- Email: [email protected]
Factor analysis of symptom profile in early onset and late onset OCD
Author’s Accepted Manuscript Factor analysis of symptom profile in early onset and late onset OCD Sandeep Grover, Siddharth Sarkar, Gourav Gupta, Natasha Kate, Abhishek Ghosh, Subho Chakrabarti, Ajit Avasthi www.elsevier.com/locate/psychres
PII: DOI: Reference:
S0165-1781(17)31012-0 https://doi.org/10.1016/j.psychres.2017.10.006 PSY10905
To appear in: Psychiatry Research Received date: 4 June 2017 Revised date: 2 September 2017 Accepted date: 1 October 2017 Cite this article as: Sandeep Grover, Siddharth Sarkar, Gourav Gupta, Natasha Kate, Abhishek Ghosh, Subho Chakrabarti and Ajit Avasthi, Factor analysis of symptom profile in early onset and late onset OCD, Psychiatry Research, https://doi.org/10.1016/j.psychres.2017.10.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
TITLE: Factor analysis of symptom profile in early onset and late onset OCD.
Authors: Sandeep Grover, Siddharth Sarkar, Gourav Gupta, Natasha Kate, Abhishek Ghosh, Subho Chakrabarti, Ajit Avasthi
Corresponding Author: Dr Sandeep Grover Additional Professor Department of Psychiatry Postgraduate Institute of Medical Education & Research Chandigarh 160012, India Phone: 9316138997 Email: [email protected]
TITLE: Factor analysis of symptom profile in early onset and late onset OCD ABSTRACT: This study aimed to assess the factor structure of early and late onset OCD. Additionally, cluster analysis was conducted in the same sample to assess the applicability of the factors. 345 participants were assessed with Yale Brown Obsessive Compulsive Scale symptom checklist. Patients were classified as early onset (onset of symptoms at age ≤ 18 years) and late onset (onset at age > 18 years) OCD depending upon the age of onset of the symptoms. Factor analysis and cluster analysis of early-onset and late-onset OCD was conducted.
The study sample
comprised of 91 early onset and 245 late onset OCD subjects. Males were more common in the early onset group. Differences in the frequency of phenomenology related to contamination related, checking, repeating, counting and ordering/ arranging compulsions were present across
the early and late onset groups. Factor analysis of YBOCS revealed a 3 factor solution for both the groups, which largely concurred with each other. These factors were named as hoarding and symmetry (factor-1), contamination (factor-2) and aggressive, sexual and religious factor (factor3). To conclude this study shows that factor structure of symptoms of OCD seems to be similar between early-onset and late-onset OCD. KEYWORDS:OCD, Factor analysis, early onset, phenomenology
INTRODUCTION Obsessive compulsive disorder (OCD) is a neuropsychiatric disorder characterized by recurrent intrusive ideas, impulses, or urges (obsessions) along with overt or covert behaviors (compulsions) aimed at reducing the distress.The prevalence of OCD and obsessive compulsive spectrum disorder ranges from 2 to 5 % in community samples (Rasmussen and Eisen, 1992; Adam et al., 2012; Fineberg et al., 2013). There have been attempts to subgroup patients with OCD to differentiate various groups of distinct symptom profile, associated neuropsychological findings and treatment response (Roth et al., 2005). It has been suggested that sub-grouping of OCD might be helpful in discerning categories with differential response to treatment (Knopp et al., 2013). Sub-classifying OCD on the basis of age of onset of symptoms into early and late onset categories has been attempted, especially in terms of finding out the phenomenological characteristics (Hemmings et al., 2004; Butwicka and Gmitrowicz, 2010; Taylor 2011). The early onset OCD has been associated with more frequent occurrence of Tourette’s disorder and tics (Hemmings et al., 2004). One study found contamination obsessions less commonly in the early
onset OCD (Butwicka and Gmitrowicz, 2010), while religious, sexual and somatic obsessions were more common for this group. Another study has found that early onset OCD was associated with male gender, greater global severity of OCD, and were likely to have comorbid tics (Taylor 2011). Though there has been a debate about what should be the cutoff for defining early onset OCD (Taylor 2011), a cutoff of 18 years looks promising and has been utilized in previous studies (Butwicka and Gmitrowicz, 2010; Katerberg et al., 2010; Kichuk et al., 2013). Factor analysis of symptom profile has been attempted to discern the phenomenological subgroups of OCD (Mataix-Cols et al., 2005; Katerberg et al., 2010). Analysis of what symptoms co-occur together might help predict particular obsessions and compulsions. Also, when used in treatment studies, it can help to know which symptom subgroups are relatively difficult to treat (Ball et al., 1996; Knopp et al., 2013). Such factor analytic studies of symptoms of OCDhave been conducted separately in young as well as adult patients (Ball et al., 1996; Girishchandra and Khanna, 2001; McKay Delorme et al., 2005; et al., 2006). However, none of the previous researches have attempted to find out factor structure in the early onset and late onset OCD as a part of the same study. Using the same study to find factor structures can help in getting more confirmatory answers as the recruitment and ratings would be similar for both the age categories and comparability of results would be higher. Hence, this study aimed to assess the factor structure of early and late onset OCD in a set of patients recruited from a hospital setting in North India. Additionally, cluster analysis was conducted in the same sample to assess the applicability of the factors. MATERIALS & METHODS Setting and participants:
The present study was conducted at the outpatient services of a tertiary care multispecialty teaching hospital in North India. All the patients were recruited after obtaining written informed consent. The study was approved by the Institute Ethics Committee. The study sample comprised of outpatients and inpatients diagnosed with OCD according to DSM IV TR (American Psychiatric Association; 2000). To be included in the study, the participants were required to be clinically diagnosed with OCD. Patients with comorbid substance dependence, chronic physical illnesses, organic brain syndrome and intellectual disability were excluded. All the consenting patients were evaluated using Yale Brown Obsessive Compulsive Checklist (YBOCS) for eliciting the symptom profile of the OCD. Instruments: Yale-Brown Obsessive–Compulsive Checklist (Goodman et al, 1989; Mataix-Cols et al., 2004): The clinician-administered version of the Y–BOCS Symptom Checklist was used to ascertain the presence of symptom dimensions. Symptoms in each dimension were rated as present or absent, and present included both current symptoms and symptoms in the lifetime. Ratings were based on information provided by the patient and their caregivers, as well as clinical observations. Statistical analysis The analysis was carried out using SPSS version 21 (IBM Corp, Armonk, NY). Descriptive analysis was carried out using mean and standard deviation with range for continuous variables while frequency and percentages were used for discontinuous ones. Factor analysis was done for the YBOCS symptom profile in both the age groups. For factor analysis, principal component analysis was used to generate the factors. The Kaiser-Meyer-Olkin (KMO) test was used for determining the sample adequacy. A value of more than 0.5 has been
considered adequate to perform factor analysis (Tabachnick and Fidell, 2007). The Bartlett test of Sphericity was used to determine to homogeneity of the data. A Bartlett test p value of less than 0.05 is considered significant and useful for factor analysis (Tabachnick and Fidell, 2007). Varimax rotation was used after the initial factor solution. The optimal number of factors was assessed from the scree plot. Hierarchical cluster analysis was also conducted on the samples of early onset and late onset OCD. Hierarchical cluster analysis was done as a complementary approach for the factor analysis. Between groups linkage method was used for cluster analysis for squared Euclidean distance interval. A dendrogram was constructed for both the age groups.
