Metabolomics in cardiovascular diseases

Journal of Pharmaceutical and Biomedical Analysis

Volumn 113, Issue , 2015, Pages 121-136

  Kordalewska, Marta ^a   Markuszewski, Michał J ^a  

^a MEDICAL UNIVERSITY OF GDANSK   (Poland)

Author keywords

Biomarkers; Cardiovascular diseases; Metabolomics; Systems biology

Indexed keywords

ATHEROSCLEROSIS; ATRIAL FIBRILLATION; CARDIOVASCULAR DISEASE; CEREBROVASCULAR ACCIDENT; COR PULMONALE; HEART FAILURE; HUMAN; HYPERTENSION; ISCHEMIC HEART DISEASE; KIDNEY DISEASE; METABOLIC DISORDER; METABOLOMICS; PERIPHERAL OCCLUSIVE ARTERY DISEASE; PRIORITY JOURNAL; REVIEW; ANIMAL; CARDIOVASCULAR DISEASES; METABOLISM; PROCEDURES; TRENDS;

ANIMALS; CARDIOVASCULAR DISEASES; HUMANS; METABOLOMICS;

EID: 84939269942     PISSN: 07317085     EISSN: 1873264X     Source Type: Journal    
DOI: 10.1016/j.jpba.2015.04.021     Document Type: Review

References (76)

