Evidence map›Paper›PMID 41965343›Full record

ReviewTranslational psychiatry2026

Advancing translational research in binge-eating: Integrating insights from clinical practice into animal models.

Rachel Dufour, Uri Shalev, Linda Booij

Abstract readReview
In one paragraph

Review in Translational psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rachel DufourDepartment of Psychology, Concordia University, Montreal, QC, Canada.
Uri ShalevDepartment of Psychology, Concordia University, Montreal, QC, Canada.
Linda BooijDepartment of Psychology, Concordia University, Montreal, QC, Canada. linda.booij@mcgill.ca.ORCID http://orcid.org/0000-0002-0863-8098

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Binge-eating behaviors are key components of several types of eating disorders, yet their etiology remains unclear. Animal models have provided valuable insights by enabling experimentally controlled investigations of biological, behavioral, and environmental factors contributing to eating disorders. This narrative review examines the clinical relevance of animal models in advancing our understanding of binge-eating-related disorders. We propose five translational priorities focused on clinically-meaningful features of binge eating and their relevance for improving animal-model development: (1) loss of control and compulsivity, (2) negative affect and stress responsivity, (3) developmental timing and sex differences, (4) individual differences and variability, and (5) treatment responsiveness. Various animal models, including food restriction, stress-induced, and addiction-based paradigms, have been developed to study binge eating. Limitations include the inability of animal models to fully capture the psychological and sociocultural dimensions of binge eating, such as the sense of loss of control, stigma, distress, and body-image concerns. While existing models capture key biological and behavioral components of binge eating, closer alignment with clinically defining features, for example, through the inclusion of emotional stressors and varied outcome measures, could improve translational impact. By refining current models to match clinical reality, animal research may continue to enhance our understanding of eating disorders and inform the development of novel treatment approaches.

Indexed as

Binge-Eating DisorderBulimiaDisease Models, AnimalTranslational Research, BiomedicalAnimalsHumansStress, Psychological

Identifiers

PMID41965343
PMCPMC13184250

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.