Article

Assessment of Cardiovascular Risk - The Impact and Future of Non-traditional Cardiovascular Risk Markers

Register or Login to View PDF Permissions
Permissions× For commercial reprint enquiries please contact Springer Healthcare: ReprintsWarehouse@springernature.com.

For permissions and non-commercial reprint enquiries, please visit Copyright.com to start a request.

For author reprints, please email rob.barclay@radcliffe-group.com.

  • Views: 502

  • Likes: 0

  • Citations: 2

Average (ratings)
No ratings
Your rating

Abstract

In relatively young patients (men <55 and women <65 years of age), first-time hospitalisation for cardiovascular disease (CVD) strikes without warning since the traditional cardiovascular risk factors are often normal or only slightly elevated. Therefore, we need non-traditional cardiovascular risk markers more closely related to CVD that can reliably predict future CVD in individuals, making better targeted prevention and more individualised treatment possible. However, it has been difficult to find non-traditional cardiovascular risk markers suitable for risk assessment, underlining the importance of future research into the complex mechanisms that lead to CVD. Better understanding of these complex mechanisms might enable us to find better risk markers and improve future cardiovascular risk assessment and treatment.

Keywords

Cardiovascular disease, risk assessment, markers, individualised treatment, HeartScore, American Heart Association guidelines,

Disclosure:The author has no conflicts of interest to declare.

Received:

Accepted:

DOI:https://doi.org/10.15420/ecr.2010.6.2.10

Correspondence Details:Michael Hecht Olsen, Unit of Cardiovascular Research, Division of Cardiology, Department of Internal Medicine, Glostrup University Hospital, Nordre Ringvej, 2600 Glostrup, Denmark. E: mho@dadlnet.dk

Copyright Statement:

The copyright in this work belongs to Radcliffe Medical Media. Only articles clearly marked with the CC BY-NC logo are published with the Creative Commons by Attribution Licence. The CC BY-NC option was not available for Radcliffe journals before 1 January 2019. Articles marked ‘Open Access’ but not marked ‘CC BY-NC’ are made freely accessible at the time of publication but are subject to standard copyright law regarding reproduction and distribution. Permission is required for reuse of this content.

Shortcomings of Traditional Cardiovascular Risk Factors

Many years of epidemiological and clinical cardiovascular research has taught us which risk factors typically lead to the development of cardiovascular disease (CVD). Since the damaging effects of these traditional cardiovascular risk factors are partly additive, researchers have developed different tools to assess cardiovascular risk in individuals. In Europe and the US, the HeartScore and Framingham risk score1–3 have been developed to calculate an individual’s risk of developing CVD within the next 10 years, which is crucial for making decisions about prevention and treatment.4–6 Unfortunately, large registry studies have shown that approximately half of the patients hospitalised for coronary artery disease have normal values of some of these traditional risk factors.7–9 Furthermore, it has been shown that only 25% of relatively young patients (men <55 and women <65 years of age) with first-time hospitalisation due to myocardial infarction fulfil the conventional criteria for prevention with cholesterol-lowering medicine immediately prior to their hospitalisation,10 making primary prevention very difficult. This is due to the fact that the traditional risk factors that have been found to be important in large groups of patients are not closely enough related to CVD to predict development of CVD in individuals (see Figure 1).

General Considerations for Cardiovascular Risk Markers/Factors

The need for better cardiovascular risk assessment is growing in order to enable the healthcare system to prioritise its limited health resources in a world with increasing life expectancy and an obesity epidemic. Therefore, many new biomarkers have been tested almost randomly during the past 10 years, with diverging results. Some studies have demonstrated that the use of single11 or multiple new risk markers12–14 can help predict future CVD, whereas others have not.15–16 These differences might be due to differences in populations with regard to age, gender and overall cardiovascular risk.

