Directional dependence of dysregulated eating, weight stigma, and psychological distress among children with overweight or obesity: a gaussian graphical model and Bayesian network approach

doi:10.21203/rs.3.rs-4616435/v1

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Directional dependence of dysregulated eating, weight stigma, and psychological distress among children with overweight or obesity: a gaussian graphical model and Bayesian network approach

https://doi.org/10.21203/rs.3.rs-4616435/v1

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Background

Childhood obesity is a pressing global health concern, with its etiology involving complex interactions among dysregulated eating, weight stigma, and psychological distress. Elucidating the directional dependencies and potential causal pathways among these factors is crucial for intervention development.

Method

In this study, 572 obese or overweight children aged 10-17completed self-report measures assessing weight stigma, internalized stigma, dysregulated eating (restrained, emotional, and external eating), depression, social anxiety, and school bullying. Gaussian graphical models (GGMs) and Bayesian networks were used to investigate the intricate directional relationships among these variables.

Results

GGMs revealed weight stigma as the most central node in the network, exhibiting the highest strength and expected influence. Bayesian networks indicated that school bullying was a significant antecedent, directly influencing both weight stigma and depression. Weight stigma emerged as a crucial mediator, transmitting the effects of school bullying to internalized stigma, emotional eating, and psychological distress. Internalized stigma was the sole direct predictor of restrained eating. Depression, influenced by school bullying and internalized stigma, directly affected social anxiety.

Conclusion

This study emphasizes the importance of addressing weight stigma to alleviate dysregulated eating and psychological distress in obese or over weight children. The findings suggest the need for targeted interventions to combat weight stigma and its negative effects. Comprehending these complex directional dependencies offers a foundation for devising more effective strategies to tackle childhood obesity and related psychological comorbidities.

Childhood obesity

dysregulated eating

weight stigma

psychological distress

Gaussian graphical models

Bayesian networks

This study looked at how being overweight or obese affects children's eating habits and mental health. We surveyed 572 children aged 10-17 who were overweight or obese. We found that weight stigma (negative attitudes towards people because of their weight) plays a central role in children's eating behaviors and psychological well-being. Children who experienced more weight stigma were more likely to have unhealthy eating habits and feel depressed or anxious. School bullying was a major cause of weight stigma and depression. Children who internalized negative attitudes about their weight were more likely to restrict their eating. Our findings suggest that addressing weight stigma could be key to improving both the mental health and eating behaviors of overweight children. This research helps us better understand the complex relationships between weight stigma, eating habits, and mental health in overweight children, which could lead to more effective ways to help them.

Childhood obesity has become a global public health issue, with a rapidly increasing prevalence over the past few decades. New study released by the Lancet shows that, in 2022, more than 1 billion people in the world are now living with obesity. Worldwide, obesity among adults has more than doubled since 1990, and has quadrupled among children and adolescents (5 to 19 years of age)[1]. This alarming trend not only places a significant burden on healthcare systems but also poses substantial risks to the physical and mental well-being of affected children, both in the short and long term.obese or overweight children have a higher risk of developing various physical health problems, such as type 2 diabetes, cardiovascular disease, sleep apnea, and musculoskeletal disorders[2, 3]. Furthermore, childhood obesity or overweight is associated with several mental health challenges, including weight stigma [4], depression[5] and social anxiety[6].

Childhood obesity or overweight and Dysregulated eating behavior

Childhood obesity or overweight is a multifactorial condition, with dysregulated eating patterns, weight stigma, and psychological distress being key contributors to its development and maintenance. Dysregulated eating patterns, such as emotional, restrained, and external eating, have been consistently linked to the development and maintenance of childhood obesity[7, 8]. For example, a study by Preuss et al. found that children who engaged in restrained eating were more likely to experience disinhibited eating and binge eating episodes[9]. Restrained eating, characterized by intentional efforts to limit food intake for weight control purposes, has been associated with increased levels of disinhibited eating and binge eating episodes[10]. Emotional eating, defined as consuming food in response to negative emotions, has been linked to higher caloric intake and a preference for energy-dense foods[11]. Children with emotional eating consumed more calories and had a higher body mass index compared to those who did not[12]. External eating, which refers to eating in response to external food cues, has been associated with overconsumption and a reduced sensitivity to internal hunger and satiety signals[13]. These findings suggest that dysregulated eating behaviors can significantly contribute to the development and maintenance of childhood obesity. Furthermore, the relationship between dysregulated eating patterns and childhood obesity is likely bidirectional. Not only do these eating behaviors contribute to weight gain, but the experience of being overweight or obese can also exacerbate dysregulated eating.

