Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment

Machine learning and the prediction of suicide in psychiatric populations: a systematic review

Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.

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.

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.

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.

Altered gray matter volumes and plasma IL-6 level in major depressive disorder patients with suicidal ideation

BACKGROUNDS

Suicidal ideation (SI) is one of the most serious consequences of major depressive disorder (MDD). Understanding the unique mechanism of MDD with SI (MDD + S) is crucial for treatment development. While abundant research has studied MDD, past studies have not reached a consensus on the mechanism of MDD + S. The study aimed to investigate the abnormalities of the gray matter volumes (GMVs) and plasma IL-6 level in MDD + S to further reveal the mechanism of MDD + S.

METHODS

We tested the plasma IL-6 level using Luminex multifactor assays and collected the Structural Magnetic Resonance Imaging (SMRI) data from 34 healthy controls (HCs), 36 MDD patients without SI (MDD - S) and 34 MDD + S patients. We performed a partial correlation between the GMVs of the brain regions with significant differences and plasma IL-6 level with age, sex, medication, scores of HAMD-17 and HAMA as the covariates.

RESULTS

Compared with HCs and MDD - S, MDD + S had significantly decreased GMVs in the left cerebellum Crus I/II and significantly increased plasma IL-6 level; compared with HCs, both the MDD + S and MDD - S had significantly decreased GMVs in right precentral and postcentral gyri. No significant correlation was found between the GMVs and the plasma IL-6 level in the MDD + S and MDD - S, respectively. While the GMVs of the right precentral and postcentral gyri negatively correlated with the level of IL-6 in the whole MDD (r = -0.28, P = 0.03). The GMVs of the left cerebellum Crus I/II (r = -0.47, P = 0.02), and the right precentral and postcentral gyri (r = -0.42, P = 0.04) negatively correlated with the level of IL-6 in HCs.

CONCLUSION

The altered GMVs and the plasma IL-6 level may provide a scientific basis to understand the pathophysiological mechanisms of MDD + S.

Identification of Risk Factors for Suicide and Insights for Developing Suicide Prevention Technologies: A Systematic Review and Meta-Analysis

Suicide is a termite that engulfs close to seven hundred thousand people worldwide each year. Existing work on risk factors that predict suicide lacks statistical associations, does not consider most countries, and has a wide range of risk factor domains. The goal of this systematic review and meta-analysis is to enhance our current understanding of suicidality by identifying risk factors that are most strongly associated with suicide and their impact on developing technological interventions for suicide prevention. A search strategy was carried out on four databases: (1) PsycINFO, (2) IEEE Xplore, (3) the ACM Digital Library, and (4) PubMed, and twenty-five studies were included based on the inclusion criteria. Factors statistically associated with suicide are any diagnosed mental disorder, adverse life events, past suicide attempts, low education level, loneliness or high levels of isolation, bipolar disorder, depression, multiple chronic health conditions, family history of suicide, sexual trauma, and being female. Domain-wise, comorbid disorders, and behavior-related risk factors are most strongly associated with suicide. We present a new hierarchical model of risk factors for suicide that advances our understanding of suicide and its causes. Finally, we present open research directions and considerations for developing suicide prevention technologies.

Polyphenic risk score shows robust predictive ability for long-term future suicidality

Suicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient’s risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.

Role of Omega-3 Fatty Acids as Non-Photic Zeitgebers and Circadian Clock Synchronizers

Omega-3 fatty acids (ω-3 FAs) are well-known for their actions on immune/inflammatory and neurological pathways, functions that are also under circadian clock regulation. The daily photoperiod represents the primary circadian synchronizer (‘zeitgeber’), although diverse studies have pointed towards an influence of dietary FAs on the biological clock. A comprehensive literature review was conducted following predefined selection criteria with the aim of updating the evidence on the molecular mechanisms behind circadian rhythm regulation by ω-3 FAs. We collected preclinical and clinical studies, systematic reviews, and metanalyses focused on the effect of ω-3 FAs on circadian rhythms. Twenty animal (conducted on rodents and piglets) and human trials and one observational study providing evidence on the regulation of neurological, inflammatory/immune, metabolic, reproductive, cardiovascular, and biochemical processes by ω-3 FAs via clock genes were discussed. The evidence suggests that ω-3 FAs may serve as non-photic zeitgebers and prove therapeutically beneficial for circadian disruption-related pathologies. Future work should focus on the role of clock genes as a target for the therapeutic use of ω-3 FAs in inflammatory and neurological disorders, as well as on the bidirectional association between the molecular clock and ω-3 FAs.

