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. Author manuscript; available in PMC: 2012 May 1.
Published in final edited form as: Psychol Med. 2010 Sep 14;41(5):1041–1050. doi: 10.1017/S0033291710001662

Borderline Personality Disorder Comorbidity: Relationship to the Internalizing-Externalizing Structure of Common Mental Disorders

Nicholas R Eaton 1, Robert F Krueger 1, Katherine M Keyes 2, Andrew E Skodol 2,3, Kristian E Markon 4, Bridget F Grant 5, Deborah S Hasin 2
PMCID: PMC3193799  NIHMSID: NIHMS329086  PMID: 20836905

Abstract

Background

Borderline personality disorder (BPD) shows high levels of comorbidity with an array of psychiatric disorders. The meaning and causes of this comorbidity are not fully understood. Our objective was to investigate and clarify the complex comorbidity of BPD by integrating it into the structure of common mental disorders.

Methods

We conducted exploratory and confirmatory factor analyses on diagnostic interview data from a representative U. S. population-based sample of 34,653 civilian, non-institutionalized individualized aged 18 and older. We modeled the structure of lifetime DSM-IV diagnoses of borderline and antisocial personality disorders, major depressive disorder, dysthymic disorder, panic disorder with agoraphobia, social phobia, specific phobia, generalized anxiety disorder, post-traumatic stress disorder, alcohol dependence, nicotine dependence, marijuana dependence, and any other drug dependence.

Results

In both women and men, the internalizing-externalizing structure of common mental disorders captured the comorbidity among all disorders including BPD. While BPD was unidimensional in terms of its symptoms, BPD as a disorder showed associations with both the distress sub-factor of the internalizing dimension and the externalizing dimension.

Conclusions

The complex patterns of comorbidity observed with BPD represent connections to other disorders at the level of latent internalizing and externalizing dimensions. BPD is meaningfully connected with liabilities shared with common mental disorders, and these liability dimensions provide a beneficial focus for understanding BPD’s comorbidity, etiology, and treatment.

Keywords: Borderline personality disorder, internalizing-externalizing, latent structure, comorbidity

Borderline personality disorder (BPD) is a serious form of psychopathology associated with distress, suicide, impaired functioning, and considerable healthcare costs (Skodol et al., 2002; Yen et al., 2004; Ansell et al., 2007). The clinical presentation, treatment, and disability of individuals with BPD is complicated by its high degree of comorbidity with other major mental disorders (Zanarini et al., 1998, Zimmerman & Mattia, 1999), as about 75% of individuals with a lifetime BPD diagnosis meet criteria for a lifetime mood disorder and about 73% meet criteria for a lifetime substance use disorder (Grant et al., 2008). Because BPD typically presents along with other disorders that have high social costs, a better understanding of the associations of BPD and its comorbidity with other psychiatric disorders has important public health and etiological implications.

BPD comorbidity is usually examined through bivariate approaches (e.g., odds ratios) that demonstrate the diagnostic co-occurrence between BPD and another disorder. This approach has been useful in indicating the high levels of comorbidity between BPD and many other DSM-IV disorders, for example, an association between lifetime BPD and generalized anxiety disorder demonstrated by an odds ratio of 8.3 (p < .01; Grant et al., 2008). The bivariate approach to BPD comorbidity can also be useful in understanding specific pair-wise relations between disorders as well as patterns of risk (e.g., increased risk of major depression in individuals with BPD). However, simultaneous consideration of a larger number of disorders may indicate important patterns of relationships between BPD and other disorders that would not emerge from bivariate analyses.

