Open Access

The cardiovascular risk factors associated with the plaque pattern on coronary computed tomographic angiography in subjects for health check-up

  • Dong-Hyeon Lee1,
  • Ho-Joong Youn1Email author,
  • Hae-Ok Jung1,
  • Kiyuk Chang1,
  • Yun-Seok Choi1 and
  • Jung Im Jung2
Clinical Hypertension201723:6

DOI: 10.1186/s40885-017-0062-4

Received: 18 October 2016

Accepted: 3 January 2017

Published: 2 March 2017

Abstract

Background

Although it is known that coronary computed tomographic angiography (CCTA) offers highly negative predictive value to exclude obstructive coronary lesions, the plaque pattern on CCTA has not been fully understood. The purpose of this study was to explore the difference of the plaque patterns on CCTA and to assess the cardiovascular risks in healthy subjects.

Methods

A total of 3914 subjects (mean age: 55 ± 10 years, M : F = 2649 : 1265) who underwent CCTA for health check-up between January 2009 and December 2012 were enrolled. According to coronary artery calcium score (CACS) and plaque pattern on CCTA, subjects were categorized into four groups (group 1: normal; group 2: “non-calcified” plaque; group 3: “calcified” plaque; group 4: mixed plaque). We analyzed cardiovascular risks and Framingham risk score (FRS) among the groups.

Results

The incidence of each group was group 1 in 55.0% (2152/3914), group 2 in 5.1% (200/3914), group 3 in 8.2% (319/3914), and group 4 in 7.2% (280/3914), respectively. There was no difference of FRS among the groups (6.4 ± 6.4%; 6.5 ± 4.6%; 8.2 ± 5.8%; 7.7 ± 5.7% p = 0.086). In multivariate analysis, HbA1c (OR = 2.285; 95%CI = 1.029 - 5.071; p = 0.042) in group 2; age (OR = 1.115; 95%CI = 1.034 - 1.202; p = 0.005) and smoking status (OR = 3.386; 95%CI = 1.124 - 10.202; p = 0.030) in group 3; and age (OR = 1.054; 95%CI = 1.011 - 1.099; p = 0.014) and hypertension (OR = 3.087; 95%CI = 1.536 - 6.202; p = 0.001) in group 4 were independent factors.

Conclusions

Our data suggest that more individualized therapy for reduction of cardiovascular risks associated with plaque pattern on CCTA could be considered in healthy subjects.

Keywords

Computed tomography Coronary artery disease Atherosclerosis Calcium Dual-source CT

Background

Now it is well known that coronary artery disease (CAD) is the leading mortality cause due to sudden death or myocardial infarction in healthy subjects and a major public health problem in the world [1]. Therefore, it is potentially important that the endeavor to identify subclinical coronary atherosclerosis can lead to reduction in the rate of cardiovascular events. Although Framingham risk score (FRS), 10-year risk for coronary heart disease (CHD) represents a very useful diagnostic tool, there are some limitations for the estimation of the risk of cardiovascular morbidity and mortality such as overestimation in a low-risk population or underestimation the risk in a high-risk population [2].

Conventional invasive coronary angiography has been the standard method for diagnosing CAD [3, 4]. In addition, the recent multidetector coronary computed tomographic angiography (CCTA) using dual-source computed tomography (DSCT) was introduced as a useful, non-invasive tool for the evaluation of coronary atherosclerosis and the prediction of cardiovascular morbidity [5, 6]. However, the screening of coronary artery calcium score (CACS) using CCTA should not be recommended in asymptomatic individuals with low-risk (0 to 1 risk factor or a 10-year risk <10%) or high-risk (CHD risk equivalents or a 10-year risk >20%) according to Framingham criteria, but considered useful in patients with intermediate- risk (more than two risk factors or a 10-year risk 10-20%) [7, 8]. Nevertheless, CACS has not only an excellent negative predictive value to exclude the presence of significant CAD [9, 10], but provides also more important prognostic information for cardiovascular risk stratification than the biomarker, such as C-reactive protein [11, 12].

Until now, the usefulness of CCTA for estimating and predicting of subclinical coronary atherosclerosis in healthy subjects has not been well established, although past studies mostly included symptomatic patients with significant or obstructive CAD including acute coronary syndrome [13, 14]. The purpose of this study was to explore the difference of the plaque patterns on CCTA such as “non-calcified” or “calcified” plaque and to assess the cardiovascular risks in healthy subjects.

Methods

Study population

Between January 2009 and December 2012, a total of 3914 subjects (mean age: 55 ± 10 years, M : F = 2649 : 1265) who underwent CCTA for health check-up at the Heath Promotion Center of Seoul St. Mary’s Hospital (The Catholic University, Seoul, Korea), were enrolled.

