CSF Aβ40 Levels Do Not Correlate with the Clinical Manifestations of Alzheimer’s Disease
Bibliographic record
Abstract
INTRODUCTION: Cerebrospinal fluid (CSF) biomarker quantification provides physicians with a reliable diagnosis of Alzheimer's disease (AD). However, the relationship between their concentration and disease course has not been clearly elucidated. This work aimed to investigate the clinical and prognostic significance of Aβ40 CSF levels. METHODS: A retrospective cohort of 76 patients diagnosed with AD using a decreased Aβ42/Aβ40 ratio was subclassified into hyposecretors (Aβ40 <7,755 pg/mL), normosecretors (Aβ40 7,755-16,715 pg/mL), and hypersecretors (Aβ40 >16,715 pg/mL). Potential differences in AD phenotype, Montreal Cognitive Assessment (MoCA) scores, and Global Deterioration Scale (GDS) stages were assessed. Correlation tests for biomarker concentrations were also performed. RESULTS: Participants were classified as hyposecretors (n = 22, median Aβ40 5,870.500 pg/mL, interquartile range [IQR] 1,431), normosecretors (n = 47, median Aβ40 10,817 pg/mL, IQR 3,622), and hypersecretors (n = 7, 19,767 pg/mL, IQR 3,088). The distribution of positive phosphorylated Tau (p-Tau) varied significantly between subgroups and was more common in the normo- and hypersecretor categories (p = 0.003). Aβ40 and p-Tau concentrations correlated positively (ρ = 0.605, p < 0.001). No significant differences were found among subgroups regarding age, initial MoCA score, initial GDS stage, progression to the dementia stage, or changes in the MoCA score. CONCLUSION: In this study, we found no significant differences in clinical symptoms or disease progression in AD patients according to their CSF Aβ40 concentration. Aβ40 was positively correlated with p-Tau and total Tau concentrations, supporting their potential interaction in AD pathophysiology.
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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 | 0.003 |
| 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) | 0.002 | 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".