Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder

ABSTRACT: Background: While broad, the current diagnostic category of Bipolar Disorder (BD) may not cover or describe the full clinical spectrum of this clinical condition. One potentially important factor lacking in previous research is the exact ancestral composition of the clinical samples. Objec...

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Autores:
Ovadia Cardona, Laura Margarita
Tipo de recurso:
Article of investigation
Fecha de publicación:
2020
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/17484
Acceso en línea:
http://hdl.handle.net/10495/17484
Palabra clave:
Bipolar Disorder
Trastorno Bipolar
Latent Class Analysis
Análisis de Clases Latentes
Genetic Markers
Marcadores Genéticos
Rights
embargoedAccess
License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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network_acronym_str UDEA2
network_name_str Repositorio UdeA
repository_id_str
dc.title.spa.fl_str_mv Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder
title Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder
spellingShingle Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder
Bipolar Disorder
Trastorno Bipolar
Latent Class Analysis
Análisis de Clases Latentes
Genetic Markers
Marcadores Genéticos
title_short Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder
title_full Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder
title_fullStr Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder
title_full_unstemmed Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder
title_sort Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorder
dc.creator.fl_str_mv Ovadia Cardona, Laura Margarita
dc.contributor.advisor.none.fl_str_mv Vargas Upegui, Cristian
dc.contributor.author.none.fl_str_mv Ovadia Cardona, Laura Margarita
dc.subject.decs.none.fl_str_mv Bipolar Disorder
Trastorno Bipolar
Latent Class Analysis
Análisis de Clases Latentes
Genetic Markers
Marcadores Genéticos
topic Bipolar Disorder
Trastorno Bipolar
Latent Class Analysis
Análisis de Clases Latentes
Genetic Markers
Marcadores Genéticos
description ABSTRACT: Background: While broad, the current diagnostic category of Bipolar Disorder (BD) may not cover or describe the full clinical spectrum of this clinical condition. One potentially important factor lacking in previous research is the exact ancestral composition of the clinical samples. Objective: To define subtypes of BD in a genetic isolate (paisa population) using sociodemographic, clinical and ancestral composition variables, in efforts to generate various conglomerates that might be useful for determining the natural history of BD. Methods: We performed a cross-sectional study on a clinical sample of 164 patients diagnosed as Bipolar I or Bipolar II disorder as defined by DSM-IV-TR. Latent Classes Analyses were used to examine the interaction of the ethnic composition of the sample, rigorously defined using specific genetic markers, with clinical variables (number of previous episodes) and the therapeutic response to treatment with lithium. Results: the best fitting model resulting from the analyses consisted of six latent classes (LC) including ancestry composition, number of depressive and manic episodes, and clinical response to lithium treatment: LC-1 consisted of patients with predominantly European ancestry, having a small number of clinical episodes of mania/depression, and showing a good response to lithium; LC-2 consisted of patients with predominant Amerindian ancestry, presenting few clinical episodes, and showing an intermediate response to lithium; LC-3 consisted of patients with predominant African ancestry, intermediate number of clinical episodes, and showing a poor response to lithium. LC-4 grouped patients with predominant Amerindian ancestry, with an intermediate number of clinical episodes, and showing a partial response to lithium; LC- 5 consisted of patients with predominant Amerindian ancestry, multiple clinical episodes, and showing a poor response to lithium; LC-6 consisted of patients with predominant European ancestry, multiple clinical episodes, and showing the best response to lithium. Conclusion: On the basis of these results, we propose six latent subtypes of BD. We show that a rigorously defined ancestry component significantly influences clinical and treatment outcomes. This latent classification may be useful in describing the genesis, course and evolution of this heterogeneous disorder and may lead to a more personalized management of individual cases
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-11-23T15:55:22Z
dc.date.available.none.fl_str_mv 2020-11-23T15:55:22Z
dc.date.issued.none.fl_str_mv 2020
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dc.type.local.spa.fl_str_mv Artículo de investigación
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dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10495/17484
url http://hdl.handle.net/10495/17484
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.format.extent.spa.fl_str_mv 27
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dc.publisher.group.spa.fl_str_mv Grupo de Investigación en Psiquiatría (GIPSI)
dc.publisher.place.spa.fl_str_mv Medellín, Colombia
institution Universidad de Antioquia
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repository.name.fl_str_mv Repositorio Institucional Universidad de Antioquia
