Identifying COVID-19 phenotypes using cluster analysis and assessing their clinical outcomes

Multiple clinical phenotypes have been proposed for COVID-19, but few have stemmed from data-driven methods. We aimed to identify distinct phenotypes in patients admitted with COVID-19 using cluster analysis, and compare their respective characteristics and clinical outcomes. We analyzed the data from 547 patients hospitalized with COVID-19 in a Canadian academic hospital from January 1, 2020, to January 30, 2021. We compared four clustering algorithms: K-means, PAM (partition around medoids), divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 hours of admission to train our algorithm. We then conducted survival analysis to compare clinical outcomes across phenotypes and trained a classification and regression tree (CART) to facilitate phenotype interpretation and phenotype assignment. We identified three clinical phenotypes, with 61 patients (17%) in Cluster 1, 221 patients (40%) in Cluster 2 and 235 (43%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile, but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Mortality, mechanical ventilation and ICU admission risk were all significantly different across phenotypes. We conducted a phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. Further research is needed to determine how to properly incorporate those phenotypes in the management of patients with COVID-19.

Circulating proteins to predict adverse COVID-19 outcomes

Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4,701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict adverse COVID-19 outcomes in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4,701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different adverse COVID-19 outcomes were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of adverse COVID-19 outcomes. Further research is needed to understand how to incorporate protein measurement into clinical care.

Covid-19 vaccine immunogenicity in people living with HIV-1

IntroductionCOVID-19 vaccine efficacy has been evaluated in large clinical trials and in real-world situation. Although they have proven to be very effective in the general population, little is known about their efficacy in immunocompromised patients. HIV-infected individuals response to vaccine may vary according to the type of vaccine and their level of immunosuppression. We evaluated immunogenicity of an mRNA anti-SARS CoV-2 vaccine in HIV-positive individuals. MethodsHIV-positive individuals (n=121) were recruited from HIV clinics in Montreal and stratified according to their CD4 counts. A control group of 20 health care workers naive to SARS CoV-2 was used. The participants Anti-RBD IgG responses were measured by ELISA at baseline and 3 to 4 weeks after receiving the first dose of an mRNA vaccine). ResultsEleven of 121 participants had anti-COVID-19 antibodies at baseline, and a further 4 had incomplete data for the analysis. Mean anti-RBD IgG responses were similar between between the HIV negative control group (n=20) and the combined HIV+ group (n=106) (p = 0.72). However, these responses were significantly lower in the group with <250 CD4 cells/mm3. (p<0.0001). Increasing age was independently associated with decreased immunogenicity. ConclusionHIV-positive individuals with CD4 counts over 250 cells/mm3 have an anti-RBD IgG response similar to the general population. However, HIV-positive individuals with the lowest CD4 counts (<250 cells/mm3) have a weaker response. These data would support the hypothesis that a booster dose might be needed in this subgroup of HIV-positive individuals, depending on their response to the second dose.

Integrated immunovirological profiling validates plasma SARS-CoV-2 RNA as an early predictor of COVID-19 mortality

Despite advances in COVID-19 management, it is unclear how to recognize patients who evolve towards death. This would allow for better risk stratification and targeting for early interventions. However, the explosive increase in correlates of COVID-19 severity complicates biomarker prioritisation. To identify early biological predictors of mortality, we performed an immunovirological assessment (SARS-CoV-2 viral RNA, cytokines and tissue injury markers, antibody responses) on plasma samples collected from 144 hospitalised COVID-19 patients 11 days after symptom onset and used to test models predicting mortality within 60 days of symptom onset. In the discovery cohort (n=61, 13 fatalities), high SARS-CoV-2 vRNA, low RBD-specific IgG levels, low SARS-CoV-2-specific antibody-dependent cellular cytotoxicity, and elevated levels of several cytokines and lung injury markers were strongly associated with increased mortality in the entire cohort and the subgroup on mechanical ventilation. Model selection revealed that a three-variable model of vRNA, age and sex was very robust at identifying patients who will succumb to COVID-19 (AUC=0.86, adjusted HR for log-transformed vRNA=3.5; 95% CI: 2.0-6.0). This model remained robust in an independent validation cohort (n=83, AUC=0.85). Quantification of plasma SARS-CoV-2 RNA can help understand the heterogeneity of disease trajectories and identify patients who may benefit from new therapies.

Identification of SARS-CoV-2-specific immune alterations in acutely ill patients

Dysregulated immune profiles have been described in symptomatic SARS-CoV-2-infected patients. Whether the reported immune alterations are specific to SARS-CoV-2 infection or also triggered by other acute illnesses remains unclear. We performed flow cytometry analysis on fresh peripheral blood from a consecutive cohort of i) patients hospitalized with acute SARS-CoV-2 infection; ii) patients of comparable age/sex hospitalized for other acute disease (SARS-CoV-2 negative); and iii) healthy controls. Using both data-driven and hypothesis-driven analyses, we found several dysregulations in immune cell subsets (e.g. decreased proportion of T cells) that are similarly associated with acute SARS-CoV-2 infection and non-COVID-19 related acute illnesses. In contrast, we identified specific differences in myeloid and lymphocyte subsets that are associated with SARS-CoV-2 status (e.g. elevated proportion of ICAM-1+ mature/activated neutrophils, ALCAM+ monocytes, and CD38+CD8+ T cells). A subset of SARS-CoV-2-specific immune alterations correlated with disease severity, disease outcome at 30 days and mortality. Our data provides novel understanding of the immune dysregulation that are specifically associated with SARS-CoV-2 infection among acute care hospitalized patients. Our study lays the foundation for the development of specific biomarkers to stratify SARS-CoV-2+ patients at risk of unfavorable outcome and uncover novel candidate molecules to investigate from a therapeutic perspective.

Circulating Proteins Influencing COVID-19 Susceptibility and Severity: a Mendelian Randomization Study

Proteins detectable in peripheral blood may influence COVID-19 susceptibility or severity. However, understanding which circulating proteins are etiologically involved is difficult because their levels may be influenced by COVID-19 itself and are also subject to confounding factors. To identify circulating proteins influencing COVID-19 susceptibility and severity we undertook a large-scale two-sample Mendelian randomization (MR) study, since this study design can rapidly scan hundreds of circulating proteins and reduces bias due to reverse causation and confounding. We identified genetic determinants of 931 circulating proteins in 28,461 SARS-CoV-2 uninfected individuals, retaining only single nucleotide polymorphism near the gene encoding the circulating protein. We found that a standard deviation increase in OAS1 levels was associated with reduced COVID-19 death or ventilation (N = 4,336 cases / 623,902 controls; OR = 0.54, P = 7x10-8), COVID-19 hospitalization (N = 6,406 / 902,088; OR = 0.61, P = 8x10-8) and COVID-19 susceptibility (N = 14,134 / 1,284,876; OR = 0.78, P = 8x10-6). Results were consistent in multiple sensitivity analyses. We then measured OAS1 levels in 504 patients with repeated plasma samples (N=1039) with different COVID-19 outcomes and found that increased OAS1 levels in a non-infectious state were associated with protection against very severe COVID-19, hospitalization and susceptibility. Further analyses suggested that a Neanderthal isoform of OAS1 affords this protection. Thus, evidence from MR and a case-control study supported a protective role for OAS1 in COVID-19 outcomes. Available medicines, such as phosphodiesterase-12 inhibitors, increase OAS1 and could be explored for their effect on COVID-19 susceptibility and severity.