The Association of Lung Function and Carotid Intima-Media Thickness in a Japanese Population: The Tohoku Medical Megabank Community-Based Cohort Study

Masato Takase; Mitsuhiro Yamada; Tomohiro Nakamura; Naoki Nakaya; Mana Kogure; Rieko Hatanaka; Kumi Nakaya; Ikumi Kanno; Kotaro Nochioka; Naho Tsuchiya; Takumi Hirata; Yohei Hamanaka; Junichi Sugawara; Tomoko Kobayashi; Nobuo Fuse; Akira Uruno; Eiichi N Kodama; Shinichi Kuriyama; Ichiro Tsuji; Atsushi Hozawa

doi:10.5551/jat.63826

Abstract

Aim: Impaired lung function is associated with atherosclerotic vascular events. Carotid intima-media thickness (cIMT) is a marker for subclinical atherosclerosis. However, few studies have examined the association between lung function and cIMT among never smokers or individuals stratified by age. We investigated the association between lung function and cIMT in the Japanese population.

Methods: We conducted a cross-sectional study of 3,716 men and 8,765 women aged 20 years or older living in Miyagi Prefecture, Japan. Lung function was evaluated using forced expiratory volume at 1 s (FEV₁) and forced vital capacity (FVC) was measured using spirometry. The maximum common carotid artery was measured using high-resolution B-mode ultrasound. An analysis of covariance was used to assess associations between lung function and cIMT and adjusted for potential confounders. A linear trend test was conducted by scoring the categories from 1 (lowest) to 4 (highest) and entering the score as a continuous term in the regression model.

Results: After adjusting for potential confounders including passive smoking, lower FEV₁ and FVC were associated with higher cIMT in both men and women (P＜0.001 for linear trend). This association was confirmed even when we restricted our study to never smokers. Furthermore, even when we stratified by age, an inverse association between lung function and cIMT was confirmed in middle-aged (40–64 years) and elderly participants (65–74 years).

Conclusions: Lower lung function was associated with higher cIMT in the Japanese population independent of age and smoking. Assessment of atherosclerosis or lung function may be required for individuals with lower lung function or atherosclerosis.

Introduction

Cardiovascular diseases (CVD) are considered the leading cause of death globally¹⁾. Previous studies have reported that lower lung function is related to an increased risk of coronary heart disease (CHD), stroke, and CVD^2-6). Carotid intima-media thickness (cIMT) is widely used as a marker for atherosclerosis, and several studies have reported that a higher cIMT is related to the incidence of CHD, stroke, and CVD^7-11). Therefore, many studies have investigated the association between lung function and cIMT and shown that poor lung function is associated with increased cIMT^12-18).

However, several issues remain to be resolved. First, smoking is strongly correlated with lung function and atherosclerosis^{19, 20)}. Hence, a stratified analysis of smoking status is necessary to determine the association between lung function and atherosclerosis, independent of smoking status. However only four studies have previously performed stratified analyses according to smoking status^{13, 15-17)}. One study showed that lung function was not associated with cIMT among never smokers¹³⁾, whereas the other studies determined that lower lung function was associated with increased cIMT among never smokers^15-17). Second, although active and passive smoking are associated with increased cIMT²¹⁾, no study has investigated the association between lung function and cIMT, taking into account passive smoking. Third, older age was associated with lower lung function and higher cIMT^{7, 14, 19, 22, 23)}. However, no study has stratified the association between lung function and cIMT by age. Fourth, only two studies have examined the association between lung function and cIMT in Japanese men^{14, 18)}. Furthermore, no studies have been conducted on the Japanese population stratified by smoking status. We hypothesized that an inverse association between lung function and cIMT would be observed independently of age, smoking, and passive smoking.

Aim

To test this hypothesis, we examined the cross-sectional association between lung function and cIMT in the Japanese population. Furthermore, we stratified the participants by age or limited the study to never smokers and investigated an association under these conditions.

Materials

Study Design and Population

We conducted a cross-sectional study using data from the Tohoku Medical Megabank Community-based Cohort Study (TMM CommCohort Study), the design of which has been described in detail elsewhere^{24, 25)}. This study was approved by the Institutional Review Board of the Tohoku Medical Megabank Organization (approval no. 2021-4-028; approval date: May 31, 2021). All the participants were recruited between May 2013 and March 2016. We used three approaches to recruit participants. The type 1 survey (40,433 participants) was conducted at specific municipal health check-up sites. The type 1 additional survey (664 participants) was conducted on different dates at specific municipal health checkups. The type 2 survey (13,855 participants) was conducted at the community support center. The source population for the study comprised men and women aged ≥ 20 years, living in Miyagi Prefecture, northeastern Japan. Informed consent was obtained from 54,952 patients.

Since several physiological measurements, including lung function, were conducted only in the type 2 survey, we only used data from 13,855 participants who had undergone these measurements. We excluded participants who withdrew from the study by July 13, 2021, failed to return the self-reported questionnaire, did not undergo physiological measurements, or had missing data regarding forced expiratory volume at 1 s (FEV₁) and forced vital capacity (FVC), cIMT, height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), glucose, glycated hemoglobin A1c (HbA1c), total cholesterol (TC), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) (n=1,374). Finally, data from 12,481 participants (3,716 men and 8,765 women) were analyzed.

Assessment of Lung Function

Lung function parameters, including FEV₁, FVC, and vital capacity (VC), were measured using a spirometer (HI-801; Chest M.I., Incorporation, Tokyo, Japan). Spirometry was performed in the sitting position using an attached nose clip. The FEV₁ was categorized into the following sex-specific cutoff points of the quartiles: Q1, ＜2.56 L; Q2, 2.56–2.95 L; Q3, 2.96–3.43 L; and Q4, ≥ 3.43 L for men and Q1, ＜1.96 L; Q2, 1.96–2.25 L; Q3, 2.26–2.58 L; and Q4, ≥ 2.58 L for women. The FVC was also categorized into the following sex-specific quartiles: Q1, ＜3.28 L; Q2, 3.28–3.73 L; Q3, 3.74–4.26 L; and Q4, ≥ 4.26 L for men and Q1, ＜2.44 L; Q2, 2.44–2.77 L; Q3, 2.78–3.14 L; and Q4, ≥ 3.14 L for women. Restrictive ventilatory impairment was defined as a reduced VC of ＜80% of predicted, and obstructive ventilatory impairment was defined as a reduced ratio of ＜70% of FEV₁ to FVC.

Assessment of Carotid Intima-Media Thickness

The cIMT was assessed using ultrasound imaging equipment (GM-72P00A; Panasonic Healthcare, Co., Ltd., Tokyo, Japan). The left and right cIMT were measured at a plaque-free site 10 mm proximal to the carotid bifurcation. Previous studies have confirmed excellent reproducibility and validity using this automated device, and it has been used in a previous study with the Japanese population^26-28). We analyzed the left and right maximum common carotid arteries because the Cardiovascular Health Study has adopted the maximum cIMT as a parameter more closely associated with cardiovascular risk factors than the mean cIMT¹⁰⁾. The analysis used the average of the maximum IMT values from the left and right IMT.

Potential Confounders

We administered a self-report questionnaire to assess the participants’ demographic characteristics, smoking status, and drinking status. Smoking status was classified into four categories: never smokers (had smoked ＜100 cigarettes in their lifetime), ex-smokers (had smoked ≥ 100 cigarettes in their lifetime and were not current smokers), current smokers (smoked ≥ 100 cigarettes in their lifetime and were currently smoking), and unknown status²⁹⁾. Passive smoking was defined as those who claimed that they had been exposed to the smoke of others at work or home in the previous year. Drinking status was classified into five categories: never drinkers (had consumed little or no alcohol or were constitutionally incapable of alcohol consumption), ex-drinkers (had stopped drinking alcohol), current drinker (＜23 g/day), current drinker (≥ 23 g/day), and unknown^{30, 31)}. We set the cutoff value at 23 g, as this is the most common unit, in Japan, for measuring the amount of alcohol consumed^30-33). Educational status was classified into the following categories: below high school, vocational school, junior or technical college, university or graduate school, other, and unknown. Metabolic equivalents (METs) assigned to each physical activity were used to quantify leisure-time physical activity³⁴⁾. We used the quartile of physical activity in our model, and missing data were categorized as an unknown group.

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, Korea). Body mass index was calculated as weight (kg) divided by height (m²).

Blood pressure (BP) was measured at the community support center. After resting in a sitting position for ≥ 2 min, BP was measured twice in the upper right arm using a digital automatic BP monitor (HEM-9000AI; Omron Healthcare Co., Ltd., Kyoto, Japan). The mean value of the two measurements was used in the analysis. Hypertension was defined as SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, and/or receiving treatment for hypertension. Blood samples were collected under nonfasting conditions. Plasma glucose and HbA1c levels were measured using enzymatic methods. The presence of diabetes was defined as plasma glucose ≥ 200 mg/dL, HbA1c ≥ 6.5%, and/or receiving treatment for diabetes. TC was measured using the ultra violet-end method with cholesterol dehydrogenase. TG levels were measured using an enzymatic method. HDL-C levels were measured using a direct method. Hypercholesterolemia was defined as TC ≥ 240 mg/dL and/or receiving treatment for dyslipidemia according to the International Conference on Low Blood cholesterol³⁵⁾. Participants answered whether they have a history of the following diseases: asthma, chronic bronchitis, chronic obstructive pulmonary disease (COPD), stroke, or myocardial infarction.

Statistical Analysis

All statistical analyses were performed using R version 4.1.2. (R Core Team, Vienna, Austria). All analyses were performed separately for men and women because the distributions of FEV₁, FVC, and cIMT differed between them. Data are presented as mean (standard deviation [SD]) or median (interquartile range) for continuous variables and as number (%) for categorical variables.

With respect to the characteristics of FEV₁ quartiles, a trend test was performed on continuous variables using a simple linear model to evaluate linearity. We also conducted a chi-square test to compare the characteristics of the categorical variables among the FEV₁ quartiles. We performed a similar analysis to evaluate the linear associations and compare the characteristics of the categorical variables among the quartile groups for FVC.

