RETRACTED: A nomogram for predicting metabolic-associated fatty liver disease in non-obese newly diagnosed type 2 diabetes patients

Year: 2025

Type: article

Abstract: Background: Metabolic-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. Methods: A 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. Results: Seven 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. Conclusion: The 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.

Source: Frontiers in Endocrinology

DOI: 10.3389/fendo.2025.1521168

Authors: Yuliang Cui, Fenghua Li, Tingting Li, Wanjing Sun, Haiyan Shi, Yunyun Cheng

Institution: Qilu Hospital of Shandong University

Language: English

Topic: Liver Disease Diagnosis and Treatment

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801