doi: 10.1161/CIRCULATIONAHA.104.482570
(Circulation. 2006;113:2335-2362.)
© 2006 American Heart Association, Inc.
Basic Science for Clinicians |
Biomarkers of Cardiovascular Disease
Molecular Basis and Practical Considerations
From the National Heart, Lung, and Blood Institute’s Framingham Heart Study, and the Cardiology Section and Department of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, Mass.
Correspondence to Ramachandran S. Vasan, MD, Framingham Heart Study, 73 Mount Wayte Ave, Suite 2, Framingham, MA 01702-5803. E-mail vasan{at}bu.edu
Key Words: atherosclerosis • epidemiology • genetics • genomics • proteins
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Introduction |
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 Top Introduction What Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality in the United States.1 Primary prevention and secondary prevention of CVD are public health priorities.2 Substantial data indicate that CVD is a life course disease that begins with the evolution of risk factors that in turn contribute to the development of subclinical atherosclerosis.3,4 Subclinical disease culminates in overt CVD.5,6 The onset of CVD itself portends an adverse prognosis with greater risk of recurrent events, morbidity, and mortality.7,8 It is also increasingly clear that although clinical assessment is the keystone of patient management, such evaluation has its limitations.9–12 Clinicians have used additional tools to aid clinical assessment and to enhance their ability to identify the "vulnerable" patient at risk for CVD, as suggested by a recent National Institutes of Health (NIH) panel.13,14 Biomarkers are one such tool to better identify high-risk individuals, to diagnose disease conditions promptly and accurately, and to effectively prognosticate and treat patients with disease. This review provides an overview of the molecular basis of biomarker discovery and selection and the practical considerations that are a prerequisite to their clinical use.
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What Is a Biomarker? Definition and Types |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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The term biomarker (biological marker) was introduced in 1989 as a Medical Subject Heading (MeSH) term: "measurable and quantifiable biological parameters (eg, specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc." In 2001, an NIH working group standardized the definition of a biomarker as "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention" and defined types of biomarkers (Table 1).15
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A biomarker may be measured on a biosample (as a blood, urine, or tissue test), it may be a recording obtained from a person (blood pressure, ECG, or Holter), or it may be an imaging test (echocardiogram or CT scan).
Biomarkers can indicate a variety of health or disease characteristics, including the level or type of exposure to an environmental factor, genetic susceptibility, genetic responses to exposures, markers of subclinical or clinical disease, or indicators of response to therapy. Thus, a simplistic way to think of biomarkers is as indicators of disease trait (risk factor or risk marker), disease state (preclinical or clinical), or disease rate (progression).16 Accordingly, biomarkers can be classified as antecedent biomarkers (identifying the risk of developing an illness), screening biomarkers (screening for subclinical disease), diagnostic biomarkers (recognizing overt disease), staging biomarkers (categorizing disease severity), or prognostic biomarkers (predicting future disease course, including recurrence and response to therapy, and monitoring efficacy of therapy).15
Biomarkers may also serve as surrogate end points (Table 1).15 Although there is limited consensus on this issue, a surrogate end point is one that can be used as an outcome in clinical trials to evaluate safety and effectiveness of therapies in lieu of measurement of the true outcome of interest. The underlying principle is that alterations in the surrogate end point track closely with changes in the outcome of interest.17–19 Surrogate end points have the advantage that they may be gathered in a shorter time frame and with less expense than end points such as morbidity and mortality, which require large clinical trials for evaluation. Additional values of surrogate end points include the fact that they are closer to the exposure/intervention of interest and may be easier to relate causally than more distant clinical events. An important disadvantage of surrogate end points is that if clinical outcome of interest is influenced by numerous factors (in addition to the surrogate end point), residual confounding may reduce the validity of the surrogate end point. It has been suggested that the validity of a surrogate end point is greater if it can explain at least 50% of the effect of an exposure or intervention on the outcome of interest.20
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Characteristics of an Ideal Biomarker |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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The overall expectation of a CVD biomarker is to enhance the ability of the clinician to optimally manage the patient. For instance, in a person with chronic or atypical chest pain, a biomarker (eg, treadmill stress test or dobutamine stress echocardiogram) may be expected to facilitate the identification of patients with chest pain of an ischemic etiology (angina). In a patient presenting to the emergency department with acute severe chest pain (suspected acute coronary syndrome), a biomarker may help to differentiate patients with an acute myocardial infarction (MI) from those with unstable angina (eg, troponin I or T), acute pulmonary embolism (eg, D-dimer or ventilation perfusion scan), or an aortic dissection (eg, transesophageal echocardiogram) in a timely fashion to facilitate targeted management. In a patient with an established acute MI, a biomarker may be able to assess the likelihood of the following: a therapeutic response (eg, ECG ST-segment elevation indicating need for thrombolysis); the extent of myocardial damage (eg, troponin); the severity of underlying coronary disease (eg, coronary angiography); the degree of left ventricular dysfunction (eg, echocardiography); the risk of future recurrences (eg, exercise stress test); and progression to heart failure (eg, B-type natriuretic peptide [BNP]).
Regardless of the purpose for its use, a new biomarker will be of clinical value only if it is accurate, it is reproducibly obtained in a standardized fashion, it is acceptable to the patient, it is easy to interpret by clinicians, it has high sensitivity and high specificity for the outcome it is expected to identify, it explains a reasonable proportion of the outcome independent of established predictors consistently in multiple studies, and there are data to suggest that knowledge of biomarker levels changes management (Table 2
).21 Table 2 displays the desirable properties of biomarkers overall and of biomarkers of screening, diagnosis, and prognosis.22–29
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The desirable properties of biomarkers vary with their intended use.30 For screening biomarkers, features such as high sensitivity, specificity, and predictive values, large likelihood ratios (discussed below), and low costs are important. For diagnostic markers of acute cardiac disease (such as acute MI), in addition to the aforementioned characteristics, rapid sustained elevation, high tissue specificity (indicating myocardial origin), release proportional to disease extent, and assay features conducive to point-of-care testing are critical.22 For biomarkers monitoring disease progression or response to therapy, features such as sensitivity or specificity are less important because the patient serves as his or her own control (baseline values are compared with follow-up values). Narrow intraindividual variation and tracking with disease outcome or therapy are critical. Costs may be less important for prognostic markers because only people with disease are tested. Some biomarkers (eg, exercise stress test) may be used for both diagnostic and prognostic purposes. Establishing the prognostic utility of a biomarker is more challenging because it requires a larger sample and a prospective design, whereas demonstrating its value as a diagnostic test requires a smaller sample and a cross-sectional design.31
Regardless of the intended use, it is important to remember that biomarkers that do not change disease management cannot affect patient outcome and therefore are unlikely to be cost-effective (judged in terms of quality-adjusted life-years gained). Typically, for a biomarker to change management, it is important to have evidence that risk reduction strategies should vary with biomarker levels, and a biomarker-guided approach translates into better patient outcomes compared with a management scheme (usually the current standard of care) without biomarker levels. It also means that biomarker levels should be directly or indirectly modifiable by therapy on the basis of evidence from prospective clinical trials. Biomarkers that do not result in medical intervention may still serve other useful purposes, such as the reassurance value of a negative exercise test in an asymptomatic airline pilot.32 There are other examples of psychological benefits accruing from negative biomarker test results such as testing for genetic susceptibility for cancer33 or Alzheimer disease.34 In other situations, biomarkers may serve as research tools by providing insights into disease mechanisms.
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Defining Abnormal Biomarker Values |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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Defining abnormal values is a critical step before the clinical use of a biomarker.30 It is important to characterize the distribution of the markers in people in the community and in patient samples on whom the biomarker will be tested. Thus, variation in levels with age, sex, ethnicity, and prevalent disease and the relations of biomarkers to known risk factors must be characterized.35
At least 3 potential approaches exist for defining abnormal biomarker levels (Figure 1). Reference limits are generated with the use of cross-sectional analyses of a reference sample (usually a healthy sample free of the disease of interest), and an arbitrary percentile cutpoint (typically the 95th or 97.5th percentile) is chosen to define abnormality.36–38 The reference range is the interval between the minimum and the maximum reference values. Approximately 200 individuals are required within each category for the formulation of reference limits for subgroups (eg, defined by age and sex).39 Cutpoints that define abnormality are typically the lower and the upper bounds of the 95% reference interval (between the lower 2.5th percentile and upper 97.5th percentile), but they may vary on the basis of the intent. The reference interval may be moved up or down according to the tradeoff between the implications (medical, ethical, social, psychological, and economic) of false-negative and false-positive results, ie, the consequences of missing disease, the availability and efficacy of treatment for people with abnormal values, and the costs associated with follow-up of abnormal results. For instance, the 99th percentile value has been used to define an abnormal troponin or creatine kinase–MB value; values exceeding this limit would indicate the presence of myocardial necrosis and an acute MI.40,41 When less specific markers of myocardial necrosis are used, a higher threshold may be used; for example, if total creatine kinase is used for the diagnosis of acute MI, a value twice the upper reference limit is recommended.40
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Several issues must be considered when reference limits are interpreted. First, a select proportion of "normal" individuals will exceed the reference limits on the basis of the percentile chosen. Second, values that lie within statistically defined reference limits may not indicate health in a given individual, especially when the person comes from a group inherently different from the one used to derive the reference limits. Third, a change in values within the reference range may indicate pathology. Accordingly, delta limits have been formulated to evaluate the change in biomarker values within an individual (in response to disease or therapy) relative to the physiological intraindividual fluctuation of values. Fourth, a value within the reference range may not necessarily be desirable, especially when the prevalence of undesirable values of a biomarker in the population is high. Thus, abnormal blood pressure or cholesterol values are not defined on the basis of the distributions of these risk factors in the community; rather, "desirable" levels are defined (see below).
Discrimination limits are also used to indicate abnormal biomarker values.42 Such limits are generated by evaluating the degree of overlap between patients with and without disease in cross-sectional studies.42 Discrimination limits trigger decisions (they are referred to as decision thresholds). The 99th percentile value of troponin for a reference sample is in essence a discrimination limit because it identifies the presence of a MI. The discrimination thresholds can be varied depending on the relative importance of missing disease versus that of misclassifying nondiseased individuals. For example, a plasma BNP value >100 pg/mL with the Biosite assay may trigger suspicion of heart failure in a dyspneic individual.43 The reference limits of the Biosite assay exceed this threshold in women aged >65 years (95th percentile is 120 pg/mL).44 A plasma value >200 pg/mL has been suggested as a threshold indicating heart failure.45
A third method is to define "undesirable" biomarker levels by relating values to the incidence of disease and seeking a threshold beyond which risk escalates. For instance, a desirable systolic blood pressure may be 
115 mm Hg because incidence of vascular disease increases continuously above this level.46 On a parallel note, low-density lipoprotein cholesterol levels 100 mg/dL are deemed to be optimal.47 For most CVD risk factors, there is a continuous gradient of risk across the range of risk factors, and a majority of individuals in a population could be classified as having undesirable levels. "Treatment" levels (especially for pharmacological treatment) of risk factors may therefore differ from undesirable levels, being defined by the risk factor thresholds for which there is good evidence (typically from large randomized controlled trials) that treatment for values above a limit does more benefit than harm. Often such treatment levels may be defined not only by the level of the specific risk factor being evaluated but by taking into consideration absolute risk of disease based on the values of several other risk factors.48 Thus, a blood pressure level of 140 (systolic) or 90 (diastolic) mm Hg or more indicates systemic hypertension.49 However, experts have argued that blood pressure levels above or below this threshold could be treated on the basis of the absolute risk of CVD events, which in turn depends on the concomitant burden of other risk factors.48 For other biomarkers, the choice of the optimal cutpoint defining abnormality remains to be described and may vary with the purpose. For instance, Framingham data indicate that a plasma BNP value exceeding the 80th percentile value in the cohort (20.0 pg/mL for men and 23.3 pg/mL for women; Shionogi assay) is associated with a 76% increased risk of CVD and a tripling of congestive heart failure hazard.50 These values are below the 95th percentile value of a healthy reference sample at any age (Shionogi assay).51 Therefore, cutpoints of plasma BNP that identify discrimination limits for a diagnosis of congestive heart failure may differ from the upper reference limit, which in turn may vary from desirable levels.
Once abnormal thresholds of markers are formulated by any of the 3 aforementioned methods, biomarker performance can be assessed with the use of principles outlined in the next section.
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Evaluation of Biomarker Performance: General Principles |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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The exact yardstick for evaluating the performance of a biomarker varies on the basis of the intended use. Good-quality biomarker studies make an independent masked comparison of the performance of a given biomarker with a reference standard in an appropriate sample of consecutive patients that represents an adequate spectrum of the disease.25 In general, the performance of biomarkers is seldom as good in a second sample as in the sample in which they were initially assessed. Consequently, it is desirable that biomarkers be evaluated initially in a derivation or training set and then investigated in a validation or test set.52 Standards have been proposed for designing and reporting the results of studies evaluating the performance of biomarkers for diagnosis25 and for prognosis.26
The accuracy of a biomarker test is evaluated in terms of its sensitivity (detection of disease when disease is truly present, ie, identifying true-positives) and its specificity (recognition of absence of disease when disease is truly absent, ie, identifying true-negatives) at select cutpoints. Several CVD biomarkers are continuously distributed quantitative variables, although there are some notable exceptions (for example, gender, race, diabetes, hypertension, genotypes). It is therefore critical to evaluate the information content of a biomarker over a range of values, often with the use of receiver operating characteristic (ROC) curves.53–55 The ROC curves illustrate the tradeoff between sensitivity and specificity when biomarker levels are used clinically to identify disease. Each point on the ROC curve indicates the conditional probability of a positive test result from a random diseased individual exceeding that from a random nondiseased person.56 Likelihood ratios57 (LR) are calculated with the use of sensitivity and specificity data (Table 2
) and may be more helpful to clinicians by answering the question of interest: how likely are we to obtain a positive test result in someone with disease compared with someone without disease (LR+), and how likely are we to get a negative result in someone with disease compared with someone without disease (LR–)?
For example, if a biomarker is to be used to screen for an uncommon condition in asymptomatic people (eg, preclinical left ventricular systolic dysfunction), it should have high specificity because a "rule in" (or confirm diagnosis) strategy is more important in this situation (also called the SpIN rule27). Expressed in terms of LR, a test with a greater LR+ (typically >10) is needed; this is because the costs of mislabeling a healthy individual (predicting disease when health is likely) may outweigh the costs of missing a rare condition. Sometimes when multiple tests are considered for screening, they are obtained in series.58 When multiple tests are obtained in series and disease is considered positive when all tests are positive ("AND rule"), specificity is enhanced but sensitivity is diminished.23,59 For instance, Ng et al60 have proposed that a sequential strategy of checking people with an initial urine N-terminal pro-BNP (N-BNP) test followed by a plasma N-BNP test (in urine "positive" cases) may facilitate screening for asymptomatic left ventricular systolic dysfunction in the community by reducing the need for follow-up echocardiograms. When multiple tests are obtained in parallel and disease is considered to be present when any of the tests is positive ("OR rule"), sensitivity is increased at the cost of specificity.23,59 For a biomarker to be accepted as a routine screening test it is important to demonstrate that a strategy of measuring the biomarker improves patient outcomes relative to a conventional strategy that does not include the biomarker measurement, usually in the context of a randomized controlled clinical trial.61 Such clinical trials prove the effectiveness of screening and also provide valuable data for cost-effectiveness analyses.
If a biomarker is used to diagnose a potentially life-threatening condition in a symptomatic patient (eg, acute MI in a patient with chest pain), it should have a high sensitivity because a "rule out" (exclude disease) strategy is critical in this setting (also called the SnOUT rule27). Expressed in terms of LR, a test with a lower LR– (typically <0.10) is needed; this is because the costs of missing disease (projecting health when disease is likely) outweigh the costs of any additional testing or a false diagnosis.
Appropriate use of biomarker results requires use of a Bayesian approach,62,63 ie, integrating pretest probabilities with biomarker test results (expressed as sensitivity/specificity or as LR) to estimate the posttest probability of disease. Predictive values use this concept to facilitate interpretation of test results, taking into consideration disease prevalence. Even for a test with high sensitivity and specificity, false-positive tests will outnumber true-positive tests when disease prevalence is very low, and false-negative tests will outnumber true-negative tests when disease prevalence is very high. Pretest probabilities for estimating predictive values may be generated on the basis of the published literature combined with clinical experience. A nomogram is available that uses the pretest probability of disease and the LR of a diagnostic test to generate posttest probability of the condition.64
Biomarkers (whether for screening, diagnosis, or prognosis) are also evaluated in terms of their discrimination and calibration65–67 capabilities. Discrimination refers to the ability of the biomarker (by itself or as part of a composite score) to distinguish "case" from "noncase" in cross-sectional studies or to differentiate "those who will develop disease" from "those who will not" in longitudinal investigations. Typically, the c statistic (or concordance index) is used as the metric of model discrimination and is equivalent to the area under the ROC curve. The c statistic is the probability that in 2 randomly paired individuals (one with and one without disease), a given test correctly identifies the one with disease. It is important to note that the c statistic is a metric of overall performance. It is possible for 2 tests to have the same c statistic, yet one biomarker may be superior to the other in terms of performance at select thresholds.
