492
views
0
recommends
+1 Recommend
1 collections
    5
    shares

      CVIA now indexed by SCOPUS from February 2024. CVIA received its first Journal Impact Factor (0.5) in the 2023 Journal Citation Reports Release. 

      Interested in becoming a CVIA published author?

      • Platinum Open Access with no APCs. 
      • Fast peer review/Fast publication online after article acceptance.

      Submissions should be made electronically at: https://mc04.manuscriptcentral.com/cvia-journal.

      Please refer to the Author Guidelines at https://cvia-journal.org/instructions-to-authors/ before submission.

       

      scite_
       
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Precision Medicine in Cardiovascular Diseases

      review-article
      1 , 1 ,
      Cardiovascular Innovations and Applications
      Compuscript
      precision medicine, cardiovascular diseases, multiomics
      Bookmark

            Abstract

            Since President Obama announced the Precision Medicine Initiative in the United States, more and more attention has been paid to precision medicine. However, clinicians have already used it to treat conditions such as cancer. Many cardiovascular diseases have a familial presentation, and genetic variants are associated with the prevention, diagnosis, and treatment of cardiovascular diseases, which are the basis for providing precise care to patients with cardiovascular diseases. Large-scale cohorts and multiomics are critical components of precision medicine. Here we summarize the application of precision medicine to cardiovascular diseases based on cohort and omic studies, and hope to elicit discussion about future health care.

            Main article text

            Introduction

            What is precision medicine? The concepts of “P4” systems medicine (predictive, preventive, personalized, and participatory medicine) and individualized medicine have been proposed in recent years, and have a meaning similar to precision medicine. P4 medicine, proposed by Leroy Hood, denotes a revolution in medicine [13]. P4 medicine promises more than a shift from a population-based “one size fits all” medicine to a particular individual-based “personalized medicine”; it also represents a movement from a reactive to a prospective and proactive approach to disease prevention, and emphasizes the importance of physician and patient participation in health care. P4 medicine is based on holistic and systems approaches to disease, emerging technologies, and analytical tools. In 2014, Topol [4] suggested the term “individualized medicine” to avoid confusion with personalized medicine, and he explained that individualized medicine relates not only to medicine that is particularized to individual patients but also to the future impact of digital technology on individual health care. Using multiple biological omic tools, we can digitize a human and create a geographic information system for the individual. In 2011, a committee representing the National Academy of Sciences in the United States proposed the term “precision medicine,” which refers to “tailoring of medical treatment to the individual characteristics of each patient through the ability to classify individuals into subpopulations with development of the Knowledge Network of Disease” [5].

            Enthusiasm for precision medicine has grown significantly in the last few years, especially after President Obama announced the precision medicine initiative (PMI) in his State of the Union Address on January 20, 2015. President Obama asked Congress for $215 million in his 2016 budget to fund the PMI and lead to a new era of medicine. Shortly afterward, the Chinese Ministry of Science and Technology held the first National Expert Conference of Precision Medicine in March 2015, and planned to invest ¥20 billion by 2030 to support the PMI. In 2016, the Chinese Ministry of Science and Technology started the National Key Research and Development Program on Precision Medicine. The US PMI used $130 million to fund a research cohort and $70 million to intensify efforts to identify molecular drivers of cancer and to apply that knowledge to drug development. In contrast, the Chinese precision medicine program includes five subjects: development of a new generation of omic technologies for clinical application; a large-scale population-based cohort study; an integration, storage, usage, and sharing platform to obtain big precision medical data; precision strategies for disease prevention, diagnosis, and therapy; and the construction of precision medicine application and demonstration systems.

            Cardiovascular disease (CVD) is the leading cause of death worldwide. Patients with the same disease tend to receive similar treatment: so-called one size fits all medicine. However, treatment effects differ across individuals. Thus individual treatment based on a person’s unique characteristics could enable the provision of more precise care, which may eventually maximize treatment benefits and minimize harm.

            Two components are critical in precision medicine. The first is a large-scale cohort, which provides the basis of genetic and risk factor studies. The second is the study of genomics and pharmacogenomics, which identifies genetic characteristics to help predict and treat disease. Big data can be produced by the collection of medical records from cohort and omic studies. However, the analysis and integration of these data to find associations between phenotypes and genotypes is a difficult task.

