Where to now in cardiovascular disease prevention

Osman Najam, MBChB, MRes, MRCP1, Kausik K Ray, BSc (hons), MBChB,

MD, MPhil (Cantab), FRCP (lon), FRCP (ed), FACC, FESC, FAHA2

1 academic clinical fellow in cardiovascular medicine 2 professor of cardiovascular disease prevention

Correspondence k.ray@imperial.ac.uk

1, Cardiovascular Sciences Research Centre, St George’s University, Cranmer terrace, London SW17 0RE, United Kingdom 2,

Department of Primary Care and Public Health, The Reynolds Building, Imperial College London, St Dunstan’s Road, London W6

8RP, United Kingdom

Professor Ray has received honoraria (modest) for consulting and is a member of the speaker’s bureaus and advisory

committees from Pfizer Inc., AstraZeneca Pharmaceuticals LP, Merck Sharp & Dohme, Abbott Laboratories, Roche Therapeutics

Inc., Regeneron Pharmaceuticals, Inc., Aegerion Pharmaceuticals, Inc., The Sanofi-Aventis Group, Novartis Pharmaceuticals

Corporation, Novo Nordisk Inc., Eli Lilly and Co., Daiichi-Sankyo, Inc., Amgen Inc., and Bristol-Myers Squibb Co.

1

Abstract

Clinical trials have been instrumental in reducing the morbidity and mortality

associated with cardiovascular disease, especially in the developed world. Recently

however this improvement has plateaued, highlighting the importance of optimising

current strategies and considering alternative practises. Inequalities in global

healthcare, the changing patient profile as a result of an obesity and diabetes

epidemic, and inadequate utilisation of evidence-based treatments are partly

responsible. Despite pharmacotherapies such as statins having substantial evidence

for cardiovascular benefit, patient response may be variable with genetic factors

thought to be partly responsible. Although randomised controlled trials remain the

backbone of clinical research, they have limitations including time taken to complete

a trial and the financial costs associated with it. In this opinion-based paper, we

discuss some of the key considerations for the future of cardiovascular disease

prevention.

Keywords

Clinical trials; population; cardiometabolic risk; registry-based randomised trials;

pharmacogenomics; lipoprotein(a); triglyceride-rich lipoprotein cholesterol

2

Addressing the inequalities in global healthcare

Despite advances in medical therapy, cardiovascular disease (CVD) remains the

leading cause of death worldwide, with an estimated annual mortality of 18 million.1

Significant region specific disparities however exist. Whilst CVD is the leading cause

of death in high income countries, it is fourth and fifth in low income countries.1

With escalating rates of obesity and diabetes globally, CVD is set to remain as the

leading cause of death well into the latter half of this century. The growing

healthcare burden of CVD continues to provide a significant challenge to healthcare

systems worldwide. It has been estimated that no data on the precise cause of death

is available in 89.8% of the population in sub-Saharan Africa, 48.1% in the Middle

East and 24.2% in South Asia.2,3 To date, most of the large-scale CVD prevention trials

have been performed in the developed world, which form the basis of our

guidelines. Therefore it surprising that 80% of all CV related deaths actually occur in

low and middle income regions, and it is this patient population that has not been

studied comprehensively.2,4 The healthcare systems of developing countries already

face a battle with excessive burden from communicable diseases and, maternal and

child mortality. The rise in CVD has further led to a diversion of these scarce

resources. Although projections of CVD burden in developing countries are worrying,

there remains a great paucity of data about CVD and its risk factors from many of

these regions.3 Early identification of at-risk groups is therefore crucial, with risk

prediction models potentially contributing to this decision making process. Despite

the large number of algorithms developed, only a minority are eventually used in

clinical practise.3 Current models are based on data obtained from populations with

differing economic and health burdens, and whether these models should be used

universally is debatable. For these models to be of most clinical use, they should

instead be developed from populations with similar risk profile or calibrated to the

target population to help local practises. Future trials need to be conducted

exploring patient populations from low and middle-income countries, where 10

million more CVD related deaths occur.2 Availability of many interventions may be

limited by financial restrictions or complexity, and in this context CV research in

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areas of primary prevention, clinical guidelines, health services and epidemiology is

needed.

Improving uptake of existing therapies

Patients with established CVD remain at risk of recurrent events.5,6 Benefit of many

secondary prevention medications in reducing CV mortality, re-infarction or stroke is

already well established. These evidence based preventative therapies although

effective, are vastly underused globally. The WHO-PREMISE study was a cross-

sectional survey of 10,000 CVD patients in three low-income and seven middle-

income countries assessing secondary prevention of CVD.7 In patients with coronary

artery disease (CAD), 18.8% were noted not to receive aspirin, 51.9% did not receive

beta-blockers, 60.2% did not receive ACE inhibitors and 79.2% did not receive

statins. Quite alarmingly, 10% of patients with CAD were not receiving any

medications. This is in comparison to European surveys that have noted better

uptake. EUROASPIRE I-IV are cross sectional surveys which began in 1995-1996 to

track lifestyle, risk factor control and cardioprotective drug use in Europe.8 Although

trends across the surveys have noted increased use of preventative medications,

there has been a worsening of lifestyle with increased obesity and high prevalence of

smoking, especially in young patients.8,9 Despite increased use of anti-hypertensive

and lipid-lowering drugs, target levels were still not being achieved. Reasons include

inadequate dosing of the drug, failure to use a combination of drug therapies and

poor adherence, with large variation amongst countries.8

The larger Prospective Urban Rural Epidemiological (PURE) study was established to

investigate associations between social, behavioural, genetic and environmental

factors, and CVD in 17 countries.10 Individuals with CVD from communities in

countries at various stages of economic development were recruited to assess the

use of proven effective secondary preventative drugs.10 Vast differences in uptake

were noted with less than 20% of patients in low-income countries using any blood

pressure lowering agent, compared to 75% in high-income regions.10 Of additional

concern was that only 3% were on statin therapy, with a 20-fold difference between

low-income and high-income countries despite the well-established benefits and low

