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: Clustering analysis was conducted based on age, BMI, hemoglobin A Results: Across the three cohorts, distinct risk patterns emerged for MACE, LRE, and T2DM among various MASLD clusters. For MACE, the cardiometabolic cluster exhibited the highest risk in all cohorts: WRW (hazard ratio [HR] 1.315, 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.