An analysis of covariance (ANCOVA) was used to calculate the least squares means of cIMT according to the FEV₁ category. In Model 1, we adjusted for age, height, and education level. In Model 2, we further adjusted for major risk factors for CVD, such as smoking status, passive smoking, weight, hypertension, diabetes, hypercholesterolemia and drinking status. Furthermore, to examine whether the association between lung function and cIMT could be explained by physical activity, Model 3 was further adjusted for METs. The association between FVC and cIMT was also examined using ANCOVA and the same models. The p-values for the analysis of linear trends were calculated by scoring the categories from 1 (the lowest category) to 4 (the highest category) and entering the number as a continuous term in the regression model. The multiple linear regression analyses were performed using lung function parameters as a continuous term and the above same model to confirm the robustness of the results.

Since smoking is related to reduced lung function and increased cIMT^{19, 20)}, to eliminate the effects on smokers and ex-smokers, we restricted the participants to those who had never smoked. Additionally, several analyses were conducted to test the robustness of our findings. First, since age is associated with lower lung function and higher cIMT^{7, 14, 19, 22, 23)}, we conducted an analysis stratified by young (20–39 years), middle-aged (40–64 years), elderly (65–75 years), and very old (≥ 75 years) participants. In Japan, all participants aged 40 years or older were eligible for specific municipal health check-ups aimed at screening individuals with CVD risk factors. Furthermore, the Japanese government defines the early-stage elderly as those aged 65–74 years and the later-stage elderly as those aged 75 years or older. Therefore, we set the cutoff values as young (20–39 years), middle-aged (40–64 years), elderly (65–75 years), and very old (≥ 75 years) participants. Second, to exclude the effects of respiratory disease, we restricted the participants to those who had no restrictive ventilatory impairment, obstructive ventilatory impairment, or history of respiratory diseases such as asthma, chronic bronchitis, and COPD. Third, we restricted the participants to those who had no history of CVD because individuals with CVD have a higher cIMT³⁶⁾. Finally, lipid-lowering drugs are associated with both reduced cIMT and increased lung function^{10, 37)}. Furthermore, not only do antihypertensive and antidiabetic drugs reduce cIMT but also they are associated with reduced lung function^{10, 38, 39)}. Therefore, to eliminate the effects of treatment for hypertension, diabetes, and dyslipidemia, we selected only individuals who were not undergoing treatment for hypertension, diabetes, and dyslipidemia. A p-value ＜0.05 was considered significant.

Results

Data of 3,716 men and 8,765 women, who fulfilled all the inclusion criteria, were included in the analyses. The mean age (±SD) of the study participants was 60.1 (±14.0) years for men and 56.2 (±13.4) years for women. The FEV₁ was higher in men than in women. Similarly, the FVC was higher in men than in women. Mean cIMT was also larger in men (0.65 [0.14] mm) than in women (0.60 [0.12] mm). Both men and women with higher FEV₁ were lower in age and had reduced cIMT, the prevalence of hypertension, diabetes, hypercholesterolemia, METs, and passive smoking. Participants with higher FEV₁ were taller, had a higher education level, and had a current-smoking status (Table 1). Similar patterns were observed when the FVC quartiles were used (Table 2).

Table 1. Participant characteristics according to FEV₁

Variables	Men	The quartile groups of FEV₁				p-value	Women	The quartile groups of FEV₁				p-value
Variables	Men	Q1 (＜2.56)	Q2 (2.56-2.95)	Q3 (2.96-3.43)	Q4 (≥ 3.43)	p-value	Women	Q1 (＜1.96)	Q2 (1.96-2.25)	Q3 (2.26-2.58)	Q4 (≥ 2.58)	p-value
Number	3716	916	934	921	945		8765	2190	2165	2220	2190
Age (years)	60.1(14.0)	70.9 (7.7)	65.4 (8.1)	59.5 (10.7)	45.1 (13.0)	＜0.001	56.2 (13.4)	67.1 (8.3)	61.0 (9.0)	54.1 (10.8)	42.7 (11.0)	＜0.001
20-39 years	436 (11.7)	5 (0.5)	11 (1.2)	60 (6.5)	360 (38.1)		1213 (13.8)	21 (1.0)	53 (2.4)	240 (10.8)	899 (41.1)
40-64 years	1512 (40.7)	133 (14.5)	357 (38.2)	516 (56.0)	506 (53.5)		4795 (54.7)	670 (30.6)	1282 (59.2)	1605 (72.3)	1238 (56.5)
65-74 years	1349 (36.3)	494 (53.9)	475 (50.9)	311 (33.8)	69 (7.3)		2284 (26.1)	1129 (51.6)	759 (35.1)	344 (15.5)	52 (2.4)
≥ 75 years	419 (11.3)	284 (31.0)	91 (9.7)	34 (3.7)	10 (1.1)		473 (5.4)	370 (16.9)	71 (3.3)	31 (1.4)	1 (0.0)
Height (cm)	167.5 (6.3)	163.3 (5.5)	165.6 (5.1)	168.7 (5.3)	172.5 (5.3)	＜0.001	155.8 (5.8)	151.7 (5.2)	154.3 (4.7)	156.9 (4.7)	160.2 (4.8)	＜0.001
Weight (kg)	66.8 (10.2)	63.4 (9.1)	65.3 (9.3)	67.6 (9.5)	71.0 (11.3)	＜0.001	54.2 (8.8)	52.4 (8.3)	53.5 (8.3)	54.7 (8.7)	56.2 (9.4)	＜0.001
BMI (kg/m²)	23.8 (3.1)	23.7 (2.9)	23.8 (3.0)	23.7 (2.9)	23.9 (3.5)	0.526	22.4 (3.5)	22.8 (3.5)	22.5 (3.4)	22.2 (3.5)	21.9 (3.5)	＜0.001
SBP (mmHg)	133.9 (16.0)	138.9 (16.2)	136.3 (16.2)	133.1 (15.4)	127.6 (13.8)	＜0.001	126.0 (17.8)	133.5 (17.4)	129.4 (17.1)	124.6 (17.2)	116.6 (14.6)	＜0.001
