https://orcid.org/0000-0001-9404-7069
Figures
Abstract
Background
The increase of blood pressure (BP) variability (BPV) is recognized as an important additional cardiovascular risk factor in both normotensive subjects and hypertensive patients. Aging-induced atherosclerosis and autonomic dysfunction impair the baroreflex and, in turn, augment 24-hour BPV. In small and large animal experiments, impaired baroreflex steepens the slope of the power spectrum density (PSD) of continuous BP in the frequency range of 0.01 to 0.1 Hz. Although the repeated oscillometric BP recording over 24 hours or longer is a prerequisite to quantify BPV in humans, how the very short-term continuous BP recording reflects BPV remains unknown. This study aimed to evaluate the impact of aging on the very short-term (30-min) BPV in healthy human subjects by frequency analysis.
Methods
We recorded continuous BP tonometrically for 30 min in 56 healthy subjects aged between 28 and 85 years. Considering the frequency-dependence of the baroreflex dynamic function, we estimated the PSD of BP in the frequency range of 0.01 to 0.1 Hz, and compared the characteristics of PSD among four age groups (26–40, 41–55, 56–70 and 71–85 years).
Results
Aging did not significantly alter mean and standard deviation (SD) of BP among four age groups. PSD was nearly flat around 0.01 Hz and decreased gradually as the frequency increased. The slope of PSD between 0.01 and 0.1 Hz was steeper in older subjects (71 years or older) than in younger subjects (55 years or younger) (p < 0.05).
Citation: Mano J, Saku K, Kinoshita H, Mannoji H, Kanaya S, Sunagawa K (2021) Aging steepens the slope of power spectrum density of 30-minute continuous blood pressure recording in healthy human subjects. PLoS ONE 16(3): e0248428. https://doi.org/10.1371/journal.pone.0248428
Editor: Giacomo Pucci, University of Perugia, ITALY
Received: September 3, 2020; Accepted: February 25, 2021; Published: March 18, 2021
Copyright: © 2021 Mano et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data that support the findings of this study are available within the article and its Supporting information.
Funding: This work was supported by Research and Development of Supportive Device Technology for Medicine Using ICT from Japan Agency for Medical Research and Development in the form of a grant awarded to Kenji Sunagawa (18he1102003h0004), the Japan Society for the Promotion of Science in the form of a Grant-in-Aid for Early-Career Scientists awarded to Keita Saku (18K15893) and Hiroshi Mannoji (20K17156), and Omron Healthcare Co., Ltd. in the form of a research grant awarded to Keita Saku and salaries for Jumpei Mano and Hiroyuki Kinoshita. The specific roles of these authors are articulated in the ‘author contributions’ section. Omron Healthcare Co., Ltd. contributed to data collection, but played no further role in study design, data analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have read the journal’s policy and have the following competing interests: Jumpei Mano and Hiroyuki Kinoshita are employees of Omron Healthcare Co., Ltd. Kenji Sunagawa worked at the Department of Therapeutic Regulation of Cardiovascular Homeostasis, Center for Disruptive Cardiovascular Medicine, Kyushu University, which was endowed by Omron Healthcare Co., Ltd. and works at the Circulatory System Research Foundation, which is endowed by Omron Healthcare Co., Ltd. Keita Saku worked at the Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, which was endowed by Omron Healthcare Co., Ltd. and receives a research grant from Omron Healthcare Co., Ltd. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents and marketed products associated with this research to declare. The wearable wrist-type tonometric blood pressure monitor was an in-house developed device which was institutionally approved for this clinical trial.
Abbreviations: ABPM, ambulatory blood pressure monitoring; ANOVA, analysis of variance; BMI, body mass index; BP, blood pressure; BPV, blood pressure variability; BRS, baroreflex sensitivity; DBP, diastolic blood pressure; ECG, electrocardiogram; FFT, Fast Fourier Transform; HF, high-frequency; HR, heart rate; HRV, heart rate variability; LF, low-frequency; LV, left ventricular; NS, not significant; PSD, power spectrum density; RAAS, renin-angiotensin-aldosterone system; RESP, respiratory signal; SBP, systolic blood pressure; SD, standard deviation
Introduction
Cardiovascular disease accounts for more than 17 million deaths per year worldwide [1], and appropriate management of blood pressure (BP) is essential to prevent serious complications. The absolute value of oscillometric BP measurement has been used for risk stratification. Recently, BP variability (BPV) has been recognized as an independent predictor of cardiovascular mortality in both normotensive subjects and hypertensive patients [2–9]. Kawai et al. [3] reported that the visit-to-visit BPV using office BP measurement correlated significantly with the incidence of cardiovascular disease. Kikuya et al. [7] also reported that daily BPV assessed by standard deviation (SD) of 24-h ambulatory BP monitoring (ABPM) every 30 min was an independent predictor of cardiovascular mortality in the general population. Furthermore, Mena et al. [9] reported that high daily BPV assessed by average real variability index of ABPM was associated with the presence and progression of subclinical organ damage, as well as the incidence of cardiovascular events. Therefore, in addition to absolute BP, BPV contributes to better BP management and risk stratification.
