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ScienceDirect Comprehensive Psychiatry 58 (2015) 11 – 17 www.elsevier.com/locate/comppsych
Are personality disorder dimensions related over time? An examination over the course of ten years using multivariate growth modeling Rachel Hershenberg a, b,⁎, Thomas M. Olino c , Margaret W. Dyson d , Joanne Davila e , Daniel N. Klein e a
VISN 4 Mental Illness Research, Education and Clinical Center at Philadelphia VA Medical Center, Philadelphia PA, USA b Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA c Department of Psychology, Temple University, Philadelphia, PA, USA d Department of Psychiatry, University of California, San Diego, CA, USA e Department of Psychology, Stony Brook University, Stony Brook, NY, USA
Abstract Objective: Despite the well-documented literature on cross-sectional comorbidity, there is a paucity of data on the associations between personality disorders (PDs) over time. Using multivariate growth modeling, the present study examined the inter-relationships between pairs of PD disorder dimensions. Methods: We tested these associations in a sample of 142 depressed outpatients followed-up five times over the course of 10 years. Results: We found cross-sectional associations between the initial levels of severity of many pairs of PD dimensions. However, there was limited support for longitudinal associations between PD dimensions. Conclusion: These findings suggest that the course of PD dimensions is fairly independent of each other, and that initial levels of PD dimensions have relatively little prognostic value for predicting the course of other PD dimensions. Published by Elsevier Inc.
1. Introduction There are considerable data on the cross-sectional cooccurrence between personality disorders (PDs). Indeed, the co-occurrence of categorical PDs appears to be the rule rather than the exception . Cross-sectional studies of large samples of inpatients [e.g., 2], mixed inpatients and outpatients , outpatients [4,5] and community participants [6,7] reveal that over 50% of those meeting criteria for one PD will also meet criteria for two or more PDs. Overall, borderline, narcissistic, schizotypal, and paranoid PDs demonstrate the highest rates of co-occurrence with the other PDs, whereas histrionic, avoidant, and obsessive ⁎ Corresponding author at: VISN 4 MIRECC, Philadelphia VA Medical Center, 3900 Woodland Ave, Philadelphia PA 19104. Tel.: +1 215 823 5800x2390. E-mail addresses: [email protected]
(R. Hershenberg), [email protected]
(T.M. Olino), [email protected]
(M.W. Dyson), [email protected]
(J. Davila), [email protected]
(D.N. Klein). http://dx.doi.org/10.1016/j.comppsych.2014.12.002 0010-440X/Published by Elsevier Inc.
compulsive tend to demonstrate the lowest rates of cooccurrence [2,4–6]. Research has also examined the longitudinal stability and course of PDs [e.g., 8], as well as the relations between PDs and other disorders over time [e.g., 9,10]. However, given the large literature on cross-sectional comorbidity, there is a surprising absence of data on the relation between PDs over time. For example, we are not aware of any research investigating whether initial levels or change in one PD (e.g., borderline) have prognostic utility for predicting change in another PD (e.g., dependent). Examining the longitudinal associations between different PDs would provide prognostic information as well as implications for the conceptualization and classification of PDs. Thus, the purpose of the present study was to examine the inter-relationship between pairs of PDs in a sample of depressed outpatients over the course of 10 years. The current DSM classification, with 10 separate PDs, implies that the course of individual PDs should exhibit some degree of independence. Accordingly, given evidence that individual PDs are likely to change over time [8,11],
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they should do so relatively independently of one another. From this perspective, we would not expect substantial covariation between the PDs over time. However, consistent with the high rates of comorbidity, a number of studies have shown that the PDs can be described by a smaller number of underlying trait dimensions [12,13]. For example, five of the ten disorders (paranoid, schizotypal, borderline, avoidant, and dependent) demonstrate consistently positive associations with neuroticism, and five (paranoid, schizotypal, borderline, antisocial, and narcissistic) exhibit negative associations with agreeableness . Similarly, there is also considerable overlap between PDs at the level of personality trait facets . For example, within neuroticism, paranoid and schizotypal share high levels of anxiousness, angry hostility, depressiveness, self-consciousness, and vulnerability and, within agreeableness, low levels of trust . 