Targeting metacognitive change mechanisms in acute inpatients with psychotic symptoms: feasibility and acceptability of a modularized group intervention.

Emerging evidence suggests the usefulness of psychological interventions targeting metacognitive change mechanisms in patients experiencing psychosis. Although many of these patients are treated in acute psychiatric contexts, only few studies have adapted such interventions for acute inpatient settings. The present study aimed to assess the feasibility, acceptability, and preliminary clinical outcomes of a novel modularized group intervention focusing on different aspects of metacognitive change mechanisms. In particular, the intervention aims to reduce patients' acute symptoms by enhancing cognitive insight and to relieve distress via cognitive defusion (i.e. coping). A sample of 37 participants with acute psychosis received up to nine sessions of the intervention. Baseline and post-intervention assessments were conducted for general psychopathology, psychotic symptoms, global functioning, and symptom distress. Measures of change mechanisms were assessed before and after the respective treatment module. Participants' experiences were explored in feedback questionnaires and interviews. Recruitment, retention, and attendance rate met the pre-set feasibility benchmark of 80%. The intervention was well received by participants, who emphasised the group's clear structure, positive atmosphere, and helpful contents. Response rates were high and linear mixed models revealed significant medium-to-large time effects on all clinical outcomes. As expected, increase in hypothesised change mechanisms cognitive insight and decrease in cognitive fusion was found. However, the uncontrolled design limits interpreting clinical effects. The study provides evidence that an intervention based on a metacognitive model is feasible and acceptable for acute inpatients with psychosis. Positive results on clinical outcomes and change mechanisms warrant further exploration in a randomized controlled trial.

Genetic structure of major depression symptoms across clinical and community cohorts.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.

Developing a mechanism-based therapy for acute psychiatric inpatients with psychotic symptoms: an Intervention Mapping approach.

Background: Treatment guidelines for psychosis recommend offering psychotherapy already in the acute illness phase. However, there is a lack of available interventions adapted to the specific needs and key change mechanisms of inpatients experiencing severe symptoms and crisis. In this article we outline the scientific development process of a needs-oriented and mechanism-based group intervention for acute psychiatric inpatients with psychosis (MEBASp). Methods: To guide our intervention design, we used Intervention Mapping (IM), a six-step framework for developing evidence-based health interventions that consisted of an extensive literature review, an in-depth problem definition and needs analysis, the modeling of change mechanisms and outcomes and the production of an intervention prototype. Results: Our low-threshold modularized group intervention consists of nine stand-alone sessions (two per week) within three modules and targets different aspects of metacognitive and social change mechanisms. Module I and II aim to reduce acute symptoms by fostering cognitive insight, Module III focuses on reducing distress via cognitive defusion. Therapy contents are adapted from existing metacognitive treatments such as the Metacognitive Training and presented in a destigmatizing, simply understandable and experience-oriented way. Conclusion: MEBASp is currently evaluated in a single-arm feasibility trial. Using a systematic and rigorous development methodology and providing a detailed description of the development steps demonstrated to be invaluable in improving the intervention's scientific foundation, validity, and replicability for similar research.

Weight-gain independent effect of mirtazapine on fasting plasma lipids in healthy men.

