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Bibliography Tag: biomonitoring

Gillezeau et al., 2020

Gillezeau, C., Lieberman-Cribbin, W., & Taioli, E.; “Update on human exposure to glyphosate, with a complete review of exposure in children;” Environmental Health, 2020, 19(1), 115; DOI: 10.1186/s12940-020-00673-z.

ABSTRACT:

BACKGROUND: Glyphosate, a commonly used pesticide, has been the topic of much debate. The effects of exposure to glyphosate remains a contentious topic. This paper provides an update to the existing literature regarding levels of glyphosate exposure in occupationally exposed individuals and focuses or reviewing all the available published literature regarding glyphosate exposure levels in children.

METHODS: A literature review was conducted and any articles reporting quantifiable exposure levels in humans published since January 2019 (the last published review on glyphosate exposure) were reviewed and data extracted and standardized.

RESULTS: A total of five new studies reporting exposure levels in humans were found including 578 subjects. Two of these studies focused on occupationally exposed individuals while three of them focused on glyphosate exposure levels in children. Given the sparse nature of the new data, previously identified studies on exposure to glyphosate in children were included in our analysis of children’s exposure. The lowest average level of glyphosate exposure reported was 0.28 μg/L and the highest average exposure levels reported was 4.04 μg/L.

CONCLUSION: The literature on glyphosate exposure levels, especially in children, remains limited. Without more data collected in a standardized way, parsing out the potential relationship between glyphosate exposure and disease will not be possible. FULL TEXT

Pellizzari et al., 2019

Pellizzari, E. D., Woodruff, T. J., Boyles, R. R., Kannan, K., Beamer, P. I., Buckley, J. P., Wang, A., Zhu, Y., & Bennett, D. H.; “Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure: Opportunities for Biomonitoring and Health-Related Research;” Environmental Health Perspectives, 2019, 127(12), 126001; DOI: 10.1289/EHP5133.

ABSTRACT:

BACKGROUND: The National Institutes of Health’s Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemicals for biomonitoring that may present health risk concerns.

OBJECTIVES: Our aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nationwide and to identify gaps needing additional research.

METHODS: We searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemicals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency exposure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring.

RESULTS: For the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in vivo endocrine, developmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro assays. Positive results for endocrine, developmental, neurotoxicity, and obesity were observed for 32, 11, 35, and 60 chemicals, respectively. Predictive modeling results suggested 90% are toxicants. Biomarkers were reported for 76 chemicals. Thirty-six were recommended for biomonitoring, 108 deferred pending additional research, and 11 as low priority for biomonitoring.

DISCUSSION: The 108 deferred chemicals included those lacking biomonitoring methods or toxicity data, representing an opportunity for future research. Our evaluation was, in general, limited by the large number of unmeasured or untested chemicals.  FULL TEXT

Buckley et al., 2020

Buckley, J. P., Barrett, E. S., Beamer, P. I., Bennett, D. H., Bloom, M. S., Fennell, T. R., Fry, R. C., Funk, W. E., Hamra, G. B., Hecht, S. S., Kannan, K., Iyer, R., Karagas, M. R., Lyall, K., Parsons, P. J., Pellizzari, E. D., Signes-Pastor, A. J., Starling, A. P., Wang, A., Watkins, D. J., Zhang, M., Woodruff, T. J., & program collaborators for, Echo; “Opportunities for evaluating chemical exposures and child health in the United States: the Environmental influences on Child Health Outcomes (ECHO) Program;” Journal of exposure science & environmental epidemiology, 2020, 30(3), 397-419; DOI: 10.1038/s41370-020-0211-9.