Calibration tells us how the ability of a biomarker (or a model incorporating the biomarker) to predict risk relates to the actual observed risk in subgroups of the population. The Hosmer-Lemeshow goodness-of-fit statistic is often used as an indicator of model calibration.68 For this purpose, the sample is divided into deciles of risk, and the observed number of events is compared with the expected number of events. Calibration is particularly important in counseling of individuals when the question of interest is the numeric probability of disease in a given patient (rather than how sick they are relative to other persons with disease).67 Thus, risk prediction algorithms have been developed that incorporate select biomarkers and enable clinicians to predict the absolute event rates of disease; examples include estimating the risk of coronary heart disease (CHD) given values of vascular risk factors,69 assessing the risk of death or stroke in patients with atrial fibrillation,70 and appraising the risk of death in patients with established heart failure.71 Models can be recalibrated if they uniformly underestimate or overestimate risk. For example, the Framingham CHD risk score overestimated risk in a Chinese cohort. A recalibration of the risk functions (with the use of mean values of risk factors and mean CHD incidence rates in the Chinese cohort) substantially improved CHD risk prediction.72
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Evaluation of the Incremental Value of New Biomarkers and the Multimarker Concept |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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To evaluate the incremental value of a new biomarker, investigators must demonstrate the elevated risk of an outcome associated with higher levels of the new biomarker with adjustment for other established risk factors. These results are typically presented as hazards ratios (relative risk estimates from a Cox model) and a probability value test of significance of the marker in the multivariable models. It has been argued that such an interpretation of a new marker’s association with risk as a reflection of its prognostic value may be inappropriate because the "hazard ratio is dependent on the measurement scale of the marker, cutoff(s) used for the novel marker, and the manner in which established variables are modeled."73 In other words, a high hazard ratio for a marker in relation to a disease outcome does not necessarily indicate better predictive performance. Indeed, very strong associations of markers with disease are required for a given biomarker to have good discrimination properties.74 Even when a biomarker threshold is associated with very high odds of disease, it often will identity only a small proportion of people with disease if false-positive rates are to be kept low.74 For example, the relative risk for CHD mortality comparing the top with the lowest decile of the distribution of serum cholesterol was &3 in a large study, indicating a strong association.75 However, if one were to accept a serum cholesterol treatment cutpoint that yields only a 5% false-positive rate (the threshold often used for screening studies), only 12% of the people who would later die of CHD would be identified by that threshold.75 In other words, risk factors for disease may not necessarily make good screening tools.76 This is because, notwithstanding an association of a risk factor with disease, the distributions of the risk factor levels in people with and without disease may overlap substantially.76
When a new biomarker X is evaluated, it is important to remember that the question of interest is not whether X is a better predictor of disease than a previously known biomarker Y.77 Rather, the pertinent question is whether X improves the predictive accuracy of the best available model (representing the standard of care for that disease) that incorporates several known predictors of disease including Y.77 Thus, the relative added values of new biomarkers is best evaluated by estimating the increment to the c statistic compared with that from a model that incorporates other previously known predictors.73,77,78 For example, the Framingham CHD risk score may be thought of as a composite of several biomarkers with a c statistic (a metric of predictive accuracy) varying between 0.74 and 0.76, values considered consistent with a "fair" test performance.69 Few risk factors of interest in terms of CHD risk prediction can enhance the c statistic beyond that provided by the Framingham risk score78; for instance, whereas C-reactive protein (CRP) was associated with vascular risk in 2 separate studies,79,80 addition of CRP did not improve the predictive accuracy of a model incorporating established risk factors that represent the current standard of care. Another way to evaluate novel risk factors is to assess whether knowledge of a putative risk factor alters the probability of disease (eg, changes the risk category from low to intermediate risk) estimated with the use of the global CHD risk score such as to change the recommended target threshold for a modifiable risk factor (eg, change the target low-density lipoprotein cholesterol from 130 to 100 mg/dL).81,82
There is considerable interest in generating multimarker scores that use a composite of several biomarkers (measured in parallel) for the purpose of predicting disease risk and patient outcomes.83–91 The comparison of several putative biomarkers and the generation of multimarker scores must take several factors into consideration. First, comparisons of biomarkers measured on the same set of individuals must account for their inherent correlation (people with high values of one marker will likely have high values of another).92 Second, the incremental utility of adding a new biomarker to a known panel of biomarkers is often estimated by ROC analysis. It is important to realize that the ability of the biomarker to identify cases not captured by the usual sets of predictors (conditional or multi-ROC analysis) requires specification of thresholds for the usual set of markers, and the performance of the new marker is conditional on the choice of those cutpoints.93 Sometimes, a multivariate formulation of several markers can be generated with the use of techniques such as neural networks to increase diagnostic accuracy.94
Risk prediction equations that incorporate multiple markers are often used for CVD risk prediction.69,94–97 The challenges associated with the development and application of such risk scores have been reviewed elsewhere.98 Nonetheless, use of a global risk score based on assessment of multiple risk factors is critical because of their synergistic influences and the importance of targeting undesirable levels of several risk factors to maximize patient benefits.99 Risk scores formulated on the basis of a sample should be demonstrated to be reproducible in the same population (with the use of data resampling techniques such as bootstrapping).67 Additionally, to become a routine part of clinical practice, risk scores should be "transportable": geographically to diverse locations; to different ethnicities; to a wide spectrum of patients; or for predicting events over a different duration of follow-up compared with what was used to develop the score.67 Risk scores derived in one sample may need to be recalibrated when applied to a very different population.
Although it is generally believed that new biomarkers should add to the c statistic to be useful, there are exceptions to this rule.99 Novel biomarkers (eg, homocysteine) that are not incremental to known risk factors may be measured in select clinical situations99,100 such as in the following: asymptomatic individuals without obviously elevated conventional risk factors but with very strong family history of vascular disease; patients with premature vascular disease but no obvious risk factors; and patients with aggressive recurrent vascular disease in the face of well-controlled levels of conventional risk factors.
In the case of studies in which genetic biomarkers are used, there is a major concern about false-positive associations with disease (or phenotypes) resulting from numerous additional factors. A detailed discussion of the factors contributing to the lack of replication of several genetic association tests is beyond the scope of this review but includes true genetic heterogeneity across samples, publication bias, confounding by population structure, misclassification of outcomes, allelic heterogeneity, small sample sizes, and failure to account for multiple testing (including the possibility that findings are due to chance).101,102 Measures to address these issues have been proposed, including but not limited to considering pretest probabilities of associations and using false discovery rates (estimated by permutation or bootstrap methodology).103–105 Replication of findings in multiple independent samples remains the gold standard for genetics of complex diseases.106
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Evaluation of Biomarker Performance: Laboratory Factors |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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The above discussion of biomarker performance assumes a perfect laboratory and limited biological variability. In practice, preanalytical, analytical, and postanalytical factors are important contributors to biomarker performance. The greater the "noise" introduced by these factors, the lesser the "signal-to-noise ratio" offered by a biomarker.
Preanalytical variability refers to biological variability and stability over time,107,108 whereas analytical variability relates to the performance of the test in the laboratory. Low analytical variability is a fundamental requirement of all biomarkers (Table 3).109–114
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Guidelines for maintaining quality control within laboratories have been proposed.110 Analytical variability means good accuracy and precision. Accuracy refers to the degree of agreement with a reference standard for the analyte and is quantified in terms of percent bias.114 Standardization of an assay means use of a reference measurement procedure and reference materials.114,115 International reference standards have been established for several biomarkers, including interleukin-6,116 interleukin-8,117 serum amyloid A protein,118 fibrinogen,119 and high-sensitivity CRP.120 Precision refers to consistent measurement on replicates114 and is quantified in terms of the coefficient of variation (continuous markers) or the kappa statistic (qualitative markers). Analytical standards have been proposed for several CVD biomarkers, including lipids,121–124 troponins,40,41 and high-sensitivity CRP.125,126
If analytical imprecision is greater than biological variability, samples should be assayed in replicate, and quality control procedures that improve assay methodology and/or operating procedures should be instituted. This is critical for biomarkers used for point-of-care testing because imprecision may be greater in this setting compared with standard laboratory measurements. If biological variability is greater than analytical imprecision, the patient should be sampled on >1 occasion to obtain a true estimate of a biomarker. Biological variability can also be reduced by instituting a standardized protocol for phlebotomy if applicable (such as the requirement of a fasting state, supine posture, or an early morning specimen). Quality control protocols to enhance analytical precision for imaging studies have been proposed.111 In the case of newer technologies such as genotyping and microarray (discussed in a subsequent section), the possibility of analytical error is of a different order of magnitude. Standards for detecting errors in genotyping112 and in microarrays113 have been proposed as well.
Postanalytical factors affecting biomarker performance include the processes of approval and transmission and the appropriate display of test results with the use of the laboratory’s information management systems. As noted above, the quality control requirements for biomarkers also may vary depending on the mode of delivery/use of the test: an automated platform from a centralized laboratory, a point-of-care testing device, or a device used for home monitoring of analytes. Point-of-care testing usually involves small benchtop analyzers or hand-held devices that facilitate rapid decision making, earlier treatment, reduced incidence of complications, quicker optimization of treatment, reduction in hospital stay, greater patient satisfaction, and economic benefits.127
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Biomarker Discovery: Challenges and Approaches |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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The development of CVD biomarkers is challenging for several reasons. As summarized in a recent consensus document,13,14 the patient vulnerable to CVD is likely harboring a triad of abnormalities: vulnerable plaque, vulnerable blood, and vulnerable myocardium. In terms of developing biomarkers, 2 of these 3 components (vulnerable plaque and myocardium) are less directly accessible relative to the third (vulnerable blood). In the case of atherosclerotic cerebrovascular disease, biomarkers that may be elevated as a consequence of brain injury may not be detectable in large amounts in the peripheral circulation because of the blood-brain barrier. It is challenging to identify diagnostic biomarkers in the peripheral blood within 3 hours of stroke onset, the critical time window for thrombolytic therapy. Furthermore, biomarkers selected to reflect a clinical phenotype may be confounded by the inaccuracies in the characterization of the phenotype. Conversely, there may truly be a poor correlation between biomarkers and the clinical phenotype itself. For example, in the setting of an acute MI, a majority of culprit ruptured plaques occur in nonstenotic coronary lesions.128 In addition, the process of atherosclerotic CVD is inherently so complex that it would be simplistic to assume that a parsimonious set of biomarkers could capture most of the interindividual variation in propensity to develop CVD or its sequelae.
The aforementioned caveats notwithstanding, 3 parallel developments have revolutionized the field of biomarker discovery. First, the completion of the Human Genome Project129 and the HapMap Project130 and the development of microarrays, proteomics, and nanotechnology together provide new avenues for developing exceptionally informative biomarkers of CVD, including high-throughput, highly sensitive, functional assays. Second, the advances in bioinformatics coupled with cross-disciplinary collaborations (eg, of biologists, clinicians, chemists, computer scientists, physicists) have greatly enhanced our ability to retrieve, characterize, and analyze large amounts of data generated by the technological advances noted above. Third, there is increased recognition that diseases arise out of the dynamic dysregulation of several gene regulatory networks, proteins, and metabolic alterations, reflecting complex perturbations (genetic and environmental) of the "system."131,132 The expectation that single biomarkers can capture these intricate derangements and can unambiguously identify disease or that targeting single molecules or signaling pathways is adequate for treating complex pathology is simplistic. Rather, a "systems biology" approach that investigates multiple components of malfunctioning regulatory networks (referred to as multiparameter analysis of tens of hundreds of molecules) may provide better insights into disease diagnosis, prognosis, and treatment.131
The development of biomarkers in CVD can be thought of as consisting of 2 potential approaches: the first strategy is "knowledge-based" (deductive method), and the second one is more "unbiased" (inductive strategy). These 2 approaches are complementary rather than mutually exclusive. The knowledge-based strategy relies on a direct understanding of the biological processes that underlie the process of atherosclerosis and the evolution of its sequelae. It may consist of improving existing biomarkers to enhance their performance, or it may comprise designing assays for attractive new candidate markers informed by the biology of the disease process. The unbiased approach involves trolling through tens of thousands of molecules with the use of current technological advances to characterize the biomolecular profile of a stage of the disease.
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Biomarker Discovery: Molecular Biology Tools |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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The systems biology tools applied to biomarker discovery investigate the hierarchical organizational of biological information: the gene itself, the mRNA that it produces, the protein coded by the mRNA, biomodules or networks, cells, organs, individuals, populations, and ecologies.131 Table 4 provides an overview of some key techniques used for identifying putative CVD biomarkers.133 Table 5 defines broadly these "Omics" tools.134–137 Table 6 provides information about some of the mathematical and molecular biological techniques within the "Omics" toolbox.134–140 In the section below, an overview of strategies used to analyze different components of this hierarchical sequence is presented.
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Genetic Studies
Genetic biomarkers are variants in the DNA code that alone or in combination are associated with disease susceptibility, disease expression, and disease outcome, including therapeutic responses. Single nucleotide polymorphisms (SNPs; DNA sequence variation when a single nucleotide in the genome sequence is altered) have been evaluated extensively in relation to CVD. The 2 classic complementary approaches used for relating genetic sequence variation to CVD risk are the linkage approach and the association strategy.141
The linkage approach investigates families with a whole genome scan consisting of hundreds of anonymous markers to identify genetic loci that may be related to disease susceptibility. The linkage strategy identifies a segment of the genome (typically involving millions of bases of DNA) that segregates with disease. Fine mapping within these segments may lead to the identification of a gene related to disease susceptibility. To date, the linkage approach has been successful in detecting genes for single-gene disorders with large genetic effects. However, linkage studies have provided very modest yields for investigating complex traits like CVD.142
The association strategy evaluates the relation of genetic variants, typically in unrelated individuals, to the presence versus absence of disease or to variation in values of a quantitative trait.143 The scientific rationale behind association studies is that common genetic variants with modest effects contribute to the variation of complex disease in the population.141,144–146 Association studies have the ability to detect more modest genetic effects (relative to linkage). The recognition that groups of neighboring polymorphisms in the genome are highly correlated147 (in linkage disequilibrium, ie, inherited together and not easily separated by recombination) has led to the concept of tag SNPs, which can be used as proxies for most of the common genetic variants in a region of linkage dysequilibrium.148 The identification of tag SNPs is expected to greatly facilitate association studies because fewer markers need to be genotyped.149 The availability of dense SNP maps of the human genome has also fuelled interest in genomewide association analyses, studies that survey the whole genome for causal common genetic variants with the use of a dense set of SNPs.150–152 It is important to emphasize that use of SNP databases is challenged by constant updating, the need for SNP verification and/or primary resequencing (given sequencing errors and rare or population-specific variants), and variation in the linkage disequilibrium patterns across different populations that can influence the selection of tag SNPs.106
Both linkage153–160 and association161–171 studies have provided valuable insights into genetic markers with a role in pathogenesis of CVD. New putative susceptibility genes for CVD have been identified, including cytokine lymphotoxin-
(LTA, on 6p21.3 for MI), galectin-2 (LGALS2, an LTA-interacting protein on 22q12-q13 for MI), 5-lipoxygenase activating protein involved in synthesizing potent proinflammatory leukotrienes (ALOX5AP, on 13q12-q13 for MI and stroke), phosphodiesterase 4D (PDE4D, on 5q12 for ischemic stroke), and the myocyte enhancer factor 2 (MEF2) signaling pathway of vascular endothelium.172
Gene Expression
The availability of rapid, high-throughput analytical platforms has facilitated molecular phenotyping of disease states by analyzing the transcriptome. The global analysis of gene expression represents a paradigm shift from the traditional single-molecule approach to the evaluation of gene regulatory networks.173–175 The National Heart, Lung, and Blood Institute has launched a multicenter Program in Genomic Applications (http://www.nhlbi.nih.gov/resources/pga/) to advance functional genomic research related to heart, lung, blood, and sleep health and diseases.
Changes in mRNA expression of select genes in tissues can be evaluated by several techniques (such as Northern blotting, RNA differential display, RNase protection assay, and various polymerase chain reaction–based methods including real-time polymerase chain reaction). Quantitative assessments of mRNA expression on a genomewide basis can be accomplished with techniques such as the serial analysis of gene expression176 and DNA "microarrays"173; genes are grouped into expression clusters, and upregulated and downregulated clusters in disease states can be recognized. Expression analysis facilitates recognition of dysregulated gene clusters and the identification of candidate genes for association tests177 and may suggest therapeutic targets. The protein products of highly upregulated genes may be candidate biomarkers if they are secreted extracellularly.
High-throughput sequencing of randomly selected clones from human heart cDNA libraries has been used to generate a compendium of expressed sequence tags.178 A cDNA microarray called the CardioChip (containing 10 368 redundant and randomly selected sequenced expressed sequence tags) has been developed on the basis of human heart and arterial tissue cDNA libraries.179 Gene expression analyses have been performed on myocardial tissue to identify specific patterns in cardiac hypertrophy,180–182 MI,183,184 different forms of heart failure,185,186 and cardiac transplants.187 Such gene expression analysis may enable molecular profiling of patients with dilated cardiomyopathy, including the correlation of therapeutic responses with transcriptional changes.188 On a parallel note, the differential patterns of gene expression in ischemic and nonischemic heart failure subsets may have therapeutic relevance.189,190 Likewise, gene expression profiles of hypertrophic and dilated cardiomyopathy have been demonstrated to be different, thereby providing clues to molecular mechanisms underlying the conditions as well as identifying distinct biomarkers for each condition.191–193 DNA microarrays have also been applied to analyze molecular signatures of atherosclerotic lesions,194–196 vascular endothelial cells subjected to shear stress,197,198 and vascular smooth muscle cells.199 These investigations have provided valuable clues to genes implicated in atherosclerosis,194,195,200 plaque rupture,201 and vascular remodeling. Genomic techniques have also been extended to peripheral circulating blood cells (progenitor cells202 and blood cells203) to evaluate the effect of statins and to identify transcripts that are altered in coronary disease; these observations raise the exciting possibility of using more readily accessible tissues (blood cells) for genomic screening. The utility of expression profiles may be extended to predicting perioperative outcomes in patients undergoing cardiac surgery.204
Whereas global gene expression profiling offers a unique opportunity for molecular profiling of CVD with implications for diagnosis, prognostication, and treatment (including identifying disease subtypes) and for identifying new therapeutic targets, technical and conceptual challenges may limit its use. The technical limitations include the limited number of transcripts on available chips, the possibility of false-positives (emphasizing the need for confirmation of results with an independent approach such as real-time polymerase chain reaction), and the challenge of isolating cell types from heterogeneous cell populations in tissues.174,205 The availability of techniques such as laser capture microdissection has facilitated the isolation of cell populations, however.206 The conceptual challenges lie in the fact that there may be a poor correlation between mRNA expression and the proteome (because all transcripts may not be translated) and with protein function (due to inability to detect alternate splicing, posttranslational modification, subcellular localization, and interactions among proteins that can influence function).207 Additional gain- or loss-of-function studies are necessary for mechanistic interpretations.174,205
Proteomics
Proteomic approaches to the identification of disease biomarkers rely principally on the comparative analysis of protein expression in normal and disease tissues to identify aberrantly expressed proteins that may represent new biomarkers, analysis of secreted proteins (in cell lines and primary cultures), and direct serum protein profiling. Proteomics methodologies include assessment of protein expression (by Western blotting and enzyme-linked immunosorbent assay and by other antibody-based methods) and the isolation, identification, and quantification of proteins in biosamples with high-resolution 2-dimensional gel electrophoresis, high-performance liquid chromatography, surface chromatography by adsorbion of proteins to activated surfaces (matrix-assisted laser desorption–ionization technology), or via peptide ionization procedures and mass spectroscopy. Mass spectrometry can yield a comprehensive profile of peptides and proteins in biosamples without the need for initial protein separations, thereby facilitating biomarker identification with reduced sample requirements and a high throughput.
The parallel development of a human protein reference database (Human Proteome Organization; www.HUPO.org) has enabled the annotation identification of proteins detected in biosamples. The Human Proteome Organization initiative includes the mapping of proteomes in biological compartments such as the plasma, urine, brain, liver, and heart.208,209 Protein profiling with the use of multidimensional automated platforms is interfaced with database search tools to facilitate the rapid identification of constituent proteins. An important caveat in the use of proteomics is that biomarkers identified by such technology may not be consistent with those generated from mRNA expression profiling.
Proteomic databases of cardiac proteins have been constructed,210–212 and alterations of several cardiac proteins have been described in both experimental and human cardiomyopathies.213 For instance, the upregulation of ubiquitin carboxyl-terminal hydrolase in experimental214 and human215 cardiomyopathic tissues is consistent with the notion that inappropriate ubiquination and proteolysis of select cardiac proteins may play a role in promoting ventricular systolic dysfunction in human heart failure.213 Several programs of the NIH support proteomics technology development, and an NIH Roadmap initiative emphasizes the importance of studying dynamic systems.216 Advances in computational biology have facilitated computer-based sophisticated cellular and whole organ modeling of the various protein-protein interactions to reconstruct the physiological processes in the heart.217
Molecular Imaging
Noninvasive molecular imaging can enable clinicians to quantitatively identify the causative molecular constituents of disease in time and space. Molecular imaging will likely facilitate targeted therapy of CVD on the basis of the molecular elements delineated in diseased tissue.218 For example, newer targeted contrast agents are being developed for plaque characterization: "by identifying fibrin within plaque microfissures,219 adhesion or thrombogenic molecules expressed on endothelium of vulnerable plaques,220 matrix metalloproteinases in the cores of progressive lesions,221 or the early angiogenic expansion of the vasa vasorum that supports plaque development."222,223 Plaques that look morphologically similar (in terms of lipid core and fibrous cap) may be distinguished with techniques such as thermography,224 multicontrast MRI,219,225 and intravascular optical coherence tomography.226
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Biomarker Development: The Processes From Discovery to Delivery |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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Figure 2 displays the various stages from the discovery of a biomarker in a laboratory with the use of the "Omics" technologies to development of an assay and finally to its delivery, ie, application in clinical practice.227 Briefly, the process begins with the identification of target biomarkers with the use of standardized technology platforms, followed by validation of the assays,228,229 statistical evaluation of biomarker distributions in reference samples and in those with disease, and assessment of the correlation between biomarker levels (or expression patterns of biomarkers) and clinical measurements that define disease status.227
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The processes involved in biomarker discovery are best exemplified by the National Cancer Institute’s Early Detection Research Network (http://www3.cancer.gov/prevention/cbrg/edrn/), which supports the integration of discovery, evaluation, and validation of biomarkers.230 The Early Detection Research Network has 4 main components: the Biomarkers Developmental Laboratories, which lead the identification of new biomarkers or refinement of old biomarkers including assay development; the Biomarkers Validation Laboratories, which facilitate the standardization of assays; the Clinical Epidemiological Centers, which extend the investigation of biomarkers to clinical samples; and the Data Management and Coordination Center, which provides the infrastructure for statistical analyses and bioinformatics.230
The development of CVD biomarkers will transition through similar stages akin to that noted in Figure 2. As noted in a recent review of inflammatory biomarkers,35 before using a new biomarker, clinicians must seek answers to several key questions related to its measurement, its validation, and the assessment of its potential clinical use (Table 7). Answers to these questions are part of the evidentiary process that is critical for assessing whether the information gained from a new biomarker is worth its cost to the healthcare system. Such answers require the performance of large population-based studies of multiethnic samples to evaluate the relations of biomarkers to subclinical and clinical CVD phenotypes of interest and, when applicable, the conduction of clinical trials to relate biomarker profiles (encompassing a comprehensive combination of genomic, expression-based, proteomic, and metabolomic data) to disease risk and to therapeutic responses.