Treatment with mirtazapine, a widely prescribed antidepressant, has been linked to weight gain and dyslipidemia. Whether dyslipidemia occurs secondary to increased appetite due to antidepressant treatment, or due to direct pharmacological effects of mirtazapine is unknown. The aim of this analysis is to complement our previously published results of the effect of mirtazapine on metabolism and energy substrate partitioning from a proof-of-concept, open-label clinical study (ClinicalTrials.gov NCT00878540) in 12 healthy males (20-25 years). We report the effect of a seven-day administration of mirtazapine 30 mg per day on weight and lipid metabolism in healthy men under highly standardized conditions with respect to diet, physical activity and day-night-rhythm and under continuous clinical observation. After a 7-day administration of mirtazapine 30 mg, we observed a statistically significant increase in triglyceride levels (mean change + 4.4 mg/dl; 95% CI [- 11.4; 2.6]; p = 0.044) as well as TG/HDL-C ratio (mean change + 0.2; 95% CI [- 0.4; 0.1]; p = 0.019) and a decrease in HDL-cholesterol (mean change - 4.3 mg/dl; 95% CI [2.1; 6.5]; p = 0.004), LDL-cholesterol (mean change - 8.7 mg/dl; 95% CI [3.8; 13.5]; p = 0.008), total cholesterol (mean change - 12.3 mg/dl; 95% CI [5.4; 19.1]; p = 0.005), and non-HDL-C (mean change - 8.0 mg/dl; 95% CI [1.9; 14.0]; p = 0.023). Notably, weight (mean change - 0.6 kg; 95% CI [0.4; 0.8]; p = 0.002) and BMI (mean change - 0.2; 95% CI [0.1; 0.2]; p = 0.002) significantly decreased. No change in waist circumference (mean change - 0.4 cm; 95% CI [- 2.1; 2.9]; p = 0.838) or waist-to-hip-ratio (mean change 0.0; 95% CI [- 0.0; 0.0]; p = 0.814) was observed. This is the first study showing unfavorable changes in lipid metabolism under mirtazapine in healthy individuals despite highly standardized conditions including dietary restriction, and despite the observation of a decrease of weight. Our findings support the hypothesis that mirtazapine has direct pharmacological effects on lipid metabolism. ClinicalTrials.gov: NCT00878540.

Recurrent suicide attempts affect normalization of HPA axis dysregulation after recovery from major depression.

More than 700,000 people worldwide die by suicide every year, and the number of suicide attempts is estimated as 20 times higher, most of them being associated with psychiatric disorders, especially major depression. Knowledge about effective methods for preventing suicide attempts in individuals at high risk for suicide is still scarce. Dysregulation of the neuroendocrine stress response system, i.e., the hypothalamic-pituitary-adrenocortical (HPA) axis, is one of the most consistent neurobiological findings in both major depression and suicidality. While the HPA axis is mostly overactive in depression, individuals with a history of suicide attempts exhibit an attenuated hormonal response to stress. It is unknown, however, whether the HPA axis is constantly attenuated in repeated suicide attempters or whether it regains normal responsivity after recovery from depression. Using the combined dexamethasone suppression/corticotropin-releasing hormone (dex/CRH) test, we assessed HPA axis regulation in acute depression (N = 237) and after recovery with respect to previous suicide attempts. Patients without previous suicide attempts show normalization of the stress hormone response to the second dex/CRH (basal ACTH response and cortisol response) after recovery from acute depression, while patients with multiple previous SA show an increased ACTH response. The change in HPA axis responsivity in patients with only one previous SA lies between the response patterns of the other groups with no change in HPA axis reactivity. Our findings suggest that patients with a history of suicide attempts belong to a subgroup of individuals that exhibit a distinct pattern of stress hormone response during acute depression and after recovery. Future studies may extend our approach by investigating additional psychological stress tasks to gain a broader understanding of the stress pathology of recurrent suicide attempters.

Effects of Pharmacokinetic Gene Variation on Therapeutic Drug Levels and Antidepressant Treatment Response.

INTRODUCTION: Pharmacogenetic testing is proposed to minimize adverse effects when considered in combination with pharmacological knowledge of the drug. As yet, limited studies in clinical settings have investigated the predictive value of pharmacokinetic (pk) gene variation on therapeutic drug levels as a probable mechanism of adverse effects, nor considered the combined effect of pk gene variation and drug level on antidepressant treatment response. METHODS: Two depression cohorts were investigated for the relationship between pk gene variation and antidepressant serum concentrations of amitriptyline, venlafaxine, mirtazapine and quetiapine, as well as treatment response. For the analysis, 519 patients (49% females; 46.6±14.1 years) were included. RESULTS: Serum concentration of amitriptyline was associated with CYP2D6 (higher concentrations in poor metabolizers compared to normal metabolizers), of venlafaxine with CYP2C19 (higher concentrations in intermediate metabolizers compared to rapid/ultrarapid metabolizers) and CYP2D6 (lower metabolite-to-parent ratio in poor compared to intermediate and normal metabolizers, and intermediate compared to normal and ultrarapid metabolizers). Pk gene variation did not affect treatment response. DISCUSSION: The present data support previous recommendations to reduce starting doses of amitriptyline and to guide dose-adjustments via therapeutic drug monitoring in CYP2D6 poor metabolizers. In addition, we propose including CYP2C19 in routine testing in venlafaxine-treated patients to improve therapy by raising awareness of the risk of low serum concentrations in CYP2C19 rapid/ultrarapid metabolizers. In summary, pk gene variation can predict serum concentrations, and thus the combination of pharmacogenetic testing and therapeutic drug monitoring is a useful tool in a personalized therapy approach for depression.

