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. Author manuscript; available in PMC: 2025 Sep 1.
Published in final edited form as: Eat Disord. 2024 Feb 25;32(5):473–492. doi: 10.1080/10640266.2024.2322322

Preliminary associations of body weight, weight bias, and dietary restriction with eating disorder diagnosis in women experiencing food insecurity

Heather A Davis 1, Meredith Kells 2, Anna Gabrielle G Patarinski 1, Jennifer E Wildes 3
PMCID: PMC11927387  NIHMSID: NIHMS1969489  PMID: 38402578

Abstract

Understanding the co-occurrence of food insecurity and eating disorders is a pressing concern. Several factors have been hypothesized to increase risk for eating disorders in women with food insecurity including dietary restriction, body weight, and weight-related bias, but few studies have tested these factors simultaneously to determine which are associated most strongly with eating disorder status. We tested cross-sectional associations of dietary restriction, current body mass index (BMI), weight suppression (i.e., the difference between current weight and highest weight), and weight bias with eating disorder diagnosis in a sample of 99 self-identified women with current food insecurity (54% White; mean [SD] age = 40.26 [14.33] years). Participants completed two virtual study visits consisting of electronic questionnaires and interviews. A binary logistic regression model was conducted to test relations between the hypothesized correlates and eating disorder diagnostic status in the past 12 months, controlling for age, food insecurity severity, and body dissatisfaction. Higher levels of weight suppression and weight bias, but not current BMI, were significantly associated with the presence of an eating disorder. Contrary to our hypothesis, greater dietary restriction was associated with lower likelihood of eating disorder diagnosis. Results suggest high levels of weight bias and weight suppression characterize women with food insecurity who meet criteria for an eating disorder. Women who experience food insecurity and have lost a relatively great deal of weight and/or hold biases about high weight should be screened for eating pathology in clinical settings.

Keywords: eating disorders, food insecurity, weight bias, weight, weight bias, eating disorders, weight, food insecurity

Introduction

Eating disorders are serious psychiatric illnesses characterized by negative health consequences, high chronicity, and elevated mortality rates (Chesney et al., 2014; Romano et al., 2020; Udo & Grilo, 2019). A large body of research suggests that individuals experiencing food insecurity (FI), defined as limited or uncertain access to sufficient quantity or variety of nutritious and safe foods (Coleman-Jensen, 2009), are at elevated risk for an eating disorder or eating-related psychopathology (Abene et al., 2023; Hazzard et al., 2022; Hazzard et al., 2020). Studies consistently indicate that rates of engagement in eating disorder behaviors and positive screenings for eating disorder diagnoses are higher among individuals experiencing FI compared to those with food security (Barry et al., 2021; Laboe et al., 2023; Lydecker & Grilo, 2019; Rasmusson et al., 2019). However, prior work examining factors that contribute to greater risk for eating disorder diagnosis among this vulnerable population is relatively limited. In this study, we sought to understand the association of several conceptually-relevant factors – namely, dietary restriction, body size, weight suppression (i.e., the discrepancy between a person’s highest adult body weight and their current weight), and weight bias (i.e., generalized negative beliefs about high weight that are not necessarily internalized) – with eating disorder diagnostic status among women experiencing current FI.

Dietary restriction is common among individuals who experience FI due to food scarcity (Middlemass et al., 2021). Dietary restriction also is a symptom endorsed in multiple different eating disorders (Fairburn et al., 2003). Across eating disorders, dietary restriction is typically motivated by a desire for weight loss or maintenance, or to exert a sense of control over one’s food intake (Elran-Barak et al., 2015). Individuals experiencing FI report restricting their diet and skipping meals for many reasons, including food scarcity, the need to reserve food for others in the family such as children, and, sometimes, to facilitate weight loss (Taylor et al., 2020). Importantly, food restriction due to food scarcity, desire for weight loss, or any other reason is associated with the later development of binge eating, preoccupation with food, and a diagnosable eating disorder, even in the absence of prior weight and shape concerns (Keys et al., 1950; Polivy, 1996). Moreover, greater dietary restriction is associated with eating disorder symptoms in individuals experiencing FI (Middlemass et al., 2021), suggesting that it may be a significant contributor to eating disorder diagnosis in this population.

