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Early identification of cardiovascular risk using genomics and proteomics

Abstract

Coronary heart disease (CHD) will soon become the leading cause of death and morbidity in the world. Early detection and treatment of CHD is thus imperative to improve global health. Atherosclerosis of the coronary arteries is a complex multifactorial disease process involving multiple pathways that can be influenced by both genetic and environmental factors. With the recent advances in genomics and proteomics, many new risk factors with small-to-moderate effects are likely to be identified. Additionally, individualized risk stratification and targeted therapy may become feasible; each individual could potentially be assessed with a panel of tests for genomic and proteomic markers and, on the basis of the individual's composite risk profile, preventive and therapeutic steps could then be undertaken. With a multimarker approach, it may also be possible to identify alterations in pathways involved in atherogenesis, rather than focus on individual risk factors. In this article, we use the specific example of atherosclerosis to discuss the role of genomics and proteomics in cardiovascular risk assessment.

Key Points

  • Conventional risk factors for atherosclerosis are prevalent in much of the general population and, therefore, available risk-prediction algorithms based on such risk factors have less than desired accuracy

  • New genomic and proteomic markers are likely to have an important role in refining cardiovascular risk assessment, thereby permitting individualized risk stratification and targeted therapy

  • Genome-wide association studies have identified variants that have modest effects on disease propensity; sequencing of entire genomes, or parts thereof, might allow identification of rare variants with stronger effects

  • Proteomic discovery technology is improving and holds promise for identifying new biomarkers that will refine cardiovascular risk stratification

  • In the future, individuals could potentially be assessed with a panel of tests for the many new genomic and proteomic markers that each have small-to-moderate effects

  • A multimarker approach might also enable us to identify alterations in specific pathways of atherogenesis as a means to assess cardiovascular risk, rather than assessing risk by focusing on individual risk factors

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Figure 1: Flow diagram illustrating the use of GWAS to identify new genetic markers.
Figure 2: Flow diagram illustrating the use of MS to identify new protein biomarkers.

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References

  1. Murray, C. J. L. & Lopez, A. D. Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet 349, 1498–1504 (1997).

    Article  CAS  PubMed  Google Scholar 

  2. Yusuf, S., Reddy, S., Ounpuu, S. & Anand, S. Global burden of cardiovascular diseases: Part II: variations in cardiovascular disease by specific ethnic groups and geographic regions and prevention strategies. Circulation 104, 2855–2864 (2001).

    Article  CAS  PubMed  Google Scholar 

  3. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 285, 2486–2497 (2001).

  4. Wilson, P. W. et al. Prediction of coronary heart disease using risk factor categories. Circulation 97, 1837–1847 (1998).

    Article  CAS  PubMed  Google Scholar 

  5. Kullo, I. J. & Ballantyne, C. M. Conditional risk factors for atherosclerosis. Mayo Clin. Proc. 80, 219–230 (2005).

    Article  PubMed  Google Scholar 

  6. Cooper, J. A., Miller, G. J. & Humphries, S. E. A comparison of the PROCAM and Framingham point-scoring systems for estimation of individual risk of coronary heart disease in the Second Northwick Park Heart Study. Atherosclerosis 181, 93–100 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Wald, N. J., Hackshaw, A. K. & Frost, C. D. When can a risk factor be used as a worthwhile screening test? BMJ 319, 1562–1565 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fuster, V., Lois, F. & Franco, M. Early identification of atherosclerotic disease by noninvasive imaging. Nat. Rev. Cardiol. doi:10.1038/nrcardio.2010.54.

    Article  PubMed  Google Scholar 

  9. Mayr, M. Metabolomics: ready for the prime time? Circ. Cardiovasc. Genet. 1, 58–65 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Morrison, A. C. et al. Prediction of coronary heart disease risk using a genetic risk score: The Atherosclerosis Risk in Communities Study. Am. J. Epidemiol. 166, 28–35 (2007).

    Article  PubMed  Google Scholar 

  11. Khoury, M. J., Jones, K. & Grosse, S. D. Quantifying the health benefits of genetic tests: the importance of a population perspective. Genet. Med. 8, 191–195 (2006).

    Article  PubMed  Google Scholar 

  12. Cortese, D. A. A vision of individualized medicine in the context of global health. Clin. Pharmacol. Ther. 82, 491–493 (2007).

    Article  CAS  PubMed  Google Scholar 

  13. Kim, C. X. et al. Sex and ethnic differences in 47 candidate proteomic markers of cardiovascular disease: The Mayo Clinic Proteomic Markers of Arteriosclerosis Study. PLoS ONE 5, e9065 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lusis, A. J. Atherosclerosis. Nature 407, 233–241 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Guttmacher, A. E., Collins, F. S. & Carmona, R. H. The family history—more important than ever. N. Engl. J. Med. 351, 2333–2336 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Scheuner, M. T. Family history: where to go from here. Genet. Med. 5, 66–68 (2003).

