A paradigm shift in fatty liver disease nomenclature has introduced steatotic liver disease (SLD) as an umbrella term, encompassing metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-related liver disease (MetALD), and alcohol-associated liver disease (ALD).These SLD subtypes exist along a dynamic continuum shaped by the synergistic interplay of metabolic dysfunction and alcohol exposure.Accumulating evidence indicates that SLD exhibits distinct outcome-based clusters reflecting heterogeneity in pathogenic drivers and clinical trajectories.Cardiometabolic clusters, most prominently observed in MASLD, are characterized by systemic metabolic dysfunction and confer increased risks of cardiovascular disease (CVD).In contrast, liver-specific clusters demonstrate progressive fibrosis and cirrhosis, hepatic decompensation, and hepatocellular carcinoma.In large-scale cohorts, CVD accounts for 40-50% of deaths in MASLD, while liver-related mortality predominates in ALD (>60%).The relative dominance of cardiometabolic versus liver-specific clusters shifts progressively across the MASLD-MetALD-ALD spectrum.While CVD remains a major driver of mortality, particularly in MASLD, hepatic outcomes increase stepwise with advancing fibrosis and greater alcohol exposure.Advanced fibrosis is the predominant shared determinant amplifying both CVD and hepatic risks across subtypes.This review synthesizes cardiovascular and hepatic outcome patterns across the MASLD-MetALD-ALD spectrum, highlighting the prognostic reorientation across the continuum, in which hepatic outcomes rise stepwise with alcohol exposure while cardiovascular risk remains an important but less subtype-differentiated burden.These findings advocate outcome-oriented risk stratification integrating noninvasive fibrosis assessment, alcohol biomarkers (PEth), and cardiovascular risk calculators within the Cardiovascular-Kidney-Metabolic 4 framework.Understanding SLD as a modifiable continuum enables individualized management to optimize multisystem outcomes.
Lee et al. (Fri,) studied this question.