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A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes

  • Park, Seokjae (Department of Brain & Cognitive Sciences, Daegu Gyeongbuk Institute of Science & Technology) ;
  • Sadanala, Krishna Chaitanya (Neurometabolomics Research Center, Daegu Gyeongbuk Institute of Science & Technology) ;
  • Kim, Eun-Kyoung (Department of Brain & Cognitive Sciences, Daegu Gyeongbuk Institute of Science & Technology)
  • Received : 2015.05.15
  • Accepted : 2015.05.26
  • Published : 2015.07.31

Abstract

Obesity and diabetes arise from an intricate interplay between both genetic and environmental factors. It is well recognized that obesity plays an important role in the development of insulin resistance and diabetes. Yet, the exact mechanism of the connection between obesity and diabetes is still not completely understood. Metabolomics is an analytical approach that aims to detect and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics to the identification of disease biomarkers, with a number of well-known biomarkers identified. Metabolomics is a potent approach to unravel the intricate relationships between metabolism, obesity and progression to diabetes and, at the same time, has potential as a clinical tool for risk evaluation and monitoring of disease. Moreover, metabolomics applications have revealed alterations in the levels of metabolites related to obesity-associated diabetes. This review focuses on the part that metabolomics has played in elucidating the roles of metabolites in the regulation of systemic metabolism relevant to obesity and diabetes. It also explains the possible metabolic relation and association between the two diseases. The metabolites with altered profiles in individual disorders and those that are specifically and similarly altered in both disorders are classified, categorized and summarized.

Keywords

References

  1. Abu Bakar, M.H., Sarmidi, M.R., Cheng, K.K., Ali Khan, A., Suan, C.L., Zaman Huri, H., and Yaakob, H. (2015). Metabolomics- the complementary field in systems biology: a review on obesity and type 2 diabetes. Mol. Biosyst. [Epub ahead of print].
  2. Adams, S.H. (2011). Emerging perspectives on essential amino acid metabolism in obesity and the insulin-resistant state. Adv. Nutr. 2, 445-456. https://doi.org/10.3945/an.111.000737
  3. Backman, L., Hallberg, D., and Kallner, A. (1975). Amino acid pattern in plasma before and after jejuno-ileal shunt operation for obesity. Scand J. Gastroenterol. 10, 811-816.
  4. Bak, J.F., Moller, N., Schmitz, O., Saaek, A., and Pedersen, O. (1992). In vivo insulin action and muscle glycogen synthase activity in type 2 (non-insulin-dependent) diabetes mellitus: effects of diet treatment. Diabetologia 35, 777-784.
  5. Bala, L., Ghoshal, U.C., Ghoshal, U., Tripathi, P., Misra, A., Gowda, G.A., and Khetrapal, C.L. (2006). Malabsorption syndrome with and without small intestinal bacterial overgrowth: a study on upper-gut aspirate using 1H NMR spectroscopy. Magn. Reson. Med. 56, 738-744. https://doi.org/10.1002/mrm.21041
  6. Bao, Y., Zhao, T., Wang, X., Qiu, Y., Su, M., and Jia, W. (2009). Metabonomic variations in the drug-treated type 2 diabetes mellitus patients and healthy volunteers. J. Proteome Res. 8, 1623-1630. https://doi.org/10.1021/pr800643w
  7. Barbas, C., Moraes, E.P., and Villasenor, A. (2011). Capillary electrophoresis as a metabolomics tool for non-targeted fingerprinting of biological samples. J. Pharm. Biomed. Anal. 55, 823-831. https://doi.org/10.1016/j.jpba.2011.02.001
  8. Beger, R.D. (2013). A review of applications of metabolomics in cancer. Metabolites 3, 552-574. https://doi.org/10.3390/metabo3030552
  9. Bentley-Lewis, R., Xiong, G., Lee, H., Yang, A., Huynh, J., and Kim, C. (2014). Metabolomic analysis reveals amino acid responses to an oral glucose tolerance test in women with prior history of gestational diabetes mellitus. J. Clin. Transl. Endocrinol. 1, 38- 43. https://doi.org/10.1016/j.jcte.2014.03.003
