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一种通过简单倾斜动作寻找直立不耐受的网络方法。

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

作者信息

Karemaker John M

机构信息

Department of Medical Biology, Section Systems Physiology, Amsterdam University Medical Centers, Amsterdam, Netherlands.

出版信息

Front Netw Physiol. 2023 Feb 6;3:1125023. doi: 10.3389/fnetp.2023.1125023. eCollection 2023.

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寻找并量化一个人的生理组中密切相关和远相关方面之间的关联性。在本研究中,我将一种受网络启发的分析方法应用于一组测量数据,这些数据是为了在注定要执行为期两周太空任务的人群中检测潜在的体位性不耐受受试者而收集的。这种方法的优势在于它本质上是无模型的:无需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需要找出“脱颖而出”个体的数据集。该数据集由22名参与者(4名女性/18名男性;12名未来宇航员/航天员,10名健康对照)在仰卧位、+30°和+70°直立倾斜位测量的生理变量组成。手指血压的稳态值以及由此得出的平均动脉压、心率、每搏输出量、心输出量、全身血管阻力;倾斜位的大脑中动脉血流速度和呼气末二氧化碳分压对每个参与者相对于仰卧位进行(%)标准化。这产生了每个变量的平均反应,并带有统计分布。所有变量,即“普通人的反应”以及定义每个参与者的一组%值,都以雷达图形式呈现,以使每个总体清晰明了。对所有值进行多变量分析得出了明显的相关性以及一些意想不到的相关性。最有趣的是个体参与者如何维持他们的血压和脑血流量。事实上,13/22的参与者在+30°和+70°时所有的标准化Δ值(即相对于组平均值的偏差,并根据标准差进行标准化)都在95%范围内。其余组表现出各种反应模式,有一个或多个较大的Δ值,但对体位性低血压无影响。一名未来航天员的值显得可疑。然而,返回地球后12小时内(未补充容量)的清晨站立血压未显示晕厥。本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获得的常识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f598/10012999/c7f706a04d85/fnetp-03-1125023-g001.jpg

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