# Distinct personality profiles associated with disease risk and diagnostic status in eating disorders

Zuo Zhang, Lauren Robinson, Iain Campbell, Madeleine Irish, Marina Bobou, Jeanne Winterer, Yuning Zhang, Sinead King, Nilakshi Vaidya, M. John Broulidakis, et al.

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Zuo Zhang, Lauren Robinson, Iain Campbell, Madeleine Irish, Marina Bobou, et al. Distinct personality profiles associated with disease risk and diagnostic status in eating disorders. Journal of Affective Disorders, 2024, 360, pp.146-155. 10.1016/j.jad.2024.05.132. hal-05160094

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Research paper

# Distinct personality profiles associated with disease risk and diagnostic status in eating disorders

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Zuo Zhang<sup>a,1</sup>, Lauren Robinson<sup>b,1</sup>, Iain Campbell<sup>b</sup>, Madeleine Irish<sup>b</sup>, Marina Bobou<sup>a</sup>, Jeanne Winterer<sup>c,d</sup>, Yuning Zhang<sup>e</sup>, Sinead King<sup>a,f,g</sup>, Nilakshi Vaidya<sup>h</sup>, M. John Broulidakis<sup>i,j</sup>, Betteke Maria van Noort<sup>k</sup>, Argyris Stringaris<sup>l</sup>, Tobias Banaschewski<sup>m</sup>, Arun L.W. Bokde<sup>n</sup>, Rüdiger Brühl<sup>o</sup>, Juliane H. Fröhner<sup>p</sup>, Antoine Grigis<sup>q</sup>, Hugh Garavan<sup>r</sup>, Penny Gowland<sup>s</sup>, Andreas Heinz<sup>c</sup>, Sarah Hohmann<sup>m</sup>, Jean-Luc Martinot<sup>t</sup>, Marie-Laure Paillère Martinot<sup>u</sup>, Frauke Nees<sup>m,v,w</sup>, Dimitri Papadopoulos Orfanos<sup>q</sup>, Tomás Paus<sup>x,y</sup>, Luise Poustka<sup>z</sup>, Julia Sinclair<sup>i</sup>, Michael N. Smolka<sup>p</sup>, Henrik Walter<sup>c</sup>, Robert Whelan<sup>aa</sup>, Gunter Schumann<sup>h,ab</sup>, Ulrike Schmidt<sup>b,ac</sup>, Sylvane Desrivières<sup>a,∗</sup>, IMAGEN, ESTRA, STRATIFY Consortia

<footnote><sup>a</sup> Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom</footnote>

<footnote><sup>b</sup> Department of Psychological Medicine, Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom</footnote>

<footnote><sup>c</sup> Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany</footnote>

<footnote><sup>d</sup> Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany</footnote>

<footnote><sup>e</sup> Psychology Department, B44 University Rd, University of Southampton, Southampton SO17 1PS, United Kingdom</footnote>

<footnote><sup>f</sup> School of Medicine, Center for Neuroimaging, Cognition and Genomics, National University of Ireland (NUI) Galway, Ireland</footnote>

<footnote><sup>g</sup> Beaumont Hospital, Royal College of Surgeons, Ireland</footnote>

<footnote><sup>h</sup> Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Germany</footnote>

<footnote><sup>i</sup> Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom</footnote>

<footnote><sup>j</sup> Department of Psychology, College of Science, Northeastern University, Boston, MA, USA</footnote>

<footnote><sup>k</sup> Department of Psychology, MSB Medical School Berlin, Rüdesheimer Str. 50, 14197 Berlin, Germany</footnote>

<footnote><sup>l</sup> Division of Psychiatry and Department of Clinical, Educational & Health Psychology, University College London, United Kingdom</footnote>

<footnote><sup>m</sup> Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany</footnote>

<footnote><sup>n</sup> Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland</footnote>

<footnote><sup>o</sup> Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany</footnote>

<footnote><sup>p</sup> Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany</footnote>

<footnote><sup>q</sup> NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France</footnote>

<footnote><sup>r</sup> Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA</footnote>

<footnote><sup>s</sup> Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom</footnote>

<footnote><sup>t</sup> Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental trajectories & psychiatry", Université Paris-Saclay, Université Paris Cité, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli UMR9010, Gif-sur-Yvette, France</footnote>

<footnote><sup>u</sup> Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 "Trajectoires développementales & psychiatrie", University Paris-Saclay, CNRS, Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, and AP-HP, Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpetrière Hospital, Paris, France</footnote>

<footnote><sup>v</sup> Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany</footnote>

<footnote><sup>w</sup> Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany</footnote>

<footnote><sup>x</sup> Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada</footnote>

<footnote><sup>y</sup> Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada</footnote>

<footnote><sup>z</sup> Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075 Göttingen, Germany</footnote>

<footnote><sup>aa</sup> School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland</footnote>

<footnote><sup>ab</sup> Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China</footnote>

<footnote>Abbreviations: EDs, eating disorders; AN, anorexia nervosa; BN, bulimia nervosa; DEBs, disordered eating behaviours; MDD, major depressive disorder; GAD, generalised anxiety disorder; DSH, deliberate self-harm.</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: sylvane.desrivières@kcl.ac.uk (S. Desrivières).</footnote>
ARTICLE INFO

Keywords:

Eating disorders

Personality

Risk factor

Diagnostic marker

Comorbidity

ABSTRACT

**Background:** Personality traits have been associated with eating disorders (EDs) and comorbidities. However, it is unclear which personality profiles are premorbid risk rather than diagnostic markers.

