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. Author manuscript; available in PMC: 2014 Apr 17.
Published in final edited form as: Am J Med Genet B Neuropsychiatr Genet. 2011 Aug 2;0(7):817–825. doi: 10.1002/ajmg.b.31226

Borderline Personality Traits and Adult Attention-Deficit Hyperactivity Disorder Symptoms: A Genetic Analysis of Comorbidity

Marijn A Distel 1,2,*, Angela Carlier 1, Christel M Middeldorp 1,3,4,5, Catherine A Derom 6, Gitta H Lubke 7, Dorret I Boomsma 1,2,5
PMCID: PMC3990457  NIHMSID: NIHMS570445  PMID: 21812103

Abstract

Previous research has established the comorbidity of adult Attention-Deficit Hyperactivity Disorder (ADHD) with different personality disorders including Borderline Personality Disorder (BPD). The association between adult ADHD and BPD has primarily been investigated at the phenotypic level and not yet at the genetic level. The present study investigates the genetic and environmental contributions to the association between borderline personality traits (BPT) and ADHD symptoms in a sample of 7,233 twins and siblings (aged 18–90 years) registered with the Netherlands Twin Register and the East Flanders Prospective Twin Survey (EFPTS). Participants completed the Conners’ Adult ADHD Rating Scales (CAARS-S:SV) and the Personality Assessment Inventory-Borderline Features Scale (PAI-BOR). A bivariate genetic analysis was performed to determine the extent to which genetic and environmental factors influence variation in BPT and ADHD symptoms and the covariance between them. The heritability of BPT and ADHD symptoms was estimated at 45 and 36%, respectively. The remaining variance in BPT and ADHD symptoms was explained by unique environmental influences. The phenotypic correlation between BPT and ADHD symptoms was estimated at r = 0.59, and could be explained for 49% by genetic factors and 51% by environmental factors. The genetic and environmental correlations between BPT and ADHD symptoms were 0.72 and 0.51, respectively. The shared etiology between BPT and ADHD symptoms is thus a likely cause for the comorbidity of the two disorders.

Keywords: attention-deficit hyperactivity disorder, borderline personality disorder, comorbidity, twin study, etiology, genetics

INTRODUCTION

Attention-Deficit Hyperactivity Disorder (ADHD) is characterized by inattentive, impulsive, and hyperactive behavior [American Psychiatric Association, 2000]. ADHD has a high prevalence in children with 5% of the individuals under the age of 18 affected worldwide [Polanczyk et al., 2007]. Although ADHD is most commonly first diagnosed during childhood, a large percentage of the patients still meet full or sub threshold DSM-IV criteria for ADHD in adulthood [Rasmussen and Gillberg, 2000; Barkley et al., 2002; Mannuzza et al., 2003; Faraone et al., 2006; Biederman et al., 2010]. Based on epidemiological data, the prevalence of ADHD in adulthood is estimated at 2–5% [Faraone et al., 2003; Faraone and Biederman, 2005; Kooij et al., 2005; Kessler et al., 2006; Fayyad et al., 2007; Simon et al., 2009; De Graaf et al., 2011].

Adult ADHD is highly comorbid with other axis-I DSM-IV disorders. Most commonly associated with adult ADHD are mood disorders, anxiety disorders, and substance use disorders [Murphy and Barkley, 1996; McGough et al., 2005; Biederman et al., 2006; Kessler et al., 2006; Fayyad et al., 2007; Cumyn et al., 2009]. Personality disorders also often co-occur with adult ADHD. In a clinical sample of 447 adults with ADHD, Cumyn et al. [2009] reported that around 50% had a comorbid personality disorder. Particularly the risk of developing cluster B personality disorders in late adolescence is increased in children with ADHD [Miller et al., 2008]. The relationship between ADHD and borderline personality disorder (BPD) has received most attention because of the similar clinical features of the two disorders. BPD is characterized by identity disturbances, interpersonal difficulties, impulsivity, and emotional lability [American Psychiatric Association, 2000]. Deficits in affect regulation and impulse control are characteristic to both disorders. Two studies retrospectively investigated childhood ADHD as a potential precursor of BPD in adulthood [Fossati et al., 2002; Philipsen et al., 2008]. In both studies ADHD was assessed using the Wender UTAH Rating Scale [WURS; Ward, 1993], a 25-item questionnaire. Based on a sample of 42 patients diagnosed with BPD, Fossati et al. [2002] found that 59.5% of the BPD subjects had a WURS-score indicating a probable childhood ADHD diagnosis. Philipsen et al. [2008] found a some-what lower prevalence rate of childhood ADHD (41.5%) in a sample of 118 women diagnosed with BPD. Miller et al. [2008] conducted a prospective follow up study of 96 adolescents diagnosed with ADHD in childhood and also found that ADHD portends risk for adult (borderline) personality disorders. A total of 13.5% of the individuals with ADHD had co-occurring BPD in contrast to 1.2% of the control group. The co-occurrence of BPD and ADHD is associated with frequent substance abuse and a higher rate of suicidal behavior [Miller et al., 2008; Ferrer et al., 2010].

