Skip to main content

Chronic Diseases and Lifestyle Biomarkers Identification by Metabolomics

  • Chapter
  • First Online:
Book cover Metabolomics: From Fundamentals to Clinical Applications

Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 965))

Abstract

Chronic diseases, also known as noncommunicable diseases (NCDs), are complex disorders that last for long periods of time and progress slowly. They currently account for the major cause of death worldwide with an alarming increase in rate both in developed and developing countries. In this chapter, the principal metabolomic-based investigations on chronic diseases (cardiovascular diseases, diabetes, and respiratory chronic diseases) and their major risk factors (particularly overweight/obesity) are described by focusing both on metabolites and metabolic pathways. Additional information on the contribution of metabolomics strategies in the ambit of the biomarker discovery for NCDs is also provided by exploring the major prospective studies of the last years (i.e., Framingham Heart Study, EPIC, MONICA, KORA, FINRIK, ECLIPSE). The metabolic signature of diseases, which arises from the metabolomic-based investigation, is therefore depicted in the chapter by pointing out the potential of metabolomics to explain the pathophysiological mechanisms underlying a disease, as well as to propose new therapeutic targets for alternative treatments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

2AA:

2-Aminoadipic acid

ArAA:

Aromatic amino acids

AUC:

Area under the curve

BA:

Bile acid

BAIBA:

Beta-aminoisobutyric

BCAA:

Branched-chain amino acids

BCKDH:

Branched-chain alpha-keto acid dehydrogenase

BMI:

Body mass index

BWHHS:

British Women’s Heart and Health Study cohort

CE:

Capillary electrophoresis

COPD:

Chronic obstructive pulmonary disease

CVD:

Cardiovascular disease

DM-AA:

Diabetes-predictive amino acid

ECLIPSE:

Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points

EPIC:

European Prospective Investigation into Cancer and Nutrition

FA:

Fatty acids

FAHFA:

Fatty acid esters of hydroxy fatty acid

FAO:

Fatty acids oxidation

FIA:

Flow injection analysis

FINRISK:

National FINRISK study

FSH:

Framingham Heart Study

GC:

Gas chromatography

GD:

Gestational diabetes

HFA:

Hydroxy fatty acids

IFG:

Impaired fasting glycemia

IGT:

Impaired glucose tolerance

IR:

Insulin resistance

KORA:

Cooperative Health Research in the Region Augsburg

LC:

Liquid chromatography

LDLs:

Low-density lipoproteins

LysoPC:

Lysophosphocholine

LysoPEs:

Lysophosphoethanolamines

MDC-CC:

Malmö Diet and Cancer Study-Cardiovascular Cohort

MONICA:

Multinational monitoring of trends and determinants in cardiovascular disease

MS:

Mass spectrometry

MS/MS:

Tandem mass spectrometry

mTOR:

Mammalian target of rapamycin

NCDs:

Noncommunicable diseases

NGT:

Normal glucose tolerance

NMR:

Nuclear magnetic resonance

PC:

Phosphocholine

PCa:

Alkyl-phosphatidylcholines

PL:

Phospholipids

ROC:

Receiver-operating characteristic

SABRE:

Southall And Brent REvisited cohort

S-AMP:

Adenylosuccinate

T1D:

Type 1 diabetes

T2D:

Type 2 diabetes

TMAO:

Trimethylamine N-oxide

References

  1. WHO. Noncommunicable diseases 2015. Available from: http://www.who.int/mediacentre/factsheets/fs355/en/. Accessed May 2016.

  2. WHO. Global status report on noncommunicable diseases 2014. 2014. Available from: http://www.who.int/nmh/publications/ncd-status-report-2014/en/. Accessed May 2016.

  3. Nugent R. Chronic diseases in developing countries: health and economic burdens. Ann N Y Acad Sci. 2008;1136:70–9.

    Article  PubMed  Google Scholar 

  4. National Center for Chronic Disease Prevention and Health Promotion. Chronic Disease Overview 2016. Available from: http://www.cdc.gov/chronicdisease/overview/. Accessed May 2016.

  5. WHO. Cardiovascular diseases 2016. Available from: http://www.euro.who.int/en/health-topics/noncommunicable-diseases/cardiovascular-diseases. Accessed May 2016.

