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Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques

Bayesian kernel machine regression (BKMR) analysis was used to evaluate the univariate contaminant exposure effect as well as the contaminant mixture effects on levels of thyroid hormones. Significant and positive associations were found between total T3 and PC-2 (high positive nickel and cadmium loadings), total T3 and PC-3 (negative association with negative loading for nickel and positive loading for cadmium) and TSH and PC-1 (high positive loadings for organic contaminants). | Environmental Advances 4 2021 100054 Contents lists available at ScienceDirect Environmental Advances journal homepage www.elsevier.com locate envadv Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques Eric N. Liberda a Aleksandra M. Zuk b David S. Di a Robert J. Moriarity c Ian D. Martin c Leonard J.S. Tsuji c a School of Occupational and Public Health and Environmental Applied Science and Management 350 Victoria St Ryerson University Toronto M5B2K3 Ontario Canada b School of Nursing Queen s University Kingston K7L 3N6 Ontario Canada c Department of Physical and Environmental Sciences University of Toronto Toronto M1C 1A4 Ontario Canada a r t i c l e i n f o a b s t r a c t Keywords Evaluating complex mixtures and their associated health effects poses a challenge in human populations. Herein Thyroid we assess the association between 17 organic and metal contaminants in blood with thyroid hormones in a remote Machine learning Indigenous First Nations region from Quebec Canada n 526 . Using principal component analysis PCA to Contaminants reduce the number of variables we generated varimax rotated principal component PC loadings of contaminants Indigenous on these uncorrelated synthetic axes. Associations with levels of thyroid hormones TSH free T4 and total T3 Exposure BKMR were conducted using multivariable linear regression methods with the participant PC loadings and adjusting for Principal component analysis covariates. Additionally Bayesian kernel machine regression BKMR analysis was used to evaluate the univariate contaminant exposure effect as well as the contaminant mixture effects on levels of thyroid hormones. Significant and positive associations were found between total T3 and PC-2 high positive nickel and cadmium loadings total T3 and PC-3 negative association with negative loading for nickel and positive loading for cadmium and TSH and PC-1 high positive .