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. 2024 Sep 17;33(1):148–162. doi: 10.1002/erv.3136

A network analysis of eating disorder, PTSD, major depression, state‐trait anxiety, and quality of life measures in eating disorder patients treated in residential care

Timothy D Brewerton 1,2,3, Maren C G Kopland 4,5,✉, Ismael Gavidia 3, Giulia Suro 3, Molly M Perlman 3,6
PMCID: PMC11617816  PMID: 39289909

Abstract

Background

The network approach in the eating disorder (ED) field has confirmed important links between EDs and posttraumatic stress disorder (PTSD) symptoms. However, studies including comorbid symptoms are scarce, which limits our understanding of potentially important connections. We hypothesised that anxiety, depression and poor quality of life (QOL) would provide a more complete picture of central, maintaining factors.

Methods

Network analysis using R was performed in 2178 adult ED patients (91% female) admitted to residential treatment. Assessments included the ED Examination Questionnaire (EDEQ), the Eating Disorders Inventory (EDI‐2), the PTSD Checklist for DSM‐5 (PTSD clusters (PCL‐5)), the Patient Health Questionnaire (PHQ‐9), the Spielberger State‐Trait Anxiety Scale (STAI), and the ED QOL Scale (EDQOL), which measure symptoms of EDs, PTSD, major depression, state‐trait anxiety, and QOL, respectively.

Results

EDI‐2 ineffectiveness showed the highest centrality (expected influence) followed by EDI‐2 interoceptive awareness, STAI state anxiety, EDEQ shape concern, EDQOL psychological subscale, and PTSD cluster D (hyperarousal) symptoms. Eating Disorder Quality of Life psychological and physical‐cognitive subscales and PHQ‐9 major depressive, STAI state anxiety and PCL‐5 PTSD cluster E (negative alterations in mood and cognition) symptoms showed the highest bridge expected influence, suggesting their interactive role in maintaining ED‐PTSD comorbidity.

Conclusions

This is the first network analysis of the interaction between ED and PTSD symptoms to include the comorbid measures of depression, anxiety, and QOL in a large clinical sample of ED patients. Our results indicate that several symptom clusters are likely to maintain ED‐PTSD comorbidity and may be important targets of integrated treatment.

Keywords: anxiety, depression, eating disorders, network analysis, PTSD, quality of life, trauma

Highlights

  • This study represents the first network analysis investigating the connection between eating disorder (ED) and posttraumatic stress disorder (PTSD) symptoms including comorbid measures of state‐trait anxiety, major depressive symptoms, and ED quality of life (QOL).

  • Ineffectiveness showed the highest centrality (expected influence) followed by interoceptive awareness, state anxiety, shape concern, psychological QOL, and intrusive PTSD symptoms.

  • Psychological QOL, physical‐cognitive QOL, major depression, state anxiety and PTSD cluster E symptoms showed the highest bridge expected influence, suggesting their role in maintaining ED‐PTSD comorbidity.

1. INTRODUCTION

Psychiatric comorbidity is commonly seen in association with EDs of all types, and traumatic experiences and resultant posttraumatic stress disorder (PTSD) have been shown to be important etiological components of EDs and related comorbid symptoms, including depression, anxiety, and poor QOL (Afari et al., 2021; Brewerton, 2022; Brewerton & Brady, 2014; Brewerton et al., 2024; Hambleton et al., 2022; Molendijk et al., 2017). In addition, patients with EDs in association with PTSD show greater severity and complexity of symptoms and are more likely to remain symptomatic following treatment (Brewerton, Perlman, et al., 2020; Brewerton, Gavidia, et al., 2022; Brewerton et al., 2023; Cassioli et al., 2022; Hazzard et al., 2021; Scharff et al., 2021). Conversely, individuals with PTSD have been reported to have higher rates of EDs and ED symptoms than those without PTSD (Arditte Hall et al., 2018; Brewerton, Ralston, et al., 2020b; Ferrell et al., 2020; Huston et al., 2019; Mitchell et al., 2016; Mitchell & Wolf, 2016).