Criteria for exclusion included: the patients (1) who underwent prior coronary artery bypass graft (CABG: n = 4, 0.10%); (2) who underwent prior percutaneous coronary intervention (PCI: n = 39, 1.0%) using stents, or the subjects (3) with irregular heartbeats (e.g., atrial fibrillation), very severe obesity (body mass index; BMI ≥40 kg/m2) [15], or inability to comply with instructions for breath holding; or (4) with CACS >10 or more than mild (≥25%) luminal stenosis represented as group 5, as a dropout (Fig. 1) [1618].
Fig. 1

Flow diagram. Group 1 (e.g., normal lumen / no plaque) represented as “normal” coronary arteries: CACS = 0 & normal CCTA; Group 2 (e.g., <25% lumen / “non-calcified or soft” plaque): CACS = 0 & CCTA of minimal disease (luminal diameter <25%); Group 3 (e.g., <25% lumen or plaque with negligible impact on lumen / “calcified” plaque): CACS >0 & normal CCTA (CACS rage in our data: 0.42–7.9); Group 4 (e.g., <25% lumen / “mixed” plaque): CACS ≤ 10 & CCTA of minimal disease (luminal diameter <25%); Group 5 the subjects with CACS >10 or more than mild (≥25%) luminal stenosis [1618]. CABG, coronary artery bypass graft; PCI, percutaneous coronary intervention; BMI, body mass index; CACS, coronary artery calcium score; CCTA, coronary computed tomographic angiography

According to the coronary artery calcium score (CACS) and plaque pattern on CCTA, we assigned as a inclusion criteria, subjects were categorized into four groups: (1) group 1 (e.g., normal lumen / no plaque) represented as “normal” coronary arteries: CACS = 0 & normal CCTA; (2) group 2 (e.g., <25% lumen / “non-calcified or soft” plaque): CACS = 0 & CCTA of minimal disease (luminal diameter <25%); (3) group 3 (e.g., <25% lumen or plaque with negligible impact on lumen / “calcified” plaque): CACS >0 & normal CCTA (CACS rage in our data: 0.42–7.9); (4) group 4 (e.g., <25% lumen / “mixed” plaque): CACS ≤ 10 & CCTA of minimal disease (luminal diameter <25%) who met the inclusion criteria, were included in the study [1618]. After ruling out the subjects with CACS >10 or more than mild (≥25%) luminal stenosis represented as group 5 in present study, we compared FRS and the other traditional cardiovascular risks among the four groups.

This study was approved by the Institutional Review Committee of St Mary’s Hospital, the Catholic University of Korea and conducted in agreement with the Declaration of Helsinki. The participants were informed of the investigative nature of the study and written informed consent was obtained before enrollment (XC11RIMI0091S).

Anthropometric parameters

Each participant also underwent a complete physical examination including anthropometric measurements. Heights were measured to the nearest 0.1 cm with a portable stadiometer (InBody 720; Biospace Ltd., Seoul, Korea) and body weights were measured to the nearest 0.1 kg using a digital scale wearing a standardized health check-up gown. BMI was calculated as the weight in kilograms, divided by the height in meters squared. Systolic blood pressure (BP), diastolic BP, and heart rate were measured using an automatic sphygmomanometer (BP203RV-II; Nippon Colin, Komaki, Japan) with subjects in a seated position after resting quietly for 10 min.

Biochemical parameters

Blood samples were taken at health check-up day. The lipid profile, including total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-cholesterol), and low-density lipoprotein cholesterol (LDL-cholesterol) levels were measured using enzymatic method by an automatic analyzer (7600-210; Hitachi Medical Corp., Tokyo, Japan). HbA1c was measured by G8 HbA1c analyzer (Tosoh Corporation, Tokyo, Japan). The biochemistry including fasting blood glucose and C-reactive protein from blood samples were measured by a biochemistry analyzer (7600-210; Hitachi Medical Corp., Tokyo, Japan).

Framingham risk score

The 10-year risk for myocardial infarction and coronary death is estimated from total points and all participants were categorized into three different CHD risk groups according to the National Cholesterol Education Program (NCEP) guidelines: (1) low-risk (10-year risk <10%); (2) moderate- or intermediate- risk (10-year risk 10–20%); and (3) high-risk group (10-year risk >20%) [19].

Coronary artery calcium score assessment

We measured coronary artery calcification with a DSCT (SOMATOM Definition; Siemens Healthcare, Forchheim, Germany). The participants did not receive additional premedications, such as β-blockers, for control of their heart rate. DSCT parameters were as follows: tube voltage = 120 kVp, gantry rotation time = 0.33 s, slice collimation = 64 × 0.6 mm, reconstruction slice width = 0.75 mm, reconstruction slice interval = 0.4 mm, kernel = B26f, field of view = 25 cm. Eighty mL of contrast agent (Iohexol, IOBRIX INJ 300; Tae Joon Pharm. Ind. Co., Ltd, Seoul, Korea) using a dual-head power injector (CT Stellant; Medrad Inc., Indianola, Pennsylvania, USA) was injected intravenously at 5 mL/s for 16 s.

All post-processing examinations were performed using retrospective electrocardiography (ECG)-gating. Scans were analyzed by consensus of two observers (YS Choi and JI Jung) with more than 3 years experience in cardiac CT imaging. CACS for vascular calcification were analyzed using a software (syngo.CT CaScoring; Siemens Healthcare; Forcheim, Germany).

For defining the quantity of coronary calcium, Agatston score, standard parameter, was used as the product of the area of calcification per coronary tomographic segment and a factor rated 1 through 4 dictated by the maximal calcium x-ray density in that segment, as described elsewhere [20]. The sum of all lesion scores for each major coronary artery including left main (LM), left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) was used to generate the total CACS.

