Effects of Once-Weekly Semaglutide on Kidney Disease Outcomes by KDIGO Risk Category in the SUSTAIN 6 Trial
Bibliographic record
Abstract
Introduction Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are recommended by Kidney Disease: Improving Global Outcomes (KDIGO) as risk-based treatment for hyperglycemia, weight management, and cardiovascular (CV) risk reduction in people with type 2 diabetes (T2D) and chronic kidney disease (CKD). The aim of this post hoc analysis was to assess treatment effects of once weekly semaglutide on kidney disease outcomes by KDIGO risk category and on changes in KDIGO risk category, compared with placebo. Methods Participants with T2D and established CV disease or at high CV risk treated with once weekly semaglutide or placebo in SUSTAIN 6 (NCT01720446) were stratified by baseline KDIGO risk category (low [ n = 1596], moderate [ n = 831], high [ n = 445], very high [ n = 366]). Treatment effect was analyzed for a kidney disease composite end point (macroalbuminuria, serum creatinine doubling and estimated glomerular filtration rate [eGFR] < 45 ml/min per 1.73 m 2 , kidney replacement therapy, or death due to kidney disease) from baseline to 2 years. Results The treatment effect of semaglutide versus placebo was consistent across KDIGO categories for the kidney disease composite end point (hazard ratio [95% confidence interval (CI)]: 0.35 [0.07–1.72], 0.42 [0.25–0.72], 0.87 [0.45–1.71], and 0.72 [0.42–1.23] for low, moderate, high, and very high risk categories, respectively; P interaction = 0.28). Participants receiving semaglutide were more likely to move to a lower KDIGO risk category (odds ratio: 1.69; 95% CI: [1.32–2.16]) and less likely to move to a higher KDIGO risk category versus placebo (odds ratio: 0.71; 95% CI: [0.59–0.86]). Conclusion Once weekly semaglutide versus placebo reduced risks of kidney disease end points and improved risk categories irrespective of baseline KDIGO risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".