Cardiology Journal Club

1. Cardiology Journal Club Sanjay Dravid, M.D. January 17, 2006

2. MULTIPLE BIOMARKERS FOR THE PREDICTION OF FIRST MAJOR CARDIOVASCULAR EVENTS AND DEATH • Wang, Thomas J., et al. • Massachusetts General Hospital. • NEJM. Volume 355(25), 21 December 2006, pp 2631-2639.

3. Overview • To evaluate the incremental usefulness of multiple biomarkers from various pathways. • Established risk factors, including smoking, htn, DM, and dyslipidemia. • Significant interest in new biomarkers for risk stratification of ambulatory persons.

4. Novel Approach • Many individual biomarkers have been studied. • “Multimarker” Approach • Simultaneous measurement may enhance risk stratification?

5. Outcomes Analysis • 1. Death from any cause • 2. 1st Major cardiovascular event (MI, coronary insufficiency, heart failure, and stroke. • Reviewed by a committee of three investigators

6. Study Sample • Large, community based cohort study • Participants from the sixth examination cycle (1995-1998) of the Framingham Offspring Study • IRB of Boston University Medical Center approval • Written informed consent was obtained • H & P, PE, and Lab Assessment

7. Exclusion Criteria • Serum creatinine levels greater than 2.0 mg/dL • Missing covariates • Prior event when determining outcome of major cardiovascular event • Triglycerides > 400

8. Biomarker Selection • 1. Marker of inflammation- hsCRP • 2. Markers of neurohormonal activity- BNP, aldosterone, renin, N-terminal pro-atrial natriuretic peptide • 3. Marker of thrombosis and inflammation- fibrinogen • 4. Marker of fibrinolytic potential and endothelial function- plasminogen-activator

9. Biomarker cont’d • Inhibitor type 1 • 5. Marker of thrombosis- D-dimer • 6. Marker of endotheial function and oxidant stress- homocysteine • 7. Marker of glomerular endothelial function- urinary albumin-to-creatinine ratio

11. Lab Protocol • Fasting blood and urine samples collected in morning after patient supine for ~10 minutes. Immediately centrifuged and stored at -70 degreesC. • Standardized Assay Methods

12. Statistical Analysis • Multivariable proportional-hazards model (2 sets of analyses for each outcome due to urine subgroups) • Logarithmic transformation used to normalize the distribution of biomarkers • To reduce the number of false positives from multiple testing:

13. Statistics cont’d • 1) Multivariable Cox regression model • 2) Backward elimination • 3) Construction of multimarker score • 4) Quintiles categorized • 5) Cumulative probability curves constructed by the Kaplan-Meier method for low, intermediate and high mulitmarker scores

14. Statistics cont’d • Then calculated hazard ratios for death and major cardiovascular events for the mulitmarker score groups • Adjusted for age, sex, conventional risk factors including htn, smoking, dm, etc. • “C statistic” • ROC curves

15. Statistics cont’d • Secondary Analysis adjusting for medication use • Repeated a Cox proportional-hazards model for major cardiovascular events adjusting for “nonmajor events”  angina, intermittent claudication, TIA • SAS software, version 8 (SAS Institute)

16. C Statistic • Defined as the probability of concordanc among persons who can be compared. • Estimated as the sum of concordance values divided by the number of comparable pairs. • Better able to measure discrimination than relative risk.

17. Results • Total of 3532 persons- 21 excluded for serum creatinine and 302 for missing covariates. • 10 year follow-up (median 7.4 years) 3209 available for study. • 207 (6%) died, of whom 72 were women • 169 (6%, excluding prevalent CV disease at baseline) had a major cardiovascular event, of whom 68 were women

18. Results cont’d • Biomarker panel for nine: P<0.001 for death and P=0.005 for cardiovascular events • Biomarker panel for ten (2750 persons): P<0.001 for death and P=0.04 for cardiovascular events

19. Results cont’d • Backward elimination models: final statistical model included only the following biomarkers: BNP, homocysteine, urinary albumin-to-creatinine ratio and renin for death. BNP and urinary albumin-to-creatinine ratio for major cardiovascular events.

21. Utility of Multimarker Scores • Backward elimination biomarkers selected as statistically significant were incorporated into mulitmarker scores. • Restricted to urine sample patients: 1) death from any cause, the number of events and number at risk were 172 and 2750, respectively; 2) major cardiovascular events, 133 and 2598, respectively.

25. Utility? • Persons with high multimarker scores had a risk of death four times as great and a risk of major cariovascular events almost two times as great as persons with low mulitmarker scores. • (P<0.001 and P=0.02, respectively)

26. Discussion • ~10 year study of biomarkers indicating BNP, hsCRP, homocysteine, renin, and alb/Cr ratio as most informative for predicting death, while BNP and alb/Cr ration as significant for predicting cardiovascular outcome. • Although high multimarker scores conferred greater risk for death and major cardiovascular events…

27. Conclusion • Mulitmarker scores (combination of biomarkers) add only moderately to conventional risk factors as evidenced by small changes in C statistic. • Single biomarkers may have correlation with predicting outcomes • Panel likely will not be useful or cost-effective in ambulatory setting for further risk stratification

28. Limitations • Biomarker selection: omission of lipoprotein-associated phospholipase A2 • Each individual marker not independently tested • Not a true cohort study to asses for primary prevention as “nonmajor” cardiovascular events adjusted • Adiposity or insulin resistance not taken into account

29. Summary • Biomarkers from multiple, biologically distinct pathways are associated with the risks of death and major cardiovascular events. • However, only moderately adds to conventional risk factors currently.