Combined application of NMR- and GC-MS-based metabonomics yields a superior urinary biomarker panel for bipolar disorder

Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study

RATIONALE

The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied.

OBJECTIVES

We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures.

METHODS

Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel.

RESULTS

In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD.

CONCLUSIONS

Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.

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.

Exploration of urine metabolic biomarkers for new-onset, untreated pediatric epilepsy: A gas and liquid chromatography mass spectrometry-based metabolomics study

OBJECTIVE

The discovery of objective indicators for recent epileptic seizures will help confirm the diagnosis of epilepsy and evaluate therapeutic effects. Past studies had shortcomings such as the inclusion of patients under treatment and those with various etiologies that could confound the analysis results significantly. We aimed to minimize such confounding effects and to explore the small molecule biomarkers associated with the recent occurrence of epileptic seizures using urine metabolomics.

METHODS

This is a multicenter prospective study. Subjects included pediatric patients aged 2 to 12 years old with new-onset, untreated epilepsy, who had had the last seizure within 1 month before urine collection. Controls included healthy children aged 2 to 12 years old. Those with underlying or chronic diseases, acute illnesses, or recent administration of medications or supplements were excluded. Targeted metabolome analysis of spot urine samples was conducted using gas chromatography (GC)- and liquid chromatography (LC)-tandem mass spectrometry (MS/MS).

RESULTS

We enrolled 17 patients and 21 controls. Among 172 metabolites measured by GC/MS/MS and 41 metabolites measured by LC/MS/MS, only taurine was consistently reduced in the epilepsy group. This finding was subsequently confirmed by the absolute quantification of amino acids. No other metabolites were consistently altered between the two groups.

CONCLUSIONS

Urine metabolome analysis, which covers a larger number of metabolites than conventional biochemistry analyses, found no consistently altered small molecule metabolites except for reduced taurine in epilepsy patients compared to healthy controls. Further studies with larger samples, subjects with different ages, expanded target metabolites, and the investigation of plasma samples are required.

Comparing the metabolic pathways of different clinical phases of bipolar disorder through metabolomics studies

This study identified the metabolic biomarkers for different clinical phases of bipolar disorder (BD) through metabolomics. BD patients were divided into three groups: patients with BD and depressive episodes (BE, n = 59), patients with BD and mania/hypomania episodes (BH, n = 16), patients with BD and mixed episodes (BM, n = 10), and healthy controls (HC, n = 10). Serum from participants was collected for metabolomic sequencing, biomarkers from each group were screened separately by partial least squares analysis, and metabolic pathways connected to the biomarkers were identified. Compared with the controls, 3-D-hydroxyacetic acid and N-acetyl-glycoprotein showed significant differences in the BE, BH, and BM groups. This study suggests that different clinical types of BD share the same metabolic pathways, such as pyruvate, glycolysis/gluconeogenesis, and ketone body metabolisms. In particular, abnormal glycine, serine, and threonine metabolism was specific to BM; β-glucose, glycerol, lipids, lactate, and acetoacetate metabolites were specific to depressive episodes; the guanidine acetic acid metabolites specific to BH; and the acetic and ascorbic acids were metabolites specific to manic and BM. We screened potential biomarkers for different clinical phases of BD, which aids in BD typing and provides a theoretical basis for exploring the molecular mechanisms of BD.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses

NMR-based metabolomics approaches have been used in a wide range of applications, for example, with medical, plant, and marine samples. One-dimensional (1D) ¹H NMR is routinely used to find out biomarkers in biofluids such as urine, blood plasma, and serum. To mimic biological conditions, most NMR studies have been carried out in an aqueous solution where the high intensity of the water peak is a major problem in obtaining a meaningful spectrum. Different methods have been used to suppress the water signal, including 1D Carr-Purcell-Meiboom-Gill (CPMG) presat, consisting of a T₂ filter to suppress macromolecule signals and reduce the humped curve in the spectrum. 1D nuclear Overhauser enhancement spectroscopy (NOESY) is another method for water suppression that is used routinely in plant samples with fewer macromolecules than in biofluid samples. Other common 1D ¹H NMR methods such as 1D ¹H presat and 1D ¹H ES have simple pulse sequences; their acquisition parameters can be set easily. The proton with presat has just one pulse and the presat block causes water suppression, while other 1D ¹H NMR methods including those mentioned above have more pulses. However, it is not well known in metabolomics studies because it is used only occasionally and in a few types of samples by metabolomics experts. Another effective method is excitation sculpting to suppress water. Herein, we evaluate the effect of method selection on signal intensities of commonly detected metabolites. Different classes of samples including biofluid, plant, and marine samples were investigated, and recommendations on the advantages and limitations of each method are presented.

Metabolomic Profiling of Bipolar Disorder by 1H-NMR in Serbian Patients

Bipolar disorder (BD) is a brain disorder that causes changes in a person's mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A ¹H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD.

Relationship between Urinary Metabolomic Profiles and Depressive Episode in Antarctica

Antarctic expeditions have a high risk of participant depression owing to long stays and isolated environments. By quantifying the stress state and changes in biomolecules over time before the onset of depressive symptoms, predictive markers of depression can be explored. Here, we evaluated the psychological changes in 30 participants in the Japanese Antarctic Research Expedition using the Patient Health Questionnaire-9 (PHQ-9). Urinary samples were collected every three months for a year, and comprehensive urinary metabolomic profiles were quantified using liquid chromatography time-of-flight mass spectrometry. Five participants showed major depressive episodes (PHQ-9 ≥ 10) at 12 months. The urinary metabolites between these participants and the 25 unaffected participants were compared at individual metabolite and pathway levels. The individual comparisons showed the most significant differences at 12 months in 14 metabolites, including ornithine and beta-alanine. Data from shorter stays showed less significant differences. In contrast, pathway and enrichment analyses showed the most significant difference at three months and a less significant difference at longer stays. These time transitions of urinary metabolites could help in the development of urinary biomarkers to detect subjects with depressive episodes at an early stage.

