Discovery and validation of blood biomarkers for suicidality

Next-generation precision medicine for suicidality prevention

Suicidality remains a clear and present danger in society in general, and for mental health patients in particular. Lack of widespread use of objective and/or quantitative information has hampered treatment and prevention efforts. Suicidality is a spectrum of severity from vague thoughts that life is not worth living, to ideation, plans, attempts, and completion. Blood biomarkers that track suicidality risk provide a window into the biology of suicidality, as well as could help with assessment and treatment. Previous studies by us were positive. Here we describe new studies we conducted transdiagnostically in psychiatric patients, starting with the whole genome, to expand the identification, prioritization, validation and testing of blood gene expression biomarkers for suicidality, using a multiple independent cohorts design. We found new as well as previously known biomarkers that were predictive of high suicidality states, and of future psychiatric hospitalizations related to them, using cross-sectional and longitudinal approaches. The overall top increased in expression biomarker was SLC6A4, the serotonin transporter. The top decreased biomarker was TINF2, a gene whose mutations result in very short telomeres. The top biological pathways were related to apoptosis. The top upstream regulator was prednisolone. Taken together, our data supports the possibility that biologically, suicidality is an extreme stress-driven form of active aging/death. Consistent with that, the top subtypes of suicidality identified by us just based on clinical measures had high stress and high anxiety. Top therapeutic matches overall were lithium, clozapine and ketamine, with lithium stronger in females and clozapine stronger in males. Drug repurposing bioinformatic analyses identified the potential of renin-angiotensin system modulators and of cyclooxygenase inhibitors. Additionally, we show how patient reports for doctors would look based on blood biomarkers testing, personalized by gender. We also integrated with the blood biomarker testing social determinants and psychological measures (CFI-S, suicidal ideation), showing synergy. Lastly, we compared that to machine learning approaches, to optimize predictive ability and identify key features. We propose that our findings and comprehensive approach can have transformative clinical utility.

Precision medicine for psychotic disorders: objective assessment, risk prediction, and pharmacogenomics

Psychosis occurs inside the brain, but may have external manifestations (peripheral molecular biomarkers, behaviors) that can be objectively and quantitatively measured. Blood biomarkers that track core psychotic manifestations such as hallucinations and delusions could provide a window into the biology of psychosis, as well as help with diagnosis and treatment. We endeavored to identify objective blood gene expression biomarkers for hallucinations and delusions, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We were successful in identifying biomarkers that were predictive of high hallucinations and of high delusions states, and of future psychiatric hospitalizations related to them, more so when personalized by gender and diagnosis. Top biomarkers for hallucinations that survived discovery, prioritization, validation and testing include PPP3CB, DLG1, ENPP2, ZEB2, and RTN4. Top biomarkers for delusions include AUTS2, MACROD2, NR4A2, PDE4D, PDP1, and RORA. The top biological pathways uncovered by our work are glutamatergic synapse for hallucinations, as well as Rap1 signaling for delusions. Some of the biomarkers are targets of existing drugs, of potential utility in pharmacogenomics approaches (matching patients to medications, monitoring response to treatment). The top biomarkers gene expression signatures through bioinformatic analyses suggested a prioritization of existing medications such as clozapine and risperidone, as well as of lithium, fluoxetine, valproate, and the nutraceuticals omega-3 fatty acids and magnesium. Finally, we provide an example of how a personalized laboratory report for doctors would look. Overall, our work provides advances for the improved diagnosis and treatment for schizophrenia and other psychotic disorders.

Sex-Specific Regulation of Stress Susceptibility by the Astrocytic Gene Htra1

Major depressive disorder (MDD) is linked to impaired structural and synaptic plasticity in limbic brain regions. Astrocytes, which regulate synapses and are influenced by chronic stress, likely contribute to these changes. We analyzed astrocyte gene profiles in the nucleus accumbens (NAc) of humans with MDD and mice exposed to chronic stress. Htra1 , which encodes an astrocyte-secreted protease targeting the extracellular matrix (ECM), was significantly downregulated in the NAc of males but upregulated in females in both species. Manipulating Htra1 in mouse NAc astrocytes bidirectionally controlled stress susceptibility in a sex-specific manner. Such Htra1 manipulations also altered neuronal signaling and ECM structural integrity in NAc. These findings highlight astroglia and the brain's ECM as key mediators of sex-specific stress vulnerability, offering new approaches for MDD therapies.

Depicting the molecular features of suicidal behavior: a review from an "omics" perspective

Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning

Background

Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide.

Methods

We conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide.

Results

Out of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance.

Conclusion

Studies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.

Navigating the Intersection of Technology and Depression Precision Medicine

This chapter primarily focuses on the progress in depression precision medicine with specific emphasis on the integrative approaches that include artificial intelligence and other data, tools, and technologies. After the description of the concept of precision medicine and a comparative introduction to depression precision medicine with cancer and epilepsy, new avenues of depression precision medicine derived from integrated artificial intelligence and other sources will be presented. Additionally, less advanced areas, such as comorbidity between depression and cancer, will be examined.

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Background

Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets.

Method

A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array.

Results

Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. The biosignature includes both genes connected before to bipolar disorder, depression, psychosis or epilepsy, as well as genes not linked before with any psychiatric disease. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed participation of 4 identified features in 6 pathways which have also been associated with bipolar disorder.

Conclusion

Automated machine learning (AutoML) managed to identify accurately 25 genes that can jointly - in a multivariate-fashion - separate bipolar patients from healthy controls with high predictive power. The discovered features lead to new biological insights. Machine Learning (ML) analysis considers the features in combination (in contrast to standard differential expression analysis), removing both irrelevant as well as redundant markers, and thus, focusing to biological interpretation.

Apolipoproteins and Suicide: A Potential Psychiatric Biomarker

Every year around 800,000 people commit suicide, this represents one death every 40 s. In the search for possible biological biomarkers associated with suicide and/or psychiatric disorders, serum cholesterol levels have been extensively explored. Several studies indicate that cholesterol and associated proteins, especially apolipoproteins (Apos), may play an important role in the diagnosis, prognosis, and susceptibility of suicidal behavior. Here, we describe the current knowledge and findings in the relationship between apolipoproteins and suicide.HIGHLIGHTSThis is the first systematic review of Apos in relation to suicidal behavior.Dysregulations of Apos expression has been observed in patients with suicidal behavior.Apos seem to be associated with cognitive dysfunction in suicide attempters.ApoE is a potential biomarker regarding suicidal behavior.

Multiple serum biomarkers for predicting suicidal behaviours in depressive patients receiving pharmacotherapy

BACKGROUND

Predictive values of multiple serum biomarkers for suicidal behaviours (SBs) have rarely been tested. This study sought to evaluate and develop a panel of multiple serum biomarkers for predicting SBs in outpatients receiving a 12-month pharmacotherapy programme for depressive disorders.

METHODS

At baseline, 14 serum biomarkers and socio-demographic/clinical characteristics including previous suicidal attempt and present suicidal severity were evaluated in 1094 patients with depressive disorders without a bipolar diagnosis. Of these, 884 were followed for increased suicidal severity and fatal/non-fatal suicide attempt outcomes over a 12-month treatment period. Individual and combined effects of serum biomarkers on these two prospective SBs were estimated using logistic regression analysis after adjustment for relevant covariates.

RESULTS

Increased suicidal severity and fatal/non-fatal suicide attempt during the 12-month pharmacotherapy were present in 155 (17.5%) and 38 (4.3%) participants, respectively. Combined cortisol, total cholesterol, and folate serum biomarkers predicted fatal/non-fatal suicide attempt, and these with interleukin-1 beta and homocysteine additionally predicted increased suicidal severity, with clear gradients robust to adjustment (p values < 0.001).

CONCLUSIONS

Application of multiple serum biomarkers could considerably improve the predictability of SBs during the outpatient treatment of depressive disorders, potentially highlighting the need for more frequent monitoring and risk appraisal.

