Advertisement

Metabolomics

, 12:3 | Cite as

Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults

  • Danxia Yu
  • Steven C. Moore
  • Charles E. Matthews
  • Yong-Bing Xiang
  • Xianglan Zhang
  • Yu-Tang Gao
  • Wei Zheng
  • Xiao-Ou ShuEmail author
Original Article

Abstract

Metabolomic studies have identified several metabolites associated with type 2 diabetes (T2D) in populations of European ancestry. East Asians, a population of particular susceptibility to T2D, were generally not included in previous studies. We examined the associations of plasma metabolites with risk and prevalence of T2D in 976 Chinese men and women (40–74 years of age) who were participants of two prospective cohort studies and had no cardiovascular disease or cancer at baseline. Sixty-eight prevalent and 73 incident T2D cases were included. Non-targeted metabolomics was conducted that detected 689 metabolites with known identities and 690 unknown metabolites. Multivariable logistic and Cox regressions were used to evaluate the associations of standardized metabolites with diabetes risk and prevalence. We identified 36 known metabolites and 10 unknown metabolites associated with prevalent and/or incident T2D at false discovery rate <0.05. The known metabolites are involved in metabolic pathways of glycolysis/gluconeogenesis, branched-chain amino acids, other amino acids, fatty acids, glycerophospholipids, androgen, and bradykinin. Six metabolites showed independent associations with incident T2D: 1,5-anhydroglucitol, mannose, valine, 3-methoxytyrosine, docosapentaenoate (22:5n3), and bradykinin-hydroxy-pro(3). Each standard deviation increase in these metabolites was associated with a 40–150 % change in risk of developing diabetes (30–80 % after further adjustment for glucose). Risk prediction was significantly improved by adding these metabolites in addition to known T2D risk factors, including central obesity and glucose. These findings suggest that hexoses, branched-chain amino acids, and yet to be validated novel plasma metabolites may improve risk prediction and mechanistic understanding of T2D in Chinese populations.

Keywords

Metabolomics Type 2 diabetes Epidemiology Prospective cohort study Chinese populations 

Notes

Acknowledgments

We thank Dr. Joshua Sampson for his comments on the paper. We thank Ms. Nancy Kennedy for her assistance on preparing the manuscript. We thank the research team and participants of the Shanghai Women’s Health Study and the Shanghai Men’s Health Study for their foundation work for this study.

Compliance with Ethical Standards

Conflict of interest

The authors, including Danxia Yu, Steven C. Moore, Charles E. Matthews, Yong-Bing Xiang, Xianglan Zhang, Yu-Tang Gao, Wei Zheng, and Xiao-Ou Shu, have no conflict of interest to declare. The study uses existing data and specimens that have already been collected by the parent studies, i.e., the Shanghai Women’s Health Study and the Shanghai Men’s Health Study. All study participants provided informed consent to the parent studies.

Funding

This work was supported, in part, by the US National Institutes of Health [R37 CA070867 and UM1 CA182910 to Dr. W. Zheng, UM1 CA173640, R01 HL079123 and NO2-CP11010-66 to Dr. X.O. Shu]. This work was also supported, in part, by the Breast Cancer Research Stamp Fund, awarded through competitive peer review and the Intramural Research Program of the National Cancer Institute, National Institutes of Health.

