Metabolomics and lipidomics analyses by 1H nuclear magnetic resonance of schizophrenia patient serum reveal potential peripheral biomarkers for diagnosis

Phospholipidomics in Clinical Trials for Brain Disorders: Advancing our Understanding and Therapeutic Potentials

Phospholipidomics is a specialized branch of lipidomics that focuses on the characterization and quantification of phospholipids. By using sensitive analytical techniques, phospholipidomics enables researchers to better understand the metabolism and activities of phospholipids in brain disorders such as Alzheimer's and Parkinson's diseases. In the brain, identifying specific phospholipid biomarkers can offer valuable insights into the underlying molecular features and biochemistry of these diseases through a variety of sensitive analytical techniques. Phospholipidomics has emerged as a promising tool in clinical studies, with immense potential to advance our knowledge of neurological diseases and enhance diagnosis and treatment options for patients. In the present review paper, we discussed numerous applications of phospholipidomics tools in clinical studies, with a particular focus on the neurological field. By exploring phospholipids' functions in neurological diseases and the potential of phospholipidomics in clinical research, we provided valuable insights that could aid researchers and clinicians in harnessing the full prospective of this innovative practice and improve patient outcomes by providing more potent treatments for neurological diseases.

The relationship between alterations in plasma metabolites and treatment responses in antipsychotic-naïve female patients with schizophrenia

This study aimed to explore the relationship between alterations in plasma metabolites and treatment responses amongst antipsychotic-naïve female patients with schizophrenia. A total of 38 antipsychotic-naïve female schizophrenia patients (ANS) and 19 healthy female controls (HC) were recruited. Plasma samples were obtained from all participants, and targeted metabolomics were measured with FIA-MS/MS and LC-MS/MS. The positive and negative syndrome scale (PANSS) was used to assess the severity of psychotic symptoms before and after eight weeks of treatment. Receiver operator characteristics (ROC) curves were used to predict diagnostic and therapeutic responses. A total of 186 metabolites passed quality control procedures and were used in statistical analysis to identify potential biomarkers. Before treatment, the ANS patients had lower levels of γ -Aminobutyric Acid (GABA) and higher levels of Cholesteryl esters (CE) (20:3), Cholic Acid (CA) and Glycocholic Acid (GCA) compared to the HCs. These four differential metabonomic markers were synthesised into a combinatorial biomarker panel. This panel significantly distinguished ANS from HC. Moreover, this biomarker panel was able to effectively predict therapeutic responses. Our results suggest that plasma CE (20:3), CA, GCA, and GABA levels may be useful for diagnosing and predicting antipsychotic efficacy amongst female schizophrenia patients.

Lactate levels in the brain and blood of schizophrenia patients: A systematic review and meta-analysis

BACKGROUND

The pathophysiological mechanisms of schizophrenia are still unclear. Converging evidence suggests that energy metabolism abnormalities are involved in schizophrenia, and support its role in the pathophysiology of this disease. Lactate plays an important role in energy metabolism. Many studies have reported changes in the levels of lactate in the brain and serum of schizophrenia patients; however, the results from these studies are not consistent. To overcome this limitation, the goal of the present meta-analysis is to analyze the changes in lactate levels in the brain and blood of schizophrenia patients.

METHODS

For this systematic review and meta-analysis, we performed a thorough search of relevant literature in the English language, using the MEDLINE, Cochrane, and Embase databases.

RESULTS

In the present meta-analysis, 20 studies were scrutinized, including 13 studies on brain lactate levels, which involved 322 schizophrenia patients and 324 healthy individuals as controls. 7 studies on blood lactate levels, involving 234 schizophrenia patients and 238 healthy individuals, were also included. Brain lactate levels were elevated in schizophrenia patients, both in vivo and in post-mortem studies. Nevertheless, blood lactate levels in schizophrenia patients have revealed no statistically significant difference, as compared with control individuals.

