Genome-Based Therapeutic Drugs For Depression Research Articles

Methodology for clinical genotyping of CYP2D6 and CYP2C19

Many antidepressants, atomoxetine, and several antipsychotics are metabolized by the cytochrome P450 enzymes CYP2D6 and CYP2C19, and guidelines for prescribers based on genetic variants exist. Although some laboratories offer such testing, there is no consensus regarding validated methodology for clinical genotyping of CYP2D6 and CYP2C19. The aim of this paper was to cross-validate multiple technologies for genotyping CYP2D6 and CYP2C19 against each other, and to contribute to feasibility for clinical implementation by providing an enhanced range of assay options, customizable automated translation of data into haplotypes, and a workflow algorithm. AmpliChip CYP450 and some TaqMan single nucleotide variant (SNV) and copy number variant (CNV) data in the Genome-based therapeutic drugs for depression (GENDEP) study were used to select 95 samples (out of 853) to represent as broad a range of CYP2D6 and CYP2C19 genotypes as possible. These 95 included a larger range of CYP2D6 hybrid configurations than have previously been reported using inter-technology data. Genotyping techniques employed were: further TaqMan CNV and SNV assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel, and, for samples with CYP2D6 hybrid configurations, long-range polymerase chain reactions (L-PCRs) with Sanger sequencing and Luminex. Agena MassARRAY was also used for CYP2C19. This study has led to the development of a broader range of TaqMan SNV assays, haplotype phasing methodology with TaqMan adaptable for other technologies, a multiplex genotyping method for efficient identification of some hybrid haplotypes, a customizable automated translation of SNV and CNV data into haplotypes, and a clinical workflow algorithm.

Estimating dose-response for time to remission with instrumental variable adjustment: the obscuring effects of drug titration in Genome Based Therapeutic Drugs for Depression Trial (GENDEP): clinical trial data

BackgroundThreshold regression, in which time to remission is modelled as a stochastic drift towards a boundary, is an alternative to the proportional hazards survival model and has a clear conceptual mechanism for examining the effects of drug dose. However, for both threshold regression and proportional hazard models, when dose titration occurs during treatment, the estimated causal effect of dose can be biased by confounding. An instrumental variable analysis can be used to minimise such bias.MethodWeekly antidepressant dose was measured in 380 men and women with major depression treated with escitalopram or nortriptyline for 12 weeks as part of the Genome Based Therapeutic Drugs for Depression (GENDEP) study. The averaged dose relative to maximum prescribing dose was calculated from the 12 trial weeks and tested for association with time to depression remission. We combined the instrumental variable approach, utilising randomised treatment as an instrument, with threshold regression and proportional hazard survival models.ResultsThe threshold model was constructed with two linear predictors. In the naïve models, averaged daily dose was not associated with reduced time to remission. By contrast, the instrumental variable analyses showed a clear and significant relationship between increased dose and faster time to remission, threshold regression (velocity estimate: 0.878, 95% confidence interval [CI]: 0.152–1.603) and proportional hazards (log hazards ratio: 3.012, 95% CI: 0.086–5.938).ConclusionsWe demonstrate, using the GENDEP trial, the benefits of these analyses to estimate causal parameters rather than those that estimate associations. The results for the trial dataset show the link between antidepressant dose and time to depression remission. The threshold regression model more clearly distinguishes the factors associated with initial severity from those influencing treatment effect. Additionally, applying the instrumental variable estimator provides a more plausible causal estimate of drug dose on treatment effect. This validity of these results is subject to meeting the assumptions of instrumental variable analyses.Trial registrationEudraCT, 2004–001723-38; ISRCTN, 03693000. Registered on 27 September 2007.

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response.