Towards precision medicine for anxiety disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

Anxiety disorders are increasingly prevalent, affect people's ability to do things, and decrease quality of life. Due to lack of objective tests, they are underdiagnosed and sub-optimally treated, resulting in adverse life events and/or addictions. We endeavored to discover blood biomarkers for anxiety, using a four-step approach. First, we used a longitudinal within-subject design in individuals with psychiatric disorders to discover blood gene expression changes between self-reported low anxiety and high anxiety states. Second, we prioritized the list of candidate biomarkers with a Convergent Functional Genomics approach using other evidence in the field. Third, we validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects with clinically severe anxiety. Fourth, we tested these candidate biomarkers for clinical utility, i.e. ability to predict anxiety severity state, and future clinical worsening (hospitalizations with anxiety as a contributory cause), in another independent cohort of psychiatric subjects. We showed increased accuracy of individual biomarkers with a personalized approach, by gender and diagnosis, particularly in women. The biomarkers with the best overall evidence were GAD1, NTRK3, ADRA2A, FZD10, GRK4, and SLC6A4. Finally, we identified which of our biomarkers are targets of existing drugs (such as a valproate, omega-3 fatty acids, fluoxetine, lithium, sertraline, benzodiazepines, and ketamine), and thus can be used to match patients to medications and measure response to treatment. We also used our biomarker gene expression signature to identify drugs that could be repurposed for treating anxiety, such as estradiol, pirenperone, loperamide, and disopyramide. Given the detrimental impact of untreated anxiety, the current lack of objective measures to guide treatment, and the addiction potential of existing benzodiazepines-based anxiety medications, there is a urgent need for more precise and personalized approaches like the one we developed.

Suicidality in civilian women with PTSD: Possible link to childhood maltreatment, proinflammatory molecules, and their genetic variations

Background

Posttraumatic stress disorder (PTSD) is a robust risk factor for suicide. Studies have suggested an association between suicide and elevated inflammatory markers, although such evidence in PTSD is scarce. Suicide risk, PTSD, and inflammatory molecules are all shown to be associated with childhood maltreatment and genetic factors.

Methods

We examined the association between suicidal ideation/risk and inflammatory markers in 83 civilian women with PTSD, and explored the possible influence of childhood maltreatment and inflammatory genes. Suicidal ideation and risk were assessed using the Beck Depression Inventory-II and the Mini-International Neuropsychiatric Interview. Childhood maltreatment history was assessed with the Childhood Trauma Questionnaire (CTQ). Blood levels of high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6) and high-sensitivity tumor necrosis factor-α were measured. Genetic polymorphisms of CRP rs2794520 and IL6 rs1800796 were genotyped.

Results

Suicidal ideation was significantly positively correlated with hsCRP (p = 0.002) and IL-6 (p = 0.015) levels. Suicide risk weighted score was significantly positively correlated with hsCRP (p = 0.016) levels. The risk alleles of CRP rs2794520 and IL6 rs1800796 leading to increased respective protein levels were dose-dependently associated with higher risk of suicide (p = 0.007 and p = 0.029, respectively). The CTQ total score was significantly correlated with suicidal ideation and risk, but not with inflammatory marker levels. Furthermore, a multivariate regression analysis controlling for PTSD severity and potential confounders revealed that rs2794520 and rs1800796, but not hsCRP or IL-6 levels, significantly predicted suicidal ideation (p < 0.001) and risk (p = 0.007), respectively.

Conclusion

Genetic variations within inflammatory genes might be useful in detecting PTSD patients at high risk of suicide.

The Impact of BDNF, NTRK2, NGFR, CREB1, GSK3B, AKT, MAPK1, MTOR, PTEN, ARC, and SYN1 Genetic Polymorphisms in Antidepressant Treatment Response Phenotypes

This study aimed to investigate the influence of genetic variants in neuroplasticity-related genes on antidepressant treatment phenotypes. The BDNF-TrkB signaling pathway, as well as the downstream kinases Akt and ERK and the mTOR pathway, have been implicated in depression and neuroplasticity. However, clinicians still struggle with the unpredictability of antidepressant responses in depressed patients. We genotyped 26 polymorphisms in BDNF, NTRK2, NGFR, CREB1, GSK3B, AKT, MAPK1, MTOR, PTEN, ARC, and SYN1 in 80 patients with major depressive disorder treated according to the Texas Medical Algorithm for 27 months at Hospital Magalhães Lemos, Porto, Portugal. Our results showed that BDNF rs6265, PTEN rs12569998, and SYN1 rs1142636 SNP were associated with treatment-resistant depression (TRD). Additionally, MAPK1 rs6928 and GSK3B rs6438552 gene polymorphisms were associated with relapse. Moreover, we found a link between the rs6928 MAPK1 polymorphism and time to relapse. These findings suggest that the BDNF, PTEN, and SYN1 genes may play a role in the development of TRD, while MAPK1 and GSK3B may be associated with relapse. GO analysis revealed enrichment in synaptic and trans-synaptic transmission pathways and glutamate receptor activity with TRD-associated genes. Genetic variants in these genes could potentially be incorporated into predictive models of antidepressant response.

Implementing Evidence-Based Suicide Prevention Strategies for Greatest Impact

Suicide remains a leading cause of death in the United States and globally. In this review, epidemiological trends in mortality and suicide risk are presented, with consideration given to the impact of the COVID-19 pandemic. A public health model of suicide prevention with a community and clinical framework, along with advances in scientific discovery, offer new solutions that await widespread implementation. Actionable interventions with evidence for reducing risk for suicidal behavior are presented, including universal and targeted strategies at community, public policy, and clinical levels. Clinical interventions include screening and risk assessment; brief interventions (e.g., safety planning, education, and lethal means counseling) that can be done in primary care, emergency, and behavioral health settings; psychotherapies (cognitive-behavioral, dialectical behavior, mentalization therapy); pharmacotherapy; and systemwide procedures for health care organizations (training, policies, workflow, surveillance of suicide indicators, use of health records for screening, care steps). Suicide prevention strategies must be prioritized and implemented at scale for greatest impact.

The promise and challenges of extracellular vesicles in the diagnosis of neurodegenerative diseases

Extracellular vesicles (EVs) have emerged as essential means of intercommunication for all cell types, and their role in CNS physiology is increasingly appreciated. Accumulating evidence has demonstrated that EVs play important roles in neural cell maintenance, plasticity, and growth. However, EVs have also been demonstrated to spread amyloids and inflammation characteristic of neurodegenerative disease. Such dual roles suggest that EVs may be prime candidates for neurodegenerative disease biomarker analysis. This is supported by several intrinsic properties of EVs: Populations can be enriched by capturing surface proteins from their cell of origin, their diverse cargo represent the complex intracellular states of the cells they derive from, and they can pass the blood-brain barrier. Despite this promise, there are important questions outstanding in this young field that will need to be answered before it can fulfill its potential. Namely, overcoming the technical challenges of isolating rare EV populations, the difficulties inherent in detecting neurodegeneration, and the ethical considerations of diagnosing asymptomatic individuals. Although daunting, succeeding to answer these questions has the potential to provide unprecedented insight and improved treatment of neurodegenerative disease in the future.

Peripheral blood cytokines as potential diagnostic biomarkers of suicidal ideation in patients with first-episode drug-naïve major depressive disorder

Objective

Major Depressive Disorder (MDD) is a leading cause of disability, with a high risk of suicidal ideation (SI). Few studies have evaluated the potential of multiple cytokines as biomarkers for SI in patients with MDD. In the present study, we examined the serum levels of multiple cytokines in patients with first-episode drug-naïve MDD, with the aim to discover and identify serum cytokines-based biomarkers for identification of SI in MDD.

Methods

A total of 55 patients with first-episode drug-naïve MDD were enrolled and divided into two groups: 26 MDD patients without SI and 29 MDD patients with SI. Beck Scale for Suicide Ideation was used to estimate SI. A total of 37 cytokines were measured using Multiplex Luminex Assays. The levels of serum cytokines between MDD patients without SI and MDD patients with SI were compared and diagnostic values of different cytokines were evaluated using the receiver operating characteristic (ROC) curve method for discriminating MDD patients with SI from MDD patients without SI. The relationship between the group and the abnormal cytokines were investigated in multiple linear regression models, with adjustments for age, gender, BMI, smoking, and Hamilton Depression Rating Scale-24 (HAMD-24) scores.