CCTA image acquisition and analysis

Reconstructed DSCT angiograms were analyzed on three- dimensional workstation (Advantage Windows Workstation 4.3, GE Healthcare, Milwaukee, Wisconsin, USA), using a software (Card IQ; GE Healthcare, Milwaukee. Wisconsin, USA). From a previously described standard American Heart Association (AHA) segmentation model [21], DSCT angiographic analysis was performed by YS Choi and JI Jung without knowledge of clinical findings. Two experienced intra- and inter- observers visually assessed each coronary segment using standard transaxial (2-dimensional) image stacks (“raw data”), oblique multiplanar reconstructions (MPRs), oblique maximum intensity projections (MIPs), curved multiplanar reformations (cMPRs), and volume-rendering (3-dimensional) technique (VRT) reconstructions [22], and performed manual computed tomography– based quantitative coronary analysis (CTQCA) using the most representative longitudinal image and a simplified calculation that estimates normal tapering of the coronary artery based on the initial method described by Reiber et al [23]. Maximal diameter stenosis severity was visually determined and were categorized as 0 to 5 (0 = no stenosis, 1 = 1% to 24%, 2 = 25% to 49%, 3 = 50% to 69%, 4 = 70% to 89%, 5 = 90% to 100%) [1618].

Statistical analysis

All data are expressed as mean ± standard deviation (SD) for continuous variables and as a frequency percentage for categorical variables and statistical analysis was performed using the SAS statistical software version 9.2 (SAS Institute, Cary, NC, USA). Analysis among the groups for continuous variables was performed using ANOVA test and analysis of categorical data was performed using the Tukey’s b-test as a post-hoc t-test. Analysis between the two groups was performed using unpaired t-test for continuous variables and chi-squared test for categorical data. The clinical variables related to FRS were assessed using Pearson correlation coefficient. To identify independent factors associated with the plaque pattern on CCTA, we used multiple logistic regression analysis and calculated odds ratios (OR) and 95% confidence intervals (95% CI). All statistical tests were 2-tailed and p <0.05 was considered statistically significant.

Results

Clinical characteristics

The mean age of a total of 3914 participants was 55 ± 10 years; there were more male (n = 2649) than female (n = 1265) subjects. A total of 977 of 1265 (78.8%) female subjects were postmenopausal. The prevalence of hypertension, diabetes mellitus, dyslipidemia, familial history of cardiovascular risk and current smoking state were 32.5, 12.8, 16.0, 84.2 and 20.3%, respectively.

Mean total CACS was 52 ± 200 mm3. Average scan heart rate was 67 ± 10 beats per minute.

Of 2951/3914 (75.4%) subjects enrolled for this study, the prevalence of each group according to the CACS and plaque pattern on CCTA was group 1 in 55.0% (2152/3914), group 2 in 5.1% (200/3914), group 3 in 8.2% (319/3914), and group 4 in 7.2% (280/3914), respectively. Baseline clinical characteristics including demographic data, laboratory findings and FRS in four groups are presented in Table 1.
Table 1

Baseline clinical and laboratory characteristics

Total = 2951

Group 1

(n = 2152)

Group 2

(n = 200)

Group 3

(n = 319)

Group 4

(n = 280)

p value

Demographic data

Age, year *‡

52 ± 10

56 ± 9

57 ± 9

56 ± 9

<0.001

Gender, male, n (%) ¶

887 (41.2)

98 (49.0)

151 (47.3)

199 (71.1)

0.003

Hypertension, n (%) ¶

567 (26.3)

57 (28.5)

18 (5.6)

142 (50.7)

0.949

Diabetes mellitus, n (%)

191 (8.9)

30 (15.0)

9 (2.8)

27 (9.6)

0.359

Dyslipidemia, n (%)

284 (13.2)

26 (13.0)

15 (4.7)

44 (15.7)

0.569

Familial history of CVA, n (%)

573 (26.6)

61 (30.5)

24 (7.5)

93 (33.2)

0.245

Body mass index, Kg/m2

24.6 ± 3.7

24.9 ± 3.4

25.8 ± 4.1

25.6 ± 4.0

< 0.001

Systolic BP, mm Hg ‡

122 ± 14

127 ± 14

127 ± 17

126 ± 13

< 0.001

Diastolic BP, mm Hg *‡

72 ± 10

75 ± 10

76 ± 11

75 ± 10

< 0.001

Heart rate, beats per minute *

64 ± 10

65 ± 11

65 ± 10

64 ± 11

0.054

Smoking status, n (%)

94 (4.4)

6 (3.0)

13 (0.9)

78 (27.9)

0.003

Laboratory findings

Total cholesterol, mg/dL

204 ± 36

198 ± 39

203 ± 40

204 ± 35

0.416

Triglyceride, mg/dL

116 ± 81

126 ± 99

120 ± 79

133 ± 79

0.045

HDL-C, mg/dL ‡

52 ± 13

51 ± 11

50 ± 12

49 ± 11

0.006

LDL-C, mg/dL *

125 ± 32

129 ± 30

125 ± 36

124 ± 33

0.313

Fasting blood glucose, mg/dL *‡

94 ± 21

102 ± 34

101 ± 24

99 ± 21

0.008

HbA1c,% *‡

5.6 ± 0.7

6.0 ± 1.4

5.9 ± 0.8

5.7 ± 0.6

< 0.001

C reactive protein, mg/dL

0.16 ± 0.41

0.16 ± 0.27

0.19 ± 0.32

0.18 ± 0.30

0.521

Data are expressed as means ± standard deviation or number of patients (percentage)

Group 1, CACS = 0 & normal CCTA; Group 2, CACS = 0 & CCTA of minimal disease (luminal diameter < 25%); Group 3, CACS >0 & normal CCTA (CACS rage: 0.42–7.9); Group 4, CACS ≤ 10 or CCTA of minimal disease (luminal diameter < 25%) [1618].

