Genome-wide transcriptomic and biochemical profiling of major depressive disorder: Unravelling association with susceptibility, severity, and antidepressant response.

Identifying biomarkers for diagnosing Major Depressive Disorder (MDD), assessing its severity, and guiding treatment is crucial. We conducted whole genome transcriptomic study in North Indian population, and analyzed biochemical parameters. Our longitudinal study investigated gene-expression profiles from 72 drug-free MDD patients and 50 healthy controls(HCs) at baseline and 24 patients after 12-weeks of treatment. Gene expression analyses identified differentially expressed genes(DEGs) associated with MDD susceptibility, symptom severity and treatment response, independently validated by qPCR. Hierarchical clustering revealed distinct expression patterns between MDD and HCs, also between mild and severe cases. Enrichment analyses of significant DEGs revealed inflammatory, apoptosis, and immune-related pathways in MDD susceptibility, severity, and treatment response. Simultaneously, we assessed thirty biochemical parameters in the same cohort, showed significant differences between MDD and HCs in 13 parameters with monocytes, eosinophils, creatinine, SGPT, and total protein remained independent predictors of MDD in a multivariate-regression model. Our study supports the role of altered immune/inflammatory signaling in MDD pathophysiology, offering clinically relevant biochemical parameters and insights into transcriptomic gene regulation in MDD pathogenesis and treatment response.

Structure-function coupling and hierarchy-specific antidepressant response in major depressive disorder.

BACKGROUND: Extensive research has explored altered structural and functional networks in major depressive disorder (MDD). However, studies examining the relationships between structure and function yielded heterogeneous and inconclusive results. Recent work has suggested that the structure-function relationship is not uniform throughout the brain but varies across different levels of functional hierarchy. This study aims to investigate changes in structure-function couplings (SFC) and their relevance to antidepressant response in MDD from a functional hierarchical perspective. METHODS: We compared regional SFC between individuals with MDD (n = 258) and healthy controls (HC, n = 99) using resting-state functional magnetic resonance imaging and diffusion tensor imaging. We also compared antidepressant non-responders (n = 55) and responders (n = 68, defined by a reduction in depressive severity of >50%). To evaluate variations in altered and response-associated SFC across the functional hierarchy, we ranked significantly different regions by their principal gradient values and assessed patterns of increase or decrease along the gradient axis. The principal gradient value, calculated from 219 healthy individuals in the Human Connectome Project, represents a region's position along the principal gradient axis. RESULTS: Compared to HC, MDD patients exhibited increased SFC in unimodal regions (lower principal gradient) and decreased SFC in transmodal regions (higher principal gradient) (p < 0.001). Responders primarily had higher SFC in unimodal regions and lower SFC in attentional networks (median principal gradient) (p < 0.001). CONCLUSIONS: Our findings reveal opposing SFC alterations in low-level unimodal and high-level transmodal networks, underscoring spatial variability in MDD pathology. Moreover, hierarchy-specific antidepressant effects provide valuable insights into predicting treatment outcomes.

The association of FKBP5 gene polymorphism with genetic susceptibility to depression and response to antidepressant treatment- a systematic review.

BACKGROUND: Given the inconsistencies in current studies regarding the impact of FKBP5 gene polymorphisms on depression, arising from variations in study methods, subjects, and treatment strategies, this paper provides a comprehensive review of the relationship between FKBP5 gene polymorphisms and genetic susceptibility to depression, as well as their influence on response to antidepressant treatment. METHODS: Electronic databases were searched up to April 11, 2023, for all literature in English and Chinese on depression, FKBP5 gene polymorphisms, and antidepressant treatment. Data extraction and quality assessment were performed for key study characteristics. Qualitative methods were used to synthesize the study results. RESULTS: A total of 21 studies were included, with the majority exhibiting average to moderate quality. Six SNPs (rs3800373, rs1360780, rs9470080, rs4713916, rs9296158, rs9394309) were broadly implicated in susceptibility to depression, while rs1360780 and rs3800373 were linked to antidepressant treatment sensitivity. Additionally, rs1360780 was associated with adverse reactions to antidepressant drug treatment. However, these associations were largely unconfirmed in replication studies. CONCLUSIONS: Depression is recognized as a polygenic genetic disorder, with multiple genes contributing, each exerting relatively small effects. Future studies should explore not only multiple gene interactions but also epigenetic changes. Presently, research on FKBP5 in affective disorders remains notably limited, highlighting the necessity for further investigations in this domain.

