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Dynamic and static baroreflex control of muscle sympathetic nerve activity (SNA) parallels that of renal and cardiac SNA during physiological change in pressure

¹Department of Cardiovascular Dynamics, National Cardiovascular Center Research Institute, Osaka; and ²Department of Cardiovascular Medicine, Kyusyu University Graduate School of Medical Sciences, Fukuoka, Japan

Submitted 14 June 2005 ; accepted in final form 26 July 2005

	ABSTRACT

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Despite accumulated knowledge on human baroreflex control of muscle sympathetic nerve activity (SNA), whether baroreflex control of muscle SNA parallels that of other SNAs, in particular renal and cardiac SNAs, remains unclear. Using urethane and -chloralose-anesthetized, vagotomized and aortic-denervated rabbits (n = 10), we recorded muscle SNA from tibial nerve by microneurography, simultaneously with renal and cardiac SNAs by wire electrode. To produce a baroreflex open-loop condition, we isolated the carotid sinuses from systemic circulation and altered the intracarotid sinus pressure (CSP) according to a binary white noise sequence of operating pressure ± 20 mmHg (for investigating dynamic characteristics of baroreflex) or in stepwise 20-mmHg increments from 40 to 160 mmHg (for investigating static characteristics of baroreflex). Dynamic high-pass characteristics of baroreflex control of muscle SNA, assessed by the increasing slope of transfer gain, showed that more rapid change of arterial pressure resulted in greater response of muscle SNA to pressure change and that these characteristics were similar to cardiac SNA but greater than renal SNA. However, numerical simulation based on the transfer function shows that the differences in dynamic baroreflex control at various organs result in detectable differences among SNAs only when CSP changes at unphysiologically high rates (i.e., 5 mmHg/s). On the other hand, static reverse-sigmoid characteristics of baroreflex control of muscle SNA agreed well with those of renal or cardiac SNAs. In conclusion, dynamic-linear and static-nonlinear baroreflex control of muscle SNA is similar to that of renal and cardiac SNAs under physiological pressure change.

carotid sinus pressure

Despite accumulated knowledge on baroreflex control of muscle SNA, whether the control of muscle SNA parallels that of other visceral organs innervated by the sympathetic nerve system, including the kidney and heart, remains unclear. This is because the human microneurographic technique is mainly limited to the upper and lower limbs (16, 18). Although earlier human studies reported that microneurographical muscle SNA correlated with norepinephrine spillovers in the kidney and heart at rest (30, 31), these studies did not assess baroreflex control of SNA. Because of a lack of definitive evidence, the impact of investigating baroreflex control of muscle SNA could be somewhat limited. Accordingly, we tested whether dynamic and static baroreflex control of muscle SNA is similar to that of renal and cardiac SNAs. We recorded muscle SNA by microneurography simultaneously with renal and cardiac SNAs in anesthetized rabbits. We then compared the dynamic-linear and static-nonlinear characteristics of baroreflex control for the three forms of SNAs.

	MATERIALS AND METHODS

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Preparation. Animals were cared for in strict accordance with the "Guiding Principles for the Care and Use of Animals in the Field of Physiological Science" approved by the Physiological Society of Japan. The experimental protocol was approved by the animal experiment committee of Japan Aerospace Exploration Agency. Ten Japanese white rabbits weighing 2.4–3.3 kg were initially anesthetized by intravenous injection (2 ml/kg) of a mixture of urethane (250 mg/ml) and -chloralose (40 mg/ml). Anesthesia was maintained by continuously infusing the anesthetics at a rate of 0.33 ml·kg^–1·h^–1 using a syringe pump (CFV-3200, Nihon Kohden, Tokyo). The rabbits were mechanically ventilated with oxygen-enriched room air. Bilateral carotid sinuses were isolated vascularly from the systemic circulation by ligating the internal and external carotid arteries and other small branches originating from the carotid sinus regions. The isolated carotid sinuses were filled with warmed physiological saline preequilibrated with atmospheric air through catheters inserted via the common carotid arteries. The intracarotid sinus pressure (CSP) was controlled by a servo-controlled piston pump (model ET-126A, Labworks; Costa Mesa, CA). Bilateral vagal and aortic depressor nerves were sectioned in the middle of the neck region to eliminate reflexes from the cardiopulmonary region and the aortic arch. The systemic arterial pressure (AP) was measured using a high-fidelity pressure transducer (Millar Instruments; Houston, TX) inserted retrogradely from the right common carotid artery below the isolated carotid sinus region. Body temperature was maintained at 38°C with a heating pad.