Results

The levels of CCL26 and VEGF in MDD patients with SI were significantly lower than those in MDD patients without SI (all P < 0.05). On the contrary, the levels of IL-17C, CXCL10, and TNF-β in MDD patients with SI were significantly higher than those in MDD patients without SI (all P < 0.05). Moreover, the results of multiple linear regression revealed that group was a significant independent predictor of serum IL-17C, CCL-26, VEGF, and TNF-β levels (all P < 0.05). In terms of CXC10, group was also likely to be a significant independent predictor (β = 0.257, P = 0.063). Furthermore, the AUC values of IL-17C and TNF-β were 0.728 and 0.732, respectively. Additionally, a combined panel of IL-17C and TNF-β achieved a high accuracy in discriminating MDD patients with SI from MDD patients without SI (AUC = 0.848, sensitivity = 75.9%, specificity = 72.7%).

Conclusions

These results suggested that circulating IL-17C and TNF-β may hold promise in the discovery of biomarkers for identification of SI in MDD.

Cerebrospinal fluid concentration of complement component 4A is increased in first episode schizophrenia

Postsynaptic density is reduced in schizophrenia, and risk variants increasing complement component 4A (C4A) gene expression are linked to excessive synapse elimination. In two independent cohorts, we show that cerebrospinal fluid (CSF) C4A concentration is elevated in patients with first-episode psychosis (FEP) who develop schizophrenia (FEP-SCZ: median 0.41 fmol/ul [CI = 0.34-0.45], FEP-non-SCZ: median 0.29 fmol/ul [CI = 0.22-0.35], healthy controls: median 0.28 [CI = 0.24-0.33]). We show that the CSF elevation of C4A in FEP-SCZ exceeds what can be expected from genetic risk variance in the C4 locus, and in patient-derived cellular models we identify a mechanism dependent on the disease-associated cytokines interleukin (IL)-1beta and IL-6 to selectively increase neuronal C4A mRNA expression. In patient-derived CSF, we confirm that IL-1beta correlates with C4A controlled for genetically predicted C4A RNA expression (r = 0.39; CI: 0.01-0.68). These results suggest a role of C4A in early schizophrenia pathophysiology.

Suicidal behavior-advances in clinical and neurobiological research and improvement of prevention strategies

Suicide is the 14^th leading cause of death worldwide. It is responsible for 1%-5% of all mortality. This article highlights the latest developments in universal, selective, and indicated prevention strategies. Concerning universal suicide prevention, current research has shown that strategies such as restricting access to lethal means (e.g., control of analgesics and hot-spots for suicide by jumping) and school-based awareness programs are most efficacious. Regarding selective prevention, substantial progress can be expected in psychological screening methods for suicidal behavior. The measurement of implicit cognition proved to be more valid in predicting future suicide attempts than classic clinical assessment. Latest developments are smartphone-based interventions and real-time monitoring of suicidal behavior. Great effort has been made to establish valid neurobiological screening methods (e.g., genetic and epigenetic risk factors for suicide, hypothalamic-pituitary-adrenal axis) without yielding a major bre-akthrough. Potentially, multiple biomarkers rather than a single one are necessary to identify individuals at risk. With regard to indicated prevention in form of psychopharmacological treatment, recent pharmacoepidemiological studies and meta-analyses have supported a protective role of antidepressants, lithium, and clozapine. However, the data concerning a specific anti-suicidal effect of these drugs are currently not consistent. Promising results exist for ketamine in reducing suicidal ideation, independently of its antidepressant effect. Concerning psychotherapy, recent findings suggest that psychotherapeutic interventions specifically designed to prevent suicide re-attempts are most efficacious. Specifically, cognitive behavioral therapy and psychodynamic therapy approaches proved to decrease the number of suicide re-attempts significantly.

Single, Fixed-Dose Intranasal Ketamine for Alleviation of Acute Suicidal Ideation. An Emergency Department, Trans-Diagnostic Approach: A Randomized, Double-Blind, Placebo-Controlled, Proof-of-Concept Trial

BACKGROUND

Suicidal patients often present to the emergency department, where specific anti-suicidal treatment is lacking. Ketamine, a Glutamate modulator and a rapidly acting antidepressant with anti-suicidal properties, might offer relief.

AIMS

Evaluation of single, fixed-dosed intranasal ketamine for acute suicidal ideation in the emergency department.

METHODS

Between August 2016 and April 2018, 30 eligible suicidal subjects, scheduled for psychiatric hospitalization, independently of their psychiatric diagnosis, were randomized to intranasal ketamine 40 mg or saline placebo. Safety and efficacy evaluations were scheduled for 30, 60, 120 and 240 min post administration and on days 1, 2, 3, 4, 5, 7, 21 and 28. Primary outcome was suicidal ideation.

RESULTS

Fifteen subjects were randomized for each study group. All were analyzed for primary and secondary outcomes. Four hours post administration, the mean difference in suicidal symptoms between the groups, measured by the Montgomery-Åsberg Depression Rating Scale (MADRS) item of suicidal thoughts (MADRS-SI), was 1.267 (95% confident interval 0.1-2.43, p < 0.05) favoring treatment. Remission from suicidal ideation was evident in 80% for the ketamine group compared with 33% for the controls (p < 0.05). The mean difference in depressive symptoms, measured by MADRS, at the same time was 9.75 (95% confident interval 0.72-18.79, p < 0.05) favoring ketamine. Treatment was safe and well-tolerated.

CONCLUSIONS

Single, fixed-dose, intranasal ketamine alleviated suicidal ideation and improved depressive symptoms four hours post administration. We present here an innovative paradigm for emergency department management of suicidal individuals. Future larger-scale studies are warranted. ClinicalTrials.gov Identifier: NCT02183272.

Differential Methylation Analysis of Suicidal Ideation Severity in Schizophrenia with the Illumina MethylationEPIC Array

There is a multitude of factors that makes difficult to identify those at risk for suicide, especially among schizophrenia patients. Suicide cannot be explained by genetics alone, therefore epigenetic mechanisms including DNA methylation are thought to play a role. DNA methylation could be a valuable tool in helping predict those at-risk individuals. This cross-sectional study comprised 112 subjects diagnosed with schizophrenia spectrum disorders, and were grouped according to the current suicidal ideation severity. DNA methylation across the genome was measured with the Infinium® MethylationEPIC BeadChip. We utilized the dmpFinder and bumphunter functions within the Bioconductor minfi package to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs), respectively. Following quality control, we removed one sample from the analysis and reported the most significant DMPs and DMRs associated with suicidal ideation severity. All positions and regions identified in this analysis were only found to have suggestive levels of significance at the genome-wide level. The present study was one of the first to investigate genome-wide methylation and suicidal ideation severity. While there were many strengths of our study, including investigating both differentially methylated positions and regions, further larger-scale studies are necessary to replicate, support, and validate our findings presented here.

FOXN3 and GDNF Polymorphisms as Common Genetic Factors of Substance Use and Addictive Behaviors

Epidemiological and phenomenological studies suggest shared underpinnings between multiple addictive behaviors. The present genetic association study was conducted as part of the Psychological and Genetic Factors of Addictions study (n = 3003) and aimed to investigate genetic overlaps between different substance use, addictive, and other compulsive behaviors. Association analyses targeted 32 single-nucleotide polymorphisms, potentially addictive substances (alcohol, tobacco, cannabis, and other drugs), and potentially addictive or compulsive behaviors (internet use, gaming, social networking site use, gambling, exercise, hair-pulling, and eating). Analyses revealed 29 nominally significant associations, from which, nine survived an FDRbl correction. Four associations were observed between FOXN3 rs759364 and potentially addictive behaviors: rs759364 showed an association with the frequency of alcohol consumption and mean scores of scales assessing internet addiction, gaming disorder, and exercise addiction. Significant associations were found between GDNF rs1549250, rs2973033, CNR1 rs806380, DRD2/ANKK1 rs1800497 variants, and the “lifetime other drugs” variable. These suggested that genetic factors may contribute similarly to specific substance use and addictive behaviors. Specifically, FOXN3 rs759364 and GDNF rs1549250 and rs2973033 may constitute genetic risk factors for multiple addictive behaviors. Due to limitations (e.g., convenience sampling, lack of structured scales for substance use), further studies are needed. Functional correlates and mechanisms underlying these relationships should also be investigated.

