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Objective: To test the equivalence of two doses of intravenous iron (ferric carboxymaltose) in pregnancy. Design: Parallel, two-arm equivalence randomised controlled trial with an equivalence margin of 5%. Setting: Single centre in Australia. Population: 278 pregnant women with iron deficiency. Methods: Participants received either 500 mg (n = 152) or 1000 mg (n = 126) of intravenous ferric carboxymaltose in the second or third trimester. Main outcome measures: The proportion of participants requiring additional intravenous iron (500 mg) to achieve and maintain ferritin >30 microg/L (diagnostic threshold for iron deficiency) at 4 weeks post-infusion, and at 6 weeks, and 3-, 6- and 12-months postpartum. Secondary endpoints included repeat infusion rate, iron status, birth and safety outcomes. Results: The two doses were not equivalent within a 5% margin at any time point. At 4 weeks post infusion, 26/73 (36%) participants required a repeat infusion in the 500-mg group compared with 5/67 (8%) in the 1000-mg group: difference in proportions, 0.283 (95% confidence interval [CI] 0.177–0.389). Overall, participants in the 500-mg arm received twice the repeat infusion rate (0.81 [SD = 0.824] versus 0.40 [SD = 0.69], rate ratio 2.05, 95% CI 1.45–2.91). Conclusions: Administration of 1000 mg ferric carboxymaltose in pregnancy maintains iron stores and reduces the need for repeat infusions. A 500- mg dose requires ongoing monitoring to ensure adequate iron stores are reached and sustained. Refereed/Peer-reviewed

Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium’s therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients (N = 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SCZ) were computed using lassosum and in a model including all three PRSs and other covariates, and the PRS of ADHD (β = −0.14; 95% confidence interval [CI]: −0.24 to −0.03; p value = 0.010) and MDD (β = −0.16; 95% CI: −0.27 to −0.04; p value = 0.005) predicted worse quantitative lithium response. A higher SCZ PRS was associated with higher rates of medication nonadherence (OR = 1.61; 95% CI: 1.34–1.93; p value = 2e−7). This study indicates that genetic risk for ADHD and depression may influence lithium treatment response. Interestingly, a higher SCZ PRS was associated with poor adherence, which can negatively impact treatment response. Incorporating genetic risk of ADHD, depression, and SCZ in combination with clinical risk may lead to better clinical care for patients with BD.

In adolescents and young adults, depressive symptoms are highly prevalent and dynamic. For clinicians, it is difficult to determine whether a young person reporting depressive symptoms is at risk of developing ongoing mood difficulties, or whether symptoms form part of a transient maturational process. Hence, giving personalized treatment recommendations is a challenge in this group; current clinical practice guidelines promote a “watch and wait” approach, where generic and low-level treatments are offered first, while more intensive therapies, such as medication, are reserved for cases who do not benefit from first-line interventions. Trajectory analyses of longitudinally assessed symptoms in large cohorts have the potential to untangle clinical heterogeneity by determining subgroups or classes of symptom courses and their risk factors. Further, they explore the impact of known or suspected risk factors on a trajectory slope and intercept, and can trace the interrelation between depressive symptoms and other clinical outcomes over time. These studies suggest that young people fall into common mood trajectory classes, and that class membership and symptom course are mediated by biological and environmental risk factors. Studies also provide evidence that high and persistent depressive symptoms are associated with a range of concurrent health- and behavioral outcomes. These findings could assist in informing personalized and preventive strategies for clinical practice.

Outcomes for people with mental illness are difficult to predict due to the syndromal nature of diagnosis and the complex relationships among clinical and biological predictors and outcomes. Due to the small amounts of variance explained by individual predictors, the application of multivariate modeling techniques is key to improving the accuracy of outcome prediction. The combination of data from multiple modes of clinical and biological assessment shows potential to increase the accuracy of such models. Superior performance may be achieved by combining clinical data with imaging, electrophysiology, and blood-based biomarkers. Multimodal multivariate modeling techniques are central to the development of personalized psychiatry, affording the potential for patient stratification and individual outcome prediction. This chapter outlines the range of multimodal data available, study design, and modeling techniques.

