Disordered eating and mental health: The role of depression and loneliness in college students

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Claydon, Rose Marie Ward, Christian Garcia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8894936/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective Previous work has showed five distinct latent profiles of the Eating Pathology Symptoms Inventory (EPSI) among college students, as well as their relationship with aspects of anxiety. Building on this work, the current study examines how the identified disordered eating (DE) profiles are connected with depression and loneliness, while accounting for peak alcohol use which have been increasing issues among college students. Methods Students ( n = 1,362) from a midwestern university participated in an annual online health survey. They answered validated questionnaires for disordered eating, depression, loneliness, and alcohol use (peak drinking). Analyses of the EPSI scale profiles were run to determine any differences on depression or loneliness. Results All profiles had moderate to moderately severe levels of depression. Profile 1 (high levels of excessive exercise and muscle building) was also most likely to have participants that were men and had the lowest levels of depression and loneliness. Profile 2 (Lowest levels of DE, largest profile, high proportion of women) and profile 3 (high levels of body dissatisfaction, moderate binge eating and restricting, 20% of sample) had the highest levels of depression and loneliness. CONCLUSIONS This study’s findings illustrate that depression and loneliness vary by DE symptom profile. Colleges need to be aware that students face intersectional psychological issues and may require unique and multi-faceted interventions. university students disordered eating depression loneliness surveys Figures Figure 1 Introduction Disordered eating (DE) is prevalent among college students, with over one-third of students experiencing DE behaviors[1]. There are a number of psychosocial outcomes currently linked to DE in college students including depression, anxiety, substance use, and loneliness [2]. Moreover, feelings of loneliness combined with depression and DE were particularly heightened during the COVID-19 pandemic [3]. Students reported rising depressive thoughts during this time along with fewer social interactions because of social distancing practices [4]. Many students also tried to cope with these challenges and feelings with negative coping strategies, such as disordered eating or substance use. Depression is highly comorbid with disordered eating, particularly among college students. Even at subclinical levels of disordered eating, increases in DE correspond with a rise in depressive symptoms [5]. It is estimated that 40% to 70% of those diagnosed with an eating disorder experience symptoms of a mood disorder (e.g., depression [6]) This is especially significant because individuals with depression and eating disorders are more likely to experience suicidal thoughts [7]. Addressing DE among these individuals could also help address some concomitant depression. Loneliness was particularly amplified among college students during the pandemic. This was also strongly associated with a positive eating disorder screen. A large longitudinal study found a bidirectional relationship between loneliness and disordered eating over the 13 years of the study [8]. Loneliness has also been associated with greater alcohol-related problems, among individuals with high levels of food and alcohol disturbance (FAD; i.e. restricting caloric intake prior to drinking [9]). Additionally, gender played a role in this relationship, with men reporting greater loneliness having a higher risk of eating disorder symptoms than women [10]. The Eating Pathology Symptoms Inventory (EPSI) is a multidimensional measure to assess disordered eating pathology across a variety of symptom areas. It was created specifically to address the gap in measures that were narrower in scope and only captured one or limited aspects of DE. The EPSI has also been shown to be invariant across the lifespan with good-to-excellent internal consistency and factor structure quality in both adolescents and adults. Prior latent profile analyses with the EPSI in college students have focused on eating disorders (EDs) rather than DE, have not considered psychosocial covariates, or were used specifically for excessive exercise.[11, 12] One LPA in university students focused on dietary restriction and the EPSI with a validation analysis considering emotion dysregulation and harmful alcohol use This LPA found five specific DE groups. Similar to the Trolio and Racine’s (2023)[13] LPA, previous research has focused on facets of anxiety in relation to the EPSI, indicating the presence of five different profiles of students with different levels and presentations of DE [14]. These profiles were, by size: Low Disordered Eating, Body Dissatisfaction & Binge Restrict Cycle, Excessive Exercise & Muscle Building, Moderate DE & Binging, and High DE. This research suggests that students who had the highest levels of anxiety should be provided more targeted services and support. Using the data from Claydon et al. (2025)[14] and additional variables, the current study considers how these previously identified latent profiles of DE symptoms map onto the psychosocial outcomes of depression and loneliness. Due to the connection between loneliness and FAD, we also controlled for peak drinking (the highest number of drinks an individual had on one occasion). Our primary aim was to understand which profiles had the highest levels of loneliness and depression (while accounting for peak alcohol use), hypothesizing profiles with higher levels of DE would also have corresponding higher levels of loneliness and depression. Extending Claydon et al.’s (2025)[14] profiles to depression and loneliness is key due to the strong relationship between depression and loneliness and disordered eating. In short, depression is one of the most common mental health diagnoses with disordered eating, particularly dysthemia and bulimia nervosa.[15] In addition, depression level (most notably, the absence of major depressive disorder) improves the long-term likelihood of recovery from eating disorders.[16] If certain profiles have more problematic levels of depression and/or loneliness, these profiles might warrant further study to inform more effective prevention and intervention studies. Methods Procedures Full-time undergraduate and graduate students (N = 17,114) at a midsized midwestern university in the United States were invited via email to participate in an anonymous online Student Health survey in March 2022. This survey is conducted annually and remained open for 3 weeks after initial invitation. Participants received a $3 e-gift card upon study completion. Of those invited, 18.07% completed the survey. To reduce order effects, measures were presented in random order, and some of the measures were only given to a subset of participants, providing some planned missingness. Because the EPSI was one of these measures, the final analytic sample included 1,362 participants (44.05% of respondents). Mental health resources were provided to all students in the consent and debriefing forms. This study was approved by Miami University’s Institutional Review Board (IRB#: 01191r). Participants A majority of the participants (67.1%, n = 914) identified as a woman and heterosexual (74.7%, n = 1018; 11.9%, n = 162, bisexual; and 3.2%, n = 43 identified as queer). A majority of the participants identified as White (88.2%, n = 1201). There was a slight over sampling of first year students (36.5%, n = 497; 23.9%, n = 325, second year students; 17.2%, n = 234, third year; 12.2%, and n = 166, fourth year. The average age was 20.44 ( SD = 3.16) years. With respect to indicators of social economic status, 14.8% ( n = 202) were first generation students and 12.9% ( n = 176) were eligible for Pell grants. In addition, 55.9% ( n = 761) of the participants lived on campus. Most participant characteristics are similar to those of the student population from which it was sampled, except for an oversampling of people who identify as women. Measures Disordered Eating symptoms measured by the Eating Pathology Symptom Inventory (EPSI ) . This measured disordered eating symptoms [17], consisting of 45 Likert scale items (ranges: Never (0) to Very Often (4)) with eight unique subscales: Body Dissatisfaction (e.g., “I tried on different outfits, because I did not like how I looked.”), Binge Eating (e.g., I ate until I was uncomfortably full.”), Cognitive Restraint (e.g., “I tried to exclude “unhealthy” foods from my diet.”), Purging (e.g., “I thought laxatives are a good way to lose weight.”), Restricting (e.g., “People told me that I do not eat very much.”), Excessive Exercise (“I felt that I needed to exercise nearly every day.”), Negative Attitudes towards Obesity (e.g., “I thought that obese people lack self-control.”), and Muscle Building (e.g., “I thought about taking steroids as a way to get more muscular.”). The EPSI has excellent estimates of validity, internal consistency (α = 0.84-0.89) and test-retest reliability (Pearson r = 0.73 [17]). Additionally, we found high internal reliability in