Obsessive–compulsive disorder: Influence of age at onset on comorbidity patterns

Maria Alice de Mathisa, d, , , Maria Conceição do Rosarioa, d, f, Juliana Belo Diniza, d, Albina Rodrigues Torresb, d, Roseli Gedanki Shavitta, d, Ygor Arzeno Ferrãoc, d, Victor Fossaluzaa, e, Carlos Alberto de Bragança Pereirae and Eurípedes Constantino Miguela, d

aDepartment of Psychiatry, University of Sao Paulo Medical School, Rua Dr. Ovídio Pires de Campos, 785, 05403-010 São Paulo, SP, Brazil bDepartment of Neurology and Psychiatry, Botucatu Medical School, Brazil cPorto Alegre Institute University Center, Brazil dObsessive–Compulsive Disorder Brazilian Consortium (C-TOC), Brazil eMath and Stat Institute, University of Sao Paulo, Brazil fDepartment of Psychiatry, Federal University of Sao Paulo, Brazil Received 15 November 2007; revised 10 January 2008; accepted 11 January 2008. Available online 7 March 2008.

Abstract

Purpose

This study investigates the influence of age at onset of OCS on psychiatric comorbidities, and tries to establish a cut-off point for age at onset.

Methods

Three hundred and thirty OCD patients were consecutively recruited and interviewed using the following structured interviews: Yale-Brown Obsessive Compulsive Scale; Yale Global Tic Severity Scale and the Structured Clinical Interview for DSM-IV. Data were analyzed with regression and cluster analysis.

Results

Lower age at onset was associated with a higher probability of having comorbidity with tic, anxiety, somatoform, eating and impulse–control disorders. Longer illness duration was associated with lower chance of having tics. Female gender was associated with anxiety, eating and impulse–control disorders. Tic disorders were associated with anxiety disorders and attention-deficit/hyperactivity disorder. No cut-off age at onset was found to clearly divide the sample in homogeneous subgroups. However, cluster analyses revealed that differences started to emerge at the age of 10 and were more pronounced at the age of 17, suggesting that these were the best cut-off points on this sample.

Conclusions

Age at onset is associated with specific comorbidity patterns in OCD patients. More prominent differences are obtained when analyzing age at onset as an absolute value.

Keywords: Obsessive–compulsive disorder; Age at onset; Comorbidity; Early onset; Late onset

Article Outline

1. Introduction2. Materials and methods2.1. Subjects2.2. Clinical assessment2.3. Statistical analyses3. Results3.1. The cut-off point for age at onset4. DiscussionReferences

1. Introduction

The complexity and diversity of obsessive–compulsive disorder (OCD) clinical manifestations have intrigued psychiatrists for a long time. Recent studies have reinforced the idea that OCD is a highly heterogeneous condition, probably composed of distinct subtypes [38], [39] and [42] and severe impact on public health [59].

The recognition of homogeneous subgroups of OCD patients is essential for the search of etiological factors and more effective treatment strategies. Many different strategies have been used as an attempt to define homogeneous subgroups, such as age at onset of the obsessive–compulsive symptoms (OCS) [10], [19], [43], [52], [53], [56] and [61]; the presence of comorbid disorders [5] and [46]; different kinds of OCS [20]; or symptom dimensions [31] and [37].

The concept of an early onset OCD subgroup was developed from the observation that OCD had two peaks of incidence: one in childhood and a second one in early adult years, with different sex distributions [21] and [57]. Since then, this hypothesis has been reinforced by clinical, genetic, neuroimaging and treatment response studies.

For instance, clinical studies suggest that an early onset is related to tics comorbidity [10], [14], [41], [43] and [52]; higher frequency of symmetry/exactness obsessions, religious obsession, hoarding and tic-like compulsions [52] and [61] and male gender [19], [21], [57] and [61]. Early onset was also associated with a chronic course of illness and schizotypal personality disorder comorbidity [3]. In Diniz et al.'s study [14] early age at onset was associated with tic disorders, eating disorders, kleptomania, trichotillomania and bipolar disorder. Family studies indicated that early onset OCS might be a subgroup of major genetic component based on findings of a higher familial aggregation among relatives of early onset probands [25], [32], [34], [48], [44] and [53]. Neuroimaging studies have reported different patterns of brain activation in childhood onset, compared to adult OCD onset patients in areas involving the striatum (caudate and putamen) [35] and [54] or the cerebellum [7]. Neuropsychological studies found executive functions impairment in patients with early onset OCS [23]. Some treatment response studies suggest worse response in early age at onset compared to late onset [1], [19], [28], [51] and [52], while others do not [2], [4], [17] and [55].

