Abstract
Bipolar disorder (BD) is a debilitating mental disorder. However, there are no biomarkers available to support objective laboratory testing for this disorder. Here, a nuclear magnetic resonance spectroscopy-based metabonomic method was used to characterize the urinary metabolic profiling of BD subjects and healthy controls in order to identify and validate urinary metabolite biomarkers for BD. Four metabolites, α-hydroxybutyrate, choline, isobutyrate, and N-methylnicotinamide, were defined as biomarkers. A combined panel of these four urinary metabolites could effectively discriminate between BD subjects and healthy controls, achieving an area under the receiver operating characteristic curve (AUC) of 0.89 in a training set (n = 60 BD patients and n = 62 controls). Moreover, this urinary biomarker panel was capable of discriminating blinded test samples (n = 26 BD patients and n = 34 controls) with an AUC of 0.86. These findings suggest that a urine-based laboratory test using these biomarkers may be useful in the diagnosis of BD.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andreazza, A. C., et al. (2008). Oxidative stress markers in bipolar disorder: A meta-analysis. Journal of Affective Disorders, 111, 135–144.
Beckwith-Hall, B., et al. (1998). Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chemical Research in Toxicology, 11, 260–272.
Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30, 1145–1159.
Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20, 341–351.
Cloarec, O., et al. (2005). Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Analytical Chemistry, 77, 517–526.
Fernstrom, J. D. (2005). Branched-chain amino acids and brain function. The Journal of nutrition, 135, 1539S–1546S.
Frey, B. N., et al. (2007). Abnormal cellular energy and phospholipid metabolism in the left dorsolateral prefrontal cortex of medication-free individuals with bipolar disorder: An in vivo 1H MRS study. Bipolar Disorders, 9, 119–127.
Ge, Y., Sealfon, S. C., & Speed, T. P. (2008). Some step-down procedures controlling the false discovery rate under dependence. Statistica Sinica, 18, 881.
Hirschfeld, R. M. A., Lewis, L., & Vornik, L. A. (2003). Perceptions and impact of bipolar disorder: How far have we really come? Results of the national depressive and manic-depressive association 2000 survey of individuals with bipolar disorder. Journal of Clinical Psychiatry, 64, 161–174.
Janowsky, D., & Overstreet, D. (1995). The role of acetylcholine mechanisms in mood disorders. In F. E. Bloom & D. J. Kupfer (Eds.), Psychopharmacology: The fourth generation of progress (pp. 945–956). New York: Raven Press.
Jung, Y., Lee, J., Kwon, J., Lee, K. S., Ryu, D. H., & Hwang, G. S. (2010). Discrimination of the geographical origin of beef by 1H NMR-based metabolomics. Journal of agricultural and food chemistry, 58(19), 10458–10466.
Jung, J. Y., et al. (2011). 1H-NMR-based metabolomics study of cerebral infarction. Stroke, 42, 1282–1288.
Kaddurah-Daouk, R., Kristal, B. S., & Weinshilboum, R. M. (2008). Metabolomics: A global biochemical approach to drug response and disease. Annual Review of Pharmacology and Toxicology, 48, 653–683.
Lan, M., et al. (2008). Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of bipolar disorder. Molecular Psychiatry, 14, 269–279.
Landaas, S. (1975). The formation of 2-hydroxybutyric acid in experimental animals. Clinica Chimica Acta, 58, 23–32.
Lester, G. (1971). End-product regulation of the tryptophan-nicotinic acid pathway in Neurospora crassa. Journal of Bacteriology, 107, 448–455.
MacIntyre, D., et al. (2010). Serum metabolome analysis by 1H-NMR reveals differences between chronic lymphocytic leukaemia molecular subgroups. Leukemia, 24, 788–797.
Mahadevan, S., Shah, S. L., Marrie, T. J., & Slupsky, C. M. (2008). Analysis of metabolomic data using support vector machines. Analytical Chemistry, 80, 7562–7570.
Merikangas, K. R., et al. (2011). Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Archives of General Psychiatry, 68, 241.
Micheel, C., Nass, S., & Omenn, G. (2012). Evolution of translational omics: Lessons learned and the path forward (Institute of Medicine Consensus Report). Washington, DC: National Academies Press.
Michell, A. W., Mosedale, D., Grainger, D. J., & Barker, R. A. (2008). Metabolomic analysis of urine and serum in Parkinson’s disease. Metabolomics, 4, 191–201.
Miller, C. L., Llenos, I. C., Dulay, J. R., & Weis, S. (2006). Upregulation of the initiating step of the kynurenine pathway in postmortem anterior cingulate cortex from individuals with schizophrenia and bipolar disorder. Brain Research, 1073, 25–37.
Müller-Oerlinghausen, B., Berghöfer, A., & Bauer, M. (2002). Bipolar disorder. The Lancet, 359, 241–247.
Murray, C. J. L., & Lopez, A. D. (1996). Evidence-based health policy—lessons from the Global Burden of Disease Study. Science, 274, 740–743.
Nicholson, J. K., & Lindon, J. C. (2008). Systems biology: Metabonomics. Nature, 455, 1054–1056.
Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). Metabonomics’: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29, 1181–1189.
Noga, M. J., et al. (2012). Metabolomics of cerebrospinal fluid reveals changes in the central nervous system metabolism in a rat model of multiple sclerosis. Metabolomics, 8, 253–263.
Oquendo, M., Currier, D., & Mann, J. (2006). Prospective studies of suicidal behavior in major depressive and bipolar disorders: What is the evidence for predictive risk factors? Acta Psychiatrica Scandinavica, 114, 151–158.
Pharmaceutica, J. (2000). The nature of bipolar disorder. The Journal of clinical psychiatry, 61, 42–57.
Sussulini, A., et al. (2009). Metabolic profiling of human blood serum from treated patients with bipolar disorder employing 1H NMR spectroscopy and chemometrics. Analytical Chemistry, 81, 9755–9763.
Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16, 119–128.
Yang, J., et al. (2011). Potential metabolite markers of schizophrenia. Molecular psychiatry, 18(1), 67–78. doi:10.1038/mp.2011.131.
Yap, I. K. S., Angley, M., Veselkov, K. A., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2010). Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and age-matched controls. Journal of Proteome Research, 9, 2996–3004.
Zheng, P., et al. (2012). Plasma metabonomics as a novel diagnostic approach for major depressive disorder. Journal of Proteome Research, 11, 1741–1748.
Zheng, P., et al. (2013). Identification and validation of urinary metabolite biomarkers for major depressive disorder. Molecular and Cellular Proteomics, 12, 207–214.
Acknowledgments
Our gratitude is extended to Professors Huaqing Meng, Delan Yang, and Hua Hu for their efforts in sample collection. We also thank Dr. N. D. Melgiri for editing and proofreading of the manuscript. This work was supported by the National Basic Research Program of China (973 Program, Grant No. 2009CB918300) and the National Natural Science Foundation of China (Grant No. 30900456).
Conflict of interest
The authors have declared no conflict of interest in the submission of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Peng Zheng, You-Dong Wei, and Guo-En Yao contributed equally to this work.
Rights and permissions
About this article
Cite this article
Zheng, P., Wei, YD., Yao, GE. et al. Novel urinary biomarkers for diagnosing bipolar disorder. Metabolomics 9, 800–808 (2013). https://doi.org/10.1007/s11306-013-0508-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11306-013-0508-y