• Chi-Fa Hung, Gerome Breen, Darina Czamara, Tanguy Corre, Christiane Wolf, Stefan Kloiber, Sven Bergmann, Nick Craddock, Michael Gill, Florian Holsboer, Lisa Jones, Ian Jones, Ania Korszun, Zoltan Kutalik, Susanne Lucae, Wolfgang Maier, Ole Mors, Michael J Owen, John Rice, Marcella Rietschel, Rudolf Uher, Peter Vollenweider, Gerard Waeber, Ian W Craig, Anne E Farmer, Cathryn M Lewis, Bertram Müller-Myhsok, Martin Preisig, Peter McGuffin, and Margarita Rivera.
• MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, Box PO82, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK. chifa.hung@gmail.com.
• Bmc Med. 2015 Apr 17; 13: 86.

BackgroundObesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD.MethodsLinear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity.ResultsIn the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P < 0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P < 0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results.ConclusionsA GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.

24 keywords...

hide…