In summary, age at onset is an important characteristic for a better understanding of the OCD heterogeneity. Unfortunately, despite its relevance, there is no consensus among researchers about what age should be the ideal threshold for determining an “early onset” of the OCS. Some authors propose that an early onset should be considered when symptoms begin before age 7 [57], 10 [21],

[48] and [52], 15 [10], [26] and [43], and 17 [3], [19], [56] and [61]. Therefore, the best age limit to consider early or late onset seems to be an arbitrary decision, and previous studies were not designed to validate a specific threshold [12].

Another controversy in the literature is about the definition on how to assess age at onset. Some studies have considered age at onset as the earliest age when the patient or a family member first noticed the presence of an OCS [7], [14] and [52]. Others consider age at onset when subjects display clinically significant distress or impairment associated with OCS [19] and [61]. Another possibility is to consider age at onset as the age when the patient would have a DSM-IV OCD diagnosis [56].

This disagreement on how to approach age at onset may produce misleading results. The achievement of a consensus regarding these age at onset questions could lead to more reliable and consistent conclusions in future studies.

The present study was designed with the main goal of analyzing the influence of OCS age at onset on the presence of psychiatric comorbid disorders. As a consequence of the lack of consensus in the literature, a secondary goal was to investigate what would be the best way of using age at onset information and whether there would be an ideal cut-off point to define an early OCD onset subgroup, taking into consideration the expression of psychiatric comorbidities.

2. Materials and methods

2.1. Subjects

The data set was composed of 330 consecutive outpatients with OCD diagnosis according to DSM-IV criteria. Patients were evaluated from 1996 to 2006, in three university hospitals from Sao Paulo (253 patients), Botucatu (27 patients) and Porto Alegre (50 patients).

This project was approved by the Ethics Committees from the three university hospitals involved. After a thorough description of the study to the patients and the assurance that their decision to participate in the study would not interfere with their access to treatment, all participants gave their written informed consent.

2.2. Clinical assessment

All patients were directly interviewed by clinical psychologists or psychiatrists with a large experience in treating OCD patients.

Age at onset of OCS was defined as the earliest age that the patient remembered having OCS. Whenever possible, a family member was also interviewed to confirm the patients' information about age of OCS onset.

All subjects were interviewed with the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) [22]; the Yale Global Tic Severity Scale (YGTSS) [29]; and the Structured Clinical Interview for DSM-IV Axis I Disorders-patient edition (SCID-I) and for impulse–control disorders [18].

2.3. Statistical analyses

The Statistical Package for Social Science (SPSS v. 12.0) for Windows and R: A Language and Environment for Statistical Computing 2.4.0 were used to perform all statistical analyses.

Logistic regression analyses were used in order to verify the influence of age at onset on comorbidities, adjusting for possible confounders. The dependent variable (Y) was the presence (Y = 1) or absence (Y = 0) of each of the studied comorbid diagnosis group. Dependent variables were tic disorders, ADHD, mood disorders, anxiety disorders, somatoform disorders, eating disorders, and impulse–control disorders. Independent variables were age at onset, presence of tics, gender and illness duration. Table 1 presents the rationality for the chosen independent variables.

Table 1.

Dependent and independent variables

Dependents variable Tic disorder, ADHD, mood disorder, anxiety disorder, somatoform disorder, eating disorder, and impulse–control disorder

Independents variable Rational

Age at onset Described above

Tic disorder (except when it was considered as dependent variable)

Is related to age at OCS onset and to other OCD characteristics and comorbidities. Tic-related OCD has been considered as the best described OCD subgroup when in association with the following patterns: early age at onset [27], [30], [40] and [52]; preponderance of males [27] and [30]; higher frequency of intrusive violent and sexual images or thoughts; and hoarding, counting and tic-like compulsions [27], [58] and [62]. Regarding comorbidities, tic-related OCD presents higher rates of trichotillomania, body dysmorphic disorder, mood disorder, social phobia, other anxiety disorders and attention-deficit hyperactivity disorder [11] and [50].

