A genome-wide association study of suicide attempts in the million veterans program identifies evidence of pan-ancestry and ancestry-specific risk loci

Shared genetics linking sociability with the brain's default mode network

The brain's default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. In the present study, we examined the genetic relationship between sociability and DMN-related resting-state functional magnetic resonance imaging (rs-fMRI) traits. To this end, we used genome-wide association summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N=34,691-342,461). First, we examined global and local genetic correlations between sociability and the rs-fMRI traits. Second, to assess putatively causal relationships between the traits, we conducted bi-directional Mendelian randomisation (MR) analyses. Finally, we prioritised genes influencing both sociability and rs-fMRI traits by combining three methods: gene-expression eQTL MR analyses, the CELLECT framework using single-nucleus RNA-seq data, and network propagation in the context of a protein-protein interaction network. Significant local genetic correlations were found between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the frontal/cingulate and angular/temporal cortices. Sociability affected 12 rs-fMRI traits when allowing for weakly correlated genetic instruments. Combing all three methods for gene prioritisation, we defined 17 highly prioritised genes, with DRD2 and LINGO1 showing the most robust evidence across all analyses. By integrating genetic and transcriptomics data, our gene prioritisation strategy may serve as a blueprint for future studies. The prioritised genes could be explored as potential biomarkers for social dysfunction in the context of neuropsychiatric disorders and as drug target genes.

Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with an alcohol use disorder

Research has identified clinical, genomic, and neurophysiological markers associated with suicide attempts (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA. We examined lifetime SA in 4,068 individuals with DSM-IV alcohol dependence from the Collaborative Study on the Genetics of Alcoholism (23% lifetime suicide attempt; 53% female; mean age: 38). Within participants with an AUD diagnosis, we explored risk across other clinical conditions, polygenic scores (PGS) for comorbid psychiatric problems, and neurocognitive functioning for lifetime suicide attempt. Participants with an AUD who had attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, and other substance use disorders compared to those who had not attempted suicide. Polygenic scores for suicide attempt, depression, and PTSD were associated with reporting a suicide attempt (ORs = 1.22 - 1.44). Participants who reported a SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small. Overall, individuals with an AUD who report a lifetime suicide attempt appear to experience greater levels of trauma, have more severe comorbidities, and carry polygenic risk for a variety of psychiatric problems. Our results demonstrate the need to further investigate suicide attempts in the presence of substance use disorders.

Polygenic risk for suicide attempt is associated with lifetime suicide attempt in US soldiers independent of parental risk

BACKGROUND

Suicide is a leading cause of death worldwide. Whereas some studies have suggested that a direct measure of common genetic liability for suicide attempts (SA), captured by a polygenic risk score for SA (SA-PRS), explains risk independent of parental history, further confirmation would be useful. Even more unsettled is the extent to which SA-PRS is associated with lifetime non-suicidal self-injury (NSSI).

METHODS

We used summary statistics from the largest available GWAS study of SA to generate SA-PRS for two non-overlapping cohorts of soldiers of European ancestry. These were tested in multivariable models that included parental major depressive disorder (MDD) and parental SA.

RESULTS

In the first cohort, 417 (6.3 %) of 6573 soldiers reported lifetime SA and 1195 (18.2 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.26, 95%CI:1.13-1.39, p < 0.001] per standardized unit SA-PRS]. In the second cohort, 204 (4.2 %) of 4900 soldiers reported lifetime SA, and 299 (6.1 %) reported lifetime NSSI. In a multivariable model that included parental history of MDD and parental history of SA, SA-PRS remained significantly associated with lifetime SA [aOR = 1.20, 95%CI:1.04-1.38, p = 0.014]. A combined analysis of both cohorts yielded similar results. In neither cohort or in the combined analysis was SA-PRS significantly associated with NSSI.

CONCLUSIONS

PRS for SA conveys information about likelihood of lifetime SA (but not NSSI, demonstrating specificity), independent of self-reported parental history of MDD and parental history of SA.

LIMITATIONS

At present, the magnitude of effects is small and would not be immediately useful for clinical decision-making or risk-stratified prevention initiatives, but this may be expected to improve with further iterations. Also critical will be the extension of these findings to more diverse populations.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.

Association study of the complement component C4 gene and suicide risk in schizophrenia

Schizophrenia is a severe mental illness and a major risk factor for suicide, with approximately 50% of schizophrenia patients attempting and 10% dying from suicide. Although genetic components play a significant role in schizophrenia risk, the underlying genetic risk factors for suicide are poorly understood. The complement component C4 gene, an immune gene involved in the innate immune system and located in the major histocompatibility complex (MHC) region, has been identified to be strongly associated with schizophrenia risk. In addition, recent findings have also suggested that the MHC region has been associated with suicide risk across disorders, making C4 a potential candidate of interest for studying suicidality in schizophrenia patients. Despite growing interest in investigating the association between the C4 gene and schizophrenia, to our knowledge, no work has been done to examine the potential of C4 variants as suicide risk factors in patients with schizophrenia. In this study, we investigated the association between different C4 copy number variants and predicted C4 brain expression with suicidal outcomes (suicide attempts/suicidal ideation). We directly genotyped 434 schizophrenia patients to determine their C4A and C4B copy number variants. We found the C4AS copy number to be marginally and negatively associated with suicide risk, potentially being protective against suicide attempts (OR = 0.49; p = 0.05) and suicidal ideation (OR = 0.65; p = 0.07). Furthermore, sex-stratified analyses revealed that there are no significant differences between males and females. Our preliminary findings encourage additional studies of C4 and potential immune dysregulation in suicide.

