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Psychological vulnerability in depressive and anxiety disorders Struijs, S.Y.

2019

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This thesis had two sub aims. First, it examined the role of psychological vulnerabilities in the onset of depressive and anxiety disorders. Therefore, we studied cross-sectional (Chapter 2) and prospective (Chapter 3, first part) associations of approach-avoidance tendencies with the onset of depressive and anxiety disorders together with prospective associations of cognitive vulnerability and personality dimensions with general symptoms of depression and the mood/cognition and anxiety/arousal subscale symptom dimensions of depression (Chapter 4). Second, it examined the role of psychological vulnerabilities in the chronicity of depressive and anxiety disorders. Hence, we studied prospective associations of approach- avoidance tendencies (Chapter 3, second part) as well as cognitive vulnerability and personality dimensions (Chapter 5) with the chronicity of depressive and anxiety disorders. We also assessed the temporal stability of various measurements of symptoms of affective (i.e., depressive and anxiety) disorders, cognitive vulnerability and personality (Chapter 6). This chapter includes a summary of the main findings and a discussion of the main findings in relation to the literature. Furthermore, methodological considerations will be addressed and possible clinical implications will be discussed. Finally, possibilities for future research will be proposed and an overall conclusion will be provided.

Summary of findings regarding onset of disorders An integration of the main findings of this thesis for the first sub aim is presented in Table 1. In Chapter 2 we examined whether there were differences between patients with depression and anxiety and healthy controls in Approach and Avoidance (AA) tendencies using both behavioral based, implicit measures and self-reported, explicit trait measures. Following the design of Rinck (2007), we studied automatic approach and avoidance behaviour using the Approach and Avoidance Task (AAT). Self-reported AA behaviour was measured by the BIS/BAS questionnaires (Carver & White, 1994). Participants were subdivided at baseline in healthy controls (n=405), remitted patients (877) and currently anxious (217), depressed (154) or comorbid (154) patients. We did not find any consistent associations of automatic approach-avoidance tendencies with psychiatric variables. In contrast, medium to large differences in BIS scores showed increased trait avoidance tendencies in all patient groups relative to healthy controls.

Table 1. Summary of findings on associations between psychological vulnerability variables and the onset of depressive and anxiety disorders (Chapters 2, 3 and 4)

Depression1 Anxiety2

Approach-avoidance behavior

Trait avoidance behavior + +

Trait approach behavior - -

Implicit avoidance behavior - -

Implicit approach behavior - -

Cognitive vulnerability

Cognitive reactivity + +

Hopelessness + -

Rumination + +

Explicit self-depressive assumptions + -

Explicit self-depressive assumptions * negative + - life events experienced

Implicit self-depressive assumptions - -

Personality

Locus of control + +

+ indicates a significant association; - indicates no association. All significant associations were positive.

We found no association between diagnostic status and BAS drive scores. We did find a dose-response relationship between a higher severity of depressive symptoms and lower BAS drive scores. This discrepancy probably results from the fact that more variance can be explained in a continuous outcome measure versus a dichotomous outcome measure. Next, in the first part of Chapter 3 we examined whether there were group differences in the prospective associations of AA behavior with the onset of depressive and anxiety disorders. We used the same predictors as in Chapter 2. Participants without (n =

1 In order to give an overview several outcomes are aggregated in this measure: cross-sectional associations between vulnerability variables and depressive disorders, the prospective onset of depressive disorders, and prospective associations of vulnerability variables with general depressive symptoms and the mood/cognition symptom dimension of depression.

2 Multiple outcomes are aggregated within this measure in a similar fashion for anxiety disorders and the anxiety/arousal symptom dimension of depression.

1,636) a current anxiety or depression diagnosis at baseline were selected. Clinical diagnoses were assessed after 2-year follow-up. Analyses were adjusted for socio-demographics (basic adjustment) and for severity and history of psychopathology (full adjustment). Stronger trait avoidance tendencies predicted increased risk of onset of anxiety disorders after full adjustment (Odds ratio onset = 1.55, P < .001). The association between stronger trait avoidance tendencies and increased risk of onset of depressive disorders was no longer significant after full adjustment. In contrast, trait approach tendencies and automatic AA tendencies were not related to onset of disorders. In Chapter 4 we examined whether the prospective associations of multiple psychological vulnerability factors were associated with changes in depressive symptoms and determined whether these associations are differentially related to the mood/cognition and anxiety/arousal symptom dimensions of depression. Baseline and 1-year follow-up data were obtained from 2981 NESDA participants. Multivariate regression analyses were carried out on cognitive reactivity, locus of control and implicit and explicit self-depressive associations. Cognitive reactivity, locus of control, explicit self-depressive associations and explicit self-depressive associations interacting with negative life-events were independently associated with changes in depressive symptoms after adjustment for covariates and baseline severity. Locus of control and explicit self-depressive associations were more strongly associated with the mood/cognition than the anxiety/arousal symptom dimension of depression. A post-hoc analysis additionally revealed that hopelessness was specifically associated with the mood/cognition symptom dimension, while rumination was associated with both symptom dimensions.

