Editor's Note: For another perspective on this topic, please see the accompanying Expert Analysis.

As reported in the New York Times,¹ most drugs work for only 50% of the people who take them. This is a poor therapeutic response and a poor financial return on investment considering the annual drug budget in the United States exceeds $300 billion. All human disease consists of an interaction between genes and the environment. Although the DNA sequence of the human genome is 99.5% identical among individuals, the 0.5% represents approximately 15 million different sequences.² The 0.5% difference in DNA sequence accounts for individual variations such as hair color, eye color, and predisposition to disease and allergens. Fortunately, it has been shown that most of these variations are due to changes in single nucleotides referred to as single nucleotide polymorphisms (SNPs).³ The number of SNPs present in the human genome is fairly constant at about 3.5 million.³ In addition to individual genetic differences, there are also environmental and lifestyle differences among individuals (Figure 1). Thus, it is not surprising that in medicine, one size does not fit all. Hence, the need for personalized medicine.

Figure 1

Definition of Personalized Medicine

Personalized medicine is defined as treatment customized to one's individual genetic variants, which can be detected directly by analyzing the DNA sequence or indirectly by its expressed product of mRNA or protein in body fluids such as blood, urine, or saliva. Personalized medicine can be used to optimize treatment and reduce the number of adverse drug reactions (ADRs), which the FDA estimates is the fourth most common cause of death in the United States.⁴ A study published in 2015 found that 7% of FDA-approved drugs and 18% of prescriptions are affected by known and testable genetic variants.⁵

Digital Medicine, an Enabler of Personalized Medicine

Digital medicine is a necessary enabler in the delivery of personalized medicine. The mainstay of digital medicine is the electronic health record (EHR), but mobile devices, hospital and laboratory systems, and nationwide medical information systems also play a role (Figure 2). The US government is promoting digital and personalized medicine initiatives, such as the Electronic Medical Records and Genomics (eMERGE) Network and the Precision Medicine Initiative, that connect physicians, patients, communities, hospitals, and even states and countries.⁶ Similar programs are under way in the United Kingdom, China, and across Europe. Despite its advantages, digital medicine will not solve all problems. However, participation in digital medicine is necessary to realize the potential of personalized medicine. It can also aid in addressing medical errors, the third most common cause of death in hospitals in the United States.⁷

Figure 2

Genetic Risk Variants Predict Risk of Coronary Artery Disease

Coronary artery disease (CAD) is now a pandemic disease having become the most common cause of death in the developed and developing world (Figure 3).⁸ Evidence from clinical trials has shown consistently that 30% to 40% of cardiac events can be prevented by modifying known conventional risk factors.⁸ Epidemiologists have shown for decades that 40% to 60% of CAD risk is attributable to genetic factors.⁹ Thus, comprehensive prevention of CAD requires modification of both acquired and genetic risk factors. In 2007, two separate groups independently identified 9p21, the first genetic risk factor for CAD.^10,11 The discovery of 9p21 indicated that the risk of CAD, mediated by 9p21, was independent of known conventional risk factors; namely, cholesterol, blood pressure, smoking, diet, diabetes and exercise.^10,11 Secondly, 9p21, as expected, occurs commonly with over 75% of the world's population harboring that genetic variant (Figure 4). Lastly, the potency of 9p21 as a risk factor was modest with only a 25% increase in relative risk for CAD per copy. These results suggested to us, as initially expected, there are many genetic risk variants responsible for CAD, each imparting only minimal to moderate risk. To pursue their discovery would require performing genome-wide association studies which necessitates genotyping the DNA of tens of thousands of cases and controls with millions of DNA markers. The international consortium, CARDIoGRAM (Coronary ARtery DIsease Genome wide Replication and Meta-analysis), was formed in an attempt to pool the necessary expertise, sample size, and digital platforms.¹²

Figure 3

Figure 4: Shown here is the chromosome 9 with the first genetic risk factor on the small arm (p) at band 2.1 (9p21)

