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Application of an analytical framework for multivariate mediation analysis of environmental data

Author

Listed:
  • Max T. Aung

    (University of Michigan School of Public Health)

  • Yanyi Song

    (University of Michigan School of Public Health)

  • Kelly K. Ferguson

    (Research Triangle Park)

  • David E. Cantonwine

    (Brigham and Women’s Hospital, Harvard Medical School)

  • Lixia Zeng

    (University of Michigan)

  • Thomas F. McElrath

    (Brigham and Women’s Hospital, Harvard Medical School)

  • Subramaniam Pennathur

    (University of Michigan
    University of Michigan
    University of Michigan)

  • John D. Meeker

    (University of Michigan School of Public Health)

  • Bhramar Mukherjee

    (University of Michigan School of Public Health
    University of Michigan School of Public Health)

Abstract

Diverse toxicological mechanisms may mediate the impact of environmental toxicants (phthalates, phenols, polycyclic aromatic hydrocarbons, and metals) on pregnancy outcomes. In this study, we introduce an analytical framework for multivariate mediation analysis to identify mediation pathways (q = 61 mediators) in the relationship between environmental toxicants (p = 38 analytes) and gestational age at delivery. Our analytical framework includes: (1) conducting pairwise mediation for unique exposure-mediator combinations, (2) exposure dimension reduction by estimating environmental risk scores, and (3) multivariate mediator analysis using either Bayesian shrinkage mediation analysis, population value decomposition, or mediation pathway penalization. Dimension reduction demonstrates that a one-unit increase in phthalate risk score is associated with a total effect of 1.07 lower gestational age (in weeks) at delivery (95% confidence interval: 0.48–1.67) and eicosanoids from the cytochrome p450 pathway mediated 26% of this effect (95% confidence interval: 4–63%). Eicosanoid products derived from the cytochrome p450 pathway may be important mediators of phthalate toxicity.

Suggested Citation

  • Max T. Aung & Yanyi Song & Kelly K. Ferguson & David E. Cantonwine & Lixia Zeng & Thomas F. McElrath & Subramaniam Pennathur & John D. Meeker & Bhramar Mukherjee, 2020. "Application of an analytical framework for multivariate mediation analysis of environmental data," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19335-2
    DOI: 10.1038/s41467-020-19335-2
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    Cited by:

    1. Yanyi Song & Xiang Zhou & Jian Kang & Max T. Aung & Min Zhang & Wei Zhao & Belinda L. Needham & Sharon L. R. Kardia & Yongmei Liu & John D. Meeker & Jennifer A. Smith & Bhramar Mukherjee, 2021. "Bayesian sparse mediation analysis with targeted penalization of natural indirect effects," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1391-1412, November.
    2. Cai, Xizhen & Zhu, Yeying & Huang, Yuan & Ghosh, Debashis, 2022. "High-dimensional causal mediation analysis based on partial linear structural equation models," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

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