Pharmacogenomics with antidepressants in the STAR*D study

Inference of social cognition in schizophrenia patients with neurocognitive domains and neurocognitive tests using automated machine learning

AIM

It has been suggested that single neurocognitive domain or neurocognitive test can be used to determine the overall cognitive function in schizophrenia using machine learning algorithms. It is unknown whether social cognition in schizophrenia patients can be estimated with machine learning based on neurocognitive domains or neurocognitive tests.

METHODS

To predict social cognition in schizophrenia, we applied an automated machine learning (AutoML) framework resulting from the analysis of predictive factors such as six neurocognitive domain scores and nine neurocognitive test scores of 380 schizophrenia patients in the Taiwanese population. Four clinical parameters (i.e., age, gender, subgroup, and education) were also used as predictive factors. We utilized an AutoML framework called Tree-based Pipeline Optimization Tool (TPOT) to generate predictive pipelines automatically.

RESULTS

The analysis revealed that all neurocognitive domains and tests except the reasoning and problem solving domain/test showed significant associations with social cognition. In addition, a TPOT-generated pipeline can best predict social cognition in schizophrenia using seven predictive factors, including five neurocognitive domains (i.e., speed of processing, sustained attention, working memory, verbal learning and memory, and visual learning and memory) and two clinical parameters (i.e., age and gender). This predictive pipeline consists of machine learning algorithms such as function transformers, an approximate feature map, independent component analysis, and linear regression.

CONCLUSION

The study indicates that an AutoML framework such as TPOT may provide a promising way to produce truly effective machine learning pipelines for predicting social cognition in schizophrenia using neurocognitive domains and/or neurocognitive tests.

Treatment-resistant depression in children and adolescents

Major depressive disorder (MDD) in children and adolescents is a significant health problem, causing profound impairments in social, academic, and family functioning and substantial morbidity and mortality. Up to 15% of children and adolescents suffer from MDD, and a proportion, around 30 to 40% of them, failed to respond to initial selective serotonin reuptake inhibitor (SSRI) treatment. The only evidence-based recommendation is medication switching to another SSRI and augmentation with cognitive behavioral therapy. Newly developing treatment, including ketamine, transcranial magnetic stimulation, psychotherapy other than cognitive behavioral therapy, and combined pharmacotherapy with other interventions, requires further longitudinal controlled trials regarding efficacy and safety in this vulnerable population.

A bagging ensemble machine learning framework to predict overall cognitive function of schizophrenia patients with cognitive domains and tests

BACKGROUND

It has been indicated that the interplay between functional outcomes and cognitive functions in schizophrenia is arbitrated by clinical symptoms, where cognitive functions are evaluated by cognitive domains and cognitive tests.

METHODS

To determine which single cognitive domain or test can best predict the overall cognitive function of schizophrenia, we established a bagging ensemble framework resulting from the analysis of factors such as 7 cognitive domain scores and 11 cognitive test scores of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, linear regression, support vector machine, and random forests.

RESULTS

The analysis revealed that among the 7 cognitive domains, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. In addition, among the 11 cognitive tests, the visual learning and memory test can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. Finally, among the 7 cognitive domains and 11 cognitive tests, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework.

CONCLUSION

The study implicates that the bagging ensemble framework may provide an applicable approach to develop tools for forecasting overall cognitive function in schizophrenia using cognitive domains and/or cognitive tests.

Logistic ridge regression to predict bipolar disorder using mRNA expression levels in the N-methyl-D-aspartate receptor genes

BACKGROUND

It is hypothesized that demographic variables and mRNA expression levels in the N-methyl-D-aspartate receptor (NMDAR) genes can be employed as potential biomarkers to predict bipolar disorder using artificial intelligence and machine learning approaches.

METHODS

To determine bipolar status, we established a logistic ridge regression model resulting from the analysis of age, gender, and mRNA expression levels in 7 NMDAR genes in the blood of 51 bipolar patients and 139 unrelated healthy individuals in the Taiwanese population. The NMDAR genes encompasses COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR. We also compared our approach with various state-of-the-art algorithms such as support vector machine and C4.5 decision tree.

RESULTS

The analysis revealed that the mRNA expression levels of COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR were associated with bipolar disorder. Moreover, the logistic ridge regression model (area under the receiver operating characteristic curve = 0.922) performed maximally among predictive models to infer the complicated relationship between bipolar disorder and biomarkers. Additionally, the results for the age- and gender-matched cohort were similar to those of the unmatched cohort.

LIMITATIONS

The cross-sectional study design limited the predictive value.

CONCLUSION

This is the first study demonstrating that the mRNA expression levels in the NMDAR genes may be altered in patients with bipolar disorder, thereby supporting the NMDAR hypothesis of bipolar disorder. The study also indicates that the mRNA expression levels in the NMDAR genes could serve as potential biomarkers to distinguish bipolar patients from healthy controls using artificial intelligence and machine learning approaches.

Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection

Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia’ functional outcomes such as Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we leveraged a bagging ensemble machine learning method with a feature selection algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble machine learning algorithm with other state-of-the-art models such as linear regression, support vector machine, multilayer feedforward neural networks, and random forests. The analysis reported that the bagging ensemble algorithm with feature selection outperformed other predictive algorithms to forecast the QLS functional outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. Furthermore, the bagging ensemble algorithm with feature selection surpassed other predictive algorithms to forecast the GAF functional outcome of schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the bagging ensemble machine learning algorithm with feature selection might present an applicable approach to provide software tools for forecasting the functional outcomes of schizophrenia using molecular biomarkers.