The left renal sympathetic nerve was exposed retroperitoneally, and the left cardiac sympathetic nerve was exposed through a middle thoracotomy. A pair of stainless steel wire electrodes (Bioflex wire AS633, Cooner Wire) was attached to each of these nerves to record renal and cardiac SNAs. The left tibial nerve was exposed at the right popliteal fossa through incising the flexors in the dorsal middle region of the thigh. A tungsten microelectrode (model 26–05-1, Frederick Haer; Bowdoinham, ME) was inserted into the right tibial nerve to record muscle SNA, based on human (16, 26) and animal (20) microneurography. We identified muscle SNA by the following discharge characteristics: 1) afferent activity induced by tapping of the calf muscles but not by gently touching the skin, and 2) excitatory and inhibitory responses induced by decreasing and increasing CSP, respectively.

The nerve fibers peripheral to electrodes were ligated securely and crushed to eliminate afferent signals. The nerve and electrodes were covered with a mixture of silicone gel (Silicon Low Viscosity, KWIK-SIL, World Precision Instrument) to insulate and immobilize the electrodes. The preamplified SNA signals were band-pass filtered at 150–1,000 Hz. These nerve signals were full-wave rectified and low-pass filtered with a cutoff frequency of 30 Hz to quantify the nerve activity.

Evaluation of baroreflex control of SNA: dynamic-linear and static nonlinear characteristics. Baroreflex controls of cardiac and renal SNAs have dynamic-linear high-pass and static-nonlinear reverse-sigmoidal characteristics (14). The dynamic high-pass characteristics indicate that more rapid change of arterial pressure is associated with greater SNA response. Identifying the transfer function from baroreceptor pressure input to SNA is the most powerful tool to quantify dynamic-linear characteristics. Importantly, the transfer function can predict linear SNA responses to any baroreceptor pressure input. On the other hand, the baroreflex control also has static-nonlinear reverse-sigmoidal characteristics that cannot be explained by dynamic-linear characteristics, particularly under steady-state condition (14). Accordingly, we evaluated baroreflex control of SNA by assessing both dynamic-linear and static-nonlinear characteristics while opening the baroreflex feedback loop independently for muscle, cardiac, and renal SNAs.

Protocols. After the surgical preparation, the rabbit was maintained supine. Protocols 1 and 2 described below were conducted to assess static-nonlinear and dynamic-linear characteristics, respectively, in randomized order with an interval of at least 5 min. Both protocols were conducted in all animals (n = 10). In both protocols, bilateral CSP was controlled by a servo-controlled piston pump (14).

In protocol 1, CSP was increased stepwise from 40 to 160 mmHg in increments of 20 mmHg. Each pressure step was maintained for 60 s. The three SNAs, CSP, and AP were recorded for 7 min at a sampling rate of 200 Hz using a 12-bit analog-to-digital converter. Data were stored on the hard disk of a dedicated laboratory computer system.

In protocol 2, CSP was first matched with systemic AP to obtain the operating AP under the baroreflex closed-loop condition. After at least 5 min of stabilization, the three SNAs, CSP, and AP were recorded for 10 min and stored as in protocol 1. The average AP over 10 min was defined as the operating AP. Then, after at least 5 min of stabilization, CSP was randomly assigned at 20 mmHg above or below the operating AP every 500 ms according to a binary white noise sequence in which the input power spectrum of CSP was reasonably flat up to 1 Hz (14). The three SNAs, CSP, and AP were recorded for 10 min and stored for analysis.

Data analysis. SNA signals were normalized by the following steps. First, for each type of SNA, 0 arbitrary unit (au) was assigned to the postmortem noise level. Second, SNA signals were averaged for the last 10 s at CSP level of 40 mmHg in protocol 1; 100 au were then assigned to the average SNA. Last, the other SNA signals in both protocols 1 and 2 were then normalized to these values.

In protocol 1, muscle, renal and cardiac SNAs were averaged for the last 10 s of each CSP level. The static-nonlinear relation between CSP and each SNA was parameterized using a four-parameter logistic equation model as follows:

(1)

where P₁ is the response range of SNA (i.e., the difference between the maximum and minimum SNA), P₂ is the coefficient of gain, P₃ is the midpoint CSP of the logistic function, and P₄ is the minimum SNA. We calculated the instantaneous gain from the first derivative of the logistic function, and the maximum gain from –P₁P₂/4 at x = P₃.