RESULTS A total of 345 participants were included in the analysis. The sample was divided into early onset (onset of symptoms at age ≤ 18 years) and late onset (onset at age > 18 years) OCD. The clinical and demographic details of early and late onset groups are shown in Table 1. As expected, early onset OCD group had a lower mean age of presentation. Also, males were more frequent in the early onset OCD group. The early onset group of OCD patients was less likely to be married, less likely to be employed and more likely to belong to nuclear family than the late onset group. As expected, the mean age of onset in the early onset group (15.5±2.7 years) was different from the late onset groups (27.4± 7.7 years; t = 14.340, p < 0.001). Table 1: Demographic and clinical variables across early and late onset Variables
Late onset (n = 254) 34.5 (±9.9)
Test (significance)
Age in years
Early onset (n = 91) 24.8 (±8.7)
t = (<0.001)*
8.290
Gender Male Female
68 (74.7%) 23 (25.3%)
128 (50.4%) 126 (49.6%)
χ2 = (<0.001)*
16.166
Marital status Married Not married Education Up to tenth grade Above tenth grade Occupation Employed Not employed Family Nuclear Other Locality Rural Urban
21 (23.1%) 70 (76.9%)
177 (69.7%) 77 (30.3%)
χ2 = (<0.001)*
31 (34.1%) 60 (65.9%)
103 (40.6%) 151 (59.4%)
χ2 = 1.186 (0.276)
26 (28.6%) 65 (71.4%)
109 (42.9%) 145 (57.1%)
χ2 = 5.786 (0.016)*
69 (75.8%) 22 (24.2%)
161 (63.4%) 93 (36.6%)
χ2 = 4.664 (0.031)*
59 (64.8%) 32 (35.2%)
171 (67.3%) 83 (32.7%)
χ2 = 0.187 (0.666)
59.516
Symptom profile: The symptom profile of both the groups is shown in Table-2. The most common obsessions in both the groups were those relating to contamination, followed by symmetry related obsessions in the early onset group, and aggressive and religious obsessions in the late onset group. While checking compulsions were more common in the early onset group, contamination related compulsions were most common in the late onset group. While late onset group had significantly more number of patients with contamination related compulsions, the early onset group had greater number of checking, repeating, counting and ordering/ arranging compulsions. Table 2: Obsessive Compulsive Symptoms as assessed by using YBOC Checklist Early onset (n = 91) Obsessions Contamination Aggressive Sexual Religious Hoarding Symmetry related Miscellaneous Compulsions Contamination related Checking
58 (63.7%) 25 (27.5%) 20 (22%) 25 (27.5%) 27 (29.7%) 28 (30.8%) 16 (17.6%) 53 (58.2%) 58 (63.7%)
Late onset (n = Test (significance) 254) 169 (66.5%)
χ2 = 0.233 (0.629)
57 (22.4%) 53 (20.9%) 57 (22.4%) 54 (21.3%) 55 (21.7%) 42 (16.5%)
χ2 = 0.936 (0.333) χ2 = 0.050 (0.824) χ2 = 0.936 (0.333) χ2 = 2.638 (0.104) χ2 = 3.047 (0.081) χ2 = 0.053 (0.819)
182 (71.7%) 127 (50.0%)
χ2 = 5.549 (0.018)* χ2 = 5.083 (0.024)*
Repeating Counting Ordering/ arranging Hoarding/collecting Miscellaneous
30 (33%) 12 (13.2%) 25 (27.5%) 6 (6.6%) 40 (44%)
χ2 = 5.773 (0.016)* χ2 = 4.262 (0.039)* χ2 = 5.121 (0.024)* χ2 = 0.144 (0.705) χ2 = 0.931 (0.335)
52 (20.5%) 16 (6.3%) 42 (16.5%) 14 (5.5%) 97 (38.2%)
* Significant at p < 0.05 The factor analysis of the two age-groups is shown in table-3. For both the groups, a three factor solution was most tenable based upon the scree plot. For the early-onset group, the first factor comprised of hoarding and symmetry related obsessions and checking, repeating, counting, ordering/ arranging, hoarding/collecting and miscellaneous compulsions. The second factor had contamination related obsessions and compulsions. The third factor had aggressive, sexual and religious obsessions. For the late-onset group, the first factor had similar obsessions and compulsions, except hoarding/collecting compulsions. The second factor was identical to the early onset group and had loaded contamination related obsessions and compulsions. The third factor had aggressive, sexual and religious obsessions and hoarding/collecting compulsions. These factors were named as hoarding and symmetry (factor-1), contamination (factor-2) and aggressive, sexual & religious factor (factor-3). It should be reckoned that the hoarding compulsions in the late onset OCD group loaded on the another factor (aggressive, sexual & religious factor), rather than the hoarding and symmetry factor.
Table 3: Factor analysis of symptoms in the two age groups Early onset group 0.678 of 367.352 (p <0.001) (p
KMO value Bartlett test sphericity χ2 value) Initial factor solution 3 (number of factors) % variance 52.1% explained
Late onset group 0.647 737.999 (p<0.001)
3 45.3%
Eigen value for each factor after varimax rotation % of variance explained Factor loading
Contamination Aggressive Sexual Religious Hoarding Symmetry related Miscellaneous Contamination related Checking Repeating Counting Ordering/ arranging Hoarding/collecting Miscellaneous
3.368
2.283
1.376
2.942
2.032
1.340
26.0%
16.3%
9.8%
21.0%
14.5%
9.6%
Factor 1 Hoarding and symmetry
Factor 2 Contamination
Factor 3 Aggressive, sexual & religious
Factor 1 Hoarding and symmetry
Factor 2 Contamination
Factor 3 Aggressive, sexual & religious
0.895
0.829 0.614 0.770 0.765
0.669 0.747
0.513 0.719 0.566 0.523 0.753 -0.503 0.875
0.909 0.430 0.642 0.703 0.770 0.693 0.497
0.468 0.562 0.732 0.505 0.572
The hierarchical cluster analysis of variables of the domains of YBOCS was conducted. The dendrograms for both the age groups is shown in Figure 1. The cluster analysis segregated out contamination related obsessions and compulsions as a distinct entity in both the age groups. While aggressive, sexual and religious obsessions tended to co-occur in the early onset group, these obsessions did not tend to co-occur together in the late onset group.
