Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function
Abstract 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 image...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Brain and Behavior |
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| Online Access: | https://doi.org/10.1002/brb3.3381 |
| _version_ | 1827319022461386752 |
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| author | Hyug‐Gi Kim Yunan Tian Sue Min Jung Soonchan Park Hak Young Rhee Chang‐Woo Ryu Geon‐Ho Jahng |
| author_facet | Hyug‐Gi Kim Yunan Tian Sue Min Jung Soonchan Park Hak Young Rhee Chang‐Woo Ryu Geon‐Ho Jahng |
| author_sort | Hyug‐Gi Kim |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-07T23:34:51Z |
| format | Article |
| id | doaj.art-c0ef4de38462425da6a69a0f1a13a2fc |
| institution | Directory Open Access Journal |
| issn | 2162-3279 |
| language | English |
| last_indexed | 2024-04-25T00:12:43Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain and Behavior |
| spelling | doaj.art-c0ef4de38462425da6a69a0f1a13a2fc2024-03-13T10:15:39ZengWileyBrain and Behavior2162-32792024-01-01141n/an/a10.1002/brb3.3381Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive functionHyug‐Gi Kim0Yunan Tian1Sue Min Jung2Soonchan Park3Hak Young Rhee4Chang‐Woo Ryu5Geon‐Ho Jahng6Department of Radiology Kyung Hee University Hospital Seoul Republic of KoreaDepartment of Medicine, Graduate School Kyung Hee University College of Medicine Seoul Republic of KoreaDepartment of Biomedical Engineering, Undergraduate School, College of Electronics and Information Kyung Hee University Yongin‐si Gyeonggi‐do Republic of KoreaDepartment of Radiology Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine Seoul Republic of KoreaDepartment of Neurology Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine Seoul Republic of KoreaDepartment of Radiology Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine Seoul Republic of KoreaDepartment of Radiology Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine Seoul Republic of KoreaAbstract 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.https://doi.org/10.1002/brb3.3381agingapolipoprotein E ε4 statusbrain atrophycognitive declinemachine learningprediction |
| spellingShingle | Hyug‐Gi Kim Yunan Tian Sue Min Jung Soonchan Park Hak Young Rhee Chang‐Woo Ryu Geon‐Ho Jahng Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function Brain and Behavior aging apolipoprotein E ε4 status brain atrophy cognitive decline machine learning prediction |
| title | Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function |
| title_full | Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function |
| title_fullStr | Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function |
| title_full_unstemmed | Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function |
| title_short | Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function |
| title_sort | predicting the apolipoprotein e ε4 allele carrier status based on gray matter volumes and cognitive function |
| topic | aging apolipoprotein E ε4 status brain atrophy cognitive decline machine learning prediction |
| url | https://doi.org/10.1002/brb3.3381 |
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