A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.

Hung, Chi-FaBreen, GeromeCzamara, DarinaCorre, TanguyWolf, ChristianeKloiber, StefanBergmann, SvenCraddock, NickGill, MichaelHolsboer, FlorianJones, LisaJones, IanKorszun, AniaKutalik, ZoltanLucae, SusanneMaier, WolfgangMors, OleOwen, Michael JRice, JohnRietschel, MarcellaUher, RudolfVollenweider, PeterWaeber, GerardCraig, Ian WFarmer, Anne ELewis, Cathryn MMüller-Myhsok, BertramPreisig, MartinMcGuffin, PeterRivera, Margarita2016-08-082016-08-082015-04-17Hung 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; 13:86http://hdl.handle.net/10668/2317Journal Article; Research Support, Non-U.S. Gov't;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.enBody mass indexGenetic risk scoreMajor depressive disorderObesityÁrea bajo la curvaÍndice de masa corporalEstudios de casos y controlesTrastorno depresivo mayorEstudio de asociación del genoma completoModelos logísticosObesidadPolimorfismo de nucleótido simpleCurva ROCRiesgoMedical Subject Headings::Named Groups::Persons::Age Groups::Adult::AgedMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Area Under CurveMedical Subject Headings::Phenomena and Processes::Physiological Phenomena::Body Constitution::Body Weights and Measures::Body Mass IndexMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Case-Control StudiesMedical Subject Headings::Psychiatry and Psychology::Mental Disorders::Mood Disorders::Depressive Disorder::Depressive Disorder, MajorMedical Subject Headings::Check Tags::FemaleMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Genetic Techniques::Genetic Association Studies::Genome-Wide Association StudyMedical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::HumansMedical Subject Headings::Check Tags::MaleMedical Subject Headings::Check Tags::MaleMedical Subject Headings::Diseases::Nutritional and Metabolic Diseases::Nutrition Disorders::Overnutrition::ObesityMedical Subject Headings::Phenomena and Processes::Genetic Phenomena::Genetic Variation::Polymorphism, Genetic::Polymorphism, Single NucleotideMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Sensitivity and Specificity::ROC CurveMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Probability::RiskMedical Subject Headings::Named Groups::Persons::Age Groups::AdultMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Models, Statistical::Logistic ModelsA genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.research article25903154open access10.1186/s12916-015-0334-31741-7015PMC4407390