  6. WHO. Prevention of Recurrences of Myocardial Infarction and Stroke Study 2016. Available from: http://www.who.int/cardiovascular_diseases/priorities/secondary_prevention/country/en/index1.html. Accessed May 2016.

  7. WHO. Chronic respiratory diseases 2016. Available from: http://www.who.int/respiratory/en/. Accessed May 2016.

  8. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Centers for disease control and prevention. Child Health 2016. Available from: http://www.cdc.gov/nchs/fastats/child-health.htm. Accessed May 2016.

  10. Pauwels RA, Rabe KF. Burden and clinical features of chronic obstructive pulmonary disease (COPD). Lancet. 2004;364(9434):613–20.

    Article  PubMed  Google Scholar 

  11. WHO. Global surveillance, prevention and control of chronic respiratory diseases. A comprehensive approach 2007. Available from: http://www.who.int/respiratory/publications/global_surveillance/en/. Accessed May 2016.

  12. WHO. Diabetes 2016. Available from: http://www.who.int/mediacentre/factsheets/fs312/en/. Accessed May 2016.

  13. WHO. Global report on diabetes 2016. Available from: http://www.who.int/diabetes/global-report/en/. Accessed May 2016.

  14. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–53.

    Article  CAS  PubMed  Google Scholar 

  15. WHO. Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy 2013. Available from: http://www.who.int/diabetes/publications/Hyperglycaemia_In_Pregnancy/en/. Accessed May 2016.

  16. van Belle TL, Coppieters KT, von Herrath MG. Type 1 diabetes: etiology, immunology, and therapeutic strategies. Physiol Rev. 2011;91(1):79–118.

    Article  PubMed  Google Scholar 

  17. WHO. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus 2011. Available from: http://www.who.int/diabetes/publications/diagnosis_diabetes2011/en/. Accessed May 2016.

  18. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003;26(3):725–31.

    Article  PubMed  Google Scholar 

  19. WHO. Chronic diseases and their common risk factors 2016. Available from: http://www.who.int/chp/chronic_disease_report/information_sheets/en/. Accessed May 2016.

  20. WHO. Obesity and overweight 2015. Available from: http://www.who.int/mediacentre/factsheets/fs311/en/. Accessed May 2016.

  21. WHO. BMI classification 2016. Available from: http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed May 2016.

  22. WHO. Waist circumference and waist–hip ratio. Report of a WHO expert consultation, Geneva, 8–11 December 2008. 2011. Available from: http://www.who.int/nutrition/publications/obesity/WHO_report_waistcircumference_and_waisthip_ratio/en/. Accessed May 2016.

  23. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997;337(13):869–73.

    Article  CAS  PubMed  Google Scholar 

  24. Skinner AC, Perrin EM, Moss LA, Skelton JA. Cardiometabolic risks and severity of obesity in children and young adults. N Engl J Med. 2015;373(14):1307–17.

    Article  PubMed  Google Scholar 

  25. Daniels SR. The consequences of childhood overweight and obesity. Future Child. 2006;16(1):47–67.

    Article  PubMed  Google Scholar 

  26. Barker DJ. Fetal origins of coronary heart disease. BMJ. 1995;311(6998):171–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. WHO. Programming of chronic disease by impaired fetal nutrition. Evidence and implications for policy and intervention strategies Geneva 2002. Available from: http://www.who.int/nutrition/publications/obesity/WHO_NHD_02.3/en/. Accessed May 2016.

  28. Godfrey KM, Barker DJ. Fetal nutrition and adult disease. Am J Clin Nutr. 2000;71(5 Suppl):1344s–52.

    CAS  PubMed  Google Scholar 

  29. Wahlqvist ML, Krawetz SA, Rizzo NS, Dominguez-Bello MG, Szymanski LM, Barkin S, et al. Early-life influences on obesity: from preconception to adolescence. Ann N Y Acad Sci. 2015;1347:1–28.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Eaton SB, Konner M, Shostak M. Stone agers in the fast lane: chronic degenerative diseases in evolutionary perspective. Am J Med. 1988;84(4):739–49.

