Novel and Conventional Biomarkers for Prediction of Incident Cardiovascular Events in the Community

Novel and conventional biomarkers for the prediction of incidentcardiovascular events in the community

Olle Melander, MD PhD, Christopher Newton-Cheh, MD MPH, Peter Almgren, MSc, BoHedblad, MD PhD, Göran Berglund, MD PhD, Gunnar Engström, MD PhD, MargarethaPersson, PhD, J. Gustav Smith, MD, Martin Magnusson, MD PhD, Anders Christensson,MD PhD, Joachim Struck, PhD, Nils G. Morgenthaler, MD, Andreas Bergmann, PhD,Michael Pencina, PhD, and Thomas J. Wang, MDDepartment of Clinical Sciences, Lund University, Malmö (OM, PA, JGS, MM, MP, GB, GE, BH,AC); Cardiology Division (CN-C, TJW), Center for Human Genetic Research (CN-C),Cardiovascular Research Center (CN-C, TJW), Massachusetts General Hospital, HarvardMedical School, Boston, MA; Program in Medical and Population Genetics (CN-C, JGS), BroadInstitute of Harvard and MIT, Cambridge, MA; Framingham Heart Study, Framingham, MA (CN-C, MP, TJW); Research Department (JS, NGM, AB), BRAHMS AG, Hennigsdorf, Germany;Department of Mathematics (MP), Boston University, Boston, MA

AbstractContext—Prior studies have conflicted regarding how much information novel biomarkers add tocardiovascular risk assessment.

Objective—To evaluate the utility of biomarkers for predicting cardiovascular risk when addedto conventional risk factors, using contemporary biomarkers and newer statistical approaches.

Design, Setting, Participants—Between 1991 and 1994, 5067 participants (mean age 58,60% women) without cardiovascular disease from a prospective cohort in Malmö, Swedenunderwent measurement of C-reactive protein (CRP), mid-regional-pro-atrial natriuretic peptide,N-terminal pro-B-type natriuretic peptide (N-BNP), mid-regional-pro-adrenomedullin (MR-proADM), lipoprotein-associated phospholipase-2, and cystatin C. Participants were followeduntil 2006. First cardiovascular events (myocardial infarction, stroke, coronary death) wereascertained using the Swedish national hospital discharge and cause-of-death registers. Low-,intermediate-, and high-risk were defined as 10-year risks of <6%, 6–19%, or ≥20%, respectively.

Main Outcome Measures—Incident cardiovascular and coronary events.

Results—During median follow-up of 12.8 years, there were 418 cardiovascular and 230coronary events. Models with conventional risk factors had c-statistics of 0.758 (95% confidenceinterval [CI], 0.734–0.781) and 0.760 (0.730–0.789) for cardiovascular and coronary events.Biomarkers retained in backward-elimination models were N-BNP and CRP for cardiovascularevents, and N-BNP and MR-proADM for coronary events, which raised the c-statistic by 0.007(p=0.04) and 0.009 (p=0.08), respectively. The proportion of participants reclassified was modest

Address for correspondence: Olle Melander, MD PhD, Department of Clinical Sciences, Lund University, Clinical Research Center(CRC), Entrance 72, Bldg 91, Floor 12, Malmö University Hospital, SE 205 02 Malmö, Sweden, [email protected] ORThomas J. Wang, MD, Cardiology Division, Massachusetts General Hospital, 55 Fruit Street, GRB-847, Boston, MA 02114,[email protected]. Struck, Morgenthaler and Bergmann are employees of and Dr. Bergmann holds stock in BRAHMS, AG. BRAHMS, AG holdspatent rights on the midregional pro-ANP assay and the mid-regional pro-adrenomedullin assay. Drs. Melander, Newton-Cheh,Struck, and Wang are listed as co-inventors on a patent application for the use of pro-adrenomedullin for risk stratification in primaryprevention. Apart from this, there are no conflicts of interest in connection with this paper.

NIH Public AccessAuthor ManuscriptJAMA. Author manuscript; available in PMC 2011 May 10.

Published in final edited form as:JAMA. 2009 July 1; 302(1): 49–57. doi:10.1001/jama.2009.943.

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(8% for cardiovascular risk, 5% for coronary risk). The net reclassification improvement (NRI)was non-significant for cardiovascular events (0.0%, 95%CI, −4.3%–4.3%) and coronary events(4.7%, −0.76%–10.1%). Greater improvements were observed in analyses restricted tointermediate-risk individuals (cardiovascular events: 7.4%, 95%CI, 0.7%–14.1% [p=0.03];coronary events: 14.6%, 5.0%–24.2% [p=0.003]). However, correct re-classification was almostentirely confined to down-classification of individuals without events, rather than up-classificationof those with events.

Conclusion—Selected biomarkers may be used to predict future cardiovascular events, but thegains over conventional risk factors are minimal. Risk classification improved in intermediate-riskindividuals, mainly through the identification of those unlikely to develop events.

BACKGROUNDCost-effective cardiovascular prevention relies on the accurate identification of individualsat risk. However, a large proportion of individuals with cardiovascular events have one orfewer of the conventional risk factors, including smoking, diabetes, hypertension, orhyperlipidemia.1 As a result, the use of novel biomarkers to augment standard riskalgorithms has attracted increasing attention in recent years. This interest has furtherintensified with the publication of the JUPITER trial, which showed that statin therapyreduced cardiovascular risk in a group of individuals with C-reactive protein (CRP) levels ≥2 mg/L.2

However, prior studies have reached differing conclusions regarding the utility ofbiomarkers for cardiovascular risk prediction. Some reports indicate that biomarkers such asCRP aid risk prediction,3,4 whereas other studies conclude that such biomarkers contributerelatively little incremental information.5,6 A number of factors influence how wellbiomarkers predict outcomes, including the population studied, the statistical methods forevaluating the biomarkers, and the specific biomarkers selected. Studies focusing on high-risk populations often yield favorable estimates of biomarker performance,4,7 but thegreatest need for new risk markers exists in low- to intermediate-risk populations, for whomthe data are most conflicting.8 With regard to the statistical approaches to evaluating newbiomarkers, it is widely accepted that basic association measures such as hazard ratios orodds ratios alone are insufficient to assess prognostic utility.9 Newer metrics assess howwell biomarkers assign patients to clinical risk categories,3,10 but studies are only beginningto incorporate such metrics.4 Another important consideration is the selection of biomarkers.Although several biomarkers consistently predict cardiovascular events after adjustment forconventional risk factors,11 few population based studies have incorporated multipleinformative biomarkers simultaneously, an approach that has the greatest prospect ofproviding incremental information.8

