<i>APOE</i> ε4 carriership associates with microglial activation independently of Aβ plaques and tau tangles
Bibliographic record
Abstract
Abstract Microglial activation is an early phenomenon in Alzheimer’s disease (AD) that may occur prior to and independently of amyloid-β (Aβ) aggregation. Recent studies in transgenic animal models suggest that the apolipoprotein E ε4 ( APOE ε4) allele may be a culprit of early microglial activation in AD. However, it is unclear whether the APOE ε4 genotype is associated with microglial reactivity in the living human brain. Here, we tested whether APOE ε4 carriership is associated with microglial activation in individuals across the aging and AD spectrum. We studied 118 individuals who had positron emission tomography (PET) for Aβ ([ 18 F]AZD4694), tau ([ 18 F]MK6240), and microglial activation ([ 11 C]PBR28), as well as clinical, genetic, and magnetic resonance imaging data. We found that APOE ε4 carriership was associated with increased microglial activation mainly in early Braak-staging regions within the medial temporal cortex, and this effect of APOE ε4 was independent of Aβ and tau deposition. Furthermore, microglial activation mediated the Aβ-independent effects of APOE ε4 on downstream tau accumulation, neurodegeneration, and clinical impairment. Interestingly, the physiological distribution of APOE mRNA expression, obtained from the Allen Human Atlas, predicted the patterns of APOE ε4-related microglial activation in our population, suggesting that the deleterious effects of APOE ε4 occur at the level of gene expression. These results support a model in which the APOE ε4 has Aβ-independent effects on AD pathogenesis by activating microglia in brain regions associated with early tau deposition. Our findings provide a rationale for the development of novel AD therapies targeting the interplay between ApoE and neuroinflammation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".