Identification of potential blood biomarkers associated with suicide in major depressive disorder

Suicides have increased to over 48,000 deaths yearly in the United States. Major depressive disorder (MDD) is the most common diagnosis among suicides, and identifying those at the highest risk for suicide is a pressing challenge. The objective of this study is to identify changes in gene expression associated with suicide in brain and blood for the development of biomarkers for suicide. Blood and brain were available for 45 subjects (53 blood samples and 69 dorsolateral prefrontal cortex (DLPFC) samples in total). Samples were collected from MDD patients who died by suicide (MDD-S), MDDs who died by other means (MDD-NS) and non-psychiatric controls. We analyzed gene expression using RNA and the NanoString platform. In blood, we identified 14 genes which significantly differentiated MDD-S versus MDD-NS. The top six genes differentially expressed in blood were: PER3, MTPAP, SLC25A26, CD19, SOX9, and GAR1. Additionally, four genes showed significant changes in brain and blood between MDD-S and MDD-NS; SOX9 was decreased and PER3 was increased in MDD-S in both tissues, while CD19 and TERF1 were increased in blood but decreased in DLPFC. To our knowledge, this is the first study to analyze matched blood and brain samples in a well-defined population of MDDs demonstrating significant differences in gene expression associated with completed suicide. Our results strongly suggest that blood gene expression is highly informative to understand molecular changes in suicide. Developing a suicide biomarker signature in blood could help health care professionals to identify subjects at high risk for suicide.

Genetics and Brain Transcriptomics of Completed Suicide

OBJECTIVE

The authors sought to study the transcriptomic and genomic features of completed suicide by parsing the method chosen, to capture molecular correlates of the distinctive frame of mind of individuals who die by suicide, while reducing heterogeneity.

METHODS

The authors analyzed gene expression (RNA sequencing) from postmortem dorsolateral prefrontal cortex of patients who died by suicide with violent compared with nonviolent means, nonsuicide patients with the same psychiatric disorders, and a neurotypical group (total N=329). They then examined genomic risk scores (GRSs) for each psychiatric disorder included, and GRSs for cognition (IQ) and for suicide attempt, testing how they predict diagnosis or traits (total N=888).

RESULTS

Patients who died by suicide by violent means showed a transcriptomic pattern remarkably divergent from each of the other patient groups but less from the neurotypical group; consistently, their genomic profile of risk was relatively low for their diagnosed illness as well as for suicide attempt, and relatively high for IQ: they were more similar to the neurotypical group than to other patients. Differentially expressed genes (DEGs) associated with patients who died by suicide by violent means pointed to purinergic signaling in microglia, showing similarities to a genome-wide association study of Drosophila aggression. Weighted gene coexpression network analysis revealed that these DEGs were coexpressed in a context of mitochondrial metabolic activation unique to suicide by violent means.

CONCLUSIONS

These findings suggest that patients who die by suicide by violent means are in part biologically separable from other patients with the same diagnoses, and their behavioral outcome may be less dependent on genetic risk for conventional psychiatric disorders and be associated with an alteration of purinergic signaling and mitochondrial metabolism.

Proteins associated with future suicide attempts in bipolar disorder: A large-scale biomarker discovery study

Suicide is a major cause of death worldwide. Several biological systems have been implicated in suicidal behavior but studies of candidate biomarkers have failed to produce clinically relevant biomarkers for suicide prediction. The objective of the present study was to identify novel candidate biomarkers for suicidal behavior. We used a nested case-control study design where a large cohort of patients with bipolar disorder (N = 5 110) were followed up to 8 years after blood sampling. We included patients that attempted suicide during follow-up (N = 348) and matched bipolar disorder patients from the same cohort who did not attempt suicide during the study period (N = 348) and analyzed a total of 92 proteins with a neuro exploratory multiplex panel. Using a multivariate classification algorithm devised to minimize bias in variable selection, we identified a parsimonious set of proteins that best discriminated bipolar disorder patients with and without prospective suicide attempts. The algorithm selected 16 proteins for the minimal-optimal classification model, which outperformed 500 models with permuted outcome (p = 0.0004) but had low sensitivity (53%) and specificity (64%). The candidate proteins were then entered in separate logistic regression models to calculate protein-specific associations with prospective suicide attempts. In individual analyses, three of these proteins were significantly associated with prospective suicide attempt (SCGB1A1, ANXA10, and CETN2). Most of the candidate proteins are novel to suicide research.

Persistent Depressive Symptoms and the Changes in Serum Cystatin C Levels in the Elderly: A Longitudinal Cohort Study

BACKGROUND

The burden of depression in the elderly is increasing worldwide with global aging. However, there is still a lack of research on the relationship between depressive symptoms and the progression of renal function. Our aim is to evaluate the longitudinal association between baseline depressive symptoms and the changes in serum cystatin C levels over 10 years' follow-up period.

METHODS

We used longitudinal data from the Health and Retirement Study (HRS), an existing community based nationally representative aging cohort study which enrolled individuals over age 50 in the USA. Depressive symptoms were determined using an eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD) at wave 7 (2004) and wave 8 (2006). Persistent depressive symptoms were defined as both CESD scores measured at waves 7 and 8 were ≥3; episodic depressive symptoms were defined as CESD scores ≥3 at wave 7 or wave 8. A linear mixed model was used to evaluate the correlation between baseline depressive symptoms and future changes in cystatin C levels.

RESULTS

The mean age of the 7,642 participants was 63.8 ± 10.8 years, and 60.9% were women. Among the participants, 1,240 (16.2%) had episodic depressive symptoms and 778 (10.2%) had persistent depressive symptoms. Compared with participants with no depressive symptoms at both waves, a significant increase in serum cystatin C levels was found among those with persistent depressive symptoms.

CONCLUSIONS

Our results showed that baseline persistent depressive symptoms were significantly associated with an increased rate of serum cystatin C levels. The level of serum cystatin C should be monitored in the elderly with persistent depressive symptoms.

Psilocybin, a Naturally Occurring Indoleamine Compound, Could Be Useful to Prevent Suicidal Behaviors

The available interventions for people who are at risk of suicide have limited efficacy. Recently, research on new mental health treatments has started to consider psychedelic compounds, particularly psilocybin, a molecule with a few thousand years of history of use in human societies. The possible effects of psilocybin on suicidal ideation and behaviors have not been specifically studied yet; however, the current knowledge on the suicidal process and the available data on es/ketamine suggest that psylocibin could be used to modulate the thoughts and behavioral patterns in individuals who are at risk of suicidal behaviors. Here, we summarize the available evidence on the possible mechanisms underlying psilocybin positive effects on suicide risk. Major pathways related to suicidal behaviors that might be modulated by psylocibin include serotonin receptors. Specifically, psylocibin directly stimulates the serotonin 2A receptor (5HT_2A), targeting the inflammatory and oxidative stress pathways and leading to a rapid increase in brain plasticity and inflammation suppression and increases in cognitive flexibility, spirituality, and empathy. We also present preliminary epidemiological data and provide a rationale for studying psilocybin in individuals with suicidal ideation or who are at risk of suicidal behaviors. This review presents a framework to understand the basis for psilocybin use in individuals who are at risk of suicidal behaviors and calls for clinical studies.

Associations of serum cystatin C with depressive symptoms and suicidal ideation in major depressive disorder

BACKGROUND

Individuals with major depressive disorder (MDD) have high suicidal ideation. There is evidence that serum cystatin C (Cys C) may be involved in the pathophysiology of MDD. The present study aimed to investigate Cys C concentration in patients with MDD and clarify its possible association with depressive symptoms and suicidal ideation.

METHODS

An online cross-sectional survey of 159 patients diagnosed with MDD was conducted. Serum Cys C levels were measured using ADVIA 2400 biochemical analyzer. The 24-item Hamilton Depression Scale (HAMD-24) was administered to evaluate the depressive symptoms. Generalized linear regression, logistic regression and restricted cubic spline models were used to examine the association of serum Cys C levels with depressive symptoms and suicidal ideation.

