Due to the increased incidence of atherosclerotic cardiovascular disease (CVD) in HIV-infected patients, there is a need to accurately predict CVD risk in order to identify which patients need to undergo lifestyle modifications and/or take cholesterol-lowering drugs such as statins. Currently, risk-prediction models are used to calculate the 5 and 10 year risk of having a CVD event based on a number of biomarkers which are inputted into an algorithm. These prediction models are inaccurate and there is a need to identify more accurate prognostic biomarkers involved in the disease process which can improve CVD risk-prediction in HIV patients. Molecular biomarkers of inflammation (Interleukin-6 and C-Reactive Proteins), thrombosis (D-dimer), and the use of imaging as biomarkers have the potential to increase the accuracy of CVD risk prediction in HIV patients. However, controlled clinical-trials with hard clinical endpoints are required to assess whether these additional biomarkers would improve CVD risk-prediction beyond the models which are used clinically today.

Introduction

Atherosclerosis is the process whereby lipids, leukocytes, calcium and other substances deposit in the intima of an artery, forming a plaque.1 Atherosclerosis usually occurs in medium to large arteries.1 Plaques can grow large enough to significantly reduce blood flow through an artery, or they may rupture leading to the formation of blood clots (thrombosis) that can travel to smaller arteries, causing complete blockage.[1] Cardiovascular disease (CVD) is a general term referring to a group of diseases of the heart and blood vessels.[2] There are four main types of CVD; coronary heart disease, stroke, peripheral arterial disease and aortic disease.

Since the introduction of antiretroviral therapy (ART), there have been huge improvements in life-expectancy and quality of life for HIV-patients.[3] However, non-HIV related co-morbidities are of increasing concern in this ageing population.[4] Premature atherosclerotic cardiovascular disease is one of the leading non-AIDS-related causes of morbidity and mortality in HIV patients.[5] HIV-infected individuals are at increased risk of developing cardiovascular disease compared to HIV-negative controls matched for age, gender, race and smoking status.[6] This increased risk is thought to be caused by a combination of increased incidence of traditional risk factors (such as smoking, hypertension and dyslipidemia), chronic-inflammation and the effects of exposure to certain anti-retroviral therapies, especially protease inhibitors (PIs).[7]

Current Biomarkers Used in CVD Risk Prediction Models

Due to the increased risk of CVD in HIV-infected patients, screening and prevention is vital in managing CVD risk. Frequent CVD risk-assessing is carried out by clinicians using risk-assessment models developed following clinical trials.4 A risk assessment model involves inputting data for a number of relevant CVD risk factors or biomarkers (age, gender, total cholesterol etc.) into an algorithm which estimates the 5 or 10 year CVD-risk score and a lifetime risk score for the patient.[8] Clinicians then use the risk score to help determine whether the patient should receive preventative treatment such as statins and undertake lifestyle modifications.[8] CVD risk-calculators, such as the Framingham 10-year risk calculator, are used in the general population. However, HIV-specific CVD risk calculators, such as the DAD 5-year risk calculator, are thought to give more accurate predictions for HIV patients as they incorporate more HIV-specific CVD risk factors and biomarkers.[4] Risk-calculators will be reviewed in detail later in this report; their benefits, risks and the studies in which they have been involved. 

Biomarkers of the Future

There are other types of biomarkers strongly-associated with atherosclerotic cardiovascular disease pathogenesis in HIV patients that are not currently used in assessing risk for CVD. It is believed that these biomarkers could be incorporated into risk-assessment algorithms and regular screening to improve the accuracy of CVD risk-prediction in HIV patients.[5] These include molecular biomarkers of inflammation, atherosclerosis and thrombosis and the use of imaging techniques as biomarkers of CVD progression.[5] 

Cardiovascular Disease Risk Assessment Models

The original and most clinically-used CVD-risk model is the Framingham Risk Score, also known as the Framingham Calculator.[4] The Framingham Risk Score, which was first developed in 1998 based on data obtained from the longitudinal Framingham Heart Study, is an algorithm that estimates the 10-year risk of a patient developing CVD.9 The Framingham study researchers were the first to identify the major CVD risk factors or biomarkers; hypertension, hypercholesterolemia, smoking, obesity, diabetes, and physical-inactivity.[10] The most recent version of the Framingham risk score, published in 2008, predicts 10-year risk based on age, gender, total cholesterol, HDL cholesterol, systolic blood pressure, hypertension-treatment, Diabetes Mellitus and whether the patient is a current smoker. One of the limitations of the Framingham Risk Score, is that it tends to under-predict CVD risk in HIV patients.[4]

A HIV-specific CVD risk score was developed following the Data Collection on Adverse Effects of Anti-HIV Drugs (DAD) Study.[8] The DAD study is a prospective, observational study involving the collaboration of 11 cohorts of HIV patients from Europe, America and Australia.[8] The DAD Risk Score, predicts 5-year risk of a CVD event based on relevant risk-factors.[8] In contrast with the Framingham risk score; the DAD Risk Score does not factor-in whether the patient is on anti-hypertensive medication but does take into account 5 other CVD risk factors or biomarkers which are relevant for HIV patients, such as whether the patient is currently being treated with indinavir, lopinavir or abacavir, number of years taking indinavir or lopinavir, previous smoker, diabetes and family history of CVD.[8] Although the validation of the DAD risk score has some limitations, it has been shown to predict CVD more accurately in HIV-infected patients than the standard Framingham risk score.[4]

Future Biomarkers to Improve CVD Risk Prediction

Although CVD risk scores are useful, they are not accurate in HIV-infected patients and tend to under-predict CVD risk, as the algorithms do not consider other factors such as chronic-inflammation and exposure to ART.[11] Therefore there is a need to incorporate more specific and measurable CVD risk factors and biomarkers of the disease process. Molecular biomarkers of inflammation and atherothrombosis have the potential to be incorporated into risk prediction models and improve CVD risk prediction and prevention in HIV patients.[5] These biomarkers could also be used to improve the understanding of how HIV and ART-exposure have an effect on the underlying CVD pathogenesis.[5]  

Relationship between Inflammation and Atherosclerosis

In recent years, it has become widely recognised that atherosclerosis does not merely involve the passive build-up of cholesterol in arteries.[12] In fact, inflammation plays a pivotal role in all stages of atherosclerosis.[13] As a result of viral replication, there is an increased inflammatory response in HIV patients, contributing to a heightened risk of CVD. Inflammation reduces following viral suppression by taking ART, however the level of inflammation does not normalise.[14] Therefore, molecules released into circulation during the disease process of atherothrombosis, such as IL-6, CRP and D-dimer have the potential to improve CVD risk prediction in HIV patients. Because these biomarkers are released into circulation, they can be easily measured with a clinical blood test. A number of studies, which are reviewed below, have investigated the correlation between biomarker levels and the incidence of CVD events in HIV patients. There are other promising molecular biomarkers, but this report will focus primarily on IL-6, CRP and D-Dimer. 

Similarly, imaging techniques could be used to improve CVD risk-stratification in HIV patients. The relevant methods of imaging which could be used for this purpose will be discussed below. 
 

Biomarkers of Inflammation; IL-6 and hsCRP

Interleukin-6 (IL-6) is a cytokine released by leukocytes and endothelial cells which plays an important role in the inflammatory cascade involved in atherosclerosis.5  C-reactive protein (CRP) is produced by the liver and released into circulation by macrophages and T-cells following IL-6 secretion, in order to activate the complement system.[5] High-sensitivity CRP (hs-CRP) can be measured down to extremely low concentrations using laser nephelometry.[5] This level of sensitivity enables hs-CRP levels to be used as a biomarker of low-grade chronic inflammation.[5] Higher levels of IL-6 and hsCRP in the general population correlate with an increased risk of CVD events and death from any cause.[5] As a result of the chronic inflammation associated with HIV infection, monitoring levels of inflammatory markers such as IL-6 and hsCRP could improve CVD risk-prediction in HIV patients.

Biomarkers of Thombosis; D-dimer

Following atherosclerosis, plaques may become fragile and rupture leading to blood-clotting (thrombosis), which can cause artery-occlusion and myocardial infarction or stroke.[1] D-dimer is a protein released into circulation following degradation of fibrin-clots by fibrinolysis, which can be used as a biomarker of thrombosis.[5] However, D-dimer is a non-specific marker which reflects increased thrombotic-activity and may be elevated in response to inflammatory stimuli.[5]

Clinical trials

A study conducted by Duprez et al. investigated the predictive value of hsCRP, IL-6 and D-dimer for CVD morbidity and mortality in HIV-infected patients enrolled in The SMART Study beyond other measured CVD risk-factors.[15] Out of the 5098 patients enrolled in the trial, 252 patients had a CVD event over a median follow-up of 29 months.[15] The researchers found that the addition of the 3 biomarkers to a pre-existing model significantly improved risk-prediction.[15] Area under the curve (AUC), which measures the performance of biomarkers, significantly improved with inclusion of the 3 biomarkers to a pre-existing model from 0.741 to 0.771 (p<0.001).[15] 

In a study by Nordell et al., the researchers investigated the prognostic value of inflammatory and thrombotic biomarkers IL-6, hsCRP and D-dimer for fatal outcomes among HIV patients that experience a CVD event.[16] Data was used from 3 international HIV trials; the SMART trial, the ESPRIT trial and the SILCAAT trial. Biomarker levels were measured at baseline for the 9,764 patients, all of whom were HIV-positive with no history of CVD.[16] Of these patients, the researchers focused on the 288 that experienced either a fatal (n=74) or nonfatal (n=214) CVD event over a median of 5 years.[16] The researchers found that IL‐6 and D‐dimer levels at baseline were significantly higher for those patients who experienced a fatal CVD event compared with the patients that experienced a non-fatal CVD event.[16] The level of hsCRP was also higher in these patients, however this was not statistically significant.[16] It was concluded that the chronic inflammation and thrombosis associated with HIV leads to a poor outcome when a CVD event occurs.[16]

Another study undertaken by De Luca et al. investigated whether certain biomarkers can independently predict CVD risk in HIV-infected patients.[17] The researchers conducted a retrospective nested case-control study in HIV patients already enrolled in 2 studies; the Icona Foundation Cohort and the CUSH study.[17] The researchers included patients aged 35-69 that were being treated with ART.17 Patients which had undergone a major CVD event were included in the case group (n=35) and patients that were free from CVD events for at least 5 years from starting ART were included in the control group (n=74).[17] The control patients were matched to the cases for diabetic and smoking status.[17] Levels of hsCRP, D-dimer, P-selectin, IL-6, tissue plasminogen activator and plasminogen activator inhibitor-1 were measured and statistical analysis was carried out.[17] The researchers found that high levels of hsCRP was associated with CVD risk, independent of traditional risk factors, HIV replication and the type of ART received at the time of sampling.[17] It was also found that higher IL-6 and P-selectin levels were independently associated with increased CVD risk, although this association was weaker than for hsCRP.[17] 

Imaging Biomarkers

Various techniques of arterial imaging have potential for use as predictive biomarkers  to improve screening for CVD risk assessment in HIV-infected patients and in helping to better understand the mechanisms underlying the correlation between HIV-infection and atherosclerotic CVD.[18]  Some of the promising imaging techniques for this purpose include carotid ultrasound, cardiac computed tomography, brachial artery ultrasound and positron image tomography.[18] 

Carotid Ultrasound is a technique which can be used to measure carotid intima-media thickness (IMT) and assess for plaque presence in the carotid arteries of the neck.[18] Increased carotid IMT is associated with an increased risk of myocardial infarction and stroke.[18] Over 20 studies have measured carotid IMT in HIV-infected patients. Combining data from these studies shows an average carotid IMT 0.04 mm thicker in the HIV population compared to HIV-negative controls.[18]

Cardiac computed tomography is a technique used to measure coronary artery calcium (CAC) or for angiography (CTA) to assess the presence and nature of coronary plaques.[18] Both CAC and CTA are strong predictors of CVD risk in the general population.[18] Currently, CTA is showing more promise for clinical use as it can identify the presence of both calcified and non-calcified plaque.[18] Studies involving the Multicentre AIDS Cohort, described an increased prevalence of non-calcified plaque in HIV-infected individuals compared to HIV-negative controls.[18]

Brachial artery ultrasound is a technique used for brachial arterial reactivity testing (BART) in order to assess  flow-mediated dilation, a marker of endothelial function that predicts future CVD risk.[18] Studies involving brachial artery ultrasound have demonstrated that HIV-infected patients show a much higher prevalence of endothelial dysfunction when compared to control groups without HIV-infection.[18] 

Another imaging technique, positron image tomography (PET) can be used to assess arterial inflammation, a process heavily involved in atherosclerosis and subsequent thrombosis.  
 

Conclusion and Future Perspective

Accurate CVD risk screening and prediction in HIV patients is vital as the patients are at an increased risk of CVD due to; an increased incidence of traditional CVD risk-factors, chronic inflammation which does not normalise following treatment and the effects of certain ART. More accurate CVD risk-prediction would allow physicians to prescribe lifestyle modifications and cholesterol-lowering medication such as statins to patients with high CVD risk. One might wonder why all HIV patients cannot be administered statins as prophylaxis to prevent the occurrence of atherosclerotic CVD. This would not be clinically beneficial due to the many side-effects associated with statins such as; myalgia and potential rhabdomyolysis, liver damage and increased incidence of Type II Diabetes, in addition to the many side-effects caused by the anti-retroviral therapy.[19] Therefore the potential benefit would not outweigh the many risks involved in such a strategy.  

There is a need for accurate methods of CVD risk prediction in HIV patients to identify the patients at risk and inhibit their progression of atherosclerotic CVD. The current tools used clinically consist of standard CVD risk assessment models or more HIV-specific CVD risk assessment models, neither of which have proven to be accurate in HIV patients and are known to either under- or over-predict CVD risk. Molecular biomarkers of inflammation and thrombosis, and imaging biomarkers have all shown potential for use in improving CVD risk prediction. However, there is a need for properly-designed, large clinical trials with hard clinical endpoints to fully investigate whether the incorporation of these biomarkers into risk-prediction models can more-accurately improve CVD risk-prediction. 

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