Genetic factors associated with suicidal behaviors and alcohol use disorders in an American Indian population

American Indians (AI) demonstrate the highest rates of both suicidal behaviors (SB) and alcohol use disorders (AUD) among all ethnic groups in the US. Rates of suicide and AUD vary substantially between tribal groups and across different geographical regions, underscoring a need to delineate more specific risk and resilience factors. Using data from over 740 AI living within eight contiguous reservations, we assessed genetic risk factors for SB by investigating: (1) possible genetic overlap with AUD, and (2) impacts of rare and low-frequency genomic variants. Suicidal behaviors included lifetime history of suicidal thoughts and acts, including verified suicide deaths, scored using a ranking variable for the SB phenotype (range 0-4). We identified five loci significantly associated with SB and AUD, two of which are intergenic and three intronic on genes AACSP1, ANK1, and FBXO11. Nonsynonymous rare and low-frequency mutations in four genes including SERPINF1 (PEDF), ZNF30, CD34, and SLC5A9, and non-intronic rare and low-frequency mutations in genes OPRD1, HSD17B3 and one lincRNA were significantly associated with SB. One identified pathway related to hypoxia-inducible factor (HIF) regulation, whose 83 nonsynonymous rare and low-frequency variants on 10 genes were significantly linked to SB as well. Four additional genes, and two pathways related to vasopressin-regulated water metabolism and cellular hexose transport, also were strongly associated with SB. This study represents the first investigation of genetic factors for SB in an American Indian population that has high risk for suicide. Our study suggests that bivariate association analysis between comorbid disorders can increase statistical power; and rare and low-frequency variant analysis in a high-risk population enabled by whole-genome sequencing has the potential to identify novel genetic factors. Although such findings may be population specific, rare functional mutations relating to PEDF and HIF regulation align with past reports and suggest a biological mechanism for suicide risk and a potential therapeutic target for intervention.

Uncommon protein-coding variants associated with suicide attempt in a diverse sample of US Army soldiers

BACKGROUND

Suicide is a societal and public health concern of global scale. Identifying genetic risk factors for suicide attempt can characterize underlying biology and enable early interventions to prevent deaths. Recent studies have described common genetic variants for suicide-related behaviors. Here, we advance this search for genetic risk by analyzing the association between suicide attempt and uncommon variation, exome-wide in a large, ancestrally diverse sample.

METHODS

We sequenced whole genomes of 13,584 soldiers from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS), including 979 with a history of suicide attempt. Uncommon, non-silent protein coding variants were analyzed exome-wide for association with suicide attempt by gene-collapsed and single variant analyses.

RESULTS

We identified 19 genes with variants enriched in individuals with suicide attempt, either through gene-collapsed or single variant analysis (Bonferroni PAdjusted< 0.05). These genes were CIB2, MLF1, HERC1, YWHAE, RCN2, VWA5B1, ATAD3A, NACA, EP400, ZNF585A, LYST, RC3H2, PSD3, STARD9, SGMS1, ACTR6, RGS7BP, DIRAS and KRTAP10-1. Most genes had variants across multiple genomic ancestry groups. Seventeen of these genes were expressed in healthy brain tissue, with seven genes expressed at highest levels in brain vs. other tissues. Brains from individuals deceased from suicide aberrantly expressed RGS7BP (PAdjusted=0.035) in addition to nominally significant genes including YWHAE, DIRAS2 and ACTR6, all of which have reported associations with other mental disorders.

CONCLUSIONS

These results advance the molecular characterization of suicide attempt behavior and support the utility of whole genome sequencing for complementing the findings of GWAS in suicide research.

High intensity interval training exercise increases dopamine D2 levels and modulates brain dopamine signaling

Background

Previous research has outlined the health benefits of exercise including its therapeutic potential for substance use disorders (SUD). These data have already been utilized and it is now common to find exercise as part of SUD treatment and relapse prevention programs. However, we need to better understand different exercise regimens and determine which would be the most beneficial for SUDs. Recently, high intensity interval training (HIIT) has gained attention in comparison with aerobic and resistance exercise. Little is known regarding the neurobiological mechanisms of HIIT, including its effects on dopamine signaling and receptor levels in the brain. The present study examined the effects of chronic HIIT exercise on dopamine signaling as measured by dopamine type 1-like receptor (D1R)-like, dopamine type 2-like receptor (D2R)-like, and tyrosine hydroxylase (TH) quantification in the brains of male and female rats as measured by [3H] SCH 23390 and [3H] spiperone autoradiography, and TH-immunoreactive optical density values.

Methods

Rats were separated in two groups: sedentary and HIIT exercise. Exercise was on a treadmill for 30 min daily (10 3 min cycles) for six weeks with progressive speed increased up to 0.8 mph (21.5 m/min).