**Methods:** We explored associations between personality and ED-related mental health symptoms using canonical correlation analyses. We investigated personality risk profiles in a longitudinal sample, associating personality at age 14 with onset of mental health symptoms at ages 16 or 19. Diagnostic markers were identified in a sample of young adults with anorexia nervosa (AN, *n* = 58) or bulimia nervosa (BN, *n* = 63) and healthy controls (*n* = 47).

**Results:** Two significant premorbid risk profiles were identified, successively explaining 7.93 % and 5.60 % of shared variance (R²). The first combined neuroticism (canonical loading, r = 0.68), openness (r = 0.32), impulsivity (r = 0.29), and conscientiousness (r = 0.27), with future onset of anxiety symptoms (r = 0.87) and dieting (r = 0.58). The other, combined lower agreeableness (r = −0.60) and lower anxiety sensitivity (r = −0.47), with future deliberate self-harm (r = 0.76) and purging (r = 0.55). Personality profiles associated with “core psychopathology” in both AN (R² = 80.56 %) and BN diagnoses (R² = 64.38 %) comprised hopelessness (r = 0.95, 0.87) and neuroticism (r = 0.93, 0.94). For BN, this profile also included impulsivity (r = 0.60). Additionally, extraversion (r = 0.41) was associated with lower depressive risk in BN.

**Limitations:** The samples were not ethnically diverse. The clinical cohort included only females. There was non-random attrition in the longitudinal sample.

**Conclusions:** The results suggest neuroticism and impulsivity as risk and diagnostic markers for EDs, with neuroticism and hopelessness as shared diagnostic markers. They may inform the design of more personalised prevention and intervention strategies.

# 1. Introduction

Eating disorders (ED), including anorexia nervosa (AN) and bulimia nervosa (BN), are serious and potentially life-threatening psychiatric disorders characterised by disturbed attitudes and abnormal behaviours regarding weight, body shape, and eating. According to the Diagnostic and Statistical Manual of Mental Disorders - Fifth Edition (DSM-5) (American Psychiatric Association, 2013), AN is characterised by intense fear of gaining weight and distorted perception of body image. A significantly low body weight is maintained through severe dietary restrictions for the restrictive subtype, or inappropriate compensatory purging behaviours (i.e., self-induced vomiting, or the misuse of laxatives, diuretics, or enemas) following episodes of binge eating, for the binge-eating/purging type. BN is characterised by recurrent episodes of uncontrollable binge eating, inappropriate compensatory purging behaviours to prevent weight gain, and being overly concerned with body shape and weight. However, unlike individuals with AN binge-eating/ purging type, those with BN maintain body weight at or above a normal level. With 13.6 million people affected worldwide in 2019, accounting for 17,361.5 years of life lost, the global burden of AN and BN is significant (GBD 2019 Mental Disorders Collaborators, 2022). This burden is greater in females than males, and among adolescents and young adults (aged 15–25 years). Both AN and BN also carry a substantial risk of medical complications and psychological distress. Particularly, AN exhibited the highest mortality rate among all psychiatric disorders, secondary only to substance use disorder (Chesney et al., 2014). Mortality and high prevalence of psychiatric comorbidities, such as mood and anxiety disorders, addiction, and personality disorders (Swanson et al., 2011) make EDs burdensome on sufferers and carers, and difficult to treat (Mehler, 2017). Given that a longer duration of untreated illness and psychiatric comorbidities are associated with poorer outcomes (Austin et al., 2021), early intervention and comprehensive treatment are important for facilitating recovery (Allen et al., 2020).

The peak age of onset during the teens and early twenties renders adolescents and young adults particularly at risk for AN and BN (Sant TOMAURO et al., 2021; Solmi et al., 2022). Symptoms of AN may emerge

at a young age, with a considerable proportion of individuals showing symptoms during childhood and early adolescence (Nagl et al., 2016). Over time, patients with AN may cross over to BN, while transitioning from BN to AN is rare (Eddy et al., 2008). Sub-clinical EDs or disordered eating behaviours (DEBs) are associated with future symptoms of major depressive disorder (MDD), generalised anxiety disorder (GAD), and deliberate self-harm (DSH) in adolescents (Anestis et al., 2008; Micali et al., 2015; Zhang et al., 2021) suggesting some shared aetiology between ED, MDD, GAD, and DSH.

The aetiology of EDs is complex and multi-factorial, with biological, psychological, and environmental factors contributing to the development of the disease (Treasure et al., 2020). Among these, personality traits may act as a predisposing phenotype (Agras et al., 2004). A recent meta-analytic review comparing adolescents with or without an ED revealed that those with an ED presented a greater propensity for personality traits related to negative affectivity, detachment, and conscientiousness (Dufresne et al., 2020). Studies using the five-factor model (Costa Jr and McCrae, 1992), a validated personality inventory, have found higher levels of neuroticism and openness and lower levels of agreeableness and conscientiousness in women with a lifetime history of EDs compared to those without (Dufresne et al., 2020; Ghaderi and Scott, 2000; Tasca et al., 2009). Personality differences among ED subtypes have also been observed, such as greater impulsivity in patients with BN compared to those with AN (Farstad et al., 2016).

