Carotid Intima Media Thickness and Risk Factor for Atherosclerosis: Tohoku Medical Megabank Community-Based Cohort Study

Abstract

Aim: We examined the association between the carotid intima medica thickness (cIMT) and risk factors for atherosclerosis based on the Japan Atherosclerosis Society 2022 Atherosclerosis Prevention Guideline.

Methods: Using data from the Tohoku Medical Megabank Community-based Cohort Study, we performed a cross-sectional study that enrolled 13,366 participants (age ≥ 20 years) with an analysis of covariance to assess associations between cIMT and risk factors for atherosclerosis. The maximum common carotid artery was measured using high-resolution B-mode ultrasound. Analysis was conducted in the model adjusted for age, sex, smoking status, drinking status, body mass index (BMI), systolic blood pressure (SBP), glycated hemoglobin (HbA1c), high-density lipoprotein-cholesterol (HDL-C), non-high-density lipoprotein-cholesterol (non-HDL-C), and height.

Results: In this study cohort, the average age and cIMT were 57.3 (13.8) years and 0.61 (0.13) mm, respectively, which included 3,988 males (29.8%). Males had a higher cIMT than did the females. Age, height, BMI, SBP, HbA1c, and non-HDL-C were positively associated with cIMT. HDL-C was inversely associated with cIMT. Compared with never drinkers, current drinkers (≥ 46.0 g/day) had a significantly decreased cIMT.

Conclusions: The cIMT was associated with atherosclerosis risk factors including age, sex, BMI, SBP, HbA1c, non-HDL-C, and HDL-C, and adequate control of risks in high-risk individuals might be required to prevent atherosclerotic cardiovascular diseases.

Introduction

The 2022 Japan Atherosclerosis Society (JAS) Guideline for Prevention of Atherosclerotic Cardiovascular Diseases indicated several risk factors for atherosclerosis, such as aging, smoking, dyslipidemia, hypertension, and diabetes¹⁾. In the JAS guideline, intima media thickness (IMT) is shown as one of the methods to assess subclinical atherosclerosis to prevent atherosclerotic cardiovascular disease (CVD)¹⁾. Carotid intima media thickness (cIMT) is not only a strong predictor of future CVD but is also associated with atherosclerosis risk factors^2-14). Previous studies in Japan showed that cIMT was associated with age, body mass index (BMI), systolic blood pressure (SBP), fasting blood glucose, smoking, total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C)^{4, 7)}. In addition, cIMT has been shown to be mainly affected by age and blood pressure^{3, 6)}. However, previous studies had limited sample sizes, and large population studies are required to better understand the contribution of atherosclerotic risk factors in cIMT.

The Tohoku Medical Megabank (TMM) Project conducted by the Tohoku University Tohoku Medical Megabank Organization and Iwate Medical University Iwate Tohoku Medical Megabank Organization has been launched to realize creative reconstruction and to solve medical problems in the aftermath of the Great East Japan Earthquake (GEJE)¹⁵⁾. In this project, we established two large-scale prospective cohorts: the TMM Birth and Three-Generation Cohort Study¹⁶⁾, and the TMM Community-based Cohort Study (TMM CommCohort Study)¹⁷⁾. In these studies, we obtained and analyzed data on lifestyle, blood, and urinary parameters. For some participants, we conducted further detailed measurements, including cIMT.

The large data set obtained from the abovementioned studies has enabled a detailed reconfirmation of the relationship between risk factors for atherosclerosis, which was proposed by the JAS Guideline for Prevention of Atherosclerotic Cardiovascular Diseases, and cIMT¹⁾. Moreover, the data from these studies have facilitated a comparison of the attribution of risk factors for cIMT.

Aim

We examined the association between cIMT and the risk factors for atherosclerosis, including age, sex, BMI, SBP, non-HDL-C, HDL-C, glycated hemoglobin (HbA1c), and smoking status in a Japanese population.

