Research Review: What we have learned about the causes of eating disorders - a synthesis of sociocultural, psychological, and biological research.

Journal of Child Psychology and Psychiatry 56:11 (2015), pp 1141–1164

doi:10.1111/jcpp.12441

Research Review: What we have learned about the causes of eating disorders – a synthesis of sociocultural, psychological, and biological research Kristen M. Culbert,1 Sarah E. Racine,2 and Kelly L. Klump3 1

Department of Psychology, University of Nevada, Las Vegas, NV; 2Department of Psychology, Ohio University, Athens, OH; 3Department of Psychology, Michigan State University, East Lansing, MI, USA

Background: Eating disorders are severe psychiatric disorders with a complex etiology involving transactions among sociocultural, psychological, and biological influences. Most research and reviews, however, focus on only one level of analysis. To address this gap, we provide a qualitative review and summary using an integrative biopsychosocial approach. Methods: We selected variables for which there were available data using integrative methodologies (e.g., twin studies, gene-environment interactions) and/or data at the biological and behavioral level (e.g., neuroimaging). Factors that met these inclusion criteria were idealization of thinness, negative emotionality, perfectionism, negative urgency, inhibitory control, cognitive inflexibility, serotonin, dopamine, ovarian hormones. Literature searches were conducted using PubMed. Variables were classified as risk factors or correlates of eating disorder diagnoses and disordered eating symptoms using Kraemer et al.’s (1997) criteria. Findings: Sociocultural idealization of thinness variables (media exposure, pressures for thinness, thin-ideal internalization, thinness expectancies) and personality traits (negative emotionality, perfectionism, negative urgency) attained ‘risk status’ for eating disorders and/or disordered eating symptoms. Other factors were identified as correlates of eating pathology or were not classified given limited data. Effect sizes for risk factors and correlates were generally small-to-moderate in magnitude. Conclusions: Multiple biopsychosocial influences are implicated in eating disorders and/or disordered eating symptoms and several can now be considered established risk factors. Data suggest that psychological and environmental factors interact with and influence the expression of genetic risk to cause eating pathology. Additional studies that examine risk variables across multiple levels of analysis and that consider specific transactional processes amongst variables are needed to further elucidate the intersection of sociocultural, psychological, and biological influences on eating disorders. Keywords: Eating disorder, disordered eating, risk, etiology, biopsychosocial.

Introduction Eating disorders are severe psychiatric illnesses (Klump, Bulik, Kaye, Treasure, & Tyson, 2009) that are associated with numerous negative outcomes, including medical complications and disruptions in cognitive, emotional, and social functioning (American Psychiatric Association (APA), 2013). The DSM-5 recognizes three primary diagnoses: anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED) (see Table 1). Eating disorder presentations that do not fit within these diagnoses (~20%–40% of cases) are classified under residual categories (Other Specified or Unspecified Feeding or Eating Disorders; APA, 2013). Notably, although diagnostic distinctions are made between the eating disorders, several component symptoms (e.g., weight/shape concerns, dietary restriction, binge eating, compensatory behaviors) are shared across diagnoses (see Table 1). Understanding the causes of eating disorders is important for child and adolescent psychologists and psychiatrists given that mid-to-late adolescence is a

Conflict of interest statement: No conflicts declared.

peak period of risk for eating disorders and their component symptoms (Abebe, Lien, & von Soest, 2012; Stice, Marti, & Rohde, 2013). Approximately 13% of youth will experience at least one eating disorder (AN, BN, BED, or Other Specified or Unspecified Feeding or Eating Disorders) by age 20 (Stice, Marti, et al., 2013), and a large proportion (i.e., 15%– 47%) of youth endorse significant disordered eating cognitions and behaviors (e.g., Culbert, Burt, McGue, Iacono, & Klump, 2009; Jones, Bennett, Olmsted, Lawson, & Rodin, 2001). Furthermore, subthreshold eating disorder syndromes are associated with similar levels of functional impairment and emotional distress as threshold eating disorders (Keel, Brown, Holm-Denoma, & Bodell, 2011; Stice, Marti, et al., 2013). These data underscore the need to identify factors that contribute to risk for eating pathology and the necessity of early prevention and intervention.

Methods Factor identification and inclusion criteria The etiology of eating disorders is complex and, similar to other psychiatric disorders, likely involves the intersection of many causal factors. Despite recognition of the likely interplay

© 2015 Association for Child and Adolescent Mental Health. Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA 02148, USA

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Kristen M. Culbert, Sarah E. Racine, and Kelly L. Klump

J Child Psychol Psychiatr 2015; 56(11): 1141–64

Table 1 Definitions and terminology Disordered eating symptoms Cognitive features Body dissatisfaction Weight concerns Overevaluation of shape/Weight Behavioral features Binge eating

Dissatisfaction with size and/or shape of one’s body or body parts (e.g., stomach) Preoccupation with one’s body weight, a desire to lose weight, the pursuit of thinness Undue influence of body shape and weight on one’s scheme for self-evaluation

Consumption of a large amount of food in a short period of time (e.g., 2 hr) and a sense of a lack of control over eating during the binge episode Compensatory behaviors Inappropriate behaviors to compensate for food consumed and to prevent weight gain, e.g. self-induced vomiting, misuse of laxatives, diuretics, or other medications; fasting; excessive exercise Dietary restriction Behavioral attempts to restrict food intake for weight loss, e.g., skipping meals, avoiding specific foods/rules about what one should or should not eat in the pursuit of weight loss. Emotional eating Increased eating in response to emotions, often negative emotions (e.g., sadness, distress) Low weight status Low body weight considering one’s age, gender, developmental trajectory, and physical health status Core features of eating disorder diagnoses Anorexia nervosa (AN) Core features: low weight status and dietary restriction Typically present: overevaluation of body shape/weight, body dissatisfaction, weight concerns Sometimes present: Binge eating and compensatory behaviors (i.e., binge eating/purging subtype) Bulimia nervosa (BN) Core features: binge eating, compensatory behaviors, and overevaluation of body shape/weight Typically present: body dissatisfaction, weight concerns, and dietary restriction Sometimes present: emotional eating Binge eating disorder (BED) Core features: binge eating (in absence of compensatory behaviors) Sometimes present: emotional eating, overevaluation of body shape/weight, body dissatisfaction, weight concerns, dietary restriction Risk factor terminology Correlate Factor is associated with outcome (e.g., cross-sectional/case-control associations) Risk factor Factor precedes and prospectively predicts outcome (e.g., prospective longitudinal associations) Causal risk factor Factor alters the risk of outcome when manipulated (e.g., experimental change in outcome) Behavioral genetic methodology Additive genetic effects Cumulative effect of many genes on the phenotypic outcome; heritability estimate (h2) Shared environment effects Environmental influences that act to make twins/siblings similar on outcome Nonshared environment effects Environmental influences that make siblings dissimilar on the outcome – different environmental factors or differential reactions to the same environmental factor; Estimates also include measurement error Genetic correlation (ra) Degree to which genetic influences on two phenotypes overlap Nonshared environment Degree to which non-shared environmental influences on two phenotypes overlap correlation (re)

among biological, psychological, and sociocultural factors, previous studies and reviews have often examined biological and psychosocial risk factors in isolation (see Striegel-Moore & Bulik, 2007 for a description). We have taken an integrative, biopsychosocial approach in this qualitative review to address this gap in the literature, as studies that examine factors across varying levels of analysis will be important for fully elucidating the etiology of eating disorders. Factors included were required to have some data using methodologies that examined biological and environmental influences (i.e., twin studies, gene-environment interplay) and/or data at both the biological and behavioral level (i.e., neuroimaging). A range of factors met these inclusion criteria, including variables related to idealization of thinness, negative emotionality, perfectionism, negative urgency, inhibitory control, cognitive inflexibility, serotonin, dopamine, and ovarian hormones. Our discussion of parental factors and abuse history is included in the integrative context of gene-environment interaction studies. We excluded factors that have not been examined using integrative methodologies (e.g., peer influences, prenatal/ perinatal factors) as well as those that are likely secondary to the illness (e.g., gastrointestinal hormones). We also refrained

from reviewing DSM diagnoses (e.g., anxiety or mood disorders) given their complexity, and instead, focused on trait-level variables that contribute to DSM diagnoses. Studies were identified by at least one of the authors’ (KMC or SER) via the PubMed database using the following keywords: eating disorders, anorexia nervosa, bulimia nervosa, binge eating disorder, disordered eating, eating pathology, sociocultural, psychosocial, thin-ideal internalization, media, pressures for thinness, thinness expectancies, personality, negative emotionality, neuroticism, perfectionism, impulsivity, negative urgency, neurocognitive, cognitive flexibility, inhibitory control, genetic, epigenetic, DNA methylation, mRNA, candidate gene, genome-wide association, gene-environment, puberty, hormones, estradiol, progesterone, and meta-analysis. Identified studies were reviewed by one of the authors’ (KMC or SER) to determine eligibility according to the inclusion criteria (see above).

Factor classification We required a minimum of two conducted studies and used the operational definitions provided by Kraemer et al. (1997) (see © 2015 Association for Child and Adolescent Mental Health.

Sample type

© 2015 Association for Child and Adolescent Mental Health.