Results

Results showed for D2R-like binding, a significant effect across the ventral caudate putamen (V CPU) between sexes, such that mean D2R-like binding was 14% greater for males than females. In the nucleus accumbens shell (Nac Shell), the HIIT Exercise rats showed 16% greater D2R-like binding as compared to the sedentary rats. No significant effects of HIIT exercise were found across groups for brain D1R-like binding levels or TH expression.

Conclusion

These results suggest that HIIT exercise can modulate dopamine signaling by way of increased D2R. These findings support the premise that HIIT exercise plays an important role in dopamine signaling and, may provide a potential mechanism for how HIIT exercise can impact the brain and behavior.

Are We Getting High Cause the Thrill is Gone?

In the USA alone, opioid use disorder (OUD) affects approximately 27 million people. While the number of prescriptions may be declining due to increased CDC guidance and prescriber education, fatalities due to fentanyl-laced street heroin are still rising. Our laboratory has extended the overall concept of both substance and non-substance addictive behaviors, calling it "Reward Deficiency Syndrome (RDS)." Who are its victims, and how do we get this unwanted disorder? Is RDS caused by genes (Nature), environment (Neuro-epigenetics, Nurture), or both? Recent research identifies resting-state functional connectivity in the brain reward circuitry as a crucial factor. Analogously, it is of importance to acknowledge that the cumulative discharge of dopamine, governed by the nucleus accumbens (NAc) and modulated by an array of additional neurotransmitters, constitutes a cornerstone of an individual's overall well-being. Neuroimaging reveals that high-risk individuals exhibit a blunted response to stimuli, potentially due to DNA polymorphisms or epigenetic alterations. This discovery has given rise to the idea of a diminished 'thrill,' though we must consider whether this 'thrill' may have been absent from birth due to high-risk genetic predispositions for addiction. This article reviews this issue and suggests the general concept of the importance of "induction of dopamine homeostasis." We suggest coupling a validated genetic assessment (e.g., GARS) with pro-dopamine regulation (KB220) as one possible frontline modality in place of prescribing potent addictive opioids for OUD except for short time harm reduction. Could gene editing offer a 'cure' for this undesirable genetic modification at birth, influenced by the environment and carried over generations, leading to impaired dopamine and other neurotransmitter imbalances, as seen in RDS? Through dedicated global scientific exploration, we hope for a future where individuals are liberated from pain and disease, achieving an optimal state of well-being akin to the proverbial 'Garden of Eden'.

Identification of gene-level methylation for disease prediction

DNA methylation is an epigenetic alteration that plays a fundamental part in governing gene regulatory processes. The DNA methylation mechanism affixes methyl groups to distinct cytosine residues, influencing chromatin architectures. Multiple studies have demonstrated that DNA methylation's regulatory effect on genes is linked to the beginning and progression of several disorders. Researchers have recently uncovered thousands of phenotype-related methylation sites through the epigenome-wide association study (EWAS). However, combining the methylation levels of several sites within a gene and determining the gene-level DNA methylation remains challenging. In this study, we proposed the supervised UMAP Assisted Gene-level Methylation method (sUAGM) for disease prediction based on supervised UMAP (Uniform Manifold Approximation and Projection), a manifold learning-based method for reducing dimensionality. The methylation values at the gene level generated using the proposed method are evaluated by employing various feature selection and classification algorithms on three distinct DNA methylation datasets derived from blood samples. The performance has been assessed employing classification accuracy, F-1 score, Mathews Correlation Coefficient (MCC), Kappa, Classification Success Index (CSI) and Jaccard Index. The Support Vector Machine with the linear kernel (SVML) classifier with Recursive Feature Elimination (RFE) performs best across all three datasets. From comparative analysis, our method outperformed existing gene-level and site-level approaches by achieving 100% accuracy and F1-score with fewer genes. The functional analysis of the top 28 genes selected from the Parkinson's disease dataset revealed a significant association with the disease.

"TO BE OR NOT TO BE" GWAS Ends the Controversy about the DRD2 Gene as a Determinant of Reward Deficiency Syndrome (RDS)

Since 1990, there have been thousands of published studies on addiction psychiatry. Several from Blum et al showed the clinical relevance of the Genetic Addiction Risk Severity (GARS) test in identifying risk for reward deficiency behaviors in cohorts from polysubstance abuse and pain clinics, post-surgical bariatrics, and DWI offenders facing prison time. Since Blum et al first published in JAMA (1990) concerning the association of the DRD2 gene polymorphism and severe alcoholism, reactions have been mixed. More recently, however, a meta-analysis of 62 studies showed a significant association between DRD2 rs1800497 and Alcohol Use Disorder (AUD). Other studies from Yale University showed that a haplotype block of the DRD2 gene A1 allele was associated with AUD and heroin dependence. GWAS studies of depression and suicide in 1.2 million veterans confirmed the first psychiatric candidate gene study finding from Blum et al 1990; a significant association between the minor DRD2 allele, Taq A1 and severe alcoholism. Additionally, the DRD2 rs1800497 is robustly associated with suicidal behaviors. Furthermore, DNA polymorphic alleles underlying substance use disorder (SUD) with multiple substances were mapped via chromatin refolding, revealing that the DRD2 gene and associated polymorphism(s) as the top gene signal. Based on these investigations, we conclude that GWAS should end the controversy about the DRD2 gene being one determinant of Reward Deficiency Syndrome (RDS) first reported in 1996.

