Serum metabolomics profile of type 2 diabetes mellitus in a Brazilian rural population
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The development of common forms of diabetes comes from the interaction between environmental and genetic factors, and the consequences of poor glycemic control in these patients could result in several complications. Metabolomic studies for type 2 diabetes mellitus in serum/plasma have reported changes in numerous metabolites, which might be considered possible targets for future mechanistic research. However, the specific role of a particular metabolite as cause or consequence of diabetes derangements is difficult to establish.
As type 2 diabetes is a disease that changes the metabolic profile in several levels, this work aimed to compare the metabolomic profiles of type 2 diabetes mellitus and non-diabetic participants. In addition, we exploited our family-based study design to bring a better understanding of the causal relationship of identified metabolites and diabetes.
In the current study, population based metabolomics was applied in 939 subjects from the Baependi Heart Study. Participants were separated into two groups: diabetic (77 individuals) and non-diabetic (862 individuals), and the metabolic profile was performed by GC/MS technique.
We have identified differentially concentrated metabolites in serum of diabetic and non-diabetic individuals. We identified 72 metabolites up-regulated in type 2 diabetes mellitus compared to non-diabetics. It was possible to recapitulate the main pathways that the literature shows as changed in diabetes. Also, based on metabolomic profile, we separated pre-diabetic individuals (with glucose concentration between 100–125 mg/dL) from non-diabetics and diabetics. Finally, using heritability analysis, we were able to suggest metabolites in which altered levels may precede diabetic development.
Our data can be used to derive a better understanding of the causal relationship of the observed associations and help to prioritize diabetes-associated metabolites for further work.
KeywordsMetabolomics Heritability Type 2 diabetes mellitus Gas chromatography–mass spectrometry
We acknowledge the Agilent Technologies Brasil Ltda, Life Sciences & Chemical Analysis for the use of the GC/MS system (7890B gas chromatograph coupled to a mass selective detector model 5977A-Agilent) for support data collection.
This work was supported by Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP 2012/05447-0, 2012/12042-7, 2013-17368-0, PROADI_ SUS Project nº 25000.180664/2011-35).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All experiments were carried out in accordance with the ethics committee of the Hospital das Clinicas, University of São Paulo, Brazil (protocol number 3759/12/015), approved the study protocol.
All participants signed a written informed consent.
- Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. application in 1H NMR metabonomics. Analytical Chemistry, 78(13), 4281–4290. doi: 10.1021/ac051632c.CrossRefPubMedGoogle Scholar
- Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., et al. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6(7), 1060–1083. doi: 10.1038/nprot.2011.335.CrossRefPubMedGoogle Scholar
- Fiehn, O., Garvey, W. T., Newman, J. W., Lok, K. H., Hoppel, C. L., & Adams, S. H. (2010). Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One, 5(12), e15234. doi: 10.1371/journal.pone.0015234.CrossRefPubMedPubMedCentralGoogle Scholar
- Gall, W. E., Beebe, K., Lawton, K. A., Adam, K.-P., Mitchell, M. W., Nakhle, P. J., et al. (2010). Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS One, 5(5), e10883. doi: 10.1371/journal.pone.0010883.CrossRefPubMedPubMedCentralGoogle Scholar
- Horimoto, A. R. V. R., Giolo, S. R., Oliveira, C. M., Alvim, R. O., Soler, J. P., de Andrade, M., et al. (2011). Heritability of physical activity traits in Brazilian families: The Baependi Heart Study. BMC Medical Genetics, 12(1), 155. doi: 10.1186/1471-2350-12-155.CrossRefPubMedPubMedCentralGoogle Scholar
- Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., et al. (2013). A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metabolism, 9(4), 311–326. doi: 10.1016/j.cmet.2009.02.002.A.CrossRefGoogle Scholar
- Sharma, A., Chavali, S., Mahajan, A., Tabassum, R., Banerjee, V., Tandon, N., et al. (2005). Genetic association, post-translational modification, and protein-protein interactions in type 2 diabetes mellitus. Molecular & Cellular Proteomics: MCP, 4(8), 1029–1037. doi: 10.1074/mcp.M500024-MCP200.CrossRefGoogle Scholar
- Swan, J. W., Anker, S. D., Walton, C., Godsland, I. F., Clark, A. L., Leyva, F., et al. (1997). Insulin resistance in chronic heart failure: Relation to severity and etiology of heart failure. Journal of the American College of Cardiology, 30(2), 527–532. doi: 10.1016/S0735-1097(97)00185-X.CrossRefPubMedGoogle Scholar
- van den Berg, R. A., Hoefsloot, H. C. J., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC genomics, 7, 142. doi: 10.1186/1471-2164-7-142.CrossRefPubMedPubMedCentralGoogle Scholar
- Wang, S., Kuo, C., & Tseng, Y. J. (2013). Batch normalizer: A fast total abundance regression calibration method to simultaneouly adjust batch and injection order effects in liquid chromatography/time-of-fligth mass spectrometry-based metabolomics data and comparison with current calibration meth. Analytical Chemistry, 85, 1037–1046.CrossRefPubMedGoogle Scholar
- WHO. (2003). Screening for type 2 diabetes.Google Scholar
- WHO. (1999) Definition, diagnosis and classification of diabetes mellitus and its complications.Google Scholar
- Xu, F., Tavintharan, S., Sum, C. F., Woon, K., Lim, S. C., & Ong, C. N. (2013). Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. The Journal of Clinical Endocrinology and Metabolism, 98(6), E1060–E1065. doi: 10.1210/jc.2012-4132.CrossRefPubMedGoogle Scholar
- Zelena, E., Dunn, W. B., Broadhurst, D., Francis-McIntyre, S., Carroll, K. M., Begley, P., et al. (2009). Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Analytical Chemistry, 81(4), 1357–1364. doi: 10.1021/ac8019366.CrossRefPubMedGoogle Scholar