Precision medicine in cardiology

Key Points

  • Precision medicine is an evolving strategy for disease prevention and tailored treatment that incorporates individual genetic, environmental, and experiential variability

  • Cardiovascular specialists are well positioned to use precision medicine to facilitate discovery science and make clinical research more efficient, with the goal of providing more precise information to improve the health of individuals and populations

  • Implementation of precision medicine will require construction of a digital ecosystem and overcoming sociopolitical barriers — issues that are beginning to be addressed by cardiovascular investigators

  • Paradigm shifts in our approach to health and disease, as well as education of health-care providers and the lay public, are necessary to realize the benefits of precision medicine

Abstract

The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance our understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including 'omic' data, exposome and social determinants of health, the microbiome, behaviours and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer a passive entity on whom measurements are made, but instead are central stakeholders who contribute data and participate actively in shared decision-making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Essential features and goals of a precision medicine system.
Figure 2: Precision medicine — describing individuals.
Figure 3: Enrichment strategies for clinical trials.
Figure 4: Precision prescribing.
Figure 5: Examples of biomarker-guided trials.

References

  1. 1

    Mozaffarian, D. et al. Heart disease and stroke statistics — 2016 update: a report from the American Heart Association. Circulation 133, e38–e360 (2016).

    PubMed  Google Scholar 

  2. 2

    Ford, E. S. et al. Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. N. Engl. J. Med. 356, 2388–2398 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. 3

    Luepker, R. V. Falling coronary heart disease rates: a better explanation? Circulation 133, 8–11 (2016).

    Article  PubMed  Google Scholar 

  4. 4

    Mannsverk, J. et al. Trends in modifiable risk factors are associated with declining incidence of hospitalized and nonhospitalized acute coronary heart disease in a population. Circulation 133, 74–81 (2016).

    Article  PubMed  Google Scholar 

  5. 5

    Mozaffarian, D. et al. Heart disease and stroke statistics — 2015 update: a report from the American Heart Association. Circulation 131, e29–e322 (2015).

    PubMed  Google Scholar 

  6. 6

    Casper, M. et al. Changes in the geographic patterns of heart disease mortality in the United States 1973 to 2010. Circulation 133, 1171–1180 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7

    Roth, G. A. et al. Estimates of global and regional premature cardiovascular mortality in 2025. Circulation 132, 1270–1282 (2015).

    Article  PubMed  Google Scholar 

  8. 8

    Shepard, D. et al. Ischemic heart disease worldwide, 1990 to 2013: estimates from the Global Burden of Disease Study 2013. Circ. Cardiovasc. Qual. Outcomes 8, 455–456 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9

    Ribeiro, A. L. et al. Cardiovascular health in Brazil: trends and perspectives. Circulation 133, 422–433 (2016).

    Article  PubMed  Google Scholar 

  10. 10

    Bauer, U. E., Briss, P. A., Goodman, R. A. & Bowman, B. A. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet 384, 45–52 (2014).

    Article  PubMed  Google Scholar 

  11. 11

    Lloyd-Jones, D. M. et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's Strategic Impact Goal through 2020 and beyond. Circulation 121, 586–613 (2010).

    Article  PubMed  Google Scholar 

  12. 12

    World Health Organization. Global action plan for the prevention and control of noncommunicable diseases 2013–2020. http://www.who.int/nmh/publications/ncd-action-plan/en/ (2013).

  13. 13

    Yusuf, S., Perel, P., Wood, D. & Narula, J. Reducing cardiovascular disease globally: the World Heart Federation's roadmaps. Glob. Heart 10, 93–95 (2015).

    Article  PubMed  Google Scholar 

  14. 14

    Auffray, C. et al. From genomic medicine to precision medicine: highlights of 2015. Genome Med. 8, 12 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Green, E. D. & Guyer, M. S. Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204–213 (2011).

    Article  CAS  PubMed  Google Scholar 

  16. 16

    International HapMap Consortium. The international HapMap project. Nature 426, 789–796 (2003).

