Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach

Next-generation precision medicine for suicidality prevention

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

Using the Strategy of Genome-Wide Association Studies to Identify Genetic Markers of Suicidal Behavior: A Narrative Review

BACKGROUND

Several studies involving various suicidal phenotypes based on the strategy of the search of genome-wide associations with single nucleotide polymorphisms have been performed recently. These studies need to be generalized.

AIM

To systematize the findings of a number of genome-wide association studies (GWAS) for suicidal phenotypes, annotate the identified markers, analyze their functionality, and possibly substantiate the hypothesis holding that these phenotypes reflect a nonspecific set of gene variants that are relevant as relates to stress-vulnerability as a key endophenotype of suicidal behavior (SB).

METHODS

A search on the PubMed and related resources using the combinations "suicide AND GWAS" and "suicidal behavior AND GWAS" was performed. It yielded a total of 34 independent studies and meta-analyses.

RESULTS

For the 10 years since such studies emerged, they have undergone significant progress. Estimates of the SNP heritability of SB in some cases are comparable with estimates of heritability based on the twin method. Many studies show a high genetic correlation with the genomic markers of the most common mental disorders (depression, bipolar disorder, schizophrenia, post-traumatic stress disorder). At the same time, a genomic architecture specific to SB is also encountered. Studies utilizing the GWAS strategy have not revealed any associations of SB with candidate genes that had been previously studied in detail (different neurotransmitters, stress response system, polyamines, etc.). Frequently reported findings from various studies belong in three main groups: 1) genes involved in cell interactions, neurogenesis, the development of brain structures, inflammation, and the immune responses; 2) genes encoding receptors for neurotrophins and various components of the intracellular signaling systems involved in synaptic plasticity, embryonic development, and carcinogenesis; and 3) genes encoding various neuro-specific proteins and regulators.

CONCLUSION

In general, GWAS in the field of suicidology mainly serve the purpose of a deeper understanding of the pathophysiology of suicidal behavior. However, they also demonstrate growing capability in terms of predicting and preventing suicide, especially when calculating the polygenic risk score among certain populations (psychiatric patients) and in combination with tests of different modalities. From our point of view, there exists a set of markers revealed by the GWAS strategy that seems to point to a leading role played by stress vulnerability, an endophenotype that is formed during early development and which subsequently comes to play the role of key pathogenetic mechanism in SB.

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.

The rs7208505 Polymorphism and Differential Expression of the SKA2 Gene in the Prefrontal Cortex of Suicide Victims from the Mexican Population

OBJECTIVE

The main aim of the current study was to investigate whether SKA2 gene expression in the postmortem brain of rs7208505 genotype are altered in suicide victims from a Mexican population.

METHODS

In this study, we report a genetic analysis of expression levels of the SKA2 gene in the prefrontal cortex of the postmortem brain of suicidal subjects (n = 22) compared to subjects who died of causes other than suicide (n = 22) in a Mexican population using RT-qPCR assays. Additionally, we genotyped the rs7208505 polymorphism in suicide victims (n = 98) and controls (n = 88) and we evaluate the association of genotypes for the SNP rs7208505 with expression level of SKA2.

RESULTS

The results showed that the expression of the SKA2 gene was significantly higher in suicide victims compared to control subjects (p = 0.044). Interestingly, we observed a greater proportion of allele A of the rs7208505 in suicide victims than controls. Even though there was no association between the SNP with suicide in the study population we found a significative association of the expression level from SKA2 with the allele A of the rs7208505 and suicide.

CONCLUSION

The evidence suggests that the expression of SKA2 in the prefrontal cortex may be a critical factor in the etiology of suicidal behavior.

Smoking affects symptom improvement in schizophrenia: a prospective longitudinal study of male patients with first-episode schizophrenia

Patients with schizophrenia (SCZ) smoke up to three times more than general people. However, there are conflicting results regarding the relationship between tobacco smoke and clinical symptom severity in SCZ. The aim of this study was to assess the impact of smoking on clinical symptoms after antipsychotic treatment in a 12-week cohort study after controlling for confounding factors. One hundred and forty-five male patients with drug-naïve first-episode (DNFE) SCZ received antipsychotic monotherapy for 12 weeks. Symptom severity was assessed at baseline and at week 12 by the Positive and Negative Syndrome Scale (PANSS). We found no differences in clinical symptoms among male smokers with SCZ compared with male nonsmokers. However, male smokers showed greater improvement in negative symptoms after 12 weeks of treatment, controlling for age, years of education, onset age, and baseline body mass index (BMI). Our study showed that after 12 weeks of treatment with antipsychotics, male smokers showed greater improvement in negative symptoms than male nonsmokers.

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.

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.

Navigating the Intersection of Technology and Depression Precision Medicine

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

Ablation of KIF2C in Purkinje cells impairs metabotropic glutamate receptor trafficking and motor coordination in male mice