Multivariate methods to understand better the comorbidity associated with BPD are available, and they provide the potential for new insights into the nature of comorbidity. As their name suggests, multivariate models consider multiple disorders simultaneously, with the aim of uncovering underlying structures that account for observed comorbidity. Multivariate modeling of comorbidity has converged on a model with two broad dimensions as providing a good fit to data on a diverse group of common mental disorders (Krueger et al., 1998; Eaton et al., 2010). The first dimension, internalizing, represents the propensity to experience unipolar mood and anxiety disorders such as major depression, generalized anxiety disorder, panic disorder, social and specific phobias. The second dimension, externalizing, represents the propensity to experience disinhibitory disorders such as substance use disorders, antisocial PD, and conduct disorder. Some studies have also shown that internalizing encompasses two sub-factors: distress and fear (Krueger, 1999; Vollebergh et al., 2001; Slade & Watson, 2006). Distress is associated with disorders such as major depression, dysthymia, and generalized anxiety disorder, while fear is associated with disorders such as panic disorder, social phobia, and simple phobia (Eaton et al., 2010).

Before now, the position of BPD in the internalizing and externalizing structure of comorbidity has not been extensively examined, although some authors have suggested links between BPD and internalizing and externalizing forms of psychopathology (e.g., Crowell et al., 2009). We are aware of only one previous study that addressed this issue in a sample of young adults in South Florida (James & Taylor, 2008). The study concluded that BPD may be best conceptualized in men as reflecting a confluence of both the distress sub-factor of internalizing (referred to by some authors as “anxious-misery”) and the externalizing dimensions; in women, results indicated BPD could either be conceptualized as relating to (1) both distress and externalizing, or (2) distress alone (James & Taylor, 2008). While this study provided an important first step in understanding multivariate BPD comorbidity, its generalizability is limited due to several factors, including its focus on individuals aged 19-22, its strict geographic constraints, and its sample size (N = 1,197). A larger, more representative U. S. sample could ensure generalizability, yield more precise model estimates, and clarify the somewhat ambiguous results obtained regarding structural connections between BPD and common mental disorders in women. The present study addresses these issues.

Our aim is to integrate BPD into the internalizing-externalizing model in a general population sample, which would provide at least two benefits. First, the overall model would be more thoroughly explicated. As more disorders are examined for their role in this model, multivariate patterns of comorbidity and the latent dimensions that account for them become clearer. Incorporating a variety of disorders helps assure that the “universe of content” for each broad dimension is adequately sampled. Extending investigations of this model to disorders not yet studied is important in evaluating its ability to organize more diverse forms of psychopathology, and may also delineate other dimensions beyond those linked to the most studied common mental disorders (i.e., mood, anxiety, substance use, and antisocial disorders; Andrews et al., 2009).

Second, unlike many other disorders, the symptoms of BPD appear to incorporate features of both the internalizing and externalizing dimensions. This possibility makes BPD a potentially informative disorder in the internalizing-externalizing framework. For instance, diagnostic criteria such as affective instability due to mood reactivity would seem to relate more strongly to internalizing, while others, such as impulsivity and inappropriate, intense anger would seem to relate more strongly to externalizing. Therefore, BPD could be understood as a confluence of internalizing and externalizing, which could have important implications for structural work on psychopathology and for conceptualizing BPD itself. Empirical findings that BPD cross-loads on both internalizing and externalizing would demonstrate that the BPD diagnosis, as currently conceptualized, is linked to at least two dimensions, rather than a single dimension. In the present study, we investigate how BPD fits into the internalizing-externalizing framework in a large and representative sample of community dwelling adults in the United States.

Method

Participants

This study utilized data from 34,653 individuals who participated in the Wave 2 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) in 2004-2005, which was a second assessment of the individuals from the Wave 1 NESARC study conducted in 2001-2002. Previous reports have detailed both Wave 1 and Wave 2 methods (Grant & Kaplan, 2005; Grant et al., 2005). Briefly, Wave 1 (N = 43,093) consisted of a representative sample of the civilian, non-institutionalized U. S. population aged 18 and older with oversampling of African-Americans, Hispanics, and young adults. Race/ethnicity was assessed via census-defined categories selected by the respondent. Participants from Wave 1 were contacted to participate in Wave 2, and 86.7% of eligible individuals agreed to participate. Forty-eight percent of the Wave 2 sample were women. Participants ranged in age from 20 years to over 90 years: 25.4% were less than 35 years old, 31.1% were 35-49, 24.1% were 50 to 64, and 19.3% were 65 or older. White subjects comprised 70.9% of the sample, African-Americans, 11.1%, Hispanics, 11.6%, Asian or Pacific Islander, 4.3%, and American Indians and Alaska Natives, 2.2%. After complete description of the study to the subjects, written informed consent was obtained.

