Association of Anthropometric and CT-Based Obesity Indices with Subclinical Atherosclerosis

Yosuke Higo; Takashi Hisamatsu; Yoshihisa Nakagawa; Yuichi Sawayama; Yuichiro Yano; Aya Kadota; Akira Fujiyoshi; Sayaka Kadowaki; Sayuki Torii; Keiko Kondo; Yoshiyuki Watanabe; Hirotsugu Ueshima; Katsuyuki Miura

doi:10.5551/jat.64096

Abstract

Aim: Few studies have compared the strength in the associations of anthropometric and computed tomography (CT)-based obesity indices with coronary artery calcification (CAC), aortic artery calcification (AoAC), and aortic valve calcification (AVC).

Methods: We assessed cross-sectcional associations of anthropometric and CT-based obesity indices with CAC, AoAC, and AVC. Anthropometric measures included body mass index (BMI), waist circumference, hip ircumference, waist-to-hip circumference ratio, and waist-to-height ratio in 931 men (mean age, 63.7 years) from a population-based cohort. CT images at the L4/5 level were obtained to calculate the areas of abdominal visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), total adipose tissue (TAT), VAT-to-SAT ratio (VSR), and VAT-to-TAT ratio (VTR). CAC, AoAC, and AVC were quantified using the Agatston score based on CT scanning.

Results: CAC, AVC, and AoAC were present in 348 (62.6%), 173 (18.6%), and 769 (82.6%) participants, respectively. In multivariable models adjusting for age, lifestyle factors, and CT types (electron beam CT and multidetector row CT), anthropometric and CT-based obesity indices were positively associated with CAC (p＜0.01). Conversely, VAT-to-SAT ratio and VAT-to-TAT ratio were positively associated with AoAC (p＜0.01). Any obesity indices were not associated with AVC.

Conclusions: The strength of the associations of obesity indices with subclinical atherosclerosis varied according to the anatomically distinct atherosclerotic lesions, among men.

See editorial vol. 31: 19-20

Introduction

Obesity is associated with metabolic abnormalities, including increased blood pressure, dyslipidemia, and type 2 diabetes mellitus (DM), and links to atherosclerotic cardiovascular diseases (CVDs)¹⁾. Although anthropometric measures, including the body mass index (BMI) and waist circumference (WC), are available as an adiposity index in clinical practices, they may not be sufficient to assess visceral adiposity since BMI and WC include both visceral and subcutaneous adipose tissue and muscle mass²⁾. Imaging techniques, e.g, computed tomography (CT) can assess the distribution of adipose tissue. Excess visceral adipose tissue (VAT) based on CT imaging is associated with cardiometabolic abnormalities^{3, 4)} and the atherosclerotic burden^{5, 6)}.

Coronary artery calcification (CAC), aortic artery calcification (AoAC), and aortic valve calcification (AVC) are markers of subclinical atherosclerosis. Although they are closely correlated with one another, each has independent prognostic value for CVDs^{7, 8)}. Additionally, all of CAC, AoAC, and AVC have shared traditional CVD risk factors^{7, 9)}. However, the association of subclinical atherosclerosis with cardiovascular risk factors may not be identical across them¹⁰⁾. Results from studies assessing the association of anthropometric and CT-based obesity indices with both CAC and AoAC have been inconsistent. For example, one study suggested a positive association of only anthropometric measures with CAC and that of both measures with AoAC¹¹⁾, whereas another showed no associations between them¹²⁾. No studies have assessed the association between adiposity indices and AVC¹³⁾. Furthermore, few studies have assessed whether the associations of anthropometric and CT-based obesity indices with atherosclerosis differed across CAC, AoAC, and AVC.

Aim

In this study, we cross-sectionally investigated the relationships between anthropometric (BMI, WC, hip circumference [HC], waist-to-hip circumference ratio [WHR], waist-to-height ratio [WHtR]) and CT-based (VAT, subcutaneous adipose tissue [SAT], VAT-to-SAT ratio [VSR], VAT-to-total adipose tissue ratio [VTR]) obesity indices with anatomically distinct atherosclerotic lesions (CAC, AoAC, and AVC) in Japanese men from a population-based cohort. This information is of great interest and importance in East Asian populations, where the distribution of obesity is quite different from that in Western populations¹⁴⁾.

Methods

Study Population

This was a cross-sectional investigation of an observational population-based study. The Shiga Epidemiological Study of Subclinical Atherosclerosis (SESSA) is a study of subclinical atherosclerosis and its determinants in a sample of Japanese residents, and details of the enrollment methods have been previously reported¹⁵⁾. In brief, from 2006 to 2008, we randomly selected and invited 2379 Japanese men aged 40 to 79 years who were residents of Kusatsu City, Shiga, based on the city’s Basic Residents’ Register (which contains information on residents’ name, sex, birth date, and address). In total, 1094 men agreed to a baseline examination. After excluding those with a history of stroke or myocardial infarction (n=63), a history of coronary revascularization or aortic valve replacement (n=20), triglycerides of ≥ 400 mg/dL (n=15) because the Friedewald formula was applied to estimate low-density lipoprotein cholesterol (LDL-C) levels, and missing information (CT images [n=9] and anthropometric measures [n=56]), 931 participants were analyzed (mean age, 63.7 years; standard deviation [SD], 10.3). All participants provided written informed consent. This study was performed in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) and approved by the institutional review board of Shiga University of Medical Science.

