Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach
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 PON...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Antioxidants |
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| Online Access: | https://www.mdpi.com/2076-3921/10/6/991 |
| _version_ | 1827689141660286976 |
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| author | Elisabet Rodríguez-Tomàs Simona Iftimie Helena Castañé Gerard Baiges-Gaya Anna Hernández-Aguilera María González-Viñas Antoni Castro Jordi Camps Jorge Joven |
| author_facet | Elisabet Rodríguez-Tomàs Simona Iftimie Helena Castañé Gerard Baiges-Gaya Anna Hernández-Aguilera María González-Viñas Antoni Castro Jordi Camps Jorge Joven |
| author_sort | Elisabet Rodríguez-Tomàs |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:12:38Z |
| format | Article |
| id | doaj.art-5c1c4e575ba947f2ad97927ecc35945f |
| institution | Directory Open Access Journal |
| issn | 2076-3921 |
| language | English |
| last_indexed | 2024-03-10T10:12:38Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antioxidants |
| spelling | doaj.art-5c1c4e575ba947f2ad97927ecc35945f2023-11-22T01:05:26ZengMDPI AGAntioxidants2076-39212021-06-0110699110.3390/antiox10060991Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning ApproachElisabet Rodríguez-Tomàs0Simona Iftimie1Helena Castañé2Gerard Baiges-Gaya3Anna Hernández-Aguilera4María González-Viñas5Antoni Castro6Jordi Camps7Jorge Joven8Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainDepartment of Internal Medicine, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainUnitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainUnitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainUnitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainDepartment of Internal Medicine, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainDepartment of Internal Medicine, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainUnitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainUnitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43201 Reus, SpainSARS-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.https://www.mdpi.com/2076-3921/10/6/991biomarkerschemokinesCOVID-19galectin-3machine learningparaoxonase-1 |
| spellingShingle | Elisabet Rodríguez-Tomàs Simona Iftimie Helena Castañé Gerard Baiges-Gaya Anna Hernández-Aguilera María González-Viñas Antoni Castro Jordi Camps Jorge Joven Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach Antioxidants biomarkers chemokines COVID-19 galectin-3 machine learning paraoxonase-1 |
| title | Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach |
| title_full | Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach |
| title_fullStr | Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach |
| title_full_unstemmed | Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach |
| title_short | Clinical Performance of Paraoxonase-1-Related Variables and Novel Markers of Inflammation in Coronavirus Disease-19. A Machine Learning Approach |
| title_sort | clinical performance of paraoxonase 1 related variables and novel markers of inflammation in coronavirus disease 19 a machine learning approach |
| topic | biomarkers chemokines COVID-19 galectin-3 machine learning paraoxonase-1 |
| url | https://www.mdpi.com/2076-3921/10/6/991 |
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