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Clin Psychol Sci. Author manuscript; available in PMC 2017 March 01. Published in final edited form as: Clin Psychol Sci. 2016 March 1; 4(2): 216–228. doi:10.1177/2167702615583228.
Coping Styles in Twins Discordant for Schizophrenia, Bipolar Disorder, and Depression Rebecca G. Fortgang1, Christina M. Hultman2, and Tyrone D. Cannon1 1 2
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Abstract
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Schizophrenia, bipolar disorder, and major depression share several clinical and etiological factors. Coping is a critical mediator of the relationship between stress and psychopathology and a point of clinical intervention for all three disorders. However, little is known about their degree of overlap in coping style, or the influence of unique or shared genetic diathesis. In this study, we examined five factors of coping within and across disorder proband and co-twin groups, modeled heritability, and tested for endophenotypic pattern in a sample of twin pairs recruited from the Swedish Twin Registry (N = 420). Although there was substantial phenotypic overlap across disorders, including low levels of Productive, Problem-Focused coping and high levels of Disengagement, each disorder was associated with a unique profile across other dimensions of coping. We also found evidence of heritability for three of five factors, yet little evidence of genotypic overlap among disorders contributing to similar strategy-use. Although initially conceptualized as entirely distinct syndromes, schizophrenia, bipolar disorder, and major depression are multi-determined phenomena that share certain clinical features as well as risk factors. At the same time, the cardinal symptoms of these disorders are different, and it is generally assumed that some of the causal contributors will not be overlapping. Where there are common risk factors and mechanisms, common treatment approaches would be indicated, and where there are disparate risk factors and mechanisms, different treatment approaches would likely be appropriate. A key question is thus discerning whether a particular risk factor or mechanism operates similarly or differently across these illness contexts.
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Stressful life events play a particularly strong role in depression (Kendler, Karkowski, & Prescott, 1999), but these factors are also elevated in patients with bipolar disorder (Hlastala et al., 2000) and schizophrenia (Norman & Malla, 1993). Regardless of disorder, coping with stress would need to be considered as a critical bottleneck process in evaluating how stress may contribute to psychopathology – a routing of all environmental stressors through a relatively narrow array of available strategies, in some cases inadequate to meet the demand. Coping may be defined as the process of responding to and potentially regulating stress, and it is a multidimensional and multi-determined set of constructs. It is fundamental
Corresponding Author: Rebecca Fortgang, 2 Hillhouse Ave., New Haven, CT 06511, [email protected], 617.840.3102.
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to our understanding of psychopathology, as it represents the interaction of stress and diathesis (Kendler, Kessler, Heath, Neale, & Eaves, 1991) and partially mediates the relationship between stress and psychopathology (e.g. McLaughlin & Hatzenbuehler, 2009; Meng, Tao, Wan, Yan, & Wang, 2011). Coping with stress and with symptoms is also a critical point of intervention in psychotherapy and predicts symptom severity within these disorders (e.g. Meyer, 2001; Van Rheenen, Murray, & Rossell, 2015).
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Kendler et al. (1991) suggested that in some disorders, coping may be an endophenotype that marks one avenue of genetic influence on the etiology of psychopathology, in interaction with exposure to stress. Endophenotypes are “risk traits” that lie on the pathways between the genetic and syndromal elements of various forms of psychopathology (Gottesman & Gould, 2003). Some coping styles, as assessed using various measures, have in fact been found to be moderately heritable in the general population (Table 1s; e.g. Busjahn, Faulhaber, Freier, & Luft, 1999; Kendler et al., 1991; Kozak, Strelau, & Miles, 2005) and could thus be considered as candidate endophenotypes for psychiatric disorders. Alternatively, proclivity to a particular coping style may be an epiphenomenon of a disorder itself – whether by way of symptoms, medication, or another factor.
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Although criteria for endophenotype status initially included specificity to one disorder (Tsuang, Faraone, & Lyons, 1993), this criterion does not hold up against more recent discoveries about shared genetics among disorders. It stands to reason that the higher the degree of genetic overlap, the more likely it is that the disorders share endophenotypes. Schizophrenia and bipolar disorder share a very high level of genetic overlap – on the order of 50–65% (Craddock, O’Donovan, & Owen, 2006; Lichtenstein et al., 2009; Purcell et al., 2009), and bipolar disorder shares approximately 25% of its genetic factors with major depression (McGuffin et al., 2003). If any coping style is an endophenotype for one disorder, it may thus also be an endophenotype for the other disorders with which it overlaps genetically.
