Machine Learning Analysis of Nutrient Associations with Peripheral Arterial Disease: Insights from NHANES 1999-2004.

Background: Peripheral arterial disease (PAD) is a common manifestation of atherosclerosis, affecting over 200 million people worldwide. The incidence of PAD is increasing due to the aging population. Common risk factors include smoking, diabetes, and hyperlipidemia, but its exact pathogenesis remains unclear. Nutritional intake is associated with the onset and progression of PAD, although relevant studies remain limited. Some studies suggest that certain nutritional elements may influence the development of PAD. This study aims to explore the relationship between nutrition and PAD using machine learning techniques. Unlike traditional statistical methods, machine learning can effectively capture complex, nonlinear relationships, providing a more comprehensive analysis of PAD risk factor.

Methods: Data from National Health and Nutrition Examination Survey (NHANES 1999-2004) were analyzed, including demographic, clinical, and dietary information. Nutrient intake was assessed through 24-h dietary recalls using computer-assisted dietary interview system (CADI) and automated multiple pass method (AMPM) methods. PAD was defined as an ankle-brachial index (ABI) < 0.9. Six ML models-extreme gradient boosting (XGBoost), random Forest (RF), naive bayes classifier (NB), support vector machine (SVM), logistic regression (LR), and decision tree (DT)-were trained on a 70/30 train-test split, with missing data imputed and sample imbalance addressed via synthetic minority oversampling technique (SMOTE). Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, precision, recall, and F1 score. Shapley additive explanations (SHAP) analysis was used to identify key features. In addition, to further enhance the interpretability of the model, we applied SHAP analysis to identify the features that have a significant impact on PAD prediction. This approach allowed us to determine the contribution of different variables to the model's output, providing deeper insights into how each feature influences the prediction of PAD outcomes.

Results: Of 31,126 participants, 4,520 met the inclusion criteria (mean age 61.2 ± 13.5 years; 48.8% male), and 441 (9.7%) had ABI < 0.9. XGBoost outperformed other models, achieving an AUROC of 0.913 (95% CI, 0.891-0.936) and F1 score of 0.932. With SMOTE, its AUROC improved to 0.926 (95% CI, 0.889-0.936) and F1 score to 0.937. SHAP analysis identified vitamin C, saturated fatty acids, selenium, phosphorus, and protein intake as key predictors of PAD.

Conclusion: This is the first study to apply ML algorithms to examine nutrient intake and PAD in a general population. Vitamin C and phosphorus showed negative correlations with PAD, while saturated fatty acids, protein, and selenium exhibited positive associations.