Central Systolic Blood Pressure as a Risk Factor for Accelerated Progression of Arterial Stiffness

Abstract

Aims: In the arterial tree, a pressure gradient of the systolic blood pressure (SBP) is observed from the center to the periphery, with the pressure being higher in the periphery because of pressure wave reflection. However, this gradient is attenuated, with elevation of the central SBP (cSBP), in cases with abnormal pressure wave reflection in the arterial tree. It remains unclear if increase of the cSBP might be an independent risk factor for accelerated progression of arterial stiffness. We conducted this prospective observational study using latent growth curve model (LGCM) analyses to examine if elevated cSBP might be an independent risk factor for accelerated progression of the arterial stiffness in middle-aged Japanese men.

Methods: In this 9-year prospective observational study, we analyzed the data of 3862 middle-aged Japanese men (43±10years old) without cerebrocardiovascular disease at the study baseline who had undergone repeated annual measurements of the brachial-ankle pulse wave velocity (baPWV) and cSBP, as represented by the second peak of the radial pressure waveform (SBP2) in radial pressure waveform analysis.

Results: During the follow-up period (6.3±2.5years), significant increases of both the baPWV and SBP2 were observed in all the subjects. Analysis using the LGCM confirmed that the SBP2, a marker of the cSBP (B=0.260, P＜0.001), was a significant determinant of the slope of the annual changes of the baPWV during the study period.

Conclusions: Our finding may appear to confirm elevated cSBP as an independent risk factor for accelerated progression of the arterial stiffness in middle-aged Japanese men.

Introduction

Arterial stiffness is an independent risk factor for the development of cardiovascular disease (CVD), and it is important to clarify the mechanisms of increase of the arterial stiffness in greater detail^1-6). Meta-analyses have suggested that elevated blood pressure is a major determinant of accelerated increase of the arterial stiffness⁷⁾.

In the arterial tree, contraction of the heart pump generates a forward pressure wave on the arterial wall, and peripheral arterial resistance generates a backward pressure wave, and a summation of the two pressure waves occurs somewhere in between in the arterial tree^{1, 2, 4-6)}. Therefore, within the arterial tree, the systolic blood pressure (SBP) exhibits a pressure gradient from the center to the periphery, being higher in the periphery. Abnormal pressure wave reflection caused by vascular damage in arterial tree shifts the point of summation of the forward and antegrade pressure waves more centrally, which results in elevation of the central systolic blood pressure (cSBP) even in the absence of any change of the blood pressure level in the peripheral arteries^{1, 2, 4-6)}. cSBP is estimated by radial arterial pressure waveform analysis and is represented by the second peak of the radial pressure waveform (SBP2). In a previous study, we demonstrated a longitudinal association of the cSBP, as represented by the SBP2, with the rate of progression of the arterial stiffness using mixed model linear regression (MIX) analyses⁸⁾. However, a recent article described the differences between the mixed-effects and latent-curve approaches to growth modeling, and reported the limitations of MIX analyses for assessing the assocation between cSBP and rate of progression of arterial stiffness (i.e., time-unstructured data and unclarified non-linear association)⁹⁾. Furthermore, we also did not examine the association of the degree of increase of the cSBP with the rate of change of the brachial-ankle pulse wave velocity (baPWV) during the study period in our previous study. Therefore, further analyses are needed to clarify if elevated cSBP might be an independent risk factor for accelerated progression of arterial stiffness.

Aim

The latent growth curve model (LGCM) analysis is a useful statistical approach for analyzing time-unstructured data with unclarified non-linear relationships and for estimating the rate of change over time from longitudinally measured data^{9, 10)}. Therefore, to verify the association between the cSBP and the rate of progression of arterial stiffness, we conducted this prospective observational study using LGCM analysis to examine if elevated cSBP might be an independent risk factor for accelerated progression of arterial stiffness in middle-aged Japanese men.

Methods

Design and Subjects

For the present study, we used the same data that we used for our previously reported prospective observational studies^{11, 12)}. The study was conducted in employees at the headquarters of a single large Japanese construction company located in downtown Tokyo (all the study participants had desk jobs). Informed consent for participation in the study was obtained from each of the study participants prior to their enrollment in this study. The study was conducted with the approval of the Ethics Guidelines Committee of Tokyo Medical University (No. 209 and No. 210 in 2003).