A major limitation to studying the role of personality in the aetiology of EDs is that most of the literature in the field comes from studies examining different personality inventories, which reduces comparability between studies. Also, comorbidities in EDs justify a comprehensive approach that integrates psychiatric comorbidities into the study of ED development, which is currently lacking in the literature. And importantly, current knowledge mostly derives from cross-sectional clinical studies, providing correlational information that may be confounded by secondary effects and may reflect symptom persistence rather than onset. Longitudinal adolescent studies are needed to clarify the associations between personality and ED psychopathology but these are scarce. Findings indicate that neuroticism, in particular its depressed affect component (Robinson et al., 2020), negative urgency (the tendency to act rashly when distressed, associated with impulsivity) and negative affect (Pearson et al., 2012; Pearson et al., 2015) predicted subsequent increases in binge eating in children and adolescents. As

<footnote n="1">¹ Zuo Zhang and Lauren Robinson contributed equally to this paper.</footnote>
most existing research has employed univariate analyses, it remains
unclear how combinations of personality traits and clusters of symptoms
are associated.

Here, we applied multivariate models to identify personality risk and diagnostic markers for EDs and comorbid disorders, correlating personality assessed using the NEO five-factor inventory (NEO-FFI) (Costa and McCrae, 1989) and the Substance Use Risk Profile Scale (SURPS) (Jurk et al., 2015) with mental health symptoms, including eating disorders, anxiety, and depressive symptoms, and self-harm or suicidal risk. For personality risk profiles, a longitudinal adolescent sample was used, in which correlations between personality profiles at age 14 and onset of mental health symptoms at ages 16 or 19 were investigated. For diagnostic markers, a cohort of young adults, with AN or BN diagnoses and healthy controls, was used to investigate concurrent associations. In this sample, we hypothesised that both AN and BN would exhibit personality traits related to negative affectivity, while BN would be specifically characterised by traits associated with impulsivity.

**2. Methods**

**2.1. Samples**

The studies included in our analyses have been designed to be comparable and complementary. IMAGEN, a longitudinal population-based adolescent cohort, was sampled with the aim to identify psycho-biological mechanisms of clinically relevant symptoms. Its longitudinal design was aimed at identifying early markers and risk factors for mental disorders. The studies of clinical groups (STRATIFY and ESTRA) were established to test if markers/factors identified in IMAGEN are still applicable in patients. Participants from these studies were assessed using the same standardised protocols, including the same study questionnaires. Identical data collection and quality control procedures were also used across the three studies.

**2.1.1. Population sample**

IMAGEN participants were recruited from eight study sites in Europe at age 14 years (baseline), and followed up at ages 16 (follow-up 1, FU1), 19 (FU2), and 23 (FU3). Details about recruitment, inclusion criteria, and characteristics of the sample are in the Supplemental Methods and Schumann et al. (2010). In this study, we analysed data collected from ages 14 to 19 years. Included in our study, were participants who did not report any symptoms of binge eating, purging, dieting, depression, generalised anxiety, or DSH at age 14 years. The outcome variables were binary mental health symptoms: individuals who reported a mental health symptom at either age 16 or 19 were classified as “developers”; those who remained asymptomatic across the three ages were defined as “controls”. Therefore, we created six binary outcome variables for the development of binge eating, purging, dieting, depression, generalised anxiety, and DSH, respectively (see Supplemental Fig. 1A for a flow chart). Given that none of the selected participants reported any of the mental health symptoms at age 14, the exposure — personality traits at age 14 — predated the onset of symptoms at ages 16/19.

**2.1.2. Clinical, case-control sample**

Participants from this sample were derived from two related and complementary clinical studies, ESTRA and STRATIFY, that used protocols harmonised to match the IMAGEN protocol. STRATIFY was designed to generate a neurobehavioural framework for stratification of psychopathology for some of the most prevalent mental disorders: major depression (MDD), alcohol use disorder (AUD), attention deficit hyperactivity disorder (ADHD), and Psychosis. These disorders are often comorbid with EDs (Catone, 2023) and have in common dysfunctional reinforcement-related behaviours, particularly in reward processing (Baskin-Sommers and Foti, 2015; Kesby et al., 2023). ESTRA focussed on the recruitment of ED participants. ESTRA and STRATIFY were therefore designed to be complementary.

2.1.2.1. ESTRA. Participants from this study were recruited in London. In addition to age (18–25 years), inclusion criteria were DSM-5-based (American Psychiatric Association, 2013) diagnostic criteria for AN or BN, based on the Eating Disorder Diagnostic Scale (EDDS DSM5 version) (Bohon and Stice, 2017). All participants from this sample were female by biological sex and Caucasian.

2.1.2.2. **STRATIFY.** Participants with MDD, AUD, ADHD or Psychosis were recruited in the UK (London and Southampton) and Germany (Berlin). In addition to age (18–30 years), inclusion criteria were: current moderate-severe depression, assessed by PHQ-9 (Kroenke et al., 2001), with a total score of 15 or above; moderate-severe alcohol use disorder, assessed by AUDIT (Saunders et al., 1993), with a total score of 15 or above; schizophrenia, schizotypal and delusional disorders based on ICD-10 diagnosis (World Health Organization, 1993); ADHD diagnosis based on the Adult ADHD Rating Scale (DuPaul et al., 1998). To complement the ESTRA sample, we also included in our analyses STRATIFY participants who were female and met diagnostic criteria for AN or BN. See Supplementary Fig. 1 for details.

2.1.2.3. *Healthy controls (HCs)*. Healthy controls were also recruited as part of ESTRA and STRATIFY. Included in our analyses were participants who were female, screened negative for any ED diagnosis based on the EDDS, screened negative for MDD and GAD diagnoses, and had low suicidal risk (current suicide risk score ≤ 8) based on the MINI neuro-psychiatric interview (Lecrubier et al., 1997). See Supplemental Methods for recruitment details and Supplemental Fig. 1B for a flow chart of the sampling procedure.

