Glucagon-Like Peptide 1 Receptor Agonists and the Risk of Incident Diabetic Retinopathy
Bibliographic record
Abstract
OBJECTIVE: Previous studies suggested that glucagon-like peptide 1 receptor agonists (GLP-1 RAs) may initially worsen and possibly increase the risk of diabetic retinopathy. However, data on this possible association remain limited. Thus, this population-based study aimed to determine whether use of GLP-1 RAs is associated with an increased risk of incident diabetic retinopathy. RESEARCH DESIGN AND METHODS: Using the U.K. Clinical Practice Research Datalink (CPRD), we conducted a cohort study among 77,115 patients with type 2 diabetes initiating antidiabetic drugs between January 2007 and September 2015. Adjusted hazard ratios (HRs) and 95% CIs of incident diabetic retinopathy were estimated using time-dependent Cox proportional hazards models, comparing use of GLP-1 RAs with current use of two or more oral antidiabetic drugs. In an ancillary analysis, new users of GLP-1 RAs were compared with new users of insulin. RESULTS: During 245,825 person-years of follow-up, 10,763 patients were newly diagnosed with diabetic retinopathy. Compared with current use of two or more oral antidiabetic drugs, use of GLP-1 RAs was not associated with an increased risk of incident diabetic retinopathy overall (HR 1.00, 95% CI 0.85-1.17). Compared with insulin, GLP-1 RAs were associated with a decreased risk of diabetic retinopathy (HR 0.67, 95% CI 0.51-0.90). CONCLUSIONS: The associations with diabetic retinopathy varied according to the type of comparator. When compared with use of two or more oral antidiabetic drugs, use of GLP-1 RAs was not associated with an increased risk of incident diabetic retinopathy. The apparent lower risk of diabetic retinopathy associated with GLP-1 RAs compared with insulin may be due to residual confounding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".