1 As normal-range personality traits change over the lifespan , we might expect PDs that share similar traits and facets to covary longitudinally, waxing and waning in concert over time. From this perspective, we would expect particularly close longitudinal associations between pairs of PDs that share common trait variance, such as paranoid and schizotypal, paranoid and borderline, and schizotypal and borderline PDs. 1.1. Present study The current study sought to evaluate whether the longitudinal trajectories of pairs of PD dimensions were linked by using multivariate growth curve models. We used dimensional symptom scores rather than categorical diagnoses given evidence that dimensional scores have greater inter-rater reliability , stability over time , concurrent validity when compared to informants' reports , and predictive validity with respect to external criteria . Although our sample was limited to depressed patients, depressive disorders are the most common axis I condition in outpatient settings  and exhibit very high rates of PD comorbidity . Multivariate latent growth models (LGMs) can estimate the longitudinal trajectories of two outcomes simultaneously. In doing so, this allows for testing if the initial level (i.e., intercept) and rate of change (i.e., slope) in one variable are related to these characteristics in the other. In the case of PD dimensions, multivariate growth models allow for the testing of three sets of relationships. First, LGM examines the associations between intercepts for pairs of PD dimensions, which tests if the baseline severity of symptoms of one PD dimension is associated with the initial severity of the other PD dimension. Second, LGM examines the associations between slopes for pairs of PDs, which tests if the rate of 1 Because so many comparisons between PDs at the facet level emerged as statistically significant in the Samuel & Widiger (2008) metaanalysis  (due to the large number of studies and high total n of the samples), consistent with the author's interpretations, we provided examples of disorders that shared correlations of .20 or higher.
change in one PD symptom dimension is related to the rate of change in another. Third, LGM examines the associations between the intercept of one PD dimension and the slope of another PD dimension, which tests if the initial level of symptoms of one PD is associated with change in another PD over time. Thus, LGM offers a powerful analytic technique for the study of change across time in pairs of PD dimensions. We examined the longitudinal relationships between pairs of PD dimensions in 142 outpatients with depressive disorders who were assessed 5 times over the course of 10 years. On the one hand, the traditional DSM model would predict that the course of individual PD symptom dimensions would be at least partially independent from one another. On the other hand, a trait model of PDs, such as the model included in DSM-5 section 3 , would predict strong positive longitudinal associations between many pairs of dimensional PD scores due to shared underlying trait dimensions, such as paranoid and schizotypal, paranoid and borderline, and schizotypal and borderline. 2. Method 2.1. Participants The original sample consisted of 142 (72.5% female) outpatients with major depressive disorder (MDD) and/or dysthymic disorder (DD) according to the Diagnostic and Statistical Manual of Mental Disorders [3rd ed., rev.; DSMIII-R; 22]. Participants were between the ages of 18 and 60 years, and the mean age of the participants was 32.0 years (SD = 9.2). The sample was predominately Caucasian (89.4%), middle-class, English-speaking, and had an average of 13.5 years of education. At the baseline assessment, 30.3% of the participants were married, 21.8% were separated or divorced, 46.5% had never been married, and 1.4% were widowed. No participants were currently psychotic nor had ever been psychotic outside of an MDD episode. Recruitment procedures have been described in greater detail elsewhere [see 8]. At the baseline assessment, DSM-III-R diagnoses included: MDD without lifetime DD (31.7%), DD without current MDD (29.9%), and double depression (DD with superimposed MDD episode) (39.4%). The mean Global Assessment Functioning [GAF; 23] score was 56.6 (SD = 10.6). Four follow-up assessments were conducted over a period of 10 years with the following rates of participation: 108 (76.1%) of the participants participated in the 2.5-year follow-up, 111 (78.2%) at 5-year follow-up, 109 (76.8%) at 7.5-year follow-up, and 101 (71.1%) at 10-year follow-up. Overall, of the original sample of 142 patients, 125 (88.0%) had at least one follow-up assessment. 2.2. Procedures 2.2.1. Baseline assessment At the initial assessment, participants were assessed using the Structured Clinical Interview for DSM-III-R [SCID; 23]
