Journal of Personality Disorders, 29, 2015, 197 © 2015 The Guilford Press

COMORBIDITY OF BORDERLINE PERSONALITY DISORDER AND LIFETIME SUBSTANCE USE DISORDERS IN A NATIONALLY REPRESENTATIVE SAMPLE Ryan W. Carpenter, MA, Phillip K. Wood, PhD, and Timothy J. Trull, PhD

Borderline personality disorder (BPD) is comorbid with substance use disorders (SUDs). However, most epidemiological work on BPD and SUDs has collapsed nonalcohol substances into a drug use disorder indicator, potentially obscuring patterns of association between BPD and individal SUDs. Using a nationally representative sample (National Epidemiologic Survey on Alcohol and Related Conditions; N = 34,481), the authors examined the association between lifetime BPD and nine lifetime SUDs. First, the authors examined the bivariate association of BPD and each SUD. BPD was associated with all nine SUDs. Second, they added relevant covariates (demographic variables, additional psychopathology) to each model. Seven SUDs remained significant. Finally, to account for shared variance across SUDs, the authors conducted a multivariate logistic regression with the nine SUDs and covariates as predictors. Alcohol, cocaine, and opiate use disorder were the only significant SUD predictors, indicating a unique association between BPD and these three SUDs. Future research should explore factors involved in the association of BPD with these specific SUDs.

Borderline personality disorder (BPD) is a severe mental disorder that affects 1%–3% of the general population and is the most commonly diagnosed personality disorder (PD; Lenzenweger, Lane, Loranger, & Kessler, 2007; Trull, Tomko, Brown, & Scheiderer, 2010). BPD is characterized by affective instability, impulsivity, and interpersonal dysfunction (American Psychiatric Association, 2013) and involves impairment in multiple areas of functioning. BPD is comorbid with other PDs and mood, trauma, and anxiety disorders (Skodol et al., 2002). In particular, BPD is highly related to substance use disorders (SUDs; Skodol, Oldham, & Gallaher, 1999; Trull et al., in press; Trull, Jahng, Tomko, Wood, & Sher, 2010). From University of Missouri–Columbia. This work was supported by the National Institute of Health (R01 AA016392 and P60 AA11998). We would like to thank Kristofer J. Hagglund, Wendy S. Slutske, and Jarrod M. Ellingson for their feedback on an earlier draft of this article. Address correspondence to Ryan W. Carpenter, 210 McAlester Hall, Department of Psychological Sciences, University of Missouri, Columbia, MO 65211. E-mail: [email protected]

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This is especially true for alcohol use disorder (AUD). In their review of the literature from 1990 through 1997, Trull, Sher, Minks-Brown, Durbin, and Burr (2000) found that nearly half (48.8%) of BPD participants met criteria for AUD and 14.3% of participants with AUD met criteria for BPD. In an update of this earlier review, Trull et al. (in press) reported similar estimates, finding that, across studies published since 2000, 48.1% of BPD participants met criteria for AUD and 16.5% of individuals with an AUD diagnosis met criteria for BPD. Other individual studies, not included in these estimates, have also consistently found evidence for the relationship of BPD and AUD (Skodol et al., 1999; Trull, Jahng, et al., 2010; Zimmerman & Mattia, 1999). Drug use disorder (DUD), incorporating problematic use of all nonalcohol substances, has also been found to be comorbid with BPD. Trull et al. (2000) found, across studies, that 38.0% of BPD participants met criteria for DUD and 27.4% of individuals with an SUD (including AUD) met criteria for BPD, while Trull et al. (in press) found that 45.9% of BPD participants met criteria for at least one DUD and 41.9% of participants with a DUD diagnosis met criteria for BPD. Not included in these estimates, Trull, Jahng, et al. (2010), using data from the nationally representative National Epidemiologic Survey on Alcohol and Related Conditions study, found that 22.76% of BPD individuals had a lifetime DUD, and Zimmerman and Mattia (1999), in a sample of several hundred outpatients, found that BPD, compared to other psychiatric disorders, was associated with lifetime, but not current, DUD. The presence of a current DUD diagnosis among BPD individuals may also be linked to age, with diagnosis more common among younger, as compared to older, individuals with BPD (Morgan, Chelminski, Young, Dalrymple, & Zimmerman, 2013). Evidence has also suggested that BPD is comorbid with DUD not only cross-sectionally, but also over time (Walter et al., 2009; Zanarini et al., 2011). Although epidemiological studies show a strong association between BPD and DUD, few studies have examined the pattern of association between BPD and individual nonalcohol SUDs. Skodol et al. (1999) reported, in a sample of psychiatric patients, that BPD was more associated with increased risk for lifetime cannabis, stimulant (cocaine and stimulants), and other (hallucinogen, opioids, sedative, polysubstance) use disorders than other personality disorders. Most studies, however, have examined DUD only in aggregate. The rationale for combining different nonalcohol substances into a single DUD category may be related to the difficulty involved in obtaining a sufficient number of individuals with problematic use across individual substances, because many DUDs have relatively low prevalence rates. Studies that have examined the association of BPD with individual DUDs have used samples too small to identify differential patterns of association (e.g., Hatzitaskos, Soldatos, Kokkevi, & Stefanis, 1999; Thomas, Melchert, & Banken, 1999). Using a single DUD category, however, inevitably results in the loss of information. Different drugs have significantly different physiological and psychological effects. For example, drugs may have depressant (e.g., cannabis, opiates, tranquilizers, sedatives), stimulative (e.g., amphetamine, cocaine), or hallucinogenic (e.g., hallucinogens, cannabis) effects. Collapsing drugs with differing effects into a single category may obscure differential patterns of

