Sex differences in shared genetic determinants between severe mental disorders and metabolic traits.

High rates of metabolic risk factors contribute to premature mortality in patients with severe mental disorders, but the molecular underpinnings of this association are largely unknown. We performed the first analysis on shared genetic factors between severe mental disorders and metabolic traits considering the effect of sex. We applied an integrated analytical pipeline on the largest sex-stratified genome-wide association datasets available for bipolar disorder (BD), major depressive disorder (MDD), schizophrenia (SZ), and for body mass index (BMI) and waist-to-hip ratio (WHR) (all including participants of European origin). We observed extensive genetic overlap between all severe mental disorders and variants associated with BMI in women or men and identified several genetic loci shared between BD, or SZ and BMI in women (24 and 91, respectively) or men (13 and 208, respectively), with mixed directions of effect. A large part of the identified genetic variants showed sex differences in terms of location, genes modulated in adipose tissue and/or brain regions, and druggable targets. By providing a complete picture of disorder specific and cross-disorder shared genetic determinants, our results highlight potential sex differences in the genetic liability to metabolic comorbidities in patients with severe mental disorders.

Exploring the use of immunomethylomics in the characterization of depressed patients: A proof-of-concept study.

Alterations in DNA methylation and inflammation could represent valid biomarkers for the stratification of patients with major depressive disorder (MDD). This study explored the use of DNA-methylation based immunological cell-type profiles in the context of MDD and symptom severity over time. In 119 individuals with MDD, DNA-methylation was assessed on whole blood using the Illumina Infinium MethylationEPIC 850 k BeadChip. Quality control and data processing, as well as cell type estimation was conducted using the RnBeads package. The cell type composition was estimated using epigenome-wide DNA methylation signatures, applying the Houseman method, considering six cell types (neutrophils, natural killer cells (NK), B cells, CD4 + T cells, CD8 + T cells and monocytes). Two cytokines (IL-6 and IL-1ß) and hsCRP were quantified in serum. We performed a hierarchical cluster analysis on the six estimated cell-types and tested the differences between these clusters in relation to the two cytokines and hsCRP, depression severity at baseline, and after 6 weeks of treatment (celecoxib/placebo + vortioxetine). We performed a second cluster analysis with cell-types and cytokines combined. ANCOVA was used to test for differences across clusters. We applied the Bonferroni correction. After quality control, we included 113 participants. Two clusters were identified, cluster 1 was high in CD4 + cells and NK, cluster 2 was high in CD8 + T-cells and B-cells, with similar fractions of neutrophils and monocytes. The clusters were not associated with either of the two cytokines and hsCRP, or depression severity at baseline, but cluster 1 showed higher depression severity after 6 weeks, corrected for baseline (p = 0.0060). The second cluster analysis found similar results: cluster 1 was low in CD8 + T-cells, B-cells, and IL-1ß. Cluster 2 was low in CD4 + cells and natural killer cells. Neutrophils, monocytes, IL-6 and hsCRP were not different between the clusters. Participants in cluster 1 showed higher depression severity at baseline than cluster 2 (p = 0.034), but no difference in depression severity after 6 weeks. DNA-methylation based cell-type profiles may be valuable in the immunological characterization and stratification of patients with MDD. Future models should consider the inclusion of more cell-types and cytokines for better a prediction of treatment outcomes.

Lower Plasma Levels of Selective VGF (Non-Acronymic) Peptides in Bipolar Disorder: Comparative Analysis Reveals Distinct Patterns across Mood Disorders and Healthy Controls.