    Article  CAS  PubMed  Google Scholar 

  31. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181–9.

    Article  CAS  PubMed  Google Scholar 

  32. Fiehn O. Metabolomics–the link between genotypes and phenotypes. Plant Mol Biol. 2002;48(1–2):155–71.

    Article  CAS  PubMed  Google Scholar 

  33. German JB, Hammock BD, Watkins SM. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics. 2005;1(1):3–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hivert MF, Perng W, Watkins SM, Newgard CS, Kenny LC, Kristal BS, et al. Metabolomics in the developmental origins of obesity and its cardiometabolic consequences. J Dev Orig Health Dis. 2015;6(2):65–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Du F, Virtue A, Wang H, Yang XF. Metabolomic analyses for atherosclerosis, diabetes, and obesity. Biomark Res. 2013;1(1):17.

    Article  PubMed  PubMed Central  Google Scholar 

  36. 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. United States. 2009;9:311–26.

    Google Scholar 

  37. Wurtz P, Makinen VP, Soininen P, Kangas AJ, Tukiainen T, Kettunen J, et al. Metabolic signatures of insulin resistance in 7,098 young adults. Diabetes. 2012;61(6):1372–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Nobakht MGBF, Aliannejad R, Rezaei-Tavirani M, Taheri S, Oskouie AA. The metabolomics of airway diseases, including COPD, asthma and cystic fibrosis. Biomarkers. 2015;20(1):5–16.

    Article  Google Scholar 

  40. Klein MS, Shearer J. Metabolomics and type 2 diabetes: translating basic research into clinical application. J Diabetes Res. 2016;2016:3898502.

    Article  PubMed  Google Scholar 

  41. Roberts LD, Gerszten RE. Toward new biomarkers of cardiometabolic diseases. Cell Metab. 2013;18(1):43–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. United States. 2011;17:448–53.

    Google Scholar 

  43. Parikh NI, Vasan RS. Assessing the clinical utility of biomarkers in medicine. Biomark Med. 2007;1(3):419–36.

    Article  CAS  PubMed  Google Scholar 

  44. Roe CR, Millington DS, Maltby DA. Identification of 3-methylglutarylcarnitine. A new diagnostic metabolite of 3-hydroxy-3-methylglutaryl-coenzyme A lyase deficiency. J Clin Invest. 1986;77(4):1391–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Jellum E, Kvittingen EA, Stokke O. Mass spectrometry in diagnosis of metabolic disorders. Biomed Environ Mass Spectrom. 1988;16(1–12):57–62.

    Article  CAS  PubMed  Google Scholar 

  46. Kim KR, Park HG, Paik MJ, Ryu HS, Oh KS, Myung SW, et al. Gas chromatographic profiling and pattern recognition analysis of urinary organic acids from uterine myoma patients and cervical cancer patients. J Chromatogr B Biomed Sci Appl. 1998;712(1–2):11–22.

    Article  CAS  PubMed  Google Scholar 

  47. Kimura M, Yamamoto T, Yamaguchi S. Automated metabolic profiling and interpretation of GC/MS data for organic acidemia screening: a personal computer-based system. Tohoku J Exp Med. 1999;188(4):317–34.

    Article  CAS  PubMed  Google Scholar 

  48. Zhang A, Sun H, Wang X. Power of metabolomics in biomarker discovery and mining mechanisms of obesity. Obes Rev. 2013;14(4):344–9.

    Article  CAS  PubMed  Google Scholar 

  49. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36.

    Article  CAS  PubMed  Google Scholar 

  50. Pencina MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23(13):2109–23.

    Article  PubMed  Google Scholar 

  51. Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011;33:46–62.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Janes H, Pepe MS, Gu W. Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med. 2008;149(10):751–60.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795–802.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell. 2008;134(5):714–7.

    Article  CAS  PubMed  Google Scholar 

  55. Dettmer K, Aronov PA, Hammock BD. Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2007;26(1):51–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kim JY, Park JY, Kim OY, Ham BM, Kim HJ, Kwon DY, et al. 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. 2010;9(9):4368–75.

    Article  CAS  PubMed  Google Scholar 

  57. 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(9):1695–700.

