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© The European Society of Cardiology 2006. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

Dyslipidaemia and global cardiovascular risk: clinical issues

Gerd Assmann

Institute of Arteriosclerosis Research, University of Münster, Domagkstrasse 3, 48149 Münster, Germany

Corresponding author. Tel: +49 251 83 47 222; fax: +49 251 83 47 225. E-mail address: assmann{at}uni-muenster.de


   Abstract
Top
 Abstract
Introduction
 Prediction of global...
 Lipid parameters and...
 Future challenges in global...
 Managing global cardiovascular...
 Conclusions
 References
 
Improvements in cardiovascular outcomes have occurred in recent decades, but a serious burden of cardiovascular morbidity and mortality remains. Data from large, prospective observational studies, such as the PROspective CArdiovascular Münster (PROCAM) Study, have facilitated the design of simple risk factor scoring systems suitable for the estimation of global cardiovascular risk in routine clinical practice. These systems show that dyslipidaemia remains an important contributor to global cardiovascular risk, despite the increased success in controlling LDL cholesterol in recent years. Data from PROCAM have shown that low HDL cholesterol is prognostically important, especially in patients at high global risk due to the presence of concomitant risk factors and in patients with concomitant low HDL cholesterol and hypertriglyceridaemia. Newer techniques, including neural network analysis, myocardial imaging, and genotyping, are likely to add to the precision with which patients requiring cardiovascular intervention are identified in the future. The management of global cardiovascular risk is based on addressing individual cardiovascular risk factors, including hypercholesterolaemia, low HDL cholesterol, and hypertension. In particular, consideration should be given to correcting low HDL cholesterol by the addition of nicotinic acid or a fibrate to the therapeutic regimen in addition to treatments to control LDL cholesterol.

Key Words: Cardiovascular risk • Coronary heart disease • Cardiovascular risk engines • Dyslipidaemia


   Introduction
Top
 Abstract
 Introduction
Prediction of global...
 Lipid parameters and...
 Future challenges in global...
 Managing global cardiovascular...
 Conclusions
 References
 
Death rates from cardiovascular disease have fallen in recent decades in Europe1,2 and in the USA,3 but a major burden of premature cardiovascular mortality remains. For example, latest available data (2003) from the World Health Organization (WHO) for the European Union (EU) confirm that circulatory disease was the largest single cause of death with a standardized death rate (270/100 000 individuals) that outstripped the corresponding death rate from malignant neoplasms (186/100 000).4 About 1.9 million people in the EU die from cardiovascular disease each year.1 Thus, more than 5000 individuals die from cardiovascular disease everyday, which corresponds to about one cardiovascular death every 20 s. The estimated total annual cost of cardiovascular disease to the EU has been estimated as {euro}169 billion.1

The development of cardiovascular disease is largely asymptomatic, and patients often do not develop symptoms until disease is well advanced. For example, the Euro Heart Survey of acute coronary syndromes, a prospective survey of 10 484 patients with acute coronary syndromes from 103 hospitals in 25 countries, found that about one-third of patients (33%) did not have coronary symptoms (angina) preceding the development of their coronary ischaemic event.5 Similarly, of a series of 400 patients in the USA, 44% did not experience symptoms of angina before their myocardial infarction.6 Prognosis following acute myocardial infarction remains poor. Data from the WHO MONICA project, gathered from 21 countries worldwide, suggest that about half of patients who develop an acute myocardial infarction die within 28 days.7 Moreover, patients with hyperglycaemia on admission, or with pre-existing diabetes, are less likely to survive a myocardial infarction than their counterparts without these conditions.8–11

It is clearly important to optimize the risk stratification of patients at risk of myocardial infarction, in order to guide appropriate therapeutic intervention. Large observational cohort studies play a crucial role in defining the natural history and evolution of cardiovascular disease and underpin the evidence base for such interventions. The Framingham study, for example, has been achieving this objective in the USA since its first publication in the early 1950s.12 Difficulties in extrapolating the results of one cohort to another remain a major limitation of such studies; however, data relevant to defined local populations are required to optimize risk stratification.13,14

To this end, the PROspective CArdiovascular Münster (PROCAM) study has collected data on more than 50 physiological parameters from more than 40 000 European subjects in Germany since its initiation in 1979.15 This review will consider the lessons from the PROCAM study, with special reference to the contribution of dyslipidaemia to global cardiovascular risk. (Readers should note that the PROCAM project is an active research collaboration, and analyses were performed at different times. These data are provided in each case, either in the text or in legends to figures or tables.)


