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

Rongpeng Gong

Rongpeng Gong Medical College of Qinghai University, Xining, People’s Republic of China

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Yuanyuan Liu

Yuanyuan Liu Medical College of Qinghai University, Xining, People’s Republic of China

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Gang Luo

Gang Luo Medical College of Qinghai University, Xining, People’s Republic of China

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Jiahui Yin

Jiahui Yin College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China

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Zuomiao Xiao

Zuomiao Xiao Department of Clinical Laboratory, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, China

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Tianyang Hu

Tianyang Hu Precision Medicine Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China

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regression analysis identified eight candidate factors, and after exclusion of collinearity interference, a total of seven factors were finally included, as shown in the nomogram ( Figs 2 and 3 ). The included factors were HDL, LDL, ALB, GLB, GLU, BMI, and