Department of Medicine for the Elderly, University of Leicester, The Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK. 1Department of Medical Physics, ...
q 1999, British Geriatrics Society
Age and Ageing 1999; 28: 347–353
Older subjects show no age-related decrease in cardiac baroreceptor sensitivity SUZANNE L. DAWSON, THOMPSON G. ROBINSON, JANE H. YOUDE, ALISON MARTIN, MARTIN A. JAMES, PHILIP J. WESTON, RONNEY B. PANERAI1, JOHN F. POTTER Department of Medicine for the Elderly, University of Leicester, The Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK 1
Department of Medical Physics, University of Leicester, Leicester Royal Infirmary, Infirmary Square, Leicester LE1 5WW, UK
Address correspondence to: S. L. Dawson. Fax: (þ44) 116 232 2976
Abstract Objective: to examine the relationship between age, blood pressure and cardiac baroreceptor sensitivity derived from spectral analysis, the Valsalva manoeuvre and impulse response function. Methods: we studied 70 healthy normotensive volunteers who were free from disease and not taking medication with cardiovascular or autonomic effects. We measured beat-to-beat arterial blood pressure and used standard surface electrocardiography to record pulse interval under standardized conditions with subjects resting supine as well as during three Valsalva manoeuvres. We performed single, multiple and stepwise regression of patient characteristics against cardiac baroreceptor sensitivity results. Results: there is a non-linear decline in cardiac baroreceptor sensitivity with advancing age, increasing systolic blood pressure and heart rate values (except for the Valsalva-derived result), but little further decline after the fourth decade. Only age significantly influenced values derived using the Valsalva manoeuvre and impulse response analysis. Using spectral analysis, age, systolic and diastolic blood pressure and heart rate influenced cardiac baroreceptor sensitivity, age contributing to 50% of the variability. Age also influenced the relationship between pulse interval and blood pressure, possibly indicating more non-baroreceptor-mediated changes with advancing age. Conclusions: although age is the dominant factor influencing cardiac baroreceptor sensitivity in this normotensive population, there is little change in mean values after 40 years of age. The differences in the relationship between pulse interval and blood pressure with advancing age have implications for the calculation of cardiac baroreceptor sensitivity using spectral analysis. Keywords: age, blood pressure, cardiac baroreflex sensitivity, impulse response function, phase response, regression analysis
Introduction The cardiac-baroreceptor reflex arc is one of the many physiological mechanisms involved in the short-term control of arterial blood pressure. Researchers have examined factors influencing cardiac baroreceptor sensitivity (BRS) [1–5]. Gribbin et al. [1] were the first to identify an age-related decline; others have confirmed this [2–5], but few subjects over 60 years have been investigated. This is partly because of the invasive nature of traditional methods of measuring beat-to-beat changes in arterial blood pressure.
Increasing age is associated with a rise in blood pressure. There is debate as to which factor influences BRS more. Gribbin et al. [1] felt that age and blood pressure acted independently and that increasing values were inversely associated with BRS. Conversely, work from our department, using pharmacological and non-invasive methods of measuring BRS, found that age contributed only 7% of the variance in cardiac BRS compared with 27–45% for systolic blood pressure (SBP) in subjects over 60 years [4]. Spectral methods of calculating cardiac BRS have several advantages over conventional invasive pharmacological methods and give similar BRS values [6].
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S. L. Dawson et al. Power spectral analysis involves the detection of rhythmicity in computer-derived tachograms of beatto-beat recordings of blood pressure and pulse interval (PI); various algorithms can then be used to assess the number, frequency and amplitude of the oscillatory components. However, this analysis requires the prior selection of frequency bands for blood pressure and PI. Previous studies favoured either the low-frequency band (0.05–0.15 Hz) or the combined low- and highfrequency bands (0.15–0.35 Hz)—the a value [7–12]. Two other measures are important in spectral analysis: phase and coherence. Phase, in cardiac BRS settings, is the time relationship between changes in SBP and PI. A negative phase value implies that changes in SBP are leading changes in PI, reflecting a baroreceptor-mediated sequence. A positive phase implies PI leading SBP and reflects system noise or non-baroreceptor-mediated sequences. Coherence is a measure of input–output coupling. It assesses the statistical significance between the dynamic change in arterial blood pressure and the resulting change in PI. It is similar to the correlation coefficient, with a range of 0 (no relation) to 1.0 (very strongly correlated). A value for squared coherence that is significantly 160 mmHg or diastolic blood pressure (DBP) phase V >95 mmHg.
