机器学习辅助优化饮食干预对抗痴呆风险的饮食干预
Si-Jia Chen1, Hui Chen2, Jia You3
1Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
Nature human behaviour
|July 2, 2025
概括
一种新的机器学习辅助优化饮食干预对抗痴呆风险 (MODERN) 饮食可能会降低痴呆风险. 这种优先考虑特定食物组的饮食,与MIND饮食相比,与较低的痴呆风险有更强的关联.
科学领域:
- 营养流行病学 营养流行病学
- 在健康领域的机器学习.
- 神经科学是一个神经科学.
背景情况:
- 饮食显著影响认知健康和痴呆风险.
- 现有的饮食指南,如MIND饮食,旨在减轻痴呆症.
- 大规模的数据分析对于完善饮食建议至关重要.
研究的目的:
- 开发和验证一种新的饮食干预措施,现代饮食,使用机器学习来降低痴呆风险.
- 为了比较MODERN饮食与已建立的MIND饮食的疗效.
- 探索现代饮食对痴呆风险的影响背后的生物学途径.
主要方法:
- 利用了来自185,012名参与者的英国生物库数据,其中1987人在10年内患上痴呆症.
- 采用机器学习来识别和排名与痴呆风险相关的25个食物组.
- 建立并外部验证了MODERN评分,并将其与MIND饮食评分进行了比较.
主要成果:
- 与MIND饮食 (HR 0.75) 相比,MODERN饮食优先考虑八个关键食物组,与降低痴呆风险 (HR 0.64) 有更强的关联.
- 现代饮食显示出与精神/行为障碍风险降低的显著关联.
- 多模式分析 (神经成像,代谢学,炎症,蛋白质学) 表明了潜在的生物机制.
结论:
- 机器学习衍生的MODERN饮食为预防痴呆症提供了一个潜在的更有效的饮食策略.
- 现代饮食的好处可能延伸到其他健康结果,特别是精神和行为障碍.
- 对已识别的途径的进一步研究可以优化现代饮食对认知健康的作用.
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