REVIEW URRENT C OPINION
The role of cardiac biomarkers in cardiovascular disease risk assessment Paul Collinson a,b
Purpose of review A range of cardiovascular biomarkers have come into the routine clinical use for the diagnosis and assessment of patients with symptomatic coronary disease. The review will consider the current evidence base for the role of measurement of a number of these biomarkers in asymptomatic patients. Recent findings Measurement of the cardiac troponins, cardiac troponin T and cardiac troponin I, using sensitive methods, defines a true reference population, and has demonstrated risk of cardiovascular events with small elevations above the reference interval and a gradient of risk across the reference range even in normal healthy individuals. Similar results can be seen for the measurement of the ventricular stress marker B-type natriuretic peptide. In an asymptomatic population, there is occult cardiovascular disease which can be detected by cardiovascular biomarker measurement. Novel markers of myocardial inflammation and fibrosis, such as growth differentiation factor 15, are also powerful predictors of mortality. Measurement of these cardiovascular biomarkers provides additional risk prediction when added to risk prediction models based on the conventional risk factors. Summary The measurement of cardiovascular biomarkers has the potential to provide additional risk stratification beyond conventional risk factors. The challenge is to translate biomarker measurements to treatment strategies which will reduce long-term cardiovascular risk. Keywords B-type natriuretic peptide, cardiovascular biomarker, cardiovascular risk assessment, growth differentiation factor 15, high-sensitivity cardiac troponin
INTRODUCTION The assessment of cardiovascular risk for primary prevention of coronary heart disease has traditionally been based on the use of risk scoring algorithms derived from the early work on the Framingham Heart Study. Although they have undergone different incarnations and modifications, all of the current risk algorithms retain certain core commonalities. The problem with these algorithms is that their efficiency is relatively low. Studies that have used rigorous statistical analysis tend to show an area under the receiver operator characteristic (ROC) curve in the range 0.65–0.85. This is the level at which a test is considered to be just acceptable but not to have particularly good performance. It is, therefore, an attractive concept to combine additional biomarkers with conventional risk stratification algorithms to improve risk prediction. To date, the use of additional biomarkers has attracted controversy in terms of both the findings [1,2] and www.co-cardiology.com
the discussion of the relevance of statistical techniques to validate their utility [3,4]. A range of biomarkers have been progressively developed over the last 10 years for use as diagnostic tests for patients presenting with chest pain and heart failure. Interest has arisen as to whether or not these can be used for the routine assessment of cardiovascular risk in otherwise asymptomatic individuals rather than as tests for acute disease. The hypothesis is based upon the concept that these biomarkers offer an assessment of the impact of a
Department of Clinical Blood Sciences and bDepartment of Cardiology, St George’s Hospital and Medical School, London, UK Correspondence to Paul Collinson, Professor, Department of Clinical Blood Sciences, St George’s Hospital and Medical School, Cranmer Terrace, London, SW17 0RE, UK. E-mail: paul.collinson@stgeorges. nhs.uk Curr Opin Cardiol 2014, 29:366–371 DOI:10.1097/HCO.0000000000000081 Volume 29 Number 4 July 2014
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KEY POINTS A range of routinely available cardiac biomarkers can be measured in patients asymptomatic for coronary disease which have prognostic significance. Addition of these cardiac biomarkers to risk models based on the conventional risk factors produces a significant improvement in the model performance for risk stratification. The most promising markers for the purpose of risk stratification are cardiac troponin measured by a highsensitivity assay, B-type natriuretic peptide and growth differentiation factor 15. There is, as yet, no evidence that this enhanced risk stratification can be converted into improved clinical outcome and clinical studies are required.
plaque formation and destabilization which has resulted in occult myocardial injury. The reason for a reappraisal of these markers for risk stratification rather than for disease assessment is two-fold. First, there has been transition of these novel cardiac biomarkers from the research laboratory to the clinical laboratory for routine patient management. These cardiac biomarkers are now available on a range of high-throughput analytical systems in real time. The second factor is improvement in methodology. Analytical performance, particularly the sensitivity of the assays and analytical imprecision, has progressively improved. Improved sensitivity and analytical imprecision allows precise and accurate measurement at very low levels. The ability to measure discrete values down to levels across the reference interval of an analyte allows more sophisticated analysis than simply using an arbitrary decision threshold. Cardiac biomarkers can be broadly divided into three categories: biomarkers of myocardial injury, biomarkers of myocardial and circulatory stress and biomarkers of myocardial fibrosis and inflammation.
BIOMARKERS OF MYOCARDIAL INJURY The use of biomarkers of myocardial injury has been part of the diagnostic workup of patients with chest pain and plays a significant role in the diagnosis of non-ST elevation myocardial infarction. The utility of these tests was limited by lack of tissue specificity. The cardiac troponins, cardiac troponin T (cTnT) 37 kilodaltons (kDa) and cardiac troponin I (cTnI, 22 kDa), form part of the troponin–tropomyosin complex which regulates muscular contraction. The ability to measure cTnT and cTnI produced a paradigm shift in the biomarkers used for the
diagnosis and management strategies in acute chest pain. The universal definition of myocardial infarction incorporates cTn measurement as one of the three diagnostic criteria and is, arguably, the dominant single component [5]. Interest in the potential role of cTn as a risk prediction biomarker has accompanied the progressive improvements in measurement methodology and, in particular, the development of ‘high-sensitivity’ troponin assays [6]. Until recently, the majority of troponin assays have not been able to measure cTn in normal healthy individuals. There has been development of assays of progressively greater sensitivity which can measure either the top 25–50% of values in apparently healthy individuals [7] or, more recently, can define a full range of values in healthy normals with an apparently Gaussian (normal) distribution curve [8,9]. A number of studies have examined apparently healthy populations using high-sensitivity assays and have shown that cTn levels are associated with evidence of occult myocardial dysfunction or injury. Measurement of troponin levels in 545 individuals from a primary care population demonstrated that the value obtained for the upper limit of normal, the 99th percentile, progressively fell for cTnT from 29.9 to 14.4 ng/l and for cTnI from 66.8 to 43.8 ng/l on progressively excluding individuals on the basis of a health questionnaire, simple biochemical tests and cardiac imaging [8]. This finding has been confirmed by two other studies. One