Methods All subjects were examined using a standardized protocol [15]. They were asked to attend in the morning after abstinence from caffeine, nicotine or alcohol-containing products for at least 12 h and at least 2 h after breakfast. The study was performed in a quiet laboratory kept at a constant temperature (20–248C), with subdued lighting. Subjects wore loose comfortable clothing and were asked to micturate just before the study. Subjects lay supine on a couch, the head supported by two pillows. Standard surface electrocardiographic limb leads were attached for continuous monitoring. A Finapres cuff (Finapres NIBP Ohmeda System, CO, USA) [16–18] of appropriate size was placed around the right middle finger for beat-to-beat arterial blood pressure measurement. The arm was supported at atrial height on a specifically designed rest attached to the couch. Subjects were asked to breathe at a rate of 15 breaths per minute; if necessary, a metronome was used to aid this. After 10–15 min when the readings had stabilized and subjects were accustomed to the setting, PI and blood pressure were recorded directly onto a dedicated microcomputer fitted with a 12-bit analogue– digital converter with a sampling frequency of 200 samples/s per channel. The servo-adjust mechanism of the Finapres was disconnected and restarted between recording periods. We made three consecutive 5-min recordings while subjects lay quietly. They were asked not to talk. Each subject was then asked to sit up and with their arm supported at atrial height to perform three consecutive Valsalva manoeuvres (which had been practised beforehand) [19, 20]. They were asked to obtain a mouth pressure of 40 mmHg for 15 s. This pressure was visually displayed on a transducer to aid compliance. A constant bleed device was integral to the system. We recorded mouth pressure, heart rate and blood pressure changes throughout the procedure and for 1 min afterwards. When outputs had returned to baseline levels, the manoeuvre was repeated.
Data analysis We analysed the data using specially designed software
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Age-related decline in cardiac baroreceptor sensitivity [21]. First, data were calibrated from the Finapres recording at the start of each session and then edited. If the Valsalva manoeuvre was being analysed, then the pressure transducer recording was also calibrated. The PI was marked from the Finapres trace: this is less affected by external stimuli (e.g. muscle activity during the Valsalva manoeuvre) than the surface electrocardiography trace. There is no difference between BRS values calculated using PI derived from the electrocardiographm or blood pressure trace [22]. Spectral analysis in the frequency domain using fast Fourier transformation (FFT) and then the Valsalva-derived BRS were calculated [7–12, 19, 20]. In each case, we took the mean of the three recordings. Signals were low-pass filtered with an eighth-order Butterworth digital filter with a cut-off frequency of 20 Hz. Beat-to-beat sequences of SBP and PI were extracted and were linearly interpolated in the event of an ectopic beat. We rejected recordings of more than four ectopics. We employed FFT which used 512 samples; beat-to-beat changes were interpolated using a third-order polynomial and then resampled with a 0.5-s interval. The power spectra were averaged over three readings and smoothed with a 13-point triangular window. We calculated the baroreflex sensitivity index, ~ , from the mean of the square roots of the ratios of the spectral powers of SBP and PI in the low-frequency (0.05–0.15 Hz) and high-frequency (0.15–0.35 Hz) bandwidths. We calculated the Valsalva-derived BRS using the linear regression of SBP and PI during phase IV of the Valsalva manoeuvre. IRF was calculated from the same three recordings. A transfer function was computed by dividing the cross spectrum by the power spectrum of SBP. We applied a cosine-shaped low-pass filter with a cut-off frequency of 0.5 Hz in the frequency domain and obtained the IRF with an inverse FFT [14]. The highest value obtained is termed the IRFpeak; by using this value and averaging it and the two neighbouring values the IRFsmooth is obtained. We used this latter value as our measure of IRF [14].
