Abstract
Aims: Carotid intima–media thickness (IMT) measurement is used to assess subclinical atherosclerosis. We aimed to examine the association between the maximum IMT by location and the occurrence of silent brain infarction (SBI).
Methods: Overall, 280 Japanese individuals (92 females, 52.6±5 years old) underwent a medical check-up at our hospital in Tokyo in 2015. Carotid IMT was measured at each site on ultrasound images (common carotid artery [CCA], internal carotid artery, or bifurcation). The risk factors for arterial dysfunction were evaluated. SBI was assessed using magnetic resonance imaging (MRI). The cross-sectional relationship between carotid maximum IMT and SBI was evaluated.
Results: Of the 280 individuals, 18 (6.4%) were diagnosed with SBI on MRI. The mean age of the SBI(–) and SBI(+) groups was 51.9±10.6 and 63.6±18.6 years, respectively. The correlation coefficients between the carotid maximum IMT at each location were very weak (correlation coefficient range: 0.180–0.253). The percentage of participants with SBI increased significantly with increasing maximum CCA and bIMT values. After adjusting for confounders, SBI was found to be significantly associated with the maximum bIMT (per 0.1-mm increase) (adjusted odds ratio [aOR], 1.10; 95% confidence interval [CI]: 1.03–1.17). When bIMT was categorized according to three groups (＜1.0 mm, 1.0–＜2.0 mm, and ≥ 2.0 mm), a significant SBI risk was also observed with an increase by each category of bIMT (aOR: 3.96, 95% CI: 1.63–9.52, P=0.002).
Conclusion: The maximum bIMT was found to be the main determinant of SBI. A significant SBI risk was associated with an increase in each category of the maximum bIMT. Therefore, the maximum bIMT might be a useful predictor of future stroke in Japanese stroke-free medical check-up participants.
Introduction
Japan ranks highest in the world in terms of longevity, with an average life expectancy of 81.5 years in men and 87.6 years in women in 2021. Cardiovascular diseases (primarily stroke and ischemic heart disease) are major non-communicable diseases that significantly contribute to life expectancy1). Stroke is the leading cause of serious long-term adult disability in Japan and its consequences have become a major social problem2, 3). Therefore, there is an urgent need in Japan to develop a diagnostic method that allows the identification of people at a high risk of stroke in a population without a history of stroke.
Measuring the carotid intima-media thickness (IMT) is an established and useful noninvasive method for assessing subclinical and early stage atherosclerosis, and it is independently associated with the risk of stroke and cardiovascular events4-6). The assessment of maximum IMT is associated with the risk of atherosclerotic diseases6,7), whereas the presence of silent brain infarction (SBI) on brain magnetic resonance imaging (MRI) is strongly associated with the development of stroke8-10). The presence of SBI has been reported to more than double the risk of subsequent stroke in a meta-analysis of 13 studies11).
Although it is difficult to predict future stroke in stroke-free individuals undergoing medical check-ups, we believe that it is useful to assess SBI using brain MRI to identify the risk of stroke development. However, so far few reports have been published on the relationship between the ultrasonographic measurements of carotid IMT by location and asymptomatic stroke12).
Aim
This study aimed to examine whether the assessment of maximum IMT by location is associated with SBI in Japanese stroke-free participants.
Methods
Study Population
This study was conducted during an annual checkup at our hospital in Tokyo, Japan, in 2015. The total study population consisted of 303 individuals who underwent annual comprehensive medical examinations to assess their physical condition. Of these individuals, 23 (7.5%) were excluded because of a history of cardiocerebrovascular diseases (CCVD), such as cardiovascular disease (14 individuals [4.6%]), transient ischemic attack or stroke (five individuals [1.6%]), or missing data (four individuals [1.3%]). Furthermore, this study excluded any participants who reported a history of stroke to ensure that no clinically apparent stroke was mistaken for an SBI. Ultimately, 280 participants were included in this study.
