C L I N I C A L Q U E S T I O N

What is the role of alternative biomarkers for coronary heartdisease?

Abhimanyu Garg

Division of Nutrition and Metabolic Diseases, Department of Internal Medicine, Center for Human Nutrition, UT Southwestern Medical

Center, Dallas, TX, USA

Summary

Predictive models for future risk of coronary heart disease (CHD)

based on traditional risk factors, such as age, male gender, LDL

cholesterol, HDL cholesterol, diabetes mellitus, hypertension,

smoking and family history of premature CHD, are quite robust

but leave room for further improvement. Thus, efforts are being

made to assess additional biomarkers for CHD, such as, lipoprotein

(a), C-reactive protein, fibrinogen, lipoprotein-associated phos-

pholipase A2, homocysteine and others. However, none of the

novel biomarkers has demonstrated improved prediction beyond

traditional risk factor models in a consistent fashion across multi-

ple cohorts. Many criteria have to be fulfilled before a biomarker

can be considered clinically relevant. Another way is to develop

new models predicting long-term or life-time risk of CHD. Further

research using novel biomarkers and long-term predictive models

has the potential to improve CHD risk prediction.

(Received 20 January 2011; returned for revision 7 February 2011;

finally revised 11 March 2011; accepted 11 March 2011)

Significance of the clinical problem

During the past decades, various observational and long-term pro-

spective epidemiologic studies have contributed immensely to our

understanding of risk factors for coronary heart disease (CHD).1–3

Clearly, CHD is a multifactorial disease with both genetic factors

and lifestyle choices recognized to contribute. Based on the data

from the Framingham Study,1 which have been confirmed by other

studies, several risk factors have become well-established such as

age, male gender, high levels of low density lipoprotein (LDL) cho-

lesterol or nonhigh density lipoprotein (non-HDL) cholesterol,

low levels of HDL cholesterol, smoking, hypertension, diabetes

mellitus and family history of premature CHD (men <55 years and

women <65 years).2,3 The risk factors are used to calculate an indi-

vidual’s 10-year future risk of CHD (Framingham Risk Score)

(<10%, low risk; 10–20%, intermediate risk; and >20%, high

risk).4,5 The National Cholesterol Education Programme’s Adult

Treatment Panel III recommends targets for lowering of LDL or

non-HDL cholesterol based on the calculated future risk of CHD.

Aggressive measures and lower target values for LDL or non-HDL

cholesterol are suggested for those with CHD, CHD risk equiva-

lents or those at high risk while less aggressive measures and higher

target values are suggested for those at intermediate or low risk of

CHD.4,5 However, the traditional risk factors used in calculating

Framingham risk score account for most of the risk for CHD but

do not explain all the excess risk.

Barriers to optimal practice

Ideally, we should be able to fully predict the risk of CHD and

therefore current methodology for CHD risk prediction needs to

be improved. Many investigators are trying to improve the predict-

ability of the Framingham risk score by calculating longer term risk

(20-year, 30-year or lifetime risk of CHD).6 Others are trying to

find additional serum markers to increase predictability. These

efforts are justified for the following reasons:

• Improve CHD risk prediction to nearly 100%.

• Approximately 10–20% of patients with CHD do not have any

identifiable traditional risk factor.

• About 35% of patients with CHD have total cholesterol

<200 mg/dl.

• Improvement in risk classification, i.e., classify individuals with

low risk, intermediate risk or high risk more accurately to other

categories.

Many new CHD biomarkers have been identified which repre-

sent various stages of atherosclerotic lesions, i.e., atherosclerotic

plaque, unstable plaque, plaque rupture, thrombosis, ischaemia,

necrosis and heart remodelling (Table 1).7 Some of these novel

CHD biomarkers have been extensively evaluated in epidemiologic

studies. These include lipoprotein (Lp)-related biomarkers such as

apolipoprotein (apo) B, apo A1, Lp(a), serum or Lp-associated

phospholipase A2 (PLA2), small dense LDL and triglycerides;

inflammatory biomarkers such as C-reactive protein (CRP), inter-

leukin (IL)-18, IL-6 and IL-10 and tumour-necrosis factor-a;

thrombosis-related biomarkers such as fibrinogen, D-dimer,

Correspondence: Abhimanyu Garg, 5323 Harry Hines Blvd., Dallas,

TX 75390-8537. Tel.: +1(214)648-2895; Fax: +1(214)648-0553;

