Growth associated protein 43 (GAP-43) predicts brain amyloidosis in Alzheimer’s dementia continuum: an [18 F] AV-45 study
Bibliographic record
Abstract
BACKGROUND: Growth-associated protein 43 (GAP-43) is a key protein involved in neuronal growth and synaptic plasticity. Alterations in GAP-43 levels have been associated with Alzheimer's Disease (AD), potentially reflecting synaptic dysfunction. We evaluated the potential of GAP-43 as a biomarker for AD and explored its association with amyloid-beta (Aβ) levels, as well as its correlation with Aβ plaque burden in the brain. METHODS: We screened 1,639 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. A total of 226 individuals met the eligibility criteria and were enrolled. Participants were classified into three groups: 77 cognitively normal (CN) individuals, 111 with mild cognitive impairment (MCI), and 38 with a diagnosis of AD. The associations between cerebrospinal fluid (CSF) GAP-43 levels with other biomarkers as well as [¹⁸F] AV-45 (Florbetapir) PET Standardized Uptake Value Ratios (SUVR) were investigated. RESULTS: Our findings revealed significantly elevated CSF GAP-43 levels in individuals with AD compared to CN and MCI groups. Furthermore, GAP-43 levels showed a significant positive correlation with tau pathology. Notably, we observed a significant association between GAP-43 and [¹⁸F] Florbetapir PET SUVR in the MCI group, suggesting that GAP-43 may serve as a reliable biomarker in the early stages of AD. CONCLUSION: This study provides evidence supporting the role of GAP-43 as a potential biomarker for AD, particularly in relation to predicting the amyloid pathology pattern in the brain in the MCI stage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".