Independent associations of plasma GFAP with amyloid‐β and tau in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Glial fibrillary acidic protein (GFAP) is a reactive astrogliosis biomarker, shown to increase in individuals with preclinical Alzheimer’s disease (AD). First thought to be a great marker of amyloid‐β (Aβ) pathology, recent post‐mortem research has linked it to tau accumulation only. Here we investigate the independent associations of plasma GFAP with imaging markers of AD along the disease spectrum. Method 126 individuals from TRIAD cohort underwent [18F]MK6240 tau‐PET and [18F]AZD4694 Aβ‐PET and plasma GFAP assessment using an in‐house assay. Voxel and region of interest regression models evaluated the relationship between PET tracers and plasma GFAP. Models with [18F]AZD4694‐PET as the outcome were adjusted for [18F]MK6240‐PET voxel‐wise and vice‐versa. All models were corrected for age and sex, and RFT was used to correct for multiple comparisons. Result Plasma GFAP was associated with [18F]AZD4694 signal throughout the cortex (Figure1A), independently of [18F]MK6240. Additionally, it was positively correlated with [18F]MK6240 in the medial temporal and occipital lobes and the anterior cingulate cortex, independently of [18F]AZD4694 (Figure1B). Voxel‐wise findings were confirmed by region‐of‐interest‐based analyses. Conclusion Our findings indicate that plasma GFAP is independently associated with both Aβ and tau pathologies in AD. Aβ‐PET showed associations throughout the entire cortex, suggesting its effectiveness as a reliable marker of Aβ pathology across the AD spectrum. On the other hand, tau‐PET correlations were observed in specific regions associated with memory and behavioral impairments that exhibit early deposition of tau. Overall, these results demonstrate that GFAP, an astrogliosis marker, is independently associated with Aβ and tau.
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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 | 0.001 |
| 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) | 0.002 | 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".