    Article  CAS  Google Scholar 

  58. She P, Van Horn C, Reid T, Hutson SM, Cooney RN, Lynch CJ. Obesity-related elevations in plasma leucine are associated with alterations in enzymes involved in branched-chain amino acid metabolism. Am J Physiol Endocrinol Metab. 2007;293(6):E1552–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Laferrere B, Reilly D, Arias S, Swerdlow N, Gorroochurn P, Bawa B, et al. Differential metabolic impact of gastric bypass surgery versus dietary intervention in obese diabetic subjects despite identical weight loss. Sci Transl Med. United States. 2011;3:80re2.

    Google Scholar 

  60. Shah SH, Crosslin DR, Haynes CS, Nelson S, Turer CB, Stevens RD, et al. Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss. Diabetologia. 2012;55(2):321–30.

    Article  CAS  PubMed  Google Scholar 

  61. Halvatsiotis PG, Turk D, Alzaid A, Dinneen S, Rizza RA, Nair KS. Insulin effect on leucine kinetics in type 2 diabetes mellitus. Diabetes Nutr Metab. 2002;15(3):136–42.

    CAS  PubMed  Google Scholar 

  62. Tessari P, Coracina A, Kiwanuka E, Vedovato M, Vettore M, Valerio A, et al. Effects of insulin on methionine and homocysteine kinetics in type 2 diabetes with nephropathy. Diabetes. 2005;54(10):2968–76.

    Article  CAS  PubMed  Google Scholar 

  63. 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(4):757–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Shin AC, Fasshauer M, Filatova N, Grundell LA, Zielinski E, Zhou JY, et al. Brain insulin lowers circulating BCAA levels by inducing hepatic BCAA catabolism. Cell Metab. 2014;20(5):898–909.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol. England. 2014;10:723–36.

    Google Scholar 

  66. Newgard CB. Interplay between lipids and branched-chain amino acids in development of insulin resistance. Cell Metab. United States, 2012 Elsevier Inc. 2012;15:606–14.

    Google Scholar 

  67. 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(3):605–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wang Z, Tang WH, Cho L, Brennan DM, Hazen SL. Targeted metabolomic evaluation of arginine methylation and cardiovascular risks: potential mechanisms beyond nitric oxide synthase inhibition. Arterioscler Thromb Vasc Biol. 2009;29(9):1383–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wang L, Hou E, Wang Y, Yang L, Zheng X, Xie G, et al. Reconstruction and analysis of correlation networks based on GC-MS metabolomics data for young hypertensive men. Anal Chim Acta. 2015;854:95–105.

    Article  CAS  PubMed  Google Scholar 

  70. Wedes SH, Wu W, Comhair SA, McDowell KM, DiDonato JA, Erzurum SC, et al. Urinary bromotyrosine measures asthma control and predicts asthma exacerbations in children. J Pediatr. 2011;159(2):248–55.e1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Jung J, Kim SH, Lee HS, Choi GS, Jung YS, Ryu DH, et al. Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis. Clin Exp Allergy. 2013;43(4):425–33.

    Article  CAS  PubMed  Google Scholar 

  72. Kutsuzawa T, Shioya S, Kurita D, Haida M. Plasma branched-chain amino acid levels and muscle energy metabolism in patients with chronic obstructive pulmonary disease. Clin Nutr. 2009;28(2):203–8.

    Article  CAS  PubMed  Google Scholar 

  73. Ubhi BK, Cheng KK, Dong J, Janowitz T, Jodrell D, Tal-Singer R, et al. Targeted metabolomics identifies perturbations in amino acid metabolism that sub-classify patients with COPD. Mol Biosyst. 2012;8(12):3125–33.

    Article  CAS  PubMed  Google Scholar 

  74. Wang L, Tang Y, Liu S, Mao S, Ling Y, Liu D, et al. Metabonomic profiling of serum and urine by (1)H NMR-based spectroscopy discriminates patients with chronic obstructive pulmonary disease and healthy individuals. PLoS One. 2013;8(6):e65675.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Saude EJ, Skappak CD, Regush S, Cook K, Ben-Zvi A, Becker A, et al. Metabolomic profiling of asthma: diagnostic utility of urine nuclear magnetic resonance spectroscopy. J Allergy Clin Immunol. 2011;127(3):757–64.e1–6.