* p < 0.05, p values was analyzed using Student’s t-test for the relationship between group 1 and 2

p < 0.05, p value was analyzed using chi-square test for the relationship between group 1 and 2

p < 0.05, p value was analyzed using Student’s t-test for the relationship between group 1 and 3

§ p <0.05, p value was analyzed using chi-square test for the relationship between group 1 and 3

p < 0.05, p value was analyzed using Student’s t-test for the relationship between group 1 and 4

p <0.05, p value was analyzed using chi-square test for the relationship between group 1 and 4

CVA cerebrovascular accidents, BP blood pressure, HDL-cholesterol high density lipoprotein cholesterol, LDL-cholesterol low density lipoprotein cholesterol

“Non-calcified” plaque on CCTA

Age (56 ± 9 year vs. 52 ± 10 year, p <0.001), diastolic BP (75 ± 10 mmHg vs. 72 ± 10 mmHg, p <0.001) in demographic data, and plasma LDL-cholesterol (129 ± 30 mg/dL vs. 125 ± 32 mg/dL, p = 0.031), fasting blood glucose (102 ± 34 mg/dL vs. 94 ± 21 mg/dL, p <0.001) and HbA1c (6.0 ± 1.4% vs. 5.6 ± 0.7%, p <0.001) concentration in laboratory findings showed a significant difference between group 2 and 1, respectively (Table 1). However, there were no significant gender difference (group 1, M : F = 887: 1265; group 2, M : F = 98 : 102; p = 0.067) and in FRS (6.4 ± 6.4% vs. 6.5 ± 4.6%, p = 0.869; Fig. 2a) between group 2 and 1.
Fig. 2

Comparison of Framingham risk score among the groups (a) and according to the risk stratification of Framingham risk score (b). Group 1, CACS = 0 & normal CCTA; Group 2, CACS = 0 & CCTA of minimal disease (luminal diameter <25%); Group 3, CACS >0 & normal CCTA (CACS rage: 0.42–7.9); Group 4, CACS ≤ 10 or CCTA of minimal disease (luminal diameter <25%) [1618]. *, p value was analyzed using Student’s t-test for the relationship between two groups; †, p value was analyzed using ANOVA test for the relationship among the four groups. CACS, coronary artery calcium score; CCTA, coronary computed tomographic angiography

“Calcified” plaque on CCTA

Age (57 ± 9 year vs. 52 ± 10 year, p <0.001), BMI (25.8 ± 4.1 kg/m2 vs. 24.6 ± 3.7 kg/m2, p <0.001), systolic BP (127 ± 17 mmHg vs. 122 ± 14 mmHg, p <0.001), and diastolic BP (76 ± 11 mmHg vs. 72 ± 10 mmHg, p <0.001) in demographic data, and plasma fasting blood glucose (101 ± 24 mg/dL vs. 94 ± 21 mg/dL, p = 0.001) and HbA1c (5.9 ± 0.8% vs. 5.6 ± 0.7%, p <0.001) concentration in laboratory findings showed a significant difference between group 3 and 1, respectively (Table 1). Plasma HDL-cholesterol (50 ± 12 mg/dL vs. 52 ± 13 mg/dL, p = 0.016) showed a significant lower in group 3 greater than group 1 (Table 1). However, there was no a significant gender difference (group 1, M : F = 887 : 1265; group 3, M : F = 151 : 168; p = 0.062) and in FRS (6.4 ± 6.4% vs. 8.2 ± 5.8%, p = 0.105; Fig. 2a) between group 1 and 3, respectively.

“Mixed” plaque on CCTA

Age (56 ± 9 year vs. 52 ± 10 year, p <0.001), male ratio (71.1% vs. 41.2%; p = 0.012), prevalence of hypertension (71.1% vs. 26.3%; p = 0.038), BMI (25.6 ± 4.0 kg/m2 vs. 24.6 ± 3.7 kg/m2, p = 0.001), systolic BP (126 ± 13 mmHg vs. 122 ± 14 mmHg, p = 0.001), and diastolic BP (75 ± 10 mmHg vs. 72 ± 10 mmHg, p <0.001) in demographic data, and triglyceride (133 ± 79 mg/dL vs. 116 ± 81 mg/dL, p = 0.017) concentration in laboratory findings showed a significant higher in group 4 greater than group 1, respectively (Table 1). Plasma HDL-cholesterol (49 ± 11 mg/dL vs. 52 ± 13 mg/dL, p = 0.017) showed a significant lower in group 4 greater than group 1 (Table 1). However, there was no a significant difference in FRS (6.4 ± 6.4% vs. 7.7 ± 5.7%, p = 0.057; Fig. 2a) between group 1 and 4.