Most clinical evidence of BPV has been provided by 24-h ABPM, indicating the importance of daily BPV as a clinical risk index. Various factors including mental stress, behavior, environmental temperature and food/drink intake may contribute to daily BPV [10]. At the same time, the baroreflex is a robust negative feedback system that stabilizes daily BPV through sympathetic modulation [11].
The usefulness of frequency analysis of biological data for the stratification of cardiovascular patients is well known. Heart rate (HR) variability (HRV) is most commonly analyzed in clinical settings. The high-frequency (HF) component of HRV (0.15‒0.4 Hz) reflects the vagal nerve modulated HR change by respiratory fluctuation [12]. On the other hand, the low-frequency (LF) component of HRV is reported to indicate concomitant activity of sympathetic and vagal nerve. In addition, the LF/HF ratio reflects sympatho-vagal balance or the activation of sympathetic nervous system [12]. Frequency analysis focusing on continuous BP recording has been developed using the data of standard catheter-manometer systems or noninvasive plethysmographic devices. The LF component (0.04‒0.15 Hz) of systolic BP (SBP) is also known to be an index of sympathetic modulation directed to blood vessels [13]. Thus, the LF of SBP has been reported to increase during tilt [14, 15], mental stress [15], and even several cardiovascular diseases [16, 17].
The baroreflex is a dominant regulatory system of BPV from seconds to hours [18]. Studies in various animal species have also demonstrated that the baroreflex has a higher gain in the low-frequency range (low-pass characteristics) with the cutoff frequency at around 0.05 Hz [19, 20]. Thus, baroreflex dysfunction increases BPV in the low-frequency range. Our previous study has shown that the slope of power spectral density (PSD) of BP in the frequency range of 0.01 to 0.1 Hz increases with the decrease in baroreflex gain in rats [21]. Therefore, the PSD analysis of BP in the baroreflex frequency range would reflect the baroreflex modulated BPV.
Our goal is to derive new indices to evaluate BPV from very short-term continuous BP recording. As the first step, we analyzed the PSD of very short-term continuous BP recordings from healthy adults in various age groups. Since aging is known to worsen the baroreflex function [10, 22], we hypothesized that aging steepens the slope of PSD of BP in the baroreflex operating frequency range.
Materials and methods
Subjects and devices
Between December 2017 and April 2018, we recruited 80 healthy volunteers of various age groups (41 women and 39 men; mean age 57.4, range 28–85 years), who were not prescribed antihypertensive drugs or cardiovascular drugs. We excluded the volunteers who were prescribed drugs for dyslipidemia and diabetes mellitus, because these diseases may affect the circulatory regulation via autonomic or baroreflex dysfunction. None of the subjects presented clinical signs of neurological and autonomic dysfunction or received prescriptions for neurological diseases. Omron Expert Link Co., Ltd. (Kyoto, Japan) was entrusted with recruitment of volunteers. The institutional review board of Omron Healthcare Co., Ltd. (Kyoto, Japan) approved this study, and all participants provided written informed consent to participate.
We used an in-house developed, institutionally approved wearable wrist-type tonometric BP monitor for noninvasive continuous BP recording [10, 23]. We simultaneously recorded electrocardiogram (ECG) and respiratory signal (RESP) from thoracic impedance (BP-A308; Omron Healthcare Co., Ltd., Kyoto, Japan). We also measured oscillometric BP in the upper arm (BP-203RPEIII; Omron Healthcare Co., Ltd., Kyoto, Japan).
Protocol
We attached the wearable tonometric BP monitor to the wrist and the oscillometric BP monitor to the ipsilateral upper arm. We placed three electrodes for ECG and RESP on the chest. After the preparation, we waited for 5–15 min until all signals were stabilized. We then recorded continuous BP, ECG and RESP for 30 min with the subject resting in a supine position. We measured the oscillometric BP before and after the continuous BP recordings (Fig 1).
After waiting for 5–15 min until all signals were stabilized, continuous BP, ECG and RESP were recorded for 30 min with the subject resting in a supine position. The oscillometric BP was measured before and after the continuous BP recording. BP, blood pressure; ECG, electrocardiogram; RESP, respiratory signal.