CONCLUSIONS

In comparison with healthy individuals, schizophrenia patients had higher lactate levels in the brain, rather than in the blood. These findings suggest independent regulatory mechanisms of lactate levels in the brain and peripheral tissues. Abnormal lactate metabolism in the brain may be an important pathological mechanism in schizophrenia.

Inhibition of glycolysis prevents behavioural changes in mice with MK801-induced SCZ model by alleviating lactate accumulation and lactylation

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with a complex aetiology. Cognitive symptoms and hippocampal changes have been implicated in the pathophysiology of SCZ. Changes in metabolites level and up-regulated glycolysis have been reported in previous studies, which may be related to the hippocampal dysfunction in SCZ. However, the pathological mechanism of glycolysis involved in the pathogenesis of SCZ remains unclear. Therefore, the change of glycolysis level and the involvement in SCZ need to be further studied. In our study, MK801 was used to induce an SCZ mouse model and cell model in vivo and in vitro. Western blotting was performed to evaluate the levels of glycolysis, metabolites, and lactylation in hippocampal tissue of mice with SCZ or cell models. The level of high mobility group protein 1 (HMGB1) in the medium of MK801-treated primary hippocampal neurons was examined. Apoptosis was evaluated in HMGB1-treated hippocampal neurons by flow cytometry. The glycolysis inhibitor 2-DG prevented behavioural changes in the MK801-induced SCZ mouse model. The lactate accumulation and level of lactylation were alleviated in the hippocampal tissue of MK801-treated mice. Glycolysis was enhanced, and lactate accumulated in MK-801-treated primary hippocampal neurons. In addition, the level of HMGB1 increased in the medium and induced apoptosis in primary hippocampal neurons. Together, the data showed that glycolysis and lactylation increased in the MK801-induced SCZ model in vivo and in vitro, and this effect could be prevented by 2-DG (a glycolysis inhibitor). Glycolytic related HMGB1 upregulation may induce apoptosis in hippocampal neurons downstream.

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.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

NMR Metabolomics in Serum Fingerprinting of Schizophrenia Patients in a Serbian Cohort

Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia.

Metabolic phenotyping reveals a potential link between elevated faecal amino acids, diet and symptom severity in individuals with severe mental illness

The brain-gut axis is increasingly recognized as an important contributing factor in the onset and progression of severe mental illnesses such as schizophrenia spectrum disorders and bipolar disorder. This study investigates associations between levels of faecal metabolites identified using ¹H-NMR, clinical parameters, and dietary components of forty-two individuals diagnosed in a transdiagnostic approach to have severe mental illness. Faecal levels of the amino acids; alanine, leucine, and valine showed a significant positive correlation with psychiatric symptom severity as well as with dairy intake. Overall, this study proposes a diet-induced link between the brain-gut axis and the severity of psychiatric symptoms, which could be valuable in the design of novel dietary or therapeutic interventions to improve psychiatric symptoms.

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.

An integrated pathological research for precise diagnosis of schizophrenia combining LC-MS/1H NMR metabolomics and transcriptomics

BACKGROUND

Lack of clinically specific biomarkers has impeded the precise diagnosis of schizophrenia, meanwhile, limited comprehending of pathogenesis for schizophrenia has restricted the effective treatment.

METHOD

An integrated multi-omic approach, combining metabolomic platform (LC-MS and ¹H NMR) and transcriptomic platform, was established to differentiate healthy subjects from schizophrenia patients. Based on filtered metabolites and genes, characteristic spectrums were further built. Then, representative metabolites and genes were screened out through Boruta algorithm. Moreover, characteristic diagnostic formulas were established via LASSO regression analysis.

RESULT

As a result, 86 differential metabolites (in line with amino acid metabolism, etc.) and 189 differential expression genes (involving in amino acid metabolic process, etc.) were obtained as potential biomarkers for schizophrenia. The latent interaction between metabolites with genes, such as HMGCLL1 with energy metabolism, etc., was further studied through the analysis of pathway-based integration. Moreover, fine predictive ability was attributed to characteristic metabolomic/transcriptomic diagnostic spectrums/formulas.

