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Metabolomics for Biomarkers of Type 2 Diabetes Mellitus: Advances and Nutritional Intervention Trends

  • Lipids (L Parnell and J Ordovas, Section Editors)
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Abstract

Metabolic characterization of type 2 diabetes mellitus (T2DM) is crucial for the identification of individuals at risk for developing diabetes and T2DM-related vascular complications as well as for monitoring disease progression. The application of metabolomics to diabetes research may lead to the identification and discovery of diagnostic and prognostic T2DM biomarkers, in addition to elucidating disease pathways. In the present review, we summarize the distinct classes of metabolites that have been proposed as potential biomarkers for progressing stages of T2DM by metabolomic approaches. Several studies have demonstrated that the metabolism of carbohydrates, lipids, and amino acids is considerably altered in prediabetes and continue to vary over the course of T2DM progression. The identification of intermediate metabolites involved in glycolysis, gluconeogenesis, the tricarboxylic acid cycle, lipolysis, and proteolysis have provided evidence of these metabolic dysfunctions. Finally, given the increasing worldwide incidence of T2DM and its related complications, research should focus on the impact of lifestyle factors, particularly diet, at the metabolomic level for better understanding and improved healthcare strategies.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance, •• Of major importance

  1. Paneni F, Costantino S, Cosentino F. Insulin resistance, diabetes, and cardiovascular risk. Curr Atheroscler Rep. 2014;16:419.

    Article  PubMed  Google Scholar 

  2. Herder C, Karakas M, Koenig W. Biomarkers for the prediction of type 2 diabetes and cardiovascular disease. Clin Pharmacol Ther. 2011;90:52–66.

    Article  CAS  PubMed  Google Scholar 

  3. Kolberg JA, Jørgensen T, Gerwien RW, Hamren S, McKenna MP, Moler E, et al. Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care. 2009;32:1207–12.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Zhang X, Gregg EW, Williamson DF, Barker LE, Thomas W, Bullard KM, et al. A1C level and future risk of diabetes: a systematic review. Diabetes Care. 2010;33:1665–73.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Gieger C, Geistlinger L, Altmaier E, Hrabé de Angelis M, Kronenberg F, Meitinger T, et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008;4:e1000282.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Wang L, Athinarayanan S, Jiang G, Chalasani NZhang M, Liu W. Fatty acid desaturase 1 (FADS1) gene polymorphisms control human hepatic lipid composition. Hepatology. 2015;61:119–28.

  7. Preet A, Karve TM, Rizk N, Cheema AK. Metabolomics: approaches and applications to diabetes research. J Diabetes Metab. 2012; S6:001.

  8. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomics applied to diabetes research: moving from information to knowledge. Diabetes. 2009;58:2429–43.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Friedrich N. Metabolomics in diabetes research. J Endocrinol. 2012;215:29–42.

    Article  CAS  PubMed  Google Scholar 

  10. Suhre K. Metabolic profiling in diabetes. J Endocrinol. 2014;221:R75–85.

    Article  CAS  PubMed  Google Scholar 

  11. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37 Suppl 1:S81–90.

  12. Cobb J, Gall W, Adam KP, Nakhle P, Button E, Hathorn J, et al. A novel fasting blood test for insulin resistance and prediabetes. J Diabetes Sci Technol. 2013;7:100–10.

  13. Shaham O, Wei R, Wang TJ, Ricciardi C, Lewis GD, Vasan RS, et al. Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol Syst Biol. 2008;4:214.

    Article  PubMed Central  PubMed  Google Scholar 

  14. Lam SM, Shui G. Lipidomics as a principal tool for advancing biomedical research. J Genet Genomics. 2013;40:375–90.

    Article  CAS  PubMed  Google Scholar 

  15. Kotronen A, Velagapudi VR, Yetukuri L, Westerbacka J, Bergholm R, Ekroos K, et al. Serum saturated fatty acids containing triacylglycerols are better markers of insulin resistance than total serum triacylglycerol concentrations. Diabetologia. 2009;52:684–90.