ABSTRACT:

The Environmental Influences on Child Health Outcomes (ECHO) Program will evaluate environmental factors affecting children’s health (perinatal, neurodevelopmental, obesity, respiratory, and positive health outcomes) by pooling cohorts composed of >50,000 children in the largest US study of its kind. Our objective was to identify opportunities for studying chemicals and child health using existing or future ECHO chemical exposure data. We described chemical-related information collected by ECHO cohorts and reviewed ECHO-relevant literature on exposure routes, sources, and environmental and human monitoring. Fifty-six ECHO cohorts have existing or planned chemical biomonitoring data for mothers or children. Environmental phenols/parabens, phthalates, metals/metalloids, and tobacco biomarkers are each being measured by ≥15 cohorts, predominantly during pregnancy and childhood, indicating ample opportunities to study child health outcomes. Cohorts are collecting questionnaire data on multiple exposure sources and conducting environmental monitoring including air, dust, and water sample collection that could be used for exposure assessment studies. To supplement existing chemical data, we recommend biomonitoring of emerging chemicals, nontargeted analysis to identify novel chemicals, and expanded measurement of chemicals in alternative biological matrices and dust samples. ECHO’s rich data and samples represent an unprecedented opportunity to accelerate environmental chemical research to improve the health of US children. FULL TEXT

Franke et al., 2020

Franke, A. A., Li, X., & Lai, J. F.; “Analysis of glyphosate, aminomethylphosphonic acid, and glufosinate from human urine by HRAM LC-MS;” Analytical and Bioanalytical Chemistry, 2020; DOI: 10.1007/s00216-020-02966-1.

ABSTRACT:

Aminomethylphosphonic acid (AMPA) is the main metabolite of glyphosate (GLYP) and phosphonic acids in detergents. GLYP is a synthetic herbicide frequently used worldwide alone or together with its analog glufosinate (GLUF). The general public can be exposed to these potentially harmful chemicals; thus, sensitive methods to monitor them in humans are urgently required to evaluate health risks. We attempted to simultaneously detect GLYP, AMPA, and GLUF in human urine by high-resolution accurate-mass liquid chromatography mass spectrometry (HRAM LC-MS) before and after derivatization with 9-fluorenylmethoxycarbonyl chloride (Fmoc-Cl) or 1-methylimidazole-sulfonyl chloride (ImS-Cl) with several urine pre-treatment and solid phase extraction (SPE) steps. Fmoc-Cl derivatization achieved the best combination of method sensitivity (limit of detection; LOD) and accuracy for all compounds compared to underivatized urine or ImS-Cl-derivatized urine. Before derivatization, the best steps for GLYP involved 0.4 mM ethylenediaminetetraacetic acid (EDTA) pre-treatment followed by SPE pre-cleanup (LOD 37 pg/mL), for AMPA involved no EDTA pre-treatment and no SPE pre-cleanup (LOD 20 pg/mL) or 0.2-0.4 mM EDTA pre-treatment with no SPE pre-cleanup (LOD 19-21 pg/mL), and for GLUF involved 0.4 mM EDTA pre-treatment and no SPE pre-cleanup (LOD 7 pg/mL). However, for these methods, accuracy was sufficient only for AMPA (101-105%), while being modest for GLYP (61%) and GLUF (63%). Different EDTA and SPE treatments prior to Fmoc-Cl derivatization resulted in high sensitivity for all analytes but satisfactory accuracy only for AMPA. Thus, we conclude that our HRAM LC-MS method is suited for urinary AMPA analysis in cross-sectional studies. FULL TEXT

Connolly et al., 2020

Connolly, A., Coggins, M. A., & Koch, H. M.; “Human Biomonitoring of Glyphosate Exposures: State-of-the-Art and Future Research Challenges;” Toxics, 2020, 8(3); DOI: 10.3390/toxics8030060. https://www.ncbi.nlm.nih.gov/pubmed/32824707.