Anthropometric Obesity Indices

Body weight and height were measured while the participant was wearing light clothing without shoes. BMI was calculated as weight divided by the square of the height. WC and HC were measured twice at the umbilical level and maximal protrusion of the hip in an upright standing position, respectively. All analyses used the mean of two measures. WHR was calculated as WC divided by HC. WHtR was calculated as WC divided by height¹⁶⁾ (Fig.1A).

Fig.1. Anthropometric and CT-based obesity indices

(A) Waist circumference (WC) and hip circumference (HC) were measured at the umbilical level and maximal protrusion of the hip in an upright standing position, respectively. Body mass index, waist-to-hip circumference ratio, and waist-to-height ratio were calculated from height, weight, WC, and HC.

(B) Abdominal adipose tissue at the L4/5 vertebral space was adopted as a CT-based obesity index. Visceral adipose tissue (VAT) was defined as the fat enclosed by the inner aspect of the abdominal wall. Subcutaneous adipose tissue (SAT) was defined as the fat outside the outer aspects of the abdominal wall, but not including that located within the muscular fascia. The VAT-to-SAT ratio and VAT-to-total adipose tissue ratio were calculated from the VAT and SAT areas.

CT-Based Obesity Indices

We adopted areas of VAT and SAT as CT-based obesity indices. Abdominal VAT was defined as the fat enclosed by the inner aspect of the abdominal wall. Abdominal SAT was defined as the fat outside the outer aspects of the abdominal wall, but not including that located within the muscular fascia. While participants were supine, serial CT images were obtained using a protocol similar to that in a previous report¹⁷⁾. A single CT image of the L4/5 vertebral space was selected to estimate areas of VAT and SAT. Adipose tissue was identified as showing attenuation between −190 and −30 HU combined with anatomical interpretation by a reader¹⁷⁾. The inner and outer aspects of the abdominal walls were manually tracked, and respective areas were calculated using image analysis software (sliceOmatic; TomoVision, Montreal, Canada). Two CT techniques were used: electron beam CT (EBCT) (Imatron C-150; GE Healthcare, Chicago, IL, USA) (slice thickness, 6 mm) for participants examined from May 2006 through August 2007 and 16-row multidetector row CT (MDCT) (Aquilion 16; Toshiba Medical Systems, Tochigi, Japan) (slice thickness, 7 mm) for participants examined thereafter. All CT images were analyzed at Shiga University of Medical Science by a trained physician researcher who was blinded to the participants’ characteristics. The abdominal total adipose tissue (TAT) area was calculated using the sum of the VAT and SAT areas. VSR and VTR were calculated as VAT area divided by SAT area and VAT area divided by TAT area, respectively (Fig.1B).

Assessment of Subclinical Atherosclerosis

The detailed methods to assess CAC, AoAC, and AVC have been published elsewhere^{18, 19)}. We assessed CAC, AoAC, and AVC by either EBCT or MDCT²⁰⁾. Images were obtained every 3 mm from the level of the aortic root through the heart to evaluate CAC and AVC and every 6 mm from the aortic arch to the iliac bifurcation to evaluate AoAC, with a scan time of 100 ms (EBCT) or 320 ms (MDCT). The presence of CAC, AoAC, and AVC was defined as a minimum of three contiguous pixels (area=1 mm²) with a density of ≥ 130 HU using AccuImage software (AccuImage Diagnostics, South San Francisco, CA, USA), which implements the widely accepted Agatston method²¹⁾. The total CAC was obtained by multiplying the pixel area by a density score (1, 130–199 HU; 2, 200–299 HU; 3, 300–399 HU; and 4, ≥ 400 HU) derived from the maximal HU within this area. AoAC and AVC scores were measured and quantified for any calcified lesion located from the aortic arch to the iliac bifurcation and within the aortic valve leaflets, respectively, with the same definition as CAC.

Covariate Assessment

Blood specimens were obtained early in the clinic visit after a 12-hour fast and used for laboratory testing, including lipid and glucose measurements, at a single laboratory (Shiga Laboratory, MEDIC, Shiga, Japan). Lipid measurements were standardized annually according to the protocol of the Centers for Disease Control and Prevention/Cholesterol Reference Method Laboratory Network. Total cholesterol and triglyceride levels were measured using enzymatic assays, and high-density lipoprotein cholesterol (HDL-C) levels were measured using a direct method. The Friedewald formula was used to calculate LDL-C when the triglyceride concentration was ＜400 mg/dL. Plasma glucose levels were determined by sodium fluoride-treated plasma using a hexokinase glucose-6-phosphate-dehydrogenase enzymatic assay. Glycated hemoglobin (HbA1c) was measured using a latex agglutination assay according to the standardized method of the Japanese Diabetes Society (JDS). We then converted JDS values to National Glycohemoglobin Standardization Program (NGSP) values using the formula recommended by the JDS: HbA1c (NGSP)=1.02×HbA1c (JDS)＋0.25 (%)²²⁾. DM was defined as fasting plasma glucose of ≥ 126 mg/dL, HbA1c (NGSP) of ≥ 6.5%, or use of diabetic medication. Dyslipidemia was defined as LDL-C of ≥ 140 mg/dL, HDL-C of ＜40 mg/dL, triglycerides of ≥ 150 mg/dL, or use of dyslipidemic medication.

