Effects of oral semaglutide on cardiovascular outcomes: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Cardiovascular disease remains a leading cause of morbidity and mortality in type 2 diabetic mellitus (T2DM) patients. While injectable GLP-1 RAs have demonstrated efficacy in reducing cardiovascular risk, an oral formulation of semaglutide was developed to improve accessibility and treatment adherence, however, its therapeutic potential has yet to be fully elucidated. This meta-analysis evaluates the efficacy of oral semaglutide on cardiovascular outcomes to better inform clinical decision-making. Methods A systematic search was conducted in major databases for randomized controlled trials (RCTs) up to May 2025, with a primary outcome of cardiovascular events, expanded outcomes included nonfatal stroke, death from cardiovascular cause, and nonfatal myocardial infarction, and the secondary outcome was all-cause mortality. Statistical analysis was performed using RStudio, with heterogeneity assessed via I 2 statistics. Results A total of 13,875 patients were included, of whom 6935 received oral semaglutide and 6940 received placebo from the 5 eligible studies. A significant difference was observed for cardiovascular events with a risk ratio (RR) of 0.86 (95 % CI: 0.78–0.95; p = 0.0029; I 2 = 0 %) and hazard ratio (HR) of 0.85 (95 % CI: 0.77–0.95), respectively. In contrast, no significant difference was found for nonfatal stroke, all-cause mortality, death from cardiovascular causes, and nonfatal myocardial infarction. Conclusion The meta-analysis revealed that oral semaglutide was cardioprotective in T2DM patients with consistent reductions in cardiovascular event risk. However, the lack of significance in the remaining outcomes underscores the need for further investigation.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".