Author, as appears in the article.: Castañé, H; Iftimie, S; Baiges-Gaya, G; Rodríguez-Tomás, E; Jiménez-Franco, A; López-Azcona, AF; Garrido, P; Castro, A; Camps, J; Joven, J
Department: Medicina i Cirurgia
URV's Author/s: Baiges Gaya, Gerard / Camps Andreu, Jorge / Castañé Vilafranca, Helena / Castro Salomó, Antoni / Iftimie Iftimie, Simona Mihaela / Jiménez Franco, Andrea / Joven Maried, Jorge / Rodriguez Tomas, Elisabet
Keywords: Machine learning Lipidomics Lipid metabolism Covid-19 Artificial intelligence machine learning lipidomics lipid metabolism covid-19
Abstract: Background: Lipids are involved in the interaction between viral infection and the host metabolic and immunological responses. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, as well as with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases. Methods: We analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. A semi-targeted lipidomics analysis was performed using liquid chromatography coupled to mass spectrometry. Two-hundred and eighty-three lipid species were identified and quantified. Results were interpreted by machine learning tools. Results: We identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients who had other causes of inflammation. Conversely, lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids were the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients. Conclusion: This study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, and which differentiate them from the healthy population. The most notable alterations were observed in oxylipins, while alterations in bile acids and glycerophospholipis best distinguished between COVID-19-positive and COVID-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.
Thematic Areas: Saúde coletiva Odontología Nutrição Medicina iii Medicina ii Medicina i Interdisciplinar General medicine Farmacia Engenharias ii Enfermagem Endocrinology, diabetes and metabolism Endocrinology & metabolism Endocrinology Educação física Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciência de alimentos Biotecnología Antropologia / arqueologia
Author's mail: andrea.jimenez@urv.cat simonamihaela.iftime@urv.cat jorge.camps@urv.cat helena.castane@estudiants.urv.cat gerard.baiges@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat jorge.joven@urv.cat antoni.castro@urv.cat
Author identifier: 0000-0003-0714-8414 0000-0002-3165-3640 0000-0003-2749-4541 0000-0001-5441-6333
Record's date: 2024-09-07
Papper version: info:eu-repo/semantics/submittedVersion
Link to the original source: https://www.metabolismjournal.com/article/S0026-0495(22)00075-0/fulltext#relatedArticles
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Metabolism-Clinical And Experimental. 131 155197-
APA: Castañé, H; Iftimie, S; Baiges-Gaya, G; Rodríguez-Tomás, E; Jiménez-Franco, A; López-Azcona, AF; Garrido, P; Castro, A; Camps, J; Joven, J (2022). Machine learning and semi-targeted lipidomics identify distinct serum lipid signatures in hospitalized COVID-19-positive and COVID-19-negative patients. Metabolism-Clinical And Experimental, 131(), 155197-. DOI: 10.1016/j.metabol.2022.155197
Article's DOI: 10.1016/j.metabol.2022.155197
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications
Repositori URV