INTRODUCTION
Many prospective cohort studies have shown that reduced lung function, that is, forced expiratory volume at 1 s (FEV1) and forced vital capacity (FVC), is associated with a higher risk of stroke, myocardial infarction, and cardiovascular mortality [1–4]. Furthermore, several studies have shown that these associations remained, even when restricted to never-smokers [1–3].
Hypertension is a well known leading risk factor for cardiovascular disease [5]. Therefore, several studies have examined the association between lung function and hypertension [6–14], and these studies have shown that lung function is inversely associated with blood pressure (BP) and hypertension [6–13]. However, only a few studies have reported an association between lung function and hypertension among never-smokers [7,8], although smoking is closely associated with reduced lung function and is positively associated with BP [15–18]. In addition, age is closely associated not only with increased BP but also with decreased lung function because of decreased elastic recoil [10,15,19–21]. Hence, age-related confounding may have a strong impact on the association between lung function and hypertension; however, no studies have reported an association between lung function and hypertension, stratified age. Furthermore, previous studies showed that home BP is better than casual BP at predicting the onset of cardiovascular disease [22–25]. However, the association between lung function and home hypertension is unknown. Finally, to the best of our knowledge, no reports on the association between lung function and hypertension have yet been reported in a Japanese population.
Thus, we examined the cross-sectional association between lung function and the prevalence of hypertension and home hypertension in a Japanese Tohoku Medical Megabank Community-Based Cohort Study (TMM CommCohort Study) population.
METHODS
Study design and population
We conducted a cross-sectional study using data from the TMM CommCohort Study. The details have been published elsewhere [26,27]. In brief, this cohort study was aimed to contribute to the development of personalized healthcare and medicine worldwide, for which many genomic and epidemiological studies have been conducted [26–33]. To recruit participants, we used three approaches: a type 1 survey (40 433 participants) was conducted at specific municipal health check-up sites; an additional type 1 survey (664 participants) was conducted on different dates at specific municipal health check-ups; and a type 2 survey (13 855 participants) was conducted at a community support center. The same basic information was obtained through blood and urine samples, a questionnaire, and municipal health check-ups among the three approaches. Additionally, several physiological measurements (i.e. body composition and lung function tests) were performed only in type 2 survey. This study included men and women aged at least 20 years living in the Miyagi Prefecture, northeastern Japan. The survey and recruitment were conducted between May 2013 and March 2016. Informed consent was obtained from 54 952 participants. This study was approved by the Institutional Review Board of the Tohoku Medical Megabank Organization (approval number: 2022-4-047; approval date: 30 June 2022).
In this study, we only used data from type 2 survey (n = 13 855) participants who underwent several physiological measurements, including lung function tests. We excluded those who withdrew from the study by 13 July 2021, failed to return the self-reported questionnaire, did not undergo physiological measurements, or had missing data regarding lung function, SBP, DBP, plasma glucose, glycated hemoglobin A1c (HbA1c), total cholesterol (TC), triglyceride, high-density lipoprotein cholesterol (HDL-C), urinary creatinine, estimated urinary 24-h sodium excretion, estimated urinary 24 h potassium excretion and white blood cell (WBC) count (n = 1332). Finally, data from 12 523 participants (3728 men and 8795 women) were analyzed.
Assessment of the lung function
Lung function, including FEV1, FVC, and vital capacity (VC), was measured using a spirometer (HI-801; Chest M. I., Incorporation). The measurements were taken with the participants in a sitting position with a nose clip attached. The FEV1 and FVC were categorized into the following sex-specific quartiles: For men, Q1 (<2.56), Q2 (2.56–2.95), Q3 (2.96–3.43), Q4 (≥3.43) in FEV1; and Q1 (<3.28), Q2 (3.28–3.73), Q3 (3.74–4.26), Q4 (≥4.26) in FVC. For women, Q1 (<1.96), Q2 (1.96–2.25), Q3 (2.26–2.58), Q4 (≥2.58) in FEV1; and Q1 (<2.44), Q2 (2.44–2.77), Q3 (2.78–3.14), Q4 (≥3.14) in FVC. Restrictive and obstructive ventilatory impairments were defined as reduced vital capacity of less than 80% of the predicted and reduced FVC ratio of less than 70% of FEV1 : FVC, respectively.
Hypertension
After resting for at least 2 min in a sitting position, BP was measured twice in the upper right arm using a digital automatic BP monitor (HEM-9000AI; Omron Healthcare Co., Ltd., Kyoto, Japan) at the community support center. The mean values of the two recorded measurements were used. Hypertension was defined as SBP at least 140 mmHg, and/or DBP at least 90 mmHg, and/or self-reported treatment for hypertension. Home BP was measured using a cuff-oscillometric device (HEM-7080IC; Omron Healthcare Co., Ltd.) and was recorded for 10 days in the morning [34,35]. Home hypertension was defined as morning home SBP at least 135 mmHg, and/or home DBP at least 85 mmHg, and/or self-reported treatment for hypertension [36].
Other measurements
We used a self-reported questionnaire to assess demographic characteristics, smoking status, drinking status, education level, physical activity, and history of respiratory disease. Age was determined at the time of visit to the community support center. 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) [37], and unknown status. 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. To calculate the amount of ethanol consumed, alcohol types were classified into six categories: sake, distilled spirits, shochu-based beverages, beer, whiskey, and wine. Alcohol intake frequency was also classified into the following six categories: almost never, 1–3 days/month, 1–2 days/week, 3–4 days/week, 5–6 days/week, and daily. To calculate the amount of ethanol, for each type of alcohol, we multiplied the frequency of alcohol by the amount. We set the cutoff value at 23 g of ethanol, which is the traditional Japanese unit of sake [30,31,38]. Education level was classified into five categories: below high school; vocational school, junior college, or technical college; university or graduate school; other; and unknown. To calculate the amount of leisure-time physical activity (METs-min/week), the average frequency (times/week) and duration (min/time) of normal walking, brisk walking, moderate-intensity exercise, and hard-intensity exercise during leisure time were obtained using a self-reported questionnaire. Metabolic equivalents (METs) were assigned for each physical activity [39]. The value of METs-min/week was calculated by multiplying the corresponding METs, duration, and frequency. Participants answered whether they had a history of asthma, chronic bronchitis, or chronic obstructive pulmonary disease (COPD).
Height was measured to the nearest 0.1 cm using a stadiometer (AD6400; A&D Co., Ltd., Tokyo, Japan). Weight was measured in increments of 0.1 kg, and 1.0 kg was subtracted to account for the weight of the participant's clothing using a body composition analyzer (InBody720; Biospace Co., Ltd., Seoul, Korea). The BMI was calculated as weight (kg) divided by height [meters squared (m2)].
Blood samples were collected under nonfasting conditions. Plasma glucose and HbA1c levels were measured using enzymatic methods. Diabetes was defined as plasma glucose at least 200 mg/dl, HbA1c at least 6.5%, and/or self-reported treatment for diabetes. TC was measured using an ultraviolet-end method with cholesterol dehydrogenase. The TG levels were measured using an enzymatic method. HDL-C levels were measured using a direct method. We could not calculate the low-density lipoprotein cholesterol (LDL-C) because the Friedewald formula, used to calculate LDL-C, only holds for fasting blood samples [40]. Hypercholesterolemia was defined as TC at least 240 mg/dl and/or treatment for dyslipidemia. The cut-off point was set according to the International Conference on Low Blood Cholesterol [41]. The WBC count was measured using the sheath flow electrical resistance method and sodium lauryl sulfate hemoglobin method. Casual spot urine samples from each participant were collected. Estimates of 24-h urinary excretion of sodium and potassium from the spot urine samples were calculated using the Tanaka formula [42].
Statistical analysis
Data are presented as mean [standard deviation (SD)] or median [interquartile range (IQR)] for continuous variables, and as numbers (%) for categorical variables. All analyses were performed separately for men and women because the distributions of lung function and the prevalence of hypertension differed between them.
In terms of the characteristics of the FEV1 quartiles, a trend test was performed for continuous variables using a simple linear model to evaluate the linear association. We also conducted a chi-square test to compare the characteristics of 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 FVC quartile groups.
Multivariate logistic regression analysis was used to examine the association between FEV1 and the prevalence of hypertension. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. In model 1, we adjusted for age, height, and education level. In model 2, we further adjusted for smoking status, weight, diabetes, hypercholesterolemia, drinking status, estimated 24-h urinary excretion of sodium and potassium. Furthermore, in model 3, METs and WBC counts were included to confirm whether the association between lung function and hypertension could be explained by physical activity and inflammation. 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. We also examined the association between FVC and prevalence of hypertension using the same statistical models. Furthermore, we performed an analysis with home hypertension as outcome using above same model, among participants who measured home BP more than 10 times, in order to investigate whether the inverse association between lung function and hypertension observed in casual blood pressure is also observed in home BP (n = 2526 for men and n = 6119 for women).
