A metabolite-augmented FIB-4 machine learning panel achieves superior liver fibrosis staging in chronic liver disease.

Abstract

Accurate, non-invasive liver fibrosis detection is essential for chronic liver disease management, particularly with rising metabolic dysfunction-associated liver disease (MASLD) and chronic hepatitis B (CHB). While the Fibrosis-4 (FIB-4) index is widely used, its performance for advanced fibrosis is limited. We develop Met-FIB using metabolomics and machine learning, integrating FIB-4 parameters (age, aspartate aminotransferase, alanine aminotransferase, and platelet count) with tyrosine and taurocholic acid identified in a CHB discovery cohort (n = 3,251). Validation includes one CHB cohort (n = 729) and two MASLD cohorts (n = 149, n = 155). Met-FIB outperforms FIB-4, FibroScan, and other serum markers across all fibrosis stages. In CHB, Met-FIB achieves 96.3% rule-out sensitivity and 85.4% rule-in specificity for significant fibrosis, with rule-in specificity reaching 98.6% and 98.8% for advanced fibrosis and cirrhosis. In MASLD, corresponding values are 93.9% and 90.2% for significant fibrosis, with >97.9% specificity for late-stage disease. Met-FIB demonstrates clinical utility for non-invasive fibrosis staging across diverse etiologies.