Proteomic resolution of the MASLD cardiometabolic spectrum identifies sex-driven endotypes and predicts systemic mortality

Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous condition where overlapping cardiometabolic risk factors challenge accurate prognostic prediction. Objectives We aimed to define the molecular heterogeneity underlying MASLD clinical presentation, resolving disease patterns and identifying sub-phenotypes that account for prognostic differences in the general population. Design We applied latent class analysis (LCA) to 48 806 UK Biobank participants with MRI-based proton density fat fraction (MRI-PDFF) data to define phenotypic subtypes over a >15-year follow-up. Large-scale proteomic profiling characterised the molecular basis of the identified prognostic patterns. This led to the derivation of a four-protein score (PS4), which was robustly validated for all-cause mortality prediction in an expanded cohort with/ without MRI-PDFF data (n=115 971). We used mediation analysis to assess the potential contribution of PS4 proteins to the observed class–outcome associations. Results The LCA identified clinically distinct MASLD subtypes, revealing pronounced sexual dimorphism in disease severity and identifying high-risk prognostic subgroups. The derived PS4 score significantly outperformed established non-invasive scores in predicting all-cause mortality across the expanded cohort. The PS4 components, linked to hepatic lipid/one-carbon metabolism (FABP1, FTCD) and immune/vascular integrity (ADGRG1, GAST) pathways, were found to account for a significant fraction (10–20%) of the estimated effect of the patient’s phenotypic class on mortality outcome. Conclusions MASLD-driven proteomic shifts reflect biological pathways that connect hepatic metabolic dysfunction with long-term survival. By resolving the MASLD cardiometabolic spectrum, we identified high-risk endotypes that are not captured by traditional liver-centric scores. The PS4 represents a biologically grounded framework for deciphering the systemic consequences of MASLD.