Nonclinical/population-based



Suisman et al., 2011

Weight preoccupation Klump et al., 2000


Weight/Shape Concerns (Combined Construct) Klump, Burt, Nonclinical/population-based et al., 2010; Wade et al., 2013 Nonclinical/population-based Mage~11 Mage~23 Mage~13 Mage~14 Mage~16

Mage~11 Mage~17 Mage~18


h2

Mage~18


h2

Mage~18


Body dissatisfaction Klump et al., 2000

h2

Mage~18



N/A

h2

Non-clinical/population-based

N/A N/A

h2

N/A

h2

h2

N/A

N/A

h2

h2

N/A

h2

N/A

N/A

N/A

N/A

h2


Klump, McGue, & Iacono, 2003 Klump, Burt, et al., 2007 Suisman, Burt, McGue, Iacono, & Klump, 2011 Binge eating Racine, Burt, Iacono, McGue, & Klump, 2011 Suisman et al., 2011

N/A

h2


Culbert et al., 2009

N/A

h2


Silberg & Bulik, 2005

N/A

h2

SNP (risk allele)

N/A

Genetic effect/Gene

h2

Age

Mage~11 Mage~17 Mage~11 Mage~14 Mage~18 Mage~12 Mage~16 Mage~11 Mage~13 Mage~21 Mage~11 Mage~17 Mage~14

Behavioral genetic models Phenotypic outcome Overall disordered eating Klump, McGue, & Nonclinical/population-based Iacono, 2000 Klump, Burt, et al., Non-clinical/population-based 2007

Study

Table 3 Results from behavioral genetic and candidate gene 9 environment interaction studies in females

N/A

= 1,678)

= 680)

= 680)

= 172)

versus

versus 13–41 years(n

= 397)

= 2,446)

= 602)

versus

Age, longitudinal(n = 702): 13 years versus 14 years versus 16 years

Age: 11 years(n

= 397)

602)

= 602)

versus

versus

versus 17 years(n Parental Divorce: Divorced(n Intact(n = 1,413)

Age: 11 years(n

= 397)

= 397)


Age: 11 years(n

Parental Divorce: Divorced(n Intact(n = 1,413)

Dietary Restraint(n


Pubertal Status: Pre-puberty(n = 452) versus Mid-puberty(n = 78) versus Post-puberty(n = Advancing Pubertal Status(n = 510)

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

= 324)

= 602)

Age, longitudinal: ≤13 years(n = 1,266) versus ≥ 14 years(n = 1,104) Pubertal Status: Prepuberty(n = 168) versus Mid-puberty(n = 142) versus Post-puberty(n

versus 17 years(n

N/A

= 680)

Genetic Main Effect


Age: 11 years(n

Environmental moderator (sample size, n’s)

No

Yes

No

Yes

Yes

No

No

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

G9E

Significant? Yes/No

doi:10.1111/jcpp.12441 Causes of eating disorders

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Causes of eating disorders

Table 4 Summary of neural serotonin and dopamine disturbances in women recovered from an eating disorder

Biological marker Serotonin system 5-HT1a receptor Bailer et al., 2005

Age

PET effect

Mage~24 Mage~23

↑BP ↑BP

Mage~27

↑BP

Ages: 18–30

↑BP

5-HT2a receptor Frank et al., 2002

Mage~25

↓BP

Bailer et al., 2004

Mage~24

↓BP

Mage~25

↓BP

Mage~25

↓BP

Mage~25

↑BP

Mage~29

↓BP

Mage~24 Mage~24 Mage~27 Mage~27

↑BP ↑BP ↑BP ↓BP

Mage~25

↑BP

Galusca et al., 2008

Kaye et al., 2001 5-HT Transporter Bailer et al., 2007 Pichika et al., 2012 Dopamine system D2/D3 receptor Frank et al., 2005

Recovered ED (sample size, n’s)

Brain region

Mesial temporal cortexa; Dorsal raphea Mesial temporal cortex; Subgenual cingulate; Lateral temporal cortexa; Orbitofrontal cortexa; Parietal cortexa Prefrontal cortex; Mesial temporal cortex; Lateral temporal cortex; Orbitofrontal cortex; Supragenual cingulate; Subgenual cingulate; Pregenual cingulate; Parietal cortex; Dorsal raphe Superior temporal gyrus; Inferior frontal gyrus; Parietal operculum; Temporoparietal junction

AN-R(n = 13) > controls(n = 18) ↑harm avoidance AN-R(n = 11)

Mesial temporal cortex; Subgenual cingulate; Pregenual cingulate; Sensorimotor cortexa Lateral temporal cortexa; Subgenual cingulate; Parietal cortex; Occipital cortex Lateral temporal cortex; Subgenual cingulate; Pregenual cingulate Parietal cortex Mesial temporal cortex; Subgenual cingulate; Pregenual cingulate Mesial temporal cortex; Left temporal cortex; Subgenual cingulate; Occipital cortex Lateral temporal cortexa; Orbitofrontal cortex; Sensorimotor cortexa

AN(n

Dorsal raphe; Antero-ventral striatum Antero-ventral striatum Anterior cingulate; Superior temporal gyrus Midbrain; Superior and inferior cingulate

AN-R(n = 11) > AN-BP(n = 7) BN(n = 9) > AN-BP(n = 7) BN(n = 8) > controls(n = 8) BN(n = 8) < controls(n = 8)

Antero-ventral striatum; Ventral putamena; Dorsal caudatea; Middle caudatea Mage~24 ↑BP Dorsal caudate; Dorsal putamen Bailer et al., 2013 Mage~27 ↑BP Antero-ventral striatuma Mage~27 ↑BP Ventral putamena Mage~27 ↑BP Dorsal caudate; Dorsal putamen Endogenous dopamine release (following amphetamine administration) Bailer et al., 2012 Mage~27 ↓ΔBP Antero-ventral striatuma Mage~26 ↓ΔBP Dorsal caudate Mage~28 ↓ΔBP Antero-ventral striatum

AN-BP(n

AN-R(n

= 12)

= 9)

= 16)

AN-BP(n

> controls(n

> controls(n

< controls(n

= 10)

= 18)

= 7)

= 23)

< controls(n

= 16)

↑drive for thinness AN-BP(n ↑novelty seeking AN-BP(n

= 9)

↑harm avoidance AN-BP(n BN(n

AN(n

= 9)

< controls(n

= 10)

= 9)

= 9)

= 12)

> controls(n

= 12)

↑harm avoidance AN AN-R(n = 17) > controls(n = 21) BN(n = 14) > controls(n = 21) ↑harm avoidance AN/BN(n = 27) AN(n = 10) < controls(n = 9) ↑Δanxiety AN(n = 10) ↑euphoria controls(n = 9)

BP, binding potential; PET, positron emission tomography; AN, anorexia nervosa, combined restricting and binge-purge subtype; AN-R, anorexia nervosa, restricting subtype; AN-BP, anorexia nervosa, binge-purge subtype; BN, bulimia nervosa; AN/BN, combined anorexia nervosa and bulimia nervosa group. a Nonsignificant/trend-level effect that is medium magnitude based on Cohen’s d effect size calculations (d’s ≥ .50).

(see Tables 3 and 5). Herein, we focus on three sets of interesting findings that deserve note: serotonin and G 9 E effects, dopamine and epigenetic effects, and age/puberty and G 9 E effects.

Serotonin and G 9 E Parental factors (e.g., parental pressures and criticism; low parental contact) and abuse history have been identified as retrospective correlates of eating pathology (e.g., for systematic review, see Jacobi et al., 2004). Nonetheless, abuse history is often considered a risk factor for eating disorders since some retrospective reports indicate the occurrence of © 2015 Association for Child and Adolescent Mental Health.

abuse prior to eating disorder onset (Jacobi et al., 2004), and childhood abuse/neglect has been shown to prospectively predict elevated risk for disordered eating and eating disorder onset in a longitudinal study (Johnson, Cohen, Kasen, & Brook, 2002). Interestingly, studies have begun to explore interactions between these environmental experiences and biological factors. The short allele of 5-HTTLPR has been shown to interact with parenting style (e.g., perceived parental control, criticism, and underinvolvement) and abuse history (e.g., physical and/ or sexual) in the prediction of AN, bulimic symptoms, and drive for thinness (see Table 4). Effect sizes have ranged from small-to-large in magnitude. These

