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The persistence of coronavirus disease 2019 (COVID-19)-related hospitalization severely threatens medical systems worldwide and has increased the need for reliable detection of acute status and prediction of mortality. We applied a systems biology approach to discover acute-stage biomarkers that could predict mortality. A total 247 plasma samples were collected from 103 COVID-19 (52 surviving COVID-19 patients and 51 COVID-19 patients with mortality), 51 patients with other infectious diseases (IDCs) and 41 healthy controls (HCs). Paired plasma samples were obtained from survival COVID-19 patients within 1 day after hospital admission and 1-3 days before discharge. There were clear differences between COVID-19 patients and controls, as well as substantial differences between the acute and recovery phases of COVID-19. Samples from patients in the acute phase showed suppressed immunity and decreased steroid hormone biosynthesis, as well as elevated inflammation and proteasome activation. These findings were validated by enzyme-linked immunosorbent assays and metabolomic analyses in a larger cohort. Moreover, excessive proteasome activity was a prominent signature in the acute phase among patients with mortality, indicating that it may be a key cause of poor prognosis. Based on these features, we constructed a machine learning panel, including four proteins [C-reactive protein (CRP), proteasome subunit alpha type (PSMA)1, PSMA7, and proteasome subunit beta type (PSMB)1)] and one metabolite (urocortisone), to predict mortality among COVID-19 patients (area under the receiver operating characteristic curve: 0.976) on the first day of hospitalization. Our systematic analysis provides a novel method for the early prediction of mortality in hospitalized COVID-19 patients., (© 2024. The Author(s).)

As the long-term consequences of coronavirus disease 2019 (COVID-19) have not been defined, it is necessary to explore persistent symptoms, long-term respiratory impairment, and impact on quality of life over time in COVID-19 survivors. In this prospective cohort study, convalescent individuals diagnosed with COVID-19 were followed-up 2 and 3 years after discharge from hospital. Participants completed an in-person interview to assess persistent symptoms and underwent blood tests, pulmonary function tests, chest high-resolution computed tomography, and the 6-min walking test. There were 762 patients at the 2-year follow-up and 613 patients at the 3-year follow-up. The mean age was 60 years and 415 (54.5%) were men. At 3 years, 39.80% of the participants had at least one symptom; most frequently, fatigue, difficulty sleeping, joint pain, shortness of breath, muscle aches, and cough. The participants experienced different degrees of pulmonary function impairment, with decreased carbon monoxide diffusion capacity being the main feature; results remained relatively stable over the 2-3 years. Multiple logistic regression analysis demonstrated that female sex and smoking were independently associated with impaired diffusion capacity. A subgroup analysis based on disease severity was performed, indicating that there was no difference in other parameters of lung function except forced vital capacity at 3-year follow-up. Persistent radiographic abnormalities, most commonly fibrotic-like changes, were observed at both timepoints. At 3 years, patients had a significantly improved Mental Component Score compared with that at 2 years, with a lower percentage with anxiety. Our study indicated that symptoms and pulmonary abnormalities persisted in COVID-19 survivors at 3 years. Further studies are warranted to explore the long-term effects of COVID-19 and develop appropriate rehabilitation strategies., (© 2024 The Authors. Journal of Medical Virology published by Wiley Periodicals LLC.)