Comorbidity in trichotillomania (hair-pulling disorder): A cluster analytical approach.


Journal

Brain and behavior
ISSN: 2162-3279
Titre abrégé: Brain Behav
Pays: United States
ID NLM: 101570837

Informations de publication

Date de publication:
12 2019
Historique:
received: 06 06 2019
revised: 08 08 2019
accepted: 23 09 2019
pubmed: 7 11 2019
medline: 17 6 2020
entrez: 7 11 2019
Statut: ppublish

Résumé

A promising approach to reducing the phenotypic heterogeneity of psychiatric disorders involves the identification of homogeneous subtypes. Careful study of comorbidity in obsessive-compulsive disorder (OCD) contributed to the identification of the DSM-5 subtype of OCD with tics. Here we investigated one of the largest available cohorts of clinically diagnosed trichotillomania (TTM) to determine whether subtyping TTM based on comorbidity would help delineate clinically meaningful subgroups. As part of an ongoing international collaboration, lifetime comorbidity data were collated from 304 adults with pathological hair-pulling who fulfilled criteria for DSM-IV-TR or DSM-5 TTM. Cluster analysis (Ward's method) based on comorbidities was undertaken. Three clusters were identified, namely Cluster 1: cases without any comorbidities (n = 63, 20.7%) labeled "simple TTM," Cluster 2: cases with comorbid major depressive disorder only (N = 49, 16.12%) labeled "depressive TTM," and Cluster 3: cases presenting with combinations of the investigated comorbidities (N = 192, 63.16%) labeled "complex TTM." The clusters differed in terms of hair-pulling severity (F = 3.75, p = .02; Kruskal-Wallis [KW] p < .01) and depression symptom severity (F = 5.07, p = <.01; KW p < .01), with cases with any comorbidity presenting with increased severity. Analysis of the temporal nature of these conditions in a subset suggested that TTM onset generally preceded major depressive disorder in (subsets of) Clusters 2 and 3. The findings here are useful in emphasizing that while many TTM patients present without comorbidity, depression is present in a substantial proportion of cases. In clinical practice, it is crucial to assess comorbidity, given the links demonstrated here between comorbidity and symptom severity. Additional research is needed to replicate these findings and to determine whether cluster membership based on comorbidity predicts response to treatment.

Sections du résumé

BACKGROUND
A promising approach to reducing the phenotypic heterogeneity of psychiatric disorders involves the identification of homogeneous subtypes. Careful study of comorbidity in obsessive-compulsive disorder (OCD) contributed to the identification of the DSM-5 subtype of OCD with tics. Here we investigated one of the largest available cohorts of clinically diagnosed trichotillomania (TTM) to determine whether subtyping TTM based on comorbidity would help delineate clinically meaningful subgroups.
METHODS
As part of an ongoing international collaboration, lifetime comorbidity data were collated from 304 adults with pathological hair-pulling who fulfilled criteria for DSM-IV-TR or DSM-5 TTM. Cluster analysis (Ward's method) based on comorbidities was undertaken.
RESULTS
Three clusters were identified, namely Cluster 1: cases without any comorbidities (n = 63, 20.7%) labeled "simple TTM," Cluster 2: cases with comorbid major depressive disorder only (N = 49, 16.12%) labeled "depressive TTM," and Cluster 3: cases presenting with combinations of the investigated comorbidities (N = 192, 63.16%) labeled "complex TTM." The clusters differed in terms of hair-pulling severity (F = 3.75, p = .02; Kruskal-Wallis [KW] p < .01) and depression symptom severity (F = 5.07, p = <.01; KW p < .01), with cases with any comorbidity presenting with increased severity. Analysis of the temporal nature of these conditions in a subset suggested that TTM onset generally preceded major depressive disorder in (subsets of) Clusters 2 and 3.
CONCLUSIONS
The findings here are useful in emphasizing that while many TTM patients present without comorbidity, depression is present in a substantial proportion of cases. In clinical practice, it is crucial to assess comorbidity, given the links demonstrated here between comorbidity and symptom severity. Additional research is needed to replicate these findings and to determine whether cluster membership based on comorbidity predicts response to treatment.

Identifiants

pubmed: 31692297
doi: 10.1002/brb3.1456
pmc: PMC6908854
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e01456

Subventions

Organisme : NIDDK NIH HHS
ID : P30 DK092949
Pays : United States

Informations de copyright

© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

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Auteurs

Christine Lochner (C)

SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa.

Nancy J Keuthen (NJ)

Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Erin E Curley (EE)

Department of Psychology, Temple University, Philadelphia, PA, USA.

Esther S Tung (ES)

Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA.

Sarah A Redden (SA)

Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA.
Department of Psychology, Florida State University, Tallahassee, FL, USA.

Emily J Ricketts (EJ)

Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.

Christopher C Bauer (CC)

Department of Psychology, Marquette University, Milwaukee, WI, USA.

Douglas W Woods (DW)

Department of Psychology, Marquette University, Milwaukee, WI, USA.

Jon E Grant (JE)

Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA.

Dan J Stein (DJ)

SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa.

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