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
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.
Subscribe to Journal
Get full journal access for 1 year
only $17.42 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Mozaffarian, D. et al. Heart disease and stroke statistics — 2016 update: a report from the American Heart Association. Circulation 133, e38–e360 (2016).
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).
Luepker, R. V. Falling coronary heart disease rates: a better explanation? Circulation 133, 8–11 (2016).
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).
Mozaffarian, D. et al. Heart disease and stroke statistics — 2015 update: a report from the American Heart Association. Circulation 131, e29–e322 (2015).
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).
Roth, G. A. et al. Estimates of global and regional premature cardiovascular mortality in 2025. Circulation 132, 1270–1282 (2015).
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).
Ribeiro, A. L. et al. Cardiovascular health in Brazil: trends and perspectives. Circulation 133, 422–433 (2016).
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).
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).
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).
Yusuf, S., Perel, P., Wood, D. & Narula, J. Reducing cardiovascular disease globally: the World Heart Federation's roadmaps. Glob. Heart 10, 93–95 (2015).
Auffray, C. et al. From genomic medicine to precision medicine: highlights of 2015. Genome Med. 8, 12 (2016).
Green, E. D. & Guyer, M. S. Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204–213 (2011).
International HapMap Consortium. The international HapMap project. Nature 426, 789–796 (2003).
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).
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).
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).
Wende, A. R. Post-translational modifications of the cardiac proteome in diabetes and heart failure. Proteomics Clin. Appl. 10, 25–38 (2016).
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).
Altman, R. B. & Ashley, E. A. Using 'big data' to dissect clinical heterogeneity. Circulation 131, 232–233 (2015).
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).
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).
Peters, S. G. & Khan, M. A. Electronic health records: current and future use. J. Comp. Eff. Res. 3, 515–522 (2014).
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).
Contreras, J. L. & Reichman, J. H. Sharing by design: data and decentralized commons. Science 350, 1312–1314 (2015).
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).
Swan, M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1, 85–99 (2013).
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).
Scruggs, S. B. et al. Harnessing the heart of big data. Circ. Res. 116, 1115–1119 (2015).
Mooney, S. J., Westreich, D. J. & El-Sayed, A. M. Commentary: epidemiology in the era of big data. Epidemiology 26, 390–394 (2015).
Shah, S. H. et al. Opportunities for the cardiovascular community in the Precision Medicine Initiative. Circulation 133, 226–231 (2016).
Snyderman, R. & Drake, C. D. Personalized health care: unlocking the potential of genomic and precision medicine. J. Precision Med. 1, 38–41 (2015).
Naylor, S. What's in a name? The evolution of 'P-Medicine'. J. Precision Med. 1, 15–29 (2015).
Stanley, K. Design of randomized controlled trials. Circulation 115, 1164–1169 (2007).
Moye, L. Statistical methods for cardiovascular researchers. Circ. Res. 118, 439–453 (2016).
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).
Lagakos, S. W. The challenge of subgroup analyses — reporting without distorting. N. Engl. J. Med. 354, 1667–1669 (2006).
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).
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).
Menche, J. et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).
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).
Myers, M. B. Targeted therapies with companion diagnostics in the management of breast cancer: current perspectives. Pharmgenomics Pers. Med. 9, 7–16 (2016).
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).
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).
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).
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).
Roychowdhury, S. & Chinnaiyan, A. M. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J. Clin. 66, 75–88 (2016).
Wright, J. T. Jr et al. A randomized trial of intensive versus standard blood-pressure control. N. Engl. J. Med. 373, 2103–2116 (2015).
Ahmad, T. et al. Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J. Am. Coll. Cardiol. 64, 1765–1774 (2014).
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).
Ayer, J., Charakida, M., Deanfield, J. E. & Celermajer, D. S. Lifetime risk: childhood obesity and cardiovascular risk. Eur. Heart J. 36, 1371–1376 (2015).
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).
Lewington, S. et al. The burden of hypertension and associated risk for cardiovascular mortality in China. JAMA Intern. Med. 176, 524–532 (2016).
Barabasi, A. L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
Chan, S. Y. & Loscalzo, J. The emerging paradigm of network medicine in the study of human disease. Circ. Res. 111, 359–374 (2012).
Loscalzo, J. & Barabasi, A. L. Systems biology and the future of medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 619–627 (2011).
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).
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).
Ji, W. et al. Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat. Genet. 40, 592–599 (2008).
Leow, M. K. Environmental origins of hypertension: phylogeny, ontogeny and epigenetics. Hypertens. Res. 38, 299–307 (2015).
Zhang, W. Epigenetics of epithelial Na+ channel-dependent sodium uptake and blood pressure regulation. World J. Nephrol. 4, 363–366 (2015).
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).
Bress, A. P. et al. Generalizability of SPRINT results to the U.S. adult population. J. Am. Coll. Cardiol. 67, 463–472 (2016).
Gradman, A. H. SPRINT: to whom do the results apply? J. Am. Coll. Cardiol. 67, 473–475 (2016).
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).
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).
Mehta, C. et al. Optimizing trial design: sequential, adaptive, and enrichment strategies. Circulation 119, 597–605 (2009).
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).
Sheiner, L. B. Learning versus confirming in clinical drug development. Clin. Pharmacol. Ther. 61, 275–291 (1997).
