Systemic Perturbations of Key Metabolites in Diabetic Rats During the Evolution of Diabetes Studied by Urine Metabonomics

Abstract

BackgroundElucidation of metabolic profiles during diabetes progression helps understand the pathogenesis of diabetes mellitus. In this study, urine metabonomics was used to identify time-related metabolic changes that occur during the development of diabetes mellitus and characterize the biochemical process of diabetes on a systemic, metabolic level.Methodology/Principal FindingsUrine samples were collected from diabetic rats and age-matched controls at different time points: 1, 5, 10, and 15 weeks after diabetes modeling. 1H nuclear magnetic resonance (1H NMR) spectra of the urine samples were obtained and analyzed by multivariate data analysis and quantitative statistical analysis. The metabolic patterns of diabetic groups are separated from the controls at each time point, suggesting that the metabolic profiles of diabetic rats were markedly different from the controls. Moreover, the samples from the diabetic 1-wk group are closely associated, whereas those of the diabetic 15-wk group are scattered, suggesting that the presence of various of complications contributes significantly to the pathogenesis of diabetes. Quantitative analysis indicated that urinary metabolites related to energy metabolism, tricarboxylic acid (TCA) cycle, and methylamine metabolism are involved in the evolution of diabetes.Conclusions/SignificanceThe results highlighted that the numbers of metabolic changes were related to diabetes progression, and the perturbed metabolites represent potential metabolic biomarkers and provide clues that can elucidate the mechanisms underlying the generation and development of diabetes as well as its complication.

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Identification of Energy Metabolism Changes in Diabetic Cardiomyopathy Rats Using a Metabonomic Approach

“…The detailed acquisition protocol of 1 H-NMR spectra is described in our previous reports [19,22]. All NMR spectra were phase-and baseline-corrected, and then data-reduced to 1100 integrated regions with widths of 0.01 ppm corresponding to the region of δ10 to -1 using the Topspin 2.1 software package (Bruker Corp., Karlsruhe, Germany) for multivariate pattern recognition analysis.…”

“…We previously used this approach and subsequently observed significant changes in energy metabolites, including higher levels of lipids, ketone bodies, and lactate, in the serum of T2DM rats [15,16]. We also have shown that a reduction in energy and the disequilibrium of lipids and methylamine metabolism are closely related to the pathogenic progression of diabetic nephropathy (DN) [17][18][19]. In the present study, we used quantitative and correlative analysis of 1 H NMR spectra of isolated cardiac tissues obtained from diabetic rats to identify the characteristic changes in energy metabolism.…”