Résumé
Objective:To construct a new model for assessing insulin resistance(IR) in newly diagnosed type 2 diabetic patients by combining anthropometry parameters and biochemical parameters.Methods:A total of 677 newly diagnosed type 2 diabetic patients were included in this study. Clinical data, biochemical indicators, and body composition measurements were collected, and a predictive model was constructed using logistic regression analysis.Results:The IR prediction model was constructed based on five indicators: triglycerides(TG), fasting plasma glucose(FPG), visceral fat area(VFA), alanine aminotransferase(ALT), and uric acid(UA). The formula for the new predictive model was as follows: y=-17.765+ 1.389×ln VFA+ 1.045×ln UA+ 0.91×ln ALT+ 2.167×ln FPG+ 0.805×ln TG. The receiver operating characteristic curve(ROC) area under the curve(AUC) for the model was 0.82, with an optimal cutoff value of 1.67, sensitivity of 0.80, and specificity of 0.71. The AUC values for the triglyceride glucose(TyG) index, lipid accumulation product(LAP), and triglyceride/high-density lipoprotein cholesterol ratio(THR) were 0.75, 0.75, and 0.70, respectively. The corresponding sensitivities were 0.66, 0.84, and 0.71, and the specificities were 0.71, 0.59, and 0.60. The optimal cutoff values were 1.81, 30.31, and 1.14, respectively. Conclusion:The new model constructed using TG, FPG, VFA, ALT, and UA as indicators showed high predictive value and can serve as a new model for assessing IR in newly diagnosed type 2 diabetic patients.