Mental disorders may emerge as the result of interactions between observable symptoms. Such interactions can be analyzed using network analysis. Several recent studies have used network analysis to examine eating disorders, indicating a core role of overvaluation of weight and shape. However, no studies to date have applied network models to binge-eating disorder (BED), the most prevalent eating disorder.

We constructed a cross-sectional graphical LASSO network in a sample of 788 individuals with BED. Symptoms were assessed using the Eating Disorders Examination Interview. We identified core symptoms of BED using expected influence centrality.

Overvaluation of shape emerged as the symptom with the highest centrality. Dissatisfaction with weight and overvaluation of weight also emerged as highly central symptoms. On the other hand, behavioral symptoms such as binge eating, eating in secret, and dietary restraint/restriction were less central. The network was stable, allowing for reliable interpretations (centrality stability coefficient = 0.74).

Overvaluation of shape and weight emerged as core symptoms of BED. This trend is consistent with past network analyses of eating disorders more broadly, as well as literature that suggests a primary role of shape and weight concerns in BED. Although DSM-5 diagnostic criteria for BED does not currently include a cognitive criterion related to body image or shape/weight overvaluation, our results provide support for including shape/weight overvaluation as a diagnostic specifier.