One year health outcomes associated with systemic corticosteroids for COVID-19: a longitudinal cohort study (preprint)

BackgroundIn patients with COVID-19 requiring supplemental oxygen, dexamethasone reduces acute severity and improves survival, but longer-term effects are unknown. We hypothesised that systemic corticosteroid administration during acute COVID-19 would be associated with improved health-related quality of life (HRQoL) one year after discharge. MethodsAdults admitted to hospital between February 2020 and March 2021 for COVID-19 and meeting current guideline recommendations for dexamethasone treatment were included using two prospective UK cohort studies. HRQoL, assessed by EQ-5D-5L utility index, pre-hospital and one year after discharge were compared between those receiving corticosteroids or not after propensity weighting for treatment. Secondary outcomes included patient reported recovery, physical and mental health status, and measures of organ impairment. Sensitivity analyses were undertaken to account for survival and selection bias. FindingsIn 1,888 participants included in the primary analysis, 1,149 received corticosteroids. There was no between-group difference in EQ-5D-5L utility index at one year (mean difference 0.004, 95% CI: -0.026 to 0.034, p = 0.77). A similar reduction in EQ-5D-5L was seen at one year between corticosteroid exposed and non-exposed groups (mean (SD) change -0.12 (0.22) vs -0.11 (0.22), p = 0.32). Overall, there were no differences in secondary outcome measures. After sensitivity analyses modelled using a larger cohort of 109,318 patients admitted to hospital with COVID-19, EQ-5D-5L utility index at one year remained similar between the two groups. InterpretationSystemic corticosteroids for acute COVID-19 have no impact on the large reduction in HRQoL one year after hospital discharge. Treatments to address this are urgently needed. Take home messageSystemic corticosteroids given for acute COVID-19 do not affect health-related quality of life or other patient reported outcomes, physical and mental health outcomes, and organ function one year after hospital discharge

Cohort Profile: Post-hospitalisation COVID-19 study (PHOSP-COVID) (preprint)

Abstract [bullet] PHOSP-COVID is a national UK multi-centre cohort study of patients who were hospitalised for COVID-19 and subsequently discharged. [bullet] PHOSP-COVID was established to investigate the medium- and long-term sequelae of severe COVID-19 requiring hospitalisation, understand the underlying mechanisms of these sequelae, evaluate the medium- and long-term effects of COVID-19 treatments, and to serve as a platform to enable future studies, including clinical trials. [bullet] Data collected covered a wide range of physical measures, biological samples, and Patient Reported Outcome Measures (PROMs). [bullet] Participants could join the cohort either in Tier 1 only with remote data collection using hospital records, a PROMs app and postal saliva sample for DNA, or in Tier 2 where they were invited to attend two specific research visits for further data collection and biological research sampling. These research visits occurred at five (range 2-7) months and 12 (range 10-14) months post-discharge. Participants could also participate in specific nested studies (Tier 3) at selected sites. [bullet] All participants were asked to consent to further follow-up for 25 years via linkage to their electronic healthcare records and to be re-contacted for further research. [bullet] In total, 7935 participants were recruited from 83 UK sites: 5238 to Tier 1 and 2697 to Tier 2, between August 2020 and March 2022. [bullet] Cohort data are held in a Trusted Research Environment and samples stored in a central biobank. Data and samples can be accessed upon request and subject to approvals.

The Incidence of Immune Mediated Inflammatory Diseases Following COVID-19: a Matched Cohort Study in UK Primary Care (preprint)

