1932

Abstract

Risk assessments are integral for the prevention and management of cardiometabolic disease (CMD). However, individuals may develop CMD without traditional risk factors, necessitating the development of novel biomarkers to aid risk prediction. The emergence of omic technologies, including genomics, proteomics, and metabolomics, has allowed for assessment of orthogonal measures of cardiometabolic risk, potentially improving the ability for novel biomarkers to refine disease risk assessments. While omics has shed light on novel mechanisms for the development of CMD, its adoption in clinical practice faces significant challenges. We review select omic technologies and cardiometabolic investigations for risk prediction, while highlighting challenges and opportunities for translating findings to clinical practice.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-med-042418-010924
2020-01-27
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/med/71/1/annurev-med-042418-010924.html?itemId=/content/journals/10.1146/annurev-med-042418-010924&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Kannel WB, Dawber TR, Kagan A et al. 1961. Factors of risk in the development of coronary heart disease—six year follow-up experience. The Framingham Study. Ann. Intern. Med. 55:33–50
    [Google Scholar]
  2. 2. 
    Morrow DA, de Lemos JA 2007. Benchmarks for the assessment of novel cardiovascular biomarkers. Circulation 115:949–52
    [Google Scholar]
  3. 3. 
    de Lemos JA, Rohatgi A, Ayers CR 2015. Applying a big data approach to biomarker discovery: running before we walk. ? Circulation 132:2289–92
    [Google Scholar]
  4. 4. 
    Wilson PW, D'Agostino RB, Levy D et al. 1998. Prediction of coronary heart disease using risk factor categories. Circulation 97:1837–47
    [Google Scholar]
  5. 5. 
    Goff David C, Lloyd-Jones DM, Bennett G et al. 2014. 2013 ACC/AHA guideline on the assessment of cardiovascular risk. Circulation 129:S49–73
    [Google Scholar]
  6. 6. 
    Ridker PM, Buring JE, Rifai N, Cook NR 2007. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: The Reynolds Risk Score. JAMA 297:611–19
    [Google Scholar]
  7. 7. 
    Hippisley-Cox J, Coupland C, Brindle P 2017. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ Clin. Res. Ed. 357:j2099
    [Google Scholar]
  8. 8. 
    Bang H, Edwards AM, Bomback AS et al. 2009. Development and validation of a patient self-assessment score for diabetes risk. Ann. Intern. Med. 151:775–83
    [Google Scholar]
  9. 9. 
    Heikes KE, Eddy DM, Arondekar B, Schlessinger L 2008. Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care 31:1040–45
    [Google Scholar]
  10. 10. 
    Khot UN, Khot MB, Bajzer CT et al. 2003. Prevalence of conventional risk factors in patients with coronary heart disease. JAMA 290:898–904
    [Google Scholar]
  11. 11. 
    Wang TJ. 2011. Assessing the role of circulating, genetic, and imaging biomarkers in cardiovascular risk prediction. Circulation 123:551–65
    [Google Scholar]
  12. 12. 
    Hoefer IE, Steffens S, Ala-Korpela M et al. 2015. Novel methodologies for biomarker discovery in atherosclerosis. Eur. Heart J. 36:2635–42
    [Google Scholar]
  13. 13. 
    Pepe MS, Fan J, Feng Z et al. 2015. The net reclassification index (NRI): a misleading measure of prediction improvement even with independent test data sets. Stat. Biosci. 7:282–95
    [Google Scholar]
  14. 14. 
    Marenberg ME, Risch N, Berkman LF et al. 1994. Genetic susceptibility to death from coronary heart disease in a study of twins. N. Engl. J. Med. 330:1041–46
    [Google Scholar]
  15. 15. 
    Musunuru K, Kathiresan S. 2019. Genetics of common, complex coronary artery disease. Cell 177:132–45
    [Google Scholar]
  16. 16. 
    Kathiresan S, Srivastava D. 2012. Genetics of human cardiovascular disease. Cell 148:1242–57
    [Google Scholar]
  17. 17. 
    Abul-Husn NS, Manickam K, Jones LK et al. 2016. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 354:6319aaf7000
    [Google Scholar]
  18. 18. 
    Estrada K, Aukrust I, Bjorkhaug L et al. 2014. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311:2305–14
    [Google Scholar]
  19. 19. 
    Talmud PJ, Cooper JA, Palmen J et al. 2008. Chromosome 9p21.3 coronary heart disease locus genotype and prospective risk of CHD in healthy middle-aged men. Clin. Chem. 54:467–74
    [Google Scholar]
  20. 20. 
    Deloukas P, Kanoni S, Willenborg C et al. 2013. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45:25–33
    [Google Scholar]
  21. 21. 
    Nikpay M, Goel A, Won HH et al. 2015. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47:1121–30
    [Google Scholar]
  22. 22. 