PTSD commonly co‐occurs with EDs, especially in higher levels of care. Studies of ED patients admitted to residential treatment have documented prevalence rates of current PTSD as high as 50% (Brewerton, Perlman, et al., 2020; Brewerton et al., 2023; Rienecke et al., 2020), while rates in outpatient samples average 25% (Ferrell et al., 2020). Furthermore, many patients have histories of prior traumas and resulting PTSD symptoms that fall below the diagnostic threshold yet they are also nevertheless adversely affected (Inniss et al., 2011; Mitchell, Mazzeo, et al., 2012; Scharff et al., 2019). Mechanisms by which adverse childhood experiences, other traumas, and resultant PTSD symptoms may negatively impact the course of EDs and contribute to their morbidity and mortality have been previously described in detail (Brewerton, 2022; Mitchell et al., 2021). However, further elucidation of how the complicated array of concurrent symptoms interact with each other in large clinical samples of affected patients using newer approaches, such as network analysis, is warranted.

Studies using network analysis have so far also substantiated the interconnectedness between PTSD and EDs. Vanzhula and colleagues were the first to delineate specific illness pathways between very specific ED and PTSD symptoms in two separate samples, one consisting of 158 individuals diagnosed with an ED and another consisting of a nonclinical sample of 300 college students (Vanzhula et al., 2019). They described three illness pathways, including one between binge eating and irritability, one between desire for a flat stomach and disturbing dreams, and another between concentration problems and weight and shape‐related concentration problems. Liebman and co‐investigators reported results using a community sample of 120 adults with a history of childhood abuse and some evidence of ED symptoms (Liebman et al., 2021). They found that reexperiencing symptoms had the highest strength centrality that bridged the PTSD and ED clusters. In addition, cognitive restraint was a bridge to all PTSD symptoms. Finally, Nelson and associates conducted a network analysis on 344 freshman undergraduates and reported several findings, including that negative alterations in cognitions and mood (NACM) associated with traumatic experiences were highly influential in the ED‐PTSD network (Nelson et al., 2021). They also noted an important pathway between binge eating and the inability to experience positive emotions, suggesting affect regulation may be impacted via binge eating.

Despite the advances illuminated by these analyses, there are important limitations to these studies that indicate the need for further research in this area. All three studies included individuals without clinically diagnosed EDs, and two of them focused exclusively on nonclinical samples (Liebman et al., 2021; Nelson et al., 2021). Only the study by Vanzhula and colleagues included 158 patients diagnosed with EDs who had been recently discharged from either residential or partial hospital care (Vanzhula et al., 2019). Previous studies include relatively small sample sizes, especially those involving ED patients (Liebman et al., 2021; Nelson et al., 2021; Vanzhula et al., 2019) which limits the number of symptoms or symptom clusters that can be effectively investigated using network analysis (Sacha Epskamp et al., 2018; Isvoranu & Epskamp, 2021). As network analysis generally demands higher sample sizes the more items one wants to investigate, it is understandable that all three of these studies focused exclusively on ED and PTSD symptoms. None of them included other related comorbid symptoms, particularly depression and anxiety, or poor QOL, all of which are associated with EDs and may be important in understanding their aetiology, interrelationships, and perpetuation (Brewerton, Perlman, et al., 2020; Brewerton, Gavidia, et al., 2022). Other studies showing links between ED, anxiety and depression symptoms have been reported but have not included PTSD (Elliott et al., 2019; Forrest et al., 2019; Olatunji et al., 2018; Smith et al., 2019; Solmi et al., 2018).

In this study we present results from a network analysis of a relatively large group of adult ED patients admitted to residential treatment using not only measures of ED and PTSD symptoms, but also those of major depression, state‐trait anxiety, and QOL. The aims of this study were to perform a more comprehensive network analysis of ED and PTSD symptoms than has been done previously that included the potential influence of the commonly co‐occurring comorbid symptom complexes of state and trait anxiety, major depression, and ED QOL. Specifically, we hypothesised that symptom constructs of state and trait anxiety, depression and poor QOL do in fact play major roles in bridging ED and PTSD symptoms and will add important information regarding mechanisms that maintain ED‐PTSD comorbidity. Although many studies have focused on single symptoms, others have used recognised symptom complexes or subscales as nodes, a strategy which we have employed in this study (Carbone et al., 2023; Galderisi et al., 2018; Sala et al., 2023).