Data analysis
We recruited 80 subjects. Subjects were excluded from analysis if they met any of the following exclusion criteria:
- Subject who showed SBP ≥ 140 mmHg or diastolic BP (DBP) ≥ 90 mmHg (averaged value of oscillometric BP measurements during the protocol).
- Subject who could not complete the stable continuous BP recording for 30 min because of motion, respiration noise and recording failure.
- Subject who had irregular beats more than 10 times during the 30-min recording.
Finally, we excluded 24 subjects from analysis, and studied the remaining 56 subjects.
Continuous BP analysis.
We digitized continuous BP at 1.0 kHz using a 16-bit analog-to-digital converter (Power Lab 8/35; AD Instruments, Sydney, Australia). We derived SBP (maximum BP), mean BP, DBP (minimum BP) and HR in every beat. We estimated BPV and HRV in the time domain as the SD of beat-by-beat BP (SBP, mean BP and DBP) and HR, respectively, for 30 min.
For frequency analysis of continuous BP, we resampled the mean BP time series at 5 Hz and divided them into 200-second segments with 50% overlap. In each segment, after removing a linear trend, we applied the Hanning window. We applied the fast Fourier transform (FFT) using the Welch’s periodogram [24] and estimated PSD in the frequency range of 0.01 to 0.1 Hz. The integrated PSD area reflects BPV in the frequency range.
We chose this frequency range because previous studies in various animal species indicate that the baroreflex function approximates distinctive low-pass filter characteristics [19, 20]. The baroreflex cannot operate above 0.1 Hz and fully operates at 0.01 Hz; hence the baroreflex strongly attenuates BPV at around 0.01 Hz. To quantify the impact of baroreflex on PSD, we characterized PSD at 0.01 Hz (PSD0.01Hz) and 0.1 Hz (PSD0.1Hz) and derived the slope of PSD (S1 Fig).
We also assessed baroreflex sensitivity (BRS), which has been extensively used for assessing baroreflex function in clinical settings [25, 26]. BRS is HR response to BP change in the closed-loop condition. In this study, BRS was assessed using the sequence method [27]. BRS was calculated from the slope of linear regression plots of SBP versus RR interval on spontaneous sequences, in which SBP and RR interval concurrently increase (up sequences) or decrease (down sequences) for three or more consecutive beats.
Statistical analysis.
We performed statistical analyses using commercially available software (BellCurve for Excel version 3.21, Social Survey Research Information Co., Ltd., Tokyo, Japan). We used Pearson correlation coefficients to assess the relationship between age and each measurement variable for all age groups. We divided the subjects into four age groups: 26–40, 41–55, 56–70 and 71–85 years. We compared all age groups by one-way factorial analysis of variance (ANOVA) followed by the Tukey-Kramer test. We considered differences to be statistically significant at p < 0.05.
Results
Baseline characteristics
Table 1 shows the baseline characteristics. The proportion of males was relatively low in the 26–40 age group. Body mass index (BMI) did not differ among age groups. In this cohort, SBP and DBP increased slightly with age (S2 Fig).
Impact of aging on BP and BPV from continuous BP recordings
Shown in Fig 2 is a representative time series of continuous tonometric BP recording for 30 min. Both SBP and DBP fluctuated continuously without a large change in mean BP. Averaged BP and HR did not differ among the four age groups (Table 2). SBP increased slightly with age (S3 Fig).
Fig 3 demonstrates the relationship of age with BPVs and HRV expressed by SD. The upper panels show the scatter plots of individual data between age and BPVs or HRV. While BPVs did not correlate significantly with age, HRV correlated weakly with age and tended to decrease with aging. The lower panels compare the BPVs and HRV among four age groups. There were no significant differences in BPVs and HRV among the four groups.
The relationship of age with BPVs and HRV: individual data (A-D) and group data (E-H). SD of beat-by-beat SBP, mean BP, DBP and HR were calculated. Upper panels present scatter plots of individual data for the relationship of age with BPVs and HRV analyzed by Pearson’s correlation coefficient (r). Straight lines indicate linear regression lines. Lower panels present the comparisons of BPVs and HRV among four age groups analyzed by one-way factorial ANOVA. BPV, blood pressure variability; HRV, heart rate variability; SBP, systolic blood pressure; BP, blood pressure; DBP, diastolic blood pressure; HR, heart rate; SD, standard deviations; ANOVA, analysis of variance.
Fig 4 demonstrates the relationship between age and BRS, and the relationship between the slope of PSD and BRS. Fig 4A shows the scatter plot of individual data between age and BRS. Fig 4B compares BRS among the four age groups. BRS correlated significantly with age and tended to decrease as age increases (Fig 4A). BRS was significantly smaller in the 71–85 age group than in the 26–40 and 41–55 age groups (Fig 4B). On the other hand, BRS did not correlate significantly with the slope of PSD (Fig 4C).
