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HOME > Cardiovasc Prev Pharmacother > Volume 1(1); 2019 > Article
Review Article Challenges and Future in Precision Cardiovascular Medicine
Sang Hong Baek, MD, PhDorcid
Cardiovascular Prevention and Pharmacotherapy 2019;1(1):10-18.
DOI: https://doi.org/10.36011/cpp.2019.1.e3
Published online: July 31, 2019
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Division of Cardiovascular Medicine, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea

Correspondence to Sang Hong Baek, MD, PhD Division of Cardiovascular Medicine, The Catholic University of Korea, Seoul St. Mary's Hospital, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea. E-mail: whitesh@catholic.ac.kr
• Received: June 18, 2019   • Accepted: July 10, 2021

Copyright © 2019 Korean Society of Cardiovascular Disease Prevention; International Society of Cardiovascular Pharmacotherapy, Korea Chapter.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

  • Cardiovascular disease (CVD) still remains the global leading cause of mortality and also impose major burdens on morbidity, quality of life, and societal costs despite of the remarkable progress of cardiovascular (CV) treatment over the past 50 years. CVD therapy improves CV outcomes in less than half of patients. Precision medicine is an attractive and advancing strategy to enhance for disease prevention, diagnosis, and tailored treatment and allocate limited resources more wisely and effectively. We are now in the middle of fourth industrial revolution by a robust confluence of biotechnology, physical science and information technologies. This approach is in its premature so far, but has begun to yield useful information that moves from the conventional ‘average response’ approach to more specific and targeted approaches governed by individual variability. This review aims to how precision medicine, genomics, and epigenetics work together to create a new era of CV precision medicine.
The definitions of precision medicine are varying depending on the idea of peoples. A common sense defines precision medicine as new health-care model that facilitates efficient and accurate identification of the optimal health care for individuals. According to the Precision Medicine Initiative, precision medicine is “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.”1)
Precision medicine identifies the unique aspects of an individual related to health and disease to choice optimal and effective therapy. We may consider that precision medicine can identify probabilistic risks and the likelihood of various interventions to yield improved health outcomes in patients with common phenotype characteristics. Personalized medicine may refer to implementation of these individual health information into a person-specific tailored treatment plan or, the creation of a treatment plan for an individual based on known biomarkers.2-6) They are not identical, but rather related and overlapping disciplines.
The current approach to reducing CV morbidity and mortality in at-risk individuals or those with current established CV disease (CVD) is based on a traditional preventive and therapeutic strategies with evidence-based medicine. However, CVD remains the leading cause of mortality and morbidity in the developed countries.7-9) According to advances in biological and information sciences in decades, we might expect that more smart and effective strategies for prevention, diagnosis, and treatment could be delivered at both the individual and population levels.2)3) However, there is a growing gap of unmet need between the development of biotechnology and clinical application in the global CV healthcare system.10) It suggests that significant fundamental changes to existing systems for R&D, translation, and health care delivery are required.
The goal of precision medicine is to identify optimal health care of an individual based on a distinctive personal profile rather than that of the average population. The power of precision medicine depends on the data and requires the synthesis of rapidly changing data sets, ranging from traditional clinical, imaging, and laboratory testing to next-generating sequencing, panomics (genomics, transcriptomics, epigenomics, metabolomics, proteomics, and microbiomics) studies to historical health record data.11) For example, using a precision medicine approach, individual undergo deep phenotyping with data analysis performed using network analysis from endophenotype categories to cluster categories. This methodology can be used to optimal changes towards health, predict and prognosticate disease, identify biomarkers for disease, enrich clinical studies, select rational polypharmacy, and optimally tailored environmental exposures.
Current clinical trials approach CVDs (e.g. hypertension) by simplifying the biological continuum (e.g. ideal health - CV risk factors - CVD), thereby considering that all trial participants have a common phenotype (e.g. elevated blood pressure), and reporting the trial results as the average finding in a given treatment arm, such as the median blood pressure.2)12)
A principle of precision medicine is establishing the precise phenotype for any given health or disease disorder to improve individual and population outcome. Precision medicine focuses on deep profiling of an individual, understanding the determinants of responses to treatments, and finally identifying the optimal treatment tools on the basis of an individual's profile.
