A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression

Genetic profile for dopamine signaling predicts brain functional reactivity to repetitive transcranial magnetic stimulation

Research integrating molecular and imaging data provides important insights into how the genetic profile associated with dopamine signaling influences inter-individual differences in brain functions. However, the effects of genetic variations in dopamine signaling on the heterogeneity of brain changes induced by repetitive transcranial magnetic stimulation (rTMS) still remain unclear. The current study examined the composite effects of genetic variations in dopamine-related genes on rTMS-induced brain responses in terms of the functional network connectivity and working memory performance. Healthy individuals (n = 30) participated in a randomized, double-blind, sham-controlled study with a crossover design of five consecutive days where active rTMS or sham stimulation sessions were administered over the left dorsolateral prefrontal cortex (DLPFC) of the brain. Participants were mostly women (n = 29) and genotyped for polymorphisms in the catechol-O-methyltransferase and D2 dopamine receptor genes and categorized according to their genetic composite scores: high vs. low dopamine signaling groups. Pre- and post-intervention data of resting-state functional magnetic resonance imaging and working memory performance were obtained from 27 individuals with active rTMS and 30 with sham stimulation sessions. The mean functional connectivity within the resting-state networks centered on the DLPFC increased in the high dopamine signaling group. Working memory performance also improved with rTMS in the high dopamine signaling group compared to that in the low dopamine signaling group. The present results suggest that genetic predisposition to higher dopamine signaling may be a promising neurobiological predictor for rTMS effects on cognitive enhancement.Trial registration: ClinicalTrials.gov (NCT02932085).

Investigating genetic variants for treatment response to selective serotonin reuptake inhibitors in syndromal factors and side effects among patients with depression in Taiwanese Han population

Major depressive disorder (MDD) is associated with high heterogeneity in clinical presentation. In addition, response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably among patients. Therefore, identifying genetic variants that may contribute to SSRI treatment responses in MDD is essential. In this study, we analyzed the syndromal factor structures of the Hamilton Depression Rating Scale in 479 patients with MDD by using exploratory factor analysis. All patients were followed up biweekly for 8 weeks. Treatment response was defined for all syndromal factors and total scores. In addition, a genome-wide association study was performed to investigate the treatment outcomes at week 4 and repeatedly assess all visits during follow-up by using mixed models adjusted for age, gender, and population substructure. Moreover, the role of genetic variants in suicidal and sexual side effects was explored, and five syndromal factors for depression were derived: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. Subsequently, several known genes were mapped to suggestive signals for treatment outcomes, including single-nucleotide polymorphisms (SNPs) in PRF1, UTP20, MGAM, and ENSG00000286536 for psychomotor-insight and in C4orf51 for anorexia. In total, 33 independent SNPs for treatment responses were tested in a mixed model, 12 of which demonstrated a p value <0.05. The most significant SNP was rs2182717 in the ENSR00000803469 gene located on chromosome 6 for the core syndromal factor (β = -0.638, p = 1.8 × 10^-4) in terms of symptom improvement over time. Patients with a GG or GA genotype with the rs2182717 SNP also exhibited a treatment response (β = 0.089, p = 2.0 × 10^-6) at week 4. Moreover, rs1836075352 was associated with sexual side effects (p = 3.2 × 10^-8). Pathway and network analyses using the identified SNPs revealed potential biological functions involved in treatment response, such as neurodevelopment-related functions and immune processes. In conclusion, we identified loci that may affect the clinical response to treatment with antidepressants in the context of empirically defined depressive syndromal factors and side effects among the Taiwanese Han population, thus providing novel biological targets for further investigation.

Predicting treatment outcome in depression: an introduction into current concepts and challenges

Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.

Precision Medicine in Antidepressants Treatment

Precision medicine uses innovative approaches to improve disease prevention and treatment outcomes by taking into account people's genetic backgrounds, environments, and lifestyles. Treatment of depression is particularly challenging, given that 30-50% of patients do not respond adequately to antidepressants, while those who respond may experience unpleasant adverse drug reactions (ADRs) that decrease their quality of life and compliance. This chapter aims to present the available scientific data that focus on the impact of genetic variants on the efficacy and toxicity of antidepressants. We compiled data from candidate gene and genome-wide association studies that investigated associations between pharmacodynamic and pharmacokinetic genes and response to antidepressants regarding symptom improvement and ADRs. We also summarized the existing pharmacogenetic-based treatment guidelines for antidepressants, used to guide the selection of the right antidepressant and its dose based on the patient's genetic profile, aiming to achieve maximum efficacy and minimum toxicity. Finally, we reviewed the clinical implementation of pharmacogenomics studies focusing on patients on antidepressants. The available data demonstrate that precision medicine can increase the efficacy of antidepressants and reduce the occurrence of ADRs and ultimately improve patients' quality of life.

BDNF as a Mediator of Antidepressant Response: Recent Advances and Lifestyle Interactions

Conventional antidepressants are widely employed in several psychiatric and neurologic disorders, yet the mechanisms underlying their delayed and partial therapeutic effects are only gradually being understood. This narrative review provides an up-to-date overview of the interplay between antidepressant treatment and Brain-Derived Neurotrophic Factor (BDNF) signaling. In addition, the impact of nutritional, environmental and physiological factors on BDNF and the antidepressant response is outlined. This review underlines the necessity to include information on lifestyle choices in testing and developing antidepressant treatments in the future.

Routinely accessible parameters of mineralocorticoid receptor function, depression subtypes and response prediction: a post-hoc analysis from the early medication change trial in major depressive disorder

OBJECTIVES

Previous studies indicated a relationship between aldosterone, the mineralocorticoid receptor (MR), and antidepressant treatment outcome. Physiological indicators of MR function (blood pressure and electrolytes) are easily accessible and may therefore serve as useful predictors. Thus, our aim was to investigate the predictive value of peripheral MR-related markers for antidepressant treatment outcomes.

METHODS

826 MDD patients who had participated in the randomised-controlled Early Medication Change (EMC) trial were analysed. Depression severity and MR-related markers were assessed weekly. In 562 patients, genetic variation of five MR-related genes was determined.