2. METHOD

2.1. Setting

Monte Nido and Affiliates is a multi‐site, multi‐level comprehensive treatment programme across 15 U.S. states for adolescent and adult patients with severe EDs who require higher levels of care. This report analyzes data generated from adults admitted to 12 residential treatment sites over a 4.5‐year period.

2.2. Ethics

This research was approved by the Salus Institutional Review Board. All participants gave written informed consent for the use of their assessment results.

2.3. Participants

The inclusion criteria for this study comprised of all patients admitted to an adult residential treatment programme at Monte Nido and Affiliates for treatment of an ED. There were 3093 adult participants (≥18 years old) with DSM‐5 EDs, as determined by preadmission staff and admitting psychiatrists using a standardised, semi‐structured interview, entering residential treatment between 24 October 2017, and 30 June 2022, and (2602, 84%) gave written informed consent to participate in research. Of these, 2178 (84%) completed admission assessments, and all of these patients' data were included in the analyses. Otherwise, there were no exclusion criteria. Eating disorder diagnoses were as follows: anorexia nervosa, restricting type (AN‐R) (n = 668); anorexia nervosa, binge‐purge type (AN‐BP) (n = 456); bulimia nervosa (BN) (n = 399); other specified feeding and eating disorder (OSFED) (n = 498); binge eating disorder (BED) (n = 71); avoidant restrictive food intake disorder (ARFID) (n‐63). The average age at admission of the participants was 26.0 ± 9.0 years, and the average age of ED onset was 15.1 ± 5.9 years.

Of these, those with PTSD+ accounted for 46% of patients, while the remaining 54% were PTSD‐. Categorisation by gender identity was as follows: 90.5% female, 3.5% male, 4.0% non‐binary, 0.7% transman, 0.6% transwoman, 0.7% other. Categorisation by race was as follows: 93.0% white, 3.9% Asian, 2.1% black or African American, 0.7% American Indian/Native Alaskan, and 0.3% native Hawaiian/other Pacific Islander. The majority (91.1%) of patients identified as of non‐Hispanic origin. Regarding the highest level of education attained, 13.6% completed high school, 4.0% had not, 5.9% had an associate degree, 38.2% completed some college, 21.7% had a bachelor's degree, 4.4% completed some postgraduate education, and 12.2% achieved a master's degree or beyond. Reported total household income was as follows: <$50,000: 16.2%; $50,000–$99,999: 14.6%; $100,000–$199,999: 17.8%; > $200,000: 12.7%; and 38.7% did not respond.

2.4. Assessments

Validated assessment measures used in this study have been described previously (Brewerton, Perlman, et al., 2020) and included the following: ED Examination Questionnaire (EDEQ) (Luce & Crowther, 1999; Mond et al., 2004), ED Inventory‐2 (EDI‐2) (Garner, 1991), Patient Health Questionnaire (PHQ‐9) (Kroenke et al., 2001), Spielberger State‐Trait Anxiety Inventory (STAI) (Spielberger et al., 1983), ED QOL (EDQOL) scale (Engel et al., 2006), Life Events Checklist for DSM‐5 (LEC‐5) (Gray et al., 2004; Weathers et al., 2013), and PTSD Checklist for DSM‐5 (PTSD clusters (PCL‐5)) (Blevins et al., 2015).

Several of these assessment instruments have subscale scores, which were used as symptom clusters in these network analyses. The EDEQ is divided into 4 subscale scores related to recent ED behaviours (restraint, eating concern, shape concern, and weight concern). The EDI‐2 has 11 subscale scores, three of which are related to core ED symptomatology (drive for thinness, body dissatisfaction, and bulimia), and eight subscales related to associated ED psychopathology (ascetism, impulse regulation, interoceptive awareness, ineffectiveness, interpersonal distrust, maturity fears, perfectionism, and social insecurity). The STAI has 2 subscale scores (state anxiety and trait anxiety). The EDQOL has 4 domain subscales (psychological, physical‐cognitive, financial, and work/school). Finally, the PCL‐5 can be divided into 4 cluster scores (B, C, D, and E).

Provisional diagnoses of PTSD according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐5) were made via the Life Events Checklist for DSM‐5 (LEC‐5) for criterion A and the PCL‐5 for criteria B through E (American Psychiatric Association, 2013). The inclusion criteria for being classified as having a provisional diagnosis of PTSD (PTSD+) comprised: (a) endorsement of at least one life‐threatening event, serious accident or sexual assault that happened to the individual, (b) having a PCL‐5 total score of ≥33, and (c) endorsing each of the B through E DSM‐5 criteria for PTSD as determined by PCL‐5 responses (Brewerton, Perlman, et al., 2020).

2.5. Statistics

Statistical analyses were conducted using SPSS version 27 and the R‐package, version 4.2.1 (R Core Team, 2021). The complete R‐code and additional information about the analyses can be found in the Supplementary Materials. The goldbricker function in the R package networktools (Jones, Williams, & McNally, 2021) was used to identify redundant variables (Hittner et al., 2003). The goldbricker analysis recommended the removal of two items: (a) either PTSD cluster C (avoidance symptoms) or B (intrusive symptoms), and (b) either EDEQ Shape Concern or Weight Concern. We eliminated cluster C (avoidance) given that cluster B (intrusive) symptoms are considered the most diagnostically “classic” symptoms unique to the PTSD construct and are often the most severe (Bryant et al., 2017; Iyadurai et al., 2019; Jovanovic et al., 2010; Liebman et al., 2021; McNally et al., 2017; Bryant et al., 2017; Iyadurai et al., 2019; Liebman et al., 2021; McNally et al., 2017; Jovanovic et al., 2010; Wendlandt et al., 2022). We also eliminated the EDEQ weight concern subscale given that the EDEQ shape concern subscale has been reported to be more central in other studies (Kopland et al., 2023; Wang et al., 2019). In addition, EDEQ weight and shape concern subscales are highly correlated with each other (Rand‐Giovannetti et al., 2020). Apart from this, the analysis provided further support for the validity of the theoretical selections.

Following recent recommendations (Isvoranu & Epskamp, 2021) addressing which estimation method to use for the research question of interest (estimate network with >3000 patients), unregularised graphical Gaussian models were used to estimate the network structures. The nodes represent ED subscales (EDEQ and EDI‐2), PCL‐5, state and trait anxiety subscale scores (STAI), total number of major depression symptoms (PHQ‐9), and EDQOL subscales. The edges between them represent partial correlations between variables when all other variables are held constant. The Fruchterman–Reingold algorithm was used to visualise the network (see Figure 1). Here, the nodes with the highest centrality are drawn to the centre of the network, and less important nodes are placed in the periphery (Fruchterman & Reingold, 1991). In addition, the algorithm also functions to minimise the number of crossing edges.

FIGURE 1.

FIGURE 1

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Network structure of eating disorder (ED), PTSD, depression, state‐trait anxiety and quality of life (QOL) measures. Blue edges represent positive relations, whereas red edges represent negative relations.

A common centrality measure was obtained for each variable across the network. Here, expected influence centrality (Robinaugh et al., 2016) was calculated with standardised z‐scores on the x‐axis (low z‐scores correspond to the low importance of the node in the network). Raw‐score estimates are provided in the Supplementary Materials (see Figures S4 and S5). Expected influence reflects greater importance of a node in the network. Bridge centrality indices were obtained through the networktools package (Jones, Williams, & McNally, 2021), which displays the most central nodes in bridging identified communities in the network. We estimated the 1‐step and 2‐step bridge expected influence to estimate both the direct and indirect impact of the nodes on neighbouring communities (McNally, 2021). One‐step bridge expected influence estimates a node's total connectivity with nodes in other communities (1 step away from the node). Two‐step bridge expected influence is similar to 1‐step, but can estimate how node A is connected to both the nearest cluster, but also takes into account the indirect effects that node A has on other communities via other nodes (Jones, Ma, & McNally, 2021; Robinaugh et al., 2016). This statistic is valuable for networks with both positive and negative edges, and it is thought to be useful for researchers seeking to identify influential nodes for clinical treatment purposes (Jones, Ma, & McNally, 2021; Robinaugh et al., 2016). In our study, ED symptom subscales, ED QOL, PTSD subscales and comorbid subscales were predefined as four separate communities using GroupObject. Thus, it allowed us to determine which ED symptoms were most closely related to all PTSD symptoms and vice versa.

The accuracy of edge weights was assessed by nonparametric bootstrapping (1000 iterations) with 95% confidence intervals using the R package bootnet (Epskamp et al., 2018) and is reported in the supplements (Figure S2). A CS‐coefficient indicates the maximum proportion of cases that can be dropped to retain, with 95% certainty, a correlation with the original centrality indices of 0.70 or higher. The CS‐coefficient should preferably be 0.50 or higher (Epskamp et al., 2018).

3. RESULTS

3.1. Cronbach's alpha

The internal consistency or Cronbach's alpha coefficient was calculated for each of the psychometric measures and ranged from 0.884 to 0.909. The overall Cronbach's alpha for the sample was 0.897.

3.2. Network estimation

The resulting network structure is visualised in Figure 1. The blue edges represent positive partial correlations between nodes, whereas the red edges represent negative partial correlations.

Visual inspection of the network indicates particularly strong positive connections between (a) EDI‐2 body dissatisfaction (ED2) and EDEQ shape concern (ED14), (b) EDEQ eating concern (ED13) and EDI‐2 bulimia (ED3), and (c) EDI‐2 social insecurity (ED11) and EDI‐2 interpersonal distrust (ED8) (see Figure 1). There were also strong connections between trait (A1) and state (A2) anxiety scores and between PTSD cluster scores. In addition, all of the PCL‐5 (P1, P2, P3) were associated with each other.

3.3. Expected influence (centrality)

To investigate which nodes are highly central we looked at expected influence, which is reflected in the network where several nodes had the most edges attached to it (see Figure 2). These included EDI‐2 ineffectiveness (ED7), EDI‐2 interoceptive awareness (ED6), EDEQ shape concern (ED14), EDQOL psychological subscale (Q1), STAI state score (A2), and PCL‐5 D (P2) and E (P3) scores, all of which displayed high node expected influence (≥1 z‐score) When looking at significant differences between edges, they are all significantly different from the items <1 in z‐score (see Figure S5 representing centrality difference test). Less central nodes (<−1 z‐score) were EDI‐2 maturity fears (ED9), EDI‐2 bulimia (ED3), and EDI‐2 perfectionism (ED10) subscales, indicating that these groups of symptoms are not maintaining an ED‐PTSD network.

FIGURE 2.

FIGURE 2

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Expected influence estimates of the network.

3.4. 1‐Step bridge expected influence (bridge symptoms)

The 1‐step bridge expected influence is presented in Supplementary Figure S2, which displays the nodes with the strongest connections to other communities of symptoms. Between our predefined four communities of (a) PTSD symptoms, (b) ED symptoms, (c) EDQOL symptoms, and (d) comorbid symptoms (trait anxiety, state anxiety, and depression), depression (D1) and EDQOL psychological (Q1) and physical/cognitive (Q4) subscales showed the highest bridge expected influence (>1 z‐score). This suggests that they are highly connected to the nearest clusters and maintain the ED‐PTSD through their direct effects. PTSD clusters hyperarousal (P3) and STAI state anxiety (A2) also showed high centrality. Less central (<−1 z‐score) was shape concern, restraint concern, perfectionism, maturity fears, interoceptive awareness, and body dissatisfaction.

3.5. 2‐Step bridge expected influence (bridge symptoms)

The 2‐step bridge expected influence for each node is presented in Figure 3 To access which node(s) are likely to be involved in bridging the distinct clusters of symptoms, 2‐step bridge expected influence indexes were obtained. The 2‐step bridge expected influence procedure was chosen because it can account for the influence of intermediary clusters that are two steps away (Jones, Ma, & McNally, 2021), and therefore, it is a more comprehensive representation of expected influence than the 1‐step procedure. Correspondingly, the EDQOL psychological subscale (Q1) was the most substantial bridge (>2 z‐scores), followed by the EDQOL physical/cognitive subscale (Q2), the total PHQ‐9 score (D1), the STAI State anxiety score (A1), and the PTSD cluster E score (P4). All of these measures were substantial bridge expected influence indices (≥1 z‐score) and thereby indicated the relevance of these nodes in bridging the constructs together (see Figure S6 for centrality difference test). This also shows that these nodes have the highest sums of edge weights.