We conducted several sensitivity analyses. First, as smoking is strongly associated with lung function and hypertension, we stratified the analysis by smoking status (never-smoker, ex-smoker, current smoker). Second, we stratified the analysis by age (young, 20–39 years; middle-aged 40–64 years; elderly, 65–74 years; and very old, ≥75 years). Third, to rule out 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. Forth, previous studies have shown that antihypertensive drugs are associated with reduced lung function [11,43]. Hence, to rule out the influence of hypertension treatment, we selected only participants who were not undergoing treatment for hypertension.
P < 0.05 was considered significant. All analyses were performed using R, version 4.1.2. (R Core Team, Vienna, Austria).
RESULTS
Tables 1 and 2 presents the characteristics of the study participants. The data of 3728 men and 8795 women, who met all inclusion criteria were analyzed. The mean age (±SD) of the study participants was 60.1 years (±14.0 years) for men and 56.2 years (±13.4 years) for women. Seven hundred and fifty-four (20.2%) men and 622 (7.1%) women were current smoker. The FEV1 and FVC were higher in men than in women. The prevalence of hypertension was also higher in men [1994 (53.5%)] than in women [2992 (34.0%)]. Among the participants, the higher the FEV1, the lower the age, prevalence of hypertension, diabetes, and hypercholesterolemia. Moreover, participants with higher FEV1 had higher education levels and were current smokers (Table 1). Similar patterns were observed when the FVC quartiles were used (Table 2).
TABLE 1 -
Participant's characteristics according to forced expiratory volume at 1 s quartile
|
|
The quartile groups of FEV1 (range) |
|
|
The quartile groups of FEV1 (range) |
|
| 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 |
3728 |
919 |
934 |
925 |
950 |
|
8795 |
2198 |
2174 |
2166 |
2257 |
|
| Age (year) |
60.1 (14.0) |
70.9 (7.7) |
65.4 (8.1) |
59.5 (10.6) |
45.1 (13.0) |
<0.001 |
56.2 (13.4) |
67.2 (8.3) |
61.0 (9.0) |
54.2 (10.8) |
42.9 (11.1) |
<0.001 |
| 20–39 |
439 (11.8) |
5 (0.5) |
11 (1.2) |
60 (6.5) |
363 (38.2) |
|
1218 (13.8) |
21 (1.0) |
53 (2.4) |
227 (10.5) |
917 (40.6) |
|
| 40–64 |
1517 (40.7) |
134 (14.6) |
356 (38.1) |
519 (56.1) |
508 (53.5) |
|
4809 (54.7) |
673 (30.6) |
1287 (59.2) |
1568 (72.4) |
1281 (56.8) |
|
| 65–74 |
1351 (36.2) |
494 (53.8) |
476 (51.0) |
312 (33.7) |
69 (7.3) |
|
2292 (26.1) |
1132 (51.5) |
762 (35.1) |
340 (15.7) |
58 (2.6) |
|
| ≥75 |
421 (11.3) |
286 (31.1) |
91 (9.7) |
34 (3.7) |
10 (1.1) |
|
476 (5.4) |
372 (16.9) |
72 (3.3) |
31 (1.4) |
1 (0.0) |
|
| Height (cm) |
167.5 (6.3) |
163.3 (5.5) |
165.6 (5.1) |
168.6 (5.3) |
172.5 (5.3) |
<0.001 |
155.8 (5.8) |
151.7 (5.2) |
154.4 (4.7) |
156.9 (4.8) |
160.1 (4.8) |
<0.001 |
| Weight (kg) |
66.8 (10.2) |
63.4 (9.1) |
65.3 (9.2) |
67.5 (9.5) |
71.0 (11.3) |
<0.001 |
54.2 (8.8) |
52.4 (8.3) |
53.5 (8.3) |
54.8 (8.8) |
56.2 (9.4) |
<0.001 |
| BMI (kg/m2) |
23.8 (3.1) |
23.8 (2.9) |
23.8 (3.0) |
23.7 (2.9) |
23.9 (3.5) |
0.536 |
22.4 (3.5) |
22.8 (3.5) |
22.5 (3.4) |
22.3 (3.4) |
21.9 (3.5) |
<0.001 |
| SBP (mmHg) |
133.9 (16.0) |
138.9 (16.1) |
136.3 (16.2) |
133.0 (15.4) |
127.6 (13.8) |
<0.001 |
126.0 (17.8) |
133.5 (17.5) |
129.4 (17.1) |
124.8 (17.2) |
116.7 (14.7) |
<0.001 |
| DBP (mmHg) |
80.9 (10.8) |
78.8 (10.8) |
81.5 (10.8) |
82.5 (10.3) |
80.8 (11.1) |
<0.001 |
76.5 (10.5) |
77.2 (10.4) |
77.5 (10.4) |
77.2 (10.6) |
74.3 (10.4) |
<0.001 |
| Prevalence of hypertension (%) |
1994 (53.5) |
641 (69.7) |
583 (62.4) |
487 (52.6) |
283 (29.8) |
<0.001 |
2992 (34.0) |
1144 (52.0) |
877 (40.3) |
655 (30.2) |
316 (14.0) |
<0.001 |
| Glucose (mg/dl) |
92.8 (20.8) |
96.7 (24.8) |
94.7 (21.6) |
91.8 (19.0) |
88.1 (15.8) |
<0.001 |
86.8 (14.4) |
90.2 (17.6) |
87.9 (14.1) |
85.8 (12.3) |
83.3 (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 (0.6) |
5.6 (0.5) |
5.5 (0.4) |
5.3 (0.4) |
<0.001 |
| Prevalence of diabetes (%) |
389 (10.4) |
156 (17.0) |
113 (12.1) |
77 (8.3) |
43 (4.5) |
<0.001 |
405 (4.6) |
183 (8.3) |
126 (5.8) |
68 (3.1) |
28 (1.2) |
<0.001 |
| TC (mg/dl) |
201.3 (34.9) |
197.7 (33.4) |
201.6 (34.7) |
203.8 (35.5) |
202.2 (35.8) |
0.002 |
212.4 (35.5) |
216.0 (34.7) |
219.1 (34.6) |
215.3 (35.5) |
199.8 (34.1) |
<0.001 |
| TG (mg/dl) |
101.0 [72.0–149.0] |
104.0 [73.5–147.5] |
102.0 [76.0–152.0] |
101.0 [71.0–147.0] |
96.0 [66.3–146.0] |
0.595 |
79.0 [58.0–112.0] |
91.0 [67.0–124.0] |
84.0 [62.0–119.0] |
79.0 [58.0–109.0] |
65.0 [49.0–90.0] |
<0.001 |
| HDL-C (mg/dl) |
57.2 (15.1) |
56.3 (14.9) |
57.4 (15.8) |
57.6 (14.7) |
57.3 (15.2) |
0.144 |
67.6 (16.2) |
66.0 (16.3) |
67.5 (16.2) |
68.4 (16.5) |
68.6 (15.8) |
<0.001 |
| Prevalence of hypercholesterolemia (%) |
862 (23.1) |
215 (23.4) |
227 (24.3) |
240 (25.9) |
180 (18.9) |
0.003 |
2749 (31.3) |
887 (40.4) |
827 (38.0) |
686 (31.7) |
349 (15.5) |
<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] |
3.78 [3.58–4.04] |
<0.001 |
2.26 [1.96–2.58] |
1.76 [1.60– 1.86] |
2.12 [2.04–2.18] |
2.40 [2.33–2.48] |
2.83 [2.69–3.03] |
<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] |
3.37 [3.20–3.61] |
<0.001 |
| %VC (%) |
101.9 (13.5) |
93.4 (13.3) |
101.2 (11.4) |
105.2 (12.4) |
107.6 (12.3) |
<0.001 |
103.1 (13.6) |
95.1 (12.6) |
103.0 (12.2) |
105.6 (13.0) |
108.5 (12.9) |
<0.001 |
| Restrictive ventilatory impairment (%) |
164 (4.4) |
132 (14.4) |
17 (1.8) |
10 (1.1) |
5 (0.5) |
<0.001 |
285 (3.2) |
206 (9.4) |
36 (1.7) |
34 (1.6) |
9 (0.4) |
<0.001 |
| FEV1/FVC (%) |
79.0 (7.1) |
74.2 (8.7) |
78.7 (5.5) |
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) |
84.6 (5.1) |
<0.001 |
| Obstructive ventilatory impairment (%) |
313 (8.4) |
224 (24.4) |
57 (6.1) |
22 (2.4) |
10 (1.1) |
<0.001 |
249 (2.8) |
186 (8.5) |
34 (1.6) |
25 (1.2) |
4 (0.2) |
<0.001 |
| METs (MET-min/week) |
91.5 [12.9–250.7] |
138.0 [37.9–306.0] |
126.0 [27.0–280.2] |
90.0 [18.0–252.0] |
36.0 [0.0–137.7] |
<0.001 |
66.3 [8.4–192.9] |
121.7 [28.9–252.0] |