Dopaminergic dysfunction: Role for genetic & epigenetic testing in the new psychiatry

Reward Deficiency Syndrome (RDS), particularly linked to addictive disorders, costs billions of dollars globally and has resulted in over one million deaths in the United States (US). Illicit substance use has been steadily rising and in 2021 approximately 21.9% (61.2 million) of individuals living in the US aged 12 or older had used illicit drugs in the past year. However, only 1.5% (4.1 million) of these individuals had received any substance use treatment. This increase in use and failure to adequately treat or provide treatment to these individuals resulted in 106,699 overdose deaths in 2021 and increased in 2022. This article presents an alternative non-pharmaceutical treatment approach tied to gene-guided therapy, the subject of many decades of research. The cornerstone of this paradigm shift is the brain reward circuitry, brain stem physiology, and neurotransmitter deficits due to the effects of genetic and epigenetic insults on the interrelated cascade of neurotransmission and the net release of dopamine at the Ventral Tegmental Area -Nucleus Accumbens (VTA-NAc) reward site. The Genetic Addiction Risk Severity (GARS) test and pro-dopamine regulator nutraceutical KB220 were combined to induce "dopamine homeostasis" across the brain reward circuitry. This article aims to encourage four future actionable items: 1) the neurophysiologically accurate designation of, for example, "Hyperdopameism /Hyperdopameism" to replace the blaming nomenclature like alcoholism; 2) encouraging continued research into the nature of dysfunctional brainstem neurotransmitters across the brain reward circuitry; 3) early identification of people at risk for all RDS behaviors as a brain check (cognitive testing); 4) induction of dopamine homeostasis using "precision behavioral management" along with the coupling of GARS and precision Kb220 variants; 5) utilization of promising potential treatments include neuromodulating modalities such as Transmagnetic stimulation (TMS) and Deep Brain Stimulation(DBS), which target different areas of the neural circuitry involved in addiction and even neuroimmune agents like N-acetyl-cysteine.

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors

OBJECTIVE

Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures.

METHODS

This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses.

RESULTS

Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors.

CONCLUSIONS

This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Genetic Correlates as a Predictor of Bariatric Surgery Outcomes after 1 Year

This study analyzed genetic risk assessments in patients undergoing bariatric surgery to serve as a predictive factor for weight loss parameters 1 year after the operation. Thirty (30) patients were assessed for Genetic Addiction Risk Severity (GARS), which analyzes neurogenetic polymorphisms involved in addiction and reward deficiency. Genetic and psychosocial data collected before the operation were correlated with weight loss data, including changes in weight, body mass index (BMI), and percent of expected weight loss (%EWL). Results examined correlations between individual gene risk alleles, 1-year body weight data, and psychosocial trait scores. Spearman's correlations revealed that the OPRM1 (rs1799971) gene polymorphism had significant negative correlation with 1-year weight (rs = -0.4477, p < 0.01) and BMI (rs = -0.4477, p < 0.05). In addition, the DRD2 risk allele (rs1800497) was correlated negatively with BMI at 1 year (rs = -0.4927, p < 0.05), indicating that one risk allele copy was associated with lower BMI. However, this allele was positively correlated with both ∆Weight (rs = 0.4077, p < 0.05) and %EWL (rs = 0.5521, p < 0.05) at 1 year post-surgery. Moreover, the overall GARS score was correlated with %EWL (rs = 0.4236, p < 0.05), ∆Weight (rs = 0.3971, p < 0.05) and ∆BMI (rs = 0.3778, p < 0.05). Lastly, Food Cravings Questionnaire (FCQ) scores were negatively correlated with %EWL (rs = -0.4320, p < 0.05) and ∆Weight at 1 year post-surgery (rs = -0.4294, p < 0.05). This suggests that individuals with a higher genetic addiction risk are more responsive to weight loss treatment, especially in the case of the DRD2 polymorphism. These results should translate clinically to improve positivity and attitude related to weight management by those individuals born with the risk alleles (rs1800497; rs1799971).

Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts

Introduction

Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework.

Methods

We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model.

Results

Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features.