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Currently Available CVD Biomarkers: An Overview |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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A detailed review of CVD biomarkers (circulating, structural, functional, and genomic) was published in a recent supplement of Circulation.193,231–233 A comprehensive assessment of inflammatory biomarkers and their use in clinical cardiology has also been published recently.35,82,126,234–237 The present review will not attempt to replicate these reviews. Table 8 provides an overview of CVD biomarkers, including a display of the evidence linking them to CVD and methodological issues. 238–406 The list of biomarkers in the table is not intended to be exhaustive; rather, a brief summary of some key biomarkers is provided.
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To illustrate the opportunities and challenges related to the use of CVD biomarkers, let us consider as an example biomarkers of acute coronary syndromes. An understanding of the pathobiology of atherosclerosis and the molecular events implicated in the progression from subclinical disease to overt disease has enabled the development of CVD biomarkers.22,407 Acute coronary syndromes are accompanied by progressive mechanical obstruction, dynamic obstruction, and plaque inflammation, instability, and rupture, followed by superimposed thrombosis. Myocardial ischemia and necrosis are the sequelae, followed over time by ventricular remodeling. Thus, activation of select markers and enzymes during the different phases of the process can be detected in the peripheral circulation (Figure 3).13,14,408 The time period preceding the onset of an acute coronary syndrome is characterized by atherosclerotic arterial lesions prone to rupture: such lesions are rich in macrophages (which release lytic enzymes like metalloproteinases) and are associated with a reduction in smooth muscle, presence of a low-grade stenosis, and a thin fibrous cap. Plaque rupture is associated with release of soluble CD40 ligand, placental growth factor, pregnancy-associated plasma protein A, and adhesion molecules.408 Superimposed thrombosis may be manifest as elevations of circulating D-dimer, plasminogen activator inhibitor-1, and von Willebrand factor.408 The onset of symptomatic ischemia is antedated by release of ischemia-modified albumin by a few hours and the development of myocardial necrosis by time-dependent release of myocyte components such as troponins, myoglobin, and creatine kinase-MB. Troponin elevation in a patient with non-ST-segment elevation MI is a marker not only of myocyte necrosis but also of intracoronary thrombus formation and the distal microembolization of platelet microaggregates.409 Thus, troponin release in acute coronary syndromes also serves as an indicator of increased likelihood of response to antiplatelet and antithrombin therapy.409 The hemodynamic consequences of ischemia and/or infarction are reflected by elevation of plasma natriuretic peptide levels. The choice of the biomarker(s) in patients with suspected acute coronary syndrome depends on the answers to several questions410: Where is the test being performed (emergency department, physician’s office, coronary care unit)? What is the time interval from the onset of ischemic symptoms? Which biomarker and what immunoassay should be used? What discrimination limits should be used for the chosen biomarker(s)? Is the test being performed for diagnosis or for prognostication? The prognostic significance of biomarkers after infarction may vary with specific end points.409 Thus, elevated natriuretic peptides are key predictors of mortality risk but weaker correlates of the risk of recurrent ischemia. Therefore, multimarker schemes may need to weigh individual biomarkers differentially on the basis of the end point being predicted.409
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Cardiovascular Biomarkers: Future Directions |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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It is generally believed that the biomarker industry will continue to rapidly expand and flourish in the near future. The burgeoning research in biomarker development mandates a systematic organization of data with the use of standardized taxonomies that facilitate the online sharing of biomarker metadata among researchers. Large epidemiological and clinical studies will be required to assess the cost-effectiveness of biomarkers. Screening biomarkers will likely compete for limited healthcare budgets, and only those with excellent performance characteristics will find utility in primary care settings. It is conceivable that some biomarkers may find use as over-the-counter tests as the public continues its informed interest in its own health. Biomarkers that are cost-effective in preventing late sequelae of CVD will likely survive such competition. Diagnostic markers will find use in point-of-care testing in emergency departments and by the bedside. Biomarkers that perform well and cost-effectively in the testing of rapid "rule out" or "rule in" strategies and those that help to triage patients into low- and high-risk treatment strategies will be integrated into clinical decision-making protocols. Biomarkers (including pharmacogenetic ones) that facilitate choice of the most appropriate drug, that enable titration of drug dose to avoid side effects, and that maximize therapeutic effects are likely to be attractive to clinicians.
Biomarker development must be associated with concurrent advances in physician training to use the array of biomarkers available so that clinicians can order tests appropriately and interpret them correctly. Parallel advances must be made in medical information systems, in the quality control procedures within clinical laboratories, and in the interpretive reporting of biomarker tests. The advent of genomic biomarkers has generated a number of ethical411 and regulatory issues412 that must be addressed concomitantly. Ultimately, the evolution of CVD biomarkers will represent the coordinated and concerted effort of basic scientists, clinicians, technology experts, epidemiologists, statisticians, federal and industrial sponsors, and regulatory agencies within a cooperative framework.
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Conclusions |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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Biomarkers, defined as alterations in the constituents of tissues or body fluids, provide a powerful approach to understanding the spectrum of CVD with applications in at least 5 areas: screening, diagnosis, prognostication, prediction of disease recurrence, and therapeutic monitoring. Advances in functional genomics, proteomics, metabolomics, and bioinformatics have revolutionized unbiased inquiries into numerous putative markers that may be informative with regard to the various stages of atherogenesis including overt CVD and its sequelae. A prerequisite for the clinical use of biomarkers is elucidation of the specific indications, standardization of analytical methods, characterization of analytical features, assessment of performance characteristics, incremental yield of different markers for given clinical indications, and demonstration of cost-effectiveness. Technological advances will likely facilitate the use of multimarker profiling to individualize treatment of CVD in the future.
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Acknowledgments |
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This work was supported through NIH/National Heart, Lung and Blood Institute contracts N01-HC-25195, 1R01HL67288, 1RO1HL71039, NO1-HV-28178, U01 HL 66585 and 2K24HL04334 (Dr Vasan). The author would like to acknowledge the assistance of Drs Thomas Wang, Sekar Kathiresan, and Sudha Seshadri for their thoughtful reviews of the manuscript.
Disclosures
None.
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References |
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TopIntroductionWhat Is a Biomarker?...Characteristics of an Ideal...Defining Abnormal Biomarker...Evaluation of Biomarker...Evaluation of the Incremental...Evaluation of Biomarker...Biomarker Discovery: Challenges...Biomarker Discovery: Molecular...Biomarker Development: The...Currently Available CVD...Cardiovascular Biomarkers:...ConclusionsReferences |
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1. American Heart Association. Heart Disease and Stroke Statistics: 2005 Update. Dallas, Tex: American Heart Association; 2005.
2. Pearson TA, Blair SN, Daniels SR, Eckel RH, Fair JM, Fortmann SP, Franklin BA, Goldstein LB, Greenland P, Grundy SM, Hong Y, Houston Miller N, Lauer RM, Ockene IS, Sacco RL, Sallis JF Jr, Smith SC Jr, Stone NJ, Taubert KA. AHA guidelines for primary prevention of cardiovascular disease and stroke: 2002 update: consensus panel guide to comprehensive risk reduction for adult patients without coronary or other atherosclerotic vascular diseases. Circulation. 2002; 106: 388–391.
3. Berenson GS, Srinivasan SR, Bao W, Newman WP, Tracy RE, Wattigney WA. Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults: the Bogalusa Heart Study. N Engl J Med. 1998; 338: 1650–1656.
4. Raitakari OT, Juonala M, Kahonen M, Taittonen L, Laitinen T, Maki-Torkko N, Jarvisalo MJ, Uhari M, Jokinen E, Ronnemaa T, Akerblom HK, Viikari JSA. Cardiovascular risk factors in childhood and carotid artery intima-media thickness in adulthood: the Cardiovascular Risk in Young Finns Study. JAMA. 2003; 290: 2277–2283.
5. Kuller LH, Shemanski L, Psaty BM, Borhani NO, Gardin J, Haan MN, O’Leary DH, Savage PJ, Tell GS, Tracy R. Subclinical disease as an independent risk factor for cardiovascular disease. Circulation. 1995; 92: 720–726.
6. Psaty BM, Furberg CD, Kuller LH, Bild DE, Rautaharju PM, Polak JF, Bovill E, Gottdiener JS. Traditional risk factors and subclinical disease measures as predictors of first myocardial infarction in older adults: the Cardiovascular Health Study. Arch Intern Med. 1999; 159: 1339–1347.
7. Guidry UC, Evans JC, Larson MG, Wilson PWF, Murabito JM, Levy D. Temporal trends in event rates after Q-wave myocardial infarction: the Framingham Heart Study. Circulation. 1999; 100: 2054–2059.
8. Jokhadar M, Jacobsen SJ, Reeder GS, Weston SA, Roger VL. Sudden death and recurrent ischemic events after myocardial infarction in the community. Am J Epidemiol. 2004; 159: 1040–1046.
9. Kita Chun A, McGee SR. Bedside diagnosis of coronary artery disease: a systematic review. Am J Med. 2004; 117: 334–343.[CrossRef][Medline] [Order article via Infotrieve]
10. Panju AA, Hemmelgarn BR, Guyatt GH, Simel DL. Is this patient having a myocardial infarction? JAMA. 1998; 280: 1256–1263.
11. Pope JH, Aufderheide TP, Ruthazer R, Woolard RH, Feldman JA, Beshansky JR, Griffith JL, Selker HP. Missed diagnoses of acute cardiac ischemia in the emergency department. N Engl J Med. 2000; 342: 1163–1170.
12. Swap CJ, Nagurney JT. Value and limitations of chest pain history in the evaluation of patients with suspected acute coronary syndromes. JAMA. 2005; 294: 2623–2629.
13. Naghavi M, Libby P, Falk E, Casscells SW, Litovsky S, Rumberger J, Badimon JJ, Stefanadis C, Moreno P, Pasterkamp G, Fayad Z, Stone PH, Waxman S, Raggi P, Madjid M, Zarrabi A, Burke A, Yuan C, Fitzgerald PJ, Siscovick DS, de Korte CL, Aikawa M, Juhani Airaksinen KE, Assmann G, Becker CR, Chesebro JH, Farb A, Galis ZS, Jackson C, Jang IK, Koenig W, Lodder RA, March K, Demirovic J, Navab M, Priori SG, Rekhter MD, Bahr R, Grundy SM, Mehran R, Colombo A, Boerwinkle E, Ballantyne C, Insull W Jr, Schwartz RS, Vogel R, Serruys PW, Hansson GK, Faxon DP, Kaul S, Drexler H, Greenland P, Muller JE, Virmani R, Ridker PM, Zipes DP, Shah PK, Willerson JT. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: part I. Circulation. 2003; 108: 1664–1672.
14. Naghavi M, Libby P, Falk E, Casscells SW, Litovsky S, Rumberger J, Badimon JJ, Stefanadis C, Moreno P, Pasterkamp G, Fayad Z, Stone PH, Waxman S, Raggi P, Madjid M, Zarrabi A, Burke A, Yuan C, Fitzgerald PJ, Siscovick DS, de Korte CL, Aikawa M, Airaksinen KEJ, Assmann G, Becker CR, Chesebro JH, Farb A, Galis ZS, Jackson C, Jang IK, Koenig W, Lodder RA, March K, Demirovic J, Navab M, Priori SG, Rekhter MD, Bahr R, Grundy SM, Mehran R, Colombo A, Boerwinkle E, Ballantyne C, Insull W Jr, Schwartz RS, Vogel R, Serruys PW, Hansson GK, Faxon DP, Kaul S, Drexler H, Greenland P, Muller JE, Virmani R, Ridker PM, Zipes DP, Shah PK, Willerson JT. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: part II. Circulation. 2003; 108: 1772–1778.
15. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001; 69: 89–95.[CrossRef][Medline] [Order article via Infotrieve]
16. Fox N, Growdon JH. Biomarkers and surrogates. Neuro Rx. 2004; 1: 181.[CrossRef]
17. Prentice RL. Surrogate endpoints in clinical trials: definition and operational criteria. Stat Med. 1989; 8: 431–440.[Medline] [Order article via Infotrieve]
18. Colburn WA. Optimizing the use of biomarkers, surrogate endpoints, and clinical endpoints for more efficient drug development. J Clin Pharmacol. 2000; 40: 1419–1427.[Abstract]
19. De Gruttola VG, Clax P, DeMets DL, Downing GJ, Ellenberg SS, Friedman L, Gail MH, Prentice R, Wittes J, Zeger SL. Considerations in the evaluation of surrogate endpoints in clinical trials: summary of a National Institutes of Health workshop. Control Clin Trials. 2001; 22: 485–502.[CrossRef][Medline] [Order article via Infotrieve]
20. Freedman LS, Graubard BI, Schatzkin A. Statistical validation of intermediate endpoints for chronic diseases. Stat Med. 1992; 11: 167–178.[Medline] [Order article via Infotrieve]
21. Manolio T. Novel risk markers and clinical practice. N Engl J Med. 2003; 349: 1587–1589.
22. Morrow DA, de Lemos JA, Sabatine MS, Antman EM. The search for a biomarker of cardiac ischemia. Clin Chem. 2003; 49: 537–539.
23. Vitzthum F, Behrens F, Anderson NL, Shaw JH. Proteomics: from basic research to diagnostic application: a review of requirements & needs. J Proteome Res. 2005; 4: 1086–1097.[CrossRef][Medline] [Order article via Infotrieve]
24. Zolg JW, Langen H. How industry is approaching the search for new diagnostic markers and biomarkers. Mol Cell Proteomics. 2004; 3: 345–354.
25. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM, Lijmer JG, Moher D, Rennie D, de Vet HCW. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Clin Chem. 2003; 49: 1–6.
26. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. Reporting recommendations for tumor marker prognostic studies. J Clin Oncol. 2005; 23: 9067–9072.
27. Sackett DL, Haynes RB, Guyatt GH, Tugwell P. The Interpretation of Diagnostic Data: Clinical Epidemiology, a Basic Science for Clinical Medicine. Boston, Mass: Little, Brown; 1991: 69–152.
28. Batstone G. Practising by the evidence: the role of pathology. J Clin Pathol. 1997; 50: 447–448.
29. Rembold CM. Number needed to screen: development of a statistic for disease screening. BMJ. 1998; 317: 307–312.
30. LaBaer J. So, you want to look for biomarkers. J Proteome Res. 2005; 4: 1053–1059.[CrossRef][Medline] [Order article via Infotrieve]
31. Hlatky MA. Exercise testing to predict outcome in patients with angina. J Gen Intern Med. 1999; 14: 63–65.[Medline] [Order article via Infotrieve]
32. Mushlin AI, Kern LM, Paris M, Lambert DR, Williams G. The value of diagnostic information to patients with chest pain suggestive of coronary artery disease. Med Decis Making. 2005; 25: 149–157.
33. Rowley PT. Inherited susceptibility to colorectal cancer. Ann Rev Med. 2005; 56: 539–554.[CrossRef][Medline] [Order article via Infotrieve]
34. Marteau TM, Roberts S, LaRusse S, Green RC. Predictive genetic testing for Alzheimer’s disease: impact upon risk perception. Risk Anal. 2005; 25: 397–404.[Medline] [Order article via Infotrieve]
35. Fortmann SP, Ford E, Criqui MH, Folsom AR, Harris TB, Hong Y, Pearson TA, Siscovick D, Vinicor F, Wilson PF. CDC/AHA workshop on markers of inflammation and cardiovascular disease: application to clinical and public health practice: report from the Population Science Discussion Group. Circulation. 2004; 110: e554–e559.
36. Solberg HE. Approved recommendations (1986) on the theory of reference values, I: the concept of reference values. J Clin Chem Clin Biochem. 1987; 25: 337–342.[Medline] [Order article via Infotrieve]
37. Solberg HE. Approved recommendations (1987) on the theory of reference values, II: selection of individuals for the production of reference values. J Clin Chem Clin Biochem. 1987; 25: 639–644.
38. Solberg HE. Approved recommendations (1987) on the theory of reference values, V: statistical treatment of collected reference values: determination of reference limits. J Clin Chem Clin Biochem. 1987; 25: 645–656.
39. Lott JA, Mitchell LC, Moeschberger ML, Sutherland DE. Estimation of reference ranges: how many subjects are needed? Clin Chem. 1992; 38: 648–650.
40. Antman E, Bassand JP, Klein W, Ohman M, Lopez Sendon JL, Ryden L, Simoons M, Tendera M, for the the Joint European Society of Cardiology/American College of Cardiology Committee. Myocardial infarction redefined: a consensus document of the Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol. 2000; 36: 959–969.
41. Apple FS, Parvin CA, Buechler KF, Christenson RH, Wu AHB, Jaffe AS. Validation of the 99th percentile cutoff independent of assay imprecision (CV) for cardiac troponin monitoring for ruling out myocardial infarction. Clin Chem. 2005; 51: 2198–2200.
42. Sunderman FW Jr. Current concepts of "normal values," "reference values," and "discrimination values" in clinical chemistry. Clin Chem. 1975; 21: 1873–1877.[Abstract]
43. Dao Q, Krishnaswamy P, Kazanegra R, Harrison A, Amirnovin R, Lenert L, Clopton P, Alberto J, Hlavin P, Maisel AS. Utility of B-type natriuretic peptide in the diagnosis of congestive heart failure in an urgent-care setting. J Am Coll Cardiol. 2001; 37: 379–385.
44. Redfield MM, Rodeheffer RJ, Jacobsen SJ, Mahoney DW, Bailey KR, Burnett J. Plasma brain natriuretic peptide concentration: impact of age and gender. J Am Coll Cardiol. 2002; 40: 976–982.
45. Shapiro BP, Chen HH, Burnett JC Jr, Redfield MM. Use of plasma brain natriuretic peptide concentration to aid in the diagnosis of heart failure. Mayo Clin Proc. 2003; 78: 481–486.
46. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R; Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002; 360: 1903–1913.[CrossRef][Medline] [Order article via Infotrieve]
47. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. 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.
