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Article

Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach

1
Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, Spain
2
Department of Internal Medicine, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Academic Editor: Carlo Cervellati
Antioxidants 2021, 10(6), 991; https://doi.org/10.3390/antiox10060991
Received: 7 June 2021 / Revised: 17 June 2021 / Accepted: 17 June 2021 / Published: 21 June 2021
(This article belongs to the Section Health Outcomes of Antioxidants and Oxidative Stress)
SARS-CoV-2 infection produces a response of the innate immune system causing oxidative stress and a strong inflammatory reaction termed ‘cytokine storm’ that is one of the leading causes of death. Paraoxonase-1 (PON1) protects against oxidative stress by hydrolyzing lipoperoxides. Alterations in PON1 activity have been associated with pro-inflammatory mediators such as the chemokine (C-C motif) ligand 2 (CCL2), and the glycoprotein galectin-3. We aimed to investigate the alterations in the circulating levels of PON1, CCL2, and galectin-3 in 126 patients with COVID-19 and their interactions with clinical variables and analytical parameters. A machine learning approach was used to identify predictive markers of the disease. For comparisons, we recruited 45 COVID-19 negative patients and 50 healthy individuals. Our approach identified a synergy between oxidative stress, inflammation, and fibrogenesis in positive patients that is not observed in negative patients. PON1 activity was the parameter with the greatest power to discriminate between patients who were either positive or negative for COVID-19, while their levels of CCL2 and galectin-3 were similar. We suggest that the measurement of serum PON1 activity may be a useful marker for the diagnosis of COVID-19. View Full-Text
Keywords: biomarkers; chemokines; COVID-19; galectin-3; machine learning; paraoxonase-1; SARS-CoV-2 biomarkers; chemokines; COVID-19; galectin-3; machine learning; paraoxonase-1; SARS-CoV-2
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MDPI and ACS Style

Rodríguez-Tomàs, E.; Iftimie, S.; Castañé, H.; Baiges-Gaya, G.; Hernández-Aguilera, A.; González-Viñas, M.; Castro, A.; Camps, J.; Joven, J. Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach. Antioxidants 2021, 10, 991. https://doi.org/10.3390/antiox10060991

AMA Style

Rodríguez-Tomàs E, Iftimie S, Castañé H, Baiges-Gaya G, Hernández-Aguilera A, González-Viñas M, Castro A, Camps J, Joven J. Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach. Antioxidants. 2021; 10(6):991. https://doi.org/10.3390/antiox10060991

Chicago/Turabian Style

Rodríguez-Tomàs, Elisabet, Simona Iftimie, Helena Castañé, Gerard Baiges-Gaya, Anna Hernández-Aguilera, María González-Viñas, Antoni Castro, Jordi Camps, and Jorge Joven. 2021. "Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach" Antioxidants 10, no. 6: 991. https://doi.org/10.3390/antiox10060991

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