Advances in Multi-Omics Applications in HBV-Associated Hepatocellular Carcinoma

Hepatitis B virus (HBV) specifically infects liver cells, leading to progressive liver cirrhosis and significantly increasing the risk of hepatocellular carcinoma (HCC). The maturity of sequencing technology, improvement in bioinformatics data analysis and progress of omics technologies had improved research efficiency. The occurrence and progression of HCC are affected by multisystem and multilevel pathological changes. With the application of single-omics technologies, including genomics, transcriptomics, metabolomics and proteomics in tissue and body fluid samples, and even the novel development of multi-omics analysis on a single-cell platform, HBV-associated HCC changes can be better analyzed. The review summarizes the application of single omics and combined analysis of multi-omics data in HBV-associated HCC and proposes the importance of multi-omics analysis in the type of HCC, which provide the possibility for the precise diagnosis and therapy of HBV-associated HCC.

An integrative approach based on GC-qMS and NMR metabolomics data as a comprehensive strategy to search potential breast cancer biomarkers

INTRODUCTION

Globally, breast cancer (BC) is leading at the top of women's diseases and, as a multifactorial disease, there is the need for the development of new approaches to aid clinicians on monitoring BC treatments. In this sense, metabolomic studies have become an essential tool allowing the establishment of interdependency among metabolites in biological samples.

OBJECTIVE

The combination of nuclear magnetic resonance (NMR) and gas chromatography-quadrupole mass spectrometry (GC-qMS) based metabolomic analyses of urine and breast tissue samples from BC patients and cancer-free individuals was used.

METHODS

Multivariate statistical tools were used in order to obtain a panel of metabolites that could discriminate malignant from healthy status assisting in the diagnostic field. Urine samples (n = 30), cancer tissues (n = 30) were collected from BC patients, cancer-free tissues were resected outside the tumor margin from the same donors (n = 30) while cancer-free urine samples (n = 40) where obtained from healthy subjects and analysed by NMR and GC-qMS methodologies.

RESULTS

The orthogonal partial least square discriminant analysis model showed a clear separation between BC patients and cancer-free subjects for both classes of samples. Specifically, for urine samples, the goodness of fit (R²Y) and predictive ability (Q²) was 0.946 and 0.910, respectively, whereas for tissue was 0.888 and 0.813, revealing a good predictable accuracy. The discrimination efficiency and accuracy of tissue and urine metabolites was ascertained by receiver operating characteristic curve analysis that allowed the identification of metabolites with high sensitivity and specificity. The metabolomic pathway analysis identified several dysregulated pathways in BC, including those related with lactate, valine, aspartate and glutamine metabolism. Additionally, correlations between urine and tissue metabolites were investigated and five metabolites (e.g. acetone, 3-hexanone, 4-heptanone, 2-methyl-5-(methylthio)-furan and acetate) were found to be significant using a dual platform approach.

CONCLUSION

Overall, this study suggests that an improved metabolic profile combining NMR and GC-qMS may be useful to achieve more insights regarding the mechanisms underlying cancer.

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.

Measuring the health effects of food by metabolomics

Metabolomics of human biological fluids or tissues is used to discover markers for diseases by comparing the metabolome of the patients against healthy individuals. Ultimately, these markers can be used in drug discovery to determine how medications normalize (at least in part) the human metabolome at specific disease stages to homeostatic. Likewise, the health effects of food can be studied. Even metabolomics of the food can be combined with metabolomics of the treated patients to correlate compounds from food with measurable health effects from clinical studies. Various chemometric analyses of these metabolomics data are used to identify markers for diseases and to obtain evidence for health effects. This review discusses recent researches (published from 2013 to 2021) on whether specific dietary intervention to humans suffering from metabolic disorders may improve their pathological status. The scope is limited to those associated with major lifestyle diseases such as diabetes, obesity, and cardiovascular diseases, for which food is thought may have detrimental as well as beneficial effects on human health. It includes metabolites characterization of different biological samples such as the human serum/plasma, urine, saliva, feces, or ileal fluid. Whether the study results supported the claimed health benefits and whether the research was conducted with appropriate study design, was criticized.

Pre- and Post-treatment Levels of Plasma Metabolites in Patients With Bipolar Depression

Background: Bipolar disorder (BD) is a serious mental disease with complex clinical manifestations and high recurrence rate. The purpose of this study was to detect metabolites related to the diagnosis and efficacy evaluation of bipolar depression in plasma samples by metabolomics. Methods: Thirty-one bipolar depression patients were recruited and completed 8 weeks medication and a matched group of 47 healthy controls (HCs) was recruited. Nuclear magnetic resonance spectroscopy was used to profile plasma samples of bipolar depression patients at baseline and after 8 weeks medication, and HCs. Then Multivariate statistical analysis was performed to analyze differences of plasma metabolites among the three groups. Results: We detected seven specific differential metabolites in bipolar depression. Six of the metabolites were returned to the normal levels in different degrees after 8 weeks medication, only Glycine continuously decreased in the acute and significant improvement stages of bipolar depression (VIP > 1 and p < 0.05). These differential metabolites involved several metabolic pathways. Limitations: The small sample size was one of the most prominent limitations. Each BD patient was given an individualized medication regimen according to the clinical guidelines. Conclusion: There were metabolites changes before and after 8 weeks medication. Glycine may be a characteristic marker of bipolar depression and does not change with the improvement of bipolar depression, while other 6 differential metabolites may be biomarkers associated with the pathological development or the improvement of bipolar depression. And, these differential metabolites mainly related to energy metabolism, amino acid metabolism and gut microbiota metabolism.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

The effect of acupuncture on depression and its correlation with metabolic alterations: A randomized controlled trial

BACKGROUND

Electroacupuncture (EA) treatment has antidepressant effect and when patients were treated with EA and antidepressants, the effect could be maintained for a longer time. However, the effect of EA combined with antidepressants based on metabolism is still in the initial observation stage, which requires further research.

