RT Journal Article
T1 A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.
A1 Hung, Chi-Fa
A1 Breen, Gerome
A1 Czamara, Darina
A1 Corre, Tanguy
A1 Wolf, Christiane
A1 Kloiber, Stefan
A1 Bergmann, Sven
A1 Craddock, Nick
A1 Gill, Michael
A1 Holsboer, Florian
A1 Jones, Lisa
A1 Jones, Ian
A1 Korszun, Ania
A1 Kutalik, Zoltan
A1 Lucae, Susanne
A1 Maier, Wolfgang
A1 Mors, Ole
A1 Owen, Michael J
A1 Rice, John
A1 Rietschel, Marcella
A1 Uher, Rudolf
A1 Vollenweider, Peter
A1 Waeber, Gerard
A1 Craig, Ian W
A1 Farmer, Anne E
A1 Lewis, Cathryn M
A1 Müller-Myhsok, Bertram
A1 Preisig, Martin
A1 McGuffin, Peter
A1 Rivera, Margarita
K1 Body mass index
K1 Genetic risk score
K1 Major depressive disorder
K1 Obesity
K1 Área bajo la curva
K1 Índice de masa corporal
K1 Estudios de casos y controles
K1 Trastorno depresivo mayor
K1 Estudio de asociación del genoma completo
K1 Modelos logísticos
K1 Obesidad
K1 Polimorfismo de nucleótido simple
K1 Curva ROC
K1 Riesgo
AB 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.
PB BioMed Central
YR 2015
FD 2015-04-17
LK http://hdl.handle.net/10668/2317
UL http://hdl.handle.net/10668/2317
LA en
NO Hung CF, Breen G, Czamara D, Corre T, Wolf C, Kloiber S, et al. A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder. BMC Med. 2015; 13:86
NO Journal Article; Research Support, Non-U.S. Gov't;
DS RISalud
RD Apr 9, 2025