Abstract
Background and aims:
Long-term blood pressure variability (BPV) is associated with cardiovascular events independent of mean blood pressure (BP); however, little is known about its predictors.
Methods:
Using data from the CARDIA study, we investigated the association between peak lung-function and long-term BPV in 2917 individuals (mean age 24.8 years, 45.3% males, 58.6% whites) who were not taking antihypertensive medications. Lung-function was measured using forced vital capacity (FVC) and forced expiratory volume in 1-s (FEV1) at years 0, 2, 5, 10 and 20 and the maximum score attained was considered as peak lung-function. Variability independent of the mean (VIM) and coefficient of variation (CV) of BP were calculated to quantify BPV since achieving peak lung-function across 9 visits over 30 years.
Results:
In a multivariate linear regression models, individuals in the 2nd (−0.64 mmHg; 95% CI: −1.06, −0.19), 3rd (−0.96; −1.47, −0.45), and 4th (−0.85: −1.53, −0.17) quartiles of FVC had lower VIM of systolic BP than the those in quartile 1 (p-trend = 0.005). CV of systolic BP was also lower by −0.58 (−0.98, −0.19), −0.92 (−1.42, −0.43), and −0.74 (−1.40, −0.08) percentage points, in the three progressively higher quartiles of FVC compared to quartile 1 (p-trend = 0.008). Similar findings were observed when the outcome was diastolic BPV. There was no association of FEV1 and FEV1-to-FVC ratio with BPV.
Conclusions:
These findings suggest that smaller lung volume or restrictive lung disease during young adulthood, which result in lower peak FVC, may independently increase the risk of higher long-term BPV during middle adulthood.
Keywords: Long-term blood pressure variability, Visit-to-visit blood, Pressure variability, Lung function
1. Introduction
Long-term blood pressure (BP) variability refers to fluctuations in BP that occur over weeks, months, and years [1]. Long-term BP variability has been recently identified as an independent risk factor for the development and progression of vascular events [1–4]. Higher long-term BP variability was shown to correlate with progression of subclinical outcomes such as carotid intima-media thickness [5], aortic and carotid stiffness [6], and microalbuminuria [7] independent of mean BP. In addition, independent of mean BP, long term variability in BP was associated with development of coronary heart disease, heart failure, cardiovascular mortality [8], stroke [9], chronic kidney disease [3], and cognitive function [4]. Although arterial stiffness [6], poor BP control [10], and noncompliance to medications [10] have been suggested as potential predictors of long-term BP variability, little is known about other factors that may predict long-term BP variability.
Lower lung function was associated independently with CVD and all-cause mortality [11,12] and these associations have been observed even among individuals without evident respiratory symptoms [13]. Lower lung function is believed to contribute to CVD events partly by increasing the risk of hypertension [14,15]. In addition to independently predicting mean BP and future incident hypertension [14,15], lower lung function may also be a risk factor for higher long-term BP variability. In fact, lower lung function was associated with a decrease in central vascular elasticity [16]. This may in turn play a role at increasing long-term BP variability because lower aortic distensibility was independently associated with higher long-term BP variability [6]. The objective of this study was to investigate the association of peak lung function with long-term variability in systolic and diastolic BPs.
2. Materials and methods
2.1. Study design and participants
The Coronary Artery Risk Development in Young Adults (CARDIA) study is a longitudinal population based study with an aim of investigating factors that influence the development of cardiovascular diseases (CVD) in young adults [17]. Participants were 5115 blacks and whites and men and women aged 18—30 years at baseline (Year 0, 1985 to 1986) and were recruited from 4 centers across the United States (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and Oakland, California). The study protocol was approved by the institutional review board at all 4 field centers and all participants consented to participate in the study. Participants were recruited by random-digit dialing from total communities or specific census tracts in Chicago, Birmingham, and Minneapolis [17]. In Oakland, participants were randomly selected from the Kaiser Permanente Medical Care Program (KPMCP) [17]. KPMCP is the largest not-for-profit, integrated health care delivery system in the United States [18]. Further information about the KPMCP can be found in McCarthy et al. [18] Fifty percent of the invited participants were enrolled and formed the CARDIA cohort. Eight follow-up examinations were conducted during 1987—1988 (Year 2), 1990—1991 (Year 5), 1992—1993 (Year 7), 1995—1996 (Year 10), 2000—2001 (Year 15), 2005—2006 (Year 20), 2010—2011 (Year 25), and 2015—2016 (Year 30). Details on design and objectives of CARDIA can be found in Friedman et al. [17].
