Precision medicine in cardiology

Nature Reviews Cardiology

Volumn 13, Issue 10, 2016, Pages 591-602

  Antman, Elliott M ^a   Loscalzo, Joseph ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIOLOGY; CLINICAL RESEARCH; CLINICAL TRIAL (TOPIC); HEALTH STATUS; HUMAN; HYPERTENSION; PATIENT CARE; PERSONALIZED MEDICINE; PRIORITY JOURNAL; REVIEW; CARDIOVASCULAR DISEASE; INTEGRATED HEALTH CARE SYSTEM; MORTALITY; PATHOPHYSIOLOGY; PROCEDURES; PROGNOSIS; TRENDS;

CARDIOLOGY; CARDIOVASCULAR DISEASES; DELIVERY OF HEALTH CARE, INTEGRATED; HUMANS; PRECISION MEDICINE; PROGNOSIS;

EID: 84976448008     PISSN: 17595002     EISSN: 17595010     Source Type: Journal    
DOI: 10.1038/nrcardio.2016.101     Document Type: Review

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47. James, P A., et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 311 507-520 2014).
48. Laragh, J. H. Vasoconstriction-volume analysis for understanding and treating hypertension: the use of renin and aldosterone profiles. Am. J. Med. 55, 261-274 (1973).
49. Roychowdhury, S., & Chinnaiyan, A. M. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J. Clin. 66, 75-88 (2016).
50. Wright, J. T. Jr et al. A randomized trial of intensive versus standard blood-pressure control. N. Engl. J. Med. 373, 2103-2116 (2015).
51. Ahmad, T., et al. Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J. Am. Coll. Cardiol. 64, 1765-1774 (2014).
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54. Khoury, M. J., Gwinn, M. L., Glasgow, R. E., & Kramer, B. S. A population approach to precision medicine. Am. J. Prev. Med. 42, 639-645 (2012).
55. Lewington, S., et al. The burden of hypertension and associated risk for cardiovascular mortality in China. JAMA Intern. Med. 176, 524-532 (2016).
56. Barabasi, A. L., Gulbahce, N., & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56-68 (2011).
57. Chan, S. Y., & Loscalzo, J. The emerging paradigm of network medicine in the study of human disease. Circ. Res. 111, 359-374 (2012).
58. Loscalzo, J., & Barabasi, A. L. Systems biology and the future of medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 619-627 (2011).
59. Parikh, V. N., et al. MicroRNA 21 integrates pathogenic signaling to control pulmonary hypertension: results of a network bioinformatics approach. Circulation 125, 1520-1532 (2012).
60. Sharma, A., et al. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum. Mol. Genet. 24, 3005-3020 (2015).
61. Ji, W., et al. Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat. Genet. 40, 592-599 (2008).
62. Leow, M. K. Environmental origins of hypertension: phylogeny, ontogeny and epigenetics. Hypertens. Res. 38, 299-307 (2015).
63. Zhang, W. Epigenetics of epithelial Na+ channel dependent sodium uptake and blood pressure regulation. World J. Nephrol. 4, 363-366 (2015).
64. Scott, R. A., et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci. Transl. Med. 8, 341ra76 (2016).
65. Bress, A. P., et al. Generalizability of SPRINT results to the U.S. adult population. J. Am. Coll. Cardiol. 67, 463-472 (2016).
66. Gradman, A. H. SPRINT: to whom do the results apply?. J. Am. Coll. Cardiol. 67, 473-475 (2016).
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71. Sheiner, L. B. Learning versus confirming in clinical drug development. Clin. Pharmacol. Ther. 61, 275-291 (1997).
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82. No authors listed.] ADAPTABLE, the aspirin study-A patient-centered trial. Adaptable http://theaspirinstudy.org (2016).
83. Choudhry, N. K., et al. Full coverage for preventive medications after myocardial infarction. N. Engl. J. Med. 365, 2088-2097 (2011).
84. Ogedegbe, G., et al. Counseling African Americans to control hypertension: cluster-randomized clinical trial main effects. Circulation 129, 2044-2051 (2014).
