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Turtle L, Thorpe M, Drake TM, Swets M, Palmieri C, Russell CD, Ho A, Aston S, Wootton DG, Richter A, de Silva TI, Hardwick HE, Leeming G, Law A, Openshaw PJM, Harrison EM, Baillie JK, Semple MG, Docherty AB. Outcome of COVID-19 in hospitalised immunocompromised patients: An analysis of the WHO ISARIC CCP-UK prospective cohort study. PLoS Med 2023; 20:e1004086. [PMID: 36719907 PMCID: PMC9928075 DOI: 10.1371/journal.pmed.1004086] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/14/2023] [Accepted: 01/11/2023] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Immunocompromised patients may be at higher risk of mortality if hospitalised with Coronavirus Disease 2019 (COVID-19) compared with immunocompetent patients. However, previous studies have been contradictory. We aimed to determine whether immunocompromised patients were at greater risk of in-hospital death and how this risk changed over the pandemic. METHODS AND FINDINGS We included patients > = 19 years with symptomatic community-acquired COVID-19 recruited to the ISARIC WHO Clinical Characterisation Protocol UK prospective cohort study. We defined immunocompromise as immunosuppressant medication preadmission, cancer treatment, organ transplant, HIV, or congenital immunodeficiency. We used logistic regression to compare the risk of death in both groups, adjusting for age, sex, deprivation, ethnicity, vaccination, and comorbidities. We used Bayesian logistic regression to explore mortality over time. Between 17 January 2020 and 28 February 2022, we recruited 156,552 eligible patients, of whom 21,954 (14%) were immunocompromised. In total, 29% (n = 6,499) of immunocompromised and 21% (n = 28,608) of immunocompetent patients died in hospital. The odds of in-hospital mortality were elevated for immunocompromised patients (adjusted OR 1.44, 95% CI [1.39, 1.50], p < 0.001). Not all immunocompromising conditions had the same risk, for example, patients on active cancer treatment were less likely to have their care escalated to intensive care (adjusted OR 0.77, 95% CI [0.7, 0.85], p < 0.001) or ventilation (adjusted OR 0.65, 95% CI [0.56, 0.76], p < 0.001). However, cancer patients were more likely to die (adjusted OR 2.0, 95% CI [1.87, 2.15], p < 0.001). Analyses were adjusted for age, sex, socioeconomic deprivation, comorbidities, and vaccination status. As the pandemic progressed, in-hospital mortality reduced more slowly for immunocompromised patients than for immunocompetent patients. This was particularly evident with increasing age: the probability of the reduction in hospital mortality being less for immunocompromised patients aged 50 to 69 years was 88% for men and 83% for women, and for those >80 years was 99% for men and 98% for women. The study is limited by a lack of detailed drug data prior to admission, including steroid doses, meaning that we may have incorrectly categorised some immunocompromised patients as immunocompetent. CONCLUSIONS Immunocompromised patients remain at elevated risk of death from COVID-19. Targeted measures such as additional vaccine doses, monoclonal antibodies, and nonpharmaceutical preventive interventions should be continually encouraged for this patient group. TRIAL REGISTRATION ISRCTN 66726260.
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Swann OV, Pollock L, Holden KA, Munro APS, Bennett A, Williams TC, Turtle L, Fairfield CJ, Drake TM, Faust SN, Sinha IP, Roland D, Whittaker E, Ladhani SN, Nguyen-Van-Tam JS, Girvan M, Donohue C, Donegan C, Spencer RG, Hardwick HE, Openshaw PJM, Baillie JK, Harrison EM, Docherty AB, Semple MG. Comparison of UK paediatric SARS-CoV-2 admissions across the first and second pandemic waves. Pediatr Res 2023; 93:207-216. [PMID: 35449394 PMCID: PMC9876790 DOI: 10.1038/s41390-022-02052-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 01/29/2023]
Abstract
BACKGROUND We hypothesised that the clinical characteristics of hospitalised children and young people (CYP) with SARS-CoV-2 in the UK second wave (W2) would differ from the first wave (W1) due to the alpha variant (B.1.1.7), school reopening and relaxation of shielding. METHODS Prospective multicentre observational cohort study of patients <19 years hospitalised in the UK with SARS-CoV-2 between 17/01/20 and 31/01/21. Clinical characteristics were compared between W1 and W2 (W1 = 17/01/20-31/07/20,W2 = 01/08/20-31/01/21). RESULTS 2044 CYP < 19 years from 187 hospitals. 427/2044 (20.6%) with asymptomatic/incidental SARS-CoV-2 were excluded from main analysis. 16.0% (248/1548) of symptomatic CYP were admitted to critical care and 0.8% (12/1504) died. 5.6% (91/1617) of symptomatic CYP had Multisystem Inflammatory Syndrome in Children (MIS-C). After excluding CYP with MIS-C, patients in W2 had lower Paediatric Early Warning Scores (PEWS, composite vital sign score), lower antibiotic use and less respiratory and cardiovascular support than W1. The proportion of CYP admitted to critical care was unchanged. 58.0% (938/1617) of symptomatic CYP had no reported comorbidity. Patients without co-morbidities were younger (42.4%, 398/938, <1 year), had lower PEWS, shorter length of stay and less respiratory support. CONCLUSIONS We found no evidence of increased disease severity in W2 vs W1. A large proportion of hospitalised CYP had no comorbidity. IMPACT No evidence of increased severity of COVID-19 admissions amongst children and young people (CYP) in the second vs first wave in the UK, despite changes in variant, relaxation of shielding and return to face-to-face schooling. CYP with no comorbidities made up a significant proportion of those admitted. However, they had shorter length of stays and lower treatment requirements than CYP with comorbidities once those with MIS-C were excluded. At least 20% of CYP admitted in this cohort had asymptomatic/incidental SARS-CoV-2 infection. This paper was presented to SAGE to inform CYP vaccination policy in the UK.
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Liew F, Talwar S, Cross A, Willett BJ, Scott S, Logan N, Siggins MK, Swieboda D, Sidhu JK, Efstathiou C, Moore SC, Davis C, Mohamed N, Nunag J, King C, Thompson AAR, Rowland-Jones SL, Docherty AB, Chalmers JD, Ho LP, Horsley A, Raman B, Poinasamy K, Marks M, Kon OM, Howard L, Wootton DG, Dunachie S, Quint JK, Evans RA, Wain LV, Fontanella S, de Silva TI, Ho A, Harrison E, Baillie JK, Semple MG, Brightling C, Thwaites RS, Turtle L, Openshaw PJM. SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination. EBioMedicine 2023; 87:104402. [PMID: 36543718 PMCID: PMC9762734 DOI: 10.1016/j.ebiom.2022.104402] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 11/22/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. METHODS In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. FINDINGS Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. INTERPRETATION The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. FUNDING This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript.
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Grundmann A, Wu C, Hardwick M, Baillie JK, Openshaw PJM, Semple MG, Böhning D, Pett S, Michael BD, Thomas RH, Galea I. Fewer COVID-19 Neurological Complications with Dexamethasone and Remdesivir. Ann Neurol 2023; 93:88-102. [PMID: 36261315 PMCID: PMC9874556 DOI: 10.1002/ana.26536] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/17/2022] [Accepted: 10/17/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The objective of this study was to assess the impact of treatment with dexamethasone, remdesivir or both on neurological complications in acute coronavirus diease 2019 (COVID-19). METHODS We used observational data from the International Severe Acute and emerging Respiratory Infection Consortium World Health Organization (WHO) Clinical Characterization Protocol, United Kingdom. Hospital inpatients aged ≥18 years with laboratory-confirmed severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection admitted between January 31, 2020, and June 29, 2021, were included. Treatment allocation was non-blinded and performed by reporting clinicians. A propensity scoring methodology was used to minimize confounding. Treatment with remdesivir, dexamethasone, or both was assessed against the standard of care. The primary outcome was a neurological complication occurring at the point of death, discharge, or resolution of the COVID-19 clinical episode. RESULTS Out of 89,297 hospital inpatients, 64,088 had severe COVID-19 and 25,209 had non-hypoxic COVID-19. Neurological complications developed in 4.8% and 4.5%, respectively. In both groups, neurological complications were associated with increased mortality, intensive care unit (ICU) admission, worse self-care on discharge, and time to recovery. In patients with severe COVID-19, treatment with dexamethasone (n = 21,129), remdesivir (n = 1,428), and both combined (n = 10,846) were associated with a lower frequency of neurological complications: OR = 0.76 (95% confidence interval [CI] = 0.69-0.83), OR = 0.69 (95% CI = 0.51-0.90), and OR = 0.54 (95% CI = 0.47-0.61), respectively. In patients with non-hypoxic COVID-19, dexamethasone (n = 2,580) was associated with less neurological complications (OR = 0.78, 95% CI = 0.62-0.97), whereas the dexamethasone/remdesivir combination (n = 460) showed a similar trend (OR = 0.63, 95% CI = 0.31-1.15). INTERPRETATION Treatment with dexamethasone, remdesivir, or both in patients hospitalized with COVID-19 was associated with a lower frequency of neurological complications in an additive manner, such that the greatest benefit was observed in patients who received both drugs together. ANN NEUROL 2023;93:88-102.