(A): Scatter plot of individual data for the relationship between age and BRS. Straight line indicates linear regression line. Pearson correlation coefficient (r) was used to assess the goodness-of-fit of the linear regression. (B): Age group comparison of BRS. One-way factorial ANOVA followed by Tukey-Kramer test was used to compare the relationship among four age groups. Data are shown as mean ± SD. (C): Scatter plot of individual data for the relationship between the slope of PSD and BRS. BRS did not correlate significantly with the slope of PSD. *p < 0.05 versus 26–40 age group. **p < 0.01 versus 26–40 age group. †p < 0.05 versus 41–55 age group. BRS, baroreflex sensitivity; PSD, power spectrum density; ANOVA, analysis of variance; SD, standard deviations.
Impact of aging on characteristics of PSD of BP
Fig 5 shows the individual and group PSD estimated from 30-min continuous BP recordings. Both PSD and frequency axes are logarithmically scaled. In all age groups, PSD was relatively flat around 0.01 Hz and decreased as the frequency increased. As shown in Fig 5E, the slope of PSD between 0.01 and 0.1 Hz in the 71–85 age group was steeper than that in the 26–40 age group.
(A-D): Individual (fine lines) and mean PSD (bold line) estimated from 30-min continuous BP recordings. (E): Comparison of mean PSD between the 26–40 and 71–85 age groups. The slope of PSD between 0.01 and 0.1 Hz was steeper in the 71–85 age group than in the 26–40 age group. PSD, power spectrum density; BP, blood pressure.
Fig 6 demonstrates the relationship of age with PSD characteristics. The upper panels show scatter plots of individual data between age and PSD characteristics. The lower panels compare PSD characteristics among the four age groups. PSD0.01Hz did not correlate with age in individual data (Fig 6A) or the group data (Fig 6D). On the other hand, PSD0.1Hz correlated significantly with age and tended to decrease as age increased (Fig 6B). As a result, the slope of PSD between 0.01 and 0.1 Hz correlated significantly with age and tended to increase as age increased (Fig 6C). The slope of PSD between 0.01 and 0.1 Hz was significantly larger in the 71–85 age group than in the 26–40 and 41–55 age groups (Fig 6F).
(A-C): Scatter plots of individual data for the relationship between age and PSD characteristics. Straight lines indicate linear regression lines. Pearson correlation coefficient (r) was used to assess the goodness of fit of the linear regression. (D-F): Age group comparison of PSD characteristics. One-way factorial ANOVA followed by the Tukey-Kramer test was used to compare the relationship among four age groups. Data are shown as mean ± SD. The slope of PSD between 0.01 to 0.1 Hz correlated significantly with age. *p < 0.05 versus 26–40 age group. †p < 0.05 versus 41–55 age group. PSD, power spectrum density; ANOVA, analysis of variance; SD, standard deviations.
The PSD characteristics between males and females in each age group was also compared. Although the sample size was small, the slope of PSD within the same age group did not differ significantly between males and females. The general trend of age-related alteration in the slope of PSD was also the same between males and females (S1 Table, S4 and S5 Figs).
Discussion
This study aimed to investigate in healthy human subjects the very short-term BPV focusing on the baroreflex operating frequency range between 0.01 and 0.1 Hz. Major findings of this study include: (1) we can stably estimate PSD of BP from 30-min continuous BP recording in the baroreflex operating frequency range; (2) PSD of BP showed low-pass filter characteristics; (3) aging significantly steepened the slope of PSD of BP; (4) aging did not alter SD of BP time series, which indicate BPV in the time domain.
Characteristics of PSD estimated from 30-min continuous BP recording in humans
The PSD of BP in healthy human subjects was nearly flat around 0.01 Hz and decreased gradually with the increase in frequency. We previously reported that the PSD in rats decreased with the increase in frequency with an inflection point at around 0.1 Hz [21]. In both humans and rats, PSD of BP decreased in the frequency range between 0.01 to 0.1 Hz. However, the precise shape of the PSD differs between the two species, especially the flatness around 0.01 Hz in humans and the inflection point at around 0.1 Hz in rats. Since the baroreflex dynamic function, which is the major regulator of BPV in the frequency range of 0.01 to 0.1 Hz, is similar in various animal species [19, 20, 28], we speculate that factors such as the effect of respiration on BP and neural disturbance may contribute to the differences in the shape of the PSD of BP. Further investigation is needed to characterize the PSD of BP in humans.
