PDF(Pubmed)
Abstract：
The approach introduced by Network Physiology intends to find and quantify connectedness between close- and far related aspects of a person\'s Physiome. In this study I applied a Network-inspired analysis to a set of measurement data that had been assembled to detect prospective orthostatic intolerant subjects among people who were destined to go into Space for a two weeks mission. The advantage of this approach being that it is essentially model-free: no complex physiological model is required to interpret the data. This type of analysis is essentially applicable to many datasets where individuals must be found that \"stand out from the crowd\". The dataset consists of physiological variables measured in 22 participants (4f/18 m; 12 prospective astronauts/cosmonauts, 10 healthy controls), in supine, + 30° and + 70° upright tilted positions. Steady state values of finger blood pressure and derived thereof: mean arterial pressure, heart rate, stroke volume, cardiac output, systemic vascular resistance; middle cerebral artery blood flow velocity and end-tidal pCO2 in tilted position were (%)-normalized for each participant to the supine position. This yielded averaged responses for each variable, with statistical spread. All variables i.e., the \"average person\'s response\" and a set of %-values defining each participant are presented as radar plots to make each ensemble transparent. Multivariate analysis for all values resulted in obvious dependencies and some unexpected ones. Most interesting is how individual participants maintained their blood pressure and brain blood flow. In fact, 13/22 participants had all normalized Δ-values (i.e., the deviation from the group average, normalized for the standard deviation), both for +30° and +70°, within the 95% range. The remaining group demonstrated miscellaneous response patterns, with one or more larger Δ-values, however of no consequence for orthostasis. The values from one prospective cosmonaut stood out as suspect. However, early morning standing blood pressure within 12 h after return to Earth (without volume repletion) demonstrated no syncope. This study demonstrates an integrative way to model-free assess a large dataset, applying multivariate analysis and common sense derived from textbook physiology.
摘要：
网络生理学引入的方法旨在发现和量化人的生理组的紧密相关方面之间的联系。在这项研究中，我将网络启发的分析应用于一组测量数据，这些数据已被收集起来，以检测注定要进入太空进行两周任务的人们中的前瞻性直立不耐受受试者。这种方法的优点在于它基本上是无模型的:不需要复杂的生理模型来解释数据。这种类型的分析基本上适用于许多数据集，其中必须找到“从人群中脱颖而出”的个人。该数据集包括在22名参与者中测量的生理变量（4f/18m；12名准宇航员/宇航员，10个健康对照），仰卧,+30°和+70°直立倾斜位置。手指血压的稳态值及其推导：平均动脉压，心率,每搏输出量,心输出量,全身血管阻力;倾斜体位的大脑中动脉血流速度和潮气末pCO2对仰卧位的每位参与者进行归一化（％）。这产生了每个变量的平均响应，统计传播。所有变量，即，“普通人的反应”和一组定义每个参与者的％值显示为雷达图，以使每个集合透明。对所有值的多变量分析导致了明显的依赖性和一些意外的依赖性。最有趣的是个体参与者如何保持血压和大脑血流量。事实上,13/22参与者具有所有归一化的Δ值(即，与集团平均水平的偏差，归一化为标准偏差)，+30°和+70°，在95%的范围内。其余小组表现出各种反应模式，具有一个或多个较大的Δ值，然而对矫正没有影响。一位准宇航员的价值观令人怀疑。然而,返回地球后12小时内的清晨站立血压（无容量补充）显示无晕厥。这项研究展示了一种无模型评估大型数据集的综合方法，应用多变量分析和从教科书生理学中得出的常识。
