Cargando…
A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity
Associations between inherited Killer Immunoglobulin-like Receptor (KIR) genotypes and the severity of multiple RNA virus infections have been reported. This prospective study was initiated to investigate if such an association exists for COVID-19. In this cohort study performed at Ankara University...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8449213/ https://www.ncbi.nlm.nih.gov/pubmed/34536086 http://dx.doi.org/10.1007/s00251-021-01227-4 |
_version_ | 1784569382731513856 |
---|---|
author | Beksac, Meral Akin, Hasan Yalim Gencer-Oncul, Emine Begum Yousefzadeh, Mahsa Cengiz Seval, Guldane Gulten, Ezgi Akdemir Kalkan, Irem Cinar, Gule Memikoglu, Osman Karaagaoglu, Ergun Dalva, Klara |
author_facet | Beksac, Meral Akin, Hasan Yalim Gencer-Oncul, Emine Begum Yousefzadeh, Mahsa Cengiz Seval, Guldane Gulten, Ezgi Akdemir Kalkan, Irem Cinar, Gule Memikoglu, Osman Karaagaoglu, Ergun Dalva, Klara |
author_sort | Beksac, Meral |
collection | PubMed |
description | Associations between inherited Killer Immunoglobulin-like Receptor (KIR) genotypes and the severity of multiple RNA virus infections have been reported. This prospective study was initiated to investigate if such an association exists for COVID-19. In this cohort study performed at Ankara University, 132 COVID-19 patients (56 asymptomatic, 51 mild-intermediate, and 25 patients with severe disease) were genotyped for KIR and ligands. Ankara University Donor Registry (n:449) KIR data was used for comparison. Clinical parameters (age, gender, comorbidities, blood group antigens, inflammation biomarkers) and KIR genotypes across cohorts of asymptomatic, mild-intermediate, or severe disease were compared to construct a risk prediction model based on multivariate binary logistic regression analysis with backward elimination method. Age, blood group, number of comorbidities, CRP, D-dimer, and telomeric and centromeric KIR genotypes (tAA, tAB1, and cAB1) along with their cognate ligands were found to differ between cohorts. Two prediction models were constructed; both included age, number of comorbidities, and blood group. Inclusion of the KIR genotypes in the second prediction model exp (-3.52 + 1.56 age group - 2.74 blood group (type A vs others) + 1.26 number of comorbidities - 2.46 tAB1 with ligand + 3.17 tAA with ligand) increased the predictive performance with a 92.9% correct classification for asymptomatic and 76% for severe cases (AUC: 0.93; P < 0.0001, 95% CI 0.88, 0.99). This novel risk model, consisting of KIR genotypes with their cognate ligands, and clinical parameters but excluding earlier published inflammation-related biomarkers allow for the prediction of the severity of COVID-19 infection prior to the onset of infection. This study is listed in the National COVID-19 clinical research studies database. GRAPHICAL ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-8449213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-84492132021-09-20 A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity Beksac, Meral Akin, Hasan Yalim Gencer-Oncul, Emine Begum Yousefzadeh, Mahsa Cengiz Seval, Guldane Gulten, Ezgi Akdemir Kalkan, Irem Cinar, Gule Memikoglu, Osman Karaagaoglu, Ergun Dalva, Klara Immunogenetics Original Article Associations between inherited Killer Immunoglobulin-like Receptor (KIR) genotypes and the severity of multiple RNA virus infections have been reported. This prospective study was initiated to investigate if such an association exists for COVID-19. In this cohort study performed at Ankara University, 132 COVID-19 patients (56 asymptomatic, 51 mild-intermediate, and 25 patients with severe disease) were genotyped for KIR and ligands. Ankara University Donor Registry (n:449) KIR data was used for comparison. Clinical parameters (age, gender, comorbidities, blood group antigens, inflammation biomarkers) and KIR genotypes across cohorts of asymptomatic, mild-intermediate, or severe disease were compared to construct a risk prediction model based on multivariate binary logistic regression analysis with backward elimination method. Age, blood group, number of comorbidities, CRP, D-dimer, and telomeric and centromeric KIR genotypes (tAA, tAB1, and cAB1) along with their cognate ligands were found to differ between cohorts. Two prediction models were constructed; both included age, number of comorbidities, and blood group. Inclusion of the KIR genotypes in the second prediction model exp (-3.52 + 1.56 age group - 2.74 blood group (type A vs others) + 1.26 number of comorbidities - 2.46 tAB1 with ligand + 3.17 tAA with ligand) increased the predictive performance with a 92.9% correct classification for asymptomatic and 76% for severe cases (AUC: 0.93; P < 0.0001, 95% CI 0.88, 0.99). This novel risk model, consisting of KIR genotypes with their cognate ligands, and clinical parameters but excluding earlier published inflammation-related biomarkers allow for the prediction of the severity of COVID-19 infection prior to the onset of infection. This study is listed in the National COVID-19 clinical research studies database. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2021-09-18 2021 /pmc/articles/PMC8449213/ /pubmed/34536086 http://dx.doi.org/10.1007/s00251-021-01227-4 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Beksac, Meral Akin, Hasan Yalim Gencer-Oncul, Emine Begum Yousefzadeh, Mahsa Cengiz Seval, Guldane Gulten, Ezgi Akdemir Kalkan, Irem Cinar, Gule Memikoglu, Osman Karaagaoglu, Ergun Dalva, Klara A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity |
title | A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity |
title_full | A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity |
title_fullStr | A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity |
title_full_unstemmed | A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity |
title_short | A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity |
title_sort | model integrating killer immunoglobulin-like receptor (kir) haplotypes for risk prediction of covid-19 clinical disease severity |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8449213/ https://www.ncbi.nlm.nih.gov/pubmed/34536086 http://dx.doi.org/10.1007/s00251-021-01227-4 |
work_keys_str_mv | AT beksacmeral amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT akinhasanyalim amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT genceronculeminebegum amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT yousefzadehmahsa amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT cengizsevalguldane amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT gultenezgi amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT akdemirkalkanirem amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT cinargule amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT memikogluosman amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT karaagaogluergun amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT dalvaklara amodelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT beksacmeral modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT akinhasanyalim modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT genceronculeminebegum modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT yousefzadehmahsa modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT cengizsevalguldane modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT gultenezgi modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT akdemirkalkanirem modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT cinargule modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT memikogluosman modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT karaagaogluergun modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity AT dalvaklara modelintegratingkillerimmunoglobulinlikereceptorkirhaplotypesforriskpredictionofcovid19clinicaldiseaseseverity |