Quantifying dimensional severity of obsessive-compulsive disorder for neurobiological research
Carregando...
Citações na Scopus
3
Tipo de produção
article
Data de publicação
2017
Título da Revista
ISSN da Revista
Título do Volume
Editora
PERGAMON-ELSEVIER SCIENCE LTD
Autores
ALONSO, Pino
ZAI, Gwyneth
ROSARIO, Maria Conceicao do
FONTENELLE, Leonardo
Citação
PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY, v.79, p.206-212, 2017
Resumo
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.
Palavras-chave
Obsessive-compulsive disorder, Phenotype, Algorithm, Dimensional assessment
Referências
- Aleman A., 2016, SCHIZOPHR RES
- Alonso P, 2011, PSYCHONEUROENDOCRINO, V36, P473, DOI 10.1016/j.psyneuen.2010.07.022
- Alonso P, 2012, J PSYCHIATR NEUROSCI, V37, P273, DOI 10.1503/jpn.110109
- American Psychiatric Association, 2013, DIAGN STAT MAN MANT
- BASU D, 1982, J STAT PLAN INFER, V6, P345, DOI 10.1016/0378-3758(82)90004-0
- Bloch MH, 2008, AM J PSYCHIAT, V165, P1532, DOI 10.1176/appi.ajp.2008.08020320
- Burmeister M, 2008, NAT REV GENET, V9, P527, DOI 10.1038/nrg2381
- Cappi C, 2016, TRANSL PSYCHIAT, V6, DOI 10.1038/tp.2016.30
- Cavallini MC, 2002, AM J MED GENET, V114, P347, DOI 10.1002/ajmg.1700
- Pereira CAD, 2008, REVSTAT-STAT J, V6, P199
- De Luca V, 2011, J AFFECT DISORDERS, V133, P300, DOI 10.1016/j.jad.2011.03.041
- Delorme R, 2006, BMC PSYCHIATRY, V6, DOI 10.1186/1471-244X-6-1
- do Rosario-Campos MC, 2001, AM J PSYCHIAT, V158, P1899, DOI 10.1176/appi.ajp.158.11.1899
- GOODMAN WK, 1989, ARCH GEN PSYCHIAT, V46, P1006
- GOODMAN WK, 1989, ARCH GEN PSYCHIAT, V46, P1012
- Harrison BJ, 2013, BIOL PSYCHIAT, V73, P321, DOI 10.1016/j.biopsych.2012.10.006
- Hasler G, 2007, BIOL PSYCHIAT, V61, P617, DOI 10.1016/j.biopsych.2006.05.040
- Hosmer DW, 2013, WILEY SER PROBAB ST, P1, DOI 10.1002/9781118548387
- Iervolino AC, 2011, ARCH GEN PSYCHIAT, V68, P637, DOI 10.1001/archgenpsychiatry.2011.54
- Jhung K, 2014, PROG NEURO-PSYCHOPH, V53, P149, DOI 10.1016/j.pnpbp.2014.04.007
- Katerberg H, 2010, BEHAV GENET, V40, P505, DOI 10.1007/s10519-010-9339-z
- Katerberg H, 2010, AM J MED GENET B, V153B, P167, DOI 10.1002/ajmg.b.30971
- Kohlrausch FB, 2016, PSYCHIAT RES, V243, P152, DOI 10.1016/j.psychres.2016.06.027
- Leckman JF, 1997, AM J PSYCHIAT, V154, P911
- Lecrubier Y, 2008, EUR ARCH PSY CLIN N, V258, P6, DOI 10.1007/s00406-007-1003-0
- Lennertz L, 2014, EUR NEUROPSYCHOPHARM, V24, P86, DOI 10.1016/j.euroneuro.2013.07.003
- Mataix-Cols D, 2004, ARCH GEN PSYCHIAT, V61, P564, DOI 10.1001/archpsyc.61.6.564
- Mataix-Cols D, 2002, AM J PSYCHIAT, V159, P263, DOI 10.1176/appi.ajp.159.2.263
- Mataix-Cols D, 2013, JAMA PSYCHIAT, V70, P709, DOI 10.1001/jamapsychiatry.2013.3
- Mattheisen M, 2015, MOL PSYCHIATR, V20, P337, DOI 10.1038/mp.2014.43
- Miguel EC, 2008, REV BRAS PSIQUIATR, V30, P185, DOI 10.1590/S1516-44462008000300003
- Pauls DL, 2014, NAT REV NEUROSCI, V15, P410, DOI 10.1038/nrn3746
- Pauls David L, 2010, Dialogues Clin Neurosci, V12, P149
- PAULS DL, 1995, AM J PSYCHIAT, V152, P76
- PAULS DL, 1992, PSYCHIAT CLIN N AM, V15, P759
- Pertusa A, 2010, CLIN PSYCHOL REV, V30, P371, DOI 10.1016/j.cpr.2010.01.007
- R Core Team, 2014, R LANG ENV STAT COMP
- Rosario-Campos MC, 2006, MOL PSYCHIATR, V11, P495, DOI 10.1038/sj.mp.4001798
- Rufer M, 2005, J AFFECT DISORDERS, V88, P99, DOI 10.1016/j.jad.2005.06.003
- Schooler C, 2008, DEPRESS ANXIETY, V25, P680, DOI 10.1002/da.20444
- Shavitt RG, 2014, J PSYCHIATR RES, V57, P141, DOI 10.1016/j.jpsychires.2014.06.010
- Stewart SE, 2013, MOL PSYCHIATR, V18, P788, DOI 10.1038/mp.2012.85
- Taj MJRJ, 2013, PROG NEURO-PSYCHOPH, V41, P18, DOI 10.1016/j.pnpbp.2012.10.023
- Venables W. N., 2002, MODERN APPL STAT S
- Via E, 2014, BRIT J PSYCHIAT, V204, P61, DOI 10.1192/bjp.bp.112.123364
- Waszczuk M.A., 2017, J ABNORM PSYCHOL
- Yue WH, 2016, SCI REP-UK, V6, DOI 10.1038/srep31333