Identification and validation of urinary metabolite biomarkers for major depressive disorder

Metabolite profiling in posttraumatic stress disorder

BACKGROUND

Traumatic stress does not only increase the risk for posttraumatic stress disorder (PTSD), but is also associated with adverse secondary physical health outcomes. Despite increasing efforts, we only begin to understand the underlying biomolecular processes. The hypothesis-free assessment of a wide range of metabolites (termed metabolite profiling) might contribute to the discovery of biological pathways underlying PTSD.

METHODS

Here, we present the results of the first metabolite profiling study in PTSD, which investigated peripheral blood serum samples of 20 PTSD patients and 18 controls. We performed liquid chromatography (LC) coupled to Quadrupole/Time-Of-Flight (QTOF) mass spectrometry. Two complementary statistical approaches were used to identify metabolites associated with PTSD status including univariate analyses and Partial Least Squares Discriminant Analysis (PLS-DA).

RESULTS

Thirteen metabolites displayed significant changes in PTSD, including four glycerophospholipids, and one metabolite involved in endocannabinoid signaling. A biomarker panel of 19 metabolites classifies PTSD with 85% accuracy, while classification accuracy from the glycerophospholipid with the highest differentiating ability already reached 82%.

CONCLUSIONS

This study illustrates the feasibility and utility of metabolite profiling for PTSD and suggests lipid-derived and endocannabinoid signaling as potential biological pathways involved in trauma-associated pathophysiology.

Risk assessment and predicting outcomes in patients with depressive symptoms: a review of potential role of peripheral blood based biomarkers

Depression is one of the major global health challenges and a leading contributor of health related disability and costs. Depression is a heterogeneous disorder and current methods for assessing its severity in clinical practice rely on symptom count, however this approach is unreliable and inconsistent. The clinical evaluation of depressive symptoms is particularly challenging in primary care, where the majority of patients with depression are managed, due to the presence of co-morbidities. Current methods for risk assessment of depression do not accurately predict treatment response or clinical outcomes. Several biological pathways have been implicated in the pathophysiology of depression; however, accurate and predictive biomarkers remain elusive. We conducted a systematic review of the published evidence supporting the use of peripheral biomarkers to predict outcomes in depression, using Medline and Embase. Peripheral biomarkers in depression were found to be statistically significant predictors of mental health outcomes such as treatment response, poor outcome and symptom remission; and physical health outcomes such as increased incidence of cardiovascular events and deaths, and all-cause mortality. However, the available evidence has multiple methodological limitations which must be overcome to make any real clinical progress. Despite extensive research on the relationship of depression with peripheral biomarkers, its translational application in practice remains uncertain. In future, peripheral biomarkers identified with novel techniques and combining multiple biomarkers may have a potential role in depression risk assessment but further research is needed in this area.

Berberine ameliorates nonbacterial prostatitis via multi-target metabolic network regulation

Metabolomics has been increasingly applied to discovering biomarkers and identifying perturbed pathways. Berberine has been shown to exhibit anti-inflammatory, antioxidant, and anticancer properties, but its mechanisms for treating nonbacterial prostatitis (NBP) remain unclear completely. We developed the untargeted metabolomics approach based on UPLC-Q-TOF-HDMS to profile the metabolite changes in urine samples in order to discover novel potential biomarkers to clarify mechanisms of berberine in treating a rat model of capsaicin-induced nonbacterial prostatitis (NBP). The changes in metabolic profiling were restored to their base-line values after berberine treatment according to the principal component analysis (PCA) score plots. Fourteen different potential biomarkers and five acutely perturbed metabolic pathways contributing to the treatment of NBP were discovered and identified. Specifically, the berberine-treated rats are located closer to the normal group, indicating that the NBP-induced disturbances to the metabolic profile were partially reversed by berberine treatment. After treatment with berberine, the relative contents of 12 potential biomarkers were effectively regulated, which suggested that the therapeutic effects of berberine on NBP may involve regulating disturbances to the metabolism. Our results show that the protective effect of berberine occurs in part through a reversal of the NBP-caused disturbances.