48. Jackson R, Lawes CM, Bennett DA, Milne RJ, Rodgers A. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual’s absolute cardiovascular risk. Lancet. 2005; 365: 434–441.[Medline] [Order article via Infotrieve]
49. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, Jones DW, Materson BJ, Oparil S, Wright JT Jr, Roccella EJ. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003; 289: 2560–2572.
50. Wang TJ, Larson MG, Levy D, Benjamin EJ, Leip EP, Omland T, Wolf PA, Vasan RS. Plasma natriuretic peptide levels and the risk of cardiovascular events and death. N Engl J Med. 2004; 350: 655–663.
51. Wang TJ, Larson MG, Levy D, Leip EP, Benjamin EJ, Wilson PWF, Sutherland P, Omland T, Vasan RS. Impact of age and sex on plasma natriuretic peptide levels in healthy adults. Am J Cardiol. 2002; 90: 254–258.[CrossRef][Medline] [Order article via Infotrieve]
52. Wade A. Derivation versus validation. Arch Dis Child. 2000; 83: 459–460.
53. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982; 143: 29–36.
54. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988; 240: 1285–1293.
55. Baker SG. The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer. J Natl Cancer Inst. 2003; 95: 511–515.
56. Pepe MS. An interpretation for the ROC curve and inference using GLM procedures. Biometrics. 2000; 56: 352–359.[CrossRef][Medline] [Order article via Infotrieve]
57. Deeks JJ, Altman DG. Diagnostic tests 4: likelihood ratios. BMJ. 2004; 329: 168–169.
58. Newman TB, Browner WS, Cummings SR. Designing studies of medical tests. In: Hulley SB, Cummings SR, Browner WS, Grady D, Hearst N, Newman TB, eds. Designing Clinical Research. Philadlephia, Pa: Lippincott Williams & Wilkins; 2001: 175–194.
59. Griner PF, Mayewski RJ, Mushlin AI, Greenland P. Selection and interpretation of diagnostic tests and procedures: principles and applications. Ann Intern Med. 1981; 94: 557–592.[Medline] [Order article via Infotrieve]
60. Ng LL, Loke IW, Davies JE, Geeranavar S, Khunti K, Stone MA, Chin DT, Squire IB. Community screening for left ventricular systolic dysfunction using plasma and urinary natriuretic peptides. J Am Coll Cardiol. 2005; 45: 1043–1050.
61. Lindholt JS, Juul S, Fasting H, Henneberg EW. Screening for abdominal aortic aneurysms: single centre randomised controlled trial. BMJ. 2005; 330: 750.
62. Diamond GA, Denton TA, Berman DS, Cohen I. Prior restraint: a Bayesian perspective on the optimization of technology utilization for diagnosis of coronary artery disease. Am J Cardiol. 1995; 76: 82–86.[CrossRef][Medline] [Order article via Infotrieve]
63. Diamond GA, Kaul S. Prior convictions: Bayesian approaches to the analysis and interpretation of clinical megatrials. J Am Coll Cardiol. 2004; 43: 1929–1939.
64. Fagan TJ. Nomogram for Bayes theorem. N Engl J Med. 1975; 293: 257.[Medline] [Order article via Infotrieve]
65. Gail MH, Pfeiffer RM. On criteria for evaluating models of absolute risk. Biostatistics. 2005; 6: 227–239.[Medline] [Order article via Infotrieve]
66. van Houwelingen HC. Validation, calibration, revision and combination of prognostic survival models. Stat Med. 2000; 19: 3401–3415.[CrossRef][Medline] [Order article via Infotrieve]
67. Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999; 130: 515–524.
68. Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982; 115: 92–106.
69. Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998; 97: 1837–1847.
70. Wang TJ, Massaro JM, Levy D, Vasan RS, Wolf PA, D’Agostino RB, Larson MG, Kannel WB, Benjamin EJ. A risk score for predicting stroke or death in individuals with new-onset atrial fibrillation in the community: the Framingham Heart Study. JAMA. 2003; 290: 1049–1056.
71. Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. JAMA. 2003; 290: 2581–2587.
72. Liu J, Hong Y, D’Agostino RB Sr, Wu Z, Wang W, Sun J, Wilson PWF, Kannel WB, Zhao D. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-provincial Cohort Study. JAMA. 2004; 291: 2591–2599.
73. Kattan MW. Judging new markers by their ability to improve predictive accuracy. J Natl Cancer Inst. 2003; 95: 634–635.
74. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004; 159: 882–890.
75. Wald NJ, Law M, Watt HC, Wu T, Bailey A, Johnson AM, Craig WY, Ledue TB, Haddow JE. Apolipoproteins and ischaemic heart disease: implications for screening. Lancet. 1994; 343: 75–79.[CrossRef][Medline] [Order article via Infotrieve]
76. Wald NJ, Hackshaw AK, Frost CD. When can a risk factor be used as a worthwhile screening test? BMJ. 1999; 319: 1562–1565.
77. Kattan MW. Evaluating a new marker’s predictive contribution. Clin Cancer Res. 2004; 10: 822–824.
78. Greenland P, O’Malley PG. When is a new prediction marker useful? a consideration of lipoprotein-associated phospholipase A2 and C-reactive protein for stroke risk. Arch Intern Med. 2005; 165: 2454–2456.
79. Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med. 2002; 347: 1557–1565.
80. Wilson PWF, Nam BH, Pencina M, D’Agostino RB Sr, Benjamin EJ, O’Donnell CJ. C-reactive protein and risk of cardiovascular disease in men and women from the Framingham Heart Study. Arch Intern Med. 2005; 165: 2473–2478.
81. Greenland P, Smith SC Jr, Grundy SM. Improving coronary heart disease risk assessment in asymptomatic people: role of traditional risk factors and noninvasive cardiovascular tests. Circulation. 2001; 104: 1863–1867.
82. Smith SC Jr, Anderson JL, Cannon RO III, Fadl YY, Koenig W, Libby P, Lipshultz SE, Mensah GA, Ridker PM, Rosenson R. CDC/AHA workshop on markers of inflammation and cardiovascular disease: application to clinical and public health practice: report from the Clinical Practice Discussion Group. Circulation. 2004; 110: e550–e553.
83. Kip KE, Marroquin OC, Shaw LJ, Arant CB, Wessel TR, Olson MB, Johnson BD, Mulukutla S, Sopko G, Merz CN, Reis SE. Global inflammation predicts cardiovascular risk in women: a report from the Women’s Ischemia Syndrome Evaluation (WISE) study. Am Heart J. 2005; 150: 900–906.[CrossRef][Medline] [Order article via Infotrieve]
84. Baldus S, Heeschen C, Meinertz T, Zeiher AM, Eiserich JP, Munzel T, Simoons ML, Hamm CW, on behalf of the CAPTURE Investigators. Myeloperoxidase serum levels predict risk in patients with acute coronary syndromes. Circulation. 2003; 108: 1440–1445.
85. Bodi V, Sanchis J, Llacer A, Facila L, Nunez J, Pellicer M, Bertomeu V, Ruiz V, Chorro FJ. Multimarker risk strategy for predicting 1-month and 1-year major events in non-ST-elevation acute coronary syndromes. Am Heart J. 2005; 149: 268–274.[CrossRef][Medline] [Order article via Infotrieve]
86. de Lemos JA, Morrow DA, Bentley JH, Omland T, Sabatine MS, McCabe CH, Hall C, Cannon CP, Braunwald E. The prognostic value of B-type natriuretic peptide in patients with acute coronary syndromes. N Engl J Med. 2001; 345: 1014–1021.
87. Heeschen C, Dimmeler S, Fichtlscherer S, Zeiher AM, Hamm CW, Simoons ML. Pregnancy-associated plasma protein-A levels in patients with acute coronary syndromes: comparison with markers of systemic inflammation, platelet activation, and myocardial necrosis. J Am Coll Cardiol. 2005; 45: 229–237.
88. James SK, Lindahl B, Wallentin L, Siegbahn A, Stridsberg M, Venge P, Armstrong P, Barnathan ES, Califf R, Topol EJ, Simoons ML. N-Terminal pro-brain natriuretic peptide and other risk markers for the separate prediction of mortality and subsequent myocardial infarction in patients with unstable coronary artery disease: a Global Utilization of Strategies to Open Occluded Arteries (GUSTO)-IV substudy. Circulation. 2003; 108: 275–281.
89. Newby LK, Storrow AB, Gibler WB, Garvey JL, Tucker JF, Kaplan AL, Schreiber DH, Tuttle RH, McNulty SE, Ohman EM. Bedside multimarker testing for risk stratification in chest pain units: the Chest Pain Evaluation by Creatine Kinase-MB, Myoglobin, and Troponin I (CHECKMATE) study. Circulation. 2001; 103: 1832–1837.
90. Ng SM, Krishnaswamy P, Morissey R, Clopton P, Fitzgerald R, Maisel AS. Ninety-minute accelerated critical pathway for chest pain evaluation. Am J Cardiol. 2001; 88: 611–617.[CrossRef][Medline] [Order article via Infotrieve]
91. Sabatine MS, Morrow DA, de Lemos JA, Gibson CM, Murphy SA, Rifai N, McCabe C, Antman EM, Cannon CP, Braunwald E. Multimarker approach to risk stratification in non-ST elevation acute coronary syndromes: simultaneous assessment of troponin I, C-reactive protein, and B-type natriuretic peptide. Circulation. 2002; 105: 1760–1763.
92. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988; 44: 837–845.[CrossRef][Medline] [Order article via Infotrieve]
93. Shultz EK. Multivariate receiver-operating characteristic curve analysis: prostate cancer screening as an example. Clin Chem. 1995; 41: 1248–1255.
94. Voss R, Cullen P, Schulte H, Assmann G. Prediction of risk of coronary events in middle-aged men in the Prospective Cardiovascular Munster Study (PROCAM) using neural networks. Int J Epidemiol. 2002; 31: 1253–1262.
95. Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the Prospective Cardiovascular Munster (PROCAM) Study. Circulation. 2002; 105: 310–315.
96. Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, De Backer G, De Bacquer D, Ducimetiere P, Jousilahti P, Keil U, Njolstad I, Oganov RG, Thomsen T, Tunstall-Pedoe H, Tverdal A, Wedel H, Whincup P, Wilhelmsen L, Graham IM, on behalf of the SCORE Project Group. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003; 24: 987–1003.
97. Jackson R. Updated New Zealand cardiovascular disease risk-benefit prediction guide. BMJ. 2000; 320: 709–710.
98. Hense HW. Observations, predictions and decisions: assessing cardiovascular risk assessment. Int J Epidemiol. 2004; 33: 235–239.
99. Hackam DG, Anand SS. Emerging risk factors for atherosclerotic vascular disease: a critical review of the evidence. JAMA. 2003; 290: 932–940.
100. Malinow MR, Bostom AG, Krauss RM. Homocyst(e)ine, diet, and cardiovascular diseases: a statement for healthcare professionals from the Nutrition Committee, American Heart Association. Circulation. 1999; 99: 178–182.
101. Colhoun HM, McKeigue PM, Smith GD. Problems of reporting genetic associations with complex outcomes. Lancet. 2003; 361: 865–872.[CrossRef][Medline] [Order article via Infotrieve]
102. Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004; 96: 434–442.
103. Benjamini Y, Yekutieli D. Quantitative trait loci analysis using the false discovery rate. Genetics. 2005; 171: 783–790.
104. Efron B, Tibshirani R. Empirical Bayes methods and false discovery rates for microarrays. Genet Epidemiol. 2002; 23: 70–86.[CrossRef][Medline] [Order article via Infotrieve]
105. Fernando RL, Nettleton D, Southey BR, Dekkers JCM, Rothschild MF, Soller M. Controlling the proportion of false positives in multiple dependent tests. Genetics. 2004; 166: 611–619.
106. Palmer LJ, Cardon LR. Shaking the tree: mapping complex disease genes with linkage disequilibrium. Lancet. 2005; 366: 1223–1234.[CrossRef][Medline] [Order article via Infotrieve]
107. Sebastian-Gambaro MA, Liron-Hernandez FJ, Fuentes-Arderiu X. Intra- and inter-individual biological variability data bank. Eur J Clin Chem Clin Biochem. 1997; 35: 845–852.[Medline] [Order article via Infotrieve]
108. Guder WG. Preanalytical factors and their influence on analytical duality specifications. Scand J Clin Lab Invest. 1999; 59: 545–549.[CrossRef][Medline] [Order article via Infotrieve]
109. Fraser CG, Hyltoft Petersen P. Analytical performance characteristics should be judged against objective quality specifications. Clin Chem. 1999; 45: 321–323.
110. Petersen PH, Ricos C, Stockl D, Libeer JC, Baadenhuijsen H, Fraser C, Thienpont L. Proposed guidelines for the internal quality control of analytical results in the medical laboratory. Eur J Clin Chem Clin Biochem. 1996; 34: 983–999.[Medline] [Order article via Infotrieve]
111. Gardin JM. How reliable are serial echocardiographic measurements in detecting regression in left ventricular hypertrophy and changes in function? J Am Coll Cardiol. 1999; 34: 1633–1636.
112. Pompanon F, Bonin A, Bellemain E, Taberlet P. Genotyping errors: causes, consequences and solutions. Nat Rev Genet. 2005; 6: 847–846.[CrossRef][Medline] [Order article via Infotrieve]
113. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FCP, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M. Minimum Information About a Microarray Experiment (MIAME): toward standards for microarray data. Nat Genet. 2001; 29: 365–371.[CrossRef][Medline] [Order article via Infotrieve]
114. Clinical and Laboratory Standards Institute (CLSI), formerly National Committee for Clinical Laboratory Standards (NCCLS). Harmonized terminology database: 2005. Available at: http://www.clsi.org/Content/NavigationMenu/StandardsDevelopment/CLSIStandardsDevelopment/StandardsDevelopmentResources/HarmonizedTechnologyDatabase/default.htm. Accessed January 31, 2006.
115. Buttner J. Reference materials and reference methods in laboratory medicine: a challenge to international cooperation. Eur J Clin Chem Clin Biochem. 1994; 32: 571–577.[Medline] [Order article via Infotrieve]
116. Gaines Das RE, Poole S. The international standard for interleukin-6: evaluation in an international collaborative study. J Immunol Methods. 1993; 160: 147–153.[CrossRef][Medline] [Order article via Infotrieve]
117. Mire-Sluis AR, Gaines DR, Thorpe R. Implications for the assay and biological activity of interleukin-8: results of a WHO international collaborative study. J Immunol Methods. 1997; 200: 1–16.[CrossRef][Medline] [Order article via Infotrieve]
118. Poole S, Walker D, Gaines Das RE, Gallimore JR, Pepys MB. The first international standard for serum amyloid A protein (SAA): evaluation in an international collaborative study. J Immunol Methods. 1998; 214: 1–10.[CrossRef][Medline] [Order article via Infotrieve]
119. Whitton CM, Sands D, Hubbard AR, Gaffney PJ. A collaborative study to establish the 2nd International Standard for Fibrinogen, Plasma. Thromb Haemost. 2000; 84: 258–262.[Medline] [Order article via Infotrieve]
120. Whicher JT. BCR/IFCC reference material for plasma proteins (CRM 470): Community Bureau of Reference: International Federation of Clinical Chemistry. Clin Biochem. 1998; 31: 459–465.[CrossRef][Medline] [Order article via Infotrieve]
121. Current status of blood cholesterol measurement in clinical laboratories in the United States: a report from the Laboratory Standardization Panel of the National Cholesterol Education Program. Clin Chem. 1988; 34: 193–201.
122. Bachorik PS, Ross JW, for the National Cholesterol Education Program Working Group on Lipoprotein Measurement. National Cholesterol Education Program recommendations for measurement of low-density lipoprotein cholesterol: executive summary. Clin Chem. 1995; 41: 1414–1420.
123. Stein EA, Myers GL, for the National Cholesterol Education Program Working Group on Lipoprotein Measurement. National Cholesterol Education Program recommendations for triglyceride measurement: executive summary. Clin Chem. 1995; 41: 1421–1426.
124. Warnick GR, Wood PD, for the National Cholesterol Education Program Working Group on Lipoprotein Measurement. National Cholesterol Education Program recommendations for measurement of high-density lipoprotein cholesterol: executive summary. Clin Chem. 1995; 41: 1427–1433.
125. Ledue TB, Rifai N. Preanalytic and analytic sources of variations in C-reactive protein measurement: implications for cardiovascular disease risk assessment. Clin Chem. 2003; 49: 1258–1271.
126. Roberts WL. CDC/AHA workshop on markers of inflammation and cardiovascular disease: application to clinical and public health practice: laboratory tests available to assess inflammation—performance and standardization: a background paper. Circulation. 2004; 110: e572–e576.
127. Price CP. Point of care testing. BMJ. 2001; 322: 1285–1288.
128. Ambrose JA, Tannenbaum MA, Alexopoulos D, Hjemdahl-Monsen CE, Leavy J, Weiss M, Borrico S, Gorlin R, Fuster V. Angiographic progression of coronary artery disease and the development of myocardial infarction. J Am Coll Cardiol. 1988; 12: 56–62.[Abstract]
129. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, Stange-Thomann N, Stojanovic N, Subramanian A, Wyman D, Rogers J, Sulston J, Ainscough R, Beck S, Bentley D, Burton J, Clee C, Carter N, Coulson A, Deadman R, Deloukas P, Dunham A, Dunham I, Durbin R, French L, Grafham D, Gregory S, Hubbard T, Humphray S, Hunt A, Jones M, Lloyd C, McMurray A, Matthews L, Mercer S, Milne S, Mullikin JC, Mungall A, Plumb R, Ross M, Shownkeen R, Sims S, Waterston RH, Wilson RK, Hillier LW, McPherson JD, Marra MA, Mardis ER, Fulton LA, Chinwalla AT, Pepin KH, Gish WR, Chissoe SL, Wendl MC, Delehaunty KD, Miner TL, Delehaunty A, Kramer JB, Cook LL, Fulton RS, Johnson DL, Minx PJ, Clifton SW, Hawkins T, Branscomb E, Predki P, Richardson P, Wenning S, Slezak T, Doggett N, Cheng JF, Olsen A, Lucas S, Elkin C, Uberbacher E, Frazier M, Gibbs RA, Muzny DM, Scherer SE, Bouck JB, Sodergren EJ, Worley KC, Rives CM, Gorrell JH, Metzker ML, Naylor SL, Kucherlapati RS, Nelson DL, Weinstock GM, Sakaki Y, Fujiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Kawagoe C, Watanabe H, Totoki Y, Taylor T, Weissenbach J, Heilig R, Saurin W, Artiguenave F, Brottier P, Bruls T, Pelletier E, Robert C, Wincker P, Smith DR, Doucette-Stamm L, Rubenfield M, Weinstock K, Lee HM, Dubois J, Rosenthal A, Platzer M, Nyakatura G, Taudien S, Rump A, Yang H, Yu J, Wang J, Huang G, Gu J, Hood L, Rowen L, Madan A, Qin S, Davis RW, Federspiel NA, Abola AP, Proctor MJ, Myers RM, Schmutz J, Dickson M, Grimwood J, Cox DR, Olson MV, Kaul R, Raymond C, Shimizu N, Kawasaki K, Minoshima S, Evans GA, Athanasiou M, Schultz R, Roe BA, Chen F, Pan H, Ramser J, Lehrach H, Reinhardt R, McCombie WR, de la Bastide M, Dedhia N, Blocker H, Hornischer K, Nordsiek G, Agarwala R, Aravind L, Bailey JA, Bateman A, Batzoglou S, Birney E, Bork P, Brown DG, Burge CB, Cerutti L, Chen HC, Church D, Clamp M, Copley RR, Doerks T, Eddy SR, Eichler EE, Furey TS, Galagan J, Gilbert JG, Harmon C, Hayashizaki Y, Haussler D, Hermjakob H, Hokamp K, Jang W, Johnson LS, Jones TA, Kasif S, Kaspryzk A, Kennedy S, Kent WJ, Kitts P, Koonin EV, Korf I, Kulp D, Lancet D, Lowe TM, McLysaght A, Mikkelsen T, Moran JV, Mulder N, Pollara VJ, Ponting CP, Schuler G, Schultz J, Slater G, Smit AF, Stupka E, Szustakowski J, Thierry-Mieg D, Thierry-Mieg J, Wagner L, Wallis J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Yang SP, Yeh RF, Collins F, Guyer MS, Peterson J, Felsenfeld A, Wetterstrand KA, Patrinos A, Morgan MJ, de Jong P, Catanese JJ, Osoegawa K, Shizuya H, Choi S, Chen YJ; International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001; 409: 860–921.[CrossRef][Medline] [Order article via Infotrieve]
130. Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P. A haplotype map of the human genome. Nature. 2005; 437: 1299–1320.[CrossRef][Medline] [Order article via Infotrieve]
131. Marko-Varga G, Lindberg H, Lofdahl CG, Jonsson P, Hansson L, Dahlback M, Lindquist E, Johansson L, Foster M, Fehniger TE. Discovery of biomarker candidates within disease by protein profiling: principles and concepts. J Proteome Res. 2005; 4: 1200–1212.[CrossRef][Medline] [Order article via Infotrieve]
132. Westont AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res. 2004; 3: 179–196.[CrossRef][Medline] [Order article via Infotrieve]
133. Ilyin SE, Belkowski SM, Plata-Salaman CR. Biomarker discovery and validation: technologies and integrative approaches. Trends Biotechnol. 2004; 22: 411–416.[CrossRef][Medline] [Order article via Infotrieve]
134. Chitty M, ed. Biopharmaceutical glossary, taxonomies and guide to 21st century therapeutics, technologies and trends. Cambridge Healthtech Institute; 2005. Available at: http://www.genomicglossaries.com/. Accessed January 31, 2006.
