Astrocyte reactivity associates with tau phosphorylation and neurodegeneration before detectable amyloid‐β pathology
Bibliographic record
Abstract
Abstract Background Aß plaques are the first detectable signs of AD pathology. Our group recently demonstrated that the astrocyte activation marker, glial fibrillary acidic protein (GFAP), has a pivotal role in the association between Aß burden and tau phosphorylation. However, the role of astrocyte activation in individuals that do not present detectable Aß pathology using biomarkers is still underexplored. Here we sought to understand the association between plasma GFAP with tau phosphorylation and neurodegeneration in Aß negative individuals. Method 598 study participants [cognitively unimpaired, (CU)=200, and cognitively impaired, (CI)=398] from two cohorts (TRIAD, Canada and BICWALZ, South Korea) were assessed for Aß‐PET, plasma GFAP, p‐tau217, NfL, and hippocampal volume measures. Pearson correlations between plasma GFAP and plasma p‐tau217, plasma NfL along with hippocampal volume was performed in Aß‐PET negative individuals. Result Remarkably, we found that plasma GFAP levels positively correlate with plasma p‐tau217 levels in Aß‐PET negative individuals in both cohorts [TRIAD cohort: r=0.23, p=0.01; BICWALZ cohort: r=0.21, p=0.0001; Figure: 1A, B]. Further, the correlation between plasma GFAP with plasma NfL has also been found to be positively correlated among the two cohorts [TRIAD cohort: r=0.45, p=0.0001; BICWALZ cohort: r=0.49, p=0.0001; Figure: 2A, B]. We also observed that hippocampal volume, as a neurodegeneration marker, reveals a significant negative Pearson correlation with plasma GFAP levels in Aß‐PET negative human individuals from both the cohorts [TRIAD cohort: r=‐0.27, p=0.0009; BICWALZ cohort: r=‐0.24, p=0.0001; Figure: 3A, B]. Conclusion Our study provides evidence from research and clinical cohorts that astrocyte reactivity may be associated with very early tau phosphorylation and neurodegeneration in the absence of Aß pathology.
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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 | 0.001 |
| 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".