment levels that was present throughout the testing session, with the strongest effects occurring in the postexercise recovery portions of the protocol, a period of cardiac vulnerability in patients with coronary artery disease (Frolkis et al. 2003). There was no effect of pollution on within-testing session changes in the magnitude of ST-segment depression. The risk of ST-segment depression of ≥0.5 mm was elevated with higher pollution; new ECG depression of this magnitude has been associated with increased risk of adverse cardiac events among patients with acute coronary syndrome (Cannon et al. 1997).
Although we found pollution to be associated with ST-segment depression sustained throughout the testing session, the Finnish portion of the ULTRA study found associations of pollution with reversible exercise-induced ST-segment depression (Pekkanen et al. 2002). The etiology of the ST-segment depression we observed is unclear but may represent the consequences of subclinical myocardial ischemia, inflammation, or both.
Although a minority of our subjects had documented coronary disease, many had risk factors predisposing them to subclinical disease and possible ischemia. Particle pollution may decrease myocardial oxygen supply and increase the risk of cardiac ischemia due to epicardial coronary disease through potentially interrelated mechanisms, including systemic inflammation, oxidative stress, endothelial dysfunction, and/or autonomic dysfunction (Gold et al. 2000; Liao et al. 1999). Coronary artery disease is now considered, in large part, an inflammatory process (Ridker et al. 2000), and transient increases in air pollution could lead to transient exacerbation in vascular inflammation. Particle pollution has been linked to ST-segment changes in healthy canines (Godleski et al. 2000) and to reduction of the time to ischemic changes in canines with partial coronary artery occlusion (Wellenius et al. 2003). Brachial artery diameter, which is correlated with coronary artery diameter, was diminished in healthy subjects after exposure in a chamber to concentrated ambient particles (Brook et al. 2002), concomitant with elevated levels of endothelin.
Rather than causing subclinical ischemia, pollution-associated systemic inflammation may lead to low-grade myocardial inflammation, with associated subtle repolarization changes, including sustained ST-segment depression. A series of epidemiologic studies have found associations of particle pollution with elevation of measures of systemic inflammation, including plasma viscosity (Peters et al. 1997), fibrinogen (Gardner et al. 2000), neutrophil count, vascular cellular adhesion molecule and soluble intracellular adhesion molecule (Salvi et al. 1999), and C-reactive protein (Peters et al. 2001).
In this same study, in the entire cohort, we found that BC was associated with a decrease in heart rate variability, suggesting traffic-particle–associated autonomic dysfunction (Schwartz et al. In press). Future work will focus on whether ambient pollution leads to ST-segment depression and autonomic dysregulation through related pathways (e.g., inflammation) or through separate pathways.
BC can be viewed as a surrogate for traffic-related particle pollution; exhaust emissions from diesel-powered vehicles have been identified as the main source of BC or elemental carbon in urban areas (Janssen et al. 2002; Schauer et al. 1996). Laden et al. (2000), in a study of six U.S. cities, found that traffic particles were more strongly associated with cardiovascular deaths than were particles from coal burning. Although BC influenced ST-segment depression, we did not find independent effects of CO on ST-segment level, perhaps because of the low levels of exposure. In one study, short-term exposure to CO, producing carboxyhemoglobin levels of 2–3.9%, were associated with ischemic ST-segment changes in exercising subjects with coronary disease (Allred et al. 1989), although these low-level effects were not reproduced in a study by Sheps et al. (1987). ST-segment depression during exercise was associated with PM2.5 and CO in the Finnish study of subjects with stable coronary heart disease who performed repeated biweekly submaximal exercise tests over a 6-month period (Pekkanen et al. 2002). In that study, correlation between the two pollutants made it more difficult to separate their effects. In our Boston setting, CO was not an independent predictor of ST-segment depression. An alternative explanation for the lack of independent associations of the gases with ST-segment depression is more misclassification of exposure, particularly because all the gases other than CO were measured at distances farther than the site where BC and PM2.5 were measured, which was very close to the health effects testing site (discussed above).
This study was limited by lack of personal exposure measurements for CO and particles. However, ambient levels were measured on the same busy city street as the participant residences, < 0.5 km away, and studies in Boston have shown that ambient concentrations are good surrogates of personal exposures to PM2.5 of ambient origin (Rojas-Bracho et al. 2000). Moreover, the consequence of using ambient particle measures to estimate exposure is likely to be a modest underestimation of pollution effects (Zeger et al. 2000). Our ability to investigate interactions between participant characteristics such as beta-blocker use and particle effects was limited by the size of the population. Confidence in and generaliz-ability of our estimates for our dichotomous outcome were limited by small numbers of observations. Although our cohort was vulnerable on the basis of age, previous smoking, or hypertension history, our potential to document overt ischemic episodes was also limited by the choice of a population, only 18% of whom had diagnosed clinical coronary artery disease. Even in the ULTRA study of a population with doctor-diagnosed coronary artery disease subjected to submaximal exercise, sufficient episodes to examine the outcome of ischemia were documented only among Finnish participants and not among participants from the two other countries (Pekkanen et al. 2002). Our primary analyses did include one individual who, on 3 of 12 visits, reported smoking one to three cigarettes or cigarillos within the previous 48 hr. His data met the inclusion criteria for examining the dichotomous ST-segment depression ≥0.5 mm only during the exercise period; exclusion of this individual from analyses did not influence our findings. In our continuous analyses, we included only those whom we considered vulnerable on the basis of ST-segment depression (23 of 28). A sensitivity analysis showed that although inclusion of the entire cohort somewhat attenuated the magnitude and significance of the results, a significant association of 5-hr BC with ST-segment depression was still detectable during the postexercise period [second rest and paced breathing; e.g., second rest effect estimates: −0.11 vs. −0.08, p = 0.001 vs. 0.007, for a subcohort with at least two episodes of ST-segment depression vs. the entire cohort (233 vs. 317 observations)].
In conclusion, in a population of elders susceptible to cardiovascular pollution effects on the basis of age or underlying cardiovascular disease, we found an association between traffic-related particles and ST-segment depression that may represent ischemia or myocardial inflammation.
Figure 1 Estimates of the effects of BC on mean ST-segment level during paced breathing, scaled to the difference between the 10th and the 90th percentile in levels for individual hourly lags. Error bars indicate 95% CIs.
Table 1 Participant characteristics [n (%)].
ST-segment analysis
Characteristic Entire cohort (n = 28) Continuous outcomea (n = 23) Dichotomous outcomeb (n = 13)
Sex
Male 7 (25) 5 (22) 3 (23)
Female 21 (75) 18 (78) 10 (77)
Race/ethnicity
Black, non-Hispanic 8 (29) 6 (26) 3 (23)
White 19 (68) 16 (70) 9 (69)
Other 1 (4) 1 (4) 1 (8)
Cigarette smoking
Never 11 (39) 10 (43) 4 (31)
Former 16 (57) 13 (57) 8 (62)
Current 1 (4) 0 1 (8)
Ever asthmac 1 (4) 1 (4) 1 (8)
Coronary artery disease (ever angina or heart attack) 5 (18) 5 (22) 4 (31)
Ever congestive heart failure 2 (7) 2 (9) 1 (8)
Ever hypertensionc 11 (39) 10 (43) 5 (38)
Medication use
Beta-blocker 5 (18) 4 (17) 1 (8)
Calcium channel blocker 3 (11) 3 (13) 2 (15)
Angiotensin-converting enzyme inhibitor 7 (25) 7 (30) 4 (31)
Age [median years (range)] 73 (60–89) 71 (61–88) 76 (62–88)
Percentages may not add up to 100 because of rounding.
a Analyses assess the association of pollution with ST-segment level.
b Analyses assess the association of pollution with ST-segment depression ≥0.5 mm.
c Report of doctor’s diagnosis of disease.
Table 2 Mediana heart rate and ST-segment level for six protocol periods.
First rest Blood pressure Standing Exercise Second rest Paced breathing
Heart rate (beats/min) 65b — 78 86 67 65
ST-segment level, modified V5 lead (mm) −0.13 −0.10 −0.08 −0.29 −0.27 −0.17
ST-segment level, modified aVF lead (mm) 0.12 0.12 0.11 0.10 0.05 0.10
a Median of the mean values for each part of the protocol, for observations included in analyses. Based on 269 observations on the 24 subjects in analyses using either the continuous or dichotomous outcomes.
b Median heart rate for the period that includes both first rest and blood pressure portions of the protocol.
Table 3 Ambient pollution and temperature levels during Holter monitoring (n = 269).a
Pollutant 10th percentile 50th percentile 90th percentile Maximum
BC (μg/m3)
5-hrb 0.66 1.28 2.25 4.34
12-hr meanc 0.79 1.14 1.68 2.23
PM 2.5 (μg/m3)
5-hrb 3.8 9.5 25.6 41.0
12-hr mean 4.1 9.8 25.9 35.6
CO (ppm)
5-hrb 0.20 0.53 1.08 1.55
12-hr mean 0.38 0.56 0.81 1.04
O3 (ppb)
1-hr 8.5 27.1 54.9 95.4
5-hrb 2.9 13.3 28.8 57.7
12-hr mean 8.2 19.7 34.2 58.9
NO2 (ppb)
5-hrb 11.9 22.4 35.6 53.1
12-hr mean 14.3 21.4 35.2 48.9
SO2 (ppb)
5-hrb 1.3 3.5 8.6 17.4
12-hr mean 2.0 4.3 6.5 11.5
Temperature (°C) 17.2 23.3 28.9 33.3
a Pollutants include daily BC, PM2.5, O3, NO2, SO2, and CO. Temperature is current 1-hr mean.
b The distribution of the levels (total n = 269) during the fifth hour before Holter monitoring.
c The mean of the levels during the 24 hr before Holter monitoring.
Table 4 Ambient BC as a predictor of ST-segment level for five protocol periods.
Outcome variable No. of observations 5-hr BCa p-Value 12-hr mean BCa p-Value
Estimated ST-segment change in mm (95% CI), for continuous outcomeb
First rest 207 −0.11 (−0.20 to −0.02) 0.02 −0.10 (−0.19 to −0.01) 0.03
Blood pressure 209 −0.09 (−0.16 to −0.01) 0.02 −0.08 (−0.15 to −0.01) 0.03
Standing 196 −0.11 (−0.21 to −0.01) 0.03 −0.09 (−0.19 to 0.01) 0.09
Exercise 257 −0.08 (−0.17 to 0.00) 0.06 −0.02 (−0.11 to 0.06) 0.57
Second rest 233 −0.11 (−0.18 to −0.05) 0.001 −0.07 (−0.14 to −0.01) 0.03
Paced breathing 219 −0.11 (−0.17 to −0.04) 0.001 −0.08 (−0.14 to −0.01) 0.02
Estimated relative risk (95% CI), for ST-segment depression ≥0.5 mm
First rest 90 (29)c 5.1 (0.9 to 28.0) 0.06 3.8 (0.7 to 21.3) 0.11
Blood pressure 66 (22) 6.0 (0.8 to 44.8) 0.07 5.7 (0.6 to 56.3) 0.11
Standing 66 (28) 9.2 (1.1 to 78.3) 0.05 8.3 (0.8 to 81.9) 0.06
Exercise 114 (38) 0.9 (0.2 to 4.7) 0.86 0.6 (0.1 to 3.1) 0.53
Second rest 90 (48) 10.4 (1.3 to 83.0) 0.03 2.8 (0.5 to 14.3) 0.19
Paced breathing 66 (22) 6.6 (0.9 to 50.0) 0.06 3.5 (0.5 to 23.6) 0.15
a Estimated for a 10th to 90th percentile change in BC.
b Repeated-measures regression models contain pollution concentration, a cubic effect of current temperature, and a linear trend of time.
c Numbers in parentheses in this column represent the number of positive events with ST-depression ≥0.5 mm.
Table 5 5-hr BC and CO as predictors of continuous ST-segment level in single- and multiple-pollutant models.
Outcome variable, model Predictor variable Coefficient Estimated effect [mm (95% CI)] p-Value
Second rest
1 BC −0.07 −0.11 (−0.17 to −0.05) 0.001
2 CO −0.15 −0.13 (−0.22 to −0.04) 0.007
3 BC −0.06 −0.09 (−0.17 to 0.03) 0.04
CO −0.05 −0.05 (−0.17 to 0.07) 0.45
4 PM2.5 −0.0002 −0.004 (−0.08 to 0.07) 0.92
5 O3 1.38 0.04 (−0.05 to 0.12) 0.39
6 NO2 −1.96 −0.05 (−0.12 to 0.03) 0.22
7 SO2 −3.19 −0.02 (−0.10 to 0.05) 0.53
Paced breathing
1 BC −0.07 −0.11 (−0.17 to −0.04) 0.001
2 CO −0.11 −0.09 (−0.19 to 0.00) 0.05
3 BC −0.07 −0.11 (−0.20 to −0.03) 0.01
CO 0.01 0.01 (−0.11 to 0.13) 0.87
4 PM2.5 −0.0008 −0.02 (−0.09 to 0.05) 0.64
5 O3 0.85 0.02 (−0.06 to 0.11) 0.60
6 NO2 −1.54 −0.04 (−0.11 to 0.04) 0.33
7 SO2 −5.15 −0.04 (−0.11 to 0.03) 0.30
Repeated-measures regression models contain pollution concentration, a cubic effect of current temperature, and a linear trend of time. All models except model 3 include only the single pollutant described. Model 3, for second rest and for paced breathing, includes both BC and CO; thus, the coefficient for BC is adjusted for CO. Results presented are estimated for a 10th to 90th percentile change in BC.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7830ehp0113-00088816002378ResearchEnvironmental MedicineGrand Rounds: Latex-Induced Occupational Asthma in a Surgical Pathologist Green-McKenzie Judith Hudes Debra *University of Pennsylvania Medical Center, Division of Occupational and Environmental Medicine, Philadelphia, Pennsylvania, USAAddress correspondence to J. Green-McKenzie, 3400 Spruce St., Division of Occupational and Environmental Medicine, Ground Silverstein, Philadelphia, PA 19104-4283. Telephone: (215) 662-4439. Fax: (215) 349-5100. E-mail:
[email protected]*Current address: Division of Occupational Medicine, Temple University Hospital, Philadelphia, PA.
This article is based on an oral presentation at the 23rd annual national meeting of the Society of General Internal Medicine, Boston, MA, 4–6 May 2000.
The authors declare they have no competing financial interests.
7 2005 31 3 2005 113 7 888 893 1 12 2004 31 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Context: Latex allergy and sensitization have been an important problem facing health care workers. Providing a latex-safe environment is the intervention of choice.
Case Presentation: A 46-year-old surgical pathologist presented with increasing shortness of breath for the previous 4 years. Twenty years before presentation, he noted a pruritic, erythematous rash on his hands, associated with latex glove use. Fourteen years before presentation, during pathology residency, he developed a nonproductive cough, wheezing, and an urticarial rash, temporally associated with use of powdered latex gloves. These symptoms improved while away from work. At presentation, he had one-flight dyspnea. His skin prick test was positive for latex, and pulmonary function testing showed mild obstruction, which was reversible with bronchodilator use. Because the patient was at risk for worsening pulmonary function and possible anaphylaxis with continued exposure, he was removed from the workplace because no reasonable accommodation was made for him at that time.
Discussion: The patient’s presentation is consistent with latex-induced occupational asthma. Initially noting dermal manifestations, consistent with an allergic contact dermatitis secondary to accelerators present in latex gloves, he later developed urticaria, flushing, and respiratory symptoms, consistent with a type I hypersensitivity reaction to latex. He also has reversible airways disease, with significant improvement of peak expiratory flow rate and symptoms when away from work.
Relevance to Clinical or Professional Practice: The ideal treatment for latex sensitization is removal from and avoidance of exposure. Clinicians should consider occupational asthma when patients present with new-onset asthma or asthmatic symptoms that worsen at work.
formaldehydehealth care workerlatex allergyoccupational asthmapathologyxylene
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Case Presentation
A 46-year-old male surgical pathologist presented to our clinic complaining of a 4-year history of increasing shortness of breath. He had been in good health until 20 years prior while in medical school, when he noted a pruritic, erythematous rash on the dorsal aspect of his hands whenever he wore latex gloves. He often applied steroid cream to the rash, but it usually did not resolve unless he refrained from using latex gloves. This rash, associated with latex glove use, persisted during his internal medicine residency. Approximately 14 years before presentation, at the beginning of his pathology residency, he noted that the rash involved his arms. He developed an episodic, nonproductive cough, wheezing, and occasional chest tightness, which occurred at work when he used powdered latex gloves. These symptoms were mild and did not interfere with his vigorous exercise program. He did not seek medical attention.
After completing his residency, the patient worked as a hospital-based surgical pathologist. Typical daily activities involved cutting tissue and frozen sections and preparing slides. He changed gloves several times each day. He did reasonably well until 4 years before presentation (1993), when his symptoms worsened. He then experienced cough and dyspnea within 30 min of starting work. These symptoms, which continued throughout the workday and improved once he left work, seemed especially severe on the first day of the workweek and worsened as the week progressed. The use of xylene and formaldehyde exacerbated his symptoms. He noted an intermittent rash on his upper extremities and torso, occasional flushing with exposure to latex, postnasal drip, progressive dyspnea on exertion, and dyspnea and coughing when he laughed. He noted heavy breathing if he “flipped” his gloves off, and he described an episode of “passing out” 1 year earlier when he “flipped” his gloves off and placed his hands over his mouth and nose. He was taken to a local emergency department, where he was diagnosed as having had a vasovagal episode. He was returned to work without intervention.
The patient’s wife and co-workers started commenting on his cough, noting that he “breathed heavily.” He became self-conscious about his cough and about constantly having to clear his throat. There was no seasonal variation to his symptoms. The patient attempted to reduce his exposure to powdered natural rubber latex (NRL) gloves, formaldehyde, and xylene. For example, he switched to non-powdered latex gloves, although his co-workers continued to use the powdered form. He replaced eyecups on the microscope once he realized that they contained latex. He instructed his staff to allow an hour for drying slides fixed with formaldehyde and xylene before sending them to him to be read. His symptoms persisted, however, prompting him to seek medical attention.
The patient subsequently consulted with an allergist, an otorhinolaryngologist, and a dermatologist. Skin biopsy of his rash revealed changes consistent with acute urticaria. Latex skin prick tests were positive to latex glove extracts. Skin prick tests were positive to dust, cat dander, and mold antigens, and a computerized tomography (CT) scan of the sinuses revealed nasal polyps in the maxillary sinus. He was diagnosed with chronic sinusitis, asthma, and allergic rhinitis. Treatment included antibiotics and a steroid taper. The patient was started on Serevent (GlaxoSmithKline, Research Triangle Park, NC), Flovent (GlaxoSmithKline), and Proventil (Schering, Kenilworth, NJ) inhalers and returned to work with the recommendation that he use a surgical mask while at work. His symptoms continued to progress, and he presented to us 2 months later, by which time he was experiencing single-flight dyspnea.
The patient’s past medical history was remarkable for hypertension, nasal polyps, and near syncope. He denied any previous diagnosis of asthma, allergy, hives, or anaphylaxis. His family history was remarkable for asthma in a sister and a paternal uncle. He denied use of alcohol, cigarettes, or illegal drugs and denied allergies to medications or environmental substances. He gave a history of chest tightness when he ate fruit such as banana, avocado, and kiwi. His occupational history was remarkable for work in the medical field (Table 1). On physical examination, he was a well-nourished, well-developed white male in no acute distress whose vital signs were within normal limits. His examination was remarkable for a body mass index of 30, hyperemic conjunctivae, boggy nasal mucosa, an erythematous urticarial rash on his right shoulder, and diffuse expiratory wheezing.
Laboratory evaluation revealed a normal electrocardiogram. Chest X ray showed poor inspiration; CT of the chest showed mild bronchial wall thickening consistent with mild airways disease; pulmonary function tests (PFTs) were remarkable for mild obstruction with acute bronchodilator response (Table 2); and a radioallergoimmuno-absorbent assay (RAST) test for latex IgE antibody was negative. His peak expiratory flow rate (PEFR) diary during an 11-day work period and a subsequent 6-day vacation period showed significant improvement (20% in the morning, 22% in the evening) while he was away from work (Table 3) and progressive improvement during successive days of vacation (Figure 1).
The provision of a latex-safe environment was explored with hospital administration and deemed not feasible at that time. A full-face dual-cartridge respirator was recommended and tried in consultation with a certified industrial hygienist. However, it interfered with the patient’s ability to communicate, and he was unable to tolerate wearing it for an 8-hr day. We felt that he was at risk for potentially fatal anaphylaxis, as well as irreversible and impending structural damage to his lungs, given his long history of exposure and disease severity. In order to eliminate exposure to NRL, the patient was removed from the work-place. He was advised to avoid contact with latex, carry injectable epinephrine, and wear a MedicAlert bracelet (MedicAlert Foundation International, Turlock, CA). Despite removal from the workplace shortly after presentation, the patient’s pulmonary status did not improve. He is maintained on steroids and immunosuppressive agents and has not been able to return to work as a surgical pathologist.
Discussion
Latex allergy and sensitization.
The use of powdered high-protein NRL gloves is recognized as the major environmental risk factor for latex sensitization and allergy in the health care field (Levy et al. 1999; Wild and Lopez 2003). The widespread use of NRL gloves in the health care industry started in the 1980s as health care facilities complied with Universal Precautions [Occupational Safety and Health Administration (OSHA) 1991]. After the first report of a case of immediate hypersensitivity to NRL (Nutter 1979), NRL allergy became increasingly recognized as a problem among health care workers (Garabrant and Schweitzer 2002). NRL, used in the production of latex gloves, is derived from the milky sap of the commercial rubber tree, Hevea brasiliensi (Atkins 1999). The sap of this tree is a complex mixture of protein, lipid, and phospholipid. The protein content varies depending on country of harvest location, environmental conditions, and manufacturing process. Sixty of the 240 proteins in NRL have been found to be allergenic (Levy et al. 1999).
Freshly harvested latex is treated with ammonia and other preservatives to prevent its deterioration during transport to factories; it is then treated with antioxidants and accelerators before being shaped into the final product. Increased washing time in glove manufacture can lead to a decrease in the amount of soluble protein in the final product (Yunginger et al. 1994), hence decreasing the antigenicity of the glove. The product is frequently dry-lubricated with cornstarch or talc powder to improve ease of donning the glove. Latex allergen elutes onto the powder, providing a source for respiratory exposure (Yunginger et al. 1994). Notably, synthetic rubber elastomers (butyl rubber, polymers of 2-chlorobutadiene, co-polymers of butadiene and acrylonitrile) do not cause or contribute to allergic sensitization; people who are sensitized to NRL proteins can safely use products made from synthetic rubbers (OSHA 1999; Renaud 1993).