with a very similar design demonstrated a fall of cTnI from 20.4 to 11.1 ng/l and for cTnT from 20.4 to 15.9 ng/l [9]. The second study found that higher cTnI concentrations were predicted by hypertension, left ventricular mass, systolic and diastolic dysfunction and coronary artery disease. The conclusion is that occult cardiac disease is causing minor degrees of cardiac damage with consequent troponin elevation [10]. These elevations are of more than academic interest. To date, five studies have examined the ability of background troponin levels to predict outcome; four of these examined cTnT and one cTnI. In all cases, it has been a consistent finding that there is a risk gradient corresponding to increased risk of an adverse cardiac event with the rise in troponin level even within the reference interval. Measurement of cTnT in 4221 communitydwelling adults of 65 years or over with repeat measurement 2–3 years later found that an increase in cTnT levels of greater than 50% predicted an increased risk of death or heart failure [11]. In the Dallas Heart Study, 3546 individuals from 30 to 65 years had cTnT measured and cardiac imaging [12]. By quintile of cTnT value, the risk of death
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increased from 1.9 to 28.4% from the lowest to the highest, despite adjustment for traditional risk factors, C-reactive protein level (CRP), chronic kidney disease and levels of N-terminal pro B-type natriuretic peptide (NTpro-BNP). Troponin elevation correlated with the presence of left ventricular hypertrophy and left ventricular systolic dysfunction. The Atherosclerosis Risk in Communities (ARIC) study of 9698 participants 54–74 years of age free of vascular disease at baseline measured cTnT [13]. By quintile, from the lowest to the highest, there was a progressive increase in the risk of death from cardiovascular disease, all-cause mortality and risk of heart failure. In multivariate risk analysis, addition of cTnT measurement was superior to CRP for risk prediction. Measurement of high-sensitivity troponin has been combined with other biomarkers. Measurement of cTnI using a high-sensitivity experimental cTnI assay (Erenna cTnI, Singulex, Alameda, CA, USA) was combined with additional biomarker measurement in the Framingham Heart Study in 3428 individuals with follow-up over 11.3 years to generate a multimarker risk score [14 ]. In a model adjusted for conventional risk factors, cTnI was strongly associated with death, heart failure and major cardiovascular events. In multivariate adjusted risk, cTnI predicted heart failure and major cardiovascular events but not death or coronary heart disease events. Measurement of cTnI in a Scottish cohort of 15 340 individuals has similarly shown a gradation of risk of cardiovascular events according to progressive troponin elevation [15 ]. This study used a fully adjusted prediction model incorporating age, sex and conventional cardiovascular risk predictors, and was able to demonstrate that incorporation of cTnI improved both the net reclassification index and also the area under the ROC curve (the c statistic). Imaging studies have shown that elevated troponin is associated with severity of coronary heart disease, left ventricular mass, left ventricular ejection fraction and regional left ventricular dysfunction [12]. In patients with known vascular disease, cTnT elevation has been correlated with plaque morphology [16] and shown to correlate with the presence of noncalcified plaque [17]. It has been suggested that chronic silent plaque rupture with occult micro-embolization might account for these troponin elevations. This is in accordance with the concept that remodelling plaques are associated with significantly greater risk of developing acute coronary syndrome. The conclusion is that cTn measurements using high-sensitivity assays that detect troponin levels within the reference interval can be used for risk prediction in the primary care population. &&
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BIOMARKERS OF MYOCARDIAL AND CIRCULATORY STRESS The natriuretic peptides are a closely related family of ring-shaped peptides involved in sodium and water balance. There are two natriuretic peptides of clinical interest: A-type natriuretic peptide (ANP) and B-type natriuretic peptide (BNP). They are synthesized within the myocardium as prohormones, prepro-ANP and prepro-BNP, secreted as pro-ANP and pro-BNP and undergo subsequent modification by cleavage to N-terminal pro-ANP (NTpro-ANP) and ANP (biologically active) and N-terminal pro-BNP (NTpro-BNP) and BNP (biologically active). BNP is measured routinely in the clinical practice by measurement of NTpro-BNP or BNP. The majority of research studies have measured NT-proBNP as this molecule is extremely stable. Release of BNP occurs in response to myocardial stretch, so levels are an index of overall myocardial function. Clinically, measurement of BNP is used for the differential diagnosis of shortness of breath. A landmark study by Kragelund [18] demonstrated that measurement of NTpro-BNP was an independent marker of long-term mortality in patients with stable coronary artery disease beyond conventional risk factors and degree of left ventricular systolic dysfunction. BNP measurement has been shown to be predictive across the entire range of cardiovascular disease from acute coronary syndrome patients [19] to asymptomatic populations [20]. BNP measurement has been combined with coronary artery calcium scoring and shown to be an independent and complementary risk predictor [21]. Although BNP has been shown to be a biomarker of risk in the primary care population, the findings across different studies have not been consistent when comparing BNP with other biomarkers. Direct comparison of CRP and BNP (as NT-proBNP) has suggested that BNP is superior [22 ]. In the Framingham cohort described above [14 ], BNP predicted death, heart failure and major cardiovascular events. It was the best marker for predicting heart failure, but CRP was superior to both BNP and cTnI as a predictor of death and coronary heart disease events. In the ARIC study [13], adding cTnT or NTpro-BNP was superior to adding CRP to risk prediction models for improving risk prediction for all outcomes. In the Scottish study, BNP predicted cardiovascular events and was superior to CRP but did not predict coronary death [15 ]. Other biomarkers of circulatory stress, copeptin, the C-terminal part of pro-arginine vasopressin (proAVP) (CT-proAVP) [23], and adrenomedullin, a member of the calcitonin gene-related peptide (CGRP) family [24], have been studied as potential &
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markers of circulatory dysfunction, usually in chest pain and heart failure. Copeptin is a risk predictor in patients with symptomatic coronary disease [25], but was not found useful in the asymptomatic Minnesota Heart Survey population [26]. Evidence for adrenomedullin is contradictory. A large population-based study (5000 individuals, age 35–74) found adrenomedullin elevation was associated with classical risk factors, evidence of cardiac dysfunction and manifest cardiac disease [27]. A study of healthy free-living 70-year-olds showed no independent associations to other cardiovascular risk factors or vascular pathologies, but adrenomedullin appeared to act as an
overall risk integrator [28]. The Minnesota Heart Survey population did not find adrenomedullin measurement useful [26]. The major problem with these markers is that they are nonspecific and affected by a range of other clinical conditions.