Assessment

DSM-IV diagnoses were made using the Alcohol Use Disorder and Associated Disabilities Interview Schedule—DSM-IV Version (AUDADIS-IV; Grant et al., 1995; Grant et al., 2003; Ruan et al., 2008). The AUDADIS is a structured interview designed for administration by experienced lay interviewers. AUDADIS-IV test-retest estimates are similar to other structured interviews (e.g., the DIS, the CIDI) used in large-scale psychiatric epidemiologic surveys (reviewed in Wittchen, 1994). The present study uses lifetime diagnoses (representing the combination of lifetime diagnostic assessments from Wave 1 AUDADIS with a “since last interview” diagnostic assessment from Wave 2 AUDADIS, i.e., if the person met lifetime diagnostic criteria at Wave 1 or in the interval between Wave 1 and Wave 2, they received a lifetime diagnosis at Wave 2). There were two exceptions: (1) BPD, which was assessed as a lifetime diagnosis at Wave 2 only, and (2) antisocial personality disorder (ASPD), which was represented in our analyses by the lifetime diagnosis at wave 1 (i.e., we did not incorporate Wave 2 information into our ASPD diagnostic variable because ASPD is conceptualized in DSM-IV as a disorder that should not emerge de novo in adults, in the time interval between the two interviews).

We included two AUDADIS-IV assessed mood disorders: major depressive disorder and dysthymic disorder, and five anxiety disorders: panic disorder with agoraphobia, social phobia, specific phobia, generalized anxiety disorder, and post-traumatic stress disorder. Reliability values for these diagnoses ranged from fair (kappa = .42, specific phobia) to good (kappa = .64, major depressive disorder; Canino et al., 1999; Grant et al., 2003; Ruan et al., 2008).

We also included four substance use disorders: alcohol dependence, nicotine dependence, marijuana dependence, and any other drug dependence. A variable representing any other drug dependence was created for the present study to increase variation in these somewhat infrequent behaviors and thus to allow for meaningful estimates of covariation with other disorders. This drug dependence variable was a compilation of the following: dependence on sedatives, tranquilizers, opioids, amphetamines, hallucinogens, cocaine, inhalants/solvents, heroin, or any other drug. Good to excellent AUDADIS test-retest reliability for alcohol and drug dependence (kappa = .70-.84) is documented in clinical and general population samples (Grant et al., 1995; Chatterji et al., 1997; Hasin, Carpenter et al., 1997; Grant et al., 2003) as was good to excellent convergent, discriminant, and construct validity of AUDADIS alcohol and drug dependence diagnoses in studies examining United States (Hasin et al., 1994; Hasin, Van Rossem et al., 1997; Hasin et al., 1999; Hasin et al., 2007) and international samples (Cottler et al., 1997;Canino et al., 1999; Nelson et al., 1999; Hasin, Grant et al., 1997, Pull et al., 1997; Ustun et al., 1997; Vrasti et al., 1997), including clinical reappraisals. The any other drug dependence variable we created had good internal consistency (Cronbach’s alpha = .77).

Finally, we included two personality disorders: ASPD and BPD. Although the other diagnoses included in the present study were Axis I disorders, ASPD has consistently been demonstrated to be an indicator of externalizing in previous research (Eaton et al., 2010). BPD diagnosis required that respondents endorse five or more of the DSM-IV diagnostic criteria, at least one of which was associated with social or occupational dysfunction.i, The AUDADIS-IV uses multiple questions to assess DSM-IV personality disorder criteria. For example, the first BPD criterion regards “frantic efforts to avoid real or imagined abandonment.” One of the AUDADIS-IV items used to assess this criterion asks, “Have you often become frantic when you thought that someone you really cared about was going to leave you?” Test-retest reliability of the BPD diagnoses showed good reliability (kappa = .71; Ruan et al., 2008). ASPD diagnosis required the endorsement of sufficient childhood symptoms before age 15 in addition to lifetime adult symptoms occurring since age 15. Test-retest reliability of the ASPD diagnoses showed good reliability (kappa = .67; Grant et al., 2003).