Annual health checkup data of the study subjects obtained from year 2007 through year 2015 were used for the present study. A flow diagram of selection of the study subjects for this longitudinal study is shown in Fig.1. Of the total of 5857 subjects who were working at the headquarters of a company in Tokyo, subjects who had undergone health checkup only once (n=834) were excluded from this study. Subjects with ABI＜0.95 on either side, with atrial fibrillation, who were undergoing maintenance hemodialysis, or with heart disease and/or cerebrovascular disease (n=334) were also excluded. Among the remaining 4689 subjects, 673 women were also excluded, because their number was relatively small. Male subjects with an unreliable accuracy of the measured radial augmentation index (rAI) (i.e., subjects with a standard deviation of the rAI, calculated from ten radial pressure waveforms of ≥ 6%) were also excluded (n=154). Finally, we analyzed the data of 3862 subjects (Fig.1).

Fig.1.

Flow diagram of subject enrollment in the study

Measurement of the Brachial-Ankle Pulse Wave Velocity and SBP2

The baPWV was measured using a volume-plethysmographic apparatus (Form/ABI, Omron Healthcare Co., Ltd., Kyoto, Japan), as previously described^11-14). Briefly, occlusion cuffs connected to both the plethysmographic and oscillometric sensors were tied around both the upper arms and ankles of the subjects lying in the supine position. The blood pressures were measured by oscillometric sensors, once on the left side and once on the right side, and not duplicated; the measurements were conducted after the subjects had rested for at least 5 minutes in the supine position in an air-conditioned room (maintained at 24℃) designated exclusively for this study.

Measurement of the SBP2 was conducted after the subjects had rested for at least 5 minutes in the seated position. The left radial arterial waveform was recorded using an arterial applanation tonometry probe equipped with an array of 40 micropiezo-resistive transducers (HEM-9010AI; Omron Healthcare Co., Ltd.). Subsequently, the first and second peaks of the radial pressure waveform (SBP1 and SBP2) and brachial diastolic pressure (DP) were automatically detected using the fourth derivatives for each radial arterial waveform, and then averaged.

SBP1 is a marker related to the peripheral systolic blood pressure and SBP2 is a marker related to the cSBP. The radial augmentation index (rAI), a marker of the amptitude of the pressure wave reflection in the arterial tree, was calculated as follows: (SBP2−brachial DP)/(SBP1−brachial DP)×100 (%)¹⁵⁾.The turns for measurement of the baPWV and SBP2 as the first measurement were randomly allocated. The mean blood pressure (mBP) was calculated as the diastolic blood pressure (DBP)＋(SBP1−DBP)/3. Acceptable reproducibility and accuracy of these measurements are reported elsewhere^14-19).

Laboratory Measurements

We confirmed the smoking status (i.e., current smoker or not), daily ethanol intake, medication history, and history of illness based on the results of a questionnaire. The serum concentrations of triglycerides (TG), uric acid (UA), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL) and creatinine, as well as the plasma concentrations of glucose and glycohemoglobin A1c (HbA1c) were measured using standard enzymatic methods (Falco Biosystems Co. Ltd, Tokyo). All the blood samples were obtained in the morning after the patients had fasted overnight.

Statistical Analysis

Data are expressed as the means±SD. The differences in the measured values between the study baseline and final year of measurement for this study (hereinafter, study completion) were assessed by the paired t-test for continuous variables, and McNemar’s non-parametric test for categorical variables.

We used the LGCM model to determine the rate of change of the baPWV in relation to the SBP1/SBP2 from longitudinally measured data during the study period¹⁰⁾. In this analysis, we used the conventional cardiovascular risk factors as covariates (the details are described in the Results section). While the LGCM analysis in panel data was conducted using the Amos software (version 23.0; IBM/SPSS Inc., Tokyo, Japan), all the other analyses were conducted using the SPSS software (version 26.0; IBM/SPSS Inc., Armonk, NY, USA). P＜0.05 was considered as being indicative of a statistically significant difference in all the statistical tests.

