Exploring Predictors of Diabetes Within Animal and Plant-Based Dietary Patterns with the XGBoost Machine Learning Classifier: NHANES 2013-2016

Background: /Objectives: Understanding the relationship between dietary patterns, nutrient intake, and chronic disease risk is critical for informing public health strategies. However, confounding from lifestyle and individual factors complicates the assessment of diet-disease associations. Emerging machine learning (ML) techniques offer novel approaches to clarifying the importance of multifactorial predictors. This study investigated the associations between animal-sourced and plant-based dietary patterns and diabetes history, accounting for diet-lifestyle patterns employing the XGBoost algorithm. Methods: Using data from the National Health and Nutrition Examination Survey (NHANES) from 2013-2016, individuals consuming animal-sourced foods (ASF) and plant-based foods (PBF) were matched on key confounders, including age, gender, body composition, energy intake, and activity levels. Predictors of diabetes history were analyzed using the XGBoost classifier, with feature importance derived from Shapley plots. Lifestyle and dietary patterns derived from principal component analysis (PCA) were incorporated as predictors, and high multicollinearity among predictors was examined. Results: The top predictors by importance to diabetes prediction included age, percent body fat, recent BMI changes, and physical activity. Higher protein and fat intake from ASFs and PBFs were associated with lower risk, while unhealthy lifestyle factors exacerbated risk. The XGBoost model achieved an accuracy of 91.4% and an AUROC of 89%. The dietary and serum omega-6 to omega-3 fatty acids ratio emerged as significant dietary predictors. Conclusions: This study underscores the complex interactions between diet, lifestyle, and body composition in diabetes risk. Machine learning techniques like XGBoost provide valuable insights into these multifactorial relationships by mitigating confounding and identifying key predictors. Future research should focus on prospective studies incorporating detailed nutrient analyses and ML approaches to refine prevention strategies and dietary recommendations for type 2 diabetes.