            Large-Scale Cohorts

            Both in the United States and in China, a major aim of the PMI is to develop large-scale research cohorts. Several famous cohorts for cardiovascular research were developed before the concept of precision medicine was proposed. In 1948, the US Public Health Service embarked upon the first large-scale cardiovascular study: the Framingham Heart Study [6, 7]. Over the past 67 years, three generations of volunteers have participated in this study and more than 4000 research papers have been published based on studies using this cohort. For decades, the study has provided substantial insights into CVD epidemiology and risk factors. In 2006, the SNP Health Association Resource Program began, which supports genome-wide genotyping across all the Framingham cohorts. Hundreds of common genetic variants that affect the risk of CVD have been identified among 550,000 SNPs [7]. The Chinese Multi-provincial Cohort Study (CMCS) is a prospective cardiovascular cohort with more than 30,000 participants recruited from 11 provinces of China since 1992 [8]. Investigators have evaluated the validity of using the Framingham prediction model for the Chinese population, by comparing the results of the CMCS and Framingham studies. They found that the original Framingham model overestimated the coronary heart disease risk for CMCS participants, and recalibrated the Framingham functions using the CMCS cohort to improve the performance of Framingham functions in the Chinese population [8]. The China Kadoorie Biobank was set up to investigate the main genetic and environmental causes of common chronic diseases in the Chinese population; during 2004–2008, over 0.5 million participants were recruited [9].

            One project of the Chinese precision medicine Program is to develop a CVD cohort of more than 50,000 participants and a follow-up greater than 5 years. Another project fund is allocated for precise subtyping of CVD based on multiple omic data obtained from cohort studies.

            One study using a Chinese hypertension cohort showed that 75% of hypertension patients in China have hyperhomocysteinemia, a much higher level than found in other countries [10]. Moreover, the China Stroke Primary Prevention Trial confirmed the efficacy of folic acid (which can reduce the level of homocysteine) therapy in primary stroke prevention among Chinese hypertension patients [11]. Using a Chinese hypertrophic cardiomyopathy (HCM) cohort, Zou et al. [12] found a greater frequency of multiple mutations in Chinese HCM patients than in the white population, and described the correlation between genotypes and phenotypes.

            These cohorts will provide big data, which are so large and complex that they cannot be analyzed with traditional data processing methods. Big data are commonly characterized by the “3Vs”: volume, velocity, and variety [1315]. Volume refers to the amount or size of the data, which determines whether they can be considered as big data. Velocity depicts the speed of data processing. Scientists need to analyze increasing volumes of data with greater rapidity, which requires new software and hardware support. Variety describes the number of different types of data. Several types of data can be used to analyze biological samples, such as genomic, transcriptomic, proteomic, and metabolomic data. Greater data variety provides more opportunities to understand diseases and biological processes. How are big data applied in cardiovascular precision medicine? The sources of big data include clinical registries, electronic health records, patient-reported information, biometric data, medical imaging data, multiomic data, and Internet data. These data are further combined into an analytical platform and used in the prediction of risk and resource use, population management, drug and medical device surveillance, disease and treatment heterogeneity, clinical decision support, quality of care and performance measurement, public health, and research, all of which aim to improve cardiovascular quality and outcomes of care [15]. Although big data provide opportunities to implement cardiovascular precision medicine, physicians and scientists also face challenges, such as how to integrate data from various sources. We need to develop new analytical tools and apply these tools in clinical practice.

            Multiomics

            Genomics

            On what basis should patients with the same disease receive tailored therapy? Genetic features might be a suitable foundation. The Human Genome Project, which was completed in 2003, provided the first opportunity to understand all human genes [16]. Genetic variations play an important role in the pathogenesis of multiple CVDs. HCM is the leading cause of sudden death in young people, and more than 50% of HCM patients have a family history of the disease [17]. Studies have identified disease-causing mutations in more than 11 genes that encode cardiac sarcomere proteins; these account for 80% of familial HCM cases [1820]. To identify the function of these variations detected in HCM patients, mice containing these mutation sites have been produced. One recent study designed a small molecule, MYK-461, that reduces cardiomyocyte contractility by decreasing the adenosine triphosphatase activity of cardiac myosin heavy chain, and eventually suppresses the development of HCM [21] in mice with mutations in the β-cardiac myosin heavy chain 7 gene (MYH7). The study suggested a paradigm for treating diseases on the basis of their genetic features.

            Aortic aneurysm often results from cystic medial degeneration, and can lead to dissection or rupture. As many as 40% of people with aortic dissections die instantly [22]. At least 19% of patients have a family history of thoracic aortic aneurysm (TAA), and the presentation shows classic Mendelian inheritance, indicating the contribution of a single gene to this disease [23]. Some familial TAAs have syndromic presentations, such as Marfan syndrome and Loeys-Dietz syndrome, which result from mutations in the fibrillin 1 gene (FBN1) and genes encoding transforming growth factor β receptors respectively. In some cases, the TAA treatment strategy depends on the patient’s genotype. According to European and American guidelines on treatment of TAA, regardless of the cause, patients who have a maximal aortic diameter of 55 mm or greater should undergo surgery [24, 25]. However, earlier interventions have been proposed for aortic diameters greater than 42 mm in patients with Loeys-Dietz syndrome [24, 25] because TAA growth in patients with Loeys-Dietz syndrome is much faster (more than 10 cm/year), resulting in a mean age of death of 26 years [26]. In the past two decades researchers have identified increasing numbers of genes responsible for nonsyndromic TAA. However, most TAA cases are sporadic, and the genetic predisposition of this disease is still under investigation. Using genome-wide association studies, LeMaire et al. [27] found that common SNPs in a region encompassing FBN1 are associated with sporadic TAA. Using next-generation sequencing technology, we identified FBN1 variants in 15.75% of patients with sporadic TAA and dissections, and expanded the mutation spectrum of FBN1, which will be helpful for genetic counseling of Chinese patients [28].

            Atrial fibrillation (AF) is the most frequent cardiac arrhythmia. There are more than 10 million AF patients in China, and the disease is responsible for one-third of strokes in elderly people. In 2003, Chinese clinicians identified the first genetic mutation underlying AF in a four-generation family [29]. More than 20 genes associated with AF have been identified, which facilitates the individual treatment of AF.

            Pharmacogenomics

            Many drugs do not work in the same way for every patient. Many medications produce benefits in some patients but no response or adverse reactions in other patients. Pharmacogenomics offers the opportunity to predict drug response on the basis of a patient’s genotype, and to provide clinical decision support to increase the safety and effectiveness of drug therapy. This makes it an important area of precision medicine [30]. In the early years of the first decade of this century, the US Food and Drug Administration (FDA) began to require the pharmaceutical industry to submit pharmacogenomic information [31]. In 2009 the Clinical Pharmacogenetics Implementation Consortium was formed, and it has published multiple dosing guidelines for drugs. To date, more than 200 drug labels contain pharmacogenomic information approved by the US FDA, the European Medicines Agency, the Pharmaceuticals and Medical Devices Agency in Japan, and Health Canada. In the field of CVDs, pharmacogenomic tests have been increasingly used to provide clinically valuable information about individualized prescriptions of drugs such as warfarin, clopidogrel, aspirin, statins, and antihypertensive drugs.

            Warfarin, the most commonly used oral anticoagulant drug for CVDs, is the leading cause of hospitalization for adverse drug events in the United States [32]. Variants of the cytochrome P450 (CYP) family 2 subfamily C member 9 gene (CYP2C9) and the vitamin K epoxide reductase complex subunit 1 gene (VKORC1) are associated with sensitivity to warfarin, and up to 60% of the variability in the dose requirement of warfarin can be explained by clinical factors plus common variants of CYP2C9 and VKORC1 [3335]. Thus, in 2007, the US FDA approved pharmacogenomic data for warfarin labeling, which was further revised in 2010 to produce a dosing table based on CYP2C9 and VKORC1 genotypes, indicating the desirability of genetic testing before warfarin use.