This assumption is supported by previous findings by our group that demonstrated that high-sensitivity C-reactive protein (hsCRP), a marker of early atherosclerosis, predicted CVD primarily in younger, low-risk subjects,17–19 whereas N-terminal pro-brain natriuretic peptide (Nt-proBNP), a marker of subclinical cardiovascular damage, predicted CVD in older, high-risk subjects.17–19 This again underlines the importance of choosing the right marker for a given population based on which part of the CVD process the marker reflects. However, a given marker might reflect different parts of the process at different time-points. Low-grade inflammation assessed by hsCRP reflected early atherosclerosis in younger, low-risk subjects,19 but also reflected unstable atherosclerotic plaques in high-risk subjects.20–21 Cardiovascular risk markers that do not actively contribute to the development of CVD,22 such as hsCRP and Nt-proBNP, can be useful in risk prediction (see Figure 2). However, the risk of not being able to reproduce the association with CVD in other populations is higher,12 and reduction of the marker may not improve prognosis.23

Second, the definition of cut-off values for the different cardiovascular risk factors/markers is also controversial because the cardiovascular risk associated with most factors/markers increases continuously with increasing levels of the factor/marker. Sometimes this is further complicated by the fact that some markers predict different outcomes at different levels. Urine albumin/creatinine ratio (UACR) ≥2.5mg/mmol in men or ≥3.5mg/mmol in women predicts nephropathy in patients with diabetes, whereas UACR ≥1mg/mmol, a level close to the 90th percentile in an apparently healthy population and previously regarded as normal, has been demonstrated to predict the composite cardiovascular end-point (CEP) consisting of cardiovascular death, myocardial infarction or stroke in an apparently healthy population24 as well as in patients with hypertension.25 However, the positive predictive value (risk of cardiovascular death, myocardial infarction or stroke within 10 years of follow-up) for UACR ≥1mg/mmol was approximately 15% in an apparently healthy population, and a possible beneficial effect of treatment has never been tested in the absence of diabetes or hypertension.26

Examples Evaluating How Cardiovascular Risk Markers May Supplement HeartScore

In apparently healthy subjects with a 10-year risk of cardiovascular death lower than 5% based on HeartScore,1 and therefore not eligible for primary prevention,4 the actual 10-year risk of cardiovascular death exceeded 5% in a small subgroup of subjects with hsCRP >5.6mg/l,19 which was close to the pre-specified gender-adjusted cut-off value of 6.0mg/l for men and 7.3mg/l for women (90th percentile) (see Figure 3).17 As hsCRP ≥6.0/7.3mg/l was found in only 124 subjects, predicting only six CEPs, and as 82% of the subjects in the low- to moderate-risk group were 41 or 51 years of age,19 one could argue for a lower cut-off value accepting intervention at a lower absolute 10-year cardiovascular risk if the relative risk was high. This would be especially relevant in subjects with moderate cardiovascular risk as recommended by the Centers for Disease Control and Prevention (CDC) and American Heart Association (AHA) in 2003.27 However, our data also suggested that hsCRP did not add new prognostic information in subjects with low to moderate cardiovascular risk, if younger subjects were regarded as being 60 years of age when calculating cardiovascular risk19 in order to avoid withholding intervention that would be recommended only if the subjects were older.4 However, this method almost doubled the number of subjects eligible for primary prevention due to high cardiovascular risk based on HeartScore, which is not rational. The impact of measuring hsCRP is still controversial. Ridker et al.28 and others29 have previously found hsCRP to predict cardiovascular events independently of Framingham risk score, and recently claimed that a new risk score using hsCRP as a continuous variable together with traditional cardiovascular risk factors in subjects with moderate cardiovascular risk can re-classify 40–50% of subjects to either higher or lower CV risk,30 whereas Danesh et al.31 questioned the additive predictive value of hsCRP.

In the same low- to moderate-risk group, the actual 10-year risk of cardiovascular death exceeded 5% for UACR >1.6mg/mmol (see Figure 4), providing an indication for primary prevention4 in a small subgroup of 61 subjects (4.3%).19 However, as most of the subjects were 41 or 51 years of age, with over-representation of women,19 intervention might be relevant at a lower absolute 10-year risk of cardiovascular death.

