The Trajectory of Cerebrospinal Fluid Growth-Associated Protein 43 in the Alzheimer’s Disease Continuum: A Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Synaptic degeneration has been suggested as an early pathological event that strongly correlates with severity of dementia in Alzheimer's disease (AD). However, changes in longitudinal cerebrospinal fluid (CSF) growth-associated protein 43 (GAP-43) as a synaptic biomarker in the AD continuum remain unclear. OBJECTIVE: To assess the trajectory of CSF GAP-43 with AD progression and its association with other AD hallmarks. METHODS: CSF GAP-43 was analyzed in 788 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including 246 cognitively normal (CN) individuals, 415 individuals with mild cognitive impairment (MCI), and 127 with AD dementia based on cognitive assessments. The associations between a multimodal classification scheme with amyloid-β (Aβ), tau, and neurodegeneration, and changes in CSF GAP-43 over time were also analyzed. RESULTS: CSF GAP-43 levels were increased at baseline in MCI and dementia patients, and increased significantly over time in the preclinical (Aβ-positive CN), prodromal (Aβ-positive MCI), and dementia (Aβ-positive dementia) stages of AD. Higher levels of CSF GAP-43 were also associated with higher CSF phosphorylated tau (p-tau) and total tau (t-tau), cerebral amyloid deposition and hypometabolism on positron emission tomography, the hippocampus and middle temporal atrophy, and cognitive performance deterioration at baseline and follow-up. Furthermore, CSF GAP-43 may assist in effectively predicting the probability of dementia onset at 2- or 4-year follow-up. CONCLUSION: CSF GAP-43 can be used as a potential biomarker associated with synaptic degeneration in subjects with AD; it may also be useful for tracking the disease progression and for monitoring the effects of clinical 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".