our sample (α = 0.77 to 0.91 across the subscales, demonstrating acceptable to excellent internal reliability). Depression measured by the Patient Health Questionnaire-9 (PHQ-9) The PHQ-9 is a nine item self-report questionnaire that connects with the nine DSM-IV depression criteria [18]. These criteria are written as nine statements about the past two weeks and are paired with a four-point Likert response from Not at all to Nearly every day , which is scored from 0-3 for each. There are a total of 27 points, with increasing scores indicating greater depression. Example items include “little interest or pleasure in doing things” and “feeling down depressed, or hopeless.” Participants reported an average score of 16.06 ( SD = 6.26). The measure demonstrated excellent internal reliability (α = .91). Loneliness measured by the UCLA Loneliness Measure The UCLA Loneliness Measure is a 20-item self-assessment rated on a 4-point Likert scale from I often feel this way to I never feel this way [19]. The measure detects both subjective loneliness and social isolation questions such as: “I cannot tolerate being alone” and “I feel shut out and excluded by others.” Based on total scoring with each item reverse scored and “nevers” given lower scores, higher scores indicate greater loneliness. Participants scored a mean of 5.54 ( SD =1.93), and the measure demonstrated good internal reliability (α = .85). Alcohol indicated by Peak Drinking Based on the definition of a standard drink of alcohol (i.e., one 12-ounce beer, one 1.5 ounce shot of liquor, or a five-ounce glass of wine), participants were asked: “During the last 30 days, what is the highest number of drinks that you drank on any one occasion?” Participants were also asked how many days they have at least one drink containing alcohol in a typical week, and how many drinks they have on a typical day when drinking. Participants reported a mean of 4.29 drinks ( SD = 4.50). Data Analysis To identify potential subgroups, profiles, or latent groups from the EPSI, Claydon et al. (2025)[14] used Latent Profile Analysis (LPA) in Mplus 8.8, utilizing robust maximum likelihood estimation. LPA is a person-centered method, as opposed to a variable-centered approach, employing latent variable mixture modeling with a categorical latent variable and continuous manifest variables. It follows a stepwise procedure to identify the optimal solution. In LPA, the optimal model is expected to: 1) define the number of profiles, 2) ensure each participant belongs to only one profile, and 3) maintain homogeneity among participants within a profile. The process begins with researchers identifying the target population and selecting appropriate indicators. In the second phase, they test the modeling assumptions and analyze patterns of missing data. The third phase involves model estimation, including profile enumeration and model formulation. Subsequently, the models are assessed based on fit and classification diagnostics (Akaike Information Criteria; Bayesian Information Criteria; Lo-Mendell-Rubin likelihood ratio test; Entropy; latent class posterior probability; and interpretability; see Claydon et al., 2025[14] for more information). Researchers then decide whether to incorporate distal outcomes or covariates. Finally, the profiles are examined and compared concerning other variables not included in the model-building phase. Using the profiles determined by Claydon et al. (2025)[14], we added peak drinking was as a covariate along with distal outcomes (PHQ-9, loneliness), to assess their impact on the solution.[20] We enhanced the profile descriptions and assessed the solution's validity by incorporating covariates using the R3STEP method and distal outcomes through the BCH method (Bolck, Croon, and Hagenaars method).[21] The R3STEP method involves three steps: First, perform the LPA to identify profiles without covariates. Next, assign individuals to profiles using their posterior probabilities from the LPA, creating a most likely class membership variable. Finally, predict latent profile membership by modeling the relationship between covariates and the class membership variable, typically through multinomial logistic regression. The BCH is used for distal outcomes after the latent profiles have been identified. By incorporating other variables using the BCH method[21], we enhanced the description of the profiles and assessed the validity of our solution. The BCH method allows for the integration of auxiliary variables post-identification of the latent profiles. Finally, the profiles were analyzed and compared against additional variables not included in the model-building phase, such as demographic variables. Chi-square tests of independence were used to assess the profiles across demographic categories, while follow-up pairwise Wald tests evaluated differences among the profiles concerning outcome variables. Results Statistics of the EPSI with psychosocial risk factors Means, standard deviations, and Cronbach’s alpha estimates are provided in Table 1 for each study measure. Demographics commonly associated with DE were assessed (e.g., gender identity, sexual orientation, race/ethnicity, age, fraternity/sorority status) in additional to the psychosocial risk factors. Means, standard deviations, and Cronbach’s alpha estimates are provided in Table 1 for each study measure. Table 1 Correlations, Means, and standard deviations of the EPSI with psychosocial risk factors. EPSI Body Dissatisfaction Binge Eating Excessive Exercise Cognitive Restraint Purging Neg. Att. Towards Obesity Restricting Muscle Building Alpha M (SD) PHQ-9 .46*** .34*** .03 .24*** .19*** .09*** .42*** .07* .91 16.06 (6.26) Loneliness .34*** .27*** .02 .18*** .12*** .13*** .24*** .05* .85 5.54 (1.93) Peak Drinking − .04 .05* .20*** .04 .01 .18*** − .01 .23*** -- 4.29 (4.50) Alpha 0.89 0.86 0.88 0.77 0.91 0.91 0.89 0.82 M (SD) 10.74 (7.21) 6.19 (5.04) 5.53 (5.19) 3.98 (3.07) 1.71 (3.72) 3.91 (4.54) 5.73 (5.52) 3.35 (4.11) Note. M (SD) = Mean (Standard Deviation). * p < .05; ** p < .01; *** p < .001. PHQ—9 = depression screener; Peak drinking = the highest number of standard drinks in the past 30 days. Neg. Att. Towards Obesity = Negative Attitudes towards Obesity. Profiles of the EPSI scales Fit statistics for the unconditional 1–6 LPA Models for the EPSI scales are presented in Claydon et al. (2025)[ 14 ]. [ 12 ] The fit criteria or information indexes (e.g., BIC) decrease as the number of profiles is tested. Based on interpretability, the LMR likelihood ratio test, a 5-class solution was determined to be optimal (entropy > .82) from the unconditional model. See Claydon et al. (2025) for more details concerning the model selection.[ 14 ] Figure 1 displays the average Z-scores for each profile across the subscales used to develop them. Profile 1, characterized by above average levels of excessive exercise, negative attitudes, and muscle building, comprised 9.7% ( n = 132) of the sample. Profile 2, representing 54.1% ( n = 734) of the sample, exhibited the lowest levels (below average levels) of disordered eating tendencies in the model. Profile 3 accounted for 20.2% ( n = 274) of the sample and was marked by moderate levels of body dissatisfaction, binge eating and restricting, body dissatisfaction, and cognitive restraint. Profile 4 included 8.0% ( n = 109) of the sample and showed the moderate levels of body dissatisfaction, binge eating, purging, and cognitive restraint. Profile 5 was the smallest, comprising 7.9% ( n = 107) of the sample, and generally had the highest scores on each subscale, except for excessive exercise and muscle building. In particular, participants grouped in profile 5 had a higher-than-average score on the purge scale. One covariate (peak drinking) was added to the final 5-profile solution using the 3-step procedure. In short, peak drinking differentiates between profile 1 and the other profiles. Profile 1 has lower peak drinking levels than the other profiles. See Claydon et al. (2025) for more information about multinomial logistic regression comparing the profiles [ 14 ]. Psychosocial Risk factors The psychosocial risk factors of depression and loneliness were mapped against the five distinct LPA profiles. We used the BCH procedure in Mplus to compare the profiles across the continuous outcome variables (i.e., the psychosocial risk factors). The BCH process uses Wald tests to compare the mean scores of the variables across the profiles. See Table 2 for the Wald tests. For the depression screener (PHQ-9) and loneliness measure, profile 1 (above average levels of excessive exercise, negative attitudes, and muscle building) had significantly lower levels of depression and loneliness than all the profiles except profile 4 (moderate levels of body dissatisfaction, binge eating, purging, and cognitive restraint; highest proportion of women). Profile 1 also had the highest proportion of men and highest peak drinking level. Another consistent pattern across the depression and loneliness measures is that profiles 2 (below average levels of DE), 3 (moderate levels of body dissatisfaction, binge eating and restricting, body dissatisfaction, and cognitive restraint), and 5 (high levels of DE especially purging) had the highest levels on average of depression and loneliness and high proportions of women in