生物标志物 生物标志物
Akira A Nair1, Zixuan Wen1, Zexuan Wang1
1University of Pennsylvania, Philadelphia, PA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 24, 2025
概括
阿尔茨海默病的进展可以使用粉样β斑块,神经纤维状结和神经元损失生物标志物分阶段. 像PHATE,Slingshot和SuStaIn这样的计算方法揭示了这些生物标志物的独特但融合的轨迹,有助于疾病预测.
科学领域:
- 神经科学是一个神经科学.
- 生物标志物发现发现
- 计算生物学 计算生物学
背景情况:
- 阿尔茨海默病 (AD) 的特点是粉样β斑块 (A),神经纤维状结 (T) 和神经元损失 (N),统称为A/T/N.
- 了解神经退行症的空间进展对于预测AD轨迹和结果至关重要.
研究的目的:
- 使用A/T/N生物标志物探索AD患者的病期和伪时间.
- 利用来自阿尔茨海默病神经成像计划 (ADNI) 的 pozitron发射断层扫描 (PET) 和磁共振成像 (MRI) 数据.
主要方法:
- 应用PHATE来减少维度,以可视化A/T/N模式的疾病进展轨迹.
- 使用Slingshot将PHATE嵌入式转换为1D伪时间值.
- 使用SuStaIn,一种机器学习算法,来预测患者的病期和生物标志物序列.
- 将SustaIn阶段的预测与PHATE/Slingshot伪时值进行比较,以评估强度.
主要成果:
- 在所有模式中,SuStaIn预测的阶段与PHATE/Slingshot伪时值密切结合.
- 粉样蛋白PET和蛋白PET显示出比基于MRI的体积更强的轨迹对齐.
- 来自SustaIn的生物标志物事件序列与PHATE伪时间告知模型一致.
- 粉样和形态表现出中度的伪物质时间相关性,但不同的驱动生物标志物事件.
结论:
- 对计算方法的综合分析提高了对疾病进展时间的信心.
- 粉样蛋白和蛋白生物标志物似乎遵循不同的皮质轨迹.
- 不同的计算方法独立地产生了关于疾病进展的类似发现.
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