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Prior work has compared coping styles of patients with schizophrenia, bipolar disorder, and major depressive disorder with healthy volunteers in separate studies, using a diverse set of coping measures (e.g. Lam & Wong, 2005; Van den Bosch, Van Asma, Rombouts, & Louwerens, 1992). In general, these studies have found that patients with each of these disorders show lower levels of active, productive coping than healthy volunteers. There are also findings suggestive of potentially diverse coping mechanisms across these disorders, in that patients with schizophrenia tend to use distancing, avoiding, and disengaging coping styles (e.g. Tait, Birchwood, & Trower, 2004), those with depression tend to use ruminative styles (Nolen-Hoeksema, 2000) and escapism (Rohde, Lewinsohn, Tilson, & Seeley, 1990), and those with bipolar disorder may also make greater use of avoidance (Goossens, Knoppert-van der Klein, & van Achterberg, 2008), and stimulation-avoidance and modifying excessive behavior during prodrome reduces probability of manic episodes (Lam, Wong, & Sham, 2001). Bipolar disorder has also been associated with emotion regulation abnormalities that predict aspects of symptomatology (Van Rheenen et al., 2015) and are linked with reduced frontal-amygdala connectivity (Kanske, Schönfelder, Forneck, & Wessa, 2015). However, no prior study has directly compared these three groups of patients to each other on the same measures of coping, making it difficult to discern whether these
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patterns are truly disorder specific. Further, no prior study has examined whether coping styles represent endophenotypes of these three disorders using a genetically informative design. It thus remains unclear whether any coping style is shared phenotypically across schizophrenia, bipolar disorder, and depression, whether any aspect of coping reflects an underlying genetic diathesis for any of these disorders, and whether any such coping-related endophentoype is shared with one or both of the other disorders due to their genetic overlap.
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To address these questions, we evaluated coping styles in samples of twins concordant and discordant for schizophrenia, bipolar disorder, and major depression, along with healthy comparison twin pairs. This approach permits testing not only whether a coping style is shared phenotypically across disorders, but also whether it is shared by non-clinicallyaffected co-twins of probands with the three diagnoses. The non-clinically-affected co-twins are at increased genetic risk for the disorder shown by their affected co-twin, but because they do not have the disorder overtly, these subjects represent a form of control for phenotypic expression of illness, exposure to treatments, and other secondary factors. In other words, the non-ill co-twins provide the basis for evaluating whether a particular coping style is likely to be associated with a genetic diathesis to each illness and whether any aspect of coping helps to define that part of the genetic diatheses that overlap across these three disorders. Given the limited scope of the prior literature on these issues, as summarized above, there is little basis for specifying a model that predicts discrete areas of overlap and non-overlap in terms of coping styles across disorders or across non-affected co-twins. Rather, we used the logic of the discordant twin pair design to parse the various possible patterns empirically, with an appropriate control for multiple testing, hoping to provide a set of observations useful for subsequent model-building.
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Methods Subjects
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Subjects were identified through the Swedish Twin Registry, managed by the Karolinska Institutet. Full recruitment procedures are described by Lichtenstein et al. (2006). Twin pairs were eligible for inclusion if they were same sex, between the ages of 25 and 65, and born in Sweden between 1940 and 1985 (inclusive). The age range was intended to exclude individuals who were young enough that they had not yet developed an emerging disorder or old enough that they had already developed signs of dementia. Other exclusion criteria were presence of a neurological disorder, history of significant head injury with loss of consciousness, mental retardation, history of substance dependence within 6 months of the screening interview, inability to read or comprehend spoken and written Swedish, or pregnancy/lactation at time of evaluation. To ascertain twin pairs with psychopathology, this population of twins was screened using hospital admission and discharge diagnosis information from the Swedish National Patient Registry. Screening for pairs comprising at least one twin with a diagnosis of schizophrenia or bipolar disorder yielded 562 potential probands: 257 male and 305 female. Monozygotic (MZ) and dizygotic (DZ) pairs were recruited randomly from this population. Zygosity was
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determined for twin pairs using either DNA testing or a well-validated screening measure administered to parents and twins (Lichtenstein et al., 2006), yielding 177 complete twin pairs with impulsivity data, of whom 77 were monozygotic, 97 were dizygotic, and 3 had undetermined zygosity. Healthy control tin pairs were recruited to match proband pairs on age, sex, and zygosity. Healthy controls were excluded if they had a family history of schizophrenia or bipolar disorder.
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Diagnostic interviewing was used in conjunction with hospital records to determine diagnosis for each individual, and twin pairs were then classified as controls or as concordant or discordant for schizophrenia or bipolar disorder, regardless of the initial recruitment classification. Individuals with schizoaffective disorder were included in the schizophrenia group. Discordant co-twins of probands were also included regardless of a history of non-psychotic psychopathology such as depression. Individuals recruited as controls were also included regardless of history of depression, creating for our purposes another diagnostic group of participants with major depressive disorder, without a twin affected by schizophrenia or bipolar disorder. Individuals diagnosed with a different psychotic disorder were excluded from analyses comparing diagnostic groups. Tests of Sample Representativeness—We tested whether the studied probands were comparable to the remainder of the twin proband population in terms of sex, age, age at first hospitalization, and number of hospitalizations. For sex, we used a chi-square test of independence and phi (ϕ) as an estimator of effect size. For other variables, we used Cohen’s d. Cohen (1992) suggested that values below 0.2 may be considered small effect sizes, 0.5 is a medium effect size, and 0.8 is a large effect size.
