Effect of semaglutide versus placebo on cardiorenal outcomes by prior cardiovascular disease and baseline body mass index: Pooled post hoc analysis of <scp>SUSTAIN</scp> 6 and <scp>PIONEER</scp> 6
Bibliographic record
Abstract
Abstract Aims Cardiorenal effects of semaglutide in people with type 2 diabetes (T2D) at high cardiovascular (CV) risk were investigated. Materials and Methods Post hoc analyses of pooled SUSTAIN 6 (NCT01720446) and PIONEER 6 (NCT02692716) data assessed time to primary major adverse CV events (MACE; CV death, non‐fatal myocardial infarction, or non‐fatal stroke), expanded MACE (MACE + hospitalisation for unstable angina or heart failure), CV death, all‐cause death, and new or worsening nephropathy. The impact of body weight (BW) changes on primary MACE risk was also evaluated. Participants were stratified by prior CV disease (CVD) status and baseline body mass index (BMI). Results Semaglutide significantly reduced the risk of primary and expanded MACE, with a nonsignificant risk reduction of CV and all‐cause death versus placebo in the overall population; the effect was consistent across all subgroups ( p INT >0.05 for all comparisons). Semaglutide consistently reduced nephropathy risk versus placebo in the SUSTAIN 6 population (HR [95% CI]: 0.64 [0.46; 0.88], p = 0.0054) and across all subgroups ( p INT >0.05 for all comparisons). When accounting for BW changes, treatment effects on primary MACE risk in the overall population and by BMI subgroups remained similar compared with the results of the main analysis. Conclusions Semaglutide treatment improved cardiorenal outcomes versus placebo in people with T2D, regardless of prior CVD and baseline BMI. This improvement was observed even when accounting for changes in BW, indicating direct effects of semaglutide on the cardiorenal system. This analysis supports the broad efficacy of semaglutide in a diverse T2D population.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".