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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

174
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
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使用循环血液生物标志物的生物年龄估计.

Jordan Bortz1,2, Andrea Guariglia3,4, Lucija Klaric3

  • 1Humanity Inc, Humanity, 177 Huntington Ave, Ste 1700, Humanity Inc - 91556, Boston, MA, 02115, USA. jordan.bortz@humanity.email.

Communications biology
|October 26, 2023
PubMed
概括

这项研究使用机器学习和来自英国生物银行的25种血液生物标志物开发了更准确的生物年龄估计. 与现有模型相比,新方法改善了死亡风险预测,为评估生理衰老提供了实用工具.

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科学领域:

  • 生物医学科学 生物医学科学
  • 老年学是指老年学的学科.
  • 计算生物学 计算生物学

背景情况:

  • 生物年龄,反映生理衰退,是健康的更好的指标,而不是时间的年龄.
  • 血液生物标志物为估计生物年龄和指导干预提供了一个有希望的途径.
  • 现有的生物年龄模型,如PhenoAge,在预测准确性方面存在局限性.

研究的目的:

  • 通过机器学习和一套全面的循环生物标志物来提高生物年龄估计.
  • 开发一种比目前基于血液生物标志物的模型更准确的死亡风险预测器.
  • 建立一个实用且具有成本效益的生物年龄评估方法.

主要方法:

  • 利用了来自英国生物库的306,116个人的数据集,其中有60个循环生物标志物.
  • 实施了Elastic-Net衍生Cox模型来选择25个关键生物标志物用于死亡风险预测.
  • 估计的生物年龄基于个体的死亡风险相对于同性人口.

主要成果:

  • 开发的模型实现了0.778的C指数,超过了PhenoAge模型 (C指数=0.750).
  • 这代表死亡风险预测值相对增加了11%.
  • 使用常见的临床测定面板与归算并没有显著降低预测准确度.

结论:

  • 为生物年龄估计开发了一种实用且具有成本效益的机器学习模型.
  • 这种改进的生物年龄测量,从比时间年龄年轻20年到比时间年龄年长20年,揭示了血液中显著的衰老信号.
  • 该方法可供一般人群使用,有助于健康跨度研究和干预.