90.0 [16.8–222.3] |
57.9 [3.0–165.6] |
28.9 [0.0–126.0] |
<0.001 |
| WBC count (/μl) |
5963. (1585) |
6102 (1613) |
5951 (1536) |
5856. (1582) |
5944 (1602) |
0.015 |
5585 (1489) |
5592 (1370) |
5507 (1440) |
5506 (1560) |
5731 (1567) |
<0.001 |
| Sodium excretion (g/day) |
3.0 (1.2) |
3.0 (1.1) |
3.0 (1.1) |
3.0 (1.2) |
3.0 (1.3) |
0.281 |
2.5 (1.1) |
2.4 (1.0) |
2.5 (1.1) |
2.5 (1.1) |
2.8 (1.2) |
<0.001 |
| Potassium excretion (g/day) |
1.4 (0.8) |
1.3 (0.7) |
1.4 (0.7) |
1.4 (0.8) |
1.5 (0.8) |
<0.001 |
1.3 (0.8) |
1.2 (0.7) |
1.2 (0.7) |
1.3 (0.7) |
1.4 (0.9) |
<0.001 |
| Education status (%) |
|
|
|
|
|
<0.001 |
|
|
|
|
|
<0.001 |
| Below high school |
2084 (55.9) |
607 (66.1) |
556 (59.5) |
504 (54.5) |
417 (43.9) |
|
4837 (55.0) |
1459 (66.4) |
1287 (59.2) |
1118 (51.6) |
973 (43.1) |
|
| Vocational school, junior college, or technical college |
454 (12.2) |
70 (7.6) |
103 (11.0) |
102 (11.0) |
179 (18.8) |
|
2847 (32.4) |
557 (25.3) |
677 (31.1) |
779 (36.0) |
834 (37.0) |
|
| University or graduate school |
1131 (30.3) |
221 (24.0) |
262 (28.1) |
307 (33.2) |
341 (35.9) |
|
1005 (11.4) |
135 (6.1) |
186 (8.6) |
253 (11.7) |
431 (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 |
41 (1.1) |
13 (1.4) |
9 (1.0) |
10 (1.1) |
9 (0.9) |
|
75 (0.9) |
32 (1.5) |
20 (0.9) |
10 (0.5) |
13 (0.6) |
|
| Smoking status (%) |
|
|
|
|
|
<0.001 |
|
|
|
|
|
<0.001 |
| Never-smoker |
1077 (28.9) |
257 (28.0) |
257 (27.5) |
269 (29.1) |
294 (30.9) |
|
6918 (78.7) |
1906 (86.7) |
1818 (83.6) |
1658 (76.5) |
1536 (68.1) |
|
| Ex-smoker |
1875 (50.3) |
495 (53.9) |
519 (55.6) |
486 (52.5) |
375 (39.5) |
|
1203 (13.7) |
183 (8.3) |
214 (9.8) |
348 (16.1) |
458 (20.3) |
|
| Current smoker |
754 (20.2) |
160 (17.4) |
152 (16.3) |
166 (17.9) |
276 (29.1) |
|
622 (7.1) |
89 (4.0) |
127 (5.8) |
151 (7.0) |
255 (11.3) |
|
| Unknown |
22 (0.6) |
7 (0.8) |
6 (0.6) |
4 (0.4) |
5 (0.5) |
|
52 (0.6) |
20 (0.9) |
15 (0.7) |
9 (0.4) |
8 (0.4) |
|
| Drinking status (%) |
|
|
|
|
|
<0.001 |
|
|
|
|
|
<0.001 |
| Never drinker |
665 (17.8) |
181 (19.7) |
157 (16.8) |
160 (17.3) |
167 (17.6) |
|
4573 (52.0) |
1339 (60.9) |
1184 (54.5) |
1080 (49.9) |
970 (43.0) |
|
| Ex-drinker |
143 (3.8) |
59 (6.4) |
35 (3.7) |
24 (2.6) |
25 (2.6) |
|
164 (1.9) |
37 (1.7) |
38 (1.7) |
37 (1.7) |
52 (2.3) |
|
| <23 g |
1439 (38.6) |
340 (37.0) |
335 (35.9) |
354 (38.3) |
410 (43.2) |
|
3295 (37.5) |
704 (32.0) |
786 (36.2) |
853 (39.4) |
952 (42.2) |
|
| ≥23 g |
1468 (39.4) |
336 (36.6) |
403 (43.1) |
384 (41.5) |
345 (36.3) |
|
736 (8.4) |
106 (4.8) |
157 (7.2) |
194 (9.0) |
279 (12.4) |
|
| Unknown |
13 (0.3) |
3 (0.3) |
4 (0.4) |
3 (0.3) |
3 (0.3) |
|
27 (0.3) |
12 (0.5) |
9 (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 |
590 (6.7) |
155 (7.1) |
135 (6.2) |
143 (6.6) |
157 (7.0) |
0.673 |
| Chronic bronchitis |
26 (0.7) |
8 (0.9) |
9 (1.0) |
4 (0.4) |
5 (0.5) |
0.442 |
85 (1.0) |
34 (1.5) |
13 (0.6) |
25 (1.2) |
13 (0.6) |
0.002 |
| 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 (0.01) |
1 (0.01) |
0 (0.0) |
0.003 |
Values are expressed as mean (standard deviation) or median [interquartile range] for continuous variables, or as number (%) for categorical variables. Hypertension was defined as SBP at least 140 mmHg and/or DBP at least 90 mmHg or receiving treatment for hypertension. Diabetes was defined as nonfasting glucose at least 200 mg/dl and/or HbA1c at least 6.5%, or receiving treatment for diabetes. Hypercholesterolemia was defined as TC at least 240 mg/dl and/or treatment for dyslipidemia. Restrictive ventilatory impairment was defined as reduced VC of <80% of predicted. Obstructive ventilatory impairment was defined as reduced a ration in FEV1 to FVC of <70%. 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, TC, total cholesterol; TG, triglyceride; VC, vital capacity; WBC, white blood cell.
TABLE 2 -
Participant's characteristics according to forced vital capacity quartile
|
|
The quartile groups of FVC (range) |
|
|
The quartile groups of FVC (range) |
|
| 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 |
3728 |
930 |
917 |
938 |
943 |
|
8795 |
2156 |
2194 |
2218 |
2227 |
|
| Age (year) |
60.1 (14.0) |
70.6 (7.9) |
64.5 (9.7) |
58.7 (11.4) |
46.9 (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 |
439 (11.8) |
6 (0.6) |
30 (3.3) |
76 (8.1) |
327 (34.7) |
|
1218 (13.8) |
26 (1.2) |
95 (4.3) |
316 (14.2) |
781 (35.1) |
|
| 40–64 |
1517 (40.7) |
155 (16.7) |
352 (38.4) |
520 (55.4) |
490 (52.0) |
|
4809 (54.7) |
683 (31.7) |
1265 (57.7) |
1520 (68.5) |
1341 (60.2) |
|
| 65–74 |
1351 (36.2) |
494 (53.1) |
433 (47.2) |
310 (33.0) |
114 (12.1) |
|
2292 (26.1) |
1098 (50.9) |
746 (34.0) |
345 (15.6) |
103 (4.6) |
|
| ≥75 |
421 (11.3) |
275 (29.6) |
102 (11.1) |
32 (3.4) |
12 (1.3) |
|
476 (5.4) |
349 (16.2) |
88 (4.0) |
37 (1.7) |
2 (0.1) |
|
| Height (cm) |
167.5 (6.3) |
162.8 (5.3) |
165.7 (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) |
63.5 (9.4) |
65.1 (9.1) |
67.6 (10.3) |
71.2 (10.5) |
<0.001 |
54.2 (8.8) |
52.5 (8.5) |
53.5 (8.6) |
54.4 (8.6) |
56.6 (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) |
0.534 |
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) |
136.7 (16.2) |
132.7 (15.7) |
128.2 (13.9) |
<0.001 |
126.0 (17.8) |
133.7 (17.6) |
128.7 (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 (10.6) |
74.7 (10.3) |
<0.001 |
| Prevalence of hypertension (%) |
1994 (53.5) |
647 (69.6) |
575 (62.7) |
468 (49.9) |
304 (32.2) |
<0.001 |
2992 (34.0) |
1138 (52.8) |
865 (39.4) |
634 (28.6) |
355 (15.9) |
<0.001 |
| Glucose (mg/dl) |
92.8 (20.8) |
96.8 (24.7) |
94.3 (22.4) |
92.0 (18.5) |
88.0 (15.4) |
<0.001 |
86.8 (14.4) |
90.7 (18.0) |
87.5 (13.9) |
85.7 (13.0) |
83.4 (10.8) |
<0.001 |
| HbA1c (%) |
5.6 (0.6) |
5.8 (0.73) |
5.6 (0.6) |
5.6 (0.6) |
5.4 (0.5) |
<0.001 |
5.5 (0.5) |
5.7 (0.6) |
5.6 (0.5) |
5.5 (0.5) |
5.3 (0.3) |
<0.001 |
| Prevalence of diabetes (%) |
389 (10.4) |
160 (17.2) |
108 (11.8) |
85 (9.1) |
36 (3.8) |
<0.001 |
405 (4.6) |
197 (9.1) |
