Abstract

Background
Environmental chemical exposures rarely occur as isolated compounds. Instead, individuals are typically exposed to complex mixtures originating from multiple sources. Therefore, evaluating the combined health effects of correlated environmental chemicals through mixture-based approaches is essential.
Methods
This study applied quantile g-computation, a regression-based method designed to estimate the joint effects of correlated exposures. Among 4,279 participants in cycle 5 of the Korean National Environmental Health Survey (KoNEHS), 1,383 participants were included in the final analysis. The exposure mixture included persistent organic pollutants (POPs), specifically organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). Associations with cardiometabolic outcomes, including HbA1c and lipid profile indicators, were evaluated using six hierarchical regression models with increasing levels of covariate adjustment and stratification. For statistically significant results, the relative contribution and directional influence of individual chemicals within the mixture were quantified using estimated mixture weights.
Results
Across the hierarchical regression models, mixture effects were more pronounced for lipid metabolism than for glycemic outcomes. Within the OCP mixture, β-hexachlorocyclohexane and p,p’-DDE consistently showed the largest positive contributions. Within the PCB mixture, PCB74 and PCB153 were the dominant contributors, particularly for lipid-related outcomes.
Conclusions
Overall, these findings support the importance of mixture-based analytical approaches and suggest that dyslipidemia may represent a key pathway linking POP exposure to cardiometabolic risk.

• Keywords: persistent organic pollutants; cardiometabolic outcomes; mixture analysis; quantile g-computation; KoNEHS