AD Resemblance Atrophy Index of Brain Magnetic Resonance Imaging in Predicting the Progression of Mild Cognitive Impairment Carrying Apolipoprotein E-ε4 Allele.

Abstract

Background and Objective:

Early identification is important for timely Alzheimer's disease (AD) treatment. Apolipoprotein E ε4 allele (APOE-ε4) is an important genetic risk factor for sporadic AD. The AD-Resemblance Atrophy Index (RAI)-a structural magnetic resonance imaging-derived composite index-was found to predict the risk of progression from mild cognitive impairment (MCI) to AD. Therefore, we investigated whether the AD-RAI can predict cognitive decline and progression to AD in patients with MCI carrying APOE ε4.

Methods:

We included 733 participants with MCI from the Alzheimer's Disease Neuroimaging Initiative Database (ADNI). Their APOE genotypes, cognitive performance, and levels of AD-RAI were assessed at baseline and follow-up. Linear regression models were used to test the correlations between the AD-RAI and baseline cognitive measures, and linear mixed models with random intercepts and slopes were applied to investigate whether AD-RAI and APOE-ε4 can predict the level of cognitive decline. Cox proportional risk regression models were used to test the association of AD-RAI and APOE status with the progression from MCI to AD.

Results:

The baseline AD-RAI was higher in the MCI converted to AD group than in the MCI stable group (P < 0.001). The AD-RAI was significantly correlated with cognition, and had a synergistic effect with APOE-ε4 to predict the rate of cognitive decline. The AD-RAI predicted the risk and timing of MCI progression to AD. Based on the MCI population carrying APOE-ε4, the median time to progression from MCI to AD was 24 months if the AD-RAI > 0.5, while the median time to progression from MCI to AD was 96 months for patients with an AD-RAI ≤ 0.5.

Conclusion:

The AD-RAI can predict the risk of progression to AD in people with MCI carrying APOE ε4, is strongly correlated with cognition, and can predict cognitive decline.

• Authors: Yingren Mai, Zhiyu Cao, Jiaxin Xu, Qun Yu, Shaoqing Yang, Jingyi Tang, Lei Zhao, Wenli Fang, Yishan Luo, Ming Lei, Vincent C T Mok, Lin Shi, Wang Liao, Jun Liu, Alzheimer’s Disease Neuroimaging Initiative
• Journal: Frontiers in aging neuroscience (Front Aging Neurosci) Vol. 14 Pg. 859492 ( 2022) ISSN: 1663-4365 [Print] Switzerland
• PMID: 35572149 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Mai, Cao, Xu, Yu, Yang, Tang, Zhao, Fang, Luo, Lei, Mok, Shi, Liao, Liu and The Alzheimer’s Disease Neuroimaging Initiative.