A Method for Identifying Prevalent Chemical Combinations in the U.S. Population

Abstract

Background:Through the food and water they ingest, the air they breathe, and the consumer products with which they interact at home and at work, humans are exposed to tens of thousands of chemicals, many of which have not been evaluated to determine their potential toxicities. Furthermore, while current chemical testing tends to focus on individual chemicals, the exposures that people actually experience involve mixtures of chemicals. Unfortunately, the number of mixtures that can be formed from the thousands of environmental chemicals is enormous, and testing all of them would be impossible.Objectives:We seek to develop and demonstrate a method for identifying those mixtures that are most prevalent in humans.Methods:We applied frequent itemset mining, a technique traditionally used for market basket analysis, to biomonitoring data from the 2009–2010 cycle of the continuous National Health and Nutrition Examination Survey (NHANES) to identify combinations of chemicals that frequently co-occur in people.Results:We identified 90 chemical combinations consisting of relatively few chemicals that occur in at least 30% of the U.S. population, as well as three supercombinations consisting of relatively many chemicals that occur in a small but nonnegligible proportion of the U.S. population.Conclusions:We demonstrated how FIM can be used in conjunction with biomonitoring data to narrow a large number of possible chemical combinations down to a smaller set of prevalent chemical combinations. https://doi.org/10.1289/EHP1265

Biomarkers of exposure to SVOCs in children and their demographic associations: The TESIE Study

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Regulatory assessment and risk management of chemical mixtures: challenges and ways forward

“…The methodology usually assesses the frequency of exceedances of safety thresholds in time and space as well as looking at the degree of exceedance. Other approaches focus on identifying prevalent combinations of chemicals by statistical analysis of co-exposure patterns (Kapraun et al 2017). Considering use-dependent mixture exposures (modeling exposure concentrations, for example, based on agricultural, domestic and urban run-off scenarios) was suggested as a means of prioritizing environmental MRA in river catchments (Posthuma et al 2018).…”