Statistical Analyses

All analyses were conducted in Mplus version 5.21 using the Mplus defaults: the WLSMV estimator and delta parameterization for confirmatory factor analyses, and the WLSM estimator and oblique Geomin rotation for exploratory factor analyses (Muthén & Muthén, 2010) The WLSM and WLSMV estimators allow for fitting models to data derived from a complex sampling design (e.g., NESARC) with categorical observed variables. All diagnoses were treated as categorical variables, and all analyses incorporated the Wave 2 weighting, clustering, and stratification variables. Sample weights were re-estimated at Wave 2 to ensure that the sample remained representative of the U. S. population in the year 2000.

Exploratory factor analyses utilized the scree test, available fit indices, and factor interpretability to determine dimensionality; confirmatory factor analyses utilized available fit indices. The fit indices used for model evaluation were the comparative fit index (CFI), Tucker-Lewis index (TLI), root mean squared error of approximation (RMSEA), and the number of free parameters in the model.ii Values of CFI/TLI greater than .95, and values of RMSEA less than .06, are commonly used guidelines for inferring reasonably good model fit (Hu & Bentler, 1999). The number of free parameters in a model represents how many parameters were free to be estimated, rather than being constrained to a certain value (e.g., constrained to be equal to another parameter or to zero); the smaller the number of free parameters in a model, the less complex the model, and thus the greater parsimony it shows in fitting the data. In the confirmatory factor analyses, we thus defined the optimal model as the model with the best fit as judged by CFI, TLI, RMSEA; in the case where two models might have very similar fit indices, the most parsimonious model (the model with the fewest freely estimated parameters) was defined as the more optimal model. Confirmatory factor analysis was first conducted to determine the fit of the baseline internalizing-externalizing model (with distress and fear internalizing sub-factors) in each sex. We then fit models with BPD loading on distress, fear, and/or externalizing. To ensure that the higher-order internalizing-externalizing model was identified, the loadings of distress and fear on internalizing were constrained to equality.

Results

We began by investigating the structure of BPD in the present study’s data to determine its dimensionality. If later analyses were to demonstrate that BPD showed cross-loadings on two or more dimensions (e.g., distress, fear, and externalizing), such a finding could be due to BPD being a multidimensional diagnostic construct. The scree test, examination of fit indices, and interpretability of factors indicated that, in these data, BPD is best represented by a single factor. That is, a one-factor model provided a nearly perfect fit to the data (CFI = .99; TLI = .99; RMSEA = .03; all standardized loadings on the single factor were > .68; in a two-factor solution, the two factors were correlated .83). Thus, any findings in the following analyses that BPD cross-loaded on two or more dimensions would not be due to BPD itself consisting of more than one underlying dimension.

Because BPD status was not assessed at wave 1, we considered whether it might have impacted which individuals participated at wave 2. We identified four wave 1 BPD-related constructs that significantly correlated with wave 2 BPD, which could be used to infer whether BPD was related to attrition: past year breakup of a marriage/steady relationship (χ2 [1] = 0.49, p = 0.49), previous suicide attempt status (χ2 [1] = 6.73, p = 0.01), and wave 1 antisocial (χ2 [1] = 1.80, p = 0.18) and histrionic (χ2 [1] = 0.16, p = 0.69) PDs. Only having attempted suicide was significantly related to respondent status—individuals who responded at wave 2 were more likely to have attempted suicide. Given that only one of these BPD-related constructs was related to attrition, it appears unlikely that differential follow-up by BPD status substantially impacted our results.

