Comparative Efficacy of Glucagon-Like Peptide 1 Receptor Agonists for Cardiovascular Outcomes in Asian Versus White Populations: Systematic Review and Meta-analysis of Randomized Trials of Populations With or Without Type 2 Diabetes and/or Overweight or Obesity
Bibliographic record
Abstract
BACKGROUND: Cardiovascular outcome trials (CVOTs) suggest glucagon-like peptide 1 receptor agonists (GLP-1RAs) provide greater cardiovascular (CV) benefits in Asian compared with White individuals. PURPOSE: Compare CV efficacy of GLP-1RAs between Asian and White individuals. DATA SOURCES: Systematic review of PubMed and ClinicalTrials.gov (1 January 2015 to 1 November 2024). STUDY SELECTION: Randomized placebo-controlled CVOTs of GLP-1RAs. Risk of bias was assessed (RoB 2). DATA EXTRACTION: Ethnicity-specific hazard ratios (HRs) for major adverse cardiovascular events (MACE). DATA SYNTHESIS: Random-effects meta-analyses per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines included eight trials (5,909 Asian individuals, 55,855 White individuals). GLP-1RA-associated MACE HR was 0.69 (95% CI 0.58, 0.83) in Asian people and 0.85 (95% CI 0.79, 0.91) in White people (Pinteraction = 0.045). Absolute MACE risk reduction was 2.9% (95% CI 1.5, 4.2) in Asian people versus 1.4% (0.9, 1.9) in White people. LIMITATIONS: Lack of individual patient-level data precluded detailed subclassification of the Asian group. CONCLUSIONS: GLP-1RAs may offer greater MACE reductions in Asian compared with White individuals.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".