Semaglutide (SUSTAIN and PIONEER) reduces cardiovascular events in type 2 diabetes across varying cardiovascular risk
Bibliographic record
Abstract
AIM: To investigate the effects of semaglutide versus comparators on major adverse cardiovascular events (MACE: cardiovascular [CV] death, nonfatal myocardial infarction [MI] and nonfatal stroke) and hospitalization for heart failure (HF) in the SUSTAIN (subcutaneous semaglutide) and PIONEER (oral semaglutide) trials across subgroups of varying CV risk. METHODS: Post hoc analyses of individual patient-level data combined from SUSTAIN 6 and PIONEER 6 were performed to assess MACE and HF. MACE were analysed in subjects with and without: established CV disease and/or chronic kidney disease; prior MI or stroke; and prior HF. MACE in the SUSTAIN and PIONEER glycaemic efficacy trials were also assessed. RESULTS: In SUSTAIN 6 and PIONEER 6 combined, the hazard ratio (HR) for effect of semaglutide versus placebo on overall MACE was 0.76 (95% CI 0.62, 0.92), which was mainly driven by the effect on nonfatal stroke (HR 0.65 [95% CI 0.43, 0.97]). The HR for hospitalization for HF was 1.03 (95% CI 0.75, 1.40). The HRs for MACE were <1.0 in all subgroups, except for those with prior HF (HR 1.06 [95% CI 0.72, 1.57]); P-values for interaction of subgroup on treatment effect were >0.05, except for HF (0.046). In the combined glycaemic efficacy trials, the HR for effect of semaglutide versus comparators on MACE was 0.85 (95% CI 0.55, 1.33). CONCLUSIONS: In SUSTAIN and PIONEER combined, glucagon-like peptide-1 analogue semaglutide showed consistent effects on MACE versus comparators across varying CV risk. No effect of semaglutide on MACE was observed in subjects with prior HF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".