Interconnections of Dysregulated Eating, Weight Stigma, and Psychological Distress in obese or overweight children

In addition to dysregulated eating, weight stigma and psychological distress are pervasive and particularly detrimental to children with obesity or overweight. Weight stigma refers to the societal devaluation and discrimination of individuals based on their weight[14]. Psychological distress encompasses troubling mental and physical symptoms like depression, anxiety, and stress. These factors are interconnected, forming a complex combine effect that perpetuates the long-term effects of child obesity. A review study by Puhl & Lessard indicates that weight stigma is highly prevalent among higher-weight adolescents and puts adolescents at risk for psychological distress and adverse physical health consequences, including maladaptive eating behaviors and weight gain[15]. Faced with the negative emotions resulting from weight stigma, children with obesity or overweight engage in dysregulated eating as a coping mechanism [16]. Emotional eating, which involves consuming food in response to negative emotions, and external eating, which is characterized by eating in response to external food cues, may provide temporary relief from the psychological distress experienced by obese or overweight children[17]. However, while these dysregulated eating offer short-term alleviation of negative emotions, they can ultimately exacerbate psychological distress in the long run, as they contribute to further weight gain and perpetuate the cycle of obesity and mental health problems[18]. In addition to emotional and external eating, restrained eating, which involves deliberately restricting food intake to control weight, has also been linked to psychological distress in obese or overweight children[19]. A study conducted by Linardon et al. found that dieting behaviors can exacerbate symptoms of depression and anxiety, creating a vicious cycle wherein the child engages in restrictive eating to cope with negative emotions, but the act of dieting itself leads to increased psychological distress[20]. This bidirectional interaction between psychological distress and dysregulated eating may contribute to the persistence and worsening of childhood obesity, as the child becomes trapped in a self-perpetuating cycle of maladaptive coping strategies and mental health problems. Further research is needed to elucidate the complex interplay between weight stigma, psychological distress, and dysregulated eating in children with obesity or overweight, ultimately breaking the vicious cycle of psychological distress and maladaptive eating behaviors.

The gaps between the literature and the current study

Despite the wealth of research highlighting the complex interplay among dysregulated eating, weight stigma, and psychological distress in children with obesity or overweight, previous studies have primarily focused on linear correlations or unidirectional causal models, which may not fully capture the intricate and dynamic nature of these relationships[21, 22]. Especially, the directional dependence and causal relationships among these variables remain unclear. To address these gaps, the present study aimed to combine Gaussian graphical models and Bayesian networks to disentangle the complex relationships between dysregulated eating, psychological factors. By employing these advanced statistical techniques, this study seeks to contribute to a more comprehensive understanding of the factors underlying childhood obesity, ultimately informing the development of more effective prevention and treatment strategies. Gaussian graphical models, which represent the conditional dependence structure among a set of variables, have been increasingly applied in the field of psychology to identify patterns of relationships among multiple variables[23]. By employing this technique, the current study seeks to uncover the direct associations between dysregulated eating and psychological factors, while controlling for the influence of other variables in the network. In addition to Gaussian graphical models, the study also employed Bayesian networks, which are probabilistic graphical models that represent the conditional dependencies among a set of variables[24]. Bayesian networks have been used in various domains, including psychology and medicine, to infer causal relationships from observational data [25]. By applying this approach to the study of childhood obesity, the present research aims to shed light on the directional relationships between dysregulated eating and psychological factors, providing valuable insights into the potential causal pathways underlying this complex condition.

Participants and Procedure

The study involved 598 children with overweight or obesity who were assessed at the Child Care Center of the Children’s Hospital Affiliated with Wenzhou Medical University from February to November 2023. Wenzhou, known for its economic development, has been experiencing a rise in the obesity rate among children and adolescents. In order to qualify for the study, participants needed to fulfill the following criteria: (1) fall within the 10 to 17 age range; (2) meet the overweight or obesity standards outlined by the National Health and Health Commission of China in 2018, which assess body weight and height; (3) express willingness to partake and provide informed consent. Ultimately, 572 participants completed the survey, yielding a response rate of 95.65%.

Prior to the survey, a pre-questionnaire was administered to ensure user-friendliness and logical consistency. Subsequently, eligible participants were provided with an informed consent form detailing the questionnaire’s objectives and stressing voluntary participation. The survey involved face-to-face structured interviews conducted one-on-one by trained researchers who had received systematic training before the formal survey. After the interviews, participants completed the questionnaire electronically on a tablet PC provided by the researchers. Both the participants and their parents consented to the interview survey, in line with the Declaration of Helsinki. The study was reviewed and approved by the Ethics Committee of Wenzhou Medical University (No.2023022).

Measurements

Demographic information

Demographic information, including age, gender, education level and body mass index (BMI), was collected through self-administered questionnaires. Age was treated as a continuous variable, measured in years. Gender and education level were considered categorical variables. The categories for gender were male and female, while education level was classified into three categories: primary school or below, middle school, and high school or above. BMI was calculated using the standard formula: weight (kg) divided by height squared (m²). Detailed information on the demographic characteristics of the study participants is presented in Table 1.

Weight stigma

The "Weight Teasing" subscale of the Perception of Teasing Scale was used to assesses participants' experiences of weight stigma. This subscale consists of 6 items that measure the frequency and extent to which individuals encounter weight-related teasing or stigmatization. Participants responded to each item using a 4-point Likert scale, higher scores indicate a greater degree of perceived weight stigma experienced. In previous studies, the Weight Teasing subscale has demonstrated good internal consistency reliability among Chinese adolescents, with Cronbach's α = 0.88[26]. In the current study, the Cronbach's α for the Weight Teasing subscale was 0.92.