            Clopidogrel is an oral thienopyridine prodrug that is widely used in antiplatelet therapy for patients undergoing percutaneous coronary intervention. Conversion of clopidogrel to its active metabolite requires the participation of multiple CYPs in the liver, particularly CYP2C19. The common loss-of-function allele CYP2C19*2 (681G>A, rs4244285) is associated with impaired clopidogrel antiplatelet activity [3638]. In addition to CYP2C19, variants of the genes ABCA1, CES1, P2RY12, and PON1 contribute to the interindividual variability of clopidogrel responsiveness [39].

            Statins are the most commonly used cholesterol-lowering drugs, and can inhibit the activity of 3-hydroxy-3-methylglutaryl-CoA reductase, a key enzyme for cholesterol synthesis. Variants of the genes encoding 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and low-density lipoprotein receptor (LDLR) are associated with variability in the effect of statins in lowering low-density lipoprotein cholesterol levels [4042]. Other variants of genes such as APOE, CYP3A4, CYP2C9, and ABCB1 are also associated with efficacy of statin response by affecting low-density lipoprotein transport or statin pharmacokinetics [42]. Another critical application of pharmacogenetics in statin therapy is to predict individual adverse drug reactions. SLCO1B1 521C polymorphism decreases simvastatin transport into hepatocytes and thus increases systemic simvastatin concentrations, which leads to increased risk of myopathy. On the basis of these findings, SLCO1B1 genetic testing is recommended before simvastatin is used [43, 44].

            There is evidence that two genes have a genetic association with the antihypertensive effect of drugs. The NEDD4L polymorphism rs4149601G>A predicts greater sensitivity to thiazide diuretics, and polymorphisms of the β1-adrenergic receptor gene (ADRB1) are associated with the efficiency of the blood pressure lowering response to β-blockers [45]. Homozygous ADRB1 389R carriers show greater antihypertensive effects and greater increases in left ventricular ejection fraction when treated with β-blockers [46, 47].

            The use of large-scale cohorts in pharmacogenomic studies will make it easier to establish links between genetic variants and drug response. This will greatly help physicians to select drugs. At the same time, new technologies for sequencing have been developing rapidly, which has enabled many hospitals to begin genetic testing.

            Other Omics

            Precision medicine is based on increased knowledge of the relation between genotype and phenotype, which characterizes the expression of disease in individuals. Unlike cancer, most CVDs are complex and involve substantial gene-environment interactions; thus, genomic studies are insufficient to solve all the challenges in this area. Other omic technologies, such as transcriptomics, proteomics, metabolomics, epigenomics, exomics, glycomics, and phenomics, should also be used in cardiovascular precision medicine.

            Proteomics and metabolomics are mass spectrometry–based omics. Proteomics is widely used for identification of biomarkers, diagnosis, and therapy selection [48]. With use of a targeted proteomics approach, osteoprotegerin, growth/differentiation factor 15, and matrix metalloprotease (MMP)-12 have been identified as new risk factors for carotid artery plaque in a large human sample [49]. Other biomarkers, such as trimethylamine N-oxide, MMP-2, MMP-9, MMP10, neutrophil gelatinase-associated lipocalin, and galectin 3 are also associated with CVD [36, 37, 50, 51]. Using a holistic proteomic approach, Hage et al. [39] identified novel biomarkers of inflammation that predict severity and prognosis in heart failure patients with preserved ejection fraction. Proteomics has also been used to explore the effects of drugs, such as antiplatelet agents [52]. Studies have analyzed the platelet protein map [40, 52, 53], and compared the platelet proteome profiles of aspirin-resistant and aspirin-sensitive patients with acute coronary syndrome [38]. These two groups show differences in the proteins involved in energy metabolism, cytoskeleton, oxidative stress, and cell survival pathways [38]. Individual proteomics analysis could help to predict which antiplatelet drug is suitable for a patient at risk of thrombosis.

            Precision medicine has been recognized as a new era in health care. With multiomic analysis, data collected from analysis of genetic variants, biomarker detection, and phenotype assessment can be integrated and used to improve strategies for preventing, diagnosing, and treating diseases in individual patients. Precision medicine both offers promise and presents challenges to the future care of individuals with CVDs.

