Mental Health and Behavioral Sciences Service, Providence VA Medical Center, Providence, Rhode Island
Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, USA
Correspondence: Amir A. Khan, MD, Mental Health and Behavioral Sciences Service, Providence VA Medical Center, 830 Chalkstone Ave., Providence, RI 02908, USA. E-mail: amir.khan2{at}med.va.gov
Funding detailed in Acknowledgements.
See editorial, pp.
182184, this
issue.
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ABSTRACT |
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Aims To study the degree to which variation in normal personality accounts for the comorbidity of eight common psychiatric and substance use disorders.
Method Internalising disorders (major depression, generalised anxiety and panic disorders, phobias), externalising disorders (alcohol and drug dependence, antisocial personality and conduct disorders) and personality dimensions of neuroticism, extraversion and novelty seeking were assessed in 7588 participants from a population-based twin registry. The proportion of comorbidity explained by each personality dimension was calculated using structural equation modelling.
Results Neuroticism accounted for the highest proportion of comorbidity within internalising disorders (2045%) and between internalising and externalising disorders (1988%). Variation in neuroticism and novelty seeking each accounted for a modest proportion (1012% and 714%, respectively) of the comorbidity within externalising disorders. Extraversion contributed negligibly.
Conclusions High neuroticism appears to be a broad vulnerability factor for comorbid psychiatric disorders. Novelty seeking is modestly important for comorbid externalising disorders.
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INTRODUCTION |
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METHOD |
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The male-male and male-female (MM/MF) twin pairs, covering the birth years 1940-1974, were ascertained in a separate study beginning in 1993. We interviewed 72% of the eligible sample, usually by telephone, in our wave 1 study. This sample was followed up in a second wave of face-to-face interviews (1994-1998) that were completed with 79.4% of eligible participants.
We examine here the results of combined data from the MM/MF and FF samples, based on the second and fourth wave of interviews, respectively, because these were the most recent waves in which we had measured both personality and psychiatric diagnoses. Our sample consisted of 7588 individual twins, with 4240 males (55.9%) and 3348 females (44.1%). All participants were Caucasian, ranging in age from 20 to 58 years (mean=36.8, s.d.=8.9) at the time of the interview. Informed consent was obtained from all participants prior to assessment.
Measures
Psychiatric disorders
The outcome measures of interest, as outlined in the introduction, were
lifetime diagnoses of common psychiatric disorders. In order to facilitate the
discussion, we will use the concepts of internalising (propensity to express
distress inwards, including major depression, GAD, panic disorder, any phobia)
and externalising (propensity to express distress outwards, including alcohol
and drug dependence, antisocial personality disorder, conduct disorder)
disorders as described by Krueger et al
(Krueger, 1999; Krueger & Markon, 2001).
With the exception of any phobia, all disorders were assessed
using the Structured Clinical Interview for DSM-III-R
(Spitzer & Williams,
1985). Diagnostic algorithms for GAD, panic disorder and alcohol
dependence were modified to reflect DSM-IV criteria
(American Psychiatric Association,
1994), whereas major depression, drug dependence, antisocial
personality disorder and conduct disorder were based on DSM-III-R criteria
(American Psychiatric Association,
1987) owing to the lack of items corresponding to DSM-IV criteria.
The drug dependence diagnosis included dependence on marijuana, cocaine,
opiates, hallucinogens, stimulants, sedatives or other drugs. Phobias were
assessed with an adaptation of the phobic disorders section of the Diagnostic
Interview Schedule, version III-A (Robins
& Helzer, 1985), and the diagnosis of any phobia
included agoraphobia, social, situational, animal, blood and miscellaneous
phobias. The diagnostic algorithm for phobias has been described in detail
previously (Kendler et al,
2002).
Interviewers were carefully trained and supervised, and had at least a
masters degree in a mental health-related field or a bachelors
degree in such a field and two years of clinical experience. Diagnoses for
conduct disorder and antisocial personality disorder were based on self-report
questionnaires; all other diagnoses were assessed using personal interview.
