ArticleFrontiers in endocrinology2025
A nomogram for predicting metabolic-associated fatty liver disease in non-obese newly diagnosed type 2 diabetes patients.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A TyG-UHR-based machine learning model for screening lean MAFLD: development and external validation.Biomedical engineering online · 2026Article
- Metabolic dysfunction-associated fatty liver disease exacerbates hematoma expansion in intracerebral hemorrhage: an explainable machine learning approach.Frontiers in endocrinology · 2026Article
- An interpretable machine learning model for predicting metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes.Diabetes, obesity & metabolism · 2026Article
- Glycogen metabolic dysfunction in T2DM with MASLD: linking α-hydroxybutyrate to GYS2 downregulation.Frontiers in nutrition · 2026Article
- Analysis of the correlation and predictive performance of GNRI, CONUT, and PNI with MAFLD in Chinese adult physical examination population.Frontiers in nutrition · 2026Article
- A nomogram for assessing the risk of MAFLD based on the albumin-to-gamma-glutamyl transferase ratio.European journal of medical research · 2025Article
- The association of multiple metabolic disorders with the severity of fasting blood glucose and hepatic steatosis in a Chinese population with MAFLD: a cross-sectional study of over 3000 participants.Scientific reports · 2025Article
- Association Between Thyroid Hormone Sensitivity and Non-Alcoholic Fatty Liver Disease in Individuals with Normal Thyroid Function.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
Corrections and comments
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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