The study protocol was approved by the Ethics Committee of our hospital (Tokyo, Japan; approval number: 27-16), and data were collected using an opt-out method on the hospital bulletin board. All the procedures were conducted in accordance with the tenets of the latest version of the Declaration of Helsinki.
Data Collection
We used a self-report questionnaire as previously described13,14). Physical activity and excessive alcohol consumption were defined in a previous study15). In short, the amount of alcohol consumption was estimated using responses to question items on frequency, usual daily amount, and type of beverage. Physical activity was assessed as the frequency (almost never, 1–3 days/month, 1–2 days/week, 3–4 days/week, and almost daily) of participation in non-occupational physical activity. Anthropometric measurements and blood samples were collected in a room maintained at 22±2℃. Hypertension was defined as systolic blood pressure (BP) ≥ 140 mmHg, diastolic BP ≥ 90 mmHg, and/or the use of antihypertensive medication.
Carotid Artery Ultrasonography
Carotid IMT was measured in each individual by an experienced technician who was blinded to the clinical data. Carotid IMT was assessed using an Xario XG ultrasonography system (Canon Medical Systems, Tochigi, Japan) equipped with a broadband linear array transducer. The patients were examined in the supine position without a pillow, with their necks tilted slightly away from the carotid artery being examined, and their chin angled slightly upward. First, the carotid artery segment observed using short-axis imaging was scanned from the proximal (heart) side toward the distal (head) side to confirm the course and location of the blood vessel. Long-axis images of the blood vessels were then produced parallel to the probe as much as possible. Images of the distal common carotid artery (CCA) bifurcation and internal carotid artery (ICA) bifurcation were obtained in all patients as recommended by the American Society of Echocardiography Carotid Intima-Media Thickness Task Force5).
A previous report used the maximal rather than the mean IMT as the key variable6). IMT was visualized and measured bilaterally at three sites: the level of the common carotid (10 mm proximal to the dilation of the carotid bulb), the carotid bifurcation (10 mm proximal to the flow divider), and the internal carotid artery (10 mm distal to the flow divider)16-18).
The thickest portion on the left or right side was used as the maximum IMT. If only one side could be measured owing to a high bifurcation, then that side was used as the numerical value. When the IMT is less than 0.4 mm, it is difficult to measure accurately; therefore, we standardized it to 0.2 mm. The bifurcation IMT (bIMT) was calculated at the thickest point, including any plaque of left and right carotid bulb origins6,19).
Biochemical Measurements
We also measured blood samples from the antecubital vein after overnight fasting as described previously13,14). Diabetes was defined as a fasting plasma glucose (FPG) level ≥ 126 mg/dL, HbA1c ≥ 6.5%, or the use of glucose-lowering medications. Dyslipidemia was defined as a low-density lipoprotein cholesterol level ≥ 140 mg/dL, high-density lipoprotein cholesterol level ＜40 mg/dL, triglyceride level ≥ 150 mg/dL, and/or use of lipid-lowering medications. The estimated glomerular filtration rate (eGFR) was defined by the Japanese Society of Nephrology as follows: eGFR (mL/min/1.73 m2)=193×serum creatinine−1.094×age−0.287 20).
SBI Definitions
Brain MRI examinations were performed by certified technologists using a standardized imaging protocol and MAGNETOM Aera 1.5 T machine (Siemens Healthineers, Erlangen, Germany). The axial MRI was rotated according to the anterior-to-posterior commissure axis in the transaxial plane. Moreover, the Sylvian fissure was defined as a reference anatomical landmark in the anteroposterior direction. Images were captured in contiguous 5-mm slices. T1-weighted images were acquired with a repetition time of 567 ms and echo time of 12 ms. T2-weighted images were captured using a repetition time of 4000 ms and echo time of 90 ms. All images were examined by a certified neurologist and radiologist. Periventricular hyperintensities, deep subcortical white matter hyperintensities, and SBI were assessed using MRI. SBI was defined as a focal lesion ≥ 3 mm in diameter, with a signal intensity corresponding to liquor ( hyperintense on T2-weighted images and hypointense on T1-weighted images)11).
