Multiple Biomarkers for the Prediction of First Major Cardiovascular Events and Death
Thomas J. Wang, M.D., Philimon Gona, Ph.D., Martin G. Larson, Sc.D., Geoffrey H. Tofler, M.D., Daniel Levy, M.D., Christopher Newton-Cheh, M.D., M.P.H., Paul F. Jacques, D.Sc., Nader Rifai, Ph.D., Jacob Selhub, Ph.D., Sander J. Robins, M.D., Emelia J. Benjamin, M.D., Sc.M., Ralph B. D'Agostino, Ph.D., and Ramachandran S. Vasan, M.D.
Background Few investigations have evaluated the incrementalusefulness of multiple biomarkers from distinct biologic pathwaysfor predicting the risk of cardiovascular events.
Methods We measured 10 biomarkers in 3209 participants attendinga routine examination cycle of the Framingham Heart Study: thelevels of C-reactive protein, B-type natriuretic peptide, N-terminalpro–atrial natriuretic peptide, aldosterone, renin, fibrinogen,D-dimer, plasminogen-activator inhibitor type 1, and homocysteine;and the urinary albumin-to-creatinine ratio.
Results During follow-up (median, 7.4 years), 207 participantsdied and 169 had a first major cardiovascular event. In Coxproportional-hazards models adjusting for conventional riskfactors, the following biomarkers most strongly predicted therisk of death (each biomarker is followed by the adjusted hazardratio per 1 SD increment in the log values): B-type natriureticpeptide level (1.40), C-reactive protein level (1.39), the urinaryalbumin-to-creatinine ratio (1.22), homocysteine level (1.20),and renin level (1.17). The biomarkers that most strongly predictedmajor cardiovascular events were B-type natriuretic peptidelevel (adjusted hazard ratio, 1.25 per 1 SD increment in thelog values) and the urinary albumin-to-creatinine ratio (1.20).Persons with "multimarker" scores (based on regression coefficientsof significant biomarkers) in the highest quintile as comparedwith those with scores in the lowest two quintiles had elevatedrisks of death (adjusted hazard ratio, 4.08; P<0.001) andmajor cardiovascular events (adjusted hazard ratio, 1.84; P=0.02).However, the addition of multimarker scores to conventionalrisk factors resulted in only small increases in the abilityto classify risk, as measured by the C statistic.
Conclusions For assessing risk in individual persons, the useof the 10 contemporary biomarkers that we studied adds onlymoderately to standard risk factors.
Established cardiovascular risk factors, including dyslipidemia,smoking, hypertension, and diabetes mellitus, have been incorporatedinto algorithms for risk assessment in the general population,1,2but these characteristics do not fully explain cardiovascularrisk.3,4,5 There is substantial interest in the use of newerbiomarkers to identify persons who are at risk for the developmentof cardiovascular disease and who could be targeted for preventivemeasures.6 Many individual biomarkers have been related to cardiovascularrisk in ambulatory persons, including levels of C-reactive protein,7,8B-type natriuretic peptide,9 fibrinogen,10 D-dimer,11 and homocysteine.12Measurement of several biomarkers simultaneously (the "multimarker"approach) could enhance risk stratification of ambulatory persons.We therefore evaluated the usefulness of 10 previously reportedbiomarkers for predicting death and major cardiovascular eventsin a large, community-based cohort.
Methods
Study Sample
Participants attending the sixth examination cycle (1995 through1998) of the Framingham Offspring Study were eligible for inclusionin this study. The institutional review board of Boston UniversityMedical Center approved the protocol, and participants providedwritten informed consent.
All participants provided a medical history and underwent aphysical examination and laboratory assessment of cardiovascularrisk factors. We assessed the participants for cigarette smokingand diabetes mellitus and measured blood pressure, body-massindex, total cholesterol levels, high-density lipoprotein (HDL)cholesterol levels, and serum creatinine levels. Medicationuse was recorded. For this study, we excluded persons who hadserum creatinine levels greater than 2.0 mg per deciliter (176.8µmol per liter) or missing covariates.
