基于健康和生活方式特征的计算算法,用于对NUTRiMDEA队列中的生命元类型进行分类
Andrea Higuera-Gómez1, Víctor de la O2,3, Rodrigo San-Cristobal1,4,5
1Precision Nutrition and Cardiometabolic Health, IMDEA-Food Institute (Madrid Institute for Advanced Studies) Campus of International Excellence (CEI) UAM+CSIC, Madrid, Spain.
Scientific reports
|October 22, 2024
概括
研究人员开发了一种机器学习工具,根据健康,生活方式和个人数据将个人分为五个"生命类型"集群. 这使得可以针对慢性疾病预防和健康管理制定个性化策略.
科学领域:
- 营养科学与公共卫生
- 计算生物学和生物信息学
- 个性化医疗是个性化的医疗.
背景情况:
- 精准医学需要根据复杂的健康和生活方式数据对个人进行分类.
- 代谢类型和生活方式表型是慢性疾病风险的关键指标.
- 机器学习为数据驱动的健康分层提供了新的方法.
研究的目的:
- 开发和验证基于机器学习的聚类工具,用于将个人分为不同的健康和生活方式群体.
- 为个性化健康干预确定可操作的"生命metabotypes".
- 利用在线队列进行可扩展和可访问的健康评估.
主要方法:
- 使用32个可访问问题的NUTRiMDEA在线队列 (n=17,332) 数据分析.
- 探索性因子分析,集群定义和机器学习算法的应用.
- 开发用于快速集群分配的计算算法.
主要成果:
- 确定了五个不同的群体:"西化千一代"",健康"",地中海年轻人"",病前"和"病前".
- 集群在年龄,性别分布,生活习惯,情绪和健康状况方面存在显著差异.
- "亲病"群体包括健康和生活方式较差的老年人,而"地中海年轻成年人"显示了最高的饮食坚持.
结论:
- 机器学习驱动的"生命metabotypes"的分类是有效的基于健康和生活方式的个人分层.
- 这种方法促进了针对慢性疾病预防和管理的个性化干预.
- 开发的算法支持有针对性的健康维护和精确的生活方式建议的在线方法.
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