their groups. Table 2 Equality Tests of Means Across Profiles using the BCH Procedure PHQ 9 M SE M SE Profile 1 14.227 .209 Profile 2 19.104 .467 Profile 3 19.810 .720 Profile 4 15.034 .498 Profile 5 17.737 .792 Chi-square p-value Chi-square p-value Overall Test 133.457 < .001 Profile 1 v. 2 81.425 < .001 Profile 1 v. 3 55.423 < .001 Profile 1 v. 4 2.214 .137 Profile 1 v. 5 18.354 < .001 Profile 2 v. 3 0.648 .421 Profile 2 v. 4 33.674 < .001 Profile 2 v. 5 2.208 .137 Profile 3 v. 4 29.404 < .001 Profile 3 v. 5 3.616 .057 Profile 4 v. 5 8.291 .004 Loneliness M SE M SE Profile 1 5.116 .073 Profile 2 6.295 .134 Profile 3 6.128 .191 Profile 4 5.456 .174 Profile 5 5.881 .198 Chi-square p-value Chi-square p-value Overall Test 70.911 < .001 Profile 1 v. 2 52.669 < .001 Profile 1 v. 3 24.508 < .001 Profile 1 v. 4 3.217 .073 Profile 1 v. 5 13.110 < .001 Profile 2 v. 3 0.489 .484 Profile 2 v. 4 13.820 < .001 Profile 2 v. 5 2.991 .084 Profile 3 v. 4 6.675 .010 Profile 3 v. 5 .780 .377 Profile 4 v. 5 2.572 .109 Note. PHQ-9 = Physician’s Health Questionnaire- 9, a screener for depression; Loneliness = UCLA Loneliness Scale. Discussion The current study expands understanding of disordered eating (DE) among college students by identifying five latent profiles of DE symptoms and examining the patterns of depression and loneliness, while accounting for peak alcohol consumption across the profiles. This person-centered approach revealed heterogeneity in DE presentations, highlighting patterns that would not be captured by traditional correlational methods. Overall Elevated Depression and Loneliness One striking finding is that most profiles reported moderate to moderately severe levels of depression, even among those profiles with relatively lower DE symptoms. The averages for each profile were above the cut point of 10, which indicates major depression with 88% sensitivity and 88% specificity [ 18 ]. This may reflect contextual factors unique to the sample, which was assessed in Spring 2022 as students were transitioning out of the COVID-19 pandemic. National surveys have documented elevated depressive symptoms and loneliness among college students during this period due to disrupted routines, social distancing, and academic pressures [ 10 ]. Additionally, since most of these students were first or second-year students, most would have spent a majority of their high school experience remotely or via hybrid options. This could have contributed to depression and may have also created a more challenging transition to college. Our data suggest that the lingering psychosocial effects of the pandemic may have contributed to higher overall levels of depression across profiles. Profile-Specific Patterns The relationships between depression and loneliness were not uniform across DE profiles, underscoring the need for subgroup-specific analyses. For instance, Profile 2, the largest profile, characterized by the lowest levels of DE and a high proportion of women; Profile 3, marked by body dissatisfaction and moderate binge eating/restricting; and Profile 5, high levels of DE especially purging, reported the highest levels of depression and loneliness (see Table 2 ). Contrary to expectations that profiles with more severe DE symptomatology would demonstrate the greatest psychosocial difficulties (i.e., highest levels of depression and loneliness), the findings suggest that for some students, even moderate levels of body dissatisfaction and binge eating (e.g., Profile 3) may be strongly tied to loneliness and depression. This relationship has been observed in other studies [ 22 , 23 ] and in Forbush et al. (2013)[ 24 ] that found that the EPSI Body Dissatisfaction and Binge Eating scales had the strongest relationship with depression compared to the other EPSI scales. Subgroup analyses in future research may clarify whether unique psychosocial mechanisms drive distress in these moderate-symptom groups. Profile 1 (roughly 10% of the sample), characterized by high levels of excessive exercise and muscle building, was predominantly male and had the lowest levels of depression and loneliness. This gendered pattern is consistent with literature indicating that men are more likely to endorse muscularity-oriented DE symptoms, often tied to cultural ideals of strength and performance rather than distress per se [ 25 ]. Due to the communal culture of exercise and the societal view of acceptance on exercise and muscle building, this population may experience less distress around these patterns but still have problematic DE behaviors. Notably, this profile also reported relatively high alcohol use, suggesting a potentially distinct risk pattern for men that warrants further exploration. These populations may also not be found with typical screening tools, suggesting greater need for understanding the distinctions of DE presentations. Another unexpected result emerged in Profile 5, the “most severe” group with elevated scores across nearly all EPSI subscales and an above average level of purge. Participants in this profile reported moderately high levels of depression and loneliness. While these students were still classified in the moderately severe range for depression, the findings complicate the assumption that more severe DE is linearly associated with psychosocial impairment. In short, we expected this pattern of DE to have significantly higher levels of depression and loneliness – especially when compared to profile 2, which had below average levels of DE. Instead, these results suggest that different DE groups may carry distinct psychosocial burdens, reinforcing the importance of person-centered methods. Profile 4 (moderate levels of body dissatisfaction, binge eating, purging, and cognitive restraint; highest proportion of women) warrants further investigation given that they reported relatively lower depression and loneliness compared to profile 3, and the pattern of the purge scale seems visually to separate the two groups. It is also critical to determine what types of interventions might work for this specific population. Implications for Research and Intervention Together, these findings demonstrate that depression and loneliness do not uniformly map onto levels of DE but instead vary by symptom profile. A bivariate approach would likely obscure these nuances, whereas latent profile modeling illuminates which subgroups are more vulnerable (e.g., profile 3 vs. profile 4). For campus health services, these distinctions are critical: students in profiles characterized by body dissatisfaction and binge eating may be at especially high risk for co-occurring depression and loneliness, even if their DE symptoms are not the most severe. Conversely, students engaged in muscularity-oriented behaviors may require different outreach strategies that integrate gender-sensitive approaches and consider alcohol use. The association between loneliness and pattern of DE also underscores the need for prevention and intervention programs that address social connection and belongingness on college campuses. Holt-Lunstad and colleagues [ 26 ] highlight that loneliness is not only a mental health concern but also a public health issue with broad implications for morbidity and mortality. Interventions that reduce social isolation, through peer support programs, mentorship initiatives, and campus-wide inclusion efforts, may therefore indirectly mitigate DE risk while also addressing depression. Additionally, these findings show the importance of screening for a variety of psychological concerns among students. DE screening or eating disorder screening are not standardized across college campuses and some measures may capture a more nuanced view of those disorders than others. With the vast differences in DE profiles across students, this could lead to some groups being missed based on their distinct presentations. Strengths and Limitations There are several strengths inherent in this study. We have a large sample size of university students that are representative of the total university population. Additionally, we employ a randomized survey design, which allows us to collect data on a wider array of topics without increasing respondent burden or fatigue. Third, the study utilized well-validated measures, including the Eating Pathology Symptom Inventory (EPSI [ 17 ]), the Patient Health Questionnaire-9 (PHQ-9 [ 18 ]), and the UCLA Loneliness Measure [ 19 ], all of which demonstrated strong internal reliability in the present sample. Finally, we included peak drinking in our models, which allowed us to account for alcohol consumption, a relevant behavioral health factor in college populations that is often linked to disordered eating. Along with these strengths, some limitations warrant consideration. All data were derived from cross-sectional, self-report measures, which limits causal inference and leaves the study open to potential reporting biases. In addition, gender identity was not included as a covariate in the final models, despite differences across profiles, because sample sizes for some gender identities were too small to be used meaningfully in subgroup analyses. Further, the randomized survey design included planned missingness, though the analytic sample retained sufficient power for the LPA and outcome analyses. While peak drinking was included, this