**2.2. Measures**

**2.2.1. Personality measures**

Broad dimensions of personality were estimated using the 60-item of the NEO five-factor inventory (NEO-FFI), including measures of extraversion, agreeableness, conscientiousness, neuroticism, and openness (Costa and McCrae, 1989). We also used the Substance Use Risk Profile Scale (SURPS) (Woicik et al., 2009) that includes 23 items on a four-level Likert scale. This self-reported questionnaire measuring impulsivity, anxiety sensitivity, hopelessness, and sensation seeking is a reliable measure in adults (Castellanos-Ryan et al., 2016) and adolescents (Krank et al., 2011).

**2.2.2. Mental health symptoms in the population sample**

In IMAGEN, mental health symptoms were assessed with the Development and Well-Being Assessment (DAWBA), a package of questionnaires, interviews, and rating techniques designed to generate psychiatric diagnoses (Goodman et al., 2000). We created binary variables to indicate the presence/absence of each symptom at each age, including binge eating, purging, dieting, MDD, GAD, and deliberate self-harm (DSH). More details are provided in the Supplemental Methods.

**2.2.3. Mental health diagnoses in the clinical sample**

All the ED patients and healthy controls were also assessed for MDD and GAD diagnoses, and suicide risk (SR) based on the MINI neuro-psychiatric interview (Lecrubier et al., 1997). A binary SR variable (MINI suicidality score > 0) was used in the following analyses, indicating the presence of any suicidal risk.

**2.3. Statistical analyses**

**2.3.1. Canonical correlation analyses (CCA)**

Canonical correlation analysis (Sherry and Henson, 2005) was used to correlate between personalities and mental health symptoms, using the ‘canon’ command in Stata (Release 17. College Station, TX: Stata-Corp LLC). Personality traits from both the NEO-FFI and SURPS were
included in all CCA analyses. For longitudinal analyses in the population sample, personality variables assessed at age 14 years were tested against mental health variables comprising the development of symptoms for binge-eating, purging, dieting, depression, GAD, and DSH (i.e., onset of symptoms after age 14 years). Sex and study site were regressed out from each of the personality traits to obtain residual scores, which were then used in the CCA models. In the clinical sample, cross-sectional analyses were performed for the AN and BN groups separately. Personality variables were tested against mental health variables comprising diagnoses of ED (AN or BN), MDD, GAD, and SR. The statistical significance of the canonical correlations was evaluated by using approximate *F* statistics of Wilks's lambda (λ) (Sherry and Henson, 2005). Given that this statistical test requires multinormal distributions, significant canonical correlations were additionally verified by using permutation tests – which do not require specific distributions of the variables. The permutation tests were conducted using the CCP package in R (https://CRAN.R-project.org/package=CCP). In more detail, a permuted distribution of Wilks's λ was obtained by randomly shuffling the variables 10,000 times and calculating the λ values. *P* values were derived by comparing the original λ obtained from unpermutated data against the permuted distribution.

## 2.4. Ethics statement

This study was performed in accordance with the Declaration of Helsinki. The ESTRA study was approved by a UK research ethics committee. The STRATIFY study was approved by research ethics committees in the UK and Germany. The IMAGEN study was approved by the ethics research committees of the academic centres at which the study was conducted (London, Nottingham, UK; Dublin, Ireland; Paris, France; Berlin, Hamburg, Mannheim, and Dresden, Germany). Parents, guardians, or next of kin provided written informed consent for all the participants under 18 years to join this study. All the adult participants provided written informed consent.

# 3. Results

## 3.1. Sample characteristics

### 3.1.1. Population sample

A total of 2315 IMAGEN participants took part in the baseline assessments at age 14, among which 1804 individuals provided completed personality and mental health data. Compared to participants who were lost to follow up, i.e., completed neither FU1 (age 16) nor FU2 (age 19) (n = 267, dropout rate 17.37 %), those who completed FU1 or FU2 (n = 1537) were more likely to be female (odds ratio, OR = 1.16, *p* = 0.027) and, at age 14, less likely to engage purging (OR = 0.85, *p* = 4.5E-03). Regarding personality, they were also more open and agreeable, and less impulsive and anxiety-sensitive (ps < 0.05) at age 14 (Supplemental Table 1).

We identified 383 individuals (32.6 % female) as controls, who did not report any mental health symptoms across the three ages. 157 individuals (45.9 % female) were identified as “developers”, who did not report any mental health symptoms at age 14, but developed one or more symptoms at age 16 or 19 (Supplemental Table 2). A final sample of 540 individuals (383 developers and 157 controls) were involved in the CCA analysis. In more detail, among the developers, 18 individuals (66.7 % female) developed binge eating, 31 (67.7 % female) developed purging, 42 (64.3 % female) developed dieting behaviours, 77 (44.2 % female) developed MDD symptoms, 33 (51.5 % female) developed GAD symptoms, and 69 (42.0 % female) developed DSH. These developers were not mutually exclusive, that is, they overlapped with one another, indicating tendencies of co-development (Supplemental Table 2).

### 3.1.2. Clinical sample

The ESTRA study included 63 AN, 48 with BN, and 20 HC

participants. This sample was augmented by including STRATIY participants who met the criteria of AN (N = 8), BN (N = 27), or HC (N = 30). After excluding participants who had incomplete data, a final sample of 58 AN, 63 BN and 47 HC participants were included in the analyses, who were all female. Between 49.2 % and 70.7 % of participants with EDs had either co-occurring MDD, GAD, or risk of suicidality (Supplemental Table 3).

