COVID-19 Phenotypes Research Articles

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records.

There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes. The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management. This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission. A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83. Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

Mechanisms by which the cystic fibrosis transmembrane conductance regulator may influence SARS-CoV-2 infection and COVID-19 disease severity.

Patients with cystic fibrosis (CF) exhibit pronounced respiratory damage and were initially considered among those at highest risk for serious harm from SARS-CoV-2 infection. Numerous clinical studies have subsequently reported that individuals with CF in North America and Europe-while susceptible to severe COVID-19-are often spared from the highest levels of virus-associated mortality. To understand features that might influence COVID-19 among patients with cystic fibrosis, we studied relationships between SARS-CoV-2 and the gene responsible for CF (i.e., the cystic fibrosis transmembrane conductance regulator, CFTR). In contrast to previous reports, we found no association between CFTR carrier status (mutation heterozygosity) and more severe COVID-19 clinical outcomes. We did observe an unexpected trend toward higher mortality among control individuals compared with silent carriers of the common F508del CFTR variant-a finding that will require further study. We next performed experiments to test the influence of homozygous CFTR deficiency on viral propagation and showed that SARS-CoV-2 production in primary airway cells was not altered by the absence of functional CFTR using two independent protocols. On the contrary, experiments performed in vitro strongly indicated that virus proliferation depended on features of the mucosal fluid layer known to be disrupted by absent CFTR in patients with CF, including both low pH and increased viscosity. These results point to the acidic, viscous, and mucus-obstructed airways in patients with cystic fibrosis as unfavorable for the establishment of coronaviral infection. Our findings provide new and important information concerning relationships between the CF clinical phenotype and severity of COVID-19.

The causal association between polycystic ovary syndrome and susceptibility and severity of COVID-19: a bidirectional Mendelian randomization study using genetic data.

Observational studies have reported an association between polycystic ovary syndrome (PCOS) and COVID-19, but a definitive causal relationship has not been established. This study aimed to assess this association using two-way two-sample Mendelian randomization (MR). A summary of PCOS characteristics was compiled using the PCOS summary statistics from the Apollo University of Cambridge Repository. COVID-19 susceptibility and severity statistics, including hospitalization and extremely severe disease, were obtained from genome-wide association studies from the COVID-19 Host Genetics Initiative. The primary analysis used the inverse variance-weighted method, supplemented by the weighted median, MR-Egger, and MR-PRESSO methods. The forward MR analysis showed no significant impact of PCOS on COVID-19 susceptibility, hospitalization, or severity (OR = 0.983, 1.011, 1.014; 95% CI = 0.958-1.008, 0.958-1.068, 0.934-1.101; and p = 0.173, 0.68, 0.733; respectively). Similarly, reverse MR analysis found no evidence supporting COVID-19 phenotypes as risk or protective factors for PCOS (OR = 1.041, 0.995, 0.944; 95% CI = 0.657-1.649, 0.85-1.164, 0.843-1.058; and p = 0.864, 0.945, 0.323; respectively). Consequently, no significant association between any COVID-19 phenotype and PCOS was established. This MR study suggested that PCOS is not a causal risk factor for the susceptibility and severity of COVID-19. The associations identified in previous observational studies might be attributable to the presence of comorbidities in the patients.

Mendelian randomization reveals no causal relationship between COVID-19 susceptibility, hospitalization, or severity and epilepsy.