The natural history of psychotic illness is one of fluctuating symptoms and functions, from prodromal illness, to first episode, to episodic relapse, and recovery or chronic illness. Recent evidence suggests that complex biological, symptomatic, and functional trajectory patterns underlie clinical presentations and outcomes. Statistical techniques for the analysis of longitudinal data have evolved to provide better estimates of trends and the determinants of growth and change for individual patients. The analysis of complex multimodal trajectories may be key to the personalization of psychosis treatment. This chapter will discuss the advantages and disadvantages of trajectory modeling techniques for psychosis research.

This chapter gives an overview of the proteomic techniques currently in use for proteomic profiling of biospecimens from individuals suffering from psychotic disorders such as schizophrenia or bipolar disorder. Discovery and targeted mass spectrometry techniques are introduced and compared with antibody-based proteomic profiling platforms, highlighting the strengths and weaknesses of each approach. Recent findings in the field of blood biomarkers for drug-naive psychotic disorders are reviewed and considered in the context of future potential for personalized psychiatry.

Clozapine is the most effective anti-psychotic medication for treatment refractory schizophrenia. A growing number of case reports have linked infection to high clozapine levels and associated adverse outcomes. We present a systematic review of published cases to clarify the relationship between infection and elevated clozapine levels. The case reports were located through PubMed and Embase. In addition, 8 new cases from two Australian states were included. Demographics, psychiatric diagnoses and medical morbidities, medications, clinical symptoms, clozapine levels, inflammatory markers and final clinical outcome were extracted. 40 cases were identified in 23 publications that demonstrated elevated clozapine levels associated with infection. Infections were commonly respiratory in origin. Adverse events, typically sedation, were associated with raised clozapine levels during infection. In many cases the signs of infection such as fever and white blood cell count were reduced. Severe adverse effects were uncommon, with one case each of seizure, myocarditis and neutropenia. The relationship between infection, clozapine levels and adverse events is complex and multi-factorial. Monitoring of clozapine levels is essential during hospitalisation for infection and consideration should be given to gradual dose reduction to minimise dose related side effects.

Personalized medicine (personalized psychiatry in a specific setting) is a new model towards individualized care, in which knowledge from genomics and other omic pillars (microbiome, epigenomes, proteome, and metabolome) will be combined with clinical data to guide efforts to new drug development and targeted prescription of the existing treatment options. In this review, we summarize pharmacogenomic studies in mood disorders that may lay the foundation towards personalized psychiatry. In addition, we have discussed the possible strategies to integrate data from omic pillars as a future path to personalized psychiatry. So far, the progress of uncovering single nucleotide polymorphisms (SNPs) underpinning treatment efficacy in mood disorders (e.g., SNPs associated with selective serotonin re-uptake inhibitors or lithium treatment response in patients with bipolar disorder and major depressive disorder) are encouraging, but not adequate. Genetic studies have pointed to a number of SNPs located at candidate genes that possibly influence response to; (a) antidepressantsCOMT,HTR2A,HTR1A,CNR1,SLC6A4, NPY,MAOA,IL1B,GRIK4,BDNF,GNB3,FKBP5,CYP2D6,CYP2C19, andABCB1and (b) mood stabilizers (lithium)5-HTT,TPH,DRD1,FYN,INPP1,CREB1,BDNF,GSK3β,ARNTL,TIM,DPB,NR3C1,BCR,XBP1, andCACNG2. We suggest three alternative and complementary strategies to implement knowledge gained from pharmacogenomic studies. The first strategy can be to implement diagnostic, therapeutic, or prognostic genetic testing based on candidate genes or gene products. The second alternative is an integrative analysis (systems genomics approach) to combine omics data obtained from the different pillars of omics investigation, including genomics, epigenomes, proteomics, metabolomics and microbiomes. The main goal of system genomics is an identification and understanding of biological pathways, networks, and modules underlying drug-response. The third strategy aims to the development of multivariable diagnostic or prognostic algorithms (tools) combining individual’s genomic information (polygenic score) with other predictors (e.g., omics pillars, neuroimaging, and clinical characteristics) to finally predict therapeutic outcomes. An integration of molecular science with that of traditional clinical practice is the way forward to drug discoveries and novel therapeutic approaches and to characterize psychiatric disorders leading to a better predictive, preventive, and personalized medicine (PPPM) in psychiatry. With future advances in the omics technology and methodological developments for data integration, the goal of PPPM in psychiatry is promising.