F93. CYP2D6 Revisited in GENDEP, a Multicenter Clinical Trial of Nortriptyline and Escitalopram

The polymorphic CYP2D6 enzyme is estimated to be responsible for the oxidation of 20-25% of all drugs in clinical use. However, genotyping for its variants is not routinely clinically available. The gene is particularly complex, with different platforms analysing different subsets of variants. GENDEP (Genome-based therapeutic drugs for depression) is a multicentre clinical trial which found an association between CYP2D6 genotype and nortriptyline concentration, but no association between CYP2D6 or CYP2C19 and response to nortriptyline. The CYP2D6 data were produced using the AmpliChip CYP450 Test, which had weaknesses, such as lack of coverage of hybrid alleles between CYP2D6 and its adjacent pseudogene (CYP2D7).

M83 - GENETIC DETERMINANTS OF WEIGHT CHANGE DURING ANTIDEPRESSANT TREATMENT WITH SELECTIVE SEROTONIN RE-UPTAKE INHIBITORS: GENOME-WIDE STUDY AND META-ANALYSIS

Background Weight change is a common adverse effect of antidepressant treatment. The purpose of this study was to identify genetic variants associated with change in body weight during antidepressant treatment for Major Depressive Disorder (MDD) with Selective Serotonin Reuptake Inhibitors (SSRIs). Methods Genotyping and objectively measured weight change data were available for 340 unrelated adults with Major Depressive Disorder (MDD) treated with escitalopram over a 26-week period in the Genome-Based Therapeutic Drugs for Depression (GENDEP) project. We performed a genome-wide association analysis of change in Body Mass Index (BMI). Next, we meta-analyzed the GENDEP results with those obtained from two cohorts of 195 and 144 individuals treated with SSRIs with objective measurement of weight and genotyped as part of the Partners HealthCare Biobank initiative. Finally, we conducted gene-wide analyses to identify genes associated with weight change during SSRI treatment. Results We identified 3 independent associations (p Discussion We provide preliminary evidence that uncommon and common genetic variation contributes to weight change during treatment with antidepressant drugs. Confirmation in larger samples is needed to identify clinically useful variants.

Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: Meta-analysis of three treatment cohorts.

There are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 760). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10–5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.

Genetic disposition to inflammation and response to antidepressants in major depressive disorder

BackgroundInflammation may play an important role in depression and its treatment. A previous study found that increased C-reactive protein (CRP), a marker of systemic inflammation, is associated with worse response to the serotonergic antidepressant escitalopram and better response to the noradrenergic antidepressant nortriptyline. It is unclear whether this reflects genetic disposition to inflammation. MethodsWe analyzed genotype data and weekly Montgomery-Åsberg Depression Rating Scale scores (MADRS) from 755 unrelated individuals obtained over a 12-week period in the Genome-Based Therapeutic Drugs for Depression (GENDEP) study. We calculated a polygenic risk score for CRP level based on genome-wide meta-analysis results from the CHARGE Consortium. ResultsA higher polygenic risk score for CRP was associated with slightly better response to escitalopram and slightly worse response to nortriptyline, reflected in a statistically significant interaction between polygenic risk score and drug (beta = 1.07, 95% CI = 0.26–1.87, p = 0.0093). DiscussionA differential association between CRP-PRS and antidepressant drug that is in a direction opposite to that found with serum CRP measurement suggests that previously observed effect of inflammation on antidepressant efficacy may be driven by state factors distinct from genetic influences on systemic inflammation.

Differential change on depressive symptom factors with antidepressant medication and cognitive behavior therapy for major depressive disorder

