Association of Presynaptic Loss with Alzheimer's Disease and Cognitive Decline
Bibliographic record
Abstract
Objective Increased presynaptic dysfunction measured by cerebrospinal fluid (CSF) growth‐associated protein‐43 (GAP43) may be observed in Alzheimer's disease (AD), but how CSF GAP43 increases relate to AD‐core pathologies, neurodegeneration, and cognitive decline in AD requires further investigation. Methods We analyzed 731 older adults with baseline β‐amyloid (Aβ) positron emission tomography (PET), CSF GAP43, CSF phosphorylated tau181 (p‐Tau181), and 18F‐fluorodeoxyglucose PET, and longitudinal residual hippocampal volume and cognitive assessments. Among them, 377 individuals had longitudinal 18F‐fluorodeoxyglucose PET, and 326 individuals had simultaneous longitudinal CSF GAP43, Aβ PET, and CSF p‐Tau181 data. We compared baseline and slopes of CSF GAP43 among different stages of AD, as well as their associations with Aβ PET, CSF p‐Tau181, residual hippocampal volume, 18F‐fluorodeoxyglucose PET, and cognition cross‐sectionally and longitudinally. Results Regardless of Aβ positivity and clinical diagnosis, CSF p‐Tau181‐positive individuals showed higher CSF GAP43 concentrations (p < 0.001) and faster rates of CSF GAP43 increases (p < 0.001) compared with the CSF p‐Tau181‐negative individuals. Moreover, higher CSF GAP43 concentrations and faster rates of CSF GAP43 increases were strongly related to CSF p‐Tau181 independent of Aβ PET. They were related to more rapid hippocampal atrophy, hypometabolism, and cognitive decline (p < 0.001), and predicted the progression from MCI to dementia (area under the curve for baseline 0.704; area under the curve for slope 0.717) over a median 4 years of follow up. Interpretation Tau aggregations rather than Aβ plaques primarily drive presynaptic dysfunction measured by CSF GAP43, which may lead to sequential neurodegeneration and cognitive impairment in AD or neurodegenerative diseases. ANN NEUROL 2022;92:1001–1015
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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 | 0.000 | 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".