A CONNECTION BETWEEN OBESITY AND DEPRESSION. A GENETIC RISK SCORE IN THE SPANISH POPULATION STUDY PISMA-EP

Introduction: Depression and obesity are highly prevalent, and leading causes of disease burden and disability worldwide. Both conditions are major risk factors for chronic physical diseases. The reason why these disorders cluster together is not totally understood. Different mechanisms are implicated in this association, including biological and genetic factors. Objectives: Our aim is to investigate whether a genetic risk score (GRS) combining 52 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 429 individuals (37 depression cases, 391 controls) from the PISMA-ep study, a cross-sectional epidemiological study of mental disorders based on a representative sample of the adult population of Andalusia, Spain. The MINI interview was used to diagnose depression according to the DSM-IV and BMI was calculated. All individuals were genotyped for 52 candidate polymorphisms. Logistic regression models were conducted to predict depression. We calculated an unweighted GRS by summation of the number of risk alleles. Receiver operating characteristic (ROC) analyses were used to compare the discriminatory ability of predictors of depression. We constructed three predictive models using the GRS and adding traditional risk factors: 1. only GRS; 2. GRS, sex and age; 3. GRS, sex, age and BMI. Results: We found an association between the unweighted GRS and depression (p=0.0002; OR=1.23; SE=3.72) which explained approximately 7.89% of variance of depression. Adding 'traditional' risk factors (sex and age) to GRS improved the predictive ability with the area under the curve (AUC) in the ROC analysis from 0.688 to 0.698. The best model was achieved using all genetic information, traditional risk factors and BMI (AUC= 0.716, 95% CI: 0.633 – 0.799). Conclusions: The GRS constructed in our study was associated with depression and was implemented in different predictive models. The model combining genetic information, traditional risk factors and BMI improved 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.