The consortium genotyped over 200,000 cases and controls utilizing 11 million SNPs. The CARDIoGRAM consortium, comprised of the United States, Canada, UK, Germany, and Iceland, was during peak performance electronically transferring over 100 million genotypes to each other per week.¹² The pursuit of genes for polygenic disorders such as CAD was made possible by the development of platforms enabling the genotyping and analysis of massive sample sizes. The efforts of CARDIoGRAM have identified more than 60 genetic risk variants associated with CAD (Figure 5).^13,14 All of these genetic variants are of genome-wide significance and all have been replicated in independent popluations.^13,14 A recent study has shown these genetic risk markers stratify for risk of CAD independently of conventional risk factors.¹⁵ These genetic risk variants also predict the therapeutic response to statin therapy. These results have subsequently been confirmed by several studies.^16-22

Figure 5

Prospective Use of Genetic Risk Score for CAD Risk Stratification

A recent prospective study stratified the risk for CAD using a genetic risk score. Individuals adhering to a healthy lifestyle had a 40% reduction in cardiac events compared to individuals with an unhealthy lifestyle.²² Stratification of risk by DNA variants has several advantages over that of conventional risk factors. They are uninfluenced by diet, drugs, age, or sex so can be sampled in the non-fasting or fasting state. A crucial difference for DNA risk variants is that they remain the same throughout one's lifetime so risk for CAD can be determined anytime from birth till death, as opposed to conventional risk factors which are age dependent (Figure 6). Risk stratification in an asymptomatic population could induce a paradigm shift in primary prevention of CAD. This is illustrated by the example of a 45-year-old premenopausal woman with a plasma LDL-cholesterol of 170mg/dl with no other risk factors for CAD. According to the most recent medical guidelines for management of CAD, this woman would be observed without specific treatment until she develops another risk factor for CAD such as myocardial infarction, hypertension, or the plasma LCL/C increases to greater than 190mg/dl. In contrast, if tested for genetic risk and found to be in the intermediate or high risk group she would be advised regarding a change in lifestyle and possibly treated with statin therapy. The genetic risk provides a means to assist CAD risk in asymptomatic individuals at any age. The asymptomatic premenopausal female could be treated for primary prevention before the development of heart disease.

Figure 6

As illustrated by CARDIoGRAM, digital medicine was essential to the pursuit and discovery of genetic variants for CAD (Figure 7). Digital medicine is also needed to translate this genetic knowledge into the four Ps of medicine—prediction, prevention, personalization, participatory—thus realizing the potential of personalized medicine to provide the right drug in the right dose to the right patient (Figure 8).⁶

Figure 7

Figure 8

Rebuttal

The studies conducted by CARDIoGRAM have shown that more than 60 genetic risk variants identified by the consortium do improve risk prediction and are likely to revolutionize primary prevention of CAD. In single gene disorders, such as familial hypertrophic cardiomyopathy (FHCM), genetic testing is essential for appropriate management. Most of these disorders are autosomal dominant, which means only 50% of the offspring will inherit the gene for this disease; thus, there is great need to identify the 50% who do not have the gene and will not require decades of follow-up. Furthermore, these individuals do not have to worry about passing on the gene for FHCM to their children. Individuals who have the gene will benefit from serial follow-up with appropriate testing and management. Another example can be found with clopidogrel, which is still the most commonly used antiplatelet drug in the Western world. A meta-analysis of 15 studies has shown that the incidence of stroke or heart attack increases twofold among individuals who do not express the gene to convert the drug into its active form.²³ Physicians should not prescribe clopidogrel without determining whether the patient has the appropriate gene to convert clopidogrel into its active metabolite. It is estimated that about 20% of Caucasians and 30% of East Asians lack the gene to convert clopidogrel into its active metabolite.²⁴ The cost of a clopidogrel tablet is a few cents whereas the substitute drug is a few dollars.²⁵ This indicates that most people can be treated at cost of a few cents per day. Personalized medicine can be cost-effective in some diseases, but only if prior genetic testing is performed. The oncologist almost routinely performs genetic testing to determine the most appropriate drug, particularly for cancer of the breast and leukemia (Figure 9). Individual variability is primarily determined by 3.5 million SNPs. It is expected that most genetic determinates of therapeutic response to drugs will be identified in the future. It is imperative that with electronic records and knowledge of genetic variants we will in the future come closer to delivering the right drug in the right dose for the right disease (Figure 10).

Figure 9

Figure 10

Acknowledgments

I would like to acknowledge Miss Arlene Guadalupe Campillo, MPH, for her support in preparation of the manuscript.

