Long-term changes in disordered eating in patients with obesity after bariatric surgery

Long-term changes in disordered eating in patients with obesity after bariatric surgery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term changes in disordered eating in patients with obesity after bariatric surgery Hui-Lin Zhang, Chuan-Yu Yin, Su-Fang Peng, Lin Liu, Xu-Yan Ban, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7276684/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Background: There may be differences in disordered eating symptoms between healthy individuals and patients with obesity, and bariatric surgerymay influence postoperative disordered eating behaviors in patients with obesity. This study aims to explore the differences in network analysis structures of factors associated with disordered eating symptoms in healthy individuals versus patients with obesity, and further analyze changes in disordered eating symptoms in patients with obesity following BS. Methods: Between September 2020 and June 2023 in Shanghai, China, participants were stratified into two groups based on body mass index (BMI) criteria: a healthy control group (HC, n=61; BMI 18.5-24 kg/m²) and an obese group (OB, n=78; BMI ≥30 kg/m²). All participants completed the Eating Disorders Inventory-2. The OB group subsequently underwent laparoscopic sleeve gastrectomy at participating institutions, with monthly postoperative EDI-2 assessments conducted. Network analysis was employed to examine differences in factors associated with disordered eating symptoms between healthy individuals and patients with obesity, while linear mixed model combined with time series hierarchical cluster analysis were systematically applied to evaluate the interventional effects of BS on disordered eating. Result: EDI-2 total score was significantly higher in obese patients than in the healthy population ( p < 0.05). Network analysis showed that the core symptom nodes for disordered eating symptoms were Social Insecurity in the healthy population and Interoceptive Awareness, Ineffectiveness and Bulimia in the obese patients. The analysis of the postoperative follow-up data showed that the EDI-2 total score of obese patients decreased significantly from the 3rd month after surgery and remained stable ( p < 0.05). The factors related to disordered eating symptoms in obese patients showed three modes of improvement after surgery: Bulimia factor improved significantly in the early postoperative period (2nd month); Impulse regulation, Maturity Fears, Driver for Thinness and Body Dissatisfaction factors showed progressive improvement (3-24 months); Interpersonal Distrust, Social Insecurity, Perfectionism, Interoceptive Awareness, Ineffectiveness and Asceticism factors showed no significant change or even worsened. Conclusion: This study elucidates the network characteristics of disordered eating symptoms in obese patients prior to bariatric surgery, providing an evidence-based foundation for preoperative psychological interventions. Furthermore, by investigating postoperative trajectories of disordered eating symptoms and related factors, our findings highlight the critical need for future research to: (1) monitor long-term (>2 years) evolution of disordered eating symptoms, and (2) systematically evaluate psychological factor dynamics during postoperative follow-up. These investigations should inform the development of tailored psychological therapies to optimize surgical outcomes. Obesity Disordered eating Bariatric surgery EDI-2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Obesity and disordered eating represent two escalating global health concerns [1] . Obesity is typically defined as excessive adipose tissue accumulation leading to body weight beyond normal ranges, while disordered eating encompasses a spectrum of abnormal eating behaviors including binge-eating disorder, anorexia nervosa, and bulimia nervosa [2] . Although theoretically and clinically distinct, these conditions share interconnected etiological factors, risk profiles, and maintenance mechanisms, with neither constituting a singular disease entity [2] . In recent years, network analysis has gained prominence in investigating symptom characteristics of obesity and disordered eating. This methodology constructs and analyzes relational networks among variables, revealing complex interrelationships and identifying pivotal factors in disease progression [3] . Studies employing disordered eating symptom networks have identified binge eating and body dissatisfaction as central nodes [4] , while obesity research has highlighted anxiety and depression as key network components strongly associated with unhealthy eating patterns and physical inactivity [4] . Despite these advances, applications of network analysis in characterizing preoperative disordered eating symptoms among bariatric surgery (BS) candidates remain limited. Obesity patients frequently present complex psych behavioral comorbidities emotional dysregulation, body image disturbances, and aberrant eating behaviors that may significantly impact surgical outcomes and postoperative recovery [5] . Comparative network modeling between healthy controls and preoperative obesity patients could elucidate population specific symptom profiles in dietary behaviors and psychological factors, thereby informing targeted psychological interventions and personalized management strategies. Current therapeutic approaches for disordered eating primarily involve psychological and pharmacological interventions. Cognitive behavioral therapy (CBT), while widely implemented, demonstrates gradual efficacy only a minority of bulimia nervosa patients achieve recovery within 2-4 years, with approximately half requiring six years, while showing limited effectiveness for adult anorexia nervosa [6] . Pharmacological options remain constrained by modest efficacy and adverse effect profiles [7] . These limitations underscore the need for more rapid and effective interventions. Notably, many obese individuals with disordered eating symptoms pursue weight reduction strategies for symptom alleviation. Some studies report non-worsening or even improvement of disordered eating symptoms during obesity treatment and early follow-up [8] , with concomitant enhancements in psychological metrics including depression, anxiety, self-esteem, body image, and quality of life following weight management interventions [9] . Within this context, BS as a prevalent obesity intervention has garnered attention for its potential effects on disordered eating. However, research outcomes remain divergent: while some studies demonstrate symptom improvement postoperative, others report limited efficacy or negligible impact on disordered eating symptoms [10–12] . Therefore, this study employed the EDI-2 scale to evaluate disordered eating symptoms and related factors in both healthy controls and obese patients. Subsequently, network models of disordered eating related factors were constructed and compared between these two groups to identify differences in their network structural characteristics. Then, linear mixed model and time series hierarchical cluster analysis methods were applied to investigate the changing trends of disordered eating symptoms and associated factors in obese patients after BS. The study proposes the following hypotheses: (1) Significant differences exist in disordered eating symptoms and related factors between healthy individuals and obese patients before BS; (2) The network model structures of disordered eating related factors differ between healthy individuals and obese patients prior to surgery; (3) BS can effectively improve disordered eating symptoms in obese patients; (4) The improvement effects of BS on disordered eating related factors vary across different symptom domains. Methods and Materials Participants Between September 2020 and June 2023, participants were enrolled into either an obesity group (OB group) or healthy control group (HC group) based on BMI criteria and eligibility for sleeve gastrectomy. The obesity group comprised 78 participants (61 females, 17 males), all of whom subsequently underwent sleeve gastrectomy. The healthy control group consisted of 61 participants (47 females, 14 males). Inclusion and Exclusion Criteria: OB group Inclusion Criteria: BMI ≥ 30 kg/m² meeting indications for sleeve gastrectomy [13] ; Absence of anxiety, depression, sleep disorders or other major illnesses; Age range 18-60 years; Ability to comprehend questionnaire items; Willingness to participate with signed informed consent. Obesity Group Exclusion Criteria: Conditions potentially affecting metabolism/weight (e.g., hyper/hypothyroidism, Cushing syndrome) [13] ; Psychiatric disorders (assessed by psychiatrists) including severe eating disorders (bulimia nervosa, binge-eating disorder), schizophrenia, or major depressive disorder; Prior BS (e.g., gastric bypass) or long-term use of metabolism affecting medications (e.g., corticosteroids, antipsychotics); Pregnancy or malignancy [13] . HC group Inclusion Criteria: BMI 18.5-24 kg/m² not meeting surgical indications [112] ; Absence of anxiety, depression, sleep disorders or major illnesses; Age and sex distribution matched to obesity group; Ability to comprehend questionnaire items; Willingness to participate with signed informed consent. HC group Exclusion Criteria (ensuring comparability): BMI ≥ 24 kg/m²; Recent (6-month) use of weight-loss medications, hormonal drugs, or participation in structured weight-loss programs; Gastrointestinal disorders potentially affecting eating behavior assessment (e.g., chronic gastritis, GERD, functional dyspepsia) [13] ; Psychiatric disorders (psychiatrist assessed). Ethical Considerations: This study was approved by the Ethics Committee of Shanghai Sixth People's Hospital (Approval No 2020-219-(1)). All procedures complied with the Declaration of Helsinki. Methods Data Collection Demographic and anthropometric data including age, gender, height, weight, and BMI were collected for both the healthy control group and obese patient group prior to surgery. Assessment of Disordered Eating Symptoms The Eating Disorders Inventory-2 (EDI-2) was administered to evaluate disordered eating symptoms and related factors in both the HC group and the OB group. The EDI-2 is a standardized self-report questionnaire primarily designed to assess the presence of disordered eating symptoms and associated behavioral and psychological issues. Originally developed by David Garner and Paul Garfinkel in 1979, the questionnaire consists of 91 items grouped into 11 subscales: 3 behavioral subscales related to disordered eating symptoms: Drive for Thinness (Dft), Bulimia (Bul), and Body Dissatisfaction (Bod); 8 psychological subscales: Ineffectiveness (Ine), Perfectionism (Per), Interpersonal Distrust (Dis), Maturity Fears (Fea), Interoceptive Awareness (Awa), Asceticism (Asc), Impulse Regulation (Imp), and Social Insecurity (Soc) [14–16] . In this study, each subscale was scored using a 4-point Likert scale, where 0 indicated the least symptomatic response and 3 represented the most symptomatic response. The EDI-2 assesses disordered eating based on a comprehensive analysis of multiple subscales rather than individual subscale scores. Subscale scores were calculated as the mean of summed item scores, while the total score was derived from the mean of all subscale scores. Clinicians can determine the risk of disordered eating by analyzing subscale and total scores, with higher scores indicating greater symptom severity [14–16] . In this study, the Cronbach’s α coefficient for the EDI-2 was 0.978 (Table S1). LSG LSG is a safe and effective bariatric procedure with long-lasting weight loss results. It was performed in this study by specialized surgeons trained in BS [17] . The LSG procedure is performed with the patient under general anesthesia. The surgeon makes approximately five small incisions in the patient’s abdomen using a long, thin telescope with a tiny camera at the end. LSG causes the stomach to become smaller and fill up quickly at mealtime, causing the patient to eat less. LSG also increases the release of hormones such as growth hormone releasing peptide, which in turn reduces hunger and helps the patient lose weight [17] . Data