Most reactions associated with NRL can be classified into three main categories: irritant contact dermatitis (ICD), allergic contact dermatitis (ACD), and an immediate hypersensitivity reaction (Felt-Ahmed et al. 2003). ICD is confined to the skin and occurs when the skin has direct contact with the glove. ICD represents a type of contact dermatitis and is not allergic in nature. The second type of reaction, ACD, is a delayed hypersensitivity reaction (type IV) thought to be a result of exposure to the accelerators, which can lead to the activation and release of lymphokines by sensitized T lymphocytes rather than to the latex itself (Atkins 1999). Endotoxins, which may be present as contaminants, have also been implicated as causing ACD (Charous et al. 1997). Features of ACD are pruritic rash, local erythema, swelling, blistering, weeping, and crusting. These symptoms generally occur 1–2 days after exposure but also may occur from several hours to several days postexposure (Felt-Ahmed et al. 2003).
The third type of reaction, the type I, immediate-type hypersensitivity reaction, relies on previous sensitization of the immune system to latex antigens and to the generation of IgE antibodies directed specifically at latex proteins and is the most serious of the three (Atkins 1999; Vandenplas et al. 1995). Signs and symptoms include asthma, rhinitis, conjunctivitis, generalized urticaria, and mucous membrane swelling. Anaphylaxis, the most dreaded complication, may also occur in a sensitized patient and has been recorded to have occurred as a result of donning gloves, being in the presence of others who have put on gloves, during surgery, and during dental and medical examinations (Vandenplas et al. 1995). In 1991, a latex barium enema tip associated with 16 deaths was recalled by the Food and Drug Administration (FDA); this led to an increased awareness of the risk of life-threatening type I allergy associated with NRL devices (Gelfand 1991). Sensitization occurs after multiple exposures over a highly variable time, the latency period ranging from several weeks to as long as 30 years (Malo et al. 1992). Once sensitization occurs, there is considerable variability in the type and severity of allergic symptoms, occurring from within 30 min (anaphylaxis, angioedema) to more than hours and days after exposure. Asthma symptoms are highly variable in their onset, duration, and intensity, the more severe cases being associated with multiple and prolonged exposures occurring over many months to years (Felt-Ahmed et al. 2003).
The prevalence of latex sensitization has been estimated to be between 5 and 17% in health care workers (Malo et al. 1992), versus between 5 and 10% in the general population (Felt-Ahmed et al. 2003). The factors associated with an increase in the risk of latex sensitization among health care workers include the duration of exposure and the intensity of exposure to NRL gloves. Intensity of exposure is measured by the number of pairs of gloves used per day and the amount of powdered glove use (Garabrant and Schweitzer 2002). The mechanical and irritant reaction to the powder may lead to a breakdown of the skin barrier, further enhancing exposure to the latex protein (Levy et al. 1999). In addition, the powder disseminates into the environment, carrying the latex protein with it, providing a respiratory route of exposure (Baur et al. 1993). An increase in latex sensitization is seen with particular jobs and departments in health care probably as a result of a relatively higher exposure to NRL gloves. Laboratory workers have been found to have the highest incidence of latex sensitization, 4% per year, whereas the incidence of latex sensitization among health care workers in general has been estimated at 1–2.5% per year; pathology staff has been found to have a 14% prevalence of latex sensitization (Garabrant and Schweitzer 2002).
Atopic individuals are more easily sensitized to allergens and, as such, are at greater risk of developing a latex allergy than are individuals who are not atopic (Felt-Ahmed et al. 2003). Atopy is a hypersensitivity state or allergy with hereditary predisposition. Atopic individuals may have a personal or family history of eczema, asthma, or hay fever or a tendency to develop specific IgE antibodies after exposure to common environmental substances, although many do not. The tendency to develop some form of allergy is inherited, but the specific clinical form, such as hay fever, asthma, or eczema, is not (Wild and Lopez 2003). Skin tests to common environmental allergens such as pollen, animal dander, molds, and house dust mites are used to evaluate atopic status. One looks for the immediate IgE-mediated wheal and flare reaction. Clinical associations have been reported between latex allergy and allergy to several fruits and vegetables, such as avocado, kiwi fruit, banana, potato, tomato, chestnut, and papaya (Beezhold et al. 1996). Several latex allergens (e.g., Heb b2, 5, 6.02, and 7) have varying degrees of amino acid sequence homology with allergens in seed-producing plants (Wagner and Breiteneder 2002). Some patients report that food allergy preceded the latex allergy, and others report the converse (Beezhold et al. 1996).
Sensitization can be documented by the use of a skin prick test using extracts prepared from suspected substances, such as latex, in the work environment. Detection of specific IgE antibodies suggests a cause-and-effect relationship. Licensed extracts of latex for skin testing, available in Europe, have been found to be safe and reliable for detecting latex-specific IgE. The United States does not have licensed commercial latex extracts. As a result, skin testing is done with unstandardized office-prepared latex extracts, which vary widely in allergen content (Ownby 2003). Specific IgE antibodies can also be studied in vitro using a blood test, the RAST assay (Wild and Lopez 2003). Tests for latex-specific IgE such as the RAST are less sensitive and specific than are skin prick tests, with sensitivity ranging between 73 and 80% and specificity ranging between 90 and 97% (Ownby 2003). The laboratory to which this patient’s RAST was sent reports a 30% false-negative rate (Hamilton 1999).
Latex-induced occupational asthma.
Occupational asthma (OA) can be defined as the presence of variable airflow obstruction and bronchial hyperresponsiveness caused by a substance found in the workplace (Tilles and Jerath-Tatum 2003). OA differs from preexisting asthma, which is exacerbated by exposure to agents in the workplace (Wild and Lopez 2003). However, OA may occur in conjunction with preexisting asthma, because OA involves the new onset of sensitization to a workplace antigen or allergen with the development of respiratory disease. A person with preexisting asthma and allergies may develop OA to a workplace allergen. Another feature of OA is the occurrence of nasal, ocular, or contact urticarial symptoms that precede asthma symptoms. The presence of these symptoms is helpful, but not necessary, in establishing the diagnosis.
Other features include the association of prolonged exposure with worsening asthma symptoms at work, the development of more pervasive symptoms while at work, and the presence of a latency period between the initial exposures to the inciting agent where symptoms may develop from weeks to > 20 years after exposure (Chan-Yeung 1987; Tilles and Jerath-Tatum 2003; Wild and Lopez 2003). Reactive airways dysfunction syndrome (RADS) is a form of OA that does not require a latency period. RADS can occur acutely, within 24 hr, after one single exposure to an irritant (Tilles and Jerath-Tatum 2003). OA symptoms may resolve in some individuals, whereas others remain symptomatic for years. Approximately 10% of adult asthma cases are attributed to an occupational etiology (Blanc and Toren 1999). More than 250 agents encountered in the workplace have been shown to induce asthma in susceptible individuals (Wild and Lopez 2003).
Atopic individuals are at greater risk of developing OA, especially when working in an industry where high-molecular-weight proteins such as latex proteins are present. Other high-molecular-weight proteins known to cause OA are flour and animal antigens (Wild and Lopez 2003). Allergic OA is seen in individuals who develop sensitization to a specific chemical agent in the workplace. Persons with allergic OA tend to develop bronchospasm and airway inflammation upon exposure, even to low concentrations of the specific workplace agent to which they are sensitized (Paggiaro et al. 1994). NRL-induced OA, an IgE-mediated process, is initiated when the allergen-bearing particles deposit onto the mucosal surfaces of the respiratory tract. Of the health care workers estimated to be sensitized to latex, 41–69% of them are estimated to have respiratory symptoms with exposure (Lagier et al. 1992).
Various criteria are used in making the diagnosis of OA. A significant postbronchodilator response is considered to have occurred if PFTs demonstrate an increase in forced vital capacity (FVC) or forced expiratory volume in 1 sec (FEV1) of 12% above baseline and an absolute change of 0.2 L (American Thoracic Society 1991). Methacholine challenge testing, the gold standard for establishing the diagnosis of asthma, can also be used to show non-specific bronchial hyperreactivity. An abnormal test result is defined by the concentration of methacholine that drops the baseline FEV1 by 20% (Tan and Spector 2003). Medical and work histories may be used to help ascertain a temporal association between the patient’s symptoms and work, as well as to rule out other causes for the symptoms.
One recommendation for confirming the diagnosis of OA, using pre- and postshift spirometry or PEFR, is by showing a significantly decreased obstructive pattern at work compared with being away from work. For example, the PEFR should be measured approximately every 2–3 hr during a 2-week period at work and during a 1–2 week period away from work. OA is confirmed by finding a ≥20% reduction in PEFR at work versus away from work or by finding at least a 20% diurnal variability of mean work PEFR, with the disappearance of this variability when away from work (Tilles and Jerath-Tatum 2003). PFTs are most useful in suggesting an occupational cause for asthma when they show a decrease in FEV1 of at least 15% when comparing results obtained before and after a period of work (Greaves 2003). The diagnosis of OA is usually confirmed by a combination of findings. The history and physical exam should be consistent with this diagnosis; spirometry or methacholine challenge testing should demonstrate variable airflow obstruction; and serial peak flows should confirm that bronchial hyperreactivity is triggered by work-place exposures to specific agents.
Role of formaldehyde and xylene.
Formaldehyde is an upper respiratory tract irritant, exacerbating bronchial airflow obstruction or hyperreactivity. It can exacerbate asthma and precipitate wheezing in those with underlying asthma or bronchial hyperreactivity. Formaldehyde may cause an immune response by forming a hapten, a complex of a protein and a low-molecular-weight compound, which can induce an IgE response, although this is uncommon (Rutchik 1999). Xylene, an aromatic hydrocarbon used in medical technology as a solvent and fixative, may exacerbate asthma and rhinitis. Other agents to which our patient may have been exposed during his daily work as a pathologist that he did not identify as specific triggers to his symptoms—but that are associated with respiratory and dermatologic symptoms—are glutaraldehyde, phenol, and ethylene glycol (Rutchik 1999).
Treatment and workplace accommodation.
Disability from occupationally induced allergies is compensable under Workers’ Compensation law (Phillips et al. 1999). A worker with OA or NRL-induced anaphylaxis is considered to be 100% impaired from performing his or her specific job if the job entails exposure to the causative agent (American Thoracic Society 1993; Bernstein 2002). Under the Americans with Disabilities Act (1990), reasonable work-place accommodation must be made to allow a disabled worker to perform the “essential functions” of the job. The ideal treatment for latex sensitization is prevention of exposure, best achieved by identifying and removing all latex-containing products in the workplace. Latex aeroallergen levels are significantly reduced when medical centers eliminate powdered NRL gloves from the work environment, replacing them with nonpowdered synthetic rubber gloves (Swanson et al. 1994). This workplace modification has been found to be most effective and is associated with an improvement in respiratory and dermatologic symptoms in health care workers and with a reduction in the number of new cases of latex sensitization and allergy (Bernstein et al. 2003; Hunt et al. 2002; Saary et al. 2002; Swanson et al. 1994). It has also been shown to be cost-effective, considering the cost incurred by disability from latex allergy and asthma (Allmers et al. 2002; Phillips et al. 1999).
Many medical devices and products, as well as many common household and everyday items, contain NRL. However, identifying latex-containing products was made simpler when the FDA mandated that all NRL-containing medical devices be labeled as such and that health care sites provide non-latex-containing alternatives (FDA 1997). The FDA concluded that this intervention is affordable for manufacturers (FDA 1997). Extensive lists of NRL-containing products and latex-safe alternatives are also available (Spina Bifida Association of America 2004). Despite this, however, it is difficult to render and maintain an environment completely latex-free. Furthermore, NRL-containing items may also be inadvertently brought into an area. As a result, “latex-safe” is the preferred term.
Prevention of exposure may also be carried out through engineering and industrial hygiene controls and through the use of personal protective equipment. Latex aeroallergen levels may be monitored, and engineering controls can include exhaust ventilation equipment (Reiter 2002), although the use of a laminar flow glove-changing station has not been shown to reduce latex aeroallergens (Swanson et al. 1994). Work-practice controls, such as cleaning the area, might help to eliminate or minimize the hazard. Environmental controls such as HEPA-filtered vacuuming and wet wiping of surfaces with isopropyl alcohol may reduce latex allergen on surfaces (Reiter 2002).
The worker may also use personal protective equipment such as a respirator. Respirators can provide additional protection and mitigate the hazard but are not the method of choice for controlling exposures. There are various categories of respirators. Air-purifying respirators may use negative pressure (the user pulls air through the respirator), or air is supplied through a powered source (powered air-purifying respirator). They remove much of the toxicant from the inhaled air by filtration, adsorption, or absorption. Atmosphere-supplying respirators, such as the self-contained breathing apparatus (supplies air from a source such as a tank carried by the user), and the airline respirator (uses air supplied via a hose from a distant source), provide air from an independent source as opposed to purifying ambient air.
Most respirators require a tight seal between the mask and the user’s face, although some are loose fitting. Masks are quarter, half, or full face depending on the portion of the face that is covered [Harber et al. 2005; National Institute for Occupational Safety and Health (NIOSH) 2005]. Laminar flow HEPA-filtered helmets have been found to be effective in reducing the symptoms of latex-induced asthma, rhinitis, and conjunctivitis (Laoprasert et al. 1998). Respirators may interfere with vision, hearing, mobility, ability to communicate, and the use of tools such as stethoscopes and microscopes. They may be uncomfortably warm, with tight-fitting head straps, and may also lead to increases in resistance to breathing, dead space, and physical load. These factors, among others, may contribute to a functional inability to keep the respirator on for more than a brief period of time in some persons. Recommendations of a certified industrial hygienist should be used when available (Harber et al. 2005; NIOSH 2005).
Sensitized workers with severe asthma and other life-threatening allergic reactions must be removed from the workplace if exposure cannot be prevented, because the asthmatic response can occur at minute levels of exposure (Ehrlich 1994). Although not documented in individuals with OA due to NRL, evidence from other sensitizing agents, such as western red cedar asthma and toluene diisocyanate, indicates that repeated exposures to the inciting agent can increase the severity of the asthma, and the disease process may even progress after removal from exposure (Banks et al. 1990; Butcher et al. 1982; Chan-Yeung et al. 1982; Cote et al. 1990). Ultimately, irreversible lung damage and death can result from repeated exposure (Banks et al. 1990; Chan-Yeung 1987).
Removing the employee from the work-place has personal, social, and economic implications. The latex-allergic health care worker may experience psychological distress secondary to coping with the adjustment and may respond with anger, depression, anxiety, and denial. Self-esteem, interpersonal relationships, and economic well-being may be adversely affected when an individual is unable to maintain his current profession with the possible loss of future earnings or forced early retirement. These factors, among others, may lead the health care worker to delay seeking much needed medical attention (Charous et al. 2002a). In addition to eliminating exposure to latex, the treatment for OA is the same as for other types of asthma (Wild and Lopez 2003). Workers with latex sensitization and latex-induced OA should be counseled to wear a MedicAlert bracelet and carry injectable epinephrine with them at all times. They should also be counseled as to what items contain latex and to avoid dermal, mucosal, or serosal contact with them (Howarth 2001).
Conclusion
This case describes a surgical pathologist whose presentation is consistent with a diagnosis of latex-induced OA. It shows how exposure to a high-molecular-weight protein, latex, led to ACD. Repeated exposure to the inciting agent over a latency period of several years led to latex sensitization and ultimately to latex-induced OA in this atopic indiviual. He did not give a clear history of anaphylaxis, but he was diagnosed with “near syncope” of unknown etiology after flipping his gloves off and placing his hand over his nose and mouth, after which he was returned to work without intervention. Skin prick test, which is diagnostic for the presence of IgE-mediated allergy to latex, was positive to several latex-containing extracts. Although his serum IgE, or RAST, to one type of latex protein was negative, false-negative tests do occur (Hamilton 1999). The patient’s medical and occupational history, in combination with his spirometry and PEFR measurements, supports the diagnosis of OA, reversible airways disease responding to bronchodilators with symptoms that are worse at work and improve away from work. Formaldehyde and xylene probably acted as irritants, exacerbating his pulmonary symptoms.
The mainstay of treatment for latex-induced OA is to prevent contact of the worker with the inciting agents. Creating a latex-safe environment is the provision of choice (Charous et al. 2002b). However, this provision was not made at the time. Given the long period of the patient’s exposure and the severity of his disease, there was concern that his pulmonary function would continue to decline with continued exposure and that he was at risk for anaphylaxis. We thought removal from the workplace was the best way to protect the patient from exposure. Despite removal from inciting agents, the patient’s symptoms and pulmonary status did not improve. He remains out of work and is maintained on steroids and immunosuppressive agents. If his condition been identified and removal from exposure occurred sooner, his disease may not have progressed. Prompt identification of latex allergy and sensitization, as well as reduction or elimination of the hazard, may allow the patient to continue working in his environment and prevent progression of disease. Clinicians should consider OA in patients who present with new-onset asthma or who present with asthma symptoms that worsen during or after work.
Figure 1 The patient’s morning and evening PEFRs recorded in 1997 on 11 consecutive days while at work (Sunday, 2 November, through Wednesday, 12 November) and on 6 consecutive days while on vacation (Wednesday, 13 November, through Tuesday, 18 November).
Unversity of Pennsylvania Medical Center
Table 1 The chronological relationship between the patient’s occupational exposure and the appearance of symptoms.
Year Occupation Symptoms
1977 Medical student Rash on dorsum of hands with latex glove use; does not clear with steroid use
1979 Internal medicine resident Continued rash on dorsum of hands with latex glove use
1984 Pathology resident Rash on hands and arms, urticaria, wheezing, chest tightness, chronic cough
1987 Surgical pathologist Diagnosed with nasal polyps
1993 Surgical pathologist Notes dyspnea within 30 min of work and with coughing and laughing
1996 Surgical pathologist Allergist evaluation results in diagnosis of asthma and allergic rhinitis; emergency department evaluation results in diagnosis of “near syncope” after he flipped off gloves and covered mouth and nose with hands
1997 Surgical pathologist Presents to our clinic with single flight dyspnea; removed from workplace because no reasonable accommodation made at work
Table 2 Spirometry results before and after bronchodilator use showing FEV1 and FVC.
Prebronchodilator Percent predicted Postbronchodilator Percent predicted Percent change
FEV1 (L) 2.65 67 2.98 75 13
FVC (L) 3.96 81 4.47 91 13
FEV1/FVC 67 — 67 — —
Abbreviations: FEV1, forced expiratory volume in 1 sec; FVC, forced vital capacity.
Table 3 Mean morning and evening PEFRs while at work and during vacation, measured in the morning and in the evening at bedtime both before using asthma medication.
Mean PEFR
Time Work Vacation Percent increase
Morning (L/min) 368 443 20
Evening (L/min) 361 441 22
Percent increase −2 −0.5 —
PEFR range (L/min) 320–425 340–550 —
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7688ehp0113-00089416002379ResearchChildren's HealthLow-Level Environmental Lead Exposure and Children’s Intellectual Function: An International Pooled Analysis Lanphear Bruce P. 12Hornung Richard 123Khoury Jane 12Yolton Kimberly 1Baghurst Peter 4Bellinger David C. 5Canfield Richard L. 6Dietrich Kim N. 12Bornschein Robert 2Greene Tom 7Rothenberg Stephen J. 89Needleman Herbert L. 10Schnaas Lourdes 11Wasserman Gail 12Graziano Joseph 13Roberts Russell 141Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA2Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA3Institute for Health Policy and Health Services Research, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA4Women and Children’s Hospital, North Adelaide, South Australia5Department of Neurology, Children’s Hospital Boston and Harvard Medical School, Boston, Massachusetts, USA6Division of Nutritional Sciences, Cornell University, Ithaca, New York, USA7Department of Biostatistics and Epidemiology, Cleveland Clinic Foundation, Cleveland, Ohio, USA8Center for Research in Population Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico9Drew University, Los Angeles, California, USA10University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA11National Institute of Perinatology, Mexico City, Mexico12Department of Child Psychiatry, Columbia University, New York, New York, USA13Department of Environmental Health Sciences, Columbia University, New York, New York, USA14School of Applied Psychology, Griffith University, Queensland, AustraliaAddress correspondence to B.P. Lanphear, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Mail Location 7035, Cincinnati, OH 45229-3039 USA. Telephone: (513) 636-3778. Fax: (513) 636-4402. E-mail:
[email protected] study was funded, in part, by the National Institute of Environmental Health Sciences, the Centers for Disease Control and Prevention, and the U.S. Environmental Protection Agency.
The authors declare they have no competing financial interests.
7 2005 18 3 2005 113 7 894 899 22 10 2004 17 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Lead is a confirmed neurotoxin, but questions remain about lead-associated intellectual deficits at blood lead levels < 10 μg/dL and whether lower exposures are, for a given change in exposure, associated with greater deficits. The objective of this study was to examine the association of intelligence test scores and blood lead concentration, especially for children who had maximal measured blood lead levels < 10 μg/dL. We examined data collected from 1,333 children who participated in seven international population-based longitudinal cohort studies, followed from birth or infancy until 5–10 years of age. The full-scale IQ score was the primary outcome measure. The geometric mean blood lead concentration of the children peaked at 17.8 μg/dL and declined to 9.4 μg/dL by 5–7 years of age; 244 (18%) children had a maximal blood lead concentration < 10 μg/dL, and 103 (8%) had a maximal blood lead concentration < 7.5 μg/dL. After adjustment for covariates, we found an inverse relationship between blood lead concentration and IQ score. Using a log-linear model, we found a 6.9 IQ point decrement [95% confidence interval (CI), 4.2–9.4] associated with an increase in concurrent blood lead levels from 2.4 to 30 μg/dL. The estimated IQ point decrements associated with an increase in blood lead from 2.4 to 10 μg/dL, 10 to 20 μg/dL, and 20 to 30 μg/dL were 3.9 (95% CI, 2.4–5.3), 1.9 (95% CI, 1.2–2.6), and 1.1 (95% CI, 0.7–1.5), respectively. For a given increase in blood lead, the lead-associated intellectual decrement for children with a maximal blood lead level < 7.5 μg/dL was significantly greater than that observed for those with a maximal blood lead level ≥7.5 μg/dL (p = 0.015). We conclude that environmental lead exposure in children who have maximal blood lead levels < 7.5 μg/dL is associated with intellectual deficits.
blood lead concentrationchildrenenvironmentepidemiologyintelligenceleadlead toxicity
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The preponderance of experimental and human data indicates that there are persistent and deleterious effects of blood lead levels > 10 μg/dL on brain function, including lowered intelligence, behavioral problems, and diminished school performance (Baghurst et al. 1992; Bellinger et al. 1992; Cory-Slechta 1997; Dietrich et al. 1993; Ernhart et al. 1989; National Research Council 1993; Needleman and Gatsonis 1990; Pocock et al. 1994; Rice 1993; Wasserman et al. 1997; Yule et al. 1981). Lead toxicity, defined as whole blood lead ≥10 μg/dL, was based on numerous cross-sectional and prospective studies [Bellinger et al. 1987; Centers for Disease Control and Prevention (CDC) 1991; World Health Organization (WHO) 1995]. These studies generally, but not always, found adverse consequences of childhood lead exposure (CDC 1991; WHO 1995). Still, most of the children in those studies had blood lead levels > 10 μg/dL. The WHO and the CDC recognized that there was no discernable threshold for the adverse effects of lead exposure, but too few studies had examined children with blood lead levels < 10 μg/dL to support any firm conclusions (CDC 1991; WHO 1995).