Sex differences in the genetic regulation of the blood transcriptome response to glucocorticoid receptor activation.

Substantial sex differences have been reported in the physiological response to stress at multiple levels, including the release of the stress hormone, cortisol. Here, we explore the genomic variants in 93 females and 196 males regulating the initial transcriptional response to cortisol via glucocorticoid receptor (GR) activation. Gene expression levels in peripheral blood were obtained before and after GR-stimulation with the selective GR agonist dexamethasone to identify differential expression following GR-activation. Sex stratified analyses revealed that while the transcripts responsive to GR-stimulation were mostly overlapping between males and females, the quantitative trait loci (eQTLs) regulation differential transcription to GR-stimulation was distinct. Sex-stratified eQTL SNPs (eSNPs) were located in different functional genomic elements and sex-stratified transcripts were enriched within postmortem brain transcriptional profiles associated with Major Depressive Disorder (MDD) specifically in males and females in the cingulate cortex. Female eSNPs were enriched among SNPs linked to MDD in genome-wide association studies. Finally, transcriptional sensitive genetic profile scores derived from sex-stratified eSNPS regulating differential transcription to GR-stimulation were predictive of depression status and depressive symptoms in a sex-concordant manner in a child and adolescent cohort (n = 584). These results suggest the potential of eQTLs regulating differential transcription to GR-stimulation as biomarkers of sex-specific biological risk for stress-related psychiatric disorders.

Examining sex differences in neurodevelopmental and psychiatric genetic risk in anxiety and depression.

Anxiety and depression are common mental health disorders and have a higher prevalence in females. They are modestly heritable, share genetic liability with other psychiatric disorders, and are highly heterogeneous. There is evidence that genetic liability to neurodevelopmental disorders, such as attention deficit hyperactivity disorder (ADHD) is associated with anxiety and depression, particularly in females. We investigated sex differences in family history for neurodevelopmental and psychiatric disorders and neurodevelopmental genetic risk burden (indexed by ADHD polygenic risk scores (PRS) and rare copy number variants; CNVs) in individuals with anxiety and depression, also taking into account age at onset. We used two complementary datasets: 1) participants with a self-reported diagnosis of anxiety or depression (N = 4,178, 65.5% female; mean age = 41.5 years; N = 1,315 with genetic data) from the National Centre for Mental Health (NCMH) cohort and 2) a clinical sample of 13,273 (67.6% female; mean age = 45.2 years) patients with major depressive disorder (MDD) from the Psychiatric Genomics Consortium (PGC). We tested for sex differences in family history of psychiatric problems and presence of rare CNVs (neurodevelopmental and >500kb loci) in NCMH only and for sex differences in ADHD PRS in both datasets. In the NCMH cohort, females were more likely to report family history of neurodevelopmental and psychiatric disorders, but there were no robust sex differences in ADHD PRS or presence of rare CNVs. There was weak evidence of higher ADHD PRS in females compared to males in the PGC MDD sample, particularly in those with an early onset of MDD. These results do not provide strong evidence of sex differences in neurodevelopmental genetic risk burden in adults with anxiety and depression. This indicates that sex may not be a major index of neurodevelopmental genetic heterogeneity, that is captured by ADHD PRS and rare CNV burden, in adults with anxiety and depression.

Effects of weariness of life, suicide ideations and suicide attempt on HPA axis regulation in depression.

