用于多维生物年龄估计的新型机器学习框架揭示了器官系统的异质衰老
1Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Computer methods and programs in biomedicine
|December 5, 2025
概括
这项研究引入了用于多器官生物年龄 (BA) 评估的新机器学习框架. 它准确地预测死亡率并捕捉个人衰老差异,提供临床见解.
科学领域:
- 生物遗传学 生物遗传学
- 机器学习在医学中的应用
- 系统生物学 系统生物学
背景情况:
- 当前的生物年龄 (BA) 模型往往是单维的,无法解释衰老的复杂,多器官性质.
- 不同器官老龄化的异质性需要更全面的评估方法.
研究的目的:
- 开发一个统一的,基于机器学习的框架来评估和解释多器官生物衰老.
- 为了捕捉不同器官的衰老过程的多维性和异质性.
主要方法:
- 综合器官特定的BA估计来自英国生物库数据在一个合体学习框架内.
- 开发了一个"Fusion BA"作为生物年龄的总体估计.
- 分析了不同的衰老模式及其与健康结果的相关性.
主要成果:
- 融合BA与时间学年龄有显著的相关性 (MAE:4.473年,r=0.718).
- 加速融合BA预测了10年死亡率 (HR=1.504).
- 器官特定的BA与疾病风险相关,并确定了独特的衰老特征.
结论:
- 开发的框架允许系统性和器官特异性衰老评估.
- 为临床风险分层和个性化医学提供了有价值的工具和见解.
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