Analysis Analysis of Demographic Characteristics Demographic characteristics of both healthy and obese groups were analyzed using jamovi 2.4.11 and SPSS 22.0 software. Continuous variables were assessed as follows: normality was examined using the Shapiro-Wilk test, while homogeneity of variance was evaluated with Levene's test. Based on these assessments: For data meeting both normality and homogeneity of variance assumptions, Student's t-test was employed, with results presented as mean ± standard deviation (Mean ± SD); For data meeting normality but not homogeneity of variance assumptions, Welch's t-test was used, with results also expressed as Mean ± SD; For non-normally distributed data, the Mann-Whitney U test was applied, with results reported as median (25th percentile, 75th percentile) [Median (P25, P75)]. Categorical variables were analyzed using χ² tests, with results presented as number (percentage) [n (%)]. For missing data on social insecurity factors in the OB group: Normality of preoperative social insecurity data was first confirmed by Shapiro-Wilk test ( p > 0.05); Subsequently, mean imputation was performed to address missing values (Table S1). network analysis Incorporate the subscale scores of the HC group and OB group EDI-2 to construct a network analysis model. The network analysis model was built in R (version 4.3.1) using the quickNet package and the rlang package from the open-source toolkit LeiGuo0812/quickNet on GitHub. In the network model, nodes (various subscales of EDI-2) are represented by circles, with larger circles indicating stronger network properties of the node. Connections between nodes are represented by lines (the thicker the line between two nodes, the stronger the association between them), and the numbers on the lines indicate the correlation coefficients between the two nodes (positive numbers indicate a positive correlation, while negative numbers indicate a negative correlation) [18] . Strength, Closeness, Betweenness, and Expected influence are centrality metrics of the network analysis model, which can measure the network properties of the model [19] . Linear mixed model Linear mixed model was created by jamovi 2.3.26 software and used to analyze the correlation between the EDI-2 total score and the postoperative time. Comparing the changes in the EDI-2 total scores from baseline to 24 months for the OW/OB group, the dependent variable was the EDI-2 total score in the Ow/Ob group, the independent variable was the postoperative time, and the random factor was the participants No. in the Ow/Ob group, based on the formula EDI-2 total score ~ 1 + time+( 1 | Participant No.) for the calculation. Time-series hierarchical cluster analysis A linear mixed model was built by jamovi 2.3.26 to calculate the mean score of each factor of the EDI-2 for each postoperative month, and a time-series hierarchical cluster analysis was performed using the mean scores of these factors. The pheatmap package was used in R 4.3.1, with the factors as columns and time as rows to build the matrix for the time-series hierarchical cluster analysis. The time-series hierarchical cluster analysis was performed using z-scores to normalize the columns; i.e., the data of the postoperative factors were normalized with the preoperative data, and the ward.D algorithm was used to perform hierarchical clustering in which the clusters were separated by the Euclidean distance, the rows were arranged visually by time points, and the columns were arranged by factors [20] . Results Baseline characteristics There were no statistically significant differences in gender, age, Per, Dis, or Soc between the HC group and the OB group ( p > 0.05). However, the OB group had significantly higher BMI, Dft, Bul, Bod, Awa, Ine, Fea, Asm, Imp, and total EDI-2 scores compared to the HC group ( p < 0.05) (Tab.1). Stability of network analysis models The scores of each subscale of the EDI-2 before BS were included for both the HC group and the OB group, and network analysis models were constructed for each group to reveal the characteristics of disordered eating symptoms in healthy individuals and obese patients before BS. In the HC group's network analysis model, the stability of Betweenness centrality and Strength centrality both exceeded 0.5. In the OB group's network analysis model, the stability of Betweenness centrality, Closeness centrality, and Strength centrality all exceeded 0.5. This indicates that the network analysis models of both groups have good structural stability, and the relationships between nodes are relatively reliable (Fig. 1). Structural characteristics of the network analysis model in the healthy population The network analysis model of the HC group revealed weak associations among the 11 disordered eating related factors, with most nodes showing no significant correlations. Stronger positive correlations were observed between Soc and Dis (r = 0.28) as well as Ine (r = 0.15). Bul demonstrated only a weak positive association with Awa (r = 0.08). In contrast, Imp, Per, Fea, Asc, Bod, and Dft showed no connections with other nodes (Fig. 2a). Centrality metrics indicated that Soc had high values for Strength, Closeness, Betweenness, and Expected influence, suggesting its central role in the network. In comparison, Per, Fea, Imp, Dft, Bod, and Asc scored zero across all centrality indices, indicating minimal influence. Dis exhibited moderate strength and expected influence but low closeness and zero betweenness. Awa and Ine showed low strength, zero betweenness, and weak expected influence. Bul had low strength, zero betweenness, and limited expected influence. In summary, the HC group's network model demonstrated generally weak connections among disordered eating related factors, with Soc emerging as the central element, while other factors played relatively minor or indirect roles (Fig. 2b). Structural characteristics of the network analysis model of disordered eating symptoms in obese patients The network analysis model of the OB group revealed strong interconnections among the 11 disordered eating symptom related factors. A strong positive correlation was observed between Dis and Soc (r = 0.53), and a moderately strong positive correlation with Ine (r = 0.28). Ine showed moderately strong positive correlations with Soc (r = 0.19), Bul (r = 0.19), and Awa (r = 0.17), while exhibiting weaker positive correlations with Bod (r = 0.05), Imp (r = 0.04), and Asc (r = 0.1). Awa demonstrated moderately strong positive correlations with Per (r = 0.14), Bul (r = 0.16), Imp (r = 0.2), Asc (r = 0.25), and Fea (r = 0.17), but weaker correlations with Bul (r = 0.01), Dft (r = 0.01), and Bod (r = 0.01). Bul showed moderately strong positive correlations with Bod (r = 0.17), Imp (r = 0.28), Awa (r = 0.16), and Per (r = 0.17), while having a weaker correlation with Dft (r = 0.07). Imp exhibited a strong positive correlation with Asc (r = 0.31), but weaker correlations with Per (r = 0.04) and Bod (r = 0.08). Asc showed a weak positive correlation with Dft (r = 0.06). Bod had a moderately strong positive correlation with Dft (r = 0.25), while Dft demonstrated a moderately strong correlation with Fea (r = 0.11) (Fig. 3a). Centrality metrics indicated that Awa had the highest strength, closeness, betweenness, and expected influence, making it the core factor in this network. Ine and Bul showed high strength and expected influence, along with high betweenness, suggesting their strong regulatory effects on other factors in the network. Imp exhibited relatively high strength and moderate betweenness, indicating a certain degree of influence. Dis had high strength but zero betweenness, implying strong direct effects but limited indirect influence. Soc and Asc showed moderate strength but zero betweenness, indicating direct effects without indirect mediation. Per, Fea, Dft, and Bod displayed varying levels of strength (moderate to low) and inconsistent betweenness, suggesting complex roles in the network (Fig. 3b). In summary, Awa, Ine, and Bul emerged as core factors of disordered eating symptoms in obese patients, while the roles of other factors appeared more complex (Fig. 3b). BS improves disordered eating symptoms A linear mixed model was utilized to investigate changes in disordered eating symptoms during the two-year period following BS in obese patients. As shown in Figure 4a, the EDI-2 total scores demonstrated a significant decrease starting at 3 months post-surgery ( t = -2.00, p = 0.046), with this reduction remaining stable from month 4 ( t = -4.14, p < 0.001) through month 24 ( t = -3.56, p < 0.001) (Fig. 4a). Additionally, the EDI-2 total scores at 18 months post-surgery were significantly lower than those at 17 months ( p < 0.05) (Fig. 4b). Changes in disordered eating symptom related factors following BS To examine the evolving trends of disordered eating symptom related factors following BS, this study incorporated monthly postoperative factor scores from the obese cohort, employing linear mixed model and time series hierarchical cluster analysis for analysis. The analysis revealed three distinct patterns among the 11 factors based on Figure5: The Bul factor demonstrated measurable improvement beginning at 2 months post-surgery; Factors including Imp, Fea, Dft, and Bod exhibited progressive improvement throughout the 3-24 month postoperative period; The cluster comprising Dis, Soc, Per, Awa, Ine, and Asc factors showed no clinical improvement after surgery, with some demonstrating worsening symptoms (Fig. 5, Fig. S2-S3). Discussion This study employed network analysis to reveal the structural characteristics of network models associated with disordered eating symptoms in healthy individuals and obese patients before BS. The main findings were: (1) The severity of disordered eating symptoms in obese patients before surgery was significantly higher than that in healthy individuals; (2) The associations between factors related to disordered eating symptoms were weak or absent in healthy individuals, whereas obese patients exhibited complex positive correlations among these factors; (3) In the network model of disordered eating symptoms in healthy individuals, Soc was the central node, while the central nodes in obese patients were Awa, Ine, and Bul. In this study, the network structure of factors associated with disordered eating symptoms in healthy individuals was relatively loose, with only three positive correlations among the 11 factors (Soc-Dis, Soc-Ine, and Bul-Awa), all of which had correlation coefficients below 0.3. This aligns with the findings of Borsboom et al., suggesting that psychological networks in healthy populations typically exhibit low connectivity density. Similarly, Robinaugh et al. observed in their network analysis of depression that the strength of associations among depressive symptoms in healthy groups was significantly weaker than in clinical groups [ 21 ] . This study also found that centrality measures for factors related to disordered eating symptoms were generally low in healthy individuals. Although Soc had relatively high betweenness centrality, its actual influence on other factors was limited due to the overall sparse network connectivity. This finding contradicts Fairburn et al.'s conclusion that Soc is a major contributing factor in disordered eating [ 22 ] . Additionally, the study suggests that the lack of synergistic interactions among these factors in healthy individuals may help prevent pathological effects. However, further research is needed to confirm this hypothesis. The network model of factors associated with disordered eating symptoms in obese patients exhibited significant complexity, forming a core structure centered around Ine, Awa, and Bul. In this network, Bul was not only directly linked to Ine and Awa but also indirectly influenced Asc through Imp. This tightly interconnected network structure aligns with the findings of Levinson et al., who suggested that binge eating acts as a "central hub" in overweight populations, perpetuating a vicious cycle by reinforcing negative emotions and maladaptive cognitions [ 23 ] . The central role of Awa in our study is consistent with Klabunde et al.'s fMRI research, which demonstrated that abnormal interoceptive awareness in overweight individuals may