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and the Personality Disorder Examination [PDE; 24]. All interviews were conducted by a licensed clinical psychologist, master's level psychologist, master's level social worker, advanced graduate students in clinical psychology, and a doctoral level clinical psychology research fellow. The PDE is a semi-structured interview that assesses DSM-III-R PD diagnoses and dimensional scores. The PDE assigns probable diagnoses when patients fall one symptom short of meeting full criteria. In this sample, across the five assessments, the median proportion of participants with definite or probable PD diagnoses was as follows: paranoid (12%), schizoid (3.7%), schizotypal (5.5%), antisocial (3.0%), borderline (11.9%), histrionic (14.8%), narcissistic (4.2%), avoidant (22.5%), dependent (5.5%), and obsessivecompulsive (6.3%) (see  for rates of each PD at each assessment wave). PDE dimensional scores are composed of summed ratings (0–2 scale) for each PD feature. Thus, scores can range from 0 to 14 (for PDs with 7 criteria) and 0 to 18 (for PDs with 9 criteria). For antisocial personality disorder, child conduct disorder items were not included, as those could not change over time. To assess interrater reliability, a second interviewer independently rated 20 videotaped PDE interviews; intraclass correlations (ICCs) for dimensional PD scores ranged from .72 to .92, with a median of .84. 2.2.2. Follow-up assessments During all follow-up assessments (i.e., 2.5, 5, 7.5, 10 years), participants were re-evaluated with the PDE. All interviewers were blind to baseline assessments in order to reduce bias. Again, to assess interrater reliability, a second interviewer independently rated 20 videotaped PDE interviews drawn from several follow-up waves; there was no effort to include the same patients who were used in the reliability estimates for the baseline assessment. Ratings were based on the preceding 30 month interval. ICCs for dimensional PD scores ranged from .76 to .90, with a median of .84. 2.3. Data analytic plan We took a model building approach, beginning with estimating growth trajectories of individual PD symptoms and then estimating multivariate (i.e., parallel process) growth models. All models were estimated using Mplus 7.2 . Growth models were estimated in a multilevel framework and random effects for the intercept and slope parameters were specified. Analyses were implemented using the TWOLEVEL and RANDOM options in Mplus. Previous simulation work  reports that stable model estimation requires at least 50 clustering units (i.e., participants with repeated longitudinal measurements). Analyses relied on the MLF estimator due to one of the PD dimensions (Antisocial PD) yielding model estimation problems using the robust maximum likelihood estimator. Thus, to maintain parallel treatment across all outcomes, we used MLF for all analyses. The MLF estimator yields
maximum likelihood estimates with standard errors approximated by first-order derivatives and a typical chi-square test statistic . This method implements full information maximum likelihood estimation that uses all available data to produce model parameters. This process yields results identical to those obtained by multiple imputation . Unconditional growth models were estimated for each disorder individually. All models began with fixed and random effects for linear, quadratic, and cubic effects on the course of disorder symptoms. Time was centered such that the baseline assessment was t = 0. Since follow-up assessments continue through 120 months, time since baseline for each assessment was estimated as time since baseline divided by 120 to make computations less problematic. Non-significant terms were trimmed from the model beginning with the random effects for the growth parameters followed by the fixed effects. In the interest of parsimony of presentation, we only provide information for the final models (i.e., on the fixed effects that remained in the models and whether the corresponding random effects were significant). As our interest here is in the relationships between changes in PD symptoms over time, bivariate growth models were only estimated for pairs of PDs that demonstrated significant variability on at least one slope parameter. Thus, we did not examine models including PD dimensions that did not demonstrate significant individual differences in rates of change. Models were specified to include covariance paths between intercept and slope parameters for the included PD dimensions. Models also included within-time associations between PD symptoms. To provide effect sizes, we estimated correlations with the following formula: = √(t 2)/(t 2 + df) .
