Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue

The aim of present paper is to identify clinical phenotypes in a cohort of patients affected of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Ninety-one patients and 22 healthy controls were studied with the following questionnaires, in addition to medical history: visual analogical scale for...

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Detalles Bibliográficos
Autores: Murga, Iñigo, Aranburu, Larraitz, Gargiulo, Pascual Angel, Gomez Esteban, Juan Carlos, Lafuente, Jose Vicente
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/157197
Acceso en línea:http://hdl.handle.net/11336/157197
Access Level:acceso abierto
Palabra clave:LONG COVID-19
MYALGIC ENCEPHALOMYELITIS
CHRONIC FATIGUE SYNDROME
POST-VIRAL FATIGUE
DYSAUTONOMIA
COVID-19
https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
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repository_id_str
spelling Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 FatigueMurga, IñigoAranburu, LarraitzGargiulo, Pascual AngelGomez Esteban, Juan CarlosLafuente, Jose VicenteLONG COVID-19MYALGIC ENCEPHALOMYELITISCHRONIC FATIGUE SYNDROMEPOST-VIRAL FATIGUEDYSAUTONOMIACOVID-19https://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3The aim of present paper is to identify clinical phenotypes in a cohort of patients affected of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Ninety-one patients and 22 healthy controls were studied with the following questionnaires, in addition to medical history: visual analogical scale for fatigue and pain, DePaul questionnaire (post-exertional malaise, immune, neuroendocrine), Pittsburgh sleep quality index, COMPASS-31 (dysautonomia), Montreal cognitive assessment, Toulouse-Piéron test (attention), Hospital Anxiety and Depression test and Karnofsky scale. Co-morbidities and drugs-intake were also recorded. A hierarchical clustering with clinical results was performed. Final study group was made up of 84 patients, mean age 44.41 ± 9.37 years (66 female/18 male) and 22 controls, mean age 45 ± 13.15 years (14 female/8 male). Patients meet diagnostic criteria of Fukuda-1994 and Carruthers-2011. Clustering analysis identify five phenotypes. Two groups without fibromyalgia were differentiated by various levels of anxiety and depression (13 and 20 patients). The other three groups present fibromyalgia plus a patient without it, but with high scores in pain scale, they were segregated by prevalence of dysautonomia (17), neuroendocrine (15), and immunological affectation (19). Regarding gender, women showed higher scores than men in cognition, pain level and depressive syndrome. Mathematical tools are a suitable approach to objectify some elusive features in order to understand the syndrome. Clustering unveils phenotypes combining fibromyalgia with varying degrees of dysautonomia, neuroendocrine or immune features and absence of fibromyalgia with high or low levels of anxiety-depression. There is no a specific phenotype for women or men.Fil: Murga, Iñigo. Universidad del País Vasco; EspañaFil: Aranburu, Larraitz. Universidad del País Vasco; EspañaFil: Gargiulo, Pascual Angel. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Gomez Esteban, Juan Carlos. Universidad del País Vasco; EspañaFil: Lafuente, Jose Vicente. Universidad del País Vasco; EspañaFrontiers Media2021-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/157197Murga, Iñigo; Aranburu, Larraitz ; Gargiulo, Pascual Angel; Gomez Esteban, Juan Carlos; Lafuente, Jose Vicente; Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue; Frontiers Media; Frontiers in Psychiatry; 10-2021; 1-91664-0640CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3389/fpsyt.2021.735784info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:58:02Zoai:ri.conicet.gov.ar:11336/157197instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982024-05-08 13:58:02.645CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
title Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
spellingShingle Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
Murga, Iñigo
LONG COVID-19
MYALGIC ENCEPHALOMYELITIS
CHRONIC FATIGUE SYNDROME
POST-VIRAL FATIGUE
DYSAUTONOMIA
COVID-19
https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
title_short Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
title_full Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
title_fullStr Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
title_full_unstemmed Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
title_sort Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
dc.creator.none.fl_str_mv Murga, Iñigo
Aranburu, Larraitz
Gargiulo, Pascual Angel
Gomez Esteban, Juan Carlos
Lafuente, Jose Vicente
author Murga, Iñigo
author_facet Murga, Iñigo
Aranburu, Larraitz
Gargiulo, Pascual Angel
Gomez Esteban, Juan Carlos
Lafuente, Jose Vicente
author_role author
author2 Aranburu, Larraitz
Gargiulo, Pascual Angel
Gomez Esteban, Juan Carlos
Lafuente, Jose Vicente
author2_role author
author
author
author
dc.subject.none.fl_str_mv LONG COVID-19
MYALGIC ENCEPHALOMYELITIS
CHRONIC FATIGUE SYNDROME
POST-VIRAL FATIGUE
DYSAUTONOMIA
COVID-19
https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
topic LONG COVID-19
MYALGIC ENCEPHALOMYELITIS
CHRONIC FATIGUE SYNDROME
POST-VIRAL FATIGUE
DYSAUTONOMIA
COVID-19
https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
description The aim of present paper is to identify clinical phenotypes in a cohort of patients affected of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Ninety-one patients and 22 healthy controls were studied with the following questionnaires, in addition to medical history: visual analogical scale for fatigue and pain, DePaul questionnaire (post-exertional malaise, immune, neuroendocrine), Pittsburgh sleep quality index, COMPASS-31 (dysautonomia), Montreal cognitive assessment, Toulouse-Piéron test (attention), Hospital Anxiety and Depression test and Karnofsky scale. Co-morbidities and drugs-intake were also recorded. A hierarchical clustering with clinical results was performed. Final study group was made up of 84 patients, mean age 44.41 ± 9.37 years (66 female/18 male) and 22 controls, mean age 45 ± 13.15 years (14 female/8 male). Patients meet diagnostic criteria of Fukuda-1994 and Carruthers-2011. Clustering analysis identify five phenotypes. Two groups without fibromyalgia were differentiated by various levels of anxiety and depression (13 and 20 patients). The other three groups present fibromyalgia plus a patient without it, but with high scores in pain scale, they were segregated by prevalence of dysautonomia (17), neuroendocrine (15), and immunological affectation (19). Regarding gender, women showed higher scores than men in cognition, pain level and depressive syndrome. Mathematical tools are a suitable approach to objectify some elusive features in order to understand the syndrome. Clustering unveils phenotypes combining fibromyalgia with varying degrees of dysautonomia, neuroendocrine or immune features and absence of fibromyalgia with high or low levels of anxiety-depression. There is no a specific phenotype for women or men.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/157197
Murga, Iñigo; Aranburu, Larraitz ; Gargiulo, Pascual Angel; Gomez Esteban, Juan Carlos; Lafuente, Jose Vicente; Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue; Frontiers Media; Frontiers in Psychiatry; 10-2021; 1-9
1664-0640
CONICET Digital
CONICET
url http://hdl.handle.net/11336/157197
identifier_str_mv Murga, Iñigo; Aranburu, Larraitz ; Gargiulo, Pascual Angel; Gomez Esteban, Juan Carlos; Lafuente, Jose Vicente; Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue; Frontiers Media; Frontiers in Psychiatry; 10-2021; 1-9
1664-0640
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3389/fpsyt.2021.735784
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 15.812429

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