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Swann OV, Lone NI, Harrison EM, Tomlinson LA, Walker AJ, Seaborne MJ, Pollock L, Farrell J, Hall PS, Seth S, Williams TC, Preston J, Ainsworth JS, Semple FF, Baillie JK, Katikireddi SV, Akbari A, Lyons R, Simpson CR, Semple MG, Goldacre B, Brophy S, Sheikh A, Docherty AB. Studying the Long-term Impact of COVID-19 in Kids (SLICK). Healthcare use and costs in children and young people following community-acquired SARS-CoV-2 infection: protocol for an observational study using linked primary and secondary routinely collected healthcare data from England, Scotland and Wales. BMJ Open 2022; 12:e063271. [PMID: 36356998 PMCID: PMC9659708 DOI: 10.1136/bmjopen-2022-063271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/20/2022] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION SARS-CoV-2 infection rarely causes hospitalisation in children and young people (CYP), but mild or asymptomatic infections are common. Persistent symptoms following infection have been reported in CYP but subsequent healthcare use is unclear. We aim to describe healthcare use in CYP following community-acquired SARS-CoV-2 infection and identify those at risk of ongoing healthcare needs. METHODS AND ANALYSIS We will use anonymised individual-level, population-scale national data linking demographics, comorbidities, primary and secondary care use and mortality between 1 January 2019 and 1 May 2022. SARS-CoV-2 test data will be linked from 1 January 2020 to 1 May 2022. Analyses will use Trusted Research Environments: OpenSAFELY in England, Secure Anonymised Information Linkage (SAIL) Databank in Wales and Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 in Scotland (EAVE-II). CYP aged ≥4 and <18 years who underwent SARS-CoV-2 reverse transcription PCR (RT-PCR) testing between 1 January 2020 and 1 May 2021 and those untested CYP will be examined.The primary outcome measure is cumulative healthcare cost over 12 months following SARS-CoV-2 testing, stratified into primary or secondary care, and physical or mental healthcare. We will estimate the burden of healthcare use attributable to SARS-CoV-2 infections in the 12 months after testing using a matched cohort study of RT-PCR positive, negative or untested CYP matched on testing date, with adjustment for confounders. We will identify factors associated with higher healthcare needs in the 12 months following SARS-CoV-2 infection using an unmatched cohort of RT-PCR positive CYP. Multivariable logistic regression and machine learning approaches will identify risk factors for high healthcare use and characterise patterns of healthcare use post infection. ETHICS AND DISSEMINATION This study was approved by the South-Central Oxford C Health Research Authority Ethics Committee (13/SC/0149). Findings will be preprinted and published in peer-reviewed journals. Analysis code and code lists will be available through public GitHub repositories and OpenCodelists with meta-data via HDR-UK Innovation Gateway.
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Butler-Laporte G, Povysil G, Kosmicki JA, Cirulli ET, Drivas T, Furini S, Saad C, Schmidt A, Olszewski P, Korotko U, Quinodoz M, Çelik E, Kundu K, Walter K, Jung J, Stockwell AD, Sloofman LG, Jordan DM, Thompson RC, Del Valle D, Simons N, Cheng E, Sebra R, Schadt EE, Kim-Schulze S, Gnjatic S, Merad M, Buxbaum JD, Beckmann ND, Charney AW, Przychodzen B, Chang T, Pottinger TD, Shang N, Brand F, Fava F, Mari F, Chwialkowska K, Niemira M, Pula S, Baillie JK, Stuckey A, Salas A, Bello X, Pardo-Seco J, Gómez-Carballa A, Rivero-Calle I, Martinón-Torres F, Ganna A, Karczewski KJ, Veerapen K, Bourgey M, Bourque G, Eveleigh RJM, Forgetta V, Morrison D, Langlais D, Lathrop M, Mooser V, Nakanishi T, Frithiof R, Hultström M, Lipcsey M, Marincevic-Zuniga Y, Nordlund J, Schiabor Barrett KM, Lee W, Bolze A, White S, Riffle S, Tanudjaja F, Sandoval E, Neveux I, Dabe S, Casadei N, Motameny S, Alaamery M, Massadeh S, Aljawini N, Almutairi MS, Arabi YM, Alqahtani SA, Al Harthi FS, Almutairi A, Alqubaishi F, Alotaibi S, Binowayn A, Alsolm EA, El Bardisy H, Fawzy M, Cai F, Soranzo N, Butterworth A, Geschwind DH, Arteaga S, Stephens A, Butte MJ, Boutros PC, Yamaguchi TN, Tao S, Eng S, Sanders T, Tung PJ, Broudy ME, Pan Y, Gonzalez A, Chavan N, Johnson R, Pasaniuc B, Yaspan B, Smieszek S, Rivolta C, Bibert S, Bochud PY, Dabrowski M, Zawadzki P, Sypniewski M, Kaja E, Chariyavilaskul P, Nilaratanakul V, Hirankarn N, Shotelersuk V, Pongpanich M, Phokaew C, Chetruengchai W, Tokunaga K, Sugiyama M, Kawai Y, Hasegawa T, Naito T, Namkoong H, Edahiro R, Kimura A, Ogawa S, Kanai T, Fukunaga K, Okada Y, Imoto S, Miyano S, Mangul S, Abedalthagafi MS, Zeberg H, Grzymski JJ, Washington NL, Ossowski S, Ludwig KU, Schulte EC, Riess O, Moniuszko M, Kwasniewski M, Mbarek H, Ismail SI, Verma A, Goldstein DB, Kiryluk K, Renieri A, Ferreira MAR, Richards JB. Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative. PLoS Genet 2022; 18:e1010367. [PMID: 36327219 PMCID: PMC9632827 DOI: 10.1371/journal.pgen.1010367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 07/29/2022] [Indexed: 11/05/2022] Open
Abstract
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.
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Grants
- U24 CA224319 NCI NIH HHS
- RG/13/13/30194 British Heart Foundation
- C18281/A29019 Cancer Research UK
- MC_PC_20004 Medical Research Council
- UL1 TR001873 NCATS NIH HHS
- RG/18/13/33946 British Heart Foundation
- CH/12/2/29428 British Heart Foundation
- CanCOGeN HostSeq
- Fonds de Recherche Québec Santé (FRQS)
- Génome Québec
- Public Health Agency of Canada
- Canadian Institutes of Health Research (CIHR)
- Lady Davis Institute of the Jewish General Hospital
- Canadian Foundation for Innovation
- NIH Foundation
- McGill Interdisciplinary Initiative in Infection and Immunity (MI4)
- Jewish General Hospital Foundation
- McGill University
- Calcul Québec and Compute Canada
- Compute Canada
- Vagelos College of Physicians & Surgeons Office for Research
- Biomedical Informatics Resource of the Columbia University Irving Institute for Clinical and Translational Research (CTSA)
- National Center for Advancing Translational Sciences, National Institutes of Health
- German Research Foundation
- NGS Competence Center Tübingen
- West German Genome Center
- Stiftung Universitätsmedizin Essen
- Technical University of Munich
- BONFOR program of the Medical Faculty, University of Bonn
- Emmy-Noether programm of the German Research Foundation
- State of Saarland
- Dr. Rolf M. Schwiete Foundation
- Munich Clinician Scientist Programm
- Netzwerk-Universitaetsmedizin-COVIM
- Federal Ministry of Education and Research
- Swiss National Science Foundation
- Leenaards Foundation
- Santos-Suarez Foundation
- Carigest
- MIUR project “Dipartimenti di Eccellenza 2018-2020”
- Bando Ricerca COVID-19 Toscana
- charity fund 2020 from Intesa San Paolo
- Italian Ministry of University and Research
- Istituto Buddista Italiano Soka Gakkai
- Instituto de Salud Carlos III
- GePEM
- DIAVIR
- Resvi-Omics
- ReSVinext
- Enterogen
- Agencia Gallega para la Gestión del Conocimiento en Salud
- BI-BACVIR
- CovidPhy
- Agencia Gallega de Innovación (GAIN):
- GEN-COVID
- Framework Partnership Agreement between the Consellería de Sanidad de la XUNTA de Galicia
- GENVIP-IDIS
- consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias
- F. Hoffmann-La Roche Ltd
- U.S. Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response, and Biomedical Advanced Research and Development Authority
- Nevada Governor's Office of Economic Development
- Renown Health and the Renown Health Foundation
- Ratchadapiseksompotch Fund, Faculty of Medicine, Chulalongkorn University
- Healthcare-associated Infection Research Group STAR (Special Task Force for Activating Research)
- Grant for Development of New Faculty Staff, Ratchadaphiseksomphot Endowment Fund
- e-ASIA Joint Research Program (National Science and Technology Development Agency)
- Health Systems Research Institute, TSRI Fund
- Thailand Research Fund
- Ratchadapiseksompotch Fund
- Ratchadapiseksompotch Fund, Faculty of Medicine,Chulalongkorn University, Bangkok, Thailand
- Health Systems Research Institute
- Ratchadapisek Sompoch Endowment Fund, Chulalongkorn University
- NHS Blood and Transplant
- National Institute for Health Research
- UK Medical Research Council
- Japan Agency for Medical Research and Development
- Japan Science and Technology Agency
- National Center for Global Health and Medicine
- Agency for Medical Research and Development
- Polish National Science Centre
- Medical Research Agency
- Perelman School of Medicine at University of Pennsylvania
- Smilow family
- National Center for Advancing Translational Sciences of the National Institutes of Health
- Polish Medical Research Agency
- Qatar Foundation for Education, Science and Community Development
- Saudi Ministry of Health
- King Abdulaziz City for Science and Technology
- European Union’s Horizon 2020 research and innovation program
- Science for Life Laboratory
- Swedish Research Council
- Knut and Alice Wallenberg Foundation
- OCRC
- Microsoft COVID Compute Funding
- Illumina
- UCLA David Geffen School of Medicine - Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Award Program
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Wang B, Law A, Regan T, Parkinson N, Cole J, Russell CD, Dockrell DH, Gutmann MU, Baillie JK. Systematic comparison of ranking aggregation methods for gene lists in experimental results. Bioinformatics 2022; 38:4927-4933. [PMID: 36094347 PMCID: PMC9620830 DOI: 10.1093/bioinformatics/btac621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/24/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022] Open
Abstract
MOTIVATION A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. RESULTS In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20 000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets. AVAILABILITY AND IMPLEMENTATION The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Gonçalves BP, Hall M, Jassat W, Balan V, Murthy S, Kartsonaki C, Semple MG, Rojek A, Baruch J, Reyes LF, Dasgupta A, Dunning J, Citarella BW, Pritchard M, Martín-Quiros A, Sili U, Baillie JK, Aryal D, Arabi Y, Rashan A, Angheben A, Caoili J, Carrier FM, Harrison EM, Gómez-Junyent J, Figueiredo-Mello C, Douglas JJ, Mat Nor MB, Chow YP, Wong XC, Bertagnolio S, Thwin SS, Streinu-Cercel A, Salazar L, Rishu A, Rangappa R, Ong DSY, Hashmi M, Carson G, Diaz J, Fowler R, Kraemer MUG, Wils EJ, Horby P, Merson L, Olliaro PL. An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients. eLife 2022; 11:e80556. [PMID: 36197074 PMCID: PMC9534549 DOI: 10.7554/elife.80556] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome. Funding Bronner P. Gonçalves, Peter Horby, Gail Carson, Piero L. Olliaro, Valeria Balan, Barbara Wanjiru Citarella, and research costs were supported by the UK Foreign, Commonwealth and Development Office (FCDO) and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z]; and Janice Caoili and Madiha Hashmi were supported by the UK FCDO and Wellcome [222048/Z/20/Z]. Peter Horby, Gail Carson, Piero L. Olliaro, Kalynn Kennon and Joaquin Baruch were supported by the Bill & Melinda Gates Foundation [OPP1209135]; Laura Merson was supported by University of Oxford's COVID-19 Research Response Fund - with thanks to its donors for their philanthropic support. Matthew Hall was supported by a Li Ka Shing Foundation award to Christophe Fraser. Moritz U.G. Kraemer was supported by the Branco Weiss Fellowship, Google.org, the Oxford Martin School, the Rockefeller Foundation, and the European Union Horizon 2020 project MOOD (#874850). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Contributions from Srinivas Murthy, Asgar Rishu, Rob Fowler, James Joshua Douglas, François Martin Carrier were supported by CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and coordinated out of Sunnybrook Research Institute. Contributions from Evert-Jan Wils and David S.Y. Ong were supported by a grant from foundation Bevordering Onderzoek Franciscus; and Andrea Angheben by the Italian Ministry of Health "Fondi Ricerca corrente-L1P6" to IRCCS Ospedale Sacro Cuore-Don Calabria. The data contributions of J.Kenneth Baillie, Malcolm G. Semple, and Ewen M. Harrison were supported by grants from the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. All funders of the ISARIC Clinical Characterisation Group are listed in the appendix.
