医学
疾病
内科学
心血管健康
纵向研究
心脏病学
物理疗法
血压
老年学
握力
流行病学
物理医学与康复
重症监护医学
糖尿病
人口
梅德林
入射(几何)
作者
Jing Li,Junna Sui,Lian Tang,Cong Zhao,Ruhua Liu,Junjie Guo,Tingting Zhou,Deng Pan,Qingwu Tian,Chao Xuan
标识
DOI:10.1016/j.ajpc.2025.101359
摘要
Objective: The study aimed to investigate the relationships between grip strength-to-weight ratio (GSWR) and all-cause mortality, cardiovascular mortality, and prevalence of cardiovascular disease (CVD). Methods: Data were analyzed from two nationally representative cohorts: the National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS). Weighted Cox proportional hazards models were used to estimate hazard ratios (HRs) for mortality outcomes, while weighted logistic regression evaluated the association between GSWR and CVD prevalence. Multivariable-adjusted restricted cubic splines (RCS) were used to examine potential non-linear relationships. Results: Over median follow-up of 82.19 months and 100.07 months, individuals in the highest GSWR quartile exhibited a 73 % reduction in all-cause mortality (HR: 0.27, 95 % CI: 0.16-0.43) in American adults, and a 47 % reduction (HR: 0.53, 95 % CI: 0.43-0.65) in Chinese middle-aged and elderly populations, compared to those in the lowest quartile. After a median follow-up of 83.66 months, cardiovascular mortality was reduced by 69 % (HR: 0.31, 95 % CI: 0.22-0.44), 69 % (HR: 0.31, 95 % CI: 0.16-0.59), and 79 % (HR: 0.21, 95 % CI: 0.09-0.48) for each increase in GSWR quartile among American adults. An typical l-shaped non-linear relationship was observed between GSWR and both all-cause and cardiovascular mortality, with a similar non-linear association identified for CVD prevalence. Conclusion: The GSWR was non-linearly associated with all-cause mortality, cardiovascular mortality, and prevalence of CVD. It can serve as a valuable health index, encouraging strength training among middle-aged and elderly individuals to maintain and enhance functional abilities.
科研通智能强力驱动
Strongly Powered by AbleSci AI