Figure 1: Dendrograms of hierarchical cluster analysis
Early onset OCD sample
Late onset OCD sample
The odds ratios of the gender across the three factors for early and late onset OCD is shown in table 4. While hoarding and symmetry related obsessions along with checking, repeating, counting, ordering/ arranging, hoarding/collecting and miscellaneous compulsions (factor 1) were common in males in early onset group, cleaning obsessions and compulsions (factor 2) were more common in females. Similarly, the late onset group, factor 1 and 3 were more commonly endorsed in males, and factor 2 (cleaning obsessions and compulsions in females). Table 4: Gender distribution across the factors Early onset OCD Factor 1 Factor 2 Factor 3 Late onset OCD Factor 1 Factor 2 Factor 3
Odds ratio for male gender (Confidence intervals) 3.81 (1.17 to 12.45) 0.06 (<0.01 to 0.48) 1.47 (0.56 to 3.84) 5.29 (2.78 to 10.09) 0.24 (0.19 to 0.46) 2.48 (1.49 to 4.12)
Significant relationships highlighted in bold
DISCUSSION Assessment of the differences in the phenomenology and characteristics of early and late onset OCD is important from a clinical perspective. Differentiating early and late onset OCD might help to discern further the type of symptoms experiences, the comorbidities that are expected and estimate the prognosis of the case. Further research implication of differentiating OCD into early and late subtypes lies in exploring the genetic and neurobiological determinants of OCD, and predicating the best treatment options. As in other studies, males were more common in the early-onset group (Fontenelle et al., 2003; Taylor 2011). Similarly, contamination was the commonest obsession in both the groups. There were differences in the frequency of some of the obsessions and compulsions across the early and late OCD, as has been found elsewhere (Narayanaswamy et al., 2012).
Some differences were found in the symptom profile of the early and the late onset OCD, which is in keeping with the existing literature (Butwicka and Gmitrowicz, 2012; Narayanaswamy et al, 2012). This study suggests that factor structure of symptoms of OCD is similar between the early-onset and late-onset OCD. Previous factor analysis studies of OCD have reported 3 to 5 factors solutions for reducing the symptom dimensions of OCD (Bloch et al., 2008). The present study had found 3 factors as the most coherent factor solution. Studies across the globe are inconsistent about which symptoms cluster together. When adult studies are considered, there are studies which find sexual and religious obsessions club together with aggressive obsessions (Hasler et al., 2007), similar to the present findings. However there is literature suggesting that sexual and religious obsessions form a different factor altogether (Tek and Ulug, 2001; Kim et al., 2005). One consistent theme from both the models was that contamination related obsessions and cleaning compulsions comprise of one factor, while aggressive, sexual and religious obsessions loaded on another factor. Previous factor analytic studies from India have also found that contamination related factor stand out distinctly (Girishchandra and Khanna, 2001; Taj et al., 2013). Our study did not find hoarding to have a separate factor loading, as compared to previous studies and analysis from collated data (Bloch et al., 2008). However, such a finding has been reported from another study from the region which did not find separate factor loading for hoarding related obsessions and compulsions (Girishchandra and Khanna, 2001). Another consistent finding across both the age groups was that the factor related to cleaning and contamination related obsessions and compulsions were more common in females. This in line with other literature which has shown that contamination related obsessions and compulsions are more common in females (Mataix-Cols et al., 1999; Denys et al., 2004). In the present study, cluster analysis conformed fairlyto the factor analysis for the early and late onset OCD sample. One of the reasons could be that cluster analysis and factor analysis process the data in a slightly different manner. The strengths of the study includes that it attempted to explore the factor dimensions of OCD in early as well as late onset population recruited by the same methodology, evaluated by the same assessors and in the same treatment facility.Hierarchical cluster analysis was also conducted to
complement the factor analysis. Also, it was the first study from the region to look specifically at the phenomenological differences between early and late onset OCD. Some limitations should also be considered while drawing inferences from the study. The recruitment of the participants was done through purposive sampling and hence selection bias cannot be completely ruled out. The presence of various symptoms and the age of onset of the symptoms (the age at which first symptoms of OCD occurred) were based upon retrospective recall of the participants and may be subjected to recall bias. One of the factors in the factor analysis contained only two symptoms (items) in the early onset OCD group, resulting in possible over-factoring. Phenomenological sub-grouping provides one method of classifying patients with OCD, and there may be other ways of classification of patients with OCD (Delucci et al, 2011; Pauls et al., 2014). To conclude, the study furthers the evidence that early-onset OCD has a largely similar factor structure as compared to late-onset OCD, though some differences were present in factor structure and relative frequency of obsessions and compulsions. Understanding differences can have bearing on the clinical assessment, intervention and prognosis. Further genetic and neuroimaging based research if attempted (Tek and Ulug, 2001; Bloch et al., 2008), may provide more insights into the validity of these diagnostic subgroups.
REFERENCES:
Adam, Y., Meinlschmidt, G., Gloster, A.T., Lieb, R., 2012. Obsessive-compulsive disorder in the community:
12-month
prevalence,
comorbidity
and
impairment.
Soc
Psychiatry
PsychiatrEpidemiol;47(3):339–49. American Psychiatric Association; 2000. Diagnostic and Statistical Manual of Mental Disorders, 4th ed, Text Revision. Washington DC:
Ball, S.G., Baer, L., Otto, M.W., 1996. Symptom subtypes of obsessive-compulsive disorder in behavioral treatment studies: a quantitative review. Behav Res Ther 34(1):47–51. Bloch, M.H., Landeros-Weisenberger, A., Rosario, M.C., Pittenger, C., Leckman, J.F., 2008. Meta-analysis of the symptom structure of obsessive-compulsive disorder. Am J Psychiatry 165(12):1532–42. Butwicka, A., Gmitrowicz, A., 2010. Symptom clusters in obsessive-compulsive disorder (OCD): influence of age and age of onset. Eur Child Adolesc Psychiatry 19(4):365–70. Delorme, R., Golmard, J.L., Chabane, N., Millet, B., Krebs, M.O., Mouren-Simeoni, M.C., Leboyer, M., 2005. Admixture analysis of age at onset in obsessive-compulsive disorder. Psychol Med 35(2):237–43. Delucchi, K.L., Katerberg, H., Stewart, S.E., Denys, D.A., Lochner, C., Stack, D.E., den Boer J.A., van Balkom, A.J., Jenike, M.A., Stein, D.J., Cath, D.C., 2011. Latent class analysis of the Yale-Brown Obsessive-Compulsive Scale symptoms in obsessive-compulsive disorder. Compr Psychiatry;52(3):334-41. Denys, D., de Geus, F., van Megen, H.J.G.M., Westenberg, H.G.M., 2004. Use of factor analysis to detect potential phenotypes in obsessive-compulsive disorder. Psychiatry Res 128(3):273–80. Fineberg, N.A., Hengartner, M.P., Bergbaum, C., Gale, T., Rössler, W., Angst, J., 2013 Lifetime comorbidity of obsessive-compulsive disorder and sub-threshold obsessive-compulsive symptomatology in the community: impact, prevalence, socio-demographic and clinical characteristics. Int J Psychiatry Clin Pract 17(3):188–96.