    Article  CAS  PubMed  Google Scholar 

  16. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, Mccabe E, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121:1402-11.

  17. Li M, Yang L, Bai Y, Liu H. Analytical methods in lipidomics and their applications. Anal Chem. 2014;86:161–75.

    Article  CAS  PubMed  Google Scholar 

  18. Quehenberger O, Armando AM, Brown AH, Milne SB, Myers DS, Merrill AH, et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res. 2010;51:3299–305.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Zhao X, Fritsche J, Wang J, Chen J, Rittig K, Schmitt-Kopplin P, et al. Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics. 2010;6:362–74.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9:311–26.

  21. Huffman KM, Shah SH, Stevens RD, Bain JR, Muehlbauer M, Slentz CA, et al. Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care. 2009;32:1678–83.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Würtz P, Mäkinen V-P, Soininen P, Kangas AJ, Tukiainen T, Kettunen J, et al. Metabolic signatures of insulin resistance in 7,098 young adults. Diabetes. 2012;61:1372–80.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Palmer ND, Stevens RD, Antinozzi PA, Anderson A, Bergman RN, Wagenknecht LE, et al. A metabolomic profile associated with insulin resistance and conversion to diabetes in the Insulin Resistance Atherosclerosis Study. J Clin Endocrinol Metab. 2014;jc20142357.

  24. Klimentidis YC, Divers J, Casazza K, Beasley T, Allison DB, Fernandez JR. Ancestry-informative markers on chromosomes 2, 8 and 15 are associated with insulin-related traits in a racially diverse sample of children. Hum Genomics. 2011;5:79–89.

  25. Tai ES, Tan ML, Stevens RD, Low YL, Muehlbauer MJ, Goh DL, et al. Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia. 2010;53:757–67.

  26. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012;8:615. Authors identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with NGT. These biomarkers were first identified in the KORA cohort and further validated in EPIC-Postdam cohort.

  27. Gall WE, Beebe K, Lawton KA, Adam KP, Mitchell MW, Nakhle PJ, et al. Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE. 2010;5:e10883.

  28. Ferrannini E, Natali A, Camastra S, Nannipieri M, Mari A, Adam KP, et al. Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance. Diabetes. 2013;62:1730–7. In this papers α-HB and L-GPC are proposed as novel and independent potent predictors for incident dysglycemia after their confirmation in two observational cohorts: RISC study and Botnia study.

  29. Menni C, Fauman E, Erte I, Perry JR, Kastenmüller G, Shin SY, et al. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes. 2013;62:4270–6.

  30. Kelder T, Eijssen L, Kleemann R, van Erk M, Kooistra T, Evelo C. Exploring pathway interactions in insulin resistant mouse liver. BMC Syst Biol. 2011;5:127.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Kutmon M, Evelo CT, Coort SL. A network biology workflow to study transcriptomics data of the diabetic liver. BMC Genomics. 2014;15:971.

  32. Messana I, Forni F, Ferrari F, Rossi C, Giardina B, Zuppi C. Proton nuclear magnetic resonance spectral profiles of urine in type II diabetic patients. Clin Chem. 1998;44:1529–34.

    CAS  PubMed  Google Scholar 

  33. Van Doorn M, Vogels J, Tas A, van Hoogdalem EJ, Burggraaf J, Cohen A, et al. Evaluation of metabolite profiles as biomarkers for the pharmacological effects of thiazolidinediones in Type 2 diabetes mellitus patients and healthy volunteers. Br J Clin Pharmacol. 2007;63:562–74.

    Article  PubMed Central  PubMed  Google Scholar 

  34. Salek RM, Maguire ML, Bentley E, Rubtsov D V, Hough T, Cheeseman M, et al. A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol Genomics. 2007;29:99–108.

  35. Yuan K, Kong H, Guan Y, Yang J, Xu G. A GC-based metabonomics investigation of type 2 diabetes by organic acids metabolic profile. J Chromatogr B Analyt Technol Biomed Life Sci. 2007;850:236–40.