Participants were eligible for the present analysis if they: (i) had at least 3 B P measurements since achieving peak lung function because ≥3 values are required to calculate indices of BP variability [19] and (ii) were not on antihypertensive medications at all the visits because BP medication can mask pathophysiologic variations in BP [10]. Participants who met eligibility and had complete data on adjusted covariates and peak lung function were 2833 for FVC and 2917 for FEV1 and were included in our analyses.
2.2. Peak lung function
Lung function was measured at year 0 (baseline) and follow-up years 2, 5, 10 and 20 usingforced vital capacity (FVC) - the amount of air that can be maximally and forcibly expelled from the lungs after a maximal inhalation and forced expiratory volume in 1-s (FEV1) - the volume of air that can be forcibly exhaled in 1 s after full inspiration [20]. Following the American Thoracic Society recommendations [21–24], lung function was measured with the participants standing using Collins Survey 8-L water sealed spirometer and an Eagle II Microprocessor (Warren E. Collins, Inc, Braintree, MA) at years 0, 2, 5, and 10 and using dry rolling seal OMI spirometer (Viasys Corp, Loma Linda, CA) at year 20. A comparability study between the Collins Survey and OMI spirometers was performed on 25 volunteers and there was consistency between the readings with an average difference of 6 mL for FVC and 21 mL for FEV1 [14,25]. To minimize methodological error, daily assessment for leaks, volume calibration with a 3-L syringe, and weekly calibration in the 4- to 7-L range were performed on the testing machines. The largest value of FEV1 and FVC were obtained from five technically satisfactory maneuvers, and the difference between the two largest reading of the FEV1 and FVC were within 150 mL in almost all cases. Peak FVC and FEV1 were defined as the maximum score of FVC and FEV1, respectively, attained during years 0, 2, 5, 10 and 20.
2.3. Long-term blood pressure variability
At each examination, brachial BP was measured on the right arm while in a seated position after participants were seated for 5 min in a quiet room. Trained and certified research staff took three measurements of BP, each separated by 1 min, and the average of the second and third readings was used in the analysis. Hawksley random zero sphygmomanometer (WA Baum Company, Copiague, NY) was used to measure BP from baseline to year 15 examination, and an automated oscillometric BP monitor (Omron HEM-907XL; Online Fitness, Santa Monica, CA) was used at examination years 20—30. A calibration study was performed, and year 20—30 B P values were standardized to the sphygmomanometric measures [14].
Two indices were used to quantify long-term systolic and diastolic BP variability since achieving peak lung function: within-individual coefficient of variation (CV - ratio of standard deviation to the mean) and variability independent of mean (VIM) of BP. VIM was calculated as the SD*(M/x)P where SD is standard deviation of within-individual BP measurements, M is average value of BP in the cohort, and x is within-individual mean BP since attaining peak lung function. p is the regression coefficient on the basis of regressing natural logarithm of SD on natural logarithm of the multiplication of M and x [26]. VIM was shown to correlate highly with other indices of BP variability [27] while its correlation with mean BP level is almost zero [9,28]. In our study, the correlation of VIM with CV was 0.97 for systolic and 0.99 for diastole BP while its correlation with mean was 0.01 for both systolic and diastolic BPs. VIM allows assessing the association of risk factor with BP variability while removing the confounding effect of mean BP level.
Demographic, anthropometric, behavioral factors, laboratory data, and history of chronic diseases for the present study were obtained from baseline and were measured following standard procedures. See Supplemental Data 1 for detailed description of the measurement of adjusted covariates.
