Advertisement

Metabolic Signatures of Redox-Dependent Cardiovascular Diseases

  • Stephen T. Vernon
  • John F. O’Sullivan
  • Gemma A. FigtreeEmail author
Chapter

Abstract

Dysregulated redox signalling plays a central role in the development and progression of cardiovascular disease (CVD). To date only a small number of biomarkers that reflect cellular redox status have been identified and these markers have not been utilised in the clinical setting. There are currently no good circulating biomarkers that closely represent sub-cellular dysregulated redox signalling in the tissue, arterial wall or myocardium. Improved prognostic ability, and potentially early disease detection and risk stratification may be achieved through a more reflective biomarker of the dysregulated signalling microdomain. Whilst there is no currently identified circulating redox biomarker reflecting the intricacies of sub-cellular redox dysregulation in cardiovascular disease, there are some markers that are either directly or indirectly related to redox state that have been associated with cardiovascular diseases. Metabolomics platforms allow for the measurement of metabolites that are related to or result from lipid and protein oxidation.

Metabolomics is an unbiased approach that allows the identification and quantification of small molecules within a biological fluid. Advances in metabolomic platform technologies as well as bioinformatic approaches may allow for the rapid identification and utilisation of novel biomarkers that accurately reflect intracellular redox status relevant to the development of CVD. Omics studies allow incorporation and analysis of large amounts of data that represent the entirety of a particular biological parameter within a biological fluid or tissue. Metabolic signatures identified through metabolomic analysis may be relatively simple, involving a small number of metabolites, or may be complex and may include permutations of hundreds or even thousands of metabolites. These diverse metabolic signatures have a vast array of potential utility including: early disease detection and diagnosis; disease activity and treatment monitoring; and in identifying new biological pathways and potential treatment targets. Systems biology provides a platform to try to unpack the underlying relationships, interconnected networks and mechanisms contained within the complex signatures.

Advances in metabolomics technologies, and bioinformatics capabilities will assist in identification and precise measurement of both candidate and unsuspected metabolites in the circulation that reflect dysregulated redox signalling and may be of relevance to clinical practice and our efforts to improve cardiovascular health.

