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Traffic Density Exposure, Oxidative Stress Biomarkers and Plasma Metabolomics in a Population-Based Sample: The Hortega Study

dc.contributor.authorSánchez Rodríguez, Laura
dc.contributor.authorGalvez-Fernandez, Marta
dc.contributor.authorRojas-Benedicto, Ayelén
dc.contributor.authorDomingo-Relloso, Arce
dc.contributor.authorAmigo, Nuria
dc.contributor.authorRedon, Josep
dc.contributor.authorMonleon, Daniel
dc.contributor.authorSaez, Guillermo
dc.contributor.authorTellez-Plaza, Maria
dc.contributor.authorMartin-Escudero, Juan Carlos
dc.contributor.authorRamis, Rebeca
dc.contributor.funderAgencia Estatal de Investigación (España)
dc.contributor.funderInstituto de Salud Carlos III
dc.contributor.funderMinisterio de Economía y Competitividad (España)
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)
dc.contributor.funderGeneralitat Valenciana (España)
dc.date.accessioned2024-01-11T13:49:05Z
dc.date.available2024-01-11T13:49:05Z
dc.date.issued2023-12-15
dc.description.abstractExposure to traffic-related air pollution (TRAP) generates oxidative stress, with downstream effects at the metabolic level. Human studies of traffic density and metabolomic markers, however, are rare. The main objective of this study was to evaluate the cross-sectional association between traffic density in the street of residence with oxidative stress and metabolomic profiles measured in a population-based sample from Spain. We also explored in silico the potential biological implications of the findings. Secondarily, we assessed the contribution of oxidative stress to the association between exposure to traffic density and variation in plasma metabolite levels. Traffic density was defined as the average daily traffic volume over an entire year within a buffer of 50 m around the participants' residence. Plasma metabolomic profiles and urine oxidative stress biomarkers were measured in samples from 1181 Hortega Study participants by nuclear magnetic resonance spectroscopy and high-performance liquid chromatography, respectively. Traffic density was associated with 7 (out of 49) plasma metabolites, including amino acids, fatty acids, products of bacterial and energy metabolism and fluid balance metabolites. Regarding urine oxidative stress biomarkers, traffic associations were positive for GSSG/GSH% and negative for MDA. A total of 12 KEGG pathways were linked to traffic-related metabolites. In a protein network from genes included in over-represented pathways and 63 redox-related candidate genes, we observed relevant proteins from the glutathione cycle. GSSG/GSH% and MDA accounted for 14.6% and 12.2% of changes in isobutyrate and the CH2CH2CO fatty acid moiety, respectively, which is attributable to traffic exposure. At the population level, exposure to traffic density was associated with specific urine oxidative stress and plasma metabolites. Although our results support a role of oxidative stress as a biological intermediary of traffic-related metabolic alterations, with potential implications for the co-bacterial and lipid metabolism, additional mechanistic and prospective studies are needed to confirm our findings.es_ES
dc.description.peerreviewedes_ES
dc.description.sponsorshipThis research was funded by the State Agency for Research (PID2019-108973RB-C21 and C22), by Strategic Action for Research in Health Sciences (PI15/00071 and PI22CIII/00029) from the Spanish Ministry of Economy and Competitiveness and co-funded with European Funds for Regional Development (FEDER), and IDIFEDER/2021/072, CIAICO/2022/181 and INVEST/2023/180 from the Generalitat Valenciana of Spain.es_ES
dc.format.number12es_ES
dc.format.page2122es_ES
dc.format.volume12es_ES
dc.identifier.citationAntioxidants (Basel). 2023 Dec 15;12(12):2122.es_ES
dc.identifier.doi10.3390/antiox12122122es_ES
dc.identifier.issn2076-3921es_ES
dc.identifier.journalAntioxidants (Basel, Switzerland)es_ES
dc.identifier.pubmedID38136241es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/16937
dc.language.isoenges_ES
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PID2019-108973RB-C21es_ES
dc.relation.projectFECYTinfo:eu-repo/grantAgreement/ES/PID2019-108973RB-C22es_ES
dc.relation.projectFISinfo:fis/Instituto de Salud Carlos III/null/null/Subprograma de proyectos de investigacion en salud (AES 2015). Modalidad proyectos en salud. (2015)/PI15/00071es_ES
dc.relation.projectFISinfo:fis/Instituto de Salud Carlos III///PI22-ISCIII Proyectos de I+D+I en salud (AES 2022).Intramurales (2022)/PI22CIII/00029es_ES
dc.relation.publisherversionhttps://doi.org/10.3390/antiox12122122es_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.repisalud.centroISCIII::Escuela Nacional de Sanidades_ES
dc.repisalud.institucionISCIIIes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAir pollutiones_ES
dc.subjectMetabolomicses_ES
dc.subjectPopulation-basedes_ES
dc.subjectTraffic densityes_ES
dc.titleTraffic Density Exposure, Oxidative Stress Biomarkers and Plasma Metabolomics in a Population-Based Sample: The Hortega Studyes_ES
dc.typeresearch articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
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