Kinesin family member 2C (KIF2C)/mitotic centromere-associated kinesin (MCAK), is thought to be oncogenic as it is involved in tumour progression and metastasis. Moreover, it also plays a part in neurodegenerative conditions like Alzheimer's disease and psychiatric disorders such as suicidal schizophrenia. Our previous study conducted on mice demonstrated that KIF2C is widely distributed in various regions of the brain, and is localized in synaptic spines. Additionally, it regulates microtubule dynamic properties through its own microtubule depolymerization activity, thereby affecting AMPA receptor transport and cognitive behaviour in mice. In this study, we show that KIF2C regulates the transport of mGlu1 receptors in Purkinje cells by binding to Rab8. KIF2C deficiency in Purkinje cells results in abnormal gait, reduced balance ability and motor incoordination in male mice. These data suggest that KIF2C is essential for maintaining normal transport and synaptic function of mGlu1 and motor coordination in mice. KEY POINTS: KIF2C is localized in synaptic spines of hippocampus neurons, and regulates excitatory transmission, synaptic plasticity and cognitive behaviour. KIF2C is extensively expressed in the cerebellum, and we investigated its functions in development and synaptic transmission of cerebellar Purkinje cells. KIF2C deficiency in Purkinje cells alters the expression of metabotropic glutamate receptor 1 (mGlu1) and the AMPA receptor GluA2 subunit at Purkinje cell synapses, and changes excitatory synaptic transmission, but not inhibitory transmission. KIF2C regulates the transport of mGlu1 receptors in Purkinje cells by binding to Rab8. KIF2C deficiency in Purkinje cells affects motor coordination, but not social behaviour in male mice.

Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder

Introduction

Suicide is one of the leading causes of death across different age groups. The persistence of suicidal ideation and the progression of suicidal ideations to action could be related to impulsivity, the tendency to act on urges with low temporal latency, and little forethought. Quantifying impulsivity could thus help suicidality estimation and risk assessments in ideation-to-action suicidality frameworks.

Methods

To model suicidality with impulsivity quantification, we obtained questionnaires, behavioral tests, heart rate variability (HRV), and resting state functional magnetic resonance imaging measurements from 34 participants with mood disorders. The participants were categorized into three suicidality groups based on their Mini-International Neuropsychiatric Interview: none, low, and moderate to severe.

Results

Questionnaire and HRV-based impulsivity measures were significantly different between the suicidality groups with higher subscales of impulsivity associated with higher suicidality. A multimodal system to characterize impulsivity objectively resulted in a classification accuracy of 96.77% in the three-class suicidality group prediction task.

Conclusions

This study elucidates the relative sensitivity of various impulsivity measures in differentiating participants with suicidality and demonstrates suicidality prediction with high accuracy using a multimodal objective impulsivity characterization in participants with mood disorders.

Epigenome-Wide Associations of Placental DNA Methylation and Behavioral and Emotional Difficulties in Children at 3 Years of Age

The placenta is a key organ for fetal and brain development. Its epigenome can be regarded as a biochemical record of the prenatal environment and a potential mechanism of its association with the future health of the fetus. We investigated associations between placental DNA methylation levels and child behavioral and emotional difficulties, assessed at 3 years of age using the Strengths and Difficulties Questionnaire (SDQ) in 441 mother-child dyads from the EDEN cohort. Hypothesis-driven and exploratory analyses (on differentially methylated probes (EWAS) and regions (DMR)) were adjusted for confounders, technical factors, and cell composition estimates, corrected for multiple comparisons, and stratified by child sex. Hypothesis-driven analyses showed an association of cg26703534 (AHRR) with emotional symptoms, and exploratory analyses identified two probes, cg09126090 (intergenic region) and cg10305789 (PPP1R16B), as negatively associated with peer relationship problems, as well as 33 DMRs, mostly positively associated with at least one of the SDQ subscales. Among girls, most associations were seen with emotional difficulties, whereas in boys, DMRs were as much associated with emotional than behavioral difficulties. This study provides the first evidence of associations between placental DNA methylation and child behavioral and emotional difficulties. Our results suggest sex-specific associations and might provide new insights into the mechanisms of neurodevelopment.

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.

Unlocking the Secrets: Exploring the Biochemical Correlates of Suicidal Thoughts and Behaviors in Adults with Autism Spectrum Conditions

Involving 1 million people a year, suicide represents one of the major topics of psychiatric research. Despite the focus in recent years on neurobiological underpinnings, understanding and predicting suicide remains a challenge. Many sociodemographical risk factors and prognostic markers have been proposed but they have poor predictive accuracy. Biomarkers can provide essential information acting as predictive indicators, providing proof of treatment response and proposing potential targets while offering more assurance than psychological measures. In this framework, the aim of this study is to open the way in this field and evaluate the correlation between blood levels of serotonin, brain derived neurotrophic factor, tryptophan and its metabolites, IL-6 and homocysteine levels and suicidality. Blood samples were taken from 24 adults with autism, their first-degree relatives, and 24 controls. Biochemical parameters were measured with enzyme-linked immunosorbent assays. Suicidality was measured through selected items of the MOODS-SR. Here we confirm the link between suicidality and autism and provide more evidence regarding the association of suicidality with increased homocysteine (0.278) and IL-6 (0.487) levels and decreased tryptophan (-0.132) and kynurenic acid (-0.253) ones. Our results suggest a possible transnosographic association between these biochemical parameters and increased suicide risk.

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.