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Mirchandani AS, Jenkins SJ, Bain CC, Sanchez-Garcia MA, Lawson H, Coelho P, Murphy F, Griffith DM, Zhang A, Morrison T, Ly T, Arienti S, Sadiku P, Watts ER, Dickinson RS, Reyes L, Cooper G, Clark S, Lewis D, Kelly V, Spanos C, Musgrave KM, Delaney L, Harper I, Scott J, Parkinson NJ, Rostron AJ, Baillie JK, Clohisey S, Pridans C, Campana L, Lewis PS, Simpson AJ, Dockrell DH, Schwarze J, Hirani N, Ratcliffe PJ, Pugh CW, Kranc K, Forbes SJ, Whyte MKB, Walmsley SR. Author Correction: Hypoxia shapes the immune landscape in lung injury and promotes the persistence of inflammation. Nat Immunol 2022; 23:1394. [PMID: 35854097 PMCID: PMC9295113 DOI: 10.1038/s41590-022-01286-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Zhang S, Cooper-Knock J, Weimer AK, Shi M, Kozhaya L, Unutmaz D, Harvey C, Julian TH, Furini S, Frullanti E, Fava F, Renieri A, Gao P, Shen X, Timpanaro IS, Kenna KP, Baillie JK, Davis MM, Tsao PS, Snyder MP. Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity. Cell Syst 2022; 13:598-614.e6. [PMID: 35690068 PMCID: PMC9163145 DOI: 10.1016/j.cels.2022.05.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/02/2022] [Accepted: 05/18/2022] [Indexed: 01/26/2023]
Abstract
The determinants of severe COVID-19 in healthy adults are poorly understood, which limits the opportunity for early intervention. We present a multiomic analysis using machine learning to characterize the genomic basis of COVID-19 severity. We use single-cell multiome profiling of human lungs to link genetic signals to cell-type-specific functions. We discover >1,000 risk genes across 19 cell types, which account for 77% of the SNP-based heritability for severe disease. Genetic risk is particularly focused within natural killer (NK) cells and T cells, placing the dysfunction of these cells upstream of severe disease. Mendelian randomization and single-cell profiling of human NK cells support the role of NK cells and further localize genetic risk to CD56bright NK cells, which are key cytokine producers during the innate immune response. Rare variant analysis confirms the enrichment of severe-disease-associated genetic variation within NK-cell risk genes. Our study provides insights into the pathogenesis of severe COVID-19 with potential therapeutic targets.
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Evans RA, Leavy OC, Richardson M, Elneima O, McAuley HJC, Shikotra A, Singapuri A, Sereno M, Saunders RM, Harris VC, Houchen-Wolloff L, Aul R, Beirne P, Bolton CE, Brown JS, Choudhury G, Diar-Bakerly N, Easom N, Echevarria C, Fuld J, Hart N, Hurst J, Jones MG, Parekh D, Pfeffer P, Rahman NM, Rowland-Jones SL, Shah AM, Wootton DG, Chalder T, Davies MJ, De Soyza A, Geddes JR, Greenhalf W, Greening NJ, Heaney LG, Heller S, Howard LS, Jacob J, Jenkins RG, Lord JM, Man WDC, McCann GP, Neubauer S, Openshaw PJM, Porter JC, Rowland MJ, Scott JT, Semple MG, Singh SJ, Thomas DC, Toshner M, Lewis KE, Thwaites RS, Briggs A, Docherty AB, Kerr S, Lone NI, Quint J, Sheikh A, Thorpe M, Zheng B, Chalmers JD, Ho LP, Horsley A, Marks M, Poinasamy K, Raman B, Harrison EM, Wain LV, Brightling CE, Abel K, Adamali H, Adeloye D, Adeyemi O, Adrego R, Aguilar Jimenez LA, Ahmad S, Ahmad Haider N, Ahmed R, Ahwireng N, Ainsworth M, Al-Sheklly B, Alamoudi A, Ali M, Aljaroof M, All AM, Allan L, Allen RJ, Allerton L, Allsop L, Almeida P, Altmann D, Alvarez Corral M, Amoils S, Anderson D, Antoniades C, Arbane G, Arias A, Armour C, Armstrong L, Armstrong N, Arnold D, Arnold H, Ashish A, Ashworth A, Ashworth M, Aslani S, Assefa-Kebede H, Atkin C, Atkin P, Aung H, Austin L, Avram C, Ayoub A, Babores M, Baggott R, Bagshaw J, Baguley D, Bailey L, Baillie JK, Bain S, Bakali M, Bakau M, Baldry E, Baldwin D, Ballard C, Banerjee A, Bang B, Barker RE, Barman L, Barratt S, Barrett F, Basire D, Basu N, Bates M, Bates A, Batterham R, Baxendale H, Bayes H, Beadsworth M, Beckett P, Beggs M, Begum M, Bell D, Bell R, Bennett K, Beranova E, Bermperi A, Berridge A, Berry C, Betts S, Bevan E, Bhui K, Bingham M, Birchall K, Bishop L, Bisnauthsing K, Blaikely J, Bloss A, Bolger A, Bonnington J, Botkai A, Bourne C, Bourne M, Bramham K, Brear L, Breen G, Breeze J, Bright E, Brill S, Brindle K, Broad L, Broadley A, Brookes C, Broome M, Brown A, Brown A, Brown J, Brown J, Brown M, Brown M, Brown V, Brugha T, Brunskill N, Buch M, Buckley P, Bularga A, Bullmore E, Burden L, Burdett T, Burn D, Burns G, Burns A, Busby J, Butcher R, Butt A, Byrne S, Cairns P, Calder PC, Calvelo E, Carborn H, Card B, Carr C, Carr L, Carson G, Carter P, Casey A, Cassar M, Cavanagh J, Chablani M, Chambers RC, Chan F, Channon KM, Chapman K, Charalambou A, Chaudhuri N, Checkley A, Chen J, Cheng Y, Chetham L, Childs C, Chilvers ER, Chinoy H, Chiribiri A, Chong-James K, Choudhury N, Chowienczyk P, Christie C, Chrystal M, Clark D, Clark C, Clarke J, Clohisey S, Coakley G, Coburn Z, Coetzee S, Cole J, Coleman C, Conneh F, Connell D, Connolly B, Connor L, Cook A, Cooper B, Cooper J, Cooper S, Copeland D, Cosier T, Coulding M, Coupland C, Cox E, Craig T, Crisp P, Cristiano D, Crooks MG, Cross A, Cruz I, Cullinan P, Cuthbertson D, Daines L, Dalton M, Daly P, Daniels A, Dark P, Dasgin J, David A, David C, Davies E, Davies F, Davies G, Davies GA, Davies K, Dawson J, Daynes E, Deakin B, Deans A, Deas C, Deery J, Defres S, Dell A, Dempsey K, Denneny E, Dennis J, Dewar A, Dharmagunawardena R, Dickens C, Dipper A, Diver S, Diwanji SN, Dixon M, Djukanovic R, Dobson H, Dobson SL, Donaldson A, Dong T, Dormand N, Dougherty A, Dowling R, Drain S, Draxlbauer K, Drury K, Dulawan P, Dunleavy A, Dunn S, Earley J, Edwards S, Edwardson C, El-Taweel H, Elliott A, Elliott K, Ellis Y, Elmer A, Evans D, Evans H, Evans J, Evans R, Evans RI, Evans T, Evenden C, Evison L, Fabbri L, Fairbairn S, Fairman A, Fallon K, Faluyi D, Favager C, Fayzan T, Featherstone J, Felton T, Finch J, Finney S, Finnigan J, Finnigan L, Fisher H, Fletcher S, Flockton R, Flynn M, Foot H, Foote D, Ford A, Forton D, Fraile E, Francis C, Francis R, Francis S, Frankel A, Fraser E, Free R, French N, Fu X, Furniss J, Garner L, Gautam N, George J, George P, Gibbons M, Gill M, Gilmour L, Gleeson F, Glossop J, Glover S, Goodman N, Goodwin C, Gooptu B, Gordon H, Gorsuch T, Greatorex M, Greenhaff PL, Greenhalgh A, Greenwood J, Gregory H, Gregory R, Grieve D, Griffin D, Griffiths L, Guerdette AM, Guillen Guio B, Gummadi M, Gupta A, Gurram S, Guthrie E, Guy Z, H Henson H, Hadley K, Haggar A, Hainey K, Hairsine B, Haldar P, Hall I, Hall L, Halling-Brown M, Hamil R, Hancock A, Hancock K, Hanley NA, Haq S, Hardwick HE, Hardy E, Hardy T, Hargadon B, Harrington K, Harris E, Harrison P, Harvey A, Harvey M, Harvie M, Haslam L, Havinden-Williams M, Hawkes J, Hawkings N, Haworth J, Hayday A, Haynes M, Hazeldine J, Hazelton T, Heeley C, Heeney JL, Heightman M, Henderson M, Hesselden L, Hewitt M, Highett V, Hillman T, Hiwot T, Hoare A, Hoare M, Hockridge J, Hogarth P, Holbourn A, Holden S, Holdsworth L, Holgate D, Holland M, Holloway L, Holmes K, Holmes M, Holroyd-Hind B, Holt L, Hormis A, Hosseini A, Hotopf M, Howard K, Howell A, Hufton E, Hughes AD, Hughes J, Hughes R, Humphries A, Huneke N, Hurditch E, Husain M, Hussell T, Hutchinson J, Ibrahim W, Ilyas F, Ingham J, Ingram L, Ionita D, Isaacs K, Ismail K, Jackson T, James WY, Jarman C, Jarrold I, Jarvis H, Jastrub R, Jayaraman B, Jezzard P, Jiwa K, Johnson C, Johnson S, Johnston D, Jolley CJ, Jones D, Jones G, Jones H, Jones H, Jones I, Jones L, Jones S, Jose S, Kabir T, Kaltsakas G, Kamwa V, Kanellakis N, Kaprowska S, Kausar Z, Keenan N, Kelly S, Kemp G, Kerslake H, Key AL, Khan F, Khunti K, Kilroy S, King B, King C, Kingham L, Kirk J, Kitterick P, Klenerman P, Knibbs L, Knight S, Knighton A, Kon O, Kon S, Kon SS, Koprowska S, Korszun A, Koychev I, Kurasz C, Kurupati P, Laing C, Lamlum H, Landers G, Langenberg C, Lasserson D, Lavelle-Langham L, Lawrie A, Lawson C, Lawson C, Layton A, Lea A, Lee D, Lee JH, Lee E, Leitch K, Lenagh R, Lewis D, Lewis J, Lewis V, Lewis-Burke N, Li X, Light T, Lightstone L, Lilaonitkul W, Lim L, Linford S, Lingford-Hughes A, Lipman M, Liyanage K, Lloyd A, Logan S, Lomas D, Loosley R, Lota H, Lovegrove W, Lucey A, Lukaschuk E, Lye A, Lynch C, MacDonald S, MacGowan G, Macharia I, Mackie J, Macliver L, Madathil S, Madzamba G, Magee N, Magtoto