The impact of menopause on antidepressant response: an explorative analysis from a real-world study.

This study endeavors to deepen our understanding of the subject matter by exploring, within a real-world sample, the impact of menopausal status on the antidepressant treatments response. The whole sample included a total of 447 patients, 156 male and 291 female, 110 pre-menopause and 181 post-menopause. In our sample post-menopause women showed a worse response to antidepressants than pre-menopause women (p = 0.006), and this difference seems to be unrelated to age or brain aging.

Treatment response of venlafaxine induced alterations of gut microbiota and metabolites in a mouse model of depression.

Antidepressants remain the first-line treatment for depression. However, the factors influencing medication response are still unclear. Accumulating evidence implicates an association between alterations in gut microbiota and antidepressant response. Therefore, the aim of this study is to investigate the role of the gut microbiota-brain axis in the treatment response of venlafaxine. After chronic social defeat stress and venlafaxine treatment, mice were divided into responders and non-responders groups. We compared the composition of gut microbiota using 16 S ribosomal RNA sequencing. Meanwhile, we quantified metabolomic alterations in serum and hippocampus, as well as hippocampal neurotransmitter levels using liquid chromatography-mass spectrometry. We found that the abundances of 29 amplicon sequence variants (ASVs) were significantly altered between the responders and non-responders groups. These ASVs belonged to 8 different families, particularly Muribaculaceae. Additionally, we identified 38 and 39 differential metabolites in serum and hippocampus between the responders and non-responders groups, respectively. Lipid, amino acid, and purine metabolisms were enriched in both serum and hippocampus. In hippocampus, the concentrations of tryptophan, phenylalanine, gamma-aminobutyric acid, glutamic acid, and glutamine were increased, while the level of succinic acid was decreased in the responders group, compared with the non-responders group. Our findings suggest that the gut microbiota may play a role in the antidepressant effect of venlafaxine by modulating metabolic processes in the central and peripheral tissues. This provides a novel microbial and metabolic framework for understanding the impact of the gut microbiota-brain axis on antidepressant response.

Mirtazapine blood levels and antidepressant response.

OBJECTIVE: Therapeutic drug monitoring (TDM) is an important tool for treatment optimisation. Its usefulness has recently been demonstrated for some first-line antidepressants; however, few studies have been reported on the relationship between blood levels of mirtazapine and its antidepressant effects. The aim of this study was to investigate the association between blood concentration of mirtazapine and antidepressant response. METHODS: 59 outpatients treated with mirtazapine for depression were recruited and followed up for three months in a naturalistic setting. Hamilton Depression Rating Scale-21 (HAMD-21) was administered at baseline, month 1, and month 3 to assess antidepressant response. Mirtazapine serum concentration was measured at steady state. Linear regression analysis and nonlinear least-squares regression were used to estimate association between serum concentration of mirtazapine and antidepressant response. RESULTS: Our results showed no overall association between serum concentration of mirtazapine and symptom improvement at month 1 and month 3. A marginally significantly higher serum concentration of mirtazapine was found in responders vs non-responders at month 3. CONCLUSIONS: The study suggests that serum concentration of mirtazapine is not strongly associated with the antidepressant efficacy of mirtazapine. This is probably attributed to its pharmacodynamic profile, even though higher blood levels seem to be marginally more effective.

Mirtazapine plasma levels association with response is mild and do not follow the same curve of other antidepressantsMirtazapine higher plasma levels may show some benefit in a subgroup of patientsTherapeutic drug monitoring may help during antidepressant treatment.

