Precision medicine in MASLD: Integrating genetics, multi-omics, and artificial intelligence for risk stratification and targeted therapy.

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly termed nonalcoholic fatty liver disease (NAFLD), encompasses a heterogeneous clinical spectrum ranging from isolated hepatic steatosis to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis, and hepatocellular carcinoma. Differences in susceptibility to disease, progression and extrahepatic complications between individuals are due to many different variables, such as genetic, metabolic and environmental factors, sex, reproductive status and gut microbiota. Precision medicine offers a framework to navigate this complexity by combining clinical phenotyping with inherited genetic variation and large-scale multi-omics data including transcriptomics, epigenomics, proteomics, metabolomics, lipidomics, glycomics and microbiome analysis. Here we review the current evidence on important susceptibility genes, novel multi-omics biomarkers and polygenic risk scores, and discuss how these tools can improve risk prediction and sub-phenotyping. We also discuss artificial intelligence and machine learning algorithms that integrate clinical, imaging and molecular information to allow for non-invasive diagnosis and staging. Finally, we highlight precision-based therapeutic approaches such as oligonucleotide-based agents, genotype-stratified lifestyle and pharmacological interventions, and digitally supported personalized lifestyle programs. We outline the remaining challenges and research priorities needed to integrate precision hepatology into routine care, highlighting the identification of clinically actionable MASLD subtypes and the equitable translation of findings into improved long-term outcomes.