Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity: Protocol for a Retrospective Observational Study.

COVID-19 clinical research comorbidity electronic health record multiomics severity

Journal

JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504

Informations de publication

Date de publication:
14 Feb 2024
Historique:
received: 11 07 2023
accepted: 09 11 2023
revised: 23 10 2023
medline: 14 2 2024
pubmed: 14 2 2024
entrez: 14 2 2024
Statut: epublish

Résumé

Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression. The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19. The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk. An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P<.001), grouped according to the International Classification of Diseases 10 codes. This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants. DERR1-10.2196/50733.

Sections du résumé

BACKGROUND BACKGROUND
Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression.
OBJECTIVE OBJECTIVE
The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19.
METHODS METHODS
The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk.
RESULTS RESULTS
An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P<.001), grouped according to the International Classification of Diseases 10 codes.
CONCLUSIONS CONCLUSIONS
This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) UNASSIGNED
DERR1-10.2196/50733.

Identifiants

pubmed: 38354037
pii: v13i1e50733
doi: 10.2196/50733
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e50733

Informations de copyright

©Andrew English, Darren McDaid, Seodhna M Lynch, Joseph McLaughlin, Eamonn Cooper, Benjamin Wingfield, Martin Kelly, Manav Bhavsar, Victoria McGilligan, Rachelle E Irwin, Magda Bucholc, Shu-Dong Zhang, Priyank Shukla, Taranjit Singh Rai, Anthony J Bjourson, Elaine Murray, David S Gibson, Colum Walsh. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 14.02.2024.

Auteurs

Andrew English (A)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.
National Horizons Centre, Teesside University, Middlesbrough, United Kingdom.

Darren McDaid (D)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Seodhna M Lynch (SM)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Joseph McLaughlin (J)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Eamonn Cooper (E)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Benjamin Wingfield (B)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Martin Kelly (M)

Western Health Social Care Trust, Londonderry, United Kingdom.

Manav Bhavsar (M)

Western Health Social Care Trust, Londonderry, United Kingdom.

Victoria McGilligan (V)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Rachelle E Irwin (RE)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Magda Bucholc (M)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Shu-Dong Zhang (SD)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Priyank Shukla (P)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Taranjit Singh Rai (TS)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Anthony J Bjourson (AJ)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Elaine Murray (E)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

David S Gibson (DS)

Personalised Medicine Centre, School of Medicine, Ulster University, Derry/Londonderry, United Kingdom.

Colum Walsh (C)

Department of Biomedical and Clinical Sciences, Linköping University, Uppsala, Sweden.

Classifications MeSH