BIOMARKERS OF MYOCARDIAL FIBROSIS AND INFLAMMATION Three markers in this category have been studied: ST2, growth differentiation factor 15 (GDF-15) and Galectin-3. The major interest clinically has been as markers of myocardial fibrosis and dysfunction in
Table 1. Comparison of biomarker studies with risk prediction based on individual biomarkers and biomarker combinations when added to risk models based on conventional risk factors Marker Outcome
Study (reference) F [14 ]
CRP M1
&&
M2
Death
cTn
BNP
xxxx xxx
ns
ARIC [13]
ns
ns
ns
DHS [12]
ns
xxx
xxxx
DHS [29]
xx
xxx
[31 ]
M2 ARIC [14 ]
xxxx ns ns
&&
DHS [33] &&
M1 M2
ARIC [13] Cardiovascular death or major adverse cardiovascular events
xx
xxx
ns
ns
xx
xxx
xxxx ns
xx
xx
ns
ns
ns
DHS [12]
ns
xx
xxxx
[15 ]
xxx
xxxx
ns
xx
xx
xxx
&
[26] [32]
M3 &&
M2 Cardiovascular events
[15 ] &
[32]
xxxx
xx
xxx
xx xxxx
xxxx
x
xx
xx xx
M1
ARIC [13]
xxxx
xxx
M4 F [14 ]
xx
xxx
M1
F [14 ]
xx
xxx
M4
Heart failure or myocardial dysfunction
xxxx
xxxx
xxx
M3 &&
xxxx
xxxx
ns
&
F [14 ]
ST2
xxxx
DHS [30] [32]
GDF-15
xxxx xx
ns
ns
xx
xx
ns
xx
xxxx
xx
xxxx
xxxx
ns
ns
M3
xxx
M4
xxx
Comparisons based on addition to conventional risk factor models and, where indicated, extended risk models incorporating biomarkers were used. Single cells indicate direct comparison data was available. Joined cells indicate comparison was against a composite model containing all the markers. Blank cells indicate no comparison data was available. ARIC, Atherosclerosis Risk in Communities study; DHS, Dallas Heart study; F, Framingham Heart Study; M1, Model 1, conventional cardiovascular risk factors with separate models for each biomarker studied; M2, Model 2, conventional cardiovascular risk factors adjusted for all biomarkers together; M3, Model 3, conventional cardiovascular risk factors; M4, Model 4, conventional cardiovascular risk factors and cTnT, NT-proBNP and CRP. x, P ¼ 0.05; xx, P < 0.05–0.001; xxx, P < 0.0010.0001; xxxx, P < 0.0001.
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patients with cardiac failure. ST2 (also known as IL1RL1, DER4, T1 and FIT-1) is a member of the Toll-like/IL-1-receptor superfamily. The ligand for ST2 is an 18-kDa protein IL-33 (also known as IL-1F11) of the IL-1 interleukin family. IL-33/ST2 signalling is a mechanically activated, cardioprotective, fibroblast–cardiomyocyte paracrine system [34]. In the Dallas Heart Study, soluble ST2 did not associate with traditional cardiovascular risk factors or nonfatal cardiovascular events and is associated with increased all-cause and cardiovascular mortality. It did not appear to add significantly to a risk model containing CRP, NT-proBNP and cTnT, but did improve net reclassification [29]. In the Framingham Study, ST2 was associated with death, heart failure and major cardiovascular events [14 ], but not with echocardiographic indices of myocardial dysfunction [33]. In the Minnesota Heart Survey, although a low ST2 was associated with an adverse outcome, cTnI was a significantly better marker [26]. GDF-15, also known as MIC-1, is a secreted member of the transforming growth factor (TGF)-b superfamily. It has been proposed that GDF-15 is a cytokine released in an autocrine or paracrine way that displays antihypertrophic and cardioprotective features [35]. In a population of healthy elderly (70 years), GDF-15 was associated with biomarkers of endothelial activation and inflammation [36]. Both baseline (at 70 years) and change in GDF-15, 5 years later, predicted all-cause mortality [31 ]. In a similar older cohort (age 71), GDF-15 predicted total and cardiovascular mortality [32]. In a younger subset of the Dallas Heart Study, GDF-15 was independently associated with subclinical atherosclerosis and mortality [30]. In the Framingham Cohort, GDF-15 was associated with systolic dysfunction but was also the best mortality predictor [33]. Galectin-3 is a 26-kDa, 3-galactoside-binding lectin. Galectin-3 overexpression causes changes in the expression levels of cell cycle regulators. Galectin-3 has been shown to be a predictor of heart failure in the community in the Framingham Offspring Study [37]. &&
&
CONCLUSION It is difficult to assess the relative value of the range of biomarkers which have been studied. The individual studies are difficult to compare as comparative assessment of biomarkers is not usually performed. Although a range of publications suggest that individual biomarkers may be additive to conventional risk factors, a true large-scale multivariate 370
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study has not been performed and there is only limited comparative data from some studies. In Table 1, data has been taken from the studies cited in the text, in which the effect of adding individual biomarkers to risk models based on conventional risk factors has been studied. Although some of the studies examine individual biomarkers (expressed as individual entries), many of the newer markers have been added to a risk model, including CRP, cTnT or cTnI and BNP/NT-proBNP (represented as a single entry). Sensitive troponin measurement appears to be a good biomarker to predict cardiovascular death, whereas measurement of B-type natriuretic peptide is a better predictor of risk of overall mortality and heart failure. Both are routinely available. Of the newer markers, GDF-15 is the most promising, with the consistent ability to predict mortality across a range of studies. Ultimately, however, it will require prospective clinical studies that utilize an intervention based on biomarker measurement to prove their value. Currently, we are at the point at which we can say that it is possible to predict an increased risk of an adverse event based on a troponin, BNP or GDF-15, but we do not have a defined management strategy. The potential role will be to reclassify individuals at intermediary risk and commence them on known cardioprotective therapies. For the future, the question is whether noninvasive imaging will become sufficiently good at early changes of cardiovascular disease and so will render biomarker measurement for risk stratification obsolete. Acknowledgements None. Conflicts of interest The author is a member of the Clinical Advisory Board for Point of Care Testing for Siemens Healthcare Diagnostics and Phillips Diagnostics. The author is also a member of the Diagnostics Advisory Committee of the National Institute for Clinical and Health Excellence.
REFERENCES AND RECOMMENDED READING Papers of particular interest, published within the annual period of review, have been highlighted as: & of special interest && of outstanding interest 1. Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med 2006; 355:2631– 2639. 2. Ware JH. The limitations of risk factors as prognostic tools. N Engl J Med 2006; 355:2615–2617. 3. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007; 115:928–935. 4. Mallett S, Halligan S, Thompson M, et al. Interpreting diagnostic accuracy studies for patient care. BMJ 2012; 345:e3999.
