Diabetes, obesity & metabolism · 2026
Population-based cohort validation and replication reveals limited generalizability of cluster-based MASLD subtypes.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Wu Junjie, Xu Weiwei, Zeng Guyu, Wang Junfeng
Аннотация
AIMS: MASLD, defined as a steatotic liver disease in the presence of one or more cardiometabolic risk factors and the absence of harmful alcohol intake, exhibits substantial heterogeneity complicating risk stratification. A prior clustering model proposed liver-specific and cardiometabolic subtypes, yet its generalizability and prognostic relevance remain unclear in the broader population. We aim to validate and replicate the prior approach in a nationally representative U.S.
MATERIALS AND METHODS: We included 3300 participants with MASLD from NHANES III. For validation, participants were assigned to previously defined clusters using published medoids. For replication, de novo clusters were derived using the Partitioning Around Medoids algorithm. Cox proportional hazards models accounting for the complex survey design of NHANES III were used to estimate hazard ratios for all-cause, cardiovascular-related, and diabetes-related mortality across clusters.
RESULTS: Individual cluster assignments showed limited reproducibility between the validation and replication analyses, although the overall clustering pattern was preserved. Based on clinical profiles, clusters were categorized into cardiometabolic, liver-specific, and other subtypes. The cardiometabolic cluster consistently showed higher risks in both analyses, while the liver-specific cluster showed no significant associations.
CONCLUSIONS: The subtyping model demonstrated limited generalizability. Nonetheless, the consistent identification of broad cardiometabolic and liver-specific patterns suggests potential value for risk stratification, pending further validation.
MATERIALS AND METHODS: We included 3300 participants with MASLD from NHANES III. For validation, participants were assigned to previously defined clusters using published medoids. For replication, de novo clusters were derived using the Partitioning Around Medoids algorithm. Cox proportional hazards models accounting for the complex survey design of NHANES III were used to estimate hazard ratios for all-cause, cardiovascular-related, and diabetes-related mortality across clusters.
RESULTS: Individual cluster assignments showed limited reproducibility between the validation and replication analyses, although the overall clustering pattern was preserved. Based on clinical profiles, clusters were categorized into cardiometabolic, liver-specific, and other subtypes. The cardiometabolic cluster consistently showed higher risks in both analyses, while the liver-specific cluster showed no significant associations.
CONCLUSIONS: The subtyping model demonstrated limited generalizability. Nonetheless, the consistent identification of broad cardiometabolic and liver-specific patterns suggests potential value for risk stratification, pending further validation.