METHODS

A total of 60 patients with moderate depression were assigned into 2 groups at a ratio of 1:1, the EA group (receiving EA and antidepressants) and the control group (taking antidepressants only) in this randomized controlled pilot trial. The EA treatment was performed 3 times a week for 8 consecutive weeks and then follow up for 4 weeks. The patients' depressive mood was measured by the Hamilton Depression scale (HAMD) at baseline, week 4, week 8 and week 12. Before and after 8-week treatment, morning urine samples from all patients were analyzed by the gas chromatography-mass spectrometry (GC-MS) to find possible metabolic markers of depression and of EA treatment related changes.

RESULTS

Compared with the control group, the EA group showed more significant improvements in depressive symptoms measured by HAMD at week 4 (16.89 ± 5.74 vs 25.58 ± 7.03, P < .001), week 8 (9.59 ± 5.13 vs 25.04 ± 7.49, P < .001) and week 12 (11.07 ± 6.85 vs 27.25 ± 7.14, P < .001). The significant differences in urinary specific metabolites before and after EA treatment were malonic acid (fatty acid biosynthesis), cysteine (glutamate metabolism), glutathione (glutamate metabolism), tryptophan (tryptophan metabolism), proline (glutamate metabolism), and N-acetyl-5-hydroxytryptamine. These metabolites are involved in tryptophan metabolism, glutamate metabolism, and fatty acid biosynthesis.

CONCLUSION

EA treatment combined with antidepressants is more effective in improving depressive symptoms than antidepressants alone. EA may treat depression by acting on tryptophan metabolism, glutamate metabolism, and fatty acid biosynthesis.

TRIAL REGISTRATION

Chinese Clinical Trial Registry: ChiCTR-2000030786.

Overview of Brain-to-Gut Axis Exposed to Chronic CNS Bacterial Infection(s) and a Predictive Urinary Metabolic Profile of a Brain Infected by Mycobacterium tuberculosis

A new paradigm in neuroscience has recently emerged - the brain-gut axis (BGA). The contemporary focus in this paradigm has been gut → brain ("bottom-up"), in which the gut-microbiome, and its perturbations, affects one's psychological state-of-mind and behavior, and is pivotal in neurodegenerative disorders. The emerging brain → gut ("top-down") concept, the subject of this review, proposes that dysfunctional brain health can alter the gut-microbiome. Feedback of this alternative bidirectional highway subsequently aggravates the neurological pathology. This paradigm shift, however, focuses upon non-communicable neurological diseases (progressive neuroinflammation). What of infectious diseases, in which pathogenic bacteria penetrate the blood-brain barrier and interact with the brain, and what is this effect on the BGA in bacterial infection(s) that cause chronic neuroinflammation? Persistent immune activity in the CNS due to chronic neuroinflammation can lead to irreversible neurodegeneration and neuronal death. The properties of cerebrospinal fluid (CSF), such as immunological markers, are used to diagnose brain disorders. But what of metabolic markers for such purposes? If a BGA exists, then chronic CNS bacterial infection(s) should theoretically be reflected in the urine. The premise here is that chronic CNS bacterial infection(s) will affect the gut-microbiome and that perturbed metabolism in both the CNS and gut will release metabolites into the blood that are filtered (kidneys) and excreted in the urine. Here we assess the literature on the effects of chronic neuroinflammatory diseases on the gut-microbiome caused by bacterial infection(s) of the CNS, in the context of information attained via metabolomics-based studies of urine. Furthermore, we take a severe chronic neuroinflammatory infectious disease - tuberculous meningitis (TBM), caused by Mycobacterium tuberculosis, and examine three previously validated CSF immunological biomarkers - vascular endothelial growth factor, interferon-gamma and myeloperoxidase - in terms of the expected changes in normal brain metabolism. We then model the downstream metabolic effects expected, predicting pivotal altered metabolic pathways that would be reflected in the urinary profiles of TBM subjects. Our cascading metabolic model should be adjustable to account for other types of CNS bacterial infection(s) associated with chronic neuroinflammation, typically prevalent, and difficult to distinguish from TBM, in the resource-constrained settings of poor communities.

Urinary metabolite signature in bipolar disorder patients during depressive episode

The first few episodes of bipolar disorder (BD) are highly likely to be depressive. This phenomenon causes many BD patients to be misdiagnosed as having major depression. Therefore, it is very important to correctly diagnose BD patients during depressive episode. Here, we conducted this study to identify potential biomarkers for young and middle-aged BD patients during depressive episode. Both gas chromatography-mass spectroscopy (GC-MS) and nuclear magnetic resonance (NMR) spectroscopy were used to profile the urine samples from the recruited subjects. In total, 13 differential metabolites responsible for the discrimination between healthy controls (HCs) and patients were identified. Most differential metabolites had a close relationship with energy homeostasis. Meanwhile, a panel consisting of five differential metabolites was identified. This panel could effectively distinguish the patients from HCs with an AUC of 0.998 in the training set and 0.974 in the testing set. Our findings on one hand could be helpful in developing an objective diagnostic method for young and middle-aged BD patients during depressive episode; on the other hand could provide critical insight into the pathological mechanism of BD and the biological mechanisms responsible for the transformation of different episodes.

Dual Metabolomic Platforms Identified a Novel Urinary Metabolite Signature for Hepatitis B Virus-Infected Patients with Depression

OBJECTIVE

Depression could make the treatment outcome worse. However, up to now, no objective methods were developed to diagnose depression in hepatitis B virus (HBV)-infected patients. Therefore, the dual metabolomic platforms were used here to identify potential biomarkers for diagnosing HBV-infected patients with depression (dHB).

METHODS

Both gas chromatography-mass spectrometry-based and nuclear magnetic resonance-based metabolomic platforms were used to conduct urine metabolic profiling of dHB subjects and HBV-infected patients without depression (HB). Orthogonal partial least-squares discriminant analysis was used to identify the differential metabolites between dHB subjects and HB subjects, and the step-wise logistic regression analysis was used to identify potential biomarkers.