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

Building a Comprehensive Biopsychosocial Database to Identify Underlying Causes of Suicide and Improve Suicide Prevention

In June, 2022, the United States Department of Veterans Affairs (VA) announced an initiative to reduce death due to suicide in US Veterans. This column is based on a proposal written for that initiative, as well as on an earlier psychopharmacology column in this journal that reviewed the statistics and the genetics of suicide, and the US medicolegal death investigation system. This system is composed of 3137 county coroner or medical examiner offices across the country that are responsible under state and local law for investigating deaths that are not explained by natural causes and are suspicious and/or unattended. Thus, this system gathers data concerning all deaths due to suicide. Currently this death investigation system costs US taxpayers ∼$660 million per year, and it has determined that ∼45,000 Americans die from suicide each year. In the conduct of these investigations, a large amount of data is collected, including biological samples. While the demographic data are reported to the Centers for Disease Control (CDC), little-if anything-is done with the collected biological material beyond its use in determining the cause of death of the individual. The earlier column on this topic advocated for the establishment of a central database to retain and utilize this information to further understand the biopsychosocial causes of suicide, with the goal of preventing suicides. This column describes a proposal submitted to the VA system for how such a system could initially be piloted in a small group of VA medical centers and then expanded to the entire system. This initial effort could then, in turn, serve as a model for expanding such data gathering to the entire US medicolegal death investigation system.

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.

Rare protein-coding variants implicate genes involved in risk of suicide death

Identification of genetic factors leading to increased risk of suicide death is critical to combat rising suicide rates, however, only a fraction of the genetic variation influencing risk has been accounted for. To address this limitation, we conducted the first comprehensive analysis of rare genetic variation in suicide death leveraging the largest suicide death biobank, the Utah Suicide Genetic Risk Study (USGRS). We conducted a single-variant association analysis of rare (minor allele frequency <1%) putatively functional single-nucleotide polymorphisms (SNPs) present on the Illumina PsychArray genotyping array in 2,672 USGRS suicide deaths of non-Finnish European (NFE) ancestry and 51,583 NFE controls from the Genome Aggregation Database. Secondary analyses used an independent control sample of 21,324 NFE controls from the Psychiatric Genomics Consortium. Five novel, high-impact, rare SNPs were identified with significant associations with suicide death (SNAPC1, rs75418419; TNKS1BP1, rs143883793; ADGRF5, rs149197213; PER1, rs145053802; and ESS2, rs62223875). 119 suicide decedents carried these high-impact SNPs. Both PER1 and SNAPC1 have other supporting gene-level evidence of suicide risk, and psychiatric associations exist for PER1 (bipolar disorder, schizophrenia), and for TNKS1BP1 and ESS2 (schizophrenia). Three of the genes (PER1, TNKS1BP1, and ADGRF5), together with additional genes implicated by genome-wide association studies on suicidal behavior, showed significant enrichment in immune system, homeostatic and signal transduction processes. No specific diagnostic phenotypes were associated with the subset of suicide deaths with the identified rare variants. These findings suggest an important role for rare variants in suicide risk and implicate genes and gene pathways for targeted replication.

Identifying the biomarkers and pathways associated with hepatocellular carcinoma based on an integrated analysis approach

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death worldwide. The molecular mechanism underlying HCC is still unclear. In this study, we conducted a comprehensive analysis to explore the genes, pathways and their interactions involved in HCC.

METHODS

We analysed the gene expression datasets corresponding to 488 samples from 10 studies on HCC and identified the genes differentially expressed in HCC samples. Then, the genes were compared against Phenolyzer and GeneCards to screen those potentially associated with HCC. The features of the selected genes were explored by mapping them onto the human protein-protein interaction network, and a subnetwork related to HCC was constructed. Hub genes in this HCC specific subnetwork were identified, and their relevance with HCC was investigated by survival analysis.

RESULTS

We identified 444 differentially expressed genes (177 upregulated and 267 downregulated) related to HCC. Functional enrichment analysis revealed that pathways like p53 signalling and chemical carcinogenesis were eriched in HCC genes. In the subnetwork related to HCC, five disease modules were detected. Further analysis identified six hub genes from the HCC specific subnetwork. Survival analysis showed that the expression levels of these genes were negatively correlated with survival rate of HCC patients.

CONCLUSIONS

Based on a systems biology framework, we identified the genes, pathways, as well as the disease specific network related to HCC. We also found novel biomarkers whose expression patterns were correlated with progression of HCC, and they could be candidates for further investigation.

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.

Non-CG methylation and multiple histone profiles associate child abuse with immune and small GTPase dysregulation

Early-life adversity (ELA) is a major predictor of psychopathology, and is thought to increase lifetime risk by epigenetically regulating the genome. Here, focusing on the lateral amygdala, a major brain site for emotional homeostasis, we describe molecular cross-talk among multiple mechanisms of genomic regulation, including 6 histone marks and DNA methylation, and the transcriptome, in subjects with a history of ELA and controls. In the healthy brain tissue, we first uncover interactions between different histone marks and non-CG methylation in the CAC context. Additionally, we find that ELA associates with methylomic changes that are as frequent in the CAC as in the canonical CG context, while these two forms of plasticity occur in sharply distinct genomic regions, features, and chromatin states. Combining these multiple data indicates that immune-related and small GTPase signaling pathways are most consistently impaired in the amygdala of ELA individuals. Overall, this work provides insights into genomic brain regulation as a function of early-life experience.

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.