Internalized stigma

Internalized stigma was measured by the Weight Bias Internalization Scale (WBIS-M). The scale consists of 11 items, each rated on a 7-point Likert scale. The total score is calculated by summing the responses to all items, with higher scores indicating a greater degree of internalized weight bias. The Chinese version of the WBIS-M has been validated and found to be suitable for use among Chinese adolescents, with a Cronbach's α coefficient of 0.87[27], and Cronbach's α = 0.87 in current study.

Dysregulated eating

The Dutch Eating Behavior Questionnaire (DEBQ) was utilized to assess participants' dysregulated eating patterns. This questionnaire consists of 33 items, categorized into three subscales: restrictive eating, emotional eating, and external eating. Participants responded to each item using a 5-point Likert scale. Higher scores on each subscale indicate a greater tendency towards the respective eating behavior. The Chinese version of the DEBQ has demonstrated good psychometric properties, with Cronbach's α coefficients ranging from 0.87 to 0.90[28] and Cronbach’s α of the three subscales were 0.91, 0.97, 0.91 respectively in current study.

Depression

Depression was assessed using the Depression Self-rating Scale for Children (DSRSC)[29]. The scale consists of 18 items, each scored on a 3-point scale, with higher scores indicating higher levels of depression. In the current study, the Cronbach's α coefficient for the DSRSC was 0.86, indicating good internal consistency reliability within our sample.

Social anxiety

Social anxiety was measured using the Social Anxiety Scale for Children (SASC)[30]. The scale consists of 10 items, each scored on a 3-point scale, with higher scores indicating higher levels of social anxiety. In the current study SASC has demonstrated good psychometric properties with Cronbach's α = 0.92.

Bullying Behavior

Bullying behavior was measured using the Chinese version of the Olweus Bully/Victim Questionnaire (OBVQ)[31], which contains 16 items rated on a 4-point Likert scale. The scale evaluates participants' involvement in bullying or being bullied at school across three dimensions: physical, verbal, and relational bullying. A higher score indicates a higher level of bullying behavior. Many researchers have shown that the OBVQ is well suited to assess bullying in different ages and groups[32]. In the current study, the Cronbach's α for the scale was 0.92.

Statistics analysis

Descriptive statistics were calculated for all study variables. Means and standard deviations were used to describe continuous variables, while frequencies and percentages were used to describe categorical variables. Pearson correlation coefficients were computed to examine the bivariate associations among the study variables.

Gaussian graphical models (GGMs) were estimated using the “bootnet” package in R to further investigate the relationships among the study variables. The nodes in the network comprised weight stigma, internalized stigma, three dysregulated eating (restrained eating, emotional eating, and external eating), social anxiety, depression, and school bullying. The lines between nodes, known as edges, represent undirected partial correlations. All variables were standardized using z-score transformations prior to the analysis. The primary distinction between a standard partial correlation and the ones estimated by GGMs is the use of a regularization technique. In this study, regularization was applied using the Extended Bayesian Information Criterion (EBIC) form of the graphical least absolute shrinkage and selection operator (LASSO) with a tuning parameter gamma of 0.5[33]. Several centrality indicators, including strength, closeness, betweenness, and expected influence, were calculated to assess the importance of each node in the network. The accuracy of the GGM edges was evaluated using nonparametric bootstrapping with 1000 bootstrap samples. Lastly, node predictability, which represents the shared variance of each node, was measured as an absolute measure of interconnectedness. Node predictability is illustrated in blue around each node in the network visualization.

Bayesian networks (directed networks) were estimated using the hill-climbing algorithm, a score-based learning algorithm in R. The hill-climbing algorithm is a machine-learning process that estimates directed acyclic graphs (DAGs) by exploring single-edge additions, removals, and reversals to optimize goodness of fit based on the Bayesian Information Criteria (BIC)[34]. First, it calculates the fit of an empty DAG, defined as Score(G), and sets maxscore = Score(G). Next, it computes the modified network G* score for edge additions, deletions, or reversals that do not result in a cyclic network. If any G* has a Score(G*) > Score(G), maxscore is updated to Score(G*), and G* becomes the new candidate network; this process repeats until an optimal fitting DAG is selected. To ensure network stability, a bootstrapping method with 5000 samples was used. The technique involved creating a network for each sample, averaging all networks, and examining the frequency and direction of arcs. If an arc was present (regardless of direction) in a minimum of 85% of the bootstrapped DAGs and pointed in a given direction in at least 51% of those DAGs, it was represented in the final Bayesian network. The standardized Beta coefficient for each arc of the final network was computed, indicating the degree of influence a unit change in a parent node has on descendant nodes. The overall fit of the Bayesian networks was assessed using the BIC.

Descriptive analytics

The final 572 participants were enrolled in this study, with a mean age of 13.77 years (SD = 3.23) and a mean BMI of 25.34 (SD = 3.53). The sample was predominantly male, with 355 male participants (62.06%). The distribution of participants across educational levels was as follows: 235 (41.08%) from primary schools, 159 (27.80%) from junior high schools, and 178 (31.11%) from high schools. Descriptive statistics for the study variables are presented in Table 1.