References

  1. Alvim, R. O., Santos, P. C. J. L., Nascimento, R. M., Coelho, G. L. L. M., Mill, J. G., et al. (2012). BDKRB2 +9/− 9 polymorphism is associated with higher risk for diabetes mellitus in the Brazilian general population. Journal of Diabetes Research, 2012, e480251. doi: 10.1155/2012/480251.Google Scholar
  2. Aviles-Olmos, I., Dickson, J., Kefalopoulou, Z., Djamshidian, A., Ell, P., Soderlund, T., et al. (2013). Exenatide and the treatment of patients with Parkinson’s disease. Journal of Clinical Investigation, 123(6), 2730–2736. doi: 10.1172/JCI68295.PubMedPubMedCentralCrossRefGoogle Scholar
  3. Bain, J. R., Stevens, R. D., Wenner, B. R., Ilkayeva, O., Muoio, D. M., & Newgard, C. B. (2009). Metabolomics applied to diabetes research moving from information to knowledge. Diabetes, 58(11), 2429–2443. doi: 10.2337/db09-0580.PubMedPubMedCentralCrossRefGoogle Scholar
  4. Chan, J. C. N., Zhang, Y., & Ning, G. (2014). Diabetes in China: A societal solution for a personal challenge. The Lancet Diabetes & Endocrinology, 2(12), 969–979. doi: 10.1016/S2213-8587(14)70144-5.CrossRefGoogle Scholar
  5. Connolly, B. S., & Lang, A. E. (2014). Pharmacological treatment of parkinson disease: A review. JAMA, 311(16), 1670–1683. doi: 10.1001/jama.2014.3654.PubMedCrossRefGoogle Scholar
  6. DeFronzo, R. A. (2011). Bromocriptine: A sympatholytic, D2-dopamine agonist for the treatment of type 2 diabetes. Diabetes Care, 34(4), 789–794. doi: 10.2337/dc11-0064.PubMedPubMedCentralCrossRefGoogle Scholar
  7. Dungan, K. M., Buse, J. B., Largay, J., Kelly, M. M., Button, E. A., Kato, S., & Wittlin, S. (2006). 1,5-Anhydroglucitol and postprandial hyperglycemia as measured by continuous glucose monitoring system in moderately controlled patients with diabetes. Diabetes Care, 29(6), 1214–1219. doi: 10.2337/dc06-1910.PubMedCrossRefGoogle Scholar
  8. Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M., & Milgram, E. (2009). Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Analytical Chemistry, 81(16), 6656–6667. doi: 10.1021/ac901536h.PubMedCrossRefGoogle Scholar
  9. Ferrannini, E., Natali, A., Camastra, S., Nannipieri, M., Mari, A., Adam, K.-P., et al. (2013). Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes, 62(5), 1730–1737. doi: 10.2337/db12-0707.PubMedPubMedCentralCrossRefGoogle Scholar
  10. Floegel, A., Stefan, N., Yu, Z., Mühlenbruch, K., Drogan, D., Joost, H.-G., et al. (2013). Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes, 62(2), 639–648. doi: 10.2337/db12-0495.PubMedPubMedCentralCrossRefGoogle Scholar
  11. Gall, W. E., Beebe, K., Lawton, K. A., Adam, K.-P., Mitchell, M. W., Nakhle, P. J., et al. (2010). α-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.PubMedPubMedCentralCrossRefGoogle Scholar
  12. Goldstein, D. S., Eisenhofer, G., & Kopin, I. J. (2003). Sources and significance of plasma levels of catechols and their metabolites in humans. Journal of Pharmacology and Experimental Therapeutics, 305(3), 800–811. doi: 10.1124/jpet.103.049270.PubMedCrossRefGoogle Scholar
  13. Hodge, A. M., English, D. R., O’Dea, K., Sinclair, A. J., Makrides, M., Gibson, R. A., & Giles, G. G. (2007). Plasma phospholipid and dietary fatty acids as predictors of type 2 diabetes: Interpreting the role of linoleic acid. The American Journal of Clinical Nutrition, 86(1), 189–197.PubMedGoogle Scholar
  14. Hu, F. B. (2011). Globalization of diabetes the role of diet, lifestyle, and genes. Diabetes Care, 34(6), 1249–1257. doi: 10.2337/dc11-0442.PubMedPubMedCentralCrossRefGoogle Scholar
  15. Huang, T., Wahlqvist, M. L., Xu, T., Xu, A., Zhang, A., & Li, D. (2010). Increased plasma n-3 polyunsaturated fatty acid is associated with improved insulin sensitivity in type 2 diabetes in China. Molecular Nutrition & Food Research, 54(S1), S112–S119. doi: 10.1002/mnfr.200900189.CrossRefGoogle Scholar
  16. Kodama, K., Tojjar, D., Yamada, S., Toda, K., Patel, C. J., & Butte, A. J. (2013). Ethnic differences in the relationship between insulin sensitivity and insulin response a systematic review and meta-analysis. Diabetes Care, 36(6), 1789–1796. doi: 10.2337/dc12-1235.PubMedPubMedCentralCrossRefGoogle Scholar
  17. Kolodka, T., Charles, M. L., Raghavan, A., Radichev, I. A., Amatya, C., Ellefson, J., et al. (2014). Preclinical characterization of recombinant human tissue kallikrein-1 as a novel treatment for type 2 diabetes mellitus. PLoS ONE, 9(8), e103981. doi: 10.1371/journal.pone.0103981.PubMedPubMedCentralCrossRefGoogle Scholar
  18. Kröger, J., Zietemann, V., Enzenbach, C., Weikert, C., Jansen, E. H., Döring, F., et al. (2011). Erythrocyte membrane phospholipid fatty acids, desaturase activity, and dietary fatty acids in relation to risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam Study. The American Journal of Clinical Nutrition, 93(1), 127–142. doi: 10.3945/ajcn.110.005447.PubMedCrossRefGoogle Scholar
  19. Kusunoki, M., Tsutsumi, K., Nakayama, M., Kurokawa, T., Nakamura, T., Ogawa, H., et al. (2007). Relationship between serum concentrations of saturated fatty acids and unsaturated fatty acids and the homeostasis model insulin resistance index in Japanese patients with type 2 diabetes mellitus. The Journal of Medical Investigation, 54(3,4), 243–247. doi: 10.2152/jmi.54.243.PubMedCrossRefGoogle Scholar
  20. Li, G., Zhang, P., Wang, J., Gregg, E. W., Yang, W., Gong, Q., et al. (2008). The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: A 20-year follow-up study. Lancet, 371(9626), 1783–1789. doi: 10.1016/S0140-6736(08)60766-7.PubMedCrossRefGoogle Scholar
  21. Lu, J., Xie, G., Jia, W., & Jia, W. (2013). Metabolomics in human type 2 diabetes research. Frontiers of Medicine, 7(1), 4–13. doi: 10.1007/s11684-013-0248-4.PubMedCrossRefGoogle Scholar
  22. Lynch, C. J., & Adams, S. H. (2014). Branched-chain amino acids in metabolic signalling and insulin resistance. Nature Reviews Endocrinology, 10(12), 723–736. doi: 10.1038/nrendo.2014.171.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Ma, R. C. W., & Chan, J. C. N. (2013). Type 2 diabetes in East Asians: Similarities and differences with populations in Europe and the United States. Annals of the New York Academy of Sciences, 1281(1), 64–91. doi: 10.1111/nyas.12098.PubMedPubMedCentralCrossRefGoogle Scholar
  24. Ma, R. C. W., Lin, X., & Jia, W. (2014). Causes of type 2 diabetes in China. The Lancet Diabetes & Endocrinology, 2(12), 980–991. doi: 10.1016/S2213-8587(14)70145-7.CrossRefGoogle Scholar
  25. Marcondes, S., & Antunes, E. (2005). The plasma and tissue kininogen-kallikrein-kinin system: Role in the cardiovascular system. Current Medicinal Chemistry—Cardiovascular & Hematological Agents, 3(1), 33–44. doi: 10.2174/1568016052773351.CrossRefGoogle Scholar
  26. Mayers, J. R., Wu, C., Clish, C. B., Kraft, P., Torrence, M. E., Fiske, B. P., et al. (2014). Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nature Medicine, 20(10), 1193–1198. doi: 10.1038/nm.3686.PubMedPubMedCentralCrossRefGoogle Scholar
  27. Menni, C., Fauman, E., Erte, I., Perry, J. R. B., Kastenmüller, G., Shin, S.-Y., et al. (2013). Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes, 62(12), 4270–4276. doi: 10.2337/db13-0570.PubMedPubMedCentralCrossRefGoogle Scholar
  28. Montanari, D., Yin, H., Dobrzynski, E., Agata, J., Yoshida, H., Chao, J., & Chao, L. (2005). Kallikrein gene delivery improves serum glucose and lipid profiles and cardiac function in streptozotocin-induced diabetic rats. Diabetes, 54(5), 1573–1580. doi: 10.2337/diabetes.54.5.1573.PubMedCrossRefGoogle Scholar
  29. Mook-Kanamori, D. O., Selim, M. M. E.-D., Takiddin, A. H., Al-Homsi, H., Al-Mahmoud, K. A. S., Al-Obaidli, A., et al. (2014). 1,5-Anhydroglucitol in saliva is a noninvasive marker of short-term glycemic control. The Journal of Clinical Endocrinology & Metabolism, 99(3), E479–E483. doi: 10.1210/jc.2013-3596.Google Scholar