116 (5.3) |
69 (3.1) |
23 (1.0) |
<0.001 |
| TC (mg/dl) |
201.3 (34.9) |
197.7 (33.8) |
201.9 (36.0) |
204.0 (34.7) |
201.7 (34.9) |
0.007 |
212.4 (35.5) |
216.2 (34.9) |
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 –147.0] |
103.0 [72.0 –151.0] |
95.0 [65.5 –144.0] |
0.475 |
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 (15.1) |
57.4 (14.9) |
0.475 |
67.6 (16.2) |
65.7 (16.2) |
67.5 (16.0) |
68.4 (16.4) |
68.8 (16.2) |
<0.001 |
| Prevalence of hypercholesterolemia (%) |
862 (23.1) |
229 (24.6) |
212 (23.1) |
247 (26.3) |
174 (18.5) |
<0.001 |
2749 (31.3) |
879 (40.8) |
805 (36.7) |
672 (30.3) |
393 (17.6) |
<0.001 |
| FEV1 (l) |
2.96 [2.56 –3.43] |
2.31 [2.04–2.51] |
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 [3.85–4.11] |
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) |
91.5 (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 (%) |
164 (4.4) |
141 (15.2) |
18 (2.0) |
3 (0.3) |
2 (0.2) |
<0.001 |
285 (3.2) |
231 (10.7) |
41 (1.9) |
13 (0.6) |
0 (0.0) |
<0.001 |
| FEV1/FVC (%) |
79.0 (7.1) |
77.6 (8.9) |
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) |
82.4 (5.7) |
<0.001 |
| Obstructive ventilatory impairment (%) |
313 (8.4) |
126 (13.5) |
76 (8.3) |
66 (7.0) |
45 (4.8) |
<0.001 |
249 (2.8) |
111 (5.1) |
51 (2.3) |
46 (2.1) |
41 (1.8) |
<0.001 |
| METs (MET-min/week) |
91.5 [12.9–250.7] |
135.0 [36.0–302.5] |
124.7 [30.0–284.8] |
81.9 [9.2–238.8] |
42.0 [0.0–156.9] |
<0.001 |
66.3 [8.4–192.9] |
112.5 [27.0–246.0] |
88.9 [12.0–217.0] |
57.9 [3.0–171.0] |
33.7 [0.0, 126.5] |
<0.001 |
| WBC count (/μl) |
5963 (1585) |
6059 (1656) |
5981 (1535) |
5849 (1520) |
5965 (1621) |
0.075 |
5585 (1489) |
5627 (1370) |
5488 (1422) |
5549 (1578) |
5676 (1566) |
<0.001 |
| Sodium excretion (g/day) |
3.0 (1.2) |
3.0 (1.1) |
3.0 (1.1) |
3.0 (1.2) |
3.1 (1.3) |
0.281 |
2.5 (1.1) |
2.5 (1.0) |
2.4 (1.1) |
2.6 (1.2) |
2.7 (1.2) |
<0.001 |
| Potassium excretion (g/day) |
1.4 (0.8) |
1.4 (0.7) |
1.4 (0.7) |
1.4 (0.8) |
1.5 (0.8) |
0.002 |
1.3 (0.8) |
1.2 (0.7) |
1.2 (0.7) |
1.3 (0.8) |
1.4 (0.8) |
<0.001 |
| Education status (%) |
|
|
|
|
|
<0.001 |
|
|
|
|
|
<0.001 |
| Below high school |
2084 (55.9) |
618 (66.5) |
542 (59.1) |
503 (53.6) |
421 (44.6) |
|
4837 (55.0) |
1430 (66.3) |
1265 (57.7) |
1156 (52.1) |
986 (44.3) |
|
| Vocational school, junior college, or technical college |
454 (12.2) |
78 (8.4) |
88 (9.6) |
120 (12.8) |
168 (17.8) |
|
2847 (32.4) |
547 (25.4) |
709 (32.3) |
764 (34.4) |
827 (37.1) |
|
| University or graduate school |
1131 (30.3) |
214 (23.0) |
275 (30.0) |
298 (31.8) |
344 (36.5) |
|
1005 (11.4) |
133 (6.2) |
192 (8.8) |
280 (12.6) |
400 (18.0) |
|
| 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 |
41 (1.1) |
13 (1.4) |
8 (0.9) |
14 (1.5) |
6 (0.6) |
|
75 (0.9) |
35 (1.6) |
18 (0.8) |
13 (0.6) |
9 (0.4) |
|
| Smoking status (%) |
|
|
|
|
|
<0.001 |
|
|
|
|
|
<0.001 |
| Never-smoker |
1077 (28.9) |
284 (30.5) |
266 (29.0) |
249 (26.5) |
278 (29.5) |
|
6918 (78.7) |
1872 (86.8) |
1858 (84.7) |
1667 (75.2) |
1521 (68.3) |
|
| Ex-smoker |
1875 (50.3) |
493 (53.0) |
494 (53.9) |
494 (52.7) |
394 (41.8) |
|
1203 (13.7) |
181 (8.4) |
213 (9.7) |
365 (16.5) |
444 (19.9) |
|
| Current smoker |
754 (20.2) |
148 (15.9) |
150 (16.4) |
188 (20.0) |
268 (28.4) |
|
622 (7.1) |
81 (3.8) |
111 (5.1) |
177 (8.0) |
253 (11.4) |
|
| Unknown |
22 (0.6) |
5 (0.5) |
7 (0.8) |
7 (0.7) |
3 (0.3) |
|
52 (0.6) |
22 (1.0) |
12 (0.5) |
9 (0.4) |
9 (0.4) |
|
| Drinking status (%) |
|
|
|
|
|
<0.001 |
|
|
|
|
|
<0.001 |
| Never drinker |
665 (17.8) |
199 (21.4) |
148 (16.1) |
149 (15.9) |
169 (17.9) |
|
4573 (52.0) |
1332 (61.8) |
1187 (54.1) |
1091 (49.2) |
963 (43.2) |
|
| Ex-drinker |
143 (3.8) |
57 (6.1) |
36 (3.9) |
27 (2.9) |
23 (2.4) |
|
164 (1.9) |
34 (1.6) |
44 (2.0) |
39 (1.8) |
47 (2.1) |
|
| <23 g |
1439 (38.6) |
346 (37.2) |
342 (37.3) |
361 (38.5) |
390 (41.4) |
|
3295 (37.5) |
671 (31.1) |
810 (36.9) |
879 (39.6) |
935 (42.0) |
|
| ≥23 g |
1468 (39.4) |
325 (34.9) |
388 (42.3) |
395 (42.1) |
360 (38.2) |
|
736 (8.4) |
106 (4.9) |
145 (6.6) |
206 (9.3) |
279 (12.5) |
|
| Unknown |
13 (0.3) |
3 (0.3) |
3 (0.3) |
6 (0.6) |
1 (0.1) |
|
27 (0.3) |
13 (0.6) |
8 (0.4) |
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) |
0.115 |
590 (6.7) |
143 (6.6) |
139 (6.3) |
151 (6.8) |
157 (7.0) |
0.812 |
| Chronic bronchitis |
26 (0.7) |
8 (0.9) |
7 (0.8) |
6 (0.6) |
5 (0.5) |
0.840 |
85 (1.0) |
29 (1.3) |
21 (1.0) |
19 (0.9) |
16 (0.7) |
0.176 |
| Chronic obstructive pulmonary disease |
18 (0.5) |
6 (0.6) |
7 (0.8) |
4 (0.4) |
1 (0.1) |
0.182 |
9 (0.1) |
6 (0.3) |
2 (0.1) |
0 (0.0) |
1 (0.01) |
0.023 |
Values are expressed as mean (standard deviation) or median (interquartile range) for continuous variables, or as number (%) for categorical variables.Hypertension was defined as SBP at least 140 mmHg and/or DBP at least 90 mmHg or receiving treatment for hypertension. Diabetes was defined as nonfasting glucose at least 200 mg/dl and/or HbA1c at least 6.5% or receiving treatment for diabetes. Hypercholesterolemia was defined as TC at least 240 mg/dl and/or treatment for dyslipidemia. Restrictive ventilatory impairment was defined as reduced VC of less than 80% of predicted. Obstructive ventilatory impairment was defined as reduced a ration in FEV1 to FVC of less than 70%. 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, TC, total cholesterol; TG, triglyceride; VC, vital capacity; WBC, white blood cell.
Table 3 shows the association between FEV1 and prevalence of hypertension. We found that FEV1 was inversely associated with the prevalence of hypertension (P for linear trend <0.001). Even with the addition of several potential confounders (METs and WBC count) at adjustments, these associations remained statistically significant. In model 3, for Q1 (reference), Q2, Q3, and Q4, the multivariate ORs were 1.00, 0.92 (0.74–1.13), 0.81 (0.64–1.02), and 0.56 (0.42–0.75) in men; and 1.00, 0.94 (0.82–1.08), 0.93 (0.79–1.08), and 0.70 (0.57–0.86) in women, respectively.