Objective: In adolescents and young adults, depressive symptoms are highly prevalent and dynamic. For clinicians, it is difficult to determine whether a young person reporting depressive symptoms is at risk of developing ongoing mood difficulties or whether symptoms form part of a transient maturational process. Trajectory analyses of longitudinally assessed symptoms in large cohorts have the potential to untangle clinical heterogeneity by determining subgroups or classes of symptom course and their risk factors, by interrogating the impact of known or suspected risk factors on trajectory slope and intercept and by tracing the interrelation between depressive symptoms and other clinical outcomes over time. Method: We conducted a systematic review of trajectory studies conducted in cohorts including people aged between 15 and 25 years. Results: We retrieved 47 relevant articles. These studies suggest that young people fall into common mood trajectory classes and that class membership and symptom course are mediated by biological and environmental risk factors. Furthermore, studies provide evidence that high and persistent depressive symptoms are associated with a range of concurrent health and behavioral outcomes. Conclusion: Findings could assist in the formulation of novel concepts of depressive disorders in young people and inform preventive strategies and predictive models for clinical practice.

Recent genome-wide association studies have demonstrated that the genetic burden associated with depression correlates with depression severity. Therefore, conducting genetic studies of patients at the most severe end of the depressive disorder spectrum, those with treatment resistant depression and who are prescribed electroconvulsive therapy (ECT), could lead to a better understanding of the genetic underpinnings of depression. Despite ECT being one of the most effective forms of treatment for severe depressive disorders, it is usually placed at the end of treatment algorithms of current guidelines. This is perhaps because ECT has controlled risk and logistical demands including use of general anaesthesia and muscle relaxants and side effects such as short-term memory impairment. Better understanding of the genetics and biology of ECT response and of cognitive side effects could lead to more personalized treatment decisions. To enhance the understanding of the genomics of severe depression and ECT response, researchers and ECT providers from around the world and from various depression or ECT networks, but not limited to, such as the Psychiatric Genomics Consortium, the Clinical Alliance and Research in ECT (CARE), and the National Network of Depression Centers (NNDC) have formed the Genetics of ECT International Consortium (Gen-ECT-ic). Gen-ECT-ic will organize the largest clinical and genetic collection to date to study the genomics of severe depressive disorders and response to ECT, aiming for 30,000 patients worldwide using a GWAS approach. At this stage it will be the largest genomic study on treatment response in depression. Retrospective data abstraction and prospective data collection will be facilitated by a uniform data collection approach that is flexible and will incorporate data from many clinical practices. Gen-ECT-ic invites all ECT providers and researchers to join its efforts.

Proteomic exploration of the effects of psychotropic drugs on specific brain areas in rodents has the potential to uncover novel molecular networks and pathways affected by psychotropic medications, and may inform etiologic hypotheses on mental disorders. Haloperidol, a widely used first-generation antipsychotic, has been shown to produce structural and functional changes of the hippocampus, a brain region also implicated in the neuropathology of disorders such as schizophrenia and bipolar disorder. Seven adult male C57BL/6 mice were injected daily intraperitoneally with 0.5 mg/kg of haloperidol, for 28 days. A control group of six animals was injected with vehicle only (saline). Protein levels of postmortem hippocampus homogenate were determined using label-free LC/MS/MS. In the treatment group, 216 differentially expressed hippocampal proteins were identified as compared to controls. Ingenuity pathway analysis implicated oxidative phosphorylation and mitochondrial function as top canonical pathways, and local networks involved in tubulin-mediated cytoskeleton dynamics, clathrin-mediated endocytosis, and extracellular signal-regulated kinase and c-Jun N-terminal kinase signaling. The findings of this study could stimulate further research into the cellular mechanisms associated with haloperidol treatment and the pathophysiology of psychotic disorders, assisting treatment biomarker discovery. All MS data have been deposited in the ProteomeXchange with identifier PXD002250 (http://proteomecentral.proteomexchange.org/dataset/PXD002250).

