Construction of a new model for evaluating insulin resistance in newly diagnosed type 2 diabetic patients using anthropometry parameters
10.3760/cma.j.cn311282-20230309-00102
- VernacularTitle: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
- Author:
Xincheng WANG
1
;
Shi ZHANG
;
Yi WANG
;
Yanju ZHANG
;
Meiyang DU
;
Chunjun LI
Author Information
1. 天津市人民医院健康管理中心，内分泌科 300121
- Keywords:
Insulin resistance;
Predictive model;
Biochemical indices;
Anthropometry;
Diabetes mellitus, type 2
- From:
Chinese Journal of Endocrinology and Metabolism
2023;39(7):575-580
- CountryChina
- Language:Chinese
-
Abstract:
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.