  10. Berndt, J., Kloting, N., Kralisch, S., Kovacs, P., Fasshauer, M., Schon, M.R., Stumvoll, M., and Bluher, M. (2005). Plasma visfatin concentrations and fat depot-specific mRNA expression in humans. Diabetes 54, 2911-2916. https://doi.org/10.2337/diabetes.54.10.2911
  11. Blusztajn, J.K. (1998). Choline, a vital amine. Science 281, 794-795. https://doi.org/10.1126/science.281.5378.794
  12. Boling, C.L., Westman, E.C., and Yancy, W.S., Jr. (2009). Carbohydrate-restricted diets for obesity and related diseases: an update. Curr. Atheroscler. Rep. 11, 462-469. https://doi.org/10.1007/s11883-009-0069-8
  13. Bollard, M.E., Stanley, E.G., Lindon, J.C., Nicholson, J.K., and Holmes, E. (2005). NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR Biomed. 18, 143-162. https://doi.org/10.1002/nbm.935
  14. Bray, G.A. (2004). Medical consequences of obesity. J. Clin. Endocrinol. Metab. 89, 2583-2589. https://doi.org/10.1210/jc.2004-0535
  15. Brennan, L., Shine, A., Hewage, C., Malthouse, J.P., Brindle, K.M., McClenaghan, N., Flatt, P.R., and Newsholme, P. (2002). A nuclear magnetic resonance-based demonstration of substantial oxidative L-alanine metabolism and L-alanine-enhanced glucose metabolism in a clonal pancreatic beta-cell line: metabolism of Lalanine is important to the regulation of insulin secretion. Diabetes 51, 1714-1721. https://doi.org/10.2337/diabetes.51.6.1714
  16. Carr, P.W., Stoll, D.R., and Wang, X. (2011). Perspectives on recent advances in the speed of high-performance liquid chromatography. Anal. Chem. 83, 1890-1900. https://doi.org/10.1021/ac102570t
  17. Cha, Y.S. (2008). Effects of L-carnitine on obesity, diabetes, and as an ergogenic aid. Asia Pac. J. Clin. Nutr. 17, 306-308.
  18. Chan, J.C., Malik, V., Jia, W., Kadowaki, T., Yajnik, C.S., Yoon, K.H., and Hu, F.B. (2009). Diabetes in Asia: epidemiology, risk factors, and pathophysiology. JAMA 301, 2129-2140. https://doi.org/10.1001/jama.2009.726
  19. Charles, S., and Henquin, J.C. (1983). Distinct effects of various amino acids on 45$Ca^{2+}$ fluxes in rat pancreatic islets. Biochem. J. 214, 899-907. https://doi.org/10.1042/bj2140899
  20. Chen, J., Zhao, X., Fritsche, J., Yin, P., Schmitt-Kopplin, P., Wang, W., Lu, X., Haring, H.U., Schleicher, E.D., Lehmann, R., et al. (2009). Practical approach for the identification and isomer elucidation of biomarkers detected in a metabonomic study for the discovery of individuals at risk for diabetes by integrating the chromatographic and mass spectrometric information. Anal. Chem. 80, 1280-1289.
  21. Cheng, S., Rhee, E.P., Larson, M.G., Lewis, G.D., McCabe, E.L., Shen, D., Palma, M.J., Roberts, L.D., Dejam, A., Souza, A.L., et al. (2012). Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125, 2222-2231. https://doi.org/10.1161/CIRCULATIONAHA.111.067827
  22. Chevalier, S., Marliss, E.B., Morais, J.A., Lamarche, M., and Gougeon, R. (2005). Whole-body protein anabolic response is resistant to the action of insulin in obese women. Am. J. Clin. Nutr. 82, 355-365. https://doi.org/10.1093/ajcn/82.2.355
  23. Cho, Y., Ahmed, A., Islam, A., and Kim, S. (2015). Developments in FT-ICR MS instrumentation, ionization techniques, and data interpretation methods for petroleomics. Mass Spectrom. Rev. 34, 248-263. https://doi.org/10.1002/mas.21438
  24. Chylla, R.A., Hu, K., Ellinger, J.J., and Markley, J.L. (2011). Deconvolution of two-dimensional NMR spectra by fast maximum likelihood reconstruction: application to quantitative metabolomics. Anal. Chem. 83, 4871-4880. https://doi.org/10.1021/ac200536b
  25. Courant, F., Royer, A.L., Chereau, S., Morvan, M.L., Monteau, F., Antignac, J.P., and Le Bizec, B. (2012). Implementation of a semi-automated strategy for the annotation of metabolomic fingerprints generated by liquid chromatography-high resolution mass spectrometry from biological samples. Analyst 137, 4958- 4967. https://doi.org/10.1039/c2an35865d
  26. Despres, J.P., Moorjani, S., Lupien, P.J., Tremblay, A., Nadeau, A., and Bouchard, C. (1992). Genetic aspects of susceptibility to obesity and related dyslipidemias. Mol. Cell Biochem. 113, 151- 169.
