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Characterizing Chemical Exposure Trends from NHANES Urinary Biomonitoring Data
Citation:
Stanfield, Z., Woodrow Setzer, V. Hull, R. Sayre, K. Isaacs, AND J. Wambaugh. Characterizing Chemical Exposure Trends from NHANES Urinary Biomonitoring Data. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, 132(1):017009, (2024). https://doi.org/10.1289/EHP12188
Impact/Purpose:
Aggregate exposure of chemicals through multiple pathways (for example, the environment, consumer products, etc.) changes over time depending on a number of factors, such as use of chemicals in manufacturing, which products they occur in, and how they are regulated. Tracking chemical exposure in a population over time can provide insights as to how the exposure landscape is evolving over time and how that might translate to risk. Biomonitoring data (concentration measurements of chemicals in human media, such as blood or urine) offers a way to obtain a snapshot of aggregate exposure. However, most studies are limited in either the number of chemicals observed, the survey population size, or the number of time points. Additionally, biomonitoring data is limited to measuring metabolite concentrations, making it difficult to determine the original exposure to the parent chemicals of the metabolites in the environment. In this work, we used a Bayesian inference approach (via an R package called bayesmarker) to estimate chemical intake rates from a long-running biomonitoring study. Metabolite urine concentrations from the CDC's National Health and Nutrition Examination Survey (NHANES) between the years of 1999 and 2016 were used to infer exposure to their parent chemicals using a toxicokinetic model and a mapping between parent chemicals and their known metabolic products. Our analysis resulted in an exposure landscape which we examined at a high level by clustering exposure trends and identifying chemical classes and uses enriched in these various clusters as well as combining data from multiple NHANES cohorts to observe a more robust exposure change between two decades. We also observed specific trends for certain chemicals and population groups, many of which were intuitively explained. The result of this work is a large-scale summary of exposure to the U.S. population at the metabolite (151 chemicals) and parent chemical (179) levels spanning an 18-year time frame. These findings will be helpful for evaluating and benchmarking various exposure models as well as understanding how exposures change over time. Additionally, this framework and the R package is well-documented such that it will be simple to incorporate new years of NHANES data and chemicals as they are released to extend the exposure landscape presented in this work.
Description:
Background: Xenobiotic metabolites are widely present in human urine and can indicate recent exposure to environmental chemicals. Proper inference of which chemicals contribute to these metabolites can inform human exposure and risk. Furthermore, longitudinal biomonitoring studies provide insight into how chemical exposures change over time. Objectives: We constructed an exposure landscape for as many human-exposure relevant chemicals over as large a time span as possible to characterize exposure trends across demographic groups and chemical types. Methods: We analyzed urine data of nine 2-y cohorts (1999–2016) from the National Health and Nutrition Examination Survey (NHANES). Chemical daily intake rates (in milligrams per kilogram bodyweight per day) were inferred, using the R package bayesmarker, from metabolite concentrations in each cohort individually to identify exposure trends. Trends for metabolites and parents were clustered to find chemicals with similar exposure patterns. Exposure variation by age, gender, and body mass index were also assessed. Results: Intake rates for 179 parent chemicals were inferred from 151 metabolites (96 measured in five or more cohorts). Seventeen metabolites and 44 parent chemicals exhibited fold-changes ≥10 between any two cohorts (deltamethrin, di-n-octyl phthalate, and di-isononyl phthalate had the greatest exposure increases). Di-2-ethylhexyl phthalate intake began decreasing in 2007, whereas both di-isobutyl and di-isononyl phthalate began increasing shortly before. Intake for four parabens was markedly higher in females, especially reproductive-age females, compared with males and children. Cadmium and arsenobetaine exhibited higher exposure for individuals >65 years of age and lower for individuals <20 years of age. Discussion: With appropriate analysis, NHANES indicates trends in chemical exposures over the past two decades. Decreases in exposure are observable as the result of regulatory action, with some being accompanied by increases in replacement chemicals. Age- and gender-specific variations in exposure were observed for multiple chemicals. Continued estimation of demographic-specific exposures is needed to both monitor and identify potential vulnerable populations. https://doi.org/10.1289/EHP12188
URLs/Downloads:
DOI: Characterizing Chemical Exposure Trends from NHANES Urinary Biomonitoring Data
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10824265/