Discussion

Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

Suicidal behavior across a broad range of psychiatric disorders

Suicide is a leading cause of death worldwide. In 2020, some 12.2 million Americans seriously contemplated suicide, 3.2 million planned suicide attempts, and 1.2 million attempted suicide. Traditionally, the approach to treating suicidal behavior (SB) has been to treat the "underlying" psychiatric disorder. However, the number of diagnoses associated with SB is considerable. We could find no studies describing the range of disorders reported to be comorbid with SB. This narrative review summarizes literature documenting the occurrence of SB across the lifespan and the full range of psychiatric diagnoses, not only BPD and those that comprise MDE, It also describes the relevance of these observations to clinical practice, research, and nosology. The literature searches contained the terms "suicid*" and each individual psychiatric diagnosis and identified 587 studies. We did not include case reports, case series, studies only addressing suicidal ideation or non-suicidal self-injury (NSSI), studies on self-harm, not distinguishing between SB and NSSI and studies that did not include any individuals that met criteria for a specific DSM-5 diagnosis (n = 366). We found that SB (suicide and/or suicide attempt) was reported to be associated with 72 out of 145 diagnoses, although data quality varied. Thus, SB is not exclusively germane to Major Depressive Episode (MDE) and Borderline Personality Disorder (BPD), the only conditions for which it is a diagnostic criterion. That SB co-occurs with so many diagnoses reinforces the need to assess current and past SB regardless of diagnosis, and supports the addition of charting codes to the DSM-5 to indicate current or past SB. It also comports with new data that specific genes are associated with SB independent of psychiatric diagnoses, and suggests that SB should be managed with specific suicide prevention interventions in addition to treatments indicated for co-occurring diagnoses. SB diagnostic codes would help researchers and clinicians document and measure SB's trajectory and response to treatment over time, and, ultimately, help develop secondary and tertiary prevention strategies. As a separate diagnosis, SB would preclude situations in which a potentially life-threatening behavior is not accounted for by a diagnosis, a problem that is particularly salient when no mental disorder is present, as is sometimes the case.

Evidence for the DRD2 Gene as a Determinant of Reward Deficiency Syndrome (RDS)

Since 1990, published addiction psychiatry articles have exceeded 11,495. Several from Blum et al. showed the clinical relevance of the Genetic Addiction Risk Severity (GARS) test in identifying risk for reward deficiency behaviors in cohorts from polysubstance and pain clinics, post-surgical bariatrics, and DWI offenders facing prison time. Since Blum et al first published in JAMA (1990) concerning the association of the DRD2 gene polymorphism and severe alcoholism, confirmation has been mixed and controversial. More recently, however, a meta-analysis of 62 studies showed a significant association between DRD2 rs 1800497 and Alcohol Use Disorder (AUD). Other studies from Yale University showed that a haplotype block of the DRD2 gene A1 allele was associated with AUD and heroin dependence. GWAS studies of depression and suicide in 1.2 million veterans confirmed the first psychiatric candidate gene study finding from Blum et al. 1990; a significant association between the minor DRD2 allele, Taq A1 (rs 1800497 C>T) and severe alcoholism. Additionally, the DRD2 rs1800497 is associated with suicide behaviors robustly at P=1.77 × 10-7. Furthermore, DNA polymorphic alleles underlying SUD with multiple substances were mapped via chromatin refolding, revealed that the DRD2 gene and associated polymorphism(s) was the top gene signal (DRD2, P=7.9 × 10-12). Additionally, based on these investigations, we conclude that GWAS should end the controversy about the DRD2 gene being at least one determinant of Reward Deficiency Syndrome (RDS) first reported in the Royal Society of Medicine journaling 1996.

Genetic Factors Associated with Suicidal Behaviors and Alcohol Use Disorders in an American Indian Population

American Indians (AI) demonstrate the highest rates of both suicidal behaviors (SB) and alcohol use disorders (AUD) among all ethnic groups in the US. Rates of suicide and AUD vary substantially between tribal groups and across different geographical regions, underscoring a need to delineate more specific risk and resilience factors. Using data from over 740 AI living within eight contiguous reservations, we assessed genetic risk factors for SB by investigating: (1) possible genetic overlap with AUD, and (2) impacts of rare and low frequency genomic variants. Suicidal behaviors included lifetime history of suicidal thoughts and acts, including verified suicide deaths, scored using a ranking variable for the SB phenotype (range 0-4). We identified five loci significantly associated with SB and AUD, two of which are intergenic and three intronic on genes AACSP1, ANK1, and FBXO11. Nonsynonymous rare mutations in four genes including SERPINF1 (PEDF), ZNF30, CD34, and SLC5A9, and non-intronic rare mutations in genes OPRD1, HSD17B3 and one lincRNA were significantly associated with SB. One identified pathway related to hypoxia-inducible factor (HIF) regulation, whose 83 nonsynonymous rare variants on 10 genes were significantly linked to SB as well. Four additional genes, and two pathways related to vasopressin-regulated water metabolism and cellular hexose transport, also were strongly associated with SB. This study represents the first investigation of genetic factors for SB in an American Indian population that has high risk for suicide. Our study suggests that bivariate association analysis between comorbid disorders can increase statistical power; and rare variant analysis in a high-risk population enabled by whole-genome sequencing has the potential to identify novel genetic factors. Although such findings may be population specific, rare functional mutations relating to PEDF and HIF regulation align with past reports and suggest a biological mechanism for suicide risk and a potential therapeutic target for intervention.

Large epigenome-wide association study identifies multiple novel differentially methylated CpG sites associated with suicidal thoughts and behaviors in veterans

Introduction

The U.S. suicide mortality rate has steadily increased during the past two decades, particularly among military veterans; however, the epigenetic basis of suicidal thoughts and behaviors (STB) remains largely unknown.

Methods

To address this issue, we conducted an epigenome-wide association study of DNA methylation (DNAm) of peripheral blood samples obtained from 2,712 U.S. military veterans.