Methods

Study Design and Population

We conducted a cross-sectional study using data from the TMM CommCohort Study, which has been described elsewhere^{15, 17)}. It assessed the long-term impact of GEJE on disaster victims and genomic and omics research that lead to the implementation of personalized healthcare and medicine in the affected area in the near future for which many genomic and epidemiological studies have been conducted^15-26). The source population for the survey comprised males and females aged ≥ 20 years who were living in Miyagi prefecture, in northeastern Japan. All participants were recruited between May 2013 and March 2016 using the following three approaches. The Type 1 survey (40,433 participants) was conducted in specific municipal health check-up sites. The Type 1 additional survey (664 participants) was conducted on different dates during specific municipal health check-ups. The Type 2 survey (13,855 participants) was conducted in an assessment center. All surveys used a questionnaire, gathered data from blood and urine tests, and collected basic information from municipal health check-ups. Furthermore, several physiological measurements (carotid ultrasound imaging, body composition, respiratory function test, bone density measurement, etc.) were obtained only in the Type 2 survey¹⁷⁾. Informed consent was obtained from a total of 54,952 participants. This study was approved by the Institutional Review Board of the TMM Organization (approval number: 2022-4-047, approval date: June 30, 2022).

To be included in the analysis, participants had to undergo several physiological measurements. Thus, we enrolled only those who participated in the Type 2 survey (n=13,855). From these 13,855 participants, we excluded 489 participants for the following reasons: (1) those who withdrew from the study by October 5, 2021, failed to return the self-reported questionnaire, or did not undergo physiological measurements (n=196); and (2) had data missing on height, weight, SBP, diastolic blood pressure (DBP), non-fasting blood glucose, HbA1c, TC, triglyceride (TG), HDL-C, and cIMT (n=293). Therefore, 13,366 participants were included in the analysis dataset of this study.

Anthropometry

Height was measured to the nearest 0.1 cm using a stadiometer (AD-6400; A&D Co, Ltd, Tokyo, Japan). Weight was measured using a body composition analyzer (InBody720; Biospace Co, Ltd, Seoul, Republic of Korea). Weight was measured in increments of 0.1 kg, and 1.0 kg was subtracted to account for the weight of the participant’s clothing. BMI was calculated as the weight (kg) divided by height squared (m²).

Carotid Intima Media Thickness

Ultrasound imaging (GM-72P00A; Panasonic Healthcare, Co, Ltd, Japan) was used to measure right and left cIMT at the common carotid artery. The left and right cIMT were measured at a plaque-free site situated 10 mm proximal to the carotid bifurcation. The Cardiovascular Health Study used the maximum cIMT as a parameter because this parameter is more closely associated with cardiovascular risk factors compared with the mean cIMT²⁷⁾. The Shiga Epidemiological Study of Subclinical Atherosclerosis showed that the mean cIMT of maximum values had stronger associations with coronary calcium, a manifestation of coronary atherosclerosis and a strong surrogate marker for coronary heart disease²⁸⁾. Thus, we measured the thickness of the left and right maximum common carotid arteries. The analysis used the average of the maximum IMT values of the left and right IMT.

Measurements

We collected information on the participants’ demographic characteristics, smoking status, and drinking status using a self-reported questionnaire. Age was determined at the time of the visit to the community support center. Smoking status was classified into six categories: never smoker (had smoked ＜100 cigarettes in their lifetime), ex-smoker (had smoked ≥ 100 cigarettes in their lifetime but were not current smokers), current smoker (1–9, 10–19, or ≥ 20 cigarettes a day), and unknown²⁹⁾. Drinking status was classified into six categories: never drinker (had consumed little or no alcohol or were constitutionally incapable of alcohol consumption), ex-drinker (had stopped drinking alcohol), current drinker (＜23, 23.0–45.9, or ≥ 46.0 g/day), and unknown. Based on previous studies^18-21) that calculated the amount of ethanol consumed, the alcohol consumed was classified into six categories: sake, distilled spirits, shochu-based beverage, beer, whiskey, and wine. Furthermore, the alcohol intake frequency was classified into the following six categories: almost never, 1–3 days/month, 1–2 days/week, 3–4 days/week, 5–6 days/week, or daily. The amount of ethanol consumed was calculated by multiplying the type of alcohol by the frequency and amount of consumption. We set the cutoff value at 23 g, as this is the most common unit in Japan for measuring the amount of alcohol consumed^{20, 30)}.

After resting in a sitting position for ≥ 2 min, blood pressure was measured twice in the upper right arm using a digital automatic blood pressure monitor (HEM-9000AI; Omron Healthcare Co, Ltd, Kyoto, Japan). We collected non-fasting blood samples, and non-fasting blood glucose levels were measured using the hexokinase method. HbA1c levels were measured using the latex agglutination turbidimetry method. TC was measured using the Ultraviolet-End method with cholesterol dehydrogenase. TG was measured using an enzymatic method. HDL-C levels were measured using the direct method. The non-HDL-C level was calculated by subtracting the HDL-C levels from the TC.

