Brain arousal regulation as response predictor for antidepressant therapy in major depression.
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ABSTRACT:
SUBMITTER: Schmidt FM
PROVIDER: S-EPMC5366924 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The efficacy of antidepressant treatment for depression is controversial due to the only modest superiority demonstrated over placebo. However, neurobiological heterogeneity within depression may limit overall antidepressant efficacy. We sought to identify a neurobiological phenotype responsive to antidepressant treatment by testing pretreatment brain activation during response to, and regulation of, emotional conflict as a moderator of the clinical benefit of the antidepressant sertraline versus placebo. Using neuroimaging data from a large randomized controlled trial, we found widespread moderation of clinical benefits by brain activity during regulation of emotional conflict, in which greater downregulation of conflict-responsive regions predicted better sertraline outcomes. Treatment-predictive machine learning using brain metrics outperformed a model trained on clinical and demographic variables. Our findings demonstrate that antidepressant response is predicted by brain activity underlying a key self-regulatory emotional capacity. Leveraging brain-based measures in psychiatry will forge a path toward better treatment personalization, refined mechanistic insights and improved outcomes.
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Project description:Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n?=?309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.
| S-EPMC7145761 | biostudies-literature
Project description:The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study revealed poorer antidepressant treatment response among black compared with white participants. This racial disparity persisted even after socioeconomic and baseline clinical factors were taken into account. Some studies have suggested genetic contributions to this disparity, but none have attempted to disentangle race and genetic ancestry. Here we used genome-wide single-nucleotide polymorphism (SNP) data to examine independent contributions of race and genetic ancestry to citalopram response. Secondary data analyses included 1877 STAR*D participants who completed an average of 10 weeks of citalopram treatment and provided DNA samples. Participants reported their race as White (n=1464), black (n=299) or other/mixed (n=114). Genetic ancestry was estimated by multidimensional scaling (MDS) analyses of about 500?000 SNPs. Ancestry proportions were estimated by STRUCTURE. Structural equation modeling was used to examine the direct and indirect effects of observed and latent predictors of response, defined as change in the Quick Inventory of Depressive Symptomatology (QIDS) score from baseline to exit. Socioeconomic and baseline clinical factors, race, and anxiety significantly predicted response, as previously reported. However, direct effects of race disappeared in all models that included genetic ancestry. Genetic African ancestry predicted lower treatment response in all models. Although socioeconomic and baseline clinical factors drive racial differences in antidepressant response, genetic ancestry, rather than self-reported race, explains a significant fraction of the residual differences. Larger samples would be needed to identify the specific genetic mechanisms that may be involved, but these findings underscore the importance of including more African-American patients in drug trials.
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Project description:Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.
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Project description:Cyclic nucleotide phosphodiesterases (PDEs) hydrolyze the intracellular second messengers cAMP and cGMP to their corresponding monophosphates. PDEs play an important role in signal transduction by regulating the intracellular concentration of cyclic nucleotides. We have previously shown that the individual haplotype GAACC in the PDE11A gene was associated with major depressive disorder (MDD) based on block-by-block analysis. There are two PDE genes, PDE11A and PDE1A, located in chromosome 2q31-q32. In this study, we have further explored whether the whole region 2q31-q32 contribute to MDD or antidepressant response 278 depressed Mexican-American participants and 321 matched healthy controls. Although there is no significant interaction between the two genes, the remission rate of individual carrying the combination genotype at rs1880916 (AG/AA) and rs1549870 (GG) is significantly increased. We analyzed the global haplotype by examining 16 single-nucleotide polymorphisms (SNPs) in PDE11A and six SNPs in PDE1A. None of the haplotypes consisting of six SNPs in the PDE1A have a significant difference between depressed and control groups. Among haplotypes consisting of 16 SNPs across 440 kb in the PDE11A gene, 18 common haplotypes (with frequency higher than 0.8%) have been found in the studied population. Six haplotypes showed significantly different frequencies between the MDD group and the control group. The phylogenetic network result for the 16 SNPs showed that several historic recombination events have happened in the PDE11A gene. The frequency of one haplotype is significantly lower in the remitter group than in the nonremitter group for the depressed participants treated with either desipramine or fluoxetine. Thus, our data suggest that the PDE11A global haplotype is associated with both MDD and antidepressant drug response.
