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dc.contributor.authorFernández Garrido, Pablo
dc.contributor.authorCastillo Peinado, Laura de los Santos
dc.contributor.authorPriego Capote, Feliciano
dc.contributor.authorBarrio Beraza, Irantzu
dc.contributor.authorPiñeiro Guillén, Ángel
dc.contributor.authorDomínguez Santalla, María Jesús
dc.contributor.authorRodríguez Ruíz, Emilio
dc.contributor.authorGarcía Fandiño, Rebeca
dc.date.accessioned2024-05-15T16:09:09Z
dc.date.available2024-05-15T16:09:09Z
dc.date.issued2024-04
dc.identifier.citationJournal of Infection and Public Health 17(4) : 588-600 (2024)es_ES
dc.identifier.issn1876-0341
dc.identifier.issn1876-035X
dc.identifier.urihttp://hdl.handle.net/10810/67960
dc.description.abstractBackground The ongoing issues with post-COVID conditions (PCC), where symptoms persist long after the initial infection, highlight the need for research into blood lipid changes in these patients. While most studies focus on the acute phase of COVID-19, there's a significant lack of information on the lipidomic changes that occur in the later stages of the disease. Addressing this knowledge gap is critical for understanding the long-term effects of COVID-19 and could be key to developing personalized treatments for those suffering from PCC. Methods We employed untargeted lipidomics to analyze plasma samples from 147 PCC patients, assessing nearly 400 polar lipids. Data mining (DM) and machine learning (ML) tools were utilized to decode the results and ascertain significant lipidomic patterns. Results The study uncovered substantial changes in various lipid subclasses, presenting a detailed profile of the polar lipid fraction in PCC patients. These alterations correlated with ongoing inflammation and immune response. Notably, there were elevated levels of lysophosphatidylglycerols (LPGs) and phosphatidylethanolamines (PEs), and reduced levels of lysophosphatidylcholines (LPCs), suggesting these as potential lipid biomarkers for PCC. The lipidomic signatures indicated specific anionic lipid changes, implicating antimicrobial peptides (AMPs) in inflammation. Associations between particular medications and symptoms were also suggested. Classification models, such as multinomial regression (MR) and random forest (RF), successfully differentiated between symptomatic and asymptomatic PCC groups using lipidomic profiles. Conclusions The study's groundbreaking discovery of specific lipidomic disruptions in PCC patients marks a significant stride in the quest to comprehend and combat this condition. The identified lipid biomarkers not only pave the way for novel diagnostic tools but also hold the promise to tailor individualized therapeutic strategies, potentially revolutionizing the clinical approach to managing PCC and improving patient care.es_ES
dc.description.sponsorshipThis work was supported by the Spanish Agencia Estatal de Investigación (AEI) and the ERDF (RTI2018–098795-A-I00, PID2019–111327GB-I00, PID2019–111373RB-I00, PID2022-141534OB-I00 and PDC2022–133402-I00), by Xunta de Galicia and the ERDF (ED431F 2020/05, ED431B 2022/36 and Centro singular de investigación de Galicia accreditation 2016–2019, ED431G/09). The work of I.B. was financially supported in part by grants from the Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco [IT1456–22] and by the Ministry of Science and Innovation through BCAM Severo Ochoa accreditation [CEX2021–001142-S/MICIN/AEI/10.13039/501100011033] and through project [PID2020–115882RB-I00/AEI/10.13039/501100011033] funded by Agencia Estatal de Investigación and acronym “S3M1P4R" and also by the Basque Government through the BERC 2022–2025 program. We would like to thank Paula García Fandiño for her efficient and crucial work in gathering samples for this study.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MICIU/RTI2018–098795-A-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2019–111327GB-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2019–111373RB-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2022-141534OB-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PDC2022–133402-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2020-115882RB-I00es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/CEX2021-001142-Ses_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectlipidomic signaturees_ES
dc.subjectpost-COVID patientses_ES
dc.subjectprolonged inflammatory responsees_ES
dc.subjectpredictive modeling for inflammatory responsees_ES
dc.titleLipidomics signature in post-COVID patient sera and its influence on the prolonged inflammatory responsees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2024 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1876034124000236es_ES
dc.identifier.doi10.1016/j.jiph.2024.01.017
dc.departamentoesMatemáticases_ES
dc.departamentoeuMatematikaes_ES


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© 2024 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY
license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © 2024 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).