nach oben

Erschienen in:

04.01.2019 | Psychiatry and Preclinical Psychiatric Studies - Original Article

The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression

verfasst von: Azmeraw T. Amare, Klaus Oliver Schubert, Fasil Tekola-Ayele, Yi-Hsiang Hsu, Katrin Sangkuhl, Gregory Jenkins, Ryan M. Whaley, Poulami Barman, Anthony Batzler, Russ B. Altman, Volker Arolt, Jürgen Brockmöller, Chia-Hui Chen, Katharina Domschke, Daniel K. Hall-Flavin, Chen-Jee Hong, Ari Illi, Yuan Ji, Olli Kampman, Toshihiko Kinoshita, Esa Leinonen, Ying-Jay Liou, Taisei Mushiroda, Shinpei Nonen, Michelle K. Skime, Liewei Wang, Masaki Kato, Yu-Li Liu, Verayuth Praphanphoj, Julia C. Stingl, William V. Bobo, Shih-Jen Tsai, Michiaki Kubo, Teri E. Klein, Richard M. Weinshilboum, Joanna M. Biernacka, Bernhard T. Baune

Erschienen in: Journal of Neural Transmission | Ausgabe 1/2019

Einloggen, um Zugang zu erhalten

Abstract

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (P_T) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

Nur mit Berechtigung zugänglich

Afari N et al (2010) Depression and obesity: do shared genes explain the relationship? Depress Anxiety 27:799–806. https://doi.org/10.1002/da.20704 CrossRefPubMedPubMedCentral

Amare AT, Schubert KO, Klingler-Hoffmann M, Cohen-Woods S, Baune BT (2017a) The genetic overlap between mood disorders and cardiometabolic diseases: a systematic review of genome wide and candidate gene studies. Transl Psychiatry 7:e1007. https://doi.org/10.1038/tp.2016.261 CrossRefPubMedPubMedCentral

Amare AT, Schubert KO, Baune BT (2017b) Pharmacogenomics in the treatment of mood disorders: strategies and opportunities for personalized psychiatry. EPMA J 8:211–227. https://doi.org/10.1007/s13167-017-0112-8 CrossRefPubMedPubMedCentral

Amare AT, Schubert KOT, Schulze TG, Baune B (2018a) Polygenic scores for major depressive disorder and depressive symptoms predict response to lithium in patients with bipolar disorder. bioRxiv. https://doi.org/10.1101/449363 CrossRef

Amare AT et al (2018b) Association of the polygenic scores for personality traits and response to selective serotonin reuptake inhibitors in patients with major depressive disorder. Front Psychiatry 9:65. https://doi.org/10.3389/fpsyt.2018.00065 CrossRefPubMedPubMedCentral

Amare AT et al (2018c) Association of polygenic score for schizophrenia and hla antigen and inflammation genes with response to lithium in bipolar affective disorder: a genome-wide association study. JAMA Psychiatry 75:65–74. https://doi.org/10.1001/jamapsychiatry.2017.3433 CrossRefPubMed

Biernacka JM et al (2015) The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Transl Psychiatry 5:e553. https://doi.org/10.1038/tp.2015.47 CrossRefPubMedPubMedCentral

Chang HS et al (1996) The association of proopiomelanocortin polymorphisms with the risk of major depressive disorder and the response to antidepressants via interactions with stressful life events. J Neural transm. 122:59–68. https://doi.org/10.1007/s00702-014-1333-9. (Vienna Austria) CrossRef

Chekroud AM et al (2016) Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3:243–250. https://doi.org/10.1016/S2215-0366(15)00471-X CrossRefPubMed

Das S et al (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284–1287. https://doi.org/10.1038/ng.3656 CrossRefPubMedPubMedCentral

De Witte NAJ, Mueller SC (2017) White matter integrity in brain networks relevant to anxiety and depression: evidence from the human connectome project dataset. Brain Imaging Behav 11:1604–1615. https://doi.org/10.1007/s11682-016-9642-2 CrossRefPubMed

Dennis EL et al (2014) Obesity gene NEGR1 associated with white matter integrity in healthy young adults. NeuroImage 102 (Pt 2):548–557. https://doi.org/10.1016/j.neuroimage.2014.07.041 CrossRefPubMed

Fava M et al (2003) Background and rationale for the sequenced treatment alternatives to relieve depression (STAR*D) study. Psychiatr Clin N Am 26:457–494 xCrossRef

Funatsu N et al (1999) Characterization of a novel rat brain glycosylphosphatidylinositol-anchored protein (Kilon), a member of the IgLON cell adhesion molecule family. J Biol Chem 274:8224–8230CrossRefPubMed

Graff M et al (2013) Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course. Hum Mol Genet 22:3597–3607. https://doi.org/10.1093/hmg/ddt205 CrossRefPubMedPubMedCentral

Haljas K et al (2018) Bivariate Genome-Wide Association study of depressive symptoms with type 2 diabetes and quantitative glycemic traits. Psychosom Med 80:242–251. https://doi.org/10.1097/psy.0000000000000555 CrossRefPubMedPubMedCentral