Blood pressure was measured in the right arm using an automated sphygmomanometer (BP-8800; Omron Health Care, Kyoto, Japan) with an appropriately sized cuff. Participants were asked to empty their bladders for urinalysis and sit quietly for at least 5 minutes before blood pressure measurement. The average of two consecutive measures was used for analyses. Hypertension was defined as systolic blood pressure of ≥ 140 mmHg, diastolic blood pressure of ≥ 90 mmHg, or use of antihypertensive medication.

A self-administered questionnaire was used to obtain information on demographics, smoking habits (current, former, or never), alcohol drinking (current drinker or non-drinker), medication use (e.g., antihyperglycemic, antihypertensive, and antihyperlipidemic medications), and medical history (e.g., stroke and myocardial infarction). Upon questionnaire completion, trained nurses confirmed all responses with the participants.

Statistical Analysis

Data are presented as mean with SD or median (interquartile range) for continuous variables and as number with percentage for categorical variables. Differences in characteristics were evaluated by the unpaired Student’s t-test or chi-square test. We assessed nine obesity indices: BMI, WC, HC, WHR, WHtR, VAT area, SAT area, VSR, and VTR. We also stratified CAC and AoAC according to the Agatston score quartiles because the distributions of the CAC and AoAC scores were highly skewed. Given that 348 (37.4%) participants were free of CAC, the categories for CAC were as follows: 0 (n=348), 1st–50th percentile (n=118), 50th–75th percentile (n=233), and ＜75th percentile (n=232). We used an ordinal logistic regression to examine the multivariable association of the quartiles of CAC and AoAC with a 1-SD increment of each obesity index. Because prevalent AVC was observed in only 162 (17.4%) participants, we could not divide the indices into quartiles. We instead used Poisson regression with robust error variance to estimate the association of obesity indices with the presence of AVC. In Model 1, we adjusted for age, smoking status (current and former), and alcohol drinking (yes/no). In Model 2, we further adjusted for hypertension, DM, and dyslipidemia. In Model 3, we further adjusted for BMI. A term for the CT technique (EBCT or MDCT) was also included in all models. We recognized a priori that Models 2 and 3 may introduce overadjustment because DM, hypertension, and dyslipidemia may be intermediaries in the causal pathway between obesity and subclinical atherosclerosis. In the BMI-adjustment analyses, we simultaneously inserted BMI and another obesity index into one model to determine whether the association of one of the other eight indices was independent of BMI. We considered these models as exploratory but their use as justifiable with an aim to examine the strength of association of the obesity indices relative to these factors. As a sensitivity analysis, we performed Poisson regression with robust error variance for CAC and AoAC, setting the cut-off point at the 80th percentile of the Agatston score. As a supplementary analysis, we performed multiple linear regression using log (CAC＋1), log (AoAC＋1) and log (AVC＋1) as the dependent variable in model 1. Because the natural logarithms of CAC, AoAC and AVC were used, 1 was added to each score to include individuals with zero values for CAC, AoAC and AVC¹⁰⁾. Furthermore, we conducted a stratified analysis by statin use and then examined the multiplicative interactions between statin use and obesity indices for CAC, AoAC, and AVC. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA). A two-sided P value of ＜0.05 was considered statistically significant.

Results

Of the 931 participants, 583 (62.6%), 173 (18.6%), and 769 (82.6%) had CAC, AVC, and AoAC, respectively. Table 1 shows the participants’ characteristics. Those with CAC were older and had a higher prevalence of hypertension, dyslipidemia, and DM. Similarly, those with AVC were older and more likely to have hypertension and DM. Those with AoAC were also older and had a higher prevalence of former smoking, hypertension, and DM. Participants with CAC, AVC and AoAC had higher prevalence of medication use for hypertension and DM compared with those without CAC, AVC, and AoAC. Participants with AVC had a higher proportion of statin use than those without AVC. Participants with CAC had a higher BMI, WC, WHR, WHtR, VAT, VSR, and VTR; those with AVC had a higher WHR and WHtR; and those with AoAC had a higher WHR, WHtR, VSR, and VTR and lower BMI, HC, and SAT.

