人类衰老的全现象关联揭示了性别特异性的动态
Lee Reicher1,2,3, Noam Bar1,2, Anastasia Godneva1,2
1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Nature aging
|November 5, 2024
概括
生物年龄 (BA) 提供了比时间年龄更好的衰老指标. 这项研究使用了大型队列的机器学习来发现特定性别的衰老模式及其与健康的联系,为个性化预防铺平了道路.
科学领域:
- 老年学和衰老研究.
- 生物标志物的发现和验证.
- 个性化医疗和预防性健康.
背景情况:
- 衰老是一个复杂的过程,具有显著的个体变化.
- 现有的研究往往忽略了性别特定的衰老模式和单一模式数据.
- 从生物标志物的生物年龄 (BA) 估计可以提供更准确的衰老反映.
研究的目的:
- 用深度表型的纵向队列分析特定性别的衰老模式.
- 开发用于生物年龄估计的机器学习模型.
- 调查生物衰老得分与与年龄相关的疾病之间的关联.
主要方法:
- 对一万个个体的纵向队列 (10K项目,以色列) 进行分析,并进行了全面的表型化 (临床,生理,多核).
- 机器学习模型的开发,以计算跨不同生理系统的生物衰老得分.
- 长度随访每2年进行一次,持续25年.
主要成果:
- 通过使用生物衰老得分,确定了两性之间不同的衰老模式.
- 证明了较高的生物衰老得分与与年龄相关疾病的患病率增加之间的相关性.
- 揭示了系统特定的衰老动态.
结论:
- 生物年龄估计为个人衰老轨迹和性别差异提供了宝贵的见解.
- 深度表型的队列对于理解衰老和慢性疾病至关重要.
- 研究结果支持制定个性化的医疗预防策略.
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