Incretin‐based drugs and the risk of diabetic retinopathy among individuals with type 2 diabetes: A systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
AIM: The results from the SUSTAIN-6 trial generated some uncertainty regarding the association between incretin-based drugs [dipeptidyl peptidase-4 (DPP-4) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1 RAs)] and the risk of diabetic retinopathy. Our objective was to synthesize the available evidence from observational studies regarding the use of incretin-based drugs and the risk of diabetic retinopathy among individuals with type 2 diabetes. MATERIALS AND METHODS: We systemically searched Cochrane Library, Embase and Medline to identify observational studies of interest. Risk of bias was assessed using the ROBINS-I tool. Data from included studies were pooled using the DerSimonian and Laird random-effect model with the Hartung-Knapp extension. RESULTS: We included 14 studies in the systematic review, with 10 examining DPP-4 inhibitors and seven examining GLP-1 RAs. Nine studies investigated incident diabetic retinopathy, six investigated diabetic retinopathy progression and two investigated both outcomes. Seven studies were at moderate risk of bias, four at serious risk of bias and three at critical risk of bias. Data pooled across studies showed no association between the use of DPP-4 inhibitors (risk ratio: 0.98, 95% confidence interval: 0.83, 1.17) or GLP-1 RAs (risk ratio: 0.87, 95% confidence interval: 0.56, 1.34) and the risk of diabetic retinopathy. CONCLUSION: This study suggests that the use of incretin-based drugs is not associated with the risk of diabetic retinopathy among individuals with type 2 diabetes. However, these findings should be interpreted with caution considering the limited quality of some of the available evidence.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".