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dc.contributor.authorCortes-Canteli, Marta 
dc.contributor.authorGispert, Juan Domingo
dc.contributor.authorSalvadó, Gemma
dc.contributor.authorToribio-Fernández, Raquel 
dc.contributor.authorTristão-Pereira, Catarina
dc.contributor.authorFalcon, Carles
dc.contributor.authorOliva, Belen 
dc.contributor.authorMendiguren, Jose M
dc.contributor.authorFernandez-Friera, Leticia 
dc.contributor.authorSanz, Javier 
dc.contributor.authorGarcia-Ruiz, Jose M 
dc.contributor.authorFernandez-Ortiz, Antonio 
dc.contributor.authorSanchez-Gonzalez, Javier 
dc.contributor.authorIbanez, Borja 
dc.contributor.authorMolinuevo, José Luis
dc.contributor.authorFuster, Valentin 
dc.date.accessioned2021-03-18T12:35:46Z
dc.date.available2021-03-18T12:35:46Z
dc.date.issued2021-02-23
dc.identifier.citationJ Am Coll Cardiol. 2021; 77(7):888-898es_ES
dc.identifier.issn0735-1097
dc.identifier.urihttp://hdl.handle.net/20.500.12105/12368
dc.description.abstractAtherosclerosis has been linked to cognitive decline in late life; however, the impact of cardiovascular risk factors (CVRFs) and subclinical atherosclerosis on brain metabolism at earlier stages remains unexplored. This study sought to determine the association between brain metabolism, subclinical atherosclerosis, and CVRFs in middle-aged asymptomatic individuals. This study included 547 asymptomatic middle-aged participants (50 ± 4 years, 82% men) from the PESA (Progression of Early Subclinical Atherosclerosis) study with evidence of subclinical atherosclerosis. Participants underwent 18F-fluorodeoxyglucose (FDG)-positron emission tomography. Global brain FDG uptake and voxel-wise analyses were used to evaluate the associations of cerebral metabolism with CVRFs and atherosclerotic plaque burden in carotids and femorals assessed by 3-dimensional vascular ultrasound. Global FDG uptake showed an inverse correlation with 30-year Framingham Risk Score (FRS) (β = -0.15, p < 0.001). This association was mainly driven by the presence of hypertension (d = 0.36, p < 0.001). Carotid plaque burden was inversely associated with global brain FDG uptake (β = -0.16, p < 0.001), even after adjusting for 30-year FRS. Voxel-wise approaches revealed that the brain areas most strongly affected by hypometabolism in association with 30-year FRS, hypertension, and carotid plaque burden were parietotemporal regions (angular, supramarginal, and inferior/middle temporal gyri) and the cingulate gyrus. In asymptomatic middle-aged individuals, cardiovascular risk is associated with brain hypometabolism, with hypertension being the modifiable CVRF showing the strongest association. Subclinical carotid plaque burden is also linked to reduced brain metabolism independently of CVRFs. Cerebral areas showing hypometabolism include those known to be affected in dementia. These data reinforce the need to control CVRFs early in life in order to potentially reduce the brain's midlife vulnerability to future cognitive dysfunction.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.isversionofArtículoes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleSubclinical Atherosclerosis and Brain Metabolism in Middle-Aged Individuals: The PESA Study.es_ES
dc.typeArtículoes_ES
dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.pubmedID33602472es_ES
dc.format.volume77es_ES
dc.format.number7es_ES
dc.format.page888-898es_ES
dc.identifier.doi10.1016/j.jacc.2020.12.027es_ES
dc.contributor.funderBanco Santanderes_ES
dc.contributor.funderInstituto de Salud Carlos III - ISCIIIes_ES
dc.contributor.funderEuropean Regional Development Fund (ERDF/FEDER)es_ES
dc.contributor.funderFondo de Investigaciones Sanitariases_ES
dc.contributor.funderComunidad de Madrides_ES
dc.contributor.funderFundación La Caixaes_ES
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es_ES
dc.contributor.funderEuropean Research Counciles_ES
dc.contributor.funderProes_ES
dc.contributor.funderHorizon 2020es_ES
dc.description.peerreviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.jacc.2020.12.027es_ES
dc.identifier.journalJournal of the American College of Cardiologyes_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Imagen Cardiovascular y Estudios Poblacionaleses_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Laboratorio Traslacional para la Imagen y Terapia Cardiovasculares_ES
dc.repisalud.institucionCNICes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI15/02019es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/CP16/00174es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/MS16/00174es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI17/00590es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI20/00819es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PEJD-2018-POST/BMD-9259es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/LCF/BQ/DI19/11730052es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/RYC-2013-13054es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/ERC-2018-CoG-819775-MATRIXes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/LCF/PR/GN17/50300004es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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