Observational studies have shown an association between COVID-19 and epilepsy. However, causality remains unproven. This study aimed to investigate the causative effect of genetically predicted COVID-19 phenotypes on epilepsy risk using a two-sample Mendelian randomization (MR) analysis. We retrieved summary-level datasets for three COVID-19 phenotypes (COVID-19 susceptibility, COVID-19 hospitalization, and COVID-19 severity) and epilepsy from the genome-wide association studies conducted by the COVID-19 Host Genetics Initiative (COVID-19 HGI) and International League Against Epilepsy (ILAE) consortium, respectively. To analyze the final results, nine MR analytic methods were utilized. The inverse-variance weighted (IVW) method was chosen as the primary approach for data analysis to evaluate the potential causal effect. Other MR analytic methods (MR-Egger regression, weighted median estimator, mode based-estimator, and MR-PRESSO) were used as a supplement to IVW to ensure the robustness of the results. The IVW approach demonstrated no causal association between any genetically predicted COVID-19 phenotype and the risk of epilepsy [COVID-19 susceptibility: odds ratio (OR) = 0.99, 95% confidence interval (CI) = 0.86-1.14, p = 0.92; COVID-19 hospitalization: OR = 1.00, 95% CI = 0.96-1.04, p = 0.95; COVID-19 severity: OR = 0.99, 95% CI = 0.96-1.01, p = 0.25]. Other MR complementary methods revealed consistent results. Additionally, no evidence for heterogeneity and horizontal pleiotropy was found. This MR study revealed no genetically predicted causal relationship between COVID-19 phenotypes and epilepsy.

COVID-19 and the risk of neuromyelitis optica spectrum disorder: a Mendelian randomization study.

An increasing number of studies have elucidated a close nexus between COVID-19 phenotypes and neuromyelitis optica spectrum disorder (NMOSD), yet the causality between them remains enigmatic. In this study, we conducted a Mendelian randomization (MR) analysis employing summary data sourced from genome-wide association studies (GWAS) pertaining to COVID-19 susceptibility, hospitalization, severity, and NMOSD. The primary MR analysis employed the Inverse variance weighted (IVW) approach, which was supplemented by MR-Egger, weighted median, simple mode, and weighted mode methods. We implemented various sensitivity analyses including Cochran's Q test, MR-PRESSO method, MR-Egger intercept, leave-one-out analysis, and funnel plot. The MR results demonstrated a nominal association between COVID-19 susceptibility and the risk of AQP4+ NMOSD, as evidenced by the IVW method (OR = 4.958; 95% CI: 1.322-18.585; P = 0.018). Conversely, no causal association was observed between COVID-19 susceptibility, hospitalization, or severity and the increased risk of NMOSD, AQP4-NMOSD, or AQP4+ NMOSD. The comprehensive sensitivity analyses further bolstered the robustness and consistency of the MR estimates. Our findings provide compelling evidence for a causal effect of COVID-19 phenotype on AQP4+ NMOSD, shedding new light on the understanding of the comorbidity between COVID-19 and NMOSD.

Inflammatory phenotypes may be more important than age and comorbidities in predicting clinical outcomes in hospitalised patients with COVID-19

ObjectivesIn critically ill patients with COVID-19, distinct hyperinflammatory and hypoinflammatory phenotypes have been described, with different outcomes and responses to therapy. We investigated if similar phenotypes exist in non-severe illness. MethodsConsecutive patients with polymerase chain reaction (PCR) confirmed SARS-CoV-2 were examined. Baseline demographics and laboratory investigations were tabulated, including serum C-reactive protein. Patients were divided into those who were hyperinflammatory (defined as C-reactive protein >17 mg/l) or hypoinflammatory. Adverse outcomes, defined as requiring oxygenation, intensive care, or death, were recorded during the hospital stay. Clinical characteristics and outcomes were compared. ResultsOf the 1781 patients examined, 276 (15.5%) had a hyperinflammatory phenotype. They were older (51.8 ± 17.2 vs 40.3 ± 13.8 years, P <0.001), had a lower PCR cycle threshold (PCR cycle threshold value 19.3 ± 6.3 vs 22.7 ± 15.4, P = 0.025) at presentation, and more medical comorbidities. The hyperinflammatory phenotype was independently associated with adverse clinical outcomes, even after adjusting for age, medical history and viral load on multivariable analyses (adjusted odds ratio 5.78, 95% confidence interval 2.86-11.63). ConclusionEven in non-severe COVID-19, there are distinct hyper- and hypoinflammatory phenotypes, with the hyperinflammatory phenotype strongly associated with adverse clinical outcomes, that could be distinguished with a simple biomarker.

