Metrics of physiological network topology are novel biomarkers to capture functional disability and health.

Meng Hao, Hui Zhang, Shuai Jiang, Zixin Hu, Xiaoyan Jiang, Jingyi Wu, Yi Li, Li Jin, Xiaofeng Wang
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Abstract

Background: Physiological networks are highly complex, integrating connections among multiple organ systems and their dynamic changes underlying human aging. It is unknown whether individual-level network could serve as robust biomarkers for health and aging.

Methods: We used personalized network analysis to construct single sample network and examine the associations between network properties and functional disability in the Rugao Longevity and Aging Study (RuLAS), the China Health and Retirement Longitudinal Study (CHARLS), the Chinese Longitudinal Healthy Longevity Survey (CLHLS), and the National Health and Nutrition Examination Survey (NHANES).

Results: We observed impairments in interconnected physiological systems among long-lived adults in RuLAS. Single sample network analysis was applied to reflect the co-occurrence of these multi-system impairments at the individual level. The ADL-disabled individuals' networks exhibited notably increased connectivity among various biomarkers. Significant associations were found between network topology and functional disability across RuLAS, CHARLS, CLHLS and NHANES. Additionally, network topology served as novel biomarkers to capture risks of incident ADL disability in CHARLS. Furthermore, these metrics of physiological network topology predicted mortality across four cohorts. Sensitivity analysis demonstrated that prediction performance of network topology remained robust, regardless of the chosen biomarkers and parameters.

Conclusion: These findings showed that metrics of network topology were sensitive and robust biomarkers to capture risks of functional disability and mortality, highlighting the role of single sample physiological networks as novel biomarker for health and aging.

生理网络拓扑指标是捕捉功能性残疾和健康的新型生物标志物。
背景:生理网络非常复杂,整合了多个器官系统之间的联系,其动态变化是人类衰老的基础。个体层面的网络能否作为健康和衰老的可靠生物标志物尚不清楚:方法:我们利用个性化网络分析构建了单一样本网络,并在如皋长寿与衰老研究(RuLAS)、中国健康与退休纵向研究(CHARLS)、中国健康长寿纵向调查(CLHLS)和美国国家健康与营养调查(NHANES)中研究了网络属性与功能障碍之间的关联:结果:我们在 RuLAS 中观察到长寿成人相互关联的生理系统出现了损伤。我们采用单样本网络分析法来反映这些多系统损伤在个体层面上的共存情况。ADL障碍者的网络在各种生物标志物之间的连接性明显增加。在 RuLAS、CHARLS、CLHLS 和 NHANES 中发现,网络拓扑与功能障碍之间存在显著关联。此外,在 CHARLS 中,网络拓扑结构还是捕捉 ADL 残疾风险的新型生物标志物。此外,这些生理网络拓扑指标还能预测四个队列的死亡率。敏感性分析表明,无论选择何种生物标记物和参数,网络拓扑的预测性能都保持稳健:这些研究结果表明,网络拓扑指标是捕捉功能性残疾和死亡率风险的灵敏而稳健的生物标志物,突出了单样本生理网络作为健康和衰老的新型生物标志物的作用。
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