Fontenelle, L.F., Mendlowicz, M.V., Marques, C., Versiani, M., 2003. Early- and late-onset obsessive-compulsive disorder in adult patients: an exploratory clinical and therapeutic study. J Psychiatr Res 37(2):127–33. Girishchandra, B.G., Khanna, S., 2001. Phenomenology of obsessive compulsive disorder: a factor analytic approach. Indian J Psychiatry 43(4):306–16. Goodman, W.K., Price, L.H., Rasmussen, S.A., Mazure, C., Fleischmann, R.L., Hill, C.L., Heninger, G.R., Charney, D.S., 1989. The Yale-Brown obsessive compulsive scale: I. Development, use, and reliability. Arch Gen Psychiatry 46(11):1006–1011. Hasler, G., Pinto, A., Greenberg, B.D., Samuels, J., Fyer, A.J., Pauls, D., Knowles, J.A., McCracken, J.T., Piacentini, J., Riddle, M.A., Rauch, S.L., 2007. Familiality of factor analysisderived YBOCS dimensions in OCD-affected sibling pairs from the OCD Collaborative Genetics Study. Biol Psychiatry 61(5):617–25. Hemmings, S.M.J., Kinnear, C.J., Lochner, C., Niehaus, D.J.H., Knowles, J.A., MoolmanSmook, J.C., Corfield, V.A., Stein, D,J., 2004. Early- versus late-onset obsessive-compulsive disorder: investigating genetic and clinical correlates. Psychiatry Res 128(2):175–82. Katerberg, H., Delucchi, K.L., Stewart, S.E., Lochner, C., Denys, D.A., Stack, D.E., Andresen, J.M., Grant, J.E., Kim, S.W., Williams, K.A., den Boer, J.A., van Balkom, A.J., Smit, J.H., van Oppen, P., Polman, A., Jenike, M.A., Stein, D.J., Mathews, C.A., Cath, D.C.. 2010. Symptom dimensions in OCD: item-level factor analysis and heritability estimates. Behav Genet 40(4):505–17.
Kichuk, S.A., Torres, A.R., Fontenelle, L.F., Rosário, M.C., Shavitt, R.G., Miguel, E.C., Pittenger, C., Bloch, M.H., 2013. Symptom dimensions are associated with age of onset and clinical course of obsessive-compulsive disorder. ProgNeuropsychopharmacolBiol Psychiatry 44:233–9. Kim, S.J., Lee, H.S., Kim, C.H., 2005. Obsessive-compulsive disorder, factor-analyzed symptom dimensions and serotonin transporter polymorphism. Neuropsychobiology 52(4):176–82. Knopp, J., Knowles, S., Bee, P., Lovell, K., Bower, P., 2013. A systematic review of predictors and moderators of response to psychological therapies in OCD: do we have enough empirical evidence to target treatment? Clin Psychol Rev 33(8):1067–81. Mataix-Cols, D., Fullana, M.Á., Alonso, P., Menchón, J.M., Vallejo, J., 2004. Convergent and discriminant validity of the Yale-Brown Obsessive-Compulsive Scale symptom checklist. PsychotherPsychosom 73(3):190–196. Mataix-Cols, D., Rauch, S.L., Manzo, P.A., Jenike, M.A., Baer, L., 1999 Use of factor-analyzed symptom dimensions to predict outcome with serotonin reuptake inhibitors and placebo in the treatment of obsessive-compulsive disorder. Am J Psychiatry 156(9):1409–16. Mataix-Cols, D., Rosario-Campos, M.C., Leckman, J.F., 2005. A multidimensional model of obsessive-compulsive disorder. Am J Psychiatry 162(2):228–38. McKay, D., Piacentini, J., Greisberg, S., Graae, F., Jaffer, M., Miller, J., 2006. The structure of childhood obsessions and compulsions: dimensions in an outpatient sample. Behav Res Ther 44(1):137–46.
Narayanaswamy, J.C., Viswanath, B., Cherian, A.V., Math, S.B., Kandavel, T., Reddy, Y.J., 2012. Impact of age of onset of illness on clinical phenotype in OCD. Psychiatry Res 200(2– 3):554–9. Pauls, D.L., Abramovitch, A., Rauch, S.L., Geller, D.A., 2014. Obsessive-compulsive disorder: an integrative genetic and neurobiological perspective. Nat Rev Neurosci;15(6), 410-24. Rasmussen, S.A., Eisen, J.L., 1992. The epidemiology and differential diagnosis of obsessive compulsive disorder. J Clin Psychiatry;53 Suppl:4–10. Roth, R.M., Milovan, D., Baribeau, J., O’Connor, K., 2005. Neuropsychological functioning in early- and late-onset obsessive-compulsive disorder. J Neuropsychiatry Clin Neurosci 17(2):208–13. Tabachnick, B.G., Fidell, L.S., 2007. Using Multivariate Statistics. Boston: Pearson Education Inc; Taj, M.J.R.J., Viswanath, B., Purushottam, M., Kandavel, T., Janardhan Reddy, Y.C., Jain, S., 2013. DRD4 gene and obsessive compulsive disorder: do symptom dimensions have specific genetic correlates? ProgNeuropsychopharmacol Biol Psychiatry 41:18–23. Taylor, S., 2011. Early versus late onset obsessive-compulsive disorder: evidence for distinct subtypes. Clin Psychol Rev 31(7):1083–100. Tek, C., Ulug, B., 2001. Religiosity and religious obsessions in obsessive-compulsive disorder. Psychiatry Res 104(2):99–108.
Highlights:
Subjects with early onset (onset of symptoms at age ≤ 18 years) and late onset (onset at age > 18 years) OCD did not differ in terms of type of obsessions
Checking, counting and repeating compulsions were more common in the early onset group, contamination related compulsions were most common in the late onset group.