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association. Different substances also have different neurobiological targets in the brain or affect the same targets to different degrees. For example, some substances are relatively precise in their mechanism of action (e.g., cannabis, which primarily acts through cannabinoid receptors; Ashton, 2001), whereas others are not (e.g., alcohol, which affects multiple neurotransmitter systems; Valenzuela, 1997). Evidence that BPD is particularly associated with one or a small number of substances, especially if they had overlapping targets in the brain, would suggest that these neurobiological systems might also be involved in BPD. In addition, substances differ in their degree of availability, as some have currently accepted medical uses (as defined by the 1970 United States Controlled Substances Act; Public Law 91-513), while others do not. Even substances without documented medical uses differ widely in their degree of availability. Ultimately, however, regardless of the potential cause of differential association with BPD, previous work that has grouped drugs into a single DUD category cannot indicate whether BPD is associated with addiction to particular substances or to all substances about equally. Some studies show an elevated prevalence of BPD in samples of users of particular substances. Reviews suggest that the prevalence of BPD has most often been examined in samples of individuals with alcohol, cocaine, or opiate use disorders (Trull et al., 2000, in press). BPD was highly prevalent across these samples, with Trull et al. (2000) reporting that 14.3% of individuals with alcohol use disorder, 16.8% of individuals with cocaine use disorder, and 18.5% of individuals with opiate use disorder met criteria for BPD. However, these studies are also limited because they only examine the association of BPD and a single substance. They cannot therefore determine whether a pattern of association exists between BPD and a range of different SUDs. In sum, BPD is associated with both DUD as a whole and is prevalent in samples of users of specific substances (alcohol, cocaine, opiates). However, to our knowledge, no research has directly compared the association of BPD and different SUDs in a multivariate context within a single sample. It is well known that SUDs are often themselves comorbid with one another as well as with other forms of psychopathology. Therefore, to determine whether a unique relationship exists for individual SUDs with BPD, it is necessary to take into account the shared variance across SUDs, as well as variance due to other, potentially important, variables, such as additional psychopathology. Given that substances vary in terms of effect (both psychological and physiological), availability, and other dimensions, knowing whether certain SUDs are more closely related to BPD than others may thereby provide us with clues about the factors that influence substance use in individuals with this disorder. In addition to the implications for etiological theories, such information would have important practical value by highlighting which SUDs are more closely related to BPD. Specifically, it would inform clinicians who work with individuals with problematic use of these substances to be at heightened awareness of the potential for comorbidity with BPD. This would have implications for targeting interventions. To address these questions, the present study examined the association of BPD with nine lifetime SUDs in a large, nationally representative, epidemiologic sample. First, the association of BPD and each SUD was independently

