Predictive Role of Cytokine and Adipokine Panel in Hospitalized COVID-19 Patients: Evaluation of Disease Severity, Survival and Lung Sequelae.
Int J Mol Sci
; 24(16)2023 Aug 20.
Article
in En
| MEDLINE
| ID: mdl-37629176
ABSTRACT
Coronavirus disease 2019 (COVID-19) may determine a multisystemic chronic syndrome after resolution of SARS-CoV-2 infection in a significant percentage of patients. Persistent cytokine dysregulation can contribute to long-lasting inflammation and tissue damage, resulting in the diverse, often debilitating symptoms experienced by some patients (so-called long COVID syndrome). The aim of our study was to evaluate the value of a panel of serum biomarkers of severity and prognosis in patients hospitalized for COVID-19 and also as predictive factors for the development of post-COVID lung sequelae after discharge from the hospital. All blood sampling was performed in the first 24 h after admission to the hospital. Serum analyte concentrations of IL-4, IL-2, CXCL10 (IP-10), IL-1ß, TNF-α, CCL2 (MCP-1), IL-17A, IL-6, IL-10, IFN-γ, IL-12p70 and TGF-ß1 were quantified by bead-based multiplex LEGENDplex™ analysis and commercially available ELISA kits. A total of 108 COVID-19 patients were enrolled in the study. Comparative analysis of these proteins showed higher levels of TGF-ß and IL-6 and lower levels of RBP-4 and IL-10 in the severe group. Age, adiponectin, IL-8 and IL-32 resulted as the best predictors for survival. Moreover, IL-1ß, IL17A, TNF-α, TGF-ß, IL-4 and IL-6 were significantly higher in patients who showed HRCT evidence of fibrotic interstitial alterations at follow-up than patients who did not. The initial inflammatory status of patients on admission to the hospital with COVID-19, as reflected by the present panel of adipose tissue-related biomarkers and cytokines, offered insights into medium-term prognosis.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cytokines
/
COVID-19
Type of study:
Prognostic_studies
/
Risk_factors_studies
Aspects:
Patient_preference
Limits:
Humans
Language:
En
Journal:
Int J Mol Sci
Year:
2023
Document type:
Article