Establishment and evaluation of a novel practical tool for the screening of metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus - Zhang, Mingkang; Wang, Yazhi; Liu, Meng; Shi, Qi; Wu, Xin'an; Zhou, Yan (Frontiers in Nutrition, 2025)
BackgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) is a major comorbidity in type 2 diabetes mellitus (T2DM), yet early prediction models tailored to this population are limited. This study aimed to develop and validate a novel diagnostic predictive model for MASLD in adults with T2DM.MethodsA total of 4,726 T2DM patients were retrospectively analyzed. Candidate predictors were screened by least absolute shrinkage and selection operator (LASSO) regression, and a multivariable logistic regression model was built. Significant variables were integrated into a diagnostic predictive nomogram (DPN), with online and Excel-based calculators developed. Model performance was comprehensively evaluated and compared with four established models for fatty liver disease across training, internal, and external (NHANES) validation datasets. Subgroup analyses assessed generalizability.ResultsEight independent predictors (sex, age, body mass index, alanine aminotransferase, albumin, diabetes duration, triglycerides, and high-density lipoprotein cholesterol) were included in the final model. The DPN achieved robust discrimination in training set (AUC: 0.775, 95% CI: 0.759–0.791), validation set (0.767, 95% CI: 0.742–0.791), and test set (0.794, 95% CI: 0.749–0.839) compared to existing models. NRI and IDI confirmed improved predictive capacity (P < 0.05). Calibration curves were excellent in the training (P = 0.936, Brier score = 0.184), validation (P = 0.956, Brier score = 0.189), and test sets (P = 0.687, Brier score = 0.156). DCA and CIC further demonstrated higher clinical net benefit. Subgroup analyses confirmed stability and broad applicability.ConclusionsThe DPN is a clinically practical and resource-efficient screening tool that enables early risk stratification for MASLD in patients with T2DM. Its implementation could streamline screening pathways and facilitate timely intervention in routine clinical practice.