APOE <i>ε</i> 4-Associated Hippocampal Atrophy Trajectories Across the Alzheimer’s Disease Continuum: A Systematic Review, Meta-Analysis, and Longitudinal Validation
Bibliographic record
Abstract
Abstract Background The role of APOE- ε 4 in hippocampal atrophy is contested. We aimed to determine whether it functions as a static risk factor or an amyloid- β (A β )-dependent modulator of neurodegeneration. Methods We integrated a systematic meta-analysis of 18 studies ( N = 3, 781) with longitudinal validation using linear mixed-effects models in the NACC and ADNI cohorts ( N > 5, 000), employing biomarker stratification to test for gene-pathology interactions. Results The meta-analysis confirmed significant atrophy in APOE- ε 4 carriers but with high heterogeneity. Longitudinal analysis resolved this by identifying a crucial interaction: in A β -negative individuals, carrier atrophy rates were indistinguishable from non-carriers. However, A β positivity triggered a dramatic, dose-dependent acceleration in atrophy among carriers, with homozygotes declining over three times faster. Conclusions APOE- ε 4 acts as a potent, conditional accelerator of neurodegeneration, not an independent driver. Its deleterious effect is contingent on the presence of A β pathology. Clinical risk stratification should therefore integrate amyloid status with APOE genotype to accurately predict structural progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".