### 3.1.3. Cronbach's α

Measures of internal consistency for the personality scores in both the population and clinical samples (Supplemental Table 4) indicated that scores for the NEO-FFI subscales were reliable (above 0.7), except for agreeableness which offered a lower score of reliability (0.66) in the population sample. Internal consistency was lower for the SURPS, especially in the population sample. In this sample, Cronbach's α values for all SURPS subscales, except hopelessness (0.78), were around 0.6, meeting the criterion for acceptability with scales containing <10 items (Loewenthal and Lewis, 2001). Hopelessness scores demonstrated the highest reliability in both the population and clinical samples (0.78 and 0.93).

## 3.2. Personality profiles associated with the development of EDs and comorbid mental health symptoms

We conducted a canonical correlation analysis to evaluate the relationship between two variable sets, namely personality and development of mental health symptoms, in the IMAGEN sample. The full model was statistically significant (Functions 1 to 6: Wilks's λ = 0.82; *F*(54, 2681.58) = 1.96, *p*<sub>Wilks</sub> < 0.001, *p*<sub>permut.</sub> < 0.001), explaining 17.92 % of the variance shared between the variable sets. Six functions with canonical coefficients (*R*<sub>c</sub>) of 0.28, 0.24, 0.17, 0.12, 0.092, 0.059 for each function were identified. The first two functions that each successively explained 7.93 % and 5.60 % of shared variance (*R*<sub>s</sub><sup>2</sup>), were statistically significant (functions 2 to 6: λ = 0.89, *F*(40, 2295.58) = 1.53, *p*<sub>Wilks</sub> = 0.018, *p*<sub>permut.</sub> < 0.001). The last four functions, which only explained 5.56 % of the variance in the variable sets after the extraction of the prior functions, were not statistically significant and not considered further. Fig. 1 shows variables significantly associated with the first two functions, their standardised canonical function coefficients and canonical loadings (*r*<sub>s</sub>, correlations between variables and the canonical variates), which were used to evaluate the importance of variables in each model.

For the first canonical function, the mental health variables significantly contributing to this function were the development of GAD (*r*<sub>s</sub> = 0.87) and dieting (*r*<sub>s</sub> = 0.58). The other two DEBs – binge eating (*r*<sub>s</sub> = 0.48) and purging (*r*<sub>s</sub> = 0.21) – also showed positive but non-significant contributions to this function. The most contributing personality trait was neuroticism (*r*<sub>s</sub> = 0.68), followed by openness (*r*<sub>s</sub> = 0.32), impulsivity (*r*<sub>s</sub> = 0.29), and conscientiousness (*r*<sub>s</sub> = 0.27), all positively associated. We therefore named function 1 “personality profiles predicting generalised anxiety and dieting” (Fig. 1A, Supplemental Table 5).

Mental health variables significantly associated with the second function were the development of DSH (*r*<sub>s</sub> = 0.76) and purging (*r*<sub>s</sub> = 0.55). Binge eating (*r*<sub>s</sub> = 0.033) and dieting (*r*<sub>s</sub> = 0.042) had non-significant and low canonical loadings, indicating that these disordered eating behaviours minimally contributed. Associated personalities were lower agreeableness (*r*<sub>s</sub> = −0.60) and lower anxiety sensitivity (*r*<sub>s</sub> = −0.47) at age 14 years. We named function 2 “personality profiles predicting DSH and purging” (Fig. 1B).

## 3.3. Associations of personality traits with EDs and comorbid symptoms in a clinical sample

We then performed CCA analyses testing two multivariate models including either AN or BN along with MDD, GAD and SR as mental
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Significant functions resulting from a CCA on the longitudinal IMAGEN sample. Associations were analysed between a) the development of binge-eating, purging, dieting, depression (MDD), generalised anxiety (GAD), and deliberate self-harm (DSH) with b) personality traits from the NEO-FFI and SURPS. A, Function 1: personality profiles predicting generalised anxiety and dieting. B, Function 2: personality profiles predicting DSH and purging. Only statistically significant relationships (*p* < 0.05) are presented.</figcaption></figure>

health variables, and all nine personality traits. The full model for AN-related traits was statistically significant, with Wilks's $\lambda = 0.14$ (F(36, 346.50) = 6.78, $p_{Wilks} < 0.001$, $p_{permut} < 0.001$), explaining 86.44 % of the variance shared between the variable sets. Four functions with canonical correlation coefficients ($R_c$) of 0.90, 0.43, 0.37 and 0.080 were identified for this model. Only the first $R_c$ was statistically significant, explaining 80.56 % of shared variance. The remaining three canonical functions were not significant and will not be interpreted (Supplemental Table 6). Looking at the first canonical variate, all the mental health variables were significantly associated ($r_s$ > 0.80 for each). As for personality, relevant contributors were primarily neuroticism ($r_s$ = 0.93) and hopelessness ($r_s$ = 0.95). As opposed to the other variables, conscientiousness had positive function coefficients but negative canonical loadings ($r_s$ = -0.39), i.e., it was negatively correlated with the canonical variate. This is because conscientiousness was negatively correlated with neuroticism (Pearson's $r = -0.24$) and hopelessness ($r = -0.38$). We labelled this canonical function as "personality profiles for core AN-related psychopathology" (Fig. 2).