Inter-rater reliability for diagnosis (based on a subsample of FF twins) was
high (e.g. for major depression, mean (s.d.), =0.96 (0.04)), and
test-retest reliability (based on an average interval of 4.5 weeks, range 2-8
weeks, between base and reliability interview) was also acceptable for most
diagnoses (range=0.23-0.74, average
=0.52). Finally, the comorbidity of
antisocial personality disorder and conduct disorder was not examined because
the diagnosis of antisocial personality disorder requires the onset of conduct
disorder before age 15 years. Table
1 describes the prevalence of psychiatric disorders in our
sample.
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Personality
Neuroticism and extraversion, as conceptualised by Eysenck
(Eysenck & Eysenck, 1975; Hirschfeld et al,
1983), have been identified cross-culturally as major personality
traits by nearly all subsequent investigators
(Pervin, 1990). Neuroticism
reflects emotional instability, vulnerability to stress and anxiety proneness,
whereas extraversion measures sociability and liveliness. Novelty seeking,
another personality dimension, measures exploratory excitability,
impulsiveness, extravagance and regimentation
(Cloninger et al,
1991). Personality measures of neuroticism and extraversion were
obtained by self-report questionnaire in the MM/MF sample and were part of the
telephone interview in the FF sample. Novelty seeking was assessed by
self-report questionnaire only, in both samples. Neuroticism and extraversion
were assessed with 12 and 8 items, respectively, from the shortened version of
the Eysenck Personality Questionnaire - Revised (EPQ-R;
Eysenck et al, 1985;
Heath et al, 1992).
Novelty seeking was evaluated by 18 items from the abbreviated 54-item version
of the Tridimensional Personality Questionnaire (TPQ) of Cloninger
(Cloninger et al,
1991; Heath et al,
1994). For statistical analyses we used composite personality
measures derived from individual items for each dimension, respectively.
Missing data
Valid data on all three personality measures and all eight psychiatric
disorders were available for the vast majority (85.6%; n=6499) of the
sample. Missing data for major depression, GAD, any phobia and alcohol and
drug dependence were minimal (<0.6%). Rates of missing data for conduct
disorder and antisocial personality disorder were somewhat higher
(approximately 7-16%) because these diagnoses were assessed using a separate
self-report questionnaire. Rates of missing data for the three personality
measures were 2-16%, also due primarily to lower response rates for the
self-report questionnaire. Preliminary analyses revealed no significant
differences in mean levels of personality or psychiatric diagnosis due to
missing data on other variables (results available from the authors upon
request).
Statistical analysis
We performed logistic regression analyses to estimate the association of
each personality dimension with each psychiatric disorder. Correction for the
correlated structure of our twin data was done using generalised estimating
equations (Liang & Zeger,
1986) as implemented in the Statistical Analysis System (SAS)
procedure GEN-MOD. Multiple logistic regression analyses were performed with
all three personality measures as independent variables. Age, zygosity and
gender were used as covariates. Scores for all personality measures were
standardised to a mean of 0 and a variance of 1 to facilitate the direct
comparison of their effects on the disorder of interest. Odds ratios with 95%
confidence intervals and their statistical significance are reported. An odds
ratio of >1 represents the increase in risk of disorder associated with
each standard deviation (s.d.) increase in the score of the personality
dimension. An odds ratio of <1 represents the decrease in risk associated
with each s.d. increase in personality dimension score.
In order to calculate the proportion of comorbidity attributed to variation in normal personality, we conducted structural equation modelling analyses using the software program Mx (Neale et al, 1999). As depicted in Fig. 1, the model we used allowed us to calculate the total covariance (i.e. comorbidity) between the disorders of interest. This covariance was broken down into the covariance attributed to personality and the residual covariance, which represents any remaining comorbidity after removing the covariation attributable to personality. Covariance due to personality comprised both direct and indirect effects. Direct effects are the direct effects of each personality measure on each of the two disorders. In path analyses, the contribution of personality to comorbidity can be assessed by multiplying the direct effects of a given personality variable on each of the two disorders. Indirect effects are effects of personality on disorder and comorbidity that occur through correlated personality dimensions. Because the overall correlation across personality measures was low to moderate (between neuroticism and extraversion=-0.19, neuroticism and novelty seeking=0.04, extraversion and novelty seeking=0.34) indirect effects of personality are ignored when calculating the contribution to covariance of each individual personality dimension (although they are included as a separate category; see Table 3 below).
