陶-临床不匹配识别了具有共同病理的个体,并预测了临床轨迹
Christopher A Brown1, Nidhi S Mundada2, Katheryn A Q Cousins1
1Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
medRxiv : the preprint server for health sciences
|August 8, 2025
概括
陶-临床不匹配有效地识别出患有阿尔茨海默病 (AD) 的个体,他们更有可能具有共同病理和不同的临床轨迹. 这种方法有助于预测认知能力下降和监测AD患者的治疗反应.
科学领域:
- 神经科学是一个神经科学.
- 生物标志物 生物标志物
- 阿尔茨海默氏症疾病研究研究
背景情况:
- 预测个体阿尔茨海默病 (AD) 轨迹是具有挑战性的,因为疾病的异质性.
- 这种预测对于有效使用疾病修饰疗法至关重要.
- 在临床实用的工具中存在差距,用于识别具有共同病理和多种疾病过程的个体.
研究的目的:
- 评估tau临床不匹配,以识别患有AD的弹性和脆弱个体.
- 为了比较这些已识别的群体之间的共同病理水平和临床轨迹.
主要方法:
- 利用了来自ADNI,Penn-ADRC和Penn-ATM队列的数据 (所有都是粉样蛋白β阳性).
- 根据临床评估 (CDR-SB) 和tau负担 (Tau-PET或血p-tau217) 将参与者分为正规,弹性或脆弱的组.
- 评估了神经退行症标志物 (TDP-43,α-synuclein) 和纵向认知衰退 (CDR-SB).
主要成果:
- 图-临床不匹配模型将参与者分类为正规 (55.6-57.1%),弹性 (23.7-24.7%) 或脆弱 (19.3-19.7%).
- 弱势群体表现出较早的认知障碍和较高的共同病理 (TDP-43,α-synuclein),而弹性群体则表现出较晚的障碍.
- 模型在一个独立的数据集中得到验证,并证明在抗粉样蛋白治疗期间预测认知轨迹的实用性.
结论:
- 图-临床不匹配是识别患有阿尔茨海默病的个体具有共同病理和临床过程分歧的更高风险的有价值的工具.
- 基于等离子体的生物标志物为-PET提供了切实可行的替代品,具有可复制的结果.
- 这些模型可以提高个性化的预后,并可能在临床实践中监测治疗反应.
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