Cerebrospinal Fluid proNGF: A Putative Biomarker for Early Alzheimer's Disease
Bibliographic record
Abstract
The discovery of biomarkers for the onset of Alzheimer's disease (AD) is essential for disease modification strategies. To date, AD biomarker studies have focused on brain imaging and cerebrospinal fluid (CSF) changes in amyloid- β (Aβ) peptide and tau proteins. While reliable to an extent, this panel could be improved by the inclusion of novel biomarkers that optimize sensitivity and specificity. In this study, we determined whether CSF levels of the nerve growth factor (NGF) precursor protein, proNGF, increased during the progression of AD, mirroring its up regulation in postmortem brain samples of people who died with a clinical diagnosis of mild cognitive impairment (MCI) or AD. Immunoblot analysis was performed on ventricular CSF harvested from participants in the Rush Religious Orders Study with an antemortem clinical diagnosis of no cognitive impairment (NCI), amnestic MCI (aMCI, a putative prodromal AD stage), or mild/moderate AD. ProNGF levels were increased 55% in aMCI and 70% in AD compared to NCI. Increasing CSF proNGF levels correlated with impairment on cognitive test scores. In a complementary study, we found that proNGF was significantly increased by 30% in lumbar CSF samples derived from patients with a clinical dementia rating (CDR) of 0.5 or 1 compared to those with a CDR = 0. Notably, proNGF/Aβ1-42 levels were 50% higher in CDR 0.5 and CDR 1 compared to CDR 0 controls. By contrast, ELISA measurements of CSF brain-derived neurotrophic factor (BDNF) did not distinguish aMCI from NCI. Taken together, these results suggest that proNGF protein levels may augment the diagnostic accuracy of currently used CSF biomarker panels.
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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 | 0.000 | 0.000 |
| 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".