The heritability of ADHD and BPD has been established by twin and twin family studies. Heritability estimates around 40% have been reported for BPD and borderline personality traits (BPT) in several studies [Kendler et al., 2008; Torgersen et al., 2008; Distel et al., 2008a; Bornovalova et al., 2009;Distel et al., 2009]. Distel et al. [2009] found that heritability of BPT was explained by both additive genetic (21%) and dominant genetic (24%) factors. Several review articles summarize the results concerning the genetic and environmental factors contributing to ADHD [e.g., Faraone and Biederman, 1998; Faraone and Doyle, 2001; Albayrak et al., 2008; Derks et al., 2009; Sharp et al., 2009] showing that in childhood about 60–80% of the phenotypic variance in ADHD is explained by genetic factors. The remaining 20–40% of the variance is explained by non-shared environmental influences. Shared environmental factors have no or a weak influence. The heritability of ADHD in adults has been studied less frequently. Familial clustering of adult ADHD was reported by Antshel et al. [2009] who found in a sample of high IQ adults that ADHD was more prevalent in first degree relatives of adults with ADHD relative to controls (28% in family members of adults with ADHD versus 5% in family members of IQ matched controls). Only two twin family studies till date looked at the genetic influences on adult ADHD [Boomsma et al., 2010; Saviouk et al., 2011]. Both studies used the screening version of the Conners’ Adult Rating Scales [CAARS-S:SV; Conners et al., 1999] to assess inattentive and hyperactive symptoms and likely ADHD diagnosis(ADHD-Index) in a large sample of Dutch twins and their family members. Based on data from more than 10,000 individuals, Saviouk et al. [2011] report heritability estimates of 35, 23, and 31% for inattention, hyperactivity, and the ADHD-index, respectively. Including data from the parents of these twins and siblings (N > 12.000) Boomsma et al. [2010] report no cultural transmission from parents to offspring. No evidence for genetic dominance was found. The lower heritability in adults than in children can possibly be explained by age-by-genotype interaction, genotype-environment correlation, or by the fact that ratings of ADHD in adults are based on self-report whereas ratings of ADHD in children are based on parental or teacher reports [Boomsma et al., 2010].

Twin family data can also be used to study the shared etiology between traits. Instead of decomposing the variance of a univariate trait, the covariance (comorbidity) between two traits can be decomposed into parts that are due to genetic and environmental factors. The genetic contribution to the covariance between traits is a function of the genetic correlation between the traits and the square root of the heritabilities. Previous studies investigated the association between BPD and ADHD at the phenotypic level. In this manuscript we present a bivariate genetic study of BPT and ADHD symptoms in a large scale population based sample to determine whether shared (genetic and environmental) etiology can explain (part of) the comorbidity between the two disorders.

METHODS

Sample

The data for this study were collected by the Netherlands Twin Register [NTR; Boomsma et al., 2006a]. The NTR was established in 1987 and includes twins, their siblings, spouses, and parents. Every two to three years the twins and their family members are invited to complete questionnaires on physical and mental health, personality, and lifestyle. The data for this study are from the seventh survey carried out 2004–2005. The data collection procedure is explained in more detail in Distel et al. [2007]. Dutch-speaking twins in Belgium were also asked to participate in the survey. They were recruited through the East Flanders Prospective Twin Survey (EFPTS), a population-based register of multiple births in the Belgian Province of East Flanders which was started in 1964 [Derom et al., 2006].

