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

Sci Rep. 2014 Jul 28:4:5855. doi: 10.1038/srep05855.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Area Under Curve
  • Biomarkers / urine
  • Bipolar Disorder / diagnosis*
  • Bipolar Disorder / physiopathology
  • Bipolar Disorder / urine*
  • Case-Control Studies
  • Discriminant Analysis
  • Female
  • Gas Chromatography-Mass Spectrometry
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Metabolome*
  • Metabolomics / methods
  • Metabolomics / statistics & numerical data*
  • ROC Curve

Substances

  • Biomarkers