Recent studies indicate that GBDT-based models, such as XGBoost, outperform logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), support vector machines (SVM), artificial neural networks (ANN) and deep neural network (DNN), in predicting insulin resistance, supporting the superior predictive accuracy of GBDT-based models (40, 41), which might be an important explanation of our findings that the LightGBM model is a better choice in developing models for insulin sensitivity in the community and primary care settings. Here, INS is linked to Insulin resistance.