CONCLUSION

The functional relationships of filtered metabolites and genes were studied, which could elaborate the pathological process of schizophrenia more systemically, supplying more precise information on mechanism description and diagnostic evidence of schizophrenia.

A system biology approach based on metabolic biomarkers and protein-protein interactions for identifying pathways underlying schizophrenia and bipolar disorder

Mental disorders (MDs), including schizophrenia (SCZ) and bipolar disorder (BD), have attracted special attention from scientists due to their high prevalence and significantly debilitating clinical features. The diagnosis of MDs is still essentially based on clinical interviews, and intensive efforts to introduce biochemical based diagnostic methods have faced several difficulties for implementation in clinics, due to the complexity and still limited knowledge in MDs. In this context, aiming for improving the knowledge in etiology and pathophysiology, many authors have reported several alterations in metabolites in MDs and other brain diseases. After potentially fishing all metabolite biomarkers reported up to now for SCZ and BD, we investigated here the proteins related to these metabolites in order to construct a protein-protein interaction (PPI) network associated with these diseases. We determined the statistically significant clusters in this PPI network and, based on these clusters, we identified 28 significant pathways for SCZ and BDs that essentially compose three groups representing three major systems, namely stress response, energy and neuron systems. By characterizing new pathways with potential to innovate the diagnosis and treatment of psychiatric diseases, the present data may also contribute to the proposal of new intervention for the treatment of still unmet aspects in MDs.

Tear fluid biomarkers in major depressive disorder: Potential of spectral methods in biomarker discovery

Spectroscopic methods represent a group of analytical methods that demonstrate high potential in providing clinically relevant diagnostic information, such as biochemical, functional or structural changes of macromolecular complexes that might occur due to pathological processes or therapeutic intervention. Although application of these methods in the field of psychiatric research is still relatively recent, the preliminary results show that they have the capacity to detect subtle neurobiological abnormalities in major depressive disorder (MDD). Methods of mass spectrometry (MALDI-TOF MS), zymography, synchronous fluorescence spectroscopy (SFS), circular dichroism (CD) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy and atomic force microscopy (AFM) were used to analyze the human tear fluid of subjects with MDD. Using MALDI-TOF MS, two diagnostically significant peaks (3747 and 16 411 m/z) were identified with an AUC value of 0.89 and 0.92 in tear fluid of subjects with MDD vs controls, respectively. We also identified various forms of matrix metalloproteinase 9 in subjects with MDD using zymography and synchronous fluorescence spectra (SFS) showed a significant increase in fluorescence intensity at 280 nm. CD spectra were redshifted in tear fluid of subjects with MDD vs healthy controls. FTIR spectroscopy showed changes in the positions of peaks for amide A, I, II in tear fluid of subjects with MDD vs controls. Moreover, atomic force microscopy (AFM) showed different pattern in the crystal structures of tear fluid components in subjects with MDD. SFS, CD, FTIR spectroscopy, AFM and MALDI-TOF MS confirmed, that the human tear fluid proteome could be helpful in discriminating between the group of subjects with MDD and healthy controls. These preliminary findings suggest that spectral methods could represent a useful tool in clinical psychiatry, especially in establishing differential diagnosis, monitoring illness progression and the effect of psychiatric treatment.