represents only one facet of alcohol use and does not capture patterns such as frequency, motives, or alcohol-related problems. Finally, the diversity of the sample was limited, with nearly 90% of participants identifying as White, which restricts the generalizability of findings to more racially and ethnically diverse student populations. Conclusion This work demonstrates that DE among college students is multifaceted, with symptom patterns that do not map uniformly onto depression and loneliness. Person-centered analyses revealed that even moderate or low DE profiles may carry elevated psychosocial burdens. By highlighting subgroup differences, these findings underscore the importance of nuanced, profile-specific interventions that address the interrelated challenges of eating behaviors, social connectedness, and mental health among college students. Abbreviations DE disordered eating EDs eating disorders EPSI eating pathology symptoms inventory FAD food and alcohol disturbance LPA latent profile analysis Declarations Author Contribution Elizabeth Claydon and Rose Marie Ward developed the study idea and wrote the manuscript. Rose Marie Ward oversaw initial data collection and analyses. Elizabeth Claydon conducted the literature review and coordinated revisions. Christian Garcia assisted with the literature review, and writing the introduction and discussion. All authors edited and approved the final version being submitted. Acknowledgments – 7. Appreciation to all who participated to help us learn more about the health and well being of college students. 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Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. 2015;10(2):227–37. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Editor invited by journal 20 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 19 Feb, 2026 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-8894936","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607712303,"identity":"7c1cb06d-e096-4741-85e2-c05a02b00cee","order_by":0,"name":"Elizabeth A. Claydon","email":"data:image/png;base64,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","orcid":"","institution":"West Virginia University School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Elizabeth","middleName":"A.","lastName":"Claydon","suffix":""},{"id":607712304,"identity":"c824c30a-6112-4a73-b188-ad7670c4477d","order_by":1,"name":"Rose Marie Ward","email":"","orcid":"","institution":"University of Cincinnati","correspondingAuthor":false,"prefix":"","firstName":"Rose","middleName":"Marie","lastName":"Ward","suffix":""},{"id":607712305,"identity":"495052b5-b28f-4ccc-94b1-fbada5b99263","order_by":2,"name":"Christian Garcia","email":"","orcid":"","institution":"University of Cincinnati","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Garcia","suffix":""}],"badges":[],"createdAt":"2026-02-16 16:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8894936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8894936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105037820,"identity":"08b21077-25c4-4778-9ea3-ae5edf53eeb6","added_by":"auto","created_at":"2026-03-20 07:40:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86659,"visible":true,"origin":"","legend":"\u003cp\u003eVisual representation of the 5-profile solution of latent profiles using Z-scores of the EPSI subscales [14]\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8894936/v1/c7eeae3a820bbb02d19133f1.png"},{"id":105562840,"identity":"3b1715b5-1566-4c6f-80b2-5158923ee861","added_by":"auto","created_at":"2026-03-27 12:44:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":880711,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8894936/v1/cac82f36-6192-464a-83fe-dc67bcdb4594.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disordered eating and mental health: The role of depression and loneliness in college students","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDisordered eating (DE) is prevalent among college students, with over one-third of students experiencing DE behaviors[1]. There are a number of psychosocial outcomes currently linked to DE in college students including depression, anxiety, substance use, and loneliness [2]. Moreover, feelings of loneliness combined with depression and DE were particularly heightened during the COVID-19 pandemic [3]. Students reported rising depressive thoughts during this time along with fewer social interactions because of social distancing practices [4]. Many students also tried to cope with these challenges and feelings with negative coping strategies, such as disordered eating or substance use.\u003c/p\u003e\n\u003cp\u003eDepression is highly comorbid with disordered eating, particularly among college students. Even at subclinical levels of disordered eating, increases in DE correspond with a rise in depressive symptoms [5]. It is estimated that 40% to 70% of those diagnosed with an eating disorder experience symptoms of a mood disorder (e.g., depression [6]) This is especially significant because individuals with depression and eating disorders are more likely to experience suicidal thoughts [7]. Addressing DE among these individuals could also help address some concomitant depression.\u003c/p\u003e\n\u003cp\u003eLoneliness was particularly amplified among college students during the pandemic. This was also strongly associated with a positive eating disorder screen. A large longitudinal study found a bidirectional relationship between loneliness and disordered eating over the 13 years of the study [8]. Loneliness has also been associated with greater alcohol-related problems, among individuals with high levels of food and alcohol disturbance (FAD; i.e. restricting caloric intake prior to drinking [9]). Additionally, gender played a role in this relationship, with men reporting greater loneliness having a higher risk of eating disorder symptoms than women [10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Eating Pathology Symptoms Inventory (EPSI) is a multidimensional measure to assess disordered eating pathology across a variety of symptom areas. It was created specifically to address the gap in measures that were narrower in scope and only captured one or limited aspects of DE. The EPSI has also been shown to be invariant across the lifespan with good-to-excellent internal consistency and factor structure quality in both adolescents and adults. Prior latent profile analyses with the EPSI in college students have focused on eating disorders (EDs) rather than DE, have not considered psychosocial covariates, or were used specifically for excessive exercise.[11, 12] One LPA in university students focused on dietary restriction and the EPSI with a validation analysis considering emotion dysregulation and harmful alcohol use This LPA found five specific DE groups.\u003c/p\u003e\n\u003cp\u003eSimilar to the Trolio and Racine\u0026rsquo;s (2023)[13] LPA, previous research has focused on facets of anxiety in relation to the EPSI, indicating the presence of five different profiles of students with different levels and presentations of DE [14]. These profiles were, by size: Low Disordered Eating, Body Dissatisfaction \u0026amp; Binge Restrict Cycle, Excessive Exercise \u0026amp; Muscle Building, Moderate DE \u0026amp; Binging, and High DE. This research suggests that students who had the highest levels of anxiety should be provided more targeted services and support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing the data from Claydon et al. (2025)[14] and additional variables, the current study considers how these previously identified latent profiles of DE symptoms map onto the psychosocial outcomes of depression and loneliness. Due to the connection between loneliness and FAD, we also controlled for peak drinking (the highest number of drinks an individual had on one occasion). Our primary aim was to understand which profiles had the highest levels of loneliness and depression (while accounting for peak alcohol use), hypothesizing profiles with higher levels of DE would also have corresponding higher levels of loneliness and depression. Extending Claydon et al.\u0026rsquo;s (2025)[14] profiles to depression and loneliness is key due to the strong relationship between depression and loneliness and disordered eating. In short, depression is one of the most common mental health diagnoses with disordered eating, particularly dysthemia and bulimia nervosa.[15] In addition, depression level (most notably, the absence of major depressive disorder) improves the long-term likelihood of recovery from eating disorders.[16] If certain profiles have more problematic levels of depression and/or loneliness, these profiles might warrant further study to inform more effective prevention and intervention studies.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eProcedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFull-time undergraduate and graduate students (N = 17,114) at a midsized midwestern university in the United States were invited via email to participate in an anonymous online Student Health survey in March 2022. This survey is conducted annually and remained open for 3 weeks after initial invitation. Participants received a $3 e-gift card upon study completion. Of those invited, 18.07% completed the survey. To reduce order effects, measures were presented in random order, and some of the measures were only given to a subset of participants, providing some planned missingness. Because the EPSI was one of these measures, the final analytic sample included 1,362 participants (44.05% of respondents). Mental health resources were provided to all students in the consent and debriefing forms. This study was approved by Miami University\u0026rsquo;s Institutional Review Board (IRB#:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e01191r).