Hieronymus F, Emilsson JF, Nilsson S, Eriksson E (2016) Consistent superiority of selective serotonin reuptake inhibitors over placebo in reducing depressed mood in patients with major depression. Mol Psychiatry 21:523–530. https://doi.org/10.1038/mp.2015.53 CrossRefPubMed

Hirschfeld RM (1999) Efficacy of SSRIs and newer antidepressants in severe depression: comparison with TCAs. J Clin Psychiatry 60:326–335CrossRefPubMed

International Schizophrenia C et al (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:748–752. https://doi.org/10.1038/nature08185 CrossRef

Iosifescu DV et al (2003) The impact of medical comorbidity on acute treatment in major depressive disorder. Am J Psychiatry 160:2122–2127. https://doi.org/10.1176/appi.ajp.160.12.2122 CrossRefPubMed

Iosifescu DV et al (2004) Comorbid medical illness and relapse of major depressive disorder in the continuation phase of treatment. Psychosomatics 45:419–425. https://doi.org/10.1176/appi.psy.45.5.419 CrossRefPubMed

Kendler KS, Gardner CO, Fiske A, Gatz M (2009) Major depression and coronary artery disease in the Swedish twin registry: phenotypic, genetic, and environmental sources of comorbidity. Arch Gen Psychiatry 66:857–863. https://doi.org/10.1001/archgenpsychiatry.2009.94 CrossRefPubMedPubMedCentral

Kim H, Hwang JS, Lee B, Hong J, Lee S (2014) Newly identified cancer-associated role of human neuronal growth regulator 1 (NEGR1). J Cancer 5:598–608. https://doi.org/10.7150/jca.8052 CrossRefPubMedPubMedCentral

Kim H et al (2017) The new obesity-associated protein, neuronal growth regulator 1 (NEGR1), is implicated in Niemann–Pick disease type C (NPC2)-mediated cholesterol trafficking. Biochem Biophys Res Commun 482:1367–1374. https://doi.org/10.1016/j.bbrc.2016.12.043 CrossRefPubMed

Locke AE et al (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518:197–206. https://doi.org/10.1038/nature14177 CrossRefPubMedPubMedCentral

Lu Y et al (2016) New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat Commun 7:10495. https://doi.org/10.1038/ncomms10495 CrossRefPubMedPubMedCentral

Luppino FS et al (2010) Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 67:220–229. https://doi.org/10.1001/archgenpsychiatry.2010.2 CrossRefPubMed

Maccarrone G et al (2013) Psychiatric patient stratification using biosignatures based on cerebrospinal fluid protein expression clusters. J Psychiatry Res 47:1572–1580. https://doi.org/10.1016/j.jpsychires.2013.07.021 CrossRef

Marenberg ME, Risch N, Berkman LF, Floderus B, de Faire U (1994) Genetic susceptibility to death from coronary heart disease in a study of twins. N Engl J Med 330:1041–1046. https://doi.org/10.1056/NEJM199404143301503 CrossRefPubMed

Meylan EM, Halfon O, Magistretti PJ, Cardinaux JR (2016) The HDAC inhibitor SAHA improves depressive-like behavior of CRTC1-deficient mice: possible relevance for treatment-resistant depression. Neuropharmacology 107:111–121. https://doi.org/10.1016/j.neuropharm.2016.03.012 CrossRefPubMedPubMedCentral

Murray CJL et al (2015) Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet 386:2145–2191. https://doi.org/10.1016/S0140-6736(15)61340-X CrossRefPubMed

Nikpay M et al (2015) A comprehensive 1000 genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet 47:1121–1130. https://doi.org/10.1038/ng.3396 CrossRefPubMedPubMedCentral

Pohle K, Domschke K, Roehrs T, Arolt V, Baune BT (2009) Medical comorbidity affects antidepressant treatment response in patients with melancholic depression. Psychother Psychosom 78:359–363. https://doi.org/10.1159/000235975 CrossRefPubMed

Purcell S et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. https://doi.org/10.1086/519795 CrossRefPubMedPubMedCentral

Ramasamy A et al (2014) Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci 17:1418–1428. https://doi.org/10.1038/nn.3801 CrossRefPubMedPubMedCentral

Rush AJ et al (2004) Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Controll Clin Trials 25:119–142CrossRef

Saher G, Stumpf SK (2015) Cholesterol in myelin biogenesis and hypomyelinating disorders. Biochim Biophys Acta 1851:1083–1094. https://doi.org/10.1016/j.bbalip.2015.02.010 CrossRefPubMed

Samaan Z et al (2015) Obesity genes and risk of major depressive disorder in a multiethnic population: a cross-sectional study. J Clin Psychiatry 76:e1611–e1618. https://doi.org/10.4088/JCP.14m09720 CrossRefPubMed

Scherrer JF et al (2003) A twin study of depression symptoms, hypertension, and heart disease in middle-aged men. Psychosom Med 65:548–557CrossRefPubMed

