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相关概念视频

Neural Regulation of Blood Pressure01:18

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相关实验视频

Updated: Jan 14, 2026

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
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使用机器学习量化心血管自主衰老.

Andy Schumann1, Yubraj Gupta1, Maria Geisler1

  • 1Lab for Autonomic Neuroscience, Imaging and Cognition (LANIC), Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany.

American journal of physiology. Heart and circulatory physiology
|October 25, 2025
PubMed
概括
此摘要是机器生成的。

心血管自主年龄 (CAA) 差距,一种新的机器学习指标,揭示了心血管风险高的个体的加速衰老. 这种标志物可能有助于早期检测和监测生理衰老和心血管健康.

关键词:
弗雷明汉姆风险评分 (Framingham Risk Score) 是一个风险评分.生物衰老 生物衰老心血管疾病的风险.心率变化的心率变化.精准医学是一门精准医学.

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科学领域:

  • 心血管生理学心血管生理学
  • 生物标志物 生物标志物
  • 机器学习在医学中的应用

背景情况:

  • 精准医学和衰老研究越来越多地利用机器学习 (ML).
  • 自主心血管功能为生理衰老提供了洞察力.
  • 现有的指标可能无法完全捕捉与年龄有关的心血管衰退.

研究的目的:

  • 介绍心血管自主年龄 (CAA) 差距,这是一个基于ML的新型指标.
  • 量化ML估计的生物年龄和时间年龄之间的偏差.
  • 评估CAA差距与心血管风险的关联.

主要方法:

  • 从1060名健康个体的高分辨率心电图和连续血压记录中得出29个自主指数.
  • 在879名参与者身上训练了一种高斯过程回归模型,以估计生物年龄 (CAA).
  • 计算了CAA差距作为CAA和时间年龄之间的差异,在风险分层队列中验证.

主要成果:

  • 高心血管风险 (CVR) 组与低心血管风险 (-1年) 组相比,CAA差距显著增加 (+11年).
  • CAA与弗雷明汉风险评分 (r=0.42,P<0.001) 有积极的相关性.
  • 在高CVR组中,在风险值中持续观察到CAA的升高.

结论:

  • 该CAA差距作为心血管风险和加速生理衰老的敏感和可解释的指标.
  • 这种新型指标具有早期检测和对心血管健康的纵向评估的潜力.
  • 基于ML的自主标记器为了解衰老过程提供了新的途径.