Factor analysis of YBOCS revealed a 3 factor solution for both the groups, which largely concurred with each other.
PII: DOI: Reference:
S0165-1781(17)31012-0 https://doi.org/10.1016/j.psychres.2017.10.006 PSY10905
To appear in: Psychiatry Research Received date: 4 June 2017 Revised date: 2 September 2017 Accepted date: 1 October 2017 Cite this article as: Sandeep Grover, Siddharth Sarkar, Gourav Gupta, Natasha Kate, Abhishek Ghosh, Subho Chakrabarti and Ajit Avasthi, Factor analysis of symptom profile in early onset and late onset OCD, Psychiatry Research, https://doi.org/10.1016/j.psychres.2017.10.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
TITLE: Factor analysis of symptom profile in early onset and late onset OCD.
Authors: Sandeep Grover, Siddharth Sarkar, Gourav Gupta, Natasha Kate, Abhishek Ghosh, Subho Chakrabarti, Ajit Avasthi
Corresponding Author: Dr Sandeep Grover Additional Professor Department of Psychiatry Postgraduate Institute of Medical Education & Research Chandigarh 160012, India Phone: 9316138997 Email: [email protected]
TITLE: Factor analysis of symptom profile in early onset and late onset OCD ABSTRACT: This study aimed to assess the factor structure of early and late onset OCD. Additionally, cluster analysis was conducted in the same sample to assess the applicability of the factors. 345 participants were assessed with Yale Brown Obsessive Compulsive Scale symptom checklist. Patients were classified as early onset (onset of symptoms at age ≤ 18 years) and late onset (onset at age > 18 years) OCD depending upon the age of onset of the symptoms. Factor analysis and cluster analysis of early-onset and late-onset OCD was conducted.
The study sample
comprised of 91 early onset and 245 late onset OCD subjects. Males were more common in the early onset group. Differences in the frequency of phenomenology related to contamination related, checking, repeating, counting and ordering/ arranging compulsions were present across
the early and late onset groups. Factor analysis of YBOCS revealed a 3 factor solution for both the groups, which largely concurred with each other. These factors were named as hoarding and symmetry (factor-1), contamination (factor-2) and aggressive, sexual and religious factor (factor3). To conclude this study shows that factor structure of symptoms of OCD seems to be similar between early-onset and late-onset OCD. KEYWORDS:OCD, Factor analysis, early onset, phenomenology
INTRODUCTION Obsessive compulsive disorder (OCD) is a neuropsychiatric disorder characterized by recurrent intrusive ideas, impulses, or urges (obsessions) along with overt or covert behaviors (compulsions) aimed at reducing the distress.The prevalence of OCD and obsessive compulsive spectrum disorder ranges from 2 to 5 % in community samples (Rasmussen and Eisen, 1992; Adam et al., 2012; Fineberg et al., 2013). There have been attempts to subgroup patients with OCD to differentiate various groups of distinct symptom profile, associated neuropsychological findings and treatment response (Roth et al., 2005). It has been suggested that sub-grouping of OCD might be helpful in discerning categories with differential response to treatment (Knopp et al., 2013). Sub-classifying OCD on the basis of age of onset of symptoms into early and late onset categories has been attempted, especially in terms of finding out the phenomenological characteristics (Hemmings et al., 2004; Butwicka and Gmitrowicz, 2010; Taylor 2011). The early onset OCD has been associated with more frequent occurrence of Tourette’s disorder and tics (Hemmings et al., 2004). One study found contamination obsessions less commonly in the early
onset OCD (Butwicka and Gmitrowicz, 2010), while religious, sexual and somatic obsessions were more common for this group. Another study has found that early onset OCD was associated with male gender, greater global severity of OCD, and were likely to have comorbid tics (Taylor 2011). Though there has been a debate about what should be the cutoff for defining early onset OCD (Taylor 2011), a cutoff of 18 years looks promising and has been utilized in previous studies (Butwicka and Gmitrowicz, 2010; Katerberg et al., 2010; Kichuk et al., 2013). Factor analysis of symptom profile has been attempted to discern the phenomenological subgroups of OCD (Mataix-Cols et al., 2005; Katerberg et al., 2010). Analysis of what symptoms co-occur together might help predict particular obsessions and compulsions. Also, when used in treatment studies, it can help to know which symptom subgroups are relatively difficult to treat (Ball et al., 1996; Knopp et al., 2013). Such factor analytic studies of symptoms of OCDhave been conducted separately in young as well as adult patients (Ball et al., 1996; Girishchandra and Khanna, 2001; McKay Delorme et al., 2005; et al., 2006). However, none of the previous researches have attempted to find out factor structure in the early onset and late onset OCD as a part of the same study. Using the same study to find factor structures can help in getting more confirmatory answers as the recruitment and ratings would be similar for both the age categories and comparability of results would be higher. Hence, this study aimed to assess the factor structure of early and late onset OCD in a set of patients recruited from a hospital setting in North India. Additionally, cluster analysis was conducted in the same sample to assess the applicability of the factors. MATERIALS & METHODS Setting and participants:
The present study was conducted at the outpatient services of a tertiary care multispecialty teaching hospital in North India. All the patients were recruited after obtaining written informed consent. The study was approved by the Institute Ethics Committee. The study sample comprised of outpatients and inpatients diagnosed with OCD according to DSM IV TR (American Psychiatric Association; 2000). To be included in the study, the participants were required to be clinically diagnosed with OCD. Patients with comorbid substance dependence, chronic physical illnesses, organic brain syndrome and intellectual disability were excluded. All the consenting patients were evaluated using Yale Brown Obsessive Compulsive Checklist (YBOCS) for eliciting the symptom profile of the OCD. Instruments: Yale-Brown Obsessive–Compulsive Checklist (Goodman et al, 1989; Mataix-Cols et al., 2004): The clinician-administered version of the Y–BOCS Symptom Checklist was used to ascertain the presence of symptom dimensions. Symptoms in each dimension were rated as present or absent, and present included both current symptoms and symptoms in the lifetime. Ratings were based on information provided by the patient and their caregivers, as well as clinical observations. Statistical analysis The analysis was carried out using SPSS version 21 (IBM Corp, Armonk, NY). Descriptive analysis was carried out using mean and standard deviation with range for continuous variables while frequency and percentages were used for discontinuous ones. Factor analysis was done for the YBOCS symptom profile in both the age groups. For factor analysis, principal component analysis was used to generate the factors. The Kaiser-Meyer-Olkin (KMO) test was used for determining the sample adequacy. A value of more than 0.5 has been
considered adequate to perform factor analysis (Tabachnick and Fidell, 2007). The Bartlett test of Sphericity was used to determine to homogeneity of the data. A Bartlett test p value of less than 0.05 is considered significant and useful for factor analysis (Tabachnick and Fidell, 2007). Varimax rotation was used after the initial factor solution. The optimal number of factors was assessed from the scree plot. Hierarchical cluster analysis was also conducted on the samples of early onset and late onset OCD. Hierarchical cluster analysis was done as a complementary approach for the factor analysis. Between groups linkage method was used for cluster analysis for squared Euclidean distance interval. A dendrogram was constructed for both the age groups.
