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Metabolomics study of type 2 diabetes using ultra-performance LC-ESI/quadrupole-TOF high-definition MS coupled with pattern recognition methods

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Abstract

Type 2 diabetes (T2D), called the burden of the twenty-first century, is growing with an epidemic rate. Here, we explored the differences in metabolite concentrations between T2D patients and healthy volunteers. Metabolomics represents an emerging discipline concerned with comprehensive analysis of small molecule metabolites and provides a powerful approach to discover biomarkers in biological systems. The acquired data were analyzed by ultra-performance liquid chromatography–electrospray ionization/quadrupole time-of-flight high-definition mass spectrometry coupled with pattern recognition approach [principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)] to identify potential disease-specific biomarkers. PCA showed satisfactory clustering between patients and healthy volunteers. Biomarkers reflected the biochemical events associated with early stages of T2D which were observed in PLS-DA loading plots. These urinary differential metabolites, such as adiponectin, acylcarnitines, citric acid, kynurenic acid, 3-indoxyl sulfate, urate, and glucose, were identified involving several key metabolic pathways such as taurine and hypotaurine metabolism; cysteine and methionine metabolism; valine, leucine, and isoleucine biosynthesis metabolism, etc. Our data suggest that robust metabolomics has the potential as a noninvasive strategy to evaluate the early diagnosis of T2D patients and provides new insight into pathophysiologic mechanisms and may enhance the understanding of its cause of disease.

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Acknowledgments

This work was supported by grants from the Key Program of Natural Science Foundation of State (grant nos. 90709019, 81173500, 81302905 and 81202639), the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (grant nos. 2011BAI03B03, 2011BAI03B06, and 2011BAI03B08), the Key Science and Technology Program of Heilongjiang Province, China (grant nos. GC06C501, GA08C303, and GA06C30101), the National Specific Program on the Subject of Public Welfare (grant no. 200807014), the National Key Subject of Drug Innovation (grant no. 2009ZX09502-005), the Foundation of Heilongjiang University of Chinese Medicine (grant no. 201209), and the Key Project of Chinese Ministry of Education (grant no. 212044). We thank Gao-chen Song, Xiao-xia Yuan, Liu Li, and Ning Xie for their assistance in the experiment.

Conflict of interest

The authors declare no competing financial interests.

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Authors and Affiliations

  1. National TCM Key Lab of Serum Pharmacochemistry, Key Lab of Chinmedomics, Key Pharmacometabolomic Platform of Chinese Medicines, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China

    Ai-hua Zhang, Hui Sun, Guang-li Yan, Ye Yuan, Ying Han & Xi-jun Wang

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  1. Ai-hua Zhang
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  2. Hui Sun
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  3. Guang-li Yan
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  4. Ye Yuan
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Correspondence to Ai-hua Zhang or Xi-jun Wang.

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Zhang, Ah., Sun, H., Yan, Gl. et al. Metabolomics study of type 2 diabetes using ultra-performance LC-ESI/quadrupole-TOF high-definition MS coupled with pattern recognition methods. J Physiol Biochem 70, 117–128 (2014). https://doi.org/10.1007/s13105-013-0286-z

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  • DOI: https://doi.org/10.1007/s13105-013-0286-z

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