Association of environmental chemical exposure with metabolic dysfunction-associated steatotic liver disease: a cross-sectional and network toxicology analysis

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent chronic liver disease driven by metabolic dysregulation; however, the contribution of environmental chemical exposures to MASLD remains poorly understood. This study aimed to explore the associations between a broad range of environmental chemicals and MASLD prevalence using data from the National Health and Nutrition Examination Survey (NHANES). This cross-sectional study utilized data from 20,032 adults (≥ 18 years) participating in the NHANES 1999–2020. Weighted logistic regression and restricted cubic splines were applied to assess the associations between biomonitored chemicals and MASLD. Mediation analysis was performed to assess the roles of adiposity and inflammation, and network toxicology was utilized to identify potential biological pathways. The robustness of the findings was confirmed through a series of sensitivity analyses. Six blood chemicals—cadmium (Cd), iron (Fe), mercury (Hg), acrylamide (AA), perfluoroundecanoic acid (PFUnDA), and ethylmercury (EtHg)—were significantly and inversely associated with MASLD prevalence (all Odds Ratios [ORs] < 1). These associations were predominantly non-linear. Adiposity markers, particularly body mass index (BMI) and waist circumference (WC) were found to be the primary mediators of these associations. Network toxicology analysis implicated the insulin resistance pathway with TLR2, PTGS2, EGFR, and LEP identified as key protein hubs. This large-scale study reveals inverse associations between several environmental chemicals and MASLD prevalence in U.S. adults. These counterintuitive findings are strongly mediated by adiposity, suggesting a complex interplay between toxicants, metabolism, and liver health. Our rigorous analysis and novel mechanistic hypotheses provide a critical foundation for future longitudinal studies to elucidate the causal nature of these observations.