You are here:
Characterizing Exposure Trends from NHANES Urinary Biomonitoring Data.
Citation:
Stanfield, Z., Woodrow Setzer, V. Hull, R. Sayre, K. Isaacs, AND J. Wambaugh. Characterizing Exposure Trends from NHANES Urinary Biomonitoring Data. Society of Toxicology, San Diego, CA, March 27 - 31, 2022. https://doi.org/10.23645/epacomptox.19384004
Impact/Purpose:
N/A
Description:
Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data. It is also important to understand how these exposures might be changing over time. We employed a recently developed approach using Bayesian methodology to infer ranges of exposure to parent compounds consistent with urinary biomarker levels reported in the National Health and Nutrition and Examination Survey (NHANES), which is representative of the U.S. population. The method was made into a publicly available R package named bayesmarker and applied to all NHANES 2-year cohorts (from 1999 to 2016). Metabolites were linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts. The 151 NHANES metabolites were linked to 179 unique parent chemicals by 270 associations. Using the bayesmarker package, chemical exposure values were inferred for each NHANES cohort individually to identify temporal exposure trends. The chemicals di-isononyl phthalate, ethylbenzene, and deltamethrin exhibited the greatest increase in exposure over time while multiple chemicals with restrictions on use, production, or emission (such as triclosan, bisphenol A, and diethyl phthalate) showed corresponding decreases in exposure. Trends were stratified by chemical class as well as clustered to identify common patterns of exposure over time. Temporal forecasting was used to categorize chemicals by anticipated changes in exposure: likely to increase, decrease, or stay the same in the upcoming NHANES cohorts. Chemicals for various demographic groups were also prioritized by identifying those with the highest divergence in exposure from the average of the total population over time. Lastly, we demonstrated another aspect of the bayesmarker package by combining the NHANES data by decade (2000s and 2010s) for increased statistical power to identify more robust exposure changes. The tools and methods used to create these analyses have been thoroughly documented in preparation for peer-review and public distribution to the biomonitoring community. This abstract does not necessarily reflect U.S. EPA policy.
URLs/Downloads:
DOI: Characterizing Exposure Trends from NHANES Urinary Biomonitoring Data.
SOT2022POSTER.PDF (PDF, NA pp, 1402.471 KB, about PDF)