Twins with unknown zygosity and individuals with unknown age were excluded. We included a maximum of two brothers and two sisters per family. The final sample consisted of 7,233 subjects. Detailed information on the sample configuration is given in Table I.

TABLE I.

Number of Participants and Complete Twin Pairs by Zygosity and the Mean Age (Range)

Number of subjects Complete twin pairs Mean age (range)
Monozygotic males 840 302 34.66(18.44–76.71)
Dizygotic males 435 120 34.51(18.14–78.21)
Monozygotic females 2,180 875 35.49(18.32–86.42)
Dizygotic females 1,039 347 34.85(18.37–75.89)
Dizygotic opposite sex 1,173 340 32.65(18.04–75.19)
Brothers 569 — 42.09(17.65–90.29)
Sisters 997 — 41.60(17.56–83.99)
Total 7,233 — 35.77(17.56–90.29)
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Measures

ADHD behaviors were measured using the Conners’ Adult ADHD Rating Scales [CAARS-S:SV; Conners et al., 1999]. The CAARS-S:SV is designed for adults from 18 to 50 years and older, and consists of 30 items reflecting the DSM-IV ADHD symptom measures. The ADHD index is a subset of 12 core items designed to identify the adults that are likely to be diagnosed with ADHD. We used the ADHD Index subscale, because it “represents a measure of overall level of ADHD-related symptoms” [Conners et al., 1999]. All items have a four-answer scale (0–3; never, once in a while, often, very frequently), and were scored according to Conners’ test manual.

BPT were assessed with the Personality Assessment Inventory-Borderline Features Scale [PAI-BOR; Morey, 1991]. The PAI-BOR consists of four subscales with a total of 24 items. The subscales correspond to the different aspects of BPD (i.e., Affective Instability, Identity Problems, Negative Relationships, and Self-Harm). We used the full scale score for our study. The items have a four-answer scale (0–3; false, slightly true, mainly true, and very true), and were scored according to Morey’s test manual.

Validity and reliability of the CAARS and PAI-BOR have been evidenced by multiple studies. The studies support that the CAARS and PAI-BOR are reliable in indicating to what degree ADHD symptoms or borderline personality traits are present in an individual [Trull, 1995; Conners et al., 1999; Trull, 2001; Kooij et al., 2008]. The six-month test–retest correlation assessed on 200 unrelated individuals was 0.78 for the PAI-BOR and 0.67 for the CAARS ADHD index.

Zygosity

Zygosity of same sex twins was determined either from DNA typing (N = 2,757) or from eight self-report survey questions that were included in the questionnaire (N = 4,476). These eight are questions on twin resemblance and confusion of the twins by family members and strangers. The opposite sex twins were classified as dizygotic (DZ). Agreement between zygosity determined by DNA typing and by survey question is about 93–97% [Rietveld et al., 2000; Willemsen et al., 2005].

Statistical Analyses

Variation in human behavior may be caused by genetic and/or non-genetic (environmental) differences between individuals. The relative contributions of genotype and environment to phenotypic variation can be estimated with the classical twin design. The twin design makes use of the fact that monozygotic (MZ) twins are (nearly) genetically identical, and that DZ twins (and non-twin siblings) share on average50%of their segregating genes [Boomsma et al., 2002]. The classic twin model incorporates this fact, and, depending on which family members are included in a study, permits the estimation of the relative contributions of additive genetic effects (A), non-additive genetic effects (Dominance; D), shared environmental effects (C), and non-shared environmental effects (E). Based on data from only MZ and DZ twins and their siblings either an ADE or an ACE model can be fitted to the data. The choice between these latter two models may be based on prior knowledge or on the pattern of correlations in MZ and DZ twins. When the DZ correlation is more than half the MZ correlation, there is evidence for environmental effects shared by twins from the same family (C) and when the DZ correlation is less than half the MZ correlation, there is evidence for non-additive genetic effects (D). Based on results from previous analyses, and the correlation structure found in the present study (see results section) we fitted an ADE-model to the data [Distel et al., 2009; Boomsma et al., 2010].

