Metabolomic Biomarkers in Mental Disorders: Bipolar Disorder and Schizophrenia

Biomarkers of bipolar disorder based on metabolomics: A systematic review

Bipolar disorder (BD) is a severe affective disorder characterized by recurrent episodes of depression or mania/hypomania, which significantly impair cognitive function, life skills, and social abilities of patients. There is little understanding of the neurobiological mechanisms of BD. The diagnosis of BD is primarily based on clinical assessment and psychiatric examination, highlighting the urgent need for objective markers to facilitate the diagnosis of BD. Metabolomics can be used as a diagnostic tool for disease identification and evaluation. This study summarized the altered metabolites in BD and analyzed aberrant metabolic pathways, which might contribute to the diagnosis of BD. Search of PubMed and Web of science for human BD studies related to metabolism to identify articles published up to November 19, 2022 yielded 987 articles. After screening and applying the inclusion and exclusion criteria, 16 untargeted and 11 targeted metabolomics studies were included. Pathway analysis of the potential differential biometabolic markers was performed using the Kyoto encyclopedia of genes and genomes (KEGG). There were 72 upregulated and 134 downregulated biomarkers in the untargeted metabolomics studies using blood samples. Untargeted metabolomics studies utilizing urine specimens revealed the presence of 78 upregulated and 54 downregulated metabolites. The targeted metabolomics studies revealed abnormalities in the metabolism of glutamate and tryptophan. Enrichment analysis revealed that the differential metabolic pathways were mainly involved in the metabolism of glucose, amino acid and fatty acid. These findings suggested that certain metabolic biomarkers or metabolic biomarker panels might serve as a reference for the diagnosis of BD.

Are There Any Differences in Clinical and Biochemical Variables between Bipolar Patients with or without Lifetime Psychotic Symptoms?

INTRODUCTION

Psychotic symptoms occur in more than half of patients affected by Bipolar Disorder (BD) and are associated with an unfavorable course of the disorder. The objective of this study is to identify the differences in the clinical and biochemical parameters between bipolar patients with or without psychotic symptoms.

METHODS

A total of 665 inpatients were recruited. Demographic, clinical, and biochemical data related to the first day of hospitalization were obtained via a screening of the clinical charts and intranet hospital applications. The two groups identified via the lifetime presence of psychotic symptoms were compared using t tests for quantitative variables and χ2 tests for qualitative ones; binary logistic regression models were subsequently performed.

RESULTS

Patients with psychotic BD (compared to non-psychotic ones) showed a longer duration of hospitalization (p < 0.001), higher Young Mania Rating Scale scores (p < 0.001), lower Global Assessment of Functioning scores (p = 0.002), a less frequent history of lifetime suicide attempts (p = 0.019), less achievement of remission during the current hospitalization (p = 0.028), and a higher Neutrophile to Lymphocyte Ratio (NLR) (p = 0.006), but lower total cholesterol (p = 0.018) and triglycerides (p = 0.013).

CONCLUSIONS

Patients with psychotic BD have a different clinical and biochemical profile compared to their counterparts, characterized by more clinical severity, fewer metabolic alterations, and a higher grade of inflammation. Further multi-center studies have to confirm the results of this present study.

Application of Lipidomics in Psychiatry: Plasma-Based Potential Biomarkers in Schizophrenia and Bipolar Disorder

In this study, we obtained a lipidomic profile of plasma samples from drug-naïve patients with schizophrenia (SZ) and bipolar disorder (BD) in comparison to healthy controls. The sample cohort consisted of 30 BD and 30 SZ patients and 30 control individuals. An untargeted lipidomics strategy using liquid chromatography coupled with high-resolution mass spectrometry was employed to obtain the lipid profiles. Data were preprocessed, then univariate (t-test) and multivariate (principal component analysis and orthogonal partial least squares discriminant analysis) statistical tools were applied to select differential lipids, which were putatively identified. Afterward, multivariate receiver operating characteristic tests were performed, and metabolic pathway networks were constructed, considering the differential lipids. Our results demonstrate alterations in distinct lipid pathways, especially in glycerophospholipids, sphingolipids and glycerolipids, between SZ and BD patients. The results obtained in this study may serve as a basis for differential diagnosis, which is crucial for effective treatment and improving the quality of life of patients with psychotic disorders.