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determined using bivariate logistic regression models. Given previous work, it was predicted that BPD would be associated with all SUDs. Next, relevant demographic and psychopathology indicators were added as covariates to assess the degree to which each bivariate association could be explained by such third variable explanations. Third, all SUD indicators, as well as covariates, were entered into a single multivariate logistic regression model in order to examine whether any unique associations existed between BPD and individual SUD indicators after taking into account shared variance across SUDs. Given the absence of previous work, it was unknown which SUDs, if any, would remain significant. If BPD is generally associated with the problematic use of any substances, then none of the SUD indicators should remain significant. AUD, however, as mentioned, has frequently been specifically linked to BPD in the literature, and it was therefore predicted that BPD and AUD would remain significantly associated in the context of other SUDs. Cocaine and opiate use disorders were also tentatively predicted to be significant predictors of BPD, because most of the work examining the prevalence of BPD among individuals with a specific SUD, outside of AUD, has been conducted using individuals with cocaine or opiate use disorders. Although this fact, by itself, is not particularly strong evidence for a unique association for BPD and cocaine and opiate use disorders, it is suggestive that Trull et al. (2000) found BPD rates to be elevated among these substance users.

METHOD SAMPLE Data from Waves 1 (W1) and 2 (W2) of the National Institute on Alcohol Abuse and Alcoholism’s (NIAAA) NESARC study were used. NESARC is a nationally representative, face-to-face survey of noninstitutionalized U.S. citizens designed to collect data on the prevalence and correlates of SUDs. W1 (2001–2002) consisted of 43,093 participants (57% female). The sample was 57% White, 19% Hispanic, 19% Black, 3% Asian, and 2% Native American. Black and Hispanic households and young adults aged 18–25 years were oversampled. W2 data were used in the current study, except in the case of seven PDs, which were assessed at W1 but not at W2. W1 data were used for these PDs (see the following text). W2 (2004–2005), a 3-year follow-up, consisted of 34,653 participants. PARTICIPANTS Individuals who participated in both waves of NESARC and provided all information necessary to assess for DSM-IV BPD were included in the study (N = 34,481). All individuals were 18 years or older at W1. A more detailed description of the demographic variables investigated here (sex, age, raceethnicity, family income, marital status, education, urbanicity, and residential region) has been previously reported elsewhere (Tomko, Trull, Wood, & Sher, 2014). Briefly, 2.7% (n = 1,030) individuals met diagnostic criteria for BPD.

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Rates of the disorder were slightly elevated in females, individuals with a family income under $20,000 per year, individuals younger than 30, and individuals who were separated, divorced, or widowed. MATERIALS The NIAAA’s Alcohol Use Disorder and Associated Disabilities Interview Schedule DSM-IV Version (AUDADIS-IV; Grant, Hasin, & Dawson, 2001) was used to assess mood, anxiety, SUD, and PD symptoms, which were then used, according to DSM-IV guidelines, to calculate dichotomous disorder diagnoses (0 = absence of disorder, 1  =  presence of disorder). Both lifetime and 12-month disorders were assessed at each wave of the study, except for PDs (see the following discussion). For the present study, lifetime dichotomous disorders were used for all analyses. Personality Disorder Assessment. Lifetime PDs were assessed. W1 of NESARC included assessments of antisocial, avoidant, dependent, histrionic, obsessivecompulsive, paranoid, and schizoid PDs, and W2 assessed antisocial (incorporating W1 diagnostic information), borderline, narcissistic, and schizotypal PDs. For each DSM-IV PD criterion, participants were asked whether it was (a) descriptive of the participant and (b) a cause of problems at work/school or in personal relationships (impairment). Respondents were instructed to give responses that were context-free and not limited to transient periods of distress or substance or medication use (Grant et al., 2004). Impairment was required for each criterion to count toward diagnosis (see Trull, Jahng, et al., 2010). The requirement of impairment was different from the original AUDADIS diagnostic rules for PDs, which required impairment only in the case of at least one criterion. Our requirement of impairment yields lower prevalence rates of PDs compared to original estimates, but these rates are similar to those found by previous work using nationally representative diagnostic interview–based studies (Coid, Yang, Tyrer, Roberts, & Ullrich, 2006; Lenzenweger et al., 2007). PD diagnoses were dichotomous variables (0 = absent, 1 = present) and participants were considered to have a PD diagnosis if they met DSM-IV diagnostic criteria (e.g., five or more out of the nine criteria for BPD). Ten-week test–retest reliability estimates for the W1 and W2 PD diagnoses were fair to good. Kappas ranged from .40 to .67 for W1 and .67 to .71 for W2 PDs, and intraclass correlation coefficients (ICCs) for PD symptom counts ranged from .55 to .79 for W1 PDs and .71 to .75 for W2 PDs (Grant, Kaplan, Shepard, & Moore, 2003; Ruan et al., 2008). Mood and Anxiety Disorder Assessment. Lifetime mood diagnoses measured by AUDADIS included major depressive disorder, dysthymia, mania, and hypomania. Lifetime anxiety disorders included panic disorder (with and without agoraphobia), social phobia, specific phobia, generalized anxiety disorder, and posttraumatic stress disorder. Disorders were dichotomous variables (0 = absent, 1 = present). Previous work has found that these diagnostic categories have adequate test–retest reliability and validity (Canino et al., 1999; Grant, Harford, Dawson, Chou, & Pickering, 1995; Grant et al., 2003). Individual