1. Global Status Report on Noncommunicable Diseases 2010, WHO, Geneva, 2011, . http://www.who.int/nmh/publications/ncd_report_full_en.pdf.
2. Mathers C.D., Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006, 3:2011-2030.
3. Nichols M., Townsend N., Scarborough P., Rayner M. European Cardiovascular Disease Statistics 2012, European Heart Network and European Society of Cardiology. http://www.escardio.org/about/Documents/EU-cardiovascular-disease-statistics-2012.pdf.
4. DH Cardiovascular Disease Team Cardiovascular Disease Outcomes Strategy Improving Outcomes for People with or at Risk of Cardiovascular Disease 2013, https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/217118/9387-2900853-CVD-Outcomes_web1.pdf.
5. Prevention of Cardiovascular Disease Guidelines for Assessment and Management of Cardiovascular Risk 2007, WHO, Geneva, http://www.who.int/cardiovascular_diseases/guidelines/Full text.pdf.
6. Kopaei M.R., Setorki M., Doudi M., Baradaran A., Nasri H. Atherosclerosis: process, indicators, risk factors and new hopes. Int. J. Prev. Med. 2014, 5:927-946.
7. Robinson S.W., Fernandes M., Husi H. Current advances in systems and integrative biology. Comput. Struct. Biotechnol. J. 2014, 11:35-46.
8. German J.B., Hammock B.D., Watkins S.M. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics 2005, 1:3-9.
9. Nicholson J.K., Lindon J.C. Systems biology: metabonomics. Nature 2008, 455:1054-1056.
10. van der Greef J., van Wietmarschen H., van Ommen B., Verheij E. Looking back into the future: 30 years of metabolomics at TNO. Mass. Spectrom. Rev. 2013, 32:399-415.
11. Fiehn O. Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp. Funct. Genom. 2001, 2:155-168.
12. Nicholson J.K., Lindon J.C., Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29:1181-1189.
13. Strimbu K., Tavel J.A. What are biomarkers?. Curr. Opin. HIV AIDS 2010, 5:463-466.
14. Nicholson J.K., Holmes E., Kinross J.M., Darzi A.W., Takats Z., Lindon J.C. Metabolic phenotyping in clinical and surgical environments. Nature 2012, 491:384-392.
15. Gavaghana C.L., Holmesa E., Lenzb E., Wilsonb I.D., Nicholson J.K. An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10 J and Alpk:ApfCD mouse. FEBS Lett. 2000, 484:169-174.
16. Patti G.J., Yanes O., Siuzdak G. Metabolomics: the apogee of the omic triology. Nat. Rev. Mol. Cell Biol. 2013, 13:263-269.
17. Naz S., Vallejo M., García A., Barbas C. Method validation strategies involved in non-targeted metabolomics. J. Chromatogr. A 2014, 1353:99-105.
18. Chauton M.S., Størseth T.R., Johnsen G. High-resolution magic angle spinning 1H NMR analysis of whole cells of Thalassiosira pseudonana (Bacillariophyceae): broad range analysis of metabolic composition and nutritional value. J. Appl. Phycol. 2003, 15:533-542.
19. Pan Z., Raftery D. Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal. Bioanal. Chem. 2007, 387:525-527.
20. Ivanisevic J., Zhu Z.-J., Plate L., Tautenhahn R., Chen S., O'Brien P.J., Johnson C.H., Marletta M.A., Patti G.J., Siuzdak G. Toward 'omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal. Chem. 2013, 85:6876-6884.
21. Vorkas P.A., Isaac G., Anwar M.A., Davies A.H., Want E.J., Nicholson J.K., Holmes E. Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage: application to cardiovascular disease. Anal. Chem. 2015, 10.1021/ac503775m.
22. Lenz E.M., Wilson I.D. Analytical strategies in metabonomics. J. Proteome Res. 2007, 6:443-458.
23. Beana H.D., Hill J.E., Dimandja J.-M.D. Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data. J. Chromatogr. A 2015, 10.1016/j.chroma.2015.03.001.
24. Heeren R.M.A. Getting the picture: the coming of age of imaging MS. Int. J. Mass Spectrom. 2015, 377:672-680.
25. Tiffany Porta, Lesur Antoine, Varesio Emmanuel, Hopfgartner Gérard Quantification in MALDI-MS imaging: what can we learn from MALDI-selected reaction monitoring and what can we expect for imaging?. Anal. Bioanal. Chem. 2015, 407:2177-2187.