   Prediction of global cardiovascular risk from the PROCAM study
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 Abstract
 Introduction
 Prediction of global...
Lipid parameters and...
 Future challenges in global...
 Managing global cardiovascular...
 Conclusions
 References
 
Risk scoring algorithm
The PROCAM algorithm was based on a 10-year follow-up of 5389 men aged 35–65 years, during which 325 coronary events were observed.16 In all, 57 cardiometabolic parameters were analysed, of which eight (age, LDL cholesterol, smoking, HDL cholesterol, systolic blood pressure, family history of myocardial infarction, diabetes, and triglycerides) were independently predictive of cardiovascular outcomes (Figure 1). These parameters were used in the construction of a Cox proportional hazards model.


Figure 0401
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  		Figure 1 Independent predictors of outcome in the PROCAM cohort. Data are mean±SD (age, HDL-C, LDL-C, triglycerides (TG), and systolic blood pressure (SBP)] or per cent of the population (smoking, family history of myocardial infarction (FHMI), or diabetes mellitus (DM)]. Lipid parameters are in mmol/L and SBP is in mmHg. Drawn from data presented by Assmann et al.16

 
A simple scoring scheme was derived from the Cox proportional hazards model in order to facilitate the use of the PROCAM algorithm in risk stratification in routine cardiovascular care. Briefly, each risk factor was subdivided using convenient cut-off values, and a regression equation was used to match the cut-off values with the actual risk of adverse cardiovascular outcomes. The standardized coefficients were rounded to provide scores for different levels of each parameter (Figure 2). The total score is used to calculate the overall PROCAM risk.


Figure 0402
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  		Figure 2 Simple scoring system for cardiovascular risk factors to facilitate the use of the PROCAM algorithm in cardiovascular risk stratification. To convert cholesterol values to mmol/L multiply by 0.02586; to convert triglycerides to mmol/L multiply by 0.01129; and to convert fasting blood glucose (FBG) to mmol/L divide by 18. AH, antihypertensive. Drawn from data presented by Assmann et al.16

 
A version of the PROCAM risk calculator is available online and provides simple and rapid estimation of the 10-year risk of coronary heart disease death or non-fatal myocardial infarction.17 Although this uses a larger number of more closely-spaced cut-off values for each cardiovascular risk factor, the principle is the same as the published version, described earlier. For example, a non-diabetic male smoker with a family history of cardiovascular disease with any other cardiovascular parameters identical to the average values for the placebo group of the Scandinavian Simvastatin Survival Study (4S)18 provides an example of a high-risk patient similar to many encountered in routine clinical practice. The 10-year cardiovascular risk estimated by the PROCAM algorithm for this individual was 37%, and a diagnosis of diabetes further increased this individual's 10-year cardiovascular risk to 49%. Thus, the PROCAM risk calculator identifies patients at high cardiovascular risk and provides information useful to both physician and patient.

Neural network analysis
Typically, analyses of the influence of individual cardiovascular risk factors on outcomes in longitudinal cohort data rely on logistic regression analyses, which tend to assume that risk factors are related to outcomes in a continuous fashion. However, the factors that determine the overall risk of an adverse cardiovascular outcome are numerous and interact in a complex manner over varying time scales from childhood onwards. As a result, the reliability of logistic regression analyses based on a relatively small number of risk factors can be variable in which such aetiological complexities arise. Neural network analysis avoids some of these limitations by taking into account a large number of factors that interact in non-linear ways.19

These techniques have been applied to data from the PROCAM cohort, to investigate whether they provided improved prediction of increased risk of cardiovascular events.20 Specifically, two forms of neural network analysis were undertaken: multi-layer perceptron (MLP) and probabilistic neural network (PNN) analyses. The analysis was based on the observation of 325 men who developed coronary events and 4493 men who did not, over a 10-year follow-up period. In this case, the significantly predictive cardiovascular risk variables included in the final model were age, triglycerides, ln(triglycerides), HDL cholesterol, LDL cholesterol, systolic blood pressure, gamma glutamyl transferase, smoking, diabetes, family history of myocardial infarction, HDL cholesterol xln(HDL cholesterol), triglyceridexHDL cholesterol, and triglyceridexgamma glutamyl transferase.