FIGURE 3.

FIGURE 3

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Bridge expected influence estimates of the network.

3.6. Network accuracy and stability

Figures regarding the accuracy and stability of the estimated network are provided in Figures S3 and S4. These results indicate robustness of the network with high stability of centrality indices and accurate edge weights. The CS‐coefficients were 0.75 for expected influence and 0.75 for bridge expected influence estimates in the network. This implies that 75% of the data may be omitted to retain with 95% certainty a correlation of 0.75 with the original dataset (Epskamp et al., 2018). According to Epskamp et al., 2018 a network should preferably be over 0.50 based on recent simulation studies. Thus, our network displayed excellent stability.

4. DISCUSSION

To our knowledge this is the first network analysis of ED and PTSD symptoms that have incorporated the additional concomitant measures of state‐trait anxiety, major depression, and QOL in a large group of 2178 ED patients admitted to residential treatment. Our a priori hypothesis that comorbid symptom clusters do in fact play major roles in bridging ED and PTSD symptoms was supported. Our results contribute to our understanding of the complex interplay between EDs, PTSD, and associated psychiatric comorbidities and QOL measures. Given our large sample size, our goal of identifying the subscales with the highest expected and bridge expected influence was achieved with a high degree of reliability. We found that symptoms of ineffectiveness, interoceptive awareness, state anxiety, shape concern, hyperarousal, NACM associated with traumatic events, , and psychological QOL subscale scores had the highest expected influences. This reflects that across individuals these symptoms co‐occurred as most central in the network when controlling for all other symptoms, suggesting that they are crucial maintaining factors in ED‐PTSD comorbidity that may warrant particular focus in integrated treatment approaches. Some findings replicate previous research while others may expand our understanding of the ED‐PTSD landscape.

It is not surprizing that ineffectiveness emerged as the most central node in our analysis. The ineffectiveness subscale assesses feelings of inadequacy, worthlessness, and incompetence. It has been theorised to be of major significance in understanding ED phenomenology since the work of Hilde Bruch, who noted a “paralysing sense of inadequacy and ineffectiveness which pervades all thinking and activities of the patient” (Bruch, 1973). Since then, its role has been described and documented in multiple studies of ED patients (Garner, 1991; Solmi et al., 2018; Wagner et al., 1987). However, the synonymous concept of helplessness has also been shown to be of major importance to the understanding of the development and persistence of EDs, as well as of major depression and PTSD in the face of overwhelming traumatic events and perceived defeat (Conoscenti & Fanselow, 2019; Groleau et al., 2012; Pivovarova et al., 2016; Salcioglu et al., 2017; Troop, 2012; Troop & Baker, 2008; Troop & Treasure, 1997). Ineffectiveness has also been shown to be a specific psychopathological dimension connected to emotional abuse which promotes the maintenance of ED symptoms (Monteleone et al., 2019, 2022).

The emergence of interoceptive awareness as a central node in our network analysis is also not surprizing in light of past research. This EDI‐2 subscale refers to a relative inability to be aware of, and a difficulty in processing, inner bodily and emotional states. One aspect of this phenomena is alexithymia, which has been described in multiple studies of patients with EDs, major depression, PTSD and other trauma‐related disorders (Arunagiri & Reilly, 2020; Berke et al., 2017; Carano et al., 2006, 2012; Cochrane et al., 1993; de Groot et al., 1995; Dehghanizadeh et al., 2016; Eichhorn et al., 2014; Khan & Jaffee, 2022; Meneguzzo et al., 2022; Montebarocci et al., 2006; Nowakowski et al., 2013; Oglodek, 2022; Putica et al., 2021; Rice et al., 2022; Sexton et al., 1998; Shank et al., 2019; Spence & Courbasson, 2012; van Strien & Ouwens, 2007; Westwood et al., 2017). Alexithymia has also been shown to be a mediator between prior abuse and ED symptomatology (Eichhorn et al., 2014; Mitchell & Mazzeo, 2005; Paivio & McCulloch, 2004; Shank et al., 2019), and emotion processing deficits have been a central focus in the treatment of EDs for some time now (Bydlowski et al., 2005; Mountford et al., 2021; Schmidt et al., 2015). Interestingly, interoceptive awareness is in part mediated by circuits involving the insula cortex, which have been reported to be disturbed in both EDs (Frank, 2015; Kaye & Bailer, 2011; Leenaerts et al., 2022) and PTSD (Feduccia & Mithoefer, 2018; Lieberman et al., 2023; Szeszko & Yehuda, 2019; Xiao et al., 2022).

