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J Abnorm Psychol. Author manuscript; available in PMC 2017 January 01. Published in final edited form as: J Abnorm Psychol. 2016 January ; 125(1): 114–124. doi:10.1037/abn0000124.
Longitudinal associations between social anxiety disorder and avoidant personality disorder: A twin study Fartein Ask Torvik1, Audun Welander-Vatn1, Eivind Ystrom1,2, Gun Peggy Knudsen1, Nikolai Czajkowski1,2, Kenneth S. Kendler3, and Ted Reichborn-Kjennerud1,4 1Department
of Genetics, Environment and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
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2Department
of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
3Virginia
Institute for Psychiatric and Behavioral Genetics and Departments of Psychiatry and Human Genetics and Medical College of Virginia, Virginia Commonwealth University, Richmond, VA, USA 4Adult
Psychiatry Unit, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
Abstract
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Social anxiety disorder (SAD) and avoidant personality disorder (AvPD) are frequently cooccurring psychiatric disorders with symptomatology related to fear of social situations. It is uncertain to what degree the two disorders reflect the same genetic and environmental risk factors. The current study addresses the stability and co-occurrence of SAD and AvPD, the factor structure of the diagnostic criteria, and genetic and environmental factors underlying the disorders at two time points. SAD and AvPD were assessed in 1,761 young adult female twins at baseline and 1,471 of these approximately 10 years later. Biometric models were fitted to dimensional representations of SAD and AvPD. SAD and AvPD were moderately and approximately equally stable from young to middle adulthood, with increasing co-occurrence driven by environmental factors. At the first wave, approximately one in three individuals with AvPD had SAD, increasing to one in two at follow-up. The diagnostic criteria for SAD and AvPD had a two-factor structure with low cross-loadings. The relationship between SAD and AvPD was best accounted for by a model with separate, although highly correlated (r = .76), and highly heritable (.66 and .71) risk factors for each disorder. Their genetic and environmental components correlated .84 and .59, respectively. The finding of partially distinct risk factors indicates qualitative differences in the etiology of SAD and AvPD. Genetic factors represented the strongest time-invariant influences, whereas environmental factors were most important at the specific points in time.
Keywords Social anxiety disorder; social phobia; avoidant personality disorder; twin study
Correspondence concerning this article should be addressed to F. A. Torvik, Department of Genetics, Environment and Mental Health, Norwegian Institute of Public Health, P.O. Box 4404 Nydalen, N-0403 Oslo, Norway. [email protected].
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Social anxiety disorder (SAD) and avoidant personality disorder (AvPD) as defined by the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) (American Psychiatric Association, 2013), are frequently co-occurring psychiatric disorders with symptomatology related to fear of social situations. SAD and AvPD are conceptualized as separate forms of psychopathology, although the relationship between the two disorders has been discussed since they were introduced with the publication of DSM-III in 1980 (American Psychiatric Association, 1980).
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The original distinction between SAD and AvPD in the DSM-III was based on an understanding of SAD as an excessive fear of performing in social situations and AvPD as a problem of forming close interpersonal relationships due to feelings of personal inferiority and fear of social disapproval (Millon & Martinez, 1995). Although theoretically meaningful, this conceptualization was questioned after demonstrations of substantial cooccurrence in clinical studies (Ralevski et al., 2005). Estimates of co-occurrence in clinical studies may, however, be biased (Berkson, 1946), and population-based studies indicate moderate co-occurrence between SAD and AvPD (Grant et al., 2005; Lampe, Slade, Issakidis, & Andrews, 2003). In a large, nationally representative study in the U.S., only 36.4% of individuals with SAD were also diagnosed with AvPD (Cox, Pagura, Stein, & Sareen, 2009). However, stability over time and the degree to which SAD predicts later AvPD and vice versa are not known, because longitudinal population-based studies have not been undertaken.
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There may be differences in the stability of SAD and AvPD. Personality disorders (PD) are per definition assumed to be stable and of long duration, whereas anxiety disorders can be diagnosed when the duration is shorter (e.g., 6 months for SAD) (American Psychiatric Association, 2013). Longitudinal studies have revealed substantial fluctuations in AvPD symptoms and diagnoses (Hopwood et al., 2013; Nestadt et al., 2010; Wright, Pincus, & Lenzenweger, 2013), and SAD appears to be more chronic than other anxiety disorders (Ansell et al., 2011; Keller, 2003). A comparison between SAD and AvPD with regard to stability in adulthood has not been published.
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Another important aspect of the relationship between SAD and AvPD is the degree to which they share etiological factors. Qualitatively similar, but more severe social anxiety and functional impairment has been found in patients with both SAD and AvPD, compared to patients with only SAD (Ansell et al., 2011; van Velzen, Emmelkamp, & Scholing, 2000). Such findings have led several authors to conclude that the current diagnostic distinction is arbitrary, and that AvPD is a more severe variant of SAD (e.g., Isomura et al., 2015; Ralevski et al., 2005; Reich, 2009). Other studies indicate qualitative differences between SAD and AvPD (Hummelen, Wilberg, Pedersen, & Karterud, 2007; Huppert, Strunk, Ledley, Davidson, & Foa, 2008; Lampe & Sunderland, 2015). For instance, AvPD may include an aspect of emotional guardedness towards other people that is not well captured by the SAD construct (Marques et al., 2012), and family studies suggest that AvPD may be related to the schizophrenia-spectrum (e.g., Fogelson et al., 2010). There is no consensus on the issue of qualitative differences between the disorders (Bernstein, Arntz, & Travaglini, 2015).
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A confirmatory factor analysis of the symptoms of SAD and AvPD in a clinical sample indicated best fit for a two-factor solution (Huppert et al., 2008). To our knowledge, no such analyses have been carried out on non-clinical samples. However, a population-based study of female twins found that two different environmental risk factors influenced SAD and AvPD, whereas the genetic risk factors were identical (Reichborn-Kjennerud et al., 2007). The study was limited by a cross-sectional design and a moderate sample size, which led to broad confidence intervals around the estimates of genetic and environmental correlations. It was not possible to rule out common environmental factors influencing SAD and AvPD, and similarly, to determine whether the genetic factors were identical or only highly correlated. In addition, in cross-sectional twin studies, it is usually not possible to account for the effect of random measurement error on the results (Gillespie, Zhu, Neale, Heath, & Martin, 2003).
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We address the limitations in the previous literature on SAD and AvPD by using data from a 10-year longitudinal follow-up of individuals that participated in the above mentioned twin study. The assessment of SAD and AvPD on two occasions makes it possible to estimate common and disorder-specific risk factors more precisely. In the current study, we had the following specific aims: First, to assess the stability of SAD and AvPD from early to middle adulthood. Second, to estimate the co-occurrence between SAD and AvPD at both waves. Third, to investigate whether SAD and AvPD at the two waves are best accounted for by one or two underlying risk factors. Fourth, to examine genetic and environmental contributions to the time-invariant and time-specific risk for SAD and AvPD.
Method Participants
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The sample originated from the Norwegian Twin Registry. The twins were identified through the mandatory Norwegian Medical Birth Registry, established January 1, 1967. Between 1999 and 2004, DSM-IV axis I and axis II psychiatric disorders were assessed at interview in 2,801 twins (1,776 women) born between 1967 and 1979 (Wave 1). The response rate was 44%. Zygosity was determined by questionnaire items and genotyping. The misclassification rate was estimated to be less than 1.0%, which is unlikely to substantially bias results (Neale, 2003). Between 2010 and 2011 a second wave of interviews were conducted. Of the participants in Wave 1, 17 had withdrawn their informed consent, 14 had unknown addresses, and 12 had died, leaving 2,758 eligible twins who were invited to participate in the follow-up study. After two written reminders and a final telephone contact to non-responders, 2,284 twins (1,482 women) were interviewed in Wave 2 (82.8% of the eligible). Among women, 133 had SAD, AvPD, or both disorders at least once; for men the number of cases was 27. Even considering dimensional representations of the disorders, the prevalences were too low among men to permit biometric modelling. Only female twins were therefore included in this investigation. The mean age of female participants was 28.6 years (SD = 4.3, range 19–36) at Wave 1 and 37.8 (SD = 3.8, range 30–44) at Wave 2. From the interviews in Wave 1, 1,761 women had valid data for SAD and AvPD; 445 monozygotic (MZ) female pairs, 256 dizygotic (DZ) female pairs and 359 single female twins (either from incomplete pairs or women from dizygotic opposite sex pairs). From the J Abnorm Psychol. Author manuscript; available in PMC 2017 January 01.
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interviews in Wave 2, 1,471 women had valid data for SAD and AvPD; 354 MZ, 174 DZ, and 415 single female twins. In addition, partially complete data were available for 15 women at Wave 1 and 11 women at Wave 2. Participants with partially complete data were included in the analyses. Ethics—Approval was received from The Norwegian Data Inspectorate and the Regional Committees for Medical and Health Research Ethics, and written informed consent was obtained from all participants after complete description of the study. Measures
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Avoidant personality disorder—The Structured Interview for DSM-IV Personality (SIDP-IV) (Pfohl, Blum, & Zimmerman, 1995) was used to assess PDs in both waves. We used a Norwegian version of SIDP-IV (Helgeland, Kjelsberg, & Torgersen, 2005). The DSM-IV criterion associated with each question was rated as 0 = “not present,” 1 = “subthreshold,” 2 = “present,” and 3 = “strongly present.” The SIDP-IV uses the “5-year rule,” meaning that behaviors, cognitions, and feelings that predominated for most of the past 5 years are considered to be representative of an individual’s personality. For the structural analyses, we used a dimensional approach to AvPD, constructing variables based on the number of endorsed criteria. Dimensional representations are often considered a better conceptualization of personality disorders than categorical diagnoses (Widiger & Samuel, 2005). To optimize statistical power, we used the number of subthreshold criteria (≥1), assuming that the risk for each trait was continuous and normally distributed, i.e., that the classification (0–3) represented different degrees of severity. This assumption has been evaluated using multiple threshold tests for each of the criteria (Reichborn-Kjennerud et al., 2007). We also constructed dichotomous diagnoses of AvPD according to DSM criteria in order to estimate prevalence rates. Interrater reliability at Wave 1 was assessed based on two raters scoring of 70 audiotaped interviews. Intraclass and polychoric correlations for the number of endorsed AvPD criteria at the subthreshold level were 0.96 and 0.97 respectively. At Wave 2, two interviewers rescored 95 audiotaped interviews. Intraclass and polchoric correlations for the number of endorsed criteria at subthreshold level were 0.84 and 0.92.