    Article  CAS  PubMed  Google Scholar 

  76. 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(5):674–82.

    Article  CAS  Google Scholar 

  77. Kim M, Jung S, Kim SY, Lee SH, Lee JH. Prehypertension-associated elevation in circulating lysophosphatidlycholines, Lp-PLA2 activity, and oxidative stress. PLoS One. 2014;9(5):e96735.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Yang B, Ding F, Wang FL, Yan J, Ye XW, Yu W, et al. Association of serum fatty acid and estimated desaturase activity with hypertension in middle-aged and elderly Chinese population. Sci Rep. 2016;6:23446.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Xu F, Tavintharan S, Sum CF, Woon K, Lim SC, Ong CN. Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J Clin Endocrinol Metab. 2013;98(6):E1060–5.

    Article  CAS  PubMed  Google Scholar 

  80. Du Z, Shen A, Huang Y, Su L, Lai W, Wang P, et al. 1H-NMR-based metabolic analysis of human serum reveals novel markers of myocardial energy expenditure in heart failure patients. PLoS One. 2014;9(2):e88102.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Yore MM, Syed I, Moraes-Vieira PM, Zhang T, Herman MA, Homan EA, et al. Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects. Cell. 2014;159(2):318–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Claesson MJ, Cusack S, O’Sullivan O, Greene-Diniz R, de Weerd H, Flannery E, et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc Natl Acad Sci U S A. 2011;108 Suppl 1:4586–91.

    Article  CAS  PubMed  Google Scholar 

  83. Griffin JL, Wang X, Stanley E. Does our gut microbiome predict cardiovascular risk? A review of the evidence from metabolomics. Circ Cardiovasc Genet. 2015;8(1):187–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Boulange CL, Neves AL, Chilloux J, Nicholson JK, Dumas ME. Impact of the gut microbiota on inflammation, obesity, and metabolic disease. Genome Med. 2016;8(1):42.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444(7122):1027–31.

    Article  PubMed  Google Scholar 

  86. Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, et al. Host-gut microbiota metabolic interactions. Science. 2012;336(6086):1262–7.

    Article  CAS  PubMed  Google Scholar 

  87. Salek RM, Maguire ML, Bentley E, Rubtsov DV, 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(2):99–108.

    Article  CAS  PubMed  Google Scholar 

  88. Wahlstrom A, Sayin SI, Marschall HU, Backhed F. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 2016;24(1):41–50.

    Article  PubMed  Google Scholar 

  89. 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(3):362–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Mastrangelo A, Martos-Moreno GA, Garcia A, Barrios V, Ruperez FJ, Chowen JA, et al. Insulin resistance in prepubertal obese children correlates with sex-dependent early onset metabolomic alterations. Int J Obes (Lond). 2016;40(10):1494–502.

    Google Scholar 

  91. Suhre K, Meisinger C, Doring 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(11):e13953.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Gooding JR, Jensen MV, Dai X, Wenner BR, Lu D, Arumugam R, et al. Adenylosuccinate is an insulin secretagogue derived from glucose-induced purine metabolism. Cell Rep. 2015;13(1):157–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Roberts LD, Bostrom P, O’Sullivan JF, Schinzel RT, Lewis GD, Dejam A, et al. beta-Aminoisobutyric acid induces browning of white fat and hepatic beta-oxidation and is inversely correlated with cardiometabolic risk factors. Cell Metab. 2014;19(1):96–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. 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(4):1402–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, et al. Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation. 2012;125(18):2222–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. 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(10):4309–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Yin X, Subramanian S, Willinger CM, Chen G, Juhasz P, Courchesne P, et al. Metabolite signatures of metabolic risk factors and their longitudinal changes. J Clin Endocrinol Metab. 2016;101(4):1779–89.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Magnusson M, Lewis GD, Ericson U, Orho-Melander M, Hedblad B, Engstrom G, et al. A diabetes-predictive amino acid score and future cardiovascular disease. Eur Heart J. 2013;34(26):1982–9.