In a bivariate ADE model the association between BPT and ADHD symptoms was modeled as a function of genetic or environmental factors that influence both disorders. The MZ and DZ cross-trait cross-twin correlations provide the information needed to estimate the overlap of additive genetic and unique environmental factors. If the DZ cross-trait cross-twin correlation is half the MZ cross-trait cross-twin correlation the influence of A on the covariance of BPT and ADHD symptoms is indicated. If the DZ cross-trait cross-twin correlation is smaller than half the MZ cross-trait cross-twin correlation, this suggest that non-additive genetic factors also are of importance.

Statistical analyses were carried out using structural equation modeling in Mx by full-information maximum likelihood [Neale et al., 2006]. Data from complete and incomplete twin pairs were included in the analysis which corrects for ascertainment bias and improves estimates of means and variances. The fit of different models was compared with likelihood-ratio tests (LRT). An LRT provides a χ2-test with the degrees of freedom (df) being equal to the difference in the number of parameters in the two models. Significance indicates a worse fit of the constrained model compared to the full model. Because of the large sample size we used a more conservative P-value of 0.01.

In a saturated model we tested for heterogeneity of correlations among males and females. Tests for heterogeneity included constraining the correlations between males and females within zygosity to be equal (quantitative sex differences; MZ males = MZ females and DZ males = DZ females) and constraining the correlations between same sex DZ twins and dizygotic opposite sex (DOS) twins to be equal (qualitative sex differences; DZ males = DZ females = DOS). Based on prior analyses sex was included as regression coefficient in the means model for BPT and age was included as regression coefficient in the means model for BPT and ADHD symptoms. For the same reason, correlations were constrained to be equal for DZ twin pairs and sibling pairs and the variances were constrained to be equal for males and females [Distel et al., 2008a; Boomsma et al., 2010].

As stated above, the observed variances of BPT and ADHD symptoms, and the observed covariance between the two disorders, are decomposed in the bivariate ADE model into genetic variance (A and D), and environmental variance (E). For DZ twins and non-twin siblings the correlation of the A and D factors are fixed to 0.5 and 0.25, respectively whereas for MZ twins the correlation is 1.0 for both A and D. The full ADE bivariate Cholesky model can be tested against several more constrained models. Constraining the contribution of additive genetic effects and dominant genetic effects to zero permits testing whether A and D contribute significantly to the total variance of BPT and ADHD symptoms. Similarly, effects of A, D, and E on the covariance between BPT and ADHD symptoms (represented by the path coefficients a21, d21, and e21 in Fig. 1) can each be fixed to zero to evaluate the significance of shared genetic and environmental influences. Non-significance of the LRT test comparing the constrained to the full model can be considered as evidence that the constrained covariance factor does not play a role in explaining the correlation between BPT and ADHD symptoms.

FIG. 1.

FIG. 1

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Graphical representation of the best fitting bivariate genetic model. Squares represent the measured variables borderline personality traits (BPT) and attention-deficit hyperactivity disorder (ADHD) symptoms. A1 represents the genetic variance that is common to BPT and ADHD symptoms. E1 represents the unique environmental factors that are common to BPT and ADHD symptoms. Path coefficients a and e represent the influence of latent additive genetic and unique environmental factors on the phenotype. All latent A and E factors have unit variances. For clarity reasons only the twins are drawn in this figure although non-twin siblings were also included in the analyses.

RESULTS

Preliminary data exploration showed a slightly right-skewed distribution of the BPT data, which was addressed by a square-root transformation to approximate normality.

Testing the Correlations for Sex Differences

Table II shows the results of the tests performed in the saturated model. The phenotypic correlation between BPT and ADHD symptoms was the same for males and females (model 1). There were no quantitative or qualitative sex differences in the correlations for BPT (models 2–4) and ADHD symptoms (models 5–7) or in the covariance between them (models 8–10). This means that the heritability of BPT and ADHD symptoms is equal for males and females (no quantitative sex differences) and that the same genes influence variance in BPT and ADHD symptoms in males and females (no qualitative sex differences). Similarly, the covariance decomposition into a part due to genetic and a part due to environmental influences is equal for males and females. The (cross) correlations from the full and the most constrained saturated model (printed in bold) are shown in Table III. The MZ twin correlation was more than twice the DZ/sibling correlation for both BPT and ADHD symptoms which indicates the possible influence of non-additive genetic effects.

TABLE II.

