Mixtures modeling identifies heavy metals and pyrethroid insecticide metabolites associated with obesity.

We aim to examine the association between chemical mixtures and obesity. Blood and urinary levels of tween-six chemicals were measured in adults who participated in the KoNEHS. We identified the associations of chemicals with obesity using linear regression models. Weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) were conducted as secondary analyses. Of the 3,692 participants included in the analysis, 18.0% had obesity. In the logistic regression model, mercury (Hg), lead (Pb), and 3PBA levels were associated with obesity, and significant trends were observed for these chemical tertiles (p < 0.001). Hg, Pb, and 3PBA levels were also associated with BMI. The WQS index was significantly associated with both obesity (OR = 2.15, 95% CI: 2.11-2.20) and BMI (β = 0.39, 95% CI: 0.37-0.51). The qgcomp index also found a significant association between chemicals and both obesity (OR = 1.70, 95% CI: 1.56-1.85) and BMI (β = 0.40, 95% CI: 0.39-0.41). Hg, Pb, and 3PBA were the most heavily weighed chemicals in these models. In BKMR analysis, the overall effect of the mixture was significantly associated with obesity. Hg, Pb, and 3PBA showed positive trends and were observed as the most important factors associated with obesity. Given increasing exposure to chemicals, there is a need to investigate the associations between chemical exposures, either separately or together, and incident obesity risk factors in well-characterized cohorts of different populations, and to identify potential approaches to chemical exposure prevention.