Abstract
BACKGROUND: Insulin resistance is a key precursor to diabetes and increases the risk of cardiovascular diseases. Traditional assessment methods rely on multiple invasive tests. Developing an AI model based on minimally invasive tests, especially using only fasting blood glucose as the invasive test, can promote health monitoring in non-diabetic populations, particularly for frequent routine checks. OBJECTIVE: This study aims to develop an AI-driven model that uses only fasting blood glucose as the invasive measure to predict insulin resistance in non-diabetic populations. The goal is to facilitate health monitoring through simple, minimally invasive tests. METHODS: We selected simple and accessible input features, including age, gender, height, weight, pulse, blood pressure, waist circumference, and fasting blood glucose. Data from the National Health and Nutrition Examination Survey (NHANES, 1999-2020) were used to construct four AI-based prediction models, which were validated using data from the China Health and Retirement Longitudinal Study (CHARLS, 2015). These models were based on three commonly used insulin resistance (IR) indicators: HOMA-IR, TyG, and METS-IR. Additionally, we used SHAP values to interpret the contributions of these features to the predictions. RESULTS: The CatBoost algorithm performed excellently in classification tasks for insulin resistance. For numerical prediction of the METS-IR index, neural networks, particularly TabKANet, demonstrated superior performance in cross-dataset validation. In the NHANES test set, the AUC values for predicting insulin resistance were 0.8596 (HOMA-IR index) and 0.7777 (TyG index), with an external validation AUC of 0.7442 for the TyG index. For METS-IR prediction, our model achieved AUC values of 0.9731 (internal) and 0.9591 (external). Additionally, the AI-driven model for predicting METS-IR had RMSE values of 3.2643 (internal) and 3.057 (external). SHAP analysis identified waist circumference as a key predictor of insulin resistance, highlighting its importance in early diabetes and cardiovascular disease prediction. CONCLUSION: This study successfully developed a minimally invasive insulin resistance prediction model that relies solely on fasting blood glucose. The AI-driven models demonstrated robust performance across multiple insulin resistance assessment indicators, particularly in predicting the METS-IR index. These findings highlight the significant potential of AI in enhancing early detection and monitoring of insulin resistance in non-diabetic populations, thereby improving health monitoring strategies.