Supplemental material: Astrocyte biomarkers in Alzheimer’s disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To perform a systematic review and meta-analysis to determine whether fluid and imaging astrocyte biomarkers are altered in Alzheimer's 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 mean differences (SMD) using the Hedge’s G method with random-effects to determine biomarker performance. Adapted questions from QUADAS-2 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 GFAP, S100B, YKL-40 and AQP4 in the blood and cerebrospinal fluid (CSF), as well as MAO-B, indexed by positron emission tomography 11C-deuterium-L-deprenyl ([11C]-DED), were included. GFAP (SMD = 0.94; 95% CI = 0.71-1.18) and YKL-40 (SMD = 0.76; CI 95% = 0.63-0.89) levels in the CSF, S100B levels in the blood (SMD = 2.91; CI 95% = 1.01-4.8) were found significantly increased in AD patients. Conclusions: Despite significant progress, applications of astrocyte biomarkers in AD remain in their early days. The 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 | 0.009 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.124 | 0.004 |
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".