Methylation in MAD1L1 is associated with the severity of suicide attempt and phenotypes of depression

Depression is a multifactorial disorder representing a significant public health burden. Previous studies have linked multiple single nucleotide polymorphisms with depressive phenotypes and suicidal behavior. MAD1L1 is a mitosis metaphase checkpoint protein that has been linked to depression in GWAS. Using a longitudinal EWAS approach in an adolescent cohort at two time points (n = 216 and n = 154), we identified differentially methylated sites that were associated with depression-related genetic variants in MAD1L1. Three methylation loci (cg02825527, cg18302629, and cg19624444) were consistently hypomethylated in the minor allele carriers, being cross-dependent on several SNPs. We further investigated whether DNA methylation at these CpGs is associated with depressive psychiatric phenotypes in independent cohorts. The first site (cg02825527) was hypomethylated in blood (exp(β) = 84.521, p value ~ 0.003) in participants with severe suicide attempts (n = 88). The same locus showed increased methylation in glial cells (exp(β) = 0.041, p value ~ 0.004) in the validation cohort, involving 29 depressed patients and 29 controls, and showed a trend for association with suicide (n = 40, p value ~ 0.089) and trend for association with depression treatment (n = 377, p value ~ 0.075). The second CpG (cg18302629) was significantly hypomethylated in depressed participants (exp(β) = 56.374, p value ~ 0.023) in glial cells, but did not show associations in the discovery cohorts. The last methylation site (cg19624444) was hypomethylated in the whole blood of severe suicide attempters; however, this association was at the borderline for statistical significance (p value ~ 0.061). This locus, however, showed a strong association with depression treatment in the validation cohort (exp(β) = 2.237, p value ~ 0.003) with 377 participants. The direction of associations between psychiatric phenotypes appeared to be different in the whole blood in comparison with brain samples for cg02825527 and cg19624444. The association analysis between methylation at cg18302629 and cg19624444 and MAD1L1 transcript levels in CD¹⁴⁺ cells shows a potential link between methylation at these CpGs and MAD1L1 expression. This study suggests evidence that methylation at MAD1L1 is important for psychiatric health as supported by several independent cohorts.

Distinguishing among HCO 3- , CO 3= , and H + as Substrates of Proteins That Appear To Be "Bicarbonate" Transporters

BACKGROUND

Differentiating among HCO 3- , CO 3= , and H + movements across membranes has long seemed impossible. We now seek to discriminate unambiguously among three alternate mechanisms: the inward flux of 2 HCO 3- (mechanism 1), the inward flux of 1 CO 3= (mechanism 2), and the CO 2 /HCO 3- -stimulated outward flux of 2 H + (mechanism 3).

METHODS

As a test case, we use electrophysiology and heterologous expression in Xenopus oocytes to examine SLC4 family members that appear to transport "bicarbonate" ("HCO 3- ").

RESULTS

First, we note that cell-surface carbonic anhydrase should catalyze the forward reaction CO 2 +OH - →HCO 3- if HCO 3- is the substrate; if it is not, the reverse reaction should occur. Monitoring changes in cell-surface pH ( Δ pH S ) with or without cell-surface carbonic anhydrase, we find that the presumed Cl-"HCO 3 " exchanger AE1 (SLC4A1) does indeed transport HCO 3- (mechanism 1) as long supposed, whereas the electrogenic Na/"HCO 3 " cotransporter NBCe1 (SLC4A4) and the electroneutral Na + -driven Cl-"HCO 3 " exchanger NDCBE (SLC4A8) do not. Second, we use mathematical simulations to show that each of the three mechanisms generates unique quantities of H + at the cell surface (measured as Δ pH S ) per charge transported (measured as change in membrane current, ΔIm ). Calibrating ΔpH S /Δ Im in oocytes expressing the H + channel H V 1, we find that our NBCe1 data align closely with predictions of CO 3= transport (mechanism 2), while ruling out HCO 3- (mechanism 1) and CO 2 /HCO 3- -stimulated H + transport (mechanism 3).

CONCLUSIONS

Our surface chemistry approach makes it possible for the first time to distinguish among HCO 3- , CO 3= , and H + fluxes, thereby providing insight into molecular actions of clinically relevant acid-base transporters and carbonic-anhydrase inhibitors.

Association of HLA class II alleles with suicidal behavior in a Transylvanian population

Background: Suicide is a complex phenomenon determined by the interaction of various risk factors. The Major Histocompatibility Complex is the most polymorphic gene cluster of the entire human genome, being linked to both the regulation of the immune system and various psychiatric diseases. The aim of this study was to identify HLA-DQB1 and DRB1 alleles and genotypes susceptible to influence suicidal behavior.

Methods: We explored the association of HLA-DQB1 alleles with the suicidal behavior on a sample of 427 individuals (including 110 suicide attempters) from Transylvania, as well as the association of HLA-DRB1 alleles with the suicidal behavior on a sample of 271 individuals (including 50 suicide attempters), using the single specific primer-PCR (SSP-PCR) technique.

Results: We found that the HLA-DQB1*02, *03 and *06 alleles, the DQB1*02/*03, DQB1*02/*06, DRB1*12/*15 and DRB1*07/*13 genotypes, as well as the DQB1*06~DRB1*07 and DQB1*02~DRB1*13 haplotypes, were more frequent in suicide attempters. In contrast, the HLA-DQB1*04 and DQB1*13 alleles, the DQB1*02/*05 and DQB1*03/*05 genotypes and the DQB1*03~DRB1*13 haplotype were less frequent in the case group.

Conclusion: HLA-DQB1*02, *03 and *06 alleles and the DQB1*02/*03 and *02/*06 genotypes are susceptible to favor a suicide behavior, while the HLA-DQB1*04 and *13 alleles and the DQB1*02/*05 and *03/*05 genotypes were protective against such behavior. A similar analysis regarding the HLA-DRB1 alleles detected a possible risk for suicidal behavior among individuals possessing either the DRB1*12/*15 or the DRB1*07/*13 genotypes. DQB1*06~DRB1*07 and DQB1*02~DRB1*13 haplotypes were found susceptible to favor a suicidal behavior, while DQB1*03~DRB1*13 exhibited a protective influence.