3. Results Table 1 displays the means and standard deviations among PD dimensional scores for the baseline and all follow-up assessments. We summarize the inter-correlations among PD dimensional scores in two ways. First, we computed the mean intercorrelation among all PDs at each time point. The means ranged from .32 (SD = .21) at the baseline assessment to .38 (SD = .16) at the 60-month follow-up assessment. Thus, the general associations between PD dimensional scores were similar across time. However, this does not incorporate information about the absolute level of agreement across time. Thus, second, we estimated the intra-class correlation for the associations between pairs of disorders at each wave across all waves of assessments. By doing so, we evaluated the absolute consistency of the associations for pairs of disorders over the course of the follow-up. The intra-class correlation for consistency in correlations across assessments was .77. This indicates that the observed correlations between pairs of disorders were consistent over time.
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Table 1 Means and standard deviations for dimensional scores for 10 personality disorders (PDs).
3.2. Bivariate growth models: inter-relationships between PD dimensions over time
Based on the results of the univariate growth models, we estimated 21 bivariate growth models. The key parameters in these models were the cross-disorder associations among and between intercept and slope parameters. Correlation coefficients between growth parameters from bivariate growth models are presented in Table 3.
Paranoid Schizoid Schizotypal Borderline Narcissistic Histrionic Antisocial Avoidant Dependent OCPD
3.05 1.47 2.84 5.70 2.54 3.06 2.27 4.02 4.43 3.54
3.11 2.01 2.89 4.02 2.75 3.24 2.88 3.38 3.47 3.02
2.50 1.70 1.97 3.46 1.79 2.26 1.38 3.02 2.57 2.77
2.75 2.39 2.07 3.42 2.04 2.72 2.12 2.91 2.52 2.44
2.42 1.87 2.76 3.97 2.40 2.64 1.38 3.55 3.00 3.28
3.00 2.06 2.92 3.99 2.80 3.14 2.35 3.34 3.11 3.50
2.21 2.04 3.37 4.19 2.63 3.51 1.23 4.21 3.88 3.60
2.66 2.12 2.93 3.56 2.87 3.61 1.72 3.42 3.17 3.10
1.79 1.34 2.30 4.21 2.09 2.43 .97 3.84 3.10 2.29
2.37 1.47 2.22 3.56 2.41 2.75 1.70 3.19 3.04 2.35
OCPD = obsessive-compulsive personality disorder.
3.1. Unconditional growth models With the exception of schizotypal PD symptoms, all other PD dimensions demonstrated significant individual differences in initial levels of PD symptoms (Table 2). In addition, with the exceptions of narcissistic and schizotypal PD symptoms, all other dimensions of PDs demonstrated significant mean-level linear changes over the course of the study. We also found significant individual differences in rates of linear change for seven of the ten PD dimensions. Narcissistic, obsessive-compulsive, and schizoid PD dimensions did not demonstrate significant individual differences in slopes. Significant quadratic change was found for avoidant, borderline, dependent, and schizoid PDs, and quadratic and cubic change was found for histrionic PD. However, individual differences in these higher-order slope terms were nonsignificant. Thus, we proceeded to fit bivariate growth models for all pairs of antisocial, avoidant, borderline, dependent, histrionic, paranoid, and schizotypal PD dimensions.
3.2.1. Intercept-intercept associations As shown in Table 3, most models (15 of 21; 71.4%) found significant associations between the intercept terms. This indicates that, at baseline, there were notable cross-sectional associations between PD symptom dimensions. 3.2.2. Slope-slope associations We found less evidence for associations between rates of change in PD symptoms over time (see Table 3). Overall, four (of 21; 19.0%) of the models demonstrated that associations in one symptom dimension were associated with changes in another symptom dimension at a significant level. Specifically, rate of change in avoidant PD symptoms was associated with rates of change in borderline and schizotypal PD symptoms. Rate of change in borderline PD features was associated with rate of change in dependent PD features. Finally, rate of change in paranoid PD symptoms was associated with rate of change in schizotypal PD symptoms. This indicates that rates of change in some, but not most PD dimensions, are associated. 3.2.3. Intercept-slope associations Finally, these models examined whether the initial level of symptoms of one PD symptom dimension predicted the rate of change in another PD symptom dimension over time.
