Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations

Abstract

This study undertakes a comprehensive examination of the intricate link between diet nutrition, age, and metabolic syndrome (MetS), utilizing advanced artificial intelligence methodologies. Data from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2018 were meticulously analyzed using machine learning (ML) techniques, specifically extreme gradient boosting (XGBoost) and the proportional hazards model (COX). Using these analytic methods, we elucidated a significant correlation between age and MetS incidence and revealed the impact of age-specific dietary patterns on MetS. The study delineated how the consumption of certain dietary components, namely retinol, beta-cryptoxanthin, vitamin C, theobromine, caffeine, lycopene, and alcohol, variably affects MetS across different age demographics. Furthermore, it was revealed that identical nutritional intakes pose diverse pathogenic risks for MetS across varying age brackets, with substances such as cholesterol, caffeine, and theobromine exhibiting differential risks contingent on age. Importantly, this investigation succeeded in developing a predictive model of high accuracy, distinguishing individuals with MetS from healthy controls, thereby highlighting the potential for precision in dietary interventions and MetS management strategies tailored to specific age groups. These findings underscore the importance of age-specific nutritional guidance and lay the foundation for future research in this area.

Keywords

• machine learning
• XGBoost
• Cox
• metabolic syndrome
• diet nutrition
• age