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Social anxiety disorder—Clinical DSM-IV diagnoses were assessed using the computerized Composite International Diagnostic Interview (CIDI) (Wittchen & Pfister, 1997) in Norwegian translation (Landheim, Bakken, & Vaglum, 2003). Criteria A to E of the DSM-IV were covered in the interview. Criterion F regarding individuals below age 18 and the exclusion criteria G and H were not covered in the interview. Criterion A was covered by a list of social situations and a list of anxiety symptoms elicited by the social situations. Each of these lists was summed. Criterion E on functional impairment and distress about having the disorder was covered with two questions. The recency of SAD symptoms was assessed in the interviews. Individuals with diagnosable symptoms within the last 5 years were considered to have the disorder. In addition, individuals who feared at least one social situation and who were worried about showing anxiety symptoms, but who did not otherwise satisfy diagnostic criteria, were considered to have subthreshold SAD. This definition has been used in previous studies (Czajkowski, Kendler, Tambs, Røysamb, & Reichborn-Kjennerud, 2011). Five-year occurrence was used to facilitate comparison with
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the AvPD diagnoses. Due to skipping patterns, it was not possible to rescore audiotaped interviews. However, CIDI has shown good test-retest and interrater reliability in other countries (Wittchen, Lachner, Wunderlich, & Pfister, 1998; Wittchen, 1994). Validity—Joint analyses of CIDI and SIDP-IV data in the present sample has shown a good factor structure at Wave 1 (Røysamb et al., 2011) and both measures predict disability (Torvik et al., 2014; Østby et al., 2014), indicating construct and predictive validity. Other validity studies of Norwegian versions of the interviews have not been conducted.
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Procedure—Interviewers at Wave 1 were mainly senior clinical psychology graduate students and experienced psychiatric nurses. Interviewers at Wave 2 included senior clinical psychology graduate students, psychiatric nurses, and experienced clinical psychologists who were interviewers at Wave 1. In Wave 1, most interviews were conducted face-to-face, and the rest (n = 231, 8.3%) by telephone. All interviews were conducted by telephone in Wave 2. Different interviewers assessed each twin in a pair. Statistical analyses Stability over the life-course was approached in two ways: absolute and relative stability (Morey & Hopwood, 2013). Absolute stability refers to the average changes of traits, indicated by means or prevalences. Relative stability refers to consistency in the rank ordering of individuals on a given trait over time. It was estimated as polychoric correlations between either SAD or AvPD at the two time points. Similarly, co-occurrence between SAD and AvPD was calculated as the percentage of individuals with one disorder who also had the other disorder and as polychoric correlations. To maximize power, the phenotypes under study here were ordinal representations of SAD and AvPD, including the subthreshold level.
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The factor structure of SAD and AvPD criteria was examined in exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), including bifactor analysis. The analyses were run on the seven symptoms for AvPD and the five criteria covered for SAD, where criterion A and E consisted of two variables each. Statistical dependency between co-twins and within individuals at the two time points was accounted for by a sandwich estimator, where twin pairs were defined as the first level and time as the second level. The factor analyses was run in Mplus 7.3 (Muthen & Muthen, 2012). The variables were analyzed as ordered categories. We used the maximum likelihood estimator with robust standard errors and oblique geomin rotation in the EFA.
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In the classical twin model (Martin & Eaves, 1977), individual differences in traits are assumed to arise from three latent sources: Additive genetic factors (A) shared 100% by MZ twins and 50% by DZ twins; common or shared environment (C), which includes environmental exposures contributing to twin similarity; and individual-specific or nonshared environment (E), which includes environmental factors that contribute to differences between the twins, plus measurement error. As our variables were ordinal, we used a liability-threshold model, assuming that ordered categories are indicators of unobserved, normally distributed liabilies (Falconer, 1965).
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The two main models of the relationship between the two disorders are shown in Figure 1. First, we tested the number of underlying factors. We specified a measurement model with two time-invariant latent risk factors (Figure 1, A). One time-invariant risk factor (SADC) influences SAD equally strongly at Wave 1 and Wave 2, whereas another time-invariant risk factor (AvPDC) influences AvPD equally strongly at Wave 1 and Wave 2. These latent risk factors are decomposed into genetic and environmental factors (ac1, ac2, cc1, cc2, ec1, ec2). In this model, the time-invariant risks for SAD and AvPD are influenced by different genetic and environmental factors, which may correlate (rAc, rCc, rEc). The model also includes time-specific influences on SAD and AvPD at Wave 1 and Wave 2 (As1…4, Cs1…4, Es1…4) and correlations between these time-specific influences (rAs31, rAs42, rCs31, rCs42, rEs31, rEs42). A competing model is the common pathway model with one latent risk factor (LC) influencing both SAD and AvPD at both waves (Figure 1, B). In this model, SAD and AvPD reflect the same underlying risk, but possibly of different magnitudes.
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Simpler, more restricted variants of the models were tested by removing influences from A or C. We tested whether the disorders were equally strongly influenced by genetic and environmental factors across time by setting time-specific influences to be equal in magnitude. We then tested whether the disorders were influenced by the same genetic factors at both waves by removing influences from time-specific genetic factors. Timespecific non-shared environmental factors cannot be removed because these include measurement error. Finally, we tested the magnitude of the correlations in the final model.
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The difference in −2 times log-likelihood (Δ-2LL) is asymptotically χ2 distributed, which allows testing for significant differences in χ2 for nested submodels. If the difference in χ2 is non-significant, a simpler, more restricted model is preferred. In addition, we used the Akaike Information Criterion (AIC) (Akaike, 1987) as an index of parsimony. Models with low AIC value are preferred. Sample size-adjusted Bayesian Information Criterion (BICSSA) is provided for reference (Sclove, 1987). A reduction in BICSSA value indicates improvement in model fit. The models were fitted to raw data using Full Information Maximum Likelihood (FIML) for categorical data as the estimation procedure in OpenMx 2.0 (Neale et al., 2015) for R 3.1.2 (R Core Team, 2014). The FIML method utilizes all data, from both complete and incomplete pairs, and provides better estimates in structural equation models than traditional missing data methods (Enders & Bandalos, 2001).
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Stability of SAD and AvPD The first aim was to assess the stability of SAD and AvPD from early to middle adulthood. Table 1 shows the occurrence of full and subthreshold SAD and AvPD at the two waves. Cross-tabulated counts of dichotomous diagnoses are provided in supplementary Tables S1 and S2. In early adulthood, the 5-year prevalence of SAD among women was 3.7%, which increased to 5.1% in middle adulthood (χ2 = 4.47, df = 1, p = .034). The prevalence was stable when subthreshold disorders were also included, at 13.8% and 13.7% at Wave 1 and Wave 2, respectively (χ2 = .02, df = 1, p = .899).
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The prevalence of AvPD was 2.5% at Wave 1 and 2.1% at Wave 2 (χ2 = .36, df = 1, p = . 546). The mean number of subthreshold AvPD criteria in the sample fell from 0.94 (SD = 1.40) at Wave 1 to 0.71 (SD = 1.30) at Wave 2 (χ2 = 38.66, df = 1, p < .001). The rank-order stability of SAD was r = .56 (95% CI [.48, to .65]). AvPD symptoms were approximately equally stable, at r = .54 (95% CI [.49, .59]). The difference in stability was not statistically significant (χ2 = 0.27, df = 1, p = .601). Figure 2 shows the correlations between dimensional representations of SAD and AvPD at Wave 1 and 2 and a cross-lagged model of the longitudinal relationship between the disorders. Association between SAD and AvPD
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The second aim was to assess the degree of co-occurrence of SAD and AvPD. At Wave 1, 31.8% of those with AvPD also had SAD, whereas 21.5% of those with SAD had AvPD. At Wave 2, 54.8% of those with AvPD also had SAD, whereas 22.7% of those with SAD also had AvPD. The polychoric correlation between dimensional SAD and AvPD was .49 (95% CI [.43, .55]) at Wave 1, which increased to .61 (95% CI [.55, .67]) at Wave 2 (χ2 = 7.08; df = 1; p = .008). Across time, SAD at Wave 1 predicted AvPD at Wave 2 (b = .29, 95% CI [. 18, .41]) approximately as strongly as AvPD at Wave 1 predicted SAD at Wave 2 (b = .19, 95% CI [.08, .31], Δχ2 = 1.27, Δdf = 1). Number of underlying factors
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We tested the number of latent factors underlying SAD and AvPD in factor analyses and in biometric models. EFA of the diagnostic criteria of SAD and AvPD yielded factors with eigenvalues of 5.29, 3.37, and 1.05. This suggests the presence of two or possibly three factors, whereas a solution with one factor did not fit the data well. The three-factor solution lacked theoretical foundation and included an uninterpretable factor with only one loading above .20 (on AvPD criterion 1). In the oblique two-factor solution, the AvPD symptoms loaded primarily on one factor and the SAD items on another. The two factors correlated .54 (95% CI [.42, .65]). All cross-loadings were minor. The two-factor solution is shown in Table 2. Additional details of the EFA are provided in the supplementary materials along with CFA and bifactor analyses (pages S2-S7). In brief, the CFA indicated better fit for a two-factor solution than for a unidimensional solution. The best fit was found for a bifactor model with one general factor and two group factors. The factor loadings were not in line with an interpretation that the group factors merely represented method variance. Moreover, the group factor for SAD correlated with a third condition (alcohol use disorder). The factor analyses therefore indicate that partially distinct factors underlie SAD and AvPD symptoms.