Oligonucleotides as therapeutic tools for brain disorders: Focus on major depressive disorder and Parkinson's disease

Remarkable advances in understanding the role of RNA in health and disease have expanded considerably in the last decade. RNA is becoming an increasingly important target for therapeutic intervention; therefore, it is critical to develop strategies for therapeutic modulation of RNA function. Oligonucleotides, including antisense oligonucleotide (ASO), small interfering RNA (siRNA), microRNA mimic (miRNA), and anti-microRNA (antagomir) are perhaps the most direct therapeutic strategies for addressing RNA. Among other mechanisms, most oligonucleotide designs involve the formation of a hybrid with RNA that promotes its degradation by activation of endogenous enzymes such as RNase-H (e.g., ASO) or the RISC complex (e.g. RNA interference - RNAi for siRNA and miRNA). However, the use of oligonucleotides for the treatment of brain disorders is seriously compromised by two main limitations: i) how to deliver oligonucleotides to the brain compartment, avoiding the action of peripheral RNAses? and once there, ii) how to target specific neuronal populations? We review the main molecular pathways in major depressive disorder (MDD) and Parkinson's disease (PD), and discuss the challenges associated with the development of novel oligonucleotide therapeutics. We pay special attention to the use of conjugated ligand-oligonucleotide approach in which the oligonucleotide sequence is covalently bound to monoamine transporter inhibitors (e.g. sertraline, reboxetine, indatraline). This strategy allows their selective accumulation in the monoamine neurons of mice and monkeys after their intranasal or intracerebroventricular administration, evoking preclinical changes predictive of a clinical therapeutic action after knocking-down disease-related genes. In addition, recent advances in oligonucleotide therapeutic clinical trials are also reviewed.

Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions

It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF) Scale. To determine the outcome assessed by QLS and GAF, we established a bagging ensemble framework with a feature selection algorithm resulting from the analysis of factors such as 3 clinical symptom scales and 11 cognitive function scores of 302 patients with schizophrenia in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, support vector machine, linear regression, and random forests. The analysis revealed that the bagging ensemble model with feature selection performed best among predictive models in predicting the QLS functional outcome by using 20-item Scale for the Assessment of Negative Symptoms (SANS20) and 17-item Hamilton Depression Rating Scale (HAMD17). Moreover, to predict the GAF outcome, the bagging ensemble model with feature selection performed best among predictive models by using SANS20 and the Positive and Negative Syndrome Scale-Positive (PANSS-Positive) subscale. The study indicates that there are synergistic effects between negative (SANS20) and depressive (HAMD17) symptoms as well as between negative and positive (PANSS-Positive) symptoms in influencing functional outcome of schizophrenia using the bagging ensemble framework with feature selection.

Biomarkers of Major Depressive Disorder: Knowing is Half the Battle

Major depressive disorder (MDD) is a heterogeneous disease which is why there are currently no specific methods to accurately test the severity, endophenotype or therapy response. This lack of progress is partly attributed to the com-plexity and variability of depression, in association with analytical variability of clinical literature and the wide number of theoretically complex biomarkers. The literature accessible, indicates that markers involved in inflammatory, neuro-trophic and metabolic processes and components of neurotransmitters and neuroendocrine systems are rather strong indicators to be considered clinically and can be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomics and neuroimaging assessments. Promising biologic systems/markers found were i.e., growth biomarkers, endocrine markers, oxidant stress markers, proteomic and chronic inflammatory markers, are discussed in this review. Several lines of evidence suggest that a portion of MDD is a dopamine agonist-responsive subtype. This review analyzes concise reports on the pathophysiological biomarkers of MDD and therapeutic reactions via peripheral developmental factors, inflammative cytokines, endocrine factors and metabolic markers. Various literatures also support that endocrine and metabolism changes are associated with MDD. Accumulating evidence suggests that at least a portion of MDD patients show characteristics pathological changes regarding different clinical pathological biomarkers. By this review we sum up all the different biomarkers playing an important role in the detection or treatment of the different patients suffering from MDD. The review also gives an overview of different biomarker's playing a potential role in modulating effect of MDD.

Deep learning for the prediction of treatment response in depression

BACKGROUND

Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy on the basis of patient-specific biomarker may be a considerable step towards personalized psychiatry. Machine learning methods are gaining increasing popularity in the medical field. Once trained, the possibility to consider single patients in the analyses instead of whole groups makes them particularly appealing to investigate treatment response. Deep learning, a branch of machine learning, lately gained attention, due to its effectiveness in dealing with large neuroimaging data and to integrate them with clinical, molecular or -omics biomarkers.

METHODS

In this mini-review, we summarize studies that use deep learning methods to predict response to treatment in depression. We performed a bibliographic search on PUBMED, Google Scholar and Web of Science using the terms "psychiatry", "mood disorder", "depression", "treatment", "deep learning", "neural networks". Only studies considering patients' datasets are considered.

RESULTS

Eight studies met the inclusion criteria. Accuracies in prediction of response to therapy were considerably high in all studies, but results may be not easy to interpret.

