Human nasal beta‐amyloid 42 reflects cognition decline in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background The key in Alzheimer’s disease (AD) therapy is a timely and accurate diagnosis for prompt drug intervention. However, due to the high cost and invasiveness of conventional biomarker analyses, including brain positron emission tomography (PET) imaging and cerebrospinal fluid (CSF)‐based assays, easy accessibility to these screening tests is often hindered. There is, therefore, a great need to develop a more accessible biomarker screening test using less invasive and cost‐effective peripheral body fluid biomarkers. Previous studies examined the non‐quantitative expression of beta‐amyloid (Aβ) in normal and AD patients' nasal discharge fluid. They identified higher expression of oligomeric Aβ in AD patients, showing a correlation with cognitive decline. However, the quantitative measurements of nasal Aβ42 levels, including the full AD continuum, remain unknown. Here, we assessed whether quantified human nasal Aβ42 levels could identify patients with AD and differentiate them from non‐AD patients. Method 161 subjects (cognitively normal (CN), n=32; preclinical, n=29; mild cognitive impairment (MCI), n=73; AD, n=27) underwent neuropsychological battery tests. Their nasal discharge samples were collected, and nasal Aβ42 levels were measured via enzyme‐linked immunosorbent assay (ELISA). Result We found that the second‐highest quartile (Q3) group of nasal Aβ42 constituted the majority of patients with AD diagnosis (p=0.036). The Q3 group also outnumbered the other groups in the most cognitively impaired subjects in all three neuropsychological battery tests (p=0.033; p=0.0212; p=0.0147). Conclusion Quantified nasal Aβ42 is strongly associated with cognition measurements. Nasal Aβ42 suggests the possibility for discriminating AD from non‐AD.
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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 | 0.001 |
| 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".