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阿尔茨海默氏症成像联盟

Sarah Ko1, Hui Cao2, Mehrshad Saadatinia1

  • 1Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.

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概括
此摘要是机器生成的。

这项研究使用脑脊髓液蛋白质学开发了11个器官特异性蛋白质基生物年龄差距 (ProtBAGs). 大脑和肝脏的ProtBAG显示了最准确的衰老预测,推进了多器官衰老模型.

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科学领域:

  • 老年学是一门学科.
  • 蛋白质组学是指蛋白质组学.
  • 生物标志物 生物标志物

背景情况:

  • 多器官衰老和疾病建模是一个不断增长的研究领域.
  • 血蛋白质组学已建立用于预测生物年龄 (基于蛋白质组的生物年龄差距,ProtBAG).
  • 大脑脊髓液 (CSF) 蛋白质组学为器官特定衰老提供了一个新的窗口.

研究的目的:

  • 通过使用CSF蛋白质组学数据从阿尔茨海默氏症神经成像倡议 (ADNI) 研究中获得11个器官特定的ProtBAG.
  • 评估两种机器学习模型在预测器官特定生物年龄方面的表现.
  • 使用CSF生物标志物扩展多器官衰老时钟框架.

主要方法:

  • 来自736名ADNI参与者的CSF蛋白质组数据使用SomaScan 7k平台进行了分析.
  • 鉴定了器官丰富的蛋白质,并使用线性支向量回归 (SVR) 和LASSO回归开发了11个器官特异的ProtBAG.
  • 模型性能使用嵌套随机持久交叉验证,平均绝对误差 (MAE) 和皮尔森的r.使用评估.

主要成果:

  • 缺少蛋白质组学值的归算,缺失率为2%,产生了最好的模型性能 (r2=0.54).
  • 线性SVR和LASSO回归模型在11个器官系统中显示了可比的性能.
  • 大脑和肝脏的ProtBAG表现出最低的MAE和最高的皮尔森r值,表明在预测生物年龄方面具有卓越的准确性.
  • MAE值在4.5到6之间,与之前的大脑成像研究一致.
  • 内分泌和生殖系统的ProtBAG显示出相对较低的模型性能.

结论:

  • 这项研究成功地从CSF蛋白质组学中开发了11个器官特异性ProtBAG,增强了现有的多器官衰老时钟框架.
  • 这些发现突显了CSF蛋白质组学在评估器官特异性衰老方面的潜力.
  • 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.