A genome-wide association meta-analysis of prognostic outcomes following cognitive behavioural therapy in individuals with anxiety and depressive disorders

The genetic architecture of youth anxiety: a study protocol

BACKGROUND

Anxiety disorders are the most common psychiatric problems among Canadian youth and typically have an onset in childhood or adolescence. They are characterized by high rates of relapse and chronicity, often resulting in substantial impairment across the lifespan. Genetic factors play an important role in the vulnerability toward anxiety disorders. However, genetic contribution to anxiety in youth is not well understood and can change across developmental stages. Large-scale genetic studies of youth are needed with detailed assessments of symptoms of anxiety disorders and their major comorbidities to inform early intervention or preventative strategies and suggest novel targets for therapeutics and personalization of care.

METHODS

The Genetic Architecture of Youth Anxiety (GAYA) study is a Pan-Canadian effort of clinical and genetic experts with specific recruitment sites in Calgary, Halifax, Hamilton, Toronto, and Vancouver. Youth aged 10-19 (n = 13,000) will be recruited from both clinical and community settings and will provide saliva samples, complete online questionnaires on demographics, symptoms of mental health concerns, and behavioural inhibition, and complete neurocognitive tasks. A subset of youth will be offered access to a self-managed Internet-based cognitive behavioral therapy resource. Analyses will focus on the identification of novel genetic risk loci for anxiety disorders in youth and assess how much of the genetic risk for anxiety disorders is unique or shared across the life span.

DISCUSSION

Results will substantially inform early intervention or preventative strategies and suggest novel targets for therapeutics and personalization of care. Given that the GAYA study will be the biggest genomic study of anxiety disorders in youth in Canada, this project will further foster collaborations nationally and across the world.

Generalised anxiety and panic symptoms in the German National Cohort (NAKO)

OBJECTIVES

Anxiety disorders (AD) are common in the general population, leading to high emotional distress and disability. The German National Cohort (NAKO) is a population-based mega-cohort study, examining participants in 16 German regions. The present study includes data of the first 101,667 participants and investigates the frequency and severity of generalised anxiety symptoms and panic attacks (PA).

METHODS

The Generalised Anxiety Disorder Symptoms Scale (GAD-7) and the first part of the Patient Health Questionnaire Panic Disorder (PHQ-PD) were filled out by NAKO participants (93,002). We examined the correlation of GAD-7 and PHQ-PD with demographic variables, stress (PHQ-Stress), depression (PHQ-9) and childhood trauma (CTS).

RESULTS

The total proportion of prior lifetime diagnoses of AD in the NAKO cohort reached 7.8%. Panic attacks were reported by 6.0% and possible/probable current GAD symptoms in 5.2% of the examined participants. Higher anxiety severity was associated with female sex, lower education level, German as a foreign language and younger age as well as high perceived stress and depression.

CONCLUSIONS

Clinically relevant GAD symptoms as well as panic attacks are frequent in the NAKO and are associated with sociodemographic factors, and high anxiety symptoms are accompanied by pronounced stress and depression levels.

Swedish multimodal cohort of patients with anxiety or depression treated with internet-delivered psychotherapy (MULTI-PSYCH)

PURPOSE

Depression and anxiety afflict millions worldwide causing considerable disability. MULTI-PSYCH is a longitudinal cohort of genotyped and phenotyped individuals with depression or anxiety disorders who have undergone highly structured internet-based cognitive-behaviour therapy (ICBT). The overarching purpose of MULTI-PSYCH is to improve risk stratification, outcome prediction and secondary preventive interventions. MULTI-PSYCH is a precision medicine initiative that combines clinical, genetic and nationwide register data.