85. Brooks, G. C., et al. Accuracy and usability of a self administered 6 minute walk test smartphone application. Circ. Heart Fail 8, 905-913 (2015).
86. Jessup, M. Neprilysin inhibition-A novel therapy for heart failure. N. Engl. J. Med. 371, 1062-1064 (2014).
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88. Muller, H., Reihs, R., Zatloukal, K., & Holzinger, A. Analysis of biomedical data with multilevel glyphs. BMC Bioinformatics 15 (Suppl. 6), S5 (2014).
89. Sparrow, B., Liu, J., & Wegner, D. M. Google effects on memory: cognitive consequences of having information at our fingertips. Science 333, 776-778 (2011).
90. Schnohr, P., et al. Ranking of psychosocial and traditional risk factors by importance for coronary heart disease: the Copenhagen City Heart Study. Eur. Heart J. 36, 1385-1393 (2015).
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93. Mogensen, J., et al. The current role of next-generation DNA sequencing in routine care of patients with hereditary cardiovascular conditions: a viewpoint paper of the European Society of Cardiology working group on myocardial and pericardial diseases and members of the European Society of Human Genetics. Eur. Heart J. 36, 1367-1370 (2015).
94. Lindsey, M. L., et al. Transformative impact of proteomics on cardiovascular health and disease: a Scientific Statement from the American Heart Association. Circulation 132, 852-872 (2015).
95. Shah, S. H., & Newgard, C. B. Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease. Circ. Cardiovasc. Genet. 8, 410-419 (2015).
96. Wende, A. R. Post-Translational modifications of the cardiac proteome in diabetes and heart failure. Proteomics Clin. Appl. 10, 25-38 (2016).
97. Fox, C. S., et al. Future translational applications from the contemporary genomics era: a Scientific Statement from the American Heart Association. Circulation 131, 1715-1736 (2015).
98. Altman, R. B., & Ashley, E. A. Using ?big data? to dissect clinical heterogeneity. Circulation 131, 232-233 (2015).
99. Wang, R. S., Maron, B. A., & Loscalzo, J. Systems medicine: evolution of systems biology from bench to bedside. Wiley Interdiscip. Rev. Syst. Biol. Med. 7, 141-161 (2015).
100. Calcagno, C., Mulder, W. J., Nahrendorf, M., & Fayad, Z. A. Systems biology and noninvasive imaging of atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 36, e1-e8 (2016).
101. Peters, S. G., & Khan, M. A. Electronic health records: current and future use. J. Comp. Eff. Res. 3, 515-522 (2014).
102. Hernandez, A. F., Fleurence, R. L., & Rothman, R. L. The ADAPTABLE Trial and PCORnet: shining light on a new research paradigm. Ann. Intern. Med. 163, 635-636 (2015).
103. Contreras, J. L., & Reichman, J. H. Sharing by design: data and decentralized commons. Science 350, 1312-1314 (2015).
104. Thornquist, E., & Kirkengen, A. L. The quantified self: closing the gap between general knowledge and particular case?. J. Eval. Clin. Pract. 21, 398-403 (2015).
105. Swan, M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1, 85-99 (2013).
106. Pfiffner, P B., Pinyol, I., Natter, M. D., & Mandl, K. D. C3 PRO: connecting ResearchKit to the health system using i2b2 and FHIR. PLoS ONE 11 e0152722 2016).
107. Scruggs, S. B., et al. Harnessing the heart of big data. Circ. Res. 116, 1115-1119 (2015).
108. Mooney, S. J., Westreich, D. J., & El Sayed, A. M. Commentary: epidemiology in the era of big data. Epidemiology 26, 390-394 (2015).
109. Shah, S. H., et al. Opportunities for the cardiovascular community in the Precision Medicine Initiative. Circulation 133, 226-231 (2016).
110. Snyderman, R., & Drake, C. D. Personalized health care: unlocking the potential of genomic and precision medicine. J. Precision Med. 1, 38-41 (2015).