    Article  CAS  PubMed  Google Scholar 

  36. Bao Y, Zhao T, Wang X, Qiu Y, Su M, Jia W, Jia W. Metabonomic variations in the drug-treated type 2 diabetes mellitus patients and healthy volunteers. J Proteome Res. 2009;8:1623–30.

  37. Haus JM, Kashyap SR, Kasumov T, Zhang R, Kelly KR, Defronzo RA, et al. Plasma ceramides are elevated in obese subjects with type 2 diabetes and correlate with the severity of insulin resistance. Diabetes. 2009;58:337–43.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  38. Li X, Xu Z, Lu X, Yang X, Yin P, Kong H, et al. Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: biomarker discovery for diabetes mellitus. Anal Chim Acta. 2009;633:257–62.

    Article  CAS  PubMed  Google Scholar 

  39. Zhang X, Wang Y, Hao F, Zhou X, Han X, Tang H, et al. Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses research articles. J Proteome Res. 2009;5188–95.

  40. Zhang J, Yan L, Chen W, Lin L, Song X, Yan X, et al. Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC-oaTOF-MS system. Anal Chim Acta. 2009;650:16–22.

    Article  CAS  PubMed  Google Scholar 

  41. Zeng M, Che Z, Liang Y, Wang B, Chen X, Li H, et al. GC–MS based plasma metabolic profiling of type 2 diabetes mellitus. Chromatographia. 2009;69:941–8.

    Article  CAS  Google Scholar 

  42. Adams SH, Hoppel CL, Lok KH, Zhao L, Wong SW, Minkler PE, et al. Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid b-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. J Nutr. 2009;139:1073–81.

  43. Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One. 2010;5:e15234.

  44. Mihalik SJ, Goodpaster BH, Kelley DE, Chace DH, Vockley J, Toledo FG, et al. Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obesity (Silver Spring). 2010;18:1695–700.

  45. Suhre K, Meisinger C, Döring A, Altmaier E, Belcredi P, Gieger C, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010;5:e13953.

  46. Jung J, Jang Z, Hwang G-S. Metabolomics approach for discovering disease biomarkers and understanding metabolic pathway. J Anal Sci Technol. 2011;2:A189–93.

    Article  CAS  Google Scholar 

  47. Han LD, Xia JF, Liang QL, Wang Y, Wang YM, Hu P, et al. Plasma esterified and non-esterified fatty acids metabolic profiling using gas chromatography-mass spectrometry and its application in the study of diabetic mellitus and diabetic nephropathy. Anal Chim Acta. 2011;689:85–91.

  48. Zhu C, Liang QL, Hu P, Wang YM, Luo G. Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy. Talanta. 2011;85:1711–20.

  49. Ha CY, Kim JY, Paik JK, Kim OY, Paik YH, Lee EJ, et al. The association of specific metabolites of lipid metabolism with markers of oxidative stress, inflammation and arterial stiffness in men with newly diagnosed type 2 diabetes. Clin Endocrinol (Oxf). 2012;76:674–82.

  50. Mihalik SJ, Michaliszyn SF, de las Heras J, Bacha F, Lee S, Chace DH, et al. Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes: evidence for enhanced mitochondrial oxidation. Diabetes Care. 2012;35:605–11.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  51. Xu W, Zhang L, Huang Y, Yang Q, Xiao H, Zhang D. Discrimination of type 2 diabetes mellitus corresponding to different traditional Chinese medicine syndromes based on plasma fatty acid profiles and chemometric methods. J Ethnopharmacol. 2012;143:463–8.

    Article  PubMed  Google Scholar 

  52. Zhou Y, Qiu L, Xiao Q, Wang Y, Meng X, Xu R, et al. Obesity and diabetes related plasma amino acid alterations. Clin Biochem. 2013;46:1447–52.

    Article  CAS  PubMed  Google Scholar 

  53. Kaur P, Rizk N, Ibrahim S, Luo Y, Younes N, Perry B, et al. Quantitative metabolomic and lipidomic profiling reveals aberrant amino acid metabolism in type 2 diabetes. Mol Biosyst. 2013;9:307–17.