Biomarker Selection and Measurement
Ten biomarkers were selected because of reported associationswith death or cardiovascular events,7,9,10,12,13,14,15,16 biologicplausibility, and availability at the sixth examination cycle.We measured high-sensitivity C-reactive protein (a marker ofinflammation); B-type natriuretic peptide, N-terminal pro–atrialnatriuretic peptide, serum aldosterone, and plasma renin (markersof neurohormonal activity); fibrinogen (a marker of thrombosisand inflammation); plasminogen-activator inhibitor type 1 (amarker of fibrinolytic potential and endothelial function);D-dimer (a marker of thrombosis); homocysteine (a marker ofendothelial function and oxidant stress); and the urinary albumin-to-creatinineratio (a marker of glomerular endothelial function).
Fasting blood samples were collected in the morning, after participantshad been supine for approximately 10 minutes. Specimens wereimmediately centrifuged and stored at –70°C. The albumin-to-creatinineratio in morning urine specimens was assessed. Standard assayswere used for all biomarkers (see the Supplementary Appendix,available with the full text of this article at www.nejm.org).
Outcomes
Two outcomes were assessed for inclusion in the prediction analysis— death from any cause and major cardiovascular events.Death from any cause was assessed for all study participants.Major cardiovascular events were assessed only for those participantswho had not previously had such an event. Fatal and nonfatalmyocardial infarction, coronary insufficiency (prolonged anginawith documented electrocardiographic changes), heart failure,and stroke were classified as major cardiovascular events, whereasangina, intermittent claudication, and transient ischemic attackwere classified as "nonmajor" cardiovascular events. All suspectedmajor cardiovascular events were reviewed by a committee ofthree investigators, using previously described criteria.17
Statistical Analysis
We used multivariable proportional-hazards models to examinethe association of biomarker levels with the risks of deathand major cardiovascular events.18 For each outcome, we performedtwo sets of prespecified analyses — one that includedthe urinary albumin-to-creatinine ratio and one that did not— because urine samples were available for only a subgroupof the participants. Logarithmic transformation was performedto normalize the distribution of the biomarkers.
To reduce the number of false positive results from multipletesting, we used a sequential approach. First, we fitted a multivariableCox regression model, entering the biomarkers as a set, afterconfirming that the assumption of proportionality was met. Amultivariable P value for the set was determined with the useof a likelihood-ratio test, obtained by subtracting –2log likelihood for the larger model (clinical covariates andbiomarkers) from that for the smaller model (clinical covariatesonly). Subsequent analyses were performed if the multivariableP value was less than 0.05. Second, a parsimonious set of biomarkerswas selected with the use of backward elimination (retentionthreshold, P<0.05). Third, we used the following equationto construct a multimarker score (H) based on the biomarkerschosen from the previous step: H=(1xbiomarker A)+(2xbiomarkerB)+(3xbiomarker C), and so on, where 1, 2, and 3 denote theestimates of beta coefficients for biomarkers A, B, and C andwere obtained by fitting the multivariable Cox model for theoutcome of interest. Participants were categorized accordingto quintiles of the multimarker score, with the lowest two quintileslabeled low risk, the third and fourth quintiles labeled intermediaterisk, and the top quintile labeled high risk. Cumulative probabilitycurves were constructed for subjects with low, intermediate,and high multimarker scores with the use of the Kaplan–Meiermethod.
We then calculated hazard ratios for death and major cardiovascularevents for the low-, intermediate-, and high-risk strata ofthe multimarker score. The hazard ratios were adjusted for age,sex, and conventional risk factors, including cigarette smokingon a regular basis in the past year, blood-pressure categories(a systolic pressure below 120 mm Hg and a diastolic pressurebelow 80 mm Hg, a systolic pressure of 120 to 129 mm Hg or adiastolic pressure of 80 to 84 mm Hg, a systolic pressure of130 to 139 mm Hg or a diastolic pressure of 85 to 89 mm Hg,a systolic pressure of 140 to 159 mm Hg or a diastolic pressureof 90 to 99 mm Hg, a systolic pressure of 160 mm Hg or higheror a diastolic pressure of 100 mm Hg or higher or use of antihypertensivetherapy), total-cholesterol categories (less than 160 mg perdeciliter [4.1 mmol per liter], 160 to 199 mg per deciliter[4.1 to 5.1 mmol per liter], 200 to 239 mg per deciliter [5.2to 6.2 mmol per liter], 240 to 279 mg per deciliter [6.2 to7.2 mmol per liter], and 280 mg per deciliter [7.2 mmol perliter] or higher), HDL categories (less than 35 mg per deciliter[0.9 mmol per liter], 35 to 44 mg per deciliter, 45 to 49 mgper deciliter, 50 to 59 mg per deciliter, and 60 mg per deciliteror higher), and diabetes (fasting glucose level of 126 mg perdeciliter [7.0 mmol per liter] or higher or the use of antidiabetesmedication). Analyses also adjusted for body-mass index andserum creatinine level. A previous major cardiovascular eventwas an exclusion factor in models for major cardiovascular eventsand a covariate in models for death.