Although CRP and N-terminal pro-B-type natriuretic peptide (N-BNP) are relatively well-studied in the primary prevention setting,6 a variety of newer biomarkers have generatedinterest as well.12–15 Lipoprotein-associated phospholipase 2 (Lp-PLA2) has been related tocardiovascular risk16,17 and attracted interest because of the development ofpharmacological agents inhibiting Lp-PLA2.18 Cystatin C is a novel marker of renalfunction that predicts cardiovascular events better than serum creatinine.13 Mid-regionalpro-atrial natriuretic peptide (MR-proANP) and pro-adrenomedullin (MR-proADM) arenewer biomarkers that predict prognosis in patients post-myocardial infarction.14,19

The present investigation was undertaken to address limitations of prior studies assessingbiomarkers for primary cardiovascular prevention. We studied a large, middle-agedpopulation-based cohort without cardiovascular disease, using a variety of newer statisticalmeasures designed specifically to evaluate risk prediction models. We assessed both older

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(CRP, N-BNP) and newer cardiovascular biomarkers (Lp-PLA2, cystatin C, MR-proANP,MR-proADM), individually and in combination, compared with a basic model comprisingconventional risk factors.

METHODSStudy population

The Malmö Diet and Cancer (MDC) study is a population-based, prospective epidemiologiccohort of 28,449 persons enrolled between 1991 and 1996. From this cohort, 6,103 personswere randomly selected to participate in the MDC Cardiovascular Cohort, which wasdesigned to investigate the epidemiology of carotid artery disease.16 We excludedparticipants with prior myocardial infarction or stroke at baseline (n=143). Of the remainingparticipants, fasting plasma was available on 5,400 persons, among whom complete data onconventional cardiovascular risk factors were available on 5,067. The individual plasmabiomarkers were successfully measured in 4,713 to 4,936 out of the 5,067 subjects (Table1). Subjects with measurement of biomarkers did not differ from eligible subjects in theoriginal MDC Cardiovascular Cohort with regard to mean age, gender, mean systolic anddiastolic blood pressure, mean body mass index, and smoking prevalence.

All participants gave written informed consent and the study was approved by the EthicalCommittee at Lund University, Lund, Sweden.

Clinical examination and assaysParticipants underwent a medical history, physical examination, and laboratory assessment.Blood pressure was measured using a mercury-column sphygmomanometer after 10 minutesof rest in the supine position. Hypertension was defined as systolic or diastolic bloodpressure ≥140/90 mmHg or use of antihypertensive medication. Diabetes mellitus wasdefined as a fasting whole blood glucose >109 mg/dl (6.0 mmol/L), a self-reported physiciandiagnosis of diabetes, or use of anti-diabetic medication. Cigarette smoking was elicited by aself-administered questionnaire, with current cigarette smoking defined as any use withinthe past year. We measured fasting total cholesterol, HDL cholesterol, and triglyceridesaccording to standard procedures at the Department of Clinical Chemistry, UniversityHospital Malmö. LDL cholesterol was calculated according to Friedewald’s formula.

Cardiovascular biomarkers were analyzed in fasting EDTA plasma specimens that had beenfrozen at −80°C immediately after collection. The selection of biomarkers was based on theresults of prior population-based and hospital-based studies.3,6,12–15 The principalinvestigators (OM, CNC, TW) initiated and designed the study, selected the biomarkers, andperformed all analyses. Industry sponsors provided support for the biomarker measurements,but had no access to the primary study data.

C-reactive protein was measured by high-sensitivity assay (Roche Diagnostics, Basel,Switzerland). Lp-PLA2 activity was measured in duplicate using [3H]-platelet activatingfactor as substrate.16 N-BNP was determined using the Dimension RxL N-BNP (Dade-Behring, Germany).20 Cystatin C was measured using a particle-enhanced immuno-nephelometric assay (N Latex Cystatin C, Dade Behring, IL).13 MR-proANP and MR-proADM were measured using immunoluminometric sandwich assays targeted againstamino acids in the mid-regions of the respective peptide (BRAHMS, AG, Germany).21,22

The minimum detection limits for MR-proANP and MR-proADM were 6 pmol/L and 0.08nmol/L, respectively. Censoring by the lower detection limit occurred in 3 individuals forMR-proANP, and 7 individuals for MR-proADM. The maximum detection limits for MR-proANP and MR-proADM were 3000 pmol/L and 25 nmol/L, respectively, and noindividuals were censored at these thresholds.



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Mean inter-assay coefficients of variation were 4.6% for CRP, 5.8% for Lp-PLA2, ≤10% forMR-proANP and MR-proADM, 2.7% for N-BNP, and 4.3% for cystatin C.

Clinical endpointsWe examined two primary outcomes: coronary events and cardiovascular events. Theprocedure for ascertaining outcome events has been detailed previously.23,24 Coronaryevents were defined as fatal or non-fatal myocardial infarction or death due to ischemicheart disease. Cardiovascular events were defined as coronary events or fatal or non-fatalstroke. Events were identified through linkage of the 10-digit personal identification numberof each Swedish citizen with three registries: the Swedish Hospital Discharge Register, theSwedish Cause of Death Register, and the Stroke in Malmö register. Myocardial infarctionwas defined on the basis of International Classification of Diseases 9th and 10th Revisions(ICD9 and ICD10) codes 410 and I21, respectively. Death due to ischemic heart disease wasdefined on the basis of codes 412 and 414 (ICD9) or I22–I23 and I25 (ICD10). Fatal ornonfatal stroke was defined using codes 430, 431, 434 and 436 (ICD9) and I60, I61, I63, andI64 (ICD10). Classification of outcomes using these registries has been previouslyvalidated,25,26 and the sensitivity of the registry for detecting events such as myocardialinfarction has been shown to exceed 90%.27 Follow-up for outcomes extended to January 1,2006.

We also analyzed 2 secondary outcomes: total mortality and total cardiovascular events(including heart failure). Heart failure was defined from the Swedish Hospital DischargeRegister using codes 429 (ICD9) and I50 (ICD 10). The primary diagnosis of heart failure inthe Swedish Hospital Discharge Register has been shown to have an accuracy of 95%.28

Statistical analysesContinuous biomarker variables with right skewed distributions (MR-proANP, N-BNP andCRP) were log-transformed before analysis. We performed multivariable Cox proportionalhazards models to examine the association between biomarkers and incident events. Allmodels were adjusted for age, sex, systolic blood pressure, diastolic blood pressure, use ofanti-hypertensive therapy, current smoking, diabetes, LDL cholesterol, HDL cholesterol,and body mass index. We confirmed that the proportionality of hazards assumption was met.Hazards ratios were expressed per standard deviation (SD) increment in the respectivebiomarker.

