Changes in epigenetic marks such as DNA methylation and histone acetylation are associated with a broad range of disease traits, including cancer, asthma, metabolic disorders, and various reproductive conditions. It seems plausible that changes in epigenetic state may be induced by environmental exposures such as malnutrition, tobacco smoke, air pollutants, metals, organic chemicals, other sources of oxidative stress, and the microbiome, particularly if the exposure occurs during key periods of development. Thus, epigenetic changes could represent an important pathway by which environmental factors influence disease risks, both within individuals and across generations. We discuss some of the challenges in studying epigenetic mediation of pathogenesis and describe some unique opportunities for exploring these phenomena.

Per- and polyfluoroalkyl substances (PFASs) exposure is ubiquitous among the US population and has been linked to adverse health outcomes including cardiometabolic diseases, immune dysregulation and endocrine disruption. However, the metabolic mechanism underlying the adverse health effect of PFASs exposure is unknown. The aim of this project is to investigate the association between PFASs exposure and altered metabolic pathways linked to increased cardiometabolic risk in young adults. A total of 102 young adults with 82% overweight or obese participants were enrolled from Southern California between 2014 and 2017. Cardiometabolic outcomes were assessed including oral glucose tolerance test (OGTT) measures, body fat and lipid profiles. High-resolution metabolomics was used to quantify plasma exposure levels of three PFAS congeners and intensity profiles of the untargeted metabolome. Fasting concentrations of 45 targeted metabolites involved in fatty acid and lipid metabolism were used to verify untargeted metabolomics findings. Bayesian Kernel Machine Regression (BKMR) was used to examine the associations between PFAS exposure mixture and cardiometabolic outcomes adjusting for covariates. Mummichog pathway enrichment analysis was used to explore PFAS-associated metabolic pathways. Moreover, the effect of PFAS exposure on the metabolic network, including metabolomic profiles and cardiometabolic outcomes, was investigated. Higher exposure to perfluorooctanoic acid (PFOA) was associated with higher 30-minute glucose levels and glucose area under the curve (AUC) during the OGTT (p < 0.001). PFAS exposure was also associated with altered lipid pathways, which contributed to the metabolic network connecting PFOA and higher glucose levels following the OGTT. Targeted metabolomics analysis indicated that higher PFOA exposure was associated with higher levels of glycerol (p = 0.006), which itself was associated with higher 30-minute glucose (p = 0.006). Increased lipolysis and fatty acid oxidation could contribute to the biological mechanisms linking PFAS exposure and impaired glucose metabolism among young adults. Findings of this study warrants future experimental studies and epidemiological studies with larger sample size to replicate.

Despite the considerable enthusiasm about the yield of novel and replicated discoveries of genetic associations from the new generation of genome-wide association studies (GWAS), the proportion of the heritability of most complex diseases that have been studied to date remains small. Some of this “dark matter” could be due to gene-environment (G × E) interactions or more complex pathways involving multiple genes and exposures. We review the basic epidemiologic study design and statistical analysis approaches to studying G × E interactions individually and then consider more comprehensive approaches to studying entire pathways or GWAS data. In addition to the usual issues in genetic association studies, particular care is needed in exposure assessment, and very large sample sizes are required. Although hypothesis-driven, pathway-based and agnostic GWA study approaches are generally viewed as opposite poles, we suggest that the two can be usefully married using hierarchical modeling strategies that exploit external pathway knowledge in mining genome-wide data.