There is emerging evidence that lead-associated intellectual deficits occur at blood lead levels < 10 μg/dL. In the Rochester Longitudinal Study, there was an estimated reduction of 7.4 IQ points associated with an increase in lifetime mean blood lead from 1 to 10 μg/dL (Canfield et al. 2003). In a reanalysis of a Boston, Massachusetts, cohort, a similar finding was observed among children whose maximal blood lead level was < 10 μg/dL (Bellinger and Needleman 2003). Questions about an effect of lead at blood lead levels < 10 μg/dL persist, however, because of the relatively small numbers of children with maximal blood lead levels < 10 μg/dL in the Rochester Longitudinal Study (Rogan and Ware 2003). Other studies were limited because they involved children whose blood lead levels may have exceeded 10 μg/dL at some point in their lifetime or because important covariates, such as maternal IQ scores, were not always available (Fulton et al. 1987; Lanphear et al. 2000; Schwartz 1994; Schwartz and Otto 1991; Walkowiak et al. 1998). Because of the policy implications of this research, it is critical to estimate with greater precision the exposure–response relationship at blood lead levels < 10 μg/dL.
The primary objective of this pooled analysis was to estimate the quantitative relationship between children’s performance on IQ tests and selected measures of blood lead concentration among children followed prospectively, from infancy through 5–10 years of age in seven prospective cohort studies. We also sought to test whether the lead-associated IQ deficit was greater for a given change in exposure among children who had maximal blood lead levels < 10 μg/dL compared with children who had higher blood lead concentrations.
Materials and Methods
We contacted investigators for all eight prospective lead cohorts that were initiated before 1995, and we were able to retrieve data sets and collaboration from seven. The participating sites were Boston (Bellinger et al. 1992); Cincinnati (Dietrich et al. 1993) and Cleveland, Ohio (Ernhart et al. 1989); Mexico City, Mexico (Schnaas et al. 2000); Port Pirie, Australia (Baghurst et al. 1992); Rochester, New York (Canfield et al. 2003); and Yugoslavia (Wasserman et al. 1997). The Sydney, Australia, study was not included because we were unable to contact the investigators (Cooney et al. 1989). The data for the Rochester Longitudinal Study and for Mexico City, collected when the children were about 6 years of age, have not been published elsewhere. The eligibility criteria and methods for each of the cohorts are described elsewhere (Baghurst et al. 1992; Bellinger et al. 1992; Canfield et al. 2003; Dietrich et al. 1993; Ernhart et al. 1989; Schnaas et al. 2000; Wasserman et al. 1997). All studies were approved by their respective institutional review boards.
Outcome measures.
The primary outcome measure was the full-scale IQ, which is a composite score of verbal and performance tests. The children were administered a version of the Wechsler Intelligence Scales for Children [Wechsler Intelligence Scale for Children–Revised (WISC-R; Wechsler 1974), Wechsler Intelligence Scale for Children–III (WISC-III; Wechsler 1991), Wechsler Preschool and Primary Scales of Intelligence (WPPSI; 1967), and Wechsler Intelligence Scale for Children–Spanish Version (WISC-S; Wechsler 1981)] under uniform conditions within each study. The IQ test was administered when the children were between 4 years 10 months and 7 years of age for all but one cohort. In the Boston cohort, we used blood lead tests taken at 5 years of age and the nearest available full-scale IQ score, which was done at 10 years of age.
Venous or fingerstick capillary blood samples were obtained using standard protocols. Cord blood lead was collected in a subsample of the subjects. During each child’s examination, demographic and health information were obtained from the parent (usually the biologic mother). IQ tests were administered to the mother. We also obtained data on other factors that might confound the relation of lead exposure and IQ, including child’s sex, birth order, birth weight, maternal education, maternal age, marital status, prenatal alcohol exposure, prenatal tobacco exposure, and the Home Observation for Measurement of the Environment (HOME) Inventory score. The HOME Inventory is an index that reflects the quality and quantity of emotional and cognitive stimulation in the home environment (Caldwell and Bradley 1984).
Measures of exposure.
We examined four measures of blood lead: concurrent blood lead (defined as the blood lead measured closest to the IQ test), maximum blood lead level (defined as the peak blood lead measured at any time before IQ test), average lifetime blood lead (defined as the mean blood lead from 6 months to concurrent blood lead tests), and early childhood blood lead (defined as the mean blood lead from 6 to 24 months). The blood sampling intervals varied across studies. To enhance comparability across studies, we used the following blood sampling intervals (based on children’s age): 6, 12 (or 15), 36, 48, and 60 months. We used mean blood lead rather than area under the curve (AUC) to maintain the same units of analysis for all four lead indices. The AUC and mean provided essentially the same information about children’s lead exposure (r = 0.97).
Statistical methods.
To estimate the quantitative relationship between children’s performance on IQ tests and selected measures of blood lead concentration, we examined the potential confounding effects of other factors associated with IQ scores using multiple regression analysis. Ten factors were available from individual sites: HOME Inventory, child’s sex, birth weight, birth order, maternal education, maternal IQ, maternal age, marital status, prenatal smoking status, and prenatal alcohol use.
The development of the regression model involved a multistep process beginning with a simple unadjusted model relating each blood lead measure to IQ while controlling for site. The first step was to test whether the linear model of the relationship between blood lead and IQ, applied in most of the individual cohort analyses, provided a good fit over the wider range of blood lead levels represented in the pooled data. First, a linear model adjusted for the seven sites was estimated, and then quadratic and cubic terms for blood lead were added to test for linearity. A restricted cubic spline function was fit to the data to produce a curve that followed the data in the absence of any assumptions about the functional form of the relationship.
After an initial model was chosen, we examined each of the 10 available confounders individually and in combination with the other covariates to assess potential confounding of the IQ–blood lead relationship. Careful attention was paid to the stability of the parameter estimates as each additional term was added. This process was halted when either no more significant terms (p < 0.10) entered the model or the inclusion of additional terms caused no substantial change (i.e., > 10%) in the blood lead coefficient.
In all models, we tested the interaction of blood lead and site to determine whether a summary measure of the IQ–blood lead relationship could be used for all cohorts. After an initial model was selected, the tests of linearity and the restricted cubic spline models were recomputed to ensure that our initial model was still appropriate after adjustment for covariates (Harrell 2001). We also produced separate linear models for each of the seven cohorts adjusted for the covariates selected in the combined model.
After the multiple regression models were developed, regression diagnostics were employed to ascertain whether the lead coefficient was affected by collinearity or influential observations (Belsley et al. 1980). After regression diagnostics were examined and homogeneity of the blood lead coefficients across sites was evaluated, the fit of all four measures of blood lead was compared using the magnitude of the model R2. The blood lead measure with the largest R
2 (adjusted for the same covariates) was selected a priori as the preferred blood lead index relating blood lead to IQ.
Several approaches were investigated to evaluate the stability of the final model. Although the seven cohorts were not randomly sampled from a larger population of studies, an assumption could be made that they were representative of a larger population of children. Accordingly, we evaluated the results of applying a random-effects model (with sites random) rather than a fixed-effects model (Littell et al. 1996). We also examined the effect of any one site on the overall model by calculating the blood lead coefficient in seven identical models, each omitting one of the seven cohorts (Efron and Tibshirani 1993).
After the final model was selected using the full-scale IQ as the outcome variable, we fit similar models for verbal and performance IQ scores. We also examined interactions of covariates with blood lead concentration (effect modification) and tested the effect of including race as a confounder in the U.S. cohort studies. Finally, we examined the relationship of prenatal lead exposure (cord blood) and IQ score in the subsample for which cord blood lead tests were available.
Results
Of the 1,581 eligible children from the seven cohorts, data on all 10 covariates were available for 1,308 (83%) children; 1,333 (84%) children had data on the four major covariates that were selected for the final model (Table 1). Blood lead levels were highest in Yugoslavia and lowest in Rochester and Boston for all lead exposure indices (Table 2). The median peak or maximal blood lead concentration was 18 μg/dL; the mean age when children’s blood lead levels peaked was 2.5 years. By 5–7 years of age, the median blood level had declined to 9.7 μg/dL (concurrent blood lead concentration). The lifetime average blood lead concentration was 12.4 μg/dL; 244 (18%) children had a maximal blood lead concentration < 10 μg/dL, and 103 (8%) had a maximal blood lead concentration < 7.5 μg/dL.
The mean IQ of all children was approximately 93. Child IQ was highest in the Boston cohort and lowest in the Yugoslavia cohort (Table 2). In univariate regression analyses, children’s IQ was significantly related to site, maternal IQ, the HOME score, maternal education, marital status, birth weight, maternal age, birth order, race (for U.S. cohorts only), and prenatal tobacco exposure. In contrast, child’s sex and prenatal alcohol consumption were not significantly associated with a deficit in IQ score (Table 3).
We examined the relationship of the four blood lead indices with IQ (Table 4). Although all four blood lead measures were highly correlated (r range = 0.74–0.96), the concurrent blood lead variable exhibited the strongest relationship with IQ, as measured by R
2. Although the means differed for the different blood lead indices, the results of the regression analyses were very similar. In all subsequent analyses and figures, the concurrent blood lead measure was used as the primary lead exposure index.
The shape of the exposure–response relationship was determined to be nonlinear insofar as the quadratic and cubic terms for concurrent blood lead were statistically significant (p < 0.001 and p = and 0.003, respectively). Because the restrictive cubic spline indicated that a log-linear model provided a good fit to the data, we used the log of concurrent blood lead in all subsequent analyses of the pooled data (Figure 1).
The multivariable analysis resulted in a six-term model: log of concurrent blood lead, site, maternal IQ, HOME Inventory, birth weight, and maternal education, which we consider our preferred model (Table 4). Linear models of concurrent blood lead and IQ are shown for each of the seven cohorts, adjusted for the same covariates (Figure 2). The additional six terms we considered (child’s sex, birth order, maternal age, marital status, prenatal smoking status, and prenatal alcohol use) contributed very little to the overall fit of the model, and their inclusion in the model resulted in virtually no change to the coefficient for blood lead (i.e., < 5%). None of the six terms was statistically significant (data not shown).
The shape of the log-linear model and the spline function indicated that the steepest declines in IQ were at blood lead levels < 10 μg/dL (Figures 3 and 4). The log-linear model estimated a decrement of 6.9 IQ points [95% confidence interval (CI), 4.2–9.4] for an increase in concurrent blood lead levels from 2.4 to 30 μg/dL, representing the 5th to the 95th percentile for blood lead values in the data set (Table 4). But the lead-associated decrement was greatest in the lower ranges of blood lead. The estimated IQ decrements associated with an increase in blood lead from 2.4 to 10 μg/dL, 10 to 20 μg/dL, and 20 to 30 μg/dL were 3.9 (95% CI, 2.4–5.3), 1.9 (95% CI, 1.2–2.6), and 1.1 (95% CI, 0.7–1.5), respectively (Table 4).
To investigate further whether the lead-associated decrement was greater at lower blood lead concentrations, we divided the data at two cut-points a priori (i.e., maximal blood lead above and below 10 μg/dL, and maximal blood lead above and below 7.5 μg/dL) (Figure 4). We then fit separate linear models to the data in each of these ranges and compared the blood lead coefficients for the concurrent blood lead index. The coefficient for the 103 children with maximal blood lead levels < 7.5 μg/dL was significantly greater than the coefficient for the 1,230 children with a maximal blood lead ≥7.5 μg/dL [linear β= −2.94 (95% CI, −5.16 to −0.71) vs. −0.16 (95% CI, −2.4 to −0.08); p = 0.015]. The coefficient for the 244 children who had a maximal blood lead < 10 μg/dL was not significantly greater than the coefficient for the 1,089 children who had a maximal blood lead ≥10 μg/dL [linear β= −0.80 (95% CI, −1.74 to −0.14) vs. β= −0.13 (95% CI, −2.3 to −0.03); p = 0.103].
To assess the model stability, we employed a random-effects model with sites assumed to be randomly selected from a larger set of populations. Results were similar to the preferred fixed-effects model, with the random-effects model producing a blood lead coefficient that was 3.7% lower (−2.6 vs. −2.7). As an additional measure of model stability, we fit seven identical log-linear models with each model omitting data from one of the sites. The range of coefficients leaving one site out at a time was −2.36 (Rochester) to −2.94 (Yugoslavia), or a percent change ranging from −2.6 to +8.9%. These analyses provide evidence of the stability of our final preferred fixed-effects model and indicate that the results of the pooled analysis did not depend on the data from any single study.
We also examined the relation of blood lead concentration to verbal and performance IQ scores, adjusting for the same covariates used in the full-scale IQ model. The coefficient for the log of blood lead related to performance IQ was similar to the coefficient for log of blood lead in the full-scale IQ model (β= −2.73 vs. −2.70), whereas the coefficient for log of blood lead related to verbal IQ was somewhat lower than the coefficient for the log of blood lead in the full-scale IQ model (β= −2.07 vs. −2.70). The difference between the coefficient for verbal and performance IQ was not statistically significant (p = 0.196).
We did not identify any significant interactions between the covariates and the log of concurrent blood lead. In the U.S. sites, race was not significantly associated with IQ after inclusion of the four covariates in the preferred model, nor did it alter the estimated relationship of blood lead concentration and IQ. In unadjusted analyses involving the 696 children who had cord blood lead levels, the log of cord blood lead concentration was significantly associated with child’s IQ (β= −1.69, SE = 0.60; p = 0.005). After adjusting for the log of concurrent blood concentration, the log of cord blood lead was no longer associated with children’s IQ scores (p = 0.21). In contrast, the log of concurrent blood lead was significantly associated with children’s IQ scores even with log cord blood lead concentration in the model (β= −1.73, SE = 0.74; p = 0.019). Finally, we identified and removed 65 potentially influential observations from the data and refit the model. The change in the coefficient for log of blood lead was 1.4%, from −2.70 to −2.74.
Discussion
Before 1970, undue lead exposure was defined by a blood lead level of 60 μg/dL or higher—a level often associated with overt signs or symptoms of lead toxicity, such as abdominal colic, anemia, encephalopathy, and death. Since then, the blood lead concentration for defining undue lead exposure has been reduced: from 60 to 40 μg/dL in 1971, to 30 μg/dL in 1978, and to 25 μg/dL in 1985 (CDC 1991). In 1991, the CDC, and subsequently the WHO (1995), further reduced the blood lead value defining undue lead exposure to 10 μg/dL (CDC 1991). These ongoing reductions in the acceptable levels of children’s blood lead were motivated by evidence showing that blood lead concentrations as low as 10 μg/dL were associated with adverse effects, such as lower intelligence (CDC 1991; WHO 1995).
In this pooled analysis, we found evidence of lead-related intellectual deficits among children who had maximal blood lead levels < 7.5 μg/dL. Indeed, we found no evidence of a threshold. Other studies reported a similar finding, but questions about the relationship at lower levels remained because they involved smaller numbers of children with blood lead < 10 μg/dL or they did not adjust for important covariates (Canfield et al. 2003; Fulton et al. 1987; Lanphear et al. 2000; Schwartz 1994; Schwartz and Otto 1991; Walkowiak et al. 1998). In the pooled analysis, we estimated the blood lead–IQ relationship with data from the 5th to 95th percentile of the concurrent blood lead level at the time of IQ testing, which tends to underestimate the adverse effects of blood lead levels. For the entire pooled data set, the observed decline of 6.2 IQ points (95% CI, 3.8–8.6) for an increase in blood lead levels from < 1 to 10 μg/dL was comparable with the 7.4 IQ decrement for an increase in lifetime mean blood lead levels from < 1 to 10 μg/dL observed in the Rochester Longitudinal Study (Canfield et al. 2003).
Consistent with other studies (Bellinger and Needleman 2003; Canfield et al. 2003; Fulton et al. 1987; Lanphear et al. 2000; Schwartz 1994; Schwartz and Otto 1991; Walkowiak et al. 1998), the lead-associated IQ deficits observed in this pooled analysis were significantly greater at lower blood lead concentrations. In a meta-analysis, the observed decrement was greater in study cohorts in which children with blood lead levels < 15 μg/dL were more heavily represented (Schwartz 1994). In the Rochester Longitudinal Study, there was an estimated reduction of 7.4 IQ points for an increase in lifetime mean blood lead from 1 to 10 μg/dL (Canfield et al. 2003). In contrast, IQ scores declined 2.5 points for an increase in blood lead concentration from 10 to 30 μg/dL (Canfield et al. 2003). The larger sample size of this pooled analysis permitted us to show that the lead-associated intellectual decrement was significantly greater for children with a maximal blood lead of < 7.5 μg/dL than for those who had a maximal blood lead of ≥7.5 μg/dL. Although the difference in coefficients associated with the IQ decrement for children who had a maximal blood lead concentration < 10 μg/dL versus ≥10 μg/dL was not statistically significant, the results were consistent with the analysis using 7.5 μg/dL as a cut-point.
We found that concurrent blood lead levels or average lifetime estimates of lead exposure were generally stronger predictors of lead-associated intellectual deficits than was maximal measured (peak) or early childhood blood lead concentration. Although this finding conflicts with the widely held belief that 2-year (or peak) blood lead levels are the most salient measure of lead toxicity, there is increasing evidence that lifetime mean blood lead and concurrent blood lead levels are stronger predictors of IQ in older children (Baghurst et al. 1992; Canfield et al. 2003; Dietrich et al. 1993; Factor-Litvak et al. 1999). The stronger effects of concurrent and lifetime measures of lead exposure may be due to chronicity of exposure (Bellinger and Dietrich 1994). Alternatively, the weaker association with blood lead measured during early childhood may be due to exposure misclassification from the greater within-child variability of blood lead in younger children. Nevertheless, because blood lead concentrations taken in early childhood track closely with subsequent blood lead levels (Baghurst et al. 1992; Canfield et al. 2003; Dietrich et al. 1993), we cannot entirely resolve the question of whether children are more vulnerable to lead exposure during the first 2 years of life. Still, young children do ingest more lead during the first 2 years of life and may absorb it more efficiently than do older children and adults (Clark et al. 1985; Lanphear et al. 2002; Ziegler et al. 1978). Thus, efforts to prevent lead exposure must occur before pregnancy or a child’s birth.
The specific mechanisms for lead-induced intellectual deficits have not been fully elucidated. There are several plausible mechanisms for the greater lead-associated intellectual deficits observed at blood lead levels < 10 μg/dL (Lidsky and Schneider 2003; Markovac and Goldstein 1988; Schneider et al. 2003), but it is not yet possible to link any particular mechanism with the deficits observed in this pooled analysis. Nevertheless, efforts can be taken to reduce environmental lead exposure without full elucidation of the underlying mechanism (Wynder 1994).
The observational design of this study limits our ability to draw causal inferences. Instead, we must rely on the consistency of findings from numerous epidemiologic and experimental studies in rodents and nonhuman primates, including evidence that environmental lead exposure is associated with intellectual deficits at blood lead levels < 10 μg/dL. There are potential limitations of the tools we used to measure important covariates. The HOME Inventory was not conducted at the same age for children in all of the sites, and the HOME Inventory and IQ tests have not been validated in all cultural or ethnic communities. Nonetheless, because these covariates were standardized and adjusted for study site, these problems do not pose any limitations to the interpretation of the pooled analysis results. There are other predictors of neurodevelopmental outcomes that we did not examine in this pooled analysis, such as maternal depression. The omission of unmeasured variables may produce residual confounding (Pocock et al. 1994). Still, in studies that did examine other relevant covariates, such as breast-feeding and iron status, the estimated effect of lead was not altered appreciably (Canfield et al. 2003; Needleman et al. 1990; Tong and Lu 2000). Finally, each of the cohorts has unique limitations that raise questions about the validity and generalizability of their findings. Nevertheless, the results of these analyses indicate that the results are robust and not dependent on the data from any one site.
The impact of low-level environmental lead exposure on the health of the public is substantial. This pooled analysis focused on intellectual deficits, but environmental lead exposure has been linked with an increased risk for numerous conditions and diseases that are prevalent in industrialized society, such as reading problems, school failure, delinquent behavior, hearing loss, tooth decay, spontaneous abortions, renal disease, and cardiovascular disease (Borja-Aburto et al. 1999; Dietrich et al. 2001; Factor-Litvak et al. 1999; Lin et al. 2003; Moss et al. 1999; Nash et al. 2003; Needleman et al. 2002; Schwartz and Otto 1991). Although only a few studies have examined the association of these conditions or diseases among individuals with blood lead levels < 10 μg/dL (Borja-Aburto et al. 1999; Lanphear et al. 2000; Moss et al. 1999; Schwartz and Otto 1991), the evidence is growing.
In conclusion, the results of this pooled analysis underscore the increasing importance of primary prevention as the consequences of lower blood lead concentrations are recognized. Although blood lead concentrations < 10 μg/dL in children are often considered “normal,” contemporary blood lead levels in children are considerably higher than those found in pre-industrial humans (Patterson et al. 1991). Moreover, existing data indicate that there is no evidence of a threshold for the adverse consequences of lead exposure. Collectively, these data provide sufficient evidence to eliminate childhood lead exposure by banning all nonessential uses of lead and further reducing the allowable levels of lead in air emissions, house dust, soil, water, and consumer products (Lanphear 1998; Rosen and Mushak 2001).