Statistical Analysis
Continuous variables other than triglyceride levels are expressed as the mean±standard deviation. The triglyceride levels were expressed as medians and interquartile ranges. Categorical data were expressed as the number of individuals (percentage). The chi-square (χ2) test for categorical variables was used to compare the clinical characteristics. Univariate and multivariate logistic regression analyses were performed to evaluate the determinants of SBI. Variables included in the univariate analysis were age, a diagnosis of hypertension, a diagnosis of diabetes, and a family history of CCVD, whereas the HbA1c level, eGFR, maximum IMT CCA, and maximum IMT BIF were explanatory variables. Variables with pre-specified P-values (＜0.1, univariable analysis) were included in the multivariate model. Additionally, a logistic regression analysis was performed to evaluate the determinants of SBI according to the maximum IMT at each location. All tests were performed using the SPSS software program version 27.0 (IBM, Armonk, NY, USA). Statistical significance was set at P＜0.05.
Results
Baseline Characteristics of the Study Population
This study included 280 Japanese health check-up individuals (198 males, 92 females; mean age 52.6±10.6 years). Of the 280 participants, 18 (6.4%) had SBI on MRI. The clinical characteristics of the patients are summarized in Table 1. The mean age in the SBI(–) and SBI(+) groups was 51.9±10.6 and 63.6±18.6 years, respectively. The age, diagnosis of hypertension, medication for hypertension and diabetes, FPG level, hemoglobin A1c (HbA1c) level, eGFR, maximum CCA IMT, and maximum bIMT were all significantly different between the groups.
Table 1.Baseline characteristics of the study subjects (
n = 280)
|
All |
SBI (-) |
SBI (+) |
P value
|
| Subjects, n
|
280 |
262 |
18 |
|
| Age, years |
52.6±10.6 |
51.9±10.6 |
63.6±18.6 |
＜0.001 |
| Sex |
|
|
|
|
| Female, n (%)
|
92 (32.9) |
86 (32.8) |
6 (33.3%) |
0.97 |
| Asian ethnicity, n
|
280 (100%) |
262 (100%) |
18 (100%) |
1.00 |
| Body-mass index (kg/m2) ＊
|
24.1±3.6 |
24.1±3.7 |
24.6±3.0 |
0.384 |
| Mean blood pressure (BP), n
|
159 |
133 |
21 |
|
| Systolic BP, mmHg |
128.0±16.9 |
127.5±16.8 |
135.3±17.2 |
0.058 |
| Diastolic BP, mmHg |
77.3±12.6 |
77.2±12.7 |
78.8±11.1 |
0.568 |
| Comorbidities, n
|
280 |
262 |
18 |
|
| Hypertension, n (%)
|
108 (38.6) |
96 (36.6) |
12 (66.7) |
0.013 |
| Dyslipidemia, n (%)
|
142 (50.7) |
133 (50.8) |
9 (50.0) |
0.950 |
| Diabetes, n (%)
|
31 (11.1) |
23 (8.8) |
8 (44.4) |
＜0.001 |
| Current smoking, n (%)
|
96 (34.3) |
90 (34.4%) |
6 (33.3%) |
0.930 |
| Excess alcohol intake, n (%)
|
123 (43.9) |