The presence of FI also is associated with high body weight and weight gain (Basiotis & Lino, 2003; Franklin et al., 2012; Laboe et al., 2023; Rasmusson et al., 2019). The link between high weight and FI is thought to be explained by the type of diet that is most accessible to individuals experiencing FI. Across studies, people experiencing FI report lower consumption of fresh foods (e.g., fruits, vegetables) and greater consumption of highly processed, energy-dense foods (O’Brien et al., 2016). This is likely due to the greater availability and affordability of highly-processed and energy-dense foods in low-income versus high-income areas, and the foods available using food assistance programs or benefits. Of note, the link between weight gain and FI is most consistently demonstrated among women (Eisenmann et al., 2011). Although eating disorders were historically thought to mainly afflict people of low body weight, it is now widely acknowledged that eating disorders occur in people of all body sizes (Duncan et al., 2017), and rates may be increasing most among people in higher weight bodies (da Luz et al., 2017). There also is increasing recognition that levels of eating-related psychopathology are similar to or higher among individuals with eating disorders and high body weights compared to those of individuals with eating disorders and low or average body weights (Duncan et al., 2017; Hay, 2023; Walsh et al., 2023). As such, investigating associations between body weight and eating disorder diagnosis among individuals experiencing FI, who on average have higher body weights, may reveal important information to help clarify eating disorder risk in this population.

Weight suppression is another potential factor that might contribute to risk for eating disorder diagnosis among individuals experiencing FI. There is extensive evidence documenting that weight suppression is a correlate and risk factor for eating disorder diagnosis (Lowe et al., 2018). Greater weight suppression is associated with eating disorder symptom maintenance (Keel & Heatherton, 2010) and poor treatment outcome (Vall & Wade, 2015). As individuals experiencing FI have a propensity toward excess weight gain and higher body weight (Basiotis & Lino, 2003; Franklin et al., 2012; Laboe et al., 2023; Rasmusson et al., 2019) and report a desire and efforts to lose weight (Taylor et al., 2020), they may experience fluctuations in weight that result in weight suppression and contribute to eating disorder risk.

Finally, an additional factor that may relate to eating disorder diagnosis in the context of FI is the presence of weight bias, defined as negative attitudes, beliefs, assumptions, and judgments toward individuals in higher weight bodies. Weight bias is distinct from internalized weight bias or weight self-stigma, which occurs when one turns negative beliefs about individuals in higher weight bodies inward, toward oneself (Durso & Latner, 2008). Qualitative work indicates that individuals experiencing FI also experience weight-related criticism from family and friends (Frayn et al., 2022), which may promote negative beliefs about individuals in higher weight bodies. Although individuals experiencing FI report greater weight self-stigma than individuals with food security (Gastón-Panthaki et al., 2023), and internalized weight bias associated with eating pathology (Becker et al., 2021), the relationship between weight bias and eating disorder diagnosis in this population is unclear. Importantly, it is critical that research investigate weight bias among individuals experiencing FI, even if it is not internalized toward oneself, because biases against individuals in higher-weight bodies are associated with eating pathology (Barnes et al., 2014; Shao et al., 2023). Prior work has not yet (1) examined bias about high weight in general, not just toward oneself, in relation to eating disorder diagnosis among individuals experiencing FI or (2) accounted for the potential simultaneous contributions of weight status, weight history, dietary restriction, or other variables known to be associated with eating disorder behaviors (e.g., body dissatisfaction). Because each of these considerations (e.g., body size, weight history, weight bias) may have important implications for treating eating disorders in this population, understanding their potentially unique contributions to eating disorder risk is pertinent.

The Current Study

The current study sought to examine cross-sectional associations of dietary restriction, current body weight, weight suppression, weight bias, and eating disorder diagnosis among women with current FI. We hypothesized that higher levels of dietary restriction, weight bias, current body weight, and weight suppression would be associated with the presence of an eating disorder.

As a core symptom of most eating disorders, we controlled for body dissatisfaction in our analyses (McLean & Paxton, 2019). Given our focus on understanding the unique cross-sectional associations of weight bias, weight suppression, and dietary restriction, we sought to control for the likely confound of body dissatisfaction in predicting an eating disorder diagnosis (dichotomized as presence or absence). Further, we wanted to control for the overlap between body dissatisfaction, weight bias, weight suppression, and dietary restriction, to ensure that associations between those variables and eating disorder diagnosis could not be attributed to the presence of body dissatisfaction, and were instead above and beyond any association between body dissatisfaction and disordered eating. We also considered FI severity in our analyses. Across studies, greater FI severity is associated with the presence of an eating disorder diagnosis (Becker et al., 2017; Hazzard et al., 2022; Lydecker & Grilo, 2019; Rasmusson et al., 2019). We controlled for FI severity status in our model to allow for the understanding of the association of each hypothesized correlate with eating disorder diagnostic status, regardless of FI severity.