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disorders were combined to create a lifetime dichotomous (any) mood disorder indicator and a lifetime dichotomous (any) anxiety disorder indicator. Substance Use Disorder Assessment. Substance use disorder was assessed for the following substances: opioid painkillers, heroin, alcohol, cocaine, amphetamines, cannabis, hallucinogens, tranquilizers, sedatives, and inhalant/solvents. Opioid painkillers and heroin were combined to create a single opiate category. Participants were considered to have a use disorder for the respective substance if they met DSM-IV criteria for abuse and/or dependence (i.e., one or more of the four abuse criteria for abuse, three or more of the seven dependence criteria for dependence), and thus SUD diagnoses were dichotomous variables (0 = no abuse or dependence, 1 = abuse and/or dependence). Only nonmedical use of substances was assessed, defined as use “without a prescription, in greater amounts, more often, or longer than prescribed, or for a reason other than a doctor said you should use them.” Previous reports have suggested adequate to good test–retest reliabilities for alcohol and drug diagnoses (Grant et al., 1995; Hasin, Carpenter, McCloud, Smith, & Grant, 1997). SUDs were examined, as opposed to substance use, because the present work was specifically focused on whether BPD was associated with problematic substance use. STATISTICAL ANALYSIS SASTM 9.3 was used to compute prevalence rates and to conduct regression analyses. To adjust for oversampling, analyses were weighted to reflect true population estimates. Logistic regression was used to determine the odds ratios (OR) for variables of interest given relevant predictors. Logistic regression was used because the criterion (dependent variable; lifetime BPD) was dichotomous in nature. Due to the large size of the survey, 99% confidence intervals (CIs) were used. Demographic variables (sex, age, race-ethnicity, family income, marital status, education, urbanicity, and region lived in) and psychopathology other than BPD and SUDs (mood disorders, anxiety disorders, and non-BPD PDs) were next included as covariates. This was done in order to account for variables that might reasonably be expected to influence the relationship of BPD and different SUDs (e.g., Vergés et al., 2012). Including these covariates, thus, allowed us to test a number of third-variable explanations for significant associations found in initial analyses. Finally, all independent variables were included in a single multivariate logistic model to account for shared variance across SUD indicators. This provided a more stringent test of specificity.

RESULTS Prevalence rates for use disorder for each substance assessed (opiate, alcohol, cocaine, amphetamines, cannabis, hallucinogens, tranquilizers, sedatives, and inhalant/solvents) are presented in Table 1. First, we conducted nine separate logistic regressions with the lifetime dichotomous BPD indicator as the criterion and the nine lifetime dichotomous SUD indicators, respectively, as predictors. Results of these logistic regressions are reported in the first column

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TABLE 1. Prevalence (N) of Lifetime Substance Use Disorders Among Individuals With and Without Lifetime Borderline Personality Disorder (BPD) Substance use disorder

Non-BPD % (N = 33,451)

Non-BPD SE

BPD % (N = 1,030)

BPD SE

33.77 (10,661)

0.32

63.08 (604)

1.70

Alcohol use disorder Amphetamine use disorder

1.96 (603)

0.10

9.43 (90)

1.14

Cannabis use disorder

9.06 (2,811)

0.20

30.98 (281)