LIMITATIONS

The major limitation for the current studies is the small sample size, which constitutes an issue for machine learning methods.

CONCLUSIONS

Deep learning shows promising results in terms of prediction of treatment response, often outperforming regression methods and reaching accuracies of around 80%. This could be of great help towards personalized medicine. However, more efforts are needed in terms of increasing datasets size and improved interpretability of results.

Peripheral Markers of Depression

Major Depressive Disorder (MDD) is a leading cause of disability worldwide, creating a high medical and socioeconomic burden. There is a growing interest in the biological underpinnings of depression, which are reflected by altered levels of biological markers. Among others, enhanced inflammation has been reported in MDD, as reflected by increased concentrations of inflammatory markers-C-reactive protein, interleukin-6, tumor necrosis factor-α and soluble interleukin-2 receptor. Oxidative and nitrosative stress also plays a role in the pathophysiology of MDD. Notably, increased levels of lipid peroxidation markers are characteristic of MDD. Dysregulation of the stress axis, along with increased cortisol levels, have also been reported in MDD. Alterations in growth factors, with a significant decrease in brain-derived neurotrophic factor and an increase in fibroblast growth factor-2 and insulin-like growth factor-1 concentrations have also been found in MDD. Finally, kynurenine metabolites, increased glutamate and decreased total cholesterol also hold promise as reliable biomarkers for MDD. Research in the field of MDD biomarkers is hindered by insufficient understanding of MDD etiopathogenesis, substantial heterogeneity of the disorder, common co-morbidities and low specificity of biomarkers. The construction of biomarker panels and their evaluation with use of new technologies may have the potential to overcome the above mentioned obstacles.

An Ensemble Approach to Predict Schizophrenia Using Protein Data in the N-methyl-D-Aspartate Receptor (NMDAR) and Tryptophan Catabolic Pathways

In the wake of recent advances in artificial intelligence research, precision psychiatry using machine learning techniques represents a new paradigm. The D-amino acid oxidase (DAO) protein and its interaction partner, the D-amino acid oxidase activator (DAOA, also known as G72) protein, have been implicated as two key proteins in the N-methyl-D-aspartate receptor (NMDAR) pathway for schizophrenia. Another potential biomarker in regard to the etiology of schizophrenia is melatonin in the tryptophan catabolic pathway. To develop an ensemble boosting framework with random undersampling for determining disease status of schizophrenia, we established a prediction approach resulting from the analysis of genomic and demographic variables such as DAO levels, G72 levels, melatonin levels, age, and gender of 355 schizophrenia patients and 86 unrelated healthy individuals in the Taiwanese population. We compared our ensemble boosting framework with other state-of-the-art algorithms such as support vector machine, multilayer feedforward neural networks, logistic regression, random forests, naive Bayes, and C4.5 decision tree. The analysis revealed that the ensemble boosting model with random undersampling [area under the receiver operating characteristic curve (AUC) = 0.9242 ± 0.0652; sensitivity = 0.8580 ± 0.0770; specificity = 0.8594 ± 0.0760] performed maximally among predictive models to infer the complicated relationship between schizophrenia disease status and biomarkers. In addition, we identified a causal link between DAO and G72 protein levels in influencing schizophrenia disease status. The study indicates that the ensemble boosting framework with random undersampling may provide a suitable method to establish a tool for distinguishing schizophrenia patients from healthy controls using molecules in the NMDAR and tryptophan catabolic pathways.

Epigenetics and Depression: An Update

OBJECTIVE

Depression is associated with various environmental risk factors such as stress, childhood maltreatment experiences, and stressful life events. Current approaches to assess the pathophysiology of depression, such as epigenetics and gene-environment (GxE) interactions, have been widely leveraged to determine plausible markers, genes, and variants for the risk of developing depression.

METHODS

We focus on the most recent developments for genomic research in epigenetics and GxE interactions.

RESULTS

In this review, we first survey a variety of association studies regarding depression with consideration of GxE interactions. We then illustrate evidence of epigenetic mechanisms such as DNA methylation, microRNAs, and histone modifications to influence depression in terms of animal models and human studies. Finally, we highlight their limitations and future directions.

CONCLUSION

In light of emerging technologies in artificial intelligence and machine learning, future research in epigenetics and GxE interactions promises to achieve novel innovations that may lead to disease prevention and future potential therapeutic treatments for depression.

CYP2C19 genotype, physician prescribing pattern, and risk for long QT on serotonin selective reuptake inhibitors

Aim: To examine the impact of CYP2C19 genotype on selective serotonin reuptake inhibitor (SSRI) prescribing patterns. Patients & methods: Observational cohort containing 507 unique individuals receiving an SSRI prescription with CYP2C19 genotype already in their electronic medical record. Genotype was distributed as follows: n = 360 (71%) had no loss of function alleles, 136 (26.8%) had one loss of function allele and 11 (2.2%) had two loss of function alleles. Results & conclusion: For poor metabolizers exposed to sertraline, citalopram or escitalopram, providers changed prescribing patterns in response to alerts in the electronic medical record by either changing the drug, changing the dose or monitoring serial EKGs longitudinally. For intermediate metabolizers exposed to sertraline, citalopram or escitalopram, no alert was needed (mean QTc = 440.338 ms [SD = 31.1273] for CYP2C19*1/*1, mean QTc = 440.371 ms [SD = 29.2706] for CYP2C19*1/*2; p = 0.995).

