The atherosclerotic pathologic changes of peripheral arteries could reflect subclinical atherosclerosis to some extent in community-based asymptomatic populations. Subclinical atherosclerosis refers to subclinical carotid atherosclerosis or atherosclerosis in general. Published results of the Multi-Ethnic Study of Atherosclerosis (MESA) suggested that both carotid intima-media thickness (CIMT) and carotid atherosclerotic plaque were strongly associated with cardiovascular events.1 2,3
For cardiovascular health (CVH) promotion, the American Heart Association (AHA) proposed ideal CVH metrics in detail, which were defined by the presence of ideal health behaviors and health factors.4 5
Noncontrast chest computed tomography (CT) could be routinely acquired through an appropriate scan protocol regardless of whether it is electrocardiography (ECG) gated or not. The coronary artery calcium score (CACS) and epicardial adipose tissue volume (EATv) are the most widely used parameters gained from noncontrast chest CT. Both CACS and EATv were related to subclinical atherosclerosis in several studies in recent years.6–8
In this study, we hypothesized that noncontrast chest CT parameters, such as CACS and EATv, could provide certain incremental value for predicting subclinical carotid atherosclerosis above the traditional risk assessment model based on ICHS.
MATERIALS AND METHODS
Study Design and Participants
The PERSUADE study is an observational, longitudinal, and prospective cohort study in a target population of asymptomatic subjects based on Jidong Oilfield (Tangshan city, Hebei province, China).9 Fig. 1 ). Questionnaires and physical examinations were performed by trained medical professionals from Jidong Oilfield Hospital. The research was performed according to the Helsinki Declaration guidelines and approved by the Ethics Committee of Jidong Oilfield Hospital. All participants signed written informed consents.
FIGURE 1: Flowchart illustrating the study populations. AF indicates atrial fibrillation; CAS, carotid arterial stenosis; HF, heart failure; MI, myocardial infarction.
Assessment of ICHS
In accordance with the AHA guidelines, we defined ideal CVH metrics into 7 items under 3 levels.4 https://links.lww.com/JTI/A221
Data on current smoking, physical activity, and healthy diet were assessed through questionnaires. Body mass index was calculated as the body weight (kg) divided by the square of height (m2 ). Blood pressure was measured using a mercury sphygmomanometer. The researchers recorded the average of 2 readings of systolic blood pressure and diastolic blood pressure after an interval at which all the participants rested in the seated position for at least 5 minutes. Blood samples were obtained and separated according to previous methods. Biochemical variables were measured using an autoanalyzer (Olympus, AU400, Japan) at the central laboratory of the Jidong Oilfield Hospital.
Assessment of Carotid Doppler Sonography
Carotid Doppler sonography was performed by two experienced sonographers, who were blinded to the information of participants with a portable machine (EME Companion, Nicolet, Madison, WI). All participants received bilateral common carotid artery, carotid bifurcation, internal carotid artery, and external carotid artery examinations in a supine position with their heads turned to the contralateral side.10 11,12 13 14,15
Assessment of Noncontrast Chest CT
Noncontrast chest CT examination was scanned using a 64-slice CT scanner (Siemens Sensation 64-Slice CT, Forchheim, Germany) for extra CACS and EATv quantification. All CT DICOM files were transferred to semiautomatic software (Syngo MultiModality Workplace, Siemens, Germany) after a standard non–ECG-gated prospective scan protocol. Two experienced cardiologists who were blinded to the patients’ clinical information were responsible for quantification assessment of CACS and EATv. An example for imaging quantification is provided in Figure 2 .
FIGURE 2: Example for CACS and EATv quantification in a 58-year-old male participant from Jidong Oilfield community. Transverse CT sections obtained from non–ECG-gated noncontrast chest CT imaging are shown. A–C, The figures illustrate CACS quantification manually, and calcification in each coronary artery is represented in different colors. D–F, Demonstrate total EATv quantification tracing the contour of the pericardium semiautomatically based on Hounsfield unit (HU) attenuation.
