Imaging Biomarkers

C H A P T E R

22 Imaging Biomarkers: Carotid Intima-Media Thickness and Aortic Stiffness as Predictors of Cardiovascular Disease Costas Tsioufisa and Charalambos Vlachopoulosb a

1st Department of Cardiology, Athens Medical School, Hippokration Hospital, Athens, Greece; bHypertension Unit and Peripheral Vessels Unit, 1st Department of Cardiology, Athens Medical School, Hippokration Hospital, Athens, Greece

INTRODUCTION Accurate assessment of cardiovascular (CV) risk is essential for clinical decision making; many scores have been developed over the years to classify patients into low-, medium-, or high-CV-risk group (SCORE, Framingham, etc.). However, a small, yet significant gap exists between predicted and actual event rates. Additional tools to further stratify the risk of patients are biomarkers (characteristics that are objectively measured and evaluated as indicators of processes) that when they fulfill certain criteria they can substitute for a clinical endpoint (surrogate endpoint). Arterial biomarkers have the potential to integrate the damage of risk factors on the arterial wall over a long period, together with the impact of genetic background. Thus, they have the ability to predict a person’s overall CV risk above and beyond classical risk factors, fitting within the concept of early vascular aging (EVA). Aortic stiffness and carotid intima media thickness (cIMT) are two such biomarkers that have demonstrated important potential to predict CV events.

CAROTID INTIMA MEDIA THICKNESS AND CV PROGNOSIS Atherosclerosis, the leading cause of death worldwide [1], is considered a chronic inflammatory disease related to increasing age that has a long, slow asymptomatic phase. Atherosclerotic cardiovascular disease (CVD) becomes clinically apparent only when atherosclerosis is well advanced and often at the point of an acute event (thrombus formation following acute rupture or erosion of nonstenotic plaques). Therefore, little potential exists to use clinical signs and symptoms to identify those at high risk for CVD early enough to prevent events [2]. Different algorithms utilizing well-known CV risk factors in order to predict CV risk have been developed (i.e., the Framingham risk score [3], SCORE [4], ASSIGN [5], and QRISK2 [6,7]) for use in the general population but their predictive value have been shown to be inaccurate in both high-risk and low-risk populations [8]. Early detection of atherosclerosis has become possible due to new noninvasive imaging techniques for patients with risk factors allowing us to detect subclinical atherosclerosis and minimal modifications in the vascular wall that can be potentially corrected by receiving preventive treatment. In these lines, measurement of cIMT has received special attention as a relatively simple to perform predictor of future CVD risk.

How to Assess cIMT by Duplex Ultrasound Carotid IMT is defined as the distance between the lumen intima interface and the media adventitia interface [9]. Subjects are assessed supine, with the neck extended and head turned to the side that is contralateral to the side on

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© 2015 Elsevier Inc. All rights reserved.

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22. IMAGING BIOMARKERS: CAROTID INTIMA-MEDIA THICKNESS AND AORTIC STIFFNESS AS PREDICTORS OF CARDIOVASCULAR DISEASE

which the measurement is being taken, allowing maximal access to the great arteries of the neck. When the carotid artery wall is visualized by B-mode ultrasound, the intima media complex can be identified as the double line density of the intimal luminal and the medial adventitial interfaces (the characteristic “double echo” of the intima media complex) on both the near and far wall of the carotid artery. Studies comparing ultrasound measurements with histology suggest that far wall cIMT measurements are more representative of the true thickness of the arterial wall. Near-wall cIMT measurements, in comparison, are limited by their dependence on the axial resolution and gain settings of the equipment used and hence are less accurate and reproducible [10]. It has also been recommended to measure cIMT at the end of diastole, as measurement during systole is affected by stretching of the arterial wall, and subsequent reduction in cIMT [11]. Carotid IMT can be measured in several segments of the carotid tree: the common carotid artery (CCA), the carotid bifurcation (bulb), or the internal carotid artery (ICA). Practically, visualization of the CCA is easier than visualization of the ICA or bulb and since CCA is a tubular structure and is perpendicular to the ultrasound beam, measurement and reproducibility of IMT in this region are greater than for IMT measurements in the bulb or ICA [10]. It is recommended to measure cIMT in areas free of plaque (defined by Mannheim consensus as a focal thickening that encroaches into the lumen by 0.5 mm or by 50% of the surrounding IMT or where IMT is .1.5 mm) in order to improve reproducibility and accuracy [12]. Carotid plaque itself has been shown to correlate with concomitant CVD and is considered to be an indicator of high vascular risk [13]. Therefore, in terms of assessing subsequent vascular events, both cIMT and plaque should be considered separately. Carotid IMT can be quantified using either measurement of the mean IMT or the maximum IMT. The mean IMT is estimated as the mean of all IMT measurements made over single or multiple segments of the carotid arteries from the right and left sides and from the near and far walls of the arteries. The maximum IMT is the highest value of IMT measured over the carotid artery segments. As shown in Table 22.1, TABLE 22.1

Major Epidemiological Studies of Predictive Ability of cIMT

Study

Outcome

IMT measurement

Adjusteda HR for cIMT (95% CI)

RR for cIMT (95% CI)

1,257

MI

Max CCA

2

2

12,841

MI

Mean CCA, bulb, ICA, combined measure

F 5.07 (3.08 8.36)

2

Mean CCA, bulb, ICA, combined measure

F 8.54 (3.52 20.74)

