Changes in aqueous and vitreous inflammatory cytokine levels in diabetic macular oedema: a systematic review and meta‐analysis
Bibliographic record
Abstract
Diabetic macular oedema (DME) is considered a chronic inflammatory disease associated with aberrations in many intraocular cytokines. Studies assessing the role of these cytokines as biomarkers in the diagnosis and management of DME have demonstrated inconsistent findings. We quantitatively summarized data related to 116 candidate aqueous and vitreous inflammatory cytokines as biomarkers in DME. A systematic search without year limitation was performed up to 19 October 2020. Studies were included if they provided data on aqueous or vitreous cytokine concentrations in patients with DME. Effect sizes were generated as standardized mean differences (SMDs) of cytokine concentrations between patients with DME and controls. Data were extracted from 128 studies that included 4163 study eyes with DME and 1281 control eyes. Concentrations (standard mean difference, 95% confidence interval and p-value) of aqueous IL-6 (1.28, 0.57-2.00, p = 0.004), IL-8 (1.06, 0.74-1.39, p < 0.00001), MCP-1 (1.36, 0.57-2.16, p = 0.0008) and VEGF (1.31, 1.01-1.62, p < 0.00001) and vitreous VEGF (2.27, 1.55-2.99, p < 0.00001) were significantly higher in patients with DME (n = 4163) compared to healthy controls (n = 1281). No differences, failed sensitivity analyses or insufficient data were found between patients with DME and healthy controls for the concentrations of the remaining cytokines. This analysis implicates multiple cytokine biomarker candidates other than VEGF in DME and clarifies previously reported inconsistent associations. As the therapeutic options for DME expand to include multiple agents with multiple targets, it will be critical to manage the treatment burden with tailored therapy that optimizes outcomes and minimizes treatment burden. Intraocular cytokines have the promise of providing a robust individualized assessment of disease status and response to therapy. We have identified key candidate cytokines that may serve as biomarkers in individualized treatment algorithms.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".