ABSTRACT:

Glyphosate continues to attract controversial debate following the International Agency for Research on Cancer carcinogenicity classification in 2015. Despite its ubiquitous presence in our environment, there remains a dearth of data on human exposure to both glyphosate and its main biodegradation product aminomethylphosphonic (AMPA). Herein, we reviewed and compared results from 21 studies that use human biomonitoring (HBM) to measure urinary glyphosate and AMPA. Elucidation of the level and range of exposure was complicated by differences in sampling strategy, analytical methods, and data presentation. Exposure data is required to enable a more robust regulatory risk assessment, and these studies included higher occupational exposures, environmental exposures, and vulnerable groups such as children. There was also considerable uncertainty regarding the absorption and excretion pattern of glyphosate and AMPA in humans. This information is required to back-calculate exposure doses from urinary levels and thus, compared with health-based guidance values. Back-calculations based on animal-derived excretion rates suggested that there were no health concerns in relation to glyphosate exposure (when compared with EFSA acceptable daily intake (ADI)). However, recent human metabolism data has reported as low as a 1% urinary excretion rate of glyphosate. Human exposures extrapolated from urinary glyphosate concentrations found that upper-bound levels may be much closer to the ADI than previously reported. FULL TEXT

Berkowitz et al., 2004

Berkowitz, G. S., Wetmur, J. G., Birman-Deych, E., Obel, J., Lapinski, R. H., Godbold, J. H., Holzman, I. R., & Wolff, M. S.; “In utero pesticide exposure, maternal paraoxonase activity, and head circumference;” Environmental Health Perspectives, 2004, 112(3), 388-391; DOI: 10.1289/ehp.6414.

ABSTRACT:

Although the use of pesticides in inner-city homes of the United States is of considerable magnitude, little is known about the potentially adverse health effects of such exposure. Recent animal data suggest that exposure to pesticides during pregnancy and early life may impair growth and neurodevelopment in the offspring. To investigate the relationship among prenatal pesticide exposure, paraoxonase (PON1) polymorphisms and enzyme activity, and infant growth and neurodevelopment, we are conducting a prospective, multiethnic cohort study of mothers and infants delivered at Mount Sinai Hospital in New York City. In this report we evaluate the effects of pesticide exposure on birth weight, length, head circumference, and gestational age among 404 births between May 1998 and May 2002. Pesticide exposure was assessed by a prenatal questionnaire administered to the mothers during the early third trimester as well as by analysis of maternal urinary pentachlorophenol levels and maternal metabolites of chlorpyrifos and pyrethroids. Neither the questionnaire data nor the pesticide metabolite levels were associated with any of the fetal growth indices or gestational age. However, when the level of maternal PON1 activity was taken into account, maternal levels of chlorpyrifos above the limit of detection coupled with low maternal PON1 activity were associated with a significant but small reduction in head circumference. In addition, maternal PON1 levels alone, but not PON1 genetic polymorphisms, were associated with reduced head size. Because small head size has been found to be predictive of subsequent cognitive ability, these data suggest that chlorpyrifos may have a detrimental effect on fetal neurodevelopment among mothers who exhibit low PON1 activity. FULL TEXT

Thomas et al., 2010b

Thomas, K. W., Dosemeci, M., Coble, J. B., Hoppin, J. A., Sheldon, L. S., Chapa, G., Croghan, C. W., Jones, P. A., Knott, C. E., Lynch, C. F., Sandler, D. P., Blair, A. E., & Alavanja, M. C.; “Assessment of a pesticide exposure intensity algorithm in the agricultural health study;” Journal of Exposure Analysis and Environmental Epidemiology, 2010, 20(6), 559-569; DOI: 10.1038/jes.2009.54.

