Objective: We reviewed evidence regarding a possible relationship between mood disorders and obesity to better inform mental health professionals about their overlap.
Method: We performed a MEDLINE search of the English-language literature for the years 1966-2003 using the following terms: obesity, overweight, abdominal, central, metabolic syndrome, depression, mania, bipolar disorder, binge eating, morbidity, mortality, cardiovascular, diabetes, cortisol, hypertriglyceridemia, sympathetic, family history, stimulant, sibutramine, antiobesity, antidepressant, topiramate, and zonisamide. We evaluated studies of obesity (and related conditions) in persons with mood disorders and of mood disorders in persons with obesity. We also compared studies of obesity and mood disorders regarding phenomenology, comorbidity, family history, biology, and pharmacologic treatment response.
Results: The most rigorous clinical studies suggest that (1). children and adolescents with major depressive disorder may be at increased risk for developing overweight; (2). patients with bipolar disorder may have elevated rates of overweight, obesity, and abdominal obesity; and (3). obese persons seeking weight-loss treatment may have elevated rates of depressive and bipolar disorders. The most rigorous community studies suggest that (1). depression with atypical symptoms in females is significantly more likely to be associated with overweight than depression with typical symptoms; (2). obesity is associated with major depressive disorder in females; and (3). abdominal obesity may be associated with depressive symptoms in females and males; but (4). most overweight and obese persons in the community do not have mood disorders. Studies of phenomenology, comorbidity, family history, biology, and pharmacologic treatment response of mood disorders and obesity show that both conditions share many similarities along all of these indices.
Conclusion: Although the overlap between mood disorders and obesity may be coincidental, it suggests the two conditions may be related. Clinical and theoretical implications of this overlap are discussed, and further research is called for.
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http://dx.doi.org/10.4088/jcp.v65n0507 | DOI Listing |
JMIR Res Protoc
January 2025
Psychiatry Department, Weill Cornell Medicine, New York, NY, United States.
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January 2025
Key Laboratory of Adolescent Cyberpsychology and Behavior (Ministry of Education), Wuhan, China.
Bipolar disorder (BD) is a complex psychiatric condition marked by significant mood fluctuations that deeply affect quality of life. Understanding the neural mechanisms underlying BD is critical for improving diagnostic accuracy and developing more effective treatments. This study utilized resting-state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity within the ventral and dorsal attention networks in 52 patients with BD and 51 healthy controls.
View Article and Find Full Text PDFEur Child Adolesc Psychiatry
January 2025
University of Edinburgh, Edinburgh, United Kingdom.
Objective: This study aimed to investigate the longitudinal bi-directional relationship between self-reported restrictive eating behaviours and sleep characteristics within a sample of UK adolescents from the Millennium Cohort Study (MCS).
Method: Using a Structural Equation Modelling approach, the present study investigated the prospective associations between individual sleep behaviours (e.g.
Brain
January 2025
Department of Child and Adolescent Psychopathology, CHU de Lyon, F-69000 Lyon, France; Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS & Université Claude Bernard Lyon 1, F-69000 Lyon, France.
Computational neuropsychiatry is a leading discipline to explain psychopathology in terms of neuronal message passing, distributed processing, and belief propagation in neuronal networks. Active Inference (AI) has been one of the ways of representing this dysfunctional signal processing. It involves that all neuronal processing and action selection can be explained by maximizing Bayesian model evidence, or minimizing variational free energy.
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