The neuropathological mechanisms leading to suicidality are still unknown, which, in view of an annual toll of around 1 million completed suicides constitutes an urgent clinical and societal problem. Alterations of stress hormone (ACTH and cortisol, representing the activity of the hypothalamic-pituitary-adrenocortical, HPA axis) regulation has been repeatedly studied in context of suicidality. Following a suicide attempt, stress hormone activity seems to be blunted, while depressed patients with suicidal ideation often present with elevated HPA axis activity. METHODS: We investigated the effects of different forms of suicidality on HPA axis regulation in 568 hospitalized patients of the Munich Antidepressant Response Signature (MARS) project. All patients had a diagnosis of a depressive disorder; 62 patients reported a recent suicide attempt, 192 patients suicide ideation, and 171 patients expressed weariness of life as the weakest form of suicidality, the latter not being analyzed in studies so far. All patients participated in the combined dexamethasone/corticotropin releasing hormone (dex/CRH) test for assessing HPA axis regulation shortly after admission to the hospital. RESULTS: We found an increased ACTH and cortisol response following the dex/CRH-test in patients that were weary of life. In contrast, stress hormone response in suicide attempters and suicide ideators did not differ from non-suicidal patients. Further, repeated suicide attempts in patients' history were associated with more pronounced stress hormone attenuation. CONCLUSION: In this so far largest study analyzing the HPA axis with respect to suicidality, we could not confirm the assumption of a general attenuation of HPA axis response in depressed suicide ideators and attempters. Conversely, HPA axis appears to be influenced by divergent effects of a specific suicidal psychopathology as well as outlasting effects of previous suicide attempts. We discuss these findings in the light of recent concepts of suicidality, pointing to multifactorial effects of acute and predisposing conditions on HPA axis reactivity in depressed patients.

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study.

BACKGROUND: About every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis. METHODS: This study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n = 110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n = 1058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n = 1143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three consistency criteria were defined to appraise robustness and replicability of results across estimation methods, network bootstrapping, and samples. RESULTS: Network analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our consistency criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two consistency criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods. CONCLUSIONS: Genetic predisposition to higher systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue, consistent with previous studies based on circulating levels of inflammatory markers. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

The association between genetically determined ABO blood types and major depressive disorder.

ABO blood types and their corresponding antigens have long been assumed to be related to different human diseases. So far, smaller studies on the relationship between mental disorders and blood types yielded contradicting results. In this study we analyzed the association between ABO blood types and lifetime major depressive disorder (MDD). We performed a pooled analysis with data from 26 cohorts that are part of the MDD working group of the Psychiatric Genomics Consortium (PGC). The dataset included 37,208 individuals of largely European ancestry of which 41.6% were diagnosed with lifetime MDD. ABO blood types were identified using three single nucleotide polymorphisms in the ABO gene: rs505922, rs8176746 and rs8176747. Regression analyses were performed to assess associations between the individual ABO blood types and MDD diagnosis as well as putative interaction effects with sex. The models were adjusted for sex, cohort and the first ten genetic principal components. The percentage of blood type A was slightly lower in cases than controls while blood type O was more prominent in cases. However, these differences were not statistically significant. Our analyses found no evidence of an association between ABO blood types and major depressive disorder.

Schema therapy versus cognitive behavioral therapy versus individual supportive therapy for depression in an inpatient and day clinic setting: study protocol of the OPTIMA-RCT.