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costs.11 Even the use of aspirin varied by seven-fold. 80% of those at risk in these

developing regions did not receive any treatment at all, compared to 11% in

developed countries. These wide-ranging inequalities in healthcare underline the

urgency of improving the availability and uptake of these inexpensive treatments.

Although the reason for the poor uptake of these evidence-based therapies is

unclear, it may include unaffordability of the drug or of visiting a practitioner, limited

drug availability in low and middle-income countries, transportation difficulties,

absence of a national healthcare preventative programme and lack of awareness of

the importance of lifelong therapy.10,12–14 Prospective multi-national studies are

needed to explore this with qualitative research and surveys, and assessment of

existing national and community databases. Many of the evidence-based therapies,

such as statins are affordable and readily available. An increase in global exposure to

those with unmet needs would have a greater absolute risk reduction in CVD than

any new treatment, which may be expensive and available to a select few.

Absolute risk is what really counts

Whilst the projected increase in CVD prevalence is alarming, it need not become a

reality.15 CVD is preventable. Absolute CVD risk is the probability of a CV event

occurring within a defined time period.16 It is dependent on a combination of

modifiable risk factors such as smoking, blood pressure, lipid levels, and non-

modifiable risk factors such as age, gender and family history.16 The cumulative

effects of these are synergistic and more accurate in predicting absolute risk than

any individual risk factor alone.17 For trial results to be of most clinical use, the level

of global risk needs to be taken into account. Five and ten year risk estimates have

been adopted in recent guidelines.18,19 It would be reasonable to expect a CVD

prevention strategy based on estimated absolute lifetime risk (including the total

number of events and not just the first event) to be a more effective and efficient

use of resources, rather than the traditional method of identifying and managing

individual risk factors. Despite the prognostic value of using cardiometabolic risk

prediction, it is yet to be widely utilised in trials for patient selection. Trial population

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selection is usually rigid and often does not directly translate into a real-life clinical

setting. With escalating cardiometabolic risk in low to middle income countries, this

is clearly relevant. Absolute risk may vary by more than 20-fold in patients with the

same blood pressure or cholesterol levels.20,21 Post hoc analysis of the Treating to

New Targets (TNT) study demonstrated increased CV risk with features of metabolic

syndrome. Patients with one to two features of metabolic syndrome receiving a

potent dose of atorvastatin had a residual CV risk of 5-18%, whilst those with all five

features had a residual risk of 32%.22 This emphasises the limitations of categorising

patients as simply having hypertension or hypercholesterolemia. Therefore an

individual with low cholesterol or blood pressure may in fact have higher absolute

CV risk than someone with a high level of one of these risk factors. In clinical

practise, accurate risk assessment is vital to effective clinical management. Whilst

the Framingham Risk Score is one of the most validated and widely used predictive

scores, other multivariable risk models have also been developed in an attempt to

improve prediction of major clinic outcomes.23

Whilst scores such as ASSIGN, Reynolds Risk Score, PROCAM Risk Score and QRISK all

have advantages and disadvantages, not one model can be used universally and this

is reflected in different recommendations across nations.24–26 Analysis of relative

prognostic performance has shown inconsistencies across studies with potential

biases and methodological variation making direct comparisons difficult.24 Although

randomised controlled trials comparing different predictive models would be ideal in

assessing their clinical effectiveness, in reality this may prove difficult due to costs

and complexity. Whilst comparing models, future studies should attempt to

standardise performance measures such as discrimination and calibration and

perform consistent statistical analysis. Head to head comparisons are needed to

further our understanding. The performance of a risk prediction model is dependent

on its population characteristics and although ideally developed locally, they should

at least be validated in their local populations.17 With improvements in information

technologies and medical record keeping, it may be possible to obtain data on risk

factor and disease event to allow development of scores appropriate for a specific

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population.17,27 With our increased understanding of CVD, physicians should move

away from treating individual risk factors and instead focus on absolute risk.

Utilisation of cardiac imaging

Advances in imaging have provided a range of diagnostic tools to identify high-risk

atherosclerotic coronary plaques. When combined with conventional screening

measures such as lipid and non-lipid variables, CVD risk assessment algorithms and

clinical judgement, the likelihood of identifying individuals deemed to be at higher

risk of future clinical events is increased.28 In trials however, use of traditional and

emerging atherosclerosis imaging platforms to select patients is largely limited. This

is partly due to the complexities of recruiting large number of patients and having

appropriate resources to allow these imaging facilities to be available. Despite the

many non-invasive methods used to identify patients with high risk of CVD, none of

these allow the direct identification or visualisation of the culprit vulnerable

atherosclerotic lesion, provide detail on its composition or molecular activity.