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Cardiovascular disease risk assessment Collinson 5. Thygesen K, Alpert JS, Jaffe AS, et al. Third universal definition of myocardial infarction. Eur Heart J 2012; 33:2551–2567. 6. Apple FS, Collinson PO. Analytical characteristics of high-sensitivity cardiac troponin assays. Clin Chem 2012; 58:54–61. 7. Collinson PO, Clifford-Mobley O, Gaze D, et al. Assay imprecision and 99th-percentile reference value of a high-sensitivity cardiac troponin I assay. Clin Chem 2009; 55:1433–1434. 8. Collinson PO, Heung YM, Gaze D, et al. Influence of population selection on the 99th percentile reference value for cardiac troponin assays. Clin Chem 2012; 58:219–225. 9. Koerbin G, Abhayaratna WP, Potter JM, et al. Effect of population selection on 99th percentile values for a high sensitivity cardiac troponin I and T assays. Clin Biochem 2013; 46:1636–1643. 10. McKie PM, Heublein DM, Scott CG, et al. Defining high-sensitivity cardiac troponin concentrations in the community. Clin Chem 2013; 59:1099–1107. 11. DeFilippi CR, de Lemos JA, Christenson RH, et al. Association of serial measures of cardiac troponin T using a sensitive assay with incident heart failure and cardiovascular mortality in older adults. JAMA 2010; 304:2494– 2502. 12. De Lemos JA, Drazner MH, Omland T, et al. Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population. JAMA 2010; 304:2503–2512. 13. Saunders JT, Nambi V, de Lemos JA, et al. Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study. Circulation 2011; 123:1367–1376. 14. Wang TJ, Wollert KC, Larson MG, et al. Prognostic utility of novel biomarkers && of cardiovascular stress: the Framingham Heart Study. Circulation 2012; 126:1596–1604. To date, the only published study that has systematically compared the putative readily available cardiac biomarkers for risk prediction in the primary care population. A range of outcomes have been studied, showing GDF-15 to be the best overall mortality predictor and BNP the best predictor of heart failure. 15. Zeller T, Tunstall-Pedoe H, Saarela O, et al. High population prevalence of & cardiac troponin I measured by a high-sensitivity assay and cardiovascular risk estimation: the MORGAM Biomarker Project Scottish Cohort. Eur Heart J 2014; 35:271–281. The most recent study that has examined the role of high-sensitivity troponin as a prognostic risk marker. Its findings are complementary to the other studies cited in the text. 16. Ndrepepa G, Braun S, Schulz S, et al. High-sensitivity troponin T level and angiographic severity of coronary artery disease. Am J Cardiol 2011; 108: 639–643. 17. Korosoglou G, Lehrke S, Mueller D, et al. Determinants of troponin release in patients with stable coronary artery disease: insights from CT angiography characteristics of atherosclerotic plaque. Heart 2011; 97:823–831. 18. Kragelund C, Gronning B, Kober L, et al. N-terminal pro-B-type natriuretic peptide and long-term mortality in stable coronary heart disease. N Engl J Med 2005; 352:666–675. 19. De Lemos JA, Morrow DA, Bentley JH, et al. The prognostic value of B-type natriuretic peptide in patients with acute coronary syndromes. N Engl J Med 2001; 345:1014–1021. 20. Wang TJ, Larson MG, Levy D, et al. Plasma natriuretic peptide levels and the risk of cardiovascular events and death. N Engl J Med 2004; 350:655– 663.
21. Kara K, Mahabadi AA, Berg MH, et al. Predicting risk of coronary events and all-cause mortality: role of B-type natriuretic peptide above traditional risk factors and coronary artery calcium scoring in the general population: the Heinz Nixdorf Recall Study. Eur J Prev Cardiol 2013. [Epub ahead of print] 22. Leistner DM, Klotsche J, Pieper L, et al. Prognostic value of NT-pro-BNP and & hs-CRP for risk stratification in primary care: results from the populationbased DETECT study. Clin Res Cardiol 2013; 102:259–268. A large-scale primary care study demonstrating the additive value of BNP measurement to conventional risk stratification models. 23. Morgenthaler NG, Struck J, Jochberger S, Dunser MW. Copeptin: clinical use of a new biomarker. Trends Endocrinol Metab 2008; 19:43–49. 24. Yanagawa B, Nagaya N. Adrenomedullin: molecular mechanisms and its role in cardiac disease. Amino Acids 2007; 32:157–164. 25. Von Haehling S, Papassotiriou J, Morgenthaler NG, et al. Copeptin as a prognostic factor for major adverse cardiovascular events in patients with coronary artery disease. Int J Cardiol 2012; 162:27–32. 26. Apple FS, Steffen LM, Pearce LA, et al. Increased cardiac troponin I as measured by a high-sensitivity assay is associated with high odds of cardiovascular death: the Minnesota Heart Survey. Clin Chem 2012; 58:930–935. 27. Neumann JT, Tzikas S, Funke-Kaiser A, et al. Association of MR-proadrenomedullin with cardiovascular risk factors and subclinical cardiovascular disease. Atherosclerosis 2013; 228:451–459. 28. Eggers KM, Venge P, Lindahl B, Lind L. Associations of mid-regional proadrenomedullin levels to cardiovascular and metabolic abnormalities, and mortality in an elderly population from the community. Int J Cardiol 2013; 168:3537–3542. 29. Chen LQ, de Lemos JA, Das SR, et al. Soluble ST2 is associated with allcause and cardiovascular mortality in a population-based cohort: the Dallas Heart Study. Clin Chem 2013; 59:536–546. 30. Rohatgi A, Patel P, Das SR, et al. Association of growth differentiation factor15 with coronary atherosclerosis and mortality in a young, multiethnic population: observations from the Dallas Heart Study. Clin Chem 2012; 58:172–182. 31. Eggers KM, Kempf T, Wallentin L, et al. Change in growth differentiation factor & 15 concentrations over time independently predicts mortality in communitydwelling elderly individuals. Clin Chem 2013; 59:1091–1098. A large-scale study in the healthy elderly demonstrating that GDF-15 changes with time predict mortality. 32. Wallentin L, Zethelius B, Berglund L, et al. GDF-15 for prognostication of cardiovascular and cancer morbidity and mortality in men. PLoS One 2013; 8:e78797. 33. Xanthakis V, Larson MG, Wollert KC, et al. Association of novel biomarkers of cardiovascular stress with left ventricular hypertrophy and dysfunction: implications for screening. J Am Heart Assoc 2013; 2:e000399. 34. Sanada S, Hakuno D, Higgins LJ, et al. IL-33 and ST2 comprise a critical biomechanically induced and cardioprotective signaling system. J Clin Invest 2007; 117:1538–1549. 35. Kempf T, Eden M, Strelau J, et al. The transforming growth factor-beta superfamily member growth-differentiation factor-15 protects the heart from ischemia/reperfusion injury. Circ Res 2006; 98:351–360. 36. Eggers KM, Kempf T, Lind L, et al. Relations of growth-differentiation factor-15 to biomarkers reflecting vascular pathologies in a population-based sample of elderly subjects. Scand J Clin Lab Invest 2012; 72:45–51. 37. Ho JE, Liu C, Lyass A, et al. Galectin-3, a marker of cardiac fibrosis, predicts incident heart failure in the community. J Am Coll Cardiol 2012; 60:1249– 1256.
