Abstract
The primary goals of personalized medicine are to optimize diagnostic and treatment strategies by tailoring them to the specific characteristics of an individual patient. In this Review, we summarize basic concepts and methods of personalizing cardiovascular medicine. In-depth characterization of study participants and patients in general practice using standardized methods is a pivotal component of study design in personalized medicine. Standardization and quality assurance of clinical data are similarly important, but in daily practice imprecise definitions of clinical variables can reduce power and introduce bias, which limits the validity of the data obtained as well as their potential clinical applicability. Changes in statistical methods with personalized medicine include a shift from dichotomous outcomes towards continuously measured variables, predictive modelling, and individualized medical decisions, subgroup analyses, and data-mining strategies. A variety of approaches to personalized medicine exist in cardiovascular research and clinical practice that might have the potential to individualize diagnostic and therapeutic procedures. For some of the emerging methods, such as data mining, the most-efficient way to use these tools is not yet fully understood. In addition, the predictive models—although promising—are far from mature, and are likely to be greatly improved by using available large-scale data sets.
Key Points
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The primary goals of personalized medicine are to optimize diagnostic and treatment strategies by tailoring them to the specific characteristics of an individual patient
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With some exceptions in antiplatelet and anticoagulation therapy, knowledge of genetic markers currently has little practical application in personalizing cardiovascular medicine
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Large, comprehensive, and standardized studies will uncover as yet unknown subgroups from apparently clinically homogeneous populations, but clinical applicability critically depends on standardization of diagnostic procedures in clinical practice
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To enter clinical practice, novel risk markers generated from '-omics' or imaging technologies have to provide additional predictive value beyond established markers
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Statistical methods are available to identify subgroups of patients characterized by specific combinations of predictors, but these methods are not commonly applied in cardiovascular research
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The most-efficient way to use some emerging methods, such as data mining, is not yet fully understood, and the predicative models—although promising—are far from mature
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References
Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).
International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).
Graham, I. et al. European guidelines on cardiovascular disease prevention in clinical practice: full text. Fourth Joint Task Force of the European Society of Cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Eur. J. Cardiovasc. Prev. Rehabil. 14 (Suppl. 2), S1–S113 (2007).
Stergiou, G. S. & Salgami, E. V. New European, American and international guidelines for hypertension management: agreement and disagreement. Expert Rev. Cardiovasc. Ther. 2, 359–368 (2004).
Mancia, G. et al. 2007 guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur. Heart J. 28, 1462–1536 (2007).
Bhatia, R. S. et al. Outcome of heart failure with preserved ejection fraction in a population-based study. N. Engl. J. Med. 355, 260–269 (2006).
Owan, T. E. et al. Trends in prevalence and outcome of heart failure with preserved ejection fraction. N. Engl. J. Med. 355, 251–259 (2006).
Borden, E. C. & Raghavan, D. Personalizing medicine for cancer: the next decade. Nat. Rev. Drug Discov. 9, 343–344 (2010).
Collins, F. Has the revolution arrived? Nature 464, 674–675 (2010).
O'Donnell, C. J. & Nabel, E. G. Cardiovascular genomics, personalized medicine, and the National Heart, Lung, and Blood Institute: part I: the beginning of an era. Circ. Cardiovasc. Genet. 1, 51–57 (2008).
Vasan, R. S. et al. Genetic variants associated with cardiac structure and function: a meta-analysis and replication of genome-wide association data. JAMA 302, 168–178 (2009).
Reilly, M. P. et al. Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and association of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet 377, 383–392 (2011).
Strawbridge, R. J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624–2634 (2011).
Villard, E. et al. A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur. Heart J. 32, 1065–1076 (2011).
Manolio, T. A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166–176 (2010).
Thanassoulis, G. & Vasan, R. S. Genetic cardiovascular risk prediction: will we get there? Circulation 122, 2323–2334 (2010).
Ashley, E. A. et al. Clinical assessment incorporating a personal genome. Lancet 375, 1525–1535 (2010).
Daly, A. K. Pharmacogenomics of anticoagulants: steps toward personal dosage. Genome Med. 1, 10 (2009).
Cooper, G. M. et al. A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood 112, 1022–1027 (2008).