The PSD analysis of continuous BP recording provides insight into how BPV relates to autonomic regulation. Castiglioni et al. [29] reported that PSD between 0.0001 and 0.1 Hz exhibited low-pass filter characteristics in human subjects. Our observation is consistent with the previous report that showed the frequency-dependent attenuation of PSD in humans. To characterize baroreflex-regulated BPV, we focused on the PSD in the frequency range above 0.01 Hz.
Impact of aging on characteristics of PSD of BP
We previously reported that a decrease in baroreflex total loop gain increases the slope of PSD of BP in the frequency range between 0.01 and 0.1 Hz in rats. Thus, we have shown that the PSD slope is reciprocal to the baroreflex open-loop gain [21]. Conci et al. [30] reported that brain death steepened the slope of PSD of BP, indicating compromised autonomic regulation of BPV. Omboni et al. [31] reported that the slope of PSD between 0.01 to 0.1 Hz was higher in patients with autonomic dysfunction than in healthy subjects. These findings indicate that the slope of PSD of BP in the frequency range of 0.01 to 0.1 Hz closely reflects the underlying baroreflex function.
To evaluate the clinical utility of PSD analysis of 30-min continuous BP recording, we examined the relationship of PSD with aging. It is well known that aging impairs baroreflex function, one of the most powerful mechanisms in regulating BPV. Omboni al. [31] also reported that PSD of BP in older subjects showed lower power at around 0.1 Hz and higher power between 0.02 and 0.07 Hz, compared to younger subjects. Orthostatic hypotension and postprandial hypotension are common pathophysiology in older people [32–34], and aging-related autonomic dysfunction could play a significant role in worsening hypotension. Aging-related atherosclerosis and reduced arterial distensibility may blunt the baroreflex afferent loop function and result in baroreflex dysfunction [35]. Interestingly, only the frequency domain analysis of BP recording can detect the alteration of BPV with aging.
In this study, we found no significant correlation between age and SD of BP, indicating that aging does not significantly alter BPV in healthy subjects. PSD0.01Hz did not change significantly with age, while PSD0.1Hz was significantly reduced in older subjects. We previously reported that left ventricular (LV) dysfunction induced by myocardial infarction decreased integrated PSD above 0.01 Hz [21]. Since PSD is the Fourier transform of the variance of BP time series, integrated PSD is proportional to BP variance in this frequency range. Berry et al. [36] also reported that ABPM-assessed BPV decreased in heart failure patients. Thus, we need to interpret the amount of BPV carefully considering cardiac function, especially when evaluating patients with depressed cardiac function.
BRS is a useful method for the estimation of baroreflex function. Tank et al. [37] and Boettger et al. [38] reported that BRS was significantly lower in older than in younger subjects. As shown in Fig 4, BRS clearly decreased in an age-dependent manner, while there was no correlation between BRS and the slope of PSD. Since BRS is a HR response to BP change and reflects the cardiac vagal baroreflex, the slope of PSD, which strongly enhances the baroreflex pressure stabilizing function, is not equal to BRS. Further investigations may be needed to clarify the difference in the major determinant of age-related changes in BRS and the slope of PSD.
The renin-angiotensin-aldosterone system (RAAS) may also regulate BPV strongly. In older people, the secretion of aldosterone increases, while the renin activity decreases. This results in a blunted ability to secrete aldosterone against changes in renal blood flow, such as in sodium restriction [39]. Hence, RAAS dysregulation in older people worsens hormonal BP regulation and increases BPV. In addition, inadequate RAAS activation also increases BP and deteriorates baroreflex function through activation of sympathetic nerve activity [40]. Therefore, age-related RAAS dysregulation may steepen the slope of PSD in older subjects. Further investigations may be needed to deepen the understanding of age-related alteration of very short-term BPV in terms of RAAS regulation.
Clinical application of very short-term BPV analysis
In this study, we recorded continuous BP for 30 min and estimated the PSD in the baroreflex operating frequency range. The PSD analysis enables us not only to estimate BPV from the very short-term data but also characterize the BPV regulatory system, the baroreflex system. As mentioned above, BPV is conventionally evaluated by oscillometric devices. Since 24-h ABPM and visit-to-visit BP study assess the variability of intermittent BP measurements, these methods cannot address the frequency characteristics of BPV in the baroreflex operating frequency range. In addition, these methods require long-term device attachment (at least during the night) or a prolonged period of observation (at least three visits), which pose a burden on patients. Our proposed method has the potential as a novel measurement of BPV because it is noninvasive, and the recording time is only 30 min. The PSD analysis makes it possible to stratify BPV considering the BPV regulatory system.