  17. 17

    Mogensen, J. et al. The current role of next-generation DNA sequencing in routine care of patients with hereditary cardiovascular conditions: a viewpoint paper of the European Society of Cardiology working group on myocardial and pericardial diseases and members of the European Society of Human Genetics. Eur. Heart J. 36, 1367–1370 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. 18

    Lindsey, M. L. et al. Transformative impact of proteomics on cardiovascular health and disease: a Scientific Statement from the American Heart Association. Circulation 132, 852–872 (2015).

    Article  CAS  PubMed  Google Scholar 

  19. 19

    Shah, S. H. & Newgard, C. B. Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease. Circ. Cardiovasc. Genet. 8, 410–419 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Wende, A. R. Post-translational modifications of the cardiac proteome in diabetes and heart failure. Proteomics Clin. Appl. 10, 25–38 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. 21

    Fox, C. S. et al. Future translational applications from the contemporary genomics era: a Scientific Statement from the American Heart Association. Circulation 131, 1715–1736 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22

    Altman, R. B. & Ashley, E. A. Using 'big data' to dissect clinical heterogeneity. Circulation 131, 232–233 (2015).

    Article  PubMed  Google Scholar 

  23. 23

    Wang, R. S., Maron, B. A. & Loscalzo, J. Systems medicine: evolution of systems biology from bench to bedside. Wiley Interdiscip. Rev. Syst. Biol. Med. 7, 141–161 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24

    Calcagno, C., Mulder, W. J., Nahrendorf, M. & Fayad, Z. A. Systems biology and noninvasive imaging of atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 36, e1–e8 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Peters, S. G. & Khan, M. A. Electronic health records: current and future use. J. Comp. Eff. Res. 3, 515–522 (2014).

    Article  PubMed  Google Scholar 

  26. 26

    Hernandez, A. F., Fleurence, R. L. & Rothman, R. L. The ADAPTABLE Trial and PCORnet: shining light on a new research paradigm. Ann. Intern. Med. 163, 635–636 (2015).

    Article  PubMed  Google Scholar 

  27. 27

    Contreras, J. L. & Reichman, J. H. Sharing by design: data and decentralized commons. Science 350, 1312–1314 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Thornquist, E. & Kirkengen, A. L. The quantified self: closing the gap between general knowledge and particular case? J. Eval. Clin. Pract. 21, 398–403 (2015).

    Article  PubMed  Google Scholar 

  29. 29

    Swan, M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1, 85–99 (2013).

    Article  PubMed  Google Scholar 

  30. 30

    Pfiffner, P. B., Pinyol, I., Natter, M. D. & Mandl, K. D. C3-PRO: connecting ResearchKit to the health system using i2b2 and FHIR. PLoS ONE 11, e0152722 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Scruggs, S. B. et al. Harnessing the heart of big data. Circ. Res. 116, 1115–1119 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Mooney, S. J., Westreich, D. J. & El-Sayed, A. M. Commentary: epidemiology in the era of big data. Epidemiology 26, 390–394 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33

    Shah, S. H. et al. Opportunities for the cardiovascular community in the Precision Medicine Initiative. Circulation 133, 226–231 (2016).

    Article  PubMed  Google Scholar 

  34. 34

    Snyderman, R. & Drake, C. D. Personalized health care: unlocking the potential of genomic and precision medicine. J. Precision Med. 1, 38–41 (2015).

    Google Scholar 

  35. 35

    Naylor, S. What's in a name? The evolution of 'P-Medicine'. J. Precision Med. 1, 15–29 (2015).

    Google Scholar 

  36. 36

    Stanley, K. Design of randomized controlled trials. Circulation 115, 1164–1169 (2007).

    Article  PubMed  Google Scholar 

  37. 37

    Moye, L. Statistical methods for cardiovascular researchers. Circ. Res. 118, 439–453 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Blaus, A. et al. Personalized cardiovascular medicine today: a Food and Drug Administration/Center for Drug Evaluation and Research perspective. Circulation 132, 1425–1432 (2015).

    Article  CAS  PubMed  Google Scholar 

  39. 39

    Lagakos, S. W. The challenge of subgroup analyses — reporting without distorting. N. Engl. J. Med. 354, 1667–1669 (2006).