contribute to emotional eating behaviors [ 24 ] . Additionally, our findings highlighted the prominence of Ine in both betweenness centrality and strength, supporting Hilbert et al.'s conclusion that Ine can directly trigger compensatory eating and may indirectly exacerbate disordered eating symptoms by impairing emotion regulation [ 25 ] . Notably, while Fea showed no significant associations with other factors in healthy individuals, it appeared to play a role in the development of disordered eating symptoms in obese patients. This may be related to weight stigma, as Puhl et al. found that overweight adolescents subjected to weight based bullying experienced delayed psychological maturation, which in turn promoted extreme dietary behaviors [ 26 ] . Our study identified Bul, Ine, and Awa as the core factors in disordered eating symptoms among obese patients, which contrasts with the findings of Olatunji et al. [ 27 ] . Their study included participants with BMIs ranging from 18.51 to 55.09 kg/m², encompassing normal weight to severely obese individuals, potentially introducing confounding effects from non-obese populations (e.g., normal weight or overweight individuals). In comparison, our research specifically examined obese patients scheduled for BS a more homogeneous group with narrower BMI ranges and typically more severe eating pathology. This population difference likely accounts for the observed variation in core network factors, making our findings more representative of the preoperative disordered eating symptom network in bariatric candidates. Based on our network analysis of preoperative disordered eating factors in obese patients, we propose the following evidence based preoperative interventions to optimize surgical outcomes. For impaired Awa: Implement mindfulness based interventions to enhance bodily signal recognition and disrupt its pathological connections with Imp and Bul [ 28 ] . For Ine related issues: Apply cognitive restructuring techniques to improve self-efficacy and prevent progression to binge eating [ 29 ] . For established Bul with strong Bul-Imp associations: Incorporate targeted impulse control regulation strategies. For significant Bod-Dft correlations: Administer cognitive behavioral therapy combined with body image acceptance interventions to mitigate disordered eating symptoms [ 30 , 31 ] . Additionally, this study utilized linear mixed models and time series hierarchical cluster analysis to investigate the effects of BS on disordered eating symptoms and related factors in obese patients. The results demonstrated that patients' overall disordered eating symptoms showed significant improvement and stabilization within 24 months post-surgery, with the EDI-2 total scores exhibiting a consistent decline from the 3rd to the 24th postoperative month. These improvements appear closely associated with physiological regulatory mechanisms induced by BS. Specifically, the surgery may modulate gastrointestinal hormone secretion (e.g., GLP-1, ghrelin) [ 32 ] , it enhances appetite regulation through the hypothalamic arcuate nucleus, it induces functional remodeling in brain regions including the amygdala and suprachiasmatic nucleus [ 33 ] . These physiological changes collectively contribute to reduced binge eating behaviors and facilitated weight loss. Notably, emerging evidence suggests postoperative alterations in taste and olfactory sensitivity may further suppress food intake [ 34 ] , thereby accelerating improvement in disordered eating symptoms. Our in depth analysis revealed differential surgical effects across 11 disordered eating related factors. Time series hierarchical cluster analysis categorized these factors into three distinct improvement patterns: Marked improvement: Bul; Moderate improvement: Imp, Fea, Dft, and Bod; Minimal improvement or worsening: Dis, Soc, Per, Awa, Ine, and Asc. It is noteworthy that our study identified Bul as a central factor in preoperative disordered eating symptoms among obese patients undergoing BS. Building on this finding, we further observed that BS led to significant improvement in Bul symptoms. However, the other two core factors Ine and Awa showed no substantial postoperative improvement. This differential response suggests that BS may have limited efficacy in addressing the psychological dimensions of disordered eating, a finding consistent with Beck et al.'s research [ 35 ] .This phenomenon may be explained by the surgery's neurobiological mechanisms: while effectively modulating appetite regulating brain regions (such as the hypothalamus and amygdala), it appears to have minimal impact on psychological traits mediated by the prefrontal cortex's social cognitive functions [ 36 ] . Consequently, improvements in psychosocial factors tend to lag behind physiological changes, and these unresolved psychological issues may pose potential risks for the recurrence of disordered eating behaviors post-surgery. These findings highlight the clinical importance of implementing adjunctive psychological interventions following BS. Such complementary therapies could significantly contribute to sustained improvement in disordered eating symptoms by addressing the psychological components that surgical intervention alone cannot adequately modify. Limitations This study has several limitations that should be acknowledged. First, the cross-sectional design only allowed us to examine preoperative associations between factors, without capturing their postoperative trajectories. Future longitudinal cohort studies are needed to investigate the dynamic changes in these factors over time. Second, our study exclusively focused on obese patients scheduled for BS, without including a non-surgical control group. This selection bias may limit the generalizability of our findings, and future research should incorporate broader patient populations to enhance the external validity of the results. Conclusion In this study, we found that there were differences in the network model structure and core nodes of factors related to disordered eating symptoms in healthy people and obese patients, and that therapeutic measures can be taken to address the network characteristics of disordered eating symptoms in obese patients. BS can improve disordered eating symptoms in obese patients, especially for Bul, Imp, Fea, Dft and Bod factors, but not for Dis, Soc and other psychological factors or even aggravate, so we can provide appropriate psychological treatment to promote the improvement of disordered eating symptoms in obese patients after surgery. Declarations Author Contributions Huilin Zhang: data curation, formal analyses, methodology, visualization, writing—original draft. Chuanyu Yin: writing—reviewing and editing. Sufang Peng: writing—reviewing and editing. Lin Liu: writing—reviewing and editing. Xuyan Ban: writing—reviewing and editing. Hongwei Zhang: writing—reviewing and editing. Ting Xu, investigation, writing—reviewing and editing. Chen Wang: writing—reviewing and editing. Gang Peng: writing—reviewing and editing. Xiao-Dong Han: supervision. Hui Zheng: conceptualization and project administration. Jian-Zhong Di: funding acquisition. Conflicts of interest The authors each declare that they have no conflict of interest. Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed Consent Informed consent was obtained from all individual participants included in the study. Funding Support was provided by the Shanghai Health Commission project (NO. 20214098); Shanghai Science and Technology Committee project (NO. 22dz1204700), National Science and Technology Major Project of the Ministry of Science and Technology of China (NO. 2022YFC2407004). References Anonymous. Obesity: causes, consequences, treatments, and challenges[J]. Journal of Molecular Cell Biology, 2021, 13(7): 463-465. Zhang X, Ha S, Lau H C-H, et al . Excess body weight: Novel insights into its roles in obesity comorbidities[J]. Seminars in Cancer Biology, 2023, 92: 16-27. Tie D, He M, Li W, et al . Advances in the application of network analysis methods in traditional Chinese medicine research[J]. Phytomedicine, 2025, 136: 156256. Sala M, Kressel M, Schechter A, et al . The study of eating disorders from a network perspective: A scoping systematic review.[J]. Clinical Psychology: Science and Practice, 2024, 31(3): 296-322. Kubik J F, Gill R S, Laffin M, et al . The Impact of Bariatric Surgery on Psychological Health[J]. Journal of Obesity, 2013, 2013: 1-5. Vall E, Wade T D. Predictors of treatment outcome in individuals with eating disorders: A systematic review and meta‐analysis[J]. International Journal of Eating Disorders, 2015, 48(7): 946-971. Hay P. Current approach to eating disorders: a clinical update[J]. Internal Medicine Journal, 2020, 50(1): 24-29. Tsompanaki E, Koutoukidis D A, Wren G, et al . The impact of weight loss interventions on disordered eating symptoms in people with overweight and obesity: a systematic review & meta-analysis[J]. EClinicalMedicine, 2025, 80: 103049. Tan E J, Raut T, Le L K-D, et al . The association between eating disorders and mental health: an umbrella review[J]. Journal of Eating Disorders, 2023, 11(1): 51. Rubino F, Cummings D E, Eckel R H, et al . Definition and diagnostic criteria of clinical obesity[J]. The Lancet Diabetes & Endocrinology, 2025, 13(3): 221-262. Wang Y, Guo X, Lu X, et al . Mechanisms of Weight Loss After Sleeve Gastrectomy and Adjustable Gastric Banding: Far More Than Just Restriction[J]. Obesity, 2019, 27(11): 1776-1783. Diamantis T, Apostolou K G, Alexandrou A, et al . Review of long-term weight loss results after laparoscopic sleeve gastrectomy[J]. Surgery for Obesity and Related Diseases, 2014, 10(1): 177-183. Tchang B G, Aras M, Kumar R B, et al . Pharmacologic Treatment of Overweight and Obesity in Adults[M/OL]. Allen K L, Crosby R D, Oddy W H, et al . Eating disorder symptom trajectories in adolescence: effects of time, participant sex, and early adolescent depressive symptoms[J]. Journal of Eating Disorders, 2013, 1(1): 32. Salbach-Andrae H, Schneider N, Bürger A, et al . Psychometrische Gütekriterien des Eating Disorder Inventory (EDI-2) bei Jugendlichen[J]. Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie, 2010, 38(3): 219-228. Aloi M, Rania M, Caroleo M, et al . Decision making, central coherence and set-shifting: a comparison between Binge Eating Disorder, Anorexia Nervosa and Healthy Controls[J]. BMC Psychiatry, 2015, 15(1): 6. Palermo M, Serra E. Laparoscopic Sleeve Gastrectomy: How Do I Do It[J]. Journal of Laparoendoscopic & Advanced Surgical Techniques, 2020, 30(1): 2-5. De Vos J A, Radstaak M, Bohlmeijer E T, et al . The psychometric network structure of mental health in eating disorder patients[J]. European Eating Disorders Review, 2021, 29(4): 559-574. Forrest L N, Jones P J, Ortiz S N, et a l. Core psychopathology in anorexia nervosa and bulimia nervosa: A network analysis[J]. International Journal of Eating Disorders, 2018, 51(7): 668-679. Depommier C, Vitale R M, Iannotti F A, et al . Beneficial Effects of Akkermansia muciniphila Are Not Associated with Major Changes in the Circulating Endocannabinoidome but Linked to Higher Mono-Palmitoyl-Glycerol Levels as New PPARα Agonists[J]. Cells, 2021, 10(1): 185. Bringmann L F, Albers C, Bockting C, et al . Psychopathological networks: Theory, methods and practice[J]. Behaviour Research and Therapy, 2022, 149: 104011. Fairburn C G, Cooper Z, Shafran R. Cognitive behaviour therapy for eating disorders: a “transdiagnostic” theory and treatment[J]. Behaviour Research and Therapy, 2003, 41(5): 509-528. Levinson C A, Cash E, Welch K, et al . Personalized networks of eating disorder symptoms predicting eating disorder outcomes and remission[J]. International Journal of Eating Disorders, 2020, 53(12): 2086-2094. Cosío-Guirado R, Tapia-Medina M G, Kaya C, et al . A comprehensive systematic review of fMRI studies on brain connectivity in healthy children and adolescents: Current insights and future directions[J]. Developmental Cognitive Neuroscience, 2024, 69: 101438. Giovannini E N. Bridging the gap between analytic and synthetic geometry: Hilbert’s axiomatic approach[J]. Synthese, 2016, 193(1): 31-70. Puhl R M, Luedicke J, Heuer C. Weight-Based Victimization Toward Overweight Adolescents: Observations and Reactions of Peers[J]. Journal