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.

Transcriptomic Studies of Antidepressant Action in Rodent Models of Depression: A First Meta-Analysis

Antidepressants (ADs) are, for now, the best everyday treatment we have for moderate to severe major depressive episodes (MDEs). ADs are among the most prescribed drugs in the Western Hemisphere; however, the trial-and-error prescription strategy and side-effects leave a lot to be desired. More than 60% of patients suffering from major depression fail to respond to the first AD they are prescribed. For those who respond, full response is only observed after several weeks of treatment. In addition, there are no biomarkers that could help with therapeutic decisions; meanwhile, this is already true in cancer and other fields of medicine. For years, many investigators have been working to decipher the underlying mechanisms of AD response. Here, we provide the first systematic review of animal models. We thoroughly searched all the studies involving rodents, profiling transcriptomic alterations consecutive to AD treatment in naïve animals or in animals subjected to stress-induced models of depression. We have been confronted by an important heterogeneity regarding the drugs and the experimental settings. Thus, we perform a meta-analysis of the AD signature of fluoxetine (FLX) in the hippocampus, the most studied target. Among genes and pathways consistently modulated across species, we identify both old players of AD action and novel transcriptional biomarker candidates that warrant further investigation. We discuss the most prominent transcripts (immediate early genes and activity-dependent synaptic plasticity pathways). We also stress the need for systematic studies of AD action in animal models that span across sex, peripheral and central tissues, and pharmacological classes.

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

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

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.

Determinants of Suicide Ideation in the Romanian Population

BACKGROUND

Suicide ideation and behaviors are directly linked to the risk of death by suicide. In Romania, as well as worldwide, increased suicide rates were observed in the recent past, more so in the context of the COVID-19 pandemic. The purpose of this study was to investigate the influence of psychosocial factors, quality of life (QOL), and loneliness dimensions and adverse life antecedents on suicide ideation (SI) and prolonged sadness (PS).

METHODS

This cross-sectional quantitative research study used a CATI data gathering method to investigate 1102 randomly selected individuals over 18 years of age regarding various determinants of SI and PS. Data were collected in June 2021. Descriptive, inferential, and multivariate statistics were used for data analysis.

RESULTS

SI was negatively correlated with all the assessed psychosocial factors, more significantly with family relationships, wealth, health, social relationships, and affective life. Stronger correlations were observed when investigating the state of prolonged sadness, sex, and affective lives along with health and income, which were more influential. SI was negatively correlated with QOL and positively correlated with adverse life events and total loneliness scores. Lesser educated youngsters with reduced overall happiness and a history of depression, self-harm, and trauma were at greater risk of developing SI.

CONCLUSIONS

This is the first national study exploring the suicide ideation and prolonged sadness in relation to psychosocial factors, quality of life, and adverse life events. These results have important implications for suicide prevention programs, which should be designed in accordance with similar studies.

mTORC1 inhibitor effects on rapid ketamine-induced reductions in suicidal ideation in patients with treatment-resistant depression

Suicide is a public health crisis with limited treatment options. Ketamine has demonstrated rapid and robust improvements in suicidal ideation (SI). The parent study for the secondary pilot analyses presented here was a double-blind, cross-over trial that found pretreatment with the mechanistic target of rapamycin complex 1 (mTORC1) prolonged the antidepressant effects of ketamine. Here we examined the effect of mTORC1 inhibition on ketamine's antisuicidal effects. Twenty patients in a major depressive episode were randomized to pretreatment with oral rapamycin (6 mg) or placebo prior to IV ketamine (0.5 mg/kg). We found ketamine administration resulted in significant improvements across all measures with the largest effect at 24 h with only the Beck Scale for Suicide remaining significant at the two-week follow-up. There were no significant main effects of pretreatment. While these analyses are pilot in nature and overall severity of SI was relatively low, the antisuicidal findings (no effect of rapamycin) being in contrast to the antidepressant effects (prolonged effect with rapamycin), suggest the rapid-acting antisuicidal and antidepressant effects of ketamine may be mechanistically distinct and the trajectories of response, recovery, and relapse may be independent. These findings provide additional evidence of ketamine's antisuicidal effects and highlight the importance of future studies that continue to examine potential differences in mechanisms and trajectory of outcomes.

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.

Predictors of suicide attempt in patients with obsessive-compulsive disorder: an exploratory study with machine learning analysis

BACKGROUND

Patients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear - whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm.

METHODS

A total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables.

RESULTS

The prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95.

CONCLUSIONS

This is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms.

KIF2C regulates synaptic plasticity and cognition in mice through dynamic microtubule depolymerization

Dynamic microtubules play a critical role in cell structure and function. In nervous system, microtubules are the major route for cargo protein trafficking and they specially extend into and out of synapses to regulate synaptic development and plasticity. However, the detailed depolymerization mechanism that regulates dynamic microtubules in synapses and dendrites is still unclear. In this study, we find that KIF2C, a dynamic microtubule depolymerization protein without known function in the nervous system, plays a pivotal role in the structural and functional plasticity of synapses and regulates cognitive function in mice. Through its microtubule depolymerization capability, KIF2C regulates microtubule dynamics in dendrites, and regulates microtubule invasion of spines in neurons in a neuronal activity-dependent manner. Using RNAi knockdown and conditional knockout approaches, we showed that KIF2C regulates spine morphology and synaptic membrane expression of AMPA receptors. Moreover, KIF2C deficiency leads to impaired excitatory transmission, long-term potentiation, and altered cognitive behaviors in mice. Collectively, our study explores a novel function of KIF2C in the nervous system and provides an important regulatory mechanism on how activity-dependent microtubule dynamic regulates synaptic plasticity and cognition behaviors.

