Investigating the Influence of Heavy Metals and Environmental Factors on Metabolic Syndrome Risk Based on Nutrient Intake: Machine Learning Analysis of Data from the Eighth Korea National Health and Nutrition Examination Survey (KNHANES).

This study delves into the complex interrelations among nutrient intake, environmental exposures (particularly to heavy metals), and metabolic syndrome. Utilizing data from the Korea National Health and Nutrition Examination Survey (KNHANES), machine learning techniques were applied to analyze associations in a cohort of 5719 participants, categorized into four distinct nutrient intake phenotypes. Our findings reveal that different nutrient intake patterns are associated with varying levels of heavy metal exposure and metabolic health outcomes. Key findings include significant variations in metal levels (Pb, Hg, Cd, Ni) across the clusters, with certain clusters showing heightened levels of specific metals. These variations were associated with distinct metabolic health profiles, including differences in obesity, diabetes prevalence, hypertension, and cholesterol levels. Notably, Cluster 3, characterized by high-energy and nutrient-rich diets, showed the highest levels of Pb and Hg exposure and had the most concerning metabolic health indicators. Moreover, the study highlights the significant impact of lifestyle habits, such as smoking and eating out, on nutrient intake phenotypes and associated health risks. Physical activity emerged as a critical factor, with its absence linked to imbalanced nutrient intake in certain clusters. In conclusion, our research underscores the intricate connections among diet, environmental factors, and metabolic health. The findings emphasize the need for tailored health interventions and policies that consider these complex interplays, potentially informing future strategies to combat metabolic syndrome and related health issues.