115 (43.9) |
8 (44.4) |
0.964 |
| Regular exercise, n (%)
|
50 (17.9) |
46 (17.6) |
4 (22.2) |
0.627 |
| Family history of CCVD, n (%)
|
89 (31.8) |
80 (30.5) |
9 (50.0) |
0.097 |
| Medicine, n
|
280 |
262 |
18 |
|
| Anti-hypertensive agents, n (%)
|
52 (18.6) |
44 (16.8) |
8 (44.4) |
0.009 |
| Statin, n (%)
|
32 (11.4) |
29 (11.1) |
3 (16.7) |
0.494 |
| Anti-diabetic drugs, n (%)
|
15 (5.4) |
9 (3.4) |
6 (33.3) |
＜0.001 |
| Labs, n
|
280 |
262 |
18 |
|
| Total-cholesterol (mg/dL) |
207.8±34.8 |
208.8±35.1 |
207.4±30.1 |
0.679 |
| LDL-cholesterol (mg/dL) |
122.5±30.6 |
122.5±30.9 |
122.8±25.7 |
0.749 |
| HDL-cholesterol (mg/dL) |
59.5±16.8 |
59.6±16.9 |
57.1±13.8 |
0.624 |
| Trygricerides (mg/dL) †
|
131 (119, 143) |
131 (123,139) |
129 (108, 150) |
0.921 |
| Fasting plasma glucose (mg/dL) |
103.8±23.8 |
102.7±22.7 |
118.8±33.1 |
0.005 |
| HbA1c (%) |
5.8±0.9 |
5.8±0.9 |
6.4±1.0 |
＜0.001 |
| C reactive protein (mg/dL) |
0.20±0.77 |
0.20±0.79 |
0.21±0.46 |
0.691 |
| eGFR (mL/min/1.73m2)
|
81.0±17.4 |
81.5±17.4 |
73.0±14.8 |
0.049 |
| Physical examination, n
|
280 |
262 |
18 |
|
| maximum IMT CCA (mm) |
0.85±0.39 |
0.83±0.36 |
1.24±0.51 |
＜0.001 |
| maximum IMT bifurcation (mm) |
0.96±0.87 |
0.88±0.81 |
2.08±0.91 |
＜0.001 |
| maximum IMT ICA (mm) |
0.43±0.64 |
0.42±0.63 |
0.58±0.77 |
0.138 |
＊Mann-Whitney test or chi-square test for SBI.†median (IQR). CCVD, cardio-cerebrovascular diseases; LDL, low-density lipoprotein; HDL, high- density lipoprotein; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; IMT, intima media thickness; CCA, common carotid artery; ICA, internal carotid artery
In the univariate analysis, age, a diagnosis of hypertension, a diagnosis of diabetes, medication for hypertension, medication for diabetes, HbA1c level, eGFR, maximum CCA IMT, and maximum bIMT were identified as determinants of SBI (Supplementary Table 1). In the multivariable analysis, to differentiate the effect of similar factors, we adjusted for multiple potential confounders such as age, a diagnosis of hypertension, a diagnosis of diabetes, a family history of CCVD, HbA1c, and eGFR, as well as the maximum CCA IMT or maximum bIMT separately (Model 1), and adjusted for age, medication for hypertension, medication for diabetes, family history of CCVD, HbA1c level, and eGFR, as well as both the maximum CCA IMT and maximum bIMT simultaneously (Model 2). As a result, only the maximum bIMT, but not the maximum CCA or maximum ICA, was significantly associated with SBI (per 0.1-mm increase) (model 1: adjusted odds ratio [aOR]: 1.10, 95% confidence interval [CI]: 1.03–1.17, P=0.003; model 2: aOR, 1.09; 95% CI: 1.01–1.17; P=0.005; Table 2).
