Deciphering the molecular mechanism of post-acute sequelae of COVID-19 through comorbidity network analysis

The post-acute sequelae of COVID-19 (PASC) poses a significant health challenge in the post-pandemic world. However, the underlying biological mechanisms of PASC remain intricate and elusive. Network-based methods can leverage electronic health record data and biological knowledge to investigate the impact of COVID-19 on PASC and uncover the underlying biological mechanisms. This study analyzed territory-wide longitudinal electronic health records (from January 1, 2020 to August 31, 2022) of 50 296 COVID-19 patients and a healthy non-exposed group of 100 592 individuals to determine the impact of COVID-19 on disease progression, provide molecular insights, and identify associated biomarkers. We constructed a comorbidity network and performed disease-protein mapping and protein-protein interaction network analysis to reveal the impact of COVID-19 on disease trajectories. Results showed disparities in prevalent disease comorbidity patterns, with certain patterns exhibiting a more pronounced influence by COVID-19. Overlapping proteins elucidate the biological mechanisms of COVID-19's impact on each comorbidity pattern, and essential proteins can be identified based on their weights. Our findings can help clarify the biological mechanisms of COVID-19, discover intervention methods, and decode the molecular basis of comorbidity associations, while also yielding potential biomarkers and corresponding treatments for specific disease progression patterns.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-02-01
DOI
https://doi.org/10.1063/5.0250923
Primary Topic
Long-Term Effects of COVID-19
Type
article
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article

Deciphering the molecular mechanism of post-acute sequelae of COVID-19 through comorbidity network analysis

Lue Tian, Hao Luo, Eric Yuk Fai Wan, Esther W. Chan et al.
Chaos An Interdisciplinary Journal of Nonlinear Science
Long-Term Effects of COVID-19
article

Deciphering the molecular mechanism of post-acute sequelae of COVID-19 through comorbidity network analysis

Lue Tian, Hao Luo, Eric Yuk Fai Wan, Esther W. Chan, Qingpeng Zhang, Ian Chi Kei Wong, Shuyu Li, Sze Ling Celine Chui
article en

Abstract

The post-acute sequelae of COVID-19 (PASC) poses a significant health challenge in the post-pandemic world. However, the underlying biological mechanisms of PASC remain intricate and elusive. Network-based methods can leverage electronic health record data and biological knowledge to investigate the impact of COVID-19 on PASC and uncover the underlying biological mechanisms. This study analyzed territory-wide longitudinal electronic health records (from January 1, 2020 to August 31, 2022) of 50 296 COVID-19 patients and a healthy non-exposed group of 100 592 individuals to determine the impact of COVID-19 on disease progression, provide molecular insights, and identify associated biomarkers. We constructed a comorbidity network and performed disease-protein mapping and protein-protein interaction network analysis to reveal the impact of COVID-19 on disease trajectories. Results showed disparities in prevalent disease comorbidity patterns, with certain patterns exhibiting a more pronounced influence by COVID-19. Overlapping proteins elucidate the biological mechanisms of COVID-19's impact on each comorbidity pattern, and essential proteins can be identified based on their weights. Our findings can help clarify the biological mechanisms of COVID-19, discover intervention methods, and decode the molecular basis of comorbidity associations, while also yielding potential biomarkers and corresponding treatments for specific disease progression patterns.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(2)
University of Waterloo (CA), City University of Hong Kong (HK), Aston University (GB), Chinese University of Hong Kong (HK), University of Hong Kong (HK)
Good health and well-being
Openalex Percentile: Top 95%
Long-Term Effects of COVID-19
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