通过预测血糖反应来个性化营养
David Zeevi1, Tal Korem1, Niv Zmora2
1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Cell
|November 23, 2015
概括
通过机器学习制定个性化的营养计划, 有效地控制血糖升. 这种方法比一般的饮食建议更有前途,用于控制食后葡萄糖水平.
科学领域:
- 代谢健康
- 营养科学
- 个性化医疗
背景情况:
- 餐后血糖升高是一个全球性的健康问题,增加了糖尿病前期和II型糖尿病的风险.
- 目前的饮食策略对血糖控制的有效性是有限的,原因是个体对食的反应具有很高的变化.
研究的目的:
- 调查食后血糖反应对相同的食物的变化.
- 开发和验证用于预测个性化的食后血糖反应的机器学习算法.
- 根据算法评估个性化饮食干预的有效性.
主要方法:
- 在800人队伍中持续监测血糖,分析46,898餐的反应.
- 开发一个机器学习算法,整合血液参数,饮食,人体测量,体力活动和肠道微生物群.
- 在独立的队列和盲目的随机控制的饮食干预中进行验证.
主要成果:
- 对相同的食物的血糖反应观察到显著的变化,质疑普遍的饮食建议.
- 机器学习算法准确地预测了个性化的食后血糖反应.
- 个性化饮食干预导致食后葡萄糖水平显著降低,肠道微生物群发生变化.
结论:
- 由机器学习驱动的个性化饮食方法可以有效地管理食后高血糖症.
- 这一策略为缓解与血糖升高相关的代谢后果提供了一个有希望的途径.
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