Omics-derived signatures in metabolic dysfunction-associated steatotic liver disease: from molecular characterization to clinical translation

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent systemic metabolic disorder with heterogeneous clinical trajectories. Although liver biopsy remains the histological reference standard, its invasiveness and limited scalability restrict routine use, while established non-invasive tests mainly capture fibrosis-related structural changes and provide limited insight into molecular heterogeneity, active steatohepatitis and early fibrotic transition. Multi-omics technologies offer a mechanistically informed framework for resolving this heterogeneity. In this Review, we summarize recent advances in genomic, transcriptomic, single-cell and spatial, proteomic, lipidomic and metabolomic signatures and candidate biomarkers in MASLD. Genomic studies define inherited susceptibility and risk enrichment, whereas transcriptomic and spatial approaches reveal disease-associated pathways, cell states and tissue niches. Proteomics provides blood-accessible signals of steatohepatitis activity, matrix remodelling and systemic metabolic risk, while lipidomics and metabolomics capture lipotoxic lipid composition, mitochondrial stress, bile acid dysregulation and gut-liver metabolic perturbation. We further discuss how integrative multi-omics, systems biology and artificial intelligence can organize high-dimensional molecular data into disease modules, molecular subtypes, biomarker signatures and candidate therapeutic nodes. In addition, omics-informed studies of bioactive compounds and precision nutrition illustrate how nutritional modulation may be evaluated through molecular response profiles. Despite promising progress, most omics-derived biomarkers remain limited by cohort specificity, platform dependence, incomplete external validation and unclear clinical thresholds. Future translation will require longitudinal, spatially resolved and clinically anchored integration to support risk stratification, at-risk metabolic dysfunction-associated steatohepatitis detection, fibrosis assessment, treatment-response prediction and precision medicine in MASLD.