Carotid intima media thickness (CIMT) is a marker of atherosclerosis associated with

CAD. METEOR, a randomised, double-blinded, placebo-controlled trial of individuals

with elevated LDL cholesterol assessed whether statin therapy slowed the

progression of CIMT, as assessed by ultrasound.29 In middle-aged adults with low

Framingham risk score, therapy was associated with reduction in the rate of

progression of maximum CIMT when compared to placebo. Despite this the clinical

utility of CIMT is uncertain. In the Framingham Offspring Study, CIMT measurement

was noted to improve CVD risk prediction.30 A meta-analysis of 14 studies however

concluded that although addition of CIMT measurements to a risk score were

associated with a small improvement in 10-year CV risk prediction, this was not of

clinical significance.31 Coronary artery calcium score (CACS) is thought to be a better

predictor of future CV events.32,33 The MESA Study found CACS to be a strong

predictor of CAD in with a score of greater than 300 associated with a 10-fold

increased event risk.32 Its addition to a risk prediction model was seen to correctly

reclassify patients - 23% into high risk, and a further 13% into low risk groups.34 In a

comparison of risk markers of CVD prediction, CAC has provided superior

7

discrimination and risk reclassification than CIMT, high-sensitivity CRP, ankle brachial

index or family history.35 Similar findings have been noted elsewhere with several

studies evaluating CHD risk markers side by side, and CACS providing the most

additive predictive value.36–38

Although there are undeniable barriers to their widespread use in trials, there is a

need to develop existing and emerging atherosclerosis imaging platforms to allow a

more direct visualisation of the vulnerable plaque, which in combination with

existing predictive techniques would allow a more accurate assessment of a patient’s

risk or response to treatment.

Considering the population likely to derive the most benefit

In drug development, the first trials should reflect principally the populations that

are likely to derive the most benefit. Failure to follow this principle may explain why

some clinical trials fail to demonstrate clinical benefit. The efficacy and safety of a

therapy in a trial or routine clinical practise is dependent on appropriate patient

selection. The path from initial clinical testing to regulatory approval is long and

costly, averaging 8 years.39 Only 1 out of 10 therapies that enter development in

phase 1 is expected to advance to eventual regulatory approval.40 CVD trials have the

lowest success rates for drug development amongst trials of all indications.40 In an

analysis of failures of clinical trials submitted for the Food and Drug Administration

(FDA) approval between 2000 and 2012, 10% were deemed to have done so because

the populations that were studied did not reflect the populations likely to use the

drug.41

The Heart Protection Study 2 – Treatment of HDL to Reduce the Incidence of

Vascular Events (HPS2-THIRVE) study failed to demonstrate an outcome benefit for

the addition of extended release niacin-laropiprant in patients with vascular

disease.42 It also reported a significant increase in the risk of serious adverse effects.

Emerging evidence has indicated that Chinese patients have increased sensitivities to

niacin and high dose simvastatin, and in the HPS2-THRIVE study where 43% of the

8

population were of Chinese origin, there was a 10-fold higher incidence of myopathy

than European patients (0.53%/year vs 0.03%/year).42,43 Additionally those patients

who had the highest baseline LDL-C, had strong trend towards risk reduction

(borderline significance), but were underpowered in subgroups. Despite the failure

to demonstrate outcome benefit, results of the trial are important, as it has allowed

the identification of a population likely to experience adverse effects, and allowed

more appropriate selection of patients for future trials such as those with higher

baseline LDL-C levels.

Whilst randomised controlled trials (RCT) provide one of the most rigorous methods

to determine whether a cause-effect relationship exists between a therapy and

outcome, the vast costs, complexity and time required may prove prohibitive and

may risk stifling development. Over the past three decades, multiple large high-

quality registries have been established worldwide successfully collecting vast

amounts of data, and provide valuable information complementary to prospective

RCTs. Results from observational studies have limited value due to the absence of

randomisation, with selection bias and confounding factors (adjusted and

unadjusted).44 Recently, registry-based randomised trials (RRCT), a new paradigm has

begun to emerge which may potentially reduce costs considerably, allow the

identification and enrolment of large number of patients quickly and provide

accurate follow-up.44

The TASTE trial investigators utilised the RRCT concept by designing a multicentre,

prospective, randomised, controlled, open-label clinical trial to assess the mortality

benefit of routine intracoronary thrombus aspiration before primary percutaneous

coronary intervention (PCI) in patients with ST-segment elevation myocardial

infarction.45 Patients from the SWEDEHEART registry, a national registry of 29

Swedish and 1 Icelandic coronary intervention centre of patients admitted with

symptoms suggestive of an acute coronary syndrome and those undergoing

coronary intervention (medical or surgical) were enrolled.46 As with a RCT, patients

with pre-determined selection criteria were included from the registry and randomly

assigned treatment in a 1:1 ratio, to thrombus aspiration followed by PCI or to PCI