E-mail: abhimanyu.garg@utsouthwestern.edu

Clinical Endocrinology (2011) 75, 289–293 doi: 10.1111/j.1365-2265.2011.04045.x

� 2011 Blackwell Publishing Ltd 289

plasminogen activator inhibitor (PAI-1), von Willebrand factor

(vWF) and homocysteine; and others.7,8 A detailed review of the

methodology for measurement of various biomarkers, their link to

CHD prospectively, whether they are additive to the Framingham

risk score and whether they track with disease treatment was pub-

lished by Vasan.7 Other detailed reviews have also been published

recently.9–11

Recent large-scale genome-wide association studies (GWAS)

provide another tool for the identification of novel loci for CHD.

So far, these studies have identified five loci which are known to

be associated with lipid metabolism (CELSR2-PSRC1-SORT1;

LDLR; PCSK9; HNF1A; LPA) and eight others with unknown

functions (CDKN2A-CDKN2B-ANRIL; SLC5A3-MRPS6-KSNE2;

MIA3; CXCL12; PHACTR1; WDR12; SH2B3; MRAS). Besides

these and the loci for other Mendelian lipid disorders, MEF2A,

LRP6, ALOX5AP and LTA4H have been linked with risk of myo-

cardial infarction in family studies; however, these associations

have not been replicated in GWA studies.12 Interestingly, these

studies did not identify any locus associated with inflammatory or

thrombotic markers.12 As we learn the biological role of these

unknown loci in contributing to CHD, we may discover many

other biomarkers.

All novel biomarkers have to undergo rigorous scrutiny

before they can be deemed clinically relevant. The criteria for

evaluating incremental yield of a new biomarker include the fol-

lowing:

• Optimal screening characteristics using detection rate 5%

(DR5).13 The detection rate (sensitivity) is defined as the propor-

tion of affected individuals with positive results when false posi-

tive rate is 5%.

• Improve discrimination by changing C-statistic (area under the

receiver operated curve) (Fig. 1).

• Improve calibration by refining estimated risk.

• Compare deciles of observed and predicted risks.

• Improve risk classification.

The biomarkers should finally result in changing the manage-

ment of individuals and should be cost effective as defined by low

number needed to screen to prevent one CHD event.14 Some inves-

tigators consider improvement in C-statistic to be too stringent

and have recommended ‘proportion reclassified’ and ‘new reclassi-

fication improvement (NRI)’ as measures of usefulness of novel

biomarkers.15

With this background, I will discuss the predictive role of a few

novel biomarkers including fibrinogen, Lp(a), Lp-PLA2 and CRP.

Table 1. Alternative biomarkers for coronary heart disease

Lipoprotein-associated

Apolipoprotein B

Lipoprotein (a)

LDL particle size

LDL particle number

Triglycerides

Cholesterol ester transfer protein level

Lipoprotein-associated phospholipase A2

Small-dense LDL

Oxidized LDL

Apolipoprotein A1

Paraxonase-1

Inflammatory

C-reactive protein

Interleukin-18

Interleukin-6

Interleukin-10

Tumour necrosis factor-aSerum amyloid A

Serum inter-cellular adhesion molecule-1

Myeloperoxidase

sCD40

Vascular cell adhesion molecule

Interleukin-1 receptor antagonist

Thrombosis-related

Fibrinogen

Fibrin D-dimer

Plasminogen activator inhibitor-1

von Willebrand factor

Soluble CD40 ligand

Homocysteine

Factor VIII

Others

Matrix metalloproteases-9

Tissue inhibitor of metalloproteinases-1

Natriuretic peptides

Insulin

E-selectin

Adiponectin

Leptin

Ferritin

Fasting glucose

Haemoglobin A1c

Urinary albumin

Carotid intima media thickness

Ankle-brachial flow index

Coronary artery calcium score

Fig. 1 The area under the curve (AUC) or receiver-operating characteristic

(ROC) curve for a hypothetical model with and without the biomarker.

Without the biomarker, the AUC is 0Æ6 (solid line) and with the biomarker

(interrupted line), the AUC is 0Æ7. If the difference is statistically significant,

the biomarker may be clinically relevant in increasing the predictability of

the model.

