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Prior studies have conflicted regarding how much information novel biomarkers add to cardiovascular risk assessment.
To evaluate the utility of biomarkers for predicting cardiovascular risk when added to conventional risk factors, using contemporary biomarkers and newer statistical approaches.
Between 1991 and 1994, 5067 participants (mean age 58, 60% women) without cardiovascular disease from a prospective cohort in Malmö, Sweden underwent 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 followed until 2006. First cardiovascular events (myocardial infarction, stroke, coronary death) were ascertained 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.
Incident cardiovascular and coronary events.
During median follow-up of 12.8 years, there were 418 cardiovascular and 230 coronary events. Models with conventional risk factors had c-statistics of 0.758 (95% confidence interval [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 cardiovascular events, 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 (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 to intermediate-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 almost entirely confined to down-classification of individuals without events, rather than up-classification of those with events.
Selected biomarkers may be used to predict future cardiovascular events, but the gains over conventional risk factors are minimal. Risk classification improved in intermediate-risk individuals, mainly through the identification of those unlikely to develop events.
Cost-effective cardiovascular prevention relies on the accurate identification of individuals at risk. However, a large proportion of individuals with cardiovascular events have one or fewer of the conventional risk factors, including smoking, diabetes, hypertension, or hyperlipidemia.1 As a result, the use of novel biomarkers to augment standard risk algorithms has attracted increasing attention in recent years. This interest has further intensified with the publication of the JUPITER trial, which showed that statin therapy reduced 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 of biomarkers for cardiovascular risk prediction. Some reports indicate that biomarkers such as CRP aid risk prediction,3,4 whereas other studies conclude that such biomarkers contribute relatively little incremental information.5,6 A number of factors influence how well biomarkers predict outcomes, including the population studied, the statistical methods for evaluating the biomarkers, and the specific biomarkers selected. Studies focusing on high-risk populations often yield favorable estimates of biomarker performance,4,7 but the greatest need for new risk markers exists in low- to intermediate-risk populations, for whom the data are most conflicting.8 With regard to the statistical approaches to evaluating new biomarkers, it is widely accepted that basic association measures such as hazard ratios or odds ratios alone are insufficient to assess prognostic utility.9 Newer metrics assess how well biomarkers assign patients to clinical risk categories,3,10 but studies are only beginning to incorporate such metrics.4 Another important consideration is the selection of biomarkers. Although several biomarkers consistently predict cardiovascular events after adjustment for conventional risk factors,11 few population based studies have incorporated multiple informative biomarkers simultaneously, an approach that has the greatest prospect of providing 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 generated interest as well.12–15 Lipoprotein-associated phospholipase 2 (Lp-PLA2) has been related to cardiovascular risk16,17 and attracted interest because of the development of pharmacological agents inhibiting Lp-PLA2.18 Cystatin C is a novel marker of renal function that predicts cardiovascular events better than serum creatinine.13 Mid-regional pro-atrial natriuretic peptide (MR-proANP) and pro-adrenomedullin (MR-proADM) are newer biomarkers that predict prognosis in patients post-myocardial infarction.14,19
The present investigation was undertaken to address limitations of prior studies assessing biomarkers for primary cardiovascular prevention. We studied a large, middle-aged population-based cohort without cardiovascular disease, using a variety of newer statistical measures designed specifically to evaluate risk prediction models. We assessed both older (CRP, N-BNP) and newer cardiovascular biomarkers (Lp-PLA2, cystatin C, MR-proANP, MR-proADM), individually and in combination, compared with a basic model comprising conventional risk factors.
The Malmö Diet and Cancer (MDC) study is a population-based, prospective epidemiologic cohort of 28,449 persons enrolled between 1991 and 1996. From this cohort, 6,103 persons were randomly selected to participate in the MDC Cardiovascular Cohort, which was designed to investigate the epidemiology of carotid artery disease.16 We excluded participants with prior myocardial infarction or stroke at baseline (n=143). Of the remaining participants, fasting plasma was available on 5,400 persons, among whom complete data on conventional cardiovascular risk factors were available on 5,067. The individual plasma biomarkers were successfully measured in 4,713 to 4,936 out of the 5,067 subjects (Table 1). Subjects with measurement of biomarkers did not differ from eligible subjects in the original MDC Cardiovascular Cohort with regard to mean age, gender, mean systolic and diastolic blood pressure, mean body mass index, and smoking prevalence.
All participants gave written informed consent and the study was approved by the Ethical Committee at Lund University, Lund, Sweden.
Participants underwent a medical history, physical examination, and laboratory assessment. Blood pressure was measured using a mercury-column sphygmomanometer after 10 minutes of rest in the supine position. Hypertension was defined as systolic or diastolic blood pressure ≥140/90 mmHg or use of antihypertensive medication. Diabetes mellitus was defined as a fasting whole blood glucose >109 mg/dl (6.0 mmol/L), a self-reported physician diagnosis of diabetes, or use of anti-diabetic medication. Cigarette smoking was elicited by a self-administered questionnaire, with current cigarette smoking defined as any use within the past year. We measured fasting total cholesterol, HDL cholesterol, and triglycerides according to standard procedures at the Department of Clinical Chemistry, University Hospital Malmö. LDL cholesterol was calculated according to Friedewald’s formula.
Cardiovascular biomarkers were analyzed in fasting EDTA plasma specimens that had been frozen at −80°C immediately after collection. The selection of biomarkers was based on the results of prior population-based and hospital-based studies.3,6,12–15 The principal investigators (OM, CNC, TW) initiated and designed the study, selected the biomarkers, and performed 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 activating factor 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 against amino 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 0.08 nmol/L, respectively. Censoring by the lower detection limit occurred in 3 individuals for MR-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 no individuals were censored at these thresholds.
Mean inter-assay coefficients of variation were 4.6% for CRP, 5.8% for Lp-PLA2, ≤10% for MR-proANP and MR-proADM, 2.7% for N-BNP, and 4.3% for cystatin C.
We examined two primary outcomes: coronary events and cardiovascular events. The procedure for ascertaining outcome events has been detailed previously.23,24 Coronary events were defined as fatal or non-fatal myocardial infarction or death due to ischemic heart disease. Cardiovascular events were defined as coronary events or fatal or non-fatal stroke. Events were identified through linkage of the 10-digit personal identification number of each Swedish citizen with three registries: the Swedish Hospital Discharge Register, the Swedish Cause of Death Register, and the Stroke in Malmö register. Myocardial infarction was 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 was defined on the basis of codes 412 and 414 (ICD9) or I22–I23 and I25 (ICD10). Fatal or nonfatal stroke was defined using codes 430, 431, 434 and 436 (ICD9) and I60, I61, I63, and I64 (ICD10). Classification of outcomes using these registries has been previously validated,25,26 and the sensitivity of the registry for detecting events such as myocardial infarction 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 Discharge Register using codes 429 (ICD9) and I50 (ICD 10). The primary diagnosis of heart failure in the Swedish Hospital Discharge Register has been shown to have an accuracy of 95%.28
Continuous biomarker variables with right skewed distributions (MR-proANP, N-BNP and CRP) were log-transformed before analysis. We performed multivariable Cox proportional hazards models to examine the association between biomarkers and incident events. All models were adjusted for age, sex, systolic blood pressure, diastolic blood pressure, use of anti-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 respective biomarker.
Each biomarker was individually tested in models for cardiovascular and coronary events, with adjustment for conventional risk factors. The initial analyses used all participants with available data for the biomarker being studied. Thus, sample sizes for these analyses ranged from 4,713 (for N-BNP and cystatin C) to 4,937 (for Lp-PLA2), corresponding to the number of participants in whom the biomarker was measured.
We then examined the joint and comparative value of biomarkers for predicting cardiovascular and coronary events. These analyses included only the biomarkers with a statistically significant association with the endpoint in the initial stage (5 biomarkers for cardiovascular events and 3 biomarkers for coronary events). In order to standardize the number of subjects for the biomarker-risk factor and biomarker-biomarker comparisons, we restricted subsequent analyses to participants with complete data on all biomarkers being studied for the respective endpoint (n=4,483 for cardiovascular events and n=4,600 for coronary events). In this common sample, we examined models with no biomarkers, models with individual biomarkers, and models with multiple biomarkers. For models with multiple biomarkers, we entered all biomarkers into a backward elimination model, with the conventional risk factors forced in and a retention p-value <0.05. With the observed incidence rates, we had 80% power at α=0.05 to detect hazards ratios of 1.17 for cardiovascular 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 conventional risk factors with and without biomarkers.29 To assess global calibration of the risk models, we calculated modified Hosmer-Lemeshow statistics for models with and without biomarkers.30 We also evaluated the ability of biomarkers to reclassify risk, following methods suggested previously.3,10 Using multivariable risk models with the clinical covariates noted above, participants were initially classified as low, intermediate, or high risk 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, with the exception of sex, cigarette smoking, use of anti-hypertensive therapy, and diabetes, which were entered as dichotomous variables. Participants were then allowed to be reclassified into different categories with the addition of the biomarker data. We assessed the number of participants reclassified, and also calculated the Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI).10 In secondary analyses, we repeated the reclassification analysis using the National Cholesterol Education Program Adult Treatment Panel III (ATP 3) algorithm as the base clinical model.31 Under ATP3, individuals with diabetes are automatically assigned to the highest risk category.
All analyses were performed using Stata software version 8.0 (StataCorp) except for the tests for the proportionality of hazards assumption which were performed using the survival package 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.
Characteristics 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 or with 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 between N-BNP and MR-proANP (r=0.47, 95% confidence interval [CI], 0.45–0.49), and between MR-proADM and cystatin C (r=0.47, 95% CI, 0.45–0.49).
The proportionality of hazards criterion was met in all analyses of biomarkers in relation to cardiovascular and coronary events. The 10-year incidence of cardiovascular events was 7.8%. After adjustment for conventional risk factors, 5 of 6 biomarkers examined individually showed a significant relationship with incident cardiovascular events (Supplementary Table 1A). The comparative performance of the biomarkers was assessed in the 4,483 participants with data on all 5 biomarkers, in whom 364 experienced a first incident cardiovascular event during follow up. Multivariable-adjusted hazard ratios for each biomarker 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 individual biomarkers to conventional risk factors (Table 2). A model based on conventional risk factors had a c-statistic of 0.758 (95% CI, 0.734–0.781), and the addition of individual biomarkers resulted in small increases in the c-statistic (all changes less than 0.005, Table 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.
The 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 after multivariable adjustment (Supplementary Table 1B). The adjusted hazards ratio associated with CRP had borderline significance (p=0.05).
Results based on the 4,600 participants with data on the 3 significant biomarkers, in whom there were 216 first incident coronary events, are shown in Table 3. Elevations in N-BNP and MR-proADM were associated with the highest hazards for coronary events, with adjusted hazard ratios per SD increment of 1.28 (95% confidence interval, 1.12–1.47) and 1.21 (95% confidence interval, 1.07–1.37), respectively. The c-statistic associated with conventional 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-statistic appreciably (Table 3). Model calibration was good (Hosmer-Lemeshow p>0.05) with or without 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).
In backward elimination models, 2 biomarkers were retained for prediction of cardiovascular events (N-BNP and CRP), and 2 biomarkers were retained for prediction of coronary events (N-BNP and MR-proADM). Results of multivariable Cox proportional hazards models are shown in Table 4, for both outcomes. Incorporation of the set of significant biomarkers into prediction models for cardiovascular and coronary events led to small increments (approximately 0.01) in the c-statistics. The NRI was non-significant for cardiovascular events (p=0.99) and coronary events (p=0.09). The IDI had p-values of 0.08 for cardiovascular events and 0.03 for coronary events. Results for c-statistics, NRI, and IDI were unchanged when models were modified to include all biomarkers retained at p<0.10, or all biomarkers regardless of p-value.
Table 5 shows the number of participants reclassified using the panels of informative biomarkers 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 moved 335 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-year predicted risk ≥10%, rather than 20%, the proportion of individuals reclassified to high-risk using biomarkers remained small (2.3% for cardiovascular events and 1.2% for coronary events).
Calibration was essentially the same in models with and without biomarkers. For cardiovascular events, actual event rates in the low, intermediate, and high risk groups were 2%, 11%, and 24% with conventional risk factors, and 2%, 11%, and 25% with risk factors and 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 ATP algorithm, the NRI was significant for cardiovascular events, although the net proportion correctly reclassified was still modest (6.2%, p=0.004). The NRI was non-significant for coronary events (p=0.89).
We performed additional analyses restricted to “intermediate risk” participants (10-year predicted risk 6% to <20%). Most intermediate risk participants (57% for cardiovascular events and 59% for coronary events) had 10-year predicted risks <10%. Women comprised 44% of the intermediate risk group for cardiovascular events, and 26% of the intermediate risk group for coronary events.
For cardiovascular disease, 13% of the overall number of intermediate-risk individuals were down-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 individuals without events who were correctly down-classified (133 out of 973, 14%). Among those with events, a greater number (n=10, or 8%) were inappropriately down-classified than appropriately up-classified (n=6, or 4%). Similarly, for coronary disease events, 19% were down-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.
Simple “multimarker” risk scores were constructed for each endpoint. For each participant, standardized values of each biomarker (expressed in SD units from the mean), were summed to produce a score. Score values were then divided into quartiles (with the lowest scores defining quartile 1). The median multimarker scores in each quartile for cardiovascular events 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 (Panel B) events, according to quartiles of the biomarker risk scores. In multivariable-adjusted models, hazards ratios associated with the 2nd through 4th quartiles of the score for cardiovascular 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 to conventional 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 events to conventional risk factors increased the c-statistic by 0.009, with NRI 5.5% (p=0.055) and IDI (P=0.02).
There were 392 all-cause deaths in the follow-up period. In the stepwise prediction model for mortality, 3 biomarkers were retained: N-BNP (multivariable-adjusted hazard ratio 1.13 per 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 biomarkers increased the c-statistic for predicting total mortality from 0.700 to 0.711. The IDI was significant (p<0.001). The NRI was not calculated due to the absence of clinical risk categories for mortality.
The addition of heart failure to the cardiovascular endpoint (481 events overall) did not change the biomarkers retained in the stepwise model: N-BNP (multivariable-adjusted hazard 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).
We investigated a panel of contemporary biomarkers for predicting cardiovascular risk above and beyond conventional risk factors, in a population-based cohort with more than 50,000 person years of longitudinal follow up. When considered individually, 5 biomarkers predicted future cardiovascular events, and 3 predicted future coronary events in models adjusting for conventional risk factors. The best combinations of biomarkers were N-BNP and CRP for predicting cardiovascular events, and N-BNP and MR-proADM for predicting coronary events. The use of multiple biomarkers modestly improved the accuracy of risk prediction models over and above conventional cardiovascular risk factors, and did not reclassify a substantial proportion of individuals to higher or lower risk categories.
Whether novel biomarkers add useful information for risk prediction has been the focus of intense scrutiny in the cardiovascular literature.4,6,32 Conflicting findings have been attributed to a variety of factors. Inadequate statistical power, use of older biomarkers, and lack of consideration of measures such as calibration and reclassification have been invoked to explain the poor performance of biomarkers in some studies.3 Conversely, it has been argued that other studies over-estimate the relative utility of biomarkers by examining homogenous or highly-selected samples, or using endpoints such as mortality that are poorly predicted 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 strengths and limitations of potential biomarker strategies in primary prevention. With use of biomarkers, 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, a finding that confirms observations from several prior studies.6,34 Because the c-statistic has been criticized as being insensitive to small changes in predictive accuracy,35 we also calculated a newer measure called the IDI.10 This metric improves when novel markers correctly assign individuals to higher or lower probabilities of having events. The multimarker 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 and MR-proADM) and total mortality (due to N-BNP and CRP).
What may be relevant to clinical care, however, is not whether changes in predicted probabilities are statistically significant, but whether they result in individuals being assigned to new, clinically-meaningful risk categories (reclassification) that would be targeted for preventive therapies. Our data indicate that a relatively small proportion of individuals are moved to new risk categories by the addition of biomarkers; 8% or fewer when 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 III guidelines. Furthermore, these reclassifications result in only modest improvements in the overall concordance between risk categories and actual event rates, as measured by the NRI.10
Rather than screening the entire adult population with biomarkers, an alternate strategy would be to focus on those individuals deemed to be “intermediate risk,” often defined as having a 10-year predicted event rate of 6–19%.3 Our estimates of NRI are higher in this group (7.5%, p=0.03 for cardiovascular events; 14.6%, p=0.003 for coronary events). The NRI in intermediate-risk individuals has been described as the “clinical NRI,” emphasizing the potential application to clinical screening.36 However, it is notable that the significance of the NRI in this setting is driven primarily by the down-classification of individuals who do not have events. Although informative, such shifts are much less likely to lead to changes in 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 categories into the intermediate risk group.37 For instance, imagine a marker that reclassifies every person with an event from intermediate risk to high risk, and every person without an event from intermediate risk to low risk, but at the same time moved the same number of events from high to intermediate risk and non-events from low to intermediate risk. Such a marker would have a perfect clinical NRI (100%), but a true NRI (when considering the whole sample) of 0%.
It is possible that the performance of the biomarkers would have been superior in a higher risk cohort. Some of the biomarkers studied, including N-BNP, have shown better discriminative 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 large number of cardiovascular events derive from this group, and individuals in this group are the least likely to be targeted for proven, preventive therapies.
Statins for primary prevention confer benefit in individuals across a broad range of baseline cardiovascular risk.2,38,39 However, treating unselected individuals with statins may not be practical if absolute event rates are low or therapies are expensive. Thus, reclassifying individuals as low- or high-risk could have immediate clinical relevance with regard to identifying candidates for statin therapy. Our findings support the premise that biomarkers could be used to refine these classifications, but also highlight the relatively low proportion of individuals meaningfully reclassified with existing biomarkers.
These data do not exclude a future role for circulating biomarkers as adjuncts to conventional risk factors, nor do they minimize the potential for biomarkers to provide insight into underlying mechanisms of disease. Several biomarkers studied did lead to shifts in predictive accuracy that were at least statistically significant. The challenge will be to find new cardiovascular biomarkers that, alone or in combination with existing biomarkers, can bring about improvements in risk assessment that are not just statistically significant, but clinically significant as well.40
The 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 of Siemens Diagnostics, with the performance of the biomarker assays.
Dr. Melander was supported by grants from the Swedish Medical Research Council, the Swedish Heart and Lung Foundation, the Medical Faculty of Lund University, Malmö University Hospital, the Albert Påhlsson Research Foundation, the Crafoord foundation, the Ernhold Lundströms Research Foundation, the Region Skane, the Hulda and Conrad Mossfelt Foundation, the King Gustaf V and Queen Victoria Foundation, the Lennart Hanssons Memorial Fund, and the Wallenberg Foundation. Dr. Newton-Cheh was supported by NIH K23-HL-080025, a Doris Duke Charitable Foundation Clinical Scientist Development Award, and a Burroughs Wellcome Fund Career Award 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 and the accuracy of the data analysis.
The funding organizations had no role in the design and conduct of the study, collection, management, analysis, and interpretation of the data, or preparation or approval of the manuscript.
Drs. Struck, Morgenthaler and Bergmann are employees of and Dr. Bergmann holds stock in BRAHMS, AG. BRAHMS, AG holds patent 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 primary prevention. Apart from this, there are no conflicts of interest in connection with this paper.