Enrichment strategies for more efficient clinical trials are to decrease heterogeneity, or to increase prognostic enrichment, or to increase predictive enrichment. The predictive enrichment strategy utilizes both the characteristics of the trial participant and data obtained either from pre-trial experiments or during the trial (adaptive design) to predict which participants are likely to have a more powerful response to the treatment being tested. This strategy suites the principles of precision medicine and, compared with other enrichment strategies, is likely to result in more efficient clinical trials, improves the benefit–risk relationship of trial participants, and has the potential to be more informative for daily clinical practice than the other enrichment strategies.
A US Food and Drug Administration guidance document describes 5 categories of predictive enrichment strategies, all of which are applicable to the development of CV therapeutics.13)
(1) Empirical strategy: probable responders are selected on the basis of observations during a screening period or on previous responses to the test drug or to drugs from the same class (e.g. Paradigm-heart failure [HF] trial).14)
(2) Pathophysiological strategy: it selects probable responders on the basis of their individual physiology (e.g. Rutherford-2 trial).15) Probable responders can also be selected on the basis of the characteristics of the disease under study (e.g. low renin hypertension trial).16)
(3) Empirical genomic strategy: it selects probable responders on the basis of their genetic profile (e.g. Elevate-TIMI 56 trial).17)
(4) Randomized withdrawal strategy: in a randomized withdrawal trial, participants receiving a test treatment for a specified time are randomly assigned to continue that treatment or to discontinue the treatment (switching to placebo can be used to maintain blinding) (e.g. Radiance trial).18)
(5) Studies in non-responders or patients intolerant of other therapy (e.g. Odyssey Alternative trial).19)
The use of predictive enrichment strategies for CV clinical trials is expected to increase with advances in systems biology, and network medicine. An important aspect of a predictive enrichment strategy in CV therapeutic trials is the identification of a biomarker.3) Biomarkers that predict a clinical end point might ultimately constitute a validated surrogate end point for a clinical trial. As the precision medicine system matures with increasingly complicated biomedical informatics, we could be able to make greater use of the electronic health care record as a bio-research platform. Large, web-based, cohort studies that have the potential to include randomization have also been launched by CV trialists.
The CV medical community moves toward more precise and accurate with current diagnoses and treatment, as clinical genome sequencing becomes more common and basic science techniques like clustered regularly interspaced short palindromic repeats (CRISPR) begin to transition toward clinical applicability. Using knowledge gained from population sequencing projects, such as genome-wide association studies, and disease models in the laboratory, we can begin to develop deeper understandings of disease at a molecular level to predict the risk of a patient for developing a CVD and present them with the right preventive and treatment strategy at right time.20)21)
CV precision medicine integrates basic science techniques with genomic information as followings: 1) utilization of clinical genome sequencing: genomic sequencing identifies disease-causing mutations in patients and family members (e.g. cascade screening in familiar hypercholesterolemia), and direct disease treatment (e.g. long-QT syndrome subtype-informed drug selection) and clarify disease diagnoses22-24); 2) development and evaluation of genetic risk scores: genetic risk scores can help to predict the CV risk of complex disease calculated from the influence of many variants (e.g. in coronary artery disease, CV risk score discussion can affect statin usage). Association of genetic risk with CVD outcomes can be as strong as lifestyle risk25-27); 3) development of targeted therapeutics: Targeted therapeutics can tackle molecular underpinnings of specific disease