Quantifying dimensional severity of obsessive-compulsive disorder for neurobiological research

Roseli Gedanke Shavitt, Guaraci Requena, Pino Alonso, Gwyneth Zai, Daniel L.C. Costa, Carlos Alberto de Bragança Pereira, Maria Conceição do Rosário, Ivanil Morais, Leonardo Fontenelle, Carolina Cappi, James L. Kennedy, Jose Manuel Menchon, Euripedes Constantino Miguel, Peggy M.A. Richter

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2 Citations (Scopus)


Current research to explore genetic susceptibility factors in obsessive-compulsive disorder (OCD) has resulted in the tentative identification of a small number of genes. However, findings have not been readily replicated. It is now broadly accepted that a major limitation to this work is the heterogeneous nature of this disorder, and that an approach incorporating OCD symptom dimensions in a quantitative manner may be more successful in identifying both common as well as dimension-specific vulnerability genetic factors. As most existing genetic datasets did not collect specific dimensional severity ratings, a specific method to reliably extract dimensional ratings from the most widely used severity rating scale, the Yale-Brown Obsessive Compulsive Scale (YBOCS), for OCD is needed. This project aims to develop and validate a novel algorithm to extrapolate specific dimensional symptom severity ratings in OCD from the existing YBOCS for use in genetics and other neurobiological research. To accomplish this goal, we used a large data set comprising adult subjects from three independent sites: the Brazilian OCD Consortium, the Sunnybrook Health Sciences Centre in Toronto, Canada and the Hospital of Bellvitge, in Barcelona, Spain. A multinomial logistic regression was proposed to model and predict the quantitative phenotype [i.e., the severity of each of the five homogeneous symptom dimensions of the Dimensional YBOCS (DYBOCS)] in subjects who have only YBOCS (categorical) data. YBOCS and DYBOCS data obtained from 1183 subjects were used to build the model, which was tested with the leave-one-out cross-validation method. The model's goodness of fit, accepting a deviation of up to three points in the predicted DYBOCS score, varied from 78% (symmetry/order) to 84% (cleaning/contamination and hoarding dimensions). These results suggest that this algorithm may be a valuable tool for extracting dimensional phenotypic data for neurobiological studies in OCD.

Original languageEnglish
Pages (from-to)206-212
Number of pages7
JournalProgress in Neuro-Psychopharmacology and Biological Psychiatry
Publication statusPublished - 3 Oct 2017
Externally publishedYes


  • Algorithm
  • Dimensional assessment
  • Obsessive-compulsive disorder
  • Phenotype

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