Bilateral repetitive transcranial magnetic stimulation for treatment-resistant depression: a systematic review and meta-analysis of randomized controlled trials

There has been concern regarding the use of controversial paradigms for repetitive transcranial magnetic stimulation (rTMS) to manage treatment-resistant depression (TRD). This meta-analysis assessed the efficacy of bilateral rTMS compared with unilateral and sham rTMS in patients with TRD. PubMed, Embase, CENTRAL, PsycINFO, Web of Science, EAGLE and NTIS databases were searched to identify relevant studies, and randomized controlled trials (RCTs) on bilateral rTMS for TRD patients were included. The response was defined as the primary outcome, and remission was the secondary outcome. Ten RCTs that included 634 patients met the eligibility criteria. The risk ratio (RRs) of both the primary and secondary outcomes of bilateral rTMS showed non-significant increases compared to unilateral rTMS (RR=1.01, P=0.93; odds ratio [OR]=0.77, P=0.22). Notably, the RR of the primary bilateral rTMS outcome was significantly increased compared to that for sham rTMS (RR=3.43, P=0.0004). The results of our analysis demonstrated that bilateral rTMS was significantly more effective than sham rTMS but not unilateral rTMS in patients with TRD. Thus, bilateral rTMS may not be a useful paradigm for patients with TRD.

Is there a "metabolic-mood syndrome"? A review of the relationship between obesity and mood disorders

Obesity and mood disorders are highly prevalent and co-morbid. Epidemiological studies have highlighted the public health relevance of this association, insofar as both conditions and its co-occurrence are associated with a staggering illness-associated burden. Accumulating evidence indicates that obesity and mood disorders are intrinsically linked and share a series of clinical, neurobiological, genetic and environmental factors. The relationship of these conditions has been described as convergent and bidirectional; and some authors have attempted to describe a specific subtype of mood disorders characterized by a higher incidence of obesity and metabolic problems. However, the nature of this association remains poorly understood. There are significant inconsistencies in the studies evaluating metabolic and mood disorders; and, as a result, several questions persist about the validity and the generalizability of the findings. An important limitation in this area of research is the noteworthy phenotypic and pathophysiological heterogeneity of metabolic and mood disorders. Although clinically useful, categorical classifications in both conditions have limited heuristic value and its use hinders a more comprehensive understanding of the association between metabolic and mood disorders. A recent trend in psychiatry is to move toward a domain specific approach, wherein psychopathology constructs are agnostic to DSM-defined diagnostic categories and, instead, there is an effort to categorize domains based on pathogenic substrates, as proposed by the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC). Moreover, the substrates subserving psychopathology seems to be unspecific and extend into other medical illnesses that share in common brain consequences, which includes metabolic disorders. Overall, accumulating evidence indicates that there is a consistent association of multiple abnormalities in neuropsychological constructs, as well as correspondent brain abnormalities, with broad-based metabolic dysfunction, suggesting, therefore, that the existence of a "metabolic-mood syndrome" is possible. Nonetheless, empirical evidence is necessary to support and develop this concept. Future research should focus on dimensional constructs and employ integrative, multidisciplinary and multimodal approaches.

Advances in biomarkers of major depressive disorder

Major depressive disorder (MDD) is characterized by mood, vegetative, cognitive, and even psychotic symptoms and signs that can cause substantial impairments in quality of life and functioning. Biomarkers are measurable indicators that could help diagnosing MDD or predicting treatment response. In this chapter, lipid profiles, immune/inflammation, and neurotrophic factor pathways that have long been implicated in the pathogenesis of MDD are discussed. Then, pharmacogenetics and epigenetics of serotonin transport and its metabolism pathway, brain-derived neurotrophic factor, and abnormality of hypothalamo-pituitary-adrenocortical axis also revealed new biomarkers. Lastly, new techniques, such as proteomics and metabolomics, which allow researchers to approach the studying of MDD with new directions and make new discoveries are addressed. In the future, more data are needed regarding pathophysiology of MDD, including protein levels, single nucleotide polymorphism, epigenetic regulation, and clinical data in order to better identify reliable and consistent biomarkers for diagnosis, treatment choice, and outcome prediction.