subtypes (e.g. PCSK9 inhibition with human monoclonal antibodies in familiar hypercholesterolemia. And sequencing can aid placement of patients into appropriate clinical trials28)29); 4) CRISPR genome editing and its medical application: CRISPR genome editing can help potential to stop CVD before it starts (e.g. editing in hypertrophic cardiomyopathy [HCM] embryos), and target CVD at the DNA level (e.g. editing of Duchenne muscular dystrophy (DMD) in mice can alleviate disease symptoms)30)31); 5) Utilization of CV pharmacogenomics: CV pharmacogenomics can help to individualize CV drug selection and drug use to avoid adverse drug reaction, and to maximize CV drug efficacy32)33); and 6) induced pluripotent stem cells (iPSCs) and its medical application; iPSCs can help to make a CVD model and test new therapies in vitro (e.g. testing calmodulin knock-out in long QT syndrome [LQTS]), and potential source of autologous stem cells for transplantation (e.g. iPSC-erythroblasts to treat β-thalassemia).34) The diagnostic power of genetic testing is significant across the spectrum of CVDs, ranging from cardiomyopathies to life-threatening arrhythmias to aortic disease.35-37)
In the clinic, genetic testing can 1) clarify CVD diagnoses: genetic testing can help to clarify the diagnosis of diseases that cause similar clinical presentation (e.g., cardiac hypertrophy could be transthyretin (TTR) amyloidosis, Fabry disease, or sarcomeric HCM); 2) facilitate cascade screening: genetic testing can help to identify relatives at risk for CVD before disease symptoms manifest if a disease-associated variant is found on a proband and then screened for in relatives; 3) direct more precise therapy: genetic testing can help physicians choose appropriate treatments and plan the appropriate timing of those treatments. For example, inherited connective tissue disease due to variants in ACTA2, MYH11, or TGFBR2 might prompt consideration of surgical intervention at a smaller aortic aneurysm diameter38); and 4) identify patients for targeted therapies: targeted medical therapies, including antibody-based therapeutics, gene editing, and silencing technologies, are available or under development for several genetic diseases, including LQTS, DMD, TTR cardiac amyloidosis, and Fabry disease.38-40)
Advances in genetics have identified novel pathways and targets that operate in numerous CVDs, paving the way for precision medicine. However, the inherited genome determines only part of an individual's CV risk profile. Consequently, standard genomic approaches do not take into account the regulation of gene expression by modifications of the epigenome.
Variants in the genes that encode the beta-1 adrenergic receptor (ADRB1) associate with response to beta-blockers in patients with HF. Because the 389Arg allele associates with greater production of cyclic adenosine monophosphate compared with the 389Gly allele, ‘hyper-responders’ carrying the 389Arg allele might benefit to a greater extent from beta-blockers. An important BEST substudy which investigated the effect of the beta-blocker bucindolol in patients with HF showed that bucindolol reduced mortality and hospitalization in homozygotes for the Arg389 allele compared with the Gly allele carriers.41) However, MERIT-HF substudy have reported more equivocal results.42) One Korean HF study showed that the ADRB1 Gly389X genotype showed greater response to bisoprolol than the Arg389Arg genotype, suggesting the potential of individually tailoring β-blocker therapy according to genotype.43) Given the heterogeneity in pharmacological properties of beta-blockers in HF, these inconsistencies could reflect differences between these agents or the analysis of a smaller sample size.