Brain Functional and Structural Alterations in Women With Bipolar Disorder and Suicidality

Objective: Suicide is the leading cause of death from bipolar disorder (BD). At least 25-50% of the patients with BD will attempt suicide, with suicide rates much higher in women patients than in men. It is crucial to explore the potential neural mechanism underlying suicidality in women with BD, which will lead to understanding and detection of suicidality and prevent death and injury from suicide. Methods: Brain function and structure were measured by amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV) in 155 women [30 women with BD and a history of suicidality, 50 women with BD without suicidality, and 75 healthy controls (HC)]. The differences in ALFF and GMV across the BD with suicidality, BD without suicidality, and HC groups were investigated. Results: BD with suicidality showed significantly increased ALFF in the left and right cuneus compared with BD without suicidality and HC groups. Moreover, the GMV in the left lateral prefrontal cortex and left cuneus in BD with suicidality were significantly lower than those in BD without suicidality and HC groups, while the GMV of the right ventral prefrontal cortex was significantly decreased in both BD with and without suicidality groups. Conclusions: This study, combining functional and structural neuroimaging techniques, may help to identify specific pathophysiological changes in women with BD and suicidality. Increased ALFF and less GMV in cuneus might represent the neuroimaging features of suicidality in women with BD. Investigating this potential neuromarker for suicidality in women with BD may lead to the ability to prevent suicidality.

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.

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).

Targeted sequencing of the LRRTM gene family in suicide attempters with bipolar disorder

Glutamatergic signaling is the primary excitatory neurotransmission pathway in the brain, and its relationship to neuropsychiatric disorders is of considerable interest. Our previous attempted suicide genome-wide association study, and numerous studies investigating gene expression, genetic variation, and DNA methylation have implicated aberrant glutamatergic signaling in suicide risk. The glutamatergic pathway gene LRRTM4 was an associated gene identified in our attempted suicide genome-wide association study, with association support seen primarily in females. Recent evidence has also shown that glutamatergic signaling is partly regulated by sex-related hormones. The LRRTM gene family encodes neuronal leucine-rich transmembrane proteins that localize to and promote glutamatergic synapse development. In this study, we sequenced the coding and regulatory regions of all four LRRTM gene members plus a large intronic region of LRRTM4 in 476 bipolar disorder suicide attempters and 473 bipolar disorder nonattempters. We identified two male-specific variants, one female- and five male-specific haplotypes significantly associated with attempted suicide in LRRTM4. Furthermore, variants within significant haplotypes may be brain expression quantitative trait loci for LRRTM4 and some of these variants overlap with predicted hormone response elements. Overall, these results provide supporting evidence for a sex-specific association of genetic variation in LRRTM4 with attempted suicide.

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.

Convergence of recent GWAS data for suicidality with previous blood biomarkers: independent reproducibility using independent methodologies in independent cohorts

Recent genetic studies for suicidality, including four independent GWAS, have not reproduced each other's top implicated genes. While arguments of heterogeneity, methodology, and sample sizes can be invoked, heterogeneity is a feature, not a "bug" (as is well understood in biology and in personalized medicine). A comprehensive body of work on blood biomarkers for suicidality has previously been published by our group. We examine the issue of reproducibility using these different approaches, and provide reassuring evidence for convergence of findings, as well as some generalizable insights.

Biological bases of suicidal behaviours: A narrative review

Suicidal behaviour is a multifaceted phenomenon that concerns all human populations. It has been suggested that a complex interaction between the individual genetic profile and environmental factors throughout life underlies the pathophysiology of suicidal behaviour. Although epidemiological and genetic studies suggest the existence of a genetic component, exposure to biological and psychosocial adversities, especially during critical developmental periods, also contributes to altering the biological responses to threat and pleasure. This results in amplified maladaptive cognitive and behavioural traits and states associated with suicidal behaviours. Alterations in the cognitive inhibition and decision-making capacity have been implicated in suicidal behaviours. Structural and functional changes in key brain regions and networks, such as prefrontal cortex, insula and default mode network, may underlie this relationship. Furthermore, the shift from health to suicidal behaviour incorporates complex and dynamic changes in the immune and stress responses, monoaminergic system, gonadal system and neuroplasticity. In this review, we describe the major findings of epidemiological, genetic, neuroanatomical, neuropsychological, immunological and neuroendocrinological studies on suicide behaviours to provide a solid background for future research in this field. This broad overview of the biological bases of suicide should promote neuroscience research on suicidal behaviours. This might lead to improved biological models and to the identification of evidence-based biomarkers, treatment options and preventive strategies.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force

OBJECTIVES

The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD.

METHOD

A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD.

RESULTS

The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding.

CONCLUSION

Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Assessing Risk of Future Suicidality in Emergency Department Patients

BACKGROUND

Emergency departments (ED) are the first line of evaluation for patients at risk and in crisis, with or without overt suicidality (ideation, attempts). Currently employed triage and assessments methods miss some of the individuals who subsequently become suicidal. The Convergent Functional Information for Suicidality (CFI-S) 22-item checklist of risk factors, which does not ask directly about suicidal ideation, has demonstrated good predictive ability for suicidality in previous studies in psychiatrict patients but has not been tested in the real-world setting of EDs.

METHODS

We administered CFI-S prospectively to a convenience sample of consecutive ED patients. Patients were also asked at triage about suicidal thoughts or intentions per standard ED suicide clinical screening (SCS), and the treating ED physician was asked to fill a physician gestalt visual analog scale (VAS) for likelihood of future suicidality spectrum events (SSE; ideation, preparatory acts, attempts, completed suicide). We performed structured chart review and telephone follow-up at 6 months post-index visit.