Results

Three DNAm probes were significantly associated with suicide attempts, surpassing the multiple testing threshold (FDR q-value <0.05), including cg13301722 on chromosome 7, which lies between the genes SLC4A2 and CDK5; cg04724646 in PDE3A; and cg04999352 in RARRES3. cg13301722 was also found to be differentially methylated in the cerebral cortex of suicide decedents in a publicly-available dataset (p = 0.03). Trait enrichment analysis revealed that the CpG sites most strongly associated with STB in the present sample were also associated with smoking, alcohol consumption, maternal smoking, and maternal alcohol consumption, whereas pathway enrichment analysis revealed significant associations with circadian rhythm, adherens junction, insulin secretion, and RAP-1 signaling, each of which was recently associated with suicide attempts in a large, independent genome-wide association study of suicide attempts of veterans.

Discussion

Taken together, the present findings suggest that SLC4A2, CDK5, PDE3A, and RARRES3 may play a role in STB. CDK5, a member of the cyclin-dependent kinase family that is highly expressed in the brain and essential for learning and memory, appears to be a particularly promising candidate worthy of future study; however, additional work is still needed to replicate these finding in independent samples.

How to save a life: From neurobiological underpinnings to psychopharmacotherapies in the prevention of suicide

The impact of suicide on our societies, mental healthcare, and public health is beyond questionable. Every year approximately 700 000 lives are lost due to suicide around the world (WHO, 2021); more people die by suicide than by homicide and war. Although suicide is a key issue and reducing suicide mortality is a global imperative, suicide is a highly complex biopsychosocial phenomenon, and in spite of several suicidal models developed in recent years and a high number of suicide risk factors identified, we still have neither a sufficient understanding of underpinnings of suicide nor adequate management strategies to reduce its prevalence. The present paper first overviews the background of suicidal behavior including its epidemiology, prevalence, age and gender correlations and its association with neuropsychiatric disorders as well as its clinical assessment. Then we give an overview of the etiological background, including its biopsychosocial contexts, genetics and neurobiology. Based on the above, we then provide a critical overview of the currently available intervention options to manage and reduce risk of suicide, including psychotherapeutic modalities, traditional medication classes also providing an up-to-date overview on the antisuicidal effects of lithium, as well as novel molecules such as esketamine and emerging medications and further molecules in development. Finally we give a critical overview on our current knowledge on using neuromodulatory and biological therapies, such as ECT, rTMS, tDCS and other options.

Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data

Genome-wide association studies (GWAS) of suicidal thoughts and behaviors support the existence of genetic contributions. Continuous measures of psychiatric disorder symptom severity can sometimes model polygenic risk better than binarized definitions. We compared two severity measures of suicidal thoughts and behaviors at the molecular and functional levels using genome-wide data. We used summary association data from GWAS of four traits analyzed in 122,935 individuals of European ancestry: thought life was not worth living (TLNWL), thoughts of self-harm, actual self-harm, and attempted suicide. A new trait for suicidal thoughts and behaviors was constructed first, phenotypically, by aggregating the previous four traits (termed "suicidality") and second, genetically, by using genomic structural equation modeling (gSEM; termed S-factor). Suicidality and S-factor were compared using SNP-heritability (h2) estimates, genetic correlation (rg), partitioned h2, effect size distribution, transcriptomic correlations (ρGE) in the brain, and cross-population polygenic scoring (PGS). The S-factor had good model fit (χ2 = 0.21, AIC = 16.21, CFI = 1.00, SRMR = 0.024). Suicidality (h2 = 7.6%) had higher h2 than the S-factor (h2 = 2.54, Pdiff = 4.78 × 10-13). Although the S-factor had a larger number of non-null susceptibility loci (πc = 0.010), these loci had small effect sizes compared to those influencing suicidality (πc = 0.005, Pdiff = 0.045). The h2 of both traits was enriched for conserved biological pathways. The rg and ρGE support highly overlapping genetic and transcriptomic features between suicidality and the S-factor. PGS using European-ancestry SNP effect sizes strongly associated with TLNWL in Admixed Americans: Nagelkerke's R2 = 8.56%, P = 0.009 (PGSsuicidality) and Nagelkerke's R2 = 7.48%, P = 0.045 (PGSS-factor). An aggregate suicidality phenotype was statistically more heritable than the S-factor across all analyses and may be more informative for future genetic study designs interested in common genetic factors among different suicide related phenotypes.

Associations Between Natural Language Processing-Enriched Social Determinants of Health and Suicide Death Among US Veterans

Importance

Social determinants of health (SDOHs) are known to be associated with increased risk of suicidal behaviors, but few studies use SDOHs from unstructured electronic health record notes.

Objective

To investigate associations between veterans' death by suicide and recent SDOHs, identified using structured and unstructured data.

Design, Setting, and Participants

This nested case-control study included veterans who received care under the US Veterans Health Administration from October 1, 2010, to September 30, 2015. A natural language processing (NLP) system was developed to extract SDOHs from unstructured clinical notes. Structured data yielded 6 SDOHs (ie, social or familial problems, employment or financial problems, housing instability, legal problems, violence, and nonspecific psychosocial needs), NLP on unstructured data yielded 8 SDOHs (social isolation, job or financial insecurity, housing instability, legal problems, barriers to care, violence, transition of care, and food insecurity), and combining them yielded 9 SDOHs. Data were analyzed in May 2022.