#6774 CAUSAL ASSOCIATION OF COVID-19 AND KIDNEY FUNCTION: A MENDELIAN RANDOMIZATION ANALYSIS

Abstract Background and Aims Previous observational studies suggest that there are potential relationships between COVID-19 and kidney functions. However, whether COVID-19 had causal effects on different kidney traits is currently unclear. We aimed to investigate the causal link between genetically determined COVID-19 and following kidney traits. Method We performed univariate Mendelian randomization (MR) studies using summary genome-wide association studies (GWAS) statistics of COVID-19 severity (n case/n control: 13,769/1,072,442 for European, n case/n control: 794/4862 for Asian), COVID-19 hospitalization (n case/n control: 32,519/2,062,805 for European, n case/n control:2882/31,200 for Asian) and COVID-19 susceptibility (n case/n control: 122,616/2,475,240 for European, n case/n control: 4459/36,121 for Asian) derived from THE COVID-19 Host Genetics Initiative (r7) as exposure. Phenotypic outcomes analyzed for Europeans included chronic kidney disease (CKD), creatinine-based estimated glomerular filtration rate, Cystatin C-based estimated glomerular filtration rate, Urine albumin-to-creatinine ratio, acute and chronic glomerulonephritis, membranous nephropathy, diabetic nephropathy, acute and chronic nephritic syndrome, acute renal failure, glomerular diseases and IgA nephropathy. While for Asians, outcomes including CKD, estimated glomerular filtration rate, serum creatinine, acute and chronic renal failure, diabetic nephropathy, IgA nephropathy, nephrotic syndrome, acute and chronic glomerulonephritis as well as membranous nephropathy were analyzed. The inverse variance weighted (IVW) MR with multiplicative random effects model was performed as primary analysis to evaluate the causal effects of COVID-19 on different kidney traits, and potential pleiotropy as well as heterogeneity was evaluated by using MR Egger and heterogeneity test, respectively. A series of sensitivity analyses, including weighted median MR, weighted mode MR, MR Pleiotropy Residual Sum and Outlier (MR PRESSO) were performed. In addition, the causal effects of COVID-19 on different kidney traits were validated by using additional GWAS summary data of critical COVID-19 (n case/n control: 5989/42891 for European, n case/n control: 274/366 for Asian) from GenOMICC database (r6) as exposure. After multiple corrections, a P value &amp;lt; 0.0036 for Europeans and P value &amp;lt; 0.0045 for Asians were considered as significant. Results IVW MR indicated that none of the COVID-19 phenotypes were associated with CKD (OR, 0.99: P = 0.16 for COVID-19 severity; OR, 0.99: P = 0.41 for COVID-19 hospitalization; OR, 0.95: P = 0.26 for COVID-19 susceptibility), nor other kidney traits for Europeans. As for Asian ancestry, consistent with the findings in Europeans, none of the COVID-19 phenotypes were associated with CKD (OR, 0.89: P = 0.16 for COVID-19 severity; OR, 0.93: P = 0.42 for COVID-19 hospitalization; OR, 0.95: P = 0.61 for COVID-19 susceptibility), nor other kidney traits as mentioned above. All sensitivity analyses also consistently indicated the absence of causal effects of COVID-19 severity, hospitalization and susceptibility on different kidney traits, neither in the European nor in the Asian population. Conclusion There is no significant evidence supporting the causal effects of genetically predicted COVID-19 phenotype on the following different kidney traits. More deeply explorations focused on the association between COVID-19 and kidney traits should be performed with more data would be available in the future.

