Using Physiological System Networks to Elaborate Resilience Across Frailty States
Open accessAbstract
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.
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Multimodal frameworks that combine EEG and fMRI outperform clinical features alone and support precision sequencing in prospective designs.
“Multimodal frameworks that combine EEG and fMRI outperform clinical features alone and support precision sequencing in prospective designs [214,372,373].”
From Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery (2026) · cites this paper as [373]
The Construction of biomarker networks and Discussion passages describe physiological biomarkers, frailty states, and network resilience, not EEG–fMRI frameworks. Searches for EEG, fMRI, clinical-feature comparisons, and precision sequencing found no passage addressing the attributed claim; page numbers were not available in our copy. (LLM, conf=0.77)
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Cited by
Sorted by citations
Synaptic Plasticity—Intrinsic Excitability and Antidepressant Discovery
Tanaka, M. (2026) · Biomedicines · doi:10.3390/biomedicines14061265
- Authors
- Hao, M., Zhang, H., Li, Y., Hu, X., Hu, Z., Jiang, X., Wang, J., Sun, X., Liu, Z., Davis, D., Jin, L., & Wang, X.
- Affiliations
- Fudan University (CN)
- Tongji University (CN)
- Zhejiang University (CN)
- MRC Unit for Lifelong Health and Ageing (GB)
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University , Shanghai , China
- Corresponding author
- Wang, Xiaofeng
- ORCID
- Venue
- The Journals of Gerontology: Series A
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-10-12
- Volume
- 79
- Issue
- 10
- ISSN
- 1079-5006 (ISSN-L)
- 1758-535X
- OpenAlex
- W4387564105
- Cited by
- 12
- References
- 37
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 3
- FWCI
- 1.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journals of Gerontology Series A (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Natural Science Foundation of China (82301768, 32300533, 32288101, 32200536)
- Shanghai Sailing Program (23YF1430500)
- Shanghai Municipal Science and Technology Major Project (2017SHZDZX01)
- Medical Research Council (MC_UU_00019/1)
- Topics
- Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- frailty, resilience, older adults, biomarkers, network topology, beta2-microglobulin, platelet count, recovery dynamics, pre-frailty
- MeSH terms
- Frailty, Aged, Aged, 80 and over, Aging, Female, Humans, Longitudinal Studies, Male, Biomarkers, Geriatric Assessment, Frail Elderly
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