Environmental Advances (Jul 2021)
Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques
Abstract
Evaluating complex mixtures and their associated health effects poses a challenge in human populations. Herein, we assess the association between 17 organic and metal contaminants in blood with thyroid hormones in a remote Indigenous (First Nations) region from Quebec, Canada (n=526). Using principal component analysis (PCA) to reduce the number of variables, we generated varimax rotated principal component (PC) loadings of contaminants on these uncorrelated synthetic axes. Associations with levels of thyroid hormones (TSH, free T4, and total T3) were conducted using multivariable linear regression methods with the participant PC loadings and adjusting for 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 loadings for organic contaminants). No significant observations were observed for free T4. BKMR provided additional insight into the PCA results and suggested that nickel, and not cadmium, was responsible for driving the observed effects with this effect remaining when evaluating the entire mixture. BKMR analysis did not support the association of TSH with organic contaminants that were found in the PCA regression. Our findings reinforced other studies which showed that metals such as nickel may alter thyroid hormone levels and highlighted how complex environmental mixtures interact with each other. These observations represent an important step to determining how complex mixtures of contaminants can be assessed in human populations, especially those living a subsistence lifestyle who may have high body burdens of contaminants, and to help understand the resultant net effect of exposures on endogenous thyroid hormones utilizing novel machine learning statistical methods.
