帕金森病的进展亚型通过数据驱动的多队列分析确定
Tom Hähnel1,2, Tamara Raschka3,4, Stefano Sapienza5,6
1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany. tom.haehnel@scai-extern.fraunhofer.de.
NPJ Parkinson's disease
|May 2, 2024
概括
帕金森病 (PD) 的异质性是由两个不同的进展亚型解释的:快速和缓慢. 识别这些亚型可以通过专注于快速进展的患者来显著减少临床试验的大小.
科学领域:
- 神经学 神经学
- 数据科学数据科学数据科学
- 临床试验 临床试验
背景情况:
- 帕金森病 (PD) 的进展在患者之间有很大的差异,使治疗策略和临床试验设计复杂化.
- 不同的PD亚型可能需要量身定制的治疗方法.
研究的目的:
- 用数据驱动方法识别和验证不同的帕金森病进展亚型.
- 调查PD中观察到的异质性是否可以归因于潜在的进展亚型.
主要方法:
- 分析来自三大PD队列的多式纵向数据,并进行跨队列验证.
- 应用潜时联合混合效应模型 (LTJMM) 进行时间尺度对齐.
- 使用与复发的变异深嵌入 (VaDER) 识别进展亚型.
主要成果:
- 在整个队列中确定了两个稳定的PD进展亚型 (快速和缓慢).
- 亚型在运动/非运动症状,生存率,治疗反应,成像,步态和阿尔茨海默病的病理学方面有所不同.
- 预测模型实现了个人患者亚型预测的ROC-AUC为0.79.
- 模拟表明,通过丰富快速进展的患者,临床试验规模减少了43%.
结论:
- PD异质性可以通过两个不同的,稳定的进展亚型来解释.
- 这些亚型可能与大脑优先与身体优先的概念保持一致,提供生物学见解.
- 预测模型可以通过使快速进步的患者队列的丰富来优化临床试验.
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