CACS is calculated by the Agatston scoring method,16
The measurement range of EATv is from bifurcation of the pulmonary artery to the diaphragm. A threshold attenuation value of −195 to −45 HU is defined as the adipose tissue. According to the size of the heart, cardiologists manually extract 7 to 10 parallel and equidistant axial contours of the pericardium. Finally, the EATv is calculated automatically in accordance with the above ranges and threshold attenuation value.17
Intraclass correlation coefficient tests of both CACS and EATv assessment are provided in Supplementary Table 2 (Supplemental Digital Content 1, https://links.lww.com/JTI/A221
Statistical Analysis
Continuous variables are presented as the mean and SD or median and interquartile range. In the case of a normal distribution, the Student t test was used for intergroup comparisons; otherwise, the rank sum test was used. Categorical variables are represented by percentages, and the χ2 or Fisher exact test was used for comparisons between groups. Multivariate logistic regression analysis was used to reveal the association between ICHRS combined with noncontrast chest CT parameters and subclinical carotid atherosclerosis by calculating the odds ratios (ORs) and 95% confidence interval (CI). ICHRS was generated based on ICHS during the analysis of multivariate logistic regression. We recalculated log-transformed CACS as well. I-EATv was defined as EATv divided by body mass index to correct for the effect of obesity as previously described.18 P value <0.05 was considered to be statistically significant.
RESULTS
Demographic Characteristics of Participants
There were 2195 individuals (55.47±7.68 y of age, 47.6% male) enrolled in this cross-sectional study for further analysis. The prevalence of hypertension, diabetes mellitus, and dyslipidemia was 24.6%, 8.7%, and 15.1%, respectively. Detailed demographic characteristics, as well as information regarding noncontrast chest CT parameters and ICHS, are summarized in Table 1 .
TABLE 1 -
Demographic Characteristic of Enrolled Individuals
Parameters
Overall (N=2195)
Subclinical Carotid Atherosclerosis (N=451)
Normal (N=1744)
P
Sex (male)
1045 (47.6)
283 (62.7)
762 (43.7)
<0.001
Age (y)
55.47±7.68
60.60±7.10
54.14±7.26
<0.001
Age level
<0.001
45-
150 (6.9)
6 (1.3)
144 (8.3)
—
45-55
880 (40.1)
83 (18.4)
797 (45.7)
—
55-65
927 (42.2)
257 (57.0)
670 (38.4)
—
65+
238 (10.8)
105 (23.3)
133 (7.6)
—
BMI (kg/m2 )
25.12±3.25
25.64±3.08
24.99±3.29
<0.001
SBP (mm Hg)
132.98±20.00
139.69±21.10
131.25±19.34
<0.001
DBP (mm Hg)
84.10±13.21
85.72±13.20
83.68±13.19
0.004
Tobacco use
503 (22.9)
147 (32.6)
356 (20.4)
<0.001
Alcohol
638 (29.1)
149 (33.0)
489 (28.0)
0.037
Hypertension
541 (24.6)
174 (38.6)
367 (21.0)
<0.001
Diabetes mellitus
191 (8.7)
72 (16.0)
119 (6.8)
<0.001
Dyslipidemia
331 (15.1)
86 (19.1)
245 (14.0)
0.008
Glucose (mmol/L)
5.60±1.47
5.96±1.86
5.51±1.34
<0.001
TC (mmol/L)
4.75±0.90
4.87±0.87
4.72±0.91
0.002
TG (mmol/L)
1.71±1.38
1.81±1.15
1.69±1.43
0.103
LDLC (mmol/L)
2.68±0.60
2.81±0.60
2.65±0.60
<0.001
HDLC (mmol/L)
1.22±0.28
1.17±0.26
1.23±0.28
<0.001
CIMT (mm)
0.85±0.18
0.95±0.21
0.82±0.16
<0.001
EATv (ml)
123.97 (94.63, 160.63)
150.91 (119.30, 187.11)
117.63 (90.04, 151.98)
<0.001
i-EATv
4.97 (3.88, 6.24)
5.89 (4.89, 7.14)
4.72 (3.74, 5.93)
<0.001
CACS
0.00 (0.00, 0.00)
0.00 (0.00, 38.10)
0.00 (0.00, 0.00)
<0.001
log-CACS
0.00 (0.00, 0.00)
0.00 (0.00, 1.59)
0.00 (0.00, 0.00)
<0.001
ICHS
10.00 (8.00, 11.00)
9.00 (7.00, 10.00)
10.00 (8.00, 11.00)
<0.001
BMI indicates body mass index; DBP, diastolic blood pressure; HDLC, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride.