RESULTS

Patients with blood pressure <120mmHg showed higher depression severity (p = 0.005) than patients with blood pressure ≥120mmHg. Patients with a melancholic subtype had significantly lower blood pressures (p = 0.004). Na⁺/K⁺ ratio was positively and K⁺-concentration was negatively correlated to depression severity and to relative changes in HAMD from baseline to day 14, and 56 respectively (p < 0.001). For none of the MR-related genes, genetic variation was associated with treatment outcomes.

CONCLUSIONS

We confirmed early observations of an altered peripheral MR sensitivity, reflected by lower blood pressure, low K⁺ or high Na⁺/K⁺ ratio in patients with more severe depression. These routinely collected biomarkers may potentially be useful for risk stratification in an early stage of treatment. Trial Registration: clinicaltrials.gov Identifier: NCT00974155; https://www.clinicaltrials.gov/ct2/results?term=NCT00974155.

A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.

Association between CYP metabolizer phenotypes and selective serotonin reuptake inhibitors induced weight gain: a retrospective cohort study

BACKGROUND

Prescription medications such as selective serotonin reuptake inhibitors (SSRIs), commonly used to treat depression, are associated with weight gain. The role of pharmacogenomics in predicting SSRI-induced weight gain is unclear.

METHODS

In this retrospective cohort study from participants in the Mayo Clinic RIGHT study who were prescribed citalopram, paroxetine, sertraline, or fluoxetine, our aim was to evaluate the association of metabolizer phenotype and total body weight after 6 months of SSRIs initiation. We evaluated the metabolizer phenotypes (poor/intermediate, normal, and rapid/ultra-rapid) of the cytochromes P450 enzymes genes: CYP2C9, CYP2C19, and CYP2D6 known to influence the metabolism of SSRI medications: CYP2C19 for citalopram, CYP2D6 for paroxetine, CYP2D6 and CYP2C19 for sertraline, and CYP2D6 and CYP2C9 fluoxetine. In addition, we assessed the association of metabolizer phenotype and total body weight change at six months following SSRI prescription using parametric analysis of covariance adjusted for baseline body weight and multivariate regression models.

RESULTS

CYP2C19 poor/intermediate metabolizers prescribed citalopram gained significantly more weight than normal or rapid/ultra-rapid metabolizers at 6 months (TBWG %: 2.6 [95% CI 1.3-4.1] vs. 0.4 [95% CI -0.5 - 1.3] vs. -0.1 [-95% CI -1.5-1.1]; p = 0.001). No significant differences in weight outcomes at six months of treatment with paroxetine, sertraline, or fluoxetine were observed by metabolizer status.

CONCLUSIONS

Weight gain observed with citalopram may be mediated by CYP2C19 metabolizer status.

Saikosaponin D exerts antidepressant effect by regulating Homer1-mGluR5 and mTOR signaling in a rat model of chronic unpredictable mild stress

BACKGROUND

Many studies about depression have focused on the dysfunctional synaptic signaling in the hippocampus that drives the pathophysiology of depression. Radix Bupleuri has been used in China for over 2000 years to regulate liver-qi. Extracted from Radix Bupleuri, Saikosaponin D (SSD) is a pharmacologically active substance that has antidepressant effects. However, its underlying mechanism remains unknown.

MATERIALS AND METHODS

A chronic unpredictable mild stress (CUMS) paradigm was used as a rat model of depression. SD rats were randomly assigned to a normal control (NC) group or one exposed to a CUMS paradigm. Of the latter group, rats were assigned to four subgroups: no treatment (CUMS), fluoxetine-treated (FLU), high-dose and low-dose SSD-treated (SSDH and SSDL). SSD was orally administrated of 1.50 mg/kg and 0.75 mg/kg/days for three weeks in the SSDH and SSDL groups, respectively. Fluoxetine was administrated at a dose of 2.0 mg/kg/days. SSD's antidepressant effects were assessed using the open field test, forced swim test, and sucrose preference test. Glutamate levels were quantified by ELISA. Western blot and immunochemical analyses were conducted to quantify proteins in the Homer protein homolog 1 (Homer1)-metabotropic glutamate receptor 5 (mGluR5) and mammalian target of rapamycin (mTOR) pathways in the hippocampal CA1 region. To measure related gene expression, RT-qPCR was employed.

RESULTS

CUMS-exposed rats treated with SSD exhibited increases in food intake, body weight, and improvements in the time spent in the central are and total distance traveled in the OFT, and less pronounced pleasure-deprivation behaviors. SSD also decreased glutamate levels in CA1. In CA1 region of CUMS-exposed rats, SSD treatment increased mGluR5 expression while decreasing Homer1 expression. SSD also increased expressions of postsynaptic density protein 95 (PSD95) and synapsin I (SYP), and the ratios of p-mTOR/mTOR, p-p70S6k/p70S6k, and p-4E-BP1/4E-BP1 in the CA1 region in CUMS-exposed rats.

CONCLUSIONS

SSD treatment reduces glutamate levels in the CA1 region and promotes the expression of the synaptic proteins PSD-95 and SYP via the regulation of the Homer1-mGluR5 and downstream mTOR signaling pathways. These findings suggest that SSD could act as a natural neuroprotective agent in the prevention of depression.

Predicting the Treatment Outcomes of Antidepressants Using a Deep Neural Network of Deep Learning in Drug-Naïve Major Depressive Patients

Predicting the treatment response to antidepressants by pretreatment features would be useful, as up to 70–90% of patients with major depressive disorder (MDD) do not respond to treatment as expected. Therefore, we aim to establish a deep neural network (DNN) model of deep learning to predict the treatment outcomes of antidepressants in drug-naïve and first-diagnosis MDD patients during severe depressive stage using different domains of signature profiles of clinical features, peripheral biochemistry, psychosocial factors, and genetic polymorphisms. The multilayer feedforward neural network containing two hidden layers was applied to build models with tenfold cross-validation. The areas under the curve (AUC) of the receiver operating characteristic curves were used to evaluate the performance of the models. The results demonstrated that the AUCs of the model ranged between 0.7 and 0.8 using a combination of different domains of categorical variables. Moreover, models using the extracted variables demonstrated better performance, and the best performing model was characterized by an AUC of 0.825, using the levels of cortisol and oxytocin, scales of social support and quality of life, and polymorphisms of the OXTR gene. A complex interactions model developed through DNN could be useful at the clinical level for predicting the individualized outcomes of antidepressants.