Figure 1 Restricted cubic splines and log-linear model for concurrent blood lead concentration. The dotted lines are the 95% CIs for the restricted cubic splines.
Figure 2 Linear models for each cohort study in the pooled analysis, adjusted for maternal IQ, HOME score, maternal education, and birth weight. The figure represents the 5th to 95th percentile of the concurrent blood lead level at the time of IQ testing.
Figure 3 Log-linear model (95% CIs shaded) for concurrent blood lead concentration, adjusted for HOME score, maternal education, maternal IQ, and birth weight. The mean IQ (95% CI) for the intervals < 5 μg/dL, 5–10 μg/dL, 10–15 μg/dL, 15–20 μg/dL, and > 20 μg/dL are shown.
Figure 4 Log-linear model for concurrent blood lead concentration along with linear models for concurrent blood lead levels among children with peak blood lead levels above and below 10 μg/dL.
Table 1 Characteristics of the children and of their mothers in the pooled analysis (n = 1,333).
Characteristic Value
Child characteristics
Femalea 669 (50.2)
Birth weightb (g) 3,286 ± 503
Gestation at deliveryb (weeks) 39.6 ± 1.9
Birth orderc 2.0 (1–5)
Blood lead concentrationc
Concurrent 9.7 (2.5–33.2)
Peak 18.0 (6.2–47.0)
Early childhood 12.7 (4.0–34.5)
Lifetime average 12.4 (4.1–34.8)
Peak blood lead concentration < 10 μg/dLa 244 (18.3)
Peak blood lead concentration < 7.5 μg/dLa 103 (7.7)
IQb 93.2 ± 19.2
Age at IQ testingb (years) 6.9 ± 1.2
Maternal characteristics
Age at deliveryb (years) 25.4 ± 5.4
Maternal IQb 88.2 ± 18.5
Education at deliveryb (grade) 11.1 ± 2.8
HOME scoreb 37.0 ± 8.4
Marrieda 896 (67.3)
Smoked during pregnancya 453 (34.1)
Alcohol use during pregnancya 278 (21.2)
HOME score was standardized to preschool test. Early childhood blood lead concentration was defined as the mean of 6- to 24-month blood lead tests. Lifetime average blood lead concentration was defined as the mean of blood lead tests taken from 6 months through the concurrent blood lead test.
a No. (%).
b Mean ± SD.
c Median (5th–95th percentiles).
Table 2 Characteristics of 1,333 children and their mothers in seven cohort studies of environmental lead exposure and IQ.
Characteristic Boston (n = 116) Cincinnati (n = 221) Cleveland (n = 160) Mexico (n = 99) Port Pirie (n = 324) Rochester (n = 182) Yugoslavia (n = 231)
Percent femalea 60 (51.7) 108 (48.9) 73 (45.6) 50 (50.5) 174 (53.7) 89 (48.9) 115 (49.8)
Birth weightb (g) 3,412 ± 510 3,144 ± 457 3,199 ± 498 3,254 ± 432 3,393 ± 502 3,226 ± 506 3,328 ± 526
Gestation at deliveryb (weeks) 40.0 ± 1.8 39.6 ± 1.7 39.6 ± 1.2 40.2 ± 1.1 39.9 ± 1.7 39.1 ± 1.8 39.3 ± 2.9
Birth orderb 1.6 ± 1.0 2.6 ± 1.4 2.2 ± 1.1 1.8 ± 0.9 2.0 ± 1.1 2.4 ± 1.4 2.6 ± 1.7
IQ test WISC-R WISC-R WPPSI WISC-S WISC-R WPPSI WISC-III
IQ scoreb 116.0 ± 14.2 87.0 ± 11.4 86.7 ± 16.2 107.8 ± 11.0 106.0 ± 13.7 84.9 ± 14.4 74.2 ± 13.3
Age at IQ testing (years) 10 7 4.8 7 7 6 7
Blood lead concentrationsc
Concurrent blood lead 5.4 (0.8–12.7) 7.5 (3.5–20.0) 14.2 (7.0–28.5) 7.0 (3.0–16.5) 13.0 (6.0–24.0) 4.0 (1.5–12.0) 15.9 (4.7–47.8)
Peak blood lead 12.0 (5.4–27.0) 17.9 (9.0–38.0) 18.0 (9.0–34.0) 15.0 (6.0–40.0) 27.0 (15.0–46.0) 9.0 (3.5–23.3) 23.8 (7.6–61.5)
Early childhood 8.1 (3.3–18.0) 12.0 (6.6–26.6) 13.4 (7.9–24.8) 11.4 (4.3–26.8) 20.5 (11.0–33.3) 5.8 (2.4–13.1) 14.1 (4.3–44.0)
Lifetime mean 7.6 (3.6–15.2) 11.7 (5.8–24.9) 14.5 (8.1–25.3) 10.6 (4.5–21.3) 18.6 (10.8–30.2) 5.5 (2.4–12.8) 15.8 (5.6–49.3)
Peak blood lead < 10 μg/dLa 41 (35.3) 23 (10.4) 11 (6.9) 20 (20.2) 0 (0.0) 103 (56.6) 46 (19.9)
Peak blood lead < 7.5 μg/dLa 13 (11.2) 1 (0.4) 1 (0.6) 8 (8.1) 0 (0.0) 69 (37.9) 11 (4.8)
Maternal characteristics
Age at delivery (years)b 30.5 ± 4.2 22.7 ± 4.3 22.2 ± 3.8 27.1 ± 5.9 26.0 ± 4.2 24.8 ± 6.6 26.6 ± 5.1
Race (nonwhite)a 5 (4.3) 197 (89.1) 69 (43.1) NA NA 130 (71.4) NA
Maternal IQb 124.2 ± 16.2 75.2 ± 9.4 73.4 ± 13.2 93.4 ± 11.9 94.4 ± 11.0 81.1 ± 12.6 87.3 ± 14.8
Education at delivery (grade)b 15.2 ± 2.0 11.2 ± 1.4 10.6 ± 1.6 11.4 ± 3.5 10.6 ± 1.0 12.2 ± 2.0 8.8 ± 3.9
HOME scoreb 50.5 ± 3.5 32.7 ± 6.2 38.1 ± 6.7 36.8 ± 6.7 42.3 ± 4.6 31.9 ± 6.3 30.4 ± 6.8
Marrieda 107 (92.2) 30 (13.6) 82 (51.2) 88 (88.9) 298 (92.0) 60 (33.2) 231 (100)
Tobacco use during pregnancya 29 (25.0) 111 (50.2) 128 (80.0) 6 (6.1) 79 (24.6) 41 (22.6) 59 (25.5)
Alcohol use during pregnancya 61 (52.6) 31 (14.0) 75 (46.9) 6 (6.1) 82 (25.3) 9 (5.5) 14 (6.1)
NA, Not applicable. HOME score was standardized to preschool scale. Concurrent blood lead tests taken at 5 years of age were used as the concurrent blood lead test for the Boston cohort, and the IQ test was done at 10 years. Test scores of children in the Yugoslavia cohort are low because of adjustments in adapting tests where no standardization existed; rather than deriving appropriate analogues, some culturally driven items were removed, resulting in lower scores.
a No. (%).
b Mean ± SD.
c Geometric mean (5th–95th percentiles).
Table 3 Concurrent blood lead concentration and mean IQ scores by characteristics of children and their mothers (n = 1,333).
Covariate No. Median concurrent blood lead (μg/dL) (5th–95th percentiles) IQ ± SD
Child
Female 669 9.0 (2.4–31.4) 93.8 ± 18.3
Male 664 9.9 (2.6–35.7) 92.5 ± 20.0
Birth weight (g)
< 3,000 359 10.0 (2.2–28.7) 88.6 ± 18.0
3,000 to < 3,500 519 9.9 (2.4–34.2) 93.6 ± 19.3
≥3,500 455 9.1 (2.8–34.7) 96.3 ± 19.3
Gestation at delivery (weeks)
< 38 144 8.9 (3.1–37.9) 83.5 ± 18.6
38 to < 42 1,071 9.8 (2.5–33.2) 94.1 ± 18.6
≥42 115 10.0 (3.2–24.8) 96.3 ± 22.1
Birth order
1 479 9.0 (2.1–32.6) 96.7 ± 18.9
2 407 10.0 (2.6–31.4) 93.6 ± 19.2
≥3 446 10.0 (3.0–36.9) 89.0 ± 18.7
Maternal
Race (only U.S. cohorts)
White 278 7.9 (1.3–22.0) 100.6 ± 20.1
Nonwhite 401 7.1 (2.8–21.5) 84.9 ± 12.8
Age at delivery (years)
< 25 650 10.5 (3.0–32.0) 89.6 ± 17.2
≥25 683 9.0 (2.1–34.7) 96.5 ± 20.3
Maternal IQ
< 85 618 10.0 (2.9–32.0) 83.3 ± 15.0
≥85 715 9.0 (2.1–34.3) 101.6 ± 18.3
Education at delivery (grade)
< 12 710 12.0 (4.1–35.5) 90.4 ± 18.8
12 397 8.7 (2.4–34.3) 91.1 ± 17.7
≥12 226 5.5 (1.1–15.2) 105.5 ± 18.0
HOME score
< 30 276 9.4 (3.0–43.0) 77.9 ± 14.9
30 to < 40 561 10.0 (2.8–32.2) 88.3 ± 15.4
≥40 496 9.5 (2.0–22.0) 107.0 ± 15.8
Married
Yes 896 10.0 (2.7–37.5) 96.2 ± 20.5
No 436 8.1 (2.4–22.0) 87.0 ± 14.3
Prenatal smoking
Yes 453 11.5 (3.2–33.2) 89.5 ± 17.2
No 876 8.7 (2.2–33.6) 94.9 ± 19.9
Prenatal alcohol ingestion
Yes 278 10.1 (2.2–25.0) 99.3 ± 19.4
No 1,035 9.5 (2.7–34.3) 91.7 ± 18.8
Table 4 Mean unadjusted and adjusteda changes in full-scale IQ score associated with an increase in blood lead concentration (log scale), from the 5th to 95th percentile of the concurrent blood lead level at the time of IQ testing.
Blood lead variable Unadjusted estimates [β(95% CI)] Adjusted estimates [β(95% CI)] Blood lead concentration (5th to 95th percentile, μg/dL) IQ deficits [5th to 95th percentile (95% CI)]
Early childhood −3.57 (−4.86 to −2.28) −2.04 (−3.27 to −0.81) 4.1–34.8 4.4 (1.7–7.0)
Peak −4.85 (−5.16 to −3.54) −2.85 (−4.10 to −1.60) 4.0–34.5 6.1 (3.4–8.8)
Lifetime average −5.36 (−6.69 to −4.03) −3.04 (−4.33 to −1.75) 6.1–47.0 6.2 (3.6–8.8)
Concurrent −4.66 (−5.72 to −3.60) −2.70 (−3.74 to −1.66) 2.4–33.1 7.1 (4.4–9.8)
a Adjusted for site, HOME score, birth weight, maternal IQ, and maternal education. The addition of child’s sex, tobacco exposure during pregnancy, alcohol use during pregnancy, maternal age at delivery, marital status, and birth order did not alter the estimate, and these were not included in the model. The estimates for the covariates in the concurrent blood lead model were HOME score (β= 4.23, SE = 0.54), birth weight/100 g (β= 1.53, SE = 0.35), maternal IQ (β= 4.77, SE = 0.57), and maternal education (β= 1.12, SE = 0.46).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7453ehp0113-00090016002380ResearchChildren's HealthBlood Lead Level and Risk of Asthma Joseph Christine L.M. 1Havstad Suzanne 1Ownby Dennis R. 2Peterson Edward L. 1Maliarik Mary 1McCabe Michael J. Jr.3Barone Charles 1Johnson Christine Cole 11Department of Biostatistics and Research Epidemiology, Henry Ford Health System, Detroit, Michigan, USA2Allergy–Immunology Section, Medical College of Georgia, Augusta, Georgia, USA3Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, New York, USAAddress correspondence to C.L.M. Joseph, Henry Ford Health System, Department of Biostatistics and Research Epidemiology, 1 Ford Pl., Suite 3E, Detroit, MI 48202 USA. Telephone: (313) 874-6366. Fax: (313) 874-6730. E-mail:
[email protected] acknowledge the valuable contributions of R. Rasmusson, K. Wells, and J. Zajechowski in the preparation of the manuscript.
This research was funded by the National Heart, Lung, and Blood Institute (R03 HL67462).
The authors declare they have no competing financial interests.
7 2005 3 3 2005 113 7 900 904 27 7 2004 3 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Asthma and lead poisoning are prevalent among urban children in the United States. Lead exposure may be associated with excessive production of immunoglobulin E, possibly increasing asthma risk and contributing to racial disparities. The objective of this study was to examine racial differences in the association of blood lead level (BLL) to risk of developing asthma. We established and followed a cohort prospectively to determine asthma onset, using patient encounters and drug claims obtained from hospital databases. Participants were managed care enrollees with BLL measured and documented at 1–3 years of age. We used multiple variable analysis techniques to determine the relationship of BLL to period prevalent and incident asthma. Of the 4,634 children screened for lead from 1995 through 1998, 69.5% were African American, 50.5% were male, and mean age was 1.2 years. Among African Americans, BLL ≥5 and BLL ≥10 μg/dL were not associated with asthma. The association of BLL ≥5 μg/dL with asthma among Caucasians was slightly elevated, but not significant [adjusted hazard ratio (adjHR) = 1.4; 95% confidence interval (CI), 0.7–2.9; p = 0.40]. Despite the small number of Caucasians with high BLL, the adjHR increased to 2.7 (95% CI, 0.9–8.1; p = 0.09) when more stringent criteria for asthma were used. When compared with Caucasians with BLL < 5 μg/dL, African Americans were at a significantly increased risk of asthma regardless of BLL (adjHR = 1.4–3.0). We conclude that an effect of BLL on risk of asthma for African Americans was not observed. These results demonstrate the need for further exploration of the complex interrelationships between race, asthma phenotype, genetic susceptibilities, and socioenvironmental exposures, including lead.
asthmaatopyenvironmentimmunoglobulin Eincidencelead poisoningracial disparity
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Strategies for the prevention of asthma remain elusive [Centers for Disease Control and Prevention (CDC) 2002; Hartert and Peebles 2000]. In the United States, asthma morbidity is highest among minorities and persons of low socioeconomic status (SES) (Grant et al. 2000; Miller 2000). African-American and Hispanic children in the United States have emergency department (ED) and hospitalization rates for asthma that are two to four times higher than that observed in Caucasian children, and African-American asthma mortality rates can be four times higher (CDC 2002; Grant et al. 2000).
Lead poisoning is a serious environmental health hazard for U.S. children of minority status and low SES (CDC 2001). The effects of lead poisoning include delayed cognitive development, permanent learning disabilities, and behavior problems (Lanphear et al. 1998, 2002; Needleman 1998). According to national surveys, African-American children were found to have blood lead levels (BLL) four times higher than those of Caucasians after controlling for income and urban status, and were seven times more likely than Caucasians to require medical evaluation for lead poisoning (CDC 2001). Although federal guidelines recommend intervention at BLL ≥10 μg/dL, adverse outcomes have been demonstrated at lower levels (Bernard and McGeehin 2003).
The epidemiology of pediatric asthma and that of lead poisoning are strikingly similar (Hartert and Peebles 2000; Lanphear et al. 1998). Both are prevalent among minority children, and elements in the physical environment increase risk of disease (Hartert and Peebles 2000; Lanphear et al. 1998; Rosenstreich et al. 1997). Low SES and residing in an urban setting are associated with increased risk for both conditions (Bernard and McGeehin 2003; Miller 2000).
Published analyses suggest that lead exposure may result in alterations to immune system components known to be associated with asthma (Lutz et al. 1999; Sun et al. 2003). Lead has been associated with the increased production of total immunoglobulin E (IgE), which is also observed in atopic and nonatopic individuals with asthma (Beeh et al. 2000; Romanet-Manent et al. 2002).
The immunotoxic or immunomodulatory effects of lead have been demonstrated recently in animal models, and include impaired host resistance to infections and an enhancement of alloantigenic-specific T-cell proliferation by altering antigen processing and presentation (Gupta et al. 2002; McCabe et al. 2001). Both Lutz et al. (1999) and Sun et al. (2003) reported an association between lead and increased IgE in studies of young children.
We hypothesize that differential risk of lead poisoning among urban minority children may contribute to increased risk of asthma in this population. The overall goal of this analysis was to use encounter and claims data to examine relationships between BLL and development of asthma, by race.
Materials and Methods
Design.
The methods of this study were approved by the Henry Ford Health System (HFHS) institutional review board. The base study population was enrollees of a large, nonprofit managed care organization (MCO) in southeastern Michigan served by physicians in a staff model medical group. The MCO has a stable population of > 250,000 with a mean enrollment of 9.3 years. The MCO enrollment database and the associated laboratory database (all lead screens are sent to a single central laboratory) were linked to identify children born on or between 1 January 1994 and 31 December 1997, enrolled in the MCO at time of birth, and with laboratory results for a lead screen performed between 1 January 1995 and 31 December 1998 (baseline BLL) [National Institute for Occupational Health and Safety (NIOSH) 1994]. When results existed for more than one lead screen for an individual child, the highest BLL recorded within the study ascertainment period was used as the baseline BLL. Usually this was the first BLL documented. The resulting data set was linked to the patient encounter database to obtain all ambulatory and inpatient visits, as well as demographic information, including child’s date of birth, race, and residential address. The pharmacy claims database provided information on drug claims for asthma medications [Health Employers Data Information System (HEDIS) 1999].
MCO enrollment and disenrollment dates were used to calculate the person-years that each child contributed to the cohort. Geographic information system (GIS; Mapping Solutions, LLC, Lansing, MI), a computer mapping and analysis technology capable of linking geographic with demographic information, was used in conjunction with patient address and census data (U.S. Census Bureau 2000). Each study participant was assigned the average income per person in the block group of residence (a subdivision of a census tract representing a city block) (Croner et al. 1996).
The method for obtaining birth weight for children in this cohort was approved by the State of Michigan Division for Vital Records and Health Statistics (Lansing, MI) in addition to the HFHS institutional review board. Identifiers for members of the study cohort were matched, at the State of Michigan Division for Vital Records and Health Statistics (Lansing, MI), to live birth records. Birth record identifier fields were not supplied to the researchers. Matches outside of the state were censored. The resulting match rate was 97.8%.
Blood samples were obtained by venipuncture, collected in EDTA tubes, and shipped at room temperature to the HFHS chemistry laboratory. Lead was measured in the blood using graphite furnace atomic absorption spectrophotometry, with detection limits of 1 μg/dL.
Asthma definition.
We determined asthma status using enrollee encounter and pharmacy claims databases from HFHS. Two definitions of asthma were used. For definition 1, a child was considered to have asthma if the child had four or more asthma-medication–dispensing events in 12 months or met one or more of the following criteria within a 12-month period: one or more ED visits for asthma, one or more hospitalizations for asthma, or four or more outpatient visits for asthma with at least two asthma-medication–dispensing events. For definition 2, a child was considered to have asthma if the child had four or more asthma-medication–dispensing events in 12 months, and had one or more ED visits for asthma, one or more hospitalizations for asthma, or four or more outpatient visits for asthma with at least two asthma-medication–dispensing events (HEDIS 1999). These definitions are used by HEDIS to define an MCO population of patients with persistent asthma.
Statistical analysis.
We determined period prevalence of asthma at baseline by taking the number of definition 1 or 2 asthma cases occurring from birth to 12 months after the index BLL and dividing it by the total number of children in the cohort at that time. Children who did not meet the criteria for asthma during this period were considered “asthma free” and used in the incident asthma analysis. Children meeting criteria for asthma during the postbase-line follow-up period were considered incident asthma cases. We calculated incidence density (ID) by taking the number of asthma cases that developed during the postbaseline follow-up period and dividing by the total number of person-years contributed to the cohort during this period (Pearce et al. 1998). Data were censored when a patient disenrolled from the MCO. Chi-square tests were used to compare sex, race, and baseline income proportions for children included in the study with those of children excluded from the study because of lack of a recorded BLL. We used a Wilcoxon rank-sum test to compare the distributions of BLL by race.
We assessed racial differences in the number of asthma cases using logistic regression for period prevalent cases and Cox proportional hazards for incident cases. The cutoffs used for BLL in these analyses were ≥5 μg/dL and ≥10 μg/dL. The association of period prevalent asthma to BLL was evaluated by computing odds ratios (ORs) and corresponding 95% confidence intervals (CIs). We evaluated the association of BLL to asthma incident cases by computing the hazard ratio (HR) along with the corresponding 95% CIs. We used a Cox proportional hazard to determine the independent association of BLL to asthma incident cases (Diggle et al. 1994). These models adjusted for average annual income per person, birth weight, and sex. Separate models were run for ≥5 μg/dL and ≥10 μg/dL for each race using < 5 μg/dL as the comparison group. In addition, separate models were run for African Americans and Caucasians at each cut point, using Caucasians with BLL < 5 μg/dL as the comparison group, allowing direct comparison of the risk estimates.
Results
Table 1 shows the characteristics of the study population. Of the 31,526 children born between 1 January 1994 and 31 December 1997 and enrolled in the MCO, 4,634 had lead screening results in the laboratory database and were recorded as being African American or Caucasian. Children with lead screening results differed demographically from children without lead screens in terms of sex (fewer males in study sample; p = 0.02), race (more African Americans in study sample; p < 0.001), and median annual income (lower income for children in study sample; p < 0.001). The latter was observed regardless of race.
The percentages of children with BLL ≥ 5 μg/dL and ≥10 μg/dL were 39.0 and 8.6%, respectively. The overall mean BLL for the entire sample was 4.7 μg/dL (SD = 4.0, median = 4.0 μg/dL). African Americans had a higher mean BLL when compared with Caucasians, 5.5 μg/dL (SD = 4.3, median = 4.0 μg/dL) versus 3.2 μg/dL (SD = 2.5, median = 3.0 μg/dL), respectively, p < 0.01.
The period prevalence of asthma at baseline was 7.5% for definition 1 and 2.4% for definition 2 (Table 1). The period prevalence of definition 1 asthma among African Americans at baseline was 8.9% compared with 4.1% for Caucasians, p < 0.01 (Table 2). The period prevalence of definition 2 asthma among African Americans was 2.6% compared with 1.8% for Caucasians, p = 0.12.