MLP and logistic regression each identified a similar proportion of patients (~8%) with 10-year cardiovascular risk >20% (Table 1), the threshold value commonly accepted as representing a high risk requiring prompt and intensive intervention.21 However, almost twice as many patients identified as high risk by MLP reported a cardiovascular event (Table 1). MLP and logistic regression also differed in their sensitivity, which corresponds to the proportion of all cardiovascular events observed: MLP identified a group that reported about three-quarters of all cardiovascular events, compared with about half of all cardiovascular events for logistic regression. The PNN technique of neural network analysis identified a lower proportion of high-risk patients (~4%), who accounted for only about one-third of all cardiovascular events. The specificity (the proportion of patients without cardiovascular events correctly identified by the algorithms) was high in all cases (94–97%).


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  		Table 1 Prediction of coronary risk in the PROCAM cohort using neural network analysis and logistic regression20

 
Research on the PROCAM cohort is continuing in order to refine the risk prediction provided by the PROCAM algorithms. Indeed, recruitment into the study is continuing with a target population of 50 000 individuals, one-third women, by 2007. The results of a more recent analysis on a slightly larger cohort, comparing the relationships between the outputs of PROCAM algorithms and 10-year cardiovascular risk, are shown in Figure 3. An algorithm based on the neural network analysis identified a larger proportion of subjects with MI during follow-up, compared with the Cox model, in a manner consistent with the results described earlier. The 10-year incidence of myocardial infarction among the 10% of patients at highest cardiovascular risk according to the neural network analysis was 41%, compared with <30% for the Cox proportional hazards model, and <20% if LDL cholesterol alone was used as the sole predictor of cardiovascular risk.


Figure 0403
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  		Figure 3 (A) Ten-year risk of myocardial infarction based on quintiles of the PROCAM score and evaluated using neural network analysis or logistic regression. Data are from separate unpublished analyses based on 325 events in 4818 men (neural network) or 406 events in 7152 men (logistic regression). Independent variables included in each analysis were as follows. Logistic regression: age, systolic blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, diabetes, smoking, and family history of myocardial infarction; neural networks: age, systolic and diastolic blood pressures, LDL cholesterol, HDL cholesterol, triglycerides, fasting blood glucose, diabetes, smoking, family history of myocardial infarction, body mass index, and antihypertensive treatment. (B) Comparison of 10-year risk of myocardial infarction using only LDL cholesterol as an index of risk, using the Cox proportional hazards model, or neural network analysis. Data are from the highest decile of cardiovascular risk in each case and based on 325 events in 4818 men.

 

   Lipid parameters and cardiovascular risk in the PROCAM cohort
Top
 Abstract
 Introduction
 Prediction of global...
 Lipid parameters and...
Future challenges in global...
 Managing global cardiovascular...
 Conclusions
 References
 
HDL cholesterol
The PROCAM study identified low HDL cholesterol as an independent risk factor for cardiovascular events after adjustment for other cardiovascular risk factors,22,23 thus confirming and extending the results of Framingham24 and other observational studies. Figure 4 shows the 10-year risk of myocardial infarction in an analysis from the PROCAM cohort after stratification of patients for both HDL cholesterol and LDL cholesterol. Reduced HDL cholesterol increased the overall cardiovascular risk at any given level of LDL cholesterol. The relationship was steepest at higher levels of LDL cholesterol, which correspond to patients at higher global cardiovascular risk. A further analysis confirmed this relationship (Figure 5). Larger decreases in cardiovascular risk were observed in patients in the highest decile for global cardiovascular risk according to the PROCAM algorithm when compared with patients in the lowest decile for global cardiovascular risk. These data suggest that interventions in high-risk patients to correct low HDL cholesterol are likely to deliver the greatest benefit in terms of cardiovascular protection.