BACKGROUND: Major depressive disorder represents (MDD) a major cause of disability and disease burden. Beside antidepressant medication, psychotherapy is a key approach of treatment. Schema therapy has been shown to be effective in the treatment of psychiatric disorders, especially personality disorders, in a variety of settings and patient groups. Nevertheless, there is no evidence on its effectiveness for MDD in an inpatient nor day clinic setting and little is known about the factors that drive treatment response in such a target group. METHODS: In the current protocol, we outline OPTIMA (OPtimized Treatment Identification at the MAx Planck Institute): a single-center randomized controlled trial of schema therapy as a treatment approach for MDD in an inpatient and day clinic setting. Over the course of 7 weeks, we compare schema therapy with cognitive behavioral therapy and individual supportive therapy, conducted in individual and group sessions and with no restrictions regarding concurrent antidepressant medication, thus approximating real-life treatment conditions. N = 300 depressed patients are included. All study therapists undergo a specific training and supervision and therapy adherence is assessed. Primary outcome is depressive symptom severity as self-assessment (Beck Depression Inventory-II) and secondary outcomes are clinical ratings of MDD (Montgomery-Asberg Depression Rating Scale), recovery rates after 7 weeks according to the Munich-Composite International Diagnostic Interview, general psychopathology (Brief Symptom Inventory), global functioning (World Health Organization Disability Assessment Schedule), and clinical parameters such as dropout rates. Further parameters on a behavioral, cognitive, psychophysiological, and biological level are measured before, during and after treatment and in 2 follow-up assessments after 6 and 24 months after end of treatment. DISCUSSION: To our knowledge, the OPTIMA-Trial is the first to investigate the effectiveness of schema therapy as a treatment approach of MDD, to investigate mechanisms of change, and explore predictors of treatment response in an inpatient and day clinic setting by using such a wide range of parameters. Insights from OPTIMA will allow more integrative approaches of psychotherapy of MDD. Especially, the identification of intervention-specific markers of treatment response can improve evidence-based clinical decision for individualizing treatment. TRIAL REGISTRATION: Identifier on clinicaltrials.gov : NCT03287362 ; September, 12, 2017.

Genetic comorbidity between major depression and cardio-metabolic traits, stratified by age at onset of major depression.

It is imperative to understand the specific and shared etiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic etiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in 11 data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease, and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic etiology irrespective of the age depression first presents.

Citalopram-induced pathways regulation and tentative treatment-outcome-predicting biomarkers in lymphoblastoid cell lines from depression patients.

Antidepressant therapy is still associated with delays in symptomatic improvement and low response rates. Incomplete understanding of molecular mechanisms underlying antidepressant effects hampered the identification of objective biomarkers for antidepressant response. In this work, we studied transcriptome-wide expression followed by pathway analysis in lymphoblastoid cell lines (LCLs) derived from 17 patients documented for response to SSRI antidepressants from the Munich Antidepressant Response Signatures (MARS) study upon short-term incubation (24 and 48 h) with citalopram. Candidate transcripts were further validated with qPCR in MARS LCLs from responders (n = 33) vs. non-responders (n = 36) and afterward in an independent cohort of treatment-resistant patients (n = 20) vs. first-line responders (n = 24) from the STAR*D study. In MARS cohort we observed significant associations of GAD1 (glutamate decarboxylase 1; p = 0.045), TBC1D9 (TBC1 Domain Family Member 9; p = 0.014-0.021) and NFIB (nuclear factor I B; p = 0.015-0.025) expression with response status, remission status and improvement in depression scale, respectively. Pathway analysis of citalopram-altered gene expression indicated response-status-dependent transcriptional reactions. Whereas in clinical responders neural function pathways were primarily up- or downregulated after incubation with citalopram, deregulated pathways in non-responders LCLs mainly involved cell adhesion and immune response. Results from the STAR*D study showed a marginal association of treatment-resistant depression with NFIB (p = 0.068) but not with GAD1 (p = 0.23) and TBC1D9 (p = 0.27). Our results propose the existence of distinct pathway regulation mechanisms in responders vs. non-responders and suggest GAD1, TBC1D9, and NFIB as tentative predictors for clinical response, full remission, and improvement in depression scale, respectively, with only a weak overlap in predictors of different therapy outcome phenotypes.

The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes.

