机器学习和数据驱动的代谢学反向建模揭示了活跃衰老的关键过程
Jiahang Li1,2, Martin Brenner1, Iro Pierides1
1Molecular Systems Biology Lab (MOSYS), Department of Functional and Evolutionary Ecology, University of Vienna, Vienna, Austria.
NPJ systems biology and applications
|September 24, 2025
概括
身体不活动是一个全球性的健康问题. 这项研究确定阿斯巴甜酸盐是老年人身体健康的关键血液生物标志物,将其与活跃衰老和潜在的痴呆症风险联系起来.
科学领域:
- 老年学是一门学科.
- 代谢学 代谢学 代谢学
- 生物标志物发现发现
背景情况:
- 身体不活动和体力不佳是全球健康面临的重大挑战.
- 代谢学提供了一种整合性方法,将健身水平与分子变化联系起来.
- 了解这些联系对于促进健康老龄化至关重要.
研究的目的:
- 确定与老年人身体健康相关的关键代谢物生物标志物.
- 通过使用先进的分析方法,探索体能差异背后的分子动力学.
- 建立代谢特征,身体活动和衰老之间的联系.
主要方法:
- 分析不同治疗组的老年参与者的血液代谢组.
- 应用机器学习分类器来识别重要的代谢物生物标志物.
- 利用一种新的方法,COVRECON (基于CORrelation的VERified REConstruction),进行代谢网络相互作用分析.
- 定义了一个身体活动指数 (BAI),以根据身体活动水平对队列进行分层.
主要成果:
- 亚斯巴酸盐始终被确定为身体健康的主导生物标志物.
- 身体活动指数 (BAI) 有效地区分了高活动和低活动队列.
- COVRECON的分析强调了阿斯巴酸氨基转移酶 (AST) 是一个关键的差异化过程.
- 常规血液检查证实了健身组之间的AST和ALT水平存在显著差异.
- 加强了酸盐作为痴呆症生物标志物的作用,与身体健康有关.
结论:
- 机器学习和COVRECON有效地识别了与身体健康相关的代谢生物标志物和分子动态.
- 酸盐和AST是老年人身体健康状况的重要指标.
- 这些发现支持通过代谢洞察力开发促进活跃衰老的策略.
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