The term ‘epigenetics’ originally embraced the process by which a fertilized zygote develops into a mature, complex organism, but underwent expansion as findings showed that cells having the same DNA can exhibit differential modulation of gene activity. And the transmission of epigenetic changes to daughter cells where it actively regulates gene expression.44) Epigenetic modifications defined as ‘heritable changes to the genome that do not involve changes in DNA sequence’ have emerged as a new aspect of biological regulation in CVD and could advance individualized risk assessment as well as devising and deploying tailored therapies. Genetic mutations acquired during the life course represent an irreversible process, whereas plastic epigenetic changes of DNA/histone complexes are reversible to pharmacological reprogramming. Epigenetic modifications fall into 3 main categories: 1) chemical modifications of DNA (i.e. DNA methylation); 2) post-translational modifications of histone tails; and 3) regulation of gene expression by noncoding RNAs (i.e. microRNAs, PIWI-interacting RNAs, endogenous short interfering RNAs, long non-coding RNAs).45)46)
An array of environmental factors significantly contributes to build our individual epigenetic background over time that includes DNA methylation changes, post-translational histone modifications and altered expression of non-coding RNAs. Investigating the individual epigenetic landscape provides an important snapshot of the epigenetic machinery that can be finally used to customize diagnostic and therapeutic approaches in CVD prevention. Available technologies for the study of the epigenome may furnish detailed epigenetic maps based on DNA-histone interactions and non-coding RNAs landscape. Individual epigenetic maps could represent a novel tool in the clinical practice to stratify CV risk beyond traditional or genome-based risk calculations. Epigenetic information also helps in interpreting inter-and intra-personal variation in individual drug response. Adverse epigenetic patterns are modifiable to pharmacological reprogramming of chromatin modifying drugs or non-coding RNAs.47)
Precision medicine changes the current standard clinical practice and draws from clinical testing, electronic health records, panomics profiling, big data sets, and novel analytical methods, such as systems biology and network science, to create a person-specific phenotype that can be used to identify an optimal treatment.8) The obvious benefits of this strategy to patients, clinicians, and researchers are numerous and include individual phenotype specificity, identification of individuals with a similar molecular phenotype, selection of optimal therapies with maximal efficacy and limited adverse reactions, efficient selection and enrichment of clinical trial participants, potential to improve adherence and reduce health care costs, and creating a paradigm shift in how CV care is delivered. To accomplish this goal, the medical community at large, public, big pharmaceutical companies, and other policy-maker stakeholders will need to overcome barriers to implementation that range from technical to social to political.48) Other barriers are related to big data set collection and focuses on methods to ensure data accuracy, computational power, security and privacy of data sets, renewal of accruing data, and continuous development and refinement of analytical methods. Finally, continuous education, affordability, and public acceptance of the strategy play key roles in its ultimate implementation.49)50) Precision medicine is ready to become the next revolution in the medicine, as well as the prevention, diagnosis and treatment of CVD.
Precision medicine represents a new health care strategy in an integrative approach to disease prevention and treatment. Precision CV medicine compromises basic science and techniques with genomic information; clinical genome sequencing, genetic risk score, genomic editing, targeted therapeutic, and pharmacogenomic. Predictive enrichment strategies for clinical trials utilize both the characteristics of the trial participants and data to predict who is likely to have a more response to the treatments being tested. Epigenetic modifications have emerged as a new landscape of biological regulation in CVD and could advance individualized risk assessment as well as design personalized epigenetic based therapies. Many challenges still remain to be solved in precision CV medicine.

Conflict of Interest

The author has no financial conflicts of interest.

  • 1. Precision Medicine Initiative (PMI) Working Group. The Precision Medicine Initiative cohort program— building a research foundation for 21st century medicine [Internet] Bethesda, MD: PMI Working Group; 2015 Available from https://www.nih.gov/sites/default/files/research-training/initiatives/pmi/pmiworking-group-report-20150917-2.pdf