2.4. Statistical analysis
The association between peak lung function and long-term BP variability was evaluated using linear regression model while adjusting for potential confounders. We drew a priori directed acyclic graph [29] and applied Pearl’s back-door criterion [30] using DAGitty [31] to identify potential confounders (see Supplemental Data 2). Age, sex, race, education, pack-years of cigarette smoking, physical activity, alcohol intake, height, BMI, hypercholesterolemia, blood glucose, GFR, and asthma were identified as potential confounders and adjusted. In addition, we adjusted for study site. Separate regressions were used for the different indicators of peak lung function (FVC and FEV1) and BP variability (VIM and CV of systolic and diastolic BPs). Quartiles of peak lung function were regressed on BP variability indicators using the lowest quartile as a reference group. Test of trend across the quartiles was assessed by including an indicator of quartiles as a continuous ordinal variable. Interaction of sex, race, and pack-years with lung function indicators was assessed. In all the analyses, statistical significance was set at p < 0.05. Plots of the residuals against the fitted values were checked for all the models to assess assumptions of linearity and homoscedasticity and to check outlier observations in the data [32]. All analyses were performed using Stata 13 (StataCorp. 2013, TX: StataCorp LP) [33].
3. Results
3.1. Participant characteristics
The participants’ age at baseline ranged from 18 to 30 years (mean age: 24.8 years), 45.3% were male, and 58.6% were White (Table 1). Compared with individuals in the lowest quartile, those in the three progressively higher quartiles of FVC were more likely to be male, white, taller, physically active, drinkers, and to have higher systolic and diastolic BPs, fasting glucose, and lower total cholesterol, HDL cholesterol and GFR (Table 1). Similarly, participants in the 2nd, 3rd, and 4th quartiles of FEV1 were more likely to be male, white, taller, physically active, more alcohol drinkers, and had higher systolic and diastolic BPs, and fasting glucose, and lower total cholesterol, HDL cholesterol, and GFR than those in the 1st quartile (Supplemental Table 1).
Table 1.
Participant baseline characteristics by quartiles of peak forced volume capacity.
| Characteristics | Quartiles of peak FVC (Liters) |
|||||
|---|---|---|---|---|---|---|
| All participants (N = 2833) |
Quartile 1 (1.77–3.43) |
Quartile 2 (3.44–3.83) |
Quartile 3 (3.84–4.27) |
Quartile 4 (4.28–6.25) |
p-trend | |
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | ||
| Age (years) | 24.8 (3.6) | 24.4 (3.9) | 24.9 (3.6) | 24.8 (3.7) | 24.9 (3.5) | 0.02 |
| Male (%) | 45.3 | 5.5 | 18.9 | 61.6 | 95.9 | <0.001 |
| White (%) | 58.6 | 34.6 | 60.7 | 58.4 | 80.78 | <0.001 |
| Height (cm) | 170.6 (9.4) | 161.6 (6.2) | 167 (6.1) | 173.3 (6.3) | 180.5 (6.2) | <0.001 |
| Physical activity intensitya | 440.7 (300.9) | 315.2 (248) | 412.9 (273) | 492.1 (311.9) | 544.2 (315.6) | <0.001 |
| Alcohol (# of drinks/wk) | 12.0 (22.1) | 6.7 (13.1) | 8.8 (18.4) | 15.5 (29.4) | 17.2 (22.4) | <0.001 |
| Life time pack-years (years) | 2.1 (4.3) | 2.1 (4.3) | 1.7 (3.6) | 2.3 (4.7) | 2.2 (4.6) | 0.78 |
