Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GUMS and PLS-LDA

Metabolic profiling has increasingly been used as a probe in disease diagnosis and pharmacological analysis. Herein, plasma fatty acid metabolic profiling including non-esterified fatty acid (NEFA) and esterified fatty acid (EFA) was investigated using gas chromatography/mass spectrometry (GC/MS) followed by multivariate statistical analysis. Partial least squares-linear discrimination analysis (PLS-LDA) model was established and validated to pattern discrimination between type 2 diabetic mellitus (DM-2) patients and health controls, and to extract novel biomarker information. Furthermore, the PLSLDA model visually represented the alterations of NEFA metabolic profiles of diabetic patients with abdominal obesity in the treated process with rosiglitazone. The GC/MS-PLS-LDA analysis allowed comprehensive detection of plasma fatty acid, enabling fatty acid metabolic characterization of DM-2 patients, which included biomarkers different from health controls and dynamic change of NEFA profiles of patients after treated with medicine. This method might be a complement or an alternative to pathogenesis and pharmacodynamics research. (c) 2006 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.