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Procedures All measures were approved by the Regional Ethics Review Board, Stockholm, Sweden.
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Clinical Evaluation—Final diagnostic status was determined by consensus using both clinical interviews and register data, which included hospital records and lifetime history of hospital admissions from 1973 until the time of evaluation. A clinical psychiatrist interviewed each participant using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID; First, Spitzer, Gibbon, & Williams, 1997) and the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997). The SCID has demonstrated superior validity over standard clinical intake interviews (First, Spitzer, et al., 1997). Current symptoms were also rated using the Hamilton Depression Rating Scale (HAM-D; Hamilton, 1960), Young Mania Rating Scale (YMRS; Young, Biggs, Ziegler, & Meyer, 1978), Scale for Assessment of Negative Symptoms (SANS; Andreasen, 1983), and Scale for Assessment of Positive Symptoms (SAPS; Andreasen, 1984). Acceptable validity and reliability have been demonstrated for each of these measures (Peralta, Cuesta, & De Leon, 1995; Williams, 2001; Young et al., 1978). Consensus diagnosis was determined by the clinical evaluation team at the Karolinska Institutet, which comprised two psychiatrists and one clinical psychologist (Dr. Hultman). All participants were clinically stable at the time of evaluation. No changes to medication regimen were made in relation to participation in the study.
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Zygosity—Zygosity was initially determined using a self-report measure that includes physical similarity ratings and family conclusions about “identical” or “fraternal” status. This measure has demonstrated good validity (Reed et al., 2005). For participants with available DNA information, DNA zygosity tests were then conducted to determine percent of allele sharing between twins. This procedure modeled Hannelius et al. (2007) and used a highly multiplexed 47 single nucleotide polymorphism (SNP) panel, including a sex-specific marker. Likelihood of zygosity for each pair was calculated assuming a 1% genotyping error rate (false positives).
Statistical Analyses
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Prior studies have used a wide variety of measures of coping including factor scores derived from other samples as well as their own samples. Several studies have derived higher-order structures of the longer version of the COPE scale using factor analysis, and there is substantial variability among reported results (Hasking & Oei, 2002). Three-factor (e.g. Lyne & Roger, 2000) and four-factor (Carver, Scheier, & Weintraub, 1989) solutions have been found, with inconsistencies in which items load on which factors. Very few factor analyses of the Brief COPE have been reported (Hasking, Lyvers, & Carlopio, 2011), but some support has been found for a three-factor structure containing problem-focused coping, emotion-focused coping, and either dysfunctional coping (Cooper, Katona, & Livingston, 2008) or avoidant coping (Hasking et al., 2011). Factor structure may vary as a function of sampling and in particular the relative numbers of individuals with different forms of psychopathology. Given this, and as suggested by the author of the Brief COPE (Carver, 2007), we undertook an analysis of its factor structure in the sample under study.
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Brief COPE Scale (Carver, 1997)—The Brief COPE is an abbreviated coping measure that assesses 14 distinct types of coping. Carver (1997) found that the measure had adequate validity and reliability, and Meyer (2001) found evidence of concurrent and predictive validity in a psychiatric sample. A subset of 18 items was administered and scored on a fourpoint scale from 0–3, and possible total scores range from 0–54. The scale directed participants to indicate their typical coping behaviors, not their responses to an individual stressor and not during a particular time frame. Thirty-five participants missed items. These participants were excluded from analyses of the derived factors for which they missed items.
Our analysis used principal axis factoring with varimax rotation. Fit of the factor model was evaluated using the Tucker Lewis Index (TLI) and the Root Mean Square Error of Approximation (RMSEA). The TLI, or Non-Normed Fit Index (NNFI), is an incremental fit index that penalizes for complexity. Hu and Bentler (1999) suggest using ≥0.95 as a cut-off value for the TLI to indicate good model fit. The RMSEA is an absolute measure of fit, and it also penalizes complexity. Lower values on the RMSEA indicate better fit, and MacCallum, Browne, and Sugawara (1996) have used 0.01, 0.05, and 0.08 as indicators of excellent, good, and mediocre fit respectively. Factor scores were derived and carried forward into subsequent analyses. To facilitate interpretation of factor content, we examined the highest loadings on each factor. In our interpretation, loadings less than 0.3 were excluded, and in the case of double-loadings, items were assigned to the factor on which their loading was strongest. For an item to be included in a factor, its loading on that factor
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was required to be at least 0.05 higher than its loading on any other factor. Factor scores based on simple addition of these items were also derived and tested to ensure robustness of results, but factor scores were chosen because of relatively small differences in loadings on multiple factors (0.3 on a factor. Theoretically, although the two-factor model supports the elevation of maladaptive coping broadly in psychopathology, it obfuscates the underlying relationships between coping behaviors and disorders that aggregate to appear as comparable total sums. In the context of psychopathology, there is apparent merit to considering more differentiated models.