Year of Subjects publication (n)

Kuopio Ischaemic Heart Disease 1993 Study (KIHD) [15] Atherosclerosis Risk in Communities (ARIC) [16]

1997

Atherosclerosis Risk in Communities (ARIC) [17]

2000

Cardiovascular Health Study (CHS) [18]

1999

4,476

MI/Stroke

Max CCA, ICA, combined measure

2

3.15 (2.19 4.52)

Rotterdam Study [19]

2002

5,851

MI

Max CCA, bulb, ICA, combined measure

2

Combined cIMT 1.38 (1.21 1.58)

Rotterdam Study [20]

2003

5,479

Stroke

Mean CCA

2

2.23 (1.48 3.36)

Malmo Diet and Cancer Study (MDCS) [21]

2005

5,163

MI

Mean CCA

1.23 (1.07 1.41)

2

Malmo Diet and Cancer Study (MDCS) [22]

2005

5,163

Stroke

Mean CCA

1.21 (1.02 1.44)

2

Carotid Atherosclerosis Progression Study (CAPS) [23]

2006

6,962

MI/Stroke

Mean CCA, bulb, ICA

1.16 (1.05 1.27)

2

Multi-Ethnic Study of Atherosclerosis (MESA) [24]

2008

6,698

CHD, stroke, Max CCA, ICA all CVD

1.2 (1.0 1.3)

2

Framingham Offspring Study [25]

2011

2,965

All CVD

14,214

Stroke

M 1.87 (1.28 2.69)

M 3.62 (1.45 9.15)

Mean CCA and max ICA Mean CCA 1.13 (0.000 0.007)

2

Max ICA 1.21 (1.13 1.29) a

Age, sex, and risk factor adjusted, except ARIC—age, race, and center-adjusted only. MI, myocardial infarction; CHD, coronary heart disease; CVD, cardiovascular disease; CCA, common carotid artery; bulb, bifurcation; ICA, internal carotid artery; cIMT, carotid intima media thickness; F, female; M, male; HR, hazard ratio; RR, relative risk. Source: Modified based on Robertson et al. [14].

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methodologies used in large epidemiological studies vary widely [15 25]. Besides the variety of different methodologies used, measurement of cIMT is also prone to variability from a number of sources, including the type of the ultrasound machine used and most importantly the experience of both the sonographer taking the images and the reader making the measurement [14]. For reduction of the above variability standardized protocols for performing IMT measurement as well as the use of automated edge detection programs have been proposed. Contrary to earlier studies where IMT measurements were performed by visually detecting the leading edges of the blood intima and media adventitia interfaces, most recent studies use an automated edge-tracking method. The latter involves determining ultrasound interfaces using pixel intensity and employs a multi-step gradient-based algorithm to accurately identify the intima media complex. This technique distinguishes IMT values to within 0.01 mm and reduces operator dependency but also requires high-quality images [26]. Therefore, making comparisons of findings across different studies and defining a normal range for cIMT is very difficult. In this setting, the normal range of IMT in the CCA in healthy middle-aged adults approximates between 0.6 and 0.7 mm [27]. The definition of the upper limit of normal is arbitrary but is frequently set at the 75th percentile of cIMT distribution for the determination of increased relative CVD risk.

Associations Between cIMT and Traditional CV Risk Factors Data coming from large epidemiological studies showed that IMT correlates with well-known CV risk factors. In the Atherosclerosis Risk in Communities (ARIC) study, men had higher mean cIMT values than women at all ages. Investigators showed that IMT increases with age in both sexes and in all arterial segments. Indeed, the rates of progression of IMT in the ICA and the bulb were higher (from 0.015 to 0.020 mm/year) than the one in CCA (0.01 mm/year), reflecting the propensity for atherosclerotic plaque formation in these arterial segments [16]. Hypertensive subjects have also been shown to have greater values of cIMT compared to normotensive subjects. Cross-sectional studies have found a significant correlation with systolic blood pressure (BP) whereas the correlation with diastolic BP is less consistent [28 30]. Moreover, cIMT has been associated with low-density lipoprotein (LDL) cholesterol levels with the Kuopio Ischaemic Heart Disease Study showing greater progression of IMT values over a 2-year period in men with high LDL cholesterol levels [31], whereas patients with familiar hypercholesterolemia are also found to have significantly increased cIMT values [32,33]. To support the relationship between cIMT and modifiable CV risk factors, such as arterial hypertension and hypercholesterolemia, several studies assessing the role of antihypertensive and lipid-lowering therapy in cIMT progression were published. Most of the studies were collected in two meta-analyses showing a slight but significant association of cIMT slow progression with BP reduction and with lipid-lowering therapy, respectively [34,35]. Furthermore, cIMT has been correlated with smoking and type 2 diabetes. A recent meta-analysis of 21 studies of patients with type 2 diabetes found that they had 13% greater cIMT values when compared with nondiabetic individuals. Carotid IMT values were also greater in subjects with impaired glucose tolerance although to a lesser extent than in those with overt diabetes [36]. In addition, according to the Insulin Resistance and Atherosclerosis study, diabetic patients without known coronary heart disease (CHD) had IMT values similar to those of nondiabetic patients with coronary artery disease (CAD). The progression of IMT was 25% greater in diabetic patients compared to nondiabetic ones, even after adjustment for known CV risk factors [37].

