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Updated: Oct 20, 2026

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
Published on: May 10, 2022
Psychological Process-Based Profiles in the Binge-Eating Spectrum: Clinical Implications Beyond DSM-5 Subtypes
Geovanny Genaro Reivan-Ortiz1, María Quezada-Gómez2, Laura Maraver-Capdevila3
1Universidad Catolica de Cuenca, Cuenca, Azuay, Ecuador.
Background And Objectives:
Binge-spectrum eating disorders (BSEDs) are clinically impactful, yet the psychological mechanisms underlying their persistence and heterogeneity remain insufficiently understood. Clarifying these mechanisms is essential for refining diagnostic models and improving treatment. This study focused on young to early-middle adults, a developmental period with elevated risk for the onset and aggravation of BSED, to identify empirical subgroups based on eating- and psychological-related variables.
Methods:
A two-step cluster analysis was performed in 187 patients (men and women, aged 20-45 years) diagnosed with different BSED subtypes. Variables considered included emotion-related eating behaviors, impulsivity, emotion dysregulation, decision-making, stress, anxiety-depression, and obsessive-compulsive symptoms.
Results:
A three-cluster solution provided the best fit, yielding distinct patient profiles. Anxiety-depression and hunger emerged as the most influential variables for the clustering. No significant differences between the clusters were found in sociodemographic characteristics and eating disorder subtype. Clear gradients were observed in clinical severity and psychological functioning, ranging from high psychopathological burden and dysregulation in Cluster 1 (10.2% of the sample) to markedly more adaptive functioning and reduced distress in Cluster 3 (56.7%). The largest between-cluster differences involved hunger, emotion regulation deficits, and anxiety-depression levels.
Conclusions:
The findings delineate clinically meaningful BSED subgroups, spanning severely dysregulated to more adaptive profiles. This differentiation may enhance diagnostic classification and inform process-based treatment approaches, enabling personalized interventions targeting core features (particularly anxiety-depression, emotion regulation, and hunger) to optimize clinical outcomes.
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