Sullivan PF, Neale MC, Kendler KS (2000) Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry 157:1552–1562. https://doi.org/10.1176/appi.ajp.157.10.1552 CrossRefPubMed

Tamasi V et al (2014) Transcriptional evidence for the role of chronic venlafaxine treatment in neurotrophic signaling and neuroplasticity including also Glutamatergic—and insulin-mediated neuronal processes. PLoS One 9:e113662. https://doi.org/10.1371/journal.pone.0113662 CrossRefPubMedPubMedCentral

Whiteford HA et al (2013) Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 382:1575–1586. https://doi.org/10.1016/S0140-6736(13)61611-6 CrossRefPubMed

Willer CJ et al (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41:25–34. https://doi.org/10.1038/ng.287 CrossRefPubMed

Wray NR et al (2012) Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Mol Psychiatry 17:36–48. https://doi.org/10.1038/mp.2010.109 CrossRefPubMed

Yang Q, Wang Y (2012) Methods for analyzing multivariate phenotypes in genetic association studies. J Probab Stat 2012:652569, https://doi.org/10.1155/2012/652569 CrossRefPubMedPubMedCentral

Yang Q, Wu H, Guo CY, Fox CS (2010) Analyze multivariate phenotypes in genetic association studies by combining univariate association tests. Genet Epidemiol 34:444–454. https://doi.org/10.1002/gepi.20497 CrossRefPubMedPubMedCentral

Titel: The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression
verfasst von: Azmeraw T. Amare
Klaus Oliver Schubert
Fasil Tekola-Ayele
Yi-Hsiang Hsu
Katrin Sangkuhl
Gregory Jenkins
Ryan M. Whaley
Poulami Barman
Anthony Batzler
Russ B. Altman
Volker Arolt
Jürgen Brockmöller
Chia-Hui Chen
Katharina Domschke
Daniel K. Hall-Flavin
Chen-Jee Hong
Ari Illi
Yuan Ji
Olli Kampman
Toshihiko Kinoshita
Esa Leinonen
Ying-Jay Liou
Taisei Mushiroda
Shinpei Nonen
Michelle K. Skime
Liewei Wang
Masaki Kato
Yu-Li Liu
Verayuth Praphanphoj
Julia C. Stingl
William V. Bobo
Shih-Jen Tsai
Michiaki Kubo
Teri E. Klein
Richard M. Weinshilboum
Joanna M. Biernacka
Bernhard T. Baune
Publikationsdatum: 04.01.2019
Verlag: Springer Vienna
Erschienen in: Journal of Neural Transmission / Ausgabe 1/2019
Print ISSN: 0300-9564
Elektronische ISSN: 1435-1463
DOI: https://doi.org/10.1007/s00702-018-01966-x

Leitlinien kompakt für die Neurologie

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Neu im Fachgebiet Neurologie

Niedriger diastolischer Blutdruck erhöht Risiko für schwere kardiovaskuläre Komplikationen

25.04.2024 Hypotonie Nachrichten

Wenn unter einer medikamentösen Hochdrucktherapie der diastolische Blutdruck in den Keller geht, steigt das Risiko für schwere kardiovaskuläre Ereignisse: Darauf deutet eine Sekundäranalyse der SPRINT-Studie hin.

Frühe Alzheimertherapie lohnt sich

25.04.2024 AAN-Jahrestagung 2024 Nachrichten

Ist die Tau-Last noch gering, scheint der Vorteil von Lecanemab besonders groß zu sein. Und beginnen Erkrankte verzögert mit der Behandlung, erreichen sie nicht mehr die kognitive Leistung wie bei einem früheren Start. Darauf deuten neue Analysen der Phase-3-Studie Clarity AD.

Viel Bewegung in der Parkinsonforschung

25.04.2024 Parkinson-Krankheit Nachrichten

Neue arznei- und zellbasierte Ansätze, Frühdiagnose mit Bewegungssensoren, Rückenmarkstimulation gegen Gehblockaden – in der Parkinsonforschung tut sich einiges. Auf dem Deutschen Parkinsonkongress ging es auch viel um technische Innovationen.

Demenzkranke durch Antipsychotika vielfach gefährdet

23.04.2024 Demenz Nachrichten

Wenn Demenzkranke aufgrund von Symptomen wie Agitation oder Aggressivität mit Antipsychotika behandelt werden, sind damit offenbar noch mehr Risiken verbunden als bislang angenommen.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 1/2019

Application of pharmacogenetics in clinical practice: problems and solutions

Genetic variations in the ADCK1 gene predict paliperidone palmitate efficacy in Han Chinese patients with schizophrenia

Patient characteristics driving clinical utility in psychiatric pharmacogenetics: a reanalysis from the AB-GEN multicentric trial

An analysis of allele, genotype and phenotype frequencies, actionable pharmacogenomic (PGx) variants and phenoconversion in 5408 Australian patients genotyped for CYP2D6, CYP2C19, CYP2C9 and VKORC1 genes

Blood-based biomarkers predicting response to antidepressants