PARTICIPANTS

MULTI-PSYCH includes 2668 clinically well-characterised adults with major depressive disorder (MDD) (n=1300), social anxiety disorder (n=640) or panic disorder (n=728) assessed before, during and after 12 weeks of ICBT at the internet psychiatry clinic in Stockholm, Sweden. All patients have been blood sampled and genotyped. Clinical and genetic data have been linked to several Swedish registers containing a wide range of variables from patient birth up to 10 years after the end of ICBT. These variable types include perinatal complications, school grades, psychiatric and somatic comorbidity, dispensed medications, medical interventions and diagnoses, healthcare and social benefits, demographics, income and more. Long-term follow-up data will be collected through 2029.

FINDINGS TO DATE

Initial uses of MULTI-PSYCH include the discovery of an association between PRS for autism spectrum disorder and response to ICBT, the development of a machine learning model for baseline prediction of remission status after ICBT in MDD and data contributions to genome wide association studies for ICBT outcome. Other projects have been launched or are in the planning phase.

FUTURE PLANS

The MULTI-PSYCH cohort provides a unique infrastructure to study not only predictors or short-term treatment outcomes, but also longer term medical and socioeconomic outcomes in patients treated with ICBT for depression or anxiety. MULTI-PSYCH is well positioned for research collaboration.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Genetics of nonpharmacological treatments of depression

Nonpharmacological antidepressant treatments are effective and well tolerated in selected patients. However, response is heterogeneous and validated biomarkers would be precious to aid treatment choice. We searched Pubmed, Scopus, and Google Scholar until May 2022 for original articles evaluating the association of genetic variables with the efficacy of nonpharmacological treatments for major depressive episodes. Most studies analyzed small sample sizes using the candidate gene approach, leading to poorly replicated findings that need to be interpreted cautiously. The few available methylome-wide and genome-wide association studies (GWASs) considered only electroconvulsive therapy (ECT) and cognitive-behavioral therapy in small samples, providing interesting findings by using polygenic risk scores. A deeper knowledge of the genetic factors implicated in treatment response may lead to a better understanding of the neurobiological mechanisms of nonpharmacological therapies for depression, and depression itself. Future GWAS are going to expand their sample size, thanks to consortia such as the gen-ECT-ic consortium.

Evidence for distinct genetic and environmental influences on fear acquisition and extinction

BACKGROUND

Anxiety disorders are highly prevalent with an early age of onset. Understanding the aetiology of disorder emergence and recovery is important for establishing preventative measures and optimising treatment. Experimental approaches can serve as a useful model for disorder and recovery relevant processes. One such model is fear conditioning. We conducted a remote fear conditioning paradigm in monozygotic and dizygotic twins to determine the degree and extent of overlap between genetic and environmental influences on fear acquisition and extinction.

METHODS

In total, 1937 twins aged 22-25 years, including 538 complete pairs from the Twins Early Development Study took part in a fear conditioning experiment delivered remotely via the Fear Learning and Anxiety Response (FLARe) smartphone app. In the fear acquisition phase, participants were exposed to two neutral shape stimuli, one of which was repeatedly paired with a loud aversive noise, while the other was never paired with anything aversive. In the extinction phase, the shapes were repeatedly presented again, this time without the aversive noise. Outcomes were participant ratings of how much they expected the aversive noise to occur when they saw either shape, throughout each phase.

RESULTS

Twin analyses indicated a significant contribution of genetic effects to the initial acquisition and consolidation of fear, and the extinction of fear (15, 30 and 15%, respectively) with the remainder of variance due to the non-shared environment. Multivariate analyses revealed that the development of fear and fear extinction show moderate genetic overlap (genetic correlations 0.4-0.5).

CONCLUSIONS

Fear acquisition and extinction are heritable, and share some, but not all of the same genetic influences.

Psychotherapies and digital interventions for OCD in adults: What do we know, what do we need still to explore?

BACKGROUND

Despite significant advances in the understanding and treatment of obsessive compulsive disorder (OCD), current treatment options are limited in terms of efficacy for symptom remission. Thus, assessing the potential role of iterative or alternate psychotherapies is important. Also, the potential role of digital technologies to enhance the accessibility of these therapies, should not be underestimated. We also need to embrace the idea of a more personalized treatment choice, being cognisant of clinical, genetic and neuroimaging predictors of treatment response.