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The structural equation models were fitted to the raw data using maximum likelihood estimation, which allowed us to use all valid data, even if some responses or observations for a given individual are missing. Psychiatric disorders were coded as binary (1=present, 0=absent); thus, data were treated as ordinal, and thresholds for each disorder were estimated using z scores that corresponded to the prevalence of the given diagnosis. These thresholds were allowed to vary by gender to accommodate gender differences in the rates of psychiatric disorders. To test for significant gender differences, we constrained the thresholds to be equal for men and women and evaluated the overall fit of the model (using Akaikes information criteria, AIC) compared with the model where thresholds were allowed to vary by gender. Models with the lowest AIC values were considered to be the best-fitting models. We also tested for gender differences in the overall pattern of covariance by constraining the parameter estimates to be the same in males and females, and comparing the pattern of covariance with a model where parameters were allowed to vary by gender. Because Mx currently lacks the capability to analyse continuous and ordinal traits simultaneously, the continuously measured personality traits were divided into categories based on the maximum number of responses possible, and thresholds corresponding to the proportions of individuals in each category were estimated. For example, scores on the neuroticism variable were in the range 0-12. Thus, we used 12 thresholds to estimate the proportion of individuals within each response category.
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RESULTS |
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We also tested for interactions between gender and each of our three
personality measures for each of the disorders. Out of 24 possible
interactions (8 disordersx3 interactions), only the interaction between
gender and neuroticism for alcohol dependence was significant (ß=0.06,
s.e.=0.02, Wald 2=5.22, P<0.05). In this case, the
relationship between neuroticism and alcohol dependence was stronger for
females than for males. However, it should be noted that this significant
interaction may be a stochastic effect. Thus, for the structural equation
modelling analyses of personality and comorbidity, males and females were
combined into a single sample, although thresholds corresponding to
psychiatric disorder were estimated separately for males and females.
Structural equation modelling of personality effects on comorbidity
For ease of interpretation, the results of the structural equation
modelling analyses are depicted graphically in
Fig. 2. The height of the bar
represents the total phenotypic comorbidity of any two given disorders, and
the differently shaded segments depict the direct covariance accounted for by
each individual personality dimension, as well as any indirect effects, and
the residual covariance. For example, the comorbidity (phenotypic correlation)
between major depression and GAD is 0.41. Neuroticism accounts for the 0.16 of
this comorbidity whereas the remaining comorbidity (0.25) was residual
covariance. Extraversion, novelty seeking and indirect effects accounted for
negligible (and negative) covariance. In order to facilitate the description,
results from these analyses have been presented also as percentages
of the total comorbidity (Table
3). Thus, in the case of comorbidity between major depression and
GAD, Table 3 shows that 0.41 is
total comorbidity. Neuroticism accounts for 39% of this comorbidity, with the
remaining comorbidity due primarily to residual covariance (61%).
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The overall pattern of results, as shown in Fig. 2 and Table 3, indicates that neuroticism accounts for the highest proportion of comorbidity within internalising disorders (20-45%, arithmetic average=31%) and between internalising and externalising disorders (19-88%, arithmetic average=36.8%). Neuroticism also explained 10-12% of the comorbidity within externalising disorders. Extraversion explained only a very small proportion of the comorbidity (-4.9 to 7.4%). Novelty seeking accounted for a negligible proportion of comorbidity within internalising disorders (-0.8 to 0.7%) and between internalising and externalising disorders (-13.2% to 5.8%); however, novelty seeking did account for 7.4-14% of the comorbidity within externalising disorders. Residual covariance (i.e. due to factors other than personality) accounted for most of the comorbidity, with an arithmetic average of 65%. Negative values in Fig. 2 and Table 3 reflect the effects of low extraversion (introversion) and low novelty seeking on comorbidity, although the majority of these effects are quite small.