The ability to classify risk was assessed with the use of theC statistic.19 The overall C statistic is defined as the probabilityof concordance among persons who can be compared. Two subjectscan be compared if it can be determined who had a longer timeto event (time to event vs. time to event, or time to eventvs. time to censoring, if time to censoring was longer thantime to event). Subjects are considered concordant if theirpredicted event probabilities and their actual survival timesgo in the same direction; if their predicted probabilities aretied, they are considered 0.5 concordant. The C statistic isestimated as the sum of concordance values divided by the numberof comparable pairs. Also, receiver-operating-characteristic(ROC) curves were plotted for models with biomarkers and forthose without biomarkers. Because standard methods do not existfor deriving ROC curves for time-to-event data, we used occurrenceas compared with nonoccurrence of events within 5 years as theoutcome for these analyses.
In secondary analyses, we adjusted for medication use, evaluatedwhether the association of biomarkers with outcomes varied accordingto age or sex, and replaced total-cholesterol categories withlow-density lipoprotein (LDL) cholesterol categories (less than100 mg per deciliter [2.6 mmol per liter], 100 to 129 mg perdeciliter [2.6 to 3.3 mmol per liter], 130 to 159 mg per deciliter[3.4 to 4.1 mmol per liter], 160 to 189 mg per deciliter [4.1to 4.9 mmol per liter], and 190 mg per deciliter [4.9 mmol perliter] or higher).1 The Friedewald equation20 was used to estimateLDL cholesterol levels, excluding participants with triglyceridelevels of 400 mg per deciliter (4.5 mmol per liter) or higher.We also repeated a Cox proportional-hazards model for majorcardiovascular events, adjusting for previous "nonmajor" cardiovascularevents (angina, intermittent claudication, or transient ischemicattack). Analyses were performed with the use of SAS software,version 8 (SAS Institute).
Results
A total of 3532 persons attended the sixth examination cycleof the Framingham Offspring Study. Of these, 21 were excludedfor serum creatinine levels above 2.0 mg per deciliter and 302were excluded for missing covariates. Characteristics of theremaining 3209 persons who constituted the study sample areshown in Table 1. The mean age of participants at the time ofstudy enrollment was 59±10 years. Fifty-three percentof the participants were women, and 6% had prevalent major cardiovasculardisease. Median levels of the biomarkers are noted in Table 1;all biomarkers were available for all participants except theurinary albumin-to-creatinine ratio, which was available for2750 of the participants (86%).
Table 1. Baseline Characteristics of the Study Participants.
During up to 10 years of follow-up (median, 7.4 years), 207of 3209 participants (6%) died, of whom 72 were women, and 169of 3028 participants (6%, excluding 181 with prevalent cardiovasculardisease at baseline) had a major cardiovascular event, of whom68 were women. The biomarker panel was associated with bothoutcomes in models that adjusted for conventional risk factors.In analyses restricted to the nine biomarkers in blood, multivariableP values for the biomarker panel were as follows: P<0.001for death and P=0.005 for major cardiovascular events. For all10 biomarkers (including the urinary albumin-to-creatinine ratio),multivariable P values were as follows: P<0.001 for deathand P=0.04 for major cardiovascular events.
In backward-elimination models, the following five biomarkerswere retained as predictors of death in analyses restrictedto blood biomarkers: levels of C-reactive protein, N-terminalpro–atrial natriuretic peptide, homocysteine, plasma renin,and D-dimer. When the urinary albumin-to creatinine ratio wasincluded, it replaced D-dimer, and B-type natriuretic peptidereplaced N-terminal pro–atrial natriuretic peptide. Thus,the final model contained the following biomarkers: B-type natriureticpeptide level (adjusted hazard ratio, 1.40 per 1 SD incrementin the log value), C-reactive protein level (1.39), urinaryalbumin-to-creatinine ratio (1.22), homocysteine level (1.20),and renin level (1.17) (see the Supplementary Appendix).