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1-3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

Machine learning and systems genomics approaches for multi-omics data

In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.

Seeking an objective diagnosis of depression

Major depressive disorder (MDD: unipolar depression) is widely distributed in the USA and world-wide populations and it is one of the leading causes of disability in both adolescents and adults. Traditional diagnostic approaches for MDD are based on patient interviews, which provide a subjective assessment of clinical symptoms which are frequently shared with other maladies. Reliance upon clinical assessments and patient interviews for diagnosing MDD is frequently associated with misdiagnosis and suboptimal treatment outcomes. As such, there is increasing interest in the identification of objective methods for the diagnosis of depression. Newer technologies from genomics, transcriptomics, proteomics, metabolomics and imaging are technically sophisticated and objective but their application to diagnostic tests in psychiatry is still emerging. This brief overview evaluates the technical basis for these technologies and discusses how the extension of their clinical performance can lead to an objective diagnosis of MDD.

Genetic variation in the tryptophan hydroxylase 2 gene moderates depressive symptom trajectories and remission over 8 weeks of escitalopram treatment

The serotonin system plays an important role in the pathogenesis of major depressive disorder (MDD) and genetic variations in serotonin-related genes affect the efficacy of antidepressants. The aim of this study was to investigate the relationship between genotypic variation in six candidate serotonergic genes (ADCY9, HTR1B, GNB3, HTR2A, TPH2, SLC6A4) and depressive and anxiety symptom severity trajectories as well as remission following escitalopram treatment. A total of 166 Chinese patients with MDD were treated with escitalopram (open-label) for 8 weeks. TPH2 rs4570625 GG carriers were more likely to achieve depressive and anxiety symptom remission compared with T-allele carriers. At the trend level (P(corrected)=0.05), depressive symptom severity trajectories were moderated by TPH2 rs4570625. Patients with the GT or the GG genotype showed more favorable depressive symptom severity trajectories compared with TT genotype carriers. Polymorphisms in ADCY9, HTR1B, and HTR2A were nominally associated with symptom remission, but did not withstand correction for multiple comparisons. The HTTLPR polymorphism was not included in our final analysis because of a high percentage of missing data. These results suggested that genotypic variation in TPH2 may moderate the therapeutic response to esciatlopram among Chinese patients with MDD.

Genome-wide association studies in pharmacogenomics of antidepressants

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Doctors must prescribe antidepressants based on educated guesses due to the fact that it is unmanageable to predict the effectiveness of any particular antidepressant in an individual patient. With the recent advent of scientific research, the genome-wide association study (GWAS) is extensively employed to analyze hundreds of thousands of single nucleotide polymorphisms by high-throughput genotyping technologies. In addition to the candidate-gene approach, the GWAS approach has recently been utilized to investigate the determinants of antidepressant response to therapy. In this study, we reviewed GWAS studies, their limitations and future directions with respect to the pharmacogenomics of antidepressants in MDD.

Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy

Major depressive disorder (MDD) is a serious health concern worldwide. Currently there are no predictive tests for the effectiveness of any particular antidepressant in an individual patient. Thus, doctors must prescribe antidepressants based on educated guesses. With the recent advent of scientific research, genome-wide gene expression microarray studies are widely utilized to analyze hundreds of thousands of biomarkers by high-throughput technologies. In addition to the candidate-gene approach, the genome-wide approach has recently been employed to investigate the determinants of MDD as well as antidepressant response to therapy. In this review, we mainly focused on gene expression studies with genome-wide approaches using RNA derived from peripheral blood cells. Furthermore, we reviewed their limitations and future directions with respect to the genome-wide gene expression profiling in MDD pathogenesis as well as in antidepressant therapy.

Pharmacogenomics in psychiatry: the relevance of receptor and transporter polymorphisms

The treatment of severe mental illness, and of psychiatric disorders in general, is limited in its efficacy and tolerability. There appear to be substantial interindividual differences in response to psychiatric drug treatments that are generally far greater than the differences between individual drugs; likewise, the occurrence of adverse effects also varies profoundly between individuals. These differences are thought to reflect, at least in part, genetic variability. The action of psychiatric drugs primarily involves effects on synaptic neurotransmission; the genes for neurotransmitter receptors and transporters have provided strong candidates in pharmacogenetic research in psychiatry. This paper reviews some aspects of the pharmacogenetics of neurotransmitter receptors and transporters in the treatment of psychiatric disorders. A focus on serotonin, catecholamines and amino acid transmitter systems reflects the direction of research efforts, while relevant results from some genome-wide association studies are also presented. There are many inconsistencies, particularly between candidate gene and genome-wide association studies. However, some consistency is seen in candidate gene studies supporting established pharmacological mechanisms of antipsychotic and antidepressant response with associations of functional genetic polymorphisms in, respectively, the dopamine D2 receptor and serotonin transporter and receptors. More recently identified effects of genes related to amino acid neurotransmission on the outcome of treatment of schizophrenia, bipolar illness or depression reflect the growing understanding of the roles of glutamate and γ-aminobutyric acid dysfunction in severe mental illness. A complete understanding of psychiatric pharmacogenomics will also need to take into account epigenetic factors, such as DNA methylation, that influence individual responses to drugs.