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The role of cardiac biomarkers in cardiovascular disease risk assessment Paul Collinson a,b
Purpose of review A range of cardiovascular biomarkers have come into the routine clinical use for the diagnosis and assessment of patients with symptomatic coronary disease. The review will consider the current evidence base for the role of measurement of a number of these biomarkers in asymptomatic patients. Recent findings Measurement of the cardiac troponins, cardiac troponin T and cardiac troponin I, using sensitive methods, defines a true reference population, and has demonstrated risk of cardiovascular events with small elevations above the reference interval and a gradient of risk across the reference range even in normal healthy individuals. Similar results can be seen for the measurement of the ventricular stress marker B-type natriuretic peptide. In an asymptomatic population, there is occult cardiovascular disease which can be detected by cardiovascular biomarker measurement. Novel markers of myocardial inflammation and fibrosis, such as growth differentiation factor 15, are also powerful predictors of mortality. Measurement of these cardiovascular biomarkers provides additional risk prediction when added to risk prediction models based on the conventional risk factors. Summary The measurement of cardiovascular biomarkers has the potential to provide additional risk stratification beyond conventional risk factors. The challenge is to translate biomarker measurements to treatment strategies which will reduce long-term cardiovascular risk. Keywords B-type natriuretic peptide, cardiovascular biomarker, cardiovascular risk assessment, growth differentiation factor 15, high-sensitivity cardiac troponin
INTRODUCTION The assessment of cardiovascular risk for primary prevention of coronary heart disease has traditionally been based on the use of risk scoring algorithms derived from the early work on the Framingham Heart Study. Although they have undergone different incarnations and modifications, all of the current risk algorithms retain certain core commonalities. The problem with these algorithms is that their efficiency is relatively low. Studies that have used rigorous statistical analysis tend to show an area under the receiver operator characteristic (ROC) curve in the range 0.65–0.85. This is the level at which a test is considered to be just acceptable but not to have particularly good performance. It is, therefore, an attractive concept to combine additional biomarkers with conventional risk stratification algorithms to improve risk prediction. To date, the use of additional biomarkers has attracted controversy in terms of both the findings [1,2] and www.co-cardiology.com
the discussion of the relevance of statistical techniques to validate their utility [3,4]. A range of biomarkers have been progressively developed over the last 10 years for use as diagnostic tests for patients presenting with chest pain and heart failure. Interest has arisen as to whether or not these can be used for the routine assessment of cardiovascular risk in otherwise asymptomatic individuals rather than as tests for acute disease. The hypothesis is based upon the concept that these biomarkers offer an assessment of the impact of a
Department of Clinical Blood Sciences and bDepartment of Cardiology, St George’s Hospital and Medical School, London, UK Correspondence to Paul Collinson, Professor, Department of Clinical Blood Sciences, St George’s Hospital and Medical School, Cranmer Terrace, London, SW17 0RE, UK. E-mail: paul.collinson@stgeorges. nhs.uk Curr Opin Cardiol 2014, 29:366–371 DOI:10.1097/HCO.0000000000000081 Volume 29 Number 4 July 2014
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KEY POINTS A range of routinely available cardiac biomarkers can be measured in patients asymptomatic for coronary disease which have prognostic significance. Addition of these cardiac biomarkers to risk models based on the conventional risk factors produces a significant improvement in the model performance for risk stratification. The most promising markers for the purpose of risk stratification are cardiac troponin measured by a highsensitivity assay, B-type natriuretic peptide and growth differentiation factor 15. There is, as yet, no evidence that this enhanced risk stratification can be converted into improved clinical outcome and clinical studies are required.
plaque formation and destabilization which has resulted in occult myocardial injury. The reason for a reappraisal of these markers for risk stratification rather than for disease assessment is two-fold. First, there has been transition of these novel cardiac biomarkers from the research laboratory to the clinical laboratory for routine patient management. These cardiac biomarkers are now available on a range of high-throughput analytical systems in real time. The second factor is improvement in methodology. Analytical performance, particularly the sensitivity of the assays and analytical imprecision, has progressively improved. Improved sensitivity and analytical imprecision allows precise and accurate measurement at very low levels. The ability to measure discrete values down to levels across the reference interval of an analyte allows more sophisticated analysis than simply using an arbitrary decision threshold. Cardiac biomarkers can be broadly divided into three categories: biomarkers of myocardial injury, biomarkers of myocardial and circulatory stress and biomarkers of myocardial fibrosis and inflammation.
BIOMARKERS OF MYOCARDIAL INJURY The use of biomarkers of myocardial injury has been part of the diagnostic workup of patients with chest pain and plays a significant role in the diagnosis of non-ST elevation myocardial infarction. The utility of these tests was limited by lack of tissue specificity. The cardiac troponins, cardiac troponin T (cTnT) 37 kilodaltons (kDa) and cardiac troponin I (cTnI, 22 kDa), form part of the troponin–tropomyosin complex which regulates muscular contraction. The ability to measure cTnT and cTnI produced a paradigm shift in the biomarkers used for the
diagnosis and management strategies in acute chest pain. The universal definition of myocardial infarction incorporates cTn measurement as one of the three diagnostic criteria and is, arguably, the dominant single component [5]. Interest in the potential role of cTn as a risk prediction biomarker has accompanied the progressive improvements in measurement methodology and, in particular, the development of ‘high-sensitivity’ troponin assays [6]. Until recently, the majority of troponin assays have not been able to measure cTn in normal healthy individuals. There has been development of assays of progressively greater sensitivity which can measure either the top 25–50% of values in apparently healthy individuals [7] or, more recently, can define a full range of values in healthy normals with an apparently Gaussian (normal) distribution curve [8,9]. A number of studies have examined apparently healthy populations using high-sensitivity assays and have shown that cTn levels are associated with evidence of occult myocardial dysfunction or injury. Measurement of troponin levels in 545 individuals from a primary care population demonstrated that the value obtained for the upper limit of normal, the 99th percentile, progressively fell for cTnT from 29.9 to 14.4 ng/l and for cTnI from 66.8 to 43.8 ng/l on progressively excluding individuals on the basis of a health questionnaire, simple biochemical tests and cardiac imaging [8]. This finding has been confirmed by two other studies. One with