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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
Open accessPublished version of a preprint
Abstract
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 nonsevere 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% versus 32.3%; p=0.008). Severity rates for patients with NLRhiIgGhi, NLRhiIgGlo, NLRloIgGhi, and NLRloIgGlo phenotype were 72.3%, 48.5%, 33.3%, and 15.6%, respectively (p<0.0001). Furthermore, severe patients with NLRhiIgGhi, NLRhiIgGlo had higher proinflammatory cytokines levels including IL-2, IL-6 and IL-10, and decreased CD4+ T cell count compared to those with NLRloIgGlo phenotype (p<0.05). Recovery rates for severe patients with NLRhiIgGhi, NLRhiIgGlo, NLRloIgGhi, and NLRloIgGlo 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 NLRhiIgGhi and NLRhiIgGlo 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 nonsevere COVID-19 patients, and further predict their clinical outcome.
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Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationThe third international consensus definitions for sepsis and septic shock (Sepsis-3).
Singer, M., Deutschman, C. S., Seymour, C. W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G. R., Chiche, J.-D., Coopersmith, C. M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0287 · Cited by 28,250
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
A new coronavirus associated with human respiratory disease in China
Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.-W., Tian, J.-H., Pei, Y.-Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2008-3 · Cited by 11,688
Early goal-directed therapy in the treatment of severe sepsis and septic shock
Rivers, E., Nguyen, B., Havstad, S., Ressler, J., Muzzin, A., Knoblich, B., Peterson, E., & Tomlanovich, M. (2001) · New England Journal of Medicine · doi:10.1056/nejmoa010307 · Cited by 10,743
Pathological findings of COVID-19 associated with acute respiratory distress syndrome
Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30076-x · Cited by 8,545
Zhang, C., Wu, Z., Li, J.-W., Zhao, H., & Wang, G.-Q. (2020) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2020.105954 · Cited by 1,758
Liu, J., Li, S., Liu, J., Liang, B., Wang, X., Wang, H., Li, W., Tong, Q., Yi, J., Zhao, L., et al. (2020) · eBioMedicine · doi:10.1016/j.ebiom.2020.102763 · Cited by 1,702
Self-citationLiu, J., Liu, Y., Xiang, P., Pu, L., Xiong, H., Li, C., Zhang, M., Tan, J., Xu, Y., Song, R., et al. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02374-0 · Cited by 1,082
Research works printed but not linked: 1
- Zhang, 2020, Immune phenotyping based on neutrophil-to-lymphocyte ratio and IgG predicts disease severity and outcome for patients with COVID-19, medRxiv, 10.1101/2020.03.12.20035048 · DOI
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COVID-19: immunopathology and its implications for therapy
Cao, X. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0308-3 · Cited by 1,609
A single-cell atlas of the peripheral immune response in patients with severe COVID-19
Wilk, A. J., Rustagi, A., Zhao, N. Q., Roque, J., Martínez-Colón, G. J., McKechnie, J. L., Ivison, G. T., Ranganath, T., Vergara, R., Hollis, T., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0944-y · Cited by 1,493
Immunology of COVID-19: Current State of the Science
Vabret, N., Britton, G. J., Gruber, C., Hegde, S., Kim, J., Kuksin, M., Levantovsky, R., Malle, L., Moreira, A., Park, M. D., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.05.002 · Cited by 1,623
Aging in COVID-19: Vulnerability, immunity and intervention
Chen, Y., Klein, S. L., Garibaldi, B. T., Li, H., Wu, C., Osevala, N. M., Li, T., Margolick, J. B., Pawelec, G., & Leng, S. X. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101205 · Cited by 1,007
Leisman, D. E., Ronner, L., Pinotti, R., Taylor, M. D., Sinha, P., Calfee, C. S., Hirayama, A. V., Mastroiani, F., Turtle, C. J., Harhay, M. O., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30404-5 · Cited by 855
Metadata
- Authors
- Zhang, B., Zhou, X., Zhu, C., Song, Y., Feng, F., Qiu, Y., Feng, J., Jia, Q., Song, Q., Zhu, B., & Wang, J.
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2020-07-03
- Volume
- 7
- Pages
- 157-157
- ISSN
- 2296-889X (ISSN-L)
- OpenAlex
- W3045525085
- Cited by
- 528
- References
- 18
- FWCI
- 14.55
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zhang, B., Zhou, X., Zhu, C., Song, Y., Feng, F., Qiu, Y., Feng, J., Jia, Q., Song, Q., Zhu, B., & Wang, J. (2020). Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19. Frontiers in Molecular Biosciences. https://doi.org/10.3389/fmolb.2020.00157
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