The ID for the entire cohort was 2.4 and 0.9 per 100 person-years for definition 1 and definition 2 asthma, respectively. The ID of definition 1 asthma among African Americans was 3.0/100 person-years compared with 1.2/100 person-years for Caucasians, p < 0.01. The ID of definition 2 asthma among African Americans was 1.1/100 person-years versus 0.4/100 person-years for Caucasians, p = 0.01.
Table 2 shows a univariate analysis of the association of study variables to asthma prevalent and incident cases. African-American race, male sex, birth weight ≤2,500 g, and annual income ≤$10,027 (the median income per person for the study population) were significantly related to prevalent asthma (all p-values < 0.01). When compared with the reference group of BLL < 5 μg/dL, the OR (95% CI) for prevalent asthma was 1.2 (0.9–1.5), p = 0.14, for BLL ≥5 μg/dL, and 1.0 (0.7–1.6), p = 0.87, for BLL ≥10 μg/dL. African-American race, male sex, and birth weight ≤2,500 g were significantly associated with incident asthma (all p-values < 0.01). The HR for BLL ≥5 μg/dL and incident asthma was 1.2 (1.0–1.6), p = 0.09 and for BLL ≥10 μg/dL was 1.0 (0.6–1.6), p = 0.97.
Results of Cox proportional hazards analysis are shown in Table 3. All analyses were adjusted for annual income ≤$10,027, birth weight, and sex. Among Caucasians, the adjusted HR (adjHR) for definition 1 asthma was only slightly elevated for BLL ≥5 μg/dL and was not statistically significant (adjHR = 1.4; 95% CI, 0.7–2.9; p = 0.40). Again, there was no association between BLL ≥10 μg/dL and asthma. The risk estimate for definition 2 asthma was elevated for Caucasians with BLL ≥ 5 μg/dL but did not reach statistical significance (adjHR = 2.7; 95% CI, 0.9–8.1; p = 0.09). Among African Americans, BLL was not associated with developing definition 1 or 2 asthma.
We also conducted the Cox proportional hazards analysis for the association of BLL to incident asthma, using Caucasians with BLL < 5 μg/dL as reference (Table 4). Results were similar to that shown in Table 3, in that the adjHR for developing definition 1 asthma for Caucasians with BLL ≥5 μg/dL was elevated, but not significant, and BLL was not associated to incident asthma among Caucasians with BLL ≥10 μg/dL when compared with the reference group. AdjHRs (95% CIs) for African Americans with BLL < 5 μg/dL and BLL ≥5 μg/dL were 1.6 (1.4–2.0) and 1.4 (1.2–1.6), respectively, when compared with the reference group (both p < 0.01). At BLL ≥10 μg/dL, the adjHR (95% CI) for risk of asthma for African Americans was 2.1 (1.2–3.6), p = 0.01, for definition 1 and 3.0 (1.2–7.1), p = 0.01, for definition 2.
Discussion
Lead poisoning and asthma jeopardize the health and quality of life of urban minority children in the United States (Bernard and McGeehin 2003; Lanphear et al. 2002). We sought to evaluate the contribution of BLL to the increased risk of asthma among African Americans. BLL was less a predictor of asthma than was race and did not affect the relationship of race to prevalent or incident asthma. Because lead poisoning and asthma share risk factors that are heavily influenced by SES, it is difficult to obtain an unbiased estimate of the true relationship (Bernard and McGeehin 2003; Lanphear et al. 1996; Needleman 1998). Previous studies have shown an association between BLL and serum IgE, and because serum IgE is observed in both atopic and nonatopic asthma, it was of interest to determine whether a relationship between BLL and development of asthma could be demonstrated using secondary data sources. To our knowledge, there are no studies that have looked at BLL and the incidence of asthma by race.
We observed an elevated risk of asthma among children exposed to lead, although these associations were not always statistically significant and were observed only for certain subgroups. Three interesting findings can be garnered from this study. First, a trend toward elevated risk estimates for asthma was observed for BLL at a cut point lower than what is currently considered toxic (Bernard and McGeehin 2003; Burns et al. 1999; Needleman and Landrigan 2004). Second, in addition to a trend toward increased risk at lead levels ≥5 μg/dL, the elevated risk was observed consistently only for Caucasians. Although the risk of developing asthma was significantly increased for African Americans when compared with Caucasians with BLL < 5 μg/dL, the risk was not dependent on BLL; that is, African Americans with BLL < 5 μg/dL were also at increased risk of asthma. Third, although our results are inconclusive regarding a dose–response relationship for BLL and asthma, among African Americans BLL ≥10 μg/dL held a higher risk of asthma than did BLL ≥5 μg/dL. Among Caucasians, the adjHR for BLL and incident asthma increased as the asthma definition became more stringent. However, because BLL is an inadequate dosimeter of lead exposure, a dose–response relationship between BLL and asthma may not be observed in our data, if such a relationship exists.
The trend toward an elevated risk of asthma observed among Caucasians with BLL ≥5 μg/dL could be a residual effect of factors unadjusted for in our analysis. If so, these risk estimates may indicate the presence of environmental exposures related to both BLL and asthma. Because the baseline BLL in this study could have been measured as late as 3 years of age, exposure to factors related to asthma may have already occurred. If this is true, BLL ≥5 μg/dL recorded during early infancy could be an indicator that risk factors for asthma are also present in the environment. There is growing evidence that exposures and events occurring during the first year of life are important determinants of the development of atopy and asthma (Holt 1998; Johnson et al. 1996 Johnson et al. 2002; Joseph et al. 2002; Ownby et al. 2002).
The racial differences observed are of interest. It was clear that African Americans were at a significantly higher risk of developing asthma when compared with Caucasians, regardless of BLL. The effect of BLL on the immune system of African-American children may be masked by more influential factors working to increase risk (Holt 1998; Joseph et al. 2000, 2002). Again, BLL may signal the presence of indoor environmental risk factors for asthma that play a greater role in development of the disease for African Americans. Racial differences in factors related to asthma, both environmental and otherwise, have been previously reported. Differential sensitization for indoor and outdoor allergens by race has been documented in at least two studies (Celedon et al. 2004; Joseph et al. 2000). Another possible explanation is the racial difference observed in IgE (Joseph et al. 2000; Oettgen and Geha 1999). In a previous study, we found that total IgE was higher for African Americans when compared with Caucasians among children with and without asthma, and that total IgE in African Americans was not related to bronchial hyperresponsiveness, despite the observed association in Caucasians (Joseph et al. 2000). Perhaps Caucasians are more sensitive to the effect of low levels of lead, whereas the BLLs studied were not high enough to induce an effect in African Americans.
Differences in lead sources may explain variations observed. A study conducted by Lanphear et al. (1998) reported differences in housing conditions and exposures to lead-contaminated house dust that contributed to observed racial differences in BLL. Although lead-contaminated soil was a risk factor for both racial groups, African-American children were more likely to be exposed to indoor environmental sources of lead (e.g., lead-contaminated house dust, painted surfaces, and floors in poor condition), whereas outdoor sources were more likely for Caucasian children (Lanphear et al. 1998).
Genetic variation may explain racial differences in susceptibility to lead poisoning. The C282Y mutation in the HFE gene causing hemochromatosis, and the gene coding for δ-aminolevulinic acid dehydratase, an enzyme of heme synthesis, are both associated with increased lead absorption. The vitamin D receptor gene can lead to increased production of calcium-binding proteins, also resulting in increased lead absorption. These genetic variations have not been shown to explain racial differences in lead toxicity (Lanphear et al. 1996; Onalaja and Claudio 2000; Wright et al. 2004).
The role of environmental lead in the development of atopic asthma is hypothesized to be mediated through IgE. The division of asthma into two clinical variants based on atopy continues to be controversial, but high total IgE is actually characteristic of both groups (Beeh et al. 2000; Romanet-Manent et al. 2002). It has been proposed that lead acts to increase production of IgE through direct or indirect stimulation of B-cells or through the binding and subsequent alteration of allergens that stimulate the allergenic immune response (Annesi-Maesano et al. 2003; Lutz et al. 1999). Several studies report an association between lead and IgE, but we found only one study exploring the relationship between lead and an asthma diagnosis: The study by Bener et. al. (2001), conducted in United Arab Emirates, found that industrial workers had significantly higher BLL (77.5 μg/dL, SD = 42.8) compared with non-industrial workers (19.8 μg/dL, SD = 12.3) and that the former also had a higher prevalence of asthma and respiratory symptoms.
Lead levels below those recognized as unsafe have been shown to inhibit production of interferon-γ , a TH1 immune response, and enhance TH2 responses [e.g., interleukin (IL)-4, IL-5, IL-10, IL-13, and IgE)] (Annesi-Maesano et al. 2003; Miller et al. 1998). Results of a laboratory study by Snyder et al. (2000) found evidence for maternal transfer of lead both transplacentally and lactationally in pregnant BALB/c mice and their offspring. The authors found that mouse neonates exposed to lead transplacentally and/or lactationally had significantly higher plasma IgE levels. Higher IgE levels among individuals exposed to lead have been corroborated in several human studies. Lutz et al. (1999) conducted a study of BLL and IgE in a predominantly Caucasian study population of 279 young children participating in the WIC (Women, Infants, Children) Nutritional Support Program and selected lead prevention programs active in Greene County, Missouri during the study period. In this study, BLL was significantly and positively associated with serum IgE levels. No relationship between cytokines measured in the blood and BLL was observed. Boscolo and colleagues (1999 (2000) examined the role of trace metals, including lead, in expression of lymphocyte subpopulations and cytokine serum levels in asymptomatic, atopic urban men and women. Atopy was defined as “evident clinical history of allergic symptoms.” In men, blood lead (mean BLL = 11 μg/dL) had an immunomodulatory effect on CD4+ and B-lymphocytes that appeared to enhance the production of TH2-like cytokines and IgE (Boscolo et al. 1999). Women 19–49 years of age had slightly higher BLL among those that were atopic (median BLL = 64 μg/dL for atopic vs. 55 μg/dL for nonatopic), but although serum IgE levels were higher in atopic women, TH2-like cytokines and blood lymphocyte subpopulations did not differ significantly by atopic status (Boscolo et al. 2000). The authors suggested that differences in lead metabolism or hormonal secretion by sex may explain the dissimilar results. Sun et al. (2003) conducted a study in a small number of preschool children (n = 73) in the People’s Republic of China. Overall, serum concentrations of IgE were higher in the high BLL group (≥10 μg/dL), but the association was of borderline significance (p = 0.069). When stratifying by sex, Sun et al. found that serum IgE levels were significantly higher only for females in the high BLL group (p = 0.027).
Limitations to this analysis include restricting this study to children enrolled in the MCO with results of lead screens in the laboratory database. This study population was more likely to be African American and had lower annual incomes per person than did those without a recorded BLL in the HFHS laboratory database. It is reasonable that African Americans and persons of low SES would be favored for lead screening in the MCO. According to the CDC and other sources, African-American and poor children in the United States are at a higher risk of lead poisoning when compared with Caucasian and with affluent children (Bernard and McGeehin 2003). The median BLL in this study (4.0 μg/dL) was higher than that reported for U.S. children 1–5 years of age (2.2 μg/dL) and for those with family incomes less than poverty level (2.8 μg/dL), according to national data for 1999–2000, indicating that children at risk are overrepresented in this cohort [U.S. Environmental Protection Agency (EPA) 2004].
Also excluded from this study would be children who received a lead screen using a finger or heel stick. In the laboratory database, lead levels are the result of venipuncture, which is considered more reliable than other methods (e.g., finger or heel stick). This was also a strength in that it permitted assessment of BLLs < 10 μg/dL.
Variables for analysis were limited to those collected in our hospital database. Consequently, there was no information on potential sources of lead. Parasitic infection is more prevalent among low SES groups, as is lead toxicity, potentially confounding a relationship between BLL and asthma, especially if the lead source is outdoors (Hagel et al. 2004). Moreover, we did not have information on other environmental exposures known to be associated with both asthma and BLL (e.g., environmental tobacco smoke, diesel exhaust) or other medical risk factors potentially associated with risk of asthma (e.g., family history of asthma or allergy, breast feeding, diet) (Johnson et al. 1996; Mannino et al. 2003). Using a definition of asthma based on encounters and prescription claims did not allow an investigation of differing asthma phenotypes, such as allergic asthma or transient wheeze (Martinez 2002; Romanet-Manent et al. 2002). Use of these databases, however, did allow a noninvasive exploration of the relationship of BLL and asthma in a population with both African-American and Caucasian representation. Using the MCO patient population may have reduced biases due to disparities in health care access, and having addresses allowed for geocoding that resulted in the ability to adjust for surrogate measures of SES (annual income per person).
We observed a trend toward an elevated risk of developing asthma in Caucasian children with evidence of BLL of ≥5 μg/dL before the age of 3 years. Assessment of the effect of BLL on IgE may provide a better understanding of the etiology and prevention of atopy and asthma. African Americans were at an increased risk of asthma when compared with Caucasians, but if there were any effects related to BLL, they were not observed. The racial differences observed in this study illustrate the need for further exploration of the role of race in the interrelationships between genetic susceptibility, socioenvironmental exposures, and risk of asthma.
Table 1 Characteristics of the study population.
Characteristic No. Value
Age (years) at baseline BLL [mean ± SD (range)] 4,634 1.2 ± 0.5 (0.4–3.0)
Annual household income per person [US$ mean ± SD (range)] 4,450 10,579 ± 5,615 (1,819–47,077)
Sex
Male 2,340 50.5
Female 2,294 49.5
Race
African American 3,220 69.5
Caucasian 1,414 30.5
BLL (μg/dL)
≥5 1,808 39.0
≥10 401 8.6
Period prevalence of asthmaa
Definition 1 346 7.5
Definition 2 109 2.4
Values are percentage except where otherwise noted.
a Asthma cases occurring from birth up to 12 months after the index BLL.
Table 2 Association of study variables to asthma period prevalence and incidence.a
Prevalent asthma
Incident asthmab
Variable Asthma [n (%)] No asthma [n (%)] OR (95% CI) p-Value No. of cases Person-years HR (95% CI) p-Value
African American 288 (8.9) 2,932 (91.1) 2.3 (1.7–3.1) < 0.01 235 7,734 2.5 (1.8–3.4) < 0.01
Caucasian 58 (4.1) 1,356 (95.9) 47 4,053
Male 213 (9.1) 2,127 (90.9) 1.6 (1.3–2.0) < 0.01 166 5,876 1.4 (1.1–1.8) < 0.01
Female 133 (5.8) 2,161 (94.2) 116 5,912
BW ≤2,500 g 63 (13.5) 405 (86.5) 2.1 (1.6–2.8) < 0.01 38 1,054 1.5 (1.1–2.1) < 0.02
BW > 2,500 g 277 (6.9) 3,754 (93.1) 240 10,427
Income ≤$10,132c 206 (9.1) 2,054 (90.9) 1.6 (1.3–2.0) < 0.01 138 5,218 1.1 (0.9–1.4) 0.35
Income > $10,132 128 (5.8) 2,062 (94.2) 139 6,205
BLL < 5 μg/dL 198 (7.0) 2,628 (93.0) 1.0 166 7,639 1.0
BLL ≥5 μg/dL 148 (8.2) 1,660 (91.8) 1.2 (0.9–1.5) 0.14 116 4,148 1.2 (1.0–1.6) 0.09
BLL ≥10 μg/dL 29 (7.2) 372 (92.8) 1.0 (0.7–1.6) 0.87 20 875 1.0 (0.6–1.6) 0.97
BW, birth weight.
a Definition 1 asthma; all persons with definition 2 asthma also fulfilled criteria for definition 1.
b Asthma cases per 100 person-years of enrollment ascertained during follow-up period (12 months postbaseline).
c Median income for the study sample.
Table 3 Results of Cox proportional hazards multivariable analysis of the association of BLL to incident asthma by race.a
Definition/race BLL (μg/dL) No. With asthma [n (%)] AdjHR (95% CI) p-Value
Definition 1 asthma
Caucasian < 5 1,065 37 (3.5) 1.0
≥5 218 10 (4.6) 1.4 (0.7–2.9) 0.40
≥10 27 1 (3.7) 1.1 (0.2–8.4) 0.91
African American < 5 1,472 129 (8.8) 1.0
≥5 1,351 106 (7.9) 1.0 (0.8–1.3) 0.94
≥10 322 19 (5.9) 0.9 (0.5–1.4) 0.58
Definition 2 asthma
Caucasian < 5 1,085 12 (1.1) 1.0
≥5 221 5 (2.2) 2.7 (0.9–8.1) 0.09
≥10 28 0 —
African American < 5 1,580 51 (3.2) 1.0
≥5 1,444 43 (3.0) 1.1 (0.8–1.7) 0.53
≥10 340 9 (2.7) 1.3 (0.6–2.6) 0.54
—, not calculated.
a Models adjusted for average annual income per person, birth weight, and sex. Separate models were created for ≥ 5 μg/dL and ≥10 μg/dL, both using < 5 μg/dL as the comparison group.
Table 4 Results of Cox proportional hazards multivariable analysis of the association of BLL to incident asthma using one race-exposure reference group.a
Definition/race BLL (μg/dL) No. With asthma [n (%)] AdjHR (95% CI) p-Value
Definition 1 asthma
Caucasian < 5 1,065 37 (2.9) 1.0
≥5 218 10 (4.6) 1.4 (0.7–2.9) 0.33
≥10 27 1 (3.7) 1.1 (0.1–7.7) 0.96
African American < 5 1,472 129 (8.8) 1.6 (1.4–2.0) < 0.01
≥5 1,351 106 (7.9) 1.4 (1.2–1.6) < 0.01
≥10 322 19 (5.9) 2.1 (1.2–3.6) 0.01
Definition 2 asthma
Caucasian < 5 1,085 12 (1.1) 1.0
≥5 221 5 (2.2) 2.3 (0.8–6.7) 0.12
≥10 28 0 —
African American < 5 1,580 51 (3.2) 1.8 (1.3–2.4) < 0.01
≥5 1,444 43 (3.0) 1.5 (1.2–1.8) < 0.01
≥10 340 9 (2.7) 3.0 (1.2–7.1) 0.01
—, not calculated.
a Models adjusted for average annual income per person, birth weight, and sex. Data represent five separate models, all using Caucasian with BLL < 5 μg/dL as the comparison group.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7842ehp0113-00090516002381ResearchChildren's HealthUmbilical Cord Mercury Concentration as Biomarker of Prenatal Exposure to Methylmercury Grandjean Philippe 12Budtz-Jørgensen Esben 13Jørgensen Poul J. 4Weihe Pál 151Institute of Public Health, University of Southern Denmark, Odense, Denmark2Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA3Department of Biostatistics, Institute of Public Health, University of Copenhagen, Copenhagen, Denmark4Institute of Clinical Research, Odense University Hospital, Odense, Denmark5Faroese Hospital System, Tórshavn, Faroe IslandsAddress correspondence to P. Grandjean, Institute of Public Health, University of Southern Denmark, Winslowparken 17, 5000 Odense, Denmark. Telephone: 45-6550-3769. Fax: 45-6591-1458. E-mail:
[email protected] gratefully acknowledge the technical support by B. Andersen.
This study was supported by the U.S. National Institute of Environmental Health Sciences (ES09797) and the Danish Medical Research Council. The contents of this article are solely the responsibility of the authors and do not represent the official views of the National Institute of Environmental Health Sciences, National Institutes of Health, or any other funding agency.
The authors declare they have no competing financial interests.
7 2005 31 3 2005 113 7 905 908 10 12 2004 31 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Biomarkers are often applied to assess prenatal exposure to methylmercury in research and surveillance. In a prospective study in the Faroe Islands, the main exposure biomarkers were the mercury concentrations in cord blood and maternal hair obtained at parturition. We have now supplemented these exposure biomarkers with mercury analyses of umbilical cord tissue from 447 births. In particular, when expressed in relation to the dry weight of the tissue, the cord mercury concentration correlated very well with that in cord blood. Structural equation model analysis showed that these two biomarkers have average total imprecision of about 30%, which is much higher than the laboratory error. The imprecision of the dry-weight–based concentration was lower than that of the wet-weight–based parameter, and it was intermediate between those of the cord blood and the hair biomarkers. In agreement with this finding, regression analyses showed that the dry-weight cord mercury concentration was almost as good a predictor of methylmercury-associated neuropsychologic deficits at 7 years of age as was the cord-blood mercury concentration. Cord mercury analysis can therefore be used as a valid measure of prenatal methylmercury exposure, but appropriate adjustment for the imprecision should be considered.
biomarkerexposure assessmentfood contaminationhair analysismercury/analysismethylmercury compounds/analysisorganomercury compounds/bloodpregnancyprenatal exposure delayed effectspreschool childseafoodumbilical cord
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Exposure assessment is a crucial aspect of environmental epidemiology but remains an inexact science, where validity must be optimized within the confines of efficiency and practicality. Dietary questionnaires constitute a crucial instrument in nutritional epidemiology (Marshall 2003), but they are less useful for food contaminants, because their concentrations usually vary much more than do those of essential nutrients. Instead, environmental epidemiology is relying to an increasing extent on measurements of contaminant concentrations in human tissue samples (Grandjean 1995). Such exposure biomarkers are generally thought to constitute valid measures when laboratory error is carefully controlled. Studies incorporating exposure biomarkers therefore rarely take into account the measurement imprecision.
The ideal exposure biomarker should show a clear-cut relationship to the degree of exposure (Grandjean et al. 1994), but the reality is often that up to several imprecise measures may be available, none of them necessarily an accurate indicator of the true exposure. In regard to methylmercury, substantial information is now available on daily intake levels (European Food Safety Authority 2004), and experimental studies in human volunteers have demonstrated how the dietary intakes may be translated into mercury concentrations in blood (Sherlock et al. 1984) or hair (Hislop et al. 1983). However, these two commonly used exposure biomarkers show only scattered associations (Budtz-Jørgensen et al. 2004), suggesting that their total imprecision significantly exceed routine laboratory errors.
In the first etiologic studies of the so-called Minamata disease, researchers took advantage of the local tradition of saving a dried piece of umbilical cord. Using the cord mercury concentration as an exposure bio-marker, much higher levels were found in patients with Minamata disease compared with control groups (Harada 1977). These retrospective exposure assessments were later extended (Akagi et al. 1998; Dalgård et al. 1994). More recently, mercury was analyzed in a selection of umbilical cords collected from a British birth cohort (Daniels et al. 2004). A sample of umbilical cord is easily collected in connection with births, and the validity of determining mercury as an exposure biomarker therefore deserves to be assessed. However, several factors may affect the characteristics of a cord sample. Vessel contractions within the first couple of minutes after birth (Yao and Lind 1974) will determine the blood content of the cord sample. Umbilical cords differ in thickness and overall appearance, largely due to varying amounts of Wharton’s jelly (Scott and Wilkinson 1978), the amount of which decreases with the duration of gestation (Sloper et al. 1979). The cord mercury concentration is therefore usually expressed in terms of dry weight (Akagi et al. 1998; Dalgård et al. 1994).
The most frequently used sample for methylmercury exposure assessment is scalp hair, especially in field studies (Grandjean et al. 2002). Sampling of hair is noninvasive and painless, and it is a feasible and efficient procedure under most field study conditions. Depending on the rate of hair growth, the mercury concentrations along the hair shaft can represent a calendar of past exposures. Yet environmental mercury vapor may bind to the hair (Yamaguchi et al. 1975), whereas hair permanent treatments can remove much of the endogenous mercury from the hair (Yamamoto and Suzuki 1978; Yasutake et al. 2003). Also, hair color or structure may affect the incorporation of mercury into the hair (Grandjean et al. 2002).
The blood concentration is generally considered the appropriate indicator of the absorbed dose and the amount systemically available. This biomarker is also subject to possible variation. Methylmercury binds to hemoglobin, and the high affinity to fetal hemoglobin results in a higher mercury concentration in cord blood than in maternal blood (Sakamoto et al. 2004). Further, whole-blood mercury concentrations are affected by the hematocrit, and some researchers therefore prefer to measure the mercury concentration in erythrocytes (Sakamoto et al. 2004), although this procedure is more cumbersome. Routine analyses for total mercury concentrations also include inorganic mercury, but cord-blood mercury is almost entirely of the methylated form, for which the placenta does not constitute a barrier (Kelman et al. 1982).
In the absence of a gold standard, statistical correlations can be used to ascertain interrelationships between biomarkers. However, all biomarkers are subject to imprecision, and such data will not provide the validation desired. Factor analysis may be used to determine the total imprecision—the combination of laboratory imprecision and preanalytical variation—of each biomarker (Budtz-Jørgensen et al. 2003). The predictive validity of the biomarkers may also be assessed from their associations with known outcome variables (Grandjean et al. 1999). An extended analysis can be carried out using a structural equation model, where confounders and effect variables are included (Budtz-Jørgensen et al. 2002). Our previous experience using this approach has shown that mercury concentrations in cord blood and in maternal hair are subject to substantial variation, the latter to a greater extent than the former (Budtz-Jørgensen et al. 2004).
The present study was carried out to determine the usefulness of the cord mercury concentration as an exposure biomarker in comparison with more commonly used bio-markers of prenatal methylmercury exposure from maternal seafood consumption. We obtained tissue samples for mercury analysis and relevant information in connection with a prospective birth cohort study initiated in the Faroe Islands (Grandjean et al. 1992). The children were examined in regard to possible developmental neurotoxicity effects at 7 years of age (Grandjean et al. 1997), and the exposure biomarkers could therefore also be compared regarding their predictive validity.
Materials and Methods
Cohort formation and sample collection.
A birth cohort of 1,022 subjects was formed from consecutive births between 1 March 1986 and the end of 1987 at the three Faroese hospitals (Grandjean et al. 1992). In connection with each birth, we collected umbilical cord tissue, cord blood, and maternal hair. A questionnaire was administered by the midwife to obtain basic information on the general course of the pregnancy and nutritional habits, including frequencies of dinners based on pilot whale meat or fish, use of alcohol, and tobacco smoking. The study was carried out in accordance with the Helsinki convention and with the approval of the ethical review committee for the Faroe Islands and the institutional review board in the United States.
According to routine obstetric procedures, the cord was clamped 1 min after delivery. Cord blood for mercury analysis was then collected directly from the cord and frozen for later analysis (Grandjean et al. 1992). A 5-cm piece of the cord was cut off with a pair of scissors, stored in a glass vial, and frozen until analysis.
Cord-tissue analysis.
Upon thawing, the wet weight of the cord tissue sample was determined. No attempt was made to remove any remaining blood. The procedure for mercury analysis has been previously described (Dalgård et al. 1994), but changes in equipment necessitated some adjustments. The specimen was freeze-dried for 48 hr before determination of the dry weight. The heating program for the microwave oven was 10 min at 100% power followed by 5 min at 5% and 10 min at 100% power. The volume of the digested sample used for analysis was 500 μL. The mercury analysis was performed by flow-injection cold-vapor atomic absorption spectrometry (FIMS-400 and AS-90; PerkinElmer, Wellesley, MA, USA). The standard curve was generated by using 0, 2, 4, and 6 μg Hg/L solutions in 4.3 M HNO3 (with the addition of 5 mL gold solution, 1 g/L, to 1 L HNO3). The analytical method for blood samples was the same, except that freeze-drying and the microwave digestion were omitted. Because umbilical cords from children born in 1986 were used for determination of organochlorine contaminants (Grandjean et al. 2001), many samples were exhausted, and the 447 samples analyzed therefore almost entirely represent the younger cohort children born in 1987 and examined in 1994. Wet weight was not recorded in one analytical series of 25 cords.
In connection with the quality assurance of the cord analyses, tissue-based reference materials with low mercury concentrations were analyzed: BCR 184 (bovine muscle) and BCR 185 (bovine liver; both from IRMM, Geel, Belgium). The total analytical imprecision was estimated to be 20 and 6.3% at mercury concentrations of 0.0045 and 0.0392 μg/g (dry weight), respectively. Given the very low concentrations in these materials, the accuracy was deemed acceptable, with average mercury results of 0.0045 μg/g (certified value, 0.0026 μg/g) and 0.039 μg/g (certified value, 0.044 μg/g), respectively. The cord water content of the cord was mostly about 85–90%, but the total range was 62–95%. In 10 split samples, the wet-weight–based mercury concentration showed an average coefficient of variation (CV) of 17%, whereas concentrations in previously analyzed split freeze-dried samples showed an average CV of 4% (Dalgård et al. 1994), that is, similar to the normal laboratory error.
Other methylmercury exposure biomarkers have been previously described (Grandjean et al. 1992). In addition to full-length hair (~ 9 cm), we also analyzed the proximal 2-cm segment close to the root (Grandjean et al. 2003b). These two approaches represent the exposure during the full pregnancy period and during the third trimester. For some cohort members, one or more specimens were not available, and some hair samples were sufficient only for the full-length analysis.
Clinical follow-up.
Follow-up of this cohort included an extensive neurobehavioral examination at 7 years of age, where five main outcome tests were selected to represent different brain functions [details provided by Grandjean et al. (1997)]: finger tapping with the preferred hand (motor speed); continued performance test reaction time (attention); Bender Visual Motor Gestalt Test (visuospatial); Boston Naming Test (language); and California Verbal Learning Test—Children Short-term Reproduction (verbal memory). Based on the associations with exposure biomarkers, the main effects were seen in attention and language, with lesser impact on motor speed, verbal memory, and visuospatial performance.
Statistical analysis.
Following descriptive analyses, logarithmic transformations were used for mercury concentrations that showed skewed distributions, and geometric means were calculated. Interrelationships between the transformed exposure biomarkers were determined by correlation coefficients.
A structural equation model analysis was then carried out using only the exposure biomarkers (Budtz-Jørgensen et al. 2002). In a structural equation model, each of these markers (M-Hg) was assumed to be manifestations of the true (unobserved) exposure (Hg): log(M-Hg) = αm + λm log(Hg) + ɛm. We expressed the true exposure on the scale of the cord-blood concentrations. Thus, the factor loading (λm) is fixed at 1 for this bio-marker, and the intercept (αm) is 0. In an additional equation, Hg was assumed to depend on the frequency of maternal pilot whale dinners during pregnancy, as indicated by a dietary questionnaire.
In this type of analysis, measurement errors (ɛm) in different markers are usually assumed to be independent. However, we anticipated dependence between error terms in the two hair measurements and between errors in the cord-based measurement. To adjust for such local dependence, we allowed ɛm for the three cord measures to be associated; likewise, we introduced correlation between the ɛm terms for the two hair concentrations. We also carried out separate analyses based only on two biomarkers at a time (one based on cord, one on hair) to examine the robustness of the model and to avoid adjustment for local dependence.
In this analysis, standard deviations of error in natural log-transformed variables can be interpreted as error CVs in the untransformed concentrations. In addition, meaningful comparisons of the biomarkers can be obtained from their estimated correlations with the true exposure.
Children with incomplete information on the five exposure variables were included in a missing-data analysis based on the maximum likelihood principle (Little and Rubin 1987). Compared with standard complete case analysis, this approach is more powerful and less likely to yield biased results. Under the usual assumption that the likelihood ratio test statistic follows a chi-squared distribution, the hypothesis of pairs of error terms being of similar size can be tested.
Outliers identified from scatter plots were excluded in additional analyses.
Using the main outcomes at 7 years of age, we then carried out multiple regression analyses that included the same set of confounders that was originally selected (Grandjean et al. 1997). Instead of the cord-blood mercury concentration (Budtz-Jørgensen et al. 2002; Grandjean et al. 1999), we now used a cord-tissue mercury concentration as the exposure variable. The mercury effect is expressed in terms of the change in the response variable relative to the standard deviation of the response that was associated with a doubling in the mercury concentration (Grandjean et al. 1999).
Results
All exposure biomarkers showed wide ranges, where the highest concentration approached 1,000-fold the lowest (Table 1, Figure 1). The medians were very close to the geometric means. The correlations between the bio-markers showed that mercury concentrations in cord tissue and cord blood were closely associated (Figure 1), as were the two hair parameters (Table 2). Overall, the dry-weight cord measurement showed stronger correlations with other mercury biomarkers than did the wet-weight concentration.
The structural equation model provided an excellent fit to the data (p = 0.46 for difference between observed and predicted covariances). The cord-blood measurement was the most precise exposure marker, and the dry-weight cord-tissue measure was only slightly inferior, as reflected by the correlations with the true exposure (Table 3). The imprecision of the cord-blood concentration was smaller than that of the other exposure biomarkers (p < 0.05). An additional pairwise comparison showed that the dry-weight–based cord-tissue concentration also had a lower imprecision than did the wet-weight parameter (p < 0.05). Further analyses were then carried out in submodels including only one cord-based marker and one hair-based marker at a time. The results obtained were very similar to those shown in Table 3, thus supporting the robustness of the model. Likewise, exclusion of outliers changed the results only minimally, although the imprecision of the cord-tissue analysis decreased slightly.
We then performed regression analyses to compare the predictive validity of the exposure biomarkers regarding adverse effects on neurobehavioral development at 7 years of age. The regression coefficients (Table 4) for cord-tissue concentrations generally showed results similar to those previously obtained for cord blood (Grandjean et al. 1999), although some are based on much smaller cohort subgroups with complete data for the cord-tissue biomarkers. For four of five outcome variables, the cord concentration measured in terms of dry weight appeared to be a better predictor than the one expressed in regard to the wet weight.
Discussion
An imprecise exposure assessment will tend to underestimate the true effect of the exposure and may also complicate confounder adjustment (Carroll 1998). Validation of exposure biomarkers, therefore, is a key to environmental epidemiology studies. However, even superb laboratory repeatability results cannot substantiate the validity of a biomarker in regard to a causative exposure and the associated disease risk. A valid exposure marker must reflect the actual exposure, which is usually unknown.
The present study has employed different statistical strategies to explore this issue. The results show that analysis of cord blood or cord tissue is likely to provide better precision than does maternal hair. Our previous application of structural equation models showed that the imprecision in hair mercury analyses is substantial and can produce underdetermination of neurotoxic impacts of methylmercury exposures (Grandjean et al. 2003a). Other authors have shown a highly scattered association between maternal hair mercury concentrations and subsequent mercury concentrations in the child’s brain obtained at autopsy (Huang et al. 2003). These data are in accordance with the measurement error for the hair mercury parameter found in the present study using a structural equation model. Furthermore, the regression coefficients obtained from using the two cord mercury parameters as exposure variable approximate the results obtained for cord blood (Grandjean et al. 1997, 1999).
Given the large imprecision of the hair mercury parameter and its known variation with hair type and hair color (Grandjean et al. 2002), a better exposure biomarker for prenatal methylmercury is desirable. Cord blood has been recommended as the best available parameter (National Research Council 2000), but sampling of cord blood must consider that coagulation starts soon after clamping of the cord, and clinical circumstances may prevent blood collection in time. The umbilical cord offers advantages because it is easy to sample by noninvasive means, the tissue otherwise being discarded after parturition. The cord is formed mainly during the second and third trimesters, and it reaches two-thirds of its full length by the end of the second trimester (Kaufmann and Scheffen 1998). Assuming a biologic half-life of about 45 days for methyl-mercury (Smith and Farris 1996), the cord mercury concentration is likely to represent a measure of the average mercury burden during the third trimester. It will likely be less sensitive to short-term changes than will the cord-blood mercury concentration.
However, certain caveats must be considered in regard to the variability of cord tissue. The appearance of the umbilical cord varies substantially and is mainly due to differences in water content retained by the gelatinous Wharton’s jelly that surrounds the blood vessels (Scott and Wilkinson 1978; Sloper et al. 1979). The mean water content decreases with increasing duration of gestation, and the fetal end of the cord has a higher water content than does the placental end (Sloper et al. 1979). Because of these considerations, the dry-weight–based mercury concentration would seem to be a more precise parameter than the level expressed on a wet-weight basis. As a contributing factor, the blood content of the cord will depend on the time of clamping, because the cord vessels contract, especially during the first minute after parturition (Yao and Lind 1974).
The analytical reproducibility data document that the dry-weight–based mercury concentration is more precise than the one expressed on a wet-weight basis. Although these laboratory comparisons were based on the intraindividual variability, the interindividual variation in water content is probably greater. In agreement with this finding, the structural equation model shows that the dry-weight cord parameter has a better correlation to the true mercury exposure. Likewise, the predictive validity in regard to neurobehavioral deficits at 7 years of age also favors the dry-weight biomarker.
The findings on biomarker imprecision also need to be considered in light of the literature on methylmercury neurotoxicity. The fact that all exposure biomarkers are much more imprecise than suggested by laboratory quality data suggests that dose–effect relationships may have been underestimated, not just in the Faroes cohort (Grandjean et al. 2003a). Substantial imprecision of an exposure parameter also means that inclusion of confounders in the regression analysis may add to the bias toward the null hypothesis (Budtz-Jørgensen et al. 2003).
Other pollutants in seafood, such as poly-chlorinated biphenyls (PCBs), may also affect the neurobehavioral outcomes (Grandjean et al. 2001) and may also be measured with substantial imprecision. However, structural equation modeling has shown that, even if substantial imprecision is assumed in regard to the Faroese data, PCB exposure does not explain the mercury-associated deficits (Budtz-Jørgensen et al. 2002). Also, as expected for a persistent pollutant such as PCB, this exposure is more closely associated with the hair mercury concentration as a long-term measure of seafood intake, although this marker is clearly inferior to the cord-blood concentration as a marker of methylmercury exposure.
The findings of this study support the use of cord blood as the best available exposure biomarker for methylmercury. Cord tissue is clearly an appropriate alternative, especially when the mercury concentration is measured in relation to the dry weight. Although appropriate for use as an exposure biomarker, adjustment for its imprecision should always be considered.
Figure 1 Association between mercury concentrations in cord blood and cord tissue in 447 children from a Faroese birth cohort (r = 0.94).
Table 1 Geometric means, 25th–75th percentiles, and total ranges of prenatal methylmercury exposure biomarkers used in a Faroese birth cohort.
Exposure biomarker No. Geometric mean Interquartile range Total range
Cord blood (μg/L) 996 22.35 13.1–40.4 0.90–351
Cord (μg/g dry weight) 447 0.210 0.132–0.36 0.000–1.28
Cord (μg/g wet weight) 422 0.0249 0.0149–0.044 0.0024–0.23
Full-length hair (μg/g) 1,019 4.17 2.52–7.7 0.17–39.1
Proximal hair (μg/g) 683 4.46 2.76–14.6 0.34–40.5
Table 2 Pairwise correlation coefficients for logarithmic transformations of biomarkers of prenatal methylmercury exposure used in a Faroese birth cohort.
Cord blood Cord (dry) Cord (wet) Hair (full-length) Hair (proximal)
Cord (dry) 0.940 1
Cord (wet) 0.907 0.942 1
Hair (full-length) 0.784 0.732 0.690 1
Hair (proximal) 0.837 0.781 0.730 0.926 1
Table 3 Factor loading (λ), standard deviation of the error term (ɛ), and correlation to the estimated true exposure calculated for five biomarkers of prenatal methylmercury exposure.
Biomarker sample Factor loading Error SD Correlation to true exposure
Cord blood 1 0.29 0.94
Cord (dry) 0.89 0.33 0.91
Cord (wet) 0.87 0.40 0.87
Hair (full-length) 0.84 0.45 0.83
Hair (proximal) 0.88 0.37 0.89
Table 4 Numerical change (expressed as percentage of the standard deviation) in five different response variables associated with a doubling in cord-tissue mercury concentrations after adjustment for confounders.
Cord tissue
Dry weight
Wet weight
Cord blood
Response No. β (p-value) No. β(p-value) No. β(p-value)
Motor speed 411 3.00 (0.47) 388 1.38 (0.74) 820 5.37 (0.05)
Attention 89 29.6 (0.01) 72 27.3 (0.03) 390 15.9 (< 0.0001)
Visuospatial 406 1.70 (0.66) 384 1.63 (0.69) 818 3.83 (0.15)
Language 402 11.3 (0.006) 379 10.1 (0.01) 791 10.5 (< 0.0001)
Verbal memory 392 7.45 (0.08) 370 8.04 (0.07) 797 6.64 (0.019)
For comparison, data for cord blood are also shown (Grandjean et al. 1999). The direction of all effects is toward increasing deficit at higher exposures.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7680ehp0113-00090916002382ResearchChildren's HealthRisk of Brain Tumors in Children and Susceptibility to Organophosphorus Insecticides: The Potential Role of Paraoxonase (PON1) Nielsen Susan Searles 12Mueller Beth A. 12De Roos Anneclaire J. 12A. Viernes Hannah-Malia 3Farin Federico M. 3Checkoway Harvey 1231Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA2Department of Epidemiology and3Department of Environmental and Occupational Health Sciences, School of Public Health and Community Medicine, University of Washington, Seattle, Washington, USAAddress correspondence to S. Searles Nielsen, Fred Hutchinson Cancer Research Center, P.O. Box 19024, 1100 Fairview Ave. North, MS M4-C308, Seattle, WA 98109-1024 USA. Telephone: (206) 667-7613. Fax: (206) 667-5948. E-mail:
[email protected] thank the Washington State Department of Health Newborn Screening Program, M. Glass, and M. Ginder; and C. Furlong and G. Jarvik, University of Washington, Medical Genetics.
This work was supported by grants NIEHS T32ES07262, NIEHS P30ES07033 from the National Institute of Environmental Health Sciences; 1 R03 CA106011 from the National Institutes of Health; contract N01-CN-05230 from the National Cancer Institute; and Fred Hutchinson Cancer Research Center.
The authors declare they have no competing financial interests.
7 2005 18 3 2005 113 7 909 913 18 10 2004 17 3 2005 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Prior research suggests that childhood brain tumors (CBTs) may be associated with exposure to pesticides. Organophosphorus insecticides (OPs) target the developing nervous system, and until recently, the most common residential insecticides were chlorpyrifos and diazinon, two OPs metabolized in the body through the cytochrome P450/paraoxonase 1 (PON1) pathway. To investigate whether two common PON1 polymorphisms, C-108T and Q192R, are associated with CBT occurrence, we conducted a population-based study of 66 cases and 236 controls using DNA from neonatal screening archive specimens in Washington State, linked to interview data. The risk of CBT was nonsignificantly increased in relation to the inefficient PON1 promoter allele [per PON1-108T allele, relative to PON1-108CC: odds ratio (OR) = 1.4; 95% confidence interval (CI), 1.0–2.2; p-value for trend = 0.07]. Notably, this association was strongest and statistically significant among children whose mothers reported chemical treatment of the home for pests during pregnancy or childhood (per PON1-108T allele: among exposed, OR = 2.6; 95% CI, 1.2–5.5; among unexposed, OR = 0.9; 95% CI, 0.5–1.6) and for primitive neuroectodermal tumors (per PON1-108T allele: OR = 2.4; 95% CI, 1.1–5.4). The Q192R polymorphism, which alters the structure of PON1 and influences enzyme activity in a substrate-dependent manner, was not associated with CBT risk, nor was the PON1C-108T/Q192R haplotype. These results are consistent with an inverse association between PON1 levels and CBT occurrence, perhaps because of PON1’s ability to detoxify OPs common in children’s environments. Larger studies that measure plasma PON1 levels and incorporate more accurate estimates of pesticide exposure will be required to confirm these observations.
brain tumorchildrenchlorpyrifosdiazinondried blood spotsGuthrie cardsparaoxonasepesticidesPON1xenobiotic metabolism
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Some epidemiologic studies have observed increased risk of childhood brain tumors (CBTs) in relation to home pesticide use, farm residence, or parental occupation in agriculture (Bunin et al. 1994; Cordier et al. 2001; Davis et al. 1993; Efird et al. 2003; Holly et al. 1998; Kristensen et al. 1996; Pogoda and Preston-Martin 1997). Organophosphorus insecticides (OPs) target the nervous system, and it is possible that CBT occurrence is associated with prenatal or childhood exposure to OPs or a reduced ability to metabolize them. One important OP-detoxifying enzyme is paraoxonase 1 (PON1). Present in the liver and blood, PON1 hydrolyzes the acetylcholinesterase-inhibiting oxons (activated intermediates) of some OPs, including chlorpyrifos and diazinon (Costa et al. 2002), which are important in agriculture and in recent decades were the most common insecticides used in homes and yards (Donaldson et al. 2002). Although some environmental exposures influence PON1 activity (Costa et al. 2005), they appear to have much less effect than genetic variation (Jarvik et al. 2002); thus, PON1 levels are relatively stable after they reach adult levels at 6–15 months of age (Cole et al. 2003).