PROCEDURES

Non-systematic review of current literature on emerging psychological and digital therapies for OCD, as well as of potential biomarkers of treatment response.

FINDINGS

A number of 'third wave' therapies (e.g., Acceptance and Commitment Therapy, Mindfulness-Based Cognitive Therapy) have an emerging and encouraging evidence base in OCD. Other approaches entail employment of elements of other psychotherapies such as Dialectical Behaviour Therapy; or trauma-focussed therapies such as Eye Movement Desensitisation and Reprocessing, and Imagery Rescripting and Narrative Therapy. Further strategies include Danger Ideation Reduction Therapy and Habit Reversal. For these latter approaches, large-scale randomised controlled trials are largely lacking, and the precise role of these therapies in treating people with OCD, remains to be clarified. A concentrated 4-day program (the Bergen program) has shown promising short- and long-term results. Exercise, music, and art therapy have not been adequately tested in people with OCD, but may have an adjunctive role. Digital technologies are being actively investigated for enhancing reach and efficacy of psychological therapies for OCD. Biomarkers, including genetic and neuroimaging, are starting to point to a future with more 'personalised medicine informed' treatment strategizing for OCD.

CONCLUSIONS

There are a number of potential psychological options for the treatment of people with OCD who do not respond adequately to exposure/response prevention or cognitive behaviour therapy. Adjunctive exercise, music, and art therapy might be useful, albeit the evidence base for these is very small. Consideration should be given to different ways of delivering such interventions, including group-based, concentrated, inpatient, or with outreach, where appropriate. Digital technologies are an emerging field with a number of potential applications for aiding the treatment of OCD. Biomarkers for treatment response determination have much potential capacity and deserve further empirical testing.

A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder

BACKGROUND

Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors.

METHODS

EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews.

RESULTS

Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy.

LIMITATIONS

A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors.

CONCLUSIONS

The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.

Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data

This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder (MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off ≤10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and (iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness.

BDNF gene polymorphisms predicting treatment response to CBT-based rehabilitation of depression: to be submitted to: European Neuropsychopharmacology

Genetic factors were shown to play a major role in both variation of treatment response and incidence of adverse effects to medication in affective disorders. Nevertheless, there is still a lack of therapygenetic studies, investigating the prediction of psychological therapy outcomes from genetic markers. Neuroplasticity and one of its mediators, brain-derived neurotrophic factor (BDNF), are potential research targets in this field. We aimed to investigate Tag SNP polymorphisms of the BDNF gene in depressed patients treated with cognitive behavioral therapy (CBT) in the context of a standardized 6-weeks outpatient rehabilitation program. Treatment response was assessed calculating the mean differences in BDI-II (Beck Depression Inventory) scores from admission to discharge. Six BDNF SNPs, including the Val66Met polymorphism (rs6265), were genotyped. Both genotypic data and BDI-II-scores at admission and discharge were available for 277 patients. Three SNPs, rs10501087 (p = 0.005, FDRp=0.015), rs11030104 (p = 0.006, FDRp=0.012), and the Val66Met polymorphism (rs6265, p<0.001, FDRp=0.006), were significantly associated with treatment response in depressed patients, even after multiple testing correction using the false discovery rate method (FDRp). We conclude that BDNF might serve as promising genetic marker for treatment response to psychological treatment in depression. However, due to our limited sample size, further studies are needed to disentangle the role of BDNF as potential therapygenetic marker.

Genes in treatment: Polygenic risk scores for different psychopathologies, neuroticism, educational attainment and IQ and the outcome of two different exposure-based fear treatments

OBJECTIVES

Evidence for a genetic influence on psychological treatment outcome so far has been inconsistent, likely due to the focus on candidate genes and the heterogeneity of the disorders treated. Using polygenic risk scores (PRS) in homogenous patient samples may increase the chance of detecting genetic influences.