Carotid IMT as a Predictor of CV Events Apart from its associations with traditional CV risk factors, a number of studies in different clinical settings have also correlated cIMT with incident CVD, including CHD and stroke. Studies in Healthy Population (Table 22.1) CAROTID IMT AS A PREDICTOR OF CHD

In the Kuopio Ischaemic Heart Disease Study, 1,257 men, aged 43 60 years and free of CVD at baseline were followed-up for 3 years. Maximum cIMT, measured at the CCA, was found to be predictive of incident myocardial infarction (MI). More specific, a 0.1 mm increase in cIMT represented an 11% increase in risk of MI [15]. In the same lines, the ARIC study recruited 12,841 men and women aged 45 64 years with no history of CVD. Investigators measured IMT at CCA, bulb and ICA (mean of the individual IMT measurements for each section of the far wall of the carotid artery) and found that extreme mean IMT .1 mm when compared to IMT ,1 mm was associated with an increased incidence of CVD for both men and women [HR 1.87 (95% CI 1.28 2.69) and

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5.07 (95% CI 3.08 8.36), respectively] after adjustment for age, sex, and center. However, after adjustment for additional traditional CV risk factors, IMT was associated with increased risk for CVD only in the group with the highest IMT [16,17]. The Cardiovascular Health Study included 4,476 subjects with a mean age of 72.5 years with no previous CHD history and after a median follow-up of 6.2 years showed a linear increase in relative risk for MI or stroke across quintiles of maximal IMT [18]. In the Rotterdam Study, examining 5,851 men and women aged at least 55 years, increased cIMT measured in the CCA, at the bifurcation and the combined measure was found to be a strong predictor of MI after adjustment for traditional risk factors in addition to age and sex [RR per 1-standard deviation (SD) increase in IMT 1.37, 1.28, and 1.38, respectively] [19]. Nevertheless, a former study from the same investigators revealed that when added to models with conventional risk factors, IMT did not significantly improve prognosis of CHD [38]. In the Carotid Atherosclerosis Progression Study (CAPS), 5,056 participants were followed-up for a mean period of 4.2 years. Adjusted for age, sex, and CV risk factors, mean IMT at CCA and bifurcation was predictive for MI [HR 1.16 (95% CI 1.05 1.27) and 1.16 (95% CI 1.05 1.28), respectively] and the combined endpoint of MI, stroke, or death [23]. Moreover, in a 10-year cohort study of people living in Malmo, Sweden, cIMT, carotid plaque, and carotid stenosis were associated with future coronary events. The relationship for cIMT was slightly reduced after additional adjustment for carotid plaque but remained significant for those with higher cIMT [21]. Also, investigators of the Multi-Ethnic Study of Atherosclerosis (MESA) showed that cIMT was predictive of coronary events although not to the same extent as coronary artery calcium [24]. Last but not least, the Framingham Offspring study examined the predictive ability of cIMT in 2,965 men and women and showed that both mean IMT in the CCA and maximal IMT in the ICA could predict future vascular events. However, only internal carotid IMT improved clinical risk classification significantly compared to that of the CCA [25]. Well before the development of CV events, evaluation for carotid disease may provide important information for the presence of CHD. In a study from Greece, in 197 patients with chest pain that were referred for coronary angiography, carotid disease, defined as a stenosis of the lumen by $ 50%, was associated with increasing severity of CAD [39]. By means of transesophageal echocardiography, a close association of the presence of carotid plaques with increasing levels of aortic atherosclerosis has also been demonstrated [40]. CAROTID IMT AS A PREDICTOR OF STROKE

A number of studies found an association between cIMT and both prevalent and incident stroke. In the Rotterdam Study, a cIMT .0.84 mm was predictive of incident stroke even after adjustment for conventional risk factors. The relationship was found to be stronger than the one between plaque and stroke [20]. In the Malmo Diet and Cancer Study, common carotid IMT was associated with the incidence of stroke independent of the presence of carotid plaque [adjusted HR 1.21 (95% CI 1.02 1.44)] [22]. In the ARIC and the Cardiovascular Health Study increasing cIMT was associated with increased risk of stroke although the relationship was not entirely linear [17]. In contrast, the MESA study did not demonstrate an association between cIMT and incident stroke [adjusted overall HR 1.1 (95% CI 0.8 1.3)] [24]. Studies in Higher CV Risk Groups Few studies have assessed the role of cIMT as a predictor of CV events in specific high-risk groups, such as diabetic patients. Most of the existing studies in this group are cross-sectional and few prospective data are available [14]. In one of the few prospective studies, 229 diabetic patients with at least one additional CV risk factor and free of any CV complication at baseline were followed up for 5 years. Carotid IMT was an independent predictor of CV events (P 5 0.045) and provided a predictive value similar to the Framingham score for coronary events [41]. Furthermore, a recent study of 80 subjects (60 diabetic versus 20 healthy subjects) demonstrated that increased thickness of the intima media layer of the common carotid arteries correlates well with ischemic stroke in the setting of type 2 diabetes and the cutoff point for cIMT was determined by investigators to be 0.8 mm for the occurrence of ischemic stroke [42].

