Association of the COMT val158met Variant with Antidepressant Treatment Response in Major Depression

Baune, Bernhard T.; Hohoff, Christa; Berger, Klaus; Neumann, Anna; Mortensen, Sünke; Roehrs, Tilmann; Deckert, Jürgen; Arolt, Volker; Domschke, Katharina

doi:10.1038/sj.npp.1301462

Cited by 133 publications

(72 citation statements)

References 37 publications

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“…122 Nevertheless, a better response to milnacipram in the homozygous methionine genotype carriers was found in Asian 129,130 and white populations. 131,132 A significant association has also been reported in placebo response. 133 Other SNPs associated with antidepressant response have been recently described, but no replications have been produced.…”

Section: Catechol-o-methyl Transferasementioning

confidence: 72%

Pharmacogenetics of antidepressant response

Porcelli

Drago

Fabbri

et al. 2011

J Psychiatry Neurosci

160

103

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87Personalized medicine -the adaptation of therapies based on an individual's genetic and molecular profile -is one of the most promising aspects of modern medicine. The identification of the relation between genotype and drug response, including both the therapeutic effect and side effect profile, is expected to deeply affect medical practice. In this paper, we review the current knowledge about the genes related to antidepressant treatment response and provide methodologic proposals for future studies. We have mainly focused on genes associated with pharmacodynamics, for which a list of promising genes has been identified despite some inconsistency across studies. We have also synthesized the main results for pharmacokinetic genes, although so far they seem less relevant than those for pharmaco dynamic genes. We discuss possible reasons for these inconsistent findings and propose new study designs.

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Section: Catechol-o-methyl Transferasementioning

confidence: 72%

Pharmacogenetics of antidepressant response

Porcelli

Drago

Fabbri

et al. 2011

J Psychiatry Neurosci

160

103

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“…SSRIs) treatment response accounts for genetic factors, progress in uncovering the particular genetic polymorphisms has been slow [112]. As described in Table 1, candidate gene studies have pointed to a number of genes and SNPs that may influence antidepressant treatment outcomes [6, 7, 8-10, 26, 27, 54], including polymorphisms within the COMT [8,30], HTR2A [54,86,121], HTR1A [26], CNR1 [26], SLC6A4 [9], NPY [6], MAOA [27,30], IL1B [7,30], GRIK4 [64], BDNF [30,86], GNB3 [30], FKBP5 [86], CYP2D6 [31,121], CYP2C19 [31,121], and ABCB1 [12,86] genes (Table 1). For example, a negative influence of a higher activity COMT 158 val/val genotype on antidepressant treatment response was shown [].…”

Section: Ssris Treatment For Major Depressive Disordermentioning

confidence: 99%

“…Treatment response and remission were assessed using the Hamilton Depression Rating Scale (HAM-D) as the primary treatment outcome measure administered on a weekly basis at least for 6-week duration by trained psychiatrists and psychologists. Patients are of Caucasian ancestry and treatment selection was made by clinicians and included flexible antidepressant dosage and agents for augmenting [7,8,37]. .…”

Section: International Ssri Pharmacogenomics Consortium Studymentioning

confidence: 99%

“…Depression and Sequence of Treatment study The Depression and Sequence of Treatment (DAST) study is a prospective naturalistic treatment study of 746 inpatients diagnosed with a major depressive disorder of adult age (> 18 years of age) collected at the Department of Psychiatry and Psychotherapy at the University of Münster, Germany [7,8,37]. MDD was diagnosed using a structured clinical diagnostic interview (SCID).…”

Section: International Ssri Pharmacogenomics Consortium Studymentioning

confidence: 99%

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Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

2017

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Personalized medicine (personalized psychiatry in a specific setting) is a new model towards individualized care, in which knowledge from genomics and other omic pillars (microbiome, epigenomes, proteome, and metabolome) will be combined with clinical data to guide efforts to new drug development and targeted prescription of the existing treatment options. In this review, we summarize pharmacogenomic studies in mood disorders that may lay the foundation towards personalized psychiatry. In addition, we have discussed the possible strategies to integrate data from omic pillars as a future path to personalized psychiatry. So far, the progress of uncovering single nucleotide polymorphisms (SNPs) underpinning treatment efficacy in mood disorders (e.g., SNPs associated with selective serotonin re-uptake inhibitors or lithium treatment response in patients with bipolar disorder and major depressive disorder) are encouraging, but not adequate. Genetic studies have pointed to a number of SNPs located at candidate genes that possibly influence response to; (a) antidepressants COMT, HTR2A, HTR1A, CNR1, SLC6A4, NPY, MAOA, IL1B, GRIK4, BDNF, GNB3, FKBP5, CYP2D6, CYP2C19, and ABCB1 and (b) mood stabilizers (lithium) 5-HTT, TPH, DRD1, FYN, INPP1, CREB1, BDNF, GSK3β, ARNTL, TIM, DPB, NR3C1, BCR, XBP1, and CACNG2. We suggest three alternative and complementary strategies to implement knowledge gained from pharmacogenomic studies. The first strategy can be to implement diagnostic, therapeutic, or prognostic genetic testing based on candidate genes or gene products. The second alternative is an integrative analysis (systems genomics approach) to combine omics data obtained from the different pillars of omics investigation, including genomics, epigenomes, proteomics, metabolomics and microbiomes. The main goal of system genomics is an identification and understanding of biological pathways, networks, and modules underlying drug-response. The third strategy aims to the development of multivariable diagnostic or prognostic algorithms (tools) combining individual's genomic information (polygenic score) with other predictors (e.g., omics pillars, neuroimaging, and clinical characteristics) to finally predict therapeutic outcomes. An integration of molecular science with that of traditional clinical practice is the way forward to drug discoveries and novel therapeutic approaches and to characterize psychiatric disorders leading to a better predictive, preventive, and personalized medicine (PPPM) in psychiatry. With future advances in the omics technology and methodological developments for data integration, the goal of PPPM in psychiatry is promising.

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“…Clinical response to treatment was measured by the intraindividual changes of HAM-D-21 scores over the 6-week study period expressed as the weekly improvement relative to week 1 as published before in detail [14]. …”

Section: Methodsmentioning

confidence: 99%

Medical Comorbidity Affects Antidepressant Treatment Response in Patients with Melancholic Depression

Pohle

Domschke²,

Roehrs³

et al. 2009

Psychother Psychosom

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Background: This study aimed at the impact of medical comorbidity (MC) on response to antidepressant treatment over 6 weeks in diagnostic subtypes of patients with major depressive episode (MDE). Methods: In a clinical sample of 241 admitted patients with MDE, MC was assessed by medical specialists and weekly response to antidepressant treatment was assessed with the Hamilton Depression Scale (HAM-D 21). Results: Over 6 weeks of treatment, patients with melancholic depression and MC had poorer treatment response on the HAM-D scale and a worse functional outcome (GAF) as opposed to their counterparts without MC, which was first detected after 4 weeks of treatment (p = 0.02). More specifically, subjects with melancholic depression and cardiovascular or endocrinological MC showed significantly poorer treatment response over 6 weeks. Interestingly, these effects were not related to various antidepressant treatment regimens. Conclusions: MC has a negative impact on treatment response in patients with melancholic depression.

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Association of the COMT val158met Variant with Antidepressant Treatment Response in Major Depression

Cited by 133 publications

References 37 publications

Pharmacogenetics of antidepressant response

Pharmacogenetics of antidepressant response

Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Medical Comorbidity Affects Antidepressant Treatment Response in Patients with Melancholic Depression

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