METHODS

A sample of 342 phobic patients treated either for clinically relevant dental fear (n = 189) or other (mixed) phobic fears (n = 153) underwent highly standardised exposure-based CBT. A brief five-session format was used to treat dental fear, whereas longer multi-session treatments were used with the mixed-fear cohort. PRS were calculated based on large genetic studies of Neuroticism, Educational Attainment (EA), Intelligence, and four psychopathology domains. We compared PRS of post-treatment and follow-up remitters and non-remitters and regressed PRS on fear reduction percentages.

RESULTS

In the dental fear cohort, EA PRS were associated with treatment outcomes, i.e. drop-out, short- and long-term remission state, fear reduction, and attendance of subsequent dental appointments. In the mixed fear treatment cohort, no gene effects were observable.

CONCLUSIONS

Results indicate the importance of EA-related traits for outcomes following brief, but not long, standardised exposure-based CBT. Such use of PRS may help inform selection and tailoring of treatments.

Symptom-level modelling unravels the shared genetic architecture of anxiety and depression

Depression and anxiety are highly prevalent and comorbid psychiatric traits that cause considerable burden worldwide. Here we use factor analysis and genomic structural equation modelling to investigate the genetic factor structure underlying 28 items assessing depression, anxiety and neuroticism, a closely related personality trait. Symptoms of depression and anxiety loaded on two distinct, although highly genetically correlated factors, and neuroticism items were partitioned between them. We used this factor structure to conduct genome-wide association analyses on latent factors of depressive symptoms (89 independent variants, 61 genomic loci) and anxiety symptoms (102 variants, 73 loci) in the UK Biobank. Of these associated variants, 72% and 78%, respectively, replicated in an independent cohort of approximately 1.9 million individuals with self-reported diagnosis of depression and anxiety. We use these results to characterize shared and trait-specific genetic associations. Our findings provide insight into the genetic architecture of depression and anxiety and comorbidity between them.

Genetic contributions to anxiety disorders: where we are and where we are heading

Anxiety disorders are among the most common psychiatric disorders worldwide. They often onset early in life, with symptoms and consequences that can persist for decades. This makes anxiety disorders some of the most debilitating and costly disorders of our time. Although much is known about the synaptic and circuit mechanisms of fear and anxiety, research on the underlying genetics has lagged behind that of other psychiatric disorders. However, alongside the formation of the Psychiatric Genomic Consortium Anxiety workgroup, progress is rapidly advancing, offering opportunities for future research.Here we review current knowledge about the genetics of anxiety across the lifespan from genetically informative designs (i.e. twin studies and molecular genetics). We include studies of specific anxiety disorders (e.g. panic disorder, generalised anxiety disorder) as well as those using dimensional measures of trait anxiety. We particularly address findings from large-scale genome-wide association studies and show how such discoveries may provide opportunities for translation into improved or new therapeutics for affected individuals. Finally, we describe how discoveries in anxiety genetics open the door to numerous new research possibilities, such as the investigation of specific gene-environment interactions and the disentangling of causal associations with related traits and disorders.We discuss how the field of anxiety genetics is expected to move forward. In addition to the obvious need for larger sample sizes in genome-wide studies, we highlight the need for studies among young people, focusing on specific underlying dimensional traits or components of anxiety.

Integrative DNA Methylation and Gene Expression Analysis of Cognitive Behavioral Therapy Response in Children and Adolescents with Obsessive-Compulsive Disorder; a Pilot Study

Purpose

Here, we propose an integrative analysis of genome-wide methylation and gene expression to provide new insight into the biological mechanisms of Cognitive behavioral therapy (CBT) in pediatric obsessive-compulsive disorder (OCD).

Patients and Methods

Twelve children and adolescents with OCD receiving CBT for the first time were classified as responders or non-responders after eight weeks of CBT. Differentially methylated positions (DMPs) and gene co-expression modules were identified using specific R software packages. Correlations between the DMPs and gene co-expression modules were investigated.

