Effect of <i>APOE ε</i>4 genotype on amyloid‐β, glucose metabolism, and gray matter volume in cognitively normal individuals and amnestic mild cognitive impairment
Bibliographic record
Abstract
Abstract Background and purpose The presence of apolipoprotein E ε4 (APOE ε4) is associated with an increased risk of developing Alzheimer disease (AD). The aim of this study was to assess the effects of APOE ε4 on amyloid‐β (Aβ) pathology, glucose metabolism, and gray matter (GM) volume and their longitudinal changes in healthy control (HC) and amnestic mild cognitive impairment (aMCI). Methods We included 50 HCs and 109 aMCI patients from the Alzheimer's Disease Neuroimaging Initiative phase 2/GO based on availability of baseline T1‐weighted magnetic resonance imaging, 18F‐florbetapir positron emission tomography (PET), and 18F‐fluorodeoxyglucose (FDG) PET. Of these, 35 HCs and 67 aMCI patients who underwent 24‐month scans were included for follow‐up study. Results Voxelwise analysis revealed that APOE ε4 carriers exhibited greater baseline Aβ deposition than APOE ε4 noncarriers in both diagnostic groups. However, there was no significant difference between APOE ε4 noncarriers and APOE ε4 carriers in terms of 18F‐FDG PET standardized uptake value ratio and GM volume. Region of interest‐based analysis showed statistically significant greater Aβ deposition in APOE ε4 carriers than APOE ε4 noncarriers only in aMCI patients. Furthermore, APOE ε4 carriers generally exhibited a greater magnitude and spatial extent of longitudinal changes in Aβ deposition than APOE ε4 noncarriers in both diagnostic groups. Conclusions Our findings suggest a differential effect of APOE ε4 on Aβ pathology, glucose metabolism, and GM volume. Studying APOE ε4‐related brain changes with neuroimaging biomarkers in preclinical AD offers an opportunity to further our understanding of the pathophysiology of AD at an early stage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".