In protocol 2, we calculated the transfer (the gain and phase) and coherence functions from CSP input to each SNA. We resampled CSP and SNA at 10 Hz and segmented them into 10 sets of 50% overlapping bins of 2¹⁰ data points each. The segment length was 102.4 s, which yielded the lowest frequency bound of 0.01 (0.0097) Hz. We subtracted a linear trend and applied a Hanning window for each segment. We then performed fast Fourier transform to obtain frequency spectra of CSP and SNA. We ensemble-averaged the CSP power [Sxx(f)], SNA power [Syy(f)], and cross power between CSP and SNA [Syx(f)] over the 10 segments. Thereafter, we calculated the transfer function [H(f)] from CSP to SNA as follows

(2)

To quantify the linear dependence between CSP and SNA in the frequency domain, we calculated the magnitude-squared coherence function [Coh(f)] as follows

(3)

The coherence value ranges from zero to unity. Unity coherence indicates a perfect linear dependence between CSP and SNA, whereas zero coherence indicates total independence of these two signals.

Statistical analysis. All data are presented as means ± SD. We used a repeated-measures analysis of variance with post hoc multiple comparisons to compare variables among muscle, renal, and cardiac SNAs. Differences were considered significant when P < 0.05.

	RESULTS

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Static baroreflex characteristics (protocol 1). Muscle SNA decreased in response to stepwise increase in CSP in protocol 1. The change in muscle SNA appeared similar to that in renal or cardiac SNA (Fig. 1). The relation between CSP and muscle SNA was fitted to four-parameter logistic function in individual animals. The fitted logistic function of muscle SNA almost superimposed that of renal or cardiac SNA (Fig. 2). The parameters of P₁, P₂, P₃, and P₄ and the maximal gain (at the midpoint of the sigmoid curve) of muscle SNA were similar to those of renal or cardiac SNA (Fig. 2, Table 1).

View larger version (20K): [in this window] [in a new window]   Fig. 1. Representative data of one rabbit in protocol 1, showing integrated signals of muscle, cardiac, and renal sympathetic nerve activity (SNA) during stepwise increase in carotid sinus pressure (CSP). Each step is 60 s. Fine lines indicate SNA signals resampled at 10 Hz. Bold lines and closed circles indicate SNA signals averaged over the last 10 s of each CSP level, which were used to determine the static nonlinear characteristics of baroreflex control of each SNA. au, Arbitrary unit.

View larger version (11K): [in this window] [in a new window]   Fig. 2. Static nonlinear, reverse-sigmoidal baroreflex relationship between each SNA (muscle, cardiac, and renal SNA) and CSP from all animals (n = 10) in protocol 2. , Mean SNA. Error bars denote SD. The static nonlinear characteristics of muscle SNA were similar to those of cardiac and renal SNAs.

View larger version (32K): [in this window] [in a new window]   Fig. 3. Representative data of 1 rabbit in protocol 2, showing time series of CSP and muscle, cardiac, and renal SNA during CSP perturbation. CSP was changed according to a binary white noise signal with a switching interval of 500 ms.

View larger version (32K): [in this window] [in a new window]   Fig. 4. A: transfer function from CSP to renal SNA (HRenalSNA), to cardiac SNA (HCardiacSNA) and to muscle SNA (HMuscleSNA) from all animals (n = 10) in protocol 2. Gain plots (top), phase plots (middle), and coherence function (bottom) are shown. Slope of the transfer gain increases more markedly in HMuscleSNA and HCardiacSNA than in HRenalSNA. B: step responses (Step res) derived from HRenalSNA, HCardiacSNA, and HMuscleSNA. Solid and dashed lines represent mean and mean + SD values, respectively.

The coherence of the transfer function from CSP to muscle SNA was over 0.8 between 0.1 and 0.8 Hz, except at 0.35 Hz (Fig. 4A). The coherence of muscle SNA was greater than that of cardiac and renal SNAs at 0.1, 0.8, and 1 Hz (P < 0.05) (Table 2).

The step response of muscle SNA to CSP consisted of an initial decrease followed by partial recovery and then steady state (Fig. 4B). The initial decrease in muscle SNA was similar to that in cardiac SNA but greater than that in renal SNA (Fig. 4B, Table 2). However, steady-state muscle SNA was similar to that of cardiac and renal SNAs (Fig. 4B, Table 2).