Duration of illness

Is related to age at OCS onset and also influences the characterization of OCD patients. In Diniz et al.'s study [14], major depressive disorder was associated with a longer OCD duration, but not to early age at OCS onset. Longer duration of illness has also been associated with poor response in a double blind trial comparing clomipramine and placebo [13] and in an open trial with fluoxetine and clomipramine [51].

Gender

Gender might be associated with both age at onset and comorbidity [21], [60] and [62]. Males have higher frequencies of symmetry symptoms, sexual obsessions and checking/repeating compulsions than women. Women are more likely than men to present contamination and aggression obsessions, and cleaning compulsions [6], [33] and [47]. Regarding psychiatric comorbidities: men have been described to have more social phobia [6], [36] and [60]; hipomaniac episodes [6] and [33]; depersonalization disorders; substance abuse [6]; alcohol dependence [47]; and schizophrenia [60]. Female gender has been associated with eating disorders [6], [9], [16] and [47] and major depressive disorder [9] and [47].

Tic disorder: chronic motor/vocal tics and Tourette syndrome; ADHD: attention-deficit hyperactivity disorder; mood disorders: depressive disorder, bipolar mood disorder, and dysthymia; anxiety disorders: social phobia, simple phobia, generalized anxiety disorder, posttraumatic stress disorder, panic disorder, and separation anxiety disorder; somatoform disorders: hypochondriasis, body dysmorphic disorder, somatization disorder; eating disorders: anorexia nervosa, and bulimia nervosa; and impulse–control disorders: kleptomania, pyromania, pathological gambling, compulsive buying, trichotillomania, and skin picking.

Considering the hypothesis that a specific age at onset threshold could define two rather distinct groups of patients, with relevant therapeutic and prognostic implications, the optimal cut-off point to define the early and late onset groups was investigated. For this purpose, cluster analyses were performed to determine the optimal partition into two groups according to the following variables: gender, presence of tic disorders, anxiety, mood, somatoform, eating and impulse–control disorders,

and Y-BOCS severity scores. These variables were chosen based on previous studies [10], [19], [43], [52], [53], [56] and [61].

After defining the best partition point into two homogeneous groups using all relevant variables except age at onset, we compared the mean age at onset of the two groups. If a significant difference between groups was observed, it would indicate that age at onset can be considered a probably meaningful clinical feature. Following the same technique of median tests presented in Noether [24], we considered the role of all onset ages and obtained three quartiles. The first is the percentile 0.25, the second is the 0.5 percentile and the third is the percentile 0.75. Considering the two groups, we generated frequencies of individuals in each of the four new classes of age onset. If the differences between the frequency distributions obtained in this way were statistically different, we would conclude that age onset could be a good indicator for defining subgroups. To compare these distributions we used the two significant levels used nowadays: one frequentist, the p-value, and one Bayesian, the evidence value or e-value. This last index was introduced by Pereira and Stern [49]. Both p-values and e-values are indexes to measure the evidence of the data in favor of the hypotheses.

3. Results

The sample was composed of 330 patients (181 males, 149 females). The mean age at assessment was 32.85 (S.D. = 12.20) years, and the mean duration of illness was 18.81 (S.D. = 11.68) years. Most of the patients were single (58.5%). One hundred and fifteen (34.8%) subjects were married, 18 (5.5%) were divorced and 4 (1.2%) were widowed. Patients had a mean of 14.3 years of education (S.D. = 5.2). Table 2 summarizes the main clinical characteristics of the sample, including Y-BOCS severity scores and comorbidity profile.

Table 2.

Clinical characteristics and psychiatric comorbid conditions of the clinical sample

Clinical characteristics n = 330, Mean (SE)

Age at onset 14.04 (9.08)

Y-BOCS score

Obsession 11.87 (0.24)

Compulsion 12.16 (0.23)

Total 24.00 (0.42)

Comorbid disorders Cases (%)

Any comorbidity 306 (92.7)

Mood disorders 233 (70.6)

Major depressive disorder 200 (60.6)

Clinical characteristics n = 330, Mean (SE)

Bipolar I 31 (9.4)

Bipolar II 20 (6.1)

Substance-related disorders 52 (15.8)

Anxiety disorders 211 (63.9)

Panic disorder 21 (6.4)

Panic disorder/agoraphobia 37 (11.2)

Social phobia 124 (37.6)

Simple phobia 86 (26.1)

PTSD 15 (4.5)