RESULTS

The median time to complete the CFI-S was 3 minutes (first to third quartile = 3-6 minutes). Of the 338 patients enrolled, 45 (13.3%) were positive on the initial SCS, and 32 (9.5%) experienced a SSE in the 6 months of follow-up. Overall, SCS had modest diagnostic accuracy sensitivity 14/32 = 44%, (95% CI: 26-62%) and specificity 275/306 = 90%, (86-93%). The physician VAS also had moderate overall diagnostic accuracy (AUC 0.75, confidence interval [CI] = 0.66-0.85), and the CFI-S was best (AUC = 0.81, CI = 0.76-0.87). The top CFI-S differentiating items were psychiatric illness, perceived uselessness, and social isolation.

CONCLUSIONS

Using CFI-S, or some of its items, in busy EDs may help improve the detection of patients at high risk for future suicidality.

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.

Genetic associations with suicide attempt severity and genetic overlap with major depression

In 2015, ~800,000 people died by suicide worldwide. For every death by suicide there are as many as 25 suicide attempts, which can result in serious injury even when not fatal. Despite this large impact on morbidity and mortality, the genetic influences on suicide attempt are poorly understood. We performed a genome-wide association study (GWAS) of severity of suicide attempts to investigate genetic influences. A discovery GWAS was performed in Yale-Penn sample cohorts of European Americans (EAs, n = 2,439) and African Americans (AAs, n = 3,881). We found one genome-wide significant (GWS) signal in EAs near the gene LDHB (rs1677091, p = 1.07 × 10^-8) and three GWS associations in AAs: ARNTL2 on chromosome 12 (rs683813, p = 2.07 × 10^-8), FAH on chromosome 15 (rs72740082, p = 2.36 × 10^-8), and on chromosome 18 (rs11876255, p = 4.61 × 10^-8) in the Yale-Penn discovery sample. We conducted a limited replication analysis in the completely independent Army-STARRS cohorts. rs1677091 replicated in Latinos (LAT, p = 6.52 × 10^-3). A variant in LD with FAH rs72740082 (rs72740088; r² = 0.68) was replicated in AAs (STARRS AA p = 5.23 × 10^-3; AA meta, 1.51 × 10^-9). When combined for a trans-population meta-analysis, the final sample size included n = 20,153 individuals. Finally, we found significant genetic overlap with major depressive disorder (MDD) using polygenic risk scores from a large GWAS (r² = 0.007, p = 6.42 × 10^-5). To our knowledge, this is the first GWAS of suicide attempt severity. We identified GWS associations near genes involved in anaerobic energy production (LDHB), circadian clock regulation (ARNTL2), and catabolism of tyrosine (FAH). These findings provide evidence of genetic risk factors for suicide attempt severity, providing new information regarding the molecular mechanisms involved.

Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs

We endeavored to identify objective blood biomarkers for pain, a subjective sensation with a biological basis, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We studied psychiatric patients, a high risk group for co-morbid pain disorders and increased perception of pain. For discovery, we used a powerful within-subject longitudinal design. We were successful in identifying blood gene expression biomarkers that were predictive of pain state, and of future emergency department (ED) visits for pain, more so when personalized by gender and diagnosis. MFAP3, which had no prior evidence in the literature for involvement in pain, had the most robust empirical evidence from our discovery and validation steps, and was a strong predictor for pain in the independent cohorts, particularly in females and males with PTSD. Other biomarkers with best overall convergent functional evidence for involvement in pain were GNG7, CNTN1, LY9, CCDC144B, and GBP1. Some of the individual biomarkers identified are targets of existing drugs. Moreover, the biomarker gene expression signatures were used for bioinformatic drug repurposing analyses, yielding leads for possible new drug candidates such as SC-560 (an NSAID), and amoxapine (an antidepressant), as well as natural compounds such as pyridoxine (vitamin B6), cyanocobalamin (vitamin B12), and apigenin (a plant flavonoid). Our work may help mitigate the diagnostic and treatment dilemmas that have contributed to the current opioid epidemic.

The Science of Prognosis in Psychiatry: A Review

IMPORTANCE

Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry.

OBSERVATIONS

The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given.

CONCLUSIONS AND RELEVANCE

Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.

Focus on fatty acids in the neurometabolic pathophysiology of psychiatric disorders

Continuous research into the pathophysiology of psychiatric disorders, such as major depressive disorder (MDD), posttraumatic stress disorder (PTSD), and schizophrenia, suggests an important role for metabolism. This narrative review will provide an up-to-date summary of how metabolism is thought to be involved in the pathophysiology of these psychiatric disorders. We will focus on (I) the important role of fatty acids in these metabolic alterations, (II) whether fatty acid alterations represent epiphenomena or risk factors, and (III) similarities and dissociations in fatty acid alterations between different psychiatric disorders. (Historical) epidemiological evidence links fatty acid intake to psychiatric disorder prevalence, corroborated by altered fatty acid concentrations measured in psychiatric patients. These fatty acid alterations are connected with other concomitant pathophysiological mechanisms, including biological stress (hypothalamic-pituitary-adrenal (HPA)-axis and oxidative stress), inflammation, and brain network structure and function. Metabolomics and lipidomics studies are underway to more deeply investigate this complex network of associated neurometabolic alterations. Supplementation of fatty acids as disease-modifying nutraceuticals has clinical potential, particularly add-on eicosapentaenoic acid (EPA) in depressed patients with markers of increased inflammation. However, by interpreting the observed fatty acid alterations as partly (mal)adaptive phenomena, we attempt to nuance translational expectations and provide new clinical applications for these novel neurometabolic insights, e.g., to predict treatment response or depression recurrence. In conclusion, placing fatty acids in context can contribute to further understanding and optimized treatment of psychiatric disorders, in order to diminish their overwhelming burden of disease.