| S-EPMC2695232 | biostudies-literature
Project description:CONTEXT:Variations in the brain-derived neurotrophic factor gene (BDNF) have been associated with psychiatric disorders. Deep sequencing of the BDNF gene may identify new variations and bring further insight into psychiatric genetics. OBJECTIVE:To better characterize sequence variability in the BDNF gene by resequencing a genomic DNA region of 22 kilobases that contained all BDNF exons and their flanking regions. DESIGN:Case-control study. SETTING:University of California, Los Angeles, and University of Miami. PARTICIPANTS:Two hundred sixty-four controls and 272 Mexican Americans with major depressive disorder (MDD) from Los Angeles who were assessed by the same bilingual clinical research team. MAIN OUTCOME MEASURES:Identification of novel genetic polymorphisms in the BDNF gene and assessment of their frequencies and associations with MDD or antidepressant response. RESULTS:We identified 83 novel single-nucleotide polymorphisms (SNPs): 30 in untranslated regions, 4 in coding sequences, 37 in introns, and 12 in upstream regions; 3 of 4 rare novel coding SNPs were nonsynonymous. Association analyses of patients with MDD and controls showed that 6 SNPs were associated with MDD (rs12273539, rs11030103, rs6265, rs28722151, rs41282918, and rs11030101) and 2 haplotypes in different blocks (one including Val66, another near exon VIIIh) were significantly associated with MDD. One recently reported 5' untranslated region SNP, rs61888800, was associated with antidepressant response after adjusting for age, sex, medication, and baseline score on the 21-item Hamilton Depression Rating Scale. CONCLUSIONS:Our data support the concept that extensive resequencing of key candidate genes can lead to the discovery of substantial numbers of new variants. Further studies using larger independent samples are needed to confirm the association of the rs61888800 SNP with antidepressant response. TRIAL REGISTRATION:clinicaltrials.gov Identifier: NCT00265291.
| S-EPMC4272010 | biostudies-literature
Project description:OBJECTIVE It has been suggested that there is a mechanism by which nonsteroidal anti-inflammatory drugs (NSAIDs) may interfere with antidepressant response, and poorer outcomes among NSAID-treated patients were reported in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. To attempt to confirm this association in an independent population-based treatment cohort and explore potential confounding variables, the authors examined use of NSAIDs and related medications among 1,528 outpatients in a New England health care system. METHOD Treatment outcomes were classified using a validated machine learning tool applied to electronic medical records. Logistic regression was used to examine the association between medication exposure and treatment outcomes, adjusted for potential confounding variables. To further elucidate confounding and treatment specificity of the observed effects, data from the STAR*D study were reanalyzed. RESULTS NSAID exposure was associated with a greater likelihood of depression classified as treatment resistant compared with depression classified as responsive to selective serotonin reuptake inhibitors (odds ratio=1.55, 95% CI=1.21-2.00). This association was apparent in the NSAIDs-only group but not in those using other agents with NSAID-like mechanisms (cyclooxygenase-2 inhibitors and salicylates). Inclusion of age, sex, ethnicity, and measures of comorbidity and health care utilization in regression models indicated confounding; association with outcome was no longer significant in fully adjusted models. Reanalysis of STAR*D results likewise identified an association in NSAIDs but not NSAID-like drugs, with more modest effects persisting after adjustment for potential confounding variables. CONCLUSIONS These results support an association between NSAID use and poorer antidepressant outcomes in major depressive disorder but indicate that some of the observed effect may be a result of confounding.