Notably, in a systematic review of 25 network analysis studies in EDs, ineffectiveness, interoceptive awareness and affective problems emerged as core ED psychopathological constructs that also helped to reconceptualise comorbidity (Monteleone & Cascino, 2021). Together, ineffectiveness and interoceptive awareness have been found to mediate connections to all types of child maltreatment, and this combination has also been associated with non‐suicidal self‐injury, substance‐related disorders, and other comorbid symptoms (Milos et al., 2004; Monteleone et al., 2019; Noma et al., 2015; Vervaet et al., 2021). Interestingly, Mitchell and colleagues reported that successful treatment of PTSD in patients with disordered eating with cognitive processing therapy showed significant improvements in ineffectiveness and interoceptive awareness, among other EDI‐2 subscales (Mitchell, Wells, et al., 2012).

Our findings are in line with those of Nelson and colleagues, who found that NACM associated with traumatic events were highly influential in the ED‐PTSD network (Nelson et al., 2022). They suggested an important pathway between binge eating and the inability to experience positive emotions, thereby supporting the premise that binge eating serves as a form of self‐medication and affective regulation (Brewerton, 2011; Heatherton & Baumeister, 1991). Our analysis suggests that symptoms of depression and anxiety, on a between person level, seem to be “driving” and connecting the ED‐PTSD symptoms. Although these are findings from across individuals, and one should be cautious with clinical implications, it should be noted that aspects typically related to major depression (like feelings of worthlessness or anhedonia) and other aspects from the STAI (like the inability to feel calm, self‐confident, steady) might be important aspects to consider in patients with ED‐PTSD. It is noteworthy that when controlling for PTSD and ED symptoms and other relevant factors like QOL, depression and anxiety are still showing the highest bridge symptoms when controlling for the influence of nodes 2 steps away. These studies, although cross‐sectional, point to the need for a focus on symptoms of depression and anxiety during therapy for patients with ED‐PTSD. Major depression as an important and influential comorbid illness in EDs has gained increasing attention and support in the literature the last few years, especially when it occurs in relationship to PTSD, which is exceedingly common (Brewerton et al., 2024). A recent 5‐year follow‐up study from a randomised controlled trial investigating different therapies for EDs highlight the need for a focus on depression (Herzog et al., 2022).

The classical ED symptom complex involving shape concern was another factor showing high centrality in our network analysis. Shape concern has previously been found to have high centrality in network analyses from several other ED samples (Calugi et al., 2020; Christian et al., 2020; DuBois et al., 2017; Forrest et al., 2018; Smith et al., 2019; Wang et al., 2019). Shape concerns have been specifically linked to histories of childhood abuse or PTSD symptoms in previous network analyses (Rodgers et al., 2019; Vanzhula et al., 2019). Other studies have also linked weight and shape concerns with histories of child maltreatment, especially childhood sexual abuse in women (Akduman et al., 2021; Brooke & Mussap, 2013; Emery et al., 2021). A recent study investigating ED symptoms and the direction of symptom change across therapy on an individual level, found that a change in overvaluation of shape preceded and predicted changes in other ED symptoms in patients with and without childhood maltreatment (Kopland et al., 2023). These studies all point to overvaluation of shape and shape concern's pivotal and robust role in EDs.

