Cardiovascular outcomes with semaglutide by severity of chronic kidney disease in type 2 diabetes: the FLOW trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: In the FLOW trial, semaglutide reduced the risks of kidney and cardiovascular (CV) outcomes and death in participants with type 2 diabetes and chronic kidney disease (CKD). These prespecified analyses assessed the effects of semaglutide on CV outcomes and death by CKD severity. METHODS: Participants were randomized to subcutaneous semaglutide 1 mg or placebo weekly. The main outcome was a composite of CV death, non-fatal myocardial infarction (MI), or non-fatal stroke (CV death/MI/stroke) as well as death due to any cause by baseline CKD severity. CKD was categorized by estimated glomerular filtration rate < or ≥60 mL/min/1.73 m2, urine albumin-to-creatinine ratio < or ≥300 mg/g, or Kidney Disease Improving Global Outcomes (KDIGO) risk classification. RESULTS: Three thousand, five hundred and thirty-three participants were randomized with a median follow-up of 3.4 years. Low/moderate KDIGO risk was present in 242 (6.8%), while 878 (24.9%) had high and 2412 (68.3%) had very high KDIGO risk. Semaglutide reduced CV death/MI/stroke by 18% [hazard ratio (HR) 0.82 (95% confidence interval 0.68-0.98); P = .03], with consistency across estimated glomerular filtration rate categories, urine albumin-to-creatinine ratio levels, and KDIGO risk classification (all P-interaction > .13). Death due to any cause was reduced by 20% [HR 0.80 (0.67-0.95); P = .01], with consistency across estimated glomerular filtration rate categories and KDIGO risk class (P-interaction .21 and .23, respectively). The P-interaction treatment effect for death due to any cause by urine albumin-to-creatinine ratio was .01 [<300 mg/g HR 1.17 (0.83-1.65); ≥300 mg/g HR 0.70 (0.57-0.85)]. CONCLUSIONS: Semaglutide significantly reduced the risk of CV death/MI/stroke regardless of baseline CKD severity in participants with type 2 diabetes.
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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".