A systematical association analysis of 25 common virus infection and genetic susceptibility of COVID-19 infection

ObjectivesPrevious studies identified a number of diseases were associated with 2019 coronavirus disease (COVID-19). However, the associations between these diseases related viral infections and COVID-19 remains unknown now. MethodsIn this study, we utilized single nucleotide polymorphisms (SNPs) related to COVID-19 from genome-wide association study (GWAS) and individual-level genotype data from the UK biobank to calculate polygenic risk scores (PRS) of 487,409 subjects for eight COVID-19 clinical phenotypes. Then, multiple logistic regression models were established to assess the correlation between serological measurements (positive/negative) of 25 viruses and the PRS of eight COVID-19 clinical phenotypes. And we performed stratified analyses by age and gender. ResultsIn whole population, we identified 12 viruses associated with the PRS of COVID-19 clinical phenotypes, such as VZV seropositivity for Varicella Zoster Virus (Unscreened/Exposed_Negative: β = 0.1361, P = 0.0142; Hospitalized/Unscreened: β = 0.1167, P = 0.0385) and MCV seropositivity for Merkel Cell Polyomavirus (Unscreened/Exposed_Negative: β = −0.0614, P = 0.0478). After age stratification, we identified seven viruses associated with the PRS of eight COVID-19 clinical phenotypes in the age < 65 years group. After gender stratification, we identified five viruses associated with the PRS of eight COVID-19 clinical phenotypes in the women group. ConclusionOur study findings suggest that the genetic susceptibility to different COVID-19 clinical phenotypes is associated with the infection status of various common viruses.

Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.

Identification of potential biomarkers to predict organ morbidity in COVID-19: A repository based proteomics perspective

SARS-CoV-2 causes substantial extrapulmonary manifestations in addition to pulmonary disease. Some of the major organs affected are cardiovascular, hematological and thrombotic, renal, neurological, and digestive systems. These types of muti-organ dysfunctions make it difficult and challenging for clinicians to manage and treat COVID-19 patients. The article focuses to identify potential protein biomarkers that can flag various organ systems affected in COVID-19. Publicly reposited high throughput proteomic data from human serum (HS), HEK293T/17 (HEK) and Vero E6 (VE) kidney cell culture were downloaded from ProteomeXchange consortium. The raw data was analyzed in Proteome Discoverer 2.4 to delineate the complete list of proteins in the three studies. These proteins were analyzed in Ingenuity Pathway Analysis (IPA) to associate them to various organ diseases. The shortlisted proteins were analyzed in MetaboAnalyst 5.0 to shortlist potential biomarker proteins. These were then assessed for disease-gene association in DisGeNET and validated by Protein-protein interactome (PPI) and functional enrichment studies (GO_BP, KEGG and Reactome pathways) in STRING. Protein profiling resulted in shortlisting 20 proteins in 7 organ systems. Of these 15 proteins showed at least 1.25-fold changes with a sensitivity and specificity of 70%. Association analysis further shortlisted 10 proteins with a potential association with 4 organ diseases. Validation studies established possible interacting networks and pathways affected, confirmingh the ability of 6 of these proteins to flag 4 different organ systems affected in COVID-19 disease. This study helps to establish a platform to seek protein signatures in different clinical phenotypes of COVID-19. The potential biomarker candidates that can flag organ systems involved are: (a) Vitamin K-dependent protein S and Antithrombin-III for hematological disorders; (b) Voltage-dependent anion-selective channel protein 1 for neurological disorders; (c) Filamin-A for cardiovascular disorder and, (d) Peptidyl-prolyl cis-trans isomerase A and Peptidyl-prolyl cis-trans isomerase FKBP1A for digestive disorders.

Severity and mortality associated with COVID-19 among children hospitalised in tertiary care centres in India: a cohort study.

