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Quantitative methods for metabolomic analyses evaluated in the Children's Health Exposure Analysis Resource (CHEAR).


ABSTRACT: With advances in technologies that facilitate metabolome-wide analyses, the incorporation of metabolomics in the pursuit of biomarkers of exposure and effect is rapidly evolving in population health studies. However, many analytic approaches are limited in their capacity to address high-dimensional metabolomics data within an epidemiologic framework, including the highly collinear nature of the metabolites and consideration of confounding variables. In this Children's Health Exposure Analysis Resource (CHEAR) network study, we showcase various analytic approaches that are established as well as novel in the field of metabolomics, including univariate single metabolite models, least absolute shrinkage and selection operator (LASSO), random forest, weighted quantile sum (WQSRS) regression, exploratory factor analysis (EFA), and latent class analysis (LCA). Here, in a Bangladeshi birth cohort (n = 199), we illustrate research questions that can be addressed by each analytic method in the assessment of associations between cord blood metabolites (1H NMR measurements) and birth anthropometric measurements (birth weight and head circumference).

SUBMITTER: CHEAR Metabolomics Analysis Team 

PROVIDER: S-EPMC8041023 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Quantitative methods for metabolomic analyses evaluated in the Children's Health Exposure Analysis Resource (CHEAR).

Mazzella Matthew M   Sumner Susan J SJ   Gao Shangzhi S   Su Li L   Diao Nancy N   Mostofa Golam G   Qamruzzaman Qazi Q   Pathmasiri Wimal W   Christiani David C DC   Fennell Timothy T   Gennings Chris C  

Journal of exposure science & environmental epidemiology 20190923 1


With advances in technologies that facilitate metabolome-wide analyses, the incorporation of metabolomics in the pursuit of biomarkers of exposure and effect is rapidly evolving in population health studies. However, many analytic approaches are limited in their capacity to address high-dimensional metabolomics data within an epidemiologic framework, including the highly collinear nature of the metabolites and consideration of confounding variables. In this Children's Health Exposure Analysis Re  ...[more]

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