Association of glucagon-like peptide-1 (GLP-1) receptor agonists and diabetic retinopathy (DR) – a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: Previous studies have shown conflicting results on the relationship between glucagon-like peptide-1 (GLP-1) receptor agonists and diabetic retinopathy (DR). This systematic review and meta-analysis aimed to clarify the association between GLP-1 receptor agonists use and the development or progression of DR. Methods: A comprehensive search of MEDLINE (via OVID and PubMed), Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov was conducted from inception to March 2025. We included randomized controlled trials (RCTs) and observational studies reporting on the association between GLP-1 receptor agonists and DR. Screening, data extraction, and quality appraisal were performed independently and in duplicate. We assessed study quality using the Cochrane risk-of-bias tool for RCTs and the Newcastle-Ottawa Scale for observational studies. Meta-analysis was conducted using Stata 17, following PRISMA and MOOSE guidelines. Results: The search identified 6,922 studies. Of these, 39 articles (24 RCTs and 15 observational studies) met the inclusion criteria and 23 were included in the meta-analysis. The pooled analysis showed that GLP-1 receptor agonists were not significantly associated with the risk of DR compared with comparators (pooled RR = 1.00, 95% CI 0.71-1.43). Subgroup analyses by study design yielded similar non-significant results, with a pooled RR of 0.91 (95% CI 0.73-1.14) for randomized controlled trials and 2.09 (95% CI 0.47-9.19) for observational studies. After excluding studies with a high risk of bias, the pooled estimate remained non-significant (RR = 1.06, 95% CI 0.67-1.67), supporting the robustness of the overall findings. The association remained non-significant when restricted to larger studies (>500 participants; RR = 1.13, 95% CI 0.70-1.84). Conclusion: In conclusions, this systematic review found no significant association between GLP-1 receptor agonists and DR risk, though a non-significant trend toward lower risk was observed in randomized trials. Given the limited number of long-term studies, the current evidence remains inconclusive. Future studies with longer follow-up period are warranted to clarify the long-term ocular safety of GLP-1 receptor agonists. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251007882.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.045 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".