UACR above the pre-specified gender-adjusted cut-off value of 0.73mg/mmol in men and 1.06mg/mmol in women (90th percentile),17 which was found in 120 subjects with low to moderate cardiovascular risk, identified as many as 10 CEPs with a very high negative predictive value of 98%.19 High UACR still predicted CEP in subjects with low to moderate cardiovascular risk if younger subjects were regarded as being 60 years of age when calculating cardiovascular risk.19 This suggested that primary prevention in subjects with low to moderate cardiovascular risk may be relevant even at UACR levels around 1mg/mmol, which represents a practical round cut-off value close to the value at which cardiovascular risk clearly begins to increase in patients with hypertension.32 However, others have suggested a somewhat higher cut-off value.33 Combined use of UACR ≥0.73/1.06mg/mmol or hsCRP ≥6.0/7.3mg/l identified a larger subgroup of 228 subjects (16%) with high cardiovascular risk in which primary prevention may be advised19 despite low to moderate cardiovascular risk based on HeartScore1 (see Figure 5). Measuring UACR and hsCRP in subjects with low to moderate CV risk seems to be a clinically relevant supplement to HeartScore as 34% are re-classified correctly versus 15% incorrectly.

In subjects with known cardiovascular disease or diabetes, Nt-proBNP and UACR above the pre-specified 90% specificity, gender-adjusted cut-off values of 110pg/ml for men or 164pg/ml for women17 (0.73/1.06mg/mmol) predicted CEP with very high positive predictive values of approximately 37% and relatively high negative predictive values of 90%. Furthermore, combined use of UACR ≥0.73/1.06mg/mmol or high Nt-proBNP ≥110/164pg/ml in subjects with known CVD or diabetes identified a larger subgroup of 228 subjects (48%) with extremely high cardiovascular risk who should be referred for specialist care in order to optimise treatment.19 Measuring UACR and Nt-proBNP seems to be relevant in patients with known CV disease or diabetes as 49% are re-classified correctly versus 15% incorrectly. For pragmatic reasons we recommend using the accepted threshold for heart failure of 125pg/ml34 as a cut-off value in cardiovascular risk stratification instead of our gender-adjusted cut-off value of 110/164pg/ml.

Recommendations of the American Heart Association

The AHA wishes to improve the comparability of studies investigating different non-traditional cardiovascular risk factors/markers in different populations in order to ensure better evidence for these new risk factors/markers. Therefore, in 2009 the AHA wrote a scientific statement paper listing criteria for evaluation of novel markers of cardiovascular risk.35 Overall, they recommend that a paper dealing with new risk markers should:

  • follow accepted standards (design and outcomes) for observational studies;
  • calculate relative cardiovascular risks (standardised hazard ratios) for traditional cardiovascular risk factors;
  • calculate cardiovascular risk (standardised hazard ratio) for new non-traditional cardiovascular risk markers unadjusted as well as adjusted for the traditional risk factors testing the additive importance of the new marker (the likelihood ratio partial χ2);
  • evaluate the discriminative power of the new marker by comparing C-index for a risk model with and without the new marker, by measuring the extent to which the new risk marker correctly revises upward/downward the predicted risk in cases/non-cases and by displaying predicted risk in cases and non-cases separately, before and after inclusion of the new risk marker;
  • evaluate the accuracy of the new marker by displaying observed versus expected event rates across the range of predicted risk for models without and with the non-traditional risk marker (goodness-of-fit test) and by reporting the number of subjects re-classified and the event rates in the re-classified groups; and
  • demonstrate the clinical importance in a randomised prospective study with analyses of cost-effectiveness.
Conclusion

We feel that the best method to find new and better non-traditional cardiovascular risk factors/markers is to acquire a better understanding of the complex process leading from a certain gene combination through traditional and non-traditional cardiovascular risk factors/ markers to subclinical cardiovascular damage and, ultimately, CVD. Through this better understanding it will probably be possible to identify which risk factors/markers are the most important at different time-points in the development of CVD in the hope of finding the right markers with the right cut-off values for the right populations.36