The Role of Stress-Induced Changes of Homer1 Expression in Stress Susceptibility

Stress negatively affects processes of synaptic plasticity and is a major risk factor of various psychopathologies such as depression and anxiety. HOMER1 is an important component of the postsynaptic density: constitutively expressed long isoforms HOMER1b and HOMER1c bind to group I metabotropic glutamate receptors MGLUR1 (GRM1) and MGLUR5 and to other effector proteins, thereby forming a postsynaptic protein scaffold. Activation of the GLUR1-HOMER1b,c and/or GLUR5-HOMER1b,c complex regulates activity of the NMDA and AMPA receptors and Ca2+ homeostasis, thus modulating various types of synaptic plasticity. Dominant negative transcript Homer1a is formed as a result of activity-induced alternative termination of transcription. Expression of this truncated isoform in response to neuronal activation impairs interactions of HOMER1b,c with adaptor proteins, triggers ligand-independent signal transduction through MGLUR1 and/or MGLUR5, leads to suppression of the AMPA- and NMDA-mediated signal transmission, and thereby launches remodeling of the postsynaptic protein scaffold and inhibits long-term potentiation. The studies on animal models confirm that the HOMER1a-dependent remodeling most likely plays an important part in the stress susceptibility, whereas HOMER1a itself can be regarded as a neuroprotector. In this review article, we consider the effects of different stressors in various animal models on HOMER1 expression as well as impact of different HOMER1 variants on human behavior as well as structural and functional characteristics of the brain.

Genetic markers for depressive disorders with earlier age at onset

Age at onset has been considered a potential indicator of underlying genetic risk in depression research. However, the variants associated with earlier age at onset of depressive disorder have not been elucidated. To evaluate the genetic architecture of depression onset, whole-exome sequencing of samples from 1000 patients with depressive disorder was performed. Cox proportional hazard models with false discovery rate-adjusted P-values were used to estimate the hazard ratios; carriers and non-carriers of individual coding variants were compared in terms of age at onset of depression with adjustment for sociodemographic and clinical characteristics. The clinical relevance of the candidate variants was also examined. Whole-exome sequencing revealed four variants in the CCL14, FYB, GPRASP1, and CTNND2 genes associated with an increased risk of depressive disorder with earlier age at onset. Although no individual variant was associated with any clinical characteristic except AAO, together they were associated with younger AAO, younger age at visit for treatment, and recurrent and atypical depression. Our data suggest novel candidate genes for depressive disorder with earlier age at onset. These genes could serve as markers allowing early identification of patients at risk of depression, and thus earlier intervention.

Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.

Genetic factors influencing a neurobiological substrate for psychiatric disorders

A retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex-areas that constitute hub nodes of the salience network-represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n = 2271), followed by meta-analysis and replication in a fifth cohort (n = 865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

Whole-genome sequencing reveals KRTAP1-1 as a novel genetic variant associated with antidepressant treatment outcomes

Achieving remission following initial antidepressant therapy in patients with major depressive disorder (MDD) is an important clinical result. Making predictions based on genetic markers holds promise for improving the remission rate. However, genetic variants found in previous genetic studies do not provide robust evidence to aid pharmacogenetic decision-making in clinical settings. Thus, the objective of this study was to perform whole-genome sequencing (WGS) using genomic DNA to identify genetic variants associated with the treatment outcomes of selective serotonin reuptake inhibitors (SSRIs). We performed WGS on 100 patients with MDD who were treated with escitalopram (discovery set: 36 remitted and 64 non-remitted). The findings were applied to an additional 553 patients with MDD who were treated with SSRIs (replication set: 185 remitted and 368 non-remitted). A novel loss-of-function variant (rs3213755) in keratin-associated protein 1-1 (KRTAP1-1) was identified in this study. This rs3213755 variant was significantly associated with remission following antidepressant treatment (p = 0.0184, OR 3.09, 95% confidence interval [CI] 1.22-7.80 in the discovery set; p = 0.00269, OR 1.75, 95% CI 1.22-2.53 in the replication set). Moreover, the expression level of KRTAP1-1 in surgically resected human temporal lobe samples was significantly associated with the rs3213755 genotype. WGS studies on a larger sample size in various ethnic groups are needed to investigate genetic markers useful in the pharmacogenetic prediction of remission following antidepressant treatment.

Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response

Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STAR*D treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

The Impact of Lithium on Brain Function in Bipolar Disorder: An Updated Review of Functional Magnetic Resonance Imaging Studies

Lithium remains a gold standard treatment for bipolar disorder (BD), and functional magnetic resonance imaging (fMRI) studies have contributed to clarifying its impact on neural circuitries in affected individuals. However, the specific neurobiological mechanisms through which lithium exerts its effects on brain function are not fully understood. In this review, we aimed to summarize the results of recent fMRI studies evaluating the impact of lithium on brain functional activity and connectivity in patients diagnosed with BD. We performed a literature search of available sources found in the PubMed database reported in English since 2016, when the last available review on this topic was published. Five fMRI studies in resting-state condition and six studies performed during the execution of emotional tasks met the inclusion criteria. Overall, the available evidence supports normalizing effects of lithium on brain activity and connectivity. Most of these studies reported a normalization in prefrontal regions and interconnected areas involved in emotion regulation and processing, regardless of the task employed. Importantly, lithium treatment showed distinct patterns of activity/connectivity changes compared with other treatments. Finally, lithium modulation of neural circuitries was found to be associated with clinical improvement in BD. These results are consistent with the hypothesis that selective abnormalities in neural circuitries supporting emotion processing and regulation improve during lithium treatment in BD. However, the heterogeneity of the examined studies regarding study design, sample selection, and analysis methods might limit the generalizability of the findings and lead to difficulties in comparing the results. Therefore, in future studies, larger cohorts and homogeneous experimental tasks are needed to further corroborate these findings.

