Publication:
Unbiased plasma proteomics discovery of biomarkers for improved detection of subclinical atherosclerosis.

dc.contributor.authorNunez, Estefania
dc.contributor.authorFuster, Valentin
dc.contributor.authorGómez-Serrano, María
dc.contributor.authorValdivielso, José Manuel
dc.contributor.authorFernández-Alvira, Juan Miguel
dc.contributor.authorMartínez-López, Diego
dc.contributor.authorRodriguez, Jose Manuel
dc.contributor.authorBonzon-Kulichenko, Elena
dc.contributor.authorCalvo, Enrique
dc.contributor.authorAlfayate, Alvaro
dc.contributor.authorBermudez-Lopez, Marcelino
dc.contributor.authorEscola-Gil, Joan Carles
dc.contributor.authorFernández-Friera, Leticia
dc.contributor.authorCerro-Pardo, Isabel
dc.contributor.authorMendiguren, José María
dc.contributor.authorSanchez-Cabo, Fatima
dc.contributor.authorSanz, Javier
dc.contributor.authorOrdovás, José María
dc.contributor.authorBlanco-Colio, Luis Miguel
dc.contributor.authorGarcía-Ruiz, José Manuel
dc.contributor.authorIbáñez, Borja
dc.contributor.authorLara-Pezzi, Enrique
dc.contributor.authorFernández-Ortiz, Antonio
dc.contributor.authorMartín-Ventura, José Luis
dc.contributor.authorVazquez, Jesus
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es_ES
dc.contributor.funderInstituto de Salud Carlos IIIes_ES
dc.contributor.funderCentro de Investigación Biomédica en Red - CIBERCV (Enfermedades Cardiovasculares)es_ES
dc.contributor.funderCentro de Investigación Biomédica en Red - CIBERDEM (Diabetes y Enfermedades Metabólicas asociadas)es_ES
dc.contributor.funderFundación La Marató TV3es_ES
dc.contributor.funderFundación La Caixaes_ES
dc.contributor.funderBanco Santanderes_ES
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)es_ES
dc.contributor.funderFundación ProCNICes_ES
dc.date.accessioned2022-12-09T14:16:21Z
dc.date.available2022-12-09T14:16:21Z
dc.date.issued2022-02
dc.descriptionThis study was supported by competitive grants from the Spanish Ministry of Science, Innovation and Universities (BIO2015-67580-P, PGC2018-097019-B-I00, PID2019-106814RB-I00 and SAF2016-80843-R), through the Carlos III Institute of Health-Fondo de Investigacion Sanitaria grant PRB3 (IPT17/0019 - ISCIII-SGEFI / ERDF, ProteoRed), CIBERCV and CIBERDEM, the Fundacio MaratoTV3 (grant 122/C/2015) and “la Caixa” Banking Foundation (project HR17-00247). The PESA study is co-funded equally by the Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain, and Banco Santander, Madrid, Spain. The ILERVAS study was funded by the Diputacio de Lleida. The study also receives funding from the Instituto de Salud Carlos III (PI15/02019; PI18/00610; RD16/0009) and the FEDER funds. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia, Innovacion y Universidades (MCNU) and the Pro CNIC Foundation.es_ES
dc.description.abstractImaging of subclinical atherosclerosis improves cardiovascular risk prediction on top of traditional risk factors. However, cardiovascular imaging is not universally available. This work aims to identify circulating proteins that could predict subclinical atherosclerosis. Hypothesis-free proteomics was used to analyze plasma from 444 subjects from PESA cohort study (222 with extensive atherosclerosis on imaging, and 222 matched controls) at two timepoints (three years apart) for discovery, and from 350 subjects from AWHS cohort study (175 subjects with extensive atherosclerosis on imaging and 175 matched controls) for external validation. A selected three-protein panel was further validated by immunoturbidimetry in the AWHS population and in 2999 subjects from ILERVAS cohort study. PIGR, IGHA2, APOA, HPT and HEP2 were associated with subclinical atherosclerosis independently from traditional risk factors at both timepoints in the discovery and validation cohorts. Multivariate analysis rendered a potential three-protein biomarker panel, including IGHA2, APOA and HPT. Immunoturbidimetry confirmed the independent associations of these three proteins with subclinical atherosclerosis in AWHS and ILERVAS. A machine-learning model with these three proteins was able to predict subclinical atherosclerosis in ILERVAS (AUC [95%CI]:0.73 [0.70-0.74], p < 1 × 10-99), and also in the subpopulation of individuals with low cardiovascular risk according to FHS 10-year score (0.71 [0.69-0.73], p < 1 × 10-69). Plasma levels of IGHA2, APOA and HPT are associated with subclinical atherosclerosis independently of traditional risk factors and offers potential to predict this disease. The panel could improve primary prevention strategies in areas where imaging is not available. This study was supported by competitive grants from the Spanish Ministry of Science, Innovation and Universities (BIO2015-67580-P, PGC2018-097019-B-I00, PID2019-106814RB-I00 and SAF2016-80843-R), through the Carlos III Institute of Health-Fondo de Investigacion Sanitaria grant PRB3 (IPT17/0019 - ISCIII-SGEFI / ERDF, ProteoRed), CIBERCV and CIBERDEM, the Fundacio MaratoTV3 (grant 122/C/2015) and "la Caixa" Banking Foundation (project HR17-00247). The PESA study is co-funded equally by the Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain, and Banco Santander, Madrid, Spain. The ILERVAS study was funded by the Diputacio de Lleida. The study also receives funding from the Instituto de Salud Carlos III (PI15/02019; PI18/00610; RD16/0009) and the FEDER funds. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia, Innovacion y Universidades (MCNU) and the Pro CNIC Foundation.es_ES
dc.description.peerreviewedes_ES
dc.format.page103874es_ES
dc.format.volume76es_ES
dc.identifier.citationEBioMedicine. 2022 Feb;76:103874es_ES
dc.identifier.doi10.1016/j.ebiom.2022.103874es_ES
dc.identifier.e-issn2352-3964es_ES
dc.identifier.journalEBioMedicinees_ES
dc.identifier.pubmedID35152150es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/15266
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/BIO2015-67580-Pes_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PGC2018-097019-B-I00es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PID2019-106814RB-I00es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/SAF2016-80843-Res_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/IPT17/0019es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/122/C/2015es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/HR17-00247es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PI15/02019es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PI18/00610es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/RD16/0009es_ES
dc.relation.publisherversion10.1016/j.ebiom.2022.103874es_ES
dc.repisalud.institucionCNICes_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Proteómica cardiovasculares_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.meshAtherosclerosises_ES
dc.subject.meshProteomicses_ES
dc.subject.meshBiomarkerses_ES
dc.subject.meshCohort Studieses_ES
dc.subject.meshHumanses_ES
dc.subject.meshRisk Factorses_ES
dc.titleUnbiased plasma proteomics discovery of biomarkers for improved detection of subclinical atherosclerosis.es_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
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