Method

Participants

Participants included 99 self-identified women currently experiencing FI in the U.S [mean (SD) age = 40.26 (14.3) years]. See Figure 1 for a consort diagram of participant flow from eligibility screening to study visit completion. One-hundred and forty prospective participants completed the eligibility telephone screen. Of those, 20 were excluded, either due to not meeting inclusion criteria (n = 18 were food secure, n = 1 was male) or declining to participate (n =1). Of the 120 who were invited to participate, 107 completed study visit 1, and, of those, 104 completed study visit 2. At study visit 2, 5 participants were found to be ineligible due to reporting high food security at the time of the Household Food Security Survey Module interview. As such, 99 participants were included in the final sample.

Figure 1.

Figure 1.

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Consort diagram: Participant flow.

Participants were recruited using flyers on bulletin boards, social media advertisements, and ResearchMatch, a national health volunteer registry. Telephone screens were conducted to verify eligibility. Inclusion criteria were as follows: identifying as female, being 18 years of age or older, meeting USDA criteria for household FI, and having internet access to complete study questionnaires online. Exclusion criteria were as follows: unable to read English, pregnant at the time of the study, or unable to access the internet.

Measures

USDA Household Food Security Survey Module (HHFSSM; Bickel et al., 2000).

The USDA’s HHFSSM was used as a measure of FI severity. The HHFSSM is a structured interview to assess food security over the past 12 months, including 18 questions for individuals who endorse having children in the household, and 10 questions for individuals who do not endorse having children in the household. Total scores range from 0 to 10 for households with no children, and 0 to 18 for households with children, and exist on a continuum of four classifications: full, marginal, low, and very low food security. Respondents are grouped into FI severity categories based on their total score. “Full food security” (score of 0), indicates a household with very little to no trouble obtaining food. “Marginal food security” (score of 1–2), describes a household that experiences anxiety acquiring food, with no reduction in the quantity, quality, or variety of foods consumed. “Low food security” (score of 3–5 for households with no children, score of 3–7 for households with children), refers to households that experience trouble obtaining high quality and/or a variety of foods, with no reduction in the amount of food consumed. “Very low food security” (score of 6–10 for households with no children, score of 8–18 for households with children) is defined by an inadequate amount and variety of food consumed by the household. In our study, participants were coded “1” for marginal food security, “2” for low food security, and “3” for very low food security. Research supports the construct validity of the HHFSM (Marques et al., 2015). In previous studies, the HHFSM demonstrated good to excellent internal consistency and high test-retest reliability (Derrickson et al., 2000; Derrickson et al., 2001). In the current study, internal consistency was excellent (α =.92).

Eating Disorder Diagnostic Interview (EDDI; Stice et al., 2013a).

The EDDI is a semi-structured interview that generates diagnoses of eating disorders based on DSM-5 criteria and assesses weight history information, including highest weight and current weight and height. Weight suppression was calculated by subtracting participants’ self-reported current weight from their self-reported highest nonpregnancy weight. Previous research has demonstrated good agreement between objectively measured body weight and self-reported weight recalled up to 20 years later (Lowe et al., 2007). The EDDI demonstrates strong test-retest reliability and inter-rater agreement (Stice et al., 2013a). In support of the EDDI’s criterion and construct validity, the EDDI has demonstrated sensitivity to prevention and treatment interventions, prediction of depressive symptoms, and participants with EDDI-diagnosed eating disorders report greater functional impairment and emotional distress compared with participants determined by the EDDI to not have an eating disorder (Stice et al., 2008; Stice et al., 2009; Stice et al., 2013b).

Body Mass Index (BMI).

BMI was calculated for current weight, based on participants’ self-reported current height and weight. Good agreement has been found between BMI using self-reported height and weight and direct anthropometric measurements (Davies et al., 2020).

Eating Pathology Symptoms Inventory (EPSI).

The EPSI is a 45-item self-report questionnaire that assesses eight categories of eating disorder symptoms, three of which were used for the current study: Negative Attitudes Toward Obesity to assess weight bias, Body Dissatisfaction, and Restricting to assess dietary restriction. Respondents use a Likert scale anchored at 0 (“Never”) and 4 (“Very Often”) to indicate how frequently behaviors or cognitions have occurred over the past month. The EPSI demonstrates strong psychometric properties across studies (Forbush et al., 2014). In the current study, internal consistency for the subscales used was good (Negative Attitudes About Obesity, α =.89; Body Dissatisfaction, α =.80; Restricting, α =.85).

