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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".