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Pharmacopsychiatry
DOI: 10.1055/a-2313-9979
Original Paper

Integrative Genetic Variation, DNA Methylation, and Gene Expression Analysis of Escitalopram and Aripiprazole Treatment Outcomes in Depression: A CAN-BIND-1 Study

Farhana Islam
1   Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
2   Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada
,
Amanda Lisoway
1   Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
3   Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
,
Edward S. Oh
1   Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
,
Laura M. Fiori
4   McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
,
Leen Magarbeh
1   Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
2   Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada
,
Samar S. M. Elsheikh
1   Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
,
Helena K. Kim
5   Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
,
Stefan Kloiber
1   Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
2   Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada
3   Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
5   Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
,
James L. Kennedy
1   Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
3   Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
5   Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
,
Benicio N. Frey
6   Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
7   St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
,
Roumen Milev
8   Department of Psychiatry, Queen's University, Providence Care, Kingston, Ontario, Canada
,
Claudio N. Soares
8   Department of Psychiatry, Queen's University, Providence Care, Kingston, Ontario, Canada
,
Sagar V. Parikh
9   Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
,
Franca Placenza
10   Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
,
Stefanie Hassel
11   Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada; Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
,
Valerie H. Taylor
11   Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada; Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
,
Francesco Leri
12   Department of Psychology and Neuroscience, University of Guelph, Guelph, Ontario, Canada
,
Pierre Blier
13   The Royal Institute of Mental Health Research, Ottawa, Ontario, Canada
,
Rudolf Uher
14   Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
,
Faranak Farzan
15   Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada
,
Raymond W. Lam
16   Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
,
Gustavo Turecki
4   McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
,
Jane A. Foster
6   Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
10   Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
17   St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
,
Susan Rotzinger
3   Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
18   Department of Psychiatry, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
,
Sidney H. Kennedy
3   Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
5   Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
10   Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
18   Department of Psychiatry, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
19   Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada
,
Daniel J. Müller
1   Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
2   Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada
3   Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
5   Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
20   Department of Psychiatry, Psychosomatics and Psychotherapy, University Clinic of Würzburg, Würzburg, Germany
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Abstract

Introduction Little is known about the interplay between genetics and epigenetics on antidepressant treatment (1) response and remission, (2) side effects, and (3) serum levels. This study explored the relationship among single nucleotide polymorphisms (SNPs), DNA methylation (DNAm), and mRNA levels of four pharmacokinetic genes, CYP2C19, CYP2D6, CYP3A4, and ABCB1, and its effect on these outcomes.

Methods The Canadian Biomarker Integration Network for Depression-1 dataset consisted of 177 individuals with major depressive disorder treated for 8 weeks with escitalopram (ESC) followed by 8 weeks with ESC monotherapy or augmentation with aripiprazole. DNAm quantitative trait loci (mQTL), identified by SNP-CpG associations between 20 SNPs and 60 CpG sites in whole blood, were tested for associations with our outcomes, followed by causal inference tests (CITs) to identify methylation-mediated genetic effects.

Results Eleven cis-SNP-CpG pairs (q<0.05) constituting four unique SNPs were identified. Although no significant associations were observed between mQTLs and response/remission, CYP2C19 rs4244285 was associated with treatment-related weight gain (q=0.027) and serum concentrations of ESCadj (q<0.001). Between weeks 2-4, 6.7% and 14.9% of those with *1/*1 (normal metabolizers) and *1/*2 (intermediate metabolizers) genotypes, respectively, reported ≥2 lbs of weight gain. In contrast, the *2/*2 genotype (poor metabolizers) did not report weight gain during this period and demonstrated the highest ESCadj concentrations. CITs did not indicate that these effects were epigenetically mediated.

Discussion These results elucidate functional mechanisms underlying the established associations between CYP2C19 rs4244285 and ESC pharmacokinetics. This mQTL SNP as a marker for antidepressant-related weight gain needs to be further explored.

