Machine learning and semi-targeted lipidomics identify distinct serum lipid signatures in hospitalized COVID-19-positive and COVID-19-negative patients

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 differen...

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Published in:Metabolism, clinical and experimental clinical and experimental, 2022-06, Vol.131, p.155197-155197, Article 155197
Main Authors: Castañé, Helena, Iftimie, Simona, Baiges-Gaya, Gerard, Rodríguez-Tomàs, Elisabet, Jiménez-Franco, Andrea, López-Azcona, Ana Felisa, Garrido, Pedro, Castro, Antoni, Camps, Jordi, Joven, Jorge
Format: Article
Language:English
Subjects:
Artificial intelligence
Bile Acids and Salts
COVID-19
Humans
Lipid metabolism
Lipidomics
Machine Learning
Oxylipins
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Summary: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. 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. 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. 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. •COVID-19 alters lipid metabolism of the infected host.•We evaluated serum lipidome in controls and patients with or without COVID-19.•A lipidomic signature differentiated patients from controls.•A lipidomic signature differentiated COVID-19 positive from negative patients.
ISSN:0026-0495
1532-8600
DOI:10.1016/j.metabol.2022.155197