Sex-specific urinary biomarkers for diagnosing bipolar disorder

Non-Targeted Metabolomics Investigation of a Sub-Chronic Variable Stress Model Unveils Sex-Dependent Metabolic Differences Induced by Stress

Depression is twice as prevalent in women as in men, however, most preclinical studies of depression have used male rodent models. This study aimed to examine how stress affects metabolic profiles depending on sex using a rodent depression model: sub-chronic variable stress (SCVS). The SCVS model of male and female mice was established in discovery and validation sets. The stress-induced behavioral phenotypic changes were similar in both sexes, however, the metabolic profiles of female plasma and brain became substantially different after stress, whereas those of males did not. Four stress-differential plasma metabolites-β-hydroxybutyric acid (BHB), L-serine, glycerol, and myo-inositol-could yield biomarker panels with excellent performance to discern the stressed individuals only for females. Disturbances in BHB, glucose, 1,5-anhydrosorbitol, lactic acid, and several fatty acids in the plasma of stressed females implied a systemic metabolic shift to β-oxidation in females. The plasma levels of BHB and corticosterone only in stressed females were observed not only in SCVS but also in an acute stress model. These results collectively suggest a sex difference in the metabolic responses by stress, possibly involving the energy metabolism shift to β-oxidation and the HPA axis dysregulation in females.

Sex differences in bipolar disorder: The dorsolateral prefrontal cortex as an etiopathogenic region

Bipolar disorder (BD) is worldwide a prevalent mental illness and a leading risk factor for suicide. Over the past three decades, it has been discovered that sex differences exist throughout the entire panorama of BD, but the etiologic regions and mechanisms that generate such differences remain poorly characterized. Available evidence indicates that the dorsolateral prefrontal cortex (DLPFC), a critical region that controls higher-order cognitive processing and mood, exhibits biological disparities between male and female patients with psychiatric disorders, which are highly correlated with the co-occurrence of psychotic symptoms. This review addresses the sex differences in BD concerning epidemiology, cognitive impairments, clinical manifestations, neuroimaging, and laboratory abnormalities. It also provides strong evidence linking DLPFC to the etiopathogenesis of these sex differences. We emphasize the importance of identifying gene signatures using human brain transcriptomics, which can depict sexually different variations, explain sex-biased symptomatic features, and provide novel targets for sex-specific therapeutics.

Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis

INTRODUCTION

Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities.

OBJECTIVES

This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease.

METHODS

Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites.

RESULTS

Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914).

CONCLUSION

From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.

Gestational Diabetes Mellitus Changes the Metabolomes of Human Colostrum, Transition Milk and Mature Milk

Background

Gestational diabetes mellitus (GDM) is a pregnancy complication that is diagnosed by the novel onset of abnormal glucose intolerance. Our study aimed to investigate the changes in human breast milk metabolome over the first month of lactation and how GDM affects milk metabolome.

Material/Methods

Colostrum, transition milk, and mature milk samples from women with normal uncomplicated pregnancies (n=94) and women with GDM-complicated pregnancies (n=90) were subjected to metabolomic profiling by the use of gas chromatography-mass spectrometry (GC-MS).

Results

For the uncomplicated pregnancies, there were 59 metabolites that significantly differed among colostrum, transition milk, and mature milk samples, while 58 metabolites differed in colostrum, transition milk, and mature milk samples from the GDM pregnancies. There were 28 metabolites that were found to be significantly different between women with normal pregnancies and women with GDM pregnancies among colostrum, transition milk, and mature milk samples.

Conclusions

The metabolic profile of human milk is dynamic throughout the first months of lactation. High levels of amino acids in colostrum and high levels of saturated fatty acids and unsaturated fatty acids in mature milk, which may be critical for neonatal development in the first month of life, were features of both normal and GDM pregnancies.

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.

Significant increasing of DISC2 long non-coding RNA expression as a potential biomarker in bipolar disorder

Bipolar disorder (BD) is a mental disorder that is often misdiagnosed with ineffective treatment. It has strong genetic component but unknown pathophysiology. Long non-coding RNAs (lncRNAs) have been recently recognized as one of the important genetic factors and are considered as one of the regulatory mechanisms of nervous system. Given that lncRNAs may be diagnostic biomarkers for BD, we aimed to quantify the levels of DISC1 and DISC2 lncRNA transcripts. The levels of DISC1 and DISC2 lncRNA were tested in peripheral blood mononuclear cells (PBMCs) of 50 BD and 50 controls by real-time PCR. In addition, we performed ROC curve analysis as well as correlation analysis between the gene expression and some clinical features of BD cases. Computational analysis of miRNAs binding sites and CpG Islands on DISC1 and DISC2 lncRNA was performed as well. Significant down-regulation of DISC1 and up-regulation of DISC2 were observed in BD cases compared with controls. The areas under the ROC curve (AUC) for DISC1 and DISC2 lncRNA were 0.76 and 0.68 respectively. There was no significant correlation between the levels of mRNA expression in PBMCs of BD patients and clinical features. These data demonstrated that DISC1 and DISC2 lncRNA expression was potentially associated with an increased risk of bipolar disorder and might involve several molecular mechanisms. Our results revealed that the transcript levels of DISC1 and DISC2 lncRNA could be considered as a good putative biomarker for individuals with bipolar disorder.

