Korean Circulation Journal

The Korean Society of Cardiology (대한심장학회)
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Clinical Utility of Novel Biomarkers in the Prediction of Coronary Heart Disease

  • Kim, Hyeon-Chang (Department of Preventive Medicine, Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine)
  • Published : 2012.04.30
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Abstract

Coronary heart disease (CHD) is a significant cause of morbidity and mortality worldwide. Many risk prediction models have been developed in an effort to assist clinicians in risk assessment and the prevention of CHD. However, it is unclear whether the existing CHD prediction tools can improve clinical performance, and recently, there has been a lot of effort being made to improve the accuracy of the prediction models. A large number of novel biomarkers have been identified to be associated with cardiovascular risk, and studied with the goal of improving the accuracy and clinical utility of CHD risk prediction. Yet, controversy still remains with regard to the utility of novel biomarkers in CHD risk assessment, and in finding the best statistical methods to assess the incremental value of the biomarkers. This article discusses the statistical approaches that can be used to evaluate the predictive values of new biomarkers, and reviews the clinical utility of novel biomarkers in CHD prediction, specifically in the Korean population.

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References

  1. Lloyd-Jones DM. Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation 2010;121:1768-77. https://doi.org/10.1161/CIRCULATIONAHA.109.849166
  2. Kim HC, Greenland P, Rossouw JE, et al. Multimarker prediction of coronary heart disease risk: the Women's Health Initiative. J Am Coll Cardiol 2010;55:2080-91. https://doi.org/10.1016/j.jacc.2009.12.047
  3. Wood AM, Greenland P. Evaluating the prognostic value of new cardiovascular biomarkers. Dis Markers 2009;26:199-207. https://doi.org/10.1155/2009/412947
  4. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837-47. https://doi.org/10.1161/01.CIR.97.18.1837
  5. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (adult treatment panel III). Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation 2002;106:3143-421.
  6. Wood D, De Backer G, Faergeman O, Graham I, Mancia G, Pyorala K. Prevention of coronary heart disease in clinical practice: recommendations of the Second Joint Task Force of European and other Societies on Coronary Prevention. Atherosclerosis 1998;140:199-270. https://doi.org/10.1016/S0021-9150(98)90209-X
  7. De Backer G, Ambrosioni E, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice: Third Joint Task Force of European and other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of eight societies and by invited experts). Atherosclerosis 2004;173:381-91.
  8. Grundy SM, Cleeman JI, Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. J Am Coll Cardiol 2004;44:720-32. https://doi.org/10.1016/j.jacc.2004.07.001
  9. D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P; CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 2001;286:180-7. https://doi.org/10.1001/jama.286.2.180
  10. Sheridan SL, Crespo E. Does the routine use of global coronary heart disease risk scores translate into clinical benefits or harms? A systematic review of the literature. BMC Health Serv Res 2008;8:60. https://doi.org/10.1186/1472-6963-8-60
  11. Cushman M, Lemaitre RN, Kuller LH, et al. Fibrinolytic activation markers predict myocardial infarction in the elderly: the Cardiovascular Health Study. Arterioscler Thromb Vasc Biol 1999;19:493-8. https://doi.org/10.1161/01.ATV.19.3.493
  12. Mangoni AA, Jackson SH. Homocysteine and cardiovascular disease: current evidence and future prospects. Am J Med 2002;112:556-65. https://doi.org/10.1016/S0002-9343(02)01021-5
  13. Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med 2002;347:1557-65. https://doi.org/10.1056/NEJMoa021993
  14. Chambless LE, Folsom AR, Sharrett AR, et al. Coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC) Study. J Clin Epidemiol 2003;56:880-90. https://doi.org/10.1016/S0895-4356(03)00055-6
  15. Koenig W, Lowel H, Baumert J, Meisinger C. C-reactive protein modulates risk prediction based on the Framingham score: implications for future risk assessment: results from a large cohort study in southern Germany. Circulation 2004;109:1349-53. https://doi.org/10.1161/01.CIR.0000120707.98922.E3
  16. Danesh J, Wheeler JG, Hirschfield GM, et al. C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease. N Engl J Med 2004;350:1387-97. https://doi.org/10.1056/NEJMoa032804
  17. Wang TJ, Larson MG, Levy D, et al. Plasma natriuretic peptide levels and the risk of cardiovascular events and death. N Engl J Med 2004;350:655-63. https://doi.org/10.1056/NEJMoa031994
  18. Danesh J, Lewington S, Thompson SG, et al. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. JAMA 2005;294:1799-809. https://doi.org/10.1001/jama.294.14.1799