Saturated Model-Fitting Results for Borderline Personality Traits (BPT) and Attention-Deficit Hyperactivity Disorder (ADHD) Symptoms

Test −2LL χ2 Δdf P
  0. Saturated model 56629.347
  1. rphen males = rphen females 1 vs. 0 56631.427 2.080 1 0.556
Tests for sex differences in BPT correlations
  2. MZM = MZF 2 vs. 1 56631.440 0.013 1 0.909
  3. DZM = DZF 3 vs. 2 56631.621 0.181 1 0.671
  4. DZM = DZF = DOS 4 vs. 3 56633.350 1.729 1 0.189
Tests for sex differences in ADHD symptoms correlations
  5. MZM = MZF 5 vs. 4 56633.368 0.018 1 0.893
  6. DZM = DZF 6 vs. 5 56638.016 4.648 1 0.031
  7. DZM = DZF = DOS 7 vs. 6 56638.987 0.971 1 0.324
Tests for sex differences in cross correlations
  8. MZM = MZF 8 vs. 7 56639.022 0.035 1 0.852
  9. DZM = DZF 9 vs. 8 56640.222 1.200 1 0.273
  10. DZM = DZF = DOS 10 vs. 9 56641.162 0.940 1 0.332
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−2LL, −2 log likelihood; χ2, chi-square; df, degrees of freedom; P, P-value; rphen, phenotypic correlation; MZ, monozygotic; DZ, dizygotic; M, males; F, females; DOS, dizygotic opposite sex.

TABLE III.

Estimates of the Phenotypic Correlation and (Cross) Twin Correlations for Borderline Personality Traits and Attention-Deficit Hyperactivity Disorder Symptoms

Phenotypic correlation males 0.57
Phenotypic correlation females 0.59
Phenotypic correlation 0.59
Borderline personality traits
  Monozygotic male twin pairs 0.46
  Monozygotic female twin pairs 0.47
  Monozygotic twin pairs 0.47
  Male sibling pairs 0.18
  Female sibling pairs 0.22
  Sibling pairs with opposite sex 0.16
  Sibling pairs 0.19
Attention-deficit hyperactivity disorder symptoms
  Monozygotic male twin pairs 0.35
  Monozygotic female twin pairs 0.37
  Monozygotic twin pairs 0.36
  Male sibling pairs 0.05
  Female sibling pairs 0.21
  Sibling pairs with opposite sex 0.12
  Sibling pairs 0.16
Cross trait correlations
  Monozygotic male twin pairs 0.28
  Monozygotic female twin pairs 0.30
  Monozygotic twin pairs 0.29
  Male sibling pairs 0.09
  Female sibling pairs 0.16
  Sibling pairs with opposite sex 0.08
  Sibling pairs 0.12
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Bivariate Genetic Analysis

Genetic model fitting results are shown in Table IV. The contribution of D could be constrained at zero without worsening of the model fit (model 1–3). The constrained models with the effects of A (model 4 and 5) fixed at zero resulted in a significant deterioration of the model fit. Figure 1 shows a graphical representation of the bivariate model and gives the path coefficients from the best fitting model. The total variance in BPT can be written as (a211) + (e211). The heritability of BPT [calculated as (a211)/(a211) + (e211)] was estimated at 45%. The influence of E on individual differences in BPT [calculated as (e211)/(a211) + (e211)] was estimated at 55%. Total variance in ADHD symptoms can be written as (a221) + (a222) + (e221) + (e222). The heritability of ADHD symptoms [calculated as (a221) + (a222)/(a221) + (a222) + (e221) + (e222)] was estimated at 36%. The influence of E on ADHD symptoms [calculated as (e221) + (e222)/(a221) + (a222) + (e221) + (e222)] was estimated at 64%. The genetic and environmental covariance may be written as (a11 × a21) and (e11 × e21), respectively. The genetic correlation (rGa) [calculated as a11 × a21/(√(a211) × √(a221 + a222))] was estimated at 0.72 and the environmental correlation [calculated as e11 × e21/(√(e211) × √(e221 + e222))] at 0.51. The percentage of the phenotypic correlation explained by A may be calculated as √(h2bpd) × rG × √(h2adhd) divided by the phenotypic correlation. Likewise, the percentage of the phenotypic correlation explained by E may be calculated as √(e2bpd) × rE × √(e2adhd) divided by the phenotypic correlation. Based on these calculations the phenotypic correlation can be explained for 49% by A and 51% by E (see Table V).