BackgroundMajor depressive disorder (MDD) is a heterogeneous condition and individual patients are likely to be differentially responsive to specific treatments. In an exploratory factor analysis of three rating scales, the Genome-based Therapeutic Drugs for Depression (GENDEP) trial identified three factors that were differentially associated with outcome to nortriptyline and escitalopram. However, this factor analysis has neither been replicated or applied to a psychotherapy treatment. MethodsWe replicated the GENDEP analytic method in the Emory Predictors of Remission to Individual and Combined Treatments (PReDICT) study. The 17-item Hamilton Depression Rating Scale, Montgomery Asberg Depression Rating Scale, and Beck Depression Inventory were administered to 306 MDD patients in the PReDICT study, which randomized previously untreated adults to 12 weeks of treatment with cognitive behavior therapy (CBT), escitalopram, or duloxetine. Utilizing Item Response Theory methodologies, factor scores were derived from the three scales and the efficacy of the three treatments was compared for the identified factor scores. ResultsFour factors were identified: “Despair,” “Mood and Interest,” “Sleep,” and “Appetite.” These factors closely aligned with the factors identified in GENDEP. Compared to CBT, escitalopram and duloxetine produced more rapid but ultimately similar improvement on the Despair and Mood and Interest factors; no significant differences between treatments emerged on the other factors. LimitationsThe scales contained differing numbers of items pertaining to specific depressive symptoms. ConclusionThe heterogeneity of MDD can be parsed into a consistent factor structure, with the factors showing differential rapidity, but ultimately similar, improvement across treatments.

New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation.

Genome-wide association studies have generally failed to identify polymorphisms associated with antidepressant response. Possible reasons include limited coverage of genetic variants that this study tried to address by exome genotyping and dense imputation. A meta-analysis of Genome-Based Therapeutic Drugs for Depression (GENDEP) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D) studies was performed at the single-nucleotide polymorphism (SNP), gene and pathway levels. Coverage of genetic variants was increased compared with previous studies by adding exome genotypes to previously available genome-wide data and using the Haplotype Reference Consortium panel for imputation. Standard quality control was applied. Phenotypes were symptom improvement and remission after 12 weeks of antidepressant treatment. Significant findings were investigated in NEWMEDS consortium samples and Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) for replication. A total of 7062 950 SNPs were analyzed in GENDEP (n=738) and STAR*D (n=1409). rs116692768 (P=1.80e-08, ITGA9 (integrin α9)) and rs76191705 (P=2.59e-08, NRXN3 (neurexin 3)) were significantly associated with symptom improvement during citalopram/escitalopram treatment. At the gene level, no consistent effect was found. At the pathway level, the Gene Ontology (GO) terms GO: 0005694 (chromosome) and GO: 0044427 (chromosomal part) were associated with improvement (corrected P=0.007 and 0.045, respectively). The association between rs116692768 and symptom improvement was replicated in PGRN-AMPS (P=0.047), whereas rs76191705 was not. The two SNPs did not replicate in NEWMEDS. ITGA9 codes for a membrane receptor for neurotrophins and NRXN3 is a transmembrane neuronal adhesion receptor involved in synaptic differentiation. Despite their meaningful biological rationale for being involved in antidepressant effect, replication was partial. Further studies may help in clarifying their role.

T20 - New Insights Into The Pharmacogenomics Of Antidepressant Response From The Gendep And Star*D Studies: Results Of Rare Variant Analysis And High-Density Imputation