Takeuchi, F. et al. A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet. 5, e1000433 (2009).
Teichert, M. et al. A genome-wide association study of acenocoumarol maintenance dosage. Hum. Mol. Genet. 18, 3758–3768 (2009).
Roberts, J. D. et al. Point-of-care genetic testing for personalisation of antiplatelet treatment (RAPID GENE): a prospective, randomised, proof-of-concept trial. Lancet 379, 1705–1711 (2012).
Völzke, H. et al. Population imaging as valuable tool for personalized medicine. Clin. Pharmacol. Ther. 92, 422–424 (2012).
Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).
Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).
Heid, I. M. et al. Meta-analysis identifies 13 new loci associated with waist–hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 (2010).
Meschia, J. F. et al. Genomic risk profiling of ischemic stroke: results of an international genome-wide association meta-analysis. PLoS ONE 6, e23161 (2011).
Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).
Horne, B. D. et al. Genome-wide significance and replication of the chromosome 12p11.22 locus near the PTHLH gene for peripartum cardiomyopathy. Circ. Cardiovasc. Genet. 4, 359–366 (2011).
Link, E. et al. SLCO1B1 variants and statin-induced myopathy—a genomewide study. N. Engl. J. Med. 359, 789–799 (2008).
Ho, R. H. et al. Effect of drug transporter genotypes on pravastatin disposition in European- and African–American participants. Pharmacogenet. Genomics 17, 647–656 (2007).
Barber, M. J. et al. Genome-wide association of lipid-lowering response to statins in combined study populations. PLoS ONE 5, e9763 (2010).
Gurwitz, D. & Pirmohamed, M. Pharmacogenomics: the importance of accurate phenotypes. Pharmacogenomics 11, 469–470 (2010).
Hutcheon, J. A., Chiolero, A. & Hanley, J. A. Random measurement error and regression dilution bias. BMJ 340, c2289 (2010).
Jensen, M. D. Role of body fat distribution and the metabolic complications of obesity. J. Clin. Endocrinol. Metab. 93 (Suppl. 1), S57–S63 (2008).
Zimmermann, M. B. et al. Toward a consensus on reference values for thyroid volume in iodine-replete schoolchildren: results of a workshop on inter-observer and inter-equipment variation in sonographic measurement of thyroid volume. Eur. J. Endocrinol. 144, 213–220 (2001).
Völzke, H. et al. Are serum thyrotropin levels within the reference range associated with endothelial function? Eur. Heart J. 30, 217–224 (2009).
Catley, C., Stratti, H. & McGregor, C. Multi-dimensional temporal abstraction and data mining of medical time series data: trends and challenges. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008, 4322–4325 (2008).
Loukides, G., Gkoulalas-Divanis, A. & Malin, B. Anonymization of electronic medical records for validating genome-wide association studies. Proc. Natl Acad. Sci. USA 107, 7898–7903 (2010).
Epstein, R. S. et al. Warfarin genotyping reduces hospitalization rates: results from the MM-WES (Medco-Mayo Warfarin Effectiveness Study). J. Am. Coll. Cardiol. 55, 2804–2812 (2010).
Gläser, S. et al. Influence of age, sex, body size, smoking, and β blockade on key gas exchange exercise parameters in an adult population. Eur. J. Cardiovasc. Prev. Rehabil. 17, 469–476 (2010).
Ittermann, T. et al. Reference intervals for eight measurands of the blood count in a large population based study. Clin. Lab. 56, 9–19 (2010).
Koch, B. et al. Reference values for cardiopulmonary exercise testing in healthy volunteers: the SHIP study. Eur. Respir. J. 33, 389–397 (2009).
Shahabi, P., Siest, G., Herbeth, B., Ndiaye, N. C. & Visvikis-Siest, S. Clinical necessity of partitioning of human plasma haptoglobin reference intervals by recently-discovered rs2000999. Clin. Chim. Acta 413, 1618–1624 (2012).
Steyerberg, E. W. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128–138 (2010).
Pearl, J. An introduction to causal inference. Int. J. Biostat. 6, Article 7 (2010).
Anderson, K. M., Odell, P. M., Wilson, P. W. & Kannel, W. B. Cardiovascular disease risk profiles. Am. Heart J. 121, 293–298 (1991).