Summary of findings regarding chronicity of disorders An integration of the main findings for the second sub aim is presented in Table 2. In the second part of Chapter 3 we examined whether there were group differences in the prospective associations of AA behavior with the chronicity of depression and anxiety disorders. Participants were selected with a current anxiety or depression diagnosis at baseline (n = 766). Clinical diagnoses were reassessed after 2-year follow-up. Analyses were adjusted for socio-demographics (basic adjustment) and for severity and history of psychopathology (full adjustment). Stronger trait avoidance tendencies predicted increased risk of a chronic course of anxiety disorders after full adjustment (Odds ratio chronicity

=1.31, P=.03). The association between stronger trait avoidance tendencies and increased risk of chronicity of depressive disorders was no longer significant after full adjustment. In contrast, trait approach tendencies and automatic AA tendencies were not related to chronicity of disorders. In Chapter 5 we examined whether the prospective associations of different psychological vulnerabilities were associated with the chronicity of affective disorders and determined whether their associations were general to most disorders or specific to some. Participants with a current diagnosis of depression or anxiety (n=1256) were reassessed after 2 and 6 years. Diagnostic status and chronic duration (> 85% of the time) of symptoms were the outcomes. Predictors were neuroticism, extraversion, locus of control, cognitive reactivity (rumination and hopelessness reactivity), worry and anxiety sensitivity. High neuroticism, low extraversion and external locus of control predicted chronicity of various affective disorders. Rumination, however, predicted chronicity of depressive but not anxiety disorders. Worry specifically predicted chronicity of GAD and anxiety sensitivity predicted chronicity of panic disorder and social anxiety disorder. These patterns were present both at short-term and at long-term, without losing predictive accuracy.

Table 2. Summary of findings on associations between psychological vulnerability variables and the chronicity of depressive and anxiety disorders (Chapters 3, 5 and 6).

Depressive Anxiety Temporal disorders3 disorders3 stability4 Approach -avoidance behavior Trait avoidance behavior + + N.E. Trait approach behavior - - N.E. Implicit avoidance behavior - - N.E. Implicit approach behavior - - N.E.

Cognitive vulnerability Hopelessness - - .62 Rumination + - .64

Worry - + .74

Anxiety sensitivity - + N.E.

Personality

Neuroticism + + .75

Extraversion + + .75

Locus of control + + .63

+ indicates a positive relationship; - indicates no association. All significant associations were positive except for extraversion.

3 Both general and specific depressive and anxiety disorders are included as outcomes. Results from Chapter 5 (using cognitive vulnerability and personality as predictors) are summarized by using the pattern of outcomes of the multivariate stepwise logistic regression models in order to give a succinct overview. Please note that in univariate models (i.e. risk factors were individually entered as independent variables) of Chapter 5 all cognitive vulnerability and personality variables were associated with both depressive and anxiety disorders

4 Consistency estimates from the 4-year LST analyses from Chapter 6 are provided. N.E. = Not Examined. Consistency estimates of symptoms of depression, anxiety and fear are similarly high as that of the cognitive vulnerability and personality constructs (.69, .64 and .74 respectively; not tabulated).

Finally, in Chapter 6 we examined whether there were differences in the temporal stability of the measurement of symptoms of affective disorders, cognitive vulnerability and personality using two different methodologies. Participants were assessed at baseline for symptoms of affective disorders (depression, anxiety and fear), cognitive vulnerability (hopelessness, rumination and worry) and personality (neuroticism, extraversion and locus of control). They were reassessed after 2, 4, 6 and 9 years and grouped on basis of CIDI- diagnoses into stable healthy (n=768), stable diagnosis (n=352) and unstable (n=821). We determined the temporal stability of all measurements by calculating intraclass correlation coefficients (ICC) and consistency indices of latent state-trait analyses (LST) across 9 years of follow-up. Temporal stability was moderate to high for symptoms (range ICC’s .54-.73; range consistency .64- .74), cognitive vulnerability (range ICC’s .53-.76; range consistency .60-.74) and personality (range ICC’s .57-.80; range consistency.60 -.75). Consistency indices for all measures were on average a bit lower in the unstable group (ICC = .54) compared to the stable groups (ICC = .61). Overall stability was similarly high after 2, 4, 6 and 9 years.