Although the models where thresholds for psychiatric disorders were allowed to vary by gender consistently fit the data better than models assuming equal thresholds, there were no significant gender differences in the covariance structure (results available from the authors upon request). Thus, the pattern of comorbidity accounted for by personality was similar in males and females, despite the significant differences in the rates of psychiatric disorders.
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DISCUSSION |
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Extraversion
Although extraversion was significantly, albeit weakly, associated with
four of the eight psychiatric disorders in the logistic regressions, it
explained very small proportions of comorbidity. This pattern of weak effects
of extraversion on psychiatric disorders and comorbidity is inconsistent with
previous research (Sher & Trull,
1994) and probably stems from the restrictive definition of our
extraversion scale, which only reflects sociability. Eysenck revised the
extraversion scale in the EPQ-R and items that measured impulsivity were
largely moved to the psychoticism scale
(Nyborg, 1997).
Novelty seeking
High novelty seeking increased the risk for externalising disorders
significantly (Table 2) when
these disorders were examined individually. Novelty seeking also accounted for
the largest proportion of comorbidity between externalising disorders (7-14%,
arithmetic average=11.9%). Not surprisingly, novelty seeking was unrelated to
the comorbidity within internalising disorders and, for the most part, between
internalising and externalising disorders. However, somewhat surprisingly, the
contribution of neuroticism to the comorbidity within externalising disorders
was comparable with the effects of novelty seeking.
These results further support the existence of broader, underlying dimensions of core psychopathological processes. Neuroticism appears to be a robust underlying dimension not only for the comorbidity within internalising disorders but also between internalising and externalising disorders and within externalising disorders. This leads us to reconsider the issue of psychiatric classification and an age-old question of splitting neurosis (Tyrer, 1985). Our previous research has indicated that the comorbidity between major depression and GAD and, to some extent, between major depression and alcohol dependence largely results from common genetic factors (Kendler et al, 1992, 1993a) with notable gender differences (Prescott et al, 2000). In a previous report, we also found that over 50% of the genetic liability for major depression was shared with neuroticism (Kendler et al, 1993b). Thus, the possibility of common genetic liability between personality and comorbid disorders appears to be a reasonable hypothesis and will be the subject of future investigation.
Limitations
The results of this study should be interpreted in the context of four
potential methodological limitations.
First, we used scales of neuroticism and extraversion from the EPQ-R and novelty seeking from the TPQ. Although neuroticism and extraversion represent widely accepted higher dimensions of personality, there is no agreement about the lower-order dimensions among different personality researchers. Moreover, some would argue that these two scales provide an incomplete description of the structure of heritable personality differences (Heath et al, 1994). How much more of the covariation among disorders would have been explained if we used the complete EPQ-R (neuroticism, extraversion, psychoticism and lie scale) or the complete TPQ (novelty seeking, harm avoidance and reward dependence) is speculative. Similarly, although interrater agreement for diagnosis was high, test-retest reliability for some of the lower-prevalence disorders (i.e. GAD, panic disorder and antisocial personality disorder) was low (0.23-0.42). This lower reliability may have increased the variance due to random errors of measurement, lowering the strength of associations of comorbidity with personality.
Second, the cross-sectional nature of the data made it difficult to establish causality and had a potential to confound state, trait and scar effects. However, the use of lifetime diagnosis provided some assurance that the confounding effects were likely to be minimal.
Third, because of some relatively young individuals in our sample, the risk period for certain psychiatric disorders was not over. As a result, true prevalence may be underestimated in the present sample, with concomitant effects on covariance.
Fourth, the sample was limited to Caucasian individuals so the results may not be generalisable to other ethnic groups.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication August 5, 2003. Revision received September 30, 2004. Accepted for publication October 8, 2004.
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