Table 2 Unconditional univariate growth modeling results for personality disorders. Intercept Mean Est ASPD AVD BPD DPD HPD NPD OCPD PPD SZD STPD
3.46⁎⁎⁎ 3.85⁎⁎⁎ 5.40⁎⁎⁎ 4.08⁎⁎⁎ 2.95⁎⁎⁎ 2.37⁎⁎⁎ 3.41⁎⁎⁎ 3.09⁎⁎⁎ 1.53⁎⁎⁎ 2.73⁎⁎⁎
Linear slope Variance component
.60 .36 .43 .32 .43 .28 .31 .37 .27 .32
9.95⁎⁎⁎ 7.06⁎⁎⁎ 10.27⁎⁎⁎ 6.19⁎⁎⁎ 6.63⁎⁎⁎ 2.88⁎⁎⁎ 3.64⁎⁎⁎ 6.08⁎⁎⁎ 2.42 4.87⁎⁎⁎
2.52 1.18 2.07 1.29 1.44 .49 .68 1.23 .38 1.03
−2.91⁎⁎ −1.54⁎ −5.31⁎⁎⁎ −3.78⁎⁎⁎ −5.42⁎⁎ – −.53⁎ −1.19⁎⁎⁎ 1.86⁎⁎ .19
Quadratic slope a
Cubic slope a
Variance component SE
.85 .78 .93 .89 1.72 – .26 .33 .57 .28
8.84⁎ 3.71⁎ 4.69⁎⁎ 5.62⁎⁎ 2.70⁎ – – 2.89⁎⁎ – 2.29⁎
4.50 1.58 1.64 2.08 1.06 – – 1.03 – 1.11
– 1.97⁎⁎ 4.26⁎⁎⁎ 3.25⁎⁎⁎ 14.56⁎⁎⁎ – – – −1.68⁎⁎ –
– .75 .98 .87 4.18 – – – .59 –
– – –
– – –
−9.43⁎⁎⁎ – – – – –
2.63 – – – – –
−.24⁎⁎ .07 −.22 −.21 −.13 – – -.22⁎⁎ – .12
ASPD = antisocial; AVD = avoidant; BPD = borderline; DPD = dependent; HPD = histrionic; NPD = narcissistic; OCPD = obsessive compulsive; PPD = paranoid; SZD = schizoid; STPD = schizotypal. a There were no significant random effects for the quadratic or cubic slope terms in any of the models examined. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.
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Table 3 Bivariate growth models examining associations between growth parameters. PD 1 ASPD ASPD ASPD ASPD ASPD ASPD AVD AVD AVD AVD AVD BPD BPD BPD BPD DPD DPD DPD HPD HPD PPD
PD I1 & PD S1
PD I2 & PD S2
PD I1 & PD I2
PD S1 & PD S2
PD I1 & PD S2
PD I2 & PD S1
AVD BPD DPD HPD PPD STPD BPD DPD HPD PPD STPD DPD HPD PPD STPD HPD PPD STPD PPD STPD STPD
−.23⁎⁎ −.17⁎ −.21⁎ −.18⁎ −.21⁎ −.20⁎ −0.08 −0.07 −0.08 −0.05 −0.07 −0.14 −0.12 −0.13 −0.13 −0.15 −.22⁎⁎ −0.15 −0.12 −0.12 −.24⁎⁎
−0.07 −0.14 −0.15 −0.12 −.23⁎⁎ −0.13 −.17⁎ −0.14 −0.11 −.24⁎⁎ −0.13 −0.15 −0.13 −.23⁎⁎ −0.13 −0.11 −0.13 −0.13 −.23⁎⁎ −0.14 −0.12
0.03 .25⁎⁎ 0.01 0.13 −.21⁎ .16⁎ .28⁎⁎⁎ .28⁎⁎⁎ 0.07 .23⁎⁎ .32⁎⁎⁎ .28⁎⁎ .47⁎⁎⁎ .32⁎⁎⁎ .25⁎⁎ .24⁎⁎ .18⁎ 0.15 .28⁎⁎⁎ 0 .33⁎⁎⁎
−0.07 0.01 −0.08 −0.01 0.03 −0.03 .19⁎ 0.13 0.09 0.09 .21⁎ .18⁎ .16 † 0.11 0.04 0.12 0.03 0.02 0.13 −0.11 .18⁎
0.11 0 0.15 0.01 −0.07 0.02 −0.08 −0.08 −0.06 −0.09 −0.11 −0.08 −0.1 −.23⁎⁎ −0.07 −0.05 −0.01 0.06 −.25⁎⁎ 0.08 −0.11
−0.01 −0.14 0.02 −0.04 −0.08 −0.07 −0.01 0.01 0.08 0.06 −0.12 −0.12 −0.13 0.01 0.04 −0.05 −0.02 −0.01 −0.08 0.12 −.19⁎
The first two columns (PD 1 & PD 2) identify the two personality disorder (PD) symptom dimensions that are included in the bivariate growth models. Headings PD XY indicate the PD number and parameter in that column with X indicating PD 1 or PD 2 and Y indicating the intercept (I) parameter and linear slope (S) parameter. Only PDs with significant variability in linear slopes were examined. Statistics presented in the body of the table are correlation coefficients (r). ASPD = antisocial; AVD = avoidant; BPD = borderline; DPD = dependent; HPD = histrionic; PPD = paranoid; STPD = schizotypal. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001. † p b .10.