    Article  CAS  PubMed  Google Scholar 

  54. Zhang AH, Sun H, Yan GL, Yuan Y, Han Y, Wang XJ. Metabolomics study of type 2 diabetes using ultra-performance LC-ESI/quadrupole-TOF high-definition MS coupled with pattern recognition methods. J Physiol Biochem. 2014;70:117–28.

  55. Mamtimin B, Hizbulla M, Kurbantay N, You L, Yan X, Upur H. An magnetic resonance-based plasma metabonomic investigation on abnormal Savda in different complicated diseases. J Tradit Chin Med. 2014;34:166–72.

    Article  PubMed  Google Scholar 

  56. Lu H, Hu F, Zeng Y, Zou L, Luo S, Sun Y, et al. Ketosis onset type 2 diabetes had better islet β-cell function and more serious insulin resistance. J Diabetes Res. 2014;2014:510643.

    Article  PubMed Central  PubMed  Google Scholar 

  57. Xu J, Cai S, Li X, Dong J, Ding J, Chen Z. Statistical two-dimensional correlation spectroscopy of urine and serum from metabolomics data. Chemom Intell Lab Syst. 2012;112:33–40.

    Article  CAS  Google Scholar 

  58. Ståhlman M, Pham HT, Adiels M, Mitchell TW, Blanksby SJ, Fagerberg B, et al. Clinical dyslipidaemia is associated with changes in the lipid composition and inflammatory properties of apolipoprotein-B-containing lipoproteins from women with type 2 diabetes. Diabetologia. 2012;55:1156–66.

    Article  PubMed  Google Scholar 

  59. Larsen PJ, Tennagels N. On ceramides, other sphingolipids and impaired glucose homeostasis. Mol Metab. 2014;3:252–60.

  60. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17:448–53. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than five-fold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.

  61. Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost HG, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62:639–48.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  62. Wang TJ, Ngo D, Psychogios N, Dejam A, Larson MG, Vasan RS, et al. 2-Aminoadipic acid is a biomarker for diabetes risk. J Clin Invest. 2013;123:4309–17.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  63. Lorber D. Importance of cardiovascular disease risk management in patients with type 2 diabetes mellitus. Diabetes Metab Syndr Obes. 2014;7:169–83.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  64. Scalbert A, Brennan L, Manach C, Andres-Lacueva C, Dragsted LO, Draper J, et al. The food metabolome: a window over dietary exposure. Am J Clin Nutr. 2014;99:1286–308.

  65. Ley SH, Hamdy O, Mohan V, Hu FB. Prevention and management of type 2 diabetes: dietary components and nutritional strategies. Lancet. 2014;383:1999–2007. This review highlight the role of dietary components and nutritional strategies for prevention and management of T2DM. Several dietary patterns such as Mediterranean, low glycaemic index, moderately low carbohydrate, and vegetarian diets are proposed as adecuate dietary options, independently of personal and cultural food preferences.

  66. de Koning L, Chiuve SE, Fung TT, Willett WC, Rimm EB, Hu FB. Diet-quality scores and the risk of type 2 diabetes in men. Diabetes Care. 2011;34:1150–6.

  67. InterAct Consortium. Adherence to predefined dietary patterns and incident type 2 diabetes in European populations: EPIC-InterAct Study. Diabetologia. 2014;57:321–33.

  68. Ajala O, English P, Pinkney J. Systematic review and meta-analysis of different dietary approaches to the management of type 2 diabetes. Am J Clin Nutr. 2013;97:505–16.

    Article  CAS  PubMed  Google Scholar 

  69. Estruch R, Ros E, Salas-Salvadó J, Covas MI, Corella D, Arós F, et al. Primary prevention of cardiovascular disease with a Mediterranean diet. N Engl J Med. 2013;368:1279–90. In this primary prevention trial authors observed that among persons at high cardiovascular risk (including cases of T2DM), a Mediterranean diet supplemented with extra-virgin olive oil or nuts reduced significantly the incidence of major cardiovascular events.