Unveiling Chemical Interactions Between Plants and Fungi Using Metabolomics Approaches

Metabolomics has been extensively used in clinical studies in the search for new biomarkers of human diseases. However, this approach has also been highlighted in agriculture and biological sciences, once metabolomics studies have been assisting researchers to deduce new chemical mechanisms involved in biological interactions that occur between microorganisms and plants. In this sense, the knowledge of the biological role of each metabolite (virulence factors, signaling compounds, antimicrobial metabolites, among others) and the affected biochemical pathways during the interaction contribute to a better understand of different ecological relationships established in nature. The current chapter addresses five different applications of the metabolomics approach in fungal-plant interactions research: (1) Discovery of biomarkers in pathogen-host interactions, (2) plant diseases diagnosis, (3) chemotaxonomy, (4) plant defense, and (5) plant resistance; using mass spectrometry and/or nuclear magnetic resonance spectroscopy, which are the techniques most used in metabolomics.

Causality of genetically determined metabolites on anxiety disorders: a two-sample Mendelian randomization study

BACKGROUND

Although anxiety disorders are one of the most prevalent mental disorders, their underlying biological mechanisms have not yet been fully elucidated. In recent years, genetically determined metabolites (GDMs) have been used to reveal the biological mechanisms of mental disorders. However, this strategy has not been applied to anxiety disorders. Herein, we explored the causality of GDMs on anxiety disorders through Mendelian randomization study, with the overarching goal of unraveling the biological mechanisms.

METHODS

A two-sample Mendelian randomization (MR) analysis was implemented to assess the causality of GDMs on anxiety disorders. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas four different GWAS datasets of anxiety disorders were the outcomes. Notably, all datasets were acquired from publicly available databases. A genetic instrumental variable (IV) was used to explore the causality between the metabolite and anxiety disorders for each metabolite. The MR Steiger filtering method was implemented to examine the causality between metabolites and anxiety disorders. The standard inverse variance weighted (IVW) method was first used for the causality analysis, followed by three additional MR methods (the MR-Egger, weighted median, and MR-PRESSO (pleiotropy residual sum and outlier) methods) for sensitivity analyses in MR analysis. MR-Egger intercept, and Cochran's Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. Bonferroni correction was used to determine the causative association features (P < 1.03 × 10^-4). Furthermore, metabolic pathways analysis was performed using the web-based MetaboAnalyst 5.0 software. All statistical analysis were performed in R software. The STROBE-MR checklist for the reporting of MR studies was used in this study.

RESULTS

In MR analysis, 85 significant causative relationship GDMs were identified. Among them, 11 metabolites were overlapped in the four different datasets of anxiety disorders. Bonferroni correction showing1-linoleoylglycerophosphoethanolamine (OR_{fixed-effect IVW} = 1.04; 95% CI 1.021-1.06; P_{fixed-effect IVW} = 4.3 × 10^-5) was the most reliable causal metabolite. Our results were robust even without a single SNP because of a "leave-one-out" analysis. The MR-Egger intercept test indicated that genetic pleiotropy had no effect on the results (intercept = - 0.0013, SE = 0.0006, P = 0.06). No heterogeneity was detected by Cochran's Q test (MR-Egger. Q = 7.68, P = 0.742; IVW. Q = 12.12, P = 0.436). A directionality test conducted by MR Steiger confirmed our estimation of potential causal direction (P < 0.001). In addition, two significant pathways, the "primary bile acid biosynthesis" pathway (P = 0.008) and the "valine, leucine, and isoleucine biosynthesis" pathway (P = 0.03), were identified through metabolic pathway analysis.

CONCLUSION

This study provides new insights into the causal effects of GDMs on anxiety disorders by integrating genomics and metabolomics. The metabolites that drive anxiety disorders may be suited to serve as biomarkers and also will help to unravel the biological mechanisms of anxiety disorders.

