1. Utility of Biomarkers to Enhance CVD Risk Prediction James de Lemos, MD

2. Disclosures Grant support: Biosite, Inc., and Roche Diagnostics Consulting income: Biosite, Tethys Biomedical, and Ortho Clinical Diagnostics

3. Large Number of CV Events in Individuals not at High Risk Proportion in each risk category NHANES 1999-2002 76% 11% 13% Ajani UA et al, JACC 2006;48:1177 10-year CHD Events (Millions) Estimated Number of CV Events Low-risk Inter-risk High-risk

4. Identification of Susceptible Individuals for Targeted Intervention Current Algorithms Imaging Genetic Markers Bio markers

5. Variable 22 RR per 2SDC statistic Age3964.00.70 + SBP1492.50.74 + Smoking1212.90.73 + HDL860.50.73 + TC331.60.72 + LDL291.50.71 Framingham0.78 - TC391.60.77 - HDL640.60.77 - Smoking1002.60.76 - SBP1142.20.76 - Age2573.20.73 How Well do the Framingham Variables Work? Cook NR. Circulation 2007;115:928-35.

6. Variable 22 RR per 2SDC statistic Age3964.00.70 + SBP1492.50.74 + Smoking1212.90.73 + HDL860.50.73 + TC331.60.72 + LDL291.50.71 Framingham0.78 - TC391.60.77 - HDL640.60.77 - Smoking1002.60.76 - SBP1142.20.76 - Age2573.20.73 How Well do the Framingham Variables Work? Cook NR. Circulation 2007;115:928-35.

7. Are Individual Biomarkers any Better than the Risk Factors? Total Cholesterol Smoking Systolic BP CRP ESR Von Willebrand factor 124 Adjusted OR for CHD Danesh J et al. NEJM 2004;350:1387-1397 AUC 0.61 0.63 0.64 0.65 0.66 2459 CHD case, 3969 controls- 20 year f/u Reykjavic Study

8. HOPE Study: Do Multiple Inflammatory Markers Improve Risk Prediction? Blankenberg, S. et al. Circulation 2006;114:201-208

9. Should the debate be refocused from CRP to NT-proBNP? GroupEndpointC-statistic Base Model C-statistic With NT-BNP NRI Int Risk Only MenCV Events0.660.699.2%23% Heart Failure0.700.78 WomenCV Events0.730.7613.3%34% Heart Failure0.770.81 Rotterdam study n=5063; mean age 68 Rutten JHW. et al. Hypertension 2010;55:785-91

10. Wallace TW Circulation 2006; 113:1958-65 The Dallas Heart Study Troponin Elevation in the General Population 0.7% prevalence of cTnT > 0.01 mg/L 20.4 Chronic Kidney Disease 5.3 Congestive Heart Failure 5.4 LV Hypertrophy 4.6 Diabetes Odds Ratio Risk Determinant < mean 1+ SD 2+ SD 3+SD % with Elevated cTnT LV Mass

11. hs-cTnT in Chronic CAD > detection limit (0.001 μg/L) in 3594 patients (97.7%) Omland T, de Lemos JA et al. NEJM 2009; 361(26):2538-47

12. CV Mortality Omland T, de Lemos JA et al. NEJM 2009; 361(26):2538-47

13. Additional Utility of Multiple Biomarkers for Prediction of Death: FHS BiomarkerAdj HR Death per 1 SD BNP1.40 CRP1.39 Urine Alb/Cr1.22 Homocysteine1.20 Renin1.17 SCORE4.08* * HR for highest quintile v. lowest 2 quintiles 0.80 0.82 ROC Curves for Death Sensitivity 1-Specificity Wang TJ et al. NEJM 2006;355:2631

14. Multiple Biomarkers for Prediction of CV Death in Older Adults VariablesC statisticP value Established risk factors0.66Ref + cTnI0.720.002 + NT-proBNP0.75<0.001 + cystatin C0.690.07 + CRP0.690.07 + all biomarkers0.77<0.001 Zethelius B et al. N Engl J Med 2008;358:2107-2116

15. CAC and Coronary Events: The Multiethnic Study of Atherosclerosis Years to Event Cumulative Incidence Major CV Events (%) Coronary artery calcium score AUC RF RF + CAC 0.79 0.83 Detrano R et al. NEJM 2008;358:1336

16. Reclassification with CAC Scanning GroupNRI (events)NRI (no events) Total NRI Overall cohort 23%2%25% Intermed risk 29%26%55% MESA Study n=6813; mean age 62 Polonsky JAMA 2010;303:1610-16

17. An Integrated Strategy Multiple Biomarkers Non-redundant pathobiology Intermediate RiskLow Risk

18. Understanding noncardiac sources of variation “cardiac” biomarkers A prerequisite to cardiac screening

19. Relationship of CRP to BMI, Race and Sex BMI (kg/m 2 ) Median CRP (mg/L) * p<0.001 each category ** p=0.001 interaction BMI X sex Spearman ρ p-value White Men0.32<0.001 Black Men0.29<0.001 White Women0.58<0.001 Black Women0.51<0.001 Khera et al. JACC 2005;46:464-9

20. Major noncardiac sources of variation in natriuretic peptides Age Sex Body composition Sex hormones Renal function

21. Changing Targets for Biomarkers and Imaging? Budoff et al Circ 2006;114:1761-1791 Greenland et al Circ 2007;115:402-426 High Risk Intermediate Risk Low Risk ASA + LDL <100 ASA + LDL <70 CAC ≥ 300 Elevated Biomarker No ASA No Statin ?ASA Statin + CAC ≥ 100 ↑ biomarker

22. Conclusions (1) Standard risk factors alone or in combination do not predict global risk well enough –Too many events in too many lower risk individuals –Modest screening performance Individual biomarkers do not markedly improve risk prediction –Markers of existing disease (including imaging tools) more promising than nonspecific inflammatory markers Combining individual markers that reflect distinct biological pathways is a promising strategy, with several caveats –Combining multiple mediocre markers will not work –Not close to ready for clinical application

23. Conclusions (2) The bar should be extremely high before new tests are incorporated into clinical practice Need to understand noncardiac sources of variation before adopting for clinical use We must guard against –complacency (with existing tools) and – nihilism (with emerging tools)

24. Moving forward Real progress will require –Collaboration on a massive scale –Creative approaches to the problems of integrating complex data –Continued development and validation of clinically relevant metrics to evaluate novel tests –Integration of diagnostic and therapeutic research agendas