CSF GAP-43 as a biomarker of synaptic dysfunction is associated with tau pathology in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Objective：To test whether cerebrospinal fluid (CSF) growth-associated protein 43 (GAP-43) concentration is elevated in Alzheimer’s Disease (AD) dementia and its associations with other hallmarks of AD.Methods: We examined the CSF GAP-43 measurements of 787 participants (245 cognitively normal (CN), 415 individuals with mild cognitive impairment (MCI) and 127 individuals with AD dementia from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Associations were investigated between CSF GAP-43 and clinical diagnosis, Aβ/tau/neurodegeneration (AT(N)) status, CSF and blood biomarkers of AD, cognitive measurements and brain neuroimaging findings. Results: CSF GAP-43 levels were increased in patients with AD dementia (mean, 6331.05 pg/ml) compared with the CN (mean, 5001.05 pg/ml) and MCI (mean, 5118.8 pg/ml) (P < 0.001) groups. CSF GAP-43 correlated with CSF phosphorylated tau 181(p-tau) (r = 0.768, P < 0.001), and had high diagnostic accuracy in differentiating tau positive status vs. tau negative status (area under the receiver operating characteristic curve, 0.8606). CSF GAP-43 was particularly elevated among individuals with tau positive status. High CSF GAP-43 was associated with longitudinal deterioration of cognitive scores and brain neuroimaging findings. Conclusions: CSF GAP-43 was associated with a clinical diagnosis of AD dementia and with an individual’s tau status, cognitive measurements and findings from neuroimaging. This study implies that CSF GAP-43 as a biomarker of synaptic dysfunction could predict the disease progression of AD patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".