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Combined Effect of Metals, PFAS, Phthalates, and Plasticizers on Cardiovascular Disease Risk
Summary
This study used Bayesian Kernel Machine Regression on NHANES data to assess how mixtures of metals, PFAS, phthalates, and plasticizers collectively affect cardiovascular markers, finding that phthalates and plasticizers were major contributors to altered LDL, blood pressure, and cholesterol levels, with the overall mixture associated with increased cardiovascular disease risk.
This study assessed the relationship between environmental chemical mixtures-including metals, per- and polyfluoroalkyl substances (PFAS), phthalates, and plasticizers-and key cardiovascular health markers using data from the 2013-2014 National Health and Nutrition Examination Survey (NHANES). The combined effects of these pollutants on cardiovascular markers were evaluated using Bayesian Kernel Machine Regression (BKMR), a flexible, non-parametric modeling approach that accommodates nonlinear and interactive relationships among exposures. BKMR was applied to assess both the joint and individual associations of the chemical mixture with systolic blood pressure (SBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), diastolic blood pressure (DBP), total cholesterol, and triglycerides. As part of the BKMR analysis, posterior inclusion probabilities (PIPs) were estimated to identify the relative importance of each exposure within the mixture. These results highlighted phthalates as major contributors to LDL, SBP, total cholesterol, HDL, and triglycerides while plasticizers were associated with LDL, SBP, HDL, and triglycerides. Metals and PFAS were most strongly linked to LDL, DBP, total cholesterol, and SBP. The overall mixture effect indicated that cumulative exposures were associated with lower LDL and SBP and elevated DBP, suggesting an increased cardiovascular risk. Triglycerides exhibited a complex quantile-dependent trend, with higher exposures associated with reduced levels. These findings underscore the importance of mixture-based risk assessments that reflect real-world exposure scenarios.