GLP-1 receptor agonist-based therapies and cardiovascular risk: a review of mechanisms
Bibliographic record
Abstract
Cardiovascular outcome trials (CVOTs) in people living with type 2 diabetes mellitus and obesity have confirmed the cardiovascular benefits of glucagon-like peptide 1 receptor agonists (GLP-1RAs), including reduced cardiovascular mortality, lower rates of myocardial infarction, and lower rates of stroke. The cardiovascular benefits observed following GLP-1RA treatment could be secondary to improvements in glycemia, blood pressure, postprandial lipidemia, and inflammation. Yet, the GLP-1R is also expressed in the heart and vasculature, suggesting that GLP-1R agonism may impact the cardiovascular system. The emergence of GLP-1RAs combined with glucose-dependent insulinotropic polypeptide and glucagon receptor agonists has shown promising results as new weight loss medications. Dual-agonist and tri-agonist therapies have demonstrated superior outcomes in weight loss, lowered blood sugar and lipid levels, restoration of tissue function, and enhancement of overall substrate metabolism compared to using GLP-1R agonists alone. However, the precise mechanisms underlying their cardiovascular benefits remain to be fully elucidated. This review aims to summarize the findings from CVOTs of GLP-1RAs, explore the latest data on dual and tri-agonist therapies, and delve into potential mechanisms contributing to their cardioprotective effects. It also addresses current gaps in understanding and areas for further research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".