ERICH3: vesicular association and antidepressant treatment response

Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.

Genome-wide association studies of antidepressant class response and treatment-resistant depression

The "antidepressant efficacy" survey (AES) was deployed to > 50,000 23andMe, Inc. research participants to investigate the genetic basis of treatment-resistant depression (TRD) and non-treatment-resistant depression (NTRD). Genome-wide association studies (GWAS) were performed, including TRD vs. NTRD, selective serotonin reuptake inhibitor (SSRI) responders vs. non-responders, serotonin-norepinephrine reuptake inhibitor (SNRI) responders vs. non-responders, and norepinephrine-dopamine reuptake inhibitor responders vs. non-responders. Only the SSRI association reached the genome-wide significance threshold (p < 5 × 10-8): one genomic region in RNF219-AS1 (SNP rs4884091, p = 2.42 × 10-8, OR = 1.21); this association was also observed in the meta-analysis (13,130 responders vs. 6,610 non-responders) of AES and an earlier "antidepressant efficacy and side effects" survey (AESES) cohort. Meta-analysis for SNRI response phenotype derived from AES and AESES (4030 responders vs. 3049 non-responders) identified another genomic region (lead SNP rs4955665, p = 1.62 × 10-9, OR = 1.25) in an intronic region of MECOM passing the genome-wide significance threshold. Meta-analysis for the TRD phenotype (31,068 NTRD vs 5,714 TRD) identified one additional genomic region (lead SNP rs150245813, p = 8.07 × 10-9, OR = 0.80) in 10p11.1 passing the genome-wide significance threshold. A stronger association for rs150245813 was observed in current study (p = 7.35 × 10-7, OR = 0.79) than the previous study (p = 1.40 × 10-3, OR = 0.81), and for rs4955665, a stronger association in previous study (p = 1.21 × 10-6, OR = 1.27) than the current study (p = 2.64 × 10-4, OR = 1.21). In total, three novel loci associated with SSRI or SNRI (responders vs. non-responders), and NTRD vs TRD were identified; gene level association and gene set enrichment analyses implicate enrichment of genes involved in immune process.

Higher venlafaxine serum concentrations necessary for clinical improvement? Time to re-evaluate the therapeutic reference range of venlafaxine

BACKGROUND

The therapeutic reference range for venlafaxine in antidepressant treatment has been defined as 100 to 400 ng/mL. However, in an everyday setting active moiety concentrations above the therapeutic reference range were often reported.

AIM

The aim of this study was to re-evaluate the therapeutic reference range of venlafaxine.

METHODS

In-patients (⩽60 years) with major depressive episodes receiving antidepressant monotherapy with venlafaxine during routine clinical treatment were included in this observational study. Depressive symptom severity was evaluated on a weekly basis using the Hamilton Depression Rating Scale (HAMD-21), and therapeutic drug monitoring analyses were performed. Resting electrocardiograms were analyzed in week 3, week 5 and week 7 of study participation.

RESULTS

Clinical improvement from baseline to week 4 was significantly associated with increasing serum concentrations of the active moiety of venlafaxine (N = 23, Pearson correlation, p = 0.009), but not with the dose of venlafaxine. Patients achieving remission showed significantly higher serum concentrations than patients achieving response/non-response (Kruskal-Wallis test, p = 0.019). Moreover, in patients with serum concentrations above 400 ng/mL time to remission and time to response was significantly shorter than in patients with concentrations below 400 ng/mL (Mantel-COX test, p = 0.001; p = 0.010). QTc time was below the upper limit of a normal QTc time (450 ms) for all patients.

CONCLUSION

The serum concentration of the active moiety and not the dose determined the effect of venlafaxine. Shorter remission times without ECG alterations in patients with serum concentrations above the therapeutic reference range suggest a re-evaluation of the therapeutic reference range for venlafaxine in larger studies.

Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants

Planning subsequent treatment strategies based on early responses rather than waiting for delayed antidepressant action can be helpful. We identified genetic markers for later non-remission in patients exhibiting poor early improvement using whole-exome sequencing data of depressive patients treated in a naturalistic manner. Among 1000 patients, early improvement at 2 weeks (reduction in Hamilton Depression Rating Scale [HAM-D] score ≥ 20%) and remission at 12 weeks (HAM-D score ≤ 7) were evaluated. Gene- and variant-level analyses were conducted to compare patients who did not exhibit early improvement and did not eventually achieve remission (n = 126) with those who exhibited early improvement and achieved remission (n = 385). Genes predicting final non-remission in patients who exhibited poor early improvement (COMT, PRNP, BRPF3, SLC25A40, and CGREF1 in males; PPFIBPI, LZTS3, MEPCE, MAP1A, and PFAS in females; ST3GAL5 in the total population) were determined. Among the significant genes, variants in the PRNP (rs1800014), COMT (rs6267), BRPF3 (rs200565609), and SLC25A40 genes (rs3213633) were identified. However, interpretations should be made cautiously, as complex pharmacotherapy involves various genes and pathways. Early detection of poor early improvement and final non-remission based on genetic risk would be helpful for decision-making in a clinical setting.