References

  1. Conroy RM, Pyorala K, Fitzgerald AP, et al., Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project, Eur Heart J, 2003;24(11):987–1003.
    Crossref | PubMed
  2. Anderson KM, Wilson PWF, Odell PM, et al., An Updated Coronary Risk Profile. A Statement for Health Professionals, Circulation, 1991;83(1):356–62.
    Crossref | PubMed
  3. Sehestedt T, Jeppesen J, Hansen TW, et al., Risk stratification with the risk chart from the European Society of Hypertension compared with SCORE in the general population, J Hypertens, 2009;27(12):2351–7.
    Crossref | PubMed
  4. Graham IM, Guidelines on cardiovascular disease prevention in clinical practice: the European perspective, Curr Opin Cardiol, 2005;20:430–39.
    Crossref | PubMed
  5. Mancia G, De Backer G, Dominiczak A, et al., 2007 ESHESC Practice Guidelines for the Management of Arterial Hypertension: ESH-ESC Task Force on the Management of Arterial Hypertension, J Hypertens, 2007;25(9):1751–62.
    Crossref | PubMed
  6. Mancia G, Laurent S, Agabiti-Rosei E, et al., Reappraisal of European guidelines on hypertension management: a European Society of Hypertension Task Force document, J Hypertens, 2009;18(6):308–47).
    Crossref | PubMed
  7. Sachdeva A, Cannon CP, Deedwania PC, et al., Lipid levels in patients hospitalized with coronary artery disease: an analysis of 136,905 hospitalizations in Get With The Guidelines, Am Heart J, 2009;157(1):111–17.
    Crossref | PubMed
  8. Lawes CM, Vander Hoorn S, Law MR, et al., Blood pressure and the global burden of disease 2000. Part 1: estimates of blood pressure levels, J Hypertens, 2006;24(3):413–22.
    Crossref | PubMed
  9. Lawes CM, Vander Hoorn S, Law MR, et al., Blood pressure and the global burden of disease 2000. Part II: estimates of attributable burden, J Hypertens, 2006;24(3): 423–30.
    Crossref | PubMed
  10. Akosah KO, Schaper A, Cogbill C, et al., Preventing myocardial infarction in the young adult in the first place: how do the National Cholesterol Education Panel III guidelines perform? J Am Coll Cardiol, 2003;41(9):1475–9.
    Crossref | PubMed
  11. Ridker PM, Paynter NP, Rifai N, et al., C-Reactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men, Circulation, 2008;118;2243–51.
    Crossref | PubMed
  12. Blankenberg S, Zeller T, Saarela Olli, et al., for the MORGAM Project, Contribution of Thirty Biomarkers to Ten-Year Cardiovascular Risk Estimation in Two Population Cohorts; The MORGAM – Biomarker Project, Circulation, 2009, accepted.
  13. 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(25):2631–9.
    Crossref | PubMed
  14. Zethelius B, Berglund L, Sundstrom J, et al., Use of multiple biomarkers to improve the prediction of death from cardiovascular causes, N Engl J Med, 2008;358(20): 2107–16.
    Crossref | PubMed
  15. Danesh J et al., C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease, N Engl J Med, 2004;350(14):1387–97.
    Crossref | PubMed
  16. Tzoulaki I, Liberopoulos G, Ioannidis JP, Assessment of claims of improved prediction beyond the Framingham risk score, JAMA, 2009;302(21):2345–52.
    Crossref | PubMed
  17. Olsen MH, Hansen TW, Christensen MK, et al., Cardiovascular risk prediction by N-terminal pro brain natriuretic peptide and high sensitivity C-reactive protein is affected by age and sex, J Hypertens, 2008;26(1):26–34.
    Crossref | PubMed
  18. Olsen MH, Hansen TW, Christensen MK, et al., Impact of the metabolic syndrome on the predictive values of new risk markers in the general population, J Hum Hypertens, 2008;22:634–40.
    Crossref | PubMed
  19. Olsen MH, Hansen TW, Christensen MK, et al., New risk markers may change the HeartScore risk-classification significantly in one-fifth of the population, J Hum Hypertens, 2008;23(2):105–12.
    Crossref | PubMed