  27. Di Virgilio, F., and Solini, A. (2002). P2 receptors: new potential players in atherosclerosis. Br J. Pharmacol. 135, 831-842. https://doi.org/10.1038/sj.bjp.0704524
  28. Diao, C., Zhao, L., Guan, M., Zheng, Y., Chen, M., Yang, Y., Lin, L., Chen, W., and Gao, H. (2014). Systemic and characteristic metabolites in the serum of streptozotocin-induced diabetic rats at different stages as revealed by a (1)H-NMR based metabonomic approach. Mol. Biosyst. 10, 686-693. https://doi.org/10.1039/c3mb70609e
  29. Dixon, G., Nolan, J., McClenaghan, N., Flatt, P.R., and Newsholme, P. (2003). A comparative study of amino acid consumption by rat islet cells and the clonal beta-cell line BRIN-BD11-the functional significance of L-alanine. J. Endocrinol. 179, 447-454. https://doi.org/10.1677/joe.0.1790447
  30. Drogan, D., Dunn, W.B., Lin, W., Buijsse, B., Schulze, M.B., Langenberg, C., Brown, M., Floegel, A., Dietrich, S., Rolandsson, O., et al. (2015). Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study. Clin. Chem. 61, 487-497. https://doi.org/10.1373/clinchem.2014.228965
  31. Du, F., Virtue, A., Wang, H., and Yang, X.F. (2013). Metabolomic analyses for atherosclerosis, diabetes, and obesity. Biomark Res. 1, 17. https://doi.org/10.1186/2050-7771-1-17
  32. Dudzinska, W. (2014). Purine nucleotides and their metabolites in patients with type 1 and 2 diabetes mellitus. J. Biomed. Sci. Eng. 7, 38-44. https://doi.org/10.4236/jbise.2014.71006
  33. Felig, P., Marliss, E., and Cahill, G.F., Jr. (1969). Plasma amino acid levels and insulin secretion in obesity. N Engl. J. Med. 281, 811-816. https://doi.org/10.1056/NEJM196910092811503
  34. Felig, P., Wahren, J., Hendler, R., and Brundin, T. (1974). Splanchnic glucose and amino acid metabolism in obesity. J. Clin. Invest. 53, 582-590. https://doi.org/10.1172/JCI107593
  35. Fiehn, O., Garvey, W.T., Newman, J.W., Lok, K.H., Hoppel, C.L., and 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, e15234. https://doi.org/10.1371/journal.pone.0015234
  36. Floegel, A., Stefan, N., Yu, Z., Muhlenbruch, K., Drogan, D., Joost, H.G., Fritsche, A., Haring, H.U., Hrabꠓ de Angelis, M., Peters, A., et al. (2013). Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62, 639-648. https://doi.org/10.2337/db12-0495
  37. Ford, E.S., Williamson, D.F., and Liu, S. (1997). Weight change and diabetes incidence: findings from a national cohort of US adults. Am. J. Epidemiol. 146, 214-222. https://doi.org/10.1093/oxfordjournals.aje.a009256
  38. Franz, M.J. (2008). Medical nutrition therapy for diabetes mellitus and hypoglycemia of nondiabetic origin. In Food, Nutrition and Diet therapy (Canada, Saunders).
  39. Freidenberg, G.R., Reichart, D., Olefsky, J.M., and Henry, R.R. (1988). Reversibility of defective adipocyte insulin receptor kinase activity in non-insulin-dependent diabetes mellitus. Effect of weight loss. J. Clin. Invest. 82, 1398-1406. https://doi.org/10.1172/JCI113744
  40. Friedrich, N. (2012). Metabolomics in diabetes research. Metabolomics in human type 2 diabetes research. J. Endocrinol. 215, 29-42. https://doi.org/10.1530/JOE-12-0120
  41. Gibellini, F., and Smith, T.K. (2010). The Kennedy pathway--De novo synthesis of phosphatidylethanolamine and phosphatidylcholine. IUBMB Life 62, 414-428.
