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在代谢纵向数据中发现昼夜节律:贝叶斯潜伏类建模方法
Sungduk Kim1, Neil E Caporaso1, Fangyi Gu2
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Statistics in medicine
|May 26, 2023
概括
这项研究引入了一种新的统计方法来分析24小时的新陈代谢模式,区分人类新陈代谢的内部昼夜节律和外部白天变化.
科学领域:
- 生物学 生物学 生物学
- 医学 医学 医学 医学 医学
- 代谢学 代谢学 代谢学
- 统计 统计 统计 统计
背景情况:
- 昼夜节律显著影响生物机制和疾病.
- 代谢学为这些节奏提供了洞察力,但需要强大的分析方法.
- 描述各种24小时代谢模式对于理解生物功能至关重要.
研究的目的:
- 开发一种统计严格的方法来分析高维的纵向代谢学数据.
- 识别和区分人类新陈代谢中的各种24小时模式.
- 为了区分内源的昼夜节律与外源的白天变化.
主要方法:
- 开发了一种潜在类型的方法来建模代谢物概况.
- 使用了形状不变的昼夜曲线的有限混合物,包括振幅和相位变化.
- 用马尔科夫链蒙特卡洛采样进行贝叶斯后方计算.
主要成果:
- 该模型确定了两个不同的24小时模式:一个简单的阴影形 (环心节) 和一个复杂的多峰 (日间) 节奏.
- 昼夜模式在不同个体中显示出一致的相位.
- 白天模式显示了个体之间的相位差异.
结论:
- 开发的建模框架有效地分离了内源的昼夜和外源的白天代谢模式.
- 这种方法增强了对人类新陈代谢及其变异的理解.
- 它为进一步研究昼夜和昼夜对健康和疾病的影响提供了基础.
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