Eckart Adam C, Sharma Ghimire Pragya
Department of Health and Human Performance, Kean University, Union, NJ 07083, USA.
J Clin Med. 2025 Jan 13;14(2):458. doi: 10.3390/jcm14020458.
: Understanding the relationship between dietary patterns, nutrient intake, and chronic disease risk is critical for 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 Type 2 diabetes (T2D) history, accounting for diet-lifestyle patterns employing the XGBoost algorithm. : Using data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016, individuals consuming animal-sourced foods (ASF) and plant-based foods (PBF) were propensity score-matched on key confounders, including age, gender, body mass index, energy intake, and physical activity levels. Predictors of T2D 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. : A total of 2746 respondents were included in the analysis. Among the top predictors of T2D were age, BMI, unhealthy lifestyle, and the ω6: ω3 fatty acid ratio. Higher intakes of protein from ASFs and fats from PBFs were associated with lower T2D risk. The XGBoost model achieved an accuracy of 83.4% and an AUROC of 68%. : This study underscores the complex interactions between diet, lifestyle, and body composition in T2D 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 T2D.
了解饮食模式、营养摄入与慢性病风险之间的关系对于公共卫生策略至关重要。然而，生活方式和个体因素造成的混杂情况使饮食与疾病关联的评估变得复杂。新兴的机器学习（ML）技术为阐明多因素预测指标的重要性提供了新方法。本研究采用XGBoost算法，在考虑饮食 - 生活方式模式的情况下，调查了动物性和植物性饮食模式与2型糖尿病（T2D）病史之间的关联。
利用2013年至2016年美国国家健康和营养检查调查（NHANES）的数据，食用动物性食物（ASF）和植物性食物（PBF）的个体在关键混杂因素上进行了倾向得分匹配，这些因素包括年龄、性别、体重指数、能量摄入和身体活动水平。使用XGBoost分类器分析T2D病史的预测指标，并从Shapley图中得出特征重要性。将主成分分析（PCA）得出的生活方式和饮食模式作为预测指标，并检查了预测指标之间的高多重共线性。
共有2746名受访者纳入分析。T2D的主要预测指标包括年龄、体重指数、不健康的生活方式以及ω6:ω3脂肪酸比例。ASF中蛋白质摄入量较高以及PBF中脂肪摄入量较高与较低的T2D风险相关。XGBoost模型的准确率达到83.4%，曲线下面积（AUROC）为68%。
本研究强调了饮食、生活方式和身体成分在T2D风险中复杂的相互作用。像XGBoost这样的机器学习技术通过减少混杂因素并识别关键预测指标，为这些多因素关系提供了有价值的见解。未来的研究应聚焦于纳入详细营养分析和ML方法的前瞻性研究，以完善T2D的预防策略和饮食建议。