New Paradigms of Old Psychedelics in Schizophrenia

Psychedelics such as lysergic acid diethylamide (LSD), psilocybin (magic mushrooms), and mescaline exhibit intense effects on the human brain and behaviour. In recent years, there has been a surge in studies investigating these drugs because clinical studies have shown that these once banned drugs are well tolerated and efficacious in medically supervised low doses called microdosing. Psychedelics have demonstrated efficacy in treating neuropsychiatric maladies such as difficult to treat anxiety, depression, mood disorders, obsessive compulsive disorders, suicidal ideation, posttraumatic stress disorder, and also in treating substance use disorders. The primary mode of action of psychedelics is activation of serotonin 5-HT_2A receptors affecting cognition and brain connectivity through the modulation of several downstream signalling pathways via complex molecular mechanisms. Some atypical antipsychotic drugs (APDs) primarily exhibit pharmacological actions through 5-HT_2A receptors, which are also the target of psychedelic drugs. Psychedelic drugs including the newer second generation along with the glutamatergic APDs are thought to mediate pharmacological actions through a common pathway, i.e., a complex serotonin-glutamate receptor interaction in cortical neurons of pyramidal origin. Furthermore, psychedelic drugs have been reported to act via a complex interplay between 5HT_2A, mGlu2/3, and NMDA receptors to mediate neurobehavioral and pharmacological actions. Findings from recent studies have suggested that serotoninergic and glutamatergic neurotransmissions are very closely connected in producing pharmacological responses to psychedelics and antipsychotic medication. Emerging hypotheses suggest that psychedelics work through brain resetting mechanisms. Hence, there is a need to dig deeply into psychedelic neurobiology to uncover how psychedelics could best be used as scientific tools to benefit psychiatric disorders including schizophrenia.

Plasma metabolomics of schizophrenia with cognitive impairment: A pilot study

Schizophrenia (SCZ) acts as a complex and burdensome disease, in which the functional outcome can be validly predicted by cognitive impairment, as one of the core features. However, there still lack considerable markers of cognitive deficits in SCZ. Based on metabolomics, it is expected to identify different metabolic characteristics of SCZ with cognitive impairment. In the present study, 17 SCZ patients with cognitive impairment (CI), 17 matched SCZ patients with cognitive normal (CN), and 20 healthy control subjects (HC) were recruited, whose plasma metabolites were measured using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The result of metabolic profiling indicated the identification of 46 differentially expressed metabolites between HC, CN, and CI groups, with 7 differentially expressed metabolites between CN and CI groups. Four differential metabolites (imidazolepropionic acid, Homoserine, and Aspartic acid) were repeatedly found in both screenings, by which the formed biomarker panel could discriminate SCZ with cognitive impairment from matched patients (AUC = 0.974) and health control (AUC = 0.841), respectively. Several significant metabolic pathways were highlighted in pathway analysis, involving Alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism, and Citrate cycle (TCA cycle). In this study, several differentially expressed metabolites were identified in SCZ with cognitive impairment, providing novel insights into clinical treatment strategies.

Associations of C-Reactive Protein, Free Triiodothyronine, Thyroid Stimulating Hormone and Creatinine Levels with Agitation in Patients with Schizophrenia: A Comparative Cross-Sectional Study

PURPOSE

Agitation is prevalent among inpatients with schizophrenia. The aim of this study was to investigate whether biochemical parameters are associated with agitation in schizophrenia.

PATIENTS AND METHODS

Agitation was evaluated by the Positive and Negative Syndrome Scale-Excited Component questionnaire (PANSS-EC). Fasting serum levels of C-reactive protein (CRP), free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), uric acid (UA), creatinine, glucose and lipids were measured.

RESULTS

The analysis included 154 inpatients with schizophrenia (71 with agitation, 83 without agitation) and 75 healthy control subjects. Patients with schizophrenia and agitation had higher serum levels of CRP, FT3, FT4 and UA as well as lower levels of serum TSH and creatinine than patients without agitation (all P < 0.05). Multivariate logistic regression analysis indicated that serum CRP (odds ratio [OR] = 1.470, P = 0.001), FT3 (OR = 13.026, P < 0.001), TSH (OR = 0.758, P = 0.033) and creatinine (OR = 0.965, P = 0.004) were significantly associated with agitation in schizophrenia. CRP, FT3, TSH and creatinine achieved an area under the ROC curve of 0.626, 0.728, 0.620 and 0.663 respectively in discriminating schizophrenia with or without agitation.

CONCLUSION

Increased serum CRP and FT3 levels and decreased serum TSH and creatinine levels are independent risk factors for agitation in hospitalized patients with schizophrenia. Inflammation, thyroid hormones and renal function may be involved in the pathogenesis of agitation in schizophrenia.

