Body mass index in middle life and future risk of hospital admission for psychoses or depression: findings from the Renfrew/Paisley study
ABSTRACT There is evidence that greater body mass index (BMI) protects against depression, schizophrenia and suicide. However, there is a need for prospective studies.
We examined the association of BMI with future hospital admissions for psychoses or depression/anxiety disorders in a large prospective study of 7036 men and 8327 women. Weight and height were measured at baseline (1972-76) when participants were aged 45-64. Follow-up was for a median of 29 years.
Greater BMI and obesity were associated with a reduced risk of hospital admission for psychoses and depression/anxiety in both genders, with the magnitude of these associations being the same for males and females. With adjustment for age, sex, smoking and social class, a 1 standard deviation (s.d.) greater BMI at baseline was associated with a rate ratio of 0.91 [95% confidence interval (CI) 0.82-1.01] for psychoses and 0.87 (95% CI 0.77-0.98) for depression/anxiety. Further adjustment for baseline psychological distress and total cholesterol did not alter these associations.
Our findings add to the growing body of evidence that suggests that greater BMI is associated with a reduced risk of major psychiatric outcomes. Long-term follow-up of participants in randomized controlled trials of interventions that effectively result in weight loss and the use of genetic variants that are functionally related to obesity as instrumental variables could help to elucidate whether these associations are causal.
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ABSTRACT: The Economic and Social Research Council (ESRC)-funded Data Management through e-Social Sciences (DAMES) project is investigating, as one of its four research themes, how research into depression, self-harm and suicide may be enhanced through the adoption of e-Science infrastructures and techniques. In this paper, we explore the challenges in supporting such research infrastructures and describe the distributed and heterogeneous datasets that need to be provisioned to support such research. We describe and demonstrate the application of an advanced user and security-driven infrastructure that has been developed specifically to meet these challenges in an on-going study into depression, self-harm and suicide.Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 08/2010; 368(1925):3845-58. DOI:10.1098/rsta.2010.0142 · 2.86 Impact Factor