135. Nicholson JK, Lindon JC, Holmes E. "Metabonomics": understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999; 29: 1181–1189.[CrossRef][Medline] [Order article via Infotrieve]
136. Ginsburg GS, Konstance RP, Allsbrook JS, Schulman KA. Implications of pharmacogenomics for drug development and clinical practice. Arch Intern Med. 2005; 165: 2331–2336.
137. Schork N. Genetics of complex disease: approaches, problems, and solutions. Am J Respir Crit Care Med. 1997; 156: 103S–1109.
138. Tefferi A, Wieben ED, Dewald GW, Whiteman DA, Bernard ME, Spelsberg TC. Primer on medical genomics part II: background principles and methods in molecular genetics. Mayo Clin Proc. 2002; 77: 785–808.
139. Freitas RA. Nanomedicine. Foresight Institute; 2002. Available at: http://www.foresight.org/Nanomedicine/. Accessed January 31, 2006.
140. Royal Academy of Engineering. Nanotechnology and nanoscience. Royal Society; 2003. Available at: http://www.nanotec.org.uk/draftdefinition.htm. Accessed January 31, 2006.
141. Lander ES, Schork NJ. Genetic dissection of complex traits. Science. 1994; 265: 2037–2048.
142. Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M. Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet. 2001; 69: 936–950.[CrossRef][Medline] [Order article via Infotrieve]
143. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996; 273: 1516–1517.
144. Chakravarti A. Population genetics: making sense out of sequence. Nat Genet. 1999; 21: 56–60.[CrossRef][Medline] [Order article via Infotrieve]
145. Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet. 2003; 33: 177–182.[CrossRef][Medline] [Order article via Infotrieve]
146. Reich DE, Lander ES. On the allelic spectrum of human disease. Trends Genet. 2001; 17: 502–510.[CrossRef][Medline] [Order article via Infotrieve]
147. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D. The structure of haplotype blocks in the human genome. Science. 2002; 296: 2225–2229.
148. Johnson GCL, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RCJ, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SCL, Clayton DG, Todd JA. Haplotype tagging for the identification of common disease genes. Nat Genet. 2001; 29: 233–237.[CrossRef][Medline] [Order article via Infotrieve]
149. Halldorsson BV, Bafna V, Lippert R, Schwartz R, De La Vega FM, Clark AG, Istrail S. Optimal haplotype block-free selection of tagging SNPs for genome-wide association studies. Genome Res. 2004; 14: 1633–1640.
150. Carlson CS, Eberle MA, Kruglyak L, Nickerson DA. Mapping complex disease loci in whole-genome association studies. Nature. 2004; 429: 446–452.[CrossRef][Medline] [Order article via Infotrieve]
151. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005; 6: 95–108.[Medline] [Order article via Infotrieve]
152. Wang WYS, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet. 2005; 6: 109–118.[CrossRef][Medline] [Order article via Infotrieve]
153. Broeckel U, Hengstenberg C, Mayer B, Holmer S, Martin LJ, Comuzzie AG, Blangero J, Nurnberg P, Reis A, Riegger GAJ, Jacob HJ, Schunkert H. A comprehensive linkage analysis for myocardial infarction and its related risk factors. Nat Genet. 2002; 30: 210–214.[CrossRef][Medline] [Order article via Infotrieve]
154. Francke S, Manraj M, Lacquemant C, Lecoeur C, Lepr
tre F, Passa P, Hebe A, Corset L, Yan SLK, Lahmidi S, Jankee S, Gunness TK, Ramjuttun US, Balgobin V, Dina C, Froguel P. A genome-wide scan for coronary heart disease suggests in Indo-Mauritians a susceptibility locus on chromosome 16p13 and replicates linkage with the metabolic syndrome on 3q27. Hum Mol Genet. 2001; 10: 2751–2765.
155. Harrap SB, Zammit KS, Wong ZYH, Williams FM, Bahlo M, Tonkin AM, Anderson ST. Genome-wide linkage analysis of the acute coronary syndrome suggests a locus on chromosome 2. Arterioscler Thromb Vasc Biol. 2002; 22: 874–878.
156. Hauser ER, Granger CB, Dowdy E, Estabrooks S, Huang L, Pedersen B, Shah S, Schmidt S, Haynes C, West S, Asper D, Booze M, Hauser MA, Vance JM, Pericak-Vance MA, Kraus WE, Mooser V, Sharma S, Sundseth S, Middleton L, Roses AD, Crossman DC, Eggleston K, Francis S, Haines JL, McAdam B, Clevenger PW, Jones CJH, Roche K, Winkelmann BR, Wiseman AH, Muhlestein JB, Bartel AG, Dennis CA. A genomewide scan for early-onset coronary artery disease in 438 families: the GENECARD Study. Am J Hum Genet. 2004; 75: 436–447.[CrossRef][Medline] [Order article via Infotrieve]
157. Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SFA, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V, Hakonarson H, Gulcher JR, Stefansson K. The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet. 2004; 36: 233–239.[CrossRef][Medline] [Order article via Infotrieve]
158. Pajukanta P, Cargill M, Viitanen L, Nuotio I, Kareinen A, Perola M, Terwilliger JD, Kempas E, Daly M, Lilja H, Rioux JD, Brettin T, Viikari JSA, Ronnemaa T, Kaakso M, Lander ES, Peltonen L. Two loci on chromosomes 2 and X for premature coronary heart disease identified in early- and late-settlement populations of Finland. Am J Hum Genet. 2000; 67: 1481–1493.[CrossRef][Medline] [Order article via Infotrieve]
159. Wang L, Fan C, Topol SE, Topol EJ, Wang Q. Mutation of MEF2A in an inherited disorder with features of coronary artery disease. Science. 2003; 302: 1578–1581.
160. Wang Q, Rao S, Shen GQ, Li L, Cannata R, Zirzow E, Topol EJ, Moliterno DJ, Elston RC, Newby LK, Rogers WJ. Premature myocardial infarction novel susceptibility locus on chromosome 1P34–36 identified by genomewide linkage analysis. Am J Hum Genet. 2004; 74: 262–271.[CrossRef][Medline] [Order article via Infotrieve]
161. Boekholdt SM, Bijsterveld NR, Moons AHM, Levi M, Buller HR, Peters RJG. Genetic variation in coagulation and fibrinolytic proteins and their relation with acute myocardial infarction: a systematic review. Circulation. 2001; 104: 3063–3068.
162. Casas JP, Hingorani AD, Humphries SE, Bautista LE. Endothelial nitric oxide synthase genotype and ischemic heart disease: meta-analysis of 26 studies involving 23 028 subjects. Circulation. 2004; 109: 1359–1365.
163. Chiodini BD, Barlera S, Franzosi MG, Beceiro VL, Introna M, Tognoni G. APO B gene polymorphisms and coronary artery disease: a meta-analysis. Atherosclerosis. 2003; 167: 355–366.[CrossRef][Medline] [Order article via Infotrieve]
164. Juhan-Vague I, Alessi MC, Pyke SDM, Thompson SG, Jespersen J, Haverkate F. Fibrinolytic factors and the risk of myocardial infarction or sudden death in patients with angina pectoris. Circulation. 1996; 94: 2057–2063.
165. Keavney B, McKenzie C, Parish S, Palmer A, Clark S, Youngman L, Delepine M, Lathrop M, Peto R, Collins R, for the International Studies of Infarct Survival (ISIS) Collaborators. Large-scale test of hypothesised associations between the angiotensin-converting-enzyme insertion/deletion polymorphism and myocardial infarction in about 5000 cases and 6000 controls. Lancet. 2000; 355: 434–442.[Medline] [Order article via Infotrieve]
166. Mansfield MW, Stickland MH, Grant PJ. Environmental and genetic factors in relation to elevated circulating levels of plasminogen activator inhibitor-1 in Caucasian patients with non-insulin-dependent diabetes mellitus. Thromb Haemost. 1995; 74: 842–847.[Medline] [Order article via Infotrieve]
167. McCarthy JJ, Parker A, Salem R, Moliterno DJ, Wang Q, Plow EF, Rao S, Shen G, Rogers WJ, Newby LK, Cannata R, Glatt K, Topol EJ. Large scale association analysis for identification of genes underlying premature coronary heart disease: cumulative perspective from analysis of 111 candidate genes. J Med Genet. 2004; 41: 334–341.
168. Song Y, Stampfer MJ, Liu S. Meta-analysis: apolipoprotein E genotypes and risk for coronary heart disease. Ann Intern Med. 2004; 141: 137–147.
169. Tybjaerg-Hansen A, gerholm-Larsen B, Humphries SE, Abildgaard S, Schnohr P, Nordestgaard BG. A common mutation (G-455–>A) in the [beta]-fibrinogen promoter is an independent predictor of plasma fibrinogen, but not of ischemic heart disease: a study of 9,127 individuals based on the Copenhagen City Heart Study. J Clin Inv. 1997; 99: 3034–3039.[Medline] [Order article via Infotrieve]
170. Wald DS, Law M, Morris JK. Homocysteine and cardiovascular disease: Evidence on causality from a meta-analysis. BMJ. 2002; 325: 1202–1206.
171. Yamada Y, Tanaka M, Izawa H, Ichihara S, Yokota M, Takatsu F, Ishihara H, Hirayama H, Sone T. Prediction of the risk of myocardial infarction from polymorphisms in candidate genes. N Engl J Med. 2002; 347: 1916–1923.
172. Wang Q. Advances in the genetic basis of coronary artery disease. Curr Atheroscler Rep. 2005; 7: 235–241.[Medline] [Order article via Infotrieve]
173. Napoli C, Lerman LO, Sica V, Lerman A, Tajana G, de Nigris F. Microarray analysis: a novel research tool for cardiovascular scientists and physicians. Heart. 2003; 89: 597–604.
174. Cook SA, Rosenzweig A. DNA microarrays: implications for cardiovascular medicine. Circ Res. 2002; 91: 559–564.
175. Steinmetz LM, Davis RW. Maximizing the potential of functional genomics. Nat Rev Genet. 2004; 5: 190–201.[CrossRef][Medline] [Order article via Infotrieve]
176. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science. 1995; 270: 484–487.
177. Danziger RS, You M, Akil H. Discovering the genetics of complex disorders through integration of genomic mapping and transcriptional profiling. Curr Hypertens Rev. 2005; 1: 25–34.
178. Hwang DM, Dempsey AA, Wang RX, Rezvani M, Barrans JD, Dai KS, Wang HY, Ma H, Cukerman E, Liu YQ, Gu JR, Zhang JH, Tsui SKW, Waye MM, Fung KP, Lee CY, Liew CC. A genome-based resource for molecular cardiovascular medicine: toward a compendium of cardiovascular genes. Circulation. 1997; 96: 4146–4203.
179. Barrans JD, Stamatiou D, Liew CC. Construction of a human cardiovascular cDNA microarray: portrait of the failing heart. Biochem Biophys Res Commun. 2001; 280: 964–969.[CrossRef][Medline] [Order article via Infotrieve]
180. Friddle CJ, Koga T, Rubin EM, Bristow J. Expression profiling reveals distinct sets of genes altered during induction and regression of cardiac hypertrophy. Proc Natl Acad Sci U S A. 2000; 97: 6745–6750.
181. Aronow BJ, Toyokawa T, Canning A, Haghighi K, Delling U, Kranias E, Molkentin J, Dorn GW II. Divergent transcriptional responses to independent genetic causes of cardiac hypertrophy. Physiol Genomics. 2001; 6: 19–28.
182. Yussman MG, Toyokawa T, Odley A, Lynch RA, Wu G, Colbert MC, Aronow BJ, Lorenz JN, Dorn GW. Mitochondrial death protein Nix is induced in cardiac hypertrophy and triggers apoptotic cardiomyopathy. Nat Med. 2002; 8: 725–730.[Medline] [Order article via Infotrieve]
183. Sehl PD, Tai JTN, Hillan KJ, Brown LA, Goddard A, Yang R, Jin H, Lowe DG. Application of cDNA microarrays in determining molecular phenotype in cardiac growth, development, and response to injury. Circulation. 2000; 101: 1990–1999.
184. Stanton LW, Garrard LJ, Damm D, Garrick BL, Lam A, Kapoun AM, Zheng Q, Protter AA, Schreiner GF, White RT. Altered patterns of gene expression in response to myocardial infarction. Circ Res. 2000; 86: 939–945.
185. Haase D, Lehmann MH, Korner MM, Korfer R, Sigusch HH, Figulla HR. Identification and validation of selective upregulation of ventricular myosin light chain type 2 mRNA in idiopathic dilated cardiomyopathy. Eur J Heart Fail. 2002; 4: 23–31.[CrossRef][Medline] [Order article via Infotrieve]
186. Yang J, Moravec CS, Sussman MA, DiPaola NR, Fu D, Hawthorn L, Mitchell CA, Young JB, Francis GS, McCarthy PM, Bond M. Decreased SLIM1 expression and increased gelsolin expression in failing human hearts measured by high-density oligonucleotide arrays. Circulation. 2000; 102: 3046–3052.
187. Saiura A, Kohro T, Yamamoto T, Izumi A, Wada Y, Aburatani H, Sugawara Y, Hamakubo T, Taniguchi T, Naito M, Kodama T, Makuuchi M. Detection of an up-regulation of a group of chemokine genes in murine cardiac allograft in the absence of interferon-gamma by means of DNA microarray. Transplantation. 2002; 73: 1480–1486.[CrossRef][Medline] [Order article via Infotrieve]
188. Lowes BD, Gilbert EM, Abraham WT, Minobe WA, Larrabee P, Ferguson D, Wolfel EE, Lindenfeld J, Tsvetkova T, Robertson AD, Quaife RA, Bristow MR. Myocardial gene expression in dilated cardiomyopathy treated with beta-blocking agents. N Engl J Med. 2002; 346: 1357–1365.
189. Kittleson MM, Minhas KM, Irizarry RA, Ye SQ, Edness G, Breton E, Conte JV, Tomaselli G, Garcia JGN, Hare JM. Gene expression analysis of ischemic and nonischemic cardiomyopathy: shared and distinct genes in the development of heart failure. Physiol Genomics. 2005; 21: 299–307.
190. Liew CC, Dzau VJ. Molecular genetics and genomics of heart failure. Nat Rev Genet. 2004; 5: 811–825.[CrossRef][Medline] [Order article via Infotrieve]
191. Hwang JJ, Allen PD, Tseng GC, Lam CW, Fananapazir L, Dzau VJ, Liew CC. Microarray gene expression profiles in dilated and hypertrophic cardiomyopathic end-stage heart failure. Physiol Genomics. 2002; 10: 31–44.
192. Barrans JD, Allen PD, Stamatiou D, Dzau VJ, Liew CC. Global gene expression profiling of end-stage dilated cardiomyopathy using a human cardiovascular-based cDNA microarray. Am J Pathol. 2002; 160: 2035–2043.
193. Gibbons GH, Liew CC, Goodarzi MO, Rotter JI, Hsueh WA, Siragy HM, Pratt R, Dzau VJ. Genetic markers: progress and potential for cardiovascular disease. Circulation. 2004; 109: IV-47.[Medline] [Order article via Infotrieve]
194. Archacki SR, Angheloiu G, Tian XL, Tan FL, DiPaola N, Shen GQ, Moravec C, Ellis S, Topol EJ, Wang Q. Identification of new genes differentially expressed in coronary artery disease by expression profiling. Physiol Genomics. 2003; 15: 65–74.
195. Hiltunen MO, Tuomisto TT, Niemi M, Brasen JH, Rissanen TT, Toronen P, Vajanto I, Yla-Herttuala S. Changes in gene expression in atherosclerotic plaques analyzed using DNA array. Atherosclerosis. 2002; 165: 23–32.[CrossRef][Medline] [Order article via Infotrieve]
196. Wuttge DM, Sirsjo A, Eriksson P, Stemme S. Gene expression in atherosclerotic lesion of ApoE deficient mice. Mol Med. 2001; 7: 383–392.[Medline] [Order article via Infotrieve]
197. Chen B, Li Y, Zhao Y, Chen K, Li S, Lao J, Yuan S, Shyy J, Chien S. DNA microarray analysis of gene expression in endothelial cells in response to 24-h shear stress. Physiol Genomics. 2001; 7: 55–63.
198. Garcia-Cardena G, Comander J, Anderson KR, Blackman BR, Gimbrone MA Jr. Biomechanical activation of vascular endothelium as a determinant of its functional phenotype. Proc Natl Acad Sci U S A. 2001; 98: 4478–4485.
199. Feng Y, Yang JH, Huang H, Kennedy SP, Turi TG, Thompson JF, Libby P, Lee RT. Transcriptional profile of mechanically induced genes in human vascular smooth muscle cells. Circ Res. 1999; 85: 1118–1123.
200. Davies PF. Molecular phenotypes of atherosclerosis: fingering the perpetrators. Arterioscler Thromb Vasc Biol. 2004; 24: 1746–1747.
201. Faber BCG, Cleutjens KBJM, Niessen RLJ, Aarts PLJW, Boon W, Greenberg AS, Kitslaar PJEH, Tordoir JHM, Daemen MJAP. Identification of genes potentially involved in rupture of human atherosclerotic plaques. Circ Res. 2001; 89: 547–554.
202. Assmus B, Urbich C, Aicher A, Hofmann WK, Haendeler J, Rossig L, Spyridopoulos I, Zeiher AM, Dimmeler S. HMG-CoA reductase inhibitors reduce senescence and increase proliferation of endothelial progenitor cells via regulation of cell cycle regulatory genes. Circ Res. 2003; 92: 1049–1055.