Establishment, Validation, and Initial Application of a Sensitive LC-MS/MS Assay for Quantification of the Naturally Occurring Isomers Itaconate, Mesaconate, and Citraconate

Itaconate is derived from the tricarboxylic acid (TCA) cycle intermediate cis-aconitate and links innate immunity and metabolism. Its synthesis is altered in inflammation-related disorders and it therefore has potential as clinical biomarker. Mesaconate and citraconate are naturally occurring isomers of itaconate that have been linked to metabolic disorders, but their functional relationships with itaconate are unknown. We aimed to establish a sensitive high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) assay for the quantification of itaconate, mesaconate, citraconate, the pro-drug 4-octyl-itaconate, and selected TCA intermediates. The assay was validated for itaconate, mesaconate, and citraconate for intra- and interday precision and accuracy, extended stability, recovery, freeze/thaw cycles, and carry-over. The lower limit of quantification was 0.098 µM for itaconate and mesaconate and 0.049 µM for citraconate in 50 µL samples. In spike-in experiments, itaconate remained stable in human plasma and whole blood for 24 and 8 h, respectively, whereas spiked-in citraconate and mesaconate concentrations changed during incubation. The type of anticoagulant in blood collection tubes affected measured levels of selected TCA intermediates. Human plasma may contain citraconate (0.4-0.6 µM, depending on the donor), but not itaconate or mesaconate, and lipopolysaccharide stimulation of whole blood induced only itaconate. Concentrations of the three isomers differed greatly among mouse organs: Itaconate and citraconate were most abundant in lymph nodes, mesaconate in kidneys, and only citraconate occurred in brain. This assay should prove useful to quantify itaconate isomers in biomarker and pharmacokinetic studies, while providing internal controls for their effects on metabolism by allowing quantification of TCA intermediates.

The Potential Regulatory Network of Glutamate Metabolic Pathway Disturbance in Chinese Han Withdrawal Methamphetamine Abusers

Objects

To explore the long-term influence of methamphetamine abuse on metabolomics character, with gas chromatography-mass spectrometry (GS-MS) technology, and the potential regulatory network using the bioinformatics method.

Methods

Forty withdrawal methamphetamine abusers (WMA) were recruited from Shanghai Gaojing Forced Isolation Detoxification Institute. Forty healthy controls (HC) were recruited from society. GS-MS technology was used to detect metabolic products in serum. A bioinformatics method was used to build a regulatory network. Q-PCR was used to detect the candidate gene expressions, and ELISA was used to detect the regulatory enzyme expressions.

Results

Four pathways were significantly changed in the MA compared to the HC: (1) the arginine synthesis pathway, (2) alanine, aspartic acid and glutamate metabolic pathway, (3) cysteine and methionine metabolic pathway, and (4) the ascorbate and aldarate pathway (enrichment analysis p < 0.05, Impactor factor > 0.2). When focusing on the ‘Alanine, aspartate, and glutamate metabolism’ pathway, a regulatory network was established, and the expression of candidate regulatory genes and enzymes was verified. It was found that the expression of DLG2 (Discs large MAGUK scaffold protein 2), PLA2G4 (Phospholipase A2 group IVE), PDE4D (Phosphodiesterase 4D), PDE4B (Phosphodiesterase 4B), and EPHB2 (Ephrin type-B receptor 2) were significantly different between the two groups (p < 0.05), However, after adjusting for age and BMI, only DLG2, PLA2G4, and EPHB2 remained significant (p < 0.05). The expression of enzymes was not significantly different (p > 0.05).

Conclusion

Methamphetamine abuse influences the metabolic process in the long term, and DLG2, PLA2G4, and EPHB2 may regulate the glutamate metabolism pathway.

Effects of Psychostimulants and Antipsychotics on Serum Lipids in an Animal Model for Schizophrenia

