Incident allergic diseases in post-COVID-19 condition: multinational cohort studies from South Korea, Japan and the UK

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Abstract

As mounting evidence suggests a higher incidence of adverse consequences, such as disruption of the immune system, among patients with a history of COVID-19, we aimed to investigate post-COVID-19 conditions on a comprehensive set of allergic diseases including asthma, allergic rhinitis, atopic dermatitis, and food allergy. We used nationwide claims-based cohorts in South Korea (K-CoV-N; n = 836,164; main cohort) and Japan (JMDC; n = 2,541,021; replication cohort A) and the UK Biobank cohort (UKB; n = 325,843; replication cohort B) after 1:5 propensity score matching. Among the 836,164 individuals in the main cohort (mean age, 50.25 years [SD, 13.86]; 372,914 [44.6%] women), 147,824 were infected with SARS-CoV-2 during the follow-up period (2020−2021). The risk of developing allergic diseases, beyond the first 30 days of diagnosis of COVID-19, significantly increased (HR, 1.20; 95% CI, 1.13−1.27), notably in asthma (HR, 2.25; 95% CI, 1.80−2.83) and allergic rhinitis (HR, 1.23; 95% CI, 1.15−1.32). This risk gradually decreased over time, but it persisted throughout the follow-up period (≥6 months). In addition, the risk increased with increasing severity of COVID-19. Notably, COVID-19 vaccination of at least two doses had a protective effect against subsequent allergic diseases (HR, 0.81; 95% CI, 0.68−0.96). Similar findings were reported in the replication cohorts A and B. Although the potential for misclassification of pre-existing allergic conditions as incident diseases remains a limitation, ethnic diversity for evidence of incident allergic diseases in post-COVID-19 condition has been validated by utilizing multinational and independent population-based cohorts.

Abstract

SARS-CoV-2 infection has been linked to various persistent or new-onset health consequences, including disruption of the immune system. Here, the authors investigate the risk of new-onset allergic diseases following SARS-CoV-2 infection using data from South Korea, Japan, and the UK.

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COVID-19: consider cytokine storm syndromes and immunosuppression

Puja Mehta, Daniel McAuley, Michael Brown … (2020)

As of March 12, 2020, coronavirus disease 2019 (COVID-19) has been confirmed in 125 048 people worldwide, carrying a mortality of approximately 3·7%, 1 compared with a mortality rate of less than 1% from influenza. There is an urgent need for effective treatment. Current focus has been on the development of novel therapeutics, including antivirals and vaccines. Accumulating evidence suggests that a subgroup of patients with severe COVID-19 might have a cytokine storm syndrome. We recommend identification and treatment of hyperinflammation using existing, approved therapies with proven safety profiles to address the immediate need to reduce the rising mortality. Current management of COVID-19 is supportive, and respiratory failure from acute respiratory distress syndrome (ARDS) is the leading cause of mortality. 2 Secondary haemophagocytic lymphohistiocytosis (sHLH) is an under-recognised, hyperinflammatory syndrome characterised by a fulminant and fatal hypercytokinaemia with multiorgan failure. In adults, sHLH is most commonly triggered by viral infections 3 and occurs in 3·7–4·3% of sepsis cases. 4 Cardinal features of sHLH include unremitting fever, cytopenias, and hyperferritinaemia; pulmonary involvement (including ARDS) occurs in approximately 50% of patients. 5 A cytokine profile resembling sHLH is associated with COVID-19 disease severity, characterised by increased interleukin (IL)-2, IL-7, granulocyte-colony stimulating factor, interferon-γ inducible protein 10, monocyte chemoattractant protein 1, macrophage inflammatory protein 1-α, and tumour necrosis factor-α. 6 Predictors of fatality from a recent retrospective, multicentre study of 150 confirmed COVID-19 cases in Wuhan, China, included elevated ferritin (mean 1297·6 ng/ml in non-survivors vs 614·0 ng/ml in survivors; p 39·4°C 49 Organomegaly None 0 Hepatomegaly or splenomegaly 23 Hepatomegaly and splenomegaly 38 Number of cytopenias * One lineage 0 Two lineages 24 Three lineages 34 Triglycerides (mmol/L) 4·0 mmol/L 64 Fibrinogen (g/L) >2·5 g/L 0 ≤2·5 g/L 30 Ferritin ng/ml 6000 ng/ml 50 Serum aspartate aminotransferase <30 IU/L 0 ≥30 IU/L 19 Haemophagocytosis on bone marrow aspirate No 0 Yes 35 Known immunosuppression † No 0 Yes 18 The Hscore 11 generates a probability for the presence of secondary HLH. HScores greater than 169 are 93% sensitive and 86% specific for HLH. Note that bone marrow haemophagocytosis is not mandatory for a diagnosis of HLH. HScores can be calculated using an online HScore calculator. 11 HLH=haemophagocytic lymphohistiocytosis. * Defined as either haemoglobin concentration of 9·2 g/dL or less (≤5·71 mmol/L), a white blood cell count of 5000 white blood cells per mm3 or less, or platelet count of 110 000 platelets per mm3 or less, or all of these criteria combined. † HIV positive or receiving longterm immunosuppressive therapy (ie, glucocorticoids, cyclosporine, azathioprine).

