一个统一的框架,用于系统的策划和评估衰老的生物标志物
Kejun Ying1,2, Seth Paulson3, Alec Eames1
1Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Nature aging
|November 4, 2025
概括
老龄化时钟预测时间的年龄,但不预测死亡风险,强调不同的生物过程. 一个新的框架,Biolearn,标准化生物标志物评估,确定不同健康结果的最佳表现者.
科学领域:
- 生物标志物和衰老研究.
- 计算生物学和生物信息学
- 基因组学和表观遗传学
背景情况:
- 衰老生物标志物对于量化生物年龄至关重要.
- 目前的验证受到不一致的方法和数据碎片化的阻碍.
- 传统的老龄化时钟在预测时间年龄方面的准确性与死亡率预测没有相关性.
研究的目的:
- 开发和验证一个标准化的框架来评估衰老的生物标志物.
- 为了评估39个生物标志物的表现,跨越不同的队列和临床结果.
- 为了确定时间表年龄,死亡率和健康预测的强大的生物标志物.
主要方法:
- 开发了Biolearn,这是一个开源框架,用于标准化生物标志物评估.
- 评估了39个衰老生物标志物,覆盖了来自不同队伍的20,000名个人.
- 在统一数据处理管道中利用了质量控制和细胞类型解卷.
主要成果:
- 霍瓦特的皮肤和血液时钟显示出最高的时间年龄精度 (R2=0.88).
- GrimAge2显示出最强的死亡率 (HR=2.57) 和健康预测 (HR=2.00).
- 生物标志物的表现在临床结果之间有显著差异,表明特定应用的最佳选择.
结论:
- 时间学年龄预测和老化时钟的死亡率预测能力是不同的.
- 生物学习为可再生衰老研究和生物标志物开发提供了基础.
- 选择最佳的衰老生物标志物取决于具体的临床应用和结果.
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