Figure 0404
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  		Figure 4 Relative contributions of LDL cholesterol and HDL cholesterol to coronary risk in the PROCAM study. Data are from 406 coronary events observed in 7152 men followed-up for 10 years. To convert cholesterol values to mmol/L multiply by 0.02586. Adapted from Assmann et al.23 Copyright (1996) with permission from Elsevier.

 

Figure 0405
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  		Figure 5 Relationship between changes in HDL cholesterol and cardiovascular risk in the PROCAM study in subjects in the highest and lowest deciles for cardiovascular risk. Data are from an analysis of 406 coronary events in 7152 men followed for 10 years.

 
Triglycerides
The relationship between levels of triglycerides and cardiovascular risk was more complex. Unlike HDL cholesterol, triglycerides were not a statistically significant predictor of cardiovascular events after multi-variate adjustment for other risk factors.23 Consistent with this observation, the highest 10-year cardiovascular risk was observed in subjects with the lowest HDL cholesterol and highest triglycerides, in a manner analogous to the relationship between LDL cholesterol and HDL cholesterol, as shown in Figure 4. However, there was evidence that triglycerides added markedly to global cardiovascular risk in hypertriglyceridaemic patients with a high ratio of LDL cholesterol to HDL cholesterol (>5.0): although only about one subject in 25 exhibited this lipid phenotype and about one-quarter of all cardiovascular events was observed in this group.

Lipoprotein (a)
The atherogenic lipoprotein, lipoprotein (a) [Lp(a)], is an ApoB-containing lipoprotein that resembles LDL, but with an additional glycoprotein, Apo(a).25 Some prospective observational studies have identified Lp(a) as a predictor of cardiovascular events, although it is unclear whether Lp(a) was acting independently of other risk factors.26–28 In addition, 296 children with acute ischaemic stroke, normally a very rare event in this population, were about seven-fold more likely to have Lp(a) >30 mg/dL than age-matched children without stroke [odds ratio 7.2 (95% CI 3.8–13.8); P<0.0001].29 However, other studies demonstrated no association between levels of Lp(a) and adverse cardiovascular outcomes.30,31

Lp(a) was measured in a subset of patients in the PROCAM study (n=788).32 A major cardiovascular event (non-fatal myocardial infarction, fatal myocardial infarction, or sudden cardiac death) occurred in 44 of these subjects during 10 years of follow-up. Patients who suffered a major coronary event had higher Lp(a), on average, compared with those who did not (9 vs. 4 mg/dL), and the 10-year incidence of major coronary events was markedly higher in patients in the highest Lp(a) quintile (≥17 mg/dL), compared with quintiles 1–4 (11.8 vs. 2.0–6.3%). Patients with Lp(a) ≥20 mg/dL or higher were at an approximately three-fold higher risk of major coronary events, compared with the remainder of the cohort [relative risk 2.7 (95% CI 1.4–5.2]). As with HDL cholesterol, the influence of Lp(a) on cardiovascular risk differed according to global cardiovascular risk status. Elevated Lp(a) was associated with markedly increased risk of a major coronary event only in patients in the top two quintiles of global cardiovascular risk estimated using a PROCAM algorithm (Table 2).


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  		Table 2 Incidence of major cardiovascular events per 1000 subjects (non-fatal myocardial infarction, fatal myocardial infarction, or sudden cardiac death) in patients stratified for Lp(a) levels in the PROCAM study 32

 

   Future challenges in global risk assessment
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 Introduction
 Prediction of global...
 Lipid parameters and...
 Future challenges in global...
Managing global cardiovascular...
 Conclusions
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Genotyping and phenotyping
A large and growing number of genes have been identified with polymorphisms that may influence the risk of atherosclerosis, thrombosis, and myocardial infarction, particularly a-adducin, ALOX5AP, clotting factor II, interleukins, endothelial adhesion molecules, endothelial nitric oxide synthase, glycoprotein IIIa, MTHFR, and ApoE.33–37 The precise role of these genes, and their complex interactions with other genes and processes, needs to be determined, and the technique of neural network analysis is ideally suited to addressing this kind of challenge.