TABLE IV.

Bivariate Genetic Model Fitting Results of Borderline Personality Traits (BPT) and Attention-Deficit Hyperactivity Disorder (ADHD) Symptoms

Test −2LL χ2 Δdf P
0: Full model 53170.957
1: d21 = 0 1 vs. 0 53172.328 1.371 1 0.242
2: D BPT = 0 2 vs. 1 53174.349 2.021 1 0.155
3: D ADHD symptoms = 0 3 vs. 2 53174.349 0 1 1.000
4: a21 = 0 4 vs. 3 53392.697 218.348 1 <0.001
5: A = 0 5 vs. 3 53661.853 487.504 3 <0.001
6: e21 = 0 6 vs. 3 53712.868 538.519 1 <0.001
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A, genetic factors; D, non-additive genetic factors; a21, cross trait covariance additive genetic factor; d21, cross trait covariance non-additive genetic factor; e21, cross trait covariance non-shared environmental factors; −2LL, −2 log likelihood; df, degrees of freedom; P, P-value.

TABLE V.

Genetic Correlation, Environmental Correlation, and Amount of Phenotypic Correlation Between Attention-Deficit Hyperactivity Disorder Symptoms and Borderline Personality Traits Explained by Genes and Environment

Phenotypic correlation (rp) 0.59
Genetic correlation 0.72
Environmental correlation 0.51
rp explained by genes 49%
rp explained by environmental factors 51%
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DISCUSSION

This study presents a genetic analysis of the comorbidity of adult ADHD symptoms and BPT. While previous studies focused on clinical and phenotypic resemblance of the two disorders, our aim was to decompose the phenotypic correlation between BPT and ADHD symptoms in terms of shared genetic and environmental influences. A high correlation between BPT and ADHD symptoms (r = 0.59) was found in this large population based sample. The genetic analyses showed that this correlation can be explained for 49% by additive genetic factors, and for 51% by unique environmental factors. Genetic factors explained 36% of the variance in ADHD symptoms and 45% in BPT. The heritability estimates for BPT symptoms is likely to include some dominant genetic effects [see Distel et al., 2009], but for ADHD in adults genetic dominance does not seem to play a role [Boomsma et al., 2010]. It is therefore unlikely that non-additive genetic factors contribute in any major way to the covariance of the two traits.

Further research is needed to investigate the underlying sources of the genetic and non-shared environmental factors that influence both BPT and ADHD symptoms. Biological factors influencing both BPT and ADHD may contribute to dysregulation of systems that influence impulsive aggression such as the serotonergic system or the hypothalamic–pituitary–adrenal axis system [Gollan et al., 2005]. Genetic linkage studies of BPD and ADHD may provide information on the sources of shared genetic influences. Distel et al. [2008b] found that chromosomal region 9p24.1 was linked to BPT in Dutch adults (LOD = 3.55). Three other regions (1q31.1, 4p16.1, and 18q23) were suggestively linked to BPT (LOD = 1.60, 1.49, and 1.44, respectively). Around the location of the most pronounced linkage peak (9p24.1) the protein tyrosine phosphatase receptor type delta (PTPRD) gene is located. A genome wide association study conducted by Anney et al. [2008] reported an association between ADHD and the PTPRD gene. Elia et al. [2010] found four independent deletions within the PTPRD gene which frequently presented with ADHD. In addition, a genome wide linkage scan reported the region of the PTPRD gene to be associated with nicotine dependence [Li et al., 2007], a phenotype highly associated with ADHD and BPD [Wilens et al., 2008; Pulay et al., 2010]. The PTPRD gene is thus a good candidate to include in a biological pathway increasing the risk for both disorders.

