D’Rozario, Ranit ; Raychaudhuri, Deblina ; Bandopadhyay, Purbita ; Sarif, Jafar ; Mehta, Priyanka ; Liu, Chinky Shiu Chen ; Sinha, Bishnu Prasad ; Roy, Jayasree ; Bhaduri, Ritwik ; Das, Monidipa ; Bandyopadhyay, Sanghamitra ; Paul, Shekhar Ranjan ; Chatterjee, Shilpak ; Pandey, Rajesh ; Ray, Yogiraj ; Ganguly, Dipyaman (2023) Circulating interleukin-8 dynamics parallels disease course and is linked to clinical outcomes in severe COVID-19. Viruses, 15 (2). p. 549. ISSN 1999-4915
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Official URL: https://doi.org/10.3390/v15020549
Related URL: http://dx.doi.org/10.3390/v15020549
Abstract
Severe COVID-19 frequently features a systemic deluge of cytokines. Circulating cytokines that can stratify risks are useful for more effective triage and management. Here, we ran a machine-learning algorithm on a dataset of 36 plasma cytokines in a cohort of severe COVID-19 to identify cytokine/s useful for describing the dynamic clinical state in multiple regression analysis. We performed RNA-sequencing of circulating blood cells collected at different time-points. From a Bayesian Information Criterion analysis, a combination of interleukin-8 (IL-8), Eotaxin, and Interferon-γ (IFNγ) was found to be significantly linked to blood oxygenation over seven days. Individually testing the cytokines in receiver operator characteristics analyses identified IL-8 as a strong stratifier for clinical outcomes. Circulating IL-8 dynamics paralleled disease course. We also revealed key transitions in immune transcriptome in patients stratified for circulating IL-8 at three time-points. The study identifies plasma IL-8 as a key pathogenic cytokine linking systemic hyper-inflammation to the clinical outcomes in COVID-19.
Item Type: | Article |
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Source: | Copyright of this article belongs to MDPI. |
Keywords: | COVID-19; Cytokines; Machine Learning; IL-8; Survival; Biomarker |
ID Code: | 138854 |
Deposited On: | 01 Sep 2025 07:42 |
Last Modified: | 01 Sep 2025 07:42 |
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