BACKGROUND: A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathways. Many of the identified (neuro) biological dysfunctions are "transdiagnostic", meaning that they do not reflect diagnostic boundaries but are shared by different ICD/DSM diagnoses. The compromised biological validity of the current classification system for mental disorders impedes rather than supports the development of treatments that not only target symptoms but also the underlying pathophysiological mechanisms. The Biological Classification of Mental Disorders (BeCOME) study aims to identify biology-based classes of mental disorders that improve the translation of novel biomedical findings into tailored clinical applications. METHODS: BeCOME intends to include at least 1000 individuals with a broad spectrum of affective, anxiety and stress-related mental disorders as well as 500 individuals unaffected by mental disorders. After a screening visit, all participants undergo in-depth phenotyping procedures and omics assessments on two consecutive days. Several validated paradigms (e.g., fear conditioning, reward anticipation, imaging stress test, social reward learning task) are applied to stimulate a response in a basic system of human functioning (e.g., acute threat response, reward processing, stress response or social reward learning) that plays a key role in the development of affective, anxiety and stress-related mental disorders. The response to this stimulation is then read out across multiple levels. Assessments comprise genetic, molecular, cellular, physiological, neuroimaging, neurocognitive, psychophysiological and psychometric measurements. The multilevel information collected in BeCOME will be used to identify data-driven biologically-informed categories of mental disorders using cluster analytical techniques. DISCUSSION: The novelty of BeCOME lies in the dynamic in-depth phenotyping and omics characterization of individuals with mental disorders from the depression and anxiety spectrum of varying severity. We believe that such biology-based subclasses of mental disorders will serve as better treatment targets than purely symptom-based disease entities, and help in tailoring the right treatment to the individual patient suffering from a mental disorder. BeCOME has the potential to contribute to a novel taxonomy of mental disorders that integrates the underlying pathomechanisms into diagnoses. TRIAL REGISTRATION: Retrospectively registered on June 12, 2019 on ClinicalTrials.gov (TRN: NCT03984084).

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.

Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression.

BACKGROUND: The prevalence of depression is higher in individuals with autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Shared genetic etiology is a plausible explanation for the overlap, and in this study we tested whether genetic variation in the major histocompatibility complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression. METHODS: We fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 human leukocyte antigen (HLA) alleles and 4 complement component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium Major Depressive Disorder Working Group and the UK Biobank. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants, applying both a region-wide significance threshold (3.9 × 10-6) and a candidate threshold (1.6 × 10-4). RESULTS: No HLA alleles or C4 haplotypes were associated with depression at the region-wide threshold. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold for testing in HLA genes in the meta-analysis (odds ratio = 0.98, 95% confidence interval = 0.97-0.99). CONCLUSIONS: We found no evidence that an increased risk for depression was conferred by HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia. These results suggest that any HLA or C4 variants associated with depression either are rare or have very modest effect sizes.

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models.

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response.

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

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

Epigenetic upregulation of FKBP5 by aging and stress contributes to NF-κB-driven inflammation and cardiovascular risk.

Aging and psychosocial stress are associated with increased inflammation and disease risk, but the underlying molecular mechanisms are unclear. Because both aging and stress are also associated with lasting epigenetic changes, a plausible hypothesis is that stress along the lifespan could confer disease risk through epigenetic effects on molecules involved in inflammatory processes. Here, by combining large-scale analyses in human cohorts with experiments in cells, we report that FKBP5, a protein implicated in stress physiology, contributes to these relations. Across independent human cohorts (total n > 3,000), aging synergized with stress-related phenotypes, measured with childhood trauma and major depression questionnaires, to epigenetically up-regulate FKBP5 expression. These age/stress-related epigenetic effects were recapitulated in a cellular model of replicative senescence, whereby we exposed replicating human fibroblasts to stress (glucocorticoid) hormones. Unbiased genome-wide analyses in human blood linked higher FKBP5 mRNA with a proinflammatory profile and altered NF-κB-related gene networks. Accordingly, experiments in immune cells showed that higher FKBP5 promotes inflammation by strengthening the interactions of NF-κB regulatory kinases, whereas opposing FKBP5 either by genetic deletion (CRISPR/Cas9-mediated) or selective pharmacological inhibition prevented the effects on NF-κB. Further, the age/stress-related epigenetic signature enhanced FKBP5 response to NF-κB through a positive feedback loop and was present in individuals with a history of acute myocardial infarction, a disease state linked to peripheral inflammation. These findings suggest that aging/stress-driven FKBP5-NF-κB signaling mediates inflammation, potentially contributing to cardiovascular risk, and may thus point to novel biomarker and treatment possibilities.