A functional variant in the serotonin receptor 7 gene (HTR7), rs7905446, is associated with good response to SSRIs in bipolar and unipolar depression

Predicting antidepressant response has been a clinical challenge for mood disorder. Although several genome-wide association studies have suggested a number of genetic variants to be associated with antidepressant response, the sample sizes are small and the results are difficult to replicate. Previous animal studies have shown that knockout of the serotonin receptor 7 gene (HTR7) resulted in an antidepressant-like phenotype, suggesting it was important to antidepressant action. In this report, in the first stage, we used a cost-effective pooled-sequencing strategy to sequence the entire HTR7 gene and its regulatory regions to investigate the association of common variants in HTR7 and clinical response to four selective serotonin reuptake inhibitors (SSRIs: citalopram, paroxetine, fluoxetine and sertraline) in a retrospective cohort mainly consisting of subjects with bipolar disorder (n = 359). We found 80 single-nucleotide polymorphisms (SNPs) with false discovery rate < 0.05 associated with response to paroxetine. Among the significant SNPs, rs7905446 (T/G), which is located at the promoter region, also showed nominal significance (P < 0.05) in fluoxetine group. GG/TG genotypes for rs7905446 and female gender were associated with better response to two SSRIs (paroxetine and fluoxetine). In the second stage, we replicated this association in two independent prospective samples of SSRI-treated patients with major depressive disorder: the MARS (n = 253, P = 0.0169) and GENDEP studies (n = 432, P = 0.008). The GG/TG genotypes were consistently associated with response in all three samples. Functional study of rs7905446 showed greater activity of the G allele in regulating expression of HTR7. The G allele displayed higher luciferase activity in two neuronal-related cell lines, and estrogen treatment decreased the activity of only the G allele. Electrophoretic mobility shift assay suggested that the G allele interacted with CCAAT/enhancer-binding protein beta transcription factor (TF), while the T allele did not show any interaction with any TFs. Our results provided novel pharmacogenomic evidence to support the role of HTR7 in association with antidepressant response.

Extent of cortisol suppression at baseline predicts improvement in HPA axis function during antidepressant treatment

BACKGROUND

A dysregulation in the hypothalamic-pituitary-adrenal (HPA)-axis function has been repeatedly observed in major depressive disorders (MDD). Normalization of this dysregulation, i.e. of cortisol suppression after glucocorticoid receptor (GR)-stimulation, may be mandatory for clinical remission in some patient subgroups. However, there are no biological measures applied in the clinical setting to identify patient subgroups with HPA axis alterations.

OBJECTIVE

We aimed to define a suppression index of cortisol concentrations before and after GR stimulation with dexamethasone to predict the variability in improvement of HPA axis activity during antidepressant treatment.

METHODS

A modified dexamethasone suppression test (mDST) was performed with blood withdrawal for cortisol and ACTH measurement before and 3 h after 1.5 mg dexamethasone intake at 18:00 in two cohorts of depressed patients treated in a naturalistic setting. The discovery sample consisted of 106 patients, the replication sample of 117 patients. The suppression index was defined as cCORTpreDEXcCORTpostDEX.

RESULTS

The baseline suppression index explained 27.4 % of the variance in changes of HPA axis activity before and after treatment with antidepressants. Age, cCORTpreDEXcACTHpreDEX at baseline and sex explained further variance up to 56.2 % (stepwise linear regression, p = 7.8e^-8). A threshold of the suppression index at baseline was determined by ROC analysis and revealed, that only patients with a maximum index of 2.32 achieved a normalization of the HPA axis activity after antidepressant treatment. In the replication sample, the threshold was 2.86. However, the estimated suppression index was not associated with treatment response.

CONCLUSION

For the first time, by establishing a short-term suppression index of cortisol before and after GR-stimulation a threshold could be identified to predict improvement of HPA axis activity during antidepressant therapy. After replication in further studies this index may help to identify patients who benefit from a specific treatment that targets components of the HPA axis in the future.

Targeted exome sequencing identifies five novel loci at genome-wide significance for modulating antidepressant response in patients with major depressive disorder

In order to determine the role of single nucleotide variants (SNVs) in modulating antidepressant response, we conducted a study, consisting of 929 major depressive disorder (MDD) patients, who were treated with antidepressant drugs (drug-only) or in combination with a repetitive transcranial magnetic stimulation (plus-rTMS), followed by targeted exome sequencing analysis. We found that the "plus-rTMS" patients presented a more effective response to the treatment when compared to the 'drug-only' group. Our data firstly demonstrated that the SNV burden had a significant impact on the antidepressant response presented in the "drug-only" group, but was limited in the "plus-rTMS" group. Further, after controlling for overall SNV burden, seven single nucleotide polymorphisms (SNPs) at five loci, IL1A, GNA15, PPP2CB, PLA2G4C, and GBA, were identified as affecting the antidepressant response at genome-wide significance (P < 5 × 10^-08). Additional multiple variants achieved a level of correction for multiple testing, including GNA11, also shown as a strong signal for MDD risk. Our study showed some promising evidence on genetic variants that could be used as individualized therapeutic guides for MDD patients.

Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n = 4213, cases = 358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n = 3452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population-based observational studies may provide a tractable approach to achieving the necessary statistical power.

Analysis of the Deleterious Single-Nucleotide Polymorphisms Associated With Antidepressant Efficacy in Major Depressive Disorder

Major depressive disorder (MDD) is a serious mental disease with negative effects on both mental and physical health of the patient. Currently, antidepressants are among the major ways to ease or treat MDD. However, the existing antidepressants have limited efficacy in treating MDD, with a large fraction of patients either responding inadequately or differently to antidepressants during the treatment. Pharmacogenetics studies have found that the genetic features of some genes are associated with the antidepressant efficacy. In order to obtain a better understanding on the relationship between the genetic factors and antidepressant treatment response, we compiled a list of 233 single-nucleotide polymorphisms (SNPs) significantly associated with the antidepressant efficacy in treating MDD. Of the 13 non-synonymous SNPs in the list, three (rs1065852, rs3810651, and rs117986340) may influence the structures and function of the corresponding proteins. Besides, the influence of rs1065852 on the structure of CYP2D6 was further investigated via molecular dynamics simulations. Our results showed that compared to the native CYP2D6 the flexibility of the F-G loop was reduced in the mutant. As a portion of the substrate access channel, the lower flexibility of F-G loop may reduce the ability of the substrates to enter the channel, which may be the reason for the lower enzyme activity of mutant. This study may help us to understand the impact of genetic variation on antidepressant efficacy and provide clues for developing new antidepressants.

