Toward Precision Medicine in MASLD: Evolution of Disease Subtyping

Traditional classifications of metabolic dysfunction-associated steatotic liver disease (MASLD) based on histology or biochemical traits are insufficient to capture its complexity, and clinical strategies rely on generalized treatment approaches that may not target to patient heterogeneity. As MASLD is a highly prevalent and heterogeneous condition with diverse clinical trajectories and outcomes ranged from liver-related complications to multiple extrahepatic complications, emerging approaches integrating clinical features, genotypes, multi-omics (microbiota, metabolomics, proteomics, and related post-translational modifications), machine learning-based clustering, and molecular profiling have enabled more refined disease subtyping. This review underscores novel subtyping frameworks of MASLD beyond traditional methods, focusing on their clinical relevance and translational potential. We further identify gaps in standardization across studies, the limitations of existing models, and highlight the need for integrative, longitudinal approaches to improve precision medicine. In conclusion, clustering subtypes might facilitate risk stratification and guide personalized therapeutic strategies to prioritize these expensive treatments to the right phenotype of MASLD.