Table 1

Socio-demographic characteristics of participants (N = 572)
Variable	Categories	M	SD	N	Percentage
Gender
	Male			355	62.06%
	Female			217	37.94%
Educational Levels
	Primary			235	41.08%
	Junior			159	27.80%
	High			178	31.11%
Age		13.77	3.23
BMI		25.34	3.53
Weight stigma		12.45	4.67
Internalized stigma		38.93	11.50
Restrained eating		27.71	8.06
Emotional eating		33.03	13.26
External eating		33.47	7.79
Depression		9.21	5.51
School bullying		2.44	3.71
Social anxiety		8.72	5.05

The correlation matrix in Table 2 presents the bivariate correlations among the study variables. All three dysregulated eating were significantly and positively related to weight stigma and internalized stigma (ps < 0.05). It is noteworthy that emotional eating was significantly and positively correlated with depression (r = 0.21, p < 0.001), while restrained eating (r = 0.07, p > 0.05) and external eating (r = 0.03, p > 0.05) were not significantly correlated with depression. The associations between dysregulated eating and school bullying also varied. Emotional eating (r = 0.20, p < 0.001) and external eating (r = 0.12, p < 0.01) were significantly and positively correlated with school bullying. However, restrained eating was not significantly correlated with school bullying (r = 0.06, p > 0.05). Additionally, weight stigma was positively correlated with internalized stigma (r = 0.56, p < 0.001), depression (r = 0.29, p < 0.001) and social anxiety (r = 0.45, p < 0.001).

Table 2

Intercorrelations among variables used in this study (N = 572)
	1	2	3	4	5	6	7	8
1.Weight stigma	1
2.Internalized stigma	0.56^***	1
3.Restrained eating	0.14^**	0.30^***	1
4.Emotional eating	0.36^***	0.26^***	0.18^***	1
5.External eating	0.31^***	0.15^***	-0.01	0.61^***	1
6.Depression	0.29^***	0.41^***	0.07	0.21^***	0.03	1
7.School bullying	0.53^***	0.37^***	0.06	0.20^***	0.12^**	0.35^***	1
8.Social anxiety	0.45^***	0.51^***	0.11^**	0.31^***	0.33^***	0.49^***	0.32^***	1
Gender	-0.05	0.001	-0.05	0.05	-0.03	-0.01	-0.03	-0.09
Age	-0.05	0.01	0.06	-0.03	-0.03	0.02	-0.01	-0.06
Educational Levels	-0.09^*	0.01	0.04	-0.04	-0.05	0.03	-0.01	-0.07
BMI	0.22^***	0.20^***	0.05	0.04	-0.01	0.11^*	0.13^**	0.11^**
Note. ^* p < 0.05, ^ p < 0.01, ^* p < 0.001.

Gaussian graphical models (undirected networks)

Figure 1A presents the Gaussian graphical model (GGM). The estimated GGM has 19 non-zero edges out of a possible 28(67.86%), with a mean edge weight of 0.10 (edge weight matrix was showed in Supplementary Table 1). The strongest edge in the network is between emotional eating and external eating (Edge weight = 0.57). Figure 1B displays the centrality indicators for each node in the GGM. Among the centrality indicators, weight stigma WS emerges as the most important node, with the highest strength (Strength = 0.91) and expected influence (Expected Influence = 1.15). The node predictability, represented by the blue rings around each node in Fig. 1A, indicates the proportion of variance in each node that can be explained by its neighboring nodes in the network. The node predictability values range from 0.14 for restrained eating to 0.50 for weight stigma.

The GGM demonstrates very good stability (refer to Supplementary Figs. 1 and 2), particularly for edge weights (CS (cor = 0.7) = 0.75), strength (CS (cor = 0.7) = 0.67) and expected influence (CS (cor = 0.7) = 0.75). The stability of closeness is acceptable (CS (cor = 0.7) = 0.44), while the stability of betweenness is insufficient (CS (cor = 0.7) = 0.21). However, as closeness and betweenness are not the primary focus of this study, their lower stability does not significantly impact the interpretation of the results.

Bayesian networks (directed networks)

Figure 2 depicts the Bayesian networks and the beta coefficients are included in Table 3. Restrained eating and internalized stigma were conditionally independent of each other given the other variables(restrained eating ⊥ internalized stigma). This means that once controlling for the other variables, internalized stigma is the only parent variable upon restrained eating. In other words, the Bayesian network analysis indicates that internalized stigma is the sole direct predictor of restrained eating (β = 0.21).

In this Bayesian network, school bullying emerges as the highest parent node, indicating that it is a key predictor of several other variables in the network. School bullying directly influences both weight stigma (β = 0.67) and depression (β = 0.34). Weight stigma, in turn, directly influences internalized stigma (β = 1.35) and emotional eating (β = 1.03). Depression, which is influenced by both school bullying and internalized stigma, also directly affects social anxiety (β = 0.32). Emotional eating, which is directly influenced by weight stigma, appears to have a direct effect on external eating (β = 0.36). This relationship suggests that children who engage in emotional eating may also be more likely to eat in response to external cues.