MM, Mairs N, Majeed N, Major E, Malein F, Malim M, Mallison G, Mandal S, Mangion K, Manisty C, Manley R, March K, Marciniak S, Marino P, Mariveles M, Marouzet E, Marsh S, Marshall B, Marshall M, Martin J, Martineau A, Martinez LM, Maskell N, Matila D, Matimba-Mupaya W, Matthews L, Mbuyisa A, McAdoo S, Weir McCall J, McAllister-Williams H, McArdle A, McArdle P, McAulay D, McCormick J, McCormick W, McCourt P, McGarvey L, McGee C, Mcgee K, McGinness J, McGlynn K, McGovern A, McGuinness H, McInnes IB, McIntosh J, McIvor E, McIvor K, McLeavey L, McMahon A, McMahon MJ, McMorrow L, Mcnally T, McNarry M, McNeill J, McQueen A, McShane H, Mears C, Megson C, Megson S, Mehta P, Meiring J, Melling L, Mencias M, Menzies D, Merida Morillas M, Michael A, Milligan L, Miller C, Mills C, Mills NL, Milner L, Misra S, Mitchell J, Mohamed A, Mohamed N, Mohammed S, Molyneaux PL, Monteiro W, Moriera S, Morley A, Morrison L, Morriss R, Morrow A, Moss AJ, Moss P, Motohashi K, Msimanga N, Mukaetova-Ladinska E, Munawar U, Murira J, Nanda U, Nassa H, Nasseri M, Neal A, Needham R, Neill P, Newell H, Newman T, Newton-Cox A, Nicholson T, Nicoll D, Nolan CM, Noonan MJ, Norman C, Novotny P, Nunag J, Nwafor L, Nwanguma U, Nyaboko J, O'Donnell K, O'Brien C, O'Brien L, O'Regan D, Odell N, Ogg G, Olaosebikan O, Oliver C, Omar Z, Orriss-Dib L, Osborne L, Osbourne R, Ostermann M, Overton C, Owen J, Oxton J, Pack J, Pacpaco E, Paddick S, Painter S, Pakzad A, Palmer S, Papineni P, Paques K, Paradowski K, Pareek M, Parfrey H, Pariante C, Parker S, Parkes M, Parmar J, Patale S, Patel B, Patel M, Patel S, Pattenadk D, Pavlides M, Payne S, Pearce L, Pearl JE, Peckham D, Pendlebury J, Peng Y, Pennington C, Peralta I, Perkins E, Peterkin Z, Peto T, Petousi N, Petrie J, Phipps J, Pimm J, Piper Hanley K, Pius R, Plant H, Plein S, Plekhanova T, Plowright M, Polgar O, Poll L, Porter J, Portukhay S, Powell N, Prabhu A, Pratt J, Price A, Price C, Price C, Price D, Price L, Price L, Prickett A, Propescu J, Pugmire S, Quaid S, Quigley J, Qureshi H, Qureshi IN, Radhakrishnan K, Ralser M, Ramos A, Ramos H, Rangeley J, Rangelov B, Ratcliffe L, Ravencroft P, Reddington A, Reddy R, Redfearn H, Redwood D, Reed A, Rees M, Rees T, Regan K, Reynolds W, Ribeiro C, Richards A, Richardson E, Rivera-Ortega P, Roberts K, Robertson E, Robinson E, Robinson L, Roche L, Roddis C, Rodger J, Ross A, Ross G, Rossdale J, Rostron A, Rowe A, Rowland A, Rowland J, Roy K, Roy M, Rudan I, Russell R, Russell E, Saalmink G, Sabit R, Sage EK, Samakomva T, Samani N, Sampson C, Samuel K, Samuel R, Sanderson A, Sapey E, Saralaya D, Sargant J, Sarginson C, Sass T, Sattar N, Saunders K, Saunders P, Saunders LC, Savill H, Saxon W, Sayer A, Schronce J, Schwaeble W, Scott K, Selby N, Sewell TA, Shah K, Shah P, Shankar-Hari M, Sharma M, Sharpe C, Sharpe M, Shashaa S, Shaw A, Shaw K, Shaw V, Shelton S, Shenton L, Shevket K, Short J, Siddique S, Siddiqui S, Sidebottom J, Sigfrid L, Simons G, Simpson J, Simpson N, Singh C, Singh S, Sissons D, Skeemer J, Slack K, Smith A, Smith D, Smith S, Smith J, Smith L, Soares M, Solano TS, Solly R, Solstice AR, Soulsby T, Southern D, Sowter D, Spears M, Spencer LG, Speranza F, Stadon L, Stanel S, Steele N, Steiner M, Stensel D, Stephens G, Stephenson L, Stern M, Stewart I, Stimpson R, Stockdale S, Stockley J, Stoker W, Stone R, Storrar W, Storrie A, Storton K, Stringer E, Strong-Sheldrake S, Stroud N, Subbe C, Sudlow CL, Suleiman Z, Summers C, Summersgill C, Sutherland D, Sykes DL, Sykes R, Talbot N, Tan AL, Tarusan L, Tavoukjian V, Taylor A, Taylor C, Taylor J, Te A, Tedd H, Tee CJ, Teixeira J, Tench H, Terry S, Thackray-Nocera S, Thaivalappil F, Thamu B, Thickett D, Thomas C, Thomas S, Thomas AK, Thomas-Woods T, Thompson T, Thompson AAR, Thornton T, Tilley J, Tinker N, Tiongson GF, Tobin M, Tomlinson J, Tong C, Touyz R, Tripp KA, Tunnicliffe E, Turnbull A, Turner E, Turner S, Turner V, Turner K, Turney S, Turtle L, Turton H, Ugoji J, Ugwuoke R, Upthegrove R, Valabhji J, Ventura M, Vere J, Vickers C, Vinson B, Wade E, Wade P, Wainwright T, Wajero LO, Walder S, Walker S, Walker S, Wall E, Wallis T, Walmsley S, Walsh JA, Walsh S, Warburton L, Ward TJC, Warwick K, Wassall H, Waterson S, Watson E, Watson L, Watson J, Welch C, Welch H, Welsh B, Wessely S, West S, Weston H, Wheeler H, White S, Whitehead V, Whitney J, Whittaker S, Whittam B, Whitworth V, Wight A, Wild J, Wilkins M, Wilkinson D, Williams N, Williams N, Williams J, Williams-Howard SA, Willicombe M, Willis G, Willoughby J, Wilson A, Wilson D, Wilson I, Window N, Witham M, Wolf-Roberts R, Wood C, Woodhead F, Woods J, Wormleighton J, Worsley J, Wraith D, Wrey Brown C, Wright C, Wright L, Wright S, Wyles J, Wynter I, Xu M, Yasmin N, Yasmin S, Yates T, Yip KP, Young B, Young S, Young A, Yousuf AJ, Zawia A, Zeidan L, Zhao B, Zongo O. Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study. THE LANCET. RESPIRATORY MEDICINE 2022; 10:761-775. [PMID: 35472304 PMCID: PMC9034855 DOI: 10.1016/s2213-2600(22)00127-8] [Citation(s) in RCA: 154] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/23/2022] [Accepted: 03/31/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND No effective pharmacological or non-pharmacological interventions exist for patients with long COVID. We aimed to describe recovery 1 year after hospital discharge for COVID-19, identify factors associated with patient-perceived recovery, and identify potential therapeutic targets by describing the underlying inflammatory profiles of the previously described recovery clusters at 5 months after hospital discharge. METHODS The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study recruiting adults (aged ≥18 years) discharged from hospital with COVID-19 across the UK. Recovery was assessed using patient-reported outcome measures, physical performance, and organ function at 5 months and 1 year after hospital discharge, and stratified by both patient-perceived recovery and recovery cluster. Hierarchical logistic regression modelling was performed for patient-perceived recovery at 1 year. Cluster analysis was done using the clustering large applications k-medoids approach using clinical outcomes at 5 months. Inflammatory protein profiling was analysed from plasma at the 5-month visit. This study is registered on the ISRCTN Registry, ISRCTN10980107, and recruitment is ongoing. FINDINGS 2320 participants discharged from hospital between March 7, 2020, and April 18, 2021, were assessed at 5 months after discharge and 807 (32·7%) participants completed both the 5-month and 1-year visits. 279 (35·6%) of these 807 patients were women and 505 (64·4%) were men, with a mean age of 58·7 (SD 12·5) years, and 224 (27·8%) had received invasive mechanical ventilation (WHO class 7-9). The proportion of patients reporting full recovery was unchanged between 5 months (501 [25·5%] of 1965) and 1 year (232 [28·9%] of 804). Factors associated with being less likely to report full recovery at 1 year were female sex (odds ratio 0·68 [95% CI 0·46-0·99]), obesity (0·50 [0·34-0·74]) and invasive mechanical ventilation (0·42 [0·23-0·76]). Cluster analysis (n=1636) corroborated the previously reported four clusters: very severe, severe, moderate with cognitive impairment, and mild, relating to the severity of physical health, mental health, and cognitive impairment at 5 months. We found increased inflammatory mediators of tissue damage and repair in both the very severe and the moderate with cognitive impairment clusters compared with the mild cluster, including IL-6 concentration, which was increased in both comparisons (n=626 participants). We found a substantial deficit in median EQ-5D-5L utility index from before COVID-19 (retrospective assessment; 0·88 [IQR 0·74-1·00]), at 5 months (0·74 [0·64-0·88]) to 1 year (0·75 [0·62-0·88]), with minimal improvements across all outcome measures at 1 year after discharge in the whole cohort and within each of the four clusters. INTERPRETATION The sequelae of a hospital admission with COVID-19 were substantial 1 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 was reduced at 1 year compared with before hospital admission. Systematic inflammation and obesity are potential treatable traits that warrant further investigation in clinical trials. FUNDING UK Research and Innovation and National Institute for Health Research.