RESULTS

In total, 21 important metabolites responsible for distinguishing dHB subjects from HB subjects were identified. Meanwhile, seven potential biomarkers (α-ydroxyisobutyric acid, hippuric acid, azelaic acid, isobutyric acid, malonic acid, levulinic acid, and phenylacetylglycine) were viewed as potential biomarkers. The simplified biomarker panel consisting of these seven metabolites had an excellent diagnostic performance in discriminating dHB subjects from HB subjects. Moreover, this panel could yield a higher accuracy in separating dHB subjects from HB subjects than our previous panels (identified by single metabolomic platform) did.

CONCLUSION

These results suggested that the dual metabolomic platforms could yield a better urinary biomarker panel for dHB subjects than any single metabolomic platform did, and our results could be helpful for developing an objective method in future to diagnose depression in HBV-infected patients.

Metabolic Profiling in Bipolar Disorder Patients During Depressive Episodes

Bipolar disorder (BD) is a common and debilitating mental disorder. Bipolar depression is the main episode of BD. Furthermore, there are no objective biomarkers available for diagnosing the disorder. In this research, a Nuclear Magnetic Resonance (NMR) spectroscopy based on a metabonomics technique was used to analyze serum samples from 37 patients with bipolar depression and 48 healthy control participants to determine potential biomarkers for bipolar depression. In total, seven different metabolites were identified that could effectively distinguish patients from healthy controls. The metabolites indicated that disturbances of amino acid and energy metabolisms might be involved in the pathogenesis of BD. Finally, a panel consisting of four potential biomarkers (lactate, trimethylamine oxide, N-acetyl glycoprotein, and α-glucose) was identified, which showed a higher combined diagnostic ability with an area under the curve of 0.893. Our findings may contribute to the development of an objective method for diagnosing bipolar depression.

Age-specific urinary metabolite signatures and functions in patients with major depressive disorder

Major depressive disorder (MDD) patients in different age ranges might have different urinary metabolic phenotypes, because age could significantly affect the physiological and psychological status of person. Therefore, it was very important to take age into consideration when studying MDD. Here, a dual platform metabolomic approach was performed to profile urine samples from young and middle-aged MDD patients. In total, 18 and 15 differential metabolites that separately discriminated young and middle-aged MDD patients, respectively, from their respective HC were identified. Only ten metabolites were significantly disturbed in both young and middle-aged MDD patients. Meanwhile, two different biomarker panels for diagnosing young and middle-aged MDD patients, respectively, were identified. Additionally, the TCA cycle was significantly affected in both young and middle-aged MDD patients, but the Glyoxylate and dicarboxylate metabolism and phenylalanine metabolism were only significantly affected in young and middle-aged MDD patients, respectively. Our results would be helpful for developing age-specific diagnostic method for MDD and further investigating the pathogenesis of this disease.

Urinary biomarkers for diagnosing poststroke depression in patients with type 2 diabetes mellitus

BACKGROUND

Depression can seriously affect the quality of life of type 2 diabetes mellitus (T2DM) patients after stroke. However, there were still no objective methods to diagnose T2DM patients with poststroke depression (PSD). Therefore, we conducted this study to deal with this problem.

METHODS

Gas chromatography-mass spectroscopy (GC-MS)-based metabolomics profiling method was used to profile the urinary metabolites from 83 nondepressed T2DM patients after stroke and 101 T2DM patients with PSD. The orthogonal partial least-squares discriminant analysis was conducted to explore the metabolic differences in T2DM patients with PSD. The logistic regression analysis was performed to identify the optimal and simplified biomarker panel for diagnosing T2DM patients with PSD. The receiver operating characteristic curve analysis was used to assess the diagnostic performance of this biomarker panel.

RESULTS

In total, 23 differential metabolites (7 decreased and 16 increased in T2DM patients with PSD) were found. A panel consisting of pseudouridine, malic acid, hypoxanthine, 3,4-dihydroxybutyric acid, fructose and inositol was identified. This panel could effectively separate T2DM patients with PSD from nondepressed T2DM patients after stroke. The area under the curve was 0.965 in the training set and 0.909 in the validation set. Meanwhile, we found that the galactose metabolism was significantly affected in T2DM patients with PSD.

CONCLUSION

Our results could be helpful for future development of an objective method to diagnose T2DM patients with PSD and provide novel ideas to study the pathogenesis of depression.

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.

Impact of phytosterols on liver and distal colon metabolome in experimental murine colitis model: an explorative study

Phytosterols are known to reduce plasma cholesterol levels and thereby reduce cardiovascular risk. Studies conducted on human and animal models have demonstrated that these compounds have also anti-inflammatory effects. Recently, an experimental colitis model (dextran sulphate sodium-induced) has shown that pre-treatment with phytosterols decreases infiltration of inflammatory cells and accelerates mucosal healing. This study aims to understand the mechanism underlying the colitis by analysing the end-products of the metabolism in distal colon and liver excised from the same mice used in the previous work. In particular, an unsupervised gas chromatography-mass spectrometry (GC-MS) and NMR based metabolomics approach was employed to identify the metabolic pathways perturbed by the dextran sodium sulphate (DSS) insult (i.e. Krebs cycle, carbohydrate, amino acids, and nucleotide metabolism). Interestingly, phytosterols were able to restore the homeostatic equilibrium of the hepatic and colonic metabolome.

