Obesity is a global public health issue, with limited epidemiologic studies on the relationship and mechanisms between organophosphate flame retardants (OPFRs) and metabolic obesity phenotypes (MOPs). We aimed to explore the link between OPFRs metabolite (m-OPFRs) and MOPs using a combined epidemiologic and bioinformatic approach. We used cross-sectional survey data from the U.S. National Health and Nutrition Examination Survey (2011–2018) to analyze the relationship between m-OPFRs and metabolic health obesity (MHO), as well as metabolic unhealthy obesity (MUO). The dataset encompasses eligible adults to assess the impact of individual, mixed, and mediated effects on the outcome variables through multivariate logistic regression, Bayesian kernel machine regression (BKMR), and mediation analysis. Multiple logistic regression models, stratified by tertiles of exposure showed that bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) levels in the body significantly increased the risk of MHO, with OR and 95%CI of 1.454 (1.082, 1.953) for the second tertile (T2) and 1.598 (1.126, 2.268) for the third tertile (T3), compared to the first tertile (T1). Increased levels of BDCIPP in T3 (1.452(1.013, 2.081)) are associated with MUO, compared to T1. Mixed m-OPFRs and MHO risk in BMKR were positively correlated, with BDCIPP being the primary contributor. We found that the serum uric acid (SUA) and white blood cell count (WBC) indicators significantly mediated the association between BDCIPP and MHO (P < 0.05). Our study suggests that OPFRs, either individual or mixed, are associated with two distinct MOPs, with oxidative stress playing an important role. In addition, in silico analysis was used to screen for shared genes, and eight shared genes and eleven biological pathways identified during the screening process were used to construct the adverse outcome pathway, which suggests that exposure to OPFRs may activate the peroxisome proliferator-activated receptor (PPAR) pathway, thereby increasing the risk of obesity. Further studies are needed to validate our findings.
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