of School Health, 2011, 81(11): 696-703. Olatunji B O, Levinson C, Calebs B. A network analysis of eating disorder symptoms and characteristics in an inpatient sample[J]. Psychiatry Research, 2018, 262: 270-281. Wu Q, Mao X, Luo W, et al . Enhanced interoceptive attention mediates the relationship between mindfulness training and the reduction of negative mood[J]. Psychophysiology, 2022, 59(4): e13991. Larsson A, Hooper N, Osborne L A, et al . Using Brief Cognitive Restructuring and Cognitive Defusion Techniques to Cope With Negative Thoughts[J]. Behavior Modification, 2016, 40(3): 452-482. Gopan H, Rajkumar E, Gopi A, et al . Mindfulness‐based interventions for body image dissatisfaction among clinical population: A systematic review and meta‐analysis[J]. British Journal of Health Psychology, 2024, 29(2): 488-509. Furukawa Y, Sakata M, Yamamoto R, et al . Components and Delivery Formats of Cognitive Behavioral Therapy for Chronic Insomnia in Adults: A Systematic Review and Component Network Meta-Analysis[J]. JAMA Psychiatry, 2024, 81(4): 357. Mulla C M, Middelbeek R J W, Patti M. Mechanisms of weight loss and improved metabolism following bariatric surgery[J]. Annals of the New York Academy of Sciences, 2018, 1411(1): 53-64. Song J, Choi S-Y. Arcuate Nucleus of the Hypothalamus: Anatomy, Physiology, and Diseases[J]. Experimental Neurobiology, 2023, 32(6): 371-386. Ahmed K, Penney N, Darzi A, et al . Taste Changes after Bariatric Surgery: a Systematic Review[J]. Obesity Surgery, 2018, 28(10): 3321-3332. Beck N N, Mehlsen M, Støving R K. Psychological characteristics and associations with weight outcomes two years after gastric bypass surgery: Postoperative eating disorder symptoms are associated with weight loss outcomes[J]. Eating Behaviors, 2012, 13(4): 394-397. Li G, Ji G, Hu Y, et al . Bariatric surgery in obese patients reduced resting connectivity of brain regions involved with self‐referential processing[J]. Human Brain Mapping, 2018, 39(12): 4755-4765. Table Tab. 1 Demographic characteristics of the HC group and the OB group Characteristic HC group (n=61) OB group (n=78) t/χ²/U z p Female，[n (%)] a 77% 78% 0.03 - 0.871 BMI (kg/m 2 ) c 21.10±1.24 38.30±1.97 -24.6 - < .001 Age b 28.03(25.12,33.04) 29.12(24.53,33.39) 2375.5 -0.02 0.990 Dft b 0.43(0.14,0.57) 1.14 (0.71,1.75) 672.5 -7.27 < .001 Bul b 0.00(0.00,0.00) 0.29(0.00,1.143) 1009 -6.22 < .001 Bod c 1.24±0.45 2.00±0.41 -10.37 - < .001 Ine b 0.70(0.50,1.00) 0.80 (0.60,1.43) 1856 -2.23 0.026 Per b 0.67(0.33,1.33) 0.67 (0.33,1.21) 2212 -0.71 0.477 Dis b 1.43(0.71,1.86) 1.43 (0.86,1.86) 2285 -0.40 0.691 Awa b 0.30(0.20,0.40) 0.55(0.30,0.80) 1333 -4.48 < .001 Fea b 1.25(1.07,1.50) 1.38(1.25,1.91) 1742 -2.72 < .007 Asc b 0.38(0.25,0.50) 0.5(0.38,0.75) 1586 -3.44 < .001 Imp b 0.09(0.00,0.32) 0.27(0.09,0.66) 1463 -3.94 < .001 Soc c 1.45±0.58 1.52±0.58 -0.675 - 0.501 EDI-2 total score b 7.97(6.98,9.47) 11.25(8.86,13.61) 1078.5 -5.52 < .001 Note a Chi square test; b Mann-Whitney U test was employed, expressed using median (25th percentile, 75th percentile); c Student's t-test was employed，expressed using Mean ± SD. A p < 0.05 indicates statistical significance. Additional Declarations No competing interests reported. Supplementary Files Supplementalinformation.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 03 Nov, 2025 Reviews received at journal 18 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 02 Aug, 2025 First submitted to journal 02 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7276684","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495340251,"identity":"b5875c61-bf73-4f95-b9b3-97dcbbd29770","order_by":0,"name":"Hui-Lin Zhang","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui-Lin","middleName":"","lastName":"Zhang","suffix":""},{"id":495340252,"identity":"15892019-1140-4e3d-ab0c-b35d8075c51c","order_by":1,"name":"Chuan-Yu Yin","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chuan-Yu","middleName":"","lastName":"Yin","suffix":""},{"id":495340253,"identity":"a682efdb-7a03-4d18-96d9-2afeffc416c2","order_by":2,"name":"Su-Fang Peng","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Su-Fang","middleName":"","lastName":"Peng","suffix":""},{"id":495340254,"identity":"caac753f-01d8-4559-972b-8406eee5910a","order_by":3,"name":"Lin Liu","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liu","suffix":""},{"id":495340255,"identity":"d84134e0-7f05-459d-9d43-50557193b1e1","order_by":4,"name":"Xu-Yan Ban","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xu-Yan","middleName":"","lastName":"Ban","suffix":""},{"id":495340256,"identity":"99c508cb-e835-4b72-ba6b-0b6da317e01e","order_by":5,"name":"Hong-Wei Zhang","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong-Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":495340257,"identity":"b7559a5a-157a-47e8-86fe-32be1eb8cc39","order_by":6,"name":"Ting Xu","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Xu","suffix":""},{"id":495340258,"identity":"7dae141f-d6a7-4618-8b2e-a7f823871bc2","order_by":7,"name":"Chen Wang","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""},{"id":495340259,"identity":"bb372185-72eb-4a68-8ff8-ffca43bac3a4","order_by":8,"name":"Xiao-Dong Han","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Dong","middleName":"","lastName":"Han","suffix":""},{"id":495340260,"identity":"687d5c16-eaf9-4baf-a924-2f90e8e5b56a","order_by":9,"name":"Hui Zheng","email":"","orcid":"","institution":"National Center for Mental Disorders, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zheng","suffix":""},{"id":495340261,"identity":"5959e25d-e01e-49cb-8afd-6caa1557f85e","order_by":10,"name":"Jian-Zhong Di","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYDCCwwwJB4BUAhAzPmDgAYsZEK2F2YCBx4AILQcgFEgLmwRUNX4tfMcZHh74uaMuj1+6/VrlD5k/iQ3szdskGGpsonFpkQQ67GDvGbZiyTlnym7z8BgkNvAcK5NgOJaW24BDiwHIL7xtPIkbbuSk3WYAaZHIMZNgbDiMV8vBv20SYC2FP0Ba5N8Q1nKYt80AqCX9GAPYYRI8+LWA/HJYti0hceaMHGZpHh5j4zaetGKLBDx+4Tt/Jvnj27a6xH6J9Icff/bIyfazH95440ONDU4tDAw8CTCGAQNjDzB2QOwEnMpBgP0AjPGAgeEHXqWjYBSMglEwQgEA7Ptdj+inKTIAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jian-Zhong","middleName":"","lastName":"Di","suffix":""}],"badges":[],"createdAt":"2025-08-02 08:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7276684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7276684/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88500284,"identity":"f6f908a4-f130-450e-bb6e-cd2f7a3388fb","added_by":"auto","created_at":"2025-08-07 06:50:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStability of Network Analysis.\u003c/strong\u003e Figures (a)-(b) depict the stability of network analysis for the HC group and the OB group, respectively. The red line indicates the stability of Betweenness centrality among nodes, the green line signifies the stability of Closeness centrality among nodes, and the blue line denotes the stability of node Strength centrality among nodes. A stability value exceeding 0.5 suggests that the network analysis structure is stable, with higher values reflecting greater stability.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7276684/v1/7b099bfb79bff7a11bcfcfe9.png"},{"id":88500285,"identity":"6f0ec075-dd08-4d37-8ed9-8a9a38805c80","added_by":"auto","created_at":"2025-08-07 06:50:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe network analysis diagram of the 11 factors associated with disordered eating symptoms and centrality measures in the HC group.\u003c/strong\u003e (a) Circles represent nodes, which correspond to the factors related to disordered eating symptoms, specifically the 11 subscales of the EDI-2. The size of each circle indicates the strength of the node's network attributes, with larger circles denoting stronger attributes. Connections between nodes are represented by lines, and the numerical values on the lines indicate Pearson correlation coefficients. (b)The centrality metrics include strength, closeness, betweenness, and expected influence. The vertical axis represents the factors related to disordered eating symptoms, specifically the 11 subscales of the EDI-2, while the horizontal axis denotes the numerical values of the centrality metrics for each subscale.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7276684/v1/88b52d04ea755ce60b11ce62.png"},{"id":88500287,"identity":"24e7435d-a0c8-49c5-8b97-597891c2803c","added_by":"auto","created_at":"2025-08-07 06:50:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe network analysis diagram of the 11 factors associated with disordered eating symptoms and centrality measures of the OB group. \u003c/strong\u003e(a) Circles represent nodes, which correspond to the factors related to disordered eating symptoms, specifically the 11 subscales of the EDI-2. The size of each circle indicates the strength of the node's network attributes, with larger circles denoting stronger attributes. Connections between nodes are represented by lines, and the numerical values on the lines indicate Pearson correlation coefficients. (b) The centrality measures include strength, closeness centrality, betweenness centrality, and expected influence. The vertical axis represents the disordered eating symptom-related factors, which are the 11 subscales of the Eating Disorder Inventory-2 (EDI-2). The horizontal axis indicates the values of the centrality measures for each subscale.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7276684/v1/bb9d5dca342de80f94dc2866.png"},{"id":88500286,"identity":"a18e2bb4-a5af-4355-b16b-8ae6290b504b","added_by":"auto","created_at":"2025-08-07 06:50:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in changes of EDI-2 total scores after BS.\u003c/strong\u003e Figures (a)-(b) show the comparison of EDI-2 total scores each month postoperatively with preoperative scores and the comparison of EDI-2 total scores for each month postoperatively, respectively. Data are presented as mean ± standard error. \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 indicates statistical significance.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7276684/v1/150257fee0b8c82e6f8d7a39.png"},{"id":88500300,"identity":"a71e7884-3374-48e6-8f5a-176097524dde","added_by":"auto","created_at":"2025-08-07 06:50:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-series hierarchical clustering analysis of factors related to disordered eating symptoms after BS.\u003c/strong\u003e The vertical axis represents factors related to disordered eating symptoms, and the horizontal axis represents postoperative time. The colored boxes indicate the comparison of each factor's monthly postoperative scores with preoperative scores. The color of each block represents the comparison result of the factor's monthly postoperative score with the preoperative score, with a color gradient ranging from dark blue (significant decrease) to dark brown (significant increase). \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05 indicates statistical significance.