The clinical application of the PSD method requires further investigation. Although the PSD evaluation of BPV requires much shorter time of BP recordings, the 30-min BP recording remains too long as a clinical tool in routine patient care. Thus, we need to develop a novel algorithm to estimate PSD of BP from shorter (such as 5-min) BP recordings without losing the accuracy of PSD estimation.
Although the very short-term BPV analysis has the potential for stratifying the CV risk as shown by several animal studies, the predictive power is not as high as daily BPV assessed by 24-h ABPM. The major reason for the lower predictive power of very short-term BPV may be the lack of clinical evidence [41, 42]. Since the very short-term BPV analysis requires continuous BP measurement devices, the development of those devices will change this situation. In this study, we recorded continuous BP noninvasively using an in-house developed wearable wrist-type BP monitor based on arterial tonometry [10, 23]. Our algorithm of BPV analysis can be applied to any continuous BP recording system. Recently, the field of continuous BP monitor is developing rapidly due to the increasing demand for high-performance healthcare devices and optimal patient management [43]. The volume clamp method (FMS-Finapres Medical System, Arnhem, the Netherlands; CNSystems Medizintechnik AG, Graz, Austria), which can capture instantaneous BP waveform at the finger, has been used for recording continuous BP in clinical settings [44]. As for cuffless continuous noninvasive BP monitors, the Visi Mobile System (Sotera Wireless, CA, USA) based on the pulse transit time method has been shown to provide acceptable BP recording during the long-term attachment [45]. Similar wearable devices such as Caretaker (Caretaker Medical LLC, VA, USA) and BB-613 (Biobeat Technologies LTD, Israel) have been approved as medical devices in the US. With such diverse device development, translation of our method to clinical application would be rather soon.
Limitations
There are several limitations to this study. First, the number of subjects included in this study was relatively small, thus limiting statistical power. In addition, individual variation of PSD of BP was observed in each age group. This variation makes detailed analysis of PSD difficult. Thus, we need to investigate a larger number of healthy subjects to further understand the PSD of BP.
Second, we conjectured that the slope of PSD of BP reflects aging-induced baroreflex failure. Thus, we need to compare the baroreflex function by direct measurement to the data obtained by our proposed method. In addition, autonomic challenges such as cold pressure test, handgrip stress and vasoactive drug administration may deepen the understanding of autonomic function including baroreflex. However, we focused on very short-term BPV with a clinically applicable protocol without using such intervention. Further investigations are also needed to address this issue.
Lastly, the ultimate goal of this study is to derive indices from the PSD of BP for clinical risk stratification. As the first step to achieve this goal, we excluded patients with cardiovascular diseases in this study. In the next step, we need to clarify the significance of PSD-derived indices in patients with cardiovascular diseases, especially hypertension.
Conclusions
The PSD analysis of 30-min continuous BP recording characterizes the very short-term BPV in healthy human subjects. Aging steepens the slope of PSD of BP without changing the magnitude of BPV. Aging-related baroreflex impairment may contribute to the increase in the slope of PSD of BP.
Supporting information
S1 Fig. The PSD characteristics evaluated in this study.
https://doi.org/10.1371/journal.pone.0248428.s001
(PDF)
S2 Fig. Impact of aging on baseline characteristics and BP.
https://doi.org/10.1371/journal.pone.0248428.s002
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S3 Fig. Impact of aging on averaged BP and HR from 30-min continuous recordings.
https://doi.org/10.1371/journal.pone.0248428.s003
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S4 Fig. Impact of gender on baseline characteristics and BP.
https://doi.org/10.1371/journal.pone.0248428.s004
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S5 Fig. Impact of gender on characteristics of PSD of BP.
https://doi.org/10.1371/journal.pone.0248428.s005
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S1 Table. Impact of gender on baseline characteristics and BP.
https://doi.org/10.1371/journal.pone.0248428.s006
(PDF)
Acknowledgments
The authors thank the staff of Omron Healthcare Co. for technical support and Teresa Nakatani for English editing.
References
- 1.
World Health Organization. A global brief on hypertension: silent killer, global public health crisis: World Health Day 2013.