  42. Goek, O.N., Doring, A., Gieger, C., Heier, M., Koenig, W., Prehn, C., Romisch-Margl, W., Wang-Sattler, R., Illig, T., Suhre, K., et al. (2012). Serum metabolite concentrations and decreased GFR in the general population. Am. J. Kidney Dis. 60, 197-206. https://doi.org/10.1053/j.ajkd.2012.01.014
  43. Gogna, N., Krishna, M., Oommen, A.M., and Dorai, K. (2015). Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach. Mol. Biosyst. 11, 595-606. https://doi.org/10.1039/C4MB00507D
  44. Golay, A., Swislocki, A.L., Chen, Y.D., Jaspan, J.B., and Reaven, G.M. (1986). Effect of obesity on ambient plasma glucose, free fatty acid, insulin, growth hormone, and glucagon concentrations. J. Clin. Endocrinol. Metab. 63, 481-484. https://doi.org/10.1210/jcem-63-2-481
  45. Goodacre, R., Vaidyanathan, S., Dunn, W.B., Harrigan, G.G., and Kell, D.B. (2004). Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol. 22, 245-252. https://doi.org/10.1016/j.tibtech.2004.03.007
  46. Gougeon, R., Morais, J.A., Chevalier, S., Pereira, S., Lamarche, M., and Marliss, E.B. (2008). Determinants of whole-body protein metabolism in subjects with and without type 2 diabetes. Diabetes Care 31, 128-133. https://doi.org/10.2337/dc07-1268
  47. Gowda, G.A., Ijare, O.B., Somashekar, B.S., Sharma, A., Kapoor, V.K., and Khetrapal, C.L. (2006). Single-step analysis of individual conjugated bile acids in human bile using 1H NMR spectroscopy. Lipids 41, 591-603. https://doi.org/10.1007/s11745-006-5008-7
  48. Griffin, J.L., and Kauppinen, R.A. (2007). Tumour metabolomics in animal models of human cancer. J. Proteome Res. 6, 498-505. https://doi.org/10.1021/pr060464h
  49. Guan, M., Xie, L., Diao, C., Wang, N., Hu, W., Zheng, Y., Jin, L., Yan, Z., and Gao, H. (2013). Systemic perturbations of key metabolites in diabetic rats during the evolution of diabetes studied by urine metabonomics. PLoS One 8, e60409. https://doi.org/10.1371/journal.pone.0060409
  50. Halaas, J.L., Gajiwala, K.S., Maffei, M., Cohen, S.L., Chait, B.T., Rabinowitz, D., Lallone, R.L., Burley, S.K., and Friedman, J.M. (1995). Weight-reducing effects of the plasma protein encoded by the obese gene. Science 269, 543-546. https://doi.org/10.1126/science.7624777
  51. Haslam, D.W., and James, W.P. (2005). Effect of obesity on the incidence of type 2 diabetes mellitus varies with age among Indian women. Lancet 366, 1197-1209. https://doi.org/10.1016/S0140-6736(05)67483-1
  52. He, Q., Ren, P., Kong, X., Wu, Y., Wu, G., Li, P., Hao, F., Tang, H., Blachier, F., and Yin, Y. (2012). Comparison of serum metabolite compositions between obese and lean growing pigs using an NMR-based metabonomic approach. J. Nutr. Biochem. 23, 133-139. https://doi.org/10.1016/j.jnutbio.2010.11.007
  53. Heilbronn, L.K., Rood, J., Janderova, L., Albu, J.B., Kelley, D.E., Ravussin, E., and Smith, S.R. (2004). Relationship between serum resistin concentrations and insulin resistance in nonobese, obese, and obese diabetic subjects. J. Clin. Endocrinol. Metab. 89, 1844-1848. https://doi.org/10.1210/jc.2003-031410
  54. Huang, Q., Yin, P., Wang, J., Chen, J., Kong, H., Lu, X., and Xu, G. (2011). Method for liver tissue metabolic profiling study and its application in type 2 diabetic rats based on ultra performance liquid chromatography-mass spectrometry. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 879, 961-967. https://doi.org/10.1016/j.jchromb.2011.03.009
  55. Junot, C., Madalinski, G., Tabet, J.C., and Ezan, E. (2010). Fourier transform mass spectrometry for metabolome analysis. Analyst 135, 2203-2219. https://doi.org/10.1039/c0an00021c
  56. Kim, J.Y., Park, J.Y., Kim, O.Y., Ham, B.M., Kim, H.J., Kwon, D.Y., Jang, Y., and Lee, J.H. (2010). Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). J. Proteome Res. 9, 4368-4375. https://doi.org/10.1021/pr100101p