It is critical to identify high-risk groups among children with COVID-19 from low-income and middle-income countries (LMICs) to facilitate the optimum use of health system resources. The study aims to describe the severity and mortality of different clinical phenotypes of COVID-19 in a large cohort of children admitted to tertiary care hospitals in India. Children aged 0-19 years with evidence of SARS-CoV-2 infection (real time polymerase chain reaction or rapid antigen test positive) or exposure (anti-SARS-CoV-2 antibody, or history of contact with SARS-CoV-2) were enrolled in the study, between January 2021 and March 2022 across five tertiary hospitals in India. All study participants enrolled prospectively and retrospectively were followed up for three months after discharge. COVID-19 was classified into severe (Multisystem Inflammatory Syndrome in Children (MIS-C), severe acute COVID-19, 'unclassified') or non-severe disease. The mortality rates were estimated in different phenotypes. Among 2468 eligible children enrolled, 2148 were hospitalised. Signs of illness were present in 1688 (79%) children with 1090 (65%) having severe disease. High mortality was reported in MIS-C (18.6%), severe acute COVID-19 (13.3%) and the unclassified severe COVID-19 disease (12.3%). Mortality remained high (17.5%) when modified MIS-C criteria was used. Non-severe COVID-19 disease had 14.1% mortality when associated with comorbidity. Our findings have important public health implications for low resource settings. The high mortality underscores the need for better preparedness for timely diagnosis and management of COVID-19. Children with associated comorbidity or coinfections are a vulnerable group and need special attention. MIS-C requires context specific diagnostic criteria for low resource settings. It is important to evaluate the clinical, epidemiological and health system-related risk factors associated with severe COVID-19 and mortality in children from LMICs. Department of Biotechnology, Govt of India and Department of Maternal, Child and Adolescent Health and Aging, WHO, Geneva, Switzerland.

Long COVID Clinical Phenotypes up to 6 Months After Infection Identified by Latent Class Analysis of Self-Reported Symptoms.

The prevalence, incidence, and interrelationships of persistent symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection vary. There are limited data on specific phenotypes of persistent symptoms. Using latent class analysis (LCA) modeling, we sought to identify whether specific phenotypes of COVID-19 were present 3 months and 6 months post-infection. This was a multicenter study of symptomatic adults tested for SARS-CoV-2 with prospectively collected data on general symptoms and fatigue-related symptoms up to 6 months postdiagnosis. Using LCA, we identified symptomatically homogenous groups among COVID-positive and COVID-negative participants at each time period for both general and fatigue-related symptoms. Among 5963 baseline participants (4504 COVID-positive and 1459 COVID-negative), 4056 had 3-month and 2856 had 6-month data at the time of analysis. We identified 4 distinct phenotypes of post-COVID conditions (PCCs) at 3 and 6 months for both general and fatigue-related symptoms; minimal-symptom groups represented 70% of participants at 3 and 6 months. When compared with the COVID-negative cohort, COVID-positive participants had higher occurrence of loss of taste/smell and cognition problems. There was substantial class-switching over time; those in 1 symptom class at 3 months were equally likely to remain or enter a new phenotype at 6 months. We identified distinct classes of PCC phenotypes for general and fatigue-related symptoms. Most participants had minimal or no symptoms at 3 and 6 months of follow-up. Significant proportions of participants changed symptom groups over time, suggesting that symptoms present during the acute illness may differ from prolonged symptoms and that PCCs may have a more dynamic nature than previously recognized. Clinical Trials Registration. NCT04610515.

Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients: Results from a Cohort Observational Study.