TABLE 3 -
Association between forced expiratory volume at 1 s and prevalence of
hypertension
|
The quartile groups of FEV1 (range) |
|
| Men |
Q1 (<2.56) |
Q2 (2.56–2.95) |
Q3 (2.96–3.43) |
Q4 (≥3.43) |
|
| Number of participants |
919 |
934 |
925 |
950 |
P for linear trend |
| Number of cases |
641 (69.8) |
583 (62.4) |
487 (52.7) |
283 (29.8) |
|
| Crude |
1.00 (reference) |
0.72 (0.59–0.87) |
0.48 (0.40–0.58) |
0.18 (0.15–0.22) |
<0.001 |
| Model 1 |
1.00 (reference) |
0.91 (0.75–1.12) |
0.78 (0.62–0.96) |
0.52 (0.40–0.69) |
<0.001 |
| Model 2 |
1.00 (reference) |
0.90 (0.73–1.12) |
0.79 (0.63–0.99) |
0.54 (0.40–0.72) |
<0.001 |
| Model 3 |
1.00 (reference) |
0.92 (0.74–1.13) |
0.81 (0.64–1.02) |
0.56 (0.42–0.75) |
<0.001 |
|
The quartile groups of FEV1 (range) |
|
| Women |
Q1 (<1.96) |
Q2 (1.96–2.25) |
Q3 (2.26–2.58) |
Q4 (≥2.58) |
|
| Number of participants |
2198 |
2174 |
2166 |
2257 |
P for linear trend |
| Number of case |
1144 (52.1) |
877 (40.3) |
655 (30.2) |
316 (14.0) |
|
| Crude |
1.00 (reference) |
0.62 (0.55–0.70) |
0.40 (0.35–0.45) |
0.15 (0.13–0.17) |
<0.001 |
| Model 1 |
1.00 (reference) |
0.94 (0.83–1.07) |
0.93 (0.80–1.07) |
0.72 (0.59–0.88) |
0.006 |
| Model 2 |
1.00 (reference) |
0.93 (0.82–1.07) |
0.91 (0.78–1.06) |
0.69 (0.56–0.85) |
0.003 |
| Model 3 |
1.00 (reference) |
0.94 (0.82–1.08) |
0.93 (0.79–1.08) |
0.70 (0.57–0.86) |
0.005 |
Analysis by multivariable logistic regression model. Model 1 was adjusted for age (continuous), height (continuous), and educational status (below high school, vocational school, junior college, technical college, university or graduate school, others, unknown). Model 2 was adjusted for age, height, educational status, smoking status (never smoker, ex-smoker, current smoker, unknown), weight (continuous), diabetes, hypercholesterolemia, drinking status (never drinker, ex-drinker, current drinker, unknown), estimated 24-h sodium excretion, and potassium excretion. Model 3 was adjusted for Model 2 plus METs (quartile groups) and WBC count (quartile groups). 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. FEV1, forced expiratory volume at 1 s; METs, metabolic equivalents; Q, quartile; WBC, white blood cell.
We also found that FVC was inversely associated with the prevalence of hypertension (P for linear trend <0.001). This association remained significant even after adjusting for several confounding variables. In model 3, for Q1 (reference), Q2, Q3, and Q4, the multivariate ORs were 1.00, 0.99 (0.80–1.22), 0.71 (0.57–0.90), and 0.59 (0.44–0.78) in men; and 1.00, 0.91 (0.80–1.04), 0.87 (0.74–1.01), and 0.69 (0.57–0.84) in women, respectively (Table 4).
TABLE 4 -
Association between forced vital capacity and prevalence of
hypertension
|
The quartile groups of FVC (range) |
|
| Men |
Q1 (<3.28) |
Q2 (3.28–3.73) |
Q3 (3.74–4.26) |
Q4 (≥4.26) |
|
| Number of participants |
930 |
917 |
938 |
943 |
P for linear trend |
| Number of cases |
647 (69.6) |
575 (62.7) |
468 (49.9) |
304 (32.2) |
|
| Crude |
1.00 (reference) |
0.74 (0.61–0.89) |
0.44 (0.36–0.53) |
0.21 (0.17–0.25) |
<0.001 |
| Model 1 |
1.00 (reference) |
0.95 (0.78–1.17) |
0.70 (0.56–0.87) |
0.53 (0.40–0.69) |
<0.001 |
| Model 2 |
1.00 (reference) |
0.98 (0.79–1.21) |
0.70 (0.56–0.88) |
0.57 (0.43–0.75) |
<0.001 |
| Model 3 |
1.00 (reference) |
0.99 (0.80–1.22) |
0.71 (0.57–0.90) |
0.59 (0.44–0.78) |
<0.001 |
|
The quartile groups of FVC (range) |
|
| Women |
Q1 (<2.44) |
Q2 (2.44–2.77) |
Q3 (2.78–3.14) |
Q4 (≥3.14) |
|
| Number of participants |
2156 |
2194 |
2218 |
2227 |
P for linear trend |
| Number of cases |
1138 (52.8) |
865 (39.4) |
634 (28.6) |
355 (15.9) |
|
| Crude |
1.00 (reference) |
0.58 (0.52–0.66) |
0.36 (0.32–0.41) |
0.17 (0.15–0.20) |
<0.001 |
| Model 1 |
1.00 (reference) |
0.88 (0.77–1.00) |
0.82 (0.71–0.95) |
0.67 (0.55–0.80) |
<0.001 |
| Model 2 |
1.00 (reference) |
0.90 (0.79–1.03) |
0.85 (0.73–0.99) |
0.68 (0.56–0.83) |
<0.001 |
| Model 3 |
1.00 (reference) |
0.91 (0.80–1.04) |
0.87 (0.74–1.01) |
0.69 (0.57–0.84) |
<0.001 |
Analysis by multivariable logistic regression model. Model 1 was adjusted for age (continuous), height (continuous), and educational status (below high school, vocational school, junior college, technical college, university or graduate school, others, unknown). Model 2 was adjusted for age, height, educational status, smoking status (never smoker, ex-smoker, current smoker, unknown), weight (continuous), diabetes, hypercholesterolemia, drinking status (never drinker, ex-drinker, current drinker, unknown), estimated 24-h sodium excretion, and potassium excretion. Model 3 was adjusted for Model 2 plus METs (quartile groups) and WBC count (quartile groups). 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, entering the number as a continuous term in the regression model. FVC, forced vital capacity; METs, metabolic equivalents; Q, quartile; WBC, white blood cell.
Moreover, we performed an analysis with home hypertension as outcome. The prevalence of home hypertension was 1492 (59%) for men and 2187 (36%) for women. In the multivariable analysis, lower FEV1 was significantly related to an increased prevalence of home hypertension. In model 3, the multivariate ORs were 1.00, 0.68 (0.52–0.88), 0.62 (0.47–0.82), and 0.40 (0.28–0.56) in men; and 1.00, 0.77 (0.66–0.91), 0.71 (0.59–0.86), 0.50 (0.39–0.64) in women, respectively (Table 5). Similarly, FVC was inversely associated with the prevalence of home hypertension. In model 3, the multivariate ORs were 1.00, 0.81 (0.62–1.05), 0.62 (0.47–0.82), 0.41 (0.29–0.58) in men; and 1.00, 0.79 (0.67–0.93), 0.69 (0.58–0.84), 0.50 (0.40–0.64) in women, respectively (Table 6).