Generalized anxiety disorder 8.5 (25.8)

Somatoform disorders 57 (17.3)

Somatization disorder 6 (1.8)

Hypochondriasis 9 (2.7)

Body dysmorphic disorder 48 (14.5)

Eating disorders 33 (10)

Binge-eating disorder 14 (4.2)

Anorexia nervosa 11 (3.3)

Bulimia nervosa 9 (2.7)

Tic disorders 91 (27.6)

Tourette syndrome 51 (15.5)

Separation anxiety disorder 58 (17.6)

ADHD 46 (13.9)

Impulse-control disorders 77 (23.3)

Pyromania 3 (0.9)

Clinical characteristics n = 330, Mean (SE)

Kleptomania 8 (2.4)

Pathological gambling 1 (0.3)

Compulsive buying 3 (0.9)

Trichotillomania 26 (7.9)

Sckin picking 36 (11.0)

Y-BOCS: Yale-Brown Obsessive-Compulsive Scale; PTSD: posttraumatic stress disorder; and ADHD: attention-deficit hyperactivity disorder.

Results of the logistic regression analyses are shown below. Table 3 presents only the best-fit model for each disease or group of diseases, according to the Akaike Information Criterion (AIC) stepwise model selection.

Table 3.

Logistic regression

Coef. Std. error EXP (coef.) p-Value

Tic disorders

Intercept 0.81 0.38 2.25 0.034*

Age at onset −0.07 0.02 0.93 <0.001*

Illness duration −0.04 0.01 0.96 0.002*

Attention-deficit/hyperactivity disorder

Intercept −1.64 0.39 0.19 <0.001*

Age at onset −0.05 0.03 0.95 0.07**

Tic disorder 1.19 0.33 3.29 <0.001*

Mood disorders

Intercept 1.12 0.22 3.08 <0.001*

Age at onset −0.02 0.01 0.98 0.177

Anxiety disorders

Intercept 0.41 0.39 1.51 0.298

Age at onset −0.03 0.01 0.97 0.019*

Coef. Std. error EXP (coef.) p-Value

Tic disorder 0.59 0.29 1.80 0.039*

Female gender 0.79 0.26 2.20 0.002*

Illness duration 0.02 0.01 1.02 0.145

Somatoform disorders

Intercept −1.00 0.29 0.37 0.001*

Age at onset −0.04 0.02 0.96 0.035*

Eating disorders

Intercept −1.70 0.59 0.18 0.004*

Age at onset −0.06 0.03 0.94 0.024*

Female gender 1.51 0.42 4.52 <0.001*

Illness duration −0.03 0.02 0.97 0.106

Impulse–control disorders

Intercept −1.03 0.28 0,36 <0.001*

Age at onset −0.04 0.02 0.96 0.028*

Female gender 0.72 0.27 2.05 0.007*

*p-Value < 0.05; **p-value < 0.10; and coef. = coefficient.

In this table, when the coefficient is positive, the categorical independent variable (tic disorders or female gender) increases the chance of having either ADHD, anxiety disorders, eating disorders or impulsivity disorders. With continuous independent variables (age at onset and illness duration), a negative coefficient indicates that a younger age or a smaller number of years is associated with a higher probability of having the specific outcome.

As shown in Table 3, the main results are the following:

- Age at onset: the lower the age at onset, the higher the probability of having the following comorbidities: tic disorders (p ≤ 0.001); anxiety disorders (p = 0.019); somatoform disorders (p = 0.035); eating disorders (p = 0.024) and impulse–control disorders (p = 0.028).

- Illness duration: the shorter the duration of OCD, the higher the chance of having any tic disorder (p = 0.002).

- Gender: female sex enhanced the probability of having anxiety (p = 0.002), eating (p ≤ 0.001) and impulse–control disorders (p = 0.007).

- Tic disorders: participants with any tic disorder were more likely to have ADHD (p ≤ 0.001) and anxiety disorders (p = 0.04).

3.1. The cut-off point for age at onset

In order to show that there is a natural age at onset cut-off, we use an indirect argument. First, suppose that there exists a partition of the population into two distinct groups. Applying a cluster analysis to obtain the pair of sample groups with the largest distance, we proceed as following: all important variables but age at onset were considered in the determination of the patient's sample partition. Having defined the partition into two patient groups, we studied the age at onset differences by percentiles technique.