- 2. Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlöf B, et al. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet. 2010;375: 895–905. pmid:20226988
- 3. Kawai T, Ohishi M, Ito N, Onishi M, Takeya Y, Yamamoto K, et al. Alteration of vascular function is an important factor in the correlation between visit-to-visit blood pressure variability and cardiovascular disease. J Hypertens. 2013;31: 1387–95. pmid:24006037
- 4. Kikuya M, Ohkubo T, Metoki H, Asayama K, Hara A, Obara T, et al. Day-by-day variability of blood pressure and heart rate at home as a novel predictor of prognosis: the Ohasama study. Hypertension. 2008;52: 1045–1050. pmid:18981332
- 5. Hoshide S, Yano Y, Mizuno H, Kanegae H, Kario K. Day-by-Day Variability of Home Blood Pressure and Incident Cardiovascular Disease in Clinical Practice: The J-HOP Study (Japan Morning Surge-Home Blood Pressure). Hypertension. 2018;71: 177–184. pmid:29133364
- 6. Johansson JK, Niiranen TJ, Puukka PJ, Jula AM. Prognostic value of the variability in home-measured blood pressure and heart rate: the Finn-Home Study. Hypertension. 2012;59: 212–218. pmid:22215704
- 7. Kikuya M, Hozawa A, Ohkubo T, Tsuji I, Michimata M, Matsubara M, et al. Prognostic significance of blood pressure and heart rate variabilities: the Ohasama study. Hypertens. 2000;36: 901–906. pmid:11082164
- 8. Palatini P, Reboldi G, Beilin LJ, Casiglia E, Eguchi K, Imai Y, et al. Added predictive value of night-time blood pressure variability for cardiovascular events and mortality: the Ambulatory Blood Pressure-International Study. Hypertension. 2014;64: 487–493. pmid:24935939
- 9. Mena LJ, Felix VG, Melgarejo JD, Maestre GE. 24-Hour Blood Pressure Variability Assessed by Average Real Variability: A Systematic Review and Meta-Analysis. J Am Heart Assoc. 2017;6: e006895. pmid:29051214
- 10. Kario K. Evidence and Perspectives on the 24-hour Management of Hypertension: Hemodynamic Biomarker-Initiated ’Anticipation Medicine’ for Zero Cardiovascular Event. Prog Cardiovasc Dis. 2016;59: 262–281. pmid:27080202
- 11. Cowley AW Jr, Liard JF, Guyton AC. Role of baroreceptor reflex in daily control of arterial blood pressure and other variables in dogs. Circ Res. 1973;32: 564–576. pmid:4713198
- 12. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation. 1996;93: 1043–1065. pmid:8598068
- 13. Pagani M, Montano N, Porta A, Malliani A, Abboud FM, Birkett C, et al. Relationship between spectral components of cardiovascular variabilities and direct measures of muscle sympathetic nerve activity in humans. Circulation. 1997;95: 1441–1448. pmid:9118511
- 14. Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, Pizzinelli P, et al. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circulation Research. 1986;59: 178–193. pmid:2874900
- 15. Malliani A, Pagani M, Lombardi F, and Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation. 1991;84: 482–492. pmid:1860193
- 16. Diedrich A, Jordan J, Tank J, Shannon JR, Robertson R, Luft FC, et al. The sympathetic nervous system in hypertension: assessment by blood pressure variability and ganglionic blockade. J Hypertens. 2003;21: 1677–1686. pmid:12923400
- 17. Casali KR, Schaan BD, Montano N, Massierer D, M F Neto F, Teló GH, et al. Correlation between Very Short and Short-Term Blood Pressure Variability in Diabetic-Hypertensive and Healthy Subjects. Arq Bras Cardiol. 2018;110: 157–165. pmid:29561993
- 18.
Guyton AC: Arterial Pressure and Hypertension. Philadelphia: WB Saunders Co, 1980.
- 19. Dworkin BR, Tang X, Snyder AJ, Dworkin S. Carotid and aortic baroreflexes of the rat: II. Open-loop frequency response and the blood pressure spectrum. Am J Physiol Regul Integr Comp Physiol. 2000;279: 1922–1933. pmid:11049878
- 20. Kawada T, Sugimachi M. Open-loop static and dynamic characteristics of the arterial baroreflex system in rabbits and rats. J Physiol Sci. 2016;66: 15–41. pmid:26541155
- 21. Mannoji H, Saku K, Nishikawa T, Tohyama T, Kamada K, Abe K, et al. Estimation of the baroreflex total loop gain by the power spectral analysis of continuous arterial pressure recordings. Am J Physiol Heart Circ Physiol. 2019;316: 828–839. pmid:30608176
- 22. Gribbin B, Pickering TG, Sleight P, Peto R. Effect of age and high blood pressure on baroreflex sensitivity in man. Circ Res. 1971;29: 424–431. pmid:5110922
- 23. Kokubo A, Kuwabara M, Nakajima H, Tomitani N, Yamashita S, Shiga T, et al. Automatic detection algorithm for establishing standard to identify "surge blood pressure". Med Biol Eng Comput. 2020;58: 1393–1404. pmid:32281072
- 24. Welch PD. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics 1967;15: 70–73.