    Article  CAS  PubMed  Google Scholar 

  99. Floegel A, Stefan N, Yu Z, Muhlenbruch 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(2):639–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Drogan D, Dunn WB, Lin W, Buijsse B, Schulze MB, Langenberg C, et al. Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study. Clin Chem. 2015;61(3):487–97.

    Article  CAS  PubMed  Google Scholar 

  101. Jacobs S, Kroger J, Floegel A, Boeing H, Drogan D, Pischon T, et al. Evaluation of various biomarkers as potential mediators of the association between coffee consumption and incident type 2 diabetes in the EPIC-Potsdam Study. Am J Clin Nutr. 2014;100(3):891–900.

    Article  CAS  PubMed  Google Scholar 

  102. Wittenbecher C, Muhlenbruch K, Kroger J, Jacobs S, Kuxhaus O, Floegel A, et al. Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes. Am J Clin Nutr. 2015;101(6):1241–50.

    Article  CAS  PubMed  Google Scholar 

  103. Mook-Kanamori DO, Romisch-Margl W, Kastenmuller G, Prehn C, Petersen AK, Illig T, et al. Increased amino acids levels and the risk of developing of hypertriglyceridemia in a 7-year follow-up. J Endocrinol Invest. 2014;37(4):369–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Wahl S, Vogt S, Stuckler F, Krumsiek J, Bartel J, Kacprowski T, et al. Multi-omic signature of body weight change: results from a population-based cohort study. BMC Med. 2015;13:48.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Tsao CW, Vasan RS. Cohort Profile: The framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology. Int J Epidemiol. 2015;44(6):1800–13.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet. 2014;383(9921):999–1008.

    Article  PubMed  Google Scholar 

  107. Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, Fahey M, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. 2002;5(6b):1113–24.

    Article  CAS  PubMed  Google Scholar 

  108. Persson M, Hedblad B, Nelson JJ, Berglund G. Elevated Lp-PLA2 levels add prognostic information to the metabolic syndrome on incidence of cardiovascular events among middle-aged nondiabetic subjects. Arterioscler Thromb Vasc Biol. 2007;27(6):1411–6.

    Article  CAS  PubMed  Google Scholar 

  109. Boeing H, Wahrendorf J, Becker N. EPIC-Germany–A source for studies into diet and risk of chronic diseases. European Investigation into Cancer and Nutrition. Ann Nutr Metab. 1999;43(4):195–204.

    Article  CAS  PubMed  Google Scholar 

  110. Evans A, Tolonen H, Hense HW, Ferrario M, Sans S, Kuulasmaa K. Trends in coronary risk factors in the WHO MONICA project. Int J Epidemiol. 2001;30 Suppl 1:S35–40.

    Article  PubMed  Google Scholar 

  111. Holle R, Happich M, Lowel H, Wichmann HE. KORA-a research platform for population based health research. Gesundheitswesen. 2005;67 Suppl 1:S19–25.

    Article  PubMed  Google Scholar 

  112. Borodulin K, Vartiainen E, Peltonen M, Jousilahti P, Juolevi A, Laatikainen T, et al. Forty-year trends in cardiovascular risk factors in Finland. Eur J Public Health. 2015;25(3):539–46.

    Article  PubMed  Google Scholar 

  113. Vestbo J, Anderson W, Coxson HO, Crim C, Dawber F, Edwards L, et al. Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE). Eur Respir J. 2008;31(4):869–73.

    Article  CAS  PubMed  Google Scholar 

  114. Ubhi BK, Riley JH, Shaw PA, Lomas DA, Tal-Singer R, MacNee W, et al. Metabolic profiling detects biomarkers of protein degradation in COPD patients. Eur Respir J. 2012;40(2):345–55.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Coral Barbas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Mastrangelo, A., Barbas, C. (2017). Chronic Diseases and Lifestyle Biomarkers Identification by Metabolomics. In: Sussulini, A. (eds) Metabolomics: From Fundamentals to Clinical Applications. Advances in Experimental Medicine and Biology(), vol 965. Springer, Cham. https://doi.org/10.1007/978-3-319-47656-8_10

Download citation

Publish with us

Policies and ethics