Evidence map›Paper›PMID 38376028›Full record

ArticleBrain and behavior2024

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

Hyug-Gi Kim, Yunan Tian, Sue Min Jung, Soonchan Park, Hak Young Rhee, Chang-Woo Ryu, Geon-Ho Jahng

Open access · goldAbstract read
In one paragraph

Article in Brain and behavior, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Hyug-Gi KimDepartment of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea.
Yunan TianDepartment of Medicine, Graduate School, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
Sue Min JungDepartment of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, Yongin-si, Gyeonggi-do, Republic of Korea.
Soonchan ParkDepartment of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
Hak Young RheeDepartment of Neurology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
Chang-Woo RyuDepartment of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
Geon-Ho JahngDepartment of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, Seoul, Republic of Korea.ORCID 0000-0001-8881-1884
Kyung Hee University Hospital at Gangdong · KRKyung Hee University · KR

Funding

National Research Foundation of Korea (NRF) grants funded by Ministry of Science and ICT 2020R1A2C1004749
6 · The paper itself

Abstract

backgroundApolipoprotein E (ApoE) ε4 carriers have a higher risk of developing Alzheimer's disease (AD) and show brain atrophy and cognitive decline even before diagnosis.

objectiveTo predict ApoE ε4 status using gray matter volume (GMV) obtained from magnetic resonance imaging images and demographic data with machine learning (ML) methods.

methodsWe 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.

resultsThe 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.

conclusionOur 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.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAgedAllelesApolipoprotein E4Apolipoproteins EAtrophyCognitionGenotypeGray MatterHumansMagnetic Resonance ImagingApolipoprotein E4Apolipoproteins Eagingapolipoprotein E ε4 statusbrain atrophycognitive declinemachine learningprediction

Identifiers

PMID38376028
PMCPMC10772845
OpenAlexW4390668761

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.