  30. Moore, S. C., Matthews, C. E., Sampson, J. N., Stolzenberg-Solomon, R. Z., Zheng, W., Cai, Q., et al. (2014). Human metabolic correlates of body mass index. Metabolomics, 10(2), 259–269. doi: 10.1007/s11306-013-0574-1.PubMedPubMedCentralCrossRefGoogle Scholar
  31. Nagata, C., Nakamura, K., Wada, K., Tsuji, M., Tamai, Y., & Kawachi, T. (2013). Branched-chain amino acid intake and the risk of diabetes in a japanese community the takayama study. American Journal of Epidemiology, 178(8), 1226–1232. doi: 10.1093/aje/kwt112.PubMedCrossRefGoogle Scholar
  32. Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., et al. (2009). 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.PubMedPubMedCentralCrossRefGoogle Scholar
  33. Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: Systematic review. BMJ, 343, d7163. doi: 10.1136/bmj.d7163.PubMedPubMedCentralCrossRefGoogle Scholar
  34. Pan, A., Lucas, M., Sun, Q., et al. (2010). BIdirectional association between depression and type 2 diabetes mellitus in women. Archives of Internal Medicine, 170(21), 1884–1891. doi: 10.1001/archinternmed.2010.356.PubMedPubMedCentralCrossRefGoogle Scholar
  35. Pan, A., Schernhammer, E. S., Sun, Q., & Hu, F. B. (2011). Rotating night shift work and risk of type 2 diabetes: Two prospective cohort studies in women. PLoS Med, 8(12), e1001141. doi: 10.1371/journal.pmed.1001141.PubMedPubMedCentralCrossRefGoogle Scholar
  36. Patel, P. S., Sharp, S. J., Jansen, E., Luben, R. N., Khaw, K.-T., Wareham, N. J., & Forouhi, N. G. (2010). Fatty acids measured in plasma and erythrocyte-membrane phospholipids and derived by food-frequency questionnaire and the risk of new-onset type 2 diabetes: A pilot study in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Norfolk cohort. The American Journal of Clinical Nutrition, 92(5), 1214–1222. doi: 10.3945/ajcn.2010.29182.PubMedCrossRefGoogle Scholar
  37. Qin, L.-Q., Xun, P., Bujnowski, D., Daviglus, M. L., Horn, L. V., Stamler, J., & He, K. (2011). Higher branched-chain amino acid intake is associated with a lower prevalence of being overweight or obese in middle-aged east asian and western adults. The Journal of Nutrition, 141(2), 249–254. doi: 10.3945/jn.110.128520.PubMedPubMedCentralCrossRefGoogle Scholar
  38. Sampson, J. N., Boca, S. M., Shu, X. O., Stolzenberg-Solomon, R. Z., Matthews, C. E., Hsing, A. W., et al. (2013). Metabolomics in epidemiology: Sources of variability in metabolite measurements and implications. Cancer Epidemiology, Biomarkers & Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 22(4), 631–640. doi: 10.1158/1055-9965.EPI-12-1109.CrossRefGoogle Scholar
  39. Schoepf, D., Potluri, R., Uppal, H., Natalwala, A., Narendran, P., & Heun, R. (2012). Type-2 diabetes mellitus in schizophrenia: Increased prevalence and major risk factor of excess mortality in a naturalistic 7-year follow-up. European Psychiatry, 27(1), 33–42. doi: 10.1016/j.eurpsy.2011.02.009.PubMedCrossRefGoogle Scholar
  40. Shimomura, Y., Murakami, T., Nakai, N., Nagasaki, M., & Harris, R. A. (2004). Exercise promotes BCAA catabolism: Effects of BCAA supplementation on skeletal muscle during exercise. The Journal of Nutrition, 134(6), 1583S–1587S.PubMedGoogle Scholar
  41. Shu, X.-O., Li, H., Yang, G., Gao, J., Cai, H., Takata, Y., et al. (2015). Cohort profile: The shanghai men’s health study. International Journal of Epidemiology, dyv013. doi: 10.1093/ije/dyv013.
  42. Suhre, K. (2014). Metabolic profiling in diabetes. Journal of Endocrinology, 221(3), R75–R85. doi: 10.1530/JOE-14-0024.PubMedCrossRefGoogle Scholar
  43. Suhre, K., Meisinger, C., Döring, A., Altmaier, E., Belcredi, P., Gieger, C., et al. (2010). Metabolic footprint of diabetes: A multiplatform metabolomics study in an epidemiological setting. PLoS ONE, 5(11), e13953. doi: 10.1371/journal.pone.0013953.PubMedPubMedCentralCrossRefGoogle Scholar