Each biomarker was individually tested in models for cardiovascular and coronary events,with adjustment for conventional risk factors. The initial analyses used all participants withavailable data for the biomarker being studied. Thus, sample sizes for these analyses rangedfrom 4,713 (for N-BNP and cystatin C) to 4,937 (for Lp-PLA2), corresponding to thenumber of participants in whom the biomarker was measured.

We then examined the joint and comparative value of biomarkers for predictingcardiovascular and coronary events. These analyses included only the biomarkers with astatistically significant association with the endpoint in the initial stage (5 biomarkers forcardiovascular events and 3 biomarkers for coronary events). In order to standardize thenumber of subjects for the biomarker-risk factor and biomarker-biomarker comparisons, werestricted subsequent analyses to participants with complete data on all biomarkers beingstudied for the respective endpoint (n=4,483 for cardiovascular events and n=4,600 forcoronary events). In this common sample, we examined models with no biomarkers, modelswith individual biomarkers, and models with multiple biomarkers. For models with multiplebiomarkers, we entered all biomarkers into a backward elimination model, with theconventional risk factors forced in and a retention p-value <0.05. With the observed



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incidence rates, we had 80% power at α=0.05 to detect hazards ratios of 1.17 forcardiovascular events and 1.22 for coronary events, for a 1 SD increase in any biomarker.

To assess model discrimination, we calculated the c-statistic for models with conventionalrisk factors with and without biomarkers.29 To assess global calibration of the risk models,we calculated modified Hosmer-Lemeshow statistics for models with and withoutbiomarkers.30 We also evaluated the ability of biomarkers to reclassify risk, followingmethods suggested previously.3,10 Using multivariable risk models with the clinicalcovariates noted above, participants were initially classified as low, intermediate, or highrisk if their predicted 10-year risk of a coronary event was <6%, 6% to <20%, or ≥20%,respectively. Clinical covariates were entered into the model as continuous variables, withthe exception of sex, cigarette smoking, use of anti-hypertensive therapy, and diabetes,which were entered as dichotomous variables. Participants were then allowed to bereclassified into different categories with the addition of the biomarker data. We assessedthe number of participants reclassified, and also calculated the Net ReclassificationImprovement (NRI) and Integrated Discrimination Improvement (IDI).10 In secondaryanalyses, we repeated the reclassification analysis using the National Cholesterol EducationProgram Adult Treatment Panel III (ATP 3) algorithm as the base clinical model.31 UnderATP3, individuals with diabetes are automatically assigned to the highest risk category.

All analyses were performed using Stata software version 8.0 (StataCorp) except for thetests for the proportionality of hazards assumption which were performed using the survivalpackage for R, and the c-statistics, which were generated using the ROCR package for R(www.r-project.org). Tests were considered significant if the two-sided P-value was < 0.05.

RESULTSCharacteristics of the study sample are shown in Table 1. The mean age was 58 ± 6 years.Hypertension was common, with 3,204 (63%) participants on anti-hypertensive therapy orwith a blood pressure of 140/90 mm Hg or higher. Diabetes mellitus was present in 391(8%) participants. Median follow-up time was 12.8 years (interquartile range: 12.1, 13.5).

The highest age- and sex-adjusted correlations between biomarkers were observed betweenN-BNP and MR-proANP (r=0.47, 95% confidence interval [CI], 0.45–0.49), and betweenMR-proADM and cystatin C (r=0.47, 95% CI, 0.45–0.49).

Prediction of cardiovascular events using single biomarkersThe proportionality of hazards criterion was met in all analyses of biomarkers in relation tocardiovascular and coronary events. The 10-year incidence of cardiovascular events was7.8%. After adjustment for conventional risk factors, 5 of 6 biomarkers examinedindividually showed a significant relationship with incident cardiovascular events(Supplementary Table 1A). The comparative performance of the biomarkers was assessed inthe 4,483 participants with data on all 5 biomarkers, in whom 364 experienced a firstincident cardiovascular event during follow up. Multivariable-adjusted hazard ratios for eachbiomarker are shown in Table 2. The strongest associations were observed for N-BNP(adjusted hazard ratio per SD increment in N-BNP, 1.22, 95% CI, 1.10–1.36) and CRP(1.19, 95% CI, 1.07–1.32).

Several metrics were used to summarize the prognostic utility of adding individualbiomarkers to conventional risk factors (Table 2). A model based on conventional riskfactors had a c-statistic of 0.758 (95% CI, 0.734–0.781), and the addition of individualbiomarkers resulted in small increases in the c-statistic (all changes less than 0.005, Table



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http://www.r-project.org

2). Models with or without biomarkers were well-calibrated, with modified Hosmer-Lemeshow p-values >0.05. The NRI and IDI were non-significant for all biomarkers.

Prediction of coronary events using single biomarkersThe 10-year incidence of coronary events was 4.4%. Three biomarkers (N-BNP, MR-proADM, and cystatin C) were significant predictors of first incident coronary events aftermultivariable adjustment (Supplementary Table 1B). The adjusted hazards ratio associatedwith CRP had borderline significance (p=0.05).

Results based on the 4,600 participants with data on the 3 significant biomarkers, in whomthere were 216 first incident coronary events, are shown in Table 3. Elevations in N-BNPand MR-proADM were associated with the highest hazards for coronary events, withadjusted hazard ratios per SD increment of 1.28 (95% confidence interval, 1.12–1.47) and1.21 (95% confidence interval, 1.07–1.37), respectively. The c-statistic associated withconventional risk factors for predicting coronary events was 0.760 (95% CI, 0.730–0.789).As with cardiovascular events, addition of individual biomarkers did not raise the c-statisticappreciably (Table 3). Model calibration was good (Hosmer-Lemeshow p>0.05) with orwithout biomarkers, and the NRI was non-significant. The IDI was significant for MR-proADM (p=0.02), and borderline significant for N-BNP (p=0.08).

