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Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach

BACKGROUND: While depression is a frequent psychiatric comorbid condition in diabetes and has significant clinical impact, the syndromal profile of depression and anxiety symptoms has not been examined in detail. AIMS: To determine the syndromal pattern of the depression and anxiety spectrum in a la...

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Autores principales: Starkstein, Sergio E., Davis, Wendy A., Dragovic, Milan, Cetrullo, Violetta, Davis, Timothy M. E., Bruce, David G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229133/
https://www.ncbi.nlm.nih.gov/pubmed/25390370
http://dx.doi.org/10.1371/journal.pone.0112049
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author Starkstein, Sergio E.
Davis, Wendy A.
Dragovic, Milan
Cetrullo, Violetta
Davis, Timothy M. E.
Bruce, David G.
author_facet Starkstein, Sergio E.
Davis, Wendy A.
Dragovic, Milan
Cetrullo, Violetta
Davis, Timothy M. E.
Bruce, David G.
author_sort Starkstein, Sergio E.
collection PubMed
description BACKGROUND: While depression is a frequent psychiatric comorbid condition in diabetes and has significant clinical impact, the syndromal profile of depression and anxiety symptoms has not been examined in detail. AIMS: To determine the syndromal pattern of the depression and anxiety spectrum in a large series of patients with type 2 diabetes, as determined using a data-driven approach based on latent class analysis (LCA). METHOD: Type 2 diabetes participants from the observational community-based Fremantle Diabetes Study Phase II underwent assessment of lifetime depression using the Brief Lifetime Depression Scale, the Patient Health Questionnaire 9-item version (PHQ-9) for current depression symptoms, and the Generalized Anxiety Disorder Scale that was specifically developed and validated for this study. The main outcome measure was classes of patients with a specific syndromal profile of depression and anxiety symptoms based on LCA. RESULTS: LCA identified four classes that were interpreted as “major anxious depression”, “minor anxious depression”, “subclinical anxiety”, and “no anxious depression”. All nine DSM-IV/5 diagnostic criteria for major depression identified a class with a high frequency of major depression. All symptoms of anxiety had similar high probabilities as symptoms of depression for the “major depression-anxiety” class. There were significant differences between classes in terms of history of depression and anxiety, use of psychoactive medication, and diabetes-related variables. CONCLUSIONS: Patients with type 2 diabetes show specific profiles of depression and anxiety. Anxiety symptoms are an integral part of major depression in type 2 diabetes. The different classes identified here provide empirically validated phenotypes for future research.
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spelling pubmed-42291332014-11-18 Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach Starkstein, Sergio E. Davis, Wendy A. Dragovic, Milan Cetrullo, Violetta Davis, Timothy M. E. Bruce, David G. PLoS One Research Article BACKGROUND: While depression is a frequent psychiatric comorbid condition in diabetes and has significant clinical impact, the syndromal profile of depression and anxiety symptoms has not been examined in detail. AIMS: To determine the syndromal pattern of the depression and anxiety spectrum in a large series of patients with type 2 diabetes, as determined using a data-driven approach based on latent class analysis (LCA). METHOD: Type 2 diabetes participants from the observational community-based Fremantle Diabetes Study Phase II underwent assessment of lifetime depression using the Brief Lifetime Depression Scale, the Patient Health Questionnaire 9-item version (PHQ-9) for current depression symptoms, and the Generalized Anxiety Disorder Scale that was specifically developed and validated for this study. The main outcome measure was classes of patients with a specific syndromal profile of depression and anxiety symptoms based on LCA. RESULTS: LCA identified four classes that were interpreted as “major anxious depression”, “minor anxious depression”, “subclinical anxiety”, and “no anxious depression”. All nine DSM-IV/5 diagnostic criteria for major depression identified a class with a high frequency of major depression. All symptoms of anxiety had similar high probabilities as symptoms of depression for the “major depression-anxiety” class. There were significant differences between classes in terms of history of depression and anxiety, use of psychoactive medication, and diabetes-related variables. CONCLUSIONS: Patients with type 2 diabetes show specific profiles of depression and anxiety. Anxiety symptoms are an integral part of major depression in type 2 diabetes. The different classes identified here provide empirically validated phenotypes for future research. Public Library of Science 2014-11-12 /pmc/articles/PMC4229133/ /pubmed/25390370 http://dx.doi.org/10.1371/journal.pone.0112049 Text en © 2014 Starkstein et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Starkstein, Sergio E.
Davis, Wendy A.
Dragovic, Milan
Cetrullo, Violetta
Davis, Timothy M. E.
Bruce, David G.
Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach
title Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach
title_full Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach
title_fullStr Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach
title_full_unstemmed Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach
title_short Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach
title_sort diagnostic criteria for depression in type 2 diabetes: a data-driven approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229133/
https://www.ncbi.nlm.nih.gov/pubmed/25390370
http://dx.doi.org/10.1371/journal.pone.0112049
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