New research suggests that metabolic dysfunction-associated steatotic liver disease (MASLD) can be subtyped into five different conditions. Differences in genetic causes, disease progression, and clinical outcomes could support more personalized diagnosis and treatment.
The study, published in Nature Communications, combined genomic information with electronic health records from two Mayo Clinic research cohorts. It investigates why patients with the same diagnosis experience markedly different outcomes.
MASLD, previously known as nonalcoholic fatty liver disease, affects approximately 30 percent of the global population. Although commonly associated with obesity and type 2 diabetes, disease severity and progression vary considerably between patients.
Using statistical analysis of clinical characteristics, the researchers identified five patient subgroups with distinct metabolic, genetic, and disease-risk profiles. These patterns were subsequently reproduced in an independent cohort.
Two subgroups were characterized by substantial metabolic disease. One, predominantly male, showed elevated cardiovascular and kidney-related risks, while another, predominantly female, was associated with obesity, mood disorders, and greater antidepressant use. Both had high rates of type 2 diabetes and sleep apnea.
A particularly significant finding concerned a subgroup with relatively few metabolic complications but the highest overall rate of liver transplantation. Genetic analysis suggested that variants in TM6SF2, MBOAT7, and HSD17B13 contributed to this elevated risk.
Another genetically influenced subgroup, characterized by metabolic dysfunction-associated steatohepatitis (MASH), showed increased risks of advanced liver fibrosis and acute kidney failure.
These findings suggest that conventional metabolic risk indicators alone may fail to identify patients at greatest risk of severe liver disease.
The research highlights the potential value of integrating genomic risk profiles with routine clinical information to improve disease classification and identify patients requiring closer monitoring.
However, the findings are based on observational data, and the proposed subtypes require further validation before they can inform routine clinical decisions.
The researchers conclude that understanding the distinct biological mechanisms underlying MASLD could help refine risk prediction, guide surveillance strategies, and support the development of tailored treatments for individual disease profiles.
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