Neurogranin: A Potential Biomarker of Neurological and Mental Diseases

Neurogranin (Ng) is a small protein usually expressed in granule-like structures in pyramidal cells of the hippocampus and cortex. However, its clinical value is not fully clear so far. Currently, Ng is proved to be involved in synaptic plasticity, synaptic regeneration, and long-term potentiation mediated by the calcium- and calmodulin-signaling pathways. Due to both the synaptic integrity and function as the growing concerns in the pathogenesis of a wide variety of neurological and mental diseases, a series of researches published focused on the associations between Ng and these kinds of diseases in the past decade. Therefore, in this review, we highlight several diseases, which include, but are not limited to, Alzheimer’s disease, Parkinson disease, Creutzfeldt–Jakob disease, neuro-HIV, neurosyphilis, schizophrenia, depression, traumatic brain injury, and acute ischemic stroke, and summarize the associations between cerebrospinal fluid or blood-derived Ng with these diseases. We propose that Ng is a potential and promising biomarker to improve the diagnosis, prognosis, and severity evaluation of these diseases in the future.

Cross-sectional metabolic profiles of mental health in population-based cohorts of 11- to 12-year-olds and mid-life adults: The Longitudinal Study of Australian Children

OBJECTIVE

Poorer mental health in adulthood is associated with increased risk of cardiovascular disease and reduced life expectancy. However, little is known of the molecular pathways underpinning this relationship and how early in life adverse metabolite profiles relate to self-reported variation in mental health. We examined cross-sectional associations between mental health and serum metabolites indicative of cardiovascular health, in large Australian population-based cohorts at two stages of the life-course.

METHODS

We characterised cross-sectional serum nuclear magnetic resonance metabolite profiles of positively and negatively framed mental health in a large population-based sample of Australian 11- to 12-year-olds (n = 1172; 51% girls) and mid-life adults (n = 1322; mean age 45 years; 87% women). We examined multiple standard self-report mental health scales, spanning psychosocial health, general well-being, life satisfaction, and health-related quality of life. Linear regression was used to investigate the cross-sectional association between mental health and each metabolite (n = 73) in children and adults separately, unadjusted and adjusted for age, sex, socioeconomic position and body mass index.

RESULTS

Better child and adult mental health were associated with lower levels of the inflammatory marker glycoprotein acetyls, and a favourable, less atherogenic lipid/lipoprotein profile. Patterns of association in children were generally weaker than in adults. Associations were generally modest and partially attenuated when adjusted for body mass index.

CONCLUSIONS

In general, metabolite profiles associated with better child and adult mental health closely aligned with those predictive of better cardiovascular health in adults. Our findings support previous evidence for the likely bidirectional relationship between mental health and cardiovascular disease risk, by extending this evidence base to the molecular level and in children.

Assessing the Causal Effects of Human Serum Metabolites on 5 Major Psychiatric Disorders

Psychiatric disorders are the leading cause of disability worldwide while the pathogenesis remains unclear. Genome-wide association studies (GWASs) have made great achievements in detecting disease-related genetic variants. However, functional information on the underlying biological processes is often lacking. Current reports propose the use of metabolic traits as functional intermediate phenotypes (the so-called genetically determined metabotypes or GDMs) to reveal the biological mechanisms of genetics in human diseases. Here we conducted a two-sample Mendelian randomization analysis that uses GDMs to assess the causal effects of 486 human serum metabolites on 5 major psychiatric disorders, which respectively were schizophrenia (SCZ), major depression (MDD), bipolar disorder (BIP), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). Using genetic variants as proxies, our study has identified 137 metabolites linked to the risk of psychiatric disorders, including 2-methoxyacetaminophen sulfate, which affects SCZ (P = 1.7 × 10-5) and 1-docosahexaenoylglycerophosphocholine, which affects ADHD (P = 5.6 × 10-5). Fourteen significant metabolic pathways involved in the 5 psychiatric disorders assessed were also detected, such as glycine, serine, and threonine metabolism for SCZ (P = .0238), Aminoacyl-tRNA biosynthesis for both MDD (P = .0144) and ADHD (P = .0029). Our study provided novel insights into integrating metabolomics with genomics in order to understand the mechanisms underlying the pathogenesis of human diseases.