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

Abstract

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: 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= 13646) were used to validate the model. Univariate and multivariate logistic regression were 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 curves 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 7 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. Keywords: Insulin resistance; Optimal subset regression; Calibration curves; NHANES; HOMA-IR

Association Between C-Reactive Protein and Albumin Ratios and Risk of Mortality in Patients with Chronic Obstructive Pulmonary Disease

“…Diabetes laboratory diagnostic criteria were fasting blood glucose ≥ 7.0 mmol/L or glycosylated hemoglobin (HbAlc) ≥ 6.5% or self-reported diabetes or receiving hypoglycemic therapy. 31 Dyslipidemia was defined as having any one of the following: High triglyceride (TG) level: TG ≥ 150 mg/dl (3.89 mmol/l); Hypercholesterolemia: total cholesterol (TC) ≥ 200 mg/dl (5.18 mmol/l), low-density lipoprotein cholesterol (LDL-C) ≥130 mg/dl (3.37 mmol/l), and high-density lipoprotein cholesterol (HDL-C) <40 mg/dl (1.04 mmol/l [males]) and 50 mg/dl (1.30 mmol/l [females]). 32 Participants were identified as having a history of CVD if they answered “Yes” to any of the 5 questions asking about a history of congestive heart failure, coronary heart disease, angina/angina pectoris, heart attack, and stroke.…”

Comprehensive Evaluation of Ecological Functional Traits and Screening of Key Indicators of Leymus chinensis Germplasm Resources from Northern China and Mongolia

“…There are many methods for screening comprehensive evaluation indicators, and they have been widely used in research, such as correlation analysis and PCA [69,70], analytic hierarchy process [55], stepwise regression [71], and all-subsets regression method [72]. Compared with stepwise regression, the all-subsets regression method considers more variable combinations, which can ensure that the selected model belongs to the optimal regression model.…”