Animal models for depression associated with HIV-1 infection

Antiretroviral therapy has greatly extended the lifespan of people living with human immunodeficiency virus (PLHIV). As a result, the long-term effects of HIV infection, in particular those originating in the central nervous system (CNS), such as HIV associated depression, have gained importance. Animal models for HIV infection have proved very useful for understanding the disease and developing treatment strategies. However, HIV associated depression remains poorly understood and so far there is neither a fully satisfactory animal model, nor a pathophysiologically guided treatment for this condition. Here we review the neuroimmunological, neuroendocrine, neurotoxic and neurodegenerative basis for HIV depression and discuss strategies for employing HIV animal models, in particular humanized mice which are susceptible to HIV infection, for the study of HIV depression.

Influence of genetic polymorphisms in the glutamatergic and GABAergic systems and their interactions with environmental stressors on antidepressant response

Aim: To investigate the role of genetic polymorphisms in glutamatergic and GABAergic genes and their interactions with environmental stressors in antidepressant response. Methods: A set of 114 SNPs of 34 glutamatergic and GABAergic genes, mainly in promoter and coding regions, were genotyped in 281 Chinese Han major depressive disorder patients. The 17-item Hamilton Depression Rating Scale was used to evaluate the symptom severity and therapeutic efficacy. Childhood Trauma Questionnaire and Life Events Scale were used for assessing early-onset and recent stressful life events, respectively. Results: The single SNPs rs1954787 (GRIK4), rs1992647 (GABRA6), rs10036156 (GABRP) and rs3810651 (GABRQ) were significantly associated with antidepressant response, as were haplotypes in GRIK4 and GABRP genes. A genetic interaction between rs11542313 (GAD1), rs13303344 (GABRD) and rs2256882 (GABRE) was identified as impacting therapeutic response. SNPs in GRIA3 demonstrated interactions with early-onset adverse events and recent negative life stress that influence treatment outcome. Conclusion: Genetic polymorphisms in the glutamatergic and GABAergic systems and certain genetic interactions, as well as gene–environment interactions, are associated with antidepressant response.

Original submitted 9 July 2012; Revision submitted 1 January 2013

Functional biomarkers of depression: diagnosis, treatment, and pathophysiology

Major depressive disorder (MDD) is a heterogeneous illness for which there are currently no effective methods to objectively assess severity, endophenotypes, or response to treatment. Increasing evidence suggests that circulating levels of peripheral/serum growth factors and cytokines are altered in patients with MDD, and that antidepressant treatments reverse or normalize these effects. Furthermore, there is a large body of literature demonstrating that MDD is associated with changes in endocrine and metabolic factors. Here we provide a brief overview of the evidence that peripheral growth factors, pro-inflammatory cytokines, endocrine factors, and metabolic markers contribute to the pathophysiology of MDD and antidepressant response. Recent preclinical studies demonstrating that peripheral growth factors and cytokines influence brain function and behavior are also discussed along with their implications for diagnosing and treating patients with MDD. Together, these studies highlight the need to develop a biomarker panel for depression that aims to profile diverse peripheral factors that together provide a biological signature of MDD subtypes as well as treatment response.

Treatment-resistant depression in adolescents: review and updates on clinical management

Treatment-resistant depression (TRD) in adolescents is prevalent and impairing. We here review the definition, prevalence, clinical significance, risk factors, and management of TRD in adolescents. Risk factors associated with TRD include characteristics of depression (severity, level of hopelessness, and suicidal ideation), psychiatric and medical comorbidities, environmental factors (family conflict, maternal depression, and history of abuse), and pharmacokinetics and other biomarkers. Management options include review of the adequacy of the initial treatment, re-assessment for the above-noted factors that might predispose to treatment resistance, switching antidepressants, and augmentation with medication or psychotherapy. Other modalities, such as electroconvulsive therapy, vagal nerve stimulation, and repetitive transcranial magnetic stimulation, are also reviewed.

Sexual dysfunction during treatment with serotonergic and noradrenergic antidepressants: clinical description and the role of the 5-HTTLPR

OBJECTIVES

Sexual dysfunction (SD) is a frequently reported side-effect of antidepressant treatment, particularly of selective serotonin reuptake inhibitors (SSRIs). In the multicentre clinical and pharmacogenetic GENDEP study (Genome-based Therapeutic Drugs for Depression), the effect of the serotonin transporter gene promoter polymorphism 5-HTTLPR on sexual function was investigated during treatment with escitalopram (SSRI) and nortriptyline (tricyclic antidepressant).

METHODS

A total of 494 subjects with an episode of DSM-IV major depression were randomly assigned to treatment with escitalopram or nortriptyline. Over 12 weeks, depressive symptoms and SD were measured weekly with the Montgomery-Asberg Depression Rating Scale, the Antidepressant Side-Effect Checklist, the UKU Side Effect Rating Scale, and the Sexual Functioning Questionnaire.

RESULTS

The incidence of reported SD after 12 weeks of treatment was relatively low, and did not differ significantly between antidepressants (14.9% escitalopram, 19.7% nortriptyline). There was no significant interaction between the 5-HTTLPR and antidepressant on SD. Improvement in depressive symptoms and younger age were both associated with lower SD. The effect of age on SD may have been moderated by the 5-HTTLPR.