Results

Table 1 shows the clinical characteristics of the study subjects at the study baseline and at study completion. The mean follow-up period was 6.3±2.5 years, and the measurements were repeated 5.1±2.1 times in the study subjects. During the study period, while the BMI increased, the number of smokers decreased. The prevalence rate of medication for hypertension increased from 7.8% to 15.3%, the SBP decreased significantly, while the DBP increased significantly during the study period. On the other hand, the prevalence rate of medication for dyslipidemia and diabetes mellitus increased, while the serum levels of LDL and HbA1c also increased significantly during the study period. Under these longitudinal changes in the risk factors for cardiovascular disease, significant increases of the baPWV, rAI and SBP2 were observed during the study period.

Table 1.Clinical characteristics of the study subjects

Variable	At the study baseline (N= 3862)	At study completion (N= 3862)	P-Value
Age, years	43±10	49±10	＜0.001
Body mass index, kg/m2	24.1±3.1	24.2±3.2	＜0.001
Current smokers, No. (%)	1218 (31.8)	960 (24.9)	＜0.001
Daily ethanol intake (g/day)	12.8±11.2	14.6±11.9	＜0.001
baPWV, cm/s	1290±187	1343±215	＜0.001
rAI, %	69±13	72±13	＜0.001
SBP1, mm Hg	124.2±15.0	123.3±14.7	＜0.001
SBP2, mm Hg	109.5±16.6	110.2±16.2	＜0.001
DBP, mm Hg	77.7±11.0	79.0±10.2	＜0.001
HR, beats/min	65.3±9.4	64.7±9.8	＜0.001
HDL, mmol/L	1.61±0.41	1.58±0.40	＜0.001
LDL, mmol/L	3.09±0.81	3.17±0.78	＜0.001
TG, mmol/L	1.42±1.00	1.37±0.95	＜0.001
UA, μmol/L	369.37±73.71	358.43±71.74	＜0.001
HbA1c, %	5.3±0.6	5.5±0.7	＜0.001
Serum creatinine, μmol/L	75.6±10.1	76.1±10.9	＜0.001
Medications
For Hypertension, No. (%)	300 (7.8)	591 (15.3)	＜0.001
For Dyslipidemia, No. (%)	106 (2.7)	278 (7.2)	＜0.001
For Diabetes mellitus, No. (%)	95 (2.5)	167 (4.3)	＜0.001

Abbreviations: Baseline = study baseline; baPWV = brachial-ankle pulse wave velocity (mean of the values measured on the right and left sides); DBP = diastolic pressure; At study completion = at the end of the study period; HR = heart rate; HDL = serum high-density lipoprotein cholesterol; HbA1c = plasma hemoglobin A1c; LDL = serum low-density lipoprotein cholesterol; rAI = radial augmentation index; SBP1 = first peak of the radial pressure waveform; SBP2 = second peak of the radial pressure waveform; TG = serum triglycerides; UA = serum uric acid

Fig.2 illustrates the conceptual model for LGCM analysis. We used LGCM analysis to examine the association between SBP1/SBP2 and the annual rates of change of the baPWV from longitudinally measured data¹⁰⁾. In Fig.2, the interest to examine the effect of the SBP1/SBP2 measured at the baseline (i.e., the intercept of SBP1/SBP2 at the baseline) on the annual rates of change of the baPWV is represented as a large arrow. To facilitate interpretation, only the latent variables (intercept and the rate of change) are shown. Intercepts and the rate of change were also regressed on covariates. SBP1/SBP2 (1) - (9) indicate the SBP1/SBP2 values measured from the 1st to 9th years of the study period; PWV (1) – (9) indicate the baPWV values measured from the 1st to 9th years of the study period. As the covariates of the basic model (Adjusted Model 1), age, body mass index, current smoking history, current daily ethanol intake, heart rate, serum levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, HbA1c, uric acid, and creatinine, and history of medication use for hypertension, dyslipidemia, diabetes mellitus, hyperuricemia (not receiving mediation =0, receiving mediation =1; for the medication) were applied. Then, SBP1 (when SBP2 was used as the explanatory variable) or SBP2 (when SBP1 was used as the explanatory variable) or mBP was added as an additional covariate (Adjusted Model 2 and Adjusted Model 3, respectively).

Fig.2.　Results of latent growth curve model analyses to determine the relationship between the SBP1/SBP2 and the baPWV

Abbreviations: baPWV=brachial-ankle pulse wave velocity; SBP1=the first peak of radial pressure waveform; SBP2=the second peak of radial pressure waveform

As the covariates of the basic model (Adjusted Model 1), age, body mass index, current smoking history, current daily ethanol intake, heart rate, serum levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, HbA1c, uric acid, and creatinine, and history of medication use for hypertension, dyslipidemia, diabetes mellitus, hyperuricemia (not receiving mediation=0, receiving mediation=1; for the medication) were applied. Then, SBP1/SBP2 or mBP was added as an additional covariate (Adjusted Model 2 and Adjusted Model 3, respectively).

Table 2 shows the results of the LGCM analyses. In the crude model, the SBP1 and SBP2 at the study baseline showed a significant relationship with the annual rates of change of the baPWV. Even after adjustments for the covariates in Adjusted Model 1, Adjusted Model 2 and Adjusted Model 3, these associations were significant (Table 2).

Table 2.Results of LGCM analyses conducted to assess the associations of the SBP1 and SBP2 measured at the baseline with the annual rate of changes of the baPWV

Variable	Crude		Adjusted model 1		Adjusted model 2		Adjusted model 3
	B	P-value	B	P-value	B	P-value	B	P-value
Outcome variable = the rate of annual change of baPWV
SBP1	0.279	＜0.001	0.277	＜0.001	0.572	＜0.001	0.394	＜0.001
SBP2	0.302	＜0.001	0.260	＜0.001	0.536	＜0.001	0.628	＜0.001

Abbreviations: Adjusted model 1 = adjusted for basic covariates; Adjusted model 2 = adjusted for basic covariates + SBP1 (when SBP2 was used as the explanatory variable)/SBP2 (when SBP1 was used as the explanatory variable); Adjusted model 3 = adjusted for basic covariates + mBP (mean blood pressure at the time of measurement of the radial augmentation index); B = unstandardized co-efficient; Crude = without adjustment; LGCM = latent growth curve model; SBP1 = first peak of the radial pressure wave form; SBP2 = second peak of the radial pressure waveform; a marker of central systolic blood pressure; Other abbreviations are as described in the footnote for Table 1. Basic covariates used for the adjustments were age, body mass index, current smoking history, current daily ethanol intake, heart rate, serum levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, hemoglobin A1c, uric acid, and creatinine, and history of medication use for hypertension, dyslipidemia, diabetes mellitus, hyperuricemia (not receiving medication = 0, receiving medication = 1; for the medication).

Discussion

In a previous study, using MIX analyses, we demonstrated the existence of a longitudinal relationship between the cSBP and progression of arterial stiffness⁸⁾. However, there are the following limitations to using only MIX analyses for validation⁹⁾, and it would be necessary to validate the association between the cSBP and progression of arterial stiffness using LGCM analyses^{9, 10)}: 1) the association between elevation of the cSBP and increase in arterial stiffness is temporally unstructured: blood pressure, arterial stiffness and pressure wave reflection are all affected by the age²⁰⁾, but their associations with age varies in individual cases, and the timing of elevation of the cSBP in relation to increased arterial stiffness varies from case to case; 2) we have previously confirmed that the association between the pressure wave reflection and arterial stiffness is non-linear²¹⁾, but the details of the association between the cSBP and progression of arterial stiffness (function) have not yet been fully clarified. Considering these limitations, LGCM analysis may be more suitable to confirm the significance of the association of the cSBP with the progression of arterial stiffness^{9, 10)}. To the best of our knowledge, this is the first prospective observational study conducted in a Japanese occupational cohort to show that the SBP2, a marker of the cSBP, bears a significant independent relationship with the slope of the annual changes of the baPWV. The strengths of the present study were as follows: 1) LGCM analyses were performed on repeated-measurement data (an average of 5.1±2.1 times) to reduce the influence of confounding factors; 2) the average observation period of 6.3±2.5 years is a sufficiently long observation period for evaluating changes in the rate of progression of arterial stiffness.