Schizophrenia (SCZ) treatment is essentially limited to the use of typical or atypical antipsychotic drugs, which suppress the main symptoms of this mental disorder. Metabolic syndrome is often reported in patients with SCZ under long-term drug treatment, but little is known about the alteration of lipid metabolism induced by antipsychotic use. In this study, we evaluated the blood serum lipids of a validated animal model for SCZ (Spontaneously Hypertensive Rat, SHR), and a normal control rat strain (Normotensive Wistar Rat, NWR), after long-term treatment (30 days) with typical haloperidol (HAL) or atypical clozapine (CLZ) antipsychotics. Moreover, psychostimulants, amphetamine (AMPH) or lisdexamfetamine (LSDX), were administered to NWR animals aiming to mimic the human first episode of psychosis, and the effects on serum lipids were also evaluated. Discrepancies in lipids between SHR and NWR animals, which included increased total lipids and decreased phospholipids in SHR compared with NWR, were similar to the differences previously reported for SCZ patients relative to healthy controls. Administration of psychostimulants in NWR decreased omega-3, which was also decreased in the first episode of psychosis of SCZ. Moreover, choline glycerophospholipids allowed us to distinguish the effects of CLZ in SHR. Thus, changes in the lipid metabolism in SHR seem to be reversed by the long-term treatment with the atypical antipsychotic CLZ, which was under the same condition described to reverse the SCZ-like endophenotypes of this validated animal model for SCZ. These data open new insights for understanding the potential influence of the treatment with typical or atypical antipsychotics on circulating lipids. This may represent an outcome effect from metabolic pathways that regulate lipids synthesis and breakdown, which may be reflecting a cell lipids dysfunction in SCZ.

Metabolic dysfunctions in the kynurenine pathway, noradrenergic and purine metabolism in schizophrenia and bipolar disorders

BACKGROUND

We aimed at exploring potential pathophysiological processes across psychotic disorders, applying metabolomics in a large and well-characterized sample of patients and healthy controls.

METHODS

Patients with schizophrenia and bipolar disorders (N = 212) and healthy controls (N = 68) had blood sampling with subsequent metabolomics analyses using electrochemical coulometric array detection. Concentrations of 52 metabolites including tyrosine, tryptophan and purine pathways were compared between patients and controls while controlling for demographic and clinical characteristics. Significant findings were further tested in medication-free subsamples.

RESULTS

Significantly decreased plasma concentrations in patients compared to healthy controls were found for 3-hydroxykynurenine (3OHKY, p = 0.0008), xanthurenic acid (XANU, p = 1.5×10-5), vanillylmandelic acid (VMA, p = 4.5×10-5) and metanephrine (MN, p = 0.0001). Plasma concentration of xanthine (XAN) was increased in the patient group (p = 3.5×10-5). Differences of 3OHKY, XANU, VMA and XAN were replicated across schizophrenia spectrum disorders and bipolar disorders subsamples of medication-free individuals.

CONCLUSIONS

Although prone to residual confounding, the present results suggest the kynurenine pathway of tryptophan metabolism, noradrenergic and purinergic system dysfunction as trait factors in schizophrenia spectrum and bipolar disorders. Of special interest is XANU, a metabolite previously not found to be associated with bipolar disorders.

Peripheral biomarkers allow differential diagnosis between schizophrenia and bipolar disorder

Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental disorders that pose important challenges for diagnosis by sharing common symptoms, such as delusions and hallucinations. The underlying pathophysiology of both disorders remains largely unknown, and the identification of biomarkers with potential to support diagnosis is highly desirable. In a previous study, we successfully discriminated SCZ and BD patients from healthy control (HC) individuals by employing proton magnetic resonance spectroscopy (¹H-NMR). In this study, ¹H-NMR data treated by chemometrics, principal component analysis (PCA) and supervised partial least-squares discriminant analysis (PLS-DA), provided the identification of metabolites present only in BD (as for instance the 2,3-diphospho-D-glyceric acid, N-acetyl aspartyl-glutamic acid, monoethyl malonate) or only in SCZ (as isovaleryl carnitine, pantothenate, mannitol, glycine, GABA). This may represent a set of potential biomarkers to support the diagnosis of these mental disorders, enabling the discrimination between SCZ and BD, and among these psychiatric patients and HC (as 6-hydroxydopamine was present in BD and SCZ but not in HC). The presence or absence of these metabolites in blood allowed the categorization of 182 independent subjects into one of these three groups. In addition, the presented data suggest disturbances in metabolic pathways in SCZ and BD, which may provide new and important information to support the elucidation and/or new insights into the neurobiology underlying these mental disorders.

