从多个可穿戴生物传感器中发现个性化的葡萄糖反应和昼夜节律,使用贝叶斯动态建模
Nicholas E Phillips1,2, Tinh-Hai Collet2,3, Felix Naef1
1Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Cell reports methods
|September 6, 2023
概括
可穿戴生物传感器可以跟踪每天的葡萄糖,活动和心率. 个性化模型揭示昼夜节律和压力反应,通过整合多个生理信号来改善葡萄糖预测.
科学领域:
- 生物医学工程 生物医学工程
- 生理学 生理学 生理学
- 计算生物学 计算生物学
背景情况:
- 可穿戴生物传感器和智能手机应用程序使日常生活中的持续生理监测成为可能.
- 了解葡萄糖动态,体力活动,心率 (HR) 和心率变化 (HRV) 对于健康管理至关重要.
研究的目的:
- 开发一个个性化的贝叶斯推理框架来量化昼夜节律和生理反应.
- 研究食物/饮料摄入,体力活动,HR/HRV和葡萄糖动态之间的关系.
- 评估整合多个生物传感器数据对葡萄糖变异性的预测能力.
主要方法:
- 收集了25名健康参与者的14天的食物/饮料摄入量,葡萄糖,体力活动,HR和HRV数据.
- 开发了贝叶斯推理框架来建模个人昼夜节律和对压力因素的反应.
- 分析了摄入事件和昼夜节律对葡萄糖水平的影响.
- 量化了由身体活动和昼夜节律解释的HR和HRV的差异.
- 综合活动,HR和HRV数据来预测葡萄糖变化.
主要成果:
- 较慢的葡萄糖衰变动力学与较大的食后葡萄糖峰值有关.
- 确定了葡萄糖的昼夜基线节律,在一些人中具有显著的幅度.
- 身体活动和昼夜节律解释了40-65%的HR变异,并显示了对HRV的异质影响.
- 整合活动,HR和HRV数据解释了高达15%的额外葡萄糖变化.
结论:
- 从可穿戴生物传感器对生理数据的个性化建模可以揭示个体健康模式.
- 循环节律和身体活动显著影响心率和葡萄糖动态.
- 整合来自多个生物传感器的数据可以提高葡萄糖变量的预测,这对于个性化健康洞察至关重要.
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