Multiple biomarkers for cardiovascular and coronary eventsIn backward elimination models, 2 biomarkers were retained for prediction ofcardiovascular events (N-BNP and CRP), and 2 biomarkers were retained for prediction ofcoronary events (N-BNP and MR-proADM). Results of multivariable Cox proportionalhazards models are shown in Table 4, for both outcomes. Incorporation of the set ofsignificant biomarkers into prediction models for cardiovascular and coronary events led tosmall increments (approximately 0.01) in the c-statistics. The NRI was non-significant forcardiovascular events (p=0.99) and coronary events (p=0.09). The IDI had p-values of 0.08for cardiovascular events and 0.03 for coronary events. Results for c-statistics, NRI, and IDIwere unchanged when models were modified to include all biomarkers retained at p<0.10, orall biomarkers regardless of p-value.

Table 5 shows the number of participants reclassified using the panels of informativebiomarkers for cardiovascular events (n=238) and coronary events (n=144), respectively,during the first 10 years of follow-up. For cardiovascular events, use of biomarkers moved335 participants (7.5%) into a higher or lower risk category. Only 35 participants (0.8%)were moved into the high risk category (10-year predicted risk ≥20%). For coronary events,231 (5.0%) participants were reclassified into a higher or lower risk category, with only 22(0.5%) moved into the high risk category. When “high-risk” was redefined as a 10-yearpredicted risk ≥10%, rather than 20%, the proportion of individuals reclassified to high-riskusing biomarkers remained small (2.3% for cardiovascular events and 1.2% for coronaryevents).

Calibration was essentially the same in models with and without biomarkers. Forcardiovascular events, actual event rates in the low, intermediate, and high risk groups were2%, 11%, and 24% with conventional risk factors, and 2%, 11%, and 25% with risk factorsand biomarkers. Corresponding event rates for coronary disease were 2%, 9%, and 27%with conventional risk factors, and 2%, 10%, and 23% with risk factors and biomarkers.

We also assessed reclassification using the ATP3 algorithm as the base clinical model,rather than a model fitted using the Malmö data. With the addition of biomarkers to the ATPalgorithm, the NRI was significant for cardiovascular events, although the net proportion



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correctly reclassified was still modest (6.2%, p=0.004). The NRI was non-significant forcoronary events (p=0.89).

Analyses in “intermediate risk” participantsWe performed additional analyses restricted to “intermediate risk” participants (10-yearpredicted risk 6% to <20%). Most intermediate risk participants (57% for cardiovascularevents and 59% for coronary events) had 10-year predicted risks <10%. Women comprised44% of the intermediate risk group for cardiovascular events, and 26% of the intermediaterisk group for coronary events.

For cardiovascular disease, 13% of the overall number of intermediate-risk individuals weredown-classified, and only 3% were up-classified. The NRI for this subgroup was significant,7.4% (95% CI, 0.7%–14.1%; p=0.03), although this was driven solely by individualswithout events who were correctly down-classified (133 out of 973, 14%). Among thosewith events, a greater number (n=10, or 8%) were inappropriately down-classified thanappropriately up-classified (n=6, or 4%). Similarly, for coronary disease events, 19% weredown-classified and only 4% were up-classified. The NRI was 14.6% (95% CI, 5.0%–24.2%; p=0.003), due to the high proportion of individuals without events who were down-classified (107 out of 525, 20%). Among intermediate-risk individuals with coronary events,3 (6%) were inappropriately down-classified and 2 (4%) were appropriately up-classified.

Multimarker scoresSimple “multimarker” risk scores were constructed for each endpoint. For each participant,standardized values of each biomarker (expressed in SD units from the mean), were summedto produce a score. Score values were then divided into quartiles (with the lowest scoresdefining quartile 1). The median multimarker scores in each quartile for cardiovascularevents were −1.66 (range −5.47, −1.01), −0.52 (−1.01, −0.04), 0.37 (−0.04, 0.90), and 1.65(0.90, 5.62). The median multimarker scores in each quartile for coronary events were −1.62(−5.14, −1.02), −0.50 (−1.02, −0.06), 0.38 (−0.06, 0.88), and 1.60 (0.88, 11.65).

Figure 1 depicts the cumulative incidence of cardiovascular (Panel A) or coronary (PanelB) events, according to quartiles of the biomarker risk scores. In multivariable-adjustedmodels, hazards ratios associated with the 2nd through 4th quartiles of the score forcardiovascular events were 1.07 (95% CI, 0.75–1.52), 1.36 (0.98–1.89), and 1.61 (1.17–2.23; p for trend=0.001). Adding this cardiovascular disease biomarker score toconventional risk factors resulted in small improvements in the c-statistic (0.007), the NRI(0.0%, p=0.88), and the IDI (P=0.09). Adjusted hazards ratios associated with the 2nd

through 4th quartiles of the score for coronary events were 1.01 (95% CI, 0.64–1.59), 1.11(0.71–1.73), and 1.86 (1.22–2.83; p for trend =0.001). Adding the score for coronary eventsto conventional risk factors increased the c-statistic by 0.009, with NRI 5.5% (p=0.055) andIDI (P=0.02).

Secondary endpointsThere were 392 all-cause deaths in the follow-up period. In the stepwise prediction modelfor mortality, 3 biomarkers were retained: N-BNP (multivariable-adjusted hazard ratio 1.13per SD increment in N-BNP, 95% CI, 1.02–1.26; p=0.02), CRP (1.16, 95% CI, 1.03–1.28;p=0.007), and MR-ADM (1.14, 95% CI, 1.03–1.26; p=0.01). The addition of biomarkersincreased the c-statistic for predicting total mortality from 0.700 to 0.711. The IDI wassignificant (p<0.001). The NRI was not calculated due to the absence of clinical riskcategories for mortality.



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The addition of heart failure to the cardiovascular endpoint (481 events overall) did notchange the biomarkers retained in the stepwise model: N-BNP (multivariable-adjustedhazard ratio, 1.29, 95% CI, 1.17–1.43; p<0.001) and CRP (1.22, 95% CI, 1.10–1.35;p<0.001). The c-statistic rose from 0.759 to 0.770 and the IDI was significant (p=0.003).The NRI remained non-significant (p=0.52).

DISCUSSIONWe investigated a panel of contemporary biomarkers for predicting cardiovascular riskabove and beyond conventional risk factors, in a population-based cohort with more than50,000 person years of longitudinal follow up. When considered individually, 5 biomarkerspredicted future cardiovascular events, and 3 predicted future coronary events in modelsadjusting for conventional risk factors. The best combinations of biomarkers were N-BNPand CRP for predicting cardiovascular events, and N-BNP and MR-proADM for predictingcoronary events. The use of multiple biomarkers modestly improved the accuracy of riskprediction models over and above conventional cardiovascular risk factors, and did notreclassify a substantial proportion of individuals to higher or lower risk categories.

