Predictive Value and Correlation Study of HOMA2 IR-CP and TyG-BMI for Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients with Type 2 Diabetes Mellitus.

Background and Objectives: To investigate the associations of C-peptide-based insulin resistance index (HOMA2 IR-CP) and triglyceride-glucose-body mass index (TyG-BMI) with metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with type 2 diabetes mellitus (T2DM), identify independent risk factors for MASLD, construct a clinical predictive model, and evaluate its predictive performance. Methods: A total of 311 T2DM patients were enrolled and randomly assigned to a derivation cohort (218 cases) and a validation cohort (93 cases) at a 7:3 ratio. Predictive factors were screened via LASSO regression, with results integrated with those from logistic regression analysis. A nomogram was developed, and model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Restricted cubic spline (RCS) regression was used to explore dose-response relationships, and stratified analyses and interaction analyses were performed. Results: HOMA2 IR-CP and TyG-BMI as independent risk factors for MASLD in T2DM patients. The area under the curve (AUC) was 0.805 in the derivation cohort and 0.730 in the validation cohort. Calibration curves showed good agreement between predicted probabilities and actual outcomes, and DCA verified the model's substantial clinical net benefit. RCS analysis revealed a non-linear association between HOMA2 IR-CP and MASLD (P for non-linearity < 0.05), whereas TyG-BMI exhibited a linear association with MASLD (P for non-linearity = 0.139). No significant interaction effects were observed across subgroups stratified by gender, age, BMI, or HbA1c level; however, the associations were more prominent in patients with BMI < 28 kg/m2 and those with HbA1c ≥ 7%. Conclusion: HOMA2 IR-CP and TyG-BMI are independent risk factors for MASLD in T2DM patients. The nomogram model constructed based on these two factors exhibits good predictive performance and can provide a reference for MASLD risk assessment in T2DM patients.