从衰老时钟到器官网络:生物年龄驱动的器官异步和器官间相互作用塑造死亡风险
Zhenyu Li1, Jiahao Liu1, Yaoyao Dai1
1Department of Epidemiology, Institute for Applied Research in Public Health, Key Laboratory of Jiangsu Higher Education Institutions for Advanced Medical Analytics and Public Health, School of Public Health, Nantong University, Nantong, Jiangsu, China.
Ageing research reviews
|February 8, 2026
概括
新的多学科衰老时钟精确地追踪器官特定的衰老,并预测健康风险. 一个人工智能平台将这些时钟集成为个性化干预,以延长健康的寿命并减少死亡率.
科学领域:
- 老年学和计算生物学.
- 衰老的生物标志物
- 多主题数据分析数据分析.
背景情况:
- 衰老是一个复杂的,异质的过程,影响器官异步.
- 多omics技术使不同的衰老时钟 (表观遗传学,蛋白质学,代谢学,成像学) 能够预测疾病和死亡率.
- 个体器官的衰老速度各不相同,影响全身健康和生存.
研究的目的:
- 引入人工智能驱动的多omics健康平台,用于持续的生物年龄监测.
- 为了能够及早发现与年龄有关的疾病风险.
- 支持基于个体老龄化特征的个性化干预措施.
主要方法:
- 开发和整合器官特异性的多组体衰老时钟.
- 利用人工智能 (AI) 进行数据分析和风险预测.
- 整合临床数据,成像和可穿戴传感器数据.
主要成果:
- 多器官衰老时钟显示了个体内衰老速度的显著变化.
- 加快的或多器官衰老与系统性失调和增加的死亡率有关.
- 器官交叉网络 (例如,心血管-肺-大脑) 影响健康范围.
结论:
- 支持人工智能的多omics衰老时钟为理解和干预人类衰老提供了一个变革性的框架.
- 从单个器官到网络层面的评估的转变使得精确的抗衰老策略成为可能.
- 这种方法可以构成国家健康生态系统的基础,以延长健康的寿命并减少死亡率.
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