The full model for BN-related traits was also statistically significant (functions 1 to 4: Wilks's $\lambda = 0.24$; F(36, 365.24) = 4.67, $p_{Wilks} < 0.001$, $p_{permut} < 0.001$) and explained 75.77 % of the variance shared between the variable sets. Four functions, with $R_c$ of 0.80, 0.44, 0.32 and 0.25 for

each function, were identified. Given the $R_c^2$ effects for each function, only the first two were significant, explaining 64.38 % and 18.97 % of shared variance, respectively (functions 2 to 4: $\lambda = 0.68$, F(24, 284.83) = 1.69, $p_{Wilks} = 0.025$, $p_{permut} < 0.001$). Therefore, only the first two functions will be interpreted. Examination of the standardised canonical coefficients and canonical loadings in function 1, all mental health variables were important contributors ($r_s$ > 0.7 for all, Supplemental Table 7). The personality variables more closely related to this function were neuroticism ($r_s$ = 0.94), hopelessness ($r_s$ = 0.87), and impulsivity ($r_s$ = 0.60). We named this first canonical function "personality profiles for core BN-related psychopathology" (Fig. 3A). Within function 2, BN ($r_s$ = 0.47) and negative contributions of MDD ($r_s$ = -0.39) were significant mental health variables. For personality, the variables of relevance were high extraversion ($r_s$ = 0.41), low conscientiousness ($r_s$ = -0.52), and low hopelessness ($r_s$ = -0.14). We thus labelled this function "Personality profiles for BN and low MDD" (Fig. 3B).

## 4. Discussion

Our study conducted multivariate analyses in two samples, a longitudinal adolescent cohort and a clinical sample with AN or BN diagnoses, to provide a comprehensive characterisation of personality

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Significant functions resulting from a CCA on the AN and HC samples. Associations were analysed between a) AN and co-occurring depression (MDD), generalised anxiety (GAD), and suicidal risk (SR) with b) personality traits. Function 1: Personality profiles for "core AN-related psychopathology". Only statistically significant relationships (*p* < 0.05) are presented.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Significant functions resulting from a CCA on the BN and HC samples. Associations were analysed between a) BN and co-occurring depression (MDD), generalised anxiety (GAD), and suicidal risk (SR) with b) personality traits. A, Function 1: Personality profiles for "core BN-related psychopathology". B, Function 2: Personality profiles for "BN and low MDD". Only statistically significant relationships (*p* < 0.05) are presented.</figcaption></figure>

profiles associated with disease risk and current diagnosis in EDs. In the population sample, two personality risk profiles associated with future onset of ED-related symptoms were identified. The first, consisting of the combination of neuroticism, openness, impulsivity, and conscientiousness, predated the onset of generalised anxiety symptoms and dieting. The second, comprising low agreeableness and low anxiety sensitivity characterised adolescents who would develop future purging and deliberate self-harm. In the clinical sample, personality profiles associated with "core ED psychopathology" in both AN and BN comprised hopelessness and neuroticism. For BN, the personality profile also included impulsivity. These results suggest both aetiological and clinical implications.

## 4.1. Premorbid ED personality profiles

Neuroticism was the personality trait that contributed most to predicting future onset of symptoms of generalised anxiety and dieting, also showing positive, albeit non-significant associations with binge eating and purging. The higher loading of anxiety symptoms compared to dieting suggests a primary contribution of anxiety within the mental health symptoms. These results corroborate findings that consistently associate neuroticism with disordered eating and generalised anxiety (Gilmartin et al., 2022; Moreno-Peral et al., 2014). The relationship between impulsivity and dietary restraint is mixed in the literature, with some cross-sectional studies indicating positive associations (Mills et al., 2018; Nederkoorn et al., 2004). Our finding that higher impulsivity is associated with future dieting should be interpreted in the context of the co-development of anxiety, binge eating, and purging, and evidence that

dieting behaviours are often precursors to developing bulimic symptoms (Neumark-Sztainer et al., 2006). As for conscientiousness, which involves self-discipline, control, and planning, it has been shown to be positively associated with dietary restraint (Keller and Siegrist, 2015). In this personality profile, high impulsivity combined with high conscientiousness might create internal conflict as an individual struggles to reconcile these opposing traits. This conflict might manifest in regressed activities (e.g., dieting) accompanied by unexpected or unplanned behaviours (e.g., binge eating), resulting in stress and anxiety. An alternative plausible interpretation is that this personality profile captures subsets of at-risk participants sharing similar mental health outcomes of future anxiety and dieting: those scoring high on either conscientiousness or impulsivity.

In the second personality profile, lower anxiety sensitivity and lower agreeableness characterise individuals with a susceptibility to purging and self-harm. The literature has consistently found a link between self-harm and DEBs, particularly purging (Sagiv et al., 2019; Warne et al., 2021). Possible explanations for these associations include the hypothesis that both behaviours manifest as maladaptive coping strategies to gain control and ease anxiety (Jeppson et al., 2003). Along this line, purging is perceived as a form of self-harm by some individuals (Fox et al., 2019). While the associations between low agreeableness and future purging and self-harm are unsurprising (Kiekens et al., 2015; Robinson et al., 2020), the negative association with anxiety sensitivity seems counterintuitive (Anestis et al., 2008). As one of the facets of anxiety sensitivity is a fear of somatic sensations, it seems reasonable to postulate that individuals scoring high on this measure are less likely to consider DSH as a way of coping with mounting anxiety (Dixon et al.,
2019).

