Novel urinary biomarkers for diagnosing bipolar disorder

Zheng, Peng; Wei, You-Dong; Yao, Guo-En; Ren, Gaoping; Guo, Jing; Zhou, Chanjuan; Zhong, Jianqiao; Cao, Du; Zhou, Linke; Xie, Peng

doi:10.1007/s11306-013-0508-y

Cited by 36 publications

(44 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We did univariate analysis using all subjects to find metabolites that would be worthy of further analysis ( p < 0.10), which identified 67 different metabolites (18 NMR-derived and 49 GC-MS-derived metabolites) from 94 metabolites. These 94 differential metabolites including the four metabolites biomarkers (choline, N-methylnicotinamide,α-hydroxybutyrate, isobutyrate) identified by NMR and one metabolite (2,4-dihydroxypyrimidine) identified by GC/MS were included in this study1920. The 94 metabolites were described in supplementary Table S1.…”

Section: Resultsmentioning

confidence: 99%

“…Meanwhile, in our group, Zheng et al . employed NMR to identify four potential urinary metabolite biomarkers, and Xu et al employed GC-MS to identify 2,4-dihydroxypyrimidine as a potential urinary metabolite biomarker for diagnosing BD1920. These previous metabonomic studies have been helpful in developing objective laboratory-based testing for BD while providing valuable data on the physiopathologic mechanism(s) of BD.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Combined Application of NMR- and GC-MS-Based Metabonomics Yields a Superior Urinary Biomarker Panel for Bipolar Disorder

Chen

Liu

Fan

et al. 2014

Sci Rep

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Combined Application of NMR- and GC-MS-Based Metabonomics Yields a Superior Urinary Biomarker Panel for Bipolar Disorder

Chen

Liu

Fan

et al. 2014

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…10,11,39 To further evaluate the diagnostic performance of the simplified schizophrenia biomarker panel, receiver-operating characteristic (ROC) curve analysis was carried out to quantify the ability of this metabolite biomarker panel to discriminate schizophrenia subjects from healthy controls in both the training and test sets. 40 …”

Section: Identification Of Pbmc Metabolite Biomarkers For Schizophreniamentioning

confidence: 99%

GC-MS based metabolomics identification of possible novel biomarkers for schizophrenia in peripheral blood mononuclear cells

et al. 2014

Self Cite

View full text Add to dashboard Cite

Schizophrenia is a debilitating mental disorder. Currently, the lack of disease biomarkers to support objective laboratory tests constitutes a bottleneck in the clinical diagnosis of schizophrenia. Here, a gas chromatography-mass spectrometry (GC-MS) based metabolomic approach was applied to characterize the metabolic profile of schizophrenia subjects (n = 69) and healthy controls (n = 85) in peripheral blood mononuclear cells (PBMCs) to identify and validate biomarkers for schizophrenia. Multivariate statistical analysis was used to visualize group discrimination and to identify differentially expressed metabolites in schizophrenia subjects relative to healthy controls. The multivariate statistical analysis demonstrated that the schizophrenia group was significantly distinguishable from the control group. In total, 18 metabolites responsible for the discrimination between the two groups were identified. These differential metabolites were mainly involved in energy metabolism, oxidative stress and neurotransmitter metabolism. A simplified panel of PBMC metabolites consisting of pyroglutamic acid, sorbitol and tocopherol-α was identified as an effective diagnostic tool, yielding an area under the receiver operating characteristic curve (AUC) of 0.82 in the training samples (45 schizophrenia subjects and 50 healthy controls) and 0.71 in the test samples (24 schizophrenic patients and 35 healthy controls). Taken together, these findings help to develop diagnostic tools for schizophrenia.

show abstract

“…Employing these ''-omics'' approaches, our research group has performed a series of preclinical (Hu et al, 2013;Mu et al, 2007;Yang et al, 2013) and clinical (Xu et al, 2012;Zheng et al, 2012;Zheng et al, 2013aZheng et al, , 2013bZheng et al, , 2013c investigations on depression. With specific respect to the cerebellum, our research group previously investigated cerebellar metabolic changes in a CMS rodent model of depression by using a gas chromatography-mass spectrometry (GC-MS) metabolomics approach, which revealed several CMS resilience-related metabolites that may be involved in the pathophysiology underlying MDD (Shao et al, 2013).…”

mentioning

confidence: 99%

Combined Metabolomics and Proteomics Analysis of Major Depression in an Animal Model: Perturbed Energy Metabolism in the Chronic Mild Stressed Rat Cerebellum

Shao

Chen

Fan

et al. 2015

OMICS: A Journal of Integrative Biology

Self Cite

View full text Add to dashboard Cite

Major depressive disorder (MDD) is a highly prevalent, debilitating mental illness of importance for global health. However, its molecular pathophysiology remains poorly understood. Combined proteomics and metabolomics approaches should provide a comprehensive understanding of MDD's etiology. The present study reports novel ''-omics'' insights from a rodent model of MDD. Cerebellar samples from chronic mild stressed (CMS)-treated depressed rats and controls were compared with a focus on the differentially expressed proteins and metabolites using isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics and gas chromotography/mass spectrometry (GC-MS) metabolomics techniques, respectively. The combined analyses found significant alterations associated with cerebellar energy metabolism, as indicated by (1) abnormal amino acid metabolism accompanied by corresponding metabolic enzymatic alterations and disturbed protein turnover, (2) increased glycolytic and tricarboxylic acid (TCA) cycle enzyme levels paralleled by changes in the concentrations of associated metabolites, and (3) perturbation of ATP biosynthesis through adenosine accompanied by perturbation of the mitochondrial respiratory chain. To the best of our knowledge, this study is the first to integrate proteomics and metabolomics analyses to examine the pathophysiological mechanism(s) underlying MDD in a CMS rodent model of depression. These results can offer important insights into the pathogenesis of MDD.

show abstract

Novel urinary biomarkers for diagnosing bipolar disorder

Cited by 36 publications

References 32 publications

Combined Application of NMR- and GC-MS-Based Metabonomics Yields a Superior Urinary Biomarker Panel for Bipolar Disorder

Combined Application of NMR- and GC-MS-Based Metabonomics Yields a Superior Urinary Biomarker Panel for Bipolar Disorder

GC-MS based metabolomics identification of possible novel biomarkers for schizophrenia in peripheral blood mononuclear cells

Combined Metabolomics and Proteomics Analysis of Major Depression in an Animal Model: Perturbed Energy Metabolism in the Chronic Mild Stressed Rat Cerebellum

Contact Info

Product

Resources

About