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Bayesian inference of chemical exposures from NHANES urine biomonitoring data

Journal of Exposure Science & Environmental Epidemiology volume 32, pages 833–846 (2022)Cite this article

Abstract

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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Fig. 1: Depiction of the Bayesian inference methodology employed to estimate human exposure to parent chemicals of metabolites measured in urine biomonitoring data.
Fig. 2: Network representation of the parent-metabolite mapping for all NHANES metabolites.
Fig. 3: Difference in inferred chemical exposure between each population group and the total population.
Fig. 4: Inferred bioactivity:exposure ratios (BERs) for parent chemicals of NHANES metabolites.
Fig. 5: Comparison of estimated intake rates of the new population group, 3–5-year-olds, introduced in the 2015–2016 NHANES cohort with 6–11-year-olds and all participants (labeled “Total”).
Fig. 6: Comparison of bayesmarker estimated intake rates with SEEM predictions for chemicals not in the SEEM calibration set.
Fig. 7: Visualization of the influence of various potential contributors to uncertainty on inferred intake rates from urine biomonitoring data for personal care and consumer product chemicals.

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Data availability

All biomonitoring data from NHANES is hosted online by the CDC (https://wwwn.cdc.gov/nchs/nhanes). The bayesmarker input data (metabolite table, weights table, and metabolite map) along with data used to generate each figure is included in the supplemental data file. The supplemental data also includes the exposure estimates (median and 95% CIs) using the most recent NHAHES cohort data for each metabolite.

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Acknowledgements

We thank Drs. Elaina Kenyon and Jeff Minucci for their helpful U.S. EPA internal reviews of the manuscript and Dr. Caroline Ring for useful discussion.

Funding

The United States Environmental Protection Agency (EPA) through its Office of Research and Development (ORD) funded the research described here. The views expressed in this publication are those of the authors and do not necessarily represent the views or policies of the U.S. EPA. Reference to commercial products or services does not constitute endorsement

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Authors and Affiliations

  1. Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA

    Zachary Stanfield, R. Woodrow Setzer, Victoria Hull, Risa R. Sayre, Kristin K. Isaacs & John F. Wambaugh

  2. Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, 37830, USA

    Victoria Hull

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Contributions

RWS and JFW devised and wrote the original R code and Bayesian inference model. ZS made minor updates to the original R code and then adapted it into the bayesmarker R package. ZS performed data analyses and drafted figures, tables, and text. RRS built the original parent-metabolite mapping and RRS and VH helped curate the final bayesmarker input files. RWS, VH, RRS, KKI, and JFW reviewed and provided comments on the text.

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Correspondence to John F. Wambaugh.

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Stanfield, Z., Setzer, R.W., Hull, V. et al. Bayesian inference of chemical exposures from NHANES urine biomonitoring data. J Expo Sci Environ Epidemiol 32, 833–846 (2022). https://doi.org/10.1038/s41370-022-00459-0

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