Increased Homer1-mGluR5 mediates chronic stress-induced depressive-like behaviors and glutamatergic dysregulation via activation of PERK-eIF2α

Glutamatergic dysregulation has served as one common pathophysiology of major depressive disorder (MDD) and a promising target for treatment intervention. Previous studies implicate neurotransmission via metabotropic glutamate receptors (mGluRs) and Homer1 in stress-induced anhedonia, but the mechanisms have not been well elucidated. In the present study, we used two different animal models of depression, chronic social defeat stress (CSDS) and chronic restraint stress (CRS), to investigate the expression of Homer1 isoforms and functional interaction with mGluRs. We found that chronic stress selectively upregulated the expression of Homer1b/c in the hippocampus, whereas the level of Homer1a was unchanged. Additionally, there was a significant negative correlation between the levels of Homer1-mGluR5 signaling and depressive-like behaviors. Both application of paired-pulse low-frequency stimulation (PP-LFS) and the selective group 1 mGluRs agonist (RS)-3,5-dihydroxyphenylglycine (DHPG) significantly enhanced mGluR-dependent long-term depression (LTD) at CA3-CA1 pyramidal cell synapses in slices from susceptible mice, whereas there was no change in NMDAR-dependent LTD induced by LFS. Furthermore, these effects were associated with the internalization of surface AMPARs in hippocampal pyramidal neurons, including reduced the expression of AMPARs and amplitude of AMPARs-mediated mEPSC. Finally, we found that chronic stress activated the KR-like ER kinase-eukaryotic initiation factor 2α (PERK-eIF2α) signaling pathway, subsequently phosphorylated cAMP response element binding protein (CREB) at the S129 and reduced the BDNF level, eventually leading to the impairment of synaptic transmission and depressive-like behaviors. Therefore, our study suggests that PERK-eIF2α acts as a critical target downstream of Homer1-mGluR5 complex to mediate chronic stress-induced depressive-like behaviors, and highlights them as a potential target for the treatment of mood disorder.

Polymorphisms in the BDNF and BDNFOS genes are associated with hypothalamus-pituitary axis regulation in major depression

Major depression is a stress-related disorder with robust clinical and preclinical data implicating that both, dysregulation of the hypothalamus-pituitary-adrenocortical (HPA) axis and of the neurotrophin system of the brain are involved in the pathophysiology. Genetic variations within the brain-derived neurotrophic factor (BDNF) gene region, a major representative of the brain neurotrophins, are suggested to influence response to antidepressant treatment. Specifically, we recently identified two BDNF single nucleotide polymorphisms (SNP), rs2049046 and rs11030094, as associated with antidepressant treatment response in a large pharmacogenetic study of hospitalized patients. We now analyzed these two SNPs in a sub-sample for their association with HPA axis dysregulation using the combined dexamethasone suppression/corticotropin releasing hormone challenge (dex/CRH) test at hospital admission (N = 266) and at discharge (N = 190). Rs11030094, located 3' outside the coding region of BDNF, is also located in an intron of BDNFOS coding for a functional antagonist of BDNF. We further included the non-synonymous Val66Met (rs6265) polymorphism in our analysis, for which - albeit being extensively studied - conflicting results in respect to its role in antidepressant treatment response have been reported. Similar to the previous analysis, rs2049046 and rs11030094 showed a significant effect on antidepressant response. In a gene-dose dependent manner, we found significant lower cortisol responses to the dex/CRH test at discharge in carriers of the respective SNP alleles ('T' of rs2049046 and 'G' of rs11030094) that were associated with antidepressant response (beneficial alleles). These genetic effects on HPA axis regulation were independent of age, sex, medication and depressive symptomatology. Although not reaching statistical significance, the same direction of effect was observed for cortisol at admission, as well as the ACTH response at admission and discharge. An interaction analysis of both SNPs revealed highest cortisol levels in subjects that were non-carriers of both beneficial alleles. The Val66Met (rs6265) was neither associated with antidepressant response nor with HPA axis regulation. Our findings provide further evidence for an interaction of the HPA axis and the neurotrophin system in major depression. This study stresses the importance investigating BDNF variants beyond the extensively studied Val66Met polymorphism. In-depth analyses of both pathophysiologically relevant systems may point to possible new targets for pharmaceutical intervention and precision medicine of major depression in the future.

Novel genome-wide associations for anhedonia, genetic correlation with psychiatric disorders, and polygenic association with brain structure

Anhedonia is a core symptom of several psychiatric disorders but its biological underpinnings are poorly understood. We performed a genome-wide association study of state anhedonia in 375,275 UK Biobank participants and assessed for genetic correlation between anhedonia and neuropsychiatric conditions (major depressive disorder, schizophrenia, bipolar disorder, obsessive compulsive disorder and Parkinson's Disease). We then used a polygenic risk score approach to test for association between genetic loading for anhedonia and both brain structure and brain function. This included: magnetic resonance imaging (MRI) assessments of total grey matter volume, white matter volume, cerebrospinal fluid volume, and 15 cortical/subcortical regions of interest; diffusion tensor imaging (DTI) measures of white matter tract integrity; and functional MRI activity during an emotion processing task. We identified 11 novel loci associated at genome-wide significance with anhedonia, with a SNP heritability estimate (h2SNP) of 5.6%. Strong positive genetic correlations were found between anhedonia and major depressive disorder, schizophrenia and bipolar disorder; but not with obsessive compulsive disorder or Parkinson's Disease. Polygenic risk for anhedonia was associated with poorer brain white matter integrity, smaller total grey matter volume, and smaller volumes of brain regions linked to reward and pleasure processing, including orbito-frontal cortex. In summary, the identification of novel anhedonia-associated loci substantially expands our current understanding of the biological basis of state anhedonia and genetic correlations with several psychiatric disorders confirm the utility of this phenotype as a transdiagnostic marker of vulnerability to mental illness. We also provide the first evidence that genetic risk for state anhedonia influences brain structure, including in regions associated with reward and pleasure processing.

Pharmacogenetics and Depression: A Critical Perspective

Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.