Psychological and physical‐cognitive QOL symptoms, major depression symptoms, state anxiety symptoms, and hyperarousal symptoms (PTSD Cluster E) were the nodes with the highest 2‐step bridge expected influence, meaning that major depression and anxiety appear to be connecting the network of ED‐PTSD symptoms and having a greater impact in the network than other subscales. Notably, the EDQOL psychological subscale has been associated with moderate to severe ED symptoms and has been found to have high convergent validity with the Beck Depression Inventory (0.73) and neuroticism (0.71) (Engel et al., 2006). In addition, the EDQOL physical‐cognitive subscale includes a number of somatic symptoms linked to anxiety, depression and trauma, such as headache, fatigue, and difficulty concentrating (Brewerton, Perlman, et al., 2022; Harshaw, 2015; Scioli‐Salter et al., 2016).

In summary, when investigating symptoms of EDs, PTSD, major depression, state‐trait anxiety, and QOL, it was ineffectiveness, interoceptive awareness, state anxiety, shape concern, psychological QOL symptoms, NACM and hyperarousal that appeared as most central in the network. This is in line with several other network analyses, further substantiating their role in ED patients. However, in the 2‐step bridge expected influence analysis, which controlled for other more distant correlated symptoms, major depression, psychological‐physical‐cognitive QOL symptoms, and state anxiety symptoms emerged as the most important bridge symptom clusters in maintaining the ED‐PTSD network. These findings should be studied more extensively on a within‐person level to develop more accurate treatments for the ED‐PTSD group.

4.1. Limitations and strengths

Although this article reveals network connections between symptoms belonging to different symptom clusters, the cross‐sectional design has limitations regarding directionality and temporality (Bos et al., 2017). This points towards the necessity of future intensive longitudinal studies (Levinson et al., 2020, 2022) to examine the within‐person relations between nodes in the network, and more specifically how ED‐PTSD, anxiety and depression symptoms interact at the individual level over time. Subsequently, interventional studies are needed to examine whether manipulation of central nodes leads to change in the other connected nodes. Nevertheless, several strengths should be noted. This is the first study to include symptoms of QOL, depression, and state‐trait anxiety in an ED‐PTSD network with a large clinical sample. This enables the investigation of many relevant nodes which have not been examined before. Lastly, the current network displays excellent stability pointing to a robust network.

5. CONCLUSION

In conclusion, these results contribute to the expanding literature showing important and intertwined links between EDs and PTSD that extend beyond ED and PTSD symptoms proper into comorbid trauma‐related symptomatology, such as depression and state anxiety. Our hypothesis that comorbid symptoms of depression, anxiety and QOL do in fact play major roles in bridging ED and PTSD symptoms was supported. Identifying these heretofore unrevealed central symptom connections may lead to improved treatment approaches that are direly needed for this multimorbid group of patients that tend to be relatively refractory to traditional treatment approaches. Thus, our analysis suggests future temporal, longitudinal network analyses could benefit from incorporating interoceptive awareness, ineffectiveness, state anxiety, and depression to better understand the complex interactions between symptoms in an ED‐PTSD network. This might in turn contribute to the development of more effective treatments.

Supporting information

Supplementary Material

ERV-33-148-s001.docx (42.8KB, docx)

Figure S1

ERV-33-148-s007.pdf (12.2KB, pdf)

Figure S2

ERV-33-148-s006.pdf (11.5KB, pdf)

Figure S3

ERV-33-148-s004.pdf (47.1KB, pdf)

Figure S4

ERV-33-148-s003.pdf (32.6KB, pdf)

Figure S5

ERV-33-148-s005.pdf (37.5KB, pdf)

Figure S6

ERV-33-148-s002.pdf (34.1KB, pdf)

Brewerton, T. D. , Kopland, M. C. G. , Gavidia, I. , Suro, G. , & Perlman, M. M. (2025). A network analysis of eating disorder, PTSD, major depression, state‐trait anxiety, and quality of life measures in eating disorder patients treated in residential care. European Eating Disorders Review, 33(1), 148–162. 10.1002/erv.3136

Handling Editor: Hubertus Himmerich

DATA AVAILABILITY STATEMENT

Research data are not shared.

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Associated Data

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Supplementary Materials

Supplementary Material

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Figure S1

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Figure S2

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Figure S5

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Figure S6

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Data Availability Statement

Research data are not shared.

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