  57. Kim, H.J., Kim, J.H., Noh, S., Hur, H.J., Sung, M.J., Hwang, J.T., Park, J.H., Yang, H.J., Kim, M.S., Kwon, D.Y., et al. (2011). Metabolomic analysis of livers and serum from high-fat diet induced obese mice. J. Proteome Res. 10, 722-731. https://doi.org/10.1021/pr100892r
  58. Krebs, M., Brunmair, B., Brehm, A., Artwohl, M., Szendroedi, J., Nowotny, P., Roth, E., Furnsinn, C., Promintzer, M., Anderwald, C., et al. (2007). The Mammalian target of rapamycin pathway regulates nutrient-sensitive glucose uptake in man. Diabetes 56, 1600-1607. https://doi.org/10.2337/db06-1016
  59. Kussmann, M., Raymond, F., and Affolter, M. (2006). OMICSdriven biomarker discovery in nutrition and health. J. Biotechnol. 124, 758-787. https://doi.org/10.1016/j.jbiotec.2006.02.014
  60. Lagarde, M., Geloën, A., Record, M., Vance, D., and Spener, F. (2003). Lipidomics is emerging. Biochim. Biophys. Acta 1634, 61. https://doi.org/10.1016/j.bbalip.2003.11.002
  61. Lin, L., Yu, Q., Yan, X., Hang, W., Zheng, J., Xing, J., and Huang, B. (2010). Direct infusion mass spectrometry or liquid chroma tography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. Analyst 135, 2970-2978. https://doi.org/10.1039/c0an00265h
  62. Liu, L., Wang, M., Yang, X., Bi, M., Na, L., Niu, Y., Li, Y., and Sun, C. (2013). Fasting serum lipid and dehydroepiandrosterone sulfate as important metabolites for detecting isolated postchallenge diabetes: serum metabolomics via ultra-high-performance LC-MS. Clin. Chem. 59, 1338-1348. https://doi.org/10.1373/clinchem.2012.200527
  63. Madsen, R., Lundstedt, T., and Trygg, J. (2010). Chemometrics in metabolomics--a review in human disease diagnosis. Anal. Chim. Acta 659, 23-33. https://doi.org/10.1016/j.aca.2009.11.042
  64. Malik, V.S., Popkin, B.M., Bray, G.A., Despres, J.P., and Hu, F.B. (2010). Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation 121, 1356- 1364. https://doi.org/10.1161/CIRCULATIONAHA.109.876185
  65. Maskarinec, G., Grandinetti, A., Matsuura, G., Sharma, S., Mau, M., Henderson, B.E., and Kolonel, L.N. (2009). Diabetes prevalence and body mass index differ by ethnicity: the Multiethnic Cohort. Ethn. Dis. 19, 49-55.
  66. Mihalik, S.J., Michaliszyn, S.F., de las Heras, J., Bacha, F., Lee, S., Chace, D.H., DeJesus, V.R., Vockley, J., and Arslanian, S.A. (2012). Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes: evidence for enhanced mitochondrial oxidation. Diabetes Care 35, 605-611. https://doi.org/10.2337/DC11-1577
  67. Mooradian, A.D., Haas, M.J., and Wong, N.C. (2004). Transcriptional control of apolipoprotein A-I gene expression in diabetes. Diabetes 53, 513-520. https://doi.org/10.2337/diabetes.53.3.513
  68. Mooradian, A.D., Haas, M.J., Wehmeier, K.R., and Wong, N.C. (2008). Obesity-related changes in high-density lipoprotein metabolism. Obesity (Silver Spring) 16, 1152-1160. https://doi.org/10.1038/oby.2008.202
  69. Moore, S., and Stein, W.H. (1954). Procedures for the chromatographic determination of amino acids on four per cent cross-linked sulfonated polystyrene resins. J. Biol. Chem. 211, 893-906.
  70. Moore, S.C., Matthews, C.E., Sampson, J.N., Stolzenberg-Solomon, R.Z., Zheng, W., Cai, Q., Tan, Y.T., Chow, W.H., Ji, B.T., Liu, D.K., et al. (2014). Human metabolic correlates of body mass index. Metabolomics 10, 259-269. https://doi.org/10.1007/s11306-013-0574-1
  71. Newgard, C.B., An, J., Bain, J.R., Muehlbauer, M.J., Stevens, R.D., Lien, L.F., Haqq, A.M., Shah, S.H., Arlotto, M., Slentz, C.A., et al. (2009). A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311-326. https://doi.org/10.1016/j.cmet.2009.02.002
  72. Nikiforova, V.J., Giesbertz, P., Wiemer, J., Bethan, B., Looser, R., Liebenberg, V., Ruiz Noppinger, P., Daniel, H., and Rein, D. (2014). Glyoxylate, a new marker metabolite of type 2 diabetes. J. Diabetes Res. 2014, 685204.