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA majority of the participants (67.1%, \u003cem\u003en\u003c/em\u003e = 914) identified as a woman and heterosexual (74.7%, \u003cem\u003en\u003c/em\u003e = 1018; 11.9%, \u003cem\u003en\u003c/em\u003e = 162, bisexual; and 3.2%, \u003cem\u003en\u0026nbsp;\u003c/em\u003e= 43 identified as queer). A majority of the participants identified as White (88.2%, \u003cem\u003en =\u0026nbsp;\u003c/em\u003e1201). There was a slight over sampling of first year students (36.5%, \u003cem\u003en\u0026nbsp;\u003c/em\u003e= 497; 23.9%, \u003cem\u003en\u003c/em\u003e = 325, second year students; 17.2%, \u003cem\u003en\u003c/em\u003e = 234, third year; 12.2%, and \u003cem\u003en\u003c/em\u003e = 166, fourth year. The average age was 20.44 (\u003cem\u003eSD\u003c/em\u003e = 3.16) years. With respect to indicators of social economic status, 14.8% (\u003cem\u003en =\u003c/em\u003e202) were first generation students and 12.9% (\u003cem\u003en\u003c/em\u003e = 176) were eligible for Pell grants. In addition, 55.9% (\u003cem\u003en\u003c/em\u003e = 761) of the participants lived on campus. Most participant characteristics are similar to those of the student population from which it was sampled, except for an oversampling of people who identify as women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisordered Eating symptoms measured by the \u003cem\u003eEating Pathology Symptom Inventory (EPSI\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e)\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis measured disordered eating symptoms [17], consisting of 45 Likert scale items (ranges: \u003cem\u003eNever\u0026nbsp;\u003c/em\u003e(0)\u003cem\u003e\u0026nbsp;\u003c/em\u003eto \u003cem\u003eVery Often\u0026nbsp;\u003c/em\u003e(4)) with eight unique subscales: Body Dissatisfaction (e.g., \u0026ldquo;I tried on different outfits, because I did not like how I looked.\u0026rdquo;), Binge Eating (e.g., I ate until I was uncomfortably full.\u0026rdquo;), Cognitive Restraint (e.g., \u0026ldquo;I tried to exclude \u0026ldquo;unhealthy\u0026rdquo; foods from my diet.\u0026rdquo;), Purging (e.g., \u0026ldquo;I thought laxatives are a good way to lose weight.\u0026rdquo;), Restricting (e.g., \u0026ldquo;People told me that I do not eat very much.\u0026rdquo;), Excessive Exercise (\u0026ldquo;I felt that I needed to exercise nearly every day.\u0026rdquo;), Negative Attitudes towards Obesity (e.g., \u0026ldquo;I thought that obese people lack self-control.\u0026rdquo;), and Muscle Building (e.g., \u0026ldquo;I thought about taking steroids as a way to get more muscular.\u0026rdquo;). The EPSI has excellent estimates of validity, internal consistency (\u0026alpha; = 0.84-0.89) and test-retest reliability (Pearson \u003cem\u003er\u003c/em\u003e = 0.73 [17]). Additionally, we found high internal reliability in our sample (\u0026alpha; = 0.77 to 0.91 across the subscales, demonstrating acceptable to excellent internal reliability).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepression measured by the \u003cem\u003ePatient Health Questionnaire-9 (PHQ-9)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PHQ-9 is a nine item self-report questionnaire that connects with the nine DSM-IV depression criteria [18]. These criteria are written as nine statements about the past two weeks and are paired with a four-point Likert response from \u003cem\u003eNot at all\u0026nbsp;\u003c/em\u003eto \u003cem\u003eNearly every day\u003c/em\u003e, which is scored from 0-3 for each. There are a total of 27 points, with increasing scores indicating greater depression. Example items include \u0026ldquo;little interest or pleasure in doing things\u0026rdquo; and \u0026ldquo;feeling down depressed, or hopeless.\u0026rdquo; Participants reported an average score of 16.06 (\u003cem\u003eSD\u003c/em\u003e = 6.26). The measure demonstrated excellent internal reliability (\u0026alpha; = .91).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLoneliness measured by the \u003cem\u003eUCLA Loneliness Measure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UCLA Loneliness Measure is a 20-item self-assessment rated on a 4-point Likert scale from \u003cem\u003eI often feel this way\u0026nbsp;\u003c/em\u003eto \u003cem\u003eI never feel this way\u0026nbsp;\u003c/em\u003e[19]. The measure detects both subjective loneliness and social isolation questions such as: \u0026ldquo;I cannot tolerate being alone\u0026rdquo; and \u0026ldquo;I feel shut out and excluded by others.\u0026rdquo; Based on total scoring with each item reverse scored and \u0026ldquo;nevers\u0026rdquo; given lower scores, higher scores indicate greater loneliness. Participants scored a mean of 5.54 (\u003cem\u003eSD\u003c/em\u003e=1.93),\u0026nbsp;and the measure demonstrated good internal reliability (\u0026alpha; = .85).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlcohol indicated by \u003cem\u003ePeak Drinking\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the definition of a standard drink of alcohol (i.e., one 12-ounce beer, one 1.5 ounce shot of liquor, or a five-ounce glass of wine), participants were asked: \u0026ldquo;During the last 30 days, what is the highest number of drinks that you drank on any one occasion?\u0026rdquo; Participants were also asked how many days they have at least one drink containing alcohol in a typical week, and how many drinks they have on a typical day when drinking. Participants reported a mean of 4.29 drinks (\u003cem\u003eSD\u003c/em\u003e = 4.50).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify potential subgroups, profiles, or latent groups from the EPSI, Claydon et al. (2025)[14] used Latent Profile Analysis (LPA) in Mplus 8.8, utilizing robust maximum likelihood estimation. LPA is a person-centered method, as opposed to a variable-centered approach, employing latent variable mixture modeling with a categorical latent variable and continuous manifest variables. It follows a stepwise procedure to identify the optimal solution. In LPA, the optimal model is expected to: 1) define the number of profiles, 2) ensure each participant belongs to only one profile, and 3) maintain homogeneity among participants within a profile.\u003c/p\u003e\n\u003cp\u003eThe process begins with researchers identifying the target population and selecting appropriate indicators. In the second phase, they test the modeling assumptions and analyze patterns of missing data. The third phase involves model estimation, including profile enumeration and model formulation. Subsequently, the models are assessed based on fit and classification diagnostics (Akaike Information Criteria; Bayesian Information Criteria; Lo-Mendell-Rubin likelihood ratio test; Entropy; latent class posterior probability; and interpretability; see Claydon et al., 2025[14] for more information). Researchers then decide whether to incorporate distal outcomes or covariates. Finally, the profiles are examined and compared concerning other variables not included in the model-building phase.\u003c/p\u003e\n\u003cp\u003eUsing the profiles determined by Claydon et al. (2025)[14], we added peak drinking was as a covariate along with distal outcomes (PHQ-9, loneliness), to assess their impact on the solution.[20] We enhanced the profile descriptions and assessed the solution\u0026apos;s validity by incorporating covariates using the R3STEP method and distal outcomes through the BCH method (Bolck, Croon, and Hagenaars method).[21] The R3STEP method involves three steps: First, perform the LPA to identify profiles without covariates. Next, assign individuals to profiles using their posterior probabilities from the LPA, creating a most likely class membership variable. Finally, predict latent profile membership by modeling the relationship between covariates and the class membership variable, typically through multinomial logistic regression. The BCH is used for distal outcomes after the latent profiles have been identified. By incorporating other variables using the BCH method[21], we enhanced the description of the profiles and assessed the validity of our solution. The BCH method allows for the integration of auxiliary variables post-identification of the latent profiles.