Biomarkers that can predict long-term symptoms of COVID

Up to 69% of patients who recover from coronavirus disease 2019 (COVID-19) are subsequently diagnosed with acute sequelae of COVID-19 (PASC). Symptoms of PASC that can be detected more than 4 weeks after initial diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection include memory loss, gastrointestinal (GI) distress, fatigue, insomnia, and shortness of breath. there is.

The wide range of potential symptoms that can be caused by PASC, and the variety of factors that can contribute to its development, underline the urgent need to better understand the pathophysiology of this condition.

study: Multiple early factors predict acute sequelae of COVID-19. Image credit: angellodeco / Shutterstock.com

About research

recent researchers cell The study is conducting a longitudinal multi-omics study from initial diagnosis to early recovery in COVID-19 patients. Taken together, this data allowed researchers to identify, quantify and characterize various immunological factors that may contribute to the development of PASC.

A total of 209 patients who presented with acute COVID-19 made up the INCOV cohort and were paired with 457 healthy controls. Blood was collected at her three time points including T1, T2, and T3. These represent clinical diagnosis, acute illness, and convalescence, respectively. Convalescent samples were collected 2–3 months after the first symptoms of infection were detected.

Each blood sample was evaluated for the presence of autoantibodies (autoAb) and SARS-CoV-2-specific antibodies, and their proteomic and metabolomic profiles. In addition, we isolated peripheral blood mononuclear cells (PBMCs) from blood samples and performed single-cell multi-omics analysis.

Investigation result

At T3, most study participants showed anti-SARS-CoV-2 spike protein It is a receptor-binding (RBD) antibody and its levels correlate with symptom severity. The most common symptoms reported by study participants at T3 included fatigue, cough, and smell/dysgeusia, reported among his 52%, 25%, and 18% of participants, respectively. I was.

Patients with a history of type 2 diabetes, chronic obstructive pulmonary disease (COPD), T1 and high red blood cell (RBC) count, and female patients were more likely to have 3 or more PASC symptoms.

Several biomarkers were found to correlate with different PASC symptoms at T3. For example, patients previously treated with steroids during acute illness showed lower cortisol levels at T1 and T2, but not at T3.

Patients who experienced neurological symptoms were more likely to show elevated levels of proteins that negatively regulate circadian rhythms. showed both a decrease and an increase in the levels of these circadian rhythm proteins.

Previous studies have demonstrated that the development of PASC may be associated with Epstein-Barr virus (EBV) reactivation. In the current study, his 14% of patients showed her EBV viremia at her T1 and were more likely to experience symptoms of fatigue and sputum associated with PASC.

Approximately 25% of PASC patients were T1 and positive for SARS-CoV-2 RNAemia. Notably, the presence of SARS-CoV-2 RNAemia at this time point was associated with a higher likelihood of mortality.

Several autoAbs and anti-SARS-CoV-2 Abs detected in T3 correlated with various PASC symptoms. For example, patients with high levels of anti-SARS-CoV-2 nucleocapsid protein immunoglobulin G (IgG) are more likely to experience neurological symptoms associated with PASC, and various autoantibodies at T3 and T1. Increased levels were associated with GI-related symptoms of PASC and sputum production.

Patients experiencing gastrointestinal symptoms as a result of PASC were also more likely to exhibit CD8+ and CD4+ T cells. More specifically, these patients displayed undifferentiated T cells and increased cytotoxic features in T3 during acute disease.By comparison, patients with respiratory PASC symptoms showed the opposite T cells pattern.

Conclusion

The current study has provided new insights into various biological factors that may contribute to PASC. These observations may enable clinicians to identify patients at high risk of developing PASC before symptoms appear, potentially improving patient outcomes.