# Predicting amyloid status in mild cognitive impairment: the role of semantic intrusions combined with plasma biomarkers

## TL;DR

This study shows that combining semantic intrusion errors with blood biomarkers can accurately predict amyloid status in people with mild cognitive impairment.

## Contribution

The study introduces a novel predictive model combining semantic intrusion errors and plasma biomarkers for amyloid detection in MCI.

## Key findings

- Semantic intrusion errors on the BVLT are highly predictive of amyloid deposition in MCI participants.
- The combined model of p-tau217, GFAP, and semantic intrusion errors achieved an AUC of 0.964 in predicting amyloid positivity.
- p-tau217 and GFAP levels are positively associated with semantic intrusion errors in A+ MCI patients.

## Abstract

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.