Cognitive, Disability, and Treatment Outcome Implications of Symptom-Based Phenotyping in Late-Life Depression.

OBJECTIVE: Late-life depression is associated with substantial heterogeneity in clinical presentation, disability, and response to antidepressant treatment. We examined whether self-report of severity of common symptoms, including anhedonia, apathy, rumination, worry, insomnia, and fatigue were associated with differences in presentation and response to treatment. We also examined whether these symptoms improved during treatment with escitalopram. DESIGN: Eighty-nine older adults completed baseline assessments, neuropsychological testing and providing self-reported symptom and disability scales. They then entered an 8-week, placebo-controlled randomized trial of escitalopram, and self-report scales were repeated at the trial's end. Raw symptom scale scores were combined into three standardized symptom phenotypes and models examined how symptom phenotype severity was associated with baseline measures and depression improvement over the trial. RESULTS: While rumination/worry appeared independent, severity of apathy/anhedonia and fatigue/insomnia were associated with one another and with greater self-reported disability. Greater fatigue/insomnia was also associated with slower processing speed, while rumination/worry was associated with poorer episodic memory. No symptom phenotype severity score predicted a poorer overall response to escitalopram. In secondary analyses, escitalopram did not improve most phenotypic symptoms more than placebo, aside for greater reductions in worry and total rumination severity. CONCLUSION: Deeper symptom phenotype characterization may highlight differences in the clinical presentation of late-life depression. However, when compared to placebo, escitalopram did not improve many of the symptoms assessed. Further work is needed to determine whether symptom phenotypes inform longer-term course of illness, and which treatments may best benefit specific symptoms.

White matter predicts tDCS antidepressant effects in a sham-controlled clinical trial study.

Transcranial direct current stimulation (tDCS) has been used as treatment for depression, but its effects are heterogeneous. We investigated, in a subsample of the clinical trial Escitalopram versus Electrical Direct Current Therapy for Depression Study (ELECTTDCS), whether white matter areas associated with depression disorder were associated with tDCS response. Baseline diffusion tensor imaging data were analyzed from 49 patients (34 females, mean age 41.9) randomized to escitalopram 20 mg/day, tDCS (2 mA, 30 min, 22 sessions), or placebo. Antidepressant outcomes were assessed by Hamilton Depression Rating Scale-17 (HDRS) after 10-week treatment. We used whole-brain tractography for extracting white matter measures for anterior corpus callosum, and bilaterally for cingulum bundle, striato-frontal, inferior occipito-frontal fasciculus and uncinate. For the rostral body, tDCS group showed higher MD associated with antidepressant effects (estimate = -5.13 ± 1.64, p = 0.002), and tDCS significantly differed from the placebo and the escitalopram group. The left striato-frontal tract showed higher FA associated with antidepressant effects (estimate = -2.14 ± 0.72, p = 0.003), and tDCS differed only from the placebo group. For the right uncinate, the tDCS group lower AD values were associated with higher HDRS decrease (estimate = -1.45 ± 0.67, p = 0.031). Abnormalities in white matter MDD-related areas are associated with tDCS antidepressant effects. Suggested better white matter microstructure of the left prefrontal cortex was associated with tDCS antidepressant effects. Future studies should investigate whether these findings are driven by electric field diffusion and density in these areas.

Pre-treatment allostatic load and metabolic dysregulation predict SSRI response in major depressive disorder: a preliminary report.