Keywords

antidepressant - depression - escitalopram - aripiprazole - methylation - quantitative trait loci

Supplementary Material



Publication History

Received: 23 December 2023
Received: 13 March 2024

Accepted: 15 April 2024

Article published online:
25 June 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 United Nations. UN health agency reports depression now ‘leading cause of disability worldwide’. UN News. 2017; https://news.un.org/en/story/2017/02/552062-un-health-agency-reports-depression-now-leading-cause-disability-worldwide
    PubMed
  • 2 Cuijpers P, Noma H, Karyotaki E. et al. A network meta-analysis of the effects of psychotherapies, pharmacotherapies and their combination in the treatment of adult depression. World Psychiatry 2020; 19: 92-107
    CrossrefPubMedGoogle Scholar
  • 3 Rush AJ, Trivedi MH, Wisniewski SR. et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: ASTAR*D report. Am J Psychiatry 2006; 163: 1905-1917
    CrossrefPubMedGoogle Scholar
  • 4 Trivedi MH, Rush AJ, Wisniewski SR. et al. STAR*D Study Team. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry 2006; 163: 28-40
    CrossrefPubMedGoogle Scholar
  • 5 Bousman C, Arandjelovic K, Mancuso SG. et al. Pharmacogenetic tests and depressive symptom remission: A meta-analysis of randomized controlled trials. Pharmacogenomics 2019; 20: 37-47
    CrossrefPubMedGoogle Scholar
  • 6 Greden JF, Parikh SV, Rothschild AJ. et al. Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: A large, patient- and rater-blinded, randomized, controlled study. J Psychiatr Res 2019; 111: 59-67
    CrossrefPubMedGoogle Scholar
  • 7 Rosenblat JD, Simon GE, Sachs GS. et al. Treatment effectiveness and tolerability outcomes that are most important to individuals with bipolar and unipolar depression. J Affect Disord 2019; 243: 116-120
    CrossrefPubMedGoogle Scholar
  • 8 Scherf-Clavel M, Weber H, Wurst C. et al. Effects of pharmacokinetic gene variation on therapeutic drug levels and antidepressant treatment response. Pharmacopsychiatry 2022; 55: 246-254
    Article in Thieme ConnectPubMedGoogle Scholar
  • 9 Magarbeh L, Hassel C, Choi M. et al. ABCB1 gene variants and antidepressant treatment outcomes: A systematic review and meta-analysis including results from the CAN-BIND-1 Study. Clin Pharmacol Ther 2023; 114: 88-117
    CrossrefPubMedGoogle Scholar
  • 10 Islam F, Marshe VS, Magarbeh L. et al. Effects of CYP2C19 and CYP2D6 gene variants on escitalopram and aripiprazole treatment outcome and serum levels: Results from the CAN-BIND 1 study. Transl Psychiatry 2022; 12: 1-11
    CrossrefPubMedGoogle Scholar
  • 11 Fabbri C, Serretti A. Genetics of treatment outcomes in major depressive disorder: Present and future. Clin Psychopharmacol Neurosci 2020; 18: 1-9
    CrossrefPubMedGoogle Scholar
  • 12 Islam F, Gorbovskaya I, Müller DJ. Pharmacogenetic/pharmacogenomic tests for treatment prediction in depression. Adv Exp Med Biol 2021; 1305: 231-255
    CrossrefPubMedGoogle Scholar
  • 13 Fiori LM, Lin R, Ju C. et al. Chapter eleven - using epigenetic tools to investigate antidepressant response. Prog Mol Biol Transl Sci 2018; 158: 255-272
    CrossrefPubMedGoogle Scholar
  • 14 Frieling H, Tadić A. Value of genetic and epigenetic testing as biomarkers of response to antidepressant treatment. Int Rev Psychiatry 2013; 25: 572-578
    CrossrefPubMedGoogle Scholar
  • 15 Hannon E, Knox O, Sugden K. et al. Characterizing genetic and environmental influences on variable DNA methylation using monozygotic and dizygotic twins. PLOS Genetics 2018; 14: e1007544
    CrossrefPubMedGoogle Scholar