RESULTS

Serum Cys C levels were higher in MDD patients than in controls (p = 0.001) and were positively associated with scores on HAMD-24 in unadjusted (gender distribution, age, smoking, alcohol consumption, family history of depression and traumatic life events; (p = 0.003) and fully adjusted linear regression model (p = 0.005). The fully adjusted regression coefficient with 95% confidence intervals for serum Cys C levels and HAMD-24 score was 30.339 (9.602 to 51.077). The level of Cys C in the suicidal ideation (SI) group was significantly higher than that in the non-suicide ideation (non-SI) group (p = 0.001). Serum Cys C levels were positively associated with suicidal ideation in each logistic regression model (all p <  0.05).

CONCLUSION

Serum Cys C levels were elevated in MDD patients and appeared to be positively correlated with depressive symptoms and suicidal ideation. These findings suggest that the dysfunction of Cys C may be involved in the severity of depression and in the pathophysiological process of MDD. Thus, regulation of serum Cys C could potentially be an effective predictor of the severity of depression and potentially, play a role in reducing the risk of suicide in MDD patients.

Neurobiological Basis of Increased Risk for Suicidal Behaviour

According to the World Health Organization (WHO), more than 700,000 people die per year due to suicide. Suicide risk factors include a previous suicide attempt and psychiatric disorders. The highest mortality rate in suicide worldwide is due to depression. Current evidence suggests that suicide etiopathogenesis is associated with neuroinflammation that activates the kynurenine pathway and causes subsequent serotonin depletion and stimulation of glutamate neurotransmission. These changes are accompanied by decreased BDNF (brain-derived neurotrophic factor) levels in the brain, which is often linked to impaired neuroplasticity and cognitive deficits. Most suicidal patients have a hyperactive hypothalamus–pituitary–adrenal (HPA) axis. Epigenetic mechanisms control the above-mentioned neurobiological changes associated with suicidal behaviour. Suicide risk could be attenuated by appropriate psychological treatment, electroconvulsive treatment, and drugs: lithium, ketamine, esketamine, clozapine. In this review, we present the etiopathogenesis of suicide behaviour and explore the mechanisms of action of anti-suicidal treatments, pinpointing similarities among them.

The GSK3-NRF2 Axis in Suicide

Mutations in the genes coding for tryptophan-hydrolase-2 and the scaffold protein FKBP5 are associated with an increased risk of suicide. The mutation in both cases enhances the enzymatic activity of glycogen synthase kinase-3 (GSK3). Conversely, anti-suicidal medications, such as lithium, clozapine, and ketamine, indirectly inhibit the activity of GSK3. When GSK3 is active, it promotes the metabolic removal of the transcription factor NRF2 (nuclear factor erythroid 2-related factor-2), which suppresses the transcription of multiple genes that encode anti-oxidative and anti-inflammatory proteins. Notably, several suicide-biomarkers bear witness to an ongoing inflammatory process. Moreover, alterations in serum lipid levels measured in suicidal individuals are mirrored by data obtained in mice with genetic deletion of the NRF2 gene. Inflammation is presumably causally related to both dysphoria and anger, two factors relevant for suicide ideation and attempt. Preventing the catabolism of NRF2 could be a strategy to obtain novel suicide-prophylactic medications. Possible candidates are minocycline and nicotinic-α7 agonists. The antibiotic minocycline indirectly activates NRF2-transcriptional activity, whereas the activation of nicotinic-α7 receptors indirectly inhibits GSK3.

Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation

Background

Major depressive disorder (MDD) and suicide are two major health problems, but there are still no objective methods to diagnose MDD or suicidal ideation (SI). This study was conducted to identify potential biomarkers for diagnosing MDD patients with SI.

Methods

First-episode drug-naïve MDD patients with SI and demographics-matched healthy controls (HCs) were recruited. First-episode drug-naïve MDD patients without SI were also included. The serum lipids, C-reactive protein (CRP), transferring (TRSF), homocysteine (HCY) and alpha 1-antitrypsin (AAT) in serum were detected. The univariate and multivariate statistical analyses were used to identify and validate the potential biomarkers.

Results

The 86 HCs, 53 MDD patients with SI and 20 MDD patients without SI were included in this study. Four potential biomarkers were identified: AAT, TRSF, high-density lipoprotein cholesterol (HDLC), and apolipoprotein A1 (APOA1). After one month treatment, the levels of AAT and APOA1 were significantly improved. The panel consisting of these potential biomarkers had an excellent diagnostic performance, yielding an area under the ROC curve (AUC) of 0.994 and 0.990 in the training and testing set, respectively. Moreover, this panel could effectively distinguish MDD patients with SI from MDD patients without SI (AUC=0.928).

Conclusion

These results showed that these potential biomarkers could facilitate the development of an objective method for diagnosing MDD patients with SI, and the decreased AAT levels in MDD patients might lead to the appearance of SI by resulting in the elevated inflammation.

Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

Mood disorders (depression, bipolar disorders) are prevalent and disabling. They are also highly co-morbid with other psychiatric disorders. Currently there are no objective measures, such as blood tests, used in clinical practice, and available treatments do not work in everybody. The development of blood tests, as well as matching of patients with existing and new treatments, in a precise, personalized and preventive fashion, would make a significant difference at an individual and societal level. Early pilot studies by us to discover blood biomarkers for mood state were promising [1], and validated by others [2]. Recent work by us has identified blood gene expression biomarkers that track suicidality, a tragic behavioral outcome of mood disorders, using powerful longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality) [3-6]. These studies showed good reproducibility with subsequent independent genetic studies [7]. More recently, we have conducted such studies also for pain [8], for stress disorders [9], and for memory/Alzheimer's Disease [10]. We endeavored to use a similar comprehensive approach to identify more definitive biomarkers for mood disorders, that are transdiagnostic, by studying mood in psychiatric disorders patients. First, we used a longitudinal within-subject design and whole-genome gene expression approach to discover biomarkers which track mood state in subjects who had diametric changes in mood state from low to high, from visit to visit, as measured by a simple visual analog scale that we had previously developed (SMS-7). Second, we prioritized these biomarkers using a convergent functional genomics (CFG) approach encompassing in a comprehensive fashion prior published evidence in the field. Third, we validated the biomarkers in an independent cohort of subjects with clinically severe depression (as measured by Hamilton Depression Scale, (HAMD)) and with clinically severe mania (as measured by the Young Mania Rating Scale (YMRS)). Adding the scores from the first three steps into an overall convergent functional evidence (CFE) score, we ended up with 26 top candidate blood gene expression biomarkers that had a CFE score as good as or better than SLC6A4, an empirical finding which we used as a de facto positive control and cutoff. Notably, there was among them an enrichment in genes involved in circadian mechanisms. We further analyzed the biological pathways and networks for the top candidate biomarkers, showing that circadian, neurotrophic, and cell differentiation functions are involved, along with serotonergic and glutamatergic signaling, supporting a view of mood as reflecting energy, activity and growth. Fourth, we tested in independent cohorts of psychiatric patients the ability of each of these 26 top candidate biomarkers to assess state (mood (SMS-7), depression (HAMD), mania (YMRS)), and to predict clinical course (future hospitalizations for depression, future hospitalizations for mania). We conducted our analyses across all patients, as well as personalized by gender and diagnosis, showing increased accuracy with the personalized approach, particularly in women. Again, using SLC6A4 as the cutoff, twelve top biomarkers had the strongest overall evidence for tracking and predicting depression after all four steps: NRG1, DOCK10, GLS, PRPS1, TMEM161B, GLO1, FANCF, HNRNPDL, CD47, OLFM1, SMAD7, and SLC6A4. Of them, six had the strongest overall evidence for tracking and predicting both depression and mania, hence bipolar mood disorders. There were also two biomarkers (RLP3 and SLC6A4) with the strongest overall evidence for mania. These panels of biomarkers have practical implications for distinguishing between depression and bipolar disorder. Next, we evaluated the evidence for our top biomarkers being targets of existing psychiatric drugs, which permits matching patients to medications in a targeted fashion, and the measuring of response to treatment. We also used the biomarker signatures to bioinformatically identify new/repurposed candidate drugs. Top drugs of interest as potential new antidepressants were pindolol, ciprofibrate, pioglitazone and adiphenine, as well as the natural compounds asiaticoside and chlorogenic acid. The last 3 had also been identified by our previous suicidality studies. Finally, we provide an example of how a report to doctors would look for a patient with depression, based on the panel of top biomarkers (12 for depression and bipolar, one for mania), with an objective depression score, risk for future depression, and risk for bipolar switching, as well as personalized lists of targeted prioritized existing psychiatric medications and new potential medications. Overall, our studies provide objective assessments, targeted therapeutics, and monitoring of response to treatment, that enable precision medicine for mood disorders.

Serum β-endorphin levels are associated with addiction to suicidal behavior: A pilot study

The literature provides partial support for the hypothesis that some suicide attempters develop a behavioral addiction to suicidal behavior (SB). We hypothesized that major suicide repeaters (MR) (≥5 lifetime suicide attempts) are addicted to suicide attempts as measured by modified DSM-IV criteria for substance dependence. In this cross-sectional study with 13 psychiatric controls (PC), 55 non-major suicide attempters (NMR), and 9 MR we found that MR are characterized by emotional abuse and neglect, as well as higher scores on the Personality and Life Event scale (short version). The levels of 8 AM serum ACTH, cortisol and β-endorphin were elevated in all three groups. Serum β-endorphin (pg/mL) was particularly high in PC diagnosed with schizophrenia 220.34 (±56.30). The level of 8 AM serum β-endorphin rose with increased numbers of criteria met for addiction to SB from 130.31 (±88.16) (≥ 3 criteria met for addiction to SB) to 174.84 (±114.93) (≥ 6 criteria met for addiction to SB) whereas serum ACTH and cortisol did not change. SB addicts (≥ 6 criteria) displayed higher serum β-endorphin concentrations than non-addicts (174.84 ± 114.93 vs. 116.93 ± 61.70, FET p = 0.09). The present study brings some support to the addictive hypothesis of SB. Our results delineate β-endorphin as a promising biomarker of SB addiction, and offer a good basis for future studies that test whether buprenorphine can be used to prevent repetitive suicide attempts, non-suicidal-self-injury (NSSI), and the development of an addiction to SB.

Convergent Functional Genomics approach to prioritize molecular targets of risk in early life stress-related psychiatric disorders

There is an overwhelming evidence proving that mental disorders are not the product of a single risk factor - i.e. genetic variants or environmental factors, including exposure to maternal perinatal mental health problems or childhood adverse events - rather the product of a trajectory of cumulative and multifactorial insults occurring during development, such as exposures during the foetal life to adverse mental condition in the mother, or exposures to adverse traumatic events during childhood or adolescence. In this review, we aim to highlight the potential utility of a Convergent Functional Genomics (CFG) approach to clarify the complex brain-relevant molecular mechanisms and alterations induced by early life stress (ELS). We describe different studies based on CFG in psychiatry and neuroscience, and we show how this 'hypothesis-free' tool can prioritize a stringent number of genes modulated by ELS, that can be tested as potential candidates for Gene x Environment (GxE) interaction studies. We discuss the results obtained by using a CFG approach identifying FoxO1 as a gene where genetic variability can mediate the effect of an adverse environment on the development of depression. Moreover, we also demonstrate that FoxO1 has a functional relevance in stress-induced reduction of neurogenesis, and can be a potential target for the prevention or treatment of stress-related psychiatric disorders. Overall, we suggest that CFG approach could include trans-species and tissues data integration and we also propose the application of CFG to examine in depth and to prioritize top candidate genes that are affected by ELS across lifespan and generations.

Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs

Short-term memory dysfunction is a key early feature of Alzheimer's disease (AD). Psychiatric patients may be at higher risk for memory dysfunction and subsequent AD due to the negative effects of stress and depression on the brain. We carried out longitudinal within-subject studies in male and female psychiatric patients to discover blood gene expression biomarkers that track short term memory as measured by the retention measure in the Hopkins Verbal Learning Test. These biomarkers were subsequently prioritized with a convergent functional genomics approach using previous evidence in the field implicating them in AD. The top candidate biomarkers were then tested in an independent cohort for ability to predict state short-term memory, and trait future positive neuropsychological testing for cognitive impairment. The best overall evidence was for a series of new, as well as some previously known genes, which are now newly shown to have functional evidence in humans as blood biomarkers: RAB7A, NPC2, TGFB1, GAP43, ARSB, PER1, GUSB, and MAPT. Additional top blood biomarkers include GSK3B, PTGS2, APOE, BACE1, PSEN1, and TREM2, well known genes implicated in AD by previous brain and genetic studies, in humans and animal models, which serve as reassuring de facto positive controls for our whole-genome gene expression discovery approach. Biological pathway analyses implicate LXR/RXR activation, neuroinflammation, atherosclerosis signaling, and amyloid processing. Co-directionality of expression data provide new mechanistic insights that are consistent with a compensatory/scarring scenario for brain pathological changes. A majority of top biomarkers also have evidence for involvement in other psychiatric disorders, particularly stress, providing a molecular basis for clinical co-morbidity and for stress as an early precipitant/risk factor. Some of them are modulated by existing drugs, such as antidepressants, lithium and omega-3 fatty acids. Other drug and nutraceutical leads were identified through bioinformatic drug repurposing analyses (such as pioglitazone, levonorgestrel, salsolidine, ginkgolide A, and icariin). Our work contributes to the overall pathophysiological understanding of memory disorders and AD. It also opens new avenues for precision medicine- diagnostics (assement of risk) as well as early treatment (pharmacogenomically informed, personalized, and preventive).

Genome-wide methylation association with current suicidal ideation in schizophrenia

In this study, we investigate the epigenetic mechanisms associated with current suicidal ideation. Gene expression changes have been found in post-mortem brain of suicide victims. However, it is not clear how in-vivo gene expression change confers risk for suicide. DNA methylation is a form of epigenetic modification that regulates gene expression. Our primary aim is to investigate genome-wide methylation in conferring risk for current suicidal ideation (SI) in schizophrenia. The presence of current SI and genome-wide methylation patterns were assessed in 107 patients with schizophrenia. DNA methylation has been measured in white blood cells as a possible peripheral biomarker of SI. SI was the primary outcome variable in a model including methylation status of white blood cells using the Illumina 450 array. We have tested the association with genome-wide methylation levels in 19 subjects with current SI and 88 subjects without current SI and we found that higher methylation level in the CpG cg06121808 located in the gene SLC20A1 on chromosome 2 was associated with current SI (p = 0.000003; beta difference = 0.06). Furthermore, the distal promoter analysis showed that the gene SMPD2 was hypermethylated in suicide ideators (p = 0.0001; beta difference = 0.02). Thus, molecular biomarkers could advance our understanding of the molecular mechanisms of stress-related SI. Furthermore, the methylation sites that we have identified should be replicated in other suicide related phenotypes to generate robust biomarkers with high translational value for proof of concept interventions aiming at reducing SI.

A logical relationship for schizophrenia, bipolar, and major depressive disorder. Part 1: Evidence from chromosome 1 high density association screen

Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was investigated systematically (Aukes et al., Genetics in Medicine, 2012, 14, 338-341) and any two or even three of these disorders could coexist in some families. Furthermore, evidence from symptomatology and psychopharmacology also imply the existence of intrinsic connections between these three major psychiatric disorders. A total of 71,445 SNPs on chromosome 1 were genotyped on 119 SCZ, 253 BPD (type-I), 177 MDD cases and 1000 controls and further validated in 986 SCZ patients in the population of Shandong province of China. Outstanding psychosis genes are systematically revealed( ATP1A4, ELTD1, FAM5C, HHAT, KIF26B, LMX1A, NEGR1, NFIA, NR5A2, NTNG1, PAPPA2, PDE4B, PEX14, RYR2, SYT6, TGFBR3, TTLL7, and USH2A). Unexpectedly, flanking genes for up to 97.09% of the associated SNPs were also replicated in an enlarged cohort of 986 SCZ patients. From the perspective of etiological rather than clinical psychiatry, bipolar, and major depressive disorder could be subtypes of schizophrenia. Meanwhile, the varied clinical feature and prognosis might be the result of interaction of genetics and epigenetics, for example, irreversible or reversible shut down, and over or insufficient expression of certain genes, which may gives other aspects of these severe mental disorders.

Towards precision medicine for stress disorders: diagnostic biomarkers and targeted drugs

The biological fingerprint of environmental adversity may be key to understanding health and disease, as it encompasses the damage induced as well as the compensatory reactions of the organism. Metabolic and hormonal changes may be an informative but incomplete window into the underlying biology. We endeavored to identify objective blood gene expression biomarkers for psychological stress, a subjective sensation with biological roots. To quantify the stress perception at a particular moment in time, we used a simple visual analog scale for life stress in psychiatric patients, a high-risk group. Then, using a stepwise discovery, prioritization, validation, and testing in independent cohort design, we were successful in identifying gene expression biomarkers that were predictive of high-stress states and of future psychiatric hospitalizations related to stress, more so when personalized by gender and diagnosis. One of the top biomarkers that survived discovery, prioritization, validation, and testing was FKBP5, a well-known gene involved in stress response, which serves as a de facto reassuring positive control. We also compared our biomarker findings with telomere length (TL), another well-established biological marker of psychological stress and show that newly identified predictive biomarkers such as NUB1, APOL3, MAD1L1, or NKTR are comparable or better state or trait predictors of stress than TL or FKBP5. Over half of the top predictive biomarkers for stress also had prior evidence of involvement in suicide, and the majority of them had evidence in other psychiatric disorders, providing a molecular underpinning for the effects of stress in those disorders. Some of the biomarkers are targets of existing drugs, of potential utility in patient stratification, and pharmacogenomics approaches. Based on our studies and analyses, the biomarkers with the best overall convergent functional evidence (CFE) for involvement in stress were FKBP5, DDX6, B2M, LAIR1, RTN4, and NUB1. Moreover, the biomarker gene expression signatures yielded leads for possible new drug candidates and natural compounds upon bioinformatics drug repurposing analyses, such as calcium folinate and betulin. Our work may lead to improved diagnosis and treatment for stress disorders such as PTSD, that result in decreased quality of life and adverse outcomes, including addictions, violence, and suicide.

Integrative analysis of lithium treatment associated effects on brain structure and peripheral gene expression reveals novel molecular insights into mechanism of action

Lithium is a highly effective medication for bipolar disorder, but its mechanism of action remains unknown. In this study, brain MRI scans and blood samples for gene expression (total of 110 scans and 109 blood samples) were collected from 21 bipolar subjects before and after 2 and 8 weeks of lithium monotherapy and at the same time-points from untreated 16 healthy controls. We used linear mixed-effects models to identify brain structural features and genes with expression changed after lithium treatment, with correction for multiple testing, and correlated their concurrent changes to identify molecular pathways associated with lithium effects. There are significant increases in gray matter fraction, global cortical thickness, and the frontal and parietal cortices after 8 weeks of lithium treatment (corrected p < 0.05). Volume increases were also seen for putamen, hippocampus, thalamic nuclei, and thalamic substructures. Several genes showed significant expression changes, and 14 gene pathways were identified for the present integration analysis. Of these, nine pathways had significant correlations with structural changes (FDR < 0.05). Three neurotrophy-related pathways (GDNF family of ligands, NFAT immune-response, and p53-signaling pathway) correlated with structural changes in multiple regions. Mediation analysis showed that the sphingomyelin metabolism pathway is associated with HAM-D change (p < 0.01), and this effect is mediated via the volume of mediodorsal thalamus (p < 0.03). In summary, the integration of lithium effects on brain structural and peripheral gene expression changes revealed effects on several neurotrophic molecular pathways, which provides further insights into the mechanism of lithium action.

Plasma microRNA Array Analysis Identifies Overexpressed miR-19b-3p as a Biomarker of Bipolar Depression Distinguishing From Unipolar Depression

OBJECTIVES

The clinical characteristics of bipolar disorder (current major depressive episode) (BD) overlap with unipolar depressive disorder (UD), which makes it difficult to perform an accurate diagnosis. We identified plasma microRNAs (miRNAs) that distinguished BD from UD and explored the relationship between miRNA expression levels and clinical characteristics.

METHODS

Total miRNAs from blood plasma from seven UD patients, seven BD patients, and six controls were analyzed. The identified miRNAs were validated in a separate population group. Depression severity and early life adversities were assessed. Bioinformatic analysis was conducted to investigate the target genes that were identified and the pathways associated with the altered miRNAs.

RESULTS

Compared to controls, 42 miRNAs were differentially expressed in patients. miR-19b-3p, miR-3921, and miR-1180-3p were selected to validate the microarray results. Only miR-19b-3p was validated as down-regulated in patients. The primary predicted genes associated with miR-19b-3p were MAPK1, PTEN, and PRKAA1. The most relevant KEGG pathways included mTOR, FoxO, and the PI3-K/Akt signaling pathway. BD patients were more likely to have higher expression levels of miR-19b-3p and more severe childhood trauma experience compared to UD patients.

CONCLUSIONS

Plasma miR-19b-3p is a potential non-invasive biomarker that might be useful in distinguishing UD from BD. miR-19b3p was predicted to be involved in the pathway of inflammatory dysregulation associated with experiencing early childhood trauma.

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R² values of 0.961 and 0.943 for Hamilton Rating Scale for Depression-17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment.

Novel Treatment Targets Based on Insights in the Etiology of Depression: Role of IL-6 Trans-Signaling and Stress-Induced Elevation of Glutamate and ATP

Inflammation and psychological stress are risk factors for major depression and suicide. Both increase central glutamate levels and activate the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. Both factors also affect the function of the chloride transporters, Na-K-Cl-cotransporter-1 (NKCC1) and K-Cl-cotransporter-2 (KCC2), and provoke interleukin-6 (IL-6) trans-signaling. This leads to measurable increases in circulating corticosteroids, catecholamines, anxiety, somatic and psychological symptoms, and a decline in cognitive functions. Recognition of the sequence of pathological events allows the prediction of novel targets for therapeutic intervention. Amongst others, these include blockade of the big-K potassium channel, blockade of the P2X4 channel, TYK2-kinase inhibition, noradrenaline α2B-receptor antagonism, nicotinic α7-receptor stimulation, and the Sgp130Fc antibody. A better understanding of downstream processes evoked by inflammation and stress also allows suggestions for tentatively better biomarkers (e.g., SERPINA3N, MARCKS, or ¹³C-tryptophan metabolism).

Spermidine/spermine-N1-acetyltransferase ablation impacts tauopathy-induced polyamine stress response

BACKGROUND

Tau stabilizes microtubules; however, in Alzheimer's disease (AD) and tauopathies, tau becomes hyperphosphorylated, aggregates, and results in neuronal death. Our group recently uncovered a unique interaction between polyamine metabolism and tau fate. Polyamines exert an array of physiological effects that support neuronal function and cognitive processing. Specific stimuli can elicit a polyamine stress response (PSR), resulting in altered central polyamine homeostasis. Evidence suggests that elevations in polyamines following a short-term stressor are beneficial; however, persistent stress and subsequent PSR activation may lead to maladaptive polyamine dysregulation, which is observed in AD, and may contribute to neuropathology and disease progression.

METHODS

Male and female mice harboring tau P301L mutation (rTg4510) were examined for a tau-induced central polyamine stress response (tau-PSR). The direct effect of tau-PSR byproducts on tau fibrillization and oligomerization were measured using a thioflavin T assay and a N2a split superfolder GFP-Tau (N2a-ssGT) cell line, respectively. To therapeutically target the tau-PSR, we bilaterally injected caspase 3-cleaved tau truncated at aspartate 421 (AAV9 Tau ΔD421) into the hippocampus and cortex of spermidine/spermine-N¹-acetyltransferase (SSAT), a key regulator of the tau-PSR, knock out (SSAT-/-), and wild type littermates, and the effects on tau neuropathology, polyamine dysregulation, and behavior were measured. Lastly, cellular models were employed to further examine how SSAT repression impacted tau biology.

RESULTS

Tau induced a unique tau-PSR signature in rTg4510 mice, notably in the accumulation of acetylated spermidine. In vitro, higher-order polyamines prevented tau fibrillization but acetylated spermidine failed to mimic this effect and even promoted fibrillization and oligomerization. AAV9 Tau ΔD421 also elicited a unique tau-PSR in vivo, and targeted disruption of SSAT prevented the accumulation of acetylated polyamines and impacted several tau phospho-epitopes. Interestingly, SSAT knockout mice presented with altered behavior in the rotarod task, the elevated plus maze, and marble burying task, thus highlighting the impact of polyamine homeostasis within the brain.

CONCLUSION

These data represent a novel paradigm linking tau pathology and polyamine dysfunction and that targeting specific arms within the polyamine pathway may serve as new targets to mitigate certain components of the tau phenotype.

Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death

BACKGROUND

Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering clinical phenomenology improves prediction of longer term outcomes remains understudied. Hospital discharge provides an opportunity to stratify risk using readily available clinical records and details.

METHODS

We applied a validated natural language processing tool to generate estimated Research Domain Criteria (RDoC) scores for a cohort of 444,317 individuals drawn from 815,457 hospital discharges between 2005 and 2013. We used survival analysis to examine the association of this risk with suicide and accidental death, adjusted for sociodemographic features.

RESULTS

In adjusted models, symptoms in each of the five domains contributed to incremental risk (log rank P < 0.001), with greatest increase observed with positive valence. The contribution of each domain to risk was time dependent.

CONCLUSIONS

RDoC symptom scores parsed from clinical documentation are associated with suicide and illustrates that multiple domains contribute to risk in a time-varying fashion.

Child Autism Spectrum Disorder Traits and Parenting Stress: The Utility of Using a Physiological Measure of Parental Stress

Caregivers of children with autism spectrum disorder (ASD) report greater stress due to unique parenting demands (e.g.; Estes et al. in Brain Dev 35(2):133-138, 2013). Stress is often studied through self-report and has not been extensively studied using physiological measures. This study compared parenting stress in mothers of children with and without ASD traits. Twenty-seven mother-child dyads participated in an interaction task while measuring mother's heart rate variability (HRV) and mothers self-reported stress levels. Results demonstrated that while self-report and physiological stress measures were not correlated, ASD symptomology did account for HRV change score (i.e., more severe ASD symptoms were positively related to HRV change). This may reflect an atypical coping response. Implications for using physiological indicators for studying parenting stress are explored.

Association of a Noncoding RNA Postmortem With Suicide by Violent Means and In Vivo With Aggressive Phenotypes

BACKGROUND

Previous findings suggest that differences in brain expression of a human-specific long intergenic noncoding RNA (LINC01268; GRCh37/hg19: LOC285758) may be linked to suicide by violent methods. We sought to replicate and extend these findings in a new sample and translate the results to the behavioral level in living healthy subjects.

METHODS

We examined RNA sequencing data in human brains to confirm the prior postmortem association of the long intergenic noncoding RNA specifically with suicide by violent means. In addition, we used a genetic variant associated with LINC01268 expression to detect association in healthy subjects with trait aggression and with in vivo prefrontal physiology related to behavioral control. Finally, we performed weighted gene coexpression network analysis and gene ontology analysis to identify biological processes associated with a LINC01268 coexpression network.

RESULTS

In the replication sample, prefrontal expression of LINC01268 was again higher in suicides by violent means (n = 65) than in both nonsuicides (n = 78; p = 1.29 × 10^-6) and suicides by nonviolent means (n = 46; p = 1.4 × 10^-6). In the living cohort, carriers of the minor allele of a single nucleotide polymorphism associated with increased LINC01268 expression in brain scored higher on a lifetime aggression questionnaire and show diminished engagement of prefrontal cortex (Brodmann area 10) when viewing angry faces during functional magnetic resonance imaging. Weighted gene coexpression network analysis highlighted the immune response.

CONCLUSIONS

These results suggest that LINC01268 influences emotional regulation, aggressive behavior, and suicide by violent means; the underlying biological dynamics may include modulation of genes potentially engaged in the immune response.

Association of Psychotic Experiences With Subsequent Risk of Suicidal Ideation, Suicide Attempts, and Suicide Deaths: A Systematic Review and Meta-analysis of Longitudinal Population Studies

IMPORTANCE

Recent research has highlighted that psychotic experiences are far more prevalent than psychotic disorders and associated with the full range of mental disorders. A particularly strong association between psychotic experiences and suicidal behavior has recently been noted.

OBJECTIVE

To provide a quantitative synthesis of the literature examining the longitudinal association between psychotic experiences and subsequent suicidal ideation, suicide attempts, and suicide deaths in the general population.

DATA SOURCES

We searched PubMed, Excerpta Medica Database, Cumulative Index to Nursing and Allied Health Literature, and PsycINFO from their inception until September 2017 for longitudinal population studies on psychotic experiences and subsequent suicidal ideation, suicide attempts, and suicide death.

STUDY SELECTION

Two authors searched for original articles that reported a prospective assessment of psychotic experiences and suicidal ideation, suicide attempts, or suicide death in general population samples, with at least 1 follow-up point.

DATA EXTRACTION AND SYNTHESIS

Two authors conducted independent data extraction. Authors of included studies were contacted for information where necessary. We assessed study quality using the Newcastle-Ottawa Quality Assessment Scale. We calculated pooled odds ratios using a random-effects model. A secondary analysis assessed the mediating role of co-occurring psychopathology.

MAIN OUTCOMES AND MEASURES

Psychotic experiences and subsequent suicidal ideation, suicide attempts, and suicide death.

RESULTS

Of a total of 2540 studies retrieved, 10 met inclusion criteria. These 10 studies reported on 84 285 participants from 12 different samples and 23 countries. Follow-up periods ranged from 1 month to 27 years. Individuals who reported psychotic experiences had an increase in the odds of future suicidal ideation (5 articles; n = 56 191; odds ratio [OR], 2.39 [95% CI,1.62-3.51]), future suicide attempt (8 articles; n = 66 967; OR, 3.15 [95% CI, 2.23-4.45]), and future suicide death (1 article; n = 15 049; OR, 4.39 [95% CI, 1.63-11.78]). Risk was increased in excess of that explained by co-occurring psychopathology: suicidal ideation (adjusted OR, 1.59 [95% CI, 1.09-2.32]) and suicide attempt (adjusted OR, 2.68 [95% CI, 1.71-4.21]).

CONCLUSIONS AND RELEVANCE

Individuals with psychotic experiences are at increased risk of suicidal ideation, suicide attempts, and suicide death. Psychotic experiences are important clinical markers of risk for future suicidal behavior.

Perinatal exposure to the SSRI paroxetine alters the methylome landscape of the developing dentate gyrus

Evidence in humans and rodents suggests that perinatal exposure to selective serotonin reuptake inhibitor (SSRI) antidepressants can have serious long-term consequences in offspring exposed in utero or infancy via breast milk. In spite of this, there is limited knowledge of how perinatal SSRI exposure impacts brain development and adult behaviour. Children exposed to SSRIs in utero exhibit increased internalizing behaviour and abnormal social behaviour between the ages of 3 and 6, and increased risk of depression in adolescence; however, the neurobiological changes underlying this behaviour are poorly understood. In rodents, perinatal SSRI exposure perturbs hippocampal gene expression and alters adult emotional behaviour (including increased depression-like behaviour). The present study demonstrates that perinatal exposure to the SSRI paroxetine leads to DNA hypomethylation and reduces DNA methyltransferase 3a (Dnmt3a) mRNA expression in the hippocampus during the second and third weeks of life. Next-generation sequencing identified numerous differentially methylated genomic regions, including altered methylation and transcription of several dendritogenesis-related genes. We then tested the hypothesis that transiently decreasing Dnmt3a expression in the early postnatal hippocampus would mimic the behavioural effects of perinatal SSRI exposure. We found that siRNA-mediated knockdown of Dnmt3a in the dentate gyrus during the second to third week of life produced greater depression-like behaviour in adult female (but not male) offspring, akin to the behavioural consequences of perinatal SSRI exposure. Overall, these data suggest that perinatal SSRI exposure may increase depression-like behaviours, at least in part, through reduced Dnmt3a expression in the developing hippocampus.