Results

Two genes were enriched with significant DMPs (Δβ > ± 0.2, FDR-adjusted p-value < 0.05): PIWIL1 and MIR886. The yellowgreen module of co-expressed genes was associated with CBT response (FDR-adjusted p-value = 0.0003). Significant correlations were observed between the yellowgreen module and the CpGs in PIWIL1 and MIR886 (p < 0.008). Patients showing hypermethylation in these CpGs presented an upregulation in the genes in the yellowgreen module.

Conclusion

Taken together, the preliminary results of this systems-level approach, despite the study limitations, provide evidence that the epigenetic regulation of ncRNAs could be a predictor of CBT response.

Limitations

The sample size limited the statistical power, and given that the study was hypothesis-driven, our results should be seen as preliminary.

A bioinformatic study revealed serotonergic neurons are involved in the etiology and therapygenetics of anxiety disorders

Genetic factors contribute to the susceptibility of anxiety disorders (ADs) and responses to associated cognitive-behavioral therapy (CBT). However, the type of brain cell affected by the related genes remains unclear. Previous studies have indicated various important brain neurons associated with psychiatric disorders, highlighting the necessity to study the cellular basis of anxiety. We assembled 37 AD-related genes and 23 CBT-related genes from recent large-scale genome-wide association studies, and then investigated their cell-type specificity in single-cell transcriptome data via an expression weighted cell type enrichment method. Additionally, to investigate the cellular differences between ADs and other psychiatric disorders, we excluded the genes associated with major depressive disorder, bipolar disorder, and neuroticism, resulting in 29 AD-specific genes. Remarkably, results indicate that serotonergic neurons are significantly associated with both AD-related and CBT-related genes, despite the two gene sets showing no overlap. These observations provide evidence that serotonergic neurons are involved in the etiology and therapygenetics of ADs. Moreover, results also showed that serotonergic neurons are associated with AD-specific genes, providing a supplementary finding that is in opposition to previous studies that found no evidence for the association between serotonergic neurons and psychiatric disorders via the same strategy. In summary, the current study found that serotonergic neurons are involved in the etiology and therapygenetics of ADs, providing insights into their genetic and cellular basis. Further, this cellular difference study may deepen our understanding of ADs and other psychiatric disorders.

The cooperative revolution reaches clinical psychology and psychotherapy: An example from Germany

Background

Psychology is at the beginning of a cooperative revolution. Traditionally, psychological research has been conducted by individual labs, limiting its scope in clinical samples and promoting replication problems. Large-scale collaborations create new opportunities for highly powered studies in this resource-intensive research area. To present the current state of a Germany-wide platform for coordinating research across university outpatient clinics for psychotherapy.

Method

Since 1999, over 50 such clinics were created in Germany. They represent a unique infrastructure for research, training, and clinical care. In 2013, a steering committee initiated a nationwide research platform for systematic coordination of research in these clinics (German abbreviation “KODAP”). Its main goal is to aggregate and analyze longitudinal treatment data – including patient, therapist, and treatment characteristics – across all participating clinics.

Results

An initial survey (100% response rate) yielded recommendations for improved integration of data collection. Pilot data from 4,504 adult (16 clinics) and 568 child and adolescent patients (7 clinics) proved feasibility of data transfer and aggregation despite different data formats. Affective, neurotic, stress, and somatoform (adults) and anxiety and behavioral (children and adolescents) disorders were most frequent; comorbidity was high. Overcoming legal, methodological, and technical challenges, a common core assessment battery was developed, and data collection started in 2018. To date, 42 clinics have joined.

Conclusions

KODAP shows that research collaboration across university outpatient clinics is feasible. Fulfilling the need for stronger cumulative and cooperative research in Clinical Psychology will contribute to better knowledge about mental health, a core challenge to modern societies.

Data from 4,504 adult and 568 child and adolescent patients were successfully aggregated across 23 outpatient clinics.

Affective, neurotic, stress, and somatoform (adults) and anxiety and behavioral (children and adolescents) disorders were most frequent; comorbidity was high.