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Pathak GA, Karjalainen J, Stevens C, Neale BM, Daly M, Ganna A, Andrews SJ, Kanai M, Cordioli M, Polimanti R, Harerimana N, Pirinen M, Liao RG, Chwialkowska K, Trankiem A, Balaconis MK, Nguyen H, Solomonson M, Veerapen K, Wolford B, Roberts G, Park D, Ball CA, Coignet M, McCurdy S, Knight S, Partha R, Rhead B, Zhang M, Berkowitz N, Gaddis M, Noto K, Ruiz L, Pavlovic M, Hong EL, Rand K, Girshick A, Guturu H, Baltzell AH, Niemi MEK, Rahmouni S, Guntz J, Beguin Y, Cordioli M, Pigazzini S, Nkambule L, Georges M, Moutschen M, Misset B, Darcis G, Guiot J, Azarzar S, Gofflot S, Claassen S, Malaise O, Huynen P, Meuris C, Thys M, Jacques J, Léonard P, Frippiat F, Giot JB, Sauvage AS, Frenckell CV, Belhaj Y, Lambermont B, Nakanishi T, Morrison DR, Mooser V, Richards JB, Butler-Laporte G, Forgetta V, Li R, Ghosh B, Laurent L, Belisle A, Henry D, Abdullah T, Adeleye O, Mamlouk N, Kimchi N, Afrasiabi Z, Rezk N, Vulesevic B, Bouab M, Guzman C, Petitjean L, Tselios C, Xue X, Afilalo J, Afilalo M, Oliveira M, Brenner B, Brassard N, Durand M, Schurr E, Lepage P, Ragoussis J, Auld D, Chassé M, Kaufmann DE, Lathrop GM, Adra D, Hayward C, Glessner JT, Shaw DM, Campbell A, Morris M, Hakonarson H, Porteous DJ, Below J, Richmond A, Chang X, Polikowski H, Lauren PE, Chen HH, Wanying Z, Fawns-Ritchie C, North K, McCormick JB, Chang X, Glessner JR, Hakonarson H, Gignoux CR, Wicks SJ, Crooks K, Barnes KC, Daya M, Shortt J, Rafaels N, Chavan S, Timmers PRHJ, Wilson JF, Tenesa A, Kerr SM, D’Mellow K, Shahin D, El-Sherbiny YM, von Hohenstaufen KA, Sobh A, Eltoukhy MM, Nkambul L, Elhadidy TA, Abd Elghafar MS, El-Jawhari JJ, Mohamed AAS, Elnagdy MH, Samir A, Abdel-Aziz M, Khafaga WT, El-Lawaty WM, Torky MS, El-shanshory MR, Yassen AM, Hegazy MAF, Okasha K, Eid MA, Moahmed HS, Medina-Gomez C, Ikram MA, Uitterlinden AG, Mägi R, Milani L, Metspalu A, Laisk T, Läll K, Lepamets M, Esko T, Reimann E, Naaber P, Laane E, Pesukova J, Peterson P, Kisand K, Tabri J, Allos R, Hensen K, Starkopf J, Ringmets I, Tamm A, Kallaste A, Alavere H, Metsalu K, Puusepp M, Batini C, Tobin MD, Venn LD, Lee PH, Shrine N, Williams AT, Guyatt AL, John C, Packer RJ, Ali A, Free RC, Wang X, Wain LV, Hollox EJ, Bee CE, Adams EL, Palotie A, Ripatti S, Ruotsalainen S, Kristiansson K, Koskelainen S, Perola M, Donner K, Kivinen K, Palotie A, Kaunisto M, Rivolta C, Bochud PY, Bibert S, Boillat N, Nussle SG, Albrich W, Quinodoz M, Kamdar D, Suh N, Neofytos D, Erard V, Voide C, Bochud PY, Rivolta C, Bibert S, Quinodoz M, Kamdar D, Neofytos D, Erard V, Voide C, Friolet R, Vollenweider P, Pagani JL, Oddo M, zu Bentrup FM, Conen A, Clerc O, Marchetti O, Guillet A, Guyat-Jacques C, Foucras S, Rime M, Chassot J, Jaquet M, Viollet RM, Lannepoudenx Y, Portopena L, Bochud PY, Vollenweider P, Pagani JL, Desgranges F, Filippidis P, Guéry B, Haefliger D, Kampouri EE, Manuel O, Munting A, Papadimitriou-Olivgeris M, Regina J, Rochat-Stettler L, Suttels V, Tadini E, Tschopp J, Van Singer M, Viala B, Boillat-Blanco N, Brahier T, Hügli O, Meuwly JY, Pantet O, Gonseth Nussle S, Bochud M, D’Acremont V, Estoppey Younes S, Albrich WC, Suh N, Cerny A, O’Mahony L, von Mering C, Bochud PY, Frischknecht M, Kleger GR, Filipovic M, Kahlert CR, Wozniak H, Negro TR, Pugin J, Bouras K, Knapp C, Egger T, Perret A, Montillier P, di Bartolomeo C, Barda B, de Cid R, Carreras A, Moreno V, Kogevinas M, Galván-Femenía I, Blay N, Farré X, Sumoy L, Cortés B, Mercader JM, Guindo-Martinez M, Torrents D, Garcia-Aymerich J, Castaño-Vinyals G, Dobaño C, Gori M, Renieri A, Mari F, Mondelli MU, Castelli F, Vaghi M, Rusconi S, Montagnani F, Bargagli E, Franchi F, Mazzei MA, Cantarini L, Tacconi D, Feri M, Scala R, Spargi G, Nencioni C, Bandini M, Caldarelli GP, Canaccini A, Ognibene A, D’Arminio Monforte A, Girardis M, Antinori A, Francisci D, Schiaroli E, Scotton PG, Panese S, Scaggiante R, Monica MD, Capasso M, Fiorentino G, Castori M, Aucella F, Biagio AD, Masucci L, Valente S, Mandalà M, Zucchi P, Giannattasio F, Coviello DA, Mussini C, Tavecchia L, Crotti L, Rizzi M, Rovere MTL, Sarzi-Braga S, Bussotti M, Ravaglia S, Artuso R, Perrella A, Romani D, Bergomi P, Catena E, Vincenti A, Ferri C, Grassi D, Pessina G, Tumbarello M, Pietro MD, Sabrina R, Luchi S, Furini S, Dei S, Benetti E, Picchiotti N, Sanarico M, Ceri S, Pinoli P, Raimondi F, Biscarini F, Stella A, Zguro K, Capitani K, Nkambule L, Tanfoni M, Fallerini C, Daga S, Baldassarri M, Fava F, Frullanti E, Valentino F, Doddato G, Giliberti A, Tita R, Amitrano S, Bruttini M, Croci S, Meloni I, Mencarelli MA, Rizzo CL, Pinto AM, Beligni G, Tommasi A, Sarno LD, Palmieri M, Carriero ML, Alaverdian D, Busani S, Bruno R, Vecchia M, Belli MA, Mantovani S, Ludovisi S, Quiros-Roldan E, Antoni MD, Zanella I, Siano M, Emiliozzi A, Fabbiani M, Rossetti B, Bergantini L, D’Alessandro M, Cameli P, Bennett D, Anedda F, Marcantonio S, Scolletta S, Guerrini S, Conticini E, Frediani B, Spertilli C, Donati A, Guidelli L, Corridi M, Croci L, Piacentini P, Desanctis E, Cappelli S, Verzuri A, Anemoli V, Pancrazzi A, Lorubbio M, Miraglia FG, Venturelli S, Cossarizza A, Vergori A, Gabrieli A, Riva A, Paciosi F, Andretta F, Gatti F, Parisi SG, Baratti S, Piscopo C, Russo R, Andolfo I, Iolascon A, Carella M, Merla G, Squeo GM, Raggi P, Marciano C, Perna R, Bassetti M, Sanguinetti M, Giorli A, Salerni L, Parravicini P, Menatti E, Trotta T, Coiro G, Lena F, Martinelli E, Mancarella S, Gabbi C, Maggiolo F, Ripamonti D, Bachetti T, Suardi C, Parati G, Bottà G, Domenico PD, Rancan I, Bianchi F, Colombo R, Barbieri C, Acquilini D, Andreucci E, Segala FV, Tiseo G, Falcone M, Lista M, Poscente M, Vivo OD, Petrocelli P, Guarnaccia A, Baroni S, Hayward C, Porteous DJ, Fawns-Ritchie C, Richmond A, Campbell A, van Heel DA, Hunt KA, Trembath RC, Huang QQ, Martin HC, Mason D, Trivedi B, Wright J, Finer S, Akhtar S, Anwar M, Arciero E, Ashraf S, Breen G, Chung R, Curtis CJ, Chowdhury M, Colligan G, Deloukas P, Durham C, Finer S, Griffiths C, Huang QQ, Hurles M, Hunt KA, Hussain S, Islam K, Khan A, Khan A, Lavery C, Lee SH, Lerner R, MacArthur D, MacLaughlin B, Martin H, Mason D, Miah S, Newman B, Safa N, Tahmasebi F, Trembath RC, Trivedi B, van Heel DA, Wright J, Griffiths CJ, Smith AV, Boughton AP, Li KW, LeFaive J, Annis A, Niavarani A, Aliannejad R, Sharififard B, Amirsavadkouhi A, Naderpour Z, Tadi HA, Aleagha AE, Ahmadi S, Moghaddam SBM, Adamsara A, Saeedi M, Abdollahi H, Hosseini A, Chariyavilaskul P, Jantarabenjakul W, Hirankarn N, Chamnanphon M, Suttichet TB, Shotelersuk V, Pongpanich M, Phokaew C, Chetruengchai W, Putchareon O, Torvorapanit P, Puthanakit T, Suchartlikitwong P, Nilaratanakul V, Sodsai P, Brumpton BM, Hveem K, Willer C, Wolford B, Zhou W, Rogne T, Solligard E, Åsvold BO, Franke L, Boezen M, Deelen P, Claringbould A, Lopera E, Warmerdam R, Vonk JM, van Blokland I, Lanting P, Ori APS, Feng YCA, Mercader J, Weiss ST, Karlson EW, Smoller JW, Murphy SN, Meigs JB, Woolley AE, Green RC, Perez EF, Wolford B, Zöllner S, Wang J, Beck A, Sloofman LG, Ascolillo S, Sebra RP, Collins BL, Levy T, Buxbaum JD, Sealfon SC, Jordan DM, Thompson RC, Gettler K, Chaudhary K, Belbin GM, Preuss M, Hoggart C, Choi S, Underwood SJ, Salib I, Britvan B, Keller K, Tang L, Peruggia M, Hiester LL, Niblo K, Aksentijevich A, Labkowsky A, Karp A, Zlatopolsky