Katherine E. Tansey
Cardiff University
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Featured researches published by Katherine E. Tansey.
American Journal of Psychiatry | 2014
Rudolf Uher; Katherine E. Tansey; Tracy Dew; Wolfgang Maier; Ole Mors; Joanna Hauser; Mojca Zvezdana Dernovšek; Neven Henigsberg; Daniel Souery; Anne Farmer; Peter McGuffin
OBJECTIVE Major depressive disorder has been linked with inflammatory processes, but it is unclear whether individual differences in levels of inflammatory biomarkers could help match patients to treatments that are most likely to be beneficial. The authors tested the hypothesis that C-reactive protein (CRP), a commonly available marker of systemic inflammation, predicts differential response to escitalopram (a serotonin reuptake inhibitor) and nortriptyline (a norepinephrine reuptake inhibitor). METHOD The hypothesis was tested in the Genome-Based Therapeutic Drugs for Depression (GENDEP) study, a multicenter open-label randomized clinical trial. CRP was measured with a high-sensitivity method in serum samples from 241 adult men and women with major depressive disorder randomly allocated to 12-week treatment with escitalopram (N=115) or nortriptyline (N=126). The primary outcome measure was the score on the Montgomery-Åsberg Depression Rating Scale (MADRS), administered weekly. RESULTS CRP level at baseline differentially predicted treatment outcome with the two antidepressants (CRP-drug interaction: β=3.27, 95% CI=1.65, 4.89). For patients with low levels of CRP (<1 mg/L), improvement on the MADRS score was 3 points higher with escitalopram than with nortriptyline. For patients with higher CRP levels, improvement on the MADRS score was 3 points higher with nortriptyline than with escitalopram. CRP and its interaction with medication explained more than 10% of individual-level variance in treatment outcome. CONCLUSIONS An easily accessible peripheral blood biomarker may contribute to improvement in outcomes of major depressive disorder by personalizing treatment choice.
American Journal of Psychiatry | 2013
Rudolf Uher; Katherine E. Tansey; Marcella Rietschel; Neven Henigsberg; Wolfgang Maier; Ole Mors; Joanna Hauser; Anna Placentino; Daniel Souery; Anne Farmer; Katherine J. Aitchison; Ian Craig; Peter McGuffin; Cathryn M. Lewis; Marcus Ising; Susanne Lucae; Elisabeth B. Binder; Stefan Kloiber; Florian Holsboer; Bertram Müller-Myhsok; Stephan Ripke; Steven P. Hamilton; Jared Soundy; Gonzalo Laje; Francis J. McMahon; Maurizio Fava; John A. Rush; Roy H. Perlis
OBJECTIVE Indirect evidence suggests that common genetic variation contributes to individual differences in antidepressant efficacy among individuals with major depressive disorder, but previous studies may have been underpowered to detect these effects. METHOD A meta-analysis was performed on data from three genome-wide pharmacogenetic studies (the Genome-Based Therapeutic Drugs for Depression [GENDEP] project, the Munich Antidepressant Response Signature [MARS] project, and the Sequenced Treatment Alternatives to Relieve Depression [STAR*D] study), which included 2,256 individuals of Northern European descent with major depressive disorder, and antidepressant treatment outcomes were prospectively collected. After imputation, 1.2 million single-nucleotide polymorphisms were tested, capturing common variation for association with symptomatic improvement and remission after up to 12 weeks of antidepressant treatment. RESULTS No individual association met a genome-wide threshold for statistical significance in the primary analyses. A polygenic score derived from a meta-analysis of GENDEP and MARS participants accounted for up to approximately 1.2% of the variance in outcomes in STAR*D, suggesting a weakly concordant signal distributed over many polymorphisms. An analysis restricted to 1,354 individuals treated with citalopram (STAR*D) or escitalopram (GENDEP) identified an intergenic region on chromosome 5 associated with early improvement after 2 weeks of treatment. CONCLUSIONS Despite increased statistical power accorded by meta-analysis, the authors identified no reliable predictors of antidepressant treatment outcome, although they did identify modest, direct evidence that common genetic variation contributes to individual differences in antidepressant response.