Procedure

This study received Institutional Review Board approval from the Medical Center’s human subjects committee. Participants provided written informed consent prior to participation. Procedures included the completion of two 45-minute virtual study visits which occurred via Zoom or telephone. During the first visit, participants met with a member of the research staff via Zoom or telephone and were provided information about the consent process including instructions for filling out the electronic consent form while on the call. Once the participant provided consent, the research staff member sent them their personalized questionnaire link which was completed after the call ended. Research staff remained available to participants via phone and email following the conclusion of the call, to allow participants the opportunity to ask questions or obtain assistance accessing the electronic questionnaire. The second interview visit, which included the HHFSSM and EDDI, was completed via telephone or Zoom with a member of the research staff following completion of the first study visit. Following the interview, all participants were offered referrals for mental health services and food security resources. Participants were provided a $60 electronic gift card for participation in both study visits.

Data Analytic Plan

Statistical analyses were performed using SPSS version 24 (IBM, 2016).

Differences in demographic characteristics and study variables by eating disorder diagnostic status (present or absent) over the past 12 months were examined using chi-square tests for categorical variables (e.g., race, HHFSSM FI category) and independent samples t-tests for continuous variables (e.g., age, current BMI, weight suppression, weight bias, EPSI Body Dissatisfaction score). We also calculated bivariate Pearson correlations to describe associations between study variables. Next, a binary logistic regression model was constructed to test the association of the hypothesized correlates (EPSI Restricting score, current BMI, weight suppression, EPSI Negative Attitudes Toward Obesity score) with eating disorder diagnostic status (present or absent) over the past 12 months, controlling for the relevant confounders, HHFSSM FI category, age, and EPSI Body Dissatisfaction. Age and race were entered as additional covariates because participants with eating disorders were significantly older than participants without eating disorders, on average. At Step 1, HHFSSM FI category, age, race, and EPSI Body Dissatisfaction were entered. At Step 2, EPSI Restricting score, EPSI Negative Attitudes Toward Obesity score, current BMI, and weight suppression were entered. Odds ratios and 95% confidence intervals are reported to represent the likelihood of having an eating disorder diagnosis in the past 12 months based on the presence of each correlate.

Results

Descriptive Data.

All 99 participants included in analyses completed both study visits. On average, study visits were spaced out 6.47 days (range: 0 – 23 days; 1 participant completed the interview immediately following the survey). One third (n = 33; 33.33%) of the sample met criteria for an eating disorder in the past 12 months. The diagnostic breakdown was as follows: 10 participants met criteria for current atypical anorexia nervosa (AAN), 6 participants met criteria for current binge-eating disorder (BED), 4 participants met criteria for current bulimia nervosa (BN), 1 participant met criteria for current purging disorder (PD), 7 participants met criteria for current other specified feeding or eating disorder (OSFED) BED, 1 participant met criteria for current OSFED BN, 1 participant met criteria for past 12 months BED, and 3 participants met criteria for past 12 months OSFED BN.

Demographic characteristics of participants with and without an eating disorder diagnosis in the past 12 months are presented in Table 1. Participants in the eating disorder group were more likely to identify as white and were significantly older than participants in the no eating disorder group, a mean difference of 7.83 years. The eating disorder and no eating disorder groups did not differ significantly by ethnicity, FI status, household income, or level of education. Over half of participants in both groups were classified as having “very low food security.” More than 25% of participants in both groups were classified as having “low food security,” and 12 – 14% of participants in both groups were classified as having “marginal food security.”

Table 1.