References

1. Pollack A. Patient's DNA May Be Signal to Traitor Medication. New York Times. 29 Dec 2009.
2. Levy S, Sutton G, Ng PC, et al. The diploid genome sequence of an individual human. PLoS Biol 2007;5:e254.
3. Stranger BE, Forrest MS, Dunning M, et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 2007;315:848-53.
4. Lazarou J, Pomeranz B, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 1998;279:1200-5.
5. Relling M, Evans W. Pharmacogenomics in the clinic. Nature 2015;526:343-50.
6. Yang S. From Genome to 4p Medicine. Sino-American Symposium on Clinical and Translational Medicine. 2011.
7. Makary MA, Daniel M. Medical error-the third leading cause of death in the US. BMJ 2016;353:i2139.
8. Wald NJ, Law MR. A strategy to reduce cardiovascular disease by more than 80%. BMJ 2003;326:1419.
9. Vinkuyzen AA, Wray NR, Yang J, Goddard ME, Visscher PM. Estimation and partition of heritability in human populations using whole-genome analysis methods. Annu Rev Genet 2013;47:75-95.
10. McPherson R, Pertsemlidis A, Kavaslar N, et al. A common allele on chromosome 9 associated with coronary heart disease. Science 2007;316:1488-91.
11. Helgadottir A, Thorleifsson G, Manolescu A, et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 2007;316:1491-3.
12. Preuss M, Konig IR, Thompson JR, et al. Design of the coronary ARtery DIsease genome-wide replication and meta-analysis (CARDIoGRAM) study: A genome-wide association meta-analysis involving more than 22,000 cases and 60,000 controls. Circ Cardiovasc Genet 2010;3:475-83.
13. CARDIoGramplusC4D Consortium, Deloukas P, Kanoni S, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet 2013;45:25-33.
14. Assimes TL, Roberts R. Genetics: implications for prevention and management of coronary artery disease. J Am Coll Cardiol 2016;68:2797-818.
15. Mega J, Stitziel NO, Smith JG, et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 2015;385:2264-71.
16. Abraham G, Havulinna AS, Bhalala OG, et al. Genomic prediction of coronary heart disease. Eur Heart J 2016;37:3267-78.
17. Ganna A, Magnusson PK, Pedersen NL, et al. Multilocus genetic risk scores for coronary heart disease prediction. Arterioscler Thromb Vasc Biol 2013;33:2267-72.
18. Goldstein BA, Yang L, Salfati E, Assimes TL. Contemporary considerations for constructing a genetic risk score: an empirical approach. Genet Epidemiol 2015;39:439-45.
19. Iribarren C, Lu M, Jorgenson E, et al. Clinical utility of multimarker genetic risk scores for prediction of incident coronary heart disease: a cohort study among over 51 thousand individuals of European ancestry. Circ Cardiovasc Genet 2016;9:531-40.
20. Ripatti S, Tikkanen E, Orho-Melander M, et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 2010;376:1393-400.
21. Tada H, Melander O, Louie JZ, et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur Heart J 2015;37:561-7.
22. Khera AV, Emdin CA, Drake I, et al. Genetic risk, adherence to a healthy lifestyle, and coronary artery disease. N Engl J Med 2016;375:2349-58.
23. Pan Y, Chen W, Xu Y, et al. Genetic polymorphisms and clopidogrel efficacy for acute ischemic stroke or transient ischemic attack: a systematic review and meta-analysis. Circulation 2017;135:21-33.
24. Shuldiner AR, O'Connell JR, Bliden KP, et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA 2009;302:849-57.
25. Abdel-Qadir H, Roifman I, Wijeysundera H. Cost-effectiveness of clopidogrel, prasugrel and ticagrelor for dual antiplatelet therapy after acute coronary syndrome: a decision-analytic model. CMAJ Open 2015;3:E438-46.

Keywords: Geriatrics, Precision Medicine, Coronary Artery Disease, Cholesterol, LDL, Risk Factors, Platelet Aggregation Inhibitors, Genome-Wide Association Study, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Polymorphism, Single Nucleotide, Genome, Human, Genetic Markers, Blood Pressure, Genotype, Cardiomyopathy, Hypertrophic, Familial, Smoking, Nucleotides, Pharmaceutical Preparations, Pandemics, Myocardial Infarction, Genetic Testing, Hypertension, Stroke, Diabetes Mellitus, Genomics, Life Style, Primary Prevention, DNA, Drug-Related Side Effects and Adverse Reactions

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