Serotonin transporter gene expression predicts the worsening of suicidal ideation and suicide attempts along a long-term follow-up of a Major Depressive Episode

The quest for biomarkers in suicidal behaviors has been elusive so far, despite their potential utility in clinical practice. One of the most robust biological findings in suicidal behaviors is the alteration of the serotonin transporter function in suicidal individuals. Our main objective was to investigate the predictive value of the serotonin transporter gene expression (SLC6A4) for suicidal ideation and as secondary, for suicide attempts in individuals with a major depressive episode (MDE). A 30-week prospective study was conducted on 148 patients with a MDE and 100 healthy controls including 4 evaluation times (0, 2, 8 and 30 weeks). Blood samples and clinical data were collected and SLC6A4 mRNA levels were measured from peripheral blood mononuclear cells using RT-qPCR. We first demonstrated the stability and reproducibility of SLC6A4 mRNA expression measures over time in healthy controls (F=0.658; p=0.579; η²=0.008; ICC=0.91, 95% CI [0.87-0.94]). Baseline SLC6A4 expression level (OR=0.563 [0.340-0.932], p=0.026) as well as early changes in SLC6A4 expression between baseline and the 2^nd week (β=0.200, p=0.042) predicted the worsening of suicidal ideation (WSI) in the following 8 weeks. Moreover, changes in SLC6A4 expression between the 2^nd and 8^th weeks predicted the occurrence of a suicide attempt within 30 weeks (OR=10.976 [1.438-83.768], p=0.021). Altogether, the baseline level and the changes in SLC6A4 mRNA expression during a MDE might predict the WSI and the occurrence of suicidal attempts and could be a useful biomarker in clinical practice.

Altered Cortical GABA in Female Veterans with Suicidal Behavior: Sex Differences and Clinical Correlates

Background

Suicide is a public health concern in the civilian and veteran populations. Stressful life events are precipitating factors for suicide. The neurochemical underpinnings of the association between stress/trauma and suicide risk are unclear, especially in regards to sex differences. We hypothesized that gamma-amino butyric acid (GABA), the major inhibitory neurotransmitter may be a neurochemical candidate that is critical in the association between stress and suicide risk in veterans.

Methods

Proton magnetic resonance spectroscopy (1H MRS) at 3.0 Tesla was used to measure in vivo neurochemistry in the anterior cingulate cortex (ACC; predominantly the dorsal ACC) of 81 veterans (16 females), including 57 (11 females) who endorsed past suicidal ideation (SI) and/or suicide attempt (SA) and 24 (5 females) with no history of SI and/or SA. Suicidal behavior (SB) was defined as the presence of SI and/or SA.

Results

We observed no significant differences in GABA/ Creatine+phosphocreatine (Cr+PCr) between veterans with SB (SB+) and without SB (SB-). However, the female SB+ group showed significantly reduced GABA/Cr+PCr vs. the female SB- group. We observed a trend-level significant negative correlation between GABA/Cr+PCr and the defensive avoidance (DA) subscale on the Trauma Symptom Inventory (TSI) in the SB+ group. In contrast, the SB- group exhibited a positive relationship between the two variables. Furthermore, we found significant negative correlations between GABA/Cr+PCr and Hamilton Rating Scale for Depression (HAM-D) scores as well as between GABA/Cr+PCr and several subscales of the TSI in female veterans.

Conclusions

This study suggests that reduced GABA/Cr+ PCr ratio in the ACC, which may be related to altered inhibitory capacity, may underlie suicide risk in female veterans. Further, the negative association between GABA/Cr+PCr and stress symptomatology and depression scores suggests that MRS studies may shed light on intermediate phenotypes of SB.

A multi-dimensional characterization of anxiety in monozygotic twin pairs reveals susceptibility loci in humans

The etiology of individual differences in human anxiousness is complex and includes contributions from genetic, epigenetic (i.e., DNA methylation) and environmental factors. Past genomic approaches have been limited in their ability to detect human anxiety-related differences in these factors. To overcome these limitations, we employed both a multi-dimensional characterization method, to select monozygotic twin pairs discordant for anxiety, and whole genome DNA methylation sequencing. This approach revealed 230 anxiety-related differentially methylated loci that were annotated to 183 genes, including several known stress-related genes such as NAV1, IGF2, GNAS, and CRTC1. As an initial validation of these findings, we tested the significance of an overlap of these data with anxiety-related differentially methylated loci that we previously reported from a key neural circuit of anxiety (i.e., the central nucleus of the amygdala) in young monkeys and found a significant overlap (P-value < 0.05) of anxiety-related differentially methylated genes, including GNAS, SYN3, and JAG2. Finally, sequence motif predictions of all the human differentially methylated regions indicated an enrichment of five transcription factor binding motifs, suggesting that DNA methylation may regulate gene expression by mediating transcription factor binding of these transcripts. Together, these data demonstrate environmentally sensitive factors that may underlie the development of human anxiety.

