ArticleFrontiers in clinical diabetes and healthcare2026
Prediction of metabolic dysfunction-associated steatotic liver disease among patients with type 2 diabetes mellitus using liver fat indices in a resource-limited clinical setting.
Article in Frontiers in clinical diabetes and healthcare, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed non-alcoholic fatty liver disease (NAFLD), is the hepatic presentation of metabolic syndrome. It shares the common pathophysiology of insulin resistance with type 2 diabetic mellitus (T2DM). The aim of this study was to establish the predictive ability of non-invasive liver fat indices-Fatty Liver Index (FLI), Hepatic Steatosis Index (HSI), Visceral Adiposity Index (VAI), Triglyceride and Glucose Index (TyG), and NAFLD-Liver Fat Score (NAFLD-LFS)-to determine fatty liver among patients with T2DM. Methodology: A cross-sectional analytical study was conducted on 260 newly diagnosed patients with type 2 diabetes. Anthropometric measurements were taken, while ultrasound scan was performed to determine fatty liver; its grade and blood samples were used to determine biochemical parameters. Liver fat index scores were calculated using standardized equations and analyzed using the statistical software SPSS version 23.0. Results: The incidence of MASLD among patients with T2DM was 69.2%. Three of the liver fat indices-FLI, HSI, and NAFLD-LFS-showed strong statistically significant correlations ( Conclusion: The current study showed that all five liver fat indices have the ability to predict the presence or absence of fatty liver among patients with T2DM. FLI and HSI showed the highest performance among the five indices, which can be used as screening tools for fatty liver more efficiently. The findings provide reliable alternative methods for detecting fatty liver in a resource-limited clinical setting simplifying patient management.
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