TABLE 5 -
Association between forced expiratory volume at 1 s and prevalence of home
hypertension
|
The quartile groups of FEV1 (range) |
|
| Men |
Q1 (<2.53) |
Q2 (2.53–2.89) |
Q3 (2.90–3.30) |
Q4 (>3.30) |
|
| Number of participants |
623 |
638 |
625 |
640 |
P for linear trend |
| Number of case |
467 (75.0) |
410 (64.3) |
363 (58.1) |
252 (39.4) |
|
| Crude |
1.00 (reference) |
0.60 (0.47–0.77) |
0.46 (0.36–0.59) |
0.22 (0.17–0.28) |
<0.001 |
| Model 1 |
1.00 (reference) |
0.68 (0.53–0.87) |
0.58 (0.45–0.76) |
0.36 (0.45–0.76) |
<0.001 |
| Model 2 |
1.00 (reference) |
0.67 (0.52–0.87) |
0.60 (0.45–0.78) |
0.38 (0.27–0.53) |
<0.001 |
| Model 3 |
1.00 (reference) |
0.68 (0.52–0.88) |
0.62 (0.47–0.82) |
0.40 (0.28–0.56) |
<0.001 |
|
The quartile groups of FEV1 (range) |
|
| Women |
Q1(<1.94) |
Q2(1.94–2.24) |
Q3(2.25–2.55) |
Q4(>2.55) |
|
| Number of participants |
1490 |
1563 |
1523 |
1543 |
P for linear trend |
| Number of case |
849 (73.9) |
652 (41.7) |
467 (30.7) |
219 (14.2) |
|
| Crude |
1.00 (reference) |
0.54 (0.47–0.62) |
0.33 (0.29–0.39) |
0.12 (0.10–0.15) |
<0.001 |
| Model 1 |
1.00 (reference) |
0.78 (0.67–0.91) |
0.73 (0.61–0.87) |
0.53 (0.42–0.66) |
<0.001 |
| Model 2 |
1.00 (reference) |
0.76 (0.65–0.90) |
0.69 (0.58–0.84) |
0.49 (0.38–0.62) |
<0.001 |
| Model 3 |
1.00 (reference) |
0.77 (0.66–0.91) |
0.71 (0.59–0.86) |
0.50 (0.39–0.64) |
<0.001 |
Analysis by multivariable logistic regression model. Home hypertension was defined as morning home SBP at least 135 mmHg, and/or home DBP at least 85 mmHg, and/or self-reported treatment for hypertension. Model 1 was adjusted for age (continuous), height (continuous), and educational status (below high school; vocational school, junior college, or technical college; university or graduate school; others; 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; unknown), smoking status (never-smoker, ex-smoker, current smoker, unknown), weight (continuous), diabetes, hypercholesterolemia, drinking status (never drinker, ex-drinker, < 23 g, ≥ 23 g, unknown) and estimated 24-h sodium excretion, and potassium excretion. Model 3 was adjusted as for model 2 plus METs (quartile category) and WBC count (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. cIMT carotid intima–media thickness; FEV1, forced expiratory volume at 1 s; METs, metabolic equivalents; Q, quartile; WBC, white blood cell.
TABLE 6 -
Association between forced vital capacity and prevalence of home
hypertension
|
The quartile groups of FVC (range) |
|
| Men |
Q1 (<3.25) |
Q2 (3.25–3.68) |
Q3 (3.69–4.17) |
Q4 (>4.17) |
P for linear trend |
| Number of participants |
617 |
637 |
635 |
637 |
|
| Number of cases |
456 (73.9) |
420 (65.9) |
359 (56.5) |
257 (40.4) |
|
| Crude |
1.00 (reference) |
0.68 (0.54–0.87) |
0.46 (0.36–0.58) |
0.24 (0.19–0.30) |
<0.001 |
| Model 1 |
1.00 (reference) |
0.77 (0.60–0.99) |
0.58 (0.44–0.75) |
0.37 (0.27–0.51) |
<0.001 |
| Model 2 |
1.00 (reference) |
0.79 (0.61–1.03) |
0.60 (0.46–0.80) |
0.40 (0.28–0.56) |
<0.001 |
| Model 3 |
1.00 (reference) |
0.81 (0.62–1.05) |
0.62 (0.47–0.82) |
0.41 (0.29–0.58) |
<0.001 |
|
The quartile groups of FVC (range) |
|
| Women |
Q1 (<2.43) |
Q2 (2.43–2.76) |
Q3 (2.77–3.10) |
Q4 (>3.10) |
P for linear trend |
| Number of participants |
1500 |
1503 |
1551 |
1565 |
|
| Number of cases |
862 (57.5) |
622 (41.4) |
455 (29.3) |
248 (15.9) |
|
| Crude |
1.00 (reference) |
0.52 (0.45–0.60) |
0.31 (0.26–0.36) |
0.14 (0.12–0.16) |
<0.001 |
| Model 1 |
1.00 (reference) |
0.76 (0.65–0.89) |
0.64 (0.54–0.77) |
0.48 (0.38–0.59) |
<0.001 |
| Model 2 |
1.00 (reference) |
0.78 (0.66–0.92) |
0.67 (0.56–0.81) |
0.49 (0.39–0.61) |
<0.001 |
| Model 3 |
1.00 (reference) |
0.79 (0.67–0.93) |
0.69 (0.58–0.84) |
0.50 (0.40–0.64) |
<0.001 |
Analysis by multivariable logistic regression model. Home hypertension was defined as morning home SBP at least 135 mmHg, and/or home DBP at least 85 mmHg, and/or self-reported treatment for hypertension. Model 1 was adjusted for age (continuous), height (continuous), and educational status (below high school; vocational school, junior college, or technical college; university or graduate school; others; 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; unknown), smoking status (never-smoker, ex-smoker, current smoker, unknown), weight (continuous), diabetes, hypercholesterolemia, drinking status (never drinker, ex-drinker, less than 23 g, at least 23 g, unknown) and estimated 24 h sodium excretion, and potassium excretion. Model 3 was adjusted for model 2 plus METs (quartile category) and WBC count (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, entering the number as a continuous term in the regression model. FVC, forced vital capacity; METs, metabolic equivalents; Q, quartile; WBC, white blood cell.
Several sensitivity analyses were conducted. First, we performed stratified analysis by smoking status. FEV1 and FVC were inversely associated with the prevalence of hypertension in both men and women even among never-smoker (e-Tables 1 and 2, https://links.lww.com/HJH/C118). These association was also confirmed among ex-smokers (e-Tables 3 and 4, https://links.lww.com/HJH/C118). In current smoker, an inverse association between lung function and hypertension was observed in men but not in women (e-Tables 5 and 6, https://links.lww.com/HJH/C118). Second, we conducted analysis stratified by age. In general, the highest quartile of FEV1 showed a lower OR for hypertension than the lowest quartile. In terms of P for trend, FEV1 was inversely and significantly associated with the prevalence of hypertension among middle-aged, elderly, and very old men (e-Tables 7–10, https://links.lww.com/HJH/C118). In women, FEV1 was inversely and significantly associated with age only in middle-aged participants. Similarly, in FVC, an inverse and significant association was observed among middle-aged, elderly, and very old men as well as in middle-aged women (e-Tables 11–14, https://links.lww.com/HJH/C118). Third, we excluded participants with respiratory diseases. Consequently, FEV1 and FVC were inversely associated with the prevalence of hypertension in men and women (e-Tables 15 and 16, https://links.lww.com/HJH/C118). Fourth, when we excluded participants’ treatment for hypertension, FEV1 was inversely associated with the prevalence of hypertension among men but not in women (e-Tables 17, https://links.lww.com/HJH/C118). For FVC, an inverse association was observed in both men and women (e-Tables 18, https://links.lww.com/HJH/C118).
DISCUSSION
The present study showed that higher lung function was associated with a lower prevalence of hypertension in the Japanese population, even after adjustment for several potential confounders. These inverse associations were also observed among never-smokers. Furthermore, reduced lung function was also associated with higher prevalence of home hypertension.
Several studies have examined the association between lung function and hypertension. A normative aging study reported a significantly inverse association between FVC and the incidence of hypertension [6]. The People's Republic of China-United States cardiopulmonary epidemiologic study showed an inverse association between FVC and FEV1 and hypertension [8]. In the Seattle Nikkei Health Study, poor lung function was significantly associated with the prevalence of hypertension in a Japanese American population [10]. The Coronary Artery Risk Development in Young Adults Study reported that a decline in FVC is inversely associated with the incidence of hypertension [12]. Our results showed that FEV1 and FVC were inversely associated with the prevalence of hypertension, which is consistent with the results of previous studies.
In the additional analyses of the association between lung function and home hypertension, we showed that reduced lung function was associated with higher prevalence of home hypertension. Previous studies have reported that home BP have strong prediction of the risk of cardiovascular disease when compared with casual BP [22–25]. This is because home BP might avoid regression dilution bias and the white-coat effect [22,23,34,35,44,45]. However, no study has investigated the association between lung function and home hypertension, our results extend previous findings of the association between lung function and hypertension.
Although this observational study could not clarify the potential mechanisms of our findings, we raised some possibilities of confounding and performed stratified analysis. First, smoking may have been an important confounding factor. Several previous studies have shown that lung function is related to cardiovascular disease even among nonsmokers [1–3]. However, only a few studies have examined the association between lung function and hypertension among never-smokers [7,8]. The Cardiovascular Health Study showed that lung function is inversely associated with hypertension while excluding participants with a history of current smoking and ex-smokers of at least 20 pack-years [7]. In the People's Republic of China-United States cardiopulmonary epidemiologic study, lung function was inversely related to BP even among never-smokers [8]. Similar to previous studies, this study showed an inverse association even among never-smokers. Thus, we considered that the inverse association between lung function and hypertension might be independent of the smoking status.