Replication of rs300774, a genetic biomarker near ACP1, associated with suicide attempts in patients with schizophrenia: Relation to brain cholesterol biosynthesis

The aim of this study was to determine if three biomarkers for suicide attempts previously identified and replicated in a genome-wide association (GWAS) study of bipolar disorder (BD) suicide attempters also predicted suicide attempts in patients prospectively diagnosed with schizophrenia (SCZ) or schizoaffective disorder (SAD). 162 genetically-verified Caucasian patients with SCZ or SAD were phenotyped for presence (45.7%) or absence of a lifetime suicide attempt. Three single nucleotide polymorphisms (SNPs) were genotyped or partially imputed from a GWAS dataset. After controlling for genetic architecture and gender, we replicated rs300774 (p = 0.012), near ACP1 (acid phosphatase 1), the top predictor of suicide attempts in the BD study. The result of Willour et al. (2012) was replicated in males (p = 0.046) but not in females (p = 0.205). The other two SNPs, rs7296262, and rs10437629, were not associated with suicide attempts in this study. rs300774 could be a cis-eQTL for ACP1, with minor allele carriers having lower expression levels (p = 0.002). This SNP also functioned as a trans-eQTL for genes related to cholesterol biosynthesis and the wnt-β-catenin pathway (p ≤ 0.0001). Further, co-expression analysis of candidate genes in brain suggested ACP1 is important to the regulation of a number of brain mechanisms linked to suicide, including cholesterol synthesis, β-catenin-mediated signaling pathway, serotonin, GABA, and the stress response via ARHGAP35 (p190rhogap), a repressor of glucocorticoid receptor (NR3C1) transcription. This study provides an additional validation of rs300774 as a potential transdiagnostic biomarker for suicide attempts and evidence that ACP1 may have an important role in regulation of the multiple systems associated with suicide.

Gene expression associated with suicide attempts in US veterans

According to a recent report from the Office of Suicide Prevention in the US Department of Veterans Affairs, veterans represent 8.5% of the US population, but account for 18% of all deaths from suicide. The aim of this study of psychiatric patients (n=39; 87% male) was to compare blood gene expression data from veterans with a history of one or more suicide attempts to veterans who had never attempted suicide. The attempter and non-attempter groups were matched for age and race/ethnicity, and both groups included veterans with a diverse psychiatric history that included posttraumatic stress disorder (PTSD) and substance-use disorders. Veterans were interviewed for lifetime psychiatric history, including a detailed assessment of prior suicide attempts and provided a blood sample. Results of Ingenuity Pathway Analysis (IPA) identified several pathways associated with suicide attempts, including the mammalian target of rapamycin (mTOR) and WNT signaling pathways. These pathways are of particular interest, given their role in explaining pharmacological treatments for suicidal behavior, including the use of ketamine and lithium. These results suggest that findings observed in civilians are also relevant for veterans and provide a context for interpreting results observed in post-mortem samples. In conclusion, an emerging body of work that shows consistency in findings across blood and brain samples suggests that it might be possible to identify molecular predictors of suicide attempts.

Precision medicine for suicidality: from universality to subtypes and personalization