Supplementary Table 1.Determinants of subjects with SBI
|
SBI |
|
|
| Univariable |
Multivariable (model 1) |
Multivariable (model 2) |
| OR (95%CI) |
P value
|
OR |
P value
|
OR |
P value
|
| Age, years |
1.12 (1.06-1.18) |
＜0.001 |
1.07 (1.00-1.15) |
0.040 |
1.08 (1.01-1.15) |
0.030 |
| Sex |
|
|
|
|
|
|
| Femal |
Ref |
|
|
|
|
|
| Male |
0.98 (0.36-2.69) |
0.965 |
|
|
|
|
| Body-mass index (kg/m2) ＊
|
1.04 (0.92-1.19) |
0.505 |
|
|
|
|
| Comorbidities, n
|
|
|
|
|
|
|
| Hypertension, n (%)
|
3.46 (1.26-9.51) |
0.016 |
1.36 (0.42-4.35) |
0.607 |
|
|
| Dyslipidemia, n (%)
|
0.97 (0.37-2.52) |
0.950 |
|
|
|
|
| Diabetes, n (%)
|
8.31 (2.99-23.13) |
＜0.001 |
3.26 (0.70-15.30) |
0.133 |
|
|
| Current smoking, n (%)
|
0.96 (0.35-2.63) |
0.930 |
|
|
|
|
| Excess alcohol intake, n (%)
|
1.02 (0.39-2.67) |
0.964 |
|
|
|
|
| Regular exercise, n (%)
|
1.34 (0.42-4.26) |
0.618 |
|
|
|
|
| Family history of CCVD, n (%)
|
2.28 (0.87-5.95) |
0.094 |
1.51 (0.47-4.83) |
0.486 |
|
|
| Medicine, n
|
|
|
|
|
|
|
| Anti-hypertensive agents, n (%)
|
3.96 (1.48-10.61) |
0.006 |
|
|
1.23 (0.34-4.52) |
0.752 |
| Statin, n (%)
|
1.60 (0.44-5.89) |
0.474 |
|
|
|
|
| Anti-diabetic drugs, n (%)
|
14.06 (4.30-45.94) |
＜0.001 |
|
|
4.06 (0.72-22.93) |
0.113 |
| Labs, n
|
|
|
|
|
|
|
| LDL-cholesterol (per 1mg/dL increase) |
1.00 (0.99-1.02) |
0.966 |
|
|
|
|
| HDL-cholesterol (per 1mg/dL increase) |
0.99 (0.96-1.02) |
0.536 |
|
|
|
|
| Trygricerides (per 1mg/dL increase)†
|
1.00 (1.00-1.00) |
0.931 |
|
|
|
|
| Fasting plasma glucose (per 1mg/dL increase) |
1.02 (1.00-1.03) |
0.015 |
1.00 (0.98-1.02) |
0.915 |
|
|
| HbA1c (per 1% increase ) |
1.55 (1.12-2.15) |
0.008 |
|
|
1.13 (0.72-22.93) |
0.652 |
| C reactive protein (per 1mg/dL increase) |
1.02 (0.57-1.83) |
0.940 |
|
|
|
|
| eGFR (per 1mL/min/1.73m2 increase)
|
0.97 (0.94-1.00) |
0.035 |
1.00 (0.97-1.03) |
0.866 |
1.00 (0.97-1.04) |
0.921 |
| Physical examination, n
|
|
|
|
|
|
|
| maximum IMT CCA (per 0.1mm increase) |
1.19 (1.09-1.31) |
＜0.001 |
1.08 (0.995-1.23) |
0.061 |
1.07 (0.97-1.20) |
0.181 |
| maximum IMT bifurcation (per 0.1mm increase) |
1.14 (1.08-1.21) |
＜0.001 |
1.10 (1.03-1.17) |
0.005 |
1.09 (1.01-1.17) |
0.005 |
| maximum IMT ICA (per 0.1mm increase) |
1.03 (0.98-1.08) |
0.266 |
|
|
|
|
Variables that had a p＜0.1 were included in the final multivariable model. SBI, silent brain infarction; CCVD, cardio-cerebrovascular diseases; LDL, low-density lipoprotein; HDL, high-density lipoprotein; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; IMT, intima media thickness; CCA, common carotid artery; BIF, bifurcation; ICA, internal carotid artery; ABI, anke-brachial index; OR, odds ratio.
aMultivariate model 1 includes age, dianosis of hypertension, diagnosis of diabetes, family history of CCVD, and HbA1c, eGFR and maximum IMT CCA or maximum IMT bifurcation either as explanatory variables.
b Multivariate model 2 analysis includes age, dianosis of hypertension, diagnosis of diabetes, family history of CCVD, and HbA1c, eGFR, maximum IMT CCA and maximum IMT bifurcation as explanatory variables.