Biomarkers in aggression

Aggressive behavior exerts an enormous impact on society remaining among the main causes of worldwide premature death. Effective primary interventions, relying on predictive models of aggression that show adequate sensitivity and specificity are currently lacking. One strategy to increase the accuracy and precision of prediction would be to include biological data in the predictive models. Clearly, to be included in such models, biological markers should be reliably associated with the specific trait under study (i.e., diagnostic biomarkers). Aggression, however, is phenotypically highly heterogeneous, an element that has hindered the identification of reliable biomarkers. However, current research is trying to overcome these challenges by focusing on more homogenous aggression subtypes and/or by studying large sample size of aggressive individuals. Further advance is coming by bioinformatics approaches that are allowing the integration of inter-species biological data as well as the development of predictive algorithms able to discriminate subjects on the basis of the propensity toward aggressive behavior. In this review we first present a brief summary of the available evidence on neuroimaging of aggression. We will then treat extensively the data on genetic determinants, including those from hypothesis-free genome-wide association studies (GWAS) and candidate gene studies. Transcriptomic and neurochemical biomarkers will then be reviewed, and we will dedicate a section on the role of metabolomics in aggression. Finally, we will discuss how biomarkers can inform the development of new pharmacological tools as well as increase the efficacy of preventive strategies.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

An NMR-based lipidomic approach to identify Parkinson's disease-stage specific lipoprotein-lipid signatures in plasma

Disturbances in lipid composition and lipoproteins metabolism can play a crucial role in the pathogenesis of Parkinson's disease (PD) and other neurodegenerative diseases. The lipidomic strategy proposed here involves lipoprotein profiling using NMR spectroscopy and multivariate data pre-processing and analysis tools on 94 plasma samples (belonging to 38 early-stage PD patients, 10 PD-related dementia patients, 23 persons with Alzheimer's dementia, and 23 healthy control subjects) to firstly differentiate PD patients (irrespective of the stage of the disease) from persons with Alzheimer's disease (AD) as well as from controls, and then to discriminate among PD patients according to disease severity. The whole data set was subdivided into 86 training and 8 external test samples for validation purposes. A two-step classification scheme, based on linear discriminant analysis with variable selection accomplished by a stepwise orthogonalisation procedure, was proposed to optimise classification performance. Careful pre-processing of NMR signals was crucial to ensure data set quality. A total of 30 chemical shift buckets enabled differentiation between PD patients (regardless of disease severity), AD and control subjects, providing classification, cross-validation and external prediction rates of 100% in all cases. Only 15 variables were required to further discriminate between early-stage PD and PD-related dementia, again with 100% correct classifications, and internal/external predictions. The simplicity and effectiveness of the classification methodology proposed support the use of NMR spectroscopy, in combination with chemometrics, as a viable alternative diagnostic tool to conventional PD clinical diagnosis.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Metabolomics Biomarkers for Precision Psychiatry

The treatment of psychiatric disorders remains a significant challenge in part due to imprecise diagnostic criteria and incomplete understanding of the molecular pathology involved. Current diagnostic and pharmacological treatment guidelines use a uniform approach to address each disorder even though psychiatric clinical presentation and prognosis within a disorder are known to be heterogeneous. Limited therapeutic success highlights the need for a precision medicine approach in psychiatry, termed precision psychiatry. To practice precision psychiatry, it is essential to research and develop multiple omics-based biomarkers that consider environmental factors and careful phenotype determination. Metabolomics, which lies at the endpoint of the "omics cascade," allows for detection of alterations in systems-level metabolites within biological pathways, thereby providing insights into the mechanisms that underlie various physiological conditions and pathologies. The eicosanoids, a family of metabolites derived from oxygenated polyunsaturated fatty acids, play a key role in inflammatory mechanisms and have been implicated in psychiatric disorders such as anorexia nervosa and depression. This review (1) provides background on the current clinical challenges of psychiatric disorders, (2) gives an overview of metabolomics application as a tool to develop improved biomarkers for precision psychiatry, and (3) summarizes current knowledge on metabolomics and lipidomic findings in common psychiatric disorders, with a focus on eicosanoids. Metabolomics is a promising tool for precision psychiatry. This research has great potential for both discovering biomarkers and elucidating molecular mechanisms underlying psychiatric disorders.

