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A Method for Identifying Prevalent Chemical Combinations in the U.S. Population.
Kapraun, Dustin F; Wambaugh, John F; Ring, Caroline L; Tornero-Velez, Rogelio; Setzer, R Woodrow.
Affiliation
  • Kapraun DF; National Center for Computational Toxicology, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina, USA.
  • Wambaugh JF; National Center for Computational Toxicology, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina, USA.
  • Ring CL; National Center for Computational Toxicology, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina, USA.
  • Tornero-Velez R; Oak Ridge Institute for Science and Education , Oak Ridge, Tennessee, USA.
  • Setzer RW; National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina, USA.
Environ Health Perspect ; 125(8): 087017, 2017 08 24.
Article in En | MEDLINE | ID: mdl-28858827
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Environmental Monitoring / Environmental Exposure / Environmental Pollutants / Data Mining Limits: Humans Country/Region as subject: America do norte Language: En Journal: Environ Health Perspect Year: 2017 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Environmental Monitoring / Environmental Exposure / Environmental Pollutants / Data Mining Limits: Humans Country/Region as subject: America do norte Language: En Journal: Environ Health Perspect Year: 2017 Document type: Article Affiliation country: Estados Unidos