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The first step of the biometric modelling corresponds to the aim of investigating whether one or two underlying factors best account for the risk for SAD and AvPD. Correlations between co-twins on SAD and AvPD at the two time points are shown in Table 3, and model fit statistics are given in Table 4. The Cholesky decomposition (model 1) is shown as a comparison. Model 2 includes one time-invariant latent factor for SAD and one for AvPD, whereas both disorders reflect one latent factor in model 3. Model 2 with two latent factors fitted the data best, according to the χ2 and AIC value.
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Longitudinal biometric models
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We proceeded to examine the longitudinal biometric models of the relationship between SAD and AvPD, in accordance with the fourth aim. In the second step of Table 4, we compared model 2, which includes A, C, and E factors, to simpler models excluding C (model 4), A (model 5), or both (model 6) variance components. The AE model (model 4) provided the best fit in step 2. In the third step, we tested whether the relative influence of genetic and environmental factors changed from Wave 1 to Wave 2, by setting the timespecific influences to be equal across time (as1 = as2; as3 = as4; es1 = es2; es3 = es4). This led to further improvement of model fit (model 7), indicating that the heritability of the disorders was the same at both waves.
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We proceeded to test whether the same genetic factors accounted for the disorders at Wave 1 and Wave 2, by removing time-specific genetic factors. The time-specific genetic influences on SAD was estimated at .41 (95% CI [.25, .53]) in model 7. These could not be removed without a deterioration of the model fit (model 8). Removing the time-specific genetic influences on AvPD, estimated at .11 (95% CI [−.25, .25]), improved the model fit (model 9). Setting the correlation in environmental factors to be equal across time led to a worse fit, as did setting the correlations between genetic or environmental components of the timeinvariant traits to unity (step 5).
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The overall best fitting model (model 9) was the measurement model with two correlated time-invariant factors and time-specific genetic influences on SAD. This model is shown in Figure 3. Seventy-six percent of the time-invariant risk factors for SAD and AvPD were overlapping (r = .76, 95% CI [.71, .85]). The heritability of the time-invariant SAD factor was .66 (95% CI [.56, .86]) and the heritability of the corresponding AvPD factor was .71 (95% CI [.59, .79]). The genetic components of the time-invariant risk factors for SAD and AvPD correlated .84 (95% CI [.69, 1.00]). The heritability at the specific time points was lower, at .52 (95% CI [.40, .62]) for SAD and .38 (95% CI [.32, .43]) for AvPD. Thirty percent of the genetic variance in SAD (.30, 95% CI [.10, .49]) was unique to each time point. In total, the genetic influences on SAD and AvPD correlated .70 (95% CI [.58, .82]) at both Wave 1 and Wave 2.
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The correlation between environmental influences on the two time-invariant risk factors was .59 (95% CI [.23, .98]). The time-invariant environmental risk factors had modest environmentality and therefore contributed only modestly to the covariance between the two disorders. In addition, there were moderately strong effects of disorder-specific and timespecific non-shared environment, some of which is due to random measurement error. These environmental influences had a correlation of .19 (95% CI [.04, .36]) at Wave 1, and a correlation of .47 (95% CI [.30, .64]) at Wave 2, indicating that time-specific events influence the co-occurrence between SAD and AvPD.
Discussion Stability of SAD and AvPD SAD and AvPD were equally stable from early to middle adulthood, but the degree of stability was moderate for both disorders. The moderate stability of AvPD is at odds with J Abnorm Psychol. Author manuscript; available in PMC 2017 January 01.
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the conceptualization of PDs as stable and of long duration (American Psychiatric Association, 2013), but corresponds with other studies on the longitudinal course of PDs (Morey & Hopwood, 2013; Wright et al., 2013). The finding that SAD and AvPD were equally stable does not necessarily imply that either SAD or AvPD is misclassified as an anxiety or personality disorder, but rather adds additional support to studies indicating that stability is not a distinguishing feature of PDs (Krueger, 2005; Shea & Yen, 2003).
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The general decline in AvPD symptoms is in line with previous research (Lenzenweger, 1999; Shea et al., 2002; Wright et al., 2013), although one study found that AvPD symptoms increased over time (Seivewright, Tyrer, & Johnson, 2002). The increase in diagnosable SAD was unexpected. One possible reason could be that the second interview was conducted over the telephone. A previous study did not find that interview modality influenced prevalences estimates of SAD in telephone versus personal interviews (Crippa et al., 2008), although we cannot exclude that explanation in our study. It is not clear why interview modality should influence SAD and not AvPD. The increase seems, however, to reflect variation in full, but not subthreshold SAD, as the combined prevalence of SAD and subthreshold SAD was unchanged from Wave 1 to Wave 2.
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The biometric modelling suggested that the genetic influences on AvPD were identical across time. We also found high stability of genetic influences on SAD. These results are in line with previous studies finding that genetic effects on behavioral phenotypes are stable in adulthood (Nivard et al., 2015). Nevertheless, SAD was influenced by time-specific genetic variance not shared with AvPD. Thus, the genetic risk for SAD is to some extent dependent on age. Genetic innovation implies that genetic factors previously unrelated to a trait become active (Gillespie et al., 2004). Different factors with heritable components may influence SAD at different stages in life. For instance, one may speculate that this could be related to changes in career or family situation from young to middle adulthood. Although genetic innovation in anxiety in adulthood seems to be in conflict with Nivard et al. (2015), another study found genetic innovation in anxiety around age 30 (Gillespie et al., 2004). Further studies are needed to clarify whether there are age-specific genetic effects on SAD in adulthood. We also found that individual-specific environmental factors contributed moderately to the stability of SAD and AvPD, which is in line with previous results for anxiety in adult age (Gillespie et al., 2004; Nivard et al., 2015). Co-occurrence of SAD and AvPD
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The co-occurrence of SAD and AvPD was moderate. Many individuals with AvPD (approximately half to two thirds) did not fulfill the criteria for SAD, which indicates diagnostic differences. The relatively low overlap is consistent with results of other population based studies (Cox et al., 2009; Grant et al., 2005; Lampe et al., 2003), although the co-occurrence is stronger in clinical samples (Ralevski et al., 2005). Interestingly, the degree of co-occurrence between SAD and AvPD increased over time. A possible explanation for this change from early to middle adulthood is that direct causal influences may exist between SAD and AvPD and increase their co-occurrence over time. The increase in environmental correlation supports this explanation. Alternatively, the effect
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of environmental risk factors shared between SAD and AvPD become more prominent with increasing age. Underlying risk factors The factor analyses suggested a model in which the criteria for SAD and AvPD reflect distinct, but correlated constructs. In line with the factor analyses, the biometric analyses supported a model of separate, although highly correlated, risk factors for each disorder. The results did not support a model in which SAD and AvPD reflected a single latent factor. Taking into account that there were only moderate rates of SAD among individuals with AvPD, the current findings imply that there are qualitative differences between SAD and AvPD.
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Unlike the previous study in this sample (Reichborn-Kjennerud et al., 2007), we found that the genetic influences on SAD and AvPD were not identical, but highly correlated (r = 0.70). Moreover, environmental factors, both stable and time-specific, contributed to cooccurrence of the disorders. In the previous study, environmental factors did not overlap. These differences in results are most likely due to higher power in the present study by inclusion of subthreshold disorders and follow-up data. The high heritability of the timeinvariant risk factors compared to the disorders at the specific time points follows from the time-invariant risk factors being free from measurement error. In our biometric analyses, random measurement error is part of the time-specific environment. As we found differences in the time-invariant environment and in the genetic components of SAD and AvPD, it is implausible that the observed difference between SAD and AvPD merely reflects measurement error associated with each interview. The bifactor models supported this interpretation.
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The unique genetic and environmental components of AvPD indicate that AvPD is not merely a severe variant of SAD. The factor analyses also indicate that SAD and AvPD reflect distinct constructs. One possible explanation is that the AvPD diagnosis encompasses psychopathology that is not included in the SAD diagnosis. For example, schizophrenia and AvPD symptoms aggregate in the same families (e.g., Fogelson et al., 2010). The unique genetic component of AvPD may therefore be linked to the schizophrenia spectrum. It has also been proposed that AvPD represents anhedonic introversion whereas SAD represents an internalizing factor (Røysamb et al., 2011). Another possibility is that the unique components of AvPD reflect a general risk factor for personality pathology, as underlying risk factors for are shared between various PDs (Kendler, Aggen, et al., 2008). Difficulties in interpersonal functioning is a hallmark feature of PDs, and such difficulties may distinguish AvPD from SAD (Eikenaes, Hummelen, Abrahamsen, Andrea, & Wilberg, 2013; Hummelen et al., 2007). Thus, akin to a pathoplastic model, it may be that one common disorder is expressed and recognized as SAD, AvPD, or both, depending on the patient’s personality traits (c.f. Widiger & Smith, 2008). Despite partially different risk factors for SAD and AvPD, we found that the risk factors were shared to a large degree, like in previous studies (Huppert et al., 2008; Isomura et al., 2015; Lampe & Sunderland, 2015). It is plausible that both disorders reflect risk factors associated with temperamental traits such as behavioral inhibition, and this may be the J Abnorm Psychol. Author manuscript; available in PMC 2017 January 01.
