Metabonomic signature analysis in plasma samples of glioma patients based on 1H-nuclear magnetic resonance spectroscopy

• Published in: Neurology India Vol. 64; no. 2; pp. 246 - 251
• Main Authors: Kelimu, Alimujiang, Xie, Rong, Zhang, Kuiming, Zhuang, Zhongwei, Mamtimin, Batur, Sheyhidin, Ilyar
• Format: Journal Article
• Language: English
• Published: Mumbai Wolters Kluwer - Medknow Publications 01.03.2016; Medknow Publications and Media Pvt. Ltd; Medknow Publications & Media Pvt. Ltd
• Subjects: Analysis; Diagnosis; Gliomas; Metabolites; NMR; Nuclear magnetic resonance; Nuclear magnetic resonance spectroscopy; Plasma; Spectrum analysis; India
• DOI: 10.4103/0028-3886.177606
• ISSN: 0028-3886

Objective: The presence of a glioma is associated with increasing mortality. In this study, nuclear magnetic resonance (NMR) based metabonomics has been applied to investigate the metabolic signatures of a glioma in plasma. The purpose of this study was to assess the diagnostic potential of this approach and gain novel insights into the metabolism of glioma and its systemic effects. Methods: Plasma samples were collected prospectively by centrifugation of blood samples from patients with a glioma (n = 70) or a control group (n = 70). NMR spectra of these plasma samples were analyzed using orthogonal partial least square discriminant analysis (OPLS-DA) to identify the potential biomarkers. Results: The OPLS-DA model showed a good differentiation between the glioma and the control groups. A total of 20 metabolites were identified, which are closely correlating with the presence of a glioma. Compared to the control group, patients with a glioma were associated with lower concentrations of isoleucine, leucine, valine, lactate, alanine, glycoprotein, glutamate, citrate, creatine, myo-inositol, choline, tyrosine, phenylalanine, 1-methylhistidine, α-glucose, β-glucose, and higher concentrations of very low density lipoprotein, low density lipoprotein (LDL), unsaturated lipid, and pyruvate. These 20 metabolites, which are involved in energy, fatty acid, and amino acid metabolism, may be associated with a human glioma. Conclusion: Our study is the first one to identify the plasma metabolites that have the potential to distinguish between patients with a glioma and healthy subjects. NMR-based metabonomics provides a good sensitivity and selectivity in differentiating the healthy control group from patients suffering form the disease. Plasma metabolic profiling may have a potential in diagnosing a glioma in the early phase and may help in enhancing our understanding of its underlying mechanisms.