    Article  CAS  PubMed  Google Scholar 

  40. 40

    Spivack, S. B., Bernheim, S. M., Forman, H. P., Drye, E. E. & Krumholz, H. M. Hospital cardiovascular outcome measures in federal pay-for-reporting and pay-for-performance programs: a brief overview of current efforts. Circ. Cardiovasc. Qual. Outcomes 7, 627–633 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41

    Loscalzo, J., Kohane, I. & Barabasi, A. L. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol. 3, 124 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42

    Menche, J. et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Precision Medicine Initiative (PMI) Working Group. The Precision Medicine Initiative cohort program — building a research foundation for 21st century medicine. https://www.nih.gov/sites/default/files/research-training/initiatives/pmi/pmi-working-group-report-20150917-2.pdf (2015).

  44. 44

    Myers, M. B. Targeted therapies with companion diagnostics in the management of breast cancer: current perspectives. Pharmgenomics Pers. Med. 9, 7–16 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Kearney, P. M., Whelton, M., Reynolds, K., Whelton, P. K. & He, J. Worldwide prevalence of hypertension: a systematic review. J. Hypertens. 22, 11–19 (2004).

    Article  CAS  PubMed  Google Scholar 

  46. 46

    Schieb, L. J. et al. Vital signs: avoidable deaths from heart disease, stroke, and hypertensive disease — United States, 2001–2010. MMWR Morb. Mortal. Wkly Rep. 62, 721–727 (2013).

    Google Scholar 

  47. 47

    James, P. A. et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 311, 507–520 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. 48

    Laragh, J. H. Vasoconstriction-volume analysis for understanding and treating hypertension: the use of renin and aldosterone profiles. Am. J. Med. 55, 261–274 (1973).

    Article  CAS  PubMed  Google Scholar 

  49. 49

    Roychowdhury, S. & Chinnaiyan, A. M. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J. Clin. 66, 75–88 (2016).

    Article  PubMed  Google Scholar 

  50. 50

    Wright, J. T. Jr et al. A randomized trial of intensive versus standard blood-pressure control. N. Engl. J. Med. 373, 2103–2116 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. 51

    Ahmad, T. et al. Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J. Am. Coll. Cardiol. 64, 1765–1774 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52

    Hawgood, S., Hook-Barnard, I. G., O'Brien, T. C. & Yamamoto, K. R. Precision medicine: beyond the inflection point. Sci. Transl. Med. 7, 300ps17 (2015).

    Article  PubMed  Google Scholar 

  53. 53

    Ayer, J., Charakida, M., Deanfield, J. E. & Celermajer, D. S. Lifetime risk: childhood obesity and cardiovascular risk. Eur. Heart J. 36, 1371–1376 (2015).

    Article  PubMed  Google Scholar 

  54. 54

    Khoury, M. J., Gwinn, M. L., Glasgow, R. E. & Kramer, B. S. A population approach to precision medicine. Am. J. Prev. Med. 42, 639–645 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55

    Lewington, S. et al. The burden of hypertension and associated risk for cardiovascular mortality in China. JAMA Intern. Med. 176, 524–532 (2016).

    Article  PubMed  Google Scholar 

  56. 56

    Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Chan, S. Y. & Loscalzo, J. The emerging paradigm of network medicine in the study of human disease. Circ. Res. 111, 359–374 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Loscalzo, J. & Barabasi, A. L. Systems biology and the future of medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 619–627 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  59. 59

    Parikh, V. N. et al. MicroRNA-21 integrates pathogenic signaling to control pulmonary hypertension: results of a network bioinformatics approach. Circulation 125, 1520–1532 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Sharma, A. et al. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum. Mol. Genet. 24, 3005–3020 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Ji, W. et al. Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat. Genet. 40, 592–599 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Leow, M. K. Environmental origins of hypertension: phylogeny, ontogeny and epigenetics. Hypertens. Res. 38, 299–307 (2015).

    Article  PubMed  Google Scholar 

  63. 63

    Zhang, W. Epigenetics of epithelial Na+ channel-dependent sodium uptake and blood pressure regulation. World J. Nephrol. 4, 363–366 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64

    Scott, R. A. et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci. Transl. Med. 8, 341ra76 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Bress, A. P. et al. Generalizability of SPRINT results to the U.S. adult population. J. Am. Coll. Cardiol. 67, 463–472 (2016).

