A randomized phase 2b trial examined the effects of the glucagon-like peptide-1 and glucagon receptor agonist cotadutide on kidney outcomes in patients with diabetic kidney disease
Bibliographic record
Abstract
Cotadutide is a glucagon-like peptide-1 (GLP-1) and glucagon receptor agonist that may improve kidney function in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD). In this phase 2b study, patients with T2D and CKD (estimated glomerular filtration rate [eGFR] of 20 or more and under 90 mL/min per 1.73 m 2 and urinary albumin-to-creatinine ratio [UACR] over 50 mg/g) were randomized 1:1:1:1:1 to 26 weeks' treatment with standard of care plus subcutaneous cotadutide uptitrated to 100, 300, or 600 μg, or placebo daily (double-blind), or the GLP-1 agonist semaglutide 1 mg once weekly (open-label).The co-primary endpoints were absolute and percentage change versus placebo in UACR from baseline to the end of week 14. Among 248 randomized patients, mean age 67.1 years, 19% were female, mean eGFR was 55.3 mL/min per 1.73 m 2 , geometric mean was UACR 205.5 mg/g (coefficient of variation 270.0), and 46.8% were receiving concomitant sodium–glucose co-transporter 2 inhibitors. Cotadutide dose-dependently reduced UACR from baseline to the end of week 14, reaching significance at 300 μg (–43.9% [95% confidence interval −54.7 to −30.6]) and 600 μg (−49.9% [−59.3 to −38.4]) versus placebo; with effects sustained at week 26. Serious adverse events were balanced across arms. Safety and tolerability of cotadutide 600 μg were comparable to semaglutide. Thus, our study shows that in patients with T2D and CKD, cotadutide significantly reduced UACR on top of standard of care with an acceptable tolerability profile, suggesting kidney protective benefits that need confirmation in a larger study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".