Level of CSF GAP-43 and white matter microstructural changes in Alzheimer's disease
Bibliographic record
Abstract
Objectives: Several studies have reported altered cerebrospinal fluid (CSF) concentrations of presynaptic proteins, such as growth-associated protein 43 (GAP-43) in Alzheimer's disease (AD) patients. Given the potential predictive role of CSF GAP-43 for AD, the current study aimed to investigate the relationship between CSF GAP-43 levels and DTI-detected microstructural changes in the white matter (WM).Methods: Data from three groups of participants including 39 control normals (CN), 138 MCI, and 39 AD subjects were obtained from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Linear regression was used to the association of CSF-GAP43 and DTI values (including MD, RD, AxD, and FA) in the brain.Results: We found a significant association between CSF-GAP43 and FA (p-value = 0.011). Also, a negative association was found between CSF-GAP43 concentration and AD, MD, and RD values in MCI (p-value = 0.013, p-value = 0.004, p-value = 0.017). The regression models also revealed a positive association between CSF-GAP43 and FA value in AD subjects (p-value = 0.028). Furthermore, increased CSF-GAP43 level was associated with lower AD, MD, and RD values in brain WM of AD patients (p-value = 0.022, p-value = 0.033, p-value = 0.041).Conclusion: Our study provides a better understanding of the link between CSF GAP-43 and WM changes in patients with MCI and AD. Our findings support the application of CSF GAP-43 as an effective biomarker for monitoring individuals at high risk of AD in the early stages.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".