For major cardiovascular events, two biomarkers were retainedin analyses excluding the urinary albumin-to-creatinine ratio— B-type natriuretic peptide and plasminogen-activatorinhibitor type 1. When the urinary albumin-to-creatinine ratiowas included, it entered the model, and plasminogen-activatorinhibitor type 1 became marginally significant (P=0.05). Thefinal model therefore included B-type natriuretic peptide (adjustedhazard ratio, 1.25) and the urinary albumin-to-creatinine ratio(1.20). For the remaining analyses, we used models that includedthe urinary albumin-to-creatinine ratio, because it was a significantpredictor of both outcomes.
Usefulness of Multimarker Scores
Biomarkers selected with the use of backward elimination wereincorporated into multimarker scores, according to the formulasin Table 2. Because the multimarker scores included the urinaryalbumin-to-creatinine ratio, the models on which the scoresare based were restricted to participants with a urine sample.Thus, for death from any cause, the number of events and thenumber at risk were 172 and 2750, respectively, whereas formajor cardiovascular events, the number of events and the numberat risk were 133 and 2598, respectively. Figure 1A and 1B showthe Kaplan–Meier curves depicting the cumulative probabilityof death and major cardiovascular events for persons with low,intermediate, and high multimarker scores. Multivariable-adjustedhazard ratios for death and major cardiovascular events forpersons with low, intermediate, and high multimarker scoresare shown in Table 3. Persons with high multimarker scores hada risk of death four times as great and a risk of major cardiovascularevents almost two times as great as persons with low multimarkerscores (P<0.001 and P=0.02, respectively).
Table 2. Multimarker Scores for the Prediction of Death and Major Cardiovascular Events, with Cutoff Points Distinguishing Low, Intermediate, and High Risk.
Figure 1. Kaplan–Meier Curves of the Cumulative Probability of Death (Panel A) and Major Cardiovascular Events (Panel B), According to Category of Multimarker Score.
Multimarker scores were classified as low, intermediate, or high, as described in Table 2.
Table 3. Relation of Multimarker Risk Score to Outcomes.
C statistics for models of death were 0.75 (with age and sexas predictors), 0.79 (with age, sex, and multimarker score aspredictors), 0.80 (with age, sex, and conventional risk factorsas predictors), and 0.82 (with all predictors). C statisticsfor major cardiovascular events were 0.68 (with age and sexas predictors), 0.70 (with age, sex, and multimarker score aspredictors), 0.76 (with age, sex, and conventional risk factorsas predictors), and 0.77 (with all predictors). As shown inFigure 2, ROC curves overlapped for models with conventionalrisk factors with biomarkers and for models with conventionalrisk factors without biomarkers.
Figure 2. Receiver-Operating-Characteristic Curves for Death (Panel A) and for Major Cardiovascular Events (Panel B) during 5-Year Follow-up.
For each end point, curves are based on models of the prediction of risk with the use of conventional risk factors with or without biomarkers (multimarker score). Biomarkers for death were B-type natriuretic peptide, C-reactive protein, the urinary albumin-to-creatinine ratio, homocysteine, and renin. Biomarkers for major cardiovascular events were B-type natriuretic peptide and the urinary albumin-to-creatinine ratio.
Secondary Analyses
Because plasminogen-activator inhibitor type 1 was marginallysignificant (P=0.05) in the backward-elimination model for majorcardiovascular events, a secondary analysis was performed withthis variable included in the model. This analysis resultedin an adjusted hazard ratio of 1.86 (P=0.02) for high multimarkerscores and a C statistic of 0.77. Adjustment for the use ofstatins, aspirin, or antihypertensive medications or for previous"nonmajor" cardiovascular events did not alter our findingssignificantly. In addition, substituting LDL cholesterol fortotal cholesterol yielded results that were similar to thoseof the primary analyses. Interactions of age and sex with biomarkersfor death and major cardiovascular events were not statisticallysignificant.