RESULTS A total of 345 participants were included in the analysis. The sample was divided into early onset (onset of symptoms at age ≤ 18 years) and late onset (onset at age > 18 years) OCD. The clinical and demographic details of early and late onset groups are shown in Table 1. As expected, early onset OCD group had a lower mean age of presentation. Also, males were more frequent in the early onset OCD group. The early onset group of OCD patients was less likely to be married, less likely to be employed and more likely to belong to nuclear family than the late onset group. As expected, the mean age of onset in the early onset group (15.5±2.7 years) was different from the late onset groups (27.4± 7.7 years; t = 14.340, p < 0.001). Table 1: Demographic and clinical variables across early and late onset Variables
Late onset (n = 254) 34.5 (±9.9)
Test (significance)
Age in years
Early onset (n = 91) 24.8 (±8.7)
t = (<0.001)*
8.290
Gender Male Female
68 (74.7%) 23 (25.3%)
128 (50.4%) 126 (49.6%)
χ2 = (<0.001)*
16.166
Marital status Married Not married Education Up to tenth grade Above tenth grade Occupation Employed Not employed Family Nuclear Other Locality Rural Urban
21 (23.1%) 70 (76.9%)
177 (69.7%) 77 (30.3%)
χ2 = (<0.001)*
31 (34.1%) 60 (65.9%)
103 (40.6%) 151 (59.4%)
χ2 = 1.186 (0.276)
26 (28.6%) 65 (71.4%)
109 (42.9%) 145 (57.1%)
χ2 = 5.786 (0.016)*
69 (75.8%) 22 (24.2%)
161 (63.4%) 93 (36.6%)
χ2 = 4.664 (0.031)*
59 (64.8%) 32 (35.2%)
171 (67.3%) 83 (32.7%)
χ2 = 0.187 (0.666)
59.516
Symptom profile: The symptom profile of both the groups is shown in Table-2. The most common obsessions in both the groups were those relating to contamination, followed by symmetry related obsessions in the early onset group, and aggressive and religious obsessions in the late onset group. While checking compulsions were more common in the early onset group, contamination related compulsions were most common in the late onset group. While late onset group had significantly more number of patients with contamination related compulsions, the early onset group had greater number of checking, repeating, counting and ordering/ arranging compulsions. Table 2: Obsessive Compulsive Symptoms as assessed by using YBOC Checklist Early onset (n = 91) Obsessions Contamination Aggressive Sexual Religious Hoarding Symmetry related Miscellaneous Compulsions Contamination related Checking
58 (63.7%) 25 (27.5%) 20 (22%) 25 (27.5%) 27 (29.7%) 28 (30.8%) 16 (17.6%) 53 (58.2%) 58 (63.7%)
Late onset (n = Test (significance) 254) 169 (66.5%)
χ2 = 0.233 (0.629)
57 (22.4%) 53 (20.9%) 57 (22.4%) 54 (21.3%) 55 (21.7%) 42 (16.5%)
χ2 = 0.936 (0.333) χ2 = 0.050 (0.824) χ2 = 0.936 (0.333) χ2 = 2.638 (0.104) χ2 = 3.047 (0.081) χ2 = 0.053 (0.819)
182 (71.7%) 127 (50.0%)
χ2 = 5.549 (0.018)* χ2 = 5.083 (0.024)*
Repeating Counting Ordering/ arranging Hoarding/collecting Miscellaneous
30 (33%) 12 (13.2%) 25 (27.5%) 6 (6.6%) 40 (44%)
χ2 = 5.773 (0.016)* χ2 = 4.262 (0.039)* χ2 = 5.121 (0.024)* χ2 = 0.144 (0.705) χ2 = 0.931 (0.335)
52 (20.5%) 16 (6.3%) 42 (16.5%) 14 (5.5%) 97 (38.2%)
* Significant at p < 0.05 The factor analysis of the two age-groups is shown in table-3. For both the groups, a three factor solution was most tenable based upon the scree plot. For the early-onset group, the first factor comprised of hoarding and symmetry related obsessions and checking, repeating, counting, ordering/ arranging, hoarding/collecting and miscellaneous compulsions. The second factor had contamination related obsessions and compulsions. The third factor had aggressive, sexual and religious obsessions. For the late-onset group, the first factor had similar obsessions and compulsions, except hoarding/collecting compulsions. The second factor was identical to the early onset group and had loaded contamination related obsessions and compulsions. The third factor had aggressive, sexual and religious obsessions and hoarding/collecting compulsions. These factors were named as hoarding and symmetry (factor-1), contamination (factor-2) and aggressive, sexual & religious factor (factor-3). It should be reckoned that the hoarding compulsions in the late onset OCD group loaded on the another factor (aggressive, sexual & religious factor), rather than the hoarding and symmetry factor.
Table 3: Factor analysis of symptoms in the two age groups Early onset group 0.678 of 367.352 (p <0.001) (p
KMO value Bartlett test sphericity χ2 value) Initial factor solution 3 (number of factors) % variance 52.1% explained
Late onset group 0.647 737.999 (p<0.001)
3 45.3%
Eigen value for each factor after varimax rotation % of variance explained Factor loading
Contamination Aggressive Sexual Religious Hoarding Symmetry related Miscellaneous Contamination related Checking Repeating Counting Ordering/ arranging Hoarding/collecting Miscellaneous
3.368
2.283
1.376
2.942
2.032
1.340
26.0%
16.3%
9.8%
21.0%
14.5%
9.6%
Factor 1 Hoarding and symmetry
Factor 2 Contamination
Factor 3 Aggressive, sexual & religious
Factor 1 Hoarding and symmetry
Factor 2 Contamination
Factor 3 Aggressive, sexual & religious
0.895
0.829 0.614 0.770 0.765
0.669 0.747
0.513 0.719 0.566 0.523 0.753 -0.503 0.875
0.909 0.430 0.642 0.703 0.770 0.693 0.497
0.468 0.562 0.732 0.505 0.572
The hierarchical cluster analysis of variables of the domains of YBOCS was conducted. The dendrograms for both the age groups is shown in Figure 1. The cluster analysis segregated out contamination related obsessions and compulsions as a distinct entity in both the age groups. While aggressive, sexual and religious obsessions tended to co-occur in the early onset group, these obsessions did not tend to co-occur together in the late onset group.