BACKGROUND: Major depressive disorder (MDD) is associated with increased allostatic load (AL; a measure of physiological costs of repeated/chronic stress-responding) and metabolic dysregulation (MetD; a measure of metabolic health and precursor to many medical illnesses). Though AL and MetD are associated with poor somatic health outcomes, little is known regarding their relationship with antidepressant-treatment outcomes. METHODS: We determined pre-treatment AL and MetD in 67 healthy controls and 34 unmedicated, medically healthy MDD subjects. Following this, MDD subjects completed 8-weeks of open-label selective serotonin reuptake inhibitor (SSRI) antidepressant treatment and were categorized as 'Responders' (⩾50% improvement in depression severity ratings) or 'Non-responders' (<50% improvement). Logistic and linear regressions were performed to determine if pre-treatment AL or MetD scores predicted SSRI-response. Secondary analyses examined cross-sectional differences between MDD and control groups. RESULTS: Pre-treatment AL and MetD scores significantly predicted continuous antidepressant response (i.e. absolute decreases in depression severity ratings) (p = 0.012 and 0.014, respectively), as well as post-treatment status as a Responder or Non-responder (p = 0.022 and 0.040, respectively), such that higher pre-treatment AL and MetD were associated with poorer SSRI-treatment outcomes. Pre-treatment AL and MetD of Responders were similar to Controls, while those of Non-responders were significantly higher than both Responders (p = 0.025 and 0.033, respectively) and Controls (p = 0.039 and 0.001, respectively). CONCLUSIONS: These preliminary findings suggest that indices of metabolic and hypothalamic-pituitary-adrenal-axis dysregulation are associated with poorer SSRI-treatment response. To our knowledge, this is the first study to demonstrate that these markers of medical disease risk also predict poorer antidepressant outcomes.

Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder.

BACKGROUND: Alterations in gray matter (GM) have been recognized as playing an important role in the neurobiological mechanism underlying major depressive disorder (MDD) and antidepressant responses. However, little is known about white matter (WM) connectivity in MDD, leaving an incomplete understanding of the pathophysiology of the disorder. PURPOSE: To examine the functional connectivity (FC) of WM, GM, and WM-GM in MDD patients and explore the relationship between FC and antidepressant response. STUDY TYPE: Longitudinal study. SUBJECTS: In all, 129 MDD patients and 89 healthy controls (HC). FIELD STRENGTH/SEQUENCE: Whole-brain blood oxygen level-dependent (BOLD) single-shot echo planar imaging was acquired at 3.0T. ASSESSMENT: At baseline, all participants received Hamilton depression rating scale (HAMD) assessment and an fMRI scan. After 2- and 8-week antidepressant treatment, patients completed the HAMD again. The HAMD reductive rate of 2- and 8-weeks were calculated. STATISTICAL TESTS: The comparisons of age, education, HAMD scores, and FC values (false discovery rate correction) between patients and controls were calculated with a two-sample t-test. The chi-square test was employed to compare the differences of gender between these two groups. Correlations between FC and HAMD, as well as the reductive rate of HAMD, were analyzed with Pearson or Spearman correlation. Receiver operator curve analysis was performed to predict the antidepressant response. RESULTS: Compared to HC, MDD patients exhibited widespread decreases in FC of WM-GM. Furthermore, 28 GM regions and 11 WM bundles had lower connectivity in MDD patients. At baseline, four FC of WM-GM showed negative correlations with the HAMD scores. Six FC of WM-GM correlated with the 2-week reductive rate of HAMD. Moreover, FC in GM, WM, and WM-GM also exhibited significantly positive correlations with an 8-week reductive rate of HAMD. DATA CONCLUSION: The FC of WM-GM was decreased in MDD and may play a role in its pathophysiology and antidepressant responses. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 2.

Prefrontal resting-state connectivity and antidepressant response: no associations in the ELECT-TDCS trial.