Table 1.Characteristics of study participants with or without CAC, AoAC, or AVC

	overall n=931	CAC^＊			AVC^＊			AoAC^＊
	overall n=931	Absence n=348	Presence n=583	P value	Absence n=758	Presence n=173	P value	Absence n=162	Presence n=769	P value
Agatston scores			5.2 (0.0–81.0)			0.0 (0.0–0.0)			295.8 (27.4–1220.1)
Age, years	63.7±10.3	58.6±11.4	66.8±8.1	＜0.001	62.1±10.3	71.1±6.3	＜0.001	51.5±9.1	66.3±8.5	＜0.001
Hypertension, n (%)	498 (53.5)	123 (35.3)	375 (64.3)	＜0.001	376 (49.6)	122 (70.5)	＜0.001	38 (24.5)	460 (59.8)	＜0.001
Diabetes mellitus, n (%)	162 (17.4)	35 (10.6)	127 (21.8)	＜0.001	348 (16.2)	72 (22.5)	0.048	10 (6.2)	152 (20.0)	＜0.001
Dyslipidemia, n (%)	511 (54.9)	160 (46.0)	351 (60.2)	＜0.001	410 (54.1)	101 (58.4)	0.306	78 (48.2)	433 (56.3)	0.058
Medication for hypertension, n (%)	238 (25.6)	53 (15.2)	185 (31.7)	＜0.001	177 (23.4)	61 (35.3)	0.001	15 (9.3)	223 (29.0)	＜0.001
Medication for diabetes mellitus, n (%)	85 (9.1)	15 (4.3)	70 (7.5)	＜0.001	56 (7.4)	29 (16.8)	＜0.001	6 (3.7)	79 (10.3)	0.008
Statin, n (%)	82 (8.8)	23 (6.3)	59 (10.1)	0.067	59 (7.8)	23 (13.3)	0.021	12 (7.4)	70 (9.1)	0.489
Smoking Status, n (%)				0.175			0.625			＜0.001
Current	292 (31.4)	110 (31.6)	182 (31.2)		242 (31.9)	50 (28.9)		50 (30.9)	242 (31.5)
Former	470 (50.5)	165 (47.4)	305 (52.3)		377 (49.7)	93 (53.8)		64 (39.5)	406 (52.8)
Alcohol drinker, n (%)	718 (77.1)	274 (78.7)	444 (76.2)	0.365	593 (78.2)	125 (72.3)	0.091	131 (80.9)	587 (76.3)	0.212
BMI, kg/m²	23.5±3.0	23.0±2.9	23.8±3.1	＜0.001	23.5±3.0	23.6±3.2	0.688	24.0±3.1	23.4±3.0	0.028
WC, cm	85.1±8.1	83.6±7.5	86.1±8.3	＜0.001	85.1±7.9	85.5±8.9	0.559	85.7±8.2	85.0±8.0	0.295
HC, cm	92.4±5.4	92.1±5.3	92.6±5.4	0.146	92.6±5.4	91.7±5.2	0.052	94.2±5.8	92.0±5.2	＜0.001
WHR	0.92±0.05	0.91±0.05	0.93±0.05	＜0.001	0.92±0.05	0.93±0.06	0.007	0.91±0.05	0.92±0.05	0.002
WHtR	0.51±0.05	0.50±0.04	0.52±0.05	＜0.001	0.51±0.05	0.52±0.05	0.001	0.50±0.05	0.51±0.05	0.019
VAT, cm²	116±54	109±55	121±49	＜0.001	116±51	119±63	0.473	118±52	116±54	0.744
SAT, cm²	121±54	118±52	123±55	0.177	121±53	122±58	0.880	132±58	119±53	0.004
VSR	1.01±0.47	0.97±0.34	1.04±0.38	0.003	1.01±0.36	1.03±0.42	0.541	0.94±0.30	1.03±0.38	0.001
VTR	0.49±0.09	0.48±0.08	0.49±0.09	0.006	0.49±0.08	0.49±0.10	0.941	0.47±0.08	0.49±0.09	0.007

Data are presented as mean±standard deviation, median (interquartile range), or number (%).

AoAC, aortic artery calcification; AVC, aortic valve calcification; BMI, body mass index; CAC, coronary artery calcification; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; VSR, VAT-to-SAT ratio; VTR, VAT-to-TAT ratio; WC, waist circumference; WHtR, waist-to-height ratio; WHR, waist-to- hip circumference ratio

^＊Presence of CAC, AoAC, and AVC was defined as an Agatston score of ＞0.

In the ordinal logistic regression after adjustment for age, smoking status, alcohol drinking, and CT technique (Model 1) (Fig.1A), prevalent CAC was significantly associated with BMI (odds ratio [OR], 1.39; 95% confidence interval [CI], 1.23–1.58), WC (OR, 1.39; 95% CI, 1.23–1.58), HC (OR, 1.31; 95% CI, 1.15–1.50), WHR (OR, 1.35; 95% CI, 1.19–1.54), WHtR (OR, 1.42; 95% CI, 1.25–1.61), VAT (OR, 1.27; 95% CI, 1.12–1.45), and SAT (OR, 1.28; 95% CI, 1.12–1.45). After further adjustment for hypertension, DM, and dyslipidemia (Model 2), prevalent CAC was significantly associated only with anthropometric indices. After further adjustment for BMI (Model 3), the significant association between CAC and obesity indices disappeared (Fig.2A). However, prevalent AoAC was significantly associated with VSR (OR, 1.24; 95% CI, 1.09–1.41) and VTR (OR, 1.21; 95% CI, 1.07–1.37) in Model 1, and the positive associations were observed throughout the models (Fig.2B).

