结合人体成分指标在新发2型糖尿病患者中构建评估胰岛素抵抗的新模型

Construction of a new model for evaluating insulin resistance in newly diagnosed type 2 diabetic patients using anthropometry parameters

目的本研究联合人体成分测量结果和常规生化指标在新发2型糖尿病患者中构建评估胰岛素抵抗(insulin resistance, IR)的新模型。方法共纳入677例新诊断的2型糖尿病患者, 收集患者的临床资料、生化指标及人体成分测量结果, 利用logistic回归分析构建预测模型。结果构建了由三酰甘油(TG)、空腹血糖(FPG)、内脏脂肪面积(visceral fat area, VFA)、丙氨酸氨基转移酶(ALT)和尿酸5项指标组成的IR预测模型。新的预测模型公式为:y=-17.765+1.389×ln VFA+1.045×ln尿酸+0.91×ln ALT+2.167×ln FPG+0.805×ln TG, 模型的受试者工作特征(receiver operating characteristic, ROC)曲线下面积为0.82, 最佳切点值为1.67, 灵敏度为0.80, 特异度为0.71。三酰甘油葡萄糖(triglyceride glucose, TyG)指数、脂肪蓄积指数(lipid accumulation product, LAP)以及三酰甘油/高密度脂蛋白胆固醇比率(TG/HDL-C ratio, THR)ROC曲线下面积分别为0.75、0.75、0.70, 灵敏度分别为0.66、0.84、0.71, 特异度分别为0.71、0.59、0.60, 最佳临界值分别为1.81、30.31、1.14。结论利用TG、FPG、VFA、ALT和尿酸5项指标构建的新模型预测价值较高, 可作为新发2型糖尿病患者评估IR的新模型。

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.