Statistical Analysis

Data are presented as the mean (standard deviation (SD)) or median (interquartile range) for continuous variables and as the number (percentage) for categorical variables. An analysis of covariance (ANCOVA) was used to examine the association between risk factors for atherosclerosis and cIMT. The least square (LS) means of cIMT and corresponding 95% confidence intervals (CI) are presented. The multivariable-adjusted models included age (per 1-year increase), sex, height (per 10-cm increase), smoking status (never smoker, ex-smoker, current smoker [1–9, 10–19, or ≥ 20 cigarettes a day] and unknown), BMI (per 1-kg/m²), SBP (per 10 mmHg), HbA1c (＜5.5%, 5.5%–6.4%, and ≥ 6.5%), HDL-C (per 10 mg/dL), non-HDL-C (per10 mg/dL), and drinking status (never drinker, ex-drinker, and current drinker [＜23, 23.0–45.9, or ≥ 46.0 g/day] and unknown). The results were compared for each independent variable by analysis using Dunnett’s test with the following categories as references: age ＜30 years, height ＜150 cm, never smoker, BMI ＜16.0 kg/m², SBP ＜100 mmHg, HbA1c ＜5.5%, HDL-C ＜40 mg/dL, non-HDL-C ＜90 mg/dL, and never drinker. For continuous variables such as age, height, BMI, SBP, HbA1c, HDL-C, and non-HDL-C, we further calculated the p-values for the linear trend analysis by scoring the categories and entering the number as a continuous term in the regression model.

To compare the attribution of atherosclerosis risk factors for cIMT, we performed multivariable logistic regression analysis and calculated the area under the receiver operating characteristics curve (AUROC) and 95% CI by using the value of the third quartile of the cIMT measured for the entire cohort as the cutoff point of high cIMT. We compared the AUROC of the full model (age, sex, height, smoking status, BMI, SBP, HbA1c, HDL-C, non-HDL-C, and drinking status) and the AUROC without each independent variable.

Statistical significance was set at two-tailed p＜0.05. All analyses were performed using SAS version 9.4 for Windows (SAS Inc, Cary, NC, USA).

Results

Characteristics of the Study Population

In the entire study cohort, the mean (SD) values for age, cIMT, SBP, HbA1c, HDL-C, and non-HDL-C were 57.3 (13.8) years, 0.61 (0.13) mm, 128.4 (17.7) mmHg, 5.5 (0.5)%, 64.5 (16.6) mg/dL, and 144.5 (35.0) mg/dL, respectively. The proportion (%) of males, current drinkers, and current smokers were 29.8%, 55.4%, and 11.3%, respectively (Table 1) in this study cohort.

Table 1. Characteristics of participants

Number	13366
Age, years	57.3	(13.8)
Men,%	3988	(29.8)
cIMT, mm	0.61	(0.13)
Height, cm	159.3	(8.0)
Weight, cm	57.9	(11.0)
BMI, kg/m2	22.7	(3.4)
SBP, mmHg	128.4	(17.7)
DBP, mmHg	77.8	(10.8)
Glucose, mg/dL	88.6	(16.9)
HbA1c,%	5.5	(0.5)
TC, mg/dL	208.9	(35.7)
TG, mg/dL	85.0	(61.0-122.0)
HDL-C, mg/dL	64.5	(16.6)
non-HDL-C, mg/dL	144.5	(35.0)
Drinking status
Never drinker	4863	(36.4)
Ex-drinker	333	(2.5)
＜23 g/day	6207	(46.4)
23g–45.9 g/day	673	(5.0)
＞46.0 g/day	518	(3.9)
Unknown	772	(5.8)
Smoking status
Never smoker	8532	(63.8)
Ex-smoker	3280	(24.5)
1–9 cigarettes/day	312	(2.3)
10–19 cigarettes/day	634	(4.7)
＞20 cigarettes/day	563	(4.2)
Unknown	45	(0.3)

Values are expressed as mean (standard deviation) or median (interquartile range) for continuous variables or as number (percentage) for categorical variables.

