Synapse dysfunction and astrocyte reactivity are associated independently of amyloid‐β and tau pathologies
Bibliographic record
Abstract
Abstract Background Biomarkers of astrocyte reactivity have the potential to improve diagnostic precision, disease monitoring, and treatment efficacy. Glial fibrillary acidic protein (GFAP) protein is expressed in astrocytes, which is important to synaptic plasticity, cell communication, and reactive gliosis. Method Cerebrospinal fluid (CSF) biomarkers were measured in participants of the McGill TRIAD cohort. 75 individuals (>50 years old, 44 cognitively unimpaired (CU), and 31 with cognitive impairment (CI)), had available Aß and tau‐PET. We measured CSF GFAP and synaptic markers (growth‐associated protein 43 (GAP‐43), neurogranin (Ng), synaptotagmin 1 (SYT1), presynaptic protein synaptosomal‐associated protein 25 (SNAP‐25)). Linear regressions adjusted for age, sex, clinical diagnosis, and Aß/tau‐PET were used to test the associations between astrocyte reactivity and synaptic function. Result Demographic information is shown in Table 1. We found an association between CSF GFAP and presynaptic markers (GAP‐43: p<0.0001, ß = 0.1076; SYT1: p<0.0001, ß = 0.0024; SNAP‐25 long: p<0.0001, ß = 0.0008) as well as postsynaptic markers (Ng: p<0.05, ß = 0.0066) independently of Aß and tau burden (Figure 1). Conclusion Our biomarker results support experimental literature suggesting that astrocyte reactivity plays a role in downstream synaptic dysfunction independent of the brain levels of Aß and tau tangles pathologies. This supports in vitro literature suggesting that therapeutic interventions targeting astrocyte reactivity can contribute to halting AD 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".