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7276684/v1/ba3164e2eba2335aeef1d02a.png"},{"id":89062793,"identity":"130b1a20-433d-49ba-8c81-d822179465ba","added_by":"auto","created_at":"2025-08-14 09:39:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1207362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7276684/v1/2ced5ed2-2052-44c5-86a9-f4c7fa5be52b.pdf"},{"id":88500291,"identity":"014d3a7e-4d15-4e23-9677-ee313e2f7294","added_by":"auto","created_at":"2025-08-07 06:50:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":293752,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7276684/v1/c146dcf644fba180378e586a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term changes in disordered eating in patients with obesity after bariatric surgery","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity and disordered eating represent two escalating global health concerns\u003csup\u003e[1]\u003c/sup\u003e. Obesity is typically defined as excessive adipose tissue accumulation leading to body weight beyond normal ranges, while disordered eating encompasses a spectrum of abnormal eating behaviors including binge-eating disorder, anorexia nervosa, and bulimia nervosa\u003csup\u003e[2]\u003c/sup\u003e. Although theoretically and clinically distinct, these conditions share interconnected etiological factors, risk profiles, and maintenance mechanisms, with neither constituting a singular disease entity\u003csup\u003e[2]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn recent years, network analysis has gained prominence in investigating symptom characteristics of obesity and disordered eating. This methodology constructs and analyzes relational networks among variables, revealing complex interrelationships and identifying pivotal factors in disease progression\u003csup\u003e[3]\u003c/sup\u003e. Studies employing disordered eating symptom networks have identified binge eating and body dissatisfaction as central nodes\u003csup\u003e[4]\u003c/sup\u003e, while obesity research has highlighted anxiety and depression as key network components strongly associated with unhealthy eating patterns and physical inactivity\u003csup\u003e[4]\u003c/sup\u003e. Despite these advances, applications of network analysis in characterizing preoperative disordered eating symptoms among bariatric surgery (BS) candidates remain limited. Obesity patients frequently present complex psych behavioral comorbidities emotional dysregulation, body image disturbances, and aberrant eating behaviors that may significantly impact surgical outcomes and postoperative recovery\u003csup\u003e[5]\u003c/sup\u003e. Comparative network modeling between healthy controls and preoperative obesity patients could elucidate population specific symptom profiles in dietary behaviors and psychological factors, thereby informing targeted psychological interventions and personalized management strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCurrent therapeutic approaches for disordered eating primarily involve psychological and pharmacological interventions. Cognitive behavioral therapy (CBT), while widely implemented, demonstrates gradual efficacy only a minority of bulimia nervosa patients achieve recovery within 2-4 years, with approximately half requiring six years, while showing limited effectiveness for adult anorexia nervosa\u003csup\u003e[6]\u003c/sup\u003e. Pharmacological options remain constrained by modest efficacy and adverse effect profiles\u003csup\u003e[7]\u003c/sup\u003e. These limitations underscore the need for more rapid and effective interventions. Notably, many obese individuals with disordered eating symptoms pursue weight reduction strategies for symptom alleviation. Some studies report non-worsening or even improvement of disordered eating symptoms during obesity treatment and early follow-up\u003csup\u003e[8]\u003c/sup\u003e, with concomitant enhancements in psychological metrics including depression, anxiety, self-esteem, body image, and quality of life following weight management interventions\u003csup\u003e[9]\u003c/sup\u003e. Within this context, BS as a prevalent obesity intervention has garnered attention for its potential effects on disordered eating. However, research outcomes remain divergent: while some studies demonstrate symptom improvement postoperative, others report limited efficacy or negligible impact on disordered eating symptoms\u003csup\u003e[10\u0026ndash;12]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTherefore, this study employed the EDI-2 scale to evaluate disordered eating symptoms and related factors in both healthy controls and obese patients. Subsequently, network models of disordered eating related factors were constructed and compared between these two groups to identify differences in their network structural characteristics. Then, linear mixed model and time series hierarchical cluster analysis methods were applied to investigate the changing trends of disordered eating symptoms and associated factors in obese patients after BS. The study proposes the following hypotheses: (1) Significant differences exist in disordered eating symptoms and related factors between healthy individuals and obese patients before BS; (2) The network model structures of disordered eating related factors differ between healthy individuals and obese patients prior to surgery; (3) BS can effectively improve disordered eating symptoms in obese patients; (4) The improvement effects of BS on disordered eating related factors vary across different symptom domains.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween September 2020 and June 2023, participants were enrolled into either an obesity group (OB group) or healthy control group (HC group) based on BMI criteria and eligibility for sleeve gastrectomy. The obesity group comprised 78 participants (61 females, 17 males), all of whom subsequently underwent sleeve gastrectomy. The healthy control group consisted of 61 participants (47 females, 14 males).\u003c/p\u003e\n\u003cp\u003eInclusion and Exclusion Criteria:\u003c/p\u003e\n\u003cp\u003eOB group Inclusion Criteria: BMI \u0026ge; 30 kg/m\u0026sup2; meeting indications for sleeve gastrectomy\u003csup\u003e[13]\u003c/sup\u003e; Absence of anxiety, depression, sleep disorders or other major illnesses; Age range 18-60 years; Ability to comprehend questionnaire items; Willingness to participate with signed informed consent. Obesity Group Exclusion Criteria: Conditions potentially affecting metabolism/weight (e.g., hyper/hypothyroidism, Cushing syndrome)\u003csup\u003e[13]\u003c/sup\u003e; Psychiatric disorders (assessed by psychiatrists) including severe eating disorders (bulimia nervosa, binge-eating disorder), schizophrenia, or major depressive disorder; Prior BS (e.g., gastric bypass) or long-term use of metabolism affecting medications (e.g., corticosteroids, antipsychotics); Pregnancy or malignancy\u003csup\u003e[13]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHC group Inclusion Criteria: BMI 18.5-24 kg/m\u0026sup2; not meeting surgical indications \u003csup\u003e[112]\u003c/sup\u003e; Absence of anxiety, depression, sleep disorders or major illnesses; Age and sex distribution matched to obesity group; Ability to comprehend questionnaire items; Willingness to participate with signed informed consent. HC group Exclusion Criteria (ensuring comparability): BMI \u0026ge; 24 kg/m\u0026sup2;; Recent (6-month) use of weight-loss medications, hormonal drugs, or participation in structured weight-loss programs; Gastrointestinal disorders potentially affecting eating behavior assessment (e.g., chronic gastritis, GERD, functional dyspepsia)\u003csup\u003e[13]\u003c/sup\u003e; Psychiatric disorders (psychiatrist assessed).\u003c/p\u003e\n\u003cp\u003eEthical Considerations: This study was approved by the Ethics Committee of Shanghai Sixth People\u0026apos;s Hospital (Approval No 2020-219-(1)). All procedures complied with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and anthropometric data including age, gender, height, weight, and BMI were collected for both the healthy control group and obese patient group prior to surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssessment of Disordered Eating Symptoms\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Eating Disorders Inventory-2 (EDI-2) was administered to evaluate disordered eating symptoms and related factors in both the HC group and the OB group. The EDI-2 is a standardized self-report questionnaire primarily designed to assess the presence of disordered eating symptoms and associated behavioral and psychological issues. Originally developed by David Garner and Paul Garfinkel in 1979, the questionnaire consists of 91 items grouped into 11 subscales: 3 behavioral subscales related to disordered eating symptoms: Drive for Thinness (Dft), Bulimia (Bul), and Body Dissatisfaction (Bod); 8 psychological subscales: Ineffectiveness (Ine), Perfectionism (Per), Interpersonal Distrust (Dis), Maturity Fears (Fea), Interoceptive Awareness (Awa), Asceticism (Asc), Impulse Regulation (Imp), and Social Insecurity (Soc)\u003csup\u003e[14\u0026ndash;16]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, each subscale was scored using a 4-point Likert scale, where 0 indicated the least symptomatic response and 3 represented the most symptomatic response. The EDI-2 assesses disordered eating based on a comprehensive analysis of multiple subscales rather than individual subscale scores. Subscale scores were calculated as the mean of summed item scores, while the total score was derived from the mean of all subscale scores. Clinicians can determine the risk of disordered eating by analyzing subscale and total scores, with higher scores indicating greater symptom severity\u003csup\u003e[14\u0026ndash;16]\u003c/sup\u003e. In this study, the Cronbach\u0026rsquo;s \u0026alpha; coefficient for the EDI-2 was 0.978 (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLSG\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLSG is a safe and effective bariatric procedure with long-lasting weight loss results. It was performed in this study by specialized surgeons trained in BS\u003csup\u003e[17]\u003c/sup\u003e. The LSG procedure is performed with the patient under general anesthesia. The surgeon makes approximately five small incisions in the patient\u0026rsquo;s abdomen using a long, thin telescope with a tiny camera at the end. LSG causes the stomach to become smaller and fill up quickly at mealtime, causing the patient to eat less. LSG also increases the release of hormones such as growth hormone releasing peptide, which in turn reduces hunger and helps the patient lose weight\u003csup\u003e[17]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalysis of Demographic Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic characteristics of both healthy and obese groups were analyzed using jamovi 2.4.11 and SPSS 22.0 software. Continuous variables were assessed as follows: normality was examined using the Shapiro-Wilk test, while homogeneity of variance was evaluated with Levene\u0026apos;s test. Based on these assessments: For data meeting both normality and homogeneity of variance assumptions, Student\u0026apos;s t-test was employed, with results presented as mean \u0026plusmn; standard deviation (Mean \u0026plusmn; SD); For data meeting normality but not homogeneity of variance assumptions, Welch\u0026apos;s t-test was used, with results also expressed as Mean \u0026plusmn; SD; For non-normally distributed data, the Mann-Whitney U test was applied, with results reported as median (25th percentile, 75th percentile) [Median (P25, P75)]. Categorical variables were analyzed using \u0026chi;\u0026sup2; tests, with results presented as number (percentage) [n (%)]. For missing data on social insecurity factors in the OB group: Normality of preoperative social insecurity data was first confirmed by Shapiro-Wilk test (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05); Subsequently, mean imputation was performed to address missing values (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003enetwork analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncorporate the subscale scores of the HC group and OB group EDI-2 to construct a network analysis model. The network analysis model was built in R (version 4.3.1) using the quickNet package and the rlang package from the open-source toolkit LeiGuo0812/quickNet on GitHub. In the network model, nodes (various subscales of EDI-2) are represented by circles, with larger circles indicating stronger network properties of the node. Connections between nodes are represented by lines (the thicker the line between two nodes, the stronger the association between them), and the numbers on the lines indicate the correlation coefficients between the two nodes (positive numbers indicate a positive correlation, while negative numbers indicate a negative correlation)\u003csup\u003e[18]\u003c/sup\u003e. Strength, Closeness, Betweenness, and Expected influence are centrality metrics of the network analysis model, which can measure the network properties of the model\u003csup\u003e[19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLinear mixed model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear mixed model was created by jamovi 2.3.26 software and used to analyze the correlation between the EDI-2 total score and the postoperative time. Comparing the changes in the EDI-2 total scores from baseline to 24 months for the OW/OB group, the dependent variable was the EDI-2 total score in the Ow/Ob group, the independent variable was the postoperative time, and the random factor was the participants No. in the Ow/Ob group, based on the formula EDI-2 total score ~ 1 + time+( 1 | Participant No.) for the calculation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTime-series hierarchical cluster analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA linear mixed model was built by jamovi 2.3.26 to calculate the mean score of each factor of the EDI-2 for each postoperative month, and a time-series hierarchical cluster analysis was performed using the mean scores of these factors. The pheatmap package was used in R 4.3.1, with the factors as columns and time as rows to build the matrix for the time-series hierarchical cluster analysis. The time-series hierarchical cluster analysis was performed using z-scores to normalize the columns; i.e., the data of the postoperative factors were normalized with the preoperative data, and the ward.D algorithm was used to perform hierarchical clustering in which the clusters were separated by the Euclidean distance, the rows were arranged visually by time points, and the columns were arranged by factors\u003csup\u003e[20]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBaseline characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no statistically significant differences in gender, age, Per, Dis, or Soc between the HC group and the OB group (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). However, the OB group had significantly higher BMI, Dft, Bul, Bod, Awa, Ine, Fea, Asm, Imp, and total EDI-2 scores compared to the HC group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Tab.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStability of network analysis models\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scores of each subscale of the EDI-2 before BS were included for both the HC group and the OB group, and network analysis models were constructed for each group to reveal the characteristics of disordered eating symptoms in healthy individuals and obese patients before BS.\u003c/p\u003e\n\u003cp\u003eIn the HC group\u0026apos;s network analysis model, the stability of Betweenness centrality and Strength centrality both exceeded 0.5. In the OB group\u0026apos;s network analysis model, the stability of Betweenness centrality, Closeness centrality, and Strength centrality all exceeded 0.5. This indicates that the network analysis models of both groups have good structural stability, and the relationships between nodes are relatively reliable (Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStructural characteristics of the network analysis model\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;in the healthy population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe network analysis model of the HC group revealed weak associations among the 11 disordered eating related factors, with most nodes showing no significant correlations. Stronger positive correlations were observed between Soc and Dis (r = 0.28) as well as Ine (r = 0.15). Bul demonstrated only a weak positive association with Awa (r = 0.08). In contrast, Imp, Per, Fea, Asc, Bod, and Dft showed no connections with other nodes (Fig. 2a).\u003c/p\u003e\n\u003cp\u003eCentrality metrics indicated that Soc had high values for Strength, Closeness, Betweenness, and Expected influence, suggesting its central role in the network. In comparison, Per, Fea, Imp, Dft, Bod, and Asc scored zero across all centrality indices, indicating minimal influence. Dis exhibited moderate strength and expected influence but low closeness and zero betweenness. Awa and Ine showed low strength, zero betweenness, and weak expected influence. Bul had low strength, zero betweenness, and limited expected influence.\u003c/p\u003e\n\u003cp\u003eIn summary, the HC group\u0026apos;s network model demonstrated generally weak connections among disordered eating related factors, with Soc emerging as the central element, while other factors played relatively minor or indirect roles (Fig. 2b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStructural characteristics of the network analysis model of disordered eating symptoms in obese patients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe network analysis model of the OB group revealed strong interconnections among the 11 disordered eating symptom related factors. A strong positive correlation was observed between Dis and Soc (r = 0.53), and a moderately strong positive correlation with Ine (r = 0.28). Ine showed moderately strong positive correlations with Soc (r = 0.19), Bul (r = 0.19), and Awa (r = 0.17), while exhibiting weaker positive correlations with Bod (r = 0.05), Imp (r = 0.04), and Asc (r = 0.1). Awa demonstrated moderately strong positive correlations with Per (r = 0.14), Bul (r = 0.16), Imp (r = 0.2), Asc (r = 0.25), and Fea (r = 0.17), but weaker correlations with Bul (r = 0.01), Dft (r = 0.01), and Bod (r = 0.01). Bul showed moderately strong positive correlations with Bod (r = 0.17), Imp (r = 0.28), Awa (r = 0.16), and Per (r = 0.17), while having a weaker correlation with Dft (r = 0.07). Imp exhibited a strong positive correlation with Asc (r = 0.31), but weaker correlations with Per (r = 0.04) and Bod (r = 0.08). Asc showed a weak positive correlation with Dft (r = 0.06). Bod had a moderately strong positive correlation with Dft (r = 0.25), while Dft demonstrated a moderately strong correlation with Fea (r = 0.11) (Fig. 3a).\u003c/p\u003e\n\u003cp\u003eCentrality metrics indicated that Awa had the highest strength, closeness, betweenness, and expected influence, making it the core factor in this network. Ine and Bul showed high strength and expected influence, along with high betweenness, suggesting their strong regulatory effects on other factors in the network. Imp exhibited relatively high strength and moderate betweenness, indicating a certain degree of influence. Dis had high strength but zero betweenness, implying strong direct effects but limited indirect influence. Soc and Asc showed moderate strength but zero betweenness, indicating direct effects without indirect mediation. Per, Fea, Dft, and Bod displayed varying levels of strength (moderate to low) and inconsistent betweenness, suggesting complex roles in the network (Fig. 3b).\u003c/p\u003e\n\u003cp\u003eIn summary, Awa, Ine, and Bul emerged as core factors of disordered eating symptoms in obese patients, while the roles of other factors appeared more complex (Fig. 3b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBS improves disordered eating symptoms\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA linear mixed model was utilized to investigate changes in disordered eating symptoms during the two-year period following BS in obese patients. As shown in Figure 4a, the EDI-2 total scores demonstrated a significant decrease starting at 3 months post-surgery (\u003cem\u003et\u003c/em\u003e = -2.00, \u003cem\u003ep\u003c/em\u003e = 0.046), with this reduction remaining stable from month 4 (\u003cem\u003et\u003c/em\u003e = -4.14, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) through month 24 (\u003cem\u003et\u003c/em\u003e = -3.56, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Fig. 4a). Additionally, the EDI-2 total scores at 18 months post-surgery were significantly lower than those at 17 months (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Fig. 4b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eChanges in disordered eating symptom related factors following BS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the evolving trends of disordered eating symptom related factors following BS, this study incorporated monthly postoperative factor scores from the obese cohort, employing linear mixed model and time series hierarchical cluster analysis for analysis. The analysis revealed three distinct patterns among the 11 factors based on Figure5: The Bul factor demonstrated measurable improvement beginning at 2 months post-surgery; Factors including Imp, Fea, Dft, and Bod exhibited progressive improvement throughout the 3-24 month postoperative period; The cluster comprising Dis, Soc, Per, Awa, Ine, and Asc factors showed no clinical improvement after surgery, with some demonstrating worsening symptoms (Fig. 5, Fig. S2-S3).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed network analysis to reveal the structural characteristics of network models associated with disordered eating symptoms in healthy individuals and obese patients before BS. The main findings were: (1) The severity of disordered eating symptoms in obese patients before surgery was significantly higher than that in healthy individuals; (2) The associations between factors related to disordered eating symptoms were weak or absent in healthy individuals, whereas obese patients exhibited complex positive correlations among these factors; (3) In the network model of disordered eating symptoms in healthy individuals, Soc was the central node, while the central nodes in obese patients were Awa, Ine, and Bul.\u003c/p\u003e\u003cp\u003eIn this study, the network structure of factors associated with disordered eating symptoms in healthy individuals was relatively loose, with only three positive correlations among the 11 factors (Soc-Dis, Soc-Ine, and Bul-Awa), all of which had correlation coefficients below 0.3. This aligns with the findings of Borsboom et al., suggesting that psychological networks in healthy populations typically exhibit low connectivity density. Similarly, Robinaugh et al. observed in their network analysis of depression that the strength of associations among depressive symptoms in healthy groups was significantly weaker than in clinical groups\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study also found that centrality measures for factors related to disordered eating symptoms were generally low in healthy individuals. Although Soc had relatively high betweenness centrality, its actual influence on other factors was limited due to the overall sparse network connectivity. This finding contradicts Fairburn et al.'s conclusion that Soc is a major contributing factor in disordered eating\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdditionally, the study suggests that the lack of synergistic interactions among these factors in healthy individuals may help prevent pathological effects. However, further research is needed to confirm this hypothesis.\u003c/p\u003e\u003cp\u003eThe network model of factors associated with disordered eating symptoms in obese patients exhibited significant complexity, forming a core structure centered around Ine, Awa, and Bul. In this network, Bul was not only directly linked to Ine and Awa but also indirectly influenced Asc through Imp. This tightly interconnected network structure aligns with the findings of Levinson et al., who suggested that binge eating acts as a \"central hub\" in overweight populations, perpetuating a vicious cycle by reinforcing negative emotions and maladaptive cognitions\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The central role of Awa in our study is consistent with Klabunde et al.'s fMRI research, which demonstrated that abnormal interoceptive awareness in overweight individuals may contribute to emotional eating behaviors\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Additionally, our findings highlighted the prominence of Ine in both betweenness centrality and strength, supporting Hilbert et al.'s conclusion that Ine can directly trigger compensatory eating and may indirectly exacerbate disordered eating symptoms by impairing emotion regulation\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Notably, while Fea showed no significant associations with other factors in healthy individuals, it appeared to play a role in the development of disordered eating symptoms in obese patients. This may be related to weight stigma, as Puhl et al. found that overweight adolescents subjected to weight based bullying experienced delayed psychological maturation, which in turn promoted extreme dietary behaviors\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study identified Bul, Ine, and Awa as the core factors in disordered eating symptoms among obese patients, which contrasts with the findings of Olatunji et al.