Clinical Heterogeneity in ME/CFS: A Way to Understand Long-COVID19 Fatigue
- Autores
- Murga, Iñigo; Aranburu, Larraitz; Gargiulo, Pascual Angel; Gomez Esteban, Juan Carlos; Lafuente, Jose Vicente
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- 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.
Fil: Murga, Iñigo. Universidad del País Vasco; España
Fil: Aranburu, Larraitz. Universidad del País Vasco; España
Fil: Gargiulo, Pascual Angel. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Gomez Esteban, Juan Carlos. Universidad del País Vasco; España
Fil: Lafuente, Jose Vicente. Universidad del País Vasco; España - Materia
-
LONG COVID-19
MYALGIC ENCEPHALOMYELITIS
CHRONIC FATIGUE SYNDROME
POST-VIRAL FATIGUE
DYSAUTONOMIA
COVID-19 - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/157197
Ver los metadatos del registro completo
id |
CONICETDig_84afd1a2ea751d74bb2f0c698712190f |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/157197 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
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 Técnicas2025-09-29T10:05:53Zoai: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:34982025-09-29 10:05:53.612CONICET 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 |
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 |
topic |
LONG COVID-19 MYALGIC ENCEPHALOMYELITIS CHRONIC FATIGUE SYNDROME POST-VIRAL FATIGUE DYSAUTONOMIA COVID-19 |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.2 https://purl.org/becyt/ford/3 |
dc.description.none.fl_txt_mv |
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. Fil: Murga, Iñigo. Universidad del País Vasco; España Fil: Aranburu, Larraitz. Universidad del País Vasco; España Fil: Gargiulo, Pascual Angel. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina Fil: Gomez Esteban, Juan Carlos. Universidad del País Vasco; España Fil: Lafuente, Jose Vicente. Universidad del País Vasco; España |
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 |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
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 |
_version_ |
1844613900628131840 |
score |
13.070432 |