  44. Tai, E. S., Tan, M. L. S., Stevens, R. D., Low, Y. L., Muehlbauer, M. J., Goh, D. L. M., et al. (2010). Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia, 53(4), 757–767. doi: 10.1007/s00125-009-1637-8.PubMedPubMedCentralCrossRefGoogle Scholar
  45. Taneera, J., Lang, S., Sharma, A., Fadista, J., Zhou, Y., Ahlqvist, E., et al. (2012). A Systems genetics approach identifies genes and pathways for type 2 diabetes in human islets. Cell Metabolism, 16(1), 122–134. doi: 10.1016/j.cmet.2012.06.006.PubMedCrossRefGoogle Scholar
  46. The World Bank (2011) Toward a healthy and harmonious life in china: Stemming the rising tide of non-communicable diseases (No. World Bank Report Number 62318-CN).Google Scholar
  47. Tiffin, N., Adie, E., Turner, F., Brunner, H. G., van Driel, M. A., Oti, M., et al. (2006). Computational disease gene identification: A concert of methods prioritizes type 2 diabetes and obesity candidate genes. Nucleic Acids Research, 34(10), 3067–3081. doi: 10.1093/nar/gkl381.PubMedPubMedCentralCrossRefGoogle Scholar
  48. Tillin, T., Hughes, A. D., Wang, Q., Würtz, P., Ala-Korpela, M., Sattar, N., et al. (2015). Diabetes risk and amino acid profiles: Cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a South Asian and European cohort from the SABRE (Southall And Brent REvisited) Study. Diabetologia, 58(5), 968–979. doi: 10.1007/s00125-015-3517-8.PubMedPubMedCentralCrossRefGoogle Scholar
  49. Tomita, H., Sanford, R. B., Smithies, O., & Kakoki, M. (2012). The kallikrein–kinin system in diabetic nephropathy. Kidney International, 81(8), 733–744. doi: 10.1038/ki.2011.499.PubMedPubMedCentralCrossRefGoogle Scholar
  50. Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P., McCabe, E., et al. (2011). Metabolite profiles and the risk of developing diabetes. Nature Medicine, 17(4), 448–453. doi: 10.1038/nm.2307.PubMedPubMedCentralCrossRefGoogle Scholar
  51. Wang-Sattler, R., Yu, Z., Herder, C., Messias, A. C., Floegel, A., He, Y., et al. (2012). Novel biomarkers for pre-diabetes identified by metabolomics. Molecular Systems Biology, 8(1), 615. doi: 10.1038/msb.2012.43.PubMedPubMedCentralGoogle Scholar
  52. Xu, F., Tavintharan, S., Sum, C. F., Woon, K., Lim, S. C., & Ong, C. N. (2013a). Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. The Journal of Clinical Endocrinology & Metabolism, 98(6), E1060–E1065. doi: 10.1210/jc.2012-4132.CrossRefGoogle Scholar
  53. Xu, Y., Wang, L., He, J., et al. (2013b). Prevalence and control of diabetes in Chinese adults. JAMA, 310(9), 948–959. doi: 10.1001/jama.2013.168118.PubMedCrossRefGoogle Scholar
  54. Zhang, G., Sun, Q., Hu, F. B., Ye, X., Yu, Z., Zong, G., et al. (2012). Erythrocyte n-3 fatty acids and metabolic syndrome in middle-aged and older Chinese. The Journal of Clinical Endocrinology and Metabolism, 97(6), E973–E977. doi: 10.1210/jc.2011-2997.PubMedCrossRefGoogle Scholar
  55. Zheng, W., Chow, W.-H., Yang, G., Jin, F., Rothman, N., Blair, A., et al. (2005). The Shanghai Women’s Health Study: Rationale, study design, and baseline characteristics. American Journal of Epidemiology, 162(11), 1123–1131. doi: 10.1093/aje/kwi322.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Danxia Yu
    • 1
  • Steven C. Moore
    • 2
  • Charles E. Matthews
    • 2
  • Yong-Bing Xiang
    • 3
  • Xianglan Zhang
    • 1
  • Yu-Tang Gao
    • 3
  • Wei Zheng
    • 1
  • Xiao-Ou Shu
    • 1
    Email author
  1. 1.Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology CenterVanderbilt University School of MedicineNashvilleUSA
  2. 2.Division of Cancer Epidemiology and GeneticsNational Cancer InstituteBethesdaUSA
  3. 3.Shanghai Cancer Institute, Renji HospitalShanghai Jiaotong University School of MedicineShanghaiChina

Personalised recommendations