a very similar design demonstrated a fall of cTnI from 20.4 to 11.1 ng/l and for cTnT from 20.4 to 15.9 ng/l [9]. The second study found that higher cTnI concentrations were predicted by hypertension, left ventricular mass, systolic and diastolic dysfunction and coronary artery disease. The conclusion is that occult cardiac disease is causing minor degrees of cardiac damage with consequent troponin elevation [10]. These elevations are of more than academic interest. To date, five studies have examined the ability of background troponin levels to predict outcome; four of these examined cTnT and one cTnI. In all cases, it has been a consistent finding that there is a risk gradient corresponding to increased risk of an adverse cardiac event with the rise in troponin level even within the reference interval. Measurement of cTnT in 4221 communitydwelling adults of 65 years or over with repeat measurement 2–3 years later found that an increase in cTnT levels of greater than 50% predicted an increased risk of death or heart failure [11]. In the Dallas Heart Study, 3546 individuals from 30 to 65 years had cTnT measured and cardiac imaging [12]. By quintile of cTnT value, the risk of death
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increased from 1.9 to 28.4% from the lowest to the highest, despite adjustment for traditional risk factors, C-reactive protein level (CRP), chronic kidney disease and levels of N-terminal pro B-type natriuretic peptide (NTpro-BNP). Troponin elevation correlated with the presence of left ventricular hypertrophy and left ventricular systolic dysfunction. The Atherosclerosis Risk in Communities (ARIC) study of 9698 participants 54–74 years of age free of vascular disease at baseline measured cTnT [13]. By quintile, from the lowest to the highest, there was a progressive increase in the risk of death from cardiovascular disease, all-cause mortality and risk of heart failure. In multivariate risk analysis, addition of cTnT measurement was superior to CRP for risk prediction. Measurement of high-sensitivity troponin has been combined with other biomarkers. Measurement of cTnI using a high-sensitivity experimental cTnI assay (Erenna cTnI, Singulex, Alameda, CA, USA) was combined with additional biomarker measurement in the Framingham Heart Study in 3428 individuals with follow-up over 11.3 years to generate a multimarker risk score [14 ]. In a model adjusted for conventional risk factors, cTnI was strongly associated with death, heart failure and major cardiovascular events. In multivariate adjusted risk, cTnI predicted heart failure and major cardiovascular events but not death or coronary heart disease events. Measurement of cTnI in a Scottish cohort of 15 340 individuals has similarly shown a gradation of risk of cardiovascular events according to progressive troponin elevation [15 ]. This study used a fully adjusted prediction model incorporating age, sex and conventional cardiovascular risk predictors, and was able to demonstrate that incorporation of cTnI improved both the net reclassification index and also the area under the ROC curve (the c statistic). Imaging studies have shown that elevated troponin is associated with severity of coronary heart disease, left ventricular mass, left ventricular ejection fraction and regional left ventricular dysfunction [12]. In patients with known vascular disease, cTnT elevation has been correlated with plaque morphology [16] and shown to correlate with the presence of noncalcified plaque [17]. It has been suggested that chronic silent plaque rupture with occult micro-embolization might account for these troponin elevations. This is in accordance with the concept that remodelling plaques are associated with significantly greater risk of developing acute coronary syndrome. The conclusion is that cTn measurements using high-sensitivity assays that detect troponin levels within the reference interval can be used for risk prediction in the primary care population. &&
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BIOMARKERS OF MYOCARDIAL AND CIRCULATORY STRESS The natriuretic peptides are a closely related family of ring-shaped peptides involved in sodium and water balance. There are two natriuretic peptides of clinical interest: A-type natriuretic peptide (ANP) and B-type natriuretic peptide (BNP). They are synthesized within the myocardium as prohormones, prepro-ANP and prepro-BNP, secreted as pro-ANP and pro-BNP and undergo subsequent modification by cleavage to N-terminal pro-ANP (NTpro-ANP) and ANP (biologically active) and N-terminal pro-BNP (NTpro-BNP) and BNP (biologically active). BNP is measured routinely in the clinical practice by measurement of NTpro-BNP or BNP. The majority of research studies have measured NT-proBNP as this molecule is extremely stable. Release of BNP occurs in response to myocardial stretch, so levels are an index of overall myocardial function. Clinically, measurement of BNP is used for the differential diagnosis of shortness of breath. A landmark study by Kragelund [18] demonstrated that measurement of NTpro-BNP was an independent marker of long-term mortality in patients with stable coronary artery disease beyond conventional risk factors and degree of left ventricular systolic dysfunction. BNP measurement has been shown to be predictive across the entire range of cardiovascular disease from acute coronary syndrome patients [19] to asymptomatic populations [20]. BNP measurement has been combined with coronary artery calcium scoring and shown to be an independent and complementary risk predictor [21]. Although BNP has been shown to be a biomarker of risk in the primary care population, the findings across different studies have not been consistent when comparing BNP with other biomarkers. Direct comparison of CRP and BNP (as NT-proBNP) has suggested that BNP is superior [22 ]. In the Framingham cohort described above [14 ], BNP predicted death, heart failure and major cardiovascular events. It was the best marker for predicting heart failure, but CRP was superior to both BNP and cTnI as a predictor of death and coronary heart disease events. In the ARIC study [13], adding cTnT or NTpro-BNP was superior to adding CRP to risk prediction models for improving risk prediction for all outcomes. In the Scottish study, BNP predicted cardiovascular events and was superior to CRP but did not predict coronary death [15 ]. Other biomarkers of circulatory stress, copeptin, the C-terminal part of pro-arginine vasopressin (proAVP) (CT-proAVP) [23], and adrenomedullin, a member of the calcitonin gene-related peptide (CGRP) family [24], have been studied as potential &
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Cardiovascular disease risk assessment Collinson
markers of circulatory dysfunction, usually in chest pain and heart failure. Copeptin is a risk predictor in patients with symptomatic coronary disease [25], but was not found useful in the asymptomatic Minnesota Heart Survey population [26]. Evidence for adrenomedullin is contradictory. A large population-based study (5000 individuals, age 35–74) found adrenomedullin elevation was associated with classical risk factors, evidence of cardiac dysfunction and manifest cardiac disease [27]. A study of healthy free-living 70-year-olds showed no independent associations to other cardiovascular risk factors or vascular pathologies, but adrenomedullin appeared to act as an
overall risk integrator [28]. The Minnesota Heart Survey population did not find adrenomedullin measurement useful [26]. The major problem with these markers is that they are nonspecific and affected by a range of other clinical conditions.
