Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, 37830, USA.
J Expo Sci Environ Epidemiol. 2022 Nov;32(6):833-846. doi: 10.1038/s41370-022-00459-0. Epub 2022 Aug 17.
Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data.
Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty.
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.
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.
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.
了解哪些环境化学物质会导致人体代谢物的出现，对于从生物监测数据中进行有意义的暴露和风险评估是必要的。
采用一种将生物监测数据与化学代谢信息相结合的建模方法，对生物标志物尿液样本中测量到的母体化学物质的暴露摄入率进行估计，并对其不确定性进行量化。
贝叶斯方法用于推断美国国家健康和营养调查（NHANES）尿液样本中生物标志物的母体化学物质的暴露范围。使用 NHANES 报告和 PubMed 摘要的文本挖掘，将代谢物概率性地与母体化学物质联系起来。
对不同人群进行了化学暴露估计，并通过毒代动力学（TK）建模和实验数据将其转化为基于风险的优先级排序。对 2015-2016 年 NHANES 队列中首次出现的 3 至 5 岁儿童这一人群的暴露估计进行了更深入的研究。
这里描述的方法已被编译成一个 R 包 bayesmarker，并在 GitHub 上公开提供。这些推断出的暴露情况，结合通过高通量 TK 预测的有毒剂量，可以通过基于风险的生物活性-暴露比来帮助识别公共卫生优先化学物质。
