Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From...

This study aims to assess the predictive value of dietary antioxidants in diabetes-cancer comorbidity using interpretable machine learning (ML) models and to identify key clinical factors. Data were sourced from the National Health and Nutrition Examination Survey (NHANES) 2007-2010 and 2017-2018 cycles, including 44 dietary antioxidants, as well as demographic, lifestyle, and health-related features. 8 ML models (Random Forest, light Gradient Boosting Machines [LightGBM], Logistic Regression, Decision Tree, Multilayer Perceptron, Naïve Bayes, Kernel k-Nearest Neighbors, and Support Vector Machine with Radial Basis Function) were trained, with preprocessing steps for multicollinearity, class imbalance (SMOTE), and data normalization. Model performance was evaluated using AUC, accuracy, Brier scores, and calibration plots. SHapley Additive exPlanations (SHAP) values were applied to interpret feature importance. Data from 8644 participants were analyzed, including 272 individuals with confirmed diabetes-cancer comorbidity. After removing collinear features, the ML model included 30 dietary antioxidant features and 10 baseline features. The Random Forest model achieved optimal performance (AUC = 0.996, accuracy = 0.978, brier score = 0.0241), followed by LightGBM (AUC = 0.993). SHAP analysis revealed that while advanced age, cardiovascular disease, and hypertension were the primary drivers of comorbidity probability, dietary antioxidants are also influential factors. Specifically, polyphenols (daidzein, malvidin, pelargonidin, cyanidin) and essential minerals (magnesium) emerged as the most influential nutritional features. The high accuracy of the Random Forest and LightGBM models underscores their clinical utility in risk stratification for diabetes-cancer comorbidity. While advancing age and cardiometabolic dysfunction primarily drives the probability of diabetes-cancer comorbidity. This study establishes dietary antioxidants, particularly polyphenols such as daidzein and malvidin, as predictive factors for diabetes-cancer comorbidity.