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.

DNA redox modulations and global DNA methylation in bipolar disorder: Effects of sex, smoking and illness state

DNA redox modulations and methylation have been associated with bipolar disorder (BD) pathophysiology. We aimed to investigate DNA redox modulation and global DNA methylation and demethylation levels in patients with BD during euthymia, mania or depression in comparison to non-psychiatric controls. The roles of sex and smoking as susceptibility factors for DNA redox modulations and global DNA methylation and demethylation were also explored. Levels of 5-methylcytosine (5-mC), 5-hydroxymethylcytosine (5-hmC) and 8-hydroxy-2'-deoxyguanosine (8-OHdG) were assessed in DNA samples of 75 patients with DSM-IV BD type I (37 euthymic, 18 manic, 20 depressive) in comparison to 60 non-psychiatric controls. Levels of 5-mC and 5-hmC were assessed using Dot Blot as a screening process, and verified using ELISA. Levels of 8-OHdG were assessed using ELISA. The levels of 8-OHdG significantly differed among non-psychiatric control, euthymia, mania and depression groups [F (3,110) = 2.771, p = 0.046], whereas there were no alterations in the levels of 5-hmC and 5-mC. Linear regression analyses revealed the significant effects of smoking (p = 0.031) and sex (p = 0.012) as well as state of illness on the levels of 8-OHdG (p = 0.025) in patients with BD. Our results suggest that levels of 8-OHdG may be affected by sex, illness states and smoking in BD.

Sex differences in gut microbiota in patients with major depressive disorder

OBJECTIVE

Our previous studies found that disturbances in gut microbiota might have a causative role in the onset of major depressive disorder (MDD). The aim of this study was to investigate whether there were sex differences in gut microbiota in patients with MDD.

PATIENTS AND METHODS

First-episode drug-naïve MDD patients and healthy controls were included. 16S rRNA gene sequences extracted from the fecal samples of the included subjects were analyzed. Principal-coordinate analysis and partial least squares-discriminant analysis were used to assess whether there were sex-specific gut microbiota. A random forest algorithm was used to identify the differential operational taxonomic units. Linear discriminant-analysis effect size was further used to identify the dominant sex-specific phylotypes responsible for the differences between MDD patients and healthy controls.

RESULTS

In total, 57 and 74 differential operational taxonomic units responsible for separating female and male MDD patients from their healthy counterparts were identified. Compared with their healthy counterparts, increased Actinobacteria and decreased Bacteroidetes levels were found in female and male MDD patients, respectively. The most differentially abundant bacterial taxa in female and male MDD patients belonged to phyla Actinobacteria and Bacteroidia, respectively. Meanwhile, female and male MDD patients had different dominant phylotypes.

CONCLUSION

These results demonstrated that there were sex differences in gut microbiota in patients with MDD. The suitability of Actinobacteria and Bacteroidia as the sex-specific biomarkers for diagnosing MDD should be further explored.

Differential urinary metabolites related with the severity of major depressive disorder

Major depressive disorder (MDD) is a common mental disorder that affects a person's general health. However, there is still no objective laboratory test for diagnosing MDD. Here, an integrated analysis of data from our previous studies was performed to identify the differential metabolites in the urine of moderate and severe MDD patients. A dual platform approach (NMR spectroscopy and GC-MS) was used. Consequently, 14 and 22 differential metabolites responsible for separating moderate and severe MDD patients, respectively, from their respective healthy controls (HCs) were identified. Meanwhile, the moderate MDD-specific panel (N-Methylnicotinamide, Acetone, Choline, Citrate, vanillic acid and azelaic acid) and severe MDD-specific panel (indoxyl sulphate, Taurine, Citrate, 3-hydroxyphenylacetic acid, palmitic acid and Lactate) could discriminate moderate and severe MDD patients, respectively, from their respective HCs with high accuracy. Moreover, the differential metabolites in severe MDD were significantly involved in three metabolic pathways and some biofunctions. These results showed that there were divergent urinary metabolic phenotypes in moderate and severe MDD patients, and the identified potential urinary biomarkers might be useful for future developing objective diagnostic tests for MDD diagnosis. Our results could also be helpful for researchers to study the pathogenesis of MDD.

