COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model

• Published in: Briefings in bioinformatics 2021-11, Vol.22 (6)
• Main Authors: Gogate, Nikhita, Lyman, Daniel, Bell, Amanda, Cauley, Edmund, Crandall, Keith A, Joseph, Ashia, Kahsay, Robel, Natale, Darren A, Schriml, Lynn M, Sen, Sabyasach, Mazumder, Raja
• Format: Article
• Language: English
• Subjects: Problem Solving Protocol
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbab191

In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors-compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.