The PON1 gene contains several common single nucleotide polymorphisms, some of which directly affect OP metabolism. In the promoter region, adjacent to a binding site for the transcription factor Sp1 (Deakin et al. 2003), the C-108T polymorphism influences expression of the gene, with the PON1-108T allele conferring reduced PON1 levels. It is the most influential known polymorphism in the promoter region, contributing 22–25% of variation in PON1 expression in white adults (Brophy et al. 2001; Leviev and James 2000). Relative to PON1-108CC homozygotes, PON1-108TT homozygotes have, on average, 33–45% lower enzyme activity as adults (Brophy et al. 2001; Leviev and James 2000) and 63% lower as neonates (Chen et al. 2003). PON1’s OP detoxification activity is also influenced by enzyme structure, and a coding region polymorphism, Q192R, determines whether glutamine (Q) or arginine (R) is present near PON1’s catalytic center (Harel et al. 2004). The two resulting PON1192 isoforms hydrolyze some substrates at different rates. In mice, PON1R192 isoform provides significantly better protection than does PON1Q192 from chlorpyrifos oxon (Li et al. 2000). The two isoforms provide similar protection with respect to the oxon of diazinon.
Because chlorpyrifos and diazinon are common in children’s environments (Andrew Clayton et al. 2003) and PON1 genotype may influence susceptibility to these OPs (Cole et al. 2003), we examined whether the inefficient PON1 promoter allele (PON1-108T) or the allele coding for the PON1192 isoform that may provide lower chlorpyrifos protection (PON1192Q) are associated with increased risk of CBT in children.
Materials and Methods
Subject identification and specimen collection.
Institutional review board approvals were received from the Fred Hutchinson Cancer Research Center and Washington State Department of Health (WDOH) before the conduct of this study. Most subjects were drawn from a previous case–control study (Gurney et al. 1996), with additional control subjects randomly selected from similar birth years. Briefly, in the previous study, cases were diagnosed with primary tumors of the brain, cranial nerves, or meninges [International Classification of Diseases for Oncology (ICD-O; World Health Organization [WHO] 1976), codes 191.0–192.1] at < 20 years of age in 1984–1991 while residing in the Seattle–Puget Sound region of Washington State covered by a population-based cancer incidence registry affiliated with the National Cancer Institute’s Surveillance, Epidemiology, and End Results Program. Controls from the same counties were identified via random digit dialing (RDD), frequency matched to cases 2:1 by sex and age. Mothers of all participating children [134 (74%) eligible cases and 281 (79%) eligible controls] were interviewed using a structured questionnaire that included questions about home pesticide treatment, defined as chemical treatment of the house for pests such as termites, fleas, ants, cockroaches, or silverfish, during the index pregnancy and/or childhood up to the diagnosis date (cases) or similar reference date (controls).
A subset of these subjects [70 (52%) cases and 160 (57%) controls] were eligible for the present study because the child was born after 1977 and the mother resided in Washington when the child was born, so a dried blood spot (DBS) card potentially remained archived at the WDOH Newborn Screening Program. Blind to case status, staff located cards and clipped a DBS for 66 (94%) eligible cases and 137 (86%) eligible controls. Most (74%) of the unlocated subjects were born either in the earliest, uncatalogued months (14 of 27) or outside a civilian hospital (6 of 27). Case–control, race/ethnicity, and histologic tumor type proportions from the previous study were preserved in the subset for whom DBS were available. Subjects’ DBS were permanently anonymized (American Society of Human Genetics 1996) before removal from WDOH. DBS from a pilot study that randomly sampled 100 anonymous infants from the same archives and a similar range of birth years (1980–1991) were available to supplement our control group.
DNA extraction and genotyping.
DNA extraction and genotyping were conducted at the Center for Ecogenetics and Environmental Health Functional Genomics Laboratory at the University of Washington, blind to case status. Six 3-mm punches were removed from each DBS, with all instrumentation flame sterilized between specimens. DNA was then extracted using the QIAamp DNA Mini Kit (QIAGEN, Valencia, CA) according to the manufacturer’s DBS protocol.
PON1 C-108T and Q192R variants were identified with TaqMan detection system-based assays (respective probes from Integrated DNA Tehcnologies, Coralville, IA, and Applied Biosystems, Foster City, CA). Genotypes were assigned based on relative fluorescence, verified by sequencing as needed. Negative controls and DNA-sequenced positive controls representing each possible genotype were included in each batch of analyses. We included blind duplicate or quadruplicate specimens for 6% of cases and 6% of interviewed controls. All PON1 genotypes were represented, and the results were in complete agreement with the original specimens. In addition, the laboratory repeated the assays for > 10% randomly selected specimens, also in full agreement.
PON1 genotyping was completed for all subjects. One control for whom we collected two equally well-matching DBS was excluded from our CBT–PON1 analyses because PON1 genotype was different for the two possible matches. Thus, 66 cases and 236 controls were available for statistical analysis.
Statistical analysis.
Genotype and allele frequencies were tabulated, and chi-square tests were used to check Hardy-Weinberg equilibrium. Using Intercooled Stata (version 8.0; Stata Corp., College Station, TX), we conducted logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for CBT in relation to PON1 genotype (Breslow and Day 1980). Because PON1 heterozygotes have an intermediate phenotype (Brophy et al. 2001; Leviev and James 2000), each polymorphism was modeled linearly (0, 1, or 2 PON1-108T alleles; 0, 1, or 2 PON1192Q alleles). To allow for possible threshold effects (e.g., any vs. no PON1R192 isoform), the appropriateness of the linear assumption was verified using the likelihood-ratio test and by comparing modeled risk estimates to those obtained for individual genotypes. Test of trend p-values for CBT and number of PON1-108T or PON1192Q alleles were derived from the linear terms in the logistic regression models.
To consider the possible combined effect of the two polymorphisms, we investigated whether risk estimates for one polymorphism depended on the other and calculated CBT risk estimates in relation to PON1C-108T/Q192R diplotypes and haplotypes. For the latter, we modeled the number of each allele (TQ, TR, CQ, CR) linearly, using all subjects for whom haplotype could be directly inferred or accurately estimated (PHASE, version 2.0.2) (Stephens et al. 2001). We also used PHASE to test whether cases and controls had different haplotype frequencies.
CBT (Gurney et al. 1999) and the PON1-108T and PON1192Q alleles (Brophy et al. 2002; Chen et al. 2003) are more common in non-Hispanic whites than in individuals of most other races or ethnicities. To investigate whether population stratification influenced risk estimates, we repeated all analyses restricted to children whose biologic mother and father were both non-Hispanic white. Race/ethnicity was not otherwise included in models because there were few nonwhite or Hispanic subjects, and within this heterogeneous category for which prevalence of PON1 variants varies widely, there were substantial racial/ethnic differences between cases and controls.
Other potential confounders considered were the other PON1 polymorphism (C-108T by Q192R, and vice versa) and the frequency-matching variables (sex and age). These were retained in the models only if ORs or 95% CIs for the PON1 polymorphisms were altered by > 10%, and unless stated, unadjusted risk estimates are presented. We also examined whether CBT–PON1 risk estimates varied by reported home pesticide treatment, farm residence, or parental agricultural occupation, potential indicators of exposure to OPs metabolized by PON1. Statistical interaction on the multiplicative scale was assessed in logistic regression models by the p-value (α= 0.05) of the interaction term, or likelihood-ratio test when a single interaction required multiple terms. To the extent possible, we calculated risk estimates by histologic subtype: astroglial tumors (ICD-O histology codes 9380–9384, 9400–9421, 9424–9442), primitive neuroectodermal tumors (PNETs; 9362, 9470–9473, 9500), and other CBTs, using all controls as the reference group (WHO 1976).
Results
Subject characteristics.
Approximately half of cases had astroglial tumors, with the remainder evenly divided between PNETs and a heterogeneous group of other tumors (Table 1). Most cases (71%) were diagnosed before the age of 5 years. Similar proportions of cases (88%) and controls (91%) were non-Hispanic white; however, among subjects for whom the father’s race/ethnicity was known (63 cases, 135 interviewed controls), proportionally fewer cases (81%) than controls (93%) were born to two non-Hispanic white parents.
Proportionally fewer mothers of cases (31%) than controls (42%) reported that any of the child’s homes had ever been chemically treated for pests during the pregnancy or childhood before the diagnosis/reference date. In this largely urban/suburban region, few subjects had ever lived on a farm (9% cases, 3% controls) or had parents who worked in agriculture (14% cases, 10% controls).
PON1 genotype.
PON1 genotype frequencies did not significantly differ from Hardy-Weinberg equilibrium (both p > 0.20). Proportionally more cases (26%) than controls (17%) were homozygous for the inefficient PON1 promoter allele (PON1-108T), and risk of CBT was nonsignificantly increased with increasing PON1-108T alleles (for PON1-108TT, relative to PON1-108CC: OR = 2.1; 95% CI, 0.9–4.7; for PON1-108CT, OR = 1.4; 95% CI, 1.0–2.2; p-value for trend = 0.07; Table 2). The association was strongest and statistically significant in relation to the PNET histologic tumor type specifically (for each additional PON1-108T allele: OR = 2.4; 95% CI, 1.1–5.4; p-value for trend = 0.03, based on 15 PNET cases, including 6 PON1-108TT homozygotes and 7 heterozygotes; data not shown). According to PON1192 genotype, 48% cases and 42% controls had no PON1R192 isoform (Table 2). Although there was a weak suggestion of increased CBT risk in relation to increasing number of PON1192Q alleles, CIs were quite wide and the p-value for trend was nonsignificant (for PON1192QQ, relative to PON1192RR: OR = 1.5; 95% CI, 0.6–3.4; for PON1192QR: OR = 1.2; 95% CI, 0.8–1.9; p-value for trend = 0.36). None of the above risk estimates was attenuated when we restricted the analysis to children with two non-Hispanic white parents, nor were they markedly altered when we separately used either the interviewed controls identified through RDD or the anonymous WDOH archive controls as the reference group.
Logistic regression models indicated no interaction (p = 0.75) between these two PON1 polymorphisms. Indeed, we observed a positive association between CBT and the PON1-108T allele within each PON192 geno-type, and the estimated risk per PON1-108T allele was nearly identical in children with (OR = 1.5; 95% CI, 0.9–2.6) or without (OR = 1.4; 95% CI, 0.8–2.5) any PON1R192 isoform (data not shown). However, with respect to CBT–PON1Q192R, the possible weak association was absent among PON1-108TT homozygotes (per PON1192Q allele relative to PON1192RR: OR = 1.0; 95% CI, 0.4–2.8.
PON1 diplotype and haplotype.
We observed proportionally more cases than controls in each PON1C-108T/Q192R diplotype with two inefficient PON1 promoters and in the CT/QQ diplotype (no PON1R192 isoform and only one efficient promoter allele; data not shown). Proportionally fewer cases were represented in each of the other diplo-types, those with two efficient promoters or those with only one efficient promoter but some PON1R192 isoform.
PON1C-108T/Q192R haplotype frequencies were not significantly different in cases versus controls (p = 0.09). The haplotype model confirmed the earlier impression that the PON1192Q allele was not associated with CBT among PON1-108TT homozygotes: the risk of CBT relative to children with the CR/CR haplotype (homozygous for efficient promotion of PON1192R isoform) was 1.7 (95% CI, 1.0–3.2) per TQ allele, 1.7 (95% CI, 0.6–4.3) per TR allele, and 1.3 (95% CI, 0.7–2.6) per CQ allele. However, the resulting risk estimates for individual diplotypes were not markedly different from those estimated by simpler models, including one with only a single linear term for each polymorphism.
PON1 and pesticide exposure indicators.
CBT risk was associated with PON1C-108T only among children whose mothers reported that at least one of the child’s homes had been chemically treated for pests. Relative to PON1-108CC, the risk per PON1-108T allele was 2.6 (95% CI, 1.2–5.5), whereas among children whose homes were reportedly not treated, the risk was 0.9 (95% CI, 0.5–1.6; interaction p = 0.03; Table 3). Any suggestion of an interaction between PON1Q192R and home pesticide treatment was not statistically significant (p = 0.33; Table 4). These observations did not appear to be caused by case–control differences in demographic factors associated with pesticide use/reporting, such as race/ethnicity, maternal education, or smoking. Our ability to examine the CBT–PON1 relation by farm residence or parental agricultural occupation was quite limited, although there was a higher frequency of PON1-108TT cases among children who had lived on a farm (4 of 6 cases, 0 of 4 controls). Combining all three pesticide exposure indicators, the risk of CBT per PON1-108T allele was 2.0 (95% CI, 1.03–3.7) among exposed and 1.0 (95% CI, 0.5–1.9) among unexposed (interaction p = 0.15; data not shown.)
Discussion
This small population-based study suggests that having an inefficient PON1 promoter allele at position –108 is associated with an increased risk of CBT. The observed association was strongest with respect to PNET, the CBT type most consistently associated with farm residence (Bunin et al. 1994; Kristensen et al. 1996). For the most part, CBT was not associated with the PON1Q192R polymorphism, which determines the enzyme’s structure and thereby detoxification efficiency for some substrates. Our results were similar when we focused on the largest racial/ethnic group in our population, indicating that potential biases related to race/ethnicity were probably not largely responsible for our observations. In addition, risk estimates were fairly resilient to exclusion of either of the two population-based sources of controls. Nevertheless, any association between CBT and PON1C-108T, and the lack thereof between CBT and PON1Q192R, must be interpreted with caution.
First, our small numbers of subjects may have hampered our ability to observe a CBT–PON1Q192R association and simultaneously increased the probability that the apparent association between CBT and PON1C-108T is a false positive. Furthermore, no prior studies have examined this relationship. Non-Hodgkin lymphoma (Kerridge et al. 2002), multiple myeloma (Lincz et al. 2004), and prostate cancer (Antognelli et al. 2004; Marchesani et al. 2003) have been associated with other PON1 polymorphisms, including Q192R, but to our knowledge this is the first cancer study to consider the C-108T polymorphism of PON1, and the first study to examine the potential role of this enzyme in relation to childhood cancer.
Second, we obtained subjects’ DNA from an indirect source. It is possible that environmental DNA contamination occurred, which would likely bias risk estimates toward null. However, our assays do not simply detect the presence of an allele, but instead rely on relative amounts of each allele compared with sequence-verified laboratory controls to assign one of three genotypes.
Third, although our specimen retrieval rate for cases was very high and did not require cases to have survived, we cannot rule out survival-related selection bias among cases in the study from which they were drawn. However, even if such bias existed and PON1 genotype influenced cancer prognosis, it is unlikely this could account fully for the moderately strong association we observed between CBT and PON1C-108T.
To the extent that this association is not due to chance or case survival, these results suggest that CBT risk may be inversely related to PON1 enzyme levels. Ideally, fresh blood would have been available and PON1 enzyme levels measured, but the C-108T polymorphism is a significant determinant of PON1 levels in both neonates and pregnant women (Chen et al. 2003). Because PON1 may hydrolyze OPs before they can reach the brain, our results lend support to prior epidemiologic studies that have observed an increased risk of CBT in relation to possible pesticide exposure. OPs target the developing nervous system (Garcia et al. 2002; Johnson et al. 1998), and chlorpyrifos may affect replication and differentiation of glial cells (Garcia et al. 2001).
If OPs do relate to CBT risk, one would expect CBT–PON1 associations to be present mainly among those exposed to chlorpyrifos and/or diazinon. These two OPs were the most common residential insecticides during the study years, and indoor air is an important source of children’s exposure to them (Andrew Clayton et al. 2003). Therefore, it is interesting that we observed an increased risk of CBT in relation to the inefficient PON1-108T allele only among children whose homes were reportedly treated for insect pests. Still, one cannot discount the possibility that this OP-metabolizing enzyme could protect the brain via its more generic ability to metabolize oxidized lipid molecules. That PON1Q192R was not associated with CBT seems to underscore this point. However, the relative protection provided by the two resulting PON1192 isoforms depends on the OP (Li et al. 2000), and perhaps other factors such as PON1 levels. We had no direct measures of these, nor of the level of exposure to chlorpyrifos and diazinon. Because of our modest number of cases, we also had limited ability to consider other potentially relevant factors, such as diet, age at diagnosis, family history of cancer, and prenatal and childhood exposure to tobacco smoke. For example, there was some indication that the PON1192Q allele is associated with increased risk of CBT among subjects reportedly not exposed to tobacco smoke, but we could not adequately examine whether this reflects a plausible biologic effect or is simply a spurious observation due to chance or bias related to racial/ethnic differences between cases and controls and smokers and nonsmokers.
The strengths of our study are population-based identification of cases and controls, the use of a DNA source unrelated to case survival, and inclusion of children diagnosed before the residential phase-out of chlorpyrifos and diazinon began. Future studies of CBT and PON1 would benefit from larger sample sizes, more accurate indicators of exposure to chlorpyrifos and diazinon, the addition of other PON polymorphisms, measurement of plasma PON1 activity (e.g., PON1 status) (Costa and Furlong 2002), and genotyping/phenotyping of both children and mothers. It would also be useful to know whether the PON1C-108T polymorphism is associated with biomarkers of relevance to cancer, such as chromosome aberrations, as has been demonstrated in farmers with respect to PON1Q192R (Au et al. 1999). Such studies would be worthwhile in light of the possible association we observed between CBT occurrence and the predominant polymorphism in the PON1 promoter region, and because chlorpyrifos remains a leading agricultural insecticide and is detected in or on foods frequently consumed by children (Andrew Clayton et al. 2003; U.S. Department of Agriculture 2004).
Table 1 Demographic characteristics and selected exposures among children with and without brain tumors [n (%)].
Cases (n = 66) Interviewed controls (n = 136) Anonymous controls (n = 100) All controls (n = 236)
Race/ethnicitya
White 58 (88) 131 (96) 33 (75) 164 (91)
Black 0 (0) 2 (1) 4 (9) 6 (3)
Hispanic 0 (0) 1 (1) 5 (11) 6 (3)
Asian 3 (5) 0 (0) 2 (5) 2 (1)
Other 5 (8) 2 (1) 0 (0) 2 (1)
Male 42 (64) 84 (62) 57 (58)b 141 (60)b
Birth year
1978–1983 30 (45) 71 (52) 50 (50) 121 (51)
1984–1991 36 (55) 65 (48) 50 (50) 115 (49)
Age at diagnosis/reference (years)
< 5 47 (71) 89 (65) — —
5–10 19 (29) 47 (35) — —
Mother smoked during pregnancy 10 (15) 23 (17) — —
Pesticide exposure indicators
Home pesticide treatmentc 20 (31)d 57 (42) — —
Farm residencec 6 (9) 4 (3) — —
Parental agricultural occupatione 9 (14) 14 (10) — —
Histologic tumor type
Astroglial 37 (56) — — —
PNET 15 (23) — — —
Other 14 (21) — — —
—, data not available.
a As determined by maternal interview (cases and interviewed controls) or checkbox for child on DBS card (anonymous controls; not available for births before 1990); percentage excludes 56 anonymous controls for whom race/ethnicity was not reported.
b Percentage excludes two anonymous controls for whom sex was not reported on the DBS card.
c During pregnancy or childhood before diagnosis/reference date.
d Percentage excludes one case for whom pesticide use was unknown.
e In year of birth or prior 4 years: agricultural occupation/industry (Cordier et al. 2001), or occupational exposure to pesticides/weedkillers, fertilizer, “other” agricultural chemicals, farm animals, manure, or other potential indicators of chlorpyrifos/diazinon contact (domestic animals/birds for resale, unprocessed wool, hides/skins/feathers, or “other” animal products, excluding raw meat and milk).
Table 2 Risk of CBT in relation to PON1 C-108T and Q192R polymorphisms [n (%)].
All subjects
Subjects with white parentsa
PON1 genotype Cases (n = 66) Controls (n = 236) OR (95% CI)b Cases (n = 51) Controls (n = 125) OR (95% CI)b
C-108T: PON1 promotion
TT (inefficient) 17 (26) 39 (17) 2.1 (0.9–4.7) 14 (27) 22 (18) 2.6 (1.0–6.9)
CT (intermediate) 34 (52) 125 (53) 1.4 (1.0–2.2) 28 (55) 66 (53) 1.6 (1.0–2.6)
CC (efficient) 15 (23) 72 (31) 1.0 (reference) 9 (18) 37 (30) 1.0 (reference)
Q192R: PON1R192 isoform
QQ (none) 32 (48) 100 (42) 1.5 (0.6–3.4)c 27 (53) 58 (46) 1.6 (0.5–4.6)
QR (some) 28 (42) 105 (44) 1.2 (0.8–1.9)c 21 (41) 57 (46) 1.3 (0.7–2.1)
RR (all) 6 (9) 31 (13) 1.0 (reference) 3 (6) 10 (8) 1.0 (reference)
a Biologic mother and father both reportedly non-Hispanic and white; excludes 3 cases, 1 interviewed control, and 100 anonymous controls for whom father’s race/ethnicity unknown.
b For individual genotypes, with each polymorphism modeled linearly (0, 1, or 2 PON1-108T alleles; 0, 1, or 2 PON1192Q alleles) using logistic regression.
c Adjusted for PON1C-108T.
Table 3 Risk of CBT in relation to PON1C-108T genotype, by ever/never chemical treatment of home for insect pests during pregnancy or childhood.
Home pesticide treatmenta PON1C-108T genotype Cases [n = 65b (%c)] Controls [n = 136d (%c)] OR (95% CI)e OR (95% CI)f
Yes TT 9 (45) 10 (18) 1.4 (0.5–3.9) 6.6 (1.5–29.7)
CT 8 (40) 26 (46) 0.5 (0.2–1.3) 2.6 (1.2–5.5)
CC 3 (15) 21 (37) 0.2 (0.1–0.7) 1.0 (reference)
No TT 7 (16) 13 (16) 0.8 (0.3–2.7)
CT 26 (58) 47 (59) 0.9 (0.5–1.6)
CC 12 (27) 19 (24) 1.0 (reference)
a Based on maternal interview; chemical treatment of home for termites, fleas, cockroaches, ants, silverfish, or other, during index pregnancy or during childhood before diagnosis/reference date.
b Excludes one case for whom home pesticide treatment was unknown.
c Within respective pesticide category.
d Excludes anonymous controls, due to absence of pesticide interview data.
e Relative to PON1-108CC homozygotes whose homes were not chemically treated for insect pests.
f Relative to PON1-108CC homozygotes whose homes were chemically treated for insect pests.
Table 4 Risk of CBT in relation to PON1Q192R genotype, by ever/never chemical treatment of home for insect pests during pregnancy or childhood.