INTRODUCTION: Discriminating bipolar disorder (BD) from major depressive disorder (MDD) remains a challenging clinical task. Identifying specific peripheral biosignatures that can differentiate between BD and MDD would significantly increase diagnostic accuracy. Dysregulated neuroplasticity is implicated in BD and MDD, and psychotropic medications restore specific disrupted processes by increasing neurotrophic signalling. The nerve growth factor inducible vgf gene (non-acronymic) encodes a precursor protein named proVGF, which undergoes proteolytic processing to produce several VGF peptides, some of which were suggested to be implicated in mood disorders and have antidepressant effects. Since the presence of VGF peptides in humans has been exclusively investigated in brain and cerebrospinal fluid, we aimed to identify which VGF peptides are present in the plasma and to investigate whether their levels could differentiate BD from MDD as well as responders from non-responders to pharmacological interventions. METHODS: VGF peptides were investigated in plasma from patients diagnosed with MDD (n = 37) or BD (n = 40 under lithium plus n = 29 never exposed to lithium), as well as healthy controls (HC; n = 36). RESULTS: Three VGF peptides (TLQP-11, AQEE-14, and NAPP-19) were identified using spectrometry analysis of plasma from HC. These peptides were then measured in the entire sample using ELISA, which showed significantly lower levels of AQEE and NAPP in BD than in HC and MDD (p = 5.0 × 10-5, p = 0.001, respectively). CONCLUSION: Our findings suggest that lower plasma levels of NAPP and AQEE are specifically associated with BD, thus possibly representing a diagnostic biomarker in mood disorders.

Leukocyte Telomere Length and Mitochondrial DNA Copy Number in Treatment-Resistant Depression and Response to Electroconvulsive Therapy: A Pilot Longitudinal Study.

OBJECTIVES: In this study, we investigated if changes in leukocyte telomere length (LTL) and mitochondrial DNA copy number (mtDNA-cn), 2 markers of cellular aging, are associated with treatment-resistant depression (TRD) and with response to electroconvulsive therapy (ECT). METHODS: LTL and mtDNA-cn were measured in 31 TRD patients before (T0), 1 week (T1), and 4 weeks (T2) after the ECT course, as well as in a sample of 65 healthy controls. RESULTS: TRD patients had significantly shorter LTL and higher mtDNA-cn compared with healthy controls at baseline. In the TRD sample, LTL was inversely correlated with Montgomery-Åsberg Depression Rating Scale scores at baseline. Baseline levels of LTL or mtDNA-cn were not correlated with response to ECT. Similarly, changes in LTL or mtDNA-cn were not associated with response to ECT either when considered as a dichotomous trait (responders vs nonresponders) or as a percentage change in symptoms improvements. CONCLUSIONS: Ours is the first longitudinal study exploring the role of LTL and mtDNA-cn in response to ECT. Findings of this pilot investigation suggest that LTL and mtDNA-cn may constitute disease biomarkers for TRD but are not involved in response to ECT.

A Secondary Analysis of the Complex Interplay between Psychopathology, Cognitive Functions, Brain Derived Neurotrophic Factor Levels, and Suicide in Psychotic Disorders: Data from a 2-Year Longitudinal Study.

Identifying phenotypes at high risk of suicidal behaviour is a relevant objective of clinical and translational research and can facilitate the identification of possible candidate biomarkers. We probed the potential association and eventual stability of neuropsychological profiles and serum BDNF concentrations with lifetime suicide ideation and attempts (LSI and LSA, respectively) in individuals with schizophrenia (SCZ) and schizoaffective disorder (SCA) in a 2-year follow-up study. A secondary analysis was conducted on a convenience sample of previously recruited subjects from a single outpatient clinic. Retrospectively assessed LSI and LSA were recorded by analysing the available longitudinal clinical health records. LSI + LSA subjects consistently exhibited lower PANSS-defined negative symptoms and better performance in the BACS-letter fluency subtask. There was no significant association between BDNF levels and either LSI or LSA. We found a relatively stable pattern of lower negative symptoms over two years among patients with LSI and LSA. No significant difference in serum BDNF concentrations was detected. The translational viability of using neuropsychological profiles as a possible avenue for the identification of populations at risk for suicide behaviours rather than the categorical diagnosis represents a promising option but requires further confirmation.

Lithium and its effects: does dose matter?