## 4.2. Concurrent ED personality profiles

Our analysis of the clinical sample indicates that, in addition to being a risk factor for dieting and generalised anxiety, neuroticism strongly contributes to the co-occurrence of depression, anxiety, and suicidal risk in patients with AN or BN. Neuroticism is typically defined by negative emotionality, impulsiveness, and vulnerability. As such, participants scoring higher in this trait are prone to negative emotions, such as fear, sadness, anger, guilt, and shame. They are also vulnerable to stress and tend to panic, be frustrated, and feel hopeless in emergencies or stressful situations (Robert and Paul, 2005). Therefore, it is not surprising that patients with depressive and anxiety disorders consistently score higher than controls on neuroticism (Kotov et al., 2010). Our finding that neuroticism and hopelessness were the strongest correlates of AN- and BN-related core psychopathology supports the relationship between emotional difficulties and maladaptive responses to stress and their association with mental disorders and suicidal behaviour (Kotov et al., 2010). In contrast, impulsivity is specifically associated with BN-psychopathology. This is not surprising given that impulsivity makes it more likely that one will be overwhelmed by desires and succumb to temptations, as seen in BN. Our identification of impulsivity as a risk factor for future anxiety and dieting in the longitudinal sample, along with its specific association with BN in the clinical sample suggest that impulsivity is both an early risk factor and diagnostic marker for BN.

Identification of a personality profile (i.e., low conscientiousness, high extraversion, and low hopelessness) associated with lower depressive risk in BN has further clinical implications. Associations of low conscientiousness and low extraversion (and high neuroticism or hopelessness) with depressive symptoms are well-established (Hakuli-nen et al., 2015; Kuckertz et al., 2014). While neuroticism and low conscientiousness characterise many psychiatric disorders, low extraversion (i.e., introversion) may be more specific to internalising disorders such as depression (Chioqueta and Stiles, 2005), dysthymic disorder and social phobia (Kuckertz et al., 2014). Thus, as extraversion is associated with positive emotions, assertiveness and being sociable, targeted interventions designed at improving these facets in the most introverted ED patients may improve their depressive symptoms and overall clinical outcomes.

Our findings also highlight conscientiousness as a trait associated with AN and BN. Associations of low conscientiousness with ED diagnoses highlight the protective nature of a well-adjusted personality towards more positive mental health and adaptive coping skills (Claes et al., 2013; Claes et al., 2004). However, our findings highlighted the complexity of associations with conscientiousness. Higher scores on this measure predated the development of ED-related symptoms but were negatively associated to ED-related psychopathology in the clinical sample. These suggest that distinct facets of conscientiousness may have opposite effects on ED symptoms (Bollen and Wojciechowski, 2004).

## 4.3. Strengths and limitations

Our study has several strengths, including the use of both a longitudinal, adolescent sample and a young adult clinical sample with overlapping measures of psychopathology and personality. Another key strength is the longitudinal nature of our assessments, which enabled us to determine personality traits which predate the onset of symptoms and are also associated with current ED psychopathology. Some limitations are acknowledged. The participants were mostly of white ethnicity from European countries and those with AN or BN were all female, limiting the generalisability of our findings to males and other ethnicities. Also, non-random attrition in the longitudinal sample may limit the generalisability of our findings. For example, the loss of participants who were male, more impulsive and sensation seeking, and less open to experience and agreeable, have introduced unintended bias. Further research will

be needed to establish the generalisation of our findings to these groups. In the longitudinal analysis, we investigated personality traits at a single time point as premorbid risk factors but did not consider personality changes at subsequent time points. Given the instability of personality traits during adolescence, it should be noted that these traits can also be influenced by psychopathology (Widiger, 2011). Further studies are required to elucidate the reciprocal influences of personality and psychopathology over time.

## 5. Conclusions

Our comparative analyses in a longitudinal population cohort and a clinical sample revealed distinct personality risk and diagnostic markers for eating disorders. Personality profiles of neuroticism and impulsivity support their utility as early risk markers for anxiety and dieting, and as diagnostic markers for both AN and BN (i.e., neuroticism) or specifically BN (impulsivity). The emergence of hopelessness, uniquely as a diagnostic marker, associated with comorbid depression, anxiety and suicidal risk, and extraversion as a potential protective factor associated with lower depressive risk in bulimia nervosa, suggest that interventions targeting hopelessness and introversion may improve clinical outcomes. Thus our findings may inform the design of more personalised prevention and intervention strategies.

### Role of the funding source

The funders of the study had no role in study design, the collection, analysis and interpretation of data, the writing of the report, or the decision to submit the article for publication.