1.77

Cocaine use disorder

2.78 (913)

0.11

16.73 (148)

1.54

Hallucinogen use disorder

1.68 (482)

0.09

9.67 (75)

1.21

Inhalant use disorder

0.31 (98)

0.04

2.93 (25)

0.67

Opiate use disorder

1.82 (547)

0.10

13.23 (114)

1.36

Sedative use disorder

1.10 (333)

0.08

8.43 (86)

1.06

Tranquilizer use disorder

1.12 (329)

0.08

7.88 (69)

1.12

Note. Substance use disorder (0 = no abuse or dependence, 1 = abuse and/or dependence) and BPD (0 = BPD absent, 1 = BPD present) were dichotomous categorical indicators. Total N = 34,481. SE = standard error.

of Table 2. Each row in this column presents the resulting odds ratio (OR) for the respective SUD, which was the sole predictor in a logistic regression model with BPD as the criterion. Thus, the ORs for each SUD represent the unadjusted association of that SUD with BPD, without taking into account potential third-variable explanations (i.e., covariates) for the association or the variance shared between BPD and all SUDs. Each diagnosis was significantly associated with BPD. Inhalant use disorder had the largest OR (9.72 [4.89, 19.31]). This means an individual with inhalant use disorder had 9.72 times the likelihood of also being diagnosed with BPD.

TABLE 2.  Odds Ratios of Substance Use Disorders Predicting Borderline Personality Disorder, Bivariately and Multivariately, and With and Without Covariates Bivariate With No Covariates Substance use disorder Inhalant use disorder

Bivariate With All Covariates

All Substances With All Covariates

Est.

99% CI Lower-Upper

Est.

99% CI Lower-Upper

Est.

99% CI Lower-Upper

9.72

4.89

2.15

0.67

6.88

1.22

0.38

3.88 3.12

19.31

Sedative use disorder

8.30

5.57

12.38

2.39

1.36

4.18

1.38

0.61

Opiate use disorder

8.21

5.82

11.58

2.51

1.59

3.97

1.81

1.03

3.19

Tranquilizer use disorder

7.54

4.84

11.76

1.84

1.00

3.40

0.73

0.32

1.63

Cocaine use disorder

7.04

5.17

9.58

2.84

1.87

4.34

2.06

1.23

3.42

Hallucinogen use disorder

6.28

4.24

9.32

1.82

1.03

3.20

0.84

0.41

1.70

Amphetamine use disorder

5.21

3.58

7.58

1.68

1.01

2.79

0.72

0.36

1.44

Cannabis use disorder

4.51

3.58

5.67

1.89

1.38

2.59

1.27

0.89

1.82

Alcohol use disorder

3.35

2.73

4.12

2.04

1.56

2.68

1.73

1.30

2.31

Note. Substance use disorder categories are ranked in descending order of initial bivariate logistic regression odds ratio estimates. Bold number indicates significance at p < .01. N = 34,481 (borderline personality disorder N = 1,030). Substance use disorder (0 = no abuse or dependence, 1 = abuse and/or dependence) and BPD (0 = BPD absent, 1 = BPD present) were dichotomous categorical indicators. Covariates included sex, age, race-ethnicity, family income, marital status, education, urbanicity, region lived in, mood disorders, anxiety disorders, and non-BPD personality disorders. CI = confidence interval.

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The second column reports results of nine bivariate logistic regressions that also included covariates (sex, age, race-ethnicity, family income, marital status, education, urbanicity, and region lived in) and psychopathology other than BPD and SUDs (mood disorders, anxiety disorders, and non-BPD PDs). Each row presents the OR for the respective SUD, which was the sole SUD predictor in a logistic regression model with BPD as the criterion and all covariates entered as additional predictors. Thus, in this case, the ORs for each SUD represent the adjusted association for the SUD with BPD, taking into account covariates that may be associated with BPD or the respective SUD, but not the variance shared across all SUDs. Cocaine use disorder (OR = 2.84 [1.87, 4.34]), opiate use disorder (OR = 2.51 [1.59, 3.97]), sedative use disorder (OR = 2.39 [1.36, 4.18]), alcohol use disorder (OR = 2.04 [1.56, 2.68]), cannabis use disorder (OR = 1.89 [1.38, 2.59]), hallucinogen use disorder (OR = 1.82 [1.03, 3.20]), and amphetamine use disorder (OR = 1.68 [1.01, 2.79]) were each significantly associated with BPD. The third column reports the results for the final multivariate model predicting BPD based on all substance indicators, with the inclusion of covariates.1 That is, each row presents the OR for each SUD resulting from a single logistic regression model with BPD as the criterion and all nine SUD indicators and all covariates entered as predictors. In this way, the effect of covariates and the variance shared between BPD and all SUDs were accounted for, and the ORs for each SUD represent the remaining unique association between BPD and that SUD. Cocaine use disorder (OR = 2.06 [1.23, 3.42]), alcohol use disorder (OR = 1.73 [1.30, 2.31]), and opiate use disorder (OR = 1.81 [1.03, 3.19]) were significant, unique predictors of BPD.2, 3