Figure 1: Dendrograms of hierarchical cluster analysis
Early onset OCD sample
Late onset OCD sample
The odds ratios of the gender across the three factors for early and late onset OCD is shown in table 4. While hoarding and symmetry related obsessions along with checking, repeating, counting, ordering/ arranging, hoarding/collecting and miscellaneous compulsions (factor 1) were common in males in early onset group, cleaning obsessions and compulsions (factor 2) were more common in females. Similarly, the late onset group, factor 1 and 3 were more commonly endorsed in males, and factor 2 (cleaning obsessions and compulsions in females). Table 4: Gender distribution across the factors Early onset OCD Factor 1 Factor 2 Factor 3 Late onset OCD Factor 1 Factor 2 Factor 3
Odds ratio for male gender (Confidence intervals) 3.81 (1.17 to 12.45) 0.06 (<0.01 to 0.48) 1.47 (0.56 to 3.84) 5.29 (2.78 to 10.09) 0.24 (0.19 to 0.46) 2.48 (1.49 to 4.12)
Significant relationships highlighted in bold
DISCUSSION Assessment of the differences in the phenomenology and characteristics of early and late onset OCD is important from a clinical perspective. Differentiating early and late onset OCD might help to discern further the type of symptoms experiences, the comorbidities that are expected and estimate the prognosis of the case. Further research implication of differentiating OCD into early and late subtypes lies in exploring the genetic and neurobiological determinants of OCD, and predicating the best treatment options. As in other studies, males were more common in the early-onset group (Fontenelle et al., 2003; Taylor 2011). Similarly, contamination was the commonest obsession in both the groups. There were differences in the frequency of some of the obsessions and compulsions across the early and late OCD, as has been found elsewhere (Narayanaswamy et al., 2012).
Some differences were found in the symptom profile of the early and the late onset OCD, which is in keeping with the existing literature (Butwicka and Gmitrowicz, 2012; Narayanaswamy et al, 2012). This study suggests that factor structure of symptoms of OCD is similar between the early-onset and late-onset OCD. Previous factor analysis studies of OCD have reported 3 to 5 factors solutions for reducing the symptom dimensions of OCD (Bloch et al., 2008). The present study had found 3 factors as the most coherent factor solution. Studies across the globe are inconsistent about which symptoms cluster together. When adult studies are considered, there are studies which find sexual and religious obsessions club together with aggressive obsessions (Hasler et al., 2007), similar to the present findings. However there is literature suggesting that sexual and religious obsessions form a different factor altogether (Tek and Ulug, 2001; Kim et al., 2005). One consistent theme from both the models was that contamination related obsessions and cleaning compulsions comprise of one factor, while aggressive, sexual and religious obsessions loaded on another factor. Previous factor analytic studies from India have also found that contamination related factor stand out distinctly (Girishchandra and Khanna, 2001; Taj et al., 2013). Our study did not find hoarding to have a separate factor loading, as compared to previous studies and analysis from collated data (Bloch et al., 2008). However, such a finding has been reported from another study from the region which did not find separate factor loading for hoarding related obsessions and compulsions (Girishchandra and Khanna, 2001). Another consistent finding across both the age groups was that the factor related to cleaning and contamination related obsessions and compulsions were more common in females. This in line with other literature which has shown that contamination related obsessions and compulsions are more common in females (Mataix-Cols et al., 1999; Denys et al., 2004). In the present study, cluster analysis conformed fairlyto the factor analysis for the early and late onset OCD sample. One of the reasons could be that cluster analysis and factor analysis process the data in a slightly different manner. The strengths of the study includes that it attempted to explore the factor dimensions of OCD in early as well as late onset population recruited by the same methodology, evaluated by the same assessors and in the same treatment facility.Hierarchical cluster analysis was also conducted to
complement the factor analysis. Also, it was the first study from the region to look specifically at the phenomenological differences between early and late onset OCD. Some limitations should also be considered while drawing inferences from the study. The recruitment of the participants was done through purposive sampling and hence selection bias cannot be completely ruled out. The presence of various symptoms and the age of onset of the symptoms (the age at which first symptoms of OCD occurred) were based upon retrospective recall of the participants and may be subjected to recall bias. One of the factors in the factor analysis contained only two symptoms (items) in the early onset OCD group, resulting in possible over-factoring. Phenomenological sub-grouping provides one method of classifying patients with OCD, and there may be other ways of classification of patients with OCD (Delucci et al, 2011; Pauls et al., 2014). To conclude, the study furthers the evidence that early-onset OCD has a largely similar factor structure as compared to late-onset OCD, though some differences were present in factor structure and relative frequency of obsessions and compulsions. Understanding differences can have bearing on the clinical assessment, intervention and prognosis. Further genetic and neuroimaging based research if attempted (Tek and Ulug, 2001; Bloch et al., 2008), may provide more insights into the validity of these diagnostic subgroups.
REFERENCES:
Adam, Y., Meinlschmidt, G., Gloster, A.T., Lieb, R., 2012. Obsessive-compulsive disorder in the community:
12-month
prevalence,
comorbidity
and
impairment.
Soc
Psychiatry
PsychiatrEpidemiol;47(3):339–49. American Psychiatric Association; 2000. Diagnostic and Statistical Manual of Mental Disorders, 4th ed, Text Revision. Washington DC:
Ball, S.G., Baer, L., Otto, M.W., 1996. Symptom subtypes of obsessive-compulsive disorder in behavioral treatment studies: a quantitative review. Behav Res Ther 34(1):47–51. Bloch, M.H., Landeros-Weisenberger, A., Rosario, M.C., Pittenger, C., Leckman, J.F., 2008. Meta-analysis of the symptom structure of obsessive-compulsive disorder. Am J Psychiatry 165(12):1532–42. Butwicka, A., Gmitrowicz, A., 2010. Symptom clusters in obsessive-compulsive disorder (OCD): influence of age and age of onset. Eur Child Adolesc Psychiatry 19(4):365–70. Delorme, R., Golmard, J.L., Chabane, N., Millet, B., Krebs, M.O., Mouren-Simeoni, M.C., Leboyer, M., 2005. Admixture analysis of age at onset in obsessive-compulsive disorder. Psychol Med 35(2):237–43. Delucchi, K.L., Katerberg, H., Stewart, S.E., Denys, D.A., Lochner, C., Stack, D.E., den Boer J.A., van Balkom, A.J., Jenike, M.A., Stein, D.J., Cath, D.C., 2011. Latent class analysis of the Yale-Brown Obsessive-Compulsive Scale symptoms in obsessive-compulsive disorder. Compr Psychiatry;52(3):334-41. Denys, D., de Geus, F., van Megen, H.J.G.M., Westenberg, H.G.M., 2004. Use of factor analysis to detect potential phenotypes in obsessive-compulsive disorder. Psychiatry Res 128(3):273–80. Fineberg, N.A., Hengartner, M.P., Bergbaum, C., Gale, T., Rössler, W., Angst, J., 2013 Lifetime comorbidity of obsessive-compulsive disorder and sub-threshold obsessive-compulsive symptomatology in the community: impact, prevalence, socio-demographic and clinical characteristics. Int J Psychiatry Clin Pract 17(3):188–96.
