仅用空腹血糖预测胰岛素抵抗，准确率超85%。
AI-driven Prediction of Insulin Resistance in Normal Populations: Comparing Models and Criteria
- 用空腹血糖结合基础健康数据，训练轻量级AI模型
- 对胰岛素抵抗预测AUC达0.9731，外部验证仍超0.95
- 适合临床筛查早筛糖尿病与心血管风险人群
胰岛素抵抗（IR）是糖尿病和心血管病的重要前兆。传统评估需多项血液检测。本研究基于NHANES（1999–2020）与CHARLS（2015）数据，构建仅需空腹血糖的AI模型，输入特征包括年龄、性别、身高、体重、血压、腰围及空腹血糖。采用CatBoost算法，对HOMA-IR指标在NHANES中达到AUC 0.8596，TyG指数达0.7777，外部验证中为0.7442；对METS-IR预测，内部AUC达0.9731，外部为0.9591，对应RMSE分别为3.2643与3.057。SHAP分析显示腰围是关键预测因子。该模型提供一种微创高效的风险预测工具，有助于早期预防糖尿病与心血管疾病。
原文摘要 · Abstract (English)
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.
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