  73. Nyenwe, E.A., Jerkins, T.W., Umpierrez, G.E., and Kitabchi, A.E. (2011). Management of type 2 diabetes: evolving strategies for the treatment of patients with type 2 diabetes. Metabolism 60, 1-23. https://doi.org/10.1016/j.metabol.2010.09.010
  74. Oberbach, A., Bluher, M., Wirth, H., Till, H., Kovacs, P., Kullnick, Y., Schlichting, N., Tomm, J.M., Rolle-Kampczyk, U., Murugaiyan, J., et al. (2011). Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes. J. Proteome Res. 10, 4769-4788. https://doi.org/10.1021/pr2005555
  75. Oliver, S.G., Winson, M.K., Kell, D.B., and Baganz, F. (1998). Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373-378. https://doi.org/10.1016/S0167-7799(98)01214-1
  76. Oresic, M., Simell, S., Sysi-Aho, M., Nanto-Salonen, K., Seppanen- Laakso, T., Parikka, V., Katajamaa, M., Hekkala, A., Mattila, I., Keskinen, P., et al. (2008). Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. J. Exp. Med. 205, 2975-2984. https://doi.org/10.1084/jem.20081800
  77. Padberg, I., Peter, E., Gonzalez-Maldonado, S., Witt, H., Mueller, M., Weis, T., Bethan, B., Liebenberg, V., Wiemer, J., Katus, H.A., et al. (2014). A new metabolomic signature in type-2 diabetes mellitus and its pathophysiology. PLoS One 9, e85082. https://doi.org/10.1371/journal.pone.0085082
  78. Palmer, N.D., Stevens, R.D., Antinozzi, P.A., Anderson, A., Bergman, R.N., Wagenknecht, L.E., Newgard, C.B., and Bowden, D.W. (2015). Metabolomic profile associated with insulin resistance and conversion to diabetes in the insulin resistance atherosclerosis study. J. Clin. Endocrinol. Metab 100, E463-468. https://doi.org/10.1210/jc.2014-2357
  79. Pataky, Z., Armand, S., Muller-Pinget, S., Golay, A., and Allet, L. (2014). Effects of obesity on functional capacity. Obesity (Silver Spring) 22, 56-62. https://doi.org/10.1002/oby.20514
  80. Pietilainen, K.H., Sysi-Aho, M., Rissanen, A., Seppanen-Laakso, T., Yki-Jarvinen, H., Kaprio, J., and Oresic, M. (2007). Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects--a monozygotic twin study. PLoS One 2, e218. https://doi.org/10.1371/journal.pone.0000218
  81. Randle, P.J. (1998). Regulatory interactions between lipids and carbohydrates: the glucose fatty acid cycle after 35 years. Diabetes Metab. Rev. 14, 263-283. https://doi.org/10.1002/(SICI)1099-0895(199812)14:4<263::AID-DMR233>3.0.CO;2-C
  82. Rhee, E.P., Cheng, S., Larson, M.G., Walford, G.A., Lewis, G.D., McCabe, E., Yang, E., Farrell, L., Fox, C.S., O'Donnell, C.J., et al. (2011). Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J. Clin. Invest. 121, 1402-1411. https://doi.org/10.1172/JCI44442
  83. Sadanala, K.C., Jeongae, L., Bong Chul, C., and Man Ho, C. (2012). Targeted metabolite profiling: sample preparation techniques for GC-MS-based steroid analysis. Mass Spectrometry Lett. 3, 4-9. https://doi.org/10.5478/MSL.2012.3.1.004
  84. Salek, R.M., Maguire, M.L., Bentley, E., Rubtsov, D.V., Hough, T., Cheeseman, M., Nunez, D., Sweatman, B.C., Haselden, J.N., Cox, R.D., et al. (2007). A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol. Genomics 29, 99-108. https://doi.org/10.1152/physiolgenomics.00194.2006
  85. Samad, F., Hester, K.D., Yang, G., Hannun, Y.A., and Bielawski, J. (2006). Altered adipose and plasma sphingolipid metabolism in obesity: a potential mechanism for cardiovascular and metabolic risk. Diabetes 55, 2579-2587. https://doi.org/10.2337/db06-0330
  86. Saris, W.H. (2003). Sugars, energy metabolism, and body weight control. Am. J. Clin. Nutr. 78, 850S-857S. https://doi.org/10.1093/ajcn/78.4.850S
  87. Scarfe, G.B., Wright, B., Clayton, E., Taylor, S., Wilson, I., Lindon, J.C., and Nicholson, J.K. (1998). 19F-NMR and directly coupled HPLC-NMR-MS investigations into the metabolism of 2-bromo-4-trifluoromethylaniline in rat: a urinary excretion balance study without the use of radiolabelling. Xenobiotica 28, 373-388. https://doi.org/10.1080/004982598239489
  88. Sellers, M.B., Tricoci, P., and Harrington, R.A. (2009). A new generation of antiplatelet agents. Curr. Opin. Cardiol. 24, 307-312. https://doi.org/10.1097/HCO.0b013e32832e2b44
  89. Sener, A., and Malaisse, W.J. (1980). L-leucine and a nonmetabolized analogue activate pancreatic islet glutamate dehydrogenase. Nature 288, 187-189. https://doi.org/10.1038/288187a0
  90. Shah, S.H., Kraus, W.E., and Newgard, C.B. (2012). Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 126, 1110-1120. https://doi.org/10.1161/CIRCULATIONAHA.111.060368
  91. Shaham, O., Wei, R., Wang, T.J., Ricciardi, C., Lewis, G.D., Vasan, R.S., Carr, S.A., Thadhani, R., Gerszten, R.E., and Mootha, V.K. (2008). Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol. Syst. Biol. 4, 214.
