一种可解释的机器学习基于脊柱的形态参数估计了生物年龄
Zi Xu1, Yunsong Peng1, Mudan Zhang1
1Department of Radiology, Guizhou Provincial Peoples Hospital, Guiyang, 550001, People's Republic of China.
GeroScience
|October 24, 2024
概括
这项研究介绍了SpineAge,这是一种基于脊柱形态学的新生物年龄预测器. 脊柱年龄比时间年龄更好地预测死亡风险,突出了它对个性化护理的潜力.
科学领域:
- 老年学和生物信息学
- 放射学和医学成像学 医学成像学
背景情况:
- 估计生物年龄对于评估衰老和预测健康风险至关重要.
- 脊柱压缩的患病率随着年龄的增长而增加,但对生物年龄估计的脊椎形态学数据未得到充分利用.
研究的目的:
- 使用机器学习模型开发和验证一种新的脊柱生物年龄预测器,称为SpineAge.
- 评估脊柱年龄,加速衰老指数 (AAI) 和全因死亡风险之间的关联.
主要方法:
- 利用了2364名参与国家健康和营养检查调查的数据.
- 通过通过双能X射线吸收计分析的横向放射图的脊柱形态参数计算脊柱年龄.
- 采用了夏普利添加剂解释模型的可解释性,并将AAI定义为脊柱年龄减去时间年龄.
主要成果:
- 与时间表相比,SpineAge在预测2年和5年全因死亡率方面表现出卓越的表现.
- 发现AAI与增加全因死亡风险 (HR,每年1.259) 之间存在显著的关联.
- 较高的AAI四分位数显示死亡风险大幅增加,最高四分位数增加了22.9倍.
结论:
- 脊柱年龄 (SpineAge) 是一种新的,适用的生物年龄预测指标,来源于脊柱形态学.
- 开发的加速衰老指数 (AAI) 有效量化脊柱衰老的加速,并预测长期死亡率.
- 这种方法促进了个性化的预后和个性化的医疗保健策略.
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