203. Ma J, Liew CC. Gene profiling identifies secreted protein transcripts from peripheral blood cells in coronary artery disease. J Mol Cell Cardiol. 2003; 35: 993–998.[CrossRef][Medline] [Order article via Infotrieve]
204. Podgoreanu MV, Schwinn DA. New paradigms in cardiovascular medicine: emerging technologies and practices: perioperative genomics. J Am Coll Cardiol. 2005; 46: 1965–1977.
205. Podgoreanu MV, Schwinn DA. Genomics and the circulation. Br J Anaesthesiol. 2004; 93: 140–148.
206. Craven RA, Banks RE. Laser capture microdissection and proteomics: possibilities and limitation. Proteomics. 2001; 1: 1200–1204.[CrossRef][Medline] [Order article via Infotrieve]
207. Pardanani A, Wieben ED, Spelsberg TC, Tefferi A. Primer on medical genomics, part IV: expression proteomics. Mayo Clin Proc. 2002; 77: 1185–1196.
208. Hanash S. HUPO initiatives relevant to clinical proteomics. Mol Cell Proteomics. 2004; 3: 298–301.
209. Omenn GS. Advancement of biomarker discovery and validation through the HUPO plasma proteome project. Dis Markers. 2004; 20: 131–134.[Medline] [Order article via Infotrieve]
210. Evans G, Wheeler CH, Corbett JM, Dunn MJ. Construction of HSC-2DPAGE: a two-dimensional gel electrophoresis database of heart proteins. Electrophoresis. 1997; 18: 471–479.[CrossRef][Medline] [Order article via Infotrieve]
211. Muller EC, Thiede B, Zimny-Arndt U, Scheler C, Prehm J, Muller-Werdan U, Wittmann-Liebold B, Otto A, Jungblut P. High-performance human myocardial two-dimensional electrophoresis database: edition 1996. Electrophoresis. 1996; 17: 1700–1712.[CrossRef][Medline] [Order article via Infotrieve]
212. Pleissner KP, Sander S, Oswald H, Regitz-Zagrosek V, Fleck E. The construction of the World Wide Web-accessible myocardial two-dimensional gel electrophoresis protein database "HEART-2DPAGE": a practical approach. Electrophoresis. 1996; 17: 1386–1392.[CrossRef][Medline] [Order article via Infotrieve]
213. McGregor E, Dunn MJ. Proteomics of heart disease. Hum Mol Genet. 2003; 12: R135–R144.
214. Weekes J, Wheeler CH, Yan JX, Weil J, Eschenhagen T, Scholtysik G, Dunn MJ. Bovine dilated cardiomyopathy: proteomic analysis of an animal model of human dilated cardiomyopathy. Electrophoresis. 1999; 20: 898–906.[CrossRef][Medline] [Order article via Infotrieve]
215. Corbett JM, Why HJ, Wheeler CH, Richardson PJ, Archard LC, Yacoub MH, Dunn MJ. Cardiac protein abnormalities in dilated cardiomyopathy detected by two-dimensional polyacrylamide gel electrophoresis. Electrophoresis. 1998; 19: 2031–2042.[CrossRef][Medline] [Order article via Infotrieve]
216. Sheeley DM, Breen JJ, Old SE. Building integrated approaches for the proteomics of complex, dynamic systems: NIH programs in technology and infrastructure development. J Proteome Res. 2005; 4: 1114–1122.[CrossRef][Medline] [Order article via Infotrieve]
217. Noble D. Modeling the heart: from genes to cells to the whole organ. Science. 2002; 295: 1678–1682.
218. Staniloae CS, Ambrose JA. Identification of vulnerable atherosclerotic plaques. Exp Rev Cardiovasc Ther. 2003; 1: 353–365.[CrossRef]
219. Flacke S, Fischer S, Scott MJ, Fuhrhop RJ, Allen JS, McLean M, Winter P, Sicard GA, Gaffney PJ, Wickline SA, Lanza GM. Novel MRI contrast agent for molecular imaging of fibrin: implications for detecting vulnerable plaques. Circulation. 2001; 104: 1280–1285.
220. Lanza GM, Winter PM, Caruthers SD, Morawski AM, Schmieder AH, Crowder KC, Wickline SA. Magnetic resonance molecular imaging with nanoparticles. J Nucl Cardiol. 2004; 11: 733–743.[CrossRef][Medline] [Order article via Infotrieve]
221. Chen J, Tung CH, Mahmood U, Ntziachristos V, Gyurko R, Fishman MC, Huang PL, Weissleder R. In vivo imaging of proteolytic activity in atherosclerosis. Circulation. 2002; 105: 2766–2771.
222. Winter PM, Morawski AM, Caruthers SD, Fuhrhop RW, Zhang H, Williams TA, Allen JS, Lacy EK, Robertson JD, Lanza GM, Wickline SA. Molecular imaging of angiogenesis in early-stage atherosclerosis with [alpha]v[beta]3-integrin-targeted nanoparticles. Circulation. 2003; 108: 2270–2274.
223. Wickline SA. Plaque characterization: surrogate markers or the real thing? J Am Coll Cardiol. 2004; 43: 1185–1187.
224. Stefanadis C, Diamantopoulos L, Vlachopoulos C, Tsiamis E, Dernellis J, Toutouzas K, Stefanadi E, Toutouzas P. Thermal heterogeneity within human atherosclerotic coronary arteries detected in vivo: a new method of detection by application of a special thermography catheter. Circulation. 1999; 99: 1965–1971.
225. Ruehm SG, Corot C, Vogt P, Kolb S, Debatin JF. Magnetic resonance imaging of atherosclerotic plaque with ultrasmall superparamagnetic particles of iron oxide in hyperlipidemic rabbits. Circulation. 2001; 103: 415–422.
226. Jang IK, Bouma BE, Kang DH, Park SJ, Park SW, Seung KB, Choi KB, Shishkov M, Schlendorf K, Pomerantsev E, Houser SL, Aretz HT, Tearney GJ. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol. 2002; 39: 604–609.
227. Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M, Winget M, Yasui Y. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001; 93: 1054–1061.
228. Barker PE. Cancer biomarker validation: standards and process: roles for the National Institute of Standards and Technology (NIST). Ann N Y Acad Sci. 2003; 983: 142–150.[CrossRef][Medline] [Order article via Infotrieve]
229. Schulte PA, Perera FP. Validation. In: Schulte PA, Perera FP, eds. Molecular Epidemiology: Principles and Practice. San Diego, Calif: Academic Press; 1993: 79–107.
230. Srivastava S, Gopal-Srivastava R. Biomarkers in cancer screening: a public health perspective. J Nutr. 2002; 132: 2471S–2475S.
231. Cohn JN, Quyyumi AA, Hollenberg NK, Jamerson KA. Surrogate markers for cardiovascular disease: functional markers. Circulation. 2004; 109: IV-31.[Medline] [Order article via Infotrieve]
232. Mancini GBJ, Dahlof B, Diez J. Surrogate markers for cardiovascular disease: structural markers. Circulation. 2004; 109: IV-22.[Medline] [Order article via Infotrieve]
233. Ridker PM, Brown NJ, Vaughan DE, Harrison DG, Mehta JL. Established and emerging plasma biomarkers in the prediction of first atherothrombotic events. Circulation. 2004; 109: IV-6.[Medline] [Order article via Infotrieve]
234. Biasucci LM. CDC/AHA workshop on markers of inflammation and cardiovascular disease: application to clinical and public health practice: clinical use of inflammatory markers in patients with cardiovascular diseases: a background paper. Circulation. 2004; 110: e560–e567.
235. Myers GL, Rifai N, Tracy RP, Roberts WL, Alexander RW, Biasucci LM, Catravas JD, Cole TG, Cooper GR, Khan BV, Kimberly MM, Stein EA, Taubert KA, Warnick GR, Waymack PP. CDC/AHA workshop on markers of inflammation and cardiovascular disease: application to clinical and public health practice: report from the Laboratory Science Discussion Group. Circulation. 2004; 110: e545–e549.
236. Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon RO III, Criqui M, Fadl YY, Fortmann SP, Hong Y, Myers GL, Rifai N, Smith SC Jr, Taubert K, Tracy RP, Vinicor F. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003; 107: 499–511.
237. Wilson PWF. CDC/AHA workshop on markers of inflammation and cardiovascular disease: application to clinical and public health practice: ability of inflammatory markers to predict disease in asymptomatic patients: a background paper. Circulation. 2004; 110: e568–e571.
238. Marcovina SM, Albers JJ, Kennedy H, Mei JV, Henderson LO, Hannon WH. International Federation of Clinical Chemistry standardization project for measurements of apolipoproteins A-I and B-IV: comparability of apolipoprotein B values by use of international reference material. Clin Chem. 1994; 40: 586–592.
239. Pischon T, Girman CJ, Sacks FM, Rifai N, Stampfer MJ, Rimm EB. Non-high-density lipoprotein cholesterol and apolipoprotein B in the prediction of coronary heart disease in men. Circulation. 2005; 112: 3375–3383.
240. Shai I, Rimm EB, Hankinson SE, Curhan G, Manson JE, Rifai N, Stampfer MJ, Ma J. Multivariate assessment of lipid parameters as predictors of coronary heart disease among postmenopausal women: potential implications for clinical guidelines. Circulation. 2004; 110: 2824–2830.
241. Walldius G, Jungner I, Holme I, Aastveit AH, Kolar W, Steiner E. High apolipoprotein B, low apolipoprotein A-I, and improvement in the prediction of fatal myocardial infarction (AMORIS Study): a prospective study. Lancet. 2001; 358: 2026–2033.[CrossRef][Medline] [Order article via Infotrieve]
242. Sharrett AR, Ballantyne CM, Coady SA, Heiss G, Sorlie PD, Catellier D, Patsch W. Coronary heart disease prediction from lipoprotein cholesterol levels, triglycerides, lipoprotein(a), apolipoproteins A-I and B, and HDL density subfractions: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 2001; 104: 1108–1113.
243. Talmud PJ, Hawe E, Miller GJ, Humphries SE. Nonfasting apolipoprotein B and triglyceride Levels as a useful predictor of coronary heart disease risk in middle-aged UK men. Arterioscler Thromb Vasc Biol. 2002; 22: 1918–1923.
244. Lamarche B, Moorjani S, Lupien PJ, Cantin B, Bernard PM, Dagenais GR, Despres JP. Apolipoprotein A-I and B levels and the risk of ischemic heart disease during a five-year follow-up of men in the Quebec Cardiovascular Study. Circulation. 1996; 94: 273–278.
245. Ridker PM, Rifai N, Cook NR, Bradwin G, Buring JE. Non-HDL cholesterol, apolipoproteins A-I and B100, standard lipid measures, lipid ratios, and CRP as risk factors for cardiovascular disease in women. JAMA. 2005; 294: 326–333.
246. Ballantyne CM, Andrews TC, Hsia JA, Kramer JH, Shear C. Correlation of non-high-density lipoprotein cholesterol with apolipoprotein B: effect of 5 hydroxymethylglutaryl coenzyme A reductase inhibitors on non-high-density lipoprotein cholesterol levels. Am J Cardiol. 2001; 88: 265–269.[CrossRef][Medline] [Order article via Infotrieve]
247. Gotto AM Jr, Whitney E, Stein EA, Shapiro DR, Clearfield M, Weis S, Jou JY, Langendorfer A, Beere PA, Watson DJ, Downs JR, de Cani JS. Relation between baseline and on-treatment lipid parameters and first acute major coronary events in the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS). Circulation. 2000; 101: 477–484.
248. Simes RJ, Marschner IC, Hunt D, Colquhoun D, Sullivan D, Stewart RAH, Hague W, Keech A, Thompson P, White H, Shaw J, Tonkin A. Relationship between lipid levels and clinical outcomes in the Long-Term Intervention With Pravastatin in Ischemic Disease (LIPID) Trial: to what extent is the reduction in coronary events with pravastatin explained by on-study lipid levels? Circulation. 2002; 105: 1162–1169.
249. Danesh J, Collins R, Peto R. Lipoprotein(a) and coronary heart disease: meta-analysis of prospective studies. Circulation. 2000; 102: 1082–1085.
250. Otvos JD, Jeyarajah EJ, Bennett DW, Krauss RM. Development of a proton nuclear magnetic resonance spectroscopic method for determining plasma lipoprotein concentrations and subspecies distributions from a single, rapid measurement. Clin Chem. 1992; 38: 1632–1638.
251. Blake GJ, Otvos JD, Rifai N, Ridker PM. Low-density lipoprotein particle concentration and size as determined by nuclear magnetic resonance spectroscopy as predictors of cardiovascular disease in women. Circulation. 2002; 106: 1930–1937.
252. Kuller L, Arnold A, Tracy R, Otvos J, Burke G, Psaty B, Siscovick D, Freedman DS, Kronmal R. Nuclear magnetic resonance spectroscopy of lipoproteins and risk of coronary heart disease in the cardiovascular health study. Arterioscler Thromb Vasc Biol. 2002; 22: 1175–1180.
253. Mezdour H, Kora I, Parra HJ, Tartar A, Marcel YL, Fruchart JC. Two-site enzyme immunoassay of cholesteryl ester transfer protein with monoclonal and oligoclonal antibodies. Clin Chem. 1994; 40: 593–597.
254. Boekholdt SM, Kuivenhoven JA, Wareham NJ, Peters RJG, Jukema JW, Luben R, Bingham SA, Day NE, Kastelein JJP, Khaw KT. Plasma levels of cholesteryl ester transfer protein and the risk of future coronary artery disease in apparently healthy men and women: the prospective EPIC (European Prospective Investigation into Cancer and Nutrition)-Norfolk Population Study. Circulation. 2004; 110: 1418–1423.
255. Hoogeveen RC, Ballantyne CM. PLAC test for identification of individuals at increased risk for coronary heart disease. Exp Rev Mol Diagnost. 2005; 5: 9–14.[CrossRef]
256. Ballantyne CM, Hoogeveen RC, Bang H, Coresh J, Folsom AR, Heiss G, Sharrett AR. Lipoprotein-associated phospholipase A2, high-sensitivity C-reactive protein, and risk for incident coronary heart disease in middle-aged men and women in the Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 2004; 109: 837–842.
257. Ballantyne CM, Hoogeveen RC, Bang H, Coresh J, Folsom AR, Chambless LE, Myerson M, Wu KK, Sharrett AR, Boerwinkle E. Lipoprotein-associated phospholipase A2, high-sensitivity C-reactive protein, and risk for incident ischemic stroke in middle-aged men and women in the Atherosclerosis Risk in Communities (ARIC) Study. Arch Intern Med. 2005; 165: 2479–2484.
258. Blake GJ, Dada N, Fox JC, Manson JE, Ridker PM. A prospective evaluation of lipoprotein-associated phospholipase A2 levels and the risk of future cardiovascular events in women. J Am Coll Cardiol. 2001; 38: 1302–1306.
259. Brilakis ES, McConnell JP, Lennon RJ, Elesber AA, Meyer JG, Berger PB. Association of lipoprotein-associated phospholipase A2 levels with coronary artery disease risk factors, angiographic coronary artery disease, and major adverse events at follow-up. Eur Heart J. 2005; 26: 137–144.
260. Koenig W, Khuseyinova N, Lowel H, Trischler G, Meisinger C. Lipoprotein-associated phospholipase A2 adds to risk prediction of incident coronary events by C-reactive protein in apparently healthy middle-aged men from the general population: results from the 14-year follow-up of a large cohort from southern Germany. Circulation. 2004; 110: 1903–1908.
261. Oei HH, van der Meer IM, Hofman A, Koudstaal PJ, Stijnen T, Breteler MMB, Witteman JCM. Lipoprotein-associated phospholipase A2 activity is associated with risk of coronary heart disease and ischemic stroke: the Rotterdam Study. Circulation. 2005; 111: 570–575.
262. Boekholdt SM, Keller TT, Wareham NJ, Luben R, Bingham SA, Day NE, Sandhu MS, Jukema JW, Kastelein JJP, Hack CE, Khaw KT. Serum levels of type II secretory phospholipase A2 and the risk of future coronary artery disease in apparently healthy men and women: the EPIC-Norfolk Prospective Population Study. Arterioscler Thromb Vasc Biol. 2005; 25: 839–846.
263. Tsimihodimos V, Karabina SA, Tambaki AP, Bairaktari E, Goudevenos JA, Chapman MJ, Elisaf M, Tselepis AD. Atorvastatin preferentially reduces LDL-associated platelet-activating factor acetylhydrolase activity in dyslipidemias of type IIA and type IIB. Arterioscler Thromb Vasc Biol. 2002; 22: 306–311.
264. Schaefer EJ, McNamara JR, Asztalos BF, Tayler T, Daly JA, Gleason JL, Seman LJ, Ferrari A, Rubenstein JJ. Effects of atorvastatin versus other statins on fasting and postprandial C-reactive protein and lipoprotein-associated phospholipase A2 in patients with coronary heart disease versus control subjects. Am J Cardiol. 2005; 95: 1025–1032.[CrossRef][Medline] [Order article via Infotrieve]
265. Danesh J, Wheeler JG, Hirschfield GM, Pepys MB, Eda S, Eiriksdottir G, Gudnason V, Rumley A, Lowe GDO. C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease. N Engl J Med. 2004; 350: 1387–1397.
266. Malik I, Danesh J, Whincup P, Bhatia V, Papacosta O, Walker M, Lennon L, Thomson A, Haskard D. Soluble adhesion molecules and prediction of coronary heart disease: a prospective study and meta-analysis. Lancet. 2001; 358: 971–976.[CrossRef][Medline] [Order article via Infotrieve]
267. Cesari M, Penninx BWJH, Newman AB, Kritchevsky SB, Nicklas BJ, Sutton-Tyrrell K, Rubin SM, Ding J, Simonsick EM, Harris TB, Pahor M. Inflammatory markers and onset of cardiovascular events: results from the Health ABC Study. Circulation. 2003; 108: 2317–2322.
268. Lindmark E, Diderholm E, Wallentin L, Siegbahn A. Relationship between interleukin 6 and mortality in patients with unstable coronary artery disease: effects of an early invasive or noninvasive strategy. JAMA. 2001; 286: 2107–2113.
269. Luc G, Bard JM, Juhan-Vague I, Ferrieres J, Evans A, Amouyel P, Arveiler D, Fruchart JC, Ducimetiere P. C-reactive protein, interleukin-6, and fibrinogen as predictors of coronary heart disease: the PRIME Study. Arterioscler Thromb Vasc Biol. 2003; 23: 1255–1261.
270. Pai JK, Pischon T, Manson JE, Hankinson SE, Joshipura K, Curhan GC, Stampfer MJ, Rimm EB, Ma J, Rifai N, Cannuscio CC. Inflammatory markers and the risk of coronary heart disease in men and women. N Engl J Med. 2004; 351: 2599–2610.
271. Pradhan AD, Manson JE, Rossouw JE, Siscovick DS, Mouton CP, Rifai N, Wallace RB, Jackson RD, Pettinger MB, Ridker PM. Inflammatory biomarkers, hormone replacement therapy, and incident coronary heart disease: prospective analysis from the Women’s Health Initiative Observational Study. JAMA. 2002; 288: 980–987.
272. Ridker PM, Rifai N, Stampfer MJ, Hennekens CH. Plasma concentration of interleukin-6 and the risk of future myocardial infarction among apparently healthy men. Circulation. 2000; 101: 1767–1772.
273. Volpato S, Guralnik JM, Ferrucci L, Balfour J, Chaves P, Fried LP, Harris TB. Cardiovascular disease, interleukin-6, and risk of mortality in older women: the Women’s Health and Aging Study. Circulation. 2001; 103: 947–953.
