Nomogram model for the risk of insulin resistance in obese children and adolescents based on anthropomorphology and lipid derived indicators

Abstract

Abstract Objective This study aims to screen for measures and lipid-derived indicators associated with insulin resistance (IR) in obese children and adolescents and develop a nomogram model for predicting the risk of insulin resistance. Methods A total of 404 eligible obese children and adolescents aged 10–17 years were recruited for this study from a summer camp between 2019 and 2021. The risk factors were screened using the least absolute shrinkage and selection operator (LASSO)-logistic regression model, and a nomogram model was developed. The diagnostic value of the model was evaluated by plotting the receiver operator characteristic curve and calculating the area under the curve. Internal validation was performed using the Bootstrap method, with 1000 self-samples to evaluate the model stability. The clinical applicability of the model was assessed by plotting the clinical decision curve. Results On the basis of the LASSO regression analysis results, three lipid-related derivatives, TG/HDL-c, TC/HDL-c, and LDL-c/HDL-c, were finally included in the IR risk prediction model. The nomogram model AUC was 0.804 (95% CI: 0.760 to 0.849). Internal validation results show a C-Index of 0.799, and the mean absolute error between the predicted and actual risks of IR was 0.015. The results of the Hosmer–Lemeshow goodness-of-fit test show a good model prediction (χ2 = 9.523, P = 0.300). Conclusion Three early warning factors, TG/HDL-c, TC/HDL-c, and LDL-c/HDL-c, were screened, which can effectively predict the risk of developing IR in obese children and adolescents, and the nomogram model has an eligible diagnostic value.