BACKGROUND: Decades of clinical research have demonstrated the efficacy of lithium in treating acute episodes (both manic and depressive), as well as in preventing recurrences of bipolar disorder (BD). Specific to lithium is its antisuicidal effect, which appears to extend beyond its mood-stabilizing properties. Lithium's clinical effectiveness is, to some extent, counterbalanced by its safety and tolerability profile. Indeed, monitoring of lithium levels is required by its narrow therapeutic index. There is consensus that adequate serum levels should be above 0.6 mEq/L to achieve clinical effectiveness. However, few data support the choice of this threshold, and increasing evidence suggests that lithium might have clinical and molecular effects at much lower concentrations. CONTENT: This narrative review is aimed at: (1) reviewing and critically interpreting the clinical evidence supporting the use of the 0.6 mEq/L threshold, (2) reporting a narrative synthesis of the evidence supporting the notion that lithium might be effective in much lower doses. Among these are epidemiological studies of lithium in water, evidence on the antisuicidal, anti-aggressive, and neuroprotective effects, including efficacy in preventing cognitive impairment progression, Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS), of lithium; and (3) revieweing biological data supporting clinically viable uses of lithium at low levels with the delineation of a mechanistic hypothesis surrounding its purported mechanism of action. The study selection was based on the authors' preference, reflecting the varied and extensive expertise on the review subject, further enriched with an extensive pearl-growing strategy for relevant reviews and book sections. CONCLUSIONS: Clinical and molecular effects of lithium are numerous, and its effects also appear to have a certain degree of specificity related to the dose administered. In sum, the clinical effects of lithium are maximal for mood stabilisation at concentrations higher than 0.6 mEq/l. However, lower levels may be sufficient for preventing depressive recurrences in older populations of patients, and microdoses could be effective in decreasing suicide risk, especially in patients with BD. Conversely, lithium's ability to counteract cognitive decline appears to be exerted at subtherapeutic doses, possibly corresponding to its molecular neuroprotective effects. Indeed, lithium may reduce inflammation and induce neuroprotection even at doses several folds lower than those commonly used in clinical settings. Nevertheless, findings surrounding its purported mechanism of action are missing, and more research is needed to investigate the molecular targets of low-dose lithium adequately.

Symptomatic remission and recovery in major psychosis: Is there a role for BDNF? A secondary analysis of the LABSP cohort data.

Remission, relapse prevention, and clinical recovery are crucial areas of interest in schizophrenia (SCZ) research. Although SCZ is a chronic disorder with poor overall outcomes, years of research demonstrated that recovery is possible. There are considerable data linking brain-derived neurotrophic factor (BDNF) to SCZ, however, evidence on the role of BDNF in remission in SCZ is scarce. This secondary analysis of the Longitudinal Assessment of BDNF in Sardinian patients (LABSP) data aimed to investigate the relationship between serum BDNF levels and symptomatic remission, simultaneous clinical and functional remission, and recovery in patients with SCZ. A total of 105 patients with SCZ or schizoaffective disorder were recruited for a longitudinal assessment of BDNF levels over 24 months. Longitudinal data were analyzed using mixed-effects linear regression models. The study found significant associations between use of long acting injectables (χ2 = 7.075, df = 1, p = 0.008), baseline serum BDNF levels (U = 701, z = -2.543, p = 0.011), and "childhood" (U = 475, z = -2.124, p = 0.034) and "general" (U = 55, z = -2.014, p = 0.044) subscales of the Premorbid Adjustment Scale (PAS) with patients maintaining remission and recovery. The diagnosis of SCZ was significantly associated with lower BDNF levels for patients with simultaneous clinical and functional remission (Z = 2.035, p = 0.0419) and recovery (Z = 2.009, p = 0.0445) compared to those without. There were no significant associations between remission in the entire sample and longitudinal serum BDNF levels or genetic variants within the BDNF gene. These findings provide further insight into the complex relationship between BDNF and SCZ.

Dissecting the genetic overlap between severe mental disorders and markers of cellular aging: Identification of pleiotropic genes and druggable targets.