Proceedings of the National Academy of Sciences of the United States of America | 2013
Christoph Anacker; Annamaria Cattaneo; K. Musaelyan; Patricia A. Zunszain; Mark Horowitz; Raffaella Molteni; Alessia Luoni; Francesca Calabrese; Katherine E. Tansey; Massimo Gennarelli; Sandrine Thuret; Jack Price; Rudolf Uher; Marco Riva; Carmine M. Pariante
Stress and glucocorticoid hormones regulate hippocampal neurogenesis, but the molecular mechanisms mediating these effects are poorly understood. Here we identify the glucocorticoid receptor (GR) target gene, serum- and glucocorticoid-inducible kinase 1 (SGK1), as one such mechanism. Using a human hippocampal progenitor cell line, we found that a small molecule inhibitor for SGK1, GSK650394, counteracted the cortisol-induced reduction in neurogenesis. Moreover, gene expression and pathway analysis showed that inhibition of the neurogenic Hedgehog pathway by cortisol was SGK1-dependent. SGK1 also potentiated and maintained GR activation in the presence of cortisol, and even after cortisol withdrawal, by increasing GR phosphorylation and GR nuclear translocation. Experiments combining the inhibitor for SGK1, GSK650394, with the GR antagonist, RU486, demonstrated that SGK1 was involved in the cortisol-induced reduction in progenitor proliferation both downstream of GR, by regulating relevant target genes, and upstream of GR, by increasing GR function. Corroborating the relevance of these findings in clinical and rodent settings, we also observed a significant increase of SGK1 mRNA in peripheral blood of drug-free depressed patients, as well as in the hippocampus of rats subjected to either unpredictable chronic mild stress or prenatal stress. Our findings identify SGK1 as a mediator for the effects of cortisol on neurogenesis and GR function, with particular relevance to stress and depression.
Biological Psychiatry | 2013
Katherine E. Tansey; Michel Guipponi; Xiaolan Hu; Enrico Domenici; Glyn Lewis; Alain Malafosse; Jens R. Wendland; Cathryn M. Lewis; Peter McGuffin; Rudolf Uher
BACKGROUND Pharmacogenetic studies aiming to personalize the treatment of depression are based on the assumption that response to antidepressants is a heritable trait, but there is no compelling evidence to support this. METHODS We estimate the contribution of common genetic variation to antidepressant response with Genome-Wide Complex Trait Analysis in a combined sample of 2799 antidepressant-treated subjects with major depressive disorder and genome-wide genotype data. RESULTS We find that common genetic variants explain 42% (SE = .180, p = .009) of individual differences in antidepressant response. CONCLUSIONS These results suggest that response to antidepressants is a complex trait with substantial contribution from a large number of common genetic variants of small effect.
PLOS Medicine | 2012
Katherine E. Tansey; Michel Guipponi; Nader Perroud; Guido Bondolfi; Enrico Domenici; David Evans; Stephanie S.K. Hall; Joanna Hauser; Neven Henigsberg; Xiaolan Hu; Borut Jerman; Wolfgang Maier; Ole Mors; Michael Conlon O'Donovan; Timothy J. Peters; Anna Placentino; Marcella Rietschel; Daniel Souery; Katherine J. Aitchison; Ian Craig; Anne Farmer; Jens R. Wendland; Alain Malafosse; Peter Holmans; Glyn Lewis; Cathryn M. Lewis; Tine B. Stensbøl; Shitij Kapur; Peter McGuffin; Rudolf Uher
Testing whether genetic information could inform the selection of the best drug for patients with depression, Rudolf Uher and colleagues searched for genetic variants that could predict clinically meaningful responses to two major groups of antidepressants.
