Cardiovascular efficacy of liraglutide and semaglutide in individuals with diabetes and peripheral artery disease
Bibliographic record
Abstract
Abstract Aim To evaluate the cardiovascular (CV) efficacy of liraglutide and semaglutide in patients with type 2 diabetes (T2D) and peripheral artery disease (PAD). Materials and Methods LEADER and SUSTAIN 6 trials investigated subcutaneous liraglutide (≤1.8 mg/day) and semaglutide (0.5 or 1.0 mg/week), respectively, versus placebo in patients with T2D and high CV risk (median follow‐up: 3.8 and 2.1 years, respectively). The primary outcome was a composite of CV death, non‐fatal myocardial infarction or non‐fatal stroke (major adverse CV event [MACE]) according to the presence of PAD at baseline. Results Overall, 1184/9340 (12.7%) patients in LEADER and 460/3297 (14.0%) in SUSTAIN 6 had PAD at baseline. Patients with PAD were at an ~35% increased risk of MACE versus those without (LEADER: hazard ratio [HR] 1.36, 95% confidence interval [CI] 1.17‐1.58; SUSTAIN 6: HR 1.33, 95% CI 0.94‐1.83). The effects of both therapies on MACE were consistently beneficial in patients with PAD (liraglutide: HR 0.77, 95% CI 0.58‐1.01; semaglutide: 0.61, 0.33‐1.13) and without (liraglutide: HR 0.89, 95% CI 0.79‐1.00; semaglutide: HR 0.77, 95% CI 0.58‐1.01; P interaction = .34 for liraglutide and .49 for semaglutide). Absolute risk reductions for MACE were higher in patients with PAD (liraglutide: 4.13%‐point, 95% CI −0.15‐8.42; semaglutide: 4.63%‐point, 95% CI −0.58‐9.84) versus without (liraglutide:1.42%‐point, 95% CI −0.03‐2.87; semaglutide: 1.90%‐point, 95% CI 0.00‐3.80). Conclusion Both liraglutide and semaglutide reduce MACE with consistent CV efficacy regardless of PAD status.
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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 | 0.000 |
| 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".