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Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology

2019-12-06Journal article Restricted access
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dc.contributor.authorFonseca-Pinto, Rui
dc.contributor.authorLopes, Nuno Vieira
dc.contributor.authorBrito, Gabriel
dc.contributor.authorLages, Marlene
dc.contributor.authorGuarino, Maria Pedro
dc.date.accessioned2022-02-22T14:27:10Z
dc.date.available2022-02-22T14:27:10Z
dc.date.issued2019-12-06
dc.description.abstractMetabolic diseases are one of the leading causes of death worldwide. Due to its lack of clinical manifestations for long periods, metabolic diseases are generally detected in advanced stages, when the risk of cardiovascular, ocular and renal complications is high. Thus, early detection of these disorders is essential to design effective health promotion strategies. Herein we provide a preliminary approach for the early diagnosis of metabolic diseases based on Principal Component Analysis (PCA) of autonomic features of sympathovagal Balance (SVB) to characterize the activity of the carotid bodies (CB). CBs are small chemoreceptors located in the bifurcation of the carotid arteries whose overactivation is intimately linked to early stages of metabolic disease through asymptomatic deregulation of the sympathetic nervous system. Herein we discuss parameters that can be extracted from these recordings using a PCA approach in response to two different challenge tests: 100% oxygen and administration of a mixed meal in healthy and type 2 diabetes volunteers. This methodology may represent a paradigm shift in the diagnosis of metabolic diseases through the characterization of CB activity, and aims to bridge the existing gap in early assessment of metabolic dysfunction.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFonseca-Pinto, R., Lopes, N. V., Brito, G. C., Lages, M., & Guarino, M. P. (2020). Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology. Health and Technology, 10(1), 79–85. https://doi.org/10.1007/s12553-019-00384-7pt_PT
dc.identifier.doi10.1007/s12553-019-00384-7pt_PT
dc.identifier.issn2190-7188
dc.identifier.urihttp://hdl.handle.net/10400.8/6703
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationSAICT 23278/2016pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPrincipal component analysispt_PT
dc.subjectSympathovagal balancept_PT
dc.subjectEarly diagnosispt_PT
dc.subjectMetabolic syndromept_PT
dc.subjectCarotid bodiespt_PT
dc.titleAssessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodologypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage85pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage79pt_PT
oaire.citation.titleHealth and Technologypt_PT
oaire.citation.volume10pt_PT
person.familyNameFonseca-Pinto
person.familyNameVieira Lopes
person.familyNameCorreia-Brito
person.familyNameLages
person.familyNameGuarino
person.givenNameRui
person.givenNameNuno
person.givenNameGabriel
person.givenNameMarlene
person.givenNameMaria Pedro
person.identifier.ciencia-id681D-C547-B184
person.identifier.ciencia-idE117-67AC-0B45
person.identifier.ciencia-idC613-4639-10A8
person.identifier.ciencia-idF21A-BD01-2D52
person.identifier.orcid0000-0001-6774-5363
person.identifier.orcid0000-0002-2232-1839
person.identifier.orcid0000-0001-5077-4520
person.identifier.orcid0000-0002-7389-6368
person.identifier.orcid0000-0001-6079-1105
person.identifier.ridK-9449-2014
person.identifier.ridB-5594-2015
person.identifier.scopus-author-id26039086400
person.identifier.scopus-author-id26031536700
person.identifier.scopus-author-id57206777195
person.identifier.scopus-author-id56348477000
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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