| Body mass index (kg/m2) | 23.6 (4.2) | 23.8 (5.4) | 23.1 (4.2) | 23.5 (3.5) | 24.1 (3.4) | <0.001 |
| Systolic BP (mmHg) | 108.4 (10.2) | 104.7 (8.8) | 105.8 (9.7) | 109.4 (10.4) | 113.5 (9.6) | <0.001 |
| Diastolic BP (mmHg) | 67.1 (8.8) | 65.2 (8) | 66 (8.6) | 67.3 (9.3) | 69.8 (8.7) | <0.001 |
| Total cholesterol (mg/dL) | 175.4 (32.2) | 177.6 (33.3) | 174.5 (30.5) | 175.7 (32.4) | 174 (32.6) | 0.08 |
| HDL (mg/dL) | 53.9 (13) | 55.9 (13.1) | 55.9 (12.7) | 53.9 (13.3) | 49.7 (11.8) | <0.001 |
| Fasting glucose (mg/100 mL) | 81.8 (11.8) | 79.9 (16.2) | 80.7 (8.1) | 82.4 (12.6) | 84.2 (7.7) | <0.001 |
| GFR (ml/min/1.73 m2) | 122.3 (15.6) | 126.1 (17) | 121 (16.1) | 121.9 (15.9) | 120.3 (12.4) | <0.001 |
| Asthma (%) | 9.9 | 9.4 | 9.5 | 10.7 | 9.9 | 0.60 |
BP, blood pressure; FVC, forced vital capacity; GFR, glomerular filtration rate; HDL, high density lipoprotein; SD, standard deviation.
Physical activity was measured using self-reported frequency of participation in 13 activities during the previous year. Total physical activity score was calculated by multiplying an intensity score to each activity and summing the resulting value. Number of participants in each quartile is Q1 (n = 711), Q2 (n = 713), Q3 (n = 704), Q4 (n = 705).
Mean of peak FVC was 4.51 L(L) (95% confidence interval- CI: 4.47, 4.55) and mean FEV1 was 3.70 L (95% CI: 3.66, 3.72). Proportion of participants who achieved peak FVC at year 0 was 29.1%, 17.4% at year 2, 28.7% at year 5, 21.7% at year 10, and 3.0% at year 20. The proportion of those who achieved peak FEV1 were 43.6%, 21.6%, 26.9%, 7.4%, and 0.5% during the 5 progressive years where lung function was measured. Mean CV of systolic BP was 7.31% (95% CI: 7.18, 7.45), mean CV of diastolic BP was 9.45% (95% CI: 9.29, 9.61), mean VIM of systolic BP was 8.01 mmHg (95% CI: 7.87, 8.15) and mean VIM of diastolic BP was 6.54 mmHg (95% CI: 6.43, 6.66). Mean CV and VIM of systolic and diastolic BPs by the quartiles of FVC and FEV1 are shown in Table 2.
Table 2.
Mean and standard deviation of long-term blood pressure variability indices by quartiles of peak lung function indicators.
| Exposures | CV systolic BP |
VIM systolic BP |
CV diastolic BP |
VIM diastolic BP |
|---|---|---|---|---|
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | |
| Peak FVC | ||||
| Quartile 1 (n = 711) | 8.6 (4.2) | 9.5 (4.3) | 10.5 (4.8) | 7.2 (3.3) |
| Quartile 2 (n = 713) | 7.5 (3.6) | 8.3 (3.8) | 9.4 (4.2) | 6.5 (2.9) |
| Quartile 3 (n = 704) | 6.8 (3.2) | 7.4 (3.3) | 9.3 (4.5) | 6.4 (3.1) |
| Quartile 4 (n = 705) | 6.4 (3.1) | 6.8 (3.1) | 8.6 (3.8) | 6.0 (2.6) |
| All participants (N = 2833) | 7.3 (3.6) | 8.0 (3.8) | 9.5 (4.4) | 6.5 (3.0) |
| Peak FEV1 | ||||
| Quartile 1 (n = 742) | 8.5 (4.3) | 9.4 (4.3) | 10.3 (4.9) | 7.1 (3.4) |
| Quartile 2 (n = 723) | 7.1 (3.4) | 8 .0 (3.6) | 9.3 (4.4) | 6.4 (3) |
| Quartile 3 (n = 732) | 7.2 (3.6) | 7.8 (3.8) | 9.5 (4.3) | 6.6 (3.0) |
| Quartile 4 (n = 720) | 6.3 (3) | 6.6 (3.1) | 8.6 (3.8) | 6.0 (2.6) |
| All participants (N = 2917) | 7.3 (3.6) | 8.0 (3.9) | 9.4 (4.4) | 6.5 (3.1) |
BP, blood pressure; CV, coefficient of variation; FVC, forced vital capacity; FEV1, forced expiratory volume in the first 1 s; VIM, variability independent of the mean; SD, standard deviation.