Abstract Objective To assess whether there is an association between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) infection and the incidence of immune mediated inflammatory diseases (IMIDs). Design Matched cohort study. Setting Primary care electronic health record data from the Clinical Practice Research Datalink Aurum database. Participants The exposed cohort included 458,147 adults aged 18 years and older with a confirmed SARS CoV-2 infection by reverse transcriptase polymerase chain reaction (RT-PCR) or lateral flow antigen test, and no prior diagnosis of IMIDs. They were matched on age, sex, and general practice to 1,818,929 adults in the unexposed cohort with no diagnosis of confirmed or suspected SARS CoV-2 infection and no prior diagnosis of IMIDs. Main Outcome Measures The primary outcome measure was a composite of the incidence of any of the following IMIDs: 1. autoimmune thyroiditis, 2. coeliac disease, 3. inflammatory bowel disease (IBD), 4. myasthenia gravis, 5. pernicious anaemia, 6. psoriasis, 7. rheumatoid arthritis (RA), 8. Sjogrens syndrome, 9. systemic lupus erythematosus (SLE), 10. type 1 diabetes mellitus (T1DM), and 11. vitiligo. The secondary outcomes were the incidence of each of these conditions separately. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI) for the primary and secondary outcomes comparing the exposed to the unexposed cohorts, and adjusting for age, sex, ethnic group, smoking status, body mass index, relevant infections, and medications. Results 537 patients (0.11%) in the exposed cohort developed an IMID during the follow-up period over 0.29 person years, giving a crude incidence rate of 3.54 per 1000 person years. This was compared 1723 patients (0.09%) over 0.29 person years in the unexposed cohort, with an incidence rate of 2.82 per 1000 person years. Patients in the exposed cohort had a 22% relative increased risk of developing an IMID, compared to the unexposed cohort (aHR 1.22, 95% CI 1.10 to 1.34). The incidence of three IMIDs were statistically significantly associated with SARS CoV-2 infection. These were T1DM (aHR 1.56, 95% CI 1.09 to 2.23), IBD (1.52, 1.23 to 1.88), and psoriasis (1.23, 1.05 to 1.42). Conclusions SARS CoV-2 was associated with an increased incidence of IMIDs including T1DM, IBD and psoriasis. Further research is needed to replicate these findings in other populations and to measure autoantibody profiles in cohorts of individuals with COVID-19, including Long COVID and matched controls.

Clinical characteristics with inflammation profiling of Long-COVID and association with one-year recovery following hospitalisation in the UK: a prospective observational study (preprint)

Background There are currently no effective pharmacological or non-pharmacological interventions for Long-COVID. To identify potential therapeutic targets, we focussed on previously described four recovery clusters five months after hospital discharge, their underlying inflammatory profiles and relationship with clinical outcomes at one year. Methods PHOSP-COVID is a prospective longitudinal cohort study, recruiting adults hospitalised with COVID-19 across the UK. Recovery was assessed using patient reported outcomes measures (PROMs), physical performance, and organ function at five-months and one-year after hospital discharge. Hierarchical logistic regression modelling was performed for patient-perceived recovery at one-year. Cluster analysis was performed using clustering large applications (CLARA) k-medoids approach using clinical outcomes at five-months. Inflammatory protein profiling from plasma at the five-month visit was performed. Findings 2320 participants have been assessed at five months after discharge and 807 participants have completed both five-month and one-year visits. Of these, 35.6% were female, mean age 58.7 (SD 12.5) years, and 27.8% received invasive mechanical ventilation (IMV). The proportion of patients reporting full recovery was unchanged between five months 501/165 (25.6%) and one year 232/804 (28.9%). Factors associated with being less likely to report full recovery at one year were: female sex OR 0.68 (95% CI 0.46-0.99), obesity OR 0.50 (95%CI 0.34-0.74) and IMV OR 0.42 (95%CI 0.23-0.76). Cluster analysis (n=1636) corroborated the previously reported four clusters: very severe, severe, moderate/cognitive, mild relating to the severity of physical, mental health and cognitive impairments at five months in a larger sample. There was elevation of inflammatory mediators of tissue damage and repair in both the very severe and the moderate/cognitive clusters compared to the mild cluster including interleukin-6 which was elevated in both comparisons. Overall, there was a substantial deficit in median (IQR) EQ5D-5L utility index from pre-COVID (retrospective assessment) 0.88 (0.74-1.00), five months 0.74 (0.60-0.88) to one year: 0.74 (0.59-0.88), with minimal improvements across all outcome measures at one-year after discharge in the whole cohort and within each of the four clusters. Interpretation The sequelae of a hospital admission with COVID-19 remain substantial one year after discharge across a range of health domains with the minority in our cohort feeling fully recovered. Patient perceived health-related quality of life remains reduced at one year compared to pre-hospital admission. Systematic inflammation and obesity are potential treatable traits that warrant further investigation in clinical trials.