Table 2.Determinants of subjects with SBI
|
SBI |
|
|
| Univariable |
Multivariable (model 1) |
Multivariable (model 2) |
| OR (95%CI) |
P value
|
OR (95%CI) |
P value
|
OR (95%CI) |
P value
|
| maximum IMT CCA (per 0.1mm increase) |
1.19 (1.09-1.31) |
＜0.001 |
1.11 (0.995-1.23) |
0.061 |
1.07 (0.97-1.20) |
0.181 |
| maximum IMT bifurcation (per 0.1mm increase) |
1.14 (1.08-1.21) |
＜0.001 |
1.10 (1.03-1.17) |
0.003 |
1.09 (1.01-1.17) |
0.005 |
| maximum IMT ICA (per 0.1mm increase) |
1.03 (0.98-1.08) |
0.266 |
- |
|
- |
|
Variables that had a p＜0.1 were included in the final multivariable model. SBI, silent brain infarction; CCVD, cardio-cerebrovascular diseases; IMT, intima media thickness; CCA, common carotid artery; BIF, bifurcation; ICA, internal carotid artery; OR, odds ratio.
a Multivariate model 1 analysis maximum IMT CCA and IMT bifurcation separately and includes age, dianosis of hypertension, diagnosis of diabetes, family history of CCVD, and HbA1c, eGFR and maximum IMT CCA or maximum IMT bifurcation either as explanatory variables.
b Multivariate model 2 analysis includes age, dianosis of hypertension, diagnosis of diabetes, family history of CCVD, and HbA1c, eGFR, maximum IMT CCA and maximum IMT bifurcation as explanatory variables.
We examined the prevalence of SBI according to the IMT thickness of ＜1.0 mm, 1.0–＜2.0 mm, and ≥ 2.0 mm at the IMT site, and found that the prevalence of SBI increased significantly with the increasing thickness for bifurcation and CCA (Fig.1). When a stratified analysis was performed using the determinants of bIMT, a significant risk for the presence of SBI was also observed in individuals with the highest bIMT compared to that in individuals with the lowest bIMT after adjusting for multiple potential confounders (22.98, 95% CI: 2.37–221.78; P=0.007). Furthermore, the OR of the increase in each category was calculated to be significant even after adjusting for the aforementioned risk factors (OR, 3.96; 95% CI, 1.63–9.62; P=0.002; Table 3). The results of the tertile analysis also showed similar results, which are shown in Supplementary Table 2.
Table 3.Unadjusted and adjusted odds ratios of SBI according to thickness of IMT in bifurcation
|
Unadjusted |
Adjusted＊
|
| OR |
95% CI |
P value
|
OR |
95% CI |
P value
|
| Bifurcation |
IMT ＜1 mm |
1.00 |
reference |
- |
1.00 |
reference |
- |
|
IMT 1 to ＜2 mm |
11.35 |
1.40-92.19 |
0.023 |
8.05 |
0.92-70.70 |
0.060 |
|
IMT ≥ 2 mm |
50.04 |
6.07-412.50 |
＜0.001 |
22.98 |
2.37-221.78 |
0.007 |
|
For an increase by each category |
5.80 |
2.71-12.43 |
＜0.001 |
3.96 |
1.63-9.62 |
0.002 |
＊Adjusted for age, sex, hypertension, dyslipidemia, diabetes, FH of CCVD, current smoking, excess alcohol intake, and regular exercise. CCVD, cardio-cerebrovascular diseases; OR, odds ratio; CI, confidence interval.
