Mixed Exposure to Underground Air Pollutants and Metabolic Syndrome in Coal Miners: A Cross-Sectional Study Integrating Multi-Pollutant Models and Urinary Metabolomics

Abstract

To investigate the associations between single and mixed exposure to air pollutants in underground coal mine environments and the risk of metabolic syndrome (MetS) among workers, we conducted a cross-sectional study. Multivariable logistic regression, Bayesian kernel machine regression (BKMR), and quantile-based g-computation (Qgcomp) were used to evaluate the joint effects of mixed pollutant exposures and to identify the major contributing components. In addition, untargeted urinary metabolomics analysis was performed to explore the potential biological mechanisms. After adjusting for confounding factors, logistic regression analysis revealed that exposure to coal dust (CD), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) was associated with an increased risk of MetS. Furthermore, BKMR and Qgcomp models consistently indicated a significant positive association between mixed pollutant exposure and MetS risk, with CD and NO2 identified as the primary components with the strongest statistical contribution. CD exposure was mainly associated with central obesity and hypertension, whereas NO2 exposure was primarily linked to elevated blood glucose and triglyceride levels. MetS patients exhibited significant alterations in urinary metabolic profiles, with more than 113 differentially abundant metabolites identified. Notably, leukotrienes were positively correlated with NO2 exposure, while acylcarnitines were associated with CD exposure. Pathway enrichment analysis indicated significant disturbances in pyrimidine and arachidonic acid metabolism. These findings provide important evidence for developing comprehensive occupational health strategies in mining environments.