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