DBP (mmHg)	80.9 (10.8)	78.9 (10.8)	81.5 (10.8)	82.5 (10.3)	80.8 (11.2)	＜0.001	76.5 (10.5)	77.2 (10.4)	77.5 (10.4)	77.1 (10.6)	74.3 (10.4)	＜0.001
Prevalence of hypertension (%)	1991 (53.6)	639 (69.8)	583 (62.4)	487 (52.9)	282 (29.8)	＜0.001	2981 (34.0)	1138 (52.0)	874 (40.4)	669 (30.1)	300 (13.7)	＜0.001
Glucose (mg/dl)	92.8 (20.8)	96.7 (24.8)	94.7 (21.6)	91.8 (19.1)	88.0 (15.8)	＜0.001	86.7 (14.3)	90.0 (17.2)	87.9 (14.1)	85.8 (12.3)	83.2 (12.1)	＜0.001
HbA1c (%)	5.6 (0.6)	5.8 (0.7)	5.7 (0.6)	5.6 (0.6)	5.4 (0.6)	＜0.001	5.5 (0.5)	5.7 (0.5)	5.6 (0.5)	5.5 (0.4)	5.3 (0.4)	＜0.001
Prevalence of diabetes (%)	388 (10.4)	157 (17.1)	112 (12.0)	77 (8.4)	42 (4.4)	＜0.001	401 (4.6)	179 (8.2)	126 (5.8)	71 (3.2)	25 (1.1)	＜0.001
TC (mg/dl)	201.4 (34.9)	197.8 (33.3)	201.6 (34.7)	203.8 (35.5)	202.3 (35.9)	0.003	212.4 (35.5)	216.1 (34.7)	219.0 (34.5)	215.0 (35.6)	199.7 (34.1)	＜0.001
TG (mg/dl)	101.0 (72.0-149.0)	104.0 (74.0-147.3)	102.0 (76.0-152.8)	101.0 (72.0-148.0)	96.0 (66.0-146.0)	0.643	79.0 (58.0-112.0)	91.0 (67.0-124.0)	84.0 (62.0-119.0)	79.0 (58.0-108.0)	65.0 (49.0-90.0)	＜0.001
HDL-C (mg/dl)	57.2 (15.1)	56.4 (14.8)	57.4 (15.7)	57.6 (14.7)	57.3 (15.2)	0.169	67.6 (16.2)	66.0 (16.3)	67.5(16.2)	68.5(16.4)	68.5 (15.8)	＜0.001
Prevalence of hypercholesterolemia (%)	860 (23.1)	214 (23.4)	228 (24.4)	239 (26.0)	179 (18.9)	0.002	2741 (31.3)	885 (40.4)	823 (38.0)	697 (31.4)	336 (15.3)	＜0.001
FEV₁ (L)	2.96 (2.56-3.43)	2.27 (2.03-2.42)	2.76 (2.66-2.85)	3.16 (3.06-3.29)	3.77 (3.58-4.04)	＜0.001	2.26 (1.96-2.58)	1.76 (1.60-1.86)	2.12 (2.04-2.18)	2.41 (2.33-2.49)	2.84 (2.70-3.03)	＜0.001
FVC (L)	3.74 (3.28-4.26)	2.98 (2.71-3.20)	3.50 (3.32-3.70)	3.95 (3.76-4.15)	4.62 (4.34-4.95)	＜0.001	2.78 (2.44-3.14)	2.22 (2.03-2.37)	2.63 (2.51-2.74)	2.93 (2.81-3.07)	3.38 (3.21-3.61)	＜0.001
%VC	101.9 (13.5)	93.4 (13.3)	101.2 (11.4)	105.2 (12.3)	107.6 (12.3)	＜0.001	103.1 (13.6)	95.1 (12.6)	103.0 (12.2)	105.7 (13.0)	108.5 (12.9)	＜0.001
Restrictive ventilatory impairment (%)	163 (4.4)	131 (14.3)	17 (1.8)	10 (1.1)	5 (0.5)	＜0.001	284 (3.2)	205 (9.4)	36 (1.7)	34 (1.5)	9 (0.4)	＜0.001
FEV₁/FVC (%)	79.0 (7.0)	74.2 (8.6)	78.7 (5.6)	80.4 (5.3)	82.7 (5.2)	＜0.001	81.4 (5.8)	78.2 (6.5)	80.6 (4.7)	82.1 (4.9)	84.7 (5.1)	＜0.001
Obstructive ventilatory impairment (%)	312 (8.4)	222 (24.2)	58 (6.2)	22 (2.4)	10 (1.1)	＜0.001	249 (2.8)	186 (8.5)	34 (1.6)	25 (1.1)	4 (0.2)	＜0.001
cIMT (mm)	0.65 (0.14)	0.72 (0.14)	0.68 (0.13)	0.64 (0.13)	0.56 (0.11)	＜0.001	0.60 (0.12)	0.66 (0.12)	0.62 (0.11)	0.58 (0.11)	0.52 (0.09)	＜0.001
METs (MET-min/week)	90.2 (12.8-250.7)	137.9 (37.0-306.0)	126.0 (27.0-280.2)	90.0 (18.0-252.0)	36.0 (0.0-137.6)	＜0.001	66.3 (8.4-192.9)	121.7 (28.9-252.0)	90.0 (16.8-222.4)	57.9 (3.0-65.5)	28.9 (0.0-126.0)	＜0.001
Education status (%)						＜0.001						＜0.001
Below high school	2079 (55.9)	605 (66.0)	557 (59.6)	502 (54.5)	415 (43.9)		4823 (55.0)	1453 (66.3)	1283 (59.3)	1144 (51.5)	943 (43.1)
Vocational school, junior college, or technical college	451 (12.1)	69 (7.5)	102 (10.9)	102 (11.1)	178 (18.8)		2838 (32.4)	556 (25.4)	674 (31.1)	798 (35.9)	810 (37.0)
University or graduate school	1126 (30.3)	220 (24.0)	262 (28.1)	305 (33.1)	339 (35.9)		1001 (11.4)	135 (6.2)	186 (8.6)	262 (11.8)	418 (19.1)
Others	18 (0.5)	8 (0.9)	4 (0.4)	2 (0.2)	4 (0.4)		31 (0.4)	15 (0.7)	4 (0.2)	6 (0.3)	6 (0.3)
Unknown	42 (1.1)	14 (1.5)	9 (1.0)	10 (1.1)	9 (1.0)		72 (0.8)	31 (1.4)	18 (0.8)	10 (0.5)	13 (0.6)
Smoking status (%)						＜0.001						＜0.001
Never-smoker	1073 (28.9)	257 (28.1)	257 (27.5)	267 (29.0)	292 (30.9)		6894 (78.7)	1900 (86.8)	1811 (83.6)	1693 (76.3)	1490 (68.0)
Ex-smoker	1870 (50.3)	494 (53.9)	519 (55.6)	484 (52.6)	373 (39.5)		1199 (13.7)	182 (8.3)	213 (9.8)	362 (16.3)	442 (20.2)
Current smoker	750 (20.2)	157(17.1)	152 (16.3)	166 (18.0)	275 (29.1)		621 (7.1)	89 (4.1)	126 (5.8)	155 (7.0)	251 (11.5)
Unknown	23 (0.6)	8 (0.9)	6 (0.6)	4 (0.4)	5 (0.5)		51 (0.6)	19 (0.9)	15 (0.7)	10 (0.5)	7 (0.3)
Passive smoking (%)	325 (8.7)	107 (11.7)	82 (8.8)	60 (6.5)	76 (8.0)	0.001	1009 (11.5)	354 (16.2)	239 (11.0)	220 (9.9)	196 (8.9)	＜0.001
Drinking status (%)						＜0.001						＜0.001
Never-drinker	662 (17.8)	179 (19.5)	157 (16.8)	160 (17.4)	166 (17.6)		4559 (52.0)	1333 (60.9)	1179 (54.5)	1105 (49.8)	942 (43.0)
Ex-drinker	142 (3.8)	59 (6.4)	34 (3.6)	24 (2.6)	25 (2.6)		165 (1.9)	38 (1.7)	38 (1.8)	40 (1.8)	49 (2.2)
Current drinker (＜ 23g)	1434 (38.6)	339 (37.0)	335 (35.9)	353 (38.3)	407 (43.1)		3285 (37.5)	703 (32.1)	785 (36.3)	876 (39.5)	921 (42.1)
Current drinker (≥ 23g)	1464 (39.4)	335 (36.6)	404 (43.3)	381 (41.4)	344 (36.4)		731 (8.3)	105 (4.8)	155 (7.2)	197 (8.9)	274 (12.5)
Unknown	14 (0.4)	4 (0.4)	4 (0.4)	3 (0.3)	3 (0.3)		25 (0.3)	11 (0.5)	8 (0.4)	2 (0.1)	4 (0.2)
History of respiratory disease (%)
Asthma (%)	228 (6.1)	75 (8.2)	46 (4.9)	48 (5.2)	59 (6.2)	0.015	589 (6.7)	155 (7.1)	135 (6.2)	146 (6.6)	153 (7.0)	0.664
Chronic bronchitis (%)	26 (0.7)	8 (0.9)	9 (1.0)	4 (0.4)	5 (0.5)	0.446	86 (1.0)	35 (1.6)	13 (0.6)	25 (1.1)	13 (0.6)	0.001
Chronic obstructive pulmonary disease (%)	18 (0.5)	12 (1.3)	4 (0.4)	2 (0.2)	0 (0.0)	＜0.001	9 (0.1)	7 (0.3)	1 (0.0)	1 (0.0)	0 (0.0)	0.003
History of cardiovascular disease (%)	306 (8.2)	121 (13.2)	92 (9.9)	68 (7.4)	25 (2.6)	＜0.001	255 (2.9)	126 (5.8)	77 (3.6)	37 (1.7)	15 (0.7)	＜0.001

