Presynaptic loss and axonal degeneration synergistically correlate with longitudinal neurodegeneration and cognitive decline
Bibliographic record
Abstract
INTRODUCTION: Baseline and longitudinal characteristics of cerebrospinal fluid (CSF) growth-associated protein 43 (GAP-43) and plasma neurofilament light (NfL) and how they correlate interactively with neurodegeneration and cognitive decline in Alzheimer's disease (AD) are not fully understood. METHODS: We investigated dynamic changes of CSF GAP-43 and plasma NfL across different AD stages and their association with longitudinal neurodegeneration and cognitive decline up to 12 years. RESULTS: Individuals with hippocampal atrophy, AD-signature cortical thinning, or hypometabolism (N+) had faster plasma NfL increase rates than healthy individuals, regardless of amyloid/tau status. In contrast, none of these N+ imaging indicators correlated with more rapid increases in CSF GAP-43. Furthermore, CSF GAP-43 and plasma NfL synergistically predicted subsequent gray matter atrophy, cortical thinning, hypometabolism of the middle temporal region, and cognition. DISCUSSION: CSF GAP-43-associated presynaptic loss indicates tau-dependent early neurodegeneration, whereas the axonal degeneration indicated by plasma NfL is a relatively late atrophy/hypometabolism-associated fluid neurodegeneration biomarker. HIGHLIGHTS: Plasma neurofilament light (NfL) was increased in N+ or cognitively impaired individuals. Increases in tau-dependent cerebrospinal fluid CSF growth-associated protein 43 (GAP-43) before imaging neurodegeneration indicators. CSF GAP-43 and plasma NfL are synergistically related to longitudinal neurodegeneration. CSF GAP-43 and plasma NfL are synergistically related to longitudinal cognitive decline.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".