Kaufman, A. L. et al. Evidence for clinical implementation of pharmacogenomics in cardiac drugs. Mayo Clin. Proc. 90, 716–729 (2015).
FDA-NIH Biomarker Working Group. BEST (Biomarkers, 2016).
Wang, T. J. et al. Multiple biomarkers and the risk of incident hypertension. Hypertension 49, 432–438 (2007).
El Shamieh, S. & Visvikis-Siest, S. Genetic biomarkers of hypertension and future challenges integrating epigenomics. Clin. Chim. Acta 414, 259–265 (2012).
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).
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).
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).
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).
Danciu, I. et al. Secondary use of clinical data: the Vanderbilt approach. J. Biomed. Informat. 52, 28–35 (2014).
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).
[No authors listed.] ADAPTABLE, the aspirin study — a patient-centered trial. Adaptable http://theaspirinstudy.org (2016).
Choudhry, N. K. et al. Full coverage for preventive medications after myocardial infarction. N. Engl. J. Med. 365, 2088–2097 (2011).
Ogedegbe, G. et al. Counseling African Americans to control hypertension: cluster-randomized clinical trial main effects. Circulation 129, 2044–2051 (2014).
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).
Jessup, M. Neprilysin inhibition — a novel therapy for heart failure. N. Engl. J. Med. 371, 1062–1064 (2014).
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).
Muller, H., Reihs, R., Zatloukal, K. & Holzinger, A. Analysis of biomedical data with multilevel glyphs. BMC Bioinformatics 15 (Suppl. 6), S5 (2014).
Sparrow, B., Liu, J. & Wegner, D. M. Google effects on memory: cognitive consequences of having information at our fingertips. Science 333, 776–778 (2011).
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).
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).
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).
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).
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).
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).
Kotchen, T. A., Cowley, A. W. Jr & Liang, M. Ushering hypertension into a new era of precision medicine. JAMA 315, 343–344 (2016).
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).
Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).
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).
Sboner, A. & Elemento, O. A primer on precision medicine informatics. Brief Bioinform. 17, 145–153 (2016).
Gligorijevic, V., Malod-Dognin, N. & Przulj, N. Integrative methods for analyzing big data in precision medicine. Proteomics 16, 741–758 (2016).
Stein, B. & Morrison, A. The enterprise data lake: better integration and deeper analytics. Technol. Forecas. 1, 1–8 (2014).
Darcy, A. M., Louie, A. K. & Roberts, L. W. Machine learning and the profession of medicine. JAMA 315, 551–552 (2016).
Deo, R. C. Machine learning in medicine. Circulation 132, 1920–1930 (2015).
Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).
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).
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).
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).
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).
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).
Plante, T. B. et al. Validation of the instant blood pressure smartphone app. JAMA Intern. Med. 176, 700–702 (2016).
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).
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).
National Academies of Sciences Engineering and Medicine. Biomarker Tests for Molecularly Targeted Therapies: Key to Unlocking Precision Medicine (National Academies Press, 2016).
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).
Kohane, I. S. Ten things we have to do to achieve precision medicine. Science 349, 37–38 (2015).
Goldfeder, R. L. et al. Medical implications of technical accuracy in genome sequencing. Genome Med. 8, 24 (2016).
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).
Van Driest, S. L. et al. Association of arrhythmia-related genetic variants with phenotypes documented in electronic medical records. JAMA 315, 47–57 (2016).
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).
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).
Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).
Goodman, S. N. Clinical trial data sharing: what do we do now? Ann. Intern. Med. 162, 308–309 (2015).
Rosenblatt, M., Jain, S. H. & Cahill, M. Sharing of clinical trial data: benefits, risks, and uniform principles. Ann. Intern. Med. 162, 306–307 (2015).
Thompson, B. & Boiani, J. The legal environment for precision medicine. Clin. Pharmacol. Ther. 99, 167–169 (2016).
FH Foundation. About the CASCADE FH Registry™. http://www.thefhfoundation.org/fh-research/registry (2016).
Antman, E. M. & Bierer, B. E. Standards for clinical research: keeping pace with the technology of the future. Circulation 133, 823–825 (2016).
Frobert, O. et al. Thrombus aspiration during ST-segment elevation myocardial infarction. N. Engl. J. Med. 369, 1587–1597 (2013).
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).
Aronson, N. Making personalized medicine more affordable. Ann. NY Acad. Sci. 1346, 81–89 (2015).
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).
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).
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).
Mital, S. et al. Enhancing literacy in cardiovascular genetics: a scientific statement from the American Heart Association. Circ. Genetics(in press).
National Human Genome Research Institute. NHGRI Genomic Medicine Working Group. https://www.genome.gov/27549220/ (2016).
Rose, G. Sick individuals and sick populations. Int. J. Epidemiol. 14, 32–38 (1985).
McMurray, J. J. et al. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N. Engl. J. Med. 371, 993–1004 (2014).
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).
Mulatero, P., Verhovez, A., Morello, F. & Veglio, F. Diagnosis and treatment of low-renin hypertension. Clin. Endocrinol. (Oxf.) 67, 324–334 (2007).
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).
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).
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).
The authors declare no competing financial interests.
About this article
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
Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease
Journal of Atherosclerosis and Thrombosis (2020)
New England Journal of Medicine (2020)
Circulation Research (2020)
European Heart Journal (2020)
Journal of Women's Health (2020)