Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-24T00:42:01.915Z Has data issue: false hasContentIssue false

Binge eating among young adults: association with sociodemographic factors, nutritional intake, dietary n-6:n-3 ratio and impulsivity

Published online by Cambridge University Press:  14 January 2021

Maryse Khoury
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
Santa Chamsine
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
Camil Merheb
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
Edouard Arfoul
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
Maria Rached
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
Farah Younes
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
Nada El Osta
Affiliation:
Department of Removable Prosthodontics, Faculty of Dental Medicine, Saint-Joseph University of Beirut, Beirut, Lebanon Equipe d’accueil EA 4847, Centre de Recherche en Odontologie Clinique (CROC), Université Clermont Auvergne, Clermont-Ferrand, France Laboratoire de Recherche Cranio-Faciale, Unité de Santé Orale, Faculty of Dental Medicine, Saint-Joseph University of Beirut, Beirut, Lebanon
Sophie Laye
Affiliation:
Nutrition et Neurobiologie Intégrée, Inrae, Université de Bordeaux, Bordeaux, France
Carla Aoun
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon Department of Nutrition, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
Tatiana Papazian
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon Department of Nutrition, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
Lydia Rabbaa Khabbaz*
Affiliation:
Laboratoire de pharmacologie, pharmacie clinique et contrôle de qualité des médicaments, Faculty of Pharmacy, Saint-Joseph University of Beirut, Beirut, Lebanon
*
*Corresponding author: Professor Lydia Rabbaa Khabbaz, email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Binge eating behaviour (BE) is the major symptom of binge eating disorder (BED). This study aimed to compare the nutritional intake in the presence or absence of BE, with a particular focus on dietary n-6:n-3 ratio, to assess the association between BE and impulsivity and the mediating effect of BMI on this association. A total of 450 university students (age 18–28 years) participated. The self-administered questionnaires were a semi-quantitative FFQ and the UPPS-P Impulsive Behavior Scale and the binge eating scale. The average BE score was 11·6 (se 7·388), and 20 % of the total participants scored above the cut-off of 17, thus presenting BE with 95 % CI of 16·3, 23·7 %. Our study revealed that greater BMI, higher total energy intake, greater negative urgency and positive urgency scores were significantly associated with BE. Participants with high value of dietary n-6:n-3 ratio were 1·335 more at risk to present a BE compared with those with a lower value of this ratio (P = 0·017). The relationship between BE score and UPPS domains score was not mediated by the BMI. This is the first study reporting a link between high dietary n-6:n-3 ratio and BE as well as the fact that BE was linked to both, negative and positive urgencies, and that the association between BE and impulsivity was not mediated by BMI. These findings can help to deal more efficiently with people suffering from BE, a symptom that can precede the development of BED.

Type
Full Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

Binge eating disorder (BED) is the most common eating disorder (ED) and an important public health problem worldwide. It continues to be an under-recognised and undertreated condition. Patients rarely spontaneously disclose binge eating symptoms because of embarrassment or shame. Binge eating behaviour (BE) is known as a behavioural symptom of BED, and the difference between BE and BED is that the latter is recognised as a psychiatric disorder by the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR and DSM-5)(1).

BE is characterised by episodes of eating food more than a person would typically eat in a discrete period. In BED, these episodes occur at least once a week over 3 months(Reference Attia, Becker and Bryant-Waugh2,Reference Walsh and Sysko3) and are accompanied by a sense of loss of control and marked distress, in the absence of regular compensatory behaviours for weight loss(Reference Attia, Becker and Bryant-Waugh2,Reference Lydecker, Ivezaj and Grilo4) .

BE is a public concern with serious physical and mental health consequences(Reference Gan, Mohamad and Law5). It was found to be associated with depression, anxiety and substance abuse(Reference Lee-Winn, Mendelson and Mojtabai6), as well as compulsive behaviours, such as gambling(Reference Davison, Marshall-Fabien and Gondara7) and binge drinking(Reference Rolland, Naassila and Duffau8).

The lifetime prevalence of BE is estimated to be 4·9 % in women and 4·0 % in men in community samples(Reference Hudson, Hiripi and Pope9). While some reports state that BED is more common in women than men(Reference Hudson, Hiripi and Pope9,Reference Kessler, Berglund and Chiu10) , BE is reported to be comparable with similar rates of frequency and functional impairment among women and men(Reference Striegel, Bedrosian and Wang11Reference Grilo, Masheb and Brody13). The differences between BED and BE are related to the fact that many individuals who engage in BE may not meet the full criteria of BED. Obese individuals are at 2–3 times increased risk of having disordered eating, compared with normal-weight individuals(Reference Nagata, Garber and Tabler14). However, BE can also occur in healthy non-obese individuals(Reference Attia, Becker and Bryant-Waugh2,Reference Cotrufo, Barretta and Monteleone15) .

Impulsivity is a broad term referring to a disposition towards behaviours that are unduly hasty, risky, and that lead to undesirous outcomes(Reference Grant and Chamberlain16). Individuals with high impulsivity and reward sensitivity experience an addictive response to certain foods, particularly to high-sugar and high-fat foods(Reference Schulte, Grilo and Gearhardt17). One compelling premise places impulsivity at the roots of loss of control during eating episodes(Reference Loxton18). Higher trait impulsivity and poor motor inhibitory mechanisms have been highlighted in individuals suffering from emotional eating independently from their weight(Reference Aoun, Nassar and Soumi19).

Cross-sectional studies have found significant associations between impulsivity and ED that involve purging behaviour(Reference Fedorowicz, Falissard and Foulon20,Reference Lilenfeld, Adan and Kaye21) . Impulsivity has not, however, been examined extensively in association with binge eating without purging behaviour. One study reported a significant association between impulsivity and binge eating in a nationally representative sample of US adolescents(Reference Lee-Winn, Townsend and Reinblatt22). Others have shown that BE occurs in response to experiencing negative emotions(Reference Munsch, Meyer and Quartier23,Reference Stein, Kenardy and Wiseman24) and research suggests individuals with elevated impulsivity are more likely to binge eat because of their tendency to engage in reckless actions under distress(Reference Fischer, Smith and Cyders25,Reference Waxman26) . Furthermore, negative urgency was significantly linked to binge eating in preadolescents(Reference Combs, Pearson and Smith27,Reference Fischer, Settles and Collins28) .

However, according to some studies, obesity itself may be the underlying cause. Obese individuals show elevated impulsivity both on the Stop-Signal task and Barratt impulsiveness questionnaire, compared with normal-weight controls with similar levels of impulse control disorders(Reference Chamberlain, Derbyshire and Leppink29). Some studies suggest that cognitive deficits associated with BE are really primarily associated with obesity and that obese individuals with or without BE exhibit the same types and severity of cognitive deficits(Reference Wu, Giel and Skunde30Reference Davis, Patte and Curtis32).

All ED are characterised by alterations in food choice, influencing directly the quantity and quality of nutrients intake(Reference Al Massadi, Pardo and Roca-Rivada33). The bulk of published data relies on food choices from laboratory test meals, in which the selection is restricted to foods decided on by the researchers and not by the patients(Reference Segura-García, De Fazio and Sinopoli34). Furthermore, nutritional intake studies focused more on anorexia nervosa or bulimia nervosa and very few on BE. It was reported that BE was associated with snacking and eating sweets(Reference Pompili and Laghi35) and with unbalanced diets(Reference Ledoux, Choquet and Manfredi36), but researches are definitely needed on this subject. Among macro- and micronutrients, the role of PUFA in human health acquired growing interest in the last decades. The most important classes of PUFA are the n-3 fatty acids (FA), including α-linolenic acid, EPA and DHA, and n-6 FA, including linoleic and arachidonic acids. The beneficial properties of n-3 FA on inflammatory, cardiovascular and the nervous system are recognised by several investigations(Reference Lee, Gura and Kim37Reference Ruiz-León, Lapuente and Estruch40). In the brain, these agents modulate brain cell signalling, including dopaminergic and serotonergic pathways(Reference de la Presa Owens and Innis41Reference Bozzatello, Brignolo and De Grandi43). A well-balanced dietary n-6:n-3 ratio is fundamental for the development and functioning of the central nervous system(Reference Bozzatello, Rocca and Mantelli44). In recent years, the effects of PUFA, with an emphasis on EPA and DHA, were investigated in several diseases such as psychosis, major depression, bipolar disorder, anxiety disorders, obsessive–compulsive disorder, post-traumatic disorder, ED, attention deficit hyperactivity disorder, autism spectrum disorders, substance abuse and borderline personality disorder(Reference Bozzatello, Brignolo and De Grandi43,Reference Cadenhead, Minichino and Kelsven45Reference Polokowski, Shakil and Carmichael50) . Abnormal levels of n-3 and n-6 FA were observed in patients with anorexia nervosa(Reference Swenne and Rosling51,Reference Caspar-Bauguil, Montastier and Galinon52) , whereas in both BED and BE, there are no studies examining the level or intake of n-3 or n-6 FA from diet.

Hence, the objectives of this study were firstly to examine the sociodemographic variables associated with BE among young adults, secondly to compare the nutritional intake between BE individuals and those without BE, with a particular focus on dietary n-3 and n-6:n-3 ratio, and finally to assess the association between BE and impulsivity using the UPPS scale, as well as the mediating effect of BMI on this association.

Materials and methods

Ethical considerations

The protocol of the study was approved by the ethics committee of Saint-Joseph University of Beirut (reference USJ-2019-180). Informed written consent was acquired from all individuals prior to participating in the study.

Survey procedure, sampling and data collection

This cross-sectional study was based on a survey conducted by four trained research assistants among the students of Saint-Joseph University, one of the largest universities in Lebanon, with students from all districts and regions. The questionnaires were presented to the participants in the same non-randomised order. Data collection started on January and was carried on until June 2019 (6 months). Inclusion criteria were: students above 18 years old and not suffering from any cognitive or chronic illnesses. From the 550 students randomly selected from each faculty of Beirut campus (the largest campus of the university), 450 agreed to participate. The sample size was calculated according to the formula that takes into consideration the number of independent variables to be included in the model: n 50 + 8m (m is the number of explanatory variables: sociodemographic characteristics, nutritional variables and UPPS score); given that m = 16, a minimum of 178 subjects has to be included in the study(Reference Tabachnick and Fidell53).

The face-to-face interview was divided into three steps: first, a research assistant explained the study and asked for a written informed consent. Then, the assistant collected sociodemographic information by asking direct questions to the participant. Finally, and since the last part of the questionnaire was self-administered, it was handed over to the participant to be filled without assistance. The self-administered questionnaires are internationally validated and reliable, namely the FFQ(Reference Aoun, Bou Daher and El Osta54Reference Aoun, Papazian and Helou56), the UPPS scale for impulsivity and the BE questionnaire. The time required for completion of the questionnaires was about 20-25 min.

Participants and data collection

Sociodemographic variables of interest collected were age, sex, faculty, university, weight, height, living alone or not, tobacco smoking, alcohol and caffeine consumption. BMI was calculated using the formula: weight (kg)/height2 (m2). It was then analysed in two different ways: as a continuous variable and also categorised according to the WHO cut-off points (underweight <18·5, normal 18·5–24·9, overweight 25–29·9 and obese >30 kg/m2)(57). The crowding index representing the number of people living in the same house divided by the number of rooms in the house (excluding the kitchen and bathrooms) was also calculated since it could affect sleep(Reference Johnson, Drake and Joseph58) and it reflects the socio-economic status of the participants.

Study material

Impulsivity

The UPPS-P Impulsive Behavior Scale in its short version was used. It is a self-rated inventory with twenty items to measure five distinct personality pathways to impulsive behaviour(Reference Pikó and Pinczés59,Reference Geurten, Catale and Gay60) : negative urgency (four items), (lack of) perseverance (four items), (lack of) premeditation (four items), sensation seeking (four items) and positive urgency (four items). Items were rated on a four-point scale from Strongly Agree to Strongly Disagree. Average scores were calculated for each dimension. The UPPS has a good internal consistency as well as divergent and external validity. In this study, it showed a good Cronbach’s α of 0·776.

Binge eating behaviour

The binge eating scale is a sixteen-item self-report questionnaire designed to capture the behavioural (eight items, e.g. large amount of food consumed), as well as the cognitive and emotional (eight items, e.g. feeling out of control while eating, preoccupation with food and eating), features of objective binge eating in overweight and obese adults(Reference Gormally, Black and Daston61). For each item, respondents are asked to select one of three or four response options, coded zero to two or three, respectively. Individuals’ scores are summed and range from 0 to 46, with higher scores indicating more severe binge eating problems. Based on the binge eating scale total score, which ranges from 0 to 46, participants were categorised into three groups according to established severity cut-offs, which are: none (score < 17), mild-moderate (score of 18–26) and severe (score > 27)(Reference Beydoun, Fanelli Kuczmarski and Beydoun48). The binge eating scale was used as a screening measure to classify those with scores ≥17 as ‘binge-eaters’. Importantly, the binge eating scale was created before BED was officially recognised as a psychiatric diagnosis(1) and thus is not intended to detect the presence of this disorder. Rather, it has been suggested that this measure be used as a brief screening tool to identify the severity of BE in overweight and obese adults, to tailor obesity interventions and to track treatment outcomes(Reference Gormally, Black and Daston61,Reference Marcus, Wing and Hopkins62) . In this study, it showed a very good Cronbach’s α of 0·828.

Nutrient intake calculation

A semi-quantitative 150-item FFQ containing Middle Eastern foods and local meals, validated by our research team in a previous study, was administered to participants(Reference Aoun, Bou Daher and El Osta54,Reference Papazian, Hout and Sibai55) .

To help them quantify the exact amount and portion of foods consumed, standard measuring cups and spoons, plastic food models and local food photos in frequently consumed sizes were used during the interview. The FFQ was subdivided into twelve food groups with open-frequency categories, used in decreasing order: daily, weekly, monthly, all over the year and never. The weight in grams of each food was multiplied by its frequency of consumption and divided, for example, by 7, if it was consumed just once a week. Participants’ responses were then converted into average daily intake, in grams. The Nutrilog software (version 2.30) was used to analyse the food records of the FFQ, using databases from US Department of Agriculture, and n-6 and n-3 FA content of specific Lebanese food was derived from the American University of Beirut database.

Statistical analysis

The statistical analyses were carried out using SPSS software for Windows (version 24.0). The significance level was set at 0·05. The characteristics of the sample were described using the mean and standard deviation for continuous variables and percentage for categorical variables. The prevalence of BE was calculated with a 95 % CI. Kolmogorov–Smirnov tests were performed to assess the normality distribution of continuous variables. In the initial stages, the univariate analyses were carried out using the Student’s t test. Pearson was also used to evaluate the association between continuous variables. χ 2 independence tests and Fisher exact tests were performed to assess the relationship between categorical variables. Logistic regression analysis was used with categorised BE as the dependent variable. Independent variables that showed associations with a P value <0·200 in univariate analyses were candidates for the multivariate model, according to the Enter method. Collinearity among independent variables was also tested, and variables highly correlated were excluded from the model; it has already been suggested not to include two independent variables where there is a correlation of 0·7 or more(Reference Tabachnick and Fidell53). The explanatory variables included in the model were smoking, BMI, energy, MUFA as percentage of total fat, n-3, n-6:n-3 ratio, negative urgency, lack of premeditation and positive urgency.

The n-6:n-3 ratio was categorised into P66·6 (low, moderate and high) in order to further examine the association with BE. The cut-off values for n-6:n-3 ratio were chosen using the 66th percentiles; thus, the values >66·6th indicate a greater ratio. Logistic regression analysis was performed, and adjusted OR were obtained; these OR quantify better the strength of the associations between n-6:n-3 ratio and BE.

Mediation with regression analysis of BMI (as mediators M) on the relationship between impulsivity domain as the independent variable and BE as the dependent variable was conducted using a four-step approach, in which regression analyses and significance of the coefficients were examined at each step.

Results

Out of 550 students approached, 450 (81·8 %) agreed to participate. Sociodemographic characteristics of the participants are summarised in Table 1. Of the 450 participants included in the study, 54·9 % were women. Participants’ age varied between 18 and 28 years old. Mean BMI was 22·4 (sd 3·4) kg/m2, and 74·4 % of participants had a normal body weight.

Table 1. Participants’ sociodemographic factors

(Mean values and standard deviations; numbers and percentages)

Table 2 presents the nutritional intake of the participants as minimum, maximum, mean and standard deviation. Total energy was presented in kJ, macronutrients in percentage of total energy intake while sugar, n-3, n-6 in g and cholesterol in mg. In addition, n-3 and n-6 were presented as intake in g per 1000 total kcal (4184 total kJ). The ratio of n-6:n-3 was also provided as well as the distribution of participants above or below the tertile value for this ratio.

Table 2. Participants’ nutritional intake

(Mean values and standard deviations; numbers and percentages, n 450)

UPPS-P and BE scores are presented in Table 3. The average BE score was 11·6 (se 7·4), and 20 % of the total participants scored above the cut-off of 17 on the BE questionnaire, thus presenting BE with a 95 % CI of 16·3, 23·7.

Table 3. UPPS-P and binge eating behaviour (BE) scores of the participants

(Mean values and standard deviations; numbers and percentages, n 450)

Univariate analysis

Table 4 presents the associations between categorised BE score and quantitative sociodemographic and nutritional variables, while Table 5 shows the associations between BE and UPPS-P domains.

Table 4. Associations between categorised binge eating behaviour (BE) score and quantitative sociodemographic and nutritional variables

(Mean values and standard deviations; numbers and percentages, n 450)

* BE score dichotomised: None to minimal (0–17), presence of BE (≥18).

P values are significant.

Table 5. Association between categorised binge eating behaviour (BE) scores and UPPS-P domains

(Mean values and standard deviations; numbers and percentages)

* BE score dichotomised: None to minimal (0–17), presence of BE (≥18).

P values are significant.

Multivariate analysis

Independent variables highly correlated were not included in the same multivariate model (total UPPS and its domains), (Energy and Sugar), (n-6:n-3 and n-6 per 1000 kcal (4184 kJ)) (Table 6).

Table 6. Logistic regression of explanatory variables associated with categorised binge eating

(B-coefficients with their standard errors; odds ratios and 95 % confidence intervals)

* P66.6: tertile value for this ratio, with distribution of participants above or below this tertile value.

Significant.

Participants with greater BMI and higher total energy intake were more prone at risk to develop BE compared with those with lower BMI (P = 0·002 and 0·049, respectively).

Participants with greater negative (P = 0·011) and positive urgency score (P = 0·028) were more at risk to have BE compared with those with a lower score.

Participants with high value of dietary n-6:n-3 ratio were 1·335 more at risk to present BE compared with those with lower value of this ratio (P = 0·017).

The mediating effect of BMI (Mediator M) on the relationship between BE (dependent variable) and total UPPS score (independent variable) was tested with a four-step analysis (Table 7). The results showed that the relationship between BE score and UPPS domains score was not mediated by the BMI.

Table 7. Four-step analysis of the mediating effect of BMI (mediator M) on the relationship between binge eating behaviour (BE) (dependent variable) and UPPS score (independent variable)*

(B-coefficients with their standard errors; odds ratios and 95 % confidence intervals)

* Since, there were significant relationships from steps 1 through 3, the mediation was possible, and we proceed to step 4. At step 4, UPPS score was significant when BMI is controlled and this finding did not support mediation.

P values are significant.

Step 1: Single regression analysis with UPPS score predicting BE.

§ Step 2: Single regression analysis with UPPS score predicting BMI.

Step 3: Single regression analysis with BMI predicting BE.

Step 4: Multiple regression analysis with BMI and UPPS score predicting BE.

Discussion

Epidemiological BED research remains limited particularly across the Arab world, where overweight and obesity are primary public health issues(Reference Alhyas, McKay and Balasanthiran63,Reference Ng, Zaghloul and Ali64) .

Our results showed that BE occurrence among participants was 20 % and 4 % of those presented a severe form. BE reported frequency in the literature for the Western population is 1 % to 4·6 % with different tools and questionnaires(Reference Hudson, Hiripi and Pope9). Those frequencies seem higher in the Arab world, with 14·4 % previously reported in Lebanon(Reference Zeeni, Safieddine and Doumit65), moderate to severe binge eating reported by one-third of a sample of youths in the United Arab Emirates(Reference Schulte66) and up to 68·8 % in Saudi Arabia(Reference Rabie, Abo-El-Ez and Salah-El-Din67).

The majority of research conducted on ED has focused on the fact that body image concerns underlie the disorder. Furthermore, the consideration of internalised body image ideals is of particular relevance to populations from non-Western nations(Reference Fitzsimmons-Craft, Bardone-Cone and Kelly68,Reference Patmore, Meddaoui and Feldman69) . Sociocultural model of disordered eating suggests that the pressure to achieve Western ideals of thinness may engender body image concerns and disordered eating. This could be one explanation of the high prevalence of BED observed in Lebanon.

Furthermore, even though most of the studies show that BE is more frequent in women(Reference Kessler, Berglund and Chiu10), some recent studies have focused on an increase in ED amongst men, maybe due to social pressures and media influences regarding masculine and strong males’ ideal body and serve binging as a method for achieving this goal(Reference Storvoll, Strandbu and Wichstrøm70). In a recent study among Iranian college students, BE occurrence was not different between males and females(Reference Sahlan, Taravatrooy and Quick71). Our results (multivariate analysis) showed no statistical difference in BE occurrence between males and females, and this seems to highlight the fact that neither BE prevalence nor sex differences are similar between Western and Middle-eastern population owing to probable socio-cultural disparities.

Greater BMI and higher total energy intake were associated with BE in our study. Weight fluctuation is a common phenomenon in all subjects suffering from ED; however, because binge eaters do not engage in inappropriate compensatory behaviours such as purging, they seldom have a low body weight and mostly they tend to be of normal or higher than average weight. Besides, their disrupted abnormal relationship with food creates specific food rituals, mostly directed towards high energetic preferences. In an experiment conducted by Dalton et al. (Reference Dalton, Blundell and Finlayson72), among normal/lean and overweight/obese binge type and normal individual, those having higher BMI had a tendency to consume more energy content, with a net preference towards sugary and fatty food choices.

Long-chain PUFA have important physiological functions and play important structural and functional roles in the human brain and affect monoaminergic neurotransmission, dendritic arborisation, synapse formation and ion channel function(Reference Dyall73,Reference Müller, Reichel and Mühle74) . n-3 PUFA have been shown to possess anti-inflammatory and antioxidant(Reference Kiecolt-Glaser, Epel and Belury75) properties, while n-6 or n-6 PUFA are generally seen as pro-inflammatory, and a high n-6:n-3 ratio is thought to have adverse health effects(Reference Lin, Huang and Su47,Reference Simopoulos76) .

As a result of the worldwide increased consumption of ready-made and processed foods during the last decades, the dietary n-6:n-3 ratio is constantly growing(Reference Popkin and Gordon-Larsen77,Reference Drewnowski and Popkin78) .

Our results showed a dietary ratio of n-6:n-3 ratio close to 10:1, which is lower than contemporary Western diets, characterised by a ratio of about 15:1, reflecting deficient intake of n-3 FA and excessive intake of n-6 FA(Reference Simopoulos76). Published national Lebanese data had shown a net decline in fish consumption due to its high cost(Reference Nasreddine, Hwalla and Sibai79). In contrast, eating French fries, oil-fried chicken and meat is frequent because it is easier to cook and cheaper to prepare or buy than sophisticated meals, especially among our sample of university students. However, the ratio observed in our study is still lower than typical western diets, probably because Lebanese diet retains some characteristics of the Mediterranean diet, n-3 dietary sources, such as fish, walnuts and purslane.

While no differences were observed in the dietary intake of n-3 and n-6 between participants with or without BE, the ratio of dietary n-6:n-3 was significantly different and this is reported for the first time: participants with high value of dietary n-6:n-3 ratio were 1·335 more at risk to present BE compared with those with a lower value of this ratio.

It has been suggested that the ratio between n-6 and n-3 intakes might be a more important indicator of status than the absolute intake of either because it reflects the mutual competition of the two PUFA types(Reference Loef and Walach80). For example, even when n-3 intake is high, this might be counteracted by an even higher intake of n-6(Reference Rees, Miles and Banerjee81). Furthermore and even though it was speculated by some authors that n-6-docosapentaenoic acid is a buffer to prevent the possible catastrophic effects of DHA (n-3) depletion on brain and visual function(Reference Sinclair82), there is a consensus that an increased ratio of n-6:n-3 PUFA in the diet is an important risk factor of major chronic disorders(Reference Simopoulos83). In a cross-sectional study investigating the association between cognitive decline and dietary intake of PUFA(Reference González, Huerta and Fernández84), it was shown that the n-6:n-3 ratio was associated with cognitive decline. Another study also reported an association between dementia or cognitive decline and the ratio of n-6:n-3 FA(Reference Barberger-Gateau, Raffaitin and Letenneur85). In these studies, the dietary ratio of n-6:n-3 FA was not explicitly delineated. Vercambre et al. stated a mean ratio of n-6:n-3 FA of 9·4 and a positive association between increasing dietary levels n-6:n-3 and cognitive decline(Reference Vercambre, Boutron-Ruault and Ritchie86). In animal models, it was found that mice fed with a diet presenting n-6:n-3 ratio of 2·5 performed better in spatial learning and memory than animals fed with a diet presenting a n-6:n-3 ratio of 7·5(Reference Hooijmans, Rutters and Dederen87,Reference Oksman, Iivonen and Hogyes88) . As to the relation between dietary intakes of PUFA and brain concentrations, transgenic Alzheimer disease-mouse models that were fed with a diet presenting a 2·8 ratio of n-6:n-3, showed a lower brain concentration of arachidonic acid and a lower DHA:arachidonic acid ratio than mice fed with a n-6:n-3 dietary ratio of 10·4(Reference Arsenault, Julien and Tremblay89) and diets containing a low n-6:n-3 ratio resulted in relatively low ratios in total brain homogenates(Reference Calon, Lim and Yang90). These studies seem to indicate a direct relationship between dietary ratio of n-6:n-3 PUFA and brain fat composition.

Apart from memory and learning, an association between high dietary n-6:n-3 ratio and psychiatric illnesses was reported: the increased intake of n-6 essential FA and the reduced consumption of foods containing n-3 FA have been hypothesised to correlate with depression(Reference Simopoulos76). In anorexia nervosa, multiple studies have consistently demonstrated very profound distortions in serum and dietary PUFA profiles, compared with normal(Reference Scolnick91,Reference Ayton92) . As for BE, this is the first report of a link between high dietary n-6:n-3 ratio and BE occurrence. This important finding needs to be consolidated in future larger studies.

Finally, total impulsivity score and urgency domains were significantly associated with high BE scores in our study, and this association between BE and impulsivity was not mediated by BMI, as previously mentioned in some articles. Research suggests individuals with elevated impulsivity are more likely to binge eat(Reference Fischer, Smith and Cyders25,Reference Waxman26) . One study reported a significant association between impulsivity and binge eating in a nationally representative sample of US adolescents(Reference Lee-Winn, Townsend and Reinblatt22). Others have shown that BE occurs in response to experiencing negative emotions(Reference Munsch, Meyer and Quartier23,Reference Stein, Kenardy and Wiseman24) . Furthermore, negative urgency was significantly linked to binge eating in preadolescents(Reference Combs, Pearson and Smith27,Reference Fischer, Settles and Collins28) . Heightened negative urgency, or the tendency to behave impulsively when experiencing (and/or attempting to avoid) negative emotions, appears to characterise both problem eating and drinking behaviours(Reference Fischer, Smith and Cyders25,Reference Geurten, Catale and Gay60,Reference Bardone-Cone, Butler and Balk93Reference Verdejo-García, Bechara and Recknor97) . In our study, both negative and positive urgencies were higher among participants with greatest BE scores. This is the first report on the impact of positive urgency on BE showing that BE was not only linked to negative but also to positive urgency (impulsive behaviour when experiencing positive emotions). This finding, if confirmed in future investigations, will help professionals understand the full panel of emotions, negative and positive, that provoke binge eating episodes and deal more efficiently with this behaviour that was classically considered until now as a way of coping or avoiding negative emotions.

Several limitations should be considered. This was a young sample, but BE is also present in older age groups. The study was neither designed nor powered to examine the influence of co-morbidities on BE, an issue that warrants further study in future. For example, attention deficit hyperactivity disorder is common in BE and has interesting overlap neuro-biologically (including in terms of pharmacotherapy)(Reference Cortese, Bernardina and Mouren98). FFQ is a limitation in gathering data on foods consumed. An additional limitation is that the blood measures of n-6 and n-3 FA are the gold standard for estimating intakes compared with estimations based on FFQ’s and the correlation between n-6 and 3 FA in foods shows weak relationships with blood levels, especially n-3 levels. This is another limitation which must be acknowledged. Finally, the current findings may not generalise to clinical settings, since the study recruited from the university. Notwithstanding these limitations, several findings in this study are of utmost importance: this is the first study reporting a link between high dietary n-6:n-3 ratio and BE occurrence as well as the fact that BE was not only linked to negative but also to positive urgency and that the association between BE and impulsivity was not mediated by BMI. These important findings, consolidated in future larger studies, can help professionals understand the importance of a balanced dietary n-6:n-3 ratio as well as the full panel of emotions, negative and positive, that provoke binge eating episodes and deal more efficiently with this problem.

Acknowledgements

We would like to thank the participants as well as Dr Maroun Saber and Dr Joelle Najem for their help.

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

Contributions of the authors were: L. R. K.: conceptualisation, project administration, supervision and writing. M. K., S. C., C. M., E. A. and M. R.: acquisition of data, revising the article and final approval. S. L.: revising the article and final approval. N. E. O.: design of the study, analysis and interpretation, revising the article and final approval. T. P.: conception and design, revising the article and final approval.

All data are made available by authors upon request.

All authors approved the manuscript and approved submission for publication.

The authors declare that they have no conflicts of interest.

References

American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders, 5th ed. American Psychiatric Association. https://psychiatryonline.org/doi/book/10.1176/appi.books.9780890425596 (accessed February 2012).CrossRefGoogle Scholar
Attia, E, Becker, AE, Bryant-Waugh, R, et al. (2013) Feeding and eating disorders in DSM-5. Am J Psychiatry 170, 12371239.10.1176/appi.ajp.2013.13030326CrossRefGoogle ScholarPubMed
Walsh, BT & Sysko, R (2009) Broad Categories for the Diagnosis of Eating Disorders (BCD-ED): an alternative system for classification. Int J Eat Disord 42, 754764.10.1002/eat.20722CrossRefGoogle Scholar
Lydecker, JA, Ivezaj, V & Grilo, CM (2020) Testing the validity and clinical utility of the severity specifiers for binge-eating disorder for predicting treatment outcomes. J Consult Clin Psychol 88, 172178.10.1037/ccp0000464CrossRefGoogle ScholarPubMed
Gan, WY, Mohamad, N & Law, LS (2018) Factors associated with binge eating behavior among Malaysian adolescents. Nutrients 10, 66.10.3390/nu10010066CrossRefGoogle ScholarPubMed
Lee-Winn, A, Mendelson, T & Mojtabai, R (2014) Racial/ethnic disparities in binge eating: disorder prevalence, symptom presentation, and help-seeking among Asian Americans and non-Latino Whites. Am J Public Health 104, 12631265.10.2105/AJPH.2014.301932CrossRefGoogle ScholarPubMed
Davison, KM, Marshall-Fabien, GL & Gondara, L (2014) Sex differences and eating disorder risk among psychiatric conditions, compulsive behaviors and substance use in a screened Canadian national sample. Gen Hosp Psychiatry 36, 411414.10.1016/j.genhosppsych.2014.04.001CrossRefGoogle Scholar
Rolland, B, Naassila, M, Duffau, C, et al. (2017) Binge eating, but not other disordered eating symptoms, is a significant contributor of binge drinking severity: findings from a cross-sectional study among French students. Front Psychol 8, 1878.CrossRefGoogle Scholar
Hudson, JI, Hiripi, E, Pope, HG, et al. (2007) The prevalence and correlates of eating disorders in the National Comorbidity Survey Replication. Biol Psychiatry 61, 348358.10.1016/j.biopsych.2006.03.040CrossRefGoogle ScholarPubMed
Kessler, RC, Berglund, PA, Chiu, WT, et al. (2013) The prevalence and correlates of binge eating disorder in the World Health Organization World Mental Health Surveys. Biol Psychiatry 73, 904914.10.1016/j.biopsych.2012.11.020CrossRefGoogle ScholarPubMed
Striegel, RH, Bedrosian, R, Wang, C, et al. (2012) Why men should be included in research on binge eating: results from a comparison of psychosocial impairment in men and women. Int J Eat Disord 45, 233240.CrossRefGoogle ScholarPubMed
Barry, DT, Grilo, CM & Masheb, RM (2002) Gender differences in patients with binge eating disorder. Int J Eat Disord 31, 6370.10.1002/eat.1112CrossRefGoogle ScholarPubMed
Grilo, CM, Masheb, RM, Brody, M, et al. (2005) Binge eating and self-esteem predict body image dissatisfaction among obese men and women seeking bariatric surgery. Int J Eat Disord 37, 347351.10.1002/eat.20130CrossRefGoogle ScholarPubMed
Nagata, JM, Garber, AK, Tabler, JL, et al. (2018) Prevalence and correlates of disordered eating behaviors among young adults with overweight or obesity. J Gen Intern Med 33, 13371343.10.1007/s11606-018-4465-zCrossRefGoogle ScholarPubMed
Cotrufo, P, Barretta, V, Monteleone, P, et al. (1998) Full-syndrome, partial-syndrome and subclinical eating disorders: an epidemiological study of female students in Southern Italy. Acta Psychiatr Scand 98, 112115.10.1111/j.1600-0447.1998.tb10051.xCrossRefGoogle ScholarPubMed
Grant, JE & Chamberlain, SR (2014) Impulsive action and impulsive choice across substance and behavioral addictions: cause or consequence? Addic Behav 39, 16321639.10.1016/j.addbeh.2014.04.022CrossRefGoogle ScholarPubMed
Schulte, EM, Grilo, CM & Gearhardt, AN (2016) Shared and unique mechanisms underlying binge eating disorder and addictive disorders. Clin Psychol Rev 44, 125139.10.1016/j.cpr.2016.02.001CrossRefGoogle ScholarPubMed
Loxton, NJ (2018) The role of reward sensitivity and impulsivity in overeating and food addiction. Curr Addict Rep 5, 212222.10.1007/s40429-018-0206-yCrossRefGoogle Scholar
Aoun, C, Nassar, L, Soumi, S, et al. (2019) The cognitive, behavioral, and emotional aspects of eating habits and association with impulsivity, chronotype, anxiety, and depression: a cross-sectional study. Front Behav Neurosci 13, 204.10.3389/fnbeh.2019.00204CrossRefGoogle ScholarPubMed
Fedorowicz, VJ, Falissard, B, Foulon, C, et al. (2007) Factors associated with suicidal behaviors in a large French sample of inpatients with eating disorders. Int J Eat Disord 40, 589595.10.1002/eat.20415CrossRefGoogle Scholar
Lilenfeld, LRR (2011) Personality and temperament. In Behavioral Neurobiology of Eating Disorders, pp. 316 [Adan, RAH and Kaye, WH, editors]. New York: Springer-Verlag Publishing.Google Scholar
Lee-Winn, AE, Townsend, L, Reinblatt, SP, et al. (2016) Associations of neuroticism and impulsivity with binge eating in a nationally representative sample of adolescents in the United States. Personal Individ Differ 90, 6672.CrossRefGoogle Scholar
Munsch, S, Meyer, AH, Quartier, V, et al. (2012) Binge eating in binge eating disorder: a breakdown of emotion regulatory process? Psychiatry Res 195, 118124.CrossRefGoogle ScholarPubMed
Stein, RI, Kenardy, J, Wiseman, CV, et al. (2007) What’s driving the binge in binge eating disorder? A prospective examination of precursors and consequences. Int J Eat Disord 40, 195203.10.1002/eat.20352CrossRefGoogle ScholarPubMed
Fischer, S, Smith, GT & Cyders, MA (2008) Another look at impulsivity: a meta-analytic review comparing specific dispositions to rash action in their relationship to bulimic symptoms. Clin Psychol Rev 28, 14131425.CrossRefGoogle Scholar
Waxman, SE (2009) A systematic review of impulsivity in eating disorders. Eur Eat Disord Rev 17, 408425.10.1002/erv.952CrossRefGoogle ScholarPubMed
Combs, JL, Pearson, CM & Smith, GT (2011) A risk model for preadolescent disordered eating. Int J Eat Disord 44, 596604.10.1002/eat.20851CrossRefGoogle ScholarPubMed
Fischer, S, Settles, R, Collins, B, et al. (2012) The role of negative urgency and expectancies in problem drinking and disordered eating: testing a model of comorbidity in pathological and at-risk samples. Psychol Addict Behav 26, 112123.10.1037/a0023460CrossRefGoogle Scholar
Chamberlain, SR, Derbyshire, KL, Leppink, E, et al. (2015) Obesity and dissociable forms of impulsivity in young adults. CNS Spectr 20, 500507.10.1017/S1092852914000625CrossRefGoogle ScholarPubMed
Wu, M, Giel, KE, Skunde, M, et al. (2013) Inhibitory control and decision making under risk in bulimia nervosa and binge-eating disorder. Int J Eat Disord 46, 721728.10.1002/eat.22143CrossRefGoogle ScholarPubMed
Galioto, R, Spitznagel, MB, Strain, G, et al. (2012) Cognitive function in morbidly obese individuals with and without binge eating disorder. Compr Psychiatry 53, 490495.CrossRefGoogle ScholarPubMed
Davis, C, Patte, K, Curtis, C, et al. (2010) Immediate pleasures and future consequences. A neuropsychological study of binge eating and obesity. Appetite 54, 208213.10.1016/j.appet.2009.11.002CrossRefGoogle ScholarPubMed
Al Massadi, O, Pardo, M, Roca-Rivada, A, et al. (2010) Macronutrients act directly on the stomach to regulate gastric ghrelin release. J Endocrinol Invest 33, 599602.CrossRefGoogle ScholarPubMed
Segura-García, C, De Fazio, P, Sinopoli, F, et al. (2014) Food choice in disorders of eating behavior: correlations with the psychopathological aspects of the diseases. Compr Psychiatry 55, 12031211.10.1016/j.comppsych.2014.02.013CrossRefGoogle ScholarPubMed
Pompili, S & Laghi, F (2019) Binge eating and binge drinking among adolescents: the role of drinking and eating motives. J Health Psychol 24, 15051516.10.1177/1359105317713359CrossRefGoogle ScholarPubMed
Ledoux, S, Choquet, M & Manfredi, R (1993) Associated factors for self-reported binge eating among male and female adolescents. J Adolesc 16, 7591.10.1006/jado.1993.1006CrossRefGoogle ScholarPubMed
Lee, S, Gura, KM, Kim, S, et al. (2006) Current clinical applications of omega-6 and omega-3 fatty acids. Nutr Clin Pract 21, 323341.10.1177/0115426506021004323CrossRefGoogle ScholarPubMed
Pusceddu, MM, Nolan, YM, Green, HF, et al. (2016) The omega-3 polyunsaturated fatty acid docosahexaenoic acid (DHA) reverses corticosterone-induced changes in cortical neurons. Int J Neuropsychopharmacol 19, pyv130.Google ScholarPubMed
Calder, PC, Bosco, N, Bourdet-Sicard, R, et al. (2017) Health relevance of the modification of low grade inflammation in ageing (inflammageing) and the role of nutrition. Ageing Res Rev 40, 95119.10.1016/j.arr.2017.09.001CrossRefGoogle ScholarPubMed
Ruiz-León, AM, Lapuente, M, Estruch, R, et al. (2019) Clinical advances in immunonutrition and atherosclerosis: a review. Front Immunol 10, 837.10.3389/fimmu.2019.00837CrossRefGoogle ScholarPubMed
de la Presa Owens, S & Innis, SM (1999) Docosahexaenoic and arachidonic acid prevent a decrease in dopaminergic and serotoninergic neurotransmitters in frontal cortex caused by a linoleic and alpha-linolenic acid deficient diet in formula-fed piglets. J Nutr 129, 20882093.CrossRefGoogle ScholarPubMed
Sinn, N, Milte, C & Howe, PRC (2010) Oiling the brain: a review of randomized controlled trials of omega-3 fatty acids in psychopathology across the lifespan. Nutrients 2, 128170.CrossRefGoogle ScholarPubMed
Bozzatello, P, Brignolo, E, De Grandi, E, et al. (2016) Supplementation with omega-3 fatty acids in psychiatric disorders: a review of literature data. J Clin Med 5, 67.10.3390/jcm5080067CrossRefGoogle ScholarPubMed
Bozzatello, P, Rocca, P, Mantelli, E, et al. (2019) Polyunsaturated fatty acids: what is their role in treatment of psychiatric disorders? Int J Mol Sci 20, 5257.CrossRefGoogle ScholarPubMed
Cadenhead, KS, Minichino, A, Kelsven, S, et al. (2019) Metabolic abnormalities and low dietary omega 3 are associated with symptom severity and worse functioning prior to the onset of psychosis: findings from the North American Prodrome Longitudinal Studies Consortium. Schizophr Res 204, 96103.10.1016/j.schres.2018.09.022CrossRefGoogle ScholarPubMed
Sethom, MM, Fares, S, Bouaziz, N, et al. (2010) Polyunsaturated fatty acids deficits are associated with psychotic state and negative symptoms in patients with schizophrenia. Prostaglandins Leukot Essent Fatty Acids 83, 131136.10.1016/j.plefa.2010.07.001CrossRefGoogle ScholarPubMed
Lin, P-Y, Huang, S-Y & Su, K-P (2010) A meta-analytic review of polyunsaturated fatty acid compositions in patients with depression. Biol Psychiatry 68, 140147.10.1016/j.biopsych.2010.03.018CrossRefGoogle ScholarPubMed
Beydoun, MA, Fanelli Kuczmarski, MT, Beydoun, HA, et al. (2013) ω-3 Fatty acid intakes are inversely related to elevated depressive symptoms among United States women. J Nutr 143, 17431752.10.3945/jn.113.179119CrossRefGoogle ScholarPubMed
Rutkofsky, IH, Khan, AS, Sahito, S, et al. (2017) The psychoneuroimmunological role of omega-3 polyunsaturated fatty acids in major depressive disorder and bipolar disorder. Adv Mind Body Med 31, 816.Google ScholarPubMed
Polokowski, AR, Shakil, H, Carmichael, CL, et al. (2020) Omega-3 fatty acids and anxiety: a systematic review of the possible mechanisms at play. Nutr Neurosci 23, 494504.10.1080/1028415X.2018.1525092CrossRefGoogle ScholarPubMed
Swenne, I & Rosling, A (2012) Omega-3 essential fatty acid status is improved during nutritional rehabilitation of adolescent girls with eating disorders and weight loss. Acta Paediatr 101, 858861.10.1111/j.1651-2227.2012.02684.xCrossRefGoogle ScholarPubMed
Caspar-Bauguil, S, Montastier, E, Galinon, F, et al. (2012) Anorexia nervosa patients display a deficit in membrane long chain poly-unsaturated fatty acids. Clin Nutr 31, 386390.10.1016/j.clnu.2011.11.015CrossRefGoogle ScholarPubMed
Tabachnick, BG & Fidell, LS (2019) Using Multivariate Statistics, 7th ed. Boston, MA: Pearson.Google Scholar
Aoun, C, Bou Daher, R, El Osta, N, et al. (2019) Reproducibility and relative validity of a food frequency questionnaire to assess dietary intake of adults living in a Mediterranean country. PLOS ONE 14, e0218541.10.1371/journal.pone.0218541CrossRefGoogle Scholar
Papazian, T, Hout, H, Sibai, D, et al. (2016) Development, reproducibility and validity of a food frequency questionnaire among pregnant women adherent to the Mediterranean dietary pattern. Clin Nutr 35, 15501556.10.1016/j.clnu.2016.04.015CrossRefGoogle ScholarPubMed
Aoun, C, Papazian, T, Helou, K, et al. (2019) Comparison of five international indices of adherence to the Mediterranean diet among healthy adults: similarities and differences. Nutr Res Pract 13, 333343.CrossRefGoogle ScholarPubMed
WHO (1993) Physical Status: The Use of and Interpretation of Anthropometry, Report of a WHO Expert Committee. Technical Report Series no. 854. Geneva: WHO.Google Scholar
Johnson, DA, Drake, C & Joseph, CLM (2015) Influence of neighbourhood-level crowding on sleep-disordered breathing severity: mediation by body size. J Sleep Res 24, 559565.10.1111/jsr.12305CrossRefGoogle ScholarPubMed
Pikó, B & Pinczés, T (2014) Impulsivity-aggression-depression: study of adolescents’ problem behavior in light of their personality traits. Psychiatr Hung Magy Pszichiatriai Tarsasag Tudomanyos Folyoirata 29, 4855.Google Scholar
Geurten, M, Catale, C, Gay, P, et al. (2018) Measuring impulsivity in children: adaptation and validation of a short version of the UPPS-P impulsive behaviors scale in children and investigation of its links with ADHD. J Atten Disord 25, 105114.CrossRefGoogle Scholar
Gormally, J, Black, S, Daston, S, et al. (1982) The assessment of binge eating severity among obese persons. Addict Behav 7, 4755.CrossRefGoogle ScholarPubMed
Marcus, MD, Wing, RR & Hopkins, J (1988) Obese binge eaters: affect, cognitions, and response to behavioural weight control. J Consult Clin Psychol 56, 433439.10.1037/0022-006X.56.3.433CrossRefGoogle ScholarPubMed
Alhyas, L, McKay, A, Balasanthiran, A, et al. (2011) Prevalences of overweight, obesity, hyperglycaemia, hypertension and dyslipidaemia in the Gulf: systematic review. JRSM Short Rep 2, 55.10.1258/shorts.2011.011019CrossRefGoogle ScholarPubMed
Ng, SW, Zaghloul, S, Ali, HI, et al. (2011) The prevalence and trends of overweight, obesity and nutrition-related non-communicable diseases in the Arabian Gulf States. Obes Rev Off J Int Assoc Study Obes 12, 113.10.1111/j.1467-789X.2010.00750.xCrossRefGoogle ScholarPubMed
Zeeni, N, Safieddine, H & Doumit, R (2017) Eating disorders in Lebanon: directions for public health action. Commun Ment Health J 53, 117125.CrossRefGoogle ScholarPubMed
Schulte, SJ (2016) Predictors of binge eating in male and female youths in the United Arab Emirates. Appetite 105, 312319.10.1016/j.appet.2016.06.004CrossRefGoogle ScholarPubMed
Rabie, M, Abo-El-Ez, N & Salah-El-Din, M (2010) Anxiety and social anxiety symptoms among overweight females seeking treatment for obesity. Curr Psychiatry 17, 1320.Google Scholar
Fitzsimmons-Craft, EE, Bardone-Cone, AM & Kelly, KA (2011) Objectified body consciousness in relation to recovery from an eating disorder. Eat Behav 12, 302308.10.1016/j.eatbeh.2011.09.001CrossRefGoogle ScholarPubMed
Patmore, J, Meddaoui, B & Feldman, H (2019) Cultural considerations for treating Hispanic patients with eating disorders: a case study illustrating the effectiveness of CBT in reducing bulimia nervosa symptoms in a Latina patient. J Clin Psychol 75, 20062021.CrossRefGoogle Scholar
Storvoll, EE, Strandbu, A & Wichstrøm, L (2005) A cross-sectional study of changes in Norwegian adolescents’ body image from 1992 to 2002. Body Image 2, 518.CrossRefGoogle ScholarPubMed
Sahlan, RN, Taravatrooy, F, Quick, V, et al. (2020) Eating-disordered behavior among male and female college students in Iran. Eat Behav 37, 101378.10.1016/j.eatbeh.2020.101378CrossRefGoogle ScholarPubMed
Dalton, M, Blundell, J & Finlayson, G (2013) Effect of BMI and binge eating on food reward and energy intake: further evidence for a binge eating subtype of obesity. Obes Facts 6, 348–59.10.1159/000354599CrossRefGoogle ScholarPubMed
Dyall, SC (2015) Long-chain omega-3 fatty acids and the brain: a review of the independent and shared effects of EPA, DPA and DHA. Front Aging Neurosci 7, 52.10.3389/fnagi.2015.00052CrossRefGoogle ScholarPubMed
Müller, CP, Reichel, M, Mühle, C, et al. (2015) Brain membrane lipids in major depression and anxiety disorders. Biochim Biophys Acta 1851, 10521065.10.1016/j.bbalip.2014.12.014CrossRefGoogle ScholarPubMed
Kiecolt-Glaser, JK, Epel, ES, Belury, MA, et al. (2013) Omega-3 fatty acids, oxidative stress, and leukocyte telomere length: a randomized controlled trial. Brain Behav Immun 28, 1624.10.1016/j.bbi.2012.09.004CrossRefGoogle ScholarPubMed
Simopoulos, AP (2011) Evolutionary aspects of diet: the omega-6/omega-3 ratio and the brain. Mol Neurobiol 44, 203215.10.1007/s12035-010-8162-0CrossRefGoogle ScholarPubMed
Popkin, BM & Gordon-Larsen, P (2004) The nutrition transition: worldwide obesity dynamics and their determinants. Int J Obes 28, S2S9.CrossRefGoogle ScholarPubMed
Drewnowski, A & Popkin, BM (1997) The nutrition transition: new trends in the global diet. Nutr Rev 55, 3143.10.1111/j.1753-4887.1997.tb01593.xCrossRefGoogle ScholarPubMed
Nasreddine, L, Hwalla, N, Sibai, A, et al. (2006) Food consumption patterns in an adult urban population in Beirut, Lebanon. Public Health Nutr 9, 194203.10.1079/PHN2005855CrossRefGoogle Scholar
Loef, M & Walach, H (2013) The omega-6/omega-3 ratio and dementia or cognitive decline: a systematic review on human studies and biological evidence. J Nutr Gerontol Geriatr 32, 123.CrossRefGoogle ScholarPubMed
Rees, D, Miles, EA, Banerjee, T, et al. (2006) Dose-related effects of eicosapentaenoic acid on innate immune function in healthy humans: a comparison of young and older men. Am J Clin Nutr 83, 331342.10.1093/ajcn/83.2.331CrossRefGoogle Scholar
Sinclair, AJ (2019) Docosahexaenoic acid and the brain- what is its role? Asia Pac J Clin Nutr 28, 675688.Google ScholarPubMed
Simopoulos, AP (2008) The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases: Exp Biol Med 233, 674688.CrossRefGoogle ScholarPubMed
González, S, Huerta, JM, Fernández, S, et al. (2010) The relationship between dietary lipids and cognitive performance in an elderly population. Int J Food Sci Nutr 61, 217225.10.3109/09637480903348098CrossRefGoogle Scholar
Barberger-Gateau, P, Raffaitin, C, Letenneur, L, et al. (2007) Dietary patterns and risk of dementia: the three-city cohort study. Neurology 69, 19211930.CrossRefGoogle ScholarPubMed
Vercambre, M-N, Boutron-Ruault, M-C, Ritchie, K, et al. (2009) Long-term association of food and nutrient intakes with cognitive and functional decline: a 13-year follow-up study of elderly French women. Br J Nutr 102, 419427.10.1017/S0007114508201959CrossRefGoogle ScholarPubMed
Hooijmans, CR, Rutters, F, Dederen, PJ, et al. (2007) Changes in cerebral blood volume and amyloid pathology in aged Alzheimer APP/PS1 mice on a docosahexaenoic acid (DHA) diet or cholesterol enriched Typical Western Diet (TWD). Neurobiol Dis 28, 1629.10.1016/j.nbd.2007.06.007CrossRefGoogle Scholar
Oksman, M, Iivonen, H, Hogyes, E, et al. (2006) Impact of different saturated fatty acid, polyunsaturated fatty acid and cholesterol containing diets on beta-amyloid accumulation in APP/PS1 transgenic mice. Neurobiol Dis 23, 563572.10.1016/j.nbd.2006.04.013CrossRefGoogle ScholarPubMed
Arsenault, D, Julien, C, Tremblay, C, et al. (2011) DHA improves cognition and prevents dysfunction of entorhinal cortex neurons in 3xTg-AD mice. PLOS ONE 6, e17397.CrossRefGoogle ScholarPubMed
Calon, F, Lim, GP, Yang, F, et al. (2004) Docosahexaenoic acid protects from dendritic pathology in an Alzheimer’s disease mouse model. Neuron 43, 633645.CrossRefGoogle Scholar
Scolnick, B (2018) Hypothesis: clues from mammalian hibernation for treating patients with anorexia nervosa. Psychology 9, 2159.Google ScholarPubMed
Ayton, AK (2004) Dietary polyunsaturated fatty acids and anorexia nervosa: is there a link? Nutr Neurosci 7, 112.CrossRefGoogle ScholarPubMed
Bardone-Cone, AM, Butler, RM, Balk, MR, et al. (2016) Dimensions of impulsivity in relation to eating disorder recovery. Int J Eat Disord 49, 10271031.10.1002/eat.22579CrossRefGoogle ScholarPubMed
Fischer, S, Anderson, KG & Smith, GT (2004) Coping with distress by eating or drinking: role of trait urgency and expectancies. Psychol Addict Behav 18, 269274.CrossRefGoogle ScholarPubMed
Cyders, MA (2011) Impulsivity and the sexes: measurement and structural invariance of the UPPS-P Impulsive Behavior Scale. Assessment 20, 8697.Google ScholarPubMed
Fischer, S, Smith, GT, Annus, A, et al. (2007) The relationship of neuroticism and urgency to negative consequences of alcohol use in women with bulimic symptoms. Personal Individ Differ 43, 11991209.10.1016/j.paid.2007.03.011CrossRefGoogle Scholar
Verdejo-García, A, Bechara, A, Recknor, EC, et al. (2007) Negative emotion-driven impulsivity predicts substance dependence problems. Drug Alcohol Depend 91, 213219.CrossRefGoogle ScholarPubMed
Cortese, S, Bernardina, BD & Mouren, M-C (2007) Attention-deficit/hyperactivity disorder (ADHD) and binge eating. Nutr Rev 65, 404411.10.1111/j.1753-4887.2007.tb00318.xCrossRefGoogle ScholarPubMed
Figure 0

Table 1. Participants’ sociodemographic factors(Mean values and standard deviations; numbers and percentages)

Figure 1

Table 2. Participants’ nutritional intake(Mean values and standard deviations; numbers and percentages, n 450)

Figure 2

Table 3. UPPS-P and binge eating behaviour (BE) scores of the participants(Mean values and standard deviations; numbers and percentages, n 450)

Figure 3

Table 4. Associations between categorised binge eating behaviour (BE) score and quantitative sociodemographic and nutritional variables(Mean values and standard deviations; numbers and percentages, n 450)

Figure 4

Table 5. Association between categorised binge eating behaviour (BE) scores and UPPS-P domains(Mean values and standard deviations; numbers and percentages)

Figure 5

Table 6. Logistic regression of explanatory variables associated with categorised binge eating(B-coefficients with their standard errors; odds ratios and 95 % confidence intervals)

Figure 6

Table 7. Four-step analysis of the mediating effect of BMI (mediator M) on the relationship between binge eating behaviour (BE) (dependent variable) and UPPS score (independent variable)*(B-coefficients with their standard errors; odds ratios and 95 % confidence intervals)