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Because this analysis is cross-sectional, no conclusions can be drawn about whether observed associations between psychopathology and coping style suggest that poor coping predisposed to vulnerability for the disorder or that the illness reduced coping efficacy. Longitudinal studies of coping could tease apart these two possibilities, which may both contribute to observed differences in patient and healthy populations. It is also important to consider that this factor structure was derived in the context of psychopathology, which naturally leads to factors that reflect differences attributable to group differences in psychopathology. Performing the analysis excluding affected individuals produced a very similar factor structure, but the sample size left us underpowered to use this model in our final analyses. Additionally, heritability modeling was performed in the entire sample due to sample size, so heritability may be underestimated if individuals with psychopathology show larger differences from co-twins generally.
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The version of the Brief COPE used was further abbreviated, so results may not generalize to the structure of the complete scale. We also relied entirely on self-report, and memory fallibility as well as biased reporting may influence validity of the data. Additionally, only three diagnostic groups were included, so we cannot infer information about coping style across more disorders, despite its association with many. Our major depressive disorder group was also created post-hoc and was not specifically recruited. Finally, our sample size was relatively small, and a larger sample would potentially yield more accurate estimates of
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heritability and group means, as well as the comparison of monozygotic and dizygotic twins within diagnostic group. A larger sample would also allow heritability calculation in healthy controls and diagnostic groups separately, which could illustrate whether the effect of psychopathology masks predispositions for coping style.
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Despite these limitations, the observed pattern of findings represents a step toward uncovering the relationship between coping with stress and development of psychopathology. This set of analyses helps to uncover differential relationships of coping style across disorder, offering suggestions as to the plausible causal mechanisms involved in the etiology and maintenance of different symptom clusters in the face of stress, but much more is still obscured. For example, longitudinal work can help to tease apart the cases in which coping style created vulnerability and risk for psychopathology from those in which it emerged as a clinical phenotype in the context of the illness. Experience sampling methodology may illuminate what coping styles predict increases or decreases in symptoms during the course of a day or a week, providing clues about the role of coping in maintenance of psychopathology.
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Additionally, here we are still wedded to diagnostic categories as indicators of symptom clusters, but use of symptom-level data may tease apart what symptom or symptoms are specifically related to these coping factors. For example, schizophrenia comprises multiple factors of symptomatology that can be present to varying degree across individuals (e.g. Wallwork, Fortgang, Hashimoto, Weinberger, & Dickinson, 2012), and there may be coping styles that uniquely associate with symptom clusters. Additionally, a focus on context may be particularly important in understanding the process of coping and regulating emotions in psychopathology (Aldao, 2013), as flexibility in coping style is associated with lower levels of psychopathology (e.g. Bonanno, Papa, Lalande, Westphal, & Coifman, 2004). The field of coping research is rich, complex, and longstanding. As it extends into the future, there is more to be done to uncover how the structure of coping can be understood in the context of varying environments and clusters of psychiatric symptoms.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
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Figure 1.
Graph of Standardized Means and Standard Errors of Coping Factors
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Table 1
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Demographic information Dx Group
n
Age* M (S.D.)
%Male
Education Level M (S.D.)**
SCZ
64
50.12 (10.42)
53.10%
3.38 (1.51)
SCZ-CT
64
51.51 (11.21)
51.60%
3.80 (1.46)
BPD
71
49.59 (10.85)
40.80%
3.36 (1.51)
BPD-CT
51
49.59 (10.46)
47.10%
3.60 (1.34)
MDD
44
50.87 (10.20)
47.70%
3.58 (1.42)
MDD-CT
25
51.08 (9.18)
48.00%
3.33 (1.61)
OPSY
5
45.46 (9.91)
60.00%
2.80 (1.80)
OPSY-CT
2
38.94 (14.11)
50.00%
3.67 (1.16)
HV
61
45.99 (10.74)
49.20%
3.84 (1.20)
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No DX
33
52.86 (8.02)
36.40%
2.70 (1.64)
Whole Sample
420
49.63 (10.52)
47.40%
3.51 (1.47)
SCZ — Schizophrenia, SCZ-CT — Schizophrenia Co-Twins, BPD — Bipolar Disorder, BPD-CT — Bipolar Disorder Co-Twins, MDD — Major Depressive Disorder, MDD-CT — Major Depressive Disorder Co-Twins, OSPY — Other Psychotic Disorder, OSPY-CT — Other Psychotic Disorder Co-Twins, HV — Healthy Volunteers, No DX — No consensus diagnosis; *
Age at time of testing,
**
Ranges from 0=no schooling to 5=university.
4 people had missing data and 15 indicated “other.”
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Author Manuscript Active Coping, Positive Reframing, Planning
Self-Blame, Venting, Humor, Substance Use
Use of Emotional & Instrumental Support
Factor 3: Support-Seeking Behavioral Disengagement Self Distraction, Acceptance
Factor 4: Disengagement
Religion, Denial
Factor 5: Belief-Based
Black arrows indicate an association at p
Clin Psychol Sci. Author manuscript; available in PMC 2017 March 01. Published in final edited form as: Clin Psychol Sci. 2016 March 1; 4(2): 216–228. doi:10.1177/2167702615583228.
