five

Data_Sheet_1_A Prospective Study of Grip Strength Trajectories and Incident Cardiovascular Disease.docx

收藏
NIAID Data Ecosystem2026-03-12 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_A_Prospective_Study_of_Grip_Strength_Trajectories_and_Incident_Cardiovascular_Disease_docx/16626730
下载链接
链接失效反馈
官方服务:
资源简介:
Background: A single measurement of grip strength (GS) could predict the incidence of cardiovascular disease (CVD). However, the long-term pattern of GS and its association with incident CVD are rarely studied. We aimed to characterize the GS trajectory and determine its association with the incidence of CVD (myocardial infarction, angina, stroke, and heart failure). Methods: This study included 5,300 individuals without CVD from a British community-based cohort in 2012 (the baseline). GS was repeatedly measured in 2004, 2008, and 2012. Long-term GS patterns were identified by the group-based trajectory model. Cox proportional hazard models were used to examine the associations between GS trajectories and incident CVD. We identified three GS trajectories separately for men and women based on the 2012 GS measurement and change patterns during 2004–2012. Results: After a median follow-up of 6.1 years (during 2012–2019), 392 participants developed major CVD, including 114 myocardial infarction, 119 angina, 169 stroke, and 44 heart failure. Compared with the high stable group, participants with low stable GS was associated with a higher incidence of CVD incidence [hazards ratio (HR): 2.17; 95% confidence interval (CI): 1.52–3.09; P <0.001], myocardial infarction (HR: 2.01; 95% CI: 1.05–3.83; P = 0.035), stroke (HR: 1.96; 95% CI: 1.11–3.46; P = 0.020), and heart failure (HR: 6.91; 95% CI: 2.01–23.79; P = 0.002) in the fully adjusted models. Conclusions: The low GS trajectory pattern was associated with a higher risk of CVD. Continuous monitoring of GS values could help identify people at risk of CVD.
创建时间:
2021-09-16
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