Abstract
Purpose
Visit-to-visit blood pressure variability is a strong predictor of the incidence of cardiovascular events and target organ damage due to hypertension. The present study investigated whether year-to-year blood pressure variability predicts the risk of hypertension in the Japanese general population.
Materials and methods
This study analysed 2806 normotensive individuals who participated in our physical check-up program for five years in a row from 2008 to 2013. The average, standard deviation, coefficient of variation, average real variability, and highest value of systolic blood pressure in the five consecutive visits were determined and used as baseline data. The participants were followed up for the next 6 years with the development of ‘high blood pressure’, an average blood pressure level of ≥140/90 mmHg or the use of antihypertensive medications, as the endpoint.
Result
During follow-up, ‘high blood pressure’ developed in 389 participants (13.9%, 29.5 per 1 000 person-years). The incidence increased across the quartiles of standard deviation and average real variability, while the average and highest systolic blood pressure had the most prominent impact on the development of ‘high blood pressure’. Multivariate logistic regression analysis adjusted for possible risk factors indicated that the average, standard deviation, average real variability, and highest blood pressure, but not the coefficient of variation of systolic blood pressure, were significant predictors of ‘high blood pressure’.
Conclusion
Increased year-to-year blood pressure variability predicts the risk of hypertension in the general normotensive population. The highest blood pressure in the preceding years may also be a strong predictor of the risk of hypertension.
PLAIN LANGUAGE SUMMARY
What is the context
A relatively high blood pressure level recorded by chance is not usually examined further, especially in cases where the blood pressure values recorded in different opportunities were within normal levels.
However, high blood pressure observed by chance may be a result of increased blood pressure variability.
Increased blood pressure variability predicts incident hypertension in patients with diabetes, but clinical significance of increased blood pressure variability in the general population with normal blood pressure has not been studied.
What is new
The impact of blood pressure variability on the development of hypertension in the normotensive general population was investigated.
The present study demonstrated that increased blood pressure variability was the significant predictor of the development of hypertension in the general population.
What is the impact
Increased year-to-year blood pressure variability as well as the highest blood pressure observed by chance in the preceding years is a strong predictor of the development of hypertension in the general normotensive population.
Introduction
Hypertension is one of the most important risk factors for cardiovascular diseases, such as coronary heart disease, heart failure, and stroke, which seriously affect people’s healthy life expectancy and lifespan [Citation1]. The risk of cardiovascular events in individuals undergoing medical treatment is still higher, even if their blood pressure is appropriately controlled within normal levels than that in individuals without medication with similar levels of blood pressure [Citation2]. This finding suggests a residual risk in individuals who achieve the target blood pressure.
Blood pressure variability (BPV) is one of the candidate factors accounting for this residual risk. Although fluctuations in blood pressure between visits are frequently observed in clinical settings, an exaggerated visit-to-visit variability in blood pressure in patients with treated hypertension is a strong predictor of cerebrovascular events [Citation3]. Recently, many studies have confirmed that cardiovascular events and target organ damage are associated with the degree of BPV independently of blood pressure levels in patients with treated hypertension [Citation3–10], although some investigators did not observe such an association [Citation11–13]. Visit-to-visit BPV is associated with arterial stiffness [Citation14–17]; thus, chronic hypertension may increase BPV owing to the progression of arteriosclerosis. However, an increase in BPV causes target organ damage [Citation5, Citation6] and, thereby, may promote the progression of hypertension. Indeed, reduced glomerular filtration rate (eGFR), albuminuria, and increased arterial stiffness are risk factors for incident hypertension in normotensive individuals [Citation18–20]. A recent report demonstrated that increased BPV predicted hypertension in normotensive patients with type 2 diabetes [Citation21], however, clinical significance of augmented BPV in general population with normal blood pressure is currently unknown.
Most healthy individuals do not have sufficient opportunities to measure their blood pressure, but those undergoing regular physical check-ups (e.g. annual physical check-ups for employees) are able to measure their blood pressure every year. Year-to-year BPV is not usually assessed when their blood pressure levels are normal. Alternatively, a relatively high blood pressure level recorded by chance, which may result from increased BPV, is not usually examined further, especially in cases where the blood pressure values recorded in different opportunities were within normal levels. However, investigating the significance of year-to-year variability or the highest blood pressure level during a certain period may provide important information for the general population. The present study aimed to investigate whether year-to-year BPV predicts risk of hypertension in the Japanese general population with normal blood pressure.