Coping Styles in Twins Discordant for Schizophrenia, Bipolar Disorder, and Depression Rebecca G. Fortgang1, Christina M. Hultman2, and Tyrone D. Cannon1 1 2
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Abstract
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Schizophrenia, bipolar disorder, and major depression share several clinical and etiological factors. Coping is a critical mediator of the relationship between stress and psychopathology and a point of clinical intervention for all three disorders. However, little is known about their degree of overlap in coping style, or the influence of unique or shared genetic diathesis. In this study, we examined five factors of coping within and across disorder proband and co-twin groups, modeled heritability, and tested for endophenotypic pattern in a sample of twin pairs recruited from the Swedish Twin Registry (N = 420). Although there was substantial phenotypic overlap across disorders, including low levels of Productive, Problem-Focused coping and high levels of Disengagement, each disorder was associated with a unique profile across other dimensions of coping. We also found evidence of heritability for three of five factors, yet little evidence of genotypic overlap among disorders contributing to similar strategy-use. Although initially conceptualized as entirely distinct syndromes, schizophrenia, bipolar disorder, and major depression are multi-determined phenomena that share certain clinical features as well as risk factors. At the same time, the cardinal symptoms of these disorders are different, and it is generally assumed that some of the causal contributors will not be overlapping. Where there are common risk factors and mechanisms, common treatment approaches would be indicated, and where there are disparate risk factors and mechanisms, different treatment approaches would likely be appropriate. A key question is thus discerning whether a particular risk factor or mechanism operates similarly or differently across these illness contexts.
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Stressful life events play a particularly strong role in depression (Kendler, Karkowski, & Prescott, 1999), but these factors are also elevated in patients with bipolar disorder (Hlastala et al., 2000) and schizophrenia (Norman & Malla, 1993). Regardless of disorder, coping with stress would need to be considered as a critical bottleneck process in evaluating how stress may contribute to psychopathology – a routing of all environmental stressors through a relatively narrow array of available strategies, in some cases inadequate to meet the demand. Coping may be defined as the process of responding to and potentially regulating stress, and it is a multidimensional and multi-determined set of constructs. It is fundamental
Corresponding Author: Rebecca Fortgang, 2 Hillhouse Ave., New Haven, CT 06511, [email protected], 617.840.3102.
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to our understanding of psychopathology, as it represents the interaction of stress and diathesis (Kendler, Kessler, Heath, Neale, & Eaves, 1991) and partially mediates the relationship between stress and psychopathology (e.g. McLaughlin & Hatzenbuehler, 2009; Meng, Tao, Wan, Yan, & Wang, 2011). Coping with stress and with symptoms is also a critical point of intervention in psychotherapy and predicts symptom severity within these disorders (e.g. Meyer, 2001; Van Rheenen, Murray, & Rossell, 2015).
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Kendler et al. (1991) suggested that in some disorders, coping may be an endophenotype that marks one avenue of genetic influence on the etiology of psychopathology, in interaction with exposure to stress. Endophenotypes are “risk traits” that lie on the pathways between the genetic and syndromal elements of various forms of psychopathology (Gottesman & Gould, 2003). Some coping styles, as assessed using various measures, have in fact been found to be moderately heritable in the general population (Table 1s; e.g. Busjahn, Faulhaber, Freier, & Luft, 1999; Kendler et al., 1991; Kozak, Strelau, & Miles, 2005) and could thus be considered as candidate endophenotypes for psychiatric disorders. Alternatively, proclivity to a particular coping style may be an epiphenomenon of a disorder itself – whether by way of symptoms, medication, or another factor.
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Although criteria for endophenotype status initially included specificity to one disorder (Tsuang, Faraone, & Lyons, 1993), this criterion does not hold up against more recent discoveries about shared genetics among disorders. It stands to reason that the higher the degree of genetic overlap, the more likely it is that the disorders share endophenotypes. Schizophrenia and bipolar disorder share a very high level of genetic overlap – on the order of 50–65% (Craddock, O’Donovan, & Owen, 2006; Lichtenstein et al., 2009; Purcell et al., 2009), and bipolar disorder shares approximately 25% of its genetic factors with major depression (McGuffin et al., 2003). If any coping style is an endophenotype for one disorder, it may thus also be an endophenotype for the other disorders with which it overlaps genetically.
