Finally, the researchers have decided to make the data broadly available through a free web-based tool ( covid-omics.app ), enabling interactive exploration of this compendium with the hope that experts around the world will continue to mine these data. Striking hypercoagulative signature of COVID-19 unveiled
In short, the researchers mapped 219 molecular features with high significance to COVID-19 status and severity, many of them involved in complement and neutrophil activation, as well as dysregulated lipid transport and blood vessel damage.
However, the most striking observations were linked to the dysregulation of blood coagulation and platelet function. As a result, this study provided a unique insight into the COVID-19 hypercoagulation phenotype.
"Our data both confirm the striking hypercoagulative signature of COVID-19 and expand on our current understanding of its pathophysiology", further state study authors. "The data also offer several additional candidate therapeutics," they add.
For example, the authors have observed a substantial reduction in prothrombin abundance and its correlation with disease severity. Furthermore, the levels of cellular fibronectin (which is another coagulation-related protein) were highly increased in COVID-19 patients.
A dysregulation of von Willebrand factor (VWF) (a blood glycoprotein implicated in the cessation of bleeding) and circulating coagulation factors were also observed, which could provide a rationale for more tailored antithrombotic therapies – including the synergistic addition of several drugs that work at different levels. Precise forecasting with the use of multi-omics based models
"The use of machine learning revealed additional features relevant to COVID-19 severity and underlined the utility of the multi-omics based model for predictions, as this model performed better than the well-established Charlson comorbidity index", study authors summarize their main findings.
Likewise, the addition of the Charlson score (which basically quantifies a person's burden of disease and corresponding one-year mortality risk) as a variable to the proposed model did not result in improved predictive power.
This finding may indicate that the clinical score is highly collinear with the multi-omic variables utilized by this model and that the clinical observation cannot completely capture the features leading to patients' outcomes.
"All large-scale omics studies have limitations but, ideally, still stimulate the generation of numerous testable hypotheses," caution study authors in their medRxiv paper.
This comprehensive work is no exception, and it represents a starting point in our quest to define this devastating disease completely. Future research should, therefore, include a broader and larger patient population, as well as multiple sampling time points. *Important Notice
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information. Journal reference:
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