Conroy, R. M. et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur. Heart J. 24, 987–1003 (2003).
Pencina, M. J., D'Agostino, R. B. Sr, Larson, M. G., Massaro, J. M. & Vasan, R. S. Predicting the 30-year risk of cardiovascular disease: the Framingham Heart Study. Circulation 119, 3078–3084 (2009).
Parikh, N. I. et al. A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study. Ann. Intern. Med. 148, 102–110 (2008).
Schnabel, R. B. et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet 373, 739–745 (2009).
Steyerberg, E. W. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating (Springer, 2009).
Hlatky, M. A. et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 119, 2408–2416 (2009).
McGeechan, K., Macaskill, P., Irwig, L., Liew, G. & Wong, T. Y. Assessing new biomarkers and predictive models for use in clinical practice: a clinician's guide. Arch. Intern. Med. 168, 2304–2310 (2008).
Pencina, M. J., D'Agostino, R. B. Sr, D'Agostino, R. B. Jr & Vasan, R. S. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat. Med. 27, 157–172 (2008).
Pepe, M. S. et al. Integrating the predictiveness of a marker with its performance as a classifier. Am. J. Epidemiol. 167, 362–368 (2008).
Mihaescu, R. et al. Improvement of risk prediction by genomic profiling: reclassification measures versus the area under the receiver operating characteristic curve. Am. J. Epidemiol. 172, 353–361 (2010).
Goldstein, D. B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009).
Schnabel, R. B., Baccarelli, A., Lin, H., Ellinor, P. T. & Benjamin, E. J. Next steps in cardiovascular disease genomic research—sequencing, epigenetics, and transcriptomics. Clin. Chem. 58, 113–126 (2012).
Tan, P.-N., Steinbach, M. & Kumar, V. Introduction to Data Mining (Pearson Education, 2006).
Longstreth, W. T. Jr et al. Cluster analysis and patterns of findings on cranial magnetic resonance imaging of the elderly: the Cardiovascular Health Study. Arch. Neurol. 58, 635–640 (2001).
Fukuoka, Y., Lindgren, T. G., Rankin, S. H., Cooper, B. A. & Carroll, D. L. Cluster analysis: a useful technique to identify elderly cardiac patients at risk for poor quality of life. Qual. Life Res. 16, 1655–1663 (2007).
Peters, R. M., Shanies, S. A. & Peters, J. C. Fuzzy cluster analysis of positive stress tests, a new method of combining exercise test variables to predict extent of coronary artery disease. Am. J. Cardiol. 76, 648–651 (1995).
Magidson, J. & Vermunt, J. K. Latent class models for clustering: a comparison with K-means. Can. J. Marketing Res. 20, 37–44 (2002).
Skrondal, A. & Rabe-Hesketh, S. Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models (Monographs on Statistics and Applied Probability) (Chapman & Hall, 2004).
Muthén, B. Beyond SEM: general latent variable modeling. Behaviometrika 29, 81–117 (2002).
Cheng, S. et al. Correlates of echocardiographic indices of cardiac remodeling over the adult life course: longitudinal observations from the Framingham Heart Study. Circulation 122, 570–578 (2010).
Lieb, W. et al. Longitudinal tracking of left ventricular mass over the adult life course: clinical correlates of short- and long-term change in the Framingham Offspring Study. Circulation 119, 3085–3092 (2009).
Snijders, T. A. B. & Bosker, E. J. Multilevel Analysis: An Introduction To Basic and Advanced Multilevel Modeling (Sage Publications, 1999).
Kerner, B. & Muthen, B. O. Growth mixture modelling in families of the Framingham Heart Study. BMC Proc. 3 (Suppl. 7), S114 (2009).
Muthen, B. et al. General growth mixture modeling for randomized preventive interventions. Biostatistics 3, 459–475 (2002).
Muthen, B. & Brown, H. C. Estimating drug effects in the presence of placebo response: causal inference using growth mixture modeling. Stat. Med. 28, 3363–3385 (2009).
Shiroma, E. J. & Lee, I. M. Physical activity and cardiovascular health: lessons learned from epidemiological studies across age, gender, and race/ethnicity. Circulation 122, 743–752 (2010).