Patients with severe mental disorders such as bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) show a substantial reduction in life expectancy, increased incidence of comorbid medical conditions commonly observed with advanced age and alterations of aging hallmarks. While severe mental disorders are heritable, the extent to which genetic predisposition might contribute to accelerated cellular aging is not known. We used bivariate causal mixture models to quantify the trait-specific and shared architecture of mental disorders and 2 aging hallmarks (leukocyte telomere length [LTL] and mitochondrial DNA copy number), and the conjunctional false discovery rate method to detect shared genetic loci. We integrated gene expression data from brain regions from GTEx and used different tools to functionally annotate identified loci and investigate their druggability. Aging hallmarks showed low polygenicity compared with severe mental disorders. We observed a significant negative global genetic correlation between MDD and LTL (rg = -0.14, p = 6.5E-10), and no significant results for other severe mental disorders or for mtDNA-cn. However, conditional QQ plots and bivariate causal mixture models pointed to significant pleiotropy among all severe mental disorders and aging hallmarks. We identified genetic variants significantly shared between LTL and BD (n = 17), SCZ (n = 55) or MDD (n = 19), or mtDNA-cn and BD (n = 4), SCZ (n = 12) or MDD (n = 1), with mixed direction of effects. The exonic rs7909129 variant in the SORCS3 gene, encoding a member of the retromer complex involved in protein trafficking and intracellular/intercellular signaling, was associated with shorter LTL and increased predisposition to all severe mental disorders. Genetic variants underlying risk of SCZ or MDD and shorter LTL modulate expression of several druggable genes in different brain regions. Genistein, a phytoestrogen with anti-inflammatory and antioxidant effects, was an upstream regulator of 2 genes modulated by variants associated with risk of MDD and shorter LTL. While our results suggest that shared heritability might play a limited role in contributing to accelerated cellular aging in severe mental disorders, we identified shared genetic determinants and prioritized different druggable targets and compounds.

A Pilot Interaction Analysis of Gut Microbiota and Peripheral Markers of Aging in Severe Psychiatric Disorders: A Role for Lachnoclostridium?

Excessive predominance of pathological species in the gut microbiota could increase the production of inflammatory mediators at the gut level and, via modification of the gut-blood barrier, at the systemic level. This pro-inflammatory state could, in turn, increase biological aging that is generally proxied by telomere shortening. In this study, we present findings from a secondary interaction analysis of gut microbiota, aging, and inflammatory marker data from a cohort of patients with different diagnoses of severe mental disorders. We analyzed 15 controls, 35 patients with schizophrenia (SCZ), and 31 patients with major depressive disorder (MDD) recruited among those attending a community mental health center (50 males and 31 females, mean and median age 46.8 and 46.3 years, respectively). We performed 16S rRNA sequencing as well as measurement of telomere length via quantitative fluorescence in situ hybridization and high-sensitivity C-reactive protein. We applied statistical modeling with logistic regression to test for interaction between gut microbiota and these markers. Our results showed statistically significant interactions between telomere length and gut microbiota pointing to the genus Lachnostridium, which remained significantly associated with a reduced likelihood of MDD even after adjustment for a series of covariates. Although exploratory, these findings show that specific gut microbiota signatures overexpressing Lachnoclostridium and interacting with biological aging could modulate the liability for MDD.

Melatonin MT1 receptors as a target for the psychopharmacology of bipolar disorder: A translational study.

The treatment of bipolar disorder (BD) still remains a challenge. Melatonin (MLT), acting through its two receptors MT1 and MT2, plays a key role in regulating circadian rhythms which are dysfunctional in BD. Using a translational approach, we examined the implication and potential of MT1 receptors in the pathophysiology and psychopharmacology of BD. We employed a murine model of the manic phase of BD (Clock mutant (ClockΔ19) mice) to study the activation of MT1 receptors by UCM871, a selective partial agonist, in behavioral pharmacology tests and in-vivo electrophysiology. We then performed a high-resolution Nuclear Magnetic Resonance study on isolated membranes to characterize the molecular mechanism of interaction of UCM871. Finally, in a cohort of BD patients, we investigated the link between clinical measures of BD and genetic variants located in the MT1 receptor and CLOCK genes. We demonstrated that: 1) UCM871 can revert behavioral and electrophysiological abnormalities of ClockΔ19 mice; 2) UCM871 promotes the activation state of MT1 receptors; 3) there is a significant association between the number of severe manic episodes and MLT levels, depending on the genetic configuration of the MT1 rs2165666 variant. Overall, this work lends support to the potentiality of MT1 receptors as target for the treatment of BD.

Biological markers of sex-based differences in major depressive disorder and in antidepressant response.

Major depressive disorder (MDD) presents different clinical features in women and men, with women being more affected and responding differently to antidepressant treatment. Specific molecular mechanisms underlying these differences are not well studied and this narrative review aims at providing an overview of the neurobiological features underlying sex-differences in biological systems involved in MDD pathophysiology and response to antidepressant treatment, focusing on human studies. The majority of the reviewed studies were performed through candidate gene approaches, focusing on biological systems involved in MDD pathophysiology, including the stress response, inflammatory and immune, monoaminergic, neurotrophic, gamma-aminobutyric acid and glutamatergic, and oxytocin systems. The influence of the endocrine system and sex-specific hormone effects are also discussed. Genome, epigenome and transcriptome-wide approaches are less frequently performed and most of these studies do not focus on sex-specific alterations, revealing a paucity of omics studies directed to unravel sex-based differences in MDD. Few studies about sex-related differences in antidepressant treatment response have been conducted, mostly involving the inflammatory system, with less evidence on the monoaminergic system and sparse evidence in omics approaches. Our review covers the importance of accounting for sex-differences in research, optimizing patient stratification for a more precise diagnostic and individualized treatment for women and men.

RNA Biomarkers in Bipolar Disorder and Response to Mood Stabilizers.

Bipolar disorder (BD) is a severe chronic disorder that represents one of the main causes of disability among young people. To date, no reliable biomarkers are available to inform the diagnosis of BD or clinical response to pharmacological treatment. Studies focused on coding and noncoding transcripts may provide information complementary to genome-wide association studies, allowing to correlate the dynamic evolution of different types of RNAs based on specific cell types and developmental stage with disease development or clinical course. In this narrative review, we summarize findings from human studies that evaluated the potential utility of messenger RNAs and noncoding transcripts, such as microRNAs, circular RNAs and long noncoding RNAs, as peripheral markers of BD and/or response to lithium and other mood stabilizers. The majority of available studies investigated specific targets or pathways, with large heterogeneity in the included type of cells or biofluids. However, a growing number of studies are using hypothesis-free designs, with some studies also integrating data on coding and noncoding RNAs measured in the same participants. Finally, studies conducted in neurons derived from induced-pluripotent stem cells or in brain organoids provide promising preliminary findings supporting the power and utility of these cellular models to investigate the molecular determinants of BD and clinical response.

Probing the Association between Cognition, Suicidal Behavior and Tryptophan Metabolism in a Sample of Individuals Living with Bipolar Disorder: A Secondary Analysis.

Background and Objectives: Alterations in hot cognition and in the tryptophan metabolism through serotonin (5-HT) and kynurenine (KYN) pathways have been associated with an increased risk of suicidal behavior. Here, we aim at probing the association between Stroop test performances and tryptophan pathway components in a sample of individuals with bipolar disorder (BD). Materials and Methods: We explored the association between the Emotion Inhibition Subtask (EIS) performances of the Brief Assessment of Cognition for Affective Disorders (BAC-A) and plasmatic levels of 5-hydroxytriptophan (5-HTP), 5-HT, KYN, 3-hydroxykynurenine (3-HK), quinolinic acid (QA), and kynurenic acid (KYNA) among subjects reporting lifetime suicide ideation (LSI) vs. non-LSI and subjects reporting lifetime suicide attempts (LSA) vs. non-LSA. Results: In a sample of 45 subjects with BD, we found a statistically significant different performance for LSA vs. non-LSA in the color naming (CN) and neutral words (NW) EIS subtasks. There was a significant association between CN performances and plasma 5-HTP levels among LSI and LSA subjects but not among non-LSI or non-LSA. Conclusions: In our sample, patients with LSA and LSI presented lower performances on some EIS subtasks compared to non-LSA and non-LSI. Moreover, we found an inverse correlation between plasma 5-HTP concentration and some EIS performances in LSA and LSI but not among non-LSA or non-LSI. This may represent an interesting avenue for future studies probing this complex association.

A genome-wide association study of antidepressant-induced mania.

Antidepressant-induced mania (AIM) is a side effect of antidepressant treatment that is characterized by mania or hypomania after the start of medication. It is likely polygenic, but its genetic component remains largely unexplored. We aim to conduct the first genome-wide association study of AIM in 814 bipolar disorder patients of European ancestry. We report no significant findings from our single-marker or gene-based analyses. Our polygenic risk score analyses also did not yield significant results with bipolar disorder, antidepressant response, or lithium response. Our suggestive findings on the hypothalamic-pituitary-adrenal axis and the opioid system in AIM require independent replications.

Pharmacokinetic Markers of Clinical Outcomes in Severe Mental Illness: A Systematic Review.

The term severe mental illness (SMI) encompasses those psychiatric disorders exerting the highest clinical burden and socio-economic impact on the affected individuals and their communities. Pharmacogenomic (PGx) approaches hold great promise in personalizing treatment selection and clinical outcomes, possibly reducing the burden of SMI. Here, we sought to review the literature in the field, focusing on PGx testing and particularly on pharmacokinetic markers. We performed a systematic review on PUBMED/Medline, Web of Science, and Scopus. The last search was performed on the 17 September 2022, and further augmented with a comprehensive pearl-growing strategy. In total, 1979 records were screened, and after duplicate removal, 587 unique records were screened by at least 2 independent reviewers. Ultimately, forty-two articles were included in the qualitative analysis, eleven randomized controlled trials and thirty-one nonrandomized studies. The observed lack of standardization in PGx tests, population selection, and tested outcomes limit the overall interpretation of the available evidence. A growing body of evidence suggests that PGx testing might be cost-effective in specific settings and may modestly improve clinical outcomes. More efforts need to be directed toward improving PGx standardization, knowledge for all stakeholders, and clinical practice guidelines for screening recommendations.

Multi-omics data integration methods and their applications in psychiatric disorders.

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

An integrated precision medicine approach in major depressive disorder: a study protocol to create a new algorithm for the prediction of treatment response.

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients' empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

Converging Evidence Points to BDNF as Biomarker of Depressive Symptoms in Schizophrenia-Spectrum Disorders.

Brain-derived neurotrophic factor (BDNF) is a key modulator of neuroplasticity and has an important role in determining the susceptibility to severe psychiatric disorder with a significant neurodevelopmental component such as major psychoses. Indeed, a potential association between BDNF serum levels and schizophrenia (SCZ) and schizoaffective disorder (SAD) has been tested in diverse studies and a considerable amount of them found reduced BDNF levels in these disorders. Here, we aimed at testing the association of BDNF serum levels with several demographic, clinical, and psychometric measures in 105 patients with SCZ and SAD, assessing the moderating effect of genetic variants within the BDNF gene. We also verified whether peripheral BDNF levels differed between patients with SCZ and SAD. Our findings revealed that BDNF serum levels are significantly lower in patients affected by SCZ and SAD presenting more severe depressive symptomatology. This finding awaits replication in future independent studies and points to BDNF as a possible prognostic indicator in major psychoses.

Investigation of Genetic Variants Associated with Tryptophan Metabolite Levels via Serotonin and Kynurenine Pathways in Patients with Bipolar Disorder.

The kynurenine pathway (KP) may play a role in the pathophysiology of bipolar disorder (BD). We conducted a genome-wide association study (GWAS) to identify genetic variants associated with the plasma levels of the metabolites of tryptophan (TRP) via the serotonin (5-HT) and kynurenine (KYN) pathways in 44 patients with BD and 45 healthy controls. We assessed whether variants that were differentially associated with metabolite levels based on the diagnostic status improved the prediction accuracy of BD using penalized regression approaches. We identified several genetic variants that were significantly associated with metabolites (5-HT, 5-hydroxytryptophan (5-HTP), TRP, and quinolinic acid (QA) or metabolite ratios (5-HTP/TRP and KYN/TRP) and for which the diagnostic status exerted a significant effect. The inclusion of genetic variants led to increased accuracy in the prediction of the BD diagnostic status. Specifically, we obtained an accuracy of 0.77 using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The predictors retained as informative in this model included body mass index (BMI), the levels of TRP, QA, and 5-HT, the 5-HTP/TRP ratio, and genetic variants associated with the levels of QA (rs6827515, rs715692, rs425094, rs4645874, and rs77048355) and TRP (rs292212) or the 5-HTP/TRP ratio (rs7902231). In conclusion, our study identified statistically significant associations between metabolites of TRP via the 5-HT and KYN pathways and genetic variants at the genome-wide level. The discriminative performance of penalized regression models incorporating clinical, genetic, and metabolic predictors warrants a follow-up analysis of this panel of determinants.