ORIGINAL RESEARCH article
Front. Aging Neurosci.
Sec. Neurocognitive Aging and Behavior
Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1624513
Predicting amyloid status in mild cognitive impairment: the role of semantic intrusions combined with plasma biomarkers
Provisionally accepted
- 1Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
- 2Huashan Hospital, Shanghai, China
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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-tau 181, 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 3 these biomarkers with semantic intrusion errors offers a strong predictive model for assessing amyloid status in MCI patients.
Keywords: Mild Cognitive Impairment, semantic intrusions, BVLT, p-tau217, GFAP, amyloid. *, †, ‡ : Means with different superscripts are statistically significant p < 0.05 by the Bonferroni Test. Abbreviations: BVLT=Bi-list verbal learning test
Received: 07 May 2025; Accepted: 13 Jun 2025.
Copyright: © 2025 Lu, Cui, Huang, Xie and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qihao Guo, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
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