生物标志物 生物标志物
Michael J Spilka1, Mengdan Xu1, Balazs Toth2
1Winterlight Labs (Cambridge Cognition), Toronto, ON, Canada.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
数字语音生物标志物显示出跟踪阿尔茨海默病 (AD) 进展的前景. 之前开发的语音组合在临床试验中在测量随时间变化和可靠性方面表现最好.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 生物标志物 生物标志物
背景情况:
- 阿尔茨海默病 (AD) 的特点是语言逐渐衰退.
- 自然语言处理 (NLP) 为测量语言障碍提供了客观的工具.
- 语音生物标志物可以促进跟踪AD的临床进展.
研究的目的:
- 评估和比较基于数字语音的标记,用于跟踪阿尔茨海默病的进展.
- 评估语音复合材料在测量纵向变化和可靠性的性能.
- 在AD试验中将语音生物标志物与已确定的临床终点相关联.
主要方法:
- 临床痴呆症评分 (CDR) 采访记录的分析,来自于2个2期2期semorinemab试验的227名参与者的采访记录.
- 从患者的反应计算声学和语言语音特征.
- 使用不同的特征选择方法开发和验证三个语音复合分数.
主要成果:
- 这三种语音组合均显示出随时间的推移显著的纵向变化 (效果大小为0.47-0.59).
- 对复合材料观察到足够的测试重复测试可靠性 (类内相关性为0.67-0.80).
- 复合物与ADAS-Cog11和CDR-SB (斯皮尔曼 rho 0.21-0.49) 等临床终点有显著的相关性.
结论:
- 语音特征可以整合到对轻度认知障碍 (MCI) 到中度AD疾病进展的有意义指数中.
- 之前发表的Tauriel衍生生物标志物复合物表现最好,显示了最大的效果大小,最高的可靠性和节性.
- 基于语音的生物标志物提供了一个潜在的客观,低负担的措施,以补充AD临床试验中的传统终点.
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