AI-driven Prediction of Insulin Resistance in Normal Populations: Comparing Models and Criteria
This provides a minimally invasive tool for early detection of IR to prevent diabetes and cardiovascular disease, but it is incremental as it applies existing methods to new data with specific improvements.
The researchers tackled the problem of predicting insulin resistance (IR) in non-diabetic populations using a simple AI model based on fasting blood glucose and other basic health metrics, achieving high AUC values such as 0.9731 for METS-IR prediction internally and 0.9591 externally.
Insulin resistance (IR) is a key precursor to diabetes and a significant risk factor for cardiovascular disease. Traditional IR assessment methods require multiple blood tests. We developed a simple AI model using only fasting blood glucose to predict IR in non-diabetic populations. Data from the NHANES (1999-2020) and CHARLS (2015) studies were used for model training and validation. Input features included age, gender, height, weight, blood pressure, waist circumference, and fasting blood glucose. The CatBoost algorithm achieved AUC values of 0.8596 (HOMA-IR) and 0.7777 (TyG index) in NHANES, with an external AUC of 0.7442 for TyG. For METS-IR prediction, the model achieved AUC values of 0.9731 (internal) and 0.9591 (external), with RMSE values of 3.2643 (internal) and 3.057 (external). SHAP analysis highlighted waist circumference as a key predictor of IR. This AI model offers a minimally invasive and effective tool for IR prediction, supporting early diabetes and cardiovascular disease prevention.