Statistical two-dimensional correlation spectroscopy of urine and serum from metabolomics data

Statistical two-dimensional correlation spectroscopy combined with pattern recognition is demonstrated for coanalysis of NMR spectroscopic data from different sources. The urine and serum H-1 NMR spectra from metabolomics datasets of diabetes and hyperthyroidism are taken as examples. The intrinsic covariance of certain molecules between urine and serum spectra is identified. The highly urine-serum-correlated metabolites are further analyzed by using the projection to latent structure discriminant analysis (PLS-DA) method. To illustrate the applicability of the method, the metabolomics datasets of diabetes and hyperthyroidism are imported separately to calculate the corresponding two-dimensional urine-serum correlation coefficient matrixes. The results show that creatinine (delta 4.08) and succinate (delta 2.45) are found to be highly correlated between urine and serum from diabetes patients, and choline (delta 321) and pyruvate (delta 233) are highly correlated between urine and serum from hyperthyroidism patients. This study offers a new angle of view for interpreting metabolomics data and demonstrates the potential of the correlation analysis of spectra from different biological sources as a new systems biology tool. (c) 2012 Elsevier B.V. All rights reserved.NNSF of China [11074209]; Science Research Foundation of Ministry of Health [WKJ2008-2-36]; Science and Technology Bureau of Xiamen [WSK0501