Each model estimated two parameters for this question. These were the associations between the intercept for one disorder and the slope of the other disorder, and between the intercept for the other disorder and the slope of the first disorder. In these models, very few (3 of 42; 7.1%) of these parameters were significant (see Table 3). However, the significant findings were consistent. Initial borderline, histrionic, and schizotypal symptoms were each associated with rate of change in paranoid symptoms over time. The direction of these associations demonstrated that higher levels of symptoms were associated with faster rates of symptom reductions in paranoid PD over time.
4. Discussion The present study is the first, to our knowledge, to examine the interrelationships between the PDs over time. This is surprising, given the large literature documenting substantial cross-sectional associations between PDs, whether assessed as categories or dimensions. We next review key findings across the three types of associations examined in our study. First, with regard to the intercept-intercept associations, our findings were consistent with evidence for the high rates
of co-variation between PDs measured cross-sectionally [e.g., 4,5]. Such robust rates of co-occurrence may be a reflection of a general PD severity dimension  or several underlying trait dimensions [12,13]. Second, we examined if the rates of change in pairs of dimensional PDs were systematically related to one another by testing the slope-slope associations. Overall, support for slope-slope associations was relatively low, as only 19% of the potential pairs of PD dimensional scores demonstrated significant relationships. Thus, it appears that rates of change in PDs over time are largely independent. We had expected that associations would emerge between pairs of disorder dimensions sharing similar traits and facets, such as paranoid and schizotypal, paranoid and borderline, and schizotypal and borderline. Of these three pairs, only a positive slope-slope association emerged between paranoid and schizotypal. Consistent with trait models of PDs [14,15], we also observed positive slope-slope associations between avoidant and borderline, which share five of the six neuroticism facets (anxiousness, angry hostility, depressiveness, self-consciousness, vulnerability), one agreeableness facet (low trust), and two conscientiousness facets (low competence and low self-discipline). Similarly, positive slope-slope associations emerged between borderline and dependent dimensions, which share high anxiousness, depressiveness, self-consciousness, and vulnerability facets
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within neuroticism and low competence and self-discipline within conscientiousness. Finally, we observed associations between avoidant and schizotypal, which also share five of six neuroticism facets (anxiousness, angry hostility, depressiveness, self-consciousness, vulnerability), low warmth, gregariousness, and positive emotions within extraversion, and low trust within agreeableness. Thus, the pairs of PD dimensions with significantly associated trajectories are related at both the trait and facet levels. However, the number of pairs with correlated trajectories was quite small, and there are many other pairs of PD dimensions with overlapping traits and facets that did not have correlated slopes. Overall, then, it appears that the rate of change over time in various PDs is largely independent. Last, we tested the extent to which initial severity levels of one PD dimension predicted rate of change in other PD dimensions. Less than 10% of the potential pairs of disorder dimensions demonstrated significant intercept-slope associations. Thus, consistent with the relatively few slope-slope associations, initial level of PDs generally did not predict change in other PDs. Again, however, when associations were present, they were somewhat consistent with trait models. Specifically, we found associations between greater borderline, histrionic, and schizotypal symptoms at baseline and sharper decreases in symptoms of paranoid PD over time. One possible interpretation of this finding is that symptoms of paranoid PD may emerge in the context of high levels of certain PD traits (e.g., high neuroticism) but may be transient or dissipate over time. Moreover, this finding may indicate that paranoid symptoms improve based on other trait and environment factors; for example, in this treatment seeking sample, paranoid thinking might have decreased due to improvements in emotion dysregulation (e.g., neuroticism) and interpersonal functioning (e.g., low agreeableness) that are characteristic of individuals with high levels of borderline, histrionic, and schizotypal