根据饮食偏好和生活方式,估计代谢物网络
Georgios Bartzis1, Carel F W Peeters2, Hae-Won Uh3
1Mathematical and Statistical Methods group (Biometris), Wageningen University and Research, Wageningen, The Netherlands.
Metabolomics : Official journal of the Metabolomic Society
|August 11, 2025
概括
这项研究通过分析随着时间的推移在代谢组的变化来确定与饮食和生活方式相关的代谢物. 网络分析揭示了具有共同关联的代谢物组,改善了我们对代谢变异的理解.
科学领域:
- 生物医学科学 生物医学科学
- 代谢学 代谢学 代谢学
- 系统生物学 系统生物学
背景情况:
- 代谢组将遗传变异与临床表型联系起来.
- 代谢学研究由饮食,环境,时间和生活方式所影响的生理变化.
- 量化生活方式对新陈代谢变化的影响是具有挑战性的,因为其未定义的性质.
研究的目的:
- 估计时间和生活方式对代谢变化的影响.
- 识别与饮食和生活方式有类似关系的代谢物.
- 运用网络分析来理解代谢物相关性.
主要方法:
- 在两个时间点 (2007年,2014年) 与SNP和饮食数据一起测量了代谢物度.
- 线性混合效应模型用于解释重复测量,饮食,遗传学和时间.
- 随机拦截模拟为主体特定的生活方式影响,与饮食,遗传和时间不同.
主要成果:
- 开发了一种方法来隔离残留的代谢变异.
- 建立了与饮食和生活方式具有相似关联的代谢物网络.
- 根据共同的生活方式和饮食反应,确定了有意义的代谢物分组.
结论:
- 为多个代谢变化的来源进行校正,使其能够研究残留效应.
- 对残留变异的网络分析可以揭示功能相关的代谢物组.
- 这种方法提高了对饮食和生活方式如何塑造新陈代谢的理解.
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