Myocardial imaging
Visualization of coronary atherosclerosis provides an alternative means of quantifying the extent of atherosclerotic disease present. Advances in non-invasive imaging techniques, such as spiral computed tomography (CT) or electron beam CT, can detect and quantify the extent of calcification of coronary arteries, which provides a useful and well validated marker of atherosclerosis progression.38–40 Grundy41 has noted that age is effectively a surrogate for the likelihood of atherosclerosis in risk scoring methodologies and that the prognostic significance of Framingham scoring is skewed to some extent by an increasing influence of age over other cardiovascular risk factors in individuals aged >50 years. He suggests that the use of coronary calcium scores derived from CT may provide a useful adjustment to this method of global risk evaluation in older subjects.

Specifically, it is suggested that a coronary calcium score could replace the age score in the Framingham risk algorithm. For example, Framingham risk scoring accrues 6 age points for a man aged 50–54 years. For a man within the lowest quartile of coronary calcium score on CT evaluation, this would be replaced by a score of 0, with corresponding scores 6 for a man in the middle two quartiles and by a score of 10 for a man in the upper quartile of coronary calcium scores. In this way, the global risk assessment can be directly influenced by a non-invasive measure of the actual atherosclerotic burden within the coronary arteries.

Routine adjustment to global risk scoring on the basis of CT measurement is not sufficiently validated for widespread use. In addition, myocardial imaging is not sufficiently widely available as yet. Nevertheless, this is an interesting concept for the future and provides an illustration of the potential for advances in imaging techniques to contribute to global risk stratification in the coming years.


   Managing global cardiovascular risk
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 Introduction
 Prediction of global...
 Lipid parameters and...
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 Managing global cardiovascular...
Conclusions
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Smoking, hypertension, and hypercholesterolaemia are well-known sources of cardiovascular risk. Many other risk factors that contribute to global cardiovascular risk are associated with the metabolic syndrome. The presence of three or more risk factors from a list of five (high waist circumference, low HDL cholesterol, hypertriglyceridaemia, high blood pressure, and high fasting plasma glucose) is sufficient to diagnose the metabolic syndrome according to the US National Cholesterol Education Program,42 whereas high waist circumference and two others from this list are required to diagnose metabolic syndrome according to the International Diabetes Federation.43 It is important to remember, however, that the brevity of these lists reflects a need for simplified diagnosis in practice and that many more risk factors other than these are associated with the metabolic syndrome and insulin resistance. These risk factors, which include elevated fasting insulin, impaired fibrinolysis, endothelial dysfunction, and chronic low-grade inflammation, are also believed to be potent drivers of atherosclerosis.

Currently, we have no overall treatment for the metabolic syndrome, and management is based on the control of individual risk factors. Many agents are available for the control of LDL cholesterol, including the statins and cholesterol absorption inhibitors. Many antihypertensive agents are also available, although the recently published Anglo Scottish Cardiac Outcomes Trial Blood Pressure Lowering Arm (ASCOT-BPLA) has suggested that modern antihypertensive regimens (angiotensin-converting enzyme and a calcium channel blocker) may be more effective than older regimens (thiazide diuretic and ß-blocker) in terms of cardiovascular protection.44,45

The important contribution of low HDL cholesterol to overall cardiovascular risk, observed in the PROCAM study and elsewhere, provides a compelling rationale for the measurement and active management of this parameter. Nicotinic acid is the most effective drug currently available for the normalization of low HDL cholesterol and also produces a marked improvement in the levels of triglycerides.42 Fibrates also produce a clinically useful increase in the levels of HDL cholesterol.42 Consideration should be given to adding one of these agents to the regimen, in addition to a statin. A strong evidence base supports such an approach. For example, a combination of a prolonged-release formulation of Niaspan® with a statin has been shown to control both HDL cholesterol and LDL cholesterol and to induce regression of atherosclerosis over a period of 1–2 years of treatment.46,47


   Conclusions
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 Abstract
 Introduction
 Prediction of global...
 Lipid parameters and...
 Future challenges in global...
 Managing global cardiovascular...
 Conclusions
References
 
Accurate stratification of patients in terms of global cardiovascular risk is essential for the optimization of therapy aimed at reducing the risk of a morbid cardiovascular event. Risk factor scoring systems based on large, prospective observational studies (e.g. PROCAM) provide a simple and straightforward means of achieving this objective. Newer techniques, such as neural network analysis, myocardial imaging, and genotyping, will likely improve the accuracy of global cardiovascular risk determination in the future. For now, management of global cardiovascular risk is based on addressing individual cardiovascular risk factors, including hypercholesterolaemia, low HDL cholesterol, and hypertension.

