The Effects of CSF Neurogranin and APOE ε4 on Cognition and Neuropathology in Mild Cognitive Impairment and Alzheimer’s Disease
Bibliographic record
Abstract
Cerebrospinal fluid (CSF) measurements of neurogranin (Ng) have emerged as a promising biomarker for cognitive decline in mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The apolipoprotein E ε4 (APOE ε4) allele is by far the most consistent genetic risk factor for AD. However, it is not known whether the pathophysiological roles of Ng in MCI or AD are related to APOEε4. We stratified 250 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database into cognitively normal (CN) ε4 negative (CN ε4−), CN ε4 positive (CN ε4+), MCI ε4 negative (MCI ε4−), MCI ε4 positive (MCI ε4+), AD ε4 negative (AD ε4−), and AD ε4 positive (AD ε4+). CSF Ng levels were significantly increased in APOE ε4 carriers compared to APOE ε4 non-carriers with MCI. In addition, CSF Ng identified MCI ε4+ versus CN ε4−, but not MCI ε4− versus CN ε4−. Similarly, CSF Ng negatively correlated with Mini-Mental State Examination (MMSE) scores at baseline in the MCI ε4+ group. Our findings support the use of CSF Ng as a biomarker of synaptic pathology for AD. We propose that the roles of CSF Ng in the pathophysiology of MCI may be related to APOE ε4.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".