    Article  PubMed  Google Scholar 

  66. 66

    Gradman, A. H. SPRINT: to whom do the results apply? J. Am. Coll. Cardiol. 67, 473–475 (2016).

    Article  PubMed  Google Scholar 

  67. 67

    Food and Drug Administration. Guidance for industry. Enrichment strategies for clinical trials to support approval of human drugs and biological products. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm332181.pdf (2012).

  68. 68

    Jackson, N. et al. Improving clinical trials for cardiovascular diseases: a position paper from the Cardiovascular Round Table of the European Society of Cardiology. Eur. Heart J. 37, 747–754 (2016).

    Article  PubMed  Google Scholar 

  69. 69

    Mehta, C. et al. Optimizing trial design: sequential, adaptive, and enrichment strategies. Circulation 119, 597–605 (2009).

    Article  PubMed  Google Scholar 

  70. 70

    Antman, E., Weiss, S. & Loscalzo, J. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 4, 367–383 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Sheiner, L. B. Learning versus confirming in clinical drug development. Clin. Pharmacol. Ther. 61, 275–291 (1997).

    Article  CAS  PubMed  Google Scholar 

  72. 72

    Kaufman, A. L. et al. Evidence for clinical implementation of pharmacogenomics in cardiac drugs. Mayo Clin. Proc. 90, 716–729 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    FDA-NIH Biomarker Working Group. BEST (Biomarkers, 2016).

  74. 74

    Wang, T. J. et al. Multiple biomarkers and the risk of incident hypertension. Hypertension 49, 432–438 (2007).

    Article  CAS  PubMed  Google Scholar 

  75. 75

    El Shamieh, S. & Visvikis-Siest, S. Genetic biomarkers of hypertension and future challenges integrating epigenomics. Clin. Chim. Acta 414, 259–265 (2012).

    Article  CAS  PubMed  Google Scholar 

  76. 76

    Zhang, W. et al. Identification of hypertension predictors and application to hypertension prediction in an urban Han Chinese population: a longitudinal study, 2005–2010. Prev. Chronic Dis. 12, E184 (2015).

    PubMed  PubMed Central  Google Scholar 

  77. 77

    Nerurkar, S. S. et al. P38 MAPK inhibitors suppress biomarkers of hypertension end-organ damage, osteopontin and plasminogen activator inhibitor-1. Biomarkers 12, 87–112 (2007).

    Article  CAS  PubMed  Google Scholar 

  78. 78

    Antoniou, M., Jorgensen, A. L. & Kolamunnage-Dona, R. Biomarker-guided adaptive trial designs in phase II and phase III: a methodological review. PLoS ONE 11, e0149803 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Antman, E. M. & Harrington, R. A. Transforming clinical trials in cardiovascular disease: mission critical for health and economic well-being. JAMA 308, 1743–1744 (2012).

    Article  CAS  PubMed  Google Scholar 

  80. 80

    Danciu, I. et al. Secondary use of clinical data: the Vanderbilt approach. J. Biomed. Informat. 52, 28–35 (2014).

    Article  Google Scholar 

  81. 81

    Patient-Centered Outcomes Research Institute (PCORI). Aspirin dosing: a patient-centric trial assessing benefits and long-term effectiveness (ADAPTABLE). http://www.pcori.org/research-results/2015/aspirin-dosing-patient-centric-trial-assessing-benefits-and-long-term (2016).

  82. 82

    [No authors listed.] ADAPTABLE, the aspirin study — a patient-centered trial. Adaptable http://theaspirinstudy.org (2016).

  83. 83

    Choudhry, N. K. et al. Full coverage for preventive medications after myocardial infarction. N. Engl. J. Med. 365, 2088–2097 (2011).

    Article  CAS  PubMed  Google Scholar 

  84. 84

    Ogedegbe, G. et al. Counseling African Americans to control hypertension: cluster-randomized clinical trial main effects. Circulation 129, 2044–2051 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  85. 85

    Brooks, G. C. et al. Accuracy and usability of a self-administered 6-minute walk test smartphone application. Circ. Heart Fail 8, 905–913 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  86. 86

    Jessup, M. Neprilysin inhibition — a novel therapy for heart failure. N. Engl. J. Med. 371, 1062–1064 (2014).

