A genome-wide association study of a sustained pattern of antidepressant response
Introduction
Treatment with antidepressant medications is associated with significant improvements in clinical symptoms of Major Depressive Disorder (MDD), as well as improvements in functional status and quality of life. However, there is marked heterogeneity of outcomes including a subset of patients who show unsustained response (Muthén et al., 2011; Quitkin et al., 1984). Inter-individual variation in antidepressant response is under genetic influence (Tansey et al., 2012a), yet no genetic marker has shown a consistent association with clinical outcome (Tansey et al., 2012b; Uher et al., 2013). Limited progress in predicting drug efficacy may be in part due to heterogeneity in MDD related to complex gene–environment etiology (Keers and Aitchison, 2011), or the inability to separate specific response to antidepressants from naturalistic course or placebo response (Malhotra, 2010; Malhotra et al., 2012), among other factors.
The discovery of predictors of clinical response may also depend critically on the classification of outcomes. MDD trials commonly define outcomes using a predetermined cutoff score assessed at a single primary endpoint. This approach fails to account for patterns of change in clinical symptoms over time (Muthén et al., 2008) and may not reflect clinically or physiologically meaningful distinctions (Uher et al., 2010a, Uher et al., 2010b). Clinical changes over time are especially relevant when subjects exhibit alternating improvement and worsening of symptoms (Hunter et al., 2010) or a U-shaped pattern of outcome (Muthén et al., 2011; Quitkin et al., 1984). Unsustained response is clinically undesirable and may represent a “placebo” response rather than a “true drug” effect (Quitkin et al., 1984). Insofar as differences between sustained and unsustained response patterns may reflect a physiological substrate, it is of interest to examine these phenotypes for genetic association.
Advanced statistical modeling techniques have identified various response patterns, including unsustained response during acute antidepressant treatment. Growth mixture modeling (GMM) is a systematic, data-driven approach that utilizes symptom severity measures from all available time points to identify distinct trajectories of response; cluster analytic features are incorporated into GMM to reveal latent “classes” or patterns of change in symptom severity over time (Muthén and Asparouhov, 2009; Muthén and Shedden, 1999). Such techniques have been successfully applied to longitudinal data to identify response patterns of clinical relevance during pharmacotherapy interventions in MDD (Gueorguieva et al., 2011; Hunter et al., 2010; Muthén et al., 2011; Power et al., 2012; Uher et al., 2009, Uher et al., 2010a, Uher et al., 2010b, 2011).
GMM was recently applied to data from the Sequenced Treatment Alternatives to Relieve Depression trial (STAR*D) (Trivedi et al., 2006), a large open-label multi-site study that, because of its size and inclusion of “real world” patients, is especially well suited to this technique. Analyses that examined all available scores on the 16-item clinician-rated Quick Inventory of Depressive Symptoms (QIDS-C) (Rush et al., 2003b) obtained at baseline and over 12 weeks during Level 1 treatment with citalopram yielded fundamental trajectory shapes providing evidence of four classes: ‘non-responders’; ‘partial improvers’; ‘sustained responders’ (SUS) showing monotonic improvement culminating in response at week 12; and ‘unsustained responders’ (UNS), showing U-shaped response-level improvement by week 6 but with a return of baseline-level symptoms by week 12 (Fig. 1) (Muthén et al., 2011). SUS and UNS responder class sizes ranged from 32% to 45%, and 6% to19%, respectively, depending on the model (Muthén et al., 2011).
We hypothesize that sustained and unsustained response trajectories represent biologically distinct types of response to antidepressants. To test this hypothesis, we conducted a genome-wide association study (GWAS) contrasting STAR*D subjects in SUS versus UNS response trajectory classes to determine whether common DNA variation determines durability of response to antidepressant treatment. Identification of individuals unlikely to sustain antidepressant response would have great clinical utility, providing incentive for aggressive optimization of treatment in susceptible individuals.
Section snippets
Overview
GWAS was conducted in the STAR*D dataset to test for association between single-locus SNP variants and durability of response ('sustained' versus 'unsustained' response class outcomes defined using GMM). SNPs with the strongest association were then examined prospectively for replication in subjects from the Genome-based Therapeutic Drugs for Depression (GENDEP) study. Secondary, gene-based analyses were conducted to determine the association between combined effects of SNPs within individual
STAR*D clinical and demographic features
A total of 1116 subjects from STAR*D were analyzed (Supplemental Table 2). There were 869 subjects who were classified as SUS, while there were 247 subjects who were classified as UNS using our GMM algorithm. These latter subjects are described by a pattern of initial response to treatment with return of symptoms over time. Clinical and demographic variables were tested for association with the sustained response phenotype. Those that were associated with the sustained response pattern at a
Discussion
We carried out a GWAS to address the hypothesis that DNA variation influences a pattern of unsustained as compared to sustained antidepressant response in individuals with unipolar major depression in the STAR*D sample. Our strongest finding involved ACSS3, a gene not previously linked with antidepressant biology. The risk allele increased the likelihood of the unsustained response. There is no overlap between the top findings from this analysis and our previous GWAS of citalopram response in
Financial disclosures
Drs. Garriock, Hamilton, Lewis, and Power reported no biomedical financial interests or potential conflicts of interest.
Dr. Hunter is an inventor of a UCLA-assigned patent pending method to predict antidepressant effects.
Dr. Leuchter has received research support from Covidien, Neuronetics, NeuroSigma, NIMH, and Shire Pharmaceuticals. Dr. Leuchter further serves, or has served, in the following capacities for biomedical companies: Chair, Neuroscience Advisory Board, Covidien, Newton, MA
Contributors
Drs. Hunter, Leuchter, and Hamilton conceptualized the study.
Dr. Hunter managed the literature searches and analyses.
Drs. Hunter and Hamilton wrote the first draft of the manuscript with contributions from Drs. Leuchter, Power, Muthèn, Cook, and Uher.
Drs. Power, Uher, and Hamilton conducted statistical analyses.
Drs. Muthèn, McGrath, Lewis, Garriock, and McGuffin contributed critically to the design of the underlying studies and/or analyses. All authors contributed to revisions and have approved
Role of the funding source
Clinical data used in the GWAS were obtained from the limited access datasets distributed from the NIMH-supported “Sequenced Treatment Alternatives to Relieve Depression” (STAR*D) (Contract # N01MH90003 to the University of Texas Southwestern Medical Center). Genotyping of the STAR*D sample was supported by NIMH grant MH072802 to S.P. Hamilton. Analysis of STAR*D clinical data and response trajectories was supported by NIMH grant 1R34MH085933-01 to A.M. Hunter. This manuscript reflects the
Acknowledgments
A portion of this paper was presented in poster format at the 11th Annual Pharmacogenetics in Psychiatry meeting March 31, 2012, New York, NY. The trajectories that form the basis for sustained and unsustained patterns of response in STAR*D were previously reported in Muthén et al. (2011).
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