  92. She, P., Reid, T.M., and Bronson, S.K. (2007). Disruption of BCATm in mice leads to increased energy expenditure associated with the activation of a futile protein turnover cycle. Cell Metab. 6, 181-194. https://doi.org/10.1016/j.cmet.2007.08.003
  93. Sims, E.A., Danforth, E., Jr., Horton, E.S., Bray, G.A., Glennon, J.A., and Salans, L.B. (1973). Endocrine and metabolic effects of experimental obesity in man. Recent Prog. Horm. Res. 29, 457-496.
  94. Siri-Tarino, P.W., Sun, Q., Hu, F.B., and Krauss, R.M. (2010). Saturated fat, carbohydrate, and cardiovascular disease. Am. J. Clin. Nutr. 91, 502-509. https://doi.org/10.3945/ajcn.2008.26285
  95. Smith, P.A., Sakura, H., Coles, B., Gummerson, N., Proks, P., and Ashcroft, F.M. (1997). Electrogenic arginine transport mediates stimulus-secretion coupling in mouse pancreatic beta-cells. J. Physiol. 499 (Pt 3), 625-635. https://doi.org/10.1113/jphysiol.1997.sp021955
  96. Sparks, D.L., and Chatterjee, C. (2012). Purinergic signaling, dyslipidemia and inflammatory disease. Cell Physiol. Biochem. 30, 1333-1339. https://doi.org/10.1159/000343322
  97. Sparks, D.L., Doelle, H., and Chatterjee, C. (2014). Circulating nucleotide levels in health and disease. Receptors Clin. Invest. 1, e344.
  98. Stolar, M.W. (1988). Atherosclerosis in diabetes: the role of hyperinsulinemia. Metabolism 37, 1-9.
  99. Stumvoll, M., Goldstein, B.J., and van Haeften, T.W. (2005). Type 2 diabetes: principles of pathogenesis and therapy. Lancet 365, 1333-1346. https://doi.org/10.1016/S0140-6736(05)61032-X
  100. Suhre, K., Meisinger, C., Doring, A., Altmaier, E., Belcredi, P., Gieger, C., Chang, D., Milburn, M.V., Gall, W.E., Weinberger, K.M., et al. (2010). Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5, e13953. https://doi.org/10.1371/journal.pone.0013953
  101. Tremblay, F., Lavigne, C., Jacques, H., and Marette, A. (2007). Role of dietary proteins and amino acids in the pathogenesis of insulin resistance. Annu. Rev. Nutr. 27, 293-310. https://doi.org/10.1146/annurev.nutr.25.050304.092545
  102. Um, S.H., D'Alessio, D., and Thomas, G. (2006). Nutrient overload, insulin resistance, and ribosomal protein S6 kinase 1, S6K1. Cell Metab. 3, 393-402. https://doi.org/10.1016/j.cmet.2006.05.003
  103. van Kampen, J.J., Burgers, P.C., de Groot, R., Gruters, R.A., and Luider, T.M. (2011). Biomedical application of MALDI mass spectrometry for small-molecule analysis. Mass Spectrom Rev. 30, 101-120. https://doi.org/10.1002/mas.20268
  104. Villarreal-Perez, J.Z., Villarreal-Martinez, J.Z., Lavalle-Gonzalez, F.J., Torres-Sepulveda, M.d.R., Ruiz-Herrera, C., Cerda-Flores, R.M., Castillo-Garcia, E.R., Rodriguez-Sanchez, I.P., and Villarreal, L.E.M.d. (2014). Plasma and urine metabolic profiles are reflective of altered beta-oxidation in non-diabetic obese subjects and patients with type 2 diabetes mellitus. Diabetol. Metab. Syndr. 6, 129-136. https://doi.org/10.1186/1758-5996-6-129
  105. Vinayavekhin, N., Homan, E.A., and Saghatelian, A. (2010). Exploring disease through metabolomics. ACS Chem. Biol. 5, 91-103. https://doi.org/10.1021/cb900271r
  106. Wahl, S., Yu, Z., Kleber, M., Singmann, P., Holzapfel, C., He, Y., Mittelstrass, K., Polonikov, A., Prehn, C., Romisch-Margl, W., et al. (2012). Childhood obesity is associated with changes in the serum metabolite profile. Obes. Facts 5, 660-670. https://doi.org/10.1159/000343204
  107. Wang, T.J., Larson, M.G., Vasan, R.S., Cheng, S., Rhee, E.P., McCabe, E., Lewis, G.D., Fox, C.S., Jacques, P.F., Fernandez, C., et al. (2011). Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448-453. https://doi.org/10.1038/nm.2307
  108. Wang-Sattler, R., Yu, Z., Herder, C., Messias, A.C., Floegel, A., He, Y., Heim, K., Campillos, M., Holzapfel, C., Thorand, B., et al. (2012). Novel biomarkers for pre-diabetes identified by metabolomics. Mol. Syst. Biol. 8, 615-625.