CONCLUSIONS

In GENDEP, rates of reported SD during treatment were lower than those described in previous reports. There was no apparent effect of the 5-HTTLPR on the observed decline in SD.

Pharmacogenomic testing and outcome among depressed patients in a tertiary care outpatient psychiatric consultation practice

The authors tested the hypothesis that pharmacogenomic genotype knowledge is associated with better clinical and cost outcomes in depressed patients, after controlling for other factors that might differentiate tested and non-tested patients. Medical records of 251 patients, seen in the Mayo Clinic Rochester outpatient psychiatric practice, who had patient health questionnaire-9 (PHQ-9) scores before and after consultation, were reviewed. Comparisons of differences in pre-consultation and post-consultation depression scores and slopes between tested and non-tested patients and between genotype categories of tested patients, were evaluated, along with healthcare cost and utilization comparisons between tested and non-tested patients, using Kruskal-Wallis tests, Wilcoxon rank-sum tests and group mean comparisons, controlling for significant univariate demographic and clinical differences. Tested patients had significantly higher depression diagnosis frequency, baseline PHQ-9 scores, family history of depression, psychiatric hospitalization history, and higher numbers of antidepressant, mood stabilizer and antipsychotic medication trials. After controlling for these differences, there were no differences between tested and non-tested patients in post-baseline depression scores or slopes for CYP genotype categories. For patients with 5-HTTLPR testing, there was significantly more depression score improvement for patients with the long/long genotype at time 4 (N=55, χ(2)-value=8.0492, P=0.018) and at time 5 (N=44, χ(2)-value=6.1492, P=0.046). For a subgroup (n=46) with ≥two pre- and ≥two post-baseline PHQ-9 scores, the mean difference between pre-baseline and post-baseline PHQ-9 score slopes for tested patients was -0.08 (median -0.01; range -1.20 to 0.15) compared with 0.13 (median 0.02; range -0.18 to 2.16) for non-tested patients (P=0.03). Among genotype categories, mean differences between pre-consultation and post-consultation slopes were significantly better for poor CYP2D6 metabolizers than intermediate or extensive metabolizers (P=0.04); there was a trend for slope differences to be better for 5-HTTLPR long/long genotype patients (P=0.06). Subsets of local tested and consultant-adjusted non-tested controls (n=19), who had 8 years of longitudinal care within the health system, had similar overall mean healthcare costs before and after testing; however, tested patients on average had significantly fewer time-adjusted post-baseline psychiatric admissions (0.8 vs 3.8, P=0.04) and fewer time-adjusted psychiatric consultations and comprehensive mental health-specialty evaluations (4.2 vs 9.9, P=0.03). Prospective study is indicated as to whether and how pharmacogenomic testing in a psychiatric consultation practice may improve clinical and cost outcomes.

TPH1 is associated with major depressive disorder but not with SSRI/SNRI response in Taiwanese patients

RATIONALE

Tryptophan hydroxylase 1 (TPH1), which encodes the rate-limiting enzyme tryptophan hydroxylase in the biosynthesis of serotonin, is a candidate gene in the development and treatment response of major depressive disorder (MDD); however, its actual role is uncertain.

OBJECTIVES

We aimed to compare the allele frequencies of TPH1 in MDD patients and healthy controls in Taiwan, and also to investigate the association between TPH1 A218C and treatment response to either fluoxetine or venlafaxine in a Taiwanese population with MDD.

METHODS

One hundred five healthy controls and 115 outpatients diagnosed with MDD were recruited and genotyped for the TPH1 218A/C (rs1800532) polymorphism. Patients were randomized into either the fluoxetine or venlafaxine treatment group. The 21-item Hamilton rating scale for depression (HAM-D) was administered to evaluate depressive symptoms at baseline and bi-weekly over 6 weeks of treatment.

RESULTS

The TPH1 218A/C allele frequencies differed significantly between healthy controls and MDD patients in Taiwan, with a higher prevalence of the A allele in the patient group (p = 0.025). The odds ratio of the A allele to the C allele was 0.507 for the subjects with MDD. There was no significant correlation between the percentage change in HAM-D score and either TPH1 218A/C genotype or TPH1 allele frequencies.

CONCLUSIONS

This study indicated that the TPH1 218A/C genotype and allele frequencies differed between the Taiwanese healthy controls and MDD patients but could not be used to predict treatment outcome in Taiwanese MDD patients. Further research with larger sample sizes is needed to confirm the role of TPH1 218A/C.

The use of referenced-EEG (rEEG) in assisting medication selection for the treatment of depression

OBJECTIVE

To evaluate the efficacy of rEEG(®)-guided pharmacotherapy for the treatment of depression in those circumstances where rEEG and STAR*D provided different recommendations.

MATERIALS AND METHODS

This was a randomized, single-blind, parallel group, 12 center, US study of rEEG-guided pharmacotherapy vs. the most effective treatment regimens reported in the NIH sponsored STAR*D study. Relatively treatment-resistant subjects ≥18 years who failed one or more antidepressants were required to have a QIDS-16-SR score ≥13 and a MADRS score ≥26 at baseline. All subjects underwent a washout of all current medications (with some protocol-specified exceptions) for at least five half-lives before receiving a QEEG and rEEG report. Subjects randomized to rEEG were assigned a regimen based on the rEEG report. Control subjects who had failed only SSRI's in their current episode were randomized to receive venlafaxine XR. Control subjects who had failed antidepressants from ≥2 classes of antidepressants were randomized to receive a regimen from Steps 2-4 of the STAR*D study. Treatment lasted 12 weeks. The primary outcome measures were change from baseline for self-rated QIDS-SR16 and Q-LES-Q-SF.