    Liu C, Kraja AT, Smith JA et al. 2016. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat. Genet. 48:1162–70
    [Google Scholar]
  23. 23. 
    Manolio TA, Collins FS, Cox NJ et al. 2009. Finding the missing heritability of complex diseases. Nature 461:747–53
    [Google Scholar]
  24. 24. 
    Lango H, Palmer CN, Morris AD et al. 2008. Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes 57:3129–35
    [Google Scholar]
  25. 25. 
    Morris RW, Cooper JA, Shah T et al. 2016. Marginal role for 53 common genetic variants in cardiovascular disease prediction. Heart 102:1640–47
    [Google Scholar]
  26. 26. 
    Ripatti S, Tikkanen E, Orho-Melander M et al. 2010. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 376:1393–400
    [Google Scholar]
  27. 27. 
    Khera AV, Emdin CA, Drake I et al. 2016. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375:2349–58
    [Google Scholar]
  28. 28. 
    Inouye M, Abraham G, Nelson CP et al. 2018. Genomic risk prediction of coronary artery disease in 480,000 adults. Implications Primary Prev 72:1883–93
    [Google Scholar]
  29. 29. 
    Bonifacio E, Beyerlein A, Hippich M et al. 2018. Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: a prospective study in children. PLOS Med 15:e1002548
    [Google Scholar]
  30. 30. 
    Khera AV, Chaffin M, Aragam KG et al. 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50:1219–24
    [Google Scholar]
  31. 31. 
    Abraham G, Havulinna AS, Bhalala OG et al. 2016. Genomic prediction of coronary heart disease. Eur. Heart J. 37:3267–78
    [Google Scholar]
  32. 32. 
    Mahajan A, Taliun D, Thurner M et al. 2018. Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50:1505–13
    [Google Scholar]
  33. 33. 
    Martin AR, Kanai M, Kamatani Y et al. 2019. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51:584–91
    [Google Scholar]
  34. 34. 
    Wald NJ, Old R. 2019. The illusion of polygenic disease risk prediction. Genet. Med. 21:1705–7
    [Google Scholar]
  35. 35. 
    Lam MPY, Ping P, Murphy E 2016. Proteomics research in cardiovascular medicine and biomarker discovery. J. Am. Coll. Cardiol. 68:2819–30
    [Google Scholar]
  36. 36. 
    Lindsey Merry L, Mayr M, Gomes Aldrin V et al. 2015. Transformative impact of proteomics on cardiovascular health and disease. Circulation 132:852–72
    [Google Scholar]
  37. 37. 
    Benson MD, Sabatine MS, Gerszten RE 2016. The prospects for cardiovascular proteomics: a glass approaching half full. JAMA Cardiol 1:245–46
    [Google Scholar]
  38. 38. 
    Edwards AVG, White MY, Cordwell SJ 2008. The role of proteomics in clinical cardiovascular biomarker discovery. Mol. Cell. Proteom. 7:1824–37
    [Google Scholar]
  39. 39. 
    Han X, Aslanian A, Yates JR 3rd 2008. Mass spectrometry for proteomics. Curr. Opin. Chem. Biol. 12:483–90
    [Google Scholar]
  40. 40. 
    Addona TA, Shi X, Keshishian H et al. 2011. A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease. Nat. Biotechnol. 29:635–43
    [Google Scholar]
  41. 41. 
    Yin X, Subramanian S, Hwang SJ et al. 2014. Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative. Arterioscler. Thromb. Vasc. Biol. 34:939–45
    [Google Scholar]
  42. 42. 
    Gerstein HC, Pare G, McQueen MJ et al. 2015. Identifying novel biomarkers for cardiovascular events or death in people with dysglycemia. Circulation 132:2297–304
    [Google Scholar]
  43. 43. 
    Lind L, Siegbahn A, Lindahl B et al. 2015. Discovery of new risk markers for ischemic stroke using a novel targeted proteomics chip. Stroke 46:3340–47
    [Google Scholar]
  44. 44. 
    Nowak C, Sundstrom J, Gustafsson S et al. 2016. Protein biomarkers for insulin resistance and type 2 diabetes risk in two large community cohorts. Diabetes 65:276–84
    [Google Scholar]
  45. 45. 
    Nowak C, Carlsson AC, Östgren CJ et al. 2018. Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes. Diabetologia 61:1748–57
    [Google Scholar]
  46. 46. 
    Molvin J, Pareek M, Jujic A et al. 2019. Using a targeted proteomics chip to explore pathophysiological pathways for incident diabetes—the Malmo Preventive Project. Sci. Rep. 9:272
    [Google Scholar]
  47. 47. 
    Ho JE, Lyass A, Courchesne P et al. 2018. Protein biomarkers of cardiovascular disease and mortality in the community. J. Am. Heart Assoc. 7:e008108
    [Google Scholar]
  48. 48. 
