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 A1c, 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

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, p <0.001), Hong Kong CDARS (HR 1.559, p <0.001), and SingHealth Diabetes Registry (HR 1.262, p <0.001). For LRE, the liver-specific cluster showed the highest risk in the WRW (HR 1.578, p = 0.002) and SingHealth Diabetes Registry cohorts (HR 2.403, p <0.001). In contrast, in the Hong Kong CDARS cohort, both the cardiometabolic (HR 1.818, p <0.001) and liver-specific clusters (HR 1.557, p <0.001) exhibited similarly increased risks. For T2DM, the cardiometabolic cluster showed the highest risk in the WRW (HR 3.418, p <0.001) and Hong Kong CDARS cohorts (HR 2.761, p <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.