  • 2. Antman EM, Loscalzo J. Precision medicine in cardiology. Nat Rev Cardiol 2016;13:591-602.ArticlePubMed
  • 3. Leopold JA, Loscalzo J. Emerging role of precision medicine in cardiovascular disease. Circ Res 2018;122:1302-15.ArticlePubMedPMC
  • 4. Califf RM. Future of personalized cardiovascular medicine: JACC state-of-the-art review. J Am Coll Cardiol 2018;72:3301-9.PubMed
  • 5. Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med 2012;366:489-91.ArticlePubMed
  • 6. Fuster V. A first dilemma in cardiovascular medicine: adherence versus personalized therapy. J Am Coll Cardiol 2014;64:1059-60.PubMed
  • 7. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, Chiuve SE, Cushman M, Delling FN, Deo R, de Ferranti SD, Ferguson JF, Fornage M, Gillespie C, Isasi CR, Jiménez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Lutsey PL, Mackey JS, Matchar DB, Matsushita K, Mussolino ME, Nasir K, O'Flaherty M, Palaniappan LP, Pandey A, Pandey DK, Reeves MJ, Ritchey MD, Rodriguez CJ, Roth GA, Rosamond WD, Sampson UK, Satou GM, Shah SH, Spartano NL, Tirschwell DL, Tsao CW, Voeks JH, Willey JZ, Wilkins JT, Wu JH, Alger HM, Wong SS, Muntner P; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation 2018;137:e67-492.ArticlePubMed
  • 8. Ford ES, Ajani UA, Croft JB, Critchley JA, Labarthe DR, Kottke TE, Giles WH, Capewell S. Explaining the decrease in U.S. deaths from coronary disease, 1980-2000. N Engl J Med 2007;356:2388-98.ArticlePubMed
  • 9. Luepker RV. Falling coronary heart disease rates: a better explanation? Circulation 2016;133:8-11.PubMed
  • 10. Ginsburg GS, Phillips KA. Precision medicine: from science to value. Health Aff (Millwood) 2018;37:694-701.ArticlePubMedPMC
  • 11. Lau E, Wu JC. Omics, big data, and precision medicine in cardiovascular sciences. Circ Res 2018;122:1165-8.ArticlePubMedPMC
  • 12. Loscalzo J. Precision medicine. Circ Res 2019;124:987-9.ArticlePubMedPMC
  • 13. Food and Drug Administration (FDA). Enrichment strategies for clinical trials to support determination of effectiveness of human drugs and biological products guidance for industry [Internet] Silver Spring, MD: FDA; 2019 March Available from https://www.fda.gov/media/121320/download
  • 14. McMurray JJ, Packer M, Desai AS, Gong J, Lefkowitz MP, Rizkala AR, Rouleau JL, Shi VC, Solomon SD, Swedberg K, Zile MR; PARADIGM-HF Investigators and Committees. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med 2014;371:993-1004.ArticlePubMed
  • 15. Raal FJ, Stein EA, Dufour R, Turner T, Civeira F, Burgess L, Langslet G, Scott R, Olsson AG, Sullivan D, Hovingh GK, Cariou B, Gouni-Berthold I, Somaratne R, Bridges I, Scott R, Wasserman SM, Gaudet D; RUTHERFORD-2 Investigators. PCSK9 inhibition with evolocumab (AMG 145) in heterozygous familial hypercholesterolaemia (RUTHERFORD-2): a randomised, double-blind, placebo-controlled trial. Lancet 2015;385:331-40.ArticlePubMed
  • 16. Mulatero P, Verhovez A, Morello F, Veglio F. Diagnosis and treatment of low-renin hypertension. Clin Endocrinol (Oxf ) 2007;67:324-34.ArticlePubMed
  • 17. Mega JL, Hochholzer W, Frelinger AL 3rd, Kluk MJ, Angiolillo DJ, Kereiakes DJ, Isserman S, Rogers WJ, Ruff CT, Contant C, Pencina MJ, Scirica BM, Longtine JA, Michelson AD, Sabatine MS. Dosing clopidogrel based on CYP2C19 genotype and the effect on platelet reactivity in patients with stable cardiovascular disease. JAMA 2011;306:2221-8.ArticlePubMed
  • 18. Packer M, Gheorghiade M, Young JB, Costantini PJ, Adams KF, Cody RJ, Smith LK, Van Voorhees L, Gourley LA, Jolly MK. Withdrawal of digoxin from patients with chronic heart failure treated with angiotensin-converting-enzyme inhibitors. RADIANCE Study. N Engl J Med 1993;329:1-7.ArticlePubMed
  • 19. Moriarty PM, Jacobson TA, Bruckert E, Thompson PD, Guyton JR, Baccara-Dinet MT, Gipe D. 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 2014;8:554-61.ArticlePubMed
  • 20. Dainis AM, Ashley EA. Cardiovascular precision medicine in the genomics era. JACC Basic Transl Sci 2018;3:313-26.ArticlePubMedPMC