9

only. Other aspects of RCT such as ethics approval were also obeyed. Data on the

primary outcome (all-cause mortality) was obtained from the registry with 80%

powering to detect a statistical difference. Results noted no significant difference in

the primary endpoint at 30 days or 1 year. The methodology utilised in the TASTE

trial is novel with a large-scale trial conducted in little time with >90% cost saving

compared to similar RCTs.44 Concerns with RRCT however include availability of high

quality data, lack of adequate blinding and how to assure a representative

population. The TASTE trial was performed in Scandinavia, where the well-developed

healthcare and information technology systems with good links to all the major

hospitals have allowed collection of high quality data. It remains to be seen whether

these trials can be undertaken in countries where clinical data collection may be

fragmented and not of high quality.47 Other questions that remain to be answered

include how to ensure privacy and informed consent are appropriate, would blinding

be possible and would researchers be able to obtain long-term follow-up or measure

composite outcomes.47 In addition, it is yet to be established how researchers will

prevent unwanted selection bias in situations where a registry may only be able to

enrol a fraction of the population. With increasing difficulties in conducting well-

designed RCTs, RRCT provides a complementary approach in CVD prevention

research. It may act as a powerful and highly cost-effective research tool, which may

be able to explore specific areas when resources are limited.

Pharmacogenomics to identify those likely to derive the most benefit

Despite medical advances, burden from first time and recurrent CV events remains

inappropriately high. Inter-patient variability in CVD drug responsiveness is

increasingly being recognised as key, with heritable genetic polymorphisms

understood to play an important role. Successful identification of genetic

polymorphism would allow drug and dose adaptations in patients with aberrant

metabolism.48 Despite the well-established benefits of statin therapy, many patients

fail to achieve adequate cholesterol reduction. More than 40 genes have been

identified that may be responsible for the variation in response to statin therapy.48

Cholesteryl ester transfer protein (CETP) is a plasma protein that reduces HDL-C

10

levels by transferring the cholesterol ester from HDL to other lipoproteins in

exchange for triglycerides. Patients with B1B1 genotype of the CETP gene (CETP

Taq1B) for instance have been noted to derive greater benefit from statin treatment

than those with B2B2 genotype.49 This is despite untreated B2B2 patients having

lower CETP levels, higher HDL-C levels and lower risk of coronary artery disease.50

Although the causal relationship between LDL-C and CVD is established, the role of

HDL-C is often debated. An inverse association between HDL-C levels and

cardiovascular events has been shown previously.51–54 It has been difficult to prove

however that elevating HDL levels translates to CV risk reduction, with trials

involving HDL based therapies yielding disappointing results.55,56 An increase in

mortality and morbidity were noted with torcetrapib, the first potent CETP inhibitor

despite an increase in HDL-C level by almost 70%.56 Elevation in circulating

neurohormones was however noted in some patients, and may partly have

contributed to outcomes. Dal-OUTCOMES, a phase 3 trial involving dalcetrapib,

another CETP inhibitor (with no neurohormonal effect) failed to demonstrate a

benefit of reduction in CV events in patients who had a recent acute coronary

syndrome despite an increase in HDL-C levels.57 With prior epidemiological, clinical,

animal model and molecular studies suggestive of HDL as a therapeutic target, a

recent study conducted a pharmacogenomic evaluation to test whether response to

dalcetrapib varied based on the genetic profile of the patient.58,59 A genome-wide

approach was used in the dal-OUTCOMES study and in the dal-PLAQUE-2 imaging

trial to test the association of genetic factors with CV events and vascular disease

changes when treated with dalcetrapib. Polymorphisms in the ADCY9 gene were

noted to influence the effect of dalcetrapib therapy on CV outcomes and carotid

atherosclerosis, thereby highlighting that response to therapy may be determined by

a patient’s genetic profile. This potentially offers a way forward in optimising the use

of existing evidence-based treatments and raises the possibility of an individualised

and adaptable treatment regimen with the greatest efficacy and least risk of adverse

effects.

11

Despite our increasing understanding of genetic variation in patients with CVD,

translation of pharmacogenomics to clinical practise is yet to be established. Results

across the studies have been inconsistent with variable study designs, making their

collective interpretation complex. In addition, the effect of a single gene variation

may produce a small effect clinically to warrant widespread use. A recent study has

attempted to elucidate the benefit of formulating and utilising a genetic risk score to

determine if it predicts coronary events and whether the benefit of statin therapy

varied by this score.60 In an analysis of around 50,000 individuals from primary and

secondary prevention trials, increasing genetic score was associated with increased

risk for coronary heart disease. In addition, statin therapy was associated with

increasing relative risk reduction with increasing scores. In primary prevention trials,

there was a threefold decrease in the number needed to treat to prevent one

coronary heart disease event in the high genetic risk category compared to low risk.

These results are important as they demonstrate that potential use of genetic risk

scores may help target those at greatest risk, and therefore likely to derive the most

benefit. Further studies with large patient cohorts are needed before clinicians can

confidently utilise this tool to guide treatment.