    Article  PubMed  Google Scholar 

  17. Kullo, I. J. & Ding, K. Mechanisms of disease: The genetic basis of coronary heart disease. Nat. Clin. Pract. Cardiovasc. Med. 4, 558–569 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. Williams, R. R. et al. Usefulness of cardiovascular family history data for population-based preventive medicine and medical research (the Health Family Tree Study and the NHLBI Family Heart Study). Am. J. Cardiol. 87, 129–135 (2001).

    Article  CAS  PubMed  Google Scholar 

  19. Murabito, J. M. et al. Sibling cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults. JAMA 294, 3117–3123 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. Utermann, G. et al. Polymorphism of apolipoprotein E and occurrence of dysbetalipoproteinaemia in man. Nature 269, 604–607 (1977).

    Article  CAS  PubMed  Google Scholar 

  21. Clarke, R. et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med. 361, 2518–2528 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr & Hobbs, H. H. Sequence variations in pcsk9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Kullo, I. J. et al. Association of polymorphisms in NOS3 with the ankle-brachial index in hypertensive adults. Atherosclerosis 196, 905–912 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Ng, S. B. et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461, 272–276 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ding, K. & Kullo, I. J. Genome-wide association studies for atherosclerotic vascular disease and its risk factors. Circ. Cardiovasc. Genet. 2, 63–72 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Visel, A. et al. Targeted deletion of the 9p21 non-coding coronary artery disease risk interval in mice. Nature 464, 409–412 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Samani, N. J. et al. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357, 443–453 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ragoussis, J. Genotyping technologies for genetic research. Annu. Rev. Genomics Hum. Genet. 10, 117–133 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. Pollex, R. L. & Hegele, R. A. Copy number variation in the human genome and its implications for cardiovascular disease. Circulation 115, 3130–3138 (2007).

    Article  PubMed  Google Scholar 

  30. Redon, R. et al. Global variation in copy number in the human genome. Nature 444, 444–454 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. D'Agostino, R. B., Sr et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117, 743–753 (2008).

    Article  PubMed  Google Scholar 

  32. Gail, M. H. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J. Natl Cancer Inst. 100, 1037–1041 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Janssens, A. C. & van Duijn, C. M. Genome-based prediction of common diseases: advances and prospects. Hum. Mol. Genet. 17, R166–R173 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Talmud, P. J. et al. Chromosome 9p21.3 coronary heart disease locus genotype and prospective risk of CHD in healthy middle-aged men. Clin. Chem. 54, 467–474 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Brautbar, A. et al. Impact of adding a single allele in the 9p21 locus to traditional risk factors on risk classification for coronary heart disease and implications for lipid-modifying therapy in the white population of the Atherosclerosis Risk in Communities (ARIC) study. Circ. Cardiovasc. Genet. 2, 279–285 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Paynter, N. P. et al. Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3. Ann. Intern. Med. 150, 65–72 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Thomas, D. Gene–environment-wide association studies: emerging approaches. Nat. Rev. Genet. 11, 259–272 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Hunter, D. J., Khoury, M. J. & Drazen, J. M. Letting the genome out of the bottle—will we get our wish? N. Engl. J. Med. 358, 105–107 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Khoury, M. J. et al. The Scientific Foundation for personal genomics: recommendations from a National Institutes of Health-Centers for Disease Control and Prevention multidisciplinary workshop. Genet. Med. 11, 559–567 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. McGuire, A. L. & Burke, W. An unwelcome side effect of direct-to-consumer personal genome testing: raiding the medical commons. JAMA 300, 2669–2671 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Weston, A. D. & Hood, L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J. Proteome Res. 3, 179–196 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. Haddow, J. & Palomaki, G. ACCE: A model process for evaluating data on emerging genetic tests (Oxford University Press, New York, 2004).

    Google Scholar 

  43. Khawaja, F. J. et al. Association of novel risk factors with the ankle brachial index in African American and non-Hispanic white populations. Mayo Clin. Proc. 82, 709–716 (2007).

    Article  PubMed  Google Scholar 

  44. Pepe, M. S. An interpretation for the ROC curve and inference using GLM procedures. Biometrics 56, 352–359 (2000).

    Article  CAS  PubMed  Google Scholar 

  45. Domon, B. & Aebersold, R. Mass spectrometry and protein analysis. Science 312, 212–217 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Rifai, N., Gillette, M. A. & Carr, S. A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971–983 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Anderson, N. L. et al. Mass spectrometric quantitation of peptides and proteins using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA). J. Proteome Res. 3, 235–244 (2004).

    Article  CAS  PubMed  Google Scholar 

  48. Keshishian, H., Addona, T., Burgess, M., Kuhn, E. & Carr, S. A. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell Proteomics 6, 2212–2229 (2007).

    Article  CAS  PubMed  Google Scholar 

  49. Ling, M. M., Ricks, C. & Lea, P. Multiplexing molecular diagnostics and immunoassays using emerging microarray technologies. Expert Rev. Mol. Diagn. 7, 87–98 (2007).

