Association of cerebrospinal fluid neurogranin levels with cognition and neurodegeneration in Alzheimer’s disease
Bibliographic record
Abstract
Accumulating data suggest cerebrospinal fluid (CSF) neurogranin (Ng) as a potential biomarker for cognitive decline and neurodegeneration in Alzheimer disease (AD). To investigate whether the CSF Ng can be used for diagnosis, prognosis, and monitoring of AD, we examined 111 cognitively normal (CN) controls, 193 mild cognitive impairment (MCI) patients and 95 AD patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Correlations were tested between baseline CSF Ng levels and baseline core AD biomarkers and longitudinal glucose metabolism, brain atrophy and cognitive decline. We detected that CSF Ng levels increased with disease severity, and correlated with phosphorylated tau and total tau levels within each diagnostic group. High baseline CSF Ng levels correlated with longitudinal reductions in cortical glucose metabolism within each diagnostic group and hippocampal volume within MCI group during follow-up. In addition, high baseline CSF Ng levels correlated with cognitive decline as reflected by decreased cognitive scale scores. The CSF Ng levels predicted future cognitive impairment (adjusted hazard ratio:3.66, 95%CI: 1.74-7.70, P = 0.001) in CN controls. These data demonstrate that CSF Ng offers diagnostic utility for AD and predicts future cognitive impairment in CN individuals and, therefore, may be a useful addition to the current AD biomarkers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".