生物标志物 生物标志物
Sarah Ko1, Hui Cao2, Mehrshad Saadatinia1
1Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
这项研究使用脑脊液 (CSF) 蛋白质学开发了11个器官特异性蛋白质基生物年龄差距 (ProtBAGs). 大脑和肝脏显示了最准确的衰老预测,推进了多器官衰老模型.
科学领域:
- 生物化学 生化学
- 老年学是指老年学的学科.
- 蛋白质组学是指蛋白质组学.
背景情况:
- 人类的衰老和疾病越来越多地使用多器官框架进行建模.
- 血蛋白质组学是预测生物年龄的关键工具,产生基于蛋白质组的生物年龄差距 (ProtBAG).
- 这项研究利用脑脊液 (CSF) 蛋白质组来扩大器官特定的衰老时钟.
研究的目的:
- 通过使用CSF蛋白质组学数据,推导出11个器官特定的基于蛋白质组的生物年龄差距 (ProtBAGs).
- 应用两个不同的机器学习方法来开发这些器官特定的衰老时钟.
- 评估ProtBAG在不同器官系统中的性能.
主要方法:
- 分析了来自ADNI研究的CSF蛋白质组数据 (7,008种蛋白质,736名参与者).
- 鉴定了器官丰富蛋白质,并训练了两个机器学习模型 (线性SVR和LASSO).
- 通过交叉验证,使用平均绝对误差 (MAE) 和皮尔森相关系数 (r) 来评估模型性能.
主要成果:
- 具有2%缺失率的蛋白质组学归算产生了最好的模型性能 (r2=0.54).
- 大脑和肝脏的ProtBAG显示了最低的平均绝对误差 (MAE) 值,表明更高的准确性.
- 大脑和肝脏的ProtBAG也表现出最高的皮尔森r值,证实了强大的预测能力.
结论:
- 11个器官特异性ProtBAG成功地使用来自ADNI队列的CSF蛋白组进行了开发.
- 这项工作通过基于CSF的多器官洞察来增强现有的器官衰老时钟框架.
- 未来的研究将探索这些新型ProtBAG,认知功能和阿尔茨海默病进展之间的联系.
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