  • 16 Lisoway AJ, Zai CC, Tiwari AK. et al. DNA methylation and clinical response to antidepressant medication in major depressive disorder: A review and recommendations. Neurosci Lett 2018; 669: 14-23
    CrossrefPubMedGoogle Scholar
  • 17 Hannon E, Gorrie-Stone TJ, Smart MC. et al. Leveraging DNA-methylation quantitative-trait loci to characterize the relationship between methylomic variation, gene expression, and complex traits. Am J of Hum Genet 2018; 103: 654-665
    CrossrefPubMedGoogle Scholar
  • 18 Min JL, Hemani G, Hannon E. et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet 2021; 53: 1311-1321
    CrossrefPubMedGoogle Scholar
  • 19 Smith AK, Kilaru V, Kocak M. et al. Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics 2014; 15: 145
    CrossrefPubMedGoogle Scholar
  • 20 Almli LM, Stevens JS, Smith AK. et al. A genome-wide identified risk variant for PTSD is a methylation quantitative trait locus and confers decreased cortical activation to fearful faces. Am J Med Genet B Neuropsychiatr Genet 2015; 168B: 327-336
    CrossrefPubMedGoogle Scholar
  • 21 Ciuculete DM, Voisin S, Kular L. et al. meQTL and ncRNA functional analyses of 102 GWAS-SNPs associated with depression implicate HACE1 and SHANK2 genes. Clin Epigenetics 2020; 12: 99
    CrossrefPubMedGoogle Scholar
  • 22 Gamazon E, Badner J, Cheng L. et al. Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol Psychiatry 2013; 18: 340-346
    CrossrefPubMedGoogle Scholar
  • 23 Zhao S, Liu Z. Integrative analysis of genome-wide association study and common meQTLs for exploring the effects of DNA methylation on the development of neuroticism. J Affect Disord 2020; 274: 218-222
    CrossrefPubMedGoogle Scholar
  • 24 Lam RW, Milev R, Rotzinger S. et al. Discovering biomarkers for antidepressant response: Protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort. BMC Psychiatry 2016; 16: 105
    CrossrefPubMedGoogle Scholar
  • 25 Kennedy SH, Lam RW, Rotzinger S. et al. Symptomatic and functional outcomes and early prediction of response to escitalopram monotherapy and sequential adjunctive aripiprazole therapy in patients with major depressive disorder: A CAN-BIND-1 report. J Clin Psychiatry 2019; 80: 18m12202
    CrossrefPubMedGoogle Scholar
  • 26 Sheehan DV, Lecrubier Y, Sheehan KH. et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 1998; 59: 22-33
    PubMedGoogle Scholar
  • 27 Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry 1979; 134: 382-389
    CrossrefPubMedGoogle Scholar
  • 28 Ju C, Fiori LM, Belzeaux R. et al. Integrated genome-wide methylation and expression analyses reveal functional predictors of response to antidepressants. Transl Psychiatry 2019; 9: 1-12
    CrossrefPubMedGoogle Scholar
  • 29 Morris TJ, Butcher LM, Feber A. et al. ChAMP: 450k chip analysis methylation pipeline. Bioinformatics 2014; 30: 428-430
    CrossrefPubMedGoogle Scholar
  • 30 Yrondi A, Fiori LM, Frey BN. et al. Association between side effects and blood microRNA expression levels and their targeted pathways in patients with major depressive disorder treated by a selective serotonin reuptake inhibitor, escitalopram: A CAN-BIND-1 Report. Int J Neuropsychopharmacol 2019; 23: 88-95
    CrossrefPubMedGoogle Scholar
  • 31 Córdova-Palomera A, Fatjó-Vilas M, Gastó C. et al. Genome-wide methylation study on depression: Differential methylation and variable methylation in monozygotic twins. Transl Psychiatry 2015; 5: e557
    CrossrefPubMedGoogle Scholar
  • 32 Kim HK, Zai G, Müller DJ. et al. Identification of endocannabinoid predictors of treatment outcomes in major depressive disorder: A secondary analysis of the First Canadian Biomarker Integration Network in Depression (CAN-BIND 1) Study. Pharmacopsychiatry 2022; 55: 297-303
    Article in Thieme ConnectPubMedGoogle Scholar