Discussion of findings regarding onset of disorders General positive associations with the onset of both depression and anxiety: Trait avoidance tendencies, cognitive reactivity, rumination and locus of control Previous research regarding trait avoidance tendencies and affective disorders is mostly cross-sectional in nature, and report on self-reported symptoms that may not necessarily extend to psychiatric disorders. Positive associations were found for symptoms of depression (Hundt, Nelson-Gray, Kimbrel, Mitchell, & Kwapil, 2007; Jones & Day, 2008; Pinto-Meza et al., 2006), anxiety (Fullana et al., 2004; Kimbrel, Mitchell, & Nelson-Gray, 2010), or both (Beevers & Meyer, 2002; Campbell-Sills, Liverant, & Brown, 2004; Coplan, Wilson, Frohlick, & Zelenski, 2006; Johnson, Turner, & Iwata, 2003; Jorm et al., 1999; Kimbrel, Nelson-Gray, & Mitchell, 2007; Muris, Meesters, de Kanter, & Timmerman, 2005; Segarra et al., 2007; Van Meter & Youngstrom, 2015). So, our findings that trait avoidance tendencies were cross-sectionally also associated with depressive and anxiety disorders add to this large body of literature assessing the relationship between avoidance and internalizing disorders (Bijttebier, Beck, Claes, & Vandereycken, 2009; Trew, 2011). Next to the positive cross-section associations of trait avoidance tendencies with depressive and anxiety disorders, we also found that trait avoidance was predictive of the onset of both

depressive and anxiety disorders. This is in line with results from previous studies which found that increased trait avoidance tendencies predicted general symptoms of distress in healthy students and school children (Li, Xu, & Chen, 2015; Sportel, Nauta, de Hullu, & de Jong, 2013; Takahashi, Roberts, Yamagata, & Kijima, 2015). It indicates that trait avoidance tendencies constitute a transdiagnostic risk factor for the development of both depression and anxiety. The questionnaire that was used in this study to measure cognitive reactivity (LEIDS- R) was developed to measure the extent in which depressive cognitions are activated by low mood without using a negative mood induction procedure (Segal, Gemar, & Williams, 1999; Van der Does, 2002; Weissman, 1979). Previous research has shown that the LEIDS-R is associated with depressive symptoms using a cross-sectional design (Antypa, Van der Does, & Penninx, 2010; Moulds et al., 2008) and a tryptophan depletion method (Booij & Van der Does, 2007). Our results that cognitive reactivity as assessed by the LEIDS-R is predictive of the development of both depression and anxiety-related symptoms of psychopathology add to this body of literature. A post-hoc analysis we performed in chapter 4 indicated that rumination was related to the onset of both depression and anxiety related symptoms. This might indicate that rumination is a more general risk factor for depression and anxiety. The transdiagnostic risk of rumination possibly constitutes avoidance through repetitive negative thoughts (Spinhoven, van Hemert, & Penninx, 2018; P. Spinhoven, Drost, van Hemert, & Penninx, 2015). An external locus of control has been previously related to depression (Benassi, Sweeney, & Dufour, 1988) and anxiety (Chorpita & Barlow, 1998), as well as to the incidence of first depressive episodes (Ernst, Schmid, & Angst, 1992) and the onset of depression in later life (Steunenberg, Beekman, Deeg, & Kerkhof, 2006). Our finding that an external locus of control is predictive of the onset of both depression and anxiety related symptoms of psychopathology fits well with these findings and indicates that this might constitute a transdiagnostic risk factor.

Specific positive associations with the onset of depression: Hopelessness and explicit depressive self-associations A post-hoc analysis we performed in chapter 4 indicated that hopeless reactions to sad mood were specifically related to the onset of depression related symptoms. This might

indicate that hopelessness is indeed a specific risk factor for depression according to theory and previous results (Liu, Kleiman, Nestor, & Cheek, 2015) and is less relevant for the development of anxiety. Explicit depressive self-associations were also specifically predictive of the onset of depression related symptoms of psychopathology. This is in line with the findings described in the literature and seem to underline the specificity of this risk factor to depression, which increases risk to depression in combination with negative life-events (Beck, 1967; Monroe & Simons, 1991). These results need to be interpreted with caution however, because the instrument we used to assess explicit self-depressive associations has not been officially validated yet. This instrument was specifically created to be equivalent to the automatic self- associations as measured by the Implicit Association Test. The scores on this new instrument were internally consistent however and appear to be valid (Glashouwer & de Jong, 2010).

