Aims Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to angiotensin converting enzyme 2 (ACE2) enabling entrance of the virus into cells and causing the infection termed coronavirus disease of 2019 (COVID-19). Here, we investigate associations between plasma ACE2 and outcome of COVID-19. Methods and results This analysis used data from a large longitudinal study of 306 COVID-19 positive patients and 78 COVID-19 negative patients (MGH Emergency Department COVID-19 Cohort). Comprehensive clinical data were collected on this cohort, including 28-day outcomes. The samples were run on the Olink® Explore 1536 platform which includes measurement of the ACE2 protein. High admission plasma ACE2 in COVID-19 patients was associated with increased maximal illness severity within 28 days with OR = 1.8, 95%-CI: 1.4–2.3 ( P < 0.0001). Plasma ACE2 was significantly higher in COVID-19 patients with hypertension compared with patients without hypertension ( P = 0.0045). Circulating ACE2 was also significantly higher in COVID-19 patients with pre-existing heart conditions and kidney disease compared with patients without these pre-existing conditions ( P = 0.0363 and P = 0.0303, respectively). Conclusion This study suggests that measuring plasma ACE2 is potentially valuable in predicting COVID-19 outcomes. Further, ACE2 could be a link between COVID-19 illness severity and its established risk factors hypertension, pre-existing heart disease and pre-existing kidney disease.
【초록키워드】 COVID-19, coronavirus disease, SARS-CoV-2, ACE2, coronavirus, severity, Infection, outcome, risk factor, virus, hypertension, outcomes, Cohort, Patient, Kidney disease, plasma, disease, Admission, platform, association, Analysis, Emergency, ACE2 protein, COVID-19 patient, Illness severity, acute respiratory syndrome, Clinical data, enzyme, COVID-19 illness, Department, Cell, bind, Comprehensive, collected, include, condition, significantly higher, COVID-19 negative, COVID-19 positive patient, used data, 【제목키워드】 COVID-19, ACE2, outcome, predict, hospitalized patient,