|Year : 2020 | Volume
| Issue : 1 | Page : 1-6
Comparison of Traditional and Novel Markers of Subclinical Atherosclerosis for Evaluating Cardiovascular Risk in Asymptomatic Population
Rohit Tandon1, Mamta Bansal1, Namita Bansal2, Abhishek Goyal1, Bhupinder Singh1, Shibba Takkar Chabbra1, Naved Aslam1, Bishav Mohan1, Gurpreet S. Wander2
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 Submission||02-Mar-2019|
|Date of Decision||04-Apr-2019|
|Date of Acceptance||02-May-2019|
|Date of Web Publication||11-Apr-2020|
Dr. Rohit Tandon
Department of Cardiology, Hero DMC Heart Institute, Ludhiana, Punjab
Source of Support: None, Conflict of Interest: None
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|| |
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. 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., 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.,,,,
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|| |
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.
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].,,,,
|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].,,,,,
|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].,,
|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.
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|| |
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 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 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.
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].
| Discussion|| |
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%.,,,, 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.,,,,, 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.,,,,,
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.,, 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|| |
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.
- 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
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]