[Metabolic Flexibility Evaluation Method Based on Dynamic Respiratory Quotient Characteristics]

To address the need for metabolic disorder assessment, an evaluation model of metabolic flexibility was developed based on the dynamic characteristics of the respiratory quotient (RQ). This study enrolled 56 participants from the First Affiliated Hospital of University of Science and Technology of China, including 20 participants in the metabolically flexible group and 36 participants in the metabolically inflexible group. Clinical data were collected. Indirect calorimetry was used to measure RQ values at five time points during an oral glucose tolerance test. Features derived from RQ were extracted. A staged feature selection process was conducted to identify the most relevant predictors. The synthetic minority over-sampling technique (SMOTE) was applied to alleviate class imbalance. Evaluation models were established using logistic regression (LR), K-nearest neighbors (KNN), XGBoost, and random forest (RF) algorithms. All models demonstrated strong performance, with comprehensive scores exceeding 0.920. The RF model achieved the best performance, with an accuracy of 0.941, an F1-score of 0.957, an AUC of 0.985, and a comprehensive score of 0.961. The proposed method combines high predictive accuracy with physiological interpretability, providing a reliable basis for early screening of metabolic disease risk.