Hemoglobin A1c Reduction with the GLP-1 Receptor Agonist Semaglutide Is independent of Baseline eGFR: Post Hoc Analysis of SUSTAIN and PIONEER Programs
Bibliographic record
Abstract
Background: Hyperglycemia is an established risk factor for the development and progression of chronic kidney disease. The glucagon-like peptide-1 receptor agonist semaglutide is approved for the treatment of type 2 diabetes (T2D) across a wide range of estimated glomerular filtration rates (eGFRs). We investigated whether baseline eGFR affected glycated hemoglobin (HbA1c) reduction with semaglutide. Methods: This post hoc, trial-level analysis considered all SUSTAIN (1-10) and PIONEER (1-10) trials where renal impairment was not an exclusion criteria and where the number of subjects receiving semaglutide with eGFR <60 mL/min/1.73m2 was >10. It included data for once-weekly subcutaneous semaglutide (SUSTAIN 4-6, pooled 0.5 and 1.0 mg; SUSTAIN 10, 1.0 mg only) and once-daily oral semaglutide (PIONEER 5 and 6, 14 mg); comparator data were not analyzed. Subjects receiving semaglutide were grouped by baseline eGFR; the eGFR subgroups evaluated were selected according to number of subjects meeting eGFR cut-offs (≥60 and <60 mL/min/1.73 m2 in SUSTAIN 4, 5, and 10; <45, 45 to <60 and ≥60 mL/min/1.73 m2 in SUSTAIN 6 and PIONEER 5 and 6). Within each trial, absolute estimated change in HbA1c from baseline to end of treatment (EOT) was compared between eGFR subgroups using a linear mixed model. Results: Mean HbA1c at baseline ranged from 7.9% to 8.7% across the subgroups. Semaglutide significantly reduced HbA1c at a comparable magnitude across eGFR subgroups in all trials (mean reduction of 1.0-1.7% from baseline to EOT; p>0.148 for difference between eGFR subgroups within each trial; Figure). Conclusions: Semaglutide (subcutaneous and oral) is an effective glucose-lowering agent in subjects with T2D, independently of baseline eGFR, including in those with chronic kidney disease. Funding: Commercial Support - Novo Nordisk
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".