What Is the Clinical Value of cIMT Measurement? In 2007, Lorenz et al. collected all the prospective studies investigating the association between baseline cIMT values and risk of CV events and performed a meta-analysis showing that for an absolute carotid IMT difference

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of 0.1 mm, the future risk of myocardial infarction increases by 10 15% and the stroke risk increases by 13 18% [43]. Whilst the univariate relationship between cIMT and future vascular events would suggest potential usefulness for the cIMT in risk prediction, evidence for an additional benefit over and above existing risk prediction models (e.g., Framingham risk scoring) is less certain. The ARIC investigators found that adding cIMT and plaque information to traditional risk factors improved the prediction of vascular events compared with traditional risk factors alone [44]. However, according to the CAPS, IMT, when added to the Framingham risk score and SCORE models, did not significantly improve risk prediction in their cohort. Overall, 8.8% of people were reclassified following IMT measurement and of these 30% were classified correctly [45]. As such, the net reclassification, a new statistical method, introduced by Pencina et al., that goes beyond the c-statistic and has been hypothesized to better estimate the predictive role of CV markers [46], was found to be 21.41%, suggesting that cIMT may not be useful in risk prediction, despite its association with incident vascular disease [45]. A recent systematic review which examined several novel markers of risk in addition to traditional risk assessment concluded that there was strong evidence that cIMT improved risk prediction above traditional assessment. Twelve studies were identified and nine showed an increase in c-statistic when cIMT was added to the traditional risk assessment. Six of the studies reported the net reclassification index (NRI) and, of those, five showed a positive NRI, with only one showing a negative NRI (range 21.4 to 11.6%) [47]. Furthermore, the IMPROVE Study described a novel approach to measure cIMT progression, the Fastest-IMTmax-progr. This newly identified variable that reflects a focal increase of carotid IMT was associated with CV risk and the association persisted after adjustments for Framingham risk factors unlike all other IMT measures [48]. Technical aspects concerning the reproducibility of IMT measurements as well as pathophysiological issues are also crucial in understanding the role of this tool in CV risk assessment. Variability, involving the differing features between patients as well as between-sonographer and within-sonography variability in measurements, remains the main issue and may account for the conflicting results of different studies [14]. In the pathophysiological area, traditional risk factors only explain 15 17% of IMT, implying that the process of IMT is complex, depending not only by strictly atherogenic factors but mostly by reactive changes to BP and by shear stress pattern. Wall reactivity is not a marker of early atherosclerosis per se. Therefore, IMT regression does not represent a regression in atherosclerotic disease. In addition, as plaques grow longitudinally along the carotid axis, more than twice faster than it thickens, IMT seems to be a relatively insensitive measure of plaque evolution [49]. Indeed, Simon et al. published a review examining the impact of IMT and plaque on risk prediction where they reported that although IMT independently predicted CHD, it made only a modest improvement to risk prediction and suggested that carotid plaque may be a more useful tool in predicting CHD [50]. Moreover, cIMT has been claimed as a surrogate endpoint. However, if we consider what surrogate endpoint exactly means, then cIMT may not be technically defined as such. According to Prentice, a surrogate endpoint may be useful if there is a clear association between intervention-induced changes in the surrogate endpoint and changes in the clinical endpoint, which is what is exactly lacking for cIMT [51]. Preliminary findings of an ongoing prospective meta-analysis based on individual data (PROG-IMT project) failed to show an association between progression of cIMT and CV risk and investigators reported that no conclusion can be derived for the use of IMT progression as a surrogate in clinical trials [52]. Taking everything into account, the Mannheim Carotid IMT consensus [12], the ASE consensus statement [53], and the American College of Cardiology Foundation/American Heart Association guidelines [54] recommend IMT measurements for patients at intermediate CVD risk and in subjects in the following clinical circumstances: (1) a family history of premature CVD in first-degree relatives, (2) individuals ,60 years old with severe abnormalities in a single risk factor who otherwise would not be candidates for pharmacotherapy, (3) women ,60 years of age with at least two CVD risk factors, and (4) in all epidemiological and interventional trials dealing with vascular diseases to better characterize the population investigated. In the above populations, if the cIMT is increased, medical therapies should be advocated. Conventional statins are found to be efficient and safe to decrease the rate of cIMT progression in the long term through their pleiotropic effects. Evidence that aggressive statin therapy may provide superior efficacy for cIMT regression exists but its safety in the long term needs more randomized controlled trials to be proven [35]. As far as antihypertensive drug therapy is concerned, until now, there is no undisputable evidence that major drug classes differ in their ability to protect against overall CV risk or cause-specific CV events, such as stroke and myocardial infarction. Diuretics, angiotensin-converting enzyme inhibitors, calcium antagonists, angiotensin receptor antagonists, and β-blockers can all be considered suitable for initiation of antihypertensive treatment, as well as

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for its maintenance [55]. Serial scanning of cIMT is challenging in individual patients across short time intervals due to variability in measurement in relation to the rate of disease progression and is therefore not recommended in clinical settings. In conclusion, many large epidemiological studies have shown a strong relationship between IMT and incident CVD, but the evidence for the use of cIMT in clinical practice remains incomplete. Little evidence exists regarding its use in higher-risk groups, such as people with type 2 diabetes, compared to the healthy population and until now the use of IMT is recommended only for people at intermediate risk according to Framingham risk scoring, where it may add useful information. Future perspectives lie on the use of MRI techniques to evaluate cIMT, avoiding much of the variability observed with the ultrasound technique.

AORTIC STIFFNESS AND CV PROGNOSIS Arterial stiffening results primarily from arteriosclerosis (principally a disease of the media, related to normal or accelerated aging) rather than from atherosclerosis (principally a disease of the intima, affecting the vessel in a patchy and not uniform manner). Because waves travel faster in a rigid tube, loss of compliance results in increased velocity of pulse waves; therefore, a high pulse wave velocity (PWV) is a hallmark of arteriosclerosis. Arterial stiffness integrates the damage of risk factors on the arterial wall over a long period, whereas BP, glycemia, and lipids can fluctuate and their values, recorded at the time of risk assessment, may not reflect the average values damaging the arterial wall. Accordingly, arterial stiffness has the ability to predict a person’s overall CV risk above and beyond classical risk factors; this fits within the concept of EVA. A multitude of invasive and noninvasive methods for measuring arterial stiffness have been described.

Carotid-Femoral Pulse Wave Velocity The aorta is a major vessel of interest when determining regional arterial stiffness because the thoracic and abdominal aorta make the largest contribution to arterial buffering. Carotid femoral pulse wave velocity (cfPWV), that is, the velocity of the pulse as it travels from the heart to the carotid and the femoral artery, remains the most commonly used noninvasive method and is considered as the “gold standard” for the assessment of aortic stiffness [56]. Methodology cfPWV is usually measured using the “foot-to-foot” velocity method from a number of waveforms. These are usually obtained using surface tonometry probes at the right CCA and the right femoral artery; the time delay (Dt, or transit time) is measured between the “feet” of the two waveforms [57]. The “foot” of the wave is defined at the end of diastole, when the steep rise of the wavefront begins. The transit time is the time of travel of the “foot” of the wave over a known distance. A variety of different waveforms can be used including pressure [57], distension [58] and flow [59]. The distance D covered by the waves is usually assimilated to the skin distance between the two recording sites, that is, the CCA and the common femoral artery. PWV is calculated as PWV 5 D/Dt (m/s). An expert consensus document for the measurement of cfPWV in daily practice has been published [58]. Predictive Value—Use as Surrogate Endpoint A large number of studies have reported the various physiological and pathophysiological conditions associated with increased aortic stiffness [56]. Apart from the dominant effect of BP and aging, these include genetic background, CV risk factors, and diseases, and also primarily non-CV diseases (such as end-stage renal disease [60]) and chronic low-grade inflammation diseases (such as inflammatory bowel disease [61]). Aortic stiffness is a cumulative measure of their damaging effects on the arterial wall. The predictive value of aortic stiffness has been largely demonstrated. Historically, the earliest of these studies were conducted in patients belonging to high-risk groups, such as end-stage renal disease, while later studies progressively extended to other patient groups, such as patients with hypertension or CHD, and from there to the general population. Numerous studies in patients with uncomplicated essential hypertension [62 64], type 2 diabetes mellitus [59], end-stage renal disease [65], elderly subjects [66,67] and the general population have prospectively validated cfPWV [68]. The independent predictive value of arterial stiffness has also been demonstrated for neurologic functional outcome after stroke [69] and in patients with erectile dysfunction [70]. It should

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be stressed that the independent predictive value of arterial stiffness has been demonstrated after adjustment for classical CV risk factors, including brachial pulse pressure. In addition, arterial stiffness retains its predictive value for CV events after adjustment for the FRS [62] or SCORE [71], suggesting that it has an added value to a combination of CV risk factors (Table 22.1). In the latter study, in a population-based sample of 1,968 subjects without CVD or diabetes not receiving any CV, antidiabetic, or lipid-lowering treatment, traditional CV risk factors as well as left ventricular (LV) mass index, carotid artery atherosclerotic plaques, cfPWV, and urine albumin/creatinine ratio were measured and patients were followed for a median of 12.8 years. Risk of CV death was associated with LV hypertrophy, atherosclerotic plaques, and cfPWV .12 m/s, independently of SCORE risk stratification. Furthermore, these data indicated a stronger prognostic importance of cfPWV compared with other markers of subclinical organ damage in subjects with SCORE ,5% [71]. To gauge the effect of aortic stiffness on events we performed in 2010 and updated in 2014 a systematic metaanalysis of 17 longitudinal studies that evaluated aortic PWV in 15,877 subjects over a 7.7 year period (updated meta-analysis: 27 longitudinal studies, 22,611 subjects). Increased arterial stiffness was linked to a twofold increase in CV events and mortality, as well as all-cause mortality for subjects with high versus low aortic PWV (Figures 22.1 and 22.2). An increase in aortic PWV by 1 m/s corresponds to an age-, sex-, and risk factor adjusted risk increase of 14% in total CV events, 15% in CV mortality, and 15% in all-cause mortality. An increase in aortic PWV by 1 SD was associated with respective increases of 47%, 47%, and 42% [72 73]. This analysis also found that the predictive ability of arterial stiffness was higher in subjects with a higher baseline CV risk. In 2014, an individual data meta-analysis confirmed these results and showed that CV events increased by 30% per 1-SD increase of cfPWV (95% CI 1.18 1.43) after adjustment for traditional risk factors [74] (Table 22.2). Taken together, these results show that aortic PWV is a robust predictor of all-cause and CV mortality, fatal and nonfatal coronary events, and fatal strokes, thus it can be considered as an intermediate endpoint for CV events [77]. The independent association with all-cause mortality merits attention, as it indicates that the role of arterial stiffness extends beyond diseases of the CV system. An important criterion to validate a new biomarker is its ability to change (reclassify) a person’s risk in a clinically meaningful way and move them into a different risk category (clinical utility criterion of a surrogate endpoint). Three studies and an individual data meta-analysis showed that patients at intermediate risk could be reclassified into a higher or lower CV risk category when arterial stiffness was measured [68,70,71,78]. Study characteristics

A

B

Total CV events

Population

RR

Anderson 2009

GEN

.....

Blacher 1999

ESRD

4.89

2.79 – 8.57

4.89

Chol 2007

CAD

1.75

1.28 – 2.37

.....

Cruickshank 2002

DM

Laurent 2001

HTN

2.35

1.76 – 3.14

Mattaco-Raso 2006

GEN

2.39

1.76 – 3.25

Meaume 2001

GEN

4.60

1.37 – 15.40

Mitchell 2010

GEN

4.82

3.18 – 7.29

.....

Pannier 2005

ESRD

2.24

1.88 – 2.67

2.24

Shoji 2001

ESRD

2.16

1.11 – 4.20

2.16

Shokawa 2005

GEN

18.66

2.46 – 141.54

Sutton-Tyrrell 2005

GEN

1.40

1.22 – 1.61

Terai 2008

HTN

2.73

Wang 2010 (men)

GEN

Author

.....

95% Cl

RR (95% Cl)

RR

.....

.....

.....

..... 2.35 ..... 4.60

C

CV mortality 95% Cl

RR

RR (95% Cl)

.....

1.41

2.79 – 8.57 ..... ..... 1.76 – 3.14 ..... 1.37 – 15.40 .....

95% Cl

RR (95% Cl)

0.93 – 2.14

4.67

3.06 – 7.11

5.96

0.24 – 145.6

1.34

1.11 – 1.62

2.14

1.71 – 2.67

1.67

1.37 – 2.03

.....

.....

.....

.....

.....

1.88 – 2.67

CV mortality

.....

1.11 – 4.20

2.01

1.35 – 2.99

18.66

2.46 – 141.54

3.71

1.95 – 7.05

1.09 – 1.79

1.60

1.18 – 2.16

1.75 – 4.26

1.40 .....

4.61

1.58 – 13.47

1.58

1.25 – 1.94

1.58

1.25 – 1.94

1.50

1.33 – 1.69

Wang 2010 (women) GEN

1.94

1.56 – 2.42

1.94

1.56 – 2.42

1.76

1.54 – 2.01

Willum-Hansen 2006

GEN

1.50

1.38 – 1.63

1.61

1.44 – 1.80

.....

Zoungas 2007

ESRD

3.06

1.84 – 5.08

1.68

0.65 – 4.37

.....

2.26

1.89 – 2.70

2.02

Overall

0.5

1

2

5

10

Test for heterogeneity: I2=84.9%, P<0.001 Test for overall effect: Z=8.86, P<0.001

.....

1.68 – 2.12

0.5

1.90

1

2 I2=74.4%,

5

10

Test for heterogeneity: P<0.001 Test for overall effect: Z=7.49, P<0.001

..... ..... 1.61 – 2.24

0.2

0.5

1

2

5

10

I2=77.0%,

Test for heterogeneity: P<0.001 Test for overall effect: Z=7.58, P<0.001

FIGURE 22.1 Relative risk and 95% confidence intervals for high aortic PWV and clinical events. Relative risk (RR) and 95% confidence interval (CI) for high aortic pulse wave velocity (PWV) and total cardiovascular (CV) events (A), CV mortality (B), and all-cause mortality (C). Studies are listed alphabetically. Boxes represent the RR and lines represent the 95% CI for individual studies. The diamonds and their width represent the pooled RRs and the 95% CI, respectively. CAD: coronary artery disease; DM: diabetes mellitus; ESRD: end-stage renal disease; GEN: general population; HTN: hypertension. Source: Adapted from Vlachopoulos et al. [72].

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Summary data meta-analysis (27 studies; 22,611 subjects) RR

95% Cl

Total CV events

1.41

1.28–1.54

CV mortality

1.47

1.29–1.67

All-cause mortality

1.36

1.23–1.50 0.5

RR (95% Cl)

1

2

Individual data meta-analysis (16 studies; 17,635 subjects) RR

95% Cl

Total CV events

1.45

1.30–1.61

CV mortality

1.41

1.27–1.56

All-cause mortality

1.22

1.16–1.27 0.5

RR (95% Cl)

1

2

FIGURE 22.2 Relative risk and 95% confidence intervals for a 1-SD increase in aortic PWV and clinical events. Relative risks (RR) and 95% CIs for a 1-SD increase in aortic PWV (aPWV) and total CV events, CV mortality, and all-cause mortality according to summary data meta-analysis (updated analysis of Ref. [6] including 10 new studies [that reported risk for a 1-SD increase in aPWV in a literature search until September 2013] to a total of 27 studies, top panel) and individual data meta-analysis (Refs. [17,18] studies, Ben-Shlomo et al., J Am Coll Cardiol 2014, bottom panel). The diamonds and their width represent the pooled RRs and the 95% CIs, respectively. Source: Adapted from Vlachopoulos et al. [73].

TABLE 22.2

Incremental Value of Aortic Stiffness Indices for Risk Stratification Clinical NRIa

Reference

24.27% (CV mortality)

[74]

2

2

[72]

Brachial ankle PWV

2

2

[75]

Brachial ankle PWV

8.5% (CV events)

2

[76]

Study

Outcome

Markers added

Overall NRI

Individual data meta-analysis (n 5 17,635)

Total/CV mortality, CV/CHD events, stroke

Log (aortic PWV) 8.34% (CV mortality)

Meta-analysis (n 5 15,877)

Total/CV mortality, CV events

Carotid femoral PWV

Meta-analysis (n 5 8,169)

CV events

General population without known CV disease (n 5 2,916)

CV events

ARTERIAL STIFFNESS Carotid femoral pulse wave velocity

Brachial ankle PWV

a

Net reclassification index (NRI) calculated only for individuals at intermediate risk according to the Framingham Risk Score. CV, cardiovascular; CHD, coronary heart disease; PWV, pulse wave velocity.

Specifically, in the Framingham study 15.7% of patients at intermediate risk could be reclassified into a higher (14.3%) or lower (1.4%) risk category [78]. A total of 29% of patients with chronic kidney disease were reclassified into lower or higher risk for all-cause mortality when arterial stiffness was taken into account [79] and in patients with erectile dysfunction, a prognostic marker of generalized arterial disease and CV events, cfPWV reclassified 28% of patients to higher or lower risk category. In the most important approach in this issue, in the recently published individual data meta-analysis (n 5 17,635 participants), the 5-year overall NRI for CHD and stroke in intermediate risk individuals was 14.8% and 19.2%, respectively [74] (Table 22.2). From the therapeutic standpoint, aortic stiffness appears to be a worthwhile treatment target. To date, only one study has demonstrated in an indirect way that an improvement in outcomes was mediated through improvement in aortic stiffness [80]. Guerin et al. monitored 150 patients with end-stage renal disease in whom BP was controlled

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233

by adjustment of dry weight and treatment with antihypertensive medication when necessary. Independently of BP reduction, survival was associated with a reduction in aortic stiffness, while increase in PWV was predictive of all-cause and CV mortality. However, no study or randomized controlled trial has yet assessed in a direct way the potential of cfPWV as a target for therapy and whether such a strategy would result in better clinical outcomes (clinical outcomes criterion of a surrogate endpoint). Such a study is currently underway [77]. A decisive step toward clinical implementation is the determination of reference values for PWV by the Reference Values for Arterial Stiffness in 1,455 healthy subjects and a larger population of 11,092 subjects with CV risk factors [81]. Reference values have also been provided for children and teenagers [82]. Although the relationship between cfPWV and CV events is continuous, a threshold of 10 m/s has been proposed [58]. Of note, this cfPWV value refers to the full direct carotid-to-femoral distance, in order to normalize cfPWV values according to the arterial pathway. Investigators should preferentially use the direct carotid femoral distance and multiply by 0.8, only marginally overestimating the real traveled distance by 0.4% [58,83]. cfPWV meets most of the criteria to qualify as a surrogate endpoint for CV disease. Measurement of cfPWV has been incorporated in guidelines. At present, the ESC guidelines for individuals at intermediate risk acknowledge the added value of cfPWV for the stratification of patients [84]. Moreover, it should be considered for hypertensives (class IIa/B recommendation) [85], and it can add predictive value to the usual risk estimate of diabetics [86]. In contrast, it is not recommended by the ACCF/AHA guidelines for the assessment of CV risk in asymptomatic adults (class III/B recommendation) [87]; nevertheless, these need to be reappraised in light of recent data [74].

Brachial Ankle Pulse Wave Velocity Brachial ankle pulse wave velocity (baPWV) shares the same theoretical background with cfPWV, but it capitalizes on the concept that measurements over a longer arterial length may provide additional information and be easier, as it only involves wrapping of a pressure cuff around each of the four exposed extremities [88]. The method is being primarily used in Japan. Methodology baPWV is measured using a volume-plethysmographic apparatus. Occlusion cuffs, connected to both plethysmographic and oscillometric sensors, are wrapped around both upper arms and ankles of the subjects lying in the supine position. The brachial and posterior tibial arterial pressure waveforms are recorded by the plethysmographic sensor. The path lengths from the suprasternal notch to the brachium (Lb) and from the suprasternal notch to the ankle (La) are obtained from superficial measurements, corrected for the height of the individual and baPWV is calculated according to the equation: baPWV 5 (La 2 Lb)/ΔTba, where ΔTba is the time interval between the wavefront of the brachial waveform and that of the ankle waveform [89]. A baPWV value of 18 m/s has been suggested as a cutoff value in the assessment of the risk for CV disease [76,90]. Due to the fact that age and BP are major determinants of baPWV, a nomogram describing the correlation of the three variables is available [91]. Predictive Value—Use as Surrogate Endpoint baPWV has been well validated as a CV risk marker, as it is closely correlated not only with cfPWV, but also with aortic PWV assessed by the invasive method [88,92]. Similarly to cfPWV, the presence of CV risk factors is linked to elevated baPWV values [93]. For primary prevention, several prospective studies have reported that baPWV may be a useful predictor of future CV events in patients with end-stage renal disease, hypertension, and in the general population [76,94]. For secondary prevention, baPWV has been suggested as a useful predictor of the prognosis in patients with acute coronary syndromes and heart failure [90,95]. Some prospective studies also demonstrated that an elevated baPWV is a predictor of progression of pathophysiological abnormalities in the early stages of hypertension and chronic kidney disease [92]. In a meta-analysis we performed in 2012, we demonstrated that an increase in baPWV by 1 m/s was associated with an increase by 12%, 13%, and 6% in CV events, CV mortality, and allcause mortality, respectively; the independent predictive value of baPWV was established when studies that had controlled for most CV risk factors were subsequently analyzed [75] (Figure 22.3). The potential clinical advantage of baPWV over traditional risk scores has not been, however, formally proven (Table 22.2). One study has demonstrated the potential of the method to reclassify patients [76]. Nevertheless, data spanning more diverse populations should be obtained. Despite the fact that improvements in baPWV following drug

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Total CV events

(A) Author Population ESRD Kato2012 ESRD *Kitahara 2005 Matsuoka 2005 GENIELO Meguro 2009 CVO GENIELD *Miyano 2010 Morimoto 2009 ESRD Munakala 2011 ESRD *Munakala 2012 HTN Nakamura 2010 DM/CVD CVD Orlova 2009 *Sugamata 2011 CVD ESRD *Tanaka 2011 *Tomiyama 2005 CVD Yoshida 2012 DM Overall Overall (High-quality studies)

RR 3.40 7.03 68.91 5.10 9.01 7.67 1.98 2.97 1.57 5.27 1.66 1.51 5.47 1.47

95% Cl 0.71–16.36 1.49–33.12 3.90–1218.25 1.03–25.17 1.08–75.03 1.81–32.56 1.11–3.54 0.98–9.06 1.00–2.47 3.03–9.18 1.05–2.62 0.70–3.23 2.69–11.11 0.98–2.19 1.99–4.20 1.63–5.33

RR (95% Cl)

0.1

1

10

100

Test for heterogeneity: I2=66.0%, P<0.001 Test for overall effect: Z=5.57, P<0.001 (B)

CV mortality

Author Population ESRD Kato2012 ESRD *Kitahara 2005 GENIELO Matsuoka 2005 CVO Meguro 2009 GENIELD *Miyano 2010 ESRD Morimoto 2009 ESRD *Tanaka 2011 Overall Overall (High-quality studies)

RR 16.90 7.03 68.91 10.00 9.01 7.67 3.61 7.68 5.36

95% Cl 1.12–255.69 1.49–33.12 3.90–1218.25 1.32–75.85 1.08–75.03 1.81–32.56 0.97–13.44 3.91–15.07 2.17–13.27

Test for heterogeneity: I2=0.0%, P=0.686 Test for overall effect: Z=5.92, P<0.001

RR (95% Cl)

0.1

RR 2.06 5.11 1.70 1.79 2.97 4.09 1.97 2.63 6.80 2.48 5.36

10

100

All-cause mortality RR (95% Cl)

(C) Author Population Amemiya 2011 ESRD Chen 2011 RD Kato2012 ESRD ESRD *Kitahara 2005 GEN/ELD *Miyano 2010 ESRD Morimoto 2009 Nakamura 2010 DM/CVD ESRD *Tanaka 2011 GEN *Turin 2010 Overall Overall (High-quality studies)

1

95% Cl 0.52–8.11 1.53–17.08 0.64–4.49 0.93–3.46 1.25–7.07 1.61–10.42 1.01–3.84 0.89–7.79 1.40–32.91 1.82–3.37 1.56–3.86 0.1

1

10

100

Test for heterogeneity: I2=0.0%, P=0.617 Test for overall effect: Z=5.78, P<0.001

FIGURE 22.3 Prediction of CV events and all-cause mortality with brachial ankle elasticity index (baPWV). Relative risk (RR) and 95% CI for high brachial ankle elasticity index (baEI) and clinical events. RR and 95% CI for high baEI and total cardiovascular (CV) events (A), CV mortality (B), and all-cause mortality (C). Studies are listed alphabetically. Boxes represent the RR and lines represent the 95% CI for individual studies. The diamonds and their width represent the pooled RRs and the 95% CI, respectively. CVD indicates cardiovascular disease; DM, diabetes mellitus; GEN, general population; ELD, elderly; ESRD, end-stage renal disease; RD, renal disease; HTN, hypertensives.  High quality studies. Source: Adapted from Vlachopoulos et al. [75].

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therapy for hypertension, dyslipidemia, and diabetes mellitus and lifestyle modifications (exercise, weight reduction, smoking cessation) have been shown, no studies exist to date to substantiate the prospect that treatment according to baPWV values can lead to better clinical outcomes. Reference values have been published for Chinese populations [96,97]; but no data exist for non-Asian populations. At present, baPWV has not been endorsed by guidelines. baPWV shows considerable promise in fulfilling the criteria for a surrogate endpoint, not being, however, at this point as “mature” as cfPWV is. While simplicity and concurrent ankle-brachial index measurement with the same device are considerable advantages, issues that should be further addressed are those pertaining to calculation of traveled distance (height-based formulas), the need for validation in diverse populations, and the lack of reference values. Comparisons with the “gold standard” method of cfPWV are needed.

CONCLUSION Carotid IMT and aortic stiffness and are two biomarkers that have the ability to predict a person’s overall CV risk above and beyond classical risk factors, fitting within the concept of EVA. Aortic stiffness currently fulfills most criteria to be considered a surrogate endpoint, thus substituting for clinical endpoints. In contrast, cIMT offers important information but currently lacks such a status mainly limited by reclassification potential; improvement in methodology/assessment may be a means of overcoming such a drawback. A combined assessment of the two biomarkers may be advantageous in certain clinical settings or comorbidities merging their different strengths.

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