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.

Efficacy and acceptability of oxcarbazepine vs. carbamazepine with betahistine mesilate tablets in treating vestibular paroxysmia: a retrospective review

OBJECTIVES

Vestibular paroxysmia (VP) is a rare episodic peripheral vestibular disorder that can cause acute short attacks of vertigo. This study aimed to compare the efficacy and acceptability of carbamazepine (CBZ), CBZ plus betahistine mesilate tablets (BMT) and oxcarbazepine (OXC) plus BMT in treating VP within 12 weeks.

METHODS

A retrospective analysis of data from 196 VP patients treated in our hospital was conducted. There were 73 patients receiving CBZ, 65 patients receiving CBZ+BMT and 58 patients receiving OXC+BMT. The frequency of vertigo, vertigo duration, vertigo score, response rate (RR) and side effects were compared between groups to assess efficacy and acceptability at the end of 12(th) week.

RESULTS

After 12 weeks' treatment, the CBZ+BMT group had a greater reduction in the frequency of vertigo, vertigo duration and vertigo score than the other two groups. The RR was highest in the CBZ+BMT group, second in the OXC+BMT group and lowest in the CBZ group. The incidence of side-effects was highest in the CBZ group, second in the CBZ+BMT group and lowest in the OXC+BMT group. Two patients in the CBZ group were withdrawn.

CONCLUSION

These results indicated that using BMT as an augmentation for CBZ or OXC might be a good choice in treating VP.

Urinary Biomarkers of Brain Diseases

Biomarkers are the measurable changes associated with a physiological or pathophysiological process. Unlike blood, urine is not subject to homeostatic mechanisms. Therefore, greater fluctuations could occur in urine than in blood, better reflecting the changes in human body. The roadmap of urine biomarker era was proposed. Although urine analysis has been attempted for clinical diagnosis, and urine has been monitored during the progression of many diseases, particularly urinary system diseases, whether urine can reflect brain disease status remains uncertain. As some biomarkers of brain diseases can be detected in the body fluids such as cerebrospinal fluid and blood, there is a possibility that urine also contain biomarkers of brain diseases. This review summarizes the clues of brain diseases reflected in the urine proteome and metabolome.

Deep brain stimulation of pallidal versus subthalamic for patients with Parkinson's disease: a meta-analysis of controlled clinical trials

BACKGROUND

Parkinson's disease (PD) is a common neurodegenerative disorder that affects many people every year. Deep brain stimulation (DBS) is an effective nonpharmacological method to treat PD motor symptoms. This meta-analysis was conducted to evaluate the efficacy of subthalamic nucleus (STN)-DBS versus globus pallidus internus (GPi)-DBS in treating advanced PD.

METHODS

Controlled clinical trials that compared STN-DBS to GPi-DBS for short-term treatment of PD in adults were researched up to November 2015. The primary outcomes were the Unified Parkinson's Disease Rating Scale Section (UPDRS) III score and the levodopa-equivalent dosage (LED) after DBS. The secondary outcomes were the UPDRS II score and the Beck Depression Inventory (BDI) score.

RESULTS

Totally, 13 studies containing 1,148 PD patients were included in this meta-analysis to compare STN-DBS versus GPi-DBS. During the off-medication state, the pooled weighted mean difference (WMD) of UPDRS III and II scores were -2.18 (95% CI =-5.11 to 0.74) and -1.96 (95% CI =-3.84 to -0.08), respectively. During the on-medication state, the pooled WMD of UPDRS III and II scores were 0.15 (95% CI =-1.14 to 1.44) and 1.01 (95% CI =0.12 to 1.89), respectively. After DBS, the pooled WMD of LED and BDI were -254.48 (95% CI =-341.66) and 2.29 (95% CI =0.83 to 3.75), respectively.

CONCLUSION

These results indicate that during the off-medication state, the STN-DBS might be superior to GPi-DBS in improving the motor function and activities of daily living for PD patients; but during the on-medication state, the opposite result is observed. Meanwhile, the STN-DBS is superior at reducing the LED, whereas the GPi-DBS shows a significantly greater reduction in BDI score after DBS.