Legal, methodological, and technical challenges were overcome, and a common core assessment battery was developed.

42 clinics have joined a Germany-wide research platform for systematic coordination of research in these clinics. Longitudinal data collection started in 2018.

Blood Brain-Derived Neurotrophic Factor (BDNF) and Major Depression: Do We Have a Translational Perspective?

Major depressive disorder (MDD) affects millions of people worldwide and is a leading cause of disability. Several theories have been proposed to explain its pathological mechanisms, and the “neurotrophin hypothesis of depression” involves one of the most relevant pathways. Brain-derived neurotrophic factor (BDNF) is an important neurotrophin, and it has been extensively investigated in both experimental models and clinical studies of MDD. Robust empirical findings have indicated an association between increased BDNF gene expression and peripheral concentration with improved neuronal plasticity and neurogenesis. Additionally, several studies have indicated the blunt expression of BDNF in carriers of the Val66Met gene polymorphism and lower blood BDNF (serum or plasma) levels in depressed individuals. Clinical trials have yielded mixed results with different treatment options, peripheral blood BDNF measurement techniques, and time of observation. Previous meta-analyses of MDD treatment have indicated that antidepressants and electroconvulsive therapy showed higher levels of blood BDNF after treatment but not with physical exercise, psychotherapy, or direct current stimulation. Moreover, the rapid-acting antidepressant ketamine has presented an early increase in blood BDNF concentration. Although evidence has pointed to increased levels of BDNF after antidepressant therapy, several factors, such as heterogeneous results, low sample size, publication bias, and different BDNF measurements (serum or plasma), pose a challenge in the interpretation of the relation between peripheral blood BDNF and MDD. These potential gaps in the literature have not been properly addressed in previous narrative reviews. In this review, current evidence regarding BDNF function, genetics and epigenetics, expression, and results from clinical trials is summarized, putting the literature into a translational perspective on MDD. In general, blood BDNF cannot be recommended for use as a biomarker in clinical practice. Moreover, future studies should expand the evidence with larger samples, use the serum or serum: whole blood concentration of BDNF as a more accurate measure of peripheral BDNF, and compare its change upon different treatment modalities of MDD.

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.

Maternal-Fetal Inflammation in the Placenta and the Developmental Origins of Health and Disease

Events in fetal life impact long-term health outcomes. The placenta is the first organ to form and is the site of juxtaposition between the maternal and fetal circulations. Most diseases of pregnancy are caused by, impact, or are reflected in the placenta. The purpose of this review is to describe the main inflammatory processes in the placenta, discuss their immunology, and relate their short- and long-term disease associations. Acute placental inflammation (API), including maternal and fetal inflammatory responses corresponds to the clinical diagnosis of chorioamnionitis and is associated with respiratory and neurodevelopmental diseases. The chronic placental inflammatory pathologies (CPI), include chronic villitis of unknown etiology, chronic deciduitis, chronic chorionitis, eosinophilic T-cell vasculitis, and chronic histiocytic intervillositis. These diseases are less-well studied, but have complex immunology and show mechanistic impacts on the fetal immune system. Overall, much work remains to be done in describing the long-term impacts of placental inflammation on offspring health.

Working memory moderates the relation between the brain-derived neurotropic factor (BDNF) and psychotherapy outcome for depression

BACKGROUND

Insight into patient characteristics that predict response to treatment for major depressive disorder (MDD) may help to personalize treatment and improve outcomes. One mechanism that has been linked to the success of treatment for MDD is brain-derived neurotropic factor (BDNF). BDNF is implicated in learning and memory and may play a role in the effects of psychotherapy that involves changing cognitions and behaviors. In addition, only in individuals with low BDNF, low working memory capacity has been associated with increased symptoms of depression. However, the role of BDNF and working memory capacity in psychotherapy outcome is unclear. The aim of this study was to investigate the role of BDNF and its interaction with working memory capacity in psychotherapy outcomes for MDD.