Physical, cognitive and mental health impacts of COVID-19 following hospitalisation: a multi-centre prospective cohort study (preprint)

Background The impact of COVID-19 on physical and mental health, and employment following hospitalisation is poorly understood. Methods PHOSP-COVID is a multi-centre, UK, observational study of adults discharged from hospital with a clinical diagnosis of COVID-19 involving an assessment between two- and seven-months later including detailed symptom, physiological and biochemical testing. Multivariable logistic regression was performed for patient-perceived recovery with age, sex, ethnicity, body mass index (BMI), co-morbidities, and severity of acute illness as co-variates. Cluster analysis was performed using outcomes for breathlessness, fatigue, mental health, cognition and physical function. Findings We report findings of 1077 patients discharged in 2020, from the assessment undertaken a median 5 [IQR4 to 6] months later: 36% female, mean age 58 [SD 13] years, 69% white ethnicity, 27% mechanical ventilation, and 50% had at least two co-morbidities. At follow-up only 29% felt fully recovered, 20% had a new disability, and 19% experienced a health-related change in occupation. Factors associated with failure to recover were female, middle-age, white ethnicity, two or more co-morbidities, and more severe acute illness. The magnitude of the persistent health burden was substantial and weakly related to acute severity. Four clusters were identified with different severities of mental and physical health impairment: 1) Very severe (17%), 2) Severe (21%), 3) Moderate with cognitive impairment (17%), 4) Mild (46%), with 3%, 7%, 36% and 43% feeling fully recovered, respectively. Persistent systemic inflammation determined by C-reactive protein was related to cluster severity, but not acute illness severity. Interpretation We identified factors related to recovery from a hospital admission with COVID-19 and four different phenotypes relating to the severity of physical, mental, and cognitive health five months later. The implications for clinical care include the potential to stratify care and the need for a pro-active approach with wide-access to COVID-19 holistic clinical services. Funding: UKRI and NIHR

Coronavirus associated molecular mimicry common to SARS-CoV-2 peptide (preprint)

Relationship of COVID-19 and immunity is complex and can involve autoimmune reactions through molecular mimicry. We investigated autoimmunity related pathological mechanisms involving molecular mimicry that are common to certain coronaviruses, including SARS-CoV-2, by means of a selected peptide sequence (CFLGYFCTCYFGLFC). Accordingly, coronavirus-associated sequences that are homologous to that 15mer sequence in the SARS-CoV-2 proteome are attained first. Then, homologous human and coronavirus sequences are obtained, wherein the coronavirus sequences are homologous to the 15mer SARS-CoV-2 peptide. All the identified query-subject sequences contained at least 7 residue matches in the aligned regions. Finally, parts of those coronavirus and host sequences, which are predicted to have high affinity to the same human leukocyte antigen (HLA) alleles as that of the SARS-CoV-2 sequence, are selected among the query and subject epitope-pairs that were both (predicted to be) strongly binding to the same HLA alleles. The proteins or the protein regions with those predicted epitopes include, but not limited to, immunoglobulin heavy chain junction regions, phospholipid phosphatase-related protein type 2, slit homolog 2 protein, and CRB1 isoform I precursor. These proteins are potentially associated with certain pathologies, but especially the possible CRB1 related coronavirus pathogenicity could be furthered by autoimmunity risk in HLA*A24:02 serotypes. Overall, results imply autoimmunity risk in COVID-19 patients with HLA*A02:01 and HLA*A24:02 serotypes in general, through molecular mimicry. This is also common to other coronaviruses than SARS-CoV-2. These results are indicative at the current stage, they need to be validated. Yet, they can pave the way to autoimmunity treatment options to be used in COVID-19 and its associated diseases.

Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2 (preprint)

Biochemical phenotypes are major indexes for protein structure and function characterization. They are determined, at least in part, by the intrinsic physicochemical properties of amino acids and may be reflected in the protein three-dimensional structure. Modeling mutational effects on biochemical phenotypes is a critical step for understanding protein function and disease mechanism as well as enabling drug discovery. Deep Mutational Scanning (DMS) experiments have been performed on SARS-CoV-2's spike receptor binding domain and the human ACE2 zinc-binding peptidase domain - both central players in viral infection and evolution and antibody evasion - quantifying how mutations impact binding affinity and protein expression. Here, we modeled biochemical phenotypes from massively parallel assays, using convolutional neural networks trained on protein sequence mutations in the virus and human host. We found that neural networks are significantly predictive of binding affinity, protein expression, and antibody escape, learning complex interactions and higher-order features that are difficult to capture with conventional methods from structural biology. Integrating the intrinsic physicochemical properties of amino acids, including hydrophobicity, solvent-accessible surface area, and long-range non-bonded energy per atom, significantly improved prediction (empirical p<0.01) though there was such a strong dependence on the sequence data alone to yield reasonably good prediction. We observed concordance of the DMS data and our neural network predictions with an independent study on intermolecular interactions from molecular dynamics (multiple 500 ns or 1 s all-atom) simulations of the spike protein-ACE2 interface, with critical implications for the use of deep learning to dissect molecular mechanisms. The mutation- or genetically- determined component of a biochemical phenotype estimated from the neural networks has improved causal inference properties relative to the original phenotype and can facilitate crucial insights into disease pathophysiology and therapeutic design.

Early therapy with remdesivir and antibody combinations improves COVID-19 disease in mice (preprint)

Improving the standard of clinical care for coronavirus disease 2019 (COVID-19) is a global health priority. Small molecule antivirals like remdesivir (RDV) and biologics such as human monoclonal antibodies (mAb) have demonstrated therapeutic efficacy against SARS-CoV-2, the causative agent of COVID-19. However, the efficacy of single agent therapies has not been comprehensively defined over the time course of infection and it is not known if combination RDV/mAb will improve outcomes over single agent therapies. In kinetic studies in a mouse-adapted SARS-CoV-2 pathogenesis model, we show that single-agent therapies exert potent antiviral effects even when initiated relatively late after infection, but their efficacy is diminished as a function of time. RDV and a cocktail of two mAbs in combination provided improved outcomes compared to single agents alone extending the therapeutic window of intervention with less weight loss, decreased virus lung titers, reduced acute lung injury, and improved pulmonary function. Overall, we demonstrate that direct-acting antivirals combined with potent mAb can improve outcomes over single agents alone in animal models of COVID-19 thus providing a rationale for the coupling of therapies with disparate modalities to extend the therapeutic window of treatment.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced host metabolic reprogramming and identifies metabolic antiviral targets (preprint)

Tremendous progress has been made to control the COVID-19 pandemic, including the development and approval of vaccines as well as the drug remdesivir, which inhibits the SARS-CoV-2 virus that causes COVID-19. However, remdesivir confers only mild benefits to a subset of patients, and additional effective therapeutic options are needed. Drug repurposing and drug combinations may represent practical strategies to address these urgent unmet medical needs. Viruses, including coronaviruses, are known to hijack the host metabolism to facilitate their own proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM). We find that SARS-CoV-2 infection can induce recurrent and complicated metabolic reprogramming spanning a wide range of metabolic pathways. We next applied the GEM-based metabolic transformation algorithm (MTA) to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. These predictions are enriched for validated targets from various published experimental drug and genetic screens. Further analyzing the RNA-sequencing data of remdesivir-treated Vero E6 cell samples that we generated, we predicted metabolic targets that act in combination with remdesivir. These predictions are enriched for previously reported synergistic drugs with remdesivir. Since our predictions are based in part on human patient data, they are likely to be clinically relevant. We provide our top high-confidence candidate targets for their evaluation in further studies, demonstrating host metabolism-targeting as a promising antiviral strategy.

