Multifaceted environmental factors linked to metabolic dysfunction-associated fatty liver disease: an...

Environmental factors, or exposome, are non-negligible contributors to the occurrence and progression of metabolic dysfunction-associated fatty liver disease (MAFLD). Therefore, this environment-wide association study (EWAS) aimed to investigate the associations between multifarious environmental factors and MAFLD among the general adult population in the United States.

Eligible participants were obtained from the National Health and Nutrition Examination Survey 2005-2020 cycles. Survey-weighted multivariate logistic regression models were constructed to identify and tentatively validate MAFLD-associated environmental factors. The least absolute shrinkage and selection operator (LASSO) regression was conducted to identify tentatively validated environmental factors with stronger associations with MAFLD. Moreover, the importance, discrimination power, correlation patterns, subgroup-specific differences, and survey cycle heterogeneity of the identified factors were further examined by multiple statistical strategies.

A total of 14,416 participants were included in this EWAS. Among 511 candidate environmental factors, 167 were identified and tentatively validated, and 45 were preserved after the LASSO selection and correlation evaluation. In this study, most previously known factors were replicated with reduced bias, and several poorly studied environmental factors were discovered, for example, upper leg length, access to care, mid-upper arm circumference, and total trabecular bone score. Their importance, discrimination ability, pairwise correlations, subgroup variations, and heterogeneity across survey cycles were further systematically and rigorously evaluated.

This EWAS comprehensively explored the associations between environmental factors and MAFLD in the general adult population from a panoramic perspective. The findings may provide clues for further understanding this disease and promote early prevention and risk prediction strategies in the future.