Serum biomarker screening and metabolic profiling analysis of nonalcoholic fatty liver disease patients using untargeted metabolomics and machine learning techniques

Integration of untargeted metabolomics and machine learning effectively distinguishes NAFLD patients from healthy controls. cholesterol metabolism, caffeine metabolism, and the FoxO and AMPK signaling pathways may participate in NAFLD pathogenesis. ML-validated metabolites 1-methyluric acid, paraxanthine, canavaninosuccinate, and maresin one hold potential as diagnostic biomarkers and therapeutic targets for NAFLD, with 1-methyluric acid exhibiting the highest diagnostic relevance. In summary, serum metabolomics provides stable, accurate biomarkers for NAFLD early warning and diagnosis, and this study offers data and resource support for optimizing its clinical.