Materials and methods
Study design
The participants of our annual physical check-up program were included in the present cohort study. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Enshu Hospital (approval number; #2017-09-02). All participants provided written informed consent prior to the start of the study and at each study visit.
Study participants and procedures
Our physical check-up program included routine physical examination, chest radiography, electrocardiography, laboratory assessment of the cardiovascular risk factors, and a questionnaire-based survey about their health condition. Blood pressure was measured according to guidelines for the management of hypertension [Citation1]. Trained technicians measured the participants’ blood pressure levels using an automated device (BP-203RV III C, Omron Colin, Tokyo, Japan) in the morning in a seated position after an overnight fast. Three consecutive blood pressure measurements were taken at 2-min intervals, and the mean of the second and third measurements was recorded as the blood pressure.
A total of 6 499 consecutive individuals who participated in our annual physical check-up program for five years in a row during 2008 and 2013 (2008–2012 or 2009–2013) were screened for eligibility in the present study (screening period). First, participants who were prescribed antihypertensive medication at least once during the screening period (2008–2012 or 2009–2013) were excluded (n = 1 633). The average systolic and diastolic blood pressure levels were calculated using the blood pressure values recorded at five consecutive visits during the screening period, and participants with an average systolic or diastolic blood pressure of 140 or 90 mmHg, respectively, or more were further excluded (n = 243). The remaining 4 623 participants were enrolled in the study (). The average, standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and highest value of systolic blood pressure during the screening period (five visits yearly in 2008–2012 or 2009–2013) were used as indices of blood pressure at baseline. The ARV was calculated as the average of the absolute difference from the preceding blood pressure value in multiple consecutive measurements. Other baseline data were obtained at the last visit during the screening period (2012 or 2013).
Figure 1. A diagram showing the follow-up status of participants. A total of 6 499 individuals who participated in an annual physical check-up program for five years in a row during 2008 and 2013 (2008–2012 or 2009–2013) were screened (screening period). Participants who received antihypertensive medication or had an average blood pressure level of ≥140/90 mmHg were excluded. The remaining 4 623 participants were enrolled and followed up for the next 6 years. Data of 2 806 participants who underwent a physical check-up at least four times during the follow-up period were analysed.
The enrolled participants were followed up for the next 6 years (follow-up period), and the possible relationships between the indices of BPV at baseline and the development of ‘high blood pressure’ during the follow-up period were investigated. The data of 2 806 participants who underwent a physical check-up at least four times during the follow-up period were analysed (). Development of ‘high blood pressure’ was defined as an average systolic blood pressure level of ≥140 mmHg during follow-up, a diastolic blood pressure level of ≥90 mmHg during follow-up, or the use of antihypertensive medications. In another series of analyses, the impact of baseline BPV on the use of antihypertensive medications during the follow-up period was assessed using logistic regression analysis.
Diabetes mellitus was defined as a fasting plasma glucose level of ≥126 mg/dL, a haemoglobin A1c (HbA1c) level of ≥6.5% [Citation22], or the use of antidiabetic medications. Dyslipidemia was defined as a low-density lipoprotein cholesterol level of ≥140 mg/dL, a high-density lipoprotein cholesterol level of <40 mg/dL, a triglyceride level of ≥150 mg/dL [Citation23], or the use of antidyslipidemic medications. The eGFR was calculated using the modified Modification of Diet in Renal Disease study formula for the Japanese population [Citation24]. Chronic kidney disease (CKD) was defined as an eGFR of <60 mL/min per 1.73 m2. Urinary protein was determined using the dipstick method (Arkray, Kyoto, Japan), and the results were interpreted by trained technicians and recorded as –, ±, 1+, 2+, and 3+. Proteinuria was defined as the presence of 1+, 2+, or 3+ ions. The participants reported alcohol consumption ranging from 0 to 7 times/week, with frequent alcohol consumption defined as drinking 6 or 7 times/week.
Data analysis
All analyses were performed using IBM SPSS Statistics version 24 (IBM SPSS, Chicago, Illinois, USA). Data were expressed as mean ± SD or as the number and percentage of participants. The significance of any difference between two means with a normal distribution was determined using unpaired t-tests. Chi-square tests were used to compare the categorical data. The relationship between the blood pressure indices as continuous variables and the onset of hypertension was investigated using multivariate logistic regression analysis models. The odds ratios (ORs) and 95% confidence intervals (CIs) were also calculated. Receiver operating characteristics (ROC) curve analysis was performed to determine the cut-off level, area under the cure (AUC), sensitivity, and specificity. In all cases, two-tailed tests were used, and a p-value of <0.05 was considered significant.