Supplementary Table 2.Unadjusted and adjusted odds ratios of SBI according to thickness of IMT in bifurcation
|
Unadjusted |
Adjusted＊
|
| OR |
95% CI |
P-value
|
OR |
95% CI |
P value
|
| Bifurcation |
IMT Tertile 1 |
1.00 |
reference |
- |
1.00 |
reference |
- |
|
IMT Tertile 2 |
10.00 |
1.15-87.27 |
0.037 |
8.27 |
0.88-77.78 |
0.065 |
|
IMT Tertile 3 |
30.11 |
3.82-237.13 |
0.001 |
15.13 |
1.64-139.38 |
0.016 |
|
For an increase by each category |
4.41 |
2.11-9.22 |
＜0.001 |
3.17 |
1.32-7.59 |
0.010 |
＊Adjusted for age, sex, hypertension, dyslipidemia, diabetes, FH of CCVD, current smoking, excess alcohol intake, and regular exercise. CCVD, cardio-cerebrovascular diseases; OR, odds ratio; CI, confidence interval.
Significant positive correlations were observed between the maximum bIMT and maximum CCA-IMT, maximum bIMT, maximum ICA-IMT, maximum ICA-IMT, and maximum CCA-IMT. However, the correlation coefficients between each site were very weak (correlation coefficient range: 0.180–0.253) (Fig.2).
Discussion
In this cross-sectional study, we analyzed data from 280 Japanese stroke-free medical check-up participants to examine whether the assessment of the maximum IMT by location was associated with SBI. We found that (1) SBI based on MRI was observed in 6.4% of the patients free of cardiovascular disease and stroke in a population of Japanese medical check-up participants; (2) the determinant of SBI was the maximum bIMT, not the maximum CCA or ICA-IMT, and the correlation coefficients between each site were very weak (correlation coefficient ranged from 0.180 to 0.253); (3) the percentage of individuals with SBI significantly increased with an increasing bIMT; and (4) significant ORs for the presence of SBI were observed in individuals with the highest bIMT compared to those with the lowest bIMT.
A significant correlation between SBI prevalence and age has been reported, with a prevalence of approximately 5% among people in their 50s in the general community, including Japan21-24). The mean age in this study was 52.6 years, which is comparable to that reported in previous studies. Although detailed guidelines for secondary prevention in patients with stroke have been reported25), few guidelines exist for the management of patients with incidental SBI26). However, SBI was a strong predictor of future stroke in an MRI-based study of stroke-free individuals11). For example, according to a meta-analysis of 13 studies, the presence of SBI was associated with an approximately two-fold increased risk of future stroke, even after adjusting for potentially confounding vascular risk factors11). Thus, identifying the risk factors associated with SBI in stroke-free individuals is very important from a stroke prevention perspective. Furthermore, although risk factors such as aging27), hypertension28), coronary artery disease29), and carotid artery diseases24,30) have been reported to be significantly associated with SBI, few studies have so far examined their association with carotid IMT according to location12). The main finding in this study, namely that IMT thickening at the bifurcation is significantly correlated with SBI, even after adjusting for confounding factors, suggests that carotid ultrasonography may be useful as a non-invasive screening test in stroke-free individuals.
In this study, aging and the maximum bIMT remained associated with SBI after adjusting for confounding factors. As reported in many large prospective studies, the prevalence of SBI varied with the age of the study population, but consistently increased with age11,30,31). However, cardiovascular risk stratification according to the site of the carotid segment, where IMT is measured on carotid ultrasonography, still remains controversial. For example, CCA-IMT is primarily influenced by age and BP, whereas ICA-IMT is more representative of cardiovascular risk factors32). Additionally, bulb IMT has been shown to be more closely related to coronary heart disease and its risk factors than CCA-IMT19,33). There are also reports that the maximal ICA-IMT is associated with the prevalence of cardiovascular disease and future cardiovascular events compared with the mean CCA-IMT34,35). However, few studies have so far examined the association between carotid IMT according to its location and SBI. For example, in a population with SBI, a larger mean IMT was observed in a pooled analysis, although most reports used the mean CCA-IMT36). Even when restricted to stroke-free individuals, the results did not change significantly, and the maximum IMT and site-specific assessments have not yet been investigated. Thus, although differences in IMT thickening by measurement site do not clearly differentiate among cardiovascular risk factors, future cardiovascular disease, and stroke risk, the finding that maximum bIMT, but not maximum CCA or ICA-IMT, is associated with SBI as an independent and significant risk factor in this population of stroke-free individuals has important implications as an indicator of carotid ultrasonography.