Epigenetic studies in suicidal ideation and behavior

Most psychiatric disorders are associated with an elevated risk of suicide. Suicidal behavior is the product of the interaction of many risk factors, such as genetics and environmental factors. Hence, epigenetics research may help to understand the mechanisms leading to suicidal ideation and behavior. This review will discuss epigenetic studies in both suicidal ideation and behavior. Epigenetic modifications are likely to be important in both suicidal ideation and behavior. Most of the reviewed studies found significant epigenetic modifications linked with suicidal behavior rather than ideation. Although sizable research has been carried out on this topic, most studies have been done on small-scale samples, and future research is required in larger samples with better clinical characterization of suicide phenotypes to investigate these epigenetic modifications further.

Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain

Aging is a major risk factor contributing to neurodegeneration and dementia. However, it remains unclarified how aging promotes these diseases. Here, we use machine learning and weighted gene co-expression network (WGCNA) to explore the relationship between aging and gene expression in the human frontal cortex and reveal potential biomarkers and therapeutic targets of neurodegeneration and dementia related to aging. The transcriptional profiling data of the human frontal cortex from individuals ranging from 26 to 106 years old was obtained from the GEO database in NCBI. Self-Organizing Feature Map (SOM) was conducted to find the clusters in which gene expressions downregulate with aging. For WGCNA analysis, first, co-expressed genes were clustered into different modules, and modules of interest were identified through calculating the correlation coefficient between the module and phenotypic trait (age). Next, the overlapping genes between differentially expressed genes (DEG, between young and aged group) and genes in the module of interest were discovered. Random Forest classifier was performed to obtain the most significant genes in the overlapping genes. The disclosed significant genes were further identified through network analysis. Through WGCNA analysis, the greenyellow module is found to be highly negatively correlated with age, and functions mainly in long-term potentiation and calcium signaling pathways. Through step-by-step filtering of the module genes by overlapping with downregulated DEGs in aged group and Random Forest classifier analysis, we found that MAPT, KLHDC3, RAP2A, RAP2B, ELAVL2, and SYN1 were co-expressed and highly correlated with aging.

Development of a Suicide Prediction Model for the Elderly Using Health Screening Data

Suicide poses a serious problem globally, especially among the elderly population. To tackle the issue, this study aimed to develop a model for predicting suicide by using machine learning based on the elderly population. To obtain a large sample, the study used the big data health screening cohort provided by the National Health Insurance Sharing Service. By applying a machine learning technique, a predictive model that comprehensively utilized various factors was developed to select the elderly aged > 65 years at risk of suicide. A total of 48,047 subjects were included in the analysis. Individuals who died by suicide were older, and the number of men was significantly greater. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Specifically, the prescription of benzodiazepines alone was associated with a high suicide risk. Furthermore, body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group. We developed a model for predicting suicide by using machine learning based on the elderly population. This suicide prediction model can satisfy the performance to some extent by employing only the medical service usage behavior without subjective reports.

Neurobiological Basis of Increased Risk for Suicidal Behaviour

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

A Convergent Functional Genomics Analysis to Identify Biological Regulators Mediating Effects of Creatine Supplementation

Creatine (Cr) and phosphocreatine (PCr) are physiologically essential molecules for life, given they serve as rapid and localized support of energy- and mechanical-dependent processes. This evolutionary advantage is based on the action of creatine kinase (CK) isozymes that connect places of ATP synthesis with sites of ATP consumption (the CK/PCr system). Supplementation with creatine monohydrate (CrM) can enhance this system, resulting in well-known ergogenic effects and potential health or therapeutic benefits. In spite of our vast knowledge about these molecules, no integrative analysis of molecular mechanisms under a systems biology approach has been performed to date; thus, we aimed to perform for the first time a convergent functional genomics analysis to identify biological regulators mediating the effects of Cr supplementation in health and disease. A total of 35 differentially expressed genes were analyzed. We identified top-ranked pathways and biological processes mediating the effects of Cr supplementation. The impact of CrM on miRNAs merits more research. We also cautiously suggest two dose-response functional pathways (kinase- and ubiquitin-driven) for the regulation of the Cr uptake. Our functional enrichment analysis, the knowledge-based pathway reconstruction, and the identification of hub nodes provide meaningful information for future studies. This work contributes to a better understanding of the well-reported benefits of Cr in sports and its potential in health and disease conditions, although further clinical research is needed to validate the proposed mechanisms.

Nectins and Nectin-like molecules in synapse formation and involvement in neurological diseases

Synapses are interneuronal junctions which form neuronal networks and play roles in a variety of functions, including learning and memory. Two types of junctions, synaptic junctions (SJs) and puncta adherentia junctions (PAJs), have been identified. SJs are found at all excitatory and inhibitory synapses whereas PAJs are found at excitatory synapses, but not inhibitory synapses, and particularly well developed at hippocampal mossy fiber giant excitatory synapses. Both SJs and PAJs are mediated by cell adhesion molecules (CAMs). Major CAMs at SJs are neuroligins-neurexins and Nectin-like molecules (Necls)/CADMs/SynCAMs whereas those at PAJs are nectins and cadherins. In addition to synaptic PAJs, extrasynaptic PAJs have been identified at contact sites between neighboring dendrites near synapses and regulate synapse formation. In addition to SJs and PAJs, a new type of cell adhesion apparatus different from these junctional apparatuses has been identified and named nectin/Necl spots. One nectin spot at contact sites between neighboring dendrites at extrasynaptic regions near synapses regulates synapse formation. Several members of nectins and Necls had been identified as viral receptors before finding their physiological functions as CAMs and evidence is accumulating that many nectins and Necls are related to onset and progression of neurological diseases. We review here nectin and Necls in synapse formation and involvement in neurological diseases.