    Article  CAS  PubMed  Google Scholar 

  50. Schmidt, A. M., Yan, S. D., Wautier, J. L. & Stern, D. Activation of receptor for advanced glycation end products: a mechanism for chronic vascular dysfunction in diabetic vasculopathy and atherosclerosis. Circ. Res. 84, 489–497 (1999).

    Article  CAS  PubMed  Google Scholar 

  51. Cooper, L. T. Jr, et al. Genomic and proteomic analysis of myocarditis and dilated cardiomyopathy. Heart Fail. Clin. 6, 75–85 (2010).

    Article  PubMed  Google Scholar 

  52. Granger, C. B., Van Eyk, J. E., Mockrin, S. C. & Anderson, N. L. National Heart, Lung, and Blood Institute Clinical Proteomics Working Group Report. Circulation 109, 1697–1703 (2004).

    Article  PubMed  Google Scholar 

  53. Department of Health and Human Services, Centers for Disease Control and Prevention. Current CLIA Regulations (including all changes through 01/24/2004) [online], (2004).

  54. Ellington, A. A., Kullo, I. J., Bailey, K. R. & Klee, G. G. Antibody-based protein multiplex platforms: technical and operational challenges. Clin. Chem. 56, 186–193 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Ellington, A. A., Kullo, I. J., Bailey, K. R. & Klee, G. G. Measurement and quality control issues in multiplex protein assays: a case study. Clin. Chem. 55, 1092–1099 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kingsmore, S. F. Multiplexed protein measurement: technologies and applications of protein and antibody arrays. Nat. Rev. Drug Discov. 5, 310–320 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. US Food and Drugs Administration. Draft guidance for industry and FDA staff—pharmacogenetic tests and genetic tests for heritable markers [online], (2006).

  58. Rosen, S. in Lateral Flow Immunoassay (eds Wong, R. C. & Tse, H. Y.) 35–49 (Humana Press, New York, 2009).

    Google Scholar 

  59. Wang, T. J. et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N. Engl. J. Med. 355, 2631–2639 (2006).

    Article  CAS  PubMed  Google Scholar 

  60. Zethelius, B. et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N. Engl. J. Med. 358, 2107–2116 (2008).

    Article  CAS  PubMed  Google Scholar 

  61. Cook, N. R. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin. Chem. 54, 17–23 (2008).

    Article  CAS  PubMed  Google Scholar 

  62. Ware, J. H. The limitations of risk factors as prognostic tools. N. Engl. J. Med. 355, 2615–2617 (2006).

    Article  CAS  PubMed  Google Scholar 

  63. Cook, N. R. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115, 928–935 (2007).

    Article  PubMed  Google Scholar 

  64. Pencina, M. J., D'Agostino, R. B., Sr, D'Agostino, R. B. Jr, & Vasan, R. S. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat. Med. 27, 157–172 (2008).

    Article  PubMed  Google Scholar 

  65. Wang, T. J. New cardiovascular risk factors exist, but are they clinically useful? Eur. Heart J. 29, 441–444 (2008).

    Article  CAS  PubMed  Google Scholar 

  66. Prainsack, B. et al. Personal genomes: Misdirected precaution. Nature 456, 34–35 (2008).

    Article  CAS  PubMed  Google Scholar 

  67. Mosca, L. C-reactive protein—to screen or not to screen? N. Engl. J. Med. 347, 1615–1617 (2002).

    Article  PubMed  Google Scholar 

  68. Hackam, D. G. & Anand, S. S. Emerging risk factors for atherosclerotic vascular disease: a critical review of the evidence. JAMA 290, 932–940 (2003).

    Article  PubMed  Google Scholar 

  69. Manolio, T. Novel risk markers and clinical practice. N. Engl. J. Med. 349, 1587–1589 (2003).

    Article  CAS  PubMed  Google Scholar 

  70. Braunwald, E. The Simon Dack lecture. Cardiology: the past, the present, and the future. J. Am. Coll. Cardiol. 42, 2031–2041 (2003).

    Article  PubMed  Google Scholar 

  71. National Center for Biotechnology Information. The database of Genotypes and Phenotypes (dbGaP) [online], (2010).

  72. Helgadottir, A. et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316, 1491–1493 (2007).

    Article  CAS  PubMed  Google Scholar 

  73. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  74. Coronary Artery Disease Consortium et al. Large scale association analysis of novel genetic loci for coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 29, 774–780 (2009).

  75. Kathiresan, S. et al. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat. Genet. 41, 334–341 (2009).

    Article  CAS  PubMed  Google Scholar 

  76. Willer, C. J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40, 161–169 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Erdmann, J. et al. New susceptibility locus for coronary artery disease on chromosome 3q22.3. Nat. Genet. 41, 280–282 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Gudbjartsson, D. F. et al. Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction. Nat. Genet. 41, 342–347 (2009).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported in part by grants HL81331 and HG04599 from the NIH, USA, and a generous gift from the Marriot family.

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Correspondence to Iftikhar J. Kullo.

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Kullo, I., Cooper, L. Early identification of cardiovascular risk using genomics and proteomics. Nat Rev Cardiol 7, 309–317 (2010). https://doi.org/10.1038/nrcardio.2010.53

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