  • 33 Jansen RJ, Tong L, Argos M. et al. The effect of age on DNA methylation in whole blood among Bangladeshi men and women. BMC Genomics 2019; 20: 704
    CrossrefPubMedGoogle Scholar
  • 34 Rahmani E, Shenhav L, Schweiger R. et al. Genome-wide methylation data mirror ancestry information. Epigenet Chromatin 2017; 10: 1
    CrossrefPubMedGoogle Scholar
  • 35 Millstein J, Zhang B, Zhu J. et al. Disentangling molecular relationships with a causal inference test. BMC Genet 2009; 10: 23
    CrossrefPubMedGoogle Scholar
  • 36 Liu Y, Aryee MJ, Padyukov L. et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol 2013; 31: 142-147
    CrossrefPubMedGoogle Scholar
  • 37 CPIC®. CPIC® guideline for selective serotonin reuptake inhibitors and CYP2D6 and CYP2C19 [Internet]. 2019; https://cpicpgx.org/guidelines/guideline-for-selective-serotonin-reuptake-inhibitors-and-cyp2d6-and-cyp2c19/
    PubMed
  • 38 Ackermann M, Sikora-Wohlfeld W, Beyer A. Impact of natural genetic variation on gene expression dynamics. PLoS Genet 2013; 9: e1003514
    CrossrefPubMedGoogle Scholar
  • 39 Villicaña S, Bell JT. Genetic impacts on DNA methylation: Research findings and future perspectives. Genome Biol 2021; 22: 127
    CrossrefPubMedGoogle Scholar
  • 40 Aldrich SL, Poweleit EA, Prows CA. et al. Influence of CYP2C19 metabolizer status on escitalopram/citalopram tolerability and response in youth with anxiety and depressive disorders. Front Pharmacol 2019; 10: 99
    CrossrefPubMedGoogle Scholar
  • 41 Jukić MM, Haslemo T, Molden E. et al. Impact of CYP2C19 genotype on escitalopram exposure and therapeutic failure: A retrospective study based on 2,087 patients. Am J Psychiatry 2018; 175: 463-470
    CrossrefPubMedGoogle Scholar
  • 42 Fabbri C, Tansey KE, Perlis RH. et al. Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: Meta-analysis of data from genome-wide association studies. Eur Neuropsychopharmacol 2018; 28: 945-954
    CrossrefPubMedGoogle Scholar
  • 43 Tsai MH, Lin KM, Hsiao MC. et al. Genetic polymorphisms of cytochrome P450 enzymes influence metabolism of the antidepressant escitalopram and treatment response. Pharmacogenomics 2010; 11: 537-546
    CrossrefPubMedGoogle Scholar
  • 44 Ricardo-Silgado ML, Singh S, Cifuentes L. et al. Association between CYP metabolizer phenotypes and selective serotonin reuptake inhibitors induced weight gain: A retrospective cohort study. BMC Med 2022; 20: 261
    CrossrefPubMedGoogle Scholar
  • 45 Gill H, Gill B, El-Halabi S. et al. Antidepressant medications and weight change: A narrative review. Obesity 2020; 28: 2064-2072
    CrossrefPubMedGoogle Scholar
  • 46 Dhar GA, Saha S, Mitra P. et al. DNA methylation and regulation of gene expression: Guardian of our health. Nucleus (Calcutta) 2021; 64: 259-270
    CrossrefPubMedGoogle Scholar
  • 47 Sharma AR, Vohra M, Shukla V. et al. Coding SNPs in hsa-miR-1343-3p and hsa-miR-6783-3p target sites of CYP2C19 modulates clopidogrel response in individuals with cardiovascular diseases. Life Sci 2020; 245: 117364
    CrossrefPubMedGoogle Scholar
  • 48 Oh ES, Petronis A. Origins of human disease: The chrono-epigenetic perspective. Nat Rev Genet 2021; 22: 533-546
    CrossrefPubMedGoogle Scholar
  • 49 Blake LE, Roux J, Hernando-Herraez I. et al. A comparison of gene expression and DNA methylation patterns across tissues and species. Genome Res 2020; 30: 250-262
    CrossrefPubMedGoogle Scholar
  • 50 Olsson Lindvall M, Angerfors A, Andersson B. et al. Comparison of DNA methylation profiles of hemostatic genes between liver tissue and peripheral blood within individuals. Thromb Haemost 2021; 121: 573-583
    Article in Thieme ConnectPubMedGoogle Scholar