Comparison of the Proximate Composition, Vitamins (Ascorbic Acid, α-Tocopherol and Retinol), Anti-Nutrients (Phytate and Oxalate) and the GC-MS Analysis of the Essential Oil of the Root and Leaf of Rumex crispus L

Medicinal plants are a pertinent and effective remedy, employed in indigenous healthcare systems by traditional healers. This study focused on proximate parameters, minerals, vitamins, anti-nutrients and essential oil of the root and leaf of the medicinal plant; R. crispus, using the standard food analysis techniques. The result reveals that the moisture content of the leaf (7.57 ± 0.40%) and root (7.59 ± 0.08%) was not significantly different. The leaf has a higher ash, crude fat, fibre and mineral content than the root, except the carbohydrate (57.74 ± 3.06%) and Ca (1190.0 ± 0 mg/100g) values which are quite higher in the root. Traces of phytate was found in the leaf (1.15 ± 0.74%) and root (1.38 ± 0.27%) of R. crispus. The highest value of retinol, ascorbic acid and α-tocopherol was found in dried leaf (1.29 ± 0.014 mg retinol/100g), fresh leaf (159.73 ± 26.77 mg ascorbic acid/100g) and fresh root (54.90 ± 0.39 mg α-tocopherol/100g) respectively. The principal compound in the essential oil of the leaf are; 5-Eicosene, (E)-, docos-1-ene, trans-5-Octadecene, tetradecane while those found in the root are; 1-Heptacosanol, 4-Methyloctane, ethylcyclohexane, eucalyptol, m-Xylene, octadecane, phytol, and tetradecane. The research reveals that R. crispus may not only be used for medicinal purposes but could also be suitable for a complementary diet.

Integrated Proteomics and Metabolomic Analyses of Plasma Injury Biomarkers in a Serious Brain Trauma Model in Rats

Diffuse axonal injury (DAI) is a prevalent and serious brain injury with significant morbidity and disability. However, the underlying pathogenesis of DAI remains largely unclear, and there are still no objective laboratory-based tests available for clinicians to make an early diagnosis of DAI. An integrated analysis of metabolomic data and proteomic data may be useful to identify all of the molecular mechanisms of DAI and novel potential biomarkers. Therefore, we established a rat model of DAI, and applied an integrated UPLC-Q-TOF/MS-based metabolomics and isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis to obtain unbiased profiling data. Differential analysis identified 34 metabolites and 43 proteins in rat plasma of the injury group. Two metabolites (acetone and 4-Hydroxybenzaldehyde) and two proteins (Alpha-1-antiproteinase and Alpha-1-acid glycoprotein) were identified as potential biomarkers for DAI, and all may play important roles in the pathogenesis of DAI. Our study demonstrated the feasibility of integrated metabolomics and proteomics method to uncover the underlying molecular mechanisms of DAI, and may help provide clinicians with some novel diagnostic biomarkers and therapeutic targets.

Metabolomic Biomarkers in Mental Disorders: Bipolar Disorder and Schizophrenia

Psychiatric disorders are some of the most impairing human diseases. Among them, bipolar disorder and schizophrenia are the most common. Both have complicated diagnostics due to their phenotypic, biological, and genetic heterogeneity, unknown etiology, and the underlying biological pathways, and molecular mechanisms are still not completely understood. Given the multifactorial complexity of these disorders, identification and implementation of metabolic biomarkers would assist in their early detection and diagnosis and facilitate disease monitoring and treatment responses. To date, numerous studies have utilized metabolomics to better understand psychiatric disorders, and findings from these studies have begun to converge. In this chapter, we briefly describe some of the metabolomic biomarkers found in these two disorders.

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.

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.

Predictive Role of Urinary Metabolic Profile for Abnormal MRI Score in Preterm Neonates

Background and Objective

Early identification of neonates at risk for brain injury is important to start appropriate intervention. Urinary metabolomics is a source of potential, noninvasive biomarkers of brain disease. We studied the urinary metabolic profile at 2 and 10 days in preterm neonates with normal/mild and moderate/severe MRI abnormalities at term equivalent age.

Methods

Urine samples were collected at two and 10 days after birth in 30 extremely preterm infants and analyzed using proton magnetic resonance spectroscopy. A 3 T MRI was performed at term equivalent age, and images were scored for white matter (WM), cortical grey matter (cGM), deep GM, and cerebellar abnormalities. Infants were divided in two groups: normal/mild and moderately/severely abnormal MRI scores.

Results

No significant clustering was seen between normal/mild and moderate/severe MRI scores for all regions at both time points. The ROC curves distinguished neonates at 2 and 10 days who later developed a markedly less mature cGM score from the others (2 d: area under the curve (AUC) = 0.72, specificity (SP) = 65%, sensitivity (SE) = 75% and 10 d: AUC = 0.80, SP = 78%, SE = 80%) and a moderately to severely abnormal WM score (2 d: AUC = 0.71, specificity (SP) = 80%, sensitivity (SE) = 72% and 10 d: AUC = 0.69, SP = 64%, SE = 89%).

Conclusions

Early urinary spectra of preterm infants were able to discriminate metabolic profiles in patients with moderately/severely abnormal cGM and WM scores at term equivalent age. Urine spectra are promising for early identification of neonates at risk of brain damage and allow understanding of the pathogenesis of altered brain development.

Urinary biomarker panel for diagnosing patients with depression and anxiety disorders

Available data indicate that patients with depression and anxiety disorders are likely to be at greater risk for suicide. Therefore, it is important to correctly diagnose patients with depression and anxiety disorders. However, there are still no empirical laboratory methods to objectively diagnose these patients. In this study, the multiple metabolomics platforms were used to profile the urine samples from 32 healthy controls and 32 patients with depression and anxiety disorders for identifying differential metabolites and potential biomarkers. Then, 16 healthy controls and 16 patients with depression and anxiety disorders were used to independently validate the diagnostic performance of the identified biomarkers. Finally, a panel consisting of four biomarkers-N-methylnicotinamide, aminomalonic acid, azelaic acid and hippuric acid-was identified. This panel was capable of distinguishing patients with depression and anxiety disorders from healthy controls with an area under the receiver operating characteristic curve of 0.977 in the training set and 0.934 in the testing set. Meanwhile, we found that these identified differential metabolites were mainly involved in three metabolic pathways and five molecular and cellular functions. Our results could lay the groundwork for future developing a urine-based diagnostic method for patients with depression and anxiety disorders.

Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study

Major depressive disorder (MDD) is a debilitating psychiatric illness. However, there is currently no objective laboratory-based diagnostic tests for this disorder. Although, perturbations in multiple neurotransmitter systems have been implicated in MDD, the biochemical changes underlying the disorder remain unclear, and a comprehensive global evaluation of neurotransmitters in MDD has not yet been performed. Here, using a GC-MS coupled with LC-MS/MS-based targeted metabolomics approach, we simultaneously quantified the levels of 19 plasma metabolites involved in GABAergic, catecholaminergic, and serotonergic neurotransmitter systems in 50 first-episode, antidepressant drug-naïve MDD subjects and 50 healthy controls to identify potential metabolite biomarkers for MDD (training set). Moreover, an independent sample cohort comprising 49 MDD patients, 30 bipolar disorder (BD) patients and 40 healthy controls (testing set) was further used to validate diagnostic generalizability and specificity of these candidate biomarkers. Among the 19 plasma neurotransmitter metabolites examined, nine were significantly changed in MDD subjects. These metabolites were mainly involved in GABAergic, catecholaminergic and serotonergic systems. The GABAergic and catecholaminergic had better diagnostic value than serotonergic pathway. A panel of four candidate plasma metabolite biomarkers (GABA, dopamine, tyramine, kynurenine) could distinguish MDD subjects from health controls with an AUC of 0.968 and 0.953 in the training and testing set, respectively. Furthermore, this panel distinguished MDD subjects from BD subjects with high accuracy. This study is the first to globally evaluate multiple neurotransmitters in MDD plasma. The altered plasma neurotransmitter metabolite profile has potential differential diagnostic value for MDD.

Metabolomics and Lipidomics of Ischemic Stroke

Ischemic stroke is a sudden loss of brain function due to the reduction of blood flow. Brain tissues cease to function with subsequent activation of the ischemic cascade. Metabolomics and lipidomics are modern disciplines that characterize the metabolites and lipid components of a biological system, respectively. Because the pathogenesis of ischemic stroke is heterogeneous and multifactorial, it is crucial to establish comprehensive metabolomic and lipidomic approaches to elucidate these alterations in this disease. Fortunately, metabolomic and lipidomic studies have the distinct advantages of identifying tissue/mechanism-specific biomarkers, predicting treatment and clinical outcome, and improving our understanding of the pathophysiologic basis of disease states. Therefore, recent applications of these analytical approaches in the early diagnosis of ischemic stroke were discussed. In addition, the emerging roles of metabolomics and lipidomics on ischemic stroke were summarized, in order to gain new insights into the mechanisms underlying ischemic stroke and in the search for novel metabolite biomarkers and their related pathways.

Analysis on speech signal features of manic patients

Given the lack of effective biological markers for early diagnosis of bipolar mania, and the tendency for voice fluctuation during transition between mood states, this study aimed to investigate the speech features of manic patients to identify a potential set of biomarkers for diagnosis of bipolar mania. 30 manic patients and 30 healthy controls were recruited and their corresponding speech features were collected during natural dialogue using the Automatic Voice Collecting System. Bech-Rafaelsdn Mania Rating Scale (BRMS) and Clinical impression rating scale (CGI) were used to assess illness. The speech features were compared between two groups: mood group (mania vs remission) and bipolar group (manic patients vs healthy individuals). We found that the characteristic speech signals differed between mood groups and bipolar groups. The fourth formant (F4) and Linear Prediction Coefficient (LPC) (P < .05) were significantly differed when patients transmitted from manic to remission state. The first formant (F1), the second formant (F2), and LPC (P < .05) also played key roles in distinguishing between patients and healthy individuals. In addition, there was a significantly correlation between LPC and BRMS, indicating that LPC may play an important role in diagnosis of bipolar mania. In this study we traced speech features of bipolar mania during natural dialogue (conversation), which is an accessible approach in clinic practice. Such specific indicators may respectively serve as promising biomarkers for benefiting the diagnosis and clinical therapeutic evaluation of bipolar mania.

Metabolomics of Major Depressive Disorder and Bipolar Disorder: Overview and Future Perspective

Major depressive disorder (MDD) and bipolar disorder (BD) are the most common mood disorders. They are etiologically related, but clinically distinct psychiatric illnesses. Their shared clinical features result in high rates of misdiagnosis due to a lack of biomarkers that allow their differentiation. BD is more frequently misdiagnosed as MDD because of overlapping symptomology, often later onset of mania, and frequent occurrence of depressive episodes in patients with BD. Misdiagnosis is also increased when patients with BD present symptoms indicative of a clinically significant depressive episode, but are premorbid for manic symptoms, or previous manic states not recognized. Therefore, the development of specific biomarkers for these disorders would be invaluable for establishing the correct diagnosis and treatment of MDD and BD. This chapter presents an overview and future perspective of the identification of biomarkers for mood disorders using metabolomics.

Metabolite identification in fecal microbiota transplantation mouse livers and combined proteomics with chronic unpredictive mild stress mouse livers

Major depressive disorder (MDD) is a common mood disorder. Gut microbiota may be involved in the pathogenesis of depression via the microbe-gut-brain axis. Liver is vulnerable to exposure of bacterial products translocated from the gut via the portal vein and may be involved in the axis. In this study, germ-free mice underwent fecal microbiota transplantation from MDD patients and healthy controls. Behavioral tests verified the depression model. Metabolomics using gas chromatography-mass spectrometry, nuclear magnetic resonance, and liquid chromatography-mass spectrometry determined the influence of microbes on liver metabolism. With multivariate statistical analysis, 191 metabolites were distinguishable in MDD mice from control (CON) mice. Compared with CON mice, MDD mice showed lower levels for 106 metabolites and higher levels for 85 metabolites. These metabolites are associated with lipid and energy metabolism and oxidative stress. Combined analyses of significantly changed proteins in livers from another depression model induced by chronic unpredictive mild stress returned a high score for the Lipid Metabolism, Free Radical Scavenging, and Molecule Transports network, and canonical pathways were involved in energy metabolism and tryptophan degradation. The two mouse models of depression suggest that changes in liver metabolism might be involved in the pathogenesis of MDD. Conjoint analyses of fecal, serum, liver, and hippocampal metabolites from fecal microbiota transplantation mice suggested that aminoacyl-tRNA biosynthesis significantly changed and fecal metabolites showed a close relationship with the liver. These findings may help determine the biological mechanisms of depression and provide evidence about "depression microbes" impacting on liver metabolism.

