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Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinica...

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Autores principales: Elemam, Noha M., Hammoudeh, Sarah, Salameh, Laila, Mahboub, Bassam, Alsafar, Habiba, Talaat, Iman M., Habib, Peter, Siddiqui, Mehmood, Hassan, Khalid Omar, Al-Assaf, Omar Yousef, Taneera, Jalal, Sulaiman, Nabil, Hamoudi, Rifat, Maghazachi, Azzam A., Hamid, Qutayba, Saber-Ayad, Maha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9067542/
https://www.ncbi.nlm.nih.gov/pubmed/35529862
http://dx.doi.org/10.3389/fimmu.2022.865845
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author Elemam, Noha M.
Hammoudeh, Sarah
Salameh, Laila
Mahboub, Bassam
Alsafar, Habiba
Talaat, Iman M.
Habib, Peter
Siddiqui, Mehmood
Hassan, Khalid Omar
Al-Assaf, Omar Yousef
Taneera, Jalal
Sulaiman, Nabil
Hamoudi, Rifat
Maghazachi, Azzam A.
Hamid, Qutayba
Saber-Ayad, Maha
author_facet Elemam, Noha M.
Hammoudeh, Sarah
Salameh, Laila
Mahboub, Bassam
Alsafar, Habiba
Talaat, Iman M.
Habib, Peter
Siddiqui, Mehmood
Hassan, Khalid Omar
Al-Assaf, Omar Yousef
Taneera, Jalal
Sulaiman, Nabil
Hamoudi, Rifat
Maghazachi, Azzam A.
Hamid, Qutayba
Saber-Ayad, Maha
author_sort Elemam, Noha M.
collection PubMed
description Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.
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spelling pubmed-90675422022-05-05 Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling Elemam, Noha M. Hammoudeh, Sarah Salameh, Laila Mahboub, Bassam Alsafar, Habiba Talaat, Iman M. Habib, Peter Siddiqui, Mehmood Hassan, Khalid Omar Al-Assaf, Omar Yousef Taneera, Jalal Sulaiman, Nabil Hamoudi, Rifat Maghazachi, Azzam A. Hamid, Qutayba Saber-Ayad, Maha Front Immunol Immunology Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19. Frontiers Media S.A. 2022-04-20 /pmc/articles/PMC9067542/ /pubmed/35529862 http://dx.doi.org/10.3389/fimmu.2022.865845 Text en Copyright © 2022 Elemam, Hammoudeh, Salameh, Mahboub, Alsafar, Talaat, Habib, Siddiqui, Hassan, Al-Assaf, Taneera, Sulaiman, Hamoudi, Maghazachi, Hamid and Saber-Ayad https://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 Immunology
Elemam, Noha M.
Hammoudeh, Sarah
Salameh, Laila
Mahboub, Bassam
Alsafar, Habiba
Talaat, Iman M.
Habib, Peter
Siddiqui, Mehmood
Hassan, Khalid Omar
Al-Assaf, Omar Yousef
Taneera, Jalal
Sulaiman, Nabil
Hamoudi, Rifat
Maghazachi, Azzam A.
Hamid, Qutayba
Saber-Ayad, Maha
Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling
title Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling
title_full Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling
title_fullStr Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling
title_full_unstemmed Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling
title_short Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling
title_sort identifying immunological and clinical predictors of covid-19 severity and sequelae by mathematical modeling
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9067542/
https://www.ncbi.nlm.nih.gov/pubmed/35529862
http://dx.doi.org/10.3389/fimmu.2022.865845
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