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spelling Vargas Upegui, CristianOvadia Cardona, Laura Margarita2020-11-23T15:55:22Z2020-11-23T15:55:22Z2020http://hdl.handle.net/10495/17484ABSTRACT: Background: While broad, the current diagnostic category of Bipolar Disorder (BD) may not cover or describe the full clinical spectrum of this clinical condition. One potentially important factor lacking in previous research is the exact ancestral composition of the clinical samples. Objective: To define subtypes of BD in a genetic isolate (paisa population) using sociodemographic, clinical and ancestral composition variables, in efforts to generate various conglomerates that might be useful for determining the natural history of BD. Methods: We performed a cross-sectional study on a clinical sample of 164 patients diagnosed as Bipolar I or Bipolar II disorder as defined by DSM-IV-TR. Latent Classes Analyses were used to examine the interaction of the ethnic composition of the sample, rigorously defined using specific genetic markers, with clinical variables (number of previous episodes) and the therapeutic response to treatment with lithium. Results: the best fitting model resulting from the analyses consisted of six latent classes (LC) including ancestry composition, number of depressive and manic episodes, and clinical response to lithium treatment: LC-1 consisted of patients with predominantly European ancestry, having a small number of clinical episodes of mania/depression, and showing a good response to lithium; LC-2 consisted of patients with predominant Amerindian ancestry, presenting few clinical episodes, and showing an intermediate response to lithium; LC-3 consisted of patients with predominant African ancestry, intermediate number of clinical episodes, and showing a poor response to lithium. LC-4 grouped patients with predominant Amerindian ancestry, with an intermediate number of clinical episodes, and showing a partial response to lithium; LC- 5 consisted of patients with predominant Amerindian ancestry, multiple clinical episodes, and showing a poor response to lithium; LC-6 consisted of patients with predominant European ancestry, multiple clinical episodes, and showing the best response to lithium. Conclusion: On the basis of these results, we propose six latent subtypes of BD. We show that a rigorously defined ancestry component significantly influences clinical and treatment outcomes. This latent classification may be useful in describing the genesis, course and evolution of this heterogeneous disorder and may lead to a more personalized management of individual casesRESUMEN: Antecedentes: si bien es amplia, la categoría de diagnóstico actual de trastorno bipolar (BD) puede no cubrir o describir el espectro clínico completo de esta afección clínica. Un factor potencialmente importante que falta en investigaciones previas es la composición ancestral exacta de las muestras clínicas. Objetivo: Definir subtipos de EB en un aislado genético (población paisa) utilizando variables sociodemográficas, clínicas y de composición ancestral, en un esfuerzo por generar diversos conglomerados que puedan ser útiles para determinar la historia natural de la EB. Métodos: Realizamos un estudio transversal en una muestra clínica de 164 pacientes diagnosticados de trastorno Bipolar I o Bipolar II según la definición del DSM-IV-TR. Los análisis de clases latentes se utilizaron para examinar la interacción de la composición étnica de la muestra, rigurosamente definida mediante marcadores genéticos específicos, con las variables clínicas (número de episodios previos) y la respuesta terapéutica al tratamiento con litio. Resultados: el modelo de mejor ajuste resultante de los análisis consistió en seis clases latentes (CL) que incluyen la composición de la ascendencia, el número de episodios depresivos y maníacos y la respuesta clínica al tratamiento con litio: LC-1 consistió en pacientes con ascendencia predominantemente europea, que tenían una pequeña número de episodios clínicos de manía / depresión y buena respuesta al litio; LC-2 consistió en pacientes con ascendencia predominante amerindia, que presentaban pocos episodios clínicos y mostraban una respuesta intermedia al litio; LC-3 consistió en pacientes con ascendencia africana predominante, número intermedio de episodios clínicos y que mostraban una mala respuesta al litio. LC-4 agrupó a pacientes con ascendencia predominante amerindia, con un número intermedio de episodios clínicos y que mostraban una respuesta parcial al litio; LC-5 consistió en pacientes con ascendencia amerindia predominante, múltiples episodios clínicos y que mostraron una mala respuesta al litio; LC-6 consistió en pacientes con ascendencia europea predominante, múltiples episodios clínicos y que mostraron la mejor respuesta al litio. Conclusión: Sobre la base de estos resultados, proponemos seis subtipos latentes de EB. Demostramos que un componente de ascendencia rigurosamente definido influye significativamente en los resultados clínicos y del tratamiento. Esta clasificación latente puede ser útil para describir la génesis, el curso y la evolución de este trastorno heterogéneo y puede conducir a un manejo más personalizado de los casos individuales.27application/pdfenginfo:eu-repo/semantics/acceptedVersioninfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1https://purl.org/redcol/resource_type/ARTArtículo de investigacióninfo:eu-repo/semantics/embargoedAccesshttp://creativecommons.org/licenses/by-nc-nd/2.5/co/http://purl.org/coar/access_right/c_f1cfhttps://creativecommons.org/licenses/by-nc-nd/4.0/Bipolar disorder in a genetic isolate: Ancestry and subtypes of the disorderGrupo de Investigación en Psiquiatría (GIPSI)Medellín, ColombiaBipolar DisorderTrastorno BipolarLatent Class AnalysisAnálisis de Clases LatentesGenetic MarkersMarcadores GenéticosEspecialista en PsiquiatriaEspecializaciónFacultad de Medicina. Especialización en PsiquiatríaUniversidad de AntioquiaCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8823http://bibliotecadigital.udea.edu.co/bitstream/10495/17484/3/license_rdfb88b088d9957e670ce3b3fbe2eedbc13MD53ORIGINALOvadiaLaura_2020_BypolarSubtypesAncestry.pdfOvadiaLaura_2020_BypolarSubtypesAncestry.pdfTrabajo de grado de especializaciónapplication/pdf310128http://bibliotecadigital.udea.edu.co/bitstream/10495/17484/6/OvadiaLaura_2020_BypolarSubtypesAncestry.pdfc6c329dfcfcf2f991a9631cefa04bee0MD56LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://bibliotecadigital.udea.edu.co/bitstream/10495/17484/7/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5710495/17484oai:bibliotecadigital.udea.edu.co:10495/174842021-04-30 12:30:55.229Repositorio Institucional Universidad de Antioquiaandres.perez@udea.edu.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