ArticleChinese medical journal2026
Data-driven classification of metabolic-associated steatotic liver disease subtypes predicting hepatic and extrahepatic progression.
Article in Chinese medical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors.
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Abstract
backgroundMetabolic-associated steatotic liver disease (MASLD) is a heterogeneous condition with highly variable outcomes. We aimed to distinguish the subtypes of MASLD that were associated with varying risks of hepatic and extrahepatic outcomes.
methodsAn innovative multi-task deep least absolute shrinkage and selection operator (LASSO) algorithm was developed for feature selection in the discovery cohort ( n = 1111, 87.6% [973/1111] of biopsy-proved MASLD), followed by clustering analysis in MASLD. Validation was performed in 6172 individuals who undertook health check-ups (MASLD: 43.9% [2710/6172], mean follow-up 27.6 months) and 7406 participants from the Third National Health and Nutrition Examination Survey (NHANES III) (MASLD: 37.3%, mean follow-up 280.2 months).
resultsFour clusters with distinct risks of hepatic and extrahepatic outcomes were identified in the discovery cohort: Cluster 1 characterized by subcutaneous adiposity, modest metabolic disorders, but lower risk of cardiovascular disease (CVD); Cluster 2 characterized by significant hyperlipidemia, substantial liver damage, and increased hepatic fibrosis risk; Cluster 3 characterized by low muscle mass, remarkable chronic systemic inflammation, and a higher risk of cardiovascular-kidney complications; and Cluster 4 characterized by severe insulin resistance, visceral adiposity, poor glucose and lipid control, and severe liver damage, conferring a high risk of cardiovascular-liver-kidney complications. Additionally, Cluster 4 exhibited the highest frequencies of PNPLA3 risk alleles. The prognostic relevance was further confirmed in external validation cohorts. Specifically, Clusters 3 and 4 had increased risks of all-cause and CVD-related mortality.
conclusionsWe developed a novel algorithm that identified four MASLD clusters, each characterized by distinct clinical features and varying risks of hepatic and extrahepatic outcomes. This classification facilitates the precise integration of MASLD risk stratification and management within the cardiovascular-liver-kidney-metabolic framework.
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