	DISCUSSION

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Despite accumulated knowledge on baroreflex control of muscle SNA, whether the baroreflex control of muscle SNA parallels that of other visceral organs innervated by the sympathetic nervous system, including the kidney and heart, remains unclear. This study has two major new findings. First, the dynamic high-pass characteristic of baroreflex control for muscle SNA, assessed by the increasing slope of transfer gain, is similar to that of cardiac SNA but greater than that of renal SNA. However, the difference is physiologically insignificant, because it may induce detectable differences among SNAs only when AP changes at unphysiologically high rates (see below). Second, the static reverse-sigmoidal relationship between CSP and muscle SNA is almost identical to that of both cardiac and renal SNAs. These findings support our hypothesis to a large extent and indicate that dynamic-linear and static-nonlinear baroreflex control of muscle SNA is similar to that of renal and cardiac SNAs under physiological pressure changes.

The present study quantified the dynamic high-pass characteristics of baroreflex control of muscle SNA by opening the baroreflex feedback loop. In humans, responses of muscle SNA to change in AP is believed to depend on the speed of AP change; more rapid AP change is associated with greater muscle SNA response to AP change. This suggests the presence of high-pass characteristics in the baroreflex control of muscle SNA, and this was actually found in previous human study by sinusoidal modulation of muscle SNA by neck suction at varying frequencies (1). We investigated the transfer function in animals and showed that the transfer gain increased as the frequency of CSP perturbation increased when the increasing slope of transfer gain was 1.84 dB/octave (Table 2). This finding indicates that when CSP changes more rapidly with doubling of the frequency, SNA response increases 1.24 times. In addition, our calculated step response from high-pass transfer function (initial decrease followed by partial recovery, Fig. 4B) agrees with the time series of human muscle SNA observed during graded neck pressure or suction (22), supporting the validity of our system identification.

Our data revealed that the dynamic high-pass characteristics of baroreflex control of muscle SNA are similar to those of cardiac SNA but greater than those of renal SNA. In other words, more rapid AP change results in greater muscle SNA in response to pressure change, and this characteristic is similar to cardiac SNA but stronger than renal SNA. Quantitative estimation from the increasing slopes of transfer gain (Table 2) indicates that when the frequency of CSP doubles, the response of cardiac SNA response increases 1.30 times, which is statistically similar to muscle SNA (1.24 times), whereas the response of renal SNA increases 1.13 times and is lower than the muscle and cardiac SNAs. The difference between cardiac and renal SNAs is consistent with previous study (14).

Numerical simulation based on the transfer function estimated by protocol 2 shows that the differences in dynamic baroreflex control in various organs induce detectable differences among SNAs only when AP changes at very high rates (Fig. 5). According to our previous studies (15), we modeled transfer function of baroreflex control of SNA (see APPENDIX) by setting the parameters of the function to reflect our actual data. The numerical simulation shows that the faster the increasing speed of CSP, the more prominent are the differences among the responses of three SNAs; muscle and cardiac SNAs decrease more markedly than renal SNA (Fig. 5, compare D and C to B). However, the organ-dependent differences become detectable only when CSP increases at a high speed of 5 mmHg/s. In clinical situation, AP hardly increases at such high rates even with pharmacological intervention in medical treatment; therefore these SNAs may be similar under physiological pressure changes. Moreover, even if CSP increases at a very high rate, the three SNAs reach similar steady-state activities, reflecting their similar transfer gains at the lowest frequency (Fig. 4A, Table 1).

View larger version (22K): [in this window] [in a new window]   Fig. 5. Simulation of muscle, cardiac, and renal SNA in response to rapid (B), moderate (C) and slow (D) ramp increase in CSP. On the basis of the data from protocol 2, the transfer function of baroreflex control of SNA (from CSP input to SNA) is modeled (A) as described in APPENDIX. In all panels (except CSP panels), solid, thin dashed, and dashed lines represent the simulation for muscle, cardiac, and renal SNA, respectively. Data of cardiac SNA almost overlap with those of muscle SNA (except Phase panel in A). When CSP increases more rapidly (B), muscle and cardiac SNAs decrease in response to CSP change more markedly than renal SNA. Of note, time axes are different among panels. The slower the increasing speed of CSP, the smaller the difference among responses of 3 SNAs (C and D). Despite the difference in dynamic responses, however, all SNAs reach similar steady-state activity levels, regardless of the CSP increasing speed.

Our data indicate that static nonlinear characteristics of baroreflex control of muscle SNA are almost identical to those of renal and cardiac SNAs. This finding indicates that steady-state muscle SNA approximates that of renal and cardiac SNA at any baroreceptor pressure. This finding is consistent with the calculated step response of SNA to CSP change (Fig. 4B) because muscle SNA reaches the steady-state level similar in magnitude to cardiac and renal SNAs despite differences in the initial rapid decreases at the three sites. This also agrees with the numerical simulation (Fig. 5, B–D), which reveals that these SNAs reach similar steady-state levels even though they respond differently to CSP increase.