111. Naylor, S. What's in a name?. The evolution of ?P Medicine?. J. Precision Med. 1, 15-29 (2015).
112. Stanley, K. Design of randomized controlled trials. Circulation 115, 1164-1169 (2007).
113. Moye, L. Statistical methods for cardiovascular researchers. Circ. Res. 118, 439-453 (2016).
114. Blaus, A., et al. Personalized cardiovascular medicine today: a Food and Drug Administration/Center for Drug Evaluation and Research perspective. Circulation 132, 1425-1432 (2015).
115. Lagakos, S. W. The challenge of subgroup analyses-reporting without distorting. N. Engl. J. Med. 354, 1667-1669 (2006).
116. Spivack, S. B., Bernheim, S. M., Forman, H. P., Drye, E. E., & Krumholz, H. M. Hospital cardiovascular outcome measures in federal pay-for-reporting and pay-for-performance programs: a brief overview of current efforts. Circ. Cardiovasc. Qual. Outcomes 7, 627-633 (2014).
117. Loscalzo, J., Kohane, I., & Barabasi, A. L. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol. 3, 124 (2007).
118. Menche, J., et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).
119. Precision Medicine Initiative (PMI) Working Group The Precision Medicine Initiative cohort program-building a research foundation for 21st century medicine. https://www.nih.gov/sites/default/files/research-Training/initiatives/pmi/pmi-working-group-report-20150917-2.pdf (2015).
120. Myers, M. B. Targeted therapies with companion diagnostics in the management of breast cancer: current perspectives. Pharmgenomics Pers. Med. 9, 7-16 (2016).
121. Kearney, P. M., Whelton, M., Reynolds, K., Whelton, P. K., & He, J. Worldwide prevalence of hypertension: a systematic review. J. Hypertens. 22, 11-19 (2004).
122. Schieb, L. J., et al. Vital signs: avoidable deaths from heart disease, stroke, and hypertensive disease-United States, 2001-2010. MMWR Morb. Mortal. Wkly Rep. 62, 721-727 (2013).
123. James, P A., et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 311 507-520 2014).
124. Laragh, J. H. Vasoconstriction-volume analysis for understanding and treating hypertension: the use of renin and aldosterone profiles. Am. J. Med. 55, 261-274 (1973).
125. Roychowdhury, S., & Chinnaiyan, A. M. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J. Clin. 66, 75-88 (2016).
126. Wright, J. T. Jr et al. A randomized trial of intensive versus standard blood-pressure control. N. Engl. J. Med. 373, 2103-2116 (2015).
127. Ahmad, T., et al. Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J. Am. Coll. Cardiol. 64, 1765-1774 (2014).
128. Hawgood, S., Hook-Barnard, I. G., O'Brien, T. C., & Yamamoto, K. R. Precision medicine: beyond the inflection point. Sci. Transl. Med. 7, 300ps17 (2015).
129. Ayer, J., Charakida, M., Deanfield, J. E., & Celermajer, D. S. Lifetime risk: childhood obesity and cardiovascular risk. Eur. Heart J. 36, 1371-1376 (2015).
130. Khoury, M. J., Gwinn, M. L., Glasgow, R. E., & Kramer, B. S. A population approach to precision medicine. Am. J. Prev. Med. 42, 639-645 (2012).
131. Lewington, S., et al. The burden of hypertension and associated risk for cardiovascular mortality in China. JAMA Intern. Med. 176, 524-532 (2016).
132. Barabasi, A. L., Gulbahce, N., & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56-68 (2011).
133. Chan, S. Y., & Loscalzo, J. The emerging paradigm of network medicine in the study of human disease. Circ. Res. 111, 359-374 (2012).
134. Loscalzo, J., & Barabasi, A. L. Systems biology and the future of medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 619-627 (2011).
135. Parikh, V. N., et al. MicroRNA 21 integrates pathogenic signaling to control pulmonary hypertension: results of a network bioinformatics approach. Circulation 125, 1520-1532 (2012).
136. Sharma, A., et al. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum. Mol. Genet. 24, 3005-3020 (2015).
137. Ji, W., et al. Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat. Genet. 40, 592-599 (2008).
138. Leow, M. K. Environmental origins of hypertension: phylogeny, ontogeny and epigenetics. Hypertens. Res. 38, 299-307 (2015).
