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Understanding mixed environmental exposures using metabolomics via a hierarchical community network model in a cohort of California women in 1960's.


ABSTRACT: Even though the majority of population studies in environmental health focus on a single factor, environmental exposure in the real world is a mixture of many chemicals. The concept of "exposome" leads to an intellectual framework of measuring many exposures in humans, and the emerging metabolomics technology offers a means to read out both the biological activity and environmental impact in the same dataset. How to integrate exposome and metabolome in data analysis is still challenging. Here, we employ a hierarchical community network to investigate the global associations between the metabolome and mixed exposures including DDTs, PFASs and PCBs, in a women cohort with sera collected in California in the 1960s. Strikingly, this analysis revealed that the metabolite communities associated with the exposures were non-specific and shared among exposures. This suggests that a small number of metabolic phenotypes may account for the response to a large class of environmental chemicals.

SUBMITTER: Li S 

PROVIDER: S-EPMC6949431 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Understanding mixed environmental exposures using metabolomics via a hierarchical community network model in a cohort of California women in 1960's.

Li Shuzhao S   Cirillo Piera P   Hu Xin X   Tran ViLinh V   Krigbaum Nickilou N   Yu Shaojun S   Jones Dean P DP   Cohn Barbara B  

Reproductive toxicology (Elmsford, N.Y.) 20190709


Even though the majority of population studies in environmental health focus on a single factor, environmental exposure in the real world is a mixture of many chemicals. The concept of "exposome" leads to an intellectual framework of measuring many exposures in humans, and the emerging metabolomics technology offers a means to read out both the biological activity and environmental impact in the same dataset. How to integrate exposome and metabolome in data analysis is still challenging. Here, w  ...[more]

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