\u003c/p\u003e\n\u003cp\u003eFinally, the profiles were analyzed and compared against additional variables not included in the model-building phase, such as demographic variables. Chi-square tests of independence were used to assess the profiles across demographic categories, while follow-up pairwise Wald tests evaluated differences among the profiles concerning outcome variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistics of the EPSI with psychosocial risk factors\u003c/h2\u003e \u003cp\u003eMeans, standard deviations, and Cronbach\u0026rsquo;s alpha estimates are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for each study measure. Demographics commonly associated with DE were assessed (e.g., gender identity, sexual orientation, race/ethnicity, age, fraternity/sorority status) in additional to the psychosocial risk factors. Means, standard deviations, and Cronbach\u0026rsquo;s alpha estimates are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for each study measure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations, Means, and standard deviations of the EPSI with psychosocial risk factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eEPSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody Dissatisfaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinge Eating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcessive Exercise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCognitive Restraint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePurging\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNeg. Att. Towards Obesity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRestricting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMuscle Building\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAlpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.09***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.07*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.06 (6.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.54 (1.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak Drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.20***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.29 (4.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003cp\u003e(7.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.19 (5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003cp\u003e(5.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003cp\u003e(3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003cp\u003e(3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003cp\u003e(4.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003cp\u003e(5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003cp\u003e(4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eNote.\u003c/em\u003e M (SD) = Mean (Standard Deviation). * \u003cem\u003ep\u003c/em\u003e \u0026lt; .05; ** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01; *** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. PHQ\u0026mdash;9\u0026thinsp;=\u0026thinsp;depression screener; Peak drinking\u0026thinsp;=\u0026thinsp;the highest number of standard drinks in the past 30 days. Neg. Att. Towards Obesity\u0026thinsp;=\u0026thinsp;Negative Attitudes towards Obesity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProfiles of the EPSI scales\u003c/h2\u003e \u003cp\u003eFit statistics for the unconditional 1\u0026ndash;6 LPA Models for the EPSI scales are presented in Claydon et al. (2025)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] The fit criteria or information indexes (e.g., BIC) decrease as the number of profiles is tested. Based on interpretability, the LMR likelihood ratio test, a 5-class solution was determined to be optimal (entropy \u0026gt; .82) from the unconditional model. See Claydon et al. (2025) for more details concerning the model selection.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the average Z-scores for each profile across the subscales used to develop them. Profile 1, characterized by above average levels of excessive exercise, negative attitudes, and muscle building, comprised 9.7% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;132) of the sample. Profile 2, representing 54.1% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;734) of the sample, exhibited the lowest levels (below average levels) of disordered eating tendencies in the model. Profile 3 accounted for 20.2% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;274) of the sample and was marked by moderate levels of body dissatisfaction, binge eating and restricting, body dissatisfaction, and cognitive restraint. Profile 4 included 8.0% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;109) of the sample and showed the moderate levels of body dissatisfaction, binge eating, purging, and cognitive restraint. Profile 5 was the smallest, comprising 7.9% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;107) of the sample, and generally had the highest scores on each subscale, except for excessive exercise and muscle building. In particular, participants grouped in profile 5 had a higher-than-average score on the purge scale.\u003c/p\u003e \u003cp\u003eOne covariate (peak drinking) was added to the final 5-profile solution using the 3-step procedure. In short, peak drinking differentiates between profile 1 and the other profiles. Profile 1 has lower peak drinking levels than the other profiles. See Claydon et al. (2025) for more information about multinomial logistic regression comparing the profiles [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePsychosocial Risk factors\u003c/h3\u003e\n\u003cp\u003eThe psychosocial risk factors of depression and loneliness were mapped against the five distinct LPA profiles. We used the BCH procedure in Mplus to compare the profiles across the continuous outcome variables (i.e., the psychosocial risk factors). The BCH process uses Wald tests to compare the mean scores of the variables across the profiles. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the Wald tests. For the depression screener (PHQ-9) and loneliness measure, profile 1 (above average levels of excessive exercise, negative attitudes, and muscle building) had significantly lower levels of depression and loneliness than all the profiles except profile 4 (moderate levels of body dissatisfaction, binge eating, purging, and cognitive restraint; highest proportion of women). Profile 1 also had the highest proportion of men and highest peak drinking level. Another consistent pattern across the depression and loneliness measures is that profiles 2 (below average levels of DE), 3 (moderate levels of body dissatisfaction, binge eating and restricting, body dissatisfaction, and cognitive restraint), and 5 (high levels of DE especially purging) had the highest levels on average of depression and loneliness and high proportions of women in their groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEquality Tests of Means Across Profiles using the BCH Procedure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ 9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChi-square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eChi-square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 1 v. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 1 v. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 1 v. 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 1 v. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 2 v. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 2 v. 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 2 v. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 3 v. 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 3 v. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 4 v. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLoneliness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChi-square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eChi-square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 1 v. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 1 v. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 1 v. 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 1 v. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 2 v. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 2 v. 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 2 v. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 3 v. 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 3 v. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 4 v. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e PHQ-9\u0026thinsp;=\u0026thinsp;Physician\u0026rsquo;s Health Questionnaire- 9, a screener for depression; Loneliness\u0026thinsp;=\u0026thinsp;UCLA Loneliness Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study expands understanding of disordered eating (DE) among college students by identifying five latent profiles of DE symptoms and examining the patterns of depression and loneliness, while accounting for peak alcohol consumption across the profiles. This person-centered approach revealed heterogeneity in DE presentations, highlighting patterns that would not be captured by traditional correlational methods.\u003c/p\u003e\n\u003ch3\u003eOverall Elevated Depression and Loneliness\u003c/h3\u003e\n\u003cp\u003eOne striking finding is that most profiles reported moderate to moderately severe levels of depression, even among those profiles with relatively lower DE symptoms. The averages for each profile were above the cut point of 10, which indicates major depression with 88% sensitivity and 88% specificity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This may reflect contextual factors unique to the sample, which was assessed in Spring 2022 as students were transitioning out of the COVID-19 pandemic. National surveys have documented elevated depressive symptoms and loneliness among college students during this period due to disrupted routines, social distancing, and academic pressures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, since most of these students were first or second-year students, most would have spent a majority of their high school experience remotely or via hybrid options. This could have contributed to depression and may have also created a more challenging transition to college. Our data suggest that the lingering psychosocial effects of the pandemic may have contributed to higher overall levels of depression across profiles.