Bayesian inference of chemical exposures from NHANES urine biomonitoring data.

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 popula

SUBMITTER: Stanfield Z 

PROVIDER: S-EPMC9979158 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature