Metacognition and Emotional Regulation as Treatment Targets in Binge Eating Disorder: A Network Analysis Study

Background: This study aims to examine the underlying associations between eating, affective and metacognitive symptoms in patients with binge eating disorder (BED) through network analysis (NA), in order to identify key variables that may be considered the target for psychotherapeutic interventions. Methods: One hundred and fty-ve patients with BED completed measures of eating psychopathology, affective symptoms, emotion regulation and metacognition. A cross-sectional network was inferred by means of Gaussian Markov random eld estimation using graphical LASSO and extended Bayesian information criterion (EBIC-LASSO), and central symptoms of BED were identied by means of the strength centrality index. Results: Impaired self-monitoring metacognition and diculties on impulse control emerged as the symptoms with the highest centrality. Conversely, eating and affective features were less central. The centrality stability coecient of strength was above the recommended cut-off, thus indicating the stability of the network. Conclusions: According to present NA ndings, impaired self-monitoring metacognition and diculties on impulse control are the central nodes in the psychopathological network of BED while eating symptoms appear marginal. If further studies with larger samples replicate these results, metacognition and impulse control could represent new targets of psychotherapeutic interventions in the treatment of BED. In light of this, Metacognitive Interpersonal Therapy (MIT) could be a promising aid in clinical practice to develop an effective treatment for BED. ED: EDI-2: LASSO: Absolute Shrinkage Selection Operator; MIT: Metacognitive Interpersonal Therapy; MSAS: Metacognition Self-Assessment Scale; NA: Network analysis; SCL-90: Symptom checklist – 90; STAI: State-Trait Anxiety Inventory.


Plain English Summary
This study tried to seek the key symptoms for the psychotherapy of patients with binge eating disorder (BED). For this purpose, we used a statistical technique called network analysis that is capable of not only ordering symptoms in relation to their importance, but also delineating their relationships. We analyzed the responses of 155 patients with BED to a series of psychological questionnaires related to their eating behavior, affectivity, emotional regulation and metacognition. From the research, it emerged that impaired metacognition and the di culty in impulse control were the central elements of BED, while affective and eating symptoms were marginal aspects of this disorder. Therefore, metacognitive alterations and emotional dysregulation should be the targets for the psychotherapy of patients with BED.

Background
Binge eating disorder (BED) is characterized by recurrent episodes of binge eating with a sense of loss of control over eating and accompanied by negative feelings [1]. To date, guidelines recommend cognitive behavioral therapy (CBT) as the rst-line option treatment for BED [2,3]. Although CBT is quite effective in BED, about 50% do not fully respond to treatment [4][5][6]. A possible explanation of these ndings could be due to the fact that overvaluation of body shape and weight, the core of CBT protocol, is reported in a small portion of patients with BED [7]. Other treatments such as dialectical behavioral therapy (DBT) [8,9] and interpersonal psychotherapy (IPT) [10,11] have shown promising results but failed to bridge the e cacy gap in treating BED. In others words, the available data do not favor one treatment over the other.
New therapeutic approaches able to target the core elements of the complex psychopathology of BED arise as a priority. Investigating the speci c weight of each psychopathological dimension could help in developing more tailored psychological interventions for BED.
Network analysis (NA) has emerged as a novel approach to conceptualize mental disorders [12]. According to the NA approach, symptoms of psychiatric disorders are distinct entities that can in uence, maintain, and/or interact with other symptoms [13]. Mental disorders can be characterized as complex systems in which symptoms are represented as distinct nodes, connected by edges that represent the strength (e.g. strong/weak correlations) and direction (e.g. positive/negative correlations) between pairs of symptoms. Visual inspection reveals that thicker edges indicate stronger associations between symptoms, with positive associations typically illustrated in blue and negative associations typically represented in red. NA allows the identi cation of the central symptoms (i.e. when a node has many strong associations with other nodes and strong correlations with other nodes within the network) [14].
The development of network approach over the past decade has provided a theoretical framework that was adopted to identify the central symptoms of different psychiatric disorders such as bipolar disorder [15], depression [16], obsessive compulsive disorder [17], or schizophrenia [18]. More recently, researchers in the eld of eating disorders (EDs) have applied NA to examine the symptoms of Anorexia Nervosa [19][20][21][22] and Bulimia Nervosa [23][24][25].
To date, only three studies [26][27][28] dealing with binge eating disorder (BED) have used the NA approach. In the rst investigation, overvaluation of shape and weight emerged as central symptoms of BED while behavioral symptoms (i.e. binge eating, restriction, secret eating) were less central [27]. The study by Solmi et al. revealed that affective symptoms, interoceptive awareness, ineffectiveness, interpersonal functioning and drive for thinness were the most central variables among BED patients [26]. Finally, the third research showed that CBT resulted in a greater integration and connectivity of the psychopathology network in BED, suggesting an improved patient understanding of associations between binge eating and other symptoms [28].
However, no research has used NA to investigate the interconnections between the eating (i.e. binge eating and eating psychopathology), affective (i.e. anxiety and mood) and psychological (i.e. metacognition and emotional regulation) features of patients with BED. In fact, affective symptoms appear associated with BED [29][30][31] and the severity of BED seems worsened in relation to impaired selfmonitoring metacognition trough the mediation of emotional dysregulation [32]. Thus, the purpose of this study is to increase the knowledge about the underlying association of clinical variables in BED through NA, in order to identify key variables that may be considered the target for psychotherapeutic interventions. Given the explorative nature of our study, no a priori hypotheses were formulated. Participants were deemed ineligible if: a) IQ < 70; b) drug dependence and/or abuse; c) severe mental illness that could interfere with clinical assessment (i.e. psychosis); d) history of chronic medical illness or neurological conditions affecting cognitive functioning; e) other severe medical comorbidities (e.g. epilepsy); f) medical conditions that in uenced eating/weight (i.e. diagnosis of diabetes mellitus); g) history of malignant disease.
Trained psychiatrists interviewed all participants using the Structured Clinical Interview for DSM-5 Disorders-Research Version [33] for diagnostic purposes and collected sociodemographic and clinical data. Researchers informed about the aim, procedures anonymity and voluntary participation to this research. Participants gave their written informed consent to participate in accordance with the latest version of the Declaration of Helsinki [34].

Measures
The Eating Disorders Inventory-2 (EDI-2) [35,36] is a self-report questionnaire made up of 91 items, which evaluates ED psychopathology and symptomatology. The EDI-2 provides 11 subscale scores and a global measure of ED severity obtained from the sum of all the items. Cronbach's alpha for the total score in this study was good (.840).
Binge Eating Scale (BES) [37] measures the severity of BED. It consists of 16 items that describe the behaviors, feelings and cognitions associated with binge eating. Total BES scores < 17, 17-27 and > 27 respectively indicate improbable, possible and probable BED. The internal consistency in this study was .880.
Di culties in Emotion Regulation Scale (DERS) [39]. The DERS consists of 36-items and assesses emotion dysregulation across six subscales: (a) non-acceptance of emotions, (b) di culties in pursuing goals when having strong emotions, (c) di culties in controlling impulsive behaviors when experiencing negative emotions, (d) lack of emotional awareness, (e) limited access to emotion regulation strategies, and (f) lack of emotional clarity. Higher scores indicate more problems in emotional regulation. In the current study, the internal consistency ranges from .870 to .895.
State-Trait Anxiety Inventory (STAI) consists of 20 items that assess state (STAI-St) and 20 items that measure trait (STAI-Tr) anxiety [41]. The present study only included the STAI-Tr for statistical purposes. Cronbach's α was 0.795.
Network estimation and accuracy NA was performed using R, version 3.6.2, using qgraph and bootnet packages in accordance with Epskamp and colleagues [42].
The network has been inferred by means of Gaussian Markov random eld estimation, applying "Least Absolute Shrinkage and Selection Operator" (LASSO) regularization was applied to limit the number of spurious associations [43]. Moreover, the Extended Bayesian Information Criterion (EBIC) [44], a tuning parameter that sets the degree of regularization/penalty applied to sparse correlations, was set to 0.20 in the current study (values between 0 and 0.5 are typically chosen). Network estimation was performed using the estimateNetwork routine of the bootnet package [45].
The centrality of a node can be used to infer its in uence, or structural importance, in the network. Three main indices estimate the centrality: betweenness, how a node in uences the average path between other pairs of nodes; closeness, how a node is indirectly connected to the other nodes; and strength, how a node is directly connected to the other nodes. The centrality Plot function in qgraph was used to calculate indices of centrality.
According to recommendations of Epskamp et al. [46], in order to assess the internal reliability of the network, we calculated the Correlation Stability (CS) coe cient, which is the maximum proportion of the population that can be dropped so that the correlation between the re-calculated indices of the obtained networks and those of the original network is at least 0.7. It is recommended that the minimum cut-off to consider a network stable is 0.25 for betweenness, closeness and strength [46]. The CS coe cient was computed using case-drop bootstrapping (nboots = 2000). Then we estimated the accuracy of edgeweights by drawing bootstrapped con dence intervals calculated using nonparametric bootstrapping (nboots = 2000). Both for case-drop and nonparametric bootstrapping, network stability analyses were performed using the bootnet function in the bootnet package.

Sample Characteristics
In total, 155 BED patients (86.5% females), 41.2 ± 13.2 years old and 37.9 ± 10.4 kg/m 2 (body mass index) took part in the current study. Table 1 displays the clinical characteristics of the sample. Network Analysis Figure 1 illustrates the network of BED symptoms. Nodes belonging to each domain (i.e. symptoms, emotion regulation and metacognition) are generally associated and close to each other. There is a strong negative connection between self-monitoring and DERS-Clarity, and a strong positive connection among self-monitoring, differentiation and mastery. The associations between BED symptoms and depression and between EDI-tot and depression and anxiety are moderately strong. The psychopathologic variables and emotion regulation are moderately connected. BED symptoms node has a direct connection with non-acceptance of emotions, whereas the depression node is connected to both di culties in controlling impulsive behaviors and lack of emotional clarity. The strength centrality index of the variables included in the network is plotted in Fig. 2. The CS coe cient is 0.301 for strength that is above the recommended cut-off value of 0.25; instead, the CS coe cients for betweenness and closeness are below 0.25. Therefore, we decided to choose the strength index as main CS coe cient. This choice is not surprising, because the interpretation of betweenness and closeness in networks is somewhat unclear [47] and the strength index is considered a more stable centrality index than betweenness and closeness [48]. Further, since we aimed to understand the core symptoms to target with psychological treatment, we relied on the strength index because it performs exactly this function. The Additional File 1 ( Figure S1) shows the accuracy of CS indices.
The nodes with the highest strength centrality are MSAS Self-monitoring (M = 1.98) and DERS Impulse (M = 1.27) (Fig. 2). The strongest connections of MSAS Self-monitoring are with MSAS Mastery (0.352) and DERS Clarity (-0.350). The strongest connections of DERS Impulse are with DERS Goals (0.38) and DERS Strategies (0.318). The Additional File 2 ( Figure S2) reports the bootstrapped con dence intervals of estimated edge-weights.

Discussion
This is the rst study that investigated the associations between the eating (i.e. binge eating and eating psychopathology), affective (i.e. anxiety and mood) and psychological features (i.e. metacognition and emotional regulation) among BED patients through the NA method.
Our results showed that impaired self-monitoring metacognition and di culties on impulse control were the nodes with the highest centrality strength and, thus, the nodes most directly connected to the other nodes in the network [48]. According to NA approach, the activation of a node may cause the development of the connected symptoms; therefore, the most central nodes have been conceptualized as risk factors for developing further symptoms [49], in our case, BED symptoms. Although the high centrality of a node has been argued to be a possible effect of connections with other symptoms [50] and the cross-sectional design of our study does not allow causal conclusions, metacognitive and emotional regulation functions may represent targets for psychotherapeutic intervention.
This nding is in line with our previous study where we found that low self-monitoring lead BED-obese patients to express the worsening of binge severity through the mediation of emotional dysregulation [51]. Consistent with this hypothesis, other researchers found that di culties in emotion recognition could play a key role in the development and maintenance of BED [52,53].
Another important nding of the current NA was the strong correlation of Self-monitoring node with mastery strategies. According to Semerari and colleagues' theory, high level of self-monitoring allows the use of functional mastery strategies. More in detail, mastery is "the ability to work through one's representations and mental states, with a view to implementing effective action strategies, in order to accomplish cognitive tasks or cope with problematic mental states" [54]. So, it could be inferred that enhancing metacognitive abilities could lead to reduce dysfunctional strategies among patients with BED that usually manage intense emotions with binges [8,55].
It is worth noting that dysfunctional eating (i.e. BES and EDI-2 total scores) and affective symptoms (i.e. BDI and STAI) were peripheral to the network of patients with BED, indicating that they had less connection to the rest of the network as compared with other nodes. Regarding eating psychopathology, in the current study the lowest strength was found for BES (M=-1.39) and EDI-2 total score (M=-1.22). Our results con rm recent data of the literature on NA in BED that found that binge eating was not the central to the psychopathology [27,28]. Further, depressive and anxious symptoms were not either central nodes in our network model; conversely, anxiety and depression had high centrality in Solmi and colleagues' model [26]. A possible explanation of this discrepancy could be the use of different psychometric instruments. Solmi and colleagues used the symptom checklist − 90 (SCL-90), that is not so speci c and only takes into consideration the prior week; instead, the BDI-II and the STAI-tr are more speci c for diagnostic purposes and consider a longer temporal range of assessment (i.e. two weeks for BDI following DSM-5 temporal criterion for major depressive episode; "usually feeling" for STAI-tr). Therefore, their study could have overestimated the weight of anxious and depressive symptoms in BED.
The results of this study should be read in light of some limitations. First, the sample size is smaller than in other studies that used NA in BED. Nevertheless, according to the recommendations of Levinson and colleagues [56] about the use of NA in the eld of eating disorder ("to date, the best recommendation is to use the largest sample size possible and make sure that your network is stable") our model demonstrated to be stable. Second, it was not possible to evaluate the differences in NA according to sex; however, a recent NA study among patients with eating disorders showed more similarities than differences between men and women [57]. Finally, the cross-sectional design of the current study does not allow the explanation of the possible causal association between the dimensions investigated; so future longitudinal research is needed to explore whether psychotherapeutic interventions that target metacognitive and impulsive dimensions can be more effective in treating BED.

Conclusions
According to present NA ndings, impaired self-monitoring metacognition and di culties on impulse control are the central nodes in the psychopathological network of BED while eating symptoms seem to be marginal.
If further studies with larger samples of patients with BED con rm them, these results could lead to rethink the current conceptualization of BED and to consider new targets of psychotherapeutic interventions. In light of this, Metacognitive Interpersonal Therapy (MIT) could be a promising aid in clinical practice to develop an effective treatment for BED.

Declarations Funding
This study did not receive any funding.

Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate
The local Ethical Committee approved this study. Informed consent was obtained from all participants included in the study.

Consent for publication
Not applicable.

Competing interests
All the authors declare that they have no con ict of interest.
Authors' information 1 Outpatient Unit for Clinical Research and Treatment of Eating Disorders. University Hospital "Mater