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机器学习算法衍生的饮食模式网络与NutriNet-Santé队列中的心血管疾病风险之间的关联

Mélina Côté¹, Joy M Hutchinson², Mathilde Touvier³

¹Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Québec, Canada; École de Nutrition, Université Laval, Québec, Québec, Canada.

The Journal of nutrition

|September 24, 2025

概括

摄入超加工糖果和零食与法国成年人心血管疾病风险更高有关,即使整体饮食质量良好. 新的网络分析发现了这种饮食模式.

科学领域:

营养流行病学营养流行病学
计算生物学是一种计算生物学.
数据科学是数据科学.

背景情况:

数据科学和机器学习提供了新的方法,如高斯图形模型 (GGM) 和卢温算法来识别饮食模式 (DP).
了解饮食模式对于公共卫生和疾病预防至关重要.

研究的目的:

在法国人口样本中使用计算方法识别饮食模式 (DP) 网络.
调查已识别的DP网络与心血管疾病 (CVD) 风险之间的关联.

主要方法:

利用了NutriNet-Santé队列中的99,362名参与者 (年龄≥15岁) 的数据.
通过多个24小时记录评估饮食摄入量,分为42个食物组.
采用GGM与卢温算法来导出DP网络和Cox模型来评估心血管疾病风险关联.

主要成果:

确定了5个不同的DP网络:开胃菜,早餐,植物性,超加工糖果/零食和健康食品.
超加工糖果和零食DP网络显示与心血管疾病风险增加有显著的关联 (HR: 1.32;95% CI: 1.11-1.57).
在对能量摄入量和整体饮食质量进行调整后,这种关联仍然显著.

结论:

超加工糖果和零食消费的DP网络独立地与法国人口中发生的心血管疾病有关.
新的经验DP网络导出方法可以帮助确定一起消费的食物组,帮助预防心血管疾病的战略.
这项研究强调了针对特定饮食模式的重要性,比如高消费的超加工糖果,对于心血管健康.

关键词:

高斯的图形模型是高斯的.卢凡的算法卢凡的算法在NutriNet-Santé中,我们提供了健康营养.心血管疾病发生率.饮食模式饮食模式

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