Weight stigma plays a pivotal role in the network, acting as a crucial mediator between school bullying and a range of negative outcomes. It directly influences internalized stigma and emotional eating, setting off a cascade of effects on other variables in the network. Under weight stigma, internalized stigma emerges as a key parent node, directly influencing three important variables: depression (β = 0.15), restrained eating (β = 0.21), and social anxiety (β = 0.14).

Finally, social anxiety emerges as the lowest child node in the network, being directly influenced by external eating (β = 0.18), depression (β = 0.32), and internalized stigma (β = 0.14). This finding highlights the complex interplay of psychological distress, dysregulated eating, and internalized stigma in the development of social anxiety among obese or overweight children.

Table 3

Arc frequency, arc direction, and beta coefficients for the Bayesian networks.
From	To	Arc frequency	Arc direction	Beta coefficient
Emotional eating	External eating	1.00	0.75	0.36
External eating	Social anxiety	0.99	0.83	0.18
Depression	Social anxiety	1.00	0.71	0.32
School bullying	Depression	0.98	0.80	0.34
School bullying	Weight stigma	1.00	0.50	0.67
Weight stigma	Emotional eating	0.93	0.78	1.03
Weight stigma	Internalized stigma	1.00	0.62	1.35
Internalized stigma	Restrained eating	1.00	0.51	0.21
Internalized stigma	Depression	0.87	0.67	0.15
Internalized stigma	Social anxiety	0.99	0.89	0.14
Note: arc frequency refers to the proportion of times that an arc appeared in the bootstrap samples, regardless of direction. Arc direction refers to the proportion of times that the arc pointed in that given direction.

The present study investigated the intricate relationships between dysregulated eating, school bullying, weight stigma, and psychological distress in obese or overweight children by employing GGMs and Bayesian networks. The most robust findings across both networks highlighted weight stigma as a significant contributor to dysregulated eating and psychological distress in this population, corroborating existing literature. Prior research has demonstrated that weight stigma is associated with heightened risk of depression[35], social anxiety[36], and dysregulated eating[37] in children and adolescents with obesity. GGMs revealed that weight stigma emerged as the most central node, exhibiting the highest strength and expected influence. Bayesian networks provided further insights, indicating that weight stigma and internalized stigma are the parent variables upon which dysregulated eating and psychological distress depend, mediating the relationships between external stimuli and individual responses. Moreover, Bayesian networks showed that depression, influenced by both school bullying and internalized stigma, had a direct effect on social anxiety, emphasizing the complex interplay between these psychological factors.

Gaussian graphical models

Gaussian graphical model (GGM) findings unveil a complex network of interactions between dysregulated eating, weight stigma, and psychological distress among obese or overweight children. Significantly, weight stigma as a central node, indicating its pivotal role in the network of psychological distress and eating behaviors. This centrality suggests that weight stigma not only directly influences children's psychological well-being but also has a substantial impact on their eating behaviors. For instance, a study by Puhl and Luedicke (2012) found that weight-based victimization was associated with increased depressive symptoms, lower self-esteem, and unhealthy eating behaviors in adolescents[38]. Similarly, the study by Wang et al. (2019) who found that weight stigma was associated with increased emotional eating in children, suggesting that the experience of stigma related to one's weight could lead to using food as a coping mechanism for negative emotions[39]. Araiza et al. (2018) further elaborated that the stress resulting from weight stigma can trigger physiological stress responses that promote food intake, particularly of high-calorie, comfort foods[40]. The centrality of weight stigma in our GGM analysis also underscores the necessity of addressing weight stigma in interventions aimed at improving both the psychological well-being and eating behaviors of obese or overweight children. By targeting weight stigma, interventions could potentially disrupt the vicious cycle of stigma, psychological distress, and maladaptive eating behaviors, thereby addressing key factors in the perpetuation of childhood obesity. Moreover, the strong association observed between emotional eating and external eating aligns with previous research[41]. This finding suggests that obese or overweight children who engage in emotional eating also be more prone to eating in response to external food cues, potentially exacerbating their weight-related challenges.

Bayesian network

The Bayesian network analysis provided further insights into the directional relationships and potential causal pathways among the studied variables. School bullying emerged as a significant antecedent, exerting direct effects on both weight stigma and depression. This finding is pivotal, underscoring the role of school bullying as a primary stressor that triggers a cascade of psychological and behavioral consequences in obese or overweight children. The direct influence of school bullying on weight stigma aligns with the findings of Puhl and King (2013), who highlighted that experiences of bullying related to one's weight significantly contribute to the internalization of weight stigma[42]. This internalization process, in turn, fosters environments conducive to psychological distress and the adoption of maladaptive coping mechanisms, such as dysregulated eating.

