Semaglutide Reduced Risks of Major Kidney Outcomes Irrespective of CKD Severity in the FLOW Trial
Bibliographic record
Abstract
Background: Semaglutide reduced risks of major kidney outcomes, cardiovascular (CV) events, and death from any cause in participants with type 2 diabetes (T2D) and chronic kidney disease (CKD) in the FLOW trial. The aim of this analysis was to assess kidney outcomes by baseline CKD severity. Methods: Participants had T2D with eGFR 50–75 mL/min/1.73m2 and urine albumin–creatinine ratio (UACR) >300–<5000 mg/g, or eGFR 25–<50 mL/min/1.73m2 and UACR >100–<5000 mg/g. They were randomized to subcutaneous semaglutide 1 mg once weekly or placebo. The FLOW primary outcome was a composite of kidney failure (initiation of chronic kidney replacement therapy, eGFR <15 ml/min/1.73m2), >50% eGFR decline, or death due to kidney or CV causes. Participants were categorized by baseline eGFR and UACR. Results: Among 3533 participants, 30% (n=1069) were women. At baseline, their mean age was 67 years, mean eGFR was 47 mL/min/1.73 m2, and median UACR was 568 mg/g. The hazard ratio for the primary outcome was 0.76 (95% CI 0.66–0.88) for semaglutide versus placebo over a median of 3.4 years. Consistent results were observed across eGFR and UACR categories (Figure). Conclusion: Semaglutide safely reduced risks of major kidney outcomes irrespective of CKD severity defined by baseline eGFR or UACR in participants with T2D and CKD in the FLOW trial. 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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".