Metabolomics and Biomarker Discovery

Recently, metabolomics-the study of metabolite profiles within biological samples-has found a wide range of applications. This chapter describes the different techniques available for metabolomic analysis, the various samples that can be utilised for analysis and applications of both global and targeted metabolomic analysis to biomarker discovery in medicine.

Insights into the Effects of Crack Abuse on the Human Metabolome Using a NMR Approach

Approximately 255 million people consume illicit drugs every year, among which 18 million use cocaine. A portion of this drug is represented by crack, but it is difficult to estimate the number of users since most are marginalized. However, there are no recognized efficacious pharmacotherapies for crack-cocaine dependence. Inflammation and infection in cocaine users may be due to behavior adopted in conjunction with drug-related changes in the brain. To understand the metabolic changes associated with the drug abuse disorder and identify biomarkers, we performed a ¹H NMR-based metabonomic analysis of 44 crack users' and 44 healthy volunteers' blood serum. The LDA model achieved 98% of accuracy. From the water suppressed ¹H NMR spectra analyses, it was observed that the relative concentration of lactate was higher in the crack group, while long chain fatty acid acylated carnitines were decreased, which was associated with their nutritional behavior. Analyses of the aromatic region of CPMG ¹H NMR spectra demonstrated histidine and tyrosine levels increased in the blood serum of crack users. The reduction of carnitine and acylcarnitines and the accumulation of histidine in the serum of the crack users suggest that histamine biosynthesis is compromised. The tyrosine level points to altered dopamine concentration.

Toxicological effects on earthworms (Eisenia fetida) exposed to sub-lethal concentrations of BDE-47 and BDE-209 from a metabolic point

Earthworms improve the soil fertility and they are also sensitive to soil contaminants. Earthworms (Eisenia fetida), standard reference species, were usually chosen to culture and handle for toxicity tests. Metabolic responses in earthworms exposed to 2, 2', 4, 4'-tetrabromodiphenyl ether (BDE-47) and decabromodiphenyl ether (BDE-209) were inhibitory and interfered with basal metabolism. In this study, ¹H-NMR based metabolomics was used to identify sensitive biomarkers and explore metabolic responses of earthworms under sub-lethal BDE-47 and BDE-209 concentrations for 14 days. The results revealed that lactate was accumulated in earthworms exposed to BDE-47 and BDE-209. Glutamate increased significantly when the concentration of BDE-47 and BDE-209 reached 10 mg/kg. The BDE-47 exposure above 50 mg/kg concentration decreased the content of fumarate significantly, which was noticed different from that of BDE-209. Whereas, the BDE-207 or BDE-209 exposure increased the protein degradation into amino acids in vivo. The increased betaine content indicated that earthworms may maintain the cell osmotic pressure and protected enzyme activity by metabolic regulation. Moreover, the BDE-47 and BDE-209 exposure at 10 mg/kg changed most of the metabolites significantly, indicating that the metabolic responses were more sensitive than growth inhibition and gene expression. The metabolomics results revealed the toxic modes of BDE-47 and BDE-209 act on the osmoregulation, energy metabolism, nerve activities, tricarboxylic acid cycle and amino acids metabolism. Furthermore, our results highlighted that the ¹H-NMR based metabolomics is a strong tool for identifying sensitive biomarkers and eco-toxicological assessment.