Linking biomarker data to pharmacokinetic (PK) models permits comparison of absorbed dose with a toxicological benchmark, which is an important step to understanding the health implications of pesticide exposure. The purpose of this analysis was to evaluate the feasibility of reconstructing the absorbed dose of two pesticides using PK models developed from biomarker data in a study of occupational application of these compounds. Twenty-four-hour urine samples were collected from farmers 24 h before through 96 h after a typical application of chlorpyrifos or 2,4-D. PK models were used to link the amounts found in urine samples to absorbed dose. Modeled total body dose estimates (in micrograms) were compared to measured dose from time 0-96 h. Despite the complexities surrounding the interpretation of biomonitoring data from a field setting, the models developed as part of this analysis accurately estimated the absorbed dose of 2,4-D and chlorpyrifos when collection of urine samples was largely complete. Over half of the farmers were excluded from modeling due to suspected noncompliance with urine collection or confounding exposure events, which highlights the importance of these issues for designing and interpreting biomonitoring data in future studies. Further evaluation of PK models in scenarios using single void samples is warranted for improving field-based dose assessments.
[Show abstract][Hide abstract] ABSTRACT: We conducted a systematic review and meta-analysis of childhood leukemia and parental occupational pesticide exposure.
Searches of MEDLINE (1950-2009) and other electronic databases yielded 31 included studies.
Two authors independently abstracted data and assessed the quality of each study.
Random effects models were used to obtain summary odds ratios (ORs) and 95% confidence intervals (CIs). There was no overall association between childhood leukemia and any paternal occupational pesticide exposure (OR = 1.09; 95% CI, 0.88-1.34); there were slightly elevated risks in subgroups of studies with low total-quality scores (OR = 1.39; 95% CI, 0.99-1.95), ill-defined exposure time windows (OR = 1.36; 95% CI, 1.00-1.85), and exposure information collected after offspring leukemia diagnosis (OR = 1.34; 95% CI, 1.05-1.70). Childhood leukemia was associated with prenatal maternal occupational pesticide exposure (OR = 2.09; 95% CI, 1.51-2.88); this association was slightly stronger for studies with high exposure-measurement-quality scores (OR = 2.45; 95% CI, 1.68-3.58), higher confounder control scores (OR = 2.38; 95% CI, 1.56-3.62), and farm-related exposures (OR = 2.44; 95% CI, 1.53-3.89). Childhood leukemia risk was also elevated for prenatal maternal occupational exposure to insecticides (OR = 2.72; 95% CI, 1.47-5.04) and herbicides (OR = 3.62; 95% CI, 1.28-10.3).
Childhood leukemia was associated with prenatal maternal occupational pesticide exposure in analyses of all studies combined and in several subgroups. Associations with paternal occupational pesticide exposure were weaker and less consistent. Research needs include improved pesticide exposure indices, continued follow-up of existing cohorts, genetic susceptibility assessment, and basic research on childhood leukemia initiation and progression.
Environmental Health Perspectives 10/2009; 117(10):1505-13. DOI:10.1289/ehp.0900582 · 7.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Chlorpyrifos exposures were assessed in 12 Egyptian cotton field workers.
3,5,6-trichloro-2-pyridinol (TCPy) was measured in 24-hour urine samples to estimate absorbed dose. Workshift air samples were used to calculate chlorpyrifos inhalation dose.
Patches on legs had the highest chlorpyrifos loading rates among body regions sampled. Geometric mean chlorpyrifos air concentrations were 5·1, 8·2, and 45·0 μg/m(3) for engineers, technicians, and applicators, respectively; peak TCPy urinary concentrations were 75-129, 78-261, and 487-1659 μg/l, respectively; geometric mean doses were 5·2-5·4, 8·6-9·7, and 50-57 μg/kg, respectively, considering TCPy excretion half-life values of 27 and 41 hours. All worker doses exceeded the acceptable operator exposure level of 1·5 μg/kg/day. An estimated 94-96% of the dose was attributed to dermal exposure, calculated as the difference between total dose and inhalation dose.
Interventions to reduce dermal exposure are warranted in this population, particularly for the hands, feet, and legs.
International journal of occupational and environmental health 07/2012; 18(3):198-209. DOI:10.1179/1077352512Z.00000000030 · 1.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Human biomonitoring has become a primary tool for chemical exposure characterization in a wide variety of contexts: population monitoring and characterization at a national level, assessment and description of cohort exposures, and individual exposure assessments in the context of epidemiological research into potential adverse health effects of chemical exposures. The accurate use of biomonitoring as an exposure characterization tool requires understanding of factors, apart from external exposure level, that influence variation in biomarker concentrations. This review provides an overview of factors that might influence inter- and intraindividual variation in biomarker concentrations apart from external exposure magnitude. These factors include characteristics of the specific chemical of interest, characteristics of the likely route(s) and frequency of exposure, and physiological characteristics of the biomonitoring matrix (typically, blood or urine). Intraindividual variation in biomarker concentrations may be markedly affected by the relationship between the elimination half-life and the intervals between exposure events, as well as by variation in characteristics of the biomonitored media such as blood lipid content or urinary flow rate. Variation across individuals may occur due to differences in time of sampling relative to exposure events, physiological differences influencing urinary flow or creatinine excretion rates or blood characteristics, and interindividual differences in metabolic rate or other factors influencing the absorption or excretion rate of a compound. Awareness of these factors can assist researchers in improving the design and interpretation of biomonitoring studies.
Journal of Toxicology and Environmental Health Part B 03/2014; 17(1):45-61. DOI:10.1080/10937404.2013.864250 · 4.97 Impact Factor
Note: This list is based on the publications in our database and might not be exhaustive.
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