  20. Nakachi T, Kosuge M, Hibi K, et al., C-reactive protein elevation and rapid angiographic progression of nonculprit lesion in patients with non-ST-segment elevation acute coronary syndrome, Circ J, 2008;72(12): 1953–9.
    Crossref | PubMed
  21. Kashiwagi M, Tanaka A, Kitabata H, et al., Relationship between coronary arterial remodeling, fibrous cap thickness and high-sensitivity C-reactive protein levels in patients with acute coronary syndrome, Circ J, 2009;73(7): 1291–5.
    Crossref | PubMed
  22. Zacho J et al., Genetically elevated C-reactive protein and ischemic vascular disease, N Engl J Med, 2008;359(18): 1897–1908.
    Crossref | PubMed
  23. Olsen MH, Wachtell K, Tuxen C, et al., Opposite Effects of Losartan and Atenolol on Natriuretic Peptides in Patients with Hypertension and Left Ventricular Hypertrophy. A LIFE Study, J Hypertens, 2005;23(5):1083–90.
    Crossref | PubMed
  24. Olsen MH, Hansen TW, Christensen MK, et al., N-terminal pro-brain natriuretic peptide, but not high sensitivity C-reactive protein, improves cardiovascular risk prediction in the general population, Eur Heart J, 2007;28(11):1374–81.
    Crossref | PubMed
  25. Olsen MH, Wachtell K, Ibsen H, et al., Reductions in albuminuria and in electrocardiographic left ventricular hypertrophy independently improve prognosis in hypertension: the LIFE study, J Hypertens, 2006;24(4): 775–81.
    Crossref | PubMed
  26. Ibsen H, Olsen MH, Wachtell K, et al., Reduction in albuminuria translates to reduction in cardiovascular events in hypertensive patients: losartan intervention for endpoint reduction in hypertension study, Hypertens, 2005;45(2):198–202.
    Crossref | PubMed
  27. Pearson TA, Mensah GA, Alexander RW, et al., 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(3):499–511.
    Crossref | PubMed
  28. Ridker PM, Cook N, Clinical usefulness of very high and very low levels of C-reactive protein across the full range of Framingham Risk Scores, Circulation, 2004;109(16): 1955–9.
    Crossref | PubMed
  29. Koenig W, Lowel H, Baumert J, et al., C-reactive protein modulates risk prediction based on the Framingham Score: implications for future risk assessment: results from a large cohort study in southern Germany, Circulation, 2004;109(11):1349–53.
    Crossref | PubMed
  30. Ridker PM, Buring JE, Rifai N, et al., Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score, JAMA, 2007;297(6):611–19.
    Crossref | PubMed
  31. Danesh J, Wheeler JG, Hirschfield GM, et al., C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease, N Engl J Med, 2004;350(14):1387–97.
    Crossref | PubMed
  32. Olsen MH, Sehestedt T, Lyngbæk S, et al., Urine Albumin/Creatinine Ratio, High Sensitivity C-Reactive Protein and N-Terminal Pro Brain Natriuretic Peptide – Three new Cardiovascular Risk Markers – Do They Improve Risk Prediction and Influence Treatment?, Curr Vasc Pharmacol, 2010;8(1):134–9.
    Crossref | PubMed
  33. Redon J, Williams B, Microalbuminuria in essential hypertension: redefining the threshold, J Hypertens, 2002;20(3):353–5.
    Crossref | PubMed
  34. Gustafsson F, Badskjær J, Hansen FS, et al., Value of N-Terminal proBNP in the Diagnosis of Left Ventricular Systolic Dysfunction in Primary Care Patients Referred for Echocardiography, Heart Drug 3, 2003;141–6.
    Crossref
  35. Hlatky MA, Greenland P, Arnett DK, et al., American Heart Association Expert Panel on Subclinical Atherosclerotic Diseases and Emerging Risk Factors and the Stroke Council, Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association, Circulation, 2009;119(17):2408–16
    Crossref | PubMed
  36. Sehestedt T, Jeppesen J, Hansen TW, et al., Which markers of subclinical organ damage to measure in individuals with high normal blood pressure?, J Hypertens, 2009;27(6):1165–71.
    Crossref | PubMed
Previous Volume Article Next Volume Article