            Acknowledgments

            Jie Du was supported by grants from the Chinese Ministry of Science and Technology (2016YFC0903001) and the Beijing Collaborative Innovative Research Center for Cardiovascular Diseases (PXM2014_014226_000002). Yan Liu was supported by grants from the Beijing Nova Program (Z151100000315067) and the National Natural Science Foundation of China (81300120).

            Conflict of Interest

            The authors declare that they have no conflicts of interest.

            References

            1. AuffrayC, CharronD, HoodL. Predictive, preventive, personalized and participatory medicine: back to the future. Genome Med 2010;2:57.

            2. WestonAD, HoodL. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res 2004;3:17996.

            3. HoodL, FriendSH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 2011;8:1847.

            4. TopolEJ. Individualized medicine from prewomb to tomb. Cell 2014;157:24153.

            5. National Research Council (US) Committee on a Framework for Developing a New Taxonomy of Disease. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press; 2011.

            6. TsaoCW, VasanRS. The Framingham Heart Study: past, present and future. Int J Epidemiol 2015;44:17636.

            7. MahmoodSS, LevyD, VasanRS, WangTJ. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 2014;383:9991008.

            8. LiuJ, HongY, D’AgostinoRB, SrWu Z, WangW, SunJ, et al. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. J Am Med Assoc 2004;291:25919.

            9. ChenZ, ChenJ, CollinsR, GuoY, PetoR, WuF, et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int J Epidemiol 2011;40:1652-66.

            10. HuDY, XuXP. [Prevention of stroke relies on valid control “H” type hypertension]. Zhonghua Nei Ke Za Zhi 2008;47:9767.

            11. HuoY, LiJ, QinX, HuangY, WangX, GottesmanRF, et al. Efficacy of folic acid therapy in primary prevention of stroke among adults with hypertension in China: the CSPPT randomized clinical trial. J Am Med Assoc 2015;313:132535.

            12. ZouY, WangJ, LiuX, WangY, ChenY, SunK, et al. Multiple gene mutations, not the type of mutation, are the modifier of left ventricle hypertrophy in patients with hypertrophic cardiomyopathy. Mol Biol Rep 2013;40:396976.

            13. KimJ. Big data, health informatics, and the future of cardiovascular medicine. J Am Coll Cardiol 2017;69:899902.

            14. WangL, AlexanderCA. Big data in medical applications and health care. Am Med J 2015;6:18.

            15. RumsfeldJS, JoyntKE, MaddoxTM. Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol 2016;13:3509.

            16. CollinsFS, GreenED, GuttmacherAE, GuyerMS. A vision for the future of genomics research. Nature 2003;422:83547.

            17. MaronBJ, MaronMS, SemsarianC. Genetics of hypertrophic cardiomyopathy after 20 years: clinical perspectives. J Am Coll Cardiol 2012;60:70515.

            18. SeidmanCE, SeidmanJG. Identifying sarcomere gene mutations in hypertrophic cardiomyopathy: a personal history. Circ Res 2011;108:74350.

            19. HoCY. Genetics and clinical destiny: improving care in hypertrophic cardiomyopathy. Circulation 2010;122:2430–40; discussion 2440.

            20. BosJM, WillML, GershBJ, KruisselbrinkTM, OmmenSR, AckermanMJ. Characterization of a phenotype-based genetic test prediction score for unrelated patients with hypertrophic cardiomyopathy. Mayo Clin Proc 2014;89:72737.

            21. GreenEM, WakimotoH, AndersonRL, EvanchikMJ, GorhamJM, HarrisonBC, et al. A small-molecule inhibitor of sarcomere contractility suppresses hypertrophic cardiomyopathy in mice. Science 2016;351:61721.

            22. IsselbacherEM. Thoracic and abdominal aortic aneurysms. Circulation 2005;111:81628.

            23. LindsayME, DietzHC. Lessons on the pathogenesis of aneurysm from heritable conditions. Nature 2011;473:30816.

            24. ErbelR, AboyansV, BoileauC, BossoneE, BartolomeoRD, EggebrechtH, et al. 2014 ESC guidelines on the diagnosis and treatment of aortic diseases: document covering acute and chronic aortic diseases of the thoracic and abdominal aorta of the adult. The Task Force for the Diagnosis and Treatment of Aortic Diseases of the European Society of Cardiology (ESC). Eur Heart J 2014;35:2873926.