Research on the Pathological Mechanism and Drug Treatment Mechanism of Depression

Depression is one of the prevalent and persistent psychiatric illnesses. It brings heavy socioeconomic burden such as healthcare expenditures and even higher suicide rates. Despite many hypotheses about its mechanism have been put forward, so far it is still unclear, not to mention the precise and effective diagnostic or therapeutic methods. In this paper, the current conditions of pathological and pharmacological mechanism of depression were reviewed systematically. Firstly, the most recent hypotheses and metabolomics based research including hereditary, neurotransmitter systems, brain derived neurotrophic factor (BDNF), hyperactivity of the hypothalamic pituitary adrenal (HPA) axis and inflammatory as well as metabolomics were summarized. Secondly, the present situation and development on antidepressant drugs at home and abroad were reviewed. Finally, a conclusion and prospect on the pathological and pharmacological mechanism of depression were provided primarily.

Lipidomics analysis based on liquid chromatography mass spectrometry for hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Plasma lipidomics showed that four metabolites, PE(19 : 0/0 : 0), PE(18 : 2(9Z,12Z)/0 : 0), PC(14 : 0/0 : 0) and PC(18 : 0/0 : 0), were defined as biomarkers with high sensitivity and specificity, which can be used to distinguish the HCC and ICC.

D-serine plasma concentration is a potential biomarker of (R,S)-ketamine antidepressant response in subjects with treatment-resistant depression

RATIONALE

(R,S)-ketamine is a rapid and effective antidepressant drug that produces a response in two thirds of patients with treatment-resistant depression (TRD). The underlying biochemical differences between a (R,S)-ketamine responder (KET-R) and non-responder (KET-NR) have not been definitively identified but may involve serine metabolism.

OBJECTIVES

The aim of the study was to examine the relationship between baseline plasma concentrations of D-serine and its precursor L-serine and antidepressant response to (R,S)-ketamine in TRD patients.

METHODS

Plasma samples were obtained from 21 TRD patients at baseline, 60 min before initiation of the (R,S)-ketamine infusion. Patients were classified as KET-Rs (n = 8) or KET-NRs (n = 13) based upon the difference in Montgomery-Åsberg Depression Rating Scale (MADRS) scores at baseline and 230 min after infusion, with response defined as a ≥50 % decrease in MADRS score. The plasma concentrations of D-serine and L-serine were determined using liquid chromatography-mass spectrometry.

RESULTS

Baseline D-serine plasma concentrations were significantly lower in KET-Rs (3.02 ± 0.21 μM) than in KET-NRs (4.68 ± 0.81 μM), p < 0.001. A significant relationship between baseline D-serine plasma concentrations and percent change in MADRS at 230 min was determined using a Pearson correlation, r = 0.77, p < 0.001, with baseline D-serine explaining 60 % of the variance in (R,S)-ketamine response. The baseline concentrations of L-serine (L-Ser) in KET-Rs were also significantly lower than those measured in KET-NRs (66.2 ± 9.6 μM vs 242.9 ± 5.6 μM, respectively; p < 0.0001).

CONCLUSIONS

The results demonstrate that the baseline D-serine plasma concentrations were significantly lower in KET-Rs than in KET-NRs and suggest that this variable can be used to predict an antidepressant response following (R,S)-ketamine administration.