DISCUSSION The present study focused on the association of BPD with lifetime SUDs individually, in the presence of demographic and psychopathology covariates, and in the context of one another. Without covariates in the model, bivariate logistic regressions indicated significant associations between BPD and all SUDs. Across the bivariate logistic regressions, the OR for inhalant use disorder was the largest, with the diagnosis of inhalant use disorder making it 9.72 times the likelihood to also have a BPD diagnosis. However, this OR was only slightly larger than the ORs for sedative and opiate use disorder and, in general, the CIs for all point estimates strongly overlapped. Although overlap of CIs is not a precise means of determining statistical difference between ORs (Belia, 1.  The model including all SUDs was also tested without including covariates. The only change from the model with SUDs and covariates was that, in addition to alcohol, cocaine, and opiate use disorder, cannabis use disorder was also significant. 2.  The multivariate logistic model was also run with abuse and dependence indicators for each substance entered as independent predictors. Results were similar, but specific to dependence: Alcohol, cocaine, and opiate dependence were the only significant SUD predictors of BPD. 3.  In the final multivariate model, regarding psychopathology covariates, having a schizotypal PD diagnosis, narcissistic PD diagnosis, an anxiety disorder, or a mood disorder was predictive of a BPD diagnosis. Younger age, lower income, and being separated, divorced, or widowed were also predictive of a BPD diagnosis.

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Fidler, Williams, & Cumming, 2005), the degree of overlap found across the initial bivariate models suggests that there is likely not a meaningful difference between the SUD ORs in this first stage of analysis. Interestingly, AUD had the smallest OR, and its CI only overlapped with the CIs for cannabis and amphetamine use disorder. This is likely a reflection of the relatively greater prevalence of AUD among non-BPD individuals compared to other SUDs. The addition of demographic factors, history of mood and anxiety disorders, and non-BPD PDs attenuated the associations between BPD and SUDs. Two of the previously significant associations (inhalant and tranquilizer use disorder) became nonsignificant. These initial effects may have been, at least in part, due to third variables. The remaining seven SUD effects remained significant, with the largest being cocaine use disorder, followed by opiate use disorder. However, the CIs again overlapped. The final multivariate model, which included all nine SUD indicators as predictors, thus controlling for the shared effects of individual SUDs, as well as covariates, revealed unique associations for BPD with alcohol, cocaine, and opiate use disorder. These were the only significant unique SUD predictors of BPD. The OR for cocaine use disorder was largest, although the OR point estimates were highly similar, ranging from 1.73 to 2.06, with CIs overlapping. Thus, a pattern of unique associations between BPD and specific SUDs emerged, with the strongest evidence for alcohol, cocaine, and opiate use disorder. These indicators were significant across all models, suggesting that their association with BPD cannot be readily accounted for by the influence of covariates or variance shared across SUDs. That bivariate logistic models found BPD was associated with all SUDs supports previous work indicating that BPD is associated with lifetime DUD generally (Trull et al., 2000, in press; Trull, Jahng, et al., 2010; Zanarini et al., 2011; Zimmerman & Mattia, 1999). However, results from the multivariate logistic regression revealed more specific associations that could not be detected by this previous work. If BPD were generally associated with SUDs, then none of the SUD indicators in this analysis should have been significant. Instead, BPD was uniquely related to alcohol, cocaine, and opiate use disorder. This corresponds with findings of an elevated prevalence of BPD in samples of alcohol, cocaine, and opiate users (Trull et al., 2000, in press). However, although the findings of unique effects cohere with previous work, it is unclear why BPD showed associations with these three substances and not others. On the surface, there is no obvious link between these substances with each other or BPD. In terms of physiological effect, two are depressants and one is a stimulant. They also differ in terms of relative availability in the United States, with alcohol legally available (to persons age 21 and older), some opiates available by prescription while others are not, and cocaine generally available only through illicit means. It is important to note that there need not be a shared factor or factors that explain the association of BPD with all three substances. Instead, there may be specific factors behind the association of BPD with each substance. For example, BPD may be associated with each of these substances because they have different physiological effects. It is likely that both shared and specific factors are responsible for the unique effects found in the current study.