A previous meta-analysis has shown that elevated blood pressure is an important determinant of accelerated increase of the arterial stiffness⁷⁾ and prospective studies have reported that elevated peripheral blood pressure is associated with accelerated progression of the arterial stiffness^{22, 23)}. The change in arterial stiffness was assessed only at two points in these studies (i.e., at the study baseline and study completion) and did not examine the rate of change of arterial stiffness over time from repeated measurement data using LGCM analysis. On the other hand, the influence of the cSBP on the rate of progression of the arterial stiffness independent of the peripheral blood pressure remains unknown. Recently, Boczar et al. conducted a 3-year follow-up study in patients with thoracic aortic aneurysms and reported that the central blood pressure, in addition to the arterial stiffness, affected the rate of expansion of the thoracic aortic aneurysm²⁴⁾. In their study, they found no significant effect of the peripheral blood pressure on this progression, and therefore contended that the local aortic pressure (i.e., the central blood pressure) was involved in the aortic injury. Such pressure loading is thought to increase the arterial stiffness through several mechanisms, including connective tissue degeneration and vascular smooth muscle proliferation and hypertrophy; the results of the present study^{1, 2, 4-6, 25)}, suggesting that the higher SBP2, a marker of the cSBP, the higher rate of increase of the baPWV, are consistent with this speculation.

The peripheral blood pressure increases the arterial stiffness, which in turn, increases the propagation speed of the forward and backward pressure waves on the arterial wall, and the summation of the two waves occurs more proximally in the arterial tree, to cause elevation of the central blood pressure^{1, 2, 4-6)}. Thus, elevated peripheral blood pressure is known to cause elevation of the central blood pressure via increasing the arterial stiffness. However, the findings of the present study show that the SBP2 had a positive relationship with the slope of the annual change of the baPWV, independently of the peripheral blood pressure. In addition to the arterial stiffness, which affects the central hemodynamics in the arterial tree, the peripheral reflectance of the arterial tree, which affects the speed of progression of the backward pressure wave, also affects the central hemodynamics^{1, 2, 4-6, 26)}. Therefore, the findings of the present study indicate that factors other than the arterial stiffness (i.e., peripheral vascular damage) contribute to accelerated progression of the arterial stiffness via causing elevation of the central blood pressure.

Clinical Implications

Arterial stiffness is known as an independent risk factor for the development of CVD^{1, 2, 4-6, 26)}, and monitoring of the blood pressure is important in the management arterial stiffness. The results of our present study indicate the importance of evaluating not only the brachial blood pressure, but also the central blood pressure for assessing the risk of accelerated progression of the arterial stiffness. Therefore, it is necessary to confirm the usefulness of central blood pressure monitoring in relation to the treatment of hypertension (until now, antihypertensive treatment has been reported to improve both arterial stiffness and “central blood pressure”)^{26, 27)} and to confirm whether reduction of the central blood pressure might have a favorable long-term effect on the arterial stiffness.

Study Limitations

The present study had several limitations, as follows: 1) The study population consisted only of Japanese men, and the findings of the present study still need to be confirmed in women, as well as in subjects of other ethnicities; 2) the baPWV primarily reflects the stiffness of the large to middle sized arteries¹⁴⁾; however, a close relationship has been shown to exist between the baPWV and the carotid-femoral PWV, a marker of large-arterial stiffness^{14, 28)}; 3) while habitual exercise is known to be beneficial for reducing the rate of progression of arterial stiffness²⁹⁾, we did not determine the daily physical activity level of the subjects in the present study; 4) In the present study, the SBP1 decreased significantly from the study baseline to study completion. The prevalence rate of medication for hypertension increased by 7.8% to 15.3% during the study period. This increase could have had some influence in decreasing the SBP1. Therefore, we included history of medication for CV risk factors, including hypertension, as covariates for adjustment in the statistical analyses.

Conclusion

The results of LGCM analyses in the present study supported the concept, previously proposed by us based on the results of MIX analyses, that elevated cSBP is an independent risk factor for accelerated progression of arterial stiffness. Our findings appear to confirm the significance of central blood pressure monitoring by radial pressure waveform analysis to evaluate the risk of accelerated progression of arterial stiffness, which, in turn, is an independent risk factor for the development of CVD.

Acknowledgements

The authors are grateful for the contributions of all the investigators, clinical research coordinators, data managers, and laboratory technicians involved in this registry.

Sources of Funding

This study was supported by Omron Health Care Company (Kyoto, Japan) and Asahi Calpis Wellness Company (Tokyo, Japan), which awarded funds to Professor Hirofumi Tomiyama.

Disclosure

The sponsor (Omron Health Care Company) assisted in the data formatting (i.e., in transferring the data of the brachial-ankle pulse wave velocity stored in the hard disc of the equipment used for measurement of the brachial-ankle pulse wave velocity to an Excel file). The authors have no other disclosures to make.

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