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reason the two diagnoses are strongly related (Biederman et al., 2001). Moreover, a possible source of covariance between SAD and AvPD may be overlap in the diagnostic criteria. DSM-III based descriptions of AvPD included aspects of non-social avoidant behavior, whereas the diagnostic criteria in DSM-IV and DSM-5 focus exclusively on social fears (Millon & Martinez, 1995). In addition, the generalized social phobia subtype, introduced with the DSM-III-R, led to increased co-occurrence with AvPD (Alden, Laposa, Taylor, & Ryder, 2002). The lack of focus on the non-social aspects of AvPD may have decreased the discriminant validity of AvPD with SAD (Taylor, Laposa, & Alden, 2004). In order to provide optimal treatment to individuals with SAD and AvPD, future research needs to identify unique aspects of these disorders, as well as their common features. This may include non-social fears, functioning in close relationships, and normative or maladaptive personality traits. The clinical utility of emphasizing unique features also needs to be evaluated. The presence of risk factors shared by the two disorders indicates that there is a potential for treatment or prevention of the disorders by similar interventions.
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Limitations
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The strengths of the present study include structured diagnostic interviews, longitudinal follow-up, and a population based twin sample. However, some limitations must be mentioned: First, only women were included in the analyses due to low prevalences of SAD and AvPD in men. Most community studies find SAD to be more common among women than among men (e.g., Furmark et al., 1999). The gender differences are usually not as pronounced as those observed in the present study and may be specific to the sample. The findings are therefore not necessarily valid for men. Nevertheless, in one previous study it was concluded that the genetic and environmental structure of anxiety disorders was similar among men and women (Hettema, Prescott, Myers, Neale, & Kendler, 2005). Second, we studied young Norwegian adults, and generalization may be limited to individuals of similar age and ethnic background as the participants. Third, there was modest attrition from Wave 1 to Wave 2 (see supplementary materials, pages S7). SAD at Wave 1 was not associated with attrition to Wave 2. Symptoms of AvPD were associated with reduced participation at Wave 2, but the tendency was modest, and we believe not likely to bias the results of this study, as the co-occurrence of the disorders was similar among dropouts and continuous participants. Fourth, due to a limited number of SAD cases, we could not distinguish between generalized and performance-only SAD. Fifth, the use of 5 year diagnoses of SAD is not common. Our prevalence estimates of SAD are not directly comparable to other studies, typically using either lifetime or 12-month prevalences. The rationale for applying 5-year diagnoses was to have SAD and AvPD measured during the same time-period. Sixth, due to a limited sample size, we had relatively low power to detect effects of shared environmental factors. Although there were relatively small differences between certain MZ and DZ twin correlations, models including genetic, but not shared environmental factors had better fit than models including shared environmental, but not genetic factors. Likewise, previous studies also indicate that SAD and AvPD are heritable (Isomura et al., 2015; Scaini, Belotti, & Ogliari, 2014), and shared environmental effects on anxiety disorders have consistently been found to diminish from childhood to adult age (Kendler, Gardner, et al., 2008; Smoller, Block, & Young, 2009). The use of repeated measures increased statistical power in the biometric analyses, with more precise estimates of influences on the latent
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factors than on each observation. The low number of SAD and AvPD cases can also otherwise contribute to uncertain model estimates. The use of ordinal variables should partially counteract this. Conclusion AvPD and SAD were approximately equally stable over a 10-year period from early to middle adulthood, with increasing co-occurrence over time driven by environmental factors. Factor analyses of the diagnostic criteria suggested a two-factor structure with low crossloadings. The two disorders were best explained by highly correlated, but distinct risk factors. The distinct risk factors indicate the presence of qualitative differences in the etiology of SAD and AvPD. Genetic factors represented the strongest time-invariant influences, whereas environmental factors were most important at the specific points in time.
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Supplementary Material Refer to Web version on PubMed Central for supplementary material.
Acknowledgments This project was supported by the Research Council of Norway (RCN) (grant 196148/V50) and the National Institutes of Health (grant DA037558). Previous collections and analyses of twin data from this project were in part supported by the National Institutes of Health (grant MH-068643) and grants from the RCN, the Norwegian Foundation for Health and Rehabilitation, and the Norwegian Council for Mental Health.
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Figure 1.
(A) Measurement model with two correlated time-invariant latent risk factors and (B) common pathway model with one time-invariant latent risk factor. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder.
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Figure 2.
Phenotypic polychoric correlations (left) and cross-lagged associations (right) between dimensional representations of social anxiety disorder (SAD) and avoidant personality disorder (AvPD) the last 5 years at Wave 1 and Wave 2, including 95% confidence intervals.
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Figure 3.
Best fitting model (model 9), with parameter estimates and 95% confidence intervals. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder.
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Table 1
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Prevalence of SAD and AvPD, including subthreshold disorders, last 5 years. Wave 1 n (%)
Wave 2 n (%)
1523 (86.2)
1271 (86.3)
179 (10.1)
127 (8.6)
65 (3.7)
75 (5.1)
1724 (97.5)
1449 (97.9)
45 (2.5)
31 (2.1)
0
969 (54.8)
981 (66.3)
1
348 (19.7)
238 (16.1)
2
215 (12.2)
116 (7.8)
3
115 (6.5)
54 (3.7)
4
53 (3.0)
50 (3.4)
5 or more
70 (4.0)
41 (2.8)
1776 (100.0)
1482 (100.0)
Social anxiety disorder No SAD Subthreshold SAD AvPD diagnosis Not present Present Subthreshold AvPD criteria
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Total n
Note. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder.
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Table 2
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Geomin rotated two-factor solution of SAD and AvPD diagnostic criteria.
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Factor 1
Factor 2
AvPD, 1: Avoids occupational activities that involve significant interpersonal contact, because of fears of criticism, disapproval, or rejection
.69*
.22*
AvPD, 2: Is unwilling to get involved with people unless certain of being liked
.75*
.18*
AvPD, 3: Shows restraint within intimate relationships because of the fear of being shamed or ridiculed
.55*
.19*
AvPD, 4: Is preoccupied with being criticized or rejected in social situations
.65*
.20*
AvPD, 5: Is inhibited in new interpersonal situations because of feelings of inadequacy
.85*
−.02
AvPD, 6: Views self as socially inept, personally unappealing, or inferior to others
.87*
−.04
AvPD, 7: Is unusually reluctant to take personal risks or to engage in any new activities because they may prove embarrassing
.70*
.00
SAD, A (D20): Marked and persistent fear of one or more social or performance situations
.19*
.72*
SAD, A (D22): Fears that he or she will act in a way that will be humiliating or embarrassing
.05
.68*
SAD, B (D23): Exposure to the feared social situation almost invariably provokes anxiety
.15
.52*
SAD, C (D25): The person recognizes that the fear is excessive or unreasonable
.00
.67*
SAD, D (D24): The feared social or performance situations are avoided or else are endured with intense anxiety or distress.
.14*
.30*
SAD, E (D29): Interferes significantly with the person’s normal routine
−.00
.85*
SAD, E (D25P): Marked distress about having the phobia
−.04
.95*
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Note. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder. Codes in parenthesis indicate section and number of questions in the Composite International Diagnostic Interview (CIDI). Correlation between factor 1 and 2 is .54 (95% CI [.42, .65]). *
p < .05.
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Table 3
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Co-twin correlations of dimensional representations of SAD and AvPD at Wave 1 and Wave 2 among monozygotic and dizygotic twins, including 95% confidence intervals.
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Monozygotic twins
Twin A, SAD Wave 1
Twin A, SAD Wave 2
Twin A, AvPD Wave 1
Twin A, AvPD Wave 2
Twin B, SAD Wave 1
.51 [.37, .66]
.29 [.09, .49]
.40 [.27, .53]
.31 [.16, .47]
Twin B, SAD Wave 2
.35 [.15, .54]
.53 [.37, .70]
.34 [.19, .49]
.35 [.18, .51]
Twin B, AvPD Wave 1
.16 [.01, .30]
.26 [.10, .42]
.38 [.28, .48]
.38 [.26, .49]
Twin B, AvPD Wave 2
.26 [.10, .43]
.39 [.23, .55]
.41 [.29, .52]
.35 [.22, .48]
Dizygotic twins
Twin A, SAD Wave 1
Twin A, SAD Wave 2
Twin A, AvPD Wave 1
Twin A, AvPD Wave 2
Twin B, SAD Wave 1
.37 [.11, .63]
.27 [−.05, .58]
.20 [−.03, .42]
.19 [−.09, .47]
Twin B, SAD Wave 2
.41 [.14, .68]
.40 [.11, .69]
.11 [−.12, .35]
.18 [−.10, .45]
Twin B, AvPD Wave 1
.45 [.29, .62]
.25 [.03, .47]
.30 [.16, .44]
.26 [.08, .44]
Twin B, AvPD Wave 2
.45 [.25, .66]
.33 [.10, .55]
.24 [.07, .41]
.32 [.13, .51]
Note. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder.
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Author Manuscript
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Model 2 + Drop C and A (E model)
6
Model 9 + Time-invariant A correlation = 1
Model 9 + Time-invariant E correlation = 1
11
12
12.76
11.97 20
20
20
19
19
16
21
12
12
8
3
-
Δdf
.042
.067
.010
.692
.026
.997
J Abnorm Psychol. Author manuscript; available in PMC 2017 January 01. Published in final edited form as: J Abnorm Psychol. 2016 January ; 125(1): 114–124. doi:10.1037/abn0000124.