BIOMARKERS OF MYOCARDIAL FIBROSIS AND INFLAMMATION Three markers in this category have been studied: ST2, growth differentiation factor 15 (GDF-15) and Galectin-3. The major interest clinically has been as markers of myocardial fibrosis and dysfunction in
Table 1. Comparison of biomarker studies with risk prediction based on individual biomarkers and biomarker combinations when added to risk models based on conventional risk factors Marker Outcome
Study (reference) F [14 ]
CRP M1
&&
M2
Death
cTn
BNP
xxxx xxx
ns
ARIC [13]
ns
ns
ns
DHS [12]
ns
xxx
xxxx
DHS [29]
xx
xxx
[31 ]
M2 ARIC [14 ]
xxxx ns ns
&&
DHS [33] &&
M1 M2
ARIC [13] Cardiovascular death or major adverse cardiovascular events
xx
xxx
ns
ns
xx
xxx
xxxx ns
xx
xx
ns
ns
ns
DHS [12]
ns
xx
xxxx
[15 ]
xxx
xxxx
ns
xx
xx
xxx
&
[26] [32]
M3 &&
M2 Cardiovascular events
[15 ] &
[32]
xxxx
xx
xxx
xx xxxx
xxxx
x
xx
xx xx
M1
ARIC [13]
xxxx
xxx
M4 F [14 ]
xx
xxx
M1
F [14 ]
xx
xxx
M4
Heart failure or myocardial dysfunction
xxxx
xxxx
xxx
M3 &&
xxxx
xxxx
ns
&
F [14 ]
ST2
xxxx
DHS [30] [32]
GDF-15
xxxx xx
ns
ns
xx
xx
ns
xx
xxxx
xx
xxxx
xxxx
ns
ns
M3
xxx
M4
xxx
Comparisons based on addition to conventional risk factor models and, where indicated, extended risk models incorporating biomarkers were used. Single cells indicate direct comparison data was available. Joined cells indicate comparison was against a composite model containing all the markers. Blank cells indicate no comparison data was available. ARIC, Atherosclerosis Risk in Communities study; DHS, Dallas Heart study; F, Framingham Heart Study; M1, Model 1, conventional cardiovascular risk factors with separate models for each biomarker studied; M2, Model 2, conventional cardiovascular risk factors adjusted for all biomarkers together; M3, Model 3, conventional cardiovascular risk factors; M4, Model 4, conventional cardiovascular risk factors and cTnT, NT-proBNP and CRP. x, P ¼ 0.05; xx, P < 0.05–0.001; xxx, P < 0.0010.0001; xxxx, P < 0.0001.
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patients with cardiac failure. ST2 (also known as IL1RL1, DER4, T1 and FIT-1) is a member of the Toll-like/IL-1-receptor superfamily. The ligand for ST2 is an 18-kDa protein IL-33 (also known as IL-1F11) of the IL-1 interleukin family. IL-33/ST2 signalling is a mechanically activated, cardioprotective, fibroblast–cardiomyocyte paracrine system [34]. In the Dallas Heart Study, soluble ST2 did not associate with traditional cardiovascular risk factors or nonfatal cardiovascular events and is associated with increased all-cause and cardiovascular mortality. It did not appear to add significantly to a risk model containing CRP, NT-proBNP and cTnT, but did improve net reclassification [29]. In the Framingham Study, ST2 was associated with death, heart failure and major cardiovascular events [14 ], but not with echocardiographic indices of myocardial dysfunction [33]. In the Minnesota Heart Survey, although a low ST2 was associated with an adverse outcome, cTnI was a significantly better marker [26]. GDF-15, also known as MIC-1, is a secreted member of the transforming growth factor (TGF)-b superfamily. It has been proposed that GDF-15 is a cytokine released in an autocrine or paracrine way that displays antihypertrophic and cardioprotective features [35]. In a population of healthy elderly (70 years), GDF-15 was associated with biomarkers of endothelial activation and inflammation [36]. Both baseline (at 70 years) and change in GDF-15, 5 years later, predicted all-cause mortality [31 ]. In a similar older cohort (age 71), GDF-15 predicted total and cardiovascular mortality [32]. In a younger subset of the Dallas Heart Study, GDF-15 was independently associated with subclinical atherosclerosis and mortality [30]. In the Framingham Cohort, GDF-15 was associated with systolic dysfunction but was also the best mortality predictor [33]. Galectin-3 is a 26-kDa, 3-galactoside-binding lectin. Galectin-3 overexpression causes changes in the expression levels of cell cycle regulators. Galectin-3 has been shown to be a predictor of heart failure in the community in the Framingham Offspring Study [37]. &&
&
CONCLUSION It is difficult to assess the relative value of the range of biomarkers which have been studied. The individual studies are difficult to compare as comparative assessment of biomarkers is not usually performed. Although a range of publications suggest that individual biomarkers may be additive to conventional risk factors, a true large-scale multivariate 370
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study has not been performed and there is only limited comparative data from some studies. In Table 1, data has been taken from the studies cited in the text, in which the effect of adding individual biomarkers to risk models based on conventional risk factors has been studied. Although some of the studies examine individual biomarkers (expressed as individual entries), many of the newer markers have been added to a risk model, including CRP, cTnT or cTnI and BNP/NT-proBNP (represented as a single entry). Sensitive troponin measurement appears to be a good biomarker to predict cardiovascular death, whereas measurement of B-type natriuretic peptide is a better predictor of risk of overall mortality and heart failure. Both are routinely available. Of the newer markers, GDF-15 is the most promising, with the consistent ability to predict mortality across a range of studies. Ultimately, however, it will require prospective clinical studies that utilize an intervention based on biomarker measurement to prove their value. Currently, we are at the point at which we can say that it is possible to predict an increased risk of an adverse event based on a troponin, BNP or GDF-15, but we do not have a defined management strategy. The potential role will be to reclassify individuals at intermediary risk and commence them on known cardioprotective therapies. For the future, the question is whether noninvasive imaging will become sufficiently good at early changes of cardiovascular disease and so will render biomarker measurement for risk stratification obsolete. Acknowledgements None. Conflicts of interest The author is a member of the Clinical Advisory Board for Point of Care Testing for Siemens Healthcare Diagnostics and Phillips Diagnostics. The author is also a member of the Diagnostics Advisory Committee of the National Institute for Clinical and Health Excellence.
REFERENCES AND RECOMMENDED READING Papers of particular interest, published within the annual period of review, have been highlighted as: & of special interest && of outstanding interest 1. Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med 2006; 355:2631– 2639. 2. Ware JH. The limitations of risk factors as prognostic tools. N Engl J Med 2006; 355:2615–2617. 3. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007; 115:928–935. 4. Mallett S, Halligan S, Thompson M, et al. Interpreting diagnostic accuracy studies for patient care. BMJ 2012; 345:e3999.
