ABSTRACT
Introduction: SARS-CoV-2 gains cell entry via angiotensin-converting enzyme (ACE) 2, a membrane-bound enzyme of the “alternative” (alt) renin-angiotensin system (RAS). ACE2 counteracts angiotensin II by converting it to potentially protective angiotensin 1–7.Methods Using mass spectrometry, we assessed key metabolites of the classical RAS (angiotensins I–II) and alt-RAS (angiotensins 1–7 and 1–5) pathways as well as ACE and ACE2 concentrations in 159 patients hospitalized with COVID-19, stratified by disease severity (severe, n = 76; non-severe: n = 83). Plasma renin activity (PRA-S) was calculated as the sum of RAS metabolites. We estimated ACE activity using the angiotensin II:I ratio (ACE-S) and estimated systemic alt-RAS activation using the ratio of alt-RAS axis metabolites to PRA-S (ALT-S). We applied mixed linear models to assess how PRA-S and ACE/ACE2 concentrations affected ALT-S, ACE-S, and angiotensins II and 1–7.Results Median angiotensin I and II levels were higher with severe versus non-severe COVID-19 (both p < 0.05), demonstrating activation of classical RAS. The difference disappeared with analysis limited to patients not taking a RAS inhibitor. ALT-S in severe COVID-19 increased with time (days 1–6: 0.12; days 11–16: 0.22) and correlated with ACE2 concentration (r = 0.831). ACE-S was lower in severe versus non-severe COVID-19 (p < 0.001), but ACE concentrations were similar between groups and weakly correlated with ACE-S (r = 0.232). ACE2 and ACE-S trajectories in severe COVID-19, however, did not differ between survivors and non-survivors. Overall RAS alteration in severe COVID-19 resembled severity of disease-matched patients with influenza. In mixed linear models, renin activity most strongly predicted angiotensin II and 1–7 levels. ACE2 also predicted angiotensin 1–7 levels and ALT-S. No single factor or the combined model, however, could fully explain ACE-S. ACE2 and ACE-S trajectories in severe COVID-19 did not differ between survivors and non-survivors.Conclusions Angiotensin II was elevated in severe COVID-19 but markedly influenced by RAS inhibitors and driven by overall RAS activation. ACE-S was significantly lower with severe COVID-19 and did not correlate with ACE concentrations. A shift to the alt-RAS axis because of increased ACE2 could partially explain the relative reduction in angiotensin II levels.
Subject(s)
COVID-19ABSTRACT
Background: The Covid-19 pandemic has become a global public health crisis and providing optimal patient care while preventing a collapse of the health care system is a principal objective worldwide. Objective: To develop and validate a prognostic model based on routine hematological parameters to predict uncomplicated disease progression to support the decision for an earlier discharge. Design: Development and refinement of a multivariable logistic regression model with subsequent external validation. The time course of several hematological variables until four days after admission were used as predictors. Variables were first selected based on subject matter knowledge; their number was further reduced using likelihood ratio-based backward elimination in random bootstrap samples. Setting: Model development based on three Austrian hospitals, validation cohorts from two Austrian and one Swedish hospital. Participants: Model development based on 363 survivors and 78 non-survivors of Covid-19 hospitalized in Austria. External validation based on 492 survivors and 61 non-survivors hospitalized in Austria and Sweden. Outcome: In-hospital death. Main Results: The final model includes age, fever upon admission, parameters derived from C-reactive protein (CRP) concentration, platelet count and creatinine concentration, approximating their baseline values (CRP, creatinine) and change over time (CRP, platelet count). In Austrian validation cohorts both discrimination and calibration of this model were good, with c indices of 0.93 (95% CI 0.90 - 0.96) in a cohort from Vienna and 0.93 (0.88 - 0.98) in one from Linz. The model performance seems independent of how long symptoms persisted before admission. In a small Swedish validation cohort, the model performance was poorer (p = 0.008) compared with Austrian cohorts with a c index of 0.77 (0.67 - 0.88), potentially due to substantial differences in patient demographics and clinical routine. Conclusions: Here we describe a formula, requiring only variables routinely acquired in hospitals, which allows to estimate death probabilities of hospitalized patients with Covid-19. The model could be used as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system. The model could further be used to monitor whether patients should be admitted to hospital in countries with health care systems with emphasis on outpatient care (e.g. Sweden).