Whether novel biomarkers add useful information for risk prediction has been the focus ofintense scrutiny in the cardiovascular literature.4,6,32 Conflicting findings have beenattributed to a variety of factors. Inadequate statistical power, use of older biomarkers, andlack of consideration of measures such as calibration and reclassification have been invokedto explain the poor performance of biomarkers in some studies.3 Conversely, it has beenargued that other studies over-estimate the relative utility of biomarkers by examininghomogenous or highly-selected samples, or using endpoints such as mortality that are poorlypredicted by conventional cardiovascular risk factors.8,33

The present study was undertaken to address these shortcomings. As one of the largest,population-based studies of multiple biomarkers, it provides a clearer picture of the strengthsand limitations of potential biomarker strategies in primary prevention. With use ofbiomarkers, it is possible to define groups with 2-fold differences in cardiovascular risk.Nonetheless, the translation of this benefit to individual risk prediction appears minimal.

Adding biomarkers to conventional risk factors only improves the c-statistic slightly, afinding that confirms observations from several prior studies.6,34 Because the c-statistic hasbeen criticized as being insensitive to small changes in predictive accuracy,35 we alsocalculated a newer measure called the IDI.10 This metric improves when novel markerscorrectly assign individuals to higher or lower probabilities of having events. Themultimarker approach led to a near-significant change in the IDI for cardiovascular events(driven mainly by N-BNP), and significant changes for coronary events (due to N-BNP andMR-proADM) and total mortality (due to N-BNP and CRP).

What may be relevant to clinical care, however, is not whether changes in predictedprobabilities are statistically significant, but whether they result in individuals beingassigned to new, clinically-meaningful risk categories (reclassification) that would betargeted for preventive therapies. Our data indicate that a relatively small proportion ofindividuals are moved to new risk categories by the addition of biomarkers; 8% or fewerwhen one includes both upward and downward risk category movement, and fewer than 1%when one includes only the movements likely to change therapy according to the ATP IIIguidelines. Furthermore, these reclassifications result in only modest improvements in theoverall concordance between risk categories and actual event rates, as measured by theNRI.10



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Rather than screening the entire adult population with biomarkers, an alternate strategywould be to focus on those individuals deemed to be “intermediate risk,” often defined ashaving a 10-year predicted event rate of 6–19%.3 Our estimates of NRI are higher in thisgroup (7.5%, p=0.03 for cardiovascular events; 14.6%, p=0.003 for coronary events). TheNRI in intermediate-risk individuals has been described as the “clinical NRI,” emphasizingthe potential application to clinical screening.36 However, it is notable that the significanceof the NRI in this setting is driven primarily by the down-classification of individuals whodo not have events. Although informative, such shifts are much less likely to lead to changesin therapy than upward shifts, at least under current guidelines. Another shortcoming of the“clinical NRI” is that it does not account for inaccurate reclassification from other categoriesinto the intermediate risk group.37 For instance, imagine a marker that reclassifies everyperson with an event from intermediate risk to high risk, and every person without an eventfrom intermediate risk to low risk, but at the same time moved the same number of eventsfrom high to intermediate risk and non-events from low to intermediate risk. Such a markerwould have a perfect clinical NRI (100%), but a true NRI (when considering the wholesample) of 0%.

It is possible that the performance of the biomarkers would have been superior in a higherrisk cohort. Some of the biomarkers studied, including N-BNP, have shown betterdiscriminative ability in elderly4 or high-risk7 populations. However, low to intermediate-risk individuals are the group in which novel risk markers are most needed, because a largenumber of cardiovascular events derive from this group, and individuals in this group are theleast likely to be targeted for proven, preventive therapies.

Statins for primary prevention confer benefit in individuals across a broad range of baselinecardiovascular risk.2,38,39 However, treating unselected individuals with statins may not bepractical if absolute event rates are low or therapies are expensive. Thus, reclassifyingindividuals as low- or high-risk could have immediate clinical relevance with regard toidentifying candidates for statin therapy. Our findings support the premise that biomarkerscould be used to refine these classifications, but also highlight the relatively low proportionof individuals meaningfully reclassified with existing biomarkers.

These data do not exclude a future role for circulating biomarkers as adjuncts toconventional risk factors, nor do they minimize the potential for biomarkers to provideinsight into underlying mechanisms of disease. Several biomarkers studied did lead to shiftsin predictive accuracy that were at least statistically significant. The challenge will be to findnew cardiovascular biomarkers that, alone or in combination with existing biomarkers, canbring about improvements in risk assessment that are not just statistically significant, butclinically significant as well.40

Supplementary MaterialRefer to Web version on PubMed Central for supplementary material.

AcknowledgmentsThe authors wish to thank Brahms and Siemens Diagnostics for their unrestricted support of assay measurements.The authors also acknowledge the assistance of Christine Perkins, BA, and Dejan Blagovcanin, BA, both ofSiemens Diagnostics, with the performance of the biomarker assays.

Dr. Melander was supported by grants from the Swedish Medical Research Council, the Swedish Heart and LungFoundation, the Medical Faculty of Lund University, Malmö University Hospital, the Albert Påhlsson ResearchFoundation, the Crafoord foundation, the Ernhold Lundströms Research Foundation, the Region Skane, the Huldaand Conrad Mossfelt Foundation, the King Gustaf V and Queen Victoria Foundation, the Lennart HanssonsMemorial Fund, and the Wallenberg Foundation. Dr. Newton-Cheh was supported by NIH K23-HL-080025, a



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Doris Duke Charitable Foundation Clinical Scientist Development Award, and a Burroughs Wellcome Fund CareerAward for Medical Scientists. Dr. Wang was supported by NIH grants R01-HL-086875, R01-HL-083197, and R01-DK-081572, and a grant from the American Heart Association.

Dr. Melander had full access to all of the data in the study and takes responsibility for the integrity of the data andthe accuracy of the data analysis.

The funding organizations had no role in the design and conduct of the study, collection, management, analysis, andinterpretation of the data, or preparation or approval of the manuscript.

