Interactions of synaptic and inflammatory biomarkers in Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Background The sequential model predicts synaptic depletion as a downstream of amyloid, tau and neuroinflammation. However, synaptic toxicity might be consequence of toxic forms of amyloid in the absence of tau. In this study we explore the role of synaptic depletion and neuroinflammation as determinants of tau pathology. GAP‐43 and neurogranin are pre‐ and post‐synaptic biomarkers known to be detected at elevated levels in cerebrospinal fluid (CSF) of patients with Alzheimer’s disease (AD). Method We included 126 individuals from TRIAD cohort. Brain inflammation, tau tangle and amyloid‐ß (Aß) deposition were assessed via [11C]PBR28‐PET, [18F]MK6240‐PET and [18F]AZD4694‐PET, respectively. All patients had plasma GFAP quantified, and a subset of 78 individuals had CSF GFAP, CSF Neurogranin and CSF GAP‐43 quantifications available. Voxel and region of interest regression models evaluated the relationship between PET tracers and the fluid biomarkers. Models with [18F]AZD4694‐PET as the outcome were adjusted for [18F]MK6240‐PET voxel‐wise and vice‐versa. A linear regression interaction model evaluated the interaction between CSF GFAP and both synaptic biomarkers with Aß‐ and tau‐PET as the outcome. Result Positive associations were found between CSF neurogranin and Aß‐, tau‐ and brain inflammation‐PET in AD related regions; the strongest associations were found with tau‐pet and CSF neurogranin in the medial temporal lobe. GAP‐43 was also positively associated with tau‐ and TSPO‐PET, but no associations were found with Aß‐PET. Both plasma and CSF GFAP were associated with Aß‐, tau‐ and TSPO‐PET in AD related regions, with the strongest t‐values in the model including plasma GFAP and amyloid‐PET. A negative interaction was found between CSF GFAP and both synaptic biomarkers with amyloid‐ and tau‐PET as the outcomes. These associations were stronger with tau‐PET and were found in the temporal, occipital and parietal areas. Conclusion This study supports the role of synaptic dysfunction and neuroinflammation on tau load. As both synaptic biomarkers and GFAP increase as a function of tangles load, tau accumulation is linked to the relationship between synaptic abnormalities and inflammation in early stages of the disease. Our results suggest that synaptic depletion is a phenomenon that might start prior to tau tangles.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".