    Article  PubMed  Google Scholar 

  87. 87

    Goff, D. C. Jr et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 129, S49–73 (2014).

    Article  PubMed  Google Scholar 

  88. 88

    Muller, H., Reihs, R., Zatloukal, K. & Holzinger, A. Analysis of biomedical data with multilevel glyphs. BMC Bioinformatics 15 (Suppl. 6), S5 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  89. 89

    Sparrow, B., Liu, J. & Wegner, D. M. Google effects on memory: cognitive consequences of having information at our fingertips. Science 333, 776–778 (2011).

    Article  CAS  PubMed  Google Scholar 

  90. 90

    Schnohr, P. et al. Ranking of psychosocial and traditional risk factors by importance for coronary heart disease: the Copenhagen City Heart Study. Eur. Heart J. 36, 1385–1393 (2015).

    Article  PubMed  Google Scholar 

  91. 91

    Anderson, A. H. et al. Time-updated systolic blood pressure and the progression of chronic kidney disease: a cohort study. Ann. Intern. Med. 162, 258–265 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  92. 92

    Decker, C. et al. Patient-centered decision support in acute ischemic stroke: qualitative study of patients' and providers' perspectives. Circ. Cardiovasc. Qual. Outcomes 8, S109–S116 (2015).

    Article  PubMed  Google Scholar 

  93. 93

    Xie, X. et al. Effects of intensive blood pressure lowering on cardiovascular and renal outcomes: updated systematic review and meta-analysis. Lancet 387, 435–443 (2016).

    Article  PubMed  Google Scholar 

  94. 94

    Thomopoulos, C., Parati, G. & Zanchetti, A. Effects of blood pressure lowering on outcome incidence in hypertension: 7. Effects of more versus less intensive blood pressure lowering and different achieved blood pressure levels — updated overview and meta-analyses of randomized trials. J. Hypertens. 34, 613–622 (2016).

    Article  CAS  PubMed  Google Scholar 

  95. 95

    Thomopoulos, C., Parati, G. & Zanchetti, A. Effects of blood pressure-lowering on outcome incidence in hypertension: 5. Head-to-head comparisons of various classes of antihypertensive drugs — overview and meta-analyses. J. Hypertens. 33, 1321–1341 (2015).

    Article  CAS  PubMed  Google Scholar 

  96. 96

    Kotchen, T. A., Cowley, A. W. Jr & Liang, M. Ushering hypertension into a new era of precision medicine. JAMA 315, 343–344 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. 97

    Kamide, K. et al. Genome-wide response to antihypertensive medication using home blood pressure measurements: a pilot study nested within the HOMED-BP study. Pharmacogenomics 14, 1709–1721 (2013).

    Article  CAS  PubMed  Google Scholar 

  98. 98

    Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. 99

    Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

    Article  PubMed  Google Scholar 

  100. 100

    Sboner, A. & Elemento, O. A primer on precision medicine informatics. Brief Bioinform. 17, 145–153 (2016).

    Article  PubMed  Google Scholar 

  101. 101

    Gligorijevic, V., Malod-Dognin, N. & Przulj, N. Integrative methods for analyzing big data in precision medicine. Proteomics 16, 741–758 (2016).

    Article  CAS  PubMed  Google Scholar 

  102. 102

    Stein, B. & Morrison, A. The enterprise data lake: better integration and deeper analytics. Technol. Forecas. 1, 1–8 (2014).

    Google Scholar 

  103. 103

    Darcy, A. M., Louie, A. K. & Roberts, L. W. Machine learning and the profession of medicine. JAMA 315, 551–552 (2016).

    Article  CAS  PubMed  Google Scholar 

  104. 104

    Deo, R. C. Machine learning in medicine. Circulation 132, 1920–1930 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  105. 105

    Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).

