Browsing Doctoral Degrees (Psychiatry) by browse.metadata.advisor "De Bruin, Gideon P."
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- ItemSymptom dimensions in obsessive-compulsive disorder(Stellenbosch : University of Stellenbosch, 2005-12) Lochner, Christine; Stein, Dan J.; De Bruin, Gideon P.; University of Stellenbosch. Faculty of Health Sciences. Dept. of Psychiatry.Background: Obsessive-compulsive disorder (OCD) is a neuropsychiatric condition characterized by significant heterogeneity. It has been suggested that classification of OCD into more homogeneous subtypes, and identification of their associations with etiological factors (e.g. genetic variants, or psychological trauma), and outcome (e.g. disability and treatment response), may be useful. The identified subtypes are not definitive yet and continue to be subject to revision. The overall objective of this dissertation was to assess comprehensively a sample of OCD patients, and to use cluster analytic methods to delineate valid OCD subtypes. Methods: Patients meeting DSM-IV criteria for a primary diagnosis of OCD (N=261) on the Structured Clinical Interview for Axis I Disorders - Patient Version (SCID-I/P), with ages ranging from 16 to 71, took part in the study. The newly developed Structured Clinical Interview for the Diagnosis of putative Obsessive-Compulsive Spectrum Disorders (SCIDOCSD) was administered to assess OCD-related conditions not covered by the SCID-I/P. OCD subtyping, based on OCD symptomatology (assessed with the Yale-Brown Obsessive- Compulsive Symptom Checklist [YBOCS-CL]), and based on comorbidity with the OCD spectrum of disorders (assessed with the SCID-OCSD), proceeded along the following lines: Firstly, latent class cluster analysis (LCA), a categorical analogue to traditional factor analysis (FA), and with many advantages compared to FA, was implemented with the (nine) most frequently endorsed OC symptoms. Secondly, in an attempt to remedy some of the limitations of the LCA (e.g. increased potential for computational instability when additional indicators / symptoms were included), cluster analyses (Ward’s method) were performed on all of the items of the YBOCS-CL and SCID-OCSD, respectively, for all OCD patients. The associations of cluster scores with demographic variables (age, gender), clinical variables (age of onset, obsessive-compulsive symptom severity and dimensions, level of insight, temperament, childhood trauma, treatment response) and genotypes were then examined, using Spearman correlation coefficients, one-way analysis of variance (ANOVA), and Mann- Whitney U-tests, where appropriate. Results: The findings suggested that increased presentation of symptoms characteristic of each of the clusters of cases was associated with specific demographic and clinical characteristics, which substantiated the presence of distinct clinical subtypes of OCD. Cluster analysis of the 45 selected items of the YBOCS-CL in this sample of OCD patients identified 6 separate clusters; these clusters were labelled “Contamination fears / washing”, “Hoarding / collecting”, “Symmetry / ordering / counting / arranging / repeating”, “Sexual”, “Somatic, religious and diverse” and “Harm-related”. Increased presentation of symptoms characteristic of each of the clusters was associated with specific demographic, clinical and, in some cases, genetic characteristics. Of note, the findings indicated the L/L (met/met) genotype of COMT Val158Met polymorphism plays a major role in the increased manifestation of sexual, somatic, religious and diverse, and harm-related symptoms of OCD, as such contributing to the relatively limited data on OC symptom subtypes and genetics. However, the fact that the associated features did not clearly and uniquely differentiate clusters and that clusters were significantly correlated with one another suggested that the delineation of the OCD complex into OC symptom clusters is not the only way to approach the heterogeneity characteristic of OCD. Nevertheless, the significant comorbidity with OCSD’s in the identified clusters (e.g. tics associated with sexual obsessions / compulsions) highlighted the significant relationship of OCD with the OCSD’s. This again raised the question about the way in which the OCSD’s “fit” with the standard OC symptomatology outlined in the YBOCS-CL. A cluster analysis of OCSD’s in OCD patients identified a Tourette’s syndrome / tics subtype of OCD (part of the so-called “reward deficiency” cluster), as well as an impulsivity subtype, and a somatic subtype – each associated with specific clinical and demographic variables. Here, a significant relationship between the identified clusters and the investigated dopaminergic and serotonergic polymorphisms was not found, suggesting that variants in other genes in these systems should also be explored. Conclusion: The main finding was that OCD is indeed a heterogeneous disorder that may be subtyped into different symptom dimensions. The identified OCD subtypes with their associated features were to a large extent consistent with previously published data. However, in contrast to factor analysis, LCA provided a novel, appropriate and advantageous statistical analysis strategy for the data. Furthermore, to our knowledge, the attempt to classifiy OCD according to comorbid OCSD’s was the first cluster analysis based on a prospective comprehensive interview investigating a range of OCSD’s. As such, although the dimensional structure of OCD is still not entirely understood, the categorization of our OCD patients into different groups and the investigation of their respective features have gone beyond the literature and thus add another dimension to the increasing efforts to fully delineate OCD subtypes.