274. Blankenberg S, Tiret L, Bickel C, Peetz D, Cambien F, Meyer J, Rupprecht HJ, for the AtheroGene Investigators. Interleukin-18 is a strong predictor of cardiovascular death in stable and unstable angina. Circulation. 2002; 106: 24–30.
275. Blankenberg S, Luc G, Ducimetiere P, Arveiler D, Ferrieres J, Amouyel P, Evans A, Cambien F, Tiret L, on behalf of the PRIME Study Group. Interleukin-18 and the risk of coronary heart disease in European men: the Prospective Epidemiological Study of Myocardial Infarction (PRIME). Circulation. 2003; 108: 2453–2459.
276. Haverkate F, Thompson SG, Pyke SD, Gallimore JR, Pepys MB, for the European Concerted Action on Thrombosis and Disabilities Angina Pectoris Study Group. Production of C-reactive protein and risk of coronary events in stable and unstable angina. Lancet. 1997; 349: 462–466.[CrossRef][Medline] [Order article via Infotrieve]
277. Biasucci LM, Liuzzo G, Grillo RL, Caligiuri G, Rebuzzi AG, Buffon A, Summaria F, Ginnetti F, Fadda G, Maseri A. Elevated levels of C-reactive protein at discharge in patients with unstable angina predict recurrent instability. Circulation. 1999; 99: 855–860.
278. Johnson BD, Kip KE, Marroquin OC, Ridker PM, Kelsey SF, Shaw LJ, Pepine CJ, Sharaf B, Bairey Merz CN, Sopko G, Olson MB, Reis SE. Serum amyloid A as a predictor of coronary artery disease and cardiovascular outcome in women: the National Heart, Lung, and Blood Institute–sponsored Women’s Ischemia Syndrome Evaluation (WISE). Circulation. 2004; 109: 726–732.
279. Brennan ML, Penn MS, Van Lente F, Nambi V, Shishehbor MH, Aviles RJ, Goormastic M, Pepoy ML, McErlean ES, Topol EJ, Nissen SE, Hazen SL. Prognostic value of myeloperoxidase in patients with chest pain. N Engl J Med. 2003; 349: 1595–1604.
280. Zhang R, Brennan ML, Fu X, Aviles RJ, Pearce GL, Penn MS, Topol EJ, Sprecher DL, Hazen SL. Association between myeloperoxidase levels and risk of coronary artery disease. JAMA. 2001; 286: 2136–2142.
281. Halldorsdottir AM, Stoker J, Porche-Sorbet R, Eby CS. Soluble CD40 ligand measurement inaccuracies attributable to specimen type, processing time, and ELISA method. Clin Chem. 2005; 51: 1054–1057.
282. Heeschen C, Dimmeler S, Hamm CW, van den Brand MJ, Boersma E, Zeiher AM, Simoons ML, for the CAPTURE Study Investigators. Soluble CD40 ligand in acute coronary syndromes. N Engl J Med. 2003; 348: 1104–1111.
283. Schonbeck U, Varo N, Libby P, Buring J, Ridker PM. Soluble CD40L and cardiovascular risk in women. Circulation. 2001; 104: 2266–2268.
284. Varo N, Libby P, Morrow DA, Nuzzo R, Cannon CP, Braunwald E, Schonbeck U, de Lemos JA, Murphy SA, Gibson CM. Soluble CD40L: risk prediction after acute coronary syndromes. Circulation. 2003; 108: 1049–1052.
285. Yan JC, Zhu J, Gao L, Wu ZG, Kong XT, Zong RQ, Zhan LZ. The effect of elevated serum soluble CD40 ligand on the prognostic value in patients with acute coronary syndromes. Clin Chim Acta. 2004; 343: 155–159.[CrossRef][Medline] [Order article via Infotrieve]
286. Kinlay S, Schwartz GG, Olsson AG, Rifai N, Sasiela WJ, Szarek M, Ganz P, Libby P, for the Myocardial Ischemia Reduction with Aggressive Cholesterol Lowering Study Investigators. Effect of atorvastatin on risk of recurrent cardiovascular events after an acute coronary syndrome associated with high soluble CD40 ligand in the Myocardial Ischemia Reduction with Aggressive Cholesterol Lowering (MIRACL) Study. Circulation. 2004; 110: 386–391.
287. Sanguigni V, Pignatelli P, Lenti L, Ferro D, Bellia A, Carnevale R, Tesauro M, Sorge R, Lauro R, Violi F. Short-term treatment with atorvastatin reduces platelet CD40 ligand and thrombin generation in hypercholesterolemic patients. Circulation. 2005; 111: 412–419.
288. Meisinger C, Baumert J, Khuseyinova N, Loewel H, Koenig W. Plasma oxidized low-density lipoprotein, a strong predictor for acute coronary heart disease events in apparently healthy, middle-aged men from the general population. Circulation. 2005; 112: 651–657.
289. Blankenberg S, Rupprecht HJ, Bickel C, Torzewski M, Hafner G, Tiret L, Smieja M, Cambien F, Meyer J, Lackner KJ, for the AtheroGene Investigators. Glutathione peroxidase 1 activity and cardiovascular events in patients with coronary artery disease. N Engl J Med. 2003; 349: 1605–1613.
290. Schnabel R, Lackner KJ, Rupprecht HJ, Espinola-Klein C, Torzewski M, Lubos E, Bickel C, Cambien F, Tiret L, Munzel T, Blankenberg S. Glutathione peroxidase-1 and homocysteine for cardiovascular risk prediction: results from the AtheroGene Study. J Am Coll Cardiol. 2005; 45: 1631–1637.
291. Shishehbor MH, Aviles RJ, Brennan ML, Fu X, Goormastic M, Pearce GL, Gokce N, Keaney JF Jr, Penn MS, Sprecher DL, Vita JA, Hazen SL. Association of nitrotyrosine levels with cardiovascular disease and modulation by statin therapy. JAMA. 2003; 289: 1675–1680.
292. Homocysteine SC. Homocysteine and risk of ischemic heart disease and stroke: a meta-analysis. JAMA. 2002; 288: 2015–2022.
293. Koenig W, Twardella D, Brenner H, Rothenbacher D. Plasma concentrations of cystatin C in patients with coronary heart disease and risk for secondary cardiovascular events: more than simply a marker of glomerular filtration rate. Clin Chem. 2005; 51: 321–327.[Medline] [Order article via Infotrieve]
294. Luc G, Bard JM, Lesueur C, Arveiler D, Evans A, Amouyel P, Ferrieres J, Juhan-Vague I, Fruchart JC, Ducimetiere P, for the PRIME Study Group. Plasma cystatin-C and development of coronary heart disease: the PRIME Study. Atherosclerosis. 2006; 185: 375–380.[CrossRef][Medline] [Order article via Infotrieve]
295. O’Hare AM, Newman AB, Katz R, Fried LF, Stehman-Breen CO, Seliger SL, Siscovick DS, Shlipak MG. Cystatin C and incident peripheral arterial disease events in the elderly: results from the Cardiovascular Health Study. Arch Intern Med. 2005; 165: 2666–2670.
296. Shlipak MG, Sarnak MJ, Katz R, Fried LF, Seliger SL, Newman AB, Siscovick DS, Stehman-Breen C. Cystatin C and the risk of death and cardiovascular events among elderly persons. N Engl J Med. 2005; 352: 2049–2060.
297. Apple FS, Panteghini M, Ravkilde J, Mair J, Wu AHB, Tate J, Pagani F, Christenson RH, Jaffe AS, on behalf of the Committee on Standardization of Markers of Cardiac Damage of the IFCC. Quality specifications for B-type natriuretic peptide assays. Clin Chem. 2005; 51: 486–493.
298. Galvani M, Ottani F, Oltrona L, Ardissino D, Gensini GF, Maggioni AP, Mannucci PM, Mininni N, Prando MD, Tubaro M, Vernocchi A, Vecchio C, on behalf of the Italian Working Group on Atherosclerosis, Thrombosis, and Vascular Biology and the Associazione Nazionale Medici Cardiologi Ospedalieri (ANMCO). N-Terminal pro-brain natriuretic peptide on admission has prognostic value across the whole spectrum of acute coronary syndromes. Circulation. 2004; 110: 128–134.
299. Heeschen C, Hamm CW, Mitrovic V, Lantelme NH, White HD, for the Platelet Receptor Inhibition in Ischemic Syndrome Management (PRISM) Investigators. N-Terminal pro-B-type natriuretic peptide levels for dynamic risk stratification of patients with acute coronary syndromes. Circulation. 2004; 110: 3206–3212.
300. Kistorp C, Raymond I, Pedersen F, Gustafsson F, Faber J, Hildebrandt P. N-Terminal pro-brain natriuretic peptide, C-reactive protein, and urinary albumin levels as predictors of mortality and cardiovascular events in older adults. JAMA. 2005; 293: 1609–1616.
301. Kragelund C, Gronning B, Hildebrandt P, Kober L, Steffensen R. N-Terminal pro-B-type natriuretic peptide and long-term mortality in stable coronary heart disease. N Engl J Med. 2005; 352: 666–675.
302. Morrow DA, Sabatine MS, McCabe CH, Cannon CP, Braunwald E, de Lemos JA, Murphy SA, Gibson CM, Demopoulos LA, DiBattiste PM. Evaluation of B-type natriuretic peptide for risk assessment in unstable angina/non-ST-elevation myocardial infarction: B-type natriuretic peptide and prognosis in TACTICS-TIMI 18. J Am Coll Cardiol. 2003; 41: 1264–1272.
303. Morrow DA, de Lemos JA, Blazing MA, Sabatine MS, Murphy SA, Jarolim P, White HD, Fox KAA, Califf RM, Braunwald E, for the A to Z Investigators. Prognostic value of serial B-type natriuretic peptide testing during follow-up of patients with unstable coronary artery disease. JAMA. 2005; 294: 2866–2871.
304. Omland T, Richards AM, Wergeland R, Vik-Mo H. B-Type natriuretic peptide and long-term survival in patients with stable coronary artery disease. Am J Cardiol. 2005; 95: 24–28.[Medline] [Order article via Infotrieve]
305. Suzuki S, Yoshimura M, Nakayama M, Mizuno Y, Harada E, Ito T, Nakamura S, Abe K, Yamamuro M, Sakamoto T, Saito Y, Nakao K, Yasue H, Ogawa H. Plasma level of B-type natriuretic peptide as a prognostic marker after acute myocardial infarction: a long-term follow-up analysis. Circulation. 2004; 110: 1387–1391.
306. Gackowski A, Isnard R, Golmard JL, Pousset F, Carayon A, Montalescot G, Hulot JS, Thomas D, Piwowarska W, Komajda M. Comparison of echocardiography and plasma B-type natriuretic peptide for monitoring the response to treatment in acute heart failure. Eur Heart J. 2004; 25: 1788–1796.
307. Logeart D, Thabut G, Jourdain P, Chavelas C, Beyne P, Beauvais F, Bouvier E, Solal AC. Predischarge B-type natriuretic peptide assay for identifying patients at high risk of re-admission after decompensated heart failure. J Am Coll Cardiol. 2004; 43: 635–641.
308. Troughton RW, Frampton CM, Yandle TG, Espine EA, Nicholls MG, Richards AM. Treatment of heart failure guided by plasma amino-terminal brain natriuretic peptide (N-BNP) concentrations. Lancet. 2000; 355: 1126–1130.[CrossRef][Medline] [Order article via Infotrieve]
309. Teerlink T. Measurement of asymmetric dimethylarginine in plasma: methodological considerations and clinical relevance. Clin Chem Lab Med. 2005; 43: 1130–1138.[CrossRef][Medline] [Order article via Infotrieve]
310. Schwedhelm E, Tan-Andresen J, Maas R, Riederer U, Schulze F, Boger RH. Liquid chromatography-tandem mass spectrometry method for the analysis of asymmetric dimethylarginine in human plasma. Clin Chem. 2005; 51: 1268–1271.
311. Vishwanathan K, Tackett RL, Stewart JT, Bartlett MG. Determination of arginine and methylated arginines in human plasma by liquid chromatography-tandem mass spectrometry. J Chromatogr B: Biomed Sci Appl. 2000; 748: 157–166.[CrossRef][Medline] [Order article via Infotrieve]
312. Krempl TK, Maas R, Sydow K, Meinertz T, Boger RH, Kahler J. Elevation of asymmetric dimethylarginine in patients with unstable angina and recurrent cardiovascular events. Eur Heart J. 2005; 26: 1846–1851.
313. Valkonen VP, Paiva H, Salonen JT, Lakka TA, Lehtimaki T, Laakso J, Laaksonen R. Risk of acute coronary events and serum concentration of asymmetrical dimethylarginine. Lancet. 2001; 358: 2127–2128.[CrossRef][Medline] [Order article via Infotrieve]
314. Zoccali C, Bode-Boger S, Mallamaci F, Benedetto F, Tripepi G, Malatino L, Cataliotti A, Bellanuova I, Fermo I, Frolich J, Boger R. Plasma concentration of asymmetrical dimethylarginine and mortality in patients with end-stage renal disease: a prospective study. Lancet. 2001; 358: 2113–2117.[CrossRef][Medline] [Order article via Infotrieve]
315. Bae SW, Stuhlinger MC, Yoo HS, Yu KH, Park HK, Choi BY, Lee YS, Pachinger O, Choi YH, Lee SH, Park JE. Plasma asymmetric dimeth-ylarginine concentrations in newly diagnosed patients with acute myocardial infarction or unstable angina pectoris during two weeks of medical treatment. Am J Cardiol. 2005; 95: 729–733.[CrossRef][Medline] [Order article via Infotrieve]
316. Boger RH, Zoccali C. ADMA: a novel risk factor that explains excess cardiovascular event rate in patients with end-stage renal disease. Atheroscler Suppl. 2003; 4: 23–28.[Medline] [Order article via Infotrieve]
317. Mallamaci F, Tripepi G, Cutrupi S, Malatino LS, Zoccali C. Prognostic value of combined use of biomarkers of inflammation, endothelial dysfunction, and myocardiopathy in patients with ESRD. Kidney Int. 2005; 67: 2330–2337.[CrossRef][Medline] [Order article via Infotrieve]
318. Ravani P, Tripepi G, Malberti F, Testa S, Mallamaci F, Zoccali C. Asymmetrical dimethylarginine predicts progression to dialysis and death in patients with chronic kidney disease: a competing risks modeling approach. J Am Soc Nephrol. 2005; 16: 2449–2455.
319. Tarnow L, Hovind P, Teerlink T, Stehouwer CD, Parving HH. Elevated plasma asymmetric dimethylarginine as a marker of cardiovascular morbidity in early diabetic nephropathy in type 1 diabetes. Diabetes Care. 2004; 27: 765–769.
320. Blankenberg S, Rupprecht HJ, Poirier O, Bickel C, Smieja M, Hafner G, Meyer J, Cambien F, Tiret L, for the AtheroGene Investigators. Plasma concentrations and genetic variation of matrix metalloproteinase 9 and prognosis of patients with cardiovascular disease. Circulation. 2003; 107: 1579–1585.
321. Lubos E, Schnabel R, Rupprecht HJ, Bickel C, Messow CM, Prigge S, Cambien F, Tiret L, Munzel T, Blankenberg S. Prognostic value of tissue inhibitor of metalloproteinase-1 for cardiovascular death among patients with cardiovascular disease: results from the AtheroGene Study. Eur Heart J. 2006; 27: 150–156.
322. Riley WA, Barnes RW, Applegate WB, Dempsey R, Hartwell T, Davis VG, Bond MG, Furberg CD. Reproducibility of noninvasive ultrasonic measurement of carotid atherosclerosis: the Asymptomatic Carotid Artery Plaque Study. Stroke. 1992; 23: 1062–1068.
323. Bots ML, Hoes AW, Koudstaal PJ, Hofman A, Grobbee DE. Common carotid intima-media thickness and risk of stroke and myocardial infarction: the Rotterdam Study. Circulation. 1997; 96: 1432–1437.
324. Chambless LE, Heiss G, Folsom AR, Rosamond W, Szklo M, Sharrett AR, Clegg LX. Association of coronary heart disease incidence with carotid arterial wall thickness and major risk factors: the Atherosclerosis Risk in Communities (ARIC) Study, 1987–1993. Am J Epidemiol. 1997; 146: 483–494.
325. Hodis HN, Mack WJ, LaBree L, Selzer RH, Liu CR, Liu CH, Azen SP. The role of carotid arterial intima-media thickness in predicting clinical coronary events. Ann Intern Med. 1998; 128: 262–269.
326. O’Leary DH, Polak JF, Kronmal RA, Manolio TA, Burke GL, Wolfson SK, for the Cardiovascular Health Study Collaborative Research Group. Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults. N Engl J Med. 1999; 340: 14–22.
327. Lonn E. Use of carotid ultrasound to stratify risk. Can J Cardiol. 2001; 17 (suppl A): 22A–25A.[Medline] [Order article via Infotrieve]
328. Mancini GB. Carotid intima-media thickness as a measure of vascular target organ damage. Curr Hypertens Rep. 2000; 2: 71–77.[Medline] [Order article via Infotrieve]
329. Mukherjee D, Yadav JS. Carotid artery intimal-medial thickness: indicator of atherosclerotic burden and response to risk factor modification. Am Heart J. 2002; 144: 753–759.[CrossRef][Medline] [Order article via Infotrieve]
330. Arad Y, Spadaro LA, Goodman K, Lledo-Perez A, Sherman S, Lerner G, Guerci AD. Predictive value of electron beam computed tomography of the coronary arteries: 19-month follow-up of 1173 asymptomatic subjects. Circulation. 1996; 93: 1951–1953.
331. Arad Y, Goodman KJ, Roth M, Newstein D, Guerci AD. Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St. Francis Heart Study. J Am Coll Cardiol. 2005; 46: 158–165.
332. Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA. 2004; 291: 210–215.
333. Kondos GT, Hoff JA, Sevrukov A, Daviglus ML, Garside DB, Devries SS, Chomka EV, Liu K. Electron-beam tomography coronary artery calcium and cardiac events: a 37-month follow-up of 5635 initially asymptomatic low- to intermediate-risk adults. Circulation. 2003; 107: 2571–2576.
334. Vliegenthart R, Oudkerk M, Hofman A, Oei HH, van Dijck W, van Rooij FJA, Witteman JCM. Coronary calcification improves cardiovascular risk prediction in the elderly. Circulation. 2005; 112: 572–577.
335. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, Jones DW, Kurtz T, Sheps SG, Roccella EJ. Recommendations for blood pressure measurement in humans and experimental animals, part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation. 2005; 111: 697–716.
336. Corretti MC, Anderson TJ, Benjamin EJ, Celermajer D, Charbonneau F, Creager MA, Deanfield J, Drexler H, Gerhard-Herman M, Herrington D, Vallance P, Vita J, Vogel R. Guidelines for the ultrasound assessment of endothelial-dependent flow-mediated vasodilation of the brachial artery: a report of the International Brachial Artery Reactivity Task Force. J Am Coll Cardiol. 2002; 39: 257–265.
337. Kuvin JT, Patel AR, Sliney KA, Pandian NG, Sheffy J, Schnall RP, Karas RH, Udelson JE. Assessment of peripheral vascular endothelial function with finger arterial pulse wave amplitude. Am Heart J. 2003; 146: 168–174.[CrossRef][Medline] [Order article via Infotrieve]
338. Bonetti PO, Lerman LO, Lerman A. Endothelial dysfunction: a marker of atherosclerotic risk. Arterioscler Thromb Vasc Biol. 2003; 23: 168–175.
339. Brevetti G, Silvestro A, Schiano V, Chiariello M. Endothelial dysfunction and cardiovascular risk prediction in peripheral arterial disease: additive value of flow-mediated dilation to ankle-brachial pressure index. Circulation. 2003; 108: 2093–2098.