REFERENCES1. Khot UN, Khot MB, Bajzer CT, et al. Prevalence of conventional risk factors in patients with

coronary heart disease. JAMA. 2003; 290:898–904. [PubMed: 12928466]2. Ridker PM, Danielson E, Fonseca FA, et al. Rosuvastatin to prevent vascular events in men and

women with elevated C-reactive protein. N Engl J Med. 2008; 359:2195–2207. [PubMed:18997196]

3. Ridker PM, Buring JE, Rifai N, et al. Development and validation of improved algorithms for theassessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297:611–619. [PubMed: 17299196]

4. Zethelius B, Berglund L, Sundstrom J, et al. Use of multiple biomarkers to improve the prediction ofdeath from cardiovascular causes. N Engl J Med. 2008; 358:2107–2116. [PubMed: 18480203]

5. Folsom AR, Chambless LE, Ballantyne CM, et al. An assessment of incremental coronary riskprediction using C-reactive protein and other novel risk markers: the atherosclerosis risk incommunities study. Arch Intern Med. 2006; 166:1368–1373. [PubMed: 16832001]

6. Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first majorcardiovascular events and death. N Engl J Med. 2006; 355:2631–2639. [PubMed: 17182988]

7. Blankenberg S, McQueen MJ, Smieja M, et al. Comparative impact of multiple biomarkers and N-Terminal pro-brain natriuretic peptide in the context of conventional risk factors for the predictionof recurrent cardiovascular events in the Heart Outcomes Prevention Evaluation (HOPE) Study.Circulation. 2006; 114:201–208. [PubMed: 16831981]

8. de Lemos JA, Lloyd-Jones DM. Multiple biomarker panels for cardiovascular risk assessment. NEngl J Med. 2008; 358:2172–2174. [PubMed: 18480211]

9. Pepe MS, Janes H, Longton G, et al. Limitations of the odds ratio in gauging the performance of adiagnostic, prognostic, or screening marker. Am J Epidemiol. 2004; 159:882–890. [PubMed:15105181]

10. Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, et al. Evaluating the added predictive ability ofa new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–172. [PubMed: 17569110]

11. Vasan RS. Biomarkers of cardiovascular disease: molecular basis and practical considerations.Circulation. 2006; 113:2335–2362. [PubMed: 16702488]

12. Garza CA, Montori VM, McConnell JP, et al. Association between lipoprotein-associatedphospholipase A2 and cardiovascular disease: a systematic review. Mayo Clin Proc. 2007;82:159–165. [PubMed: 17290721]

13. Shlipak MG, Sarnak MJ, Katz R, et al. Cystatin C and the risk of death and cardiovascular eventsamong elderly persons. N Engl J Med. 2005; 352:2049–2060. [PubMed: 15901858]

14. Khan SQ, O'Brien RJ, Struck J, et al. Prognostic value of midregional pro-adrenomedullin inpatients with acute myocardial infarction: the LAMP (Leicester Acute Myocardial InfarctionPeptide) study. J Am Coll Cardiol. 2007; 49:1525–1532. [PubMed: 17418290]

15. Gegenhuber A, Struck J, Dieplinger B, et al. Comparative evaluation of B-type natriuretic peptide,mid-regional pro-A-type natriuretic peptide, mid-regional pro-adrenomedullin, and Copeptin topredict 1-year mortality in patients with acute destabilized heart failure. J Card Fail. 2007; 13:42–49. [PubMed: 17339002]

16. Persson M, Hedblad B, Nelson JJ, et al. Elevated Lp-PLA2 levels add prognostic information tothe metabolic syndrome on incidence of cardiovascular events among middle-aged nondiabeticsubjects. Arterioscler Thromb Vasc Biol. 2007; 27:1411–1416. [PubMed: 17431184]



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17. Corson MA, Jones PH, Davidson MH. Review of the evidence for the clinical utility oflipoprotein-associated phospholipase A2 as a cardiovascular risk marker. Am J Cardiol. 2008;101:41F–50F.

18. Mohler ER III, Ballantyne CM, Davidson MH, et al. The effect of darapladib on plasmalipoprotein-associated phospholipase A2 activity and cardiovascular biomarkers in patients withstable coronary heart disease or coronary heart disease risk equivalent: the results of a multicenter,randomized, double-blind, placebo-controlled study. J Am Coll Cardiol. 2008; 51:1632–1641.[PubMed: 18436114]

19. Khan SQ, Dhillon O, Kelly D, et al. Plasma N-terminal B-Type natriuretic peptide as an indicatorof long-term survival after acute myocardial infarction: comparison with plasma midregional pro-atrial natriuretic peptide: the LAMP (Leicester Acute Myocardial Infarction Peptide) study. J AmColl Cardiol. 2008; 51:1857–1864. [PubMed: 18466800]

20. Di SF, Ruggieri V, Varraso L, et al. Analytical evaluation of the Dade Behring Dimension RxLautomated N-Terminal proBNP (NT-proBNP) method and comparison with the Roche Elecsys2010. Clin Chem Lab Med. 2005; 43:1263–1273. [PubMed: 16232094]

21. Morgenthaler NG, Struck J, Thomas B, et al. Immunoluminometric assay for the midregion of pro-atrial natriuretic peptide in human plasma. Clin Chem. 2004; 50:234–236. [PubMed: 14709661]

22. Morgenthaler NG, Struck J, Alonso C, et al. Measurement of midregional proadrenomedullin inplasma with an immunoluminometric assay. Clin Chem. 2005; 51:1823–1829. [PubMed:16099941]

23. Rosvall M, Janzon L, Berglund G, et al. Incident coronary events and case fatality in relation tocommon carotid intima-media thickness. J Intern Med. 2005; 257:430–437. [PubMed: 15836659]

24. Rosvall M, Janzon L, Berglund G, et al. Incidence of stroke is related to carotid IMT even in theabsence of plaque. Atherosclerosis. 2005; 179:325–331. [PubMed: 15777549]

25. Engstrom G, Jerntorp I, Pessah-Rasmussen H, et al. Geographic distribution of stroke incidencewithin an urban population: relations to socioeconomic circumstances and prevalence ofcardiovascular risk factors. Stroke. 2001; 32:1098–1103. [PubMed: 11340216]

26. The National Board of Health and Welfare. Stockholm, Sweden: National Board of Health andWelfare; 2000. Evaluation of quality of diagnosis of acute myocardial infarction, inpatient register1997 and 1995.

27. Hammar N, Alfredsson L, Rosen M, et al. A national record linkage to study acute myocardialinfarction incidence and case fatality in Sweden. Int J Epidemiol. 2001; 30(Suppl 1):S30–S34.[PubMed: 11759848]

28. Ingelsson E, Arnlov J, Sundstrom J, et al. The validity of a diagnosis of heart failure in a hospitaldischarge register. Eur J Heart Fail. 2005; 7:787–791. [PubMed: 15916919]

29. Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: modelspecific population value and confidence interval estimation. Stat Med. 2004; 23:2109–2123.[PubMed: 15211606]

30. D'Agostino, RB.; Nam, BH. Evaluation of the performance of survival analysis models:discrimination and calibration measures. In: Balkrishnan, N.; Rao, CR., editors. Handbook ofStatsitics. Amsterdam, the Netherlands: Elsevier; 2004. p. 1-25.