Keywords

Cardiovascular Biomarker Metobolomics Redox signalling 

References

  1. 1.
    Vernon ST, Hansen T, Kott KA, Yang JY, O’Sullivan JF, Figtree GA (2019) Utilizing state-of-the-art “omics” technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease. Microcirculation 26:e12488PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29(11):1181–1189PubMedCrossRefPubMedCentralGoogle Scholar
  3. 3.
    Nedic Erjavec G, Konjevod M, Perkovic M, Svob D, Tudor L, Barbas C et al (2018) Short overview on metabolomic approach and redox changes in psychiatric disorders. Redox Biol 14:178–186PubMedCrossRefPubMedCentralGoogle Scholar
  4. 4.
    Dona AC, Coffey S, Figtree G (2016) Translational and emerging clinical applications of metabolomics in cardiovascular disease diagnosis and treatment. Eur J Prev Cardiol 23(15):1578–1589PubMedCrossRefPubMedCentralGoogle Scholar
  5. 5.
    Zamboni N, Saghatelian A, Patti GJ (2015) Defining the metabolome: size, flux, and regulation. Mol Cell 58(4):699–706PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Burgess S, Harshfield E (2016) Mendelian randomization to assess causal effects of blood lipids on coronary heart disease: lessons from the past and applications to the future. Curr Opin Endocrinol Diabetes Obes 23(2):124–130PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Karimi Galougahi K, Antoniades C, Nicholls SJ, Channon KM, Figtree GA (2015) Redox biomarkers in cardiovascular medicine. Eur Heart J 36(25):1576–1582PubMedCrossRefGoogle Scholar
  8. 8.
    Ganna A, Salihovic S, Sundstrom J, Broeckling CD, Hedman AK, Magnusson PK et al (2014) Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLoS Genet 10(12):e1004801PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Lewis GD, Wei R, Liu E, Yang E, Shi X, Martinovic M et al (2008) Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury. J Clin Invest 118(10):3503–3512PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Ruiz-Canela M, Hruby A, Clish CB, Liang L, Martínez-González MA, Hu FB (2017) Comprehensive metabolomic profiling and incident cardiovascular disease: a systematic review. J Am Heart Assoc 6(10):e005705PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Guarner F, Malagelada J-R (2003) Gut flora in health and disease. Lancet 361(9356):512–519PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, DuGar B et al (2011) Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472(7341):57–63PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Tang WHW, Wang Z, Levison BS, Koeth RA, Britt EB, Fu X et al (2013) Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med 368(17):1575–1584PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Wilson Tang WH, Hazen SL (2017) The gut microbiome and its role in cardiovascular diseases. Circulation 135(11):1008–1010PubMedCentralCrossRefGoogle Scholar
  15. 15.
    Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M et al (2015) Urinary metabolic signatures of human adiposity. Sci Transl Med 7(285):285ra62PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Rosolowsky ET, Skupien J, Smiles AM, Niewczas M, Roshan B, Stanton R et al (2011) Risk for ESRD in type 1 diabetes remains high despite renoprotection. J Am Soc Nephrol 22(3):545–553PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E et al (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17:448–453PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF 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(4):311–326PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Sharma K, Karl B, Mathew AV, Gangoiti JA, Wassel CL, Saito R et al (2013) Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease. J Am Soc Nephrol 24(11):1901–1912PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    The GBDOC, Ng M, Fleming T, Robinson M, Thomson B, Graetz N et al (2014) Global, regional and national prevalence of overweight and obesity in children and adults 1980–2013: a systematic analysis. Lancet (London, England) 384(9945):766–781CrossRefGoogle Scholar
  21. 21.
    Madamanchi NR, Runge MS (2013) Redox signaling in cardiovascular health and disease. Free Radic Biol Med 61:473–501PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Burgoyne JR, Mongue-Din H, Eaton P, Shah AM (2012) Redox signaling in cardiac physiology and pathology. Circ Res 111(8):1091–1106PubMedCrossRefPubMedCentralGoogle Scholar
  23. 23.
    Finkel T (1999) Signal transduction by reactive oxygen species in non-phagocytic cells. J Leukoc Biol 65(3):337–340PubMedCrossRefPubMedCentralGoogle Scholar
  24. 24.
    Bubb KJ, Birgisdottir AB, Tang O, Hansen T, Figtree GA (2017) Redox modification of caveolar proteins in the cardiovascular system- role in cellular signalling and disease. Free Radic Biol Med 109:61–74PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    Williams TM, Lisanti MP (2004) The caveolin proteins. Genome Biol 5(3):214PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Patel HH, Insel PA (2008) Lipid rafts and caveolae and their role in compartmentation of redox signaling. Antioxid Redox Signal 11(6):1357–1372CrossRefGoogle Scholar
  27. 27.