Consider other lipid targets

Despite adequate LDL-C lowering, many patients remain at risk of CVD. There is

emerging data to support the use of triglyceride-rich lipoprotein cholesterol or

remnants, and lipoprotein(a) (Lp(a)) as causal targets for intervention especially in

high-risk patients. Elevated triglycerides are thought to be associated with increased

CV risk, with their targeted reduction thought to provide benefit.61 Recent guidance

has supported the use of non-HDL cholesterol (total cholesterol minus HDL-C) as a

treatment target for reducing CVD risk.62 Non-HDL cholesterol represents circulating

atherogenic lipoproteins including LDL-C, very-low-density lipoprotein cholesterol,

intermediate-density lipoprotein cholesterol and Lp(a), with epidemiological studies

finding that it provides a better risk estimation compared to LDL-C alone. Recent

mendelian randomisation analysis provided evidence for triglyceride-rich lipoprotein

cholesterol to be a causal target.63 These may be mediated by apolipoprotein CIII

12

(apoCIII), a key regulator of triglycerides with recent evidence indicating that it may

be a causal factor for CVD, making it a valid target.64 Results from on-going phase 3

studies of anti-sense to apo CIII are keenly awaited.

The Improved Reduction of Outcomes: Vytorin Efficacy International Trial (IMPROVE-

IT) trial enrolled 18,144 patients post acute coronary syndrome. Addition of

ezetimibe to moderate intensity statin therapy was associated with improved

primary outcomes (HR 0.936 CI (0.887, 0.988), p=0.016) and an additional 24%

reduction in LDL-C levels.65 When compared to the Cholesterol Treatment Trialists’

(CTT) Collaborator’s analysis, results from IMPROVE-IT showed similar hazard ratio

for clinical benefit per mmol/L of LDL-C reduction (HR 0.80 vs 0.78).11 Although these

findings may have indirectly supported the LDL-C hypothesis (lower LDL-C results in

reduced CV events), other lipoproteins such as triglycerides (TG) and high-sensitivity

C-reactive protein (CRP) were also lowered. Such findings are interesting as they

raise the possibility of other non-statin therapies exerting similar benefit. Fibrates

have long been used as treatment for hypercholesterolemia. They are thought to

exert their clinical benefit by primarily reducing TG and LDL-C levels and increasing

high-density lipoprotein cholesterol (HDL-C) levels. A meta-analysis in 2010 noted

reduction in risk of major CV and coronary events with fibrates.66 Combination with

statins has been noted to reduce LDL-C levels by 31-46%, TG by 32-50% and increase

HDL-C by 19-34%.67,68 Although The Action to Control Cardiovascular Risk in Diabetes

(ACCORD) Lipid trial failed to demonstrate benefit on primary outcome with fibrate

and statin dual therapy, a subgroup of patients with atherogenic dyslipidemia (high

TG, small dense LDL particles and low HDL-C) were thought to experience some

clinical benefit.69 Providing evidence for the benefit of lipid modification therapy in

patients with atherogenic dyslipidemia may help identify and target a subset of

patients who remain at high CV risk, yet currently are not being adequately treated.

Although low HDL cholesterol levels are known to be an independent predictor for

atherosclerotic CVD, the precise effect of elevating these levels is uncertain. Analysis

has failed to demonstrate that raising HDL-C may translate to reduction in risk of

CVD.70 HDL is a pleiotropic molecule with diverse functions including reverse

13

cholesterol transport, immunomodulation and improvement in endothelial function.

Trials exploring therapies that aim to improve relevant properties of the HDL

molecule are likely to improve our understanding and provide key answers on

whether molecular modification may ultimately prove beneficial. Lp(a) is another

potential target that may also be causally related to premature CVD, independent of

LDL or non-HDL cholesterol levels based on genetic data.71 The cut off of 50mg/dl

where risk increases significantly represents the top quintile meaning one fifth of the

global population have high Lp(a) and increased CV risk thereof. Despite the

emergence of novel biomarkers, their use is yet to be widely adopted. Multiple

agents are currently under development and may offer future therapeutic potential,

including antisense oligonucleotides or inhibitory RNA to new targets like apoCIII and

Lp(a). With non-HDL-C better predicting CV risk than LDL-C alone, future trials of CVD

prevention should focus on these lipid targets in addition to LDL-C. Whether it

should replace LDL-C as the sole target of therapy needs to be addressed in future

work. Whilst therapies may lower a surrogate marker, outcome trials are needed to

ascertain whether this translates to a clinical and prognostic benefit.

Conclusion

With improvements in healthcare provision and advances in pharmacotherapeutic

intervention trials, mortality associated with coronary heart disease has declined in

the developed world. However despite this, CVD remains the leading cause of death

worldwide. With a global obesity and diabetes epidemic, and an increase in

cardiometabolic syndrome incidence, future trials warrant careful planning and

consideration to ensure that the trial population resembles clinical practise.

Although there is no conclusive evidence from trials for targeting non-LDL

lipoproteins, mendelian randomisation studies have demonstrated that targeting

atherogenic triglyceride-rich lipoproteins and their remnants may provide benefit.

The processes that underpin atherosclerosis and ultimately coronary artery disease

may be under complex genetic control, which in turn would be further influenced by

environmental factors. Recent work has outlined the important role of recognising

genetic variation amongst individuals, as this may directly affect response to

14

treatment. Use of genetic risk scores may provide an opportunity to identify and

target those at most risk. Trials that explore gene-gene and gene-environment

interactions may offer further potential.

15

References:

1. The top 10 causes of death [Internet]. May 2014. [cited 2015 Aug 12];Available from: http://www.who.int/mediacentre/factsheets/fs310/en/

2. Mathers CD, Salomon JA, Ezzati M, Begg S, Vander-Hoorn S LA. No Title. In: Global Burden of Disease and Risk Factors. New York,NY: Oxford University Press; 2006.