Functional and structural MRI of prefrontal cortex (PFC) may provide putative biomarkers for predicting the treatment response to transcranial direct current stimulation (tDCS) in depression. A recent MRI study from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Theror Depression Study) showed that depression improvement after tDCS was associated with gray matter volumes of PFC subregions. Based thereon, we investigated whether antidepressant effects of tDCS are similarly associated with baseline resting-state functional connectivity (rsFC). A subgroup of 51 patients underwent baseline rsFC-MRI. All patients of ELECT-TDCS were randomized to three treatment arms for 10 weeks (anodal-left, cathodal-right PFC tDCS plus placebo medication; escitalopram 10 mg/day for 3 weeks and 20 mg/day thereafter plus sham tDCS; and placebo medication plus sham tDCS). RsFC was calculated for various PFC regions and analyzed in relation to the individual antidepressant response. There was no significant association between baseline PFC connectivity of essential structural regions, nor any other PFC regions (after correction for multiple comparisons) and patients' individual antidepressant response. This study did not reveal an association between antidepressants effects of tDCS and baseline rsFC, unlike the gray matter volume findings. Thus, the antidepressant effects of tDCS may be differentially related to structural and functional MRI measurements.

Pharmacogenetic/Pharmacogenomic Tests for Treatment Prediction in Depression.

Genetic factors play a significant but complex role in antidepressant (AD) response and tolerability. During recent years, there is growing enthusiasm in the promise of pharmacogenetic/pharmacogenomic (PGx) tools for optimizing and personalizing treatment outcomes for patients with major depressive disorder (MDD). The influence of pharmacokinetic and pharmacodynamic genes on response and tolerability has been investigated, including those encoding the cytochrome P450 superfamily, P-glycoprotein, monoaminergic transporters and receptors, intracellular signal transduction pathways, and the stress hormone system. Genome-wide association studies are also identifying new genetic variants associated with AD response phenotypes, which, combined with methods such as polygenic risk scores (PRS), is opening up new avenues for novel personalized treatment approaches for MDD. This chapter describes the basic concepts in PGx of AD response, reviews the major pharmacokinetic and pharmacodynamic genes involved in AD outcome, discusses PRS as a promising approach for predicting AD efficacy and tolerability, and addresses key challenges to the development and application of PGx tests.

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and genetic information can be considered as biomarkers. Together with machine-learning methods, prediction models have proved to be valuable for baseline prediction. PURPOSE: To propose an ensemble learning modeling framework that integrates imaging and genetic information for individualized baseline prediction of early-stage treatment response of antidepressants in major depressive disorder (MDD). STUDY TYPE: Prospective. SUBJECTS: In all, 98 inpatients with MDD. FIELD STRENGTH/SEQUENCE: 3.0T MRI and gradient-echo echo-planar imaging sequence. ASSESSMENT: Participants were divided into responders and nonresponders based on reducing rates of HDRS-6 after early-stage treatment of 2 weeks. Fourteen brain regions of interest were selected according to previous studies. An ensemble learning modeling framework was used to integrate imaging data and genetic data. STATISTICAL TESTS: Support vector machine (SVM) with linear kernel was utilized to integrate multimode information and then to construct the prediction model. Leave-one-out cross-validation (LOOCV) was used to evaluate the performance. The position characteristics obtained through SVM-RFE (recursive feature elimination) algorithm and LOOCV was considered to compare each feature's relative importance for the prediction model. RESULTS: Compared with the single-level prediction model, the ensemble learning prediction model showed improvement in prediction performance (accuracy from 0.61 to 0.86 with imaging data and genetic data). Integrated with 14 priori brain regions, the region of interest (ROI) map ensemble learning prediction model can achieve a performance that is analogous with the model with information from whole-brain regions (both with accuracy of 0.81). The integration of genetic features further improved the sensitivity of prediction (sensitivity from 0.78 to 0.87 under the ensemble learning framework). DATA CONCLUSION: Our ensemble learning prediction model demonstrated significant advantages in interpretability and information integration. The findings may provide more assistance for clinical treatment selection in MDD at the individual level. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:161-171.

A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies.

In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.

Childhood maltreatment and clinical severity of treatment-resistant depression in a French cohort of outpatients (FACE-DR): One-year follow-up.

