Digital health · 2026
Dietary micronutrients, whole food patterns, and restless legs syndrome in coronary heart disease: A dual dietary profiling machine learning analysis using NHANES data.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hu Haoyang, Kong Shanshan, Yu Zekai, Wu Jingxi, Yang Fei
Аннотация
BACKGROUND: The relationships between dietary patterns and restless legs syndrome (RLS) risk in patients with coronary heart disease (CHD) are poorly understood. This study aimed to identify dietary factors associated with RLS risk in this specific population.
METHODS: We analyzed NHANES data (2005-2018) using two complementary dietary representations: micronutrient profiles and food-group profiles derived from the MyPyramid Equivalents Database linked to NHANES dietary recalls. Six machine-learning models were developed using Boruta for feature selection and SMOTE to address class imbalance. Model robustness was further evaluated through temporal validation, and SHAP and LIME were used for model interpretation.
RESULTS: Random forest demonstrated the highest predictive performance in both micronutrient and food-group models. SHAP analysis indicated that Lycopene, Caffeine, Alpha-carotene, Vitamin C, Food folate, and Magnesium showed contributions toward lower predicted RLS probability, whereas Calcium, Moisture, and Vitamin B1 showed contributions toward higher predicted RLS probability. These model-derived directions represent associations rather than causal effects. In the food-group model, total red and orange vegetables, total vegetables, and white potatoes were inversely associated with RLS. Conversely, Milk, Meat, and Added Sugars were positively associated with predicted RLS probability.
CONCLUSION: Random forest models effectively identify dietary risk factors for RLS in CHD patients. Nutrient-dense dietary patterns, particularly those characterized by higher consumption of specific vegetables and antioxidant-related nutrients, were associated with lower predicted RLS probability, whereas higher consumption of milk, meat, and added sugars was associated with higher predicted RLS probability. These findings suggest that dietary patterns characterized by specific nutrient and food-group profiles may be associated with RLS risk among CHD patients and warrant further investigation in independent prospective studies.
METHODS: We analyzed NHANES data (2005-2018) using two complementary dietary representations: micronutrient profiles and food-group profiles derived from the MyPyramid Equivalents Database linked to NHANES dietary recalls. Six machine-learning models were developed using Boruta for feature selection and SMOTE to address class imbalance. Model robustness was further evaluated through temporal validation, and SHAP and LIME were used for model interpretation.
RESULTS: Random forest demonstrated the highest predictive performance in both micronutrient and food-group models. SHAP analysis indicated that Lycopene, Caffeine, Alpha-carotene, Vitamin C, Food folate, and Magnesium showed contributions toward lower predicted RLS probability, whereas Calcium, Moisture, and Vitamin B1 showed contributions toward higher predicted RLS probability. These model-derived directions represent associations rather than causal effects. In the food-group model, total red and orange vegetables, total vegetables, and white potatoes were inversely associated with RLS. Conversely, Milk, Meat, and Added Sugars were positively associated with predicted RLS probability.
CONCLUSION: Random forest models effectively identify dietary risk factors for RLS in CHD patients. Nutrient-dense dietary patterns, particularly those characterized by higher consumption of specific vegetables and antioxidant-related nutrients, were associated with lower predicted RLS probability, whereas higher consumption of milk, meat, and added sugars was associated with higher predicted RLS probability. These findings suggest that dietary patterns characterized by specific nutrient and food-group profiles may be associated with RLS risk among CHD patients and warrant further investigation in independent prospective studies.