Glial reactivity correlates with synaptic dysfunction across aging and Alzheimer’s disease
Bibliographic record
Abstract
Previous studies suggest glial and neuronal changes may trigger synaptic dysfunction in Alzheimer’s disease (AD), but the link between their markers and synaptic abnormalities in the living brain remains unclear. We investigated the association between glial reactivity and synaptic dysfunction biomarkers in cerebrospinal fluid (CSF) from 478 individuals in cognitively unimpaired (CU) and cognitively impaired (CI) individuals. We measured amyloid-β (Aβ), phosphorylated tau (pTau181), astrocyte reactivity (GFAP), microglial activation (sTREM2), and synaptic markers (GAP43, neurogranin). CSF GFAP levels were associated with presynaptic and postsynaptic dysfunction, independent of cognitive status or Aβ presence. CSF sTREM2 levels were related to presynaptic markers in cognitively unimpaired and impaired Aβ+ individuals, and to postsynaptic markers in cognitively impaired Aβ+ individuals. Notably, CSF pTau mediated the relationships between GFAP or sTREM2 and synaptic dysfunction. Our findings, validated in two independent cohorts (TRIAD and ADNI), reveal a distinct pattern of glial contribution to synaptic degeneration. This study shows that glial reactivity is linked to synaptic dysfunction in aging and Alzheimer’s disease, with tau pathology mediating these effects–highlighting the potential of synaptic biomarkers track disease progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".