Longitudinal associations between social anxiety disorder and avoidant personality disorder: A twin study Fartein Ask Torvik1, Audun Welander-Vatn1, Eivind Ystrom1,2, Gun Peggy Knudsen1, Nikolai Czajkowski1,2, Kenneth S. Kendler3, and Ted Reichborn-Kjennerud1,4 1Department
of Genetics, Environment and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
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2Department
of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
3Virginia
Institute for Psychiatric and Behavioral Genetics and Departments of Psychiatry and Human Genetics and Medical College of Virginia, Virginia Commonwealth University, Richmond, VA, USA 4Adult
Psychiatry Unit, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
Abstract
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Social anxiety disorder (SAD) and avoidant personality disorder (AvPD) are frequently cooccurring psychiatric disorders with symptomatology related to fear of social situations. It is uncertain to what degree the two disorders reflect the same genetic and environmental risk factors. The current study addresses the stability and co-occurrence of SAD and AvPD, the factor structure of the diagnostic criteria, and genetic and environmental factors underlying the disorders at two time points. SAD and AvPD were assessed in 1,761 young adult female twins at baseline and 1,471 of these approximately 10 years later. Biometric models were fitted to dimensional representations of SAD and AvPD. SAD and AvPD were moderately and approximately equally stable from young to middle adulthood, with increasing co-occurrence driven by environmental factors. At the first wave, approximately one in three individuals with AvPD had SAD, increasing to one in two at follow-up. The diagnostic criteria for SAD and AvPD had a two-factor structure with low cross-loadings. The relationship between SAD and AvPD was best accounted for by a model with separate, although highly correlated (r = .76), and highly heritable (.66 and .71) risk factors for each disorder. Their genetic and environmental components correlated .84 and .59, respectively. The finding of partially distinct risk factors indicates qualitative differences in the etiology of SAD and AvPD. Genetic factors represented the strongest time-invariant influences, whereas environmental factors were most important at the specific points in time.
Keywords Social anxiety disorder; social phobia; avoidant personality disorder; twin study
Correspondence concerning this article should be addressed to F. A. Torvik, Department of Genetics, Environment and Mental Health, Norwegian Institute of Public Health, P.O. Box 4404 Nydalen, N-0403 Oslo, Norway. [email protected].
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Social anxiety disorder (SAD) and avoidant personality disorder (AvPD) as defined by the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) (American Psychiatric Association, 2013), are frequently co-occurring psychiatric disorders with symptomatology related to fear of social situations. SAD and AvPD are conceptualized as separate forms of psychopathology, although the relationship between the two disorders has been discussed since they were introduced with the publication of DSM-III in 1980 (American Psychiatric Association, 1980).
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The original distinction between SAD and AvPD in the DSM-III was based on an understanding of SAD as an excessive fear of performing in social situations and AvPD as a problem of forming close interpersonal relationships due to feelings of personal inferiority and fear of social disapproval (Millon & Martinez, 1995). Although theoretically meaningful, this conceptualization was questioned after demonstrations of substantial cooccurrence in clinical studies (Ralevski et al., 2005). Estimates of co-occurrence in clinical studies may, however, be biased (Berkson, 1946), and population-based studies indicate moderate co-occurrence between SAD and AvPD (Grant et al., 2005; Lampe, Slade, Issakidis, & Andrews, 2003). In a large, nationally representative study in the U.S., only 36.4% of individuals with SAD were also diagnosed with AvPD (Cox, Pagura, Stein, & Sareen, 2009). However, stability over time and the degree to which SAD predicts later AvPD and vice versa are not known, because longitudinal population-based studies have not been undertaken.
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There may be differences in the stability of SAD and AvPD. Personality disorders (PD) are per definition assumed to be stable and of long duration, whereas anxiety disorders can be diagnosed when the duration is shorter (e.g., 6 months for SAD) (American Psychiatric Association, 2013). Longitudinal studies have revealed substantial fluctuations in AvPD symptoms and diagnoses (Hopwood et al., 2013; Nestadt et al., 2010; Wright, Pincus, & Lenzenweger, 2013), and SAD appears to be more chronic than other anxiety disorders (Ansell et al., 2011; Keller, 2003). A comparison between SAD and AvPD with regard to stability in adulthood has not been published.
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Another important aspect of the relationship between SAD and AvPD is the degree to which they share etiological factors. Qualitatively similar, but more severe social anxiety and functional impairment has been found in patients with both SAD and AvPD, compared to patients with only SAD (Ansell et al., 2011; van Velzen, Emmelkamp, & Scholing, 2000). Such findings have led several authors to conclude that the current diagnostic distinction is arbitrary, and that AvPD is a more severe variant of SAD (e.g., Isomura et al., 2015; Ralevski et al., 2005; Reich, 2009). Other studies indicate qualitative differences between SAD and AvPD (Hummelen, Wilberg, Pedersen, & Karterud, 2007; Huppert, Strunk, Ledley, Davidson, & Foa, 2008; Lampe & Sunderland, 2015). For instance, AvPD may include an aspect of emotional guardedness towards other people that is not well captured by the SAD construct (Marques et al., 2012), and family studies suggest that AvPD may be related to the schizophrenia-spectrum (e.g., Fogelson et al., 2010). There is no consensus on the issue of qualitative differences between the disorders (Bernstein, Arntz, & Travaglini, 2015).
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A confirmatory factor analysis of the symptoms of SAD and AvPD in a clinical sample indicated best fit for a two-factor solution (Huppert et al., 2008). To our knowledge, no such analyses have been carried out on non-clinical samples. However, a population-based study of female twins found that two different environmental risk factors influenced SAD and AvPD, whereas the genetic risk factors were identical (Reichborn-Kjennerud et al., 2007). The study was limited by a cross-sectional design and a moderate sample size, which led to broad confidence intervals around the estimates of genetic and environmental correlations. It was not possible to rule out common environmental factors influencing SAD and AvPD, and similarly, to determine whether the genetic factors were identical or only highly correlated. In addition, in cross-sectional twin studies, it is usually not possible to account for the effect of random measurement error on the results (Gillespie, Zhu, Neale, Heath, & Martin, 2003).
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We address the limitations in the previous literature on SAD and AvPD by using data from a 10-year longitudinal follow-up of individuals that participated in the above mentioned twin study. The assessment of SAD and AvPD on two occasions makes it possible to estimate common and disorder-specific risk factors more precisely. In the current study, we had the following specific aims: First, to assess the stability of SAD and AvPD from early to middle adulthood. Second, to estimate the co-occurrence between SAD and AvPD at both waves. Third, to investigate whether SAD and AvPD at the two waves are best accounted for by one or two underlying risk factors. Fourth, to examine genetic and environmental contributions to the time-invariant and time-specific risk for SAD and AvPD.
Method Participants
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The sample originated from the Norwegian Twin Registry. The twins were identified through the mandatory Norwegian Medical Birth Registry, established January 1, 1967. Between 1999 and 2004, DSM-IV axis I and axis II psychiatric disorders were assessed at interview in 2,801 twins (1,776 women) born between 1967 and 1979 (Wave 1). The response rate was 44%. Zygosity was determined by questionnaire items and genotyping. The misclassification rate was estimated to be less than 1.0%, which is unlikely to substantially bias results (Neale, 2003). Between 2010 and 2011 a second wave of interviews were conducted. Of the participants in Wave 1, 17 had withdrawn their informed consent, 14 had unknown addresses, and 12 had died, leaving 2,758 eligible twins who were invited to participate in the follow-up study. After two written reminders and a final telephone contact to non-responders, 2,284 twins (1,482 women) were interviewed in Wave 2 (82.8% of the eligible). Among women, 133 had SAD, AvPD, or both disorders at least once; for men the number of cases was 27. Even considering dimensional representations of the disorders, the prevalences were too low among men to permit biometric modelling. Only female twins were therefore included in this investigation. The mean age of female participants was 28.6 years (SD = 4.3, range 19–36) at Wave 1 and 37.8 (SD = 3.8, range 30–44) at Wave 2. From the interviews in Wave 1, 1,761 women had valid data for SAD and AvPD; 445 monozygotic (MZ) female pairs, 256 dizygotic (DZ) female pairs and 359 single female twins (either from incomplete pairs or women from dizygotic opposite sex pairs). From the J Abnorm Psychol. Author manuscript; available in PMC 2017 January 01.
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interviews in Wave 2, 1,471 women had valid data for SAD and AvPD; 354 MZ, 174 DZ, and 415 single female twins. In addition, partially complete data were available for 15 women at Wave 1 and 11 women at Wave 2. Participants with partially complete data were included in the analyses. Ethics—Approval was received from The Norwegian Data Inspectorate and the Regional Committees for Medical and Health Research Ethics, and written informed consent was obtained from all participants after complete description of the study. Measures
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Avoidant personality disorder—The Structured Interview for DSM-IV Personality (SIDP-IV) (Pfohl, Blum, & Zimmerman, 1995) was used to assess PDs in both waves. We used a Norwegian version of SIDP-IV (Helgeland, Kjelsberg, & Torgersen, 2005). The DSM-IV criterion associated with each question was rated as 0 = “not present,” 1 = “subthreshold,” 2 = “present,” and 3 = “strongly present.” The SIDP-IV uses the “5-year rule,” meaning that behaviors, cognitions, and feelings that predominated for most of the past 5 years are considered to be representative of an individual’s personality. For the structural analyses, we used a dimensional approach to AvPD, constructing variables based on the number of endorsed criteria. Dimensional representations are often considered a better conceptualization of personality disorders than categorical diagnoses (Widiger & Samuel, 2005). To optimize statistical power, we used the number of subthreshold criteria (≥1), assuming that the risk for each trait was continuous and normally distributed, i.e., that the classification (0–3) represented different degrees of severity. This assumption has been evaluated using multiple threshold tests for each of the criteria (Reichborn-Kjennerud et al., 2007). We also constructed dichotomous diagnoses of AvPD according to DSM criteria in order to estimate prevalence rates. Interrater reliability at Wave 1 was assessed based on two raters scoring of 70 audiotaped interviews. Intraclass and polychoric correlations for the number of endorsed AvPD criteria at the subthreshold level were 0.96 and 0.97 respectively. At Wave 2, two interviewers rescored 95 audiotaped interviews. Intraclass and polchoric correlations for the number of endorsed criteria at subthreshold level were 0.84 and 0.92.