Suicide remains a clear, present and increasing public health problem, despite being a potentially preventable tragedy. Its incidence is particularly high in people with overt or un(der)diagnosed psychiatric disorders. Objective and precise identification of individuals at risk, ways of monitoring response to treatments and novel preventive therapeutics need to be discovered, employed and widely deployed. We sought to investigate whether blood gene expression biomarkers for suicide (that is, a 'liquid biopsy' approach) can be identified that are more universal in nature, working across psychiatric diagnoses and genders, using larger cohorts than in previous studies. Such markers may reflect and/or be a proxy for the core biology of suicide. We were successful in this endeavor, using a comprehensive stepwise approach, leading to a wealth of findings. Steps 1, 2 and 3 were discovery, prioritization and validation for tracking suicidality, resulting in a Top Dozen list of candidate biomarkers comprising the top biomarkers from each step, as well as a larger list of 148 candidate biomarkers that survived Bonferroni correction in the validation step. Step 4 was testing the Top Dozen list and Bonferroni biomarker list for predictive ability for suicidal ideation (SI) and for future hospitalizations for suicidality in independent cohorts, leading to the identification of completely novel predictive biomarkers (such as CLN5 and AK2), as well as reinforcement of ours and others previous findings in the field (such as SLC4A4 and SKA2). Additionally, we examined whether subtypes of suicidality can be identified based on mental state at the time of high SI and identified four potential subtypes: high anxiety, low mood, combined and non-affective (psychotic). Such subtypes may delineate groups of individuals that are more homogenous in terms of suicidality biology and behavior. We also studied a more personalized approach, by psychiatric diagnosis and gender, with a focus on bipolar males, the highest risk group. Such a personalized approach may be more sensitive to gender differences and to the impact of psychiatric co-morbidities and medications. We compared testing the universal biomarkers in everybody versus testing by subtypes versus personalized by gender and diagnosis, and show that the subtype and personalized approaches permit enhanced precision of predictions for different universal biomarkers. In particular, LHFP appears to be a strong predictor for suicidality in males with depression. We also directly examined whether biomarkers discovered using male bipolars only are better predictors in a male bipolar independent cohort than universal biomarkers and show evidence for a possible advantage of personalization. We identified completely novel biomarkers (such as SPTBN1 and C7orf73), and reinforced previously known biomarkers (such as PTEN and SAT1). For diagnostic ability testing purposes, we also examined as predictors phenotypic measures as apps (for suicide risk (CFI-S, Convergent Functional Information for Suicidality) and for anxiety and mood (SASS, Simplified Affective State Scale)) by themselves, as well as in combination with the top biomarkers (the combination being our a priori primary endpoint), to provide context and enhance precision of predictions. We obtained area under the curves of 90% for SI and 77% for future hospitalizations in independent cohorts. Step 5 was to look for mechanistic understanding, starting with examining evidence for the Top Dozen and Bonferroni biomarkers for involvement in other psychiatric and non-psychiatric disorders, as a mechanism for biological predisposition and vulnerability. The biomarkers we identified also provide a window towards understanding the biology of suicide, implicating biological pathways related to neurogenesis, programmed cell death and insulin signaling from the universal biomarkers, as well as mTOR signaling from the male bipolar biomarkers. In particular, HTR2A increase coupled with ARRB1 and GSK3B decreases in expression in suicidality may provide a synergistic mechanistical corrective target, as do SLC4A4 increase coupled with AHCYL1 and AHCYL2 decrease. Step 6 was to move beyond diagnostics and mechanistical risk assessment, towards providing a foundation for personalized therapeutics. Items scored positive in the CFI-S and subtypes identified by SASS in different individuals provide targets for personalized (psycho)therapy. Some individual biomarkers are targets of existing drugs used to treat mood disorders and suicidality (lithium, clozapine and omega-3 fatty acids), providing a means toward pharmacogenomics stratification of patients and monitoring of response to treatment. Such biomarkers merit evaluation in clinical trials. Bioinformatics drug repurposing analyses with the gene expression biosignatures of the Top Dozen and Bonferroni-validated universal biomarkers identified novel potential therapeutics for suicidality, such as ebselen (a lithium mimetic), piracetam (a nootropic), chlorogenic acid (a polyphenol) and metformin (an antidiabetic and possible longevity promoting drug). Finally, based on the totality of our data and of the evidence in the field to date, a convergent functional evidence score prioritizing biomarkers that have all around evidence (track suicidality, predict it, are reflective of biological predisposition and are potential drug targets) brought to the fore APOE and IL6 from among the universal biomarkers, suggesting an inflammatory/accelerated aging component that may be a targetable common denominator.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.

History of Lifetime suicide attempt in bipolar I disorder: its correlates and effect on illness course

OBJECTIVES

To identify the prevalence and correlates of bipolar I patients with a lifetime history of suicide attempt.

MATERIALS AND METHODS

Bipolar I disorder was diagnosed in 150 patients as per DSM-IV-TR criteria. Their lifetime suicide risk was assessed using the Columbia Suicide Severity Rating Scale. NIMH retrospective Life Chart Methodology was used to chart the illness course. Medication Adherence Rating Scale (MARS) and Pittsburgh Sleep Quality Index (PSQI) were used to assess the recent adherence and subjective sleep quality, respectively. The suicide attempters were compared with non-attempters on individual variables.

RESULTS

Around 23% had a positive lifetime history of suicide attempt. They were predominantly female, had an index (first ever) episode of depression, spent more proportion of time being ill, especially in depressive or mixed episode phase. Comorbid substance use disorder along with suicidal attempts was seen only in males. Suicide attempters displayed poor medication adherence attitudes for medications taken during the past week and reported impaired sleep quality for the previous month.

CONCLUSIONS

A positive history of lifetime suicide attempt was significantly associated with a worse course of bipolar I disorder. Effective treatment of depressive episodes, addressing non-adherence, substance use and sleep problems can reduce the suicide risk in such patients. Retrospective design of the study and recall bias are some of the limitations.

Polyunsaturated fatty acids and suicide risk in mood disorders: A systematic review