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Project description:The N-methyl-D-aspartate (NMDA) glutamate receptor antagonist ketamine has demonstrated rapid antidepressant effects in patients with treatment-resistant depression (TRD). Despite the promise of a novel and urgently needed treatment for refractory depression, concerns regarding potential adverse neurocognitive effects of ketamine remain.Although extensive research has been conducted in healthy volunteers, there is a paucity of studies examining the neurocognitive effects of ketamine in depressed patients. Therefore, the aims of the current study were to characterize the relationship between baseline neurocognition and antidepressant response to ketamine, measure the acute impact of ketamine on neurocognition, and investigate the relationship between acute neurocognitive effects of ketamine and antidepressant response.Neurocognitive functioning was assessed in 25 patients with TRD using a comprehensive battery: estimated premorbid intelligence quotient (IQ), current IQ, and tests from the MATRICS Consensus Cognitive Battery (MCCB). A subset of the MCCB was repeated immediately following a 40-min intravenous infusion of ketamine (0.5 mg/kg).Patients who responded to ketamine 24 h following treatment had poorer baseline neurocognitive performance relative to nonresponders and, in particular, slower processing speed (F?=?8.42; df?=?23; p?=?0.008). Ketamine was associated with selective impairments in memory recall, and the degree of cognitive change carried negative prognostic significance (e.g., negative cognitive effects immediately after ketamine predicted lower response rate at 24 h; Fisher's exact test two-sided p?=?0.027).Taken together, our findings suggest a potential baseline neurocognitive predictor of ketamine response and an inverse relationship between the cognitive effects of ketamine and antidepressant efficacy.
| S-EPMC3952038 | biostudies-literature
Project description:Cyclic nucleotide phosphodiesterases (PDEs) constitute a family of enzymes that degrade cAMP and cGMP. Intracellular cyclic nucleotide levels increase in response to extracellular stimulation by hormones, neurotransmitters, or growth factors and are down-regulated through hydrolysis catalyzed by PDEs, which are therefore candidate therapeutic targets. cAMP is a second messenger implicated in learning, memory, and mood, and cGMP modulates nervous system processes that are controlled by the nitric oxide (NO)/cGMP pathway. To investigate an association between genes encoding PDEs and susceptibility to major depressive disorder (MDD), we genotyped SNPs in 21 genes of this superfamily in 284 depressed Mexican Americans who participated in a prospective, double-blind, pharmacogenetic study of antidepressant response, and 331 matched controls. Polymorphisms in PDE9A and PDE11A were found to be associated with the diagnosis of MDD. Our data are also suggestive of the association between SNPs in other PDE genes and MDD. Remission on antidepressants was significantly associated with polymorphisms in PDE1A and PDE11A. Thus, we found significant associations with both the diagnosis of MDD and remission in response to antidepressants with SNPs in the PDE11A gene. We show here that PDE11A haplotype GAACC is significantly associated with MDD. We conclude that PDE11A has a role in the pathophysiology of MDD. This study identifies a potential CNS role for the PDE11 family. The hypothesis that drugs affecting PDE function, particularly cGMP-related PDEs, represent a treatment strategy for major depression should therefore be tested.
| S-EPMC1581426 | biostudies-literature
Project description:BACKGROUND:In recent years, we have accomplished a deeper understanding about the pathophysiology of major depressive disorder (MDD). Nevertheless, this improved comprehension has not translated to improved treatment outcome, as identification of specific biologic markers of disease may still be crucial to facilitate a more rapid, successful treatment. Ongoing research explores the importance of screening biomarkers using neuroimaging, neurophysiology, genomics, proteomics, and metabolomics measures. RESULTS:In the present review, we highlight the biomarkers that are differentially expressed in MDD and treatment response and place a particular emphasis on the most recent progress in advancing technology which will continue the search for blood-based biomarkers. LIMITATIONS:Due to space constraints, we are unable to detail all biomarker platforms, such as neurophysiological and neuroimaging markers, although their contributions are certainly applicable to a biomarker review and valuable to the field. CONCLUSIONS:Although the search for reliable biomarkers of depression and/or treatment outcome is ongoing, the rapidly-expanding field of research along with promising new technologies may provide the foundation for identifying key factors which will ultimately help direct patients toward a quicker and more effective treatment for MDD.
| S-EPMC5815949 | biostudies-literature