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However, while keeping this in mind, we believe it is worthwhile to briefly consider factors that may be shared by alcohol, cocaine, and opiates, but not other substances—that is, factors that might explain why these three SUDs were associated with BPD above the variance shared across all SUDs. Multiple possible explanations exist for comorbidity between different disorders (Klein & Riso, 1993; Krueger & Markon, 2006; Neale & Kendler, 1995; Trull et al., 2000), a few of which we briefly consider here. First, one disorder may cause the second. That is, BPD may lead to problematic substance use. For example, given that BPD is considered primarily a disorder of emotion dysregulation (Carpenter & Trull, 2013), those suffering from BPD may conceivably be motivated to use substances that reduce negative affect. This is consistent with the self-medication hypothesis (Khantzian, 2003), which suggests that individuals with psychopathology will seek out substances that best counter symptoms of the disorder in question. However, the self-medication hypothesis has generally received inconsistent support and, while BPD individuals may, in fact, use to reduce negative affect, most, if not all, substances have negative affect-reducing properties. Thus, this would not seem to predict the use of specific substances. It may also be the case that problematic substance use can result in symptoms similar to those seen in BPD. For example, problematic substance use can contribute to several BPD criteria, including dissociation, affective instability, impulsivity, and interpersonal problems. In the NESARC study, interviewers were instructed to rate criteria as present only if they occurred in the absence of substance use, although it is unknown to what degree they were successful at doing so. Comorbidity may also be due to criteria overlap between the comorbid disorders. For example, the impulsivity criterion of BPD can, in part, be satisfied by substance use. However, if this were the case, it does not explain why only alcohol, cocaine, and opiate dependence were uniquely related to BPD. In addition, studies have shown a relationship between SUD and BPD even when not considering substance use in determining the diagnosis of BPD (e.g., Dulit, Fyer, Haas, Sullivan, & Frances, 1990; Grilo et al., 1997). Another possible cause of comorbidity is an etiologically important, unmeasured third variable that contributes to the risk for all comorbid disorders. As noted before, different substances have differing neurobiological targets. Interestingly, alcohol, cocaine, and opiates are all known to stimulate the endogenous opioid system (EOS; Roth-Deri, Green-Sadan, & Yadid, 2008), which plays an important role in reward and reinforcement (Akil et al., 1984; Ribeiro, Kennedy, Smith, Stohler, & Zubieta, 2005). Dysregulation of the EOS has also been previously linked to BPD symptoms (Bandelow, Schmahl, Falkai, & Wedekind, 2010; Stanley & Siever, 2010). Specifically, EOS dysregulation in BPD may involve (a) a chronic deficit of endogenous opioids, coupled with (b) upregulation of EOS receptors, or an increase in the number of receptors available for binding, which occurs as an effort by the body to minimize the negative effects of the opioid deficit. Along these lines, Prossin, Love, Koeppe, Zubieta, and Silk (2010) found evidence of EOS upregulation in BPD individuals. This upregulation would potentially cause behaviors or actions that activate the EOS to have a greater effect than they would in individuals with a normal EOS. Thus, the use of alcohol, cocaine, and opiates may be highly