ABSTRACT:

The accuracy of the exposure assessment is a critical factor in epidemiological investigations of pesticide exposures and health in agricultural populations. However, few studies have been conducted to evaluate questionnaire-based exposure metrics. The Agricultural Health Study (AHS) is a prospective cohort study of pesticide applicators who provided detailed questionnaire information on their use of specific pesticides. A field study was conducted for a subset of the applicators enrolled in the AHS to assess a pesticide exposure algorithm through comparison of algorithm intensity scores with measured exposures. Pre- and post-application urinary biomarker measurements were made for 2,4-D (n=69) and chlorpyrifos (n=17) applicators. Dermal patch, hand wipe, and personal air samples were also collected. Intensity scores were calculated using information from technician observations and an interviewer-administered questionnaire. Correlations between observer and questionnaire intensity scores were high (Spearman’s r=0.92 and 0.84 for 2,4-D and chlorpyrifos, respectively). Intensity scores from questionnaires for individual applications were significantly correlated with post-application urinary concentrations for both 2,4-D (r=0.42, P<0.001) and chlorpyrifos (r=0.53, P=0.035) applicators. Significant correlations were also found between intensity scores and estimated hand loading, estimated body loading, and air concentrations for 2,4-D applicators (r-values 0.28-0.50, P-values<0.025). Correlations between intensity scores and dermal and air measures were generally lower for chlorpyrifos applicators using granular products. A linear regression model indicated that the algorithm factors for individual applications explained 24% of the variability in post-application urinary 2,4-D concentration, which increased to 60% when the pre-application urine concentration was included. The results of the measurements support the use of the algorithm for estimating questionnaire-based exposure intensities in the AHS for liquid pesticide products. Refinement of the algorithm may be possible using the results from this and other measurement studies. FULL TEXT

Curwin et al., 2007

Curwin, Brian D., Hein, Misty J., Sanderson, Wayne T., Striley, Cynthia, Heederik, Dick, Kromhout, Hans, Reynolds, Stephen J., & Alavanja, Michael C.; “Pesticide dose estimates for children of Iowa farmers and non-farmers;” Environmental Research, 2007, 105, 307-315; DOI: 10.1016/j.envres.2007.06.001.

ABSTRACT:

Farm children have the potential to be exposed to pesticides. Biological monitoring is often employed to assess this exposure; however, the significance of the exposure is uncertain unless doses are estimated. In the spring and summer of 2001, 118 children (66 farm, 52 non-farm) of Iowa farm and non-farm households were recruited to participate in a study investigating potential take-home pesticide exposure. Each child provided an evening and morning urine sample at two visits spaced approximately 1 month apart, with the first sample collection taken within a few days after pesticide application. Estimated doses were calculated for atrazine, metolachlor, chlorpyrifos, and glyphosate from urinary metabolite concentrations derived from the spot urine samples and compared to EPA reference doses. For all pesticides except glyphosate, the doses from farm children were higher than doses from the non-farm children. The difference was statistically significant for atrazine (p<0.0001) but only marginally significant for chlorpyrifos and metolachlor (p=0.07 and 0.1, respectively). Among farm children, geometric mean doses were higher for children on farms where a particular pesticide was applied compared to farms where that pesticide was not applied for all pesticides except glyphosate; results were significant for atrazine (p=0.030) and metolachlor (p=0.042), and marginally significant for chlorpyrifos (p=0.057). The highest estimated doses for atrazine, chlorpyrifos, metolachlor, and glyphosate were 0.085, 1.96, 3.16, and 0.34 μg/kg/day, respectively. None of the doses exceeded any of the EPA reference values for atrazine, metolachlor, and glyphosate; however, all of the doses for chlorpyrifos exceeded the EPA chronic population adjusted reference value. Doses were similar for male and female children. A trend of decreasing dose with increasing age was observed for chlorpyrifos. FULL TEXT

Blair et al., 2011

Blair, A., Thomas, K., Coble, J., Sandler, D. P., Hines, C. J., Lynch, C. F., Knott, C., Purdue, M. P., Zahm, S. H., Alavanja, M. C., Dosemeci, M., Kamel, F., Hoppin, J. A., Freeman, L. B., & Lubin, J. H.; “Impact of pesticide exposure misclassification on estimates of relative risks in the Agricultural Health Study;” Occupational and Environmental Medicine, 2011, 68(7), 537-541; DOI: 10.1136/oem.2010.059469.