3.2. Peak lung function and long-term blood pressure variability
CV of systolic BP was lower by −0.58 (95% CI: −0.98, −0.19), −0.92 (95% CI: −1.42, −0.43), and −0.74 (95% CI: −1.40, −0.08) percentage points among individuals in the 2nd, 3rd, and 4th quartiles respectively, when compared to those in the 1st quartile after adjusting for baseline age, sex, race, and education, study site, pack-years, alcohol intake, physical activity, height, BMI, fasting glucose, total cholesterol, HDL, GFR, and asthma (Table 3). The mean difference in CV of systolic BP decreased in a linear fashion across the quartiles of FVC (linear trend p = 0.008). Individuals in the three progressively higher quartiles of FVC also had −0.64 (95% CI: −1.06, −0.24), −0.96 (95% CI: −1.47, −0.45), and −0.85 (95% CI: −1.53, −0.17) mmHg, respectively, lower VIM of systolic BP than those in the 1st quartile (linear trend p = 0.005). Similar results were found when the outcome was long-term variability in diastolic BP. Participants in the progressively higher quartile of FVC had lower CV (linear trend p = 0.03) and lower VIM (linear trend p = 0.03) of diastolic BP compared to those in quartile 1.
Table 3.
Association between forced vital capacity and indices of long-term variability in systolic and diastolic blood pressure.
| Exposures (N = 2833) | CV systolic BP |
VIM systolic BP |
CV diastolic BP |
VIM diastolic BP |
|---|---|---|---|---|
| Coef. (95% CI) | Coef. (95% CI) | Coef. (95% CI) | Coef. (95% CI) | |
| FVC Quartile 1 (1.77–3.72) (ref) | ||||
| FVC Quartile 2 (3.73–4.38) | −0.58 (−0.98, −0.19)‡ | −0.64 (−1.06, −0.24)‡ | −0.56 (−1.05, −0.07)† | −0.40 (−0.74, −0.06)† |
| FVC Quartile 3 (4.39–5.21) | −0.92 (−1.42, −0.43)§ | −0.96 (−1.47, −0.45)§ | −0.70 (−1.31, −0.09)† | −0.51 (−0.93, −0.09)† |
| FVC Quartile 4 (5.22–8.11) | −0.74 (−1.40, −0.08)† | −0.85 (−1.53, −0.17)† | −0.86 (−1.68, −0.05)† | −0.62 (−1.18, −0.06)† |
| Trend p | 0.008 | 0.005 | 0.03 | 0.03 |
p-value:
<0.05
<0.01
<0.001.
BP, blood pressure; CI, confidence interval; Coef., regression coefficient; CV, coefficient of variation; FEV1, forced expiratory volume in first 1 s; FVC, forced vital capacity; VIM, variability independent of the mean. Models are adjusted for age, sex, race, education, study center, pack-years, alcohol intake, physical activity, height, body mass index, fasting glucose, total cholesterol, high density lipoprotein, glomerular filtration rate, and asthma at baseline. Number of participants in each quartile is Q1 (n = 711), Q2 (n = 713), Q3 (n = 704), Q4 (n = 705).
There was no evidence of linear trend in the reduction of CV of systolic BP (linear trend p = 0.11), VIM of systolic BP (linear trend p = 0.14), CV of diastolic BP (linear trend p = 0.22), and VIM of diastolic BP (linear trend p = 0.16) across the quartiles of FEV1 (Table 4). CV and VIM of systolic and diastolic BPs were not significantly lower among individuals in the higher quartiles of FEV1 as compared to those in quartile 1 after adjusting for potential confounders and there.
Table 4.
Association between forced expiratory volume in 1 s and indices of long-term variability in systolic and diastolic blood pressure.
| Exposures (N = 2917) | CV systolic BP |
VIM systolic BP |
CV diastolic BP |
VIM diastolic BP |
|---|---|---|---|---|
| Coef. (95% CI) | Coef. (95% CI) | Coef. (95% CI) | Coef. (95% CI) | |
| FEV1 Quartile 1 (1.24–3.11) (ref) | ||||
| FEV1 Quartile 2 (3.12–3.59) | −0.62 (−0.99, −0.25)‡ | −0.65 (−1.03, −0.26)‡ | −0.44 (−0.91, 0.03) | −0.34 (−0.66, 0.0) |
| FEV1 Quartile 3 (3.60–4.23) | −0.38 (−0.82, 0.06) | −0.38 (−0.83, 0.08) | −0.17 (−0.74, 0.39) | −0.16 (−0.55, 0.22) |
| FEV1 Quartile 4 (4.24–7.53) | −0.50 (−1.08, 0.08) | −0.53 (−1.13, 0.07) | −0.61 (−1.36, 0.13) | −0.46 (−0.97, 0.05) |
| Trend p | 0.14 | 0.14 | 0.22 | 0.16 |
p value:
<0.05
<0.01
<0.001.
BP, blood pressure; CI, confidence interval; Coef., regression coefficient; CV, coefficient of variation; FEV1, forced expiratory volume in first 1 s; FVC, forced vital capacity; VIM, variability independent of the mean. Models are adjusted for age, sex, race, education, study center, pack-years, alcohol intake, physical activity, height, body mass index, fasting glucose, total cholesterol, high density lipoprotein, glomerular filtration rate, and asthma at baseline. Number of participants in each quartile are Q1 (n = 742), Q2 (n = 723), Q3 (n = 732), Q4 (n = 720).
Among individuals who achieved peak FVC and FEV1 at the same visit (N = 1549), there was no association between ratio of peak FEV1-to-FVC and BP variability indicators and no linear pattern in the reduction of BP variability across the quartiles of peak FEV1-to-FVC (linear trend p ≥ 0.53 in all, Table 5). Likewise, abnormal predicted FVC and FEV1 (defined as less than the 5th or greater than the 95th percentiles of predicted FVC and FEV1) [34] were not associated with indictors of long-term variability in systolic and diastole BP (Table 5). In the fully adjusted model, there was no significant interaction of race, pack-years (less than or greater and equal to 10 pack-years), and sex with quartiles of lung function indicators (FVC, FEV1, FEV1-to-FVC, abnormal predicted FVC and abnormal predicted FVC).
Table 5.
Multivariate regression for the association between indicators of lung function and long-term variability in blood pressure.
| Exposures (N = 1549) | CV systolic BP |
VIM systolic BP |
CV diastolic BP |
VIM diastolic BP |
|---|---|---|---|---|
| Coef. (95% CI) | Coef. (95% CI) | Coef. (95% CI) | Coef. (95% CI) | |
| FEV1-FVC 1st Quartile (0.52–0.81) (ref) | ||||
| FEV1-FVC 2nd Quartile (0.81–0.85) | 0.04 (−0.44, 0.52) | 0.05 (−0.46, 0.56) | −0.08 (−0.68, 0.53) | −0.06 (−0.48, 0.35) |
| FEV1-FVC 3rd Quartile (0.85–0.89) | −0.46 (−0.95, 0.04) | −0.46 (−0.98, 0.06) | −0.24 (−0.86, 0.38) | −0.16 (−0.58, 0.27) |
| FEV1-FVC 4th Quartile (0.89–1.00) | 0.23 (−0.27, 0.74) | 0.35 (−0.18, 0.88) | 0.07 (−0.57, 0.7) | 0.04 (−0.40, 0.47) |
| Trend p | 0.80 | 0.53 | 0.97 | 0.99 |
| Abnormal predicted FVCa | 0.17 (−0.27, 0.60) | 0.17 (−0.27, 0.62) | 0.51 (−0.02, 1.03) | 0.35 (−0.01, 0.71) |
| Abnormal predicted FEV1a | 0.13 (−0.28, 0.53) | 0.15 (−0.28, 0.57) | −0.04 (−0.56, 0.48) | 0.15 (−0.27, 0.57) |
p-value:
<0.05
<0.01
<0.001.
