Mapping comorbidity patterns and associated proteins to help fight “long COVID”
Mapping comorbidity patterns and associated proteins to help fight “long COVID” lead image
Tens of millions of individuals worldwide are estimated to have experienced some form of post-acute sequelae of COVID-19 (PASC), or “long COVID,” in which symptoms persist or emerge weeks or months after initial infection.
Recognizing the role that pre-existing multimorbidity may have in driving PASC, Tian et al. analyzed territory-wide electronic health records data from the Hong Kong Hospital Authority and employed network-based methods to better understand the molecular basis for post-COVID disease progression.
“A deeper understanding of these molecular mechanisms could facilitate the development of new treatments and the repurposing of existing drugs, ultimately aiming to reduce the risk of COVID-19 reinfection and prevent the onset of PASC,” author Qingpeng Zhang said.
Their study is distinguished from previous investigations not only by its large patient cohort, but also by its consideration of diseases across multiple organs and systems and by potential COVID-19 interrelationships. In line with clinical observations, the authors’ comorbidity network showed that COVID-19 significantly impacts the respiratory, neural, gastrointestinal, and circulatory systems.
They mapped more than a dozen key proteins linking post-infection and pre-existing diseases. Those included five overlapping proteins due to COVID-19 infection that are involved in lipid metabolism — e.g., NEU1 and INHBW, potential targets for atherosclerosis and obesity therapies, respectively — which could help to identify high-risk patients.
“SARS-Cov-2 infection ‘takes the short cut’ in disease progression, with protein expression disorder effectively shortening the distance between disease A and disease B,” Zhang said.
Future work could include randomized controlled trials to substantiate causal connections between proteins and diseases. Machine learning could be leveraged to predict individual disease trajectories based on complex multimorbidity patterns.
Source: “Deciphering the molecular mechanism of post-acute sequelae of COVID-19 through comorbidity network analysis,” by Lue Tian, Eric Wan, Sze Ling Celine Chui, Shirely Li, Esther Chan, Hao Luo, Ian C. K. Wong, and Qingpeng Zhang, Chaos (2025). The article can be accessed at https://doi.org/10.1063/5.0250923 .