Prediction of Response to Drug Therapy in Psychiatric Disorders

Reprinted with permission from Open Biol. 8: 180031. The Royal Society.

[Pharmacogenetics in psychiatry: state of the art]

In this article, the current literature on pharmacogenetics of antidepressants, antipsychotics and lithium are summarized by the section of Neurobiology and Genetics of the German Society of Psychiatry, Psychotherapy and Neurology (DGPPN). The publications of international expert groups and regulatory authorities are reviewed and discussed. In Germany, a statement on pharmacogenetics was also made by the gene diagnostics committee of the Ministry of Health. The DGPPN supports two recommendations: 1) to perform CYP2D6 genetic testing prior to prescription of tricyclic antidepressants and 2) to determine the HLA-B*1502 genotype in patients of Asian origin before using carbamazepine. The main obstacle for a broad application of pharmacogenetic tests in psychiatry remains the lack of large prospective studies, for both single gene-drug pair and cobinatorial pharmacogenetic tests, to evaluate the benefits of genetic testing. Psychiatrists, geneticists and funding agencies are encouraged to increase their efforts for the future benefit of psychiatric patients.

Prediction of response to drug therapy in psychiatric disorders

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

Markers of mineralocorticoid receptor function: changes over time and relationship to response in patients with major depression

The renin-angiotensin-aldosterone system and its hormone receptors, i.e. the angiotensin and mineralocorticoid receptor (MR), have emerged as important targets for central nervous system disorders and in particular for major depression. We have recently characterized baseline MR function as a predictor for treatment outcome with standard antidepressants. The aims of this study are (i) to characterize how strongly an early biomarker change (after 2 weeks) is related to outcome and (ii) whether these biomarker changes are related to the final outcome, that is, could serve as surrogate markers for response. Twenty-four of 30 patients with unipolar major depression completed the observational trial. MR-related biomarkers were assessed at baseline, 2 weeks, and 6 weeks of standard antidepressant treatment. These biomarkers included slow wave sleep (SWS), salivary cortisol and aldosterone after awakening, heart rate variability measured as respiratory sinus arrhythmia (RSA), systolic blood pressure, salt taste intensity (STI), salt pleasantness (SP), and plasma electrolytes. The Hamilton depression rating scale with 21 items was primarily used to determine depression severity. In the overall sample, STI increased and SP decreased significantly with treatment without a clear relationship with treatment outcome. No other significant changes were observed. Reductions in cortisol and aldosterone after 2 weeks of treatment were significantly related to improvement after 6 weeks (P<0.05). SWS increase after 2 and 6 weeks was by trend (P<0.08) correlated to clinical improvement after 6 weeks. Systolic blood pressure differentiated responders and nonresponders at baseline (P<0.05), but did not change significantly during treatment. We earlier identified a relationship between clinical outcome and baseline values of STI, SP, and RSA only in male patients; therefore, changes in this subgroup were analyzed separately: in male treatment responders, a trend toward an increase in SWS occurred after 2 (P<0.07) and 6 (P<0.07) weeks. Further, a trend toward RSA reduction (P<0.07) was observed. Changes in STI and SP were similar to the total group, but did not reach levels of significance. Early changes in central MR-related biomarkers appear to influence the outcome of standard antidepressant treatment: reduced salivary cortisol, increased SWS, and reduced RSA are linked to a better treatment outcome. These features point to a mechanism involving increased central MR activation in responders to standard antidepressants, but not in nonresponders.

Childhood trauma dependent anxious depression sensitizes HPA axis function

Anxious depression is a common subtype of major depressive disorder (MDD) and is associated with greater severity and poorer outcome. Alterations of the hypothalamic-pituitary-adrenal (HPA) axis, especially of the glucocorticoid receptor (GR) function, are often observed in MDD, but evidence lacks for anxious depression. Childhood adversity is known to influence both the HPA axis and risk of MDD. Therefore, we investigated GR-function in anxious depression dependent on childhood adversity. We enrolled 144 depressed in-patients (49.3% females). Anxious depression was defined using the Hamilton Depression Rating Scale (HAM-D) anxiety/somatization factor score ≥7. Blood draws were performed at 6 pm before and 3 h after 1.5 mg dexamethasone ingestion for measurement of cortisol, ACTH and blood count to assess GR-function and the immune system. In a subgroup of n = 60 FKBP5 mRNA controlled for FKBP5 genotype was measured before and after dexamethasone. Childhood adversity was evaluated using the Childhood Trauma Questionnaire (CTQ). We identified 78 patients (54.2%) with anxious depression who showed a greater severity and worse outcome. These patients were more often exposed to sexual abuse (30% vs. 16%/p = 0.04) and emotional neglect (76% vs. 58%/p = 0.02) than patients with non-anxious depression. Anxious depressed patients showed an enhanced GR-induced FKBP5 mRNA expression (F = 5.128; p = 0.03) and reduced cortisol levels, partly dependent on sexual abuse (F = 7.730; p = 0.006). Additionally, the GR-induced leukocyte response was enhanced in patients with sexual abuse (F = 7.176; p = 0.008). Anxious depression in dependence of childhood trauma is associated with heightened sensitivity of the HPA axis and the immune system which should be considered for treatment algorithms and targets.

Precision pharmacotherapy: psychiatry's future direction in preventing, diagnosing, and treating mental disorders

Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems. However, in the last decades, the understanding of biological mechanisms underlying mental disorders has grown and can be used for the development of precision medicine, that is, to deliver a patient-tailored individual treatment. Precision medicine may incorporate genetic variants contributing to the mental disorder and the response to pharmacotherapies, but also consider gene ¥ environment interactions, blood-based markers, neuropsychological tests, data from electronic health records, early life adversity, stressful life events, and very proximal factors such as lifestyle, nutrition, and sport. Methods such as artificial intelligence and the underlying machine learning and deep learning approaches provide the framework to stratify patients, initiate specific tailored treatments and thus increase response rates, reduce adverse effects and medical errors. In conclusion, precision medicine uses measurable health parameters to identify individuals at risk of a mental disorder, to improve the diagnostic process and to deliver a patient-tailored treatment.

