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相关实验视频

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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利用机器学习来预测帕金森病,使用前症状蛋白质组学数据.

Shirley Nieves-Rodriguez1, Liping Hou1, Christopher D Whelan2

  • 1Population Analytics & Insights, AI/ML Data Science & Digital Health, Johnson & Johnson, Spring House, PA 19002, USA.

Brain : a journal of neurology
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概括

早期检测帕金森病是可能使用血蛋白分析. 机器学习识别了23种蛋白质,可以在诊断前14年预测帕金森病的风险,有助于早期干预.

关键词:
帕金森病是帕金森氏症的一种疾病.疾病预测 疾病预测高通量蛋白质组学机器学习是机器学习.血蛋白质组学研究

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科学领域:

  • 神经科学是一个神经科学.
  • 蛋白质组学是指蛋白质组学.
  • 生物化学 生化学

背景情况:

  • 帕金森病 (PD) 的非运动症状可以在运动症状出现前20年出现.
  • 准确预测PD风险仍然是一个重大的临床挑战.
  • 早期检测对于及时干预和疾病管理至关重要.

研究的目的:

  • 为了研究血蛋白质组特征在预测帕金森病风险方面的潜力.
  • 应用机器学习 (ML) 模型来识别预测性蛋白质生物标志物在症状前的个体.
  • 探索与早期PD发展相关的生物途径.

主要方法:

  • 分析了来自英国生物库参与者的2,937个血蛋白,包括发生和流行PD病例和对照.
  • 应用ML算法来预测PD诊断的时间,最早可达14年.
  • 在一个独立的队列中验证已识别的蛋白质组签名.
  • 路径丰富分析和共同表达网络分析.

主要成果:

  • 在后来患上PD的个体中,发现446种血蛋白质的调节失调.
  • 一个由23种蛋白质组成的小组实现了0.78的曲线下面积 (AUC),用于预测事件PD.
  • 蛋白质组签名在一个独立的队列中得到验证,其AUC高达0.76.6.
  • 在诊断前9年观察到PD相关途径的丰富,包括神经元死亡和粉样β清除.

结论:

  • 血蛋白质组签名可以在临床诊断前14年预测帕金森病的风险.
  • 应用于大规模蛋白质组学数据的机器学习对早期PD检测具有重大前景.
  • 不调节的蛋白质和途径为帕金森病的早期分子机制提供了洞察力.