Specifically, increased CIMT ≥1.0 mm was observed in 104 (4.7%) cases; carotid arterial stenosis was detected in 92 (4.2%) cases; and carotid plaque was present in 409 (18.6%) cases. Participants were divided into 2 groups in accordance with subclinical carotid atherosclerosis. As a result, 451 individuals were diagnosed with subclinical carotid atherosclerosis. Male residents were more likely to suffer subclinical carotid atherosclerosis, similar to subgroups of above 55 years of age (Table 1 ).
The subclinical carotid atherosclerosis group had a higher prevalence of traditional risk factors, such as hypertension, diabetes mellitus, and dyslipidemia. Glucose, total cholesterol, low-density lipoprotein cholesterol, CIMT, EATv, i-EATv, CACS, log-CACS, and ICHS were significantly greater in the subclinical carotid atherosclerosis group (Table 1 ).
Distribution of Subclinical Carotid Atherosclerosis Under Different ICHS
The included individuals were divided and regrouped by different ICHS levels. The numbers of normal subjects, as well as the cases of subclinical carotid atherosclerosis, are shown in Figure 3 . Individuals with subclinical carotid atherosclerosis accounted for more than 25% when ICHS was <8, while the proportions were <20% when the ICHS was >10. There was a decreasing trend of the prevalence of subclinical carotid atherosclerosis as ICHS increased, which demonstrated a negative correlation between ideal CVH metrics and peripheral arterial atherosclerosis (Fig. 3 ).
FIGURE 3: Distribution of subclinical carotid atherosclerosis under different ICHS.
Univariate and Multivariate Logistic Regression Analysis of Subclinical Carotid Atherosclerosis
Male sex, age level, i-EATv, log-CACS, and ICHRS demonstrated statistical significance in univariate logistic regression analysis. When the aforementioned variables were included in multivariate logistic regression analysis, the ORs for subclinical carotid atherosclerosis were male (OR=1.506, 95% CI: 1.177-1.926, P =0.001), age 55 to 65 years (OR=5.743, 95% CI: 2.460-13.405, P <0.001), age 65+ (OR=9.849, 95% CI: 4.084-23.755, P <0.001), i-EATv (OR=1.254, 95% CI: 1.173-1.341, P <0.001), and log-CACS (OR=1.701, 95% CI: 1.480-1.955, P <0.001) and ICHRS (OR=1.143, 95% CI: 1.080-1.210, P <0.001) (Table 2 ).
TABLE 2 -
Risk Factors for Subclinical Carotid Atherosclerosis
Univariate logistic regression
Multivariate logistic regression
Parameters
OR
95% CI
P
OR
95% CI
P
Sex (male)
2.171
1.754-2.686
<0.001
1.506
1.177-1.926
0.001
Age level
45-
1.000
—
—
1.000
—
—
45-55
2.499
1.071-5.832
0.034
1.965
0.829-4.655
0.125
55-65
9.206
4.017-21.096
<0.001
5.743
2.460-13.405
<0.001
65+
18.947
8.052-44.588
<0.001
9.849
4.084-23.755
<0.001
i-EATv
1.447
1.364-1.536
<0.001
1.254
1.173-1.341
<0.001
log-CACS
2.302
2.025-2.617
<0.001
1.701
1.480-1.955
<0.001
ICHRS
1.220
1.162-1.280
<0.001
1.143
1.080-1.210
<0.001
ROC Analysis of Different Models for Subclinical Carotid Atherosclerosis
Adding male sex and age level on the basis of ICHRS significantly increased the AUC for predicting subclinical carotid atherosclerosis (0.757 [95% CI: 0.732-0.781] vs. 0.627 [95% CI: 0.599-0.654], P <0.0001). The incremental improvement values of log-CACS and i-EATv were significant when added to model 2, with elevations in the AUC from 0.757 to 0.776 (model 4 vs. model 2, P =0.0002) and from 0.757 to 0.773, respectively (model 3 vs. model 2, P =0.0017). The AUC for model 5, which combined i-EATv and log-CACS on the basis of model 2 at the same time, peaked at 0.788 (95% CI: 0.765-0.812) among the total models. The differences between model 5 and model 3 (P =0.0007) showed that log-CACS outperformed i-EATv with a further increment in predicting subclinical carotid atherosclerosis (Table 3 , Fig. 4 ).