290 A. Garg

� 2011 Blackwell Publishing Ltd, Clinical Endocrinology, 75, 289–293

Fibrinogen

Fibrinogen was first isolated in 1876 and is the most abundant clot-

ting protein in circulation. It is a 300-kDa glycoprotein synthesized

in the liver and is a precursor of fibrin. It can bind to Gp 11B/111a

surface proteins on platelets. Serum fibrinogen levels also increase

during periods of inflammation.

Lp(a)

Lp(a) consists of an LDL particle with an apo(a) covalently linked

by a disulphide bond to apoB-100.16,17 It is synthesized and

secreted by the liver. Apo(a) is a plasminogen-like protein with a

number of plasminogen-like kringles and has an inactive protease

domain. Lp(a) can promote thrombosis, inflammation and foam

cell formation.

Lp-PLA2

Lp-PLA2 is also known as platelet activating factor acetylhydrolase.

This enzyme is expressed by hematopoietic cells and is bound to

LDL. It can hydrolyse oxidized phospholipids by cleaving the ester

bond at the sn-2 position resulting in the formation of lysophos-

pholipids.18 Lp-PLA2 may have a role in plaque destabilization. A

recent meta-analysis of a large number of participants

(n = 79 036) from 32 prospective studies with 17 722 incident

events reported a significant increase in relative risk of CHD, stroke

and mortality with Lp-PLA2 activity [1Æ11 (1Æ07–1Æ15)] per 1 stan-

dard deviation (SD) increase. The same meta-analysis interestingly

also reported the relative risk of CHD for some traditional risk fac-

tors, such as, systolic blood pressure [1Æ10 (1Æ10–1Æ22)], smoking

status [1Æ34 (1Æ19–1Æ15)], non-HDL cholesterol [1Æ10 (1Æ02–1Æ18)]

and HDL cholesterol [1Æ15 (1Æ05–1Æ25)].19

CRP

It is a nonglycosylated 224-residue plasma protein produced by the

liver in response to inflammatory stimuli and cytokines such as

IL-6. It is a sensitive but nonspecific marker of inflammation. At

high concentrations, it affects immune responses via complement

activation. Original CRP assays were able to distinguish markedly

high levels in inflammatory diseases (>1000 fold above normal).

Recently, highly sensitive CRP assays have made possible to dis-

criminate much smaller changes and increased levels have been

shown to be associated with CHD risk. However, CRP provides

little improvement of CHD risk prediction when assessed using

several metrics of predictive value.14 A recent meta-analysis by US

Preventive Services Task Force 20 reported a risk ratio for CHD

associated with CRP level >3Æ0 vs <1Æ0 mg/l to be 1Æ60 (1Æ43–1Æ78).

When comparing CRP levels between 1Æ0 and 3Æ0 vs <1Æ0 mg/l, the

risk ratio was 1Æ26 (1Æ17–1Æ35). Moderate, consistent evidence sug-

gests that adding CRP among intermediate risk persons improves

risk stratification. However, sufficient evidence that reducing CRP

levels prevents CHD events is lacking. 20

Recently, some investigators suggested that the results of the

JUPITER Trial, a randomized trial of rosuvastatin in the preven-

tion of cardiovascular events among 17 802 apparently healthy

men and women with LDL cholesterol <3Æ4 mmol/l and CRP

>2 mg/l, support the use of CRP as a biomarker.21 After a median

follow-up for 1Æ9 years, those on rosuvastatin had reduced hazard

ratio for cardiovascular events 0Æ53 (0Æ40–0Æ69). However, the

JUPITER trial did not address whether CRP screening identifies

patients who benefit from statin. The trial had no ‘control group’

in whom CRP levels were not tested. Further, they did not random-

ize subjects with CRP levels <2 mg/l. Nearly 80% of the subjects

screened did not meet the inclusion criteria for the trial. This raises

an issue about the ‘number needed to screen’ with CRP to prevent

one CHD event.22 Furthermore, in secondary analysis, those with

CRP >4Æ2 mg/dl (median value) benefited less than these with

lower values. Also, no benefit of statin therapy was seen in those

without any conventional CHD risk factors but who met age and

CRP criteria for enrolment.22

Another recent method to evaluate causal relationship between a

risk factor and CHD is to assess its validity through Mendelian ran-

domization studies. These studies, for example, report the associa-

tion of PCSK9 nonsense variants, which reduce LDL cholesterol,

with low risk of CHD.23 Polymorphisms at LPA locus which

increase Lp(a) concentrations are associated with increased risk of

CHD. 12 However, +1444C>T variant in CRP increases serum CRP

levels by 1Æ14 (1Æ11–1Æ18) per T allele, but the CHD risk was 0Æ96

(0Æ90–1Æ03). This study included a total of 18 637 subjects with

4 610 having CHD.24 In another landmark study, several CRP

variants combined (1081G>A, 223C>T, )390C>T>A, 3678T>G)