M, Zyndorf M, Charney AW, Beckmann ND, Schadt EE, Abul-Husn NS, Cho JH, Itan Y, Kenny EE, Loos RJF, Nadkarni GN, Do R, O’Reilly P, Huckins LM, Ferreira MAR, Abecasis GR, Leader JB, Cantor MN, Justice AE, Carey DJ, Chittoor G, Josyula NS, Kosmicki JA, Horowitz JE, Baras A, Gass MC, Yadav A, Mirshahi T, Hottenga JJ, Bartels M, de geus EEJC, Nivard MMG, Verma A, Ritchie MD, Rader D, Li B, Verma SS, Lucas A, Bradford Y, Abedalthagafi M, Alaamery M, Alshareef A, Sawaji M, Massadeh S, AlMalik A, Alqahtani S, Baraka D, Harthi FA, Alsolm E, Safieh LA, Alowayn AM, Alqubaishi F, Mutairi AA, Mangul S, Almutairi M, Aljawini N, Albesher N, Arabi YM, Mahmoud ES, Khattab AK, Halawani RT, Alahmadey ZZ, Albakri JK, Felemban WA, Suliman BA, Hasanato R, Al-Awdah L, Alghamdi J, AlZahrani D, AlJohani S, Al-Afghani H, AlDhawi N, AlBardis H, Alkwai S, Alswailm M, Almalki F, Albeladi M, Almohammed I, Barhoush E, Albader A, Alotaibi S, Alghamdi B, Jung J, fawzy MS, Alrashed M, Zeberg H, Nkambul L, Frithiof R, Hultström M, Lipcsey M, Tardif N, Rooyackers O, Grip J, Maricic T, Helgeland Ø, Magnus P, Trogstad LIS, Lee Y, Harris JR, Mangino M, Spector TD, Emma D, Moutsianas L, Caulfield MJ, Scott RH, Kousathanas A, Pasko D, Walker S, Stuckey A, Odhams CA, Rhodes D, Fowler T, Rendon A, Chan G, Arumugam P, Karczewski KJ, Martin AR, Wilson DJ, Spencer CCA, Crook DW, Wyllie DH, O’Connell AM, Atkinson EG, Kanai M, Tsuo K, Baya N, Turley P, Gupta R, Walters RK, Palmer DS, Sarma G, Solomonson M, Cheng N, Lu W, Churchhouse C, Goldstein JI, King D, Zhou W, Seed C, Daly MJ, Neale BM, Finucane H, Bryant S, Satterstrom FK, Band G, Earle SG, Lin SK, Arning N, Koelling N, Armstrong J, Rudkin JK, Callier S, Bryant S, Cusick C, Soranzo N, Zhao JH, Danesh J, Angelantonio ED, Butterworth AS, Sun YV, Huffman JE, Cho K, O’Donnell CJ, Tsao P, Gaziano JM, Peloso G, Ho YL, Smieszek SP, Polymeropoulos C, Polymeropoulos V, Polymeropoulos MH, Przychodzen BP, Fernandez-Cadenas I, Planas AM, Perez-Tur J, Llucià-Carol L, Cullell N, Muiño E, Cárcel-Márquez J, DeDiego ML, Iglesias LL, Soriano A, Rico V, Agüero D, Bedini JL, Lozano F, Domingo C, Robles V, Ruiz-Jaén F, Márquez L, Gomez J, Coto E, Albaiceta GM, García-Clemente M, Dalmau D, Arranz MJ, Dietl B, Serra-Llovich A, Soler P, Colobrán R, Martín-Nalda A, Martínez AP, Bernardo D, Rojo S, Fiz-López A, Arribas E, de la Cal-Sabater P, Segura T, González-Villa E, Serrano-Heras G, Martí-Fàbregas J, Jiménez-Xarrié E, de Felipe Mimbrera A, Masjuan J, García-Madrona S, Domínguez-Mayoral A, Villalonga JM, Menéndez-Valladares P, Chasman DI, Sesso HD, Manson JE, Buring JE, Ridker PM, Franco G, Davis L, Lee S, Priest J, Sankaran VG, van Heel D, Biesecker L, Kerchberger VE, Baillie JK. A first update on mapping the human genetic architecture of COVID-19. Nature 2022; 608:E1-E10. [PMID: 35922517 PMCID: PMC9352569 DOI: 10.1038/s41586-022-04826-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 04/29/2022] [Indexed: 01/04/2023]
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Ijaz S, Dicks S, Jegatheesan K, Parker E, Katsanovskaja K, Vink E, McClure MO, Shute J, Hope J, Cook N, Cherepanov P, Turtle L, Paxton WA, Pollakis G, Ho A, Openshaw PJM, Baillie JK, Semple MG, Tedder RS. Mapping of SARS-CoV-2 IgM and IgG in gingival crevicular fluid: Antibody dynamics and linkage to severity of COVID-19 in hospital inpatients. J Infect 2022; 85:152-160. [PMID: 35667482 PMCID: PMC9163047 DOI: 10.1016/j.jinf.2022.05.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 05/19/2022] [Accepted: 05/29/2022] [Indexed: 02/06/2023]
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Norris T, Razieh C, Zaccardi F, Yates T, Islam N, Gillies CL, Chudasama YV, Rowlands AV, Davies MJ, McCann GP, Banerjee A, Lam CSP, Docherty AB, Openshaw PJ, Baillie JK, Semple MG, Lawson CA, Khunti K. Impact of cardiometabolic multimorbidity and ethnicity on cardiovascular/renal complications in patients with COVID-19. Heart 2022; 108:1200-1208. [PMID: 34911741 PMCID: PMC8678560 DOI: 10.1136/heartjnl-2021-320047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/08/2021] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE Using a large national database of people hospitalised with COVID-19, we investigated the contribution of cardio-metabolic conditions, multi-morbidity and ethnicity on the risk of in-hospital cardiovascular complications and death. METHODS A multicentre, prospective cohort study in 302 UK healthcare facilities of adults hospitalised with COVID-19 between 6 February 2020 and 16 March 2021. Logistic models were used to explore associations between baseline patient ethnicity, cardiometabolic conditions and multimorbidity (0, 1, 2, >2 conditions), and in-hospital cardiovascular complications (heart failure, arrhythmia, cardiac ischaemia, cardiac arrest, coagulation complications, stroke), renal injury and death. RESULTS Of 65 624 patients hospitalised with COVID-19, 44 598 (68.0%) reported at least one cardiometabolic condition on admission. Cardiovascular/renal complications or death occurred in 24 609 (38.0%) patients. Baseline cardiometabolic conditions were independently associated with increased odds of in-hospital complications and this risk increased in the presence of cardiometabolic multimorbidity. For example, compared with having no cardiometabolic conditions, 1, 2 or ≥3 conditions was associated with 1.46 (95% CI 1.39 to 1.54), 2.04 (95% CI 1.93 to 2.15) and 3.10 (95% CI 2.92 to 3.29) times higher odds of any cardiovascular/renal complication, respectively. A similar pattern was observed for all-cause death. Compared with the white group, the South Asian (OR 1.19, 95% CI 1.10 to 1.29) and black (OR 1.53 to 95% CI 1.37 to 1.72) ethnic groups had higher risk of any cardiovascular/renal complication. CONCLUSIONS In hospitalised patients with COVID-19, cardiovascular complications or death impacts just under half of all patients, with the highest risk in those of South Asian or Black ethnicity and in patients with cardiometabolic multimorbidity.
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Kousathanas A, Pairo-Castineira E, Rawlik K, Stuckey A, Odhams CA, Walker S, Russell CD, Malinauskas T, Wu Y, Millar J, Shen X, Elliott KS, Griffiths F, Oosthuyzen W, Morrice K, Keating S, Wang B, Rhodes D, Klaric L, Zechner M, Parkinson N, Siddiq A, Goddard P, Donovan S, Maslove D, Nichol A, Semple MG, Zainy T, Maleady-Crowe F, Todd L, Salehi S, Knight J, Elgar G, Chan G, Arumugam P, Patch C, Rendon A, Bentley D, Kingsley C, Kosmicki JA, Horowitz JE, Baras A, Abecasis GR, Ferreira MAR, Justice A, Mirshahi T, Oetjens M, Rader DJ, Ritchie MD, Verma A, Fowler TA, Shankar-Hari M, Summers C, Hinds C, Horby P, Ling L, McAuley D, Montgomery H, Openshaw PJM, Elliott P, Walsh T, Tenesa A, Fawkes A, Murphy L, Rowan K, Ponting CP, Vitart V, Wilson JF, Yang J, Bretherick AD, Scott RH, Hendry SC, Moutsianas L, Law A, Caulfield MJ, Baillie JK. Whole-genome sequencing reveals host factors underlying critical COVID-19. Nature 2022; 607:97-103. [PMID: 35255492 PMCID: PMC9259496 DOI: 10.1038/s41586-022-04576-6] [Citation(s) in RCA: 139] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 02/23/2022] [Indexed: 12/15/2022]
Abstract
Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2-4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.