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Social anxiety disorder—Clinical DSM-IV diagnoses were assessed using the computerized Composite International Diagnostic Interview (CIDI) (Wittchen & Pfister, 1997) in Norwegian translation (Landheim, Bakken, & Vaglum, 2003). Criteria A to E of the DSM-IV were covered in the interview. Criterion F regarding individuals below age 18 and the exclusion criteria G and H were not covered in the interview. Criterion A was covered by a list of social situations and a list of anxiety symptoms elicited by the social situations. Each of these lists was summed. Criterion E on functional impairment and distress about having the disorder was covered with two questions. The recency of SAD symptoms was assessed in the interviews. Individuals with diagnosable symptoms within the last 5 years were considered to have the disorder. In addition, individuals who feared at least one social situation and who were worried about showing anxiety symptoms, but who did not otherwise satisfy diagnostic criteria, were considered to have subthreshold SAD. This definition has been used in previous studies (Czajkowski, Kendler, Tambs, Røysamb, & Reichborn-Kjennerud, 2011). Five-year occurrence was used to facilitate comparison with
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the AvPD diagnoses. Due to skipping patterns, it was not possible to rescore audiotaped interviews. However, CIDI has shown good test-retest and interrater reliability in other countries (Wittchen, Lachner, Wunderlich, & Pfister, 1998; Wittchen, 1994). Validity—Joint analyses of CIDI and SIDP-IV data in the present sample has shown a good factor structure at Wave 1 (Røysamb et al., 2011) and both measures predict disability (Torvik et al., 2014; Østby et al., 2014), indicating construct and predictive validity. Other validity studies of Norwegian versions of the interviews have not been conducted.
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Procedure—Interviewers at Wave 1 were mainly senior clinical psychology graduate students and experienced psychiatric nurses. Interviewers at Wave 2 included senior clinical psychology graduate students, psychiatric nurses, and experienced clinical psychologists who were interviewers at Wave 1. In Wave 1, most interviews were conducted face-to-face, and the rest (n = 231, 8.3%) by telephone. All interviews were conducted by telephone in Wave 2. Different interviewers assessed each twin in a pair. Statistical analyses Stability over the life-course was approached in two ways: absolute and relative stability (Morey & Hopwood, 2013). Absolute stability refers to the average changes of traits, indicated by means or prevalences. Relative stability refers to consistency in the rank ordering of individuals on a given trait over time. It was estimated as polychoric correlations between either SAD or AvPD at the two time points. Similarly, co-occurrence between SAD and AvPD was calculated as the percentage of individuals with one disorder who also had the other disorder and as polychoric correlations. To maximize power, the phenotypes under study here were ordinal representations of SAD and AvPD, including the subthreshold level.
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The factor structure of SAD and AvPD criteria was examined in exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), including bifactor analysis. The analyses were run on the seven symptoms for AvPD and the five criteria covered for SAD, where criterion A and E consisted of two variables each. Statistical dependency between co-twins and within individuals at the two time points was accounted for by a sandwich estimator, where twin pairs were defined as the first level and time as the second level. The factor analyses was run in Mplus 7.3 (Muthen & Muthen, 2012). The variables were analyzed as ordered categories. We used the maximum likelihood estimator with robust standard errors and oblique geomin rotation in the EFA.
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In the classical twin model (Martin & Eaves, 1977), individual differences in traits are assumed to arise from three latent sources: Additive genetic factors (A) shared 100% by MZ twins and 50% by DZ twins; common or shared environment (C), which includes environmental exposures contributing to twin similarity; and individual-specific or nonshared environment (E), which includes environmental factors that contribute to differences between the twins, plus measurement error. As our variables were ordinal, we used a liability-threshold model, assuming that ordered categories are indicators of unobserved, normally distributed liabilies (Falconer, 1965).
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The two main models of the relationship between the two disorders are shown in Figure 1. First, we tested the number of underlying factors. We specified a measurement model with two time-invariant latent risk factors (Figure 1, A). One time-invariant risk factor (SADC) influences SAD equally strongly at Wave 1 and Wave 2, whereas another time-invariant risk factor (AvPDC) influences AvPD equally strongly at Wave 1 and Wave 2. These latent risk factors are decomposed into genetic and environmental factors (ac1, ac2, cc1, cc2, ec1, ec2). In this model, the time-invariant risks for SAD and AvPD are influenced by different genetic and environmental factors, which may correlate (rAc, rCc, rEc). The model also includes time-specific influences on SAD and AvPD at Wave 1 and Wave 2 (As1…4, Cs1…4, Es1…4) and correlations between these time-specific influences (rAs31, rAs42, rCs31, rCs42, rEs31, rEs42). A competing model is the common pathway model with one latent risk factor (LC) influencing both SAD and AvPD at both waves (Figure 1, B). In this model, SAD and AvPD reflect the same underlying risk, but possibly of different magnitudes.
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Simpler, more restricted variants of the models were tested by removing influences from A or C. We tested whether the disorders were equally strongly influenced by genetic and environmental factors across time by setting time-specific influences to be equal in magnitude. We then tested whether the disorders were influenced by the same genetic factors at both waves by removing influences from time-specific genetic factors. Timespecific non-shared environmental factors cannot be removed because these include measurement error. Finally, we tested the magnitude of the correlations in the final model.
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The difference in −2 times log-likelihood (Δ-2LL) is asymptotically χ2 distributed, which allows testing for significant differences in χ2 for nested submodels. If the difference in χ2 is non-significant, a simpler, more restricted model is preferred. In addition, we used the Akaike Information Criterion (AIC) (Akaike, 1987) as an index of parsimony. Models with low AIC value are preferred. Sample size-adjusted Bayesian Information Criterion (BICSSA) is provided for reference (Sclove, 1987). A reduction in BICSSA value indicates improvement in model fit. The models were fitted to raw data using Full Information Maximum Likelihood (FIML) for categorical data as the estimation procedure in OpenMx 2.0 (Neale et al., 2015) for R 3.1.2 (R Core Team, 2014). The FIML method utilizes all data, from both complete and incomplete pairs, and provides better estimates in structural equation models than traditional missing data methods (Enders & Bandalos, 2001).
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Stability of SAD and AvPD The first aim was to assess the stability of SAD and AvPD from early to middle adulthood. Table 1 shows the occurrence of full and subthreshold SAD and AvPD at the two waves. Cross-tabulated counts of dichotomous diagnoses are provided in supplementary Tables S1 and S2. In early adulthood, the 5-year prevalence of SAD among women was 3.7%, which increased to 5.1% in middle adulthood (χ2 = 4.47, df = 1, p = .034). The prevalence was stable when subthreshold disorders were also included, at 13.8% and 13.7% at Wave 1 and Wave 2, respectively (χ2 = .02, df = 1, p = .899).
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The prevalence of AvPD was 2.5% at Wave 1 and 2.1% at Wave 2 (χ2 = .36, df = 1, p = . 546). The mean number of subthreshold AvPD criteria in the sample fell from 0.94 (SD = 1.40) at Wave 1 to 0.71 (SD = 1.30) at Wave 2 (χ2 = 38.66, df = 1, p < .001). The rank-order stability of SAD was r = .56 (95% CI [.48, to .65]). AvPD symptoms were approximately equally stable, at r = .54 (95% CI [.49, .59]). The difference in stability was not statistically significant (χ2 = 0.27, df = 1, p = .601). Figure 2 shows the correlations between dimensional representations of SAD and AvPD at Wave 1 and 2 and a cross-lagged model of the longitudinal relationship between the disorders. Association between SAD and AvPD
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The second aim was to assess the degree of co-occurrence of SAD and AvPD. At Wave 1, 31.8% of those with AvPD also had SAD, whereas 21.5% of those with SAD had AvPD. At Wave 2, 54.8% of those with AvPD also had SAD, whereas 22.7% of those with SAD also had AvPD. The polychoric correlation between dimensional SAD and AvPD was .49 (95% CI [.43, .55]) at Wave 1, which increased to .61 (95% CI [.55, .67]) at Wave 2 (χ2 = 7.08; df = 1; p = .008). Across time, SAD at Wave 1 predicted AvPD at Wave 2 (b = .29, 95% CI [. 18, .41]) approximately as strongly as AvPD at Wave 1 predicted SAD at Wave 2 (b = .19, 95% CI [.08, .31], Δχ2 = 1.27, Δdf = 1). Number of underlying factors
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We tested the number of latent factors underlying SAD and AvPD in factor analyses and in biometric models. EFA of the diagnostic criteria of SAD and AvPD yielded factors with eigenvalues of 5.29, 3.37, and 1.05. This suggests the presence of two or possibly three factors, whereas a solution with one factor did not fit the data well. The three-factor solution lacked theoretical foundation and included an uninterpretable factor with only one loading above .20 (on AvPD criterion 1). In the oblique two-factor solution, the AvPD symptoms loaded primarily on one factor and the SAD items on another. The two factors correlated .54 (95% CI [.42, .65]). All cross-loadings were minor. The two-factor solution is shown in Table 2. Additional details of the EFA are provided in the supplementary materials along with CFA and bifactor analyses (pages S2-S7). In brief, the CFA indicated better fit for a two-factor solution than for a unidimensional solution. The best fit was found for a bifactor model with one general factor and two group factors. The factor loadings were not in line with an interpretation that the group factors merely represented method variance. Moreover, the group factor for SAD correlated with a third condition (alcohol use disorder). The factor analyses therefore indicate that partially distinct factors underlie SAD and AvPD symptoms.
