Urinary peptidomic patterns of Long-COVID syndrome – UriCoV

Long COVID is the most frequent, yet poorly characterized sequelae of the world´s most impactful pandemic during the last 100 years. It has tremendous, yet not even foreseeable consequences on personal health and socioeconomic status of affected individuals and families, and on global macro-economic issues.
The consortium proposing this project presented and published a first-in-class urinary peptidomic classifier predicting the risk of severe COVID-19 course, by such means enabling personalised intervention to save lives and reduce hospital costs. This classifier, CoV50, is registered as IVD and in clinical use to personalize COVID-19 therapeutic intervention. The successful molecular tools developed and used shall now be applied with the aim to predict long COVID early after SARS-CoV-2 infection. If successful, this approach should allow personalised intervention to prevent or at least reduce long COVID, based on individual pathophysiological features and selection of matching available pharmaceuticals (repurposing). In-silico drug repurposing has already been successfully applied by the proposing group as well.
The study is based on the molecular, clinical and demographic data on over 1000 COVID-19 patients already collected during the European multicentre CRIT-COV study. The patients that took part in this study will be re-contacted to collect information on their health status, and long COVID symptoms. Collaboration with a national non-profit occupancy-insurance partner will provide strong links to socioeconomic dimensions of long COVID. Partners will incorporate proven expertise to approach patients and provide both clinical and molecular phenotyping. An industry-standard web-based software package will be used for GDPR compliant data retrieval and storage. Data analysis will combine artificial intelligence approaches (support vector machines and uniform manifold approximation and projection) with conventional multivariable analysis of covariables.