Sex, racial, ethnic, and geographical disparities in major adverse cardiovascular outcome of glucagon-like peptide-1 receptor agonists among patients with and without diabetes mellitus: A meta-analysis of placebo-controlled randomized controlled trials

BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been pivotal in the management of type 2 diabetes mellitus (T2DM) and in the reduction of major adverse cardiovascular events (MACE). Notably, large cardiovascular outcomes trials (CVOTs) demonstrate significant disparities in inclusion, based on sex, race, ethnicity, and geographical regions. OBJECTIVES: We examined the impact of GLP-1RA on MACE in patients with or without T2DM, based on sex, race, ethnicity, and geography. METHODS: A literature search for placebo controlled randomized controlled trials on GLP-1RA treatment was conducted. Thorough data extraction and quality assessment were carried out, focusing on key outcome, and ensuring a robust statistical analysis using a random effects model to calculate log odds ratio (OR) with 95% confidence intervals (CIs). RESULTS: A total of 8 CVOTs comprising 71,616 patients were included. Compared with placebo, GLP-1RAs significantly reduced MACE in both sexes (females: logOR-0.19, (95% CI,-0.28 to-0.10), p < 0.01) versus (males: logOR-0.17, (95% CI,-0.23 to-0.10), p < 0.01), (p interaction NS), and among Asians (logOR-34 (95% CI,-0.53 to-0.15), p < 0.01), and Whites (logOR-17 (95% CI,-0.25 to-0.09), p < 0.01), with no difference in MACE among Blacks and Hispanics. Odds of MACE were also reduced in Asia (logOR-31 (95% CI,-0.50 to-0.11), p < 0.01), and Europe (logOR-27 (95% CI,-0.40 to-0.13), p < 0.01), but there was no statistical difference in MACE in North America and Latin America. CONCLUSION: Significant reductions in MACE with GLP-1RA treatment were demonstrated between both sexes and across certain ethnicities and certain geographical regions. (c) 2024 National Lipid Association. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.