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The first step of the biometric modelling corresponds to the aim of investigating whether one or two underlying factors best account for the risk for SAD and AvPD. Correlations between co-twins on SAD and AvPD at the two time points are shown in Table 3, and model fit statistics are given in Table 4. The Cholesky decomposition (model 1) is shown as a comparison. Model 2 includes one time-invariant latent factor for SAD and one for AvPD, whereas both disorders reflect one latent factor in model 3. Model 2 with two latent factors fitted the data best, according to the χ2 and AIC value.
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Longitudinal biometric models
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We proceeded to examine the longitudinal biometric models of the relationship between SAD and AvPD, in accordance with the fourth aim. In the second step of Table 4, we compared model 2, which includes A, C, and E factors, to simpler models excluding C (model 4), A (model 5), or both (model 6) variance components. The AE model (model 4) provided the best fit in step 2. In the third step, we tested whether the relative influence of genetic and environmental factors changed from Wave 1 to Wave 2, by setting the timespecific influences to be equal across time (as1 = as2; as3 = as4; es1 = es2; es3 = es4). This led to further improvement of model fit (model 7), indicating that the heritability of the disorders was the same at both waves.
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We proceeded to test whether the same genetic factors accounted for the disorders at Wave 1 and Wave 2, by removing time-specific genetic factors. The time-specific genetic influences on SAD was estimated at .41 (95% CI [.25, .53]) in model 7. These could not be removed without a deterioration of the model fit (model 8). Removing the time-specific genetic influences on AvPD, estimated at .11 (95% CI [−.25, .25]), improved the model fit (model 9). Setting the correlation in environmental factors to be equal across time led to a worse fit, as did setting the correlations between genetic or environmental components of the timeinvariant traits to unity (step 5).
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The overall best fitting model (model 9) was the measurement model with two correlated time-invariant factors and time-specific genetic influences on SAD. This model is shown in Figure 3. Seventy-six percent of the time-invariant risk factors for SAD and AvPD were overlapping (r = .76, 95% CI [.71, .85]). The heritability of the time-invariant SAD factor was .66 (95% CI [.56, .86]) and the heritability of the corresponding AvPD factor was .71 (95% CI [.59, .79]). The genetic components of the time-invariant risk factors for SAD and AvPD correlated .84 (95% CI [.69, 1.00]). The heritability at the specific time points was lower, at .52 (95% CI [.40, .62]) for SAD and .38 (95% CI [.32, .43]) for AvPD. Thirty percent of the genetic variance in SAD (.30, 95% CI [.10, .49]) was unique to each time point. In total, the genetic influences on SAD and AvPD correlated .70 (95% CI [.58, .82]) at both Wave 1 and Wave 2.
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The correlation between environmental influences on the two time-invariant risk factors was .59 (95% CI [.23, .98]). The time-invariant environmental risk factors had modest environmentality and therefore contributed only modestly to the covariance between the two disorders. In addition, there were moderately strong effects of disorder-specific and timespecific non-shared environment, some of which is due to random measurement error. These environmental influences had a correlation of .19 (95% CI [.04, .36]) at Wave 1, and a correlation of .47 (95% CI [.30, .64]) at Wave 2, indicating that time-specific events influence the co-occurrence between SAD and AvPD.
Discussion Stability of SAD and AvPD SAD and AvPD were equally stable from early to middle adulthood, but the degree of stability was moderate for both disorders. The moderate stability of AvPD is at odds with J Abnorm Psychol. Author manuscript; available in PMC 2017 January 01.
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the conceptualization of PDs as stable and of long duration (American Psychiatric Association, 2013), but corresponds with other studies on the longitudinal course of PDs (Morey & Hopwood, 2013; Wright et al., 2013). The finding that SAD and AvPD were equally stable does not necessarily imply that either SAD or AvPD is misclassified as an anxiety or personality disorder, but rather adds additional support to studies indicating that stability is not a distinguishing feature of PDs (Krueger, 2005; Shea & Yen, 2003).
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The general decline in AvPD symptoms is in line with previous research (Lenzenweger, 1999; Shea et al., 2002; Wright et al., 2013), although one study found that AvPD symptoms increased over time (Seivewright, Tyrer, & Johnson, 2002). The increase in diagnosable SAD was unexpected. One possible reason could be that the second interview was conducted over the telephone. A previous study did not find that interview modality influenced prevalences estimates of SAD in telephone versus personal interviews (Crippa et al., 2008), although we cannot exclude that explanation in our study. It is not clear why interview modality should influence SAD and not AvPD. The increase seems, however, to reflect variation in full, but not subthreshold SAD, as the combined prevalence of SAD and subthreshold SAD was unchanged from Wave 1 to Wave 2.
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The biometric modelling suggested that the genetic influences on AvPD were identical across time. We also found high stability of genetic influences on SAD. These results are in line with previous studies finding that genetic effects on behavioral phenotypes are stable in adulthood (Nivard et al., 2015). Nevertheless, SAD was influenced by time-specific genetic variance not shared with AvPD. Thus, the genetic risk for SAD is to some extent dependent on age. Genetic innovation implies that genetic factors previously unrelated to a trait become active (Gillespie et al., 2004). Different factors with heritable components may influence SAD at different stages in life. For instance, one may speculate that this could be related to changes in career or family situation from young to middle adulthood. Although genetic innovation in anxiety in adulthood seems to be in conflict with Nivard et al. (2015), another study found genetic innovation in anxiety around age 30 (Gillespie et al., 2004). Further studies are needed to clarify whether there are age-specific genetic effects on SAD in adulthood. We also found that individual-specific environmental factors contributed moderately to the stability of SAD and AvPD, which is in line with previous results for anxiety in adult age (Gillespie et al., 2004; Nivard et al., 2015). Co-occurrence of SAD and AvPD
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The co-occurrence of SAD and AvPD was moderate. Many individuals with AvPD (approximately half to two thirds) did not fulfill the criteria for SAD, which indicates diagnostic differences. The relatively low overlap is consistent with results of other population based studies (Cox et al., 2009; Grant et al., 2005; Lampe et al., 2003), although the co-occurrence is stronger in clinical samples (Ralevski et al., 2005). Interestingly, the degree of co-occurrence between SAD and AvPD increased over time. A possible explanation for this change from early to middle adulthood is that direct causal influences may exist between SAD and AvPD and increase their co-occurrence over time. The increase in environmental correlation supports this explanation. Alternatively, the effect
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of environmental risk factors shared between SAD and AvPD become more prominent with increasing age. Underlying risk factors The factor analyses suggested a model in which the criteria for SAD and AvPD reflect distinct, but correlated constructs. In line with the factor analyses, the biometric analyses supported a model of separate, although highly correlated, risk factors for each disorder. The results did not support a model in which SAD and AvPD reflected a single latent factor. Taking into account that there were only moderate rates of SAD among individuals with AvPD, the current findings imply that there are qualitative differences between SAD and AvPD.
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Unlike the previous study in this sample (Reichborn-Kjennerud et al., 2007), we found that the genetic influences on SAD and AvPD were not identical, but highly correlated (r = 0.70). Moreover, environmental factors, both stable and time-specific, contributed to cooccurrence of the disorders. In the previous study, environmental factors did not overlap. These differences in results are most likely due to higher power in the present study by inclusion of subthreshold disorders and follow-up data. The high heritability of the timeinvariant risk factors compared to the disorders at the specific time points follows from the time-invariant risk factors being free from measurement error. In our biometric analyses, random measurement error is part of the time-specific environment. As we found differences in the time-invariant environment and in the genetic components of SAD and AvPD, it is implausible that the observed difference between SAD and AvPD merely reflects measurement error associated with each interview. The bifactor models supported this interpretation.
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The unique genetic and environmental components of AvPD indicate that AvPD is not merely a severe variant of SAD. The factor analyses also indicate that SAD and AvPD reflect distinct constructs. One possible explanation is that the AvPD diagnosis encompasses psychopathology that is not included in the SAD diagnosis. For example, schizophrenia and AvPD symptoms aggregate in the same families (e.g., Fogelson et al., 2010). The unique genetic component of AvPD may therefore be linked to the schizophrenia spectrum. It has also been proposed that AvPD represents anhedonic introversion whereas SAD represents an internalizing factor (Røysamb et al., 2011). Another possibility is that the unique components of AvPD reflect a general risk factor for personality pathology, as underlying risk factors for are shared between various PDs (Kendler, Aggen, et al., 2008). Difficulties in interpersonal functioning is a hallmark feature of PDs, and such difficulties may distinguish AvPD from SAD (Eikenaes, Hummelen, Abrahamsen, Andrea, & Wilberg, 2013; Hummelen et al., 2007). Thus, akin to a pathoplastic model, it may be that one common disorder is expressed and recognized as SAD, AvPD, or both, depending on the patient’s personality traits (c.f. Widiger & Smith, 2008). Despite partially different risk factors for SAD and AvPD, we found that the risk factors were shared to a large degree, like in previous studies (Huppert et al., 2008; Isomura et al., 2015; Lampe & Sunderland, 2015). It is plausible that both disorders reflect risk factors associated with temperamental traits such as behavioral inhibition, and this may be the J Abnorm Psychol. Author manuscript; available in PMC 2017 January 01.
