Effect of glucagon-like peptide-1 receptor agonists on atrial fibrillation incidence: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Atrial fibrillation (AF) is a growing global public health burden associated with significantly morbidity and healthcare costs. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have demonstrated substantial cardiovascular benefits, yet their potential role in AF prevention remains uncertain. While experimental studies suggest GLP-1 RAs may exert antiarrhythmic effects, clinical data on their impact on AF incidence are inconsistent. This systematic review and meta-analysis aim to evaluate the association between GLP-1 RAs and AF risk compared to placebo and other antidiabetic agents. Purpose To assess whether GLP-1 RAs influence the incidence and burden of AF compared to placebo or other antidiabetic agents. This addresses a critical gap in cardiovascular risk management strategies. Methods A literature search was conducted across PubMed, Embase, MEDLINE, Cochrane Library and ClinicalTrials.gov. 30 randomised controlled trials (RCTs) and 8 retrospective observational studies comparing GLP-1 RAs to placebo or other non-GLP-1 RA drugs with reported AF outcomes were included in this analysis. The primary outcome measure was the incidence of AF after a minimum follow-up of 24 weeks. A random-effects meta-analysis was performed to calculate pooled odds ratios (OR) and hazard ratios (HR) with 95% confidence intervals (CI). Risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB2) tool for the RCTs and the Newcastle-Ottawa Scale (NOS) for the observational studies. Results The meta-analysis of RCTs found no overall higher risk of atrial arrhythmias with GLP-1 RAs compared to placebo (OR: 0.94 [95% CI: 0.86–1.04], p = 0.23). Observational studies suggested a modest association with lower AF risk (HR: 0.93 [95% CI: 0.89–0.97], p = 0.001). Significant heterogeneity was observed in the observational data (I² = 83%). Subgroup analysis suggested a potential benefit with semaglutide, but this did not reach statistical significance. Funnel plot analyses showed no major publication bias. Conclusion This meta-analysis demonstrates that GLP-1 RAs do not significantly reduce the incidence of AF. While the observational data suggest a moderate reduction in AF incidence. Further research is required to determine whether specific GLP-1 RAs, such as semaglutide, may exert beneficial AF-related effects. Future RCTs with AF as a primary endpoint are needed to clarify the potential role of GLP-1 RAs in AF prevention and management. The findings support the continued prioritisation of established AF prevention strategies and signpost the need for future research surrounding GLP-1 RAs.RCT Forest Plot – GLP-1 RAs & AF Risk Observational Forest Plot – GLP-1 RAs &
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".