Semaglutide, reduction in glycated haemoglobin and the risk of diabetic retinopathy
Bibliographic record
Abstract
AIMS: To evaluate diabetic retinopathy (DR) data from across the SUSTAIN clinical trial programme. MATERIALS AND METHODS: The SUSTAIN clinical trial programme evaluated the efficacy and safety of semaglutide, a glucagon-like peptide-1 analogue, for the treatment of type 2 diabetes (T2D). In SUSTAIN 6, a 2-year, pre-approval cardiovascular outcomes trial, semaglutide was associated with a significant increase in the risk of DR complications (DRC) vs placebo. DR data from across the SUSTAIN trials were evaluated, and post hoc analyses of the SUSTAIN 6 data were conducted. These included subgroup analyses to identify at-risk patients and a mediation analysis with initial change in glycated haemoglobin (HbA1c; percentage-points at week 16) as a covariate, to examine the role of the magnitude of reduction in HbA1c as an intermediate factor affecting risk of DRC. RESULTS: There was no imbalance in DR adverse events across the SUSTAIN 1 to 5 and Japanese trials. The majority of the effect with semaglutide vs placebo in SUSTAIN 6 may be attributed to the magnitude and rapidity of HbA1c reduction during the first 16 weeks of treatment in patients who had pre-existing DR and poor glycaemic control at baseline, and who were treated with insulin. CONCLUSIONS: Early worsening of DR is a known phenomenon associated with the rapidity and magnitude of improvement in glycaemic control with insulin; the DRC findings in SUSTAIN 6 are consistent with this. Guidance regarding the early worsening of DR is recommended with insulin. Similar recommendations may be appropriate for semaglutide.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".