Purpose: COVID-19 presents complex pathophysiology, and evidence collected points towards an intricate interaction between viral-dependent and individual immunological mechanisms. Identifying phenotypes through clinical and biological markers may provide a better understanding of the subjacent mechanisms and an early patient-tailored characterization of illness severity. Methods: A multicenter prospective cohort study was performed in 5 hospitals in Portugal and Brazil for one year between 2020-2021. All adult patients with an Intensive Care Unit admission with SARS-CoV-2 pneumonia were eligible. COVID-19 was diagnosed using clinical and radiologic criteria with a SARS-CoV-2 positive RT-PCR test. A two-step hierarchical cluster analysis was made using several class-defining variables. Results: 814 patients were included. The cluster analysis revealed a three-class model, allowing for the definition of three distinct COVID-19 phenotypes: 407 patients in phenotype A, 244 patients in phenotype B, and 163 patients in phenotype C. Patients included in phenotype A were significantly older, with higher baseline inflammatory biomarkers profile, and a significantly higher requirement of organ support and mortality rate. Phenotypes B and C demonstrated some overlapping clinical characteristics but different outcomes. Phenotype C patients presented a lower mortality rate, with consistently lower C-reactive protein, but higher procalcitonin and interleukin-6 serum levels, describing an immunological profile significantly different from phenotype B. Conclusions: Severe COVID-19 patients exhibit three different clinical phenotypes with distinct profiles and outcomes. Their identification could have an impact on patients' care, justifying different therapy responses and inconsistencies identified across different randomized control trial results.

Causal association of the brain structure with the susceptibility, hospitalisation, and severity of COVID-19: A large-scale genetic correlation study.

Brain structure is related to its ability to resist external pathogens. Furthermore, there are several abnormal anatomical brain events and central system symptoms associated with COVID-19. This study, which was conducted based on genetic variables, aimed to identify the causal association between brain structure and COVID-19 phenotypes. We performed a two-sample bidirectional Mendelian randomization analysis using genetic variables obtained from large genome-wide association studies as instruments to identify the potential causal effects of various brain imaging-derived phenotypes (BIDPs) traits on susceptibility, hospitalisation, and severity of COVID-19. We explored the genetic correlations of 1325 BIDPs with the susceptibility, hospitalisation, and severity of COVID-19 using Linkage Disequilibrium Score Regression. We observed a causal relationship between increased cortical thickness of the left inferior temporal area and an increased risk of increased COVID-19 infection (p=4.29×10-4 ) and hospitalisation (p=3.67×10-3 ). Moreover, the larger total surface area of the whole brain was negatively correlated with the risk of hospitalisation for COVID-19. Furthermore, there was a significant causal association between increased cerebrospinal fluid volume and decreased severity of COVID-19 (p =3.74×10-3 ). We provide new insights into the causal association between BIDPs and COVID-19 phenotypes, which may help elucidate the aetiology of COVID-19. This article is protected by copyright. All rights reserved.

Causal Inference of Central Nervous System-Regulated Hormones in COVID-19: A Bidirectional Two-Sample Mendelian Randomization Study

We assessed the causal association of three COVID-19 phenotypes with insulin-like growth factor 1, estrogen, testosterone, dehydroepiandrosterone (DHEA), thyroid-stimulating hormone, thyrotropin-releasing hormone, luteinizing hormone (LH), and follicle-stimulating hormone. We used bidirectional two-sample univariate and multivariable Mendelian randomization (MR) analyses to evaluate the direction, specificity, and causality of the association between CNS-regulated hormones and COVID-19 phenotypes. Genetic instruments for CNS-regulated hormones were selected from the largest publicly available genome-wide association studies of the European population. Summary-level data on COVID-19 severity, hospitalization, and susceptibility were obtained from the COVID-19 host genetic initiative. DHEA was associated with increased risks of very severe respiratory syndrome (odds ratio [OR] = 4.21, 95% confidence interval [CI]: 1.41-12.59), consistent with multivariate MR results (OR = 3.72, 95% CI: 1.20-11.51), and hospitalization (OR = 2.31, 95% CI: 1.13-4.72) in univariate MR. LH was associated with very severe respiratory syndrome (OR = 0.83; 95% CI: 0.71-0.96) in univariate MR. Estrogen was negatively associated with very severe respiratory syndrome (OR = 0.09, 95% CI: 0.02-0.51), hospitalization (OR = 0.25, 95% CI: 0.08-0.78), and susceptibility (OR = 0.50, 95% CI: 0.28-0.89) in multivariate MR. We found strong evidence for the causal relationship of DHEA, LH, and estrogen with COVID-19 phenotypes.