Environmental exposures that influence BPD include traumatic life events, such as sexual or physical abuse and parental divorce, loss or illness. These risk factors are common in patients with BPD and thought to be etiologically linked to BPD [Zanarini et al., 1997; Helgeland and Torgersen, 2004; Bandelow et al., 2005; Horesh et al., 2008]. Rucklidge et al. [2006] found that retrospectively assessed childhood emotional abuse and neglect was more common in adults with ADHD than in a control group. However, the authors conclude that it is not clear whether children who show typical ADHD behavior are more likely to be exposed to trauma or whether experiencing a trauma in childhood increases the likelihood of developing ADHD. Lehn et al. [2007] used the discordant MZ twin design as a tool to identify environmental risk factors that may explain the discordance within identical twin pairs. Compared with unaffected co-twins, affected twins had lower birth weight and delayed physical growth and motor development. Differences between discordant and concordant groups included maternal smoking during pregnancy and living with only one parent. Although trauma has not been linked etiologically to ADHD with certainty one may speculate that for children with ADHD, adverse childhood experiences are a risk factor for developing BPD in adulthood. In other words, the interaction between a “sensitive” genotype (i.e., the genetic predisposition to ADHD) and adverse environmental influences in childhood may lead to the development of BPD in adulthood.

Some limitations of the present study should be noted. First, some selection bias may have been present in the sample. A non-response study showed that individuals from families in which only some individuals participate in the study show slightly more BPT than individuals from families in which most individuals participate in the study [Distel et al., 2007]. For the ADHD index scale of the CAARS this information is not presented in the Distel et al. [2007] publication, but applying the analyses to the ADHD index showed that, after bonferroni correction [Distel et al., 2007] there was no significant effect of non-response [F(1, 3,277) = 5.34, P = 0.021]. Second, BPT and ADHD symptoms were assessed using questionnaires. Features clinically associated with BPD and ADHD were assessed in a large population based sample with the PAI-BOR and the CAARS which both have shown to discriminate well between patients and non-patients. However, replication of the results using other measures, such as clinical interviews, is necessary. Third, although there are shared genetic and environmental risk factors that explain the association between BPT and ADHD symptoms there are alternative explanations [see e.g., Neale and Kendler, 1995; Middeldorp et al., 2006] for comorbidity some of which are nested under the general bivariate model of shared genetic etiology, such as (reciprocal) causation. Other genetically informative longitudinal and cross-sectional designs, as described by De Moor et al. [2008], should be applied to test for a causal relationship between ADHD symptoms and BPT.

Our findings are interesting in light of the proposal to include “Disruptive Mood Dysregulation Disorder” (DMDD)in the DSM-5 [American Psychiatric Association: DSM-5 Development: Proposed Revisions, 2011]. A study on severe mood dysregulation characterized by abnormal mood (anger or sadness), hyperarousal, and increased reactivity to negative emotional stimuli showed that 86.3% of these children are diagnosed with ADHD [Leibenluft, 2011]. Longitudinal studies focused on DMDD as a developmental manifestation of axis-I disorders and showed that these youths are at increased risk for depression [Brotman et al., 2006; Stringaris et al., 2009; Leibenluft, 2011]. Studies on the Child Behavior Checklist Dysregulation profile (previously known as juvenile bipolar disorder profile) show a high stability in symptoms between age 7 and age 12 and an increased risk for problems with regulating affect, behavior, and cognition [Boomsma et al., 2006b; Althoff et al., 2010]. There are no studies that investigated the risk of youths with DMDD for personality disorders. The overlapping criteria of DMDD and BPD (temper outbursts and negative mood in DMDD described as affective instability with intense episodic dysphoria in BPD) and the high comorbidity of both disorders with depression and ADHD, raises the hypothesis that the children fulfilling criteria for ADHD and DMDD may have the highest chance to develop BPD at adulthood. If ADHD and DMDD are precursors for BPD in adulthood this may have important implications for treatment. Prospective longitudinal studies spanning childhood and the adult age range are needed to further elucidate the relationship between childhood ADHD and DMDD and BPD at adulthood.

ACKNOWLEDGMENTS

Funding was obtained from the following grants: Psychometric and genetic assessments of substance use (PI Neale; NIH DA-18673, DA-026119; Genetics of Mental Illness ERC-230374); Spinozapremie (NWO/SPI 56-464-14192); Neuroscience Campus Amsterdam (NCA), and the EMGO+ institution; Center for Medical Systems Biology (NWO Genomics). CM was supported by NWO-ZonMw (NWO 916-76-125). The EFPTS has been partly supported by grants from the Fund for Scientific Research Flanders and by Twins Association for Scientific Research in Multiple Births Belgium.

Footnotes

Financial disclosures: None.

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