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Prince T, Dong X, Penrice-Randal R, Randle N, Hartley C, Goldswain H, Jones B, Semple MG, Baillie JK, Openshaw PJM, Turtle L, Hughes GL, Anderson ER, Patterson EI, Druce J, Screaton G, Carroll MW, Stewart JP, Hiscox JA. Analysis of SARS-CoV-2 in Nasopharyngeal Samples from Patients with COVID-19 Illustrates Population Variation and Diverse Phenotypes, Placing the Growth Properties of Variants of Concern in Context with Other Lineages. mSphere 2022; 7:e0091321. [PMID: 35491827 PMCID: PMC9241508 DOI: 10.1128/msphere.00913-21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/18/2022] [Indexed: 01/02/2023] Open
Abstract
New variants of SARS-CoV-2 are continuing to emerge and dominate the global sequence landscapes. Several variants have been labeled variants of concern (VOCs) because they may have a transmission advantage, increased risk of morbidity and/or mortality, or immune evasion upon a background of prior infection or vaccination. Placing the VOCs in context with the underlying variability of SARS-CoV-2 is essential in understanding virus evolution and selection pressures. Dominant genome sequences and the population genetics of SARS-CoV-2 in nasopharyngeal swabs from hospitalized patients were characterized. Nonsynonymous changes at a minor variant level were identified. These populations were generally preserved when isolates were amplified in cell culture. To place the Alpha, Beta, Delta, and Omicron VOCs in context, their growth was compared to clinical isolates of different lineages from earlier in the pandemic. The data indicated that the growth in cell culture of the Beta variant was more than that of the other variants in Vero E6 cells but not in hACE2-A549 cells. Looking at each time point, Beta grew more than the other VOCs in hACE2-A549 cells at 24 to 48 h postinfection. At 72 h postinfection there was no difference in the growth of any of the variants in either cell line. Overall, this work suggested that exploring the biology of SARS-CoV-2 is complicated by population dynamics and that these need to be considered with new variants. In the context of variation seen in other coronaviruses, the variants currently observed for SARS-CoV-2 are very similar in terms of their clinical spectrum of disease. IMPORTANCE SARS-CoV-2 is the causative agent of COVID-19. The virus has spread across the planet, causing a global pandemic. In common with other coronaviruses, SARS-CoV-2 genomes can become quite diverse as a consequence of replicating inside cells. This has given rise to multiple variants from the original virus that infected humans. These variants may have different properties and in the context of a widespread vaccination program may render vaccines less effective. Our research confirms the degree of genetic diversity of SARS-CoV-2 in patients. By comparing the growth of previous variants to the pattern seen with four variants of concern (VOCs) (Alpha, Beta, Delta, and Omicron), we show that, at least in cells, Beta variant growth exceeds that of Alpha, Delta, and Omicron VOCs at 24 to 48 h in both Vero E6 and hACE2-A549 cells, but by 72 h postinfection, the amount of virus is not different from that of the other VOCs.
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Mirchandani AS, Jenkins SJ, Bain CC, Sanchez-Garcia MA, Lawson H, Coelho P, Murphy F, Griffith DM, Zhang A, Morrison T, Ly T, Arienti S, Sadiku P, Watts ER, Dickinson RS, Reyes L, Cooper G, Clark S, Lewis D, Kelly V, Spanos C, Musgrave KM, Delaney L, Harper I, Scott J, Parkinson NJ, Rostron AJ, Baillie JK, Clohisey S, Pridans C, Campana L, Lewis PS, Simpson AJ, Dockrell DH, Schwarze J, Hirani N, Ratcliffe PJ, Pugh CW, Kranc K, Forbes SJ, Whyte MKB, Walmsley SR. Hypoxia shapes the immune landscape in lung injury and promotes the persistence of inflammation. Nat Immunol 2022; 23:927-939. [PMID: 35624205 PMCID: PMC9174051 DOI: 10.1038/s41590-022-01216-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 04/18/2022] [Indexed: 12/30/2022]
Abstract
Hypoxemia is a defining feature of acute respiratory distress syndrome (ARDS), an often-fatal complication of pulmonary or systemic inflammation, yet the resulting tissue hypoxia, and its impact on immune responses, is often neglected. In the present study, we have shown that ARDS patients were hypoxemic and monocytopenic within the first 48 h of ventilation. Monocytopenia was also observed in mouse models of hypoxic acute lung injury, in which hypoxemia drove the suppression of type I interferon signaling in the bone marrow. This impaired monopoiesis resulted in reduced accumulation of monocyte-derived macrophages and enhanced neutrophil-mediated inflammation in the lung. Administration of colony-stimulating factor 1 in mice with hypoxic lung injury rescued the monocytopenia, altered the phenotype of circulating monocytes, increased monocyte-derived macrophages in the lung and limited injury. Thus, tissue hypoxia altered the dynamics of the immune response to the detriment of the host and interventions to address the aberrant response offer new therapeutic strategies for ARDS.
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Knight SR, Gupta RK, Ho A, Pius R, Buchan I, Carson G, Drake TM, Dunning J, Fairfield CJ, Gamble C, Green CA, Halpin S, Hardwick HE, Holden KA, Horby PW, Jackson C, Mclean KA, Merson L, Nguyen-Van-Tam JS, Norman L, Olliaro PL, Pritchard MG, Russell CD, Shaw CA, Sheikh A, Solomon T, Sudlow C, Swann OV, Turtle LCW, Openshaw PJM, Baillie JK, Docherty A, Semple MG, Noursadeghi M, Harrison EM. Prospective validation of the 4C prognostic models for adults hospitalised with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol. Thorax 2022; 77:606-615. [PMID: 34810237 PMCID: PMC8610617 DOI: 10.1136/thoraxjnl-2021-217629] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 10/11/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To prospectively validate two risk scores to predict mortality (4C Mortality) and in-hospital deterioration (4C Deterioration) among adults hospitalised with COVID-19. METHODS Prospective observational cohort study of adults (age ≥18 years) with confirmed or highly suspected COVID-19 recruited into the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK) study in 306 hospitals across England, Scotland and Wales. Patients were recruited between 27 August 2020 and 17 February 2021, with at least 4 weeks follow-up before final data extraction. The main outcome measures were discrimination and calibration of models for in-hospital deterioration (defined as any requirement of ventilatory support or critical care, or death) and mortality, incorporating predefined subgroups. RESULTS 76 588 participants were included, of whom 27 352 (37.4%) deteriorated and 12 581 (17.4%) died. Both the 4C Mortality (0.78 (0.77 to 0.78)) and 4C Deterioration scores (pooled C-statistic 0.76 (95% CI 0.75 to 0.77)) demonstrated consistent discrimination across all nine National Health Service regions, with similar performance metrics to the original validation cohorts. Calibration remained stable (4C Mortality: pooled slope 1.09, pooled calibration-in-the-large 0.12; 4C Deterioration: 1.00, -0.04), with no need for temporal recalibration during the second UK pandemic wave of hospital admissions. CONCLUSION Both 4C risk stratification models demonstrate consistent performance to predict clinical deterioration and mortality in a large prospective second wave validation cohort of UK patients. Despite recent advances in the treatment and management of adults hospitalised with COVID-19, both scores can continue to inform clinical decision making. TRIAL REGISTRATION NUMBER ISRCTN66726260.
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Dong X, Penrice-Randal R, Goldswain H, Prince T, Randle N, Donovan-Banfield I, Salguero FJ, Tree J, Vamos E, Nelson C, Clark J, Ryan Y, Stewart JP, Semple MG, Baillie JK, Openshaw PJM, Turtle L, Matthews DA, Carroll MW, Darby AC, Hiscox JA. Analysis of SARS-CoV-2 known and novel subgenomic mRNAs in cell culture, animal model, and clinical samples using LeTRS, a bioinformatic tool to identify unique sequence identifiers. Gigascience 2022; 11:6593429. [PMID: 35639883 PMCID: PMC9154083 DOI: 10.1093/gigascience/giac045] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 12/08/2021] [Accepted: 04/07/2022] [Indexed: 12/30/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a complex strategy for the transcription of viral subgenomic mRNAs (sgmRNAs), which are targets for nucleic acid diagnostics. Each of these sgmRNAs has a unique 5' sequence, the leader-transcriptional regulatory sequence gene junction (leader-TRS junction), that can be identified using sequencing. High-resolution sequencing has been used to investigate the biology of SARS-CoV-2 and the host response in cell culture and animal models and from clinical samples. LeTRS, a bioinformatics tool, was developed to identify leader-TRS junctions and can be used as a proxy to quantify sgmRNAs for understanding virus biology. LeTRS is readily adaptable for other coronaviruses such as Middle East respiratory syndrome coronavirus or a future newly discovered coronavirus. LeTRS was tested on published data sets and novel clinical samples from patients and longitudinal samples from animal models with coronavirus disease 2019. LeTRS identified known leader-TRS junctions and identified putative novel sgmRNAs that were common across different mammalian species. This may be indicative of an evolutionary mechanism where plasticity in transcription generates novel open reading frames, which can then subject to selection pressure. The data indicated multiphasic abundance of sgmRNAs in two different animal models. This recapitulates the relative sgmRNA abundance observed in cells at early points in infection but not at late points. This pattern is reflected in some human nasopharyngeal samples and therefore has implications for transmission models and nucleic acid-based diagnostics. LeTRS provides a quantitative measure of sgmRNA abundance from sequencing data. This can be used to assess the biology of SARS-CoV-2 (or other coronaviruses) in clinical and nonclinical samples, especially to evaluate different variants and medical countermeasures that may influence viral RNA synthesis.
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Correia GDS, Takis PG, Sands CJ, Kowalka AM, Tan T, Turtle L, Ho A, Semple MG, Openshaw PJM, Baillie JK, Takáts Z, Lewis MR. 1H NMR Signals from Urine Excreted Protein Are a Source of Bias in Probabilistic Quotient Normalization. Anal Chem 2022; 94:6919-6923. [PMID: 35503092 PMCID: PMC9118196 DOI: 10.1021/acs.analchem.2c00466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2 = 0.423, p < 2.2 × 10-16). This correlation was significantly reduced (r2 = 0.163, p < 2.2 × 10-16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.
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Yang Z, Macdonald-Dunlop E, Chen J, Zhai R, Li T, Richmond A, Klarić L, Pirastu N, Ning Z, Zheng C, Wang Y, Huang T, He Y, Guo H, Ying K, Gustafsson S, Prins B, Ramisch A, Dermitzakis ET, Png G, Eriksson N, Haessler J, Hu X, Zanetti D, Boutin T, Hwang SJ, Wheeler E, Pietzner M, Raffield LM, Kalnapenkis A, Peters JE, Viñuela A, Gilly A, Elmståhl S, Dedoussis G, Petrie JR, Polašek O, Folkersen L, Chen Y, Yao C, Võsa U, Pairo-Castineira E, Clohisey S, Bretherick AD, Rawlik K, Esko T, Enroth S, Johansson Å, Gyllensten U, Langenberg C, Levy D, Hayward C, Assimes TL, Kooperberg C, Manichaikul AW, Siegbahn A, Wallentin L, Lind L, Zeggini E, Schwenk JM, Butterworth AS, Michaëlsson K, Pawitan Y, Joshi PK, Baillie JK, Mälarstig A, Reiner AP, Wilson JF, Shen X. Genetic Landscape of the ACE2 Coronavirus Receptor. Circulation 2022; 145:1398-1411. [PMID: 35387486 PMCID: PMC9047645 DOI: 10.1161/circulationaha.121.057888] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. METHODS We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. RESULTS We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. CONCLUSIONS Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.
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Stewart A, Sinclair E, Ng JCF, O’Hare JS, Page A, Serangeli I, Margreitter C, Orsenigo F, Longman K, Frampas C, Costa C, Lewis HM, Kasar N, Wu B, Kipling D, Openshaw PJM, Chiu C, Baillie JK, Scott JT, Semple MG, Bailey MJ, Fraternali F, Dunn-Walters DK. Pandemic, Epidemic, Endemic: B Cell Repertoire Analysis Reveals Unique Anti-Viral Responses to SARS-CoV-2, Ebola and Respiratory Syncytial Virus. Front Immunol 2022; 13:807104. [PMID: 35592326 PMCID: PMC9111746 DOI: 10.3389/fimmu.2022.807104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/15/2022] [Indexed: 11/17/2022] Open
Abstract
Immunoglobulin gene heterogeneity reflects the diversity and focus of the humoral immune response towards different infections, enabling inference of B cell development processes. Detailed compositional and lineage analysis of long read IGH repertoire sequencing, combining examples of pandemic, epidemic and endemic viral infections with control and vaccination samples, demonstrates general responses including increased use of IGHV4-39 in both Zaire Ebolavirus (EBOV) and COVID-19 patient cohorts. We also show unique characteristics absent in Respiratory Syncytial Virus or yellow fever vaccine samples: EBOV survivors show unprecedented high levels of class switching events while COVID-19 repertoires from acute disease appear underdeveloped. Despite the high levels of clonal expansion in COVID-19 IgG1 repertoires there is a striking lack of evidence of germinal centre mutation and selection. Given the differences in COVID-19 morbidity and mortality with age, it is also pertinent that we find significant differences in repertoire characteristics between young and old patients. Our data supports the hypothesis that a primary viral challenge can result in a strong but immature humoral response where failures in selection of the repertoire risk off-target effects.
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Norris T, Razieh C, Yates T, Zaccardi F, Gillies CL, Chudasama YV, Rowlands A, Davies MJ, McCann GP, Banerjee A, Docherty AB, Openshaw PJ, Baillie JK, Semple MG, Lawson CA, Khunti K. Admission Blood Glucose Level and Its Association With Cardiovascular and Renal Complications in Patients Hospitalized With COVID-19. Diabetes Care 2022; 45:1132-1140. [PMID: 35275994 PMCID: PMC9174963 DOI: 10.2337/dc21-1709] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 01/30/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To investigate the association between admission blood glucose levels and risk of in-hospital cardiovascular and renal complications. RESEARCH DESIGN AND METHODS In this multicenter prospective study of 36,269 adults hospitalized with COVID-19 between 6 February 2020 and 16 March 2021 (N = 143,266), logistic regression models were used to explore associations between admission glucose level (mmol/L and mg/dL) and odds of in-hospital complications, including heart failure, arrhythmia, cardiac ischemia, cardiac arrest, coagulation complications, stroke, and renal injury. Nonlinearity was investigated using restricted cubic splines. Interaction models explored whether associations between glucose levels and complications were modified by clinically relevant factors. RESULTS Cardiovascular and renal complications occurred in 10,421 (28.7%) patients; median admission glucose level was 6.7 mmol/L (interquartile range 5.8-8.7) (120.6 mg/dL [104.4-156.6]). While accounting for confounders, for all complications except cardiac ischemia and stroke, there was a nonlinear association between glucose and cardiovascular and renal complications. For example, odds of heart failure, arrhythmia, coagulation complications, and renal injury decreased to a nadir at 6.4 mmol/L (115 mg/dL), 4.9 mmol/L (88.2 mg/dL), 4.7 mmol/L (84.6 mg/dL), and 5.8 mmol/L (104.4 mg/dL), respectively, and increased thereafter until 26.0 mmol/L (468 mg/dL), 50.0 mmol/L (900 mg/dL), 8.5 mmol/L (153 mg/dL), and 32.4 mmol/L (583.2 mg/dL). Compared with 5 mmol/L (90 mg/dL), odds ratios at these glucose levels were 1.28 (95% CI 0.96, 1.69) for heart failure, 2.23 (1.03, 4.81) for arrhythmia, 1.59 (1.36, 1.86) for coagulation complications, and 2.42 (2.01, 2.92) for renal injury. For most complications, a modifying effect of age was observed, with higher odds of complications at higher glucose levels for patients age <69 years. Preexisting diabetes status had a similar modifying effect on odds of complications, but evidence was strongest for renal injury, cardiac ischemia, and any cardiovascular/renal complication. CONCLUSIONS Increased odds of cardiovascular or renal complications were observed for admission glucose levels indicative of both hypo- and hyperglycemia. Admission glucose could be used as a marker for risk stratification of high-risk patients. Further research should evaluate interventions to optimize admission glucose on improving COVID-19 outcomes.
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Relph KA, Russell CD, Fairfield CJ, Turtle L, de Silva TI, Siggins MK, Drake TM, Thwaites RS, Abrams S, Moore SC, Hardwick HE, Oosthuyzen W, Harrison EM, Docherty AB, Openshaw PJM, Baillie JK, Semple MG, Ho A. Procalcitonin Is Not a Reliable Biomarker of Bacterial Coinfection in People With Coronavirus Disease 2019 Undergoing Microbiological Investigation at the Time of Hospital Admission. Open Forum Infect Dis 2022; 9:ofac179. [PMID: 35531376 PMCID: PMC9070482 DOI: 10.1093/ofid/ofac179] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/01/2022] [Indexed: 11/17/2022] Open
Abstract
Admission procalcitonin measurements and microbiology results were available for 1040 hospitalized adults with coronavirus disease 2019 (from 48 902 included in the International Severe Acute Respiratory and Emerging Infections Consortium World Health Organization Clinical Characterisation Protocol UK study). Although procalcitonin was higher in bacterial coinfection, this was neither clinically significant (median [IQR], 0.33 [0.11-1.70] ng/mL vs 0.24 [0.10-0.90] ng/mL) nor diagnostically useful (area under the receiver operating characteristic curve, 0.56 [95% confidence interval, .51-.60]).
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David A, Parkinson N, Peacock TP, Pairo-Castineira E, Khanna T, Cobat A, Tenesa A, Sancho-Shimizu V, Casanova JL, Abel L, Barclay WS, Baillie JK, Sternberg MJ. A common TMPRSS2 variant has a protective effect against severe COVID-19. Curr Res Transl Med 2022; 70:103333. [PMID: 35104687 PMCID: PMC8743599 DOI: 10.1016/j.retram.2022.103333] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/22/2021] [Accepted: 01/06/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND The human protein transmembrane protease serine type 2 (TMPRSS2) plays a key role in SARS-CoV-2 infection, as it is required to activate the virus' spike protein, facilitating entry into target cells. We hypothesized that naturally-occurring TMPRSS2 human genetic variants affecting the structure and function of the TMPRSS2 protein may modulate the severity of SARS-CoV-2 infection. METHODS We focused on the only common TMPRSS2 non-synonymous variant predicted to be damaging (rs12329760 C>T, p.V160M), which has a minor allele frequency ranging from 0.14 in Ashkenazi Jewish to 0.38 in East Asians. We analysed the association between the rs12329760 and COVID-19 severity in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units recruited as part of the GenOMICC (Genetics Of Mortality In Critical Care) study. Logistic regression analyses were adjusted for sex, age and deprivation index. For in vitro studies, HEK293 cells were co-transfected with ACE2 and either TMPRSS2 wild type or mutant (TMPRSS2V160M). A SARS-CoV-2 pseudovirus entry assay was used to investigate the ability of TMPRSS2V160M to promote viral entry. RESULTS We show that the T allele of rs12329760 is associated with a reduced likelihood of developing severe COVID-19 (OR 0.87, 95%CI:0.79-0.97, p = 0.01). This association was stronger in homozygous individuals when compared to the general population (OR 0.65, 95%CI:0.50-0.84, p = 1.3 × 10-3). We demonstrate in vitro that this variant, which causes the amino acid substitution valine to methionine, affects the catalytic activity of TMPRSS2 and is less able to support SARS-CoV-2 spike-mediated entry into cells. CONCLUSION TMPRSS2 rs12329760 is a common variant associated with a significantly decreased risk of severe COVID-19. Further studies are needed to assess the expression of TMPRSS2 across different age groups. Moreover, our results identify TMPRSS2 as a promising drug target, with a potential role for camostat mesilate, a drug approved for the treatment of chronic pancreatitis and postoperative reflux esophagitis, in the treatment of COVID-19. Clinical trials are needed to confirm this.
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