Urinary peptidomic patterns of longCoVID syndrome (UriCoV)

The burden and societal impact of long-term effects from COVID-19 is well known, among researchers and lay people alike. Also known as long COVID, or (better) post-acute sequelae of SARS CoV-2 infection (PASC), long-term effects from COVID-19 have tremendous consequences for personal health and even socioeconomic status of affected individuals. Members of the consortium conducting this project have previously presented and published a prediction tool which is able to distinguish between severe COVID-19 and a more moderate disease course. This prediction tool analyzes the naturally occurring protein fragments (peptides) in urine and can be applied already at the beginning of the SARS-CoV-2 infection. Among the peptides detected in the urine and associated with a severe COVID-19 disease course were fragments of CD99: a protein involved in formation of an intact endothelial barrier. (It can thus be hypothesized that damage to the small blood vessels, for example in the lung, resulting in fluid accumulation [pulmonary edema] is depicted in urine in the form of CD99 fragments. This signal is among others- included in the urinary peptidomic classifier CoV-50, distinguishing a subsequent critical from a moderate COVID-19 disease course.) In the present project we will use similar methodology as before in order to predict long COVID early after SARS-CoV-2 infection. To this aim, we will i) follow-up COVID-patients from the previous Prospective Validation of a Proteomic Urine Test for Early and Accurate Prognosis of Critical Course Complications in Patients with SARS-CoV-2 Infection Study (Crit-CoV-U) study, ask for and thereby identify participants suffering from PASC; ii) evaluate baseline urinary peptide patterns for predictive biomarkers with regard to PASC; iii) develop diagnostic tools for the prediction of PASC; iv) investigate the molecular mechanism of PASC based on the identified PASC-associated changes; v) assess the societal cost of PASC. The study will use previously collected data from over 1000 COVID-19 patients who participated in the Crit-CoV-U study. These patients will be re-contacted to collect information on their health status, and PASC symptoms. Collaboration with patient advocacy group and occupancy-insurance partner will provide strong links to patients viewpoints and socioeconomic dimensions of PASC. Data analysis will combine machine learning approaches with conventional multivariable analysis of covariables. The Vienna team has an additional, special task. As recent, prominently published work suggests gender inequality in the course of the pandemic (Flor et al., Lancet. 2022 Jun 25;399), we will analyze, in the form of a qualitative interview study, the experiences of men and women with PASC, specifically exploring if we find evidence of gender disparity. Interviews will follow a pre-specified interview guide, will be audiotaped, transcribed verbatim and analyzed thematically, following the principles of Grounded theory This project involves research groups from Germany, Sweden, France and Poland, besides Austria. Most partners were involved in the previous Crit-CoV-U study funded by the Federal Ministry of Health in Germany and will now incorporate proven expertise to approach patients, and provide both clinical and molecular phenotyping in this multinational project. The Vienna team will be led by Manfred Hecking and consist of at least two additional investigators (one PhD student and one medical technical assistant or diploma student).

In our FWF-funded project, we worked with colleagues from Germany, Sweden, France, and Poland to study the clinical picture of Long COVID (scientific term: Post-acute sequelae of SARS-CoV-2 infection [PASC]). All of our cooperation partners (with respective national funding under the international ERA PerMed-program) had already worked together at the beginning of the COVID-pandemic, collecting urine samples from patients who had suffered an infection with SARS-CoV-2 and had to be hospitalized. The focus of our previous and current collaboration was to use the protein (peptide) signature from urine (so-called proteomics analysis) as a predictive tool [a] to distinguish severe COVID cases from milder cases (main aim of the previous CRIT-CoV-U study); [b] to be able to detect the probability of long COVID from the urine signature (as part of the current UriCoV-study). More specifically, we set ourselves three main tasks: 1) To be allowed to contact once again patients from the original collective (with appropriate approval of the respective ethics committees), such as to medically track their disease progression, and to identify those with long COVID. 2) To analyze the original urine samples and current urine samples at the proteomics level and correlate them with our outcome data. 3) To identify the general suffering of patients with long COVID, also based on any gender differences, as part of a qualitative interview study. Regarding 1 and 2: Of 893 patients from the original CRIT-CoV-U-study, (only) 651 were identified as survivors. As part of our UriCoV-project, we developed a questionnaire for long COVID, which was sent to former CRIT-CoV-patients in various countries and was answered a total of 320 times. Using the survival data, using the information from the questionnaire, and using the urine analysis, we developed three classifiers based on urine peptides. The first classifier (DP201) predicts the mortality risk after COVID. The second classifier (Post-COVIDpred70) predicts PASC-development. The third classifier (PASC195) distinguishes PASC-patients from patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and healthy individuals. Re 3: The qualitative study was conducted in three partner countries (Austria, Sweden, France) with approximately 30 participants. For this purpose, a semi-structured guide was developed based on current literature, and the results were evaluated thematically using the grounded theory-methodology. The conceptual model presents the topics derived from the interviews-from everyday stressors to understanding the disease to coping strategies and disease management-in an analytical sequence that traces the possible course of the PASC-experience. Gender acts as a connecting influencing factor in all phases. In summary, our findings from proteomics analysis form a basis for early risk detection and ultimately also for health policy planning. The qualitative results confirm the considerable suffering of affected individuals, but also their approaches to finding solutions.