3. Celermajer DS, Chow CK, Marijon E, Anstey NM, Woo KS. Cardiovascular disease in the developing world: prevalences, patterns, and the potential of early disease detection. J Am Coll Cardiol [Internet] 2012;60(14):1207–16. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22858388

4. Gersh BJ, Sliwa K, Mayosi BM, Yusuf S. Novel therapeutic concepts: the epidemic of cardiovascular disease in the developing world: global implications. Eur Heart J [Internet] 2010;31(6):642–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20176800

5. Mohan KM, Wolfe CDA, Rudd AG, Heuschmann PU, Kolominsky-Rabas PL, Grieve AP. Risk and cumulative risk of stroke recurrence: a systematic review and meta-analysis. Stroke [Internet] 2011;42(5):1489–94. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21454819

6. Fayad P. Secondary prevention measures after a stroke--should they target stroke or heart disease? Nat Clin Pract Neurol [Internet] 2006;2(12):646–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17117163

7. Mendis S, Abegunde D, Yusuf S, et al. WHO study on Prevention of REcurrences of Myocardial Infarction and StrokE (WHO-PREMISE). Bull World Health Organ [Internet] 2005;83(11):820–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16302038

8. Kotseva K, Wood D, De Backer G, et al. Cardiovascular prevention guidelines in daily practice: a comparison of EUROASPIRE I, II, and III surveys in eight European countries. Lancet (London, England) [Internet] 2009;373(9667):929–40. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19286092

9. EUROASPIRE I and II Group; European Action on Secondary Prevention by Intervention to Reduce Events. Clinical reality of coronary prevention guidelines: a comparison of EUROASPIRE I and II in nine countries. EUROASPIRE I and II Group. European Action on Secondary Prevention by Intervention to Reduce Events. Lancet (London, England) [Internet] 2001;357(9261):995–1001. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11293642

10. Yusuf S, Islam S, Chow CK, et al. Use of secondary prevention drugs for cardiovascular disease in the community in high-income, middle-income, and low-income countries (the PURE Study): a prospective epidemiological survey. Lancet (London, England) [Internet] 2011;378(9798):1231–43. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21872920

11. Baigent C, Keech A, Kearney PM, et al. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet (London, England) [Internet] 2005;366(9493):1267–78. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16214597

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12. Cameron A, Roubos I, Ewen M, Mantel-Teeuwisse AK, Leufkens HGM, Laing RO. Differences in the availability of medicines for chronic and acute conditions in the public and private sectors of developing countries. Bull World Health Organ [Internet] 2011;89(6):412–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21673857

13. van Mourik MSM, Cameron A, Ewen M, Laing RO. Availability, price and affordability of cardiovascular medicines: a comparison across 36 countries using WHO/HAI data. BMC Cardiovasc Disord [Internet] 2010;10:25. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20534118

14. Niëns LM, Cameron A, Van de Poel E, Ewen M, Brouwer WBF, Laing R. Quantifying the impoverishing effects of purchasing medicines: a cross-country comparison of the affordability of medicines in the developing world. PLoS Med [Internet] 2010;7(8). Available from: http://www.ncbi.nlm.nih.gov/pubmed/20824175

15. Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the Future of Cardiovascular Disease in the United States: A Policy Statement From the American Heart Association. Circulation [Internet] 2011;123(8):933–44. Available from: http://circ.ahajournals.org/cgi/doi/10.1161/CIR.0b013e31820a55f5

16. Lloyd-Jones DM. Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation [Internet] 2010;121(15):1768–77. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20404268

17. Jackson R, Lawes CM, Bennett DA, Milne RJ, Rodgers A. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual’s absolute cardiovascular risk. Lancet [Internet] 2005;365(9457):434–41. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0140673605178337

18. NICE. Lipid Modification: Cardiovascular Risk Assessment and the Modification of Blood Lipids for the Primary and Secondary Prevention of Cardiovascular Disease [Internet]. 2014. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25340243

19. De Backer G, Ambrosioni E, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice. Third Joint Task Force of European and other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of eight societies and by invit. Atherosclerosis [Internet] 2004;173(2):381–91. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15195638

20. Jackson R, Lawes CMM, Bennett DA, Milne RJ, Rodgers A. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual’s absolute cardiovascular risk. Lancet (London, England) [Internet] 365(9457):434–41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15680460

21. Neaton JD, Wentworth D. Serum cholesterol, blood pressure, cigarette smoking, and death from coronary heart disease. Overall findings and differences by age for 316,099 white men. Multiple Risk Factor Intervention Trial Research Group. Arch Intern Med [Internet] 1992;152(1):56–64. Available from: http://www.ncbi.nlm.nih.gov/pubmed/1728930

22. Deedwania P, Barter P, Carmena R, et al. Reduction of low-density lipoprotein cholesterol in patients with coronary heart disease and metabolic syndrome: analysis of the Treating to New Targets study. Lancet (London, England) [Internet]

17

2006;368(9539):919–28. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16962881

23. D’Agostino RB, Grundy S, Sullivan LM, Wilson P, CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA [Internet] 2001;286(2):180–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11448281

24. Siontis GCM, Tzoulaki I, Siontis KC, Ioannidis JPA. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ [Internet] 2012;344:e3318. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22628003

25. Graham I, Atar D, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice: executive summary: Fourth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (Constituted by r. Eur Heart J [Internet] 2007;28(19):2375–414. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17726041

26. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ [Internet] 2007;335(7611):136. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17615182

27. Lundin J, Lundin M, Isola J, Joensuu H. A web-based system for individualised survival estimation in breast cancer. BMJ [Internet] 2003;326(7379):29. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12511459

28. Tarkin JM, Dweck MR, Evans NR, et al. Imaging Atherosclerosis. Circ Res [Internet] 2016;118(4):750–69. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26892971

29. Crouse JR, Raichlen JS, Riley WA, et al. Effect of rosuvastatin on progression of carotid intima-media thickness in low-risk individuals with subclinical atherosclerosis: the METEOR Trial. JAMA [Internet] 2007;297(12):1344–53. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17384434

30. Polak JF, Pencina MJ, Pencina KM, O’Donnell CJ, Wolf PA, D’Agostino RB. Carotid-wall intima-media thickness and cardiovascular events. N Engl J Med [Internet] 2011;365(3):213–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21774709

31. Den Ruijter HM, Peters SAE, Anderson TJ, et al. Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. JAMA [Internet] 2012;308(8):796–803. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22910757

32. Detrano R, Guerci AD, Carr JJ, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med [Internet] 2008;358(13):1336–45. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18367736

33. Raggi P, Gongora MC, Gopal A, Callister TQ, Budoff M, Shaw LJ. Coronary artery calcium to predict all-cause mortality in elderly men and women. J Am Coll Cardiol [Internet] 2008;52(1):17–23. Available from:

18

http://www.ncbi.nlm.nih.gov/pubmed/18582630

34. Polonsky TS, McClelland RL, Jorgensen NW, et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA [Internet] 2010;303(16):1610–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20424251

35. Yeboah J, McClelland RL, Polonsky TS, et al. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA [Internet] 2012;308(8):788–95. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22910756

36. Kavousi M, Elias-Smale S, Rutten JHW, et al. Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann Intern Med [Internet] 2012;156(6):438–44. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22431676

37. Peters SAE, den Ruijter HM, Bots ML, Moons KGM. Improvements in risk stratification for the occurrence of cardiovascular disease by imaging subclinical atherosclerosis: a systematic review. Heart [Internet] 2012;98(3):177–84. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22095617

38. Blaha MJ, Budoff MJ, DeFilippis AP, et al. Associations between C-reactive protein, coronary artery calcium, and cardiovascular events: implications for the JUPITER population from MESA, a population-based cohort study. Lancet (London, England) [Internet] 2011;378(9792):684–92. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21856482

39. Kaitin KI, DiMasi JA. Pharmaceutical innovation in the 21st century: new drug approvals in the first decade, 2000-2009. Clin Pharmacol Ther [Internet] 2011;89(2):183–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21191382

40. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nat Biotechnol [Internet] 2014;32(1):40–51. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24406927

41. Sacks L V, Shamsuddin HH, Yasinskaya YI, Bouri K, Lanthier ML, Sherman RE. Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000-2012. JAMA [Internet] 311(4):378–84. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24449316

42. HPS2-THRIVE Collaborative Group, Landray MJ, Haynes R, et al. Effects of extended-release niacin with laropiprant in high-risk patients. N Engl J Med [Internet] 2014;371(3):203–12. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25014686

43. Yang Y-L, Hu M, Chang M, Tomlinson B. A high incidence of exanthematous eruption associated with niacin/laropiprant combination in Hong Kong Chinese patients. J Clin Pharm Ther [Internet] 2013;38(6):528–32. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24020480

44. James S, Rao S V, Granger CB. Registry-based randomized clinical trials--a new clinical trial paradigm. Nat Rev Cardiol [Internet] 2015;12(5):312–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25781411

19

45. Fröbert O, Lagerqvist B, Gudnason T, et al. Thrombus Aspiration in ST-Elevation myocardial infarction in Scandinavia (TASTE trial). A multicenter, prospective, randomized, controlled clinical registry trial based on the Swedish angiography and angioplasty registry (SCAAR) platform. Study design and. Am Heart J [Internet] 2010;160(6):1042–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21146656

46. Jernberg T, Attebring MF, Hambraeus K, et al. The Swedish Web-system for enhancement and development of evidence-based care in heart disease evaluated according to recommended therapies (SWEDEHEART). Heart [Internet] 2010;96(20):1617–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20801780

47. Lauer MS, D’Agostino RB. The Randomized Registry Trial — The Next Disruptive Technology in Clinical Research? N Engl J Med [Internet] 2013;369(17):1579–81. Available from: http://www.nejm.org/doi/abs/10.1056/NEJMp1310102

48. Verschuren JJW, Trompet S, Wessels JAM, et al. A systematic review on pharmacogenetics in cardiovascular disease: is it ready for clinical application? Eur Heart J [Internet] 2012;33(2):165–75. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21804109

49. Regieli JJ, Jukema JW, Grobbee DE, et al. CETP genotype predicts increased mortality in statin-treated men with proven cardiovascular disease: an adverse pharmacogenetic interaction. Eur Heart J [Internet] 2008;29(22):2792–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18957472

50. Boekholdt SM, Sacks FM, Jukema JW, et al. Cholesteryl ester transfer protein TaqIB variant, high-density lipoprotein cholesterol levels, cardiovascular risk, and efficacy of pravastatin treatment: individual patient meta-analysis of 13,677 subjects. Circulation [Internet] 2005;111(3):278–87. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15655129

51. Assmann G, Schulte H. Relation of high-density lipoprotein cholesterol and triglycerides to incidence of atherosclerotic coronary artery disease (the PROCAM experience). Prospective Cardiovascular Münster study. Am J Cardiol [Internet] 1992;70(7):733–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/1519522

52. Baseline serum cholesterol and treatment effect in the Scandinavian Simvastatin Survival Study (4S). Lancet (London, England) [Internet] 1995;345(8960):1274–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/7746058

53. Ray KK, Cannon CP, Cairns R, Morrow DA, Ridker PM, Braunwald E. Prognostic utility of apoB/AI, total cholesterol/HDL, non-HDL cholesterol, or hs-CRP as predictors of clinical risk in patients receiving statin therapy after acute coronary syndromes: results from PROVE IT-TIMI 22. Arterioscler Thromb Vasc Biol [Internet] 2009;29(3):424–30. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19122170

54. Olsson AG, Schwartz GG, Szarek M, et al. High-density lipoprotein, but not low-density lipoprotein cholesterol levels influence short-term prognosis after acute coronary syndrome: results from the MIRACL trial. Eur Heart J [Internet] 2005;26(9):890–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15764620

55. AIM-HIGH Investigators, Boden WE, Probstfield JL, et al. Niacin in patients with low

20

HDL cholesterol levels receiving intensive statin therapy. N Engl J Med [Internet] 2011;365(24):2255–67. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22085343

56. Barter PJ, Caulfield M, Eriksson M, et al. Effects of torcetrapib in patients at high risk for coronary events. N Engl J Med [Internet] 2007;357(21):2109–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17984165

57. Schwartz GG, Olsson AG, Abt M, et al. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med [Internet] 2012;367(22):2089–99. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23126252

58. Tardif J-C, Rhéaume E, Lemieux Perreault L-P, et al. Pharmacogenomic determinants of the cardiovascular effects of dalcetrapib. Circ Cardiovasc Genet [Internet] 2015;8(2):372–82. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25583994

59. Tardif J-C, Lesage F, Harel F, Romeo P, Pressacco J. Imaging biomarkers in atherosclerosis trials. Circ Cardiovasc Imaging [Internet] 2011;4(3):319–33. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21586743

60. Mega JL, Stitziel NO, Smith JG, et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet (London, England) [Internet] 2015;385(9984):2264–71. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25748612

61. Miller M, Cannon CP, Murphy SA, et al. Impact of triglyceride levels beyond low-density lipoprotein cholesterol after acute coronary syndrome in the PROVE IT-TIMI 22 trial. J Am Coll Cardiol [Internet] 2008;51(7):724–30. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18279736

62. Rabar S, Harker M, O’Flynn N, Wierzbicki AS, Guideline Development Group. Lipid modification and cardiovascular risk assessment for the primary and secondary prevention of cardiovascular disease: summary of updated NICE guidance. BMJ [Internet] 2014;349:g4356. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25035388

63. Varbo A, Benn M, Smith GD, Timpson NJ, Tybjaerg-Hansen A, Nordestgaard BG. Remnant cholesterol, low-density lipoprotein cholesterol, and blood pressure as mediators from obesity to ischemic heart disease. Circ Res [Internet] 2015;116(4):665–73. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25411050

64. Jørgensen AB, Frikke-Schmidt R, Nordestgaard BG, Tybjærg-Hansen A. Loss-of-function mutations in APOC3 and risk of ischemic vascular disease. N Engl J Med [Internet] 2014;371(1):32–41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24941082

65. Cannon CP, Blazing MA, Giugliano RP, et al. Ezetimibe Added to Statin Therapy after Acute Coronary Syndromes. N Engl J Med [Internet] 2015;372(25):2387–97. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26039521

66. Jun M, Foote C, Lv J, et al. Effects of fibrates on cardiovascular outcomes: a systematic review and meta-analysis. Lancet (London, England) [Internet] 2010;375(9729):1875–84. Available from:

21

http://www.ncbi.nlm.nih.gov/pubmed/20462635

67. Vega GL, Ma PTS, Cater NB, et al. Effects of adding fenofibrate (200 mg/day) to simvastatin (10 mg/day) in patients with combined hyperlipidemia and metabolic syndrome. Am J Cardiol [Internet] 2003;91(8):956–60. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12686335

68. Grundy SM, Vega GL, Yuan Z, Battisti WP, Brady WE, Palmisano J. Effectiveness and tolerability of simvastatin plus fenofibrate for combined hyperlipidemia (the SAFARI trial). Am J Cardiol [Internet] 2005;95(4):462–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15695129

69. ACCORD Study Group, Ginsberg HN, Elam MB, et al. Effects of combination lipid therapy in type 2 diabetes mellitus. N Engl J Med [Internet] 2010;362(17):1563–74. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20228404

70. Voight BF, Peloso GM, Orho-Melander M, et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet (London, England) [Internet] 2012;380(9841):572–80. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22607825

71. Nordestgaard BG, Chapman MJ, Ray K, et al. Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J [Internet] 2010;31(23):2844–53. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20965889

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