FRS according to the plaque pattern on CCTA

There was no difference of FRS among the groups (6.4 ± 6.4% vs. 6.5 ± 4.6% vs. 8.2 ± 5.8% vs. 7.7 ± 5.7, p = 0.086; Fig. 2a). From the analysis for FRS using unpaired t-test, there was no difference in FRS [(group 1 : 2 = 6.4 ± 6.4%: 6.5 ± 4.6%, p = 0.639) vs. (group 1 : 3 = 6.4 ± 6.4%: 8.2 ± 5.8%, p = 0.053) vs. (group 1 : 4 = 6.4 ± 6.4%: 7.7 ± 5.7%, p = 0.057), respectively; Fig. 2a].

In group 1, age (r = 0.433, p <0.001), BMI (r = 0.221, p <0.001), systolic BP (r = 0.265, p <0.001), and diastolic BP (r = 0.320, p <0.001) in demographic data and plasma total cholesterol (r = 0.292, p <0.001), triglyceride (r = 0.324, p <0.001), HDL-cholesterol (r = -0.314, p <0.001), LDL-cholesterol (r = 0.315, p <0.001), and fasting blood glucose (r = 0.138, p = 0.017) in laboratory findings was closely related FRS, respectively (Table 2). In group 2, there was no cardiovascular risk factor related FRS (Table 2). In group 3, age (r = 0.562, p <0.001) in demographic data and plasma total cholesterol (r = 0.384, p = 0.030), triglyceride (r = 0.420, p = 0.017) in laboratory findings was closely related FRS, respectively (Table 2).
Table 2

Correlation coefficients between Framingham risk score and clinical variables

Total = 2951

Group 1 (n = 2152)

Group 2 (n = 200)

Group 3 (n = 319)

Group 4 (n = 280)

r

p value

r

p value

r

p value

r

p value

Demographic data

Age, year

0.333

< 0.001

0.124

0.573

0.562

0.002

0.112

0.449

Body mass index, Kg/m2

0.221

< 0.001

0.261

0.228

0.056

0.764

0.124

0.401

Systolic BP, mm Hg

0.265

< 0.001

0.079

0.721

0.278

0.123

0.264

0.070

Diastolic BP, mm Hg

0.320

< 0.001

0.022

0.922

0.290

0.108

0.331

0.022

Heart rate, beats per minute

0.027

0.637

0.027

0.312

0.035

0.848

0.324

0.025

Laboratory findings

Total cholesterol, mg/dL

0.292

< 0.001

0.050

0.822

0.384

0.030

0.110

0.456

Triglyceride, mg/dL

0.324

< 0.001

0.025

0.911

0.420

0.017

0.301

0.037

HDL-C, mg/dL

- 0.314

< 0.001

- 0.228

0.295

- 0.175

0.339

−0.356

0.013

LDL-C, mg/dL

0.315

< 0.001

0.053

0.809

0.337

0.059

0.116

0.434

Fasting blood glucose, mg/dL

0.138

0.017

0.005

0.980

- 0.076

0.678

0.228

0.119

HemoglobinA1c,%

0.092

0.115

0.115

0.612

0.048

0.796

0.165

0.269

C reactive protein, mg/dL

- 0.015

0.802

0.116

0.628

0.270

0.212

0.166

0.277

Group 1, CACS = 0 & normal CCTA; Group 2, CACS = 0 & CCTA of minimal disease (luminal diameter <25%); Group 3, CACS >0 & normal CCTA (CACS rage: 0.42–7.9); Group 4, CACS ≤10 or CCTA of minimal disease (luminal diameter <25%) [1618].

BP blood pressure, HDL-cholesterol high density lipoprotein cholesterol, LDL-cholesterol low density lipoprotein cholesterol

According to the NCEP guidelines, the prevalence of each subgroup for 10-year CHD risk was low-risk in 48.7% (1906/3914), intermediate-risk in 21.2% (829/3914), and high-risk group 5.5% (216/3914), respectively (Fig. 2b) However, there was no difference in FRS of the subgroup, including low-risk (group 1: 2: 3: 4 = 3.6%: 4.5%: 4.1%: 4.0%, p = 0.408), intermediate-risk (group 1: 2: 3: 4 = 12.6%: 11.1%: 12.0%: 12.8%, p = 0.150) and high-risk subgroup (group 1: 2: 3: 4 = 22.8%: 21.8%: 20.0%: 22.5%, p = 0.596), respectively (Fig. 2b).

Independent cardiovascular risks

In multivariate logistic regression analysis for the cardiovascular risks, HbA1c (OR = 2.285; 95% CI = 1.029–5.071; p = 0.042) was an independent factor associated with group 2 (so called “non-calcified or soft” plaque); age (OR = 1.115; 95% CI = 1.034–1.202; p = 0.005) and smoking status (OR = 3.386 ; 95% CI = 1.124–10.202; p = 0.030) were independent factors associated with group 3 (so called “calcified” plaque); and age (OR = 1.054; 95% CI = 1.011–1.099; p = 0.014) and presence of hypertension (OR = 3.087; 95% CI = 1.536–6.202; p = 0.001) were independent factors associated with group 4 (so called “mixed” plaque), respectively (Table 3).
Table 3

Multivariate analysis

 

Oddi ratio

95% confidence interval

p value

Independent factor associated with group 2, so called “non-calcified or soft” plaque

