Efficacy and Cardiovascular Safety of GLP-1 Receptor Analogues
Bibliographic record
Abstract
Glucagon-like peptide- 1 receptor analogs (GLP-1RAs) are incretin mimetics with potent glucose-dependent insulinotropic action that translates to glycemic control in people with type- -2 diabetes mellitus (T2DM). These agents potentially have the ability to stimulate proliferation or prevent apoptosis of pancreatic β-cells, induce weight-loss and provide vascular benefits in patients with T2DM. Newer GLP-1RA, semaglutide has shown a robust reduction in HbA1c up to 1.5 - 1.8%. However, individual differences exist between the different GLP-1RAs, in terms of efficacy, pharmacokinetics, tolerability, and vascular protection. The potential of vascular protection offered by newer anti-diabetic agents has generated a lot of excitement in the field of diabetes, and to a large extent, is now driving treatment decisions. So far, six cardiovascular outcome trials of GLP-1 RAs have been published, analyzing lixisenatide (ELIXA), liraglutide (LEADER), semaglutide (SUSTAIN-6), long-acting exenatide (EXSCEL), dulaglutide (REWIND), and oral semaglutide (PIONEER 6) with a follow-up duration of 2-4 years. LEADER, REWIND and SUSTAIN-6 trials have demonstrated a reduction in rates of major adverse cardiovascular events with active GLP-1 RA treatment, but ELIXA, PIONEER 6 and EXSCEL, have been neutral. In this review, we discuss the available evidence from randomized controlled trials (RCTs) analyzing the cardiovascular effects of various GLP-1 RAs with the aim of comparing individual drugs. We have also summarized the general aspects of GLP-1RAs that can be applied in clinical practice.
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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 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".