Department of Psychiatry, University of São Paulo, School of Medicine, Rua Dr. Ovídio Pires de Campo, 785/3(o). andar-sala 7. CEP 01060-970 São Paulo, Brazil.
Institute of Mathematics and Statistics, University of Sao Paulo, R. do Matão, 1010 - Vila Universitaria, São Paulo, SP CEP 05508-090, Brazil.
Prog Neuropsychopharmacol Biol Psychiatry. 2017 Oct 3;79(Pt B):206-212. doi: 10.1016/j.pnpbp.2017.06.037. Epub 2017 Jul 1.
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.
目前,探索强迫症(OCD)遗传易感性因素的研究已经确定了少数几个基因。然而,这些发现尚未得到广泛证实。目前广泛认为,这项工作的一个主要局限性是这种疾病的异质性,并且采用定量方式纳入 OCD 症状维度的方法可能更成功地识别常见和维度特异性易感性遗传因素。由于大多数现有遗传数据集未收集特定的维度严重程度评分,因此需要一种可靠的方法从最广泛使用的严重程度评分量表,即耶鲁-布朗强迫症量表(YBOCS)中提取 OCD 的维度评分。本项目旨在开发和验证一种从现有的 YBOCS 中提取 OCD 特定维度症状严重程度评分的新算法,用于遗传学和其他神经生物学研究。为了实现这一目标,我们使用了一个包含来自三个独立地点的成年受试者的大型数据集:巴西 OCD 联合会、加拿大多伦多的 Sunnybrook 健康科学中心和西班牙巴塞罗那的 Bellvitge 医院。我们提出了一种多项逻辑回归模型,以对具有 YBOCS(分类)数据的受试者的定量表型(即 DYBOCS 的五个同质症状维度的严重程度)进行建模和预测。我们使用来自 1183 名受试者的 YBOCS 和 DYBOCS 数据构建模型,并采用留一法交叉验证方法对其进行测试。模型的拟合优度,在预测 DYBOCS 评分方面,接受高达三分的偏差,从 78%(对称/秩序)到 84%(清洁/污染和囤积维度)不等。这些结果表明,该算法可能是 OCD 神经生物学研究中提取维度表型数据的有用工具。