ArticleJournal of eating disorders2026
Disentangling food addiction-related symptom profiles in anorexia nervosa: a latent class analysis and clinical implications.
Article in Journal of eating disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe construct of Food Addiction (FA) has generated growing interest in the context of Anorexia Nervosa (AN), especially for its potential clinical implications. While AN is traditionally associated with restrictive eating patterns, recent findings suggest that FA symptoms may also be present in this population, complicating the clinical picture. This study aimed to explore FA symptomatology in individuals with AN using a person-centered approach and to identify psychological variables associated with different FA profiles.
methodsA sample of 202 individuals with a diagnosis of AN (including AN-R, AN-BP, and Atypical AN) completed the YFAS 2.0. A Latent Class Analysis (LCA) was performed to identify subgroups based on FA symptoms. Chi-square tests assessed associations between latent classes and AN subtypes. Binary logistic regressions explored psychological predictors of class membership, including eating psychopathology (EDE-Q), depressive symptoms (BDI-II), emotion dysregulation (DERS), and metacognitive abilities (MSAS).
resultsLCA identified three latent classes: Class 1 (Addicted, 11.4%), characterized by high endorsement of FA symptoms; Class 2 (High-Risk, 33.7%), displaying moderate FA symptom severity; and Class 3 (Non-Addicted, 54.9%), showing minimal FA symptomatology. No significant association emerged between class membership and AN diagnostic subtype (χ²(4) = 3.085, p=.544). Logistic regressions revealed that impulsivity significantly predicted membership in the Addicted class (OR = 4.119, p<.05), while membership in the Non-Addicted class was associated with lower eating psychopathology, better metacognitive skills, and reduced impulsivity. Belonging to the High-Risk class was predicted by higher eating concern (OR = 2.618, p<.05), lower metacognition (OR=0.566, p<.05) and greater difficulties with emotional awareness (OR = 1.62; p<.01).
conclusionsThese findings highlight the presence of distinct FA profiles within AN, independent of traditional diagnostic categories. The Addicted and High-Risk classes showed greater psychopathology and dysregulation. Notably, the High-Risk group also exhibited impaired metacognition, suggesting potential vulnerability despite moderate symptom expression. In contrast, the Non-Addicted class showed a more adaptive profile. LCA may support the development of personalized treatment strategies by targeting FA-related features, including interventions to improve emotion regulation and metacognitive functioning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.