Second, age-related confounding is a possibility. Both BP and respiratory function were strongly associated with age [10,15,19,20]. Moreover, the age-related decrease in elastic recoil might influence decreased vital capacity [21]. Therefore, we conducted stratified analysis based on age. In general, participants in the highest quartile of lung function showed lower odds of hypertension among all age–sex subgroups. Even in the stratified subgroups, lung function was inversely and significantly associated with the prevalence of hypertension among middle-aged, elderly, and very old men. In women, lung function was inversely associated with the prevalence of hypertension in middle-aged participants. Third, weight may be a common factor underlying decreased lung function and increased BP [46,47]. In this study, even after adjusting for weight, an inverse association between lung function and hypertension was observed. Therefore, we considered that the inverse association between lung function and hypertension might be independent of the smoking status, age and weight. Fourth, physical activity may explain the association between lung function and the prevalence of hypertension. Many previous studies have shown that physical activity is associated with increased lung function and a decreased risk of hypertension [48,49]. However, even after adjusting for physical activity, lung function was inversely associated with the prevalence of hypertension. Inflammation is also considered to be a potential mechanism. Several previous studies have shown that reduced lung function is associated with increased levels of markers of inflammation [50–54]. Additionally, high levels of markers of inflammation, including circulating C-reactive protein (CRP), high-sensitivity CRP, interleukin 6, and WBC count were associated with an increased risk of hypertension [53,54]. Although we adjusted for WBC count, an inverse association between lung function and hypertension remained. To elucidate the contribution of inflammation, further studies are warranted using detailed inflammatory markers such as CRP and interleukin 6.
Other potentially harmful mechanisms might be considered. The lungs are a major organ that expresses high levels of angiotensin-converting enzyme (ACE) 1 and ACE2, which play important roles in the renin–angiotensin system [55,56]. Renin converts angiotensinogen to angiotensin I. ACE1 removes the carboxy-terminal dipeptide from the angiotensin I to produce the potent vasoconstrictor angiotensin II and degrades the vasodilator bradykinin [55,56,57]. Conversely, ACE2 converts angiotensin II to the vasodilators angiotensin 1–7 thereby reducing BP [56,58–60]. These might contribute the association between lung function and hypertension, though, as far as we know, there is no study to explain the clear mechanism of this association in terms of ACEs. Further studies are required to elucidate the potential mechanisms underlying respiratory function and hypertension.
This study has several strengths. First, this was a large population-based cohort study, which allowed stratification by confounding factors including smoking status and included various confounding factors such as sodium and potassium excretion. Second, to the best of our knowledge, this is the first study to show an association between lung function and hypertension based on casual BP and home BP. Furthermore, this is also the first study to show an association between lung function and hypertension in the Japanese population.
Our study has several limitations. First, lung function might have been measured with some errors because lung function is dependent on the effort of the participants. Second, although we restricted never-smokers to eliminate the effects of smoking, we could not completely eliminate the effects of smoking because never-smokers included participants who had smoked less than 100 cigarettes in their lifetime. Third, lung function and prevalence of hypertension vary with ethnicity [61,62]. As this study included only the Japanese population, it is difficult to apply our results to other races. Fourth, as we did not collect the fasting blood samples and information on detailed inflammatory marker such as interleukin 6, tumor necrosis factor-α, and C-reactive protein, further studies using fasting blood samples and detailed inflammatory marker may be preferable. Finally, we could not confirm causal relationships as this was a cross-sectional study. Prospective cohort studies are warranted to clarify the causal relationships.
In conclusion, this study showed that reduced lung function was significantly associated with an increased prevalence of hypertension in the Japanese population after adjusting for several potential confounding factors. This association was observed among never-smokers. Additionally, we found that reduced lung function was significantly associated with an increased prevalence of home hypertension. Therefore, participants with reduced lung function or hypertension, especially middle-aged, elderly, and very old men and middle-aged women, might be required to measure their lung function or BP.
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. We are grateful to everyone who participated in or worked for the cohort to make the studies possible. The complete list of members is available at https://www.megabank.tohoku.ac.jp/english/a220901/.
Funding information: 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).
Notation of prior abstract publication/presentation: there are no prior abstract publication or presentation.
Conflicts of interest
There are no conflicts of interest.
REFERENCES
1. Schroeder EB, Welch VL, Couper D, Nieto FJ, Liao D, Rosamond WD, Heiss G.
Lung function and incident coronary heart disease: the Atherosclerosis Risk in Communities Study.
Am J Epidemiol 2003; 158:1171–1181.
2. Sin DD, Wu L, Paul-Man SF. The relationship between reduced
lung function and cardiovascular mortality: a population-based study and a systematic review of the literature.
Chest 2005; 127:1952–1959.
3. Hozawa A, Billings JL, Shahar E, Ohira T, Rosamond WD, Folsom AR.
Lung function and ischemic stroke incidence: the Atherosclerosis Risk in Communities study.
Chest 2006; 130:1642–1649.
4. Wang B, Zhou Y, Xiao L, Guo Y, Ma J, Zhou M, et al. Association of
lung function with cardiovascular risk: a cohort study.
Respir Res 2018; 19:214.
5. WHO.
Hypertension. Available at:
https://www.paho.org/en/topics/hypertension. [Accessed 4 January 2022]
6. Sparrow D, Weiss ST, Vokonas PS, Cupples LA, Ekerdt DJ, Colton T. Forced vital capacity and the risk of
hypertension. The normative aging study.
Am J Epidemiol 1988; 127:734–741.
7. Enright PL, Kronmal RA, Smith VE, Gardin JM, Schenker MB, Manolio TA. Reduced vital capacity in elderly persons with
hypertension, coronary heart disease, or left ventricular hypertrophy. The cardiovascular health study.
Chest 1995; 107:28–35.
8. Wu Y, Vollmer WM, Buist AS, Tsai R, Cen R, Wu X, et al. Relationship between
lung function and
blood pressure in Chinese men and women of Beijing and Guangzhou. PRC-USA Cardiovascular and Cardiopulmonary
Epidemiology Research Group.
Int J Epidemiol 1998; 27:49–56.
9. Engstrom G, Wollmer P, Valind S, Hedblad B, Janzon L.
Blood pressure increase between 55 and 68 years of age is inversely related to
lung function: longitudinal results from the cohort study ‘Men born in 1914’.
J Hypertens 2001; 19:1203–1208.
10. Taneda K, Namekata T, Hughes D, Suzuki K, Knopp R, Ozasa K. Association of
lung function with atherosclerotic risk factors among Japanese Americans: Seattle Nikkei health study.
Clin Exp Pharmacol Physiol 2004; 31:S31–S34.
11. Schnabel E, Nowak D, Brasche S, Wichmann HE, Heinrich J. Association between
lung function,
hypertension and
blood pressure medication.
Respir Med 2011; 105:727–733.
12. Jacobs DR Jr, Yatsuya H, Hearst MO, Thyagarajan B, Kalhan R, Rosenberg S, et al. Rate of decline of forced vital capacity predicts future arterial
hypertension: The coronary artery risk development in young adults study.
Hypertension 2012; 59:219–225.
13. Karunanayake C, Rennie DC, Pahwa PM, Chen Y, Dosman JA. Relationship between
lung function and
hypertension among rural Canadians using fractional polynomials.
Sri Lankan J Appl Stat 2012; 12:41–61.
14. Shah CH, Reed RM, Liang Y, Zafari Z. Association between
lung function and future risks of diabetes, asthma, myocardial infarction,
hypertension and all-cause mortality.
ERJ Open Res 2021; 7:00178–2021.
15. Yohannes AM, Tampubolon G. Changes in
lung function in older people from the English longitudinal study of aging.
Expert Rev Respir Med 2014; 8:515–521.
16. Bowman TS, Gaziano JM, Buring JE, Sesso HD. A prospective study of cigarette smoking and risk of incident hypertensin in women.
J Am Coll Cardiol 2007; 50:2085–2092.
17. Halperin RO, Gaziano JH, Sesso HD. Smoking and the risk of incident
hypertension in middle-aged and older men.
Am J Hypertens 2008; 21:148–152.
18. Kaplan RC, Baldoni PL, Strizich GM, Pérez-Stable EJ, Saccone NL, Peralta CA, et al. Current smoking raises risk of incident
hypertension: Hispanic community health study-study of Latinos.
Am J Hypertens 2021; 34:190–197.
19. Skloot GS. The effects of aging on lung structure and function.
Clin Geriatr Med 2017; 33:447–457.
20. Buford TW.
Hypertension and aging.
Aging Res Rev 2016; 26:96–111.
21. Taylor BJ, Johnson BD. The pulmonary circulation and exercise responses in the elderly.
Semin Respir Crit Care Med 2010; 31:528–538.
22. Ohkubo T, Imai Y, Tsuji I, Nagai K, Kato J, Kikuchi N, et al. Home
blood pressure measurement has a stronger predictive power for mortality than dose screening
blood pressure measurement: a population-based observation in Ohasama, Japan.
J Hypertens 1998; 16:971–975.
23. Hozawa A, Ohkubo T, Nagai K, Kikuya M, Matsubara M, Tsuji I, et al. Prognosis of isolated systolic
blood pressure and isolated diastolic
hypertension as assessed by self-measurement of
blood pressure at home: the Ohasama study.
Arch Intern Med 2000; 160:3301–3306.
24. Niiranen T, Hanninen MR, Johansson J, Reunanen A, Jula AM. Home-measured
blood pressure is a stronger predictor of cardiovascular risk than office
blood pressure: The Finn-home study.
Hypertension 2010; 55:1346–1351.
25. Bliziotis IA, Destounis A, Stergious GS. Home versus ambulatory and office
blood pressure in predicting target organ damage in
hypertension: a systematic review and meta-analysis.
J Hypertens 2012; 30:1289–1299.
26. Hozawa A, Tanno K, Nakaya N, Nakamura T, Tsuchiya N, Hirata T, et al. Study profile of the Tohoku medical Megabank community-based cohort study.
J Epidemiol 2021; 31:65–76.
27. Kuriyama S, Yaegashi N, Nagami F, Arai T, Kawaguchi Y, Osumi N, et al. The Tohoku medical Megabank project: design and mission.
J Epidemiol 2016; 26:493–511.
28. Nakaya N, Xie T, Scheerder B, Tsuchiya N, Narita A, Nakamura T, et al. Spousal similarities in cardiovascular risk factors: a cross-sectional comparison between Dutch and Japanese data from two large biobank studies.
Atherosclerosis 2021; 334:85–92.
29. Hirata T, Kogure M, Tsuchiya N, Miyagawa K, Narita A, Nochioka K, et al. Impacts of the urinary sodium-to-potassium ratio, sleep efficiency, and conventional risk factors on home
hypertension in a general Japanese population.
Hypertens Res 2021; 44:858–865.
30. Takase M, Nakamura T, Tsuchiya N, Kogure M, Itabashi F, Narita A, et al. Association between the combined fat mass and fat-free mass index and
hypertension: the Tohoku Medical Megabank community-based cohort study.
Clin Exp Hypertens 2021; 43:610–621.
31. Takase M, Nakamura T, Hirata T, Tsuchiya N, Kogure M, Itabashi F, et al. Association between fat mass index, fat-free mass index, and hemoglobin A1c in a Japanese population: the Tohoku medical Megabank community-based cohort study.
J Diabetes Investig 2021; 13:858–867.
32. Yamada M, Motoike IN, Kojima K, Fuse N, Hozawa A, Kuriyama S, et al. Genetic loci for
lung function in Japanese adults with adjustment for exhaled nitric oxide levels as airway inflammation indicator.
Commun Biol 2021; 4:1288.
33. Nishimoto Y, Tsubono Y, Kogure M, Nakamura T, Itabashi F, Tsuchiya N, et al. The prevalence of current smokers and alcohol drinkers among cancer survivors and subjects with no history of cancer among participants in a community-based cardiometabolic screening program in Miyagi prefecture, Japan: a comparison with nationally representative surveys in other countries.
Cancer Med 2021; 10:9000–9011.
34. Kogure M, Hirata T, Nakaya N, Tsuchiya N, Nakamura T, Narita A, et al. Multiple measurements of the urinary sodium-to-potassium ratio strongly related home
hypertension: TMM Cohort Study.
Hypertens Res 2020; 43:62–71.
35. Hirata T, Nakamura T, Kogure M, Tsuchiya N, Narita A, Miyagawa K, et al. Reduced sleep efficiency, measured using an objective device, was related to an increased prevalence of home
hypertension in Japanese adults.
Hypertens Res 2020; 43:23–29.
36. Asayama K, Satoh M, Kikuya M. Diurnal
blood pressure changes.
Hypertens Res 2018; 41:669–678.
37. PhenX Toolkit, Research domain – alcohol, tobacco and other substances. Available at:
https://www.phenxtoolkit.org/domains/view/3000#tab5content. [Accessed 28 November 2021]
38. Inoue M, Nagata C, Tsuji I, Sugawara Y, Wakai K, Tamakoshi A, et al. Research Group for the Development and Evaluation of Cancer Prevention Strategies in Japan. Impact of alcohol intake on total mortality and mortality from major causes in Japan: a pooled analysis of six large-scale cohort studies.
J Epidemiol Community Health 2021; 66:448–456.
39. Kikuchi H, Inoue S, Odagiri Y, Ihira H, Inoue M, Sawada N, et al. Intensity-specific validity and reliability of the Japan public health center-based prospective study-physical activity questionnaire.
Prev Med Rep 2020; 20:101169.
40. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.
Clin Chem 1972; 18:499–502.
41. Jacobs D, Blackburn H, Higgins M, Reed D, Iso H, McMillan G, et al. Report of the conference on low blood cholesterol: Mortality associations.
Circulation 1992; 86:1040–1060.
42. Tanaka T, Okamura T, Miura K, Kadowaki T, Ueshima H, Nakagawa H, Hashimoto T. A simple method to estimate populational 24-h urinary sodium and potassium excretion using a casual urine specimen.
J Hum Hypertens 2002; 16:97–103.
43. Schnabel E, Karrasch S, Schulz H, Gläser S, Meisinger C, Heier M, et al. Cooperative Health Research in the Region of Augsburg (KORA) Study Group. High
blood pressure, antihypertensive medication and
lung function in a general adult population.
Respir Res 2011; 12:50.
44. James GD, Pickering TG, Yee LS, Harshfield GA, Riva J, Laragh JH. The reproducibility of average ambulatory, home, and clinic pressures.
Hypertension 1988; 11:545–549.
45. Sakuma M, Imai Y, Nagai K, Watanabe N, Sakuma H, Minami N, et al. Reproducibility of home
blood pressure measurements over a 1-year period.
Am J Hypertens 1997; 10:798–803.
46. Chen Y, Horne SL, Dosman JA. Body weight and weight gain related to pulmonary function decline in adults: a six year follow up study.
Thorax 1993; 48:375–380.
47. Neter JE, Stam BE, Kok FJ, Grobbee DE, Geleijnse JM. Influence of weight reduction on
blood pressure: a meta-analysis of randomized controlled trials.
Hypertension 2003; 42:878–884.
48. Luzak A, Karrasch S, Thorand B, Nowak D, Holle R, Peters A, et al. Association of physical activity with
lung function in lung-healthy German adults: results from the KORAFF4 study.
BMC Pulm Med 2017; 17:215.
49. Liu X, Zhang D, Liu Y, Sun X, Han C, Wang B, et al. Dose-response association between physical activity and incident
hypertension: a systematic review and meta-analysis of cohort studies.
Hypertension 2017; 69:813–820.
50. Jiang R, Burke GL, Enright PL, Newman AB, Margolis HG, Cushman M, et al. Inflammatory markers and longitudinal
lung function decline in the elderly.
Am J Epidemiol 2008; 168:602–610.
51. Barnes PJ, Celli BR. Systemic manifestations and comorbidities of COPD.
Eur Respir J 2009; 33:1165–1185.
52. Wu X, Wang C, Li H, Meng H, Jie J, Fu M, et al. Circulating white blood cells and
lung function impairment: the observational studies and Mendelian randomization analysis.
Ann Med 2021; 53:1118–1128.
53. Jayedi A, Rahimi K, Rahimi K, Bautista LE, Nazarzadeh M, Zargar MS, Shab-Bidar S. Inflammation markers and risk of developing
hypertension: a meta-analysis of cohort studies.
Heart 2019; 105:686–692.
54. Ishida S, Kondo S, Funakoshi S, Satoh A, Maeda T, Kawazoe M, et al. White blood cell count and incidence of
hypertension in the general Japanese population: ISSA-CKD study.
PLoS One 2021; 16:e0246304.
55. Tipnis SR, Hooper NM, Hyde R, et al. A human homolog of angiotensin-converting enzyme. Cloning and functional expression as a captopril-insensitive carboxypeptidase.
J Biol Chem 2000; 275:33238–33243.
56. Donoghue M, Hsieh F, Baronas E, Godbout K, Gosselin M, Stagliano N, et al. A novel angiotensin-converting enzyme–related carboxypeptidase (ACE2) converts angiotensin I to angiotensin 1-9.
Circ Res 2000; 87:E1–E9.
57. Riet LT, Esch JHM, Roks AJM, Meiracker AH, Danser AHJ.
Hypertension: renin-angiotensin-aldosterone system alterations.
Circ Res 2015; 116:960–975.
58. Keidar S, Kaplan M, Gamliel-Lazarovich A. ACE2 of the heart: From angiotensin I to angiotensin (1-7).
Cardiovasc Res 2007; 73:463–469.
59. Wang W, McKinnie SMK, Farhan M, Paul M, McDonald T, McLean B, et al. Angiotension-converting enzyme 2 metabolizes and partially inactivates Pyr-Apelin-13 and Apelin-17: physiological effects in the cardiovascular system.
Hypertension 2016; 68:365–377.
60. Chamsi-Pasha MAR, Shao Z, Tang WH. Angiotensin-converting enzyme 2 as a therapeutic target for heart failure.
Curr Heart Fail Rep 2014; 11:58–63.
61. Tipnis SR, Hooper NM, Hyde R, Karran E, Christie G, Turner AJ. Multiethnic reference values for spirometry for the 3-95-yr age range: the global
lung function 2012 equations.
Eur Respir J 2012; 40:1324–1343.
62. Tsugane S. Why has Japan become the world's most long-lived country: insights from a food and nutrition perspective.
Eur J Clin Nutr 2021; 75:921–928.
