Abstract

AbstractFatty liver plays a pivotal role in the pathogenesis of the metabolic syndrome and type 2 diabetes. According to an updated classification, any individual with liver steatosis and one or more features of the metabolic syndrome, without excess alcohol consumption or other known causes of steatosis, has metabolic dysfunction-associated steatotic liver disease (MASLD). Up to 60–70% of all individuals with type 2 diabetes have MASLD. However, the prevalence of advanced liver fibrosis in type 2 diabetes remains uncertain, with reported estimates of 10–20% relying on imaging tests and likely overestimating the true prevalence. All stages of MASLD impact prognosis but fibrosis is the best predictor of all-cause and liver-related mortality risk. People with type 2 diabetes face a two- to threefold increase in the risk of liver-related death and hepatocellular carcinoma, with 1.3% progressing to severe liver disease over 7.7 years. Because reliable methods for detecting steatosis are lacking, MASLD mostly remains an incidental finding on imaging. Regardless, several medical societies advocate for universal screening of individuals with type 2 diabetes for advanced fibrosis. Proposed screening pathways involve annual calculation of the Fibrosis-4 (FIB-4) index, followed by a secondary test such as transient elastography (TE) for intermediate-to-high-risk individuals. However, owing to unsatisfactory biomarker specificity, these pathways are expected to channel approximately 40% of all individuals with type 2 diabetes to TE and 20% to tertiary care, with a false discovery rate of up to 80%, raising concerns about feasibility. There is thus an urgent need to develop more effective strategies for surveying the liver in type 2 diabetes. Nonetheless, weight loss through lifestyle changes, pharmacotherapy or bariatric surgery remains the cornerstone of management, proving highly effective not only for metabolic comorbidities but also for MASLD. Emerging evidence suggests that fibrosis biomarkers may serve as tools for risk-based targeting of weight-loss interventions and potentially for monitoring response to therapy. Graphical Abstract

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