26. Issaq H.J., Van Q.N., Waybright T.J., Muschik G.M., Veenstra T.D. Analytical and statistical approaches to metabolomics research. J. Sep. Sci. 2009, 32:2183-2199.
27. Godzien J., Ciborowski M., Angulo S., Barbas C. From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS. Electrophoresis 2013, 34:2812-2826.
28. Worley B., Powers R. Multivariate analysis in metabolomics. Curr. Metabolomics 2013, 1:92-107.
29. Fonville J.M., Richards S.E., Barton R.H., Boulange C.L., Ebbels T.M.D., Nicholson J.K., Holmes E., Dumas M.E. The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping. J. Chemom. 2010, 24:636-649.
30. http://www.mesa-nhlbi.org/.
31. Bild D.E., Bluemke D.A., Burke G.L., Detrano R., Diez Roux A.V., Folsom A.R., Greenland P., Jacob D.R., Kronmal R., Liu K., Nelson J.C., O'Leary D., Saad M.F., Shea S., Szklo M., Tracy R.P. Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 2002, 156:871-881.
32. Steffen B.T., Steffen L.M., Tracy R., Siscovick D., Hanson N.Q., Nettleton J., Tsai M.Y. Obesity modifies the association between plasma phospholipid polyunsaturated fatty acids and markers of inflammation: the Multi-Ethnic Study of Atherosclerosis. Int. J. Obes. 2012, 36:797-804.
33. Würtz P., Raiko J.R., Magnussen C.G., Soininen P., Kangas A.J., Tynkkynen T., Thomson R., Laatikainen6 R., Savolainen M.J., Laurikka J., Kuukasjärvi P., Tarkka M., Karhunen P.J., Jula A., Viikari J.S., Kähönen M., Lehtimäki T., Juonala M., Ala-Korpela M., Raitakari O.T. High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis. Eur. Heart J. 2012, 33:2307-2316.
34. Chen X., Liu L., Palacios G., Gao J., Zhang N., Li G., Lu J., Song T., Zhang Y., Lv H. Plasma metabolomics reveals biomarkers of the atherosclerosis. J. Sep. Sci. 2010, 33:2776-2783.
35. Vorkas P.A., Shalhoub J., Isaac G., Want E.J., Nicholson J.K., Holmes E., Davies A.H. Metabolic phenotyping of atherosclerotic plaques reveals latent associations between free cholesterol and ceramide metabolism in atherogenesis. J. Proteome Res. 2015, 14:1389-1399.
36. Ming-Qian S., Jian-Xun L., Lan M., Jin C., Cheng-Ren L., Lei L., Jianxun R. LC coupled with TOFMS for metabonomics study of mini-pigs with atherosclerosis. Chromatographia 2012, 75:491-497.
37. Jove M., Ayala V., Ramirez-Núñez O., Serrano J.C.E., Cassanyé A., Arola L., Caimari A., del Bas J.M., Crescenti A., Pamplona R., Portero-Otin M. Lipidomic and metabolomic analyses reveal potential plasma biomarkers of early atheromatous plaque formation in hamsters. Cardiovasc. Res. 2013, 97:642-652.
38. Teul J., Rupérez F.J., Garcia A., Vaysse J., Balayssac S., Gilard V., Malet-Martino M., Martin-Ventura J.L., Blanco-Colio L.M., Tuñón J., Egido J., Barbas C. Improving metabolite knowledge in stable atherosclerosis patients by association and correlation of GC-MS and 1H NMR fingerprints. J. Proteome Res. 2009, 8:5580-5589.
39. Rizza S., Copetti M., Rossi C., Cianfarani M.A., Zucchelli M., Luzi A., Pecchioli C., Porzio O., Di Cola G., Urbani A., Pellegrini F., Federici M. Metabolomics signature improves the prediction of cardiovascular events in elderly subjects. Atherosclerosis 2014, 232:260-264.
40. Brindle J.T., Antti H., Holmes E., Tranter G., Nicholson J.K., Bethell H.W.L., Clarke S., Schofield P.M., Mckilligin E., Mosedale D.E., Grainger D.J. Rapid and noninvasive diagnosis of the presenceand severity of coronary heart disease using 1H NMR-based metabonomics. Nat. Med. 2002, 8:1439-1444.
41. Shah S.H., Sun J.-L., Stevens R.D., Bain J.R., Muehlbauer M.J., Pieper K.S., Haynes C., Hauser E.R., Kraus W.E., Granger C.B., Newgard C.B., Califf R.M., Newby L.K. Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am. Heart J. 2012, 163:844-850.
42. Sabatine M.S., Liu E., Morrow D.A., Heller E., McCarroll R., Wiegand R., Berriz G.F., Roth F.P., Gerszten R.E. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 2005, 112:3868-3875.
43. Lva Y., Liua X., Yanb S., Lianga X., Yang Y., Dai W., Zhang W. Metabolomic study of myocardial ischemia and intervention effects of Compound Danshen Tablets in rats using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry. J. Pharmaceut. Biomed. 2010, 52:129-135.