symptoms. That said, these three findings emerged from 42 analyses, and require replication. Overall, like the few slope-slope relationships, the paucity of intercept-slope associations suggests that the longitudinal course of individual PDs is largely independent of one another. Notably, not only did we see the typical high comorbidity between PD dimensions at baseline (i.e., both the associations between intercepts and the bivariate correlations), but the cross-sectional associations remained similar at each follow-up. Taken together, these findings suggest that although the PDs maintain their cross-sectional associations over time, they demonstrate different patterns of change, and, with rare exceptions, neither the level nor change in one PD dimension systematically predicts changes in other PD dimensions. As such, in trying to improve our classification system, one implication may be that focusing on the interrelationships between PDs at one time point may lead to premature conclusions about the extent to which they are related. Prognostically, the findings of this study suggest that
initial levels of symptoms and rates of change in one PD have little utility for predicting change in other PDs. Conceptually, this finding is somewhat consistent with the current DSM nosology, in so far as symptoms of the various PDs demonstrate a fair degree of independence over time. We were somewhat surprised by these findings given the high cross-sectional associations between PDs in the literature and in our sample, as evidenced by the many intercept-intercept associations. Thus, it is important to replicate these analyses in independent samples to more fully understand the associations between PDs over time. 4.1. Strengths and limitations Overall, our study was a novel investigation of the longitudinal associations between pairs of PD dimensions. Major strengths included the systematic assessment of the full range of PD symptoms using semi-structured diagnostic interviews on 5 occasions over a 10-year period. In addition, by using a multivariate growth modeling approach, our analyses directly tested the longitudinal relationships between the trajectories of PD dimensions. This approach increased statistical power and reduced the number of statistical tests. That said, we performed a large number of statistical tests and replication is needed. However, it should be noted that type 1 errors would only strengthen our conclusion that there are few longitudinal associations between PD dimensions over time. We also relied on DSM-III-R criteria, rather than DSM-IV/5, at all five assessments. In addition, the sample was composed of treatment-seeking outpatients with major depression and/or dysthymia. Although depressive disorders are the most common axis I condition in outpatient settings  and are associated with high rates of PD comorbidity , our results may not generalize to other patient samples with other forms of psychopathology or to community samples. It is also important to note that most patients had relatively low levels of PD symptoms, which could have restricted the range with regard to change in PDs over time (i.e., a floor effect). However, this is mitigated by the relatively high standard deviations of most PD dimensions and our choice to only analyze PD dimensions that demonstrated significant variability in rate of change. Finally, we examined multivariate change across 2.5 year intervals. While this appears reasonable for examining moderately stable constructs, it is conceivable that results may differ using shorter or longer intervals. 4.2. Conclusions In conclusion, using a multivariate growth modeling approach, we examined the interrelationships between the PD dimensions over a 10-year period. Consistent with previous findings, we found cross-sectional associations between the initial levels of severity of many pairs of the PDs. However, there was less support for longitudinal associations between pairs of PD dimensions. This suggests
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that the course of individual PD dimensions is fairly independent of each other, and that initial levels of PD dimensions have relatively little prognostic value for predicting the course of other PD dimensions.
Acknowledgment This paper was prepared with the support of the VISN 4 Mental Illness Research, Education, and Clinical Center, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA. The views expressed in the article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. Dr. Olino was supported by NIH grant K01 MH09263. Dr. Klein was supported by NIH grant RO1 MH45757. There were no conflicts of interest related to this project or its authors. References
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