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProfile-Specific Patterns\u003c/h2\u003e \u003cp\u003eThe relationships between depression and loneliness were not uniform across DE profiles, underscoring the need for subgroup-specific analyses. For instance, Profile 2, the largest profile, characterized by the lowest levels of DE and a high proportion of women; Profile 3, marked by body dissatisfaction and moderate binge eating/restricting; and Profile 5, high levels of DE especially purging, reported the highest levels of depression and loneliness (see Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Contrary to expectations that profiles with more severe DE symptomatology would demonstrate the greatest psychosocial difficulties (i.e., highest levels of depression and loneliness), the findings suggest that for some students, even moderate levels of body dissatisfaction and binge eating (e.g., Profile 3) may be strongly tied to loneliness and depression. This relationship has been observed in other studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and in Forbush et al. (2013)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] that found that the EPSI Body Dissatisfaction and Binge Eating scales had the strongest relationship with depression compared to the other EPSI scales. Subgroup analyses in future research may clarify whether unique psychosocial mechanisms drive distress in these moderate-symptom groups.\u003c/p\u003e \u003cp\u003eProfile 1 (roughly 10% of the sample), characterized by high levels of excessive exercise and muscle building, was predominantly male and had the lowest levels of depression and loneliness. This gendered pattern is consistent with literature indicating that men are more likely to endorse muscularity-oriented DE symptoms, often tied to cultural ideals of strength and performance rather than distress per se [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Due to the communal culture of exercise and the societal view of acceptance on exercise and muscle building, this population may experience less distress around these patterns but still have problematic DE behaviors. Notably, this profile also reported relatively high alcohol use, suggesting a potentially distinct risk pattern for men that warrants further exploration. These populations may also not be found with typical screening tools, suggesting greater need for understanding the distinctions of DE presentations.\u003c/p\u003e \u003cp\u003eAnother unexpected result emerged in Profile 5, the \u0026ldquo;most severe\u0026rdquo; group with elevated scores across nearly all EPSI subscales and an above average level of purge. Participants in this profile reported moderately high levels of depression and loneliness. While these students were still classified in the moderately severe range for depression, the findings complicate the assumption that more severe DE is linearly associated with psychosocial impairment. In short, we expected this pattern of DE to have significantly higher levels of depression and loneliness \u0026ndash; especially when compared to profile 2, which had below average levels of DE. Instead, these results suggest that different DE groups may carry distinct psychosocial burdens, reinforcing the importance of person-centered methods. Profile 4 (moderate levels of body dissatisfaction, binge eating, purging, and cognitive restraint; highest proportion of women) warrants further investigation given that they reported relatively lower depression and loneliness compared to profile 3, and the pattern of the purge scale seems visually to separate the two groups. It is also critical to determine what types of interventions might work for this specific population.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImplications for Research and Intervention\u003c/h3\u003e\n\u003cp\u003eTogether, these findings demonstrate that depression and loneliness do not uniformly map onto levels of DE but instead vary by symptom profile. A bivariate approach would likely obscure these nuances, whereas latent profile modeling illuminates which subgroups are more vulnerable (e.g., profile 3 vs. profile 4). For campus health services, these distinctions are critical: students in profiles characterized by body dissatisfaction and binge eating may be at especially high risk for co-occurring depression and loneliness, even if their DE symptoms are not the most severe. Conversely, students engaged in muscularity-oriented behaviors may require different outreach strategies that integrate gender-sensitive approaches and consider alcohol use.\u003c/p\u003e \u003cp\u003eThe association between loneliness and pattern of DE also underscores the need for prevention and intervention programs that address social connection and belongingness on college campuses. Holt-Lunstad and colleagues [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] highlight that loneliness is not only a mental health concern but also a public health issue with broad implications for morbidity and mortality. Interventions that reduce social isolation, through peer support programs, mentorship initiatives, and campus-wide inclusion efforts, may therefore indirectly mitigate DE risk while also addressing depression.\u003c/p\u003e \u003cp\u003eAdditionally, these findings show the importance of screening for a variety of psychological concerns among students. DE screening or eating disorder screening are not standardized across college campuses and some measures may capture a more nuanced view of those disorders than others. With the vast differences in DE profiles across students, this could lead to some groups being missed based on their distinct presentations.\u003c/p\u003e\n\u003ch3\u003eStrengths and Limitations\u003c/h3\u003e\n\u003cp\u003eThere are several strengths inherent in this study. We have a large sample size of university students that are representative of the total university population. Additionally, we employ a randomized survey design, which allows us to collect data on a wider array of topics without increasing respondent burden or fatigue. Third, the study utilized well-validated measures, including the Eating Pathology Symptom Inventory (EPSI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]), the Patient Health Questionnaire-9 (PHQ-9 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]), and the UCLA Loneliness Measure [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], all of which demonstrated strong internal reliability in the present sample. Finally, we included peak drinking in our models, which allowed us to account for alcohol consumption, a relevant behavioral health factor in college populations that is often linked to disordered eating.\u003c/p\u003e \u003cp\u003eAlong with these strengths, some limitations warrant consideration. All data were derived from cross-sectional, self-report measures, which limits causal inference and leaves the study open to potential reporting biases. In addition, gender identity was not included as a covariate in the final models, despite differences across profiles, because sample sizes for some gender identities were too small to be used meaningfully in subgroup analyses. Further, the randomized survey design included planned missingness, though the analytic sample retained sufficient power for the LPA and outcome analyses. While peak drinking was included, this represents only one facet of alcohol use and does not capture patterns such as frequency, motives, or alcohol-related problems. Finally, the diversity of the sample was limited, with nearly 90% of participants identifying as White, which restricts the generalizability of findings to more racially and ethnically diverse student populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis work demonstrates that DE among college students is multifaceted, with symptom patterns that do not map uniformly onto depression and loneliness. Person-centered analyses revealed that even moderate or low DE profiles may carry elevated psychosocial burdens. By highlighting subgroup differences, these findings underscore the importance of nuanced, profile-specific interventions that address the interrelated challenges of eating behaviors, social connectedness, and mental health among college students.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edisordered eating\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeating disorders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeating pathology symptoms inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efood and alcohol disturbance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elatent profile analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eElizabeth Claydon and Rose Marie Ward developed the study idea and wrote the manuscript. Rose Marie Ward oversaw initial data collection and analyses. Elizabeth Claydon conducted the literature review and coordinated revisions. Christian Garcia assisted with the literature review, and writing the introduction and discussion. All authors edited and approved the final version being submitted.