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...
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| Veröffentlicht in: | Immunogenetics (New York) 2021-12, Vol.73 (6), p.449-458 |
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| creator | 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 |
| 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
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| doi_str_mv | 10.1007/s00251-021-01227-4 |
| format | Article |
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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</description><identifier>ISSN: 0093-7711</identifier><identifier>EISSN: 1432-1211</identifier><identifier>DOI: 10.1007/s00251-021-01227-4</identifier><identifier>PMID: 34536086</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Age ; Allergology ; Antigens ; Asymptomatic ; Biomarkers ; Biomedical and Life Sciences ; Biomedicine ; Blood groups ; Cell Biology ; Coronaviruses ; COVID-19 ; Dimers ; Gene Function ; Genotypes ; Haplotypes ; Human Genetics ; Immunoglobulins ; Immunology ; Inflammation ; Ligands ; Mathematical models ; Original ; Original Article ; Parameters ; Patients ; Performance prediction ; Prediction models ; Receptors ; Regression analysis ; Risk ; RNA viruses ; Viral diseases</subject><ispartof>Immunogenetics (New York), 2021-12, Vol.73 (6), p.449-458</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-7ddb32d0b9f182966bd5b18d2b22a992e42932c3de8e6f20d5343a615bd3b1a13</citedby><cites>FETCH-LOGICAL-c451t-7ddb32d0b9f182966bd5b18d2b22a992e42932c3de8e6f20d5343a615bd3b1a13</cites><orcidid>0000-0001-9433-2054</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00251-021-01227-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00251-021-01227-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Beksac, Meral</creatorcontrib><creatorcontrib>Akin, Hasan Yalim</creatorcontrib><creatorcontrib>Gencer-Oncul, Emine Begum</creatorcontrib><creatorcontrib>Yousefzadeh, Mahsa</creatorcontrib><creatorcontrib>Cengiz Seval, Guldane</creatorcontrib><creatorcontrib>Gulten, Ezgi</creatorcontrib><creatorcontrib>Akdemir Kalkan, Irem</creatorcontrib><creatorcontrib>Cinar, Gule</creatorcontrib><creatorcontrib>Memikoglu, Osman</creatorcontrib><creatorcontrib>Karaagaoglu, Ergun</creatorcontrib><creatorcontrib>Dalva, Klara</creatorcontrib><title>A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity</title><title>Immunogenetics (New York)</title><addtitle>Immunogenetics</addtitle><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</description><subject>Age</subject><subject>Allergology</subject><subject>Antigens</subject><subject>Asymptomatic</subject><subject>Biomarkers</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Blood groups</subject><subject>Cell Biology</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Dimers</subject><subject>Gene Function</subject><subject>Genotypes</subject><subject>Haplotypes</subject><subject>Human Genetics</subject><subject>Immunoglobulins</subject><subject>Immunology</subject><subject>Inflammation</subject><subject>Ligands</subject><subject>Mathematical models</subject><subject>Original</subject><subject>Original Article</subject><subject>Parameters</subject><subject>Patients</subject><subject>Performance prediction</subject><subject>Prediction models</subject><subject>Receptors</subject><subject>Regression analysis</subject><subject>Risk</subject><subject>RNA viruses</subject><subject>Viral diseases</subject><issn>0093-7711</issn><issn>1432-1211</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kU1rFTEUhoMo9nr1D7gKuKmL0Zwk85GNUK5flxYKRd2GzOTMNG1mMiYzhbv1l5t6i6ILF-FAzvM-JLyEvAT2Bhir3ybGeAkF4_kA53UhH5ENSMEL4ACPyYYxJYq6Bjghz1K6YQxKxaun5ETIUlSsqTbkxxkdg0VP3bTgEM3ipoGeO-8x0v04rlMYfGhX76bCu1ukV9jhvIRIT8_3V6_ptZl9WA4zJtrny-jSLZ0jWtctLkw09HR3-W3_vgBFu-xwnfHUuoQmIU14h9Eth-fkSW98whcPc0u-fvzwZfe5uLj8tN-dXRSdLGEpamtbwS1rVQ8NV1XV2rKFxvKWc6MUR8mV4J2w2GDVc2ZLIYWpoGytaMGA2JJ3R--8tiPaDqclGq_n6EYTDzoYp__eTO5aD-FON1IqDiILTh8EMXxfMS16dKlD782EYU2al7UUSgHwjL76B70Ja5zy9zKlRC1rmYvaEn6kuhhSitj_fgwwfV-xPlasc8X6V8X6PiSOoZThacD4R_2f1E9RkKjL</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Beksac, Meral</creator><creator>Akin, Hasan Yalim</creator><creator>Gencer-Oncul, Emine Begum</creator><creator>Yousefzadeh, 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B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7T5</scope><scope>7T7</scope><scope>7TK</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9433-2054</orcidid></search><sort><creationdate>20211201</creationdate><title>A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-7ddb32d0b9f182966bd5b18d2b22a992e42932c3de8e6f20d5343a615bd3b1a13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age</topic><topic>Allergology</topic><topic>Antigens</topic><topic>Asymptomatic</topic><topic>Biomarkers</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Blood groups</topic><topic>Cell Biology</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Dimers</topic><topic>Gene Function</topic><topic>Genotypes</topic><topic>Haplotypes</topic><topic>Human Genetics</topic><topic>Immunoglobulins</topic><topic>Immunology</topic><topic>Inflammation</topic><topic>Ligands</topic><topic>Mathematical models</topic><topic>Original</topic><topic>Original Article</topic><topic>Parameters</topic><topic>Patients</topic><topic>Performance prediction</topic><topic>Prediction models</topic><topic>Receptors</topic><topic>Regression analysis</topic><topic>Risk</topic><topic>RNA viruses</topic><topic>Viral diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Beksac, Meral</creatorcontrib><creatorcontrib>Akin, Hasan Yalim</creatorcontrib><creatorcontrib>Gencer-Oncul, Emine Begum</creatorcontrib><creatorcontrib>Yousefzadeh, Mahsa</creatorcontrib><creatorcontrib>Cengiz Seval, Guldane</creatorcontrib><creatorcontrib>Gulten, Ezgi</creatorcontrib><creatorcontrib>Akdemir Kalkan, Irem</creatorcontrib><creatorcontrib>Cinar, 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Beksac, Meral</au><au>Akin, Hasan Yalim</au><au>Gencer-Oncul, Emine Begum</au><au>Yousefzadeh, Mahsa</au><au>Cengiz Seval, Guldane</au><au>Gulten, Ezgi</au><au>Akdemir Kalkan, Irem</au><au>Cinar, Gule</au><au>Memikoglu, Osman</au><au>Karaagaoglu, Ergun</au><au>Dalva, Klara</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity</atitle><jtitle>Immunogenetics (New York)</jtitle><stitle>Immunogenetics</stitle><date>2021-12-01</date><risdate>2021</risdate><volume>73</volume><issue>6</issue><spage>449</spage><epage>458</epage><pages>449-458</pages><issn>0093-7711</issn><eissn>1432-1211</eissn><abstract>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.
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| source | Springer Nature - Complete Springer Journals |
| subjects | Age Allergology Antigens Asymptomatic Biomarkers Biomedical and Life Sciences Biomedicine Blood groups Cell Biology Coronaviruses COVID-19 Dimers Gene Function Genotypes Haplotypes Human Genetics Immunoglobulins Immunology Inflammation Ligands Mathematical models Original Original Article Parameters Patients Performance prediction Prediction models Receptors Regression analysis Risk RNA viruses Viral diseases |
| title | A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity |
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