BMI, body mass index; cIMT, carotid intima media thickness; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein-cholesterol; non-HDL-C, non-high-density lipoprotein-cholesterol; Q, quartile; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride

Associations between Risk Factor of Atherosclerosis and cIMT

Males had a slightly higher cIMT (0.63 [0.62–0.64] mm) as compared with that of females (0.63 [0.61–0.64] mm) after multivariable adjustment. Age was clearly and linearly associated with an increase in the cIMT, even after adjusted for atherosclerosis risk factors (P for linear trend ＜0.001). Similarly, height and SBP were linearly associated with increased cIMT (Figs.1 and 2). BMI and non-HDL-C showed a positive linear trend with cIMT (P for linear trend ＜0.001; Figs.3 and 4). HbA1c was positively associated with cIMT. The LS means (95% CI) were 0.62 mm (0.61–0.63) for HbA1c ＜5.5%, 0.62 mm (0.61–0.63) for HbA1c 5.5%–6.4%, and 0.64 mm (0.62–0.65) for HbA1c ≥ 6.5% (P for linear trend ＜0.01). The cIMT was significantly higher when HbA1c ≥ 6.5%, compared with HbA1c ＜5.5% (P for difference ＜0.001). Conversely, HDL-C was inversely associated with the cIMT (Fig.5). Compared with never smokers, ex-smokers had significantly increased cIMT (The LS means (95%CI) were 0.62 mm (0.61–0.63) for never smokers and 0.63 mm (0.62–0.64) for ex-smoker); however, there was no significant difference in the cIMT between current smokers and never smokers (The LS means (95%CI) were 0.63 mm (0.61–0.64) for current smokers who smoked 1–9 cigarettes a day, 0.62 mm (0.61–0.64) for current smokers who smoked 10–19 cigarettes a day, 0.63 mm (0.62–0.64) for current smokers who smoked ≥ 20 cigarettes a day, and 0.61 mm (0.58–0.64) for unknown). With regard to the drinking status, the cIMT LS means (95%CI) were 0.63 mm (0.62–0.64) for never drinkers, 0.64 mm (0.62–0.65) for ex-drinkers, 0.62 mm (0.62–0.63) for current drinkers who consumed ＜23.0 g alcohol/day, 0.62 mm (0.61–0.63) for current drinkers who consumed 23.0–45.9 g alcohol/day, 0.61 mm (0.59–0.62) for current drinkers who consumed ≥ 46.0 g alcohol/day, and 0.63 mm (0.62–0.64) for unknown. Compared with never drinkers, those who consumed ≥ 46.0 g ethanol/day were significantly associated with lower cIMT values.

Fig.1. Association between height and cIMT (in the model adjusted for age, sex, smoking status, BMI, SBP, HbA1c, HDL-C, non-HDL-C, and drinking status)

p for difference was derived from Dunnett’s test using height＜150 cm as the reference. Bars represent 95% confidence intervals. p for difference showed as following: ^＊p＜0.05, ^＊＊p＜0.01, ^＊＊＊p＜0.001. p for trend was calculated by scoring the height categories and entering the number as a continuous term in the regression model. Abbreviations: BMI, body mass index; cIMT, carotid intima media thickness; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein-cholesterol; LS, least square; non-HDL-C, non-high-density lipoprotein-cholesterol; SBP, systolic blood pressure.

Fig.2. Association between SBP and cIMT (in the model adjusted for age, sex, smoking status, BMI, HbA1c, HDL-C, non-HDL-C, drinking status, and height)

p for difference was derived from Dunnett’s test using SBP＜100 mmHg as the reference. Bars represent 95% confidence intervals. p for difference showed as following: ^＊p＜0.05, ^＊＊p＜0.01, ^＊＊＊p＜0.001. p for trend was calculated by scoring the SBP categories and entering the number as a continuous term in the regression model. Abbreviations: BMI, body mass index; cIMT, carotid intima media thickness; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein-cholesterol; LS, least square; non-HDL-C, non-high-density lipoprotein-cholesterol; SBP, systolic blood pressure.

Fig.3. Association between BMI and cIMT (in the model adjusted for age, sex, smoking status, SBP, HbA1c, HDL-C, non-HDL-C, drinking status, and height)

p for difference was derived from Dunnett’s test using BMI＜16.0 kg/m² as the reference. Bars represent 95% confidence intervals. p for difference showed as following: ^＊p＜0.05, ^＊＊p＜0.01, ^＊＊＊p＜0.001. p for trend was calculated by scoring the BMI categories and entering the number as a continuous term in the regression model. Abbreviations: BMI, body mass index; cIMT, carotid intima media thickness; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein-cholesterol; LS, least square; non-HDL-C, non-high-density lipoprotein-cholesterol; SBP, systolic blood pressure.

Fig.4. Association between non-HDL-C and cIMT (in the model adjusted for age, sex, smoking status, BMI, SBP, HbA1c, HDL-C, drinking status, and height)

p for difference was derived from Dunnett’s test using non-HDL＜90 mg/dl as the reference. Bars represent 95% confidence intervals. p for difference showed as following: ^＊p＜0.05, ^＊＊p＜0.01, ^＊＊＊p＜0.001. p for trend was calculated by scoring the non-HDL-C categories and entering the number as a continuous term in the regression model. Abbreviations: BMI, body mass index; cIMT, carotid intima media thickness; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein-cholesterol; LS, least square; non-HDL-C, non-high-density lipoprotein-cholesterol; SBP, systolic blood pressure.

Fig.5. Association between HDL-C and cIMT (in the model adjusted for age, sex, smoking status, BMI, SBP, HbA1c, non-HDL-C, drinking status, and height)

p for difference was derived from Dunnett’s test using HDL-C＜40 mg/dl as the reference. Bars represent 95% confidence intervals. p for difference showed as following: ^＊p＜0.05, ^＊＊p＜0.01, ^＊＊＊p＜0.001. p for trend was calculated by scoring the HDL-C categories and entering the number as a continuous term in the regression model. Abbreviations: BMI, body mass index; cIMT, carotid intima media thickness; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein-cholesterol; LS, least square; non-HDL-C, non-high-density lipoprotein-cholesterol; SBP, systolic blood pressure.

Attribution of Atherosclerosis Risk Factors for cIMT

The AUROC of the full model (age, sex, height, smoking status, BMI, SBP, HbA1c, HDL-C, non-HDL-C, and drinking status) for the third quartile value of the cIMT as the cutoff value and the AUROC without each independent variable are shown in Table 2. The AUROC of the full model was 0.826 (0.819–0.833) in this study, and the AUROC of the adjusted model, after excluding sex, HbA1c, HDL-C, and smoking status, was the same as that of the full model. The difference between the AUROCs of the full model and age-excluded model was largest (0.744 [0.735–0.753] and −0.082 for difference), followed by that of the SBP-excluded model (0.820 [0.812–0.827] and −0.006 for difference), the non-HDL-C-excluded model (0.824 [0.816–0.831] and −0.002 for difference), the BMI- and height-excluded model (0.825 [0.817–0.832] and −0.001 for difference), and the drinking status-excluded model (0.825 [0.818–0.833] and −0.001 for difference).

Table 2. Area under the ROC curve for atherosclerosis risk factors

Area under the receiver operating characteristic curve (95% CI)
Variable	Full modela		Without corresponding variables		Difference between full model and without corresponding variables
Age, years	0.826	(0.819-0.833)	0.744	(0.735-0.753)	-0.082
SBP, mmHg			0.820	(0.812-0.827)	-0.006
Non-HDL-C			0.824	(0.816-0.831)	-0.002
BMI, kg/m2			0.825	(0.817-0.832)	-0.001
Height			0.825	(0.817-0.832)	-0.001
Drinking status			0.825	(0.818-0.833)	-0.001
Sex			0.826	(0.818-0.833)	0
HbA1c			0.826	(0.818-0.833)	0
HDL-C			0.826	(0.818-0.833)	0
Smoking status			0.826	(0.818-0.833)	0

The area under the receiver operating characteristics curve and 95% CI were calculated by the multivariable logistic regression analysis using the third quartile value of cIMT as a cutoff point in the entire cohort.

^a Adjusted for age, sex, height, smoking status, BMI, SBP, HbA1c, HDL-C, non-HDL-C, and drinking status

BMI, body mass index; cIMT, carotid intima media thickness; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin A1c; HDL-C, high- density lipoprotein-cholesterol; non-HDL-C, non-high-density lipoprotein-cholesterol; Q, quartile; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride

Discussion

In this cross-sectional study, we analyzed data from 13,366 Japanese participants (aged ≥ 20 years) to investigate the association between risk factors for atherosclerosis and cIMT. We found that higher values for age, height, BMI, SBP, HbA1c, and non-HDL-C were positively associated with cIMT, whereas an inverse relationship was observed between HDL-C and the cIMT. Detailed figures can clarify that the relationship of the risk factors and cIMT essentially did not have a threshold. Males had a higher cIMT than did the females. Compared with never drinkers, current drinkers who consumed ≥ 46.0 g ethanol /day had lower cIMT. Moreover, we compared the contribution of risk factors for cIMT using AUROC, and the largest AUROC difference was owing to the exclusion of age, SBP, non-HDL-C, BMI, height, and alcohol status.

Several risk factors for atherosclerosis, such as aging, male sex, hypertension, dyslipidemia, and diabetes, have been identified in numerous epidemiological studies³¹⁾. The 2022 JAS guideline recommends the management and treatment of the abovementioned risk factors to prevent progression of atherosclerosis¹⁾. The cIMT is used as a surrogate marker of subclinical atherosclerosis and is associated with risk factors for atherosclerosis as well as a future risk of CVD^2-14). We observed that risk factors for atherosclerosis, including age, BMI, SBP, HbA1c, and non-HDL-C, were positively associated with the cIMT, whereas HDL-C was inversely associated with the cIMT, which is in accordance with the 2022 JAS guideline¹⁾. Although many previous studies have investigated the association between cIMT and alcohol consumption, the results were inconsistent: some studies showed a protective effect of light and moderate drinking^32-41), others found that alcohol is positively associated with cIMT^42-45), and yet others suggested that there was no association^46-51). An inverse relationship between alcohol consumption and the cIMT was previously reported in three large epidemiological studies (n＞4000) in the Korean and European populations^{38, 40, 41)}, and our findings concur with those of these previous studies. However, alcohol consumption may induce an increase in blood pressure and lead to the consequent increase in the cIMT. As we adjusted for blood pressure in our analysis, this may have caused an over-adjustment. Further studies are warranted to clarify the association between alcohol consumption and the cIMT and this mechanism.

There is a positive association between height and cIMT^52-54). The China Kadoorie Biobank study showed that height was associated with a higher cIMT⁵⁴⁾. In the present study, height was positively associated with the cIMT, and this result is consistent with that of previous studies^52-54). A potential mechanism underlying this association may be that, for geometric reasons, both arterial diameter and wall thickness need to adapt to maintain or increase the blood flow through the lengthening arterial tree⁵²⁾. Therefore, the cIMT may reflect not only atherosclerosis but also the physiological adaptation in hemodynamics, such as arterial diameter and thickening of the vascular wall, due to increased blood flow. Thus, height might need to be considered in risk factor analysis that includes the cIMT.

We compared the AUROC of the risk factors for atherosclerosis with that of the value of the third quartile of the cIMT as the cutoff value, and the AUROC without each atherosclerosis risk factors was used to compare the contribution of the risk factors for atherosclerosis to the increase in the cIMT. We found that the largest AUROC difference was due to the exclusion of age, SBP, non-HDL-C, BMI, height, or drinking status. Previous studies have shown that age and hypertension are strongly associated with an increase in the cIMT^{3, 6)}. Our findings are consistent with the results of previous studies and suggest that control of blood pressure might be a priority for the prevention of atherosclerosis. Additionally, as per the 2022 JAS guideline, non-HDL-C is possibly a useful indicator for predicting the risk of coronary artery disease, and obesity promotes atherosclerosis via hyperlipidemia, hyperglycemia, and hypertension or directly through the action of adipocytokines¹⁾. Therefore, lipid level management and maintenance of an appropriate body weight are important to prevent atherosclerosis.

The strength of this study is that we describe the association between the risk factors for atherosclerosis and cIMT because this study enrolled a large population of approximately 13,000 participants. However, our study had limitations. First, the presence of plaque is considered a marker of atherosclerosis and has a stronger significance in disease prediction compared with cIMT^{3, 55, 56)}. However, as our study did not measure plaque, we were only able to examine the association between the risk factors for atherosclerosis and cIMT. Second, this study had a cross-sectional design and could not definitively establish a causal relationship. However, previous studies have reported the direction of either or both relationships and causality between risk factors for atherosclerosis and cIMT^{5, 8-10)}.

In conclusion, the cIMT, as assessed using ultrasonography, is associated with the risk factors for atherosclerosis described in the 2022 JAS guideline. High-risk individuals require targeted interventions to adequately control their risk and to prevent atherosclerotic CVD.

Acknowledgements

The authors thank the members of the Tohoku Medical Megabank Organization, including the Genome Medical Research Coordinators and the office and administrative personnel for their assistance. A complete list of members is available at A complete list of members is available at https://www.megabank.tohoku.ac.jp/english/a220901/. We also thank Fuko Kisara for her analytical supports.

Funding

This work was supported by grants from the Japanese Society for the Promotion of Science [JSPS; Grant-in-Aid for Science Research (C), number 19K10637], Tohoku Medical Megabank Project from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), the Japan Agency for Medical Research and Development [AMED; JP22tm0124005] and JST SPRING, [Grant number JPMJSP2114].

Conflicts of Interest

There are no conflicts of interest.

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