Values are expressed as mean (standard deviation) or median (interquartile range) for continuous variables, or as number (%) for categorical variables. BMI, body mass index; cIMT, carotid intima-media thickness; DBP, diastolic blood pressure; FEV₁ forced expiratory volume at 1 s; FVC, forced vital capacity; HbA1c, glycated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; METs, metabolic equivalents; Q quartile, systolic blood pressure; TC, total cholesterol; TG, triglyceride; VC, vital capacity.

Hypertension was defined as SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg or receiving treatment for hypertension. Diabetes was defined as non-fasting glucose ≥ 200 mg/dl and/or HbA1c ≥ 6.5%, or receiving treatment for diabetes. Hypercholesterolemia was defined as TC ≥ 240 mg/l and/or receiving treatment for dyslipidemia.

Restrictive ventilatory impairment was defined as reduced VC of ＜80 % of predicted. Obstructive ventilatory impairment was defined as a reduced a ration of ＜70 % of FEV₁ to FVC.

Table 2. Participant characteristics according to FVC

Variables	Men	The quartile groups of FVC				p-value	Women	The quartile groups of FVC				p-value
Variables	Men	Q1 (＜3.28)	Q2 (3.28-3.73)	Q3 (3.74-4.26)	Q4 (≥ 4.26)	p-value	Women	Q1 (＜2.44)	Q2 (2.44-2.77)	Q3 (2.78-3.14)	Q4 (≥ 3.14)	p-value
Number	3716	928	912	938	938		8765	2148	2186	2211	2220
Age (years)	60.1 (14.0)	70.5 (7.9)	64.5 (9.8)	58.7 (11.4)	47.0 (13.8)	＜0.001	56.2 (13.4)	66.8 (8.5)	60.2 (10.1)	53.3 (11.9)	44.8 (11.6)	＜0.001
20-39 years	436 (11.7)	6 (0.6)	30 (3.3)	76 (8.1)	32 (34.5)		1213 (13.8)	26 (1.2)	95 (4.3)	316 (14.3)	776 (35.0)
40-64 years	1512 (40.7)	155 (16.7)	350 (38.4)	520 (55.4)	487 (51.9)		4795 (54.7)	679 (31.6)	1261 (57.7)	1515 (68.5)	1340 (60.4)
65-74 years	1349 (36.3)	494 (53.2)	430 (47.1)	310 (33.0)	115 (12.3)		2284 (26.1)	1095 (51.0)	744 (34.0)	343 (15.5)	102 (4.6)
≥ 75 years	419 (11.3)	273 (29.4)	102 (11.2)	32 (3.4)	12 (1.3)		473 (5.4)	348 (16.2)	86 (3.9)	37 (1.7)	2 (0.1)
Height (cm)	167.5 (6.3)	162.8 (5.3)	165.8 (5.0)	168.7 (5.1)	172.8 (5.1)	＜0.001	155.8 (5.8)	151.3 (5.0)	154.4 (4.7)	156.9 (4.5)	160.4 (4.7)	＜0.001
Weight (kg)	66.8 (10.2)	63.4 (9.3)	65.1 (9.1)	67.6 (10.3)	71.2 (10.5)	＜0.001	54.2 (8.8)	52.4 (8.5)	53.5 (8.7)	54.3 (8.5)	56.5 (9.1)	＜0.001
BMI (kg/m²)	23.8 (3.1)	23.9 (3.0)	23.7 (2.9)	23.7 (3.2)	23.8 (3.2)	0.574	22.4 (3.5)	22.9 (3.6)	22.4 (3.5)	22.1 (3.4)	22.0 (3.4)	＜0.001
SBP (mmHg)	133.9 (16.0)	138.2 (16.2)	136.6 (16.1)	132.7 (15.7)	128.2 (14.0)	＜0.001	126.0 (17.8)	133.7 (17.6)	128.6 (17.2)	124.1 (17.3)	117.9 (15.1)	＜0.001
DBP (mmHg)	80.9 (10.8)	78.5 (10.6)	82.0 (10.9)	82.2 (10.3)	81.0 (11.1)	＜0.001	76.5 (10.5)	77.5 (10.4)	77.2 (10.5)	76.8 (10.7)	74.7 (10.3)	＜0.001
Prevalence of hypertension (%)	1991 (53.6)	646 (69.6)	572 (62.7)	469 (50.0)	304 (32.4)	＜0.001	2981 (34.0)	1133 (52.7)	862 (39.4)	632 (28.6)	354 (15.9)	＜0.001
Glucose (mg/dl)	92.8 (20.8)	96.8 (24.7)	94.4 (22.5)	92.1 (18.5)	88.0 (15.4)	＜0.001	86.7 (14.3)	90.6 (17.9)	87.4 (13.7)	85.7 (13.0)	83.3 (10.9)	＜0.001
HbA1c (%)	5.6 (0.6)	5.8 (0.7)	5.6 (0.6)	5.6 (0.6)	5.4 (0.5)	＜0.001	5.5 (0.5)	5.7 (0.6)	5.6 (0.4)	5.5 (0.5)	5.3 (0.3)	＜0.001
Prevalence of diabetes (%)	388 (10.4)	161 (17.3)	107 (11.7)	85 (9.1)	35 (3.7)	＜0.001	401 (4.6)	194 (9.0)	115 (5.3)	69 (3.1)	23 (1.0)	＜0.001
TC (mg/dl)	201.4 (34.9)	197.9 (33.8)	201.9 (36.0)	204.0 (34.7)	201.8 (35.0)	0.007	212.4 (35.5)	216.2 (34.8)	218.0 (34.7)	213.7 (35.8)	202.1 (34.6)	＜0.001
TG (mg/dl)	101.0 (72.0-149.0)	105.0 (74.0-149.0)	100.0 (74.0-148.0)	103.5 (72.0-151.0)	96.0 (66.0-144.0)	0.525	79.0 (58.0-112.0)	92.0 (68.0-128.0)	83.0 (62.0-117.0)	77.0 (57.0-107.0)	66.0 (50.0-94.0)	＜0.001
HDL-C (mg/dl)	57.2 (15.1)	56.0 (14.9)	57.7 (15.5)	57.5 (15.2)	57.4 (14.9)	0.713	67.6 (16.2)	65.7 (16.1)	67.5 (16.0)	68.5 (16.4)	68.9 (16.2)	＜0.001
Prevalence of hypercholesterolemia (%)	860 (23.1)	229 (24.7)	211 (23.1)	247 (26.3)	173 (18.4)	＜0.001	2741 (31.3)	876 (40.8)	804 (36.8)	669 (30.3)	392 (17.7)	＜0.001
FEV₁ (L)	2.96 (2.56-3.43)	2.31 (2.05-2.51)	2.78 (2.63-2.94)	3.16 (2.99-3.35)	3.76 (3.51-4.04)	＜0.001	2.26 (1.96-2.58)	1.76 (1.60-1.89)	2.12 (2.01-2.22)	2.40 (2.28-2.52)	2.82 (2.65-3.03)	＜0.001
FVC (L)	3.74 (3.28-4.26)	2.97 (2.71-3.14)	3.51 (3.40-3.62)	3.98 (3.85-4.10)	4.63 (4.42-4.96)	＜0.001	2.78 (2.44-3.14)	2.21 (2.03-2.33)	2.62 (2.53-2.69)	2.94 (2.85-3.03)	3.39 (3.25-3.61)	＜0.001
%VC	101.9 (13.5)	91.6 (12.3)	100.5 (10.8)	105.4 (11.2)	110.0 (12.0)	＜0.001	103.1 (13.6)	93.8 (12.2)	101.8 (11.7)	105.1 (12.3)	111.2 (12.2)	＜0.001
Restrictive ventilatory impairment (%)	163 (4.4)	140 (15.1)	18 (2.0)	3 (0.3)	2 (0.2)	＜0.001	284 (3.2)	230 (10.7)	41 (1.9)	13 (0.6)	0 (0.0)	＜0.001
FEV₁/FVC (%)	79.0 (7.0)	77.7 (8.8)	79.0 (6.5)	79.3 (6.3)	80.2 (6.0)	＜0.001	81.4 (5.8)	80.3 (6.3)	81.1 (5.6)	81.7 (5.5)	82.3 (5.7)	＜0.001
Obstructive ventilatory impairment (%)	312 (8.4)	124 (13.4)	76 (8.3)	67 (7.1)	45 (4.8/)	＜0.001	249 (2.8)	111 (5.2)	51 (2.3)	46 (2.1)	41 (1.8)	＜0.001
cIMT (mm)	0.65 (0.14)	0.72 (0.14)	0.67 (0.13)	0.64 (0.13)	0.57 (0.11)	＜0.001	0.60 (0.12)	0.66 (0.12)	0.62 (0.12)	0.58 (0.11)	0.53 (0.09)	＜0.001
METs (MET-min/week)	90.2 (12.8-250.7)	135.0 (35.7-302.0)	124. (30.0-287.0)	81.0 (9.2-238.8)	42.0 (0.0-156.0)	＜0.001	66.3 (8.4-192.9)	112.5 (27.0-246.0)	89.5 (12.4-217.3)	57.9 (3.0-171.0)	33.0 (0.0-126.9)	＜0.001
Education status (%)						＜0.001						＜0.001
Below high school	2079 (55.9)	617 (66.5)	540 (59.2)	503 (53.6)	419 (44.7)		4823 (55.0)	1424 (66.3)	1262 (57.7)	1153 (52.1)	984 (44.3)
Vocational school, junior college, or technical college	451 (12.1)	77 (8.3)	87 (9.5)	120 (12.8)	167 (17.8)		2838 (32.4)	546 (25.4)	706 (32.3)	761 (34.4)	825 (37.2)
University or graduate school	1126 (30.3)	213 (23.0)	274 (30.0)	298 (31.8)	341 (36.4)		1001 (11.4)	133 (6.2)	192 (8.8)	279 (12.6)	397 (17.9)
Others	18 (0.5)	7 (0.8)	4 (0.4)	3 (0.3)	4 (0.4)		31 (0.4)	11 (0.5)	10 (0.5)	5 (0.2)	5 (0.2)
Unknown	42 (1.1)	14 (1.5)	7 (0.8)	14 (1.5)	7 (0.7)		72 (0.8)	34 (1.6)	16 (0.7)	13 (0.6)	9 (0.4)
Smoking status (%)						＜0.001						＜0.001
Never-smoker	1073 (28.9)	284 (30.6)	265 (29.1)	248 (26.4)	276 (29.4)		6894 (78.7)	1866 (86.9)	1852 (84.7)	1660 (75.1)	1516 (68.3)
Ex-smoker	1870 (50.3)	492 (53.0)	492 (53.9)	495 (52.8)	391 (41.7)		1199 (13.7)	180 (8.4)	212 (9.7)	365 (16.5)	442 (19.9)
Current smoker	750 (20.2)	146 (15.7)	149 (16.3)	188 (20.0)	267 (28.5)		621 (7.1)	81 (3.8)	110 (5.0)	177 (8.0)	253 (11.4)
Unknown	23 (0.6)	6 (0.6)	6 (0.7)	7 (0.7)	4 (0.4)		51 (0.6)	21 (1.0)	12 (0.5)	9 (0.4)	9 (0.4)
Passive smoking (%)	325 (8.7)	101 (10.9)	82 (9.0)	70 (7.5)	72 (7.7)	0.034	1009 (11.5)	347 (16.2)	241 (11.0)	225 (10.2)	196 (8.8)	＜0.001
Drinking status (%)						＜0.001						＜0.001
Never-drinker	662 (17.8)	197 (21.2)	148 (16.2)	149 (15.9)	168 (17.9)		4559 (52.0)	1326 (61.7)	1182 (54.1)	1090 (49.3)	961 (43.3)
Ex-drinker	142 (3.8)	57 (6.1)	35 (3.8)	27 (2.9)	23 (2.5)		165 (1.9)	35 (1.6)	44 (2.0)	39 (1.8)	47 (2.1)
Current drinker (＜ 23g)	1434 (38.6)	346 (37.3)	340 (37.3)	361 (38.5)	387 (41.3)		3285 (37.5)	670 (31.2)	809 (37.0)	874 (39.5)	932 (42.0)
Current drinker (≥ 23g)	1464 (39.4)	324 (34.9)	387 (42.4)	395 (42.1)	358 (38.2)		731 (8.3)	105 (4.9)	144 (6.6)	205 (9.3)	277 (12.5)
Unknown	14 (0.4)	4 (0.4)	2 (0.2)	6(0.6)	2 (0.2)		25 (0.3)	12 (0.6)	7 (0.3)	3 (0.1)	3 (0.1)
History of respiratory disease (%)
Asthma (%)	228 (6.1)	65 (7.0)	45 (4.9)	51 (5.4)	67 (7.1)	0.114	589 (6.7)	143 (6.7)	139 (6.4)	150 (6.8)	157 (7.1)	0.82
Chronic bronchitis (%)	26 (0.7)	8 (0.9)	7 (0.8)	6 (0.6)	5 (0.5)	0.841	86 (1.0)	30 (1.4)	21 (1.0)	19 (0.9)	16 (0.7)	0.126
Chronic obstructive pulmonary disease (%)	18 (0.5)	6 (0.6)	7 (0.8)	934 (0.4)	1 (0.1)	0.182	9 (0.1)	6 (0.3)	2 (0.1)	0 (0.0)	1 (0.0)	0.023
History of cardiovascular disease (%)	306 (8.2)	137 (14.8)	82 (9.0)	58 (6.2)	29 (3.1)	＜0.001	255 (2.9)	115 (5.4)	93 (4.3)	30 (1.4)	17 (0.8)	＜0.001

Values are expressed as mean (standard deviation) or median (interquartile range) for continuous variables, or as number (%) for categorical variables. BMI, body mass index; cIMT, carotid intima-media thickness; DBP, diastolic blood pressure; FEV₁ forced expiratory volume at 1 s; FVC, forced vital capacity; HbA1c, glycated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; METs, metabolic equivalents; Q quartile, SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; VC, vital capacity.

