SNAP-25 is a promising novel cerebrospinal fluid biomarker for synapse degeneration in Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Synaptic degeneration is an early pathogenic event in Alzheimer's disease, associated with cognitive impairment and disease progression. Cerebrospinal fluid biomarkers reflecting synaptic integrity would be highly valuable tools to monitor synaptic degeneration directly in patients. We previously showed that synaptic proteins such as synaptotagmin and synaptosomal-associated protein 25 (SNAP-25) could be detected in pooled samples of cerebrospinal fluid, however these assays were not sensitive enough for individual samples. RESULTS: We report a new strategy to study synaptic pathology by using affinity purification and mass spectrometry to measure the levels of the presynaptic protein SNAP-25 in cerebrospinal fluid. By applying this novel affinity mass spectrometry strategy on three separate cohorts of patients, the value of SNAP-25 as a cerebrospinal fluid biomarker for synaptic integrity in Alzheimer's disease was assessed for the first time. We found significantly higher levels of cerebrospinal fluid SNAP-25 fragments in Alzheimer's disease, even in the very early stages, in three separate cohorts. Cerebrospinal fluid SNAP-25 differentiated Alzheimer's disease from controls with area under the curve of 0.901 (P < 0.0001). CONCLUSIONS: We developed a sensitive method to analyze SNAP-25 levels in individual CSF samples that to our knowledge was not possible previously. Our results support the notion that synaptic biomarkers may be important tools for early diagnosis, assessment of disease progression, and to monitor drug effects in treatment trials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".