The Bayesian network also revealed that weight stigma's critical position within the network, acting as a mediator that transmits the effects of school bullying to internalized stigma, dysregulated eating and psychological distress. This finding is consistent with the weight bias internalization model, which posits that the internalization of weight stigma leads to self-directed stigma and, consequently, to negative psychological outcomes and disordered eating[43]. The present study extends this model by demonstrating the centrality of weight stigma in the network of psychological distress and eating behaviors among obese or overweight children, emphasizing its role as a key mediator between external experiences and internalized responses. The significant pathway from weight stigma to internalized stigma and emotional eating, as corroborated by Tomiyama (2014), underscores a vicious cycle where weight stigma fuels self-devaluation and maladaptive eating habits, thus perpetuating the link between obesity and psychological distress[44]. Interestingly, our Bayesian network analysis brings to light internalized stigma's unique position as the direct antecedent of restrained eating behaviors. This delineation suggests that children who internalize weight-based stigma are more predisposed to adopt restrictive eating practices as a coping mechanism for the negative emotions and self-perceptions stemming from such internalization. This observation is in line with studies by O'Brien et al. (2016) and Vartanian and Porter (2016), which have linked internalized weight stigma to an increase in disordered eating behaviors, including restrictive eating[43, 45]. Our findings further the understanding of these relationships by pinpointing the direct predictive role of internalized stigma in fostering restrained eating among obese or overweight children, underscoring the necessity to address internalized weight stigma in interventions aimed at mitigating disordered eating patterns. Furthermore, the Bayesian network demonstrated that depression, which was influenced by both school bullying and internalized stigma, directly affected social anxiety. This finding highlights the complex interplay between different aspects of psychological distress in obese or overweight children and suggests that depression may play a role in the development of social anxiety. Previous research has shown that depression and social anxiety are highly comorbid in children and adolescents[44] and that both conditions are associated with increased risk for obesity[48, 49]. The present study extends this knowledge by elucidating the specific pathways through which depression may contribute to the development of social anxiety in obese or overweight children.

In conclusion, the Bayesian network analysis offers a comprehensive framework for understanding the complex interactions and causal relationships among the factors contributing to obesity and its psychological comorbidities in children. By identifying critical nodes and pathways within the network, this analysis not only corroborates existing literature but also provides new directions for targeted interventions aimed at breaking the cycle of obesity, psychological distress, and dysregulated eating in obese or overweight children.

Limitation

While the present study provides valuable insights into the complex relationships between dysregulated eating, weight stigma, and psychological distress in obese or overweight children, it is important to acknowledge its limitations. Firstly, the cross-sectional design of the study precludes the establishment of causal relationships among the investigated variables. Although the Bayesian network analysis suggests potential directional relationships, longitudinal studies are needed to confirm the temporal sequence and causal nature of these associations. Secondly, the reliance on self-reported measures for assessing dysregulated eating, weight stigma, and psychological distress may be subject to response bias. Participants may have underreported or overreported certain behaviors or experiences due to social desirability or recall bias. Future research could benefit from incorporating objective measures, such as behavioral assessments or clinical interviews, to corroborate self-reported data. Finally, while the study included a comprehensive set of variables related to dysregulated eating, weight stigma, and psychological distress, it is possible that other relevant factors were not accounted for in the analyses. For instance, family dynamics, parenting styles, and socioeconomic status may play a role in the development of dysregulated eating and psychological distress in obese or overweight children[50]. Future research should consider incorporating these additional variables to provide a more comprehensive understanding of the complex interplay between the studied factors.

All procedures were performed in accordance with relevant guidelines from declaration of Helsinki statement.

Ethics approval and consent to participate

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human subjects/patients were approved by IRB in Wenzhou Medical University (No. 2023022).

Availability of data and materials

The datasets presented in this article are not readily available due to the inclusion of student-specific information. Requests to access the datasets should be directed to [email protected].

Funding Statement

This work was supported by the Social Science Foundation of Wenzhou (Project Leader：Chen Li). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors’ Contribution

Guanghui Shen and Fan Zou: Formal analysis, Writing – original draft, Writing – review & editing. Yang Zou and Li Chen : Writing – review & editing, Investigation, Conceptualization. Mengting Wang, Hong pan and Wenqian Jian: Investigation, Methodology.

Acknowledgments

We extend our heartfelt gratitude to the physical education offices of each participating school and the dedicated teams at the child health department of each participating hospital for their invaluable support and contribution to this project.

NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet Lond Engl. 2024;403:1027–50.
Sanders RH, Han A, Baker JS, Cobley S. Childhood obesity and its physical and psychological co-morbidities: a systematic review of Australian children and adolescents. Eur J Pediatr. 2015;174:715–46.
Morales Camacho WJ, Molina Díaz JM, Plata Ortiz S, Plata Ortiz JE, Morales Camacho MA, Calderón BP. Childhood obesity: Aetiology, comorbidities, and treatment. Diabetes Metab Res Rev. 2019;35:e3203.
Gow ML, Tee MSY, Garnett SP, Baur LA, Aldwell K, Thomas S, et al. Pediatric obesity treatment, self‐esteem, and body image: A systematic review with meta‐analysis. Pediatr Obes. 2020;15:e12600.
Sutaria S, Devakumar D, Yasuda SS, Das S, Saxena S. Is obesity associated with depression in children? Systematic review and meta-analysis. Arch Dis Child. 2019;104:64–74.
Rankin J, Matthews L, Cobley S, Han A, Sanders R, Wiltshire HD, et al. Psychological consequences of childhood obesity: psychiatric comorbidity and prevention. Adolesc Health Med Ther. 2016;Volume 7:125–46.
Gowey MA, Lim CS, Dutton GR, Silverstein JH, Dumont-Driscoll MC, Janicke DM. Executive function and dysregulated eating behaviors in pediatric obesity. J Pediatr Psychol. 2018;43:834–45.
Stabouli S, Erdine S, Suurorg L, Jankauskienė A, Lurbe E. Obesity and eating disorders in children and adolescents: the bidirectional link. Nutrients. 2021;13:4321.
Preuss H, Leister L, Pinnow M, Legenbauer T. Inhibitory control pathway to disinhibited eating: A matter of perspective? Appetite. 2019;141:104297.
Polivy J, Herman CP, Mills JS. What is restrained eating and how do we identify it? Appetite. 2020;155:104820.
Van Strien T. Causes of Emotional Eating and Matched Treatment of Obesity. Curr Diab Rep. 2018;18:35.
Fuente González CE, Chávez-Servín JL, de la Torre-Carbot K, Ronquillo González D, Aguilera Barreiro M de los Á, Ojeda Navarro LR. Relationship between Emotional Eating, Consumption of Hyperpalatable Energy-Dense Foods, and Indicators of Nutritional Status: A Systematic Review. J Obes. 2022;2022:4243868.
van Strien T, Konttinen HM, Ouwens MA, van de Laar FA, Winkens LH. Mediation of emotional and external eating between dieting and food intake or BMI gain in women. Appetite. 2020;145:104493.
Puhl RM, Himmelstein MS, Pearl RL. Weight stigma as a psychosocial contributor to obesity. Am Psychol. 2020;75:274.
Puhl RM, Lessard LM. Weight Stigma in Youth: Prevalence, Consequences, and Considerations for Clinical Practice. Curr Obes Rep. 2020;9:402–11.
Frayn M, Livshits S, Knäuper B. Emotional eating and weight regulation: a qualitative study of compensatory behaviors and concerns. J Eat Disord. 2018;6:23.
Jalo E, Konttinen H, Vepsäläinen H, Chaput J-P, Hu G, Maher C, et al. Emotional eating, health behaviours, and obesity in children: A 12-country cross-sectional study. Nutrients. 2019;11:351.
Troscianko ET, Leon M. Treating Eating: A Dynamical Systems Model of Eating Disorders. Front Psychol [Internet]. 2020 [cited 2024 Apr 8];11. Available from: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2020.01801/full
Ramalho SM, Trovisqueira A, de Lourdes M, Gonçalves S, Ribeiro I, Vaz AR, et al. The impact of COVID-19 lockdown on disordered eating behaviors: the mediation role of psychological distress. Eat Weight Disord - Stud Anorex Bulim Obes. 2022;27:179–88.
Linardon J. The relationship between dietary restraint and binge eating: Examining eating-related self-efficacy as a moderator. Appetite. 2018;127:126–9.
Hill DC, Moss RH, Sykes-Muskett B, Conner M, O’Connor DB. Stress and eating behaviors in children and adolescents: Systematic review and meta-analysis. Appetite. 2018;123:14–22.
Tanofsky-Kraff M, Faden D, Yanovski SZ, Wilfley DE, Yanovski JA. The perceived onset of dieting and loss of control eating behaviors in overweight children. Int J Eat Disord. 2005;38:112–22.
Epskamp S, Waldorp LJ, Mõttus R, Borsboom D. The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivar Behav Res. 2018;53:453–80.
Briganti G, Scutari M, McNally RJ. A tutorial on bayesian networks for psychopathology researchers. Psychol Methods. 2023;28:947–61.
McNally RJ, Mair P, Mugno BL, Riemann BC. Co-morbid obsessive–compulsive disorder and depression: a Bayesian network approach. Psychol Med. 2017;47:1204–14.
Chen G, He J, Zhang B, Fan X. Body weight and body dissatisfaction among Chinese adolescents: Mediating and moderating roles of weight-related teasing. Curr Psychol. 2022;41:298–306.
Pakpour AH, Tsai M-C, Lin Y-C, Strong C, Latner JD, Fung XCC, et al. Psychometric properties and measurement invariance of the Weight Self-Stigma Questionnaire and Weight Bias Internalization Scale in children and adolescents. Int J Clin Health Psychol. 2019;19:150–9.
Wu S, Cai T, Luo X. Validation of the Dutch Eating Behavior Questionnaire (DEBQ) in a sample of Chinese adolescents. Psychol Health Med. 2017;22:282–8.