No associations with the onset of either depression or anxiety: Implicit negative self- associations, trait approach, implicit approach and avoidance behavior Implicit depressed cognitions are associated with depression; however, effect sizes are usually small (Phillips, Hine, & Thorsteinsson, 2010). Implicit depressed cognitions can predict depressive symptoms over a period of 5 weeks (Haeffel et al., 2007) in healthy students, but as the results of this thesis show, this might not extend into a period of a year in a clinical/community sample. The contradictory findings regarding the impact of implicit self-depressive associations on the development of depressive symptoms warrant further research. The same pattern is noticed for trait approach tendencies. These have been associated with more severe symptoms of depression (Beevers & Meyer, 2002; Campbell- Sills et al., 2004; Hundt et al., 2007; Pinto-Meza et al., 2006), anxiety (Kimbrel et al., 2010) or both (Coplan et al., 2006). However, our results indicate that trait approach tendencies are not associated with the development of symptoms of depression and anxiety. They were not associated with the risk of developing a new disorder either. It could be that previous positive cross-sectional findings are partly due to state effects. Thus, the current findings do not indicate that decreased trait approach tendencies constitute a risk factor for the development of depression and anxiety.

Lastly The general absence of effects we found regarding implicit approach-avoidance behaviour is in contrast with some previous positive findings in depressed patients (Seidel et al., 2010), patients with social anxiety disorder (Kuckertz, Strege, & Amir, 2016; Roelofs et al., 2009), students with symptoms of depression (Bartoszek & Winer, 2015; Vrijsen, van Oostrom, Speckens, Becker, & Rinck, 2013) and students with anxiety symptoms (Heuer, Rinck, & Becker, 2007; Lange, Keijsers, Becker, & Rinck, 2008; Roelofs et al., 2010) of which the effect sizes ranged from small to large. A possible explanation for the general absence of effect could be a low reliability of the current AAT task version due to a relative low number of trials per condition. However, the same protocol was used before in another study finding significant differences between groups (Enter, Colzato, & Roelofs, 2012), and the AAT measures in this study had adequate internal consistency. The current findings indicate that, as of yet, the predictive value of the AAT using emotional facial expressions is not established. This is in line with more recent results indicating that the AAT is not predictive of daily social anxiety in university students with high levels of social anxiety (Kampmann, Emmelkamp, & Morina, 2018). Because this is the first application of the AAT in a large-scale epidemiological study, further research is warranted to establish the clinical meaningfulness of the AAT in its current form.

Concluding remarks Psychological vulnerabilities in general are related to the onset of depressive and anxiety disorders. However, there are differences between the associations of different kinds of psychological vulnerabilities with the onset of depressive and anxiety disorders. For example, increased trait avoidance is related to the onset of disorders, but not decreased trait approach. Cognitive reactivity, rumination and locus of control are associated with both the onset of depression as well as the onset of anxiety, while hopelessness and explicit self- depressive associations are associated with the onset of depression only. Implicit psychological vulnerabilities on the other hand, are not related to the onset of depressive and anxiety disorders.

Discussion of findings regarding chronicity of disorders General positive associations with the chronicity of both depression and anxiety: trait avoidance, neuroticism, extraversion and locus of control For participants with a diagnosis at baseline, trait avoidance tendencies predicted the chronicity of an affective disorder diagnosis. This result resembles that of Brown ( 2007), who showed that increased trait avoidance tendencies were predictive of a worse outcome in affective disorder psychopathology. This implies that overall threat-avoidance might indeed be an important feature in affective disorders, predicting not only disorder onset but also the chronicity of disorders. Our finding that neuroticism is predictive of a worse course for both depression and anxiety supports the idea that neuroticism is an efficient marker of non-specified risk (Kotov, Gamez, Schmidt, & Watson, 2010; Ormel et al., 2013) and adds to that idea that neuroticism predicts chronicity of diagnoses in the long term. Our results indicate that low extraversion is also predictive of a worse course of depressive and anxiety disorders. Several explanatory models linking personality and affective disorders have been proposed (Klein, Kotov, & Bufferd, 2011; Ormel et al., 2013). Common cause models in which personality and affective disorders share determinants seem to hold promise. However, the vulnerability model – in which personality sets in motion processes that lead to affective disorders – cannot be excluded. Our finding that an external locus of control is not only predictive of the onset of depressive and anxiety disorders but also of the chronicity of depressive and anxiety disorders is in accordance with a previous findings linking an external locus of control to chronic depression (Wiersma et al., 2011) and a lower chance of remission of affective disorders (Hovens, Giltay, van Hemert, & Penninx, 2016; B. Steunenberg, Beekman, Deeg, Bremmer, & Kerkhof, 2007). This adds to our notion that an external locus of control constitutes a transdiagnostic risk factor for affective disorders.