\u003c/p\u003e\u003ch2\u003eAcknowledgments \u0026ndash;\u003c/h2\u003e \u003cp\u003e7. Appreciation to all who participated to help us learn more about the health and well being of college students.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData and materials are available upon reasonable request from the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarrack MT, West J, Christopher M, Pham-Vera AM. Disordered Eating Among a Diverse Sample of First-Year College Students. J Am Coll Nutr. 2019;38(2):141\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNutley SK, Mathews CA, Striley CW. Disordered eating is associated with non-medical use of prescription stimulants among college students. Drug Alcohol Depend. 2020;209:107907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTavolacci MP, Ladner J, D\u0026eacute;chelotte P. Sharp Increase in Eating Disorders among University Students since the COVID-19 Pandemic. Nutrients 2021, 13(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon C, Hegde S, Smith A, Wang X, Sasangohar F. Effects of COVID-19 on College Students' Mental Health in the United States: Interview Survey Study. J Med Internet Res. 2020;22(9):e21279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEck KM, Byrd-Bredbenner C. Disordered eating concerns, behaviors, and severity in young adults clustered by anxiety and depression. Brain Behav. 2021;11(12):e2367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerikangas KR, He JP, Burstein M, Swanson SA, Avenevoli S, Cui L, Benjet C, Georgiades K, Swendsen J. Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication\u0026ndash;Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry. 2010;49(10):980\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel RS, Machado T, Tankersley WE. Eating Disorders and Suicidal Behaviors in Adolescents with Major Depression: Insights from the US Hospitals. Behav Sci (Basel) 2021, 11(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortes-Garcia L, Rodriguez-Cano R, von Soest T. Prospective associations between loneliness and disordered eating from early adolescence to adulthood. Int J Eat Disord. 2022;55(12):1678\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerchenroeder L, Post SM, Stock ML, Yeung EW. Loneliness and Alcohol-Related Problems among College Students Who Report Binge Drinking Behavior: The Moderating Role of Food and Alcohol Disturbance. Int J Environ Res Public Health 2022, 19(21).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanson KT, Cuccolo K, Nagata JM. Loneliness is associated with eating disorders among a national sample of U.S. college students during the COVID-19 pandemic. J Am Coll Health. 2025;73(2):462\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForbush KT, Wildes JE. Application of structural equation mixture modeling to characterize the latent structure of eating pathology. Int J Eat Disord. 2017;50:542\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConiglio KA, Davis L, Sun J, Loureiro N, Selby EA. Detecting pathological exercise in college men: An investigation using latent profile analysis. J Am Coll Health 2021:1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrolio V, Racine SE. Exploring latent profiles of disordered eating using an indicator of dietary restriction in an undergraduate sample of men and women. Int J Eat Disord. 2023;56(8):1603\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClaydon EA, Ward RM, Geyer RB, Weekley D. Mapping anxiety symptoms and disordered eating using the EPSI: a latent profile analysis accounting for peak alcohol use. J Eat Disord. 2025;13(1):96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez M, Joiner TE Jr, Lewinsohn PM. Is Major Depressive Disorder or Dysthymia More Strongly Associated with Bulimia Nervosa? In. Volume 36. US: Wiley; 2004. pp. 55\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeshishian AC, Tabri N, Becker KR, Franko DL, Herzog DB, Thomas JJ, Eddy KT. Eating disorder recovery is associated with absence of major depressive disorder and substance use disorders at 22-year longitudinal follow-up. Compr Psychiatry. 2019;90:49\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForbush KT, Wildes JE, Pollack LO, Dunbar D, Luo J, Patterson K, Petruzzi L, Pollpeter M, Miller H, Stone A, et al. Development and validation of the Eating Pathology Symptoms Inventory (EPSI). Psychol Assess. 2013;25(3):859\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell DW. UCLA Loneliness Scale (Version 3): reliability, validity, and factor structure. J Pers Assess. 1996;66(1):20\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuthen B, Asparouhov T. Latent transition analysis with random intercepts (RI-LTA). Psychol Methods. 2022;27(1):1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolck A, Croon M, Hagenaars J. Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators. Political Anal. 2004;12(1):3\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMason TB, Heron KE, Braitman AL, Lewis RJ. A daily diary study of perceived social isolation, dietary restraint, and negative affect in binge eating. Appetite. 2016;97:94\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharpe H, Griffiths S, Choo TH, Eisenberg ME, Mitchison D, Wall M, Neumark-Sztainer D. The relative importance of dissatisfaction, overvaluation and preoccupation with weight and shape for predicting onset of disordered eating behaviors and depressive symptoms over 15 years. Int J Eat Disord. 2018;51(10):1168\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForbush KT, Wildes JE, Hunt TK. Gender norms, psychometric properties, and validity for the Eating Pathology Symptoms Inventory. Int J Eat Disord. 2014;47(1):85\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray SB, Griffiths S, Mond JM. Evolving eating disorder psychopathology: conceptualising muscularity-oriented disordered eating. Br J Psychiatry. 2016;208(5):414\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. 2015;10(2):227\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"university students, disordered eating, depression, loneliness, surveys","lastPublishedDoi":"10.21203/rs.3.rs-8894936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8894936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003ePrevious work has showed five distinct latent profiles of the Eating Pathology Symptoms Inventory (EPSI) among college students, as well as their relationship with aspects of anxiety. Building on this work, the current study examines how the identified disordered eating (DE) profiles are connected with depression and loneliness, while accounting for peak alcohol use which have been increasing issues among college students.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eStudents (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,362) from a midwestern university participated in an annual online health survey. They answered validated questionnaires for disordered eating, depression, loneliness, and alcohol use (peak drinking). Analyses of the EPSI scale profiles were run to determine any differences on depression or loneliness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll profiles had moderate to moderately severe levels of depression. Profile 1 (high levels of excessive exercise and muscle building) was also most likely to have participants that were men and had the lowest levels of depression and loneliness. Profile 2 (Lowest levels of DE, largest profile, high proportion of women) and profile 3 (high levels of body dissatisfaction, moderate binge eating and restricting, 20% of sample) had the highest levels of depression and loneliness.\u003c/p\u003e\u003ch2\u003eCONCLUSIONS\u003c/h2\u003e \u003cp\u003eThis study\u0026rsquo;s findings illustrate that depression and loneliness vary by DE symptom profile. Colleges need to be aware that students face intersectional psychological issues and may require unique and multi-faceted interventions.\u003c/p\u003e","manuscriptTitle":"Disordered eating and mental health: The role of depression and loneliness in college students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 07:11:08","doi":"10.21203/rs.3.rs-8894936/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"244156284491353107001463649930433787738","date":"2026-03-17T12:30:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T12:09:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T12:11:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-20T17:41:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T20:13:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-02-19T20:08:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5bacb74-c068-4c69-8597-c611d36a79df","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-20T07:11:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 07:11:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8894936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8894936","identity":"rs-8894936","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}