Chen Shuohua, Li Weijuan, Jin Cheng, Vaidya Anand, Gao Jingli, Yang Hui, Wu Shouling, Gao Xiang
From the Health Care Center, Kailuan Medical Group, Tangshan, China (S.C.); Vanderbilt Heart and Vascular Institute, Vanderbilt University Medical Center, Nashville, TN (W.L.); Department of Cardiology (C.J., J.G., S.W.) and Department of Surgery (H.Y.), Kailuan General Hospital, Tangshan, China; Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (A.V.); and Department of Nutritional Sciences, The Pennsylvania State University, University Park (X.G.).
Arterioscler Thromb Vasc Biol. 2017 Feb;37(2):359-364. doi: 10.1161/ATVBAHA.116.308674. Epub 2016 Dec 1.
To examine whether the long-term resting heart rate (RHR) pattern can predict the risk of having arterial stiffness in a large ongoing cohort.
This community-based cohort included 12 554 participants in the Kailun study, who were free of myocardial infarction, stroke, arrhythmia, and cancer. We used latent mixture modeling to identify RHR trajectories in 2006, 2008, and 2010. We used multivariate linear regression model to examine the association between RHR trajectory patterns and the risk of having arterial stiffness, which was assessed by brachial-ankle pulse wave velocity in 2010 to 2016. We adjusted for possible confounding factors, including socioeconomic status, lifestyle factors, use of medications, comorbidities, and serum concentrations of lipids, glucose, and high-sensitivity C-reactive proteins. We identified 5 distinct RHR trajectory patterns based on their 2006 status and on the pattern of change during 2006 to 2010 (low-stable, moderate-stable, moderate-increasing, elevated-decreasing, and elevated-stable). We found that individuals with elevated-stable RHR trajectory pattern had the highest brachial-ankle pulse wave velocity value and individuals with the low-stable RHR trajectory pattern had the lowest value (adjusted mean difference=157 cm/s; P<0.001). Adjusted odds ratio for risk of having arterial stiffness (brachial-ankle pulse wave velocity ≥1400 cm/s) was 4.14 (95% confidence interval, 2.61-6.57) relative to these 2 extreme categories. Consistently, a higher average RHR, a higher annual RHR increase rate, and a higher RHR variability were all associated with a higher risk of having arterial stiffness.
Long-term RHR pattern is a strong predictor of having arterial stiffness.
在一个大型的正在进行的队列研究中，检验长期静息心率（RHR）模式是否能够预测发生动脉僵硬度的风险。
基于社区的该队列研究纳入了开滦研究中的12554名参与者，这些参与者无心肌梗死、中风、心律失常和癌症。我们使用潜在混合模型来识别2006年、2008年和2010年的RHR轨迹。我们使用多变量线性回归模型来检验RHR轨迹模式与动脉僵硬度风险之间的关联，动脉僵硬度在2010年至2016年通过臂踝脉搏波速度进行评估。我们对可能的混杂因素进行了调整，包括社会经济地位、生活方式因素、药物使用、合并症以及脂质、葡萄糖和高敏C反应蛋白的血清浓度。基于其2006年的状态以及2006年至2010年期间的变化模式，我们识别出了5种不同的RHR轨迹模式（低稳定型、中稳定型、中上升型、高下降型和高稳定型）。我们发现，高稳定型RHR轨迹模式的个体臂踝脉搏波速度值最高，而低稳定型RHR轨迹模式的个体值最低（调整后平均差异=157 cm/s；P<0.001）。相对于这两种极端类型，发生动脉僵硬度（臂踝脉搏波速度≥1400 cm/s）风险的调整后比值比为4.14（95%置信区间，2.61 - 6.57）。一致地，较高的平均RHR、较高的年RHR增长率以及较高的RHR变异性均与发生动脉僵硬度的较高风险相关。
长期RHR模式是发生动脉僵硬度的有力预测指标。