- 25. Bavanandan S, Ajayi S, Fentum B, Paul SK, Carr SJ, Robinson TG. Cardiac baroreceptor sensitivity: a prognostic marker in predialysis chronic kidney disease patients? Kidney Int. 2005;67: 1019–1027. pmid:15698440
- 26. Hesse C, Charkoudian N, Liu Z, Joyner MJ, Eisenach JH. Baroreflex sensitivity inversely correlates with ambulatory blood pressure in healthy normotensive humans. Hypertension 2007;50: 41–46. pmid:17502489
- 27. Frenneaux MP. Autonomic changes in patients with heart failure and in post-myocardial infarction patients. Heart. 2004;90: 1248–1255. pmid:15486114
- 28. Yoshida T, Harasawa Y, Kubota T, Chishaki H, Kubo T, Sunagawa K, et al. Role of carotid sinus baroreflex in attenuating systemic arterial pressure variability studied in anesthetized dogs. Am J Physiol. 1994;266: 720–729. pmid:8141373
- 29.
Castiglioni P, Frattola A, Prati G, Di Rienzo M. 1/f modelling of blood pressure and heart rate spectra: relations to ageing. In: Morucci JP, Plonsey R, Coatrieux JL, Laxminarayan S, eds. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, Paris: IEEE Engineering in Medicine and Biology Society, 1992; 465–466.
- 30. Conci F, Di Rienzo M, Castiglioni P. Blood pressure and heart rate variability and baroreflex sensitivity before and after brain death. J Neurol Neurosurg Psychiatry 2001;71: 621–631. pmid:11606674
- 31. Omboni S, Parati G, Di Rienzo M, Wieling W, Mancia G. Blood pressure and heart rate variability in autonomic disorders: a critical review. Clin Auton Res. 1996;6: 171–182. pmid:8832127
- 32. Masaki KH, Schatz IJ, Burchfiel CM, Sharp DS, Chiu D, Foley D, et al. Orthostatic hypotension predicts mortality in elderly men: the Honolulu Heart Program. Circulation. 1998;24: 2290–2295. pmid:9826316
- 33. Rutan GH, Hermanson B, Bild DE, Kittner SJ, LaBaw F, Tell GS. Orthostatic hypotension in older adults. The Cardiovascular Health Study. CHS Collaborative Research Group. Hypertension. 1992;19: 508–519. pmid:1592445
- 34. Luciano GL, Brennan MJ, Rothberg MB. Postprandial hypotension. Am J Med. 2010;123: 281. pmid:20193838
- 35. Ferrari AU, Radaelli A, Centola M. Invited review: aging and the cardiovascular system. J Appl Physiol. 2003;95: 2591–2597. pmid:14600164
- 36. Berry M, Lairez O, Fourcade J, Roncalli J, Carrié D, Pathak A, et al. Prognostic value of systolic short-term blood pressure variability in systolic heart failure. Clin Hypertens 2016;22: 16. pmid:27413538
- 37. Tank J, Baevski RM, Fender A, Baevski AR, Graves KF, Ploewka K, et al. Reference values of indices of spontaneous baroreceptor reflex sensitivity. Am J Hypertens. 2000;13: 268–275. pmid:10777031
- 38. Boettger MK, Schulz S, Berger S, Tancer M, Yeragani VK, Voss A, et al. Influence of age on linear and nonlinear measures of autonomic cardiovascular modulation. Ann Noninvasive Electrocardiol. 2010;15: 165–174. pmid:20522058
- 39. Nanba K, Vaidya A, Rainey WE. Aging and Adrenal Aldosterone Production. Hypertension. 2018;71: 218–223. pmid:29229745
- 40. Das UN. Renin–angiotensin–aldosterone system in insulin resistance and metabolic syndrome. J Transl Int Med. 2016;4: 66–72. pmid:28191524
- 41. Parati G, Ochoa JE, Lombardi C, Bilo G. Blood pressure variability: assessment, predictive value, and potential as a therapeutic target. Curr Hypertens Rep. 2015;17: 537. pmid:25790801
- 42. Rosei EA, Chiarini G, Rizzoni D. How important is blood pressure variability? Eur Heart J suppl. 2020;22; E1–E6. pmid:32523429
- 43. Stojanova A, Koceski S, Koceska N. Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL)—Review of Methodologies and Devices. J Med Syst. 2019;43: 24. pmid:30603777
- 44. Pressman GL, Newgard PM. A transducer for the continuous external measurement of arterial blood pressure. IEEE trans Biomed Eng. 1963;10: 73–81. pmid:14064061
- 45. Zhang G, McCombie SA, Greenstein R, McCombie DB. Assessing the challenges of a pulse wave velocity based blood pressure measurement in surgical patients. Conf Proc IEEE Eng Med Biol Soc. 2014;2014: 574–577. pmid:25570024