Subjects
We studied 173 healthy volunteers (89 males and 84 females) aged 22 to 75 (48 ± 1) years. The purpose and procedures of this study were explained to the volunteers, who then provided written informed consent to participate. The Ethics Committee of the Institute for Human Science and Biomedical Engineering of the National Institute of Advanced Industrial Science and Technology reviewed and approved the study protocols.
Curve relationship between cuff pressure and arterial volume
Computer simulations have suggested that the relationship between cuff pressure and arterial volume during cuff deflation can be described as a sigmoid curve (Figure 1) [8–10] and that blood volume pulses or cuff oscillations can be generated by transforming blood pressure pulses [8]. When blood pressure pulses are generated at different cuff pressures, as shown below the horizontal axis of Figure 1, corresponding blood volume pulses or cuff oscillations can be described, as shown on the left of the vertical axis. Based on this theory, local slopes of the pressure-volume curve can be calculated from the occlusive cuff pressure for pulse pressure (systolic - diastolic blood pressure) and the amplitude of cuff oscillations. Thus, we calculated local slopes from cuff pressure and cuff oscillations, and obtained pressure-volume curves by numerically integrating the local slopes.
In the experimental day, a conventional blood pressure cuff was wrapped around the left upper arm of seated participants and was inflated to 190 mmHg at 10 mmHg/s, and deflated to 10 mmHg at 3 mmHg/s. Cuff pressure during inflation and deflation measured using a pressure transducer was stored in a computer at a sampling frequency of 1 kHz for off-line analysis. After recording cuff pressure, a low-pass filter with a cutoff frequency of 0.5 Hz was applied to the raw cuff pressure, and a time series of occlusive cuff pressure was obtained (Figure 2A). A band-pass filter of 0.5 - 10 Hz was applied to the raw cuff pressure to determine cuff oscillation evoked by the blood pressure pulse (Figure 2B). The amplitudes of the cuff oscillations of every blood pressure pulse were calculated (Figure 2C). Heart rate and blood pressure were measured oscillometrically and pulse pressure was calculated from systolic and diastolic blood pressure. Using the amplitudes of all pulse oscillations and changes in the cuff pressure for pulse pressure from the pressure point evoked by the pulses, we calculated the local slopes of the curve between the cuff pressure and arterial volume (Figure 2D). The slopes at all cuff pressures were averaged to estimate the slopes at an arbitrary point on pressure-volume curves (Figure 2E). We calculated the numerical integration of the averaged slopes to generate pressure-volume curves (Figure 2F) that were fitted using the following equation to determine their characteristics:
The numerical coefficient B of above equation was used to evaluate arterial stiffness because coefficient B closely reflected the slope of the curve. We identified the numerical coefficient B as an arterial stiffness index and named it the arterial pressure-volume index (API). Cuff pressure was measured three times and the average API was calculated for each participant. The day-to-day coefficient of variation for API in a pilot study on 8 subjects (4 male and 4 female, 22 - 55 years) was 6.0 ± 1.1%.
We measured the circumference of the left upper arm, because we indirectly estimated the arterial pulse volume via the cuff oscillation and thus should examine the effects of the fat or muscle size on API.
Pulse wave velocity (PWV)
We measured brachial-ankle (baPWV) and carotid-femoral (cfPWV) pulse wave velocity using a vascular testing device (Form PWV/ABI, Omron Healthcare, Kyoto, Japan) as we described [18]. Carotid and femoral arterial pressure waveforms were stored for 30 s using applanation tonometry sensors attached to the left common carotid artery and left common femoral artery. Bilateral brachial and post-tibial arterial pressure waveforms were stored for 10 s by occlusion/sensing cuffs adapted to both arms and ankles. The waveform analyzer measured the time intervals between the carotid and femoral arterial pressure wave (Tcf), and between the brachial and post-tibial arterial pressure wave (Tba). The arterial pressure wave was identified as the start of the sharp systolic upstroke, which was automatically detected using a band-pass filter (5 - 30 Hz). The path length from the carotid to the femoral artery (Dcf) was directly assessed in duplicate with a random zero length measurement over the surface of the body with a nonelastic tape measure [19]. The path lengths from the heart to the brachial artery (Dhb), from the heart to the femur (Dhf), and from the femur to the ankle (Dfa) were automatically calculated in cm using the following equations [20]:
where height is in cm,
and then baPWV and cfPWV were calculated as:
Carotid arterial compliance
We also compared the API with carotid arterial compliance, although the sample size was limited to 92 of the 173 participants. Carotid arterial compliance was determined using a combination of ultrasound imaging of the common carotid artery and carotid arterial blood pressure as we described [21]. Carotid arterial blood pressure was noninvasively measured using applanation tonometry (Form PWV/ABI, Omron Healthcare, Kyoto, Japan). Longitudinal B-mode images of the right common carotid artery were obtained ultrasonically (SonoSite 180PLUS, SonoSite Inc., Bothell, WA, USA) with a high-resolution linear-array transducer (10 MHz) placed at 1 to 2 cm proximal to the carotid bulb, with an approximately 90° angle to the vessel so that the near and far wall interfaces were clearly discernible. The ultrasound images were recorded on digital videotapes for offline analysis. The ultrasound images were stored in a computer at 30 Hz and analyzed using image-analysis software (ImageJ 1.32J, NIH, Bethesda, MD, USA). One investigator who was blinded to the subject characteristics performed all image analyses. Carotid arterial lumen diameter was determined as the distance between the vessel far-wall boundary corresponding to the interface between the lumen and intima. The near-wall boundary was defined as the interface of the adventitia and media at minimal diastolic relaxation and at maximal systolic expansion of the vessel. Arterial lumen diameter at minimal diastolic relaxation and maximal systolic expansion of the vessel were measured at three points per video frame and then averaged. Each parameter was averaged over 10 - 15 continuous beats and statistically analyzed. Arterial compliance was determined using the equation:
where CSAs and CSAd are cross-sectional areas at the maximal systolic expansion and at the minimal diastolic relaxation of the carotid artery, and ΔP is the carotid arterial pulse pressure. In addition to arterial compliance, the β-stiffness index was analyzed using the equation:
where cSAP and cDAP are systolic and diastolic carotid arterial blood pressure, D is end-diastolic carotid lumen diameter and ΔD is the change in carotid lumen diameter between end diastole and peak systole [22]. The β-stiffness index provided an index of arterial compliance adjusted for distending pressure [22].
Experimental protocol
Subjects abstained from food and caffeine for at least 4 h before the experiment. Measurements were taken in a quiet, temperature-controlled room (24 - 26°C). API, PWV and carotid arterial compliance were measured on the same day in random order.
Statistical analysis
The relationships between baPWV, cfPWV, carotid arterial compliance, or age and the API were analyzed using a linear regression model and Pearson's correlation coefficient.
To determine which factor explained a given dependent variable, a stepwise multiple regression analysis was carried out. The selected parameters for the analysis were API, baPWV, carotid arterial compliance, mean arterial blood pressure (MAP), and the circumference of the left upper arm. Dependent variables were API, baPWV, or carotid arterial compliance. Independent variables were all of the rest. Parameters were selected with a caution of multicollinearity among the independent variables. Consequently, cfPWV and age were excluded, because cfPWV was strongly correlated with baPWV (r = 0.85) and age was significantly related with all of the parameters of arterial stiffness (baPWV, carotid compliance, and API) and MAP.
Statistical significance was defined at P < 0.05 for all data, which are expressed in the text and figures as means ± SE.