Conflict of interest: none declared.


   References
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 Future challenges in global...
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  1. European Heart Network, British Heart Foundation. (2005) European cardiovascular disease statistics. edition. www.ehnheart.org (December 2005).
  2. Unal B, Critchley JA, Capewell S. (2005) Modelling the decline in coronary heart disease deaths in England and Wales, 1981–2000: comparing contributions from primary prevention and secondary prevention. BMJ 331:614.[Abstract/Free Full Text]
  3. Jemal A, Ward E, Hao Y, Thun M. (2005) Trends in the leading causes of death in the United States, 1970–2002. JAMA 294:1255–1259.[Abstract/Free Full Text]
  4. World Health Organization, Regional Office for Europe. Mortality by 67 causes of death, age and sex (HFA-MDB). www.data.euro.who.int/hfamdb/ (December 2005).
  5. Hasdai D, Behar S, Wallentin L, Danchin N, Gitt AK, Boersma E, Fioretti PM, Simoons ML, Battler A. (2002) A prospective survey of the characteristics, treatments and outcomes of patients with acute coronary syndromes in Europe and the Mediterranean basin; the Euro Heart Survey of Acute Coronary Syndromes (Euro Heart Survey ACS). Eur Heart J 23:1190–1201.[Abstract/Free Full Text]
  6. Kouvaras G and Bacoulas G. (1987) Unstable angina pectoris as a warning symptom before acute myocardial infarction. Q J Med 64:679–684.[Web of Science][Medline]
  7. Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM, Pajak A. (1994) Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation 90:583–612.[Abstract/Free Full Text]
  8. Chun BY, Dobson AJ, Heller RF. (1997) The impact of diabetes on survival among patients with first myocardial infarction. Diabetes Care 20:704–708.[Abstract]
  9. Lowel H, Koenig W, Engel S, Hormann A, Keil U. (2000) The impact of diabetes mellitus on survival after myocardial infarction: can it be modified by drug treatment? Results of a population-based myocardial infarction register follow-up study. Diabetologia 43:218–226.[CrossRef][Web of Science][Medline]
  10. Sala J, Masia R, Gonzalez de Molina FJ, Fernandez-Real JM, Gil M, Bosch D, Ricart W, Senti M, Marrugat J. (2002) Short-term mortality of myocardial infarction patients with diabetes or hyperglycaemia during admission. J Epidemiol Commun Health 56:707–712.[Abstract/Free Full Text]
  11. Hadjadj S, Coisne D, Mauco G, Ragot S, Duengler F, Sosner P, Torremocha F, Herpin D, Marechaud R. (2004) Prognostic value of admission plasma glucose and HbA in acute myocardial infarction. Diabet Med 21:305–310.[CrossRef][Web of Science][Medline]
  12. Dawber TR, Meadors GF, Moore FEJ. (1951) Epidemiological approaches to heart disease: the Framingham Study. Am J Public Health 41:279–286.[Free Full Text]
  13. Empana JP, Ducimetiere P, Arveiler D, Ferrieres J, Evans A, Ruidavets JB, Haas B, Yarnell J, Bingham A, Amouyel P, Dallongeville J. (2003) Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME study. Eur Heart J 24:1903–1911.[Abstract/Free Full Text]
  14. Hense HW, Schulte H, Lowel H, Assmann G, Keil U. (2003) Framingham risk function overestimates risk of coronary heart disease in men and women from Germany—results from the MONICA Augsburg and the PROCAM cohorts. Eur Heart J 24:937–945.[Abstract/Free Full Text]
  15. Assmann G, Schulte H, Assmann G. (1993) Results and conclusions of the Prospective Cardiovascular Münster (PROCAM) Study. Lipid Metabolism Disorders and Coronary Heart Disease(MMV Medizin Verlag, München) pp. 19–68.
  16. Assmann G, Cullen P, Schulte H. (2002) Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study. Circulation 105:310–315.[Abstract/Free Full Text]
  17. Online PROCAM risk calculator. www.chdrisk.uni-muenster.de/calculator.php (December 2005).