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Prior work has compared coping styles of patients with schizophrenia, bipolar disorder, and major depressive disorder with healthy volunteers in separate studies, using a diverse set of coping measures (e.g. Lam & Wong, 2005; Van den Bosch, Van Asma, Rombouts, & Louwerens, 1992). In general, these studies have found that patients with each of these disorders show lower levels of active, productive coping than healthy volunteers. There are also findings suggestive of potentially diverse coping mechanisms across these disorders, in that patients with schizophrenia tend to use distancing, avoiding, and disengaging coping styles (e.g. Tait, Birchwood, & Trower, 2004), those with depression tend to use ruminative styles (Nolen-Hoeksema, 2000) and escapism (Rohde, Lewinsohn, Tilson, & Seeley, 1990), and those with bipolar disorder may also make greater use of avoidance (Goossens, Knoppert-van der Klein, & van Achterberg, 2008), and stimulation-avoidance and modifying excessive behavior during prodrome reduces probability of manic episodes (Lam, Wong, & Sham, 2001). Bipolar disorder has also been associated with emotion regulation abnormalities that predict aspects of symptomatology (Van Rheenen et al., 2015) and are linked with reduced frontal-amygdala connectivity (Kanske, Schönfelder, Forneck, & Wessa, 2015). However, no prior study has directly compared these three groups of patients to each other on the same measures of coping, making it difficult to discern whether these
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patterns are truly disorder specific. Further, no prior study has examined whether coping styles represent endophenotypes of these three disorders using a genetically informative design. It thus remains unclear whether any coping style is shared phenotypically across schizophrenia, bipolar disorder, and depression, whether any aspect of coping reflects an underlying genetic diathesis for any of these disorders, and whether any such coping-related endophentoype is shared with one or both of the other disorders due to their genetic overlap.
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To address these questions, we evaluated coping styles in samples of twins concordant and discordant for schizophrenia, bipolar disorder, and major depression, along with healthy comparison twin pairs. This approach permits testing not only whether a coping style is shared phenotypically across disorders, but also whether it is shared by non-clinicallyaffected co-twins of probands with the three diagnoses. The non-clinically-affected co-twins are at increased genetic risk for the disorder shown by their affected co-twin, but because they do not have the disorder overtly, these subjects represent a form of control for phenotypic expression of illness, exposure to treatments, and other secondary factors. In other words, the non-ill co-twins provide the basis for evaluating whether a particular coping style is likely to be associated with a genetic diathesis to each illness and whether any aspect of coping helps to define that part of the genetic diatheses that overlap across these three disorders. Given the limited scope of the prior literature on these issues, as summarized above, there is little basis for specifying a model that predicts discrete areas of overlap and non-overlap in terms of coping styles across disorders or across non-affected co-twins. Rather, we used the logic of the discordant twin pair design to parse the various possible patterns empirically, with an appropriate control for multiple testing, hoping to provide a set of observations useful for subsequent model-building.
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Methods Subjects
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Subjects were identified through the Swedish Twin Registry, managed by the Karolinska Institutet. Full recruitment procedures are described by Lichtenstein et al. (2006). Twin pairs were eligible for inclusion if they were same sex, between the ages of 25 and 65, and born in Sweden between 1940 and 1985 (inclusive). The age range was intended to exclude individuals who were young enough that they had not yet developed an emerging disorder or old enough that they had already developed signs of dementia. Other exclusion criteria were presence of a neurological disorder, history of significant head injury with loss of consciousness, mental retardation, history of substance dependence within 6 months of the screening interview, inability to read or comprehend spoken and written Swedish, or pregnancy/lactation at time of evaluation. To ascertain twin pairs with psychopathology, this population of twins was screened using hospital admission and discharge diagnosis information from the Swedish National Patient Registry. Screening for pairs comprising at least one twin with a diagnosis of schizophrenia or bipolar disorder yielded 562 potential probands: 257 male and 305 female. Monozygotic (MZ) and dizygotic (DZ) pairs were recruited randomly from this population. Zygosity was
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determined for twin pairs using either DNA testing or a well-validated screening measure administered to parents and twins (Lichtenstein et al., 2006), yielding 177 complete twin pairs with impulsivity data, of whom 77 were monozygotic, 97 were dizygotic, and 3 had undetermined zygosity. Healthy control tin pairs were recruited to match proband pairs on age, sex, and zygosity. Healthy controls were excluded if they had a family history of schizophrenia or bipolar disorder.
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Diagnostic interviewing was used in conjunction with hospital records to determine diagnosis for each individual, and twin pairs were then classified as controls or as concordant or discordant for schizophrenia or bipolar disorder, regardless of the initial recruitment classification. Individuals with schizoaffective disorder were included in the schizophrenia group. Discordant co-twins of probands were also included regardless of a history of non-psychotic psychopathology such as depression. Individuals recruited as controls were also included regardless of history of depression, creating for our purposes another diagnostic group of participants with major depressive disorder, without a twin affected by schizophrenia or bipolar disorder. Individuals diagnosed with a different psychotic disorder were excluded from analyses comparing diagnostic groups. Tests of Sample Representativeness—We tested whether the studied probands were comparable to the remainder of the twin proband population in terms of sex, age, age at first hospitalization, and number of hospitalizations. For sex, we used a chi-square test of independence and phi (ϕ) as an estimator of effect size. For other variables, we used Cohen’s d. Cohen (1992) suggested that values below 0.2 may be considered small effect sizes, 0.5 is a medium effect size, and 0.8 is a large effect size.