TABLE 3 -
ROC Analysis of Different Models
Models
AUC
95% CI
P
vs. M1
vs. M2
vs. M3
vs. M4
Model 1:ICHRS
0.627
0.599-0.654
<0.0001
—
—
—
—
Model 2:M1+male+age
0.757
0.732-0.781
<0.0001
<0.0001
—
—
—
Model 3:M2+i-EATv
0.773
0.749-0.797
<0.0001
<0.0001
0.0017
—
—
Model 4:M2+log-CACS
0.776
0.752-0.800
<0.0001
<0.0001
0.0002
0.6162
—
Model 5:M2+i-EATv+log-CACS
0.788
0.765-0.812
<0.0001
<0.0001
<0.0001
0.0007
0.0053
P <0.005 was considered to be statistically significant abided by Bonferroni rule.
ICHRS=14−ICHS.
FIGURE 4: ROCs of model 1, model 2, and model 5. Model 1 demonstrated a basic model of ICHRS with AUC=0.627 (95% CI: 0.599-0.654). Model 2 gained an ROC curve with an AUC=0.757 (95% CI: 0.732-0.781) when adding male sex and age to model 1. Further combining i-EATv and log-CACS to model 2, model 5 reached a peak AUC=0.788 (95% CI: 0.765-0.812).
DISCUSSION
In this cross-sectional study, we demonstrated the general characteristics of community-based asymptomatic subjects, including the prevalence of subclinical carotid atherosclerosis, traditional risk factors, CACS, and EATv. It was found that noncontrast chest CT-derived parameters, such as CACS and EATv, could provide significant incremental value for predicting subclinical carotid atherosclerosis on the basis of traditional risk factors.
Multiple biological and lifestyle risk factors could constantly induce cardiovascular and peripheral atherosclerosis over a period of years. Under this circumstance, early detection of arterial atherosclerotic changes demonstrated prominent merit in risk assessment.15 19 20,21 2,22
Although carotid arteries could be examined by noninvasive means, such as sonography, it seemed to be unrealistic and costly for every asymptomatic individual to undergo this type of examination because sonography assessments were laborious and time-consuming tasks that required considerable manpower with adequate operation experience. On the other hand, noncontrast CT for lung cancer screening is being widely used in adults across China, but the data derived from noncontrast CT seems to be insufficiently analyzed. Moreover, initiating systematic atherosclerosis risk assessment with appropriate scales deserves to be taken into consideration within apparently healthy individuals.20
Olatokunbo Osibogun et al23 24
Because cardiovascular atherosclerosis and peripheral atherosclerosis are closely interlinked, Madaj and Budoff25 26 27 28
In this study, we found that CACS and EATv could provide incremental performance of the conventional prediction model (M3 vs. M2, M4 vs. M2, both P <0.005). Moreover, combining these 2 noncontrast chest CT-derived parameters could provide further incremental value for AUC improvement over the traditional risk assessment model (0.788 [95% CI: 0.765-0.812] vs. 0.757 [95% CI: 0.732-0.781], P <0.0001). Our study was partly in line with the above results, and the results could be interpreted in the following ways. First, calcification is a direct sign of subclinical cardiovascular atherosclerosis and is positively associated with CIMT and the prevalence of carotid plaque.22 29 30
It should be noted that the noncontrast chest CT parameters referred to in this study came from low-dose non–ECG-gated examinations for lung disease CT screening. Additional benefits could be expected from these CT-derived indices without extra radiation burden.31 32
There were several limitations in the present study. First, this study was cross-sectional on the basis of a local community, reflecting limited regional lifestyles, demographic characteristics, and risk factors. Further cost-effective analysis should be performed to justify this approach in consideration of radiation exposure. Second, other nontraditional risk factors besides ideal CVH metrics were not included in the present study, which would bring up some bias during statistical analysis. Third, although several works support the uniformity of epicardial adipose volumetric analysis and standardization, the correlation between CT-derived EATv and other well-established methods, such as T1 Dixon MR, remains to be investigated. Finally, EAT volume but not EAT attenuation was used in the present study. Therefore, inclusion of EAT attenuation may enhance the discrimination capacity and should be further warranted.
In summary, ICHS was inadequate for predicting subclinical carotid atherosclerosis alone in Chinese community-based asymptomatic populations. Sex and age should still be included in risk assessment. Noncontrast chest CT-derived parameters, including CACS and EATv, could provide significant incremental improvement for predicting subclinical carotid atherosclerosis beyond the traditional risk assessment model.
ACKNOWLEDGMENTS
The authors appreciate all the participants and their relatives in Jidong cohort and the staff of Jidong Oilfield Hospital for their significant efforts. They sincerely thank the professionals of the Recovery Medical Technology Development Co. Ltd for their support.
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