Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease
Bibliographic record
Abstract
Experimental evidence suggests that activated microglia induce astrocyte reactivity in neurodegenerative disorders, such as Alzheimer’s disease (AD). In this study, we investigated the association between microglial activation and amyloid-β (Aβ) with reactive astrogliosis in individuals across the AD spectrum. We examined 101 individuals using positron emission tomography radiotracers to assess Aβ deposition ([18F]AZD4694), tau aggregation ([18F]MK-6240) and microglial activation ([11C]PBR28), along with plasma biomarkers for astrocyte reactivity (GFAP) and tau phosphorylation (p-tau217). We further evaluated 251 individuals with cerebrospinal fluid levels of the microglial marker sTREM2. We found that Aβ pathology was associated with astrocyte reactivity across cortical brain regions only in the presence of microglial activation. The microglia-dependent effects of Aβ on astrocyte reactivity were further related to cognitive impairment through tau phosphorylation and aggregation. Our results suggest that microglial activation plays a key role in Aβ-related astrocyte reactivity, which, in turn, contributes to downstream pathological features of AD. Microglia influence amyloid-β effects on astrocyte reactivity in the living brain of individuals with Alzheimer’s disease. This phenomenon further contributes to cognitive impairment via tau phosphorylation and aggregation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".