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.
Authors
- Lue Tian (ORCID: https://orcid.org/0009-0004-7958-0526)
- Hao Luo (ORCID: https://orcid.org/0000-0001-5089-3386)
- Eric Yuk Fai Wan (ORCID: https://orcid.org/0000-0002-6275-1147)
- Esther W. Chan (ORCID: https://orcid.org/0000-0002-7602-9470)
- Qingpeng Zhang (ORCID: https://orcid.org/0000-0002-6819-0686)
- Ian Chi Kei Wong (ORCID: https://orcid.org/0000-0001-8242-0014)
- Shuyu Li (ORCID: https://orcid.org/0000-0003-0767-5222)
- Sze Ling Celine Chui
Institutions
- University of Waterloo (CA)
- City University of Hong Kong (HK)
- Aston University (GB)
- Chinese University of Hong Kong (HK)
- University of Hong Kong (HK)
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
- Field-Weighted Citation Impact
- 0.00