Discussion
We investigated the usefulness of 10 biomarkers for predictingdeath and major cardiovascular events in approximately 3000persons followed for up to 10 years. We observed that the mostinformative biomarkers for predicting death were blood levelsof B-type natriuretic peptide, C-reactive protein, homocysteine,and renin, and the urinary albumin-to-creatinine ratio, whereasthe most informative biomarkers for predicting major cardiovascularevents were B-type natriuretic peptide and the urinary albumin-to-creatinineratio. Persons with high multimarker scores had a risk of deathfour times as great and a risk of major cardiovascular eventsalmost two times as great as persons with low multimarker scores.Nonetheless, the use of multiple biomarkers added only moderatelyto the overall prediction of risk based on conventional cardiovascularrisk factors, as evidenced by small changes in the C statistic.
These findings highlight the strengths and limitations of theuse of current biomarkers for the prediction of cardiovascularrisk in ambulatory persons. Although multiple biomarkers areassociated with a high relative risk of adverse events, evenin combination they add only moderately to the prediction ofrisk in an individual person. We used the C statistic for assessingthe clinical usefulness of biomarkers, because it measures discriminationability better than relative risk does.21,22 One reason is thatdistributions of biomarker levels in persons with and in personswithout cardiovascular events may overlap, even when large relativedifferences are present.21 In addition, relative risk ratiosmay not reflect the fact that most persons can be effectivelyrisk stratified with conventional risk factors.22
Our findings regarding the associations of biomarkers with therisks of death and incident major cardiovascular events areconsistent with results of studies of single biomarkers involvingB-type natriuretic peptide,9,23 urinary albumin-to-creatinineratio,16 C-reactive protein,24,25 homocysteine,26,27,28 or renin.29Although higher plasminogen-activator inhibitor type 1 levelshave been observed in persons with known cardiovascular disease,30previous studies relating this biomarker to incident cardiovasculardisease have been inconclusive.14,31
Few community-based data compare cardiovascular biomarkers fromdifferent pathways or assess the incremental performance ofa multimarker panel for risk prediction. A recent study reportedthat N-terminal pro–B-type natriuretic peptide and theurinary albumin-to-creatinine ratio, but not C-reactive protein,predicted the risk of death and cardiovascular events in 764elderly persons.32 Our data extend these findings to a youngerand substantially larger cohort, with a larger panel of biomarkersand prospective assessments of clinical usefulness.
In our study, C-reactive protein predicted the risk of deathbut not of major cardiovascular events, after accounting forother biomarkers. Several studies of single markers, includinga study based on an earlier examination cycle of the FraminghamHeart Study, have shown little improvement in the predictionof risk with the addition of C-reactive protein to conventionalrisk factors.33,34 Recent data indicate only a moderate associationbetween high-sensitivity C-reactive protein and cardiovascularevents, with relative risks of 1.3 to 1.5 associated with levelsin the highest third as compared with the lowest third.8,35We did not have statistical power to exclude a similarly limitedassociation between C-reactive protein and major cardiovascularevents. Nonetheless, our data suggest that B-type natriureticpeptide and the urinary albumin-to-creatinine ratio have strongerrelations with global cardiovascular risk than does C-reactiveprotein, an observation consistent with other studies assessingthese biomarkers simultaneously in high-risk populations.32,36,37
There has been interest in refining risk-stratification algorithmsby adding information from biomarkers representing pathwaysinvolved in atherogenesis or vascular function.6 Practice guidelines,such as those relating to C-reactive protein,38 have begun toaddress the use of biomarker screening for primary prevention.Our data indicate that contemporary biomarkers contribute onlymoderately to the prediction of risk once conventional riskfactors are considered.
The assessment of biomarkers may still be useful for identifyingsubgroups that would benefit most from additional testing. Sucha group may consist of persons who are at intermediate riskfor a cardiovascular event and in whom adjustments in predictedrisk may alter the aggressiveness of the modification of riskfactors such as the lowering of serum cholesterol levels orblood pressure.22,38 Furthermore, this approach may permit moreefficient targeting of populations that would be suitable fortesting new strategies of prevention.21
Cost-effectiveness also influences the clinical decision tomeasure new markers. Relatively small improvements in the abilityto predict risk may be tolerated for screening tests that aresimple and inexpensive, whereas large increments in such predictiveusefulness may be necessary to justify costlier tests. Dataregarding the costs and benefits of biomarkers in the preventivesetting are needed.