Recent trends on the role of epigenomics, metabolomics and noncoding RNAs in rationalizing mood stabilizing treatment

Mood stabilizers are the cornerstone in treatment of mood disorders, but their use is characterized by high interindividual variability. This feature has stimulated intensive research to identify predictive biomarkers of response and disentangle the molecular bases of their clinical efficacy. Nevertheless, findings from studies conducted so far have only explained a small proportion of the observed variability, suggesting that factors other than DNA variants could be involved. A growing body of research has been focusing on the role of epigenetics and metabolomics in response to mood stabilizers, especially lithium salts. Studies from these approaches have provided new insights into the molecular networks and processes involved in the mechanism of action of mood stabilizers, promoting a systems-level multiomics synergy. In this article, we reviewed the literature investigating the involvement of epigenetic mechanisms, noncoding RNAs and metabolomic modifications in bipolar disorder and the mechanism of action and clinical efficacy of mood stabilizers.

1H NMR-based Investigation of Metabolic Response to Electro-Acupuncture Stimulation

Acupuncture is a traditional Chinese medicine therapy that has been found useful for treating various diseases. The treatments involve the insertion of fine needles at acupoints along specific meridians (meridian specificity). This study aims to investigate the metabolic basis of meridian specificity using proton nuclear magnetic resonance (¹H NMR)-based metabolomics. Electro-acupuncture (EA) stimulations were performed at acupoints of either Stomach Meridian of Foot-Yangming (SMFY) or Gallbladder Meridian of Foot-Shaoyang (GMFS) in healthy male Sprague Dawley (SD) rats. ¹H-NMR spectra datasets of serum, urine, cortex, and stomach tissue extracts from the rats were analysed by multivariate statistical analysis to investigate metabolic perturbations due to EA treatments at different meridians. EA treatment on either the SMFY or GMFS acupoints induced significant variations in 31 metabolites, e.g., amino acids, organic acids, choline esters and glucose. Moreover, a few meridian-specific metabolic changes were found for EA stimulations on the SMFY or GMFS acupoints. Our study demonstrated significant metabolic differences in response to EA stimulations on acupoints of SMFY and GMFS meridians. These results validate the hypothesis that meridian specificity in acupuncture is detectable in the metabolome and demonstrate the feasibility and effectiveness of a metabolomics approach in understanding the mechanism of acupuncture.

A Novel Urinary Metabolite Signature for Non-invasive Post-stroke Depression Diagnosis

Post-stroke depression (PSD) is the most common psychiatric complication in stroke survivors that has been associated with increased physical disability, distress, poor rehabilitation, and suicidal ideation. However, there are still no biomarkers available to support objective laboratory testing for this disorder. Here, a GC-MS-based urinary metabolomics approach was used to characterize the urinary metabolic profiling of PSD (stroke) subjects and non-PSD (health controls) subjects in order to identify and validate urinary metabolite biomarkers for PSD. Six metabolites, azelaic acid, glyceric acid, pseudouridine, 5-hydroxyhexanoic acid, tyrosine, and phenylalanine, were defined as biomarkers. A combined panel of these six urinary metabolites could effectively discriminate between PSD subjects and non-PSD subjects, achieving an area under the receiver-operating characteristic curve (AUC) of 0.961 in a training set (n = 72 PSD subjects and n = 146 non-PSD subjects). Moreover, this urinary biomarker panel was capable of discriminating blinded test samples (n = 58 PSD patients and n = 109 non-PSD subjects) with an AUC of 0.954. These findings suggest that a urine-based laboratory test using these biomarkers may be useful in the diagnosis of PSD.

Brain region-specific metabolite networks regulate antidepressant effects of venlafaxine

Administration of venlafaxine significantly altered the metabolic profiles of both the hippocampus and prefrontal cortex and the altered metabolites had significant brain region specificities.

Novel urinary metabolite signature for diagnosing postpartum depression

BACKGROUND

Postpartum depression (PPD) could affect ~10% of women and impair the quality of mother-infant interactions. Currently, there are no objective methods to diagnose PPD. Therefore, this study was conducted to identify potential biomarkers for diagnosing PPD.

MATERIALS AND METHODS

Morning urine samples of PPD subjects, postpartum women without depression (PPWD) and healthy controls (HCs) were collected. The gas chromatography-mass spectroscopy (GC-MS)-based urinary metabolomic approach was performed to characterize the urinary metabolic profiling. The orthogonal partial least-squares-discriminant analysis (OPLS-DA) was used to identify the differential metabolites. The logistic regression analysis and Bayesian information criterion rule were further used to identify the potential biomarker panel. The receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of the identified potential biomarker panel.

RESULTS

Totally, 73 PPD subjects, 73 PPWD and 74 HCs were included, and 68 metabolites were identified using GC-MS. The OPLS-DA model showed that there were 22 differential metabolites (14 upregulated and 8 downregulated) responsible for separating PPD subjects from HCs and PPWD. Meanwhile, a panel of five potential biomarkers - formate, succinate, 1-methylhistidine, α-glucose and dimethylamine - was identified. This panel could effectively distinguish PPD subjects from HCs and PPWD with an area under the curve (AUC) curve of 0.948 in the training set and 0.944 in the testing set.

CONCLUSION

These results demonstrated that the potential biomarker panel could aid in the future development of an objective diagnostic method for PPD.