Accuracy of COVID-19-Like Illness Diagnoses in Electronic Health Record Data: Retrospective Cohort Study.

Electronic health record (EHR) data provide a unique opportunity to study the epidemiology of COVID-19, clinical outcomes of the infection, comparative effectiveness of therapies, and vaccine effectiveness but require a well-defined computable phenotype of COVID-19-like illness (CLI). The objective of this study was to evaluate the performance of pathogen-specific and other acute respiratory illness (ARI) International Statistical Classification of Diseases-9 and -10 codes in identifying COVID-19 cases in emergency department (ED) or urgent care (UC) and inpatient settings. We conducted a retrospective observational cohort study using EHR, claims, and laboratory information system data of ED or UC and inpatient encounters from 4 health systems in the United States. Patients who were aged ≥18 years, had an ED or UC or inpatient encounter for an ARI, and underwent a SARS-CoV-2 polymerase chain reaction test between March 1, 2020, and March 31, 2021, were included. We evaluated various CLI definitions using combinations of International Statistical Classification of Diseases-10 codes as follows: COVID-19-specific codes; CLI definition used in VISION network studies; ARI signs, symptoms, and diagnosis codes only; signs and symptoms of ARI only; and random forest model definitions. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of each CLI definition using a positive SARS-CoV-2 polymerase chain reaction test as the reference standard. We evaluated the performance of each CLI definition for distinct hospitalization and ED or UC cohorts. Among 90,952 hospitalizations and 137,067 ED or UC visits, 5627 (6.19%) and 9866 (7.20%) were positive for SARS-CoV-2, respectively. COVID-19-specific codes had high sensitivity (91.6%) and specificity (99.6%) in identifying patients with SARS-CoV-2 positivity among hospitalized patients. The VISION CLI definition maintained high sensitivity (95.8%) but lowered specificity (45.5%). By contrast, signs and symptoms of ARI had low sensitivity and positive predictive value (28.9% and 11.8%, respectively) but higher specificity and negative predictive value (85.3% and 94.7%, respectively). ARI diagnoses, signs, and symptoms alone had low predictive performance. All CLI definitions had lower sensitivity for ED or UC encounters. Random forest approaches identified distinct CLI definitions with high performance for hospital encounters and moderate performance for ED or UC encounters. COVID-19-specific codes have high sensitivity and specificity in identifying adults with positive SARS-CoV-2 test results. Separate combinations of COVID-19-specific codes and ARI codes enhance the utility of CLI definitions in studies using EHR data in hospital and ED or UC settings.

SARS-CoV-2 getting into the brain; neurological phenotype of COVID-19, and management by nano-biotechnology.

SARS-CoV-2 getting into the brain; neurological phenotype of COVID-19, and management by nano-biotechnology.