We next sought to replicate the internalizing-externalizing structure of common mental disorders, excluding BPD. Each diagnosis was parameterized to load on one of three factors as identified by previous research: (1) major depression, dysthymia, generalized anxiety disorder, and post-traumatic stress disorder loaded on the distress sub-factor of internalizing; (2) panic disorder with agoraphobia, social phobia, and specific phobia loaded on the fear sub-factor of internalizing; and (3) ASPD, alcohol dependence, marijuana dependence, nicotine dependence, and other drug dependence loaded on the externalizing dimension. The distress and fear factors were subsumed under a higher-order internalizing dimension, which was correlated with the externalizing dimension. Exploratory and confirmatory factor analyses supported our use of sub-factors. Distress and fear were correlated (r = .77, CI99% = .73-.80 in women; r = .72, CI99% = .67-.77 in men) significantly less than unity. This internalizing-externalizing parameterization (Table 1) provided similarly good fit in women (CFI = .990, TLI = .992, RMSEA = .010) and men (CFI = .989, TLI = .991, RMSEA = .008).

Table 1.

Fit Indices and Number of Parameters for Models Fit

Model CFI TLI RMSEA # Free Parameters
Women (N = 20,089)
 Baseline INT-EXT .990 .992 .010 26
 Distress .985 .988 .012 28
 Fear .980 .984 .014 28
 EXT .965 .971 .019 28
 Distress/Fear .987 .989 .012 29
  Distress/EXT .989 .991 .011 29
 Fear/EXT .983 .986 .013 29
  Distress/Fear/EXT .989 .991 .011 30
Men (N = 14,564)
 Baseline INT-EXT .989 .991 .008 26
 Distress .983 .986 .010 28
 Fear .974 .978 .013 28
 EXT .958 .964 .016 28
 Distress/Fear .984 .986 .010 29
  Distress/EXT .989 .990 .009 29
 Fear/EXT .978 .982 .012 29
 Distress/Fear/EXT .988 .990 .009 30
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Note. CFI: comparative fit index; TLI: Tucker-Lewis index; RMSEA: root mean squared error of approximation; # Free Parameters: number of freely estimated parameters; EXT: externalizing. “Baseline INT-EXT” models indicate the fit of the higher-order model of internalizing-externalizing (including distress and fear internalizing sub-factors) without the inclusion of BPD. Other models indicate on which (sub-)factor(s) BPD was parameterized to load (e.g., “Distress/EXT” indicates BPD loaded on distress and externalizing). Bolded models are the best-fitting models within women and men indicated by fit indices.

After establishing the good fit of internalizing-externalizing in both women and men, we turned our attention to the location of BPD within this framework. We fit seven possible models of how BPD might fit into the internalizing-externalizing structure. For example, BPD could load on a single dimension—distress (model 1), fear (model 2), or externalizing (model 3). BPD could also be influenced by multiple latent propensities toward distress, fear, and externalizing. In such a case, BPD could load on both distress and fear (model 4); distress and externalizing (model 5); fear and externalizing (model 6); or on distress, fear, and externalizing (model 7). We refer to these models as distress, fear, externalizing, distress/fear, distress/externalizing, fear/externalizing, and distress/fear/externalizing, respectively. BPD could also load on different factors in women and men.

To identify the optimal location of BPD in internalizing-externalizing, each of the seven models above were fit separately within each sex (see Table 1). In women, all models fit the data well, but, relatively speaking, two models fit best when considering all three fit indices: distress/externalizing (CFI = .989, TLI = .991, RMSEA = .011, 29 free parameters) and distress/fear/externalizing (CFI = .989, TLI = .991, RMSEA = .011, 30 free parameters). The two models have identical fit indices, but the distress/externalizing model has one fewer freely estimated parameter. In keeping with our criterion for defining the optimal model, distress/externalizing was chosen, as its fit was identical to that of distress/fear/externalizing, but it was more parsimonious in terms of parameterization. In men, as in women, all models fit well, but the distress/externalizing model fit best (CFI = .989, TLI = .990, RMSEA = .009, 29 free parameters); the distress/fear/externalizing model showed only a slightly worse fit (CFI = .988, TLI = .990, RMSEA = .009, 30 free parameters) but one additional parameter. The distress/externalizing model was thus chosen as optimal in men, because it provided better fit and greater parsimony than all other models. Therefore, in both genders, the optimal model indicated that BPD was related to both distress and externalizing. This model and its parameter estimates for women and men are depicted in Figure 1.

