The <i>APOE</i> ε4 variant and hippocampal atrophy in Alzheimer’s disease and Lewy body dementia: a systematic review of magnetic resonance imaging studies and therapeutic relevance
Bibliographic record
Abstract
Introduction: The apolipoprotein E ɛ4-allele (APOE-ɛ4) increases the risk not only for Alzheimer’s disease (AD) but also for Parkinson’s disease dementia and dementia with Lewy bodies (collectively, Lewy body dementia [LBD]). Hippocampal volume is an important neuroimaging biomarker for AD and LBD, although its association with APOE-ɛ4 is inconsistently reported. We investigated the association of APOE-ε4 with hippocampal atrophy quantified using magnetic resonance imaging in AD and LBD.Areas covered: Databases were searched for volumetric and voxel-based morphometric studies published up until December 31st, 2020. Thirty-nine studies (25 cross-sectional, 14 longitudinal) were included. We observed that (1) APOE-ε4 was associated with greater rate of hippocampal atrophy in longitudinal studies in AD and in those who progressed from mild cognitive impairment to AD, (2) association of APOE-ε4 with hippocampal atrophy in cross-sectional studies was inconsistent, (3) APOE-ɛ4 may influence hippocampal atrophy in dementia with Lewy bodies, although longitudinal investigations are needed. We comprehensively discussed methodological aspects, APOE-based therapeutic approaches, and the association of APOE-ε4 with hippocampal sub-regions and cognitive performance.Expert opinion: The role of APOE-ɛ4 in modulating hippocampal phenotypes may be further clarified through more homogenous, well-powered, and pathology-proven, longitudinal investigations. Understanding the underlying mechanisms will facilitate the development of prevention strategies targeting APOE-ɛ4.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".