Age, year

1.019

0.968–1.073

0.473

Gender, male

2.452

0.686–8.767

0.168

Systolic BP

0.977

0.918–1.039

0.455

Diastolic BP

1.052

0.973–1.137

0.201

LDL-cholesterol

1.009

0.995–1.005

0.218

Fasting blood glucose

0.980

0.947–1.015

0.260

HbA1c

2.285

1.029–5.071

0.042

Smoking status

0.988

0.352–2.773

0.981

Independent factor associated with group 3, so called “calcified” plaque

Age, year

1.115

1.034–1.202

0.005

Gender, male

6.066

0.974–37.799

0.053

Body mass index

0.905

0.708–1.156

0.425

Systolic BP

1.039

0.985–1.095

0.159

Diastolic BP

0.982

0.919–1.049

0.593

HDL-cholesterol

1.011

0.979–1.045

0.500

Fasting blood glucose

0.994

0.970–1.018

0.593

HbA1c

1.681

0.817–3.459

0.159

Smoking status

3.386

1.124–10.202

0.030

Independent factor associated with group 4, so called “mixed” plaque

Age, year

1.054

1.011–1.099

0.014

Gender, male

1.294

0.520–3.220

0.580

Hypertension

3.087

1.536–6.202

0.001

Body mass index

0.866

0.710–1.098

0.183

Systolic BP

1.002

0.960–1.072

0.954

Diastolic BP

1.014

0.969–1.005

0.410

Heart rate

1.027

0.993–1.061

0.119

Triglyceride

1.000

0.995–1.005

0.973

HDL-cholesterol

0.999

0.999–1.030

0.942

Fasting blood glucose

0.989

0.996–1.013

0.378

HbA1c

1.349

0.662–2.746

0.410

Smoking status

1.817

0.736–4.485

0.195

Group 2, CACS = 0 & CCTA of minimal disease (luminal diameter <25%); Group 3, CACS >0 & normal CCTA (CACS rage: 0.42–7.9); Group 4, CACS ≤10 or CCTA of minimal disease (luminal diameter <25%) [1618].

BP blood pressure, LDL-cholesterol low density lipoprotein cholesterol, HDL-cholesterol high density lipoprotein cholesterol

Discussion

The accumulation of atherosclerotic plaque without significant coronary stenosis happens over many years prior to acute cardiovascular events, including myocardial infarction or sudden cardiac death. Furthermore, CACS on CCTA, as a recent diagnostic tool, has been shown to be helpful in patients with low- and intermediate-risk who presents with atypical cardiac symptoms [7, 8]. At the same time, effective strategies for earlier identification of subclinical coronary atherosclerosis requires in healthy subjects. In the present study, unlike previous research using CCTA in symptomatic patients with significant or obstructive CAD [13, 14], we focused on the different cardiovascular risk factors associated with the plaque pattern on CCTA in healthy subjects. As a result, in this single-center, cross-sectional study of healthy subjects comparing CCTA with FRS, our data revealed that CCTA is reliable and effective for the estimation of the different cardiovascular risk factors associated with the plaque pattern on CCTA in healthy subjects. However, to predict the presence of subclinical coronary atherosclerosis, further investigations are required prospective study in larger populations via multicenter trials.

Association with FRS and the plaque pattern on CCTA

FRS or CACS on CCTA for cardiovascular risk stratification is a useful tool. However, these tools alone may insufficient to identify subclinical coronary atherosclerosis in some part of the population. In addition, the combination of FRS and CACS may provide more accurate estimation of the risk of cardiovascular events [24]. In our study, although our data showed a weak correlation between FRS and traditional cardiovascular risk factors such as older age, obesity indicators, blood pressure and plasma lipid profile in subjects with “normal” coronary arteries on CCTA, there was no difference of FRS among the groups classified according to the plaque pattern on CCTA. As described in several previous published studies [7, 8], these findings are particularly consistent with the facts that CACS on CCTA in clinical application can provide valuable prognostic evaluation and serve as an important tool for cardiovascular risk stratification of asymptomatic or healthy individuals, although CACS on CCTA should not be recommended as a tool to diagnose significant obstructive CAD in symptomatic patients.

Difference of the plaque pattern on CCTA

In analysis for the cardiovascular risk stratification of the development and progression of subclinical coronary atherosclerosis and the difference of the plaque pattern on CCTA such as “non-calcified” or “calcified” plaque, North et al. demonstrated the role of smoking status in the pathogenesis of “calcified” coronary plaque, similar to our result [25]. On the other hand, from the ROMICAT trial as a prospective, observational cohort study, Lehman et al. reported that smoking were independently associated with coronary atherosclerotic plaque burden progression on CCTA in patients with acute chest pain over 2 years, although rate of progression is dependent on plaque composition and may be higher for “non-calcified” when compared to “calcified” plaque [26]. In addition, in patients referred to the emergency department with chest pain, Yoon et al. reported that the patients ≥50% CAD of “non-calcified” plaque on CCTA were younger and had a higher prevalence of smoking [27].

Otherwise, the present study may have an important or interesting clinical implication in the association between HbA1c, as a key marker of diabetes control and “non-calcified” plaque from this observation. Interestingly, Hausleiter et al. demonstrated the role of “non-calcified” plaque, characterized by significantly higher total cholesterol, LDL-C, and C-reactive protein levels in patients with acute coronary syndrome [28]. Furthermore, more recent studies suggested that effective prevention has to be focused on the type of plaque composition [2931]. Nicholls et al. demonstrated that “calcified” plaques are more resistant to undergoing changes in size in response to systemic interventions targeting atherosclerotic risk factors. On the contrary, “non-calcified” plaque might have a higher tendency to regress in response to established medical therapies [29]. Several studies also suggested that “mixed” plaque could convey a higher coronary risk including of acute coronary syndromes [29, 30]. Thus, to overcome these various issues and problems for identifying subclinical coronary atherosclerosis, we need to conduct further research in a larger population including of ethnic differences.