Genome-wide association studies have generally failed to identify polymorphisms associated with antidepressant response. Possible reasons are limited coverage of genetic variants, phenotypic heterogeneity and small sample size. This study investigated the genetic predictors of antidepressant efficacy in Genome-Based Therapeutic Drugs for Depression (GENDEP) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D) samples trying to address some of the reported limitations. In detail: 1) coverage of genetic variants was increased by adding exome array data to previously available genome-wide data and by genotype imputation using the largest available reference panel (Haplotype Reference Consortium (HRC)); 2) a meta-analysis was performed using SNP methods and multi-marker tests at gene and pathway level. Each dataset was imputed using Minimac3 and the HRC panel after standard quality control. Both samples included patients with diagnosis of major depressive disorder. In GENDEP, patients were partially randomized to escitalopram or nortriptyline; STAR*D patients were treated with citalopram. Escitalopram is the active isomer of citalopram, thus the whole GENDEP sample and the escitalopram-treated subgroup were meta-analyzed with STAR*D. The phenotypes were depressive symptom improvement and remission at week 12 according to standard scales. SNP-level analysis was performed using PLINK (pngu.mgh.harvard.edu/~purcell/plink/); gene and pathway analyses were performed using MAGMA (http://ctglab.nl/software/magma). Covariates were age, baseline severity, center of recruitment and ancestry-informative principal components. NEWMEDS (http://www.newmeds-europe.com) consortium samples, excluding GENDEP, served for replication. 7,062,950 SNPs were analysed in GENDEP (n=738) and STAR*D (n=1409). There was no evidence of genomic inflation (lambda values were ≤ 1.01). No SNP was associated with symptom improvement or remission in the full meta-analysis. In the citalopram/escitalopram analysis, rs116692768 (MAF=0.033, p=1.87e-08, ITGA9 or integrin alpha 9 gene) and rs76191705 (MAF=0.012, p=2.39e-08, NRXN3 or neurexin 3 gene) were associated with symptom improvement. At gene level (whole sample), OR4K2 was associated with improvement (corrected p=0.04), but its effect was inconsistent between the samples. At pathway level (whole sample), the Gene Ontology terms GO:0005694 (chromosome) and GO:0044427 (chromosomal part) were associated with improvement (corrected p=0.007 and 0.045, respectively). Genome-wide significant SNPs were not replicated in NEWMEDS SSRI-treated sample (n=751) (p>0.05). ITGA9 and NRXN3 show a meaningful biological rationale for being involved in antidepressant effect. ITGA9 codes for a membrane glycoprotein receptor for neurotrophins and NRXN3 is a transmembrane neuronal adhesion receptor involved in post-synaptic and pre-synaptic differentiation, with relevant implications for synaptic activity and synaptic plasticity. Interestingly, rs76191705 is a non-sense mediated decay transcript variant and a polymorphism in complete LD with it (rs79302561) acts as an enhancer of gene expression (Ensembl GRCh37 release 84). However, no convincing replication of these findings was achieved and further studies may be useful to clarify the role of these genes in antidepressant efficacy.

An inflammatory biomarker as a differential predictor of outcome of depression treatment with escitalopram and nortriptyline.

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). 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. 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. An easily accessible peripheral blood biomarker may contribute to improvement in outcomes of major depressive disorder by personalizing treatment choice.

Investigation of blood mRNA biomarkers for suicidality in an independent sample.

Changes in the blood expression levels of SAT1, PTEN, MAP3K3 and MARCKS genes have been reported as biomarkers of high versus low suicidality state (Le-Niculescu et al.). Here, we investigate these expression biomarkers in the Genome-Based Therapeutic Drugs for Depression (GENDEP) study, of patients with major depressive disorder on a 12-week antidepressant treatment. Blood gene expression levels were available at baseline and week 8 for patients who experienced suicidal ideation during the study (n=20) versus those who did not (n=37). The analysis is well powered to detect the effect sizes reported in the original paper. Within either group, there was no significant change in the expression of these four genes over the course of the study, despite increasing suicidal ideation or initiation of antidepressant treatment. Comparison of the groups showed that the gene expression did not differ between patients with or without treatment-related suicidality. This independent study does not support the validity of the proposed biomarkers.

Genetic relationships between suicide attempts, suicidal ideation and major psychiatric disorders: A genome‐wide association and polygenic scoring study

Epidemiological studies have recognized a genetic diathesis for suicidal behavior, which is independent of other psychiatric disorders. Genome-wide association studies (GWAS) on suicide attempt (SA) and ideation have failed to identify specific genetic variants. Here, we conduct further GWAS and for the first time, use polygenic score analysis in cohorts of patients with mood disorders, to test for common genetic variants for mood disorders and suicide phenotypes. Genome-wide studies for SA were conducted in the RADIANT and GSK-Munich recurrent depression samples and London Bipolar Affective Disorder Case-Control Study (BACCs) then meta-analysis was performed. A GWAS on suicidal ideation during antidepressant treatment had previously been conducted in the Genome Based Therapeutic Drugs for Depression (GENDEP) study. We derived polygenic scores from each sample and tested their ability to predict SA in the mood disorder cohorts or ideation status in the GENDEP study. Polygenic scores for major depressive disorder, bipolar disorder and schizophrenia from the Psychiatric Genomics Consortium were used to investigate pleiotropy between psychiatric disorders and suicide phenotypes. No significant evidence for association was detected at any SNP in GWAS or meta-analysis. Polygenic scores for major depressive disorder significantly predicted suicidal ideation in the GENDEP pharmacogenetics study and also predicted SA in a combined validation dataset. Polygenic scores for SA showed no predictive ability for suicidal ideation. Polygenic score analysis suggests pleiotropy between psychiatric disorders and suicidal ideation whereas the tendency to act on such thoughts may have a partially independent genetic diathesis. © 2014 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics published by Wiley Periodicals, Inc.

The endogenous and reactive depression subtypes revisited: integrative animal and human studies implicate multiple distinct molecular mechanisms underlying major depressive disorder.

BackgroundTraditional diagnoses of major depressive disorder (MDD) suggested that the presence or absence of stress prior to onset results in either ‘reactive’ or ‘endogenous’ subtypes of the disorder, respectively. Several lines of research suggest that the biological underpinnings of ‘reactive’ or ‘endogenous’ subtypes may also differ, resulting in differential response to treatment. We investigated this hypothesis by comparing the gene-expression profiles of three animal models of ‘reactive’ and ‘endogenous’ depression. We then translated these findings to clinical samples using a human post-mortem mRNA study.MethodsAffymetrix mouse whole-genome oligonucleotide arrays were used to measure gene expression from hippocampal tissues of 144 mice from the Genome-based Therapeutic Drugs for Depression (GENDEP) project. The study used four inbred mouse strains and two depressogenic ‘stress’ protocols (maternal separation and Unpredictable Chronic Mild Stress) to model ‘reactive’ depression. Stress-related mRNA differences in mouse were compared with a parallel mRNA study using Flinders Sensitive and Resistant rat lines as a model of ‘endogenous’ depression. Convergent genes differentially expressed across the animal studies were used to inform candidate gene selection in a human mRNA post-mortem case control study from the Stanley Brain Consortium.ResultsIn the mouse ‘reactive’ model, the expression of 350 genes changed in response to early stresses and 370 in response to late stresses. A minimal genetic overlap (less than 8.8%) was detected in response to both stress protocols, but 30% of these genes (21) were also differentially regulated in the ‘endogenous’ rat study. This overlap is significantly greater than expected by chance. The VAMP-2 gene, differentially expressed across the rodent studies, was also significantly altered in the human study after correcting for multiple testing.ConclusionsOur results suggest that ‘endogenous’ and ‘reactive’ subtypes of depression are associated with largely distinct changes in gene-expression. However, they also suggest that the molecular signature of ‘reactive’ depression caused by early stressors differs considerably from that of ‘reactive’ depression caused by late stressors. A small set of genes was consistently dysregulated across each paradigm and in post-mortem brain tissue of depressed patients suggesting a final common pathway to the disorder. These genes included the VAMP-2 gene, which has previously been associated with Axis-I disorders including MDD, bipolar depression, schizophrenia and with antidepressant treatment response. We also discuss the implications of our findings for disease classification, personalized medicine and case-control studies of MDD.

Genetic predictors of antidepressant side effects: A grouped candidate gene approach in the Genome-Based Therapeutic Drugs for Depression (GENDEP) study

