Hemoglobin A1c Reduction With the GLP-1 Receptor Agonist Semaglutide Is Independent of Baseline eGFR: post hoc Analysis of the SUSTAIN and PIONEER Programs
Bibliographic record
Abstract
Introduction Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are effective treatments for reducing hemoglobin A1c (HbA1c) in people with type 2 diabetes (T2D), including those with reduced kidney function. Methods This post hoc analysis assessed the HbA1c-lowering efficacy of semaglutide, a GLP-1RA, in participants with a range of kidney functions in the SUSTAIN 4–6 and 10 (subcutaneous semaglutide) and PIONEER 5 and 6 (oral semaglutide) clinical trials. Trial-level changes from baseline to end of treatment (EOT) in HbA1c and body weight (BW) were assessed in participants with estimated glomerular filtration rate (eGFR) >15 ml/min per 1.73 m 2 by subgroups categorized according to baseline eGFR. Adverse events were also evaluated. Results The analysis included 8859 participants. The mean comparator-adjusted reduction in HbA1c from baseline to EOT with semaglutide ranged from 0.6% to 1.6% points across trials, with similar reductions across the eGFR subgroups (interaction P -value ≥ 0.33 for difference between eGFR subgroups within each trial). Greater weight loss from baseline to EOT with semaglutide versus comparator was observed across almost all baseline eGFR subgroups, with nominally greater weight loss with lower versus higher eGFR in SUSTAIN 6 and 10 and PIONEER 5 and 6 (interaction P < 0.05). No new safety concerns with semaglutide were identified. Conclusion The HbA1c-lowering effect of semaglutide in participants with T2D was comparable irrespective of eGFR, which ranged upwards from eGFR >15 ml/min per 1.73 m 2 .
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".