Home pesticide treatmenta PON1Q192R genotype Cases [n = 65b (%c)] Controls [n = 136d (%c)] OR (95% CI)e OR (95% CI)f
Yes QQ 8 (40) 26 (46) 0.8 (0.2–2.4) 0.6 (0.1–3.3)
QR 10 (50) 27 (47) 1.0 (0.3–2.7) 0.8 (0.4–1.8)
RR 2 (10) 4 (7) 1.2 (0.3–5.2) 1.0 (reference)
No QQ 23 (51) 33 (42) 1.7 (0.6–5.4)
QR 18 (40) 37 (47) 1.3 (0.7–2.3)
RR 4 (9) 9 (11) 1.0 (reference)
a Based on maternal interview; chemical treatment of home for termites, fleas, cockroaches, ants, silverfish, or other, during index pregnancy or during childhood before diagnosis/reference date.
b Excludes one case for whom home pesticide treatment was unknown.
c Within respective pesticide category.
d Excludes anonymous controls, due to absence of pesticide interview data.
e Relative to PON1192RR homozygotes whose homes were not chemically treated for insect pests.
f Relative to PON1192RR homozygotes whose homes were chemically treated for insect pests.
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Costa LG Li WF Richter RJ Shih DM Lusis AJ Furlong CE 2002. PON1 and Organophosphate Toxicity. In: Paraoxonase (PON1) in Health and Disease: Basic and Clinical Aspects (Costa L, Furlong CE, eds). Boston:Kluwer Academic Publishers, 165–183.
Costa LG Vitalone A Cole TB Furlong CE 2005 Modulation of paraoxonase (PON1) activity Biochem Pharmacol 69 541 550 15670573
Davis JR Brownson RC Garcia R Bentz BJ Turner A 1993 Family pesticide use and childhood brain cancer Arch Environ Contam Toxicol 24 87 92 8466294
Deakin S Leviev I Brulhart-Meynet MC James RW 2003 Paraoxonase-1 promoter haplotypes and serum paraoxonase: a predominant role for polymorphic position -107, implicating the Sp1 transcription factor Biochem J 372 643 649 12639220
Donaldson D Kiely T Grube A 2002. 1998/1999 Pesticide Sales and Usage Report. Washington, DC:U.S. Environmental Protection Agency.
Efird JT Holly EA Preston-Martin S Mueller BA Lubin F Filippini G 2003 Farm-related exposures and childhood brain tumours in seven countries: results from the SEARCH International Brain Tumour Study Paediatr Perinat Epidemiol 17 201 211 12675788
Garcia SJ Seidler FJ Crumpton TL Slotkin TA 2001 Does the developmental neurotoxicity of chlorpyrifos involve glial targets? Macromolecule synthesis, adenylyl cyclase signaling, nuclear transcription factors, and formation of reactive oxygen in C6 glioma cells Brain Res 891 54 68 11164809
Garcia SJ Seidler FJ Qiao D Slotkin TA 2002 Chlorpyrifos targets developing glia: effects on glial fibrillary acidic protein Brain Res Dev Brain Res 133 151 161
Gurney J Smith M Bunin G 1999. CNS and miscellaneous intracranial and intraspinal neoplasms. In: Cancer Incidence and Survival among Children and Adolescents: United States SEER Program 1975–1995 (Ries LAG, Smith MA, Gurney JG, Linet M, Tamra T, Young JL, et al., eds). NIH Pub. No. 99-4649. Bethesda, MD:National Institutes of Health, 51–63.
Gurney JG Mueller BA Davis S Schwartz SM Stevens RG Kopecky KJ 1996 Childhood brain tumor occurrence in relation to residential power line configurations, electric heating sources, and electric appliance use Am J Epidemiol 143 120 128 8546112
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Holly EA Bracci PM Mueller BA Preston-Martin S 1998 Farm and animal exposures and pediatric brain tumors: results from the United States West Coast Childhood Brain Tumor Study Cancer Epidemiol Biomarkers Prev 7 797 802 9752988
Jarvik GP Tsai NT McKinstry LA Wani R Brophy VH Richter RJ 2002 Vitamin C and E intake is associated with increased paraoxonase activity Arterioscler Thromb Vasc Biol 22 1329 1333 12171796
Johnson DE Seidler FJ Slotkin TA 1998 Early biochemical detection of delayed neurotoxicity resulting from developmental exposure to chloropyrifos Brain Res Bull 45 143 147 9443830
Kerridge I Lincz L Scorgie F Hickey D Granter N Spencer A 2002 Association between xenobiotic gene polymorphisms and non-Hodgkin’s lymphoma risk Br J Haematol 118 477 481 12139735
Kristensen P Andersen A Irgens LM Bye AS Sundheim L 1996 Cancer in offspring of parents engaged in agricultural activities in Norway: incidence and risk factors in the farm environment Int J Cancer 65 39 50 8543394
Leviev I James RW 2000 Promoter polymorphisms of human paraoxonase PON1 gene and serum paraoxonase activities and concentrations Arterioscler Thromb Vasc Biol 20 516 521 10669651
Li WF Costa LG Richter RJ Hagen T Shih DM Tward A 2000 Catalytic efficiency determines the in-vivo efficacy of PON1 for detoxifying organophosphorus compounds Pharmacogenetics 10 767 779 11191881
Lincz LF Kerridge I Scorgie FE Bailey M Enno A Spencer A 2004 Xenobiotic gene polymorphisms and susceptibility to multiple myeloma Haematologica 89 628 629 15136237
Marchesani M Hakkarainen A Tuomainen TP Kaikkonen J Pukkala E Uimari P 2003 New paraoxonase 1 polymorphism I102V and the risk of prostate cancer in Finnish men J Natl Cancer Inst 95 812 818 12783936
Pogoda JM Preston-Martin S 1997 Household pesticides and risk of pediatric brain tumors Environ Health Perspect 105 1214 1220 9370522
Stephens M Smith NJ Donnelly P 2001 A new statistical method for haplotype reconstruction from population data Am J Hum Genet 68 978 989 11254454
U.S. Department of Agriculture 2004. Pesticide Data Program: Annual Summary Calendar Year 2002. Washington, DC:U.S. Department of Agriculture, Agricultural Marketing Service.
WHO 1976. International Classification of Diseases for Oncology. 1st ed. Geneva:World Health Organization.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0043816002355PerspectivesEditorialGuest Editorial: Contaminant Source Zones: Remediation or Perpetual Stewardship? Abriola Linda M. Department of Civil and Environmental Engineering, Tufts University, Medford, Massachusetts, E-mail:
[email protected] M. Abriola is dean of Engineering and professor of Civil and Environmental Engineering at Tufts University. Her primary research focus is the integration of mathematical modeling and laboratory experiments to investigate the transport, fate, and remediation of non-aqueous phase liquid organic contaminants. The author of more than 100 refereed publications, She recently served on the NRC's Committee on Source Removal of Contaminants in the Subsurface. She is a Fellow of the American Geophysical Union and a member of the National Academy of Engineering.
The author declares she has no competing financial interests.
7 2005 113 7 A438 A439 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
It has been some 20 years since I published my first paper on organic liquid contamination of the subsurface. That article was among the first to model the infiltration of organic solvents into aquifer systems. Before the mid-1980s, the importance of separate phase liquid pollutants was not appreciated, and most investigations into groundwater contamination had focused on solute (dissolved constituent) transport. Since that time, substantial resources have been dedicated to research on the behavior of what have become known as nonaqueous phase liquids (NAPLs). Within 5–10 years of those first articles, practitioners started to identify particular classes of NAPLs on the basis of their environmental persistence and ease of subsurface detection. The two most common classes are a) NAPLs composed of fuel hydrocarbons that are lighter (LNAPLs) than water and, thus, more easily detected, because they tend to remain within the unsaturated zone or capillary fringe areas of an aquifer; and b) organic solvent or dense NAPLs (DNAPLs) that tend to migrate deep into formations, becoming entrapped in irregular finger-like structures or pooled on low permeability strata. Laboratory evidence, coupled with a series of careful field case studies, soon revealed that fuel hydrocarbon plumes emanating from LNAPL contamination sites tended to stabilize with time, due to a series of microbial transformation processes (commonly termed “natural attenuation”). As a consequence of these investigations, monitored natural attenuation has become an accepted environmental management strategy for plumes at many LNAPL sites [e.g., U.S. Environmental Protection Agency (EPA) 1999; Wiedemeier et al. 1999].
For subsurface contaminant plumes that are attributable to organic solvent sources (of which estimates suggest there are as many as 25,000 in the United States alone), however, characterization and environmental remedy prescription have proven more elusive, and clean-up investments have often failed to deliver their promised outcome [National Research Council (NRC) 2005; Stroo et al. 2003; U.S. EPA 2003]. As I look back over the past two decades of research on DNAPLs, I am struck simultaneously by two realizations. First, we have certainly come a long way in improving our understanding of the migration, persistence, and recovery of DNAPLs in the subsurface environment. Second, we have failed to adequately incorporate this understanding into a long-term environmental management strategy. Below, I have attempted to summarize what I believe to be the most significant research findings and what I see as the most imposing challenges to implementing them. Although this editorial is devoted to the DNAPL problem, similar observations can also be made about any long-term contaminant source issue.
We now know that spatial variability in DNAPL mass distribution within a source region is almost inevitable, and, consequently, that mass detection is extremely difficult and uncertain [e.g., Interstate Technology & Regulatory Council (ITRC) 2003]. Migration pathways of DNAPLs will depend almost totally on the organic release characteristics (location, volume, composition, and rate), which are often unknown, and on small-scale subsurface textural variations that cannot be described deterministically (e.g., Kueper et al. 1993). Nevertheless, we have refined our conceptual and mathematical models of DNAPL migration to the point that we are confident in our predictions of migration pathways and equilibrium mass distributions for a fully characterized release scenario (e.g., Rathfelder et al. 2001e.g., Rathfelder et al. 2003). Furthermore, given a specific DNAPL distribution, we are also confident in our ability to model DNAPL dissolution, the process that will control source longevity and plume concentrations (e.g., Miller et al. 1998).
Given our current level of understanding, it has become clear that characterization of the source zone and the degree of uncertainty associated with that characterization are of critical importance in site assessment. Several DNAPL source zone characterization tools have been developed and demonstrated in the last few years (ITRC 2003). Although each of these has its potential uses, it is my personal perspective that a thorough delineation of mass distribution with these tools will not be feasible in the foreseeable future. However, reduction of uncertainty will likely be possible through the application of novel multistage characterization tools and protocols that incorporate knowledge obtained from initial characterization efforts into follow-on measurements (NRC 2005).
Considerable effort has also been directed toward the development and demonstration of so-called innovative remedial technologies. Many of these technologies involve flushing of the formation with various chemical amendments to achieve mass recovery or in-place mass destruction (e.g., surfactant flushing, in situ chemical oxidation). Others are predicated on creating in situ phase changes to facilitate mass removal (e.g., air sparging, six-phase heating). Although each technology has its proponents and some have been more fully refined than others, no single technology will work effectively under all conditions, nor will any technology be capable of achieving complete DNAPL mass removal and/or reduction of contaminant concentration levels to meet drinking water standards (NRC 2005). Because innovative technologies typically come with a large price tag and little guarantee of achieving regulatory end points, it is not surprising that there is a reluctance to implement these in the field. These issues, coupled with mass characterization difficulties, have led many to consider the DNAPL problem essentially intractable and to argue for containment as the best management strategy (Cherry et al. 1997; Freeze 2000).
Indeed, on an economic basis, simple cost/benefit analyses for remedial alternatives at DNAPL sites will typically lead to the selection of containment as a presumptive remedy. Few, however, seem to look beyond the 30-year present value cost horizon in such analyses. They also often fail to appreciate that the factors that limit the potential success of source remediation (insufficient characterization) may also limit the success of any proposed containment strategy. On the basis of simple mass partitioning calculations, one can derive estimates of typical DNAPL source zone longevities that span centuries under natural gradient conditions, or even longer time periods if hydraulic isolation is attempted. Such long time frames present an enormous challenge to site managers. Can we confidently guarantee adequate and consistent stewardship of physical or hydraulic containment strategies? Will the likelihood of containment failure and the costs of continued monitoring be factored into the cost/benefit analysis? Furthermore, will monitoring strategies be sufficient for timely detection of containment failure? A summary of 5-year reviews of existing physical and institutional controls under the U.S. EPA-administered National Contingency Plan suggests the answer to these questions is “no.” Although some progress been made since a 1995 U.S. EPA assessment, 5-year reviews continue to be characterized as paperwork exercises that result in few executable recommendations (Nakamura and Church 2000).
Perhaps the most promising news on the horizon is that, since the late 1990s, researchers have been identifying and isolating organisms and microbial consortia that are capable of transforming chlorinated solvents under a variety of subsurface conditions (Bradley 2003). Unlike the fuel-hydrocarbon scenario, however, the natural rates of these processes have typically proven too slow to handle contaminant loadings, and solvent plumes have been documented to persist and continue to expand for decades without apparent biotic attenuation. Fortunately, recent research suggests that coupling of innovative remedial technologies (partial mass removal) with biostimulation may lead to more effective remediation (Christ et al. 2005). For example, in a recent pilot-scale source zone remedial demonstration, our research group observed the stimulation of indigenous microbial populations after active flushing with a nonionic surfactant solution (Abriola et al. 2005; Ramsburg et al. 2004). This microbial activity resulted in the continued decline of source zone contaminant concentrations 450 days after active treatment. Although promising, much research is still needed to refine the design and explore the potential efficacy of such coupled treatment approaches.
The above observations lead to the conclusion that—from the standpoint of risk reduction—containment and perpetual stewardship as a DNAPL site management strategy is not easily justifiable. Even at sites where, at present, physical and chemical complexities permit no other viable management alternative, we routinely have failed to adequately estimate the uncertainty associated with the containment and monitoring plan. Uncertainty is difficult to quantify and thus often neglected. However, until we can evaluate the level of uncertainty associated with the observations and conceptual models upon which we base our site management decisions, assessing the cost and benefit of any characterization or remedial activity will be nearly impossible. Only after the development and employment of tools capable of quantifying uncertainty will we be able to assure the public that the actions taken are truly reducing risk. In tandem with the development of uncertainty tools, we must continue to pursue research into promising long-term source zone management strategies that couple aggressive remediation (mass removal or destruction) technologies with source zone biotic attenuation. Such a two-pronged approach promises substantial returns in the next 5 years.
==== Refs
References
Abriola LM Drummond CD Hahn EJ Kibbey TCG Lemke LD Pennell KD 2005 Pilot-scale demonstration of surfactant-enhanced PCE solubilization at the Bachman road site. 1. Site characterization and test design Environ Sci Technol 39 1178 1790
Bradley PM 2003 History and ecology of chloroethene biodegradation: a review Bioremediation J 7 81 109
Cherry JA Feenstra S Mackay DM 1997. Developing rational goals for in situ remedial technologies. In: Subsurface Restoration (Ward CH, Cherry JA, Scalf MR, eds). Chelsea, MI:Ann Arbor Press, 75–98.
Christ JA Ramsburg CA Abriola LM Pennell KD Löffler FE 2005 Coupling aggressive mass removal with microbial reductive dechlorination for remediation of DNAPL source zones: a review and assessment Environ Health Perspect 113 465 477 15811838
Freeze RA 2000. The Environmental Pendulum. Berkeley, CA:University of California Press.
ITRC 2003. Technology Overview: An Introduction to Characterizing Sites Contaminated with DNAPLs. Washington, DC:Interstate Technology & Regulatory Council.
Kueper BH Redman D Starr RC Reitsma S Mah M 1993 A field experiment to study the behavior of tetrachloroethylene below the water table: spatial distribution of residual and pooled DNAPL Ground Water 31 756 766
Miller CT Christakos G Imhoff PT McBride JF Pedit JA Trangenstein JA 1998 Multiphase flow and transport modeling in heterogeneous porous media: challenges and approaches Adv Water Resour 21 77 120
Nakamura R Church T 2000. Reinventing Superfund: An Assessment of EPA’s Administrative reforms. Washington DC: National Academy of Public Administration. Available: http://www.napawash.org/pc_economy_environment/epafile15.pdf [accessed 1 June 2005].
NRC (National Research Council) 2005. Contaminants in the Subsurface: Source Zone Assessment and Remediation. Washington, DC:National Academy Press.
Ramsburg CA Abriola LM Pennell KD Löffler FE Gamache M Amos BK 2004 Stimulated microbial reductive dechlorination following surfactant treatment at the Bachman road site Environmental Science and Technology 38 5902 5914 10.1021/es049675i.15573588
Rathfelder KM Abriola LM Singletary MA Pennell KD 2003 Influence of surfactant-facilitated interfacial tension reduction on organic liquid migration in porous media: observations and numerical simulation J Contam Hydrol 64 227 252 12814882
Rathfelder KM Abriola LM Taylor TP Pennell KD 2001 Surfactant enhanced recovery of tetrachloroethylene from a porous medium containing low permeability lenses. 2. Numerical simulations J Contam Hydrol 48 351 374 11285938
Stroo HF Unger M Ward CH Kavanaugh MC Vogel C Leeson A 2003 Remediating chlorinated solvent source zones Environ Sci Technol 37 224A 230A
U.S. EPA 1999. Use of Monitored Natural Attenuation at Superfund, RCRA Corrective Action, and Underground Storage Tank Sites. Office of Solid Waste and Emergency Response Directive 9200.4-17P. Washington, DC:US Environmental Protection Agency.
U.S. EPA 2003. The DNAPL Remediation Challenge: Is There a Case for Source Depletion? EPA 600/R-03/143. Washington, DC:US Environmental Protection Agency.
Weidemeier TH Wilson JT Kampbell DH Miller RN Hansen JE 1999. Technical Protocol for Implementing Intrinsic Remediation with Long-Term Monitoring for Natural Attenuation of Fuel Contamination Dissolved in Groundwater, Vol 1. San Antonio, TX:Air Force Center for Environmental Excellence.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a00439PerspectivesEditorialNote from the Editors: Striving for a More Reader-Friendly Journal Goehl Thomas J. Editor-in-Chief, EHP, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, E-mail:
[email protected] 2005 113 7 A439 A439 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
We at EHP continually strive to make our content more accessible to our readers. With this issue, we are pleased to unveil some journal format changes that we hope will help with this goal. Our Table of Contents has a new easier-to-read format. Titles are now in boldface, and keyword descriptors have been added. Research Articles for which nonspecialist summaries (Science Selections) have been prepared are identified as such with a small red arrow.
We have also changed the grouping of our peer-reviewed articles. Commentaries & Reviews are now grouped into their own new category. Toxicogenomics articles are being incorporated into the Research section to reflect the rapid acceptance of this critical tool into the environmental health research arsenal. All other peer-reviewed articles are included within an expanded Research section.
As always, the editors welcome your comments as we try to further enhance the value of EHP to the environmental health community.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0113-a0044016002356PerspectivesDirector's PerspectiveThe NIEHS and the National Toxicology Program: An Integrated Scientific Vision Portier Christopher J. PhDAssociate Director, NTP E-mail:
[email protected] David A. MDDirector, NIEHS and NTP E-mail:
[email protected] 2005 113 7 A440 A440 Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
The National Toxicology Program (NTP) is an interagency program1 whose mission is to coordinate, conduct, and communicate toxicological research findings across the U.S. government. The NTP is administratively housed at the NIEHS, and David Schwartz serves as the director of both the NTP and the NIEHS. The NTP and the NIEHS share an integrated vision that serves to enhance the productivity of each program by promoting extensive collaboration across the broad spectrum of environmental health sciences. One of the emerging challenges for the NTP and the NIEHS is to use the best science to create, validate, and implement in environmental health research novel, robust, and efficient biological assays that will more effectively predict the risk of human disease and protect the health of our public.
Recently the NTP developed a vision statement, “Toxicology in the 21st Century: The Role of the National Toxicology Program,” and a roadmap for implementing this vision (available at http://ntp-server.niehs.nih.gov/). The NTP vision is to develop and use the best science possible to achieve a greater understanding of the mechanisms of toxicity, and to apply this understanding to the study of a broad array of environmental agents through the most effective and efficient use of resources. This vision is consistent with the need of the NIEHS to identify and understand the human biologic and pathophysiologic response to toxicants. As we begin to develop a strategic plan for the NIEHS, an understanding of the NTP vision and roadmap can help to inform and guide this process.
The NTP roadmap has identified four key scientific areas as priorities. First, the NTP needs to modernize the mammalian assays used to screen for toxicity. NIEHS research will lead the development of such assays. Critical to this process is increasing our understanding of the similarities and differences between laboratory species and humans. It is clear that both the quality and the interpretability of toxicity data will improve through the strategic use of new approaches to comparative biology.
Second, the NTP needs to develop and implement high- and medium-throughput screens to identify and understand potential targets for environmentally mediated disease. Such screens, ranging in complexity from simple subcellular fractions to complicated mixtures of primary cultures, can address a variety of biochemical, mechanistic, and functional end points. The availability of these screens will allow the NTP to establish priorities for full-scale, resource-intensive mammalian assays, and will provide direct links into the hypothesis-driven research supported by the NIEHS. As part of this process, the NTP and the NIEHS are collaborating with investigators in the NIH Roadmap Molecular Libraries and Imaging Initiative who are screening more than 100,000 compounds against multiple cellular targets to identify possible therapeutic agents and basic biologic responses. The in vivo toxicity data in the NTP archives will be incorporated into this high-throughput screen, and will serve as a cornerstone for the NIEHS and others to develop linkages between basic biologic responses and pathophysiologic outcomes.
Third, recent developments in biomedical research and molecular genetics have created a tremendous need to develop better ways to store, retrieve, analyze, and interpret vast amounts of data. The need for databases and repositories is critical for evaluating the toxicity of potentially hazardous agents. The NTP and the NIEHS will play important and complementary roles in developing these resources, and will partner with others to develop similar tools for the wider range of toxicologic, biologic, genetic, genomic, and biochemical information.
Finally, training the next generation of scientists is critical to the environmental health sciences. In collaboration with the NTP member agencies and our colleagues at the National Institutes of Health, we will support the development of training programs focused on creating integrated teams of scientists to understand and attack environmental health problems of concern to the public.
The NTP and the NIEHS share an integrated vision that serves to enhance the productivity of each program by promoting extensive collaboration across the broad spectrum of environmental health sciences.
The fields of toxicology and environmental health sciences are intimately linked, and the future for both is challenging. We are excited by the possibilities posed by the new directions of the NTP and the NIEHS, and look forward to the continued evolution of this vital and productive relationship.
1The other major contributors to the NTP are the National Center for Toxicological Research of the Food and Drug Administration and the National Institute of Occupational Safety and Health of the Centers for Disease Control and Prevention.
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