in �50 000 subjects of whom 6545 had CHD could increase CRP

concentrations by 64% but these variants were not associated with

an increase in CHD risk.25 These findings do not support causal

association of CRP with CHD.24,25

Vasan14 evaluated the predictive utility and clinical usefulness of

CRP as a risk factor for CHD. While there was a positive associa-

tion with CHD, it was judged to improve calibration and risk

reclassification, and its effects on C-statistic and change in manage-

ment were not clear. CRP did not improve performance (DR5).

There were no data related to numbers needed to screen and cost-

effectiveness of CRP as a CHD risk marker. Thus, overall it did not

achieve all the criteria required for an ideal biomarker.

Multimarker risk prediction

Only a few epidemiological studies have evaluated multimarker

prediction of CHD risk. In the Women’s Health Initiative study, a

case–control study compared a multitude of 18 biomarkers among

321 cases of CHD with 743 control subjects.26 Of these, only IL-6

(P = 0Æ0027), D-dimer (P = 0Æ004), factor VIII (P = 0Æ0016), vWF

(P = 0Æ0001) and homocysteine (P = 0Æ0122) showed significant

association with CHD over and above the prediction by the tradi-

tional risk factor model 26. However, only D-dimer marginally

improved C-statistic (P = 0Æ0423). Notably, CRP, fibrinogen and

Lp(a) showed no association with CHD.26

Recently, Blankenberg et al.27 reported predictive value of 30

biomarkers in two prospective epidemiologic studies with 10-year

follow-up. In the FINRISK97 cohort, there were 3870 men and

4045 women aged 25–74 years and in the BELFAST Prime Men

Role of alternative biomarkers for CHD 291

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cohort, they followed 2551 men aged 50–59 years of age. After

adjusting for traditional risk factors, only N-terminal pro BNP and

CRP showed significantly increased hazard ratio per 1-standard

deviation increment. As far as discrimination criteria are con-

cerned, CRP did not significantly improve C-index when added to

the model in two cohorts (FINRISK 97 men and women) and only

improved the C-index marginally (P = 0Æ044) in PRIME men from

Belfast. Many other biomarkers such as homocysteine, PLA2 activ-

ity and mass did not show significantly increased hazard ratio.27

Long-term CHD risk prediction

Pencina et al.6 recently reported long-term (30 years) risk of CHD

in Framingham study in 2333 women and 2173 men aged

36Æ3 ± 9Æ3 and 37Æ3 ± 9Æ2 years, respectively. In comparison with

10-year risk prediction, a 30-year risk prediction revealed more

than 3-fold increase in estimated per cent risk of hard cardiovascu-

lar disease among 25-year-old men and women and for 45-year-old

women but not for 45-year-old men. The Framingham risk score is

usually criticized for categorizing many women as having low risk

of CHD. It seems that 20-years, 30-years or lifetime risk prediction

algorithms may be more accurate for women.

Conclusions

In conclusion, traditional risk factors are good at predicting CHD

risk and it is difficult to improve prediction by other nontraditional

risk markers beyond traditional models. These conclusions are sup-

ported by other recent publications.9–11 So far, many of the nontra-

ditional risk factors have not been validated in multiple cohorts.

This may be attributed to collinearity between biomarkers and tra-

ditional risk factors, such as, smoking, dyslipidaemias, diabetes and

obesity. Furthermore, the predictive value of biomarkers may not

be the same in different populations. New models predicting long-

term CHD risk may be more accurate. There is also a need to

develop accurate risk prediction models for ethnically diverse pop-

ulations, such as, Africans, Asians, and Hispanics; for children; and

for high-risk populations with monogenic dyslipidaemias and type

2 diabetes mellitus.

Note added in proof:

Three new genome-wide association studies in European, South

Asian and Chinese Han populations have identified 18 additional

susceptibility loci for CHD, some of which are associated with tra-

ditional risk factors such as lipoproteins and hypertension.28–30

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