            25. HiratzkaLF, BakrisGL, BeckmanJA, BersinRM, CarrVF, CaseyDE, Jr, et al. 2010 ACCF/AHA/AATS/ACR/ASA/SCA/SCAI/SIR/STS/SVM guidelines for the diagnosis and management of patients with Thoracic Aortic Disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, American Association for Thoracic Surgery, American College of Radiology, American Stroke Association, Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, Society of Interventional Radiology, Society of Thoracic Surgeons, and Society for Vascular Medicine. Circulation 2010;121:e266369.

            26. KuzmikGA, SangAX, ElefteriadesJA. Natural history of thoracic aortic aneurysms. J Vasc Surg 2012;56:56571.

            27. LeMaireSA, McDonaldML, GuoDC, RussellL, MillerCC, 3rd, JohnsonRJ, et al. Genome-wide association study identifies a susceptibility locus for thoracic aortic aneurysms and aortic dissections spanning FBN1 at 15q21.1. Nat Genet 2011;43:9961000.

            28. GuoJ, CaiL, JiaL, LiX, XiX, ZhengS, et al. Wide mutation spectrum and frequent variant Ala27Thr of FBN1 identified in a large cohort of Chinese patients with sporadic TAAD. Sci Rep 2015;5:13115.

            29. ChenYH, XuSJ, BendahhouS, WangXL, WangY, XuWY, et al. KCNQ1 gain-of-function mutation in familial atrial fibrillation. Science 2003;299:2514.

            30. BellGC, CrewsKR, WilkinsonMR, HaidarCE, HicksJK, BakerDK, et al. Development and use of active clinical decision support for preemptive pharmacogenomics. J Am Med Inform Assoc 2014;21:e939.

            31. JohnsonJA, CavallariLH. Pharmacogenetics and cardiovascular disease—implications for personalized medicine. Pharmacol Rev 2013;65:9871009.

            32. BudnitzDS, LovegroveMC, ShehabN, RichardsCL. Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med 2011;365:200212.

            33. AithalGP, DayCP, KestevenPJ, DalyAK. Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications. Lancet 1999;353:7179.

            34. WangD, ChenH, MomaryKM, CavallariLH, JohnsonJA, SadeeW. Regulatory polymorphism in vitamin K epoxide reductase complex subunit 1 (VKORC1) affects gene expression and warfarin dose requirement. Blood 2008;112:101321.

            35. WeekeP, RodenDM. Applied pharmacogenomics in cardiovascular medicine. Annu Rev Med 2014;65:8194.

            36. DanielsLB, CloptonP, LaughlinGA, MaiselAS, Barrett-ConnorE. Galectin-3 is independently associated with cardiovascular mortality in community-dwelling older adults without known cardiovascular disease: the Rancho Bernardo Study. Am Heart J 2014;167:67482.

            37. DanielsLB, Barrett-ConnorE, CloptonP, LaughlinGA, IxJH, MaiselAS. Plasma neutrophil gelatinase-associated lipocalin is independently associated with cardiovascular disease and mortality in community-dwelling older adults: the Rancho Bernardo Study. J Am Coll Cardiol 2012;59:11019.

            38. Mateos-CaceresPJ, MacayaC, AzconaL, ModregoJ, MahilloE, BernardoE, et al. Different expression of proteins in platelets from aspirin-resistant and aspirin-sensitive patients. Thromb Haemost 2010;103:16070.

            39. HageC, MichaelssonE, LindeC, DonalE, DaubertJC, GanLM, et al. Inflammatory biomarkers predict heart failure severity and prognosis in patients with heart failure with preserved ejection fraction: a holistic proteomic approach. Circ Cardiovasc Genet 2017;10:e001633.

