Genes, Environmental Pollutants, and Endocrine System Disruption

In this study, we explored the associations between endocrine-disrupting chemicals (EDCs) and endocrine system disruption including hyperglycemic outcomes, lipid profiles, and thyroid hormone levels. We aimed to investigate the associations considering interrelationships between EDCs and genetic predisposition, potential reverse causality in cross-sectional study, and collective effects of environmental mixtures. In the first paper, we evaluated interactions between genetic polymorphisms and persistent organic pollutants (POPs) on hyperglycemic outcomes using polygenic risk scores (PRS) for diabetes. We observed associations between the PRS and incident diabetes and prediabetes, but not with POPs. Yet, we found significant gene-environment interaction between PRS and POPs among individuals with higher exposure to POPs. The results of the first paper implied that managing modifiable risk factors can contribute to decrease risk of adverse health outcomes attributed to unmodifiable genetic risk. In the second paper, we assessed the associations between POPs and longitudinal change of lipid profiles. We observed associations between POPs and lower HDL cholesterol in longitudinal analyses. Our findings with longitudinal analyses deviated from cross-sectional analyses, which suggested that the results from cross-sectional studies might be biased and emphasized the importance of longitudinal studies in this field of study. In the third paper, we investigated the association of exposure to multiple metals with thyroid hormones. We used quantile g-computation (QGCOMP) to address the collective effects of metals as a mixture. We observed monotonic and non-monotonic associations between single metals and thyroid hormones. Moreover, our results from mixture analyses suggested that the single metal associations with thyroid hormones may be modified under the existence of concentrations of other metals. Our findings suggest the necessity of extending traditional regressions with mixtures analyses. We anticipate our study will contribute to understanding the associations between EDCs and adverse health outcomes, and current challenges in epidemiologic methods.