AUTHOR=Yu Zhenjun , Chen Mengdie , Gu Shicheng , Wang Chaohui , Feng Ping , Lin Gang 

TITLE=Sugar-sweetened beverage consumption predicts metabolic associated fatty liver disease in patients with type 2 diabetes mellitus

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1651370

DOI=10.3389/fendo.2025.1651370

ISSN=1664-2392

ABSTRACT=BackgroundMetabolic associated fatty liver disease (MAFLD) is a leading cause of chronic liver disease worldwide, with heightened prevalence and progression risks in individuals with type 2 diabetes mellitus (T2DM). Emerging evidence suggests dietary factors, particularly sugar-sweetened beverage (SSB) consumption, may exacerbate metabolic dysregulation, yet this relationship remains underexplored in MAFLD populations.MethodWe enrolled 3,305 T2DM patients from Taizhou University Hospital, classifying them into MAFLD and non-MAFLD groups via liver ultrasonography. SSB consumption was quantified as weekly intake. Clinical parameters and SSB consumption were analyzed using logistic regression. External validation leveraged NHANES data, focusing on total sugar intake and surrogate markers.ResultsMAFLD patients exhibited significantly higher BMI, waist/hip ratios, and SSB consumption than non-MAFLD counterparts (p<0.001). SSB consumption emerged as an independent MAFLD risk factor, with dose-dependent escalation in MAFLD odds. The MAFLD model based on glycometabolism (MMBG), integrating SSB consumption, C-peptide, and glucose, outperformed traditional indices, such as TyG, VAI, and AIP, achieving superior AUC (0.712 vs. 0.631–0.666), enhanced clinical utility and higher Brier scores (p<0.05, respectively). NHANES validation confirmed BMI, central obesity, hyperglycemia, and sugar intake as MAFLD predictors.ConclusionSSB consumption independently predicts MAFLD risk in T2DM patients, with synergistic effects from dysregulated glycometabolism. The MMBG model, incorporating SSB consumption and glycometabolic parameters, offers a robust tool for early MAFLD risk identification and personalized interventions.