Result
The baseline characteristics of the participants at the end of the screening period (2012 or 2013) are listed in . The blood pressure values and other characteristics of the final analysed participants (n = 2 806) were quite similar to those of all enrolled participants (n = 4 623). The BPV indices were calculated using data obtained during the screening period (five consecutive annual physical check-ups from 2008 to 2013) and were used as baseline data for BPV. During the follow-up period, ‘high blood pressure’ developed in 389 participants (13.9%, 29.5 per 1 000 person-years), with a higher incidence in men (15.8%; 34.0 per 1 000 person-years) than in women (10.7%; 22.0 per 1 000 person-years). The results of the retrospective comparison between the participants who did and did not develop ‘high blood pressure’ during the follow-up period are presented in . The average, SD, ARV, and highest blood pressure levels during the screening period were greater in participants who developed ‘high blood pressure’ later than in those who did not, while the CV of systolic blood pressure did not differ between participants who did and did not develop ‘high blood pressure’. Most baseline data, other than the blood pressure indices, also showed differences between the two groups.
Table 1. Baseline characteristics of study participants.
Table 2. Baseline characteristics of study participants; retrospective analysis.
The participants were divided into four groups according to the quartile of each index of BPV, and the incidence of ‘high blood pressure’ during the follow-up period was compared (). The incidence of ‘high blood pressure’ increased across the quartiles of SD and ARV, while the average and highest systolic blood pressure levels had the most prominent impact on the development of ‘high blood pressure’. The predictive value of BPV for the development of ‘high blood pressure’ was investigated using logistic regression analysis (). Univariate analysis demonstrated that the average, SD, ARV, and highest systolic blood pressure levels, but not the CV of systolic blood pressure during the screening period, were significant predictors for the development of ‘high blood pressure’. Similar results were obtained after adjusting for age, sex, body mass index, heart rate, serum creatinine, fasting plasma glucose, low-density lipoprotein cholesterol, triglyceride, current smoking habits, frequent alcohol consumption, and family history of hypertension at baseline (, Models 1 and 2). Further adjustment for the average systolic blood pressure in the five consecutive visits during the screening period did not abolish the statistical significance of SD, ARV, and the highest systolic blood pressure levels as predictors of the development of ‘high blood pressure’ (, Model 3). Analysis of subgroup of participants without diabetes showed similar ORs [CIs] for SD, ARV, and the highest systolic blood pressure (1.066 [1.023–1.110], p = 0.002; 1.042 [1.010–1.076], p = 0.011; 1.051 [1.022–1.080], p < 0.001; respectively). ROC curve analysis discriminating participants who developed ‘high blood pressure’ revealed that the cut-off level of the highest systolic blood pressure during the screening period was 130 mmHg (). To confirm the impact of BPV on future blood pressure elevation, logistic regression analysis was performed with the antihypertensive drug prescription as the endpoint (). Antihypertensive drugs were started to be prescribed in 309 participants during the follow-up. The average, SD, ARV, and highest systolic blood pressure levels during the screening period were significant predictors for the prescription of antihypertensive drugs, as shown in the univariate analysis. After adjustment, the average, SD, and highest systolic blood pressure levels at baseline were independent predictors of future antihypertensive medication use.
Figure 2. Bar graphs showing the relationship between blood pressure variability at baseline and the development of hypertension. The participants were divided into four groups according to the quartile (Q1–4) of average (Ave, A), standard deviation (SD, B), coefficient of variation (CV, C), average real variability (ARV, D), and highest value (E) of systolic blood pressure (SBP) during the screening period. The incidence of hypertension during the follow-up period was compared using Pearson’s chi-square test followed by z-analysis with Bonferroni’s correction (*p < 0.05 vs. Q1, #p < 0.05 vs. Q2, $p < 0.05 vs. Q3).
Figure 3. Results of receiver operating characteristics curve analysis to determine the cut-off level of the highest systolic blood pressure during the screening period discriminating participants with future development of ‘high blood pressure’. The cut-off level, area under the curve, sensitivity, and specificity are 130 mmHg, 0.810, 79.7%, and 69.0%, respectively (95% CI: 0.787-0.833, p < 0.001).
