SARS-CoV-2 is the virus responsible for the ongoing COVID-19 outbreak. The virus uses ACE2 receptor for viral entry. ACE2 is part of the counter-regulatory renin-angiotensin-aldosterone system and is also expressed in the lower respiratory tract along the alveolar epithelium. There is, however, significant controversy regarding the role of ACE2 expression in COVID-19 pathogenesis. Some have argued that decreasing ACE2 expression would result in decreased susceptibility to the virus by decreasing available binding sites for SARS-CoV-2 and restricting viral entry into the cells. Others have argued that, like the pathogenesis of other viral pneumonias, including those stemming from previous severe acute respiratory syndrome (SARS) viruses, once SARS-CoV-2 binds to ACE2, it downregulates ACE2 expression. Lack of the favourable effects of ACE2 might exaggerate lung injury by a variety of mechanisms. In order to help address this controversy, we conducted a literature search and review of relevant preclinical and clinical publications pertaining to SARS-CoV-2, COVID-19, ACE2, viral pneumonia, SARS, acute respiratory distress syndrome and lung injury. Our review suggests, although controversial, that patients at increased susceptibility to COVID-19 complications may have reduced baseline ACE2, and by modulating ACE2 expression one can possibly improve COVID-19 outcomes. Herein, we elucidate why and how this potential mechanism might work.

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Introduction and aim:Patients undergoing exercise echocardiography with no evidence of myocardial ischemia are considered a low-risk group; however, this group is likely heterogeneous in terms of short-term adverse events and subsequent cardiac testing. We hypothesized that unsupervised cluster modeling using clinical and stress characteristics can detect heterogeneity in cardiovascular risk and need for subsequent cardiac testing among these patients. Methods:We retrospectively studied 445 patients who had exercise echocardiography results negative for myocardial ischemia. All patients were followed for adverse cardiovascular events, subsequent cardiac testing, and nonacute coronary syndrome (ACS) revascularization. The heterogeneity of the study subjects was tested using computational clustering, an exploratory statistical method designed to uncover invisible natural groups within data. Clinical and stress predictors of adverse events were extracted and used to construct 3 unsupervised cluster models: clinical, stress, and combined. The study population was split into training (357 patients) and testing sets (88 patients). Results:In the training set, the clinical, stress, and combined cluster models yielded 5, 4, and 3 clusters, respectively. Each model had 1 high-risk and 1 low-risk cluster while other clusters were intermediate. The combined model showed a better predictive ability compared to the clinical or stress models alone. The need for future testing mirrored the pattern of adverse cardiovascular events. A risk score derived from the combined cluster model predicted end points with acceptable accuracy. The patterns of risk and the calculated risk scores were preserved in the testing set. Conclusions:Patients with no evidence of ischemia on exercise stress echocardiography represent a heterogeneous group. Cluster-based modeling using combined clinical and stress characteristics can expose this heterogeneity. The findings can help better risk-stratify this group of patients and aid cost-effective healthcare utilization toward better diagnostics and therapeutics.

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