  19. Shlipak MG, Sarnak MJ, Katz R, et al. Cystatin C and the risk of death and cardiovascular events among elderly persons. N Engl J Med 2005;352:2049-60. https://doi.org/10.1056/NEJMoa043161
  20. Zethelius B, Johnston N, Venge P. Troponin I as a predictor of coronary heart disease and mortality in 70-year-old men: a community-based cohort study. Circulation 2006;113:1071-8. https://doi.org/10.1161/CIRCULATIONAHA.105.570762
  21. Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med 2006;355:2631-9. https://doi.org/10.1056/NEJMoa055373
  22. Folsom AR, Chambless LE, Ballantyne CM, et al. An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the atherosclerosis risk in communities study. Arch Intern Med 2006;166:1368-73. https://doi.org/10.1001/archinte.166.13.1368
  23. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 2007;297:611-9. https://doi.org/10.1001/jama.297.6.611
  24. Zethelius B, Berglund L, Sundström J, et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N Engl J Med 2008;358:2107-16. https://doi.org/10.1056/NEJMoa0707064
  25. McGeechan K, Macaskill P, Irwig L, Liew G, Wong TY. Assessing new biomarkers and predictive models for use in clinical practice: a clinician's guide. Arch Intern Med 2008;168:2304-10. https://doi.org/10.1001/archinte.168.21.2304
  26. Blumenthal RS, Foody JM, Wong ND, Braunwald E. Preventive Cardiology: a Companion to Braunwald's Heart Disease. Philadelphia, PA: Elsevier Saunders;2011.
  27. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115:928-35 https://doi.org/10.1161/CIRCULATIONAHA.106.672402
  28. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York: John Wiley & Sons, Inc.;2000.
  29. Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-72. https://doi.org/10.1002/sim.2929
  30. Brotman DJ, Walker E, Lauer MS, O'Brien RG. In search of fewer independent risk factors. Arch Intern Med 2005;165:138-45. https://doi.org/10.1001/archinte.165.2.138
  31. Van der Meer IM, de Maat MP, Kiliaan AJ, van der Kuip DA, Hofman A, Witteman JC. The value of C-reactive protein in cardiovascular risk prediction: the Rotterdam Study. Arch Intern Med 2003;163:1323-8. https://doi.org/10.1001/archinte.163.11.1323
  32. Wilson PW, Nam BH, Pencina M, D'Agostino RB Sr, Benjamin EJ, O'Donnell CJ. C-reactive protein and risk of cardiovascular disease in men and women from the Framingham Heart Study. Arch Intern Med 2005;165:2473-8. https://doi.org/10.1001/archinte.165.21.2473
  33. Ridker PM, Buring JE, Cook NR, Rifai N. C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: an 8-year follow-up of 14 719 initially healthy American women. Circulation 2003;107:391-7. https://doi.org/10.1161/01.CIR.0000055014.62083.05
  34. Pischon T, Hu FB, Rexrode KM, Girman CJ, Manson JE, Rimm EB. Inflammation, the metabolic syndrome, and risk of coronary heart disease in women and men. Atherosclerosis 2008;197:392-9. https://doi.org/10.1016/j.atherosclerosis.2007.06.022
  35. May M, Lawlor DA, Brindle P, Patel R, Ebrahim S. Cardiovascular disease risk assessment in older women: can we improve on Framingham? British Women's Heart and Health prospective cohort study. Heart 2006;92:1396-401. https://doi.org/10.1136/hrt.2005.085381
  36. Everett BM, Kurth T, Buring JE, Ridker PM. The relative strength of C-reactive protein and lipid levels as determinants of ischemic stroke compared with coronary heart disease in women. J Am Coll Cardiol 2006;48:2235-42. https://doi.org/10.1016/j.jacc.2006.09.030
  37. Cook NR, Buring JE, Ridker PM. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann Intern Med 2006;145:21-9. https://doi.org/10.7326/0003-4819-145-1-200607040-00128
  38. Shah T, Casas JP, Cooper JA, et al. Critical appraisal of CRP measurement for the prediction of coronary heart disease events: new data and systematic review of 31 prospective cohorts. Int J Epidemiol 2009;38:217-31. https://doi.org/10.1093/ije/dyn217
  39. Buckley DI, Fu R, Freeman M, Rogers K, Helfand M. C-reactive protein as a risk factor for coronary heart disease: a systematic review and meta-analyses for the U.S. Preventive Services Task Force. Ann Intern Med 2009;151:483-95. https://doi.org/10.7326/0003-4819-151-7-200910060-00009
  40. Shlipak MG, Fried LF, Cushman M, et al. Cardiovascular mortality risk in chronic kidney disease: comparison of traditional and novel risk factors. JAMA 2005;293:1737-45. https://doi.org/10.1001/jama.293.14.1737
  41. Helfand M, Buckley DI, Freeman M, et al. Emerging risk factors for coronary heart disease: a summary of systematic reviews conducted for the U.S. Preventive Services Task Force. Ann Intern Med 2009;151:496-507. https://doi.org/10.7326/0003-4819-151-7-200910060-00010
  42. Jee SH, Park JW, Lee SY, et al. Stroke risk prediction model: a risk profile from the Korean study. Atherosclerosis 2008;197:318-25. https://doi.org/10.1016/j.atherosclerosis.2007.05.014
  43. Barzi F, Patel A, Gu D, et al. Cardiovascular risk prediction tools for populations in Asia. J Epidemiol Community Health 2007;61:115-21. https://doi.org/10.1136/jech.2005.044842

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