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

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Long COVID: major findings, mechanisms and recommendations

Hannah E. Davis, Lisa McCorkell, Julia Moore Vogel … (2023)

Long COVID is an often debilitating illness that occurs in at least 10% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. More than 200 symptoms have been identified with impacts on multiple organ systems. At least 65 million individuals worldwide are estimated to have long COVID, with cases increasing daily. Biomedical research has made substantial progress in identifying various pathophysiological changes and risk factors and in characterizing the illness; further, similarities with other viral-onset illnesses such as myalgic encephalomyelitis/chronic fatigue syndrome and postural orthostatic tachycardia syndrome have laid the groundwork for research in the field. In this Review, we explore the current literature and highlight key findings, the overlap with other conditions, the variable onset of symptoms, long COVID in children and the impact of vaccinations. Although these key findings are critical to understanding long COVID, current diagnostic and treatment options are insufficient, and clinical trials must be prioritized that address leading hypotheses. Additionally, to strengthen long COVID research, future studies must account for biases and SARS-CoV-2 testing issues, build on viral-onset research, be inclusive of marginalized populations and meaningfully engage patients throughout the research process. Long COVID is an often debilitating illness of severe symptoms that can develop during or following COVID-19. In this Review, Davis, McCorkell, Vogel and Topol explore our knowledge of long COVID and highlight key findings, including potential mechanisms, the overlap with other conditions and potential treatments. They also discuss challenges and recommendations for long COVID research and care.

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Author and article information

Contributors

Hayeon Lee:

ORCID: http://orcid.org/0009-0000-2403-6241

wwhy28@khu.ac.kr

Jinseok Lee:

ORCID: http://orcid.org/0000-0002-8580-490X

gonasago@khu.ac.kr

Dong Keon Yon:

ORCID: http://orcid.org/0000-0003-1628-9948

yonkkang@gmail.com

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 2 April 2024

Publication date PMC-release: 2 April 2024

Publication date Collection: 2024

Volume: 15

Electronic Location Identifier: 2830

Affiliations

[1 ]Department of Medicine, Kyung Hee University College of Medicine, ( https://ror.org/01zqcg218) Seoul, South Korea

[2 ]Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, ( https://ror.org/01zqcg218) Seoul, South Korea

[3 ]Department of Regulatory Science, Kyung Hee University, ( https://ror.org/01zqcg218) Seoul, South Korea

[4 ]Department of Precision Medicine, Sungkyunkwan University School of Medicine, ( https://ror.org/04q78tk20) Suwon, South Korea

[5 ]Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, ( https://ror.org/01zqcg218) Seoul, South Korea

[6 ]Research and Development Unit, Parc Sanitari Sant Joan de Deu, ( https://ror.org/02f3ts956) Barcelona, Spain

[7 ]Centre for Health, Performance and Wellbeing, Anglia Ruskin University, ( https://ror.org/0009t4v78) Cambridge, UK

[8 ]Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, ( https://ror.org/05a0ya142) Cambridge, MA USA

[9 ]Department of Biomedical Engineering, Kyung Hee University, ( https://ror.org/01zqcg218) Yongin, South Korea

[10 ]Department of Electronics and Information Convergence Engineering, Kyung Hee University, ( https://ror.org/01zqcg218) Yongin, South Korea

[11 ]Department of Pediatrics, Kyung Hee University College of Medicine, ( https://ror.org/01zqcg218) Seoul, South Korea

Author information

Minji Kim http://orcid.org/0000-0003-0293-0570

Seung Won Lee http://orcid.org/0000-0001-5632-5208

Sang Youl Rhee http://orcid.org/0000-0003-0119-5818

Min Seo Kim http://orcid.org/0000-0003-2115-7835

Hayeon Lee http://orcid.org/0009-0000-2403-6241

Jinseok Lee http://orcid.org/0000-0002-8580-490X

Dong Keon Yon http://orcid.org/0000-0003-1628-9948

Article

Publisher ID: 47176

DOI: 10.1038/s41467-024-47176-w

PMC ID: 10987608

PubMed ID: 38565542

SO-VID: 08f00a4d-3a12-491a-ac54-151beefbe914

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 14 September 2023

Date accepted : 20 March 2024

Funding

Funded by: FundRef https://doi.org/10.13039/501100003725, National Research Foundation of Korea (NRF);

Award ID: RS-2023-00248157

Award Recipient : Dong Keon Yon

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ScienceOpen disciplines: Uncategorized

Keywords: epidemiology,viral infection,sars-cov-2

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ScienceOpen disciplines: Uncategorized

Keywords: epidemiology, viral infection, sars-cov-2

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Incident allergic diseases in post-COVID-19 condition: multinational cohort studies from South Korea, Japan and the UK

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Novel Coronavirus Disease COVID-19

Most cited references 47

COVID-19: consider cytokine storm syndromes and immunosuppression

An interactive web-based dashboard to track COVID-19 in real time

Long COVID: major findings, mechanisms and recommendations

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