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Because of its quantitative character and capability for high-throughput screening, 1 H nuclear magnetic resonance (NMR) spectroscopy is used extensively in the profiling of biofluids such as urine and blood plasma. However, the narrow frequency bandwidth of 1 H NMR spectroscopy leads to a severe overlap of the spectra of components present in the complex mixtures such as biofluids. Therefore, 1 H NMR-based metabolomics analysis is focused on targeted studies related to concentrations of the small number of metabolites. Here, we propose a library-based approach to quantify proportions of overlapping metabolites from 1 H NMR mixture spectra. The method boils down to the linear non-negative least squares (NNLS) problem, whereas proportions of the pure components contained in the library stand for the unknowns. The method is validated on an estimation of the proportions of ( i ) the 78 pure spectra, presumably related to type 2 diabetes mellitus (T2DM), from their synthetic linear mixture; ( ii ) metabolites present in 62 1 H NMR spectra of urine of subjects with T2DM and 62 1 H NMR spectra of urine of control subjects. In both cases, the in-house library of 210 pure component 1 H NMR spectra represented the design matrix in the related NNLS problem. The proposed method pinpoints 63 metabolites that in a statistically significant way discriminate the T2DM group from the control group and 46 metabolites discriminating control from the T2DM group. For several T2DM-discriminative metabolites, we prove their presence by independent analytical determination or by pointing out the corresponding findings in the published literature.

Non-negative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine

Non-negative Least Squares Approach to Quantification of ¹H Nuclear Magnetic Resonance Spectra of Human Urine