空间粉样蛋白信息的多模式大脑年龄作为阿尔茨海默病相关脆弱性和风险分层的早期标志物
Liang Cui1, Qing-Min Wang2, Zhen Zhang1
1Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
The journal of prevention of Alzheimer's disease
|February 7, 2026
概括
一个粉样蛋白信息的大脑年龄差距模型改善了阿尔茨海默病的检测. 这种多模式的方法增强了认知衰退的预测,并识别了早期的AD病理,帮助风险分层.
科学领域:
- 神经成像是一种神经成像.
- 阿尔茨海默氏症疾病研究研究
- 机器学习在医学中的应用
背景情况:
- 来自MRI的脑年龄差距 (BAG) 反映了神经生物学衰老,但在检测阿尔茨海默病 (AD) 病理学方面存在局限性.
- 将粉样β (Aβ) 定子发射断层扫描 (PET) 纳入BAG模型用于早期AD检测的实用性尚不清楚.
研究的目的:
- 开发一个集成Aβ-PET数据的多式 BAG 模型.
- 评估模型与认知功能,血生物标志物和AD频谱中的功能连接的关联.
主要方法:
- 使用集成机器学习对中国临床前阿尔茨海默病研究 (CPAS) 数据进行横截面分析.
- 包括990名具有正常认知,主观认知衰退 (SCD),轻度认知障碍 (MCI) 或痴呆症的成年人.
- 利用区域Aβ-PET,结构MRI,认知测试,血生物标志物 (p-tau217,p-tau181,NfL,GFAP,Aβ42/40),以及休息状态fMRI用于海马-默认模式网络 (DMN) 连接.
主要成果:
- 较高的BAG与增加SCD,MCI或痴呆的几率相关,特别是在Aβ阳性个体中.
- BAG解释了比全球Aβ负担更多的认知差异,并与多域认知缺陷联系在一起.
- 升高的BAG与改变的AD生物标志物相对应 (增加p-tau217,p-tau181,NfL,GFAP;减少Aβ42/40) 和降低海马-DMN连接.
结论:
- 一个基于粉样蛋白的多式 BAG 模型有效地捕捉了与AD相关的病理,生物标志物变化和认知脆弱性.
- 这个模型为个性化风险评估和AD的预防策略提供了价值.
- 该方法显示了早期检测和分层可能患AD的个体的风险潜力.
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