    Article  CAS  PubMed  Google Scholar 

  106. 106

    Capobianco, E. & Lio, P. Electronic health systems: golden mine for precision medicine? Current bottlenecks and future opportunities associated with Big Data. J. Precision Med. 2, 60–65 (2015).

    Google Scholar 

  107. 107

    Mandl, K. D., Mandel, J. C. & Kohane, I. S. Driving innovation in health systems through an apps-based information economy. Cell Syst. 1, 8–13 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. 108

    Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S. & Ramoni, R. B. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J. Am. Med. Inform. Assoc. http://dx.doi.org/10.1093/jamia/ocv189 (2016).

  109. 109

    Antman, E. M. et al. Acquisition, analysis, and sharing of data in 2015 and beyond: a survey of the landscape: a conference report from the American Heart Association Data Summit 2015. J. Am. Heart Assoc. 4, e002810 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  110. 110

    Burke, L. E. et al. Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation 132, 1157–1213 (2015).

    Article  PubMed  Google Scholar 

  111. 111

    Plante, T. B. et al. Validation of the instant blood pressure smartphone app. JAMA Intern. Med. 176, 700–702 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  112. 112

    de Lemos, J. A., Rohatgi, A. & Ayers, C. R. Applying a big data approach to biomarker discovery: running before we walk? Circulation 132, 2289–2292 (2015).

    Article  PubMed  Google Scholar 

  113. 113

    Brugts, J. J. & Simoons, M. L. Genetic influences of angiotensin-converting enzyme inhibitor response: an opportunity for personalizing therapy? Expert Rev. Cardiovasc. Ther. 10, 1001–1009 (2012).

    Article  CAS  PubMed  Google Scholar 

  114. 114

    National Academies of Sciences Engineering and Medicine. Biomarker Tests for Molecularly Targeted Therapies: Key to Unlocking Precision Medicine (National Academies Press, 2016).

  115. 115

    Hall, J. L. et al. Merging electronic health record data and genomics for cardiovascular research: a science advisory from the American Heart Association. Circ. Cardiovasc. Genet. 9, 193–202 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  116. 116

    Kohane, I. S. Ten things we have to do to achieve precision medicine. Science 349, 37–38 (2015).

    Article  CAS  PubMed  Google Scholar 

  117. 117

    Goldfeder, R. L. et al. Medical implications of technical accuracy in genome sequencing. Genome Med. 8, 24 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. 118

    Andersen, J. R. et al. Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials. Br. J. Clin. Pharmacol. 79, 660–668 (2015).

    Article  PubMed  Google Scholar 

  119. 119

    Van Driest, S. L. et al. Association of arrhythmia-related genetic variants with phenotypes documented in electronic medical records. JAMA 315, 47–57 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    Feero, W. G. Establishing the clinical validity of arrhythmia-related genetic variations using the electronic medical record: a valid take on precision medicine? JAMA 315, 33–35 (2016).

    Article  CAS  PubMed  Google Scholar 

  121. 121

    National Academies of Sciences Engineering and Medicine. DIGITizE: displaying and integrating genetic information through the EHR. http://www.nationalacademies.org/hmd/Activities/Research/GenomicBasedResearch/Innovation-Collaboratives/EHR.aspx (2015).

  122. 122

    Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. 123

    Goodman, S. N. Clinical trial data sharing: what do we do now? Ann. Intern. Med. 162, 308–309 (2015).

    Article  PubMed  Google Scholar 

  124. 124

    Rosenblatt, M., Jain, S. H. & Cahill, M. Sharing of clinical trial data: benefits, risks, and uniform principles. Ann. Intern. Med. 162, 306–307 (2015).

    Article  PubMed  Google Scholar 

  125. 125

    Thompson, B. & Boiani, J. The legal environment for precision medicine. Clin. Pharmacol. Ther. 99, 167–169 (2016).

    Article  CAS  PubMed  Google Scholar 

  126. 126

    FH Foundation. About the CASCADE FH Registry. http://www.thefhfoundation.org/fh-research/registry (2016).

  127. 127

    Antman, E. M. & Bierer, B. E. Standards for clinical research: keeping pace with the technology of the future. Circulation 133, 823–825 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  128. 128

    Frobert, O. et al. Thrombus aspiration during ST-segment elevation myocardial infarction. N. Engl. J. Med. 369, 1587–1597 (2013).

