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Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19
Introduction: A recently emerging respiratory disease named coronavirus disease 2019 (COVID-19) has quickly spread across the world. This disease is initiated by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and uncontrolled cytokine storm, but it remains unknown as to whether a robus...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350507/ https://www.ncbi.nlm.nih.gov/pubmed/32719810 http://dx.doi.org/10.3389/fmolb.2020.00157 |
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author | Zhang, Bicheng Zhou, Xiaoyang Zhu, Chengliang Song, Yuxiao Feng, Fan Qiu, Yanru Feng, Jia Jia, Qingzhu Song, Qibin Zhu, Bo Wang, Jun |
author_facet | Zhang, Bicheng Zhou, Xiaoyang Zhu, Chengliang Song, Yuxiao Feng, Fan Qiu, Yanru Feng, Jia Jia, Qingzhu Song, Qibin Zhu, Bo Wang, Jun |
author_sort | Zhang, Bicheng |
collection | PubMed |
description | Introduction: A recently emerging respiratory disease named coronavirus disease 2019 (COVID-19) has quickly spread across the world. This disease is initiated by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and uncontrolled cytokine storm, but it remains unknown as to whether a robust antibody response is related to clinical deterioration and poor outcome in COVID-19 patients. Methods: Anti-SARS-CoV-2 IgG and IgM antibodies were determined by chemiluminescence analysis (CLIA) in COVID-19 patients at a single center in Wuhan. Median IgG and IgM levels in acute and convalescent-phase sera (within 35 days) for all included patients were calculated and compared between severe and non-severe patients. Immune response phenotyping based on the late IgG levels and neutrophil-to-lymphocyte ratio (NLR) was characterized to stratified patients into different disease severities and outcomes. Results: A total of 222 patients were included in this study. IgG was first detected on day 4 of illness, and its peak levels occurred in the fourth week. Severe cases were more frequently found in patients with high IgG levels, compared to those with low IgG levels (51.8 vs. 32.3%; p = 0.008). Severity rates for patients with NLR(hi)IgG(hi), NLR(hi)IgG(lo), NLR(lo)IgG(hi), and NLR(lo)IgG(lo) phenotype were 72.3, 48.5, 33.3, and 15.6%, respectively (p < 0.0001). Furthermore, severe patients with NLR(hi)IgG(hi), NLR(hi)IgG(lo) had higher inflammatory cytokines levels including IL-2, IL-6 and IL-10, and decreased CD4+ T cell count compared to those with NLR(lo)IgG(lo) phenotype (p < 0.05). Recovery rates for severe patients with NLR(hi)IgG(hi), NLR(hi)IgG(lo), NLR(lo)IgG(hi), and NLR(lo)IgG(lo) phenotype were 58.8% (20/34), 68.8% (11/16), 80.0% (4/5), and 100% (12/12), respectively (p = 0.0592). Dead cases only occurred in NLR(hi)IgG(hi) and NLR(hi)IgG(lo) phenotypes. Conclusions: COVID-19 severity is associated with increased IgG response, and an immune response phenotyping based on the late IgG response and NLR could act as a simple complementary tool to discriminate between severe and non-severe COVID-19 patients, and further predict their clinical outcome. |
format | Online Article Text |
id | pubmed-7350507 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73505072020-07-26 Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19 Zhang, Bicheng Zhou, Xiaoyang Zhu, Chengliang Song, Yuxiao Feng, Fan Qiu, Yanru Feng, Jia Jia, Qingzhu Song, Qibin Zhu, Bo Wang, Jun Front Mol Biosci Molecular Biosciences Introduction: A recently emerging respiratory disease named coronavirus disease 2019 (COVID-19) has quickly spread across the world. This disease is initiated by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and uncontrolled cytokine storm, but it remains unknown as to whether a robust antibody response is related to clinical deterioration and poor outcome in COVID-19 patients. Methods: Anti-SARS-CoV-2 IgG and IgM antibodies were determined by chemiluminescence analysis (CLIA) in COVID-19 patients at a single center in Wuhan. Median IgG and IgM levels in acute and convalescent-phase sera (within 35 days) for all included patients were calculated and compared between severe and non-severe patients. Immune response phenotyping based on the late IgG levels and neutrophil-to-lymphocyte ratio (NLR) was characterized to stratified patients into different disease severities and outcomes. Results: A total of 222 patients were included in this study. IgG was first detected on day 4 of illness, and its peak levels occurred in the fourth week. Severe cases were more frequently found in patients with high IgG levels, compared to those with low IgG levels (51.8 vs. 32.3%; p = 0.008). Severity rates for patients with NLR(hi)IgG(hi), NLR(hi)IgG(lo), NLR(lo)IgG(hi), and NLR(lo)IgG(lo) phenotype were 72.3, 48.5, 33.3, and 15.6%, respectively (p < 0.0001). Furthermore, severe patients with NLR(hi)IgG(hi), NLR(hi)IgG(lo) had higher inflammatory cytokines levels including IL-2, IL-6 and IL-10, and decreased CD4+ T cell count compared to those with NLR(lo)IgG(lo) phenotype (p < 0.05). Recovery rates for severe patients with NLR(hi)IgG(hi), NLR(hi)IgG(lo), NLR(lo)IgG(hi), and NLR(lo)IgG(lo) phenotype were 58.8% (20/34), 68.8% (11/16), 80.0% (4/5), and 100% (12/12), respectively (p = 0.0592). Dead cases only occurred in NLR(hi)IgG(hi) and NLR(hi)IgG(lo) phenotypes. Conclusions: COVID-19 severity is associated with increased IgG response, and an immune response phenotyping based on the late IgG response and NLR could act as a simple complementary tool to discriminate between severe and non-severe COVID-19 patients, and further predict their clinical outcome. Frontiers Media S.A. 2020-07-03 /pmc/articles/PMC7350507/ /pubmed/32719810 http://dx.doi.org/10.3389/fmolb.2020.00157 Text en Copyright © 2020 Zhang, Zhou, Zhu, Song, Feng, Qiu, Feng, Jia, Song, Zhu and Wang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Zhang, Bicheng Zhou, Xiaoyang Zhu, Chengliang Song, Yuxiao Feng, Fan Qiu, Yanru Feng, Jia Jia, Qingzhu Song, Qibin Zhu, Bo Wang, Jun Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19 |
title | Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19 |
title_full | Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19 |
title_fullStr | Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19 |
title_full_unstemmed | Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19 |
title_short | Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19 |
title_sort | immune phenotyping based on the neutrophil-to-lymphocyte ratio and igg level predicts disease severity and outcome for patients with covid-19 |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350507/ https://www.ncbi.nlm.nih.gov/pubmed/32719810 http://dx.doi.org/10.3389/fmolb.2020.00157 |
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