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Cardiovascular disease risk assessment Collinson 5. Thygesen K, Alpert JS, Jaffe AS, et al. Third universal definition of myocardial infarction. Eur Heart J 2012; 33:2551–2567. 6. Apple FS, Collinson PO. Analytical characteristics of high-sensitivity cardiac troponin assays. Clin Chem 2012; 58:54–61. 7. Collinson PO, Clifford-Mobley O, Gaze D, et al. Assay imprecision and 99th-percentile reference value of a high-sensitivity cardiac troponin I assay. Clin Chem 2009; 55:1433–1434. 8. Collinson PO, Heung YM, Gaze D, et al. Influence of population selection on the 99th percentile reference value for cardiac troponin assays. Clin Chem 2012; 58:219–225. 9. Koerbin G, Abhayaratna WP, Potter JM, et al. Effect of population selection on 99th percentile values for a high sensitivity cardiac troponin I and T assays. Clin Biochem 2013; 46:1636–1643. 10. McKie PM, Heublein DM, Scott CG, et al. Defining high-sensitivity cardiac troponin concentrations in the community. Clin Chem 2013; 59:1099–1107. 11. DeFilippi CR, de Lemos JA, Christenson RH, et al. Association of serial measures of cardiac troponin T using a sensitive assay with incident heart failure and cardiovascular mortality in older adults. JAMA 2010; 304:2494– 2502. 12. De Lemos JA, Drazner MH, Omland T, et al. Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population. JAMA 2010; 304:2503–2512. 13. Saunders JT, Nambi V, de Lemos JA, et al. Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study. Circulation 2011; 123:1367–1376. 14. Wang TJ, Wollert KC, Larson MG, et al. Prognostic utility of novel biomarkers && of cardiovascular stress: the Framingham Heart Study. Circulation 2012; 126:1596–1604. To date, the only published study that has systematically compared the putative readily available cardiac biomarkers for risk prediction in the primary care population. A range of outcomes have been studied, showing GDF-15 to be the best overall mortality predictor and BNP the best predictor of heart failure. 15. Zeller T, Tunstall-Pedoe H, Saarela O, et al. High population prevalence of & cardiac troponin I measured by a high-sensitivity assay and cardiovascular risk estimation: the MORGAM Biomarker Project Scottish Cohort. Eur Heart J 2014; 35:271–281. The most recent study that has examined the role of high-sensitivity troponin as a prognostic risk marker. Its findings are complementary to the other studies cited in the text. 16. Ndrepepa G, Braun S, Schulz S, et al. High-sensitivity troponin T level and angiographic severity of coronary artery disease. Am J Cardiol 2011; 108: 639–643. 17. Korosoglou G, Lehrke S, Mueller D, et al. Determinants of troponin release in patients with stable coronary artery disease: insights from CT angiography characteristics of atherosclerotic plaque. Heart 2011; 97:823–831. 18. Kragelund C, Gronning B, Kober L, et al. N-terminal pro-B-type natriuretic peptide and long-term mortality in stable coronary heart disease. N Engl J Med 2005; 352:666–675. 19. De Lemos JA, Morrow DA, Bentley JH, et al. The prognostic value of B-type natriuretic peptide in patients with acute coronary syndromes. N Engl J Med 2001; 345:1014–1021. 20. Wang TJ, Larson MG, Levy D, et al. Plasma natriuretic peptide levels and the risk of cardiovascular events and death. N Engl J Med 2004; 350:655– 663.
21. Kara K, Mahabadi AA, Berg MH, et al. Predicting risk of coronary events and all-cause mortality: role of B-type natriuretic peptide above traditional risk factors and coronary artery calcium scoring in the general population: the Heinz Nixdorf Recall Study. Eur J Prev Cardiol 2013. [Epub ahead of print] 22. Leistner DM, Klotsche J, Pieper L, et al. Prognostic value of NT-pro-BNP and & hs-CRP for risk stratification in primary care: results from the populationbased DETECT study. Clin Res Cardiol 2013; 102:259–268. A large-scale primary care study demonstrating the additive value of BNP measurement to conventional risk stratification models. 23. Morgenthaler NG, Struck J, Jochberger S, Dunser MW. Copeptin: clinical use of a new biomarker. Trends Endocrinol Metab 2008; 19:43–49. 24. Yanagawa B, Nagaya N. Adrenomedullin: molecular mechanisms and its role in cardiac disease. Amino Acids 2007; 32:157–164. 25. Von Haehling S, Papassotiriou J, Morgenthaler NG, et al. Copeptin as a prognostic factor for major adverse cardiovascular events in patients with coronary artery disease. Int J Cardiol 2012; 162:27–32. 26. Apple FS, Steffen LM, Pearce LA, et al. Increased cardiac troponin I as measured by a high-sensitivity assay is associated with high odds of cardiovascular death: the Minnesota Heart Survey. Clin Chem 2012; 58:930–935. 27. Neumann JT, Tzikas S, Funke-Kaiser A, et al. Association of MR-proadrenomedullin with cardiovascular risk factors and subclinical cardiovascular disease. Atherosclerosis 2013; 228:451–459. 28. Eggers KM, Venge P, Lindahl B, Lind L. Associations of mid-regional proadrenomedullin levels to cardiovascular and metabolic abnormalities, and mortality in an elderly population from the community. Int J Cardiol 2013; 168:3537–3542. 29. Chen LQ, de Lemos JA, Das SR, et al. Soluble ST2 is associated with allcause and cardiovascular mortality in a population-based cohort: the Dallas Heart Study. Clin Chem 2013; 59:536–546. 30. Rohatgi A, Patel P, Das SR, et al. Association of growth differentiation factor15 with coronary atherosclerosis and mortality in a young, multiethnic population: observations from the Dallas Heart Study. Clin Chem 2012; 58:172–182. 31. Eggers KM, Kempf T, Wallentin L, et al. Change in growth differentiation factor & 15 concentrations over time independently predicts mortality in communitydwelling elderly individuals. Clin Chem 2013; 59:1091–1098. A large-scale study in the healthy elderly demonstrating that GDF-15 changes with time predict mortality. 32. Wallentin L, Zethelius B, Berglund L, et al. GDF-15 for prognostication of cardiovascular and cancer morbidity and mortality in men. PLoS One 2013; 8:e78797. 33. Xanthakis V, Larson MG, Wollert KC, et al. Association of novel biomarkers of cardiovascular stress with left ventricular hypertrophy and dysfunction: implications for screening. J Am Heart Assoc 2013; 2:e000399. 34. Sanada S, Hakuno D, Higgins LJ, et al. IL-33 and ST2 comprise a critical biomechanically induced and cardioprotective signaling system. J Clin Invest 2007; 117:1538–1549. 35. Kempf T, Eden M, Strelau J, et al. The transforming growth factor-beta superfamily member growth-differentiation factor-15 protects the heart from ischemia/reperfusion injury. Circ Res 2006; 98:351–360. 36. Eggers KM, Kempf T, Lind L, et al. Relations of growth-differentiation factor-15 to biomarkers reflecting vascular pathologies in a population-based sample of elderly subjects. Scand J Clin Lab Invest 2012; 72:45–51. 37. Ho JE, Liu C, Lyass A, et al. Galectin-3, a marker of cardiac fibrosis, predicts incident heart failure in the community. J Am Coll Cardiol 2012; 60:1249– 1256.
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