Demographic characteristics by eating disorder diagnostic status

ED (n = 33) No ED (n = 66)
Variable Mean (SD) t p
Age 45.48 (11.49) 37.65 (14.96) 2.64 .01
n (%) X 2 p
Race
 White 20 (60.60) 34 (51.51) 14.43 .03
 Black 7 (21.21) 23 (34.85)
 Asian 0 4 (6.06)
 Pacific Islander 1 (3.03) 0
 American Indian 0 1 (1.52)
 Biracial 5 (15.15) 1 (1.52)
 Other (unspecified) 0 3 (4.55)
Ethnicity
 Hispanic/Latinx 2 (6.06) 8 (12.12) 1.46 .48
 Non-Hispanic/Latinx 28 (84.84) 51 (77.27)
 Not reported 0 1 (1.51)
Food Security Status .62 .73
 Marginal food security 4 (12.1) 9 (13.6%)
 Low food security 11 (33.3%) 17 (25.8%)
 Very low food security 18 (54.5%) 40 (60.6%)
Household Income 7.95 .72
 Unknown/Not reported 0 3
 $0 - $9,999 6 10
 $10,000 – 19,999 9 14
 $20,000 – 29,999 6 9
 $30,000 – 39,999 5 11
 $40,000 – 49,999 2 4
 $50,000 – 59,999 0 5
 $60,000 – 69,999 1 4
 $70,000 – 79,999 2 4
 $80,000 – 89,999 0 1
 $90,000 – 99,999 1 0
 $100,000 or more 1 1
Level of Education 8.71 .27
 Less than high school 0 0
 High school graduate 0 4
 Some college, no degree 10 13
 Associate’s degree 4 13
 Bachelor’s degree 9 25
 Master’s degree 9 8
 Professional degree 0 1
 Doctoral degree 0 1
 Other 1 1
Employment Status 4.39 .73
 Unknown/Not reported 0 1
 Full-time job 8 18
 Part-time job 7 15
 Stay-at-home parent 2 4
 Homemaker 1 1
 In school 1 6
 Unable to work/receiving disability 11 12
 Other 2 4
 Unemployed 1 5
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Note: ED = Participants who met criteria for an eating disorder in the past 12 months; No ED = Participants who did not meet criteria for an eating disorder in the past 12 months.

Clinical characteristics of participants with and without an eating disorder diagnosis in the past 12 months appear in Table 2. Participants who met criteria for an eating disorder in the past 12 months reported significantly higher current BMI, greater weight suppression, higher weight bias, and higher body dissatisfaction than participants without an eating disorder diagnosis in the past 12 months. Participants who met criteria for an eating disorder in the past 12 months reported having lost, on average, double the amount of weight from their highest weight reported compared with participants without an eating disorder. Participant groups did not differ significantly on dietary restriction. Table 3 provides the results of bivariate Pearson correlations between study variables. Across the sample, eating disorder diagnosis was positively correlated with age, body dissatisfaction, weight bias, and current BMI.

Table 2.

Scores on study variables by eating disorder diagnostic status

ED (n = 33) No ED (n = 66)
Variable Mean (SD) t p
EPSI Body Dissatisfaction 18.67 (5.37) 13.28 (5.70) 4.51 <.001
EPSI Restricting 9.00 (6.89) 10.35 (5.89) 1.01 .31
EPSI Negative Attitudes Toward Obesity 8.24 (4.47) 4.88 (5.34) 3.11 .002
Current Body Mass Index 32.55 (8.97) 28.26 (9.19) 2.21 .03
Weight Suppression (pounds) 41.58 (46.20) 18.80 (24.85) 3.12 <.001
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Note: ED = Participants who met criteria for an eating disorder in the past 12 months; No ED = Participants who did not meet criteria for an eating disorder in the past 12 months.

EPSI = Eating Pathology Symptoms Inventory

Table 3.

Correlations between study variables

1 2 3 4 5 6 7 8 9
1. Eating Disorder Diagnosis -- -- -- -- -- -- -- -- --
2. Age .26** -- -- -- -- -- -- -- --
3. Food Insecurity Severity -.03 -.02 -- -- -- -- -- -- --
4. Racea -.09 -.14 .07 -- -- -- -- -- --
5. Body Dissatisfaction .42*** .15 -.03 -.19 -- -- -- -- --
6. Dietary Restriction -.10 -.16 .30** .00 .10 -- -- -- --
7. Weight Bias .30** .05 .05 .47*** .46*** .67*** -- -- --
8. Current BMI .22*** .22** -.06 -.15 .19 -.25* .02 -- --
9. Weight Suppression .31 .19 .14 -.00 .08 .16 .17 .06 --
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Note. BMI = Body Mass Index.

a

To preserve power. race was coded dichotomous (white or nonwhite).

*

p < .05

**

p < .01

***

p < .001.

Hypothesis Tests.