  70. Viscogliosi G, Cipriani E, Liguori ML, Marigliano B, Saliola M, Ettorre E, et al. Mediterranean dietary pattern adherence: associations with prediabetes, metabolic syndrome, and related microinflammation. Metab Syndr Relat Disord. 2013;11:210–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  71. Koloverou E, Esposito K, Giugliano D, Panagiotakos D. The effect of Mediterranean diet on the development of type 2 diabetes mellitus: a meta-analysis of 10 prospective studies and 136,846 participants. Metabolism. 2014;63:903–11.

    Article  CAS  PubMed  Google Scholar 

  72. Salas-Salvadó J, Bulló M, Babio N, Martínez-González MÁ, Ibarrola-Jurado N, Basora J, et al. Reduction in the incidence of type 2 diabetes with the Mediterranean diet: results of the PREDIMED-Reus nutrition intervention randomized trial. Diabetes Care. 2011;34:14–9.

    Article  PubMed Central  PubMed  Google Scholar 

  73. Salas-Salvadó J, Bulló M, Estruch R, Ros E, Covas MI, Ibarrola-Jurado N, et al. Prevention of diabetes with Mediterranean diets: a subgroup analysis of a randomized trial. Ann Intern Med. 2014;160:1–10.

    Article  PubMed  Google Scholar 

  74. Magnusdottir OK, Landberg R, Gunnarsdottir I, Cloetens L, Åkesson B, Önning G, et al. Plasma alkylresorcinols reflect important whole-grain components of a healthy Nordic diet. J Nutr. 2013;143:1383–90.

  75. Magnusdottir OK, Landberg R, Gunnarsdottir I, Cloetens L, Akesson B, Landin-Olsson M, et al. Plasma alkylresorcinols C17:0/C21:0 ratio, a biomarker of relative whole-grain rye intake, is associated to insulin sensitivity: a randomized study. Eur J Clin Nutr. 2014;68:453–8.

    Article  CAS  PubMed  Google Scholar 

  76. Akesson A, Andersen LF, Kristjánsdóttir AG, Roos E, Trolle E, Voutilainen E, et al. Health effects associated with foods characteristic of the Nordic diet : a systematic literature review. Food Nutr Res. 2013;57. doi:10.3402/fnr.v57i0.22790.

  77. Kanerva N, Rissanen H, Knekt P, Havulinna AS, Eriksson JG, Männistö S. The healthy Nordic diet and incidence of type 2 diabetes—10-year follow-up. Diabetes Res Clin Pract. 2014;106:e34–7.

    Article  CAS  PubMed  Google Scholar 

  78. Stella C, Beckwith-Hall B, Cloarec O, Holmes E, Lindon JC, Powell J, et al. Susceptibility of human metabolic phenotypes to dietary modulation. J Proteome Res. 2006;5:2780–8.

    Article  CAS  PubMed  Google Scholar 

  79. Walsh MC, Brennan L, Pujos-guillot E, Sébédio J, Scalbert A, Fagan A, et al. Influence of acute phytochemical intake on human urinary metabolomic profiles. Am J Clin Nutr. 2007;86:1687–93.

    CAS  PubMed  Google Scholar 

  80. Rasmussen LG, Winning H, Savorani F, Toft H, Larsen TM, Dragsted LO, et al. Assessment of the effect of high or low protein diet on the human urine metabolome as measured by NMR. Nutrients. 2012;4:112–31.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  81. Heinzmann SS, Merrifield CA, Rezzi S, Kochhar S, Lindon JC, Holmes E, et al. Stability and robustness of human metabolic phenotypes in response to sequential food challenges. J Proteome Res. 2012;11:643–55.

    Article  CAS  PubMed  Google Scholar 

  82. May DH, Navarro SL, Ruczinski I, Hogan J, Ogata Y, Schwarz Y, et al. Metabolomic profiling of urine: response to a randomised, controlled feeding study of select fruits and vegetables, and application to an observational study. Br J Nutr. 2013;110:1760–70.