Hypertension was defined as an SBP ≥ 140 mmHg and/or a DBP ≥ 90 mmHg or receiving treatment for hypertension. Diabetes was defined as non-fasting glucose ≥ 200 mg/dl and/or HbA1c ≥ 6.5%, or receiving treatment for diabetes.

Hypercholesterolemia was defined as TC ≥ 240 mg/dl and/or receiving treatment for dyslipidemia. Restrictive ventilatory impairment was defined as reduced VC of ＜80 % of predicted.

Obstructive ventilatory impairment was defined as a reduced a ration of ＜70 % of FEV₁ to FVC.

Table 3 shows the associations between FEV₁ and cIMT. A higher FEV₁ was associated with lower cIMT in both men and women after adjustment for several potential confounders (P＜0.001 for linear trend). The association remained significant after adjustment for METs (P＜0.001 for linear trend).

Table 3. Association between FEV₁ and cIMT

Men	Crude			Model 1			Model 2			Model 3
FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜2.56)	0.724	(0.716-0.732)	＜0.001	0.680	(0.665-0.695)	＜0.001	0.688	(0.668-0.707)	＜0.001	0.690	(0.670-0.709)	＜0.001
Q2 (2.56-2.95)	0.679	(0.670-0.687)		0.660	(0.646-0.675)		0.671	(0.652-0.690)		0.673	(0.653-0.692)
Q3 (2.96-3.43)	0.640	(0.632-0.647)		0.649	(0.634-0.664)		0.661	(0.642-0.681)		0.663	(0.643-0.682)
Q4 (≥ 3.43)	0.559	0.550-0.567)		0.637	(0.622-0.653)		0.653	(0.633-0.673)		0.654	(0.6340.674)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.09		＜0.001	-0.03		＜0.001	-0.02		0.004	-0.02		0.004
Women
Q1 (＜1.96)	0.664	(0.659-0.668)	＜0.001	0.611	(0.602-0.620)	＜0.001	0.624	(0.612-0.637)	＜0.001	0.626	(0.614-0.639)	＜0.001
Q2 (1.96-2.25)	0.624	(0.620-0.629)		0.604	(0.595-0.613)		0.619	(0.607-0.630)		0.620	(0.608-0.632)
Q3 (2.26-2.58)	0.581	(0.576-0.585)		0.596	(0.588-0.605)		0.611	(0.599-0.623)		0.613	(0.601-0.625)
Q4 (≥ 2.58)	0.519	(0.514-0.523)		0.596	(0.587-0.606)		0.612	(0.600-0.624)		0.613	(0.601-0.626)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.12		＜0.001	-0.02		＜0.001	-0.01		＜0.001	-0.02		＜0.001

Model 1 was adjusted for age (continuous), height (continuous), and educational status (below high school, vocational school or junior college or technical college, university or graduate school, others, and unknown).

Model 2 was adjusted for age, height, educational status (below high school, vocational school, junior college or technical college, university or graduate school, others, and unknown), smoking status (never smoker, ex-smoker, current smoker), passive smoking, weight (continuous), hypertension, diabetes, hypercholesterolemia, and drinking status (never drinker, ex-drinker, current drinker (＜23 g), current drinker (≥ 23 g), and unknown).

Model 3 was adjusted for Model 2 and METs (quartile category).

The p values for the analysis of linear trends were calculated by scoring the FEV₁ category from 1 for the lowest to 4 for the highest and entering the number as a continuous term in the regression model.

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; FEV1 forced expiratory volume at 1 s; LS, least squares; METs, metabolic equivalents; Q, quartile.

Table 4 shows the association between FVC and cIMT. A higher FVC was also associated with lower cIMT in both men and women after adjusting for several potential confounders (P＜0.001 for linear trend). After adjustment for METs, the association remained significant (P＜0.001 for linear trend).

Table 4. Association between FVC and cIMT

Men	Crude			Model 1			Model 2			Model 3
FVC (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜3.28)	0.719	(0.711-0.727)	＜0.001	0.676	(0.661-0.692)	＜0.001	0.684	(0.664-0.703)	＜0.001	0.686	(0.666-0.705)	＜0.001
Q2 (3.28-3.73)	0.673	(0.665-0.6802		0.659	(0.644-0.674)		0.670	(0.651-0.690)		0.672	(0.652-0.691)
Q3 (3.74-4.26)	0.639	(0.631-0.647)		0.652	(0.637-0.667)		0.665	(0.646-0.684)		0.667	(0.647-0.686)
Q4 (≥ 4.26)	0.569	(0.561-0.578)		0.640	(0.625-0.656)		0.656	(0.637-0.676)		0.658	(0.638-0.678)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.08		＜0.001	-0.02		＜0.001	-0.01		＜0.001	-0.01		＜0.001
Women
Q1 (＜2.44)	0.662	(0.657-0.666)	＜0.001	0.612	(0.603-0.621)	＜0.001	0.624	(0.612-0.636)	＜0.001	0.626	(0.614-0.638)	＜0.001
Q2 (2.44-2.77)	0.619	(0.615-0.624)		0.603	(0.595-0.612)		0.618	(0.606-0.630)		0.620	(0.608-0.632)
Q3 (2.78-3.14)	0.579	(0.574-0.584)		0.599	(0.591-0.608)		0.615	(0.603-0.627)		0.617	(0.605-0.629)
Q4 (≥ 3.14)	0.529	(0.525-0.534)		0.594	(0.585-0.603)		0.610	(0.597-0.622)		0.611	(0.599-0.623)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.10		＜0.001	-0.01		＜0.001	-0.01		＜0.001	-0.01		＜0.001

Model 3 was adjusted for Model 2 and METs (quartile category).

The p values for the analysis of linear trends were calculated by scoring the FVC category from 1 for the lowest to 4 for the highest and entering the number as a continuous term in the regression model.

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; FVC, forced vital capacity; LS, least squares; METs, metabolic equivalents; Q, quartile.

Several subgroup analyses were conducted. First, when we restricted our analysis to never smokers, significant inverse associations between FEV₁ and cIMT were observed (Table 5). Regarding FVC, an inverse association with cIMT was observed only in female never smokers (Table 6). Second, to examine the effect of age on the association between lung function and cIMT, we conducted an analysis stratified by age. An inverse association between FEV₁ and cIMT was observed among middle-aged (40–64 years) and elderly participants (65–75 years) (Table 7). Similarly, for FVC, significant inverse associations with cIMT were observed in middle-aged and elderly participants (Table 8). Third, to eliminate the effect of respiratory disease, we restricted participants to those who had no restrictive ventilatory impairment, obstructive ventilatory impairment, or history of respiratory diseases such as asthma, chronic bronchitis, and COPD. However, the results remained markedly unchanged compared with those using all participants (Supplemental Tables 1 and 2). Fourth, we restricted participants to those who had no history of CVD to exclude the effect of CVD. The results were largely unchanged compared to those with all participants (Supplemental Tables 3 and 4). Fifth, even when we restricted participants to those who were not undergoing treatment for hypertension, diabetes, and dyslipidemia, FEV₁ and FVC were inversely associated with cIMT (Supplemental Tables 5 and 6). Finally, we performed multiple linear regression analyses using lung function parameters as continuous terms in all analyses to confirm the robustness of our results. The results are substantially unchanged compared with the results using an ANCOVA.

Table 5. Association between FEV₁ and cIMT in never-smokers

Men	Crude			Model 1			Model 2			Model 3
FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜2.57)	0.715	(0.700-0.730)	＜0.001	0.674	(0.644-0.704)	＜0.001	0.680	(0.646-0.715)	＜0.001	0.684	(0.630-0.738)	＜0.001
Q2 (2.57-2.99)	0.682	(0.667-0.697)		0.668	(0.639-0.697)		0.677	(0.644-0.711)		0.681	(0.627-0.734)
Q3 (3.00-3.46)	0.617	(0.601-0.632)		0.633	(0.605-0.662)		0.644	(0.611-0.677)		0.647	(0.595-0.700)
Q4 (≥ 3.46)	0.537	(0.522-0.552)		0.631	(0.600-0.662)		0.644	(0.609-0.679)		0.647	(0.593-0.701)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.10		＜0.001	-0.02		0.011	-0.02		0.051	-0.02		0.043
Women
Q1 (＜1.92)	0.670	(0.664-0.675)	＜0.001	0.614	(0.604-0.625)	＜0.001	0.617	(0.588-0.646)	＜0.001	0.627	(0.579-0.674)	＜0.001
Q2 (1.92-2.21)	0.635	(0.630-0.641)		0.610	(0.599-0.620)		0.612	(0.583-0.641)		0.622	(0.574-0.669)
Q3 (2.21-2.53)	0.593	(0.588-0.598)		0.598	(0.588-0.609)		0.601	(0.572-0.631)		0.611	(0.564-0.659)
Q4 (≥ 2.53)	0.528	(0.523-0.533)		0.600	(0.589-0.611)		0.603	(0.574-0.632)		0.613	(0.566-0.660)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.12		＜0.001	-0.02		＜0.001	-0.02		＜0.001	-0.02		＜0.001

Model 2 was adjusted for age, height, educational status (below high school, vocational school or junior college or technical college, university or graduate school, others, and unknown), passive smoking, weight (continuous), hypertension, diabetes, hypercholesterolemia, and drinking status (never drinker, ex-drinker, current drinker (＜23 g), current drinker (≥ 23 g), and unknown).

Model 3 was adjusted as for model 2 and METs (quartile category).

The p values for the analysis of linear trends were calculated by scoring the FEV1 category from 1 for the lowest to 4 for the highest and entering the number as a continuous term in the regression model.

ANCOVA, analysis of covariance; CI, confidence interval; cIMT carotid intima-media thickness; FEV₁ forced expiratory volume at 1 s; LS, least squares; METs metabolic equivalents; Q, quartile.

Table 6. Association between FVC and cIMT in never-smokers

Men	Crude			Model 1			Model 2			Model 3
FVC (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜3.25)	0.709	(0.693-0.725)	＜0.001	0.667	(0.636-0.697)	0.028	0.672	(0.638-0.707)	0.114	0.674	(0.620-0.728)	0.110
Q2 (3.25-3.70)	0.672	(0.656-0.688)		0.658	(0.628-0.687)		0.667	(0.633-0.701)		0.668	(0.615-0.721)
Q3 (3.71-4.27)	0.616	(0.601-0.632)		0.639	(0.611-0.667)		0.652	(0.619-0.684)		0.653	(0.601-0.705)
Q4 (≥ 4.27)	0.552	(0.536-0.567)		0.641	(0.611-0.670)		0.655	(0.620-0.690)		0.656	(0.602-0.710)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.08		＜0.001	-0.01		0.051	-0.01		0.204	-0.01		0.184
Women
Q1 (＜2.41)	0.665	(0.660-0.671)	＜0.001	0.613	(0.603-0.624)	＜0.001	0.615	(0.586-0.644)	0.004	0.625	(0.577-0.672)	0.003
Q2 (2.41-2.72)	0.630	(0.625-0.636)		0.609	(0.598-0.619)		0.611	(0.582-0.640)		0.621	(0.574-0.669)
Q3 (2.73-3.08)	0.592	(0.586-0.597)		0.602	(0.592-0.613)		0.606	(0.577-0.635)		0.616	(0.569-0.663)
Q4 (≥ 3.08)	0.537	(0.532-0.543)		0.598	(0.588-0.609)		0.603	(0.574-0.632)		0.613	(0.565-0.660)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.10		＜0.001	-0.01		＜0.001	-0.01		＜0.001	-0.01		＜0.001

Model 3 was adjusted for Model 2 and METs (quartile category).

ANCOVA, analysis of covariance; CI, confidence interval; cIMT carotid intima-media thickness; FVC forced vital capacity; LS, least squares; METs metabolic equivalents; Q, quartile.

Table 7. Association between FEV1 and cIMT stratified by age groups

Men	Model 3
20-39 years old				40-64 years old				65-74 years old				75 years or older
FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend	FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend	FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend	FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜3.56)	0.507	(0.464-0.550)	0.430	Q1 (＜2.85)	0.662	(0.623 -0.701)	0.002	Q1 (＜2.40)	0.737	(0.703-0.771)	0.006	Q1 (＜2.00)	0.761	(0.693-0.830)	0.402
Q2 (3.56-3.88)	0.518	(0.476-0.561)		Q2 (2.85-3.17)	0.652	(0.615-0.688)		Q2 (2.40-2.69)	0.740	(0.705-0.774)		Q2 (2.00-2.29)	0.757	(0.692-0.822)
Q3 (3.89-4.18)	0.516	(0.475-0.557)		Q3 (3.18-3.54)	0.644	(0.608-0.680)		Q3 (2.70-3.01)	0.723	(0.689-0.758)		Q3 (2.30-2.64)	0.769	(0.701-0.838)
Q4 (≥ 4.18)	0.516	(0.473-0.558)		Q4 (≥ 3.54)	0.630	(0.594-0.666)		Q4 (≥ 3.01)	0.711	(0.676-0.745)		Q4 (≥ 2.64)	0.737	(0.669-0.806)
Multiple linear regression model	β		p value		β		p value		β		p value		β		p value
	0.00		0.473		-0.02		0.002		-0.03		＜0.001		-0.02		0.195
Women	Model 3
Q1 (＜2.58)	0.500	(0.467-0.532)	0.108	Q1 (＜2.10)	0.596	(0.579-0.612)	0.007	Q1 (＜1.75)	0.692	(0.664-0.720)	0.001	Q1 (＜1.50)	0.837	(0.778-0.896)	0.885
Q2 (2.58-2.82)	0.504	(0.471-0.537)		Q2 (2.10-2.33)	0.593	(0.576-0.610)		Q2 (1.75-1.95)	0.680	(0.651-0.708)		Q2 (1.50-1.70)	0.809	(0.748-0.871)
Q3 (2.83-3.07)	0.495	(0.463-0.528)		Q3 (2.34-2.60)	0.590	(0.573-0.607)		Q3 (1.96-2.17)	0.683	(0.655-0.711)		Q3 (1.71-1.91)	0.823	(0.761-0.885)
Q4 (≥ 3.07)	0.494	(0.461-0.528)		Q4 (≥ 2.60)	0.584	(0.567-0.601)		Q4 (≥ 2.17)	0.666	(0.638-0.694)		Q4 (≥ 1.91)	0.837	(0.776-0.899)
Multiple linear regression model	β		p value		β		p value		β		p value		β		p value
	-0.01		0.136		-0.01		0.017		-0.02		0.020		-0.01		0.758

Model 3 was adjusted for age (continuous), height (continuous), education status (below high school, vocational school, junior college or technical college, university or graduate school, others, and unknown), weight (continuous), hypertension, diabetes, hypercholesterolemia, smoking status (never smoker, ex-smoker, current smoker and unknown), passive smoking, drinking status (never drinker, ex-drinker, current drinker (＜23 g), current drinker (≥ 23 g), and unknown), and METs (quartile category).

The P values for the analysis of linear trends were calculated by scoring the FEV₁ category from 1 for the lowest to 4 for the highest and entering the number as a continuous term in the regression model.

ANCOVA, analysis of covariance; CI, confidence interval; cIMT carotid intima-media thickness; FEV₁ forced expiratory volume at 1 s; LS, least squares; METs metabolic equivalents; Q, quartile.

Table 8. Association between FVC and cIMT stratified by age groups

Men	Model 3
20-39 years old				40-64 years old				65-74 years old				75 years or older
FVC (L)	LS means cIMT (mm)	95 % CI	p for trend	FVC (L)	LS means cIMT (mm)	95 % CI	p for trend	FVC (L)	LS means cIMT (mm)	95 % CI	p for trend	FVC (L)	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜4.24)	0.515	(0.472-0.557)	0.896	Q1 (＜3.59)	0.656	(0.618-0.695)	0.057	Q1 (＜3.11)	0.750	(0.716-0.784)	0.001	Q1 (＜2.66)	0.756	(0.688-0.823)	0.612
Q2 (4.24-4.62)	0.516	(0.474-0.558)		Q2 (3.59-3.97)	0.646	(0.610-0.683)		Q2 (3.11-3.44)	0.734	(0.699-0.769)		Q2 (2.66-3.05)	0.759	(0.690-0.828)
Q3 (4.63-5.01)	0.514	(0.472-0.555)		Q3 (3.98-4.37)	0.641	(0.606-0.677)		Q3 (3.45-3.84)	0.713	(0.679-0.747)		Q3 (3.06-3.44)	0.763	(0.697-0.830)
Q4 (≥ 5.01)	0.517	(0.474-0.559)		Q4 (≥ 4.37)	0.637	(0.601-0.673)		Q4 (≥ 3.84)	0.715	(0.680-0.749)		Q4 (≥ 3.44)	0.743	(0.675-0.811)
Multiple linear regression model	β		p value		β		p value		β		p value		β		p value
	0.00		0.656		-0.01		0.119		-0.02		0.001		-0.01		0.648
Women	Model 3
Q1 (＜3.01)	0.498	(0.466-0.531)	0.332	Q1 (＜2.60)	0.594	(0.577-0.611)	0.030	Q1 (＜2.21)	0.688	(0.660-0.716)	0.036	Q1 (＜1.93)	0.836	(0.776-0.895)	0.569
Q2 (3.01-3.27)	0.498	(0.465-0.531)		Q2 (2.60-2.86)	0.593	(0.576-0.609)		Q2 (2.21-2.45)	0.682	(0.654-0.710)		Q2 (1.93-2.18)	0.818	(0.756-0.879)
Q3 (3.28-3.56)	0.499	(0.465-0.532)		Q3 (2.87-3.18)	0.593	(0.576-0.610)		Q3 (2.46-2.71)	0.682	(0.654-0.710)		Q3 (2.19-2.47)	0.826	(0.765-0.887)
Q4 (≥ 3.56)	0.493	(0.460-0.526)		Q4 (≥ 3.18)	0.583	(0.567-0.600)		Q4 (≥ 2.71)	0.672	(0.643-0.700)		Q4 (≥ 2.47)	0.844	(0.781-0.907)
Multiple linear regression model	β		p value		β		p value		β		p value		β		p value
	-0.01		0.127		-0.01		0.027		-0.02		0.020		0.00		0.886

Model 3 was adjusted for age (continuous), height (continuous), education status (below high school, vocational school, junior college or technical college, university or graduate school, others, and unknown), weight (continuous), hypertension, diabetes, hypercholesterolemia, smoking status (never smoker, ex-smoker, current smoker and unknown), passive smoking, drinking status (never drinker, ex-drinker, current drinking (＜23 g), current drinking (≥ 23 g), and unknown), and METs (quartile category).

The P values for the analysis of linear trends were calculated by scoring the FVC category from 1 for the lowest to 4 for the highest and entering the number as a continuous term in the regression model.

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; FVC, forced vital capacity; LS, least squares; METs, metabolic equivalents; Q, quartile

Supplemental Table 1. Association between FEV₁ and cIMT excluding participants with restrictive ventilatory impairment, obstructive ventilatory impairment, and a history of respiratory diseases such as asthma, chronic bronchitis, and COPD

Men	Crude			Model 1			Model 2			Model 3
FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜2.67)	0.717	(0.702-0.732)	＜0.001	0.677	(0.646-0.707)	＜0.001	0.683	(0.648-0.717)	＜0.001	0.683	(0.649-0.718)	＜0.001
Q2 (2.67-3.03)	0.679	(0.664-0.694)		0.666	(0.637-0.696)		0.675	(0.641-0.708)		0.675	(0.641-0.709)
Q3 (3.04-3.47)	0.613	(0.598-0.628)		0.632	(0.603-0.660)		0.642	(0.609-0.675)		0.643	(0.610-0.676)
Q4 (≥ 3.47)	0.536	(0.521-0.551)		0.628	(0.597-0.659)		0.641	(0.605-0.676)		0.641	(0.606-0.677)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.01		＜0.001	-0.02		＜0.001	-0.01		0.022	-0.01		0.015
Women
Q1 (＜2.00)	0.669	(0.664-0.675)	＜0.001	0.615	(0.604-0.626)	＜0.001	0.617	(0.588-0.646)	＜0.001	0.617	(0.588-0.647)	＜0.001
Q2 (2.00-2.27)	0.635	(0.630-0.640)		0.610	(0.599-0.620)		0.612	(0.583-0.641)		0.612	(0.583-0.642)
Q3 (2.28-2.53)	0.593	(0.588-0.598)		0.599	(0.588-0.610)		0.603	(0.573-0.632)		0.603	(0.574-0.632)
Q4 (≥ 2.32)	0.528	(0.523-0.533)		0.601	(0.590-0.612)		0.604	(0.575-0.634)		0.604	(0.575-0.634)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.13		＜0.001	-0.01		＜0.001	-0.01		0.001	-0.01		0.001

Model 2 was adjusted for age, height, educational status (below high school, vocational school, junior college or technical college, university or graduate school, others, and unknown), smoking status (never smoker, ex-smoker, current smoker and unknown), passive smoking, weight (continuous), hypertension, diabetes, hypercholesterolemia, and drinking status (never drinker, ex-drinker, current drinker (＜23 g), current drinker (≥ 23 g), and unknown).

Model 3 was adjusted for Model 2 and METs (quartile category).

Restrictive ventilatory impairment was defined as reduced VC of ＜80% of the predicted.

Obstructive ventilatory impairment was defined as a reduced ratio of FEV₁ to FVC of ＜70%.

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; COPD, chronic obstructive pulmonary disorder; FEV₁ forced expiratory volume at 1 s; LS, least squares; METs, metabolic equivalents; Q, quartile.

Supplemental Table 2. Association between FVC and cIMT excluding participants with restrictive ventilatory impairment, obstructive ventilatory impairment, and a history of respiratory disease such as asthma, chronic bronchitis, and COPD

Men	Crude			Model 1			Model 2			Model 3
FVC (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜3.35)	0.716	(0.707-0.725)	＜0.001	0.674	(0.656-0.692)	0.030	0.684	(0.661-0.707)	0.003	0.687	(0.664-0.710)	0.002
Q2 (3.35-3.79)	0.665	(0.656-0.674)		0.651	(0.634-0.668)		0.663	(0.641-0.685)		0.666	(0.643-0.688)
Q3 (3.80-4.30)	0.630	(0.621-0.639)		0.647	(0.630-0.664)		0.662	(0.640-0.684)		0.665	(0.643-0.687)
Q4 (≥ 4.30)	0.567	(0.558-0.576)		0.641	(0.623-0.658)		0.659	(0.636-0.682)		0.661	(0.638-0.684)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.08		＜0.001	-0.01		0.003	-0.01		0.082	-0.01		0.059
Women
Q1 (＜2.48)	0.660	(0.655-0.665)	＜0.001	0.612	(0.602-0.621)	＜0.001	0.626	(0.613-0.638)	0.001	0.628	(0.615-0.640)	0.001
Q2 (2.48-2.79)	0.621	(0.616-0.626)		0.605	(0.596-0.614)		0.621	(0.609-0.633)		0.623	(0.610-0.635)
Q3 (2.80-3.16)	0.576	(0.572-0.581)		0.599	(0.590-0.608)		0.616	(0.604-0.629)		0.618	(0.606-0.631)
Q4 (≥ 3.16)	0.528	(0.524-0.533)		0.595	(0.586-0.605)		0.613	(0.602-0.627)		0.615	(0.602-0.627)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.10		＜0.001	-0.01		＜0.001	-0.01		0.006	-0.01		0.006

Model 2 was adjusted for age, height, educational status (below high school, vocational school, junior college or technical college, university or graduate school, others, and unknown), smoking status (never smoker, ex-smoker, current smoker and unknown), passive smoking, weight (continuous), hypertension, diabetes, hypercholesterolemia, and drinking status (never drinker, ex-drinker, current driker (＜23 g), current drinker (≥ 23 g), and unknown).

Model 3 was adjusted for Model 2 and METs (quartile category).

Restrictive ventilatory impairment was defined as reduced VC of ＜80% of the predicted.

Obstructive ventilatory impairment was defined as a reduced ratio of FEV₁ to FVC of ＜70%.

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; COPD, chronic obstructive pulmonary disorder; FVC, forced vital capacity; LS, least squares; METs, metabolic equivalents; Q, quartile.

Supplemental Table 3. Association between FEV₁ and cIMT excluding participants with a history of cardiovascular disease

Men	Crude			Model 1			Model 2			Model 3
FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜2.59)	0.722	(0.713-0.730)	＜0.001	0.674	(0.658-0.690)	＜0.001	0.686	(0.666-0.706)	＜0.001	0.688	(0.668-0.708)	＜0.001
Q2 (2.59-2.99)	0.674	(0.665-0.682)		0.654	(0.638-0.669)		0.668	(0.648-0.688)		0.670	(0.650-0.689)
Q3 (3.00-3.46)	0.633	(0.624-0.641)		0.638	(0.623-0.654)		0.655	(0.635-0.675)		0.657	(0.637-0.676)
Q4 (≥ 3.46)	0.552	(0.543-0.560)		0.626	(0.610-0.643)		0.646	(0.625-0.666)		0.647	(0.626-0.667)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.09		＜0.001	-0.03		＜0.001	-0.02		＜0.001	-0.02		＜0.001
Women
Q1 (＜1.96)	0.662	(0.657-0.667)	＜0.001	0.609	(0.600-0.618)	＜0.001	0.622	(0.610-0.634)	＜0.001	0.624	(0.612-0.636)	＜0.001
Q2 (1.96-2.26)	0.621	(0.617-0.626)		0.602	(0.592-0.611)		0.616	(0.604-0.628)		0.618	(0.605-0.630)
Q3 (2.27-2.60)	0.578	(0.574-0.583)		0.595	(0.586-0.604)		0.609	(0.597-0.621)		0.611	(0.599-0.623)
Q4 (≥ 2.60)	0.518	(0.513-0.523)		0.594	(0.585-0.604)		0.609	(0.597-0.622)		0.611	(0.598-0.623)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.12		＜0.001	-0.02		＜0.001	-0.01		＜0.001	-0.01		＜0.001

Model 2 was adjusted for age, height, educational status (below high school, vocational school, junior college or technical college, university or graduate school, others, and unknown), smoking status (never-smoker, ex-smoker, current smoker, and unknown), passive smoking, weight (continuous), hypertension, diabetes, hypercholesterolemia, and drinking status (never drinker, ex-drinker, current drinker (＜23 g), current drinker (≥ 23 g), and unknown).

Model 3 was adjusted for Model 2 and METs (quartile category).

The p values for the analysis of linear trends were calculated by scoring the FEV1 category from 1 for the lowest to 4 for the highest, entering the number as a continuous term in the regression model.

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; FEV₁ forced expiratory volume at 1 s; LS, least squares; METs, metabolic equivalents; Q, quartile.

Supplemental Table 4. Association between FVC and cIMT excluding participants with a history of cardiovascular disease

Men	Crude			Model 1			Model 2			Model 3
FVC (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜3.32)	0.716	(0.708-0.725)	＜0.001	0.670	(0.654-0.686)	＜0.001	0.682	(0.661-0.702)	＜0.001	0.684	(0.664-0.704)	＜0.001
Q2 (3.32-3.77)	0.667	(0.659-0.676)		0.649	(0.634-0.665)		0.664	(0.644-0.684)		0.665	(0.645-0.685)
Q3 (3.78-4.30)	0.630	(0.622-0.639)		0.642	(0.626-0.657)		0.659	(0.639-0.678)		0.66	(0.641-0.680)
Q4 (≥ 4.30)	0.565	(0.557-0.574)		0.633	(0.617-0.649)		0.653	(0.632-0.673)		0.654	(0.633-0.674)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.08		＜0.001	-0.02		＜0.001	-0.01		＜0.001	-0.01		＜0.001
Women
Q1 (＜2.45)	0.660	(0.655-0.664)	＜0.001	0.609	(0.600-0.619)	＜0.001	0.622	(0.609-0.634)	＜0.001	0.624	(0.611-0.636)	＜0.001
Q2 (2.45-2.78)	0.616	(0.611-0.621)		0.601	(0.592-0.610)		0.615	(0.603-0.627)		0.617	(0.605-0.629)
Q3 (2.79-3.15)	0.577	(0.572-0.582)		0.597	(0.588-0.606)		0.612	(0.600-0.624)		0.614	(0.602-0.626)
Q4 (≥ 3.15)	0.529	(0.524-0.533)		0.592	(0.583-0.602)		0.608	(0.595-0.620)		0.609	(0.597-0.622)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.10		＜0.001	-0.01		＜0.001	-0.01		＜0.001	-0.01		＜0.001

Model 2 was adjusted for age, height, educational status (below high school, vocational school or junior college or technical college, university or graduate school, others, and unknown), smoking status (never-smoker, ex-smoker, current smoker, and unknown), weight (continuous), passive smoking, hypertension, diabetes, hypercholesterolemia, and drinking status (never drinker, ex-drinker, current drinker (＜23 g), current drinker (≥23 g), and unknown).

Model 3 was adjusted for Model 2 and METs (quartile category).

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; FVC, forced vital capacity; LS, least squares; METs, metabolic equivalents; Q, quartile.

Supplemental Table 5. Association between FEV₁ and cIMT excluding participants undergoing treatment for hypertension, diabetes, and dyslipidemia

Men	Crude			Model 1			Model 2			Model 3
FEV₁ (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜2.68)	0.715	(0.706-0.725)	＜0.001	0.650	(0.632-0.668)	＜0.001	0.651	(0.627-0.674)	＜0.001	0.653	(0.629-0.677)	＜0.001
Q2 (2.68-3.09)	0.656	(0.647-0.666)		0.624	(0.607-0.642)		0.627	(0.604-0.651)		0.629	(0.606-0.653)
Q3 (3.10-3.60)	0.601	(0.591-0.610)		0.605	(0.588-0.622)		0.610	(0.587-0.633)		0.612	(0.588-0.635)
Q4 (≥ 3.60)	0.537	(0.528-0.547)		0.606	(0.588-0.624)		0.613	(0.589-0.637)		0.615	(0.590-0.639)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.10		＜0.001	-0.03		＜0.001	-0.02		＜0.001	-0.02		＜0.001
Women
Q1 (＜2.05)	0.645	(0.640-0.650)	＜0.001	0.591	(0.582-0.601)	＜0.001	0.617	(0.602-0.633)	＜0.001	0.618	(0.603-0.634)	＜0.001
Q2 (2.05-2.34)	0.600	(0.595-0.605)		0.581	(0.572-0.591)		0.607	(0.591-0.622)		0.608	(0.592-0.623)
Q3 (2.35-2.68)	0.555	(0.550-0.560)		0.575	(0.565-0.584)		0.600	(0.585-0.616)		0.602	(0.586-0.617)
Q4 (≥ 2.68)	0.506	(0.501-0.511)		0.577	(0.567-0.587)		0.603	(0.587-0.618)		0.604	(0.588-0.619)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.11		＜0.001	-0.01		＜0.001	-0.02		＜0.001	-0.02		＜0.001

Model 2 was adjusted for age, height, educational status (below high school, vocational school, junior college or technical college, university or graduate school, others, and unknown), smoking status (never-smoker, ex-smoker, current smoker, and unknown), passive smoking, weight (continuous), hypertension, diabetes, hypercholesterolemia, and drinking status (never drinker, ex-drinker, current drinker (＜23 g), current drinker (≥ 23 g), and unknown).

Model 3 was adjusted for Model 2 and METs (quartile category).

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; FEV1 forced expiratory volume at 1 s; LS, least squares; METs, metabolic equivalents; Q, quartile.

Supplemental Table 6. Association between FVC and cIMT excluding participants undergoing treatment for hypertension, diabetes, and dyslipidemia

Men	Crude			Model 1			Model 2			Model 3
FVC (L)	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend	LS means cIMT (mm)	95 % CI	p for trend
Q1 (＜3.42)	0.712	(0.702-0.722)	＜0.001	0.650	(0.632-0.668)	＜0.001	0.649	(0.625-0.673)	＜0.001	0.652	(0.627-0.676)	＜0.001
Q2 (3.42-3.89)	0.648	(0.638-0.657)		0.620	(0.603-0.637)		0.623	(0.600-0.646)		0.625	(0.601-0.648)
Q3 (3.90-4.44)	0.602	(0.593-0.612)		0.612	(0.595-0.629)		0.616	(0.593-0.639)		0.618	(0.594-0.642)
Q4 (≥ 4.44)	0.548	(0.538-0.558)		0.608	(0.590-0.626)		0.615	(0.592-0.639)		0.617	(0.593-0.641)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.08		＜0.001	-0.02		＜0.001	-0.02		＜0.001	-0.02		＜0.001
Women
Q1 (＜2.55)	0.639	(0.634-0.644)	＜0.001	0.588	(0.579-0.598)	＜0.001	0.614	(0.599-0.630)	＜0.001	0.616	(0.600-0.631)	＜0.001
Q2 (2.55-2.86)	0.592	(0.587-0.598)		0.583	(0.573-0.592)		0.608	(0.593-0.623)		0.609	(0.594-0.624)
Q3 (2.87-3.23)	0.558	(0.553-0.563)		0.580	(0.570-0.589)		0.606	(0.590-0.621)		0.607	(0.591-0.622)
Q4 (≥ 3.23)	0.518	(0.513-0.523)		0.576	(0.566-0.586)		0.602	(0.586-0.617)		0.603	(0.587-0.618)
Multiple linear regression model	β		p value	β		p value	β		p value	β		p value
	-0.09		＜0.001	-0.01		＜0.001	-0.01		＜0.001	-0.01		＜0.001

Model 2 was adjusted for age, height, educational status (below high school, vocational school or junior college or technical college, university or graduate school, others, and unknown), smoking status (never-smoker, ex-smoker, current smoker, and unknown), weight (continuous), passive smoking, hypertension, diabetes, hypercholesterolemia, and drinking status (never drinker, ex-drinker, current drinker (＜23 g), current drinker (≥23 g), and unknown).

Model 3 was adjusted for Model 2 and METs (quartile category).

ANCOVA, analysis of covariance; CI, confidence interval; cIMT, carotid intima-media thickness; FVC, forced vital capacity; LS, least squares; METs, metabolic equivalents; Q, quartile.

Discussion

The present study showed that lower lung function variables such as FEV₁ and FEV were associated with increased cIMT. These inverse associations were significant after adjusting for several potential risk factors. Even when we restricted our study to never smokers, lung function was inversely associated with cIMT in both men and women. Furthermore, even when stratified by age, an inverse association between lung function and cIMT was observed in middle-aged and elderly participants. We also observed these inverse associations using a multiple linear regression model.

Many previous studies have reported that lung function is inversely associated with cIMT^10-18). However, studies demonstrating the association between lung function and cIMT among never smokers are limited^{13, 15-17)}. Additionally, this association is unknown among Japanese never smokers. In the cross-sectional analysis of the ARIC study, individuals with lower FEV₁ tended to have higher cIMT among the smoking groups; however, the association was eliminated after adjustment for CVD risk factors in never smokers¹³⁾. Conversely, previous studies stratified by smoking status showed that low lung function was associated with increased cIMT even among never smokers^15-17). In the Japanese population, there have been only two studies on the association between lung function and cIMT^{14, 18)}. Although these studies also showed that lower lung function was associated with increased cIMT, they were not conducted using analysis by stratified smoking status and included only male participants. Our study showed that FEV₁ and FVC were associated with elevated cIMT in both men and women. Furthermore, even when restricted to never smokers, FEV₁ and FVC were associated with elevated cIMT in both men and women. Our results were consistent with previous studies^15-17) and extend previous findings of the Japanese population.

Although the detailed mechanisms underlying these effects have not been elucidated, several potentially harmful mechanisms of association between lung function and subclinical atherosclerosis have been considered. First, although smoking may have a strong impact on the association between lung function and cIMT, an inverse association between lung function and cIMT was observed, even when we restricted our analysis to never smokers. Second, age-related confounding factors also may have a strong impact on the association between lung function and cIMT. However, lung function was inversely associated with cIMT in the middle-aged and elderly populations. Third, physical inactivity may be a common factor underlying decreased lung function and increased cIMT because previous studies have demonstrated that physical activity is associated with increased lung function and a lower risk of CVD^40-42). However, an inverse association was observed even after adjustment for physical activity. Fourth, although passive smoking may be a confounding factor because it is associated with decreased lung function and increased cIMT^{43, 44)}, an inverse association between lung function and cIMT was observed even after adjustment for passive smoking. Thus, our findings indicated that smoking, age, physical activity, and passive smoking cannot fully explain the association between lung function and cIMT.

Other potentially harmful mechanisms that were not considered in this study were systemic inflammation and oxidative stress. Systemic inflammation may not only lead to reducing lung function by inducing endothelial dysfunction and subsequently causing lung alveolar destruction^{45, 46)}. Oxidative stress also plays a role in processes of reduced pulmonary function. Lung tissues are particularly susceptible to oxidative damage because of direct contact with oxidants from ambient air^{47, 48)}. Furthermore, many studies support that systemic inflammation and oxidative stress participate pivotally in the pathogenesis of atherosclerosis^{49, 50)}. In brief, systematic inflammation and oxidative stress might be considered common factors underlying reduced lung function and the development of atherosclerosis. However, Engström et al. showed that lung function is inversely associated with inflammation, the relationship contributed to but could not fully explain the increased cardiovascular risk with lower lung function⁵¹⁾. Further studies are required to elucidate the underlying mechanisms.

This study has several strengths. First, to the best of our knowledge, this is the first study to show that lung function was associated with cIMT, including smokers, ex-smokers, current smokers, and women, in a Japanese population after adjusting for several potential confounders such as cardiovascular risk factors and passive smoking. Second, since we used a relatively large population of 12,481 participants, we were able to stratify sex and age and limit to never smokers.

However, our study also has some limitations. First, lung function might have been measured with some degree of error because lung function is dependent on the effort of the participants. Second, previous studies showed that decreased lung function is associated with arterial stiffness, endothelial dysfunction, and cIMT^52-54). Therefore, although arterial stiffness and endothelial dysfunction may be confounding factors for the association between lung function and cIMT, we could not consider them because we did not collect information on them. Third, this study had a cross-sectional design, and a causal relationship between lung function and cIMT could not be definitively established. Therefore, prospective cohort studies are required to clarify this causal relationship. Fourth, our study enrolled a Japanese population, with the results limited to the Japanese population only. Therefore, it is difficult to generalize our results to other populations.

Conclusion

The findings of this study revealed that lower lung function was associated with elevated cIMT in the Japanese population, despite several confounding factors. This association was also confirmed when the analysis was limited to never smokers. Additionally, even when stratified by age, an inverse association was confirmed between lung function and cIMT in middle-aged and elderly participants. Our findings suggest that assessment of atherosclerosis or lung function may be necessary for individuals with atherosclerosis or decreased lung function. However, further studies are required to elucidate the underlying mechanisms. Moreover, it is important to explore factors common to lung function and atherosclerosis to prevent 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 https://www.megabank.tohoku.ac.jp/english/a200601/.

Funding

This work was supported by grants from the Japanese Society for the Promotion of Science [JSPS; Grant-in-Aid for Science Research (C), no. 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].

Conflict of Interest

The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript.

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