Divergent Urinary Metabolic Phenotypes between Major Depressive Disorder and Bipolar Disorder Identified by a Combined GC-MS and NMR Spectroscopic Metabonomic Approach

Bipolar disorder (BD) is a complex debilitating mental disorder that is often misdiagnosed as major depressive disorder (MDD). Therefore, a large percentage of BD subjects are incorrectly treated with antidepressants in clinical practice. To address this challenge, objective laboratory-based tests are needed to discriminate BD from MDD patients. Here, a combined gas chromatography-mass spectrometry (GC-MS)-based and nuclear magnetic resonance (NMR) spectroscopic-based metabonomic approach was performed to profile urine samples from 76 MDD and 43 BD subjects (training set) to identify the differential metabolites. Samples from 126 healthy controls were included as metabolic controls. A candidate biomarker panel was identified by further analyzing these differential metabolites. A testing set of, 50 MDD and 28 BD subjects was then used to independently validate the diagnostic efficacy of the identified panel using an area under the receiver operating characteristic curve (AUC). A total of 20 differential metabolites responsible for the discrimination between MDD and BD subjects were identified. A panel consisting of six candidate urinary metabolite biomarkers (propionate, formate, (R*,S*)2,3-dihydroxybutanoic acid, 2,4-dihydroxypyrimidine, phenylalanine, and β-alanine) was identified. This panel could distinguish BD from MDD subjects with an AUC of 0.913 and 0.896 in the training and testing sets, respectively. These results reveal divergent urinary metabolic phenotypes between MDD and BD. The identified urinary biomarkers can aid in the future development of an objective laboratory-based diagnostic test for distinguishing BD from MDD patients.

Beneficial effect of phosphatidylcholine supplementation in alleviation of hypomania and insomnia in a Chinese bipolar hypomanic boy and a possible explanation to the effect at the genetic level

INTRODUCTION

Recent studies indicated that supplementation of phosphatidylcholine has been found to be beneficial for psychiatric diseases and Diacylglycerol Kinase, Eta (DGKH) protein was involved in regulating the metabolism of phosphatidic acid and diacylglycerol. This study reported a case of a 16-year-old Chinese boy with bipolar hypomania symptoms receiving supplementation of phosphatidylcholine, and a genetic study of a risk variant of DGKH gene was performed in an attempt to provide an explanation for the potential beneficial effect of phosphatidylcholine supplementation.

CASE DESCRIPTION

We described a case of a 16-year-old boy with bipolar disorder, who suffered from monthly episodes of insomnia accompanied by hypomania for 5 months despite adherence to medication. After supplementation of phosphatidylcholine, he returned to a normal sleeping pattern and recovered from hypomania symptoms for approximately 14 months. Furthermore, genotyping results showed that this boy carries the risk genotype (G/C) in DGKH variant rs77072822 (adjusted p-value = 0.025 after 2000 permutation tests).

DISCUSSION AND EVALUATION

The 16-year-old boy appears to have benefited from the supplementation with phosphatidylcholine and recovered from hypomania symptoms. He carries a risk genotype in rs77072822 which lies in the first intron of DGKH gene that was mostly reported to be associated with bipolar disorder. Thus, this finding is consistent with the hypothesis that alleviating the phosphatidylcholine deficiencies might accompany with the risk variants of DGKH gene, which might improve the efficacies of such supplementation and design new treatment strategies for bipolar disorder.

CONCLUSIONS

This study illustrated that a 16-year-old boy with hypomania symptoms responded well to supplementation of phosphatidylcholine and the boy carries a risk genotype in DGKH gene for bipolar disorder, which provides a possible explanation for the boy's beneficial effect at the genetic level.

Comparison of paroxetine and agomelatine in depressed type 2 diabetes mellitus patients: a double-blind, randomized, clinical trial

BACKGROUND

Comorbid depression/anxiety in type 2 diabetes mellitus (DM) patients is highly prevalent, affecting both diabetes control and quality of life. However, the best treating method for depression/anxiety in type 2 DM patients is still unclear. This study was conducted to compare the efficacy of paroxetine and agomelatine on depression/anxiety and metabolic control of type 2 DM patients.

METHODS

A total of 116 depressed, type 2 DM patients were recruited for 12 weeks treatment. Patients were randomly assigned to receive either paroxetine or agomelatine. Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale were used to assess depression and anxiety, respectively. Hemoglobin A1c, fasting plasma glucose, and body mass index were assessed at baseline and at the end of the trial.

RESULTS

At the end of the trial, there were 34 (60.7%) responders and 22 (39.3%) remissions in paroxetine group; and 38 (63.3%) responders and 26 (43.3%) remissions in agomelatine group. Compared to paroxetine group, lower depression scores were observed in agomelatine group. Fasting plasma glucose and body mass index were not significantly different after 12 weeks treatment between the two groups, but agomelatine group had a significantly lower final hemoglobin A1c level compared to paroxetine group. The two antidepressants had comparable acceptability.

CONCLUSION

These results showed that compared to paroxetine, agomelatine might have some advantages in treating symptoms of depression/anxiety and glycemic control in depressed type 2 DM patients. The clinical applicability of agomelatine shows greater promise and should be explored further. Limited by the relatively small samples, future studies are needed to verify and support our findings.