By using histograms (Graph 1) and percentis graphs (Graph 2) of age at onset it is possible to observe that there was a difference only in the higher age at onset level.

Display Full Size version of this image (28K)

Graph 1. Histogram of age at onset in each cluster.

Display Full Size version of this image (33K)

Graph 2. Percentis of age at onset in the two clusters patients.

The frequencies of mood and anxiety disorders were high in both clusters, but more frequent in cluster 1, which also had more females, lower mean age at onset (11.88 S.E. = 0.56 versus 15.18 S.E. = 0.76 years) and higher mean Y-BOCS total score (25.15 S.E. = 0.61 versus 22.80 S.E. = 0.58). Tic disorders and eating disorders were less frequent than mood and anxiety disorders in both clusters, but were also more frequent in cluster 1 than in cluster 2. Somatoform, impulse–control disorders and ADHD were totally absent in cluster 2, but present in cluster 1.

To compare the two groups in relation to age at onset, we considered a categorization of age at onset based on the quartiles of the whole sample: 8, 11, and 17. We recorded the frequencies of patients, for the two groups, in the following categories: patients under 8, from 8 up to under 11, from 11 up to under 17 and finally from 17 on. Using the Chi-square test for homogeneity (3 df), we reject the homogeneity between the two groups (e-value = 0.113 and p-value = 0.038). By considering the Chi-squared partition statistics, we did not reject homogeneity (2 df) if considering only the three first categories (e-value = 0.9873, p-value = 0.84). Taking now the category of 17 or older (in contrast with the other three together), we reject the homogeneity (1 df) between the two groups (e-value = 0.021 and p-value = 0.006). Interesting to notice that Graph 2 corroborates with this result. Note that the two lines, Blue and Red, start to increase differences just after the level 0.75, the level of the quartiles.

A cluster analysis partitioning the sample into three homogeneous groups was also performed. Using again the Chi-squared partition statistics, with this clustering type, we did not find statistical significance differences among groups in relation to age at onset when considering the two groups

in early ages. However, by considering the older age at onset group (in contrast with the other two together) again we obtain statistical significance results.

4. Discussion

In order to analyze the influence of OCS age at onset on the presence of psychiatric comorbid disorders, the present study evaluated a large clinical sample of OCD patients with structured instruments, applied by experienced and carefully trained clinicians. Previous studies performed these analyses using different age thresholds, based on arbitrary criteria [3], [10], [19], [21], [26], [43], [52], [56], [57] and [61]. Therefore, we also investigated what would be the best way of using age at onset information according to comorbidity patterns.

Regarding the main goal of the study, we found significant and independent associations between earlier age at OCS onset and comorbidity with tics, anxiety, somatoform, eating and impulse–control disorders. These results confirm previous findings that specific comorbidity patterns may be related to an earlier age of OCS onset [15]. On the other hand, this results highlight that there are psychiatric comorbidities that do not relate to age at onset of OCD. Major depression, for example, is a highly prevalent comorbidity that does not seem to be associated with any specific pattern of age at onset. Therefore, it is possible that some OCD characteristics, including some psychiatric comorbidities, are independent of age at onset and may be associated with different markers of etiology.

Regarding illness duration, the only association found was that the longer the illness duration, the smaller the chance of having any tic disorder. These data corroborate previous findings from the Diniz et al. [15] study in which a longer illness duration was associated with depressive disorder and social phobia but not with tic disorders. This finding could also be a spurious finding secondary to lower current mean ages of patients with OCD without tic disorders (34.72) and OCD with tic disorders (28.61).

Consistent with previous findings, being female increased the chances of having comorbidity with anxiety and eating disorders [6], [8], [9], [16] and [47].

It is important to mention that these results focused on comorbidity patterns are in accordance with family genetic studies, which have reported that the earlier the age at onset, the higher the patient's risk of presenting specific comorbidity patterns or a positive family history of OCD and tic disorders [12], [44], [48] and [53]. Therefore, it could be conjectured that the presence of some comorbid conditions might be associated with patterns of familial aggregation. It is known that tic disorders comorbidity is related to high familial aggregation of OCD [48] and [53]. However, for other psychiatric diagnoses such as somatoform, mood and anxiety disorders, the association with familial aggregation of OCD is not so clear [45]. The understanding of the relation of comorbidities and familial aggregation is not under the scope of this study, but our findings may reflect the association between presence of familial aggregation and higher psychiatric morbidity.