Luo, W. et al. Interaction of current alcohol consumption and abdominal obesity on hypertension risk. Physiol. Behav. http://dx.doi.org/10.1016/j.physbeh.2012.10.004.
Cao, J. J. et al. Association of carotid artery intima–media thickness, plaques, and C-reactive protein with future cardiovascular disease and all-cause mortality: the Cardiovascular Health Study. Circulation 116, 32–38 (2007).
Ndiaye, N. C., Azimi Nehzad, M., El Shamieh, S., Stathopoulou, M. G. & Visvikis-Siest, S. Cardiovascular diseases and genome-wide association studies. Clin. Chim. Acta 412, 1697–1701 (2011).
Greenland, S. Interactions in epidemiology: relevance, identification, and estimation. Epidemiology 20, 14–17 (2009).
VanderWeele, T. J. Sufficient cause interactions and statistical interactions. Epidemiology 20, 6–13 (2009).
Mitchell, T. M. Machine Learning (McGraw-Hill Higher Education, 1997).
Wang, Y. et al. A classification approach for risk prognosis of patients on mechanical ventricular assistance. Proc. Int. Conf. Mach. Learn. Appl. 12, 293–298 (2010).
Vepa, J. Classification of heart murmurs using cepstral features and support vector machines. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 2539–2542 (2009).
Rodin, A., Mosley, T. H. Jr, Clark, A. G., Sing, C. F. & Boerwinkle, E. Mining genetic epidemiology data with Bayesian networks application to APOE gene variation and plasma lipid levels. J. Comput. Biol. 12, 1–11 (2005).
Vapnik, V. N. The Nature of Statistical Learning Theory (Springer, 1995).
Witten, I. H., Frank, E. & Hall, M. A. Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn (Morgan Kaufmann, 2011).
Klein, T. E. et al. Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med. 360, 753–764 (2009).
Xu, R. & Wunsch, D. 2nd Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 645–678 (2005).
Ng, A. Y. & Jordan, M. I. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Advances in Neural Information Processing Systems 2, 841–848 (2002).
Qazi, M. et al. Automated heart wall motion abnormality detection from ultrasound images using Bayesian networks. Proc. IJCAI 519–525 (2007).
Kim, J. et al. A novel data mining approach to the identification of effective drugs or combinations for targeted endpoints—application to chronic heart failure as a new form of evidence-based medicine. Cardiovasc. Drugs Ther. 18, 483–489 (2004).
Strandberg, T. E. Lipid-lowering drugs and heart failure: where do we go after the statin trials? Curr. Opin. Cardiol. 25, 385–393 (2010).
Kim, J. et al. Impact of blockade of histamine H2 receptors on chronic heart failure revealed by retrospective and prospective randomized studies. J. Am. Coll. Cardiol. 48, 1378–1384 (2006).
Liao, Y. et al. Control of plasma glucose with α-glucosidase inhibitor attenuates oxidative stress and slows the progression of heart failure in mice. Cardiovasc. Res. 70, 107–116 (2006).
Acknowledgements
This work is part of the research project Greifswald Approach to Individualized Medicine (GANI_MED). The GANI_MED consortium is funded by the Federal Ministry of Education and Research (03IS2061A/C) and the Ministry of Cultural Affairs of the Federal State of Mecklenburg–West Pomerania, Germany. Matthias Schwab is supported by the Deutsche Forschungsgemeinschaft (Grant SCHW 858/1-1). Henry Völzke, Marcus Dörr, and Stephan B. Felix are also members of the German Center for Cardiovascular Research at the partner site in Greifswald, Germany.
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H. Völzke, C. O. Schmidt, S. E. Baumeister, T. Ittermann, G. Fung, H. E. Meyer zu Schwabedissen, and M. Dörr researched data for the article and wrote the manuscript. M. Schwab and W. Lieb also researched data for the article. All the authors contributed substantially to discussion of its content, and reviewed/edited the manuscript before submission.
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Völzke, H., Schmidt, C., Baumeister, S. et al. Personalized cardiovascular medicine: concepts and methodological considerations. Nat Rev Cardiol 10, 308–316 (2013). https://doi.org/10.1038/nrcardio.2013.35
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DOI: https://doi.org/10.1038/nrcardio.2013.35
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