Exposures

Occurrence of SDOHs over a maximum span of 2 years compared with no occurrence of SDOH.

Main Outcomes and Measures

Cases of suicide death were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. Suicide was ascertained by National Death Index, and patients were followed up for up to 2 years after cohort entry with a study end date of September 30, 2015. Adjusted odds ratios (aORs) and 95% CIs were estimated using conditional logistic regression.

Results

Of 6 122 785 veterans, 8821 committed suicide during 23 725 382 person-years of follow-up (incidence rate 37.18 per 100 000 person-years). These 8821 veterans were matched with 35 284 control participants. The cohort was mostly male (42 540 [96.45%]) and White (34 930 [79.20%]), with 6227 (14.12%) Black veterans. The mean (SD) age was 58.64 (17.41) years. Across the 5 common SDOHs, NLP-extracted SDOH, on average, retained 49.92% of structured SDOHs and covered 80.03% of all SDOH occurrences. SDOHs, obtained by structured data and/or NLP, were significantly associated with increased risk of suicide. The 3 SDOHs with the largest effect sizes were legal problems (aOR, 2.66; 95% CI, 2.46-2.89), violence (aOR, 2.12; 95% CI, 1.98-2.27), and nonspecific psychosocial needs (aOR, 2.07; 95% CI, 1.92-2.23), when obtained by combining structured data and NLP.

Conclusions and Relevance

In this study, NLP-extracted SDOHs, with and without structured SDOHs, were associated with increased risk of suicide among veterans, suggesting the potential utility of NLP in public health studies.

Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans

Importance

Suicide is a leading cause of death; however, the molecular genetic basis of suicidal thoughts and behaviors (SITB) remains unknown.

Objective

To identify novel, replicable genomic risk loci for SITB.

Design, Setting, and Participants

This genome-wide association study included 633 778 US military veterans with and without SITB, as identified through electronic health records. GWAS was performed separately by ancestry, controlling for sex, age, and genetic substructure. Cross-ancestry risk loci were identified through meta-analysis. Study enrollment began in 2011 and is ongoing. Data were analyzed from November 2021 to August 2022.

Main Outcome and Measures

SITB.

Results

A total of 633 778 US military veterans were included in the analysis (57 152 [9%] female; 121 118 [19.1%] African ancestry, 8285 [1.3%] Asian ancestry, 452 767 [71.4%] European ancestry, and 51 608 [8.1%] Hispanic ancestry), including 121 211 individuals with SITB (19.1%). Meta-analysis identified more than 200 GWS (P < 5 × 10-8) cross-ancestry risk single-nucleotide variants for SITB concentrated in 7 regions on chromosomes 2, 6, 9, 11, 14, 16, and 18. Top single-nucleotide variants were largely intronic in nature; 5 were independently replicated in ISGC, including rs6557168 in ESR1, rs12808482 in DRD2, rs77641763 in EXD3, rs10671545 in DCC, and rs36006172 in TRAF3. Associations for FBXL19 and AC018880.2 were not replicated. Gene-based analyses implicated 24 additional GWS cross-ancestry risk genes, including FURIN, TSNARE1, and the NCAM1-TTC12-ANKK1-DRD2 gene cluster. Cross-ancestry enrichment analyses revealed significant enrichment for expression in brain and pituitary tissue, synapse and ubiquitination processes, amphetamine addiction, parathyroid hormone synthesis, axon guidance, and dopaminergic pathways. Seven other unique European ancestry-specific GWS loci were identified, 2 of which (POM121L2 and METTL15/LINC02758) were replicated. Two additional GWS ancestry-specific loci were identified within the African ancestry (PET112/GATB) and Hispanic ancestry (intergenic locus on chromosome 4) subsets, both of which were replicated. No GWS loci were identified within the Asian ancestry subset; however, significant enrichment was observed for axon guidance, cyclic adenosine monophosphate signaling, focal adhesion, glutamatergic synapse, and oxytocin signaling pathways across all ancestries. Within the European ancestry subset, genetic correlations (r > 0.75) were observed between the SITB phenotype and a suicide attempt-only phenotype, depression, and posttraumatic stress disorder. Additionally, polygenic risk score analyses revealed that the Million Veteran Program polygenic risk score had nominally significant main effects in 2 independent samples of veterans of European and African ancestry.

Conclusions and Relevance

The findings of this analysis may advance understanding of the molecular genetic basis of SITB and provide evidence for ESR1, DRD2, TRAF3, and DCC as cross-ancestry candidate risk genes. More work is needed to replicate these findings and to determine if and how these genes might impact clinical care.