Table 1. Subject characteristics: demographics and cardiac baroreceptor sensitivity calculated by three different methods Variable
Mean (SD) Range
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Age, years 52 (18) 24.1 (3.6) Body mass index, kg/m2 Clinic blood pressure, mmHg Systolic 131 (16) Diastolic 76 (9) Pulse, bpm 69 (11) Cardiac baroreceptor sensitivity, ms/mmHg FFT 13.7 (8.3) Valsalva 5.8 (3.4) IRF 9.0 (7.1)
22–82 18.0–33.9 97–160 57–92 48–93 2.6–35.4 0.9–14.8 0.6–27.6
n ¼ 70 (32 male). FFT, baroreceptor sensitivity derived by power spectral analysis using fast Fourier transformation; Valsalva, baroreceptor sensitivity derived from phase IV of the Valsalva manoeuvre; IRF, baroreceptor sensitivity derived from the peak-smoothed impulse response function.
two groups: 50 years. This age division was made ‘post-analysis’ since most decline in BRS was seen in the third and fourth decades and data on phase and coherence were missing in the few subjects aged 45–50 years. We calculated values for each in the low-frequency (0.05–0.15 Hz) and highfrequency (0.15–0.35 Hz) bandwidths and compared them both graphically and by parametric tests. The study had local ethical committee approval and all subjects gave written informed consent.
Results We studied 70 subjects (32 male) with a mean age of 52 years [range 22–82], SBP of 131 mmHg [range 97–160] and DBP of 76 mmHg [range 57–92] (Table 1).
Statistical analysis We analysed data using the Minitab 10 release software program. These are presented as mean 6 SD after assessing for normality, using Shapiro–Wilk normality plots. Where necessary we carried out log transformation to achieve normality; the use of logarithmic transformation was an arbitrary decision. For each BRS measurement (FFT, Valsalva-derived, IRF), we performed single regression, multiple linear regression and forward stepwise regression analyses using the different subject characteristics as the determinant parameters. Statistical significance was set at the 5% level. The influence of age on phase and coherence was examined by dividing the study population into
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Figure 1. Plot of fast Fourier transformation-derived baroreceptor sensitivity (BRS FFT) versus age.
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Figure 2. Plot of logarithmically transformed fast Fourier transformation-derived baroreceptor sensitivity (BRS FFT) data versus age.
The changes in cardiac BRS with age, as calculated using FFT, are illustrated in Figure 1. These indicate a non-linear decline in BRS with age. All three methods gave similar results, with little further decline after the fourth and fifth decades (only FFT data are shown graphically). Logarithmic transformation yields a more linear relation, with linear regression residuals that are normally distributed (Figure 2). All three BRS data sets were, therefore, logarithmically transformed; subject characteristics did not require transformation. There was a highly significant inverse relationship between the transformed BRS and age for all three measurements [P < 0:001; illustrated for FFT in Figure 2 (r ¼ ¹0:69)]. For further statistical analysis we used the transformed BRS values.
The univariate correlates for the three methods of BRS calculation and age, BMI, gender, blood pressure and heart rate are shown in Table 2. The regression coefficients for age and clinic SBP were significant (P < 0:001) for all three methods of measuring BRS— for example, logFFT ¼ 1.68–0.0118age (i.e. every year the BRS falls by 1.2% of the previous year’s value) and logFFT ¼ 2.0–0.00711SBP (i.e. for every 1 mmHg increase in SBP BRS falls by 0.7% of the previous value). The only other measure which achieved statistical significance was heart rate with FFT and IRF. Multiple linear regression using the same independent variables as in the univariate analysis showed that age, clinic SBP, DBP and heart rate were significant predictors of FFT BRS variability (P < 0:05), but that only age was significant for Valsalva-derived (P < 0:05) and IRF-derived (P < 0:01) BRS variance. We placed these significant predictors for FFT variability in a stepwise linear regression model to examine their individual and cumulative contribution. Again, age was strongly predictive, contributing 50.3% of the variability. Although heart rate was a significant factor, only a further 4.2% was added to the predictability, and clinic SBP was rejected from this model. Single regression of age and SBP and age and DBP were highly significant (age ¼ ¹11:9 þ 0:486SBP, P < 0:001; age ¼ 11:4 þ 0:528DBP, P < 0:012), but neither age and heart rate nor heart rate and SBP were significantly related. The plots of phase versus frequency for the older (