44. Zhang Q., Wang G., Ma J.A.B., Dua Y., Zhu L., Wu D. Metabonomic profiling of diet-induced hyperlipidaemia in a rat model. Biomarkers 2010, 15:205-216.
45. Wang L., Hou E., Wang Z., Sun N., He L., Chen L., Liang M., Tian Z. Analysis of metabolites in plasma reveals distinct metabolic features between Dahl salt-sensitive rats and consomic SS.13BN rats. Biochem. Biophys. Res. Commun. 2014, 450:863-869.
46. Kim M., Jung S., Kim S.Y., Lee S.-H., Lee J.H. Prehypertension-associated elevation in circulating lysophosphatidlycholines, Lp-PLA2 activity, and oxidative stress. PLoS ONE 2014, 9:e96735.
47. Wanga L., Houa E., Wangc L., Wangd Y., Yange L., Zhenge X., Xiea G., Suna Q., Liangb M., Tian Z. Reconstruction and analysis of correlation networks based on GC-MS metabolomics data for young hypertensive men. Anal. Chim. Acta 2015, 854:95-105.
48. Stamler J., Elliott P., Dennis B., Dyer A.R., Kesteloot H., Liu K., Ueshima H., Zhou B.F. INTERMAP: background, aims, design, methods, and descriptive statistics (nondietary),. J. Hum. Hypertens. 2003, 17:591-608.
49. Stamler J., Brown I.J., Yap I.K.S., Chan Q., Wijeyesekera A., Garcia-Perez I., Chadeau-Hyam M., Ebbels T.M.D., De Iorio M., Posma J., Daviglus M.L., Carnethon M., Holmes E., Nicholson J.K., Elliott P. Dietary and urinary metabonomic factors possibly accounting for higher blood pressure of African-Americans compared to White Americans--The INTERMAP Study RR. Hypertension 2013, 62:1074-1080. the INTERMAP Research Group.
50. Jung J.Y., Lee H.-S., Kang D.-G., Kim N.S., Cha M.H., Bang O.-S., Ryu D.H., Hwang G.-S. 1H-NMR-based metabolomics study of cerebral infarction. Stroke 2011, 42:1282-1288.
51. Taylor Kimberly W., Wang Y., Pham L., Furie K.L., Gerszten R.E. Metabolite profiling identifies a branched chain amino acid signature in acute cardioembolic stroke. Stroke 2013, 44:1389-1395.
52. Zhang H.-Y., Chen X., Hu P., Liang Q.-L., Liang X.-P., Wang Y.-M., Luo G.-A. Metabolomic profiling of rat serum associated with isoproterenol-induced myocardial infarction using ultra-performance liquid chromatography/time-of-flight mass spectrometry and multivariate analysis. Talanta 2009, 79:254-259.
53. Barba I., Jaimez-Auguets E., Antonio Rodriguez-Sinovas A., Garcia-Dorado D. 1H NMR-based metabolomic identification of at-risk areas after myocardial infarction in swine. MAGMA 2007, 20:265-271.
54. Kristensen M., Savorani F., Ravn-Haren G., Poulsen M., Markowski J., Larsen F.H., Dragsted L.O., Engelsen S.B. NMR and interval PLS as reliable methods for determination of cholesterol in rodent lipoprotein fractions. Metabolomics 2010, 6:129-136.
55. Dunn W.B., Broadhurst D.I., Deepak S.M., Buch M.H., McDowell G., Spasic I., Ellis D.I., Brooks N., Kell D.B., Neysesc L. Serum metabolomics reveals many novel metabolic markers of heart failure, including pseudouridine and 2-oxoglutarate,. Metabolomics 2007, 3:413-426.
56. Wang Z., Wilson Tang W.H., Cho L., Brennan D.M., Hazen S.L. Targeted metabolomic evaluation of arginine methylation and cardiovascular risks potential mechanisms beyond nitric oxide synthase inhibition. Arterioscler. Thromb. Vasc. Biol. 2009, 29:1383-1391.
57. Vallejo M., García A., Tuñón J., García-Martínez D., Angulo S., Martin-Ventura J.L., Blanco-Colio L.M., Almeida P., Egido J., Barbas C. Plasma fingerprinting with GC-MS in acute coronary syndrome. Anal. Bioanal. Chem. 2009, 394:1517-1524.
58. Teul J., Garcia A., Tũnón J., Martin-Ventura J.L., Tarín N., Bescós L.L., Egido J., Barbas C., Rupérez F.J. Targeted and non-targeted metabolic time trajectory in plasma of patients after acute coronary syndrome. J. Pharmaceut. Biomed. 2011, 56:343-351.
59. Mayr M., Yusuf S., Weir G., Chung Y.-L., Mayr U., Yin X., Ladroue C., Madhu B., Roberts N., De Souza A., Fredericks S., Stubbs M., Griffiths J.R., Jahangiri M., Xu Q., Camm A.J. Combined metabolomic and proteomic analysis of human atrial fibrillation. J. Am. Coll. Cardiol. 2008, 51:585-594.