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reason the two diagnoses are strongly related (Biederman et al., 2001). Moreover, a possible source of covariance between SAD and AvPD may be overlap in the diagnostic criteria. DSM-III based descriptions of AvPD included aspects of non-social avoidant behavior, whereas the diagnostic criteria in DSM-IV and DSM-5 focus exclusively on social fears (Millon & Martinez, 1995). In addition, the generalized social phobia subtype, introduced with the DSM-III-R, led to increased co-occurrence with AvPD (Alden, Laposa, Taylor, & Ryder, 2002). The lack of focus on the non-social aspects of AvPD may have decreased the discriminant validity of AvPD with SAD (Taylor, Laposa, & Alden, 2004). In order to provide optimal treatment to individuals with SAD and AvPD, future research needs to identify unique aspects of these disorders, as well as their common features. This may include non-social fears, functioning in close relationships, and normative or maladaptive personality traits. The clinical utility of emphasizing unique features also needs to be evaluated. The presence of risk factors shared by the two disorders indicates that there is a potential for treatment or prevention of the disorders by similar interventions.
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Limitations
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The strengths of the present study include structured diagnostic interviews, longitudinal follow-up, and a population based twin sample. However, some limitations must be mentioned: First, only women were included in the analyses due to low prevalences of SAD and AvPD in men. Most community studies find SAD to be more common among women than among men (e.g., Furmark et al., 1999). The gender differences are usually not as pronounced as those observed in the present study and may be specific to the sample. The findings are therefore not necessarily valid for men. Nevertheless, in one previous study it was concluded that the genetic and environmental structure of anxiety disorders was similar among men and women (Hettema, Prescott, Myers, Neale, & Kendler, 2005). Second, we studied young Norwegian adults, and generalization may be limited to individuals of similar age and ethnic background as the participants. Third, there was modest attrition from Wave 1 to Wave 2 (see supplementary materials, pages S7). SAD at Wave 1 was not associated with attrition to Wave 2. Symptoms of AvPD were associated with reduced participation at Wave 2, but the tendency was modest, and we believe not likely to bias the results of this study, as the co-occurrence of the disorders was similar among dropouts and continuous participants. Fourth, due to a limited number of SAD cases, we could not distinguish between generalized and performance-only SAD. Fifth, the use of 5 year diagnoses of SAD is not common. Our prevalence estimates of SAD are not directly comparable to other studies, typically using either lifetime or 12-month prevalences. The rationale for applying 5-year diagnoses was to have SAD and AvPD measured during the same time-period. Sixth, due to a limited sample size, we had relatively low power to detect effects of shared environmental factors. Although there were relatively small differences between certain MZ and DZ twin correlations, models including genetic, but not shared environmental factors had better fit than models including shared environmental, but not genetic factors. Likewise, previous studies also indicate that SAD and AvPD are heritable (Isomura et al., 2015; Scaini, Belotti, & Ogliari, 2014), and shared environmental effects on anxiety disorders have consistently been found to diminish from childhood to adult age (Kendler, Gardner, et al., 2008; Smoller, Block, & Young, 2009). The use of repeated measures increased statistical power in the biometric analyses, with more precise estimates of influences on the latent
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factors than on each observation. The low number of SAD and AvPD cases can also otherwise contribute to uncertain model estimates. The use of ordinal variables should partially counteract this. Conclusion AvPD and SAD were approximately equally stable over a 10-year period from early to middle adulthood, with increasing co-occurrence over time driven by environmental factors. Factor analyses of the diagnostic criteria suggested a two-factor structure with low crossloadings. The two disorders were best explained by highly correlated, but distinct risk factors. The distinct risk factors indicate the presence of qualitative differences in the etiology of SAD and AvPD. Genetic factors represented the strongest time-invariant influences, whereas environmental factors were most important at the specific points in time.
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Supplementary Material Refer to Web version on PubMed Central for supplementary material.
Acknowledgments This project was supported by the Research Council of Norway (RCN) (grant 196148/V50) and the National Institutes of Health (grant DA037558). Previous collections and analyses of twin data from this project were in part supported by the National Institutes of Health (grant MH-068643) and grants from the RCN, the Norwegian Foundation for Health and Rehabilitation, and the Norwegian Council for Mental Health.
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Figure 1.
(A) Measurement model with two correlated time-invariant latent risk factors and (B) common pathway model with one time-invariant latent risk factor. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder.
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Figure 2.
Phenotypic polychoric correlations (left) and cross-lagged associations (right) between dimensional representations of social anxiety disorder (SAD) and avoidant personality disorder (AvPD) the last 5 years at Wave 1 and Wave 2, including 95% confidence intervals.
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Figure 3.
Best fitting model (model 9), with parameter estimates and 95% confidence intervals. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder.
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Table 1
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Prevalence of SAD and AvPD, including subthreshold disorders, last 5 years. Wave 1 n (%)
Wave 2 n (%)
1523 (86.2)
1271 (86.3)
179 (10.1)
127 (8.6)
65 (3.7)
75 (5.1)
1724 (97.5)
1449 (97.9)
45 (2.5)
31 (2.1)
0
969 (54.8)
981 (66.3)
1
348 (19.7)
238 (16.1)
2
215 (12.2)
116 (7.8)
3
115 (6.5)
54 (3.7)
4
53 (3.0)
50 (3.4)
5 or more
70 (4.0)
41 (2.8)
1776 (100.0)
1482 (100.0)
Social anxiety disorder No SAD Subthreshold SAD AvPD diagnosis Not present Present Subthreshold AvPD criteria
Author Manuscript
Total n
Note. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder.
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Table 2
Author Manuscript
Geomin rotated two-factor solution of SAD and AvPD diagnostic criteria.
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Factor 1
Factor 2
AvPD, 1: Avoids occupational activities that involve significant interpersonal contact, because of fears of criticism, disapproval, or rejection
.69*
.22*
AvPD, 2: Is unwilling to get involved with people unless certain of being liked
.75*
.18*
AvPD, 3: Shows restraint within intimate relationships because of the fear of being shamed or ridiculed
.55*
.19*
AvPD, 4: Is preoccupied with being criticized or rejected in social situations
.65*
.20*
AvPD, 5: Is inhibited in new interpersonal situations because of feelings of inadequacy
.85*
−.02
AvPD, 6: Views self as socially inept, personally unappealing, or inferior to others
.87*
−.04
AvPD, 7: Is unusually reluctant to take personal risks or to engage in any new activities because they may prove embarrassing
.70*
.00
SAD, A (D20): Marked and persistent fear of one or more social or performance situations
.19*
.72*
SAD, A (D22): Fears that he or she will act in a way that will be humiliating or embarrassing
.05
.68*
SAD, B (D23): Exposure to the feared social situation almost invariably provokes anxiety
.15
.52*
SAD, C (D25): The person recognizes that the fear is excessive or unreasonable
.00
.67*
SAD, D (D24): The feared social or performance situations are avoided or else are endured with intense anxiety or distress.
.14*
.30*
SAD, E (D29): Interferes significantly with the person’s normal routine
−.00
.85*
SAD, E (D25P): Marked distress about having the phobia
−.04
.95*
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Note. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder. Codes in parenthesis indicate section and number of questions in the Composite International Diagnostic Interview (CIDI). Correlation between factor 1 and 2 is .54 (95% CI [.42, .65]). *
p < .05.
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Table 3
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Co-twin correlations of dimensional representations of SAD and AvPD at Wave 1 and Wave 2 among monozygotic and dizygotic twins, including 95% confidence intervals.
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Monozygotic twins
Twin A, SAD Wave 1
Twin A, SAD Wave 2
Twin A, AvPD Wave 1
Twin A, AvPD Wave 2
Twin B, SAD Wave 1
.51 [.37, .66]
.29 [.09, .49]
.40 [.27, .53]
.31 [.16, .47]
Twin B, SAD Wave 2
.35 [.15, .54]
.53 [.37, .70]
.34 [.19, .49]
.35 [.18, .51]
Twin B, AvPD Wave 1
.16 [.01, .30]
.26 [.10, .42]
.38 [.28, .48]
.38 [.26, .49]
Twin B, AvPD Wave 2
.26 [.10, .43]
.39 [.23, .55]
.41 [.29, .52]
.35 [.22, .48]
Dizygotic twins
Twin A, SAD Wave 1
Twin A, SAD Wave 2
Twin A, AvPD Wave 1
Twin A, AvPD Wave 2
Twin B, SAD Wave 1
.37 [.11, .63]
.27 [−.05, .58]
.20 [−.03, .42]
.19 [−.09, .47]
Twin B, SAD Wave 2
.41 [.14, .68]
.40 [.11, .69]
.11 [−.12, .35]
.18 [−.10, .45]
Twin B, AvPD Wave 1
.45 [.29, .62]
.25 [.03, .47]
.30 [.16, .44]
.26 [.08, .44]
Twin B, AvPD Wave 2
.45 [.25, .66]
.33 [.10, .55]
.24 [.07, .41]
.32 [.13, .51]
Note. SAD = Social anxiety disorder. AvPD = Avoidant personality disorder.
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Author Manuscript
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Model 2 + Drop C and A (E model)
6
Model 9 + Time-invariant A correlation = 1
Model 9 + Time-invariant E correlation = 1
11
12
12.76
11.97 20
20
20
19
19
16
21
12
12
8
3
-
Δdf
.042
.067
.010
.692
.026
.997
