Aqueous humour biomarker profiles in angle-closure glaucoma: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate aqueous humour biomarker concentrations in patients with angle-closure glaucoma (ACG) compared to cataract controls. METHODS: A systematic review and meta-analysis were performed as per the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A comprehensive literature search was conducted using Ovid MEDLINE, Embase, and the Cochrane Library from January 2000 to August 2024 to identify comparative studies assessing aqueous humour biomarkers in adult patients with ACG versus cataract controls. Biomarkers analyzed included cytokines, chemokines, growth factors, matrix metalloproteinases (MMPs), and tissue inhibitors of metalloproteinases (TIMPs), among others. The primary analysis compared biomarker levels between ACG and cataract controls. Meta-analyses were performed using a random-effects model, with heterogeneity evaluated using the I² statistic. RESULTS: Seventeen observational studies involving 919 eyes (500 ACG [54.4%] and 419 cataract controls [45.6%]) were included. ACG eyes had significantly elevated levels of VEGF (standard mean difference [SMD] = 2.73; 95% CI = 0.61-4.86]; p = 0.01), IL-8 (SMD = 2.87, 95% CI = 0.03-5.70; p = 0.05), MMP-2 (SMD = 1.14, 95% CI = 0.53-1.75; p = 0.0002), monocyte chemoattractant protein-1 (SMD = 1.00, 95% CI = 0.52-1.49; p < 0.0001), and TIMP-1 (SMD = 1.75, 95% CI = 0.72-2.78; p = 0.0009). Subgroup analysis revealed significantly higher tumour necrosis factor-α (SMD = 3.41, 95% CI = 0.58, 6.23; p = 0.02) and IL-6 (SMD = 2.43, 95% CI = [0.00, 4.85]; p = 0.05) levels in patients with acute primary angle closure compared to controls. CONCLUSIONS: Several biomarkers were elevated in the aqueous humour of patients with ACG compared to cataract controls, providing insights into the molecular mechanisms of angle closure and elevated intraocular pressure. Future studies should focus on the potential for biomarker-driven diagnostics and longitudinal designs to further advance personalized management strategies.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".