Lu Yao, Cui Liang, Huang Lin, Xie Fang, Guo Qi-Hao
Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China.
Front Aging Neurosci. 2025 Jun 25;17:1624513. doi: 10.3389/fnagi.2025.1624513. eCollection 2025.
The efficacy of traditional semantic intrusion measurements in identifying amyloid deposition in mild cognitive impairment (MCI) patients remains suboptimal. It is anticipated that integrating innovative cognitive assessments with blood biomarker analyses will enhance the effectiveness of screening for Alzheimer's disease (AD).
The research included 204 participants from the Chinese Preclinical Alzheimer's Disease Study cohort, assessed between March 2019 and February 2023. The Bi-list Verbal Learning Test (BVLT) was utilized to measure semantic intrusions, while amyloid burden was quantified using neuroimaging with 18F-florbetapir PET/CT scans. Additionally, the study analyzed Apolipoprotein E loci and plasma biomarkers, including Aβ42, Aβ40, Tau, p-tau181, p-tau217, Nfl, and GFAP.
The study revealed that semantic intrusion errors on the BVLT are highly predictive of amyloid deposition in MCI participants. Binary logistic regression analysis confirmed that semantic intrusion errors on the Bi-list Verbal Learning Test, along with p-tau217 levels and GFAP levels, can effectively predict amyloid positive MCI. Correlation analysis further established a positive association between p-tau217, GFAP, and semantic intrusion errors among patients with A+ MCI. The combined predictors (p-tau217, GFAP, semantic intrusion errors) demonstrated outstanding performance in ROC analysis, achieving an AUC of 0.964, with a sensitivity of 92.7% and a specificity of 85.7%.
The study suggests that semantic intrusion errors from the BVLT, along with plasma biomarkers p-tau217 and GFAP, may serve as sensitive indicators for AD-related MCI. Combining these biomarkers with semantic intrusion errors offers a strong predictive model for assessing amyloid status in MCI patients.
传统语义干扰测量在识别轻度认知障碍（MCI）患者淀粉样蛋白沉积方面的效果仍不尽人意。预计将创新的认知评估与血液生物标志物分析相结合，将提高阿尔茨海默病（AD）筛查的有效性。
该研究纳入了204名来自中国临床前阿尔茨海默病研究队列的参与者，评估时间为2019年3月至2023年2月。使用双向言语学习测验（BVLT）测量语义干扰，同时通过18F-氟代贝他吡PET/CT扫描的神经影像学方法对淀粉样蛋白负荷进行定量。此外，该研究分析了载脂蛋白E基因座和血浆生物标志物，包括Aβ42、Aβ40、Tau、p-tau蛋白181、p-tau蛋白217、神经丝轻链（Nfl）和胶质纤维酸性蛋白（GFAP）。
该研究表明，BVLT上的语义干扰错误对MCI参与者的淀粉样蛋白沉积具有高度预测性。二元逻辑回归分析证实，双向言语学习测验上的语义干扰错误，以及p-tau蛋白217水平和GFAP水平，可以有效预测淀粉样蛋白阳性的MCI。相关性分析进一步证实，在A+ MCI患者中，p-tau蛋白217、GFAP与语义干扰错误之间存在正相关。联合预测指标（p-tau蛋白217、GFAP、语义干扰错误）在ROC分析中表现出色，曲线下面积（AUC）为0.964，灵敏度为92.7%，特异性为85.7%。
该研究表明，BVLT的语义干扰错误，以及血浆生物标志物p-tau蛋白217和GFAP，可能是AD相关MCI的敏感指标。将这些生物标志物与语义干扰错误相结合，为评估MCI患者的淀粉样蛋白状态提供了一个强大的预测模型。
