Advancements in APOE and dementia research: Highlights from the 2023 AAIC Advancements: APOE conference
Bibliographic record
Abstract
INTRODUCTION: The apolipoprotein E gene (APOE) is an established central player in the pathogenesis of Alzheimer's disease (AD), with distinct apoE isoforms exerting diverse effects. apoE influences not only amyloid-beta and tau pathologies but also lipid and energy metabolism, neuroinflammation, cerebral vascular health, and sex-dependent disease manifestations. Furthermore, ancestral background may significantly impact the link between APOE and AD, underscoring the need for more inclusive research. METHODS: In 2023, the Alzheimer's Association convened multidisciplinary researchers at the "AAIC Advancements: APOE" conference to discuss various topics, including apoE isoforms and their roles in AD pathogenesis, progress in apoE-targeted therapeutic strategies, updates on disease models and interventions that modulate apoE expression and function. RESULTS: This manuscript presents highlights from the conference and provides an overview of opportunities for further research in the field. DISCUSSION: Understanding apoE's multifaceted roles in AD pathogenesis will help develop targeted interventions for AD and advance the field of AD precision medicine. HIGHLIGHTS: APOE is a central player in the pathogenesis of Alzheimer's disease. APOE exerts a numerous effects throughout the brain on amyloid-beta, tau, and other pathways. The AAIC Advancements: APOE conference encouraged discussions and collaborations on understanding the role of APOE.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".