BackgroundLithium is a first-line medication for bipolar disorder (BD), but only ~30% of patients respond optimally to the drug. Since genetic factors are known to mediate lithium treatment response, we hypothesized whether polygenic susceptibility to the spectrum of depression traits is associated with treatment outcomes in patients with BD. In addition, we explored the potential molecular underpinnings of this relationship.MethodsWeighted polygenic scores (PGSs) were computed for major depressive disorder (MDD) and depressive symptoms (DS) in BD patients from the Consortium on Lithium Genetics (ConLi+Gen; n=2,586) who received lithium treatment. Lithium treatment outcome was assessed using the ALDA scale. Summary statistics from genome-wide association studies (GWAS) in MDD (130,664 cases and 330,470 controls) and DS (n=161,460) were used for PGS weighting. Associations between PGSs of depression traits and lithium treatment response were assessed by binary logistic regression. We also performed a cross-trait meta-GWAS, followed by Ingenuity® Pathway Analysis.OutcomesBD patients with a low polygenic load for depressive traits were more likely to respond well to lithium, compared to patients with high polygenic load (MDD: OR =1.64 [95%CI: 1.26-2.15], lowest vs highest PGS quartiles; DS: OR=1.53 [95%CI: 1.18-2.00]). Associations were significant for type 1, but not type 2 BD. Cross-trait GWAS and functional characterization implicated voltage-gated potassium channels, insulin-related pathways, mitogen-activated protein-kinase (MAPK) signaling, and miRNA expression.InterpretationGenetic loading to depression traits in BD patients lower their odds of responding optimally to lithium. Our findings support the emerging concept of a lithium-responsive biotype in BD.FundingSee attached details

IMPORTANCE Lithium is a first-line mood stabilizer for the treatment of bipolar affective disorder (BPAD). However, the efficacy of lithium varies widely, with a nonresponse rate of up to 30%. Biological response markers are lacking. Genetic factors are thought to mediate treatment response to lithium, and there is a previously reported genetic overlap between BPAD and schizophrenia (SCZ). OBJECTIVES To test whether a polygenic score for SCZ is associated with treatment response to lithium in BPAD and to explore the potential molecular underpinnings of this association. DESIGN, SETTING, AND PARTICIPANTS A total of 2586 patients with BPAD who had undergone lithium treatment were genotyped and assessed for long-term response to treatment between 2008 and 2013.Weighted SCZ polygenic scores were computed at different P value thresholds using summary statistics from an international multicenter genome-wide association study (GWAS) of 36 989 individuals with SCZ and genotype data from patients with BPAD from the Consortium on Lithium Genetics. For functional exploration, a cross-trait meta-GWAS and pathway analysis was performed, combining GWAS summary statistics on SCZ and response to treatment with lithium. Data analysis was performed from September 2016 to February 2017. MAIN OUTCOMES AND MEASURES Treatment response to lithiumwas defined on both the categorical and continuous scales using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. The effect measures include odds ratios and the proportion of variance explained. RESULTS Of the 2586 patients in the study (mean [SD] age, 47.2 [13.9] years), 1478 were women and 1108 were men. The polygenic score for SCZ was inversely associated with lithium treatment response in the categorical outcome, at a threshold P < 5 ? 10-2. Patients with BPAD who had a low polygenic load for SCZ responded better to lithium, with odds ratios for lithium response ranging from 3.46 (95%CI, 1.42-8.41) at the first decile to 2.03 (95%CI, 0.86-4.81) at the ninth decile, compared with the patients in the 10th decile of SCZ risk. In the cross-trait meta-GWAS, 15 genetic loci that may have overlapping effects on lithium treatment response and susceptibility to SCZ were identified. Functional pathway and network analysis of these loci point to the HLA antigen complex and inflammatory cytokines. CONCLUSIONS AND RELEVANCE This study provides evidence for a negative association between high genetic loading for SCZ and poor response to lithium in patients with BPAD. These results suggest the potential for translational research aimed at personalized prescribing of lithium.