31. Executive Summary of The 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). JAMA. 2001; 285:2486–2497. [PubMed: 11368702]

32. Cook NR, Buring JE, Ridker PM. The effect of including C-reactive protein in cardiovascular riskprediction models for women. Ann Intern Med. 2006; 145:21–29. [PubMed: 16818925]

33. Wang TJ, Kathiresan S, Lloyd-Jones DM. Algorithms for assessing cardiovascular risk in women.JAMA. 2007; 298:176–178. [PubMed: 17622597]

34. Shlipak MG, Fried LF, Cushman M, et al. Cardiovascular mortality risk in chronic kidney disease:comparison of traditional and novel risk factors. JAMA. 2005; 293:1737–1745. [PubMed:15827312]

35. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction.Circulation. 2007; 115:928–935. [PubMed: 17309939]



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36. Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve globalcardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008; 118:2243–2251. [PubMed: 18997194]

37. Janes H, Pepe MS, Gu W. Assessing the value of risk predictions by using risk stratification tables.Ann Intern Med. 2008; 149:751–760. [PubMed: 19017593]

38. Shepherd J, Cobbe SM, Ford I, et al. Prevention of coronary heart disease with pravastatin in menwith hypercholesterolemia. West of Scotland Coronary Prevention Study Group. N Engl J Med.1995; 333:1301–1307. [PubMed: 7566020]

39. Downs JR, Clearfield M, Weis S, et al. Primary prevention of acute coronary events with lovastatinin men and women with average cholesterol levels: results of AFCAPS/TexCAPS. Air Force/Texas Coronary Atherosclerosis Prevention Study. JAMA. 1998; 279:1615–1622. [PubMed:9613910]

40. Gerszten RE, Wang TJ. The search for new cardiovascular biomarkers. Nature. 2008; 451:949–952. [PubMed: 18288185]



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Figure 1.Kaplan-Meier plot showing one minus cumulative cardiovascular event-free survival duringfollow up in quartiles (Q1 to Q4 with Q1 representing subjects with lowest values) of amultimarker score based on the summed standardized values (expressed as number of SDdeviation from the mean) of N-BNP and CRP.



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Figure 2.Kaplan-Meier plot showing one minus cumulative coronary event-free survival duringfollow up in quartiles (Q1 to Q4 with Q1 representing subjects with lowest values) of amultimarker score based on the summed standardized values (expressed as number of SDdeviation from the mean) of N-BNP and MR-proADM.



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Table 1

Characteristics of the study sample (n=5067)*

Clinical variable

Age, years 58 ± 6

Male gender, No. (%) 2044 (40)

Systolic blood pressure, mm Hg 141 ± 19

Diastolic blood pressure, mm Hg 87 ± 9

Antihypertensive treatment, No. (%) 809 (16)

Hypertension, No. (%) 3204 (63)

Body mass index, kg/m2 25.7 ± 3.9

LDL cholesterol, mg/dl 162 ± 39

HDL cholesterol, mg/dl 54.1 ± 15.5

Diabetes mellitus, No. (%) 391 (8)

Current smoking, No. (%) 1363 (27)

MR-proADM, nmol/L (n=4814) 0.46 ± 0.13

MR-proANP, pmol/L (n=4815) 66 (51–86)

N-BNP, pg/mL (n=4713) 61 (34–110)

Cystatin C, mg/L (n=4713) 0.78 ± 0.15

CRP, mg/L (n=4852) 1.3 (0.7–2.7)

Lp-PLA2 activity, nmol/min/mL (n=4936) 45 ± 13

Predicted 10-year Coronary Heart Disease Risk (Framingham Risk Score), % 4.8 ± 9.8

Normally distributed data are given as mean ± SD. Skewed variables are given as median (IQR).

*Number of subjects with complete data on conventional risk factors (age, sex, systolic blood pressure, diastolic blood pressure, use of anti-

hypertensive therapy, current smoking, diabetes, LDL cholesterol, HDL cholesterol, and body mass index). For biomarkers, the number of subjectswith complete data on conventional risk factors and the biomarker are shown in parentheses


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Tabl

e 2

Indi

vidu

al b

iom

arke

rs a

nd in

cide

nt c

ardi

ovas

cula

r eve

nts

Bio

mar

ker

Adj

uste

d H

R95

% C

IP

(HR

)Δ

C*

P (Δ

C)

NR

IP

(NR

I)†

P(ID

I)†

N-B

NP

1.22

(1.1

0–1.

36)

<0.0

010.

004

0.12

0.4%

0.84

0.08

MR

-pro

AD

M1.

12(1

.01–

1.24

)0.

040.

003

0.03

−1.4%

0.37

0.11

MR

-pro

AN

P1.

12(1

.00–

1.25

)0.

040.

001

0.49

−0.2%

0.92

0.12

Cys

tatin

C1.

13(1

.03–

1.23

)0.

006

0.00

40.

010.

6%0.

690.

66

CR

P1.

19(1

.07–

1.32

)0.

002

0.00

30.

141.

1%0.

570.

39

Ana

lyse

s are

bas

ed o

n a

sam

ple

rest

ricte

d to

indi

vidu

als w

ith a

ll bi

omar

kers

show

n (n

=4,4

83; 3

64 e

vent

s). A

ll ha

zard

ratio

s are

exp

ress

ed p

er S

D in

crem

ent i

n bi

omar

ker c

once

ntra

tion.

Mul

tivar

iabl

e C

oxpr

opor

tiona

l haz

ards

mod

els w

ere

adju

sted

for a

ge, s

ex, a

ntih

yper

tens

ive

treat

men

t, sy

stol

ic b

lood

pre

ssur

e, d

iast

olic

blo

od p

ress

ure,

bod

y m

ass i

ndex

, dia

bete

s, LD

L, H

DL,

and

cur

rent

smok

ing.

* Incr

emen

t in

C-s

tatis

tic in

a m

odel

with

con

vent

iona

l ris

k fa

ctor

s and

the

biom

arke

r, co

mpa

red

with

con

vent

iona

l ris

k fa

ctor

s alo

ne (C

=0.7

58).

† P-va

lue

for i

ncre

ase

in N

et R

ecla

ssifi

catio

n In

dex

(NR

I) o

r int

egra

ted

disc

rimin

atio

n in

dex

(ID

I) in

a m

odel

with

con

vent

iona

l ris

k fa

ctor

s and

the

biom

arke

r, co

mpa

red

with

con

vent

iona

l ris

k fa

ctor

sal

one.

CI,

conf

iden

ce in

terv

al; H

R, h

azar

d ra

tio.


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Tabl

e 3

Indi

vidu

al b

iom

arke

rs a

nd in

cide

nt c

oron

ary

even

ts

Bio

mar

ker

Adj

uste

d H

R95

% C

IP

(HR

)Δ

C*

P (Δ

C)

NR

IP

(NR

I)†

P(ID

I)†

N-B

NP

1.28

(1.1

2–1.

47)

<0.0

010.

006

0.21

1.2%

0.60

0.08

MR

-pro

AD

M1.

21(1

.07–

1.37

)0.

002

0.00

40.

142.

4%0.

350.

02

Cys

tatin

C1.

15(1

.04–

1.27

)0.

006

0.00

40.

090.

9%0.

630.

32

Ana

lyse

s are

bas

ed o

n a

sam

ple

rest

ricte

d to

indi

vidu

als w

ith a

ll bi

omar

kers

show

n (n

=4,6

00; 2

16 e

vent

s). A

ll ha

zard

ratio

s are

exp

ress

ed p

er S

D in

crem

ent i

n bi

omar

ker c

once

ntra

tion.

Mul

tivar

iabl

e C

oxpr

opor

tiona

l haz

ards

mod

els w

ere

adju

sted

for a

ge, s

ex, a

ntih

yper

tens

ive

treat

men

t, sy

stol

ic b

lood

pre

ssur

e, d

iast

olic

blo

od p

ress

ure,

bod

y m

ass i

ndex

, dia

bete

s, LD

L, H

DL,

and

cur

rent

smok

ing.

* Incr

emen

t in

C-s

tatis

tic in

a m

odel

with

con

vent

iona

l ris

k fa

ctor

s and

the

biom

arke

r, co

mpa

red

with

con

vent

iona

l ris

k fa

ctor

s alo

ne (C

=0.7

60).

† P-va

lue

for i

ncre

ase

in N

et C

lass

ifica

tion

Inde

x (N

RI)

or i

nteg

rate

d di

scrim

inat

ion

inde

x (I

DI)

in a

mod

el w

ith c

onve

ntio

nal r

isk

fact

ors a

nd th

e bi

omar

ker,

com

pare

d w

ith c

onve

ntio

nal r

isk

fact

ors a

lone

.

CI,

conf

iden

ce in

terv

al; H

R, h

azar

d ra

tio.


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Tabl

e 4

Mul

tiple

bio

mar

kers

and

inci

dent

car

diov

ascu

lar a

nd c

oron

ary

even

ts

Bio

mar

kers

ret

aine

d by

back

war

ds e

limin

atio

nA

djus

ted

HR

95%

CI

P (H

R)

Δ C

*P

(ΔC

)N

RI

P (N

RI)

†P(

IDI)

†

Firs

t car

diov

ascu

lar

even

ts

N-B

NP

1.21

(1.0

8–1.

34)

0.00

10.

007

0.04

0.0%

0.99

0.08

CR

P1.

17(1

.05–

1.30

)0.

005

Firs

t cor

onar

y ev

ents

N-B

NP

1.24

(1.0

8–1.

42)

0.00

20.

009

0.08

4.7%

0.09

0.03

MR

-pro

AD

M1.

16(1

.02–

1.32

)0.

02

All

haza

rd ra

tios a

re e

xpre

ssed

per

SD

incr

emen

t in

biom

arke

r con

cent

ratio

n. M

ultiv

aria

ble

Cox

pro

porti

onal

haz

ards

mod

els w

ere

adju

sted

for a

ge, s

ex, a

ntih

yper

tens

ive

treat

men

t, sy

stol

ic b

lood

pre

ssur

e,di

asto

lic b

lood

pre

ssur

e, b

ody

mas

s ind

ex, d

iabe

tes,

LDL,

HD

L, a

nd c

urre

nt sm

okin

g. F

or e

ach

endp

oint

, all

biom

arke

rs sh

own

wer

e en

tere

d to

geth

er in

to th

e C

ox m

odel

.

* Incr

emen

t in

C-s

tatis

tic in

a m

odel

with

con

vent

iona

l ris

k fa

ctor

s and

the

biom

arke

r set

.

† P-va

lue

for i

ncre

ase

in N

et C

lass

ifica

tion

Inde

x (N

RI)

or i

nteg

rate

d di

scrim

inat

ion

inde

x (I

DI)

in a

mod

el w

ith c

onve

ntio

nal r

isk

fact

ors a

nd th

e bi

omar

ker s

et, c

ompa

red

with

con

vent

iona

l ris

k fa

ctor

sal

one.

CI,

conf

iden

ce in

terv

al; H

R, h

azar

d ra

tio.


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Table 5

Reclassification of 10-year predicted risk

Cardiovascular Events

Model with conventional risk factors aloneModel with conventional risk factors and biomarkers

<6% 6% to <20% ≥20% Total

<6%

Total N 3,092 123 0 3,215

N with events 65 7 0 72

% with events 2.1% 5.7% 0% 2.2%

6% to <20%

Total N 143 920 35 1,098


% with events 7.0% 11.8% 17.1% 11.4%

≥20%

Total N 0 34 136 170


% with events 0% 11.8% 27.2% 24.1%

Total

Total N 3,235 1,077 171

N with events 75 120 43

% with events 2.3% 11.1% 25.1%

Coronary Events

Model with conventional risk factors aloneModel with conventional risk factors and biomarkers

<6% 6% to <20% ≥20% Total

<6%

Total N 3,891 85 0 3,976


% with events 1.8% 10.6% 0% 2.0%

6% to <20%

Total N 110 443 22 575


% with events 2.7% 10.2% 9.1% 8.7%

≥20%

Total N 0 14 35 49


% with events 0 14.3% 31.4% 26.5%

Total

Total N 4,001 542 57

N with events 75 56 13

% with events 1.9% 10.3% 22.3%

The number of events differ from the main analyses, because the table is restricted to events occuring during the first 10 years of follow up.


Novel and Conventional Biomarkers for Prediction of Incident Cardiovascular Events in the Community

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