Gene × Environment Interaction in Developmental Disorders: Where Do We Stand and What's Next?

Although the field of psychiatry has witnessed the proliferation of studies on Gene × Environment (G×E) interactions, still limited is the knowledge we possess of G×E interactions regarding developmental disorders. In this perspective paper, we discuss why G×E interaction studies are needed to broaden our knowledge of developmental disorders. We also discuss the different roles of hazardous versus self-generated environmental factors and how these types of factors may differentially engage with an individual's genetic background in predicting a resulting phenotype. Then, we present examplar studies that highlight the role of G×E in predicting atypical developmental trajectories as well as provide insight regarding treatment outcomes. Supported by these examples, we explore the need to move beyond merely examining statistical interactions between genes and the environment, and the motivation to investigate specific genetic susceptibility and environmental contexts that drive developmental disorders. We propose that further parsing of genetic and environmental components is required to fully understand the unique contribution of each factor to the etiology of developmental disorders. Finally, with a greater appreciation of the complexities of G×E interaction, this discussion will converge upon the potential implications for clinical and translational research.

Genetic variants in major depressive disorder: From pathophysiology to therapy

In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.

Role of Ginkgo biloba extract as an adjunctive treatment of elderly patients with depression and on the expression of serum S100B

OBJECTIVE

To explore the effect of ginkgo biloba extract (EGb) as an adjunctive treatment of elderly patients with depression and the effect on the expression of serum S100B.

METHODS

136 elderly patients with depression were divided into EGb +  citalopram (Cit) group and Cit group equally. Efficacy was evaluated by Hamilton Depression Rating Scale (HAMD). Wisconsin Card Classification Test (WCST) was used to evaluate cognitive function. Serum S100B expression was measured with ELISA. The relationship of S100B with HAMD, Hamilton Anxiety Scale (HAMA) score, and WCST results was evaluated subsequently.

RESULTS

The time of onset of efficacy was significantly shorter in EGb + Cit group. There were significant differences in HAMD and HAMA scores after treatment than before treatment between groups (all P < .05). After treatment, total number of WCST test, the number of continuous errors and non-persistent errors in both groups were less than those before treatment. The correct number and classifications number were increased than before treatment. In EGb + Cit group, correct numbers and classifications were increased, and the number of persistent errors was decreased. After treatment, S100B level was decreased, and S100B levels change in EGb + Cit group was greater than in Cit group. Serum S100B level was positively correlated with HAMD and HAMA scores before treatment and positively correlated with persistent errors number in WCST.

CONCLUSION

EGb, as an adjunctive treatment, can effectively improve depressive symptoms and reduce expression of serum S100B, which is a marker of brain injury, suggesting that EGb restores neurologic function during the treatment of depression in elderly patients and S100B participates in the therapeutic mechanism. EGb combined with depressive drugs plays synergistic role, and the time of onset of efficacy is faster than single antidepressants.

Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years

BACKGROUND

Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising.

OBJECTIVE

Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers.

METHODS

We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process.

RESULTS

Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294-1.138), cognition (0.929-1.022), proteins/nucleotides (0.520-1.18), and genetics (0.021-0.515).

CONCLUSION

Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.

Peripheral biomarkers of major depression and antidepressant treatment response: Current knowledge and future outlooks

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.

Association of Novel ALX4 Gene Polymorphisms with Antidepressant Treatment Response: Findings from the CO-MED Trial

Genome-wide association studies (GWAS) were conducted in participants of the CO-MED (Combining Medications to Enhance Depression Outcomes) trial, a randomized, 3-treatment arm clinical trial of major depressive disorder (MDD) designed to identify markers of differential treatment outcome (response and remission). The QIDS-SR (Quick Inventory of Depressive Symptomatology, Self-Reported version) was used to measure response at week 6 (QIDS-SR ≤5) and remission at week 12 (QIDS-SR ≤6 and ≤8 at the last two study visits). Three treatment groups (escitalopram monotherapy, escitalopram + bupropion, and venlafaxine + mirtazapine) were evaluated. GWAS identified a potentially regulatory SNP rs10769025 in the ALX4 gene on chromosome 11 with a strong association (p value = 9.85925E-08) with response to escitalopram monotherapy in Caucasians. Further, haplotype analysis on 7 ALX4 variants showed that a regulatory haplotype CAAACTG was significantly associated (odds ratio = 3.4, p = 2.00E-04) with response to escitalopram monotherapy at week 6. Ingenuity pathway analyses in the present study suggest that ALX4 has an indirect connection with antidepressant gene pathways in MDD, which may account for the genetic association with treatment outcome. Functional genomics studies to investigate the role of ALX4 in antidepressant treatment outcome will be an interesting future direction.

The MAKE Biomarker Discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) Study: Design and Methodology

OBJECTIVE

Depression is associated with a major disease burden, and many individuals suffer from depressive symptoms due to an insufficient response to ostensibly adequate antidepressant treatment. Therefore, it is important to identify reliable treatment response predictors for use in developing personalized treatment strategies.

METHODS

The MAKE Biomarker discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) study was performed to identify predictors of antidepressant response using a 2-year naturalistic prospective design. Participants in the MAKE BETTER study were consecutively recruited from patients who visited the Psychiatry Department of Chonnam National University Hospital, Gwangju, South Korea for treatment of a depressive disorder. Data on demographic and clinical characteristics, genetic markers measured by whole-exome sequencing, and blood markers were obtained. The types and doses of antidepressants were determined based on the clinical judgment of the psychiatrist, and the treatment outcomes (e.g., depressive and other psychiatric symptoms and issues related to safety) were assessed.

RESULTS

We will be able to use the data collected in this study to develop a treatment-response prediction index composed of biomarkers.

CONCLUSION

The MAKE BETTER study will provide an empirical basis for a personalized medicine approach to depression by enabling the prediction of antidepressant treatment response according the characteristics of each patient. It will thereby support evidence-based decision-making that decreases the use of a trial-and-error approach to the treatment of depressive disorders.