Astrocyte Biomarkers in Alzheimer Disease
Bibliographic record
Abstract
Objective To perform a systematic review and meta-analysis to determine whether fluid and imaging astrocyte biomarkers are altered in Alzheimer disease (AD). Methods PubMed and Web of Science databases were searched for articles reporting fluid or imaging astrocyte biomarkers in AD. Pooled effect sizes were determined with standardized mean differences (SMDs) using the Hedge G method with random effects to determine biomarker performance. Adapted questions from the Quality Assessment of Diagnostic Accuracy Studies were applied for quality assessment. A protocol for this study has been previously registered in PROSPERO (registration number: CRD42020192304). Results The initial search identified 1,425 articles. After exclusion criteria were applied, 33 articles (a total of 3,204 individuals) measuring levels of glial fibrillary acidic protein (GFAP), S100B, chitinase-3-like protein 1 (YKL-40), and aquaporin 4 in the blood and CSF, as well as monoamine oxidase-B indexed by PET 11C-deuterium-l-deprenyl, were included. GFAP (SMD 0.94, 95% confidence interval [CI] 0.71–1.18) and YKL-40 (SMD 0.76, 95% CI 0.63–0.89) levels in the CSF and S100B levels in the blood (SMD 2.91, 95% CI 1.01–4.8) were found to be significantly increased in patients with AD. Conclusions Despite significant progress, applications of astrocyte biomarkers in AD remain in their early days. This meta-analysis demonstrated that astrocyte biomarkers are consistently altered in AD and supports further investigation for their inclusion in the AD clinical research framework for observational and interventional studies.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".