METHOD

Adult patients with MDD were randomized to weekly or twice weekly sessions of cognitive behavioral therapy or interpersonal psychotherapy. BDNF Val66Met polymorphism (rs6265) (n = 138) was defined and serum BDNF was quantified before (n = 138) and after psychotherapy (n = 82).

RESULTS

Baseline serum BDNF and the Val66Met polymorphism were not associated with outcome and associations did not differ between treatment conditions. Working memory capacity significantly moderated the relation between baseline serum BDNF and outcome: high serum BDNF at baseline was related to less depressive symptoms following psychotherapy in the presence of high working memory capacity, but not low working memory capacity.

DISCUSSION

These findings, if replicated, might indicate that while BDNF may not be related to psychotherapy outcomes in general, they may play a role in the presence of specific learning processes such as working memory capacity.

Enriching CBT by Neuroscience: Novel Avenues to Achieve Personalized Treatments

Although cognitive behavioral therapy (CBT) is an established and efficient treatment for a variety of common mental disorders, a considerable number of patients do not respond to treatment or relapse after successful CBT. Recent findings and approaches from neuroscience could pave the way for clinical developments to enhance the outcome of CBT. Herein, we will present how neuroscience can offer novel perspectives to better understand (a) the biological underpinnings of CBT, (b) how we can enrich CBT with neuroscience-informed techniques (augmentation of CBT), and (c) why some patients may respond better to CBT than others (predictors of therapy outcomes), thus paving the way for more personalized and effective treatments. We will introduce some key topics and describe a selection of findings from CBT-related research using tools from neuroscience, with the hope that this will provide clinicians and clinical researchers with a brief and comprehensible overview of the field.

The influence of common polygenic risk and gene sets on social skills group training response in autism spectrum disorder

Social skills group training (SSGT) is a frequently used behavioral intervention in autism spectrum disorder (ASD), but the effects are moderate and heterogeneous. Here, we analyzed the effect of polygenic risk score (PRS) and common variants in gene sets on the intervention outcome. Participants from the largest randomized clinical trial of SSGT in ASD to date were selected (N = 188, 99 from SSGT, 89 from standard care) to calculate association between the outcomes in the SSGT trial and PRSs for ASD, attention-deficit hyperactivity disorder (ADHD), and educational attainment. In addition, specific gene sets were selected to evaluate their role on intervention outcomes. Among all participants in the trial, higher PRS for ADHD was associated with significant improvement in the outcome measure, the parental-rated Social Responsiveness Scale. The significant association was due to better outcomes in the standard care group for individuals with higher PRS for ADHD (post-intervention: β = −4.747, P = 0.0129; follow-up: β = −5.309, P = 0.0083). However, when contrasting the SSGT and standard care group, an inferior outcome in the SSGT group was associated with higher ADHD PRS at follow-up (β = 6.67, P = 0.016). Five gene sets within the synaptic category showed a nominal association with reduced response to interventions. We provide preliminary evidence that genetic liability calculated from common variants could influence the intervention outcomes. In the future, larger cohorts should be used to investigate how genetic contribution affects individual response to ASD interventions.

Mapping gene expression in social anxiety reveals the main brain structures involved in this disorder

Social Anxiety Disorder (SAD) is characterized by emotional and attentional biases as well as distorted negative self-beliefs. According this, we proposed to identify the brain structures and hub genes involved in SAD. An analysis in Pubmed and TRANSFAC was conducted and 72 genes were identified. Using Microarray data, from Allen Human Brain Atlas, it was possible to identify three modules of co-expressed genes from our gene set (R package WGCNA). Higher mean gene expression was found in cortico-medial group, basomedial nucleus, ATZ in amygdala and in head and tail of the caudate nucleus, nucleus accumbens and putamen in striatum. Our enrichment analysis identified the followed hub genes: DRD2, HTR1A, JUN, SP1 and HDAC4. We suggest that SAD is explained by delayed extinction of circuitry for conditioned fear. Caused by reduced activation of the dopaminergic and serotonergic systems,due diminished expectation of reward during social interactions.