            40. GarciaA, PrabhakarS, BrockCJ, PearceAC, DwekRA, WatsonSP, et al. Extensive analysis of the human platelet proteome by two-dimensional gel electrophoresis and mass spectrometry. Proteomics 2004;4:65668.

            41. ChasmanDI, PosadaD, SubrahmanyanL, CookNR, StantonVP, Jr, RidkerPM. Pharmacogenetic study of statin therapy and cholesterol reduction. J Am Med Assoc 2004;291:28217.

            42. MangraviteLM, ThornCF, KraussRM. Clinical implications of pharmacogenomics of statin treatment. Pharmacogenomics J 2006;6:36074.

            43. KitzmillerJP, MikulikEB, DaukiAM, MurkherjeeC, LuzumJA. Pharmacogenomics of statins: understanding susceptibility to adverse effects. Pharmgenomics Pers Med 2016;9:97106.

            44. CanestaroWJ, BrooksDG, ChaplinD, ChoudhryNK, LawlerE, MartellL, et al. Statin pharmacogenomics: opportunities to improve patient outcomes and healthcare costs with genetic testing. J Pers Med 2012;2:15874.

            45. Cooper-DeHoffRM, JohnsonJA. Hypertension pharmacogenomics: in search of personalized treatment approaches. Nat Rev Nephrol 2016;12:11022.

            46. KarlssonJ, LindL, HallbergP, MichaelssonK, KurlandL, KahanT, et al. Beta1-adrenergic receptor gene polymorphisms and response to beta1-adrenergic receptor blockade in patients with essential hypertension. Clin Cardiol 2004;27:34750.

            47. ChenL, MeyersD, JavorskyG, BurstowD, LolekhaP, LucasM, et al. Arg389Gly-beta1-adrenergic receptors determine improvement in left ventricular systolic function in nonischemic cardiomyopathy patients with heart failure after chronic treatment with carvedilol. Pharmacogenet Genomics 2007;17:9419.

            48. DuarteTT, SpencerCT. Personalized proteomics: the future of precision medicine. Proteomes 2016;4:29.

            49. LindL, ArnlovJ, LindahlB, SiegbahnA, SundstromJ, IngelssonE. Use of a proximity extension assay proteomics chip to discover new biomarkers for human atherosclerosis. Atherosclerosis 2015;242:20510.

            50. FrieseRS, RaoF, KhandrikaS, ThomasB, ZieglerMG, Schmid-SchonbeinGW, et al. Matrix metalloproteinases: discrete elevations in essential hypertension and hypertensive end-stage renal disease. Clin Exp Hypertens 2009;31:52133.

            51. HobeikaMJ, ThompsonRW, MuhsBE, BrooksPC, GagnePJ. Matrix metalloproteinases in peripheral vascular disease. J Vasc Surg 2007;45:84957.

            52. LewandrowskiU, WortelkampS, LohrigK, ZahediRP, WoltersDA, WalterU, et al. Platelet membrane proteomics: a novel repository for functional research. Blood 2009;114:e109.

            53. GarciaA. Proteome analysis of signaling cascades in human platelets. Blood Cells Mol Dis 2006;36:1526.

            Author and article information

            Journal
            CVIA
            Cardiovascular Innovations and Applications
            CVIA
            Compuscript (Ireland )
            2009-8782
            2009-8618
            February 2017
            June 2017
            : 2
            : 2
            : 155-161
            Affiliations
            [1] 1Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Collaborative Innovative Research Center for Cardiovascular Diseases, Beijing 100029, China
            Author notes
            Correspondence: Dr. Jie Du, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China,Tel.: +86-10-64456030, Fax: +86-10-64456095, E-mail: jdu@ 123456bcm.edu
            Article
            cvia20170003
            10.15212/CVIA.2017.0003
            9df0ac15-be7d-42dd-8e0b-26a30b215bf3
            Copyright © 2017 Cardiovascular Innovations and Applications

            This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

            History
            : 19 December 2016
            : 28 March 2016
            : 31 March 2017
            Categories
            Reviews

            General medicine,Medicine,Geriatric medicine,Transplantation,Cardiovascular Medicine,Anesthesiology & Pain management
            cardiovascular diseases,multiomics,precision medicine

            Comments

            Comment on this article