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Their study included participants with BMIs ranging from 18.51 to 55.09 kg/m\u0026sup2;, encompassing normal weight to severely obese individuals, potentially introducing confounding effects from non-obese populations (e.g., normal weight or overweight individuals). In comparison, our research specifically examined obese patients scheduled for BS a more homogeneous group with narrower BMI ranges and typically more severe eating pathology. This population difference likely accounts for the observed variation in core network factors, making our findings more representative of the preoperative disordered eating symptom network in bariatric candidates. Based on our network analysis of preoperative disordered eating factors in obese patients, we propose the following evidence based preoperative interventions to optimize surgical outcomes. For impaired Awa: Implement mindfulness based interventions to enhance bodily signal recognition and disrupt its pathological connections with Imp and Bul\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. For Ine related issues: Apply cognitive restructuring techniques to improve self-efficacy and prevent progression to binge eating\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. For established Bul with strong Bul-Imp associations: Incorporate targeted impulse control regulation strategies. For significant Bod-Dft correlations: Administer cognitive behavioral therapy combined with body image acceptance interventions to mitigate disordered eating symptoms\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdditionally, this study utilized linear mixed models and time series hierarchical cluster analysis to investigate the effects of BS on disordered eating symptoms and related factors in obese patients. The results demonstrated that patients' overall disordered eating symptoms showed significant improvement and stabilization within 24 months post-surgery, with the EDI-2 total scores exhibiting a consistent decline from the 3rd to the 24th postoperative month. These improvements appear closely associated with physiological regulatory mechanisms induced by BS. Specifically, the surgery may modulate gastrointestinal hormone secretion (e.g., GLP-1, ghrelin)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, it enhances appetite regulation through the hypothalamic arcuate nucleus, it induces functional remodeling in brain regions including the amygdala and suprachiasmatic nucleus\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. These physiological changes collectively contribute to reduced binge eating behaviors and facilitated weight loss.\u003c/p\u003e\u003cp\u003eNotably, emerging evidence suggests postoperative alterations in taste and olfactory sensitivity may further suppress food intake\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, thereby accelerating improvement in disordered eating symptoms. Our in depth analysis revealed differential surgical effects across 11 disordered eating related factors. Time series hierarchical cluster analysis categorized these factors into three distinct improvement patterns: Marked improvement: Bul; Moderate improvement: Imp, Fea, Dft, and Bod; Minimal improvement or worsening: Dis, Soc, Per, Awa, Ine, and Asc.\u003c/p\u003e\u003cp\u003eIt is noteworthy that our study identified Bul as a central factor in preoperative disordered eating symptoms among obese patients undergoing BS. Building on this finding, we further observed that BS led to significant improvement in Bul symptoms. However, the other two core factors Ine and Awa showed no substantial postoperative improvement. This differential response suggests that BS may have limited efficacy in addressing the psychological dimensions of disordered eating, a finding consistent with Beck et al.'s research\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.This phenomenon may be explained by the surgery's neurobiological mechanisms: while effectively modulating appetite regulating brain regions (such as the hypothalamus and amygdala), it appears to have minimal impact on psychological traits mediated by the prefrontal cortex's social cognitive functions\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Consequently, improvements in psychosocial factors tend to lag behind physiological changes, and these unresolved psychological issues may pose potential risks for the recurrence of disordered eating behaviors post-surgery. These findings highlight the clinical importance of implementing adjunctive psychological interventions following BS. Such complementary therapies could significantly contribute to sustained improvement in disordered eating symptoms by addressing the psychological components that surgical intervention alone cannot adequately modify.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be acknowledged. First, the cross-sectional design only allowed us to examine preoperative associations between factors, without capturing their postoperative trajectories. Future longitudinal cohort studies are needed to investigate the dynamic changes in these factors over time. Second, our study exclusively focused on obese patients scheduled for BS, without including a non-surgical control group. This selection bias may limit the generalizability of our findings, and future research should incorporate broader patient populations to enhance the external validity of the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we found that there were differences in the network model structure and core nodes of factors related to disordered eating symptoms in healthy people and obese patients, and that therapeutic measures can be taken to address the network characteristics of disordered eating symptoms in obese patients. BS can improve disordered eating symptoms in obese patients, especially for Bul, Imp, Fea, Dft and Bod factors, but not for Dis, Soc and other psychological factors or even aggravate, so we can provide appropriate psychological treatment to promote the improvement of disordered eating symptoms in obese patients after surgery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuilin Zhang: data curation, formal analyses, methodology, visualization, writing\u0026mdash;original draft. Chuanyu Yin: writing\u0026mdash;reviewing and editing. Sufang Peng: writing\u0026mdash;reviewing and editing. Lin Liu: writing\u0026mdash;reviewing and editing. Xuyan Ban: writing\u0026mdash;reviewing and editing. Hongwei Zhang: writing\u0026mdash;reviewing and editing. Ting Xu, investigation, writing\u0026mdash;reviewing and editing. Chen Wang: writing\u0026mdash;reviewing and editing. Gang Peng: writing\u0026mdash;reviewing and editing. Xiao-Dong Han: supervision. Hui Zheng: conceptualization and project administration. Jian-Zhong Di: funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflicts of interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors each declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed Consent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupport was provided by the Shanghai Health Commission project (NO. 20214098); Shanghai Science and Technology Committee project (NO. 22dz1204700), National Science and Technology Major Project of the Ministry of Science and Technology of China (NO. 2022YFC2407004).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnonymous. Obesity: causes, consequences, treatments, and challenges[J]. Journal of Molecular Cell Biology, 2021, 13(7): 463-465.\u003c/li\u003e\n \u003cli\u003eZhang X, Ha S, Lau H C-H, \u003cem\u003eet al\u003c/em\u003e. Excess body weight: Novel insights into its roles in obesity comorbidities[J]. Seminars in Cancer Biology, 2023, 92: 16-27.\u003c/li\u003e\n \u003cli\u003eTie D, He M, Li W, \u003cem\u003eet al\u003c/em\u003e. Advances in the application of network analysis methods in traditional Chinese medicine research[J]. Phytomedicine, 2025, 136: 156256.\u003c/li\u003e\n \u003cli\u003eSala M, Kressel M, Schechter A, \u003cem\u003eet al\u003c/em\u003e. The study of eating disorders from a network perspective: A scoping systematic review.[J]. Clinical Psychology: Science and Practice, 2024, 31(3): 296-322.\u003c/li\u003e\n \u003cli\u003eKubik J F, Gill R S, Laffin M, \u003cem\u003eet al\u003c/em\u003e. The Impact of Bariatric Surgery on Psychological Health[J]. Journal of Obesity, 2013, 2013: 1-5.\u003c/li\u003e\n \u003cli\u003eVall E, Wade T D. Predictors of treatment outcome in individuals with eating disorders: A systematic review and meta‐analysis[J]. International Journal of Eating Disorders, 2015, 48(7): 946-971.\u003c/li\u003e\n \u003cli\u003eHay P. Current approach to eating disorders: a clinical update[J]. Internal Medicine Journal, 2020, 50(1): 24-29.\u003c/li\u003e\n \u003cli\u003eTsompanaki E, Koutoukidis D A, Wren G, \u003cem\u003eet al\u003c/em\u003e. The impact of weight loss interventions on disordered eating symptoms in people with overweight and obesity: a systematic review \u0026amp; meta-analysis[J]. EClinicalMedicine, 2025, 80: 103049.\u003c/li\u003e\n \u003cli\u003eTan E J, Raut T, Le L K-D, \u003cem\u003eet al\u003c/em\u003e. The association between eating disorders and mental health: an umbrella review[J]. Journal of Eating Disorders, 2023, 11(1): 51.\u003c/li\u003e\n \u003cli\u003eRubino F, Cummings D E, Eckel R H, \u003cem\u003eet al\u003c/em\u003e. Definition and diagnostic criteria of clinical obesity[J]. The Lancet Diabetes \u0026amp; Endocrinology, 2025, 13(3): 221-262.\u003c/li\u003e\n \u003cli\u003eWang Y, Guo X, Lu X, \u003cem\u003eet al\u003c/em\u003e. Mechanisms of Weight Loss After Sleeve Gastrectomy and Adjustable Gastric Banding: Far More Than Just Restriction[J]. Obesity, 2019, 27(11): 1776-1783.\u003c/li\u003e\n \u003cli\u003eDiamantis T, Apostolou K G, Alexandrou A, \u003cem\u003eet al\u003c/em\u003e. Review of long-term weight loss results after laparoscopic sleeve gastrectomy[J]. Surgery for Obesity and Related Diseases, 2014, 10(1): 177-183.\u003c/li\u003e\n \u003cli\u003eTchang B G, Aras M, Kumar R B, \u003cem\u003eet al\u003c/em\u003e. Pharmacologic Treatment of Overweight and Obesity in Adults[M/OL].\u003c/li\u003e\n \u003cli\u003eAllen K L, Crosby R D, Oddy W H, \u003cem\u003eet al\u003c/em\u003e. Eating disorder symptom trajectories in adolescence: effects of time, participant sex, and early adolescent depressive symptoms[J]. Journal of Eating Disorders, 2013, 1(1): 32.\u003c/li\u003e\n \u003cli\u003eSalbach-Andrae H, Schneider N, B\u0026uuml;rger A, \u003cem\u003eet al\u003c/em\u003e. Psychometrische G\u0026uuml;tekriterien des Eating Disorder Inventory (EDI-2) bei Jugendlichen[J]. Zeitschrift f\u0026uuml;r Kinder- und Jugendpsychiatrie und Psychotherapie, 2010, 38(3): 219-228.\u003c/li\u003e\n \u003cli\u003eAloi M, Rania M, Caroleo M, \u003cem\u003eet al\u003c/em\u003e. Decision making, central coherence and set-shifting: a comparison between Binge Eating Disorder, Anorexia Nervosa and Healthy Controls[J]. BMC Psychiatry, 2015, 15(1): 6.\u003c/li\u003e\n \u003cli\u003ePalermo M, Serra E. Laparoscopic Sleeve Gastrectomy: How Do I Do It[J]. Journal of Laparoendoscopic \u0026amp; Advanced Surgical Techniques, 2020, 30(1): 2-5.\u003c/li\u003e\n \u003cli\u003eDe Vos J A, Radstaak M, Bohlmeijer E T, \u003cem\u003eet al\u003c/em\u003e. The psychometric network structure of mental health in eating disorder patients[J]. European Eating Disorders Review, 2021, 29(4): 559-574.\u003c/li\u003e\n \u003cli\u003eForrest L N, Jones P J, Ortiz S N, \u003cem\u003eet a\u003c/em\u003el. Core psychopathology in anorexia nervosa and bulimia nervosa: A network analysis[J]. International Journal of Eating Disorders, 2018, 51(7): 668-679.\u003c/li\u003e\n \u003cli\u003eDepommier C, Vitale R M, Iannotti F A, \u003cem\u003eet al\u003c/em\u003e. Beneficial Effects of Akkermansia muciniphila Are Not Associated with Major Changes in the Circulating Endocannabinoidome but Linked to Higher Mono-Palmitoyl-Glycerol Levels as New PPAR\u0026alpha; Agonists[J]. Cells, 2021, 10(1): 185.\u003c/li\u003e\n \u003cli\u003eBringmann L F, Albers C, Bockting C, \u003cem\u003eet al\u003c/em\u003e. Psychopathological networks: Theory, methods and practice[J]. Behaviour Research and Therapy, 2022, 149: 104011.\u003c/li\u003e\n \u003cli\u003eFairburn C G, Cooper Z, Shafran R. Cognitive behaviour therapy for eating disorders: a \u0026ldquo;transdiagnostic\u0026rdquo; theory and treatment[J]. Behaviour Research and Therapy, 2003, 41(5): 509-528.