Results of the binary logistic regression model examining the hypothesized correlates’ association with eating disorder diagnostic status are presented in Table 4. With FI severity, age, race, and body dissatisfaction included at Step 1, age and body dissatisfaction were significantly associated with the presence of an eating disorder in the past 12 months. FI severity and race were not associated with the presence of an eating disorder in the past 12 months. Addition of the hypothesized correlates in Step 2 revealed that higher levels of weight bias and weight suppression, and lower levels of dietary restriction, were significantly associated with the presence of an eating disorder in the past 12 months. Current BMI was not significantly associated with the presence of an eating disorder in the past 12 months. Although body dissatisfaction remained significantly associated with eating disorder diagnosis in the past 12 months, age no longer was significant following the addition of the other hypothesized correlates in the full model. FI severity and race remained not significantly associated with presence of an eating disorder diagnosis in the past 12 months.

Table 4.

Binary logistic regression model predicting 12-month eating disorder diagnostic status

Step Predictor B SE Wald df OR 95% CI R2 X 2
1 .30 23.48***
Food Insecurity Severity -.07 .34 .04 1 .85 .48, 1.83
Age .55 .25 4.76 1 1.73* 1.06, 2.82
Racea .18 .51 .12 1 1.20 .44, 3.24
Body Dissatisfaction 1.08 .30 13.13 1 2.95*** 1.64, 5.28
2 .46 38.87***
Food Insecurity Severity -.00 .39 .00 1 1.00 .46, 2.14
Age .14 .30 .20 1 1.14 .64, 2.06
Race .07 .56 .02 1 1.07 .36, 3.18
Body Dissatisfaction .18 .06 10.91 1 3.06*** 1.57, 5.95
Dietary Restriction -.65 .56 4.21 1 .52* .28, .97
Weight Bias .62 .31 4.05 1 1.87* 1.02, 3.42
Current BMI .19 .29 .44 1 1.21 .68, 2.15
Weight Suppression .74 .31 5.77 1 2.10* 1.15, 3.85
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Note. BMI = Body Mass Index.

a

To preserve power. race was coded dichotomous (white or nonwhite).

*

p < .05

**

p < .01

***

p < .001. Nagelkerke R2 is reported.

Discussion

Findings from this study demonstrated that greater weight suppression and weight bias were independently associated with the presence of an eating disorder diagnosis among women experiencing FI, but current BMI and severity of FI were not. Contrary to expectation, lower levels of dietary restriction were associated with greater odds of eating disorder diagnosis. These results contribute to an accumulating literature demonstrating the potential harms of weight bias and weight suppression with regard to eating pathology in women experiencing FI.

Findings suggest that women experiencing FI who endorse biased views and beliefs about higher weight bodies may be at elevated risk for an eating disorder diagnosis. Given existing literature indicating that the presence of weight bias is associated with eating pathology, regardless of current weight (Romano et al., 2021), this finding is not surprising. However, the relationship may be even more important to note in populations experiencing FI. Individuals with FI endorse experiencing criticism and stigmatization based on their weight (Frayn et al., 2022), and such experiences can lead to the development of negative beliefs about higher weight bodies, which may then promote the development of eating pathology. Over time, such weight bias may become internalized toward oneself, which is further associated with eating pathology and psychological distress (O’Brien et al., 2016). Given the association between FI and higher body weight among women in particular (Gooding et al., 2012), it may be that women with FI experience stigmatization due to their weight more frequently, either in their personal lives, healthcare settings, or workplaces, which could help to explain the high prevalence of eating disorders in this population. Future research should investigate the temporal relationship among experiences of weight stigmatization, the development of weight bias and internalized weight bias, and the onset of eating disorder symptoms among women experiencing FI to better understand the risk process. One practical implication of this finding is that individuals working with populations experiencing FI – including healthcare providers, Supplemental Food Assistance Program (SNAP) employees, and researchers – should be mindful of inadvertently stigmatizing individuals in higher-weight bodies. In addition, education about weight stigma and its harms may be needed in healthcare and education settings frequented by individuals experiencing FI to help facilitate the eradication of systemic weight bias.

