Validation of a data-driven clustering model for MASLD: Evidence from three large-scale Asian cohorts

Background & Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous condition that presents varying risks for liver-related and cardiovascular complications. Clustering methods have identified distinct MASLD subtypes, yet their applicability to Asian populations remains unclear. This study aims to validate a MASLD clustering model using clinical variables from three Asian cohorts: Wenzhou Real-World (WRW), Hong Kong Clinical Data Analysis and Reporting System (CDARS), and SingHealth Diabetes Registry. Methods: , alanine aminotransferase, LDL-cholesterol, and triglycerides. Outcomes included major adverse cardiovascular events (MACE), liver-related events (LRE), and new-onset type 2 diabetes (T2DM). They were analyzed using Cox regression risk models and Kaplan-Meier analyses to assess risk and incident events across MASLD clusters. Results: <0.001). Conclusions: The proposed MASLD clustering model is applicable to Asian populations, facilitating personalized treatment and optimizing outcomes. Impact and implications: This study provides scientific justification for applying a validated clustering model to metabolic dysfunction-associated steatotic liver disease (MASLD), demonstrating that patient subgroups identified by data-driven methods carry distinct risks for cardiovascular and liver-related outcomes. These findings are important for clinicians, researchers, and policymakers as they highlight that MASLD is not a uniform disease but rather comprises heterogeneous subgroups with differing prognoses. In practice, this work supports subgroup-based strategies to personalize treatment, improve risk stratification, and optimize the allocation of healthcare resources. The results also offer a foundation for future research into targeted therapeutic interventions while acknowledging the need for further validation in diverse populations.