Molecular states during acute COVID-19 reveal distinct etiologies of long-term sequelae.

Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are debilitating, clinically heterogeneous and of unknown molecular etiology. A transcriptome-wide investigation was performed in 165 acutely infected hospitalized individuals who were followed clinically into the post-acute period. Distinct gene expression signatures of post-acute sequelae were already present in whole blood during acute infection, with innate and adaptive immune cells implicated in different symptoms. Two clusters of sequelae exhibited divergent plasma-cell-associated gene expression patterns. In one cluster, sequelae associated with higher expression of immunoglobulin-related genes in an anti-spike antibody titer-dependent manner. In the other, sequelae associated independently of these titers with lower expression of immunoglobulin-related genes, indicating lower non-specific antibody production in individuals with these sequelae. This relationship between lower total immunoglobulins and sequelae was validated in an external cohort. Altogether, multiple etiologies of post-acute sequelae were already detectable during SARS-CoV-2 infection, directly linking these sequelae with the acute host response to the virus and providing early insights into their development.

Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative.

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility.

Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes that reduce COVID-19 host susceptibility is a critical next step. Using a translational genomics approach that integrates COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX), and perturbagen signatures, we identified IL10RB as the top candidate gene target for COVID-19 host susceptibility. In a series of validation steps, we show that predicted GReX upregulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes and that in vitro IL10RB overexpression is associated with increased viral load and activation of disease-relevant molecular pathways.

Inpatient Administration of Alpha-1-Adrenergic Receptor Blocking Agents Reduces Mortality in Male COVID-19 Patients.

Apha-1-adrenergic receptor antagonists (α1-blockers) can suppress pro-inflammatory cytokines, thereby potentially improving outcomes among patients with COVID-19. Accordingly, we evaluated the association between α1-blocker exposure (before or during hospitalization) and COVID-19 in-hospital mortality. We identified 2,627 men aged 45 or older who were admitted to Mount Sinai hospitals with COVID-19 between February 24 and May 31, 2020, in New York. Men exposed to α1-blockers (N = 436) were older (median age 73 vs. 64 years, P < 0.001) and more likely to have comorbidities than unexposed men (N = 2,191). Overall, 777 (29.6%) patients died in hospital, and 1,850 (70.4%) were discharged. Notably, we found that α1-blocker exposure was independently associated with improved in-hospital mortality in a multivariable logistic analysis (OR 0.699; 95% CI, 0.498-0.982; P = 0.039) after adjusting for patient demographics, comorbidities, and baseline vitals and labs. The protective effect of α1-blockers was stronger among patients with documented inpatient exposure to α1-blockers (OR 0.624; 95% CI 0.431-0.903; P = 0.012). Finally, age-stratified analyses suggested variable benefit from inpatient α1-blocker across age groups: Age 45-65 OR 0.483, 95% CI 0.216-1.081 (P = 0.077); Age 55-75 OR 0.535, 95% CI 0.323-0.885 (P = 0.015); Age 65-89 OR 0.727, 95% CI 0.484-1.092 (P = 0.124). Taken together, clinical trials to assess the therapeutic value of α1-blockers for COVID-19 complications are warranted.

In-hospital use of ACE inhibitors/angiotensin receptor blockers associates with COVID-19 outcomes in African American patients.

BACKGROUNDThe angiotensin-converting enzyme (ACE) D allele is more prevalent among African Americans compared with other races and ethnicities and has previously been associated with severe coronavirus disease 2019 (COVID-19) pathogenesis through excessive ACE1 activity. ACE inhibitors/angiotensin receptor blockers (ACE-I/ARB) may counteract this mechanism, but their association with COVID-19 outcomes has not been specifically tested in the African American population.METHODSWe identified 6218 patients who were admitted into Mount Sinai hospitals with COVID-19 between February 24 and May 31, 2020, in New York City. We evaluated whether the outpatient and in-hospital use of ACE-I/ARB is associated with COVID-19 in-hospital mortality in an African American compared with non-African American population.RESULTSOf the 6218 patients with COVID-19, 1138 (18.3%) were ACE-I/ARB users. In a multivariate logistic regression model, ACE-I/ARB use was independently associated with a reduced risk of in-hospital mortality in the entire population (OR, 0.655; 95% CI, 0.505-0.850; P = 0.001), African American population (OR, 0.44; 95% CI, 0.249-0.779; P = 0.005), and non-African American population (OR, 0.748, 95% CI, 0.553-1.012, P = 0.06). In the African American population, in-hospital use of ACE-I/ARB was associated with improved mortality (OR, 0.378; 95% CI, 0.188-0.766; P = 0.006), whereas outpatient use was not (OR, 0.889; 95% CI, 0.375-2.158; P = 0.812). When analyzing each medication class separately, ARB in-hospital use was significantly associated with reduced in-hospital mortality in the African American population (OR, 0.196; 95% CI, 0.074-0.516; P = 0.001), whereas ACE-I use was not associated with impact on mortality in any population.CONCLUSIONIn-hospital use of ARB was associated with a significant reduction in in-hospital mortality among COVID-19-positive African American patients.FUNDINGNone.

Author Correction: Sampling the host response to SARS-CoV-2 in hospitals under siege.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Intestinal Inflammation Modulates the Expression of ACE2 and TMPRSS2 and Potentially Overlaps With the Pathogenesis of SARS-CoV-2-related Disease.

BACKGROUND AND AIMS: The presence of gastrointestinal symptoms and high levels of viral RNA in the stool suggest active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication within enterocytes. METHODS: Here, in multiple, large cohorts of patients with inflammatory bowel disease (IBD), we have studied the intersections between Coronavirus Disease 2019 (COVID-19), intestinal inflammation, and IBD treatment. RESULTS: A striking expression of ACE2 on the small bowel enterocyte brush border supports intestinal infectivity by SARS-CoV-2. Commonly used IBD medications, both biologic and nonbiologic, do not significantly impact ACE2 and TMPRSS2 receptor expression in the uninflamed intestines. In addition, we have defined molecular responses to COVID-19 infection that are also enriched in IBD, pointing to shared molecular networks between COVID-19 and IBD. CONCLUSIONS: These data generate a novel appreciation of the confluence of COVID-19- and IBD-associated inflammation and provide mechanistic insights supporting further investigation of specific IBD drugs in the treatment of COVID-19. Preprint doi: https://doi.org/10.1101/2020.05.21.109124.