Figure 1.

Figure 1

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The best fitting model in women and men.

Note. Values are standardized factor loadings (all significant p < .001). Bolded values before slash are for women; values after slash are for men. Panic: panic disorder with agoraphobia. Social: social phobia. Spec: specific phobia. MDD: major depressive disorder. Dysth: dysthymic disorder. GAD: generalized anxiety disorder. PTSD: post-traumatic stress disorder. BPD: borderline PD. ASPD: antisocial PD. Nic: nicotine dependence. Alc: alcohol dependence. Marij: marijuana dependence. Drug: other drug dependence. Arrows without numbers indicate unique variances, including error.

We used DIFFTEST—a chi-square difference test for the WLSMV estimator in Mplus— to supplement our fit index results, noting that DIFFTEST may reject good models in large samples. For women, DIFFTEST preferred the least parsimonious model (distress/fear/externalizing) to our more parsimonious model (distress/externalizing). The distress/fear/externalizing model, however, was clearly not optimal: The BPD fear loading was .11, indicating fear accounted for a trivial 1.23% of BPD variance, and was the only model parameter not significantly different from zero, even given our very large sample (n = 20,089) of women. For men, with a smaller sample size (n = 14,564) and less power, DIFFTEST failed to reject distress/externalizing compared to distress/fear/externalizing (p = .29). For both genders, distress/externalizing accounted for slightly more BPD variance than did distress/fear/externalizing (e.g., for women, R2 = .567 vs. .557, respectively). We interpreted these results as being largely congruent with the fit index results.

The foregoing analyses identify BPD’s location in the internalizing-externalizing framework, but another question remains: How much of the variance in BPD is captured by distress and externalizing? It could be the case that, even when BPD is fit most optimally into the internalizing-externalizing structure, it is still not well captured. Examination of BPD R2 values in women (.57) and men (.54), however, indicated that this was not the case: More than half of the variance in BPD was captured by its associations with distress and externalizing. Further, the factor loadings of BPD on distress (.60 and .57 for women and men, respectively) were more than twice as large as the loadings of BPD on externalizing (.23 and .25); squaring these loadings indicates that distress accounted for 36% of BPD variance in women and 32.5% in men while externalizing accounted for only 5.3% and 6.3%, respectively. These results highlight the relatively stronger association of BPD with distress than externalizing.

Discussion

Previous research has established that BPD is highly comorbid with many diverse forms of psychopathology. Although its bivariate relations with other disorders have been examined, there have been limited multivariate examinations of diagnostic comorbidity. We integrated BPD into a well-established empirically derived model of common forms of psychopathology—the internalizing-externalizing model. The determination that BPD is “located” in both distress and externalizing helps explain the patterns of comorbidity of BPD and presents implications for the conceptualization and classification of mental disorders. Most notably, it appears that BPD is associated with more than one underlying dimension (i.e., the distress sub-factor of internalizing and the externalizing dimension, albeit more strongly with the former). This finding was robust across gender.

Links between BPD, Distress, and Externalizing

Our goal was to examine how BPD fits into the latent internalizing-externalizing structure of psychopathology to improve understanding of the comorbidity of BPD with other disorders. We first demonstrated that the internalizing-externalizing model fits the NESARC data quite well and then determined the location of BPD within the model. When considered within this internalizing-externalizing structure, our results indicate that BPD is best conceptualized as a distress and externalizing disorder. Analyses using data from women and men separately converged on this result. These findings extend previous results (James & Taylor, 2008) to a national and representative sample, present a similar picture of BPD’s structural location for men, and help to clarify somewhat ambiguous previous results for women (in our findings, BPD in women appears to be a disorder of distress and externalizing rather than solely distress).