RESULTS

A total of 114 subjects were randomized and 89 subjects were evaluable. rEEG-guided pharmacotherapy exhibited significantly greater improvement for both primary endpoints, QIDS-SR16 (-6.8 vs. -4.5, p<0.0002) and Q-LES-Q-SF (18.0 vs. 8.9, p<0.0002) compared to control, respectively, as well as statistical superiority in 9 out of 12 secondary endpoints.

CONCLUSIONS

These results warrant additional studies to determine the role of rEEG-guided psychopharmacology in the treatment of depression. If these results were confirmed, rEEG-guided pharmacotherapy would represent an easy, relatively inexpensive, predictive, objective office procedure that builds upon clinical judgment to guide antidepressant medication choice.

Mania associated with antidepressant treatment: comprehensive meta-analytic review

OBJECTIVE

To review available data pertaining to risk of mania-hypomania among bipolar (BPD) and major depressive disorder (MDD) patients with vs. without exposure to antidepressant drugs (ADs) and consider effects of mood stabilizers.

METHOD

Computerized searching yielded 73 reports (109 trials, 114 521 adult patients); 35 were suitable for random effects meta-analysis, and multivariate-regression modeling included all available trials to test for effects of trial design, AD type, and mood-stabilizer use.

RESULTS

The overall risk of mania with/without ADs averaged 12.5%/7.5%. The AD-associated mania was more frequent in BPD than MDD patients, but increased more in MDD cases. Tricyclic antidepressants were riskier than serotonin-reuptake inhibitors (SRIs); data for other types of ADs were inconclusive. Mood stabilizers had minor effects probably confounded by their preferential use in mania-prone patients.

CONCLUSION

Use of ADs in adults with BPD or MDD was highly prevalent and moderately increased the risk of mania overall, with little protection by mood stabilizers.

Association of mu-opioid receptor variants and response to citalopram treatment in major depressive disorder

OBJECTIVE

Because previous preclinical and clinical studies have implicated the endogenous opioid system in major depression and in the neurochemical action of antidepressants, the authors examined how DNA variation in the mu-opioid receptor gene may influence population variation in response to citalopram treatment.

METHOD

A total of 1,953 individuals from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study were treated with citalopram and genotyped for 53 single nucleotide polymorphisms (SNPs) in a 100-kb region of the OPRM1 gene. The sample consisted of Non-Hispanic Caucasians, Hispanic Caucasians, and African Americans. Population stratification was corrected using 119 ancestry informative markers and principal components analysis. Markers were tested for association with phenotypes for general and specific citalopram response as well as remission.

RESULTS

Association between one SNP and specific citalopram response was observed. After Bonferroni correction, the strongest finding was the association between the rs540825 SNP and specific response. The rs540825 polymorphism is a nonsynonymous SNP in the final exon of the mu-opioid receptor-1X isoform of the OPRM1 gene, resulting in a histidine to glutamine change in the intracellular domain of the receptor. When Hispanic and Non-Hispanic Caucasians were analyzed separately, similar results in the population-corrected analyses were detected.

CONCLUSIONS

These results suggest that rates of response to antidepressants and consequent remission from major depressive disorder are influenced by variation in the mu-opioid receptor gene as a result of either an effect on placebo response or true pharmacologic response.

MRP1 polymorphisms associated with citalopram response in patients with major depression

Multidrug resistance protein 1 (MRP1, ABCC1) transports antidepressive agents in the endothelial cells of the blood-brain barrier. Therefore, polymorphisms in the MRP1 gene may affect the treatment response of antidepressants. This study was aimed to identify the association between genetic variations in MRP1/ABCC1 and the therapeutic response to the antidepressant citalopram. One hundred and twenty-three patients who had been treated with citalopram monotherapy to control their major depressive disorder were recruited, and genotype data from 64 patients who had completed their 8-week follow-up were evaluated together with those from 100 controls. Nine MRP1 single nucleotide polymorphisms (SNPs) showing more than 5% allele frequency in the Korean population were analyzed. The c.4002G>A, a synonymous SNP in exon 28, showed a strong association with the remission state at 8 weeks (P = 0.005, odds ratio [OR], 4.7, 95% confidence interval [CI], 1.5 approximately 14.7). The c.4002G>A forms a linkage disequilibrium block with 3 other SNPs including c.5462T>A in the 3' untranslated region. Accordingly, the haplotype showed a significant association with the remission state (P = 0.014). Subsequent molecular studies also supported the association between these MRP1 polymorphisms and the citalopram response. Thus, kinetic studies using MRP1-enriched membrane vesicles revealed that citalopram is a substrate of MRP1 (Km = 1.99 microM, Vmax = 137 pmol/min per milligram protein). In addition, individuals with c.4002G>A or c.5462T>A polymorphisms showed higher MRP1 mRNA levels in peripheral blood cells. These results suggest that MRP1 polymorphisms may be a predictive marker of citalopram treatment in major depression.

