A Network approach to find poor orthostatic tolerance by simple tilt maneuvers.

Frontiers in network physiology Pub Date : 2023-02-06 eCollection Date: 2023-01-01 DOI:10.3389/fnetp.2023.1125023
John M Karemaker
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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.

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通过简单的倾斜动作发现正静态耐受性差的网络方法。
网络生理学(Network Physiology)引入的方法旨在发现和量化人的生理组中远近相关方面之间的联系。在这项研究中,我将受网络启发的分析方法应用到一组测量数据中,这组数据是为了在即将进入太空执行两周任务的人中发现潜在的正压不耐受受试者。这种方法的优点是基本上不需要模型: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必须找到 "脱颖而出 "的个体的数据集。数据集包括在仰卧、+ 30°和+ 70°直立倾斜姿势下测量的 22 名参与者(4f/18m;12 名准宇航员/宇航员,10 名健康对照组)的生理变量。每位参与者在倾斜姿势下的手指血压稳态值及其衍生值:平均动脉压、心率、每搏量、心输出量、全身血管阻力;大脑中动脉血流速度和潮气末二氧化碳浓度均(%)归一化为仰卧姿势。这样就得出了每个变量的平均响应,并进行了统计传播。所有变量,即 "平均人的反应 "和一组定义每个参与者的百分比值,都以雷达图的形式呈现,使每个集合都很透明。对所有数值进行多元分析后,得出了明显的相关性和一些意想不到的相关性。最有趣的是个别参与者是如何保持血压和脑血流量的。事实上,有 13/22 名参与者的所有归一化 Δ 值(即与组平均值的偏差,按标准偏差归一化),无论是 +30° 还是 +70°,都在 95% 的范围内。剩下的一组则表现出不同的反应模式,有一个或多个较大的Δ值,但对正位没有影响。一名未来宇航员的数值值得怀疑。然而,返回地球后 12 小时内的清晨站立血压(未补充血容量)显示没有晕厥。这项研究展示了一种综合方法,通过应用多变量分析和生理学教科书中的常识,对大型数据集进行无模型评估。
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