  109. Warram, J.H., Martin, B.C., Krolewski, A.S., Soeldner, J.S., and Kahn, C.R. (1990). Slow glucose removal rate and hyperinsulinemia precede the development of type II diabetes in the offspring of diabetic parent. Ann. Intern. Med. 113, 909-915 https://doi.org/10.7326/0003-4819-113-12-909
  110. Wenk, M.R. (2005). The emerging field of lipidomics. Nat. Rev. Drug Discov. 4, 594-610. https://doi.org/10.1038/nrd1776
  111. Williams, R., Lenz, E.M., Wilson, A.J., Granger, J., Wilson, I.D., Major, H., Stumpf, C., and Plumb, R. (2006). A multi-analytical platform approach to the metabonomic analysis of plasma from normal and Zucker (fa/fa) obese rats. Mol. Biosyst. 2, 174-183. https://doi.org/10.1039/b516356k
  112. Wylie-Rosett, J., and Davis, N.J. (2009). Low-carbohydrate diets: an update on current research. Curr. Diab. Rep. 9, 396-404. https://doi.org/10.1007/s11892-009-0061-2
  113. Xie, B., Waters, M.J., and Schirra, H.J. (2012). Investigating potential mechanisms of obesity by metabolomics. J. Biomed. Biotechnol. 2012, 805683.
  114. Xu, F., Tavintharan, S., Sum, C.F., Woon, K., Lim, S.C., and Ong, C.N. (2013). Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J. Clin. Endocrinol. Metab. 98, E1060-1065. https://doi.org/10.1210/jc.2012-4132
  115. Yamamoto, Y., Hirose, H., Saito, I., Tomita, M., Taniyama, M., Matsubara, K., Okazaki, Y., Ishii, T., Nishikai, K., and Saruta, T. (2002). Correlation of the adipocyte-derived protein adiponectin with insulin resistance index and serum high-density lipoproteincholesterol, independent of body mass index, in the Japanese population. Clin. Sci. 103, 137-142.
  116. Yan, Y., Wang, Q., Li, W., Zhao, Z., Yuan, X., Huang, Y., and Duan, Y. (2014). Discovery of potential biomarkers in exhaled breath for diagnosis of type 2 diabetes mellitus based on GC-MS with metabolomics. RSC Adv. 4, 25430-25439. https://doi.org/10.1039/c4ra01422g
  117. Yi, L.Z.., He, J., Liang, Y.Z., Yuan, D.L., and Chau, F.T. (2006). Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA. FEBS Lett. 580, 6837-6845. https://doi.org/10.1016/j.febslet.2006.11.043
  118. Zeisel, S.H. (2000). Choline: an essential nutrient for humans. Nutrition 16, 669-671. https://doi.org/10.1016/S0899-9007(00)00349-X
  119. Zeng, M., Liang, Y., Li, H., Wang, M., Wang, B., Chen, X., Zhou, N., Cao, D., and Wu, J. (2010). Plasma metabolic fingerprinting of childhood obesity by GC/MS in conjunction with multivariate statistical analysis. J Pharm Biomed Anal 52, 265-272. https://doi.org/10.1016/j.jpba.2010.01.002
  120. Zeng, M., Liang, Y., Li, H., Wang, B., and Chen, X. (2011). A metabolic profiling strategy for biomarker screening by GC-MS combined with multivariate resolution method and Monte Carlo PLS-DA. Analytical Methods 3, 438-445. https://doi.org/10.1039/C0AY00518E
  121. Zhang, A., Sun, H., Wang, P., Han, Y., and Wang, X. (2012). Modern analytical techniques in metabolomics analysis. Analyst 137, 293-300. https://doi.org/10.1039/C1AN15605E
  122. Zhang, A.H., Sun, H., Qiu, S., and Wang, X.J. (2013). Recent highlights of metabolomics in chinese medicine syndrome research. Evid Based Complement Alternat Med 2013, 402159.
  123. Zhao, X., Fritsche, J., Wang, J., Chen, J., Rittig, K., Schmitt-Kopplin, P., Fritsche, A., Haring, H.U., Schleicher, E.D., Xu, G., et al. (2010). Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics 6, 362-374. https://doi.org/10.1007/s11306-010-0203-1

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