Fig.2. Multivariable ordinal logistic regression describing the association of 1-SD higher obesity indices with (A) CAC and (B) AoAC

Solid circles and horizontal lines represent the odds ratios (ORs) and 95% confidence intervals (CIs) for quartiles of CAC and AoAC scores, respectively. ORs were calculated with a 1-SD increment in BMI (3.00 kg/m²), WC (8.05 cm), HC (5.44 cm), WHR (0.05), WHtR (0.05), VAT (53.7 cm²), SAT (54.1 cm²), VSR (0.37), and VTR (0.09).

Model 1: adjusted for age, smoking status (current and former), alcohol drinking (current drinker or non-drinker), and CT technique (electron beam vs. multidetector row). Model 2: further adjusted for hypertension, diabetes mellitus, and dyslipidemia. Model 3: further adjusted for BMI.

AoAC, aortic artery calcification; BMI, body mass index; CAC, coronary artery calcification; HC, hip circumference; SAT, subcutaneous adipose tissue; TAT, total adipose tissue; VAT, visceral adipose tissue; VSR, VAT-to-SAT ratio; VTR, VAT-to-TAT ratio; WC, waist circumference; WHtR, waist-to-height ratio; WHR, waist-to-hip circumference ratio.

Poisson regression with robust error variance showed that the presence of AVC was not associated with any obesity indices in any models (Fig.3). CAC was associated only with VAT (relative risk [RR], 1.14; 95% CI, 1.01–1.28), but the associations between CAC and obesity indices disappeared in Models 2 and 3 (Fig.4A). AoAC was significantly associated with VSR (RR, 1.14; 95% CI, 1.01–1.28) and VTR (RR, 1.14; 95% CI, 1.01–1.28) in Model 1, and the positive associations were observed throughout the models (Fig.4B).

Fig.3. Multivariable Poisson regression with robust error variance describing associations of obesity indices with AVC

Solid circles and horizontal lines indicate the relative risks (RRs) and 95% confidence intervals (CIs) for the presence of AVC, setting the cut-off point to an Agatston score of ＞0. RRs were calculated with a 1-SD increment in BMI (3.00 kg/m²), WC (8.05 cm), HC (5.44 cm), WHR (0.05), WHtR (0.05), VAT (53.7 cm²), SAT (54.1 cm²), VSR (0.37), and VTR (0.09).

AVC, aortic valve calcification; BMI, body mass index; HC, hip circumference; SAT, subcutaneous adipose tissue; TAT, total adipose tissue; VAT, visceral adipose tissue; VSR, VAT-to-SAT ratio; VTR, VAT-to-TAT ratio; WC, waist circumference; WHtR, waist-to-height ratio; WHR, waist-to-hip circumference ratio.

Fig.4. Multivariable Poisson regression with robust error variance describing associations of obesity indices with (A) CAC and (B) AoAC

Solid circles and horizontal lines indicate the relative risks (RRs) and 95% confidence intervals (CIs) for the presence of (A) CAC and (B) AoAC, setting the cut-off point to the 80th percentile of the Agatston score. RRs were calculated with a 1-SD increment in BMI (3.00 kg/m²), WC (8.05 cm), HC (5.44 cm), WHR (0.05), WHtR (0.05), VAT (53.7 cm²), SAT (54.1 cm²), VSR (0.37), and VTR (0.09).

Multivariable linear regression showed that the associations of CAC, AoAC and AVC with obesity indies were substantially equivalent to those in main analysis (Model 1, Supplementary Table 1). CAC was significantly associated with BMI (β coefficient, 0.30; 95% CI, 0.16-0.43), WC (β coefficient, 0.28; 95% CI, 0.14-0.42), HC (β coefficient, 0.23; 95% CI, 0.09-0.37), WHR (β coefficient, 0.25; 95% CI, 0.11-0.39), WHtR (β coefficient, 0.31; 95% CI, 0.18-0.45), VAT (β coefficient, 0.24; 95% CI, 0.09-0.37), SAT (β coefficient, 0.21; 95% CI, 0.07-0.34). AoAC was significantly associated with VSR (β coefficient, 0.24; 95% CI, 0.10-0.37) and VTR (β coefficient, 0.22; 95% CI, 0.08-0.35). AVC had no association with all obesity indies.

Supplemental Table1.Multiple linear regression describing the association of 1-SD higher in obesity indices with CAC, AoAC and AVC

	CAC			AoAC			AVC
	β coefficient	95% CI	P value	β coefficient	95% CI	P value	β coefficient	95% CI	P value
BMI	0.30	(0.16–0.43)	＜0.001	0.03	(-0.10–0.17)	0.598	0.07	(-0.03–0.17)	0.176
WC	0.28	(0.14–0.42)	＜0.001	0.04	(-0.10–0.17)	0.597	0.02	(-0.08–0.12)	0.702
HC	0.23	(0.09–0.37)	0.001	-0.03	(-0.17–0.11)	0.681	0.02	(-0.08–0.12)	0.754
WHR	0.25	(0.11–0.39)	0.001	0.09	(-0.04–0.23)	0.188	0.01	(-0.09–0.11)	0.809
WHtR	0.31	(0.18–0.45)	＜0.001	0.07	(-0.07–0.20)	0.328	0.03	(-0.06–0.13)	0.536
VAT	0.24	(0.09–0.37)	0.001	0.16	(0.02–0.29)	0.022	0.00	(-0.10–0.10)	0.979
SAT	0.21	(0.07–0.34)	0.003	-0.02	(-0.15–0.12)	0.788	0.01	(-0.09–0.11)	0.902
VSR	0.07	(-0.06–0.21)	0.294	0.24	(0.10–0.37)	0.001	-0.02	(-0.11–0.08)	0.677
VTR	0.05	(-0.09–0.18)	0.508	0.22	(0.08–0.35)	0.002	-0.05	(-0.14–0.05)	0.357

Agatston scores were log transformed after addition of one. The model was adjusted for age, smoking status (current and former), alcohol drinking (current drinker or non-drinker) and CT technique (electron beam vs. multidetector row) (Model 1).

AoAC, aortic artery calcification; AVC, aortic valve calcification; BMI, body mass index; CAC, coronary artery calcification; CI; confidence interval; HC, hip circumference; SAT, subcutaneous adipose tissue; TAT, total adipose tissue; VAT, visceral adipose tissue; VSR, VAT-to-SAT ratio; VTR, VAT-to-TAT ratio; WC, waist circumference; WHtR, waist-to-height ratio; WHR, waist-to-hip circumference ratio.

The associations of CAC and AoAC with obesity indices were similar between participants with and without statin use (all P value for interaction ＞0.2). However, for the association with AVC, WHR, WHtR, VAT, VSR, and VTR were more pronounced among participants taking statins (P values for interaction =0.041, 0.006, 0.011, 0.006, and 0.033, respectively, Supplemental Table 2).

Supplemental Table 2.Multivariable analysis describing the association between 1-SD higher in obesity indices with CAC, AoAC, and AVC in participants with or without statins use

	statin use	CAC		AoAC		AVC
	statin use	OR (95% CI)	p-interaction	OR (95% CI)	p-interaction	RR (95% CI)	p-interaction
BMI	yes	0.47 (0.94–2.71)	0.632	0.91 (0.54–1.53)	0.658	1.28 (1.00–1.64)	0.051
	no	1.36 (1.20–1.56)		0.98 (0.86–1.11)		1.07 (0.93–1.23)
WC	yes	1.22 (0.75–2.00)	0.594	0.93 (0.56–1.54)	0.740	1.26 (0.96–1.64)	0.065
	no	1.38 (1.21–1.57)		0.98 (0.86–1.11)		1.03 (0.90–1.18)
HC	yes	1.28 (0.80–2.04)	0.702	1.00 (0.63–1.60)	0.827	1.06 (0.80–1.42)	0.460
	no	1.31 (1.15–1.50)		0.91 (0.80–1.05)		1.03 (0.89–1.19)
WHR	yes	1.00 (0.57–1.76)	0.439	0.81 (0.45–1.46)	0.397	1.49 (1.09–2.04)	0.041
	no	1.35 (1.18–1.54)		1.05 (0.92–1.19)		1.02 (0.89–1.16)
WHtR	yes	1.40 (0.82–2.38)	0.841	0.82 (0.48–1.41)	0.468	1.47 (1.10–1.95)	0.006
	no	1.39 (1.22–1.58)		1.02 (0.89–1.16)		1.02 (0.89–1.17)
VAT	yes	1.41 (0.90–2.19)	0.827	0.99 (0.66–1.50)	0.909	1.18 (1.02–1.37)	0.011
	no	1.27 (1.11–1.45)		1.07 (0.94–1.23)		0.98 (0.85–1.14)
SAT	yes	1.20 (0.72–1.99)	0.669	0.78 (0.47–1.30)	0.257	0.97 (0.66–1.42)	0.786
	no	1.26 (1.10–1.43)		0.94 (0.82–1.07)		1.03 (0.89–1.19)
VSR	yes	1.27 (0.79–2.05)	0.415	1.28 (0.78–2.11)	0.307	1.55 (1.19–2.01)	0.006
	no	1.06 (0.94–1.21)		1.21 (1.06–1.38)		0.93 (0.81–1.08)
VTR	yes	1.15 (0.69–1.93)	0.664	1.20 (0.70–2.05)	0.315	1.62 (1.08–2.42)	0.033
	no	1.06 (0.93–1.20)		1.17 (1.03–1.33)		0.91 (0.79–1.05)

The model was adjusted for age, smoking status (current and former), alcohol drinking (current drinker or non-drinker) and CT technique (electron beam vs. multidetector row) (Model 1). Ordinal logistic regression was used to estimate the association between CAC and AoAC with obesity indices, and Poisson regression with robust error variance was used to estimate the association between AVC with obesity indices.

