Yu Linzhi, Li Yu, Ma Rulin, Guo Heng, Zhang Xianghui, Yan Yizhong, He Jia, Wang Xinping, Niu Qiang, Guo Shuxia
Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People's Republic of China.
Department of NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, People's Republic of China.
Risk Manag Healthc Policy. 2022 Apr 13;15:631-641. doi: 10.2147/RMHP.S352401. eCollection 2022.
This study aimed to explore the relationship between obesity- and lipid-related indices and insulin resistance (IR) and construct a personalized IR risk model for Xinjiang Kazakhs based on representative indices.
This cross-sectional study was performed from 2010 to 2012. A total of 2170 Kazakhs from Xinyuan County were selected as research subjects. IR was estimated using the homeostasis model assessment of insulin resistance. Multivariable logistic regression analysis, least absolute shrinkage and selection operator penalized regression analysis, and restricted cubic spline were applied to evaluate the association between lipid- and obesity-related indices and IR. The risk model was developed based on selected representative variables and presented using a nomogram. The model performance was assessed using the area under the ROC curve (AUC), the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA).
After screening out 25 of the variables, the final risk model included four independent risk factors: smoking, sex, triglyceride-glucose (TyG) index, and body mass index (BMI). A linear dose-response relationship was observed for the BMI and TyG indices against IR risk. The AUC of the risk model was 0.720 based on an independent test and 0.716 based on a 10-fold cross-validation. Calibration curves showed good consistency between actual and predicted IR risks. The DCA demonstrated that the risk model was clinically effective.
The TyG index and BMI had the strongest association with IR among all obesity- and lipid-related indices, and the developed model was useful for predicting IR risk among Kazakh individuals.
本研究旨在探讨肥胖及脂质相关指标与胰岛素抵抗（IR）之间的关系，并基于代表性指标构建新疆哈萨克族人群的个性化IR风险模型。
本横断面研究于2010年至2012年进行。选取新源县2170名哈萨克族居民作为研究对象。采用胰岛素抵抗稳态模型评估法估算IR。应用多变量逻辑回归分析、最小绝对收缩和选择算子惩罚回归分析以及限制性立方样条法评估脂质和肥胖相关指标与IR之间的关联。基于选定的代表性变量建立风险模型，并以列线图形式呈现。使用ROC曲线下面积（AUC）、Hosmer-Lemeshow拟合优度检验和决策曲线分析（DCA）评估模型性能。
在筛选出25个变量后，最终的风险模型纳入了4个独立风险因素：吸烟、性别、甘油三酯-葡萄糖（TyG）指数和体重指数（BMI）。观察到BMI和TyG指数与IR风险呈线性剂量反应关系。基于独立检验，风险模型的AUC为0.720，基于10倍交叉验证的AUC为0.716。校准曲线显示实际和预测的IR风险之间具有良好的一致性。DCA表明该风险模型具有临床有效性。
在所有肥胖和脂质相关指标中，TyG指数和BMI与IR的关联最强，所建立的模型有助于预测哈萨克族个体的IR风险。