Biological Psychiatry | 2017
Robert A. Power; Katherine E. Tansey; Henriette N. Buttenschøn; Sarah Cohen-Woods; Tim B. Bigdeli; Lynsey S. Hall; Zoltán Kutalik; S. Hong Lee; Stephan Ripke; Stacy Steinberg; Alexander Teumer; Alexander Viktorin; Naomi R. Wray; Volker Arolt; Bernard T. Baune; Dorret I. Boomsma; Anders D. Børglum; Enda M. Byrne; Enrique Castelao; Nicholas John Craddock; Ian Craig; Udo Dannlowski; Ian J. Deary; Franziska Degenhardt; Andreas J. Forstner; Scott D. Gordon; Hans J. Grabe; Jakob Grove; Steven P. Hamilton; Caroline Hayward
Background Major depressive disorder (MDD) is a disabling mood disorder, and despite a known heritable component, a large meta-analysis of genome-wide association studies revealed no replicable genetic risk variants. Given prior evidence of heterogeneity by age at onset in MDD, we tested whether genome-wide significant risk variants for MDD could be identified in cases subdivided by age at onset. Methods Discovery case-control genome-wide association studies were performed where cases were stratified using increasing/decreasing age-at-onset cutoffs; significant single nucleotide polymorphisms were tested in nine independent replication samples, giving a total sample of 22,158 cases and 133,749 control subjects for subsetting. Polygenic score analysis was used to examine whether differences in shared genetic risk exists between earlier and adult-onset MDD with commonly comorbid disorders of schizophrenia, bipolar disorder, Alzheimer’s disease, and coronary artery disease. Results We identified one replicated genome-wide significant locus associated with adult-onset (>27 years) MDD (rs7647854, odds ratio: 1.16, 95% confidence interval: 1.11–1.21, p = 5.2 × 10-11). Using polygenic score analyses, we show that earlier-onset MDD is genetically more similar to schizophrenia and bipolar disorder than adult-onset MDD. Conclusions We demonstrate that using additional phenotype data previously collected by genetic studies to tackle phenotypic heterogeneity in MDD can successfully lead to the discovery of genetic risk factor despite reduced sample size. Furthermore, our results suggest that the genetic susceptibility to MDD differs between adult- and earlier-onset MDD, with earlier-onset cases having a greater genetic overlap with schizophrenia and bipolar disorder.
Molecular Psychiatry | 2015
David H. Kavanagh; Katherine E. Tansey; Michael Conlon O'Donovan; Michael John Owen
After two decades of frustration, genetic studies of schizophrenia have entered an era of spectacular success. Advances in genotyping technologies and high throughput sequencing, increasing analytic rigour and collaborative efforts on a global scale have generated a profusion of new findings. The broad conclusions from these studies are threefold: (1) schizophrenia is a highly polygenic disorder with a complex array of contributing risk loci across the allelic frequency spectrum; (2) many psychiatric illnesses share risk genes and alleles, specifically, schizophrenia has substantial overlaps with bipolar disorder, intellectual disability, major depressive disorder and autism spectrum disorders; and (3) some convergent biological themes are emerging from studies of schizophrenia and related disorders. In this commentary, we focus on the very recent findings that have emerged in the past 12 months, and in particular, the areas of convergence that are beginning to emerge from multiple study designs.
Translational Psychiatry | 2013
Timothy R. Powell; Rebecca Smith; S Hackinger; Leonard C. Schalkwyk; Rudolf Uher; Peter McGuffin; Jonathan Mill; Katherine E. Tansey
Transcriptional differences in interleukin-11 (IL11) after antidepressant treatment have been found to correspond to clinical response in major depressive disorder (MDD) patients. Expression differences were partly mediated by a single-nucleotide polymorphism (rs1126757), identified as a predictor of antidepressant response as part of a genome-wide association study. Here we attempt to identify whether DNA methylation, another baseline factor known to affect transcription factor binding, might also predict antidepressant response, using samples collected from the Genome-based Therapeutic Drugs for Depression project (GENDEP). DNA samples from 113 MDD individuals from the GENDEP project, who were treated with either escitalopram (n=80) or nortriptyline (n=33) for 12 weeks, were randomly selected. Percentage change in Montgomery–Åsberg Depression Rating Scale scores between baseline and week 12 were utilized as our measure of antidepressant response. The Sequenom EpiTYPER platform was used to assess DNA methylation across the only CpG island located in the IL11 gene. Regression analyses were then used to explore the relationship between CpG unit methylation and antidepressant response. We identified a CpG unit predictor of general antidepressant response, a drug by CpG unit interaction predictor of response, and a CpG unit by rs1126757 interaction predictor of antidepressant response. The current study is the first to investigate the potential utility of pharmaco-epigenetic biomarkers for the prediction of antidepressant response. Our results suggest that DNA methylation in IL11 might be useful in identifying those patients likely to respond to antidepressants, and if so, the best drug suited to each individual.
Journal of Psychopharmacology | 2014
Karen Hodgson; Katherine E. Tansey; Mojca Zvezdana Dernovšek; Joanna Hauser; Neven Henigsberg; Wolfgang Maier; Ole Mors; Anna Placentino; Marcella Rietschel; Daniel Souery; Robert Peter Smith; Ian Craig; Anne Farmer; Katherine J. Aitchison; Sarah S. Belsy; Oliver S. P. Davis; Rudolf Uher; Peter McGuffin
Aims: Antidepressant response varies between patients, possibly due to differences in the rate cytochrome P450 enzymes metabolise antidepressants into inactive compounds. Drug metabolism rates are influenced by common variants in the genes encoding these enzymes. However, it remains unclear whether treatment outcomes can be predicted by either CYP450 genotype or antidepressant serum concentration. Methods: In GENDEP (a pharmacogenetic study of depressed individuals treated with either escitalopram or nortriptyline), serum concentrations of antidepressants and their primary metabolite were measured after eight weeks treatment and variants in CYP2D6 and CYP2C19 were genotyped. Results: Amongst patients taking escitalopram (n=223), the genotype CYP2C19 was significantly associated with escitalopram serum concentrations and desmethylescitalopram:escitalopram ratio. For those taking nortriptyline (n=161), the CYP2D6 genotype was significantly associated with nortriptyline and 10-hydroxynortriptyline serum concentrations and 10-hydroxynortriptyline:nortrip-tyline ratio. CYP450 genotypes conferring greater enzyme activity were linked to lower drug serum concentrations and higher metabolite:drug ratios. Nonetheless, no significant association was found between either CYP450 genotype or antidepressant serum concentration and treatment response. Conclusions: While there is a significant relationship between the CYP450 genotype and serum concentrations of escitalopram and nortriptyline, the genotypes are not predictive of differences in treatment response for either drug. Furthermore, differences in antidepressant serum concentrations are not associated with variability in treatment response.
Pharmacogenomics | 2012
Rudolf Uher; Katherine E. Tansey; Karim Malki; Roy H. Perlis
AIM To extend to biomarker studies the consensus clinical significance criterion of a three-point difference in Hamilton Rating Scale for Depression. MATERIALS & METHODS We simulated datasets modeled on large clinical trials. RESULTS In a typical clinical trial comparing active treatment and placebo, a difference of three Hamilton Rating Scale for Depression (HRSD) points at the end of treatment corresponds to 6.3% of variance in outcome explained. To achieve a similar explanatory power, genotypes with minor allele frequencies of 5, 10, 20, 30 and 50% need to attain a per allele difference of 4.7, 3.6, 2.8, 2.4 and 2.2 HRSD points, respectively. A normally distributed continuous biomarker will need an effect size of 1.5 HRSD points per standard deviation. A number needed to assess of three suggests that with this effect size, a biomarker will significantly improve the prediction of outcome in one out of every three patients assessed. CONCLUSION This report provides guidance on assessing clinical significance of biomarkers predictive of outcome in depression treatment.