1
|
Elemam NM, Hammoudeh S, Salameh L, Mahboub B, Alsafar H, Talaat IM, Habib P, Siddiqui M, Hassan KO, Al-Assaf OY, Taneera J, Sulaiman N, Hamoudi R, Maghazachi AA, Hamid Q, Saber-Ayad M. Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling. Front Immunol 2022; 13:865845. [PMID: 35529862 PMCID: PMC9067542 DOI: 10.3389/fimmu.2022.865845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/25/2022] [Indexed: 12/15/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Noha M Elemam
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Sarah Hammoudeh
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Laila Salameh
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Dubai Health Authority, Rashid Hospital, Dubai, United Arab Emirates
| | - Bassam Mahboub
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Dubai Health Authority, Rashid Hospital, Dubai, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Biomedical Engineering, College of Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Emirates Bio-Research Centre, Ministry of Interior, Abu Dhabi, United Arab Emirates
| | - Iman M Talaat
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Pathology Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Peter Habib
- School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt
| | - Mehmood Siddiqui
- Dubai Health Authority, Rashid Hospital, Dubai, United Arab Emirates
| | | | | | - Jalal Taneera
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Nabil Sulaiman
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Rifat Hamoudi
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Azzam A Maghazachi
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates
| | - Qutayba Hamid
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Meakins-Christie Laboratories, Research Institute of the McGill University Health Center, Montreal, QC, Canada
| | - Maha Saber-Ayad
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,College of Medicine, Cairo University, Giza, Egypt
| |
Collapse
|