60. Bujak R., García-álvarez A., Rupérez F.J., Nuño-Ayala M., García A., Ruiz-Cabello J., Fuster V., Ibáñez B., Barbas C. Metabolomics reveals metabolite changes in acute pulmonary embolism. J. Proteome Res. 2014, 13:805-816.
61. Zhao Y., Peng J., Lu C., Hsin M., Mura M., Wu L., Chu L., Zamel R., Machuca T., Waddell T., Liu M., Keshavjee S., Granton J., de Perrot M. Metabolomic heterogeneity of pulmonary arterial hypertension. PLoS ONE 2014, 9:e88727.
62. Huang C.-C., McDermott M.M., Liu K., Kuo C.-H., Wang S.-Y., Tao H., Tseng Y.J. Plasma metabolomic profiles predict near-term death among individuals with lower extremity peripheral arterial disease. J. Vasc. Surg. 2013, 58:989-996.
63. Suna J., Shannonb M., Andoa Y., Schnackenberga L.K., Khanc N.A., Portilla D., Begera R.D. Serum metabolomic profiles from patients with acute kidney injury: A pilot study. J. Chromatogr. B 2012, 893-894:107-113.
64. Kalim S., Clish C.B., Wenger J., Elmariah S., Yeh R.W., Deferio J.J., Pierce K., Deik A., Gerszten R.E., Thadhani R., Rhee E.P. A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients. J. Am. Heart Assoc. 2013, 2:000542.
65. Diercks D.B., Owen K., Tolstikov V., Sutter M. Urinary metabolomic analysis for the identification of renal injury in patients with acute heart failure. Acad. Emerg. Med. 2012, 19:18-23.
66. Huang C.-C., Lou B.-S., Hsu F.-L., Hou C.-C. Use of urinary metabolomics to evaluate the effect of hyperuricemia on the kidney. Food Chem. Toxicol. 2014, 74:35-44.
67. Toyohara T., Akiyama Y., Suzuki T., Takeuchi Y., Mishima E., Tanemoto M., Momose A., Toki N., Sato H., Nakayama M., Hozawa A., Tsuji I., Ito S., Soga T., Abe1 T. Metabolomic profiling of uremic solutes in CKD patients. Hypertens. Res. 2010, 33:944-952.
68. Hayashi K., Sasamura H., Hishiki T., Suematsu M., Ikeda S., Soga T., Itoh H. Use of serum and urine metabolome analysis for the detection of metabolic changes in patients with stage 1-2 chronic kidney disease. Nephro-Urol Mon. 2011, 3:164-171.
69. Wanga C., Feng R., Suna D., Li Y., Bi X., Sun C. Metabolic profiling of urine in young obese men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC/Q-TOF MS). J. Chromatogr. B 2011, 879:2871-2876.
70. Huang C.F., Cheng M.L., Fan C.M., Hong C.Y., Shiao M.S. Nicotinuric acid: a potential marker of metabolic syndrome through a metabolomics-based approach. Diabetes Care 2013, 36:1729-1731.
71. Yu Z.-R., Ning Y., Yu H., Tang N.-J. A HPLC-Q-TOF-MS-based urinary metabolomic approach to identification of potential biomarkers of metabolic syndrome. J. Huazhong Univ. Sci. Technolog. Med. Sci. 2014, 34:276-283.
72. Magnusson M., Lewis G.D., Ericson U., Orho-Melander M., Hedblad B., Engström G., Östling G., Clish C., Wang T.J., Gerszten R.E., Melander O. A diabetes-predictive amino acid score and future cardiovascular disease. Eur. Heart J. 2013, 34:1982-1989.
73. Gall W.E., Beebe K., Lawton K.A., Adam K.-P., Mitchell M.W., Nakhle P.J., Ryals J.A., Milburn M.V., Nannipieri M., Camastra S., Natali A., Ferrannini E. α-Hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE 2010, 5:10883.
74. Albrecht E., Waldenberger M., Krumsiek J., Evans A.M., Jeratsch U., Breier M., Adamski J., Koenig W., Zeilinger S., Fuchs C., Klopp N., Theis F.J., Wichmann H.-E., Suhre K., Illig T., Strauch K., Peters A., Gieger C., Kastenmüller G., Doering A., Meisinger C. Metabolite profiling reveals new insights into the regulation of serum urate in humans. Metabolomics 2014, 10:141-151.
75. Altmaier E., Emeny R.T., Krumsiek J., Lacruz M.E., Lukaschek K., Häfner S., Kastenmüller G., Römisch-Margl W., Prehn C., Mohney R.P., Evans A.M., Milburn M.V., Illig T., Adamski J., Theis F., Suhre K., Ladwig K.-H. Metabolomic profiles in individuals with negative affectivity and social inhibition: a population-based study of Type D personality. Psychoneuroendocrinology 2013, 38:1299-1309.
76. Dutton R.J., Turnbaugh P.J. Taking a metagenomic view of human nutrition. Curr. Opin. Clin. Nutr. Metab. Care 2012, 15:448-454.