  18. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet (1994) 344:1383–1389.[CrossRef][Web of Science][Medline]
  19. May M. (2002) Commentary: improved coronary risk prediction using neural networks. Int J Epidemiol 31:1262–1264.[Free Full Text]
  20. Voss R, Cullen P, Schulte H, Assmann G. (2002) Prediction of risk of coronary events in middle-aged men in the Prospective Cardiovascular Munster Study (PROCAM) using neural networks. Int J Epidemiol 31:1253–1262.[Abstract/Free Full Text]
  21. Third Joint Task Force of European and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of eight societies and by invited experts) European guidelines on cardiovascular disease prevention in clinical practice. Eur J Cardiovasc Prev Rehabil (2003) 10:S1–S10.[CrossRef][Web of Science][Medline]
  22. Assmann G and Schulte H. (1992) Relation of high-density lipoprotein cholesterol and triglycerides to incidence of atherosclerotic coronary artery disease (the PROCAM experience). Prospective Cardiovascular Munster study. Am J Cardiol 70:733–737.[CrossRef][Web of Science][Medline]
  23. Assmann G, Schulte H, von Eckardstein A, Huang Y. (1996) High-density lipoprotein cholesterol as a predictor of coronary heart disease risk. The PROCAM experience and pathophysiological implications for reverse cholesterol transport. Atherosclerosis 124:suppl., S11–S20.
  24. Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR. (1977) High density lipoprotein as a protective factor against coronary heart disease. The Framingham study. Am J Med 62:707–714.[CrossRef][Web of Science][Medline]
  25. Marcovina SM and Koschinsky ML. (2002) A critical evaluation of the role of Lp(a) in cardiovascular disease: can Lp(a) be useful in risk assessment? Semin Vasc Med 2:335–344.[CrossRef][Medline]
  26. Rosengren A, Wilhelmsen L, Eriksson E, Risberg B. (1990) a prospective case–control study in a general population sample of middle aged men. BMJ 301:1248–1251.[Abstract/Free Full Text]
  27. Stubbs P, Seed M, Lane D, Collinson P, Kendall F, Noble M. (1998) Lipoprotein(a) as a risk predictor for cardiac mortality in patients with acute coronary syndromes. Eur Heart J 19:1355–1364.[Abstract/Free Full Text]
  28. Cantin B, Gagnon F, Moorjani S, Despres JP, Lamarche B, Lupien PJ, Dagenais GR. (1998) Is lipoprotein(a) an independent risk factor for ischaemic heart disease in men? The Quebec Cardiovascular Study. J Am Coll Cardiol 31:519–525.[Abstract/Free Full Text]
  29. Nowak-Gottl U, Strater R, Heinecke A, Junker R, Koch HG, Schuierer G, von Eckardstein A. (1999) Lipoprotein (a) and genetic polymorphisms of clotting factor V, prothrombin, and methylenetetrahydrofolate reductase are risk factors of spontaneous ischaemic stroke in childhood. Blood 94:3678–3682.[Abstract/Free Full Text]
  30. Ridker PM, Hennekens CH, Stampfer MJ. (1993) A prospective study of lipoprotein(a) and the risk of myocardial infarction. JAMA 270:2195–2199.[Abstract/Free Full Text]
  31. Jauhiainen M, Koskinen P, Ehnholm C, Frick MH, Manttari M, Manninen V, Huttunen JK. (1991) Lipoprotein (a) and coronary heart disease risk: a nested case–control study of the Helsinki Heart Study participants. Atherosclerosis 89:59–67.[CrossRef][Web of Science][Medline]
  32. ton Eckardstein A, Schulte H, Cullen P, Assmann G. (2001) Lipoprotein(a) further increases the risk of coronary events in men with high global cardiovascular risk. J Am Coll Cardiol 37:434–439.[Abstract/Free Full Text]
  33. Incalcaterra E, Hoffmann E, Averna MR, Caimi G. (2004) Genetic risk factors in myocardial infarction at young age. Minerva Cardioangiol 52:287–312.[Medline]
