Development of transdiagnostic clinical risk prediction models for 12‐month onset and course of eating disorders among adolescents in the community

Abstract

Objective: To develop and internally validate risk prediction models for adolescent onset and persistence of eating disorders. Methods: N = 963 Australian adolescents (11-19 years) in the EveryBODY Study cohort completed online surveys in 2018 and 2019. Models were built to predict 12-month risk of (1) onset, and (2) persistence of a DSM-5 eating disorder. Results: Onset Model. Of the n = 687 adolescents without an eating disorder at baseline, 16.9% were identified with an eating disorder after 12 months. The prediction model was based on evidence-based risk factors for eating disorder onset available within the dataset (sex, body mass index percentile, strict weight loss dieting, history of bullying, psychological distress, weight/shape concerns). This model showed fair discriminative performance (mean AUC = .75). The most important factors were psychological distress, weight and shape concerns, and female sex. Diagnostic Persistence Model. Of the n = 276 adolescents with an eating disorder at baseline, 74.6% were identified as continuing to meet criteria for an eating disorder after 12 months. The prediction model for diagnostic persistence was based on available evidence-based risk factors for eating disorder persistence (purging, distress, social impairment). This model showed poor discriminative performance (mean AUC = .65). The most important factors were psychological distress and self-induced vomiting for weight control. Discussion: We found preliminary evidence for the utility of a parsimonious model for 12-month onset of an eating disorder among adolescents in the community. Future research should include additional evidence-based risk factors and validate models beyond the original sample.Public Significance: This study demonstrated the feasibility of developing parsimonious and accurate models for the prediction of future onset of an eating disorder among adolescents. The most important predictors in this model included psychological distress and weight and shape concerns. This study has laid the ground work for future research to build and test more accurate prediction models in diverse samples,

A network analysis of disordered eating symptoms, big‐five personality traits, and psychological distress in Chinese adults

“…was identified as a core bridge node in the network. Moreover, we found that a symptom of psychological distress (K6-6) was also a bridge node in the network, which is in line with previous research indicating that psychological distress is frequently observed among individuals with EDs (Mitchell et al, 2015;Olguin et al, 2017;Palmieri et al, 2021) and is a high-risk factor for the development of EDs (Brockmeyer et al, 2014;Mitchison et al, 2023;Rasmus et al, 2010).…”

“…Additionally, body dissatisfaction was identified as another central node in the network. This finding aligns with both empirical and conceptual studies highlighting the importance of body dissatisfaction in the development of EDs (Mitchison et al, 2023). For instance, bulimia nervosa has been strongly associated with body dissatisfaction as well as lower self-esteem, more depressive symptoms, and persistent disordered eating attitudes (Laporta-Herrero et al, 2018).…”

“…Disordered eating symptoms are strongly and positively associated with psychological distress (Kärkkäinen et al, 2018;Pullmer et al, 2019;Rasmus et al, 2010;Schaumberg et al, 2017). Psychological distress can be characterized as depressive and anxiety symptoms and is both a consequence (Patrick et al, 2011) and a risk factor for EDs (Connan et al, 2007;Hartmann et al, 2010;Mitchison et al, 2023).…”

“…Thus, it is crucial to further explore the etiology of EDs in Chinese populations given the importance of cultural influences in the development of EDs (Pike et al, 2014). Additionally, personality traits and psychological distress are well-recognized factors related to EDs in previous research from Western contexts (Cassin & von Ranson, 2005;Farstad et al, 2016;Lilenfeld et al, 2006;Mitchison et al, 2023). However, little is known about their connections or interactions in the Chinese context, especially from the perspective of network analysis.…”