Uncovering Contributors to Hypertension through Experimental and Computational Simulation (CHECS)

高血压，或称高血压，是发达国家全球发病率和死亡率的最重要原因之一。已有研究表明，高血压患者中风、心脏病发作、心力衰竭和肾功能衰竭的风险很高。2006年英格兰健康调查显示，英国高血压患病率从40-49岁年龄组的17%上升到70-79岁年龄组的77%[2]。高血压患者通常通过其收缩压或舒张压分别超过140或90毫米汞柱的阈值诊断来识别。然而，这种诊断倾向于误诊临界值内和临界值附近的大量人群中的个体，使选择适当的治疗变得困难。例如，高血压的一个重要决定因素是主动脉(从心脏发出的第一条动脉)的弹性，它随着年龄和动脉硬化而变得更加僵硬。然而，这种“僵硬”只是影响压力脉搏和高血压的其他几何和机械因素之一。因此，脉压波形的无创测量已经引起人们100多年的关注，包括眼压测量、超声和磁共振成像(MRI)。虽然波形的非侵入性测量已经变得很快，但目前对测量的波形数据的分析相对简单。特别是，某些波形特征的分析是孤立进行的，并且由于缺乏对动脉僵硬/几何形状、波反射和室/动脉相互作用对高血压的相对贡献的了解而受到阻碍。在过去的二十年里，计算模型已经成为研究心血管系统中不同参数之间相互作用的一门新学科。这些模型可以帮助分离对压力波形的不同贡献，并阐明影响高血压的参数的复杂交互作用。最近，在数值模拟中引入了患者解剖学和生理学的成像数据，以产生患者特定的模型。尽管已经开发了不同的模型来研究几何和机械因素的影响，但模型验证仍然具有挑战性，因为它需要在动物和患者中进行大量研究。这项建议旨在通过确定导致他们的压力病理性的机械因素来识别高危个人。这种方法将允许为个别患者更好地选择适当的治疗方法。为此，我们建议建立一个全面的实验动脉模型，用来确定和量化高血压的主要影响因素，并验证我们现有的计算动脉模拟框架(一维和三维)。这些技术向临床的转化将通过构建全尺寸硅胶动脉模型来促进，该模型将实验性地模拟高血压患者数据集的血流动力学。随后将在志愿者和一小部分患者队列中对计算分析工具进行临床验证。参考文献：[1]MacMahon，S.等人：与血压相关的疾病是全球健康优先事项。《柳叶刀》，2006。371：第1480-1482页。[2]NHS，2006年英格兰健康调查最新趋势。2008年：利兹。

High blood pressure, or hypertension, is one of the most important causes of global morbidity and mortality in the developed world [1]. It has been shown that hypertensive people have a high risk of stroke, heart attack, heart failure and renal failure. The Health Survey for England in 2006 demonstrated that the prevalence of hypertension in the UK increased from 17% in the age group 40-49 years to 77% in those aged 70-79 years [2]. Hypertensive patients are usually identified by a threshold diagnosis of their systolic or diastolic pressures exceeding 140 or 90 mmHg respectively. However this diagnosis tends to misdiagnose the individuals in the large population in and around the threshold making the selection for appropriate therapy difficult. For example one important determinant of hypertension is the flexibility of the aorta (the first artery leading from the heart), which becomes stiffer with age and arteriosclerosis. However, such "stiffness" is only one among other geometrical and mechanical factors that influence the pressure pulse and thus hypertension. Therefore, non-invasive measurement of pulse pressure waveforms has been of interest for more than 100 years, and includes tonometry, Ultrasound and Magnetic Resonance Imaging (MRI). Although the non-invasive measurement of waveforms has become fast, the current analysis of the measured waveform data is relatively simplistic. In particular, the analysis of certain waveform features are performed in isolation and are impeded by a lack of understanding of the relative contributions from arterial stiffness/geometry, wave reflection and ventricular/arterial interaction to hypertensive pressure. Over the last two decades, computational modelling has been established as a new discipline to study the interaction of different parameters in the cardiovascular system. These models can help to separate the various contributions to the pressure waveform and elucidate complex interaction of parameters affecting hypertension. More recently, imaging data of the patient's anatomy and physiology has been introduced in numerical simulations to produce patient-specific models. Although, different models have been developed to investigate the influence of geometrical and mechanical factors, a model validation remains challenging since it would require large studies in animals and patients. This proposal aims at the identification of high-risk individuals by determining the mechanical factors which cause their pressure to be pathological. This approach would allow a better selection of appropriate treatments for the individual patient. For this, we propose the construction of a comprehensive experimental arterial model with which to determine and quantify main contributors to hypertensive pressure as well as to validate our existing computational arterial simulation frameworks (1D and 3D). Translation of these technologies towards the clinic will be facilitated with the construction of full-scale silicone arterial model, which will experimentally simulate haemodynamics of a hypertensive patient dataset. This will be followed by a clinical validation of a computational analysis tools in volunteers and a small patient cohort. References:[1] MacMahon, S., et al.: Blood-pressure-related disease is a global healthy priority. Lancet, 2006. 371: p. 1480-1482.[2] NHS, Health survey for england 2006 latest trends. 2008: Leeds.