Specific positive associations with the chronicity of either depression and anxiety: rumination, worry and anxiety sensitivity Rumination and worry are both associated with, and exacerbate depression and anxiety (Ehring & Watkins, 2008; Spinhoven, Drost, de Rooij, van Hemert, & Penninx, 2016; Spinhoven et al., 2017; Spinhoven, Hemert, & Penninx, 2017; Watkins, 2008). We found that

both rumination and worry were univariately associated with the chronicity of depressive and anxiety disorders. However, in stepwise multivariate models rumination specifically predicted the chronicity of depressive disorders whereas worry specifically predicted generalized anxiety disorder. These findings relate well to the prior body of work and hierarchical models of psychopathology (Kotov et al., 2017) in which the transdiagnostic factor of repetitive negative thought is related to higher order dimensions of psychopathology and the specific differentiating content-related features of worry and rumination are also related to lower order dimensions such as concrete symptoms of generalized anxiety and major depression respectively (Spinhoven et al., 2018). Prior research has shown that anxiety sensitivity is strongly, but not uniquely associated with panic disorder. Previous longitudinal studies found that anxiety sensitivity is a predictor of the clinical course of anxiety disorders in general (Spinhoven et al., 2017) and panic disorder specifically (Benitez et al., 2009). The specific association of anxiety sensitivity with the chronicity of anxiety disorders in our study strengthens these findings.

No associations with the onset of either depression or anxiety: trait approach, implicit approach-avoidance behavior and hopelessness Trait approach tendencies were not associated with the chronicity of depression and anxiety. This is in contrast to results from previous studies which found that low BAS scores are related to worse outcomes in depressed patients (Kasch, Rottenberg, Arnow, & Gotlib, 2002; McFarland, Shankman, Tenke, Bruder, & Klein, 2006). Negative findings regarding trait approach and (the course of) depression are also reported however (Bijttebier et al., 2009). It could be that decreased trait approach is predictive of a specific ‘anhedonic’ subtype of depression (Hundt et al., 2007). As discussed in the paragraph on onset of disorders, the general absence of effects we found for implicit approach-avoidance behaviour corroborates the notion that, as of yet, the predictive value of the AAT using emotional facial expressions is not established. Despite the mixed findings in cross-sectional studies associating the AAT with depressive and anxiety disorders and the uncertainty about the predictive effects of the AAT on the onset and chronicity of depression and anxiety, studies are being conducted in which the AAT is being used in cognitive bias modification in order to gain (treatment) effects. For example, as a general positivity training designed to induce a stronger tendency to automatically approach

positive stimuli. This training was able to induce a positivity bias and reduce emotional vulnerability in dysphoric students (Becker et al., 2016). However, it does not have added beneficial effects on stress-responses in healthy students (Ferrari, Mobius, Becker, Spijker, & Rinck, 2018). Also, learning to avoid hair pulling stimuli by means of an AAT training was not effective in reducing symptoms or relapse rates in patients with hair pulling disorder undergoing CBT (Maas, Keijsers, Rinck, & Becker, 2018). The reduction of automatic approach bias towards cannabis related stimuli with the AAT was not effective in cannabis using adolescents. Those undergoing the training did report less days of cannabis use during the study, but also reported more days of alcohol use (Jacobus et al., 2018). Furthermore, in depressed individuals undergoing treatment as usual the general positivity training by means of the AAT did not increase automatic approach for positive material, despite the fact that depressive symptoms were reduced in comparison to a control group receiving no training (Vrijsen et al., 2018). All in all, it seems that the adoption of the AAT as clinical tool is premature. Contrary to expectations, hopelessness was related neither to the chronicity of depressive disorders nor to the chronicity of anxiety disorders across multivariate models. However, it did predict the chronicity of both depressive disorders and anxiety disorders in univariate models (when only hopelessness was entered as an independent variable). Also, it significantly predicted chronic symptoms of depression and anxiety between the baseline and 6-year follow-up measurement. It seems reasonable to assume that hopelessness constitutes a risk factor for affective disorders that overlaps with other psychological vulnerabilities. As a risk factor it is possibly less potent than some of the other risk factors we studied or specifically predictive of the most chronic long-term symptoms of depression and anxiety.