BACKGROUND: Childhood maltreatment is associated with major depressive disorder (MDD). It not only increases the risk of lifetime MDD, but it also aggravates its course. Among depressed patients, 20-30% of them experience treatment-resistance depression (TRD). We aimed to assess the association between childhood maltreatment, severity of depression in a unipolar TRD sample, and patient outcomes after one-year of follow-up. METHODS: Patients were recruited for a prospective cohort from the French network of TRD expert centers. Depressive symptom severity was assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Quick Inventory of Depressive Symptomatology self-report (QIDS-SR). Childhood maltreatment was evaluated with the Childhood Trauma Questionnaire (CTQ). RESULTS: In total, 256 patients filled in the CTQ at baseline between 2012 and 2019. At baseline, the MADRS score was associated with CTQ score (ß = .185; p = .004). QIDS was also associated with CTQ scores (ß = .27; p < .001). Regarding the different subtypes of childhood maltreatment, MADRS was associated with physical (ß = .21; p = .005) and sexual abuse (ß = .22; p = .002), while QIDS with physical abuse (ß = .304; p < .001) and physical neglect (ß = .254; p < .001). However, we did not find any significant association focusing on the other types of traumas. During a 1-year follow-up focusing on remission, CTQ scores (baseline) were less important in remittent patients [n = 38; CTQ score = 39.26 (9.68)] than in nonremittent ones [n = 92; CTQ score = 46.02 (17.53)] (p = .027). There was no significant difference among remitters and nonremitters based on trauma subtypes. At baseline, CTQ scores had a significant influence on remission at 1 year (χ2 (1) = 5.57; p < .05). We lost this influence adding MADRS scores at baseline in the model (p = .063). CONCLUSION: We highlighted a significant association between the severity of depressive disorders and childhood maltreatment in the TRD population. Information about a history of childhood maltreatment helps in identifying individuals who could be less likely to go into remission after treatment.

miRNAs in depression vulnerability and resilience: novel targets for preventive strategies.

The exposure to stressful experiences during the prenatal period and through the first years of life is known to affect the brain developmental trajectories, leading to an enhanced vulnerability for the development of several psychiatric disorders later in life. However, not all the subjects exposed to the same stressful experience develop a pathologic condition, as some of them, activating coping strategies, become more resilient. The disclosure of mechanisms associated with stress vulnerability or resilience may allow the identification of novel biological processes and potential molecules that, if properly targeted, may prevent susceptibility or potentiate resilience. Over the last years, miRNAs have been proposed as one of the epigenetic mechanisms mediating the long-lasting effects of stress. Accordingly, they are associated with the development of stress vulnerability or resilience-related strategies. Moreover, miRNAs have been proposed as possible biomarkers able to identify subjects at high risk to develop depression and to predict the response to pharmacological treatments. In this review, we aimed to provide an overview of findings from studies in rodents and humans focused on the involvement of miRNAs in the mechanisms of stress response with the final goal to identify distinct sets of miRNAs involved in stress vulnerability or resilience. In addition, we reviewed studies on alterations of miRNAs in the context of depression, showing data on the involvement of miRNAs in the pathogenesis of the disease and in the efficacy of pharmacological treatments, discussing the potential utility of miRNAs as peripheral biomarkers able to predict the treatment response.

Patient characteristics driving clinical utility in psychiatric pharmacogenetics: a reanalysis from the AB-GEN multicentric trial.

Clinical utility of commercial multi-gene pharmacogenetic tests in depression is starting to be studied with some promising results on efficacy and tolerability. Among the next steps is the definition of the patient profile that is most likely to benefit from testing. Here we present a reanalysis of data from the AB-GEN randomized clinical trial showing that clinical utility of pharmacogenetic testing can be markedly influenced by patient characteristics such as age, baseline severity and duration of current depressive episode.Trial registration ClinicalTrials.gov NCT02529462.

Investigation of miR-1202, miR-135a, and miR-16 in Major Depressive Disorder and Antidepressant Response.