Neuropathological changes of the hippocampus have been associated with psychotic disorders such as schizophrenia and bipolar disorder. Recent work has particularly implicated hippocampal GABAergic interneurons in the pathophysiology of these diseases. However, the molecular mechanisms underlying structural and cellular hippocampal pathology remain poorly understood. We used data from comprehensive difference-in-gel electrophoresis (2-D DIGE) investigations of postmortem human hippocampus of people with schizophrenia and bipolar disorder, covering the acidic (isoelectric point (pI) between pH4 and 7) and, separately, the basic (pI between pH6 and 11) sub-proteome, for Ingenuity Pathway Analysis (IPA) of implicated protein networks and pathways. Comparing disease and control cases, we identified 58 unique differentially expressed proteins in schizophrenia, and 70 differentially expressed proteins in bipolar disorder, using mass spectrometry. IPA implicated, most prominently, 14-3-3 and aryl hydrocarbon receptor signaling in schizophrenia, and gluconeogenesis/glycolysis in bipolar disorder. Both disorders were characterized by alterations of proteins involved in the oxidative stress response, mitochondrial function, and protein-endocytosis, -trafficking, -degradation, and -ubiquitination. These findings are interpreted with a focus on GABAergic interneuron pathology in the hippocampus.

ImportanceLithium is a first-line mood stabilizer for the maintenance treatment of Bipolar Disorder (BPD). However, the efficacy of lithium varies widely, with a non-response rate of up to 30%. Biological response markers and predictors are lacking.ObjectiveGenetic factors are thought to mediate lithium treatment response, and the previously reported genetic overlap between BPD and schizophrenia (SCZ) led us to test whether a polygenic score (PGS) for SCZ could predict lithium treatment response in BPD. Further, we explored the potential molecular underpinnings of this association.DesignWeighted SCZ PGSs were computed at ten p-value thresholds (PT) using summary statistics from a genome-wide association study (GWAS) of 36,989 SCZ cases, and genotype data for BPD patients from the Consortium on Lithium Genetics (ConLi+Gen). For functional exploration, we performed a cross-trait meta-GWAS and pathway analysis, combining GWAS summary statistics on SCZ and lithium treatment response.SettingInternational multicenter GWAS.ParticipantsPatients with BPD who had undergone lithium treatment were genotyped and retrospectively assessed for long-term treatment response (n=2,586).Main outcome measuresClinical treatment response to lithium was defined on both the categorical and continuous scales using the ALDA score. The effect measures include odds ratios (ORs) and the proportion of variance explained (R2), and a significant association was determined at pResultsThe PGS for SCZ was inversely associated with lithium treatment response in the categorical outcome (p=8×10−5), at PT−2. Patients with BPD who had low polygenic load for SCZ responded better to lithium, with ORs for lithium response ranging from 3.46 [95%CI: 1.42-8.41 at 1stdecile] to 2.03 [95%CI: 0.86-4.81 at the 9th decile], compared to the patients in the 10thdecile of SCZ risk. In the cross-trait meta-GWAS, 15 genetic loci that may have overlapping effects on lithium treatment response and susceptibility to SCZ were identified. Functional pathway and network analysis of these loci point to the HLA complex and inflammatory cytokines (TNFα, IL-4, IFNγ) as molecular contributors to lithium treatment response in BPD.Conclusions and RelevanceThe study provides, for the first-time, evidence for a negative association between high genetic loading for SCZ and poor response to lithium in patients with BPD. These results suggest the potential for translational research aimed at personalized prescribing of lithium.Key PointsQuestionDoes a polygenic score for Schizophrenia (SCZ) predict response to lithium in patients with Bipolar Disorder (BPD)? What are the molecular drivers of the association between SCZ and lithium treatment response?FindingsWe found an inverse association between genetic loading for SCZ risk variants and response to lithium in patients with BPD. Genetic variants in the HLA region on chromosome 6, the antigen presentation pathway and markers of inflammation (TNFα, IL-4, IFNγ) point to molecular underpinnings of lithium treatment response in BPD.MeaningIn patients with BPD, an assessment of a polygenic load for SCZ risk variants may assist in conjunction with clinical data to predict whether they would respond to lithium treatment.

Meta-analyses of genome-wide association studies (meta-GWAS) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases, and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardio-metabolic diseases risk (CMD-R) genes that are also associated with mood disorders. Firstly, we reviewed meta-GWA studies published until January 2016, for the diseases “type 2 diabetes, coronary artery disease, hypertension” and/or for the risk factors “blood pressure, obesity, plasma lipid levels, insulin and glucose related traits”. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and “depressive disorder” OR “depressive symptoms” OR “bipolar disorder” OR “lithium treatment”, OR “serotonin reuptake inhibitors treatment”. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B, and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin and dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our findings provide insights in to the shared biological mechanisms of mood disorders and cardio-metabolic diseases.

The concept of indicated prevention has proliferated in psychiatry, and accumulating evidence suggests that it may indeed be possible to prevent or delay the onset of a first episode of psychosis though adequate interventions in individuals deemed at clinical high risk (CHR) for such an event. One challenge undermining these efforts is the relatively poor predictive accuracy of clinical assessments used in practice for CHR individuals, often leading to diagnostic and therapeutic uncertainty reflected in clinical guidelines promoting a 'watch and wait' approach to CHR patients. Using data from published studies, and employing predictive models based on the odds-ratio form of Bayes' rule, we simulated scenarios where clinical interview, neurocognitive testing, structural magnetic resonance imaging and electrophysiology are part of the initial assessment process of a CHR individual (extended diagnostic approach). Our findings indicate that for most at-risk patients, at least three of these assessments are necessary to arrive at a clinically meaningful differentiation into high- intermediate-, and low-risk groups. In particular, patients with equivocal results in the initial assessments require additional diagnostic testing to produce an accurate risk profile forming part of the comprehensive initial assessment. The findings may inform future research into reliable identification and personalized therapeutic targeting of CHR patients, to prevent transition to full-blown psychosis.

Depression and major cardiometabolic disorders (in this chapter, cardiometabolic disorders refer to the cardiovascular diseases, metabolic disorders, and associated risk factors) are highly heritable, i.e., they are caused by a combination of genetic and environmental factors. Moreover, there are genetic co-heritabilities (genetic correlations) in these disorders suggesting the influence of common genes and shared biological pathways between them. Several candidate gene studies performed so far have identified risk-associated genes for depression, cardiovascular, and/or metabolic diseases. Besides, meta-analysis of genome-wide association (meta-GWA) studies reported a number of single nucleotide polymorphisms (SNPs) and candidate genes for the cardiometabolic disorders. Compared to the cardiometabolic disorders, the meta-GWA studies of depression have had limited success due to the heterogeneity of the disorder and lack of statistical power (sample size) to detect the associations. The first successfully replicated meta-GWA study for depression was published in July 2015.

Clathrin-mediated endocytosis (CME) is the best-characterized mechanism governing cellular membrane and protein trafficking. In this hypothesis review, we integrate recent evidence implicating CME and related cellular trafficking mechanisms in the pathophysiology of psychotic disorders such as schizophrenia and bipolar disorder. The evidence includes proteomic and genomic findings implicating proteins and genes of the clathrin interactome. Additionally, several important candidate genes for schizophrenia, such as dysbindin, are involved in processes closely linked to CME and membrane trafficking. We discuss that key aspects of psychosis neuropathology such as synaptic dysfunction, white matter changes and aberrant neurodevelopment are all influenced by clathrin-dependent processes, and that other cellular trafficking mechanisms previously linked to psychoses interact with the clathrin interactome in important ways. Furthermore, many antipsychotic drugs have been shown to affect clathrin-interacting proteins. We propose that the targeted pharmacological manipulation of the clathrin interactome may offer fruitful opportunities for novel treatments of schizophrenia.

Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes’ rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a mass spectrometry technique used for the analysis of macromolecules on an intact tissue of interest, thereby allowing the assessment of molecular signatures in health and disease in the anatomical context. MALDI-MSI is increasingly used to investigate neurodegenerative and psychiatric disorders at the molecular level, including Alzheimer's disease (AD), Parkinson's disease (PD), and schizophrenia (SCZ). These illnesses are characterized by complex neuropathological processes, and conventional proteomic techniques investigating brain tissue homogenates have inherent limitations in determining the precise anatomical or cellular location of proteomic findings. In this article, we review MALDI-MSI studies on neurodegenerative and psychiatric disorders, and explore whether the technique could accelerate the translation of proteomic information into improved understanding and ultimately better therapeutic applications. Refereed/Peer-reviewed

Clozapine is an atypical antipsychotic drug used in the treatment of schizophrenia, which has been shown to reverse behavioural and dendritic spine deficits in mice. It has recently been shown that deficiency of 14-3-3ζ has an association with schizophrenia, and that a mouse model lacking this protein displays several schizophrenia-like behavioural deficits. To test the effect of clozapine in this mouse model, 14-3-3ζ KO mice were administered clozapine (5 mg/kg) for two weeks prior to being analysed in a test battery of cognition, anxiety, and despair (depression-like) behaviours. Following behavioural testing brain samples were collected for analysis of specific anatomical defects and dendritic spine formation. We found that clozapine reduced despair behaviour of 14-3-3ζ KO mice in the forced swim test (FST) and altered the behaviour of wild types and 14-3-3ζ KO mice in the Y-maze task. In contrast, clozapine had no effects on hippocampal laminar defects or decreased dendritic spine density observed in 14-3-3ζ KO mice. Our results suggest that clozapine may have beneficial effects on clinical behaviours associated with deficiencies in the 14-3-3ζ molecular pathway, despite having no effects on morphological defects. These findings may provide mechanistic insight to the action of this drug. Refereed/Peer-reviewed

Degeneration of axotomized GABAergic septohippocampal neurones has been shown to be enhanced in ciliary neurotrophic factor (CNTF)-deficient mice following fimbria-fornix transection (FFT), indicating a neuroprotective function of endogenous CNTF. Paradoxically, however, the cholinergic population of septohippocampal neurones was more resistant to axotomy in these mutants. As leukaemia inhibitory factor (LIF) has been identified as a potential neuroprotective factor for the cholinergic medial septum (MS) neurones, FFT-induced responses were compared in CNTF(-/-), LIF(-/-) and CNTF/LIF double knockout mice. In CNTF(-/-) mice, FFT-induced cholinergic degeneration was confirmed to be attenuated as compared with wildtype mice. The expression of both LIF and LIF receptor beta was increased in the MS providing a possible explanation for the enhanced neuronal resistance to FFT in these animals. However, ablation of the LIF gene also produced paradoxical effects; following FFT in LIF(-/-) mice no loss of GABAergic or cholinergic MS neurones was detectable during the first postlesional week, suggesting that other efficient neuroprotective mechanisms are activated in these animals. In fact, enhanced activation of astrocytes, a source of neurotrophic proteins, was indicated by increased up-regulation of glial fibrillary acidic protein and vimentin expression. In addition, mRNA levels for neurotrophin signalling components (e.g. nerve growth factor, p75(NTR)) were differentially regulated. The positive effect on axotomized cholinergic neurones seen in CNTF(-/-) and LIF(-/-) mice as well as the increased up-regulation of astrogliose markers was abolished in CNTF/LIF double knockout animals. Our results indicate that endogenous CNTF and LIF are involved in the regulation of neuronal survival following central nervous system lesion and are integrated into a network of neurotrophic signals that mutually influence their expression and function.

It is an open question to what extent neuroprotective mechanisms involving neurotrophic proteins are activated after central nervous system (CNS) lesions. Results from previous studies have indicated that ciliary neurotrophic factor (CNTF) and other members of the family of gp130-associated cytokines have neuroprotective effects on septohippocampal projection neurons axotomized by fimbria-fornix transection (FFT). Here we demonstrate that the transcription factor STAT3, a component of the primary cytokine signal transduction pathway, is transiently activated after FFT in the medial septum and in the lateral septum deafferented by the lesion. Immunocytochemical double-labeling showed nuclear signals for phosphorylated STAT3 in both types (GABAergic and cholinergic) of axotomized medial septal neurons around day 4 postlesion. This response temporally coincided with the cell-type-specific upregulation of the cytokine receptor components CNTF receptor alpha and LIF receptor beta in the same neurons. However, neuronal STAT3-activation was not abolished in CNTF- or LIF-deficient mouse mutants. Furthermore, lesion-induced STAT3 signaling was also found in reactive GFAP-positive astrocytes of the medial and lateral septum. Interestingly, basal GFAP expression was reduced but postlesional upregulation was markedly enhanced in CNTF(-/-) animals. These results demonstrate transient activation of cytokine signaling after CNS lesion and suggest that gp130-associated cytokines have multiple functions in the lesioned CNS by directly acting on axotomized neurons and on reactive astrocytes.

Application of neurotrophic proteins including ciliary neurotrophic factor (CNTF) and leukemia inhibitory factor (LIF), members of the family of gp130-associated cytokines, can rescue CNS neurons from injury-induced degeneration. However, it is not clear so far if these effects reflect a physiological function of the endogenous cytokines. Using fimbria-fornix transection as a model, we examined whether responses of GABAergic and cholinergic septohippocampal neurons to axotomy are altered in mice lacking CNTF. In addition, we studied the cellular expression of CNTF, LIF and related cytokine receptor components in the septal complex following lesion. Degeneration of septohippocampal GABAergic neurons in the medial septum as indicated by the loss of parvalbumin-immunoreactive neurons was accelerated and permanently enhanced in CNTF(-/-) mice as compared to wild-type animals. Unexpectedly, the number of axotomized cholinergic MS neurons was significantly higher in CNTF-deficient mice during the first 2 weeks postlesion. Both in wild-type and in CNTF(-/-) mutants, expression of mRNA for the CNTF-specific alpha-subunit of the cytokine receptor complex was specifically upregulated in axotomized GABAergic septal neurons, whereas enhanced expression of the LIF-binding beta-subunit was specifically observed in axotomized cholinergic neurons. Following lesion, CNTF expression in wild-type mice was induced in activated astrocytes surrounding the axotomized neurons and at the lesion site. Expression of LIF mRNA was localized in the GABAergic and cholinergic septohippocampal neurons. These results strongly indicate that endogenous CNTF, supplied by reactive glia cells, acts as a neuroprotective factor for axotomized CNS neurons. In the septum, endogenous CNTF specifically supports lesioned GABAergic projection neurons, whereas LIF may play a similar role for the cholinergic counterparts.

Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases ‘type 2 diabetes, coronary artery disease, hypertension’ and/or for the risk factors ‘blood pressure, obesity, plasma lipid levels, insulin and glucose related traits’. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and ‘depression’ or ‘depressive disorder’ or ‘depressive symptoms’ or ‘bipolar disorder’ or ‘lithium treatment response in bipolar disorder’, or ‘serotonin reuptake inhibitors treatment response in major depression’. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases.