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 Table of Contents  
ORIGINAL RESEARCH
Year : 2020  |  Volume : 4  |  Issue : 1  |  Page : 1-6

Comparison of Traditional and Novel Markers of Subclinical Atherosclerosis for Evaluating Cardiovascular Risk in Asymptomatic Population


1 Department of Cardiology, Hero DMC Heart Institute, Ludhiana, Punjab, India
2 Department of Cardiology, Research and Development Centre, Dayanand Medical College and Hospital, Ludhiana, Punjab, India

Date of Submission02-Mar-2019
Date of Decision04-Apr-2019
Date of Acceptance02-May-2019
Date of Web Publication11-Apr-2020

Correspondence Address:
Dr. Rohit Tandon
Department of Cardiology, Hero DMC Heart Institute, Ludhiana, Punjab
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jiae.jiae_10_19

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  Abstract 

Background: There is a definite need to identify markers that could offer greater discrimination of high- and low-risk patients within the general population at risk of cardiovascular disease (CVD). We tried to estimate the cardiovascular (CV) risk using novel imaging markers and compared them with traditional risk scores and biochemical markers. The study aimed at (1) evaluating the estimation of anthropometric, biochemical measurements (body surface area, body mass index, and lipid profile) along with novel imaging markers carotid intima-media thickness test (CIMT) and epicardial adipose tissue (EAT) for subclinical atherosclerosis carotid-femoral pulse wave velocity (CFPWV) for vascular stiffness; (2) comparing anthropometric markers, biochemical markers, and imaging markers in the study population with clinical risk prediction algorithms (American College of Cardiology/American Heart Association [ACC/AHA] atherosclerotic CVD [ASCVD] Risk Calculator). Materials and Methods: This was a prospective cross-sectional study done at a tertiary-level cardiac facility for a duration of 3 months. The study population consisted of apparently healthy individuals between the age group of 40 and 65 years. Persons with established ASCVD, chronic liver disease, chronic kidney disease, history of diabetes mellitus, or hypertension >7 years were excluded from the study. Results: Two hundred and seventy-seven individuals were included and divided into two groups based on the ACC/AHA ASCVD Risk Calculator. Individuals with <5% risk of ASCVD death/myocardial infarction/stroke over 10 years were included in low-risk group while individuals with 5%–15% risk of ASCVD death/myocardial infarction/stroke over 10 years were included in moderate-risk group. The relationship of these imaging parameters with ACC/AHA risk score was 8%, 16%, and 30% for CIMT, EAT, and CFPWV, respectively, whereas for total cholesterol/high-density lipoprotein (TC/HDL) ratio, it was 13% meaning that CIMT and TC/HDL ratio have weak correlation as compared to EAT and CFPWV. Inter-marker correlation with ACC/AHA Risk Calculator was 30% (P < 0.0001) for CFPWV, 16% (P = 0.008) for EAT, 13% (P = 0.026) for TC/HDL ratio, and 8% (P = 0.181) for CIMT. Multivariate regression showed that age, mean arterial blood pressure, history of diabetes mellitus, and EAT remain statistically significant parameters. Conclusion: Our study results suggested that both EAT and CFPWV measurements add incremental value over traditional methods for ASCVD risk stratification and should be of additional value over traditional methods for estimating ASCVD risk of an individual.

Keywords: Cardiovascular disease, epicardial adipose tissue, pulse wave velocity, subclinical atherosclerosis


How to cite this article:
Tandon R, Bansal M, Bansal N, Goyal A, Singh B, Chabbra ST, Aslam N, Mohan B, Wander GS. Comparison of Traditional and Novel Markers of Subclinical Atherosclerosis for Evaluating Cardiovascular Risk in Asymptomatic Population. J Indian Acad Echocardiogr Cardiovasc Imaging 2020;4:1-6

How to cite this URL:
Tandon R, Bansal M, Bansal N, Goyal A, Singh B, Chabbra ST, Aslam N, Mohan B, Wander GS. Comparison of Traditional and Novel Markers of Subclinical Atherosclerosis for Evaluating Cardiovascular Risk in Asymptomatic Population. J Indian Acad Echocardiogr Cardiovasc Imaging [serial online] 2020 [cited 2020 Jun 1];4:1-6. Available from: http://www.jiaecho.org/text.asp?2020/4/1/1/282194




  Introduction Top


Atherosclerotic coronary heart disease is commonly asymptomatic before the sentinel clinical event and therefore is the leading cause of mortality. A variety of clinical risk prediction algorithms have been developed for use in the primary prevention setting to identify asymptomatic individuals at risk. Clinical factors, such as age, hypertension, diabetes mellitus, hyperlipidemia, and family history, are still the predominant indicators for the likelihood of developing coronary artery disease (CAD) and have been incorporated in clinical risk prediction algorithms.[1] Over the years, a multitude of tests have been proposed to allow for more accurate classification of individuals into appropriate treatment categories. Comparison between different risk markers has been historically difficult because of the paucity of high-quality prospective studies evaluating these candidate risk markers side by side in the same population. We tried to analyze all these parameters in a single study and compared them with traditional disease markers. The current trends in the primary prevention of cardiovascular disease (CVD) emphasize the need-to-treat individuals based on their global cardiovascular (CV) risk, and practice guidelines recommend approaches to classify individuals as high, intermediate, or low risk using the Framingham Risk Score and, recently, American College of Cardiology/American Heart Association (ACC/AHA) risk scores.[1],[2] However, there is increasing recognition of the imprecision of these classifications such that the intermediate-risk group actually represents a composite of high-risk individuals, for whom more aggressive therapy is warranted. The intermediate-risk group also contains low-risk individuals in whom lifestyle measures alone are required. There is definitely a need to identify markers that could offer greater discrimination of high- and low-risk patients within the intermediate-risk group. Coronary artery calcium tops the list of markers of subclinical atherosclerosis, but for mass screening of low- and intermediate-risk population, there is a concern of radiation risk and cost. Other markers such as carotid intima-media thickness test (CIMT), epicardial adipose tissue (EAT), and pulse wave velocity (PWV) show a modest correlation in predicting CV risk in disease-free population in certain populations, but studies are required in various other ethnicities to verify their clinical use.[3],[4],[5],[6],[7]

Aims and objectives

The study aimed at:

  • Evaluating the estimation of anthropometric, biochemical measurements (body surface area [BSA], body mass index [BMI], and lipid profile) along with novel imaging markers CIMT and EAT for subclinical atherosclerosis carotid-femoral PWV (CFPWV) for vascular stiffness
  • Comparing anthropometric markers, biochemical markers, and imaging markers in the study population with clinical risk prediction algorithms (ACC/AHA atherosclerotic CVD [ASCVD] Risk Calculator).



  Materials and Methods Top


This was a prospective cross-sectional study done for 3 months at a tertiary-level cardiac center. The study population was aged between 40 and 65 years and apparently healthy individuals. Persons with established ASCVD, chronic liver disease, chronic kidney disease, history of diabetes mellitus, or hypertension >7 years were excluded from the study.

Imaging protocol

Echocardiography machines equipped with both sector and linear transducers were used (HD11 and Affinity 50C Ultrasound Machine Philips). Epicardial fat thickness is an echo-free space between the outer wall of the myocardium and the visceral layer of the pericardium over the right ventricle (RV)- free wall measured perpendicularly at end diastole from the parasternal long-axis and RV-focused views in three cardiac cycles. Normal value was taken as <7 mm while a cutoff of 9 mm was taken to divide the patients into low- and intermediate-risk groups [Figure 1].[8],[9],[10],[11],[12]
Figure 1: Epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT) as measured in parasternal long-axis view by two-dimensional echocardiography

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CIMT was measured using automated calipers from the far wall of the left and right common carotid arteries, at a region 10-mm proximal to the carotid bifurcation in the longitudinal plane. The mean value of these three measurements was used for the analysis. Normal value was taken as less than 0.6 mm while a cut off value of 0.9 mm was taken to divide the subjects into two groups [Figure 2].[13],[14],[15],[16],[17],[18]
Figure 2: Carotid intima-media thickness test measurement of common carotid artery using automated calipers

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PWV measurement was performed in the supine position after 10 min rest with a linear (4–12 MHZ) probe. After locating the carotid artery along the sternocleidomastoid muscle, the distance was measured using a measuring tape from 2 cm distal to carotid bifurcation till the point where femoral pulsations were felt in the groin. Then, with the same probe, we performed pulse Doppler ultrasound, synchronized with electrocardiogram (ECG), and the average of three recordings was taken. Each recording involved two or three cardiac cycles. To find the transit time, we measured the time from the R-wave of QRS to the foot of the waveform using digital calipers. To calculate velocity in m/s, we used the formula dx/dt × 0.8.(dt is the difference between T1 and T2, T1 is the time measurement in femoral artery gating with ECG, T2 is the time measurement in carotid artery gating with ECG, and dx is the distance from the sternal notch till the femoral artery at the groin); the normal value was taken as 8 7 m/s while a cutoff of 8 m/s was taken to divide the patients into low- and intermediate-risk groups [Figure 3].[19],[20],[21]
Figure 3: Electrocardiogram gated time delay between carotid and femoral artery Doppler signals for measuring pulse wave velocity (PWV) using the standard formula

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All measurements were performed by two investigators who were blinded to clinical data of the patients.

Height, weight, and waist-to-hip ratio were measured with the participant in the standing position wearing indoor clothes and no shoes. Smoking and alcohol use were estimated from self-report and categorized as current, former, and never user. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic BP (DBP) ≥ 90 mmHg and/or use of antihypertensive medication. Diabetes mellitus was defined as treatment with insulin or oral hypoglycemic agents and/or fasting plasma venous glucose >126 mg/dl. Mean arterial pressure (MAP) was calculated as (2 × DBP + SBP)/3.

Data analysis

Individuals without evidence of overt CVD were divided into two groups according to the AHA/ACC risk score: low-risk (<5%) and intermediate-risk (>5%) groups. Individuals with prevalent CVD (i.e., CAD, peripheral artery disease, or stroke) were excluded from the analyses (n = 48). To calculate the AHA/ACC risk score, data had to be available on age, gender, race, SBP, total and high-density lipoprotein (HDL)-cholesterol levels, presence of diabetes mellitus, current smoking, and treatment for BP. As already known with complete lipid profile, there is only 50% detection of CVD, so we added another calculation which is total cholesterol (TC)/HDL ratio >5 and <5, which can most reliably predict CAD more reliably. After risk score calculation, the relations of EAT, PWV, and CIMT with AHA/ACC risk score were calculated using linear regression models. To directly compare the different types of measurements, regression analysis using coefficients was used to ascertain co-relation between risk markers and was interpreted as 1SD increase in measurement. In the second part of the analysis, determinants of EAT, PWV, and CIMT were explored using single and multiple linear regression analyses. First, crude relations of determinants with EAT, PWV, or CIMT were determined. Subsequently, determinants were added in a multivariable model; P ≤ 0.05 was considered statistically significant.


  Results Top


Two hundred and seventy-seven individuals (80 [28.9%] females and 197 [71.1%] males) from asymptomatic population were included in the study. Individuals were divided into two groups based on the ACC/AHA ASCVD Risk Calculator. Individuals with <5% risk of ASCVD death/myocardial infarction/stroke over 10 years were in low-risk group while individuals with 5%–15% risk of ASCVD death/myocardial infarction/stroke over 10 years were in moderate-risk group. There were 132 (47.7%) individuals in low-risk group. Of 132 individuals, 65 (38%) were hypertensive and 13 (7.6%) were diabetic. There were 145 (52.3%) individuals in moderate-risk group; of 145 individuals, 67 (63.2%) were hypertensive and 31 (29.2%) were diabetic. The overall prevalence of abnormal values of three imaging parameters was in 127 (45%) individuals and was as follows: 11.2% of individuals had abnormal CIMT, 17% had abnormal PWV, and 29.6% had abnormal EAT. The cutoff values of these imaging markers for differentiating between the two groups were 0.72 ± 0.4 mm for mean CIMT, 8.3 ± 5 mm for mean EAT, and 8.75 ± 2.8 m/s for mean CFPWV. In univariate analysis for correlation of these parameters with ACC/AHA Risk Calculator, EAT and CFPWV showed significant correlation as 16% (P = 0.008) and 30% (P ≤ 0.0001), respectively, while CIMT and TC/HDL ratio showed a weak correlation as 8% (P = 0.181) and 13% (P = 0.026), respectively, thereby meaning that EAT and CFPWV are complimentary to the overall risk score [Table 1].
Table 1: General characteristics of study population (n=277)

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[Table 2] shows statistically significant difference in two groups for all three imaging markers (PWV, EAT, and CIMT) while there was no statistical difference for anthropometric measurements such as BMI, waist-to-hip ratio, and lipid values. Corroborated in receiver operating characteristic curve and area under the curve (AUC) graphs depicting maximum difference between CIMT values which differed most among groups, AUC was 0.695 [Figure 4]. Univariate regression in [Table 3] showed that with CIMT increase of 1SD, there was 0.08% increase in risk score for TC/HDL ratio, while it was 0.13%, for EAT 0.16% and for CFPWV there was increase of 0.30% in risk score, thereby highlighting the influence of these parameters in overall risk calculation.
Table 2: Imaging anthropometric and biochemical parameters

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Figure 4: Flow chart

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Table 3: Relations between markers for vascular damage and ACC/AHA score

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General parameters which affect these parameters individually are shown in [Table 4]. EAT and PWV are influenced by person's age and history of diabetes mellitus; in addition, PWV is also influenced by MAP. Surprisingly, BMI did not affect any of these parameters although studies have shown that there is a linear correlation of these parameters with BMI values. CIMT somehow showed only weak correlation with age.
Table 4: Independent determinants of CIMT, EAT and CFPWV

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Multivariate regression showed that age, mean arterial BP, history of diabetes mellitus, and EAT remain statistically significant parameters which influence ACC/AHA risk, and among these, EAT had maximum impact in moderate group. With addition of age, mean arterial BP, and history of diabetes mellitus in >5% risk population, the effect of CIMT and PWV lost significance while EAT measurement was not affected [Table 5].
Table 5: Regression analysis of various risk factors

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  Discussion Top


Using the current AHA/ACC risk factor calculator, a number of individuals fall into the low- and moderate-risk categories, which is of debatable significance. On individual basis, additional diagnostic studies are required to gauge the actual risk. Approximately 13% of intermediate-risk individuals can be reclassified by the addition of these novel markers to the risk factor calculation. Furthermore, a number of low-risk individuals, including younger adults, are up-classed when incorporating these novel markers into their risk score. Multi-Ethnic Study of Atherosclerosis (MESA) study followed up 4227 individuals free of diabetes mellitus, hypertension, and ASCVD for 10 years and concluded that ACC/AHA Risk Calculator overestimates individual's CVD risk by 78% (at the sensitivity of 62% and specificity of 35% for predicting ASCVD risk). It has limited value in women and individuals younger than 45 years. In the Copenhagen General Population Study, ACC/AHA risk score fared better than European Society of Cardiology/European Atherosclerosis Society score in assigning ASCVD risk although the MESA study had reported that, as compared to the predicted rate of 9.16% for 10 years, the observed rate for ASCVD was only 5.16%.[2],[5],[6],[14],[16] There is a need for a risk factor which can differ between the individuals with and without outcomes, predict future outcomes, add predictive information on top of established risk markers, reclassify patients' predicted risk to a sufficient extent, improve outcomes, provide novel and clinically relevant information beyond that provided by standard risk factors, and be cost-effective. In this study imaging based markers could easily outscore routinely used biochemical and anthropometric measurements in differentiating low risk and intermediate risk population. Recent research has demonstrated that the mean CIMT varied across obesity phenotypes; a higher CIMT value and positive associations with carotid plaque and carotid atherosclerosis were observed in only the metabolically abnormal obese subtype. These contradictory findings suggested that obesity does not affect vascular parameters related to early atherosclerosis, warranting more research to identify the correlation between different fat distributions and CIMT.[11],[12],[13],[14],[15],[16] Epicardial fat is a unique fat compartment between the myocardium and the visceral pericardium sharing a common embryologic origin with the visceral fat depot. Its local effects on cardiac function are incorporated in the complex pathomechanism of CAD. Importantly, EAT may produce several adipocytokines and chemokines that may influence the development and progression of coronary atherosclerosis. Echocardiography provides a simple, cheap, and readily available assessment which directly pictures the EAT thickness on the free wall of the RV. In a meta-analysis, EAT was 7.5 ± 0.1 mm in thickness in metabolic syndrome (n = 427) compared to 4.0 ± 0.1 mm in controls (n = 301), and EAT correlated significantly with the components of the metabolic syndrome.[8],[9],[10],[11],[12],[22]

In our study, the principal determinant of EAT and CIMT was age while PWV was dependent on age, mean arterial BP, and diabetes status, thereby indicating that in an individualistic approach, we have to take these determinants into consideration. The relationship of these imaging parameters with ACC/AHA risk score was 8%, 16%, and 30% for CIMT, EAT, and CFPWV, respectively, whereas for TC/HDL ratio, it was 13% meaning that CIMT and TC/HDL ratio have weak correlation as compared to EAT and CFPWV. These findings correlate with recent trial findings on the value of EAT and coronary flow velocity (CFV) in risk stratification of patients with ASCVD and also their high prevalence in abnormal range in this population. CIMT has poorer correlation probably that it has a more direct relation to cerebrovascular accident transient ischemic attack and as such carotid artery is a larger arterial tree. Moreover, it is now well understood that vascular stiffness plays a significant role in vascular damage and its consequences. EAT as such represents higher atheromatous load. Comparing the other two measures, EAT had a higher discriminative value for subjects with low risk, while CFV provided better discrimination for subjects with high risk according to the traditional risk factors. Among the three parameters, EAT is easier to measure as it does not require high-end equipment as for CFPWV. Keeping this in mind, we had taken PWV measurements with the help of linear ultrasound transducers, which are now a well-validated mode of PWV measurement. Initially, we had taken ankle-brachial velocity ratio, but recent reports have refuted the limited ability of this parameter as a surrogate for vascular stiffness due to its farther distance from the aorta. Our study is one of its kinds where we have taken both anatomical and functional parameters of vascular damage together and compared them with predetermined risk score based on an established risk calculator. A recent meta-analysis predicted that for 1 m/s increase in aortic PWV, there was a 7% increased risk of CV events occurring for a 60-year-old man who is a nonsmoker, nondiabetic, and not on any BP medication, with SBP of 120 mmHg, TC of 5.5 mmol/l, and HDL of 1.3 mmol/l.[19],[20],[21] This study throws light on few aspects such that obesity determined by anthropometric ratios (BSA and BMI) does not truly reflect vascular status of the individual. Furthermore, as previously discussed, clinical risk prediction tools have their inherent limitations, and many of the near-miss individuals can be reclassified based on these novel parameters. This study has several limitations. Overall, 34% of the patients were miscategorized as low risk by ACC/AHA Risk Calculator although they had abnormally high values of CIMT, PWV, and EAT.

This was a cross-sectional study, and as such, the associations do not necessarily imply causality and are not powered to predict CVD events. Reported R 2 values for individual contributions of variables may have been underestimated because some variability may be explained by interactions with age and BP. However, the strength of this study is that it focuses on a traditionally at-risk population for CVD. Evaluating the vulnerable subjects serves to broaden the applicability of research findings.


  Conclusion Top


Novel imaging markers for atherosclerosis should be of additional incremental value in risk stratification of both low- and intermediate-risk individuals for predicting ASCVD, especially when including EAT and CFPWV measurements to risk prediction model, keeping in view the age, mean arterial BP, and diabetic status of the individual.

Highlights

  • CIMT, PWV, and EAT are noninvasive markers for subclinical atherosclerosis
  • EAT and PWV show significant correlation with traditional markers and biochemical markers
  • For better prediction of CV risk in the asymptomatic population, imaging markers should be assessed along with traditional and biochemical markers keeping in mind the age, history of diabetes mellitus, and BP status.


Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
World Health Organization. Prevention of Cardiovascular Disease: Guideline for Assessment and Management of Cardiovascular Risk. Geneva, Switzerland: World Health Organization; 2007.  Back to cited text no. 1
    
2.
D'Agostino RB Sr., Pencina MJ, Massaro JM, Coady S. Cardiovascular disease risk assessment: Insights from Framingham. Glob Heart 2013;8:11-23.  Back to cited text no. 2
    
3.
Oliver JJ, Webb DJ. Noninvasive assessment of arterial stiffness and risk of atherosclerotic events. Arterioscler Thromb Vasc Biol 2003;23:554-66.  Back to cited text no. 3
    
4.
Amar J, Ruidavets JB, Chamontin B, Drouet L, Ferrières J. Arterial stiffness and cardiovascular risk factors in a population-based study. J Hypertens 2001;19:381-7.  Back to cited text no. 4
    
5.
Heiss G, Sharrett AR, Barnes R, Chambless LE, Szklo M, Alzola C. Carotid atherosclerosis measured by B-mode ultrasound in populations: Associations with cardiovascular risk factors in the ARIC study. Am J Epidemiol 1991;134:250-6.  Back to cited text no. 5
    
6.
Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O'Leary D, et al. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA 2012;308:788-95.  Back to cited text no. 6
    
7.
Rosito GA, Massaro JM, Hoffmann U, Ruberg FL, Mahabadi AA, Vasan RS, et al. Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: The Framingham heart study. Circulation 2008;117:605-13.  Back to cited text no. 7
    
8.
Eroglu S, Sade LE, Yildirir A, Bal U, Ozbicer S, Ozgul AS, et al. Epicardial adipose tissue thickness by echocardiography is a marker for the presence and severity of coronary artery disease. Nutr Metab Cardiovasc Dis 2009;19:211-7.  Back to cited text no. 8
    
9.
Natale F, Tedesco MA, Mocerino R, de Simone V, Di Marco GM, Aronne L, et al. Visceral adiposity and arterial stiffness: Echocardiographic epicardial fat thickness reflects, better than waist circumference, carotid arterial stiffness in a large population of hypertensives. Eur J Echocardiogr 2009;10:549-55.  Back to cited text no. 9
    
10.
Wang TD, Lee WJ, Shih FY, Huang CH, Chen WJ, Lee YT, et al. Association of epicardial adipose tissue with coronary atherosclerosis is region-specific and independent of conventional risk factors and intra-abdominal adiposity. Atherosclerosis 2010;213:279-87.  Back to cited text no. 10
    
11.
Nelson MR, Mookadam F, Thota V, Emani U, Al Harthi M, Lester SJ, et al. Epicardial fat: An additional measurement for subclinical atherosclerosis and cardiovascular risk stratification? J Am Soc Echocardiogr 2011;24:339-45.  Back to cited text no. 11
    
12.
Iacobellis G, Malavazos AE, Corsi MM. Epicardial fat: From the biomolecular aspects to the clinical practice. Int J Biochem Cell Biol 2011;43:1651-4.  Back to cited text no. 12
    
13.
Wendelhag I, Liang Q, Gustavsson T, Wikstrand J. A new automated computerized analyzing system simplifies readings and reduces the variability in ultrasound measurement of intima-media thickness. Stroke 1997;28:2195-200.  Back to cited text no. 13
    
14.
Bots ML, Hoes AW, Koudstaal PJ, Hofman A, Grobbee DE. Common carotid intima-media thickness and risk of stroke and myocardial infarction: The Rotterdam study. Circulation 1997;96:1432-7.  Back to cited text no. 14
    
15.
Bots ML, Dijk JM, Oren A, Grobbee DE. Carotid intima-media thickness, arterial stiffness and risk of cardiovascular disease: Current evidence. J Hypertens 2002;20:2317-25.  Back to cited text no. 15
    
16.
Bauer M, Delaney JA, Möhlenkamp S, Jöckel KH, Kronmal RA, Lehmann N, et al. Comparison of factors associated with carotid intima-media thickness in the multi-ethnic study of atherosclerosis (MESA) and the Heinz Nixdorf recall study (HNR). J Am Soc Echocardiogr 2013;26:667-73.  Back to cited text no. 16
    
17.
Eikendal AL, Groenewegen KA, Anderson TJ, Britton AR, Engström G, Evans GW, et al. Common carotid intima-media thickness relates to cardiovascular events in adults aged<45 years. Hypertension 2015;65:707-13.  Back to cited text no. 17
    
18.
Geisel MH, Bauer M, Hennig F, Hoffmann B, Lehmann N, Möhlenkamp S, et al. Comparison of coronary artery calcification, carotid intima-media thickness and ankle-brachial index for predicting 10-year incident cardiovascular events in the general population. Eur Heart J 2017;38:1815-22.  Back to cited text no. 18
    
19.
Mendonça MI, Reis RP, Sousa AC, Gomes S, Faria P, Pereira A, et al. Pulse wave velocity and coronary risk stratification. Rev Port Cardiol 2009;28:155-71.  Back to cited text no. 19
    
20.
Song HG, Kim EJ, Seo HS, Kim SH, Park CG, Han SW, et al. Relative contributions of different cardiovascular risk factors to significant arterial stiffness. Int J Cardiol 2010;139:263-8.  Back to cited text no. 20
    
21.
Norhayati MN, Hanif I, Rasool AH. Carotid-femoral pulse wave velocity assessment amongst 10-year cardiovascular disease risk groups of hypertensive patients in primary health care. Int J Collab Res Intern Med Public Health 2014;6:286-95.  Back to cited text no. 21
    
22.
Nagy E, Jermendy AL, Merkely B, Maurovich-Horvat P. Clinical importance of epicardial adipose tissue. Arch Med Sci 2017;13:864-74.  Back to cited text no. 22
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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