Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function

• Published in: Brain and behavior Vol. 14; no. 1; pp. e3381 - n/a
• Main Authors: Kim, Hyug‐Gi, Tian, Yunan, Jung, Sue Min, Park, Soonchan, Rhee, Hak Young, Ryu, Chang‐Woo, Jahng, Geon‐Ho
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.01.2024; John Wiley and Sons Inc; Wiley
• Subjects: Age; Aged; aging; Alleles; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - genetics; Alzheimer's disease; apolipoprotein E ε4 status; Apolipoprotein E4 - genetics; Apolipoproteins; Apolipoproteins E - genetics; Atrophy; Atrophy - pathology; Biomarkers; brain atrophy; Cognition; Cognitive ability; cognitive decline; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - genetics; Cognitive Dysfunction - pathology; Dementia; Genotype; Genotype & phenotype; Gray Matter - diagnostic imaging; Gray Matter - pathology; Health risk assessment; Humans; machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Neuropsychology; Original; prediction; prediction; aging; apolipoprotein E ε4 status; cognitive decline; machine learning; brain atrophy
• ISSN: 2162-3279; 2162-3279
• DOI: 10.1002/brb3.3381

Background Apolipoprotein E (ApoE) ε4 carriers have a higher risk of developing Alzheimer's disease (AD) and show brain atrophy and cognitive decline even before diagnosis. Objective To predict ApoE ε4 status using gray matter volume (GMV) obtained from magnetic resonance imaging images and demographic data with machine learning (ML) methods. Methods We recruited 74 participants (25 probable AD, 24 amnestic mild cognitive impairment, and 25 cognitively normal older people) with known ApoE genotype (22 ApoE ε4 carriers and 52 noncarriers) and scanned them with three‐dimensional (3D) T1‐weighted (T1W) and 3D double inversion recovery (DIR) sequences. We extracted GMV from regions of interest related to AD pathology and used them as features along with age and mini–mental state examination (MMSE) scores to train different ML models. We performed both receiver operating characteristic curve analysis and the prediction analysis of the ApoE ε4 carrier with different ML models. Results The best model of ML analyses was a cubic support vector machine (SVM3) that used age, the MMSE score, and DIR GMVs at the amygdala, hippocampus, and precuneus as features (AUC = .88). This model outperformed models using T1W GMV or demographic data alone. Conclusion Our results suggest that brain atrophy with DIR GMV and cognitive decline with aging can be useful biomarkers for predicting ApoE ε4 status and identifying individuals at risk of AD progression. The ApoE ε4 genotype might be carried by an elderly participant with a low MMSE score and GMV reduction in the amygdala and hippocampus. This result is important to identify individuals who have a high risk for AD progression in the future.