SARS-CoV-2 variant B.1.1.7 is susceptible to neutralizing antibodies elicited by ancestral Spike vaccines (preprint)

The SARS-CoV-2 Spike glycoprotein mediates virus entry and is a major target for neutralizing antibodies. All current vaccines are based on the ancestral Spike with the goal of generating protective neutralizing antibodies. Several novel SARS-CoV-2 variants with multiple Spike mutations have emerged, and their rapid spread and potential for immune escape have raised concerns. One of these variants, first identified in the United Kingdom, B.1.1.7 (also called VUI202012/01), contains eight Spike mutations with potential to impact antibody therapy, vaccine efficacy and risk of reinfection. Here we employed a lentivirus-based pseudovirus assay to show that variant B.1.1.7 remains sensitive to neutralization, albeit at moderately reduced levels (~2-fold), by serum samples from convalescent individuals and recipients of two different vaccines based on ancestral Spike (mRNA-1273, Moderna, and protein nanoparticle (NVX-CoV2373, Novavax). Some monoclonal antibodies to the receptor binding domain (RBD) of Spike were less effective against the variant while others were largely unaffected. These findings indicate that B.1.1.7 is not a neutralization escape variant that would be a major concern for current vaccines, or for risk of reinfection.

Individual factors underlie temperature variation in sickness and in health: influence of age, BMI and genetic factors in a multi-cohort study (preprint)

IntroductionAgeing affects immune function resulting in aberrant fever response to infection. We assess the effects of biological variables on basal temperature and temperature in COVID-19 infection, proposing an updated temperature threshold for older adults. MethodsParticipants: O_LIUnaffected twin volunteers: 1089 adult TwinsUK participants. C_LIO_LILondon hospitalised COVID-19+: 520 adults with emergency admission. C_LIO_LIBirmingham hospitalised COVID-19+: 757 adults with emergency admission. C_LIO_LICommunity-based COVID-19+: 3972 adults self-reporting a positive test using the COVID Symptom Study mobile application. C_LI AnalysisHeritability assessed using saturated and univariate ACE models; Linear mixed-effect and multivariable linear regression analysing associations between temperature, age, sex and BMI; multivariable logistic regression analysing associations between fever ([≥]37.8{degrees}C) and age; receiver operating characteristic (ROC) analysis to identify temperature threshold for adults [≥] 65 years. ResultsAmong unaffected volunteers, lower BMI (p=0.001), and older age (p<0.001) associated with lower basal temperature. Basal temperature showed a heritability of 47% (95% Confidence Interval 18-57%). In COVID-19+ participants, increasing age associated with lower temperatures in cohorts (c) and (d) (p<0.001). For each additional year of age, participants were 1% less likely to demonstrate a fever (OR 0.99; p<0.001). Combining healthy and COVID-19+ participants, a temperature of 37.4{degrees}C in adults [≥]65 years had similar sensitivity and specificity to 37.8{degrees}C in adults <65 years for discriminating fever in COVID-19. ConclusionsAgeing affects temperature in health and acute infection. Significant heritability indicates biological factors contribute to temperature regulation. Our observations indicate a lower threshold (37.4{degrees}C) should be considered for assessing fever in older adults. Key PointsO_LIOlder adults, particularly those with lower BMI, have a lower basal temperature and a lower temperature in response to infection C_LIO_LIBasal temperature is heritable, suggesting biological factors underlying temperature regulation C_LIO_LIOur findings support a lower temperature threshold of 37.4{degrees}C for identifying possible COVID-19 infection in older adults C_LIO_LIThis has implications for case detection, surveillance and isolation and could be incorporated into observation assessment C_LI