Deficiency of omega-3 polyunsaturated fatty acids (PUFAs) and an alteration between the ratio of omega-3 and omega-6 PUFAs may contribute to the pathogenesis of bipolar disorder and unipolar depression. Recent epidemiological studies have also demonstrated an association between the depletion of PUFAs and suicide. Our aim was to investigate the relationship between PUFAs and suicide; assess whether the depletion of PUFAs may be considered a risk factor for suicidal behavior; in addition to detailing the potential use of PUFAs in clinical practice. We performed a systematic review on PUFAs and suicide in mood disorders, searching MedLine, Excerpta Medica, PsycLit, PsycInfo, and Index Medicus for relevant epidemiological, post-mortem, and clinical studies from January 1997 to September 2016. A total of 20 articles from peer-reviewed journals were identified and selected for this review. The reviewed studies suggest that subjects with psychiatric conditions have a depletion of omega-3 PUFAs compared to control groups. This fatty acid depletion has also been found to contribute to suicidal thoughts and behavior in some cases. However, large epidemiological studies have generally not supported this finding, as the depletion of omega-3 PUFAs was not statistically different between controls and patients diagnosed with a mental illness and/or who engaged in suicidal behavior. Increasing PUFA intake may be relevant in the treatment of depression, however in respect to the prevention of suicide, the data is currently not supportive of this approach. Changes in levels of PUFAs may however be a risk factor to evaluate when assessing for suicide risk. Clinical studies should be conducted to prospectively assess whether prescriptive long-term use of PUFAs in PUFA-deficient people with depression, may have a preventative role in attenuating suicide.

Discovery and replication of a peripheral tissue DNA methylation biosignature to augment a suicide prediction model

Background

Suicide is the second leading cause of death among adolescents in the USA, and rates are rising. Methods to identify individuals at risk are essential for implementing prevention strategies, and the development of a biomarker can potentially improve prediction of suicidal behaviors. Prediction of our previously reported SKA2 biomarker for suicide and PTSD is substantially improved by questionnaires assessing perceived stress or anxiety and is therefore reliant on psychological assessment. However, such stress-related states may also leave a biosignature that could equally improve suicide prediction. In genome-wide DNA methylation data, we observed significant overlap between waking cortisol-associated and suicide-associated DNA methylation in blood and the brain, respectively.

Results

Using a custom bioinformatic brain to blood discovery algorithm, we derived a DNA methylation biosignature that interacts with SKA2 methylation to improve the prediction of suicidal ideation in our existing suicide prediction model across both blood and saliva data sets. This biosignature was independently validated in the Grady Trauma Project cohort and interacted with HPA axis metrics in the same cohort. The biosignature showed a relationship with immune status by its correlation with myeloid-derived cell proportions in all data sets and with IL-6 measures in a prospective postpartum depression cohort. Three probes showed significant correlations with the biosignature: cg08469255 (DDR1), cg22029879 (ARHGEF10), and cg24437859 (SHP1), of which SHP1 methylation correlated with immune measures.

Conclusions

We conclude that this biosignature interacts with SKA2 methylation to improve suicide prediction and may represent a biological state of immune and HPA axis modulation that mediates suicidal behavior.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-016-0279-1) contains supplementary material, which is available to authorized users.

Molecular signatures associated with cognitive deficits in schizophrenia: a study of biopsied olfactory neural epithelium

Cognitive impairment is a key feature of schizophrenia (SZ) and determines functional outcome. Nonetheless, molecular signatures in neuronal tissues that associate with deficits are not well understood. We conducted nasal biopsy to obtain olfactory epithelium from patients with SZ and control subjects. The neural layers from the biopsied epithelium were enriched by laser-captured microdissection. We then performed an unbiased microarray expression study and implemented a systematic neuropsychological assessment on the same participants. The differentially regulated genes in SZ were further filtered based on correlation with neuropsychological traits. This strategy identified the SMAD 5 gene, and real-time quantitative PCR analysis also supports downregulation of the SMAD pathway in SZ. The SMAD pathway has been important in multiple tissues, including the role for neurodevelopment and bone formation. Here the involvement of the pathway in adult brain function is suggested. This exploratory study establishes a strategy to better identify neuronal molecular signatures that are potentially associated with mental illness and cognitive deficits. We propose that the SMAD pathway may be a novel target in addressing cognitive deficit of SZ in future studies.

National Initiative to Prevent Suicide (NIPS): A New Proposal to Improve the Understanding and Prevention of Suicide

Suicide is a staggering, tragic, and growing cause of death in the United States. Despite a government-led 20-year effort, the suicide rate increased by 25% between 1999 and 2014. To prevent suicide, it is essential to understand the biological factors-genetic and epigenetic-and environmental factors that underlie it. To gain this increased understanding, the equivalent of the "War on Cancer" initiative is needed. The War on Cancer initiative, which began in the 1970s, has transformed the treatments and outcomes of cancer, and the same could occur with a similar initiative on suicide. This article proposes a National Initiative to Prevent Suicide (NIPS), with the first step being the establishment of a National Suicide Database (NSD). The NSD would be established by a government-private partnership much as was done by the National Cancer Institute in the War on Cancer. The NSD would be established under the auspices of the National Institute of Mental Health and the Centers for Disease Control and Prevention. Approximately $600 million are currently spent annually by taxpayers in the United States to support the medicolegal death investigation system, composed of 3137 county coroner or medical examiner offices across the country. In their investigation of deaths due to suicide, these offices collect extensive information, including biological samples, from the >40,000 deaths due to suicide that occur each year. The proposal presented in this column calls for this material to be stored in the NSD so that vetted government and public/private researchers can investigate the causes of suicide. This information will make possible the development of new methods, including laboratory evaluations, for assessing suicide risk as well as new treatments to prevent suicide. In support of this proposed new initiative, this article/proposal reviews the current medicolegal death investigation system and recent advances in our understanding of the biological basis of suicide. The time for action on this proposal is now.