Using Physiological System Networks to Elaborate Resilience Across Frailty States.

Meng Hao, Hui Zhang, Yi Li, Xiaoxi Hu, Zixin Hu, Xiaoyan Jiang, Jiucun Wang, Xuehui Sun, Zuyun Liu, Daniel Davis, Li Jin, Xiaofeng Wang
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Abstract

Background: Aging is characterized by loss of resilience, the ability to resist or recover from stressors. Network analysis has shown promise in investigating dynamic relationships underlying resilience. We aimed to use network analysis to measure resilience in a longitudinal cohort of older adults and quantify whole-system vulnerabilities associated with frailty.

Methods: We used data from the Rugao Longitudinal Ageing Study, including 71 biomarkers from participants classified as robust, prefrail, or frail. We quantified biomarker correlations and topological parameters. Additionally, we proposed propagation models to simulate damage and recovery dynamics, investigating network resilience under various conditions.

Results: We classified 1 754 individuals into robust (n = 369), prefrail (n = 1 103), and frail (n = 282) groups with 71 biomarkers. Several biomarkers were linked to frailty, including those related to blood pressure, electrocardiogram (ECG), kidney function, platelets, and white blood cells. Each frailty stage was associated with increased network correlations. The frail network showed increased average degree and connectance, decreased average path length and diameter, and reduced modularity compared to robust and prefrail networks. Hub biomarkers, particularly β2-microglobulin and platelet count, played a significant role, potentially propagating dysfunction across physiological systems. Simulations revealed that damage to critical hubs led to longer recovery times in the frail network than robust and prefrail networks.

Conclusions: Network analysis could serve as a valuable tool for quantifying resilience and identifying vulnerabilities in older adults with frailty. Our findings contribute to understanding frailty-related physiological disturbances and offer potential for personalized healthcare interventions targeting resilience in older populations.

利用生理系统网络来阐述脆弱状态下的恢复能力。
背景:衰老的特点是失去弹性,即抵抗压力或从压力中恢复的能力。网络分析在研究弹性背后的动态关系方面显示出了前景。我们旨在使用网络分析来衡量老年人的纵向队列中的恢复力,并量化与虚弱相关的整个系统的脆弱性。方法:我们使用了如皋纵向衰老研究的数据,包括71个来自参与者的生物标志物,这些生物标志物被分类为强壮、运动前或虚弱。我们量化了生物标志物相关性和拓扑参数。此外,我们提出了传播模型来模拟损伤和恢复动态,研究了各种条件下的网络弹性。结果:我们用71种生物标志物将1754个个体分为强壮组(n=369)、运动前组(n=1103)和虚弱组(n=282)。一些生物标志物与虚弱有关,包括与血压、心电图、肾功能、血小板和白细胞有关的生物标志物。每个虚弱阶段都与网络相关性增加有关。与稳健和预轨道网络相比,脆弱网络的平均程度和连通性增加,平均路径长度和直径减少,模块性降低。中枢生物标志物,特别是β2-微球蛋白和血小板计数,发挥了重要作用,可能在整个生理系统中传播功能障碍。模拟显示,关键枢纽的损坏导致脆弱网络的恢复时间比稳健和铁路前网络更长。结论:网络分析可以作为一种有价值的工具来量化老年体弱者的复原力和识别其脆弱性。我们的研究结果有助于理解与虚弱相关的生理障碍,并为针对老年人群的复原力的个性化医疗干预提供了潜力。
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