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that determine for whom sociocultural pressures for thinness will lead to thin-ideal internalization. Additional evidence for sociocultural risk effects on eating disorders and disordered eating symptoms comes from prevention and intervention data. Specifically, programs that aim to reduce thin-ideal internalization (e.g., the Body Project, a cognitive dissonance program) or thinness expectancies (Annus, Smith, & Masters, 2008) have led to reductions in disordered eating symptoms (e.g., body dissatisfaction, dieting, bulimic symptoms; Annus et al., 2008; Stice, Marti, Shaw, & O’Neil, 2008), and in one study, eating disorder onset (Stice et al., 2011). Effect sizes have generally ranged from small-tomedium, but larger intervention effects have been found when programs target high-risk individuals (e.g., girls with elevated thin-ideal internalization, body dissatisfaction, or an eating disorder; M€ uller & Stice, 2013; Stice et al., 2008). Interestingly, the Body Project program has also been shown to alter neural responsiveness to thin-ideal media images and statements (e.g., pre–post neural reductions in the caudate and anterior cingulate cortex; Stice, Becker, & Yokum, 2013). Together, these data demonstrate that targeted reductions of sociocultural influences (e.g., thin-ideal internalization) reduce risk for eating pathology, and importantly, that changes at the cognitive/behavioral level are linked to biological changes – further highlighting biopsychosocial interplay. Notably, these same sociocultural factors have been proposed as leading candidates for explaining gender differences and developmental changes in rates of eating pathology, although data directly examining these hypotheses are limited. Crosssectional data demonstrate that thin-ideal internalization is positively correlated with advancing pubertal maturation in girls (Hermes & Keel, 2003; Suisman et al., 2014), and adolescent and young adult females report higher mean levels of perceived pressures and internalization of cultural ideals of appearance than males (e.g., Halliwell & Harvey, 2006; McCabe & Ricciardelli, 2001; Smolak, Levine, & Thompson, 2001). These data provide indirect evidence that higher risk for eating pathology in females, relative to males, may reflect higher rates of endorsement of sociocultural factors. Nonetheless, data from most experimental and prospective studies demonstrate that males are not immune to sociocultural influences. Sociocultural factors (e.g., media exposure to sociocultural body ideals; perceived pressures to lose weight) positively predict body-image concerns (Hausenblas et al., 2013) and the development of disordered eating symptoms (e.g., dieting, weight concerns) in adolescent males, with similar effect sizes to those observed in females (i.e., small-tomedium in magnitude; Field et al., 2001; McCabe & Ricciardelli, 2005; Presnell et al., 2004; Ricciardelli & McCabe, 2003).


Taken together, sociocultural pressures for thinness are ubiquitous in Westernized cultures; however, there are individual differences (due to biological and environmental effects) in the extent to which these factors are internalized. Thus, sociocultural influences (i.e., media exposure, perceived pressures for thinness, thin-ideal internalization, thinness expectancies) are risk factors for disordered eating cognitions and behaviors, but not universally – only a subset of females and males are vulnerable to these influences. Additional work is needed to establish whether each of these factors contributes to risk for eating disorder diagnoses and to identify additional moderators of sociocultural effects on eating disorder risk. The factors reviewed below represent individual difference variables that may influence the development of eating pathology in the context of sociocultural pressures for thinness.

Personality traits Personality traits have received significant attention in etiologic models of eating disorders (Lilenfeld, Wonderlich, Riso, Crosby, & Mitchell, 2006). Selfreported personality traits are thought to index stable, individual differences in a person’s typical pattern of thinking, emotion, or behavior across situations and may influence one’s reactions to environmental events or milieus (such as sociocultural pressures for thinness – see above). In this review, we focus on negative emotionality/neuroticism, perfectionism, and impulsivity/negative urgency, since these personality traits prospectively predict the development of eating disorder symptoms (Combs, Pearson, Zapolski, & Smith, 2012; Leon, Fulkerson, Perry, Keel, & Klump, 1999; Tyrka, Waldron, Graber, & Brooks-Gunn, 2002) and have been shown to share etiologic (e.g., genetic) underpinnings with eating pathology (Klump, McGue, & Iacono, 2002; Racine et al., 2013; Wade & Bulik, 2007). Although other personality traits (e.g., harm avoidance, reward dependence) have been examined in relation to eating disorders, longitudinal and behavioral genetic data are not available for these traits (Lilenfeld et al., 2006) and thus, they will not be reviewed.

Negative emotionality/Neuroticism Negative emotionality and neuroticism represent trait-based dispositions toward experiencing unpleasant emotions (e.g., anxiety, anger). Longitudinal research consistently demonstrates that negative emotionality and neuroticism predict the development of eating pathology (e.g., drive for thinness, bulimic symptoms; Leon et al., 1999), full-threshold eating disorder diagnoses (Cervera et al., 2003; Ghaderi & Scott, 2000), AN (Bulik et al., 2006; for exception, see Tyrka et al., 2002), and bulimic syndromes (Killen et al., 1996; Tyrka et al., © 2015 Association for Child and Adolescent Mental Health.

doi:10.1111/jcpp.12441

2002). Most studies have been conducted in samples of adolescent females, with effect sizes ranging from small-to-large (see Table 2). Thus, available research supports the classification of negative emotionality/ neuroticism as a risk factor for disordered eating and eating disorders across the diagnostic spectrum (i.e., any eating disorder, AN, BN). However, limitations of this body of research include the lack of examination of BED and the fact that few studies have separately predicted the onset of AN and BN. Behavior genetic designs have been used to elucidate the etiologic nature of the relationships between disordered eating symptoms and key personality traits studied in longitudinal research, including negative emotionality/neuroticism. Similar to eating disorders and their component symptoms, personality traits have been shown to be heritable, with approximately 40%–50% of the variance accounted for by genetic factors (Polderman et al., 2015). The remaining variance is typically explained by nonshared environmental factors. Given this, it is important to understand whether the relationship between personality traits and disordered eating is a result of common causal factors (as opposed to independent etiologic factors; Lilenfeld et al., 2006) and whether these common causal factors are primarily genetic or environmental in origin. Such findings can point researchers in the direction of specific genetic/biological and/or environmental risk processes that account for both phenotypes and can inform our understanding of transactional risk processes between genetic and environmental factors in the etiology of eating pathology. Behavioral genetic research examining negative emotionality/neuroticism-disordered eating associations has been somewhat mixed. Two studies found that genetic factors largely explained the covariation between negative emotionality/neuroticism and disordered eating symptoms (i.e., binge eating, body dissatisfaction, weight preoccupation, compensatory behavior) in adolescent and young adult females, with genetic correlations (i.e., ra; see definition in Table 1) ranging from .37 to .49 across studies (Klump et al., 2002; Koren et al., 2014). Both studies reported significant non-shared environmental correlations for these personality traits and binge eating (re’s = .25–.28), whereas Klump et al. (2002) did not detect environmental overlap for weight preoccupation, body dissatisfaction, or compensatory behavior. In contrast, Wade et al. (2000) reported only nonshared environmental overlap between neuroticism and a composite measure of disordered eating (i.e., problems related to AN, BN, and obesity) in mid-adulthood. Discrepant results may be due to differences in participant age (i.e., adolescence/young adulthood vs. mid-adulthood) or assessment methodology. For example, Wade et al. (2000) used a heterogeneous measure of disordered eating, and participants reported on current personality traits (mean age = 36.5 years) but lifetime dis© 2015 Association for Child and Adolescent Mental Health.


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ordered eating. Thus, future behavioral genetic research is needed to determine the extent to which genetic and nonshared environmental influences on negative emotionality/neuroticism and disordered eating overlap as well as whether the nature of this overlap differs for specific disordered eating symptoms.

Perfectionism Perfectionism is a multidimensional trait that includes components tapping high personal standards as well overly critical evaluations of oneself (Bardone-Cone et al., 2007). Longitudinal studies generally suggest that perfectionism increases risk for eating disorder outcomes by interacting with other important factors. For example, perfectionism predicted increases in bulimic symptoms in late adolescent and young adult females, but only among participants with low self-esteem and who perceived themselves as overweight (Bardone-Cone, Abramson, Vohs, Heatherton, & Joiner, 2006; Vohs, Bardone, Joiner, & Abramson, 1999). More recent longitudinal data suggested that perfectionism interacts with body dissatisfaction to predict increases in drive for thinness and overevaluation of weight and shape in adolescent females (Boone, Soenens, & Luyten, 2014). Of the longitudinal studies that have found direct effects for perfectionism, Tyrka et al. (2002) reported that perfectionism was related to the onset of anorexic, but not bulimic, syndromes over an 8-year period, whereas Boone et al. (2014) reported direct effects of perfectionism on bulimic symptoms but not disordered eating cognitions. Effect sizes for the main and interaction effects of perfectionism on eating disorder outcomes range from small-to-moderate (see Table 2). Taken together, data suggest that perfectionism is a risk factor for disordered eating attitudes and behaviors, acting either independently or in combination with other factors. Given that few prospective studies have examined threshold eating disorder outcomes (i.e., AN, BN, or BED), additional research is needed before classifying perfectionism as a risk factor for clinical diagnoses. With regards to the etiologic association between perfectionism and disordered eating, results from two twin studies of adolescent/adult females suggest that genetic, rather than environmental factors, are most important. This is true for both the core cognitive (i.e., undue influence of weight and shape on self-esteem: ra’s = .25–.53; Wade & Bulik, 2007) and behavioral (i.e., binge eating, compensatory behaviors: ra’s = .46–.80; Spanos, 2012) symptoms. Nonshared environmental overlap was smaller and almost uniformly nonsignificant (re = .05–.14) Furthermore, female cotwins of AN probands were found to have higher levels of perfectionism than control cotwins, with results suggesting that the shared transmission of perfectionism and AN is partially

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genetic (Wade et al., 2008). Future behavioral genetic studies are needed to confirm genetic, but not environmental, associations and examine a wider range of disordered eating symptoms.

Impulsivity and negative urgency Whereas negative emotionality and perfectionism tend to be associated with eating disorder symptoms across the diagnostic spectrum, impulsivity is thought to relate specifically to binge eating and purging. Indeed, cross-sectional research tends to find greater levels of impulsivity in patients currently ill with BN, BED, and AN-binge/purge type (AN-B/P) compared to patients with AN-restricting type (AN-R) and controls (e.g., Claes, Vandereycken, & Vertommen, 2005; Rosval et al., 2006). Nonetheless, longitudinal research regarding the prospective relationship between impulsivity and binge eating and purging has been limited, and results have been mixed. An earlier set of studies found that behavioral indices of impulsivity (e.g., substance use, delinquency), but not trait impulsivity, predicted the onset of binge eating and compensatory behavior in adolescent females (Wonderlich, Connolly, & Stice, 2004). More recently, Bodell, Joiner, and Ialongo (2012) found that parent-reported impulsivity in 1st grade predicted self-reported bulimic symptoms in 10th grade, although the presence of bulimic pathology in 1st grade was not assessed. Efforts to deconstruct the multidimensional impulsivity construct (Whiteside & Lynam, 2001) have arguably led to significant advances in our understanding of the role of impulsivity in eating disorders. Accumulating evidence suggests that negative urgency (i.e., tendency to engage in rash action when distressed) is the most important form of impulsivity for binge eating and purging symptoms (Fischer, Smith, & Cyders, 2008). Recent data extend cross-sectional findings by demonstrating that negative urgency prospectively predicts increases in binge eating and purging in college women over one semester (Fischer et al., 2013) and binge eating in girls transitioning from elementary to middle school over a 1-year period (Combs et al., 2012), with effect sizes ranging from small-to-moderate (see Table 2). Thus, negative urgency is currently classified as a risk factor for disordered eating behaviors. Future longitudinal studies must focus on eating disorder diagnoses (e.g., BN, BED) to investigate whether effects generalize to threshold syndromes. The one behavioral genetic study conducted to date suggests that negative urgency shares etiologic underpinnings with binge eating (Racine et al., 2013). Specifically, a substantial genetic correlation was found between negative urgency and binge eating (ra = .77), which starkly contrasts with the small genetic associations observed when other facets of impulsivity have been examined (i.e., lack


of planning, ra’s = .10–.17; Spanos, 2012). Negative urgency and binge eating also share a smaller, yet significant, proportion of their nonshared environmental influences (re = .29; Racine et al., 2013). Additional twin study data are needed to confirm that negative urgency is a heritable risk factor for binge eating and to examine etiologic associations between negative urgency and other disordered eating symptoms. Furthermore, behavioral genetic data could be used to bolster transactional risk models (e.g., Acquired Preparedness model; Combs et al., 2012) of bulimic symptom development. The expectation that eating will reduce negative affect, a key variable in the Acquired Preparedness model, has been shown to prospectively increase risk for binge eating (Fischer et al., 2013; Smith et al., 2007) and to mediate the longitudinal association between negative urgency and increases in binge eating in youth (Combs et al., 2012). Thus, future research should investigate the impact of eating expectancies on the genetic/environmental associations between negative urgency and binge eating.

Neurocognitive processes Neurocognitive processes have increasingly gained attention as individual difference factors that influence eating disorder risk. Cognitive flexibility and inhibitory control are two neurocognitive processes that have been linked to eating disorders and have been examined in biologically informative ways (i.e., using family designs and/or neuroimaging methods). Given that no prospective, longitudinal studies have examined cognitive flexibility and inhibitory control as predictors of disordered eating or eating disorder onset, we review studies with patients who are currently ill and, when available, patients who are recovered as well as unaffected relatives.

Cognitive flexibility Cognitive flexibility can be defined as the ability to move back and forth, or ‘shift’, between multiple tasks, operations, or mental sets (Roberts, Tchanturia, Stahl, Southgate, & Treasure, 2007) and is thought to be localized in multiple brain regions, including the prefrontal cortex (PFC), anterior cingulate cortex (ACC), and posterior parietal cortex. Research consistently demonstrates impaired performance on cognitive flexibility tasks (e.g., Trail Making Test, Wisconsin Card Sorting Test) in ill adults with AN, compared to controls, with a recent meta-analysis reporting a moderate overall effect size for AN (i.e., Hedges’ g = .44; Wu et al., 2014).3 Furthermore, there is some evidence to suggest that cognitive flexibility deficits persist into recovery from AN (i.e., ill-AN > recovered-AN > controls; Lindner, Fichter, & Quadflieg, 2014; Roberts, Tchanturia, & Treasure, 2010; Shott et al., 2012; Steinglass, Walsh, & Stern, 2006; Tchanturia et al., © 2015 Association for Child and Adolescent Mental Health.

doi:10.1111/jcpp.12441

2012; Tenconi et al., 2010). However, small, nonsignificant differences in children and adolescents with AN, compared to controls (meta-analytic d’s = .005–.20; Lang, Stahl, Espie, Treasure, & Tchanturia, 2014), suggest that cognitive flexibility deficits may reflect starvation or duration of illness in AN. Although less research has examined cognitive flexibility deficits in BN and BED, the meta-analysis by Wu et al. (2014) reported moderate effect sizes for both of these disorders (g’s = .50 and .53, respectively). However, only two studies have examined BED, and it is currently unknown whether deficits persist into recovery for BN or BED. Importantly, however, familial data provide evidence for traitrelated dysfunction in both AN and BN. For example, nonaffected biological sisters of individuals with AN (Holliday, Tchanturia, Landau, Collier, & Treasure, 2005; Roberts et al., 2010; Tenconi et al., 2010) and BN (Roberts et al., 2010) demonstrated problems with cognitive flexibility compared to controls. Unaffected twins of probands with any lifetime eating disorder were also found to perform more poorly on cognitive flexibility tasks than control twins (d’s = .20–.40; Kanakam, Raoult, Collier, & Treasure, 2013). Associations between cognitive flexibility and neural activation further suggest that these cognitive deficits may reflect biological markers. For example, differential activation in neural circuits related to cognitive (e.g., less activity in ventrolateral prefrontal cortex and bilateral parahippocampal cortex; Sato et al., 2013) and behavioral (e.g., reduced activation in fronto-striato-thalamic circuitry; Zastrow et al., 2009) flexibility have been observed in individuals with eating disorders compared to controls. Nonetheless, these studies have exclusively focused on individuals currently ill with AN. Thus, we do not know whether these neural alterations are a function of the ill-state and whether these patterns apply to individuals with BN or BED. In sum, current evidence supports cognitive flexibility as a correlate of AN and BN, but less is known about the role of cognitive flexibility for BED. Cognitive flexibility deficits may represent premorbid traits in AN, given some persistence into recovery and presence in noneating disorder family members. Evidence in BN is more limited (e.g., unexamined in recovered individuals), but impairment in unaffected family members of BN probands is promising evidence for premorbid deficits. Future research examining cognitive flexibility, as well as corresponding neural alterations, must be conducted in both patients recovered from eating disorders and child/ adolescent patients early in their illness. Furthermore, efforts to deconstruct the broad construct of cognitive flexibility into component processes (i.e., attentional set-shifting and reversal learning; Wildes, Forbes, & Marcus, 2014) are necessary to more clearly understand the precise nature of the neurocognitive deficits present in eating disorders. © 2015 Association for Child and Adolescent Mental Health.


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Inhibitory Control Inhibitory control refers to the behavior of suppressing, inactivating, or over-riding a relatively automatic response in favor of a less automatic one – for simplicity, ‘inhibition’ is used as the descriptor of this process. Inhibitory control encompasses both motor inhibition (e.g., Go-No Go and Stop Signal tasks) and cognitive inhibition (e.g., Stroop interference). Brain regions involved in inhibitory control include the right inferior frontal gyrus, supplementary motor areas, basal ganglia, as well as the PFC and ACC. Inhibitory control deficits are most consistently observed in eating disorders characterized by binge eating and purging behaviors. For example, studies have reported greater commission errors, reaction time, and reaction time variability on the Go-No Go task in patients ill with BN and AN-B/P compared to patients with AN-R and healthy controls (Claes, Mitchell, & Vandereycken, 2012; Mobbs, Van der Linden, d’Acremont, & Perroud, 2008; Rosval et al., 2006). Furthermore, women with BN, but not BED, demonstrated greater stop signal reaction time compared to controls (Wu, Giel, et al., 2013). Cognitive inhibition deficits have also been observed on disorder-neutral Stroop tasks in patients with BN relative to controls (Dobson & Dozois, 2004; Wu, Hartmann, Skunde, Herzog, & Friederich, 2013). A small-to-moderate meta-analytic effect size (i.e., g = .32) for inhibitory control deficits was obtained when combining across currently ill patients with BN, BED, and AN-B/P (Wu, Hartmann, et al., 2013). Unfortunately, no study has examined inhibitory control processes in patients recovered from eating disorders or in unaffected relatives of eating disorder probands. Neuroimaging studies of inhibitory control processes have been conducted across the eating disorder spectrum (i.e., AN, BN, and BED) and generally demonstrate reduced activation in frontostriatal regions compared to controls (see Lock, Garrett, Beenhakker, & Reiss, 2011 for an exception). In addition, neural activation has been shown to inversely correlate with specific eating disorder symptoms in adults and adolescents with BN (e.g., greater binge frequency; Marsh et al., 2011, 2009) as well as in adults with BED (i.e., higher dietary restraint; Balodis et al., 2013). Interestingly, interpretations of reduced activation have differed by diagnosis. In BN and BED, attenuated recruitment of self-regulatory regions is thought to interfere with resolving the conflicting desire to consume palatable foods and avoid weight gain (Marsh et al., 2011). In contrast, decreased activation in AN-R has been proposed to reflect a need for less inhibitory resources to perform similarly to controls on inhibitory control tasks (Wierenga et al., 2014). Taken together, inhibitory control deficits appear to be correlates of eating disorders characterized by binge eating and/or purging during the ill-state.

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Additional exploration into whether these deficits occur in the absence of the active illness and in nonaffected family members will be necessary for understanding whether inhibitory control deficits are trait-like disturbances that contribute to risk for eating disorders. Studies that directly compare neural activation across eating disorder subtypes, and in relation to specific disordered eating symptoms, will also be important for elucidating the biological correlates of inhibitory control deficits. Finally, prospective studies that investigate whether cognitive flexibility and inhibitory control impairments precede and predict the development of disordered eating and eating disorders are necessary for establishing whether neurocognitive processes are risk factors for eating pathology.

(see meta-analyses of Calati, De Ronchi, Bellini, & Serretti, 2011; Lee & Lin, 2010). These polymorphisms have shown replicated associations with AN, although effect sizes are relatively small (meta-analytic odds ratio ~1.2–1.4). Associations have not been consistently observed for other eating disorder diagnoses (e.g., BN, BED) and have not been extensively examined across the spectrum of disordered eating cognitions and behaviors. Candidate gene associations await risk classification until studies with stronger designs (e.g., GWAS) replicate and confirm these effects. Accumulating data (see Formal Tests of Gene-Environment Interplay) suggest that the consideration of gene-environment interplay effects, as opposed to focusing exclusively on genetic main effects, will also be important.

Molecular genetics

Formal tests of gene-environment interplay

As noted previously, twin and adoption studies highlight that genetic influences, which predate symptom onset, substantially contribute to risk for disordered eating and eating disorders. Unfortunately, despite significant methodological advances in the study of genetic associations over the past two decades, we still know relatively little about the specific genes that contribute to eating disorder risk. Genome-wide association studies (GWAS) search the entire genome for single nucleotide polymorphisms (SNPs) that are associated with the presence of eating pathology. To date, five GWAS have been conducted for either AN or disordered eating symptoms (i.e., body dissatisfaction; bulimic behaviors; Boraska et al., 2014, 2012; Nakabayashi et al., 2009; Wade et al., 2013; Wang et al., 2011), yet no gene variant has met the significance threshold for multiple comparisons (p < 5 9 10 8). This is not surprising given the small sample sizes in these studies (~320–2,900 cases), as much larger samples have been necessary for the detection of significant GWAS effects for other psychiatric disorders (e.g., schizophrenia; n’s > 20,000; Bergen & Petryshen, 2012). Conclusions await the collection of larger samples and the examination of other eating disorder diagnoses (i.e., BN, BED). Candidate gene association studies have been used to examine genes in neurobiological systems thought to be disrupted in eating disorders (e.g., serotonin (5HT), dopamine, brain derived neurotrophic factor). These studies typically use a case– control approach whereby a higher allele or genotype frequency in eating disorder cases versus controls suggests that the gene increases risk for the disorder. Nonreplication is the norm in this literature. Possible exceptions in this regard are the 1438 G/ A polymorphism (A allele) of the 5-HT2a receptor (see meta-analyses of Gorwood, Kipman, & Foulon, 2003; Mart askov a, Slachtov a, Kemlink, Z ahor akov a, & Papezov a, 2009) and the 5-HTTLPR polymorphic region (short allele) of the serotonin transporter gene

Difficulties detecting main effects of most genetic variants on eating disorder risk may be due, in part, to the fact that genetic and environmental factors likely act in concert rather than independently. Differences in environmental risk within a sample could obscure genetic associations if gene-environment interplay is present. Gene-environment interplay effects generally occur in two forms: geneenvironment interactions (G 9 E) and epigenetic effects. G 9 E effects occur when environmental factors interact with one’s genotype to predict differential risk (e.g., certain environmental contexts/ experiences may only relate to the phenotypic outcome in those that are genetically vulnerable). Epigenetic processes occur when environmental or biological factors cause alterations in gene expression (e.g., via changes in methylation, changes in gene transcription) that modify protein production, and ultimately, affect expression of a phenotype. To date, relatively few studies have empirically examined either type of gene-environment interplay. Candidate gene and twin moderation models have been used to formally test G 9 E effects, with ‘environmental’ moderators typically falling within one of four domains: abuse history, parental factors, dieting, or development (see Table 3). Epigenetic effects have been tested by examining differences in mRNA expression and/or DNA methylation between cases and controls, primarily during the ill-state (see Table 5). Nearly all epigenetic studies have used a candidate gene, rather than whole genome, approach (for exceptions, see Global DNA analysis in Booij et al., 2015; Frieling et al., 2007; Saffrey, Novakovic, & Wade, 2014; Tremolizzo et al., 2014) and have not conducted internal replications (i.e., split-half samples). It is too early to label most G 9 E and epigenetic effects as correlates of or risk factors for eating disorders or disordered eating symptoms; significant findings await independent replications, as sample sizes have been small and most studies have explored a different combination of variables © 2015 Association for Child and Adolescent Mental Health.

Sample type






Weight preoccupation Klump et al., 2000


Weight/Shape Concerns (Combined Construct) Klump, Burt, Nonclinical/population-based et al., 2010; Wade et al., 2013 Nonclinical/population-based Mage~11 Mage~23 Mage~13 Mage~14 Mage~16



h2

Mage~18


h2

Mage~18


Body dissatisfaction Klump et al., 2000

h2

Mage~18



N/A

h2


N/A N/A

h2

N/A

h2

h2

N/A

N/A

h2

h2

N/A

h2

N/A

N/A

N/A

N/A

h2


Klump, McGue, & Iacono, 2003 Klump, Burt, et al., 2007 Suisman, Burt, McGue, Iacono, & Klump, 2011 Binge eating Racine, Burt, Iacono, McGue, & Klump, 2011 Suisman et al., 2011

N/A

h2


Culbert et al., 2009

N/A

h2


Silberg & Bulik, 2005

N/A

h2

SNP (risk allele)

N/A

Genetic effect/Gene

h2

Age

Mage~11 Mage~17 Mage~11 Mage~14 Mage~18 Mage~12 Mage~16 Mage~11 Mage~13 Mage~21 Mage~11 Mage~17 Mage~14

Behavioral genetic models Phenotypic outcome Overall disordered eating Klump, McGue, & Nonclinical/population-based Iacono, 2000 Klump, Burt, et al., Non-clinical/population-based 2007

Study

Table 3 Results from behavioral genetic and candidate gene 9 environment interaction studies in females

N/A

= 1,678)

= 680)

= 680)

= 172)

versus

versus 13–41 years(n

= 397)

= 2,446)

= 602)

versus


Age: 11 years(n

= 397)

602)

= 602)

versus

versus


Age: 11 years(n

= 397)

= 397)


Age: 11 years(n


Dietary Restraint(n


Pubertal Status: Pre-puberty(n = 452) versus Mid-puberty(n = 78) versus Post-puberty(n = Advancing Pubertal Status(n = 510)

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

= 324)

= 602)

Age, longitudinal: ≤13 years(n = 1,266) versus ≥ 14 years(n = 1,104) Pubertal Status: Prepuberty(n = 168) versus Mid-puberty(n = 142) versus Post-puberty(n

versus 17 years(n

N/A

= 680)

Genetic Main Effect


Age: 11 years(n


No

Yes

No

Yes

Yes

No

No

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

G9E

Significant? Yes/No

doi:10.1111/jcpp.12441 Causes of eating disorders

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Drive for thinness Akkermann et al., 2011

Akkermann et al., 2012 SLC6A4

Mage~18

Val66Met (Met-allele) 5-HTTLPR (s-allele)

5-HTTLPR (s-allele) T102C (c-allele)

= 344)

= 344)

= 344)

= 344)

Reduced Meal Frequency(n = Starvation(n = 397) Adverse Life Events(n = 201) Sexual Abuse(n = 201)

Dietary Restraint(n Impulsivity(n = 344) Dietary Restraint(n Impulsivity(n = 344)

Dietary Restraint(n Impulsivity(n = 344) Dietary Restraint(n Impulsivity(n = 344)

Reduced Meal Frequency(n = Starvation(n = 397) Adverse Life Events(n = 206) Physical Abuse(n = 205) Sexual Abuse(n = 205) Emotional Abuse(n = 205)

397)

397)

n = 98)

= 256)

Physical or Sexual Abuse(BN, n = 113; control,

Problematic Parenting(n


No No No No

No No No No

No No No No

No No No No No No

Yes

Yes

Genetic Main Effect

AN, anorexia nervosa; BN, bulimia nervosa; h2, heritability; N/A, not applicable; NR3C1, glucocorticoid receptor; SLC6A4, serotonin transporter; 5-HT2a, serotonin 2a receptor. a Nonsignificant/trend-level effect that is medium magnitude based on Cohen’s d effect size calculations (d’s ≥ .50).

BDNF

Mage~16

5-HT2a

SLC6A4

5-HTTLPR (s-allele) T102C (c-allele)

Val66Met (Met-allele) 5-HTTLPR (s-allele)

Bcl1 (c-allele)

5-HTTLPR (s-allele)

SNP (risk allele)

No No No Yes

No No No No

No No No No

Yes Yes Yes Yesa Yes No

Yes

Yes

G9E

Significant? Yes/No


Mage~19

Nonclinical/University students

SLC6A4

Emotional eating Racine et al., 2009

Mage~19 5-HT2a

Nonclinical/University students

SLC6A4

Mage~18

Nonclinical/ population-based

Binge eating Racine, Culbert, Larson, & Klump, 2009

BDNF

NR3C1

SLC6A4

Genetic effect/Gene

Mage~16

Mage~25

Mage~25

Age


BN versus controls

BN diagnosis Steiger et al., 2011

Bulimic symptoms Akkermann, Hiio, Villa, & Harro, 2011 Akkermann et al., 2012

Sister pairs: AN discordance

Sample type

Molecular genetic models Phenotypic outcome AN diagnosis Karwautz et al., 2011

Study

Table 3 (continued)

1152 J Child Psychol Psychiatr 2015; 56(11): 1141–64


doi:10.1111/jcpp.12441

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Table 4 Summary of neural serotonin and dopamine disturbances in women recovered from an eating disorder

Biological marker Serotonin system 5-HT1a receptor Bailer et al., 2005

Age

PET effect

Mage~24 Mage~23

↑BP ↑BP

Mage~27

↑BP

Ages: 18–30

↑BP

5-HT2a receptor Frank et al., 2002

Mage~25

↓BP

Bailer et al., 2004

Mage~24

↓BP

Mage~25

↓BP

Mage~25

↓BP

Mage~25

↑BP

Mage~29

↓BP

Mage~24 Mage~24 Mage~27 Mage~27

↑BP ↑BP ↑BP ↓BP

Mage~25

↑BP

Galusca et al., 2008

Kaye et al., 2001 5-HT Transporter Bailer et al., 2007 Pichika et al., 2012 Dopamine system D2/D3 receptor Frank et al., 2005

Recovered ED (sample size, n’s)

Brain region

Mesial temporal cortexa; Dorsal raphea Mesial temporal cortex; Subgenual cingulate; Lateral temporal cortexa; Orbitofrontal cortexa; Parietal cortexa Prefrontal cortex; Mesial temporal cortex; Lateral temporal cortex; Orbitofrontal cortex; Supragenual cingulate; Subgenual cingulate; Pregenual cingulate; Parietal cortex; Dorsal raphe Superior temporal gyrus; Inferior frontal gyrus; Parietal operculum; Temporoparietal junction

AN-R(n = 13) > controls(n = 18) ↑harm avoidance AN-R(n = 11)

Mesial temporal cortex; Subgenual cingulate; Pregenual cingulate; Sensorimotor cortexa Lateral temporal cortexa; Subgenual cingulate; Parietal cortex; Occipital cortex Lateral temporal cortex; Subgenual cingulate; Pregenual cingulate Parietal cortex Mesial temporal cortex; Subgenual cingulate; Pregenual cingulate Mesial temporal cortex; Left temporal cortex; Subgenual cingulate; Occipital cortex Lateral temporal cortexa; Orbitofrontal cortex; Sensorimotor cortexa

AN(n

Dorsal raphe; Antero-ventral striatum Antero-ventral striatum Anterior cingulate; Superior temporal gyrus Midbrain; Superior and inferior cingulate

AN-R(n = 11) > AN-BP(n = 7) BN(n = 9) > AN-BP(n = 7) BN(n = 8) > controls(n = 8) BN(n = 8) < controls(n = 8)

Antero-ventral striatum; Ventral putamena; Dorsal caudatea; Middle caudatea Mage~24 ↑BP Dorsal caudate; Dorsal putamen Bailer et al., 2013 Mage~27 ↑BP Antero-ventral striatuma Mage~27 ↑BP Ventral putamena Mage~27 ↑BP Dorsal caudate; Dorsal putamen Endogenous dopamine release (following amphetamine administration) Bailer et al., 2012 Mage~27 ↓ΔBP Antero-ventral striatuma Mage~26 ↓ΔBP Dorsal caudate Mage~28 ↓ΔBP Antero-ventral striatum

AN-BP(n

AN-R(n

= 12)

= 9)

= 16)

AN-BP(n

> controls(n

> controls(n

< controls(n

= 10)

= 18)

= 7)

= 23)

< controls(n

= 16)

↑drive for thinness AN-BP(n ↑novelty seeking AN-BP(n

= 9)

↑harm avoidance AN-BP(n BN(n

AN(n

= 9)

< controls(n

= 10)

= 9)

= 9)

= 12)

> controls(n

= 12)

↑harm avoidance AN AN-R(n = 17) > controls(n = 21) BN(n = 14) > controls(n = 21) ↑harm avoidance AN/BN(n = 27) AN(n = 10) < controls(n = 9) ↑Δanxiety AN(n = 10) ↑euphoria controls(n = 9)

BP, binding potential; PET, positron emission tomography; AN, anorexia nervosa, combined restricting and binge-purge subtype; AN-R, anorexia nervosa, restricting subtype; AN-BP, anorexia nervosa, binge-purge subtype; BN, bulimia nervosa; AN/BN, combined anorexia nervosa and bulimia nervosa group. a Nonsignificant/trend-level effect that is medium magnitude based on Cohen’s d effect size calculations (d’s ≥ .50).

(see Tables 3 and 5). Herein, we focus on three sets of interesting findings that deserve note: serotonin and G 9 E effects, dopamine and epigenetic effects, and age/puberty and G 9 E effects.

Serotonin and G 9 E Parental factors (e.g., parental pressures and criticism; low parental contact) and abuse history have been identified as retrospective correlates of eating pathology (e.g., for systematic review, see Jacobi et al., 2004). Nonetheless, abuse history is often considered a risk factor for eating disorders since some retrospective reports indicate the occurrence of © 2015 Association for Child and Adolescent Mental Health.

abuse prior to eating disorder onset (Jacobi et al., 2004), and childhood abuse/neglect has been shown to prospectively predict elevated risk for disordered eating and eating disorder onset in a longitudinal study (Johnson, Cohen, Kasen, & Brook, 2002). Interestingly, studies have begun to explore interactions between these environmental experiences and biological factors. The short allele of 5-HTTLPR has been shown to interact with parenting style (e.g., perceived parental control, criticism, and underinvolvement) and abuse history (e.g., physical and/ or sexual) in the prediction of AN, bulimic symptoms, and drive for thinness (see Table 4). Effect sizes have ranged from small-to-large in magnitude. These

controls(n = 30)

AN(n

= 20),

BN(n

= 23),

controls(n

Steiger, Labont e, Groleau, Turecki, & Israel, 2013

AN/AN-Rec(n = 30) Controls(n = 41) BN(n = 64), controls(n

controls(n

Pjetri et al., 2013

= 15),

AN(n

= 32)

= 36)

= 30)

POMC CB1

Mage~17 Mage~26

SERT NR3C1

Mage~25

Vasopressin OXTR

Mage~23

Ages: 16–60

ANP

Mage~22

Mage~25 Mage~22

Gene(s)

CB2 Leptin BDNF BDNF H19/IGF2

DRD2 DRD2

Mage~24 Ages: 16–60

Ages: 16–60

DAT1 DRD4 DRD2

Mage~25

Age

Not Examined

Not Examined

↓AN, BN versus controls ↓Overall disordered eating ↓Impulse Regulation No Differences Not Examined

↑AN versus AN-Rec., controls ↑Drive for thinness ↑AN, BN versus controls ↓Overall disordered eating ↓Body dissatisfaction ↓Bulimia symptoms ↓Drive for thinness ↓Perfectionism ↓Impulse Regulation No Differences Not Examined Not Examined Not Examined Not Examined

Not Examined Not Examined

↑AN, BN versus controls No Differences ↓AN, BN versus controls

mRNA expression

DNA methylation

↑BN versus controlsb

No Differences ↑AN versus controls ↑Overall disordered eating ↑Dietary restraint ↑Eating concerns ↑Weight concerns ↑Low body mass index ↑Depressive symptoms ↑Anxiety symptoms ↑Social/communication deficits No Differences

↑BN versus controls

Not Examined No Differences No Differences ↑BN versus controls No Differences

Not Examined

No Differences

↑AN, BN versus controls No Differences ↑AN versus controls ↑BN versus controlsa ↑BN versus controlsb No Differences

Epigenetic mechanism Kristen M. Culbert, Sarah E. Racine, and Kelly L. Klump

Kim, Kim, Kim, Shin, & Treasure, 2015

30)

= 26)

AN/AN-Rec(n = 38) Controls(n = 38) Thaler et al., 2014 BN(n = 64), controls(n = 32) Saffrey et al., 2014; AN(n = 10), controls(n = 10) Stress response, social, and/or emotional function systems Frieling et al., 2008 AN(n = 22), BN(n = 24), controls(n =

Pjetri et al., 2013

Frieling et al., 2007

Groleau et al., 2014 BN(n = 52), controls(n = 19) Pjetri et al., 2013 AN/AN-Rec(n = 40) Controls(n = 42) Feeding and energy homeostasis systems Ehrlich et al., 2010 AN(n = 31), AN-Rec(n = 30), controls(n

= 24),

BN(n

Main effects Reward-motivation systems Frieling et al., 2010 AN(n

= 22),

Participants (n’s)

Study

Sample descriptives

Table 5 Results from epigenetic studies in females

1154 J Child Psychol Psychiatr 2015; 56(11): 1141–64


AN(n

AN(n AN(n

Frieling et al., 2007

Saffrey et al., 2014 Tremolizzo et al., 2014

= 24),

controls(n controls(n

BN(n

controls(n

= 13)

= 10)

controls(n

= 15)

© 2015 Association for Child and Adolescent Mental Health. = 32)

= 30)

= 30)

Global DNA Global DNA

Mage~22 Mage~15

NR3C1 BDNF

CB1


BDNF

Mage~25

DRD2

NR3C1

Mage~25

Mage~24

DRD2

Mage~24

Mage~25

Global DNA (also see Global DNA

SNCA HERP

table note)

Gene(s)

Mage~22

Mage~25

Age

↓ED w/SIB versus ED no SIB, controls

Not Examined

Not Examined

Not Examined

Not Examined

Not Examined

Not Examined

Not Examined Not Examined

Not Examined

Not Examined

↑AN, BN versus controls No Differences

mRNA expression

DNA methylation

Not Examined

↑BN w/BPD versus BN no BPDa, controls ↑BN w/BPD versus BN no BPD, controls ↑BN w/BPD versus BN no BPD, controls

↑BN w/abuse versus BN no abuse, controls

No Differences

↑BN w/abuse versus controlsa

↓AN versus BN, controls ↓BN versus controlsb No Differences ↓AN versus controls ↓Cortisol ↓DHEA-S ↑Leptin ↓Progesterone ↓Testosterone

↑AN versus controls

↑AN versus controls No Differences

Epigenetic mechanism

AN, anorexia nervosa; BN, bulimia nervosa; AN-Rec, patients recovered from anorexia nervosa; AN/AN-Rec, combined sample of patients ill and recovered from anorexia nervosa; ED, combined sample of patients with anorexia or bulimia nervosa; BPD, borderline personality disorder; SIB, self-injurious behavior; ANP, atrial natriuretic peptide; BDNF, brain-derived neurotrophic factor; CB1, cannabinoid receptor 1; CB2, cannabinoid receptor 2; DAT1, dopamine transporter; DRD2, dopamine receptor D2; DRD4, dopamine receptor D4; H19/IGF2, H19/insulin-like growth factor 2 imprinting control region; HERP, homocysteine-induced endoplasmatic reticulum protein; NR3C1, glucocorticoid receptor; OXTR, oxytocin receptor; POMC, Pro-opiomelanocortin; SNCA, alpha synuclein promoter. In addition to the global methylation differences, results from Booij et al. (2015) indicated hypermethylation of 14 probes in women with anorexia nervosa (e.g., genes associated with dopamine and glutamate signaling, cholesterol storage and lipid transport, histone acetylation and RNA modification) and prolonged illness was associated with differential methylation in other gene pathways (e.g., genes related to spinal cord and brain development, neurocognitive deficits, anxiety). To date, epigenetic studies have not conducted internal replications of the findings, and with the exception of four studies (see Global DNA), a candidate gene approach has been used. a Nonsignificant/trend-level effect that is medium magnitude based on Cohen’s d effect size calculations (d’s ≥ .50). b Nonsignificant/trend-level effect that is small-to-medium in magnitude based on Cohen’s d effect size calculations (d ~.30).

Subphenotype effects History of childhood abuse Groleau et al., 2014 BN w/abuse(n = 14), BN no abuse(n = 38), controls(n = 19) Steiger et al., 2013 BN w/abuse(n = 32), BN no abuse(n = 32), controls(n = 32) Thaler et al., 2014 BN w/abuse(n = 15–29), BN no abuse(n = 34–49), controls(n Personality disorder Groleau et al., 2014 BN w/BPD(n = 8), BN no BPD(n = 44), controls(n = 19) Steiger et al., 2013 BN w/BPD(n = 14), BN no BPD(n = 47), controls(n = 32) Thaler et al., 2014 BN w/BPD(n = 14), BN no BPD(n = 47), controls(n =32) Self-injurious behavior Schroeder et al., 2012 ED w/SIB(n = 9), ED no SIB(n = 34), Controls(n = 26)

= 32),

= 10),

= 22),

= 29),

AN(n

controls(n

Global DNA approaches Booij et al., 2015;

= 24),

BN(n

AN(n

Miscellaneous systems Frieling et al., 2007;

= 22),

Participants (n’s)

Sample descriptives

Study

Table 5 (continued) doi:10.1111/jcpp.12441 Causes of eating disorders

1155

1156


G 9 E data provide initial evidence that environmental experiences (e.g., parenting style, childhood abuse) may serve to potentiate risk for eating disorders and disordered eating symptoms, particularly in individuals who are biologically vulnerable. However, classification of these G 9 E effects is withheld given methodological limitations (e.g., candidate gene rather than GWAS approach). Associations between serotonin genes and eating disorders, via main and G 9 E effects, align with neurobiological studies demonstrating alterations in the serotonin system in women with eating disorders. Although no longitudinal studies of serotonergic function predicting eating pathology onset exist, studies of recovered patients with AN and BN show increased cerebrospinal fluid (CSF) levels of 5-HIAA (a serotonin metabolite) compared to controls, which may reflect increased serotonin neuronal activity (Kaye, Gwirtsman, George, & Ebert, 1991; Kaye et al., 1998). Neuroimaging data also point to altered serotonin receptor activity (i.e., increased 5-HT1a and 5-HTT binding potential; reduced 5-HT2A binding potential) in several cortical and limbic regions in women recovered from AN and BN, and these serotonin receptor alterations have been associated with co-occurring features of eating disorders (i.e., increased drive for thinness and harm avoidance) (see Table 4). Reduced serotonin reuptake has been found in women in remission from BN (Steiger et al., 2005) and in unaffected first degree relatives of BN probands (Steiger et al., 2006), suggesting possible familial/heritable transmission of serotonin disturbances. One interesting study in women ill with BN found that reduced serotonin reuptake was associated with higher rates of borderline personality disorder and childhood sexual abuse, whereas elevated serotonin reuptake was associated with higher levels of perfectionism and lower rates of childhood sexual abuse (Steiger et al., 2004). These data begin to suggest that the consideration of individual difference variables may be important for fully elucidating biological (e.g., serotonin) vulnerabilities to eating pathology. Moving forward, it will also be important to establish whether neural and physiological serotonin alterations precede and predict the development of disordered eating and eating disorders.

Dopamine and epigenetic effects Interesting findings have emerged for epigenetic changes in dopaminergic genes. Two of three studies have found epigenetic alterations in the dopamine D2 receptor gene, of small-to-large effect size, in women with AN and/or BN (see Table 5). One of these studies found more robust effects in the subgroup of women with BN who had an abuse history or a personality disorder diagnosis (Groleau et al., 2014). These preliminary findings await classification, but align with emerging data from


neurobiological studies suggesting dopaminergic disturbances in patients recovered from AN, and to some extent, BN (see Table 4). Lower CSF levels of dopamine metabolites, which may reflect decreased neuronal dopamine function, have been found in women recovered from AN-R (but not those recovered from BN or AN-B/P; Kaye et al., 1998, 1991; Kaye, Frank, & McConaha, 1999). Increased binding of dopamine D2/D3 receptors (i.e., reflective of reduced intrasynaptic dopamine or elevated affinity/density of receptors) is present in women recovered from AN and, to a limited degree, in women recovered from BN (see Table 4). Women recovered from AN also showed a large (d = .75), albeit trend-level, effect for greater endogenous dopamine release following amphetamine administration compared to controls, an effect that was significantly associated with increased anxiety (see Table 4). Thus, epigenetic alterations in dopaminergic genes and disturbances in dopamine functioning may be linked to eating disorders. However, given methodological limitations (i.e., the exclusive use of ill or recovered case-control designs), it is unclear whether these epigenetic or neurobiological disturbances in dopamine are predisposing risk factors for eating pathology. Neurobiological dopamine disturbances are classified as correlates of AN. Additional studies are needed to determine whether neurobiological dopamine disturbances are correlates or risk factors of other eating disorders (e.g., BN, BED) and/or disordered eating symptoms.

Age/Puberty and G 9 E Finally, evidence for G 9 E effects has emerged from developmental twin studies. The magnitude of genetic influences on disordered eating symptoms in females varies by age and pubertal status (see Table 3), such that genetic effects substantially contribute to risk for disordered eating symptoms during mid-to-late adolescence (e.g., ≥age 13) and mid-to-late puberty, but not during pre-adolescence (e.g., Klump, Burt, McGue, & Iacono, 2007; Klump, Burt, et al., 2010) or prepuberty (e.g., Culbert et al., 2009; Klump, Perkins, Burt, McGue, & Iacono, 2007). Only one study has examined adolescent boys, but in contrast with girls, no pubertal changes in the magnitude of genetic effects on overall disordered eating symptoms were observed (i.e., h2~50% in pre-puberty and mid-puberty; Klump et al., 2012). Interestingly, ovarian hormones have been proposed as a possible mechanism underlying developmental changes in genetic risk in girls, given that these hormones rise during puberty and regulate gene transcription in key neurobiological systems (e.g., serotonin, dopamine; Ostlund, Keller, & Hurd, 2003). One prior study has investigated this possibility and found that genetic effects on overall disordered eating symptoms were negligible in girls with low levels of estradiol, whereas at high levels of © 2015 Association for Child and Adolescent Mental Health.

doi:10.1111/jcpp.12441

estradiol, substantial genetic influences on disordered eating were found (Klump, Keel, Sisk, & Burt, 2010). Additional replications are certainly needed, but such findings fit with phenotypic data implicating ovarian hormones in the expression of disordered eating symptoms. Longitudinal studies across the menstrual cycle have demonstrated that changes in estradiol and progesterone predict changes in emotional eating and/or binge eating in community samples and in women with clinical binge eating and BN (Edler, Lipson, & Keel, 2007; Klump, Keel, Culbert, & Edler, 2008; Klump et al., 2013, 2014), with effect sizes ranging from small-to-medium in magnitude. In contrast with the consistent effects of ovarian hormones on behavioral components (i.e., emotional eating and binge eating), data suggest minimal ovarian hormone effects on cognitive symptoms (e.g., weight preoccupation) (Hildebrandt et al., 2015; Racine et al., 2012). Together, findings indicate that age and pubertal maturation substantially contribute to the emergence of genetic risk for disordered eating symptoms, at least in girls, yet whether these effects extend to risk for full-threshold diagnoses remains to be investigated. The potential role for ovarian hormones in the adolescent/pubertal emergence of genetic effects on disordered eating is notable, particularly in light of the phenotypic effects of ovarian hormones on the expression of dysregulated eating in women; however, classification of ovarian hormones as moderators of genetic risk awaits additional replication.

Conclusions Our review of several possible causes of eating disorders for which integrative, multimethod data exist indicates that most factors can currently only be considered correlates of disordered eating and eating disorders. Variables that emerged as ‘risk factors’ for the development of disordered eating symptoms included: sociocultural influences (media exposure, pressures for thinness, thin-ideal internalization, and thinness expectancies) and personality characteristics (negative emotionality/ neuroticism, perfectionism, and negative urgency). With the exception of negative emotionality/neuroticism, identified factors have not been established as risk factors for full-threshold diagnoses due to a lack of investigation or need for additional replications (i.e., only one study was conducted or initial results have been mixed; see Table 2). Several other factors were identified as correlates of disordered eating symptoms (i.e., estradiol, progesterone) and/or eating disorders (i.e., cognitive flexibility, inhibitory control, serotonin disturbances, and currently for AN only: dopamine disturbances). G 9 E behavior genetic data demonstrate that age and pubertal maturation contribute to the emergence of genetic risk for disordered eating symptoms; however, © 2015 Association for Child and Adolescent Mental Health.


1157

molecular genetics (i.e., serotonin genes), other G 9 E data, and epigenetic effects were not classified as correlates or risk factor for disordered eating or eating disorders given limited data and/or methodological limitations. Across factors, effect sizes tended to range from small-to-medium in magnitude (see Table 2). From our integrative review of the literature, we have extracted several important take-home messages and propose a tentative model to describe the way in which sociocultural, psychological, and biological factors might intersect to increase risk for eating disorders. First, data suggest that the ubiquitous sociocultural messages regarding the importance of being thin are not internalized by all individuals. Indeed, individual differences in thinideal internalization are due to both genetic and environmental factors. Data also indicate neural and behavioral plasticity to sociocultural influences. Second, personality traits and neurocognitive processes are partially rooted in one’s genes and neural circuitry and share etiologic influences with eating pathology. Finally, genetic and environmental/nongenetic influences on eating disorders do not operate in isolation. Indeed, environmental experiences (e.g., abuse history) and developmental changes appear to interact with and influence the expression of genetic risk in studies of gene-environment interplay. Although data that consider all of these factors in combination do not yet exist, we posit that genetic and neurobiological (e.g., serotonin, dopamine) factors not only increase risk for features specific to eating disorders (e.g., appetite, eating, and weight) but are more generally associated with high levels of maladaptive personality traits or deficits in neurocognitive processing. Indeed, interactions between serotonin and dopamine are hypothesized to underlie high levels of negative urgency (Cyders & Smith, 2008), and components of cognitive flexibility and inhibitory control have been linked to distinct neurobiological systems (Fineberg et al., 2010). However, these biological and psychological factors as well as most environmental variables examined in G 9 E studies (e.g., abuse, parenting factors) are nonspecific or transdiagnostic, in that they are relevant for many other forms of psychopathology. Etiologic mechanisms (e.g., high negative emotionality; Tackett et al., 2013) that are shared, to varying degrees, across forms of psychopathology may underlie a general ‘psychopathology’ factor (Lahey et al., 2015), and specific factors may then intersect with this general risk to lead the expression of eating pathology in some individuals. Sociocultural influences (e.g., thin-ideal internalization, thinness expectancies) are likely one set of factors that shape a more general biopsychosocial diathesis toward psychopathology into the development of eatingspecific problems. Finally, the confluence of these variables is likely to be most impactful when occurring in females during adolescence, given the

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significant risk conferred by these epidemiological factors. Clearly, empirical research is needed to further test these hypotheses and to elucidate the nature of the risk processes contributing to both comorbidity and divergent trajectories among psychological disorders. In addition to synthesizing the available research on possible causes of eating disorders, our review highlighted several areas in need of future research. Most studies have exclusively focused on exploring risk factors and correlates of eating pathology in late adolescent and young adult females, with very few studies examining gender differences or adopting a developmental perspective. These are substantial limitations of the literature, as risk effects for eating pathology may be age, gender, and/or circumstance specific. By including males and participants across developmental stages, researchers may be better able to identify the precise causal mechanisms that place adolescent females at greatest risk for eating pathology. Furthermore, understanding what factors do and do not predict the development of eating disorders in males, children, and older adults is critical for informing research that translates our knowledge of risk processes into prevention and intervention programs. To identify antecedents and fully capture risk trajectories for eating pathology, longitudinal research would likely benefit from starting in pre-adolescence (e.g., ~age 8), utilizing high-risk samples (e.g., offspring of affected probands), and comprehensively assessing biological, psychological, and behavioral markers. Existing longitudinal databases (e.g., The National Longitudinal Study of Adolescent to Adult Health) could be used in the exploration of transactional processes that are common versus unique across dimensions of psychopathology. Despite our emphasis on studies that used prospective, longitudinal designs and that examined recovered patients, the eating disorders literature is primarily cross-sectional and conducted with patients who are currently ill. Although challenging,


the use of methodological approaches that can establish temporal precedence (e.g., prospective studies) or causation (e.g., experimental manipulations via use of animal and human models) will enhance the identification of risk factors for eating pathology. Finally, some factors exhibit specific effects for a particular eating disorder or set of component symptoms (e.g., behavioral vs. cognitive features). For this reason, studies that directly compare individuals with different eating disorder diagnoses and/or participants who vary on distinct disordered eating dimensions will be particularly useful for elucidating differential associations between risk factors and eating disorder symptoms or syndromes. In particular, etiologic research that focuses on disordered eating symptoms across the spectrum of severity can improve upon methodological limitations associated with case-control designs, including phenotypic heterogeneity within eating disorder diagnoses and long-term (e.g., ‘scar’) effects. Not only does this dimensional approach have the potential to move us closer to identifying the etiologic underpinnings of eating disorders, but it also may help refine our diagnostic classification system and improve prevention and treatment (Cuthbert, 2005).

Acknowledgments Research support from the Hilda and Preston Davis Foundation (KMC). This review was invited by the journal Editors (for which the authors have been offered a small honorarium to defray expenses) and has been the subject of full external peer review. The authors have declared that they have no competing or potential conflicts of interest.

Correspondence Kristen M. Culbert, Ph.D., Department of Psychology, University of Nevada, 4505 S. Maryland Parkway, Las Vegas, NV 89154, USA; Email: kristen.culbert@unlv. edu

Key points

• • • • •

The etiology of disordered eating and eating disorders likely involves a complex interplay of biopsychosocial effects – behavior genetic data consistently point to substantial genetic and environmental influences in risk. Confirmed risk factors for eating disorders and/or disordered eating include sociocultural influences, specifically media exposure, pressures for thinness, thin-ideal internalization, thinness expectancies, and nonspecific personality factors including negative emotionality/neuroticism, perfectionism, negative urgency; other investigated factors can currently only be considered correlates. Effect sizes for correlates and risk factors tend to range from small-to-medium in magnitude, underscoring the notion that no single factor accounts for eating pathology. Sociocultural (e.g, thin-ideal internalization) and psychological (e.g., personality) factors have substantial genetic/biological underpinnings that may partially contribute to biological risk for eating disorders. The use of methodological approaches that can establish causal effects and that examine risk variables across multiple levels of analysis is imperative for developing more nuanced etiologic models of eating disorder risk.


doi:10.1111/jcpp.12441

Notes 1. The overall prevalence estimate of ~13% takes

into account that some individuals meet criteria for more than one eating disorder over time, e.g., illness progression and diagnostic cross-over (Stice, Marti, et al., 2013). 2. We presume that this denial construct is most closely related to thinness expectancies, and thus, have classified it as such in Table 2. 3. Some, but not all, studies of cognitive flexibility and eating disorders have considered IQ, either by ensuring that case and control groups do not differ on IQ or by entering IQ as a covariate in statistical analyses. The meta-analysis by Wu et al. (2014) included all studies, even those that did not control for IQ. Ensuring that cognitive flexibility effects are independent of IQ will be imperative for future research.

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Accepted for publication: 11 May 2015 First published online: 19 June 2015


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