139. Zhang, W. Epigenetics of epithelial Na+ channel dependent sodium uptake and blood pressure regulation. World J. Nephrol. 4, 363-366 (2015).
140. Scott, R. A., et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci. Transl. Med. 8, 341ra76 (2016).
141. Bress, A. P., et al. Generalizability of SPRINT results to the U.S. adult population. J. Am. Coll. Cardiol. 67, 463-472 (2016).
142. Gradman, A. H. SPRINT: to whom do the results apply?. J. Am. Coll. Cardiol. 67, 473-475 (2016).
143. Food and Drug Administration. Guidance for industry. Enrichment strategies for clinical trials to support approval of human drugs and biological products. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm332181.pdf (2012).
144. Jackson, N., et al. Improving clinical trials for cardiovascular diseases: a position paper from the Cardiovascular Round Table of the European Society of Cardiology. Eur. Heart J. 37, 747-754 (2016).
145. Mehta, C., et al. Optimizing trial design: sequential, adaptive, and enrichment strategies. Circulation 119, 597-605 (2009).
146. Antman, E., Weiss, S., & Loscalzo, J. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 4, 367-383 (2012).
147. Sheiner, L. B. Learning versus confirming in clinical drug development. Clin. Pharmacol. Ther. 61, 275-291 (1997).
148. Kaufman, A. L., et al. Evidence for clinical implementation of pharmacogenomics in cardiac drugs. Mayo Clin. Proc. 90, 716-729 (2015).
149. FDA-NIH Biomarker Working Group BEST (Biomarkers, 2016).
150. Wang, T. J., et al. Multiple biomarkers and the risk of incident hypertension. Hypertension 49, 432-438 (2007).
151. El Shamieh, S., & Visvikis-Siest, S. Genetic biomarkers of hypertension and future challenges integrating epigenomics. Clin. Chim. Acta 414, 259-265 (2012).
152. Zhang, W., et al. Identification of hypertension predictors and application to hypertension prediction in an urban Han Chinese population: a longitudinal study, 2005-2010. Prev. Chronic Dis. 12, E184 (2015).
153. Nerurkar, S S., et al. P38 MAPK inhibitors suppress biomarkers of hypertension end-organ damage, osteopontin and plasminogen activator inhibitor 1. Biomarkers 12 87-112 2007).
154. Antoniou, M., Jorgensen, A. L., & Kolamunnage Dona, R. Biomarker-guided adaptive trial designs in phase II and phase III: a methodological review. PLoS ONE 11, e0149803 (2016).
155. Antman, E. M., & Harrington, R. A. Transforming clinical trials in cardiovascular disease: mission critical for health and economic well-being. JAMA 308, 1743-1744 (2012).
156. Danciu, I., et al. Secondary use of clinical data: the Vanderbilt approach. J. Biomed. Informat. 52, 28-35 (2014).
157. Patient-Centered Outcomes Research Institute (PCORI) Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness (ADAPTABLE). http://www.pcori.org/research-results/2015/aspirin-dosing-patient-centric-Trial-Assessing-benefits-And-long-Term (2016).
158. [No authors listed.] ADAPTABLE, the aspirin study-A patient-centered trial. Adaptable http://theaspirinstudy.org (2016).
159. Choudhry, N. K., et al. Full coverage for preventive medications after myocardial infarction. N. Engl. J. Med. 365, 2088-2097 (2011).
160. Ogedegbe, G., et al. Counseling African Americans to control hypertension: cluster-randomized clinical trial main effects. Circulation 129, 2044-2051 (2014).
161. Brooks, G. C., et al. Accuracy and usability of a self administered 6 minute walk test smartphone application. Circ. Heart Fail 8, 905-913 (2015).
162. Jessup, M. Neprilysin inhibition-A novel therapy for heart failure. N. Engl. J. Med. 371, 1062-1064 (2014).
163. Goff, D. C. Jr et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 129, S49-73 (2014).
164. Muller, H., Reihs, R., Zatloukal, K., & Holzinger, A. Analysis of biomedical data with multilevel glyphs. BMC Bioinformatics 15 (Suppl. 6), S5 (2014).
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