Deciphering the Molecular Mechanism of Post-Acute Sequelae of COVID-19 through Comorbidity Network Analysis (preprint)

IntroductionThe post-acute sequelae of COVID-19 presents a significant health challenge in the post-pandemic world. Our study aims to analyze longitudinal electronic health records to determine the impact of COVID-19 on disease progression, provide molecular insights into these mechanisms, and identify associated biomarkers. MethodWe included 58,710 patients with COVID-19 records from 01/01/2020 to 31/08/2022 and at least one hospital admission before and after the acute phase of COVID-19 (28 days) as the treatment group. A healthy control group of 174,071 individuals was established for comparison using propensity score matching based on pre-existing diseases (before COVID-19). We built a comorbidity network using Pearson correlation coefficient differences between pairs of pre-existing disease and post-infection disease in both groups. Disease-protein mapping and protein-protein interaction network analysis revealed the impact of COVID-19 on disease trajectories through protein interactions in the human body. ResultsThe disparity in the weight of prevalent disease comorbidity patterns between the treatment and control groups highlights the impact of COVID-19. Certain specific comorbidity patterns show a more pronounced influence by COVID-19. For each comorbidity pattern, overlapping proteins directly associated with pre-existing diseases, post-infection diseases, and COVID-19 help to elucidate the biological mechanism of COVID-19s impact on each comorbidity pattern. Proteins essential for explaining the biological mechanism can be identified based on their weights. ConclusionDisease comorbidity associations influenced by COVID-19, as identified through longitudinal electronic health records and disease-protein mapping, can help elucidate the biological mechanisms of COVID-19, discover intervention methods, and decode the molecular basis of comorbidity associations. This analysis can also yield potential biomarkers and corresponding treatments for specific disease patterns. Ethical approvalEthical approval for this study was granted by the Institutional Review Board of the University of Hong Kong/HA HK West Cluster (UW20-556, UW21-149 and UW21-138). RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for research articles up to Nov 30, 2022, with no language restrictions, using the terms "Post-Acute Sequelae of COVID-19" OR "PASC" OR "Long COVID" AND "comorbidity" OR "multimorbidity" OR "co-morbidity" OR "multi-morbidity". We found most related papers focus on the comorbidity or multimorbidity patterns among PASC. Some papers focus on the associations between specific diseases and PASC. However, no study investigated the biological mechanism of PASC from the perspective of comorbidity network. Added value of this studyThis study investigated the biological mechanism of PASC based on the comorbidity network including the impact of pre-existing diseases (diseases diagnosed within 730 days before COVID-19) on the development of PASC. We classified pairs of pre-existing disease and post-infection disease (new diseases diagnosed in 28 days to 180 days after COVID-19) as comorbidity associations. Through a comparison of the frequency of comorbidity associations in health people group and patients with COVID-19 infection group, we identified comorbidity patterns that are significantly influenced by COVID-19 infection and constructed a comorbidity network comprising of 117 nodes (representing diseases) and 271 edges (representing comorbidity patterns). These comorbidity patterns suggest COVID-19 patients with these pre-existing diseases have higher risk for post-infection diseases. Through the analysis of the Protein-Protein interaction (PPI) network and associations between diseases and proteins, we identified key proteins in the topological distance of each comorbidity pattern and important biological pathways by GO enrichment analysis. These proteins and biological pathways provide insights into the underlying biological mechanism of PASC. Implications of all the available evidenceThe identification of elevated-risk comorbidity patterns associated with COVID-19 infection is crucial for the effective allocation of medical resources, ensuring prompt care for those in greatest need. Furthermore, it facilitates the recovery process of patients from COVID-19, offering a roadmap for their path back to health. The key proteins identified in our study have the potential to serve as biomarkers and targets for therapeutic intervention, thereby establishing a foundation for the development of new drugs and the repurposing of existing ones. Further research should focus on drug discovery and the development of drug recommendations for patients with COVID-19 infections.

Renin-angiotensin system blockers and mortality in COVID-19: a territory-wide study from Hong Kong (preprint)

Aims Renin–angiotensin system blockers such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of adverse outcomes in COVID-19. In this study, the relationships between ACEI/ARB use and COVID-19 related mortality were examined. Methods Consecutive patients diagnosed with COVID-19 by RT-PCR at the Hong Kong Hospital Authority between 1 st January and 28 th July 2020 were included. Results This study included 2774 patients. The mortality rate of the COVID-19 positive group was 1.5% (n=42). Those who died had a higher median age (82.3[76.5-89.5] vs. 42.9[28.2-59.5] years old; P<0.0001), more likely to have baseline comorbidities of cardiovascular disease, diabetes mellitus, hypertension, and chronic kidney disease (P<0.0001). They were more frequently prescribed ACEI/ARBs at baseline, and steroids, lopinavir/ritonavir, ribavirin and hydroxychloroquine during admission (P<0.0001). They also had a higher white cell count, higher neutrophil count, lower platelet count, prolonged prothrombin time and activated partial thromboplastin time, higher D-dimer, troponin, lactate dehydrogenase, creatinine, alanine transaminase, aspartate transaminase and alkaline phosphatase (P<0.0001). Multivariate Cox regression showed that age, cardiovascular disease, renal disease, diabetes mellitus, the use of ACEIs/ARBs and diuretics, and various laboratory tests remained significant predictors of mortality. Conclusions We report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. Key Points We report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk.