基于饮食中的抗氧化剂预测心力衰竭风险建模:一种机器学习方法
Hechao Zhao1, Guanguo Shen2, Wenjie Zhang1
1The Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, China Three Gorges University, Yichang, China.
Molecular nutrition & food research
|September 9, 2025
概括
饮食中的抗氧化剂,如维生素B6,叶酸和柳科,可能会降低心力衰竭 (HF) 的风险. 一个机器学习模型根据抗氧化剂摄入量准确预测HF,帮助预防策略.
科学领域:
- 营养科学 营养科学
- 心血管健康 心血管健康
- 数据科学在医学中的数据科学
背景情况:
- 心力衰竭 (HF) 是一个重要的公共卫生问题.
- 饮食因素,特别是抗氧化剂,越来越多地被认为对心血管健康的作用.
- 识别特定的饮食成分和HF风险的预测标记对预防至关重要.
研究的目的:
- 调查饮食中抗氧化剂与心力衰竭风险之间的关联.
- 确定导致HF风险的关键抗氧化剂变量.
- 开发和验证机器学习 (ML) 模型,以基于饮食中的抗氧化剂配置文件来预测HF.
主要方法:
- 利用了来自国家健康和营养检查调查 (2005-2018) 的数据,其中有9279名参与者.
- 从24小时的回忆中提取了44种饮食抗氧化剂变量,并应用了特征选择技术 (Boruta,VIF).
- 训练和评估了六个ML模型 (XGBoost,随机森林等). 并使用WQS/Qg-comp回归来进行联合效应分析.
主要成果:
- XGBoost模型显示出高预测性能 (准确率为0.965,F1得分为0.971).
- 增加整体抗氧化剂摄入量与HF几率降低20%有关 (OR=0.80).
- 通过SHAP分析确定的关键抗氧化剂包括神胺,甘,咖啡因和维生素B1,B12,C.
结论:
- 摄入较高的特定的饮食抗氧化剂,特别是维生素B6,叶酸和甘,与降低心力衰竭风险有关.
- 开发的XGBoost模型为个性化的高频风险预测提供了一个强大的和可解释的工具.
- 研究结果支持将饮食中抗氧化剂评估纳入预防心力衰竭的基于证据的策略.
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