The notion that BPD relates to distress is in keeping with previous research indicating that emotional dysregulation seems to be a core feature of BPD; our results supplement this conceptualization by also documenting the relevance of the externalizing liability dimension to BPD (Sanislow et al., 2002; Skodol et al., 2002; Paris, 2007; Selby & Joiner, 2009). The notion that BPD is unidimensional while also being connected to the two latent dimensions of distress and externalizing pathology may seem counterintuitive. However, it is compatible with a liability threshold perspective on multiple underlying contributions to BPD risk. In a liability threshold model, BPD liability is a single dimension, ranging from very low levels of BPD symptomatology to very high levels, and a threshold demarcates the location on this dimension where the liability reaches a sufficiently high level for an individual to receive a BPD diagnosis. Our results demonstrating that BPD is a unidimensional construct indicate that there is indeed a single liability dimension for BPD. Our results demonstrating that BPD is connected to distress and externalizing suggest that these two separable liability dimensions each contribute to an individual’s liability level. Both distress and externalizing liabilities push an individual closer to the diagnostic threshold for BPD. However, distress has a much stronger relation to BPD than does EXT (Figure 1), and distress accounts for more BPD variance; thus, an increase in distress would move the individual closer to the diagnostic threshold than would an equivalent increase in EXT.

Implications

Comorbidity and classification

Our results suggest that the current conceptualization of BPD comorbidity, and perhaps of mental disorders in general, deserves reconsideration. BPD shows comorbidity with a wide array of disorders because of shared liability at a latent level. Rather than conceptualizing the prototypical BPD patient as suffering from numerous disorders, many BPD patients may be understood as having a high level of the latent internalizing and externalizing liabilities, manifesting as both BPD and other diagnoses (Livesley, 2005; Livesley, 2008).

Our findings also support the notion that rationally derived groupings of disorders, such as the Axis I-Axis II distinction in DSM-IV, may not reflect the true state of nature. In our findings, two Cluster B personality disorders (ASPD and BPD) showed links with Axis I disorders at the latent level. Indeed, the Axis II diagnoses were well integrated into the model; the factor loadings of these two personality disorders were frequently similar to, and sometimes larger than, the factor loadings of the Axis I disorders. Putatively distinct disorders seem closely related at a latent level, supporting calls for revising the nosology to reflect the continuity of Axes I and II (Siever & Davis, 1991; Krueger, 2005). Determining how additional Axis I and II disorders fit into these latent structures would be a valuable direction for future research.

Etiology

These results have implications for thinking about the origins of BPD and its comorbid disorders. BPD appears to originate from liabilities shared with a variety of other disorders. These liabilities for internalizing (and distress and fear) and externalizing are heritable, with unique environmental effects playing a role in how this liability manifests (Kendler et al., 2003). This suggests that a substantial portion of BPD’s etiology, and the etiology of the comorbidity it shows with other disorders, lies at the genetic level. Research on BPD etiology and comorbidity etiology should focus on understanding genetic predispositions to internalizing and/or externalizing as well as the specific environmental inputs that may determine how this liability is expressed (for examples of this approach, see Kender et al., 2008; Torgersen et al., 2008).

Treatment

With regard to treating BPD and its comorbid conditions, our results suggest that a compelling focus of intervention may lie at the latent liability level. Instead of treating various manifestations of underlying propensities, clinicians might address the underlying liability to experience distress and to externalize, keeping in mind that BPD is apparently more a disorder of distress than externalizing. Such an approach, if successful, would likely benefit both BPD and its concomitant disorders. For instance, rather than focusing interventions on emotional instability, depression, and anxiety in a BPD patient, a psychological or pharmacological intervention aimed at decreasing the individual’s overall tendency to experience distress might facilitate improvement across these areas more effectively and efficiently. Similarly, interventions targeting latent externalizing broadly might show diffuse impacts on problem behaviors such as impulsivity, risk taking, aggression, substance use, and self-injury frequently seen in individuals with BPD. Treatment research would thus be well served to investigate interventions that affect BPD and its comorbid conditions at the level of latent liability dimensions (Barlow et al., 2004).

