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Obesity as a behavioral addiction: moving past quick fixes and the case for inclusion in the Diagnostic and Statistical Manual of Mental Disorders

Mayank Gupta,Nihit Gupta,Priyal Khurana

Published 2025 in Academia Mental Health and Well-Being

ABSTRACT

Background: To explore the theoretical, clinical, and neurobiological underpinnings of obesity as a behavioral addiction and explore the implications for potential inclusion in future DSM (Diagnostic and Statistical Manual of Mental Disorders) revisions. Materials and Methods: A narrative review approach was employed, synthesizing findings from theoretical literature, clinical studies, and neurobiological research examining parallels between obesity and behavioral addiction. Relevant articles were identified through searches of PubMed, PsycINFO, and Google Scholar databases using keywords such as obesity, behavioral addiction, food addiction, compulsive overeating, and reward neurocircuitry. Studies discussing diagnostic frameworks, neurobiological mechanisms, and clinical outcomes were prioritized to inform the review’s objectives. Results: There are striking similarities between patterns of compulsive overeating, and the diagnostic features of substance use disorders. There are several hallmark symptoms associated with addictive disorders that are observable in individuals with obesity. Neurological studies have revealed that the mesolimbic dopamine system, which mediates reward processing, motivation, and reinforcement learning, plays a central role in both substance addiction and maladaptive eating behaviors. An absence of formal diagnostic criteria in current classification systems poses a challenge to effectively classify obesity as the result of addiction and provide targeted intervention. Conclusions: The current review highlights obesity as an outcome of compulsive, addictive eating behaviors with the neurobiological and psychological processes observed in substance use disorders. Conceptualizing certain eating behaviors through the lens of addiction may offer improved clinical utility, particularly for treatment planning and outcome prediction.

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