  34. Auer J, Weber T, Berent R, Lassnig E, Lamm G, Eber B. (2003) Genetic polymorphisms in cytokine and adhesion molecule genes in coronary artery disease. Am J Pharmacogenomics 3:317–328.[CrossRef][Medline]
  35. Green F and Humphries S. (1994) Genetic determinants of arterial thrombosis. Baillieres Clin Haematol 7:675–692.[CrossRef][Web of Science][Medline]
  36. Kajimoto K, Shioji K, Ishida C, Iwanaga Y, Kokubo Y, Tomoike H, Miyazaki S, Nonogi H, Goto Y, Iwai N. (2005) Validation of the association between the gene encoding 5-lipoxygenase-activating protein and myocardial infarction in a Japanese population. Circ J 69:1029–1034.[CrossRef][Web of Science][Medline]
  37. Morrison AC, Bray MS, Folsom AR, Boerwinkle E. (2002) ADD1 460 W allele associated with cardiovascular disease in hypertensive individuals. Hypertension 39:1053–1057.[Abstract/Free Full Text]
  38. Mielke CH, Shields JP, Broemeling LD. (2001) Coronary artery calcium, coronary artery disease, and diabetes. Diabetes Res Clin Pract 53:55–61.[CrossRef][Web of Science][Medline]
  39. Schmermund A, Mohlenkamp S, Erbel R. (2003) Coronary artery calcium and its relationship to coronary artery disease. Cardiol Clin 21:521–534.[CrossRef][Medline]
  40. Hosoi M, Sato T, Yamagami K, Hasegawa T, Yamakita T, Miyamoto M, Yoshioka K, Yamamoto T, Ishii T, Tanaka S, Itoh A, Haze K, Fujii S. (2002) Impact of diabetes on coronary stenosis and coronary artery calcification detected by electron-beam computed tomography in symptomatic patients. Diabetes Care 25:696–701.[Abstract/Free Full Text]
  41. Grundy SM. (2001) Coronary plaque as a replacement for age as a risk factor in global risk assessment. Am J Cardiol 88:8E–11E.[CrossRef][Web of Science][Medline]
  42. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. (2001) Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 285:2486–2897.[Free Full Text]
  43. Alberti KGM, Zimmet P, Shaw J. (2005) The metabolic syndrome—a new worldwide definition. Lancet 366:1059–1062.[CrossRef][Web of Science][Medline]
  44. Dahlof B, Sever PS, Poulter NR, Wedel H, Beevers DG, Caulfield M, Collins R, Kjeldsen SE, Kristinsson A, McInnes GT, Mehlsen J, Nieminen M, O'Brien E, Ostergren J. ASCOT Investigators. (2005) Prevention of cardiovascular events with an antihypertensive regimen of amlodipine adding perindopril as required versus atenolol adding bendroflumethiazide as required, in the Anglo-Scandinavian Cardiac Outcomes Trial-Blood Pressure Lowering Arm (ASCOT-BPLA): a multicentre randomised controlled trial. Lancet 366:895–906.[CrossRef][Web of Science][Medline]
  45. Poulter NR, Wedel H, Dahlof B, Sever PS, Beevers DG, Caulfield M, Kjeldsen SE, Kristinsson A, McInnes GT, Mehlsen J, Nieminen M, O'Brien E, Ostergren J, Pocock S. ASCOT Investigators. (2005) Role of blood pressure and other variables in the differential cardiovascular event rates noted in the Anglo-Scandinavian Cardiac Outcomes Trial-Blood Pressure Lowering Arm (ASCOT-BPLA). Lancet 366:907–913.[CrossRef][Web of Science][Medline]
  46. Taylor AJ, Sullenberger LE, Lee HJ, Lee JK, Grace KA. (2004) Arterial Biology for the Investigation of the Treatment Effects of Reducing Cholesterol (ARBITER) 2: a double-blind, placebo-controlled study of extended-release niacin on atherosclerosis progression in secondary prevention patients treated with statins. Circulation 110:3512–3517.[Abstract/Free Full Text]
  47. Taylor AJ, Sullenberger LE, Lee HJ. (2005) ARBITER 3: atherosclerosis regression during open-label continuation of extended-release niacin following ARBITER 2. (Abstract 943). Circulation 112:.

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J.-C. Fruchart, F. M Sacks, M. P Hermans, G. Assmann, W V. Brown, R. Ceska, M J. Chapman, P. M Dodson, P. Fioretto, H. N Ginsberg, et al.
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