High temporal stability for all constructs We found that the temporal stability of all constructs we examined is moderate to strong and diminishes only slightly over time. This is in line with results from previous studies (Aldinger et al., 2014; Anusic & Schimmack, 2016; Ferguson, 2010; Nivard et al., 2015; Ormel et al., 2013; Ormel et al., 2013; B. Roberts & DelVecchio, 2000; Rosellini, Fairholme, & Brown, 2011; Spinhoven et al., 2016; Verduijn et al., 2017; Wight, Aneshensel, Seeman, & Seeman, 2003). The results seem to imply that past a certain age, different kinds of

psychological vulnerabilities and corresponding complaints are relatively set (Caspi & Moffitt, 2018). The differences we found in temporal stability between that of symptoms of affective disorders and personality are smaller than previously reported. In previous studies directly comparing the temporal stability of personality and symptoms of affective disorders, the stability of personality is around 33% larger than that of symptoms of affective disorders (Ormel et al., 2013). Most differences of temporal stability we found between constructs were smaller, regardless of the methods we used (our largest difference was no larger than 22%). This implies that the state-trait distinction might not be as pronounced as one might think. This idea fits well with results from recent research on the course of affective disorders which revealed that a chronic course is more the rule than the exception (Kessing & Andersen, 2017; Spinhoven et al., 2016; Verduijn et al., 2017; Verhoeven, Wardenaar, Ruhe, Conradi, & de Jonge, 2018). The rather large overlap of the temporal stability estimates between constructs seems reflected by similar similarities in heritability estimates and substantial genetic correlation between personality and depressive symptomatology (Luciano et al., 2018; Ormel et al., 2013). It might also be that trait-state associations are dynamic and vary according to the individual’s developmental context (Durbin & Hicks, 2014). Persons who struggle in their development might experience both symptoms of psychopathology and dysfunctional thoughts and fail to make subsequent positive changes in their personality. This negative developmental cycle can also act in reversed order as a positive manifold (i.e. people maturing and thereby making positive changes in their personality might experience functional thoughts and an absence of symptoms of psychopathology which fosters further personal growth). Both processes might end in relatively stable states and traits across the board (Borsboom, 2017).

Concluding remarks Psychological vulnerabilities in general are related to the chronicity of depressive and anxiety disorders. However, there are differences in the associations between different kinds of psychological vulnerabilities with the chronicity of depressive and anxiety disorders. Of all approach-avoidance measures we studied only trait avoidance is of importance in the chronicity of anxiety disorders. Specific cognitive vulnerabilities are related to the chronicity of specific disorders whereas more general personality variables are related to both the

chronicity of depressive and anxiety disorders. Finally, all constructs show strong temporal stability with a modest decline over time.

Discussion of results within a wider theoretical framework As the results of this thesis indicate, several risk factors for the onset and chronicity of depressive and anxiety disorders can be classified as general and transdiagnostic, whereas others can be classified as specific. Furthermore, theoretically fluctuating symptoms of depression and anxiety are just as stable over time as these theoretically more persistent risk factors. Both findings indicate that the strict dichotomous categorization of mental disorders as separate entities might not reflect reality and that the distinction between traits and states might not be as strong as once thought. Instead, these findings support recent calls for the use of alternative classification systems of psychopathology and related traits such as the dimensional Hierarchical Taxonomy Of Psychopathology (HiTOP) system (Kotov et al., 2017) or the use of a different method to conceptualize psychopathology such as the network approach (Borsboom, 2017). Within the HiTOP system dimensional spectra of psychopathology are construed by grouping together syndromes that group together symptoms. The highest order of psychopathology, or ‘superspectrum’, represents a liability to mental disorders which has been termed by some as the ‘p factor’, which may help account for the nonspecificity within psychiatry research (Caspi & Moffitt, 2018). It has been argued however that approaches that try and find underlying common factors such as the p factor for psychopathology, the g factor for general intelligence and the general factor of personality (i.e. latent variable models), are subjected to inherent limitations (van Bork, Epskamp, Rhemtulla, Borsboom, & van der Maas, 2017). The biggest limitation being that finding a general factor model that fits the data well does not mean that a general factor actually exists. It could also be a product of a positive manifold, i.e. the almost invariable positive correlations that exist between indicators of a general factor. The network approach on the other hand, argues that symptoms are not reflecting an underlying general factor but are integral parts of it. Within this theory, the symptoms itself form dynamic causal interactions that give rise to network structures (e.g. a lack of sleep leads to fatigue which leads to concentration difficulties which can lead to a sad mood which leads to a lack of sleep etc.). In the network perspective a positive manifold of

