serval:BIB_6B8A30726C04
A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.
10.1186/s12916-015-0334-3
000353257600001
25903154
Hung
C.F.
author
Breen
G.
author
Czamara
D.
author
Corre
T.
author
Wolf
C.
author
Kloiber
S.
author
Bergmann
S.
author
Craddock
N.
author
Gill
M.
author
Holsboer
F.
author
Jones
L.
author
Jones
I.
author
Korszun
A.
author
Kutalik
Z.
author
Lucae
S.
author
Maier
W.
author
Mors
O.
author
Owen
M.J.
author
Rice
J.
author
Rietschel
M.
author
Uher
R.
author
Vollenweider
P.
author
Waeber
G.
author
Craig
I.W.
author
Farmer
A.E.
author
Lewis
C.M.
author
Müller-Myhsok
B.
author
Preisig
M.
author
McGuffin
P.
author
Rivera
M.
author
article
2015
Bmc Medicine
1741-7015
1741-7015
journal
13
86
BACKGROUND: Obesity 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.
METHODS: Linear 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.
RESULTS: In 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.
CONCLUSIONS: A 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.
eng
60_published
true
peer-reviewed
Publication types: Journal ArticlePublication Status: epublish
University of Lausanne
mailto:serval_help@unil.ch
http://www.unil.ch/serval
http://serval.unil.ch/disclaimer
https://serval.unil.ch/notice/serval:BIB_6B8A30726C04