Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.8/6703 |
Resumo: | Metabolic 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. |
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Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodologyPrincipal component analysisSympathovagal balanceEarly diagnosisMetabolic syndromeCarotid bodiesMetabolic 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.SpringerIC-OnlineFonseca-Pinto, RuiLopes, Nuno VieiraBrito, GabrielLages, MarleneGuarino, Maria Pedro2022-02-22T14:27:10Z2019-12-062019-12-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.8/6703engFonseca-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/s12553-019-00384-72190-718810.1007/s12553-019-00384-7metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-17T15:53:24Zoai:iconline.ipleiria.pt:10400.8/6703Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:49:46.206714Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology |
title |
Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology |
spellingShingle |
Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology Fonseca-Pinto, Rui Principal component analysis Sympathovagal balance Early diagnosis Metabolic syndrome Carotid bodies |
title_short |
Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology |
title_full |
Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology |
title_fullStr |
Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology |
title_full_unstemmed |
Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology |
title_sort |
Assessing autonomic control of metabolic syndrome by principal component analysis: a data driven methodology |
author |
Fonseca-Pinto, Rui |
author_facet |
Fonseca-Pinto, Rui Lopes, Nuno Vieira Brito, Gabriel Lages, Marlene Guarino, Maria Pedro |
author_role |
author |
author2 |
Lopes, Nuno Vieira Brito, Gabriel Lages, Marlene Guarino, Maria Pedro |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
IC-Online |
dc.contributor.author.fl_str_mv |
Fonseca-Pinto, Rui Lopes, Nuno Vieira Brito, Gabriel Lages, Marlene Guarino, Maria Pedro |
dc.subject.por.fl_str_mv |
Principal component analysis Sympathovagal balance Early diagnosis Metabolic syndrome Carotid bodies |
topic |
Principal component analysis Sympathovagal balance Early diagnosis Metabolic syndrome Carotid bodies |
description |
Metabolic 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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12-06 2019-12-06T00:00:00Z 2022-02-22T14:27:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.8/6703 |
url |
http://hdl.handle.net/10400.8/6703 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fonseca-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/s12553-019-00384-7 2190-7188 10.1007/s12553-019-00384-7 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136990035181568 |