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Procedures All measures were approved by the Regional Ethics Review Board, Stockholm, Sweden.
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Clinical Evaluation—Final diagnostic status was determined by consensus using both clinical interviews and register data, which included hospital records and lifetime history of hospital admissions from 1973 until the time of evaluation. A clinical psychiatrist interviewed each participant using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID; First, Spitzer, Gibbon, & Williams, 1997) and the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997). The SCID has demonstrated superior validity over standard clinical intake interviews (First, Spitzer, et al., 1997). Current symptoms were also rated using the Hamilton Depression Rating Scale (HAM-D; Hamilton, 1960), Young Mania Rating Scale (YMRS; Young, Biggs, Ziegler, & Meyer, 1978), Scale for Assessment of Negative Symptoms (SANS; Andreasen, 1983), and Scale for Assessment of Positive Symptoms (SAPS; Andreasen, 1984). Acceptable validity and reliability have been demonstrated for each of these measures (Peralta, Cuesta, & De Leon, 1995; Williams, 2001; Young et al., 1978). Consensus diagnosis was determined by the clinical evaluation team at the Karolinska Institutet, which comprised two psychiatrists and one clinical psychologist (Dr. Hultman). All participants were clinically stable at the time of evaluation. No changes to medication regimen were made in relation to participation in the study.
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Zygosity—Zygosity was initially determined using a self-report measure that includes physical similarity ratings and family conclusions about “identical” or “fraternal” status. This measure has demonstrated good validity (Reed et al., 2005). For participants with available DNA information, DNA zygosity tests were then conducted to determine percent of allele sharing between twins. This procedure modeled Hannelius et al. (2007) and used a highly multiplexed 47 single nucleotide polymorphism (SNP) panel, including a sex-specific marker. Likelihood of zygosity for each pair was calculated assuming a 1% genotyping error rate (false positives).
Statistical Analyses
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Prior studies have used a wide variety of measures of coping including factor scores derived from other samples as well as their own samples. Several studies have derived higher-order structures of the longer version of the COPE scale using factor analysis, and there is substantial variability among reported results (Hasking & Oei, 2002). Three-factor (e.g. Lyne & Roger, 2000) and four-factor (Carver, Scheier, & Weintraub, 1989) solutions have been found, with inconsistencies in which items load on which factors. Very few factor analyses of the Brief COPE have been reported (Hasking, Lyvers, & Carlopio, 2011), but some support has been found for a three-factor structure containing problem-focused coping, emotion-focused coping, and either dysfunctional coping (Cooper, Katona, & Livingston, 2008) or avoidant coping (Hasking et al., 2011). Factor structure may vary as a function of sampling and in particular the relative numbers of individuals with different forms of psychopathology. Given this, and as suggested by the author of the Brief COPE (Carver, 2007), we undertook an analysis of its factor structure in the sample under study.
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Brief COPE Scale (Carver, 1997)—The Brief COPE is an abbreviated coping measure that assesses 14 distinct types of coping. Carver (1997) found that the measure had adequate validity and reliability, and Meyer (2001) found evidence of concurrent and predictive validity in a psychiatric sample. A subset of 18 items was administered and scored on a fourpoint scale from 0–3, and possible total scores range from 0–54. The scale directed participants to indicate their typical coping behaviors, not their responses to an individual stressor and not during a particular time frame. Thirty-five participants missed items. These participants were excluded from analyses of the derived factors for which they missed items.
Our analysis used principal axis factoring with varimax rotation. Fit of the factor model was evaluated using the Tucker Lewis Index (TLI) and the Root Mean Square Error of Approximation (RMSEA). The TLI, or Non-Normed Fit Index (NNFI), is an incremental fit index that penalizes for complexity. Hu and Bentler (1999) suggest using ≥0.95 as a cut-off value for the TLI to indicate good model fit. The RMSEA is an absolute measure of fit, and it also penalizes complexity. Lower values on the RMSEA indicate better fit, and MacCallum, Browne, and Sugawara (1996) have used 0.01, 0.05, and 0.08 as indicators of excellent, good, and mediocre fit respectively. Factor scores were derived and carried forward into subsequent analyses. To facilitate interpretation of factor content, we examined the highest loadings on each factor. In our interpretation, loadings less than 0.3 were excluded, and in the case of double-loadings, items were assigned to the factor on which their loading was strongest. For an item to be included in a factor, its loading on that factor
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was required to be at least 0.05 higher than its loading on any other factor. Factor scores based on simple addition of these items were also derived and tested to ensure robustness of results, but factor scores were chosen because of relatively small differences in loadings on multiple factors (0.3 on a factor. Theoretically, although the two-factor model supports the elevation of maladaptive coping broadly in psychopathology, it obfuscates the underlying relationships between coping behaviors and disorders that aggregate to appear as comparable total sums. In the context of psychopathology, there is apparent merit to considering more differentiated models.
