Using optimal subset regression to identify factors associated with insulin resistance and construct predictive models in the US adult population

作者全名："Gong, Rongpeng; Liu, Yuanyuan; Luo, Gang; Yin, Jiahui; Xiao, Zuomiao; Hu, Tianyang"

作者地址："[Gong, Rongpeng; Liu, Yuanyuan; Luo, Gang] Qinghai Univ, Med Coll, Xining, Peoples R China; [Yin, Jiahui] Shandong Univ Tradit Chinese Med, Coll Tradit Chinese Med, Jinan, Peoples R China; [Xiao, Zuomiao] Nanchang Univ, Dept Clin Lab, Affiliated Ganzhou Hosp, Ganzhou, Peoples R China; [Hu, Tianyang] Chongqing Med Univ, Affiliated Hosp 2, Precis Med Ctr, Chongqing, Peoples R China"

通信作者："Hu, TY (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Precis Med Ctr, Chongqing, Peoples R China."

来源：ENDOCRINE CONNECTIONS

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:000885741500009

JCR分区：Q3

影响因子：2.9

年份：2022

卷号：11

期号：7

开始页：　

结束页：　

文献类型：Article

关键词：insulin resistance; optimal subset regression; calibration curves; NHANES; HOMA-IR

摘要："Background: In recent decades, with the development of the global economy and the improvement of living standards, insulin resistance (IR) has become a common phenomenon. Current studies have shown that IR varies between races. Therefore, it is necessary to develop individual prediction models for each country. The purpose of this study was to develop a predictive model of IR applicable to the US population. Method: In total, 11 cycles of data from the NHANES database were selected for this study. Of these, participants from 1999 to 2010 (n = 14931) were used to establish the model, and participants from 2011 to 2020 (n = 13,646) were used to validate the model. Univariate and multivariable logistic regression was used to analyze the factors associated with IR. Optimal subset regression was used to filter the best modeling variables. ROC curves, calibration curves, and decision curve analysis were used to determine the strengths and weaknesses of the model. Results: After screening the variables by optimal subset regression, variables with covariance were excluded, and a total of seven factors (including HDL, LDL, ALB, GLB, GLU, BMI, and waist) were finally included to establish the prediction model. The AUCs were 0.851 and 0.857 in the training and validation sets, respectively, and the Brier value of the calibration curve was 0.153. Conclusion: The optimal subset predictive model proposed in this study has a great performance in predicting IR, and the decision curve analysis shows that it has a high net clinical benefit, which can help clinicians and epidemiologists easily detect IR and take appropriate interventions as early as possible."

基金机构：Natural Science Foundation of Qinghai Province [2020-ZJ-930]

基金资助正文：This work was supported by General Project of Natural Science Foundation of Qinghai Province (2020-ZJ-930).