Higher polygenic risk scores for schizophrenia may be suggestive of treatment non-response in major depressive disorder

Up to 60% of patients with major depressive disorder (MDD) do not respond to the first treatment with antidepressants. Response to antidepressants is a polygenic trait, although its underpinning genetics has not been fully clarified. This study aimed to investigate if polygenic risk scores (PRSs) for major psychiatric disorders and trait neuroticism (NEU) were associated with non-response or resistance to antidepressants in MDD. PRSs for bipolar disorder, MDD, NEU, and schizophrenia (SCZ) were computed in 1,148 patients with MDD. Summary statistics from the largest meta-analyses of genome-wide association studies were used as base data. Patients were classified as responders, non-responders to one treatment, non-responders to two or more treatments (treatment-resistant depression or TRD). Regression analyses were adjusted for population stratification and recruitment sites. PRSs did not predict either non-response vs response or TRD vs response after Bonferroni correction. However, SCZ-PRS was nominally associated with non-response (p = 0.003). Patients in the highest SCZ-PRS quintile were more likely to be non-responders than those in the lowest quintile (OR = 2.23, 95% CI = 1.21-4.10, p = 0.02). Patients in the lowest SCZ-PRS quintile showed higher response rates when they did not receive augmentation with second-generation antipsychotics (SGAs), while those in the highest SCZ-PRS quintile had a poor response independently from the treatment strategy (p = 0.009). A higher genetic liability to SCZ may reduce treatment response in MDD, and patients with low SCZ-PRSs may show higher response rates without SGA augmentation. Multivariate approaches and methodological refinements will be necessary before clinical implementations of PRSs.

Phosphodiesterase 8A to discriminate in blood samples depressed patients and suicide attempters from healthy controls based on A-to-I RNA editing modifications

Mental health issues, including major depressive disorder, which can lead to suicidal behavior, are considered by the World Health Organization as a major threat to global health. Alterations in neurotransmitter signaling, e.g., serotonin and glutamate, or inflammatory response have been linked to both MDD and suicide. Phosphodiesterase 8A (PDE8A) gene expression is significantly decreased in the temporal cortex of major depressive disorder (MDD) patients. PDE8A specifically hydrolyzes adenosine 3',5'-cyclic monophosphate (cAMP), which is a key second messenger involved in inflammation, cognition, and chronic antidepressant treatment. Moreover, alterations of RNA editing in PDE8A mRNA has been described in the brain of depressed suicide decedents. Here, we investigated PDE8A A-to-I RNA editing-related modifications in whole blood of depressed patients and suicide attempters compared to age-matched and sex-matched healthy controls. We report significant alterations of RNA editing of PDE8A in the blood of depressed patients and suicide attempters with major depression, for which the suicide attempt took place during the last month before sample collection. The reported RNA editing modifications in whole blood were similar to the changes observed in the brain of suicide decedents. Furthermore, analysis and combinations of different edited isoforms allowed us to discriminate between suicide attempters and control groups. Altogether, our results identify PDE8A as an immune response-related marker whose RNA editing modifications translate from brain to blood, suggesting that monitoring RNA editing in PDE8A in blood samples could help to evaluate depressive state and suicide risk.

Analysis of gut microbiota and intestinal integrity markers of inpatients with major depressive disorder

Previous studies have reported on the relationship between gut microbiota and major depressive disorder (MDD). However, there remain gaps in literature concerning the role of the intestinal barrier and microflora in the pathogenesis of depression. This study analyzes the potential causative relationship between gut microbiota and inflammatory and gut integrity markers and clinical symptoms in inpatients with depressive episodes. Sixteen inpatients (50% females) being treated with escitalopram (5-20 mg daily) in standardized conditions were included in the study. The composition of fecal microbiota was evaluated at baseline and endpoint using 16S rRNA sequencing. A significant correlation between depression severity was found, as measured with HDRS24 (Hamilton Depression Rating Scale-24 item), and the following abundance in bacteria: positive correlation with Paraprevotella (r = 0.80, q = 0.012), strong, negative correlations with Clostridiales (r = -0.70, q = 0.016), Clostridia (r = -0.71, q = 0.026), Firmicutes (r = -0.67. q = 0.032), and the RF32 order (r = -0.70, p = 0.016) in the Alphaproteobacteria (r = -0.66, q = 0.031). After six weeks of treatment, clinical outcomes were found to have a negative correlation with levels of plasma intestinal fatty acid-binding protein (IFABP) at the beginning of the study. Still they had a positive correlation with changes in fecal calprotectin during hospitalization. In conclusion, gut microbiota was associated with the severity of depressive symptoms. However, these findings do not serve as predictors of symptomatic improvement during antidepressant treatment in inpatient treatment for MDD. In turn, intestinal integrity and inflammation markers were associated with the response to treatment of patients with MDD and symptom severity. Additional studies are needed to confirm and extend these findings.