Another goal was to obtain the best cut-off point to divide patients in subgroups according into the influence of age at onset on psychiatric comorbidities. In all statistical analyses performed, it was striking that the earlier the age at onset, the larger the probability of having comorbidities. However, the analyses trying to determine a cut-off point did not result in a clear age threshold, but suggested that the best way to analyze age at onset of the OCS in the investigation of its influence on comorbidity patterns is to consider it as a continuous variable. Otherwise, there could be loss of sensitivity to detect possible effects of age at onset on clinical characteristics. In other words, when using categorical subgroups of OCD patients according to age at onset, we may be creating groups

of patients that are almost as heterogeneous as the whole sample. It would be important to mention, however, that all this discussion above was based on the sample observations.

We can speculate that these findings reflect the complex and multi-factorial OCD etiology. The expression of the OCD phenotype is probably the result of the interaction between multiple genes and different environmental determinants. Therefore, the so far proposed categorical age at onset divisions might not contemplate the complexity related to this etiological mechanism.

Another study trying to investigate age at OCS onset used admixture analyses to determine the best-fitting model for the observed age at onset in a sample of 161 CD patients [12]. The main goal was to demonstrate that age at onset was a marker for different OCD subtypes. The authors reported that two groups emerged. The early onset group (composed of subjects with age at OCS onset ≤21 years), when compared to the late onset group (OCS onset >21 years), had increased frequencies of TS and higher familial aggregation of OCD. The late OCS onset group had elevated prevalences of depression and anxiety disorders. Even though their results supported the idea of dividing the sample in early and late onset subgroups, some issues should be considered. For instance, the early onset group corresponded to a much higher percentage of patients in the current study – 75% of the sample had the onset of the OCS before the age of 17.

As said before, our findings point to considering, as the best approach, age at onset as a continuous variable. However, for studies investigating the impact of age at onset in OCD, we do recognize that, for case–control studies, authors may be inclined to divide their samples by taking cut-off points. In this circumstance, if the partition is only on two subgroups, we suggest the cut-off point should be 17 years. The reason for this age is as follows: after having obtained the best clustering into two homogeneous clusters, comparing these clusters in relation to the frequencies of patients above a cut-off, 17 years is the first cut-off that the two clusters became significantly different. If for any reason one should consider two cut-offs, consequently partitioning the sample into three subgroups, our analysis suggests that the other cut-off should be 10 years. Note that the percentiles curves, Graph 2, start to differ just on 10 years. These are, in fact, the thresholds used in some previous studies [3], [19], [21], [48], [52], [56] and [61].

A limitation of this study is the possibility of recall bias since age at OCS onset was assessed retrospectively. Whenever possible, a family member was consulted about the age of OCS onset in order to increase reliability. It is of extreme importance to remind that all the analyses were performed taking into account this information. Therefore, it is recommended to evaluate the age at OCS onset with methods that can guarantee a higher reliability of this information. Another drawback was that all patients were ascertained through specialized OCD clinics, and they could be more likely to have comorbid conditions or more severe forms of OCD. Therefore, the current findings may not apply to OCD patients who are not in treatment (community samples) and it is also unclear how generalizable are our findings to other countries and cultures. Besides, this study defined age at onset as the age when the patient or a family member first noticed the presence of any OCS. It might be speculated that different results would be found if age at OCS onset was defined as the age when OCS became significant in the person's life, and its influence on other variables such as symptom dimensions, family aggregation, molecular genetic, neuroimaging, and neuropsychological findings, and treatment response.

Despite these limitations, the current results emphasize the impact of age at OCS onset on comorbidity patterns and the fact that the earlier the onset of the OCS, the stronger the differences found. The results also suggest that by dividing the sample in subgroups according to cut-off age points we might be missing important information and, therefore, it is best to analyze age at OCS onset as a continuous variable. We believe that the present results are of extreme value for pursuing

the way to convince scientists to include age at onset of OCS in future studies to better understanding the OCD heterogeneity.

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Corresponding author. Department of Psychiatry, University of Sao Paulo Medical School, Rua Dr. Ovídio Pires de Campos, 785, Room 7, 05403-010 São Paulo, SP, Brazil. Tel.: +55 11 3069 7896; fax: +55 11 3069 7895. European Psychiatry Volume 23, Issue 3, April 2008, Pages 187-194