Limitations

This study is not without its limitations. First, this study utilized diagnostic information rather than symptom-level data. While this dichotomization of the disorders was modeled statistically, it still addresses structural questions at a different level of analysis than does the use of symptom-level data (Markon, 2010). Future studies would benefit from using additional types of variables (e.g., manifest ordinal variables) and other assessment batteries. Second, the diagnoses in this study were made by extensively trained lay interviewers rather than clinicians. Another limitation is that several of our models fit well, and the necessary use of the WLSMV estimator precluded the calculation of other fit indices (e.g., BIC) that could have further clarified our results. Finally, the lifetime diagnoses required retrospective self-reporting, the accuracy of which are subject to phenomena such as memory accuracy, insight, and social desirability. That said, the robustness of our results—coupled with corroboration from previous research using dimensional manifest indicators (James & Taylor, 2008)—deserve careful consideration. Even with these caveats in mind, the results of the current study can inform thinking about BPD’s place in the structure of mental disorders. Specifically, although BPD diagnostic criteria were best represented as a single factor in the current research, BPD is connected to both distress and externalizing pathology.

Conclusions

The current study demonstrated that BPD fits well into the internalizing-externalizing structure of mental disorders, and latent liability dimensions account for more than half of the variance in the BPD diagnosis. This pattern of interconnections with underlying liability dimensions and other disorders was similar in women and men. These connections at the latent level account for BPD’s observed comorbidity and have implications for understanding classification, etiology, and treatment. These findings support the notion that it may be useful for studies of BPD to focus on connections with underlying liability dimensions. In turn, and more broadly, these findings support the notion that underlying liability dimensions may be key constructs in the search for etiology and effective interventions for comorbid mental disorders.

Acknowledgements

U01AA018111, R01DA018652 and K05AA014223 (Hasin); F31DA026689 (Keyes). The National Epidemiologic Survey on Alcohol and Related Conditions was sponsored by the National Institute on Alcohol Abuse and Alcoholism and funded, in part, by the Intramural Program, NIAAA, National Institutes of Health, with additional support from the National Institute on Drug Abuse.

Footnotes

Declarations of interest: None.

i

The prevalence rate of BPD in this sample was 6.2% in women and 5.6% in men (5.9% overall) when at least five diagnostic criteria were present and at least one was associated with impairment (Grant et al., 2008). Because this prevalence is higher than reported for other samples (see Torgersen et al., 2001), we also computed more conservative diagnoses that required each criterion to be associated with impairment for it to count toward a diagnosis (cf. Trull et al., in press). This approach yielded BPD prevalence rates of 3.0% in women and 2.4% in men (2.7% overall), which fall within the range of prevalence rates from previous studies reviewed by Torgersen et al. (2001). This more conservative diagnostic algorithm resulted in a diagnostic variable highly correlated with the less conservative algorithm (tetrachoric r = .99), and the results reported below did not differ when using the more conservative diagnostic algorithm (e.g., the fit indices for the best-fitting model were identical across diagnostic algorithms).

ii

Although frequently used as an index of model fit, the chi-square goodness of fit (CSGOF) is not reported here for two reasons. First, the CSGOF for the WLSMV estimator cannot be used for chi-square difference tests, and its degrees of freedom are estimates rather than precise values. Second, in large samples such as that in the present study, the CSGOF is often significant (indicating poor model fit) even when the model provides good fit due to a high degree of statistical power (Brown, 2006). Indeed, the CSGOF was significant (p < .001) in all models fit in the present study, including those with near perfect model fit judged by the other fit indices.

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