There are several limitations that our study includes the relatively small sample size and possibility of referral bias from one center trial. First, the proportion of sample group was lower in group 2–4 than group 1 in present study. Second, there is a lack of knowledge about the analysis including statin therapy and differential hormonal effects based on gender. Last, our investigators suggest that prospective studies via large multi-ethnic populations and long-term follow up are required to determine the potential value of identifying the development and progression of subclinical coronary atherosclerosis and to predict the prognosis of CHD.

Conclusions

Although there was no difference of FRS among the groups classified according to the plaque pattern on CCTA, our data suggest that more individualized therapy for reduction of cardiovascular risks could be considered in healthy subjects.

Declarations

Acknowledgment

The authors thank all the participants as well as the faculty & staff - division of cardiology and radiology at the Health Promotion and Cardiovascular Center of Seoul St. Mary’s Hospital (The Catholic University of Korea) for their cooperation in this study.

Availability of data and materials

Not applicable.

Authors’ contributions

LD and YH designed the study, performed the experiments, analyzed the results and wrote the manuscript. JH, CK, CY and JJ contributed data analysis. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study was approved by the Institutional Review Committee of St Mary’s Hospital, the Catholic University of Korea and conducted in agreement with the Declaration of Helsinki (XC11RIMI0091S).

Financial support

The authors declare that no competing financial interests exist.

Data sharing statement

No additional data are available.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Division of Cardiology, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea
(2)
Radiology, Seoul St. Mary’s Hospital, The Catholic University of Korea

