A nomogram for predicting metabolic-associated fatty liver disease in non-obese newly diagnosed type 2 diabetes patients - Cui, Yuliang; Li, Fenghua; Li, Tingting; Sun, Wanjing; Shi, Haiyan; Cheng, Yunyun (Frontiers in Endocrinology, 2025)
BackgroundMetabolic-associated fatty liver disease (MAFLD) is becoming increasingly prevalent in non-obese patients with type 2 diabetes mellitus (T2DM) and leads to serious liver damage in this population. The study aims to develop and validate a nomogram to predict the risk of MAFLD in non-overweight individuals with newly diagnosed T2DM.MethodsA total of 2372 non-obese patients with newly diagnosed T2DM and MAFLD were enrolled and randomly assigned to the training and validation sets in a ratio of 7:3. The independent risk factors associated with MAFLD were screened by univariate and multivariate logistic regression, and a nomogram was constructed to predict the risk of MAFLD. Receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA) were used to verify the performance and clinical utility of the model.ResultsSeven predictors, namely body mass index (BMI), alanine aminotransferase/aspartate aminotransferase (ALT/AST), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), fasting blood glucose (FBG), creatinine (Cr) and serum uric acid (SUA), were identified by multivariate logistic regression analysis from a total of 14 variables studied. The nomogram built using these seven predictors showed good prediction ability (AUC: 0.815 in the training cohort; AUC: 0.787 in the validation cohort), along with favorable calibration and clinical utility.ConclusionThe nomogram demonstrated effectiveness as a screening tool for evaluating the risk of MAFLD in T2DM individuals without obesity, facilitating early identification and supporting enhanced management strategies for MAFLD.