The present study may extend earlier studies investigating the relationship between muscle SNA and other SNAs innervating visceral organs (30, 31). These studies reported that microneurographical muscle SNA correlated with norepinephrine spillovers in the heart at rest, handgrip, and mental stress (30), and those in the kidney at rest (31). Although these studies suggested a correlation between muscle SNA and cardiac or renal SNA, they did not assess baroreflex control of SNA. In addition, the spillover measurements may be affected by neurotransmitter kinetics in synapses (release and uptake) and circulating norepinephrine independent of SNA (11), and the method has a low time resolution for assessing the dynamic baroreflex control of SNA. Accordingly, we measured the three SNAs directly and investigated the baroreflex control of each SNA.

Limitations. The present study has several limitations. First, we excluded the efferent effect of vagally mediated arterial baroreflex, which could affect the properties of baroreflex control of SNAs. Second, artificial respiration and surgical procedures used in this study could affect baroreflex. Third, anesthetic agents tend to inhibit efferent SNA and depress the gain of baroreflex control of SNA. Fourth, we used physiological saline preequilibrated with atmospheric air to perfuse the carotid sinuses. Local hypoxia could have occurred and somewhat affected baroreflex control of SNA. Last, although we held CSP for 60 s at each CSP level in protocol 1, some SNAs did not reach steady-state level within 60 s (Fig. 1). Therefore, the duration may be short to obtain steady-state SNA in all cases. However, because holding CSP for longer periods can induce SNA changes originating from factors other than CSP change itself, it is difficult to know precisely when SNA reaches steady-state level. Future study is needed to examine pure static baroreflex characteristics.

In summary, dynamic high-pass characteristics of baroreflex control of muscle SNA, assessed by the increasing slope of transfer gain, showed that more rapid change of arterial pressure resulted in greater response of muscle SNA to pressure change and that these characteristics were similar to cardiac SNA but greater than renal SNA. However, the numerical simulation based on the transfer function shows that the differences in dynamic baroreflex control at various organs result in detectable difference among SNAs only when AP changes at unphysiologically high rates (i.e., 5 mmHg/s). In addition, static reverse-sigmoid characteristics of baroreflex control of muscle SNA are almost identical with those of renal or cardiac SNAs. We conclude that dynamic-linear and static-nonlinear baroreflex control of muscle SNA is similar to that of renal and cardiac SNAs, with the exception of a mildly reduced dynamic-linear response of renal SNA to rapid pressure change outside the physiological range.

	APPENDIX

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In rabbits, the transfer function of the baroreflex neural arc (baroreceptor pressure to SNA) approximates derivative characteristics in the frequency range below 0.8 Hz and high-cut characteristics of frequencies above 0.8 Hz (15). Therefore, according to our previous study (15), we model the neural arc transfer function (H_N) using Eq. A1 as follows

(A1)

where f and j represent the frequency (in Hz) and imaginary units, respectively; K_N is static gain (in au/mmHg); f_c1 and f_c2 (f_c1 < f_c2) are corner frequencies (in Hz) for derivative and high-cut characteristics, respectively; and L is a pure delay (in s) that would represent the sum of delays in the synaptic transmission at the baroreflex central pathways and the sympathetic ganglion. The dynamic gain increases in the frequency range from f_c1 to f_c2 and decreases above f_c2. On the basis of the measured results from protocol 1, we set K_N at 1.6 and f_c2 at 0.8 similarly in all of muscle, cardiac, and renal SNAs in simulations shown in Fig. 5. In addition, we set f_c1 at 0.05, 0.05, and 0.1, respectively, in muscle, cardiac and renal SNA. We also set L at 0.3, 0.1 and 0.2, respectively, corresponding to the distance of neural pathway from carotid sinus region to the tibial, cardiac, and renal sympathetic nerve.

	GRANTS

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This study is a part of the "Ground-Based Research Announcement for Space Utilization" project promoted by Japan Space Forum. This study was also supported by Industrial Technology Research Grant Program 03A47075 from New Energy and Industrial Technology Development Organization (NEDO) of Japan.

	FOOTNOTES

 

Address for reprint requests and other correspondence: A. Kamiya, Dept. of Cardiovascular Dynamics, National Cardiovascular Center Research Institute, 5-7-1 Fujishirodai, Suita, Osaka 565-8565, Japan (e-mail: kamiya{at}ri.ncvc.go.jp)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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