We found that greater weight suppression, but not current BMI, was associated with eating disorder diagnosis among women experiencing FI. Although some research has found a higher prevalence of eating disorder diagnoses among those with high body weight, there also is evidence of comparable levels of eating pathology among those with high body weight compared to those with lower body weights (Duncan et al., 2017; Hay, 2023; Walsh et al., 2023). It may be that the presence of suppressed body weight is a better indicator of eating pathology than high body weight among women experiencing FI. Greater weight suppression has been shown to predict eating disorder diagnostic status in individuals not assessed for FI (Lowe et al., 2018), but, to our knowledge, this is the first study to demonstrate this association among women who were recruited based on their FI status. The finding that weight suppression was associated with eating disorder diagnosis in women experiencing FI has important clinical implications. Although individuals experiencing FI report that the pursuit of weight loss is important (Taylor et al., 2020), our results suggest that a lower weight relative to one’s previous weight may not always represent health or healthy behavior engagement. Of note, it is possible that weight status is a mediator of the relationship between FI and eating disorder diagnosis, which may explain why current BMI was not significantly associated with FI in this preliminary, cross-sectional study. Future research should investigate FI, weight status, and weight fluctuation in relation to eating disorder diagnosis using longitudinal designs that allow for mediational analyses. In addition, upon asking about weight history, we did not assess whether participants were intentionally or unintentionally suppressing their weight. Previous work has demonstrated that intentional weight suppression is associated with eating pathology but unintentional weight suppression is not (Chen et al., 2022). We cannot know how our results might differ if we had assessed the intentionality of weight suppression among our participants. Future work investigating weight suppression among individuals experiencing FI should clarify whether weight loss was intentional. In terms of clinical implications of this finding, providers of individuals experiencing FI should be encouraged to (1) view current weight in the context of the patient’s weight history, (2) carefully monitor large fluctuations in weight, specifically weight loss, and (3) screen for eating pathology regularly among this high-risk population.

Contrary to previous research demonstrating that higher levels of current FI are associated with higher levels of eating pathology and eating disorder diagnoses (Barry et al., 2021; Becker et al., 2017; Lydecker & Grilo, 2019; Rasmusson et al., 2019), we found no significant association between FI severity and eating disorder diagnosis. This result should be interpreted with caution. Our sample was relatively small in comparison to prior studies, did not include individuals with food security, and the majority of participants (58%) reported very low food security. Previous work using larger samples has compared individuals experiencing FI to individuals not experiencing FI (Barry et al., 2021) or individuals with low food security to individuals with very low food security (Lydecker & Grilo, 2019; Rasmusson et al., 2019), allowing for more direct and intentional comparisons of food security level in relation to eating pathology. Taken together, there may not have been enough variability in food security level for us to detect significant associations with eating disorder diagnosis in our sample. Future studies should continue to examine FI severity in relation to eating pathology.

Finally, dietary restriction was negatively associated with the presence of an eating disorder diagnosis. This finding was unexpected given that eating disorders often include dietary restriction (Fairburn et al., 2003), and previous studies have shown that (1) individuals experiencing FI engage in dietary restriction for a variety of reasons and (2) greater dietary restraint – the intention to restrict one’s food intake – among individuals experiencing FI is associated with eating pathology (Middlemass et al., 2021). Our study is the first to use the EPSI Restricting subscale to assess dietary restriction in women experiencing FI. It is possible that this measure is less sensitive than questionnaires used in previous research to the unique nature of dietary restriction of individuals experiencing FI, who must restrict their food intake for reasons other than weight loss (Taylor et al., 2020). Given previous work measuring dietary restraint’s relation to eating disorder pathology among individuals experiencing FI (Middlemass et al., 2021), it may be that the intention to restrict food is elevated among individuals experiencing FI and an eating disorder, but behavioral restriction of food is not. Future research should examine this possibility. Further, several items included in the EPSI Restricting subscale focus on how others perceive the respondent (e.g., “People told me I do not eat very much”, “People encouraged me to eat more”). Given associations between FI and high body weight (Basiotis & Lino, 2003; Franklin et al., 2012; Laboe et al., 2023; Rasmusson et al., 2019), and the ubiquity of weight stigma in Western society (Tomiyama, 2014), it may be that women experiencing FI are not often encouraged to eat more, and do not perceive themselves as eating too little because they do not perceive their body weight as too low, even if it is suppressed. Future research should further investigate motivations for dietary restriction in relation to eating disorder risk among individuals experiencing FI. Such research may include qualitative methods that assess lived experiences and perspectives which may inform the development of measures that more adequately assess the construct of dietary restriction in relation to FI.

This study had several strengths. First, we controlled for body dissatisfaction in our model to ensure that associations between study variables and eating disorder diagnosis could not be attributed to a known eating disorder correlate. Second, we used standardized, semi-structured interviews and self-report measures with strong psychometric properties to assess constructs. Third, our sample included representation of racial and ethnic minorities comparable to population studies of FI (Gomez & Perez, 2022). However, the majority of the participants experiencing an eating disorder diagnosis identified as white. Because FI disproportionately impacts racially and ethnically diverse communities, it will be important for future studies to examine the relationships demonstrated in this study in samples with greater diversity in racial and ethnic background. Our inclusion of only women is both a strength and potential limitation. Women disproportionately experience both FI (Rabbitt et al., 2022) and eating disorders (Sonneville & Lipson, 2018) and women experiencing FI may be at higher risk for eating pathology (Maynard et al., 2018). Thus, an investigation of the correlates of eating pathology among women experiencing FI is especially warranted. However, the exclusion of other genders limits our ability to generalize the study findings. To examine whether the relationships demonstrated in this study extend to other genders, future research examining associations among FI, weight, weight history, weight bias, and eating disorder diagnosis should include samples with greater gender diversity.