AoAC, aortic artery calcification; AVC, aortic valve calcification; BMI, body mass index; CAC, coronary artery calcification; CI; confidence interval; HC, hip circumference; OR; odd ratio; RR, risk ratio; SAT, subcutaneous adipose tissue; TAT, total adipose tissue; VAT, visceral adipose tissue; VSR, VAT-to-SAT ratio; VTR, VAT-to-TAT ratio; WC, waist circumference; WHtR, waist-to-height ratio; WHR, waist-to-hip circumference ratio

Discussion

Principal Findings

In this population-based, cross-sectional study of middle-aged to older Japanese men without apparent CVD, CAC was positively associated with anthropometric or CT-based obesity indices, whereas AoAC was positively associated only with CT-based obesity indices (VSR and VTR). No obesity indices were associated with AVC. In the exploratory analysis, after adjusting for cardiometabolic profiles such as DM, hypertension, and dyslipidemia, CAC was associated only with anthropometric indices, and AoAC was still associated with VSR and VTR. After further adjusting for BMI, all associations of obesity indices with CAC disappeared, while the associations of VSR and VTR with AoAC remained.

Findings in Context of Current Literature

Prior studies have investigated the association of anthropometric or CT-based obesity indices with CAC. For anthropometric indices, some population-based studies showed a positive and independent association of CAC with BMI²³⁾ or with WC and WHR²⁴⁾. For CT-based obesity indices, however, the results regarding CAC were inconsistent. Two large population-based cohorts in the United States (from the Jackson Heart Study and the Framingham Heart Study) showed that VAT was positively associated with CAC in age- and sex-adjusted models, and the association was diminished after adjustment for traditional CVD risk factors excluding BMI^{12, 25)}. However, two cohorts of patients with probable coronary artery disease in Saudi Arabia and Japan showed that VAT was still associated with CAC after adjustment for traditional CVD risk factors including BMI^{26, 27)}. One possible reason for these differences in results is the differences in the characteristics of the target population among various studies. The association between VAT and CAC is stronger in patient- than population-based studies because of the higher prevalence of obesity and coronary atherosclerosis in patients with coronary artery disease than in healthy community-dwelling individuals. Interestingly, similar to prior population-based studies, we found that the associations between CT-based obesity indices and CAC were attenuated after adjustment for cardiometabolic profiles while anthropometric indices were more strongly associated with CAC.

In our study, AoAC was associated only with abdominal adipose tissue composition indices on CT, such as VSR and VTR, and these associations persisted even after adjusting for cardiometabolic profiles and BMI. Several studies on anthropometric obesity indices showed no association with AoAC^{12, 28)}. For example, in the Framingham Heart Study, AoAC had no association with BMI and WC after adjusting for conventional CVD risk factors¹²⁾. The relationship between AoAC and CT-based obesity indices also remains inconsistent: some studies suggested a positive association^{29, 30)} whereas some suggested no association^{11, 12)}. Differences in the aortic segment used to identify calcification³¹⁾ or in the population characteristics (e.g., sex, race)^{32, 33)} may contribute to these different results among studies.

We found that AVC was not associated with any obesity indices. Previous investigations of these associations have shown conflicting results. In the Rotterdam Study, AVC was not associated with BMI or WHR in men after adjustment for age, CT technique, medication use (DM, hypertension, or dyslipidemia), and history of CVD³⁴⁾. In one population-based study of 1376 randomly selected adults in the United States, AVC was associated with BMI³⁵⁾ after adjustment for CVD risk factors. A study of 259 Japanese patients who underwent diagnostic CT angiography for coronary artery disease showed that the percentage of abdominal VAT, considered equal to VTR, was independently associated with AVC¹³⁾. The lack of association between AVC and obesity indices in our study might be explained by the differences in the participants’ characteristics, especially by the lowest BMIs (mean, 23.5 kg/m²), among the relevant studies.

Differential Associations among CAC, AoAC, and AVC

CAC, AoAC, and AVC share some traditional CVD risk factors^{7, 8)}, although our study showed clear differences in the associations among CAC, AoAC, and AVC in terms of obesity indices. CAC was independently associated with anthropometric indices even after adjustment for conventional CVD risk factors. This statistical independence may reflect multiple mechanisms to which obesity contributes, including low-grade inflammation, prothrombotic state, insulin resistance, local cytokine release from ectopic fat tissue, and endothelial dysfunction²³⁾. VAT is metabolically active by secreting adipokines, causing vascular inflammation and insulin resistance, which are associated with CVD³⁶⁾. Although abdominal adipose tissue had no association with CAC after adjustment for conventional CVD risk factors, point estimates showed a tendency toward a positive association in our study.

In our study, only VSR and VTR had significant associations with AoAC even after adjustment for conventional CVD risk factors and BMI. The reason for these significant associations remains unclear. However, it been suggested that SAT may have a protective role as a metabolic sink storing surplus energy and that VSR may be a better index of SAT dysfunction, leading to a marked increase in visceral and ectopic fat in other compartments associated with insulin resistance and inflammation³⁶⁾. SAT dysfunction can appear at a relatively early stage of metabolic complications³⁷⁾. Calcification begins in the larger arteries and progresses to the smaller arteries, and AoAC also appears in the early stage of vascular disease¹¹⁾. Hence, adverse cardiometabolic profiles due to SAT dysfunction might lead to AoAC in early atherosclerosis. Abdominal aortic calcification also appears to be a strong marker of aging and exposure to cigarette smoke¹¹⁾, and VSR is significantly associated with aging³⁸⁾ and smoking³⁹⁾. These findings suggest a strong association between VSR or VTR and AoAC.

AVC had no association with obesity indices in our study. Several studies have shown that oxidative stress and inflammatory cytokines are associated with AVC^{40, 41)}. Additionally, both anthropometric indices (e.g., BMI or WC) and CT-based indices are associated with oxidative stress or inflammation⁴²⁾. However, previous studies on AVC and anthropometric obesity indices have shown conflicting results^{35, 43, 44)}. To date, limited data have been available with respect to the association of AVC with CT-based obesity indices. In a study of 259 patients who underwent CT angiography for the diagnosis of coronary artery disease, VAT had a positive association with AVC¹³⁾. This study included patients who were older (mean age, 67 years) and had a higher prevalence of AVC (29.0%), hypertension (61.4%), and diabetes mellitus (27.8%), suggesting the patients were at higher risk of atherosclerosis, compared with participants in our study. Therefore, the difference in participants’ characteristics may contribute to the difference in results between the two studies. Taken together, previous studies examining the relationship between AVC and obesity indices have been heterogenic with regard to demographics or study design⁴⁵⁾, which may lead to the conflicting results. In our study of the population-based individuals with less advanced CVD risk factors (e.g., low BMI), no association was found between obesity indices and AVC.

In our study, AVC had significant associations with both anthropometric obesity indices and CT-based obesity indices especially among the participants using statins. Our results are in line with recent reports demonstrating an association of statin with progression in AVC or aortic valve stenosis^{46, 47)}. Statin therapy may stabilize coronary plaques by increasing CAC volume, while it may simultaneously drive the progression from fibrosis to calcification of valvular leaflets⁴⁶⁾. Nonetheless, the possibility that patients under statin therapy initially have more atherosclerosis features in terms of more advanced valve calcification with respect to no-statin patients, must be considered⁴⁶⁾.

Study Limitations

To our knowledge, this is the first population-based study to comprehensively examine the association of both anthropometric and CT-based obesity indices with three anatomically distinct atherosclerotic lesions (CAC, AoAC, and AVC). However, our study has several limitations. First, the results may not be widely generalizable because we analyzed only middle-aged to older male residents recruited from a single area of Japan. Second, because this was a cross-sectional study, the temporal relationship between obesity indices and calcifications cannot be shown. Third, our CT-based obesity indices were measured by the area, not volume, at the L4/5 level. However, these indices are widely used and highly correlated with volume⁴⁸⁾. Therefore, our conclusions are unlikely to be influenced by differences in adipose tissue measurement techniques. Forth, we carefully controlled for major known confounders; however, our findings may be partly explained by differences in unmeasured factors (e.g, pericardial fat⁴⁹⁾ and adiponectin⁵⁰⁾). Finally, the low prevalence of calcification, especially AVC, may have contributed to the lack of power to detect significant associations.

Conclusions

The findings obtained from Japanese men from this population-based cohort have important implications. First, both anthropometric and CT-based obesity indices were associated with CAC. Notably, the association of anthropometric indices with CAC persisted after adjustment for CVD risk factors such as hypertension, dyslipidemia, and DM. Second, CT-based obesity indices were associated with AoAC independent of CVD risk factors and BMI. Finally, neither anthropometric nor CT-based obesity indices were associated with AVC. These findings suggest that obesity may have a different effect on each atherosclerotic lesion.

Acknowledgements

We thank the SESSA investigators, staff, and study participants for their commitments and outstanding dedication. A full list of the SESSA investigators can be found at https://shiga-publichealth.jp/sessa/. The authors also thank Angela Morben, DVM, ELS, from Edanz (https://jp.edanz.com/ac), for editing a draft of this manuscript.

Financial Support

This work was supported by Grants-in-Aid for Scientific Research (A13307016, A17209023, A21249043, A23249036, A25253046, A15H02528, 18H04074, and 22H00493) from the Ministry of Education, Culture, Sports, Science and Technology Japan; by the National Institutes of Health, USA (R01HL068200); and by GlaxoSmithKline.

Conflict of Interest

None.

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