  • 21. Manolio TA, Abramowicz M, Al-Mulla F, Anderson W, Balling R, Berger AC, Bleyl S, Chakravarti A, Chantratita W, Chisholm RL, Dissanayake VH, Dunn M, Dzau VJ, Han BG, Hubbard T, Kolbe A, Korf B, Kubo M, Lasko P, Leego E, Mahasirimongkol S, Majumdar PP, Matthijs G, McLeod HL, Metspalu A, Meulien P, Miyano S, Naparstek Y, O'Rourke PP, Patrinos GP, Rehm HL, Relling MV, Rennert G, Rodriguez LL, Roden DM, Shuldiner AR, Sinha S, Tan P, Ulfendahl M, Ward R, Williams MS, Wong JE, Green ED, Ginsburg GS. Global implementation of genomic medicine: we are not alone. Sci Transl Med 2015;7:290. ps13.Article
  • 22. Giudicessi JR, Ackerman MJ. Genotype- and phenotype-guided management of congenital long QT syndrome. Curr Probl Cardiol 2013;38:417-55.ArticlePubMedPMC
  • 23. Mazzanti A, Maragna R, Faragli A, Monteforte N, Bloise R, Memmi M, Novelli V, Baiardi P, Bagnardi V, Etheridge SP, Napolitano C, Priori SG. Gene-specific therapy with mexiletine reduces arrhythmic events in patients with long QT syndrome type 3. J Am Coll Cardiol 2016;67:1053-8.ArticlePubMedPMC
  • 24. Abu-Zeitone A, Peterson DR, Polonsky B, McNitt S, Moss AJ. Efficacy of different beta-blockers in the treatment of long QT syndrome. J Am Coll Cardiol 2014;64:1352-8.ArticlePubMed
  • 25. Khera AV, Chaffin M, Aragam KG. Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease. bioRxivorg 2017 Nov 15 [E-pub ahead of print]. https://doi.org/10.1101/218388.Article
  • 26. Torpy JM, Burke AE, Glass RM. JAMA patient page. Coronary heart disease risk factors. JAMA 2009;302:2388. PubMed
  • 27. Kullo IJ, Jouni H, Austin EE, Brown SA, Kruisselbrink TM, Isseh IN, Haddad RA, Marroush TS, Shameer K, Olson JE, Broeckel U, Green RC, Schaid DJ, Montori VM, Bailey KR. Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MIGENES clinical trial). Circulation 2016;133:1181-8.ArticlePubMedPMC
  • 28. Schmidt AF, Pearce LS, Wilkins JT, Overington JP, Hingorani AD, Casas JP. PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database Syst Rev 2017;4:CD011748. ArticlePubMed
  • 29. Fitzgerald K, White S, Borodovsky A, Bettencourt BR, Strahs A, Clausen V, Wijngaard P, Horton JD, Taubel J, Brooks A, Fernando C, Kauffman RS, Kallend D, Vaishnaw A, Simon A. A highly durable RNAi therapeutic inhibitor of PCSK9. N Engl J Med 2017;376:41-51.ArticlePubMed
  • 30. Ding Q, Strong A, Patel KM, Ng SL, Gosis BS, Regan SN, Cowan CA, Rader DJ, Musunuru K. Permanent alteration of PCSK9 with in vivo CRISPR-Cas9 genome editing. Circ Res 2014;115:488-92.ArticlePubMedPMC
  • 31. Ma H, Marti-Gutierrez N, Park SW, Wu J, Lee Y, Suzuki K, Koski A, Ji D, Hayama T, Ahmed R, Darby H, Van Dyken C, Li Y, Kang E, Park AR, Kim D, Kim ST, Gong J, Gu Y, Xu X, Battaglia D, Krieg SA, Lee DM, Wu DH, Wolf DP, Heitner SB, Belmonte JC, Amato P, Kim JS, Kaul S, Mitalipov S. Correction of a pathogenic gene mutation in human embryos. Nature 2017;548:413-9.ArticlePubMed
  • 32. Weinshilboum RM, Wang L. Pharmacogenomics: precision medicine and drug response. Mayo Clin Proc 2017;92:1711-22.ArticlePubMed
  • 33. Food and Drug Administration (FDA).Table of pharmacogenomic biomarkers in drug labeling [Internet]. Silver Spring, MD: FDA; 2019 March. Available from https://www.fda.gov/Drugs/ScienceResearch/ucm572698.htm
  • 34. Xie F, Ye L, Chang JC, Beyer AI, Wang J, Muench MO, Kan YW. Seamless gene correction of β-thalassemia mutations in patient-specific iPSCs using CRISPR/Cas9 and piggyBac. Genome Res 2014;24:1526-33.ArticlePubMedPMC
  • 35. Harper AR, Parikh VN, Goldfeder RL, Caleshu C, Ashley EA. Delivering clinical grade sequencing and genetic test interpretation for cardiovascular medicine. Circ Cardiovasc Genet 2017;10:e001221.ArticlePubMed
  • 36. Walsh R, Thomson KL, Ware JS, Funke BH, Woodley J, McGuire KJ, Mazzarotto F, Blair E, Seller A, Taylor JC, Minikel EV; Exome Aggregation Consortium, MacArthur DG, Farrall M, Cook SA, Watkins H. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet Med 2017;19:192-203.ArticlePubMed
  • 37. Sturm AC, Hershberger RE. Genetic testing in cardiovascular medicine: current landscape and future horizons. Curr Opin Cardiol 2013;28:317-25.PubMed
  • 38. Caleshu C, Ashley E. Genetic testing for cardiovascular conditions predisposing to sudden death. Edited by Wilson MG, Drezner JA, Sharma S: IOC Manual of Sports Cardiology. Hoboken, NJ, Wiley & Sons, Ltd. 2016, pp 175-86.