psychopathology symptoms that are not bound to existing diagnostic criteria is the subject of study which offers yet another explanation to the nonspecific findings within psychiatry research. This approach formalizes the feedback loops that clinicians use in therapy. These can inform patients about the possible dysfunctional behavioral patterns that hinder them. And also offer possibilities of breaking these vicious cycles and transforming them into functional ones. The network approach has recently gained momentum within the field of psychopathology. The possibilities this approach offers are accompanied by some possible pitfalls. Currently the biggest limitations for the network approach seem to be a relative absence of strong theoretical grounding (Bringmann & Eronen, 2018)5 and a certain difficulty in replicating results within or across datasets and researchers (Fried & Cramer, 2017; Guloksuz, Pries, & van Os, 2017). This has led to some hefty debates (Borsboom et al., 2017; Forbes, Wright, Markon, & Krueger, 2017), which hopefully will help the network approach to mature further and move the fields of clinical psychology and psychiatry forward.

Methodological considerations The studies described in this thesis have several strengths: 1) A large sample size, which is inclusive of anxious, depressed and healthy individuals recruited from diverse settings measuring both depressive and anxiety symptoms as well as disorders using psychiatric criteria. Also, remitted and comorbid patients were included. The large number of participants that were included in the study provides the analysis on which the results are based with substantial statistical power; 2) Different domains of psychological vulnerabilities were assessed and analyzed simultaneously with multiple constructs per domain which confirmed that different measures each show unique predictive validity and allowed a direct comparison of the effects of predictor variables on different outcome variables. Dispositional and automatic AA tendencies were assessed using different methodologies. The BIS/BAS questionnaire measures trait like tendencies by means of self-report, whereas the AAT is a state measure using reaction times of behavioral responses; 3) The longitudinal

5 Bringmann & Eronen argue in their review that the network approach is not a specific modeling framework. Instead, according to the authors, the bounderies between the network approach and the latent variable models or common cause models are rather unclear. They do however acknowledge the added benefits of the network approach and find these benefits in the form of appealing visualisations of existing models, additional tools for analyses and the guidance of researchers and clinicians towards dynamic ways of thinking about mental disorders.

design of the study allowed inferences about the impact of cognitive vulnerability on depressive and anxiety symptoms over time (or the assessment of group differences based on the course of clinical diagnoses and the long-term follow-up period). Finally, we used multiple assessment methods for the assessment of temporal stability. Also, there are several limitations that need to be addressed: 1) In most chapters there are cases of attrition. Although common in cohort studies, study drop-out can influence results. However, drop-out was relatively low in the NESDA study and differences between completers and non-completers were small and we took measures to account for study drop-out by means of multiple imputation or related strategies, of which the results implied that missingness did not meaningfully influence our results; 2) The samples included cases with comorbid disorders that could have diminished the specificity of outcomes in some cases. However, high rates of comorbidity are inherent in affective disorders (Lamers et al., 2011; Penninx, 2015). So, while it may be possible to reveal more specific associations links by removing overlapping cases, the remaining cases will not be very representative of the target disorder. On the other hand, for some outcomes we compiled participants with different anxiety disorders into one group. This was necessary because, partly due to high comorbidity across anxiety, there were not enough patients per single anxiety disorder diagnosis (e.g., ‘pure’ GAD) to allow for separate analyses; 3) The study sample used in the onset analyses were over-inclusive of participants with a remitted diagnosis, which can hamper generalizability of the study results to the general population. It must be noted, however, that in chapter three the associations of psychological vulnerability measures were not significantly different in healthy participants with versus without a history of previous diagnoses; 4) Multiple hypothesis-testing could be an issue for some of our positive findings that are relatively weak (P > .01), and therefore these results require replication. However, within the chapters where this is of most relevance the results were related to a pattern of results, indicating significant associations of risk factors across multiple outcomes, which diminishes the probability that our findings are due to chance; 5) Item-overlap (e.g. ‘Worry Engagement’ of the PSWQ overlaps with the A-criteria of GAD) could (in part) explain the more specific associations we found in chapter five; Finally, the approach-avoidance task (AAT) was conducted at the end of an interview day, which might have caused concentration difficulties. However, the relatively low error rates and the fact that the reaction times were in the normal range suggest that the AAT was administered under fairly optimal conditions.

Clinical implications Our findings underline the importance of addressing psychological vulnerability in the assessment, treatment and aftercare of both depression and anxiety. Assessment of psychological vulnerability markers and related complaints may be useful to help determine the focus of treatment given that psychological vulnerabilities are likely (distal) causal factors in the onset and course of psychopathology. Using homogeneous measures for these assessments provide more information about the patient than heterogeneous measures. This information can be used to more specifically monitor a patient's progress or prognosis, allowing for possible treatment adjustment. Therapies could explicitly focus on changing psychological vulnerabilities next to the alleviation of the current state of affairs that brought the patient to seek help in the first place. For example, it will most likely be beneficial for patients to enlist strategies that continuously counter their overall tendency to avoid possible negative outcomes. As such, clinicians should address not only specific sets of avoidance behavior, but also the general tendency to avoid negative outcomes, in their treatment of affective disorders. Indeed, stable vulnerabilities such as personality traits seem amenable to psychological intervention. This is possible through the use of existing therapies (Roberts et al., 2017) and might even be more effective when treatments are used that are tailored specifically towards this goal (Barlow et al., 2017). Finally, from a public health perspective, additional efforts might be made to prevent the onset of (chronic) complaints. This might be possible by targeting psychological vulnerabilities in at risk populations and is especially relevant in the light of our findings that symptoms of psychopathology are relatively stable over time.

Future research Future research should focus on advancing our understanding of the underlying model of psychopathology in order to create an even better understanding of depressive and anxiety disorders and related complaints. Some recently developed concepts that attempt to do so include the HiTOP system and the network approach to psychopathology. Subsequently it can be assessed how the risk factors we studied and other possible causes and correlates are related to new concepts derived from these concepts (e.g. spectra of psychopathology or network structures). A possible general integrative guideline for such an endeavor is the

Research Domain Criteria (RDoC). This constitutes a research framework designed by the National Institute of Mental Health in order to better understand the nature of mental health and illness (Cuthbert & Insel, 2013). Within this framework observational studies should preferably be broad, inclusive and measure participants on multiple time points with different time-intervals. Finally, given the transdiagnostic nature of most risk factors we studied future research should focus on developing, refining and implementing transdiagnostic treatment protocols (Barlow et al., 2017; Norton & Paulus, 2016). Such treatment protocol might or might not be best off when complemented by add-on modules that target specific risk factors that patients present themselves with.

Conclusion Depression and anxiety disorders are highly prevalent and constitute a large burden of disease worldwide. Although many risk factors are related to either depression or anxiety, they are largely studied in isolation of other possible risk factors and outcomes. Therefore, it is still unclear to what extent these risk factors constitute risk for the onset or chronicity of disorders and whether these risk factors constitute risk for specific disorders and subtypes or are more generalized transdiagnostic risk factors. Next to that, although risk factors for depression and anxiety are thought to be inherently more stable than symptoms of depression and anxiety, some research findings indicate that this distinction might be less pronounced as theorized but this has never been closely examined with multiple kinds of risk factors on a large scale. We found that among different risk factors, trait behavioral avoidance was associated with depression and anxiety and constituted risk for the onset as well as the chronicity of depression and anxiety. In contrast, trait behavioral approach and automatic approach- avoidance behavior were not associated with depressive and anxiety disorders and did not constitute risk for either the onset or chronicity of depressive and anxiety disorders. Furthermore, we found that practically all other risk factors we studied, were related to either the onset or chronicity of depression and anxiety. Some risk factors such as neuroticisms, extraversion and locus of control were more broadly related to affective disorders, whereas other risk factors such as explicit self-depressive associations, hopelessness, worry and anxiety sensitivity constituted risk for more specific symptom dimensions of either depression or anxiety, or specific affective disorders themselves.

Finally, we found that the stability over time of the different risk we studied as well as the symptoms of affective disorders was moderate to strong and remarkably similar. These findings support the notion of specific as well as transdiagnostic predictors of the onset and course of affective disorders. They underline the importance of addressing psychological vulnerability in the prevention, treatment and relapse prevention of both depression and anxiety.

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