Food Addiction and Psychiatric Comorbidity: A Latent Profile Analysis
Year: 2026
Type: article
Abstract: OBJECTIVE: Food addiction (FA) frequently co-occurs with eating disorders (EDs), PTSD, mood/anxiety disorders, and substance use disorders (SUDs), yet few studies have examined clinically meaningful subgroups in multi-morbid treatment populations. Latent profile analysis (LPA) was used to identify subgroups in a highly comorbid sample to determine whether FA marks a distinct profile. METHOD: Seven hundred and forty seven adults admitted to a multi-site behavioral health center completed self-report measures of PTSD, complex PTSD, dissociation, depression, anxiety, substance use, ED, and FA. These nine indicators were reduced to four composites (Trauma/Internalizing, ED, FA, and other substance use) and submitted to LPA in R; a parallel analysis entering all nine indicators individually served as a robustness check. Models were fit for one to six profiles, selected on BIC, entropy, minimum profile size, and interpretability, then validated against demographics, diagnoses, and distal outcomes. RESULTS: A three-profile conditional-independence (EEI) solution fit best: Trauma/Internalizing/ED (23%), lower-acuity (65%), and FA (12%). The FA profile had by far the highest FA severity and body mass index (BMI) but did not differ from the Trauma/Internalizing/ED profile on ED or Trauma/Internalizing severity. The lower-acuity profile comprised most patients, with lower symptomatology and markedly better quality of life. The nine-indicator model recovered the FA profile essentially unchanged, while remaining patients were split by alcohol use rather than acuity. DISCUSSION: FA marks a distinct clinical subtype within trauma-burdened psychiatric populations rather than a uniform feature of ED or severe trauma, supporting routine screening and integrated treatment addressing FA alongside PTSD and ED pathology.
DOI: 10.1002/eat.70264
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,300
OpenAlex ID: W7225658586