    Article  CAS  PubMed  Google Scholar 

  129. 129

    Rao, S. V. et al. A registry-based randomized trial comparing radial and femoral approaches in women undergoing percutaneous coronary intervention: the SAFE-PCI for Women (Study of Access Site for Enhancement of PCI for Women) trial. JACC Cardiovasc. Interv. 7, 857–867 (2014).

    Article  PubMed  Google Scholar 

  130. 130

    Aronson, N. Making personalized medicine more affordable. Ann. NY Acad. Sci. 1346, 81–89 (2015).

    Article  PubMed  Google Scholar 

  131. 131

    National Human Genome Research Institute. Inter-society coordinating committee for practitioner education in genomics (ISCC). https://www.genome.gov/27554614/intersociety-coordinating-committee-for-practitioner-education-in-genomics-iscc/ (2016).

  132. 132

    Korf, B. R. et al. Framework for development of physician competencies in genomic medicine: report of the Competencies Working Group of the Inter-Society Coordinating Committee for Physician Education in Genomics. Genet. Med. 16, 804–809 (2014).

    Article  PubMed  Google Scholar 

  133. 133

    Dickson, D. J. & Pfeifer, J. D. Real-world data in the molecular era-finding the reality in the real world. Clin. Pharmacol. Ther. 99, 186–197 (2016).

    Article  CAS  PubMed  Google Scholar 

  134. 134

    Mital, S. et al. Enhancing literacy in cardiovascular genetics: a scientific statement from the American Heart Association. Circ. Genetics(in press).

  135. 135

    National Human Genome Research Institute. NHGRI Genomic Medicine Working Group. https://www.genome.gov/27549220/ (2016).

  136. 136

    Rose, G. Sick individuals and sick populations. Int. J. Epidemiol. 14, 32–38 (1985).

    Article  CAS  PubMed  Google Scholar 

  137. 137

    McMurray, J. J. et al. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N. Engl. J. Med. 371, 993–1004 (2014).

    Article  CAS  PubMed  Google Scholar 

  138. 138

    Raal, F. J. et al. PCSK9 inhibition with evolocumab (AMG 145) in heterozygous familial hypercholesterolaemia (RUTHERFORD-2): a randomised, double-blind, placebo-controlled trial. Lancet 385, 331–340 (2015).

    Article  CAS  PubMed  Google Scholar 

  139. 139

    Mulatero, P., Verhovez, A., Morello, F. & Veglio, F. Diagnosis and treatment of low-renin hypertension. Clin. Endocrinol. (Oxf.) 67, 324–334 (2007).

    Article  CAS  Google Scholar 

  140. 140

    Mega, J. L. et al. Dosing clopidogrel based on CYP2C19 genotype and the effect on platelet reactivity in patients with stable cardiovascular disease. JAMA 306, 2221–2228 (2011).

    Article  CAS  PubMed  Google Scholar 

  141. 141

    Packer, M. et al. Withdrawal of digoxin from patients with chronic heart failure treated with angiotensin-converting-enzyme inhibitors. N. Engl. J. Med. 329, 1–7 (1993).

    Article  CAS  PubMed  Google Scholar 

  142. 142

    Moriarty, P. M. et al. Efficacy and safety of alirocumab, a monoclonal antibody to PCSK9, in statin-intolerant patients: design and rationale of ODYSSEY ALTERNATIVE, a randomized phase 3 trial. J. Clin. Lipidol. 8, 554–561 (2014).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The AHA is creating an Institute for Precision Cardiovascular Medicine. The authors acknowledge the Strategic Planning Committee for the Institute; many of their contributions have been incorporated into Figures 1 and 2.

Author information

Affiliations

Authors

Contributions

Both authors contributed to researching data, discussions of content, writing the article, and to reviewing and editing of the manuscript before submission.

Corresponding author

Correspondence to Elliott M. Antman.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Antman, E., Loscalzo, J. Precision medicine in cardiology. Nat Rev Cardiol 13, 591–602 (2016). https://doi.org/10.1038/nrcardio.2016.101

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing