Having reliable sets of predictive biomarkers for diseases is at the heart of a new emerging paradigm for medicine, a paradigm I have called Personalized Predictive Preventative Participatory Medicine (PPPPM). See the blog entries Harnessing the engines of finance and commerce for life-extension, Personalized medicine – reducing the cost and improving the effectiveness of health care, and Transformed State of Medicine – 2025. This blog entry reports on progress for identifying reliable biomarkers for cardiovascular diseases.
A disease biomarker is in general a condition or substance used as an indicator of a biological state that describes a disease susceptibility or that is predictive of a disease . “It is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention(ref).” In the general sense I am using here, a biomarker can be age, gender, childhood adiposity, a clinical test score like a measurement of cholesterol or blood pressure, body-mass index, a genetic condition such as mutation of a specified gene, an epigenetic condition, a condition related to family or ethnic history, X-ray and MRI results, existing or previous disease conditions, or personal habits such as cigarette smoking.
Of the hundreds of relevant publications, I have selected only a limited number for review here to illustrate what I see as the major developmental trends for cardiovascular biomarkers.
Framingham study risk scores for cardiovascular heart disease
A biomarker system for scoring risks of various cardiovascular heart disease (CHD) was developed years ago as part of the Framingham Heart Study. “Determining 10-year (short term) risk for developing CHD is carried out using Framingham risk scoring. The risk factors included in the Framingham calculation are age, total cholesterol, HDL cholesterol, systolic blood pressure, treatment for hypertension, and cigarette smoking. Because of a larger database, Framingham estimates are more robust for total cholesterol than for LDL cholesterol. Note, however, that LDL cholesterol remains the primary target of therapy. The Framingham risk score gives estimates for “hard CHD” which includes myocardial infarction and coronary death(ref).”
As listed on the Framingham Heart Study website “Risk prediction estimates for the risk of various cardiovascular disease outcomes in different time horizons are available as score sheets and direct risk functions. The choice of the appropriate risk prediction algorithm should take into account the following components: cardiovascular outcome, population of interest, time horizon and risk factors. Outcome-specific algorithms preceded by the descriptions of the above four components are available for the following:
Atrial Fibrillation (AF) (10-year risk) and calculator
Congestive Heart Failure
Coronary Heart Disease (10-year risk)
Coronary Heart Disease (2-year risk)
General Cardiovascular Disease
Hard Coronary Heart Disease and calculator (10-year risk)
Intermittent Claudication
Recurring Coronary Heart Disease
Stroke
Stroke after Atrial Fibrillation and calculator
Stroke or Death after Atrial Fibrillation and calculator”
C-reactive protein, a CHD biomarker but not a cause
Significant research attention has been paid to finding additional biomarkers that can improve the sometimes-weak predictive capabilities of the traditional biomarker combinations used in Framingham risk scoring. One such candidate extensively studied has been C-reactive protein (CRP), known since the 1960s as a measure of inflammation and long suspected to be a predictor of CHD. A 1999 study C-Reactive Protein, a Sensitive Marker of Inflammation, Predicts Future Risk of Coronary Heart Disease in Initially Healthy Middle-Aged Men reports “We used a sensitive immunoradiometric assay to examine the association of serum C-reactive protein (CRP) with the incidence of first major coronary heart disease (CHD) event in 936 men 45 to 64 years of age. The subjects, who were sampled at random from the general population, participated in the first MONICA Augsburg survey (1984 to 1985) and were followed for 8 years. — Conclusions—These results confirm the prognostic relevance of CRP, a sensitive systemic marker of inflammation, to the risk of CHD in a large, randomly selected cohort of initially healthy middle-aged men.”
“In the Harvard Woman’s Health Study, results of the CRP test were more accurate than cholesterol levels in predicting heart problems. Twelve different markers of inflammation were studied in healthy, postmenopausal women. After three years, CRP was the strongest predictor of risk. Women in the group with the highest CRP levels were more than four times as likely to have died from coronary disease, or to have suffered a nonfatal heart attack or stroke compared to those with the lowest levels. This group was also more likely to have required a cardiac procedure such as angioplasty (a procedure that opens clogged arteries with the use of a flexible tube) or bypass surgery than women in the group with the lowest levels(ref).”
There is a significant literature relating CRP to CHD. The 2005 study C-Reactive Protein and the 10-Year Incidence of Coronary Heart Disease in Older Men and Women reports “Background— High C-reactive protein (CRP) is associated with increased coronary heart disease risk. Few long-term data in the elderly are available. — Methods and Results— Baseline CRP was measured in 3971 men and women 65 years of age without prior vascular diseases; 26% had elevated concentrations (>3 mg/L). With 10 years of follow-up, 547 participants developed coronary heart disease (CHD; defined as myocardial infarction or coronary death). With elevated CRP, the 10-year cumulative CHD incidences were 33% in men and 17% in women. — Conclusions— In older men and women, elevated CRP was associated with increased 10-year risk of CHD, regardless of the presence or absence of cardiac risk factors. A single CRP measurement provided information beyond conventional risk assessment, especially in intermediate-Framingham-risk men and high-Framingham-risk women.”
The 2006 publication The relative strength of C-reactive protein and lipid levels as determinants of ischemic stroke compared with coronary heart disease in women reported “OBJECTIVES: We sought to determine the relative strength of high-sensitivity C-reactive protein (hs-CRP) and lipid levels as markers for future ischemic stroke compared with coronary heart disease (CHD) in women. — BACKGROUND: Although hs-CRP and lipid levels are established risk determinants for vascular disease, the relative strength of these biomarkers for ischemic stroke compared with CHD is uncertain. — METHODS: Among 15,632 initially healthy women who were followed for a 10-year period, we compared hs-CRP, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoproteins A-I and B100, and lipid ratios as determinants of ischemic stroke compared with CHD. — CONCLUSIONS: In this large prospective cohort of initially healthy women, lipid levels are significant risk determinants for ischemic stroke, but with a magnitude of effect smaller than that observed for CHD. High-sensitivity CRP associates more closely with ischemic stroke than with CHD. Concomitant evaluation of lipid levels and hs-CRP may improve risk assessment for stroke as well as CHD.”
The clear association of CRP with CVD led researchers to speculate that perhaps high CRP is a cause of CVD. A series of studies shot down this speculation. The 2008 publications The association of C-reactive protein and CRP genotype with coronary heart disease: findings from five studies with 4,610 cases amongst 18,637 participants. “CONCLUSIONS: We found no association of a genetic variant, which is known to be related to CRP levels, (rs1130864) and having CHD. These findings do not support a causal association between circulating CRP and CHD risk, but very large, extended, genetic association studies would be required to rule this out.
Another 2008 study C-reactive protein (CRP) gene polymorphisms, CRP levels, and risk of incident coronary heart disease in two nested case-control studies. reported “CONCLUSIONS: Common variation in the CRP gene was significantly associated with plasma CRP levels; however, the association between common SNPs and CRP levels did not correspond to a predicted change in CHD risk. The underlying inflammatory processes which predict coronary events cannot be captured solely by variation in the CRP gene.”
Finally, a 2009 JAMA publication Genetic Loci Associated With C-Reactive Protein Levels and Risk of Coronary Heart Disease concluded “The lack of concordance between the effect on coronary heart disease risk of CRP genotypes and CRP levels argues against a causal association of CRP with coronary heart disease.”So, CRP is a biomarker of inflammation and of CHD. Inflammation may well be a cause for CHD. “Inflammation plays a key role in the pathogenesis of CHD at every stage from initiation to progression and rupture of the atherosclerotic plaque. but its marker, CRP, it not such a cause(ref).” The 2005 publication C-reactive protein comes of age concludes “It is our contention that the future will see much wider use of CRP and CRP-driven therapies in clinical medicine, improving our ability to identify and manage cardiovascular disease.” The authors were right about CRP as a biomarker but, in the light of the new knowledge, wrong about CRP as a target of therapies. This illustrates the incremental nature of biomedical knowledge.
The search for improved biomarkers
The search for better biomarkers predictive of cardiovascular diseases has continued for over 30 years now. Many publications have appeared on the topic such as the 2009 review publication Biomarkers and Cardiovascular Disease. By 2009 some researchers were starting to think that further search was unproductive. The 2009 paper Novel and Conventional Biomarkers for Prediction of Incident Cardiovascular Events in the Community reported on a “Cohort study of 5067 participants (mean age, 58 years; 60% women) without cardiovascular disease from Malmö, Sweden, who attended a baseline examination between 1991 and 1994. Participants underwent measurement of C-reactive protein (CRP), cystatin C, lipoprotein-associated phospholipase 2, midregional proadrenomedullin (MR-proADM), midregional proatrial natriuretic peptide, and N-terminal pro-B-type natriuretic peptide (N-BNP) and underwent follow-up until 2006.” — Results During median follow-up of 12.8 years, there were 418 cardiovascular and 230 coronary events. Models with conventional risk factors had C statistics of 0.758 (95% confidence interval [CI], 0.734 to 0.781) and 0.760 (0.730 to 0.789) for cardiovascular and coronary events, respectively. Biomarkers retained in backward-elimination models were CRP and N-BNP for cardiovascular events and MR-proADM and N-BNP for coronary events, which increased the C statistic by 0.007 (P = .04) and 0.009 (P = .08), respectively. The proportion of participants reclassified was modest (8% for cardiovascular risk, 5% for coronary risk). Net reclassification improvement was nonsignificant for cardiovascular events (0.0%; 95% CI, –4.3% to 4.3%) and coronary events (4.7%; 95% CI, –0.76% to 10.1%). Greater improvements were observed in analyses restricted to intermediate-risk individuals (cardiovascular events: 7.4%; 95% CI, 0.7% to 14.1%; P = .03; coronary events: 14.6%; 95% CI, 5.0% to 24.2%; P = .003). However, correct reclassification was almost entirely confined to down-classification of individuals without events rather than up-classification of those with events. The conclusions were “Selected biomarkers may be used to predict future cardiovascular events, but the gains over conventional risk factors are minimal. Risk classification improved in intermediate-risk individuals, mainly through the identification of those unlikely to develop events.”
Progress reported during 2010
Natriuretic peptides
“Natriuretic peptides are peptide hormones that are synthesized by the heart, brain and other organs. The release of these peptides by the heart is stimulated by atrial and ventricular distension, as well as by neurohumoral stimuli, usually in response to heart failure(ref).” I cite a few 2010 publications relating them to cardiovascular risk.
A 2010 publication Assessment of Conventional Cardiovascular Risk Factors and Multiple Biomarkers for the Prediction of Incident Heart Failure and Atrial Fibrillation reports “Objectives: The purpose of this study was to assess the predictive accuracy of conventional cardiovascular risk factors for incident heart failure and atrial fibrillation, and the added benefit of multiple biomarkers reflecting diverse pathophysiological pathways. — Background: Heart failure and atrial fibrillation are interrelated cardiac diseases associated with substantial morbidity and mortality and increasing incidence. Data on prediction and prevention of these diseases in healthy individuals are limited. — Methods: In 5,187 individuals from the community-based MDCS (Malmö Diet and Cancer Study), we studied the performance of conventional risk factors and 6 biomarkers including midregional pro-atrial natriuretic peptide (MR-proANP), N-terminal pro–B-type natriuretic peptide (NT-proBNP), midregional pro-adrenomedullin, cystatin C, C-reactive protein (CRP), and copeptin. — Results: During a mean follow-up of 14 years, 112 individuals were diagnosed with heart failure and 284 individuals with atrial fibrillation. — Conclusions: Conventional cardiovascular risk factors predict incident heart failure and atrial fibrillation with reasonable accuracy in middle-age individuals free from disease. Natriuretic peptides, but not other biomarkers, improve discrimination modestly for both diseases above and beyond conventional risk factors and substantially improve risk classification for heart failure.”
Another 2010 study of natriutic peptides is Amino-Terminal Pro–B-Type Natriuretic Peptide Improves Cardiovascular and Cerebrovascular Risk Prediction in the Population. “Increased circulating amino-terminal pro–B-type natriuretic (NT-proBNP) levels are a marker of cardiac dysfunction but also associate with coronary heart disease and stroke. We aimed to investigate whether increased circulating NT-proBNP levels have additive prognostic value for first cardiovascular and cerebrovascular events beyond classic risk factors. In a community-based cohort of 5063 participants free of cardiovascular disease, aged
Calcium scores
The 2010 publication Coronary Artery Calcium Score and Risk Classification for Coronary Heart Disease Prediction reports: “Context The coronary artery calcium score (CACS) has been shown to predict future coronary heart disease (CHD) events. However, the extent to which adding CACS to traditional CHD risk factors improves classification of risk is unclear. — Objective To determine whether adding CACS to a prediction model based on traditional risk factors improves classification of risk. — Design, Setting, and Participants CACS was measured by computed tomography in 6814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), a population-based cohort without known cardiovascular disease. Recruitment spanned July 2000 to September 2002; follow-up extended through May 2008. — Conclusion In this multi-ethnic cohort, addition of CACS to a prediction model based on traditional risk factors significantly improved the classification of risk and placed more individuals in the most extreme risk categories. “
Imaging biomarkers
CT scanning for calcification indicating subclinical coronary atherosclerosis may be a useful biomarker for patients already known to have other CVD risk factors. The 2010 publication Coronary Risk Stratification, Discrimination, and Reclassification Improvement Based on Quantification of Subclinical Coronary Atherosclerosis reports “Objectives: The purpose of this study was to determine net reclassification improvement (NRI) and improved risk prediction based on coronary artery calcification (CAC) scoring in comparison with traditional risk factors. — Background: CAC as a sign of subclinical coronary atherosclerosis can noninvasively be detected by CT and has been suggested to predict coronary events. — Methods: In 4,129 subjects from the HNR (Heinz Nixdorf Recall) study (age 45 to 75 years, 53% female) without overt coronary artery disease at baseline, traditional risk factors and CAC scores were measured. Their risk was categorized into low, intermediate, and high according to the Framingham Risk Score (FRS) and National Cholesterol Education Panel Adult Treatment Panel (ATP) III guidelines, and the reclassification rate based on CAC results was calculated. — Results: After 5 years of follow-up, 93 coronary deaths and nonfatal myocardial infarctions occurred — Conclusions: CAC scoring results in a high reclassification rate in the intermediate-risk cohort, demonstrating the benefit of imaging of subclinical coronary atherosclerosis. Our study supports its application, especially in carefully selected individuals with intermediate risk.”
So far the use of advanced imaging biomarkers for predicting cardiovascular disease has been relatively disappointing. The 2010 paper Cardiac computed tomography and myocardial perfusion scintigraphy for risk stratification in asymptomatic individuals without known cardiovascular disease: a position statement of the Working Group on Nuclear Cardiology and Cardiac CT of the European Society of Cardiology states “ — From available data, the use of MPS (myocardial perfusion scintigraphy) as first line testing modality for risk stratification is not recommended in any category of primary prevention subjects with the possible exception of first-degree relatives of patients with premature CAD in whom MPS may be considered. However, the Working Group recognizes that neither the use of computed tomography for calcium imaging nor of MPS have been proven to significantly improve clinical outcomes of primary prevention subjects in prospective controlled studies.”
Combinations of multiple new and old biomarkers
For the many new as well as traditional biomarkers known to be weakly predictive of CHD, 2010 saw several publications relating to different ways of combining them to create more robust predictive tests.
The 2010 publication Multimarker Prediction of Coronary Heart Disease Risk reports “Objectives: The aim of this study was to investigate whether multiple biomarkers contribute to improved coronary heart disease (CHD) risk prediction in post-menopausal women compared with assessment using traditional risk factors (TRFs) only. — Background: The utility of newer biomarkers remains uncertain when added to predictive models using only TRFs for CHD risk assessment. — Methods: The Women’s Health Initiative Hormone Trials enrolled 27,347 post-menopausal women ages 50 to 79 years. Associations of TRFs and 18 biomarkers were assessed in a nested case-control study including 321 patients with CHD and 743 controls. Four prediction equations for 5-year CHD risk were compared: 2 Framingham risk score covariate models; a TRF model including statin treatment, hormone treatment, and cardiovascular disease history as well as the Framingham risk score covariates; and an additional biomarker model that additionally included the 5 significantly associated markers of the 18 tested (interleukin-6, D-dimer, coagulation factor VIII, von Willebrand factor, and homocysteine). — Results: The TRF model showed an improved C-statistic (0.729 vs. 0.699, p = 0.001) and net reclassification improvement (6.42%) compared with the Framingham risk score model. The additional biomarker model showed additional improvement in the C-statistic (0.751 vs. 0.729, p = 0.001) and net reclassification improvement (6.45%) compared with the TRF model. Predicted CHD risks on a continuous scale showed high agreement between the TRF and additional biomarker models (Spearman’s coefficient = 0.918).”
The 2010 publication Multiple marker approach to risk stratification in patients with stable coronary artery disease found an interesting result for the population studied. “Aims: Multimarker approaches for risk prediction in coronary artery disease have remained inconsistent. We assessed multiple biomarkers representing distinct pathophysiological pathways in relation to cardiovascular events in stable angina. — Methods and results We investigated 12 biomarkers reflecting inflammation [C-reactive protein, growth-differentiation factor (GDF)-15, neopterin], lipid metabolism (apolipoproteins AI, B100), renal function (cystatin C, serum creatinine), and cardiovascular function and remodelling [copeptin, C-terminal-pro-endothelin-1, mid-regional-pro-adrenomedullin (MR-proADM), mid-regional-pro-atrial natriuretic peptide (MR-proANP), N-terminal-pro-B-type natriuretic peptide (Nt-proBNP)] in 1781 stable angina patients in relation to non-fatal myocardial infarction and cardiovascular death (n = 137) over 3.6 years. — Conclusion Comparative analysis of 12 biomarkers revealed Nt-proBNP, GDF-15, MR-proANP, cystatin C, and MR-proADM as the strongest predictors of cardiovascular outcome in stable angina. All five biomarkers taken separately offered incremental predictive ability over established risk factors. Combination of the single markers slightly improved model fit but did not enhance risk prediction from a clinical perspective.”
The 2010 publication Contribution of 30 Biomarkers to 10-Year Cardiovascular Risk Estimation in 2 Population Cohorts reports “Background— Cardiovascular risk estimation by novel biomarkers needs assessment in disease-free population cohorts, followed up for incident cardiovascular events, assaying the serum and plasma archived at baseline. We report results from 2 cohorts in such a continuing study. — Methods and Results— Thirty novel biomarkers from different pathophysiological pathways were evaluated in 7915 men and women of the FINRISK97 population cohort with 538 incident cardiovascular events at 10 years (fatal or nonfatal coronary or stroke events), from which a biomarker score was developed and then validated in the 2551 men of the Belfast Prospective Epidemiological Study of Myocardial Infarction (PRIME) cohort (260 events). No single biomarker consistently improved risk estimation in FINRISK97 men and FINRISK97 women and the Belfast PRIME Men cohort after allowing for confounding factors; however, the strongest associations (with hazard ratio per SD in FINRISK97 men) were found for N-terminal pro-brain natriuretic peptide (1.23), C-reactive protein (1.23), B-type natriuretic peptide (1.19), and sensitive troponin I (1.18). A biomarker score was developed from the FINRISK97 cohort with the use of regression coefficients and lasso methods, with selection of troponin I, C-reactive protein, and N-terminal pro-brain natriuretic peptide. Adding this score to a conventional risk factor model in the Belfast PRIME Men cohort validated it by improved c-statistics (P=0.004) and integrated discrimination (P<0.0001) and led to significant reclassification of individuals into risk categories (P=0.0008). — Conclusions— The addition of a biomarker score including N-terminal pro-brain natriuretic peptide, C-reactive protein, and sensitive troponin I to a conventional risk model improved 10-year risk estimation for cardiovascular events in 2 middle-aged European populations. Further validation is needed in other populations and age groups.”
The challenge of combining multiple biomarkers into reliable standardized tests is a significant one. An editorial from the American Heart Association’s magazine Circulation addresses this issue, Separating the Contenders From the Pretenders – Competitive High-Throughput Biomarker Screening in Large Population-Based Studies. “Despite great enthusiasm for biomarkers as tools to enhance risk prediction and to lead the way in a transformation towards personalized cardiovascular medicine, progress in the biomarker field has been painstakingly slow, particularly in the area of population screening. Some individual biomarkers such as C-reactive protein (CRP) have demonstrated consistent associations with incident cardiovascular events across multiple studies, but the magnitude of these associations is modest,1 and only small improvements in discrimination and reclassification are seen.2,3 One attractive solution to the limitations of individual biomarkers is to combine nonredundant biomarkers into panels to enhance risk assessment. However, results of studies testing multiple biomarkers for risk prediction in primary prevention populations have not provided a clear picture, with some studies showing qualified promise4–6 and others suggesting limited value.2,7,8” — Although the study represents a qualified victory for multiple biomarker panels that include CRP, NT-proBNP, and cTnI, does it represent the end of the road for the other biomarkers that were tested and failed? More important, does the failure here and in prior studies of the more novel biomarkers suggest that biomarker discovery in this area is likely to be futile? We believe such a conclusion would be premature. — Progress forward requires movement in several directions. For the established biomarkers, further clinical validation of panels containing CRP, NT-proBNP (or BNP), and a sensitive troponin assay is required in different age, race, and sex groups, given the known influences of these factors on levels of these biomarkers. We also need carefully designed observational and interventional studies to help us understand the full implications of reclassification based on these biomarkers. In particular, it is critical to establish the safety of deferral of preventive therapies for individuals who are reclassified to a lower risk category by biomarkers. With regard to the more novel biomarkers, careful thought is needed with regard to the appropriate target populations for discovery and validation, as well as the criteria used to sort out the contenders from the pretenders. “
Genomic biomarkers – SNPs
A presentation at the American Heart Association meeting a couple of weeks ago reports on progress in using single nucleotide polymorphisms (SNPs) in gene sequences to strengthen the discriminatory power of existing biomarker panels for predicting CHD. “A single-nucleotide polymorphism (SNP, pronounced snip) is a DNA sequence variation occurring when a single nucleotide — A, T, C, or G — in the genome (or other shared sequence) differs between members of a species or paired chromosomes in an individual(ref).”
As reported in Genomeweb News on November 17 2010 article Study Suggests Genetic Data May Improve Heart Attack Risk Prediction, “Incorporating genetic information into heart attack risk prediction models based on traditional risk factors can help to more accurately classify a subset of individuals, according to a team of Mayo Clinic researchers. — In a study done through the National Human Genome Research Institute-funded Electronic Medical Records & Genomics, or eMERGE, Network, the investigators brought together information on traditional heart attack factors from medical records with data on 11 heart attack risk SNPs for nearly 1,300 individuals. — Their findings, presented at the American Heart Association Scientific Sessions meeting last night, indicate that this genetic information refined heart attack risk classifications for almost a third of those evaluated. — “This study tells us that genetic information may be helpful in screening people for their risk for having a heart attack,” Mayo Clinic cardiologist Iftikhar Kullo, who is leading the study, said in a statement. — Heart attack risk is typically determined from a set of risk factors such as age, cholesterol levels, blood pressure, smoking behavior, and more. But such factors, which are brought together in a Framingham Risk Score for predicting heart risk over a decade, don’t always classify individuals accurately.”
Going on, — “The method we have been using for decades to predict heart attack risk is not ideal,” Kullo said. “[M]any people thought to be at low risk experience a heart attack.” — In an effort to find ways to refine heart attack risk profiles, Kullo and his colleagues evaluated Framingham Risk Scores for 1,262 individuals with no history of heart disease based on their medical record data. — They also genotyped the individuals at 11 SNPs thought to be associated with heart disease using DNA isolated from the individuals’ blood samples and compared the predictive value of genetic data alone with Framingham Risk Score predictions and models that included both Framingham Risk Score and SNP information. — By incorporating the SNP information, the researchers reported, they were able to reclassify 50 of the 197 individuals from the low-risk group into an intermediate-risk group and move 86 of 397 individuals in the intermediate risk group up to a higher risk (“intermediate-high”) group. — Similarly, the team found that 54 of the 430 individuals considered intermediate-high risk belonged in the high risk category. — On the other hand, 77 intermediate risk, 79 intermediate-high risk, and 39 of 238 high risk individuals were bumped down to a lower risk category when their SNP data was added to their heart attack risk profiles. — If the findings pan out in future clinical studies, the researchers said, it may be possible to provide more accurate heart attack risk information to patients — particularly those who fall into intermediate risk categories based on traditional risk factor data. — Previous research evaluating half a dozen protein biomarkers for cardiovascular disease found only modest improvements in risk prediction when these markers were combined with traditional risk factor information.”
Summary
· Biomarker combinations for identification of cardiovascular disease risk have been used for many years, particularly the Framingham heart study risk scores.
· Identification of additional predictive CVD biomarkers for has been an ongoing process for many years and several powerful newer ones have been identified starting with C-reactive protein, going on to natriuretic peptides and, more recently SNP gene variations. There appear to be well over 30 potential CVD biomarkers most of which show fairly weak associations and then in many cases only to specific cardiovascular diseases.
· Emphasis is turning to finding specific combinations of biomarkers which offer the greatest predictive power. The process is very slow because there are so many possible combinations, because there are several different cardiovascular diseases and because large cohorts of people must be followed for a number of years to get results.
· While progress is slow it appears to be steady and already various studies have already suggested predictive biomarker panels that are significantly improved over the traditional ones. Reported progress in 2010 alone appears to be significant.
