Data-driven cluster analysis identifies distinct types of metabolic dysfunction-associated steatotic liver disease

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) exhibits considerable variability in clinical outcomes. Identifying specific phenotypic profiles within MASLD is essential for developing targeted therapeutic strategies. Here we investigated the heterogeneity of MASLD using partitioning around medoids clustering based on six simple clinical variables in a cohort of 1,389 individuals living with obesity. The identified clusters were applied across three independent MASLD cohorts with liver biopsy (totaling 1,099 participants), and in the UK Biobank to assess the incidence of chronic liver disease, cardiovascular disease and type 2 diabetes. Results unveiled two distinct types of MASLD associated with steatohepatitis on histology and liver imaging. The first cluster, liver-specific, was genetically linked and showed rapid progression of chronic liver disease but limited risk of cardiovascular disease. The second cluster, cardiometabolic, was primarily associated with dysglycemia and high levels of triglycerides, leading to a similar incidence of chronic liver disease but a higher risk of cardiovascular disease and type 2 diabetes. Analyses of samples from 831 individuals with available liver transcriptomics and 1,322 with available plasma metabolomics highlighted that these two types of MASLD exhibited distinct liver transcriptomic profiles and plasma metabolomic signatures, respectively. In conclusion, these data provide preliminary evidence of the existence of two distinct types of clinically relevant MASLD with similar liver phenotypes at baseline, but each with specific underlying biological profiles and different clinical trajectories, suggesting the need for tailored therapeutic strategies.