A significant risk for SBI was observed in individuals with a bIMT ≥ 2.0 mm compared to those with bIMT ＜1.0 mm after adjusting for potential confounders. Previous studies have compared groups according to IMT thickness in two ways: by tertiles or quartiles37-39) or by dividing the groups into branches that can be easily understood from a practical clinical point of view. In the present study, IMT thickness was divided into three groups: ＜1 mm, 1–2 mm, and ≥ 2 mm as previously reported40-42). The results shown in Supplementary Table 2 are similar to those analyzed by tertiles, thus indicating that the risk of SBI increases with bIMT thickness, regardless of the classification method used.
The fact that bIMT is more strongly associated with the presence of SBI than CCA-IMT or ICA-IMT may be related to wall shear stress (WSS). Several studies have reported that carotid artery geometry and WSS are involved in the development and progression of atherosclerosis, with atherosclerosis particularly concentrated at the outlet of arteries such as the carotid bulb rather than the straight segments, such as the common carotid artery and abdominal aorta43-45). Previous studies have also shown that individual geometry, low WSS, and even oscillatory shear index, which is the instantaneous fluctuation of WSS calculated by temporal changes in the local WSS vector, at the carotid bifurcation may be involved in the development of atherosclerotic lesions46-48). A computed tomography angiography analysis has also shown that the geometry and anatomy increase the risk of carotid artery stenosis independent of conventional vascular risk factors49), as confirmed by Bijari et al., who studied bifurcation geometry and wall thickness using cardiovascular magnetic resonance in more than 1000 patients50).
This study is associated with some limitations. First, the causal relationship between an increased maximum bIMT and SBI could not be determined because this was a cross-sectional study. Second, the study was conducted with participants from the northern district of Tokyo. Therefore, it is unclear whether the results of this study can be extrapolated to other conditions, including populations in other countries or ethnic groups. Third, the number of participants and patients with SBI included in the current epidemiological study was small. This may have affected our results because of the lack of sufficient statistical power. Fourth, the maximum IMT was measured in long-axis images. Therefore, accurate IMT cannot be measured if plaque is present in the lateral wall area. Fifth, plaque characteristics were not assessed in this study. Therefore, it was not possible to evaluate the effect of plaque characteristics on SBI, or whether plaque characteristics or maximum IMT had a stronger effect on SBI. Finally, the number of SBIs was quantitatively evaluated. However, of the 18 patients with SBI, 14 had one SBI, 3 had two, and 1 had three. Therefore, we determined that analyses which considered the number of SBI would be difficult to carry out due to a lack of power. Despite these limitations, non-invasive testing of key findings, such as the presence of SBI and IMT, has a strong potential to identify stroke-free individuals at a high risk of stroke. Further prospective studies are thus required to clarify whether the maximum bIMT is a reliable marker for predicting stroke events in stroke-free individuals undergoing medical check-ups.
Conclusion
In this study, we found the main determinant of SBI to be the maximum bIMT, but not the maximum CCA or ICA-IMT. In addition, the percentage of individuals with SBI significantly increased with an increasing maximum bIMT, and a significant SBI risk was also observed in individuals with the highest bIMT compared with those with the lowest bIMT. Screening individuals with a thicker maximum bIMT during medical checkups may help to identify a population at risk for future stroke.
Ethics Approval and Consent to Participate:
The study protocol was approved by the ethics committee of our hospital (Tokyo, Japan; approval no.: 27-16), and data were collected using the opt-out method on the bulletin board of our hospital. All the study procedures were performed in accordance with the tenets of the latest version of the Declaration of Helsinki.
Acknowledgements:
We thank Yuko Arai for assistance with ultrasound data collection and Masayoshi Isa for advice on MRI conditions.
Availability of Data and Material:
All data generated or analyzed during this study are included in this published article.
Declarations of Interests:
None.
Funding:
This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.
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