There also were limitations to this study. First, our sample size was modest and findings should be replicated in larger samples. Second, given our small sample, we examined associations of weight bias and weight history with binary eating disorder diagnosis (present or absent), which allowed us power to detect effects but limited our ability to understand differences in relationships based on specific eating disorder diagnosis. Future studies should investigate the relationships demonstrated in the current study in larger samples with a variety of eating disorder diagnoses, to allow for investigation of potential differences according to diagnosis. Third, participants were English-speaking women with Internet access, and results may not generalize to non-English speaking women and women without Internet access. Fourth, it is possible that participants who self-selected into our study differed on variables of interest compared to individuals who would not have volunteered under similar circumstances. We cannot know if such differences contributed to effects observed in the study, or if findings would differ among a randomly-selected sample of women experiencing FI.

Fifth, there are likely other factors associated with eating disorder diagnosis among women experiencing FI that were not explored in this study. Participants with an eating disorder diagnosis in the past 12 months had varying sociodemographic characteristics that may be differentially related to eating disorder diagnosis; future studies should investigate the intersection of sociodemographic characteristics with FI and eating disorder diagnosis in larger samples to understand any potential differences. Relatedly, participants ranged in age from 20 years to 70 years, which may have influenced our results. Although eating disorder risk appears to vary across the lifespan (Baker & Runfola, 2016; Manwaring et al., 2006; Klump, 2013), research on FI and eating disorder diagnosis is still emerging, and it is not yet clear how eating disorder risk across adulthood may very as a function of FI status. Future work examining relations among FI, eating disorder diagnosis, weight history, and weight bias should investigate differences based on age group. Sixth, we measured weight suppression using the traditional method of subtracting current weight from lifetime highest weight. Emerging data suggest a developmentally sensitive measure of weight suppression more strongly correlates with eating pathology (Singh et al., 2021). However, we were not able to calculate weight suppression using this developmental weight suppression method because we did not collect data on premorbid age. Future work should investigate developmental weight suppression among individuals experiencing FI. Finally, the cross-sectional design precludes any inferences related to causality. Instead, this research represents a first step toward the development of a causal model of FI, weight bias, weight suppression, and eating disorder diagnosis.

Continued research on weight history and weight bias as correlates and, potentially, predictors of eating disorder diagnosis among women experiencing FI could help to inform the adaptation and development of treatments for eating disorders in this vulnerable, underserved population. Our results indicate that patients who experience FI and have lost a great deal of weight and/or hold biases about high weight should be screened for eating pathology in clinical settings. Research and clinical efforts to reduce weight stigma and bias may prove useful in preventing or reducing the occurrence of eating disorders, in general, and among this vulnerable population.

Clinical Implications.

  • The experience of food insecurity and an eating disorder, among women, is associated with suppressed weight and elevated weight bias.

  • Providers of women experiencing food insecurity should view weight in the context of the patient’s weight history, as the presence of weight loss may not necessarily indicate health or health behavior engagement and may be associated with an eating disorder.

  • Individuals working with populations experiencing food insecurity (as part of healthcare, education, or research) should be mindful of inadvertently stigmatizing their patients, students, and participants in higher-weight bodies.

  • Education about weight stigma and its harms may be needed in healthcare and education settings frequented by individuals experiencing food insecurity to help facilitate the eradication of systemic weight bias.

Funding:

This work was supported by a grant (T32 MH082761) from the National Institute of Mental Health and a Dean’s Research Fund award from the University of Chicago Medicine and Biological Sciences.

IRB statement:

This study received Institutional Review Board approval from the Human Subjects Committee at the University of Chicago Medical Center, Biological Sciences Division.

Footnotes

Conflicts of interest/Competing interest: All authors have no conflicts of interest to report.

Availability of data and material:

The data that support the findings of this study are available upon reasonable request from the University of Chicago Medicine Eating Disorders Program.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available upon reasonable request from the University of Chicago Medicine Eating Disorders Program.

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