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reinforced in BPD individuals, which may lead to increased risk of developing dependence on these substances. In addition, one possible result of an opioid deficit is chronic pain (Bruehl, McCubbin, & Harden, 1999), which is common among BPD individuals (Sansone, Mueller, Mercer, & Wiederman, 2010; Sansone & Sansone, 2012; Saper & Lake, 2002; Tragesser, Bruns, & Disorbio, 2010). Chronic pain may lead BPD individuals to seek out substances with analgesic properties, to which they may then be at increased risk for becoming addicted to these substances. Opiates, of course, are often used, as prescribed or otherwise, for pain. Alcohol is also often used by individuals for pain management (Riley & King, 2009), and, although there is little behavioral evidence suggesting that individuals use cocaine for analgesic purposes, other than as a topical analgesic, cocaine does have antinociceptive effects that can be reversed by high doses of naloxone, an opioid antagonist (Pertovaara, Mecke, & Carlson, 1991). However, while intriguing, linking the present findings to EOS dysregulation is speculative because these substances, particularly alcohol and cocaine, have many other neurobiological targets in the brain besides the EOS. In turn, the EOS is also broadly involved in reward and reinforcement (Merrer, Becker, Befort, & Kieffer, 2009). Although the evidence for EOS activation is clearest for alcohol, cocaine, and opiates, some research has linked the EOS to substances (e.g., amphetamines; Olive, Koenig, Nannini, & Hodge, 2001) that were not uniquely associated with BPD in the present study. Ultimately, while the current study offers some initial and intriguing findings, more research is needed to understand the causes behind the comorbidity of BPD and alcohol, cocaine, and opiate dependence.

LIMITATIONS, FUTURE DIRECTIONS, AND CONCLUSION Several limitations of this study should be noted. First, although the present study used a large, nationally representative sample, relatively few individuals met criteria for many SUDs. Although the NESARC data were ascertained to be representative of the U.S. population, users of certain substances are difficult to recruit and retain, possibly leading to systematic underestimation of the prevalence of many SUDs. It may be that recruiting individuals with both an SUD and BPD is even more difficult, thereby potentially attenuating the association between BPD and SUDs. Second, lifetime diagnoses were used, which require retrospection on the part of participants, potentially over long periods of time. Although 12-month diagnoses were assessed for SUDs, BPD was only assessed over the lifetime at W2. For this reason, we chose to use lifetime diagnoses for SUDs as well. Third, dichotomous variables provide reduced information relative to continuous indices (e.g., symptom counts). However, the NESARC dataset was designed with dichotomous diagnostic variables in mind, and previous research examining the comorbidity of BPD and SUDs has primarily used dichotomous variables in the context of logistic regression (Trull et al., 2000, in press). Therefore, we took a similar approach. Fourth, due to the cross-sectional nature of the data, we did not have information as to the timing of onset for SUDs and BPD relative to one another and

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could not assess temporal ordering of diagnoses. We also had no information about patterns of substance use over time or how this might relate to BPD symptomatology. There is thus a need for longitudinal studies that examine the onset and course of SUDs in BPD individuals. Fifth, some have questioned the NESARC PD interview items, which were not drawn verbatim from preexisting Axis II semistructured interviews (Lenzenweger et al., 2007). However, the agreement among these interviews has been found to be moderate to poor (e.g., Farmer, 2000), suggesting the absence of a gold standard in this area. Furthermore, past work has shown that the NESARC BPD diagnosis has good external validity (Tomko et al., 2014; Trull, Jahng, et al., 2010). With these limitations in mind, the overall size and representativeness of NESARC provides confidence in the current findings. The current study examined the pattern of associations between BPD and individual SUDs, the effect of relevant covariates, and investigated whether there were unique associations between BPD and individual SUDs. Although BPD was broadly associated with SUDs, taking into account relevant covariates reduced the number of significant effects. Finally, accounting for the shared variance between different SUD indicators, we found evidence that BPD was uniquely associated with the problematic use of alcohol, cocaine, and opiates. This finding has implications for researchers, suggesting that BPD may share risk factors with these SUDs in particular. The findings also suggest that practitioners should pay particular attention to the use of alcohol, cocaine, and opiates among BPD individuals, and vice versa, because this may be important for improving patient outcomes. This may be particularly relevant for settings in which individuals are prescribed opiates (e.g., for chronic pain; Sansone & Sansone, 2012) or treated for alcohol, cocaine, and/or opiate use disorder. Regular screening for BPD, along with referral for appropriate treatment, may be valuable in such settings. The comorbidity of BPD and SUDs is a serious issue because the presence of one can complicate the treatment of the other (Ball, 2005), especially if the comorbidity is not recognized.

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