MD, PhD Paul Brunault
email: paul.brunault@univ-tours.fr

MD, PhD Robert Courtois

PhD Ashley N Gearhardt

MD Philippe Gaillard

MD Kevin Journiac

MS Sarah Cathelain

PhD Christian Réveillère

MD, PhD Nicolas N Ballon

Validation of the French version of the DSM-5 Yale Food Addiction Scale (YFAS 2.0) in a nonclinical sample

Keywords: food addiction, addictions, behavioral addictions, substance use disorders, Yale Food Addiction Scale 2.0, psychometric, factor analysis, eating disorders, binge eating, addictive disorders

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Drug addiction is a chronic relapsing disorder characterized by compulsion to seek and take the drug, a loss of control over drug-seeking and drug-taking behaviours, and the continued use of the drug despite its adverse consequences. [START_REF]American Psychiatric Association. Diagnostic and statistical manual of mental disorders[END_REF] It has recently been suggested that addictions should also include compulsive engagement in some pleasurable activity, such as gambling, gaming, internet use, sex, exercising, eating or shopping, in addition to pharmacologic rewards. [START_REF] Grant | Introduction to behavioral addictions[END_REF] Apart from gambling disorder, which has been included in a new behavioural addiction category of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), more research is needed before considering the potential inclusion of these other activities as addictive disorders. [START_REF]American Psychiatric Association. Diagnostic and statistical manual of mental disorders[END_REF] Despite the fact that food is widely available in western societies, that eating is an essential and frequent human activity, and that certain foods have powerful addictive and rewarding effects similar to drugs, [START_REF] Volkow | How can drug addiction help us understand obesity?[END_REF][START_REF] Volkow | Food and drug reward: overlapping circuits in human obesity and addiction[END_REF] the hypothesis that some individuals are addicted to food or to their eating behaviour has only recently been proposed. [START_REF] Meule | Five years of the Yale Food Addiction Scale: Taking stock and moving forward[END_REF] Gearhardt et al. recently proposed the concept of food addiction (FA) as a specific phenotype defined by applying the DSM-IV-TR diagnostic criteria for substance dependence to certain foods, particularly processed foods high in added sugar, fat and salt. [START_REF] Gearhardt | Food addiction: an examination of the diagnostic criteria for dependence[END_REF] This led to the development of the first self-administered questionnaire assessing FA symptoms: the Yale Food Addiction Scale (YFAS, hereafter called "original YFAS"). [START_REF] Gearhardt | Preliminary validation of the Yale Food Addiction Scale[END_REF] Further studies demonstrated the excellent psychometric properties of the original YFAS, including a one-factor structure, excellent internal consistency and very good convergent validity with measures of binge eating. [START_REF] Gearhardt | Preliminary validation of the Yale Food Addiction Scale[END_REF] This questionnaire has been cross-culturally validated, including Chinese, [START_REF] Chen | The Chinese Version of the Yale Food Addiction Scale: An Examination of its Validation in a Sample of Female Adolescents[END_REF] French, [START_REF] Brunault | Validation of the French version of the Yale Food Addiction Scale: an examination of its factor structure, reliability, and construct validity in a nonclinical sample[END_REF] German, [START_REF] Meule | German translation and validation of the Yale Food Addiction Scale[END_REF] Italian, [START_REF] Innamorati | Psychometric properties of the Italian Yale Food Addiction Scale in overweight and obese patients[END_REF] Spanish [START_REF] Granero | Food addiction in a Spanish sample of eating disorders: DSM-5 diagnostic subtype differentiation and validation data[END_REF] and Turkish [START_REF] Sevinçer | Psychometric properties of the Turkish version of the Yale Food Addiction Scale among bariatric surgery patients[END_REF] versions, allowing a growing body of research in this field. [START_REF] Cathelain | Food addiction: definition, strength and limits of the concept, risk factors, and clinical and therapeutic implications[END_REF] FA was found to be more prevalent in single and obese individuals, [START_REF] Pursey | The prevalence of food addiction as assessed by the Yale Food Addiction Scale: a systematic review[END_REF] in those with higher levels of depression, higher impulsivity, higher prevalence of post-traumatic stress disorder, higher prevalence rate for diagnosis of Attention Deficit Hyperactivity Disorder in childhood, [START_REF] Murphy | Interrelationships among impulsive personality traits, food addiction, and Body Mass Index[END_REF][START_REF] Gearhardt | An examination of the food addiction construct in obese patients with binge eating disorder[END_REF][START_REF] Mason | Posttraumatic stress disorder symptoms and food addiction in women by timing and type of trauma exposure[END_REF][START_REF] Davis | Evidence that "food addiction" is a valid phenotype of obesity[END_REF] and in patients with alterations in brain circuitry similar to those found in drug addiction (elevated activation in reward circuitry in response to food cues and reduced activation of inhibitory regions in response to food intake). [START_REF] Gearhardt | Neural correlates of food addiction[END_REF] With the recent update of DSM-5 diagnostic criteria, an update of the original YFAS was needed to take into account four new criteria, namely craving, use despite interpersonal or social consequences, failure in role obligations, and use in physically hazardous situations. An updated version could also help determine whether the DSM-5 criteria for addictive disorders could be applicable to food and whether food addiction could be included in the international diagnostic classifications as an addictive disorder. To this end, Gearhardt et al. designed the US version of the YFAS 2.0 and validated it in a nonclinical population [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] :they used confirmatory factor analysis based on the 11 diagnostic criteria to compare a one-factor model and a twofactor model (seven DSM-IV-TR criteria plus craving vs. three DSM-IV-TR criteria of abuse recently added in the DSM-5). They finally retained a one-factor structure based on fit indices (the two-factor solution did not result in noticeably improved fit and the two factors of the twofactor model were highly correlated).This validation study demonstrated the excellent psychometric properties of the YFAS 2.0, with a one-factor structure and high convergent validity with measures of disinhibited eating, obesity, and weight cycling. Regarding discriminant validity, the US version of the YFAS 2.0 was not significantly correlated with dietary restraint, and approximately half the participants with an FA diagnosis did not meet criteria for an existing eating disorder. Additionally, the YFAS 2.0 exhibited incremental validity by accounting for variance in elevated BMI above and beyond binge eating frequency.

To our knowledge, there is currently no French version of the YFAS 2.0, and no studies investigating the prevalence of FA and its associated factors as assessed by the DSM-5 criteria.

This study aimed to measure the psychometric properties of a French version of the YFAS 2.0 in a nonclinical sample by establishing its factor structure (confirmatory factor analysis), its internal consistency, its construct validity with eating disorder diagnosis, binge eating symptoms, uncontrolled eating, emotional eating and emotional overeating (convergent validity) and with cognitive restraint (discriminant validity), and its incremental validity (does the YFAS 2.0 symptom count explain unique variance in BMI above and beyond binge eating frequency?). We hypothesized that the French YFAS 2.0 had a one-factor structure, good internal consistency, high convergent validity with BMI, binge eating and emotional eating, and no correlation with cognitive restraint.

Methods

Participants and Procedures

A total of 330 volunteers participated in our study between May 2014 and May 2015. They were recruited through a web-based questionnaire, created using Sphinx software (Sphinx Plus 2 version 5.1.0.4), [START_REF] Sphinx | Mise en place d'une enquête avec Sphinx, support d'utilisation[END_REF] sent to students from the Department of Psychology and Medicine of Tours University (n=164; 49.7% of the sample) and their families (n=166; 50.3% of the sample).

Students proposed the questionnaire to their family memberswho participated based on volunteering. We differentiated results from the students and family members answers. We ensured that only adults were included in the family sample. The YFAS translation procedure was the same as the one used in the validation of the French version of the original YFAS (see Brunault et al. [START_REF] Brunault | Validation of the French version of the Yale Food Addiction Scale: an examination of its factor structure, reliability, and construct validity in a nonclinical sample[END_REF] for more details regarding the translation procedure): (1) translation, (2) blindbackward translation, (3) comparison between the two versions and modification if necessary, and finally (4) test of a pilot version in 10 controls and 10 patients, and modification of the scale if necessary. The final French version of the YFAS 2.0 can be found in Table 1.

Measures

We used measures assessing the same constructs as Gearhardt et al. [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] in their YFAS 2.0 validation study, collecting socio-demographic characteristics (i.e., age, sex, marital status), current BMI, previous maximal BMI, and using self-administered questionnaires assessing the same constructs (see below and Gearhardt et al. [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] for more details).

Food addiction (Yale Food Addiction Scale version 2.0)

The YFAS 2.0 is a 35-item self-report scale designed by Gearhardt et al. [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] to assess FA symptoms over the previous 12 months based on the 11 diagnostic criteria for Substance-Related and Addictive Disorders proposed in the DSM-5. [START_REF]American Psychiatric Association. Diagnostic and statistical manual of mental disorders[END_REF] In this way, the YFAS 2.0 assesses 11 FA criteria (for a detailed description, see Supplementary Table 1), including the seven previous food-related DSM-IV-TR criteria (consumption more than planned; persistent desire or unsuccessful efforts to cut down or control consumption of certain foods; great deal of time spent; important activities given up; use despite physical/psychological consequences; tolerance; withdrawal;), [START_REF] Gearhardt | Food addiction: an examination of the diagnostic criteria for dependence[END_REF] the four new criteria in the DSM-5 (i.e., craving, use despite interpersonal or social consequences, failure in role obligations, and use in physically hazardous situations), and significant distress in relation to food.

Each item is rated on an 8-point Likert-type scale ranging from 0 (never) to 7 (every day). Each item can be rated dichotomously (endorsed or not endorsed) depending on specific cut-offs that Gearhardt et al. defined for each item by examining the specificity for each response option based on Receiver Operator Characteristic (ROC) curves (see Gearhardt et al. [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] for more details). Based on these thresholds, each of the 11 diagnostic criteria is met when one or more item representing that criterion is endorsed (see Supplementary Table 1 for the detailed scoring instructions for the YFAS 2.0). [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] The YFAS 2.0 provides two scoring options: a diagnostic version (FA is diagnosed when the participant reports two or more symptoms during the previous 12 months plus clinically significant impairment or distress) and a symptom count version (number of FA symptoms experienced in the previous 12 months, ranging from 0 to 11). FA is mild if there are two or three symptoms and clinically significant impairment/distress, moderate if there are four or five symptoms and significant impairment/distress, and severe if there are six or more symptoms and significant impairment/distress. 1 , 21 Details of the psychometric properties of the English version of the YFAS 2.0 are described above in the Introduction.

Binge eating (Binge Eating Scale)

The Binge Eating Scale (BES) is a 16-item questionnaire designed to assess severity of binge eating using behavioural, affective, and cognitive symptoms (cut-off≥18). [START_REF] Gormally | The assessment of binge eating severity among obese persons[END_REF] The BES is a reliable tool for assessing binge eating disorder and has been validated in French. [START_REF] Brunault | Validation de la version française de la Binge Eating Scale : étude de sa structure factorielle, de sa consistance interne et de sa validité de construit en population clinique et non clinique[END_REF] In our sample, Cronbach's  was .89.

Eating disorders (Questionnaire on Eating and Weight Patterns-R and the Eating Disorder Diagnostic Scale)

We screened for eating disorders based on DSM-5 criteria using the Questionnaire on Eating and Weight Patterns-R (QEWP-R) [START_REF] Spitzer | The questionnaire on eating and weight patternsrevised (QEWP-R)[END_REF] (for binge eating disorder and bulimia nervosa) and the Eating Disorder Diagnostic Scale [START_REF] Stice | Development and validation of the Eating Disorder Diagnostic Scale: a brief self-report measure of anorexia, bulimia, and binge-eating disorder[END_REF] (for anorexia nervosa).

Eating behaviour characteristics (Emotional Overeating Questionnaire, Revised 18-item version of the Three Factor Eating Questionnaire)

We assessed emotional overeating using the Emotional Overeating Questionnaire (EOQ) which identifies the frequency of overeating in response to six emotions (anxiety, sadness, loneliness, tiredness, anger, and happiness) with high internal consistency (=0.85). [START_REF] Masheb | Emotional overeating and its associations with eating disorder psychopathology among overweight patients with Binge eating disorder[END_REF] In our sample, Cronbach's was .81. We used the revised 18-item version of the Three Factor Eating Questionnaire(TFEQ) [START_REF] De Lauzon | The Three-Factor Eating Questionnaire-R18 is able to distinguish among different eating patterns in a general population[END_REF][START_REF] Karlsson | Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study[END_REF] to assess uncontrolled eating (tendency to eat more than usual due to a loss of control over intake accompanied by subjective feelings of hunger; this subscale encompasses the previous "disinhibition" and "hunger" subscales of the 51-item TFEQ), emotional eating (inability to resist emotional cues) and cognitive restraint (conscious restriction of food intake in order to control body weight or to promote weight loss). The scores were calculated as described by de Lauzon et al. [START_REF] De Lauzon | The Three-Factor Eating Questionnaire-R18 is able to distinguish among different eating patterns in a general population[END_REF] : the theoretical ranges for the items were 9-36 for uncontrolled eating, 3-12 for emotional eating and 6-24 for cognitive restraint. In our sample, Cronbach's was .89.

Statistical Analyses and Ethical Considerations

Analyses were conducted using AMOS (SPSS Inc., Chicago, IL, USA) [START_REF] Arbuckle | AMOS (Version 7.0)[END_REF] for confirmatory factor analyses and using the R statistical package version 2.15.2 [START_REF]R: A language and environment for statistical computing[END_REF] with the psych package [START_REF] Revelle | psych: Procedures for Personality and Psychological Research[END_REF] for the other statistics. Statistical analyses included descriptive statistics and examination of the psychometric properties of the scale (factor structure, item statistics, internal consistency, construct validity and incremental validity).

To test the factor structure of the French YFAS 2.0, we used the same procedure than Gearhardt et al. [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] : we conducted confirmatory factor analyses based on the 11 diagnostic criteria to compare a one-factor model and a two-factor model (seven DSM-IV-TR criteria plus craving vs. three former DSM-IV-TR criteria of abuse recently added in the DSM-5).To assess the internal consistency of the scale, we used Kuder-Richardson alpha (KR-20) and McDonald's omega. We used KMO statistics to assess the sampling adequacy.

We assessed the construct validity of both the diagnostic and the symptom count versions by examining the associations between FA (assessed using either the diagnostic or the symptom count version of the YFAS) and the following measures: socio-demographic characteristics, weight-related variables, eating disorder diagnosis, binge eating, emotional eating and emotional overeating (convergent validity) and cognitive restraint (discriminant validity). We used chi-squared tests, parametric mean comparison tests (ANOVA) or Pearson's correlation tests, as appropriate.

We used hierarchical multiple regression to test incremental validity: does the YFAS 2.0 symptom count predict elevated BMI above and beyond binge eating frequency (item 3 of the QEWP-R)?

There were no missing data, as all of the questions required a response to proceed to the next page of the survey. Our study did not require institutional review board approval because it was not considered biomedical research under French law; however, it followed the tenets of the Declaration of Helsinki.

Results

Sample Characteristics

Table 2 presents the socio-demographic characteristics, weight-related variables and prevalence of each FA symptom. Participants had a mean age of 28.9 years (SD 11.3, range=18-76) and a mean current BMI of 23.3 kg/m 2 (SD 4.9). Supplementary Table 2 presents and compares the sociodemographic characteristics of students vs. family members). Prevalence of FA was 8.2%.

Mean number of FA symptoms was 1.1 (SD 1.9), with no significant difference between men and women (0.7 SD 1.3 vs. 0.8 SD 1.4; p=0.65). Ten percent were underweight (BMI<18.5kg/m 2 ), 65.2% were normal weight (18.5kg/m2≤BMI<25kg/m2), 16.4% were overweight (25kg/m 2 ≤BMI<30kg/m 2 ), and 8.5% were obese (BMI≥30kg/m 2 ). Table 3 presents the prevalence of eating disorders and the eating behaviour characteristics of our population.

Sixteen out of the 27 participants with FA (59.3%) had significant binge eating.

Item Statistics, Factor Structure, and internal consistency

Table 4 summarizes the item statistics, including the mean, standard deviation, and item-total correlation for each item.

Results and fit indices of confirmatory factor analyses are presented in Table 5. The one-factor model had adequate fit indices (Confirmatory Fit Index=CFI was .887, Root-Mean-Square error of approximation=RMSEA was .083) with all factor loadings greater than .32. In line with Gearhardt et al. [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] YFAS 2.0 validation study and consistent with Gillespie et al.'s examination of the factor structure of substance use disorder symptoms [START_REF] Gillespie | Factor and item-response analysis DSM-IV criteria for abuse of and dependence on cannabis, cocaine, hallucinogens, sedatives, stimulants and opioids[END_REF] , we retained a onefactor solution because the two-factor model did not result in noticeably improved fit (CFI=.891, RMSEA=.082) and the two factors of this two-factor model were highly correlated (r=.69, p<.001). The internal consistency of the YFAS 2.0 (11 diagnostic criteria plus significant distress) was good (KR-20=.83, Mc Donald omega=.86) and higher than the one observed with the DSM-IV-TR diagnosticcriteria of our sample (7 diagnostic criteria plus significant distress; KR-20=.74). Sampling adequacy was very good (KMO=.86). Mean interitem correlation was .26 for the 7 DSM-IV-TR criteria and r=.30 for the 11 DSM-5 criteria.

Convergent validity of the YFAS 2.0 (diagnostic version)

Participants with FA (vs. without) were more frequently female, had higher current weight, BMI and higher previous maximal BMI (Table 2). They had a higher prevalence rate for DSM-5 binge eating disorder but not for bulimia nervosa or anorexia nervosa (Table 3). Participants with FA had higher scores for binge eating, uncontrolled eating and emotional eating (p<0.001).

They also ate more frequently in response to negative and positive emotions (i.e., anxiety, sadness, loneliness, anger, tiredness, happiness) (Table 3).

Convergent validity of the YFAS 2.0 (symptom count version)

Table 4 presents the factors associated with the YFAS symptom score: the YFAS symptom score was associated with higher current BMI and higher previous maximal BMI, but not with marital status, age or gender. The YFAS symptom score was associated with diagnosis of binge eating disorder, higher scores for binge eating, uncontrolled eating, emotional eating, and eating in response to negative or positive emotions such as anxiety, sadness, loneliness, anger, tiredness or happiness. It was not associated with diagnosis of anorexia nervosa, or bulimia nervosa.

Discriminant validity of the YFAS 2.0 (diagnostic and symptom count versions)

FA diagnosis and symptom count were associated with higher cognitive restraint (Table 4).

Incremental validity of the YFAS 2.0

In simple linear regression, binge eating frequency was a significant predictor of BMI (t=2.59, =.14, p<.01), accounting for 1.7% of the variance. In hierarchical multiple regression, when YFAS 2.0 symptom count was entered in the model, binge eating was no longer a significant predictor (t=.61, =.04, p=.55), and YFAS 2.0 symptom count was a significant predictor (t=4.71, =.27, p<.001), accounting for an additional 6% of the variance in BMI (F=14.67, p<.001).

Discussion

This study demonstrated that the French version of the YFAS 2.0 is a reliable tool to diagnose FA and FA symptoms based on the DSM-5 criteria for Substance-Related and Addictive Disorders. It has a one-factor structure, good internal consistency, and high convergent validity with measures of binge eating, emotional eating, BMI, and with diagnosis of DSM-5 binge eating disorder.We did not confirm the discriminant validity of the YFAS with cognitive restraint found in the US validation study. The YFAS symptom count predicted BMI above and beyond binge eating frequency. These results demonstrate that the addiction model is relevant and applicable to food consumption, and that FA seems to be a unitary concept and construct, related to but different from traditional eating disorders. [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] The one-factor structure and good internal consistency of the French version of the YFAS 2.0 is in line with the results obtained with the DSM-5 version of the YFAS. [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] Interestingly, the addition of the four new criteria in the DSM-5 (including craving) did not modify the one-factor structure of this scale, and even improved its internal consistency. The YFAS 2.0 also corrected a limitation of the original YFAS: the lack of reliability of items related to "persistent desire or unsuccessful efforts to cut down or control use". [START_REF] Meule | Factor structure and item analysis of the Yale Food Addiction Scale in obese candidates for bariatric surgery[END_REF] In the French YFAS 2.0, this item was endorsed by 12.1% of the sample (vs. 90.1% in the original YFAS), indicating better assessment of this diagnostic criterion in the YFAS 2.0. Prevalence of FA was higher in the US sample than in the French sample (see Supplementary Table 4 for a comparison of FA prevalence and criteria in the original French YFAS, the French YFAS 2.0 and the US YFAS 2.0), probably due to cultural differences or differences in BMI (in our sample, one participant out of four was overweight or obese vs. one out of two in the US sample [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] ). The adequate factor loadings observed for all of the 12 dichotomous diagnostic criteria (11 diagnostic DSM criteria plus significance questions) suggest that the DSM-5 addiction criteria applied to food assess a unitary concept.

Although the DSM-5 thresholds have been lowered for addictive disorders (i.e., 2 criteria out of 11 are necessary for diagnosis in the DSM-5, compared to 3 out of 7 in the DSM-IV-TR), we found no significant increase in the prevalence rate for DSM-5 FA (Supplementary Table 3). The lower thresholds for the 11 addiction criteria might have been counterbalanced by the higher YFAS 2.0 thresholds for each item.

Interestingly, FA was associated with emotional eating which is in line with the high comorbidity between FA, emotional dysregulation, [START_REF] Innamorati | Emotion regulation and mentalization in people at risk for food addiction[END_REF] and mood and anxiety disorders. [START_REF] Meule | Five years of the Yale Food Addiction Scale: Taking stock and moving forward[END_REF] , [START_REF] Eichen | Exploration of "food addiction" in overweight and obese treatment-seeking adults[END_REF] Our results are in line with Bruch and Kaplan's psychosomatic theory, which postulates that food intake increases in response to an internal state of emotional arousal, such as anxiety or depression, and that this could partly explain weight gain. [START_REF] Bruch | Eating disorders: Obesity, anorexia nervosa, and the person within[END_REF] , [START_REF] Kaplan | The psychosomatic concept of obesity[END_REF] Although FA was associated with higher BMI, it is still unclear whether FA could be a direct cause of weight gain and whether treatment for FA could improve weight. FA prevalence was higher among women than among men, but the mean number of FA symptoms did not differ between women and men. Because men have generally more difficulty than women at processing and expressing emotions, [START_REF] Collignon | Women process multisensory emotion expressions more efficiently than men[END_REF] use of the symptom count version (number of FA criteria met) may be more useful than the diagnostic version (diagnosis) for men.

We did not confirm the YFAS discriminant validity with cognitive restraint found in the US validation study, [START_REF] Gearhardt | Development of the Yale Food Addiction Scale version 2.0[END_REF] probably because of cultural differences or differences in samples (by including young students, our participants may have had higher cognitive restraint and lower BMI).

This study has a number of limitations, including use of a self-administered questionnaire (future studies should first design and validate a semi-structured diagnostic instrument to test the sensitivity and specificity of the YFAS 2.0), use of self-report for height (often overestimated) and weight (often underestimated), no assessment of the test-retest reliability of the scale, and use of the YFAS 2.0 in a nonclinical population. Future research should test its psychometric properties in patients with eating disorders or with diseases potentially associated with FA (i.e., obesity, diabetes, hypertension or other metabolic syndrome risk factors).

By validating the French version of the YFAS 2.0, this study provides an opportunity to study the "food addiction" phenotype and its associated factors in greater depth. Before including FA in the international classification of diseases -a subject that is still hotly debatedthe similarities of this phenotype with other addictions in terms of disease course, phenomenology, comorbidity, neurobiological mechanisms, and response to treatment must be identified, as well as its specificities. [START_REF] Varescon | Les addictions comportementales : aspects cliniques et psychopathologiques[END_REF] If it is included, it must then be determined whether FA should be conceptualized as a substance-related disorder, [START_REF] Ifland | Refined food addiction: a classic substance use disorder[END_REF] as a behavioural addiction [START_REF] Hebebrand | Eating addiction", rather than "food addiction", better captures addictive-like eating behavior[END_REF] or as a "mixed" substance-related and behavioural addiction. This could have important practical implications, such as choosing the most appropriate therapeutic strategy for a given patient (including psychotherapy and substitution or substance replacement therapy), and developing effective harm reduction programs or even public health policies. It also highlights the importance of investigating each patient's mental representations and values associated with food, which may explain how and why an individual chooses food rather than another substance or behaviour in a stressful situation.

Conclusions

This study demonstrated that the French version of the YFAS 2.0 is a reliable tool for assessing FA symptoms based on the DSM-5 criteria for substance-related and addictive disorders. We demonstrated that the YFAS 2.0 can be used either to diagnose FA or to assess the number of FA symptoms in a nonclinical sample. Future studies should test the validity of the YFAS 2.0 in other nonclinical and clinical samples. The assessment of a "food addiction" phenotype using this DSM-5-based questionnaire is an important preliminary step in order to identify its risk factors and prior to discussing the pro and cons of considering FA as an addictive disorder. To contribute to this debate, future studies should investigate FA in patients who experience harm related to their FA, including obesity and other metabolic syndrome risk factors or metabolic complications (e.g. type 2 diabetes, hypertension, dyslipidemia, atherosclerosis, stroke, or coronary heart disease).

TABLES Table 1. French version of the Yale Food Addiction Scale 2.0

Consignes pour remplir le questionnaire :

Ce questionnaire porte sur vos habitudes alimentaires de l'année passée. Pour chaque question, merci d'entourer le chiffre (0, 1, 2, 3, 4, 5, 6 ou 7) qui correspond le mieux à vos habitudes alimentaires des 12 derniers mois. Les gens ont parfois du mal à contrôler la quantité de nourriture qu'ils peuvent manger, comme par exemple:

-Les aliments sucrés comme les glaces ou les crèmes glacées, le chocolat, les beignets, les biscuits, les gâteaux et les bonbons.

-Les féculents comme le pain, le pain de mie, les sandwichs, les pâtes et le riz.

-Les aliments salés comme les chips, les bretzels et les biscuits apéritifs.

-Les aliments gras comme le steak, les charcuteries, le bacon, les hamburgers, les cheeseburgers, les fromages, les pizzas et les frites.

-Les boissons sucrées comme le soda, la limonade et les boissons énergétiques. Pour les questions suivantes, l'expression « CERTAINS ALIMENTS » sera utilisée. Dans ce cas, merci de penser à TOUT aliment ou boisson indiqué(e) dans la liste ci-dessus ou à TOUT AUTRE(S) aliment(s) qui vous a (ont) posé un problème au cours de l'année passée. 12. Lorsque j'ai diminué ou arrêté ma consommation de certains aliments et que j'ai eu des symptômes physiques, j'ai mangé ces aliments pour me sentir mieux.

AU COURS DES 12 DERNIERS MOIS :

Jamais

0 1 2 3 4 5 6 7
13. Lorsque j'ai diminué ou arrêté ma consommation de certains aliments et que je me suis senti(e) irritable, stressé(e) ou triste, j'ai mangé ces aliments pour me sentir mieux.

0 1 2 3 4 5 6 7
14. Lorsque j'ai diminué ou arrêté ma consommation de certains aliments, j'ai eu des symptômes physiques comme par exemple des maux de tête ou de la fatigue.

0 1 2 3 4 5 6 7
15. Lorsque j'ai diminué ou arrêté ma consommation de certains aliments, j'ai constaté que j'avais un besoin plus important ou une envie irrésistible de manger ces aliments.

0 1 2 3 4 5 6 7
16. Mon comportement vis-à-vis de la nourriture et de l'alimentation a été source de souffrance.

0 1 2 3 4 5 6 7
17. J'ai eu beaucoup de problèmes dans ma vie à cause de la nourriture et de l'alimentation, comme par exemple des problèmes pour gérer le quotidien, des problèmes au travail, à l'école, avec la famille ou encore des problèmes de santé.

0 1 2 3 4 5 6 7
18. Des fois, je me suis senti(e) si mal à cause de mon alimentation excessive que cela m'a empêché de faire des choses importantes, comme travailler ou passer du temps avec mes amis ou ma famille. 22. J'ai continué à manger le(s) même(s) type(s) d'aliment(s) ou la même quantité de nourriture bien que cela ait été responsable de problèmes psychologiques.

0 1 2 3 4 5 6 7
23. J'ai continué à manger le(s) même(s) type(s) d'aliment(s) ou la même quantité de nourriture bien que cela ait été responsable de problèmes physiques.

0 1 2 3 4 5 6 7
24. Le fait de manger la même quantité de nourriture qu'avant ne me donne plus le même plaisir qu'avant.

0 1 2 3 4 5 6 7
25. J'ai vraiment voulu diminuer ou arrêter ma consommation de certains aliments, mais je n'y suis pas arrivé.

0 1 2 3 4 5 6 7
26. J'ai eu besoin de manger de plus en plus pour avoir le même effet qu'avant, comme par exemple avoir moins de stress, avoir moins de tristesse ou avoir plus de plaisir. 28. J'ai continué à manger certains aliments même si je savais que c'était dangereux pour ma santé physique. Par exemple, j'ai continué à manger des bonbons alors que je savais que j'avais du diabète, ou j'ai continué à manger des aliments gras alors que je savais que j'avais des problèmes cardiaques.

0

19 .

 19 Mon alimentation excessive m'a empêché(e) de m'occuper correctement de ma famille ou de faire des tâches ménagères. 'ai évité des opportunités professionnelles ou relationnelles parce que je ne pouvais pas manger certains aliments dans ces situations. 'ai évité certaines activités sociales car dans ces situations, certaines personnes n'étaient pas d'accord avec la quantité de nourriture que je pouvais manger.

27 .

 27 Je n'ai pas réussi correctement au travail ou à l'école car je mangeais trop.

2 ) 4 ) 5 )

 245 635. b This two-factor model (factor 1=seven DSM-IV-TR criteria plus craving; factor 2:three former DSM-IV-TR criteria of abuse recently added in the DSM-5) was significant with the corresponding fit indices:χ 2 =138.23, df (degrees of freedom)=43; χ 2 (CMIN)/df=3.22; p=.001; CFI (Comparative fit index)=.891; IFI (Incremental fit index)=.893; RMSEA (Root Mean Square Error of Approximation)=.082 [.067 -.0098]; ECVI (Expected Cross-Validation Index)=.627. Factor 1 and Factor 2 were significantly correlated (r=.69, p<.001). *** p< .001. Each question has a different threshold: 0 = threshold not met, 1 = threshold is met 1) Once a month: #9, #10, #19, #27, #33, #35 Two to three times a month: #8, #18, #20, #21, #34 3) Once a week: #3, #11, #13, #14, #22, #28, #29 Two to three times a week: #5, #12, #16, #17, #23, #24, #26, #30, #31, #32 Four to six times a week: #1, #2, #4, #6, #7, #15, #25 After computing the threshold for each question, sum up the questions under each criterion (e.g. Tolerance, Withdrawal, Clinical Significance, etc.). If the score for the symptom criterion is ≥ 1, then the criterion has been met and is scored as 1. If the score is = 0, then the symptom criterion has not been met and is scored as 0. Example: Tolerance: (#24 =1) + (#26 = 0) = 1, Criterion Met Craving (#29=0) + (#30 = 0), Criterion Not Met Failure to fulfill role obligations (#19 =1) + (#27 = 1), Criterion Met and scored as 1 For the symptom count scoring option, add up all of the scores for each of the 11 criterion (e.g. Tolerance, Withdrawal, Use Despite Negative Consequence, …). Do not add clinical significance to the score. This score should range from 0 to 11 (0 symptoms to 11 symptoms.) For the "diagnosis" scoring option ("diagnostic version"), a participant can meet criteria for mild, moderate or severe food addiction. Both the symptom count score and the clinical significance criterion are used. No Food Addiction = 1 or fewer symptoms No Food Addiction = Does not meet criteria for clinical significance Mild Food Addiction = 2 or 3 symptoms and clinical significance Moderate Food Addiction = 4 or 5 symptoms and clinical significance Severe Food Addiction = 6 or more symptoms and clinical significance Supplementary

  En mangeant, il m'est arrivé(e) d'être tellement inattentif (inattentive) que j'aurai pu être blessé(e) (par exemple en conduisant une voiture, en traversant la rue ou en utilisant une machine ou un instrument dangereux). #2 are optionals and are not present in the US version of the YFAS 2.0. These 2 items are not used in the determination of the food addiction diagnosis nor in the calculation of the number of food addiction symptoms endorsed, but we included them here because they were included in the original YFAS and they help to determine which types of foods are problematic.

					1	2	3	4	5	6	7
	29. J'ai eu des envies si fortes pour certains aliments que je ne pouvais plus penser à autre	0	1	2	3	4	5	6	7
	chose.
	30. J'ai eu des envies si fortes pour certains aliments que c'était comme si je devais	0	1	2	3	4	5	6	7
	absolument les manger tout de suite.
	31. J'ai essayé de diminuer ou d'arrêter ma consommation de certains aliments, mais je	0	1	2	3	4	5	6	7
	n'ai pas réussi.
	32. J'ai essayé mais n'ai pas réussi à diminuer ou à arrêter de manger certains aliments.	0	1	2	3	4	5	6	7
	33. 0	1	2	3	4	5	6	7
	34. En pensant à la nourriture et à l'alimentation, il m'est arrivé(e) d'être tellement	0	1	2	3	4	5	6	7
	inattentif (inattentive) que j'aurai pu être blessé(e) (par exemple en conduisant une
	voiture, en traversant la rue ou en utilisant une machine ou un instrument dangereux).
	35. Mes amis et ma famille ont été inquiets de la quantité de nourriture que je pouvais	0	1	2	3	4	5	6	7
	manger.
	Item optionnel n°1. Merci d'entourer TOUS les aliments pour lesquels vous avez eu des problèmes (c'est-à-dire des difficultés à en contrôler la consommation).
	Glaces/Crèmes glacées	Chocolat	Pommes	Beignets		Brocolis		Biscuits
	Gâteaux	Bonbons	Pain	Pain de mie		Sandwichs	Laitues
	Pâtes	Fraises	Riz	Chips		Bretzels		Biscuits apéritifs
	Carottes	Steak	Charcuteries	Bananes		Bacon		Hamburgers
	Cheeseburgers	Fromages	Pizzas	Frites		Sodas		Aucun de ces aliments
	Item optionnel n°2. Merci d'indiquer ici s'il y a d'autre(s) aliment(s) pour lesquels vous avez eu des problèmes (c'est-à-dire des difficultés à en contrôler la consommation). Merci d'indiquer uniquement les
	aliments qui ne sont pas dans la liste ci-dessus.
	…………………………………………………………………………………………………………………………………………………………………………………………………………………………………
	………………………………………………………………………………………………………………………………………………………………………………………
	Items#1 and

Table 2 . Descriptive statistics of the overall sample, and comparison of participants with and without food addiction

 2

		Overall	Participants	Participants	Statistic	p
		sample	without food	with food	test
		(n=330)	addiction	addiction
			(n=303)	(n=27)
	Sociodemographic characteristics
	Age (years)	28.9 ± 11.3	28.8 ± 11.4	27.6 ± 11.0	F=.25	0.62
	Gender (male)**	19.7% (65)	21.5% (65)**	0% (0)**	 2 =7.21	<0.01
	Marital status (married or in a relationship)	35.8% (118)	36.3% (111)	29.6% (8)	 2 =.48	0.49
	Weight-related variables
	Weight (kg)*	65.7 ± 16.3	65.2 ± 15.7*	72.0 ± 21.5*	F=4.35	<0.05
	BMI (kg/m 2 )**	23.3 ± 4.9	23.1 ± 4.5**	25.9 ± 7.7**	F=8.32	<0.01
	Previous maximal BMI (kg/m 2 )***	24.8 ± 5.2	24.6 ± 4.7*	27.9 ± 8.8*	F=10.57	<0.001
	Food addiction prevalence and symptoms
	Prevalence of food addiction	8.2% (27)	-	-	-	-
	Mild food addiction	3.3% (11)	-	-	-	-
	Moderate food addiction	2.1% (7)	-	-	-	-
	Severe food addiction	3.0% (9)	-	-	-	-
	Number of food addiction symptoms***	1.1 ± 1.8	0.7 ± 1.4***	5.0 ± 3.5***	F=194.98 <0.001
	Food consumed in larger quantities or over	20% (66)	16.5% (50)***	59.3% (16)***	 2 =28.33 <0.001
	a longer period than intended***
	Persistent desire or unsuccessful efforts to	12.1% (40)	8.9% (27)***	48.1% (13)***	 2 =35.83 <0.001
	cut down or control consumption of certain
	foods***
	Considerable time spent to obtain, consume,	12.4% (41)	10.2% (31)***	37.0% (10)***	 2 =16.37 <0.001
	or recover from effects of food***
	Giving up important social, occupational, or	7.6% (25)	4.0% (12)***	48.1% (13)***	 2 =69.13 <0.001
	recreational activities because of food
	consumption***
	Continuing to eat certain foods despite	7.9% (26)	4.3% (13)***	48.1% (13)***	 2 =65.70 <0.001
	physical or psychological problems***
	Tolerance***	6.4% (21)	3.0% (9)***	44.4% (12)***	 2 =71.56 <0.001
	Withdrawal***	7.9% (26)	4.6% (14)***	44.4% (12)***	 2 =54.17 <0.001
	Continued consumption despite social or	13.3% (44)	8.9% (27)***	63.0% (17)***	 2 =62.68 <0.001
	interpersonal problems***
	Failure to fulfill major role obligation***	3% (10)	1.7% (5)***	18.5% (5)***	 2 =24.00 <0.001
	Use in physically hazardous situations***	9.1% (30)	6.6% (20)***	37.0% (10)***	 2 =27.79 <0.001

Table 5 . Factor loadings for the 1-and 2-factor structures of the Yale Food Addiction Scale 2.0 (confirmatory factor analyses)

 5

	One-factor	Two-factor structure b
	structure a
	Factor 1	Factor 1	Factor 2
	loadings	loadings	loadings

Table 3 (Table S3). Item statistics for the Yale Food Addiction Scale 2.0

 3

	Mean	SD	Item-total
			correlation

Acknowledgments:

We thank Manon Barbet and Cynthia Beheydt for assistance with data collection. We thank William Revelle for helping us in using the psych statistical package. We thank Elizabeth Yates for correcting the English version of the manuscript.

Declaration of conflict interests: Dr. Brunault reports personal fees from Lundbeck, personal fees from Astra-Zeneca, personal fees from DNA Pharma, outside the submitted work; Dr.

Ballon reports personal fees from Lundbeck, personal fees from Astra-Zeneca, personal fees from DNA Pharma, outside the submitted work; the other authors declare that they have no

Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

conflict of interest to disclose.


Paul Brunault
email: paul.brunault@univ-tours.fr

Anne-Laure Champagne

Grégoire Huguet

Isabelle Suzanne

Jean-Louis Senon

Gilles Body

Emmanuel Rusch

Guillaume Magnin

Mélanie Voyer

Christian Réveillère

Jean-Louis Senon

Vincent Camus

Jean- Louis Senon

are independent risk factors for lower quality of life in non-metastatic breast cancer patients

Keywords: Major depressive disorder, personality disorders and coping strategies cancer, oncology, quality of life, coping, depression

INTRODUCTION

Breast cancer has become a major public health issue because it is the most common cancer that affects women in terms of both prevalence and incidence [START_REF] Ferlay | Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008[END_REF]. Thanks to better screening and advances in treatment since the 1980s, there has been a significant reduction in the mortality rate from breast cancer [START_REF] Rachet | Population-based cancer survival trends in England and Wales up to 2007: an assessment of the NHS cancer plan for England[END_REF], allowing clinicians to focus on improvements in quality of life (QOL) and psychological well-being, rather than solely on life expectancy, in this patient population [START_REF] Montazeri | Health-related quality of life in breast cancer patients: A bibliographic review of the literature from 1974 to 2007[END_REF]. The concept of QOL, which refers to the subjective assessment of a patient's health, has become a major outcome measure in medicine (along with mortality and morbidity) because it assesses a patient's subjective view of his/her health and serves as a prognostic factor [START_REF] Leplège | Mesure de la santé perceptuelle et de la qualité de vie: méthodes et applications[END_REF].

Following a diagnosis of breast cancer, an assessment of QOL is an important clinical outcome measure because patients have to face major stressors that are likely to deteriorate their QOL, not only just after diagnosis but also in the long term (e.g., facing a life-threatening illness, painful and impairing treatments, significant role changes, and issues pertaining to body image) [START_REF] Al-Azri | Coping With a Diagnosis of Breast Cancer-Literature Review and Implications for Developing Countries[END_REF][START_REF] Helms | Body image issues in women with breast cancer[END_REF]. To design early therapeutic interventions that can improve QOL in this population, a better understanding is needed regarding which factors are associated with QOL.

Several types of factors are considered to be QOL risk factors in non metastatic breast cancer patients, such as individual variables (e.g., younger age, less frequent use of active coping, religious coping and acceptance, personality traits such as high neuroticism) [START_REF] Janz | Population-based study of the relationship of treatment and sociodemographics on quality of life for early stage breast cancer[END_REF][START_REF] Kroenke | Functional Impact of Breast Cancer by Age at Diagnosis[END_REF][START_REF] Cousson-Gélie | Psychologie de la santé et cancers: quels apports et quelles perspectives?[END_REF][START_REF] Elumelu | Impact of active coping, religion and acceptance on quality of life of patients with breast cancer in the department of radiotherapy, UCH, Ibadan[END_REF][START_REF] Härtl | Quality of life, anxiety, and oncological factors: a follow-up study of breast cancer patients[END_REF], and measures of symptoms (e.g., higher pain, higher anxiety and higher depression) [START_REF] Härtl | Quality of life, anxiety, and oncological factors: a follow-up study of breast cancer patients[END_REF][START_REF] Brunault | Impact of late treatment-related radiotherapy toxicity, depression, and anxiety on quality of life in long-term breast cancer survivors[END_REF][START_REF] Sundaresan | Patients' Perceptions of Health-related Quality of Life During and After Adjuvant Radiotherapy for T1N0M0 Breast Cancer[END_REF]. Other studies that focused on biological and physiological variables found mixed results: some authors found that alteration in physical, emotional and social QOL was not associated with tumor severity nor treatment type [START_REF] Burgess | Depression and anxiety in women with early breast cancer: five year observational cohort study[END_REF][START_REF] Reich | Depression, quality of life and breast cancer: a review of the literature[END_REF], while some others found an association with use of chemotherapy or radiotherapy [START_REF] Härtl | Personality traits and psychosocial stress: quality of life over 2 years following breast cancer diagnosis and psychological impact factors[END_REF][START_REF] Brunault | Impact of late treatment-related radiotherapy toxicity, depression, and anxiety on quality of life in long-term breast cancer survivors[END_REF]. These studies also demonstrated that QOL could differ depending on the duration of illness [START_REF] Janz | Population-based study of the relationship of treatment and sociodemographics on quality of life for early stage breast cancer[END_REF][START_REF] Kroenke | Functional Impact of Breast Cancer by Age at Diagnosis[END_REF]. Finally, social support (that encompasses tangible support, emotional informational support, affectionate support and positive social interaction) was associated with higher QOL and acted as a moderator between depressive symptoms and QOL [START_REF] Huang | Social support as a moderator between depressive symptoms and quality of life outcomes of breast cancer survivors[END_REF].

Although many studies independently assessed the potential impact of each of these variables, no study assessed the relative impact of these variables on QOL in an integrative model. To our knowledge, no studies assessed whether QOL was more strongly associated with biological variables (i.e., disease severity, treatments) or with variables related to the individual's characteristics prior to the disease and to the manner in which this individual copes with the disease (i.e., measures of symptoms such as depression; individual variables such as coping and personality). In addition, we do not know yet whether depression, personality and coping strategies are three independent risk factors for lower QOL or if their association with QOL is due to other potential confounding variables. Finally, no studies assessed depression and personality using a categorical approach (i.e., major depressive disorder and personality disorder).

To improve our knowledge of the factors that are associated with QOL in multivariate analyses, we proposed and tested a working model that aimed to assess the relative impact of biological and physiological variables (i.e., tumor severity, types of treatment used), individual variables (i.e., age, coping, personality, marital status), and measures of symptoms (i.e., depression, pain) on QOL. This modified model was based on Wilson & Cleary's QOL theoretical model [START_REF] Wilson | Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes[END_REF], which is one of the most commonly used and referenced models in the health-related QOL literature [START_REF] Bakas | Systematic review of health-related quality of life models[END_REF]. This study aimed to determine which factors were associated with physical, emotional and global QOL in non-metastatic breast cancer patients at the end of their initial treatment phase (7 months after diagnosis). We hypothesized that, even after adjusting for the severity of disease and types of treatment that were received, major depressive disorder, personality disorders and certain coping strategies (i.e., more frequent use of avoidant coping strategies and less frequent use of active coping strategies) were significant and independent risk factors for lower QOL.

MATERIAL AND METHODS

Participants and procedure

The participants in this study were recruited based on protocols from the ESPOIR study (Early Screening for Psycho-Oncological Intervention Research) that aimed to identify early psychological risk factors for psychiatric disorders in breast cancer patients at the end of their treatment phase (7 months post diagnosis). Participants were recruited between May 2006 and November 2007 from the Gynecology Department of the Tours and Poitiers University Hospitals, located in France.

Patients were considered eligible for the study if they were being diagnosed with breast cancer for the first time and if no metastasis was detected during their diagnosis. They were also required to understand French. Patients were excluded if they had metastasis, presented with a relapse of breast cancer, had a cancer of another organ in the past, had difficulties with understanding the study or the assessment instruments, or simply refused to participate. The data collection was conducted 7 months after cancer diagnosis by experienced psychologists (except for personality disorders which were assessed 3 months after cancer diagnosis). 141 out of 233 potentially eligible patients participated (response rate=60.5%).

Measured variables

Individual characteristics (age, personality, coping and marital status)

We assessed the following variables in each patient: age, occupational and marital status (i.e., single or in a relationship).

Personality disorders were assessed using the French version of the VKP questionnaire (Vragenlijst voor Kenmerken van de Persoonlijkheid) [START_REF] Enfoux | Comorbidity between personality disorders and depressive symptomatology in women: A cross-sectional study of three different transitional life stages: Depression and personality disorders[END_REF]. The VKP is a 197-item, self-administered questionnaire based on the International Personality Disorder Examination (IPDE) and evaluates the presence of each of the 10 personality disorders according to the ICD-10 and DSM-IV diagnostic criteria [START_REF] Duijsens | The VKP, a self-report instrument for DSM-III-R and ICD-10 personality disorders: construction and psychometric properties[END_REF].

We assessed coping strategies using the French version of the Brief-COPE scale [START_REF] Fillion | Validation of the shortened cope for use with breast cancer patients undergoing radiation therapy[END_REF], which is a short version of the COPE inventory [START_REF] Carver | You want to measure coping but your protocol' too long: Consider the brief cope[END_REF]. The Brief-COPE assesses 14 coping strategies (two items per coping): active coping, planning, use of instrumental support, use of emotional support, venting, behavioral disengagement, self-distraction, self-blame, positive reframing, humor, denial, acceptance, religion and substance use; a higher score indicating a more frequent occurrence of the particular coping strategy.

Biological and physiological variables (severity of disease, types of treatment received)

The severity of breast cancer was assessed in each patient using the international Tumor Node Metastasis (TNM) staging system (T0, T1, T2, T3 or T4; N0 or N1) and the detection of hormonal receptors. We also assessed medical data that was related to patient care, including the existence of chemotherapy, tumor excision surgery and/or mastectomy, axillary lymph node dissection, radiotherapy and/or hormone therapy.

Symptom measures: major depressive disorder and pain

We evaluated the presence of major depressive disorder using the Mini International Neuropsychiatric Interview (MINI) version 5.0.0. The MINI is a semi-structured interview that assesses psychiatric disorders according to the DSM-IV diagnostic criteria. The MINI has been validated by Sheehan et al. [START_REF] Sheehan | The Mini-International Neuropsychiatric Interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10[END_REF] and was translated into the French language and validated by Lecrubier et al [START_REF] Lecrubier | The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structured interview: reliability and validity according to the CIDI[END_REF]. The MINI is a reliable and valid instrument to assess major depressive disorder (sensitivity=96%, specificity=88% and kappa coefficient =0.84 [START_REF] Sheehan | The Mini-International Neuropsychiatric Interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10[END_REF][START_REF] Lecrubier | The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structured interview: reliability and validity according to the CIDI[END_REF]).

To assess pain, we utilized the Questionnaire de Douleur Saint Antoine (QDSA) [START_REF] Boureau | Comparative study of the validity of four French McGill Pain Questionnaire (MPQ) versions[END_REF], which is an adaptation and French language translation of the McGill Pain Questionnaire [START_REF] Melzack | The short-form McGill pain questionnaire[END_REF]. The QDSA is a verbal, multidimensional, self-administered scale that assesses sensorial and affective aspects of pain; higher score indicate a higher pain level. The QDSA has been validated in oncology settings [START_REF] Boureau | Comparative study of the validity of four French McGill Pain Questionnaire (MPQ) versions[END_REF].

Assessment of QOL

We assessed QOL using the French version of the Quality of Life Questionnaire-Core 30 (QLQ-C30) questionnaire [START_REF] Fournier | Pertinence d'évaluer la qualité de vie des femmes jeunes et de leur partenaire en sénologie[END_REF], which is an international 30-items questionnaire designed by the European Organization for the Research and Treatment of Cancer (EORTC) to assess cancer-specific QOL [START_REF] Aaronson | The European Organization for Research and Treatment of Cancer QLQ-C30: A Quality-of-Life Instrument for Use in International Clinical Trials in Oncology[END_REF] and that compose a global QOL scale and five functional scales (i.e., physical, emotional, social, cognitive functioning and ability to work) [START_REF] Aaronson | The European Organization for Research and Treatment of Cancer QLQ-C30: A Quality-of-Life Instrument for Use in International Clinical Trials in Oncology[END_REF]. Each scale is scored from 0 to 100; higher scores indicate better QOL. In this study, we focused on physical, emotional and global QOL because they are the most frequently studied QLQ-C30 dimensions.

Ethical considerations

This study was approved by the ethics committee of the University Hospital Center of Tours, France. All procedures have been performed in accordance with the ethical standards described in the 1964 Declaration of Helsinki. All participants provided written informed consent prior to their inclusion in the study.

Statistical analysis

The R statistical software package version 2.15.2 (R Development Core Team, Vienna, Austria), was used to perform the data analysis of this study [START_REF]R: A language and environment for statistical computing[END_REF].

To determine which factors were associated with each QOL dimension (i.e., physical, emotional and global QOL), we used a two-stage approach. First, we used univariate analyses (correlation tests and mean comparisons) to determine which variables were significantly associated with each QOL dimension. Second, we used multivariate analyses (multiple linear regression) to determine which variables were associated with each QOL dimension, after adjusting for potential confounding factors: age, initial tumor stage, use of hormone therapy, pain, presence of major depressive disorder and use of coping strategies. We verified that the basic assumptions for multiple linear regression were met for all of our analyses, especially for the normality of residuals. For each dependent variable, we specified the beta regression coefficient, its standard deviation and the associated t-and p-values. Statistical significance was considered when the p-value ≤ 0.05.

RESULTS

Population

A total of 141 patients were initially enrolled in our study shortly after diagnosis. Sixteen of these 141 patients were withdrawn from the study due to metastatic evolution (3 patients), withdrawal of consent (6 patients), or loss to follow-up (7 patients). The eligible patient population at the end of the treatment phase consisted of 125 patients; however, 5 participants had missing data from at least one questionnaire. Therefore, our final study population included 120 patients.

Sample characteristics

Table 1 shows the individual (e.g., age, occupational and marital status, personality, coping), and the biological and physiological variables (e.g., tumor characteristics, types of treatment received) of our population. Mean QOL scores were 83.7±13.4 for physical QOL, 78.5±23.3 for emotional QOL and 66.7±18.8 for global QOL.

Factors associated with physical, emotional and global QOL

Univariate analyses

Factors that were associated with each QOL dimension are presented in Table 2.

Multivariate analyses (Table 3)

Physical QOL was significantly lower in patients who had a more severe initial tumor stage or major depressive disorder, and in patients who used the behavioral disengagement coping strategy more frequently, but it was not associated with pain, existence of a personality disorder or hormone therapy. These factors explained 13.5% of the total variation in physical QOL.

Emotional QOL was significantly lower in patients who had higher pain levels, major depressive disorder, or a personality disorder, and in patients who used the self-blame coping strategy more frequently or the acceptance coping strategy less frequently, but it was not associated with age, initial tumor stage or hormone therapy. These factors explained 48.7% of the total variation in emotional QOL.

Global QOL was significantly lower in patients who had major depressive disorder, in patients who were not prescribed hormone therapy, in patients who used the self-blame coping strategy more frequently, and in patients who used the positive reframing coping strategy less frequently, but it was not associated with age, initial tumor stage, pain level or existence of a personality disorder. These factors explained 26% of the total variation in global QOL.

DISCUSSION

In this study, which was conducted in non-metastatic breast cancer patients at the end of their initial treatment phase (7 months after diagnosis), we found that QOL was associated much more to measures of symptoms (i.e., depression) and individual characteristics (i.e., age, personality, coping) than to biological variables (i.e., tumor severity and types of treatment received). We also found that even after adjustment for potential confounding factors, major depressive disorder, personality disorders and coping were three independent and important risk factors for lower QOL.

First, we demonstrated that QOL was more strongly associated to variables related to the individual's characteristics prior to the disease (i.e., age, personality) and to the manner in which this individual copes with the disease (i.e., coping, depression and pain) than to biological or physiological variables related to the severity of the cancer or to its treatment. Our results support the hypothesis that in non-metastatic breast cancer patients, the diagnosis of breast cancer (rather than the severity of this cancer) could reveal specific risk factors for lower QOL, including younger age (i.e., younger patients might experience higher personal and social distress due to their altered body image) [START_REF] Bloom | Multi-dimensional quality of life among long-term (5+ years) adult cancer survivors[END_REF], higher pain (i.e., these patients might be more susceptible to the fear of cancer recurrence and thus experience lower emotional QOL; higher pain could also be due to higher depression), existence of a psychological vulnerability prior to the diagnosis (i.e., patients with a personality disorder or with depression), and use of some coping strategies after diagnosis. Higher tumor severity could be a less important risk factor associated only with lower physical, but not with emotional nor global QOL [START_REF] Janz | Population-based study of the relationship of treatment and sociodemographics on quality of life for early stage breast cancer[END_REF][START_REF] Kroenke | Functional Impact of Breast Cancer by Age at Diagnosis[END_REF][START_REF] Burgess | Depression and anxiety in women with early breast cancer: five year observational cohort study[END_REF][START_REF] Reich | Depression, quality of life and breast cancer: a review of the literature[END_REF]. In addition, the subjective impact of the treatment rather than the types of treatment used might be risk factors for lower QOL.

Our second main result was that depression, personality disorders and coping were three independent risk factors for lower QOL. The fact that major depressive disorder was the variable associated with the strongest decrease in QOL on all three dimensions (decrease of 38.2 points on emotional QOL, 24.2 points on global QOL and 10.6 points on physical QOL) is in line with previous studies [START_REF] Brunault | Impact of late treatment-related radiotherapy toxicity, depression, and anxiety on quality of life in long-term breast cancer survivors[END_REF][START_REF] Ruo | Depressive symptoms and healthrelated quality of life: The heart and soul study[END_REF][START_REF] Ivanauskienė | Well-being of newly diagnosed women with breast cancer: which factors matter more?[END_REF]. Depression can have a direct (e.g., via a systematic cognitive bias regarding the treatment of information that leads to a negative self-assessment of health) or indirect (e.g., via a greater difficulty with acquiring new coping strategies that improve QOL) impact on QOL [START_REF] Brunault | Predictors of changes in physical, psychosocial, sexual quality of life, and comfort with food after obesity surgery: a 12month follow-up study[END_REF].

Existence of a personality disorder was another independent risk factor, but only for lower emotional QOL. To explain the previously demonstrated associations between personality dimensions (i.e., trait anxiety and neuroticism) and physical, emotional and global QOL [START_REF] Cousson-Gélie | Psychologie de la santé et cancers: quels apports et quelles perspectives?[END_REF][START_REF] Härtl | Quality of life, anxiety, and oncological factors: a follow-up study of breast cancer patients[END_REF], we can assume that the deterioration in global and physical QOL could be due to confounding factors (e.g., depression) while the deterioration in emotional QOL could be directly due to the personality disorder.

These results suggest that when a patient with a personality disorder reports low physical or global QOL, the personality disorder cannot explain this low QOL and it is important to screen for and treat other risk factors.

Interestingly, the coping strategies associated with lower QOL (i.e., low positive reframing, low acceptance, high self-blame and high behavioural disengagement) were mainly avoidant coping strategies (i.e., coping strategies that lead individuals to activities or mental states that prevent them from directly addressing stressful events) [START_REF] Bruchon-Schweitzer | Psychologie de la santé: modèles, concepts et méthodes[END_REF]. Although these strategies may be effective in the short term, if the stressor is uncontrollable, it may lead to lower psychological adjustment in the long term [START_REF] Bruchon-Schweitzer | Psychologie de la santé: modèles, concepts et méthodes[END_REF]. These strategies may also indicate patients who attempt to avoid the experience of grief, which is a natural and normal reaction to losses that are induced by cancer. Alternatively, the association between high QOL and both high positive reframing and high acceptance may also be explained by post-traumatic growth after a diagnosis of cancer (i.e., positive changes that are felt after a difficult life event) [START_REF] Morrill | The interaction of post-traumatic growth and post-traumatic stress symptoms in predicting depressive symptoms and quality of life[END_REF]. Our results support the idea that QOL is more strongly associated to the manner in which the patient actively copes with the disease (no matter the disease severity) and to the individual characteristics prior to the disease than to the severity of the disease or the types of treatment used.

This study had several limitations, including a small sample size (that did not allow us to study the association between QOL and other less prevalent psychiatric disorders), and the use of a self-administered questionnaire for personality disorders (that may have over-evaluated the prevalence of personality disorders), and the question whether our results are generalizable to other countries or cultures. Another limitation of our study was related to the timing of the assessment (end of the initial treatment phase): long-term studies could potentially identify different risk factors for lower QOL, such as the impact of various types of treatment. Our results are limited to non-metastatic breast cancer patients and to patients with similar levels of symptoms (i.e., pain, depression). Finally, we did not assessed other important risk factors for lower QOL (e.g., social support) nor other QOL dimensions (e.g., social QOL, cognitive functioning or difficulties in ability to work), and we did not use a generic QOL questionnaire to compare our population's QOL scores with the general population.

This study has several practical implications. Our results highlight the need for early screening of all the psychological factors that are associated with lower QOL in breast cancer patients, including major depressive disorder, personality disorders, and certain coping strategies (e.g., low positive reframing, low acceptance, high self-blame, high behavioral disengagement). Better screening and treatment for major depressive disorder is critical given that depressive disorders are highly prevalent in breast cancer patients [START_REF] Brunault | Impact of late treatment-related radiotherapy toxicity, depression, and anxiety on quality of life in long-term breast cancer survivors[END_REF][START_REF] Naaman | Status of psychological trials in breast cancer patients: a report of three meta-analyses[END_REF] and that these disorders are often underdiagnosed and untreated in this population, despite the existence of effective psychotherapeutic and pharmacologic treatments [START_REF] Brunault | Impact of late treatment-related radiotherapy toxicity, depression, and anxiety on quality of life in long-term breast cancer survivors[END_REF][START_REF] Dolbeault | Groupes psycho-éducationnels à destination de patients atteints de cancer: quels fondements, quels objectifs, quel format en pratique ?[END_REF]. Such interventions may not only improve QOL in these patients but also improve their tolerance and adherence to therapeutic interventions. An early screening program for personality disorders and coping styles may detect which patients are at higher risk for lower psychosocial outcomes. Subsequently, psychotherapeutic interventions may be proposed based on their personality and coping styles (e.g., therapies based on psycho-education with cognitive behavioral interventions such as group therapy, stress management techniques, assertiveness training, problem solving or cognitive restructuration) [START_REF] Hall | recent developments in our understanding of grief and bereavement[END_REF]. In all cases, therapy must be tailored to the individual person, relationship and circumstances that characterize a patient at a particular point in time as they grieve for specific losses [41].

CONCLUSION

This study demonstrated that in non-metastatic breast cancer, lower physical, emotional and global QOL scores were more strongly associated to variables related to the individual's psychological characteristics prior to the disease and to the manner in which this individual copes with the disease (e.g., major depressive disorder, personality disorders and use of coping strategies) than to biological or physiological variables (e.g., treatment types, cancer severity). Future studies should determine whether therapeutic interventions that are based on these psychological factors could improve QOL in breast cancer patients.

TABLES

Table 1 . Individual characteristics, biological and physiological variables, and symptom measures of the study population (n=120).

 1

	Mean(±SD)
	Number of patients(%)

Table 2 . Factors associated with physical, emotional and global QOL in univariate analyses.

 2

	Physical QOL	Emotional QOL	Global QOL
	Test	p	Test	p	Test	p

ACKNOWLEDGMENTS

We thank Vanessa Davy for translating the initial version of the manuscript into English. Funding: This work was supported by the French National Cancer Institute (grant number INCA05-IS/ESPOIR) with an additional funding from the Cancer League Committees (Ligue contre le Cancer) of the French departments of Indre-et-Loire and Vienne.

This work was supported by the French National Cancer Institute (grant number INCA05-IS/ESPOIR) with an additional funding from the Cancer League Committees (Ligue contre le Cancer) of the French departments of Indre-et-Loire and Vienne.

Conflict of interest disclosure:

The authors declare that they have no conflict of interest.

Conflict of interest disclosure:

The authors declare that they have no conflict of interest.


Paul Brunault
email: paul.brunault@univ-tours.fr

Pierre-Henri Ducluzeau

Céline Bourbao-Tournois

Irène Delbachian

Charles Couet

Christian Réveillère

Nicolas Ballon

Food addiction in bariatric surgery candidates: prevalence and risk factors

Keywords: bariatric surgery candidates: prevalence and risk factors Obesity, Bariatric surgery, Addictive Behaviors, Food Addiction, Quality of Life, Binge-Eating Disorder

INTRODUCTION

Bariatric surgery is an efficient treatment for severe obesity, associated with long-term weight loss and improvement in comorbidities and quality of life (QoL) [START_REF] Buchwald | Bariatric surgery: a systematic review and meta-analysis[END_REF][START_REF] Van Hout | Bariatric psychology, psychological aspects of weight loss surgery[END_REF]. Due to variations in postoperative outcomes [START_REF] Van Hout | Bariatric psychology, psychological aspects of weight loss surgery[END_REF], current research aims to determine, screen for and treat the factors associated with poor postoperative outcomes. Pre-and post-operative eating disorders and problematic eating behavior are important factors affecting postoperative QoL, comorbidities, and sometimes weight loss [START_REF] Van Hout | Bariatric psychology, psychological aspects of weight loss surgery[END_REF]. To date, studies of eating behavior in bariatric surgery patients have focused on Binge Eating Disorder (BED), the most prevalent form of eating disorder in obese patients, affecting from 5 to 50% of bariatric surgery candidates [START_REF] Van Hout | Bariatric psychology, psychological aspects of weight loss surgery[END_REF][START_REF] Niego | Binge eating in the bariatric surgery population: a review of the literature[END_REF]. BED is characterized by recurrent binge eating (i.e., consumption of unusually large amounts of food during a discrete period of time, while experiencing a sense of loss of control) without compensatory weight control behaviors [START_REF]Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition[END_REF].

To identify specific aspects of food addiction in patients exhibiting significant distress in relation to food, Gearhardt et al. adapted the DSM diagnostic criteria for substance dependence to addiction to certain foods, particularly highly processed foods high in added sugar, fat and salt [START_REF] Gearhardt | Food addiction: an examination of the diagnostic criteria for dependence[END_REF].

Food addiction describes the behavior of the subpopulation of patients who show substancedependence symptoms in their eating behavior (i.e., diminished control over food consumption; consumption despite adverse physical or psychological consequences; persistent desire or unsuccessful efforts to cut down or control food use; eating in larger amount or over longer period than intended; giving up important occupational or recreational activities because of food consumption; tolerance; withdrawal; significant distress in relation to food) [START_REF] Gearhardt | Food addiction: an examination of the diagnostic criteria for dependence[END_REF]. This definition is complementary to BED, making it possible to screen patients who experience significant distress in relation to food or loss of control over their eating behavior but do not meet the diagnostic criteria for BED (i.e., consumption of small or moderate amounts of food throughout the day; consumption of food over a long period of time; continuation of eating behavior despite physical or psychological problems). There is growing evidence for the clinical validity and utility of the concept of food addiction in obese patients: food addiction is highly prevalent in obese patients [START_REF] Meule | Five years of the Yale Food Addiction Scale: Taking stock and moving forward[END_REF] and sometimes associated with poorer weight loss [START_REF] Burmeister | Food addiction in adults seeking weight loss treatment. Implications for psychosocial health and weight loss[END_REF]. However, few studies have focused on bariatric surgery candidates, and there is a lack of studies examining the biological and psychological characteristics of food addiction in this population [START_REF] Meule | Five years of the Yale Food Addiction Scale: Taking stock and moving forward[END_REF].

This study aimed to assess the prevalence of food addiction in bariatric surgery candidates and to determine their biological (age, gender, BMI, duration and age of onset of obesity) and psychological (i.e., QoL, depression, binge eating) characteristics. We hypothesized that food addiction would be more prevalent in bariatric surgery candidates than in the overall population and associated with poor psychosocial outcome (i.e., low QoL and high levels of depression and binge eating).

MATERIALS AND METHODS

Participants and procedure

This cross-sectional study included all consecutive bariatric surgery candidates seen in the Nutrition Department of the University Hospital of Tours (France) and referred for preoperative psychiatric assessment between July 2013 and November 2015. Out of the initial population (n=204), 16 were excluded (10 did not understand the questionnaires and 6 gave incomplete data), leading to a final sample of 188 patients. Patients answered the questionnaires shortly before the systematic preoperative psychiatric consultation.

Measures

We collected data on demographics (age, gender, marital status), current body-mass index (BMI), previous maximal BMI, age of onset and duration of obesity.

We diagnosed current food addiction using the French version of the Yale Food Addiction Scale [START_REF] Gearhardt | Preliminary validation of the Yale Food Addiction Scale[END_REF][START_REF] Brunault | Validation of the French version of the Yale Food Addiction Scale: an examination of its factor structure, reliability, and construct validity in a non clinical sample[END_REF], that assesses the seven diagnostic criteria for food addiction based on the DSM-IV-TR diagnostic criteria for substance dependence as well as significant distress in relation to food. Food addiction was diagnosed when at least three of the seven symptoms were present during the previous 12 months and clinically significant impairment or distress was confirmed [START_REF] Gearhardt | Food addiction: an examination of the diagnostic criteria for dependence[END_REF].

We assessed physical, psycho-social and sexual QoL using the "Quality Of Life, Obesity and Dietetics" (QOLOD) rating scale [START_REF] Ziegler | Development and validation of a French obesityspecificquality of life questionnaire: Quality of Life, Obesity and Dietetics (QOLOD) rating scale[END_REF], which is a French tool derived from the "Impact of Weight on Quality Of Life Questionnaire", the first instrument specifically designed to assess QoL in obesity.

We assessed depression using the shortened Beck Depression Inventory, a widely used 13item self-rating scale (cut-off≥16) [START_REF] Beck | Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation[END_REF] used in bariatric surgery populations.

We assessed binge eating symptoms using the Binge Eating Scale, a 16-item self-report scale (cutoff≥18), which is a reliable screening tool for BED in obese patients [START_REF] Gormally | The assessment of binge eating severity among obese persons[END_REF].

Statistical analyses

Analyses were conducted using SPSS version 22 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.). We used mean comparison tests (Mann Whitney U test or Student"s test based on the assumption of normality of the variables) and chi-squared tests (we checked that theoretical frequencies were higher or equal to 5 for each cell and for each analysis) to compare patients with and without food addiction.

RESULTS

Descriptive data (Table 1)

Table 1 presents the characteristics of the overall population, including patients with and without food addiction. Mean age was 40.8 ± 12.6 years; 84% were female. Prevalence of current food addiction was 16.5% (significantly higher than the 8.7% prevalence observed in the overall population [START_REF] Brunault | Validation of the French version of the Yale Food Addiction Scale: an examination of its factor structure, reliability, and construct validity in a non clinical sample[END_REF]; p<.001). In our sample, the most prevalent food addiction symptoms were, by order of importance: persistent desire to control consumption of certain foods (93.1%), continuing to eat certain foods despite physical or psychological problems (40.4%), tolerance (38.8%), considerable time spent to obtain, use, or recover from the effects of certain foods (25%), significant distress in relation to these foods (22.9%), use of food in larger quantities or over longer periods than intended (18.6%), withdrawal (14.4%), and giving up important activities because of food (13.3%).

Comparison of patients with and without food addiction (Table 1)

Patients with food addiction were more frequently single than those without, but there was no difference in terms of age, gender, current BMI, previous maximal BMI, duration of obesity, or age of onset of obesity.

Patients with food addiction reported lower physical, psycho-social and sexual QoL, and higher prevalence and levels of depression and binge eating than those without addiction. Thirty-six percent of the patients with food addiction had no significant binge eating.

CONCLUSION

This study demonstrated that food addiction is very prevalent among bariatric surgery candidates (16.5%) and higher than in the overall population (8.7%), with a high prevalence rate for each food addiction criterion (especially "persistent desire to control consumption of certain foods", "continuing to eat certain foods despite physical or psychological problems" and "tolerance"). Patients with food addiction experienced lower QoL, and higher levels of depression and binge eating.

In view of the low QoL and the high comorbidity with psychiatric disorders in patients with food addiction, better screening and treatment of this condition is crucial during the preoperative period, as persistence of addiction could partly explain the persistence of psychiatric disorders. Given that it is generally easier to report one"s addiction symptoms than one"s psychiatric symptoms (i.e., psychiatric disorders are more stigmatized than addictions), screening for food addiction could also help identify this high-risk subpopulation (i.e., greater psychopathology and lower QoL).

We confirmed that binge eating and food addiction are distinct constructs (36% of patients with food addiction had no significant binge eating), demonstrating that the simultaneous assessment of BED and food addiction could improve our ability to identify patients who exhibit problematic eating behavior, compared to considering BED alone.

Finally, given that addictions share some specific risk factors, patients with food addiction may have a higher risk of switching to another addiction after surgery if not treated (we assume that this might explain the postoperative increase in alcohol addiction).

Our study"s limitations include the small sample size, the cross-sectional design, and the use of self-administered scales. Although we demonstrated that food addiction was associated with poorer preoperative psychosocial outcome in bariatric surgery candidates, future studies should determine its association with postoperative outcomes (could pre-or post-operative food addiction be a risk factor for lower weight loss, lower QoL, and/or higher prevalence of postoperative comorbidities?).

Food addiction is concept that might be of importance in bariatric surgery patients because of its prevalence and because of its potential association with long-term postoperative outcome. If confirmed, it would lead to novel management and treatment strategies that might improve postoperative outcome.

TABLES

Table 1 .

 1 Descriptive statistics of the overall population, and comparison of patients with and without food addiction.

		Overall	Patients	Patients with	p
		population	without food	food addiction
		(n=188)	addiction	(n=31)
			(n=157)
	Sociodemographic characteristics
	Age (years)	40.8 ± 12.3	40.6 ± 12.2	42.1 ± 12.7	0.54
	Gender (female)	84.0% (158)	82.8% (130)	90.3% (28)	0.30
	Marital status (married or in a relationship)*	69.1% (130)	72.6% (114)*	51.6% (16)*	<0.05
	Weight-related variables
	Weight (kg)	124.9 ± 24.9	126.0 ± 25.7	119.3 ± 19.7	0.25
	BMI (kg/m 2 )	46.0 ± 7.3	46.4 ± 7.6	43.9 ± 4.8	0.15
	Previous maximal BMI (kg/m 2 )	48.8 ± 7.9	49.3 ± 8.3	46.4 ± 5.2	0.14
	Age of onset of obesity (years)	17.7 ± 12.2	17.4 ± 12.3	19.6 ± 11.7	0.36
	Duration of obesity (years)	23.7 ± 11.3	24.0 ± 11.1	22.5 ± 12.6	0.52
	Food addiction (prevalence)	16.5%	-	-	-
	Food addiction symptoms (prevalence)
	Tolerance***	38.8% (73)	33.8% (53)*** 64.5% (20)***	<0.001
	Withdrawal***	14.4% (27)	7.6% (12)*** 45.4% (15)***	<0.001
	Food consumed in larger quantities or over a
	longer period than intended***	18.6% (35)	11.5% (18)*** 54.8% (17)***	<0.001
	Persistent desire or unsuccessful efforts to cut
	down or control consumption of certain foods	93.1% (175)	92.4% (145)	96.8% (30)	0.38
	Considerable time spent to obtain, consume,	25% (47)	17.8% (28)*** 61.3% (19)***	<0.001
	or recover from effects of food***
	Giving up important social, occupational, or
	recreational activities because of food	13.3% (25)	6.4% (10)*** 48.4% (15)***	<0.001
	consumption***
	Continuing to eat certain foods despite	40.4% (76)	33.1% (52)*** 77.4% (24)***	<0.001
	physical or psychological problem***
	Significant distress in relation to food***	22.9% (43)	7.6% (12)*** 100% (31)***	<0.001
	Quality of life
	Physical quality of life***	33.1 ± 9.2	34.4 ± 8.9***	26.7 ± 8.3***	<0.001
	Psychosocial quality of life***	34.7 ± 9.7	36.3 ± 26.1*** 26.1 ± 8.3***	<0.001
	Sexual quality of life***	13.7 ± 4.8	14.3 ± 4.6***	10.7 ± 4.8***	<0.001
	Depression
	Mean BDI score***	8.1 ± 6.1	6.9 ± 5.1***	14.1 ± 7.4***	<0.001
	Significant BDI score***	11.7% (22)	7.0% (11)*** 35.5% (11)***	<0.001
	Binge eating
	Mean BES score***	11.0 ± 7.9	9.0 ± 6.1	21.3 ± 8.1	<0.001
	Significant BES score***	17.0% (32)	7.6% (12)*** 64.5% (20)***	<0.001

Legends: Descriptive data are presented as mean ± standard deviation or percentage (number). We compared patients with and without food addiction using mean comparison tests

(

Mann Whitney U test or Student"s test) and chi-squared tests.*, ** and *** indicate variables significantly associated with food addiction diagnosis (* p<0.05; ** p<0.01; *** p<0.001). BMI: Body Mass Index; BDI: Beck Depression Inventory; BES: Binge Eating Scale.

This study was conducted without specific funding.

Conflict of Interest

We thank Elizabeth Yates for correcting the English version of the manuscript.