Loss of Y in leukocytes as a risk factor for critical COVID-19 in men

BackgroundThe COVID-19 pandemic, which has a prominent social and economic impact worldwide, shows a largely unexplained male bias for the severity and mortality of the disease. Loss of chromosome Y (LOY) is a risk factor candidate in COVID-19 due to its prior association with many chronic age-related diseases, and its impact on immune gene transcription.MethodsPublicly available scRNA-seq data of PBMC samples derived from male patients critically ill with COVID-19 were reanalyzed, and LOY status was added to the annotated cells. We further studied LOY in whole blood for 211 COVID-19 patients treated at intensive care units (ICU) from the first and second waves of the pandemic. Of these, 139 patients were subject to cell sorting for LOY analysis in granulocytes, low-density neutrophils (LDNs), monocytes, and PBMCs.ResultsReanalysis of available scRNA-seq data revealed LDNs and monocytes as the cell types most affected by LOY. Subsequently, DNA analysis indicated that 46%, 32%, and 29% of critically ill patients showed LOY above 5% cut-off in LDNs, granulocytes, and monocytes, respectively. Hence, the myeloid lineage that is crucial for the development of severe COVID-19 phenotype is affected by LOY. Moreover, LOY correlated with increasing WHO score (median difference 1.59%, 95% HDI 0.46% to 2.71%, p=0.025), death during ICU treatment (median difference 1.46%, 95% HDI 0.47% to 2.43%, p=0.0036), and history of vessel disease (median difference 2.16%, 95% HDI 0.74% to 3.7%, p=0.004), among other variables. In 16 recovered patients, sampled during ICU stay and 93–143 days later, LOY decreased significantly in whole blood and PBMCs. Furthermore, the number of LDNs at the recovery stage decreased dramatically (median difference 76.4 per 10,000 cell sorting events, 95% HDI 55.5 to 104, p=6e−11).ConclusionsWe present a link between LOY and an acute, life-threatening infectious disease. Furthermore, this study highlights LOY as the most prominent clonal mutation affecting the myeloid cell lineage during emergency myelopoiesis. The correlation between LOY level and COVID-19 severity might suggest that this mutation affects the functions of monocytes and neutrophils, which could have consequences for male innate immunity.

How sepsis parallels and differs from COVID-19.

How sepsis parallels and differs from COVID-19.

Trajectory of Functional Recovery from 6 to 12 Months in Persons Hospitalized for Severe SARS-CoV-2 Illness

<h3>Research Objectives</h3> To identify distinct post-acute COVID-19 phenotypes among adults hospitalized for severe SARS-CoV-2 infection and describe multidimensional outcomes and trajectories at 6 and 12 months post-hospitalization. <h3>Design</h3> Prospective, longitudinal data collection in functional, physical, cognitive, and psychological domains at 3, 6, and 12 months post-hospitalization. Retrospective data collection from the acute care and post-acute care settings. <h3>Setting</h3> Acute care and post-acute telephone follow-up. <h3>Participants</h3> English- and Spanish- speaking adults, with decision-making capacity, admitted for inpatient rehabilitation following inpatient rehabilitation for acute COVID-19 related illness (N = 61). <h3>Interventions</h3> N/A. <h3>Main Outcome Measures</h3> Physical, cognitive, and psychological symptoms; self-reported employment status and assistance with ADLs. <h3>Results</h3> Median age 60.8 years; 59% male; 72.1% white; 72.1% non-Hispanic; 26.2% preferred assessment in Spanish. 83% required mechanical ventilation in acute care. Comorbidities were common. We found a high prevalence of persistent symptoms at 6- and 12- months across physical, cognitive, and emotional health outcome domains. Three post-acute phenotypes were identified at 6 months; a "minimally symptomatic" subgroup with minimal symptom endorsement across all domains relative to other subjects (22.95%, n = 14), a "predominantly physical symptoms" subgroup (47.54%, n = 29), and a "globally symptomatic" subgroup (29.51%, n = 18). A similar pattern for phenotypes emerges at 12-months, with 67.21% of subjects falling into the same phenotype at both time points. In the Predominantly Physical Symptom phenotype, 31.0% declined into the Globally Symptomatic Phenotype and 10.3% improved. In the Globally Symptomatic phenotype, 11.1% of participants transitioned to the Minimally Symptomatic phenotype and 16.7% to the Predominantly Physical Symptom phenotype. Compared to premorbid level of employment (50.8%), 24.6% of participants were employed at 12-months. Phenotype at 6-months was a significant predictor of employment at 12-months (B = 2.26, p = .05, OR = 9.6). <h3>Conclusions</h3> Persons with severe COVID-19 illness experience persistent functional limitations and reduced employment up to 12 months post-hospitalization. Distinct recovery subgroups were found suggesting the need for comprehensive assessment and tailored treatment for recovery. <h3>Author(s) Disclosures</h3> The authors declare no relevant conflicts of interest.