Table 3. Logistic regression analysis for relationship between baseline blood pressure variability and future development of hypertension.
Table 4. Logistic regression analysis for relationship between baseline blood pressure variability and future antihypertensive drug prescription.
Discussion
The present study demonstrates that (1) increased year-to-year BPV predicts the development of hypertension, and (2) the average and highest blood pressure levels measured during the preceding years are strong predictors of the development of hypertension.
Logistic regression analyses adjusted for known risk factors and average blood pressure during the screening period demonstrated that the SD, ARV, and highest systolic blood pressure levels independently predicted the development of ‘high blood pressure’. ‘High blood pressure’ was defined as an average blood pressure level of ≥140/90 mmHg during follow-up or the use of antihypertensive medications. Although ‘high blood pressure’ does not exactly mean hypertension, similar results were obtained in logistic analysis where the prescription of antihypertensive medication was adopted as the endpoint. This suggests that the development of ‘high blood pressure’ used in the present study was reliable index for estimating the risk of hypertension. Increased BPV may be associated with vascular damage, such as arterial stiffening [Citation15–17], even in normotensive individuals, leading to the increase in blood pressure. Hypertension promotes arterial stiffening and vice versa [Citation18, Citation25, Citation26]. An increase in BPV, which means an increased risk of blood pressure elevation and blood pressure reduction, may cause target organ damage [Citation5, Citation6] and thereby promote the progression of hypertension. In line with these speculations, kidney function deterioration predicts the future development of hypertension [Citation19, Citation20]. Although the visit-to-visit BPV obtained in hypertensive patients under medication could be a marker of poor adherence to treatment rather than a real index of BPV [Citation27], the year-to-year variability of blood pressure in individuals without hypertension may have a different clinical significance from the visit-to-visit variability in hypertensive patients under medication. Most individuals without hypertension do not frequently or regularly measure their blood pressure; hence, these indices of long-term (year-to-year) BPV may be useful. Multivariate analyses demonstrated the profound impact of average blood pressure levels on the future development of hypertension. This finding is compatible with previous reports, which indicated that individuals with high normal blood pressure levels are at high risk of developing hypertension in the future [Citation28–30]. Notably, the highest blood pressure level over several years was also a strong risk factor for new-onset hypertension. Systolic blood pressure of 130 mmHg or more recorded by chance during several visits over several years indicates increased risk for developing hypertension. It is unrealistic to calculate the average blood pressure level measured over several years in normotensive individuals. When assessing individuals without hypertension, physicians often ignore a single high blood pressure value during several visits, but such highest value, even if different from usual, may be clinically important for assessing normotensive individuals.
The predictive value of BPV has already been studied in patients with diabetes; SD of the mean systolic blood pressure recorded in 4 visits during 2 years predicted the incidence of hypertension in patients with type 2 diabetes and normal blood pressure [Citation21]. However, most of our participants did not have diabetes (about 6% of participants were with diabetes) and the present study investigated year-to-year BPV during 4 years. Thus, the present study expanded the knowledge obtained in patients with diabetes to the general population using different index of BPV. Indeed, analysis of subgroup of our participants without diabetes showed similar impact of BPV on the risk developing hypertension.
The interpretation of the present results is limited by the following points. Various indices are used to assess the BPV, such as beat-to-beat, diurnal, day-to-night, day-to-day, month-to-month, and seasonal variability. Among these, the present study only investigated the effects of year-to-year variability on the development of hypertension. Different results may have been obtained using different variability indices. Variability was calculated using the blood pressure values obtained on five consecutive occasions, and the frequency of measurements might have affected the results. Finally, a follow-up period of 6 years may not be sufficiently long. Although a large number of participants may partially overcome some of these limitations, further study with a larger number of participants and a longer observation period is necessary to draw a definite conclusion.
Conclusion
In summary, increased year-to-year BPV predicts the development of hypertension in the normotensive general population. The highest blood pressure level in the preceding years may also be a strong predictor of the development of hypertension. Instructions to modify the lifestyles of individuals at high risk of developing hypertension may be a realistic modality for the primary prevention of hypertension. The present results provide important information for screening individuals at increased risk of hypertension.
Ethical approval
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Enshu Hospital (approval number; #2017-09-02).
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
The datasets analysed during the current study are available from the corresponding author on reasonable request.
Additional information
Funding
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