Birleson P, Hudson I, Buchanan DG, Wolff S. CLINICAL EVALUATION OF A SELF‐RATING SCALE FOR DEPRESSIVE DISORDER IN CHILDHOOD (DEPRESSION SELF‐RATING SCALE). J Child Psychol Psychiatry. 1987;28:43–60.
La Greca AM, Dandes SK, Wick P, Shaw K, Stone WL. Development of the Social Anxiety Scale for Children: Reliability and Concurrent Validity. J Clin Child Psychol. 1988;17:84–91.
Huang Y, Gan X, Jin X, Wei Z, Cao Y, Ke H. The healthy context paradox of bullying victimization and academic adjustment among Chinese adolescents: A moderated mediation model. PloS One. 2023;18:e0290452.
Cikili-Uytun M, Efendi GY, Mentese-Babayigit T. The Olweus Bully/Victim Questionnaire. In: Martin C, Preedy VR, Patel VB, editors. Handb Anger Aggress Violence [Internet]. Cham: Springer International Publishing; 2022 [cited 2024 Jun 13]. p. 1–13. Available from: https://doi.org/10.1007/978-3-030-98711-4_123-1
Yuan M, Lin Y. Model selection and estimation in the Gaussian graphical model. Biometrika. 2007;94:19–35.
Tsamardinos I, Brown LE, Aliferis CF. The max-min hill-climbing Bayesian network structure learning algorithm. Mach Learn. 2006;65:31–78.
Brochu PM. Weight stigma as a risk factor for suicidality. Int J Obes. 2020;44:1979–80.
Christian C, Nicholas JK, Penwell TE, Levinson CA. Profiles of experienced and internalized weight-based stigma in college students across the weight spectrum: Associations with eating disorder, depression, and anxiety symptoms. Eat Behav. 2023;50:101772.
Jendrzyca A, Warschburger P. Weight stigma and eating behaviours in elementary school children: A prospective population-based study. Appetite. 2016;102:51–9.
Puhl RM, Luedicke J. Weight-Based Victimization Among Adolescents in the School Setting: Emotional Reactions and Coping Behaviors. J Youth Adolesc. 2012;41:27–40.
Wang Z, Wang B, Hu Y, Cheng L, Zhang S, Chen Y, et al. Relationships among weight stigma, eating behaviors and stress in adolescents in Wuhan, China. Glob Health Res Policy. 2020;5:8.
Araiza AM, Lobel M. Stress and eating: Definitions, findings, explanations, and implications. Soc Personal Psychol Compass. 2018;12:e12378.
van Strien T, Peter Herman C, Verheijden MW. Eating style, overeating and weight gain. A prospective 2-year follow-up study in a representative Dutch sample. Appetite. 2012;59:782–9.
Puhl RM, King KM. Weight discrimination and bullying. Best Pract Res Clin Endocrinol Metab. 2013;27:117–27.
O’Brien KS, Latner JD, Puhl RM, Vartanian LR, Giles C, Griva K, et al. The relationship between weight stigma and eating behavior is explained by weight bias internalization and psychological distress. Appetite. 2016;102:70–6.
Tomiyama AJ. Weight stigma is stressful. A review of evidence for the Cyclic Obesity/Weight-Based Stigma model. Appetite. 2014;82:8–15.
Vartanian LR, Porter AM. Weight stigma and eating behavior: A review of the literature. Appetite. 2016;102:3–14.
Cummings CM, Caporino NE, Kendall PC. Comorbidity of anxiety and depression in children and adolescents: 20 years after. Psychol Bull. 2014;140:816.
Dochnal R, Vetró Á, Kiss E, Baji I, Lefkovics E, Bylsma LM, et al. Emotion Regulation Among Adolescents With Pediatric Depression As a Function of Anxiety Comorbidity. Front Psychiatry [Internet]. 2019 [cited 2024 Jun 16];10. Available from: https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2019.00722/full
Amiri S, Behnezhad S. Obesity and anxiety symptoms: a systematic review and meta-analysis. Neuropsychiatr Klin Diagn Ther Rehabil Organ Ges Osterreichischer Nervenarzte Psychiater. 2019;33:72–89.
Milaneschi Y, Simmons WK, van Rossum EFC, Penninx BW. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry. 2019;24:18–33.
Pace U, Aiello F, Zappulla C. Childhood Obesity: The Relationship Between Negative Emotionality, Emotion Regulation, and Parenting Styles. J Child Fam Stud. 2019;28:2272–9.

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Directional dependence of dysregulated eating, weight stigma, and psychological distress among children with overweight or obesity: a gaussian graphical model and Bayesian network approach

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Abstract

Background

Method

Results

Conclusion

Figures

Plain English Summary

Introduction

Childhood obesity or overweight and Dysregulated eating behavior

Interconnections of Dysregulated Eating, Weight Stigma, and Psychological Distress in obese or overweight children

The gaps between the literature and the current study

Methods

Participants and Procedure

Measurements

Demographic information

Weight stigma

Internalized stigma

Dysregulated eating

Depression

Social anxiety

Bullying Behavior

Statistics analysis

Result

Descriptive analytics

Gaussian graphical models (undirected networks)

Bayesian networks (directed networks)

Discussion

Gaussian graphical models

Bayesian network

Limitation

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1