Interaction of serotonin-related genes affects short-term antidepressant response in major depressive disorder

BACKGROUND

Four serotonin-related genes including guanine nucleotide binding protein beta polypeptide 3 (GNB3), 5-hydroxytryptamine receptor 1A (HTR1A; serotonin receptor 1A), 5-hydroxytryptamine receptor 2A (HTR2A; serotonin receptor 2A), and solute carrier family 6 member 4 (SLC6A4; serotonin neurotransmitter transporter) have been suggested to be candidate genes for influencing antidepressant treatment outcome. The aim of this study was to explore whether interaction among these genes could contribute to the pharmacogenomics of short-term antidepressant response in a Taiwanese population with major depressive disorder (MDD).

METHODS

Included in this study were 101 MDD patients who were treated with antidepressants, 35 of whom were rapid responders and 66 non-responders after 2weeks of treatment. We genotyped four single nucleotide polymorphisms (SNPs), including GNB3 rs5443 (C825T), HTR1A rs6295 (C-1019G), HTR2A rs6311 (T102C), and SLC6A4 rs25533, and employed the generalized multifactor dimensionality reduction (GMDR) method to investigate gene-gene interactions.

RESULTS

Single-locus analyses showed the GNB3 rs5443 polymorphism to be associated with short-term antidepressant treatment outcome (P-value=0.029). We did not correct for multiple testing in these multiple exploratory analyses. Finally, the GMDR approach identified a significant gene-gene interaction (P-value=0.025) involving GNB3 and HTR2A, as well as a significant 3-locus model (P-value=0.015) among GNB3, HTR2A, and SLC6A4.

CONCLUSIONS

These results support the hypothesis that GNB3, HTR2A, and SLC6A4 may play a role in the outcome of short-term antidepressant treatment for MDD in an interactive manner. Future research with independent replication using large sample sizes is needed to confirm the functions of the candidate genes identified in this study as being involved in short-term antidepressant treatment response.

Strategies to enhance the therapeutic efficacy of antidepressants: targeting residual symptoms

Major depressive disorder (MDD) is an illness of great morbidity that affects many people across the world. The current goal for treatment of MDD is to achieve remission (i.e., no depressive symptoms). However, despite scientific advances in the treatment for MDD, antidepressants as first-line agents yield only modest remission rates. In fact, a recent study indicated that only one out of three subjects who received a standard, first-line antidepressant attained remission. Not achieving remission from depressive symptoms increases the risk of a more chronic and debilitating course of illness with frequent recurrences. Although a number of reasons contribute to these modest outcomes, the presence of residual symptoms is a major problem. Residual symptoms are defined as symptoms that linger despite an adequate dose and duration of an antidepressant medication. This article reviews the prevalence and clinical impact of common residual symptoms and discusses the utility of aggressively addressing residual symptoms to enhance the efficacy of antidepressant medications.

Recent advances in bipolar disorder pharmacotherapy: focus on bipolar depression and rapid cycling

This article reviews recent advances in the evidence base for effective pharmacotherapy in bipolar disorder. We focus first on bipolar depression, since this pole of the illness forms the bulk of the burden of illness for both bipolar I and bipolar II patients. Recent studies throw doubt on the benefits of antidepressants in bipolar depression and suggest that selected mood stabilizers or second-generation antipsychotics may be effective alternatives. A second focus is on rapid-cycling bipolar disorder, a more severe phase of the illness, in which four or more episodes occur in a year. Although this form of the illness responds poorly to monotherapy, evidence is accumulating concerning which treatments are best combined in order to manage rapid cycling most effectively. Additional nonpharmacological management strategies are a vital element of the effective management of bipolar disorder but are beyond the scope of this review. Finally, suggestions are made for future research.

Pharmacogenetics of selective serotonin reuptake inhibitors in pediatric depression and anxiety

Selective serotonin reuptake inhibitors (SSRIs) are now an accepted and widely used first-line treatment for pediatric depression and anxiety. However, the data indicate that SSRI treatment achieves a clinical response in only 55-60% of children, and some may develop drug-induced suicidal behavior. Clinicians have no reliable tools to help them identify in advance those youths who are not likely to respond to an SSRI, or who are likely to develop SSRI-induced suicidality. Pharmacogenetic research attempts to identify genetic markers that are associated with response and side-effect profile. This review covers all the pharmacogenetic studies conducted as yet on pediatric samples and compares them with available data on adult samples. An emphasis is put on serotonergic genes such as the serotonin transporter (5-HTT) and additional genes known to be active in the CNS.

Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders

Major depressive disorder (MDD) is one of the most common mental disorders worldwide. Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. A common SNP in the brain-derived neurotrophic factor (BDNF) gene, a methionine (Met) substitution for valine (Val) at codon 66 (Val66Met), is a candidate SNP for influencing antidepressant treatment outcome. In this study, our goal was to determine the relationship between the Val66Met polymorphism in the BDNF gene and the rapid antidepressant response to venlafaxine in a Taiwanese population with MDD. Overall, the BDNF Val66Met polymorphism was found not to be associated with short-term venlafaxine treatment outcome. However, the BDNF Val66Met polymorphism showed a trend to be associated with rapid venlafaxine treatment response in female patients. Future research with independent replication in large sample sizes is needed to confirm the role of the BDNF Val66Met polymorphism identified in this study.