Fontenelle, L.F., Mendlowicz, M.V., Marques, C., Versiani, M., 2003. Early- and late-onset obsessive-compulsive disorder in adult patients: an exploratory clinical and therapeutic study. J Psychiatr Res 37(2):127–33. Girishchandra, B.G., Khanna, S., 2001. Phenomenology of obsessive compulsive disorder: a factor analytic approach. Indian J Psychiatry 43(4):306–16. Goodman, W.K., Price, L.H., Rasmussen, S.A., Mazure, C., Fleischmann, R.L., Hill, C.L., Heninger, G.R., Charney, D.S., 1989. The Yale-Brown obsessive compulsive scale: I. Development, use, and reliability. Arch Gen Psychiatry 46(11):1006–1011. Hasler, G., Pinto, A., Greenberg, B.D., Samuels, J., Fyer, A.J., Pauls, D., Knowles, J.A., McCracken, J.T., Piacentini, J., Riddle, M.A., Rauch, S.L., 2007. Familiality of factor analysisderived YBOCS dimensions in OCD-affected sibling pairs from the OCD Collaborative Genetics Study. Biol Psychiatry 61(5):617–25. Hemmings, S.M.J., Kinnear, C.J., Lochner, C., Niehaus, D.J.H., Knowles, J.A., MoolmanSmook, J.C., Corfield, V.A., Stein, D,J., 2004. Early- versus late-onset obsessive-compulsive disorder: investigating genetic and clinical correlates. Psychiatry Res 128(2):175–82. Katerberg, H., Delucchi, K.L., Stewart, S.E., Lochner, C., Denys, D.A., Stack, D.E., Andresen, J.M., Grant, J.E., Kim, S.W., Williams, K.A., den Boer, J.A., van Balkom, A.J., Smit, J.H., van Oppen, P., Polman, A., Jenike, M.A., Stein, D.J., Mathews, C.A., Cath, D.C.. 2010. Symptom dimensions in OCD: item-level factor analysis and heritability estimates. Behav Genet 40(4):505–17.
Kichuk, S.A., Torres, A.R., Fontenelle, L.F., Rosário, M.C., Shavitt, R.G., Miguel, E.C., Pittenger, C., Bloch, M.H., 2013. Symptom dimensions are associated with age of onset and clinical course of obsessive-compulsive disorder. ProgNeuropsychopharmacolBiol Psychiatry 44:233–9. Kim, S.J., Lee, H.S., Kim, C.H., 2005. Obsessive-compulsive disorder, factor-analyzed symptom dimensions and serotonin transporter polymorphism. Neuropsychobiology 52(4):176–82. Knopp, J., Knowles, S., Bee, P., Lovell, K., Bower, P., 2013. A systematic review of predictors and moderators of response to psychological therapies in OCD: do we have enough empirical evidence to target treatment? Clin Psychol Rev 33(8):1067–81. Mataix-Cols, D., Fullana, M.Á., Alonso, P., Menchón, J.M., Vallejo, J., 2004. Convergent and discriminant validity of the Yale-Brown Obsessive-Compulsive Scale symptom checklist. PsychotherPsychosom 73(3):190–196. Mataix-Cols, D., Rauch, S.L., Manzo, P.A., Jenike, M.A., Baer, L., 1999 Use of factor-analyzed symptom dimensions to predict outcome with serotonin reuptake inhibitors and placebo in the treatment of obsessive-compulsive disorder. Am J Psychiatry 156(9):1409–16. Mataix-Cols, D., Rosario-Campos, M.C., Leckman, J.F., 2005. A multidimensional model of obsessive-compulsive disorder. Am J Psychiatry 162(2):228–38. McKay, D., Piacentini, J., Greisberg, S., Graae, F., Jaffer, M., Miller, J., 2006. The structure of childhood obsessions and compulsions: dimensions in an outpatient sample. Behav Res Ther 44(1):137–46.
Narayanaswamy, J.C., Viswanath, B., Cherian, A.V., Math, S.B., Kandavel, T., Reddy, Y.J., 2012. Impact of age of onset of illness on clinical phenotype in OCD. Psychiatry Res 200(2– 3):554–9. Pauls, D.L., Abramovitch, A., Rauch, S.L., Geller, D.A., 2014. Obsessive-compulsive disorder: an integrative genetic and neurobiological perspective. Nat Rev Neurosci;15(6), 410-24. Rasmussen, S.A., Eisen, J.L., 1992. The epidemiology and differential diagnosis of obsessive compulsive disorder. J Clin Psychiatry;53 Suppl:4–10. Roth, R.M., Milovan, D., Baribeau, J., O’Connor, K., 2005. Neuropsychological functioning in early- and late-onset obsessive-compulsive disorder. J Neuropsychiatry Clin Neurosci 17(2):208–13. Tabachnick, B.G., Fidell, L.S., 2007. Using Multivariate Statistics. Boston: Pearson Education Inc; Taj, M.J.R.J., Viswanath, B., Purushottam, M., Kandavel, T., Janardhan Reddy, Y.C., Jain, S., 2013. DRD4 gene and obsessive compulsive disorder: do symptom dimensions have specific genetic correlates? ProgNeuropsychopharmacol Biol Psychiatry 41:18–23. Taylor, S., 2011. Early versus late onset obsessive-compulsive disorder: evidence for distinct subtypes. Clin Psychol Rev 31(7):1083–100. Tek, C., Ulug, B., 2001. Religiosity and religious obsessions in obsessive-compulsive disorder. Psychiatry Res 104(2):99–108.
Highlights:
Subjects with early onset (onset of symptoms at age ≤ 18 years) and late onset (onset at age > 18 years) OCD did not differ in terms of type of obsessions
Checking, counting and repeating compulsions were more common in the early onset group, contamination related compulsions were most common in the late onset group.
Factor analysis of YBOCS revealed a 3 factor solution for both the groups, which largely concurred with each other.