References

  1. Greenland P, Smith Jr SC, Grundy SM. Improving coronary heart disease risk assessment in asymptomatic people: role of traditional risk factors and noninvasive cardiovascular tests. Circulation. 2001;104:1863–7.View ArticlePubMedGoogle Scholar
  2. Brindle P, Beswick A, Fahey T, Ebrahim S. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review. Heart. 2006;92:1752–9.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Bax JJ, Schuijf JD. Which role for multislice computed tomography in clinical cardiology? Am Heart J. 2005;149:960–1.View ArticlePubMedGoogle Scholar
  4. Achenbach S, Daniel WG. Computed tomography of the coronary arteries: more than meets the (angiographic) eye. J Am Coll Cardiol. 2005;46:155–7.View ArticlePubMedGoogle Scholar
  5. Weustink AC, Meijboom WB, Mollet NR, Otsuka M, Pugliese F, van Mieghem C, et al. Reliable high-speed coronary computed tomography in symptomatic patients. J Am Coll Cardiol. 2007;50:786–94.View ArticlePubMedGoogle Scholar
  6. Achenbach S, Ropers U, Kuettner A, Anders K, Pflederer T, Komatsu S, et al. Randomized comparison of 64-slice single- and dual-source computed tomography coronary angiography for the detection of coronary artery disease. JACC Cardiovasc Imaging. 2008;1:177–86.View ArticlePubMedGoogle Scholar
  7. Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56:e50–103.View ArticlePubMedGoogle Scholar
  8. Taylor AJ, Cerqueira M, Hodgson JM, Mark D, Min J, O'Gara P, et al. ACCF/SCCT/ACR/AHA/ASE/ASNC/NASCI/SCAI/SCMR 2010 appropriate use criteria for cardiac computed tomography. A report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the Society of Cardiovascular Computed Tomography, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the American Society of Nuclear Cardiology, the North American Society for Cardiovascular Imaging, the Society for Cardiovascular Angiography and Interventions, and the Society for Cardiovascular Magnetic Resonance. J Am Coll Cardiol. 2010;56:1864–94.View ArticlePubMedGoogle Scholar
  9. Budoff MJ, Achenbach S, Blumenthal RS, Carr JJ, Goldin JG, Greenland P, et al. Assessment of coronary artery disease by cardiac computed tomography: a scientific statement from the American Heart Association Committee on Cardiovascular Imaging and Intervention, Council on Cardiovascular Radiology and Intervention, and Committee on Cardiac Imaging, Council on Clinical Cardiology. Circulation. 2006;114:761–91.View ArticleGoogle Scholar
  10. Hamon M, Biondi-Zoccai GG, Malagutti P, Agostoni P, Morello R, Valgimigli M, et al. Diagnostic performance of multislice spiral computed tomography of coronary arteries as compared with conventional invasive coronary angiography: a meta-analysis. J Am Coll Cardiol. 2006;48:1896–910.View ArticlePubMedGoogle Scholar
  11. Arad Y, Goodman K, Roth M, Newstein D, Guerci A. Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St. Francis study. J Am Coll Cardiol. 2005;46:158–65.View ArticlePubMedGoogle Scholar
  12. Elias-Smale S, Proenca R, Koller M, Kavousi M, van Rooij F, Hunink M, et al. Coronary calcium score improves classification of coronary heart disease risk in the elderly: the Rotterdam study. J Am Coll Cardiol. 2010;56:1407–14.View ArticlePubMedGoogle Scholar
  13. Shabestari AA, Abdi S, Akhlaghpoor S, Azadi M, Baharjoo H, Pajouh MD, et al. Diagnostic performance of 64-channel multislice computed tomography in assessment of significant coronary artery disease in symptomatic subjects. Am J Cardiol. 2007;9:1656–61.View ArticleGoogle Scholar
  14. Fernandez-Friera L, Garcia-Alvarez A, Bagheriannejad-Esfahani F, Malick W, Mirelis JG, Sawit ST, et al. Diagnostic value of coronary artery calcium scoring in low-intermediate risk patients evaluated in the emergency department for acute coronary syndrome. Am J Cardiol. 2011;107:17–23.View ArticlePubMedGoogle Scholar
  15. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation on Obesity, Geneva, 3-5 June 1997. Geneva: World Health Organization; 1998.
  16. Cheng V, Gutstein A, Wolak A, Suzuki Y, Dey D, Gransar H, et al. Moving beyond binary grading of coronary arterial stenoses on coronary computed tomographic angiography: insights for the imager and referring clinician. JACC Cardiovasc Imaging. 2008;1:460–71.View ArticlePubMedGoogle Scholar
  17. Goldstein JA, Gallagher MJ, O'Neill WW, Ross MA, O'Neil BJ, Raff GL. A randomized controlled trial of multi-slice coronary computed tomography for evaluation of acute chest pain. J Am Coll Cardiol. 2007;49:863–71.View ArticlePubMedGoogle Scholar
  18. Rumberger JA, Brundage BH, Rader DJ, Kondos G. Electron beam computed tomographic coronary calcium scanning: a review and guidelines for use in asymptomatic persons. Mayo Clin Proc. 1999;74:243–52.View ArticlePubMedGoogle Scholar
  19. National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–421.
  20. Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte Jr M, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15:827–32.View ArticlePubMedGoogle Scholar
  21. Austen WG, Edwards JE, Frye RL, Gensini GG, Gott VL, Griffith LS, et al. A reporting system on patients evaluated for coronary artery disease. Report of the ad hoc committee for grading of coronary artery disease, council on cardiovascular surgery. American Heart Association Circulation. 1975;51:5–40.PubMedGoogle Scholar
  22. Ferencik M, Ropers D, Abbara S, Cury RC, Hoffmann U, Nieman K, et al. Diagnostic accuracy of image post processing methods for the detection of coronary artery stenoses by using multidetector CT. Radiology. 2007;243:696–702.View ArticlePubMedGoogle Scholar
  23. Reiber JH, Serruys PW, Kooijman CJ, Wijns W, Slager CJ, Gerbrands JJ, et al. Assessment of short-, medium-, and long-term variations in arterial dimensions from computer-assisted quantitation of coronary cineangiograms. Circulation. 1985;71:280–8.View ArticlePubMedGoogle Scholar
  24. Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA. 2004;291:210–5.View ArticlePubMedGoogle Scholar
  25. North KE, Carr JJ, Borecki IB, Kraja A, Province M, Pankow JS, et al. QTL-specific genotype-by-smoking interaction and burden of calcified coronary atherosclerosis: the NHLBI Family Heart Study. Atherosclerosis. 2007;193:11–9.View ArticlePubMedGoogle Scholar
  26. Lehman SJ, Schlett CL, Bamberg F, Lee H, Donnelly P, Shturman L, et al. Assessment of coronary plaque progression in coronary computed tomography angiography using a semiquantitative score. JACC Cardiovasc Imaging. 2009;2:1262–70.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Yoon YE, Chang SA, Choi SI, Chun EJ, Cho YS, Youn TJ, et al. The absence of coronary artery calcification does not rule out the presence of significant coronary artery disease in Asian patients with acute chest pain. Int J Cardiovasc Imaging. 2012;28:389–98.View ArticlePubMedGoogle Scholar
  28. Hausleiter J, Meyer T, Hadamitzky M, Kastrati A, Martinoff S, Schömig A. Prevalence of noncalcified coronary plaques by 64-slice computed tomography in patients with an intermediate risk for significant coronary artery disease. J Am Coll Cardiol. 2006;48:312–8.View ArticlePubMedGoogle Scholar
  29. Nicholls SJ, Tuzcu EM, Wolski K, Sipahi I, Schoenhagen P, Crowe T, et al. Coronary artery calcification and changes in atheroma burden in response to established medical therapies. J Am Coll Cardiol. 2007;49:263–70.View ArticlePubMedGoogle Scholar
  30. Lin F, Shaw LJ, Berman DS, Callister TQ, Weinsaft JW, Wong FJ, et al. Multidetector computed tomography coronary artery plaque predictors of stress-induced myocardial ischemia by SPECT. Atherosclerosis. 2008;197:700–9.View ArticlePubMedGoogle Scholar
  31. Pundziute G, Schuijf JD, Jukema JW, Decramer I, Sarno G, Vanhoenacker PK, et al. Evaluation of plaque characteristics in acute coronary syndromes: non-invasive assessment with multi-slice computed tomography and invasive evaluation with intravascular ultrasound radiofrequency data analysis. Eur Heart J. 2008;29:2373–81.View ArticlePubMedGoogle Scholar

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© The Author(s). 2017

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