BP, blood pressure; CV, coefficient of variation; CI, confidence interval; Coef., regression coefficient; FEV1, forced expiratory volume in first 1 s; FVC, forced vital capacity; VIM, variability independent of the mean. Models adjusted for age, sex, race, education, study center, pack-years, alcohol intake, physical activity, height, body mass index, fasting glucose, total cholesterol, high density lipoprotein, glomerular filtration rate, and asthma at baseline.
Abnormal predicted FVC and FEV1 were defined as less than the fifth or greater than the 95th percentiles of predicted FVC and FEV1, respectively. Number of participants in each quartile is Q1 (n = 388), Q2 (n = 388), Q3 (n = 386), Q4 (n = 387).
4. Discussion
In this population based prospective cohort study of young adults, using different indicators of peak lung function (FVC, FEV1, FEV1-to-FVC) and indictors of long-term BP variability (CV and VIM), we found that higher peak FVC, but not FEV1 and FEV1-to-FVC, during young adulthood was associated with lower long-term systolic and diastolic BP variability during middle adulthood. These findings suggest that smaller lung volume or restrictive lung disease during young adulthood, which result in lower peak FVC, but obstructive lung disease which results in lower peak FEV1 and FEV1-to-FVC ratio, may increase long-term variability in systolic and diastolic BPs during middle adulthood.
To our knowledge, there is no study that investigated the association between peak lung-function and long-term BP variability. However, lower lung function has been consistently shown to be associated with higher future mean BP [15,35] and incident hypertension [14,36]. In a population-based study of risk factors for cardiovascular and pulmonary diseases in Malmo, Sweden, the adjusted mean increase in systolic BP over 13 years was 20.4, 18.7, 16.5 and 11.1 mmHg (trend p = 0.005) among non-hypertensive men at baseline who were in the 1st, 2nd, 3rd, 4th quartiles of baseline FVC, respectively [35]. In the CARDIA study, greater reduction in FVC over 10-years was also associated with higher risk of future incident hypertension by two fold among 3205 individuals aged 18—30 at baseline [14].
In contrast to these results on long-term BP variability, studies on the association between lung-function and short term BP variability (i.e. fluctuation in BP within 24 h) have had mixed findings [37,38]. In the Jackson Heart Study, the association between baseline lung-function measured using FVC, FEV1, and ratio of FEV1-to-FVC and short term BP variability (standard deviation and average real variability of 24 h systolic and diastolic BPs) was assessed in 1008 middle-aged African Americans [37]. No significant differences in standard deviation and average real variability of both systolic and diastolic BPs were found across the quartiles of FVC, FEV1, and ratio of FEV1-to-FVC [37]. However, among 94 individuals, of whom 60 had chronic obstructive pulmonary disease, there was a significant and inverse association between FEV1 and beat-to-beat systolic BP in 5 min after adjustment for other covariates [38].
The underlying pathophysiological mechanisms linking peak FVC to long-term BP variability as well as higher mean BP level are not clear. Lower lung function was independently associated with reduction in central vascular elasticity [16] and this may play a role at increasing long-term BP variability. In fact, in the Multi-Ethnic Study of Atherosclerosis, baseline aortic distensibility was associated with long-term systolic BP variability after adjusting for several confounders [6]. Additional plausible explanations for the association between FVC and long-term BP variability may simply be a residual confounding effect of central obesity (trunk obesity). Although the association attenuated significantly after adjusting for BMI in our analysis, BMI (a marker of overall obesity) may not appropriately adjust for trunk obesity. In support of this idea, a weak and insignificant association of FVC and BP was observed among Chinese residents of China who tend to have lower prevalence of obesity than Caucasians [39] while the association was strong among predominantly Caucasian participants in the United Kingdom and Australia [40,41].