The unwanted side effects associated with antidepressants are key determinants of treatment adherence in depression; propensity to experience these adverse drug reactions (ADRs) may be influenced by genetic variation. However, previous work attempting to ascertain the genetic variants involved has had limited success, in part due to the range of ADRs reported with antidepressants. ADRs reported with antidepressant treatment were categorised using their likely pharmacological basis; adrenergic, cholinergic, serotonergic and histaminergic. To identify genetic predictors of susceptibility to each group of ADRs, a candidate gene analysis was performed with data from 431 depressed patients (from a total sample size of 811 patients) enrolled in the Genome-Based Therapeutic Drugs for Depression (GENDEP) project, who were randomly allocated to receive treatment with escitalopram or nortriptyline. Data from 474 patients treated with citalopram or reboxetine in the GenPod project (total sample of 601 patients) were used for replication of significant findings. We found no significant predictors of presumed adrenergic, cholinergic and histaminergic ADRs. Putative serotonergic ADRs were significantly associated with variation in the gene encoding the serotonin 2C receptor (HTR2C, rs6644093, odds ratio (OR)=1.72, 95% confidence interval (CI)=1.31-2.25, p=7.43×10(-5)) in GENDEP. However, this finding was not replicated in GenPod. The association between serotonergic side effects and variation in the HTR2C gene in the GENDEP sample supports the hypothesis that serotonin receptor-mediated mechanisms underlie these adverse reactions, however this finding was not replicated in GenPod.

A genome-wide association study of a sustained pattern of antidepressant response

Genome-wide association studies (GWAS) have failed to replicate common genetic variants associated with antidepressant response, as defined using a single endpoint. Genetic influences may be discernible by examining individual variation between sustained versus unsustained patterns of response, which may distinguish medication effects from non-specific, or placebo responses to active medication. We conducted a GWAS among 1116 subjects with Major Depressive Disorder from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial who were characterized using Growth Mixture Modeling as showing a sustained versus unsustained pattern of clinical response over 12 weeks of treatment with citalopram. Replication analyses examined 585 subjects from the Genome-based Therapeutic Drugs for Depression (GENDEP) trial. The strongest association with sustained as opposed to unsustained response in STAR*D involved a single nucleotide polymorphism (SNP; rs10492002) within the acyl-CoA synthetase short-chain family member 3 gene (ACSS3, p-value=4.5×10−6, odds ratio=0.61). No SNPs met our threshold for genome-wide significance. SNP data were available in GENDEP for 18 of the top 25 SNPs in STAR*D. The most replicable association was with SNP rs7816924 (p=0.008, OR=1.58); no SNP met the replication p-value threshold of 0.003. Joint analysis of these 18 SNPs resulted in the strongest signal coming from rs7816924 (p=2.11×10−7), which resides in chondroitin sulfate N-acetylgalactosaminyltransferase 1 gene (CSGALNACT1). An exploratory genetic pathway analysis revealed evidence for an involvement of the KEGG pathway of long-term potentiation (FDR=.02). Results suggest novel genetic associations to sustained response.

Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies.

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. 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. 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. 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.

Tumor necrosis factor and its targets in the inflammatory cytokine pathway are identified as putative transcriptomic biomarkers for escitalopram response

Converging evidence suggests that the activation of the inflammatory cytokine pathway is important in the pathophysiology of unipolar depression. Antidepressants have anti-inflammatory properties and evidence suggests that inter-individual variability in response to antidepressants may reflect genetic differences in the inflammatory cytokine pathway. In particular, protein levels of Tumor Necrosis Factor (TNF) and the SNPs rs1126757 in interleukin-11 (IL11), and rs7801617 in interleukin-6 (IL6), have previously been implicated in the clinical response to the selective serotonin reuptake inhibitor (SSRI) antidepressant escitalopram. This study investigated the transcription of TNF, IL11 and IL6 as well as genes in the wider inflammatory cytokine pathway both at baseline and after escitalopram treatment in depressed patients who were either clinical “responders” (n=25) or “non-responders” (n=21). Samples were obtained as a subset of the Genome-Based Therapeutic Drugs for Depression (GENDEP) project and response status is based on changes in the Montgomery–Asberg Depression Rating Scores over a 12wk treatment period. Binary logistic regressions revealed significant expression differences at baseline between responders and non-responders in TNF, and after escitalopram treatment in TNF and IL11. Differences in IL11 after treatment were found to be driven by drug-induced allele-specific expression differences relating to rs1126757. Top hits in the wider inflammatory cytokine pathway at both baseline and after escitalopram treatment were found to be targets of TNF. The current study adds substantial support for the role of the inflammatory cytokine pathway in mediating response to the SSRI escitalopram, and is the first to identify TNF and its targets as putative transcriptomic predictors of clinical response.