ABSTRACT:

BACKGROUND: The Agricultural Health Study (AHS) is a prospective study of licensed pesticide applicators and their spouses in Iowa and North Carolina. We evaluate the impact of occupational pesticide exposure misclassification on relative risks using data from the cohort and the AHS Pesticide Exposure Study (AHS/PES).

METHODS: We assessed the impact of exposure misclassification on relative risks using the range of correlation coefficients observed between measured post-application urinary levels of 2,4-dichlorophenoxyacetic acid (2,4-D) and a chlorpyrifos metabolite and exposure estimates based on an algorithm from 83 AHS pesticide applications.

RESULTS: Correlations between urinary levels of 2,4-D and a chlorpyrifos metabolite and algorithm estimated intensity scores were about 0.4 for 2,4-D (n=64), 0.8 for liquid chlorpyrifos (n=4) and 0.6 for granular chlorpyrifos (n=12). Correlations of urinary levels with kilograms of active ingredient used, duration of application, or number of acres treated were lower and ranged from -0.36 to 0.19. These findings indicate that a priori expert-derived algorithm scores were more closely related to measured urinary levels than individual exposure determinants evaluated here. Estimates of potential bias in relative risks based on the correlations from the AHS/PES indicate that non-differential misclassification of exposure using the algorithm would bias estimates towards the null, but less than that from individual exposure determinants.

CONCLUSIONS: Although correlations between algorithm scores and urinary levels were quite good (ie, correlations between 0.4 and 0.8), exposure misclassification would still bias relative risk estimates in the AHS towards the null and diminish study power.

FULL TEXT

Coble et al., 2005

Coble, J., Arbuckle, T., Lee, W., Alavanja, M., & Dosemeci, M.; “The validation of a pesticide exposure algorithm using biological monitoring results;” Journal of Occupational and Environmental Hygiene, 2005, 2(3), 194-201; DOI: 10.1080/15459620590923343.

ABSTRACT:

A pesticide exposure algorithm was developed to calculate pesticide exposure intensity scores based on responses to questions about pesticide handling procedures and application methods in a self-administered questionnaire. The validity of the algorithm was evaluated through comparison of the algorithm scores with biological monitoring data from a study of 126 pesticide applicators who applied the herbicides MCPA or 2,4-D. The variability in the algorithm scores calculated for these applicators was due primarily to differences in their use of personal protective equipment (PPE). Rubber gloves were worn by 75% of applicators when mixing and 22% when applying pesticides, rubber boots were worn by 33% when mixing and 23% when applying, and goggles were worn by 33% and 17% of applicators when mixing and when applying, respectively. Only 2% of applicators wore all three types of PPE when both mixing and applying, and 15% wore none of these three types of PPE when either mixing or applying. Substantial variability was also observed in the concentrations of pesticides detected in the post application urine samples. The concentration of MCPA detected in urine samples collected on the second day after the application ranged from less than < 1.0 to 610 microg/L among 84 of the applicators who applied MCPA. The concentrations of 2,4-D detected in the urine samples ranged from less than < 1.0 to 514 microg/L among 41 of the applicators who applied 2,4-D. When categorized into three groups based on the algorithm scores, the geometric mean in the highest exposure group was 20 microg/L compared with 5 microg/L in the lowest exposure group for the MCPA applicators, and 29 microg/L in highest exposure group compared with 2 microg/L in the low exposure group for the 2,4-D applicators. A regression analysis detected statistically significant trends in the geometric mean of the urine concentrations across the exposure categories for both the 2,4-D and the MCPA applicators. The algorithm scores, based primarily on the use of PPE, appear to provide a reasonably valid measure of exposure intensity for these applicators, however, further studies are needed to generalize these results to other types of pesticides and application methods. FULL TEXT

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