An Update on Glutamatergic System in Suicidal Depression and on the Role of Esketamine

BACKGROUND

A research on mood disorder pathophysiology has hypothesized abnormalities in glutamatergic neurotransmission, by suggesting further investigation on glutamatergic N-methyl-Daspartate (NMDA) receptor modulators in treating Major Depressive Disorder (MDD). Esketamine (ESK), an NMDA receptor antagonist able to modulate glutamatergic neurotransmission has been recently developed as an intranasal formulation for treatment-resistant depression (TRD) and for rapid reduction of depressive symptomatology, including suicidal ideation in MDD patients at imminent risk for suicide.

OBJECTIVE

The present study aims at investigating recent clinical findings on research on the role of the glutamatergic system and ESK in treating suicidal depression in MDD and TRD.

METHODS

A systematic review was here carried out on PubMed/Medline, Scopus and the database on U.S. N.I.H. Clinical Trials (https://clinicaltrials.gov) and the European Medical Agency (EMA) (https://clinicaltrialsregister.eu) from inception until October 2019.

RESULTS

Intravenous infusion of ESK is reported to elicit rapid-acting and sustained antidepressant activity in refractory patients with MDD and TRD. In phase II studies, intranasal ESK demonstrated a rapid onset and a persistent efficacy in patients with TRD as well as in MDD patients at imminent risk for suicide. However, some data discrepancies have emerged in phase III studies.

CONCLUSION

The U.S. Food and Drug Administration (FDA) granted fast track and Breakthrough Therapy Designation to Janssen Pharmaceuticals®, Inc. for intranasal ESK in 2013 for treatment-resistant depression (TRD) and in 2016 for the treatment of MDD with an imminent risk of suicide. However, further studies should be implemented to investigate the long-term efficacy and safety of intranasal ESK.

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.

Inflamed Mind: Multiple Genetic Variants of IL6 Influence Suicide Risk Phenotypes in Interaction With Early and Recent Adversities in a Linkage Disequilibrium-Based Clumping Analysis

Background: Understanding and predicting suicide remains a challenge, and a recent paradigm shift regarding the complex relationship between the immune system and the brain brought attention to the involvement of inflammation in neuropsychiatric conditions including suicide. Among cytokines, IL-6 has been most frequently implicated in suicide, yet only a few candidate gene studies and without considering the effect of stress investigated the role of IL6 in suicidal behaviour. Our study aimed to investigate the association of IL6 variation with a linkage disequilibrium-based clumping method in interaction with childhood adversities and recent stress on manifestations along the suicide spectrum. Methods: One thousand seven hundred and sixty-two participants provided information on previous suicide attempts, current suicidal ideation, thoughts of death, and hopelessness, and were genotyped for 186 variants in IL6. Early childhood adversities were recorded with an instrument adapted from the Childhood Trauma Questionnaire, recent life events were registered using the List of Threatening Life Events. Following a 3-step quality control, logistic and linear regression models were run to explore the effect of genotype and gene-environment interactions on suicide phenotypes. All regression models were followed by a clumping process based on empirical estimates of linkage disequilibrium between clumps of intercorrelated SNPs. Interaction effects of distinct types of recent life events were also analysed. Results: No clumps with significant main effects emerged, but we identified several clumps significantly interacting with childhood adversities on lifetime suicide attempts, current suicidal ideation, and current thoughts of death. We also identified clumps significantly interacting with recent negative life events on current suicidal ideation. We reported no clumps with significant effect on hopelessness either as a main effect or in interaction with childhood adversities or recent stress. Conclusion: We identified variant clumps in IL6 influencing suicidal behaviour, but only in interaction with childhood or recent adversities. Our results may bring us a step further in understanding the role of neuroinflammation and specifically of IL-6 in suicide, towards identifying novel biological markers of suicidal behaviour especially in those exposed to stressful experiences, and to fostering the adaptation of a new paradigm and identifying novel approaches and targets in the treatment of suicidal behaviour.

The therapeutic importance of acid-base balance

Baking soda and vinegar have been used as home remedies for generations and today we are only a mouse-click away from claims that baking soda, lemon juice, and apple cider vinegar are miracles cures for everything from cancer to COVID-19. Despite these specious claims, the therapeutic value of controlling acid-base balance is indisputable and is the basis of Food and Drug Administration-approved treatments for constipation, epilepsy, metabolic acidosis, and peptic ulcers. In this narrative review, we present evidence in support of the current and potential therapeutic value of countering local and systemic acid-base imbalances, several of which do in fact involve the administration of baking soda (sodium bicarbonate). Furthermore, we discuss the side effects of pharmaceuticals on acid-base balance as well as the influence of acid-base status on the pharmacokinetic properties of drugs. Our review considers all major organ systems as well as information relevant to several clinical specialties such as anesthesiology, infectious disease, oncology, dentistry, and surgery.

Carbonic anhydrases enhance activity of endogenous Na-H exchangers and not the electrogenic Na/HCO3 cotransporter NBCe1-A, expressed in Xenopus oocytes

KEY POINTS

According to the HCO 3 - metabolon hypothesis, direct association of cytosolic carbonic anhydrases (CAs) with the electrogenic Na/HCO3 cotransporter NBCe1-A speeds transport by regenerating/consuming HCO 3 - . The present work addresses published discrepancies as to whether cytosolic CAs stimulate NBCe1-A, heterologously expressed in Xenopus oocytes. We confirm the essential elements of the previous experimental observations, taken as support for the HCO 3 - metabolon hypothesis. However, using our own experimental protocols or those of others, we find that NBCe1-A function is unaffected by cytosolic CAs. Previous conclusions that cytosolic CAs do stimulate NBCe1-A can be explained by an unanticipated stimulatory effect of the CAs on an endogenous Na-H exchanger. Theoretical analyses show that, although CAs could stimulate non- HCO 3 - transporters (e.g. Na-H exchangers) by accelerating CO2 / HCO 3 - -mediated buffering of acid-base equivalents, they could not appreciably affect transport rates of NBCe1 or other transporters carrying HCO 3 - , CO 3 = , or NaCO 3 - ion pairs.