    Tsutsumi YM, Horikawa YT, Jennings MM, Kidd MW, Niesman IR, Yokoyama U et al (2008) Cardiac-specific overexpression of caveolin-3 induces endogenous cardiac protection by mimicking ischemic preconditioning. Circulation 118(19):1979–1988PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Wypijewski KJ, Tinti M, Chen W, Lamont D, Ashford MLJ, Calaghan SC et al (2015) Identification of caveolar resident proteins in ventricular myocytes using a quantitative proteomic approach: dynamic changes in caveolar composition following adrenoceptor activation. Mol Cell Proteomics 14(3):596–608PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Pulli B, Ali M, Forghani R, Schob S, Hsieh KLC, Wojtkiewicz G et al (2013) Measuring myeloperoxidase activity in biological samples. PLoS One 8(7):e67976PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Morrow JD, Awad JA, Boss HJ, Blair IA, Roberts LJ (1992) Non-cyclooxygenase-derived prostanoids (F2-isoprostanes) are formed in situ on phospholipids. Proc Natl Acad Sci U S A 89(22):10721–10725PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Griffiths HR, Moller L, Bartosz G, Bast A, Bertoni-Freddari C, Collins A et al (2002) Biomarkers. Mol Aspects Med 23(1–3):101–208PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Kromer BM, Tippins JR (1996) Coronary artery constriction by the isoprostane 8-epi prostaglandin F2 alpha. Br J Pharmacol 119(6):1276–1280PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Patrono C, FitzGerald GA (1997) Isoprostanes: potential markers of oxidant stress in atherothrombotic disease. Arterioscler Thromb Vasc Biol 17(11):2309–2315PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Delanty N, Reilly MP, Pratico D, Lawson JA, McCarthy JF, Wood AE et al (1997) 8-epi PGF2 alpha generation during coronary reperfusion. A potential quantitative marker of oxidant stress in vivo. Circulation 95(11):2492–2499PubMedCrossRefPubMedCentralGoogle Scholar
  35. 35.
    Mallat Z, Philip I, Lebret M, Chatel D, Maclouf J, Tedgui A (1998) Elevated levels of 8-iso-prostaglandin F2alpha in pericardial fluid of patients with heart failure: a potential role for in vivo oxidant stress in ventricular dilatation and progression to heart failure. Circulation 97(16):1536–1539PubMedCrossRefGoogle Scholar
  36. 36.
    Halliwell B (2000) Lipid peroxidation, antioxidants and cardiovascular disease: how should we move forward? Cardiovasc Res 47(3):410–418PubMedCrossRefPubMedCentralGoogle Scholar
  37. 37.
    Walter MF, Jacob RF, Jeffers B, Ghadanfar MM, Preston GM, Buch J et al (2004) Serum levels of thiobarbituric acid reactive substances predict cardiovascular events in patients with stable coronary artery disease: a longitudinal analysis of the PREVENT study. J Am Coll Cardiol 44(10):1996–2002PubMedCrossRefPubMedCentralGoogle Scholar
  38. 38.
    Antonopoulos AS, Margaritis M, Coutinho P, Shirodaria C, Psarros C, Herdman L et al (2015) Adiponectin as a link between type 2 diabetes and vascular NADPH oxidase activity in the human arterial wall: the regulatory role of perivascular adipose tissue. Diabetes 64(6):2207–2219PubMedCrossRefPubMedCentralGoogle Scholar
  39. 39.
    Shishehbor MH, Aviles RJ, Brennan M et al (2003) Association of nitrotyrosine levels with cardiovascular disease and modulation by statin therapy. JAMA 289(13):1675–1680PubMedCrossRefPubMedCentralGoogle Scholar
  40. 40.
    Dalle-Donne I, Rossi R, Colombo R, Giustarini D, Milzani A (2006) Biomarkers of oxidative damage in human disease. Clin Chem 52(4):601–623PubMedCrossRefPubMedCentralGoogle Scholar
  41. 41.
    Bollineni RC, Fedorova M, Bluher M, Hoffmann R (2014) Carbonylated plasma proteins as potential biomarkers of obesity induced type 2 diabetes mellitus. J Proteome Res 13(11):5081–5093PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
    Brennan ML, Penn MS, Van Lente F, Nambi V, Shishehbor MH, Aviles RJ et al (2003) Prognostic value of myeloperoxidase in patients with chest pain. N Engl J Med 349(17):1595–1604CrossRefGoogle Scholar
  43. 43.
    Meuwese MC, Stroes ES, Hazen SL, van Miert JN, Kuivenhoven JA, Schaub RG et al (2007) Serum myeloperoxidase levels are associated with the future risk of coronary artery disease in apparently healthy individuals: the EPIC-Norfolk Prospective Population Study. J Am Coll Cardiol 50(2):159–165CrossRefGoogle Scholar
  44. 44.
    Kataoka Y, Shao M, Wolski K, Uno K, Puri R, Tuzcu EM et al (2014) Myeloperoxidase levels predict accelerated progression of coronary atherosclerosis in diabetic patients: insights from intravascular ultrasound. Atherosclerosis 232(2):377–383PubMedCrossRefGoogle Scholar
  45. 45.
    Jialal I, Devaraj S (2003) Antioxidants and atherosclerosis: don’t throw out the baby with the bath water. Circulation 107(7):926–928PubMedCrossRefPubMedCentralGoogle Scholar
  46. 46.
    Shah AM, Channon KM (2004) Free radicals and redox signalling in cardiovascular disease. Heart 90(5):486–487PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Stephen T. Vernon
    • 1
    • 2
  • John F. O’Sullivan
    • 2
    • 3
    • 4
  • Gemma A. Figtree
    • 1
    • 2
    Email author
  1. 1.Cardiothoracic and Vascular Health, Kolling Institute and Department of CardiologyRoyal North Shore Hospital, Northern Sydney Local Health DistrictSydneyAustralia
  2. 2.Sydney Medical School, Faculty of Medicine and HealthThe University of SydneySydneyAustralia
  3. 3.Charles Perkins CentreThe University of SydneySydneyAustralia
  4. 4.Heart Research InstituteSydneyAustralia

Personalised recommendations