    Ngo D, Sinha S, Shen D et al. 2016. Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease. Circulation 134:270–85
    [Google Scholar]
  49. 49. 
    Ganz P, Heidecker B, Hveem K et al. 2016. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA 315:2532–41
    [Google Scholar]
  50. 50. 
    Williams SA, Murthy AC, DeLisle RK et al. 2018. Improving assessment of drug safety through proteomics. Circulation 137:999–1010
    [Google Scholar]
  51. 51. 
    McGarrah RW, Crown SB, Zhang G-F et al. 2018. Cardiovascular metabolomics. Circ. Res. 122:1238–58
    [Google Scholar]
  52. 52. 
    Rhee EP, Gerszten RE. 2012. Metabolomics and cardiovascular biomarker discovery. Clin. Chem. 58:139–47
    [Google Scholar]
  53. 53. 
    Wang TJ, Larson MG, Vasan RS et al. 2011. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17:448
    [Google Scholar]
  54. 54. 
    Lotta LA, Scott RA, Sharp SJ et al. 2016. Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a Mendelian randomisation analysis. PLOS Med 13:e1002179
    [Google Scholar]
  55. 55. 
    Tobias DK, Lawler PR, Harada PH et al. 2018. Circulating branched-chain amino acids and incident cardiovascular disease in a prospective cohort of US women. Circ. Genom. Precision Med. 11:e002157
    [Google Scholar]
  56. 56. 
    Walford GA, Ma Y, Clish C et al. 2016. Metabolite profiles of diabetes incidence and intervention response in the diabetes prevention program. Diabetes 65:1424–33
    [Google Scholar]
  57. 57. 
    Vaarhorst AA, Verhoeven A, Weller CM et al. 2014. A metabolomic profile is associated with the risk of incident coronary heart disease. Am. Heart J. 168:45–52.e7
    [Google Scholar]
  58. 58. 
    Wurtz P, Havulinna AS, Soininen P et al. 2015. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation 131:774–85
    [Google Scholar]
  59. 59. 
    Ruiz-Canela M, Hruby A, Clish CB et al. 2017. Comprehensive metabolomic profiling and incident cardiovascular disease: a systematic review. J. Am. Heart Assoc. 6:e005705
    [Google Scholar]
  60. 60. 
    Wang Z, Klipfell E, Bennett BJ et al. 2011. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472:57–63
    [Google Scholar]
  61. 61. 
    Ganna A, Salihovic S, Sundstrom J et al. 2014. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLOS Genet 10:e1004801
    [Google Scholar]
  62. 62. 
    Stegemann C, Pechlaner R, Willeit P et al. 2014. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study. Circulation 129:1821–31
    [Google Scholar]
  63. 63. 
    Lawler PR, Akinkuolie AO, Chu AY et al. 2017. Atherogenic lipoprotein determinants of cardiovascular disease and residual risk among individuals with low low-density lipoprotein cholesterol. J. Am. Heart Assoc. 6:e005549
    [Google Scholar]
  64. 64. 
    Paynter NP, Balasubramanian R, Giulianini F et al. 2018. Metabolic predictors of incident coronary heart disease in women. Circulation 137:841–53
    [Google Scholar]
  65. 65. 
    Tebani A, Abily-Donval L, Afonso C et al. 2016. Clinical metabolomics: the new metabolic window for inborn errors of metabolism investigations in the post-genomic era. Int. J. Mol. Sci. 17:1167
    [Google Scholar]
  66. 66. 
    Ordovás JM, Smith CE. 2010. Epigenetics and cardiovascular disease. Nat. Rev. Cardiol. 7:510–19
    [Google Scholar]
  67. 67. 
    Lu D, Thum T. 2019. RNA-based diagnostic and therapeutic strategies for cardiovascular disease. Nat. Rev. Cardiol. https://doi.org/10.1038/s41569-019-0218-x
    [Crossref] [Google Scholar]
  68. 68. 
    Walford GA, Porneala BC, Dauriz M et al. 2014. Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes. Diabetes Care 37:2508–14
    [Google Scholar]
  69. 69. 
    Kullo IJ, Cooper LT. 2010. Early identification of cardiovascular risk using genomics and proteomics. Nat. Rev. Cardiol. 7:309
    [Google Scholar]
  70. 70. 
    Shah SH, Newgard CB. 2015. Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease. Circ. Cardiovasc. Genet. 8:410–19
    [Google Scholar]
  71. 71. 
    Kakadiaris IA, Vrigkas M, Yen AA et al. 2018. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J. Am. Heart Assoc. 7:e009476
    [Google Scholar]
  72. 72. 
    Rifai N, Gillette MA, Carr SA 2006. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24:971–83
    [Google Scholar]
/content/journals/10.1146/annurev-med-042418-010924
Loading
/content/journals/10.1146/annurev-med-042418-010924
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error