\u003c/li\u003e\n \u003cli\u003eLevinson C A, Cash E, Welch K, \u003cem\u003eet al\u003c/em\u003e. Personalized networks of eating disorder symptoms predicting eating disorder outcomes and remission[J]. International Journal of Eating Disorders, 2020, 53(12): 2086-2094.\u003c/li\u003e\n \u003cli\u003eCos\u0026iacute;o-Guirado R, Tapia-Medina M G, Kaya C, \u003cem\u003eet al\u003c/em\u003e. A comprehensive systematic review of fMRI studies on brain connectivity in healthy children and adolescents: Current insights and future directions[J]. Developmental Cognitive Neuroscience, 2024, 69: 101438.\u003c/li\u003e\n \u003cli\u003eGiovannini E N. Bridging the gap between analytic and synthetic geometry: Hilbert\u0026rsquo;s axiomatic approach[J]. Synthese, 2016, 193(1): 31-70.\u003c/li\u003e\n \u003cli\u003ePuhl R M, Luedicke J, Heuer C. Weight-Based Victimization Toward Overweight Adolescents: Observations and Reactions of Peers[J]. Journal of School Health, 2011, 81(11): 696-703.\u003c/li\u003e\n \u003cli\u003eOlatunji B O, Levinson C, Calebs B. A network analysis of eating disorder symptoms and characteristics in an inpatient sample[J]. Psychiatry Research, 2018, 262: 270-281.\u003c/li\u003e\n \u003cli\u003eWu Q, Mao X, Luo W, \u003cem\u003eet al\u003c/em\u003e. Enhanced interoceptive attention mediates the relationship between mindfulness training and the reduction of negative mood[J]. Psychophysiology, 2022, 59(4): e13991.\u003c/li\u003e\n \u003cli\u003eLarsson A, Hooper N, Osborne L A, \u003cem\u003eet al\u003c/em\u003e. Using Brief Cognitive Restructuring and Cognitive Defusion Techniques to Cope With Negative Thoughts[J]. Behavior Modification, 2016, 40(3): 452-482.\u003c/li\u003e\n \u003cli\u003eGopan H, Rajkumar E, Gopi A, \u003cem\u003eet al\u003c/em\u003e. Mindfulness‐based interventions for body image dissatisfaction among clinical population: A systematic review and meta‐analysis[J]. British Journal of Health Psychology, 2024, 29(2): 488-509.\u003c/li\u003e\n \u003cli\u003eFurukawa Y, Sakata M, Yamamoto R,\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. Components and Delivery Formats of Cognitive Behavioral Therapy for Chronic Insomnia in Adults: A Systematic Review and Component Network Meta-Analysis[J]. JAMA Psychiatry, 2024, 81(4): 357.\u003c/li\u003e\n \u003cli\u003eMulla C M, Middelbeek R J W, Patti M. Mechanisms of weight loss and improved metabolism following bariatric surgery[J]. Annals of the New York Academy of Sciences, 2018, 1411(1): 53-64.\u003c/li\u003e\n \u003cli\u003eSong J, Choi S-Y. Arcuate Nucleus of the Hypothalamus: Anatomy, Physiology, and Diseases[J]. Experimental Neurobiology, 2023, 32(6): 371-386.\u003c/li\u003e\n \u003cli\u003eAhmed K, Penney N, Darzi A, \u003cem\u003eet al\u003c/em\u003e. Taste Changes after Bariatric Surgery: a Systematic Review[J]. Obesity Surgery, 2018, 28(10): 3321-3332.\u003c/li\u003e\n \u003cli\u003eBeck N N, Mehlsen M, St\u0026oslash;ving R K. Psychological characteristics and associations with weight outcomes two years after gastric bypass surgery: Postoperative eating disorder symptoms are associated with weight loss outcomes[J]. Eating Behaviors, 2012, 13(4): 394-397.\u003c/li\u003e\n \u003cli\u003eLi G, Ji G, Hu Y, \u003cem\u003eet al\u003c/em\u003e. Bariatric surgery in obese patients reduced resting connectivity of brain regions involved with self‐referential processing[J]. Human Brain Mapping, 2018, 39(12): 4755-4765.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTab. 1 Demographic characteristics of the HC group and the OB group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHC group (n=61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eOB group (n=78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003et/\u0026chi;\u0026sup2;/U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eFemale，[n (%)] \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e21.10\u0026plusmn;1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e38.30\u0026plusmn;1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAge \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e28.03(25.12,33.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e29.12(24.53,33.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2375.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eDft \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.43(0.14,0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.14 (0.71,1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e672.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eBul \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.29(0.00,1.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eBod \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.24\u0026plusmn;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2.00\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eIne \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.70(0.50,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.80 (0.60,1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePer \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.67(0.33,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.67 (0.33,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eDis \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.43(0.71,1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.43 (0.86,1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAwa \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.30(0.20,0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.55(0.30,0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eFea \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.25(1.07,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.38(1.25,1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAsc \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.38(0.25,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.5(0.38,0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eImp \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.09(0.00,0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.27(0.09,0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSoc\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1.45\u0026plusmn;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.52\u0026plusmn;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.501\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eEDI-2 total score \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7.97(6.98,9.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e11.25(8.86,13.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1078.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e Chi square test; \u003csup\u003eb\u003c/sup\u003e Mann-Whitney U test was employed, expressed using median (25th percentile, 75th percentile); \u003csup\u003ec\u003c/sup\u003e Student\u0026apos;s t-test was employed，expressed using Mean \u0026plusmn; SD. A \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 indicates statistical significance.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eawd","sideBox":"Learn more about [Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity](https://www.springer.com/journal/40519)","snPcode":"40519","submissionUrl":"https://submission.nature.com/new-submission/40519/3","title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Obesity, Disordered eating, Bariatric surgery, EDI-2","lastPublishedDoi":"10.21203/rs.3.rs-7276684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7276684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThere may be differences in disordered eating symptoms between healthy individuals and patients with obesity, and bariatric surgerymay influence postoperative disordered eating behaviors in patients with obesity. This study aims to explore the differences in network analysis structures of factors associated with disordered eating symptoms in healthy individuals versus patients with obesity, and further analyze changes in disordered eating symptoms in patients with obesity following BS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Between September 2020 and June 2023 in Shanghai, China, participants were stratified into two groups based on body mass index (BMI) criteria: a healthy control group (HC, n=61; BMI 18.5-24 kg/m²) and an obese group (OB, n=78; BMI ≥30 kg/m²). All participants completed the Eating Disorders Inventory-2. The OB group subsequently underwent laparoscopic sleeve gastrectomy at participating institutions, with monthly postoperative EDI-2 assessments conducted. Network analysis was employed to examine differences in factors associated with disordered eating symptoms between healthy individuals and patients with obesity, while linear mixed model combined with time series hierarchical cluster analysis were systematically applied to evaluate the interventional effects of BS on disordered eating.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eEDI-2 total score was significantly higher in obese patients than in the healthy population (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05). Network analysis showed that the core symptom nodes for disordered eating symptoms were Social Insecurity in the healthy population and Interoceptive Awareness, Ineffectiveness and Bulimia in the obese patients. The analysis of the postoperative follow-up data showed that the EDI-2 total score of obese patients decreased significantly from the 3rd month after surgery and remained stable (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05). The factors related to disordered eating symptoms in obese patients showed three modes of improvement after surgery: Bulimia factor improved significantly in the early postoperative period (2nd month); Impulse regulation, Maturity Fears, Driver for Thinness and Body Dissatisfaction factors showed progressive improvement (3-24 months); Interpersonal Distrust, Social Insecurity, Perfectionism, Interoceptive Awareness, Ineffectiveness and Asceticism factors showed no significant change or even worsened.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study elucidates the network characteristics of disordered eating symptoms in obese patients prior to bariatric surgery, providing an evidence-based foundation for preoperative psychological interventions. Furthermore, by investigating postoperative trajectories of disordered eating symptoms and related factors, our findings highlight the critical need for future research to: (1) monitor long-term (\u0026gt;2 years) evolution of disordered eating symptoms, and (2) systematically evaluate psychological factor dynamics during postoperative follow-up. These investigations should inform the development of tailored psychological therapies to optimize surgical outcomes.\u003c/p\u003e","manuscriptTitle":"Long-term changes in disordered eating in patients with obesity after bariatric surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 06:50:45","doi":"10.21203/rs.3.rs-7276684/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-03T10:32:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T22:04:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150064553382185367926356612758429104279","date":"2025-10-07T07:27:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164710092374427237600769032238285285657","date":"2025-10-06T13:35:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T10:44:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T09:05:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-02T11:31:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","date":"2025-08-02T08:20:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eawd","sideBox":"Learn more about [Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity](https://www.springer.com/journal/40519)","snPcode":"40519","submissionUrl":"https://submission.nature.com/new-submission/40519/3","title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1eb779ab-8080-4a30-b21c-224509fab94e","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T12:09:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 06:50:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7276684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7276684","identity":"rs-7276684","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}