Sex-specific urinary biomarkers for diagnosing bipolar disorder

Sex-based differences are prominent in affective disorders, but there are no biomarkers available to support sex-specific, laboratory-based diagnostics for male and female bipolar disorder (BD) patients. Here, a NMR-based metabonomic approach was used to preliminarily identify sex-specific urinary metabolite biomarkers for diagnosing male and female BD patients. A male-specific biomarker panel consisting of four metabolites (α-hydroxybutyrate, choline, formate, and N-methylnicotinamide) effectively discriminated between male BD and healthy controls (HC) subjects, achieving an area under the receiver operating characteristic curve (AUC) of 0.942. A female-specific biomarkers panel consisting of four metabolites (α-hydroxybutyrate, oxalacetate, acetone, and N-methylnicotinamide) effectively discriminated between female BD and HC subjects, achieving an AUC of 0.909. The male-specific biomarker panel displayed low discriminatory power in the female group, and the female-specific biomarker panel displayed low discriminatory power in the male group. Moreover, several other metabolites showed different trends between male and female BD subjects. These findings suggest that male and female BD patients have distinct biomarker fingerprints and that these two sex-specific biomarker panels may serve as effective diagnostic tools in distinguishing male and female BD patients from their healthy counterparts. Our work may provide a window into the mechanisms underlying the pathoetiology of BD in both men and women.

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types—prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

Dynamic analysis of the endogenous metabolites in depressed patients treated with TCM formula Xiaoyaosan using urinary (1)H NMR-based metabolomics

ETHNOPHAMACOLOGICAL RELEVANCE

Xiaoyaosan (XYS), one of the best-known traditional Chinese medicine prescriptions with a long history of use, is composed of Bupleurum chinense DC., Paeonia lactiflora Pall., Poria cocos (Schw.) Wolf, Angelica sinensis (Oliv.) Diels, Zingiber officinale Rosc., Atractylodes macrocephala Koidz., Glycyrrhiza uralensis Fisch., and Mentha haplocalyx Briq. For centuries, XYS has been widely used in China for the treatment of mental disorders such as depression. However, the complicated mechanism underlying the antidepressant activity of XYS is not yet well-understood. This understanding is complicated by the sophisticated pathophysiology of depression and by the complexity of XYS, which has multiple constituents acting on different metabolic pathways. The variations of endogenous metabolites in depressed patients after administration of XYS may help elucidate the anti-depressant effect and mechanism of action of XYS. The aim of this study is to establish the metabolic profile of depressive disorder and to investigate the changes of endogenous metabolites in the depressed patients before and after the treatment of Xiaoyaosan using the dynamic analysis of urine metabolomics profiles based on (1)H NMR.

MATERIALS AND METHODS

Twenty-one depressed patients were recruited from the Traditional Chinese Medicine Department of the First Affiliated Hospital of Shanxi Medical University. Small endogenous metabolites present in urine samples were measured by nuclear magnetic resonance (NMR) and analyzed by multivariate statistical methods. The patients then received XYS treatment for six weeks, after which their Hamilton Depression Scale (HAMD) scores were significantly decreased compared with their baseline scores (p≤0.01). Eight components in urine specimens were identified that enabled discrimination between the pre- and post-XYS-treated samples.

RESULTS

Urinary of creatinine, taurine, 2-oxoglutarate and xanthurenic acid increased significantly after XYS treatment (p≤0.05), while the urinary levels of citrate, lactate, alanine and dimethylamine decreased significantly (p≤0.05) compared with pre-treatment urine samples. These statistically significant perturbations are involved in energy metabolism, gut microbes, tryptophan metabolism and taurine metabolism.

CONCLUSIONS

The symptoms of depression had been improved after 6 weeks׳ treatment of XYS according to evaluation of HAMD scores. The dynamic tendency of the 8 metabolites that changed significantly during the treatment of XYS is consistent with the improvement in symptoms of depression. These metabolites may be used as biomarkers for the diagnosis of depressive disorders or the evaluation of the antidepressant as well as the exploration of the mechanism of depression.

