Citation:

Stanfield, Z., R. Setzer, V. Hull, R. Sayre, K. Isaacs, AND J. Wambaugh. Bayesian inference of chemical exposures from NHANES urine biomonitoring data. Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 32:833-846, (2022). https://doi.org/10.1038/s41370-022-00459-0

Impact/Purpose:

This work details a modeling approach that can estimate chemical exposures to humans from urine biomonitoring data. The method, which has been made into an R package called bayesmarker, uses the concentrations of metabolites in urine (from a set of individuals that is a representative sample of the US population) to infer exposure to the parent chemicals of those metabolites (based on known chemical metabolism) through an approach called Bayesian inference. The latest data from the CDC NHANES survey was used and divergences in chemical exposure across different population groups were explored, specifically with a focus on child exposures. This work provides a detailed description of the approach and results in a publicly available computational tool that can be applied to any data from the NHANES survey as well as other urine biomonitoring studies. This work will allow for continued assessment of human exposure in the future and expand the set of chemicals for which we can infer exposure.

Description:

Background Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data. Objective Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty. Methods Bayesian methodology was used to infer ranges of exposure for parent chemicals of biomarkers measured in urine samples from the U.S population by the National Health and Nutrition Examination Survey (NHANES). Metabolites were probabilistically linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts. Results Chemical exposures were estimated for various population groups and translated to risk-based prioritization using toxicokinetic (TK) modeling and experimental data. Exposure estimates were investigated more closely for children aged 3 to 5 years, a population group that debuted with the 2015–2016 NHANES cohort. Significance The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.

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