    Article  CAS  PubMed  Google Scholar 

  83. Floegel A, von Ruesten A, Drogan D, Schulze MB, Prehn C, Adamski J, et al. Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. Eur J Clin Nutr. 2013;67:1100–8.

    Article  CAS  PubMed  Google Scholar 

  84. Moazzami AA, Zhang J-X, Kamal-Eldin A, Aman P, Hallmans G, Johansson JE, et al. Nuclear magnetic resonance-based metabolomics enable detection of the effects of a whole grain rye and rye bran diet on the metabolic profile of plasma in prostate cancer patients. J Nutr. 2011;141:2126–32.

  85. Vázquez-Fresno R, Llorach R, Marinic J, Tulipani S, Garcia-Aloy M, Espinosa-Martos I, et al. Urinary metabolomic fingerprinting after consumption of a probiotic strain in women with mastitis. Pharmacol Res. 2014;87:160-5.

  86. Llorach R, Urpi-Sarda M, Tulipani S, Garcia-Aloy M, Monagas M, Andres-Lacueva C. Metabolomic fingerprint in patients at high risk of cardiovascular disease by cocoa intervention. Mol Nutr Food Res. 2013;57:962–73.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This study was supported by CICYT AGL2009-13906-C02-01 from the Spanish Ministerio de Economía y Competitividad (MINECO) and PI13/01172 Project, (Plan N de I + D + i 2013-2016) co-funded by ISCII-Subdirección General de Evaluación y Fomento de la Investigación and Fondo Europeo de Desarrollo Regional (FEDER). We also thank the award of 2014SGR1566 from the Generalitat de Catalunya’s Agency AGAUR and the EU Joint Programming Initiative A Healthy Diet for a Healthy Life on Biomarkers BioNH-FOODBALL (PCIN-2014-133-MINECO-Spain). M.U.-S. would like to thank the “Ramón y Cajal” program (RYC-2011-09677) from MINECO and the Fondo Social Europeo. EAA would like to thank to CONACYT (México) for the Ph.D. fellowship. ST acknowledges the Juan de la Cierva program (MINECO).

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Conflicts of Interest

Cristina Andres-Lacueva, Francisco J Tinahones, Jordi Salas-Salvadó, Mireia Urpi-Sarda, Sara Tulipani, and Enrique Almanza-Aguilera have no conflicts of interest.

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Authors and Affiliations

  1. Biomarkers and Nutritional and Food Metabolomics Research Group, Nutrition and Food Science Department, XaRTA, INSA, Pharmacy Faculty, University of Barcelona, Avenida Joan XXIII s/n, 08028, Barcelona, Spain

    Mireia Urpi-Sarda, Enrique Almanza-Aguilera, Sara Tulipani & Cristina Andres-Lacueva

  2. Biomedical Research Institute (IBIMA), Service of Endocrinology and Nutrition, Hospital Complex (Virgen de la Victoria), Campus de Teatinos s/n, University of Málaga, 29010, Málaga, Spain

    Sara Tulipani

  3. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain

    Francisco J. Tinahones & Jordi Salas-Salvadó

  4. Endocrinology and Nutrition Service, Virgen de la Victoria Clinical Hospital, Málaga, Spain

    Francisco J. Tinahones

  5. Human Nutrition Unit, Hospital Universitari de Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, Reus, Spain

    Jordi Salas-Salvadó

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  1. Mireia Urpi-Sarda
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  2. Enrique Almanza-Aguilera
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  3. Sara Tulipani
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  4. Francisco J. Tinahones
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  5. Jordi Salas-Salvadó
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  6. Cristina Andres-Lacueva
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Correspondence to Mireia Urpi-Sarda.

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Urpi-Sarda, M., Almanza-Aguilera, E., Tulipani, S. et al. Metabolomics for Biomarkers of Type 2 Diabetes Mellitus: Advances and Nutritional Intervention Trends. Curr Cardiovasc Risk Rep 9, 12 (2015). https://doi.org/10.1007/s12170-015-0440-y

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