M34 - A GENETIC RISK SCORE, DEPRESSION AND OBESITY: EVIDENCE FROM THE SPANISH POPULATION STUDY PISMA-EP

Abstract

Background Depression and obesity are highly prevalent diseases in the general population, responsible of disability burden worldwide. Both conditions are major risk factors for chronic physical diseases such as type 2 diabetes, cardiovascular disease and hypertension among others. The reason why obesity and depression cluster together is not totally understood and several mechanisms have been proposed. There are many factors driving this observation, such as individual lifestyle choices, socioeconomic factors, psychosocial stress, disparities in health care, medication, as well as biological and genetic factors. The aim of this study is to investigate whether a Genetic Risk Score (GRS) combining 85 candidate SNPs for depression and other major psychiatric disorders is associated with depression and predicts depression in individuals with obesity. Methods The sample consists of 743 community-based individuals from the PISMA-ep study. The PISMA-ep is a cross-sectional epidemiological study of mental disorders based on a representative sample of the adult population of Andalusia, Spain. A DSM-IV diagnosis of major depression was ascertained using the MINI interview. Height and weight data reported from each individual was used to calculate Body Mass Index (BMI), as a measure of obesity, using the formula: weight(kg)/height(m)2. All individuals have been genotyped for the 85 candidate polymorphisms using TaqMan® OpenArrayTM Genotyping System. These markers were selected according to genome location, function and previous evidence. Logistic regression models will be conducted to predict depression. We will calculate an unweighted GRS by summation of the number of risk alleles, and a weighted GRS as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver Operating Characteristic (ROC) analysis will be used to compare the discriminatory ability of predictors of depression. Results We hope that both unweighted and weighted GRS will be associated with depression and explain a modest amount of variance. We also hope that adding ‘traditional’ risk factors to GRS will significantly improve the predictive ability with the area under the curve (AUC) in the ROC analysis. We further expect that the GRS will discriminate depression better in obese individuals compare to normal-weight subjects. Discussion The association between depression and obesity has repeatedly been reported in many studies. Given the high prevalence of both disorders, we expect that incorporating genetic information, traditional risk factors and obesity status may largely improve the predicting ability for depression. Addressing obesity in people with depression or vice versa is highly important as both disorders are associated with substantial personal and societal economic costs worldwide.