Several limitations of our analysis deserve comment. We selectedbiomarkers on the basis of previous experimental and clinicalstudies; we acknowledge that other biomarkers not tested, suchas lipoprotein-associated phospholipase A2,39 might have providedadditional information. Because of the concern regarding multipletesting, we did not test the association of each individualmarker with outcomes. Instead, we used a global test of thebiomarker panel, followed by backward elimination to selectthe most predictive biomarkers. The failure of a specific biomarkerto be retained in the final model does not imply that it isnot related to outcomes.
We did not include "nonmajor" cardiovascular events (angina,intermittent claudication, or transient ischemic attack) inthe cardiovascular end point or baseline exclusions. Thus, ourparticipants cannot be viewed strictly as a cohort for studyingprimary prevention. We intended for the study sample to reflecta general, unselected population with varying baseline risks.
It is possible that the association between biomarker levelsand outcomes was partly mediated by visceral adiposity or insulinresistance. Although we adjusted for body-mass index in ouranalyses, measures of insulin resistance were not availableat the baseline examination. This limitation may be particularlyrelevant for biomarkers that correlate with insulin resistance,such as C-reactive protein and plasminogen-activator inhibitortype 1.40
In summary, biomarkers from multiple, biologically distinctpathways are associated with the risks of death and major cardiovascularevents. Nonetheless, the use of contemporary biomarkers addsonly moderately to standard risk factors for risk assessmentof individual persons. These results highlight the importanceof evaluating putative biomarkers with the use of prospectivedata and explicit assessments of the ability to classify risk.The future success of biomarker strategies may depend on thediscovery of new biomarkers to complement the best existingones, perhaps with the help of new, unbiased approaches.
Supported by grants (NO1-HC-25195, K23-HL-074077, R01-HL-076784,R01-HL-48157, and 2K24-HL-04334) from the National Heart, Lung,and Blood Institute; an agreement (Agreement 58-1950-4-401)with the Department of Agriculture; the American Diabetes Association;and the American Heart Association. The natriuretic peptideassays were performed by Shionogi (Osaka, Japan).
Dr. Selhub reports serving as a consultant to Cooper Clinic,Eprova, First Horizon, and Pamlab and serving as a consultantfor a patent-infringement case involving U.S. patent 10/020,634,regarding methods of vitamin composition in the treatment ofosteoarthritis. Dr. Rifai reports receiving lecture fees fromOrtho Diagnostics and grant support from Merck. Dr. Robins reportsserving as a consultant to Hoffmann–La Roche and ReliantPharmaceuticals and receiving grant support from GlaxoSmithKline.Dr. D'Agostino reports serving as a consultant to Pfizer, Sanofi,and Bayer. No other potential conflict of interest relevantto this article was reported.
Source Information
From the Framingham Heart Study, Framingham, MA (T.J.W., P.G., M.G.L., D.L., C.N.-C., S.J.R., E.J.B., R.B.D., R.S.V.); the Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School (T.J.W., C.N.-C.), and the Department of Mathematics and Statistics, Boston University (P.G., M.G.L., R.B.D.) — both in Boston; the Royal North Shore Hospital, Sydney (G.H.T.); the National Heart, Lung, and Blood Institute, Bethesda, MD (D.L.); and the Jean Mayer Department of Agriculture Human Nutrition Research Center on Aging, Tufts University (P.F.J., J.S.), the Department of Laboratory Medicine, Children's Hospital, Harvard Medical School (N.R.), and the Preventive Medicine and Cardiology Sections (D.L., E.J.B., R.S.V.) and the Division of Endocrinology, Nutrition, and Diabetes (S.J.R.), Boston Medical Center, Boston University School of Medicine — all in Boston.
Address reprint requests to Dr. Wang at the Massachusetts General Hospital, Cardiology Division, GRB-800, 55 Fruit St., Boston, MA 02114, or at tjwang{at}partners.org.
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Biomarkers for Prediction of Cardiovascular Events
Musunuru K., Blumenthal R. S., Ridker P. M, Cook N. R., Becker D. M., Mora S., Goff D. C. Jr., Fletcher R. H., Fletcher S. W., Mints G., Shah N. R., Hauswald M., Wang T. J., Larson M. G., Vasan R. S., Ware J. H.
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