Plasma fatty acid metabolic profile coupled with uncorrelated linear discriminant analysis to diagnose and biomarker screening of type 2 diabetes and type 2 diabetic coronary heart diseases

Type 2 diabetes mellitus (T2DM) and type 2 diabetic coronary heart diseases (T2DM-CHD) are directly associated with metabolism disorder of lipid. In the present study, GC-MS followed by multivariate statistical analysis has been successfully applied to plasma free fatty acid metabolic profiling in T2DM and T2DM-CHD. Because principal component analysis and partial least squares-linear discriminant analysis both failed to the class separation among T2DM, T2DM-CHD, and control, uncorrelated linear discriminant analysis (ULDA) was proposed and successfully discriminated these three groups. The predictive correct rates were 81.03%, 85.37%, 88.89% for control and T2DM, control and T2DM-CHD, T2DM and T2DM-CHD, respectively. Furthermore, three potential biomarkers were screened. ULDA are much more efficient than PCA and PLS for discrimination analysis of complex data set. It is undoubtedly that such newly multivariate analysis method will promote and widen the application of metabonome analysis in disease clinical diagnosis.