340. Chan SY, Mancini GB, Kuramoto L, Schulzer M, Frohlich J, Ignaszewski A. The prognostic importance of endothelial dysfunction and carotid atheroma burden in patients with coronary artery disease. J Am Coll Cardiol. 2003; 42: 1037–1043.
341. Fichtlscherer S, Breuer S, Zeiher AM. Prognostic value of systemic endothelial dysfunction in patients with acute coronary syndromes: further evidence for the existence of the "vulnerable" patient. Circulation. 2004; 110: 1926–1932.
342. Gokce N, Keaney JF Jr, Hunter LM, Watkins MT, Menzoian JO, Vita JA. Risk stratification for postoperative cardiovascular events via noninvasive assessment of endothelial function: a prospective study. Circulation. 2002; 105: 1567–1572.
343. Gokce N, Keaney JF Jr, Hunter LM, Watkins MT, Nedeljkovic ZS, Menzoian JO, Vita JA. Predictive value of noninvasively determined endothelial dysfunction for long-term cardiovascular events in patients with peripheral vascular disease. J Am Coll Cardiol. 2003; 41: 1769–1775.
344. Halcox JP, Schenke WH, Zalos G, Mincemoyer R, Prasad A, Waclawiw MA, Nour KR, Quyyumi AA. Prognostic value of coronary vascular endothelial dysfunction. Circulation. 2002; 106: 653–658.
345. Heitzer T, Schlinzig T, Krohn K, Meinertz T, Munzel T. Endothelial dysfunction, oxidative stress, and risk of cardiovascular events in patients with coronary artery disease. Circulation. 2001; 104: 2673–2678.
346. Neunteufl T, Heher S, Katzenschlager R, Wolfl G, Kostner K, Maurer G, Weidinger F. Late prognostic value of flow-mediated dilation in the brachial artery of patients with chest pain. Am J Cardiol. 2000; 86: 207–210.[CrossRef][Medline] [Order article via Infotrieve]
347. Perticone F, Ceravolo R, Pujia A, Ventura G, Iacopino S, Scozzafava A, Ferraro A, Chello M, Mastroroberto P, Verdecchia P, Schillaci G. Prognostic significance of endothelial dysfunction in hypertensive patients. Circulation. 2001; 104: 191–196.
348. Schachinger V, Britten MB, Zeiher AM. Prognostic impact of coronary vasodilator dysfunction on adverse long-term outcome of coronary heart disease. Circulation. 2000; 101: 1899–1906.
349. Schindler TH, Hornig B, Buser PT, Olschewski M, Magosaki N, Pfisterer M, Nitzsche EU, Solzbach U, Just H. Prognostic value of abnormal vasoreactivity of epicardial coronary arteries to sympathetic stimulation in patients with normal coronary angiograms. Arterioscler Thromb Vasc Biol. 2003; 23: 495–501.
350. Suwaidi JA, Hamasaki S, Higano ST, Nishimura RA, Holmes DR Jr, Lerman A. Long-term follow-up of patients with mild coronary artery disease and endothelial dysfunction. Circulation. 2000; 101: 948–954.
351. Targonski PV, Bonetti PO, Pumper GM, Higano ST, Holmes DR Jr, Lerman A. Coronary endothelial dysfunction is associated with an increased risk of cerebrovascular events. Circulation. 2003; 107: 2805–2809.
352. Verma S, Buchanan MR, Anderson TJ. Endothelial function testing as a biomarker of vascular disease. Circulation. 2003; 108: 2054–2059.
353. von Mering GO, Arant CB, Wessel TR, McGorray SP, Bairey Merz CN, Sharaf BL, Smith KM, Olson MB, Johnson BD, Sopko G, Handberg E, Pepine CJ, Kerensky RA. Abnormal coronary vasomotion as a prognostic indicator of cardiovascular events in women: results from the National Heart, Lung, and Blood Institute–Sponsored Women’s Ischemia Syndrome Evaluation (WISE). Circulation. 2004; 109: 722–725.
354. Modena MG, Bonetti L, Coppi F, Bursi F, Rossi R. Prognostic role of reversible endothelial dysfunction in hypertensive postmenopausal women. J Am Coll Cardiol. 2002; 40: 505–510.
355. Brinton BS, Cotter MD, Kailasam MBBS, Brown MD, Chio P. Development and validation of a noninvasive method to determine arterial pressure and vascular compliance. Am J Cardiol. 1997; 80: 323–330.[CrossRef][Medline] [Order article via Infotrieve]
356. Karamanoglu M, Gallagher DE, Avolio AP, O’Rourke MF. Pressure wave propagation in a multibranched model of the human upper limb. Am J Physiol. 1995; 269: H1363–H1369.[Medline] [Order article via Infotrieve]
357. McVeigh GE, Bratteli CW, Morgan DJ, Alinder CM, Glasser SP, Finkelstein SM, Cohn JN. Age-related abnormalities in arterial compliance identified by pressure pulse contour analysis: aging and arterial compliance. Hypertension. 1999; 33: 1392–1398.
358. Mitchell GF, Lacourciere Y, Ouellet JP, Izzo JL Jr, Neutel J, Kerwin LJ, Block AJ, Pfeffer MA. Determinants of elevated pulse pressure in middle-aged and older subjects with uncomplicated systolic hypertension: the role of proximal aortic diameter and the aortic pressure-flow relationship. Circulation. 2003; 108: 1592–1598.
359. Blacher J, Asmar R, Djane S, London GM, Safar ME. Aortic pulse wave velocity as a marker of cardiovascular risk in hypertensive patients. Hypertension. 1999; 33: 1111–1117.
360. Boutouyrie P, Tropeano AI, Asmar R, Gautier I, Benetos A, Lacolley P, Laurent S. Aortic stiffness is an independent predictor of primary coronary events in hypertensive patients: a longitudinal study. Hypertension. 2002; 39: 10–15.
361. London GM, Blacher J, Pannier B, Guerin AP, Marchais SJ, Safar ME. Arterial wave reflections and survival in end-stage renal failure. Hypertension. 2001; 38: 434–438.
362. Safar ME, Blacher J, Pannier B, Guerin AP, Marchais SJ, Guyonvarc’h PM, London GM. Central pulse pressure and mortality in end-stage renal disease. Hypertension. 2002; 39: 735–738.
363. Stork S, van den Beld AW, von Schacky C, Angermann CE, Lamberts SWJ, Grobbee DE, Bots ML. Carotid artery plaque burden, stiffness, and mortality risk in elderly men: a prospective, population-based cohort study. Circulation. 2004; 110: 344–348.
364. Weber T, Auer J, O’Rourke MF, Kvas E, Lassnig E, Berent R, Eber B. Arterial stiffness, wave reflections, and the risk of coronary artery disease. Circulation. 2004; 109: 184–189.
365. Weber T, Auer J, O’Rourke MF, Kvas E, Lassnig E, Lamm G, Stark N, Rammer M, Eber B. Increased arterial wave reflections predict severe cardiovascular events in patients undergoing percutaneous coronary interventions. Eur Heart J. 2005; 26: 2657–2663.
366. Grey E, Bratteli C, Glasser SP, Alinder C, Finkelstein SM, Lindgren BR, Cohn JN. Reduced small artery but not large artery elasticity is an independent risk marker for cardiovascular events. Am J Hypertens. 2003; 16: 265–269.[CrossRef][Medline] [Order article via Infotrieve]
367. Schiffrin EL, Park JB, Intengan HD, Touyz RM. Correction of arterial structure and endothelial dysfunction in human essential hypertension by the angiotensin receptor antagonist losartan. Circulation. 2000; 101: 1653–1659.
368. Kaiser V, Kester ADM, Stoffers HEJH, Kitslaar PJEH, Knottnerus JA. The influence of experience on the reproducibility of the ankle-brachial systolic pressure ratio in peripheral arterial occlusive disease. Eur J Vasc Endovasc Surg. 1999; 18: 25–29.[CrossRef][Medline] [Order article via Infotrieve]
369. Matzke S, Franckena M, Alback A, Railo M, Lepantalo M. Ankle brachial index measurements in critical leg ischaemia: the influence of experience on reproducibility. Scand J Surg. 2003; 92: 144–147.[Medline] [Order article via Infotrieve]
370. Doobay AV, Anand SS. Sensitivity and specificity of the ankle-brachial index to predict future cardiovascular outcomes: a systematic review. Arterioscler Thromb Vasc Biol. 2005; 25: 1463–1469.
371. Criqui MH, Langer RD, Fronek A, Feigelson HS, Klauber MR, McCann TJ, Browner D. Mortality over a period of 10 years in patients with peripheral arterial disease. N Engl J Med. 1992; 326: 381–386.[Abstract]
372. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002; 39: S1–266.[CrossRef][Medline] [Order article via Infotrieve]
373. Agodoa LY, Appel L, Bakris GL, Beck G, Bourgoignie J, Briggs JP, Charleston J, Cheek D, Cleveland W, Douglas JG, Douglas M, Dowie D, Faulkner M, Gabriel A, Gassman J, Greene T, Hall Y, Hebert L, Hiremath L, Jamerson K, Johnson CJ, Kopple J, Kusek J, Lash J, Lea J, Lewis JB, Lipkowitz M, Massry S, Middleton J, Miller ER III, Norris K, O’Connor D, Ojo A, Phillips RA, Pogue V, Rahman M, Randall OS, Rostand S, Schulman G, Smith W, Thornley-Brown D, Tisher CC, Toto RD, Wright JT Jr, Xu S, for the African American Study of Kidney Disease and Hypertension Study Group. Effect of ramipril vs amlodipine on renal outcomes in hypertensive nephrosclerosis: a randomized controlled trial. JAMA. 2001; 285: 2719–2728.
374. Ballard DJ, Humphrey LL, Melton LJ, Frohnert PP, Chu PC, O’Fallon WM, Palumbo PJ. Epidemiology of persistent proteinuria in type II diabetes mellitus: population-based study in Rochester, Minnesota. Diabetes. 1988; 37: 405–412.[Abstract]
375. Borch-Johnsen K, Feldt-Rasmussen B, Strandgaard S, Schroll M, Jensen JS. Urinary albumin excretion: an independent predictor of ischemic heart disease. Arterioscler Thromb Vasc Biol. 1999; 19: 1992–1997.
376. Miettinen H, Haffner SM, Lehto S, Ronnemaa T, Pyorala K, Laakso M. Proteinuria predicts stroke and other atherosclerotic vascular disease events in nondiabetic and non–insulin-dependent diabetic subjects. Stroke. 1996; 27: 2033–2039.
377. Mogensen CE. Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset diabetes. N Engl J Med. 1984; 310: 356–360.[Abstract]
378. Neil A, Hawkins M, Potok M, Thorogood M, Cohen D, Mann J. A prospective population-based study of microalbuminuria as a predictor of mortality in NIDDM. Diabetes Care. 1993; 16: 996–1003.[Abstract]
379. Nelson RG, Pettitt DJ, Carraher MJ, Baird HR, Knowler WC. Effect of proteinuria on mortality in NIDDM. Diabetes. 1988; 37: 1499–1504.[Abstract]
380. Arnlov J, Evans JC, Meigs JB, Wang TJ, Fox CS, Levy D, Benjamin EJ, D’Agostino RB, Vasan RS. Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and nondiabetic individuals: the Framingham Heart Study. Circulation. 2005; 112: 969–975.
381. Fibrinogen SC. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. JAMA. 2005; 294: 1799–1809.
382. Danesh J, Whincup P, Walker M, Lennon L, Thomson A, Appleby P, Rumley A, Lowe GDO. Fibrin D-dimer and coronary heart disease: prospective study and meta-analysis. Circulation. 2001; 103: 2323–2327.
383. Longstaff C, Whitton CM. A proposed reference method for plasminogen activators that enables calculation of enzyme activities in SI units. J Thromb Haemost. 2004; 2: 1416–1421.[CrossRef][Medline] [Order article via Infotrieve]
384. Lowe GDO, Danesh J, Lewington S, Walker M, Lennon L, Thomson A, Rumley A, Whincup PH. Tissue plasminogen activator antigen and coronary heart disease: prospective study and meta-analysis. Eur Heart J. 2004; 25: 252–259.
385. Whincup PH, Danesh J, Walker M, Lennon L, Thomson A, Appleby P, Rumley A, Lowe GDO. von Willebrand factor and coronary heart disease: prospective study and meta-analysis. Eur Heart J. 2002; 23: 1764–1770.
386. Devereux RB, Alonso DR, Lutas EM, Gottlieb GJ, Campo E, Sachs I, Reichek N. Echocardiographic assessment of left ventricular hypertrophy: comparison to necropsy findings. Am J Cardiol. 1986; 57: 450–458.[CrossRef][Medline] [Order article via Infotrieve]
387. Palmieri V, Dahlof B, DeQuattro V, Sharpe N, Bella JN, de Simone G, Paranicas M, Fishman D, Devereux RB. Reliability of echocardiographic assessment of left ventricular structure and function: the PRESERVE Study. J Am Coll Cardiol. 1999; 34: 1625–1632.
388. Wachtell K, Bella JN, Liebson PR, Gerdts E, Dahlof B, Aalto T, Roman MJ, Papademetriou V, Ibsen H, Rokkedal J, Devereux RB. Impact of different partition values on prevalences of left ventricular hypertrophy and concentric geometry in a large hypertensive population: the LIFE Study. Hypertension. 2000; 35: 6–12.
389. Bikkina M, Levy D, Evans JC, Larson MG, Benjamin EJ, Wolf PA, Castelli WP. Left ventricular mass and risk of stroke in an elderly cohort: the Framingham Heart Study. JAMA. 1994; 272: 33–36.
390. Kannel WB, Abbott RD. A prognostic comparison of asymptomatic left ventricular hypertrophy and unrecognized myocardial infarction: the Framingham Study. Am Heart J. 1986; 111: 391–397.[CrossRef][Medline] [Order article via Infotrieve]
391. Koren MJ, Devereux RB, Casale PN, Savage DD, Laragh JH. Relation of left ventricular mass and geometry to morbidity and mortality in uncomplicated essential hypertension. Ann Intern Med. 1991; 114: 345–352.
392. Levy D, Garrison RJ, Savage DD, Kannel WB, Castelli WP. Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study. N Engl J Med. 1990; 322: 1561–1566.[Abstract]
393. Liao Y, Cooper RS, McGee DL, Mensah GA, Ghali JK. The relative effects of left ventricular hypertrophy, coronary artery disease, and ventricular dysfunction on survival among black adults. JAMA. 1995; 273: 1592–1597.
394. Quinones MA, Greenberg BH, Kopelen HA, Koilpillai C, Limacher MC, Shindler DM, Shelton BJ, Weiner DH. Echocardiographic predictors of clinical outcome in patients with left ventricular dysfunction enrolled in the SOLVD registry and trials: significance of left ventricular hypertrophy: Studies Of Left Ventricular Dysfunction. J Am Coll Cardiol. 2000; 35: 1237–1244.
395. Schillaci G, Verdecchia P, Porcellati C, Cuccurullo O, Cosco C, Perticone F. Continuous relation between left ventricular mass and cardiovascular risk in essential hypertension. Hypertension. 2000; 35: 580–586.
396. Vakili BA, Okin PM, Devereux RB. Prognostic implications of left ventricular hypertrophy. Am Heart J. 2001; 141: 334–341.[CrossRef][Medline] [Order article via Infotrieve]
397. Verdecchia P, Schillaci G, Borgioni C, Ciucci A, Gattobigio R, Zampi I, Reboldi G, Porcellati C. Prognostic significance of serial changes in left ventricular mass in essential hypertension. Circulation. 1998; 97: 48–54.
398. Devereux RB, Wachtell K, Gerdts E, Boman K, Nieminen MS, Papademetriou V, Rokkedal J, Harris K, Aurup P, Dahlof B. Prognostic significance of left ventricular mass change during treatment of hypertension. JAMA. 2004; 292: 2350–2356.
399. Mathew J, Sleight P, Lonn E, Johnstone D, Pogue J, Yi Q, Bosch J, Sussex B, Probstfield J, Yusuf S. Reduction of cardiovascular risk by regression of electrocardiographic markers of left ventricular hypertrophy by the angiotensin-converting enzyme inhibitor ramipril. Circulation. 2001; 104: 1615–1621.
400. Okin PM, Devereux RB, Jern S, Kjeldsen SE, Julius S, Nieminen MS, Snapinn S, Harris KE, Aurup P, Edelman JM, Wedel H, Lindholm LH, Dahlof B, for the LIFE Study Investigators. Regression of electrocardiographic left ventricular hypertrophy during antihypertensive treatment and the prediction of major cardiovascular events. JAMA. 2004; 292: 2343–2349.
401. Ekelund LG, Suchindran CM, McMahon RP, Heiss G, Leon AS, Romhilt DW, Rubenstein CL, Probstfield JL, Ruwitch JF. Coronary heart disease morbidity and mortality in hypercholesterolemic men predicted from an exercise test: the Lipid Research Clinics Coronary Primary Prevention Trial. J Am Coll Cardiol. 1989; 14: 556–563.[Abstract]
402. Gibbons LW, Mitchell TL, Wei M, Blair SN, Cooper KH. Maximal exercise test as a predictor of risk for mortality from coronary heart disease in asymptomatic men. Am J Cardiol. 2000; 86: 53–58.[Medline] [Order article via Infotrieve]
403. Rautaharju PM, Prineas RJ, Eifler WJ, Furberg CD, Neaton JD, Crow RS, Stamler J, Cutler JA. Prognostic value of exercise electrocardiogram in men at high risk of future coronary heart disease: Multiple Risk Factor Intervention Trial experience. J Am Coll Cardiol. 1986; 8: 1–10.[Abstract]
404. Panteghini M. Standardization of cardiac troponin I measurements: the way forward? Clin Chem. 2005; 51: 1594–1597.
405. Heidenreich PA, Alloggiamento T, Melsop K, McDonald KM, Go AS, Hlatky MA. The prognostic value of troponin in patients with non-ST elevation acute coronary syndromes: a meta-analysis. J Am Coll Cardiol. 2001; 38: 478–485.
406. Scirica BM, Morrow DA. Troponins in acute coronary syndromes. Prog Cardiovasc Dis. 2004; 47: 177–188.[CrossRef][Medline] [Order article via Infotrieve]
407. Libby P, Theroux P. Pathophysiology of coronary artery disease. Circulation. 2005; 111: 3481–3488.
408. Apple FS, Wu AHB, Mair J, Ravkilde J, Panteghini M, Tate J, Pagani F, Christenson RH, Mockel M, Danne O, Jaffe AS, on behalf of the Committee on Standardization of Markers of Cardiac Damage of the IFCC. Future biomarkers for detection of ischemia and risk stratification in acute coronary syndrome. Clin Chem. 2005; 51: 810–824.
409. Morrow DA, Braunwald E. Future of biomarkers in acute coronary syndromes: moving toward a multimarker strategy. Circulation. 2003; 108: 250–252.
410. Ravkilde J. Risk stratification of acute coronary syndrome patients: a multi-marker approach. Scand J Clin Lab Invest Suppl. 2005; 240: 25–29.[CrossRef][Medline] [Order article via Infotrieve]
411. Hook CC, DiMagno EP, Tefferi A. Primer on medical genomics, part XIII: ethical and regulatory issues. Mayo Clin Proc. 2004; 79: 645–650.
412. Hackett JL, Gutman SI. Introduction to the Food and Drug Administration (FDA) regulatory process. J Proteome Res. 2005; 4: 1110–1113.[CrossRef][Medline] [Order article via Infotrieve]
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|
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