ABSTRACT

The HCO 3 - metabolon hypothesis predicts that cytosolic carbonic anhydrase (CA) binds to NBCe1-A, promotes HCO 3 - replenishment/consumption, and enhances transport. Using a short step-duration current-voltage (I-V) protocol with Xenopus oocytes expressing eGFP-tagged NBCe1-A, our group reported that neither injecting human CA II (hCA II) nor fusing hCA II to the NBCe1-A carboxy terminus affects background-subtracted NBCe1 slope conductance (GNBC ), which is a direct measure of NBCe1-A activity. Others - using bovine CA (bCA), untagged NBCe1-A, and protocols keeping holding potential (Vh ) far from NBCe1-A's reversal potential (Erev ) for prolonged periods - found that bCA increases total membrane current (ΔIm ), which apparently supports the metabolon hypothesis. We systematically investigated differences in the two protocols. In oocytes expressing untagged NBCe1-A, injected with bCA and clamped to -40 mV, CO2 / HCO 3 - exposures markedly decrease Erev , producing large transient outward currents persisting for >10 min and rapid increases in [Na+ ]i . Although the CA inhibitor ethoxzolamide (EZA) reduces both ΔIm and d[Na+ ]i /dt, it does not reduce GNBC . In oocytes not expressing NBCe1-A, CO2 / HCO 3 - triggers rapid increases in [Na+ ]i that both hCA II and bCA enhance in concentration-dependent manners. These d[Na+ ]i /dt increases are inhibited by EZA and blocked by EIPA, a Na-H exchanger (NHE) inhibitor. In oocytes expressing untagged NBCe1-A and injected with bCA, EIPA abolishes the EZA-dependent decreases in ΔIm and d[Na+ ]i /dt. Thus, CAs/EZA produce their ΔIm and d[Na+ ]i /dt effects not through NBCe1-A, but endogenous NHEs. Theoretical considerations argue against a CA stimulation of HCO 3 - transport, supporting the conclusion that an NBCe1-A- HCO 3 - metabolon does not exist in oocytes.

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.

The neural correlates of low social integration as a risk factor for suicide

Low social integration is commonly described in acutely suicidal individuals. Neural mechanisms underlying low social integration are poorly understood in depressed and suicidal patients. We sought to characterize the neural response to low social integration in acutely suicidal patients. Adult depressed patients within 3 days of a suicide attempt (n = 10), depressed patients with suicidal ideation (n = 9), non-suicidal depressed patients (n = 15), and healthy controls (N = 18) were administered the Cyberball Game while undergoing functional magnetic resonance imaging. We used complementary functional connectivity and region of interest data analysis approaches. There were no group differences in functional connectivity within neural network involving the pain matrix, nor in insula neural activity or the insula during either social inclusion. Superior anterior insula activity exhibited an inverted U-shaped curve across the suicide risk spectrum during social inclusion. Superior insula activity during social inclusion correlated with depression severity and psychological pain. Dorsal anterior cingulate cortex activity during social exclusion correlated with physical pain severity. Neural responses in the anterior insula significantly correlated with depression severity and with psychological pain during social inclusion, whereas dACC activity significantly correlated with physical pain during social exclusion. Recent suicidal behavior seems associated with a distinct neural response to social exclusion independently of presence of depression or suicidal thoughts.

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).

Association between suicide risk severity and sarcopenia in non-elderly Chinese inpatients with major depressive disorder

BACKGROUND

Sarcopenia is a skeletal muscle disorder. Recent studies have shown an association between muscle health and suicide. However, there have been no previous studies on the relationship between suicide risk severity and sarcopenia in major depressive disorder (MDD). This study aimed to explore the association between suicide risk severity and sarcopenia in non-elderly Chinese inpatients with MDD.

METHODS

The first-episode drug-naïve MDD inpatients aged 20-59 years with the 24-item Hamilton Rating Scale for Depression (HAMD-24) scores of >20 were included, who were then classified into low, intermediate, high and very high suicide risk groups according to the Nurses' Global Assessment of Suicide Risk (NGASR). The HAMD-24, the Hamilton Rating Scale for Anxiety (HAMA) and the SARC-F questionnaire were used to assess depression severity, anxiety severity and sarcopenia, respectively. The plasma levels of cortisol and adrenocorticotropic hormone (ACTH) were measured.

RESULTS

A total of 192 MDD inpatients (122 females, 70 males; aged 39.3 ± 11.7 years) were included, with 12.5% meeting criteria for sarcopenia. There were significant differences in gender, HAMD score and prevalence of sarcopenia among the suicide risk groups. Adjusted ordinal regression analysis showed that sarcopenia was significantly associated with more severe suicide risk (OR = 2.39, 95%CI 1.02-5.58, p = 0.044) independent of depression severity.

CONCLUSIONS

This study revealed that sarcopenia was significantly associated with higher suicide risk in non-elderly Chinese MDD inpatients after adjustment for depression severity. Intervention of sarcopenia might be effective in reducing the risk of suicide in non-elderly MDD patients.

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.