### CRediT authorship contribution statement

**Zuo Zhang:** Writing – original draft, Visualization, Formal analysis.
**Lauren Robinson:** Writing – original draft, Visualization, Project administration, Investigation.
**Iain Campbell:** Writing – review & editing, Investigation.
**Madeleine Irish:** Writing – review & editing, Investigation.
**Marina Bobou:** Writing – review & editing, Investigation.
**Jeanne Winterer:** Writing – review & editing, Project administration, Investigation.
**Yuning Zhang:** Writing – review & editing, Project administration, Investigation.
**Sinead King:** Writing – review & editing, Project administration, Investigation.
**Nilakshi Vaidya:** Writing – review & editing, Project administration.
**M. John Broulidakis:** Writing – review & editing, Investigation.
**Betteke Maria van Noort:** Writing – review & editing, Project administration, Investigation.
**Argyris Stringaris:** Writing – review & editing, Project administration, Investigation.
**Tobias Banaschewski:** Writing – review & editing, Project administration, Investigation.
**Arun L.W. Bokde:** Writing – review & editing, Project administration, Investigation.
**Rüdiger Brühl:** Writing – review & editing, Project administration, Investigation.
**Juliane H. Fröhner:** Writing – review & editing, Project administration, Investigation.
**Antoine Grigis:** Writing – review & editing, Project administration, Investigation.
**Hugh Garavan:** Writing – review & editing, Project administration, Investigation.
**Penny Gowland:** Writing – review & editing, Project administration, Investigation.
**Andreas Heinz:** Writing – review & editing, Project administration, Investigation.
**Sarah Hohmann:** Writing – review & editing, Project administration, Investigation.
**Jean-Luc Martinot:** Writing – review & editing, Project administration, Investigation.
**Marie-Laure Paillère Martinot:** Writing – review & editing, Project administration, Investigation.
**Frauke Nees:** Writing – review & editing, Project administration, Investigation.
**Dimitri Papadopoulos Orfanos:** Writing – review & editing, Data curation.
**Tomás Paus:** Writing – review & editing, Project administration, Investigation.
**Luise Poustka:** Writing – review & editing, Project administration, Investigation.
**Julia Sinclair:** Writing – review & editing, Project administration, Investigation.
**Michael N. Smolka:** Writing – review & editing, Project administration, Investigation.
**Henrik
**Walter:** Writing – review & editing, Project administration, Investigation. **Robert Whelan:** Writing – review & editing, Project administration, Investigation. **Gunter Schumann:** Writing – review & editing, Project administration, Investigation, Funding acquisition. **Ulrike Schmidt:** Writing – review & editing, Investigation. **Sylvane Desrivières:** Writing – original draft, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interest

Dr. Banaschewski served in an advisory or consultancy role for ADHS digital, Infectopharm, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Roche, and Takeda. He received conference support or speaker's fee by Medice and Takeda. He received royalties from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press. Dr. Poustka served in an advisory or consultancy role for Roche and Viforpharm and received speaker's fee by Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. The present work is unrelated to these relationships. The other authors report no biomedical financial interests or potential conflicts of interest.

Data availability

De-identified data of the IMAGEN, STRATIFY, and ESTRA studies are available to researchers after approval of a proposal by the Executive Committee of these studies. For details, please see https://imagen-project.org/the-imagen-dataset/ (IMAGEN) and https://stratify-project.org/the-stratify-dataset/ (STRATIFY and ESTRA). The analytical codes are available on the following GitHub repository: https://github.com/crickfan/CCA-personality-mental-health.

Acknowledgements

This work received support from the following sources: The Medical Research Foundation and Medical Research Council (grants MR/ R00465X/1 and MRF-058-0004-RG-DESR: 'Neurobiological underpin- ning of eating disorders: integrative biopsychosocial longitudinal ana- lyses in adolescents'; MR/S020306/1 and MRF-058-0009-RG-DESR- C0759: 'Establishing causal relationships between biopsychosocial pre- dictors and correlates of eating disorders and their mediation by neural pathways'), the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant 'STRATIFY' (Brain network based stratifi- cation of reinforcement-related disorders) (695313), the Medical Research Council/Arts and Humanities Research Council/Economic and Social Research Council, Adolescence, Mental Health and the Devel- oping Mind initiative as part of the EDIFY programme (grant number MR/W002418/1), the UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee (10041392 and 10038599) as part of the Horizon Europe HORIZON-HLTH-2021- STAYHLTH-01 under grant agreement number 101057429. This study represents independent research partly funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Ulrike Schmidt is supported by an NIHR Senior Investigator award. Zuo Zhang is supported by a fellowship from the Medical Research Foundation (MRF-058-0014-F-ZHAN- C0866).

Further funding included the National Institutes of Health (NIH) Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence and NIH grant 1R56AG058854-01), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-

ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (MR/N027558/1), Human Brain Project (HBP SGA 2, 785907), the FP7 project MATRICS (603016), the Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the Bun- desministerium für Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940/2, NE 1383/14-1), the ANR (ANR-12-SAMA-0004, AAPG2019 – GeBra), the Eranet Neuron (AF12-NEUR0008-01 – WM2NA; and ANR- 18- NEUR00002-01 – ADORe), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites- Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de- Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l'Avenir (grant AP-RM-17-013), the Fédération pour la Recherche sur le Cerveau and the NIH, Science Foundation Ireland (16/ ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1).

IMAGEN Consortium

Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Sylvane Desrivières, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Herve Lemaitre, Tomás Paus, Luise Poustka, Sarah Hohmann, Nathalie Holz, Juliane H. Fröhner, Christian Büchel, Michael N. Smolka, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Gunter Schumann.

ESTRA Consortium

Marina Bobou, Zuo Zhang, Lauren Robinson, Gareth J. Barker, Arun L. W. Bokde, Hervé Lemaître, Dimitri Papadopoulos Orfanos, Gunter Schumann, Ulrike Schmidt, Sylvane Desrivières.

STRATIFY Consortium

Marina Bobou, M. John Broulidakis, Betteke Maria van Noort, Zuo Zhang, Lauren Robinson, Nilakshi Vaidya, Jeanne Winterer, Yuning Zhang, Sinead King, Gareth J. Barker, Arun L. W. Bokde, Hervé Lemaî- tre, Frauke Nees, Dimitri Papadopoulos Orfanos, Julia Sinclair, Argyris Stringaris, Henrik Walter, Robert Whelan, Sylvane Desrivières, Gunter Schumann.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jad.2024.05.132.

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