Omega-3 Fatty acids as Monotherapy in Treating Depression in Pregnant Women: a Meta- Analysis of Randomized Controlled Trials

Previous studies have reported inconsistent findings regarding the efficacy of omega-3 fatty acids on pregnant women with major depressive disorder (MDD). This meta-analysis was conducted to systematically evaluate the clinical applicability of omega-3 fatty acids in treating depression in pregnant women. Randomized controlled trials (RCTs) that compared omega-3 fatty acids to placebo for short-course treatment of depression in pregnant women were systematically reviewed between March 1999 and April 2015. The search terms used were 'depression', 'omega-3 fatty acids', 'fish oil', 'eicosapentaenoic acid' and 'docosahexaenoic acid'. Standardized difference in means of depression scale was used as the main outcome. Random effect model was used. The effects of baseline depression scores were studying by meta-regression analysis. patients received omega-3 fatty acids. The pooled standardized difference in means was 0.75 with 95% CI= (0.47, 1.04). The baseline depression scores had no effect on the efficacy. None of the recruited patients was withdrawn.

Efficacy of ketamine in the rapid treatment of major depressive disorder: a meta-analysis of randomized, double-blind, placebo-controlled studies

BACKGROUND

An increasing number of studies are reporting that ketamine could be treated as a novel antidepressant for major depressive disorder (MDD). Therefore, we performed this meta-analysis to comprehensively and systematically assess the efficacy of ketamine for treating patients with MDD.

METHOD

Randomized, double-blind, placebo-controlled studies on ketamine versus placebo for treating MDD were searched up to April 2016 in medical databases (PubMed, CCTR, Web of Science, Embase, CBM-disc, and CNKI). Three treatment time points (24 and 72 h, and day 7) were chosen. Response and remission rates were the main outcomes. The random effects model was used. An intention-to-treat analysis was conducted.

RESULTS

Nine high-quality studies that included 368 patients were selected to compare the efficacy of ketamine to placebo. The therapeutic effects of ketamine at 24 and 72 h, and day 7 were found to be significantly better than placebo. Response and remission rates in the ketamine group at 24 and 72 h, and day 7 were 52.2% and 20.6%; 47.9% and 23.8%; and 39.8% and 26.2%, respectively. No significant heterogeneity existed, and the Egger's test showed no publication bias.

CONCLUSION

These results indicated that ketamine could yield a good efficacy in the rapid treatment of MDD. Future large-scale clinical studies are needed to confirm our results and investigate the mid- and long-term efficacy of ketamine in treating MDD.

Identification of sex-specific urinary biomarkers for major depressive disorder by combined application of NMR- and GC-MS-based metabonomics

Women are more vulnerable to major depressive disorder (MDD) than men. However, molecular biomarkers of sex differences are limited. Here we combined gas chromatography-mass spectrometry (GC-MS)- and nuclear magnetic resonance (NMR)-based metabonomics to investigate sex differences of urinary metabolite markers in MDD, and further explore their potential of diagnosing MDD. Consequently, the metabolite signatures of women and men MDD subjects were significantly different from of that in their respective healthy controls (HCs). Twenty seven women and 36 men related differentially expressed metabolites were identified in MDD. Fourteen metabolites were changed in both women and men MDD subjects. Significantly, the women-specific (m-Hydroxyphenylacetate, malonate, glycolate, hypoxanthine, isobutyrate and azelaic acid) and men-specific (tyrosine, N-acetyl-d-glucosamine, N-methylnicotinamide, indoxyl sulfate, citrate and succinate) marker panels were further identified, which could differentiate men and women MDD patients from their respective HCs with higher accuracy than previously reported sex-nonspecific marker panels. Our findings demonstrate that men and women MDD patients have distinct metabonomic signatures and sex-specific biomarkers have promising values in diagnosing MDD.

Discriminating poststroke depression from stroke by nuclear magnetic resonance spectroscopy-based metabonomic analysis

Poststroke depression (PSD), the most common psychiatric disease that stroke survivors face, is estimated to affect ~30% of poststroke patients. However, there are still no objective methods to diagnose PSD. In this study, to explore the differential metabolites in the urine of PSD subjects and to identify a potential biomarker panel for PSD diagnosis, the nuclear magnetic resonance-based metabonomic method was applied. Ten differential metabolites responsible for discriminating PSD subjects from healthy control (HC) and stroke subjects were found, and five of these metabolites were identified as potential biomarkers (lactate, α-hydroxybutyrate, phenylalanine, formate, and arabinitol). The panel consisting of these five metabolites provided excellent performance in discriminating PSD subjects from HC and stroke subjects, achieving an area under the receiver operating characteristic curve of 0.946 in the training set (43 HC, 45 stroke, and 62 PSD subjects). Moreover, this panel could classify the blinded samples from the test set (31 HC, 33 stroke, and 32 PSD subjects) with an area under the curve of 0.946. These results laid a foundation for the future development of urine-based objective methods for PSD diagnosis and investigation of PSD pathogenesis.

Perturbational Profiling of Metabolites in Patient Fibroblasts Implicates α-Aminoadipate as a Potential Biomarker for Bipolar Disorder

Many studies suggest the presence of aberrations in cellular metabolism in bipolar disorder. We studied the metabolome in bipolar disorder to gain insight into cellular pathways that may be dysregulated in bipolar disorder and to discover evidence of novel biomarkers. We measured polar and nonpolar metabolites in fibroblasts from subjects with bipolar I disorder and matched healthy control subjects, under normal conditions and with two physiologic perturbations: low-glucose media and exposure to the stress-mediating hormone dexamethasone. Metabolites that were significantly different between bipolar and control subjects showed distinct separation by principal components analysis methods. The most statistically significant findings were observed in the perturbation experiments. The metabolite with the lowest p value in both the low-glucose and dexamethasone experiments was α-aminoadipate, whose intracellular level was consistently lower in bipolar subjects. Our study implicates α-aminoadipate as a possible biomarker in bipolar disorder that manifests under cellular stress. This is an intriguing finding given the known role of α-aminoadipate in the modulation of kynurenic acid in the brain, especially as abnormal kynurenic acid levels have been implicated in bipolar disorder.