Altered transcriptomes, cell type proportions, and dendritic spine morphology in hippocampus of suicide deaths

Suicide is a condition resulting from complex environmental and genetic risks that affect millions of people globally. Both structural and functional studies identified the hippocampus as one of the vulnerable brain regions contributing to suicide risk. Here, we have identified the hippocampal transcriptomes, gene ontology, cell type proportions, dendritic spine morphology, and transcriptomic signature in iPSC-derived neuronal precursor cells (NPCs) and neurons in postmortem brain tissue from suicide deaths. The hippocampal tissue transcriptomic data revealed that NPAS4 gene expression was downregulated while ALDH1A2, NAAA, and MLXIPL gene expressions were upregulated in tissue from suicide deaths. The gene ontology identified 29 significant pathways including NPAS4-associated gene ontology terms "excitatory post-synaptic potential", "regulation of postsynaptic membrane potential" and "long-term memory" indicating alteration of glutamatergic synapses in the hippocampus of suicide deaths. The cell type deconvolution identified decreased excitatory neuron proportion and an increased inhibitory neuron proportion providing evidence of excitation/inhibition imbalance in the hippocampus of suicide deaths. In addition, suicide deaths had increased dendric spine density, due to an increase of thin (relatively unstable) dendritic spines, compared to controls. The transcriptomes of iPSC-derived hippocampal-like NPCs and neurons revealed 31 and 33 differentially expressed genes in NPC and neurons, respectively, of suicide deaths. The suicide-associated differentially expressed genes in NPCs were RELN, CRH, EMX2, OXTR, PARM1 and IFITM2 which overlapped with previously published results. The previously-known suicide-associated differentially expressed genes in differentiated neurons were COL1A1, THBS1, IFITM2, AQP1, and NLRP2. Together, these findings would help better understand the hippocampal neurobiology of suicide for identifying therapeutic targets to prevent suicide.

Plasticity of synapses and reward circuit function in the genesis and treatment of depression

What changes in brain function cause the debilitating symptoms of depression? Can we use the answers to this question to invent more effective, faster acting antidepressant drug therapies? This review provides an overview and update of the converging human and preclinical evidence supporting the hypothesis that changes in the function of excitatory synapses impair the function of the circuits they are embedded in to give rise to the pathological changes in mood, hedonic state, and thought processes that characterize depression. The review also highlights complementary human and preclinical findings that classical and novel antidepressant drugs relieve the symptoms of depression by restoring the functions of these same synapses and circuits. These findings offer a useful path forward for designing better antidepressant compounds.

College students’ screening early warning factors in identification of suicide risk

This study aimed to explore the main influencing factors of suicide risk among Chinese students and establish an early warning model to provide interventions for high-risk students. We conducted surveys of students in their first and third years from a cohort study at Jining Medical College. Logistic regression models were used to screen the early warning factors, and four machine learning models were used to establish early warning models. There were 8 factors related to suicide risk that were eventually obtained through screening, including age, having a rough father, and CES-D, OHQ, ASLEC-4, BFI-Neuroticism, BFI-Openness, and MMC-AF-C scores. A random forest model with SMOTE was adopted, and it verified that these 8 early warning signs, for suicide risk can effectively predict suicide risk within 2 years with an AUC score of 0.947. Among the factors, we constructed a model that indicated that different personality traits affected suicide risk by different paths. Moreover, the factors obtained by screening can be used to identify college students in the same year with a high risk of suicide, with an AUC score that reached 0.953. Based on this study, we suggested some interventions to prevent students going high suicide risk.

Genetics and epigenetics of self-injurious thoughts and behaviors: Systematic review of the suicide literature and methodological considerations

Suicide is a multifaceted and poorly understood clinical outcome, and there is an urgent need to advance research on its phenomenology and etiology. Epidemiological studies have demonstrated that suicidal behavior is heritable, suggesting that genetic and epigenetic information may serve as biomarkers for suicide risk. Here we systematically review the literature on genetic and epigenetic alterations observed in phenotypes across the full range of self-injurious thoughts and behaviors (SITB). We included 577 studies focused on genome-wide and epigenome-wide associations, candidate genes (SNP and methylation), noncoding RNAs, and histones. Convergence of specific genes is limited across units of analysis, although pathway-based analyses do indicate nervous system development and function and immunity/inflammation as potential underlying mechanisms of SITB. We provide suggestions for future work on the genetic and epigenetic correlates of SITB with a specific focus on measurement issues.

Pharmacological Strategies for Suicide Prevention Based on the Social Pain Model: A Scoping Review

Suicidal behaviour is a public health problem whose magnitude is both substantial and increasing. Since many individuals seek medical treatment following a suicide attempt, strategies aimed at reducing further attempts in this population are a valid and feasible secondary prevention approach. An evaluation of the available evidence suggests that existing treatment approaches have a limited efficacy in this setting, highlighting the need for innovative approaches to suicide prevention. Existing research on the neurobiology of social pain has highlighted the importance of this phenomenon as a risk factor for suicide, and has also yielded several attractive targets for pharmacological strategies that could reduce suicidality in patients with suicidal ideation or a recent attempt. In this paper, the evidence related to these targets is synthesized and critically evaluated. The way in which social pain is related to the “anti-suicidal” properties of recently approved treatments, such as ketamine and psilocybin, is examined. Such strategies may be effective for the short-term reduction in suicidal ideation and behaviour, particularly in cases where social pain is identified as a contributory factor. These pharmacological approaches may be effective regardless of the presence or absence of a specific psychiatric diagnosis, but they require careful evaluation.

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.