ArticleFrontiers in aging neuroscience2025
Predicting amyloid status in mild cognitive impairment: the role of semantic intrusions combined with plasma biomarkers.
Article in Frontiers in aging neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of plasma p-tau217 against amyloid PET: A meta-analysis of technical platform variability and ratio versus single marker comparisons.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Advancing Alzheimer's disease research in China: insights, innovations, and future directions from the chinese preclinical Alzheimer's disease study (CPAS).Molecular psychiatry · 2026Review
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5 authors.
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Abstract
Background: 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). Methods: 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. Results: 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%. Conclusion: 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.
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