Demographic and Clinical Correlates of Body Mass Index in Older Age Bipolar Disorder: Results from the GAGE-BD Project

Background and Objectives: There are known associations between bipolar disorder and obesity, but it has not been well characterized in older adults with bipolar disorder (OABD). This study aims to examine body mass index (BMI) and its clinical correlations in OABD. Materials and Methods: A secondar...

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Detalles Bibliográficos
Autores: Chan, CK, Rothenberg, KG, Briggs, FBS, Mallu, A, Fiorelli, NM, Almeida, OP, Altinbas, K, Balanzá-Martinez, V, Barbosa, IG, Blumberg, HP, Chen, PJ, Flicker, L, Gildengers, A, Haarman, BCM, Klaus, F, Lafer, B, Nunes, PV, Olagunju, AT, Oluwaniyi, SO, Orhan, M, Patrick, RE, Pomarol-Clotet, E, Radua, J, Rej, S, Saores, JC, Teixeira, AL, Tsai, SY, Vidal-Rubio, SLL, Vieta, E, Weisenbach, SL, Eyler, LT, Dols, A, Sajatovic, M
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
Repositorio:r-FISABIO. Repositorio Institucional de Producción Científica
OAI Identifier:oai:dnet:r-fisabio___::5c52d703624261df28e5e1f8ee603662
Acceso en línea:https://fisabio.portalinvestigacion.com/publicaciones/21217
Access Level:acceso abierto
Palabra clave:bipolar disorder
older age
obesity
overweight
BMI
geriatric
Descripción
Sumario:Background and Objectives: There are known associations between bipolar disorder and obesity, but it has not been well characterized in older adults with bipolar disorder (OABD). This study aims to examine body mass index (BMI) and its clinical correlations in OABD. Materials and Methods: A secondary analysis was conducted using data from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) project, an international harmonized dataset of OABD cohorts. To examine the relationship between BMI and clinical characteristics (e.g., sex, psychiatric history, symptom severity, medication use, comorbidities), multivariable linear regression and multinomial logistic regression models with random effect for study cohort were used, with BMI as a continuous and as an ordinal (underweight vs. healthy weight vs. overweight vs. obese) dependent variable, respectively. Results: Of 1,226 OABD participants with BMI data, 405 (33.0%) were classified as overweight (BMI 25-29.99) and 462 (37.7%) as obese (BMI > 30). In linear regression models, higher BMI was associated with younger age, higher number of somatic comorbidities, and anticonvulsant use, while lower BMI was associated with lithium use. In logistic regression models, obesity was associated with cardiovascular comorbidity, musculoskeletal comorbidity and endocrine comorbidity. Conclusions: A high proportion of individuals with OABD are overweight or obese. Several demographic and clinical correlations of higher BMI were found, including younger age, higher number of medical comorbidities and anticonvulsant use. Clinicians should monitor and manage weight changes and associated comorbidities, and promote lifestyle and health interventions to minimize the risk of negative health outcomes associated with high BMI.

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