five

DataSheet_1_Exposure–Response Analysis of Cardiovascular Outcome Trials With Incretin-Based Therapies.docx

收藏
NIAID Data Ecosystem2026-03-13 收录
下载链接:
https://figshare.com/articles/dataset/DataSheet_1_Exposure_Response_Analysis_of_Cardiovascular_Outcome_Trials_With_Incretin-Based_Therapies_docx/19979756
下载链接
链接失效反馈
官方服务:
资源简介:
Our study aimed to evaluate the exposure–response relationship between incretin-based medications and the risk of major adverse cardiovascular events (MACE) using cardiovascular outcome trials (CVOTs). Eleven CVOTs with incretin-based medications were included. The median follow-up time, percentage of time exposure, and hazard ratio (HR) of MACE were obtained from each CVOT. The pharmacokinetic parameters of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 inhibitor (DPP-4) were obtained from published studies. Regression analysis was performed to assess the relationship between drug exposure and MACE HR. Cutoff values were determined from the ROC curves. The linear regression results indicated that log Cmax, log AUC0–24h, and log AUCCVOT are negatively correlated with MACE HR (R2 = 0.8494, R2 = 0.8728, and R2 = 0.8372, respectively; all p < 0.0001). The relationship between drug exposure (log Cmax, log AUC0–24h, and log AUCCVOT) and MACE HR strongly corresponded with the log (inhibitor) vs. response curve (R2 = 0.8383, R2 = 0.8430, and R2 = 0.8229, respectively). The cutoff values in the ROC curves for log Cmax, log AUC0–24h, and log AUCCVOT, were 2.556, 3.868, and 6.947, respectively (all p = 0.007). A Fisher’s exact test revealed that these cutoff values were significantly related to cardiovascular benefits (all p < 0.05). Our study revealed a linear exposure–response relationship between drug exposure and MACE HR. We conclude that the cardiovascular benefits of incretin-based therapies may occur with higher doses of GLP-1 RAs and with increased exposure.
创建时间:
2022-06-03
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作