Impaired lung function and hypoxia increase the cardiac oxygen demand-to-supply ratio and this could result in higher cardiac output and possibly higher BP variability [42,43]. However, impairment in lung function and resulting hypoxia is unlikely to explain the relationship in our study because our participants were healthy individuals aged 18—30 at baseline and most (92%) of the participants had predicted FVC and FEV1 within the normal limit [44].
Although an association between lower lung function and higher risk of CVD has been shown in several studies [45,46], the underlying mechanism of this association remains unclear. Our results suggest that lower lung function may contribute to an increase in the risk of vascular events partially through increasing long-term BP variability because long-term BP variability was shown to increase the risk of vascular events independent of mean BP [2–4]. The percent of mediation of long-term BP variability in the association between lung-function and CVD outcomes should be investigated in future studies.
Major strength of our study is that it is the first, to our knowledge, to show an association between lower peak lung-function and higher long-term BP variability independent of mean BP and other confounders. Reliability of long-term BP variability was shown to diminish when BP variability is calculated from <5 BP readings [11,47]. In this study, 97% of the participants had between 5 and 9 B P readings and this may have helped at increasing the reliability of BP variability, although a higher number of BP readings would have been preferable. However, there are some limitations in this study. Although retention rate was high in CARDIA study, the proportion of loss to follow-up was higher among blacks and smokers who are known to have lower lung-function [48] and higher long-term BP variability [49]. This may have underestimated our estimates toward the null. There may be some participants who had not achieved peak lung function by year 20 and these are likely to be younger and to have lower BP variability [50]. However, the impact of this on our result is likely negligible because only 3% of our participants had the highest FVC and 0.5% had the highest FEV1 at year 20.
In conclusion, we found that young adults with higher peak FVC showed lower long-term variation in BP over the next 20—30 years independent of mean BP and other confounders. Our findings suggest that smaller lung volume or restrictive lung disease, which result in lower peak FVC, may increase long-term variability in systolic and diastolic BPs. But obstructive lung disease, which results in lower peak FEV1 and ratio of peak FEV1-to-FVC, may not influence future variation in BP. Based on these findings, we suggest that lower FVC may qualify as a new risk factor for greater longterm BP variability, a finding that deserves further investigation.
Supplementary Material
Acknowledgments
Financial support
The Coronary Artery Risk Development in Young Adults (CARDIA) Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the University of Alabama at Birmingham (HHSN268201300025C and HHSN2 68201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging (NIA) and intra-agency agreement AG0005 between the NIA and NHLBI. Y.G. Tedla was supported by a T32 HL 069771 Ruth L. Kirschstein National Research Service Award from the National Heart, Lung, and Blood Institute to the Northwestern University, Department of Preventive Medicine. Y. Yano was supported by the American Heart Association Strategically Focused Research Network Fellow Grant to the Northwestern University, Department of Preventive Medicine.
The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and the decision to submit the manuscript for publication. The manuscript was reviewed by CARDIA staff for scientific content.
Abbreviations:
- BP
blood pressure
- CV
coefficient of variation
- FEV1
forced expiratory volume in the first 1 s
- FVC
forced vital capacity
- GFR
glomerular filtration rate
- HDL
high density lipoprotein
- VIM
variability independent of the mean
Footnotes
Conflicts of interest
The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript.
Publisher's Disclaimer: Disclaimer
The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the NHLBI, the National Institutes of Health, or the US Department of Health and Human Services.
Appendix A. Supplementary data
Supplementary data related to this article can be found at https://doi.org/10.1016/j.atherosclerosis.2018.06.816.
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