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

Bipolar disorder (BD) is a debilitating mental disorder that cannot be diagnosed by objective laboratory-based modalities. Our previous studies have independently used nuclear magnetic resonance (NMR)-based and gas chromatography-mass spectrometry (GC-MS)-based metabonomic methods to characterize the urinary metabolic profiles of BD subjects and healthy controls (HC). However, the combined application of NMR spectroscopy and GC-MS may identify a more comprehensive metabolite panel than any single metabonomic platform alone. Therefore, here we applied a dual platform (NMR spectroscopy and GC-MS) that generated a panel of five metabolite biomarkers for BD-four GC-MS-derived metabolites and one NMR-derived metabolite. This composite biomarker panel could effectively discriminate BD subjects from HC, achieving an area under receiver operating characteristic curve (AUC) values of 0.974 in a training set and 0.964 in a test set. Moreover, the diagnostic performance of this panel was significantly superior to the previous single platform-derived metabolite panels. Thus, the urinary biomarker panel identified here shows promise as an effective diagnostic tool for BD. These findings also demonstrate the complementary nature of NMR spectroscopy and GC-MS for metabonomic analysis, suggesting that the combination of NMR spectroscopy and GC-MS can identify a more comprehensive metabolite panel than applying each platform in isolation.

2,4-Dihydroxypyrimidine is a potential urinary metabolite biomarker for diagnosing bipolar disorder

Bipolar disorder (BD) is a common and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. Here, we used a gas chromatography-mass spectrometry (GC-MS) based metabonomic method to characterize the urinary metabolic profiling of BD subjects and healthy controls to identify and validate urinary metabolite biomarkers for BD. Multivariate statistical analysis was used to visualize group discrimination and identify differentially expressed urinary metabolites in BD subjects relative to the healthy controls. Multivariate statistical analysis showed that the BD group was significantly distinguishable from the healthy control. Totally, 37 urinary metabolites responsible for discriminating BD subjects from healthy controls were identified. Interestingly, of 37 differential metabolites, 2,4-dihydroxypyrimidine was identified as an effective diagnostic biomarker for BD, yielding an area under the receiver operating characteristic curve (AUC) of 0.889 in the training samples (45 BD subjects and 61 healthy controls) and 0.805 in the test samples (26 BD subjects and 33 healthy controls). Our findings suggest that 2,4-dihydroxypyrimidine is a promising candidate urinary biomarker for BD, which may facilitate development of a urine-based diagnostic test for BD.

Peripheral blood mononuclear cell-based metabolomic profiling of a chronic unpredictable mild stress rat model of depression

Major depressive disorder (MDD) is a debilitating mood disorder with various etiopathological hypotheses.

A novel urinary metabolite signature for diagnosing major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating mental disorder. Yet, there are no objective biomarkers available to support diagnostic laboratory testing for this disease. Here, gas chromatography-mass spectrometry was applied to urine metabolic profiling of 126 MDD and 134 control subjects. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to identify the differential metabolites in MDD subjects relative to healthy controls. The OPLS-DA analysis of data from training samples (82 first-episode, drug-naïve MDD subjects and 82 well-matched healthy controls) showed that the depressed group was significantly distinguishable from the control group. Totally, 23 differential urinary metabolites responsible for the discrimination between the two groups were identified. Postanalysis, 6 of the 23 metabolites (sorbitol, uric acid, azelaic acid, quinolinic acid, hippuric acid, and tyrosine) were defined as candidate diagnostic biomarkers for MDD. Receiver operating characteristic analysis of combined levels of these six biomarkers yielded an area under the receiver operating characteristic curve (AUC) of 0.905 in distinguishing training samples; this simplified metabolite signature classified blinded test samples (44 MDD subjects and 52 healthy controls) with an AUC of 0.837. Furthermore, a composite panel by the addition of previously identified urine biomarker (N-methylnicotinamide) to this biomarker panel achieved a more satisfactory accuracy, yielding an AUC of 0.909 in the training samples and 0.917 in the test samples. Taken together, these results suggest this composite urinary metabolite signature should facilitate development of a urine-based diagnostic test for MDD.