Resistance to antidepressant treatment is associated with polymorphisms in the leptin gene, decreased leptin mRNA expression, and decreased leptin serum levels

Leptin, a peptide hormone from adipose tissue and key player in weight regulation, has been suggested to be involved in sleep and cognition and to exert antidepressant-like effects, presumably via its action on the HPA-axis and hippocampal function. This led us to investigate whether genetic variants in the leptin gene, the level of leptin mRNA-expression and leptin serum concentrations are associated with response to antidepressant treatment. Our sample consisted of inpatients from the Munich Antidepressant Response Signature (MARS) project with weekly Hamilton Depression ratings, divided into two subsamples. In the exploratory sample (n=251) 17 single nucleotide polymorphisms (SNPs) covering the leptin gene region were genotyped. We found significant associations of several SNPs with impaired antidepressant treatment outcome and impaired cognitive performance after correction for multiple testing. The SNP (rs10487506) showing the highest association with treatment response (p=3.9×10−5) was analyzed in the replication sample (n=358) and the association could be verified (p=0.021) with response to tricyclic antidepressants. In an additional meta-analysis combining results from the MARS study with data from the Genome-based Therapeutic Drugs for Depression (GENDEP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR⁎D) studies, nominal associations of several polymorphisms in the upstream vicinity of rs10487506 with treatment outcome were detected (p=0.001). In addition, we determined leptin mRNA expression in lymphocytes and leptin serum levels in subsamples of the MARS study. Unfavorable treatment outcome was accompanied with decreased leptin mRNA and leptin serum levels. Our results suggest an involvement of leptin in antidepressant action and cognitive function in depression with genetic polymorphisms in the leptin gene, decreased leptin gene expression and leptin deficiency in serum being risk factors for resistance to antidepressant therapy in depressed patients.

Early and Delayed Onset of Response to Antidepressants in Individual Trajectories of Change During Treatment of Major Depression

The timing and rate of improvement after the initiation of an antidepressant has implications for establishing the mechanism of antidepressant action and for answering the clinically relevant question of how long an appropriate trial of antidepressant medication should be. We explore the individual trajectories of relative change in depression severity to establish what proportion of individuals experience early and late onset of improvement. Longitudinal latent class analysis was applied in a secondary analysis of data obtained from the Genome-Based Therapeutic Drugs for Depression (GENDEP) study. In the GENDEP trial, conducted in 9 European academic psychiatry centers from July 2004 to June 2008, 811 treatment-seeking adult subjects with DSM-IV major depression received escitalopram or nortriptyline for 12 weeks. Montgomery-Asberg Depression Rating Scale measurements were taken weekly. The secondary analysis reported in this article was conducted in 2010. A model with 9 latent classes provided a good description of the individual trajectories of symptom change over time. These classes included 3 nonresponder classes, 3 classes with varying degrees of improvement concentrated in the first 3 weeks (early improvement), and 3 classes with varying degrees of improvement that was more prominent in the second 3 weeks than in the first 3 weeks (delayed improvement). More than half of the subjects who eventually reached remission showed a pattern of delayed improvement, and their eventual outcome could not be predicted from early time points. Early marked response occurred more frequently in subjects treated with nortriptyline than in those treated with escitalopram (12.9% vs 7.5%, χ² = 6.29, P = .01). Delayed complete remission occurred more frequently in subjects treated with escitalopram than in those treated with nortriptyline (13.6% vs 6.1%, χ² = 11.52, P = .0007). Both early and delayed improvement are common. Although early changes are maintained, the eventual outcome of 12-week antidepressant treatment can be accurately predicted only after 8 weeks. http://www.controlled-trials.com Identifier: ISRCTN03693000.