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Because this analysis is cross-sectional, no conclusions can be drawn about whether observed associations between psychopathology and coping style suggest that poor coping predisposed to vulnerability for the disorder or that the illness reduced coping efficacy. Longitudinal studies of coping could tease apart these two possibilities, which may both contribute to observed differences in patient and healthy populations. It is also important to consider that this factor structure was derived in the context of psychopathology, which naturally leads to factors that reflect differences attributable to group differences in psychopathology. Performing the analysis excluding affected individuals produced a very similar factor structure, but the sample size left us underpowered to use this model in our final analyses. Additionally, heritability modeling was performed in the entire sample due to sample size, so heritability may be underestimated if individuals with psychopathology show larger differences from co-twins generally.
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The version of the Brief COPE used was further abbreviated, so results may not generalize to the structure of the complete scale. We also relied entirely on self-report, and memory fallibility as well as biased reporting may influence validity of the data. Additionally, only three diagnostic groups were included, so we cannot infer information about coping style across more disorders, despite its association with many. Our major depressive disorder group was also created post-hoc and was not specifically recruited. Finally, our sample size was relatively small, and a larger sample would potentially yield more accurate estimates of
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heritability and group means, as well as the comparison of monozygotic and dizygotic twins within diagnostic group. A larger sample would also allow heritability calculation in healthy controls and diagnostic groups separately, which could illustrate whether the effect of psychopathology masks predispositions for coping style.
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Despite these limitations, the observed pattern of findings represents a step toward uncovering the relationship between coping with stress and development of psychopathology. This set of analyses helps to uncover differential relationships of coping style across disorder, offering suggestions as to the plausible causal mechanisms involved in the etiology and maintenance of different symptom clusters in the face of stress, but much more is still obscured. For example, longitudinal work can help to tease apart the cases in which coping style created vulnerability and risk for psychopathology from those in which it emerged as a clinical phenotype in the context of the illness. Experience sampling methodology may illuminate what coping styles predict increases or decreases in symptoms during the course of a day or a week, providing clues about the role of coping in maintenance of psychopathology.
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Additionally, here we are still wedded to diagnostic categories as indicators of symptom clusters, but use of symptom-level data may tease apart what symptom or symptoms are specifically related to these coping factors. For example, schizophrenia comprises multiple factors of symptomatology that can be present to varying degree across individuals (e.g. Wallwork, Fortgang, Hashimoto, Weinberger, & Dickinson, 2012), and there may be coping styles that uniquely associate with symptom clusters. Additionally, a focus on context may be particularly important in understanding the process of coping and regulating emotions in psychopathology (Aldao, 2013), as flexibility in coping style is associated with lower levels of psychopathology (e.g. Bonanno, Papa, Lalande, Westphal, & Coifman, 2004). The field of coping research is rich, complex, and longstanding. As it extends into the future, there is more to be done to uncover how the structure of coping can be understood in the context of varying environments and clusters of psychiatric symptoms.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
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Figure 1.
Graph of Standardized Means and Standard Errors of Coping Factors
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Table 1
Author Manuscript
Demographic information Dx Group
n
Age* M (S.D.)
%Male
Education Level M (S.D.)**
SCZ
64
50.12 (10.42)
53.10%
3.38 (1.51)
SCZ-CT
64
51.51 (11.21)
51.60%
3.80 (1.46)
BPD
71
49.59 (10.85)
40.80%
3.36 (1.51)
BPD-CT
51
49.59 (10.46)
47.10%
3.60 (1.34)
MDD
44
50.87 (10.20)
47.70%
3.58 (1.42)
MDD-CT
25
51.08 (9.18)
48.00%
3.33 (1.61)
OPSY
5
45.46 (9.91)
60.00%
2.80 (1.80)
OPSY-CT
2
38.94 (14.11)
50.00%
3.67 (1.16)
HV
61
45.99 (10.74)
49.20%
3.84 (1.20)
Author Manuscript
No DX
33
52.86 (8.02)
36.40%
2.70 (1.64)
Whole Sample
420
49.63 (10.52)
47.40%
3.51 (1.47)
SCZ — Schizophrenia, SCZ-CT — Schizophrenia Co-Twins, BPD — Bipolar Disorder, BPD-CT — Bipolar Disorder Co-Twins, MDD — Major Depressive Disorder, MDD-CT — Major Depressive Disorder Co-Twins, OSPY — Other Psychotic Disorder, OSPY-CT — Other Psychotic Disorder Co-Twins, HV — Healthy Volunteers, No DX — No consensus diagnosis; *
Age at time of testing,
**
Ranges from 0=no schooling to 5=university.
4 people had missing data and 15 indicated “other.”
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Author Manuscript Active Coping, Positive Reframing, Planning
Self-Blame, Venting, Humor, Substance Use
Use of Emotional & Instrumental Support
Factor 3: Support-Seeking Behavioral Disengagement Self Distraction, Acceptance
Factor 4: Disengagement
Religion, Denial
Factor 5: Belief-Based
Black arrows indicate an association at p
