医学
平均动脉压
血压
内科学
血管阻力
血管紧张素II
心输出量
松弛素
血流动力学
全身给药
内分泌学
自发性高血压大鼠
心脏病学
心率
受体
体内
生物
生物技术
作者
Dan O. Debrah,Kirk P. Conrad,Arundhathi Jeyabalan,Lee A. Danielson,Sanjeev G. Shroff
出处
期刊:Hypertension
[Lippincott Williams & Wilkins]
日期:2005-09-20
卷期号:46 (4): 745-750
被引量:97
标识
DOI:10.1161/01.hyp.0000184230.52059.33
摘要
Chronic administration of recombinant human relaxin (rhRLX) to conscious, normotensive rats (male and female) increases cardiac output (CO) and global arterial compliance (ACg) and reduces systemic vascular resistance (SVR) with no change in mean arterial pressure (MAP). Effects (magnitude and temporal pattern) of relaxin on systemic hemodynamics and arterial properties in hypertensive animal models are not known. Accordingly, the major goal of the present study was to determine the cardiovascular effects of rhRLX in hypertensive rats using 2 models: Long-Evans rats chronically administered angiotensin II (AII) and spontaneously hypertensive rats (SHR). CO and systemic arterial load, as quantified by SVR and ACg, were obtained using methods reported previously by us. In rats with AII-induced hypertension, acute rhRLX administration (up to 6 hours) significantly increased CO and ACg (24.9+/-3.9 and 34.3+/-12.6% above baseline, respectively) and significantly decreased SVR (17.2+/-3.5%) without changing MAP. In contrast, acute rhRLX administration to SHR and normotensive rats for up to 6 hours failed to produce any significant changes in CO, ACg, SVR, or MAP. However, chronic rhRLX administration (1 to 7 days) to SHR yielded significant changes (24.0+/-8.1 and 22.3+/-6.6% increases in CO and ACg, respectively, and a 13.3+/-5.3% decrease in SVR, with no change in MAP). In conclusion, rhRLX increases CO and reduces arterial load in hypertensive rats without reducing MAP. However, the time course of response to rhRLX treatment is dependent on the model of hypertension such that rats characterized by AII-mediated hypertension responded more rapidly to rhRLX administration than SHR.
科研通智能强力驱动
Strongly Powered by AbleSci AI