Background: Major depressive disorder is a debilitating illness, which is most commonly treated with antidepressant drugs. As the majority of patients do not respond on their first trial, there is great interest in identifying biological factors that indicate the most appropriate treatment for each patient. Studies suggest that microRNA represent excellent biomarkers to predict antidepressant response. Methods: We investigated the expression of miR-1202, miR-135a, and miR-16 in peripheral blood from 2 cohorts of depressed patients who received 8 weeks of antidepressant therapy. Expression was quantified at baseline and after treatment, and its relationship to treatment response and depressive symptoms was assessed. Results: In both cohorts, responders displayed lower baseline miR-1202 levels compared with nonresponders, which increased following treatment. Conclusions: Ultimately, our results support the involvement of microRNA in antidepressant response and suggest that quantification of their levels in peripheral samples represents a valid approach to informing treatment decisions.

Efficacy of prospective pharmacogenetic testing in the treatment of major depressive disorder: results of a randomized, double-blind clinical trial.

BACKGROUND: A 12-week, double-blind, parallel, multi-center randomized controlled trial in 316 adult patients with major depressive disorder (MDD) was conducted to evaluate the effectiveness of pharmacogenetic (PGx) testing for drug therapy guidance. METHODS: Patients with a CGI-S ≥ 4 and requiring antidepressant medication de novo or changes in their medication regime were recruited at 18 Spanish public hospitals, genotyped with a commercial PGx panel (Neuropharmagen®), and randomized to PGx-guided treatment (n = 155) or treatment as usual (TAU, control group, n = 161), using a computer-generated random list that locked or unlocked psychiatrist access to the results of the PGx panel depending on group allocation. The primary endpoint was the proportion of patients achieving a sustained response (Patient Global Impression of Improvement, PGI-I ≤ 2) within the 12-week follow-up. Patients and interviewers collecting the PGI-I ratings were blinded to group allocation. Between-group differences were evaluated using χ2-test or t-test, as per data type. RESULTS: Two hundred eighty patients were available for analysis at the end of the 12-week follow-up (PGx n = 136, TAU n = 144). A difference in sustained response within the study period (primary outcome) was not observed (38.5% vs 34.4%, p = 0.4735; OR = 1.19 [95%CI 0.74-1.92]), but the PGx-guided treatment group had a higher responder rate compared to TAU at 12 weeks (47.8% vs 36.1%, p = 0.0476; OR = 1.62 [95%CI 1.00-2.61]), and this difference increased after removing subjects in the PGx-guided group when clinicians explicitly reported not to follow the test recommendations (51.3% vs 36.1%, p = 0.0135; OR = 1.86 [95%CI 1.13-3.05]). Effects were more consistent in patients with 1-3 failed drug trials. In subjects reporting side effects burden at baseline, odds of achieving a better tolerability (Frequency, Intensity and Burden of Side Effects Rating Burden subscore ≤2) were higher in the PGx-guided group than in controls at 6 weeks and maintained at 12 weeks (68.5% vs 51.4%, p = 0.0260; OR = 2.06 [95%CI 1.09-3.89]). CONCLUSIONS: PGx-guided treatment resulted in significant improvement of MDD patient's response at 12 weeks, dependent on the number of previously failed medication trials, but not on sustained response during the study period. Burden of side effects was also significantly reduced. TRIAL REGISTRATION: European Clinical Trials Database 2013-002228-18 , registration date September 16, 2013; ClinicalTrials.gov NCT02529462 , retrospectively registered: August 19, 2015.

Noncoding RNAs in Depression.

Major depressive disorder (MDD) is a common psychiatric disorder affecting millions of people worldwide, yet its etiology remains elusive. The last decades have seen great advances in our understanding of the genome structure and functional organization. Noncoding RNAs (ncRNAs) are RNAs that do not code for proteins but have important regulatory roles. The investigation of ncRNAs as regulators of gene expression has been a topic of growing interest in health research, including in studies investigating etiological and therapeutic factors in major depression. Several different species of ncRNAs have been identified in association to and have shown to be dysregulated in depressed individuals or in animal models of depression. This review will detail the complex relation between ncRNAs and major depression and the studies that propose mechanisms and pathways that specific ncRNAs may be involved in major depression.