  • 39. Benson MD, Dasgupta NR, Rissing SM, Smith J, Feigenbaum H. Safety and efficacy of a TTR specific antisense oligonucleotide in patients with transthyretin amyloid cardiomyopathy. Amyloid 2017;24:219-25.ArticlePubMed
  • 40. Parikh VN, Ashley EA. Next-generation sequencing in cardiovascular disease: present clinical applications and the horizon of precision medicine. Circulation 2017;135:406-9.ArticlePubMedPMC
  • 41. Liggett SB, Mialet-Perez J, Thaneemit-Chen S, Weber SA, Greene SM, Hodne D, Nelson B, Morrison J, Domanski MJ, Wagoner LE, Abraham WT, Anderson JL, Carlquist JF, Krause-Steinrauf HJ, Lazzeroni LC, Port JD, Lavori PW, Bristow MR. A polymorphism within a conserved beta(1)-adrenergic receptor motif alters cardiac function and beta-blocker response in human heart failure. Proc Natl Acad Sci U S A 2006;103:11288-93.ArticlePubMedPMC
  • 42. White HL, de Boer RA, Maqbool A, Greenwood D, van Veldhuisen DJ, Cuthbert R, Ball SG, Hall AS, Balmforth AJ; MERIT-HF Study Group. An evaluation of the beta-1 adrenergic receptor Arg389Gly polymorphism in individuals with heart failure: a MERIT-HF sub-study. Eur J Heart Fail 2003;5:463-8.ArticlePubMed
  • 43. Lee HY, Chung WJ, Jeon HK, Seo HS, Choi DJ, Jeon ES, Kim JJ, Shin JH, Kang SM, Lim SC, Baek SH. Impact of the β-1 adrenergic receptor polymorphism on tolerability and efficacy of bisoprolol therapy in Korean heart failure patients: association between β adrenergic receptor polymorphism and bisoprolol therapy in heart failure (ABBA) study. Korean J Intern Med 2016;31:277-87.ArticlePubMedPMC
  • 44. González-Recio O, Toro MA, Bach A. Past, present, and future of epigenetics applied to livestock breeding. Front Genet 2015;6:305. PubMedPMC
  • 45. DeLaughter DM, Bick AG, Wakimoto H, McKean D, Gorham JM, Kathiriya IS, Hinson JT, Homsy J, Gray J, Pu W, Bruneau BG, Seidman JG, Seidman CE. Single-cell resolution of temporal gene expression during heart development. Dev Cell 2016;39:480-90.ArticlePubMedPMC
  • 46. Handy DE, Castro R, Loscalzo J. Epigenetic modifications: basic mechanisms and role in cardiovascular disease. Circulation 2011;123:2145-56.PubMedPMC
  • 47. Costantino S, Libby P, Kishore R, Tardif JC, El-Osta A, Paneni F. Epigenetics and precision medicine in cardiovascular patients: from basic concepts to the clinical arena. Eur Heart J 2018;39:4150-8.ArticlePubMed
  • 48. Kohane IS. Health care policy. Ten things we have to do to achieve precision medicine. Science 2015;349:37-8.ArticlePubMed
  • 49. Joyner MJ. Precision medicine, cardiovascular disease and hunting elephants. Prog Cardiovasc Dis 2016;58:651-60.ArticlePubMed
  • 50. Fuster V. A second dilemma in cardiovascular medicine: personalized medicine versus personal interaction with the patient. J Am Coll Cardiol 2014;64:1292-3.PubMed

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